Content:
Presentation type:
NH – Natural Hazards

EGU24-4270 | Orals | NH2.1 | Highlight | Plinius Medal Lecture

Hazard forecasting: is it a matter of time? 

Jacopo Selva

Hazard models aim at making explicit our forecasting capability about future potentially adverse natural events. Hazard events are typically rare and not deterministically predictable, forcing hazard models to speak the language of certainty and uncertainty, that is, of probability. This is valid for any forecasting time window, from years to days/hours in the future (long- to short-term hazard), to the evaluation of the potential impact of an ongoing event in the next seconds/minutes/hours (warning/now-casting to urgent computing). Even though the definition of the target time window is driven by the users of the forecast (e.g. civil protections) and is not a scientific matter, the quantification of existing uncertainty given the time frame is certainly a scientific matter. Probabilistic hazard is commonly discussed mainly for long-term hazards, where large uncertainty dominates. In shorter-term forecasts, uncertainty may deacrease and practitioners are often tempted by simplified approaches that neglect uncertainty, like for eruption forecasting during volcanic crises, or for tsunami warning models after seismic or volcanic events. Nevertheless, uncertainty may still exist, and a rational scientific approach should let the results to speak about existing uncertainty, rather than to neglect it by definition. Is it possible to define a unified approach to probabilistic hazard entailing all time scales? The long-term integral hazard integrating all potential sources and generation/propagation conditions can be adapted to the different forecasting time windows, generating a unified framework in which the different time scales may feed to each other, producing homogeneous and easy-to-interpret results. This unified vision of hazard models, embracing long- to short-term hazard as well as warning and urgent computing models, is here discussed based on the recent advancements in models for volcanic, seismic and tsunami hazard and warning.

How to cite: Selva, J.: Hazard forecasting: is it a matter of time?, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-4270, https://doi.org/10.5194/egusphere-egu24-4270, 2024.

The intensification of extreme precipitation in a warming climate has been shown in observations and climate models to follow approximately theoretical Clausius-Clapeyron scaling. However, larger changes have been indicated in events of short-duration which frequently trigger flash floods or landslides, causing loss of life. Global analyses of continental-scale convection-permitting climate models (CPCMs) and new observational datasets will be presented that provide the state-of-the-art in understanding changes to extreme weather (rainfall, wind, hail, lightning) and their compounding effects with global warming. These analyses suggest that not only warming, but dynamical circulation changes, are important in the manifestation of change to some types of extreme weather, which must be addressed in the design of new CPCM ensembles. We use our projections to provide the first analyses of impacts on infrastructure systems using a new consequence forecasting framework and show the implications for adaptation. It will be argued that a shift in focus is needed towards examining extreme weather events in the context of their ‘ingredients’ through their evolution in time and space. Coupled with exploration of their causal pathways, sequencing, and compounding effects – ‘storylines’ –, this can be used to improve both early warning systems and projections of extreme weather events for climate adaptation.

How to cite: Fowler, H.: Rapidly intensifying extreme weather events in a warming world: how important are large-scale dynamics in generating extreme floods?, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-22472, https://doi.org/10.5194/egusphere-egu24-22472, 2024.

EGU24-10624 | ECS | Orals | NH10.1 | Highlight | NH Division Outstanding ECS Award Lecture

Advancing multi-(hazard)risk science: embracing complexity and cross-disciplinary collaborations 

Marleen de Ruiter

Recent disasters have demonstrated the growing challenges faced by society as a result of multi-hazards and compound events. The impacts of such disasters differ significantly from those caused by single hazard disasters: often the impacts of a multi-hazard disaster exceed those of the sum of the impacts of the individual hazards. Recognizing this complexity, the scientific community and international organizations, such as the UNDRR, have been advocating for a more integrated approach in multi-(hazard)risk research. This requires bridging across individual hazard types, but also learning from methodological advances made in neighbouring research fields such as the compound events community.

This talk aims to highlight recent advances in assessing the complexities of multi-(hazard)risk and discusses opportunities for further enhancing our modeling capabilities through multidisciplinary collaboration. A crucial challenge of modelling compound and multi-hazard risk, is that of the spatiotemporal dynamics of risk. This includes for example, an improved understanding of post-disaster recovery after multi-hazard disasters and the role of (changing) local contexts within which disasters take place such as the dynamics of socioeconomic vulnerability and the likelihood of post-disaster disease outbreaks. Embracing these challenges and opportunities can support more comprehensive and effective disaster risk management strategies in the future.

How to cite: de Ruiter, M.: Advancing multi-(hazard)risk science: embracing complexity and cross-disciplinary collaborations, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-10624, https://doi.org/10.5194/egusphere-egu24-10624, 2024.

NH1 – Hydro-Meteorological Hazards

Introduction: The increasing number of heat events of summer 2003, 2010 and 2015 induced a rising impact on heat health morbidity and mortality in Germany. A considerable proportion of urban population is affected by thermal discomfort due to the urban heat island burden during summer, particularly vulnerable people at risk. As contribution to the National Adaptation Strategy to Climate Change a federal expert working group prepared and published ‘Recommendations for Action for the Preparation of Heat Action Plans to Protect Human Health’ in 2017, on the basis of the 2008 WHO Heat Health Guidance. A first country-wide project had been conducted in Germany between 2019 and 2023 which investigated the status and impact of current or planned Heat Health Action Plans (HHAP), and adaptation measures appropriate to protect and prevent human health. One aim of this study was to conduct an online survey on the awareness and degree of use of the 2017 recommendations and the development and implementation of HHAP.

 

Methods: The online survey questionnaire referred to climate change, heat and health aspects. The survey had been conducted in May/June 2020 to assess the current state of affairs as well as factors of success and barriers in the development and implementation of HHAP, with a particular focus on municipal administrations. Therefore, various networks of national, regional and local environment and health administrations, as well as stakeholders had been invited via an Email, web and social media campaign to participate anonymously. 

 

Results: The study fell in the period of increasing incidence of COVID19 pandemic, which influenced and limited the responses from the public health sector country-wide. Nonetheless, the online survey had been conducted in May/June 2020. In total 116 questionnaires had been responded, mainly by participants from the environment sector (53%), and 32% from the public health sector. More than half of the respondents (n=67) indicated to be aware of the 2017 recommendations (very well-known at the federal state level: 90%, at county level known by 43%). The recommended health-related adaptation measures were appreciated very or helpful by 81%. Respondents from large cities (> 100 000 inhabitants) were main contributors to the study (41 of 81 large cities replied, while 34 rural counties responded). So far, no federal state responded, but four municipalities and one county indicated to start planning activities on HHAP.

 

Conclusion: Recent heat events clearly indicate the demand to protect public health against heat extremes in Germany. The results of this first country-wide survey show on the one hand that currently HHAP are being developed rarely. On the other hand, results reflect that first HHAP actions turned into force after 2017 recommendation had been launched. Federal states and municipalities feel motivated and act responsible to take health-related adaptation measures in advance, such as HHAP, to prevent heat-related illnesses and deaths sustainable. Policy and decision makers must develop structures and regulations that anchor HHAP as a nationwide instrument to be established timely. 

How to cite: Mücke, H.-G.: Climate change induced heat health challenges – a study on the development of health-related adaption measures in Germany, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-1224, https://doi.org/10.5194/egusphere-egu24-1224, 2024.

EGU24-1262 | ECS | Posters on site | NH1.1

A systematic review on climate change, trending outcomes for the care of older adults, and financial expenditure 

Keriin Katsaros and Jo-Ting Huang-Lachmann

As global populations age, life expectancy increases, and the impacts of climate change intensify, there is a corresponding rise in financial expenditure associated with both extreme climate events and a growing demand for long-term healthcare. There is a gap in research showing how climate change, the care of aging populations, and financial expenditure are interconnected.  A systematic review of literature is carried out to identify the interrelationships and to explore existing trends and evidence on senior care during the time of climate change. This work is important to identify barriers and priority action areas for adaptation, mitigation, and future planning to increase health gain and achieve positive economic outcomes.

The systematic review of existing peer-reviewed publications is carried out by following PRISMA guidelines.  The methodology is guided by recommendations from Cochrane and in the WHO Guidance on Research Methods for Health Emergency and Disaster Risk Management. Three electronic databases have been searched (PubMed, Web of Science, and Scopus) to assess available literature on climate change, the care of older persons, and financial expenditure. A risk of bias assessment is completed using CASP checklists.

Initial results of this review have identified significant adverse impacts for older society members including decreased physical and mental health during extreme weather events resulting in increased medical and care costs. Preliminary results also highlight a need for energy-efficient built environments, clean and affordable energy sources to overcome energy poverty, and a new way of rethinking how we care for senior society members, including increased support from families and communities.

This research aims to contribute to increasing transdisciplinary knowledge from the fields of health and care, energy, and climate change to create societies for older people that are friendly, affordable, and resilient to the adverse effects of climate change. The results aim to advance the transdisciplinary knowledge of climate services, health, and energy economics; co-creating synergies and actionable solutions; and working with societal actors for implementation, transfer, and upscaling of research.

How to cite: Katsaros, K. and Huang-Lachmann, J.-T.: A systematic review on climate change, trending outcomes for the care of older adults, and financial expenditure, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-1262, https://doi.org/10.5194/egusphere-egu24-1262, 2024.

EGU24-1762 | ECS | Orals | NH1.1

Assessing the impact of mobility on heat exposure 

Guo-Shiuan Lin and Gabriele Manoli

Higher temperatures are linked to elevated mortality risk and reduced economic productivity. In urban centers, human mobility and the urban heat island effect generally result in higher population density and increased temperatures. Yet, existing studies on urban heat exposure rely on static residential population data, thus neglecting population dynamics and their covariation with the spatial distribution of urban temperatures. Here, we evaluate how seasonal and daily mobility modify heat exposure and risk across 80 European cities by combining monthly daytime and nighttime population estimates with high-resolution urban climate simulations. Our findings reveal that, on a daily scale, mobility increases population and summer heat exposure in most cities due to daily commuting behavior. Conversely, commuting to warmer city centers (from colder rural areas) may be advantageous in winter. On seasonal timescales, summer populations decrease in most cities as urban residents travel outward for holidays but heat exposure increases significantly in touristic destinations where population peaks during the June-August period. These results emphasize the significant impact of mobility on the space-time distribution of heat exposure in cities and offer valuable insights for mitigating temperature-related risks in diverse climatic and urban contexts.

How to cite: Lin, G.-S. and Manoli, G.: Assessing the impact of mobility on heat exposure, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-1762, https://doi.org/10.5194/egusphere-egu24-1762, 2024.

EGU24-1899 | ECS | Posters on site | NH1.1

Heatwave social vulnerability index validated on mortality data in Europe 

Benedetta Sestito, Lena Reimann, Maurizio Mazzoleni, Wouter Botzen, and Jeroen Aerts

Climate change projections underscore an imminent temperature escalation of 1.1°C to 6.4°C above the 1990 baseline by 2100, leading to heatwaves of higher frequency, intensity, and duration. In the last decades, Europe has experienced severe heatwave events, such as the catastrophic one in summer 2003 that claimed over 70,000 lives. Thus, it is of pivotal importance to better understand the drivers of heatwave impacts to promote effective adaptation and mitigation strategies. While many studies have been carried out to explore the social vulnerability to heat-related impacts at local and regional scales, a large-scale continental analysis is still missing. This study aims at exploring the temporal and spatial dynamics of social vulnerability to heatwaves in Europe. This will be achieved by developing a dynamic spatial and temporal social vulnerability index for heatwaves in Europe. The index is validated against impact metrics such as mortality, which remains underexplored in large-scale assessments. In particular, a regression analysis between heatwave mortality and socio-economic and demographic factors, including population changes, income variations, and alterations in social infrastructure is carried out to assess the degree to which these factors are associated with heatwave mortality. The regression coefficients serve as the weights in the composite social vulnerability index. Such validation enhances the credibility, accuracy, and applicability of social vulnerability indices, bridging the divide between theoretical assessments and real-world consequences. Our empirical results indicate that diverse socio-economic and demographic variables exhibit distinct correlations with heatwave mortality. Consequently, an index incorporating ad hoc weighting of its constituent terms more effectively captures the social vulnerability component to heatwaves. This research provided new insights to better understand social vulnerability to heatwaves and allow better-informed decision-making to enable the development of resilient communities. Moreover, our findings advanced the understanding of heat risks in the broader context of escalating climate change challenges across Europe.

How to cite: Sestito, B., Reimann, L., Mazzoleni, M., Botzen, W., and Aerts, J.: Heatwave social vulnerability index validated on mortality data in Europe, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-1899, https://doi.org/10.5194/egusphere-egu24-1899, 2024.

EGU24-2014 | ECS | Posters on site | NH1.1

Developing a Multivariate System for Predicting and Mitigating the Health Effects ofHeat waves in Niterói, Rio de Janeiro 

Vitor Luiz Galves, Marcio Cataldi, and Joan Souza

This study aims to develop a heat wave forecasting system using a new multivariate index that encompasses hydration-related mitigation measures. Heatwaves have increasingly occurred with greater frequency and intensity in various regions worldwide, particularly in Europe and Asia since 1990, although they are not exclusive to these areas. The principal health effects of heatwaves on populations include heat-related illnesses and fatalities, cardiovascular and kidney diseases, as well as adverse reproductive effects. These detrimental impacts are widespread and commonly affect individuals aged 65 and above. Many nations have established metrics to assess the prevalence of this occurrence within their borders. These metrics typically utilize specific thresholds and/or temperature ranges at a height of 2 meters, which denote extreme percentiles of values from past records. While some of these metrics consider the persistence of the phenomenon, few take into account the relative humidity. It is noteworthy that, in most instances, the temperature thresholds lead to a linear escalation in conditions posing a risk to the population. This can result in a biased perception of the actual level of risk involved. To thoroughly evaluate the health hazards associated with heatwaves, it is essential to acknowledge the considerable variability in global climate, as well as the diverse responses of living organisms to extreme temperature and humidity conditions. Furthermore, factors such as individuals' gender, race, age, pre-existing medical conditions, and geographical location should be taken into account.This study is divided into several components to reach a comprehensive solution. The first step involves determining the monthly distribution curve of accumulated daily maximum temperatures for each grid point of the ERA 5 data. After completing this process, machine learning models must be developed to calibrate the temperature values to the percentile of the cumulative distribution. Subsequently, the temperature value exceeding 95% of the distribution will be applied to this coefficient Coef = (eTpe*Ur)/1000, where Tpe is the value of the distribution that exceeds 95% and Ur is the relative humidity. These adjusted values will then be used to compute the normalized index I=(Coef-0.022)/9.7, accounting for the exponential temperature increase and providing weightage to the relative humidity. Upon establishment of these functions, a time series of the index value will be generated. This value will be multiplied by the hours of the day during which the index deviates from zero, facilitating the evaluation of its correlation with hospitalization and mortality data related to diseases such as thrombosis, which may be linked to heat waves. The results of this phase will be presented at the Niterói region in Rio de Janeiro, Brazil, during the upcoming congress. Moreover, according to previous analyzes, since 2010 the frequency and intensity of heat waves have increased, being apparently modulated by Enso events and also by indices developed at LAMMOC/UFF related to anomalies of sea surface temperature of the Equatorial Atlantic Ocean and the South Atlantic Convergence Zone. Furthermore, the index data will subsequently undergo validation based on body water loss rates and their impact on blood viscosity fluctuations.

How to cite: Galves, V. L., Cataldi, M., and Souza, J.: Developing a Multivariate System for Predicting and Mitigating the Health Effects ofHeat waves in Niterói, Rio de Janeiro, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-2014, https://doi.org/10.5194/egusphere-egu24-2014, 2024.

EGU24-3061 | ECS | Posters on site | NH1.1

Links between weather variability and Dengue outbreaks in Sao Paulo, Brazil 

Falak Naz, Júlia Araújo, Sheila Oliveira, Seyma Celina, Aleš Urban, and Jiří Černý

Mosquito-borne diseases are among the most dangerous threats for all people living in tropical areas. Previous research has shown that the highest incidence of mosquito-borne diseases is associated with a particular type of weather (usually wet and hot) as mosquitos’ activity and development are highly dependent on meteorological conditions. However, short-term associations (on the scale of days up to a few weeks) have been less understood.

In this study, we collected weekly data on the incidence of Dengue on a municipality level in the state of Sao Paulo, Brazil, 2016–2022, and matched it with ERA5-based weather variables (ambient temperature, relative humidity, wind speed and precipitation). We employed a multilevel meta-regression analysis to i) analyse the links between Dengue incidence and weather variability in, and ii) develop a model to predict a Dengue fever outbreak based on actual weather conditions and socioeconomic variables.

Our preliminary results suggest a significant association of a Dengue outbreak with above-average daily mean temperature and humidity, heavy rainfalls, and calm conditions in previous 2-6 weeks. Further analysis is needed to identify spatial differences in these patterns based on socioeconomic conditions.

How to cite: Naz, F., Araújo, J., Oliveira, S., Celina, S., Urban, A., and Černý, J.: Links between weather variability and Dengue outbreaks in Sao Paulo, Brazil, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-3061, https://doi.org/10.5194/egusphere-egu24-3061, 2024.

EGU24-3081 | Orals | NH1.1

Developing a Multivariate System for Predicting and Mitigating the Health Impacts of Heatwaves 

Marcio Cataldi, Vitor Galvez, Victoria Gallardo Fernandez, Juan Pedro Montávez, Pedro Jiménez-Guerrero, Guillermo Felipe Lopez Sanchez, and Christian Jair Martínez Urrutia

The main purpose of this study is to develop a heatwave impact-based forecasting system using a new multivariate index, that also encompasses a mitigation action plan with hydration-related measures. Since 1990, heatwaves have become more frequent and intense in various regions worldwide, particularly in Europe and Asia. The principal health effects of heatwaves include organs' strain and damage, complications of cardiovascular and kidney diseases, as well as adverse reproductive effects. These detrimental impacts are widespread and commonly affect individuals aged 65 and above. Many nations have established metrics to assess the prevalence of this occurrence within their borders. These metrics typically use specific threshold values and/or ranges of the near-surface (2 m) air temperature, usually denoted by the extreme values from past records. To the best of our knowledge, only some of these metrics take into account the persistence of the phenomenon and few consider the relative humidity. It is noteworthy that in most of these metrics the temperature thresholds lead to a linear escalation of the conditions posing a risk to the population, which may lead to a misperception of the actual level of risk involved. To thoroughly evaluate the health hazards associated with heatwaves, it is essential to consider the climate variability and change at regional and local scales, as well as the diverse responses of living organisms to extreme (and long-lasting) temperature and humidity conditions. Factors such as individuals' sex, ancestry, age, pre-existing medical conditions, and geographical location should be considered too. The first step of this study consisted of the characterization of the monthly Cumulative Distribution Function of the daily maximum near-surface air temperature (TX) in summer, in recent climate. We used the ERA5-Land reanalysis dataset and performed the analysis for each grid point, considering 1960-1990 as baseline period.  Subsequently, in order to compute the index, the temperature values exceeding the 95th percentile (TX95p) were subjected to a normalized scaling function whose values grow exponentially with the magnitude of the temperature and also depend on the ambient relative humidity. The resulting index values range from 0 to 1, only being greater than zero when the temperature exceeds TX95p. To calibrate the index, we considered the hours of the day during which the index deviates from zero and its correlation with hospitalization and mortality data, mainly related to cardiovascular diseases such as thrombosis. The preliminary work concerned the Region of Murcia, in Spain. The index was validated in the period 2000-2022. Results show the sensitivity of the index, which displays its largest values in the summer of 2022, coinciding with the high number of heat-related deaths observed that year in Spain. Future research will be focused on index calibration and validation in other regions which are also subjected to extreme heat conditions.

How to cite: Cataldi, M., Galvez, V., Gallardo Fernandez, V., Montávez, J. P., Jiménez-Guerrero, P., Lopez Sanchez, G. F., and Martínez Urrutia, C. J.: Developing a Multivariate System for Predicting and Mitigating the Health Impacts of Heatwaves, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-3081, https://doi.org/10.5194/egusphere-egu24-3081, 2024.

EGU24-3446 | Orals | NH1.1

Drivers of the time-varying heat-cold-mortality association in Spain 

Hicham Achebak, Grégoire Rey, Simon Lloyd, Marcos Quijal-Zamorano, Raúl Fernando Méndez-Turrubiates, and Joan Ballester

Background: A number of studies have reported reductions in mortality risk due to heat and cold over time. However, questions remain about the drivers of these adaptation processes to ambient temperatures. We aimed to analyse the demographic and socioeconomic drivers of the downward trends in vulnerability to heat- and cold-related mortality observed in Spain during recent decades (1980-2018).

Methods: We collected data on all-cause mortality, temperature and relevant contextual indicators for 48 provinces in mainland Spain and the Balearic Islands between Jan 1, 1980, and Dec 31, 2018. Fourteen contextual indicators were analysed representing ageing, isolation, urbanicity, heating, air conditioning (AC), house antiquity and ownership, education, life expectancy, macroeconomics, socioeconomics, and health investment. The statistical analysis was separately performed for the range of months mostly causing heat- (June-September) and cold- (October-May) related mortality. We first applied a quasi-Poisson generalised linear regression in combination with distributed lag non-linear models (DLNM) to estimate province-specific temperature-mortality associations for different periods, and then we fitted univariable and multivariable multilevel spatiotemporal meta-regression models to evaluate the effect modification of the contextual characteristics on heat- and cold-related mortality risks over time.

Findings: The average annual mean temperature has risen at an average rate of 0·36°C per decade in Spain over 1980-2012, although the increase in temperature has been more pronounced in summer (0·40°C per decade in June-September) than during the rest of the year (0·33°C per decade). This warming has been observed, however, in parallel with a progressive reduction in the mortality risk associated to both hot and cold temperatures. We found independent associations for AC with heat-related mortality, and heating with cold-related mortality. AC was responsible for about 28·6% (31·5%) of the decrease in deaths due to heat (extreme heat) between 1989-1993 and 2009-2013, and heating for about 38·3% (50·8%) of the reductions in deaths due to cold (extreme cold) temperatures. Ageing (ie, proportion of population over 64 years) attenuated the decrease in cold-related mortality.

Conclusion: AC and heating are effective societal adaptive measures to heat and cold temperatures. This evidence holds important implications for climate change health adaptation policies, and for the projections of climate change impacts on human health.

How to cite: Achebak, H., Rey, G., Lloyd, S., Quijal-Zamorano, M., Méndez-Turrubiates, R. F., and Ballester, J.: Drivers of the time-varying heat-cold-mortality association in Spain, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-3446, https://doi.org/10.5194/egusphere-egu24-3446, 2024.

Humid heatwaves, defined as extreme temperatures combined with high relative humidity, can affect large populations and result in crop damage, causing public health emergencies and threatening food security. The consequences for society are even more severe when extreme rainfall follows a humid heatwave. There is evidence of the increasing occurrence of humid heatwaves—extreme rainfall compound events in several parts of the globe. Their compound impact depends on the response time, the statistical interdependency between the two interacting causal drivers, and their severity. While the correlation between temperature and rainfall tends to be negative at daily or monthly time scales, the correlation between short-duration rainfall extremes and high temperatures is often positive at shorter time scales during the summer. A positive (negative) correlation can result in a higher (lower) risk of compound heatwave-extreme rainfall events. Across the coasts, the dependence strengths between these two variables are often elusive due to the influence of large-scale atmospheric teleconnection. On a global scale, the statistical coupling between humid heatwaves and extreme rainfall events has not been investigated across the coasts. To fill this knowledge gap, this study provides an observational assessment of the compound interactions of summer heatwave amplitude (i.e., the peak temperature of the hottest day during the heatwave episode) and extreme rainfall (described by > 90th percentile threshold of daily rainfall magnitude) across 29 major coastal cities in the tropics (23.5°N - 23.5°S), subtropics (23.5°N - 35°N and 23.5°S - 35°S) and mid-latitudes (35°N – 60°N and 35°S - 60°S). It finds a significant (P < 0.05) increase in the frequency of compound humid heatwaves-extreme rainfall events in the past few decades, with a more robust increase over the northern hemisphere compared to the southern hemisphere. The mean response times between the heatwave amplitude and the peak rainfall tend to be shorter for the southern sub-tropics than the northern hemisphere sites, indicating a swift transition between two extremes in these regions. Using a multivariate probabilistic framework, we further demonstrate that a modest to substantial increase in heatwave amplitude in summer can enhance the rainfall extremes by 80%, with the most significant increase occurring in the sub-tropics. The findings reveal a strong coupling between humid heatwaves and extreme rainfall in sub-tropical climate regimes, contrasted by a relatively weak coupling across the tropics. Understanding the interactions between humid heatwaves and extreme precipitation across coastal megacities will help decision-makers and stakeholders to adapt and mitigate these compound hazards in densely populated settlements.

How to cite: Ganguli, P. and Merz, B.: A Global Assessment of Compound Humid Heatwaves-Extreme Rainfall in Major Coastal Cities, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-4088, https://doi.org/10.5194/egusphere-egu24-4088, 2024.

EGU24-4858 | Posters on site | NH1.1

Significant reduction of unequal population exposure to climate extremes by achieving the carbon neutrality 

Jung Choi, Seok-Geun Oh, Min-Jee Kang, Sujong Jeong, Seung-Ki Min, Sang-Wook Yeh, Yeon-Hee Kim, and Seok-Woo Son

Climate extremes, such as hot temperature and heavy precipitation events, have devastating effects on human societies. As the planet warms, they have become more intense and more frequent. To avoid irreversible damage from climate extremes, many countries have committed to achieving net-zero anthropogenic carbon emissions, or carbon neutrality, by 2050s. Here, we quantify the impact of carbon neutrality on population exposure to climate extremes using multi-model projections from the Coupled Model Intercomparison Project Phase 6 (CMIP6) based on the Shared Socioeconomic Pathway (SSP)1-1.9 and SSP3-7.0 scenarios. It is found that the increasing exposure of the population to hot-temperature and heavy-precipitation extremes can be substantially reduced by 87–98% in the late 21st century by achieving carbon neutrality. The benefits of carbon neutrality are particularly pronounced in Africa and Asia. The potential benefits of carbon neutrality are also significant in North America, Europe, and Oceania, where a reduction in climate extremes is more than twice as important as population decline in reducing population exposure to climate extremes. These results provide important scientific support for ongoing efforts to achieve net-zero carbon emissions by 2050s to reduce potential climate risk and its inequity across continents.

How to cite: Choi, J., Oh, S.-G., Kang, M.-J., Jeong, S., Min, S.-K., Yeh, S.-W., Kim, Y.-H., and Son, S.-W.: Significant reduction of unequal population exposure to climate extremes by achieving the carbon neutrality, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-4858, https://doi.org/10.5194/egusphere-egu24-4858, 2024.

EGU24-4898 | ECS | Orals | NH1.1

Assessment of humidity-inclusive heat index models over tropical megacities: Indonesia case 

Muhammad Rezza Ferdiansyah, Alberth Nahas, and Ardhasena Sopaheluwakan

One of the impacts on humans in megacities experiencing rapid urbanization is the increase in heat risk, primarily due to the urban heat island (UHI) phenomenon. One of the reasons for the intensification of UHI can be attributed to changes in land use and population growth. Additionally, global warming and climate change conditions that are currently occurring exacerbate this issue. For megacities located in tropical regions, such as Indonesia, there is limited available data regarding the impact of heat stress. Therefore, it is essential to develop a heat index suitable for tropical climates, characterized by high temperatures and humidity levels. Temperature and humidity are two crucial factors in quantifying the heat index to mitigate the risk of heat-related disasters. Consequently, when modeling the heat index for megacities in tropical regions, it is necessary to incorporate humidity. This study aims to compare different models for humidity-inclusive temperature indices, specifically Apparent Temperature (AT), Heat Index (HI), and Wet Bulb Globe Temperature (WBGT). These indices are computed using standard weather measurement data and re-analysis data to obtain spatial distribution. The current results demonstrate that these indices effectively captured the unusual heat conditions in Indonesia during the September 2023 period.

How to cite: Ferdiansyah, M. R., Nahas, A., and Sopaheluwakan, A.: Assessment of humidity-inclusive heat index models over tropical megacities: Indonesia case, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-4898, https://doi.org/10.5194/egusphere-egu24-4898, 2024.

EGU24-6555 | ECS | Posters on site | NH1.1

Mapping the vulnerability to heat: an application in the city of Bern 

Sujung Lee, Moritz Burger, Moritz Gubler, Stefan Brönnimann, and Ana Maria Vicedo-Cabrera

Background: Heat is widely acknowledged as one of the most hazardous climate-related risk factors affecting human health. Increasing urban development has led to an amplification of its health impacts due to the Urban Heat Island (UHI) effect. However, our understanding of neighbourhood-level vulnerability to the UHI effect remains limited. This information can be crucial for policymakers to identify high-risk areas in cities and develop more targeted public health interventions. Thus, we propose a comprehensive approach to map the vulnerability to UHI in the city of Bern (Switzerland) by (1) assessing the demographic and socio-economic factors contributing to increased UHI exposure and (2) analysing the spatial distribution of vulnerability to the UHI effect.

Methods: We collected population and household statistics at the individual level from 2012 to 2021 from the Federal Statistical Office of Switzerland. Firstly, we calculated the intensity of UHI (representing the temperature difference between the inner city and the rural surroundings) in each district of Bern using high-resolution (50mx50m) modelled urban temperature data. Next, we performed univariate logistic regression models to estimate the association between UHI exposure and population characteristics, reporting odds ratio (OR) and 95% confidence intervals (CI). We defined UHI exposure as individuals being exposed to UHI intensity exceeding the city-mean for the corresponding census year. Subsequently, we established the Heat Vulnerability Index (HVI) by selecting key determinants: 1) the elderly population (aged ≥65 years), 2) females, and 3) individuals with low socio-economic status. The overall percentile ranks for districts were calculated by summing variable rankings.

Result: First, our study identified several factors contributing to increased UHI exposure, in particular, single individuals had 60% higher odds of UHI exposure (OR:1.60; CI:1.59-1.62) compared to married individuals, and individuals aged 26-44 (1.71; 1.70-1.74) compared to those aged 0-17. Also, wealthier individuals appeared to have higher odds of UHI exposure (medium: 2.32; 2.30-2.35, high: 1.66; 1.64-1.67) compared to the lowest group. In the context of the work environment, individuals in large-size companies (≥250 employees) had an increased risk (1.85; 0.77-6.05) of UHI exposure compared to those in micro-size companies (<10 employees) and employees of public companies (1.17; 0.88-1.62) compared to their counterparts in private companies. Our results highlighted varying vulnerability patterns in different districts. In the city centre, despite a medium HVI, UHI intensity surpassed other areas, intensifying vulnerability to heat. The western part showed lower UHI but had high HVI due to a concentration of individuals with the lowest socio-economic status.

Conclusion: Our preliminary results emphasize the importance of considering demographic and socioeconomic characteristics when assessing the impact of UHI exposure on population health. Building upon these findings, we plan to develop a heat vulnerability map of the city of Bern by applying a more advanced epidemiological analysis using Bayesian methods to assess the spatial distribution of the UHI mortality risk. This investigation will provide valuable evidence and methods to improve our understanding of the impact of UHI on health and aid in developing targeted interventions to protect at-risk communities.

How to cite: Lee, S., Burger, M., Gubler, M., Brönnimann, S., and Vicedo-Cabrera, A. M.: Mapping the vulnerability to heat: an application in the city of Bern, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-6555, https://doi.org/10.5194/egusphere-egu24-6555, 2024.

EGU24-7213 | Posters on site | NH1.1

Assessing Present-Day and Future Perspectives of Climate Impact on Thermal Stress Risks in Korea from 1km High Resolution Scenarios 

Jae-Hee Lee, Hyun Min Sung, Jin-Uk Kim, Sungbo Shim, Chu-Yong Chung, and Young-Hwa Byun

Among the various thermal stress indices, apparent temperature (AT) is closely related to public health indicators, and consequently is widely used by weather agencies around the world. Therefore, in this study we estimate the changes in AT and contributing components in Korea as a whole and in five major cities (Seoul, Gwanju, Daegu, Daejeon, and Busan) using national standard climate scenarios based on the coupled model inter-comparison project (CMIP6). In the present day, high AT occurs in major cities due to high temperature (TAS) and relative humidity (RH). Our findings reveal that even when TAS is relatively low, large AT occurs with higher humidity. Notably, in future warmer climate conditions, high AT may first appear in the five major cities and then extend to the surrounding areas. An increase in TAS and RH during the pre-hot season (March to June) may lead to earlier occurrence of thermal risks in future warmer climate conditions and more frequent occurrence of high thermal stress events. Our study can serve as a reference for future information on thermal risk changes in Korea. Considering those who have not adapted to high temperature environments, our findings imply that thermal risks will become more serious and that heat adaptation strategies will be needed during the pre-hot season under future warmer climate conditions.

How to cite: Lee, J.-H., Sung, H. M., Kim, J.-U., Shim, S., Chung, C.-Y., and Byun, Y.-H.: Assessing Present-Day and Future Perspectives of Climate Impact on Thermal Stress Risks in Korea from 1km High Resolution Scenarios, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-7213, https://doi.org/10.5194/egusphere-egu24-7213, 2024.

EGU24-8032 | ECS | Posters on site | NH1.1

The interconnections between household energy, health, and climate change: a comprehensive umbrella review 

Clemens Marggraf and Jo-Ting Huang-Lachmann

Household energy is becoming increasingly important for the maintenance of good health, especially as people spend 90% of their lifetime indoors. However, there are a number of health and climate impacts associated with the generation and use of household energy. To date, there is no holistic picture in the literature describing the interlinkages between household energy, health outcomes and climate change. In order to systematically synthesize the fragmented evidence, an umbrella review will be conducted.

Therefore, a systematic review of peer-reviewed literature was carried out following PRISMA guidelines. Five electronic databases were searched (PubMed, Web of Science, Google Scholar, Cochrane and Scopus) to assess available literature on climate change, health and household energy, from January 1, 1900 through to June 5, 2023.

Preliminary findings highlight the dynamic interactions between the three issues, e.g., the impact of climate change on energy use/production and health outcomes, as well as the impact of different methods of energy use/production on climate change and health outcomes. In addition, the lack of consideration of the current literature on climate change  in the context of health and energy is a further finding of the umbrella review. Furthermore, the literature reviewed tends to ignore inclusion criteria (e.g., gender, socioeconomic or spatial aspects), which are also essential for a just transition to a more climate-friendly society in the future.

The goal of the umbrella review is to help policymakers understand the complex interrelationships between the three topics, both now and in the future, as climate change progresses and humanity is forced to adopt different mitigation and adaptation methods that affect energy use and production, as well as health outcomes.

How to cite: Marggraf, C. and Huang-Lachmann, J.-T.: The interconnections between household energy, health, and climate change: a comprehensive umbrella review, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-8032, https://doi.org/10.5194/egusphere-egu24-8032, 2024.

EGU24-8234 * | Orals | NH1.1 | Highlight

Urgency of Climate Change through the lens of COVID-19 Pandemic: the case of heat-related mortality 

Fulden Batibeniz, Sonia Isabelle Seneviratne, Srinidhi Jha, Andreia Ribeiro, Laura Suarez Gutierrez, Christoph C. Raible, Ben Armstrong, Michelle L. Bell, Eric Lavigne, Antonio Gasparrini, Yuming Guo, Masahiro Hashizume, Pierre Masselot, Susana Pereira da Silva, Dominic Royé, Francesco Sera, Shilu Tong, Aleš Urban, and Ana M. Vicedo-Cabrera and the Multi-Country Multi-City Collaborative Research Network

The COVID-19 pandemic and climate change are both urgent global health concerns. However, their impact on human lives has not been compared on the same scale. In this study, we compared mortality due to heat with COVID-19 in 38 cities worldwide, considering different levels of global warming (+1°C, +1.5°C, +2°C, and +3°C). Our findings reveal that even at a global warming level of +1.0ºC, 6 cities are already at a point where heat-related deaths could equal COVID-19 deaths within 15 years. Regardless of high or low COVID-19 mortality in the cities, the number of years to reach the level of COVID-19 mortality decreases with higher global warming levels. In 18.4% to 47.4% of the cities, heat-related mortality is projected to equal COVID-19 mortality within 15 years, ranging from +1.0ºC to +3.0ºC of global warming. The vulnerability to climate change varies among regions, with European, Mediterranean, and North American cities experiencing a significant rise in heat-related mortality with higher global warming levels. It is important to note that the given number of years represents the time required to reach COVID-19 mortality. However, unlike the peak and decline of COVID-19, climate change-driven heat-related deaths will persistently worsen unless substantial adaptation measures are taken. This emphasizes the crucial need to integrate climate change into public health discourse and policy.

How to cite: Batibeniz, F., Seneviratne, S. I., Jha, S., Ribeiro, A., Gutierrez, L. S., Raible, C. C., Armstrong, B., Bell, M. L., Lavigne, E., Gasparrini, A., Guo, Y., Hashizume, M., Masselot, P., Pereira da Silva, S., Royé, D., Sera, F., Tong, S., Urban, A., and Vicedo-Cabrera, A. M. and the Multi-Country Multi-City Collaborative Research Network: Urgency of Climate Change through the lens of COVID-19 Pandemic: the case of heat-related mortality, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-8234, https://doi.org/10.5194/egusphere-egu24-8234, 2024.

EGU24-8342 * | Orals | NH1.1 | Highlight

Global assessment of atmospheric and land surface drivers of heatwaves 

Yigit Uckan, Melissa Ruiz-Vasquez, Kelley De Polt, and Rene Orth

Heatwaves are extreme weather events characterized by exceptionally high temperatures that have severe impacts on society and ecosystems. Their magnitude and frequency are increasing with climate change in many regions. They are driven by both atmospheric and land surface processes such as advection or reduced evaporative cooling. The contributions of these individual drivers to the formation of heatwaves have been analyzed in case studies for major past events with model experiments. At the same time, the global relevance of heatwave drivers remains unclear.

We perform a global analysis with reanalysis data to determine the relation of heatwave temperatures to (i) atmospheric variables such as wind, pressure, and pressure differences, each at different geopotential heights, as well as (ii) land surface variables such as evaporative fraction, enhanced vegetation index, and surface net radiation. First, we identify the hottest day in each grid cell during the period 2001-2020. We also determine the values of the driver variables on this day. Then, for each driver variable, we select five days from the entire study period where the variables’ value most closely matched the hottest day value (=analogues). Next, we compare the averaged temperature anomalies of these analogues to those of the hottest day. The more similar the analogue temperature anomalies are (=hotter), the more relevant the driver variable is deemed. This is done for the three hottest days in each grid cell, ensuring that they are at least 15 days apart from each other to belong to separate heatwave events. 

The results show that pressure at the 500 hPa level is the most relevant driver of heatwaves in the mid-latitudes, while in the tropics a combination of variables plays a more important role than individual variables. Radiation is the second most relevant driver in many regions, particularly in tropical areas. In most cases, several drivers seem to contribute to the heatwave events such that only their aggregated analogue temperature anomalies can match the observed anomalies. These findings confirm previous case studies which highlighted the relevance of atmospheric circulation patterns such as blocking as well as reduced evaporation related to vegetation water stress. For the first time we identify the relevance of these processes across the globe, and with observation-based data. This can contribute to a better management and potential mitigation of heatwave temperatures and their impacts.



How to cite: Uckan, Y., Ruiz-Vasquez, M., De Polt, K., and Orth, R.: Global assessment of atmospheric and land surface drivers of heatwaves, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-8342, https://doi.org/10.5194/egusphere-egu24-8342, 2024.

EGU24-8448 | ECS | Posters on site | NH1.1 | Highlight

 Beyond Ambient Temperature: A Comparative Analysis of Heat Metrics for Detecting Heatwaves on a Country Level 

Tobias Monthaler, Katharina Wieser, and Chloe Brimicombe

Ambient temperature is the standard metric for the detection of heatwaves. However, when considering the impacts on humans the severity in terms of our wellbeing may be underestimated. The field of detecting human-perceived heatwaves is new and fast growing. It is important to recognise the difference in measuring heatwaves using heat metrics in comparison to the standard of ambient temperature. For this approach we develop an algorithm to track heatwaves on a country level through time for the last 30 years. Maximum daily temperature data is used in comparison to the heat metrics of WBGT and UTCI. Through all this, a substantiated knowledge basis should be established of how well the stated heat metrics are detecting heatwave impacts. The findings contribute to the development of standardized and robust approaches for utilizing reanalysis data in conjunction with heat indices, ultimately improving heatwave detection, forecasting and policy development.

How to cite: Monthaler, T., Wieser, K., and Brimicombe, C.:  Beyond Ambient Temperature: A Comparative Analysis of Heat Metrics for Detecting Heatwaves on a Country Level, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-8448, https://doi.org/10.5194/egusphere-egu24-8448, 2024.

EGU24-8713 | ECS | Orals | NH1.1

Future Exposure to Extreme Heat in Southeast Asia 

Sonali Manimaran, Dennis Wagenaar, Christine Nam, Ludwig Lierhammer, Laurens Bouwer, and David Lallemant

Southeast Asia has seen an increasing trend in extreme temperatures, with records of the highest surface air temperature being set in recent years. Heat waves are also increasing in frequency, severity and duration, with higher maximum temperatures being recorded both in daytime and nighttime. These trends are expected to intensify in the coming decades with climate change, with many implications for human health. For Southeast Asia, however, the number of studies quantifying future heat hazards and exposure are severely limited, but first analyses show that several countries in the region could see dramatic shifts in risk due to climate change. Therefore, it is imperative to quantify the heat stress that populations will be exposed to due to future extremes in the region. To this end, this study uses projections from Regional Climate Models to compute the Wet Bulb Globe Temperature (WBGT) and Universal Thermal Climate Index (UTCI) as measures of extreme heat. WBGT has been widely used in studies of extreme heat and impacts, particularly in the case of outdoor physical work, and has been shown to be representative of heat stress in hot and humid environments, such as Southeast Asia. On the other hand, the UTCI is a newer metric which has been demonstrated to be applicable in a range of climatic conditions and representative of the human physiological response. Using the novel CORDEX-CORE dataset for Asia, WBGT and UTCI metrics for Southeast Asia are developed at a 22x22 km resolution up till 2100 using a wide range of ensemble simulations. Future population exposure to extreme heat is then computed for each country in the region, across a range of Shared Socioeconomic Pathway (SSP) scenarios. Our first results show that nearly the entire region will experience strong heat stress by mid-century and some countries will experience strong heat stress for most of the year by the end of the century.

How to cite: Manimaran, S., Wagenaar, D., Nam, C., Lierhammer, L., Bouwer, L., and Lallemant, D.: Future Exposure to Extreme Heat in Southeast Asia, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-8713, https://doi.org/10.5194/egusphere-egu24-8713, 2024.

EGU24-9026 | ECS | Orals | NH1.1

The compound effect of acute respiratory infections and temperatures on mortality in the Czech Republic, 1982–2019 

Ekaterina Borisova, Aleš Urban, Hana Hanzlíková, Eva Plavcová, Jan Kyselý, Jan Kynčl, and Joan Ballester

Numerous studies have thoroughly documented the contribution of non-optimal temperatures and acute respiratory infections (ARIs) to increased mortality. However, there is still a gap in understanding how these factors interact together to affect human mortality during the cold season, and how this impacts population susceptibility to heat waves in the summer.

In this study we conduct an analysis over a period spanning 38 years (1982–2019), utilizing: a) daily all-cause mortality counts across the Czech Republic, b) daily proxies of acute respiratory infections (ARIs) incidence, interpolated from weekly healthcare surveillance data, and distinguished regarding three dominant influenza viruses (A/H3N2, A/H1N1, and B), and c) a suite of weather variables, sourced from E-OBS gridded data, including daily mean, maximum, and minimum temperatures, daily precipitation, daily mean sea level pressure, daily mean wind speed, daily mean relative humidity, and radiation level.

To investigate the complex associations between mortality rates, ARI incidence, and weather variability, we employ a distributed lag non-linear model (DLNM) with multiple cross-bases. This approach facilitates the adjustment for confounding meteorological variables and provides a better understanding of their impact as fluctuating confounders. From these refined models, we derived the fraction of mortality attributable to ARIs and low temperatures, offering a quantification of their impact on excess mortality in the cold season. Additionally, we analyse changes in seasonal patterns of mortality according to the meteorological and epidemiological characteristics and assess temporal associations between air temperature and mortality in summer considering factors like intensity of ARI outbreaks and the mean winter temperature in the previous cold season. Our results contribute to better understanding of the links between temperature variability, respiratory infection dynamics and the seasonal variations in mortality.

How to cite: Borisova, E., Urban, A., Hanzlíková, H., Plavcová, E., Kyselý, J., Kynčl, J., and Ballester, J.: The compound effect of acute respiratory infections and temperatures on mortality in the Czech Republic, 1982–2019, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-9026, https://doi.org/10.5194/egusphere-egu24-9026, 2024.

In recent years, the escalation of climate change has led to a discernible increase in occurrences of extreme heat events, subjecting individuals to heightened and more recurrent heat stress. This phenomenon is particularly pronounced among socioeconomically disadvantaged populations, the elderly, and other vulnerable groups. Prolonged exposure to elevated temperatures has been identified as a significant factor contributing to adverse health outcomes within these demographics. Concurrently, alterations in land use and land cover (LULC) exert a notable influence on the thermal environment. While rural areas experience relatively modest changes in land use compared to urban counterparts, the expansion of non-vegetative zones in these regions still engenders temperature fluctuations. 

This study endeavors to ascertain the spatial-temporal characteristics of the thermal environment and heat-related comfort levels in aging rural areas, a facet that has been largely overlooked in prior research. The focal area of investigation is Yunlin County in Taiwan, a pivotal agricultural region characterized by a noteworthy aging demographic. The research methodology involves an analysis of the spatial distribution of meteorological parameters to discern the thermal landscape in Yunlin. Additionally, a thermal comfort index, Physiological Equivalent Temperature (PET), is employed to gauge the impact of spatial characteristics on human comfortability within this aging rural enclave. LULC data are acquired through supervised classification of remote sensing images. The computation of PET and associated parameters in the Yunlin region is facilitated by the Python package, Pythermalcomfort. 

The analysis reveals thermal comfort and environmental conditions for the elderly in Yunlin, identifying specific hotspots and periods characterized by elevated PET. Notably, the influence of LULC on thermal conditions is discerned, with built-up and bare soil areas exhibiting elevated temperatures. Furthermore, projections of future scenarios indicate an escalating trend of discomfort for the elderly, manifesting in a heightened frequency of PET exceeding 42°C. The study also undertakes an exposure analysis to identify individuals susceptible to heat injuries under diverse scenarios. Ultimately, a set of preventive measures and recommendations are delineated, encompassing the augmentation of green spaces, provision of adequate shading, identification of cooler areas during heatwaves, and scheduling physically demanding activities during cooler hours (between 4:00 and 7:00). In summation, this research utilizes PET to pinpoint high-risk periods and locations for aging rural areas in Yunlin during the summer, with the overarching aim of furnishing valuable insights into heat-related risks.

How to cite: Juang, J.-Y.: Examining the temporospatial patterns of thermal risk for the elderly in an aging rural area in Taiwan, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-9262, https://doi.org/10.5194/egusphere-egu24-9262, 2024.

EGU24-11600 | ECS | Orals | NH1.1

Assessment of heat exposure under a high-end climate change scenario and projected population scenario around Africa’s Lake Victoria region. 

Delphine Ramon, Clare Heaviside, Oscar Brousse, Charles Simpson, Irene Amuron, Eddie Jjemba, Jonas Van de Walle, Wim Thiery, and Nicole P.M. van Lipzig

Recent global temperature increases and extreme heat events have raised concerns about their impact on health. The area surrounding Lake Victoria, accommodating over 45 million people and ranking among Africa's most densely populated regions, faces rapid population growth and urbanization, set to double its population by 2050 compared to 2022 for most countries in the region. Global-scale projections indicate a potential amplification in heat stress, reaching levels up to 200 times the current rates under high-end scenarios, with the Democratic Republic of the Congo and Uganda facing the most pronounced impacts. Children born in this area in 2020 may face about 1.4 times more heatwaves than their counterparts elsewhere. The combination of population growth and intensifying heat renders the region around the Lake Victoria particularly susceptible to future heat stress.

This research investigates the impacts of climate and population changes on heat exposure and heat stress in the region surrounding Lake Victoria. We analyze how dangerous heat stress by the end-of-the century could change under the ensemble mean climate change signal of the high-end SSP5-8.5 climate change scenario compared to the recent past. Furthermore, we evaluate to what extent and where the population could be affected by dangerous heat stress by these changes. Climate model simulations performed with the COSMO-CLM regional climate model at 0.025° are used, forced with ERA-5 data, applying a pseudo global warming approach for the end-of-century run. Dangerous heat stress is assessed based on categories of heat index and humidex heat stress metrics.

Results indicate a substantial rise in dangerous heat stress across the region. By the 2080s, up to 122 million people (i.e. 44% of the projected population) may experience dangerous heat stress for over 5% of the year (i.e. ~18 days), in contrast to an estimated 1 million people (i.e. 1% of the population) in 2010. Moreover, around 28% of the population (i.e. ~78 million people) might face such dangerous heat for 15% of the time (i.e. ~55 days) by the 2080s. The inhabitants most exposed to dangerous heat stress are notably clustered along the northern shores of Lake Victoria and the southern region, including their urban areas. These findings emphasize the urgent need to address the escalating threat of dangerous heat stress in this region.

How to cite: Ramon, D., Heaviside, C., Brousse, O., Simpson, C., Amuron, I., Jjemba, E., Van de Walle, J., Thiery, W., and van Lipzig, N. P. M.: Assessment of heat exposure under a high-end climate change scenario and projected population scenario around Africa’s Lake Victoria region., EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-11600, https://doi.org/10.5194/egusphere-egu24-11600, 2024.

EGU24-14155 * | ECS | Orals | NH1.1 | Highlight

Mortality burden in 35 European countries attributed to anthropogenic warming during the record-breaking summer of 2022  

Thessa M Beck, Dominik L Schumacher, Ana M Vicedo Cabrera, Sonia I Seneviratne, Hicham Achebak, and Joan Ballester

More than 61,000 heat-related deaths were associated with the record-breaking temperatures in Europe during the summer of 2022. In this study, we quantify the number of heat-related deaths that would have been avoided in the absence of anthropogenic warming.

For this study, we utilize epidemiological models calibrated for the period 2015–2019 to estimate the heat-related mortality burden in the summer of 2022 for the factual and counterfactual scenario. We derive a counterfactual scenario by removing the regional summer mean warming that arises in response to rising global mean temperatures from the factual temperatures. We use ERA5-Land temperature data and mortality counts from the Eurostat database to estimate the heat-related deaths across 823 distinct administrative regions spanning 35 European countries. 

At 1.15 °C of global warming since pre-industrial times, we obtain a population-weighted median increase over all regions in Europe of more than 2 °C in summer mean temperatures, with the Mediterranean regions being most affected by the increase. By comparing the factual and counterfactual heat-related mortality, we estimate that approx. 70% [95th CI 53.33%– 82.17%] of the total heat-related deaths would not have occurred without anthropogenic warming. Southern Europe has been the most affected by dangerous heat and consequently features the highest number of heat-related deaths attributable to climate change [64.19% of the climate change-attributable deaths]. In relative terms, however, the impact of anthropogenic warming is strongest in Central Europe where approx. 78% of the heat-related deaths are attributable to anthropogenic warming.

How to cite: Beck, T. M., Schumacher, D. L., Vicedo Cabrera, A. M., Seneviratne, S. I., Achebak, H., and Ballester, J.: Mortality burden in 35 European countries attributed to anthropogenic warming during the record-breaking summer of 2022 , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-14155, https://doi.org/10.5194/egusphere-egu24-14155, 2024.

EGU24-14273 | ECS | Posters on site | NH1.1

Heat, humidity and health impacts: How causal diagrams can help tell the complex story 

Sidharth Sivaraj, Jonathan Buzan, Olivia Romppainen-Martius, and Ana M. Vicedo-Cabrera

The global health burden associated with exposure to heat is a grave concern and is projected to further increase under global warming. While physiological studies have demonstrated the role of humidity alongside temperature in exacerbating heat stress for humans, epidemiological findings remain conflicted to date. Understanding the intricate relationships between heat, humidity, and health outcomes are crucial for future adaptation and mitigation. This project introduces 'directed acyclic graphs' (DAGs) as causal models to elucidate the analytical complexity in observational epidemiological studies focusing on humid heat related health impacts. DAGs are employed to delineate implicit assumptions often overlooked in such studies, depicting humidity as a confounder, a mediator, or an effect modifier. The complexities arising from using composite heat-stress indices such as wet-bulb temperature, emphasizing the limitations induced in extracting individual effects of humidity are also portrayed through DAGs. Theoretical generalisations for regression models corresponding to each of the causal assumptions are also discussed. The goal of the study is not to prioritize one causal model, but to explicitly discuss the potential causal models suitable for representing associations between heat, humidity, and related health impacts. In the process, we highlight the implications of selecting one model over another. The project aims to inspire further quantitative studies on the topic and motivate researchers to explicitly characterize the assumptions underlying the analytical models with DAGs, facilitating accurate interpretations of the findings. This extends beyond analysing the role of humidity in heat-related health impacts, encompassing similarly complex research questions associated with other compound events.

How to cite: Sivaraj, S., Buzan, J., Romppainen-Martius, O., and Vicedo-Cabrera, A. M.: Heat, humidity and health impacts: How causal diagrams can help tell the complex story, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-14273, https://doi.org/10.5194/egusphere-egu24-14273, 2024.

EGU24-15322 | Orals | NH1.1 | Highlight

The Role of Rainfall in Humid Heat Extremes across the Global Tropics 

Lawrence Jackson, Cathryn Birch, Guillaume Chagnoud, John Marsham, and Christopher Taylor

Extreme humid heat poses a serious risk to human health, reducing the body’s ability to cool itself through sweating. The impact on humans will increase under climate change, particularly in tropical regions, such as the Indian subcontinent, that are highly populated and already hot and humid. Whilst there is a growing body of research on dry-bulb temperature extremes, there is limited understanding of the meteorological drivers of humid heat extremes, particularly the role of moisture transport, rainfall, and evaporation of moisture from the Earth’s surface.

In this study, we use ERA5 data to identify and analyse extreme humid heat events in the global tropics during 1993-2022. In particular, we focus on the relationship between rainfall and the occurrence of humid heat and use extremes in wet-bulb temperatures to define the humid heat events.

We find that rainfall is a key driver of humid heat extremes across much of the global tropics. In monsoon regions, dry-bulb temperature extremes typically occur in the pre-monsoon period whereas wet-bulb extremes occur more frequently during the monsoon season. The role of rainfall varies between humid heat events characterised by extremes in dry-bulb temperature versus those characterised by extremes in humidity. In much of the global tropics, rainfall followed by a few days of dry clear weather primes the surface and boundary layer climates for the initiation of humid heat events. These events typically have extremes in dry-bulb temperatures accompanied by what we characterise as a sufficiently high level of humidity. In arid regions, away from irrigated areas, rainfall is critical for the initiation of humid heat and frequently occurs locally on the first day of humid heat events. These events typically have extremes in humidity whereas dry-bulb temperatures are less likely to be extreme.

These findings are a step towards greater understanding of the meteorological drivers of humid heat extremes at the regional scale. They will be valuable in the evaluation of weather and climate models, will aid the use and interpretation of climate model projections, and ultimately inform the design of much needed early warning systems for humid heat extremes.

How to cite: Jackson, L., Birch, C., Chagnoud, G., Marsham, J., and Taylor, C.: The Role of Rainfall in Humid Heat Extremes across the Global Tropics, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-15322, https://doi.org/10.5194/egusphere-egu24-15322, 2024.

EGU24-15748 | ECS | Posters virtual | NH1.1

Building a heat wave anticipatory action plan for the Sahelian city of Ouagadougou 

Rachidate Wend-layolsda D. Somdakouma, Bakari Sankara, Ilyassa Sawadogo, Emmanuel Poan, Dorothy Heinrich, and Kiswendsida Guigma

The West African Sahel is one of the hottest regions of the globe, but as in other African regions, heat waves largely remain a neglected hazard from both a preparedness and a research perspective. They are deadly and underestimated. Yet, there is a large potential to decrease their impacts through early warning and anticipatory action, particularly because they are so predictable. In this research, we present a case study of the development of a heat wave early action protocol for the Sahelian city of Ouagadougou, Burkina Faso. The work was led by the Burkina Faso Red Cross (BFRC), with support from the Red Cross Red Crescent Climate Centre and partners. Two research questions guided the process: (i) are heat waves a concern to various actors in the city of Ouagadougou and if so, why? (ii) What are the impacts of heatwaves in Ouagadougou and what can be done to mitigate them?

In collaboration with the National Meteorological Agency (ANAM), the first ever definition for heat waves in the city of Ouagadougou was developed based on statistical analyses of meteorological records and reports of historical severe events. Heat waves were thus defined as spells of three or more days where the daytime and/or night-time temperature exceeds the 90th percentile of the distribution of the hottest month of the year i.e. April in Ouagadougou. This definition has now been incorporated by ANAM into its early warning platform and will automatically alert BFRC when needed.

Faced with the lack of quantitative impact data about heatwave impacts in the Sahel region, a qualitative cross-sectional study based on focus group discussions, and key informant interviews, and a review of grey literature (especially media reports) were used. The target audience population for this study was experts/practitioners from various disaster management sectors including health, water, energy and municipal officers, vulnerable social groups and vulnerable communities living in slums.

Experts, practitioners, vulnerable social groups and communities all stressed that extreme heat is a major concern in Ouagadougou, has become more severe in recent years and should be better tackled at the individual, community and national levels. The elderly, children under the age of five and people suffering from chronic diseases such as albinism, leprosy and other conditions were frequently mentioned by interviewees as the most vulnerable to extreme heat. From a geographical perspective,

slums, which are generally located in the periphery of the city, were identified as the most vulnerable neighbourhoods, mainly because of the poverty rates and the lack of infrastructures. The most recurrent impacts found across the study were around insufficient water and power availability, increase in some diseases, thermal discomfort and subsequent social and economic impacts. Among the suggested solutions, BFRC and their partners have decided to prioritise in the Early Action Protocol: early warning dissemination, potable water distribution, medical monitoring of chronically ill people and cash distribution.

Keywords: heat waves, Ouagadougou, slums, anticipatory action, Red Cross

How to cite: Somdakouma, R. W.-D., Sankara, B., Sawadogo, I., Poan, E., Heinrich, D., and Guigma, K.: Building a heat wave anticipatory action plan for the Sahelian city of Ouagadougou, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-15748, https://doi.org/10.5194/egusphere-egu24-15748, 2024.

EGU24-17242 | ECS | Posters on site | NH1.1 | Highlight

Limits to adaptation strategies for heat impacts on rural labors filtered through stabilized climate mitigation scenarios.  

Jonathan Buzan, Yona Silvy, Fabrice Larcoix, Friedrich Burger, Jens Terhaar, Thomas Frölicher, Édouard Davin, and Fortunat Joos

With global warming, increased heat stress will substantially impact the rural labor force. Understanding and quantifying this impact is difficult, especially due to regional differences: Does the temperature increase? Is there more solar exposure? Does humidity respond non-linearly with respect to temperature changes? Furthermore, humans are resourceful, and local environments could provide adaptation methods to decrease heat impacts.

 

A policy-relevant assessment in the context of the Paris Agreement is even more difficult with existing CMIP-type simulations with prescribed greenhouse gas trajectories that lead to a different and often non-stable warming for each model. To resolve the impacts climate mitigation and adaptation on heat stress on warming levels with specific relevance for the Paris Agreement, we use the Community Earth System Model (CESM2) driven by emissions from the Adaptive Emissions Reduction Approach (AERA) to generate climate mitigation scenarios stabilized at 1.5°C, 2.0°C and 3.0°C of global warming.

 

One form of adaptation to heat stress impacts is to use the local environment for cooling. Within CESM2, we compare the direct and indirect exposure to solar radiation within the vegetated canopy as an inexpensive form adaptation. To diagnose the heat stress conditions we use the International Organization for Standardization (ISO) 7243, the Wet Bulb Globe Temperature (WBGT), realized by first principles representation of the globe, dry bulb, and natural wet bulb thermometers utilizing CESM2’s temperature, humidity, winds, and radiation. The WBGT values are transformed into labor capacity using standardized algorithms (e.g. NIOSH or Lancet) and the above canopy (no adaptation) and below canopy (with adaptation) labor capacity are directly compared to each other.

 

We show that the potential to adapt by using the local environment for cooling is not uniform across regions. For example, evaluating the hottest seasonal period (defined as a local summer), at the 3.0°C mitigation scenario in equatorial Southeast Asia, adaptation can save up to 50% of total labor capacity losses. However, in northern South Asia, adaptation saves only 10% of the seasonal labor capacity losses. These results demonstrate that rural laborers in some locations may have limited capacity to adapt to differing global mitigation strategies and may require mechanical cooling or other expensive forms of adaptation.

How to cite: Buzan, J., Silvy, Y., Larcoix, F., Burger, F., Terhaar, J., Frölicher, T., Davin, É., and Joos, F.: Limits to adaptation strategies for heat impacts on rural labors filtered through stabilized climate mitigation scenarios. , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-17242, https://doi.org/10.5194/egusphere-egu24-17242, 2024.

EGU24-17545 | Orals | NH1.1 | Highlight

Development of a deep learning based system for heatwave detection using seasonal forecast data 

Fatemeh Heidari, Qing lin, Yanet Díaz Esteban, Edgar Espitia Sarmiento1, and Elena Xoplaki

Heatwaves have been widely studied in recent years because of their major impact on human health, mortality, ecosystems, agriculture, and the economy. Globally, heatwaves are becoming more severe, longer, and recurrent with global temperature rise. Therefore, the study of heat waves and the development of an early warning system for prediction of regional heatwaves help climate preparedness and decision-making. In this research, we propose a heatwave prediction algorithm based on a deep learning model, a convolutional neural network (CNN). This CNN model is trained with reanalysis data ERA5 and real heatwave events from EMO observation data for years from 1993 to 2021. We illustrate the relationship between the patterns in geopotential height at 500 hpa (GPH), sea surface temperature (SST), and the real heatwaves that happened in the last 20 years. This study employs the hindcast data from SEAS5.1 with 25 ensemble members, available at C3S. GPH and SST from observation data are input to the model and the heatwave magnitude at every single grid point is the output. The heatwave is defined as a period of three or more consecutive hot days and nights when the daily maximum and minimum temperature (TX/TN) exceeds the long‐term (1993–2022) daily 90th percentile. For estimating the heat wave magnitude we accumulated TX exceedance the local 90th percentile for all heat wave days over a user-defined interval (monthly, seasonal, etc.) as in Zampieri et al. (2017), Toreti et al. (2019). The results show the CNN model using atmospheric circulation fields (SST and GPH) with adjusted parameters is able to forecast extreme events in Europe, and it can potentially enhance the AI-based early warning systems for extreme weather.

Zampieri, M., Ceglar, A., Dentener, F., and Toreti, A. (2017). Wheat yield loss attributable to heat waves, drought and water excess at the global, national and subnational scales. Environmental Research Letters, 12 (6), 064008. doi:10.1088/1748-9326/aa723b

Toreti, A., Cronie, O., and Zampieri, M. (2019). Concurrent climate extremes in the key wheat producing regions of the world. Scientific Reports, 9(1), 5493. doi:10.1038/s41598-019-41932-5

How to cite: Heidari, F., lin, Q., Díaz Esteban, Y., Espitia Sarmiento1, E., and Xoplaki, E.: Development of a deep learning based system for heatwave detection using seasonal forecast data, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-17545, https://doi.org/10.5194/egusphere-egu24-17545, 2024.

EGU24-18354 | ECS | Orals | NH1.1 | Highlight

Projecting heat-related excess mortality under climate change scenarios in Europe: A multi-domain analysis 

Hélder Relvas, Silvia Coelho, Vera Rodrigues, Ana Isabel Miranda, Myriam Lopes, Daniel Graça, Bruno Augusto, João Basso, and Joana Ferreira

Recent studies have highlighted the diverse health consequences associated with climate change. However, a comprehensive evaluation of the specific susceptibilities of individuals and cities to these changes remains lacking. Addressing this gap, our study offers insights into potential excess mortality risks attributable to heat-related events under various climate change scenarios across diverse European regions, within the framework of the European Project DISTENDER.

The primary objective of this research is to assess the potential impact of climate change scenarios, specifically Shared Socioeconomic Pathways (SSPs) SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5, on heat-related excess mortality in a range of European locations. Focusing on Austria, the EURAF domain (encompassing parts of Portugal and Spain), a region in the Netherlands, the metropolitan area of Turin (Italy), and the urban area of Guimarães (Portugal), our investigation spans varied socio-geographic domains.

By employing local-specific relative risk functions and daily average temperature data spanning 2015 to 2049, derived through statistical downscaling from CanESM5, EC-EARTH3, and MPI-ESM1-2-HR global climate models, our analysis encompasses resolutions ranging from 9000 to 500 meters, depending on the specific domain. This multidomain approach allows for capturing localized variations in climate impacts with high spatial resolution.

The significance of our findings lies in their contribution to informing adaptive strategies, public health policies, and urban planning efforts aimed at mitigating the effects of climate change on vulnerable populations. Preliminary results indicate a distinct upward trend in heat-related excess mortality over the years, with the highest values observed for SSP5-8.5. Furthermore, there exists considerable variability among climate models.

Acknowledgements:

The authors would also like to acknowledge the support of CESAM (UIDP/50017/2020 + UIDB/50017/2020 + LA/P/0094/2020) and C2TN (UIDB/04349/2020). Thanks are due to the DISTENDER Project (Grant agreement ID: 101056836). Thanks are due to FCT/MCTES for the contract granted to Helder Relvas (10.54499/2021.00185.CEECIND/CP1659/CT0026).

How to cite: Relvas, H., Coelho, S., Rodrigues, V., Isabel Miranda, A., Lopes, M., Graça, D., Augusto, B., Basso, J., and Ferreira, J.: Projecting heat-related excess mortality under climate change scenarios in Europe: A multi-domain analysis, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-18354, https://doi.org/10.5194/egusphere-egu24-18354, 2024.

EGU24-18980 | Posters on site | NH1.1

So, will you live an unprecedented life? 

Wim Thiery

Under a continued increase in global warming, extreme events such as heatwaves will further rise in frequency, intensity, and duration over the next decades. Climate change impact studies routinely assess hazard and exposure change across discrete time windows, but thereby ignore how vulnerability and climate risk evolve across a person’s lifetime. Conversely, demographic research has a long tradition of assessing population processes and vulnerability from a cohort perspective, but generally neglects climate impacts. 

In the soon-to-start ERC CoG project LACRIMA (LAgrangian Climate Risk and Impact Attribution), we will develop novel concepts and methodologies to express climate change impacts and risk from a cohort perspective. More specifically, the project pursues to (i) to reconstruct two iconic climate change impacts on people around the world using machine learning (heat-related mortality and burned area), (ii) to uncover age-specific vulnerability to climate extremes including heatwaves, wildfires, river floods, droughts, tropical cyclones, and crop failures, (iii) to detect and attribute changes in lifetime extreme event exposure and climate impacts on mortality across generations and regions, (iv) to quantify how these attributable cohort impacts change country-level life expectancy around the world under a range of warming scenarios, and finally (v) to project how lifetime exposure to extreme events including compound events may trigger irreversible impacts under scenarios of temporary overshooting of long-term warming targets.

By bridging physical climate science, demography, and planetary health, LACRIMA will comprehensively identify whether and where people will live an unprecedented life in terms of climate impacts, and how mitigation choices can alter the climate change burden on current young generations around the world

How to cite: Thiery, W.: So, will you live an unprecedented life?, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-18980, https://doi.org/10.5194/egusphere-egu24-18980, 2024.

EGU24-19866 | ECS | Orals | NH1.1 | Highlight

Developing a proof-of-concept within Destination Earth for heat-stress adaptation under climate change scenarios, with a special focus on public health management 

Joshua Lizundia-Loiola, Nieves Peña, Efren Feliu, Jorge Paz Jiménez, Niels Souverijns, Dirk Lauwaet, and Filip Lefebre

According to the World Health Organization, between 2000 and 2016, the number of people exposed to heat waves increased by around 125 million. In Europe in 2003, 70.000 people died as a result of the June-August events and in Spain, that summer produced a total of 6.534 deaths.  2022 summer was even worst and its record-breaking heat caused 61.672 deaths in Europe, 11.324 of them in Spain, according to Ballester et al. (2023) [1].

The most worrying thing is not that all these episodes confirm the global warming, but that what is coming is even worse. If greenhouse gas emissions are not significantly curtailed, extreme temperatures and specifically heatwaves will become more frequent, more intense and longer every year that coupled with urban population growth and the trend towards an ageing population will produce devastating impacts on human health especially on vulnerable populations (see  https://www.eea.europa.eu/publications/europes-changing-climate-hazards-1/heat-and-cold/heat-and-cold-extreme-heat). Impacts will depend on local factors related with exposure, vulnerability to climate-related stresses and the capacity to cope with, so well-prepared health systems and well-suited adaptation measures at different levels are essential to protect populations, limit adverse impacts of heat and therefore reduce the magnitude of their risks.

Current information and heat simulation models are limited in time and space due to the high computational costs, so sometimes make it too complex for multiple stakeholders to have a good understanding of heat-stress assessment. This situation leads to ambiguity among stakeholders when implementing adaptation measures.  The idea of this ongoing work is to use Destination Earth’s Climate Adaptation digital twin to develop an interactive tool that will support decision-makers in the assessment of different adaptation options for heat stress adaptation under climate change scenarios, with a special focus on public health management. The tool will allow to have a better understanding of heat-stress assessment by simulating different type of climate change scenarios and, hence, identifying hotspot areas, and high-risk populations and locating opportunities to incorporate solutions to reduce impacts.

The architecture of DestinE provides a unique opportunity to develop an operational environment in which different but interconnected components give guidance and advice to decision makers in their process of designing adaptation pathways. From the data provided by Destination Earth Data Lake, it will be developed a proof-of-concept over the Basque region based on data-driven statistical models, physically based simulations, cost-impact analysis and algorithms. The demonstrator will also include a web-based graphical interface providing easy and accessible dashboards to support decision-makers in the assessment of adaptation options for heat stress adaptation under different climate change scenarios.
Key words: Destination Earth, Climate Adaptation digital Twin, heat-stress, adaptation, health, Copernicus, Europe. 


1. Ballester, J., Quijal-Zamorano, M., Méndez Turrubiates, R.F. et al. Heat-related mortality in Europe during the summer of 2022. Nat Med 29, 1857–1866 (2023). https://doi.org/10.1038/s41591-023-02419-z

How to cite: Lizundia-Loiola, J., Peña, N., Feliu, E., Paz Jiménez, J., Souverijns, N., Lauwaet, D., and Lefebre, F.: Developing a proof-of-concept within Destination Earth for heat-stress adaptation under climate change scenarios, with a special focus on public health management, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-19866, https://doi.org/10.5194/egusphere-egu24-19866, 2024.

EGU24-20179 | Orals | NH1.1

Heat waves and health impacts in the northern part of Senegal: implementation of an early warning system to support Health National Adaptation Plan (HNAP) 

Ibrahima Sy, Birane Cissé, Babacar Ndao, Mory Touré, Abdoul Aziz Diouf, Mamadou Adama Sarr, and Ousmane Ndiaye

The Sahelian zone of Senegal experienced heat waves in the previous decades, such as 2013, 2016 and 2018 that were characterised by temperatures exceeding 45°C for up to 3 successive days. The health impacts of these heat waves are not yet analysed in Senegal although their negative efects have been shown in many countries. This study analyses the health impacts of observed extreme temperatures in the Sahelian zone of the country, focusing on morbidity and mortality by com[1]bining data from station observation, climate model projections, and household survey to investigate heat wave detection, occurrence of climate-sensitive diseases and risk factors for exposure. To do this, a set of climatic (temperatures) and health (morbidity, mortality) data were collected for the months of April, May and June from 2009 to 2019. These data have been completed with 1246 households’ surveys on risk factor exposure. Statistical methods were used to carry out univariate and bivariate analyses while cartographic techniques allowed mapping of the main climatic and health indicators. The results show an increase in temperatures compared to seasonal normal for the 1971–2000 reference period with threshold exceed[1]ances of the 90th percentiles (42°C) for the maxima and (27°C) the minima and higher temperatures during the months of May and June. From health perspective, it was noted an increase in cases of consultation in health facilities as well as a rise in declared morbidity by households especially in the departments of Kanel (17.7%), Ranérou (16.1 %), Matam (13.7%) and Bakel (13.7%). The heat waves of May 2013 were also associated with cases of death with a reported mortality (observed by medical staf) of 12.4% unequally distributed according to the departments with a higher number of deaths in Matam (25, 2%) and in Bakel (23.5%) than in Podor (8.4%) and Kanel (0.8%). The morbidity and mortality distribution according to gender shows that women (57%) were more afected than men (43%). These health risks have been associated with a number of factors including age, access to drinkable water, type of fuel, type of housing and construction materials, existence of fan and an air conditioner, and health history.The heat wave recurrence has led to a frequency in certain diseases sensitive to rising temperatures, which is increasingly a public health issue in the Sahelian zone of Senegal. The main scientific evidence and findings generated from this research initiative support the adaptation options of health national adaptation plan (HNAP) with the implementation of an early warning system for local communities and health system workers.

Keywords: Climate · Temperature · Heat waves · Diseases · Health risks, adaptation, Senegal

How to cite: Sy, I., Cissé, B., Ndao, B., Touré, M., Diouf, A. A., Sarr, M. A., and Ndiaye, O.: Heat waves and health impacts in the northern part of Senegal: implementation of an early warning system to support Health National Adaptation Plan (HNAP), EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-20179, https://doi.org/10.5194/egusphere-egu24-20179, 2024.

Employing seasonal forecasting in the domain of impact and risk assessment is particularly beneficial. It facilitates early warning systems and proactive adaptation strategies, which are essential for minimizing the adverse effects of heat waves. This proactive approach is crucial for public health, urban planning, and disaster management, where timely information can significantly alter response strategies and mitigation measures.

This study, we underscore the value of integrating high-quality climate data with impact assessment models. It demonstrates how bridging the gap between climate science and practical risk management can lead to more effective and informed decision-making processes in the face of climate change challenges.

In our study, we integrate Copernicus seasonal forecasting data with the CLIMADA platform, adopting a forward-looking approach to assess the potential impacts of heat waves on populations. This integration involved developing a bespoke pipeline to seamlessly bridge the gap between raw forecasting data from Copernicus and the analytical capabilities of CLIMADA, an ETH Zurich-developed tool for climate impact and risk assessment. The focus is not only on facilitating data integration but also on automating the processing and communication of results.

One significant aspect of this work is managing extensive datasets containing multiple simulations. To efficiently handle this, we implemented an automated system for data extraction, transformation, and loading. This is crucial in maintaining the integrity and usability of the data within CLIMADA's impact modeling framework. Part of this process also entailed resolving spatial and temporal alignment issues, a step essential to ensuring the ability of the seasonal forecasting data to reflect the potential heat wave impacts. Our approach aim to streamline the complexities of large-scale climate data, enhancing the precision and effectiveness of our assessments.

Building a pipeline that links these probabilistic forecasting with impact assessment tools has multiple benefits. It enhances the capability to identify critical data needs and model improvements, thus fostering a feedback loop that drives data and model refinement. Furthermore, it contributes to laying the groundwork for the effective use of the next generation of seasonal forecast data, potentially transforming how we prepare for and adapt to climate risks.

How to cite: Araya, D., Kropf, C. M., and Bresch, D. N.: Integrating Copernicus Seasonal Forecasting Data with CLIMADA for Heat Wave Impact Analysis: Challenges and Solutions in Pipeline Development, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-20478, https://doi.org/10.5194/egusphere-egu24-20478, 2024.

EGU24-108 | Orals | NH1.2

Reconstructing Historical Flood Events: A Monte Carlo-Based Uncertainty Approach 

Ramtin Sabeti, Thomas R. Kjeldsen, and Ioanna Stamataki

Reconstructing historical flood events can offer critical insight into past hydrological responses to extreme weather, informing contemporary flood risk management and infrastructure design. This study employs reverse engineering, based on historical data such as recorded rainfall, flood marks, visual records, and eyewitness accounts to reconstruct a flood event. Historical data was collected by the team during a workshop with the local community. The approach involves hydrological (HEC-HMS) and hydraulic (HEC-RAS) models to simulate the flood event. The July 1968 UK storm, remarkable for record rainfall reaching 175 millimetres within 18 hours, caused extensive devastation in south-west England. This study focuses on reconstructing the 1968 flash flood on the River Chew, notably the peak hydrograph in the village of Pensford. A Monte Carlo simulation approach is used in conjunction with the HEC-HMS and HEC-RAS models to produce a range of potential input hydrographs with uncertainty input parameters (primarily event rainfall and Manning’s roughness) that match the historical evidence.  In particular, the Monte Carlo approach is implemented using a series of Python scripts enabling multiple HEC-RAS simulations to be conducted and the results synthesised in the form of an uncertainty analysis of key parameters such as peak flow. 

How to cite: Sabeti, R., R. Kjeldsen, T., and Stamataki, I.: Reconstructing Historical Flood Events: A Monte Carlo-Based Uncertainty Approach, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-108, https://doi.org/10.5194/egusphere-egu24-108, 2024.

EGU24-395 | ECS | Orals | NH1.2

Beyond Extreme Temperature: Spatiotemporal Analysis of Humid Heat Stress  

Jency Maria Sojan and Jayaraman Srinivasan

Extreme humid heat stress presents significant challenges to human health and productivity. Traditional heat action plans formulated to tackle dry heat stress are insufficient to address the complexities associated with humid heat stress. Furthermore, there is limited quantitative evidence on the evolving patterns of humid heat stress under changing climate. This study investigates the spatiotemporal trends of extreme heat stress across the Global South from 1964 to 2023, distinguishing between dry and humid heat, using high-resolution ERA5 reanalysis hourly data and the Heat Index (HI).

Notably, South Asia and the Middle East experience the highest frequency of extremely humid heat stress. Specific regions in peninsular South Asia have extremely humid heat stress hours from May to June due to persistent high humidity levels. In contrast, western regions of South Asia encounter extreme dry heat stress preceding the monsoon season, followed by a transition to humid heat stress immediately after the onset of the monsoon. The temporal analysis reveals a more rapid increase in the occurrence of extremely humid heat stress compared to that of dry heat stress from May to July over the past 60 years. This underscores the evolving nature of heat stress and the intensification of humid conditions compared to dry ones.

In conclusion, this study advocates for a shift from exclusively addressing dry stress to a comprehensive approach that accounts for the diverse impacts of humid heat stress, particularly on vulnerable populations. This understanding is critical for policymakers to formulate adaptive strategies tailored to the changing landscape of extreme heat stress. 

How to cite: Sojan, J. M. and Srinivasan, J.: Beyond Extreme Temperature: Spatiotemporal Analysis of Humid Heat Stress , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-395, https://doi.org/10.5194/egusphere-egu24-395, 2024.

EGU24-737 | ECS | Posters on site | NH1.2

A database for the outer sizes of tropical cyclones over the Middle Americas 

Adolfo Perez Estrada and Christian Domínguez Sarmiento

Tropical cyclones (TCs) pose a constant threat to populations residing within tropical and subtropical regions. The direct impacts of TCs, such as intense surface winds, storm surge, and heavy precipitation near the center, are well known. However, the indirect effects (e.g, disruption of the upper-level mean wind flow resulting in continental convection, and precipitation associated with cloud bands away from the cyclone's center), are often underestimated.

It is crucial to comprehensively characterize the size of TCs, taking into account both direct and indirect effects, as this new size definition  could improve early warning systems. While various studies employ different parameterizations to describe cyclone size, many of them overlook precipitation. To address this gap, the ROCLOUD technique was developed using  a Python-based algorithm. This algorithm utilizes information on the TC’s position, the extent of cloud bands, and the size of the wind field to define an outersize for TCs located over the oceanic basins in the Middle Americas. In addition to ROCLOUD, we also developed a technique that uses the spatial distribution of TC rainfall to define the outer TC size, named as RPB algorithm. This technique  utilizes a threshold of 2.5 mm in the precipitation satellite products for depicting TC rainfall. Our dual approach provides a comprehensive understanding of TC  sizes, considering the presence of rainfall that can lead to disasters.

Our database shows  external sizes and positions of TCs (recorded every 6 hours) over the North Atlantic (NA) and Eastern Pacific (EP) Oceans during the 2000-2020 period. We got 191 and 336 positions  from the NA and EP basins, respectively. Statistical analysis reveals the coverage of oceanic basins and highlights their differences. We conclude that ROCLOUD offers an operational approximation of the external size of TCs, especially in situations where storms pose a threat to continental regions. The study discusses the utility of both versions of ROCLOUD and RPB for  the Tropical Cyclone Early Warning System over Mexico (EWS-TC), shedding light on the impact of TC sizes that can lead to disasters.

How to cite: Perez Estrada, A. and Domínguez Sarmiento, C.: A database for the outer sizes of tropical cyclones over the Middle Americas, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-737, https://doi.org/10.5194/egusphere-egu24-737, 2024.

EGU24-1525 | ECS | Orals | NH1.2

Objective Identification of Tropical Cyclones with Severe Storm Surge Potential for the North-west Pacific 

Xiaoqi Zhang, Gregor C Leckebusch, and Kelvin S Ng

Storm surges caused by tropical cyclones can significantly impact on coastal areas in East Asia, including megacities e.g., in China. To inform effective adaptation and mitigation planning, a robust storm surge hazard assessment is essential. Unfortunately, the real frequency-intensity distribution of relevant storm-surge levels can only be estimated with large uncertainly based on limited historical observations.

This study demonstrates the successful development of a two-step, objective and automated identification and selection approach of storm-surge relevant TCs for large model data sets where no ground truth verification is possible. In our approach, we combine for the first time two established identification and tracking tools originally developed for extra-tropical cyclones and storms and apply these to identify tropical cyclones. In the first step, we adapted the widely used Murray & Simmonds (1991) University of Melbourne tracking scheme (MS-Track) to the specific conditions of TC tracking in the North-west Pacific. In the second step, we apply the windstorm tracking tool WiTRACK to TC-induced severe wind fields to provide and attach the potential storm-surge relevant information in addition to just the core track provided by the MS-Track.

By validating our results with ERA5 reanalysis data and IBTrACS, we show that our method is simple yet has a well comparable performance in detecting and assessing relevant TC events than more complex tracking approaches. Based on this performance this approach is well-designed and specifically intended to specific applications in CAT modelling approaches, e.g. for the creation of physically consistent event sets for storm surges.

How to cite: Zhang, X., Leckebusch, G. C., and Ng, K. S.: Objective Identification of Tropical Cyclones with Severe Storm Surge Potential for the North-west Pacific, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-1525, https://doi.org/10.5194/egusphere-egu24-1525, 2024.

The physical mechanisms underlying extreme precipitation events linked to atmospheric moisture transport (IVT-P) are investigated in this study. It investigates changes in synoptic-scale weather patterns over the Indian subcontinent and identifies regions where IVT influences extreme precipitation. The study discovers a strong relationship between daily IVT and precipitation over the core monsoon region and the complex terrains of the Western Ghats and Himalayas. Event Coincidence Analysis reveals that extreme IVT can be used to forecast extreme precipitation in these regions. The dynamic component of moisture transport has a strong influence on daily and extreme precipitation over complex terrains. In contrast, the thermodynamic component has an influence on precipitation over regions with an abundance of water vapor and weak horizontal winds. The study also identifies synoptic features and moisture transport ahead of IVT-P events and finds intense low-pressure anomalies, the transition from ridge to trough patterns, and intense 700 hPa relative humidity in the specified regions. Overall, the study provides insights into the physical mechanisms underlying IVT-linked extreme precipitation events.

How to cite: Raghuvanshi, A. S. and Agarwal, A.: Deciphering the connections between extreme precipitation events, atmospheric moisture transport, and associated synoptic features over India, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-1625, https://doi.org/10.5194/egusphere-egu24-1625, 2024.

EGU24-1631 | ECS | Orals | NH1.2

Shift of soil moisture-temperature coupling exacerbated 2022 compound hot-dry event in eastern China 

Yueyang Ni, Bo Qiu, Xin Miao, Lingfeng Li, Jiuyi Chen, Xiaohui Tian, Siwen Zhao, and Weidong Guo

Compound hot-dry events (CHDEs) are among the deadliest climate hazards and are occurring with increasing frequency under global warming. The Yangtze River Basin in China experienced a record-breaking CHDE in the summer of 2022, causing severe damage to human societies and ecosystems. Recent studies have emphasized the role of atmospheric circulation anomalies in driving this event. However, the contribution of land–atmosphere feedback to the development of this event remains unclear. Here, we investigated the impacts of soil moisture-temperature coupling on the development of this concurrent heatwave and drought. We showed that large amounts of surface net radiation were partitioned to sensible heat instead of latent heat as the soil moisture-temperature coupling pattern shifted from energy-limited to water-limited under low soil moisture conditions, forming positive land–atmosphere feedback and leading to unprecedented hot extremes in August. The spatial heterogeneity of hot extremes was also largely modulated by the land–atmosphere coupling strength. Furthermore, enhanced land–atmosphere feedback has played an important role in intensifying CHDEs in this traditional humid region. This study improves the understanding of the development of CHDEs from three aspects, including timing, intensity, and spatial distribution, and enables more effective early warning of CHDEs.

How to cite: Ni, Y., Qiu, B., Miao, X., Li, L., Chen, J., Tian, X., Zhao, S., and Guo, W.: Shift of soil moisture-temperature coupling exacerbated 2022 compound hot-dry event in eastern China, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-1631, https://doi.org/10.5194/egusphere-egu24-1631, 2024.

EGU24-2445 | ECS | Posters virtual | NH1.2

A method of dynamic diagnosis for regional drought degree 

Ruxin Zhao, Hongquan Sun, and Lisong Xing

In view of the difficulty in quantifying the severity of regional drought, this study proposes a method that can quantify and dynamically diagnose the severity of regional drought events, and consider the cumulative superposition effect of drought in the process of spatial and temporal development and evolution. Starting from the site drought index, the method firstly establishes a regional drought index to determine whether drought occurs in the study area in the same month; secondly, it constructs a two-dimensional Copula joint probability model by counting the cumulative duration and cumulative severity of droughts; and finally, it uses the percentile method to classify the joint probability of two-dimensional cumulative drought characteristics into four degree levels of regional drought: light, moderate, severe, and extreme. In the study, the SPEI drought index from 1961 to 2022 was used as the basic data, and the drought centers of China, such as North China Plain, Yangtze River Basin, and Yunnan Province, were selected as the case validation zones, and the results showed that this method can effectively identify the historical drought events in the study area and dynamically diagnose the development process of severe and extreme droughts therein. 

How to cite: Zhao, R., Sun, H., and Xing, L.: A method of dynamic diagnosis for regional drought degree, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-2445, https://doi.org/10.5194/egusphere-egu24-2445, 2024.

        Using multi-source global station and grid monitoring data, FY-2H satellite, and ERA5 reanalysis data, the life history and precipitation characteristics of tropical cyclone "Freddy" as well as the causes of heavy precipitation in southern Mozambique were analyzed. The results show that "Freddy" had a lifespan of 35.5 days which made it the longest lived tropical cyclone in the world, as well as the widest latitude-crossing TC in the southern hemisphere. The extreme long life cycle of "Freddy" was related to favorable large-scale circulation conditions. The strong and sustained subtropical high pressure system made "Freddy" moving westward over the Southern Indian Ocean stably, without the opportunity to combine with the mid latitude trough or cold air which may cause the path turning, intensity weakening, or transformation. After the generation of "Freddy", more than 70% of its life time was over the sea, and the surrounding SST was generally abnormally high, which provided favorable conditions for the development or maintenance of TC intensity. Especially, the SST within the Mozambique Strait remained above 28 ℃, providing excellent underlying conditions for the enhancement of TC intensity, allowing "Freddy" to develop and strengthen rapidly twice after experiencing intensity weakening caused by landfall. The combined influence of favorable circulation conditions and warm sea surface temperature led to the extreme long life of "Freddy".

        "Freddy" made three landfall, bringing sustained heavy precipitation and severe floods to countries in Southeastern Africa. Especially in the southern part of Mozambique, precipitation had characteristics such as long duration, concentrated areas, and large accumulated amount. After landing in Mozambique, "Freddy" was located in a saddle field, leading to weakened steering airflow. Combined with high-level divergence and sustained transportation of warm and humid air by low-level jet, the large-scale circulation system provided favorable background conditions for the slow movement and maintenance of tropical cyclone. The development of low-level convergence and vorticity bands in lower troposphere, as well as strong and sustained water vapor transport, led to the persistence of heavy rainfall in Mozambique. The invasion of cold air induced the formation of a pseudo equivalent potential temperature high-gradient zone in southern Mozambique, and the cold air in the middle layer enhanced atmospheric instability, which was conducive to the development of convection. The southern part of Mozambique was continuously affected by several mesoscale convective systems (MCSs), which not only improved precipitation efficiency but also prolonged the duration of precipitation. The evolution of MCSs had obvious diurnal variation characteristics, with its rapid development and maturity stages almost concentrating in the afternoon to the earlier evening of local time. The increase in low-level wind speed promoted the enhancement of both water vapor and energy, and under the above conditions, the convergence of tropical cyclone wind direction and wind speed triggered the generation of MCSs continuously.

How to cite: Yang, S.: Analysis on the Characteristics of Extreme Long Life Cycle Tropical Cyclone "Freddy" and the Causes of Heavy Rainfall, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-2724, https://doi.org/10.5194/egusphere-egu24-2724, 2024.

Extreme temperature changes from one day to another, either associated with warming or cooling, can have a significant impact on health, environment, and society. Previous studies have quantified that such day-to-day temperature (DTDT) variations and extremes are typically more pronounced in mid-and high latitudes compared to tropical zones. However, the underlying physical processes and the relationship between extreme events and large-scale atmospheric circulation remain poorly understood. Here, such processes are investigated for different locations around the globe based on Observation, ERA5 reanalysis data, and Lagrangian backward trajectory calculations. In the extratropics, extreme DTDT changes are generally associated with changes in air mass transport, in particular shifts from warm to cold air advection or vice versa, linked to regionally specific synoptic-scale circulation anomalies (ridge or through patterns). Lagrangian temperature changes in the advected air masses are due to adiabatic warming, which is dominant in the local winter season, and diabatic warming, most importantly in summer. In contrast, for extreme DTDT changes in the tropics, local processes are more important than changes in advection. For instance, the strongest DTDT decreases over central South America in December-February are linked to a transition from mostly cloud-free to cloudy conditions, indicating an important role of radiative heating. The mechanistic insights into extreme DTDT changes obtained in this study can be helpful for improving the prediction of such events and anticipating future changes in their occurrence frequency and intensity.

 

How to cite: Hamal, K.: Quantification of the Physical Process Leading to Extreme Day-to-Day Temperature Changes, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-3564, https://doi.org/10.5194/egusphere-egu24-3564, 2024.

EGU24-3613 | ECS | Orals | NH1.2

Assessing windstorm hazard emerging from multiple types of storms 

Nasrin Fathollahzadeh Attar, Francesco Marra, and Antonio Canale

In the context of global climate change, windstorms pose significant environmental, ecological, and socioeconomic challenges. Mountainous and forested regions of Europe, including the Veneto region in northern Italy, have been devastated by unprecedented events such as the storms in July 2023 and Vaia in October 2018, raising the question whether such events may occur more frequently in the future. The probability of observing such extremes in present-day climate can be quantified using cumulative distribution functions of annual maxima wind speeds, obtained from extreme value analysis methods. Once these are derived, however, is it near to impossible to project future changes in these distributions as extreme wind speeds are caused by storms driven by diverse synoptic conditions, the characteristics and occurrence frequency of which may change differently in response to climate change.

This study introduces a method to derive cumulative distribution functions of annual maximum wind speeds explicitly considering the intensity and occurrence frequency of multiple types of storms. Independent windstorms are separated and their maximum hourly wind speed is isolated. Storms are then organized into types based on their local wind direction using a clustering technique. We then use a multi-type Simplified Metastatistical Extreme Value distribution (SMEV) to estimate the cumulative distribution function of annual maximum wind speed for the location of interest. The study focuses on mountainous areas, seeking a simpler relation between typical wind directions and synoptic conditions.

A thorough leave-one-out evaluation with benchmark models, including the traditional Generalized Extreme Value distribution (GEV) and a single-type SMEV, is conducted on 22 mountain stations in the Veneto region (northern Italy). We show that, overall, the proposed multi-type method provides estimates of extreme return levels that are comparable with the ones of single-type SMEV and GEV. Our results demonstrate that it is possible to derive cumulative distribution functions of annual maximum wind speeds explicitly considering storms emerging from different marginal processes. This paves the way to the use of projections of large-scale atmospheric dynamics from climate models to improve our prediction of future extreme wind speeds.

 

Keywords: Windstorm; Extreme events; Wind direction classification; Multiple types; Simplified Metastatistical Extreme Value (SMEV); Mountainous areas.

How to cite: Fathollahzadeh Attar, N., Marra, F., and Canale, A.: Assessing windstorm hazard emerging from multiple types of storms, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-3613, https://doi.org/10.5194/egusphere-egu24-3613, 2024.

EGU24-3992 | Posters on site | NH1.2

Synoptic and Mesoscale Conditions of Deep Moist Convection during the Cold Season in Croatia 

Maja Telišman Prtenjak, Domagoj Dolički, Petra Mikuš Jurković, and Damjan Jelić

In this study, thunderstorm activity during the cold part of the year was analyzed based on Thunderstorm Intensity Index (TSII) data on a pre-defined grid with a resolution of 3 km x 3 km in Croatia. The study covered a five-year period from 2016 to 2020, focusing on the months from October to March. The goal of the research was to conduct a spatial and temporal analysis of thunderstorm activity and determine the synoptic and thermodynamic conditions under which it occurs. The analysis aimed to provide an overview of the fundamental characteristics, thereby improving the understanding of deep moist convection in the cold part of the year, which poses a significant challenge in operational forecasting due to its lower frequency and more difficult intensity assessment. The occurrence of surface frontal disturbances was detected based on surface and upper-level synoptic charts, and the flow regime at the 500 hPa level was determined. Thermodynamic and kinematic parameters were calculated from radiosonde profiles from stations in San Pietro Capofiume, Brindisi, Pratica di Mare, Zagreb, and Zadar, using the thundeR free software package.

        A total of 290 convective days were selected for analysis from the observed period. The results indicate that synoptic forcing plays a significantly greater role in the development of convection during the cold part of the year compared to the warm part, while the dominant upper-level flow regime is southwest. The obtained values of CAPE (Convective Available Potential Energy) in the cold part of the year are much lower than those in the warmer part, with a significant contribution from the considerably lower amount of solar surface heating. Additionally, most thunderstorms developed under conditions of strong vertical wind shear, indicating that the atmospheric environment conducive to winter thunderstorms is predominantly a High Shear-Low CAPE (HSLC) environment.

How to cite: Telišman Prtenjak, M., Dolički, D., Mikuš Jurković, P., and Jelić, D.: Synoptic and Mesoscale Conditions of Deep Moist Convection during the Cold Season in Croatia, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-3992, https://doi.org/10.5194/egusphere-egu24-3992, 2024.

EGU24-4050 | ECS | Posters on site | NH1.2

Compound dry and hot extreme events in the Mediterranean region 

André Correia Lourenço, Ana Russo, Virgílio A. Bento, and João Lucas Geirinhas

Over the last few decades, the frequency, duration, magnitude of heatwaves in Europe have increased considerably, with major natural and socioeconomic impacts (Basarin et al., 2020; K.P. Tripathy et al., 2022). In climate change scenarios, these events are expected to present an increasing trend (Zscheischler et al., 2018) due to variations in dynamic and thermodynamic mechanisms, triggering unusually longer and more intense periods of drought and causing a reduction in agricultural production and the supply of water reservoirs. The Mediterranean region is a climate change hotspot and therefore a region susceptible to the development and intensification of single or compound hot and dry events (Giorgio and Linello, 2008).

This work aims at studying single and compound heatwaves and droughts based on ERA5 and ERA5-Land databases for Southern Europe on a 0.25º x 0.25º and 0.1º x 0.1º spatial resolution, respectively.

The results show positive trends for the duration and intensity of heatwaves and droughts and, conversely, negative trends for soil moisture. Most of the study area shows statistically significant negative trends when aggregating spatially. On the other hand, the annual temperature means tends to migrate towards higher values and precipitation means show a small decrease. Furthermore, the relation between large scale climatic patterns such as the North Atlantic Oscillation (NAO) and compound drought and heatwaves are studied here.

It is expected that compound hot and dry events will have a positive trend in their frequency, duration and intensity, as a consequence of climatic phenomena, such as the synoptic systems, or even due to previous dry characteristics of the soil. Our findings highlight the intricate interplay between different mechanisms in the occurrence of extreme events in Mediterranean Europe, putting into evidence the need for better representation this interplay in climate models.

A.L., A.R., V.B. and J.G. have been supported by the Fundação para a Ciência e a Tecnologia (FCT) I.P./MCTES through national funds (PIDDAC, grant no. UIDB/50019/2020, https://doi.org/10.54499/UIDP/50019/2020, and LA/P/0068/2020, https://doi.org/10.54499/LA/P/0068/2020, to Instituto Dom Luiz; project DHEFEUS, https://doi.org/10.54499/2022.09185.PTDC). J.G. acknowledges Fundação para a Ciência e a Tecnologia (FCT) for the PhD Grant 2020.05198.BD.

 

References:

Basarin, Biljana, Tin Lukić, and Andreas Matzarakis. 2020. "Review of Biometeorology of Heatwaves and Warm Extremes in Europe" Atmosphere 11, no. 12: 1276. https://doi.org/10.3390/atmos11121276.

Giorgi, F., Lionello, P., 2008. Climate change projections for the Mediterranean region. Global Planet. Change 63 (2), 90–104.

Tripathy, K. P., & Mishra, A. K. (2023). How unusual is the 2022 European compound drought and heatwave event? Geophysical Research Letters, 50, e2023GL105453. https://doi.org/10.1029/2023GL105453.

Zscheischler, J., Westra, S., van den Hurk, B. J. J. M., Seneviratne, S. I., Ward, P. J., Pitman, A., et al. (2018). Future climate risk from compound events. Nat. Clim. Change 8, 469–477. doi: 10.1038/s41558-018-0156-3.

How to cite: Lourenço, A. C., Russo, A., Bento, V. A., and Geirinhas, J. L.: Compound dry and hot extreme events in the Mediterranean region, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-4050, https://doi.org/10.5194/egusphere-egu24-4050, 2024.

Rainfall return levels are guiding hazard protection, insurance models, infrastructure design, construction, and planning of cities. When deriving information about the frequency and intensity of extremes by fitting extreme value models to pointwise observations, the regionalization of these models is challenging. Rain gauges are distributed unevenly, where some regions suffer from data scarcity in space and time. To address this, topographical and/or climatological covariates are often used for the spatial interpolation. On the other hand, high-resolution climate simulations are available to provide spatial information on rainfall extremes. However, these simulations are still governed by model biases, where the bias adjustment of extremes at ungauged locations is also inducing uncertainty.   

In this study, we propose a combination of observations and a high-resolution convection-permitting climate model simulation in the framework of smooth spatial Generalized Extreme Value (GEV) models in order to estimate spatial rainfall return levels. We choose a study area over southern Germany with complex terrain, which is densely monitored with 1132 rain gauges providing more than 30-year daily rainfall observations. There, a 30-year simulation of the Weather and Forecasting Research (WRF) model is available at 1.5 km resolution driven by ERA5 reanalysis data. We combine observations and covariates from the WRF simulation in the spatial GEV and refer to this approach as sGEV-WRF.

We want to answer three research questions to assess the added value of the proposed framework:

  • Is it worth the effort? Does the sGEV-WRF improve the generation of rainfall return levels compared to the WRF alone?
  • Does the WRF simulation as covariate add value? Can the sGEV-WRF outperform a topography-only spatial GEV?
  • Does the dynamical downscaling at high resolution add value? Can sGEV-WRF outperform a spatial GEV based on observations and covariates from the coarser resolution ERA5?

By evaluating the percentage bias, mean absolute error, and root-mean-square error, we show that the combination of observations and WRF can improve the representation of 10-year and 100-year return levels of daily rainfall.

In addition, we aim to assess the performance of this framework under data-scarce conditions. Therefore, we devise an extensive cross-validation study. We select 5%, 10%, 20%, 50%, 80%, 90%, and 95% of all 1132 rain gauges to re-build the spatial GEV models with 1000 random folds each. We show that the performance is robust under these conditions, highlighting the potential for the application in data-scarce regions. Furthermore, in a non-stationary setup with climate model future projections, it can serve as a reliable tool to assess climate change effects on heavy rainfall.     

How to cite: Poschlod, B. and Koh, J.: Combining observations and a high-resolution climate model for the generation of spatial rainfall return levels, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5310, https://doi.org/10.5194/egusphere-egu24-5310, 2024.

EGU24-5441 | ECS | Orals | NH1.2

Compounding drivers amplify the severity of river floods  

Shijie Jiang, Larisa Tarasova, Guo Yu, and Jakob Zscheischler

Estimating the risk of river flooding under global warming is challenging, mainly due to the compound nature of the various drivers, which is not yet fully understood. Our study aims to quantitatively unravel the complex dynamics of multiple factors that interact and influence river flooding. Using interpretable machine learning techniques, we analyzed thousands of global catchments to identify the role of compounding drivers in river flooding. Our results indicate that these compounding drivers have played a significant role in increasing the magnitude of river floods over the past four decades. In particular, the influence of the interaction effects between these drivers becomes more pronounced with increasing flood magnitude, and the degree is modulated by specific physioclimatic conditions. Importantly, traditional flood analysis will underestimate the magnitude of extreme floods due to insufficient consideration of the varying compounding effects in flood generation. Overall, our results emphasize the need to more carefully incorporate compounding factors to improve extreme flood estimates.

How to cite: Jiang, S., Tarasova, L., Yu, G., and Zscheischler, J.: Compounding drivers amplify the severity of river floods , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5441, https://doi.org/10.5194/egusphere-egu24-5441, 2024.

EGU24-5446 | ECS | Posters on site | NH1.2

Enhancing flood forecasting and prevention: The multidisciplinary approach of Flood2Now project and its innovative solutions 

Carlo Guzzon, Raül Marcos, Maria Carmen Llasat, Montserrat Llasat-Botija, Dimitri Marinelli, Albert Diaz Guilera, Luis Mediero, Luis Garrote, Alicia Cabañas Ibañez, Javier Arbaizar Gonzalez, and Olga Varela

Spain and the Mediterranean coast are largely affected by flash floods, which are generated by intense, localized storms within smaller basins, typically less than 100 km2 (Gaume et al., 2016). Predicting these events remains challenging as they are frequently triggered by convective systems operating at scales below the resolution of conventional meteorological models. In Spain, floods are the country's primary recurring natural disaster, accounting for nearly 70% of the compensation amount issued by the Consorcio de Compensación de Seguros (CCS, 2011). 

In this hydrogeological risk context, the ultimate goal of the Flood2Now project is to support the population and mitigate the risk associated with this natural hazard, through the implementation of an automatic real-time warning system in two basins (Francolí and Arga) located in the north-east part of the Iberian peninsula. Multidisciplinarity plays a pivotal role in defining this system, integrating various disciplines and information sources, ranging from complex systems physics and hydrometeorological data to citizen science and socio-economic statistics.

Flood2Now embodies a collaborative effort between universities, companies, and social foundations, to explore the following technical aspects: (i) establishing a comprehensive digital database spanning four decades of flood occurrences; (ii) exploring complex systems methodologies to discern interrelationships among various factors influencing flood impacts; (iii) studying weather patterns associated with diverse flood events, accounting for their impact; (iv) implementing analogous methodologies to enhance flood risk forecasting; and (v) integrating this knowledge to enhance operational systems aiding flood-related decision-making.

This research extends its impact on society by implementing citizen science methodologies to gather supplementary data for flood risk management, enhancing early warning systems' precision, and raising community awareness of flood risks and climate change. Innovative approaches include integrating historical and citizen-collected data into decision-making, employing ensemble prediction systems, and implementing advanced hydrological modeling techniques for streamflow prediction and decision support.

This contribution shows the selected basins and case studies, the proposed applied hydrometeorological chain to forecast flash flood impacts, and the improvements that citizen science can provide, on the one hand, in obtaining flow data and the state of rivers, especially in ungauged basins, and, on the other, in increasing risk awareness.

This research has been done in the framework of the Flood2Now project, Grant PLEC2022-009403 funded by MCIN/AEI/10.13039/501100011033 and by the European UnionNextGenerationEU/PRTR. 

 

References:

Gaume, E., Llasat M.C., et al., 2016. Mediterranean extreme floods and flash floods. Into Hydro-meteorological extremes, chapter 3, The Mediterranean Region under Climate Change. A Scientific Update (coordinated byAllEnvi).133-144. ISBN : 978-2-7099-2219-7.

CCS, 2021, Estadística riesgos extraordinarios. Serie 1971-2020. Available at: https://www.consorseguros.es/web/documents/1018/4419/Estadistica_Riesgos_Extraordinarios_1971_2014/14ca6778-2081-4060-a86d-728d9a17c522

 

 

How to cite: Guzzon, C., Marcos, R., Llasat, M. C., Llasat-Botija, M., Marinelli, D., Diaz Guilera, A., Mediero, L., Garrote, L., Cabañas Ibañez, A., Arbaizar Gonzalez, J., and Varela, O.: Enhancing flood forecasting and prevention: The multidisciplinary approach of Flood2Now project and its innovative solutions, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5446, https://doi.org/10.5194/egusphere-egu24-5446, 2024.

Hail is by far the greatest contributor worldwide to insured losses from severe convective storms on an annual basis. Individual outbreaks can cause losses well above EUR 1 bn. In Italy, severe convective storm losses have been dominating the market in the last 5-7 years, with a record of EUR 1.4 bn in 2019 prior to year 2023. On 18-25 July 2023 an unprecedented outbreak brought large hail and strong winds to Lombardy, Veneto, Friuli-Venezia Giulia, Piedmont and Emilia-Romagna, with affected cities including Parma, Turin, Milan and Venice. There were many reports of large hailstones, causing significant damage to property and motor vehicle. The European hail record was breached too. Twice. On 19 July, a hailstone measuring 16 cm in diameter was recorded in Carmignano di Brenta, and broke the previous largest hail record in Europe, which was held by a 15 cm stone found in Romania in 2016. Just five days later, a new record was set, when a 19 cm hailstone was found in the town of Azzano Decimo. This is very close to the all-time largest hail recorded of 20.3 cm, found in 2010 in South Dakota, US. Total loss estimates, of which hail was by far the largest contributor, exceeds EUR 3 bn, of which 70-80% in the property sector (residential and commercial buildings), and the remaining 20-30% in the motor vehicle sector. These were the largest hail events in Italy in recorded history, and the costliest cat event in the third quarter of 2023 for the global insurance market.

Following in the footsteps of the severe convective storm outbreak that impacted France in June 2022, these storms came after a record-hot air mass that languished over Southern Europe much of the week prior. Persistent meteorological conditions conducive to rotating supercell thunderstorms were observed for several consecutive days. These compounded with local conditions favorable for the development of severe hail over the Po Valley. In this study we present a reconstruction of these events based on event reports from European Severe Weather Database. We analyze the synoptic configurations and pre-convective environments that characterized them, with focus on those properties and features that are peculiar to severe hail-forming thunderstorms. We look at different formulations of CAPE and vertical wind shear, as well as composite parameters such as the Significant Hail Parameter and the Supercell Composite Parameter. We make use of Gallagher Re’s Severe Convective Storm Index to contextualize these events historically, and to discuss climate change trends over Northern Italy. Finally, we discuss the implications that such events and their expected frequency under climate change have on the (re)insurance market.

How to cite: Panosetti, D. and Tomassetti, U.: The July 2023 Northern Italy hailstorms from a climatological and (re)insurance market perspective, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5962, https://doi.org/10.5194/egusphere-egu24-5962, 2024.

The consensus of climatic research indicates that the likelihoods of extreme precipitation events are going to change significantly, but specific trends depend on the type of dominant weather system, and regional landscape and climate details. Despite the effort of increasingly accurate and converging global circulation models, the uncertainties in the ensemble of CMIP6 models, and the often course spatial resolution make translation of climate models to actionable information of flood forecasts complex and uncertain. We carried out an analysis of a large ensemble of Global Circulation Models (GCMs) of the CMIP6 ensemble that were downscaled statistically as part of the NASA NEX-GDDP-CMIP6 dataset. The analysis looked at segmented windowed return period analysis using the method of l moments to fit general extreme value distributions to global climate models. With analysis of 1, 3 and 7 day duration, median, 15 and 85 percent quantiles, between 5 and 100 year return period, and global spatial coverage, the results show variations in how precipitation events of various return periods and durational are predicted to change in GCMs, and what the associated uncertainty is for various regions of the world. Intermediate analysis outputs show artifacts in yearly extreme precipitation due to the applied statistical downscaling, but relative factors to be used in precipitation scaling under climate change resolves these. Average increases in precipitation extremes of percent are observed globally (+5.1%), with many local outliers for the SSP585 scenario in 2050 (e.g. regions such as the Himalayan region (+23.4 percent median), the Sahel region(+21.6%) or South-Western Spain (-3.9%)). The other SSP scenarios change the global average factors to +3.75% and +4.32% for SPP245 and SSP370 Respectively. Very low variability in the changes is observed for return periods, indicating that the intensity probability curves shift uniformly in the model output. Precipitation events duration does more significantly alter the analysis outputs, and various areas show differences here that correlate with flash and fluvial flood susceptibility. Finally, we open-source the analysis code and link the output as a built-in dataset in the fastflood.org rapid flood simulation platform. Here, automatically derived extreme precipitation events from era5 datasets can be rescaled under climate change conditions by applying the scaling factors derived in this work.

How to cite: van den Bout, B.: Global changes in extreme precipitation linked with rapid flood simulation tools, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-7617, https://doi.org/10.5194/egusphere-egu24-7617, 2024.

EGU24-8116 | ECS | Posters on site | NH1.2

IMERG-E and IMERG-L: A Comprehensive Evaluation of the Medicane Daniel in Thessaly, Greece 

Evangelos Leivadiotis, Silvia Kohnová, and Aris Psilovikos

On 4 September 2023, the area of Thessaly (Greece) experienced a catastrophic flood as a result of the Daniel hurricane sequence. This severe phenomenon is characterized by extreme rainfall records ranging from 305 mm to 1096 mm between 4 and 7 September, causing severe damage to infrastructure, agriculture and buildings. Seventeen casaulties were recorded. The aim of the study is to complete the integrated multi-satellite harvesting of the Global Precipitation Measurement Mission (IMERG) using 10 precipitation stations distributed in the Thessaly region. Specifically, two precipitation products (IMERG-E and IMERG-L) were used to evaluate the early and late extreme precipitation events of IMERG version 7. In order to obtain the rainfall data needed for the research, a time period of 4 September 2023 (0000UTC) to 7 September 2023 (2330UTC) was chosen. This window corresponds to the approximate time at which Daniel's storm convective zone was on the area of interest. The National Meteorological Agency collected six of the ten precipitation stations and four of the Public Electricity Agency. The evaluation process was divided into two parts: the first part aimed at estimating the total rainfall of IMERG-E and IMERG-L, and the second part aimed at estimating the total daily rainfall of both products. Two statistical assessment indicators were used: the Pearson correlation coefficient and the root mean square error (RMSE) to quantitatively assess the performance of satellite precipitation products using rain-gauge data. Firstly, the correlation coefficient between IMERG-E, IMERG-L and total precipitation at IMERG-E, IMERG-L and IMERG-L is -0.03 and 0.27, respectively. Early products did not correlate with ground data, but later versions showed weak positive linear relationships. The RMSE values are 0.8 and 0.52, respectively. The daily analyses of IMERG-E showed moderate negative correlations on September 4 (-0.29), September 5 (-0.15), and September 7 (-0.25), and moderate positive correlations on September 6 (0.37). In terms of daily performance, the correlation coefficients suggest weak positive correlations (0.22 in 4 September, 0.13 in 5 September, 0.23 in 7 September), with the exception of -0.3 in 6 September. RMSE values remain low (0.31 on 4 September, 0.34 on 5 September, 0.20 on 7 September), except for 6, September, where values (0.95) indicate high levels of error. Overall, the late version is more efficient than the early version, but there are rooms for improvements when the IMERG final version will be available.

How to cite: Leivadiotis, E., Kohnová, S., and Psilovikos, A.: IMERG-E and IMERG-L: A Comprehensive Evaluation of the Medicane Daniel in Thessaly, Greece, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-8116, https://doi.org/10.5194/egusphere-egu24-8116, 2024.

EGU24-9412 | Orals | NH1.2

Extratropical intrusions and their role in tropical flood events: A South Pacific perspective 

Romain Pilon, Andries de Vries, and Daniela Domeisen

Extratropical Rossby waves are a potential source of instability for driving convective disturbances in the tropics. In the South Pacific, island nations are subject to flooding associated with such convective disturbances, yet these have not been conclusively linked to any large-scale processes. Using an object-based approach, this study specifically explores in particular how Rossby waves propagating into the tropics can contribute to the formation of extratropical-tropical cloud bands, which can cause flooding events. These cloud bands are associated with substantial precipitation events and serve as easily detectable proxies to identify when such intrusions occur. Building upon this foundation we use ERA5 reanalysis along with a detection analysis for tropical-extratropical cloud bands and potential vorticity streamers and cutoffs to establish a climatology of such intrusions and cloud bands. This allows us to demonstrate the statistical association of extratropical intrusions with intensified deep convection, in particular over the tropical central South Pacific. We find that these intrusions contribute significantly to the occurrence of floods in the Polynesian islands. In summary, this study allows us to connect the interaction between the extratropics and the tropics with flood events in the South Pacific.

How to cite: Pilon, R., de Vries, A., and Domeisen, D.: Extratropical intrusions and their role in tropical flood events: A South Pacific perspective, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-9412, https://doi.org/10.5194/egusphere-egu24-9412, 2024.

EGU24-10058 | ECS | Orals | NH1.2

Detection of past extreme precipitation events and connection to recorded impacts: a multi-data and multi-method assessment over the Central-Eastern Alps 

Katharina Enigl, Alice Crespi, Sebastian Lehner, Klaus Haslinger, and Massimiliano Pittore

Extreme hydro-meteorological events are increasingly observed in southern Europe and especially in the European Alps, where they threaten ecological and socio-economic systems. To detect such events and analyse the changes in their occurrence, a proper definition of an extreme event is needed. Statistically, we define extremes from the tails of the probability distributions. However, these events are not necessarily extreme in terms of impact, and impact-related thresholds may vary spatially and temporally, i.e., single absolute thresholds do not necessarily reflect the extremes at all locations, in all time periods and all seasons. Moreover, the availability of harmonized and consistent datasets is crucial for investigating extremes in a transnational context. In this study, we focus on the identification and characterisation of extreme hydro-meteorological events affecting a transboundary Alpine region between Austria and Italy from 2003 to 2021 based on different definitions of extreme events considering spatiotemporal aspects and multiple datasets. Daily accumulated precipitation is used as the main proxy parameter to describe the potential for severe consequences, as it as it is the most broadly available quantity across different datasets compared to e.g., sub-daily precipitation sums. Moreover, its role as a triggering factor for various hazards (e.g., landslides, debris flows, pluvial and fluvial floods) is widely recognised. We analyse three different statistical methods for the detection of extreme events: (i) the identification of the highest daily precipitation amounts on a regional scale, (ii) the detection of daily precipitation values of high intensity on a local scale and (iii) the identification of exceptional daily precipitation records not in absolute terms but with respect to average conditions associated to a specific period of the year. All detection algorithms are applied to four gridded precipitation datasets, including both observation and reanalysis products, with different technical specifications. Subsequently, identified events for each method-dataset combination are blended with existing records of gravitational mass movements and fluvial floods in the Austrian-Italian border region to analyse the suitability of each combination to detect actual occurred impacts. First results indicate that most detected precipitation extremes relate to actual observed impacts (e.g., 74% for regional scale identification with reanalysis data). However, different method-dataset combinations have different strengths and weaknesses, which reflect inherent characteristics of the dataset and/or of the statistical method employed. Furthermore, some combinations show lower performance in detecting impactful events, because the dataset and method applied conflict with each other (e.g., a coarse-resolution dataset not resolving local-scale features conflicts with a statistical method searching for locally high intensities). The findings could contribute to better inform civil protection authorities about risks related to extreme hydrometeorological events, possibly affected by climate change.

How to cite: Enigl, K., Crespi, A., Lehner, S., Haslinger, K., and Pittore, M.: Detection of past extreme precipitation events and connection to recorded impacts: a multi-data and multi-method assessment over the Central-Eastern Alps, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-10058, https://doi.org/10.5194/egusphere-egu24-10058, 2024.

EGU24-10451 | Posters on site | NH1.2

Influence of Design Storm Profiles on Flood Peak Discharge in a Small River Catchment 

Kazimierz Banasik, Leszek Hejduk, Adam Krajewski, Donald E. Woodward, Andrzej Wałęga, and Beniamin Więzik

Estimations of flood peak discharges of low probability of exceedance are required for designing and maintaining hydraulic and road structures (reservoirs, weirs, water intakes, bridges, culverts) as well as for flood protection, including assessment of the risk of flooding. Rainfall-runoff models are usually the only alternative for such estimations in case of small catchments, as there is a lack of sufficient, good quality historic data to be used for applying the traditional i.e. statistical methods. The aim of this study was to check responses of a small agro-forested, lowland catchment located in center of Poland to rainfall of assumed probability of exceedance and of three profiles of intensity (i.e. a/ constant intensity, b/ asymmetric one with highest intensity between 0.3 and 0.5 its duration, c/ symmetric one with single peaked intensity) and various storm duration.

A regional formula, developed by state hydrological service, on relationship of intensity-duration-frequency, applicable also for region of center of Poland, has been used to find rainfall depths of the events with probability of exceedance of 1% (return period of 100 years) and various duration (i.e. D = 6, 12, 18, 24, 30, 36, 42, 48, 60 and 72 h), as input data for runoff hydrograph simulation. As the catchment, which area is 82.4 km2, has long term monitoring history, the model parameters, as Curve Number of NRCS (Natural Resources Conservation Service), used for extracting the effective rainfall (direct runoff) from total rainfall depth and parameters of Nash model, used for transformation of effective rainfall into direct runoff hydrograph, were estimated from recorded rainfall-runoff events. Over 50-year-continuous discharge record allowed us to estimate the 100 year flood, by applying statistical method for the investigated catchment, as 25,6 m3/s which form a base for comparison of the results of application of the rainfall-runoff model.

Results of modelling of the of rainfall-runoff process indicate: a/ that critical rainfall duration (producing highest peak discharges) of the three storm profiles were between 24 and 60 hours, and b/ higher peak discharges at critical rainfall durations of the three storm profiles than one of statistical method. The differences (overestimates) were from 1.6% for the constant intensity to 30.0% for the symmetric single peaked intensity.

How to cite: Banasik, K., Hejduk, L., Krajewski, A., Woodward, D. E., Wałęga, A., and Więzik, B.: Influence of Design Storm Profiles on Flood Peak Discharge in a Small River Catchment, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-10451, https://doi.org/10.5194/egusphere-egu24-10451, 2024.

EGU24-10531 | Orals | NH1.2

Evaluating Standard Precipitation Index (SPI) using MIROC6 historicclimate simulations and ERA 5 reanalysis data as a tool to map theimpacts of climate change in rainfall regime in Brazil 

Gean Paulo Michel, Aimée Guida Barroso, Franciele Zanandrea, Márcio Vinicius Aguiar Soares, Gabriel Ferreira Subtil de Almeida, Marcio Cataldi, Priscila Esposte Coutinho, Lívia Sancho, and Vitor Luiz Galves

Rising global average temperatures, as a consequence of climate change, have worsened the occurrences of extreme weather events, causing disruptions in rainfall patterns around the world. In Brazil, such effects are already observed with the increase of heat waves, floods, droughts, and wildfires. The correlation between disruptions in precipitation patterns and fires is complex, nevertheless, the intensity, frequency, and duration of drought events have significant impacts on fuel flammability and fire behavior. Drought monitoring is particularly relevant in Brazil, where the vast majority of forest fires have an anthropogenic ignition and prolonged dry periods favor such fires to spread out of control. The Standardized Precipitation Index (SPI) is one of the most important tools used to evaluate precipitation variability, offering simple yet robust statistical information on the distribution, duration, and frequency of rainfalls and, consequently, droughts. The SPI uses precipitation as input data to standardize the deviation of cumulated rainfall from the mean of historical precipitation, detecting water deficit (negative values) or water surplus (positive values) for a given location. In doing so, this index allows direct spatial comparability between arid and humid regions. This is an advantageous characteristic when drought analysis is applied to a country with different regional rainfall regimes, such as Brazil. The applicability of SPI as a source of drought prediction was investigated by observing its performance with historical climate simulations of the 6th phase of the Model for Interdisciplinary Research on Climate (MIROC6) and the fifth generation ECMWF atmospheric reanalysis of the global climate, ERA5. The direct comparison of the SPI data, employing the climatology extending from 1980-2014 in Brazil, derived both from the climate simulation model and the reanalysis data - which combines observations and models – has provided valuable insights. Preliminary results show an overall consistency in the calculated indexes from both sources, which are in line with seasonal regional rainfall patterns in Brazil. On average, the SPI indexes recognize water deficits for the North-east, north of the South-east and central regions of Brazil. During the months of winter, both indexes detect droughts in these regions, with ERA-5 SPI index registering severe droughts in central Brazil. These results suggest that the SPI index calculated using the reanalysis data seems to register droughts with greater severity and longer duration, identifying more precisely periods with little to no rainfall, whilst the SPI derived from the MIROC6 simulation data, although able to acceptably identify and delimitate droughts, records less severity for the same period. These findings are important to recognize the MIROC6-derived SPI index as a valuable tool in drought prediction. However, they also highlight the necessity of acknowledging the limitations of the model regarding the severity of droughts. The understanding and prediction of precipitation anomalies is fundamental to coping with the impacts of climate change on water resources, agriculture, and biodiversity, guiding mitigation and adaptation strategies in Brazil.

How to cite: Michel, G. P., Guida Barroso, A., Zanandrea, F., Aguiar Soares, M. V., Ferreira Subtil de Almeida, G., Cataldi, M., Esposte Coutinho, P., Sancho, L., and Galves, V. L.: Evaluating Standard Precipitation Index (SPI) using MIROC6 historicclimate simulations and ERA 5 reanalysis data as a tool to map theimpacts of climate change in rainfall regime in Brazil, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-10531, https://doi.org/10.5194/egusphere-egu24-10531, 2024.

EGU24-10737 | ECS | Orals | NH1.2

Projecting Extreme Rainfall in Sicily: Integrating Simple Scaling and Hourly Projections into Depth-Duration-Frequency Analysis 

Gaetano Buonacera, David J. Peres, Nunziarita Palazzolo, and Antonino Cancelliere

In this present work, we propose a robust methodology for the derivation of future rainfall depth-duration-frequency curves (DDFs), utilizing hourly projections, the assumption of simple scaling of precipitation, and the application of the method of moments for parameter estimation in dimensionless precipitation height distributions. The methodology introduced herein involves the application of change factors derived from climate projections to precipitation averages across various durations (1, 3, 6, 12, and 24 hours) and to the dimensionless moments of the precipitation series. To implement this methodology, we leverage regional scale models (RCM) from the EURO-CORDEX initiative, characterized by hourly temporal resolution. The direct utilization of hourly projection data allows to bypass the necessity for temporal disaggregation techniques. Change factors are calculated through an analysis of annual maxima derived from both future and control series (1971-2000) generated via RCMs. We consider two distinct emission scenarios, namely RCP (Representative Concentration Pathways) 4.5 and 8.5, spanning three future periods: near future (2021-2050), middle future (2051-2070), and far future (2071-2100). Our methodology is applied to multiple rain gauges located across the Sicily region. The outcomes of our investigation underscore an upward trend in future DDFs, particularly pronounced in the RCP 4.5 scenario and during the far future period. This trend is attributed to an observed intensification in the variability of rainfall events. Depending on the specific geographic location, chosen emission scenario, and future time period, future Depth-Duration-Frequency (DDF) curves may correspond to return periods that more than double those observed in the control climate. The methodology, given the easy availability of the exploited data, can turn useful for updating hydrological design criteria for flood mitigation.  

 

How to cite: Buonacera, G., Peres, D. J., Palazzolo, N., and Cancelliere, A.: Projecting Extreme Rainfall in Sicily: Integrating Simple Scaling and Hourly Projections into Depth-Duration-Frequency Analysis, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-10737, https://doi.org/10.5194/egusphere-egu24-10737, 2024.

EGU24-10848 | ECS | Orals | NH1.2

Extreme precipitation – temperature scaling: disentangling causality and covariation 

Sarosh Alam Ghausi, Erwin Zehe, Subimal Ghosh, Yinglin Tian, and Axel Kleidon

Warmer temperatures are expected to cause more intense rainfall, primarily due to the rise in atmospheric moisture at the rate of 7%/K, as indicated by the Clausius-Clapeyron (CC) equation. To evaluate this effect, studies use a statistical approach known as precipitation-temperature scaling that involves fitting an exponential regression between observations of extreme rainfall events and local temperatures, resembling how saturation-vapor pressure scales with temperature. However, the estimated sensitivities (also called scaling rates), exhibit notable deviations from the CC scaling (7%/K). These rates remain mostly negative in the tropics as the rainfall extremes exhibit a general monotonic decrease with temperature and “hook-shape” structures in most parts of tropics and mid-latitudes.

Here we show that most of the variability in the observed scaling rates arises from the confounding radiative effect of clouds associated with rainfall events. Clouds substantially reduce the net radiative heating of the surface during the storms by up to 100 W/m2 in the tropics, leading to the cooling of surface temperatures by up to 8K. This cloud-induced cooling results in a covariation between precipitation and local temperature, inducing a two-way causality in the observed scaling rates. To isolate this cooling effect, we used a thermodynamically constrained surface energy balance model and force it with radiative fluxes under both "clear" and "cloudy" sky conditions. We then quantified the changes in surface temperatures due to clouds and remove it from temperature observations during rainy days. After removing this effect, we found positive scaling across the global land areas, closely aligning with CC rates of 7%/K. We demonstrate that cloud radiative effects alone can explain the observed negative and hook-shaped relationships found in precipitation-temperature scaling.

Our findings imply that projected intensification of rainfall extremes with temperature by climate models is consistent with observations after the cloud-cooling effect is corrected for. Our results emphasize on making a clear distinction between causality and covariation by explicitly separating the temperatures before the rainfall event that are shaped by less clouds from temperature during the rainfall event which include clouds. This adds a crucial effect to the debate of interpreting observed precipitation - temperature scaling rates. Furthermore, our methodology of removing cloud effects on temperatures can be extended to estimate climate sensitivities from observations beyond precipitation extremes.

How to cite: Ghausi, S. A., Zehe, E., Ghosh, S., Tian, Y., and Kleidon, A.: Extreme precipitation – temperature scaling: disentangling causality and covariation, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-10848, https://doi.org/10.5194/egusphere-egu24-10848, 2024.

EGU24-11716 | ECS | Posters on site | NH1.2

Tropical Cyclone Rainfall Asymmetries Inferred from GPM-IMERG: A Focus on Lesser Antilles 

Catherine Nabukulu, Janneke Ettema, Victor Jetten, and Bastian van den Bout

Abstract

This study utilizes GPM-IMERG satellite rainfall estimates to assess the asymmetric rainfall patterns in 27 tropical cyclones (TCs) across the Lesser Antilles region from 2000 to 2020. The aim is to evaluate whether there is a persistent relationship between precipitation and wind characteristics, which could support improved TC-related flood risk assessment for these islands. With a focus on hurricane and tropical storm categories, the 30-minute precipitation variability was assessed within a radius of 500 km from the TC’s eye during its path in the study area. In addition, TC’s forward speed and wind characteristics, like  TC’s category and the extent of 34-knot winds (R34), are included. The analysis reveals temporal trends, indicating increased TC rainfall events in the study area during the second decade. Correlations show positive relationships between rainfall total (RT), rainfall area (RA), and rainfall intensity at the 90th percentile (RI0.9), with RT and RI0.9 showing the strongest link in the majority of the observations. Contrary to conventional assumptions, this research challenges the idea that highest category TCs in the wind intensity always produce higher rainfall, as we see that higher-category hurricanes such as H4 (209-251km/hr) and H5 (>=252km/hr) were often associated with lower rainfall values in RT and RI0.9 compared to tropical storms (63 - 118 km/hr). Tropical storms, like higher-category hurricanes, can display large rainfall areas. In addition,  quadrant analysis of rainfall zones around the TC eye highlights that the NE and SE quadrants in TC have significantly more rainfall impact. However, it also reveals the danger posed by weaker quadrants in wind characteristics such as SW and NW, as they can exhibit high rainfall values in RA and RT. The study indicates complex, non-linear relationships between TC’s wind and precipitation characteristics in the Lesser Antilles region. Incorporating the rainfall variability observed in TC dynamics into early warning systems and risk assessment is essential for a more effective emergency response and mitigation planning.

General methodology

The satellite rainfall estimates were obtained within a defined buffer of a diameter of 500km around the TC eye while following the TC trajectory. The buffer was further dissected into quadrant spatial zones  (NE, SE, SW and NW) to provide a detailed perspective on rainfall distribution in different parts of the TC impact area. For each eye position, rainfall characteristics (RT, RA and  RI0.9) were computed for the whole buffer and later individual quadrant partitions. The computed rainfall characteristics were then investigated for potential correlation relationships with the TC wind intensity. In addition, quadrant rainfall patterns were analyzed for persistence throughout the TC duration.

 

How to cite: Nabukulu, C., Ettema, J., Jetten, V., and van den Bout, B.: Tropical Cyclone Rainfall Asymmetries Inferred from GPM-IMERG: A Focus on Lesser Antilles, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-11716, https://doi.org/10.5194/egusphere-egu24-11716, 2024.

EGU24-12191 | ECS | Posters on site | NH1.2

Prediction and predictability of drought events in the Spree region 

Clara Hauke, Uwe Ulbrich, and Henning Rust

The predictability of drought events in the Spree region is analyzed, aiming at developing hydrological extreme events forecast and warning systems and long-term solutions regarding sustainable, interdisciplinary and integrated water resources management in the project SpreeWasser:N.

Predictors acting as potential indicators of imminent drought risk are inferred from statistical analyses, modeling and literature. Connections between certain states of the atmosphere (large-scale weather patterns) and local drought events are drawn, focussing mainly on agriculture as a user group. Special attention is paid to the succession of certain weather patterns and their impact on precipitation.

A drought forecast based on k-nearest neighbor regression is being developed using an algorithm which automatically selects the meteorological variables and regions yielding the largest forecast skill as input predictor variables during a hindcast period. This machine learning approach supports the discovery of underlying physical links in atmospheric phenomena.

The analysis and software development is based on ECMWF ERA5 reanalysis data and the objective weather type classification by the German Weather Service (DWD), spanning the years 1980 to 2021.

How to cite: Hauke, C., Ulbrich, U., and Rust, H.: Prediction and predictability of drought events in the Spree region, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-12191, https://doi.org/10.5194/egusphere-egu24-12191, 2024.

Tornadoes represent major meteorological hazards, in terms of damages to buildings, vehicles and structures and casualties. Because of their small space scale (order of 1km or less), duration (order of 1000s), strongly nonlinear and chaotic dynamics, tornadoes cannot be reproduced in operational weather prediction and climate models. It is important to develop approaches overcoming this limitation and capable of delivering reliable early warnings by civil protection services and estimating whether frequency and strength of tornadoes will change because of anthropogenic climate change. Recently, a probabilistic approach has been developed that resulted in analytical expressions of the probability of tornadoes occurrence based on meteorological parameters that can be extracted from weather prediction and climate models, such as WMAX (updraft maximum parcel vertical velocity, linked to the Convective Available Potential Energy CAPE), WS700 (the wind shear in the lower troposphere), LCL (the lifting condensation level), SRH900 (low-level storm relative helicity). An example is the formula log10(P)=-6.6+WMAX/(3.1+5.2 · WMAX/WS700), which is meant to describe dependence of probability P of occurrence of a tornadoes  on the surrounding environmental conditions and to distinguish among conditions with low and high probability. In this study this and similar formulas are applied to hindcasting the probability of tornadoes using ERA5 data. The purpose is to assess the skill of the method for operational prediction and explore its validity for climate change studies.

The methodology supporting this formula is extensively described in Ingrosso, R., Lionello, P., Miglietta, M. M., and Salvadori, G.: Brief communication: Towards a universal formula for the probability of tornadoes, Nat. Hazards Earth Syst. Sci., 23, 2443–2448, https://doi.org/10.5194/nhess-23-2443-2023, 2023.

How to cite: Lionello, P. and Muhammadi, A.: Testing the skill of an analytical expression for the probability of occurrence of tornadoes, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-12741, https://doi.org/10.5194/egusphere-egu24-12741, 2024.

EGU24-15012 | ECS | Orals | NH1.2

Drought projections and associated uncertainties over the Arabian Peninsula from CMIP6 models 

Md Saquib Saharwardi, Hari Prasad Dasari, Waqar Ul Hassan, Harikishan Gandham, Raju Pathak, Karumuri Ashok, and Ibrahim Hoteit

Drought frequency and severity have increased over the water-stressed Arid regions. This research employs multiple CMIP6 global climate models (GCMs) for projecting droughts over the Arabian Peninsula (AP) until the end of the 21st century. We utilized the standardized precipitation index (SPI) and standardized precipitation evapotranspiration index (SPEI) to generate projected future statistics of droughts along with uncertainties assessment from inter-model spread, scenarios, timescale, and methods therein.

For this purpose, after a meticulous analysis, we first identify the most suitable GCMs for better representation of AP's drought spatiotemporal pattern over the historical period (1985-2014). Our results indicate an increase in potential evapotranspiration (PET), which dominates simulated drought statistics relative to the precipitation. The projected evolution of the SPEI, which is derived from both precipitation and PET, indicates droughts  consistently increasing from low to high emission scenarios, In contrast, the SPI, owing to relatively-weaker amplification of the precipitation shows a moderately increasing wetness, except for a few northern regions where both indices evolve in agreement The fidelity of the simulated precipitation by many models over the historical period is also relatively poor compared to the PET, which may also be potentially adding to the uncertainties. In general, the principal sources of uncertainty in drought projections evolve from the choices of index, followed by scenarios, and inter-model variability, whereas methods and timescale mostly impact the magnitude of the trend in drought statistics.  

How to cite: Saharwardi, M. S., Dasari, H. P., Hassan, W. U., Gandham, H., Pathak, R., Ashok, K., and Hoteit, I.: Drought projections and associated uncertainties over the Arabian Peninsula from CMIP6 models, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-15012, https://doi.org/10.5194/egusphere-egu24-15012, 2024.

EGU24-15295 | Orals | NH1.2

Creation of an automatic workflow for a National Flood assessment in Aotearoa New Zealand 

Alice Harang, Emily Lane, Cyprien Bosserelle, Rose Pearson, Celine Cattoën-Gilbert, Trevor Carey-Smith, Hisako Shiona, Sam Dean, Raghav Srinivasan, Graeme Smart, and Matt Wilkins

To manage current flood hazard and help develop climate change adaptation strategies, the government-funded project “Mā te haumaru ō ngā puna wai: Increasing flood resilience across Aotearoa” aims to better understand flood hazard and risk across all Aotearoa New Zealand, now and in the future. A crucial part of this project is the generation of nationally consistent flood maps across the whole country for the current climate and future climate projections.

First, the workflow requires as input the identification of independent floodplains. Each floodplain will be associated to its catchment and be considered a computational unit. For each domain, a design storm is generated for a given scenario (Annual Exceedance Probability, climate projection, antecedent conditions) or an historical storm is used for validation purposes. The runoff and flow routing of streams and rivers on the steep part of the catchment are simulated with the NIWA TopNET model (McMillan et al. 2016). Used uncalibrated, this hydrological model was modified to include a physically realistic soil conductivity and provide a consistent response between gauge and ungauged catchments. The model is spun up to an average base flow with consistent soil and ground water antecedent conditions. The design storm is then run through the model to provide realistic flow boundary conditions to the hydrodynamic model in the populated lower catchment. Before the inundation modelling, the spatial maps are generated, using the GeoFabrics suite (Pearson et al. 2023), across the lower catchment, based on the latest LiDAR data available and complementary databases such as OpenStreetMap for infrastructure. This process produces a hydrologically conditioned DEM (Digital Elevation Model), including waterways opening and a basic riverbed estimation, associated to a roughness length map. Finally, the flood is simulated using the hydrodynamic model BG_Flood (Bosserelle et al. 2022). The model is a GPU-enabled inundation model using a modern shock-capturing St Venant solver. The model uses a quadtree type mesh that is well suited for GPU computation and allows iterative refinement of the mesh. A first coarse resolution run is used to define the expected flood extent. This flood extent and external data such as stop bank locations, is then used to produce a refinement map defining areas where higher resolution is needed. The model is then run a second time using the variably refined mesh.

Figure 1: Scheme of the cascade of model used to develop consistent flood maps in Aotearoa New Zealand.

This workflow has been validated on several historic flood events including a fluvial flood in Westport, ANZ (56h duration, 60-year flood), a fluvial and pluvial flood in Waikanae, ANZ (12h duration, 80-year flood) and the floods in the Hawkes Bay and Tairāwhiti regions (ANZ) following the Tropical Cyclone Gabrielle in February 2023 (over 100-year flood in some areas).

This workflow is based on open-sources tools; it is modular and automated for continual improvement, to enable data update and to facilitate the creation of new scenarios.

How to cite: Harang, A., Lane, E., Bosserelle, C., Pearson, R., Cattoën-Gilbert, C., Carey-Smith, T., Shiona, H., Dean, S., Srinivasan, R., Smart, G., and Wilkins, M.: Creation of an automatic workflow for a National Flood assessment in Aotearoa New Zealand, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-15295, https://doi.org/10.5194/egusphere-egu24-15295, 2024.

EGU24-15755 | ECS | Orals | NH1.2

The September 2023 flood in Derna, Libya: an extreme weather event or man-made disaster? 

Elad Dente, Moshe Armon, and Yuval Shmilovitz

Storm Daniel, the deadliest recorded Mediterranean tropical-like (medicane) storm, led to severe floods in large parts of the eastern-central Mediterranean, including Greece and northern Libya. Extreme rainfall, reaching more than 400 mm day-1, triggered a flash flood in Wadi Derna (Libya)– an ephemeral river with a drainage area of 575 km2 that crosses the city of Derna at its outlet to the Mediterranean Sea. Historical measures to mitigate flood risks included dam construction in the Wadi Dernah basin since the 1970s. However, during Storm Daniel, at least two of the dams were breached, resulting in a devastating flood that inundated much of the city of Derna, with over 4,000 casualties, 8,000 missing persons, and the displacement of tens of thousands. The devastating event was the focus of media coverage for a long time, but questions regarding the role of dams and their collapse remain open, and are relevant for other dammed regions as well: How extreme was the storm? How extreme the flood would have been if the dams had not been breached? What would the outcomes of the flood look like if dams were not built in the first place?

To analyze the characteristics of the storm over Wadi Derna, the catchment’s hydrological response, and the impact of the flood on the city of Derna, we integrate various datasets and models. Satellite-based precipitation estimations (IMERG) were used to quantify spatiotemporal storm properties and the catchment-scale rainfall, which were fed into the KINEROS2 hydrological model to quantify surface runoff upstream of the collapsed dams. The modeled flood hydrograph is then fed into a 2D hydraulic model (HEC-RAS) to test three end-member scenarios: (a) dam filling, overflow, and collapse, (b) dam overflow but no collapse, and (c) no dams exist in the wadi. This combination of methods reveals that the peak discharge during the flood was ~1,400 m3 s-1, just below the expected maximum extreme flood for this region. In the dam-collapse scenario, the populated flooded area is 40% larger than the no-dam scenario. These results emphasize the anthropogenic influence of damming natural streams on flood impacts. Given the high variability of precipitation in arid and semi-arid areas and the projected increase in extreme precipitation intensity with climate change, the Wadi Derna flood should serve as a warning sign for other populated areas downstream of a man-made dam in similar environments.

How to cite: Dente, E., Armon, M., and Shmilovitz, Y.: The September 2023 flood in Derna, Libya: an extreme weather event or man-made disaster?, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-15755, https://doi.org/10.5194/egusphere-egu24-15755, 2024.

EGU24-16370 | ECS | Posters on site | NH1.2

Analysis of historical flood events in Denmark with information from digital news media 

Jonas Wied Pedersen, Peter Steen Mikkelsen, and Michael Brian Butts

Reliable information on historical flood events is critical for flood risk analysis, climate change adaptation, verification of forecast models, etc. Unfortunately, such information is often difficult to find, due to e.g. lack of monitoring equipment at the location of a flood. In Denmark, management of water has traditionally been the responsibility of local authorities, which means there is a limited national overview of historical events and their consequences. Previous studies have employed different strategies for compiling a flood event inventory, including mining information from (1) insurance data, (2) social media data, and (3) newspaper archives. The aim of this study is to exploit a comprehensive digital news media archive to compile an inventory of Danish flood events in the period 2007-2020 with information on the time and location of the event, to classify the type of flood, and note any available information on local consequences and damages.

We have gained access to the company Infomedia’s large digital media archive, which consists of digitized articles from news sources ranging from major national newspapers to small, local outlets. The archive contains more than 75 million news articles with the earliest articles dating back to 1990. The archive is searchable through calls to an API with a custom search language that combine user-specified keywords. A hydrologist has read all articles that match the keywords, noting all the relevant information.

1,118 distinct flooded locations where identified over the 14-year period of 2007-2020. Results show that there is large year-to-year variability in the different types of floods. Urban pluvial floods are experienced somewhere in Denmark every single year, while the number of both fluvial and storm surge floods are very low (or entirely missing) in some years. Urban pluvial floods occur throughout the year but are highly concentrated in the summer months with a mean date of occurrence in late July, while storm surges are observed only between September and March with a mean date in mid-December. Fluvial floods are the least concentrated type of floods and occur throughout the year with a slight overweight in winter months (mean date in early January). The spatial distribution of floods is uneven with four out the 10 municipalities that experience the highest number of floods being located in Eastern Jutland (Vejle, Horsens, Kolding, Aarhus) and another four located in the Northern half of Zealand (Copenhagen, Roskilde, Gribskov, Holbæk).

Storm surge events occur over large geographical areas and we therefore speculate that they are more likely to be reported in news media than urban pluvial floods, which are often local events due to the small-scale nature of convective rainfall cells. Ongoing work is trying to quantify these aspects and validate the individual flood events in the inventory using additional data sources.

How to cite: Pedersen, J. W., Mikkelsen, P. S., and Butts, M. B.: Analysis of historical flood events in Denmark with information from digital news media, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-16370, https://doi.org/10.5194/egusphere-egu24-16370, 2024.

EGU24-16614 | Posters on site | NH1.2

A 172-year Drought Atlas for Romania  

Mihai-Gabriel Cotos, Monica Ionita, Catalin-Constantin Roibu, Adrian-Bogdan Antonescu, Petru-Cosmin Vaideanu, and Viorica Nagavciuc

In this study, we have created a 172-year historic drought catalogue for Romania by applying both the Standardized Precipitation Index (SPI) and the Standardized Precipitation Evapotranspiration Index (SPEI) to 16 long-term meteorological records/stations, covering the period 1852 – 2023. The long-term meteorological records together with documentary sources (e.g., newspapers, meteorological archives) spanning the last 172 years, are used to analyze the spatio-temporal patterns of variability, trends, and potential drivers of drought conditions, thus contributing to a nuanced understanding of Romania's hydroclimatic conditions over time. The results based on the SPEI point to the fact that the southern and eastern parts of Romania are becoming drier due to an increase in the potential evapotranspiration and mean air temperature, especially after the 1990’s. By contrast, the SPI drought index does not reveal these changes in the drought variability, mainly due to the fact that the precipitation does not exhibit a significant change. Five major drought-rich periods, in terms of duration and severity, were identified at the country level from 1852–2023, based on SPEI: 1866 – 1867, 1918 – 1920, 1947 – 1948, 2000 – 2001, and 2019 – 2022, respectively. The most pronounced drought event occurred during 2019 – 2022, followed by the 1866 – 1867 event. When analyzing the SPI-based events, similar results are found over the period 1852 – 1980, but the drought event from 2019 – 2022 is not captured by the SPI index. The most pronounced drought event, based on SPI, is the 1866 – 1867 event, followed by the 1919 – 1920 event. Nevertheless, due to the influence of the Carpathian Mountains, there are also strong regional differences in the drought events and their magnitude, with the southern and eastern parts of Romania being more affected by long-lasting drought events compared to the north-western part. Highlighting the above, a Drought Atlas for Romania (1852 – 2023) was developed using long-term meteorological data, which can provide comprehensive information on drought occurrence, magnitude and impacts over a period that goes beyond the currently available products.

How to cite: Cotos, M.-G., Ionita, M., Roibu, C.-C., Antonescu, A.-B., Vaideanu, P.-C., and Nagavciuc, V.: A 172-year Drought Atlas for Romania , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-16614, https://doi.org/10.5194/egusphere-egu24-16614, 2024.

EGU24-17041 | ECS | Orals | NH1.2

Evaluation of the performance of hydrological model LISFLOOD using the ECMWF seasonal meteorological forecast at 1arcmin-1day spatiotemporal resolution over German catchments 

Edgar Fabian Espitia Espitia, Yanet Diaz Esteban, Fatemeh Heidari, Qing Lin, and Elena Xoplaki

Floods and their devastating effects on society and economy have increased dramatically in Germany, and Europe in recent years. At the end of 2023, rivers and streams across Germany burst their banks due to heavy rainfall, affecting property, transport and power supplies and necessitating rescue operations and evacuations to protect human lives. One measure to deal with flooding and safeguard lives and property is the implementation of early warning systems, such as the European Flood Awareness System (EFAS), which provides short-term hydrological forecasts in real time. However, preparedness is essential along the responders value chain and longer term forecasts are important to anticipate, take precautions, raise awareness and generally mitigate the effects of flooding. The objective of this study is to evaluate the performance of hydrological forecasting using the seasonal meteorological forecast at a spatio-temporal resolution of 1 arcmin and day over Germany including all transboundary catchments for the period from 1990 to 2020. The hydrological model used was LISFLOOD. In the first step, LISFLOOD was calibrated using the meteorological observations, the EMO 1arcmin dataset and the discharge data from the transnational hydrological portal for all federal states and neighboring countries. The characteristics of land use, land cover, soil, groundwater, and human activity referred to as surface fields for global environmental modelling, were provided by EFAS. The second step, downscaling of the seasonal (long-term) forecast meteorological forcing to 1arcmin, is performed using a Deep Residual Neural Network (DRNN), and a bilinear interpolation approach over the seasonal forecast information of atmospheric conditions up to seven months into the future provided by the European Center for Medium-Range Weather Forecasts (ECMWF), 25 ensemble members in total. In the third step, the discharge is simulated by feeding the LISFLOOD model with two meteorological forcing scenarios, the DRNN downscaled and the bilinear approach of the seasonal meteorological forecast, to finally compare the performance with the observed runoff using the modified Kling-Gupta efficiency criteria (KGE'). The calibrated and validated LISFLOOD parameters showed a good and acceptable performance in all catchments, KGE' between 0.6 and 0.9. The DRNN downscaling technique shows a promising result, providing a good agreement between downscaled and observed dataset. Finally, the hydrological performance, KGE', is expected to be improved by 0.05 to 1 in the hydrological stations with good and poor performance, respectively, by using the DRNN downscaled seasonal forecast.

How to cite: Espitia Espitia, E. F., Diaz Esteban, Y., Heidari, F., Lin, Q., and Xoplaki, E.: Evaluation of the performance of hydrological model LISFLOOD using the ECMWF seasonal meteorological forecast at 1arcmin-1day spatiotemporal resolution over German catchments, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-17041, https://doi.org/10.5194/egusphere-egu24-17041, 2024.

EGU24-18684 | ECS | Orals | NH1.2

Modeling Uncertainty of Copula-based Joint Return Period of Flood Events under Climate Change 

Ankita Manekar and Meenu Ramadas

Modeling the joint behavior of flood characteristics under climate change is necessary for understanding the potential changes in associated flood risk and hazards. In this study, we assessed the changes in flood duration, peak, and volume between historical and future periods through copula-based flood frequency analysis, employing the Soil and Water Assessment Tool (SWAT) hydrological model for modeling flood risk in a tropical watershed (Govindpur) lying in eastern India. Observed streamflow at the watershed outlet is obtained for the baseline period (1990-2014) for flood analysis. A suitable copula model is selected for bivariate flood frequency analysis while assuming copula parameters vary between baseline and future periods under climate change. In this study, high-resolution (12-km) climate reanalysis dataset from the Indian Monsoon Data Assimilation and Analysis (IMDAA) and future climate projections from general circulation models (BCC-CSM2-MR, MPI-ESM1-2-HR) after downscaling and bias correction, are used for simulating flood events using SWAT. The use of high-resolution climate data for hydrological modeling and flood frequency analysis is a novel aspect of the presented study. Uncertainty in the estimation of joint return periods of flood events under climate change due to climate model selection and assumption of stationarity is also quantified in this study for the near future (2041-2070) period under the shared socio-economic pathway (SSP585) scenario. Among the GCMs used, BCC-CSM2-MR performed relatively better in simulating baseline period streamflow in the study watershed. In this study, the Clayton copula is obtained as the most suitable based on its lowest Akaike information criterion (AIC) value, and joint return periods are then derived with the help of a conditional copula. It is found that flood events are projected to become more severe in the near future; the flood peak value increased by more than 90%, while the duration is projected to decrease. Flood volume may likely double in the future, as per our analysis, suggesting the need for mitigation and precautionary measures to reduce flood risk in the watershed. Based on the analysis, uncertainty in flood return period estimation under changed future climate is to be accounted for extreme event studies, and that can aid in managing and minimizing the flood-associated risks.

Keywords: Climate Change, Flood Frequency Analysis, Soil and Water Assessment Tool, Copula, General Circulation Model, Uncertainty Analysis

How to cite: Manekar, A. and Ramadas, M.: Modeling Uncertainty of Copula-based Joint Return Period of Flood Events under Climate Change, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-18684, https://doi.org/10.5194/egusphere-egu24-18684, 2024.

The flood events in Germany during the summer of 2021 have once again brought to the forefront the challenges in translating scientific knowledge into effective disaster risk management practices. This paper examines the critical gap between the scientific understanding of flood risks and the practical needs of those who manage these risks. We delve into the limitations of current scientific approaches, such as flood risk and hazard mapping, in fully addressing the complexities and nuances required for practical disaster risk management, especially in the face of uncertain climate change impacts. We examine the dynamics of how flood risk information, inclusive of uncertainties, is perceived and acted upon, highlighting the psychological factors influencing these processes. The paper discusses the challenges and opportunities in translating scientific risk assessments and forecasts into practical, actionable strategies for communities and stakeholders. By highlighting the disconnects and potential areas for improvement in the science-practice interface, this paper seeks to foster a more coherent and comprehensive approach to disaster risk management. Within the framework of the Safe Development Paradox, the importance of communicating uncertainties and evaluating their potential impacts on planning and emergency responses is discussed. This paper addresses uncertainties at multiple levels and for different stakeholders, highlighting the integration of uncertainty information as a vital step in preparing for surprises and ambiguities in the context of extreme meteorological and hydrological events induced by severe weather and climate change.

How to cite: Höllermann, B.: Navigating Uncertainty in Flood Risk Perception in the Context of Climate-Induced Extremes , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-18692, https://doi.org/10.5194/egusphere-egu24-18692, 2024.

EGU24-19701 | Orals | NH1.2

Counterfactual floods: What if the storm track would have taken a different path? 

Bruno Merz, Viet Dung Nguyen, Guse Björn, Li Han, Xiaoxiang Guan, Oldrich Rakovec, Luis Samaniego, Bodo Ahrens, and Sergiy Vorogushyn

When a flood disaster occurs, there is an opportunity for affected individuals and decision-makers to learn from the experience. However, this learning tends to be narrowly focused on the specific event, missing the chance to discuss and prepare for even more severe or different events. For instance, regions that have been spared from havoc might feel safe and underestimate the risk. We suggest spatial counterfactual floods to encourage society to engage in discussions about exceptional events and appropriate risk management strategies. We create a series of floods across Germany by spatially shifting the rainfall fields of the 10 most expensive floods, arguing that past storm tracks could have occurred several tens of kilometers away from their actual paths. The set of spatial counterfactual floods generated includes events that are more than twice as severe as the most devastating flood in Germany since 1950. Our approach obtains peak flows that exceed the current flood-of-record at more than 70% of the gauges (369 out of 516). Spatial counterfactuals are proposed as an easy-to-understand approach to overcome society's unwillingness to consider and prepare for exceptional floods, which are expected to occur more frequently in a warmer world.

How to cite: Merz, B., Nguyen, V. D., Björn, G., Han, L., Guan, X., Rakovec, O., Samaniego, L., Ahrens, B., and Vorogushyn, S.: Counterfactual floods: What if the storm track would have taken a different path?, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-19701, https://doi.org/10.5194/egusphere-egu24-19701, 2024.

EGU24-19897 | Orals | NH1.2

Long-Term Trends and Drivers of Hailstorms in Switzerland 

Lena Wilhelm, Olivia Martius, Katharina Schröer, and Cornelia Schwierz

Climate change affects the severity and frequency of extreme meteorological events, including hailstorms. In this regard, it is imperative to understand the factors driving the intra- and interannual variability of hailstorms. In Switzerland, this remains insufficiently understood. To address this knowledge gap, our study conducts a long-term analysis to identify potential drivers and precursors of Swiss hailstorm variability. Due to the lack of long-term data on Swiss hailstorms, we developed statistical models reconstructing hail days from 1959 to 2022, utilizing radar-based hail observations and environmental data from ERA-5. Our hailday time series shows a statistically significant positive trend in yearly hail days in both southern and northern Switzerland. This trend is mainly attributed to heightened atmospheric instability and moisture content evident in recent decades' ERA-5 data. Noteworthy natural variability is observed in both regions. To delve into the large-scale mechanisms influencing Swiss hail activity, our study uses composites to explore potential drivers and precursors. Those include soil moisture conditions, sea surface temperature anomalies, large-scale variability patterns (Piper and Kunz 2017), central European weather types (e.g., Rohrer et al. 2018), cold fronts (Schemm et al. 2015, 2016), and atmospheric blocks (e.g. Barras et al. 2021). 

How to cite: Wilhelm, L., Martius, O., Schröer, K., and Schwierz, C.: Long-Term Trends and Drivers of Hailstorms in Switzerland, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-19897, https://doi.org/10.5194/egusphere-egu24-19897, 2024.

EGU24-19970 | ECS | Posters on site | NH1.2

Freva for ClimXtreme: helping to systematize holistic analysis of extreme events 

Etor E. Lucio-Eceiza, Christopher Kadow, Martin Bergemann, Andrej Fast, and Thomas Ludwig

Climate change is responsible for more extreme weather situations with damaging consequences. Public interest projects such as ClimXtreme [1, 2] were conceived to improve our knowledge on extreme events, the role of climate change, and their impacts. Focusing on an integrated approach, ClimXtreme evaluates the physical processes behind the extremes, their statistical assessment and their societal impact. On its second phase ClimXtreme [3] aims to open up its findings to a wider stakeholder base of different kinds.

Frameworks such as Freva (Free Evaluation System Framework [4, 5]) offer an efficient solution to handle customisable evaluation systems of large research projects, institutes or universities in the Earth system community [6-8] via the HPC environment and in a centralised manner. Mainly written in Python, Freva offers:

  • Centralised access. Freva can be accessed via command line interface, web, and a Python module with similar functionality.
  • Standardised data search. Freva allows quick and intuitive integration and searching of multiple, centrally stored data sets.
  • Flexible analysis. Freva provides a common interface for user-defined data analysis routines to be plugged into the system, regardless of the programming language. These plugins are able to search from and integrate their own results back into Freva. This environment enables an ecosystem of plugins that promotes the exchange of results and ideas between researchers, and facilitates the portability to any other research project using a Freva instance.
  • Transparent and reproducible results. Every analysis run through Freva (including parameter configuration and plugin version information) is stored in a central database and can be viewed, shared, modified and re-run by anyone within the project. Freva optimises the use of computing and storage resources and paves the way for traceability in line with the FAIR data principles [9].

The Freva instance of ClimXtreme (XCES [7]), hosted at DKRZ, provides fast access to more than 10 million data files from models (e.g. CMIP, CORDEX), observations (e.g. ERA5, HYRAS, stations) and plugin outputs. The ClimXtreme community has actively contributed plugins to XCES, its biggest asset, with nearly 20 plugins of different disciplines available to all within the project.

We would like to show a practical application of the capabilities of XCES by using it to systematise the characterisation (e.g. return periods, severity, co-occurrence...) of several past extreme events extracted from the ClimXtreme Phase 1 catalogue. Such an application can be extended to create workflows focused, for example, on the rapid assessment of the analysis of currently occurring events, allowing a quicker response to stakeholders or the public in general.

 

References:

[1] https://www.fona.de/de/massnahmen/foerdermassnahmen/climxtreme.php

[2] https://www.climxtreme.net/index.php/en/

[3] https://www.fona.de/de/aktuelles/nachrichten/2023/231207_ClimXtreme_Phase_2_b.php

[4] http://doi.org/10.5334/jors.253

[5] https://github.com/FREVA-CLINT/freva-deployment

[6] freva.met.fu-berlin.de

[7] https://www.xces.dkrz.de/

[8] www-regiklim.dkrz.de

[9] https://www.go-fair.org/fair-principles/

 

 

How to cite: Lucio-Eceiza, E. E., Kadow, C., Bergemann, M., Fast, A., and Ludwig, T.: Freva for ClimXtreme: helping to systematize holistic analysis of extreme events, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-19970, https://doi.org/10.5194/egusphere-egu24-19970, 2024.

EGU24-20455 | Posters virtual | NH1.2

Investigating the effects of initial soil moisture and the uncertainty of Manning friction coefficient on flood hazard estimation and mapping. 

Athanasios Loukas, Anastasios Katsiolas, and George Papaioannou

Floods are among the most devastating water-related hazards and are primarily responsible for the loss of human life and destruction of the natural and man-made environment. This study addresses the estimation and mapping of flood hazard in small mountain watersheds with urban areas at the lowlands and the related uncertainty. Specifically, this research studies the flood hazard for the Metropolitan city of Volos in Central Greece, which is frequently affected by intense storms that cause flash floods. The above study area is crossed by three (3) streams.The methodology used in the study is divided into three stages. At first the 24-hour design storm hydrographs were constructed for the three sub-basins of the study area with using the mean IDF parameters and the relevant confidence limits. The Alternating Block Method was used for the design hyetographs for return periods, T = 50-year, T=100-year and T=1000-year (worst-case scenario). The second stage concerns the hydrological analysis using a rainfall-runoff model. Firstly, the net rainfall was estimated by using the U.S. Soil Conservation Service (SCS-CN) method for three (3) soil's Antecedent Moisture Conditions (AMC) for dry-average-wet conditions. Then, the net rainfall was transformed by using the Instantaneous Unit Clark hydrograph into discharge and the flood hydrographs for each return period were estimated. At the final stage, the flood hydrograph estimated for each watershed was routed through the hydrographic network using the HEC-RAS 2D hydraulic-hydrodynamic simulation (2D) model.  For the flow routing, Manning’s n was estimated for various cross sections by visual inspection and corresponding values reported in international reports. The “upper” and “lower” boundaries of Manning’s n were estimated as the -50% and +50% of the average Manning’s n values, respectively. In this simulation approach, flood hazard maps for three return periods, T=50, T=100 and T=1000 years considering three different soil moisture conditions and three different values of Manning’s n have been estimated. The values of Manning’s n in the flood plain were estimated by using land cover/land use data.  The flow routing with in the urban areas was simulated by the block rising method. In total twenty-seven (27) flood scenarios have been simulated for each watershed. The results were validated with the flooded areas during a specific historical flood event using the Critical Success Index (CSI) method and reports and photographs of the historical flood event. The results of hydrological analysis and hydraulic simulation were also compared with the results of the Greek Flood Hazard Management Plans.

How to cite: Loukas, A., Katsiolas, A., and Papaioannou, G.: Investigating the effects of initial soil moisture and the uncertainty of Manning friction coefficient on flood hazard estimation and mapping., EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-20455, https://doi.org/10.5194/egusphere-egu24-20455, 2024.

EGU24-22160 | Posters on site | NH1.2

Novel approach to quantifying long-term rainfall distribution variation: the region of Europe 

Andrew Barnes and Ioanna Stamataki

Climate change is changing rainfall and flood regimes across the world with severe and widespread impacts on society. Rainfall extremes are intensifying in frequency and magnitude due to the effects of climate change, and thus in this research, we introduce a new, novel framework for understanding how rainfall distributions are changing through time, enabling more accurate flood risk analysis. The framework offers two approaches to comparing rainfall distributions, the first of these utilises a stagnant benchmark distribution and the second highlights a moving benchmark approach. When combined the framework enables the identification of significant sudden and gradual changes in the distributions without the need to fit statistical distributions to the data.

 The region of Europe is selected as the case study and analysed in the four UN regions of Europe: Northern Europe, Eastern Europe, Southern Europe, and Western Europe. Using daily precipitation data generated using the ERA5 Reanalysis hourly data from the ECMWF’s Copernicus data store, the case study is used to highlight the capability of both frameworks to capture different forms of rainfall distribution shift.

 Comparing the frameworks presented revealed similar long term changes in the rainfall variation. The stagnant comparison showed that rainfall distributions have intensified since 1940 with a clear increase across all four regions of Europe regarding the percentage of days with rainfall, averaging at 2.75% across Europe. The largest changes seen are in the last comparison period for Eastern Europe (1960-1975) at 3.07% and in the latest comparison period (2005-2020) for Northern Europe (2.64%). The moving comparison method unveiled the strongest changes between the periods 1940-1960 and 1960-1980 with an average of 2.09% of rainfall days being intensified across all Europe. The most considerable shifts in rainfall variability occurred in Eastern (2.39%) and Western Europe (2.72%) during the 1960-1980 period.

 By applying it over the European region, this paper demonstrated how this novel approach can be used to identify long-term rainfall variation in the 20th century. The suggested frameworks do not rely on fitting statistical distributions and thus enable both long and short term change identification, providing flood risk managers a new solution to understanding local, regional and global rainfall variability and quantification. The analysis of the changing dynamics of precipitation patterns and the increase of the intensity of precipitation events, offers considerable potential for further investigations in the mitigation strategies of a resilient future.

How to cite: Barnes, A. and Stamataki, I.: Novel approach to quantifying long-term rainfall distribution variation: the region of Europe, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-22160, https://doi.org/10.5194/egusphere-egu24-22160, 2024.

The intensification of extreme precipitation in a warming climate has been shown in observations and climate models to follow approximately theoretical Clausius-Clapeyron scaling. However, larger changes have been indicated in events of short-duration which frequently trigger flash floods or landslides, causing loss of life. Global analyses of continental-scale convection-permitting climate models (CPCMs) and new observational datasets will be presented that provide the state-of-the-art in understanding changes to extreme weather (rainfall, wind, hail, lightning) and their compounding effects with global warming. These analyses suggest that not only warming, but dynamical circulation changes, are important in the manifestation of change to some types of extreme weather, which must be addressed in the design of new CPCM ensembles. We use our projections to provide the first analyses of impacts on infrastructure systems using a new consequence forecasting framework and show the implications for adaptation. It will be argued that a shift in focus is needed towards examining extreme weather events in the context of their ‘ingredients’ through their evolution in time and space. Coupled with exploration of their causal pathways, sequencing, and compounding effects – ‘storylines’ –, this can be used to improve both early warning systems and projections of extreme weather events for climate adaptation.

How to cite: Fowler, H.: Rapidly intensifying extreme weather events in a warming world: how important are large-scale dynamics in generating extreme floods?, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-22472, https://doi.org/10.5194/egusphere-egu24-22472, 2024.

EGU24-532 | ECS | Orals | NH1.3 | Highlight

Rapid unsupervised economic assessment of urban flood damage using SAR images 

jeremy Eudaric, Andres Camero, Kasra Rafiezadeh Shahi, Heidi Kreibich, Sandro Martinis, and Xiao Xiang Zhu

Climate change projections for 2030 indicate a concerning increase in the frequency of floods, which is expected to result in significant economic damages and losses on a global scale. The growth of urbanization has indeed increased flood risk, highlighting the need for a prompt evaluation of economic losses to facilitate rapid response and effective reconstruction. However, providing timely and accurate economic damage assessment immediately after a flood event is difficult and associated with high uncertainty. Remote sensing  data can support this task, but challenges such as cloud cover, infrequent return times from satellites, and the lack of ground truth data make supervised approaches challenging. To address these challenges, we propose a new economic damage assessment approach based on the analysis of multi-temporal and multi-source, Synthetic Aperture Radar (SAR) images before and after the flood peak with an unsupervised change detection method. This method utilizes computer vision techniques, specifically a pixel-based approach with SAR data (Sentinel-1 and TerraSAR-X/TanDEM-X) to monitor changes in buildings and the flood extension. It employs various threshold techniques and parameters to determine the optimal threshold values for highlighting changes and the presence of water. By using this method, our aim is to obtain an economic model based on pixels, which represents the volume of water surrounding or on each building and the flood extension. The purpose of this study is to support governments in decision-making processes and enable insurers to efficiently assess and compensate for damages caused by flood events. 

How to cite: Eudaric, J., Camero, A., Rafiezadeh Shahi, K., Kreibich, H., Martinis, S., and Zhu, X. X.: Rapid unsupervised economic assessment of urban flood damage using SAR images, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-532, https://doi.org/10.5194/egusphere-egu24-532, 2024.

EGU24-957 | ECS | Posters on site | NH1.3

Flood Mapping Using High-Resolution Topography and Crowdsourced Data with the Geomorphic HAND Approach in Rural Plains 

Hassan Sabeh, Marie-George Tournoud, Nanée Chahinian, Chadi Abdallah, Roger Moussa, and Rouya Hdeib

Flood mapping is essential for risk management and emergency response. The most common approach is hydraulic modelling, a method that is still challenging and demanding in terms of data and computation. Low complexity models are an increasingly adopted alternative that are capable of achieving good results while using minimal data input and low calculation time. Yet, the reliability and effectiveness of such approaches remain unclear in flat and engineered plains. In this study we aim to optimize flood hazard mapping based on the Height Above Nearest Drainage (HAND) geomorphic approach by utilizing a high-resolution digital elevation model (15 cm) with crowdsourced data. The approach is tested on the Ostouane river basin (144 km2) in Lebanon, and validated using crowdsourced data of the January 2019 flood, which was the most intense flood within the past decade. The workflow begins by developing a database of spatial and topographic information, including the digital elevation model, bathymetry, land use and crowdsourced flood depths. Five scenarios representing different terrain configurations with varying levels of hydro-conditioning and feature inclusion (e.g. bathymetry, canals and levees) are simulated. The model’s thresholding is then optimized by integrating rating curves produced by 1D HEC-RAS hydraulic model to assess and correct HAND based synthetic rating curves (SRC). Results shows that extensive hydro-conditioning is necessary to improve the inundation extents within the floodplains. Correcting synthetic rating curves is essential to overcome errors produced by terrain conditioning. Overall, the model is able to yield high accuracy of flood extent when ensuring hydrologic connectivity between the river and floodplain and within the floodplain itself. Our findings indicate that leveraging high-resolution topography and crowdsourced inputs can enhance the accuracy of flood mapping results. However, achieving this precision necessitates a meticulous optimization procedure.

How to cite: Sabeh, H., Tournoud, M.-G., Chahinian, N., Abdallah, C., Moussa, R., and Hdeib, R.: Flood Mapping Using High-Resolution Topography and Crowdsourced Data with the Geomorphic HAND Approach in Rural Plains, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-957, https://doi.org/10.5194/egusphere-egu24-957, 2024.

EGU24-1081 | ECS | Orals | NH1.3

CFD modelling to investigate hydrodynamic forces on bridges in case of submergence and material deposition  

Natasha Petruccelli, Diego Panici, Alessio Domeneghetti, and Armando Brath

Increasingly frequent and intense flood events, combined with the remarkable industrialization process of cities, are placing transportation networks under stress. The loads that roads and railways must resist are nowadays often greater than those considered for their design; furthermore, their state of ageing is such that any disturbance (flood, earthquake, landslide) could cause a total or partial interruption of traffic resulting in socio-economic losses.
Bridges represent the most vulnerable component of a transport system and their failure can compromise the functionality of the entire network, as well as causing loss of life. During floods, bridges can be partially or completely submerged, having to withstand higher hydrodynamic loads which can lead to the collapse of the structure itself. Furthermore, accumulations of large wood and scour total around the bridge piers can reduce the load-bearing capacity of the structure and therefore its structural integrity.
In this study, we investigated the hydrodynamic actions and the 3-dimensional flow field at a model bridge (comprising deck and pier) using CFD (Computational Fluid Dynamics) modelling. Drag and lift forces acting on the rectangular-shaped deck were estimated for different submergence values to evaluate the structure's maximum permissible load. In particular, drag and lift coefficients were calculated by simulating various flow conditions (Froude number varying between  0.16 and 0.50) and adopting three different turbulence models (RNG, k-ε, k-ω).
In addition, the effect on the drag coefficient of the accumulation of large wood around the pier was also examined, considering different geometries. Numerical simulations, performed for both fixed and live river bed conditions, were validated using experimental data. However, the trends of the synthetic curves constructed so far have presented characteristics similar to those present in the literature, with all positive values ​​for the drag coefficient and negative ​​for the lift coefficient. 
The emerging evaluations allow us to provide useful indications to designers to evaluate the possible state of stresses on existing bridges and improve knowledge for designing new ones.

How to cite: Petruccelli, N., Panici, D., Domeneghetti, A., and Brath, A.: CFD modelling to investigate hydrodynamic forces on bridges in case of submergence and material deposition , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-1081, https://doi.org/10.5194/egusphere-egu24-1081, 2024.

EGU24-1594 | ECS | Orals | NH1.3

Drivers of coastal real estate demand under flood model predictions in the UK 

Avidesh Seenath, Scott Mark Romeo Mahadeo, Matthew Blackett, and Jade Catterson

Flood model predictions are becoming increasingly available online through open access flood risk maps and communications. While these predictions are important for flood management, their inherent uncertainty presents a considerable risk for real estate markets, a leading indicator of macroeconomic performance.  We, therefore, need to understand the factors influencing real estate demand in an era of open access flood model predictions. Here, we investigate the role of gender, education, employment, place of residence, caring responsibilities, income, insurance, location preferences, level of risk aversion, and flood experience and awareness on coastal real estate demand decisions in the UK in response to flood model predictions. Here, our objective is test whether access to flood predictions is a leading driver of real estate demand decisions or whether alternative factors influence how people perceive such predictions. We achieve this by applying an inter-disciplinary approach, involving numerical flood modelling, a novel experimental willingness-to-pay real estate survey of UK residents in response to flood model outputs, statistical and geospatial modelling, and thematic analysis. Our preliminary findings indicate that access to flood model predictions is the primary factor influencing real estate demand decisions, whereas alternative factors considered have negligible impact. Such preliminary findings suggest that we need to re-think how flood model predictions are communicated in order to minimise real estate risks.  

How to cite: Seenath, A., Mahadeo, S. M. R., Blackett, M., and Catterson, J.: Drivers of coastal real estate demand under flood model predictions in the UK, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-1594, https://doi.org/10.5194/egusphere-egu24-1594, 2024.

EGU24-1692 | Orals | NH1.3 | Highlight

A Fresh Start for Flood Estimation in Ungauged Catchments 

Ross Woods, Yiming Yin, Giulia Evangelista, Pierluigi Claps, Giulia Giani, Yanchen Zheng, Gemma Coxon, Roberto Quaglia, Dawei Han, and Miguel Rico-Ramirez

Flood estimation in ungauged basins is important for flood design, and for improving our understanding of the sensitivity of flood magnitude to changes in climate and land cover. Flood estimates in ungauged basins by current methods (e.g. statistical regression, unit hydrograph) have high uncertainty, even in places with dense observing networks (e.g. +/- 50-100% in the UK). Reductions in this uncertainty are being sought by using alternative methods, such as continuous simulation using hydrological models (spatially-distributed or lumped), and event-scale derived distribution approaches. The very significant challenges for reliable application of continuous simulation models in ungauged catchments are well known. So far there has been only limited application of machine learning techniques to this problem, but it seems an obvious route to try, but to exploit the big-data strengths of this approach, the problem must be recast to extract information from many more events at each site than just annual maximum events.

The event-scale derived distribution approach also has challenges, which we explore below. The derived distribution approach at the event scale typically combines the following elements: a stochastic rainfall model, an event-scale rainfall-runoff model (including “losses” and a “baseflow” component), and a runoff routing model. In principle, every element of this approach may be considered as a (seasonally varying) random variable. The flood peak distribution is obtained by integrating over joint distributions of the model elements. After giving an overview of our approach, I will focus on challenges regarding the catchment response time associated with flood events.

How should we define catchment response time? Why do we need this quantity and how will it be used? What are the relative merits of empirical and model/theory-based approaches? Specifically, I will discuss the empirical DMCA method for catchment response time of Giani et al, https://doi.org/10.1029/2020wr028201). How is it relevant for ungauged catchments? What does DMCA really measure? How do we assign hydrological meaning to this empirical response time? How does this response time vary between events and catchments?

How to cite: Woods, R., Yin, Y., Evangelista, G., Claps, P., Giani, G., Zheng, Y., Coxon, G., Quaglia, R., Han, D., and Rico-Ramirez, M.: A Fresh Start for Flood Estimation in Ungauged Catchments, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-1692, https://doi.org/10.5194/egusphere-egu24-1692, 2024.

EGU24-3629 | ECS | Orals | NH1.3

Flood plain inundation modeling with explicit description of land surface macrotopography 

Simone Pizzileo, Giovanni Moretti, and Stefano Orlandini

Land surface topography plays an essential role in flood plain inundation modeling. High-resolution digital surface models (DSMs) based on LiDAR surveys have become increasingly accessible in various geographical areas. Nevertheless, common practice involves filtering out land surface macrostructures, such as trees and buildings, by using obtained digital terrain models (DTMs) to represent the land surface hydraulic geometry. This is done by letting resistance coefficients represent the effects of both micro and macrostructures on surface flow propagation. In addition, significant information loss is observed when digital terrain models are coarsened for computational efficiency.

In the present study, physically meaningful unstructured meshes are automatically extracted from high-resolution digital surface models to explicitly describe land surface macrostructures. This is achieved by extracting relevant ridges at a selected level of representation without applying any coarsening or depression filling pre-processing. The effects of these macrostructures on floodwater propagation are evaluated by comparing simulations obtained by using digital terrain models and related Manning coefficients, simulations obtained by using digital surface models representing land surface macrostructures and related Manning coefficients, and observations for a real flood inundation event occurred after a levee failure in the lowlands adjoining the Panaro River in Northern Italy in 2020.

The explicit description of land surface macrostructures based on a 1-m digital surface model is found to yield a 42% improvement in the prediction of flooded area extent, a 36% improvement in the prediction of flooded areal position, and a 24% improvement in the prediction of flood plain inundation travel time with respect to the case in which resistance coefficients representing both land surface micro and macrostructures are used. Unstructured meshing of land surface macrostructures based on extracted ridge networks is essential for achieving a detailed description of land surface hydraulic geometry without altering the original topographic data, while also preserving computational efficiency. The obtained results highlight the role of natural and human-made macrotopographic structures in delineating flood plain inundation models and generating flood hazard mapping. These tools represent valuable assets in the context of Emergency Action Planning (EAP) and prevention strategies.

How to cite: Pizzileo, S., Moretti, G., and Orlandini, S.: Flood plain inundation modeling with explicit description of land surface macrotopography, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-3629, https://doi.org/10.5194/egusphere-egu24-3629, 2024.

EGU24-3747 | Posters on site | NH1.3

Developing a flood image detection model using deep learning algorithms 

Cheng-Lin Yang

As the impact of climate change intensifies, the frequency of short-duration heavy rainfall events gradually increases, posing a serious challenge to urban infrastructure and underground drainage systems. Assessing flood-prone areas and disaster extents relies heavily on manual surveys, lacking real-time and effective methodologies. Our study uses Mask R-CNN deep learning and closed-circuit television (CCTV) flood images to develop a real-time and effective flood detection model. The results of our study demonstrate that the proposed flood image recognition model achieves a precision of 60.6%, a recall rate of 92.2%, and an F1 score of 73.1 for the flood category. These results signify the model's exceptional capability of the model in flood detection. Additionally, through on-site measurements of road dimensions and binary matrix-based area estimation, the average error is only 1.6%. This model can be applied effectively and serves as a reference for authorities to promptly determine the occurrence of flooding and the extent of the disaster, thus facilitating the formulation of more effective disaster response measures. The developed model exhibits promising potential for real-time flood detection in urban disaster management, providing a valuable tool for authorities to respond promptly to the dynamic challenges posed by climate change.

How to cite: Yang, C.-L.: Developing a flood image detection model using deep learning algorithms, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-3747, https://doi.org/10.5194/egusphere-egu24-3747, 2024.

EGU24-4842 | ECS | Posters on site | NH1.3

Substantial increase in future fluvial flood risk projected in China's major urban agglomerations 

Ruijie Jiang, Hui Lu, Kun Yang, Deliang Chen, Jiayue Zhou, Dai Yamazaki, Ming Pan, Wenyu Li, Nan Xu, Yuan Yang, Dabo Guan, and Fuqiang Tian

Floods are one of the most destructive natural disasters and projecting future flood risk is essential for protecting lives and livelihoods. China is in the process of rapid urbanization, and most of the urban agglomerations are distributed on floodplains, facing high fluvial flood risk. The effect of urban spatial expansion, instead of densification of assets within existing urban cells, on flood risk has rarely been reported. Here, based on the latest projected urban land data and bias-corrected CMIP6 outputs, we project the future flood risk of seven urban agglomerations in China, home to over 750 million people. The inundated urban land areas in the future are projected to be 4 to 19 times that at present, with southern China facing the greatest increase. Although climate change is the main driver for this strong projected rise in flood risk, the inundated urban land areas will be underestimated by 10-50% if the urban spatial expansion is not considered. Urban land is more likely to be inundated than non-urban land, and the newly-developed urban land will be inundated more easily than the historical urban land due to the marginal expansion of urban land. The results demonstrate the urgency of integrating climate change mitigation, reasonable urban land expansion, and increased flood protection levels to minimize the flood risk in urban land.

How to cite: Jiang, R., Lu, H., Yang, K., Chen, D., Zhou, J., Yamazaki, D., Pan, M., Li, W., Xu, N., Yang, Y., Guan, D., and Tian, F.: Substantial increase in future fluvial flood risk projected in China's major urban agglomerations, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-4842, https://doi.org/10.5194/egusphere-egu24-4842, 2024.

EGU24-5935 | ECS | Orals | NH1.3

Assessing the flooding hazard through a probabilistic approach including earthen levees vulnerability estimate     

Bianca Bonaccorsi, Silvia Barbetta, and Giuseppe Tito Aronica

Levees collapse cause huge financial and social losses, especially in highly developed areas of many countries. Since that 2563 floods occurred in Europe between 1980 and 2010 (EEA, 2018), the European Parliament issued the Floods Directive, approved in 2009, in which EU Member States are invited to minimise this risk of failure, improving methods and finding simple solutions for large-scale application. For this reason, the scientific community is gradually performing stochastic approaches allow a large number of simulations runs in a Monte Carlo framework, providing the basis for a probabilistic risk assessment considering also the influence of levee breaches on the flood risk (Apel et al., 2006, Castellarin et al., 2011). Indeed, in many studies, seepage analyses account only the hydraulic boundary conditions, i.e.  the water head upstream of the embankment (Tracy et al., 2016, 2020).  

In this context, the present work is focused on the evaluation of the residual flood risk through the analysis of earthen levees’ seepage vulnerability. In particular, the levee fragility curves determined with the use of simplified and expeditious approaches and those assessed by using geotechnical finite element models (i.e. PLAXIS 2D) are compared. Furthermore, the goal of this study is to find the relation between the frequency of levee’s failure due to hydraulic and geotechnical conditions, to aim of define the conditional probability of the residual flood risk.

 

References

Apel, H., Annegret, H. T., Bruno, M., & Günter, B. (2006). A probabilistic modelling system for assessing flood risks. Natural Hazard, 38, 79-100. https://doi.org/10.1007/s11069-005-8603-7.

Castellarin, A., Di Baldassare, G., & Brath, A. (2011). Floodplain management strategies for flood attenuation in the River Po. River Research and Applications, 27(8), 1037 –1047. https://doi.org/10.1002/rra.1405.

EEA, European Environment Agency. (2018). European past floods [Online]. Copenhagen, Denmark: Author. Retrieved from https://www.eea.europa.eu/data-and-maps/data/european-past-floods/ .

Tracy, F.T., Brandon, T. L., Corcoran, M.K. (2016). Transient seepage analyses in levee engineering practice, Technical Report TR-16-8, U.S. Army Engineer Research and Development Center, Vicksburg, MS, http://acwc.sdp.sirsi.net/client/en_US/search/asset/1050667.

Tracy, F.T., Ryder, J.L., Schultz, M.T., Ellithy, G.S., Breland, B.R., Massey, T.C., Corcoran, M.K. (2020). Monte Carlo Simulations of Coupled Transient Seepage Flow and Soil Deformation in Levees. Scalable Computing Practice and Experience 21(1):147-156. https://doi.org/10.12694/scpe.v21i1.1629.

How to cite: Bonaccorsi, B., Barbetta, S., and Aronica, G. T.: Assessing the flooding hazard through a probabilistic approach including earthen levees vulnerability estimate    , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5935, https://doi.org/10.5194/egusphere-egu24-5935, 2024.

EGU24-6509 | Orals | NH1.3

Dam operating strategies and hydrodynamic modeling to mitigate floods in the lower Tagus valley similar to the 1979 catastrophic event  

Diego Fernández-Nóvoa, Alexandre M. Ramos, José González-Cao, Orlando García-Feal, Cristina Catita, Moncho Gómez-Gesteira, and Ricardo M. Trigo

The lower valley of the Tagus River, one of the most important rivers in the Iberian Peninsula, is a particularly relevant and vulnerable area in terms of flood impact. This valley is characterized by a flattened and large alluvial plain, which implies that floods can affect large areas of territory, causing significant damage and affecting a large number of people. Although several floods have occurred in the lower Tagus valley, the one in February 1979 stands out, since the vast flooded area affected around 10,000 people, many of whom were evacuated or made homeless. The Tagus River flow in its lower valley is controlled, to a large extent, by the functioning of the Alcántara dam, which has the largest water storage in the Tagus basin. In this context, this study aims to develop strategies to take advantage of this infrastructure to effectively mitigate floods in the lower Tagus valley. For that, dam operating strategies, focused on flood mitigation, are developed sustained on a sequence of logical principles, such as avoiding inducing man-made floods or maintaining average water storage similar to the actual one. The effectiveness of the proposed strategies, in terms of flood mitigation, is analyzed by applying the Iber+ hydrodynamic model. For this, the numerical model is validated in the lower Tagus valley by evaluating its ability to reproduce the outstanding flood of 1979. Additionally, several Digital Elevation Models (DEMs) are also analyzed to determine which is the most accurate for the area under scope. The results show that Iber+ model, coupled with Copernicus DEM, is able to provide an efficient reproduction of this flood. In particular, the simulation shows good agreement with some descriptions and watermarks available for the 1979 event. This also allows the analysis of this historical event from a hydrologic-hydraulic perspective, which contributes to improving knowledge and understanding of how floods occur and develop in the lower Tagus valley.

Regarding flood mitigation, results indicate that, since 1970, when data is available, the frequency of floods is reduced by more than 80%, compared to the natural flow regime, with the application of the proposed strategies. In addition, the mitigation of the most extreme floods that occurred during the analyzed period, is also achieved. In particular, peak river flows are reduced for the most extreme events. This implies that flood extension is reduced by around 5-10% in the lower Tagus valley. A more efficient mitigation is achieved for flood indicators closely linked to the damage caused by these events. Thus, water depth is reduced by around 25% and water velocity by around 25-30%, in the flooded areas, for the most extreme events. This corroborates the effectiveness of the proposed dam operating strategies to mitigate floods in the lower Tagus valley through an adequate dam functioning.

The developed proposal provides an affordable approach to flood mitigation in comparison with the construction of additional structural measures, which could also be applicable to other areas vulnerable to floods affected by dam-regulated rivers.

How to cite: Fernández-Nóvoa, D., Ramos, A. M., González-Cao, J., García-Feal, O., Catita, C., Gómez-Gesteira, M., and Trigo, R. M.: Dam operating strategies and hydrodynamic modeling to mitigate floods in the lower Tagus valley similar to the 1979 catastrophic event , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-6509, https://doi.org/10.5194/egusphere-egu24-6509, 2024.

Developing reliable and efficient flood modelling systems on a large scale is crucial for addressing errors and inconsistencies in both observations and modelling. However, the computational demands of hydrodynamic models have constrained their widespread application to coarse resolutions (30m-1km), compromising accuracy by neglecting the local and small-scale features that may significantly influence flooding, especially in urban areas. Furthermore, traditional models struggle to effectively incorporate river bathymetry, especially given the significant flood volume conveyed by the river channel during floods. These models often rely on surveyed cross-sections for river channel representation, leading to missing topography between cross-sections and hindering the resolution of complex floodplain flow paths. To resolve small-scale effects in limited areas while simulating large domains, grid adaptation methodologies are implemented in this project to locally adjust the resolution of the computation in a static or a dynamic way. A hybrid 1D-2D flood model is developed, incorporating the static/dynamic adaptive mesh generation and an integrated sub-/super grid channel model. The sub-/super grid channel is applied to accommodate situations where river channel width exceeds or fall below the grid resolution. Parallelized with GPU architecture, the performance of hybrid 1D-2D with either static or dynamics nonuniform structured grid was thoroughly evaluated, benchmarked with the full resolution CPU solver, shedding light on their effectiveness in enhancing flood modelling approach.

How to cite: Rong, Y., Bates, P., and Neal, J.: Towards a large-scale locally relevant flood modelling using adaptive mesh generation and an integrated sub-/super grid channel solver, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-6787, https://doi.org/10.5194/egusphere-egu24-6787, 2024.

Recent climate change has led to extreme floods surpassing levee design criteria, posing a threat to safety. Consequently, there is a demand for the development of technologies capable of handling such severe floods. In this study, a method assessing failure probabilities, represented by fragility curves, was developed for the levee slope under rapid drawdown. The time-dependent probabilistic stability assessment of the levee slope due to a water level drop was explored. Integrating seepage analysis results from finite element analysis with slope stability analysis, Monte Carlo simulations were conducted to scrutinize the time-dependent behavior of the levee slope under rapid drawdown conditions. The probability of failure was calculated to develop fragility curves for the levee slope. The developed fragility curves were significantly influenced by the drawdown rate. Since the drawdown rate is determined through hydraulic analysis based on flood scenarios, the stability of the water-side slope of the embankment due to a water level drop will be greatly affected by climate change. The fragility curves obtained using the proposed methods are valuable for risk assessment, offering information to evaluate the performance of the levee under various water level drawdown conditions.

How to cite: Cho, S. E.: Fragility assessment of levee based on time-dependent reliability analysis under rapid drawdown, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-7672, https://doi.org/10.5194/egusphere-egu24-7672, 2024.

Levees are linear structures that can be thousands of kilometers long and play a very important role in flood protection. They are usually monitored by traditional direct survey techniques, such as CPTU or coring, or piezometers, which provide high accuracy, but are localized and performed in predetermined locations.

As a result, long distances between investigated sections limit the detailed analysis of the entire structure. In addition, predetermined locations may not cover areas of actual potential weakness. Recently, new survey technologies from aerial media (drones) have been successfully applied to obtain a first level of levee investigation in order to identify the location of possible weak areas or potential locations of levee failure, so as to plan further local investigations in those areas.

Usually, levee failures are localized in the presence of:

(i) concrete or other materials structures passing the levee;

(ii) large trees, which can be dangerous because their roots are a preferred route for water infiltration. In addition, at higher erosion levels of the river bank, large trees can promote bank collapse due to their weight (i.e. cantilever failure);

(iii) sections where unfavorable conditions of the levee body, such as soils with high permeability or the presence of animal burrows crossing the levee or obstructed drains, prevent proper drainage and bring the phreatic surface close to the levee surface.

From previous experience, we have noticed that several times levee failures have occurred at sections previously vegetated by reeds. Reed canes usually grow on sandy soils and, in addition, are characterized by very deep and large roots, possible routes of localized infiltration through the body of the levee. From these observations comes the idea of using reedbeds as indicators of sandy soils and possible weak levee sections;

Thus, we performed two UAV-supported surveys on the same test area aimed at identifying the position and extension of the reeds vegetated areas, in combination with local on-site surveys with soil sampling along levee transversal sections, to compare and combine the obtained results. The RGB orthophotos obtained by the two surveys have been elaborated to determine the DSM and the vegetation cover map of the embankment, to compare them in different seasons. The obtained data have been calibrated with on-site surveys conducted by vegetation experts. To facilitate the identification of reedbeds, the first campaign has been carried out in winter, when reedbeds are yellowish in color, unlike short grass. In areas identified as reedbed vegetated, the soil has been sampled by coring and fully classified in the geotechnical laboratory to check if reedbed can effectively be an indicator of sandy soils. Similarly, other samples have been taken from sections not covered by reeds for comparison.

The final aim is to test the possibility of using vegetation maps as an indicator of weak sections of the embankment, thus to develop an innovative method of low-cost aerial monitoring of levee structures that can provide an initial state of information and identify areas in need of further direct investigation in order to define the necessary maintenance works, decreasing associated risks.

How to cite: Dalla Santa, G. and Simonini, P.: Reeds influence of levee hazards: detection through UAV survey and soil geotechnical analysis, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-8071, https://doi.org/10.5194/egusphere-egu24-8071, 2024.

EGU24-9050 | ECS | Orals | NH1.3 | Highlight

Is 3D modelling necessary for simulating long-duration urban flooding? 

Shuyue Yu, Xuefang Li, Vasileios Kitsikoudis, Guilhem Dellinger, and Benjamin Dewals

Urban flood modeling within complex urban environments demands sophisticated methodologies. While 2D computational models have historically served as foundational tools, their inherent limitations in capturing the intricate three-dimensional dynamics necessitate further exploration. Our research endeavors to expand this understanding by delving into 3D computational simulations, providing a more holistic perspective on urban flood dynamics.

In the current research, we conducted 3D simulations to replicate urban flood processes, drawing comparisons with earlier 2D modeling results and experimental observations. The simulations were executed considering various urban layouts and turbulence closure models. The urban layouts include two groups, totaling 13 architectural models. These models feature varying numbers or positions of openings on their exterior walls to represent architectural elements such as doors and windows that could allow floodwaters to enter in the interior of the buildings. As for the turbulence equations, k-omega SST and k-epsilon were considered. By analyzing the surface velocity, flow depth, and flowrate distribution, preliminary findings indicate that 3D simulations offer enhanced accuracy in capturing intricate flow patterns within urban settings compared to their 2D counterparts. Moreover, the tested simulations from various turbulence models influence the 2D and 3D simulations in different ways. This direct comparison allowed us to dissect and understand the influence of turbulence modeling on the accuracy of 3D simulations, thereby enhancing the robustness of our findings.

After obtaining the relevant results, we applied them to flood risk analysis. Compared to traditional 2D analyses, we derived some new insights to guide informed decision-making, enhancing the applicability of our approach. By integrating sophisticated modeling techniques and risk evaluations, this study paves the way for more resilient and adaptive urban planning strategies, ensuring safer and more sustainable urban environments in the face of increasing flood challenges.

How to cite: Yu, S., Li, X., Kitsikoudis, V., Dellinger, G., and Dewals, B.: Is 3D modelling necessary for simulating long-duration urban flooding?, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-9050, https://doi.org/10.5194/egusphere-egu24-9050, 2024.

EGU24-9555 | ECS | Orals | NH1.3

A tracer-aided criterion to discretize pluvial and fluvial flood hazard maps in catchment scale shallow water models 

Pasquale Perrini, Luis Cea, Andrea Gioia, and Vito Iacobellis

Knowing the nature of the flood hazard is a crucial factor for improving the resilience of the urban areas, since awareness, preparedness and early warning systems are based on the scientific tools such as the 2D depth-averaged shallow water models. Inland flood hazard primarily stems from pluvial and fluvial inundations, typically modeled separately respecting the pertaining spatial domains of the assessment, namely the urban areas and the riverine floodplains. Considering the high computational power and efficiency of both hardware and codes, the catchment scale hydrological-hydrodynamic modeling is becoming an increasingly adopted approach in flood hazard assessments. Since a complete rainfall-induced routing is preserved, these simulators determine fluvial, pluvial and compound inundations caused by heavy storm events within the entire watershed.

However, this approach leads to flood extent maps in which the inundations such as those resulting from pluvial and fluvial processes, are usually not differentiated, even if significant disparity in the space-time scales and volumes of water are involved. Indeed, these two hazards follow distinct normative and regulatory flood risk management rules among different countries. 

With such a rationale we established a tracer-aided criterion to systematically categorize and map pluvial and fluvial hazard in a catchment scale shallow water model, exploiting the advection process of a conservative tracer. The physically based methodology, implemented in the GPU-parallelized Iber+ software and its water-quality module (IberWQ+), is applied in a small urban catchment for multiple probabilistic scenarios. The results demonstrate the effectiveness of nesting transport and shallow water equations, univocally discretizing the two inundation sources in function of the computational cells reached by the tracer. This enables to define the spatial domains of the pluvial and fluvial processes, providing valuable insights for holistic catchment-scale flood risk management. Additionally, the advancements achieved by the proposed method are showcased in comparison to commonly employed modeling techniques for mapping fluvial inundations. As the tracers continue to improve our understanding of catchment sciences, we conceptualized them role through an abstraction that can aid surface hydrodynamic modelling to identify pluvial and fluvial sources of hazard.

How to cite: Perrini, P., Cea, L., Gioia, A., and Iacobellis, V.: A tracer-aided criterion to discretize pluvial and fluvial flood hazard maps in catchment scale shallow water models, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-9555, https://doi.org/10.5194/egusphere-egu24-9555, 2024.

EGU24-11106 | ECS | Posters virtual | NH1.3

Impact of vegetation on urban open-channel flow: Practical experiment with 2D IBER hydraulic simulations in Monterrey, Mexico. 

Jacob Jesús Nieto Butrón, Nelly Lucero Ramírez Serrato, Selene Barco Coyote, Fabiola Doracely Yépez Rincon, and Mariana Patricia Jácome Paz

Flooding is a constant danger in many cities. To prevent and mitigate their impacts, mathematical modeling is carried out to simulate the behavior of the flow within the environment and define the possible flood zones. Incorporating vegetation in hydraulic models is pivotal for understanding its impact on flow characteristics, sediment transport, and channel morphology.

The Santa Catarina River in Monterrey, Nuevo León, Mexico, grapples with irregular water flows. During dry seasons, minimal water levels promote unchecked vegetation growth along its banks and bed, potentially obstructing normal flow. Conversely, extreme weather events like hurricanes lead to rapid surges, sweeping away vegetation and debris. Balancing this fluctuation—from sparse to intense flows—presents challenges in managing the river's vegetation, necessitating strategies that reconcile environmental preservation with urban infrastructure resilience.

For this purpose, This study utilized two hydraulic models through IBER to assess vegetation's impact on flood simulations. One model employed a Digital Elevation Model (DEM), portraying terrain topography. The second model used a Digital Surface Model (DSM) integrating manually digitized vegetation from (2020) Google Earth imagery. Assigned heights of 3m for shrubs and 15m for trees emulated their impact on water flow. Both the DEM and DSM, with a 5-meter resolution, were obtained via LiDAR techniques from the INEGI government web platform. the models also utilized a land use classification obtained from a Sentinel-2 satellite image (from 2023). Hydrological data for both models were derived from the cumulative rainfall during Hurricane Alex in 2010.

The findings highlight significant changes in flood patterns attributed to vegetation. Its presence alters the flow, shifting the flood zone towards a southwest residential-commercial area. In this integrated model, the maximum depth reaches 16.78 meters, compared to 10.70 meters in the DEM-based hydraulic model. Additionally, the consistently affected area deepens from 2 meters to 4.37 meters when considering the vegetation-inclusive DSM-based approach.

These findings underscore the crucial role of vegetation in shaping flood pathways within urban environments, emphasizing the need to consider both natural and human-introduced elements in flood risk management strategies. Future research directions could explore the evolving impact on populations across varied flood zones and conduct comprehensive cost evaluations regarding risk mitigation, recovery efforts, and infrastructure fortification. These avenues present promising trajectories for further studies, offering insights into the socio-economic and financial implications of diverse flooding patterns in urban settings.

How to cite: Nieto Butrón, J. J., Ramírez Serrato, N. L., Barco Coyote, S., Yépez Rincon, F. D., and Jácome Paz, M. P.: Impact of vegetation on urban open-channel flow: Practical experiment with 2D IBER hydraulic simulations in Monterrey, Mexico., EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-11106, https://doi.org/10.5194/egusphere-egu24-11106, 2024.

EGU24-11521 | ECS | Orals | NH1.3

Assessment of sewer network data quality on urban pluvial flood modeling with a 2D/1D dual drainage model 

Carlos Montalvo, Paolo Tamagnone, and Luis Cea

Urban pluvial floods are one of the most common water-related hazards and are going to become more frequent and severe looking at the upsetting climate projections. These events mainly occur due to intense and short precipitation events, leading to the overload of the sewer network and resulting in physical, economic, and even human losses. To address this hazard, effective methods are needed to estimate the scale and impact of pluvial flood events and to develop mitigation strategies. In this context, 2D/1D dual drainage models have become one of the most useful tools for these purposes, being able to simulate all hydraulic phenomena occurring on and beneath the surface. However, these models require detailed information about the topography and geometrical specifications of the sewer network, which are not always readily accessible or, when available, are often incomplete or of poor quality, particularly in large urban environments.

In this work, considering that pluvial flood studies are becoming more popular and several numerical tools are available, we wanted to address a recurrent question raised by the flood modeler community: is the effort/level of complexity of implementing a detailed dual drainage model worth it? To answer this question, we assess the influence of sewer network data quality on the results of water depth and velocity obtained with a 2D/1D dual drainage model applied to urban flood modelling. For this purpose, an ad-hoc 2D/1D hydraulic model was implemented to simulate the complex network system of the city of Differdange (LU) exploiting the recently developed Iber-SWMM. This city was chosen as study case because it has experienced several flooding events in recent years, such as those recorded in 2021, and it has an extensive dataset of detailed geospatial data available, enabling the setup of a high-resolution resolution and fully coupled 2D/1D dual drainage model.

Sewer network links were classified based on their physical properties, such as diameter and length. The sewer network layout was gradually simplified, starting from the minor links to the more complex segments of the network, obtaining new simplified versions of the network that could represent incomplete or poor-quality scenarios. These simplified versions were successively implemented in the 2D/1D model. The comparison between the results of the complete and comprehensive model and the simplified scenarios reveals the impacts of the quality of the sewer network information on pluvial flood modeling.

How to cite: Montalvo, C., Tamagnone, P., and Cea, L.: Assessment of sewer network data quality on urban pluvial flood modeling with a 2D/1D dual drainage model, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-11521, https://doi.org/10.5194/egusphere-egu24-11521, 2024.

EGU24-11904 | ECS | Posters on site | NH1.3

The role of predisposing factors in determining the rainfall intensity necessary to cause flash floods in Portugal 

Caio Vidaurre Nassif Villaça, José Luís Zêzere, and Pedro Pinto Santos

Flash floods are often responsible for deaths and damage to infrastructure. The general objective of this work is to create data-based models to understand how the predisposing factors influence the triggering factor (precipitation) in the case of flash floods in the continental area of Portugal. Flash floods occurrences were extracted from the DISASTER database, which contains the location and date of historical flood events in the study region. Historical daily rainfall data was collected automatically from the Copernicus database. We extracted the accumulated precipitation for 3 days preceding each event and calculated the rainfall intensity. The predisposing factors were extracted considering the whole basin that corresponds to each flood event. The  analyzed predisposing factors were: accumulated flow, average slope, average elevation, predominant slope aspect, predominant lithology and soil properties (percentage of clay, coarse sand and coarse elements and field capacity). Elevation can often define different climatic and vegetation zones, while slope influences both the concentration and the infiltration capacity. The slope aspect can influence the amount and intensity of rainfall that affects the hillslope, as well as the amount and intensity of solar radiation. Lithology represents the properties of bedrock and the soils properties influence water infiltration and percolation. The Random Forest algorithm and the Leave-One-Out cross-validation technique were used to evaluate the model's performance and create a final model that identifies the relationship between the predisposing factors and the different rainfall intensities related to each flash flood occurrence. The final model obtained a root mean square error (RMSE) value of 3, an acceptable value for the objectives of the work. The percentage of coarse elements in the soil, average slope and field capacity were defined as the most important factors in the model for defining the amount of rainfall needed for flash floods to occur in mainland Portugal. The model developed can help to predict flash flood occurrence and future work involves combining the susceptibility model with the model created in this project to create a warning system that can be updated in real time, taking into account rainfall forecasts.

Acknowledgements: This work was financed by national funds through the FCT – Fundação Portuguesa para a Ciência e Tecnologia, I.P., under the grant to support the completion of the doctoral dissertation with the reference 2022.14473.BD.

How to cite: Vidaurre Nassif Villaça, C., Luís Zêzere, J., and Pinto Santos, P.: The role of predisposing factors in determining the rainfall intensity necessary to cause flash floods in Portugal, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-11904, https://doi.org/10.5194/egusphere-egu24-11904, 2024.

EGU24-12195 | ECS | Orals | NH1.3

Modeling flood fatalities in the Italian context: an empirical approach 

Mina Yazdani, Christian N. Gencarelli, Paola Salvati, and Daniela Molinari

Floods are among the most frequent and damaging natural hazards, affecting millions of people worldwide, and the risk of catastrophic losses due to flooding is expected to increase as a result of climate change. The possibility of predicting and estimating the expected fatalities in flood-prone regions is among the top priorities of decision-makers in flood risk management. Thus, predicting the conditions leading to loss of life is crucial for assessing the risk to the population. Here we focus on the Po River District in Northern Italy which covers the largest Italian hydrographic basin. We demonstrate that the occurrence of flood-related fatalities can be estimated by utilizing a random forest (RF) algorithm applied to a dataset of fatalities that occurred in this area from 1970 to 2019. This method relies on nine explanatory variables that describe the hazard intensity, and the environmental and sociodemographic conditions leading to fatalities. The proposed model is a primary attempt to estimate the probability of flood-related fatalities in the Italian context, and it provides a proxy for the quantitative estimation of flood risk to the population.

How to cite: Yazdani, M., N. Gencarelli, C., Salvati, P., and Molinari, D.: Modeling flood fatalities in the Italian context: an empirical approach, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-12195, https://doi.org/10.5194/egusphere-egu24-12195, 2024.

EGU24-12935 | ECS | Orals | NH1.3

Estimating flood exceedance probabilities for several levee breach scenarios for an urban riverine environment in Toronto, Canada 

Florence Mainguenaud, Usman Khan, Laurent Peyras, Claudio Carvajal, Jitendra Sharma, and Bruno Beullac

Assessing flood risk requires the combination of flood hazard, exposure, and vulnerability.  Hence, flood hazard is a key component of flood risk assessments. As flood propagation is impacted by hydraulic structures built along the river, flood defense such as levees have gained attention as they are rarely included in large scaled flood risk assessments. However, flood events such as hurricane Katrina showcased the impact that levee failure has on flow depth, velocity, and flood extent. Therefore, its consideration should be regularly implemented in flood risk assessments. However, with current flood risk assessment methods, considering different levee failure scenarios results in numerous flood scenarios, simulations, and hazard maps. The multiplication of simulations and maps increases the complexity of flood risk management. We propose to improve flood hazard assessments by considering a single probabilistic flood map accounting for several flood events and levee breaching scenarios. For flood events enabling the performance assessment of the levee (i.e. levee breaching), we assessed levee failure probabilities, associating each levee segment to a fragility curve. Then, we defined breaching and non-breaching scenarios and ran flood simulations using HEC-RAS and its integrated parametric levee breaching model. We propose a new method to compute flood scenario probabilities and flood exceedance probabilities. The cumulative flood exceedance probability provides a curve for every location of the flooded area. Using GIS, we applied this method to the entire flooded area, resulting in an interactive flood hazard map. An application to the Etobicoke Creek located in the Greater Toronto Area showed that this new approach provides an operational levee breaching flood hazard method that can be used in integrated flood risk assessments.

How to cite: Mainguenaud, F., Khan, U., Peyras, L., Carvajal, C., Sharma, J., and Beullac, B.: Estimating flood exceedance probabilities for several levee breach scenarios for an urban riverine environment in Toronto, Canada, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-12935, https://doi.org/10.5194/egusphere-egu24-12935, 2024.

EGU24-15306 | ECS | Posters on site | NH1.3

Numerical modelling of flood hazard mitigation strategies: the case of the Baganza River after the 2014 inundation of the city of Parma 

Alessia Ferrari, Renato Vacondio, and Paolo Mignosa

Urban flood risk mitigation is a paramount priority given the increasing frequency of flood events, which have become predominant natural disasters in recent decades. Over half of the global populace now resides in urbanized areas, amplifying the vulnerability to such events that is further accentuated by climatic shifts and rapid urban sprawl. In addressing these challenges, sophisticated flood risk management strategies often integrate advanced numerical models for precise hydrological assessments. These models can support e.g. urban planning, emergency response preparedness and the design of structural measures. In the present work, the Baganza River in the city of Parma (Northern Italy) is investigated with particular emphasis on recent modifications that have been designed with outcomes deriving from a computationally efficient parallel 2D numerical model solving the Shallow Water Equations (SWEs).

On October 2014, a severe flood event occurring on the Baganza River caused the inundation of the southwestern part of the city of Parma. Since the urban river reach showed limitations in the propagation of the flood wave, a comprehensive re-evaluation of the river's hydraulic conveyance capacities was required. Thus, in 2015, hydraulic authorities started designing and realizing several modifications along this river reach, including levee modification and removal, in order to increase its conveyance. With the aim of assessing the effectiveness of these strategies, the PARFLOOD numerical model, which solves the 2D-SWEs on a finite volume scheme and ensures high computational efficiency due to its parallel implementation on GPU, was adopted. The model was initially calibrated and adopted to simulate the 2014 flood event. Thereafter, leveraging a refined spatial resolution and incorporating detailed urban topographies, the model delineated residual flood hazard maps, facilitating evidence-based mitigation strategy refinements.

Once the most promising strategies were outlined and implemented over these last ten years, a new high-resolution Digital Terrain Model (DTM) deriving from a LiDAR survey was provided in 2023. By simulating the same synthetic discharge hydrograph, e.g. with a return period of 100 and 200 years, using both the 2014 DTM and the 2023 one, it clearly emerged that the current asset strongly reduces the residual flood hazard in these districts of the city of Parma, both in terms of flood extent and magnitude.

How to cite: Ferrari, A., Vacondio, R., and Mignosa, P.: Numerical modelling of flood hazard mitigation strategies: the case of the Baganza River after the 2014 inundation of the city of Parma, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-15306, https://doi.org/10.5194/egusphere-egu24-15306, 2024.

With the worsening of climate change, extreme weather events are on the rise, leading to more frequent occurrences of climate-related disasters. Analyzing people's perceptions and attitudes towards disasters after they occur can help determine the spatial pattern of the disaster intensity and the post-disaster needs of different populations. The implication is to provide references for disaster assessment and post-disaster relief needs analysis.

 

Starting from July 29, 2023, due to the influence of Typhoon Dusrayi and Typhoon Canu, the Beijing-Tianjin-Hebei region in China suffered from catastrophic rainfall, resulting in severe flooding in multiple areas. This study utilized web crawlers to collect relevant Weibo data during the disaster, applied machine learning models to conduct public opinion analysis on the flooding disaster, developed the evolutionary patterns of public opinions on the disaster, and obtained heat maps and sentiment indicators for different cities. The results will contribute to the rapid assessment of post-disaster losses and guide the resource allocation in the initial emergency rescue process after the disaster.

How to cite: Zhang, X.:  A rapid disaster intensity assessment method using social media data: a case study of the flood disaster in the Beijing-Tianjin-Hebei region, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-15415, https://doi.org/10.5194/egusphere-egu24-15415, 2024.

EGU24-15605 | ECS | Orals | NH1.3

Establishing a Germany-wide Standardized Indication Map Representing the Flood Situation Caused by Heavy Rainfall 

Lukas Wimmer, Michael Hovenbitzer, and Patrick Merita

Recent studies on climate change show an increasing trend in the frequency of extreme weather events (IPCC, 2021; Tradowsky et al., 2023). These include storms with high-intensity precipitation, known as heavy rainfall. During those events the amounts of precipitation can be so high in a very short period of time that catastrophic flooding can also develop far away from rivers and lakes. Heavy rainfall events have occurred more frequently in Germany in recent years, resulting in severe damage and therefore focusing attention on risk management and prevention.

Contributing to an optimal preparation for the consequences of heavy rainfall events the Federal Agency for Cartography and Geodesy (BKG) is working with federal and state authorities to develop a Germany-wide indication map representing simulated flood situations after heavy rainfall events based on standardized guidelines. Once the mapping has been completed within the first half of 2024, it will be freely available as OpenData to politicians, the public administration and the general public for damage prevention and civil protection.

Geodata of the federal and state governments are essential for the hydronumerical two-dimensional modelling. A digital terrain model with a grid width of one meter forms the basis. Road culverts with corresponding dimensions, 3D building models, pumping stations as well as land cover data representing the surface roughness are integrated into this model in order to achieve a hydrologically effective modification and thus a realistic discharge.

The heavy rainfall indication map shows realistic simulation events for possible flooding scenarios that follow the heavy rainfall index according to Schmitt et al., 2018. The index describes the hazardous character of heavy rainfall events based on the return period and is commonly used in heavy rainfall risk communication by German federal and state authorities. Two scenarios are simulated: First, a 100-year event based on KOSTRA data from the German Weather Service (DWD), a dataset including regionalized precipitation heights as a function of precipitation duration and annularity. Second, an extreme heavy rainfall event with a precipitation of 100 mm/h. For both scenarios flood depths, flow velocities and flow directions are simulated.

The indication map for heavy rainfall provides an initial assessment of the risk potential, which, in combination with existing local expertise, should considerably simplify the planning of measures. It serves as an important tool for identifying areas at risk from heavy rainfall. This enables local authorities, planners and emergency services throughout Germany to derive appropriate measures, both preventively and in the event of an actual disaster.

 

References

Intergovernmental Panel on Climate Change (Ed.): Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC). Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, <doi:10.1017/9781009157896>.

Schmitt, T. G. et al.: Einheitliches Konzept zur Bewertung von Starkregenereignissen mittels Starkregenindex, KA Korrespondenz Abwasser, Abfall, 2018(65), Nr. 2.

Tradowsky, J.S., Philip, S.Y., Kreienkamp, F. et al.: Attribution of the heavy rainfall events leading to severe flooding in Western Europe during July 2021. Climatic Change 176, 90 (2023). <https://doi.org/10.1007/s10584-023-03502-7>.

How to cite: Wimmer, L., Hovenbitzer, M., and Merita, P.: Establishing a Germany-wide Standardized Indication Map Representing the Flood Situation Caused by Heavy Rainfall, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-15605, https://doi.org/10.5194/egusphere-egu24-15605, 2024.

The subject of the study is the considerable uncertainty in determining flood risk when long-term climate change projections are developed. Risk management decision-making involves comparing options based on their benefits and costs. The purpose of the analysis is to reveal the uncertainty robustness of alternative flood protection measures. The treatment of different sources of uncertainty is done by using probabilistic net present value (NPV) analysis as well as by using Information-gap decision theory (IGDT). The case study is a settlement in northern Bulgaria with a record of severe flooding in the past, for which different climate change projections are generated under RCP 4.5 and RCP 8.5 scenarios. The behaviour of three civil protection options under these uncertainty conditions is investigated for an extended 30-year time period to 2050. A probabilistic analysis with NPV performance criterion is performed sequentially, followed by Info-gap decision theory analysis.

After discussing the results, the advantages and disadvantages of the two methods are compared. Some limitations and advantages of the Information gap theory are discussed. Finally, it is highlighted that when making decisions about long-term flood protection, it is recommended to use multiple methods that differ in data and assumptions, necessarily taking into account the hydrological uncertainty arising from climate change, which can radically change our choices.

How to cite: Mavrova-Guirguinova, M.: Impact of Uncertainty on the Choice of Long-Term Flood Protection Option under Climate Change, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-16798, https://doi.org/10.5194/egusphere-egu24-16798, 2024.

EGU24-17663 | ECS | Posters on site | NH1.3

Flood Risk Mapping Through Advanced Machine Learning Techniques and Geomorphic Data Integration 

Jorge Saavedra Navarro, Ruodan Zhuang, Cinzia Albertini, and Salvatore Manfreda

Flood events rank among the most destructive natural hazards, necessitating comprehensive risk management strategies to mitigate their impact on human health, the environment, cultural heritage, and economic activities. In this context, various approaches have been developed for identifying flood-prone areas, but there is still a need to enhance their capabilities due to dynamic changes in landscape and infrastructure.

In recent years, there has been a proliferation of remote sensing observations that can support dynamic and continuous mapping of flood-prone areas by integrating the most updated information. This study explores the potential of machine learning (ML) techniques, including Random Forest, Support Vector Machine, and Navies Bayer model, utilizing geomorphic information such as slope, elevation, precipitation, land use/land cover, elevation difference to the nearest river, and others as predictor variables. The best model and set of variables were explored by adopting approximately 30 variables spanning types, hydrologic, topographic, and categorical categories. Careful consideration was given to avoiding high correlations between variables in test subsets, ensuring relevance, and avoiding redundancy. Calibration and validation of the model employ Copernicus Emergency Management Service maps from Sentinel-2 satellite coupled with regional maps of past flood events.

Results highlight that the best ML technique is represented by the Random Forest, adopting a range of 5 to 8 variables for effective delineation of flood-prone areas. Among the selected variables, the most relevant ones include Rainfall, Geomorphic Flood index - GFI, Lithology, and others. The study demonstrates that a minimal amount of information (between 0.1% and 10%) suffices for optimal model performance (AUC greater than 0.8).

The study covered the entire territory of Italy, resulting in a flood-prone map at a 90m resolution, validated with flood maps provided by national agencies and obtained through traditional hydraulic models.

Keywords: satellite images, flood-prone areas, Machine Learning, GFI, flood risk.

How to cite: Saavedra Navarro, J., Zhuang, R., Albertini, C., and Manfreda, S.: Flood Risk Mapping Through Advanced Machine Learning Techniques and Geomorphic Data Integration, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-17663, https://doi.org/10.5194/egusphere-egu24-17663, 2024.

EGU24-18219 | ECS | Posters on site | NH1.3

Meta-heuristic Algorithms Applied to Urban Flood Evacuation Routes: A case study in Beijing, China 

Chuannan Li, Changbo Jiang, Reza Ahmadian, Man Yue Lam, and Jie Chen

Abstract: Climate change and urbanization have increased the occurrence of natural disasters, including floods, tsunamis and hurricanes. Among these disasters, floods occur with high frequency, impact a large number of people, cause high economic losses, and lead to high toll of deaths. Examples include floods in Indonesia in 2021 and Pakistan in 2022. The flood in Indonesia affected about 1 million people. The flood in Pakistan affected 33 million and killed 1,739 people and costed US$15 billion in economic damage. Flood risk assessment and evacuation are effective mitigation measures to create flood-resilient cities. Previous studies have focused on flood modelling and risk assessment, yet it is recently recognized that optimal evacuation routes are necessary and critical for social adaptation to flood risks. To date, there are limited research on evacuation route optimisation problem.

There are two approaches for evacuation route optimisation: namely exact methods and meta-heuristic methods. The exact methods such as linear programming, weighted summation, and mixed integer programming have been widely applied. Nevertheless, meta-heuristic algorithms are gaining attention as flexible, non-problem-specific, and computationally efficient optimisation methods. The principle of Meta-heuristic algorithms is based on simulating the optimisations that occur naturally in biological or physicochemical processes. For example, there is a commonality between an animal herd searching for routes and a population searching for routes in a flood disaster. Commonly applied meta-heuristic algorithms are Genetic Algorithms, Ant Colony Algorithms, Particle Swarm Algorithms, and Sparrow’s Algorithms. This is because these algorithms have simple structures and high adaptability, desirable local and global convergence properties and require few parameters.

In this study, the flood in Beijing, China, in late July and early August 2023 will be simulated. The flood claimed at least 33 lives, damaged 209,000 homes and more than 15,000 hectares of cropland and caused 127 thousand people to evacuate. The flood extent, water depth and flow velocity will be obtained from a two-dimensional hydrodynamic flood model. The flood risk for pedestrians or vehicles will be estimated with the hydrodynamic model result and a mechanic-based stability method. Optimal evacuation routes will be obtained with Genetic Algorithm, Ant Colony Algorithm, Particle Swarm Algorithm, and Sparrow’s Algorithm. The performance of the optimisation algorithms will be compared and evaluated.  This study contributes to the scientific planning of urban flood evacuation routes and provides insight for urban planners and managers to enhance urban resilience.

Keywords: Urban floods, evacuation routes, Meta-heuristic algorithms.

How to cite: Li, C., Jiang, C., Ahmadian, R., Lam, M. Y., and Chen, J.: Meta-heuristic Algorithms Applied to Urban Flood Evacuation Routes: A case study in Beijing, China, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-18219, https://doi.org/10.5194/egusphere-egu24-18219, 2024.

EGU24-18287 | ECS | Orals | NH1.3

Sensitivity Analysis of Image Augmentation Methods to Improve Flooded Area Detection Performance 

Seon Woo Kim, Soon Ho Kwon, Sanghoon Jun, and Donghwi Jung

Recently, detecting flooded areas in CCTV images was performed based on semantic segmentation models (e.g., U-Net, FCN, etc.). However, these flooded area detection techniques are based on large-scale manually annotated images, which consume manpower and time. Image augmentation is one of the ways to overcome the limitations mentioned above. Some previous studies have used image augmentation to improve the performance of flooded area detection by combining two or more methods. However, there has been no study quantifying which augmentation methods are reasonable. This study aims to verify which image augmentation method is reasonable to improve the performance of urban flooded area detection techniques. First, this study develops a flood area detection technology corresponding to training images augmented with five different methods (Brightness, Blur, Contrast, Rotation, Crop). Subsequently, the performance changes for each technique were quantified, and characteristics related to the performance variations of each method were examined.

How to cite: Kim, S. W., Kwon, S. H., Jun, S., and Jung, D.: Sensitivity Analysis of Image Augmentation Methods to Improve Flooded Area Detection Performance, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-18287, https://doi.org/10.5194/egusphere-egu24-18287, 2024.

EGU24-22491 | ECS | Posters virtual | NH1.3

Application of Advanced Deep Learning Models for Flood Image Processing and Semantic Segmentation 

Sai Praneeth Dulam, Vidya Samadi, and Carlos Toxtli-Hernández

In developing Version 2.0 of our Flood Image Classifier, we underscore the significant role of Convolutional Neural Networks (CNNs), mainly Faster R-CNN and YOLOv3, in detecting and segmenting flood-related labels in images. Additionally, our research delves into the potential of Vision Transformers (ViT) for advanced object detection and image classification for flood-related images extracted for the USGS river cameras. Transformer methods offer improved predictions of flood depth and inundation areas, marking a substantial step forward in flood vision technology. The integration of advanced image processing techniques, the enhancement of CNN capabilities, and the incorporation of cutting-edge detection and classification models are pivotal in developing a comprehensive, real-time flood monitoring system. This system is designed to equip frontline decision-makers and emergency responders with essential insights into flooding conditions, thereby significantly contributing to disaster management and response through the innovative use of our flood image classifier, Version 2.0.

How to cite: Dulam, S. P., Samadi, V., and Toxtli-Hernández, C.: Application of Advanced Deep Learning Models for Flood Image Processing and Semantic Segmentation, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-22491, https://doi.org/10.5194/egusphere-egu24-22491, 2024.

EGU24-529 | ECS | Posters on site | NH1.5

The Attachment Process of Negative Connecting Leader to the Lateral Surface of Downward Positive Leader in a +CG Lightning Flash 

Qi Qi, Bin Wu, Weitao Lyu, Ying Ma, Lyuwen Chen, Fanchao Lyu, and Yan Gao

In the lightning attachment process, the leader connecting behavior is an interesting topic. In the attachment process of a negative cloud-to-ground lightning flash, the “Tip to the lateral surface” connection type has been widely observed, and researchers have carried out a series of studies and discussions on the characteristics and the physical mechanisms of the leader connecting behavior. However, is there also a “Tip to the lateral surface” connecting behavior in the attachment process of the positive cloud-to-ground lightning flash? In this study, using high-speed video cameras operating with framing rates of 20 and 50 kiloframes per second, we captured an attachment process during a positive cloud-to-ground flash, which demonstrates the connection of the negative connecting leader (NCL) to the lateral surface of the downward positive leader (DPL) for the first time. When the NCL was initiated, the tip of the DPL had passed the initiation position of the NCL for about 50 m. A common streamer zone (CSZ) was observed when the three-dimensional distance between the NCL tip and the lateral surface of DPL was about 30 m. It is remarkable to note that a luminous segment (space stem/leader) with a length of about 7 m was captured within the CSZ during the attachment process. The connection between the NCL tip and the lateral surface of the DPL was caused by the development of the CSZ and its inner space leader.

How to cite: Qi, Q., Wu, B., Lyu, W., Ma, Y., Chen, L., Lyu, F., and Gao, Y.: The Attachment Process of Negative Connecting Leader to the Lateral Surface of Downward Positive Leader in a +CG Lightning Flash, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-529, https://doi.org/10.5194/egusphere-egu24-529, 2024.

EGU24-1238 | ECS | Posters on site | NH1.5

Optical observations of needles evolving into negative leaders in a positive cloud-to-ground lightning flash 

Bin Wu, Qi Qi, Weitao Lyu, Ying Ma, Lyuwen Chen, and Vladimir Rakov

High-speed video records of a single-stroke positive cloud-to-ground (+CG) flash were used to examine the evolution of eight needles developing more or less radially from the +CG channel. All these eight needles occurred during the later return-stroke stage and the following continuing current stage. Six needles, after their initial extension from the lateral surface of the parent channel core, elongated via bidirectional recoil events, which are responsible for flickering, and two of them evolved into negative stepped leaders. For the latter two, the mean extension speed decreased from 5.3 × 10^6 to 3.4 × 10^5 and then to 1.3 × 10^5 m/s during the initial, recoil-event, and stepping stages, respectively. The initial needle extension ranged from 70 to 320 m (N = 8), extension via recoil events from 50 to 210 m (N = 6), and extension via stepping from 810 to 1,870 m (N = 2). Compared with needles developing from leader channels, the different behavior of needle flickering, the longer length, the faster extension speed, and the higher flickering rate observed in this work may be attributed to a considerably higher current (rate of charge supply) during the return-stroke and early continuing-current stages of +CG flashes.

How to cite: Wu, B., Qi, Q., Lyu, W., Ma, Y., Chen, L., and Rakov, V.: Optical observations of needles evolving into negative leaders in a positive cloud-to-ground lightning flash, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-1238, https://doi.org/10.5194/egusphere-egu24-1238, 2024.

EGU24-1639 | Orals | NH1.5

Modelling the collision of streamers using the AMReX framework 

Christoph Köhn, Angel Ricardo Jara, Morten Jung Westermann, Mathias Gammelmark, and Elloise Fangel-Lloyd

Streamers, precursors of the hot, long lightning leaders, are small filamentary discharges with high electric fields at their tips. Experiments of laboratory discharges have shown that streamers in their corona can approach each other and it has been suggested that such collisions enhance the electric field in-between beyond the thermal runaway electric field accelerating electrons to the runaway regime thus generating X-rays. Streamer collision also plays a role in the interaction of wind turbine blades with lightning when streamers locally incept from the surface of blades and attract the downward moving lightning leader. Despite the relevance of streamer collisions in the runaway process or their role in the interaction of lightning with wind turbine blades, there have only been a few numerical studies due to computational limitations. We have therefore developed a novel 3D fluid model for streamer propagation implemented in the AMREX framework. AMREX allows us to solve drift-diffusion and Poisson equation using parallelization and GPU support to accelerate the block structured adaptive mesh refinement. We will present details of the implementation as well as a parameter study on typical streamer parameters (electron density, electric field, tip width and velocity,…) during streamer collision in various ambient fields and for various initial electron densities. We will also study various geometries with different displacements of the initial electrons perpendicular to the ambient electric field. Finally, we will interpret our results with respect to the runaway process and wind turbine-lightning interaction.

How to cite: Köhn, C., Jara, A. R., Westermann, M. J., Gammelmark, M., and Fangel-Lloyd, E.: Modelling the collision of streamers using the AMReX framework, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-1639, https://doi.org/10.5194/egusphere-egu24-1639, 2024.

The ground-level potential gradient (PG) or the atmospheric electric field, the air-Earth current density as well as the main Global Electric Circuit (GEC) parameters such as the ionospheric potential, global resistance and the total current, can be obtained from the EGATEC engineering model of the GEC (Odzimek et al. 2010) at the resolution of 3 hours. The model input data based on satellite cloud and lightning observation datasets from the period 1998-2006 for evaluating the activity of the GEC cloud generators, and the summer/winter and low/high solar activity conductivity model of Tinsley and Zhou (2006) allow calculating the GEC parameters in the summers and winters of the period. In this work we compare the modelling results to observations from the Stanislaw Kalinowski Geophysical Observatory in Świder, Poland (52°07' N, 21°14' E) of the ground-level potential gradient and conduction current density calculated from the newly digitised PG and positive conductivity data from 1965-2005. We also look for connections in the time variations of the model meteorological input and atmospheric electricity observational data. The work is supported by the Polish National Science Centre grant no 2021/41/B/ST10/04448.

How to cite: Odzimek, A., Tacza, J., Pawlak, I., and Kępski, D.: Analysis of time variations in the Global Electric Circuit parameters from the EGATEC model and Świder atmospheric electricity data, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-1658, https://doi.org/10.5194/egusphere-egu24-1658, 2024.

The different morphologies of lightning channels are caused by different electrical environments within the cloud, the charge distribution determines the lightning channel morphology, and the lightning morphology can reflect the charge structure to some extent. The distribution of charges is mainly determined by the dynamics and microphysical conditions in clouds, and turbulence plays a significant role in the distribution of charges. Due to the dependence of lightning morphology on the distribution of thunderstorm charges, which is regulated by thunderstorm dynamic effects, a relationship can be established between lightning morphology and thunderstorm dynamic effects.

In this study, the lightning channel was obtained from three-dimensional radiation source localization data from the Lightning Mapping Array at the Langmuir Laboratory of the New Mexico Institute of Mining and Technology. The fractal dimension was used to characterize the complexity of lightning channels, which was calculated by the box-counting method. The S-band dual-polarization Doppler radar data was used to estimate the cube root of the eddy dissipation rate (EDR, the EDR was estimated using the Python Turbulence Detection Algorithm). The EDR and radar radial velocity were used to represent the thunderstorm dynamic characteristics.

Superimposing EDR and radar radial velocities with LMA radiation sources, our analysis shows that the overall morphology and detailed morphology of the lightning channel correspond to different EDR characteristics. Lightning with complex channel morphology has a larger average FD and occurs in regions with large EDRs. In single lightning events, channels that extend directly within a certain height range without significant bifurcation and turning tend to propagate in the direction of decreasing EDRs, while channel bifurcations and turns usually occur in regions with large radial velocity gradients and large EDRs. This study shows the relationship between channel morphology and thunderstorm dynamics and provides a new method for the direct application of channel-level localization data to understand thunderstorm dynamics characteristics.

How to cite: Li, Y., Zhang, Y., Zhang, Y., and Krehbiel, P. R.: Analysis of the Relationship between the Morphological Characteristics of Lightning Channels and Turbulent Dynamics Based on the Localization of VHF Radiation Sources, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-2144, https://doi.org/10.5194/egusphere-egu24-2144, 2024.

EGU24-2536 | Posters on site | NH1.5

Lightning Activities near the Red Sea: Effects of Aerosols Morphology and Local Meteorology 

Ashraf Farahat and Maher Dayeh

Lightning activity is one of the global natural hazards that pose significant risks to human life and numerous aspects of society's technological infrastructure. Understanding the linkage between aerosols present in the atmosphere and lightning activity is important to further advance our knowledge of the global lightning activity cycle.

Saudi Arabia and Yemen host one of the world’s largest desert areas namely the Empty Quarter (al-Rubea Al-Khali). Moreover, Saudi Arabia is one of the world’s largest oil exporters with many water desalination, petrochemical, and cement industrial plants, while large cities in both Saudi Arabia and Yemen have large construction projects and vehicle emissions. This increases both natural and anthropogenic aerosol loading in both countries.  Meanwhile, the inland regions close to the Red Sea are one of the 500 hottest lightning regions in the world. This work identifies a possible correlation between lightning activity and aerosol loading.

Using data of individual lightning strokes from the Global Lightning Detection Network (GLD360), in conjunction with remote sensing measurements of the aerosol optical depth (AOD) obtained at 500 nm from the Moderate Resolution Imaging Spectroradiometer (MODIS) instrument onboard the Terra and Aqua satellites during active lightning days, we examine the evolution of lightning activity in two geographically and topologically different regions over Saudi Arabia and Yemen. One region extends inland to the desert (R1) and the other is in the southwest mountainous region that is close to the Red Sea (R2). In both regions, results from thunder days only indicate that lightning is strongly and positively correlated with the AOD loading, up to AOD ~ 0.8, after which the trend flattens or reverses direction. Results suggest the two opposite effects that aerosols could indirectly have on lightning activity are at play. The mountainous region exhibits a much stronger linear relation compared to the inland region. Furthermore, both regions exhibit seasonal and asynchronous lightning activity and AOD loading. The year 2018 in R1 shows very high lightning activity, likely linked to the 2018 intense dust storms in the region.

How to cite: Farahat, A. and Dayeh, M.: Lightning Activities near the Red Sea: Effects of Aerosols Morphology and Local Meteorology, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-2536, https://doi.org/10.5194/egusphere-egu24-2536, 2024.

EGU24-3358 | Orals | NH1.5

Investigation of the Electric Fields Related to Elves Simulations 

Petr Kaspar, Ivana Kolmasova, Ondrej Santolik, and Martin Popek

Elves are transient luminous events occurring above thunderclouds. They appear as an expanding ring of light at altitudes of 85 – 95 km with diameters of more than 200 km and lasting less than 1 ms. The elves are produced by electromagnetic pulses emitted by underlying high-peak current lightning discharges, which excite nitrogen molecules at the bottom of the ionosphere. We develop an electromagnetic model of elves, which consists of two steps. As the first step, we compute the horizontal part of the electric field at a height of 15 km from transmission line return stroke (RS) models without damping, with linear, and/or exponential damping of the current wave. Subsequently, we solve Maxwell’s equations self consistently for altitudes from 15 km to 95 km, including finite neutral and electron densities, and nonlinearities related to heating, ionization, and attachment of free electrons caused by the RS transient electric field. We show computed electric fields and optical emission rates at the heights of the development of elves. This procedure allows us to distinguish between the electrostatic, induction, and radiation part of the electric field and to investigate their role in the evolution of elves in the full wave simulations.

How to cite: Kaspar, P., Kolmasova, I., Santolik, O., and Popek, M.: Investigation of the Electric Fields Related to Elves Simulations, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-3358, https://doi.org/10.5194/egusphere-egu24-3358, 2024.

EGU24-3628 | Orals | NH1.5

Glow-terminating terrestrial gamma-ray flashes observed during the ALOFT Campaign 

Steven Cummer, Yunjiao Pu, Andrew Mezentsev, Marni Pazos, Morris Cohen, Nikolai Ostgaard, Mark Stanley, Timothy Lang, Martino Marisaldi, J. Eric Grove, Mason Quick, Hugh Christian, Christopher Schultz, Richard Blakeslee, Ian Adams, Phillip Bitzer, Martin Fullekrug, Bilal Qureshi, Bendik Husa, and Gerald Heymsfield and the additional members of ALOFT team

The ALOFT campaign targeted aircraft measurements of terrestrial gamma-ray flashes (TGFs) through NASA ER-2 overflights of strong thunderstorms.  We report here the analysis of glow-terminating TGFs (GT-TGFs) that occur at the end of some gamma-ray glows.  GT-TGFs were generated by most of the observed storms during the campaign and were prolifically generated by two specific storms that were particularly active in gamma ray production.  One unique feature of GT-TGFs is that they always occur within several tens of microseconds of a narrow bipolar event (NBE).  The characteristics of GT-TGFs and the associated NBE radio emissions will be described in detail.

How to cite: Cummer, S., Pu, Y., Mezentsev, A., Pazos, M., Cohen, M., Ostgaard, N., Stanley, M., Lang, T., Marisaldi, M., Grove, J. E., Quick, M., Christian, H., Schultz, C., Blakeslee, R., Adams, I., Bitzer, P., Fullekrug, M., Qureshi, B., Husa, B., and Heymsfield, G. and the additional members of ALOFT team: Glow-terminating terrestrial gamma-ray flashes observed during the ALOFT Campaign, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-3628, https://doi.org/10.5194/egusphere-egu24-3628, 2024.

EGU24-3670 | ECS | Orals | NH1.5

Using meteorological reanalysis to identify weather conditions for classifying atmospheric electricity data  

Hripsime Mkrtchyan, Giles Harrison, and Keri Nicoll

Atmospheric electricity Potential Gradient (PG) data has typically been classified by local weather conditions, such as by identifying data recorded during “fair weather” (FW) or in the absence of rainfall “no hydrometeors” (NH), to try and obtain globally representative values. In general, this approach is essential in obtaining global atmospheric circuit (GEC) signals. The weather information needed to do this is, however, only available from some of the sites providing atmospheric electricity measurements. For other sites, meteorological reanalysis – of which there are many products available, spanning different times and scales - may provide a data source for such classification of PG data. This study investigates the integration of ERA5 meteorological reanalysis data to identify FW and NH conditions and improves the quality of data used in long-term atmospheric electricity studies.  

Initial findings investigating the meteorological quantities show a strong correlation between wind speed, total cloud coverage and total precipitation from ERA5 and observed ground-based measurements at the Eskdalemuir and Lerwick sites. This is to be applied to classifying past atmospheric electricity data, specifically of the hourly potential gradient (PG), which were obtained at the Lerwick observatory from 1925 to 1984, and Eskdalemuir observatory, which made atmospheric electricity measurements from 1911-1981 (Harrison & Riddick, 2022; Märcz & Harrison, 2003). 

Identified criteria from ERA5 which best match for FW and NH conditions are implemented in historical data from the Lerwick and Eskdalemuir observatories, enhancing the reliability of past studies which is important for atmospheric electricity analyses. This supports the potential of ERA5 data for providing information to identify FW and NH conditions. From this, we are evaluating a range of methods to use the meteorological reanalysis, with the aim of recovering representative FW data at sites lacking meteorological measurements. 

How to cite: Mkrtchyan, H., Harrison, G., and Nicoll, K.: Using meteorological reanalysis to identify weather conditions for classifying atmospheric electricity data , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-3670, https://doi.org/10.5194/egusphere-egu24-3670, 2024.

EGU24-4116 | Orals | NH1.5

Stream Machine Learning for Lightning Nowcasting - Harnessing the Power of Continuously Updated Data 

Cesar Beneti, Luis Pavam, Luiz Oliveira, Marco Alves, Leonardo Calvetti, and Fernanda Verdelho

Uninterrupted access to electricity is a fundamental feature of civilization. In its absence, an all-embracing cessation of activities occurs, ranging from essential services to more frivolous activities. The maintenance of the energy supply is critical for society's day-to-day functions. The Brazilian state of Paraná (PR) is home to the world's second-largest hydropower plant, Itaipu, which, in conjunction with other power plants in the state, provides almost one-third of the power energy production in Brazil. The transmission lines that pervade PR are essential to Brazil's power distribution system, for hydropower generation is typically made far away from the regions that most demand it, being transported by transmission lines in an interconnected power grid. This type of asset mainly depends on the forecast of Cloud-to-Ground (CG) lightning, as it is one of the leading weather-related causes of power outages. Lightning and wind gusts are the two leading weather-related causes of disruptions, representing at least 23% of the known causes of energy disruption, as declared by the local power distribution company. Our study of lightning incidence and power outages from 2017-2021 indicates a correlation of 0.98 between these events, denoting that more outages must be lightning-related. Reliable CG lightning forecasts are crucial for proactive hazard mitigation. This work expounds on developing a Machine Learning (ML) model for CG lightning forecasting for PR. Our ML model predicts the occurrence or lack of CG lightning near power company assets in PR, defining a binary classification task. The model makes its predictions based on the past spatio-temporal conditions of lightning occurrences, requiring only past lightning data to forecast lightning. We chose to use a stream ML method, i.e., the model is continuously trained as new data arrives. Using a stream ML, we intend to harness the machine's capacity to continuously learn the patterns of lightning occurrence and power outages in real-time -- thus constructing an ever-updating model capable of adapting to transient weather conditions. Given its rapid training time and aptitude for classification tasks, the chosen algorithm was a Very Fast Decision Tree. The stream ML classifier outperforms a classic static ML model by 30% regarding the ROC AUC metric (stream: 71.80%, static: 40.85%) and 50% considering the Micro-f1 score (stream: 91.05%, static: 40.91%). These results arise from the highly dynamic nature of lightning, defining an ideal phenomenon for prediction based on a constantly updated stream of data. An automatic system for CG lightning forecasting for power company assets is helpful for risk management and operational planning. Future steps include increasing the lead time from ten min. to up to one hour, allowing for more time to prepare and anticipate hazards, preventing power outages, and optimizing personnel allocation.

How to cite: Beneti, C., Pavam, L., Oliveira, L., Alves, M., Calvetti, L., and Verdelho, F.: Stream Machine Learning for Lightning Nowcasting - Harnessing the Power of Continuously Updated Data, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-4116, https://doi.org/10.5194/egusphere-egu24-4116, 2024.

EGU24-4214 | Posters on site | NH1.5

Bottom-heavy charge structure and lightning discharges in Tibetan Plateau thunderstorms 

Xiushu Qie, Zhuling Sun, Fengquan Li, Lei Wei, Chunfa Sun, Kexin Zhu, Shanfneg Yuan, Dongxia Liu, and Rubin Jiang

The main charge region in thunderstorms over Lhasa city with an elevation of 3700 m is investigated by using a VHF interferometer, incorporating with fast antenna, weather radar and cloud-to-ground lightning location. The evolution of charge structure and its effects on lightning discharges were discussed in a bottom-heavy thunderstorm. During the early developing stage, the thunderstorm exhibited an inverted dipolar charge structure with negative charge center over the positive, and lower negative intracloud (IC) lightning occurred in between. Then an upper positive charge region appeared as the convection intensifying, and the charge structure exhibited obvious tripolar pattern and with large lower positive charge center (LPCC), and fewer positive IC discharges occurred in the upper dipole but lower negative IC lightning still dominated. As the thunderstorm entered the later mature stage, both negative IC between the lower dipole and positive IC between the upper dipole observed simultaneously. With gradually depleting of the positive charge carriers by precipitation, the LPCC weakened, the positive IC lightning between the upper dipole dominated, and two negative CG flashes were able to occur. In the later stage, positive IC dominated, although not much.  The study further confirms the previous conclusion (Qie et al., GRL, 2005) that weak thunderstorms are characterized by a bottom-heavy charge structure, and in the vigorous stage of thunderstorm, it may exhibit tripolar charge structure with a large LPCC, which has a significant impact on lightning types.

How to cite: Qie, X., Sun, Z., Li, F., Wei, L., Sun, C., Zhu, K., Yuan, S., Liu, D., and Jiang, R.: Bottom-heavy charge structure and lightning discharges in Tibetan Plateau thunderstorms, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-4214, https://doi.org/10.5194/egusphere-egu24-4214, 2024.

The evolution of charge structure plays a crucial role in thunderstorm electrification. In this paper, the signatures related to the upper charge regions consisting of charged ice crystals are analyzed in an isolated thunderstorm, observed by an X-band dual-polarized phased array weather radar (DP-PAWR), which operates in its normal operational mode which performs a volume scan with 110 elevations at a temporal resolution of 30 seconds. The radar data quality control is applied to polarized parameters of DP-PAWR, including the horizontal reflectivity ZH, differential propagation phase shift, and specific differential phase. The lightning data was obtained by a lightning detection system called LIDEN (LIghtning DEtection Network system) operated by the JMA. A flash group algorithm is employed to group lightning discharges into flash branches according to a spatial range, azimuth interval, and time criterion.

 

To explore the mean structure of upper charge regions in the convective part of the thunderstorms, an expanded quasi-vertical profile method is applied to examine the temporal evolution of microphysical processes of upper charge regions. The convective part in the isolated thunderstorm is defined as one separated from nearby storms by an area of composite ZH larger than 40 dBZ at and above -10℃ layer, and a criterion of correlation coefficient ρHV greater than 0.8 is used to remove poor quality radar data. Meanwhile, only the lightning flashes within the given volume are used to calculate the IC lighting flash rate and explore the signatures with the upper charge regions.

 

The results indicate that during the different stages from the early developing stage of isolated thunderstorms to the end of the mature stage, the upper charge regions above the -10 ℃ layer experienced an evolution process from initiation to development accompanied by the rise of the charge region in the updraft and the enhancement of charge concentration. In the mature stage of thunderstorm, the upper charge regions extended from the -30℃ layer to the cloud top, followed by a decay process in the upper charge region at the end of the mature stage, in which the IC lightning flash rate is larger than 60 flashes/min. At the same time, the mean structure evolution of the upper charge regions exhibited a good relationship with the in-cloud lightning flash rate.

How to cite: Wang, S.: Analysis of the Signatures Related to the Upper Charge Regions in an Isolated Thunderstorm Observed by Dual-Polarized Phased Array Weather Radar, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-4254, https://doi.org/10.5194/egusphere-egu24-4254, 2024.

Winter thunderstorms often exhibit compact vertical dimensions and lower heights of the major charge centers and are often accompanied by strong wind shear, with a propensity for positive cloud-to-ground strokes that can produce mesospheric transient luminous events (e.g. sprites, haloes, elves and jets). There are many optical observations confirming this over the Sea of Japan and the Mediterranean Sea, which are known to be the most convectively active regions during Northern Hemisphere winter.

We use a 3D quasi-electrostatic model (Haspel et al., 2022) with wintertime thunderstorm charge configurations to evaluate sprite inception regions in the mesosphere under various conditions typical of the Eastern Mediterranean. This is a is a relatively new, numerically robust model based on an analytical solution to Poisson’s equation that was developed specifically to handle non-symmetric charge configurations in a large 3D domain.  We address several key questions related to the onset of sprites in winter: (a) the minimum charge that enables sprite inception under the compact thunderstorm structures, (b) the effect of wind shear (lateral offsets of 3-5 km between the cloud charge centers) on the electric field and the location of the area of possible sprite inception, and (c) how the time difference between consecutive strokes in adjacent cumulonimbus clouds affects the size and location of the area of possible sprite inception. Additionally, we will present results of sensitivity studies on the discharge time and profile, showing how the area of possible sprite inception depends on this factor.

 

Reference

Haspel, C., G. Kurtser and Y. Yair (2022). The feasibility of a 3D time-dependent model for predicting the area of possible sprite inception in the mesosphere based on an analytical solution to Poisson's equation. Jour. Atmos. Sol. Terr. Phys.,230, 105853, doi:10.1016/j.jastp.2022.105853.

How to cite: Haspel, C. and Yair, Y.: Numerical simulations of the mesospheric region for sprite inception in winter thunderstorms over the Eastern Mediterranean, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-4618, https://doi.org/10.5194/egusphere-egu24-4618, 2024.

EGU24-4634 | ECS | Orals | NH1.5

Regional differences in thunderstorm intensity driven by monsoon and westerlies over the Tibetan Plateau 

Lei Wei, Xiushu Qie, Zhuling Sun, and Chen Xu

Thunderstorms are weak but frequent, and exhibit unique charge structures over the Tibetan Plateau (TP) where the average elevation is higher than 4 km. In this study, all detected thunderstorms over the TP between 1998 and 2013 by TRMM were divided into four intensity categories: weak, median, severe and extreme. This classification was based on the 75%, 90%, and 99% values of flash rate, maximum 40 dBZ height, minimum 85 GHz polarization-corrected temperature (PCT), and minimum 37 GHz PCT, respectively. The monthly distributions of thunderstorm intensity show that all categories mostly occur in summer over most regions of the TP, and in spring near the Himalayas. Although the peaks of thunderstorms occur during 1300-1600 LT, the thunderstorms occurring in the early morning and evening have a high probability of developing into severe and extreme thunderstorms. This is distinct from the thunderstorms over the Sichuan Basin, the surrounding areas, and the middle and lower reaches of the Yangtze River at the same latitude. On the basis of westerlies- and monsoon-dominated regions, as well as the altitude, the TP was divided into four regions: the eastern, northern, southern and western regions of the TP (namely ETP, NTP, STP and WTP, respectively). The ETP and STP are primarily influenced by the monsoon, with the ETP at a lower altitude than the STP. Conversely, the WTP and NTP are affected by the westerlies, with the WTP situated at a higher altitude than the NTP. Thunderstorms over the ETP are more likely to be severe and extreme than those over the NTP. The percentage of weak thunderstorms is highest over the WTP. It is found that the maximum top height, development depth, horizontal development area, and development volume at 20 dBZ, 30 dBZ, and 40 dBZ echoes are largest over the ETP, followed by the NTP and STP, while being smallest over the WTP. The results imply that thunderstorms influenced by the monsoon are larger and more likely to be severe and extreme than those influenced by the westerlies.

How to cite: Wei, L., Qie, X., Sun, Z., and Xu, C.: Regional differences in thunderstorm intensity driven by monsoon and westerlies over the Tibetan Plateau, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-4634, https://doi.org/10.5194/egusphere-egu24-4634, 2024.

EGU24-5346 | Posters on site | NH1.5

Spectral Analysis of High-Energy Radiation Events Observed during the ALOFT 2023 Campaign 

David Sarria, Nikolai Østgaard, Martino Marisaldi, Timothy Lang, Eric Grove, Mason Quick, Hugh Christian, Chris Schultz, Richard Blakeslee, Ian Adams, Rachael Kroodsma, Gerald Heymsfield, Andrey Mezentsev, Ingrid Bjørg Engeland, Anders Fuglestad, Nikolai Lehtinen, Kjetil Ullaland, Shiming Yang, Bilal Hasan Quresh, and Jens Søndergaard and the ALOFT Team

The Airborne Lighting Observatory for FEGS and TGFs (ALOFT) is equipped with a comprehensive set of instruments on-board a NASA ER-2 research aircraft for observing Terrestrial Gamma-ray Flashes (TGFs) and gamma-ray glows from thunderclouds. The ER-2 research aircraft flew at about 20 km altitude, above thunderstorms, from July 1st to July 30th, 2023, for a total flight time of about 60 hours.  The onboard instrument suite comprised several X/gamma-ray detectors, which spanned a dynamic range of four orders of magnitude in flux and covered the entire energy spectrum associated with the gamma-ray transients.

    During the campaign, we observed over 130 short gamma-ray transients, along with hundreds of gamma-ray glows. Several of these detections consisted of thousands of photon counts, allowing precise and unprecedented spectral analyses.

    In this study, we present a comprehensive spectral analysis of various events using a forward modeling technique and Monte-Carlo simulations. This approach enables us to constrain the source characteristics of these events, including their source energy spectrum, production altitude and offset, spatial extension, and the brightness (fluence) of the source RREA electrons.

How to cite: Sarria, D., Østgaard, N., Marisaldi, M., Lang, T., Grove, E., Quick, M., Christian, H., Schultz, C., Blakeslee, R., Adams, I., Kroodsma, R., Heymsfield, G., Mezentsev, A., Bjørg Engeland, I., Fuglestad, A., Lehtinen, N., Ullaland, K., Yang, S., Hasan Quresh, B., and Søndergaard, J. and the ALOFT Team: Spectral Analysis of High-Energy Radiation Events Observed during the ALOFT 2023 Campaign, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5346, https://doi.org/10.5194/egusphere-egu24-5346, 2024.

Lightning now has designated as an Essential Climate Variable in the Global Climate Observing System to understand the climate change. Lightning detection from geostationary satellites enables continuous monitoring of lightning activity. The satellite-borne lightning imagers take advantage of optical imaging technology combined with multiple filtering methods to extract the weak signals of lightning from very strong background signals and eventually clustering to reconstruct the original lightning flashes. By using the observation data of Fengyun-4A Lightning Mapper Imager (LMI), the first geostationary satellite-borne lightning imager developed in China, the lightning activity and the optical characteristics of lightning flashes in China were analyzed. The lightning activity observed by LMI exhibits obvious regional, seasonal and diurnal variation properties. The flashes are mainly concentrated in the southeastern coastal region in China and the southwestern China. During the pre-monsoon period (March-May), LMI detected lightning outbreaks in southwestern China and its surrounding areas, while during the monsoon period (June-September), both eastern southwestern China and southeastern coastal region in China show a significant dense distribution of lightning flashes. The climatic characteristics of lightning activity and the simultaneous observations of Lightning Imaging Sensor (LIS) on the International Space Station (ISS) confirm the LMI observations. However, there is a difference between the absolute amounts of the LMI and LIS observations. The overall number of lightning flashes observed by LMI is relatively lower than that observed by LIS. In addition, the detection capability of LMI is higher at low latitudes compared to mid-latitudes, and is higher during daytime hours than that during nighttime hours. As for the flash properties, which mainly refer to the optical radiance, area, and duration of lightning flashes, there are also regional differences for these properties observed by LMI. The high values of flash properties are concentrated in southern China. The LMI observations are related to the radiometric response of its detector and the difference in spatial resolution within the large field of view of geostationary orbit observations.

How to cite: Hui, W. and Zhang, W.: Lightning Activity in China and Its Optical Characteristics Observed by Geostationary Satellite, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5363, https://doi.org/10.5194/egusphere-egu24-5363, 2024.

EGU24-5400 | ECS | Posters on site | NH1.5

The intensity distribution of Terrestrial Gamma-ray Flashes from the ALOFT flight campaign 

Anders Fuglestad and the ALOFT team

In July 2023, the Airborne Lightning Observatory for FEGS and TGFs (ALOFT) flight campaign took place using a NASA ER-2 research aircraft flying over the Gulf of Mexico and the Caribbean Sea. The campaign consisted of about 60 flight hours at a cruise altitude of 20 km, using live telemetry to target gamma-ray glowing thunderclouds.

The payload consisted of several instruments including gamma-ray detectors with a dynamic range spanning four orders of magnitude in flux, an imaging array of optical photometers, electric field change meters, radiometers, and radar systems. In addition to several ground stations measuring very low frequency, low frequency, and very high frequency radio signals.

96 TGFs were detected by ALOFT. For 44 of these events, it was possible to get an estimate of the location of the source using both correlated optical pulses and lightning detection networks.

With the estimate of the source location and the gamma-ray observation from ALOFT. Monte Carlo simulations were used to get an estimate of the source intensity of the TGFs.

Based on the results it was determined that the vast majority of the 44 TGFs investigated have source intensities below the threshold needed to be observed from current satellite instruments, which indicates a large population of low intensity TGFs that has gone previously undetected. These results contribute to the open debate on the rarity of TGFs.

How to cite: Fuglestad, A. and the ALOFT team: The intensity distribution of Terrestrial Gamma-ray Flashes from the ALOFT flight campaign, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5400, https://doi.org/10.5194/egusphere-egu24-5400, 2024.

EGU24-6398 | ECS | Orals | NH1.5

Side discharges on positively charged lightning leaders 

Shanfeng Yuan, Xiushu Qie, Rubin Jiang, and Dongfang Wang

Recent observations unveiled two types of side discharges associated with positive leaders: needle discharges and nearby bidirectional leaders. The formation mechanism and connections of two phenomena remained unclear due to the lack of synchronous optical detection and radio mapping data. Here we present the first high-speed video and low-frequency lightning mapping results. Negative branches of nearby bidirectional leaders can propagate after connecting to the parent positive channel, and needle discharges act as positive connecting leaders. Our research shows that positive leaders exhibit unconventional channel extensions, maintained by frequent recoil leaders, sharing characteristics with streamer discharges. Notably, when two approaching positive leaders develop in this manner, they can eventually collide. These findings significantly advance our understanding of side discharges on positive leaders, offering fresh insights into these intriguing phenomena.

How to cite: Yuan, S., Qie, X., Jiang, R., and Wang, D.: Side discharges on positively charged lightning leaders, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-6398, https://doi.org/10.5194/egusphere-egu24-6398, 2024.

EGU24-6468 | Posters on site | NH1.5

On the impact of thunder on cloud droplets and ice crystals  

Konstantinos Kourtidis and Stavros Stathopoulos

In the lightning channel pressures can be of the order of 100 atm and hence in the produced thunder, sound pressure levels (SPL) can be very high. Additionally, the thunder frequency spectra have peaks for peal and claps at around 100 Hz and around 50 Hz for rumble sounds, with intracloud lightning having peaks at even fewer Hz. These low frequencies are ideal for acoustically induced orthokinetic agglomeration of droplets. Thunder occurs in cloud environments where not only large numbers of droplets are present, but additionally the shockwave front expands at supersonic velocities and hence could cause near the lightning channel modulations of droplet size distributions and increase ice crystals numbers through e.g. vibrational breakup. We present calculations for the two mechanisms above (orthokinetic agglomeration and vibrational breakup) for typical cloud droplet sizes and concentrations, including also clouds containing desert dust. In thunderstorm conditions, it is found that acoustic orthokinetic agglomeration of droplets can be very effective and can produce very rapidly changes in the mean cloud droplet diameter. Also, it is found that the critical flow velocities, over which breakup occurs, is easily exceeded near the lightning channel and will lead to droplet and ice crystal breakup. We note that all models of ice crystal generation in clouds substantially underestimate the observed ice crystal numbers, and the mechanism presented here may be responsible for the discrepancy. We also note that these processes need further study to assess how they could interfere with the lightning generation process itself, through both charge redistribution in the modified droplet size distribution spectra, as well as the increase in vertical and turbulent transport velocities of the smaller ice crystals resulting from breakup. 

How to cite: Kourtidis, K. and Stathopoulos, S.: On the impact of thunder on cloud droplets and ice crystals , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-6468, https://doi.org/10.5194/egusphere-egu24-6468, 2024.

EGU24-6523 | Posters on site | NH1.5

ESTHER: a small project to investigate gamma-ray emissions in thunderstorms and volcanic lightning 

Alessandro Ursi and Danilo Reitano

Detecting terrestrial gamma-ray flashes (TGFs) from the ground is a relatively new frontier in atmospheric science and has opened up new avenues for research. Also, the recent detection of a TGF produced during the massive Hunga Tonga–Hunga Ha'apai eruption, pointed out the possibility that even volcanic lightning might produce gamma-ray emissions at MeV energies.

In this context, we present the Experiment to Study Thunderstorm High-Energy Radiation (ESTHER), a small project funded by the Italian National Institute for Astrophysics (INAF), aimed at monitoring from the ground gamma-ray emissions produced during thunderstorms and, possibly, by volcanic lightning. The ESTHER set-up consists of a gamma-ray detection system and a VLF radio receiver, to be installed on the top of the Etna volcano (Italy). The selected installation site is the Etnean Observatory of the Italian National Institute of Geophysics and Volcanology (INGV), located at 2,818 m altitude and laying less than 2.7 km from the main volcano craters.

An extensive analysis of the flash rate recorded at Mt. Etna in the last eight years pointed out that the mountain top is interested by strong lightning activity in the summer months, making it a suitable location for the investigation of lightning and associated high-energy phenomena. In particular, the largest fraction of discharges turned out to cluster nearby the mountain peak and right above the main volcano craters, where the frequent presence of volcanic ashes possibly increases the electrical conductivity, under conditions of humid air typical of thunderstorms, making the region above the volcano's top a natural trigger for lightning. Moreover, as for other volcanoes around the world, Etna has been documented to produce volcanic lightning (last times in 2015 and 2022). As a consequence, given the proximity of the Etnean Observatory to the main craters, ESTHER will enjoy a privileged location for investigating potential gamma-ray emissions produced either by thunderstorms and volcanic lightning. In conditions of clear sky, ESTHER will also provide an as much as possible continuous monitoring of the environmental gamma-ray background, allowing to point out potential variations of it before, during, or after volcanic eruptions. The ESTHER set-up will be installed and start its first data acquisitions in spring 2024.

How to cite: Ursi, A. and Reitano, D.: ESTHER: a small project to investigate gamma-ray emissions in thunderstorms and volcanic lightning, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-6523, https://doi.org/10.5194/egusphere-egu24-6523, 2024.

EGU24-7273 | Orals | NH1.5

Observation of positive Narrow Bipolar Events in the Mediterranean region 

Ivana Kolmašová, Ondřej Santolík, Serge Soula, Eric Defer, Yanan Zhu, Radek Lán, Stéphane Pedeboy, and Andrea Kolínská

Narrow Bipolar Events (NBEs) are brief intracloud (IC) discharge processes that generate powerful radiation in the HF and VHF radio bands. NBEs typically occur in isolation, but they have also been identified as initial events in IC lightning flashes. Their incidence is statistically correlated with the strength of convection. NBEs can exhibit both polarities and usually occur in the upper regions of the thundercloud.

We present, for the first time, properties of NBEs detected in the Mediterranean region. The dataset comprises 37 events recorded by broadband magnetic loops located at two sites in France. The events were identified using the list of NBEs from 2022 provided by the Earth Network. The frequency range of our broadband sensors enabled us to obtain detailed shapes of NBE pulses. We calculated rise times, full width at half maximum times, and zero-crossing times of NBE pulses to facilitate comparisons with observations of NBEs in other parts of the world. The majority of NBE pulses observed in the Mediterranean region were isolated events occurring above the land and displaying a simple bipolar waveform with an overshoot peak of the opposite polarity. For two events, we supplemented our observation with the data from the SAETTA (Suivi de l’Activité Electrique Tridimensionnelle Totale de l’Atmosphère) lightning mapping array. Additionally, we estimated the altitude of the NBE events and placed our observations in the meteorological contexts to determine why NBE occurrences in the Mediterranean region have been overlooked until now.

 

How to cite: Kolmašová, I., Santolík, O., Soula, S., Defer, E., Zhu, Y., Lán, R., Pedeboy, S., and Kolínská, A.: Observation of positive Narrow Bipolar Events in the Mediterranean region, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-7273, https://doi.org/10.5194/egusphere-egu24-7273, 2024.

EGU24-7900 | Orals | NH1.5

TGF and gamma-ray glow highlights from the ALOFT 2023 flight campaign 

Nikolai Ostgaard, Timothy Lang, Martino Marisaldi, Eric Grove, Mason Quick, Hugh Christian, Cristopher Schultz, Richard Blakeslee, Ian Adams, Rachael Kroodsma, Gerald Heymsfield, Andrey Mezentsev, David Sarria, Ingrid Bjorg Engeland, Anders Fuglestad, Nikolai Lehtinen, Kjetil Ullaland, Shiming Yang, Bilal Hasan Qureshi, and Jens Sondergaard and the ALOFT team

During the summer of 2023 the  Airborne Lighting Observatory for FEGS and TGFs (ALOFT) field campaign was performed. With a NASA ER-2 research aircraft, flying at 20 km altitude, ALOFT was searching for Terrestrial Gamma ray Flashes (TGF) and gamma-glowing thunderclouds in Central America and Caribbean. The ALOFT payload included a comprehensive number of instruments:

1) Several gamma-ray detectors covering four orders of magnitude dynamic range in flux as well as the full energy range for TGF/gamma-ray glow detection (UIB-BGO and ISTORM).

2) Fly’s Eye GLM Simulator (FEGS), an imaging array of photometers sensitive to different wavelengths, and electric field change meters.

3) Lightning Instrument Package (LIP), giving three component electric field measurements.

4) a suite of microwave radiometers and radars for cloud characterization: the Advanced Microwave Precipitation Radiometer (AMPR), Configurable Scanning Submillimeter-wave Instrument/Radiometer (CoSSIR), Cloud Radar System (CRS), and X-band Radar (EXRAD)

 

5) An extensive set of ground-based radio observations.

 

For all the 10 flights, 60 hours total, realtime gamma-ray detections were downlinked. Due to this simple but novel mission concept, we knew in real time if the aircraft was passing a gamma-glowing cloud and the pilot was instructed to return to the same thundercloud as long as the cloud was glowing. During the campaign ALOFT observed a total of 130 transient gamma-ray events and hundreds of gamma-ray glows. With the richness of the ALOFT observations we learned that thundercloud can glow for much longer than minute scale and over much larger areas than previously reported. We also learned that transient gamma-ray events come in a large variety and new types of events were discovered.  In this presentation we will give an overview of the main results and discoveries by the ALOFT campaign

 

How to cite: Ostgaard, N., Lang, T., Marisaldi, M., Grove, E., Quick, M., Christian, H., Schultz, C., Blakeslee, R., Adams, I., Kroodsma, R., Heymsfield, G., Mezentsev, A., Sarria, D., Bjorg Engeland, I., Fuglestad, A., Lehtinen, N., Ullaland, K., Yang, S., Hasan Qureshi, B., and Sondergaard, J. and the ALOFT team: TGF and gamma-ray glow highlights from the ALOFT 2023 flight campaign, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-7900, https://doi.org/10.5194/egusphere-egu24-7900, 2024.

EGU24-7927 | Orals | NH1.5

A novel view of gamma-ray glows from the ALOFT 2023 flight campaign 

Martino Marisaldi, Nikolai Østgaard, Timothy J. Lang, J. Eric Grove, Mason Quick, Hugh Christian, Christopher J. Schultz, Richard Blakeslee, Ian S. Adams, Rachael A. Kroodsma, Gerald M. Heymsfield, Andrey Mezentsev, David Sarria, Ingrid Bjørge-Engeland, Anders Fuglestad, Nikolai Lehtinen, Kjetil Ullaland, Shiming Yang, Bilal Hasan Qureshi, and Jens Søndergaard and the ALOFT team

The Airborne Lightning Observatory for FEGS and TGFs (ALOFT) was a field campaign targeted at Terrestrial Gamma-ray Flashes (TGFs) and gamma-ray glows from thunderclouds. The campaign was successfully carried out during July 2023, for a total of 60 flight hours in the Gulf of Mexico and the Caribbean. The scientific payload was flown on a NASA ER-2 research aircraft, capable to fly at 20 km altitude above thunderclouds. The payload included a suite of gamma-ray detectors spanning four orders of magnitude dynamic range in flux, and a complete suite of instruments for the characterisation of the electrical and optical activity, and the thundercloud environment. A key asset of the mission was the real-time downlink of gamma-ray count rates, which enabled the immediate identification of gamma-ray glowing regions. The pilot was then instructed to turn and pass over the same glowing region to explore its spatial extension and duration.

ALOFT resulted in the detection of hundreds of gamma-ray glows, anticipating a revolution in our understanding of the phenomenon. Thunderclouds were observed to glow for hours and over several thousands of square kilometers, making glows a much more pervasive phenomenon than previously reported. Glows show significant time variability from seconds down to millisecond time scale, suggesting a relation to short transients such as TGFs more complex than previously thought. Glows are observed in association with the overpass of active convective cores, 20-25 km in size, yet their time variability and intensity modulation suggest a more complex spatial structure.

These observations challenge the current view of glows as quasi-stationary phenomena related to relatively stable electrification conditions. The observed glows show highly dynamic temporal and spatial structures and are closely related to the development phases of active thunderclouds. These observations call for a rethinking of the assumptions at the basis of current modeling efforts.

How to cite: Marisaldi, M., Østgaard, N., Lang, T. J., Grove, J. E., Quick, M., Christian, H., Schultz, C. J., Blakeslee, R., Adams, I. S., Kroodsma, R. A., Heymsfield, G. M., Mezentsev, A., Sarria, D., Bjørge-Engeland, I., Fuglestad, A., Lehtinen, N., Ullaland, K., Yang, S., Qureshi, B. H., and Søndergaard, J. and the ALOFT team: A novel view of gamma-ray glows from the ALOFT 2023 flight campaign, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-7927, https://doi.org/10.5194/egusphere-egu24-7927, 2024.

EGU24-7940 | Orals | NH1.5

LOFAR Observations of the Initial Stage of IC Dart Leaders 

Brian Hare, Olaf Scholten, Paulina Ťureková, Steven Cummer, Joseph Dwyer, Ningyu Liu, Chris Sterpka, and Sander ter Veen

In previous work we have found that dart leaders quench needle activity; where dart leaders are charge pulses that re-trace previously established lightning leader channels, and needles are small repeating negative discharges that propagate away from positive lightning channels. We hypothesized that dart leaders could be quenching needles by carrying negative charge away from the region of needle activity. Therefore, in order to further explore the interactions between dart leaders and needles, we are investigating the beginnings of different dart leaders with the LOFAR radio telescope, which uses hundreds of antennas in northern Netherlands to image lightning in the 30-80 MHz band with meter and nanosecond level accuracy. We have found that, consistent with previous work, dart leaders start slow with weak radio emission and then accelerate over a period roughly around 50 µs in duration until they reach a maximum speed and radio intensity. However, we also observe that the power of the radio emissions from the dart leaders exhibits large, randomly-timed, variations. These variations do not appear to be a form of leader stepping. The time-differences between individual peaks in the time trace is significantly longer than the width of each peak (or pulse) that is dominated by the antenna function, (FWHM ~ 50 ns). One possible explanation could be that the power fluctuations are consistent with Poisson statistical variations of radio sources (possibly streamers), which would imply that at any point in time the radio emission is dominated by a small number of strong emitters, as opposed to millions of small streamers. A second possible explanation is that the fluctuations could be due to small-scale structural variations along the previously established plasma channel, which we have observed in previous work.

How to cite: Hare, B., Scholten, O., Ťureková, P., Cummer, S., Dwyer, J., Liu, N., Sterpka, C., and ter Veen, S.: LOFAR Observations of the Initial Stage of IC Dart Leaders, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-7940, https://doi.org/10.5194/egusphere-egu24-7940, 2024.

EGU24-7982 | ECS | Posters virtual | NH1.5

Enhancement of Catastrophic Positive Cloud to Ground Lightning in recent years over Maharashtra (India): Role of Dust Aerosols 

Abhijeet Gangane, Sunil Pawar, Prajna Priyadarshini, and Venkatachalam Gopalakrishnan

Many studies have shown that aerosols can influence microphysical processes inside thunderclouds that could affect charge-generation processes. Cloud to Ground (CG) lightning data from Ground-based observations (IITM-LLN) over the State of Maharashtra, India, from 2014 to 2023, have been analyzed here to study the percentage and physical mechanism associated with the enhancement of catastrophic Positive CG in total CG lightning. Our analysis shows that the average positive CG percentage remains above 25% during the monsoon (July-September) and post-monsoon (October-November). This increased percentage of positive CG is attributed to elevated dust aerosol concentration over the study region during the monsoon and post-monsoon periods. An enormous amount of dust can be seen during the Indian Summer Monsoon (ISM) over the Arabian Desert and neighborhood extending up to the western Indian (Maharashtra) region. Dust aerosol intrusion into the thunderstorm acts as Ice nuclei (IN) as well as Cloud Condensation Nuclei (CCN) and can influence charge separation processes inside the cloud. In recent years, we observed an enhancement of Dust AOT over Maharashtra state, indicating that the increasing trend in Positive CG lightning is closely linked to the transport of desert dust from the Middle East and elevated aerosol content during the post-monsoon season. Here, we propose that these high concentrations of dust aerosols near the cloud base acting as IN produce a high concentration of ice crystals in the lower portion of the cloud, which can form a strong positive charge region in the lower part of the mixed-phase region by non-inductive charging mechanism. This strong positive charge region in the lower portion of the mixed phase region may be responsible for the observed increased percentage of positive CG over the study region.

How to cite: Gangane, A., Pawar, S., Priyadarshini, P., and Gopalakrishnan, V.: Enhancement of Catastrophic Positive Cloud to Ground Lightning in recent years over Maharashtra (India): Role of Dust Aerosols, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-7982, https://doi.org/10.5194/egusphere-egu24-7982, 2024.

EGU24-7996 | ECS | Posters on site | NH1.5

Monte Carlo Error Analysis of Lightning Interferometry with LOFAR 

Paulina Turekova, Brian Hare, Olaf Scholten, Steven Cummer, Joseph Dwyer, Ningyu Liu, Chris Sterpka, and Sander ter Veen
The LOFAR radio telescope works on a principle of radio interferometric imaging. It coherently sums the signal of hundreds of antennas in northern Netherlands, covering the 30-80 MHz window of the very high frequency (VHF) band of 30-300 MHz. We are using the TRI-D algorithm to extract 3-D polarization data of a lightning flash observed by LOFAR. TRI-D functions by coherently summing recorded voltages, accounting for the antenna function, polarization, and geometric time delay for each voxel. The result is split into time slices. A coherent intensity is calculated for each time slice, and the maximum of this value is set as a source location. The outcome is a reconstructed source location and polarization as seen by the LOFAR antennas. We are now exploring the accuracy of TRI-D in response to realistic parameters. In this work, we perform a Monte Carlo error analysis which simulates the voltages on each antenna from an assumed dipole emitter, adds normally distributed noise, and then reconstructs the source properties with TRI-D. The difference between the simulated input and the reconstruction gives us an estimate of the resulting error bars. We will show a detailed account of the interferometry technique that produces our data, the Monte Carlo simulation that tests the accuracy of our model and finally, our polarization results.

How to cite: Turekova, P., Hare, B., Scholten, O., Cummer, S., Dwyer, J., Liu, N., Sterpka, C., and ter Veen, S.: Monte Carlo Error Analysis of Lightning Interferometry with LOFAR, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-7996, https://doi.org/10.5194/egusphere-egu24-7996, 2024.

EGU24-8002 | ECS | Posters on site | NH1.5

Measuring evaporation-condensation charging of individual aerosol particles 

Andrea Stoellner, Isaac Christopher David Lenton, Caroline Muller, and Scott Russell Waitukaitis

Although cloud electrification has been studied for hundreds of years, it is still not fully understood [1]. The most promising charging mechanism – ice crystal-graupel collision charging – answers some of our questions, but leaves us with others. Why do ice crystals and graupel charge on collision in the first place? And why do they reverse their charging behavior below a certain temperature? To get some insights we take a step back and look at the charging behavior of individual aerosol particles in a humid environment. Shavlov et al. [2] suggest that the hydroxide and hydronium ions formed by the autodissociation of water are sufficient to cause charging during evaporation and condensation of water droplets or surface-adsorbed water on solid particles. This small amount of charge could be a precursor to bigger charge exchange during collision.

            We aim to test this hypothesis by levitating individual aerosol particles in an optical trap and measuring their charge while varying humidity. Our setup allows for trapping of different types of solid and liquid particles in the micrometer size range, like water droplets and silica microspheres. In the future we also hope to study ice crystals. Figure 1 shows an illustration of the measurement principle. The particle’s charge is measured by applying a sinusoidal electric field and observing the resulting particle motion. The Mie scattering pattern of the particle furthermore gives information about the particle’s size and refractive index, both at equilibrium and during evaporation/condensation. The experiment allows us to control the relative humidity, pressure and air ion concentration around as well as air flow across the particle.

Ultimately we hope to contribute to a better understanding of the microphysical processes involved in thundercloud electrification and adjacent electrical phenomena in the atmosphere. 

FIGURE 1. Optical tweezers (wavelength λ = 532 nm) holding a solid or liquid aerosol particle. A sinusoidal electric field is applied between the two electrodes and the resulting particle motion as well as the particle’s Mie scattering pattern are recorded.

Acknowledgments

This project has received funding from the European Research Council (ERC) under the European Union’s Starting Grant (A. Stoellner, I.C.D. Lenton & S.R. Waitukaitis received funding from ERC No. 949120, C. Muller received funding from ERC No. 805041).

 

References

  • Berdeklis, P. and List, R. (2001) J Aerosol Sci. 58(18) 2751–2770.
  • Shavlov A. et al. (2018) J Aerosol Sci. 123 17-26.

How to cite: Stoellner, A., Lenton, I. C. D., Muller, C., and Waitukaitis, S. R.: Measuring evaporation-condensation charging of individual aerosol particles, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-8002, https://doi.org/10.5194/egusphere-egu24-8002, 2024.

EGU24-9355 | Posters on site | NH1.5

Dynamics of global lightning activity on different time scales as indicated by Schumann resonance frequency variations 

Gabriella Sátori, Tamás Bozóki, Earle Williams, Ernő Prácser, Raidiel Puig, and Rachel Albrecht

The electromagnetic waves in the Schumann resonance (SR) frequency range (<100 Hz) radiated by natural “lightning antennas” excite the Earth-ionosphere cavity confined between the Earth’s surface and the ionospheric D-region of ~100 km height. This contribution provides observational evidence for the relationships between the variations of peak frequencies of the first three modes and the global/regional lightning dynamics based on SR observations of the vertical electric field component, EZ, at Nagycenk (NCK), Hungary, Central Europe. Lightning source-observer distance-dependent frequency variations are considered on the annual, seasonal and diurnal time scale as well as during specific events when squall-line formation of lightning activity in South America moves toward NCK. The observations are interpreted with model calculations. The distance-dependent frequency variation has important applications to climate issues as well.

How to cite: Sátori, G., Bozóki, T., Williams, E., Prácser, E., Puig, R., and Albrecht, R.: Dynamics of global lightning activity on different time scales as indicated by Schumann resonance frequency variations, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-9355, https://doi.org/10.5194/egusphere-egu24-9355, 2024.

EGU24-9526 | Orals | NH1.5

On the radio wave polarization of Saturn lightning 

Georg Fischer, Ulrich Taubenschuss, David Pisa, and Masafumi Imai

The radio waves with Saturn lightning origin have been studied since the first detection by Voyager 1, but their wave polarization has rarely been explored. Fischer et al. (2007, JGR 112, A12308) examined lightning from a storm located at 35° south latitude and found its radio emissions below 2 MHz to be highly polarized (80%) in a right-handed circular sense with respect to the wave propagation direction. They explained this by absorption of the extraordinary mode in Saturn's ionosphere and the dominance of the ordinary mode emission, as the radio waves are propagating against a direction of the magnetic field when coming from a source in the southern hemisphere. A limited examination of Saturn lightning from the so-called Great White Spot at 35° north latitude by Fischer et al. (2011, Nature 475, 75-77) revealed radio wave polarization in the left-handed sense. In this presentation we will show the radio wave polarization of lightning from various other storms in Saturn's atmosphere, which have not been examined until today. In this way we want to corroborate the hypothesis that the sense of the circular radio wave polarization of Saturn lightning depends on the hemispherical location of the storm.

How to cite: Fischer, G., Taubenschuss, U., Pisa, D., and Imai, M.: On the radio wave polarization of Saturn lightning, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-9526, https://doi.org/10.5194/egusphere-egu24-9526, 2024.

EGU24-9986 | ECS | Orals | NH1.5 | Highlight

Potential gradient as a predictor of fog 

Caleb Miller, Keri Nicoll, Chris Westbrook, and R. Giles Harrison

Although fog is an important weather phenomenon, it remains difficult to predict using traditional methods. This could be improved by new observations-based nowcasting systems. It has long been understood that fog affects measurements of atmospheric electricity. However, there has been disagreement in the literature on whether these changes contain information which is valuable for fog prediction beyond other commonly used methods. Here, results are presented which show that the potential gradient (PG), a measure of atmospheric electricity, could be used as an additional diagnostic in predicting fog for timescales of several hours. A much larger dataset of fog and PG is examined than has been previously possible, which allows for a more robust understanding of the behaviour of the PG during radiation fog. It is found to increase by a median of 58 V/m by the start of the event. In addition, this increase is found to begin over two hours in advance of the fog, 30% of the time. This shows that PG may contain useful fog nowcasting information. A number of individual fog case studies are presented and the applicability of the general results to these specific cases is discussed. 

How to cite: Miller, C., Nicoll, K., Westbrook, C., and Harrison, R. G.: Potential gradient as a predictor of fog, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-9986, https://doi.org/10.5194/egusphere-egu24-9986, 2024.

EGU24-10352 | Orals | NH1.5

Cloud Microphysical Characteristics Associated with Blue Corona Discharges at thundercloud tops 

Dongshuai Li, Alejandro Luque, Torsten Neubert, Olivier Chanrion, Yanan Zhu, Jeff Lapierre, Nikolai Østgaard, and Víctor Reglero

Blue corona discharges are bursts of streamer discharges often observed at the top of thunderclouds, but the conditions in the clouds that generate them are not well understood.

The cloud microphysical parameters related to them are important for future empirical studies and for theoretical models and simulations. Previous studies modeled the scattering and absorption emissions from blue corona discharges by assuming mean particle radius of 10–20 μm and densities of 1–2.5 × 10^8 m^−3, resulting in photon mean free paths of 1–20 m.

Here we present the first-ever estimate of important microphysical parameters related to blue corona discharges based on data measurements from the CALIPSO lidar. The results showed that most blue corona discharges were associated with ice particles with a radius of ∼50 μm and a number density of ∼ 2 × 10^7 m^−3, resulting in a photon mean free path of ∼3 m.

Around 20% of the blue corona discharges coincide with Narrow Bipolar Events (NBEs) indentified from the Earth Networks Total Lightning Network.The altitudes of blue corona discharges that were identified as NBEs are derived from both the optical and radio bands. It revealed that in six out of nine cases, the R^2 value was greater than 0.85, indicating a good agreement between the two methods and supporting our estimate of the photon mean free path as 3 m. However, in the shallowest and deepest cases, there was some discrepancy between the altitudes determined by the two methods, suggesting more complex cloud microphysical parameters. Possible reasons for the discrepancy, such as the homogeneous approximation for the cloud's microphysical parameters and the simplification of the source length, will be discussed.

How to cite: Li, D., Luque, A., Neubert, T., Chanrion, O., Zhu, Y., Lapierre, J., Østgaard, N., and Reglero, V.: Cloud Microphysical Characteristics Associated with Blue Corona Discharges at thundercloud tops, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-10352, https://doi.org/10.5194/egusphere-egu24-10352, 2024.

EGU24-11101 | Orals | NH1.5

Mapping out lightning processes in both the VHF and VLF using LOFAR and the Met Office’s lightning detection system, LEELA 

Graeme Marlton, Brian Hare, Olaf Scholten, Mike Protts, Ed Stone, Sue Twelves, and Francesco Devoto

Lightning is one of the most destructive meteorological phenomena being a hazard to people and objects on the ground as well as aircraft. In addition to the strong currents and optical emission from a lightning stroke broadband radio emissions are also produced from the VLF to VHF. The LOw Frequency ARray (LOFAR) telescope centred in the Netherlands consists of a large array of VHF (30-300 MHz) receivers which can be configured to image a lightning strike in the 30-80 MHz bandwidth. The Met Office Lightning Electromagnetic Emission Location using Arrival time differencing LEELA system operates in the VLF (3-30 kHz). It also archives the raw incoming VLF data allowing the individual VLF waveforms to be analysed. From a lightning flash recorded in June 2021 over the Netherlands, 8 distinct events were detected by both systems. Here we present an analysis of these 8 events which include dart leaders, negative leaders, an intensely radiating negative leader and a cloud to ground strike. Initial results show that while both systems co-locate the events they are sensitive to different processes within the lightning strike process. VHF emission from a lightning strike is observed for periods of 30-40 ms and captures the development of the lightning channel. However, VLF emission is observed for much shorter periods of a few ms likely corresponding to the rapid vertical movement of charge during the strikes.

How to cite: Marlton, G., Hare, B., Scholten, O., Protts, M., Stone, E., Twelves, S., and Devoto, F.: Mapping out lightning processes in both the VHF and VLF using LOFAR and the Met Office’s lightning detection system, LEELA, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-11101, https://doi.org/10.5194/egusphere-egu24-11101, 2024.

EGU24-11257 | Orals | NH1.5

Locating charged regions in extensive layer cloud 

R.Giles Harrison and Keri Nicoll

Extensive layer clouds are common in Earth’s atmosphere. They acquire charge at their upper and lower boundaries, from the vertical current flowing in the global atmospheric electric circuit. The quantity of charge collected is related to the current, the transition distance from clear air to cloudy air at the cloud boundary, and the background cosmic ray ionisation. The transition distance is the region in which a change in conductivity occurs, which determines the charge acquisition. This differs between cloud top and cloud base. At cloud top, the boundary transition distance is closely related to the temperature inversion, which can be less than the transition distance at cloud base. At cloud base, the transition distance depends on droplet growth rate and updraft speed. The combined effects of the local ionisation, current flow and conductivity gradient leads to droplet charging.

Using instrumentation carried on enhanced meteorological radiosondes, the extent of the charged region in extensive layer clouds has been observed with specially developed cloud sensors operating at multiple optical wavelengths, simultaneously with the in situ electrical measurements. (Further, in some situations, ceilometer measurements of backscatter are also available). These soundings are compared with modelled profiles of droplet properties and layer cloud charges, for situations characteristic of mid-latitude and polar clouds. Effects of the droplet size distribution on the layer cloud electrification are also investigated, and responses to variations in cosmic ray ion production.

Charging is known to affect some aspects of the microphysical behaviour of droplets, such as their evaporation and growth rates. This may in turn influence properties of layer clouds in the climate system.

How to cite: Harrison, R. G. and Nicoll, K.: Locating charged regions in extensive layer cloud, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-11257, https://doi.org/10.5194/egusphere-egu24-11257, 2024.

EGU24-11482 | Orals | NH1.5 | Highlight

Thundercloud high-energy radiation production by long streamers 

Nikolai Lehtinen, David Sarria, Martino Marisaldi, Andrey Mezentsev, Nikolai Østgaard, Steven Cummer, and Yunjiao Pu

The novel Streamer Parameter Model (SPM) [Lehtinen, 2021, doi:10.1007/s11141-021-10108-5] allows to quickly calculate the shape, velocity, and electric field of an electric streamer in air, without resorting to lengthy hydrodynamic simulations. A streamer propagates faster as its length grows. When the streamer length exceeds several meters, the velocity may become comparable to the speed of light, which necessitates correcting the model for relativistic effects. Such long streamers may describe the experimentally observed fast positive and negative breakdown. We propose that they may produce large quantities of relativistic runaway electrons, and therefore x-rays. This is facilitated by several conditions: (1) electric fields at the streamer tip may be sufficiently close to the so-called thermal runaway threshold (~30 MV/m), at which free electrons may accelerate from thermal energies up to relativistic energies; (2) in negative streamers, the energetic electrons are synchronized in velocity with the streamer front; (3) the streamer tip radius may exceed tens of centimeters, providing a large volume of the high field where the thermal runaway acceleration may take place.

We apply SPM to long streamer propagation inside a thundercloud and calculate the relativistic runaway electron production, as well as radio, optical and x-ray radiation. The calculations are compared to the observations of Narrow Bipolar Events (NBE), Terrestrial Gamma Flashes (TGF), and luminous phenomena obtained during the recent ALOFT campaign.

How to cite: Lehtinen, N., Sarria, D., Marisaldi, M., Mezentsev, A., Østgaard, N., Cummer, S., and Pu, Y.: Thundercloud high-energy radiation production by long streamers, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-11482, https://doi.org/10.5194/egusphere-egu24-11482, 2024.

EGU24-11937 | Orals | NH1.5

Feedback Effects in Positive Corona and Relativistic Runaway Discharges 

Victor Pasko, Sebastien Celestin, Anne Bourdon, Reza Janalizadeh, and Jaroslav Jansky

We discuss characteristic scales and direct physical analogy between the photoionization feedback in conventional positive corona discharges in air and the photoelectric feedback in discharges driven by relativistic runaway electrons in air. In a positive corona system the avalanche of electrons in bulk of discharge volume is initiated by specific distribution of photoionization far away from the electrode.  Under inception conditions in positive corona each electron arriving at the anode creates on average just enough seed electrons in discharge volume through photoionization to replicate itself. Under these self-sustained steady state conditions, photoionization feedback produces just enough secondary electrons upstream of the avalanche to maintain the system in steady state. Analogically, in case of relativistic electron avalanches a feedback process is realized when X-rays emitted by these electrons travel backwards with respect to the electron motion and generate new relativistic electron seeds due to the photoelectric absorption in air. It is demonstrated that terrestrial gamma-ray flashes are produced by growth of long bidirectional lightning leader system consisting of positive and stepping negative leaders. The spatial extent of streamer zones of a typical lightning leader with tip potential exceeding several tens of megavolts is on the order of 10–100 m. The photoelectric absorption of bremsstrahlung radiation generated by avalanching relativistic runaway electrons occurs efficiently on the same spatial scales. The intense multiplication of these electrons is triggered when the size of the negative leader streamer zone crosses a threshold of approximately 100 m (for sea-level air pressure conditions) allowing self-replication of these avalanches due to the upstream relativistic electron seeds generated by the photoelectric absorption.

References: 
Pasko et al., GRL, 50, e2022GL102710, 2023, https://doi.org/10.1029/2022GL102710
Pasko et al., PSST, 32, 075014, 2023, https://doi.org/10.1088/1361-6595/ace6d0

How to cite: Pasko, V., Celestin, S., Bourdon, A., Janalizadeh, R., and Jansky, J.: Feedback Effects in Positive Corona and Relativistic Runaway Discharges, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-11937, https://doi.org/10.5194/egusphere-egu24-11937, 2024.

Terrestrial gamma-ray flashes (TGFs), powerful bursts of gamma-rays produced within our atmosphere, often occur in association with lightning. However, the mechanisms for generating the large number of runaway electrons required to account for the TGF luminosities remain uncertain. For example, TGFs might be produced by cold-runaway electron production from streamer heads and/or leader tips in the high-field regions near lightning, or TGFs might be produced by the self-sustained production of runaway electrons by relativistic feedback involving backward propagating runaway positrons and backscattered x-rays. Because both mechanisms could possibly occur in the presence of lightning leaders, it has been challenging to test which TGF production mechanisms are important. In this work, detailed simulations are used to test whether TGFs may be produced by thunderstorm electrification alone, without the presence if lightning. It is found that rapid thunderstorm charging may first produce strong gamma-ray glows, followed by large pulses of gamma-rays, followed by multi-pulsed TGFs similar to the TGFs first observed by CGRO/BATSE. Furthermore, the ionization produced by the high-energy particles partially discharges the electric field in some regions while amplifying the field in other regions, potentially allowing for the initiation of narrow bipolar events (NBEs) and/or lightning. If confirmed, such sequence of events would be strong evidence for the relativistic feedback mechanism.

How to cite: Dwyer, J. and Liu, N.: Gamma-ray glows and terrestrial gamma-ray flashes produced by thunderstorm electrification without lightning , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-12103, https://doi.org/10.5194/egusphere-egu24-12103, 2024.

EGU24-12247 | ECS | Orals | NH1.5

Investigating Storm Charge Distribution Trends with Intracloud Lightning Polarity Data 

Elizabeth DiGangi, Jeff Lapierre, and Yanan Zhu

There are, at present, two accepted primary paradigms for thunderstorm charge distribution using a simple tripole model: “normal” polarity storms, which are characterized by a central negative charge region, an upper positive charge region, and sometimes a lower positive charge region; and “inverted” polarity storms, which are characterized by a central positive charge region, an upper negative charge region, and sometimes a lower negative charge region. The real distribution of thunderstorm charge is known to be more complex than the tripole model can represent, but the normal/inverted paradigm is still widely used in the field. Characterizing storms as having a normal or inverted polarity has been a subject of interest in lightning research since discovering that inverted storms produce a larger-than-average fraction of positive amplitude cloud-to-ground (CG) lightning compared with normal storms. +CG lightning is understood to have generally higher peak currents and a much greater probability of producing continuing current than -CGs, which is relevant for research into subjects like lightning-initiated wildfires and transient luminous events. Thunderstorm charge distribution is also directly related to storm microphysics and thermodynamics, which, in turn, links it to the meteorological characteristics of storms and storm environments.

Most published research on storm polarity has either investigated large-scale trends in +CG versus -CG frequency from long-range lightning detection systems (LDSs), or has used LDSs which map lightning in 3D to infer storm polarity directly from intracloud (IC) lightning leader propagation patterns. Data on IC lightning from long-range LDSs is a resource which, to our knowledge, has not yet been used to study bulk storm charge structures. It stands to reason that if inverted storms favor the production of more +CGs than normal storms, then they would also favor the production of more -ICs. The goal of this study is therefore to interrogate several years of lightning data from the Earth Networks Total Lightning Network (ENTLN) to determine whether or not IC peak current information can be used to study storm charge structure and the geographic distributions of inverted and normal polarity storms.

How to cite: DiGangi, E., Lapierre, J., and Zhu, Y.: Investigating Storm Charge Distribution Trends with Intracloud Lightning Polarity Data, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-12247, https://doi.org/10.5194/egusphere-egu24-12247, 2024.

EGU24-12606 | Posters on site | NH1.5

CubeSpark: Space-based 3-D Lightning Mapping using a Constellation of Radio Frequency Sensors 

Sonja Behnke, Kim Katko, Harald Edens, Patrick Gatlin, Timothy Lang, William Haynes, Paul Snow, Jeremiah Rushton, Joellen Renck, Charley Weaver, Larry Bronisz, Jacob Pratt, Steven Dobson, Nikhil Pailoor, Jackson Remington, and Sarah Stough

CubeSpark is a new concept for a constellation of CubeSats that combines bi-spectral optical lightning imaging with radio frequency (RF) sensing to provide a 3-D lightning detection capability with global coverage from low-Earth Orbit. The development of CubeSpark is a collaboration between Los Alamos National Laboratory and NASA Marshall Space Flight Center. CubeSpark innovates over current ground and space-based global lightning capabilities by determining the altitude of lightning radiation sources, enabling new science in thunderstorm processes and the impact of lightning on climate. The key to determining the altitude of lightning is using a constellation of RF sensors to make coordinated measurements of impulsive RF radiation sources, similar to the approach of a ground-based lightning mapping array. The RF measurements will be enhanced with bi-spectral optical sensors to improve overall lightning detection efficiency and provide additional, complementary information about lightning processes.

This presentation introduces the CubeSpark mission concept and science applications with a focus on the RF hardware under development. Two challenges of space-based RF lightning detection are ionospheric effects and RF noise from both the satellite bus and anthropogenic sources from Earth. While the process of removing ionospheric dispersion from broadband waveforms for time-of-arrival (TOA) estimation is well established, CubeSpark further reduces ionospheric impacts on TOA by using a circularly polarized antenna, which suppresses one of the birefringent wave modes. For noise reduction, the CubeSpark receiver leverages programmable high- and low-pass filters to allow for on-orbit modifications of its passband. A benchtop demonstration of the RF hardware has been completed.

How to cite: Behnke, S., Katko, K., Edens, H., Gatlin, P., Lang, T., Haynes, W., Snow, P., Rushton, J., Renck, J., Weaver, C., Bronisz, L., Pratt, J., Dobson, S., Pailoor, N., Remington, J., and Stough, S.: CubeSpark: Space-based 3-D Lightning Mapping using a Constellation of Radio Frequency Sensors, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-12606, https://doi.org/10.5194/egusphere-egu24-12606, 2024.

EGU24-12658 | Posters on site | NH1.5

GLM lightning flashes observed during ASIM triggers over Tropical South America 

Carlos Morales, Joan Montanyà, Jesus Lopéz, Oscar Van Der Velde, Nicolai Østgaard, Torsten Neubert, and Víctor Reglero

The Atmosphere-Space Interactions Monitor (ASIM) on board the International Space Station (ISS) is collecting data of lightning and Terrestrial Gamma Flashes (TGF) over the globe since April 2018 by means of two suites: i) modular multispectral imaging array (MMIA); and ii) modular X and gamma-ray sensors (MXGS). MMIA responds to lightning flashes, while high energy detector (HED) and low energy detector (LED) of MXGS are employed to estimate TGF spectra and source. Based on these features, ASIM is providing a large dataset of MMIA, LED and HED triggers that are used identify potential TGF events that require an extra imaging analysis to depict the exact location and validation. Upon such measurements, this study employs coincident ASIM and GLM lightning flashes over Tropical South America (90-30W and 20S-10N) to inspect if the electrically active thunderstorms present unequivocal features associated with each ASIM trigger, i.e., MMIA, LED, HED and TGF. Electrically active thunderstorms were identified as contiguous GLM lightning flashes clustered at 0. 1 x 0.1 degrees on ± 30 minutes of ASIM trigger time following Barnes et al. (2015) and Morales et al. (2021) procedures. During the period of 2018 and 2021, we were able to find 30,417 active thunderstorms that have lightning flashes within ± 3 seconds of trigger time (19,546 during the night and 10,871 during the day). Of those thunderstorms, 343 (1,745) were identified with HED, 278 (1,752) with LED, 12,858 (27811) with MMIA and 49 (116) with TGF within 0-200 ms (200ms-3 sec) of the trigger time. The spatial distribution of those thunderstorms do not show any lightning hot spot. MMIA thunderstorms coincide with the location of HED and LED thunderstorms, except HED thunderstorms over the Peruvian Andes mountain range. Moreover, we did not find any TGF thunderstorms along the mountain regions, especially in Peru and Ecuador. The 60 minutes lightning activity (# flashes/per minute) reveal that TGF thunderstorms show higher lightning flash rates than the MMIA, HED and LED triggered thunderstorms, in addition of a sudden lightning flash rate increase prior to the TGF trigger and sustained high lightning activity for the following 10 minutes. HED and LED show similar lightning temporal evolution (flash rate increase before the trigger and decay afterwards), but LED triggered thunderstorms have higher flash rates over the entire 60 minutes time period. MMIA triggered thunderstorms show the lowest flash rates and almost steady lightning activity during the entire 60 minutes. Based on 90% confidence level of T-Student test, we found that TGF and MMIA thunderstorms are statistical different during the entire 60 minute time period, meaning that not all MMIA thunderstorms produce TGFs. In another hand, we can state that HED and LED triggers are good indicators of TGF emissions, since they are not statistically different, meaning that these parameters could be used as triggers to identify TGF occurrences.

How to cite: Morales, C., Montanyà, J., Lopéz, J., Van Der Velde, O., Østgaard, N., Neubert, T., and Reglero, V.: GLM lightning flashes observed during ASIM triggers over Tropical South America, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-12658, https://doi.org/10.5194/egusphere-egu24-12658, 2024.

EGU24-13074 | ECS | Posters on site | NH1.5 | Highlight

A Deep Learning Approach to Lightning Nowcasting and Forecasting 

Randall Jones, Joel Thornton, Dale Durran, Lyatt Jaeglé, Christopher Wright, and Robert Holzworth

Lightning plays a fundamental role in Earth’s climate system and is a frequently occurring natural hazard. However, lightning remains a relatively unpredictable area of meteorology, especially in terms of lightning frequency per convective event, with limited ability for nowcasting and forecasting of lightning occurrence. The goal of this study is to develop a deep learning algorithm able to replicate lightning stroke density on a climatological average, as well as on a convective feature basis. We use a convolutional neural network (CNN) containing combinations of the following variables at 0.5-degree by 0.5-degree spatial resolution and a 3-hourly temporal-resolution over a domain that encompasses most of the Western Hemisphere: lightning from the World-Wide Lightning Location Network (WWLLN), precipitation rate from NASA’s Integrated Multi-satellite Retrievals for GPM (IMERG) and convective available potential energy (CAPE), cloud base height (CBH), two-meter temperature (T2M) and zero degree level (ZDL) from the European Centre for Medium-Range Weather Forecasting (ECMWF). We train the CNN on the years from 2010 to 2018, and tested on the years 2019 to 2022. Model performance was evaluated on a four-year average through changes to the initial seed used to train the model, the loss function used, transformations to the lightning dataset, and changing the spatial and temporal resolution of the input datasets. We further examined the value of 11 input variable combinations, from single variables to all five variables used in training. Preliminary results show that changing the initial seed, as well as changing the loss function from mean squared error to mean-squared logarithmic error, does not greatly impact model performance when running the model with more than one input variable. Results vary amongst the variable combinations, but amongst the different initial seeds and loss functions, the r-squared values remain above 0.75 for every model configuration over both land and ocean. Model performance is improved when using higher time resolution training set but not necessarily a higher spatial resolution. For example, a 1-degree by 1-degree spatial resolution and a 3-hourly time resolution resulted in an r-squared between predicted and observed lightning frequency 0.1 higher than that using 0.5-degree by 0.5-degree spatial resolution and a daily time resolution. The model is able to reproduce the approximate evolution of lightning stroke density of individual convective events, but tends to overestimate the stroke density on a 3-hourly basis. Future work will include a steeper penalty for overestimating lightning occurrence during training. These results show that larger-scale weather forecasting and earth system models could significantly improve lightning stroke density parameterizations by incorporating deep learning results.

How to cite: Jones, R., Thornton, J., Durran, D., Jaeglé, L., Wright, C., and Holzworth, R.: A Deep Learning Approach to Lightning Nowcasting and Forecasting, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-13074, https://doi.org/10.5194/egusphere-egu24-13074, 2024.

EGU24-13383 | ECS | Posters on site | NH1.5

Peak currents of terminating flashes in thunderstorm ground enhancements around Mt Aragats, Armenia 

Gayane Karapetyan, Earle Williams, Hripsime Mkrtchyan, and Reik V. Donner

Thunderstorm ground enhancements (TGEs) are high-energy particle fluxes detected at the ground level during thunderstorms. It has been observed that some TGEs experience abrupt termination by lightning strikes (Chilingarian 2015, Tsuchiya 2013, Williams et al., 2022) often accompanied by simultaneous reductions in flux. Understanding the origin and parameters of terminating lightning can provide insights into the distribution of electric fields and potential within thunderclouds. 

Thundercloud potential is a key factor in determining the maximum peak current of lightning. One expects a linear relationship between peak current and cloud potential because the charge that is deposited on the leader channel is proportional to the leader potential (e.g. Chronis et. al. 2015). 

This study evaluates peak currents in terminating flashes documented in TGEs observed around Mt Aragats (Armenia) using a ground-based VLF lightning detection network, GLD360. A total of 71 terminating flashes have been identified over a period of 6 years (2017-2022). The events documented at Aragats were detected by particle detectors that showed the abrupt decrease in flux associated with lightning. These events were accurately timed using an EFM100 electric field mill (resolution of 2Hz). Thereafter, correlations between these events and the corresponding GLD360 lightning events were established, using millisecond precision times of GLD360 and electric field mill.

Our findings show that the mean peak current of this collection of terminating flashes (45 kA) is 3.4 times higher than that of the general population of lightning flashes measured in the same location (13.6 kA) over a similar period of time. However, it is difficult to define the relationship between the change in electric field during TGE or lightning and the peak currents. It appears that lightning with smaller peak currents tends to have larger values of the change of electric field, while lightning with larger peak currents is characterized by an average change in the electric field.

This research provides insights into peak currents of terminating lightning flashes with general parameters of the TGEs, such as duration and flash rate. Additionally, it shows that flashes with extremely high peak currents occur during thunderstorms with smaller flash rates and are located within 10 km distance from the particle detectors. Furthermore, flash rates of thunderstorms with terminating lightning are larger compared to general thunderstorms without TGEs.

How to cite: Karapetyan, G., Williams, E., Mkrtchyan, H., and Donner, R. V.: Peak currents of terminating flashes in thunderstorm ground enhancements around Mt Aragats, Armenia, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-13383, https://doi.org/10.5194/egusphere-egu24-13383, 2024.

EGU24-13989 | Posters on site | NH1.5

C3IEL, the Cluster for Cloud evolution ClImatE and Lightning mission to study convective clouds at high spatial and temporal resolutions 

Eric Defer, Celine Cornet, Daniel Rosenfeld, Cecile Cheymol, Adrien Deschamps, Alex Frid, Laurene Gillot, Vadim Holodovsky, Avner Kaidar, Raphael Peroni, Colin Price, Didier Ricard, Antoine Rimboud, Yoav Schechner, Aviad Shmaryahu, and Yoav Yair

The French-Israeli space-borne C3IEL (Cluster for Cloud evolution, ClImate and Lightning) mission aims at providing new insights on convective clouds, at high spatial and temporal resolutions, close to the scales of the individual convective eddies. The mission will simultaneously characterize the convective cloud dynamics, the interactions of clouds with the surrounding water vapor, and the lightning activity.

The C3IEL mission consists in a short-baseline (~150 km) train of 2 synchronized nano-satellites. Each nano-satellite carries a visible camera (670 nm) for cloud imagery at a spatial resolution of ~20 meters, near-infrared water vapor imagers (1.04, 1.13 et 1.37 µm) measuring in and near the water vapor absorption bands, and a lightning imager (777.4 nm) and at least one photometer (777.4 nm).

The scientific objectives of the C3IEL mission, i.e. documenting the 3D evolution of the clouds’ surface, entrainment of water vapor, and electrification, will be first reminded. Then, we will introduce the satellite train configuration, the different sensors of the mission and the innovative and different observational strategy that will be applied during daytime and nighttime. We will then detail the expected observations and products, including the ones related to lightning.

How to cite: Defer, E., Cornet, C., Rosenfeld, D., Cheymol, C., Deschamps, A., Frid, A., Gillot, L., Holodovsky, V., Kaidar, A., Peroni, R., Price, C., Ricard, D., Rimboud, A., Schechner, Y., Shmaryahu, A., and Yair, Y.: C3IEL, the Cluster for Cloud evolution ClImatE and Lightning mission to study convective clouds at high spatial and temporal resolutions, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-13989, https://doi.org/10.5194/egusphere-egu24-13989, 2024.

EGU24-14531 | ECS | Posters virtual | NH1.5

Identifying atmospheric conditions for intermittent, small-scale lightning discharges near the top of thunderstorms 

Reinaart van Loon, Jelle Assink, Olaf Scholten, Brian H Hare, and Hidde Leijnse

Despite its impact on society, many aspects of lightning, including the initiation and propagation, remain poorly understood. This also applies to a distinct type of intermittent small-scale lightning discharges recorded by the Low-Frequency Array (LOFAR) in the Netherlands (Scholten et al., 2023). The so-called “sparkles” seem uncorrelated and occur relatively high up in thunder clouds near the tropopause. This research investigates the meteorological conditions under which sparkles exist.  

Previous literature suggests a correlation between sparkles and strong updrafts. One hypothesis proposes that powerful updrafts overshooting the level of neutral buoyancy causes a charged screening layer aloft to be entrained into the cloud, resulting in charge pockets. Alternatively, some hypothesize that turbulence plays a vital role in discharge initiation and charge sedimentation. Therefore, intense turbulence near the top of strong updrafts could not only initiate numerous discharges, but could also influence the lightning structures through the spatial charge distribution. 

This project aims to improve the understanding of sparkles by comparing high- resolution LOFAR lighting data with meteorological data. Specifically, thunderstorm dynamics are studied using data from satellites, radar and the HARMONIE weather forecast model. Following the hypotheses, relations are explored between sparkling activity and factors such as updrafts strength, turbulence, mixing, and entrainment of the air aloft.

Scholten, O., Hare, B. M., Dwyer, J., Liu, N., Sterpka, C., Assink, J., ... & Veen, S. T. (2023). Small‐Scale Discharges Observed Near the Top of a Thunderstorm. Geophysical Research Letters, 50(8), e2022GL101304. 

How to cite: van Loon, R., Assink, J., Scholten, O., Hare, B. H., and Leijnse, H.: Identifying atmospheric conditions for intermittent, small-scale lightning discharges near the top of thunderstorms, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-14531, https://doi.org/10.5194/egusphere-egu24-14531, 2024.

EGU24-14754 | Orals | NH1.5

Quantitative detection device for NOx of centimeters-discharge and its preliminary applications in laboratory long spark and rocket-triggered lightning 

Rubin Jiang, Yufan Ren, Ruiling Chen, Hongbo Zhang, Mingyuan Liu, Xinran Xia, Jianwen Wu, Dongfang Wang, Kun Liu, and Xiushu Qie

A quantitative detection device for nitrogen oxides (NOx) produced by the centimeters-scale discharge channel is designed, consisting of a container made of high-strength acrylic Plexiglas, two copper metal electrodes fixed to the top and bottom of the container, a pumping system and a back-end NOx detector. Inside the container, the gap between the two copper electrodes is 4 cm in length. When a discharge occurs between the electrodes, the NOx produced by the air ionization are confined within the container to provide a quantitative measurement. The device can be used in the laboratory long spark and rocket-triggered lightning scenarios, with container volumes of 12.2 L and 58.8 L, respectively, both of which ensure an accurate measurement of the discharge current. In the laboratory long spark scenario, the device is placed under the discharge electrode of the Marx generator. As the discharge is generated, the discharge strikes the upper copper metal electrode and leads to the gap breakdown within the container, then the current is released through the bottom copper metal electrode to the ground. In the rocket-triggered lightning scenario, the device is fixed between the current sensor and the grounding system. The triggered discharge leads to the gap breakdown within the container, and the current is also released through the bottom copper metal electrode to the ground. After the discharge, the gas in the canister is pumped to the NOx concentration meter. The instruments used are the Thermo, which uses a chemical method to measure NO and NOx concentrations with a time resolution of 1 minute, and the LGR-NO2, which uses an optical method to measure NO2 concentrations with a time resolution of 1 second. The preliminary experiment shows that the 4 cm long discharge due to the laboratory long spark with a peak current of about 2 kA produced 6.8×1017 NO2 molecules. In an unsuccessful triggering lightning case, the discharges due to the precursors also lead to significant NOx signals.

How to cite: Jiang, R., Ren, Y., Chen, R., Zhang, H., Liu, M., Xia, X., Wu, J., Wang, D., Liu, K., and Qie, X.: Quantitative detection device for NOx of centimeters-discharge and its preliminary applications in laboratory long spark and rocket-triggered lightning, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-14754, https://doi.org/10.5194/egusphere-egu24-14754, 2024.

EGU24-15389 | ECS | Posters on site | NH1.5

TGFs observed by the ALOFT 2023 flight campaign during an ISS overpass 

Ingrid Bjørge-Engeland, Nikolai Østgaard, Timothy Lang, Martino Marisaldi, J. Eric Grove, Mason Quick, Hugh Christian, Christopher Schultz, Richard Blakeslee, Ian Adams, Rachael Kroodsma, Gerald Heymsfield, Andrey Mezentsev, David Sarria, Anders Fuglestad, Nikolai Lehtinen, Kjetil Ullaland, Shiming Yang, Bilal Hasan Qureshi, and Jens Søndergaard and the ALOFT team

During the Airborne Lightning Observatory for FEGS and TGFs (ALOFT) campaign in July 2023, the International Space Station (ISS), at an altitude of approximately 410 km, passed over the same region as covered by ALOFT within a short time period on the 24th of July. The ALOFT campaign, which carried gamma-ray detectors, photometers, and instruments for characterizing the electrical activity and the cloud environment, flew at an altitude of approximately 20 km and covered thunderstorms over the Gulf of Mexico and Caribbean during its 60 flight hours. The Atmosphere-Space Interactions Monitor (ASIM) is mounted on the ISS, with its Modular X- and Gamma-ray Sensor (MXGS) designed for observing TGFs. During the ISS overpass, ALOFT observed six TGFs within less than two minutes that were all within the field of view of the ASIM instrument. However, none of the TGFs were detected by ASIM. Here we present the six TGFs observed by ALOFT during the ISS overpass and discuss their source properties. The ASIM non-detection provides a strong upper limit on the TGF fluence.

How to cite: Bjørge-Engeland, I., Østgaard, N., Lang, T., Marisaldi, M., Grove, J. E., Quick, M., Christian, H., Schultz, C., Blakeslee, R., Adams, I., Kroodsma, R., Heymsfield, G., Mezentsev, A., Sarria, D., Fuglestad, A., Lehtinen, N., Ullaland, K., Yang, S., Hasan Qureshi, B., and Søndergaard, J. and the ALOFT team: TGFs observed by the ALOFT 2023 flight campaign during an ISS overpass, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-15389, https://doi.org/10.5194/egusphere-egu24-15389, 2024.

EGU24-15691 | ECS | Posters on site | NH1.5

Supervised Machine Learning for the Automatic Classification of Triggers from ASIM/MXGS on board the ISS 

Jose E. Adsuara, Javier Navarro-González, Paul Connell, Víctor Reglero, Nikolai Østgaard, and Torsten Neubert

During ASIM operations from June 2018 until the end of 2019, 486 TGFs were observed. For this task, the ASDC (ASIM Science Data Center) dealt with numerous triggers from the instrument (5000 per week). The relocation of the instrument from EPF SDX to EPF SDN (Starboard Deck Nadir) of the Columbus ISS Module on January 10, 2022, demonstrated that the MXGS location capabilities could be used not only for TGF location but also for imaging GRB events, as its Field of View in the SDN port encompasses both Earth and space.

It's worth noting that only a few of the ASIM triggers correspond to TGF events. There is a screening process employing a series of algorithms to detect and discard false positives (triggers that are not TGFs). Nevertheless, the ASIM archive retains all data from every trigger. Due to the extended operational time, there is currently a sufficiently large database that enables us to present the initial results here using novel machine learning methods, such as kernel methods or neural networks, for the automatic categorization of both present and future events.

Furthermore, our interest goes beyond mere classification, as we are currently investigating whether various explainability methods applied to these techniques can assist in identifying the relevant features of the signal for such classification. The aim of this work is to provide a tool to quantify new physical processes that could be the cause of instrument triggers and to examine whether there is a connection with the Earth-Space global circuit.

How to cite: Adsuara, J. E., Navarro-González, J., Connell, P., Reglero, V., Østgaard, N., and Neubert, T.: Supervised Machine Learning for the Automatic Classification of Triggers from ASIM/MXGS on board the ISS, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-15691, https://doi.org/10.5194/egusphere-egu24-15691, 2024.

EGU24-16929 | Orals | NH1.5

Constraining electrification of volcanic plumes through numerical simulation 

Michael Herzog, Vishnu Nair, Alexa Van Eaton, and Ted Mansell

Technological improvements over the past decade have dramatically increased lightning detection from explosive eruptions worldwide. The underwater Hunga Tonga-Hunga Ha’apai volcano eruption in January 2022 in Tonga produced more lightning than any storm yet documented in the modern satellite era. These observations of volcanic lightning capture the imagination of the public and provide novel ways to monitor explosive hazards in near real time. In this presentation, we present the first results from the numerical simulation of the electrification of a volcanic plume using the volcanic plume model ATHAM. The electrification mechanisms of fracto-emission and triboelectrification along with the macroscopical transport of the charge carrying plume components have been modelled in ATHAM to make this the first numerical model to quantify volcanic electrification. We also present first results of discrete lightning discharges which are diagnosed as continuous branching regions defined by local net charge density and electric potential. 

The enhanced modelling capability of ATHAM opens new routes into the study of explosive eruptions and nowcasting of volcanic ash hazards for aviation and downwind communities. 

How to cite: Herzog, M., Nair, V., Van Eaton, A., and Mansell, T.: Constraining electrification of volcanic plumes through numerical simulation, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-16929, https://doi.org/10.5194/egusphere-egu24-16929, 2024.

EGU24-17483 | ECS | Orals | NH1.5

Characterisation and modelling of lightning strikes as point events in time and space 

Uldis Zandovskis, Davide Pigoli, and Bruce D. Malamud

Lightning, a spatio-temporal phenomenon, comprises of individual strikes with specific occurrence times and spatial coordinates. This study models and characterises lightning strikes from single thunderstorms, treating each strike as a point event. Utilising real-world datasets, we characterise and model lightning strikes' physical properties. Our analysis involves two severe UK thunderstorm systems, selected based on published synoptic analyses. These systems enable subdivision of the lightning dataset into subsets, each representing a distinct thunderstorm. Our two major storm systems feature three thunderstorms each: Storm system A with 7955, 11988, and 5655 strikes over the English Midlands on 28 June 2012; Storm system B with 4218, 455, and 1926 strikes characterised over the northern England on 1-2 July 2015. These six datasets exemplify individual thunderstorms and a total of three physical attributes are : movement speed, lightning inter-event time distribution, and spatial spread about the storm track. Applying least-squares plane and linear fits in the spatio-temporal and lag spaces, we estimate movement speeds of 47-59 km/h and 67-111 km/h for Storm systems A and B, respectively. The inter-event time distribution ranges from 0.01 to 100 seconds, with density peaks around 0.1 seconds and at 1-10 seconds. Autocorrelation analysis in natural time reveals significant autocorrelation in all storms, varying from short-range to long-range. For spatial spread, calculated as the distance from the storm track to the strikes, we employ a linear filter to establish the storm track. This analysis yields typical spatial spreads up to 80 km in either northing or easting dimensions, with an outlier of 226 km in the northing dimension for one storm. The paper concludes with a synthetic lightning strike model. This model allows selection of individual storms' starting points, directions, and movement speeds, generating point events based on our characterisation findings. This comprehensive study of lightning strikes in time and space accurately reflects severe thunderstorms' behaviour and informs statistical models for simulating lightning events.

How to cite: Zandovskis, U., Pigoli, D., and Malamud, B. D.: Characterisation and modelling of lightning strikes as point events in time and space, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-17483, https://doi.org/10.5194/egusphere-egu24-17483, 2024.

EGU24-18529 | Orals | NH1.5

Analysis of an upward discharge above a thundercloud over Mediterranean Sea 

Serge Soula, Gabriel Hausknost, Axel Ventre, Sylvain Coquillat, Janusz Mlynarczyk, and Alex Hermant

On the night of November 1st, 2022, several weather photographers obtained remarkable photos showing a jet-like phenomenon with long blue filaments above a Mediterranean storm. An unprecedented set of optical and electrical data, including two pictures, one movie, VHF sources from a Lightning Mapping Array (LMA), detections from a LLS and Current Moment Waveforms from an ELF detection, makes it possible to characterize this event. It consists of a two-part upward luminous channel emerging from the cloud top at 11.8 km of altitude, developing up to 14.2 km and topped with blue streamers up to 17.2 km. It is embedded in a flash which starts with a positive 25 kA-discharge followed by a continuing current during 75 ms associated with VHF sources at 10 km. Contrary to blue jets and blue starters which have a positive polarity, the luminous event above the cloud is identified as a negative leader followed by three channel brightenings linked to the negative charge of a positive cloud dipole. The luminous event-producing flash is preceded by a convective surge and a production of positive flashes within the same region of the cloud. The triggering conditions and mechanisms of the event share similarities with gigantic jets, especially its polarity and a reduced upper positive charge.

How to cite: Soula, S., Hausknost, G., Ventre, A., Coquillat, S., Mlynarczyk, J., and Hermant, A.: Analysis of an upward discharge above a thundercloud over Mediterranean Sea, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-18529, https://doi.org/10.5194/egusphere-egu24-18529, 2024.

EGU24-18594 | Orals | NH1.5 | Highlight

Observations of thunderstorms with a neuromorphic camera: First results of the THOR-DAVIS experiment on the International Space Station. 

Olivier Chanrion, Nicolas Pedersen, Yoav Yair, Martin Stendel, Andreas Mogensen, Dongshuai Li, Andreas Stokholm, and Torsten Neubert

THOR-DAVIS is an experiment on the International Space Station to observe thunderclouds and their electrical activity with a neuromorphic camera and a co-aligned video camera. A neuromorphic camera, or 'event camera,' only reads pixels when there is a change in pixel illumination, allowing for a temporal resolution that may reach 10 microseconds. Launched by the SpaceX Commercial Resupply Service mission on June 5, 2023, THOR-DAVIS was part of Danish ESA astronaut Andreas Mogensen’s Huginn mission. The scientific focus was to conduct video observations of electrical activity at the cloud tops and the stratosphere above and to extract their altitudes. The technical objective was to test the neuromorphic concept for observations of thunderstorms from space. Andreas Mogensen performed 15 days of observations, passing over 48 thunderstorms, most forecasted by us a day in advance following a procedure inherited from previous ISS experiments (THOR (2015), ILAN-ES (2022)) and some at his own initiative. In all, 36 thunderstorms were recorded in both cameras, totaling ~3 hours of observations. Most notably, Andreas Mogensen secured the first observations of sprites and of an elve with a neuromorphic camera. In addition, numerous lightning flashes, including spider lightning with leader branches extending above the clouds, were observed. The presentation will provide an overview of the THOR-DAVIS payload design, laboratory measurements, and some of the observations from the ISS.

How to cite: Chanrion, O., Pedersen, N., Yair, Y., Stendel, M., Mogensen, A., Li, D., Stokholm, A., and Neubert, T.: Observations of thunderstorms with a neuromorphic camera: First results of the THOR-DAVIS experiment on the International Space Station., EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-18594, https://doi.org/10.5194/egusphere-egu24-18594, 2024.

EGU24-18787 | Orals | NH1.5

Machine Learning to predict Downbursts in Japan Based on Total Lightning and Ground Precipitation Data 

Alexander Shvets, Yasuhide Hobara, Shiho Miyashita, Hiroshi Kikuchi, Jeff Lapierre, and Elizabeth DiGangi(

In this study, using total lightning data from the Japan Total Lightning Network (JTLN) and precipitation data from X-band MP radar, machine learning with a Random Forest model was used to successfully classify the occurrence of downbursts in Japan. TL data associated with the event is collected from JTLN which consists of 11 Earth Networks Total Lightning Sensorsover Japan (data set in 2017, currently 16 stations nationwide) deployed by the UECand jointly operated with Earth Networks. These sensors can detect lightning pulses with a spatial resolution of 500 m. TL parameters such as types of lightning (IC and CG), time of occurrence (UT), location (latitude-longitude), and lightning polarity were collected for each lightning discharge. Ground precipitation data (temporal resolution of 1min, spatial resolution of 250m) from the Ministry of Land, Infrastructure, Transport, and Tourism’s eXtended RAdar Information Network (XRAIN) composed of 26 C-band radars and 39 X-band multiparameter (X-MP) radars are used. Fourteen thunderstorms causing gusty winds and 33 of those without causing gusty winds that occurred in Japan from 2014 to 2017 were analyzed. AITCC (Algorithm for the Identification and Tracking of Convective Cells) was applied to track both precipitation cell and associated lightning discharges. By using Random Forest model, the importance of variables was derived, and it was shown that lightning jump is one of the most important variables for predicting downbursts. This implies that the updrafts inside the clouds are closely related to the occurrence of a significant increase in lightning, followed by a downburst. The prediction accuracy was highest for models that included both lightning and precipitation, followed by lightning-only and precipitation-only models, confirming the importance of data fusion for improving prediction accuracy.

How to cite: Shvets, A., Hobara, Y., Miyashita, S., Kikuchi, H., Lapierre, J., and DiGangi(, E.: Machine Learning to predict Downbursts in Japan Based on Total Lightning and Ground Precipitation Data, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-18787, https://doi.org/10.5194/egusphere-egu24-18787, 2024.

EGU24-20590 | Orals | NH1.5

Re-discharges on preexisting negative leader channels of a positive cloud-to-ground lightning flash  

Zhuling Sun, Xiushu Qie, Mingyuan Liu, and Fengquan Li

Using the lightning VHF interferometer, three types of discharges on the preexisting negative leader channels of a positive cloud-to-ground lightning flash were observed. The first type involved small-scale cluster discharges during the simultaneous development of the upper horizontally negative leader and downward positive leader before the return stroke. These discharges exhibited similar characteristics and radiation features as the needle-like discharges on the positive leader. Over time, their occurrence positions progressed toward the head of the negative leader, and some cluster discharges had the potential to develop into new negative branches. The other two types of re-discharges occurred after the return stroke. Immediately after the return stroke, rapid discharges initiated near the head of the negative leader, developed along the preexisting negative leader channel, and caused the decayed negative leaders to progress forward again. Subsequently, numerous lateral discharges breaking down the air occurred, distributed widely throughout the negative leader channel. These discharges developed rapidly, gradually slowing down over time until the long continuous current ended. In comparison to the positive leader discharges before the return stroke, which showed no obvious recoil leader discharges, the negative leader channel was more prone to extinguish. These re-discharges on the preexisting negative leader channel were influenced by both radial and longitudinal electric fields of flash channels, and they could also generate a backward surging current wave to sustain the discharge process on the positive leader or grounded channel.

How to cite: Sun, Z., Qie, X., Liu, M., and Li, F.: Re-discharges on preexisting negative leader channels of a positive cloud-to-ground lightning flash , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-20590, https://doi.org/10.5194/egusphere-egu24-20590, 2024.

EGU24-20630 | Orals | NH1.5

eLMA: Supercells over the Upgraded Ebro 3D Lightning Mapping Array and High-speed Observations of Lightning in the Near-Ultraviolet 

Oscar van der Velde, David Romero, Jesús López, Joan Montanyà, and Nicolau Pineda

In 2011, the Ebro 3D Lightning Mapping Array was the first LMA installed outside the USA, consisting of 12 stations in 2012-2014 and was subsequently split in half in 2015 to facilitate a LMA in Colombia.

Thanks to research infrastructure service funding from the Spanish government and the European Union (MCIN/AEI/10.13039/501100011033/ and NextGenerationEU, project EQC2021-006957-P), the Ebro LMA has been upgraded to a wider area network operating 15 latest technology LMA stations operating on solar power in the Ebro Valley region (western Catalonia and eastern Aragón). New services are offered: (1) Real-time tracking of lightning flashes across northeastern Spain, available to the public via the website elma.upc.edu. (2) Archive of plotted data that can be browsed freely, including for the old Ebro LMA data. (3) Raw/processed data can be requested. (4) LMA rental is possible for field campaigns.

We developed a new processing & visualization tool for flash/thunderstorm analysis and future integration with the new EUMETSAT Lightning Imager (LI). It is written in the Julia programming language for its speed of processing with the Makie interactive graphics package. Additionally, we present a new tool for displaying regional maps of actual (not computed) LMA station contributions to assess the network performance. The capabilities of the new Ebro LMA are showcased with record-setting horizontal lightning flashes, several large-hail producing supercells with high flash rates, a lightning hole, and rising turrets of small flashes and sparks at the cloud top, which can now be isolated and analyzed with the Julia visualization tool. The electrical evolution features of these supercells are examined in relation to their timeline of severe weather production.

Additionally, a Vision Research Phantom TMX 6410 and UV-sensitive Lambert HiCATT 25 image intensifier with optics and filters were acquired, and is also available to third parties via eLMA rental services. During spring/summer 2023 it has been successfully used to image lightning leaders through a 337 nm optical narrowband filter (10 nm width) similar to imaging systems of the Atmosphere-Space Interactions Monitor (ASIM), at speeds of 65,000 to 320,000 frames per second. We found that observation distances <4 km are needed in order to be able to see the stepped leader in negative cloud-to-ground flashes. However, in only one case, of an intense burst of horizontal leader activity below the cloud base, negative streamers can be clearly distinguished and the stepping process analyzed over time. At greater distances only return strokes and dart leaders are tracked through the 337 nm filter. In fact, a >418 ms long continuing current negative return stroke (cut off by end of buffer) was observed. Also, the system captured elves, nocturnal optical emissions at the base of the ionosphere (85 km) over Mediterranean winter thunderstorms, clearly showing the typical double-wave structure originally reported by photometer arrays.

How to cite: van der Velde, O., Romero, D., López, J., Montanyà, J., and Pineda, N.: eLMA: Supercells over the Upgraded Ebro 3D Lightning Mapping Array and High-speed Observations of Lightning in the Near-Ultraviolet, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-20630, https://doi.org/10.5194/egusphere-egu24-20630, 2024.

EGU24-20763 | ECS | Orals | NH1.5

Low-frequency sferics associated with consecutive Terrestrial Gamma-ray Flashes  

Hongbo Zhang, Xiushu Qie, and Gaopeng Lu

Terrestrial Gamma-ray Flashes (TGFs) are brief and intense emissions of hard X-rays and gamma-rays originating inside thunderstorms. It has been observed that TGF occurs much less frequently than lightning. However, the TGF generation conditions and mechanism of are not clear, such as why just the TGF-associated lightning produces TGF while others not. Consecutive TGFs detected by space-based platform are usually several seconds to 1-2 minutes apart, and they come from same meteorological environment and even from the same storm cells. This provides a possibility to understand the relationship between lightning and TGF. Based on Fermi high-energy photons observations and the ground low-frequency (LF) lightning sferics measurements, more than 10 pairs of consecutive TGFs with synchronous LF lightning waveform are analyzed. Preliminary results show that the sferics of each TGF pairs are almost same, while they vary with different pairs. More details will be shown. In addition, some TGFs detected by ASIM and the associated lightning will also be introduced.

How to cite: Zhang, H., Qie, X., and Lu, G.: Low-frequency sferics associated with consecutive Terrestrial Gamma-ray Flashes , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-20763, https://doi.org/10.5194/egusphere-egu24-20763, 2024.

Based on the work of several PhD students in Amsterdam, we now have a verified model for positive streamers in air. For streamer propagation a fluid model is sufficient, while for branching the discreteness of the photo-ionization events has to be taken into account. The model results on propagation and branching have both been validated on experiments in Eindhoven, and hence a few streamers can now be modeled quantitatively in 3D. However,  bursts or coronas with hundreds and more streamers are computationally not feasible. Instead of this, models of dielectric breakdown type should be developed, but based on the now known microscopic basis. We present two results in this direction: 1. The identification of steady positive and negative streamers and a revision of the concept of the stability field. 2. The analysis of streamer heads as coherent structures which allows a macroscopic characterization of the streamer head dynamics by few parameters such as radius, velocity, maximal and minimal field, ionization degree etc. (up to now only for positive streamers). Together with the branching simulations, these are stepping stones towards a reduced model of dielectric breakdown type for multi-streamer structures.

The models were developed and evaluated by the PhD students Dennis Bouwman, Hani Francisco, Baohong Guo, Xiaoran Li and Zhen Wang under the supervision of Jannis Teunissen and Ute Ebert in Amsterdam, and the experiments used for model validation were performed by Ph.D. students Siebe Dijcks and Yihao Guo under the supervision of Sander Nijdam in Eindhoven. For the reduced model, we collaborate with Alejandro Luque in Granada, Spain.

How to cite: Ebert, U.: Towards quantitative modeling of multi-streamer processes in 3D, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-20811, https://doi.org/10.5194/egusphere-egu24-20811, 2024.

EGU24-20835 | ECS | Orals | NH1.5

3D Geolocation of Simulated Lightning Sources from Low-Earth Orbit 

Jackson Remington, Sonja Behnke, Harald Edens, Patrick Gatlin, Timothy Lang, Nikhil Pailoor, Mason Quick, and Sarah Stough

The recent removal of the Lightning Imaging Sensor from the International Space Station has left an observational gap in lightning detection from low-Earth orbit (LEO). However, new studies have demonstrated the potential for 3D geolocation of lightning sources using orbiting sensors. The Cubespark mission concept aims to take advantage of these developments by deploying a constellation of satellites with radio frequency (RF) sensors and optical imagers to not only map lightning locations, but also to collect bi-spectral flash images. These new capabilities include mapping storm charge structure, flash channel structure, and distinguishing microphysical processes throughout flash development, helping link microphysics and convective processes with overall flash and storm structure around the globe from LEO.

In this study, we simulate lightning RF sources in the very high frequency (VHF) band, extrapolate their signals to space-based detection using an improved ionospheric model, and reconstruct their 3D locations using a time-of-arrival (TOA) minimization algorithm. Various constellation configurations, locations, and atmospheric conditions are considered in order to identify and quantify the three main sources of geolocation error: geometric, ionospheric, and instrumental effects. The promising results of this study emphasize the potential of space-based 3D lightning mapping under diverse conditions. 3D resolution is shown to be better than 1-2 km in many cases, enabling new global applications in meteorology and climate sciences. Here we present a selection of these geolocation results as seen from space alongside recent advancements, paving the way for a future generation of LEO lightning mappers.

How to cite: Remington, J., Behnke, S., Edens, H., Gatlin, P., Lang, T., Pailoor, N., Quick, M., and Stough, S.: 3D Geolocation of Simulated Lightning Sources from Low-Earth Orbit, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-20835, https://doi.org/10.5194/egusphere-egu24-20835, 2024.

EGU24-20882 | ECS | Orals | NH1.5

Mixed Mode of Charge Transfer During an Upward Positive Flash at Säntis Tower 

Toma Oregel-Chaumont, Antonio Šunjerga, Marcos Rubinstein, and Farhad Rachidi

The term “mixed mode of charge transfer to ground for initial continuous current (ICC) pulses” in the context of upward lightning flashes was first proposed by Zhou et al. 2011 [1] to describe fast pulses, distinct from the classical M-component mode of charge transfer, superposed on the slowly varying initial-stage current of upward negative flashes they observed at the Gaisberg Tower in Austria. The pulses in question were associated with leader/return-stroke processes occurring in decayed or newly created branches of the plasma channel connecting to the grounded, current-carrying channel, with junction points below the cloud base (height < 1 km) [1,2].

Herein, we report, to the best of our knowledge, the first observation of a mixed-mode-type pulse during the initial stage of an upward positive flash that was initiated from the Säntis Tower in Switzerland. The Mt. Säntis Lightning Research Facility, which recorded the flash, consists of a current measurement system installed in the mountaintop tower (2.5 km ASL), slow and fast electric field sensors and X-ray detectors 20 m from the tower base, an additional fast E-field sensor 15 km away, as well as full HD cameras and a high-speed camera (HSC) at various distances, among other systems (see Šunjerga et al. 2021 for details [3]).

The observed flash, categorized as a Type 1 from its current waveform (see Romero et al. 2013 for definition [4]), occurred at 16:24:03 UTC on July 24th, 2021, during the Laser Lightning Rod project [5]. Its “return stroke”-like main pulse was confirmed from HSC footage to have been triggered by a downward-connecting leader with a junction height of approximately 369±5 m AGL, well below the defined cut-off of 1 km. Interestingly, though the 12 kA peak current is reasonable for a mixed-mode pulse, the current and E-field risetimes were both >10 μs, more characteristic of a M-component-type ICC pulse [2].

These observations are important to improving our understanding of the charge transfer mechanisms in upward lightning flashes, which regularly damage wind turbines, telecommunications towers, and airplanes during take-off and landing.

 

References:

[1] Zhou, H., Diendorfer, G., Thottappillil, R., Pichler, H., Mair, M. (2011). Mixed mode of charge transfer to ground for initial continuous current pulses in upward lightning. In 2011 7th Asia-Pacific International Conference on Lightning (pp. 677–681). Chengdu, China: IEEE. https://doi.org/10.1109/APL.2011.6110212

[2] Zhou, H., Rakov, V. A., Diendorfer, G., Thottappillil, R., Pichler, H., Mair, M. (2015). A study of different modes of charge transfer to ground in upward lightning. Journal of Atmospheric and Solar-Terrestrial Physics, 125–126, 38–49. https://doi.org/10.1016/j.jastp.2015.02.008

[3] Šunjerga, A., Mostajabi, A., Paolone, M., Rachidi, F., Romero, C., Hettiarachchi, P., … Smith, D. (2021). Säntis Lightning Research Facility Instrumentation. International Conference on Lightning Protection, 6.

[4] Romero, C., Rachidi, F., Rubinstein, M., Paolone, M., Rakov, V. A., Pavanello, D. (2013). Positive lightning flashes recorded on the Säntis tower from May 2010 to January 2012: POSITIVE LIGHTNING SÄNTIS TOWER. Journal of Geophysical Research: Atmospheres, 118(23), 12,879-12,892. https://doi.org/10.1002/2013JD020242

[5] Houard, A., Walch, P., Produit, T., Moreno, V., Mahieu, B., Šunjerga, A., … Wolf, J.-P. (2023). Laser-guided lightning. Nature Photonics, 17(3), 231–235. https://doi.org/10.1038/s41566-022-01139-z

How to cite: Oregel-Chaumont, T., Šunjerga, A., Rubinstein, M., and Rachidi, F.: Mixed Mode of Charge Transfer During an Upward Positive Flash at Säntis Tower, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-20882, https://doi.org/10.5194/egusphere-egu24-20882, 2024.

EGU24-20981 | Orals | NH1.5 | Highlight

Energy from extraterrestrial sources is driving arctic lightning 

Eija Tanskanen and Marzieh Khansari

The possible effect of solar activity on lightning has been studied for a long period of time. Specifically, the relationship between sunspot number and lightning activity has been investigated, although the results still remain inconclusive across regions and time. In some regions, a positive correlation is found, in others a negative one. Thus, it is important to explore other solar-geomagnetic variables possibly influencing lightning activity.

In order to examine the possible relationship between solar activity and lightning activity we will study lightning and geomagnetic activity at the latitudes of 50° to 70° together with the solar and solar wind observations (SDO, ACE, OMNI database).  Data from the Nordic lightning location system (NORDLIS) was used for lightning strikes and geomagnetic measurements from Sodankylä Geophysical Observatory, INTERMAGNET and IMAGE for geomagnetic disturbances. Our analysis showed a strong correlation between high-speed streams and lightning activity as well as with geomagnetic activity during solar cycle 23. All parameters peaked in 2003 during the early declining phase of solar cycle 23 and showed similar trends over the solar cycle. The correlation was strong and significant between latitudes 62° and 66°.  The best coupling was found at 63° and 65°, where solar wind variability explained 86% and 88% of the variability of lightning activity, respectively. We hypothesize that this correlation is because of a much larger number of energetic particles due to an exceptionally high number of HSS during solar cycle 23. Penetration of these highly energetic particles to the atmosphere and production of high energetic secondary electrons can lead to runaway breakdown in thunderclouds and initiation of lightning.

How to cite: Tanskanen, E. and Khansari, M.: Energy from extraterrestrial sources is driving arctic lightning, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-20981, https://doi.org/10.5194/egusphere-egu24-20981, 2024.

EGU24-21048 | Orals | NH1.5

Employing VLF and field mill measurements to predict lightning activity 

Moacir Lacerda and Carlos Augusto Morales Rodrigues

The STORM-T Laboratory of University of São Paulo (USP) – Brazil operates a VLF long range lightning detection network known as STARNET (Morales et al., 2014) and a local field mill network. We have developed and implemented two operational schemes to predict the thunderstorm activity and propagation for the next 30 minutes (Now-STARNET) and the probability of occurrence of lightning strikes in a local area within 10 minutes (YANSA – Lacerda et al., 2022). Now-STARNET scheme is based on the cell-tracking algorithm proposed by Betz et al. (2008) to identify active thunderstorms over South America (90-30W and 60S-10N). STARNET lightning measurements are hourly accumulated over grids of 0.1 x 0.1 degrees and those cumulative grids are used to identify active thunderstorms that are defined as contiguous lightning grids. For each identified thunderstorm, we retrieve the lightning activity every 1 minute and the area, speed and direction of propagation every 5 minutes. Based on these temporal and dynamical features we adjust polynomial functions to forecast the position of active thunderstorms (must have lightning activity in the last 5 minutes) for the next 30 minutes every 5 minutes. Finally, the projected areas are used to identify the Brazilian cities that will have lightning activity to issue warnings. YANSA tool uses the temporal variation of the vertical electrical field observed by field mills to compute the time between the first lightning pulse and the first cloud-to-ground stroke as defined by Rodrigues and Lacerda (2022). Based on the elapsed time and the magnitude of the electrical field, YANSA issues different warning messages (no-risk, low, moderate, high and extreme risk) that help the users to know the probability of CG occurrence and time spam for lighting activity. YANSA was configured to use 4 field mills deployed in the USP campus transmitting every 1 minute and issue warning of lightning activity in area of the university. For the conference we will present the skills of both Now-STARNET and YANSA tools in predicting lightning activity and lightning strikes by means of contingency table tests, i.e., POD, FAR and CSI. For Now-STARNET we will use STARNET measurements of 2022 and 2023 and explore how the skills change with thunderstorm size and location. For YANSA, we will use LINET and STARNET lightning strokes observed in the vicinity of the University of São Paulo during the period of 2023 to validate each message.

Rodrigues F. and M. Lacerda, "Warning of lightning risk for the first lightning produced by a thunderstorm using electric field mill network records," 2022 36th International Conference on Lightning Protection (ICLP), Cape Town, South Africa, 2022, pp. 720-723, doi: 10.1109/ICLP56858.2022.9942596.

Lacerda, M. Rodrigues, F., Verly, R., Morales C.A.R. (2023). Monitoring lightning activity by using the YANSA platform to emit warnings of lightning risk in real time with an electric field mill network. risk for the first lightning produced by a thunderstorm using electric field mill network records," 2022 36th International Conference on Lightning Protection (ICLP), Cape Town, South Africa, 2022,

 

How to cite: Lacerda, M. and Morales Rodrigues, C. A.: Employing VLF and field mill measurements to predict lightning activity, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-21048, https://doi.org/10.5194/egusphere-egu24-21048, 2024.

Lightning is an essential climate variable that could be influenced by climate change processes. In this study, wintertime lightning data over the Mediterranean Sea (MS) during the period 2009-2019 from the World-Wide Lightning Location Network were analyzed together with corresponding observational and modeled data of solar activity, atmospheric dynamics and seawater chemistry. The results of this analysis demonstrate that solar activity is the dominant parameter that influences lightning activity over the MS. Where, wintertime lightning intensity and frequency for lightning with energy >0.5 MJ over the MS is 237 and 517 times greater during the solar maximum compared to the minimum, respectively. In contrast, lightning activity parameters have a significantly smaller dependence on climate change parameters, including convective available potential energy, seawater salinity, pH and total alkalinity. Therefore, it is highly unlikely that trends in lightning activity over the MS due to climate change will be detectable in the near future.

How to cite: Asfur, M., Price, C., and Silverman, J.: Is winter cloud-to-sea-surface lightning activity over the Mediterranean Sea during 2009-2019 strongly influenced by solar activity?, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-22350, https://doi.org/10.5194/egusphere-egu24-22350, 2024.

EGU24-22447 | ECS | Orals | NH1.5

3D location estimation of lightning charges using electrostatic field changes 

Sho Yui, Yukihiro Takahashi, and Mitsutero Sato

Floods caused by the development of cumulonimbus clouds cause significant damage, especially in tropical areas, such as the Southeast Asian region. Lightning strikes in cumulonimbus clouds have been shown to correlate with a time lag of several tens of minutes preceding heavy rainfall. Therefore, it is expected that lightning observations will help us to forecast heavy rainfall. Especially, if we could know the 3-dimensional distribution of lightning charges, this information might be a good proxy way of knowing thunderstorm development.  Here, we improved 3D estimation of lightning charges using electrostatic field measurement. In this method the electrostatic field changes caused by lightning stroke are observed with a network consisting  of sensors installed at multiple locations at about 5 km interval. Based on those data, three-dimensional location and amount of charges removed by lightning stroke can be estimated. A previous study using same kind of data conducted a brute force calculation, which is not practical because it takes about 2 minutes longer than the typical interval of lightning stroke in the active thunderstorm. In this study, we propose a new method using interpolation analysis by kriging, which results in significant reduction of the estimation time to about 8 seconds. This improving will allow us to analyse more data we took so far and make the new model of thunderstorms.

This research is supported by Science and Technology Research, Partnership for Sustainable Development (SATREPS), Japan Science and Technology Agency (JST) / Japan International Cooperation Agency (JICA).

How to cite: Yui, S., Takahashi, Y., and Sato, M.: 3D location estimation of lightning charges using electrostatic field changes, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-22447, https://doi.org/10.5194/egusphere-egu24-22447, 2024.

EGU24-18 | ECS | Posters on site | HS2.4.3

Non-stationary design rainfalls for Australia 

Lalani Jayaweera, Conrad Wasko, Rory Nathan, and Fiona Johnson

Action needs to be taken in response to the changes in future flood risk due to the impact of global warming on the magnitude and frequency of extreme rainfalls. Projected changes in extreme rainfalls can be used to estimate the associated changes in design flood estimates using Intensity-Frequency-Duration (IFD) curves in combination with event-based flood models. IFD curves are estimated from records of historical annual maxima across different storm durations and exceedance probabilities. Past studies investigating changes in extreme rainfall across Australia have been limited in scope as they have focused on single durations, single exceedance probabilities, or limited regional extents. This means that we do not yet have a comprehensive understanding of how projected changes in extreme rainfalls impact on IFD curves.

Here, to fill this gap, we investigate the changes in extreme rainfall changes across different storm durations and exceedance probabilities across 42 stations which span the entire continent of Australia. We begin with examining the trend in annual maximum rainfall across 16 different storm durations (6 min to 7 day) using the Theil-Sen slope estimator, testing for statistical significance using the Mann-Kendall test. To extrapolate 1% annual exceedance probability, we fit non-stationary Generalized Extreme Value Distributions (GEVs) at each site. Non-stationarity was assessed by varying the location parameter, varying the scale parameter, and varying both the location and scale parameters as a linear trend in time.

We find that the short duration (<1 hr) annual maximum rainfalls have increased across Australia, but longer duration annual maxima (>1 hr and 1 day) show fewer positive trends with some sites exhibiting negative trends. Based on Akaike Information Criteria, the GEV models which varied either the location parameter, or both the scale and location parameters, were found to be superior. However, when changes in quantile estimates were examined for rare exceedance probabilities (up to the 1 in 100 AEP), it was found the GEV model which only varied the location parameter was unable to capture the increased rate of change in extreme rainfalls. Accordingly, we conclude that changes in extreme rainfalls is best represented by non-stationary models that incorporate changes in both location and scale parameters.

How to cite: Jayaweera, L., Wasko, C., Nathan, R., and Johnson, F.: Non-stationary design rainfalls for Australia, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-18, https://doi.org/10.5194/egusphere-egu24-18, 2024.

EGU24-361 | ECS | Orals | HS2.4.3

Evolution of Hydro-Meteorological Whiplash Events (Compound Floods and Droughts) over India 

Debankana Bhattacharjee and Chandrika Thulaseedharan Dhanya

In recent decades, the heightened frequency of extreme hydro-meteorological events such as floods and droughts has emerged as a global concern. These events not only pose a significant threat to individual societies but also exert lasting impacts on entire ecosystems. Of particular concern is the occurrence of whiplash events, where rapid transitions from wet to dry spells or vice versa amplify the already substantial impacts on various spatial and temporal scales. This study delves into the potential risks associated with the immediate succession of dry spells following wet spells and the heightened likelihood of intense compound occurrences fueled by concentrated rainfall distribution. Spanning 7 decades from 1951 to 2019, this research employs Event Coincidence Analysis or ECA to examine the aggregated whiplash behaviour in the Indian subcontinent. Our investigation focuses on the frequency of compound whiplash events, specifically dry spells followed by wet spells. Intriguingly, the findings reveal that, on average, 45 to 60% of dry spells across the majority of India are followed by wet spells within a 3-month window or 90 days. Moreover, our analysis demonstrates that the rate of wet spells triggering subsequent dry spells surpasses the reverse scenario. Consistent with the overall trend, compound flash floods and droughts, categorised by high intensity but brief duration, have been notably prevalent from 1951 to 2019. Although the spatial coverage of these events remains relatively small, recent decades have witnessed a discernible increase of 7–9%, primarily in arid, semi-arid, and tropical monsoon regions. Limited occurrences in tropical savannahs and humid subtropical regions were also noted. While the spatial structures associated with increased whiplash frequency appear less organised compared to individual dry and wet spells, the study underscores significantly higher ratios. This suggests that, despite the modest spatial coverage, whiplash events have experienced a notable increase in frequency over the past three decades. This comprehensive analysis contributes valuable insights into the evolving landscape of hydro-meteorological extremes, emphasising the growing importance of understanding compound events for effective climate resilience and adaptation strategies.

How to cite: Bhattacharjee, D. and Dhanya, C. T.: Evolution of Hydro-Meteorological Whiplash Events (Compound Floods and Droughts) over India, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-361, https://doi.org/10.5194/egusphere-egu24-361, 2024.

The global hydrological cycle is substantially influenced by climate change, leading to notable alterations in hydroclimatic extremes. This encompasses extreme precipitation and temperature events, ultimately amplifying the frequency and intensity of floods. Analyzing the trends in floods and the related covariates provides insight into regional patterns of flood changes and shifts in flood generation mechanisms within the selected catchments. An improved understanding of the processes driving the historical changes in this natural hazard can provide basic information to enhance our preparation and mitigation efforts. Differences in significant trends (non-stationarities) in the magnitude and frequency of flood-related characteristics are determined for the river basins of Peninsular India through analysis of AMS (Annual Maximum Series) and POT (Peaks Over Threshold) series of streamflow over the period 1979–2019. Scrutiny of the trend detection results provided a better understanding of the strengths and limitations of AMS and PDS approaches in analyzing flood characteristics. Non-stationarity in the flood peaks is attributed to precipitation and temperature dynamics. This is accomplished by developing Generalised Pareto regression models to establish a relationship between the flood peaks and basin-averaged precipitation and temperature at different time scales preceding the flood events. Our findings emphasize the importance of understanding climatic conditions driving flood events and incorporating the same for assessing hydroclimatic risks with changing climate patterns, ultimately fostering more resilient and sustainable strategies.

How to cite: K.v., A. and V.v., S.: Detection and attribution of non-stationarity of flood characteristics across the Peninsular Basins of India, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-445, https://doi.org/10.5194/egusphere-egu24-445, 2024.

EGU24-855 | ECS | Orals | HS2.4.3

Meteorological Droughts in India under Climate Change Conditions: A Complex Networks-based Approach 

Devika Chandrababu Salini and Bellie Sivakumar

Droughts pose substantial challenges to water resources, ecosystems, and agriculture. Climate change is anticipated to result in more frequent and greater magnitude droughts in the future. The present study assesses meteorological droughts in India under climate change conditions using a complex networks-based approach. The Standardized Precipitation Index (SPI) values at a duration of 1, 3, 6, and 12 months are used to assess the meteorological droughts. Observed precipitation data from the India Meteorological Department (IMD) and precipitation outputs from 53 GCMs participating in the Coupled Model Intercomparison Project Phase 6 (CMIP6) are used. The data considered are at a spatial resolution of 1° x 1°, covering a total of 288 grids across India. The Shortest Path Length is used as a network measure to rank the GCMs. First, the network is constructed by treating each grid as a node and identifying the links between any pair of grids according to certain threshold conditions in correlations in SPI values. Next, the GCMs are individually ranked for each of the 288 grids based on the difference in the shortest path length between the observed and GCM-simulated SPI networks. Then, the Group Decision-Making (GDM) approach is applied toidentify the top-performing GCMs across all the 288 grids. Finally, the inclusion of a comprehensive rating metric (RM) value provides a unified approach to combine the ranks obtained for GCMs across various duration (1, 3, 6, and 12 months). The results indicate that NorESM2-MM, CESM2-FV2, KACE-1-0-G, SAM0-UNICON, and CMCC-CM2-SR5 are the top five models in terms of performance. Data from these five models are then studied using Event Synchronization (ES) to uncover the spatial connections in drought events across space. This novel approach contributes to a better understanding of the spatial dynamics of meteorological droughts, especially under climate change.

How to cite: Chandrababu Salini, D. and Sivakumar, B.: Meteorological Droughts in India under Climate Change Conditions: A Complex Networks-based Approach, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-855, https://doi.org/10.5194/egusphere-egu24-855, 2024.

EGU24-960 | Posters virtual | HS2.4.3

Examining the spatial and temporal characteristics of hydrological drought in the largest basin of the Indian Peninsula 

Meghomala Ghosal, Somil Swarnkar, and Soumya Kundu

The intensified warming conditions have substantially impacted the occurrence, duration, and magnitude of severe hydroclimatic events worldwide. Consequently, economic conditions have experienced considerable influence in the past decades. Droughts, in particular, are complex catastrophic events, rendering them extremely unpredictable and hard to comprehend. It is a gradual and prolonged catastrophe marked by insufficient rainfall, leading to a scarcity of water. In addition, drought is often defined as a period of reduced rainfall resulting in water shortage. It is frequently assessed by examining combinations of many factors, such as precipitation, temperature, and soil moisture. Specifically, hydrological droughts are precisely characterized as prolonged periods when water levels in rivers and streams fall below a preset threshold value. Furthermore, frequent occurrences of hydrological drought pose a significant threat to freshwater resources. Thus, identifying the spatiotemporal characteristics of preceding droughts is crucial for the effective management of future water resources. Hence, this work focuses on analyzing the spatial and temporal patterns of hydrological drought events that occurred between 1964 and 2020 in the Godavari River Basin (GRB) located in the peninsular area of India. The GRB has an area of roughly 0.3 million square kilometers, making it the biggest river basin in peninsular India. Over the last several decades, the GRB has been confronted with severe drought conditions. Therefore, the present analysis utilized the dataset of daily observed water discharge data collected at 21 gauging stations by the Central Water Commission (CWC). In addition to eliminating minor droughts and aggregating droughts, the 'Variable Threshold' concept is utilized to derive hydrological drought characteristics at various stations, including intensity, deficit, and duration. According to our findings, significant spatial and temporal variation is evident in the regional hydrological drought characteristics of the GRB. Additionally, flash drought conditions have been reported at multiple stations. The results derived from this research contribute to the advancement of knowledge regarding the spatiotemporal patterns of droughts in the GRB.

How to cite: Ghosal, M., Swarnkar, S., and Kundu, S.: Examining the spatial and temporal characteristics of hydrological drought in the largest basin of the Indian Peninsula, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-960, https://doi.org/10.5194/egusphere-egu24-960, 2024.

EGU24-980 | ECS | Posters virtual | HS2.4.3

Spatio-temporal characteristics of floods in the largest basin of the Indian Peninsula 

Shreejit Pandey, Somil Swarnkar, and Soumya Kundu

The intensification of the hydrological cycle is a consequence of the rising global temperatures caused by global warming. This has worsened extreme hydrological occurrences, including floods. Flooding is a substantial global hazard that endangers human livelihoods, infrastructure, and economies. Furthermore, the combination of rising temperatures and human activities has significantly modified the flood patterns that have been documented worldwide by several scientists. More precisely, a substantial area of the Indian sub-continent is greatly impacted by regular instances of flooding. Previous studies have indicated an increase in both the magnitude and frequency of flood events in the Indian river basins during the past several decades. The Godavari River Basin (GRB), which is the biggest peninsular basin in India with an area of 312,812 square kilometers, has been prone to frequent and devastating flood events in recent decades. Nevertheless, the comprehensive flood attributes, such as the maximum intensity, total amount, and length, in the GRB remain unidentified. Hence, in this study, we have employed the peak-over-threshold and Master Recession Curve (MRC) techniques to evaluate the flood features in the GRB. We have utilized the daily recorded water flow information obtained from the Central Water Commission (CWC) from 21 gauging stations in the Godavari River Basin (GRB). The 21 gauging stations are categorized into four main geographical zones. The results of our research indicate that there are notable differences in the regional flood characteristics of the GRB in terms of both spatial and temporal scales. The majority of stations in the GRB exhibit substantial fluctuations in flood characteristics after 1995. More precisely, the western GRB exhibits a notable decrease in the amount, length, and intensity of floods after 1995. The data suggest that human actions have a significant role in the flood generation process in the western GRB area. The conclusions derived from this research will be valuable to policymakers and many stakeholders in their efforts to reduce flooding and promote equitable growth in the GRB.

How to cite: Pandey, S., Swarnkar, S., and Kundu, S.: Spatio-temporal characteristics of floods in the largest basin of the Indian Peninsula, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-980, https://doi.org/10.5194/egusphere-egu24-980, 2024.

EGU24-1108 | Orals | HS2.4.3

Climate Change Effects on Flood Hazard and Risk in Harami̇dere Basin 

Egemen Firat, Buse Özer, Koray K. Yılmaz, Gülçin Türkkan Karaoğlu, and Esra Fitoz

In flood hazard and risk assessment studies, modeling is generally done by examining the hydrometeorological events that have developed by using past datasets. Recently, increasing rainfall per unit time due to climate change may cause flash floods. Hydrographs, which are input to 1D/2D hydrodynamic models, are also likely to change as a result of climate change. Hydrological calculations based on past data may underestimate the predicted values. Therefore, flood risks produced from the results of flood depth and hazard models may also remain at low values. In this study, firstly, a hydrological modeling study was carried out on the streams in Haramdere Basin by using hydrometeorological measurements between 2010-2022 and hydrographs were produced for Q2, Q5, Q10, Q25, Q50, Q100, Q500 and Q1000 returning periods. In mapping studies, river structures, stream geometry, digital surface and terrain model were determined using ground measurements and flight data. Then, flood depth and hazard maps were created with 1D and 2D hydrodynamic models. Economic risk calculation was made using these maps. Then, RCP8.5 scenarios known as having high precipitation anomalies for all climate models included in CMIP6 were re-run in the hydrological model. In this way, flow data were generated for each climate model RCP8.5 scenario. Then, 3 different climate change impacts (worst, medium and best) for Haramidere Basin in regards to flood hazard and risk will be revealed by analyzing the rainfall and runoff extremes produced from the hydrological model for all climate models. In the worst case scenario, the climate model with the highest rainfall and runoff extremes, in the medium case, the average of the values calculated from all climate models and in the best case scenario, the climate model with the lowest extremes will be selected. In this way, climate models specific to this basin will be determined for these 3 different Scenarios. Then, from these selected climate models, coefficients will be determined to be used in hydrological calculations for the effect of climate change on flooding events. Finally, flood risk calculations will be made for these 3 scenarios and the economic value of climate change in terms of flood risk will be quantified by comparing with the flood risk calculated with the measurements between 2010-2022.

How to cite: Firat, E., Özer, B., Yılmaz, K. K., Türkkan Karaoğlu, G., and Fitoz, E.: Climate Change Effects on Flood Hazard and Risk in Harami̇dere Basin, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-1108, https://doi.org/10.5194/egusphere-egu24-1108, 2024.

EGU24-1125 | ECS | Posters on site | HS2.4.3

Climate-Informed-Seasonal Mixing Approach to Estimate Flood Quantiles 

Abinesh Ganapathy, Bruno Merz, Sergiy Vorogushyn, and Ankit Agarwal

Traditional flood frequency analysis assumes that the probability distribution is stationary over time. However, this assumption has been challenged, given widespread changes in catchments and climate. One of the inherent handicaps of the stationarity assumption is its non-inclusion of changes in extremes associated with future climatic conditions. To overcome this handicap, climate covariates can be incorporated into the estimation of flood probability through the non-stationary Climate-Informed Flood Frequency Analysis (CIFFA). The CIFFA methodology comprises 1) selection of predictands (usually seasonal maxima), 2) identification of suitable predictors (large-scale climate indices), and 3) derivation of a statistical link between predictands and predictors. Since CIFFA typically considers the flood peaks in the dominant season, its applicability to gauges, where flood extremes occur in several seasons, is limited. Here, we develop and test a novel non-stationary Climate-Informed-Seasonal-Mixing approach across various European basins. In the proposed Climate-Informed-Seasonal-Mixing approach, we fit the seasonal peak distribution (boreal seasons) with the location parameter conditioned on the selected covariate using the Bayesian Inference. The best climate covariates for each season among a set of predictors are identified based on widely applicable information criterion (WAIC), which computes log posterior predictive density and adjusts the overfitting using the effective number of parameters. Even the traditional stationary model could be a preferred model for any season if it has a minimum WAIC value. Following the estimation of seasonal distribution parameters, the annual flood quantiles are derived by multiplicatively mixing all the seasonal distributions. In order to demonstrate the performance of the proposed approach, we split the entire period into calibration and validation sets, fitting the model based only on calibration samples. The projected quantiles during the validation period are then compared with a benchmark model (traditional model fitted solely with validation samples). Our results suggest that for many gauges, the flood quantiles estimated by the proposed Climate-Informed-Seasonal-Mixing approach align with the baseline estimates where the traditional approaches fall short.

How to cite: Ganapathy, A., Merz, B., Vorogushyn, S., and Agarwal, A.: Climate-Informed-Seasonal Mixing Approach to Estimate Flood Quantiles, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-1125, https://doi.org/10.5194/egusphere-egu24-1125, 2024.

EGU24-1235 | ECS | Posters on site | HS2.4.3

How unusual was Australia's 2017-2019 Tinderbox Drought? 

Georgina Falster and Sloan Coats

Australia’s Murray-Darling Basin experienced three consecutive years of meteorological drought across 2017–2019, collectively named the ‘Tinderbox Drought’. Rainfall deficits during the three-year drought were focussed in the Australian cool season (April to September), and deficits in both the cool season and the annual total were unprecedented in the instrumental record. However, at ~120 years long, Australian rainfall records are not long enough to have captured the full possible range of variability, particularly for multi-year extreme events. That is, observations are an incomplete sampling of the full possible range of rainfall variability. Climate model simulations may provide longer timeseries, however climate models have known biases in Australian rainfall (Grose et al. 2020). Therefore, to determine if the Tinderbox Drought was outside the expected range of internal variability, we constructed Linear Inverse Models (LIMs) that simulate internal variability in Australian rainfall and associated global sea surface temperature (SST) anomalies. We used the LIMs to produce 10000-year-long rainfall records that emulate the stationary statistics of observed Australian rainfall, hence reflecting more of the full possible range of variability.

 

Overall, we find that rainfall deficits were most severe 1) in the northern Murray-Darling Basin; and 2) during the final year of the drought (2019). Global SST anomalies during the drought mostly did not resemble the pattern that is most reliably associated with low rainfall over the Murray-Darling Basin (warm anomalies in the central tropical Pacific and the western Indian Ocean). In fact, global SST anomalies observed during the Tinderbox Drought are not reliably associated with negative rainfall anomalies across the Murray-Darling Basin—this is particularly the case for the first two years of the drought. In terms of single-year rainfall anomalies, the only aspect of the Tinderbox Drought that was beyond the expected natural range was annual-total rainfall over the northern Murray-Darling Basin during 2019. However, when considered in terms of basin-wide rainfall over the full three years, negative anomalies during the Tinderbox Drought were beyond the expected natural range in terms of both cool season and annual rainfall. This suggests an anthropogenic contribution to the severity of the drought. Additionally, we find that Linear Inverse Models are a valuable tool for estimating whether or not an observed extreme rainfall event falls within the expected natural range.

References

Grose, M. R., Narsey, S., Delage, F. P., Dowdy, A. J., Bador, M., Boschat, G., et al. (2020). Insights from CMIP6 for Australia's future climate. Earth's Future, 8, e2019EF001469.

How to cite: Falster, G. and Coats, S.: How unusual was Australia's 2017-2019 Tinderbox Drought?, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-1235, https://doi.org/10.5194/egusphere-egu24-1235, 2024.

EGU24-1256 | ECS | Posters virtual | HS2.4.3

Runoff variation and progressive aridity during drought in catchments in southern-central Chile 

Guillermo Barrientos, Rafael Rubilar, Efrain Duarte, and Alberto Paredes

Persistent drought events frequently intensify the aridity of ecosystems and cause catchments runoff depletion. Here, using large and long-term data sets of meteorological and hydrologic variables (precipitation, runoff, temperature and potential evapotranspiration) investigated the major causes that modulated catchment runoff depletion between years 1980 and 2020 in southern central Chile. We identify the hydrological years where aridity index intensified and analyzed its relationship with annual runoff, and evaluated the effect of annual evaporation index and annual aridity index on water balance of 44 catchments with different precipitation regimes located between 35° and 40°S. Our results showed that observed precipitation and runoff significantly decreased between 1980 and 2020 in 64% of the catchments in the study area. Potential evapotranspiration increased significantly in 39% of the catchments. The runoff value decreased as the aridity index increased from 0.3 to 6.7, and the Budyko curve captured 98.5% of the annual variability of all catchments. Furthermore, for an extreme aridity index (e.g. 6.5), potential evapotranspiration far exceeds mean annual runoff and precipitation. Catchment runoff is modulated by the aridity index, which is a key indicator of insufficient precipitation. As expected, for any type of drought, precipitation and evapotranspiration are key factors modulating catchment runoff response. Hydrological years in which precipitation decreased, showed a decreased runoff trend. This result suggest that meteorological droughts tend to significantly decrease observed runoff. However, our results suggest that runoff in catchments, under consecutive years of water stress, will suffer from an even more severe water deficit in today’s rapidly changing global climate with negative impacts on ecosystem services and human activities.

How to cite: Barrientos, G., Rubilar, R., Duarte, E., and Paredes, A.: Runoff variation and progressive aridity during drought in catchments in southern-central Chile, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-1256, https://doi.org/10.5194/egusphere-egu24-1256, 2024.

EGU24-1738 | ECS | Orals | HS2.4.3

Understanding past changes in Australian droughts and their drivers 

Matt Grant, Anna Ukkola, Elisabeth Vogel, Sanaa Hobeichi, Andy Pitman, and Andrew Hartley

Australia is frequently exposed to considerable impacts from severe and widespread droughts. Despite this, a comprehensive understanding of the past trends and drivers of Australian droughts remains elusive. Existing studies have often characterised past trends based on changes in mean values rather than the extremes. However, given Australia’s exceptionally variable climate, this may fail to capture the full nature of the country’s drought trends. Furthermore, studies often rely on a limited number of drought indicators and may not encompass the diverse meteorological, hydrological and ecological conditions contributing to drought.

This work explores past drought trends in Australia using multiple drought indicators. We analyse changes in traditional drought metrics, including precipitation, runoff and soil moisture, defining droughts as time periods below the 15th percentile. We complement these metrics with an impacts-based drought indicator built from government drought reports using machine learning. We explore the drivers of drought trends using explainable machine learning methods, and consider multiple drivers including large-scale climate features, land surface properties and past conditions. Using a diverse range of metrics allows for a more comprehensive analysis of drought changes experienced over the past decades and will provide greater insight into the main drivers behind Australian droughts.

How to cite: Grant, M., Ukkola, A., Vogel, E., Hobeichi, S., Pitman, A., and Hartley, A.: Understanding past changes in Australian droughts and their drivers, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-1738, https://doi.org/10.5194/egusphere-egu24-1738, 2024.

EGU24-3964 | Orals | HS2.4.3

Extreme flood analysis on the Garonne river at Agen, using historical information since 1435 

Michel Lang, Jérôme Le Coz, Felipe Mendez-Rios, Perrine Guillemin, David Penot, and Didier Scopel

The safety of nuclear power plants in France is assessed based on 1000-year flood estimates, with a safety factor accounting for uncertainty. Previous studies on the Garonne River near Agen, France, used threshold exceedance values from a continuous 85 year-long series at two hydrometric stations: Malause (1915-1966) and Lamagistère (1967-2000), and historical data at Agen since 1770. The estimate of the design flood was very sensitive to the choice of an Exponential or of a Generalized Pareto distribution, yielding 12 600 and 16 000 m3/s, respectively. This communication presents a more comprehensive study based on a GEV distribution fitted from the annual maximum values of a continuous series since 1852 (adding Agen 1852-1914) and historical data at Agen since 1435. The statistical framework accounts for both discharge and sampling uncertainty components. The first uncertainty component is about 3% for the recent years and 35% for the oldest years. The statistical framework is able to account for a multiplicative error on rating curves. This leads to corrections in peak discharge values, with better agreement between historical data at Agen and hydrometric data at Malause-Lamagistère. The final estimate of the design flood is around 10 500-11 600 m3/s, without or with the largest known historical flood of 1435. It confirms the safety of the nuclear plant, based on extensive historical information.

How to cite: Lang, M., Le Coz, J., Mendez-Rios, F., Guillemin, P., Penot, D., and Scopel, D.: Extreme flood analysis on the Garonne river at Agen, using historical information since 1435, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-3964, https://doi.org/10.5194/egusphere-egu24-3964, 2024.

EGU24-4044 | Posters on site | HS2.4.3

HYDROLOGICAL DROUGHT – Processes and Estimation Methods for Streamflow and Groundwater 

Lena M. Tallaksen, Henny A.J. Van Lanen, Jamie Hannaford, Hege Hisdal, Daniel G. Kingston, Gregor Laaha, Christel Prudhomme, James H. Stagge, Kerstin Stahl, Anne F. Van Loon, and Niko Wanders

Drought is a worldwide phenomenon that originates from a prolonged deficiency in precipitation, often combined with high evaporation, over an extended region. The resultant meteorological water balance deficiency may cause a hydrological drought to develop into below normal levels of streamflow, lakes, and groundwater. Contemporary knowledge and experiences from an international team of drought experts are consolidated in a textbook (Tallaksen and van Lanen et al., 2023), which builds on an earlier edition from 2004 (URL 1), with significant new material added. An updated synthesis was requested given the high relevance and severe impacts of drought seen in many regions of the world in recent years, along with the increasing knowledge gained over the last two decades. A majority of these studies focus on climate and climatology approaches, whereas the textbook addresses hydrological drought in particular. The textbook consists of three parts; Part I (Drought as a natural hazard) discusses the drought phenomenon, its main features, regional diversity and controlling processes. Part II (Estimation methods) presents contemporary approaches to drought estimation, including data and hydrological drought characteristics, statistical analysis of drought series, incl. frequency analysis, time series analysis and regionalisation procedures, as well as process-based modelling. Part III (Living with drought) addresses aspects related to the interactions between water and people. Topics include historical and future drought, how human interventions influence drought, drought impacts and Drought Early Warning Systems. Knowledge and experiences shared in the book are from regions all over the world although somewhat biased to Europe and rivers that flow most of the year.

This presentation aims to introduce the textbook, its motivation and content to a wide audience. The textbook is supported with worked examples and self-guided tours that are elaborated more extensively on GitHub. Worked examples include online procedures, code, and details of the calculation procedures that enable readers to repeat calculations in a stepwise manner, either with their own data or by using online datasets, and we encourage user’s feedbacks and experiences in testing these. Self-guided tours are demonstrations of advanced methodologies that involve several calculation steps and are given as online presentations. Four datasets are included on GitHub; an international, a regional and two local datasets. The international dataset illustrates the drought phenomenon and its diversity across the world, whereas regional data and local aspects of drought are studied using a combination of hydroclimatological time series and catchment information. Hopefully, the textbook will contribute to an increased awareness of one of our main natural hazards, and thereby increase the preparedness and resilience of society to drought.

How to cite: Tallaksen, L. M., Van Lanen, H. A. J., Hannaford, J., Hisdal, H., Kingston, D. G., Laaha, G., Prudhomme, C., Stagge, J. H., Stahl, K., Van Loon, A. F., and Wanders, N.: HYDROLOGICAL DROUGHT – Processes and Estimation Methods for Streamflow and Groundwater, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-4044, https://doi.org/10.5194/egusphere-egu24-4044, 2024.

EGU24-4127 | ECS | Orals | HS2.4.3

Emerging river flow and hydrological drought trends in Great Britain 

Wilson Chan, Maliko Tanguy, Amulya Chevuturi, and Jamie Hannaford

Hydrological drought frequency and severity is projected to increase for the UK. However, there is not yet robust observational evidence for decreasing river flows and increasing hydrological drought severity. This lack of evidence may stem from short observational records, human influences on river flows and internal climate variability. As a result, river flow trends in the past and in the near-term may be different to the trend induced by long-term climate change. This lack of congruency poses significant challenges for decision-makers faced with uncertain future projections on the one hand and an apparent lack of observed changes on the other: underscoring the need for approaches that bridge this gap. Single-Model-Initial-Condition-Large Ensembles (SMILEs) provide an ideal opportunity to reconcile past observations and future projections as they isolate the effect of internal climate variability. Here, we use the 50-member CRCM5 12km SMILE to drive GR6J catchment hydrological models for 190 catchments across Great Britain. Results show that observed trends in precipitation and river flows are within the spread of the large ensemble, which includes both robust wetting and drying trends over the historical period that could have arisen from internal climate variability. We further estimate the time of emergence for each catchment, i.e. the decade at which river flow changes exceed natural climate variability. Winter river flows increase with warming and are estimated to exceed natural climate variability before the 2050s for many catchments, with implications for flood risk. Summer river flows are estimated to reduce with warming, including hotspots in southwest Britain with an early time of emergence, exacerbating existing pressures on water resources. Autumn flows for catchments in southeast England are estimated to decrease but are not estimated to exceed natural climate variability until late 21st century. Establishing water management and adaptation strategies is crucial well in advance of catchments reaching their time of emergence (i.e. before a statistically significant trend is detectable). These results highlight the potential to use SMILEs to explore plausible alternative realisations and explore storylines of low-likelihood, high-impact hydrological extremes.

How to cite: Chan, W., Tanguy, M., Chevuturi, A., and Hannaford, J.: Emerging river flow and hydrological drought trends in Great Britain, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-4127, https://doi.org/10.5194/egusphere-egu24-4127, 2024.

Several severe drought events occurred in the past years and droughts will likely occur more frequent and be more intense in the future. Hydrological drought, which reflects the shortage of water in the river system, can lead to economic losses and can have severe negative impacts on aquatic ecosystems. Therefore being able to predict and increase insights in which rivers are more vulnerable to hydrological droughts, based on catchment characteristics and human interactions, can be of relevance for water managers. In this analysis, the drought sensitivity of rivers is predicted at a regional scale (Flanders, Belgium). Hereby the interests of multiple stakeholders is taken into account by considering four drought metrics, namely the yearly summer volume, the number of dry days, the drought intensity and drought severity. Whereby the latter three are based on the ecological flow. To predict each of these drought metrics, five models ranging from statistical to tree-based methods are applied using twelve input variables ranging from catchment characteristics to human interactions. Hereby random forest without bootstrap and XGBoost outperforms the other methods. To increase the interpretability of the results, the XGBoost models are used to calculate the SHAP and SHAP interaction values. As a result, the impact of the different input variables on the model results is assessed.

From this analysis, some general conclusions can be drawn. Irrigation is the most important variable for each of the considered drought metrics. However, not for every drought metric a clear, unique dependence between the irrigation and the drought sensitivity of a river could be observed. Rivers which have sand as dominant soil texture in their drainage area are less vulnerable to drought. When there are more human interaction in the drainage area, the river is more vulnerable to drought. Beside this, several other dependencies are observed of which many can be explained by the difference in ease of water transferability between sandy soils and clay soils. Next to this, it became clear that the impact of forest and agricultural area on the drought sensitivity of a river is complex, whereby especially its interaction with soil texture and human activities needs further investigation. The applied method can predict the drought sensitivity of a river based on catchment characteristics and human interactions, and therefore define rivers that are more vulnerable to drought. They moreover can provide additional insights in the importance of catchment characteristics and human interactions, and their relation to the drought sensitivity of a river.

How to cite: De Meester, J. and Willems, P.: Analysing spatial variability in drought sensitivity of rivers using explainable artificial intelligence, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-4758, https://doi.org/10.5194/egusphere-egu24-4758, 2024.

EGU24-5903 | Orals | HS2.4.3

The expansion of forests and the practice of irrigated agriculture contribute to reduced river flows in southern Europe during dry years 

Sergio Martín Vicente Serrano, Ahmed El Kenawy, Dhais Peña-Angulo, Jorge Lorenzo-Lacruz, Conor Murphy, Jamie Hannaford, Simon Dadson, Kerstin Stahl, Iván Noguera, Magí Franquesa, Beatriz Fernández-Duque, and Fernando Domínguez-Castro

This research examines the changes in annual streamflow across Europe from 1962 to 2017, with a specific focus on the correlation between streamflow trends and climate dynamics, as well as physiographic and land cover characteristics. The spatial distribution of streamflow trends aligns closely with climate patterns, suggesting a climate-related influence. However, a detailed analysis at the basin scale reveals that the significant decline in streamflow in southern Europe cannot be solely attributed to climate dynamics. Instead, a discernible negative trend linked to non-climate factors becomes apparent. Specifically, our study indicates that the primary drivers of negative streamflow trends in southern Europe, especially during dry years, are forest growth and irrigated agriculture. This is attributed to the higher proportion of green water consumption compared to blue water generation. These findings hold substantial implications, particularly in the context of widely adopted nature-based solutions for addressing climate change. This includes concerns about carbon sequestration through forests and the planned expansion of irrigated agricultural lands in central and northern European countries to meet growing crop water demands. Such developments may potentially reduce the availability of water resources, leading to an increased frequency and severity of low flow periods.

How to cite: Vicente Serrano, S. M., El Kenawy, A., Peña-Angulo, D., Lorenzo-Lacruz, J., Murphy, C., Hannaford, J., Dadson, S., Stahl, K., Noguera, I., Franquesa, M., Fernández-Duque, B., and Domínguez-Castro, F.: The expansion of forests and the practice of irrigated agriculture contribute to reduced river flows in southern Europe during dry years, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5903, https://doi.org/10.5194/egusphere-egu24-5903, 2024.

EGU24-5969 | ECS | Posters on site | HS2.4.3

New estimation models for determining the Q347 

Yanick Dups, Daniela Pavia Santolamazza, Philipp Staufer, and Henning Lebrenz

In Switzerland, low flows are described by the five percent quantile denoted by Q347. This threshold value not only has consequences for the planning, but also necessitates authorities to adjust the operation of pertinent infrastructure to mitigate ecological impacts on watercourses. Given a discharge timeseries spanning at least a ten-year period, determination of the Q347 can be done using the duration curve. Typically, said timeseries are not available for smaller catchments necessitating the estimation of the threshold value Q347. In Switzerland, the utilization of multiple linear regression has been established to estimate the area-specific discharge q347.

The primary objective of these investigations is to estimate the Q347 value for 383 ungauged catchments in the Canton of Solothurn, each covering an area less than 100 km². Daily discharge, precipitation and temperature timeseries ranging from 1990 to 2020 were collected from 56 gauged catchments smaller than 500 km² surrounding the target area. 30 “static” parameters delineating geometry, topography, geology, land use, and drainage along with nine “climatic” parameters describing temperatures, precipitation distributions, and potential evapotranspiration were defined and computed to characterize gauged and ungauged catchments. Alongside comparing three regression methods, coupled with two adjustment techniques supplementing truncated discharge timeseries, three parameter selection methods are evaluated. The validation of the proposed models shows reduced errors and increased linear correlations between estimated and observed values compared to currently applied models. Notably, a spatially more homogeneous yet catchment-specific distribution of estimated values is observable. Particularly when timeseries remain unadjusted or adjustment is done using the Antecedent Precipitation Index (API) and the flow duration curve from a donor basin (Ridolfi, E.; Kumar, H.; Bárdossy, A., 2020), the proposed models yield promising results.

Furthermore, the temporal variability of low flow events for the glacier-free catchments in the study area has been analysed. The frequency of low flow events below the threshold systematically increased over the last 30 years, while the 10-year Q347 value of said catchments has systematically decreased in the same period. The increase in low flow days leads to large errors in the estimation of the Q347 value, especially when its estimation is based on truncated timeseries. As further changes in runoff behaviour are to be expected due to climate change, extending the definition of "low flow" to include event duration and intensity alongside a fixed threshold value could offer a more suitable description.

How to cite: Dups, Y., Pavia Santolamazza, D., Staufer, P., and Lebrenz, H.: New estimation models for determining the Q347, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5969, https://doi.org/10.5194/egusphere-egu24-5969, 2024.

EGU24-5997 | ECS | Orals | HS2.4.3 | Highlight

Surprising megafloods in Europe – learning from the big picture 

Miriam Bertola and Günter Blöschl and the Team members

Megafloods that far exceed previously observed records at a given location can take citizens and flood managers by surprise. Existing methods based on local and regional information rarely go beyond national borders and cannot predict these floods well because of limited data on megafloods, and because flood generation processes of such extremes differ from those of smaller, more frequently observed events. Here we analyse the most comprehensive dataset of annual maximum discharges in Europe available to date, to assess whether recent locally surprising megafloods could have been anticipated using observations in hydrologically similar catchments across the continent.

We base our analysis on annual maximum river discharge observations from 8023 gauging stations for the period 1810–2021. We identify about 500 “target” catchments where recent (i.e., after 1999) megafloods have occurred that are surprising based on local data. We perform a hindcast experiment of predicting their peak discharge with regional envelope curves, using flood observations from similar “donor” catchments up to the year before their occurrence. From this group of donor catchments we construct an envelope curve which we compare with the megaflood that occurred later in the target catchments. We repeat this analysis for all the detected megafloods in the target catchments.

Our analysis shows that, in 95.5% of the target catchments, the discharge of the envelope is larger than that of the observed megaflood, suggesting that, from a European perspective, almost none of the events can be considered a regional surprise. Similar results are obtained by repeating the analysis on two consecutive sub-periods, indicating that megafloods have not changed much in time relative to their spatial variability. In conclusion, our findings show that recent megafloods could have been anticipated from observations in other parts of Europe, which would not be possible using only national data.

How to cite: Bertola, M. and Blöschl, G. and the Team members: Surprising megafloods in Europe – learning from the big picture, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5997, https://doi.org/10.5194/egusphere-egu24-5997, 2024.

Droughts are recurrent phenomena that present a large variety of space and time patterns making rather difficult the assessment of their rarity and the comparison between events. Our study focuses on the space-time “memory effect” of meteorological drought over France using gridded precipitation from the SAFRAN reanalysis over 1950-2022. The proposed easy tool of rarity matrix analyzes how drought events build and persist across time and space. The approach is purely statistic, assuming that drought consequences over a given area depend on the probability of non exceedance (“rarity”) of antecedent rainfall accumulations. In order to cover a large spectrum of “memory effects”, we consider a continuum of accumulation periods ranging from a few weeks to several years and moving windows of size 80x80 to 480x480 km2 over France. The rarity matrix of a given year displays the most severe rarity values encountered during the year as a function of the various accumulation periods and the various spatial scales.

Over the study period of 1950-2022 we show how the shape of rarity matrix discriminate short- and long-term historical droughts, as well as regional to national droughts.

As an additional asset, the rarity matrix is also able to analyze the rarity of precipitation excess over several weeks to months or years, as it was the case in fall 2023 in France.

How to cite: Blanchet, J., Chagnaud, G., and Creutin, J.-D.: The multi-scale rarity matrix – a comprehensive tool to analyze the space-time severity of meteorological drought, with application to France, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-6086, https://doi.org/10.5194/egusphere-egu24-6086, 2024.

EGU24-6166 | ECS | Orals | HS2.4.3

Heavy-tailed flood peak distributions: What is the effect of the spatial variability of rainfall and runoff generation? 

Elena Macdonald, Bruno Merz, Viet Dung Nguyen, and Sergiy Vorogushyn

The distributions of many observed time series of daily precipitation and streamflow show heavy tail behaviour. This means that the occurrence of extreme events has a higher probability than would be the case if the tail was receding exponentially. To avoid underestimating extreme flood events in their occurrence probability or their magnitude, a robust estimation of the tail behaviour is required. However, this is often hindered due to the limited length of time series. One way of overcoming this is to enhance the understanding of the processes that govern the tail behaviour of flood peak distributions. Here, we analyse how the spatial variability of rainfall and runoff generation along with the tail behaviour of rainfall affect the flood peak tail behaviour in catchments of various size. To do so, a modelling chain consisting of a stochastic weather generator and a conceptual rainfall-runoff model is used. For a large synthetic catchment (>100,000 km²), long time series of daily rainfall with varying tail behaviour and varying degree of spatial variability are generated and used as input for the rainfall-runoff model. In the rainfall-runoff model, spatially variable runoff is generated by setting respective model parameters accordingly. The tail behaviour of the simulated precipitation and streamflow time series is characterized with the shape parameter of the Generalized Extreme Value (GEV) distribution.

Our analysis shows that heavy-tailed rainfall tends to result in heavy-tailed flood peak distributions, independent of the catchment size. In contrast, first results regarding the effect of the spatial variability of rainfall on flood peak tail behaviour indicate that this relation varies with the size of the catchment. In large catchments, attenuating effects, for example through river routing, might have a stronger impact than in small basins. Regarding the runoff generation, the tail of flood peak distributions tends to be heavier when a fast runoff component is triggered simultaneously in a larger share of the catchment rather than when this is the case only very localized. This in turn is linked to more homogeneous catchment characteristics and rainfall patterns. The results of this study can help with improving the estimation of occurrence probabilities of extreme flood events.

How to cite: Macdonald, E., Merz, B., Nguyen, V. D., and Vorogushyn, S.: Heavy-tailed flood peak distributions: What is the effect of the spatial variability of rainfall and runoff generation?, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-6166, https://doi.org/10.5194/egusphere-egu24-6166, 2024.

There seem to be two contrasting views on flooding after drought. Subsurface hydrologists pose that with dry antecedent conditions there is more storage available, which leads to lower flood peaks. Surface hydrologists pose that dry, hydrophobic soils support less infiltration and more surface runoff, which leads to higher flood peaks. But which theory is true? Or can both be true? And what happens if you put people and their actions in the mix? In this presentation is discuss the scientific and empirical evidence related to drought-flood events. I draw on scientific literature, global data analysis, a review of reports and news articles, qualitative case studies, and science communication examples. I will mostly focus on hydrological processes, but also highlight some meteorological and anthropogenic aspects.

How to cite: Van Loon, A. and the PerfectSTORM team: On the drought-flood conundrum: do droughts cause more or less flooding? Let’s discuss the science, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-7811, https://doi.org/10.5194/egusphere-egu24-7811, 2024.

EGU24-8302 | ECS | Posters virtual | HS2.4.3

Projected changes in extreme precipitation and floods in central India 

Nikhil Kumar, Evan G.R. Davies, Manish Kumar Goyal, and Monireh Faramarzi

Precipitation extremes are expected to rise in a warming climate; however, the corresponding increases in flood magnitudes remain a complex and underexplored issue. This study employs the annual maxima approach to assess the relationship between extreme precipitation and floods, using a process-based hydrological model, the Soil & Water Assessment Tool (SWAT), in four river basins of central India (Brahmani and Baitarni, Subarnarekha, Mahanadi and Narmada) for past (1984-2014) and future (2030-2060 and 2070-2100). First, the SWAT models underwent rigorous data selection (climate and land cover data), calibration and validation to ensure a reliable representation of the hydrologic conditions of these basins at a daily scale, based on observations from 26 hydrometric stations for the 1988–2019 period. Second, climate projections from four CMIP6 GCMs were statistically downscaled using Bias Correction/Constructed Analogues with Quantile mapping reordering (BCCAQ) for the SSP245 and SSP585 scenarios. Finally, the SWAT models were used to project future changes in extreme precipitation and flood characteristics in the selected river basins. Considering both daily model performance (Nash-Sutcliffe Efficiency-NSE > 0.60) and catchment representativeness, we selected 10 from 26 hydrometric stations for the extreme value analysis. The analysis of the ensemble mean of the 95th percentile of four GCMs and the modelled 20-year return levels show a future increase in both precipitation (0.27 to 27.93 % and 6.19 to 50.06 %) and discharge (1.31 to 50.35 % and 5.42 to 100.73 %) at 6 out of 10 selected stations, with a more significant increase under the SSP585 scenario than the SSP245 scenario, highlighting a clear link between increased precipitation and discharge The modelling framework developed in this study will improve understanding of processes involved and the thresholds at which the central Indian catchments correspond to extreme precipitation. The findings will help the projection of future flood risks and could help to shape effective adaptation strategies in the region.

How to cite: Kumar, N., G.R. Davies, E., Kumar Goyal, M., and Faramarzi, M.: Projected changes in extreme precipitation and floods in central India, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-8302, https://doi.org/10.5194/egusphere-egu24-8302, 2024.

EGU24-8320 | Orals | HS2.4.3

Agricultural Drought Propagation over India: A Complex Network Theory Approach  

Kasi Venkatesh and Bellie Sivakumar

Agricultural drought has emerged as a significant threat to global food security and sustainable development. Despite the progress made in the identification and analysis of various characteristics for the assessment and early warning of agricultural drought, our knowledge of the mechanisms governing agricultural drought propagation remains limited. This study aims to address this gap by employing complex network theory. Specifically, the study uses complex network measures to investigate the spatial propagation of agricultural drought propagation across India during 1950–2014. Spatial drought networks are constructed using event synchronization (ES) for mild drought conditions derived from the Standardized Soil Moisture Index (SSMI) at a 3-month aggregated scale (SSMI-3). The investigation delves into the mechanisms of spatial propagation of drought, including propagation source and sink, distance and orientation using directed networks. Several metrics, including network divergence, in-degree, and out-degree, inward and outward distance, inward and outward orientation are used. These metrics play a crucial role in identifying specific locations, namely source and sink regions, propagation distance and orientation, where drought onsets extend to other areas within the regional spatial networks. The results indicate that the northwest India acts as the source region and the west central India and peninsular India act as sinks. The central and east India are identified as vulnerable regions playing crucial roles in spatial drought propagation. The results also reveal that the dominant directions of propagation lead towards the northwestern parts of India. For inward distances, shorter propagation distances of less than 50 km are observed in the peninsular, central, and some parts of the northeastern regions, while longer propagation distances are observed in the western parts of India, exceeding 150 km. For outward distances, shorter propagation distances below 20 km are observed in hilly regions, while longer propagation distances are observed in the peninsular regions of India, exceeding 80 km. These results suggest that most regions propagate droughts inward and outward, covering distances of even hundreds of kilometres. Understanding the dominant inward and outward orientation of drought propagation could play a crucial role in developing early warning systems for droughts.

How to cite: Venkatesh, K. and Sivakumar, B.: Agricultural Drought Propagation over India: A Complex Network Theory Approach , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-8320, https://doi.org/10.5194/egusphere-egu24-8320, 2024.

EGU24-8350 | Posters on site | HS2.4.3

Subway Flooding Simulation with US EPA SWMM: A Case Study of the Tamsui-Xindian Line During Taiwan's 2001 Typhoon Nari 

Yong-Jun Lin, Hsiang-Kuan Chang, Jihn-Sung Lai, and Yih-Chi Tan

There have been frequent reports of subway station flooding incidents in recent years. For instance, on October 30, 2012, Hurricane Sandy in the United States caused a storm surge combined with astronomical tide that submerged seven subway lines in New York City. It was the most severe disaster in the New York City subway system. On July 20, 2021, a flooding incident occurred in Zhengzhou, Henan Province, China, with a record hourly rainfall of 201.9mm. The heavy rain caused severe water accumulation at the Wulongkou yard of Zhengzhou Metro Line 5 and its surrounding areas. The temporary flood barrier was breached, allowing water to flood into the subway, with a maximum water depth of 1.75 meters inside the carriages and the flooding length extending approximately 1 kilometer.

In 2001, Typhoon Nari caused flooding at the Taipei Station, with 16 MRT stations also inundated. Surface roads were extensively flooded, and the Taiwan Railways Administration stations in Taipei, Wanhua, and Banqiao were submerged, resulting in a 90-day suspension of the Taipei MRT station. How to quickly evaluate the impacts of subway station flooding is crucial for the extreme weather in the future.

Therefore, this study utilized the US EPA SWMM to simulate the flooding situation of the Tamsui-Xindian line during Typhoon Nari in 2001. The SWMM calculations showed varying degrees of flooding at different stations at different times. For example, Guting Station was not affected by human intervention, while the simulated flooding depth at Taipei Station was only 0.09 m different from the actual depth. Additionally, the September 17, 2001 flood profile at 17:34 showed that Taipei Station was submerged, with water flowing to Zhongshan and Shuanglian stations. The National Taiwan University Hospital station experienced minimal flooding due to its higher elevation. The simulation also displayed the water ingress situation at different stations at various times. However, there were some inaccuracies due to the lack of detailed flood progression and inflow data and the use of a simplified station model. Nonetheless, the overall simulation results reflected the related flooding process.

How to cite: Lin, Y.-J., Chang, H.-K., Lai, J.-S., and Tan, Y.-C.: Subway Flooding Simulation with US EPA SWMM: A Case Study of the Tamsui-Xindian Line During Taiwan's 2001 Typhoon Nari, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-8350, https://doi.org/10.5194/egusphere-egu24-8350, 2024.

The aridity of a region plays a pivotal role in shaping a diverse range of hydrological processes, encompassing critical aspects such as the sensitivity of evaporation to variations in temperature and precipitation, water use efficiency, and the intricate interactions between precipitation, soil moisture, and evaporation. These processes, in turn, influence the response of hydrological extremes, such as drought and flood, to global warming. Understanding the impact of aridity on these extreme events in the context of changing climate conditions across global terrestrial ecosystems is essential for comprehending water availability and ecological resilience in different regions. This study investigates the relationships of changes in drought and flood intensities for the end of the twenty-first century with background aridity across global terrestrial ecosystems. Background aridity is quantified using an aridity index, calculated as the ratio between precipitation and evaporation. Drought is characterized by the standardized precipitation index (SPI), and flood by fitting a generalized extreme value distribution (GEV) to the annual maxima flow time series of the Inter-Sectoral Impact Model Intercomparison Project models. The results show opposite responses of drought and floods to background aridity under climate change across global terrestrial ecosystems. As aridity decreases from dry to wet regions, the intensification of flood events in the future is expected to increase. In contrast, drought intensification is more pronounced in dry and semi-dry regions. These findings hold significant implications for developing effective and region-specific water resource management policies to address hydrological extremes in a changing climate.

How to cite: Tabari, H.: Contrasting responses of drought and floods to background aridity in a changing climate across global terrestrial ecosystems, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-8460, https://doi.org/10.5194/egusphere-egu24-8460, 2024.

EGU24-9058 | ECS | Posters on site | HS2.4.3

Evaluation of soil moisture droughts under climate change in Germany 

Friedrich Boeing, Andreas Marx, Thorsten Wagener, Luis Samaniego, Oldrich Rakovec, Rohini Kumar, and Sabine Attinger

It is projected that the likelyhood and duration of extreme soil moisture (SM) droughts will increase in Germany under future warming scenarios. Annual precipitation changes are small under climate change in Germany with increases in winter and decreasing precipitation in summer for some parts of Germany. Generally, the climate ensemble spread in the future precipitation signal is large. Furthermore, impacts of SM droughts depend largely on the soil volume evaluated. We identified a gradient of stronger soil drying in shallow SM compared to deeper SM under global warming, leading to different effects on shallow-rooted vegetation compared to deep-rooted vegetation (agriculture versus forestry). In addition, spatial characteristics such as soil properties can strongly influence the dynamics of SM and thus shape the response of SM drought to changing meteorological conditions. 
In this work we evaluate the impact of the considered soil depth and spatial features on simulated changes in SM droughts in Germany. We compare this influence to the uncertainty in meteorological changes. We use a large climate ensemble based on Euro-Cordex regional climate model simulations, which were bias-adjusted and spatially disaggregated to run the mesoscale hydrological model (mHM) (mhm-ufz.org) with a high spatial resolution of 1.2x1.2km. 
This work aims to expand the picture of climate change impacts on SM droughts in Germany. The results can contribute to an improved definition of sector-specific drought indicators that will support national efforts to ensure climate change resilient water management.

How to cite: Boeing, F., Marx, A., Wagener, T., Samaniego, L., Rakovec, O., Kumar, R., and Attinger, S.: Evaluation of soil moisture droughts under climate change in Germany, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-9058, https://doi.org/10.5194/egusphere-egu24-9058, 2024.

EGU24-9081 | ECS | Posters on site | HS2.4.3

Suitability Mapping for Subsurface Floodwater Storage Schemes 

Lea Augustin and Thomas Baumann

Co-management strategies for floods and droughts offer a promising solution for dealing with two extremes that are increasingly close in time and space. Techniques originally developed for drought prevention, such as managed groundwater recharge (MAR), could use floods as a source of water (Flood-MAR) to simultaneously protect against flooding.

The project Smart-SWS aims to develop this concept further by capturing the flood waves in a river and infiltrating them into aquifers nearby. Subsurface storage is created through geotechnical measures in the aquifer. This storage could secure the seasonal water supply while protecting downstream settlements from flooding.

The main attributes of Smart-SWS sites mirror the overall objective: On the one hand, potential sites for such a system are located in areas that are regularly flooded and, at the same time, have problems with groundwater scarcity. In order to infiltrate large volumes of water into the aquifer and store this water for extended periods, the characteristics of the aquifer, the surface, and the water source must be taken into account to assess the suitability of these sites.

In this work, we have identified suitable sites for such an underground flood storage system by applying a GIS-based multi-criteria decision analysis (MCDA). The workflow for the suitability mapping is based on publicly available data and implemented in Python. The results are shown for the administrative district of Swabia in Bavaria, Germany, where approximately 35% of the study area was identified as having varying degrees of suitability. The robustness of the MCDA is validated with a sensitivity analysis, and the results are checked against expert opinions based on field data.

How to cite: Augustin, L. and Baumann, T.: Suitability Mapping for Subsurface Floodwater Storage Schemes, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-9081, https://doi.org/10.5194/egusphere-egu24-9081, 2024.

EGU24-9215 | ECS | Posters on site | HS2.4.3

Exploring extreme flow events and associated patterns in Switzerland: a Dense feed-forward Neural Network approach 

Maria Grazia Zanoni, Marius Floriancic, Hansjörg Seybold, and James W. Kirchner

Switzerland relies significantly on sustainable water management to meet its diverse socio-economic and environmental needs. As climate change introduces heightened uncertainty in weather patterns, accurate forecasting of extreme flows from climatic data has become essential for efficient water resource management in the country. Furthermore, these events are likely shaped by nonlinear hydroclimatic and compound conditions distinct from typical average cases. A thorough understanding of these phenomena is therefore crucial for effective adaptation to changing climatic conditions. In this regard, data-driven techniques, such as Machine Learning algorithms, have proven capable of extracting knowledge from vast amounts of data, providing valuable insights into the underlying climate and societal dynamics driving extreme flow events.

The aim of the present study is therefore twofold. First, we evaluate the ability of a Dense feed-forward Neural Network (DNN) model to predict drought and peak flow events in Switzerland based on anthropogenic, environmental and climatic data. On the other side, we investigate the role of each driver in the prediction and we study the temporal trends of the target and the features. The analysis was conducted on a large dataset consisting of daily discharge data from more than 400 sites across the country, from 1999 to 2019.  First, we evaluated the flow distribution at each individual site, considering only the extreme events and developing two distinct DNN models for droughts and for peaks. The DNN performed better in modeling droughts, achieving in the test set a mean Nash-Sutcliffe efficiency coefficient of 0.6 and a mean Kling-Gupta efficiency coefficient of 0.8, compared to 0.1 and 0.38, respectively, for the peaks.  A sensitivity analysis of the features, such as the cumulative precipitation and mean air temperature in the preceding weeks of the event, was performed. In addition, we delved into a detailed examination of the temporal trends of the climatic drivers and the extreme flow rates over the 20 years of the study. In the subsequent phase of the project, we explored a multi-site modeling approach to address the issue of the DNN model's poor performance in predicting peak flows.  We introduced geographic, land use and other anthropogenic factors specific to each watershed. 

By revealing the predictive potential of data-driven models, this study serves as a valuable foundation and resource for addressing extreme flow events and the hydroclimatic and anthropogenic patterns behind them.

How to cite: Zanoni, M. G., Floriancic, M., Seybold, H., and Kirchner, J. W.: Exploring extreme flow events and associated patterns in Switzerland: a Dense feed-forward Neural Network approach, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-9215, https://doi.org/10.5194/egusphere-egu24-9215, 2024.

Climate change affects several sectors and environmental conditions, in particular the statistical characteristics of the runoff processes of a certain watershed. As a consequence of the higher temperature values and the altered precipitation distribution, the intensity and timing of floods and droughts, as well as their severity, may change in the coming decades. In order to develop adaptation strategies and implement an adequate water management, it is necessary to project the future trends of variables that can essentially influence water management, taking into account possible climate change scenarios, including the quantification of uncertainty.

Our aim is to investigate the runoff conditions with a special focus on the frequency of critical low water levels and the different levels of flood warnings for selected river sections (i.e. Tiszabecs, Uszti Csorna, Rahiv) in the Uppest-Tisza Basin, located in Central-Eastern Europe. For this purpose, simulations with the physically based, distributed DIWA hydrological model driven by a regional climate model simulation are completed. In order to analyse the projected changes, simulations are made for a historical period (1972–2001) as well as for two future periods (2021–2050 and 2069–2098). We also investigate how the choice of the RCP scenario (i.e. RCP2.6, RCP4.5 or RCP8.5) affects the output of the hydrological simulation. In order to assess uncertainty, time series of meteorological parameters (providing inputs for the hydrological model) are generated by a weather-generator embedded in a Monte-Carlo cycle. Therefore, several hundreds of scenarios with equal probability are available, by using only one climate model. Furthermore, a bias-correction of the climate model simulation is implemented for which the weather-generator is used by fitting the crucial distribution parameters to the reference, i.e. the so-called CARPATCLIM database.

How to cite: Kis, A., Pongrácz, R., and Szabó, J. A.: Analysis of the frequency of critical low water levels and flood warning water levels for the Uppest-Tisza catchment for the 21st century, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-9343, https://doi.org/10.5194/egusphere-egu24-9343, 2024.

EGU24-9553 | ECS | Orals | HS2.4.3

Climate adaptation to change in high-flows: Comparison of high-resolution climate model projections 

Aparna Chandrasekar, Friedrich Boeing, Andreas Marx, Oldrich Rakovec, Sebastian Mueller, Ehsan Sharifi, Jeisson Javier Leal Rojas, Luis Samaniego, and Stephan Thober

Climate change is altering the water cycle from the global to the local scale. The increase in temperatures and changing precipitation patterns intensify not only mean values, but also the frequency and severity of extreme weather events, leading to alterations in water availability and distribution.

This study assesses the impact of climate change on flood patterns (maximum annual river discharge) in Germany. Climate models ranging from different spatial scales will be compared for the five largest German catchments outlets (including headwaters). The climate model ensembles from the EURO-CORDEX initiative, and the ICON climate model from the Destination Earth Initiative / NextGEMS project will be used along with the mHM (mhm-ufz.org) model. The river discharge values produced from the mHM model will be used to calculate the Q90 (90th percentile of daily discharge) and the Qmax (maximum annual discharge) parameters.

Initial results from the EURO-CORDEX initiative predict a 5-15% reduction in Q90 and Qmax in the summer half year, and a 5-30% increase in Q90 and Qmax in the winter half year, in the alpine regions in Germany. In the Elbe and Oder catchments (north-eastern part of Germany) there in a greater increase in Q90 and Qmax in the summer half year than the winter half year. This increase becomes more prominent with increasing warming. However, there is a large spread in the ensemble predictions, with uncertainty reducing with increasing warming. These parameters and results will be compared with the results from the ICON climate model to understand the contribution of spatial and/or temporal resoltion towards flood prediction.

How to cite: Chandrasekar, A., Boeing, F., Marx, A., Rakovec, O., Mueller, S., Sharifi, E., Leal Rojas, J. J., Samaniego, L., and Thober, S.: Climate adaptation to change in high-flows: Comparison of high-resolution climate model projections, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-9553, https://doi.org/10.5194/egusphere-egu24-9553, 2024.

EGU24-9626 | ECS | Orals | HS2.4.3

From drought to Storm Daniel: an overall assessment on the fragility of the Mediterranean region 

Junliang Qiu, Wei Zhao, Luca Brocca, and Paolo Tarolli

In 2022, Europe experienced an unprecedented drought. 2023 marked the warmest year globally on meteorological records, leading to droughts and wildfires in Greece during the summer. In July 2023, certain areas of the Mediterranean experienced sea surface temperatures 5.5°C higher than the annual average, contributing to severe summer heatwaves and wildfires in the Greek region. These conditions also provided ample thermal energy for the formation of Storm Daniel. From September 4th to 6th, Storm Daniel struck Greece, resulting in significant rainfall and flooding. Coordinated satellite monitoring revealed that the flooded area in central Greece reached 875.28 km². On September 10th, Storm Daniel hit Libya, leading to dam collapses and claiming the lives of over 11,000 people. Concurrently, the flood area in the northern deserts of Libya exceeded 1,000 km². From a global perspective, Europe has witnessed an increased frequency of extreme droughts and floods in recent years, while North Africa grapples with geopolitical instability. Climate change-induced natural disasters are further heightening the vulnerability of the Mediterranean region. Consequently, this study underscores the importance of (1) enhancing hydrological monitoring in arid and semi-arid regions of the Mediterranean, (2) developing a Mediterranean scale early warning system, and (3) stressing the imperative for the European Union and North African countries to collaboratively establish climate change adaptation strategies, aiming to avert humanitarian disasters triggered by climate crises.

How to cite: Qiu, J., Zhao, W., Brocca, L., and Tarolli, P.: From drought to Storm Daniel: an overall assessment on the fragility of the Mediterranean region, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-9626, https://doi.org/10.5194/egusphere-egu24-9626, 2024.

EGU24-9639 | ECS | Orals | HS2.4.3

Interpretable Machine Learning to Uncover Key Compound Drivers of Hydrological Droughts 

Georgios Blougouras, Markus Reichstein, Mirco Migliavacca, Alexander Brenning, and Shijie Jiang

Hydrological drought (negative streamflow anomalies) can have significant societal and ecosystem impacts, and understanding its drivers is crucial for interpreting past and present droughts, as well as assessing future drought risk. However, despite recent research advancements, a comprehensive multivariate perspective on the drivers of hydrological drought remains elusive, particularly in the context of global warming, where distributional changes in drivers could result in an increased frequency of complex, compound events. In order to address this, quantifying the contribution of each driver is necessary. In our research, we devise an interpretable machine learning framework that can explain which hydrometeorological variables contribute to streamflow predictions. This is done by encoding a conceptual hydrological model into a neural network architecture, creating a physics-encoded hybrid model that allows us to maintain physical consistency and ensure a more causal understanding. We apply our framework to numerous North American basins across spatiotemporal scales and quantify the contribution of each potential driver to identified streamflow deficit events. We also investigate the mechanisms associated with compound drivers and assess if drought drivers are becoming increasingly complex due to climate change based on the defined compoundness index.  Overall, our framework has managed to capture the contribution of diverse drought drivers to events across different hydroclimatological regimes. The results demonstrate the effectiveness of our novel method in improving hydrological drought process understanding, especially the mechanisms and severity of droughts associated with compound drivers, thereby facilitating increased preparedness for future drought risks.

How to cite: Blougouras, G., Reichstein, M., Migliavacca, M., Brenning, A., and Jiang, S.: Interpretable Machine Learning to Uncover Key Compound Drivers of Hydrological Droughts, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-9639, https://doi.org/10.5194/egusphere-egu24-9639, 2024.

Understanding and quantifying severe low flows is crucial for the management of hydropower or thermal power plants. Moreover, low flows are strongly related to the climatic regime and will be affected by climate change. Therefore, we propose a modelling chain to estimate severe low flow values for several human-influenced catchments in France, both under current and future climate.

Firstly, a bivariate weather generator (Touron, 2019) of daily temperature and precipitation, representing the average of 28 catchments spread out over France, was trained, and used to generate 1000 meteorological time series over a 30-year period. Average daily precipitation and temperature are then spatially disaggregated to produce 1000 local time series for each of the 28 catchments using an analogue approach. Thirdly, MORDOR-SD a lumped conceptual rainfall-runoff model, developed and used at EDF (Garavaglia et al. 2017), combined with upstream-downstream propagation and water management module was forced by the 1000 local meteorological time series. The resulting 1000 time series of simulated river flows are then used to calculate an empirical rare percentile estimate of low flows across 12 large catchments of interest.

The methodology is applied on historical period (1981-2010) using precipitation and temperature observations to train the weather generator. The robustness of the method is evaluated by comparing return levels of low flows obtained through the proposed method and the ones estimated through river flow observations available. Finally, to assess the impact of climate change, the weather generator is also trained/used with 5 downscaled climate projections from the CMIP5 experiments corresponding to: (1) the historical period (1981-2010) and, (2) 4 storylines representing different levels of warming/drying (2036-2065).

The comparison over the historical period has shown the relative agreement between simulated and observed severe low flows. Furthermore, under future conditions, the climatic differences between the 4 storylines lead to logical differences in the estimation of severe low flows, i.e. warmer/drier storylines lead to lower estimation of severe low flows.

How to cite: Devers, A., Gailhard, J., Parey, S., and Froidurot, S.: Estimating severe low flows on human-influenced catchments by combining weather generator, analogue spatial disaggregation, and hydrological modelling under historical and future climate., EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-10393, https://doi.org/10.5194/egusphere-egu24-10393, 2024.

EGU24-10718 | ECS | Orals | HS2.4.3

Exploring Extreme Climate Transitions in Kerala, India: A Multi-Decadal Investigation (1980-2020) 

Meera G Mohan, Arathy Nair Geetha Raveendran Nair, and Adarsh Sankaran

In response to the escalating challenges posed by climate change, this study addresses the critical need to understand the dynamics of extreme climatic events within Kerala, India. Focusing on the years spanning 1980 to 2020, specifically during 12 identified drought years within, we meticulously examine transitions between droughts and floods, recognizing the profound impact on the region's hydrological landscape. With a strategic selection of 17 stream gauge locations covering high, mid, and low lands, representing varied climatic zones, our investigation delves into the intricacies of climatic shifts. The study deals with the analysis of discernible trend of increasing frequency in extreme events over time, by employing a thorough approach incorporating statistical significance testing, frequency analysis of extreme events, and lag analysis and then to unravel the intricate relationships between streamflow and precipitation during distinct phases such as pre-drought, drought, and post-drought years. The research findings illustrate an erratic pattern in the occurrence of contradictory extremes, such as transitions between drought and flood. The timing and duration of these transitions are also found to be inconsistent, showing varying periods in-between and occasionally consecutive occurrences of the same extremes, which in turn highlights the complexity and irregularity of extreme event patterns present in Kerala. Notably, our analysis reveals a concerning trend where the frequency of extreme events is progressively increasing, indicating a higher occurrence of climatic extremes over the years. Specifically, from 2015 to 2020, the observed transitions are striking, in the case that, the total incidences of heavy rain (64.5-115.5 mm per day) were 360 across 10 months in 2015 whereas in the succeeding year (2016), followed by an unprecedented 100-year return period drought. The year 2017 again saw incidences of heavy rain climbing to a total of 360 events. Astonishingly, the anomaly continued with the recurrence of devastating floods in 2018, which persisted for a broadened period up to 2020. While extending the future dynamics for the coming decade, the study predicted the frequency and patterns of extreme events in Kerala by incorporating future General Circulation Model (GCM) precipitation data. The results indicate a substantial increase in the frequency of extreme events, coupled with the anticipated emergence of prolonged dry periods in Kerala's future hydroclimatic landscape. The integration of this data into the analysis enabled the estimation of variations in future streamflow, providing valuable insights into the evolving climatic scenario. This forward-looking approach allowed for the inference of potential patterns of extreme events over the past decade in Kerala, contributing to proactive strategies for climate resilience and adaptive water resource management in the region.

How to cite: G Mohan, M., Geetha Raveendran Nair, A. N., and Sankaran, A.: Exploring Extreme Climate Transitions in Kerala, India: A Multi-Decadal Investigation (1980-2020), EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-10718, https://doi.org/10.5194/egusphere-egu24-10718, 2024.

EGU24-11083 | ECS | Orals | HS2.4.3

Evidence of Flash/ Rapid Drought in India based on Precipitation Deficit- A new Climatic Threat 

Pallavi Kumari and Rajendran Vinnarasi

Rapid intense droughts (Flash Drought) under climatic warming are of widespread concern owing to their catastrophic impacts on agricultural production, eco-system, and nation’s economy. Several studies highlight the need to develop an improved understanding of flash drought to manage its effect better, However the lack of consistent definitions have limited progress toward its assessments. A number of variables, climatic drivers are generally linked to flash drought development thus it is possible that no single description might adequately capture the flash drought. However, it is crucial to make sure that the rapid onset, fast intensification, and severe nature of flash drought can be identified and distinguished from more conventional drought (longer duration) events. With the increasing use of flash drought term within the scientific community, this study presents an evidence-based result by identifying flash droughts using pentad-scale precipitation series across India. The results demonstrate that one of the factors causing and accelerating the flash drought – rapid drought intensification and lasts for shorter duration (3 pentads to 18 pentads) is the meteorological variable precipitation. The results of this study can be further utilised in the accurate characterization of flash drought and its assessment with the strong evidence of precipitation series in finding of flash drought events across the nation.

How to cite: Kumari, P. and Vinnarasi, R.: Evidence of Flash/ Rapid Drought in India based on Precipitation Deficit- A new Climatic Threat, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-11083, https://doi.org/10.5194/egusphere-egu24-11083, 2024.

EGU24-11404 | ECS | Orals | HS2.4.3

On the use of weather generators for the estimation oflow-frequency floods under climate change scenarios 

Carles Beneyto, José Ángel Aranda, and Félix Francés

The present work presents a novel methodology based on the use of stochastic Weather Generators (WG) for the estimation of high return period floods under climate change scenarios. Starting from the premise that the 30-years climate projections, commonly used for future flood studies, do not provide enough information to obtain accurate extreme quantile estimations (especially in arid and semi-arid climates), we propose to exploit the available information by performing a regional study of maximum precipitation of the bias-corrected climate projections (mid-term and long-term), the outputs of which will improve the WG implementation.

This methodology has been applied in a case study, Rambla de la Viuda (Spain), a typical Mediterranean ephemeral river located in eastern Spain. The river is ca. 36 km in length and 1513 km2 in catchment surface, with a remarked variability: large floods are a significant element of this irregular hydrological regime, producing up to 80% of annual discharge volume. Precipitation and temperatures were obtained from the EUROCORDEX project: twelve combinations of Global Circulation Models and Regional Circulation Models were evaluated for a RCP8.5 emissions scenario.

The results obtained shown a clear increase in maximum and minimum temperatures for both projections (up to 3.6ºC), this increase being greater for the long-term projection, where the heat waves intensify importantly in both magnitude and frequency. In terms of precipitation, the results are similar, with precipitation quantiles increasing for practically all models and for both projections, although slightly reducing the annual amount of precipitation. The long synthetic series of precipitation that fed a fully-distributed hydrological model translated into substantial shifts in the river flows regimes, presenting, in general, lower flows during the year but increasing the frequency and magnitude of extreme flood events, reaching 100 years return period quantile values up to 58% higher at the river outlet and up to 130% at a smaller upper subcatchment. 

These results have demonstrated the solidity and effectiveness of the proposed methodology. In the field of meteorological modeling, the results have been consistent and satisfactory, demonstrating the methodology's ability to accurately represent the complexities of extreme climate patterns. Likewise, in the hydrological field, the methodology has exhibited an effective capacity to represent and simulate the processes related to the water cycle, offering coherent and satisfactory results in the estimation of low frequency flood events under climate change scenarios. This consistency in the robustness of the methodology, both in meteorological and hydrological modeling, supports its applicability and reliability in diverse environments and climatic conditions.

How to cite: Beneyto, C., Aranda, J. Á., and Francés, F.: On the use of weather generators for the estimation oflow-frequency floods under climate change scenarios, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-11404, https://doi.org/10.5194/egusphere-egu24-11404, 2024.

EGU24-11499 | ECS | Posters on site | HS2.4.3

The role of meteorological drivers and initial hydrologic conditions on streamflow drought-to-flood transition events 

Eduardo Muñoz-Castro, Bailey Anderson, Paul Astagneau, Joren Janzing, Pablo A. Mendoza, and Manuela I. Brunner

Extreme hydrometeorological events such as streamflow droughts and floods may have severe impacts on infrastructure, agriculture, water supply, and hydropower generation, as well as social and political systems. Even though such impacts can be enhanced if the two types of events occur consecutively, the occurrence and drivers of drought-to-flood transitions are not well understood. Here, we ask: ‘How do the properties of drought-to-flood transitions change with different meteorological drivers and initial hydrologic conditions?’ To address this question, we configure the PCR-GlobWB hydrological model in a suite of near-natural gauged catchments, included in the quasi-global large sample dataset CARAVAN, that comprise different hydroclimatic conditions and physiographic characteristics. We run numerical experiments to understand the sensitivity of consecutive drought-to-flood properties (e.g., duration, extension, intensity, etc.) to different driver scenarios. Additionally, we perform, for each catchment, a flux-mapping analysis to explore whether different combinations of drivers can lead to a similar catchment response through different combinations of fluxes. Finally, we define clusters of catchments with similar drivers and sensitivities of consecutive hydrological extremes to the different stress tests. Ongoing analyses suggest that the drivers of drought-to-flood transitions vary substantially across catchments.

How to cite: Muñoz-Castro, E., Anderson, B., Astagneau, P., Janzing, J., Mendoza, P. A., and Brunner, M. I.: The role of meteorological drivers and initial hydrologic conditions on streamflow drought-to-flood transition events, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-11499, https://doi.org/10.5194/egusphere-egu24-11499, 2024.

EGU24-12331 | Orals | HS2.4.3

The recent European droughts within the nudged storyline context 

Oldrich Rakovec, Antonio Sanchez Benitez, Helge Gößling, and Luis Samaniego

Europe has experienced a series of hot and dry weather conditions with significant socioeconomic and environmental consequences over the past decade. Here, using a novel storyline approach, we examine the extremity of the recent European droughts, and we aim to isolate the thermodynamical component of climate change from changes in atmospheric patterns, which remain controversial in climate model simulations. Our climate analysis is currently based on an ensemble (n=5) of three storyline scenarios (pre-industrial, PI; PD, present-day; 4K warming) using a CMIP6 model (AWI-CM1) with the free-troposphere winds, including the jet stream, constrained toward ERA5 data. The meteorological variables at the land surface are further used as input to a hydrological impact modelling framework using the mesoscale Hydrologic Model (mHM). 

Regarding the 2022 drought analysis, first, using our experiments, we quantify the extremity of the present-day (PD) European drought against pre-industrial (PI) simulations. The potential evapotranspiration shows an apparent increase across the entire ensemble between the PD and PI periods in all of Europe. The same increase holds for actual evapotranspiration in northern Europe and most of central Europe, while the Mediterranean shows a relative decrease of 15%; however, there is no clear separation between PD and PI ensembles. The river runoff exhibits significant reductions of 35-50% in the Mediterranean regions, while changes between -15% and 15% occur over the rest of Europe (with less agreement on the signal). 

Second, we compare how the present 2022 droughts will be further amplified under different warming-level climate scenarios. Our results suggest that the 2022 river runoff drought would be much more strongly pronounced for the 4K world concerning the PD period, by up to 50% in the Mediterranean. A clear decline, although of slightly less extremity (-15% up to -40%), would also be projected across the majority of Central Europe. These changes align with observed trends associated with anthropogenic climate change. Our ongoing efforts aim to quantify possible stress on water resources and ecosystems, by providing insights into the potential future hydrological impact of different global warming levels. The aforementioned results will be further extended to address the multi-year drought perspective during the 2018-2022 periods. 

This work was supported by funding from the Federal Ministry of Education and Research (BMBF) and the Helmholtz Research Field Earth & Environment for the Innovation Pool Project SCENIC.

How to cite: Rakovec, O., Sanchez Benitez, A., Gößling, H., and Samaniego, L.: The recent European droughts within the nudged storyline context, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-12331, https://doi.org/10.5194/egusphere-egu24-12331, 2024.

EGU24-12387 | Posters on site | HS2.4.3

Drought and flood episodes during the 19th Century in Catalonia (NE Iberian Peninsula)  

David Pino, Josep Barriendos, Mariano Barriendos, Carles Balasch, Jordi Tuset, and Laia Andreu-Hayles

The current context of climate change is leading to an increase in hydroclimate variability in the Mediterranean region. This situation is resulting in more frequent and longer dry periods but also in an increase of torrential rainfall events. Current situation justifies the study of the behaviour of droughts and floods from an integrated long-term perspective.

This study aims to study droughts, floods and their interaction during the 19th century in Catalonia using historical and administrative documentary sources. The 19th century corresponds to a climatic period of transition between the Little Ice Age and the current climatic period that includes the appearance of different climatic forcing factors such as solar minimums and extraordinary volcanic eruptions.

In Catalonia 19th century stands out for having some of the most important droughts recorded in the instrumental series of Barcelona (1812-1825), along with experiencing some notable catastrophic flood events. Administrative documentary data allowed us to study at daily resolution flood episodes such as in August 1842, May 1853, September 1874 and January 1898, together with the duration and frequency of drought episodes. Complementarily, in order to characterize the atmospheric general patterns during these episodes, we also generated daily barometric synoptic maps using old instrumental pressure data from different points of Europe. This approach provided the identification of different atmospheric anomalies driving these extreme hydrometeorological events.

How to cite: Pino, D., Barriendos, J., Barriendos, M., Balasch, C., Tuset, J., and Andreu-Hayles, L.: Drought and flood episodes during the 19th Century in Catalonia (NE Iberian Peninsula) , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-12387, https://doi.org/10.5194/egusphere-egu24-12387, 2024.

EGU24-12436 | ECS | Orals | HS2.4.3

Detecting Trends In Hydrological Extremes And Non-Stationary Extreme Value Analysis Of Flood Data In Kwazulu-Natal, South Africa 

Demian Vusimusi Mukansi, Jeff Smithers, Katelyn Johnson, Thomas Kjeldsen, and Macdex Mutema

In this study, the annual maximum streamflow from 14 stations in KwaZulu-Natal, along the East Coast of South Africa, were analysed. Trends were investigated using the non-parametric Mann-Kendall test and the Sen Slope tests, and the results indicate that the annual maximum streamflow has been decreasing in magnitude at 78 % of stations. Extreme value analysis was performed using both stationary and non-stationary models using time and rainfall as covariates. The results show that the stationary models are superior to non-stationary models at most stations with time as a covariate. Where possible, streamflow stations were linked with rainfall stations to determine the impact of rainfall on annual maximum streamflow. The results indicate that the non-stationary model incorporating observed rainfall as a covariate performed better than the stationary and non-stationary models with only time as a covariate. Therefore, incorporating rainfall in design flood estimation should be considered to account for non-stationary trends and to mitigate the risk of failure of hydraulic structures. Regional magnification factors to account for non-stationarity were not investigated further in this study as the majority of the stations showed a negative trend, which means the application of a regional magnification factor will result in a reduction of the magnitude of the estimated design floods.

How to cite: Mukansi, D. V., Smithers, J., Johnson, K., Kjeldsen, T., and Mutema, M.: Detecting Trends In Hydrological Extremes And Non-Stationary Extreme Value Analysis Of Flood Data In Kwazulu-Natal, South Africa, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-12436, https://doi.org/10.5194/egusphere-egu24-12436, 2024.

EGU24-13019 | ECS | Orals | HS2.4.3

Analyzing drought legacy effects on streamflow with machine learning 

Anne Hoek van Dijke, Sungmin Oh, Xin Yu, and Rene Orth

Prolonged periods of below-average precipitation decrease streamflow, deplete soil moisture and groundwater reservoirs, and affect vegetation health. These effects can last for several years even after precipitation returns to normal. This way, droughts can decrease or increase streamflow for post-drought years. These drought legacy effects were found in a few local studies, but they have not yet been studied at global scale. 
Here, we study drought legacy effects on streamflow in > 1100 catchments distributed across the globe using Long-Short Term Memory (LSTM) models. This type of data-driven model is very suitable for time-series predictions with long-term dependencies, and LSTMs are therefore frequently used to model streamflow. We train our LSTM model for each catchment to predict streamflow based on meteorological forcing data. For training, we include all available data between 1980 – 2019, but we exclude the drought legacy years (the two years after each drought year). We assume that our models do therefore not know about the drought legacy effects. After training we use the LSTM models to predict streamflow for drought legacy years. We then define the legacy effects as the difference between model errors (the difference between the predicted and measured streamflow) for drought legacy years, in comparison to the model errors for normal years.
Using this methodology, we find catchments that show no, positive, or negative drought legacy effects. In the next step we will study if these legacy effects vary along climate or land cover gradients. And we additionally include satellite data of vegetation greenness, evaporation, and terrestrial water storage in the LSTM training to study two hypotheses: 1) we find negative drought legacy effects due to a depletion of groundwater, and 2) we find positive drought legacy effects, because vegetation mortality leads to decreased evaporation after the drought.
Our study offers a new perspective on understanding drought legacy effects on streamflow using observational data and demonstrates the usefulness of machine learning in uncovering complex drought impacts. 

How to cite: Hoek van Dijke, A., Oh, S., Yu, X., and Orth, R.: Analyzing drought legacy effects on streamflow with machine learning, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-13019, https://doi.org/10.5194/egusphere-egu24-13019, 2024.

EGU24-13029 | ECS | Posters on site | HS2.4.3

Variable Drought Threshold Method for Low-Flow Behavior Reveals Distinct Clustering Across the Continental United States 

Ryan van der Heijden, Ali Dadkhah, Amin Aghababaei, Xueyi Li, Eniola Webster-Esho, Prabhakar Clement, Mandar Dewoolkar, Ehsan Ghazanfari, Norm Jones, Gustavious Williams, and Donna Rizzo

Groundwater and surface water are interconnected in most climatic regions. Baseflow, the contribution of streamflow not directly associated with precipitation forcing, is a critical component of streamflow prediction and water resource allocation. Baseflow is often considered to be a low-frequency component of streamflow and many of the methods for estimating it are based on this premise. The climatic and physiographic attributes of a region will contribute to the low-flow behavior of its surface waterways. For example, baseflow in a snowmelt-driven basin may produce a distinct hydrologic signature compared to baseflow in a precipitation-driven basin.

In this study, we developed a unique metric based on the variable drought threshold method (VDTM) for characterizing historical streamflow timeseries and performed cluster analysis on a large set of gages in the continental United States (CONUS). Our study goal was to observe correlations between low-flow characteristics and distinct hydrologic, physiographic, and climatic regions to provide insight into the underlying mechanisms influencing baseflow.

The VDTM applies a non-exceedance percentile (NEP) computed based on the distribution of flow recorded at a stream gage over a given time frame (i.e., month, season) throughout the complete record of measurement. This study used daily streamflow records for 1,462 reference quality gages across the CONUS from the USGS GAGES-II data set; each gage contained at least 20 years of complete daily streamflow measurements. We computed the 10th NEP for each month at all 1,462 gages and normalized this value by the mean streamflow to develop the parameter r10. We performed K-means clustering on the monthly r10 values, forming seven clusters of low-flow behavior.

We observed clusters with distinct low-flow behavior across different ecoregions related to possible mechanisms driving streamflow and baseflow in those regions. For example, a cluster located in the intermountain-west shows unique behavior largely seen nowhere else in the CONUS, possibly a result of the predominantly snowmelt-driven shallow subsurface flow that contributes to baseflow seen in that region. Conversely, clusters located in the Pacific Northwest and parts of the Appalachians show a different behavior, possibly a result of the predominantly rainfall-driven streamflow observed in those regions. Principal components analysis suggests that the critical months associated with clustered gages are during the summer (June, July) and winter (January, February).

The spatial distribution of the clusters largely adheres to the defined physiographic and climatic regions of the CONUS despite the absence of any physiographic or climatic variables used for clustering, suggesting a possible linkage between these attributes and the low-flow behavior of surface waterways. Analysis of the trend and magnitude of r10 may provide insight into whether (and when) a stream is losing water to or gaining water from groundwater as well as the magnitude of the transfer. The results of this study suggest that using NEPs and the r10 metric may be an effective method for defining regionalization based on low-flow metrics.

How to cite: van der Heijden, R., Dadkhah, A., Aghababaei, A., Li, X., Webster-Esho, E., Clement, P., Dewoolkar, M., Ghazanfari, E., Jones, N., Williams, G., and Rizzo, D.: Variable Drought Threshold Method for Low-Flow Behavior Reveals Distinct Clustering Across the Continental United States, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-13029, https://doi.org/10.5194/egusphere-egu24-13029, 2024.

EGU24-13741 | Orals | HS2.4.3

Compound events and hydro-climate extremes – how they are impacting Australia, now and in the future 

Wendy Sharples, Sur Sharmila, Bende-Michl Ulrike, Navid Ghajarnia, Katayoon Bharamian, Jiawei Hou, Christopher Pickett-Heaps, and Elisabetta Carrara

Many natural disasters in Australia are the result of compound events, where the assessment of single climate system drivers in isolation do not fully capture hydro-climate extremes. Multivariate compound events such as ‘hot and dry’ and ‘wet and windy’ events, portend a multitude of hazards from heatwaves and bushfires through to coastal inundation and floods. The multiple drivers compounding together in these events, lead to extreme conditions ripe for natural disasters to occur. Presently, compound events are negatively impacting Australia’s ability to protect its population and environmental and economic assets, as Australia tries to adjust to the greenhouse gas driven climatic shifts, with potential projected increases in hazard severity. We aim to understand the change in frequency, duration and intensity of ‘hot and dry’ and ‘wet and windy’ compound events, at current and increased global warming levels. The ‘hot and dry’ compound event is defined as the co-occurrence of SPI drought conditions, and at least 3 consecutive days of hot temperatures. The ‘wet and windy’ compound event is defined as the co-occurrence of both extreme wind and precipitation. These two compound events were chosen to begin with due to the historic severity of their associated impacts. However further research is planned to understand all types of compound events including preconditioned, and, spatially and temporally compounding, in order to fully gauge Australia’s potential vulnerability to natural disasters now and in the future.

How to cite: Sharples, W., Sharmila, S., Ulrike, B.-M., Ghajarnia, N., Bharamian, K., Hou, J., Pickett-Heaps, C., and Carrara, E.: Compound events and hydro-climate extremes – how they are impacting Australia, now and in the future, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-13741, https://doi.org/10.5194/egusphere-egu24-13741, 2024.

The Harz mountains in Germany have experienced floods and droughts in recent years. On the one hand, a major flood event affected the city of Goslar in 2017, and on the other hand, drought conditions since 2018 have led to tree mortality in the forested catchment area upstream. The frequency of such extreme events is expected to increase as a result of climate change. Here, we aim at assessing the impacts of the ongoing tree mortality on hydrology. To this end, we employ the ecohydrological model SWAT+ to assess changes in water balance components. The model is specifically calibrated for extreme conditions by evaluating the model performance for different segments of the flow duration curve. Satellite-derived changes in forest cover are used to assess the impact on water balance components. The analysis of the model performance indicates that the calibration strategy improved model performance for drought conditions. Furthermore, first model results indicate that tree mortality led to a decrease in evapotranspiration and an increase in surface runoff. The spatial assessment suggests stronger effects at the sub-catchment scale than at the catchment scale. However, the faster response of the catchment due to tree mortality potentially increases the severity of flood events and the flood risk in downstream areas. Therefore, afforestation with climate-resilient trees is needed to improve both flood and drought resilience in the Harz mountains.

How to cite: Wagner, P. and Fohrer, N.: Impacts of hydrological extremes in a mountainous forest catchment: Experiences from the Harz mountains, Germany, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-14209, https://doi.org/10.5194/egusphere-egu24-14209, 2024.

EGU24-14703 | Posters on site | HS2.4.3

Evaluating the seasonal patterns of low flow in  Nakdong River basin using SWAT 

Wonjin Kim, Si Jung Choi, and Seong Kyu Kang

ABSTRACT

This study focuses on the seasonal patterns of low flow in the Nakdong River basin (23,635 km2), considering its vital role as a seasonal phenomenon and integral component of the flow regime. Low flow, derived from groundwater discharge or surface discharge from lakes and reservoirs, exhibits varying magnitudes and durations under seasonal changes, thereby holding significant implications for agricultural activities, aquatic species, and water quality. In the absence of gauge stations for small streams, Soil and Water Assessment Tool (SWAT) was employed to ensure reliable simulation for low flow along the target watershed, and the model was calibrated for the period of ten years (2010~2019) using observed data from multipurpose dams and multifunctional weirs within the target watershed. Based on the model results, spatio-temporal variations of low flow were estimated, and seasonality indices were adopted by means of understanding and analysing low flow characteristics. The indices include seasonlity histograms (SHs) depicting the monthly distribution of low flows, seasonlity index (SI) representing the average timing of low flows within a year, and seasonality ratio (SR) showing the ratio of summer to winter low flows. Subsequently, seasonal patterns of low flow in target watershed were evaluated under three indices to figure out the response of low flow in relation to watershed characteristics and climate variability.

 

Acknowledgements

Research for this paper was carried out under the KICT Research Program, Development of IWRM-Korea Technical Convergence Platform Based on Digital New Deal) funded by the Ministry of Science and ICT.

How to cite: Kim, W., Choi, S. J., and Kang, S. K.: Evaluating the seasonal patterns of low flow in  Nakdong River basin using SWAT, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-14703, https://doi.org/10.5194/egusphere-egu24-14703, 2024.

EGU24-14737 | ECS | Posters on site | HS2.4.3

Identifying Flash Flood-Prone Subbasins in India Using Geomorphological and Meteorological Parameters  

Nandana Dilip K, Urmin Vegad, and Vimal Mishra

Flash Floods are one of the crucial disasters in India which causes high mortality and damage due to its sudden onset and devastating impact. These events are projected to increase in India due to the warming climate and increasing unplanned urbanization.  However, India still lacks a robust analysis on flash flood susceptibility at a subbasin scale. In our study, we have considered meteorological and geomorphological factors to improve the susceptibility mapping, as flash floods are the result of high intensity rainfall in a short period of time and the geomorphology of the basin. We analyzed 17 different geomorphological factors of drainage, relief and areal aspects. Further, we calculated the flashiness index for all the subbasins within India using the model simulated streamflow. We forced a hydrodynamic routing model with reanalysis data to simulate streamflow at the subbasin outlets. We prepared subbasin-level flash flood susceptibility maps based on geomorphology, flashiness index and a combination of both. The integrated use of geomorphology and meteorology will provide a more robust framework for identifying the flash flood prone subbasins in India. This will help the authorities in focusing on the probable regions to plan mitigation strategies.  

How to cite: Dilip K, N., Vegad, U., and Mishra, V.: Identifying Flash Flood-Prone Subbasins in India Using Geomorphological and Meteorological Parameters , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-14737, https://doi.org/10.5194/egusphere-egu24-14737, 2024.

EGU24-14818 | ECS | Posters virtual | HS2.4.3

Beyond the Extremes: Interpretable insights based on Attention framework  

Ashish Pathania and Vivek Gupta

Extreme weather events significantly impact the economy, agriculture, infrastructure, and ecosystems of a region. According to the Center for Science and Environment (CSE), extreme weather events caused the loss of nearly 3000 lives, 2 million hectares of crops, and the death of 90,000 cattle in India in 2023 alone. Effective mitigation and adaptation strategies for the region necessitate a reliable forecasting system. The spatiotemporal interactions of several hydroclimatic components at different scales make it difficult to provide reliable forecasts for a region with multiple climate zones. The present research proposes an encoder-decoder-based deep learning framework with an attention mechanism to develop a reliable forecasting model. Attention frameworks have exhibited considerable potential in learning contextual awareness within the time series domain which will help in identifying the temporal dependencies between the meteorological variables and extreme events. The proposed architecture of the forecasting model is made interpretable as it is crucial to comprehend the underlying mechanism of climatic extremes. It recognizes the contributing variables influencing the intensity and frequency of extreme events. The study employed 0.12° × 0.12° high-resolution IMDAA (Indian Monsoon Data Assimilation and Analysis) dataset encompassing climatic variables like precipitation and temperature.

Various studies have supported the association of the ENSO parameters with the anomalous climatic conditions over India. The present study also aims to ascertain the distinct contributions of ENSO variables through the implementation of an interpretable framework. Explainability results underscore the significance of precipitation patterns while forecasting drought conditions in the region. Moreover, the results highlight the complex interaction of climatic variables that affect the intensity of the extremes.

How to cite: Pathania, A. and Gupta, V.: Beyond the Extremes: Interpretable insights based on Attention framework , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-14818, https://doi.org/10.5194/egusphere-egu24-14818, 2024.

Reliable Tropical Cyclone (TC) precipitation and flood nowcasting play an important role in disaster prevention and mitigation. Especially for small-scale reservoirs, timely and accurate inflow forecasts are required to provide safe space for capturing high flows without having to resort to hazardous and damaging releases. Numerous studies have investigated the ability of deep learning in TC precipitation nowcasts. However, few of them focus on the skill of deep-learned TC precipitation forecasts in inflow flood forecasts. In this study, a novel framework is developed by introducing TC track information together with antecedent precipitation in the Convolution LSTM model (PTC-ConvLSTM). The ConvLSTM forecast precipitation is then input to an event-based Xinanjiang hydrological model for inflow flood forecasting, and the propagation of errors from TC track forecasts to inflow forecasts is further analyzed. The results show that TC track information enables a further 5% improvement compared to outputs from ConvLSTM with only precipitation information. PTC-ConvLSTM precipitation nowcasts present a probability of detection (POD) greater than 0.34 for a threshold of 5mm/h in a lead time of 6h. The nowcasts-driven flood forecasts have an NSE greater than 0 with a lead time of 5h at least. It is also indicated that the 100km error in TC track forecasts could generally result in a 10% degradation in precipitation forecasts and a further 8% deterioration in the driven flood forecasts. The effectiveness of our model indicates that the precipitation nowcasts from deep learning have strong applicability in disaster mitigation.

How to cite: Liu, L., Xu, Y.-P., and Gu, H.: Enhanced tropical cyclone inflow flood forecasts by using deep learning and spatial‑temporal information, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-14963, https://doi.org/10.5194/egusphere-egu24-14963, 2024.

Heatwaves and droughts are among the natural hazards with frequencies and severities expected to increase due to climate change. Furthermore, they are responsible for a large range of social and economic impacts, such as agricultural losses, energy shortages, heat related mortality, etc. Previous works have shown that co-occurring drought and heatwave events lead to higher significant socio-economic damages compared to independent events. However, limited knowledge is available on quantifying spatial patterns of co-occurring droughts and heatwaves events, their severity, and frequency of occurrence, especially at high spatial and temporal resolution.

The aim of this study is to quantify spatio-temporal changes of compound drought and heat wave events in a large anthropized alpine Italian basin, the Adige basin, located in the North of Italy, with area greater than 10,000km2 and containing a wide range of elevation from 160m to 3905m. We quantify changes in single and multiple drought and heat wave hazards during the period 1980-2018, based on hydrological simulations performed using a recently produced hydrological digital twin model at high spatial (5 km2) and temporal (daily) resolution. The model also includes artificial reservoirs and the combination of high resolution hydrological modeling and compound hazard estimation framework has a key advantage that: i) it captures single hazard evolution at daily time scale and ii) explicitly estimate the dependence between co-occurred events directly mapping critical susceptible regions.

Preliminary results show increasing trends in number and severity of compound heat waves and drought events. Ongoing work aim to quantify the spatial distribution of the analysed compound events and the exposure in terms of population impacted and main land cover types. The proposed modeling framework may help improve the prediction and assessment of occurrences of compound heat waves and droughts events and the possible implementation of mitigation actions. The authors are supported by the WATERSTEM MUR PRIN 2020 (Prot. Number 20202WF53Z) and the COACH-WAT PRIN 2022 (Prot. Number 2022FXJ3NN).

How to cite: Formetta, G. and Morlot, M.: Compound heatwave and drought hazard quantification in a large anthropized alpine basin., EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-15136, https://doi.org/10.5194/egusphere-egu24-15136, 2024.

EGU24-15522 | ECS | Orals | HS2.4.3

Flood control capacity of a large reservoir under moderate and extreme flood conditions 

Pratik Chakraborty, Sophie De Kock, Pierre Archambeau, Michel Pirotton, Sébastien Erpicum, and Benjamin Dewals

Dams, prevalent globally as major hydraulic structures, play essential roles in water supply, hydroelectric power, and flood management. However, they are known to significantly transform hydrological regimes by, among others, regulating flood and base flow dynamics. This, in turn, necessitates a meticulous understanding of the nature of these alterations.

Focused on the Eupen dam in Belgium, this study examines its storage dynamics in relation to moderate and extreme flood events. The study analyses time-series of inflow and outflow discharges at the dam for the period from 1995 to 2023. The inclusion of the July 2021 extreme event provides valuable insights into the dam’s performance (or lack thereof) during such mega-events. Notable aspects of the methodology include adjustments for an ungauged sub-basin, the use of a Savitsky-Golay filter to refine (field-)data quality without compromising peak details and a fundamental mass-balance approach to compute outflow data from the inflow time-series.

The examination of 18 flood events during this period reveals significant findings: the dam's ability to reduce peak discharge by 8.6 to 91%, delay peak discharge by up to 68 hours, decrease flood volume by 2 to 94%, and reduce the rising rate by 1.09 to 11.16 times. Distinctly, the study also reveals a strong correlation between the damping ratio of the flood wave and the ratio of the volumes of the incoming flood to that available in the reservoir (at the start of an event). The outcomes of the flood frequency analysis are also presented and interpreted in detail.

The present study features a marked shift from existing dam-effects research, wherein the analysis is often focused on mean annual flow characteristics or aggregated data across numerous dams. It highlights the rewards of such a singular case study, in terms of being able to scrutinise individual flood events. This, in turn, provides the scope to understand more complex underlying conditions that prompt a dam's effects on streamflow characteristics. Finally, this research evidences the benefits provided by dam reservoirs on flood wave damping, but also their limits in doing so.

How to cite: Chakraborty, P., De Kock, S., Archambeau, P., Pirotton, M., Erpicum, S., and Dewals, B.: Flood control capacity of a large reservoir under moderate and extreme flood conditions, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-15522, https://doi.org/10.5194/egusphere-egu24-15522, 2024.

EGU24-15968 | ECS | Posters virtual | HS2.4.3

Regional Flood Frequency Analysis Utilizing LH-moment based framework for Godavari River Basin 

Amit Singh and Sagar Chavan

Regional flood frequency analysis (RFFA) helps in estimating the design flood at ungauged locations within a hydrologically homogeneous region. LH moment is a statistical method often used in hydrology for estimating distribution parameter. The LH moment are the linear combination of higher probability weighted moment. It offers an alternative to traditional moments and is particularly useful when dealing with skewed distributions. The Godavari River, one of the major river systems in India, experiences varying hydro climatic conditions across the basin. This study presents a comprehensive regional flood frequency analysis (RFFA) conducted in the Godavari River basin employing LH moment as a robust statistical tool. The present study incorporates the formation of region using region of Influence approach (ROI) approach. In this study, five probability distributions namely generalized extreme value (GEV), generalized logistic (GLO), Pearson Type III (PE3), Generalized Normal (GNO) and generalized Pareto (GPA) are considered for performing RFFA for estimating ungauged flood quantiles corresponding to various return periods (e.g., 50, 100, and, 200 years) in the Godavari River basin. The discordancy measure and heterogeneity measure in LH-Moment framework are considered for screening of peak flow data and checking the heterogeneity of the region formed using ROI. The suitability of GEV, GLO, PE3, GNO, and GPA distribution is judged through the LH-moment ratio diagram and the Z-statistic criteria. The performance of LH-moment based RFFA is evaluated through Leave-One-Out Cross Validation (LOOCV). Results indicate that the LH-moment based RFFA yields more reliable estimates of flood quantiles.

How to cite: Singh, A. and Chavan, S.: Regional Flood Frequency Analysis Utilizing LH-moment based framework for Godavari River Basin, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-15968, https://doi.org/10.5194/egusphere-egu24-15968, 2024.

Three homogeneity tests were carried out on the lower Columbia River basin namely, Standard normal homogeneity test, Pettit test and Buishand test for daily gridded rainfall data having spatial resolution of 0.5o spanning a period of 43 years from 1980 to 2022. These tests were employed to estimate the breakpoint year for each grid and plotted on a map for spatial visualization. It was observed that a close relation follows between the elevation of a place, its changepoint year and the land use land cover of that area. The elevation of an area affects the direction of propagation of moisture laden winds that are developed over the Southwestern part of US from Pacific Ocean and gulf of California. And eventually governing where and how much precipitation they will bring. Additionally, the land cover of an area governs the amount of evapotranspiration and hence the pressure difference between the moisture laden winds and the atmosphere over that land cover. When the daily precipitation records of 4 decades were analysed for homogeneity and changepoint year observed spatially, it was noted that a particular elevation and land cover showed similar breakpoint and a trend is being followed. This study provides a novel way of understanding the behaviour of changing precipitation patterns taking into account the long-term variability of 4 decades.

Keywords: Homogeneity test, Change point analysis, Land use land cover, Lower Columbia River basin

How to cite: Hamid, I. and Jothiprakash, V.: Analysing the relation between changepoint year, elevation, and land cover over lower Columbia River basin in North America, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-16296, https://doi.org/10.5194/egusphere-egu24-16296, 2024.

EGU24-16427 | ECS | Posters on site | HS2.4.3

Development of a river breakup prediction model 

Marie Sæteren, Kolbjørn Engeland, Ånund S. Kvambekk, and Lena M. Tallaksen

The dynamic breakup of river ice can initiate ice runs where large masses of ice floes accumulate as ice jams. These ice jams can cause severe inundation and infrastructure damage. Several Norwegian rivers are prone to ice run events, however there are currently no models available in Norway for predicting this specific hydrological phenomenon. Ice-related problems are often dealt with on a site-to-site basis and rely heavily on local knowledge. Other countries, such as Canada and Sweden, have implemented statistical, machine learning and process-based modelling approaches. Being able to accurately predict the timing and severity of ice run and ice jam events improves the ability to take suitable mitigation measures and limit negative consequences. The aim of this work is to develop a model to predict ice run events in two Norwegian rivers, the Beiarn River and the Stjørdal River, and thereby address the need for predicting this hydrological hazard.

The work presented here is part of a master thesis study that will be completed by May 2023. Both Stjørdal and Beiarn River have been monitored by NVE in the latter half of the 20th century, and the timing and severity of historical ice run events are obtained from this data. The predictors are given by hydrometeorological and ice thickness data, both observed and modelled. The Distance Distribution Dynamics (DDD) model developed by NVE is used for simulating daily discharge, and a simple ice growth model from NVE is used for modelling ice thickness. The prediction model itself is a work in progress, initially taking a logistic regression approach. If time allows, other approaches within machine learning such as random forest will be attempted. The dataset is severely imbalanced given the rarity of ice run events and the limited length of the observed series. Different methods are evaluated in terms of their ability to deal with this issue. The ultimate objective of this project is to develop a model providing daily probabilistic forecasts of the likelihood of ice run events in the coming days.

How to cite: Sæteren, M., Engeland, K., Kvambekk, Å. S., and Tallaksen, L. M.: Development of a river breakup prediction model, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-16427, https://doi.org/10.5194/egusphere-egu24-16427, 2024.

EGU24-17185 | Posters on site | HS2.4.3

Using ERA-5 reanalysis to characterize extreme rainfall in Italy 

Francesco Chiaravalloti, Roberto Coscarelli, and Tommaso Caloiero

Heavy precipitation events are likely to become more frequent in most parts of Europe; yet, records of hourly precipitation are often insufficient to study trends and changes in heavy rainfall. Atmospheric reanalyses are an important source of long-term meteorological data, often considered as a solution to overcome the unavailability of direct measurements. The reanalysis procedure makes use of a large amount of heterogeneous historical observations, both sensed and remotely measured (in situ, satellite, etc), assimilated within a dynamical model to reconstruct the state of the atmosphere, land surface and oceans in the past. Among the available reanalyses, the ERA5 dataset released by the ECMWF, can be considered one of the state-of-the-art products. Atmospheric and surface variables are provided hourly, from 1950 to almost real time, with a horizontal resolution of 31 km. The land model of the ERA5, driven by the downscaled meteorological forcing from the lowest ERA5 model level, and with an elevation correction for the thermodynamic near-surface state, is also used to derive the ERA5-land dataset, characterized by a higher spatial resolution (9 km) and finer precipitation distribution details.

In this paper, data from the ERA5-land reanalysis dataset were used to characterize the 1-hour maximum yearly rainfall values in Italy. Specifically, 3215 grid series of 1-hour rainfall for the period 1950-2020 have been first extracted. Then, for each grid series the 71 1-hour maximum yearly rainfall values have been evaluated. Moreover, the time frame 1950-2020 has been divided into several intervals, and for each one, the frequency distribution of the months recording the annual maxima was calculated. Finally, a cluster analysis has been performed to evaluate the area with a similar monthly distribution of these values. Results showed that, considering the data over the whole of Italy, the monthly distribution of occurrences of annual maxima of 1-hr rainfall is characterized by a peak in September occurring in all the time windows considered. Furthermore, clustering cells with a similar distribution of annual hourly rainfall maxima, using k-means, allowed to identify three groups characterised by different months with the highest frequency of occurrence of the maximum.

This work was funded by the Next Generation EU - Italian NRRP, Mission 4, Component 2, Investment 1.5, call for the creation and strengthening of 'Innovation Ecosystems', building 'Territorial R&D Leaders' (Directorial Decree n. 2021/3277) - project Tech4You - Technologies for climate change adaptation and quality of life improvement, n. ECS0000009. This work reflects only the authors’ views and opinions, neither the Ministry for University and Research nor the European Commission can be considered responsible for them.

How to cite: Chiaravalloti, F., Coscarelli, R., and Caloiero, T.: Using ERA-5 reanalysis to characterize extreme rainfall in Italy, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-17185, https://doi.org/10.5194/egusphere-egu24-17185, 2024.

EGU24-17249 | ECS | Posters on site | HS2.4.3

Global trend and drought analysis of near-natural river flows: The ROBIN Initiative 

Amit Kumar, Jamie Hannaford, Stephen Turner, Lucy J. Barker, Harry Dixon, Adam Griffin, Gayatri Suman, and Rachael Armitage

With hydrological extremes becoming more frequent and intense in a changing world, the impact on livelihoods, infrastructure, and economies is crucial. River flow data is a valuable resource and can be used to understand and analyse trends in both flow and extreme events. It is essential to systematically examine trends and anomalies within river flow across the globe. To capture the true natural trends, the river flow data should be from natural catchments and free from anthropogenic influences, such as the construction of dams, alterations in land use, and extraction of water from rivers. Special attention must be directed towards delineating these factors to enhance our understanding of the complex dynamics governing river systems. 

Existing challenges in attributing trends in river flows to climate change demands for a comprehensive, worldwide Reference Hydrometric Network (RHN) with minimal human impacts, to ensure integrity of climate change signals in river flow data. This global initiative, the Reference Observatory of Basins for INternational hydrological climate change detection (ROBIN) is a global collaboration to bring together the first global RHN. Currently consisting of partners from almost 30 countries spanning every continent, the first iteration of the ROBIN dataset is now available – a consistently defined network of over 3,000 near-natural catchments. 

The ROBIN team estimated the first truly global analysis of trends in river flows using near-natural catchments for periods of 40 (1975-2016) and 60 (1956-2016) years. This research showcases the first global drought assessment using the subset of ROBIN network, investigating variations in river flow trends and their impact on drought events, and trends at a global scale. The research focused on the spatial and temporal variability of trends and drought characteristics in different countries and hydro-belts across the ROBIN network. It also shows the great potential of serving as benchmark for future hydrological trend assessments. 

Efforts are ongoing to broaden the ROBIN network to bring together more countries, incorporating additional catchments representing diverse geographical characteristics. With the support of international organizations such as WMO, UNESCO, and IPCC, ROBIN establishes the groundwork for a sustainable network of catchments, enabling comprehensive assessments of climate-induced trends, variability, and occurrences of drought on a global scale. This initiative makes a substantial contribution to enhancing our understanding of the impact of climate change on river flows and the corresponding global patterns of drought. 

How to cite: Kumar, A., Hannaford, J., Turner, S., Barker, L. J., Dixon, H., Griffin, A., Suman, G., and Armitage, R.: Global trend and drought analysis of near-natural river flows: The ROBIN Initiative, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-17249, https://doi.org/10.5194/egusphere-egu24-17249, 2024.

EGU24-17464 | Orals | HS2.4.3

Patterns of extreme high flows in relation to their dominant generating processes across Sweden 

Yeshewatesfa Hundecha, Jonas Olsson, Lennart Simonsson, and Jörgen Rosberg

Understanding the nature of flooding of a region is key for flood management. The impact of flooding depends on how spatially extensive it is and this, in turn, is influenced by the processes generating the flood. In this study, we investigated the relative importance of rainfall and snowmelt in the generation of floods of different magnitudes and characterized their spatial patterns in different climate regions of Sweden. We generated a large number of spatially diverse extreme river flow scenarios across Sweden that are statistically consistent with the observations by employing a multi-site weather generator and a highly resolved semi-distributed hydrological model. The extreme flows within each of the main rivers were classified based on their generating meteorological forcing and the spatial distribution of the flow magnitudes was assessed. The results reveal that rainfall is the main contributor of extreme flows of all magnitudes in the southern part followed by rain-on-snow, while in the northern part, rain-on-snow is the main process resulting in extreme flows followed by rainfall. Pure snowmelt is the least contributor of extremes in all regions and its contribution decreases with increasing magnitude of the flow. The proportion of events generated by rainfall increases with the magnitude of the flow in all regions. Extremes of lower magnitudes are generally more spatially widespread than the higher extremes and events generated by snowmelt and rain-on-snow are spatially more widespread than events generated by rainfall.

The possible impact of climate change was also assessed by generating extreme flows for end-of-century climate change scenarios by perturbing the weather inputs generated by the weather generator using data from a set of regional climate models and using them to force the hydrologic model. The results show that the main generating processes in each region remain the same. However, the proportion of rainfall generated events will be markedly higher than under the present climate. 

How to cite: Hundecha, Y., Olsson, J., Simonsson, L., and Rosberg, J.: Patterns of extreme high flows in relation to their dominant generating processes across Sweden, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-17464, https://doi.org/10.5194/egusphere-egu24-17464, 2024.

EGU24-17479 | Posters on site | HS2.4.3

MAKAHO: An interactive cartographic Tool for Trend Analysis of hydrological extremes in France  

Louis Héraut, Michel Lang, Benjamin Renard, Éric Sauquet, and Jean-Philippe Vidal

Analysing the significance of trends in hydrological variables across different components of the streamflow regime, from low flows to high flows, provides an overview of the state of a region in the context of ongoing global changes. This information is crucial for decision-making regarding adaptation but also for evaluating hydrological projections.

 

MAKAHO (MAnn-Kendall Analysis of Hydrological Observations) is an interactive cartographic visualization system designed to examine trends in hydrometric observations from the 232 stations belonging to the French Reference Hydrometric Network (Giuntoli et al., 2013). These stations show a high measurement quality, time series with a historical depth of over 30 years, and they crucially gauge near-natural catchments. The statistical test used for trend detection is a variant of the Mann-Kendall test accounting for first-order autocorrelation. The trend slope is provided by the Theil-Sen estimator.

 

The hydrological situation in France shows a marked contrast between the northern and southern regions. Between 1968 and 2020, 22 % of stations show a significantly trend in the annual maximum daily streamflow at the 90 % confidence level. Of these stations, 27 % exhibit an upward trend, with an average increase of 13 % per decade. Almost all of these stations are located in the northern part of the country.

 

This north-south divide is also visible for low flows, with the demarcation line extending further north. 39 % of stations show a decreasing trend in the annual minimum monthly discharge, with an average intensity of about 11 % per decade. The signal in the northern part of the country is less significant. The duration of low flows has significantly increased in the south, particularly in the southwest, with an average of more than ten days per decade, reaching almost a month in extreme cases.

 

The tool, developed using the R Shiny library, takes the form of an online graphical interface (https://makaho.sk8.inrae.fr/). It enables direct communication with the R Exstat package (https://github.com/super-lou/EXstat), which is essential for data aggregation and trend analysis. Calculations are performed on the fly, allowing greater customisation of analyses. MAKAHO users can choose the analysis period, the hydrological variable (from low flows to high flows), display time series for the variable of interest and extract summary sheets for a set of hydrometric stations. The interactive map and graphs allow switching from an overview to a detailed view of the results for each station. MAKAHO has been designed based on previous research projects involving stakeholders to encourage water managers to develop robust strategies for adapting to climate change and has received financial support from the French Ministry of Ecology.

 

Giuntoli, I., Renard, B., Vidal, J.-P., and Bard, A. (2013). Low flows in france and their relationship to large-scale climate indices. Journal of Hydrology, 482:105–118. https:/doi.org/10.1016/j.jhydrol.2012.12.038

How to cite: Héraut, L., Lang, M., Renard, B., Sauquet, É., and Vidal, J.-P.: MAKAHO: An interactive cartographic Tool for Trend Analysis of hydrological extremes in France , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-17479, https://doi.org/10.5194/egusphere-egu24-17479, 2024.

EGU24-18032 | ECS | Orals | HS2.4.3

Challenges in analysing and modelling extreme floods: The case study of Ahr catchment 

Bora Shehu and Axel Bronstert

The extreme weather conditions of July 2021 caused major flooding’s in multiple tributaries of the Meuse and Rhine rivers. Particularly the Ahr Valley in Germany was greatly affected, where exceptional damage and severe human loss was registered. Since then, several studies have been conducted to understand the extremity, the major driving forces, the particular mechanisms of this flood and the possible impacts of climate change on the generation of such an event. Here the main objective is to perform a hydrological analysis of the July 2021 Ahr event and discuss the challenges in modelling or analysing this event.

First, we show that particularly the rainfall field is associated with high uncertainty, as seen by the high variability between the different rainfall products available. The average areal rainfall volume can differ between products with as much as 50mm/day, which constitutes almost 55% of the rainfall volume estimated by Radolan. To capture the full uncertainty-range of the rainfall field, rainfall simulations conditioned both on radar and station observations are implemented.  

Next, based on rainfall simulations and reconstructed discharge data, runoff coefficients (Rc) are shown to be ranging between 0.6 to 1.2 (median 0.7). These values are clearly higher than expected in continental climate (Rc ~ 0.20-0.51) and the latest 100-year return flood observed in 2016 (with Rc ~ 0.4). The high lower range suggests, that the dominant processes have changed, with slower components of surface runoff shifting to faster ones. This agrees well with the observed traces of erosion, surface water and flow paths in parts of the catchment.

Lastly, the reconstructed discharge data are also subjected to uncertainty due to lack of observations and the non-representativeness of the stage-discharge curve during the flood wave. Hence, high Rc values do not only originate from underestimated rainfall but as well from possible overestimated flood volume. For this purpose, discharge was estimated with the Larsim model. As expected due to the change of the dominant processes, the pre-event parameter set underestimates considerably the flood volume, while the post-calibration one agrees better with the reconstructed data. On both cases, the computed runoff coefficient ranges between 0.4 to 0.7.

To conclude, extraordinary events such as the July 2021 in Ahr Catchment, are accompanied with high uncertainty and as such are difficult to be analysed or modelled. Nevertheless, the results dictate that the surface runoff played an important role in July 2021. At the same time, it is clear that the landscape still has a considerable retention effect, between 30% and 50%, even for such a heavy rainfall.

How to cite: Shehu, B. and Bronstert, A.: Challenges in analysing and modelling extreme floods: The case study of Ahr catchment, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-18032, https://doi.org/10.5194/egusphere-egu24-18032, 2024.

EGU24-18080 | ECS | Posters on site | HS2.4.3

Investigation of combined regional trends of extreme precipitation and temperature in southern Italy 

Gholamreza Nikravesh, Alfonso Senatore, and Giuseppe Mendicino

This contribution proposes an integrated analysis of climate regime trends in southern Italy (Calabria Region), focusing on both extreme precipitation and temperature events. Provided several precipitation and temperature observations available in the period 1955-2023 for a relatively dense monitoring network (approximately a rain gauge station per 110 km2 and a temperature station per 100 km2), four precipitation-related variables like total precipitation (PRCPTOT), maximum one-day precipitation (RX1day), maximum five-day precipitation (RX5day) and Consecutive Dry Days (CDD) were chosen. Also, three temperature-based variables were selected, i.e., the maximum of the maximum daily temperatures (TXx), the mean of the mean daily temperatures (Tmean), and the minimum of the minimum daily temperatures (TNn). The trends of these seven selected variables were assessed and combined through three approaches at the annual and seasonal scales, considering each available monitoring station (namely, 134 precipitation and 148 temperature stations). First, we combined PRCPTOT and RX1day to highlight which stations have an increased probability of both drought and flood risks, developing a novel integrated climate regime index (ICRI). Then, we considered the three temperature indices, TXx, Tmean, and TNn, to pinpoint stations that have experienced more robust rising trends. The third analysis combined PRCPTOT, RX1day and temperature (using alternatively TXx, TNn and Tmean) to investigate the compound risk of flood, drought and, to a certain extent, wildfires. The results indicate a rather homogeneous increase of all temperature-related variables, especially starting from 1990, and that since 1955, a considerable number of stations have experienced increasing trends for RX1day and falling trends for PRCTOT. Therefore, most of the territory of the region is more likely to confront water stress, flood and forest fires.

How to cite: Nikravesh, G., Senatore, A., and Mendicino, G.: Investigation of combined regional trends of extreme precipitation and temperature in southern Italy, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-18080, https://doi.org/10.5194/egusphere-egu24-18080, 2024.

EGU24-18942 | Orals | HS2.4.3

Changes in maximum and minimum runoff of Eurasian Arctic rivers during the climate change epoch 

Maria Kireeva, Dmitriy Magritskiy, and Natalia Frolova

The study is devoted to the analysis of daily time series of river runoff in the Arctic zone of Eurasia. Unique data on daily water discharges in the closing gauges of Arctic rivers were collected and processed in the package grwat (https://cloud.r-project.org/web/packages/grwat/index.html ), which identifies genetic components of runoff. As a result, 53 runoff characteristics were obtained for each of the 25 rivers flowing into the Arctic Ocean and the contribution of snowmelt, rainfall, and groundwater components to the total runoff was analyzed. Particular attention was paid to extreme characteristics - maximum water discharges of spring freshet, rain events and minimum 1, 5, 10-averaged discharges during summer and winter.
The study of maximum water discharges has shown that, in general there are trends of decreasing annual maximums for both large and medium-sized Arctic rivers. This trend, however, is not yet statistically significant everywhere. The most intensive decrease in maximums localized in the Northern Dvina, Ob, and Yenisei rivers, for which flow regulation by reservoirs has a significant impact. For the Kolyma, Yana and Indigirka rivers, there are periods of increase in maximums and their decrease lasting 5-7 years, with a general tendency to increase during 1960-2001 up to 15-20%.
In contrast, the minimum discharges with different averaging intervals increases by 25-56 % everywhere; this trend is presumably related to the general climate warming, increased infiltration and the role of groundwater flow, and for the rivers in the eastern part of the Arctic zone - to the degradation of permafrost.
The study also included analysis of the runoff signature transformation in Arctic zone by every year, as well as on average for the modern and historical period. The typing methodology consisted in classifying hydrographs according to two main features: a) exceedance of maximum discharge relative to the average annual discharge b) the share of flood runoff volume in the total annual runoff. The analysis showed a noticeable increase in the frequency of occurrence of smoothed hydrographs on the rivers of the Arctic zone of the Asia-Pacific region, for some basins the number of such years increased by 1.5-2 times (Polui, Turukhan, Ob rivers).

How to cite: Kireeva, M., Magritskiy, D., and Frolova, N.: Changes in maximum and minimum runoff of Eurasian Arctic rivers during the climate change epoch, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-18942, https://doi.org/10.5194/egusphere-egu24-18942, 2024.

EGU24-19269 | ECS | Posters on site | HS2.4.3

Dynamics of Intraseasonal Compound Whiplash Event: A Retrospective Analysis in India 

Aman Kumar and Dhyan Singh Arya

Hydroclimatic Whiplash events refer to the extreme variability characterising the rapid transitions from one hydroclimatic extreme to another, occurring in consecutive periods. Rapid transition from one extreme to the other. The compound consecutive extremes impact often exceeds the magnitude of individual events given their occurrence at distinct times. This study introduces a comprehensive investigation into Intraseasonal compound whiplash occurrences in India, focusing on the rapid shifts between drought/heat and pluvial conditions. The data used in the study is taken from IMD precipitation of 0.25˚ x 0.25˚ and temperature at 1˚x 1˚ for the time span 1901 to 2022. This study involves distinct thresholds for duration and intensity to identify the heat dry and wet events. Dry events are characterised by prolonged low rainfall and sustained minimum temperatures throughout the dry period. Conversely, wet events exhibit high intensity within relatively shorter durations. Emphasising the 70th percentile for temperature thresholds acknowledges that extreme conditions in each component aren't mandatory for a compound event's occurrence. Our study delves into the frequency of individual extremes and compound whiplash occurrences, calculating swing severity using mean temperature quantiles for warm/dry spells alongside precipitation anomalies. The Mann-Kendall test and Sen’s slope is used for the check frequency and severity evolution at the grid level. Results highlight diverse regions witnessing increasing trends in wet and dry events, signifying a notable surge in compound whiplash incidents. This is especially worrying in areas that have typically been dry because the increase in rain can disrupt the usual climate there. This concerning trend raises alarms for local ecosystems, water resources, and socio-economic activities. Recognising these evolving patterns is critical for making strategies and long-term planning in the recent climate variability.

How to cite: Kumar, A. and Arya, D. S.: Dynamics of Intraseasonal Compound Whiplash Event: A Retrospective Analysis in India, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-19269, https://doi.org/10.5194/egusphere-egu24-19269, 2024.

Hydroclimatic whiplash, a lagged compound hazard combined with preceding drought (flood) and following flood (drought), may induce significant environmental, hydrological, and socio-economic impacts worldwide. North America is one of the hot spots becoming susceptible to the transitions or shifts between two extremes. These compound events are also expected to become more frequent and intense in the future under climate change. To better understand the climate influence, overall decadal changes in climate variables and related hydroclimatic swing events need to be analyzed considering two components: anthropogenic external forcing and natural internal variability. External forcing is induced by anthropogenic activities imposing greenhouse gas emissions on the climate system, resulting in the signal of global warming. Internal climate variability (ICV), also termed climate noise, is an irreducible uncertainty induced by the chaotic nature thus unpredictable evolution of the climate system. In this study, we use four single-model initial-condition large ensembles (SMILEs) under historical and future forcing scenarios (RCP8.5), CanLEAD-EWEMBI, CanLEAD-S14FD, CanRCM4-LE, and GFDL-SPEAR, to quantify the relative role of external forcing and ICV on variations in compound dry-wet swing events across North America. The SMILE enables the robust quantification of the externally forced response and internal variability via computation of ensemble statistics, provided the ensemble size is large enough. On the virtue of this advantage, the standardized precipitation evaporation index is estimated to identify dry and wet spells and their transitions based on ensemble pooling and threshold-based event extraction methods. Frequency, intensity, transition time, transition intensity, and relative role of preceding and following spells, etc. are quantified for each warming period (1.5°C-4 °C global warming levels) and compared with those of the baseline period to investigate their projected changes and trends. The relative contribution of ACC and ICV to compound dry-wet spells is quantified by the ratio of changes and trends in the ensemble mean and the spread (standard deviation) among the ensemble members of each SMILE, respectively. The results of this study suggest that hydroclimatic swing events across North America are expected to become more frequent and intensified in a warmer climate, which is induced by significant emergence of external forcing. In addition, the transition time and transition intensity are projected to be more dominated by anthropogenic forcing over ICV than other characteristics indicating that more abrupt and severe shifts can occur in the future. The findings of this study support the necessity of developing appropriate measures for mitigating the anthropogenic forcing impact because it increases the risk of lagged compound floods and droughts that can lead to severe disasters in North America.

How to cite: Najafi, M. R., Na, W., and Grgas-Svirac, A.: Relative Contribution of External Forcing and Internal Climate Variability to Lagged Dry-Wet Swing Projections in North America, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-20728, https://doi.org/10.5194/egusphere-egu24-20728, 2024.

Desertification is a worldwide issue receiving broad attention due to deforestation, climate change, and land abuse. In India, nearly 81.4 million ha are undergoing the desertification process. A long-term assessment of the key drivers of desertification and land degradation (DLD) was done over the state of Jharkhand in the central highlands of India. The region is highly vulnerable to desertification and land degradation due to its unique geographical and climatic features, with 68.77% (5.48 Mha) of the total geographical area of 7.97 Mha undergoing DLD. This study aims to quantify desertification in Jharkhand using various satellite imageries and supervised classification using machine learning (ML) techniques. The results showed five distinct classes of DLD cases, i.e., severe, intense, moderate, light, and no desertification. The severe and intense class areas make up about 5.11 Mha (64.43%) of the total geographic area (TGA). The moderate and light classes of DLD make up 0.93 Mha (11.79%) and 1.40 Mha (17.73%) of TGA, respectively. Remarkably, the districts of Giridih, Gumla, Ranchi, Dumka, Jamtara, Deoghar, Garhwa, and Palamu are considered to be the most prone regions to DLD. This study will help to demonstrate the application of remote sensing techniques to quantify DLD-prone regions and severity over the regions, which can help policymakers manage the local administrative bodies and state government departments to demarcate the region to continuously monitor and lay policies to tackle desertification.

Keywords: Desertification, Central Highlands, GEE, Random Forest, Vulnerability, Machine Learning

How to cite: Mahato, T. and Kumar, M.: Understanding the Drivers of Desertification and Land Degradation (DLD) over the Central Highlands of India, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-20795, https://doi.org/10.5194/egusphere-egu24-20795, 2024.

EGU24-82 | ECS | Orals | HS7.5

Proposal for a new meteotsunami intensity index. 

Clare Lewis

Atmospherically generated coastal waves labelled as meteotsunami are known to cause destruction, injury and fatality due to their rapid onset and unexpected nature. These progressive shallow water waves with a period of 2 to 120 minutes tend to be initiated by sudden pressure changes (±1 mb over a few tens of minutes) and wind stress from moving atmospheric systems out on the open water. As these waves arrive at the shoreline they are amplified by localised resonances. Unlike other related coastal hazards such as tsunami, there exists no standardised means of quantifying this phenomenon which is crucial for understanding its impacts and to establish a shared language and framework for meteotsunami analysis and comparison.

In this study, we present a new 5-level Lewis Meteotsunami Intensity Index (LMTI) primarily trialled in the United Kingdom (UK) but designed for global applicability. A comprehensive dataset of meteotsunami events recorded in the UK were verified and applied to the index which yielded results that identified a predominant occurrence of Level 2 or moderate intensity meteotsunamis (69%), with distinct hotspots identified in Southwest England and Scotland. Further trial implementation and calibration of the LMTI in a global capacity revealed its adaptability to other meteotsunami prone regions facilitating the potential for further research into preparedness and hazard mitigation strategies.

How to cite: Lewis, C.: Proposal for a new meteotsunami intensity index., EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-82, https://doi.org/10.5194/egusphere-egu24-82, 2024.

EGU24-611 | ECS | Posters virtual | HS7.5

Hydrological Analysis of Monsoon Rain Spells in the Indian Ganga Basin over the Last Century 

Amit Kumar Maurya, Somil Swarnkar, and Shivendra Prakash

The Indian Ganga Basin (IGB) is a highly prominent socioeconomic region in the Indian subcontinent. The IGB supports about 500 million individuals by providing sufficient freshwater for agro-industrial activities, mainly through the contribution of Indian Summer Monsoon (ISM) rainfall, which accounts for around 85% of the total rainfall received throughout the IGB. Any modifications in ISM patterns would substantially impact the availability of freshwater, and consequently, the socio-economic activities of the IGB region will be affected. This study aims to evaluate the historical changes in the monsoon rainfall characteristics from 1901 to 2019. Here, we conducted a detailed rainfall analysis in different sub-basins of the IGB where changes in monsoon rain spells are most noticeable and examined the hydrological extremes. We found that monsoon rain spell peaks have significantly increased across the major sub-basins of the IGB after 1960, implying the increased probability of flash flood hazards. At the same time, the monsoon rain spell has been depleted across the IGB after 1960, especially in the lower Indo-Gangetic plains. These results imply a rise in the occurrence of droughts. In addition, our interpretations also indicate a growing potential for combined hydrological extremes in the IGB. Further, the continuous rise in temperature and human-induced perturbations might exacerbate the existing extreme hydrological conditions. Thus, the findings of this study will be beneficial in implementing river basin management methods to assess the complex patterns of major hydrological catastrophes in the IGB.

How to cite: Maurya, A. K., Swarnkar, S., and Prakash, S.: Hydrological Analysis of Monsoon Rain Spells in the Indian Ganga Basin over the Last Century, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-611, https://doi.org/10.5194/egusphere-egu24-611, 2024.

EGU24-669 | ECS | Orals | HS7.5

Assessing Local Community Vulnerability to Landslides and Floods: A Household Survey Approach in North-Western Rwanda  

Clemence Idukunda, Caroline Michellier, Emmanuel Twarabamenye, Florence De Longueville, and Sabine Henry

North-Western Rwanda's hilly and mountainous topography, high elevation, frequent torrential rainfall, and high population density render it highly susceptible to landslides and floods. A comprehensive understanding of community vulnerability to these hazards is crucial for effective risk assessment and mitigation strategies. To address data scarcity in the region, this study is based on a household survey approach that incorporates hazard-specific variables to compare vulnerability across three hazard categories: landslides, floods, and a combination of both. The survey encompasses 904 households across 50 cells (local administrative units), purposively selected according to hazard susceptibility distribution. Principal Component Analysis (PCA) was applied to derive a contextualized Social Vulnerability Index (SoVI). Five principal components accounting for 73.2% of the variance were identified. The first component, contributing 23.4%, highlights the vulnerability associated with unplanned settlements and low income. The second component, representing 19.5% of the variance, emphasizes demographic and social factors. The third component (12.6% of the variance) points to the vulnerability of households solely reliant on agriculture for their income. The fourth component (9% variance) is associated with land ownership, with households lacking land assets experiencing lower vulnerability. The fifth component (8.7% variance) underlines the relevance of household structure variables, indicating the high vulnerability of single-person households. SoVI scores classified 19 cells in the very high or high vulnerability category, predominantly those prone to landslides. These highly vulnerable cells are concentrated in the Northern Province, emphasizing the need to prioritize interventions in this region, such as effective land use planning and livelihood improvement strategies. This study provides a comprehensive vulnerability assessment and valuable insights for prioritizing interventions. The inclusion of hazard-specific variables and a comparative vulnerability approach across areas susceptible to landslides, floods, and both hazard types enhances the specificity and applicability of the findings. These insights are invaluable for local policymakers and disaster prevention and management authorities, enabling them to develop context-specific strategies to improve community resilience and reduce vulnerability to natural hazards.

Keywords: Community Vulnerability, Landslides, Floods, Noth-Western Rwanda, Social Vulnerability Index

How to cite: Idukunda, C., Michellier, C., Twarabamenye, E., De Longueville, F., and Henry, S.: Assessing Local Community Vulnerability to Landslides and Floods: A Household Survey Approach in North-Western Rwanda , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-669, https://doi.org/10.5194/egusphere-egu24-669, 2024.

EGU24-677 | ECS | Orals | HS7.5 | Highlight

A new climate impact database using generative AI 

Ni Li, Wim Thiery, Jakob Zscheischler, Gabriele Messori, Liane Guillou, Joakim Nivre, Olof Görnerup, Seppe Lampe, Clare Flynn, Mariana Madruga de Brito, and Aglae Jezequel

Storms, heat waves, wildfires, floods, and other extreme weather climate-related disasters pose a significant threat to society and ecosystems, which in many cases is being aggravated by climate change. Understanding and quantifying the impacts of extreme weather climate events is thus a crucial scientific and societal challenge. Disaster databases are extremely useful for establishing the link between climate events and socio-economic impacts. However, publicly available data on impacts is generally scarce. Apart from existing open disaster databases such as EM-DAT, robust data on the impacts of climate extremes can also be found in textual documents, such as newspapers, reports and Wikipedia articles. Here we present a new climate impact database that has been built based on multiple public textual entries using a pipeline of data cleaning, key information extraction and validation. In particular, we constructed the database by using the state-of-the-art generative artificial intelligence language models GPT4, Llama2 and other advanced natural language processing techniques. We note that our dataset contains more records in the early time period of 1900-1960 and in specific areas such as than the benchmark database EM-DAT. Our research highlights the opportunities of natural language processing to collect data on climate impacts, which can complement existing open impact datasets to provide a more robust information on the impacts of weather and climate events.

How to cite: Li, N., Thiery, W., Zscheischler, J., Messori, G., Guillou, L., Nivre, J., Görnerup, O., Lampe, S., Flynn, C., Madruga de Brito, M., and Jezequel, A.: A new climate impact database using generative AI, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-677, https://doi.org/10.5194/egusphere-egu24-677, 2024.

Climate change, an increasing urban population, and poor urban planning have increased flood-risk and the accompanying solid waste challenge in many coastal urban areas in developing countries. These challenges are more pronounced in informal settlements because: (a) they are often built on environmentally fragile locations such as river banks and coastal shores with high exposure to floods, (b) high poverty levels among residents resulting in low adaptive capacity, and (c) marginalisation of these localities emanating from their non-recognition in the larger city framework. Against this background, flood-risk assessments and response initiatives in these areas have primarily been informed by scientific approaches such as geographical information systems, without adequate incorporation of other forms of knowledge. Using the case of the coastal city of Durban, South Africa, our project explores the benefits of combining perspectives from different knowledge systems in understanding flood-risk and the accompanying solid waste challenge in urban informal settlements, towards developing solutions that are based on contextual and experiential aspects. Methodological techniques used include interviews and workshops with key experts and with informal settlement residents, and extensive reviews of literature.  Emerging findings show that holders of scientific, practitioner, and local knowledge vis-à-vis flood risk and waste management are active in the selected case study informal settlement. They have, in isolated cases, collaborated particularly around a) generation and distribution of flood early warnings, b) river clean-up initiatives, and c) catchment rehabilitation projects, with clear benefits for flood resilience and solid waste management. We find that there is need for a clear framework for integrating knowledge systems towards flood resilience and solid waste management in these contexts and the project has developed a draft framework. Integrating knowledge systems will: i) ensure the participation of different actors in mapping flood risk thereby creating a sense of ownership and ensuring uptake of and support for solutions crafted to deal with flood risk and the solid waste challenge; and ii) open up opportunities for coordinated support from various actors for a range of decisions around flood risk response preparation, flood and waste infrastructural design and mitigation of waste-induced flood destruction of infrastructure.

How to cite: Johnson, K. and Nyamwanza, A.: Integrated knowledge systems towards flood resilience and sustainable solid waste management in South African urban informal settlements, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-867, https://doi.org/10.5194/egusphere-egu24-867, 2024.

EGU24-2163 | ECS | Posters on site | HS7.5

Climate risk-reduction potential of gridded precipitation data for agricultural index-based insurance development 

Sarvarbek Eltazarov, Ihtiyor Bobojonov, and Lena Kuhn

Index insurance has been introduced as an innovative and potential solution to mitigate several challenges caused by climate change in the agricultural sector. Despite the promising potential of index insurance, dissemination in developing countries is slow due to a lack of reliable weather data, which is essential for the design and operation of index insurance products. The increasing availability of model- and satellite-based data could ease the constraints of data access. However, their accuracy and suitability have to undergo a thorough assessment. Therefore, this study statistically and financially analyzes and compares the risk reduction potential of index insurance products designed employing various in-situ-, model- and satellite-based precipitation products (e.g., CMOPH, CPC, IMERG, GSMaP, MERRA, GLDAS, ERA5, PERSIANN, MSWEP, and MERRA2). This study employed county-level spring wheat yield data between 1982 and 2018 from 56 counties overall in Kazakhstan and Mongolia. The results showed that in the majority of cases in both countries, the hedging effectiveness of index insurance products designed based on IMERG is the highest. Moreover, among other data sources, the index insurance products designed using the PERSIANN, GLDAS and FLDAS showed higher risk reduction potential. Overall, this study highlights that satellite- and model-based precipitation products have higher accuracy and potential for index insurance design and operation than in-situ-based precipitation data.

How to cite: Eltazarov, S., Bobojonov, I., and Kuhn, L.: Climate risk-reduction potential of gridded precipitation data for agricultural index-based insurance development, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-2163, https://doi.org/10.5194/egusphere-egu24-2163, 2024.

A severe and complex, polygenetic flood event occurred in Muktinath area of Mustang, Nepal on the evening of August 13, 2023 causing significant damage to property and infra-structures worth approximately of USD 7.4 million at Kagbeni Village, which is nestled along both banks of Kagkhola, a major left bank tributary of the Kali Gandaki River. About 29 houses, 1 motorable bridge, 1 steel truss bridge and 3 temporary bridges were destroyed, while more than 25 cows and other livestock were killed. Fortunately, human lives were spared because the community was warned to move to safety before the mud and sludge hit the village. A study was conducted in order to know what had caused this unusual flash-flood in Mustang. Kagbeni (2810 m) lies in the north Himalayan, rain-shadow area and normally receives few rainfall (<300 mm/yr). However, for several years, the trend (confirmed by local residents) has been towards increased rainfall, leading to more landslides and floods. Although rainfall data from the nearest monitoring station, Jomsom (2720 m), shows that rainfall was high, there is not detailed information about the rainfall amount at Jhong (3600 m), and Muktinath  (3760 m), source area of Kagbeni flood. From the video taken there (Jhong, Muktinath) during this flash-flood event (hyper-concentrated flow), it can be concluded that it was a landslide lake outburst flood. However due to the difficult terrain and inaccessible path, it has not yet been possible to visit the source area of the landslide in detail. Heavy rainfall over a short period and flash-flood-like disasters are becoming a trend in the mountain regions in Nepal. Furthermore, this part of Mustang is fragile (Spiti shales), and heavy rainfalls have an immediate impact, since there is little soil to absorb the excess water. Former studies have also shown that temperature in Mustang is rising which is causing the monsoon air to move northward and upward. As a result, more rainfall is taking place in Trans-Himalayan areas like Mustang and Manang (North of Annapurna Himal, 8091 m). Therefore, it is believed that climate change and the rise in temperature could be the significant reasons for heavy rainfall that caused such a flash-flood in Kagbeni, Mustang. On the other hand, people are inviting disaster in Kagbeni by settling on the very low terraces or in flood-plains and encroaching on the bed of the local Kagkhola. Given the fragile geology of upstream area of Kagkhola, ongoing anthropogenic activities (agriculture and tourism) and the effect of climate change, the possibility of flash floods reoccurring in the future at Kagbeni remains high. Sadly, locals at Kagbeni have already started rebuilding houses damaged by the recent Kagbeni flood and continue to live in potentially threatened flood plains.   

How to cite: Fort, M., Gurung, N., Arnaud-Fassetta, G., and Bell, R.: Retrospect of the polygenetic Kagbeni flood event (August 13, 2023) in Mustang, Nepal. Are rapid hydromorphological processes relays and sediment cascades in the catchment well taken into account in the risk equation?, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-2563, https://doi.org/10.5194/egusphere-egu24-2563, 2024.

EGU24-3076 | ECS | Orals | HS7.5

Assessing Surface Drainage Efficiency in Urban Pluvial Flood Hazard and Risk Mitigation: A Case Study of Braunschweig City 

Shahin Khosh Bin Ghomash, Heiko Apel, Kai Schroeter, and Max Steinhausen

Due to rapid urbanization and the increase of extreme precipitation events driven by climate change, urban areas have experienced more frequent and severe pluvial floods in recent years. This trend is anticipated to continue in the future. One of the causes of flooding in these urban zones is the limited effectiveness or temporary reduction in surface drainage capacity, even when storm sewers adhere to technical standards. A notable instance was the June 2023 flooding in Braunschweig, situated in Lower Saxony, Germany, where the city received 60 liters per square meter of rainfall within a short time span, largely excessing sewer system capacity and leading to widespread inundation.

This research investigates the impact of implementing diverse strategies aimed at expanding urban drainage capacity to mitigate pluvial flood risk in Braunschweig. To accomplish this, a moderately detailed hydrodynamic model for the city was set up using the RIM2D hydrodynamic model, allowing for quick computational processing times which enabled the exploration of various measures through sensitivity analysis. The setup involved employing a high-resolution digital elevation model and various remote sensing data for land classification. The model incorporated high-resolution precipitation radar data from the 2023 event and additional precipitation scenarios of varying occurrence probabilities. Validation of the model against available event data and existing flood hazard maps specific to Braunschweig was conducted.

The validated model was then utilized to assess the effectiveness of different surface de-sealing scenarios within the city. These scenarios aim to enhance drainage capacity by means of increased infiltration to complement the existing sewer drainage system. The evaluation of these de-sealing scenarios focused on reducing surface inundation and anticipated damage, serving as a foundational aspect for conducting a cost-benefit analysis and detailed planning. This analysis can contribute to future-oriented urban pluvial flood risk management plans for the city.

How to cite: Khosh Bin Ghomash, S., Apel, H., Schroeter, K., and Steinhausen, M.: Assessing Surface Drainage Efficiency in Urban Pluvial Flood Hazard and Risk Mitigation: A Case Study of Braunschweig City, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-3076, https://doi.org/10.5194/egusphere-egu24-3076, 2024.

EGU24-3170 | ECS | Orals | HS7.5

Influences of moisture transport on changes in extreme precipitation in Central Plains Urban Agglomeration, China  

Yufan Chen, Shuyu Zhang, Deliang Chen, and Junguo Liu

In recent decades, the Central Plains Urban Agglomeration of China (CPUA) has faced recurring extreme precipitation events (EPEs), causing severe flood disasters, endangering residents, and inducing significant property losses. This study examines the spatiotemporal patterns of summer EPEs in the CPUA from 1961 to 2022. The Hybrid Single-Particle Lagrangian Integrated Trajectory model was used to trace the water vapor trajectories associated with these events and the atmospheric circulations linked to diverse moisture transports were identified. The findings reveal an overall increase in both the intensity and frequency of summer EPEs, particularly intensifying over urban areas while displaying more frequent yet weaker precipitation in mountainous regions. Moisture contributing to these events originates from sources including Eurasia, the northern and southern Western North Pacific, as well as the Bay of Bengal and South China Sea. Notably, contributions from Eurasia and the Northern Western North Pacific have increased, whereas those from the Bay of Bengal and the South China Sea have decreased. Events fueled by Western North Pacific moisture show intensified impacts on urban areas, driven by anomalous anticyclonic patterns and the formation of the Huang-Huai cyclone, inducing vigorous convective activity over the CPUA. The proliferation of the Western North Pacific Subtropical High facilitates warm air transport, converging with colder air from inland areas, resulting in extreme precipitation.

How to cite: Chen, Y., Zhang, S., Chen, D., and Liu, J.: Influences of moisture transport on changes in extreme precipitation in Central Plains Urban Agglomeration, China , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-3170, https://doi.org/10.5194/egusphere-egu24-3170, 2024.

EGU24-3602 | ECS | Posters on site | HS7.5

FLOODGAMA: the new INUNGAMA. Beyond a flood events database for Catalonia 

Montserrat Llasat-Botija, Maria Carmen Llasat, Dimitri Marinelli, Raül Marcos, Carlo Guzzon, and Albert Díaz

Floods represent a complex natural hazard, influenced not only by meteorological factors but also by geophysical aspects such as terrain topography, social factors such as the value of exposed assets, and cultural factors like risk awareness. For this reason, the study of these phenomena requires a holistic approach. This requires the correct organization of the information. In addition, given that the information comes from different sources, the traceability of the data must also be contrasted and preserved in order to guarantee its quality and robustness. Databases make it possible to conserve and document historical information, to analyze it and to support smart flood risk management.

With this objective in mind, in 2000 the GAMA team developed the INUNGAMA flood database, following the example of other natural hazards databases. This communication will present the new version of this database, FLOODGAMA, and the main results of its analysis. FLOODGAMA contains information on 456 flood events that affected Catalonia (NE of Spain), between 1900 and 2020, which have caused 1,253 casualties. The events are classified according to the impacts. It includes linked tables with information on event dates, descriptions, fatalities, economic damages, affected municipalities, recorded rainfall and recorded flow. Other tables contain historical marks, codifications and the geographical information of municipalities, counties, basins and rivers, as well as meteorological stations. Its structure has been simplified and standardized with Python and migrated to PostgreSQL (PostGIS) from an ACCESS format. The new database allows for more general and straightforward analysis, introduces GIS tool compatibility, and simplifies the addition of new data and new data sources. This last point has been one of the key points in this transformation as it will provide the database with the flexibility to respond to the challenges posed by the digital transformation that is currently taking place and as a tool for the improvement of adaptation.

The contribution shows the structure of this flood database and the results obtained after its analysis that allows the characterization of flood events in Catalonia.

This research has been done in the framework of the C3Riskmed project, Grant PID2020-113638RB-C22 funded by MCIN/AEI/10.13039/501100011033 and Flood2Now project, Grant PLEC2022-009403 funded by MCIN/AEI/10.13039/501100011033 and by the European Union Next Generation EU/PRTR.

How to cite: Llasat-Botija, M., Llasat, M. C., Marinelli, D., Marcos, R., Guzzon, C., and Díaz, A.: FLOODGAMA: the new INUNGAMA. Beyond a flood events database for Catalonia, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-3602, https://doi.org/10.5194/egusphere-egu24-3602, 2024.

EGU24-3951 | ECS | Orals | HS7.5

Multi-day precipitation extremes ranking and their association with atmospheric moisture fluxes over India 

Tomás Gaspar, Ricardo M. Trigo, Alexandre M. Ramos, Akash Singh Raghuvanshi, Ana Russo, Pedro M.M. Soares, Tiago Ferreira, and Ankit Agarwal

The Indian subcontinent is characterized by a pronounced summer monsoon season with substantial rainfall from June to September and a less intense autumn monsoon, albeit both posing major challenges to the densely populated regions through flash floods and landslides. During monsoons, different regions of India are affected by extreme precipitation events with distinct durations and triggered by several mechanisms. Here, considering 10 different regions of India characterized by different climatic regimes, we apply an objective ranking of extreme precipitation events, across various time scales, ranging from 1 to 10 days, making use of a high-resolution daily precipitation dataset covering the entire Indian territory from 1951 to 2022. The results confirm that the method accurately detects and ranks the most extreme precipitation events in each region, providing information on the daily evolution of the magnitude (and spatial extent affected) of high precipitation values in each region. Moreover, results show that top rank events can be associated with different types of storms affecting the four main coastal regions of India. In particular, some top rank events can be critically linked to long duration events (e.g., 10 days), which can be missed in ranks for shorter duration (e.g., 1-3 days) periods, thus stressing the need to employ multi-day precipitation extremes ranking. Finally, an in-depth analysis of the large-scale atmospheric circulation and moisture transport is presented for the top 10-day events affecting four coastal regions of India. Overall, we are confident that our findings are valuable in advancing disaster risk reduction strategies, optimizing water resource management practices, and formulating climate change adaptation strategies specifically tailored for the Indian subcontinent.

 

R.M.T., A.R., S.P. and A.T.M. thank Fundação para a Ciência e a Tecnologia (FCT) I.P./MCTES through national funds (PIDDAC) – UIDB/50019/2020 (https://doi.org/10.54499/UIDP/50019/2020) and LA/P/0068/2020 (https://doi.org/10.54499/LA/P/0068/2020). A.R. and R.M.T. thank also FCT (https://doi.org/10.54499/2022.09185.PTDC, http://doi.org/10.54499/JPIOCEANS/0001/2019, https://doi.org/10.54499/DRI/India/0098/2020). A.R. was supported by FCT through https://doi.org/10.54499/2022.01167.CEECIND/CP1722/CT0006.

 

How to cite: Gaspar, T., M. Trigo, R., M. Ramos, A., Singh Raghuvanshi, A., Russo, A., M.M. Soares, P., Ferreira, T., and Agarwal, A.: Multi-day precipitation extremes ranking and their association with atmospheric moisture fluxes over India, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-3951, https://doi.org/10.5194/egusphere-egu24-3951, 2024.

EGU24-5587 | ECS | Orals | HS7.5

Socio-Economic Vulnerability assessment and validation in Seoul, South Korea  

Chi Vuong Tai, Dongkyun Kim, Soohyun Kim, Yongchan Kim, Hyojeong Choi, and Jeonghun Lee

Vulnerability is regarded as a crucial element in disaster risk reduction, garnering increasing attention from researchers. However, these assessments typically conclude with the spatial representation and analysis of vulnerability index values, with very few attempts made on vulnerability validation. This study has employed Principal Component Analysis (PCA) algorithm for the entire 38 selected socio-economic features, resulting in 9 principal components (or factors) to estimate Socio-Economic Vulnerability Index (SEVI). The results reveal consistent vulnerability levels in over half of the dongs (administrative units), compared with SEVI estimated from a subjective weighting scheme based on expert experience. Meanwhile, the remaining dongs exhibit a change in only one level of vulnerability. SEVI values and ranks from PCA were subsequently internally validated through global uncertainty and sensitivity analyses using Monte Carlo method. The vulnerability scores of all input features were randomly generated based on their fitted probability distribution functions, serving as input parameters for 39,936 Monte Carlo simulations. The median statistic was employed to evaluate the vulnerability uncertainty based on both bias of estimated SEVI values and ranks in comparison with simulated data. The findings from this analysis revealed that medium-low and medium vulnerability levels tend to be underestimated, while medium-high and high levels primarily witness an overestimation tendency. The bias in SEVI ranks was further employed to assess the vulnerability uncertainty. In the sensitivity test, a tornado diagram was created to illustrate the explanation of each feature to the overall SEVI variability. The results indicate that the feature with highest explanation of SEVI variability is the number of families with only children and a mother, accounting for more than 5%. The methodology employed in this study is applicable to areas with limited social and economic data sources. Based on our findings, we suggest that the areas with low bias on SEVI values or ranks are reliable for developing disaster risk mitigation strategies, while other areas require further consideration. Additionally, the results from the sensitivity test provide valuable support for future research when selecting input features for socio-economic vulnerability assessment.

Acknowledgement:

This study was supported by: (1) The National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. NRF-2022R1A4A3032838) (50 % grant); (2) Korea Environment Industry & Technology Institute (KEITI) through R&D Program for Innovative Flood Protection Technologies against Climate Crisis Project, funded by Korea Ministry of Environment (MOE) (RS-2023-00218873) (50 % grant).

How to cite: Vuong Tai, C., Kim, D., Kim, S., Kim, Y., Choi, H., and Lee, J.: Socio-Economic Vulnerability assessment and validation in Seoul, South Korea , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5587, https://doi.org/10.5194/egusphere-egu24-5587, 2024.

EGU24-5774 | Orals | HS7.5

myDewetra-VOLTALARM: a transboundary impact-based early warning system increasing resilience of Volta basin communities against hydrometeorological hazards 

Anna Mapelli, Andrea Libertino, Giulia Ercolani, Mirko D'Andrea, Nicola Testa, Matteo Darienzo, Simone Gabellani, Marco Massabò, Rafatou Fofana, Salifou Dene, Boukary Niampa, Maxime Teblekou, and Ramesh Tripathi and the Voltalarm member states national agencies

The Volta Basin, spanning six countries in West Africa, faces significant challenges from both floods and extreme precipitation. To address these challenges, the myDewetra-VOLTALARM system was developed as a collaborative transboundary early warning system (EWS) through the joint efforts of an international Consortium, composed by the Volta Basin Authority (VBA), the Global Water Partnership for West Africa (GWP-WA) and the World Meteorological Organization (WMO), and national institutions of the six riparian countries.  

myDewetra-VOLTALARM embraces an impact-based forecasting approach, focusing on the potential consequences of severe hydrological events on vulnerable communities. This is achieved through state-of-the-art hydro-meteorological modelling chain generating precipitation and discharge forecast with lead times of up to five days, coupled with impact assessment tools that translate these forecasts into actionable warnings based on real-time risk information for sectors like civil protection, agriculture and livelihoods, protected areas. By focusing on potential impacts,  myDewetra-VOLTALARM empowers stakeholders to make risk-informed decisions and implement timely mitigation actions, thereby reducing vulnerabilities and enhancing community resilience. The strength of myDewetra-VOLTALARM hinges on the collaboration, built-up through the implementation process, among the riparian countries, fostering data exchange and enabling a comprehensive understanding of hydrological dynamics across the entire basin. Harmonized risk assessments lead to consistent warning products and mitigation strategies, while the publication of the results on the open-source  myDewetra-VOLTALARM platform ensures transparency and accessibility for all stakeholders. 

A cornerstone of myDewetra-VOLTALARM's impact is the co-produced flood and heavy rainfall impact bulletin, issued jointly by national and regional authorities twice per week. This bulletin provides critical information, enriching and validating the model results with the expertise and local information/measurements of the national institutions, on which the Volta Basin Authority bases its advisories, tailored to specific locations and sectors. The Flood and Heavy Rainfall Impact Bulletin ensures a consistent flow of information at the basin scale and it integrates in the existing national procedures for early warning and civil protection, allowing all the stakeholders to stay informed and adapt their preparedness measures as the hydrometeorological situation evolves. 

myDewetra-VOLTALARM serves as a model for effective early warning systems in shared river basins. Its impact-based forecasting, transboundary cooperation, and co-produced Flood and Heavy Rainfall Impact Bulletin hold the potential to significantly reduce the impacts of floods and extreme precipitation, contributing to a more resilient and sustainable future for the Volta Basin communities.

How to cite: Mapelli, A., Libertino, A., Ercolani, G., D'Andrea, M., Testa, N., Darienzo, M., Gabellani, S., Massabò, M., Fofana, R., Dene, S., Niampa, B., Teblekou, M., and Tripathi, R. and the Voltalarm member states national agencies: myDewetra-VOLTALARM: a transboundary impact-based early warning system increasing resilience of Volta basin communities against hydrometeorological hazards, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5774, https://doi.org/10.5194/egusphere-egu24-5774, 2024.

EGU24-6088 | ECS | Posters on site | HS7.5

Modelling and Prediction of Unprecedented Heavy Rainfall Event Over North India  

Rohtash Saini and Raju Attada

Widespread and multi-day heavy rainfall events, recorded during 08-09 July 2023 in northwest India, significantly impacted Himachal Pradesh, Punjab, and the Chandigarh region. These events resulted in devastating floods and extensive landslides, causing a substantial loss of lives and properties. Understanding such extreme weather phenomena is imperative for enhancing predictive capabilities and mitigating associated impacts. However, due to the complex topography of the Himalayas and limited observational data, poses challenges for investigating precipitation extremes. Against the background, in this study, we employ the Weather Research and Forecasting (WRF) model to investigate the atmospheric processes that led to unprecedented extreme precipitation. The innermost domain is configured with a horizontal grid spacing of 3 km, successfully reproduces the observed extreme rainfall. To assess the performance of different microphysics schemes in capturing key characteristics associated with heavy rainfall events, sensitivity experiments were conducted with five distinct schemes. Preliminary findings reveal that the Goddard microphysics scheme demonstrates good agreement with observations, closely followed by the Thompson scheme. Statistical analyses, including skill scores, further suggest that the Goddard microphysics scheme skillfully simulates the observed rainfall, displaying robust reflectivity values exceeding 35 dBZ in the core regions. The strong reflectivity indicates substantial hydrometeor concentrations, suggesting potential locations of deep convective activity associated with heavy rainfall. Detailed results of simulating the rainfall extremes over northwest India, along with feasible mechanisms influencing atmospheric conditions during extreme will be comprehensively discussed.

How to cite: Saini, R. and Attada, R.: Modelling and Prediction of Unprecedented Heavy Rainfall Event Over North India , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-6088, https://doi.org/10.5194/egusphere-egu24-6088, 2024.

EGU24-6148 | ECS | Posters on site | HS7.5

Functionality assessment of road network combining flood roadworthiness and graph topology 

Ke He, Maria Pregnolato, Neil Carhart, Jeffrey Neal, and Raffaele De Risi

In the realm of critical infrastructure, the road network plays an indispensable role in facilitating daily activities, communication, and economic interactions. However, it remains susceptible to the persistent challenge of flood hazards, leading to both structural and non-structural damages (e.g., physical collapse and service interruption). In normal flood disasters, physical collapse may not occur, but service interruptions often occur. Such disruptions manifest in the form of increased travel distances, prolong the travel times, and, in severe cases, complete travel impossibility. This has resulted in a reduction in transportation efficiency, leading to an increase in the social cost of transportation.

This study presents a novel approach that integrated flood hazard, transportation network topology, and vehicle vulnerability to evaluate the functionality of road network. A severity factor is defined to assess the accessibility of expected links (roads and bridges), considering different vehicle types such as cars and SUVs. Then, this study analyses the overall road network functionality loss under varying flood return periods by evaluating the severity of each network link based on the different types of vehicles. Identification of links with the lowest functionality allows for the assessment of the entire network’s performance using topology-based measures, including the average node degree, average clustering, average shortest path, and reachable areas (isochrones). This research employs the transportation network of Bristol, UK, as a case study to investigate the dynamic relationship between the network status and vehicle typology in the context of flooding events. Findings reveal a discernible correlation, wherein the resilience of the network in influenced by the specific characteristics of different vehicle types. Notably, SUVs emerge as inherently more resistant to flood-related disruptions compared to conventional cars.

The insights presented in this paper hold significant implications for the development of robust mitigation strategies geared towards bolstering the resilience of road networks and optimizing the rerouting of emergency response vehicles in flood-prone areas. By elucidating the interplay between vehicle characteristics, network functionality, and flood impacts, the research provides a foundation for informed decision-making in the formulation and implementation of effective preparedness measures. The outcomes of this study offer a strategic roadmap for authorities and policymakers, enabling them to proactively address the challenges posed by future flood events and enhance the overall adaptability and responsiveness of road networks in emergency situations.

How to cite: He, K., Pregnolato, M., Carhart, N., Neal, J., and De Risi, R.: Functionality assessment of road network combining flood roadworthiness and graph topology, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-6148, https://doi.org/10.5194/egusphere-egu24-6148, 2024.

EGU24-7026 | ECS | Orals | HS7.5

Characteristics of Disaster-causing Heavy Rainfall in Taipei City and Its Application 

Chi-June Jung, Radiant Rong-Guang Hsiu, Yu-Cheng Kao, Mon-Liang Chiang, Wen-Bin Hung, Jing-Ting Wang, and Ben Jong-Dao Jou

The most challenging weather phenomenon for disaster response in Taipei City is localized short-duration heavy rainfall. The capacity of each administrative district to withstand rainfall intensity varies, leading to incidents of flooding even when the rainfall falls short of the designed protection standard of 78.8 mm/h for drainage systems. To enhance disaster response, the Taipei City Fire Department conducts investigations and reports based on rainfall conditions. By integrating the intelligence and reporting system and raising the dispatching standard from 20 to 40 mm/h, the "Heavy Rainfall Response Process Improvement" project has successfully reduced response operation time and alleviated service burdens, advocating for adopting higher standards.

This study explores the correlation between intense rainfall and disaster occurrences, examining thunderstorm events that caused significant flooding in over three administrative districts. The study compares the earliest reported flooding time in each district with the corresponding rainfall, revealing that several districts experienced flooding with less than 60 mm/h of rainfall at the onset, indicating heightened vulnerability. Additionally, the study delves into the relationship between rainfall patterns and disaster potentials. When it accumulates 40 mm of rainfall within 30 minutes, there is a 63% chance of reaching 60 mm accumulation in the following 10 to 20 minutes. This analysis underscores the potential application of cumulative rainfall within the first 30 minutes for predicting subsequent rainfall trends and issuing disaster warnings.

How to cite: Jung, C.-J., Hsiu, R. R.-G., Kao, Y.-C., Chiang, M.-L., Hung, W.-B., Wang, J.-T., and Jou, B. J.-D.: Characteristics of Disaster-causing Heavy Rainfall in Taipei City and Its Application, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-7026, https://doi.org/10.5194/egusphere-egu24-7026, 2024.

EGU24-7347 | Posters on site | HS7.5

Impacts comparison by using different hydraulic models on the 2011 flood in Thailand 

Morgane Terrier and Mathis Joffrain

The 2011 flood event in Thailand was devastating both in terms of lives and economic losses. Following this event, the (re)insurance industry have deeply transformed its underwriting practices and used new modeling tools, both external and internal.

A loss is linked both to hazard and sites characteristics. As an insurer's exposure changes, losses for the same event can differ greatly from past observations. Therefore, hazard maps representing a past event can be used to estimate losses as of today.

Building an internal flood risk model requires to create a large set of spatial grids of flood depth. The water depth spatialisation, based on the water level of identified rivers, is a crucial part of the modeling and called the hydraulic modeling.

This poster will :

(i) the use of two hydraulic models to obtain a flood footprint: The software Super-Fast Inundation of CoastS (SFINCS) (Leijnse et al., 2021), a 2D open-source fast numerical model, and LISFLOOD-FP (Bates, 2004).

(ii) calculate insured losses on a fictive portfolio in Thailand using these two models with the same inputs.

(iii) describe and explain the discrepancies steming from (ii).

How to cite: Terrier, M. and Joffrain, M.: Impacts comparison by using different hydraulic models on the 2011 flood in Thailand, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-7347, https://doi.org/10.5194/egusphere-egu24-7347, 2024.

EGU24-7770 | Posters on site | HS7.5

Historical database for multi-hazard zonation and damage trend analysis in a Mediterranean study area (southern Italy) 

Olga Petrucci, Massimo Conforti, Giovanni Cosentini, and Graziella Emanuela Scarcella

The occurrence of extreme hydro-meteorological events is globally on the rise, due to the combined effects of climate change and increasing urban development in vulnerable areas. Each year, landslides, floods, urban flooding, storm surges, snow and thunderstorm events cause casualties, huge damage to urban areas, farmland, and communication infrastructures. This work presents the preliminary results of an historical research aiming to identify the series of geo-hydrological events which affected the municipality of Catanzaro (Calabria, South Italy), having an area of 112.7 km2 and a population density of 746.84 ab./km², throughout the latest two Centuries. The purpose is to implement a GIS-platform using the historical series of past events to realize density maps resulting is a zonation of municipal area which depict the vulnerability of municipal sectors per type of damaging phenomena and type of damaged elements, and their trends throughout the decades. We firstly extracted those events contained in the database named ASICal (Italian acronym of historically flooded areas), a catalogue collecting damaging geo-hydrological events occurred in Calabria in the latest centuries and maintained by CNR-IRPI researchers. Then, to improve and enrich our series, we performed an historical research throughout the documents of the State Archive of Catanzaro. As a total, we gathered data about around 270 events which occurred in the study area between 1830 to 2023, highlighting the strong territorial vulnerability of the selected area. Considering the average number of events per year as a proxy of events impact, we can observe as this value increases during the study period, moving from one event per year (in the period 1900 – 1950) to 3 events per year (in the period 1950 – 2023). To be uploaded in the GIS platform and mapped, the 270 events were split in around 1500 records, according to the kind of damaging phenomena (flood, landslide, urban flooding, storm surges, snow, thunderstorm) and the affected place. 44% of cases were widespread events, while the remaining 56% affected single sites. Urban flooding seems the most frequent damaging phenomena (68% of records), followed by landslides (21%), while the other phenomena show lower frequencies. As far as damaged elements, the most frequently affected were public and private buildings (64%) and road and railway network (26%), while people were affected in a few cases (5%). Data elaboration as multi-hazard maps, also crosschecked to either physical or anthropogenic data can be used to identify hazard-prone areas and to support the multi risk management in terms of monitoring, planning of remedial works, and realization/updating of civil protection plans, as far as in the realization of educational campaigns aiming to raise people awareness.

This work was funded by the Next Generation EU—Italian NRRP, Mission 4, Component 2, Investment 1.5, call for the creation and strengthening of ‘Innovation Ecosystems’, building ‘Territorial R&D Leaders’ (Directorial Decree n. 2021/3277)—project Tech4You—Technologies for climate change adaptation and quality of life improvement, n. ECS0000009. This work reflects only the authors' views and opinions, neither the Ministry for University and Research nor the European Commission can be considered responsible for them.

How to cite: Petrucci, O., Conforti, M., Cosentini, G., and Scarcella, G. E.: Historical database for multi-hazard zonation and damage trend analysis in a Mediterranean study area (southern Italy), EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-7770, https://doi.org/10.5194/egusphere-egu24-7770, 2024.

EGU24-8205 | ECS | Posters on site | HS7.5

A Multi-Criteria Analysis procedure for the evaluation and classification of flood risk mitigation strategies 

Alice Gallazzi, Daniela Molinari, Francesco Ballio, Marina Credali, Immacolata Tolone, Simona Muratori, and Panagiotis Asaridis

The study aims to provide the Lombardy Region, the primary stakeholder in the project, with a procedure for evaluating and classifying structural flood risk mitigation measures. The primary objective is to assist the regional authority in identifying priority interventions for public funding. A step-by-step procedure has been developed to assess and rank all projects submitted to the Region, selecting priority projects based on technical considerations—evaluating feasibility, effectiveness, and sustainability of the proposed measures—and the preferences of policymakers. The assessment procedure's conceptual structure was tested using case studies, including both feasibility studies and executive projects, to determine the level of technical insights required at each planning phase of public works. The methodology relies on Multiple Criteria Analysis (MCA) techniques, enabling the simultaneous consideration of various, non-directly comparable criteria in a complex decision-making context. These criteria encompass technical features of proposed works, potential territorial constraints, and interferences in the intervention area (feasibility); the effectiveness of measures in reducing flood risk and associated costs; and the environmental and social co-benefits and disbenefits of each intervention (sustainability). Specific indicators, either ad hoc defined for the study or referenced from current regulations and guidelines at national and regional levels, are employed to evaluate the criteria. Stakeholder participation, particularly from the Region, River District Authorities, and Municipalities, is crucial throughout the process, especially in the final phase of aggregating (weighting) all criteria. This aggregation produces an overall performance score for each option, enabling the derivation of a regional ranking of flood risk mitigation strategies. The collaboration between academia and public institutions is highlighted as essential for enhancing the efficiency of disaster risk reduction policies.

How to cite: Gallazzi, A., Molinari, D., Ballio, F., Credali, M., Tolone, I., Muratori, S., and Asaridis, P.: A Multi-Criteria Analysis procedure for the evaluation and classification of flood risk mitigation strategies, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-8205, https://doi.org/10.5194/egusphere-egu24-8205, 2024.

Since the 1950s, global irrigated area has expanded dramatically, with complex effects on regional climate worldwide. The North China Plain (NCP) is among the most intensively irrigated regions in the world, but the effects of historical irrigation expansion on climate extremes over multi-decadal timescale are largely uncertain. Combining statistical methods with model simulations, we found that NCP experienced a decreasing trend of 0.2–0.25 ℃ decade−1 (p < 0.1) in daily maximum temperature (Tmax) during May-June of 1961–2000 along with irrigation expansion, which is distinct from other regions experiencing strong warming such as most of western China. The cooling effect on Tmax is 0.092 ℃ decade−1 (p < 0.01), relatively lower than that in California’s Central Valley but comparable to the trend in Northwest China and larger than the trend in Tibetan Plateau. The correlation coefficients between irrigation expansion and temperature change from 1960 to 2000 for Tmax and mean air temperature (Tmean) are –0.58 and –0.33 (p < 0.01), respectively, suggesting the ability of irrigation to alleviate regional warming and temperature extremes. Such effect varies over time, continuously strengthening from 1961 to 1980 because of intensive irrigation expansion, but then remaining relatively unchanged or weakening during 1980–2005 with moderate expansion. After 2005, the cooling effect is not detectable, which implies that it is completely canceled out by other forcings such as greenhouse gas warming, compensation of urban area expansion, small irrigation expansion rate and decline in irrigation water use. Despite that, irrigation is still able to reduce the number of extreme heat days after 1980. Compared with other factors, we found that irrigation expansion is the second most important contributor (27%) to the decrease in Tmax during the study period, after aerosol pollution (54%). This work emphasizes the ability of irrigation expansion to adapt agriculture to climate change over the past decades, and highlights the need for sustainable irrigation expansion in the future.

How to cite: Yuan, T., Tai, A. P. K., and Wu, J.: Irrigation expansion in North China Plain has historically decelerated regional warming and mitigated temperature extremes, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-8291, https://doi.org/10.5194/egusphere-egu24-8291, 2024.

The occurrence probability of rare floods is linked to the right-tail behavior of flood frequency distributions. Specifically, heavy-tailed behavior of flood distributions often signals significant hazards due to the unexpected extremeness of event magnitudes. However, conducting reliable analyses of flood tail heaviness across regions remains challenging due to the varying record lengths of available data.

In this study, instead of relying solely on statistical methods to evaluate flood tail behavior, we adopt a physical-based approach—hydrograph recession analysis—to quantify the nonlinearity of catchment hydrological responses. This method has shown its efficacy in identifying heavy-tailed flood behavior across analyses with different data lengths. Our analysis covers 575 river gauges, spanning drainage areas from 4 to 40,504 km2, across Atlantic-influenced European areas, Northwestern European areas, and the Continental United States. We categorize these regions based on the Köppen climate classification to explore the relationship between physiographic/climatic conditions and heavy-tailed flood behavior, and distinguish regional characteristics using the aridity index and potential evapotranspiration.

Our findings reveal a prevalence of heavy-tailed flood propensity in Atlantic-influenced European areas, prevalent nonheavy-tailed flood propensity in Northwestern European areas, and a mixed distribution with a balanced propensity in the Continental United States. Generally, drier catchments exhibit higher nonlinearity in hydrological responses, facilitating heavy-tailed floods, while catchments in which snow dynamics dominate the flood generation process tend to present linear responses. Excessively dry catchments, however, are less likely to exhibit heavy-tail floods due to insufficient moisture. Moreover, around one-third of catchments display varying tail behavior across seasons, underscoring the potential underestimation of flood tail heaviness in annual analyses. The seasonality of flood tail behavior—where instances of heavy-tailed flood behavior increase from spring to autumn but decrease in winter—is influenced by the seasonal variation of potential evapotranspiration.

Our study contributes to advancing the understanding of the impact of inherent physiographic and climatic features on regional and seasonal patterns of heavy-tailed flood behavior, providing valuable insights into the emergence of a considerable occurrence probability associated with very large magnitudes of rare floods.

How to cite: Wang, H.-J., Merz, R., and Basso, S.: Physiographic and Climatic Controls on Heavy-Tailed Flood Behavior: Insights from Catchment Nonlinear Responses, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-8389, https://doi.org/10.5194/egusphere-egu24-8389, 2024.

EGU24-8531 | Orals | HS7.5

Appraising and reducing riverine flood risk: a case study from Central Italy 

Francesco Dottori, Matteo Darienzo, Giacomo Fagugli, Simone Gabellani, Tatiana Ghizzoni, Daria Ottonelli, Flavio Pignone, and Eva Trasforini

On 15 September 2022 a catastrophic flood event hit the Misa river basin in Central Italy. The magnitude of the event (intensity of precipitation, water discharge, debris and sediment transport) and the subsequent impacts were far more severe and extended than previous flood events in the same area, thus calling for a radical change in current practices of flood risk management. In this framework, the present study aims at 1) providing a comprehensive assessment of flood risk for the Misa river basin, and 2) designing appropriate risk reduction measures at river basin scale. We reconstructed the September 2022 event by integrating in-field surveys, hydrological data, hydraulic models, observations of the event (e.g. flood extent maps) and historical data of past flood events, taking into account the incompleteness and uncertainty of both models and observations. Moreover, we modelled exposure and vulnerability of population and economic activities in the area, using detailed surveys of observed impacts to inform the model set-up. The outcomes of these activities allowed to review the risk analysis tools currently available in the Misa river basin, and to design updated risk scenarios for present and future climate conditions. Finally, the risk scenarios have been used to explore different alternatives for flood risk reduction, in agreement with local authorities and stakeholders. We evaluated a range of structural measures (strengthening of dike systems, detention areas, river diversions) and non-structural measures (land-use planning, relocation, flood-proofing measures), considering existing risk management plans and new analyses carried out in this study (e.g. cost effectiveness of measures).

How to cite: Dottori, F., Darienzo, M., Fagugli, G., Gabellani, S., Ghizzoni, T., Ottonelli, D., Pignone, F., and Trasforini, E.: Appraising and reducing riverine flood risk: a case study from Central Italy, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-8531, https://doi.org/10.5194/egusphere-egu24-8531, 2024.

EGU24-8564 | Orals | HS7.5

High-Resolution Dynamic Flood Susceptibility Mapping Across the Mediterranean Region 

Hamidreza Mosaffa and Luca Brocca

Effective disaster prevention necessitates the production of high-resolution flood susceptibility maps (FSM) that accurately identify potential flood-prone areas. Conventional FSMs, however, provide static representations that overlook the inherent dynamicity of flood susceptibility, which is influenced by temporal variations, precipitation intensities, and other factors. Additionally, traditional FSMs often lack the high-resolution climate data required for precise risk assessment. To address these limitations, we propose a novel dynamic FSM approach that incorporates temporal variations and high-resolution climate data.

Our approach employs the Random Forest machine learning algorithm, trained on a comprehensive dataset of flooded and non-flooded areas (Global Flood Database v1). The algorithm considers seven critical factors influencing flooding events: elevation, slope, land cover, proximity to rivers, drainage density, soil moisture, and precipitation. This approach enables the generation of high-resolution (1 km) dynamic FSMs for the Mediterranean region, under varying seasonal conditions, precipitation intensities, and post-drought scenarios.

To assess and compare the model's performance, we employed both training and testing datasets, conducting evaluations using various metrics. The study results demonstrate the superior performance of the Random Forest model, establishing its efficacy as a robust tool for mapping dynamic flood susceptibility. The accuracy and reliability of the results obtained through this approach offer crucial insights for mitigating flood-related risks and enhancing disaster management strategies. This study is an integral part of the Open-Earth-Monitor Cyberinfrastructure (OEMC) project. As our next step, we aim to expand the application of our dynamic flood susceptibility mapping methodology to cover the European region.

How to cite: Mosaffa, H. and Brocca, L.: High-Resolution Dynamic Flood Susceptibility Mapping Across the Mediterranean Region, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-8564, https://doi.org/10.5194/egusphere-egu24-8564, 2024.

Floods impact natural and human systems from multiple dimensions. The vulnerability to flood consequences is intricately linked to the hydrogeomorphic and socio-economic properties of the region. In a long run flood control infrastructure such as embankments evolve with the hydrogeomorphic and socio-economic properties and co produce the new set of vulnerabilities. Assessment of maladaptive contribution of flood control infrastructure is crucial in adaptive decision making and building resilience.The study analyzed flood vulnerability of the population residing inside the embankment area of the Kosi River basin from multidisciplinary parameters. The Kosi River embankment area covers around 890 Sq Km area and is home to nearly 0.8 million people who are facing a trifecta of issues, including regular flooding, scarcity of basic amenities, and loss of livelihood. The basin went through numerous flood-related research based on geomorphology, hydrology, and other physical factors; however, the flood impact assessment of embankments and its role within the socioeconomic dimension still needs to be explored. The present study unpacks flood vulnerability in 283 villages located within the Kosi embankment. Drawing upon thirteen attributes—comprising eight socio-economic and five hydro-geomorphic parameters—the analysis incorporates data from Sentinel-2, IMD, FMIS, the 2011 census report, and other pertinent survey reports. The study utilized analytical hierarchical process (AHP) to obtain relative priority order of parameters. Through the application of GIS analysis, we systematically formulated exhaustive vulnerability maps encapsulating socio-economic, hydrogeomorphic, and composite dimensions based on the weightage assigned to the selected parameters. The analysis highlights that nearly the entire population in the embankment region is susceptible to the effects of flooding, with ∼66% of the region having high and very high flood risk and ∼26% in areas with moderate risk. Furthermore, the outcomes reveal the maladaptive consequences of infrastructure solutions, manifesting as socio-economic disparities and exclusionary effects. The population living inside the embankment region exhibit notably impoverished socio-economic characteristics,including 32 % female literacy, approximately 90 houses constructed by  around 90 percent of houses are Kachha ( mud house), and highly rely on farm labor activities, which is highly lower than the region outside the embankment. Additionally, the outcomes bring to light the maladaptive consequences of infrastructure solutions, manifesting as socio-economic disparities and exclusionary effects. Residents within the embankment area exhibit notably impoverished socio-economic indicators, including a 32% female literacy rate, approximately 90% of houses are Katchha ( made from mud and straw), and economic dependency on agriculture labor activities, which is significantly lower than outside of the embankment. Moreover, the annual flood and longer periods of waterlogging cut off the population from other parts of the state. Lastly, the study discussed the source of vulnerability and adaptation options, which could be useful in developing comprehensive flood adaptation programs, including socioeconomic considerations.

How to cite: Devda, A. and Verma, V.: Assessing Flood Vulnerability and Maladaptive Effects Associated with Embankment-Based Flood Control Infrastructure : Hydrogeomorphic and Socioeconomic Analysis Kosi River Embankment Region, Bihar, India., EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-8741, https://doi.org/10.5194/egusphere-egu24-8741, 2024.

EGU24-9209 | ECS | Posters on site | HS7.5

Spatial analysis of catastrophic flooding in the metropolitan area of Murcia over the last 100 years 

Ester García Fernández, Juan Francisco Albaladejo-Gómez, Andrina Gincheva, Salvador Gil-Guirado, and Alfredo Pérez-Morales

Floods represent the most diverse, destructive and frequent natural hazard worldwide and are one of the most significant causes of loss of economic and social assets. In recent years, an increase in the quantity and intensity of this phenomenon can be observed. The factors are manifold, but two stand out: increased hazards as a consequence of anthropogenic climate change and increased exposure and vulnerability of the population and its economic assets. One of the most conflictive areas of the planet are the Mediterranean regions, due to the combination of both factors. Among the hot spots, the Southeast of Spain stands out, with a situation aggravated by a semi-arid climate, but with a highly irregular and torrential rainfall distribution.

These factors are particularly problematic in urban areas, making it necessary to precisely locate the areas at risk in order to establish effective adaptation measures. For this reason, this paper compiles historical information on the main flood events from 1900 to the present in the metropolitan area of Murcia, the main urban area in southeast Spain. The information collected comes from newspaper sources. Subsequently, this information has been geolocated and analyzed with Geographic Information Systems. The results reveal that, in general terms, the damage is concentrated mainly in the areas near the Segura River. Additionally, and to a lesser extent, there is a significant concentration in its main tributary, the Guadalentín River. However, it should be noted that during recent flooding episodes, the areas affected are being modified, involving new urbanized areas, far from the main riverbeds and located in flood zones due to the passage of secondary watercourses such as wadis. Finally, it is worth noting that there has been an increase in the number of low-intensity damage points. However, on a positive note, it has been observed that higher intensity damage is decreasing.

How to cite: García Fernández, E., Albaladejo-Gómez, J. F., Gincheva, A., Gil-Guirado, S., and Pérez-Morales, A.: Spatial analysis of catastrophic flooding in the metropolitan area of Murcia over the last 100 years, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-9209, https://doi.org/10.5194/egusphere-egu24-9209, 2024.

EGU24-9257 | ECS | Orals | HS7.5

Multi-hazard assessment of long- and short-term extreme hydrometeorological events in southeastern South America 

M. Josefina Pierrestegui, Miguel A. Lovino, Gabriela V. Müller, and Omar V. Müller

Extreme hydrometeorological events (EHE) negatively affect ecosystems, human settlements, food production, water resources, and public health worldwide. In southeastern South America (SESA), the frequency and intensity of temperature and precipitation extremes have increased over recent decades. SESA is particularly vulnerable to EHE due to its high population rates and an economy heavily reliant on agricultural activities; therefore, advancing towards a climate-resilient development is a key goal for the region. This study presents a multi-hazard analysis of EHE and their changes over SESA.

Our study assesses the frequency, duration, and intensity of short- and long-term EHE for the 1961-1990 and 1991-2020 periods. ERA5 precipitation, soil moisture, and temperature data at multiple time scales (from daily to monthly) are used, with a spatial resolution of 0.25°×0.25° latitude-longitude grid. Long-term EHE are studied using nonparametric standardized indices—specifically, the Standardized Precipitation Index (SPI) and Standardized Soil Moisture Index (SSI)—at 3- and 18-month timescales to analyze agricultural and hydrological impacts. Short-term EHE are characterized by heavy precipitation, flash droughts, and heat waves events to analyze immediate impacts in urban areas and in agriculture. Individual hazard components are derived by multiplying the frequency, duration, and intensity of the identified events, followed by a rescaling to a 0-1 range using range normalization (with minimum and maximum values). The long-term and short-term EHE hazard indices are formulated by aggregating the rescaled individual hazard components and dividing by the total number of components. Changes in observed EHE hazard components are determined by comparing the EHE hazard indices for the 1991-2020 and 1961-1990 periods.

Our findings underscore significant precipitation excess hazard, mainly concentrated in agriculture-prone areas spanning central-eastern Argentina, Uruguay, and southern Brazil across both 3- and 18-month timescales. In contrast, precipitation deficit hazard predominantly manifests in the western regions of SESA. Regarding short-term EHE, the highest hazard magnitudes are observed in northeastern Argentina, southern Brazil, and southeastern Paraguay. Heat waves occur frequently in the region, with hazardous intensities over the northern part of SESA. Additionally, heavy precipitation events constitute a significant hazard component for urban and rural infrastructure primarily in northeastern Argentina. Flash droughts also affect agriculture-prone areas, particularly with high intensity in southern Brazil, northeastern Argentina, and Uruguay.

Our results reveal that the most significant changes are observed in short-term hazard indices in northeastern SESA. This region, which includes eastern Paraguay, northeastern Argentina, southern Brazil, and Uruguay, has experienced an increase in heat wave hazard, primarily due to a significant rise in the frequency of heat waves. Hazards associated with heavy precipitation and flash drought events have also increased, with a rise in their frequency and duration observed mainly over northeastern Argentina and southern Brazil. In contrast, long-term hazard indices exhibit non-uniform patterns of change. Our findings suggest that weather-related hazards have undergone changes over the last decades. We expect that these findings provide valuable insights to enhance SESA's hydroclimatic risk management systems by identifying areas susceptible to both short- and long-term hazards.

How to cite: Pierrestegui, M. J., Lovino, M. A., Müller, G. V., and Müller, O. V.: Multi-hazard assessment of long- and short-term extreme hydrometeorological events in southeastern South America, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-9257, https://doi.org/10.5194/egusphere-egu24-9257, 2024.

EGU24-9313 | ECS | Orals | HS7.5

The Effects of Geographic Risk Complementarity on Reducing Flood Insurance Costs 

Shibo Cui and Jianshi Zhao

Flood insurance is an important non-structural measure for flood risk management. However, a significant protection gap in flood insurance exists in many countries and the high cost of flood insurance is a primary reason. Reducing the flood insurance costs for both policyholders and insurance companies is crucial for the effective implementation of flood insurance. Here, we use portfolio theory to derive fundamental principles of reducing overall insurance cost including premiums and risk reserves through geographic risk complementarity. Furthermore, we propose a reasonable premiums distribution approach among different risk agents to analyze the effect of geographic risk complementarity on individual cost, based on the cooperative game theory. We applied our method in China, which has a large territory but lacks a national flood insurance program. We show there is a low correlation of flood losses across most provinces in China. Compared to the separate insurance in each province, national flood insurance can reduce total premiums by 14.5% and total risk reserves by 61.0%. The regions with highest proportion of premium reduction are the middle and lower Yellow River reaches, which have a lower flood risk correlation with the portfolios of other regions. In conclusion, the geographic complementarity in flood risk has a significant effect on reducing flood insurance cost and the degree of cost reduction depends on the flood risk correlation among different entities. We recommend that China should utilize the geographic risk complementarity to implement a national-level flood insurance program. The method proposed can also provide references for catastrophe insurances around the world.

How to cite: Cui, S. and Zhao, J.: The Effects of Geographic Risk Complementarity on Reducing Flood Insurance Costs, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-9313, https://doi.org/10.5194/egusphere-egu24-9313, 2024.

EGU24-10245 | ECS | Orals | HS7.5

A time-dependent non-asymptotic statistical analysis of extreme precipitation events 

Matteo Pesce, Eleonora Dallan, Francesco Marra, and Marco Borga

Time-dependent precipitation frequency analyses were often hampered by the availability of relatively short data records, which result in large uncertainty in the estimation of extremes. The recently developed non-asymptotic statistical methods, based on fitting ordinary events rather than extreme events only, represent a potential solution to the problem of data scarcity and are finding wide application in literature under assumptions of stationarity. Recent studies investigated the use of non-asymptotic methods under non-stationary conditions (e.g., Vidrio-Sahagún and He, 2022) and advocated their use over other methods for non-stationary frequency analysis of extreme precipitation. In this study we formalize a non-stationary time-dependent approach for the statistical analysis of multi-duration precipitation extremes using simplified metastatistical extreme value (SMEV) approach. The study focuses on a catchment in the Eastern Italian Alps, where trends in extreme precipitation where reported (Dallan et al., 2022) and which was impacted by the exceptional Vaia event in 2018. We provide an estimation of extreme return levels of precipitation in six stations in the neighborhood of the catchment and compare them with precipitation maxima observed during Vaia storm. The results show that using a non-stationary left-censored Weibull distribution, with both scale and shape parameters linearly dependent on time, allows to properly describe the observed trends of intense precipitation for different durations. Our results suggest that the probability of observing events like Vaia increased over the past decades, leading to the need for updating local adaptation measures.

 

References:

Dallan, E., Borga, M., Zaramella, M., & Marra, F. (2022). Enhanced summer convection explains observed trends in extreme subdaily precipitation in the eastern Italian Alps. Geophysical Research Letters49(5), e2021GL096727.

Vidrio-Sahagún, C. T., & He, J. (2022). Hydrological frequency analysis under nonstationarity using the Metastatistical approach and its simplified version. Advances in Water Resources166, 104244.

How to cite: Pesce, M., Dallan, E., Marra, F., and Borga, M.: A time-dependent non-asymptotic statistical analysis of extreme precipitation events, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-10245, https://doi.org/10.5194/egusphere-egu24-10245, 2024.

EGU24-10297 | ECS | Orals | HS7.5

Projected amplification of rainfall extremes due to warming-induced reduction of snow fraction: an assessment based on convection-permitting simulations 

Petr Vohnicky, Eleonora Dallan, Francesco Marra, Giorgia Fosser, Matteo Pesce, and Marco Borga

In mountainous regions, temperature determines the state of precipitation (liquid or solid) and in turn significantly affects runoff formation and flood generation. Projected temperature increase due to global warming may therefore affect the rainfall/precipitation ratio during heavy storms, hence intensifying the flood regime. This study aims to assess the projected variations in liquid/solid fraction of precipitation during heavy precipitation events in the upper Adige River, Italy (Eastern Italian Alps). The study utilizes simulations from an ensemble of convection-permitting climate models (CPM), which are suitable to the task given their ability to explicitly represent deep convection and to resolve the mountainous topography. The CPM data provided by the CORDEX-FPS Convection project at 1-hour temporal and remapped to 3 km spatial resolution, cover historical and far-future (2090-2099) time periods under the extreme climate change scenario (RCP8.5). Observational data from the densely instrumented river system are utilized for bias evaluation. Lastly, the Simplified Metastatistical Extreme Value (SMEV) approach, known for the reduced uncertainty compared to conventional approaches, is incorporated for frequency analysis. This method proves particularly useful for analyzing extremes from short time periods, such as those in CPM simulations. The projected changes in both sub-daily mean areal precipitation and liquid rainfall return levels are examined at various spatial scales based on the sub-basins total area. Our preliminary results underscore the significance of leveraging advanced statistical techniques and high-resolution climate models to address emerging challenges in hydrology and climate science. The climate-induced shifts in return period of liquid precipitation identified in this study are expected to have implications for both water resources management and adaptation measures.

How to cite: Vohnicky, P., Dallan, E., Marra, F., Fosser, G., Pesce, M., and Borga, M.: Projected amplification of rainfall extremes due to warming-induced reduction of snow fraction: an assessment based on convection-permitting simulations, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-10297, https://doi.org/10.5194/egusphere-egu24-10297, 2024.

EGU24-10877 | ECS | Posters on site | HS7.5

Exploring Diverse Perceptions of Multiple Risks among the Public in Rome 

Mara Lucantonio, Elena Ridolfi, Patrizia Cicini, Fabio Russo, and Francesco Napolitano

Risk is given by the combination of exposure, hazard, and vulnerability, and it is perceived by individuals in different ways. Some people may be unaware of the potential occurrence of a given hazard, while others may misjudge their level of exposure, vulnerability, or both. The knowledge of the population’s risk perception is a fundamental aspect for the analysis of potentially catastrophic phenomena and for the development of prevention policies to intervene and mitigate the expected damage. Questionnaires are widely used in social science research to acquire information about the attitudes, social characteristics, beliefs, and behaviors of participants. This information when combined through a mixed method can provide robust, comprehensive, and quantifiable results, adding a valuable perspective for the development of appropriate hazard mitigation and adaptation strategies. Here we present a case study that involves the analysis of a data set based on a questionnaire submitted to around 300 citizens of the city of Rome (Italy) in spring 2023. The proposed questionnaire investigates specific areas, which are: experience and knowledge of the phenomena, probability of occurrence perceived by the respondent, potential impact, and preparedness to deal with the phenomena.The use of questionnaires to study citizens’ perception of both natural and man-made hazards enables the acquisition of valuable information for authorities dealing with emergency management. The resulting dataset has the potential to improve the communication efficiency between authorities and citizens in risk situations, and provide relevant information for future studies relying on the knowledge of citizens’ risk perception.

How to cite: Lucantonio, M., Ridolfi, E., Cicini, P., Russo, F., and Napolitano, F.: Exploring Diverse Perceptions of Multiple Risks among the Public in Rome, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-10877, https://doi.org/10.5194/egusphere-egu24-10877, 2024.

EGU24-10950 | ECS | Posters on site | HS7.5

Temporal and spatial analysis of mortality associated with landslides on São Miguel Island (Portugal) from 1900 to 2020 

Rui Fagundes Silva, Rui Marques, and José Luís Zêzere

The São Miguel Island covers an area of 744.6 km² and has a total population of 133,390, distributed across six municipalities: Ponta Delgada, Ribeira Grande, Vila Franca do Campo, Povoação, Lagoa, and Nordeste. The island features two extinct volcanic systems and three active central volcanoes with calderas connected by two fissure volcanic systems. Two distinct seasons can be identified based on rainfall patterns: from October to March (wet season) and from April to September (dry season). Since the settlement of the island in the mid-15th century, there have been records of landslides, some with significant socio-economic impact. The analysis of the spatial distribution and temporal patterns of mortality associated to landslides was carried out using the NATHA (Natural Hazards in Azores) database for the period 1900–2020. Data collection involved the analysis of more than 55,500 newspaper specimens. A total of 236 landslides events were catalogued on São Miguel Island, which caused 82 fatalities. The municipality of Povoação accounted for 48 fatalities, approximately 59% of the total. Ponta Delgada reported 14 fatalities, Ribeira Grande eight, Vila Franca do Campo seven, Nordeste three, and Lagoa two. On São Miguel Island, an average of 0.7 fatalities per year were recorded, resulting in a landslide mortality rate of 0.35 (calculated as the ratio between deaths and total events). The events with the highest number of fatalities occurred on October 31, 1997 (29 fatalities) and on October 14, 1942 (7 fatalities). The annual mortality rate per decade reveals two distinct periods with higher values: 1930-1949 and 1990-1999. No fatalities were recorded from 1900 to 1929. The landslide mortality rate has a first increase in the 1930s and 1940s (≈0.1 fatalities/10,000 inhabitants). From 1950 to 1989, there was a decrease (≈0.02 fatalities/10,000 inhabitants), with a slight increase in the 1960s. The period from 1990 to 1999 has the highest mortality rate (≈0.26 fatalities/10,000 inhabitants). However, excluding the extreme event of October 31, 1997 from the analysis reveals that the 1990s had a mortality rate in line with the previous four decades (0.02 fatalities/10,000 inhabitants). Along the two first decades of the 21st century, the mortality rate increased again, maintaining a stable trend (≈0.05 fatalities/10,000 inhabitants). The data also indicates that males had a higher frequency of fatalities. The circumstances surrounding the incidents varied, with most fatalities occurring outdoors when individuals were on foot in rural areas. However, it is noteworthy that there were also fatalities inside houses in urban areas, emphasizing the diverse contexts in which these tragic events took place. This information provides valuable insights to temporal patterns and spatial distribution of landslide-induced fatalities on São Miguel Island.

How to cite: Silva, R. F., Marques, R., and Zêzere, J. L.: Temporal and spatial analysis of mortality associated with landslides on São Miguel Island (Portugal) from 1900 to 2020, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-10950, https://doi.org/10.5194/egusphere-egu24-10950, 2024.

EGU24-11090 | ECS | Orals | HS7.5

From indices to impacts: Understanding the dynamics of drought impacts through socio-economic clustering 

Rhoda Odongo, Hans De Moel, Marthe Wens, Natalia Limones, Dim Coumou, and Anne Van Loon

Over the past decade, the Horn of Africa (HoA) has been plagued by recurrent drought events that have had devastating impacts on the population. The frequency, duration and severity of these droughts are expected to increase in the wake of global warming, leading to higher losses and damages if the vulnerability of the population is not reduced. Monitoring and early warning systems for droughts are based on various drought hazard indicators. However, assessments of how these indicators are linked to impacts are rare. For adequate drought management, it is essential to understand and characterise the drivers of drought impacts, especially in the HoA, where most studies focus either on meteorological droughts, agricultural droughts or the propagation of droughts through the hydrological cycle, without considering the relationship between hazard and impact. Drought hazard indices alone cannot capture the vulnerability of the system. In this study, we identify meaningful indices for the occurrence of region- and sector-specific impacts. We assess the effectiveness of socio-economic clustering in categorising counties based on common characteristics and their correlation with historical drought impacts (malnutrition, milk production and trekking distances to water sources). Using Random Forest (RF) and Spearman correlation analyses, we examine the link between drought indices (Standardised Precipitation Index, Standardised Precipitation Evapotranspiration Index, Standardised Soil Moisture Index, Standardised Streamflow Index and Vegetation Condition Index) with different accumulation periods and the impact data. We find that clustering regions based on vulnerability proxies significantly improves the hazard-impact relationship, emphasising the importance of considering vulnerability factors in drought risk assessment. Our results indicate an impact-specific relationship that is strongly influenced by the vulnerability of the region. In particular, household and livestock distance to water is most strongly associated with medium- to long-term precipitation-based indices (2-10 months), while milk production can be associated with a variety of indices with different accumulation periods (5-24 months), and malnutrition is correlated with precipitation- and streamflow-based indices (5-24 months). Household and livestock distance to water is well modelled by clusters reflecting low access to improved sanitation and safe water sources, high poverty, aridity and gender disparities. Malnutrition was well modelled by clusters related to aridity, average precipitation, food consumption score, access to water sources, improved sanitation and poverty levels. The type of clustering used in modelling the impact of drought on milk production does not have a major impact on the performance of the models. We then apply this relationship to hindcast drought indices to obtain impact data on individual counties for periods when no impact monitoring was done yet. With that information we estimate the associated risk under specific climatic conditions. By recognising the drivers and vulnerability factors that influence the sensitivity of counties to drought, communities can better prepare and mitigate the impacts of drought.

How to cite: Odongo, R., De Moel, H., Wens, M., Limones, N., Coumou, D., and Van Loon, A.: From indices to impacts: Understanding the dynamics of drought impacts through socio-economic clustering, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-11090, https://doi.org/10.5194/egusphere-egu24-11090, 2024.

EGU24-11493 | ECS | Posters on site | HS7.5

Dry spell frequency and duration analysis using different spell definitions 

Pedro Henrique Lima Alencar and Eva Nora Paton

Dry spells, characterized by consecutive days with little to no precipitation, pose significant challenges, particularly in agriculture, and can impact various sectors including health when compounded by high temperatures, increased evaporation rates, or pollution. However, defining the thresholds for what constitutes a significant lack of precipitation or the number of consecutive days to define a notable dry spell remains ambiguous. In this study, we investigate the occurrence of different types of dry spells across Germany using twelve diverse definitions. These definitions encompass not only the conventional criteria of low/no precipitation but also consider associations with other extreme weather conditions occurring simultaneously (such as high temperatures, and potential evapotranspiration) or following the dry spell (like intense precipitation events). Leveraging continuous weather station data spanning the last 50 years, we employ the Mann-Kendall test to analyse seasonal and regional trends in the duration and frequency of these various dry spell events across Germany. Our findings reveal positive trends in both the frequency and duration of dry spells in Germany, notably prominent in the southern regions. These trends are observed in conventional low-precipitation dry spells and compound heat-dry events. Additionally, to facilitate event identification, we have consolidated these diverse dry spell definitions into an R-package called DryER (Dry spell Events in R).

 

How to cite: Lima Alencar, P. H. and Paton, E. N.: Dry spell frequency and duration analysis using different spell definitions, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-11493, https://doi.org/10.5194/egusphere-egu24-11493, 2024.

EGU24-11733 | ECS | Orals | HS7.5

Exploring vulnerability to flash floods in a water-scarce MENA city: Challenges and possible solutions  

Clara Hohmann, Christina Maus, Ahmad Awad, Dörte Ziegler, Hanna Leberke, Maram Al Naimat, Wafaa Abuhammour, and Katja Brinkmann

Jordan is one of the water scarcest regions worldwide, but regularly hit by severe flash floods caused by heavy rainfall events. Such events will likely intensify in future and increase flash flood damages, especially in rapidly developing urban areas. Therefore, flood vulnerability analysis and assessment are urgently needed to improve urban risk management and to protect the local population. To date, however, such analyses in Jordan, as in many other MENA regions, have been hampered by the lack of spatial and temporal high-resolution climate, economic and social data. Furthermore, conducted hydrological analyses have only considered physical parameters in assessing flash flood risk.

Our aim is to investigate the vulnerability in a data scarce urban region and find solutions to overcome the challenges by combining different disciplinary perspectives with local knowledge. Jordan’s capital, Amman was selected as study region, which is a prime example of a rapidly growing city in the MENA region.

To analyze and assess the vulnerability of people, infrastructure and ecosystem to flash flood events in a watershed of Amman, a mixed-method approach was applied within a transdisciplinary research project called CapTain Rain (Capture and retain heavy rainfall in Jordan). To gain insights into flash flood risks, we explore the vulnerability dimensions exposure and sensitivity from the hydrological, hydraulic and social perspectives, and the adaptive capacity of the local population. For the assessment of each vulnerability dimension, different physical, social and ecological indicators were used. Several indicators, such as damage potential, were adapted to local conditions based on focus group discussions with Jordanian stakeholders.

The vulnerability dimensions exposure, sensitivity and adaptive capacity were assessed for the current situation and several possible scenarios with changing future conditions in climate (intensity of rainfall) and land cover (urbanization trends). As one sensitivity indicator the damage potential was analyzed. The resulting damage potential map shows e.g. the locations of critical infrastructure, and also includes the word heritage sites, which were identified as vulnerable infrastructure of high importance by the Jordanian stakeholders. Regarding future scenarios our first hydrological and hydraulic modelling results show that a moderate climate change of 20% more intense rainfall has a stronger influence compared to land cover changes. Land cover changes with more sealed surfaces have little influence on the runoff caused by the low infiltration capacity of soils in the area according to the available data.

Through interdisciplinary collaboration and local stakeholder engagement, this work demonstrates a noteworthy strategy to addressing flash flood risks in situations where data is limited. The results of the integrated scenario analysis and vulnerability assessment serve as a decision-support tool for urban planning.

How to cite: Hohmann, C., Maus, C., Awad, A., Ziegler, D., Leberke, H., Al Naimat, M., Abuhammour, W., and Brinkmann, K.: Exploring vulnerability to flash floods in a water-scarce MENA city: Challenges and possible solutions , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-11733, https://doi.org/10.5194/egusphere-egu24-11733, 2024.

EGU24-14198 | ECS | Orals | HS7.5

Assessing the Influenced Zone of Debris Flow Using Numerical Simulation 

Kai-Lun Wei, Kuo-Wei Liao, Guan-Yu Lin, Poshuan Lin, and Tsungyu Hsieh

Taiwan is located at the boundary between the Philippine Sea Plate and the Eurasian Plate, characterized by steep terrain and high river gradients. Combined with frequent events such as typhoons leading to substantial rainfall, this has resulted in disasters like debris flows. Several available tools such as HEC-RAS two-dimensional hydraulic, SRH-2D, FLO-2D and FLOW-3D are used to analyze the area of flooding and the impact of debris flow in the watershed. The simulation results are compared with historical disaster data to validate the feasibility of model. Furthermore, the results are used to evaluate the suitability of current government-designated evacuation locations and routes.

Among several analysis tools, the debris flow modeling in HEC-RAS two-dimensional hydraulic is considered as the best platform to analyze debris risk. The results show the sections of evacuation routes on the left bank of the downstream area near the estuary pass through the debris flow impact area. However, there is no suitable evacuation facility in the vicinity. Therefore, during warning issuance, residents need to be cautious and evacuate promptly. On the other hand, collaboration with government authorities can be pursued to establish new shelters or activity centers nearby, serving as alternative evacuation sites.

How to cite: Wei, K.-L., Liao, K.-W., Lin, G.-Y., Lin, P., and Hsieh, T.: Assessing the Influenced Zone of Debris Flow Using Numerical Simulation, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-14198, https://doi.org/10.5194/egusphere-egu24-14198, 2024.

EGU24-14698 | Posters on site | HS7.5

Monthly flood frequency regionalization for comprehensive flood damage assessment to crops 

Anna Rita Scorzini, Charlie Dayane Paz Idarraga, and Daniela Molinari

Quantitative flood risk assessments rely on damage models, which relate information on flood hazard and vulnerability of exposed assets to estimate expected losses. Differently from other sectors, crop damage depends not only on typical hazards variables (including water depth, flow velocity, inundation duration, water salinity, yield of sediments and/or contaminants) but also on the month of flood occurrence. Indeed, plant vulnerability changes over the different phenological phases that are strictly related to the seasonality of crop production. Considering the time of occurrence of the flood would imply a shift from the traditional representation of inundation scenarios based on annual probability to monthly-based hazard estimations. When risk assessment is carried out at large spatial scale, a detailed understanding of seasonal flood patterns is then required for the different sub-catchments of the basins, including un-gauged ones. In this study we present a clustering approach to flood frequency regionalization applied to the Po River District in Northern Italy, within the risk assessment process required by the European Floods Directive. The  area is characterized by complex climatic and topographic conditions, highlighting the representativeness of the case study for the implementation of the proposed approach in other geographical contexts. Utilizing observed monthly flow data from over 100 gauging stations, the approach combines both physical and statistical criteria to identify homogeneous regions in terms of flood generation mechanisms and seasonality. The process enables the assignment of distinct monthly flood probabilities to all catchments within the district, thereby supporting a comprehensive flood risk assessment for the agricultural sector.

How to cite: Scorzini, A. R., Paz Idarraga, C. D., and Molinari, D.: Monthly flood frequency regionalization for comprehensive flood damage assessment to crops, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-14698, https://doi.org/10.5194/egusphere-egu24-14698, 2024.

The insurance sector plays a critical role in promoting disaster resilience and recovery by providing financial protection, speeding up rebuilding and recovery, and managing the financial impact of natural disasters. To fulfill this role, insurance companies must meet the capital requirements imposed by regulators. For example, the European Solvency II regulatory framework requires insurers to hold enough capital to withstand a natural catastrophe loss with a return period of 1 in 200 years. As the historical loss data are scarce and incomplete, the insurance sector uses stochastic catastrophe models (cat models) to assess the potential cost of rare but devastating events like floods.

A stochastic event set is a crucial element of cat models. It is a collection of possible disasters with their likelihood and severity. One method to generate stochastic flood events is to use numerical models of the atmosphere to generate realistic precipitation fields, and then apply rainfall-runoff models to estimate how much water will flow into rivers and streams from precipitation and snowmelt. By running many simulations with different inputs and parameters, stochastic flood models can provide a range of possible outcomes, including floods with spatial patterns and magnitude missing in historical data.

Output of such simulations are spatio-temporal hazard grids: precipitation grids for pluvial risk and river discharge grids for fluvial risk. These grids are large as the models typically run over large geographies (countries or continents) and simulate 10,000 years or more. This contribution will (i) provide overview of existing methods how to identify flood events in such huge discharge and precipitation datasets (i.e. peak-over threshold method), (ii) show their limitations for identifying flood events, and finally (iii) propose a new methodology designed to address specific needs of reinsurance industry such as the hours-clause condition, which specifies the time period within which losses from a single event must occur in order to be covered.

As many severe floods are composed from several sub-waves (for example 2002 floods in Czech Republic), proper event identification and separation is highly relevant topic as it influences the amount of reinsurance payouts after some types of flood events and thus capital available for rebuilding and recovery. 

How to cite: Kadlec, M.: Identification of flood events in large discharge datasets - reinsurance industry perspective, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-14743, https://doi.org/10.5194/egusphere-egu24-14743, 2024.

EGU24-14898 | Posters on site | HS7.5

The diverse impacts of extreme storms in the European South. The case of Storm Daniel (2023) in Greece. 

Michalis Diakakis, Spyridon Mavroulis, Christos Filis, Yiannis Bantekas, Marilia Gogou, Katerina-Nafsika Katsetsiadou, Maria Mavrouli, Vasilis Giannopoulos, Andromachi Sarantopoulou, Panagiotis Nastos, Emmanuel Vassilakis, Aliki Konsolaki, Evelina Kotsi, Sotiris Moraitis, Eleftheria Stamati, Athanasia Bakopoulou, Emmanuel Skourtsos, Panayotis Carydis, and Efthymios Lekkas

On September 4, 2023, Storm Daniel moved inland from the Ionian Sea, intensifying due to the warmth of the post-summer Mediterranean Sea, resulting in intense rainfall and thunderstorms over the Balkans. Central Greece was particularly affected, experiencing the highest daily rainfall totals recorded in the region.

The storm caused widespread devastation, especially in the Thessaly region, with significant impacts including intense erosion, mass movement phenomena triggered by rainfall, damages from strong winds, inundation, agricultural land damage, loss of life and injuries, impacts on residences and businesses, as well as a substantial toll on the environment and cultural sites.

This study focuses on Storm Daniel and its effects in Thessaly, Greece, by creating a database of distinct impact elements based on field surveys and public records. Through this archive, the study explores the range of its impacts, developing a systematic categorization to provide an in-depth understanding of the types and mechanisms of these impacts.

Examining extreme storms through post-flood surveys and emphasizing their impacts can enhance our comprehension of associated risks. This knowledge will facilitate more accurate predictions and strategic planning for such events, contributing to improved emergency management and recovery efforts. Anticipating the impacts becomes crucial, particularly in the context of the projected increase in the frequency of such events due to climate change, thereby strengthening our preparedness.

How to cite: Diakakis, M., Mavroulis, S., Filis, C., Bantekas, Y., Gogou, M., Katsetsiadou, K.-N., Mavrouli, M., Giannopoulos, V., Sarantopoulou, A., Nastos, P., Vassilakis, E., Konsolaki, A., Kotsi, E., Moraitis, S., Stamati, E., Bakopoulou, A., Skourtsos, E., Carydis, P., and Lekkas, E.: The diverse impacts of extreme storms in the European South. The case of Storm Daniel (2023) in Greece., EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-14898, https://doi.org/10.5194/egusphere-egu24-14898, 2024.

EGU24-15286 | ECS | Posters on site | HS7.5

A 10-Year climatology of hail in France: towards an estimate of the hail hazard 

Maxime Trevisani

According to France Assureur (French insurance unions), 2022 hail damage in France is estimated at more than €6.5 billion, i.e. more than half of all climate-related damage in 2022, or 60% of all hail damage accumulated between 2013 and 2021. This record-breaking year is in line with the growing concern about hail in France among public and private stakeholders. Despite its increasing impact on society the hail hazard in France remains largely unknown or under investigated at the national level, with a single 20x20 km hail risk map produced up in 1998 by F. Vinet using economic data (insurance) and measurements (hailpad). Hail hazard is poorly studied in France due to the great difficulty of observing or modelling hailfall, which are highly localised in time and space. The emergence of social networks since the late 2000s has led to a proliferation of potential hail observers across France. These new data, combined with insurance data, make it possible to study hail at a level of resolution never seen before in France.

The main objectives of our study are therefore to update the geographical assessment of the hail hazard in France, while improving the granularity of the existing geographical hail assessment. To this end we studied the hail hazard in terms of frequency and maximum diameter at the municipal level (average 16 km²), using hail reports (Keraunos, European Sever Weather Database) and insurance data (Generali France, around 5% market share) over the period 2013-2022.

Our study thus provides a resolution 25 times finer than that of Vinet and reveals a southwest - northeast axis dividing France into two parts: the southern part is heavily affected by hail while the northern part is less affected. It also highlights 3 main geographical areas with the highest hail hazard. The Massif Central stands out as the main hail-prone area in France, with a notable maximum in its northern part. The Bordeaux-Paris axis comes second, with a local maximum in the southwest Atlantic coast. In third place comes the Provence-Alpes-Côte d'Azur region, particularly in the Pre-Alps and Pre-Atlantic massifs. There also seems to be a correlation between orography and areas of high hail hazard, particularly noticeable in the Massif Central and Pre-Alps regions, but this assumption needs to be further investigated.

How to cite: Trevisani, M.: A 10-Year climatology of hail in France: towards an estimate of the hail hazard, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-15286, https://doi.org/10.5194/egusphere-egu24-15286, 2024.

EGU24-15556 | Orals | HS7.5

Climate Stress Testing for Enhanced Understanding of the Flood Hazard and its Socioeconomic Impacts in Italy 

Francesca Perosa, Alastair Clarke, Punit Bhola, Caroline McMullan, Emma Lewington, and Bernhard Reinhardt

To contribute to a more resilient flood risk management in Italy, we employ the recently published Verisk Inland Flood Model for Italy to conduct climate stress testing. We focus on the sensitivity of modeled losses to precipitation and leverage the meteorological dataset obtained from the Climate Model Intercomparison Project Phase 6 (CMIP6) for identifying projected precipitation trends and analyzing the potential effects of climate change on inland flood losses in the future, exploring different Shared Socioeconomic Pathways (SSPs) and Representative Concentration Pathways (RCPs). The methodology involves analyzing correlations between annual or seasonal precipitation and the corresponding annual loss cost, which is defined as annual loss divided by the total insured value. By exploring these relationships, we seek to enhance our understanding of how precipitation patterns influence the financial implications of flood events in various Italian regions. Additionally, we use the 10,000-year stochastic catalog embedded in the Verisk Inland Flood Model to explore the impact of expected climate change-related changes in annual precipitation for each Italian region, addressing the climate change-based precipitation targets. This enables us to run the fully probabilistic Verisk Inland Flood model and to assess whether anticipated alterations in precipitation levels correspond to expected changes in Annual Average Loss (AAL). This approach allows us to dynamically adapt our flood risk model to varying climate scenarios, providing valuable insights for the (re)insurance industry, as well as academia and government agencies that are seeking to navigate the evolving landscape of flood-related risks.

How to cite: Perosa, F., Clarke, A., Bhola, P., McMullan, C., Lewington, E., and Reinhardt, B.: Climate Stress Testing for Enhanced Understanding of the Flood Hazard and its Socioeconomic Impacts in Italy, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-15556, https://doi.org/10.5194/egusphere-egu24-15556, 2024.

EGU24-15848 | Posters on site | HS7.5

The use of radar information for improving the knowledge about landslides and floods events: an application to Calabria region (Italy) 

Vincenzo Totaro, Simona de Sario, Francesco Chiaravalloti, and Olga Petrucci

Floods and landslides are common natural phenomena that threaten society and ecosystems causing significant losses in term of human lives and financial damages. An in-depth investigation about the past occurrences of these events is of paramount importance for providing advances in the knowledge of natural and anthropogenic factors responsible for their generation. Considering rainfall as one of the key drivers for triggering physical mechanisms responsible for the occurrences of floods and landslides, a proper description of its characteristics needs to contemplate the intrinsic spatial and temporal variability. Despite the importance of such elements, rainfall monitoring often relies on sparse rain gauges, which lead to uncertainty in the identification of real rainfall patterns, making difficult to link precipitation records with observed damages. Meteorological radar represents a relevant tool for detecting rainfall spatiotemporal variability and providing ancillary information about the evolution of the events.

Goal of the work is to develop a methodology that aims in reconcile records of landslides and floods events with the rainfall structures obtained by the joint use of data recorded by rain gauge network and radar data. The research has been carried out by moving from a consolidated catalogue of damaging events occurred in correspondence of floods and landslides in Calabria region (Italy) in 2019 and 2020. Rainfall was investigated integrating rain gauge data and maps of Surface Rainfall Intensity with resolution of 1x1 km2.

Exploiting the availability of an accurate spatiotemporal reconstruction of precipitation structures, our investigation allowed to improve the specific knowledge about dynamics responsible of selected floods and landslides events. Preliminary results are supportive of the use of the proposed approach for integrating different sources of information in the assessment of the real dynamics of damaging events and for enhancing the use of their joint scientific content in the framework of risk assessment and mitigation.

How to cite: Totaro, V., de Sario, S., Chiaravalloti, F., and Petrucci, O.: The use of radar information for improving the knowledge about landslides and floods events: an application to Calabria region (Italy), EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-15848, https://doi.org/10.5194/egusphere-egu24-15848, 2024.

EGU24-17412 | Orals | HS7.5 | Highlight

It could have come worse –  an analysis of spatial counterfactual scenarios for the July 2021 flood in the Ahr Valley, Germany 

Sergiy Vorogushyn, Li Han, Heiko Apel, Viet Dung Nguyen, Björn Guse, Xiaoxiang Guan, Oldrich Rakovec, Husain Najafi, Luis Samaniego, and Bruno Merz

After a flood disaster, the question often arises: “What if the event had gone differently?” For example, what would be the effects of a flood if the path of a pressure system and thus the precipitation field had occurred taken a different trajectory? The analysis of such alternative scenarios of precipitation footprints (“counterfactuals”) is a valuable approach for flood risk management in addition to classical extreme value statistical analyses. It helps to think about and prepare for extremes that have not occurred in this way, but which appear quite plausible.

Here, we analyze the spatial alternative scenarios of the deadly July 2021 flood in the Ahr Valley, Germany. The hydrological model mHM is driven with precipitation fields systematically shifted in space. The resulting runoff is transformed into inundation and flood impact indicators using the high-resolution hydrodynamic model RIM2D.

The results show that even a slight shift of the precipitation field by 15-20 km, which does not seem implausible due to orographic conditions, causes an increase in peak flows at the Altenahr gauge of over 30% and at individual tributaries of up to 160%. Also, significantly larger flood volumes can be expected due to precipitation shifts. This results in markable differences in inundation depths in a number of areas along the Ahr river valley. The presented results should encourage critical thinking about precautionary measures and risk management plans for extreme and unprecedented events.

How to cite: Vorogushyn, S., Han, L., Apel, H., Nguyen, V. D., Guse, B., Guan, X., Rakovec, O., Najafi, H., Samaniego, L., and Merz, B.: It could have come worse –  an analysis of spatial counterfactual scenarios for the July 2021 flood in the Ahr Valley, Germany, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-17412, https://doi.org/10.5194/egusphere-egu24-17412, 2024.

EGU24-17516 | Posters on site | HS7.5

Spatial patterns and determinants of severe geomorphological changes due to the extreme flood event in the Ahr valley, western Germany in July 2021 

Fabian Weidt, Rainer Bell, Lothar Schrott, Alexander Brenning, Michael Dietze, Lisa Burghardt, and Joshua Groeßer

The extreme flood event of July 14/15, 2021 caused massive geomorphological changes along the Ahr river in western Germany. The processes include mass movement and bank erosion, channel displacement and widening and deposition of material at the floodplains, all of which contributed to extreme damage. With the aim of gaining a more comprehensive understanding of the factors controlling these processes, spatial patterns of geomorphological changes on a regional scale are analyzed. A differential terrain model (DoD), calculated from digital terrain models (DTM) collected before and after the event using airborne laser scanning (ALS), serves as the data basis. The course of the river is divided into 120 m wide and 100 m long segments. Analyzing the cumulated volumetric loss per segment, which represents the explained variable proxying spatial variability in flood power, is conducted by using a multiple linear regression model. The independent variables considered in this investigation include peak discharge, valley floor width and river curvature. Additionally, a time series model, incorporating ARIMA and GARCH components, is applied to unravel patterns and anomalies along the course of the river while accounting for the autocorrelative and heteroscedastic structure of data. Both the native data and the residuals of all model types are used to examine effects of bridge failure and subsequent outburst waves on volumetric loss. The analysis shows that the strongest geomorphological changes are associated with high peak discharge and a small valley floor width. River segments containing destroyed arch bridges show significantly higher volumetric loss values than segments with destroyed slab bridges, intact bridges or no bridge at all. Spatially limited amplification of volumetric loss to 200 m downstream of destroyed slab bridges suggests a more rapid decrease in outburst wave power for those type of bridges in contrast to arch bridges. These findings provide evidence that there are construction types more appropriate than traditional arch bridges to prevent local augmentation of flood power caused by outburst waves resulting from bridge clogging and failure.

How to cite: Weidt, F., Bell, R., Schrott, L., Brenning, A., Dietze, M., Burghardt, L., and Groeßer, J.: Spatial patterns and determinants of severe geomorphological changes due to the extreme flood event in the Ahr valley, western Germany in July 2021, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-17516, https://doi.org/10.5194/egusphere-egu24-17516, 2024.

EGU24-18244 | ECS | Orals | HS7.5

The effects of extreme rainfall trends on compound flood risk: A case study over Greater Boston 

Stergios Emmanouil, Andreas Langousis, Elizabeth Perry, Luke Madaus, Joshua Hacker, and Emmanouil N. Anagnostou

Climate adaptation strategies and vulnerability assessments over coastal areas require proper modeling of the interplay and nonstationary nature of the physical processes involved in compound flooding. As a result of the reported upward trajectories of rainfall intensity over the Contiguous United States, flood risk estimates are also expected to vary. However, given the systematic and random inconsistencies of traditional extreme rainfall estimation approaches and the increased uncertainty surrounding climate model projections, the effects of climate change on the estimation of flood risk from compound hazards remains an open question. In this effort we aim to: (a) combine the observed rainfall intensity trends from the past 40 years (i.e., from 1979 to 2020; see also Emmanouil et al., 2022) across various scales of temporal averaging, with storm surge and antecedent streamflow conditions, to estimate how flood inundation levels evolve, and (b) assess the effects of those trends on flood risk estimation within areas affected by compound hydrological events. In doing so, we use hydrodynamic simulations of reported flood occurrences over the Greater Boston area (MA, United States) for a period of 20 years (i.e., from 2000 to 2019), along with the parametric modeling scheme proposed by Emmanouil et al. (2023). The latter has been shown to properly weight and link the exceedance probabilities of the main flood-driving mechanisms to the return periods of the maximum inundation levels, thus providing a sufficient depiction of the conditions over the studied domain and allowing for estimation beyond the range covered by the available simulations. Assuming that the dependence structure of the driving mechanisms remains time-invariant, our findings aim to enhance the understanding of how flood risk from compound hazards has been affected by extreme rainfall trends induced by the changing climatic conditions and, therefore, support decision-making on the design and protection of critical infrastructure.

References

Emmanouil, S., Langousis, A., Nikolopoulos, E. I., & Anagnostou, E. N. (2022). The Spatiotemporal Evolution of Rainfall Extremes in a Changing Climate: A CONUS‐Wide Assessment Based on Multifractal Scaling Arguments. Earth’s Future, 10(3). https://doi.org/10.1029/2021ef002539

Emmanouil, S., Langousis, A., Perry, E., Madaus, L., Hacker, J., and Emmanouil, E.N. (2023) Decomposing the effects of compound mechanisms on flood risk estimation for urban environments: A case study over Greater Boston, UrbanRain23, 12th International Workshop on Precipitation in Urban Areas, Pontresina, Switzerland, 29 November – 2 December 2023.

How to cite: Emmanouil, S., Langousis, A., Perry, E., Madaus, L., Hacker, J., and Anagnostou, E. N.: The effects of extreme rainfall trends on compound flood risk: A case study over Greater Boston, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-18244, https://doi.org/10.5194/egusphere-egu24-18244, 2024.

EGU24-18357 | ECS | Posters on site | HS7.5

Unveiling the complexity of social vulnerability: An analysis of the Social Vulnerability Index in Sweden (SVIS) 

Konstantinos Karagiorgos, Lars Nyberg, Nikos Kavallaris, Jenni Koivisto, Tonje Grahn, Ruth Björkholm, Johanna Gustavsson, and Sven Fuchs

In recent decades, social vulnerability assessments have become a valuable tool for gaining a deeper understanding of the effects of natural hazards on societies. These assessments aim to quantify and map human characteristics that contribute to potential loss, enabling the development of capacities and capabilities to respond to the emerging threats. Assessment methods range from qualitative approaches to semi-quantitative, often spatially explicit, place-based approaches, many of them with empirical background in respective case studies around the world. Despite these efforts, it is still important to carefully examine the potential benefits and limitations of these assessments, particularly those that focus on mapping and place-based approaches, in order to fully understand their value.

The purpose of this study (Karagiorgos et al., 2023) was to systematically evaluate the Social Vulnerability Index in Sweden (SVIS) developed by Haas et al. (2022) using a sensitivity analysis approach. This evaluation focuses on the sensitivity around the impact of changing aggregation scale levels, the influence of different options in constructing the index, the weight/contribution of each factor to social vulnerability and the indicators set. The aim was to determine the influence of input factor variation on model response.

Concerning the influence of scale variations on assessment outcomes, the SVIS algorithm demonstrated robustness when employed across various scales. In contrast, the factor retention method utilized yielded considerable differences in the results. Likewise, the weights' effect exerted a noteworthy influence on the index formation. The consideration of different subsets of variables revealed a high impact in certain scenarios.

The sensitivity analysis conducted in the index construction outlined in this study, recommends that the development of indexes proceed cautiously, accompanied by expert guidance. This approach ensures that the portrayal of social vulnerability remains both reasonable and consistent. Furthermore, the existence of other dimensions of vulnerability, such as physical, economic, and institutional, suggests that the SVIS be integrated with these dimensions. This integration can offer a comprehensive perspective on vulnerability, helping to identify and comprehend the primary pillars for use in Disaster Risk Reduction (DRR). It also contributes to a deeper understanding of the connections between social vulnerability models and the outcomes of disasters.

Haas, J.; Karagiorgos, K.; Pettersson, A.; de Goër de Herve, M.; Gustavsson, J.; Koivisto, J.; Turesson, K. & L. Nyberg (2022): Social sårbarhet för klimatrelaterade hot. Delstudie 2: Generella och hotspecifika index för social sårbarhet i Sverige. Myndigheten för samhällsskydd och beredskap, (MSB) rapport nr 1978, Karlstad.

Karagiorgos, K.; Kavallaris, N.; Björnholm, R.; Koivisto, J. & S. Fuchs (2023): Evaluation of the Social Vulnerability Index (SVIS) in Sweden. Swedish Civil Contingencies Agency (MSB), MSB report nr 2185, Karlstad. 

How to cite: Karagiorgos, K., Nyberg, L., Kavallaris, N., Koivisto, J., Grahn, T., Björkholm, R., Gustavsson, J., and Fuchs, S.: Unveiling the complexity of social vulnerability: An analysis of the Social Vulnerability Index in Sweden (SVIS), EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-18357, https://doi.org/10.5194/egusphere-egu24-18357, 2024.

EGU24-19140 | ECS | Posters virtual | HS7.5

Sensitivity analysis of agricultural and hydrological droughts to rainfall deficits across India 

Syed Bakhtawar Bilal and Vivek Gupta

Drought is a natural phenomenon characterized by an extended period of insufficient rainfall for a particular area. These deficit in rainfall leads to shortage of water reserves across surface and sub-surface storages. Variations in these shortages arise from diverse factors such as regional climatic variations, geographical features, and land-use patterns. The primary objective of this study is to assess the sensitivity of agricultural and hydrological systems to rainfall deficits across different climatic zones. We aim to quantify the degree of responsiveness of agricultural and hydrological droughts to varying precipitation deficiencies using various statistical and modeling techniques. By examining the diverse responses in different regions, this research seeks to enhance our understanding of precipitation shortages on drought dynamics.

How to cite: Bilal, S. B. and Gupta, V.: Sensitivity analysis of agricultural and hydrological droughts to rainfall deficits across India, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-19140, https://doi.org/10.5194/egusphere-egu24-19140, 2024.

EGU24-19393 | ECS | Orals | HS7.5

Impact of long-lasting flood water on agricultural productivity: a case study of the May 2023 Emilia Romagna floods 

Margherita Sarcinella, Jeremy S. Pal, and Jaroslav Mysiak

Heavy rainfall events occurred in the Emilia-Romagna region in Northern Italy as a result of two major storms on May 2nd and 17th that led to the overflow of 22 rivers and triggered over 250 landslides. This event claimed 15 lives, forced 10 thousand people to evacuate and caused over 400 road closures. Due to a prior long-lasting winter drought and poor land use management that hampered effective water drainage, floodwaters stagnated for over a month in some areas, exacerbating the crisis. Over 40% of regional agricultural land was flooded leading to irreversible crop damage, in some instances, entire harvest loss. The objective of this study is to build a consistent and replicable methodology to quantify the agricultural damages and economic loss resulting from stagnated floodwater over cropland using the Emilia Romagna floods as a case study. The study emphasises the use of remote sensing data as a tool to achieve accurate impact estimates. Sentinel-1 SAR imagery is used to derive 10-meter resolution flood extent and duration maps at a revisit time of 3 to 6 days. The maps are matched with crop data available for the region from the iColt database and damages are computed as a function of ponded water duration and crop type as well as resistance to oxygen deprivation. The data, comprised of crop type, growing season and sowing date, allow for the characterization of the growth state of each crop at the time of flooding, implicitly providing insights on the probability of plant survival. The use of satellite-derived vegetation indices as markers for post-disaster crop recovery, with a focus on identifying crop-specific recovery rates and patterns is highlighted. This study highlights the need for collaborative efforts with key regional entities and can provide factual-hazard-based agricultural loss estimates to local institutions. These findings can guide targeted adaptation strategies, improve the spatial accuracy of loss assessment, and improve our comprehension of the aftermath of prolonged floods on agricultural output.

How to cite: Sarcinella, M., Pal, J. S., and Mysiak, J.: Impact of long-lasting flood water on agricultural productivity: a case study of the May 2023 Emilia Romagna floods, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-19393, https://doi.org/10.5194/egusphere-egu24-19393, 2024.

EGU24-19552 | Posters on site | HS7.5 | Highlight

Unveiling global sub-daily precipitation extremes: Insights and development of the INTENSE Project  

Hayley Fowler, Amy Green, Elizabeth Lewis, David Pritchard, Stephen Blenkinsop, Luis Patino Velasquez, and Anna Whitford

Precipitation extremes result in flooding and droughts, causing substantial damages and loss of life. Understanding the variability of precipitation extremes with climate change is challenging, as we do no fully understand processes causing extreme precipitation under current climate variability. The INTENSE project focuses on understanding of the nature and drivers of global sub-daily precipitation extremes and change on societally relevant timescales. As part of this a Global sub-daily precipitation dataset has been collected, containing hourly rainfall data from approximately 25,000 rain gauges across over 200 territories, from a wide range of sources. This has been quality controlled using a rule-based open-source methodology, combining a number of checks against neighbouring gauges, known biases and errors, and thresholds based on the Expert Team on Climate Change Detection and Indices (ETCCDI) Climate Change Indices.  

A set of global hydroclimatic indices have been produced, characterising key aspects of shorter duration precipitation variability, including intensity, duration and frequency properties. An analysis of the indices, trends and corresponding climatology is carried out, providing information on various sub-daily precipitation characteristics (including extremes) across large parts of the world. These indices are publicly available for as many gauges as possible, alongside a gridded dataset that also incorporates indices calculated for additional restricted-access gauge records. To progress further with this work, updates to the dataset are required, with work ongoing to update resources for 2016 onwards, and attempts to automate the process where open-source datasets are available. Any collaborations, information, suggested contacts and relevant resources for developing the dataset are welcomed. 

How to cite: Fowler, H., Green, A., Lewis, E., Pritchard, D., Blenkinsop, S., Patino Velasquez, L., and Whitford, A.: Unveiling global sub-daily precipitation extremes: Insights and development of the INTENSE Project , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-19552, https://doi.org/10.5194/egusphere-egu24-19552, 2024.

Draught is one of the major climate related disaster that Italy has been fighting in the recent years .It is a complex multidimensional phenomenon that is dependent upon on a wide variety of parameters ranging from climatic to socioeconomic ones. In this study we are considering watershed area of lake Bolsena, which is one of the most important water resources in central Italy, to asses in drought vulnerability using Geographical Information System (GIS)  in combination with the Analytic Hierarchy Process (AHP). GIS is used for the spatial analysis of drought for Lake Bolsena watershed area for the year 2022 which was one of the worst draught affected year in the history for the country. Parameters such as Monthly rainfall, Land use/Landcover (LULC), elevation , soil type, Normalized difference vegetation index (NDVI), Normalized Difference turbidity Index (NDTI),Normalized differentiate chlorophyl index(NDCI), Normalized Difference Water Index (NDWI),Storm power index (SPI)  were chosen and considered for the study. AHP is used to calculate weightage factors of each criterion based on the pairwise comparison matrices. The thematic maps of all the parameters were analyzed and Drought Vulnerability Assessment (DVA) map was generated using GIS. The output DVA map will provide valuable information on drought severity in the area and vulnerability related to water availability.

How to cite: Mazumdar, T., Di Francesco, S., Giannone, F., and Santini, M.: Drought vulnerability assessment and mapping using Multi-Criteria decision making (MCDM) and application of Analytic Hierarchy process (AHP) for watershed area of Lake Bolsena of Central Italy, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-19949, https://doi.org/10.5194/egusphere-egu24-19949, 2024.

Rich in biodiversity, Tumaco is a focal point for REDD+ projects that aim to combat deforestation and promote sustainable land use. Cacao farming, vital to the local economy, offers an opportunity to reconcile livelihoods and conservation. However, challenges remain in reconciling cacao and forest conservation. This study explores the benefits of sustainable cacao practices, such as agroforestry, for economic development and environmental conservation. It also looks at the challenges farmers face and the implications for the success of REDD+. Perceptions of climate change profoundly influence farmers' perspectives and behaviours in the context of REDD+ initiatives, shaping the sustainability and effectiveness of such efforts. Therefore, fostering a robust understanding of climate change among local farmers is critical to improving the integration of sustainable cacao production into REDD+ frameworks. This research aims to provide insights for policy makers and project implementers to advance both conservation and development goals in the Tumaco region, by addressing potential synergies and trade-offs between cacao production and REDD+ initiatives. The farmers' lack of knowledge is particularly worrying, not only for the fight against climate change, but also because if the cacao farmers of Tumaco do not see the incentives of carbon credits as a sustainable source of income, they will be forced to return to illegal crops, and the socio-environmental development of these communities will be compromised.

How to cite: Quiroga, S., Hernanz, V., Suarez, C., and Aguiño, J. E.: Evaluating the merit of Carbon Credits: Is there a lack of effectiveness in transitioning from direct Payments for Ecosystem Services to REDD+ community-based incentives?, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-20767, https://doi.org/10.5194/egusphere-egu24-20767, 2024.

EGU24-20944 | ECS | Posters on site | HS7.5

Towards optimizing the operation of controlled flood detention basins 

Mara Ruf and Daniel Straub

Floods are one of the most hazardous natural phenomena worldwide and they are predicted to increase both in intensity and frequency due to climate change. This necessitates comprehensive flood risk mitigation measures that are planned and controlled from a regional as well as a strategic trans-regional perspective.

Controlled flood detention basins can be effective measures for dealing with extreme flood events [1]. By temporally storing water in the detention basin, the discharge in a river is reduced. If the water is removed from the river at the optimal time, this should reduce the peak water level at downstream locations and hence the flood risk.

However, the identification of the optimal operation of flood detention basins is a non-trivial as well as non-deterministic problem. Flood forecast uncertainty, dilatation of the wave along the river channel and the uncertainty in the breaching process turns the polder operation into a stochastic optimization problem with multiple possible optimization targets. Hence, this optimization belongs to the class of sequential decision problems under uncertainty. In this contribution, we utilize a developed dynamic-probabilistic flood risk model [2] to analyze and optimize different control strategies as well as the effect of uncertainties on the optimality of the detention basin operation. We consider the case of a single detention basin as well as that of multiple detention basins that are arranged in series.

 

[1] De Kork, J.-L.; Grossmann, M. (2009): Large-scale assessment of flood risk and the effects of mitigation measures along the Elbe River. Natural Hazards (2010) 52:143-166.

[2] Ruf, M., Hoffmann, A., Straub, D. (2023): Application of a decision sensitivity measure for the cost-benefit analysis of a flood polder at the Bavarian Danube. 14th International Conference on Applications of Statistics and Probability in Civil Engineering (ICASP 14). Dublin, Ireland.

How to cite: Ruf, M. and Straub, D.: Towards optimizing the operation of controlled flood detention basins, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-20944, https://doi.org/10.5194/egusphere-egu24-20944, 2024.

EGU24-20988 | ECS | Posters on site | HS7.5

Assessment of future climate risk and vulnerability of local communities in High Mountain Asia 

Anju Vijayan Nair, Rahim Dobariya, Deo Raj Gurung, and Efthymios Nikolopoulos

Higher altitude regions like High Mountain Asia (HMA) are particularly affected by future climate change where the increasing temperature coupled with inconsistent precipitation results in rapid glacier melting during summers and less regeneration of glaciers in winters affecting the livelihoods of billions of people. Access to information on future climate change and related hazards is essential to significantly reduce the impacts on socio-economic systems in HMA. In this study, we focus on identifying the areas in northwest HMA where climate extremes are projected to increase in magnitude and/or frequency. For this, statistically downscaled climate projections (at 5km resolution) derived from a 30-member ensemble of GFDL SPEAR CMIP6 are used to evaluate the projected trends in precipitation and temperature (for years 2015 to 2100) over Afghanistan, Tajikistan, and northern Pakistan under SSP2-4.5 and SSP5-8.5 scenarios. Analysis of changes in precipitation and temperature with respect to the historic climate (1990 to 2014) is done to evaluate the vulnerability to climate hazards including droughts and heatwaves. Analysis of the changes in future climate revealed a rapid increase in the occurrence of droughts and heatwaves towards the end of the century, affecting several communities in the region. Following the methodology developed by the Implementation Platform of the EU Mission on Adaptation to Climate Change (MIP4Adapt), the climate risk and vulnerability of local communities in the region is quantified. The results of this study provide critical information to stakeholders and the local communities to proactively prepare for the anticipated climate risks in the future and to adopt appropriate mitigation measures.

How to cite: Vijayan Nair, A., Dobariya, R., Gurung, D. R., and Nikolopoulos, E.: Assessment of future climate risk and vulnerability of local communities in High Mountain Asia, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-20988, https://doi.org/10.5194/egusphere-egu24-20988, 2024.

EGU24-21687 | Orals | HS7.5 | Highlight

Understanding the dynamics of multi-sector impacts of hydro-meteorological extremes: a methods overview 

Mariana Madruga de Brito, Jan Sodoge, Alexander Fekete, Michael Hagenlocher, Elco Koks, Christian Kuhlicke, Gabriele Messori, Marleen de Ruiter, Pia-Johanna Schweizer, and Philip J. Ward

Hydro-meteorological extremes, such as droughts and floods, often trigger a series of compound and cascading impacts due to interdependencies between coupled natural and social systems. However, studies typically only consider one impact and disaster event at a time, ignoring causal chains, feedback loops, and conditional dependencies between impacts. Analyses capturing these complex patterns across space and time are thus needed to inform effective adaptation planning. Here, we present a collection of methods that can be used for assessing the dynamics of the multi-sector compound and cascading impacts (CCI) of hydro-meteorological extremes. We discuss existing challenges, good practices, and potential ways forward. Rather than pursuing a single methodological approach, we advocate for methodological pluralism. We see complementary or even convergent roles for analyses based on quantitative (e.g. data-mining, systems modeling) and qualitative methods (e.g. mental models, qualitative storylines). The data-driven and knowledge-driven methods provided here can serve as a useful starting point for understanding the dynamics of both high-frequency CCI and low-likelihood but high-impact CCI.

How to cite: Madruga de Brito, M., Sodoge, J., Fekete, A., Hagenlocher, M., Koks, E., Kuhlicke, C., Messori, G., de Ruiter, M., Schweizer, P.-J., and Ward, P. J.: Understanding the dynamics of multi-sector impacts of hydro-meteorological extremes: a methods overview, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-21687, https://doi.org/10.5194/egusphere-egu24-21687, 2024.

EGU24-969 | ECS | Posters on site | HS6.6

Integration of Sentinel-1 and Sentinel-2 Datasets for River Discharge Estimation 

Ceren Yazıgülü Tural, Koray K. Yilmaz, and Angellica Tarpanelli

Rivers are corridors of freshwater that provide vital services for sustainable development and ecosystem functioning. Moreover, increase in frequency and severity of droughts and floods due to climatic change necessitates innovative and reliable techniques enabling continuous monitoring of river discharge to effectively manage risk. Since ground-based flow gauging stations are difficult to install and maintain, especially in remote regions, remote sensing methodologies have gained attention over the last decades.

In this study, we integrate Sentinel-1 Synthetic Aperture Radar (SAR) data and Sentinel-2 optical data to make best use of their advantages, namely, observation capability on cloudy-days and higher spatio-temporal resolutions, respectively. In our methodology, we first identify the water surface area at selected river reaches where flow observations are also available. The conceptual framework for computing water surface areas within the specified study boundaries entails the utilization of water indices, specifically the Normalized Difference Water Index (NDWI) and Modified Normalized Water Index (MNDWI), for Sentinel-2 and histogram-based backscattering intensity thresholding for the Sentinel-1 platform. Later, we establish relationships between the computed surface water areas and corresponding flow measurements. The Google Earth Engine (GEE) platform serves as the operational foundation for executing the methodology. We validate the satellite-based discharge estimations using observed in-situ discharge data obtained from three selected USGS gauging stations along the Mississippi River, USA. According to our preliminary results, the coefficient of determination values between estimated and observed discharge datasets range between 0.49-0.79, 0.44-0.77 and 0.49-0.74 for the studied river reaches. The methodology is being tested for other river reaches along the globe to test and improve its river discharge estimation accuracy.

How to cite: Tural, C. Y., Yilmaz, K. K., and Tarpanelli, A.: Integration of Sentinel-1 and Sentinel-2 Datasets for River Discharge Estimation, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-969, https://doi.org/10.5194/egusphere-egu24-969, 2024.

EGU24-1141 | ECS | Orals | HS6.6

Towards Accurate Flood Mapping in Arid Regions: Sentinel-1 SAR-based insights and explainable machine learning.  

Shagun Garg, Antara Dasgupta, Sakthy Selvakumaran, Mahdi Motagh, and Sandro Martinis

Floods are not only frequent but also one of the costliest natural disasters. The use of satellite remote sensing is a cost-effective and widely adopted method for near real-time flood monitoring. Optical satellite imagery excels at distinguishing water from other land cover types by leveraging the spectral behavior in visible and infrared ranges. However, a major limitation of optical sensors is their inability to penetrate through clouds. This results in images with missing information, impeding their use for flood monitoring. In the past decade, Sentinel-1 Synthetic Aperture Radar (SAR) imagery has emerged as a valuable tool in operational flood management, overcoming the challenges posed by optical sensors. SAR is an active imaging technique that provides cloud-free images day and night by utilizing specular reflection from smooth water surfaces. In SAR imagery, water appears dark due to its unique backscatter characteristics. While SAR amplitude has been widely used for flood detection and monitoring, it tends to overestimate flooded areas, especially in arid and semi-arid regions, because the radar backscatter over sand and open water surfaces is similar. 

In our study, we explore the potential of Sentinel-1 amplitude and interferometric coherence in arid-flood mapping. We conduct multiple case studies and employ the random forest method to train, test, and validate our model predictions against flood masks derived from cloud-free optical imagery. We design several scenarios to investigate the contribution of different layers of information in improving flood mapping accuracy in arid regions along with feature importance analysis to understand the role of each feature to reduce model complexity. Our results demonstrate the effectiveness of fusing amplitude and coherence information in flood mapping,  as compared to coherence or amplitude alone. By utilizing the key features derived using permutation feature importance, flood mapping accuracy was significantly improved by approximately 50%, while also reducing response time, which is crucial for effective emergency management. The findings hold promise and emphasize the versatility of the proposed approach across different sensors and scenes. This offers significant potential for global flood mapping in arid regions, particularly in countries with limited resources. As future missions and advancements in SAR systems continue to evolve, the detection capabilities for floods will further improve, leading to enhanced flood management in arid areas. 

How to cite: Garg, S., Dasgupta, A., Selvakumaran, S., Motagh, M., and Martinis, S.: Towards Accurate Flood Mapping in Arid Regions: Sentinel-1 SAR-based insights and explainable machine learning. , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-1141, https://doi.org/10.5194/egusphere-egu24-1141, 2024.

EGU24-1789 | ECS | Posters virtual | HS6.6

Applicability Of Multi-National Digital Elevation Model (DEM) For Cross-Boundary Flash Flood Modeling 

Mohammedawel Jeneto Mohammed

In July 2021, the northwestern European continent experienced a devastating flood caused by unusually high rainfall, resulting in significant socio-economic destruction. One of the areas that was highly affected by this flooding was Southern Limburg (Geul River) in the Netherlands. The Geul River, located between Belgium, Germany, and the Netherlands, posed a challenging situation for modeling the catchment due to its cross-boundary nature. The need to harmonize input datasets from different countries with varying characteristics arose despite the abundance of available data in the study area. This study assesses the feasibility of combining multi-national Digital Elevation Models (DEM) for cross-boundary flash flood modeling purposes.

The quality of the DEM significantly impacts the accuracy of flood dynamics. However, it should be noted that elevation data from various sources creates elevation mismatches, particularly in the overlapping areas between different DEMs. A comprehensive quality assessment is indispensable to ensure the compatibility and reliability of these datasets for hydrology and flood modeling. Thus, To evaluate the accuracy of the DEMs, various statistical measures such as Root Mean Square Error (RMSE), Mean and Standard Deviation (STD) have been calculated. Initially, a pixel-by-pixel-based elevation difference map was generated. Upon analysis, it was observed that the overall elevation differences ranged from -7.0 to 7.0 meters. Despite certain pixels exhibiting pronounced differences in elevation, the overall statistical analysis indicated minimal variation. The calculated RMSE and STD values were both ≤ 0.3 meters for the overlapping parts of the DEM. These errors were considered negligible in relation to the actual slope values and had no significant impact on the flow direction within the catchment. This merged dataset provides a comprehensive representation of the terrain, enabling more accurate and reliable flood modeling simulations compared with calibrated result.

How to cite: Mohammed, M. J.: Applicability Of Multi-National Digital Elevation Model (DEM) For Cross-Boundary Flash Flood Modeling, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-1789, https://doi.org/10.5194/egusphere-egu24-1789, 2024.

EGU24-2941 | Posters on site | HS6.6

Use of Remote Sensing Flood Inundation Maps (FIM) for Evaluating Model-predicted FIM: Challenges and Strategies 

Sagy Cohen, Dan Tian, Anupal Baruah, Hongxing Liu, and Parvaneh Nikrou

Remote sensing (RS)-derived Flood Inundation Maps (RS-FIM) are, in principle, a desirable source of observed data for the development, calibration, and validation of (model) predicted FIM. Advantages of using RS-FIM for evaluating predicted FIM include its spatial continuity (compared to point observations), and the representation of real flooding events (compared to synthetic events (e.g. 100-yr) or other models). Disadvantages may include low/mismatched spatial resolution, insufficient classification accuracy, lack of water depth information, and gaps in coverage (due to dense vegetation, buildings, clouds, etc.). Gaps in inundation coverage are very common in RS-FIM. While these may not be a major issue for some RS-FIM applications, they are a major, yet unacknowledged, issue for fair and robust evaluation of predicted FIM. This is because the evaluated model may correctly predict flooding in these gaps while the (RS-FIM) benchmark data is classified as non-flooded (leading to inaccurate identification of 'False-positives'). Techniques for 'closing the gaps' in RS-FIM using hydraulic models or terrain analysis can yield improved FIM but, depending on the scale of the 'gap-filling', can result in an RS-model hybrid which undermines the observational nature of RS-FIM. Here we will demonstrate and discuss the challenges in using RS-FIM for the evaluation of predicted FIM and present tools and analysis demonstrating a new framework for fair and robust evaluation of FIM predictions using RS-FIM.

How to cite: Cohen, S., Tian, D., Baruah, A., Liu, H., and Nikrou, P.: Use of Remote Sensing Flood Inundation Maps (FIM) for Evaluating Model-predicted FIM: Challenges and Strategies, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-2941, https://doi.org/10.5194/egusphere-egu24-2941, 2024.

EGU24-4031 | Orals | HS6.6 | Highlight

 Satellite Video Remote Sensing for Flood Model Validation  

Christopher Masafu and Richard Williams

Satellite-based optical video sensors are poised as the next frontier in remote sensing. Satellite video offers the unique advantage of capturing the transient dynamics of floods with the potential to supply hitherto unavailable data for the assessment of hydraulic models. A prerequisite for the successful application of hydraulic models is their proper calibration and validation. In this investigation, we validate 2D flood model predictions using satellite video-derived flood extents and velocities. Hydraulic simulations of a flood event with a 5-year return period (discharge of 722 m3 s-1) were conducted using HEC-RAS 2D in the Darling River at Tilpa, Australia. To extract flood extents from satellite video of the studied flood event, we use a hybrid transformer-encoder convolutional neural network (CNN)-decoder deep neural network. We evaluate the influence of test-time augmentation (TTA) – the application of transformations on test satellite video image ensembles, during deep neural network inference. We employ Large Scale Particle Image Velocimetry (LSPIV) for non-contact-based river surface velocity estimation from sequential satellite video frames.When validating hydraulic model simulations using deep neural network segmented flood extents, critical success index peaked at 94% and on average improved by 9.5% when TTA was implemented. We show that TTA offers significant value in deep neural network-based image segmentation, compensating for aleatoric uncertainties. The correlations between model predictions and LSPIV velocities were reasonable and averaged 0.78. Overall, our investigation demonstrates the potential of optical space-based video sensors for validating flood models and studying flood dynamics.

How to cite: Masafu, C. and Williams, R.:  Satellite Video Remote Sensing for Flood Model Validation , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-4031, https://doi.org/10.5194/egusphere-egu24-4031, 2024.

EGU24-5438 | ECS | Posters virtual | HS6.6

A novel framework for the selection of spectral input to pixel-based river discharge estimation model using Sentinel-2 imagery 

Amirhossein Tayebi-Alashti and Mohammad Danesh-Yazdi

While access to discharge data is key to hydrologic studies, it is a serious obstacle in ungauged basins. Currently, Sentinel-2 imagery at high spatiotemporal resolution offers a unique opportunity to infer the relation between pixel-based discharge rate and surface reflectance. One promising approach in this respect has been to find the complex relationship between river discharge and the spectral ratio between two benchmark pixels, namely the wet and dry pixels, whose dynamics resembles river discharge variation. However, this has been challenging due to the adverse impact of soil moisture and mixed land cover on the spectral behavior of the dry pixel. The selection of the wet pixel must also guarantee sufficient sensitivity of its spectral response to water depth fluctuations. To tackle the above issues, in this study, we developed a novel framework that automatizes the selection of the wet and dry pixels by using Sentinel-2 imagery. We also introduced the Normalized Difference Discharge Index (NDDI), as the best band combination, to predict river discharge. We used linear regression with leave-one-out cross-validation as the prediction model, which leverages limited satellite data due to the cloud cover. By implementing the developed framework at multiple gauged points across the continental United States, the best location of the dry pixel was consistently found in urban pixels whose longwave reflectance fall within a certain range. By analyzing the pixel-wised correlation coefficient between surface reflectance at NIR band and river discharge across the studied river widths, we found that the best wet pixels are located along river banks with shallow water depth. These pixels were characterized by the average reflectance higher than the 98th percentile in the green band. Finally, by testing over 4000 band combinations as input to the river discharge prediction model, we found that the normalized difference between B11 and B4 for the wet pixel, as well as the B11 ratioing between the dry and wet pixels yielded the most accurate predictions with R2 = 0.88 and R2 = 0.73, respectively.

How to cite: Tayebi-Alashti, A. and Danesh-Yazdi, M.: A novel framework for the selection of spectral input to pixel-based river discharge estimation model using Sentinel-2 imagery, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5438, https://doi.org/10.5194/egusphere-egu24-5438, 2024.

EGU24-5543 | ECS | Posters on site | HS6.6

Satellite-based Mapping of Flood Extent in Denmark 

Mark Hansen, Jacob Vejby, and Julian Koch

Floods stand out as the most frequent and costly natural disaster in Europe. In the EU alone, there have been documented more than 1500 flood events since 1980, causing over 4300 deaths and more than €170 billion in economic damages.

Due to the compounded developments of urbanization and climate change, the frequency of floods is expected to increase with severe impacts, possibly endangering lives and leading to economic losses. Moreover, floods mobilize pollutants stored in the subsurface and urban areas. Thus, current efforts, such as coastal barriers, restoration of river courses, or resilient city and landscape planning, focus on reducing vulnerability and risks from flooding. But to implement such measures, detailed information on where and when flooding occurs is necessary. This study aims to improve and implement satellite-based mapping of flood extent under Danish conditions by presenting different methods and algorithms utilizing Sentinel-1 (S1) Synthetic Aperture Radar (SAR) imagery, digital elevation models (DEM) and river geometry. In the broader literature, various methods have been proven to successfully map flood extent, such as deep learning (DL) and change detection (CD) as employed in the Global Flood Awareness System. However, DL require extensive training and labeled data that are often not available, and CD is reliant on a comprehensive pre-processing procedure of antecedent satellite imagery or accompanied with a datacube-based algorithm that exploits the satellite orbit repetition. While these methods can provide excellent results, the steep data requirements and pre-processing procedures hinder practical usage. On the other hand, single-temporal image flood extent mapping algorithms relying on histogram analysis offering a straightforward approach potentially yielding satisfying results, especially when accompanied by techniques such as image decomposition, region-growing, active contour models or image texture algorithms. But for single-temporal image histogram analysis to work in an automated setup, the two main problems, namely class imbalance and class overlap must be addressed properly. This study proposes a novel approach for single-temporal image histogram analysis by combining automatic local histogram thresholding with two image decomposition techniques for image tiling using a quadtree and a novel combination of k-means clustering and box tiling. This study implements a bimodality test and a subsequent local-threshold selection using gaussian mixture modelling and kernel-density smoothening, followed by contextual segmentation using region-growing. Furthermore, a novel approach for improving flood extent segmentation using a combination of DEM information, geographical stream location and region-growing is presented. The proposed method is showcased for two different flood events in Denmark from 2015 to 2022 using 10 x 10 m interferometric wide swath S1 SAR imagery. Results are evaluated using Sentinel-2 optical imagery where available, and otherwise evaluated against high-precision permanent water maps. Moreover, we utilize gauged timeseries of stream water level to evaluate the temporal evolution of flood extent over the period of a flood event.  

How to cite: Hansen, M., Vejby, J., and Koch, J.: Satellite-based Mapping of Flood Extent in Denmark, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5543, https://doi.org/10.5194/egusphere-egu24-5543, 2024.

EGU24-6430 | ECS | Posters on site | HS6.6

A Stacking Ensemble Method for Comprehensive Flood Susceptibility Mapping in Yemen 

Mustafa Ghaleb, Ahmed Al-Areeq, Nabil Al-Areeq, Radhwan Saleh, Anas AbuDaqa, and Atef kawara

The necessity of flood risk mapping is critical for effective planning and disaster response, particularly in flood-prone regions like the Qaa'Jahran watersheds in Dhamar, Yemen. This research implements various machine learning methods, including Support Vector Machines (SVM), K-Nearest Neighbors (kNN), Random Forest (RF), Artificial Neural Networks (ANN), and Logistic Regression (LR), with the latter also functioning as the meta-model in our stacking ensemble approach for mapping flood susceptibility. The process began with creating a flood inventory map using SAR images and historical flood records. Our model integrates the individual strengths of each technique and employs a meta-model to synthesize their forecasts. This stacked ensemble approach demonstrated superior performance over each model alone, achieving a remarkable AUC score of 0.9848 compared to the individual scores of SVM, LR, kNN, ANN, and RF. It also surpassed two innovative models, ABRBF and TPOT, in accurately pinpointing all high-risk zones identified in historical flood data. This advancement in flood risk mapping for the Qaa'Jahran watersheds exemplifies the potential of our model in enhancing disaster management and prevention efforts. It offers a significant tool for identifying at-risk areas and guiding mitigation strategies to safeguard communities in Dhamar, Yemen, against the catastrophic impacts of flooding.

How to cite: Ghaleb, M., Al-Areeq, A., Al-Areeq, N., Saleh, R., AbuDaqa, A., and kawara, A.: A Stacking Ensemble Method for Comprehensive Flood Susceptibility Mapping in Yemen, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-6430, https://doi.org/10.5194/egusphere-egu24-6430, 2024.

EGU24-8214 | ECS | Posters on site | HS6.6

Multi-source in situ and satellite variational data assimilation into a fully distributed hydrological model for floods and droughts modeling over poorly gauged and ungauged areas 

Mouad Ettalbi, Pierre-Andre Garambois, Nicolas Baghdadi, Emmanuel Ferreira, and Ngo-Nghi-Truyen Huynh

Estimating water flows and stocks in surface hydrology is crucial for addressing important socio-economic issues, such as managing water resources and predicting extreme events like floods and droughts. These challenges become more significant with the ongoing global climate change, which may intensify the hydrological cycle. Advanced modeling tools are necessary for making precise and reliable local forecasts. However, hydrological models, regardless of their complexity and status, encounter difficulties in accurately and reliably predicting quantities of interest such as river flows or soil moisture states, and in accounting for meteorological-climatic effects on hydrology. Given the complexity of the physical processes involved and their heterogeneous and limited observability, hydrological modeling is a challenging task, and internal flows often have significant uncertainties. These uncertainties could be reduced by integrating new observations from remote sensing applied to continental surfaces, which is rapidly evolving. A variety of satellites and sensors now allow the observation of watershed surface characteristics and hydrological responses with increasing spatial-temporal resolutions. In particular, products of soil moisture, evaporation, and land use are now available at relatively high spatial-temporal resolution. This work focuses on improving the integration of satellite and in-situ land surface data into spatially distributed hydrological models. The Hybrid Data Assimilation and Parameter Regionalization (HDA-PR) approach incorporating learnable regionalization mappings, based on neural networks into the differentiable hydrological model SMASH, is modified to account for satellite moisture maps in addition to discharge at gauging stations and basins physical descriptors maps. Regional optimizations are performed on flash-flood-prone areas located in the South of France and their accuracy and robustness is evaluated in terms of simulated discharge and moisture against observations. 

How to cite: Ettalbi, M., Garambois, P.-A., Baghdadi, N., Ferreira, E., and Huynh, N.-N.-T.: Multi-source in situ and satellite variational data assimilation into a fully distributed hydrological model for floods and droughts modeling over poorly gauged and ungauged areas, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-8214, https://doi.org/10.5194/egusphere-egu24-8214, 2024.

Synthetic Aperture Radar (SAR) satellites have emerged as the predominant information source for large-scale flood mapping, owing to their ability to map the Earth's surface regardless of weather conditions. Additionally, the classification of permanent water bodies and inundated areas from SAR images appears to be relatively straightforward given that calm water surfaces show up as dark patches in SAR images. Yet, a naïve approach to water body and flood classification from single SAR images can be misleading for many reasons. Firstly, in most environments SAR sensors under-detect the surface water extent due to challenging land cover and rough water surfaces. Secondly, there are water-look-alike surfaces such as tarmac or grasslands that are misclassified as water. Last but not least, the definition of permanent water bodies, wetlands, and floods is not trivial and only possible when using historic observations as reference. Some of this challenges can be addressed by experts when classifying only a limited set of SAR images. However, the difficulty significantly increases when attempting to map water bodies and floods in a fully automatic manner without prior knowledge of the environmental conditions. This becomes essential, for instance, when investigating the dynamics of wetland areas or the recurrence of floods over extended time periods or regions, or when employing SAR data for near-real-time flood monitoring. In this presentation, I will provide an overview of these challenges, drawing on the outcomes of research on this topic carried out at TU Wien over the last two decades and the preliminary experiences gained from the operationalization of the new fully-automated Sentinel-1 based Global Flood Monitoring service, which is operated as one of the components of the Copernicus Emergency Management Service.

How to cite: Wagner, W.: Scientific challenges when using SAR images for mapping of water bodies and floods everywhere and anytime, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-8642, https://doi.org/10.5194/egusphere-egu24-8642, 2024.

EGU24-11162 | ECS | Orals | HS6.6 | Highlight

Urban Flood Classification in SAR Images 

Rotem Mayo, Tal Ikan, and Adi Gerzi Rosenthal

Detecting flooding in Synthetic Aperture Radar (SAR) satellite imagery is crucial for the ability of Google’s flood forecasting team to train predictive models and identify regions at risk of flooding, making it possible to give prior warning to people in soon to be flooded areas.  However, flood detection in urban areas is currently very poor, preventing the extension of these advanced warning systems to large parts of the population. This is a long known challenge in the field of flood detection using remote sensing methods. In this study, we discuss a possible method to overcome this problem.

SAR satellites are preferred for flood monitoring due to their effectiveness regardless of weather or environmental conditions. They operate by sending pulse signals to Earth and measuring the reflected backscatter. Smooth surfaces like water typically reflect signals away, appearing darker in SAR images. However, in urban areas, the 'Double Bounce' effect caused by 90-degree surfaces, causes larger backscatter, making water detection challenging.

Our methodology involves analyzing abnormally bright pixels in urban areas, attributed to the amplification of the double bounce effect by flooding. We deviate from the traditional thresholding per image approach used in rural settings, instead focusing on the historical brightness levels of each pixel separately to identify significant deviations. We then aggregate the data over large urban areas to infer potential flooding.

We optimize and evaluate the model using a train-validation split of a dataset consisting of approximately 70 urban flood events, manually curated from news stories and paired with corresponding SAR images. The evaluation, which compares these images with randomly selected images, yields a precision of 86% and a recall of 62%.  Acquiring high quality ground truth data proved to be one of the big challenges in this project, and we are currently working on other ways to evaluate the model and improve its accuracy.

These results demonstrate the potential of using SAR images for urban flood classification by focusing on the unique characteristics of urban areas, such as the double bounce effect. This method shows promise in providing alerts and forecasts for urban regions, a crucial need for disaster management. Further research and more accurate ground truth data could enhance the effectiveness and accuracy of detecting urban floods through SAR images.

How to cite: Mayo, R., Ikan, T., and Gerzi Rosenthal, A.: Urban Flood Classification in SAR Images, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-11162, https://doi.org/10.5194/egusphere-egu24-11162, 2024.

EGU24-11215 | ECS | Posters on site | HS6.6

Flood inundation Mapping for the Sept. 2023 Derna, Libya flood event using Sentinel-1 SAR data: An Integration of SAR intensity and interferometric coherence 

M. Sulaiman Fayez Hotaki, Mahdi Motagh, and Mahmud Haghshenas Haghighi

Flood mapping, particularly in data-scarce regions, poses challenges including inadequate observational data to understand the hydrological characteristics of the floods. This study addresses this research gap by utilizing remotely sensed data, specifically Sentinel-1 Synthetic Aperture Radar (SAR) images, to delineate flood extent related to the September 11, 2023 Derna flood event in Libya. The objective is to extract flood extent from both SAR intensity and coherence and integrate these characteristics to generate a confidence flood map.

Our approach involves radiometric terrain correction of SAR data, flood pixel identification using anomaly detection techniques based on SAR intensity, and coherence analysis of pre-and post-flood SAR images. Flooded areas are categorized into 3 main classes. These include (1) High Confidence Flood (HCF), which is the intersection of SAR intensity and coherence in VV and VH bands in both Ascending and Descending directions; (2) Medium Confidence Flood (MCF), extracted from intensity and coherence in either the Ascending or Descending direction in both VV and VH bands; and (3) Low Confidence Flood (LCF), extracted from a single direction in either VV or VH band. LCF includes all pixels not confidently identified as part of either HCF or MCF.  The effectiveness of flood segmentation utilizing the integration of anomaly detection of SAR intensity and coherence analysis method is evaluated through a comparison between Sentinel-1 SAR data and optical Planet imagery.

Our findings indicate HCF covering approximately 8 hectares, MCF covering around 24 hectares, and LCF covering more than 227 hectares. These findings offer valuable insights into the observed flood extent at varying confidence levels. However, the moderate temporal resolution of Sentinel-1 data, with a revisit time of 12 days, introduces challenges in promptly detecting the entire extent of the flood. Overall, this study underscores the significance of remote sensing technology in near-real-time flood monitoring, emphasizing its role in identifying vulnerable areas, prioritizing resources, planning for potential risks, and supporting decision-making in relief efforts.

How to cite: Hotaki, M. S. F., Motagh, M., and Haghshenas Haghighi, M.: Flood inundation Mapping for the Sept. 2023 Derna, Libya flood event using Sentinel-1 SAR data: An Integration of SAR intensity and interferometric coherence, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-11215, https://doi.org/10.5194/egusphere-egu24-11215, 2024.

EGU24-13278 | Orals | HS6.6 | Highlight

Leveraging SWOT's water elevation pixel cloud to comprehend analyse the spatial dynamics of flood events 

Nicolas Gasnier, Roger Fjørtoft, Lionel Zawadzki, Damien Desroches, Santiago Pena Luque, Pottier Claire, Thérèse Barroso, and Picot Nicolas

Satellite data have been used for over 40 years, along with airborne and in situ measurements, for monitoring extreme hydrological events, and enabled major progress in our understanding of floods. The available satellite data have long been mostly limited to imagery (SAR, optical, and thermal) providing a map of the flood extent and conventional nadir altimetry providing a 1-dimensional water elevation along the satellite ground track. Since its launch in late 20232, SWOT has opened a new dimension in space altimetry by providing two-dimensional maps of water elevation. Its main instrument is a near-nadir, bistatic, Ka-band SAR altimeter that uses interferometry to measure the elevation of the water pixels (10-60x22m resolution). Although its revisit time (at least twice per 21-day nominal cycle up to 78° latitude) and spatial resolution limits its usability for operational flood monitoring, SWOT opens new perspectives in the understanding of flood dynamics, particularly if used in synergy with high-resolution imagery and real-time in situ measurements. Indeed, water elevation maps can be used to calibrate and validate hydraulic models through their comparison with the elevation of the modeled free surface at the corresponding point in time. In addition, estimations of the river flows are part of the standard SWOT products distributed on the PODAAC and hydroweb.next platforms.

While the early results on recent flood events demonstrated the utility of the SWOT data for understanding the dynamics of floods, research efforts are still needed to fully leverage its scientific and socioeconomic benefits. On the one hand, there is a scope for improvement in the production of the water elevation pixel cloud from the SLC images: the baseline data processing is dedicated to lakes and river monitoring, and custom processing for flood events may improve the quality of the water elevation data in flooded areas. On the other hand, due to their relative novelty, further adaptations will be needed to operationalize their use for key applications (e.g., more accurate modeling of floods to engineer flood-risk infrastructure, assimilation in operational hydraulic models along with other sources of data, improved risk assessment on buildings through better forecasting of water levels,...). Further research works will be able to draw on SWOT's open data, including the calibration and validation phase, which lasted from end of  March to early July 2023 on selected orbits with a 1-day repeat cycle. This phase enabled SWOT acquisitions every 24 hours for multiple flood events, including the flooding caused by the destruction of the Kakhovka Dam in Ukraine. This high temporal revisit allows for fine-scale analysis of the temporal evolution of the water elevation of the flooded area.

In our contribution, we will present early results on selected examples of flood events, and some scientific and technical issues that we believe to be of particular interest.

How to cite: Gasnier, N., Fjørtoft, R., Zawadzki, L., Desroches, D., Pena Luque, S., Claire, P., Barroso, T., and Nicolas, P.: Leveraging SWOT's water elevation pixel cloud to comprehend analyse the spatial dynamics of flood events, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-13278, https://doi.org/10.5194/egusphere-egu24-13278, 2024.

EGU24-13550 | ECS | Posters on site | HS6.6

Initial steps towards implementation of an early warning system with distributed hydrologic-hydrodynamic modeling for an urban basin with quantitative precipitation estimation (QPE) from meteorology radar. 

Mateo Hernandez Sanchez, Luis Miguel Castillo Rapalo, Pedro Gustavo Silva, and Eduardo Mario Mediondo

Continuous megacities' development, aging infrastructure, and increasing frequency and magnitude of extreme events, the lack of flood resilience becomes a pressing issue due to inadequate planning of existing hydraulic structures to handle future threats. A more resilient urban flood risk management strategy is required to efficiently mitigate the impacts of climate change, particularly floods resulting from river and urban channel overflows. This is evident within the Aricanduva River watershed area in the east zone of São Paulo City, Brazil, a region with flood challenges arise because existent hydraulic infrastructures are ineffective in inundation control, due to extensive urbanization in the lower and middle parts of the basin. To achieve resilience in urbanized areas and reduce the risk of flash floods, the development of Early Warning Systems (EWS) is crucial. An EWS serves as a predictive tool for accurately forecasting water levels in rivers or channels in real-time, providing enough time to take action in order to reduce potential risk. Hydrologic-hydrodynamic models are increasingly employed in EWS to enhance their effectiveness. However, many urban basins lack monitoring systems, whereas products such as meteorological radar represent a feasible option since they effectively capture the spatial and temporal distribution of rainfall. In urban basins like the Aricanduva River, where the quantity and distribution of pluviometers are insufficient to spatially represent an event, the use of Quantitative Precipitation Estimation (QPE) from meteorology radar becomes essential to improve hydrological-hydrodynamic analyses. The objective of this work is to propose the presentation of a distributed hydrological-hydrodynamic model (HydroPol2D) for the Aricanduva basin, calibrated with QPEs from meteorological radar. Additionally, rainfall data from 15 gauges within and around the basin were utilized, covering a 5-year period, to generate spatial rainfall using Inverse Distance Weighted (IDW) interpolation. The results of the two rainfall databases were compared using metrics such as the Nash-Sutcliffe efficiency index, Efficiency of Kling-Gupta (KGE) index, and the percentage of bias to assess model accuracy. The findings indicate that (i) the distributed model coupled with QPEs produces favorable results and better represents the basin's dynamics, (ii) the model accurately reflects the hydraulics of existing flood control infrastructure within the basin, and (iii) the generation of an accurate and rapid rainfall-runoff model forms the initial steps in identifying risk areas, establish critical points for the early warning systems and analyzing the factors contributing to or generating the risk. The next step of this work is to assess the model with more events and to include in the model strategies to automate flow control in existing flood control infrastructures.  

Keywords: Urban flooding risk management, Early Warning Systems (EWS), Hydrological-hydrodynamic models, Radar Quantitative Precipitation Estimation (QPE), Climate Change.

How to cite: Hernandez Sanchez, M., Castillo Rapalo, L. M., Silva, P. G., and Mediondo, E. M.: Initial steps towards implementation of an early warning system with distributed hydrologic-hydrodynamic modeling for an urban basin with quantitative precipitation estimation (QPE) from meteorology radar., EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-13550, https://doi.org/10.5194/egusphere-egu24-13550, 2024.

EGU24-13922 | Orals | HS6.6

Developing near-real time flood mapping capabilities in Australia 

Jiawei Hou, Wendy Sharples, Luigi Renzullo, Fitsum Woldemeskel, Christoph Rudiger, and Elisabetta Carrara

Floods rank as the second-most deadly natural hazard in Australia, surpassed only by heatwaves. The ability to monitor flood extent and depth in near real-time is key to mitigating the loss of human life and minimizing the adverse socio-economic and environmental impacts. This study aims to discover the best way to map flood extent and depth in near-real time based on the most up-to-date  available information (i.e., gauge data, hydrological and hydrodynamic models, earth observations) in Australia. High resolution (i.e., 1-5 metres) airborne LiDAR DEMs are available across most of Australia's flood-prone east coast regions. The accessibility  of this information facilitates the creation of detailed, LiDAR-derived Height above Nearest Drainage (HAND) maps, which serve as an essential baseline for accurately mapping flood events. In gauged catchments, we utilized the Bureau of Meteorology’s environmental data management system, WISKI, an API solution that provides access to in-situ water levels at gauged locations across Australia. In ungauged catchments, we routed the Bureau’s operational runoff simulations (AWRA-L v7) using CaMa-flood to estimate flood level dynamics. By integrating these estimates into the HAND mapping approach, we generated a dynamic temporal profile of flood events in near-real time, effectively capturing the spatial-temporal onset, peak, and recession stages of flooding - essential information for emergency services. As the accuracy of the modelling approach is affected by uncertainties from runoff simulation and river morphology parameters, we additionally develop a multi-satellites-based flood monitor system to bolster the accuracy of modelled information. This system utilizes data from multiple medium-resolution satellite sources, including Sentinel-1 and -2, and Landsat -7 and -8/9. By extracting updated remote sensing imagery from Google Earth Engine and Digital Earth Australia, our approach simplifies and optimizes the process of deriving flood extent and depth from satellite and airborne LiDAR observations. Notably, this remote sensing approach significantly reduces interference from clouds, cloud shadows, terrain shadows, and vegetation cover, which are common challenges in optical remote sensing. Additionally, it effectively mitigates the 'double-bounce' effects often caused by vegetation and buildings in Synthetic Aperture Radar (SAR). To verify our end to end near real time flood mapping product, we used ICEYE (commercial SAR company) flood product to benchmark flood maps derived in this study and assessed the feasibilities of developing near-real time flood mapping network in Australia. Crucially, the immediate availability of data is essential in facilitating efficient allocation of resources and safeguarding infrastructure. Simultaneously, near real-time flood mapping plays a crucial role in enhancing community preparedness, allowing for strategic planning and swift action in response to hazardous situations.

How to cite: Hou, J., Sharples, W., Renzullo, L., Woldemeskel, F., Rudiger, C., and Carrara, E.: Developing near-real time flood mapping capabilities in Australia, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-13922, https://doi.org/10.5194/egusphere-egu24-13922, 2024.

EGU24-14669 | ECS | Posters on site | HS6.6

Improving 2D hydraulic modeling in floodplain areas with ICESat-2 data: A case study in Upstream Yellow River 

Monica Coppo Frias, Suxia Liu, Xingguo Mo, Daniel Druce, Dai Yamazaki, Aske Folkmann Musaeus, Karina Nielsen, and Peter Bauer-Gottwein

Climate change intensifies the occurrence of severe flood events, increasing the demand for flood modeling studies. Hydrodynamic models can effectively represent flood events, but they are limited by the quality of available observations. Accurate topographic elevation is essential to replicate channel-floodplain interaction. Elevation is normally retrieved using satellite-based DEMs. However, freely available DEMs have a low spatial resolution, which is a limitation in identifying small-scale channel features in complex floodplain topography. In addition, these products can present issues such as vertical offset, random noise, or vegetation biases. These issues can lead to large errors when used in hydraulic modeling to simulate water levels and inundation extent. FABDEM is a 1 arcsec DEM, that removes forest and building artifacts from Copernicus DEM, but to map complex floodplain topography, finer resolution is needed. ICESat-2 mission offers a large spatial coverage with an along-track resolution down to 70 cm in the ATL03 product. This data product has shown great potential when mapping river topography and identifying small-scale channel features. ATL03 can be used as a control point dataset, to correct biases and refine DEMs

To improve the accuracy of 2D hydraulic models, FABDEM was corrected on selected floodplain areas using supplementary data and machine learning methods. Artificial Neural Network (ANN) was used in the correction of FABDEM. This regression algorithm can predict differences between FABDEM floodplain elevation and ATL03 reference elevation, inputting data from Sentinel-2 and water occurrence maps produced from spectral and SAR imagery. The output floodplain elevation has a reduced vertical offset and a spatial resolution of 10 m, which can detect small-scale channel features. Flood inundation was simulated using the updated DEM. The high computational cost of 2D hydraulic models is a limitation when using discharge time series. To deal with computational cost, discharge classes were defined to represent different inundation scenarios that provide a good indicator for flood risk management, and steady-state inundation patterns were simulated for each discharge class.

The method is demonstrated in a section of the Upstream Yellow River characterized by large floodplains with complex topography, and small-scale channels. Discharge observations from the Jimai in-situ station are used to define discharge classes. The discharge classes are defined by calculating the exceedance probability of a discharge value. The inundation scenarios are simulated for high flow discharge values for an exceedance probability of 25% (Q25) and 10% (Q10), and medium flow discharge values with 50% (Q50), and are compared with the corresponding water occurrence map produced from spectral and SAR imagery for the given discharge class. The critical success index (CSI) of the inundation map improves by about 5% using FABDEM corrected version for Q10 and Q25, and about 4% for Q50. In addition, we observe a consistent Bias reduction of about 20% for Q10 and Q25.

How to cite: Coppo Frias, M., Liu, S., Mo, X., Druce, D., Yamazaki, D., Folkmann Musaeus, A., Nielsen, K., and Bauer-Gottwein, P.: Improving 2D hydraulic modeling in floodplain areas with ICESat-2 data: A case study in Upstream Yellow River, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-14669, https://doi.org/10.5194/egusphere-egu24-14669, 2024.

EGU24-15280 | ECS | Posters on site | HS6.6

Flood risk assessment in the Ganga Basin, India: A multi-criteria geospatial analysis with NASA’s Black Marble Nighttime light Data 

Ekta Aggarwal, Marleen C. de Ruiter, Sophie Buijs, Alexander C. Whittaker, Sanjeev Gupta, Kartikeya S. Sangwan, and Ranjay Shrestha

Changing climate, intense rainfall, and geomorphological conditions within the Indo-Gangetic Basin (IGB) have led to recurring flooding within the area in the recent past. The devastating August 2022 floods in Pakistan affected 33 million people causing severe loss of life and property. The occurrence of such flooding events has increased the need to understand the complexities of the interplay between flood hazards, exposure, vulnerability, and risk. This study delves into flood risk within India's Ganga Basin, examining the flood-inducing factors, vulnerability, and exposure through an innovative approach using NASA's Black Marble Nighttime Lights Product Suite (VNP46).  The product suite, available globally on daily, monthly, and annual composite scales, corrects extraneous sources of noise in nighttime light (NTL) radiance signals and has proven effective in disaster monitoring, risk assessment and reduction, humanitarian response, preparedness, resilience, and sustainable development.

Our work to date has successfully utilized these NTL data to quantify flood exposure and the impact of flooding in both urban and rural areas by analyzing changes in radiance across time and space. However, to improve our understanding of human response to floods, we now focus on a more intricate analysis: incorporating geomorphological and socio-hydrological factors into a risk assessment approach.

Our study evaluates flood hazard, exposure, and vulnerability as three separate entities and combines them using a multi-criterion decision tool to assess flood risk within the basin. Flood hazards are studied as a relationship between geomorphological and hydrological parameters, whereas flood vulnerability is studied using land use and land cover data. The novelty of this research is using NASA’s Black Marble nightlights as a proxy to study flood exposure. We argue that the NTL data can more effectively capture the human presence and economic activities compared to some conventional parameters for flood exposure such as population count, household density, and literacy amongst others. By integrating these diverse data layers using the robust Analytical Hierarchical Process (AHP), we generate comprehensive flood risk maps across the Ganga Basin spanning a decade. The accuracy of these maps is validated against historical flood event data from the EM-DAT database. Ultimately, our research culminates in a spatially explicit and data-driven approach to flood risk assessment, which can empower targeted mitigation strategies and proactive planning within the basin.

How to cite: Aggarwal, E., de Ruiter, M. C., Buijs, S., Whittaker, A. C., Gupta, S., Sangwan, K. S., and Shrestha, R.: Flood risk assessment in the Ganga Basin, India: A multi-criteria geospatial analysis with NASA’s Black Marble Nighttime light Data, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-15280, https://doi.org/10.5194/egusphere-egu24-15280, 2024.

EGU24-15378 | Orals | HS6.6 | Highlight

Earth Observation-Driven Flood Response for Emilia-Romagna: The SaferPlaces Platform 

Paolo Mazzoli, Valerio Luzzi, Marco Renzi, Marianne Bargiotti, Sabrina Outmani, Stefania Pasetti, Stefano Bagli, and Francesca Renzi

In May 2023, the region of Emilia-Romagna, Italy, experienced an unprecedented hydrological event when 350 million cubic meters of rain fell over 36 hours, leading to widespread flooding and landslides. This disaster, affecting 100 municipalities, was compounded by antecedent drought conditions that had decreased the soil's water absorption capacity. Earth observation (EO) data became critical, providing emergency services with the means to assess and manage the catastrophe and facilitate post-event damage evaluation.

The SaferPlaces platform, supported by the ESA InCubed programme, played a pivotal role in disaster response. It provided the Civil Protection of Emilia-Romagna with high-resolution flood water and depth maps, crucial for decision-making in the aftermath of the floods. This cloud-based platform integrates satellite data, climatic records, and AI algorithms to generate global flood forecasts.

Leveraging AI, SaferPlaces processed terrain data alongside inundated area information, combining in situ measurements with satellite data from Copernicus Sentinel-2, CosmoSky-Med, Planet, and SPOT. This was further enriched with local data from municipalities and the Emilia-Romagna Civil Protection, enhancing urban flood area accuracy.

Detailed maps illustrating flood extent in the severely hit municipalities of Faenza, Cesena, Forlì, and Conselice were generated. These contained vital data on water depth and volume, forming the basis for a preliminary Flood Damage Assessment. These assessments were crucial for authorities to estimate economic losses swiftly.

The suite of tailored algorithms within SaferPlaces, extracts flood water masks from satellite imagery. This module, accessible on-demand through a user-friendly interface, requires few parameters from users to accurately delineate flooded areas and contribute to the Global Flood Monitoring system.

The main workflow of algorithm includes the GFM procedure for baseline flood extent retrieval, the Hydraflood method for flood mask extraction via GEE, and the CommSNAP pipeline for processing commercial data. The final output is a flood mask for the area and event of interest, which can also feed into the GFI model to identify flood-prone areas.

This study underscores the essential role of integrated EO and AI technologies in managing hydrological disasters. The SaferPlaces platform's capacity to synthesize multi-source data and provide actionable intelligence marks a milestone in the power of interdisciplinary approaches in enhancing disaster resilience and preparedness.

How to cite: Mazzoli, P., Luzzi, V., Renzi, M., Bargiotti, M., Outmani, S., Pasetti, S., Bagli, S., and Renzi, F.: Earth Observation-Driven Flood Response for Emilia-Romagna: The SaferPlaces Platform, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-15378, https://doi.org/10.5194/egusphere-egu24-15378, 2024.

EGU24-15446 | ECS | Posters on site | HS6.6

Floods automatic rapid mapping through Sentinel-2 MSI multitemporal data 

Valeria Satriano, Emanuele Ciancia, Nicola Pergola, and Valerio Tramutoli

Floods are widespread natural disasters on Earth affecting the planet with increasing frequency and intensity. Climate changes are responsible of the increasing number of heavy and persistent rains generating these destructive events often resulting in fatalities, injuries, and extensive infrastructural damages. A near real time monitoring system able to provide timely and accurate information about location and extent of the flooded areas is crucial for the authorities to implement the right mitigation actions. Currently, the Copernicus Emergency Management Service (CEMS) supports at European level the crisis management activities in the immediate aftermath of a flood, exploiting multi-source satellite data to provide flood delineation with a release time ranging from 7 to 48 hours (from the satellite acquisition). Map characterization and relative information are retrieved through semi-automatic or manual methodologies which do not allow for a complete automation of the analysis crucial to speed up the procedure and shorten the release time.

In this study, carried out in the framework of the MITIGO project (funded by MIUR PON R&I 2014-2020 Program), results coming from a multi-temporal optical satellite technique able to quick detect and accurately map flooded areas will be presented. This technique, namely RST-Flood, exploits the statistical characterization of the satellite observed signal to retrieve accurate background information useful to promptly and automatically identify ground changes directly linked to events occurrence. RST-Flood has already been successfully implemented with mid-low spatial resolution (from 1000 to 375m) optical satellite data sensors (i.e., Advanced Very High Resolution Radiometer, Moderate Resolution Imaging Spectroradiometer, Visible Infrared Imaging Radiometer Suite), and here is for the first time exported to Sentinel-2 Multi Spectral Instrument (MSI) data at mid-high spatial resolution (20m) to study recent floods events. The achieved results demonstrated the easy implementation of RST-Flood to different sensors and geographic areas and its capability in providing fast (processing time less than 15 min from data availability) and robust mapping of flooded areas. Furthermore, its design developed to work in the Google Earth Engine (GEE) environment makes it suitable for global scale implementation without altering its performance.

How to cite: Satriano, V., Ciancia, E., Pergola, N., and Tramutoli, V.: Floods automatic rapid mapping through Sentinel-2 MSI multitemporal data, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-15446, https://doi.org/10.5194/egusphere-egu24-15446, 2024.

EGU24-15576 | ECS | Posters virtual | HS6.6

Resilience Assessment of Flood Detention Zones in the 2023 catchment-scale floods in Hai River Basin, China 

Yiling Lin, Xie Hu, Fang Wang, and Yong Zhao

As a typical flood-prone area, the Hai River Basin (HRB) in the Beijing-Tianjin-Hebei metropolis of China has been struck by devastating floods in history. Since the 1960s, a series of flood-control programmes in the HRB have been launched to reduce the flood risks. As planned, flood detention zones serve as the last line by storing and detaining floodwaters when the water levels exceed the defense limits of reservoirs, levees, and diversion channels. Land use crisis has been a long-lasting problem in China. People are allowed to use the flood detention zones as their residential communities when these zones are not in use. A dual role played by these specific zones requires not only an effective floodwater storage in response to floods, but also an efficient floodwater recession in the aftermath of floods. However, we lack a quantitative assessment of the functionability of flood detention zones. Our study synergizes multi-source SAR images from Sentinel-1 and Gaofen-3 satellites in the framework of deep learning to accurately and efficiently extract inundation paths which evolved for two months encompassing HBR. A joint use of digital elevation model allows us to recover the three-dimensional inundation structures. We also propose the flood detention resilient coefficient based on our derived lifespan of floodwaters. Our results demonstrate that the flood detention zones in HRB can effectively trap the floodwater within to secure lives and properties, but resilience of some flood detention zones can still be improved.

How to cite: Lin, Y., Hu, X., Wang, F., and Zhao, Y.: Resilience Assessment of Flood Detention Zones in the 2023 catchment-scale floods in Hai River Basin, China, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-15576, https://doi.org/10.5194/egusphere-egu24-15576, 2024.

EGU24-16923 | ECS | Posters on site | HS6.6

Flood Segmentation with Optical Satellite Images Under Clouds Using Physically Constrained Machine Learning 

Chloe Campo, Paolo Tamagnone, Guillaume Gallion, and Guy Schumann

Timely and accurate flood map production plays a key role in enhancing effective flood risk assessment and management. Satellite imagery is frequently employed in flood mapping as it can capture flooding across vast spatial and temporal scales. Floods are usually caused by prolonged or heavy precipitation correlated with dense cloudy conditions, posing challenges for accurate mapping.

The Synthetic Aperture Radar (SAR) active sensor is a popular option due to its feature of being weather agnostic, penetrating through clouds, fog, and darkness, providing images for the detection of flooded areas regardless of the weather conditions. However, this advantage is at the expense of low temporal resolution and double bounces in urban and heavily vegetated areas, which increase signal processing difficulty and misinterpretation. Passive microwave radiometry has also been explored for flood mapping, but its coarse spatial resolution limits the utility of the resulting flood maps. Multispectral optical imagery offers a balanced trade-off between temporal and spatial resolutions, with the only limitation that the acquired images might be hindered by the presence of clouds. Capitalizing on the utility of optical imagery, FloodSENS, a machine-learning (ML) algorithm consisting of a SENet and UNet, precisely delineates flooded areas from non-flooded areas in clear and partially clouded optical imagery. Although the current algorithm version enforces flood delineation involving topography-derived information in the ML processing, it is not capable of detecting floods under clouds; thus, we propose a new iteration of FloodSENS that utilizes auxiliary data in post-processing to improve the inferred flood maps.

The post-processing pipeline utilizes the inferred flood map generated by FloodSENS and the Digital Elevation Model (DEM) of the target area to accurately delineate the flood extent beneath clouds, adhering to the physical constraints in the topography. First, Pixels at elevations equal to or lower than the water level are designated as flooded pixels. These pixels are further refined with geoprocessing to establish hydrological connectivity and topographic consistency. Pixels that are both marked as flooded and hydrologically connected are confirmed as flooded pixels for the final flood map.

The post-processing proves essential in tropical and subtropical regions that frequently have high cloud cover during the monsoon seasons, making it imperative to map the affected areas during flooding events. The FloodSENS detection with the post-processing pipeline has been tested on partly clouded optical imagery obtained from the 2023 autumn flooding in southern Somalia.

How to cite: Campo, C., Tamagnone, P., Gallion, G., and Schumann, G.: Flood Segmentation with Optical Satellite Images Under Clouds Using Physically Constrained Machine Learning, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-16923, https://doi.org/10.5194/egusphere-egu24-16923, 2024.

EGU24-16998 | ECS | Posters virtual | HS6.6

Assessing the sensitivity of geomorphological attributes to DEM source and Spatial Resolutions  

Saroj Rana and Sagar Rohidas Chavan

Digital Elevation Models (DEMs)are used for extracting the geomorphological attributes of catchments. These attributes play crucial role in determining the hydrological responses of the catchments. However past research has highlighted the sensitivity of the geomorphological attributes to various DEM sources as well as special resolutions. This study is envisaged to assess the impact of different DEM sources and DEM resolutions on geomorphological attributes proposed by Moussa (2008) on Upper Yamuna River Basin which flows in 5 states (Uttarakhand, Himachal Pradesh, Uttar Pradesh, and Haryana) of India. For this purpose, different DEM sources, Shuttle Radar Topography Mission (SRTM), Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), and Advanced Land Observing Satellite (ALOS) Phased Array type L-band Synthetic Aperture Radar (PALSAR) DEMs are used. To investigate the impact of DEM spatial resolution, 5 different resampled scenarios (grid size 90m, 120m, 150m, 180m, 210m) for each source of DEM was considered using the SRTM 30m DEM as a base DEM. The comparative assessment revealed notable discrepancies in the derived attributes among the DEMs of different resolutions and sources. The evaluation of variation in geomorphological attributes derived from various DEM sources and resolutions, yielded insightful observations. Furthermore, variations were observed between the different satellite sources, highlighting inherent differences in elevation data acquisition and processing methodologies. These findings underscore the critical influence of spatial resolution and data source on the accuracy and reliability of geomorphological attributes derived from DEMs, emphasizing the significance of careful consideration in selecting DEMs for terrain analysis and related applications.

How to cite: Rana, S. and Chavan, S. R.: Assessing the sensitivity of geomorphological attributes to DEM source and Spatial Resolutions , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-16998, https://doi.org/10.5194/egusphere-egu24-16998, 2024.

EGU24-18873 | ECS | Orals | HS6.6 | Highlight

A Comparative Analysis of Flood Frequency Mapping Approaches for Climate-Resilience in South Sudan 

Ignacio Borlaf-Mena, Èlia Cantoni, Antonio Franco-Nieto, Marta Toro-Bermejo, Beatriz Revilla-Romero, Antonio Rodriguez Serrano, Lukas Loescher, Danielle Monsef Abboud, Carlos Domenech, and Clément Albergel

In 2022, South Sudan was ranked as the world’s most vulnerable country to climate change and the one most lacking in coping capacity. Furthermore, it is also one of the world’s most politically fragile nations. The country is facing challenges related to riverine flooding, including four consecutive years of floods (2019-2022) that have displaced hundreds of thousands of people and left many struggling to access food.

Flood extent and frequency mapping based on remote sensing products is being explored by the European Space Agency's Global Development Assistance (GDA) programme's thematic area of Climate Resilience, as a collaboration between GMV and the World Bank in South Sudan.

Floods are mapped with Synthetic Aperture Radar (SAR) imagery from Sentinel-1 (S-1), and the 5-day VIIRS flood fraction product. The former has a native pixel size of 10 m (GRD), whereas it is 375 m for the latter. This resolution disparity is bridged aggregating 9x9 S-1 pixels (which also reduces speckle “noise”) and downscaling the VIIRS product using the flood fraction and the 90 m Copernicus Digital Elevation Model to determine which pixels are more likely to be flooded.

Sentinel-1 flood delineation detects significant deviations from the standard 'dry' stratus using by-track geo-median (sigma-nought) or terrain-flattened gamma-nought image classification. The latter method includes the closest VIIRS 8-day mosaics to prevent false positives in semi-arid regions. Both approaches aim to identify flooding, even beneath vegetation canopies.

Due to the absence of in-situ data, it was not possible to validate the results but an intercomparison was conducted, including different S-1 methods. The downscaled VIIRS product yielded the largest flood extents and frequencies, likely due to its higher imaging frequency (14 h). Consequently, the deviation-based Sentinel-1 products exhibit similar spatial patterns but with lower frequencies and extents due to longer revisit times. These S-1 methods failed to detect flooding in some areas marked as high-frequency flooding by VIIRS, this is attributed to a mischaracterization when the reference image is already flooded. In contrast, the classification-based Sentinel-1 product captured actual flood frequency but was prone to omission and commission errors. Combining maximum flood frequency from both Sentinel-1 products, while masking false positives with VIIRS, reduces errors while preserving maximum spatial detail.

The resulting Earth Observation (EO)-based maps provide key information on the extent, frequency, and persistence of recent flooding seasons (2017-2022). This detailed flood hazard information can raise awareness of flood risk among local institutions and communities. For such purpose, EO data is consolidating its role in helping reduce flood risk to citizens’ lives and livelihoods, as ground data is very sparse across many countries. By combining EO-based flood hazard maps with exposure datasets such as for population, building or crops, we provide additional country-wide information on the potential impacts of recent floods. The service covers the entire country of South Sudan and enables the creation of a flood hazard and exposure index, allowing the World Bank team to detect flooding hotspots and prioritize investment accordingly. These efforts will help the government develop detailed flood risk management plans.

How to cite: Borlaf-Mena, I., Cantoni, È., Franco-Nieto, A., Toro-Bermejo, M., Revilla-Romero, B., Rodriguez Serrano, A., Loescher, L., Monsef Abboud, D., Domenech, C., and Albergel, C.: A Comparative Analysis of Flood Frequency Mapping Approaches for Climate-Resilience in South Sudan, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-18873, https://doi.org/10.5194/egusphere-egu24-18873, 2024.

EGU24-18986 | ECS | Orals | HS6.6

Rapid flood mapping: Fusion of Synthetic Aperture Radar flood extents with flood hazard maps 

Ambika Khadka, Annett Anders, and Ian Millinship

Rigorous flood monitoring by ICEYE is enabled by the large-scale and systematic availability of synthetic aperture radar (SAR) data from the satellite constellation deployed and operated by ICEYE [1, 2]. However, in dense urban areas and under tree canopy cover, using single X-band based SAR images directly for rapid flood detection inherits large uncertainties due to its complex backscattering mechanisms. This study addresses this gap by proposing an approach to rapidly detect flooding in urban areas by merging real-time SAR flood extents from surrounding rural areas with hydrodynamically modeled flood hazard maps. If a flood is fully contained within an urban area, other auxiliary flood evidences are merged with JBA’s high resolution global flood hazard maps at 5 and 30m resolution. 

 

The precomputed simulation library approach used in Mason et al. 2021 appeared as a challenge, as floods are dynamic in nature [3], they suggested the benefits of using assimilation to integrate SAR data and model outputs in dynamic situations. Thus, the proposed approach builds upon Mason et al. 2021[3] and the framework for improved near real-time flood mapping [2], wherein SAR data is assimilated to enhance future flood predictions and improve the quality of flood hazard maps. This process, in turn, enhances further real-time rapid flood mapping aiding governments, NGOs and disaster responder to make accurate timely decisions in the immediate aftermath of an event. 

 

References:

[1] Dupeyrat, A., Almaksour, A., Vinholi, J., and Friberg, T.: Deep learning for automatic flood mapping from high resolution SAR images, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-6790, https://doi.org/10.5194/egusphere-egu23-6790, 2023.

[2] Friberg, T., Khadka, A., and Dupeyrat, A.: A framework for improved near real-time flood mapping, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-8520, https://doi.org/10.5194/egusphere-egu23-8520, 2023.

[3] Mason, D.C., Bevington, J., Dance, S.L., Revilla-Romero, B., Smith, R., Vetra-Carvalho, S., Cloke, H.L.: Improving Urban Flood Mapping by Merging Synthetic Aperture Radar-Derived Flood Footprints with Flood Hazard Maps, Water 2021, 13, 1577, https://doi.org/10.3390/w13111577

How to cite: Khadka, A., Anders, A., and Millinship, I.: Rapid flood mapping: Fusion of Synthetic Aperture Radar flood extents with flood hazard maps, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-18986, https://doi.org/10.5194/egusphere-egu24-18986, 2024.

EGU24-19378 | ECS | Posters on site | HS6.6

Integrated GIS analysis for flood risk assessment in Norwegian Rivers: a case study of Sokna river 

Adina Moraru, Raffa Ahmed, Mulubirhan G. Tekle, Knut Alfredsen, and Oddbjørn Bruland

This research aims to simplify and enhance the analysis and visualization of flood-prone areas in Norwegian rivers, with a primary emphasis on Sokna river. Utilizing remote sensing and GIS analysis, our objective is to advance flood risk assessment and management by integrating hydraulic data from numerical models, remotely sensed geomorphic features, and publicly available natural hazard maps. In this study, we develop GIS models, analyze geomorphic features related to erosion and deposition processes, and optimize flood risk analysis using hydro-morphodynamic indicators such as shear stress, stream power, Froude number, Shields formula, and the Hjulström diagram.

To locate flood-prone areas and estimate their severity, different influencing factors to flood risk were identified, among them fluvial dynamics, terrain characteristics, land use, and anthropic activities. Within the 12.65 km lowermost reach of Sokna river, near its confluence with Gaula river and Lundamo urban area, we conducted a comprehensive analysis of the geomorphological features (e.g. river width, soil type), natural hazards maps, and anthropic footprint (i.e. land use, infrastructure, safety measures), supported by hydrodynamics information from HEC-RAS models. Special attention was given to the analysis of sediments, erodible materials, and land use along the riverbanks while integrating flood areas with return periods ranging from 10 to 500 years, as well as other natural hazards such as rockfall- and snow erosion and deposition areas, avalanche records, landslides, debris, and quick clay landslide areas.

A temporal analysis was conducted using orthophotos from 1956, 2011, and 2021. The river channels in these orthophotos, captured in the same month to ensure similar discharges, were digitized to assess changes in river width and deposition processes. Additionally, DEM of Differences (DoD) supported refining documented river changes. The erodible sediment particle size was estimated using the Shields formula based on HEC-RAS model outputs, including Froude number, shear stress, and stream power. The erodible fraction was plotted into Shields and Hjulström diagrams and compared with the soil map. Identified locations with erodible material were complemented with land use data and other anthropic activities. Vulnerable infrastructure to erosion and deposition processes, such as culverts and bridges, were considered in flood risk assessments, with areas having safety measures (such as channel embankments) marked as having lower flood risk.

The step-by-step workflow, integrated into a GIS model using the Model Builder feature in ArcGIS Pro, is replicable for other rivers. These findings provide insights into the factors influencing flood risk, including potential erosion areas, the impact of natural hazards, and the temporal evolution of river channels. This methodology serves as a versatile tool for flood risk assessment and management in other river systems, contributing to the broader field of fluvial geomorphology and hydraulic engineering.

How to cite: Moraru, A., Ahmed, R., Tekle, M. G., Alfredsen, K., and Bruland, O.: Integrated GIS analysis for flood risk assessment in Norwegian Rivers: a case study of Sokna river, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-19378, https://doi.org/10.5194/egusphere-egu24-19378, 2024.

EGU24-20575 | Orals | HS6.6 | Highlight

Low-latency flood inundation mapping with airborne GNSS-R 

Konstantinos Andreadis and Delwyn Moller

The Rongowai project, based in New Zealand, represents a groundbreaking initiative in earth observation using next-generation Global Navigation Satellite Systems Reflectometry (GNSS-R) sensors. A NASA-developed sensor mounted on an Air New Zealand Q300 passenger aircraft collects land-surface and coastal data daily between airport hubs across the country. This project builds upon NASA's CYGNSS constellation, initially designed for sensing ocean surface winds but later expanded to terrestrial sensing due to the sensitivity of GNSS-R measurements to various surface properties of water. The next-generation GNSS-R receiver (NGRx) offers enhanced capabilities beyond CYGNSS, providing increased simultaneous measurements and introducing new measurement capabilities like polarimetry for improved land characterization. The unique mission model of Rongowai emphasizes sustainability while maintaining high-quality observations, utilizing an existing commercial Air New Zealand aircraft for data collection, thereby achieving unprecedented spatio-temporal sampling throughout New Zealand. The Air New Zealand Q300 operates approximately 7-8 flights daily in a hub-and-spoke pattern across major centers in New Zealand, offering near-ideal operational characteristics for capturing dynamic events. Here, we present a system that leverages the flight characteristics of the Q300 to deliver low-latency inundation observations immediately after landing, providing near real-time data transmission from the preceding flight. The framework, named the Flood Assessment Spatial Triage (FAST) addresses the challenge of data latency in flood reconnaissance by providing rapid inundation detection and visualization on an on-demand flight-by-flight basis within an hour after landing. The processing chain of FAST involves geolocation of specular points, coherence detection, and overlaying transects on a high-resolution digital elevation model (DEM) using a simplified flood inundation model. Analysis of GNSS-R waveforms demonstrates the ability to robustly observe inundation even in challenging conditions such as cloud cover, nighttime, and vegetated areas. Our study period captured flooding events in New Zealand's North Island during the Southern hemisphere summer of 2023, particularly in areas affected by Cyclone Gabrielle. The inundation observations from February 2023 depicted regions with surface water not classified as permanent water bodies, and a combination with a physically-based algorithm allowed for mapping flood inundation from the relatively sparse Rongowai observations. Our results align with ground reports of flooding, highlighting the potential for valuable reconnaissance information from GNSS-R when transiting affected regions. Rongowai's higher spatial resolution, combined with its hub-and-spoke flight pattern, enables rapid revisits over affected regions, making it well-suited for dynamic and rapidly evolving processes like floods.

How to cite: Andreadis, K. and Moller, D.: Low-latency flood inundation mapping with airborne GNSS-R, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-20575, https://doi.org/10.5194/egusphere-egu24-20575, 2024.

EGU24-20702 | ECS | Posters on site | HS6.6

 DeepFuse: Towards Frequent Flood Inundation Monitoring using AI and EO 

Antara Dasgupta, Rakesh Sahu, Lasse Hybbeneth, and Björn Waske

Despite the increase in the number of Earth Observation satellites with active microwave sensors suitable for flood mapping, the frequency of observations still limits adequate characterization of inundation dynamics. Particularly, capturing the flood peak or maximum inundation extent, still remains elusive and a major research gap in the remote sensing of floods. Rapidly growing archives of multimodal satellite hydrology datasets combined with the recent deep learning revolution provide an opportunity to solve this problem adequate observation frequency. DeepFuse is a scalable data fusion methodology, leveraging deep learning (DL) and Earth Observation data, to estimate daily flood inundation at scale with a high spatial resolution. In this proof-of-concept study, the potential of Convolutional Neural Networks (CNN) to simulate flood inundation at the Sentinel-1 (S1) spatial resolution is demonstrated. Leveraging coarse resolution but temporally frequent datasets such as soil moisture/accumulated precipitation data from NASA’s SMAP/GPM missions and static topographical/land-use predictors, a CNN was trained on flood maps derived from S1 to predict high-resolution flood inundation. The proposed methodology was tested in southwest France at the confluence of its two main rivers, Adour and Luy, for the December 2019 flood event. The predicted high-resolution maps were independently evaluated against flood masks derived from Sentinel-2 using the Random Forest Classifier. First results confirm that the CNN can generalize some hydrological/hydraulic relationships leading to inundation based on the provided inputs, even for some rather complex topographies. However, further tests in catchments with strongly divergent land-use, hydrological, and elevation profiles is necessary to evaluate model sensitivity towards different land surface conditions. Achieving daily cadence for flood monitoring will enable an improved understanding of spatial inundation dynamics, as well as help develop better parametric hazard re/insurance products to effectively bridge the flood protection gap.

How to cite: Dasgupta, A., Sahu, R., Hybbeneth, L., and Waske, B.:  DeepFuse: Towards Frequent Flood Inundation Monitoring using AI and EO, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-20702, https://doi.org/10.5194/egusphere-egu24-20702, 2024.

EGU24-731 | ECS | Orals | HS4.2

Exploring Drought Monitoring in Morocco: A Review of Remote Sensing and Machine Learning Techniques 

Said El Goumi, Mustapha Namous, Abdenbi Elaloui, Samira Krimissa, and Nafia El-alaouy

The challenges of climate change and water scarcity in Morocco highlight the need for Remote Sensing (RS) and Machine Learning (ML) for drought monitoring. Droughts pose socio-economic and environmental challenges and have significant impacts on the country's agriculture-based economy and water management strategies. This study provides a comprehensive review of advanced RS technologies and ML algorithms, with a focus on their effectiveness in monitoring and forecasting drought conditions. RS provides extensive spatial coverage and captures important data on factors such as vegetation health, soil moisture, and precipitation trends, which are crucial for early detection and response to droughts. Incorporating ML algorithms significantly improves the precision and efficiency of drought prediction models, aiding in the development of comprehensive drought indices and forecasting models for agricultural planning and effective water resource management.

The study evaluates various RS methods utilized in Morocco, including the analysis of satellite imagery and vegetation indices such as NDVI, and assesses ML techniques like support vector machines (SVM) and artificial neural networks (ANN) for predicting drought-induced agricultural impacts. The combined use of these technologies provides a holistic approach to drought monitoring, enabling timely interventions to assist communities affected by drought. However, the study also highlights challenges in areas such as data availability, model validation, and associated costs. To effectively manage drought risks, the paper recommends that Moroccan policymakers and stakeholders leverage these technological advancements while emphasizing the importance of continuing research, interdisciplinary collaboration, and capacity building in these areas.

Key words: Drought,remote sensing, machine learning, climate change, Morocco

How to cite: El Goumi, S., Namous, M., Elaloui, A., Krimissa, S., and El-alaouy, N.: Exploring Drought Monitoring in Morocco: A Review of Remote Sensing and Machine Learning Techniques, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-731, https://doi.org/10.5194/egusphere-egu24-731, 2024.

Drought, as a natural calamity, has serious economic and environmental implications, especially as the impacts of climate change continue to escalate globally. In many regions, monitoring and comprehending changes in drought patterns have become imperative. As climate change increasingly influences hydrological cycles, there is a need to grasp and interpret drought behaviour in diverse geographical areas. This study is particularly focused on a landlocked state in the north-eastern region of India, which is characterised by a predominantly monsoon climate with high humidity and an annual rainfall of 1800–2500 mm. The study focuses on the state of Nagaland, India, and is aimed at evaluating the efficacy of artificial intelligence (AI) models such as Multilayer Perceptron (MLP), Long Short-Term Memory (LSTM), and Genetic-Algorithm Adaptive Neuro-Fuzzy Inference System in predicting drought. For analysing the drought conditions, the Effective Drought Index (EDI) is used. By utilising rainfall data from 1987–2021, the EDI drought index has been computed, recognising the pivotal role of rainfall in comprehending prevailing drought conditions. The drought conditions are categorised from extremely dry to near normal, excluding the wet conditions in the study region. The investigation into the effectiveness of AI in predicting and detecting drought yielded insightful results, highlighting the informative and promising capabilities of AI models. The results of the study facilitate a comparative analysis of the three models, MLP, LSTM, and GA-ANFIS, using the evaluation metrics. The study findings indicate that LSTM exhibits superior prediction accuracy in the study region in terms of its ability to predict drought conditions in the given geographical area. This outcome is crucial for understanding and addressing the impacts of drought. This study contributes to the broader understanding of drought prediction and emphasises how AI models can improve their ability to predict drought conditions, which will ultimately contribute to enhanced water resource management and climate adaptability.

How to cite: Kikon, A.: Exploring the effectiveness of Artificial Intelligence-powered insights in drought study in Nagaland, India, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-853, https://doi.org/10.5194/egusphere-egu24-853, 2024.

EGU24-1425 | Posters on site | HS4.2

Occurrence of drought in groundwater over the last 12 years 

Valeria Slivova and Michaela Kurejova Stojkovova

Groundwater is a very important component of water circulation in nature, it is indispensable in every country’s wealth. Ensuring protection of its sustainable use is the most important requirement for preserving the quality of life, health of natural conditions and economic development of each sector. Groundwater is the main source of drinking water in Slovakia. This contribution assesses groundwater drought occurrence recorded at 207 objects in the Slovak groundwater monitoring network.  This comprises 141 groundwater level boreholes and 66 spring yield gauging stations. The Sandre method was used for this assessment. This method is based on a statistical comparison of the average monthly values of the hydrological year evaluated with the long-term monthly average over the reference period 1981-2010. For each month of the reporting period, five separate categories are established on the basis of the statistical treatment of the average monthly values of spring yields and groundwater levels. The period of the last 12 years (2011-2012) has been evaluated.

 

The results show that 3 years (2012, 2019 and 2022) were assessed as the dry years, 3 years were assessed as wet (2011, 2013 and 2021) and 6 years were assessed as average (period 2014 - 2016, 2018 and 2020). Within each years, groundwater drought occurred most frequently in winter, spring and summer. The main source of groundwater is the spring melting of snow. In the last years we can see, there is earlier melting of the snow as a result of warm winters and has been a lack of snow cover in the lower positions in the Slovakia. These are the main causes of the occurrence of droughts in groundwater in the winter and spring period. During the summer period, groundwater drought is caused by high evapotranspiration and rainfall deficits. The occurrence of local storms does not have a significant impact on the replenishment of groundwater resources.

 

 

 

 

Keywords: groundwater drought, rainfall deficit, spring yield, groundwater level

How to cite: Slivova, V. and Kurejova Stojkovova, M.: Occurrence of drought in groundwater over the last 12 years, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-1425, https://doi.org/10.5194/egusphere-egu24-1425, 2024.

EGU24-1946 | ECS | Orals | HS4.2

Monitoring agricultural drought in the Mediterranean region using a high-resolution (1-km) standardized evaporation deficit index 

Irina Yu. Petrova, Diego G. Miralles, Sergio M. Vicente-Serrano, and Christian Massari

Droughts, with their far-reaching and detrimental effects across multiple domains, remain critical climate events that demand better monitoring and early warning capabilities. Regions that are highly dependent on water supply for agriculture, such as the Mediterranean, are vitally dependent on timely monitoring of drought conditions. The crop losses in the region as a consequence of drought events continue to rise. Simultaneously, climate models agree regarding the exacerbation of drought following global warming in the region. Therefore, better understanding and monitoring of drought occurrence is imperative to mitigate drought adverse effects and improve water resource management in the region and beyond.
Operational drought monitoring, whether based on models or observed data, commonly employs a set of drought indices designed to assess anomalies in land or atmosphere dryness. However, these indices are typically available at relatively coarse spatio-temporal scales, rendering them unsuitable for evaluating the local drought impacts that are relevant to agriculture and ecosystems. This limitation does not facilitate decision-making by local authorities and farmers and impedes the straightforward development of on-site adaptation strategies.
In this study, we undertake the assessment and validation of an evaporation-based drought index, the Standardized Evaporation Deficit Index (SEDI: Kim&Rhee 2016, GRL), at an unprecedentedly high resolution (1 km, daily) over the Mediterranean domain. The index is constructed using data of potential and actual evaporation derived using GLEAM (Miralles et al. 2011, HESS), as part of the ESA 4DMED-Hydrology project. Unlike most other drought indices, SEDI is directly related to plant water stress, given the significance of the evaporation deficit for plant hydraulic and physiological processes. Such approach offers the potential to provide early-warning information on ecological and agricultural plant water stress at local scales. Our study of the relationship between SEDI and vegetation stress over seven years (2015–2021) and across 28 Mediterranean river basins, sheds light on critical factors that cause differential stress in crops and natural ecosystems under drought conditions. We also explore the role of irrigation in the SEDI–vegetation stress relationship using 1 km irrigation volumes obtained during the 4DMED-Hydrology project. In the future, the framework will be extended globally, with the subsequent aim to provide valuable information for optimizing irrigation timing in major irrigated breadbasket regions. 

How to cite: Yu. Petrova, I., G. Miralles, D., M. Vicente-Serrano, S., and Massari, C.: Monitoring agricultural drought in the Mediterranean region using a high-resolution (1-km) standardized evaporation deficit index, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-1946, https://doi.org/10.5194/egusphere-egu24-1946, 2024.

EGU24-2072 | ECS | Orals | HS4.2

Soil moisture forecasting for dryland fields in Australia 

Qazi Muqeet Amir and Thomas Bishop

Australia is frequently susceptible to droughts. Major droughts within the last 50 years such as that of 1982-1983 and the Millennial drought of 1997-2010 severely impacted crop growth across the country. Soil moisture can be in deficit during droughts due to a lack of recharge and high evapotranspiration from the soil. Dryland agriculture is particularly sensitive to droughts as there is no irrigation input into the soil. Soil water availability is a critical constraint to agricultural productivity, so the ability to predict its current and future state accurately is key in informing decisions relating to irrigation, fertiliser use, and yield targets. While soil moisture forecasting has been conducted in literature previously, there is limited understanding of the spatial, seasonal, and meteorological patterns that underlie the forecastability of soil moisture in a particular field. Hence this research aims to understand the spatial, seasonal, and meteorological factors that influence the forecast accuracy of soil moisture in dryland fields in Australia. 

Across Australia an increasing number of growers have soil moisture probes, which report current and historic soil moisture. The domain of this work is in the CosmOz probe network, consisting of 26 cosmic ray soil moisture probes across Australia, accounting for various geophysical and climatic regions. The probes measure average soil moisture to depths in the soil between 10 to 50 cm. Forecasting soil moisture requires the addition of various modelled/remotely sensed data such as meteorological, vegetation type, and soil property data. Using this data and lagged soil moisture as predictors, soil moisture has been forecasted at the locations of each CosmOz probe. With up to 13 years of training data, machine learning models have been fitted to forecast soil moisture with high accuracy forecasts of up to 30 days. To improve predictions a neural network autoencoder has been employed to engineer features that account for anomalous periods in the predictors.

A key outcome of this study is identifying patterns in forecast accuracy and predictor importance with respect to region, soil type, meteorological conditions, and time of year. These patterns create a nationwide perspective of soil moisture forecastability and the potential for forecasting in areas with no soil moisture probe data available. 

How to cite: Amir, Q. M. and Bishop, T.: Soil moisture forecasting for dryland fields in Australia, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-2072, https://doi.org/10.5194/egusphere-egu24-2072, 2024.

EGU24-2075 | ECS | Orals | HS4.2

Drought duration and spatial dependence increase during propagation 

Manuela Irene Brunner and Corentin Chartier-Rescan

As droughts propagate both in time and space, their impacts increase because of changes in drought properties. Even though drought propagation has two dimensions – a temporal and spatial one – these are mostly studied separately, which neglects that the propagation of droughts through the hydrological cycle may extend from local to spatial characteristics. Therefore, it is yet unknown how the spatial extent and connectedness of droughts change as droughts propagate from the atmosphere to and through the hydrosphere.
In this study, we assess not only how local meteorological droughts propagate through the hydrological cycle to streamflow and groundwater but also how drought spatial extent and connectedness change with drought propagation. To do so, we use a large-sample dataset of 70 catchments in the Central Alps for which both observed streamflow and groundwater data are available.
We show that drought propagation from the atmosphere to the hydrosphere affects both local and spatial drought characteristics and leads to longer, delayed, and fewer droughts with larger spatial extents. 75% of the precipitation droughts propagate to P-ET or further, 20% to streamflow, and only 10% to groundwater. Of the streamflow droughts, 40% propagate to groundwater but 60% do not propagate.  Drought extent and connectedness increase during drought propagation from precipitation to streamflow thanks to synchronizing effects of the land-surface such as widespread soil moisture deficits but decrease again for groundwater because of sub-surface heterogeneity. These findings have implications for drought prediction and management. They suggest a partial predictability of streamflow and groundwater droughts by atmospheric and hydrological deficits and that large scale streamflow deficits may be partly compensated by groundwater, which shows less frequent and spatially extensive droughts than streamflow.

How to cite: Brunner, M. I. and Chartier-Rescan, C.: Drought duration and spatial dependence increase during propagation, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-2075, https://doi.org/10.5194/egusphere-egu24-2075, 2024.

EGU24-2560 | ECS | Orals | HS4.2

A novel multivariate drought severity index: study of short-term hydrological signals within Amazon river basin 

Artur Lenczuk, Christopher Ndehedehe, Anna Klos, and Janusz Bogusz

The pace of Earth’s climate warming obviously sped up, especially after 2000s. Droughts are increasingly becoming  frequent, longer and more severe, with lasting impacts on ecosystems, communities and people. Thus, addressing the problem of monitoring global (or regional) climate trends and  water storage changes is crucial. We propose a novel Multivariate Drought Severity Index (MDSI) estimated through the concept of Frank copulas that is based on DSIs determined from satellite-based geodetic data. The new multivariate approach is based on data provided by the Global Positioning System (GPS) and the Gravity Recovery and Climate Experiment (GRACE).

In this study, we analyze short-term (<9 months) signals of monthly-resampled vertical displacements for 25 GPS stations that are classified as benchmarks for hydrogeodesy within Amazon river basin. We show that despite GPS and/or GRACE limitations arising in data products or their quality, the GPS- and GRACE-based DSIs are characterized with a general coherent spatial pattern to the traditional climate indices (Standardized Precipitation Index (SPI) and Standardized Precipitation Evapotranspiration Index (SPEI)). Moreover, GPS- and GRACE-based DSIs are capable of capturing extreme hydrometeorological events reported for the Amazon basin. However, DSI variations from GPS and GRACE do not always reflect real hydrological changes as they could sometimes under- or overestimate them. Our analyses show that the newly proposed MDSI is a step towards strengthening  the credibility of combined GPS and GRACE data in drought assessment to improve  understanding of climate change impact on freshwater. We demonstrate that the MDSI recognizes the exact number of events, or one event less than index chosen as the most reliable for over 90% of selected stations. We notice that MDSI series are temporally consistent with extreme precipitation values. The wet and dry periods captured by MDSI are related with precipitation anomalies over 400 mm/month and below 100 mm/month, respectively.

How to cite: Lenczuk, A., Ndehedehe, C., Klos, A., and Bogusz, J.: A novel multivariate drought severity index: study of short-term hydrological signals within Amazon river basin, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-2560, https://doi.org/10.5194/egusphere-egu24-2560, 2024.

EGU24-2685 | ECS | Orals | HS4.2

Drought propagation from meteorological to hydrological drought in the Krishna River Basin of India 

Ajay Gupta, Manoj Kumar Jain, and Rajendra Prasad Pandey

Understanding the propagation of drought from one form to another has become a prime topic of research during recent decades. The majority of research has used a correlation-based approach to study drought propagation; however, such techniques are ineffective in areas with considerable seasonality in precipitation, such as India. Only a few studies have employed an event-based approach to study drought propagation. Moreover, none of the previous studies considered the sequential propagation of drought, starting from meteorological to hydrological drought through agricultural drought. This work aims to analyse drought propagation from meteorological to hydrological drought through agricultural drought using an event-based approach in the Krishna River Basin of India. The Standardised Precipitation Evapotranspiration Index (SPEI) represents meteorological drought, the Standardised Soil Moisture Index (SSMI) represents agricultural drought, and the Standardized Streamflow Index (SSI) represents hydrological drought is estimated at a 1-month timescale at sub-basin scale. The precipitation and temperature data are procured from the India Meteorological Department (IMD) Pune, the soil moisture data is obtained from the European Space Agency (ESA) Climate Change Initiative (CCI) v03.3, and the streamflow data is downloaded from India-WRIS. Two different cases of drought propagation are analysed: meteorological to agricultural drought (SPEI-SSMI) and agricultural to hydrological drought (SSMI-SSI). Propagation of drought is quantified through the estimation of three-time matrices: (1) the time difference between the initiation of droughts, (2) the time difference between the peak of droughts, and (3) the time difference between the termination of droughts. The results from the study revealed that the SSMI drought was initiated after 6.4 months of the SPEI drought, while the SSI drought was initiated after 8.4 months of the SSMI drought. The peak of SSMI drought is found to be after 6.3 months of the peak of SPEI drought, while the peak of SSI drought is found to be after 34.7 months of the peak of SSMI drought. Once the SPEI drought terminates, it lasts for 8.3 months for the SSMI drought to terminate, while after the SSMI drought terminates, it lasts for 30.7 months for the SSI drought to terminate. Thus, it was found that the propagation of drought from SPEI-SSMI is faster than the propagation of drought from SSMI-SSI. The present work will provide essential information on drought propagation, which will be helpful in the management and mitigation of droughts in India. 

Keywords: Drought Propagation, Propagation Time, SPEI, SSMI, SSI.

How to cite: Gupta, A., Jain, M. K., and Pandey, R. P.: Drought propagation from meteorological to hydrological drought in the Krishna River Basin of India, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-2685, https://doi.org/10.5194/egusphere-egu24-2685, 2024.

EGU24-2852 | ECS | Posters virtual | HS4.2

Predicting longer lead droughts with Bayesian model averaging ensemble vine copula (BMAViC) model 

Haijiang Wu, Xiaoling Su, and Vijay P. Singh

In the face of global anthropogenic climate warming, particularly since the 1990s, the world has witnessed numerous extreme weather and climate events (e.g., droughts, heatwaves, and extreme precipitation), leading to economic losses and ecosystem degradation. In particular, drought prediction lies at the core of overall drought risk management and is critical for food security, early warning, and drought preparedness and mitigation. However, drought prediction models generally focus on shorter lead times (1–3-months) as their performance drastically declines at longer lead times (> 3 months). The vine copula can decompose complex non-linear, multi-variates into pairwise variables via bivariate copula forms which can well depict the diverse dependencies among variables (note that a vine copula possesses numerous vine structures, especially under higher-dimensional situations), while the Bayesian model averaging (BMA) can assign different weights to each ensemble member which depends on the explanatory power of the member itself for the specified objective. We therefore developed a new drought prediction model utilizing the BMA coupled with vine copula, called the Bayesian Model Averaging ensemble Vine Copula (BMAViC) model. Two drought types, i.e., hydrological drought (characterized by the standardized streamflow index (SSFI)) and agricultural drought (depicted by standardized soil moisture index (SSI)), were predicted with different lead times based on the BMAViC model under four-dimensional situations. Our model first was applied to predict the hydrological drought with the 1–3-month lead times for five hydrological stations (i.e., Tangnaihai, Minhe, Hongqi, Zheqiao, and Xiangtang) in the Upper Yellow River basin, in which previous meteorological drought, antecedent evaporative drought, and preceding hydrological drought were selected as three predictors. The BMAViC model showed robust skills during calibration and validation periods for 1–3-month lead hydrological drought predictions. In comparison with the meta-Gaussian model (reference model), the skills of the proposed model were relatively stable and superior under diverse lead times. Good performances under the 1–3-month lead times strongly implied that the BMAViC model yielded robust and accurate hydrological drought predictions. Considering the previous meteorological drought, antecedent hot condition, and agricultural drought persistence as three predictors, our proposed BMAViC model was further leveraged to predict the agricultural drought in the summer season over China with the 1–6-month lead times. Compared with optimal vine copula (OViC), average vine copula (AViC), and persistence-based models, the BMAViC model performed better for the 1–6-month lead agricultural drought predictions. Besides, the BMAViC model yielded a good prediction ability for extreme droughts. These findings enhance our confidence in seasonal drought prediction and help us understand drought dynamics in future months.

How to cite: Wu, H., Su, X., and Singh, V. P.: Predicting longer lead droughts with Bayesian model averaging ensemble vine copula (BMAViC) model, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-2852, https://doi.org/10.5194/egusphere-egu24-2852, 2024.

In recent decades, the phenomenon of drought has become a hazard with increasing frequency, with multiple societal and environmental impacts. One of these impacts concerns water resources and their availability for various uses. Numerous indices have been used over time to quantify the severity of drought and to assess its effects on socio-economic activities and environmental components. Among the spatial indices, the most numerous belong to remote sensing, being easy to use to analyse drought consequences especially on landcover and vegetation. However, regarding the hydrological drought, the indices used are mainly calculated based on hydroclimatic data, without taking into account spatial variables, such as the topographical, geological or pedological characteristics. The aim for this study is to compute a hydrological drought index which integrates several drought control variables, using both GIS and remote sensing techniques in order to map the susceptibility to hydrological drought within the Teleorman watershed.

Located in the central-southern part of Romania, the Teleorman River has a length of 169 km and a catchment area of 1.427 km2. The most part of  the catchment overlaps the central sector of the Romanian Plain, an important agricultural area, highly sensitive to water deficit. According to Köppen-Geiger classification, the analyzed catchment has a humid continental climate with hot summers (Dfa), meaning that the drought could occur in the basin.

A series of free data and information sources has been accessed in order to compute the hydrological drought index, such as: Worldclim, Landsat Archive, Geological Map of Romania, Pedological Map of Romania, Shuttle Radar Topography Mission (SRTM), Topographical Map of Romania. The following parameters were derived from these sources: Topograhic Wetness Index (TWI); Drainage Density (resulted from hydrographic network); Normalized Difference Drough Index (NDDI), resulted from ratio between Normalized Difference Vegetation Index (NDVI) and Normalized Difference Water Index (NDWI); Temperature Condition Index (TCI), extracted from Land Surface Temperature (LST); Aridity Index (AI) computed as a ratio between Precipitation and Potential Evapotranspiration (PET); De Martonne Index (based on ratio between Precipitation and Air temperature); Lithology and Soil Texture. Because some of the parameters had a different spatial resolution, the regridding method was used to bring the database to a resolution of 30 meters. Analytic hierarchy process method (AHP) was used to determine the influence of each factor and for the bonitation process. Based on the total obtained score, 5 classes from to lowest to highest hydrological drought susceptibility resulted. Finally, the Weighted Overlay and Raster Calculator tools from ArcGisPro software were used to map the index.

The resulted map allows the identification in the studied watershed of areas the most susceptible to hydroclimatic drought allowing the focus in these areas of appropriate actions to improve drought risk management. GIS and Remote sensing proved to be useful tools in spatial analysis of drought based on a composite index integrating several drought control factors. In the future, we intend to improve the method by considering other variables controlling the hydrological drought, such as the streamflow and the groundwater depth.

How to cite: Costache, M.-Ș. and Zaharia, L.: Mapping the susceptibility to hydrological drought using GIS and remote sensing techniques in the Teleorman watershed (Romania), EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-3408, https://doi.org/10.5194/egusphere-egu24-3408, 2024.

Drought projection is critical for water resource planning and management, as well as disaster prevention and mitigation. As a strategic national water source for China, the Yangtze River Basin (YRB) plays a vital role in the connectivity of rivers and economic development, flowing through 11 provincial administrative regions and is injected into the East China Sea, with a total length of 6,397 km. The watershed covers an area of 1.8 million square kilometers, accounting for about 1/5 of China's total land area. However, frequent droughts have caused water shortages in the YRB in recent years. Based on observed meteorological and hydrological data, the CMIP6 model and SPEI (standardized precipitation evapotranspiration index) drought models were used to elucidate the risk of future simultaneous droughts in the upper and mid-lower reaches of the YRB from 2015 to 2100. SRI has been used based on SWAT model to study the transfer process of meteorological drought to hydrological drought. The results indicated that, (1) The average of 10 CMIP6 models showed a good verification of historical precipitation and temperature for drought predictions. The MMK and Sen’s slope demonstrated consistency for historical and future droughts in the YRB. From a historical perspective (1961–2019), the middle reaches of the YRB experienced intensifying drought frequency with the highest total drought (Moderate and above drought events) frequency (> 17%); (2) In the future (2020–2100), the higher emission signifies higher moderate and total drought frequency, intensity, and scope of the YRB in FF, lower in NF. The ratio of autumn severe and extreme droughts would increase in mid-twenty-first century; (3) Severe drought risk encounters were projected in the upper and meanwhile in the middle-lower reaches in YRB, especially in the 2030–2040 period. Under all three scenarios, severe droughts occurred more frequently with SPEI close to − 2. The middle-lower reaches of the YRB are forecast to witness the largest scope and highest intensity of drought under the SSP1-2.6 scenario.; (4) The future runoff in the YRB during the dry period varied less, but in May and June during the main flood season the runoff under SSP1-2.6 would be the largest. Maximum decrease in runoff in the mid-lower reaches under the SSP2-4.5 scenario would be 2045, reaching 13.9%. Extreme flooding events and extreme meteorological droughts would happen accompanying with hydrological droughts would occur more frequently and severely under different scenarios. More attention and improved strategies should be brought to bear to address future simultaneous droughts in the upper and mid-lower YRB.

How to cite: Zhang, Y. and Zhang, Z.: The increasing risk of future simultaneous droughts over the Yangtze River basin based on CMIP6 models , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-3557, https://doi.org/10.5194/egusphere-egu24-3557, 2024.

EGU24-3746 | Posters on site | HS4.2

Drought Forecasting with ML-based Regionalized Climate Indices 

Taesam Lee, Yejin Kong, Sunghyun Hwang, and Sejeong Lee

Drought forecasting in South Korea has become imperative due to the increased frequency of occurrence leading various damages such as property loss and casualties. Precipitation in South Korea is distributed with high deviation, substantially concentrated in summer. Other seasons have a comparatively low amount of precipitation resulting unbalanced water resources of each season. To overcome the skewed seasonal precipitation, numerous dams and reservoirs have been constructed and operated. The management of those water-related structures should be carried out carefully to meet seasonal requests of water resources, and the precipitation prediction for each season has become critical. However, the seasonal precipitation forecasting has been a challenging task due to complex weather systems and climate patterns. The current study proposes a novel procedure for forecasting seasonal precipitation as: (1) regionalization of climate variables; (2) extraction of features with PCA, ICA and Autoencoder; and (3) finally regression model applications. Two globally gridded climate variables, Mean Sea Level Pressure (MSLP) and Sea Surface Temperature (SST) were teleconnected with the Accumulated Seasonal Precipitation (ASP) of South Korea. The results indicate that the k-means clustering successfully regionalizes the highly correlated climate variables with the ASP and all three feature selection algorithms, PCA, ICA, and Autoencoder present their superiority in different seasons combining GLM and SVM models. Especially, the PCA performs better with the linear GLM model and the Autoencoder shows better performance with the nonlinear SVM model. Overall, it can be concluded that the proposed seasonal precipitation forecasting procedure combining ML-based algorithms can be a good alternative.

How to cite: Lee, T., Kong, Y., Hwang, S., and Lee, S.: Drought Forecasting with ML-based Regionalized Climate Indices, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-3746, https://doi.org/10.5194/egusphere-egu24-3746, 2024.

EGU24-3770 | ECS | Posters on site | HS4.2

From development of multi-sectoral drought hazard indicators to global drought hazard propagation 

Neda Abbasi, Stefan Siebert, Malte Weller, Tina Trautmann, Jan Weber, Tinh Vu, Ehsan Eyshi Rezaei, Harald Kunstmann, Harald Koethe, Christof Lorenz, and Petra Döll

Droughts pose a substantial threat to various sectors, including agriculture, human water supply but also natural ecosystems. While various studies have been conducted for drought evaluation, the majority of them have focused on a particular drought type. This may lead to a lack of comprehensive understanding of the features and progression of droughts among different drought types through time. For example, for water resources management and planning purposes, it is critical to understand the changes and temporal development of drought signals from abnormal meteorological conditions to soil moisture, groundwater levels, and streamflow. Within the OUTLAST project, which aims at developing an operational, multi-sectoral global drought hazard forecasting system, we develop a near real-time drought hazard monitoring and forecasting system which, for the first time, includes tailored indicators for various sectors, including water supply, riverine and non-agricultural land ecosystems, as well as rainfed and irrigated agriculture. In this context, the primary objectives of this study are to 1) develop different drought hazard indicators (DHI) to monitor and forecast the drought across different sectors; and 2) assess the spread and propagation of droughts across different sectors and regions at a global scale. For this purpose, DHIs were computed for a 40-year reference period (1981 to 2020) using ERA5 as meteorological forcing data to drive the DHIs using the global hydrological model (WaterGAP) and the global crop water model (GCWM). These DHIs cover meteorological (SPEI and SPI), hydrological (empirical percentiles and relative deviations of soil moisture and streamflow), as well as agricultural droughts (crop-specific DHIs for rainfed and irrigated croplands). In this project, we focus on the period 2011 to 2015, with 2012 being a year in which droughts had major impacts on various regions and sectors. The study investigates drought propagation from meteorological drought, extending to rainfed agriculture due to soil moisture deficiency, over streamflow, and eventually reaching irrigated agriculture. In doing so, region-specific features and the dependency of drought propagation on the magnitude of the drought are highlighted. Finally, as monitoring and projecting drought characteristics are important for comprehending drought-related issues, our multi-sectoral drought hazard forecasting system enables us to evaluate the state of drought propagation at a global scale. 

How to cite: Abbasi, N., Siebert, S., Weller, M., Trautmann, T., Weber, J., Vu, T., Eyshi Rezaei, E., Kunstmann, H., Koethe, H., Lorenz, C., and Döll, P.: From development of multi-sectoral drought hazard indicators to global drought hazard propagation, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-3770, https://doi.org/10.5194/egusphere-egu24-3770, 2024.

EGU24-3933 | ECS | Posters on site | HS4.2

Spatiotemporal characteristics of drought in the Yili River basin in Northwest China over the past 40 years 

Mengzhen Huang, Ruijie Lu, Peiru Li, and Yutong Han

The Yili River basin is commonly referred to as a "wet island" in the Central Asian Dry Zone. It functions as a vital security barrier in the western part of China. Droughts frequently occur in the basin due to global change and pose a significant threat to food security and ecological stability in the region. Currently, droughts in the basin have not received the attention they deserve, and the mechanisms behind the occurrence, development, and impacts of drought in the basin have not yet been clarified. Based on the Standardized Precipitation Evapotranspiration Index (SPEI), this research identified drought events over the past 40 years, extracted drought characteristics and drought trends, and explored future drought. The following results were found: 1) The basin has experienced frequent wet and dry changes on monthly and seasonal scales, and entered a period of high drought since 2005, specifically the successive severe droughts of 2007-2009 and 2012-2015. 2) There were drought events approximately one-quarter of the time in the basin. Each drought event lasted an average of 2.23 months with a medium intensity. The most prominent droughts occurred in spring and summer. Droughts in the middle and southwest of the basin had short durations but higher intensities, which significantly impacted the area. 3) Over the last 40 years, there has been a general increase in aridity in the basin, especially in spring and summer. The aridity trend was more severe in the northwestern part. 4) In the future, annual drought is predicted to decrease but will increase in summer. It’s recommended that emergency management of drought disasters in the basin be strengthened and, in particular, to improve the monitoring, early warning and prevention in summer.

How to cite: Huang, M., Lu, R., Li, P., and Han, Y.: Spatiotemporal characteristics of drought in the Yili River basin in Northwest China over the past 40 years, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-3933, https://doi.org/10.5194/egusphere-egu24-3933, 2024.

EGU24-4094 | Orals | HS4.2

Identification of low flow events by machine learning algorithms 

Henning Lebrenz, Daniela Pavia, and Philipp Staufer

An improved forecast of low flow events in catchment basins could be a valuable tool for the operation and decision making of dependent infrastructure (e.g. wastewater discharge, water abstraction) along corresponding rivers. Therefore, the classification of 6642 independent low-flow-events (being the Q347 as the discharge less than the 95%- exceedance quantile of the FDC) from 55 catchment basins within the Kanton Solothurn (Switzerland) was performed by five different machine learning algorithms (i.e. knn, decision tree, random forest, support vector machine, logistic regression). Herein, each low flow event was characterized by 47 static and dynamic parameters (i.e. description of catchment and event history), being supplemented by differently defined (near) non-low-flow events, leading up to a total population of approx. 18000 discharge events.

The validation and verification showed different qualities of the classification accuracy for the forecast of low-flow events, being dependent on the selection of the defined event populations, the selected machine learning algorithm and the definition of classes. In general, the support vector machine and random forest may lead, with the presumption of carefully selected classes, to forecast accuracies of >90%.

How to cite: Lebrenz, H., Pavia, D., and Staufer, P.: Identification of low flow events by machine learning algorithms, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-4094, https://doi.org/10.5194/egusphere-egu24-4094, 2024.

EGU24-4330 | ECS | Orals | HS4.2

Predicting Food-Security Crises in the Horn of Africa Using Machine Learning 

Tim Busker, Bart van den Hurk, Hans de Moel, Marc van den Homberg, Chiem van Straaten, Rhoda A. Odongo, and Jeroen C.J.H. Aerts

Food insecurity is a global concern resulting from various complex processes and a diverse range of drivers. Due to its complexity, it is one of the most challenging drought impacts to predict. In this study, we introduce a novel machine learning model designed to forecast food crises in the Horn of Africa up to 12 months in advance. We trained an “XGBoost” model using more than 20 different input datasets to capture key food security drivers such as drought, economic shocks, conflicts and livelihood vulnerability. The model shows a promising ability to predict food security dynamics several months in advance (R2>0.6, three months in advance). Notably, it accurately predicted 20% of crisis onsets in pastoral regions (n = 84) and 40% of crisis onsets in agro-pastoral regions (n = 23) with a 3-month lead time. We compared these results to the established FEWS NET early warning system, and found a similar performance over these regions. However, our model is clearly less skilled in predicting food security for crop-farming regions than FEWS NET. This study underscores the importance of integrating machine learning into operational early-warning systems like FEWS NET and expanding these techniques to the continental or global-scale.   

How to cite: Busker, T., van den Hurk, B., de Moel, H., van den Homberg, M., van Straaten, C., A. Odongo, R., and C.J.H. Aerts, J.: Predicting Food-Security Crises in the Horn of Africa Using Machine Learning, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-4330, https://doi.org/10.5194/egusphere-egu24-4330, 2024.

EGU24-4814 | ECS | Posters on site | HS4.2

Global dryness could intensify vegetation failure even after net-negative emission is achieved 

Sanjit Kumar Mondal, Soon-Il An, Seung-Ki Min, Tong Jiang, Buda Su, Seungmok Paik, and Soong-Ki Kim

The response of global dryness and vegetation to CO2 removal experiments, especially for net-
 negative emission is immature. Here we conducted a thorough investigation to identify hysteresis and reversibility in global dryness, as well as the vegetation productivity’s response to dry and wet episodes, considering their asymmetrical nature. The asymmetry index (AI) includes two important aspects such as positive AI indicates a dominant increase of vegetation productivity during wet episodes compared to the decline in dry episodes and negative AI implies a larger reduction of productivity in dry years compared to an increase in wet years. Aggregate results from various drought indices and vegetation productivity reveal a dominant dryness in the CO2 decrease phase. Global dryness shows strong hysteresis and irreversible behavior over half of the global land with significant regional disparity. Irreversible changes in dryness are concentrated in specific areas, i.e., hotspots, covering over 14% of the global land, particularly pronounced in Northern Africa, Southwest Russia, and Central America. Moreover, a wider spread of negative asymmetry indicates a significant decrease in vegetation productivity caused by dryness. Importantly, the potential evapotranspiration is projected to be the primary driver of global dryness as well as vegetation asymmetry. Our findings suggest only CO2 alleviation is not enough to cope with drought rather implementing advanced water management strategies is a must to mitigate the impact of drought effectively.

How to cite: Mondal, S. K., An, S.-I., Min, S.-K., Jiang, T., Su, B., Paik, S., and Kim, S.-K.: Global dryness could intensify vegetation failure even after net-negative emission is achieved, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-4814, https://doi.org/10.5194/egusphere-egu24-4814, 2024.

Hydrological drought occurs frequently all over the world and has a great impact on human beings. Hydrological drought attribution contributes to a better understanding of the mechanisms of drought occurrence, improves the accuracy of predictions of drought events, and can provide a basis for drought risk reduction. At present, hydrological models which possess physical mechanisms are widely used in attribution analysis. However, this kind of models is complex in calculation, and has very limited time scale. In this study, we developed a hydrological drought attribution method via AdaBoost algorithm. The method divided the study period into natural period unaffected by non-climatic factors and impacted period. Taken the natural period as training period, the impacted period as test, the runoff was obtained to calculate the three-months standardized runoff index (SRI-3). Based on the run-length theory, we calculated average drought characteristics in the impacted period. Finally, the proportion of the average drought characteristics obtained by simulated SRI-3 series to those obtained by observed SRI-3 series is considered as the contribution of the climatic factors to the drought events.

We applied this method in the Yangtze River Basin and the results showed that climatic factors are the dominate factors affecting hydrological droughts in this region, with the contributions at all the gauge stations are over 50%. Among all the drought characteristics, average drought severity is the most affected by the climatic factors, the corresponding contributions are all greater than 100%, shown as “excess contributions” (with non-climatic factors shown as negative contributions). Through the applications in various sub-basins of the Yangtze River Basin, the method was shown to provide new ideas for hydrological drought attribution, and the method can also be extended for applications such as meteorological hazards attribution, stock market volatility attribution and so on.

How to cite: Wang, W., She, D., and Xia, J.: Separate the impact of climate change and non-climatic factors on hydrological drought based on AdaBoost algorithm, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-4870, https://doi.org/10.5194/egusphere-egu24-4870, 2024.

EGU24-5040 | ECS | Posters on site | HS4.2

Links between heat waves, drought, and atmospheric circulation in Central Europe 

Zuzana Bešťáková, Ondřej Lhotka, Jan Stryhal, and Jan Kyselý

Heat waves and drought are phenomena associated with large negative impacts on society and environment. Their common features include increasing frequency and intensity in recent decades in many regions of Europe, as well as interconnectedness of the factors that contribute to their development. In this study, we evaluate links between heat waves and drought in Central Europe using E-OBS data and ERA-5 reanalysis in the 1979–2022 period. Heat waves are classified according to their 3-dimensional structure of positive temperature anomalies into near-surface, lower-tropospheric, higher-tropospheric, and omnipresent types. We show that the associations to soil moisture conditions and development of flash drought (based on the daily climatic water balance index) differ for the individual heat wave types; the links are most pronounced for near-surface heat waves, illustrating the compound nature of the heat-drought events. We also employ the Jenkinson–Collison classification to identify circulation types with significantly increased frequency during periods of heat waves and droughts, and study changes in their occurrence. The analysis contributes to better understanding of the interrelationships between drought, heat waves, atmospheric circulation and other driving mechanisms.

How to cite: Bešťáková, Z., Lhotka, O., Stryhal, J., and Kyselý, J.: Links between heat waves, drought, and atmospheric circulation in Central Europe, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5040, https://doi.org/10.5194/egusphere-egu24-5040, 2024.

EGU24-5291 | ECS | Orals | HS4.2

High-resolution drought monitoring with Sentinel-1: A case-study over Mozambique 

Samuel Massart, Mariette Vreugdenhil, Sebastian Hahn, Pavan Muguda Sanjeevamurthy, Carina Villegas-Lituma, and Wolfgang Wagner

Droughts are characterized by periods of below-average precipitation leading to an imbalance in the hydrological cycle and reduced water availability.
In the last decades, higher average temperatures and shifts in annual rainfall patterns have increased the frequency, intensity, and length of droughts across the globe.

With the majority of its population living in rural areas and a high economic dependency on rain-fed agriculture, Mozambique is particularly vulnerable to droughts, as water shortages have devastating environmental, agricultural, and economic impacts. Therefore, monitoring droughts in Mozambique is key to developing early warning systems and adequate planning for drought impact mitigation.

In this study, we propose a novel approach to retrieve a drought index at a kilometer-scale resolution based on surface soil moisture (SSM) products derived from Sentinel-1 (S1) and ASCAT. First, both SSM products are processed over the Mozambican region using a change detection method (Sentinel-1 sampled at 1km and ASCAT at 6.25km) and compared to SSM from ERA5-Land. Then, by combining the long-term ASCAT data record with the high spatial resolution of Sentinel-1, we generate a monthly kilometer-scale drought index for the period 2016 to 2023 over six study areas located in South-central Mozambique (Chokwé, Mabote, Massinga, Buzi, Muanza and Govuro). The S1-ASCAT indicator is then evaluated against state-of-the-art drought indices based on precipitation data (Standardised precipitation index from CHIRPS (Rainfall Estimates from Rain Gauge and Satellite Observations)) and vegetation data (Normalized difference vegetation index from the Copernicus Global Land Service.

This study explores the potential of high-resolution SSM based on active microwave remote sensing to monitor agricultural droughts. Our results show that a drought indicator based on Sentinel-1 and ASCAT can temporally and spatially capture sub-regional drought patterns over Mozambique.

How to cite: Massart, S., Vreugdenhil, M., Hahn, S., Muguda Sanjeevamurthy, P., Villegas-Lituma, C., and Wagner, W.: High-resolution drought monitoring with Sentinel-1: A case-study over Mozambique, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5291, https://doi.org/10.5194/egusphere-egu24-5291, 2024.

EGU24-5692 | ECS | Orals | HS4.2

High vulnerability yet strong resilience of China's lakes to drought 

Siyu Ma, Almudena Garcia-Garcia, Xueying Li, and Jian Peng

Lakes play a crucial role in the global hydrological cycle and biogeochemical cycle. In China, lakes are an important part of water resources, providing 40.6% of drinking water. In recent years, droughts in the middle and lower reaches of the Yangtze River in China have led to a significant shrinkage of important freshwater lakes, such as Dongting Lake and Poyang Lake, posing a threat to local water security. However, there has been limited research on the extent to which thousands of lakes across China are affected by droughts. This study used remote sensing product of lake area to comprehensively investigate the impact of drought on the area of 4,702 lakes (natural lakes and reservoirs) in China from 1985 to 2018, covering the three stages of response, shrinkage, and recovery. The results indicate that lakes in China are highly vulnerable to drought. The average response probability of lakes is 72.8%, which typically occurs within six months to two years after the onset of drought. The shrinking area of the lake is 12.7% of the original area, and the shrinking process takes an average of 14 months. Lakes also show a strong resilience to drought, with 95.7% of lakes more likely to experience an increase in area following drought-induced shrinkage. However, only 49.4% of lakes are more likely to grow beyond their pre-shrinkage levels. Compared to natural lakes, artificial reservoirs exhibit a higher response probability by 4.6%, a larger shrinkage area percentage by 1.2%, and a higher recovery probability by 2.9%. Consequently, artificial reservoirs exhibit greater vulnerability and resilience to drought, reflecting the impact of human activities. Furthermore, the spatial distribution of vulnerability and resilience is inconsistent. In Northeast China, including the Songhua and Liaohe river basins, and the Mongolian endorheic basin, lakes exhibit higher vulnerability but lower resilience. Therefore, this region is considered a hotspot where the impact of drought on lake area is particularly severe. This study is expected to provide a basis for the implementation of sustainable water resource management and effective drought mitigation measures in China.

How to cite: Ma, S., Garcia-Garcia, A., Li, X., and Peng, J.: High vulnerability yet strong resilience of China's lakes to drought, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5692, https://doi.org/10.5194/egusphere-egu24-5692, 2024.

EGU24-5875 | Posters on site | HS4.2

Parameter optimization of an agro-ecological model for regional NUTS-3 yield data 

Olga Wold, Roland Baatz, Michael Berg-Monicke, Ehsan Rezaei, Eyshi, and Claas Nendel

Climate change increasingly affects agricultural systems in Central Europe, necessitating the development of robust forecasting models for drought, heat, and fire events (DHF). These hazards pose significant threats to crop production and require proactive measures to enhance resilience and adaptation.

This research project is dedicated to constructing a thorough framework for forecasting DHF events in Central Europe. It integrates an agro-ecosystem model aimed at examining how crops respond, particularly when it comes to water availability. The focus of this research extends to the region's awareness to climate-related threats and the robustness of its agricultural systems.

We utilize the MONICA (Model for Nitrogen and Carbon in Agriculture) crop model to simulate crop growth and response across a spectrum of environmental conditions. The MONICA model is designed to represent the complexity of crop development, considering factors such as soil properties and weather variations. MONICA model has the capacity to explore various scenarios, including heat stress and drought sensitivity, providing a comprehensive view of how crops respond to these challenges.

 The used data includes high-resolution meteorological (1km resolution, daily), topographic, historical crop records and soil information for whole Germany. The dataset covers the past two decades, encompassing vital information such as crop yield records.

By sensitivity analysiswe systematically identified key parameters influencing simulated crop yield and above ground biomass, particularly in the context of drought and heat stress. These insights are invaluable for advancing our understanding of how crops respond to environmental stressors.

Moving forward, our focus shifts to the calibration and optimization routines to quantify specific parameter sets for individual NUTS-3 regions within Germany.

In the poster presentation, we look forward to sharing the newest findings from our ongoing research on advanced calibration tools and yield simulations conducted over Germany. Simulation results are compared to observed yield data, providing valuable insights into the effectiveness and real-world applicability of the modelling approaches.

How to cite: Wold, O., Baatz, R., Berg-Monicke, M., Rezaei, Eyshi, E., and Nendel, C.: Parameter optimization of an agro-ecological model for regional NUTS-3 yield data, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5875, https://doi.org/10.5194/egusphere-egu24-5875, 2024.

Given their profound socio-economic impact and increasing occurrence, compound heat and drought extremes (CHDEs) have become a focal point of widespread concern. Numerous studies have attempted to reproduce and predict these extremes using general circulation models (GCMs); however, the performance of these models in capturing extreme events remains controversial. This study presents an improved historical simulation of CHDEs over China by using the regional Climate-Weather Research and Forecasting model (CWRF) to downscale the projections of two GCMs that participated in the Coupled Model Intercomparison Project Phase 6. The CWRF downscaling improved GCMs in capturing the thresholds of extreme hot and extreme dry conditions and demonstrates a better agreement with observations in the temporal trends and spatial patterns of extreme heat and extreme drought events. The performance of CWRF downscaling to reproduce CHDEs also surpasses that of GCMs, with an even greater enhancement compared to univariate extreme events. The improvement is particularly pronounced in sub-humid areas, which is primarily attributed to the enhanced simulation of temperature-precipitation coupling relationships by CWRF downscaling. This superiority is found to be associated with the finer land surface processes and land-atmosphere interaction processes of CWRF. This study highlights the important role of land-atmosphere interactions in shaping CHDEs and the efficacy of using regional climate models to reduce uncertainty in extreme event simulations.

How to cite: Zhang, S. and Zhang, H.: Towards improved prediction of compound heat and drought extremes by CWRF downscaling, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-8013, https://doi.org/10.5194/egusphere-egu24-8013, 2024.

EGU24-9170 | ECS | Orals | HS4.2

Analysis of droughts and arid conditions in Central Asia using climate indices 

Milena Latinovic, Valeria Selyuzhenok, and Abror Gafurov

Droughts pose significant challenges to water resources, agriculture, and socioeconomic stability, particularly in regions susceptible to climatic extremes such as Central Asia (CA) with its complex topography and diverse ecosystem. In the past several years there has been a substantial decrease in water storage in the region which further could lead to socioeconomic instability. Water is mainly used for irrigation and hydropower production in the region.

In CA, the availability of ground observations is restricted, with most of the measurement stations being outdated since the Soviet era with little or no data sharing between the countries. Consequently, the utilization of widely available remotely sensed data proves advantageous in overcoming these limitations and improving the accuracy of water availability assessment in the region.

CA relies predominantly on water resources derived from the melting of snow and glaciers in the Pamir, Tian Shan, and Hindukush mountains. In the study, we consider the two largest upstream river basins, Amu Darya and Naryn, the eastern headstream of Syr Darya. These two largest rivers in CA are crucial sources of water in the region, supporting agriculture and the ecosystem in the whole of CA.

The study specifically focuses on evaluating snow cover and Snow Water Equivalent (SWE) during the winter months, especially preceding the onset of drought periods, and the Total Water Storage (TWS) in the drought months. The objective is to comprehend and quantify the correlation between these climatic elements and historical droughts, utilizing the Drought Severity Index (DSI) and the widely used Standardized Precipitation Index (SPI).  DSI is based on the TWS value that is derived from the GRACE and GRACE-FO satellite missions. It shows a significant decrease in water storage in both basins since the start of the GRACE mission in 2002, with more intense arid conditions in the last 6 years. SPI-6 and SPI-9 based on precipitation and SWE data, show a slight increase in the trend in the Amu Darya basin, while in Naryn all indices show an increase in drought periods. This indicates that the arid conditions in the summer months in the Amu Darya basins are driven by human-induced water depletion. Finally, all indices can depict severe droughts in 2008, 2011 and 2018 in both basins. The study shows the potential of using globally available TWS data for drought assessment on a regional scale such as in CA.

How to cite: Latinovic, M., Selyuzhenok, V., and Gafurov, A.: Analysis of droughts and arid conditions in Central Asia using climate indices, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-9170, https://doi.org/10.5194/egusphere-egu24-9170, 2024.

EGU24-9531 | Posters on site | HS4.2

Building a hybrid drought monitor model based on U.S. Drought Monitor 

Haiting Xu, Jianhui Wei, and Ying Pan

Drought is one of the costliest natural disasters, capable of causing significant losses in agriculture, economy, and ecosystems. Different definitions of drought from multiple perspectives made drought research complicate. Exploring droughts from a comprehensive perspective improves our understanding of the evolution and drivers of drought, while there are few such comprehensive studies. The establishment of the United States Drought Monitor (USDM) marks a significant milestone in the development of composite drought indices, amalgamating objective inputs with subjective evaluations from local experts. Its uniqueness lies in integrating subjective assessments from climate and water resource experts across the United States. However, due to the human subjectivity involved in creating USDM maps, its algorithms are challenging to apply beyond the United States. In this study, a Hybrid Drought Monitor Model (HDMM)  was built using the random forest algorithm to predict drought categories based on USDM drought categories, input drought indices, and 10 static variables. The results indicate that during the testing phase, the overall accuracy of the 0.04° resolution HDMM reached 95%, surpassing the 91% overall accuracy at 1° resolution. Among the categories, D-1 (Normal or wet conditions) drought accuracy was the highest, while D0 (Abnormally Dry) drought accuracy was the lowest. During the validation phase, the HDMM exhibited good overall prediction of drought levels, yet spatial discrepancies existed across the continent. It performed poorly in the southwestern and northern regions, with overestimation of drought severity in many areas. Case studies of the 2017 Northern Plains Drought and the 2021 Southwestern Drought demonstrate that HDMM provided reliable drought classification and possessed good predictive capability. The HDMM can be adapted to other regions worldwide, offering a promising tool for land managers and local governments to prepare for and mitigate the impacts of drought.

How to cite: Xu, H., Wei, J., and Pan, Y.: Building a hybrid drought monitor model based on U.S. Drought Monitor, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-9531, https://doi.org/10.5194/egusphere-egu24-9531, 2024.

EGU24-10512 | Orals | HS4.2 | Highlight

Water scarcity under droughts and heatwaves: understanding the complex interplay of water quality and sectoral water use 

Michelle van Vliet, Gabriel Cardenas Belleza, Duncan Graham, and Edward Jones

Droughts and heatwaves pose serious challenges for water management and severely increase water scarcity in many regions of the world. It is increasingly recognized that water scarcity represents more than just a physical lack of water, referring to the imbalance between the supply and the demand of water of suitable quality for different uses. Changes in both climate and socioeconomic systems influence the availability, use and quality of water resources. Water scarcity thus amplifies when either one or more of the following three driving mechanisms intensify: 1) decreasing water availability; 2) increasing sectoral water use, and 3) deterioration of water quality resulting in unsuitability for use. Droughts and heatwaves are particularly critical as they adversely affect all three driving mechanisms, which are also highly interrelated1. However, limited understanding exists regarding the complex interplay, particularly between water quality and sectoral water use. Here we show responses in sectoral water use and water quality under droughts and heatwaves based on reported data for 1980-2019 globally and discuss a global assessment framework to unravel water scarcity and its drivers under these hydroclimatic extremes.

Our results show that heatwaves and compound drought-heatwave events increase water use mainly for domestic and irrigation water use sectors2. River water quality tends to deteriorate during droughts and heatwaves in most cases as demonstrated based on a global literature survey3 and analyses of river water quality records of 314,046 water quality monitoring stations globally4. This showed for instance on average a 17% decrease in dissolved oxygen and 24% increase in river salinity under droughts and heatwaves over 1980-2019 globally4. Increasing sectoral water use, deterioration of water quality and decreasing water availability each amplify water scarcity in their own right, but more so together due to important interactions. For instance, a decline in water availability during a drought increases water scarcity directly, but also indirectly as less water is available to dilute pollutants, thereby leading to a deterioration of water quality3,4. This may result in higher water scarcity, when water quality thresholds for certain uses are temporary exceeded (e.g., increased salinity for irrigation). Increases in sectoral water use, such as for domestic use and irrigation2, result in higher water scarcity directly, but also indirectly due to water quality impacts. We propose a new integrated modelling framework building on the PCR-GLOBWB2 hydrological model coupled to the DynQual global surface water quality model5 to quantify water scarcity under droughts and heatwaves. Here we consider the two-way interactions between sectoral water use, water quality and water availability to improve understanding of the complex interplay between these water scarcity drivers, and test solutions options towards sustainable water management.

 

1 van Vliet, M.T.H. (2023) Nature Water 1, 902–904

2 Cárdenas Belleza, G.A., M.F.P. Bierkens, M.T.H. van Vliet (2023) Environ. Res. Lett. 18 104008

3 van Vliet, M.T.H. et al (2023) Nature Reviews Earth Environ. 4, 687–702

4 Graham D.J., M.F.P. Bierkens, M.T.H. van Vliet (2024), J. of Hydrology 629, 130590

5 Jones, E.R., M.F.P. Bierkens, N. Wanders, E.H. Sutanudjaja, L.P.H. van Beek, M.T.H. van Vliet (2023) Geosci. Model Dev. 16, 4481–4500

How to cite: van Vliet, M., Cardenas Belleza, G., Graham, D., and Jones, E.: Water scarcity under droughts and heatwaves: understanding the complex interplay of water quality and sectoral water use, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-10512, https://doi.org/10.5194/egusphere-egu24-10512, 2024.

EGU24-10886 | ECS | Orals | HS4.2

Explainable machine learning revealing the mechanism behind drought events in northern Italy: the case of the 2022 drought 

Chenli Xue, Aurora Ghirardelli, Jianping Chen, and Paolo Tarolli

Drought is a complex natural hazard involving multiple variables that, depending on the measured parameters, can be categorized into meteorological, hydrological or agricultural drought. Among them, agricultural drought, which refers to soil moisture deficits that fail to meet crop growth, has been attracting more attention for severely threatening food security worldwide. In the context of climate change and the increased occurrence of drought events, it is crucial to monitor drought drivers and progression to plan the subsequent efforts in drought prevention, adaptation, and migration. However, the comprehensive knowledge of agriculture drought still needs to be clarified. Previous works often focused on precipitation or evapotranspiration and failed to capture other potential drivers of drought. This study proposes a novel framework to comprehensively monitor agricultural drought with ensemble machine learning by constructing an integrated agriculture drought index with high temporal-spatial resolution. In addition, the Shapley Additive Explanation (SHAP) explainable model was applied to reveal the driving mechanism behind the drought event that occurred in northern Italy in the summer of 2022. Results indicate that the proposed explainable ensemble machine learning model could effectively reflect the evolution of agricultural drought with spatially continuous maps on a weekly scale. The SHAP analysis demonstrated that the severe agricultural drought in the summer of 2022 was closely related to meteorological indicators, namely precipitation and land surface temperature, crucial in controlling soil moisture. Moreover, the new findings also revealed that soil textures could significantly affect agricultural drought. By combining explainable ensemble machine learning and various earth-observation data involving meteorology, soil, geomorphology, and vegetation conditions, the study constructed an integrated index to monitor and assess agricultural drought in northern Italy. The proposed research framework could effectively contribute to improving the methodology in agricultural drought research, potentially bringing more instructive insights for drought prevention and mitigation.

How to cite: Xue, C., Ghirardelli, A., Chen, J., and Tarolli, P.: Explainable machine learning revealing the mechanism behind drought events in northern Italy: the case of the 2022 drought, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-10886, https://doi.org/10.5194/egusphere-egu24-10886, 2024.

EGU24-10948 | Posters on site | HS4.2

On the use of standardized drought indices (SPI and SPEI) for assessing future climate change impacts on drought: introducing a dynamic approach   

David J. Peres, Brunella Bonaccorso, Nunziarita Palazzolo, Antonino Cancelliere, Giuseppe Mendicino, and Alfonso Senatore

Drought is frequently monitored using standardized indices, such as the Standardized Precipitation Index (SPI) and the Standardized Precipitation-Evapotranspiration Index (SPEI). The latter was specifically designed to incorporate climate variability in terms of temperature. Consequently, by definition, it is more suitable for assessing variations in drought frequency and magnitude induced by climate change across various potential future scenarios. 

However, standardization presents a challenge when employing indices to evaluate the potential impacts of future climate change on drought. This is because, by definition, these indices are drawn from a standard normal random variable (null average and unit variance). The assessment of these impacts involves comparing occurrences in a future period and scenario with those in a historical control period. If the indices are separately calibrated for each period (one calibration for the future period and one for the control period), any differences observed may result solely from the sampling variability of a series drawn from a standard normal random variable. Numerous studies have assessed climate change impacts on droughts using this imperfect approach. Conversely, an alternative approach involves computing future indices using parameters from the control period. This represents a "worst-case scenario" as it overlooks potential climate change adaptation measures that could mitigate the impacts. To address this issue, our study introduces a dynamic approach wherein future changes are evaluated by computing climate normals using moving time windows. This approach enables an understanding of how impacts change with the timing of the implementation of adaptation measures. We apply this approach to Sicily and Calabria in Southern Italy, considering various climate change scenarios (Representative Concentration Scenarios). The results suggest that the region is likely to experience an increase in drought events due to climate change. These findings underscore the necessity for revised drought identification strategies that consider the non-stationarity in climate. 

How to cite: Peres, D. J., Bonaccorso, B., Palazzolo, N., Cancelliere, A., Mendicino, G., and Senatore, A.: On the use of standardized drought indices (SPI and SPEI) for assessing future climate change impacts on drought: introducing a dynamic approach  , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-10948, https://doi.org/10.5194/egusphere-egu24-10948, 2024.

EGU24-11049 | Orals | HS4.2 | Highlight

On the predictability of the seasonal droughts at global scale 

Luis Samaniego, Ehsan Modiri, E.H. (Edwin) Sutanudjaja, Pallav Shrestha, Alberto Martinez-de la Torre, Oldrich Rakovec, Robert Schweppe, Matthias Kelbling, Katie Facer-Childs, Amulya Chevuturi, Maliko Tanguy, Niko Wanders, Rohini Kumar, and Stephan Thober

Long-lasting droughts have become more common worldwide in recent decades, such as in Australia (2001-2009), California (2012-2014), Chile (2010-2023), and Europe (2018-2022). The combination of droughts and heatwaves has led to intense flash droughts, worsening soil moisture deficits. This has resulted in global shortages of essential food, serious public health issues, and prolonged forest fires that harm air quality in populated areas. Extended droughts also contribute to food insecurity, reduced energy production, increased health crises, and the destruction of natural landscapes, causing significant economic setbacks in various regions. International agencies, such as the WMO, and water authorities are actively promoting the advancement of seasonal soil moisture monitoring and forecasting systems. In this presentation, we'll give you an update on ULYSSES [2], the global multi-model hydrological seasonal predictions system supported by the Copernicus Climate Change Service. This fully operational system runs directly at the ECMWF's HPC and aims to be the first seamless multi-model hydrological seasonal prediction system with global coverage at a spatial resolution of 0.1 degrees.

The ULYSSES modeling chain builds on the successful EDgE proof of concept [3], employing four advanced hydrological models (Jules, HTESSEL, mHM, PCR-GLOBWB). Notably, this production chain features a distinctive aspect: the utilization of a standard set of physiographical datasets (e.g., DEM, soil properties) with consistent spatio-temporal resolutions and similar forecast inputs for all hydrological models, as well as the same multi-scale routing model (mRM). The seasonal forecasts are initialized using the ERA5-land product from ECMWF. The Equitable Thread Score (ETS) skill is employed to assess the ensemble forecasting abilities for drought events, specifically when soil moisture exceeds 80% of the time, across lead times ranging from one to three months.

In a recent assessment, the global ensemble Equitable Thread Score (ETS) for the system stands at 63%, 43%, and 34% for lead times ranging from 1 to 3 months. Notably, over Europe, the ensemble ETS is significantly higher, reaching 91%, 71%, and 61% for the corresponding lead times. Contrasting these findings with a prior study that employed the mHM initialized with E-OBS forcing and the NMME ensemble over Europe [4], our analysis suggests potential reasons for the diminished performance of the current system. These factors may include: 1) the meteorological forcings utilized for initializing the hydrological models, and/or 2) the skill level of the NWF model ensemble. In this study, we will present the sensitivity of ETS when one of the models (mHM) is initialized with different available forcings procucts available such as EM-EARTH, MSWEP, WE5E, and E-OBS. Finding of this study is key for the further improvement of the system.

References

  • [1] https://doi.org/10.1029/2021EF002394
  • [2] https://www.ufz.de/ulysses
  • [3] https://doi.org/10.1175/BAMS-D-17-0274.1
  • [4] https://doi.org/10.1175/JHM-D-12-075.1
  • [5] https://doi.org/10.1175/JHM-D-19-0095.1

How to cite: Samaniego, L., Modiri, E., Sutanudjaja, E. H. (., Shrestha, P., Martinez-de la Torre, A., Rakovec, O., Schweppe, R., Kelbling, M., Facer-Childs, K., Chevuturi, A., Tanguy, M., Wanders, N., Kumar, R., and Thober, S.: On the predictability of the seasonal droughts at global scale, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-11049, https://doi.org/10.5194/egusphere-egu24-11049, 2024.

EGU24-12175 | Posters on site | HS4.2

Evaluation of daily SPI and SPEI indices for near-real time drought monitoring over CONUS 

Olivier Prat, David Coates, Scott Wilkins, Denis Willett, Ronald Leeper, Brian Nelson, Michael Shaw, and Steve Ansari

Two drought indices; the Standardized Precipitation Index (SPI) and the Standardized Precipitation Evapotranspiration Index (SPEI) are computed over CONUS using daily precipitation and temperature estimates from the NOAA Daily U.S. Climate Gridded Dataset (NClimGrid-Daily). The NClimGrid-Daily dataset consists of four climate variables derived from the GHCN-D dataset: maximum, minimum, and average temperatures and precipitation from 1951 to the present with a 5-km grid resolution. While SPI only uses precipitation as an input to assess drought conditions, SPEI uses both precipitation and potential evapotranspiration (PET). The daily SPI and SPEI are computed over various time scales (30-, 90-, 180-, 270-, 365-, 730-day). The differences between the two indices are being evaluated focusing on the influence accumulation period, differing period of record, and various SPI (McKee et al 1993, Guttman 1999) and daily PET (Thornthwaite and Mather 1957, Camargo et al. 1999, Pereira and Pruitt 2004) formulations. The impact of the period of reference is analyzed to account for the impact of precipitation and temperature changes over time (i.e., 1952-present, 1960-1990, and 1990-2020 for instance). For the most recent period (2000-present), the NClimGrid-SPI and NClimGrid-SPEI are compared against existing droughts monitoring resources such as the weekly US Drought Monitor (USDM). The use of cloud-scale computing resources reduces considerably the computation time and allows for the near-real time computation of daily SPI and SPEI indices. The effort to transfer the SPI and SPEI from research to operation (R2O) and to provide near-real time drought monitoring capabilities is also presented.

How to cite: Prat, O., Coates, D., Wilkins, S., Willett, D., Leeper, R., Nelson, B., Shaw, M., and Ansari, S.: Evaluation of daily SPI and SPEI indices for near-real time drought monitoring over CONUS, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-12175, https://doi.org/10.5194/egusphere-egu24-12175, 2024.

EGU24-12211 | ECS | Posters on site | HS4.2

Developing a national scale drought modelling and short to medium-term forecasting framework for Scotland 

Shaini Naha, Zisis Gagkas, Nick Schurch, Johan Strömqvist, Alena Bartosova, Kit Macleod, and Miriam Glendell

Climate change is resulting in many countries including Scotland being increasingly vulnerable to periods of dry weather, impacting water users and the natural environment. In 2022, large parts of Scotland experienced water shortages, resulting in Scotland Environmental Protection Act (SEPA) suspending water abstractions for abstraction licence holders in some Scottish catchments. Managing these water scarcity events requires the development of a national-scale short- to medium- term drought forecasting capability. In this study, the applicability of widely used open source hydrological models for simulating low flows depends on how various hydrological processes are accounted for in the model structures, the use of diverse calibration criteria and analysis of the associated uncertainties. Currently, few studies exist that consider all these criteria for modelling low flow events. In this study, we choose a lumped conceptual model, GR6J and a semi distributed hydrological response unit-based model, HYPE, for simulating river discharge across 81 catchments in Scotland, used by SEPA to assess water scarcity events. Our modelling framework considered model structural uncertainties by using models of different complexities and model parametric uncertainties, through robust multi-objective model calibration. We first tested this framework on an experimental Scottish catchment where GR6J outperformed HYPE in simulating river discharge after automatic calibration against objective functions KGE and logNSE. Further, calibration against logNSE improved low flow simulation in both models. We then upscaled this methodology for 81 catchments using GR6J, resulting in overall a very good model performance in simulating river discharge in both calibration and validation period with KGE and logNSE ranging from 0.37-0.96 and 0.2-0.93 for 81 gauged catchments respectively. Our next task is to calibrate HYPE for these 81 catchments and use both calibrated models to derive an ensemble of short-term river flow forecasts using 5-days meteorological forecasts from the UK Met Office. Results in overall shall highlight the need for using ensemble of hydrological models and also indicate careful consideration of objective functions, while simulating and forecasting low flows.

How to cite: Naha, S., Gagkas, Z., Schurch, N., Strömqvist, J., Bartosova, A., Macleod, K., and Glendell, M.: Developing a national scale drought modelling and short to medium-term forecasting framework for Scotland, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-12211, https://doi.org/10.5194/egusphere-egu24-12211, 2024.

Drought is one of the natural hazard risks that badly affect both agricultural and socio-economic sectors. Hungary, which is located in Eastern Europe, has already been suffering from different drought periods, and the driest year since 1901 was 2011 when the annual precipitation in Hungary was only 72 percent of the normal value. To better understand droughts and to provide information for adaptation strategies and risk-management systems, there is a strong need for a methodological framework to simulate drought events. However, it is uncertain whether climate models can simulate extreme droughts given the well-known model bias of simulating too light rainfall too frequently. So, the aim of the current study is to investigate the effects of the different model settings on the reproduction of drought characteristics.

In order to quantify the impact of the use of different parameterization schemes on regional climate model outputs, hindcast experiments were completed applying RegCM4.7 to the Carpathian region and its surroundings at 10-km horizontal resolution using ERA-Interim reanalysis data as initial and boundary conditions. In this study, we are testing various combinations of the physics schemes (land surface, microphysics, cumulus and boundary layer schemes) for the year 2011. Each parameterization combination leads to different simulated climates, so their spread is an estimate of the model uncertainty arising from the representation of the unresolved phenomena. The analysis of the RegCM-output ensemble indicates systematic precipitation biases, which are linked to different physical mechanisms in the summer and winter seasons.

Based on the results, RegCM is sensitive to the applied convection scheme, but the interactions with the other schemes (e.g., land surface or microphysics) affect the precipitation. Due to the different treatment of moisture in the schemes, there are differences not only between the representation of the precipitation cycle, but also in other climatological variables such as soil moisture, latent and sensible heat fluxes and cloud cover, which affect the drought characteristics.

 

The research was funded by the NKFIH-471-3/2021 project (the National Multidisciplinary Laboratory for Climate Change, RRF-2.3.1-21-2022-00014).

How to cite: Kalmár, T. and Pongrácz, R.: Parameterization-based uncertainties in RegCM simulations over Hungary in a dry year – a case study , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-12456, https://doi.org/10.5194/egusphere-egu24-12456, 2024.

Agricultural drought threatens global water security, food security, and natural ecosystems. Accurate identification of agricultural drought is a crucial task to mitigate its consequences. However, it is challenging to achieve reliable and accurate regional agricultural drought assessment in both wet and dry climates at the same time. Therefore, the objective of this study is to identify a reliable and accurate agricultural drought index that performs well in both dry and wet climates. Drought indices such as the Standardized Precipitation Index (SPI), the Vegetation Condition Index (VCI), the Soil Moisture Anomaly index (SMA), and the Drought Severity Index (DSI) were calculated and compared against in situ drought information devised by official sources in China. The results showed that: (1) DSI based on the Global Land Data Assimilation System (GLDAS) products performed the best in identifying agricultural drought in both dry and wet climate regions of China. (2) Agricultural regions such as Northern arid and semiarid regions, Northeast China Plain, Huang-Huai-Hai Plain, and Loess Plateau, experienced moderate and severe agricultural droughts with a frequency of 20%. (3) The frequency of agricultural droughts observed in Northern arid and semi-arid regions and Northeast China Plain has slowed significantly over the last two decades with a significance level of 0.01. On the other hand, the number of agricultural droughts has increased in Yunnan-Guizhou Plateau since 2002.

How to cite: Pan, Y. and Xu, H.: Accuracy of agricultural drought indices and analysis of agricultural drought characteristics in China between 2000 and 2019, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-12858, https://doi.org/10.5194/egusphere-egu24-12858, 2024.

EGU24-12984 | ECS | Posters on site | HS4.2 | Highlight

Understanding the Causalities between Multiple Environmental Variables and Droughts in Amazon Basin 

Weikang Qian, Yixin Wen, Alireza Farahmand, and Jesse Kisembe

Establishing an early-warning system for droughts in the Amazon Basin holds paramount importance due to the region's critical role in global climate regulation and biodiversity. Droughts in the Amazon not only impact local ecosystems and communities but also have far-reaching effects on global weather patterns and carbon storage capabilities. To fully understand the drought mechanism and improve early-warning monitoring, it is important not only to detect drought conditions by creating indicators but also to extract signals that could describe the risk of drought outbreaks. To reach this goal, our research characterizes pre-drought signals from multiple environmental variables using causal inference and information theory. This study focuses on environmental variables, such as temperature, precipitation, vapor pressure deficit, evapotranspiration rate, and relative humidity from three perspectives, spatiotemporal characteristic, anomalies, and accumulation. Environmental variables are obtained from satellite observations and reanalysis datasets. We harness the potential of these characteristics, exploring their intricate connections as precursors to drought formation and propagation. Expanding on simple association, we introduce causal inference techniques to discover causalities among environmental variables, and between environmental variables and droughts, while information theory helps us capture non-linear relationships among environmental variables. Thereby, we identify critical thresholds and pre-drought signals where these characteristics contribute to drought onset. This causality-based approach marks a departure from traditional indices, integrating temporal dynamics with a detailed understanding of system interactions. Our findings aim to contribute to sustainable land and water management in the Amazon, ultimately aiding in the preservation of its unique ecosystems and the services they provide.

How to cite: Qian, W., Wen, Y., Farahmand, A., and Kisembe, J.: Understanding the Causalities between Multiple Environmental Variables and Droughts in Amazon Basin, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-12984, https://doi.org/10.5194/egusphere-egu24-12984, 2024.

EGU24-13224 | Posters on site | HS4.2

The GRUVO web application: Bringing groundwater level predictions across Germany to the public 

Stefan Broda, Maximilian Nölscher, Matthias Heber, Patrick Clos, Markus Zaepke, and Wolfgang Stolz

The provision of current and predicted groundwater levels across Germany has become increasingly important, particularly due to the increasing likelihood of consecutive dry years. To address this issue, we present the interactive web application GRUVO, which was developed as a first step to provide groundwater level forecasts and relevant information in a targeted manner for different user groups. We also provide an overview of the features and operation of the application in its current version.

In addition to the visualisation of current groundwater levels, this mainly includes the presentation of monthly updated groundwater level forecasts and projections for short-term (up to 3 months), medium-term (up to 10 years) and long-term (up to 2100) forecast horizons at over 100 so-called reference monitoring sites (RM) distributed throughout Germany. Each of these RMs represents the groundwater levels or dynamics of a few thousand so-called cluster monitoring sites (CMs). This mapping of RMs to CMs was previously determined using a clustering approach. The RM prediction is based on 1-D convolutional neural networks (CNN), which are trained using time series of measured groundwater level data from the responsible state offices as target variables and measured meteorological forcing data from the German Weather Service (DWD) as predictors. Forecasted or projected meteorological information from the DWD is then used to predict future groundwater levels.

Apart from the available features of the current version, this contribution highlights operational challenges and nuances. It also outlines possible extensions for future development.

How to cite: Broda, S., Nölscher, M., Heber, M., Clos, P., Zaepke, M., and Stolz, W.: The GRUVO web application: Bringing groundwater level predictions across Germany to the public, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-13224, https://doi.org/10.5194/egusphere-egu24-13224, 2024.

EGU24-13279 | ECS | Orals | HS4.2

Navigating Water Resource Management: A Forecasting Framework for Interannual Drought Projections 

Ze Jiang, Golam Kibria, and Ashish Sharma

Can CMIP6 decadal projections be effective for multi-decadal water resources planning? This is the underlying question that motivates the present research, investigating what are the key deficiencies that limit their direct use for applications, and whether cleverly formulated mathematical alternatives can be used as effective postprocessors. This study focuses on the development of a robust framework for predicting droughts over interannual to decadal scales to enhance water resource management. The proposed framework utilizes the Wavelet System Prediction (WASP) methodology, which refines the spectral attributes inherent to climate indices to improve the skill of drought forecasts. Further improvement in forecasting capability is achieved through the Hierarchical Linear Combination (HLC) logic, which incorporates forecasts from ten climate indices. These indices, including ENSO-related sea surface temperature anomalies and other climate drivers closely linked to Australian rainfall, are derived from decadal predictions of the Decadal Climate Prediction Project (DCPP). The results of projected drought indices across various scales in Australia demonstrate the substantial potential of the integrated HLC-WASP framework to significantly improve the forecast skills of medium to long-term drought scenarios. This advancement enables the water industry to adapt their strategic plans and optimize reservoir operations effectively. By providing more reliable near-term projections of water availability, this research contributes to effective water resource management, facilitating informed decision-making for water allocation and conservation initiatives.

How to cite: Jiang, Z., Kibria, G., and Sharma, A.: Navigating Water Resource Management: A Forecasting Framework for Interannual Drought Projections, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-13279, https://doi.org/10.5194/egusphere-egu24-13279, 2024.

The availability of water resources generally refers to the volume of total water resources on the surface, sub-surface, and soil. For a precise assessment of the availability of water resources, it is necessary to secure the accuracy of meteorological forecasts such as precipitation and temperature forecasting and to be able to accurately evaluate the volume of invisible water resources under the surface. Metropolitan areas around large rivers can use water stably even in the event of a drought, but the upstream areas with small and medium-sized rivers are vulnerable to water supply stability in drought season. Therefore, highly reliable evaluation and prediction of river discharge is necessary to prepare comprehensive solutions such as efficient operation of water supply facilities and optimal use of available water resources during drought season.  In this study, river discharge was evaluated for 20-16 standard basins in the Yeongsan-Seomjin river basins, respectively, among major river basins in the republic of Korea. The Dynamic Water resources Assessment Tool (DWAT) was used as a assessment model. DWAT is a water resources assessment tool that can be used free of charge worldwide and can be applied to small and medium-sized river basins for water resource planning and management that considers surface water as well as groundwater and water usage for various purposes. The calibration period was set from 2012 to 2019, and the validation period was set from 2020 to 2021. In addition, simulation accuracy was calculated through a 1:1 comparison of observed and simulated discharge data based on the calibration point, and model efficiency (Nash Sutcliffe Efficiency, NSE)

How to cite: Jang, C., Kim, D., Choi, J., Shin, H., and Kim, H.: Evaluation of the River Discharge Considering Interaction of Surface water and Groundwater in the Yeongsan-Seomjin River in the Republic of Korea Using DWAT (Dynamic Water Resources Assessment Tool, DWAT), EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-13771, https://doi.org/10.5194/egusphere-egu24-13771, 2024.

EGU24-15433 | ECS | Orals | HS4.2

Exploring the Interplay of Climatic Trends, Reservoir Fluctuations, and Vegetation Dynamics in Paphos, Cyprus: A Decade-Long Study Towards Sustainable Resource Management 

Eleni Loulli, Ioannis Varvaris, Marinos Eliades, Christiana Papoutsa, and Marios Tzouvaras

Drought is a complex phenomenon that cannot be easily detected in its early stages and advances slowly, but cumulatively. Its consequences can be short-term, such as water deficiency in rivers and dams, and long-term like saltwater intrusion and ecosystem degradation. These impacts make agricultural productivity vulnerable, exacerbate waterborne diseases and increase the risk or wildfires, posing a threat to food security, safety and sovereignty. Cyprus, characterized by a semi-arid climate, experienced in recent years prolonged and frequent droughts that had multiple impacts on agricultural production and consequently the ecosystem and the economy. In the face of a changing climate and increased frequency of droughts, monitoring and understanding such phenomena is crucial in mitigating their impacts. Our overall goal is to investigate the relationships between climatic trends, reservoir fluctuations and vegetation dynamics over the study period. Therefore, we provide a comprehensive analysis of the previously mentioned relationships for the hydrological region of Paphos, (Cyprus) for the period between 2013 and 2023. Vegetated areas are extracted using the European Space Agency WorldCover tree cover, shrubland, grassland, and cropland land cover classes. The study integrates measurements at meteorological stations and satellite-derived time series to assess the relationship between climatic variables and vegetation processes. In particular, we compare the Standardized Precipitation Index (SPI) calculated using CHIRPS data (Climate Hazards Group InfraRed Precipitation with Station Data), with hydrological drought indices provided by the Water Development Department. The latter are estimated on the basis of a drought indicator system that utilizes monthly dam Inflows and mean daily flows of hydrometric stations. Additionally, we analyze spatial climatic variables (such as the MODIS Land Surface Temperature and Evapotranspiration) and vegetation indices (such as MODIS and Sentinel-2 Normalized Difference Vegetation Index, Enhanced Vegetation Index, and Green Chlorophyll Index). Preliminary results show that vegetation dynamics and drought patterns vary based on seasons and the studied land cover classes. The findings of our study are anticipated to contribute to sustainable land and water resources management in the Paphos region.

Acknowledgements

The authors acknowledge the ‘GreenCarbonCY’: Transitioning to Green agriculture by assessing and mitigating Carbon emissions from agricultural soils in Cyprus. The ‘GreenCarbonCy project has received funding from the European Union - Next Generation, the Recovery and Resilience Plan “Cyprus_tomorrow”, and the Research & Innovation Foundation of Cyprus under the Restart 2016-2020 Program with contract number CODEVELOP-GT/0322/0023.

How to cite: Loulli, E., Varvaris, I., Eliades, M., Papoutsa, C., and Tzouvaras, M.: Exploring the Interplay of Climatic Trends, Reservoir Fluctuations, and Vegetation Dynamics in Paphos, Cyprus: A Decade-Long Study Towards Sustainable Resource Management, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-15433, https://doi.org/10.5194/egusphere-egu24-15433, 2024.

EGU24-15845 | Posters on site | HS4.2

Large scale modeling of clay shrink-swell risk for current and future climate scenarios. 

Aurelien Boiselet and Gregory Seiller

In recent years, the risk of clay shrinkage-swelling has emerged as a significant concern for land use planning and for insurance companies. These superficial clay soils exhibit vertical movement (contraction and expansion), linked to meteorological conditions. Despite the slow pace of these fluctuations, they can reach an amplitude large enough to damage buildings located on these soils. In France, this hazard appears in second rank in terms of losses with events that can generate more than one billion euros in losses .

To better mitigate this risk, the French geological and mining risks office (BRGM) conducted a detailed mapping of exposure to clay shrink-swell across France. This departmental-scale analysis is based on the lithological nature of the soil, the mineralogical composition, geotechnical behavior, but also the loss experience observed. However, the susceptibility of a soil to swell has not been studied at global scale but rather over some territories. Given the current climate change, it is also necessary to understand the conditions linked to the occurrence of these events as well as the inherent impacts. This study focus on these two aspects: exposure and impact.

To estimate whether a soil might be prone to swelling, we developed a machine learning model based on exposure maps published for France and the USA with a set of pedological parameters (CEC of clay, soil texture, bulk density, etc. coming from Soilgrids & Harmonized World Soil Database models) and geological parameters; associated with the presence of clayey soils with swelling capacity. We achieved a prediction accuracy of nearly 70% on our test set in these 2 countries. For France, this approach allows us to estimate that 52% of the territory presents a medium or high exposure to this peril, which is consistent with the BRGM analysis of 49%. With this approach we also estimated that 52% of Germany’s territory is exposed to medium to high swelling susceptibility.

The impact analysis of this hazard is performed on France based on the publication of the CatNat decrees by the French Central Reinsurance fund, the loss ratio observed by AXA and climate indicators such as the Standardized Precipitation-Evapotranspiration Index (SPEI). The SPEI is a climatic indicator that is sensitive to water-balance variations, calculated over different time scales, allowing for the assessment of both short-term and long-term climatic conditions. The SPEI is particularly useful in regions where evapotranspiration plays a significant role in moisture availability. By analyzing the SPEI in conjunction with the CatNat decrees and the loss ratio observed by AXA, we can gain a comprehensive understanding of the current clay shrink-swell risk. This multi-faceted approach allows us to not only assess the current state of the hazard but also predict future trends.

How to cite: Boiselet, A. and Seiller, G.: Large scale modeling of clay shrink-swell risk for current and future climate scenarios., EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-15845, https://doi.org/10.5194/egusphere-egu24-15845, 2024.

EGU24-16037 | ECS | Orals | HS4.2

A drought monitoring and forecasting system for Switzerland 

Vincent Humphrey, Fabia Hüsler, Simone Bircher-Adrot, and Adel Imamovic

The intensity and frequency of dry spells in Switzerland have increased in recent years and are likely to increase in the future. Meanwhile, increases in water use and competition between different actors also place a greater pressure on existing water resources. Because drought has been identified as one of the main risks for various economic sectors in Switzerland, a national monitoring and forecasting system is to be established through the joint efforts of three different governmental agencies (federal offices for the environment, meteorology and climatology, and topography). The project also actively involves stakeholders in its development.

In this contribution, we introduce the Swiss national drought project with a particular focus on user-centered design, in situ and satellite-based monitoring, and the integration of sub-seasonal forecasts. Results from a user-survey revealed that even though drought is multi-dimensional and affects stakeholders in different ways, one of their primary needs is still a holistic “combined” drought index that can serve as a common ground for discussion and decision-making. Simple, local-scale-focused designs were assessed as the most efficient and useful, whereas designs showcasing nationwide maps or scientific quantities (SPI, etc.) were the least meaningful to educated but not expert users.

Further efforts include the creation of a national in situ soil moisture monitoring network with approximately 30 stations, the development of meteorological and agricultural drought products and indices, as well as the establishment of near real time, downscaled, sub-seasonal forecasts derived from existing systems (ECMWF IFS Extended). Integrating these highly heterogeneous data streams into seamless products ranging from historical observations to sub-seasonal forecasts, all within a consistent climatological baseline, is expected to represent both a major technical challenge but also a significant step forward that will greatly benefit downstream user applications. This novel meteorological basis will directly feed into impact-relevant drought indices and hydrological models, with the aim of better supporting an early warning system that has to take into consideration the needs of a very diverse user community, such as hydropower production, navigation, agriculture, forestry, artificial snow production, or ecology.

How to cite: Humphrey, V., Hüsler, F., Bircher-Adrot, S., and Imamovic, A.: A drought monitoring and forecasting system for Switzerland, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-16037, https://doi.org/10.5194/egusphere-egu24-16037, 2024.

EGU24-16800 | Posters on site | HS4.2

Multi-Source Earth Observation Data for Drought Monitoring in the Borena Region in Southern Ethiopia 

Elias Cherenet Weldemariam, Getachew Mehabie Mulualem, Tinebeb Yohannes, Héctor Nieto, Ana Andreu, and Vicente Burchard-Levine

Drought is a recurring phenomenon in the Borena region of Southern Ethiopia. The imbalance between potential evaporation and precipitation during the growing season often results in drought conditions, posing significant threats to the biodiversity, agriculture and human activities. The zone has endured severe drought risk due to consecutive years of no rainfall, significantly impacting ecosystem services, livestock and agro-pastoralist communities. To mitigate the effects of droughts and to provide quick decision-making with timely information for an effective response, it is crucial to regularly analyze the information about its severity and its extent in terms of spatial and temporal pattern. This study analyzes the spatial and temporal pattern of drought in the Borena region, using integrated indices such the Composite Drought Index (CDI) from 2000 to 2022. The CDI, which incorporates the Precipitation Drought Index (PDI), the Temperature Drought Index (TDI), and the Vegetation Drought Index (VDI), are used as input to examine spatial and temporal drought patterns, providing a comprehensive view of drought conditions over the given area. Additionally, the Mann–Kendall trend test and Sen’s slope were employed to understand the trends of these indices and determine their magnitude of change.

The study identified the occurrence of extreme drought events in recent years during 2007, 2011, 2014, 2016, 2017, and 2021 in Borena Zone. The findings also showed a decreasing trend in rainfall, an increase in temperature, and a diminishing trend in vegetation condition during the study period. Specifically, the computed mean growing season of the Normalized Difference Vegetation Index (NDVI) values ranged between -0.02352 to 0.0312, with 57.67% of the Borena region showing a decreasing trend. Future work will incorporate actual evapotranspiration (ET) estimates based on thermal infrared (TIR) imagery within the CDI, as this has the potential to more rapidly detect water stress in vegetation compared to spectral indices such as NDVI. These findings can guide the development of climate policies, disaster risk reduction and strategies in Ethiopia, contributing to the mitigation of future drought impacts and the promotion of sustainable dryland natural resources practices, including supporting early drought warning detection systems for agro-pastoralist communities.

How to cite: Weldemariam, E. C., Mulualem, G. M., Yohannes, T., Nieto, H., Andreu, A., and Burchard-Levine, V.: Multi-Source Earth Observation Data for Drought Monitoring in the Borena Region in Southern Ethiopia, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-16800, https://doi.org/10.5194/egusphere-egu24-16800, 2024.

EGU24-17245 | Orals | HS4.2

Evaluation of opportunities to characterize and monitor moisture in the unsaturated zone above the Western Mountain Aquifer 

Peter Dietrich, Ulrich Maier, Alireza Kavousi, Anna Rieß, Irina Engelhardt, and Martin Sauter

The GRaCCE project (Groundwater Recharge and Climate Change Effects - Quantification of resilience of water resources in carbonate aquifers to drought conditions) aims to develop process-based integrated and data-driven surrogate methods for determining groundwater recharge and predicting droughts in order to support water management in semi-arid regions such as Israel, Palestine and Jordan. Previous studies have shown that the thick vadose zones (several hundred meters) prevalent in the region can be relevant for water management as long-term reservoirs and, if considered as a dynamic water resource, can contribute to mitigating supply shortages during long-term droughts. In order to evaluate this water resource, it is necessary to characterize and monitor the moisture distribution in the vadose zone. In principle, borehole- and surface-based geophysical methods as well as remote sensing data can be used for this purpose. In order to assess the possibilities of the various methods for the specific site conditions of the Western Mountain Aquifer, the water balance of the area was investigated for the period from 1950 to 2020 using a double permeability variably saturated HydroGeoSphere model. Moreover, the distribution of soil moisture content at four intervals up to a cumulative depth of two-meter was inspected utilizing FLDAS2 NASA daily dataset. The temporal development of vertical moisture profiles was extracted from the HydroGeoSphere and FLDAS2 models for some selected locations. The profiles show a strong “intra-annual variation” at soil level which is strongly dampened by a depth two meter. This variability is generally not observed in higher depth profiles, as generated by HydroGeoSphere, where the shift from wet to dry periods made some “inter-annual variation” of moisture content. This result further supports the former studies claiming the importance of vadose zone on regulation of drought periods at aquifer level. Moreover, based on this assessment, a site-specific initial assessment of the suitability of the measurement methods such as cosmic ray neutron sensing, ground penetrating radar, resistivity measurements, nuclear magnetic resonance and remote sensing was carried out.

How to cite: Dietrich, P., Maier, U., Kavousi, A., Rieß, A., Engelhardt, I., and Sauter, M.: Evaluation of opportunities to characterize and monitor moisture in the unsaturated zone above the Western Mountain Aquifer, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-17245, https://doi.org/10.5194/egusphere-egu24-17245, 2024.

EGU24-17370 | Orals | HS4.2

Empowering communities through seasonal forecasts use: a lesson learned from the Euro Mediterranean 2021-2023 drought event 

Massimiliano Pasqui, Ramona Magno, Arianna Di Paola, Sara Quaresima, Elena Rapisardi, Lenadro Rocchi, and Edmondo Di Giuseppe

In the period between 2021 and 2023, the Euro-Mediterranean region experienced a series of significant thermo-pluviometric anomalies. In particular, in the central Mediterranean, the Copernicus Climate Change Service identified exceptional temperature anomalies and a complex and intense drought, also highlighted in the "European State of the Climate (ESOTC)" reports. Prolonged periods characterised by extreme weather events pose a serious threat to both society and human activities, even in advanced countries. 

Water scarcity and water resources management play a prominent role among the climatic threats, as their impacts represent the main pressure mechanisms for human beings, ecosystems, and many human activities. Therefore, it becomes imperative to develop advanced systems for forecasting and anticipating climate variability to provide crucial information to decision-makers and users, facilitating preparation for mitigation actions. Addressing this challenge requires the implementation of operational predictive systems on a seasonal scale that are reliable, salient, and easily adaptable, aiming to enhance economic and societal resilience. To this end, the Drought Observatory (DO) of CNR IBE, a web-based climate service open to the public, has developed and maintained a prediction system based on various components: a seamless prediction system based on the European model SEAS5, coupled with a bias adjustment algorithm; a Non-Homogeneous Poisson process trend analysis of individual drought severity classes; and an evaluation of vegetation stress trough indices calculated from both atmospheric variables and remotely sensed quantities. The DO has been conceived to share both the outcomes of ever-evolving scientific research and a structured set of scientific information. Tailored to different levels of complexity, this information aims to address the informational needs of both technical experts and decision-makers, as well as a wider audience and media representatives.

The DO develops these components in close collaboration with stakeholders and users engaged in institutional activities and national and international research projects. This interaction strengthens decision-making processes for adapting to meteorological and climatic risks and adversities.

An integrated approach, that relies on “converging evidence”, has been adopted to achieve an even more pertinent level of information. The 2021-2023 period, characterized by extreme climatic conditions, has been studied as a rare multiyear event to assess the effectiveness of seasonal-scale anticipation systems for climate anomalies. Moreover, this timeframe proves particularly valuable for understanding and addressing challenges associated with climate change.

Verification analysis shows that seasonal forecast skills vary over time and geographical areas. It is thus possible to identify windows of opportunity for specific tasks in cooperation with users. Within this framework, bias-corrected seasonal forecasts provide valuable supporting information for water resources management and decision-making processes. Throughout the drought period from 2021 to 2023, the Drought Observatory played a pivotal role, extensively utilized by national and international media to disseminate precise information regarding the drought trend in Italy. This underscores the crucial requirement for timely and science-based data to enlighten the broader public.

How to cite: Pasqui, M., Magno, R., Di Paola, A., Quaresima, S., Rapisardi, E., Rocchi, L., and Di Giuseppe, E.: Empowering communities through seasonal forecasts use: a lesson learned from the Euro Mediterranean 2021-2023 drought event, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-17370, https://doi.org/10.5194/egusphere-egu24-17370, 2024.

EGU24-18825 | ECS | Posters on site | HS4.2

Groundwater Resilience under Extreme Drought  

Eleyna McGrady, Claire Walsh, Stephen Birkinshaw, and Elizabeth Lewis

Abstract:

Government guidance suggests that, by 2050, water companies should be resilient to a 1-in-500-year drought, allowing them to maintain supply in all except the most extreme droughts. However, drought is poorly defined with no universally accepted definition. This is because drought is often the result of many complex processes, is not a distinct event, and is usually only recognisable after a period of time. This leads to problems when predicting, quantifying, and assessing the impact and magnitude of drought within the environment. Consequently, how do water companies prepare themselves for an extreme drought when such drought cannot be quantified? Particularly, how do they ensure that groundwater resources are resilient, given the dependence on these resources to provide public water supply? These questions are particularly prevalent due to the predicted changes in climate and the current lack of understanding of how and to what magnitude groundwater resources will be affected.

Global warming has already been shown to affect groundwater droughts in the UK, however its impact on groundwater resources has not been quantified due to the challenges associated with defining groundwater drought onset and termination, as well as the difficulties with identifying how precursor conditions affect the magnitude and duration of groundwater drought. This lack of knowledge makes groundwater resources vulnerable to direct climate change and also to the indirect socioeconomic pressures associated with climate change.

Modelling is an important process in the assessment of the impacts of drought on groundwater, however, the principle focus of climate change research with regards to groundwater has been on assessing the likely direct impacts of a general changes in precipitation and temperature patterns, using a range of modelling techniques such as soil water balance models, empirical models, conceptual models, and distributed models. However, model development has been focusses within specific fields, for example surface hydrology and flooding, groundwater, distribution networks, and water resource systems and the integration of these separate models has been limited. Integrated, physically-based, and spatially distributed models have generally not been used in large sample studies due to their extensive time, data, and computational resource requirements, however they are key to representing surface water-groundwater interactions accurately, which is key in determining how groundwater will be affected by changes in climate, and hence drought.

Subsequently, this research uses SHETRAN, a physically-based, spatially-distributed hydrological model, in a large sample size study of UK river catchments. Through using this model, the aim of this research is to address gaps in knowledge and fully understand the response of groundwater resources to changing climate, the impact of pre-cursor conditions on drought magnitude and duration, and aims to improve the current issue that is the lack of an adequate model that can be used to investigate these issues.

How to cite: McGrady, E., Walsh, C., Birkinshaw, S., and Lewis, E.: Groundwater Resilience under Extreme Drought , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-18825, https://doi.org/10.5194/egusphere-egu24-18825, 2024.

EGU24-19051 | Orals | HS4.2

Multi-model seasonal forecasting service for meteorological droughts 

Hector Macian-Sorribes, Dariana Avila-Velasquez, and Manuel Pulido-Velazquez

Drought indicators have been proven to be powerful tools to improve drought awareness and decision-making, being a key information source for water resource management in many countries and regions over the world. However, the integration of meteorological drought indicators and seasonal forecasts is not fully explored yet, since most of the drought prediction and early warning services (e.g. European Drought Observatory, Climate Prediction Center) offer limited information on drought forecasting at the seasonal scale.

This contribution presents a multi-model seasonal forecasting service of selected meteorological drought indicators, developed in the context of the WATER4CAST project, for the Jucar River Basin (Spain). This service offers seasonal forecasts (up to 6-7 months in advance) of SPI and SPEI indicators with time aggregations of 6, 12, 18 and 24 months. Input meteorological forecasts to compute them are obtained from the Copernicus Climate Change Service (C3S) for the ECMWF-SEAS5, MétéoFrance-System8, DWD-GCFS21 and CMCC-SPSv35 forecasting systems. These forecasts are post-processed against ERA5 reference data to ensure they are tailored to the climatic patterns of the Jucar River Basin, employing artificial intelligence algorithms (fuzzy logic) trained for the 1995-2014 period. Reference evapotranspiration for the calculation of SPEI indicators is estimated using the Hargreaves method. Once meteorological forecasts are post-processed and upscaled to the monthly scale, aggregated forecasts required to compute SPI and SPEI are made by combining them with past data from ERA5 (e.g. an SPI12 forecast for the next month would require 12-month aggregated precipitation forecasts made up by combining precipitation predictions for the next month with past precipitation records for the last 11 months). Finally, aggregated forecasts of precipitation (for SPI) and precipitation less reference evapotranspiration (for SPEI) are transformed into SPI and SPEI by standardizing them using the gamma (SPI) and the loglogistic (SPEI) probability functions, fitted for each ERA5 point using reference data for the 1973-2022 period. All the calculation process is coded in Python, and it is automatically launched as soon as new seasonal forecasts are available in the C3S.

The resulting service offers seasonal forecasts at the monthly scale, from 1 to 6/7 months in advance (depending on the forecasting system), of SPI and SPEI for the aggregations given at each point of the ERA5 grid overlapping the Jucar River Basin. These forecasts are uploaded into a web platform (https://water4cast-app.upv.es/) that offers information both for a given point (in with the ensemble of SPI and SPEI forecast is displayed using box-whisker plots) and with a general picture (depicting the probability of being in a dry (index <= -1), normal (-1 < index < 1) or wet (1 <= index) period.

Acknowledgements:

This study has received funding from the SOS-WATER project, under the European Union’s Horizon Europe research and innovation programme (GA No. 101059264) and the subvencions del Programa per a la promoció de la investigación científica, el desenvolupament tecnològic i la innovació a la Comunitat Valenciana (PROMETEO) under the WATER4CAST project.

How to cite: Macian-Sorribes, H., Avila-Velasquez, D., and Pulido-Velazquez, M.: Multi-model seasonal forecasting service for meteorological droughts, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-19051, https://doi.org/10.5194/egusphere-egu24-19051, 2024.

EGU24-19099 | Orals | HS4.2

Drought’s trends over continental Chile using climatic variables of water demand and supply, soil moisture, and vegetation productivity 

Francisco Zambrano, Francisco Meza, Nicolas Raab, and Iongel Duran-Llacer

A persistent drought is impacting Chile. It affects the hydrological system and vegetation development. Research studies have focused on the central part of the country. This is due to a persistent period of water scarcity. This scarcity has been found to be a megadrought. This megadrought was defined by the Standardized Precipitation Index (SPI) of twelve months in December. The SPI only considers precipitation as a drought indicator. It does not account for atmospheric evaporative demand (AED), soil moisture, or their combined effect on vegetation productivity, which are key to understanding the impact of climate on ecological and agricultural drought. We use monthly climatic variables for precipitation, temperature, and soil moisture (1 meter depth) from the ERA5-Land reanalysis product for 1981–2023. Also, we used the Normalized Difference Vegetation Index (NDVI) from the Moderate Resolution Imaging Spectroradiometer (MODIS) for 2000–2023. We calculated the atmospheric evaporative demand (AED) using temperature and the Hargreaves-Samani equation. Then, to evaluate water supply, we derived the SPI. For water demand, we calculated the Evaporative Demand Drought Index (EDDI). We propose the standardized anomaly of cumulative soil moisture at one meter (zcSM) as a multi-scalar drought index for soil moisture. The above indices were calculated for time scales of 1, 3, 6, 12, 24, and 36 months. Lastly, we calculated a drought index for vegetation (a proxy for vegetation productivity), the standardized anomaly of the cumulative NDVI of six months (zcNDVI-6). We use the zcNDVI-6 to assess the impact of variations in water demand and supply on vegetation. We use a Mann-Kendall test to analyze the historical trend of the drought indices in continental Chile. Also, we calculated the temporal correlation between the indices of water supply, water demand, and soil moisture with the zcNDVI. To summarize the results, we divide Chile into five macrozones regarding a latitudinal gradient (north to south): i) “Norte Chico," ii) “Norte Grande," iii) "Centro," iv) "Sur," and v) "Austral." The analysis of trend showed that in the macrozones "Norte Chico," "Centro," and "Sur," the SPI has a decreasing trend that increases at longer time scales (from 1 to 36 months). The trend on EDDI reaches its maximum in the macrozones "Norte Grande" and "Norte Chico," being higher at longer time scales. Regarding the correlation with zcNDVI-6, it was higher for the drought index of soil moisture accumulated over 12 months (zcSM-12), having a r-squared of 0.49 for the “Norte Chico” and 0.44 for the "Centro." Followed by a r-squared of 0.41 with SPI-36 (precipitation accumulated over three years) in the macrozone “Norte Chico.” We conclude that Chile has a persistent decline in water supply for the central part of the country ("Norte Chico" and "Centro") and an increase in water demand in the north ("Norte Grande," "Norte Chico," and "Centro"). The combined effect has contributed to exacerbate the impact on vegetation in the "Norte Chico" and "Centro." The variability of drought conditions in vegetation can be explained in ~50% by de zcSM-12.

How to cite: Zambrano, F., Meza, F., Raab, N., and Duran-Llacer, I.: Drought’s trends over continental Chile using climatic variables of water demand and supply, soil moisture, and vegetation productivity, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-19099, https://doi.org/10.5194/egusphere-egu24-19099, 2024.

EGU24-19536 | Orals | HS4.2

Drought monitoring and early warning with satellite soil moisture data 

Mariette Vreugdenhil, Samuel Massart, Pavan Muguda Sanjeevamurthy, Carina Villegas-Lituma, Markus Enenkel, and Wolfgang Wagner

Many developing countries strongly depend on agriculture, but the sector is challenged by the increasing occurrence of droughts.  Unfortunately, advanced agricultural drought monitoring that can trigger early warning and early action is still not widely available for many countries even though it is crucial to stakeholders including local and regional governments, NGOs, farmers, and vulnerable households. Classic drought monitoring tools often rely on precipitation data, which are influenced by the density of station data. Recently, satellite soil moisture data has gained interest, because of its direct link to plant available water content and the increased availability and quality of satellite soil moisture products over remote regions.  Furthermore, when using radar observations, such as those from Sentinel-1 and Metop ASCAT spatial resolutions up to kilometers can be achieved and information on spatial variability of drought within districts can be provided. Despite advancements in the development of satellite soil moisture products, there remains a significant gap in their adoption and utilization by stakeholders in drought monitoring tools and operational systems. Although a large number of drought indicators are available (Vreugdenhil. et al. 2022), they lack rigorous quality-control with impact data and are not analysis-ready. In addition, users are not familiar with the data or its benefits and have difficulties interpreting the indicators in the context of operational decision-support. 

This study will demonstrate the potential of satellite soil moisture for drought monitoring and yield prediction over Eastern Africa, highlighting strengths and weaknesses of satellite soil moisture. Particularly during the growing season, high correlations are found between different soil moisture products from H SAF Metop ASCAT, ESA CCI and ERA5-Land. During the dry season deviations occur due to subsurface scattering effects on the soil moisture signal.  When analyzing droughts, the onset, intensity and duration of droughts differ strongly with the different indicators. For example, for the Gaza region in Mozambique, severe to extreme drought conditions occurred for 1, 4 or 47 months within a 15 year period depending on the chosen drought indicator.  The impact of using different drought indicators and thresholds on drought severity classification creates challenges for integrating satellite soil moisture drought indicators in operational systems and parametric drought insurance. 

 

This research is funded by the Austrian Space Application Programme ROSSIHNI project : Remote Sensing and Social Interest for Humanitarian Insights.

Vreugdenhil, M., Greimeister-Pfeil, I., Preimesberger, W., Camici, S., Dorigo, W., Enenkel, M., van der Schalie, R., Steele-Dunne, S., Wagner, W., 2022. Microwave remote sensing for agricultural drought monitoring: Recent developments and challenges. Frontiers in Water 4.

How to cite: Vreugdenhil, M., Massart, S., Muguda Sanjeevamurthy, P., Villegas-Lituma, C., Enenkel, M., and Wagner, W.: Drought monitoring and early warning with satellite soil moisture data, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-19536, https://doi.org/10.5194/egusphere-egu24-19536, 2024.

EGU24-20109 | Posters on site | HS4.2

Using droughts indicators as triggers for water resources management in semiarid mountain regions 

Rafael Pimentel, Pedro Torralbo, Javier Aparicio, Eva Contreras, Ana Adreu, Cristina Aguilar, and María José Polo

In the current context of global warming, droughts frequency and severity have increased in the Mediterranean Region. The past hydrological year, 2022-2023, was a clear example of water scarcity after some years with precipitation below the historical mean threshold. In mountain catchments, this reduction in precipitation has resulted in a significant decrease of the seasonal snow and a shift in the common snowfall patterns. The coastal-mountain catchments in the Sierra Nevada mountain range (southern Spain) exemplify this situation. 

The use of drought indices, which are defined using hydrometeorological information, has been the most used tool for the development of warning systems and the definition of adaptation strategies. Indexes like the Standardised Precipitation Index (SPI) or the Streamflow Drought Index (SSDI), have been widely used when characterising both meteorological and hydrological droughts. However, in high mountain areas, the role of snowfall should also be taken into account in this index definition. Snowfall patterns clearly modifies the precipitation-runoff response on a seasonal basis, changing the water balance at different time scales. Therefore, “snow drought” might result in scarcity conditions even though no warning stage has been reached regarding drought’s alerts yet, and it should also be taken into account in the defintion of these indexes. Furthermore, the intrinsic characteristics of the snow cover in these regions: seasonality, with snow generally present from mid-autumn to mid-spring; low thickness and high density; various accumulation-ablation cycles throughout the year; and, high losses due to evaposublimation, make the specific definition even more necessary.

This work aims to characterise snowfall droughts in semiarid mountains, understanding its connection to precipitation and hydrological droughts, assessing the viability of using drought indexes as tools for a better water-management decision-making. The Guadalfeo Catchment in the Sierra Nevada Mountain Range has been chosen as a representative coastal-mountain catchment of the Mediterranean basin to carry out this analysis.

Both SPI and a Standardised Snowfall Index (SSI, defined as SPI but using snowfall data) were calculated in the study area on different time scales for a reference period of 40 years (1960-2020), together with SSDI from the available streamflow time series. The joint analysis of SSI and SPI on each time scale has allowed us to classify the four potential situations in relation to the occurrence of hydrological drought in the study catchments. The results show the relevant seasonality of snowfall droughts in this area, and the importance of persistent precipitation drought as antecedent conditions for the impacts of low-snow years on the spring and summer streamflow. The validation performed points to an increase of the annual variability of the snowfall regime, very much related to a higher torrentiality of the precipitation regime on an annual basis than to changes in temperature.


Acknowledgement: This research was funded by the Spanish Ministry of Science and Innovation through the research project PID2021-12323SNB-I00, HYPOMED—“Incorporating hydrological uncertainty and risk analysis to the operation of hydropower facilities in Mediterranean mountain watersheds”.

How to cite: Pimentel, R., Torralbo, P., Aparicio, J., Contreras, E., Adreu, A., Aguilar, C., and Polo, M. J.: Using droughts indicators as triggers for water resources management in semiarid mountain regions, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-20109, https://doi.org/10.5194/egusphere-egu24-20109, 2024.

EGU24-20678 | Posters on site | HS4.2

Assessing potential future subsidence due to groundwater depletion in “Alto Genil” Basin (Southern Spain). 

Rosa Maria Mateos, Antonio Juan Collados-Lara, David Pulido-Velazquez, and Leticia Baena-Ruiz

The Vega de Granada aquifer stands out as one of the primary detrital aquifers in the "Alto Genil" Basin in Southern Spain. Its significance lies in its vast extension, covering nearly 200 km2, and its substantial renewable water resources amounting to approximately 160 hm3/yr. Positioned strategically in the metropolitan area of Granada, it holds great relevance from a social point of view. Historically, it has been a crucial water source for meeting agricultural and urban water demands in various municipalities within the Vega de Granada. Over recent decades, groundwater extraction has escalated significantly, driven by urban expansion, and especially during severe droughts that periodically impact the region, resulting in high subsidence rates related to substantial groundwater level depletions.

 

Historical subsidence rates have been monitored using remote sensing techniques, specifically Differential Interferometric Synthetic Aperture Radar (DInSAR). Previous studies utilized 3 independent sets of images from different satellites: the ENVISAT satellite (C-band) and Sentinel-1A satellites (C-band) from the European Space Agency, and the Cosmo-skyMed constellation (X-band) from the Italian Space Agency. The integration of these datasets has enhanced the definition of the affected area by ground deformation and its temporal evolution. Presently, the European Ground Motion Service from Copernicus provides user-friendly information about ground deformation rates across Europe. EGMS represents a novel tool for the study of natural/induced processes such as land subsidence.

 

We utilized compiled historical information to devise a preliminary method for assessing groundwater level depletion and its associated subsidence rates in potential future scenarios. The method simulates future groundwater level drawdowns through the application of a straightforward lumped balance equation proposed by Scott (2011). Various approaches, including simple conceptual models and machine learning techniques, were tested to simulate groundwater level dynamics. These approaches aided in a more comprehensive assessment, considering the structural uncertainty associated with different simulation methods. Additionally, we explored linear regression models and neural network approaches (such as NAR or ELMAN) to assess subsidence resulting from groundwater level depletion. Machine learning techniques proved effective in providing better insights into non-linear subsidence processes. In selected points, potential future subsidence in the horizon of 2071-2100 may double in a business-as-usual scenario within the aquifer.

Based on the analysis of potential future subsidence values, we identified constraints that should be imposed on groundwater policies due to the associated risk of land subsidence resulting from groundwater level depletion.

 

 

Acknowledgments: This research has been partially supported by the projects: STAGES-IPCC (TED2021-130744B-C21) and SIGLO-PRO (PID2021-128021OB-I00), from the Spanish Ministry of Science, Innovation and Universities.

How to cite: Mateos, R. M., Collados-Lara, A. J., Pulido-Velazquez, D., and Baena-Ruiz, L.: Assessing potential future subsidence due to groundwater depletion in “Alto Genil” Basin (Southern Spain)., EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-20678, https://doi.org/10.5194/egusphere-egu24-20678, 2024.

EGU24-21136 | Orals | HS4.2

Enabling long-lead forecasting of agriculture production shocks with soil moisture monitoring and forecasting products to support food insecurity early warning 

Shraddhanand Shukla, Frank Davenport, Eric Yoon, Barnali Das, Weston Anderson, Abheera Hazara, Kim Slinski, and Amy L. McNally

As per USAID’s Famine Early Warning System Network Team (FEWS NET) 110-120 million people are projected to need emergency food assistance across all FEWS NET-monitored countries. Climate shocks such as droughts contribute to acute food insecurity. Better identification and earlier warning of anomalous conditions leading to food insecurity are critical to support decision-making to mitigate the impacts of food insecurity on lives and livelihoods. Agricultural production outlooks are one of the critical components of the famine early warning scenario generation process. Thus far these outlooks have mainly been based on estimates of seasonal rainfall or remotely sensed indicators of vegetation greenness whereas soil moisture estimates (remotely sensed or modeled) have been used as drought indicators but not directly used for crop yield forecasting to assess production shocks, particularly in operational settings. Our past research, which focused on crop yield forecasting in southern Africa, revealed a promising level of skill when soil moisture monitoring products or forecasts were used as predictors of crop yield, relative to traditional predictors such as December to February ENSO. Additionally, a separate study focused on East Africa revealed when and where soil moisture can be the best predictor of crop yield relative to other earth observations. Building upon this initial research, here we investigate the applicability of soil moisture monitoring and forecasting products in crop yield forecasting in up to 20 FEWS NET monitored countries for which processed crop yield data are available at sub-national scale. We first use soil moisture monitoring products, both remotely sensed (such as ESA-CCI) and modeled (such as FEWS NET Land Data Assimilation System) to implement and validate machine learning based within-season crop yield forecasting. We then use seasonal-scale soil moisture forecasts (up to 6 months in future) to enhance the lead-time of crop yield forecasting and implement and validate pre-season (before the start of a crop growing season) long-lead crop yield forecasting, as earlier estimates of food insecurity can provide additional critical time needed for launching famine prevention responses by governments and donor agencies.

How to cite: Shukla, S., Davenport, F., Yoon, E., Das, B., Anderson, W., Hazara, A., Slinski, K., and McNally, A. L.: Enabling long-lead forecasting of agriculture production shocks with soil moisture monitoring and forecasting products to support food insecurity early warning, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-21136, https://doi.org/10.5194/egusphere-egu24-21136, 2024.

EGU24-118 | Posters on site | HS2.1.5

Precipitation, temperature, and vegetation indices analysis for Saudi Arabia region: Feasibility of Google Earth Engine 

Zaher Mundher Yaseen, Bijay Halder, Mohamed A. Yassin, and Sani I. Abba

Climatic disaster is continuously triggering environmental degradation and thermal diversification over the earth's surface. Global warming and anthropogenic activities are the triggering factors for thermal variation and ecological diversification. Saudi Arabia has also recorded precipitation, temperature, and vegetation dynamics over the past decades. Therefore, monitoring past precipitation, temperature, and vegetation condition information can help to prepare future disaster management plans and awareness strategies. The Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks - Climate Data Record (PERSIANN-CDR) from the Center for Hydrometeorology and Remote Sensing (CHRS) data portal and Moderate Resolution Imaging Spectroradiometer (MODIS) are applied for precipitation, Land Surface Temperature (LST), Enhance Vegetation Index (EVI), and Normalized Difference Vegetation Index (NDVI) from 2003 to 2021 respectively. Yearly mean LST, EVI, NDVI, and precipitation values are calculated through the Google Earth Engine (GEE) cloud computing platform. MODIS-based LST datasets recorded the highest temperatures is 43.02 °C (2003), 45.56 °C (2009), 47.83 °C (2015), and 49.24 °C (2021) respectively. In between nineteen years, the average mean LST increased by 6.22 °C and the most affected areas are Riyadh, Jeddah, Abha, Dammam, and Al Bahah. The mean Precipitation is recorded around 776 mm, 842 mm, 1239 mm, and 1555 mm for the four study periods, while the high precipitation area is Jazan, Asir, Baha, and Makkah provinces. In between nineteen years, 779 mm of precipitation is increasing in Saudi Arabia.  Similarly, the NDVI vegetation indices observed 0.885 (2003), 0.871 (2009), 0.891 (2015), and 0.943 (2021), while EVI observed 0.775 (2003), 0.776 (2009), 0.744 (2015), and 0.847 (2021). The R2 values of the LST and EVI correlation is 0.0239 (2003), 0.0336 (2009), 0.0136 (2015) and 0.0175 (2021) similarly correlation between LST and NDVI is 0.0352 (2003), 0.0265 (2009), 0.0183 (2015) and 0.0161 (2021) respectively. The vegetation indices indicate that the green space is gradually increasing in Saudi Arabia and the highly vegetated lands are Meegowa, An Nibaj, Tabuk, Wadi Al Dawasir, Al Hofuf, and part of Qaryat Al Ulya. This analysis indicates that the temperature is increasing but precipitation and green spaces are increasing because of the groundwater recharge through dam construction, precision agriculture, and planned build-up is helps to prepare Saudi Arabia as a green country. Therefore, more attention to preparing the strategic agricultural plants as well as other vegetation and artificial groundwater recharge can improve the country as a green nation. This analysis might help to prepare future planning, awareness, and disaster management teams to prepare for future disasters and strategic steps for sustainable development.

How to cite: Yaseen, Z. M., Halder, B., Yassin, M. A., and Abba, S. I.: Precipitation, temperature, and vegetation indices analysis for Saudi Arabia region: Feasibility of Google Earth Engine, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-118, https://doi.org/10.5194/egusphere-egu24-118, 2024.

Water is scarce in the northern Chihuahuan Desert, with ~350 mm/yr precipitation, potential evapotranspiration at 1800mm/yr, and rising mean annual temperatures by >2°C since 1960. The main water resources are the Ogallala, Pecos Valley, Dockum, and Edwards-Trinity Plateau aquifers, with depletion rates of ~1 m/yr. Despite the arid climate, the Monahans and Kermit dune fields host perched water tables 1-10 m below the surface, in up to 40 m of aeolian sand spanning the past ca. 2.6 ma, and isolated from the underlying Pecos Valley Aquifer by a Pliocene/Pleistocene fluvial gravel-rich clay. A 3D model based on borehole lithology shows a topographic inversion with a southwest-trending paleo-slope infilled with aeolian sand. The aeolian stratigraphy and basin modeling indicate progressive infilling by aeolian sand with periods of pluvial lake formation and soil development, with groundwater providing dune field stability for vertical accretion and limiting aeolian erosion. Cores of sediments retrieved from the Monahans and Kermit dune fields were sampled for OSL ages and yielded ages up to 500 ka 20 m below the surface of the dunes, with identified deposition periods between 545-470 ka, 300-260 ka, 70-45 ka and post 16 ka. A set of three monitoring wells equipped with data loggers revealed aquifer recharge of 35-40 cm in the Spring and Fall consistent with regional precipitation variability, and a daily recharge cycle of 3-8 mm potentially linked to plant uptake or gravitational forces. Deuterium and 18O isotopic ratios for the dune field aquifers indicate an evaporative enriched water source compared to the Pecos Valley Aquifer, Pecos River, and Chihuahuan Desert precipitation, consistent with local precipitation. Apparent 14C ages <1360 yr for aquifer waters from the upper 1 m indicate recent meteoric recharge. Older 14C ages of > 1.3 to 2.2 ka for waters ~30 m deep and at the western edge of the aquifer indicate mixing with Holocene recharge waters in a southwest flowing aquifer. In contrast, the Pecos Valley Aquifer yields 14C ages of ca. 0.9 to 40 ka with the youngest ages near the dune fields, which suggests recharge from these perched aquifers.

How to cite: Fournier, A. and Forman, S.: Origin, gradient, and recharge processes of perched aquifers of the Monahans and Kermit dune fields, northern Chihuahuan Desert, Texas, USA , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-765, https://doi.org/10.5194/egusphere-egu24-765, 2024.

EGU24-1165 | ECS | Orals | HS2.1.5

Agrohydrological modelling approach for assessing the impact of climate change on water resources and land management in the Messinian region, Greece. 

ismail bouizrou, Giulio Castelli, Gonzalo Cabrera, Lorenzo Villani, and Elena Bresci

The Mediterranean region is highly susceptible to the consequences of warming, leading to an increasing of extreme events such as droughts, severe heat waves, and precipitation events. The Messinia watershed (MW) is predominantly characterized by olive cultivation, encompassing approximately 70% of the landscape. These olive orchards constitute a vital component of the Mediterranean ecosystem, playing a crucial role in regional agriculture. The MW is a perfect illustration of a Mediterranean watershed significantly impacted by climate change, as well as soil degradation and a lack of effective land management practices.

In this context, agro-hydrological modelling emerges as a potent tool to address soil degradation and enhance water resource retention within the olive orchards at the watershed scale. To achieve this objective, the SWAT+ agrohydrological model was chosen for a comprehensive assessment of the potential impacts of climate change on water resources and ecosystems in the Messinia region. The adopted modelling approach involved both hard and soft calibration techniques, simulating four sub-watersheds of Messinia by incorporating remote sensing data, including evaporation and soil moisture, for multi-criteria model calibration.

The calibrated model was subsequently employed to assess the potential impacts of climate change on water resources and ecosystems in the Messinia region, utilizing various RCM climate scenarios. Our findings are valuable for addressing soil degradation, as well as for guiding land and water management practices in the Messinian watershed.

 

 

This research was carried out within the SALAM-MED project, funded by the Partnership for Research and Innovation in the Mediterranean Area Programme (PRIMA).

The content of this abstract reflects the views only of the author, and the Commission cannot be held responsible for any use that may be made of the information contained therein.

 

How to cite: bouizrou, I., Castelli, G., Cabrera, G., Villani, L., and Bresci, E.: Agrohydrological modelling approach for assessing the impact of climate change on water resources and land management in the Messinian region, Greece., EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-1165, https://doi.org/10.5194/egusphere-egu24-1165, 2024.

Desertification on the Mongolian Plateau is deepening, and sand and dust have great negative impacts on many countries in East Asia. Based on meteorological and socio-economic data in the context of climate change, this study analyzed the driving mechanisms and impacts of desertification and water body area response on the Mongolian Plateau using, among others, the GTWR model. The following conclusions were drawn: the area of the Mongolian Plateau showed a decreasing trend from 1990 to 2019, and the number of lakes larger than 1 km2 decreased by 173 or 537.3 km2 in Inner Mongolia, and by 737 or 2875.1 km2 in Mongolia, and all of them were dominated by lakes of 1-10 km2; and the analysis of the correlation between the area of the water bodies showed that the The reasons driving the change of water body area in Inner Mongolia Autonomous Region and Mongolia are similar and different, soil moisture and precipitation have obvious promotion effects, economic development and livestock numbers have different degrees of negative impacts on different countries; The GTWR model is used to represent the impacts of different influencing factors on the water body area in time and space, and the evaporation and GDP are shifted from slight inhibition to promotion, and the population and temperature are both inhibited. Soil moisture and livestock numbers are contributing; Surface water resource monitoring is important to deepen the desertification of the Mongolian Plateau and to provide better water resource recommendations and protection measures for the Mongolian Plateau.

How to cite: Yan, Y. and Cheng, Y.: Study of water body area changes in the desertification process of the Mongolian Plateau and analysis of driving factors, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-1185, https://doi.org/10.5194/egusphere-egu24-1185, 2024.

EGU24-2567 | ECS | Orals | HS2.1.5 | Highlight

GIRHAF (Gridded hIgh-resolution Rainfall for the Horn of AFrica): a new rainfall product for detailed applications in a region beset by climate hazards 

Manuel F. Rios Gaona, Katerina Michaelides, and Michael Bliss Singer

Rainfall is one of the most important inputs for applications such as hydrological modelling, water resource allocation, flood/drought analysis, and climatic risk assessments. Currently, there exist numerous (global) products offering rainfall estimates at various spatio-temporal resolutions. Nevertheless, there are still places on Earth where the coverage and/or quality of such products is limited due to sparse ground-control data, thus constraining the robustness of input rainfall for hydrological and climate applications. Located in Eastern Africa, the Horn of Africa (HOA) is a place where climate impacts like droughts and floods frequently inflict a huge toll on the lives and livelihoods of millions residing in subsistence rural communities. For places like this, high resolution rainfall data are fundamental to understanding the availability of water resources, flood hazard, and soil moisture dynamics relevant to crop yields and pasture availability.

Here we introduce GIRHAF (Gridded hIgh-resolution Rainfall for the Horn of AFrica), which is a 20-year rainfall product, with a spatio-temporal resolution of 0.05°×0.05°, every 30 minutes. GIRHAF is based on downscaling CHIMES (Climate Hazards center IMErg with Stations) a pentad operational rainfall product which corrects microwave signals in IMERG (Integrated Multi-satellitE Retrievals for GPM -Global Precipitation Measurement mission-) by in situ rain gauging networks. The goal of this product is to offer the HOA region high-resolution rainfall fields that can support more detailed mechanistic analyses of historical rainfall and can also provide the base dataset required to develop stochastic rainfall models capable of simulating forecasted or projected climate scenarios. It is our aspiration that GIRHAF will enable improved responses to climatic hazards as well as better water resources management in the HOA region, and perhaps to allow people of this region to better prepare to future climate scenarios.

How to cite: Rios Gaona, M. F., Michaelides, K., and Singer, M. B.: GIRHAF (Gridded hIgh-resolution Rainfall for the Horn of AFrica): a new rainfall product for detailed applications in a region beset by climate hazards, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-2567, https://doi.org/10.5194/egusphere-egu24-2567, 2024.

EGU24-4462 | ECS | Posters on site | HS2.1.5

Modeling the impact of climate and land use changes on future water resources dynamics in central Sicily, Italy 

Shewandagn Lemma Tekle and Brunella Bonaccorso

Drought events, worsened by climate change, produce detrimental impacts on freshwater availability especially in arid and semi-arid regions. The situation becomes more critical when these hydrologic extremes combine with land use change mainly caused by anthropogenic factors, such as urbanization, intensive farming, and industrial activities. The present study is designed to investigate the combined impacts of climate and land use changes on the future freshwater  stored in the artificial reservoirs of three adjacent river basins located in the central Sicily (Italy), i.e: Verdura (2 active reservoirs with capacities 9.2 Mmc and 4.19 Mmc), Imera Meridionale (one active reservoir with capacity 15 Mmc), and Platani (one active reservoir with capacity 20.7Mmc), using the Soil and Water Assessment Tool (SWAT) model. The reservoirs are used for irrigation, drinking water supply, and electric power generation. Future climate variables such as rainfall, minimum and maximum temperatures were derived from an ensemble Regional Climate Models for two main representative concentration pathway (RCP) scenarios, such as an intermediate emission scenario (RCP4.5) and a severe emission scenario (RCP8.5). A coupled multi-layer perceptron neural networks and cellular automata (MLP-CA) model was implemented to simulate future land use of the region considering the CORINE land cover in 2000, 2006, 2012, and 2018 as a reference dataset. The future land use is then projected until the mid-century (2048) in a six-year interval using the validated MLP-CA model. The soil data from the European soil data center (EUSDAC) was used as input for the SWAT model. The result indicated that the basins could experience a decrease in inflows to the reservoirs. The separate evaluation of climate change and land use changes indicated that the effect of climate change on streamflow variation is more pronounced than the effect of land use change only. In this study, we introduced new hydrological insights into the region by analyzing the attributions of climate change, land use change, and coupled climate and land use changes on the future freshwater availability which were overlooked in the previous studies. The implementation of the proposed approach can contribute to design environmentally sustainable and climate resilient river basin management strategies.

 

Keywords: MLP-CA, Land use change, Climate change, SWAT, Hydrological modeling, Water availability

How to cite: Tekle, S. L. and Bonaccorso, B.: Modeling the impact of climate and land use changes on future water resources dynamics in central Sicily, Italy, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-4462, https://doi.org/10.5194/egusphere-egu24-4462, 2024.

EGU24-5604 | Orals | HS2.1.5

An integrated hydrological modeling approach to assess the natural groundwater recharge trends in a Mediterranean coastal aquifer 

Anis Chekirbane, Khaoula Khemiri, Constantinos Panagiotou, and Catalin Stefan

Integrating physical models with socio-economic considerations is essential to sufficiently analyze complex hydrological systems and design effective strategies for groundwater management. This integrated approach offers an effective means of detecting links between aquifer properties and groundwater processes. This study aims to assess the impact of human activities and climate changes on groundwater resources. In particular, the final goal is to quantify the spatial distribution of natural groundwater recharge, which is needed to assess the impact of anthropogenic factors on sustainable groundwater management in the Chiba watershed, NE of Tunisia as an example of a stressed hydrosystem.

The proposed methodology is based on the estimation of natural groundwater recharge through hydrological modeling with the use of the SWAT model while considering land use/land cover changes occurring within the study area, coupled with the DPSIR (Drivers-Pressures-States-Impacts-Responses) socio-economic approach for time period 1985-2021. The surveys were constructed and processed based on the probability of occurrence for the degree of satisfaction with arguments related to the DPSIR parameter within the category of the 5-point Likert scale (ranging from level 1 - very low to level 5 - very high), including mean, standard deviation, and the consensus (CnS).
Chiba watershed was selected as a case study since its climate is representative of the Tunisian semi-arid context, and due to the high vulnerability of the existing groundwater systems with respect to human activities.

The hydrological simulations suggest a gradual decrease of 33% in the aquifer's natural recharge over the entire time period. The long-term average value of the annual recharge rate per sub-basin does not exceed 3 mm/year, keeping groundwater recharge levels in the basin relatively low. This observation is mainly attributed to climate change with CnS of 0.6 and over-exploitation of the water sources for irrigation purposes (CnS = 0.62), leading to aquifer depletion and degradation of groundwater-dependent ecosystems (CnS = 0.73). These results suggest that different management practices, such as more conservative water use (CnS = 0.6), long-term monitoring and Managed Aquifer Recharge (MAR) with wastewater (CnS = 0.76), can help rural residents to diversify their economies while preserving these water resources. However, although attempts of MAR have been undertaken, they remain insufficient to counter the pressure on the coastal aquifer, underlining the importance of preserving the fragile semi-arid landscape.

The proposed approach is applicable to other regions having similar climatic and socio-economic conditions. It also demonstrates that pure modeling solutions need to be coupled to the socio-economic approaches to be able to constitute a solid asset for sustainable water resources management of stressed hydro-systems.

 

Acknowledgments

This work is funded by National Funding Agencies from Germany,  Cyprus, Portugal, Spain, and Tunisia under the Partnership for Research and Innovation in the Mediterranean Area (PRIMA) and supported under Horizon 2020 by the European Union’s Framework for Research and Innovation.

How to cite: Chekirbane, A., Khemiri, K., Panagiotou, C., and Stefan, C.: An integrated hydrological modeling approach to assess the natural groundwater recharge trends in a Mediterranean coastal aquifer, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5604, https://doi.org/10.5194/egusphere-egu24-5604, 2024.

EGU24-6984 | ECS | Posters on site | HS2.1.5

Westerly aridity in the western Tarim Basin driven by global cooling since the mid-Pleistocene transition 

Hongye Liu, Rui Zhang, Gaowen Dai, and Yansheng Gu

To explore the relationship between the global change, westerlies, and central Asian aridity, we report ~1.1 Ma local sedimentary environment changes according to high-resolution gamma ray (GR) from downhole logging, Grain size, magnetic susceptibility (MS), rubidium/strontium (Rb/Sr) ratios and total organic carbon (TOC) of an 800-m core (KT11) from the Kashgar region in the western Tarim Basin, arid zone of China. Four dominant sedimentation types, including lacustrine facies, delta facies, fluvial facies, and aeolian dunes, were identified through lithology and grain size frequency curves. The 1.1 Ma sedimentary successions experienced delta deposits with fluvial and aeolian deposits and lacustrines (1.1-0.6 Ma), alternating fluvial and aeolian facies with the occurrence of deltas and lacustrines (0.6-0.15 Ma), and aeolian facies interbedded with deltas and fluvial facies (0.15 Ma-present). Spectral analyses of the GR, MS, and Rb/Sr data reveal cycles with ~70 m, ~30 m and ~14 m wavelengths. These cycles represent ~100-kyr short-eccentricity, ~40-kyr obliquity and ~20-kyr precession frequencies, respectively and mainly are driven by orbitally forced climate change.

Stepwise drying sedimentary conditions and enhanced desertification indicated by increasing Rb/Sr ratios and proportion of aeolian sands, and decreasing TOC since the past 1.1 Ma, implied intensified westerlies, likely resulted from ice volume expansion and ongoing global cooling according to geological record comparison and simulations during the Last Glacial Maximum compared to preindustrial conditions, which may have controlled the expansion of the permanent deserts in inland Asia. These persistent drying trends and intensified westerly circulation in arid regions during glacial periods after the mid-Pleistocene Transition indicated by larger amplitudes of aeolian sand proportion than prior to 0.6 Ma are similar to those in the interior monsoonal Asia, where the larger-amplitude of median grain size indicated enhanced East Asian Winter monsoon intensity and drier glacials.

How to cite: Liu, H., Zhang, R., Dai, G., and Gu, Y.: Westerly aridity in the western Tarim Basin driven by global cooling since the mid-Pleistocene transition, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-6984, https://doi.org/10.5194/egusphere-egu24-6984, 2024.

EGU24-7068 | ECS | Orals | HS2.1.5

Exploring Drought Patterns in the Headwaters of the Tarim River Basin through an Integrated Surface-Groundwater Drought Index 

Xiaohan Yu, Xiankui Zeng, Dongwei Gui, Dong Wang, and Jichun Wu

The Tarim River Basin, China's largest inland river, has been grappling with persistent drought challenges. Over 90% of its water resources originate from the headwaters, heavily relying on groundwater. Existing drought indices often compartmentalize considerations of surface water and groundwater variables. Consequently, there is a necessity for a comprehensive drought index that accounts for the interplay between surface water and groundwater. This study employs the Copula function to formulate the Standardized Precipitation Evapotranspiration Groundwater Index (SPEGI), incorporating surface water (precipitation minus evaporation) and groundwater (changes in total water storage observed by GRACE satellite minus changes in output from the VIC model). SPEGI is computed using a moving average approach across various time scales (1, 3, 6, 12 months) and is juxtaposed with traditional indices such as Standardized Precipitation Evapotranspiration Index (SPEI), Standardized Soil Moisture Index (SSMI), and Standardized Groundwater Index (SGI). The findings underscore that SPEGI, grounded in the integrated consideration of surface and groundwater variables, provides a more comprehensive depiction of drought conditions in the study area. In contrast to traditional indices, SPEGI not only accounts for short-term precipitation and evaporation changes but also effectively reveals the characteristics of groundwater fluctuations. Additionally, by comparing SPEGI with NDVI data, the study delves into the desertification process in the region. The research discerns that SPEGI's assessment of drought resilience is more sensitive, manifesting an increasing trend in the desertification process with the enlargement of SPEGI's sliding window. Overall, this research contributes novel methodologies and empirical evidence for fostering sustainable water resource utilization and informing climate change adaptation decisions within the basin.

How to cite: Yu, X., Zeng, X., Gui, D., Wang, D., and Wu, J.: Exploring Drought Patterns in the Headwaters of the Tarim River Basin through an Integrated Surface-Groundwater Drought Index, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-7068, https://doi.org/10.5194/egusphere-egu24-7068, 2024.

EGU24-7611 | ECS | Orals | HS2.1.5

Locating unsustainable water supplies for supporting ecological restoration in China's drylands 

Fengyu Fu, Shuai Wang, and Xutong Wu

China, with vast dryland areas, has undertaken extensive ecological restoration (ER) projects since the late 1970s. While ER is a crucial means against desertification and land degradation, it must be implemented in a water-sustainable manner to avoid exacerbating the carbon–water trade-off, especially in water-limited drylands. However, there is still limited research on accurately identifying water unsustainable ER regions in China's drylands. Here, we developed a water supply–demand indicator, namely, the water self-sufficiency (WSS), defined as the ratio of water availability to precipitation. With the use of remote sensing and multisource synthesis datasets combined with trend analysis and time series detection, we conducted a spatially explicit assessment of the water sustainability risk of ER in China's drylands over the period from 1987 to 2015. The results showed that 17.15% (6.36 Mha) of ER areas face a negative shift in the WSS (indicating a risk of unsustainability), mainly in Inner Mongolia, Shanxi, and Xinjiang provinces, driven by evapotranspiration. Moreover, 29.34% (10.9 Mha) of the total ER areas, whose area is roughly double that of water unsustainable ER areas, exhibit a potential water shortage with a significant WSS decline (-0.014 yr-1), concentrated in Inner Mongolia, Shaanxi, and Gansu provinces. The reliability of our findings was demonstrated through previous studies at the local scale and an analysis of soil moisture changes. Our findings offer precise identification of water unsustainable ER regions at the grid scale, providing more specific spatial guidance for ER implementation and adaptation in China's drylands.

How to cite: Fu, F., Wang, S., and Wu, X.: Locating unsustainable water supplies for supporting ecological restoration in China's drylands, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-7611, https://doi.org/10.5194/egusphere-egu24-7611, 2024.

EGU24-8825 | ECS | Orals | HS2.1.5

Assessing stream water scarcity and groundwater roles under global change in a Mediterranean watershed: the Onyar River basin (NE Catalonia, Spain) 

Paula Gabriela Cordoba Ariza, Ramon J. Batalla, Sergi Sabater, and Josep Mas-Pla

Mediterranean basins face significant water scarcity which requires examining long-term data to evaluate their trends in water availability and quality and assess management options. In this presentation, we explore the historical streamflow changes, the influencing climatic —streamflow, precipitation, temperature, and evapotranspiration (PET and AET)— and land-use factors, and the evolution of surface water quality in the Onyar River (Inner Catalan basins, NE Spain; 295 km2) during the last decades (1960-2020).

Results highlight a consistent decline in streamflow, most pronounced over the last two decades, accompanied by an increase in PET, and a probable decrease in groundwater recharge. These changes co-occurred with higher concentrations of river water ammonium and nitrate. We attribute these patterns to changes in land use such as afforestation and intensive fertilization, as well as increased groundwater withdrawal, particularly during irrigation seasons. Additional factors include growing urban water demand and the discharges of treated wastewater back into the river system. Evaluation of the relationship between groundwater and surface water using end-member mixing analysis of hydrochemical data points out an interesting scale-dependence behaviour: groundwater baseflow from alluvial formations was relevant in the smallest subbasins, whereas regional groundwater flow involving deeper aquifers could significantly contribute to stream discharge in the lowest zones of the basin. Since water balance alteration in the future climate scenarios will reduce the contribution of the headwater flow as well as groundwater storage and baseflow generation, reclaimed wastewater shows up as a relevant source to maintain stream runoff, yet its quality is low and might not be properly diluted by rainfall originated runoff.

These observations provide a comprehensive overview of the declining water quantity and quality in the Onyar River network, attributing these trends to an interplay of climatic and anthropogenic factors. They urge for integrated water resources management strategies to mitigate the implications of these environmental changes, such as protecting baseflow generating areas as well as controlling reclaimed wastewater quality.

Funding: G. Córdoba-Ariza acknowledges funding from Secretariat of Universities and Research from Generalitat de Catalunya and European Social Fund for her FI fellowship (2022 FI_B1 00105). 

How to cite: Cordoba Ariza, P. G., Batalla, R. J., Sabater, S., and Mas-Pla, J.: Assessing stream water scarcity and groundwater roles under global change in a Mediterranean watershed: the Onyar River basin (NE Catalonia, Spain), EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-8825, https://doi.org/10.5194/egusphere-egu24-8825, 2024.

Intermittent rivers and ephemeral streams represent half of the global river network and span all climates. The intermittent rivers and ephemeral streams is a short-hand term for all flowing water that ceases to flow or that dries up completely at some point in time and/or space They are more frequent in arid and semi-arid areas but are also present in temperate, tropical humid, boreal, and alpine areas, where they are mainly located in headwaters. Their abundance is increasing due to climate change and water withdrawals for human activities.

The objective of this study is to represent the spatio-temporal dynamics of flow intermittence at the reach level in river of the seven sub-catchments of the Maures massif (between 1.5 km² and 70 km²).

First, two hydrological continuous models of varying complexities are performed: GR6J (lumped and conceptual), and SMASH (spatially distributed and conceptual) in terms of temporal calibration/validation, by dissociating dry and wet years, to asses the models’ability to simulate observed drying event over time. The metrics are based on daily flow records observed in the 7 catchments since 1968 to 2023.

In the second part, a regionalization method is tested on the spatially distributed model (SMASH). The HDA-PR approach (Hybrid Data Assimilation and Parameter Regionalization) incorporating learnable regionalization mappings, based on multivariate regressions is used. This approach consist to search for a transfer function that quantitatively relates physical descriptors to conceptual model parameters from multi-gauge discharge in order to produce a regional model.

Flow condition observed from multiple data sources (daily flow time series from gauging stations, phototrap installed along the river network taking daily pictures from 2021-04-01 to 2023-31-12, daily conductivity measurements series from 2021-01-01) are used to validate the ability of the regional model to simulate flow intermittence (prediction of dry events) at river section level.

The distributed modelling approach, with a high-resolution conceptual hydrological modeling at 0.250 km² and coupled with Hybrid Data Assimilation and Parameter Regionalization descriptors shows results highlight the effectiveness of HDA-PR surpassing the performance of a uniform regionalization method with lumped model parameters. However, the results on smallest catchments area are lowest.

The study shows the interest of using daily photos which are a good indication of the hydrogical state of the streams to obtain intermittence data and increasing the spatial coverage of observations in order to validate regional model.

How to cite: Folton, N., De Fournas, T., Colléoni, F., and Tolsa, M.: Modelling the intermittence of watercourses in the small French Mediterranean catchments of the Maures massif (Réal Collobrier ) with the SMASH platform (Spatially distributed Modelling and ASsimilation for Hydrology) , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-9681, https://doi.org/10.5194/egusphere-egu24-9681, 2024.

EGU24-9899 | Orals | HS2.1.5

60,000 years of hydrologic connectivity on the Australian dryland margins: the case of the Willandra Lakes World Heritage Area 

Kathryn Fitzsimmons, Markus Fischer, Colin Murray-Wallace, Edward Rhodes, Tobias Lauer, Maike Nowatzki, Kanchan Mishra, and Nicola Stern

Australia is big, flat, old and arid: it is the driest inhabited continent on Earth. The catastrophic flooding of recent years has demonstrated not only the potential for extreme conditions at both ends of the hydroclimatic scale, but also how little we understand of the interplay between climatic, hydrological, and surface-process mechanisms affecting this part of the world. We know still less about long-term hydrological dynamics, particularly for the dry inland where water resources are scarce and land surfaces are susceptible to erosion, requiring careful management.

Records of past hydrological variability can help inform us about changing hydroclimate states and their impact on the land surface. The Willandra lakes system, located on the desert margins of southeastern Australia, is one of the few dryland areas which preserves long-term sedimentary records of hydrologic change. The headwaters of these lakes lie in the temperate highlands hundreds of kilometres to the east; as a result, lake filling and drying reflects the interaction between rainfall in the watershed and hydrologic connectivity across the catchment and between the lakes. Environmental change in the Willandra is recorded in the sediments of the lake shoreline dunes, preserved as semi-continuous deposition of different lake facies over 60,000 years.

Here we investigate long-term hydrologic connectivity across the Willandra lakes and their catchment. Our approach uses a novel integration of lake-level reconstruction based on lunette sedimentology, stratigraphy and luminescence geochronology, with hydrologic and palaeoclimatic modelling of key event time slices over the last 60 ky. We characterize the land-surface response to various hydroclimate states, so improving our understanding of dryland atmosphere-hydrosphere interactions.

How to cite: Fitzsimmons, K., Fischer, M., Murray-Wallace, C., Rhodes, E., Lauer, T., Nowatzki, M., Mishra, K., and Stern, N.: 60,000 years of hydrologic connectivity on the Australian dryland margins: the case of the Willandra Lakes World Heritage Area, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-9899, https://doi.org/10.5194/egusphere-egu24-9899, 2024.

EGU24-10078 | ECS | Orals | HS2.1.5 | Highlight

Wheat irrigation in Marrakech conditions: A Simulation Study using SALTMED 

El Houcine El Moussaoui, Aicha Moumni, Said Khabba, and Abderrahman Lahrouni

In Morocco, agriculture accounts for 80-90% of water resources. Available data show that the performance of current irrigation systems remains low to medium, with water losses at plots ranging from 30 to 40%, divided between percolation and evaporation. Gravity irrigation is almost total in the study area, resulting in significant percolation losses. In principle, this percolation contributes mainly to the recharge of the aquifer.

The purpose of this study was to evaluate, by simulation, the impact of irrigation techniques on wheat yield and growth using the generic agro-environmental model SALTMED under the climatic and soil conditions of zone R3, which is an irrigation area located in the region of Sidi Rahal about 40 km east of the city of Marrakech in the plain of Haouz. We started the study by calibrating the model based on two parameters: photosynthetic efficiency and harvest index. After calibration, we compared different irrigation techniques implemented in the model (surface irrigation, sprinkler irrigation, and drip irrigation). Simulation results showed that the drip irrigation technique is the most economical, exhibiting the lowest losses attributed to percolation and soil evaporation. Notably, percolation, a significant contributor to groundwater recharge, measured approximately 255.5 mm/season. In addition, the irrigation practice in the study area appears to be overestimated during the observed season and could be reduced by half, according to SALTMED. When the irrigation dose is halved, the simulated yield (grain and total biomass) decreases by only 1.33%.

How to cite: El Moussaoui, E. H., Moumni, A., Khabba, S., and Lahrouni, A.: Wheat irrigation in Marrakech conditions: A Simulation Study using SALTMED, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-10078, https://doi.org/10.5194/egusphere-egu24-10078, 2024.

EGU24-10387 | ECS | Orals | HS2.1.5

Nitrogen modeling and performance of Multi-Soil-Layering (MSL)bioreactor treating domestic wastewater in rural community 

Sofyan Sbahi, Naaila Ouazzani, Abdessamed Hejjaj, Abderrahman Lahrouni, and Laila Mandi

The multi-soil-layering (MSL) bioreactor has been considered in the latest research as an
innovative bioreactor for reducing the level of pollutants in wastewater. The efficiency of the
MSL bioreactor towards nitrogen pollution is due to the mineralization of organic nitrogen in
aerobic layers to ammonia, and reactivity of ammonia nitrogen with soil and gravel by its
adsorption into soil layers followed by nitrification and denitrification processes when the
alternating phases of oxygenated/anoxic conditions occurs in the filter. In this study, we have
examined the performance of the MSL bioreactor at different hydraulic loading rates (HLRs)
and predicted the removal rate of nitrogen. To improve the prediction accuracy of the models,
the feature selection technique was performed before conducting the Neural Network model.
The results showed a significant removal (p <0.05) efficiency for five-day biochemical
oxygen demand (BOD 5,  86%), ammonium (NH 4 + , 83%), nitrates (NO 3 − , 81%), total kjeldahl
nitrogen (TKN, 84%), total nitrogen (TN, 84%), orthophosphates (PO 4 3− , 91%), and total
coliforms (TC, 1.62 Log units). However, no significant change was observed in the nitrite
(NO 2 − ) concentration as it is an intermediate nitrogen form. The MSL treatment efficiency
demonstrated a good capacity even when HLR increased from 250 to 4000 L/m 2 /day,
respectively (e.g., between 64% and 86% for BOD 5 ). The HLR was selected as the most
significant (p < 0.05) input variable that contribute to predict the removal rates of nitrogen.
The developed models predict accurately the output variables (R 2  > 0.93) and could help to
investigate the MSL behavior.

How to cite: Sbahi, S., Ouazzani, N., Hejjaj, A., Lahrouni, A., and Mandi, L.: Nitrogen modeling and performance of Multi-Soil-Layering (MSL)bioreactor treating domestic wastewater in rural community, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-10387, https://doi.org/10.5194/egusphere-egu24-10387, 2024.

EGU24-11799 | ECS | Orals | HS2.1.5 | Highlight

Exploring the mechanisms controlling dryland hydroclimate in past 'warmer worlds' 

Monika Markowska, Hubert B. Vonhof, Huw S. Groucutt, Michael D. Petraglia, Denis Scholz, Michael Weber, Axel Gerdes, Richard Albert, Julian Schroeder, Yves S. Krüger, Anna Nele Meckler, Jens Fiebig, Matthew Stewart, Nicole Boivin, Samuel L. Nicholson, Paul S. Breeze, Nicholas Drake, Julia C. Tindall, Alan M. Haywood, and Gerald Haug

Drylands cover almost half of Earth’s land surfaces, supporting ~30% of the world’s population. The International Panel on Climate Change predicts increasing aridification and expansion of drylands over the course of this century. As we approach new climate states without societal precedent, Earth’s geological past may offer the best tool to understand hydroclimate change under previously, allowing us to elucidate responses to external forcing. Paleo-records from previously warm and high-CO2 periods in Earth’s past, such as the mid-Pliocene (~3 Ma), point towards higher humidity in many dryland regions. 

Here, we examine desert speleothems from the hyper-arid desert in central Arabia, part of the largest near-continuous chain of drylands in the world, stretching from north-western Africa to the northern China, to elucidate substantial and recurrent humid phases over the past 8 million years. Independent quantitative paleo-thermometers suggest that mean annual air temperatures in central Arabia were approximately between 1 to 5 °C warmer than today. The analyses of the isotopic composition (δ18O and δ2H) of speleothem fluid inclusion waters, representing ‘fossil rainwater’, reveal an aridification trend in Arabia from the Late Miocene to Late Pleistocene during Earth’s transition from a largely ‘ice-free’ northern hemisphere to an ‘ice-age’ world. Together, our data provide evidence for recurrent discrete wetter intervals during past warmer periods, such as the Pliocene. Data-model comparisons allow us to assess the agreement between our paleoclimate data and climate model output using the HadCM3 isotope-enabled model simulations during past ‘warmer worlds’ – namely the mid-Piacenzian warm period (3.264 to 3.025 Ma). To assess the hydroclimate response to external forcing, we examine model output from a series of sensitivity experiments with different orbital configurations allowing us to postulate the mechanisms responsible for the occurrence of humid episodes in the Arabian desert, with potential implications for other dryland regions at similar latitudes. Together, our approach unveils the long-term controls on Arabian hydroclimate and may provide crucial insights into the future variability.

How to cite: Markowska, M., Vonhof, H. B., Groucutt, H. S., Petraglia, M. D., Scholz, D., Weber, M., Gerdes, A., Albert, R., Schroeder, J., Krüger, Y. S., Meckler, A. N., Fiebig, J., Stewart, M., Boivin, N., Nicholson, S. L., Breeze, P. S., Drake, N., Tindall, J. C., Haywood, A. M., and Haug, G.: Exploring the mechanisms controlling dryland hydroclimate in past 'warmer worlds', EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-11799, https://doi.org/10.5194/egusphere-egu24-11799, 2024.

EGU24-12194 | ECS | Orals | HS2.1.5

High resolution surface soil moisture microwave products: intercomparison and evaluation over Spain 

Nadia Ouaadi, Lionel Jarlan, Michel Le Page, Mehrez Zribi, Giovani Paolini, Bouchra Ait Hssaine, Maria Jose Escorihuela, Pascal Fanise, Olivier Merlin, Nicolas Baghdadi, and Aaron Boone

Surface soil moisture (SSM) products at high spatial resolution are increasingly available, either from the disaggregation of coarse-resolution products such as SMAP and SMOS, or from high-resolution radar data such as Sentinel-1. In contrast to coarse resolution products, there is a lack of intercomparison studies of high spatial resolution products, which are more relevant for applications requiring the plot scale. In this context, the objective of this work is the evaluation and intercomparison of three high spatial resolution SSM products on a large database of in situ SSM measurements collected on two different sites in the Urgell region (Catalonia, Spain) in 2021. The satellite SSM products are: i) SSMTheia product at the plot scale derived from a synergy of Sentinel-1 and Sentinel-2 using a machine learning algorithm; ii) SSMρ product at 14 m resolution derived from the Sentinel-1 backscattering coefficient and interferometric coherence using a brute-force algorithm; and iii) SSMSMAP20m product at 20 m resolution obtained from the disaggregation of SMAP using Sentinel-3 and Sentinel-2 data. Evaluation of the three products over the entire database showed that SSMTheia and SSMρ yielded a better estimate than SSMSMAP20m, and SSMρ is slightly better than SSMTheia. In particular, the correlation coefficient is higher than 0.4 for 72%, 40% and 27% of the fields using SSMρ, SSMTheia and SSMSMAP20m, respectively. The lower performance of SSMTheia compared to SSMρ is due to the saturation of SSMTheia at 0.3 m3/m3. The time series analysis shows that SSMSMAP20m is able to detect rainfall events occurring at large scale while irrigation at the plot scale are not caught. This is explained by the use of Sentinel-2 reflectances, which are not linked to surface water status, for the disaggregation of Sentinel-3 land surface temperature. The approach can therefore be improved by using high spatial and temporal resolution thermal data in the perspective of new missions such as TRISHNA and LSTM. Finally, the results show that although reasonable estimates are obtained for annual crops using SSMTheia and SSMρ, poor performance is observed for trees, suggesting the need for better representation of canopy components for tree crops in SSM inversion approaches.

How to cite: Ouaadi, N., Jarlan, L., Le Page, M., Zribi, M., Paolini, G., Ait Hssaine, B., Escorihuela, M. J., Fanise, P., Merlin, O., Baghdadi, N., and Boone, A.: High resolution surface soil moisture microwave products: intercomparison and evaluation over Spain, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-12194, https://doi.org/10.5194/egusphere-egu24-12194, 2024.

    The Yellow River (YR) is 5464 km long and the cradle of Chinese civilization. It is also well known for being the most sediment-laden river and having the largest vertical drop over its course. Although the YR accounts for only 3% of China’s water resources, it irrigates 13% of its cropland. Exceptional historical documents have recorded frequent occurrence of YR flooding events that resulted in huge losses of lives and property.
    The earliest observational record of YR runoff, beginning in 1919 at the Shanxian gauge station, is too short to study centennial-scale variability. Since the start of the Anthropocene in the 1960s, frequent human activities have resulted in large deviation between observed streamflow. The reconstruction of annual historical natural runoff of the YR is necessary to quantify the amount of anthropogenic YR water consumption in recent decades. Tree rings, with the merits of accurate dating and annual resolution, have been widely used in runoff reconstruction worldwide. In this study, 31 moisture-sensitive tree-ring width chronologies, including 860 trees and 1707 cores, collected within the upper-middle YR basins were used to reconstruct natural runoff for the middle YR course over the period 1492–2013 CE.
    The reconstruction provides a record of natural YR runoff variability prior to large-scale human interference. Most of the extreme high/low runoff events in the reconstruction can be verified with historical documents. The lowest YR flow since 1492 CE occurred during 1926–1932 CE and the YR runoff in 1781 is the highest. These two extreme values could be regarded as a benchmark for future judicious planning of water allocation. Since the late 1980s, observed YR runoff has fallen out of its natural range of variability, and there was even no water flow for several months each year in the lower YR course during 1995 to 1998. Especially concerning was that the inherent 11-year and 24-year cycles of YR became disordered following the severe drought event in late 1920s, and eventually disappeared after the 1960s.
    Year-to-year variability in YR water consumption by human activities (WCHA) was quantified, which showed good association between crop yields and acreage in Ningxia and Inner Mongolia irrigation regions. Meanwhile, WCHA was strongly negatively correlated with sediment load at Toudaoguai and Shanxian stations, which led to a 58% reduction of sediment load in Toudaoguai (upper reach) and 29% in Shanxian (middle reach). 
    If human activities continue to intensify, future YR runoff will be further reduced, and this will negatively impact agriculture, human lives, and socioeconomic development in the middle and lower basins of the YR. To reduce the risk of recurring cutoff of streamflow in the YR lower basin, water should be allocated judiciously. Our reconstructed YR natural runoff series are important for future YR water resource management. In addition, our results also provide an important model of how to distinguish and quantify anthropogenic influence from natural variability in global change studies.

How to cite: Liu, Y.: Changes and attribution of natural runoff in the Yellow River over the past 500 years, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-13979, https://doi.org/10.5194/egusphere-egu24-13979, 2024.

EGU24-14057 | ECS | Posters on site | HS2.1.5

Turbulent fluxes at kilometer scale determined by optical-microwave scintillometry in a heterogeneous oasis cropland of the Heihe River Basin 

Feinan Xu, Weizhen Wang, Chunlin Huang, Jiaojiao Feng, and Jiemin Wang

Observations of kilometer-scale turbulent fluxes of sensible (H) and latent heat (LE) are required for the validation of flux estimate algorithms from satellite remote-sensing data and the development of parameterization schemes in the hydro-meteorological models. Since 2019, two sets of Optical and Microwave scintillometer (OMS) systems have been operated in the Heihe River Basin of northwestern China, one on an alpine grassland of upper reaches, another on an oasis cropland of middle reaches, to measure both the areal H and LE. Combined with the observations of eddy-covariance (EC) and meteorological tower systems in both sites, an improved procedure for OMS data processing is proposed. The newly proposed procedure especially improves the preprocessing of raw scintillation data, properly uses the current probably better Lüdi et al. (2005) method in deriving meteorological structure parameters, and chooses the coefficients of similarity functions by Kooijmans and Hartogensis (2016) in calculating fluxes. Evaluated with the results of rather homogeneous grassland, the area-averaged H and LE over the heterogeneous oasis are then determined. Estimates of H and LE agree reasonably well with those obtained from EC in most cases. However, the most interesting is that LE over the oasis during the early crop growing stages is clearly larger than that of EC; while both agree well during the longer crop grown periods. Footprint analysis shows that, compared with EC, the OMS has clearly larger source area that contains a slight area of orchard and shelterbelts distributed near the light path, leading to larger LE during the early stages of crop growth. The area-averaged evapotranspiration (ET) over the oasis is then analyzed more acceptably, which varies from 3 to 5 mm day-1 depending on meteorological conditions during the 39 days of the crop growing period. These results are used to validate the Penman-Menteith-Leuning Version 2 (PML-V2) scheme.

How to cite: Xu, F., Wang, W., Huang, C., Feng, J., and Wang, J.: Turbulent fluxes at kilometer scale determined by optical-microwave scintillometry in a heterogeneous oasis cropland of the Heihe River Basin, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-14057, https://doi.org/10.5194/egusphere-egu24-14057, 2024.

    Recurrent droughts in history, especially climatic aridity since the mid-20th century have aroused great social anxiety about the water resources in the Chinese Loess Plateau (CLP). Given lacking of extended instrumental-like records, new precipitation reconstructions in the CLP are badly needed for objectively evaluating the current precipitation situation, understanding the spatial-temporal differences, and serving for predicting the future. Here we present a tree-ring-based 248-year regional precipitation reconstruction (P8–7) in the Heichashan Mountain, which can significantly represent the past dry-wet variations in the eastern CLP (ECLP). P8–7 explains 48.72% of the instrumental record, reveals a wetting trend since the early 2000s and attains the second wettest period over the past 248 years in 2014–2020 AD. The 1920s/2010s is recorded as the driest/wettest decade. 1910–1932 AD ranks as the driest period over the past centuries. The 19th century is comparatively wet while the 20th century is dry. Precipitation in the ECLP and western CLP (WCLP) has changed synchronously over most time of the past two centuries. However, regional difference exists in the 1890s–1920s when a gradually drying occurred in the ECLP, while not evident in the WCLP, although the 1920s megadrought occurred in the CLP. Moreover, the 20th-century drying in the ECLP begins in the 1950s, later than the WCLP. It reveals that P8–7 variability is primarily influenced by the Asian Summer Monsoon and related large-scale circulations. The seismic phase shift of the contemporaneous Northern Hemispheric temperature may also be responsible for the 1920s megadrought.

How to cite: Cai, Q. and Liu, Y.: Hydroclimatic characteristics on the Chinese Loess Plateau over the past 250 years inferred from tree rings, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-14189, https://doi.org/10.5194/egusphere-egu24-14189, 2024.

EGU24-16291 | ECS | Orals | HS2.1.5

A New Perspective on Agricultural Drought Periods: A Mediterranean Semi-Arid Context 

Kaoutar Oukaddour, Michel Le Page, and Younes Fakir

Extreme weather events have an increasing repercussions on ecosystems in recent years. By comprehending how vegetation responds to climatic extremes, their effects may be mitigated. In a semi-arid Mediterranean region, this study examines the temporal connections of the main triggers of agricultural drought, low precipitation, vegetation growth, thermal stress, and soil water deficit. Drought periods and their characteristics were determined using a revised run theory approach. The Pearson correlations across various spatial scales revealed a moderate to low degree of concordance among the drought indices. This discrepancy can be attributed to the geographical heterogeneity and climatic variations observed among the agrosystems within the basin.

The cross-correlation analysis demonstrated the cascading impacts resulting from reduced precipitation. During drought events, the significant connection between precipitation deficits and vegetation persists for at least one month across most index pairs. This suggests that agricultural drought occurrences can be temporally linked through the selected drought indices. The study unveiled short-, mid-, and long-term effects of precipitation deficiencies on soil moisture, vegetation, and temperature. As anticipated, variables like soil moisture and surface temperature, being more instantaneous, exhibited no lag in response to precipitation. Notably, vegetation anomalies at the monthly time step displayed a two-month lag, indicating a preceding impact of vegetation on precipitation.

Employing the run theory to identify drought events and stages with different thresholds revealed substantial variability in drought characteristics namely the duration, the magnitude magnitude, and the intensity. This variability was notably influenced by the selection of both normality and drought thresholds.

How to cite: Oukaddour, K., Le Page, M., and Fakir, Y.: A New Perspective on Agricultural Drought Periods: A Mediterranean Semi-Arid Context, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-16291, https://doi.org/10.5194/egusphere-egu24-16291, 2024.

EGU24-17049 | ECS | Orals | HS2.1.5

Potential of the Photochemical Reflectance Index in Understanding Photoinhibition and Improving Irrigation Water Efficiency in the Mediterranean Zone 

Zoubair Rafi, Saïd Khabba, Valérie Le Dantec, Patrick Mordelet, Salah Er-Raki, Abdelghani Chehbouni, and Olivier Merlin

Morocco's semi-arid region faces challenges due to limited water resources, necessitating efficient irrigation practices for sustainable agriculture. Precision agriculture, coupled with advanced technologies like the Photochemical Reflectance Index (PRI), holds great potential for optimizing irrigation water usage and enhancing crop productivity in this environment. This abstract provides a comprehensive overview of integrating precision agriculture techniques, PRI, and Net Radiation (Rn) to improve irrigation water efficiency and maximize crop productivity in Morocco's semi-arid zone. The study presents and analyzes an experimental investigation of the PRI signal in a winter wheat field throughout an agricultural season to comprehend its dependence on agro-environmental parameters such as global radiation (Rg) and Rn. Rn directly impacts the energy absorbed by plants, a crucial factor for photosynthesis. Elevated Rn levels generally increase energy availability for photosynthetic processes, resulting in higher chlorophyll fluorescence and PRI values. However, excessive Rn can lead to photoinhibition, damaging the photosynthetic apparatus and reducing photosynthetic efficiency. Understanding the interplay between net radiation, PRI, and photoinhibition is crucial for optimizing agricultural practices. Monitoring and managing net radiation levels allow farmers to ensure that the energy available for photosynthesis remains within the optimal range, minimizing the risk of photoinhibition while maximizing crop productivity. Additionally, the daily water stress index based on PRI (PRIj), developed independently of structural effects related to leaf area index (LAI), showed a coefficient of determination (R2) of 0.74 between PRIj and Rn. This reflects the extent of excessive light stress experienced by the wheat field throughout the experiment. In conclusion, the integration of precision agriculture techniques, specifically PRI, offers a promising approach to enhance irrigation water efficiency in Morocco's semi-arid zone. By employing this innovative tool, farmers can optimize water usage, reduce environmental impacts, and ensure the long-term sustainability of agriculture.

How to cite: Rafi, Z., Khabba, S., Le Dantec, V., Mordelet, P., Er-Raki, S., Chehbouni, A., and Merlin, O.: Potential of the Photochemical Reflectance Index in Understanding Photoinhibition and Improving Irrigation Water Efficiency in the Mediterranean Zone, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-17049, https://doi.org/10.5194/egusphere-egu24-17049, 2024.

EGU24-17321 | ECS | Orals | HS2.1.5

Quantifying Olive Tree Evapotranspiration in Semi-Arid Regions through Remote Sensing-Based SEBAL Model: Validation with Optical-Microwave Scintillometer 

Hamza Barguache, Jamal Ezzahar, Mohamed Hakim Kharrou, Said Khabba, Jamal Elfarkh, Abderrahim Laalyej, Salah Er-Raki, and Abdelghani Chehbouni

Accurately assessing sensible (H) and latent (LE) heat fluxes, along with evapotranspiration, is crucial for comprehending the energy balance at the biosphere-atmosphere interface and enhancing agricultural water management. Although the eddy covariance (EC) method is commonly employed for these measurements, it has limitations in providing spatial representativeness beyond a few hundred meters. Addressing this challenge, optical-microwave scintillometers (OMS) have emerged as a valuable tool, directly measuring kilometer-scale H and LE fluxes. These measurements serve to validate satellite remote sensing products and model simulations, such as the Surface Energy Balance Algorithm for Land (SEBAL). In this study, OMS measurements were utilized to assess the fluxes simulated by the SEBAL model at the Agdal olive orchard near Marrakech city. The results revealed that SEBAL's estimated sensible heat fluxes were 3% higher than those measured by OMS, while latent heat fluxes were approximately 15% lower. Based on these findings, we infer that OMS can effectively validate satellite-driven surface energy balance models, thereby supporting agricultural water management.

How to cite: Barguache, H., Ezzahar, J., Kharrou, M. H., Khabba, S., Elfarkh, J., Laalyej, A., Er-Raki, S., and Chehbouni, A.: Quantifying Olive Tree Evapotranspiration in Semi-Arid Regions through Remote Sensing-Based SEBAL Model: Validation with Optical-Microwave Scintillometer, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-17321, https://doi.org/10.5194/egusphere-egu24-17321, 2024.

EGU24-17560 | ECS | Posters virtual | HS2.1.5

Estimation of Irrigation Water Demand in the Southern Mediterranean Region through Explicit Integration of Irrigation Processes in a Land Surface Model: A Case Study of the Tensift Catchment (Morocco). 

Ahmed Moucha, Lionel Jarlan, Pére Quintana-Segui, Anais Barella-Ortiz, Michel Le Page, Simon Munier, Adnane Chakir, Aaron Boone, Fathallah Sghrer, Jean-christophe Calvet, and Lahoucine Hanich

The utilization of water by various socio-economic sectors has made this resource highly sought after, especially in arid to semi-arid zones where water is already scarce and limited. In this context, effective management of this resource proves to be crucial. Our study aims to: evaluate the performance of the new irrigation module in ISBA, quantify the water balance, and assess the impact of climate change and anthropogenic factors on this resource by the horizon of 2041-2060, utilizing high-resolution futuristic forcings from the study (Moucha et al., 2021). To assess the ISBA model with its new irrigation module, we initially compared observed and predicted fluxes with and without activation of the irrigation module. Subsequently, we compared irrigation water inputs at the ORMVAH-defined irrigated perimeters within the Tensift basin. The results of this evaluation showed that the predictions of latent heat flux (LE) considering all available stations in the basin shifted from -60 W/m² for the model without irrigation to -15 W/m². This indicates that the integration of the new irrigation system into ISBA significantly improves the predictions of latent heat flux (LE) over the period 2004-2014 compared to the regular model. Considering the irrigated perimeters, the study results demonstrated that the model with the integration of the irrigation module was capable of replicating the overall magnitude and seasonality of water quantities provided by ORMVAH despite a positive bias. Exploration of the water balance at the Tensift basin level revealed the ISBA model's ability, equipped with its irrigation module, to describe complex relationships among precipitation, irrigation, evapotranspiration, and drainage. Finally, the assessment of the impact of climate change and vegetation cover for the period 2041-2060, utilizing high-resolution SAFRAN forcings projected to the same horizon (Moucha et al., 2021), revealed an increase in irrigation water needs. These results are of paramount importance in the context of sustainable water resource management in arid and semi-arid regions.

How to cite: Moucha, A., Jarlan, L., Quintana-Segui, P., Barella-Ortiz, A., Le Page, M., Munier, S., Chakir, A., Boone, A., Sghrer, F., Calvet, J., and Hanich, L.: Estimation of Irrigation Water Demand in the Southern Mediterranean Region through Explicit Integration of Irrigation Processes in a Land Surface Model: A Case Study of the Tensift Catchment (Morocco)., EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-17560, https://doi.org/10.5194/egusphere-egu24-17560, 2024.

EGU24-17649 | ECS | Orals | HS2.1.5

Comprehensive Analysis of Hydrological Dynamics and Uncertainties in the Moroccan High Atlas: A Focus on Seasonal Precipitation, Runoff, and Flood Events 

Myriam Benkirane, Abdelhakim Amazirh, El Houssaine Bouras, Adnane Chakir, and Said Khabba

The Mediterranean regions, particularly the Moroccan High Atlas, is exposed to natural risks associated with the hydrological cycle, notably intense precipitation events that trigger sudden floods. This research delves into the subtleties of hydrological dynamics in the High Atlas watersheds, specifically in the Zat watershed, to comprehend the seasonality of precipitation and runoff and elucidate the origins of floods.

The results reveal a strong correlation between observed and simulated hydrographs, affirming the model's capability to capture complex hydrological processes. Evaluation metrics, particularly the Nash coefficient, demonstrate a robust model performance during the calibration phase, ranging from 61.9% to 90%. This attests to the model's ability to reproduce the dynamic nature of hydrological systems in the Moroccan High Atlas.

It is noteworthy that the study identifies the snowmelt process as a significant factor of uncertainty in runoff flooding parameters. The complexities associated with snowmelt, especially in the context of spring precipitation, emerge as a crucial factor influencing uncertainties in the simulated results. This finding underscores the importance of accurately representing snowmelt dynamics in hydrological simulations for regions prone to natural risks.

Moreover, the integration of Probability Distribution Functions and Monte Carlo simulations, coupled with rigorous evaluation metrics, enhances our understanding of calibration parameter uncertainties and validates the model's performance. The identified influence of snowmelt on runoff flooding parameters provides crucial insights for future model improvements and the development of effective mitigation strategies in regions vulnerable to natural risks. This research contributes to advancing hydrological modeling practices in complex terrain.

 

Keywords: Seasonality, Rainfall-Runoff, Floods, Calibration, Monte Carlo simulation, Parameter Uncertainty, Hydrological Modeling, Snowmelt Dynamics, Natural Risks.

How to cite: Benkirane, M., Amazirh, A., Bouras, E. H., Chakir, A., and Khabba, S.: Comprehensive Analysis of Hydrological Dynamics and Uncertainties in the Moroccan High Atlas: A Focus on Seasonal Precipitation, Runoff, and Flood Events, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-17649, https://doi.org/10.5194/egusphere-egu24-17649, 2024.

The Mediterranean area is recognized as a hotspot for climate change challenges, with noticeable patterns of rising temperatures and dryness. Olive agroecosystems are particularly affected by the increasing aridity and global climatic changes. Despite being a symbol of the Mediterranean and traditionally grown using rainfed agricultural practices, olive growers have to adapt to cope with higher temperatures, drought, and more frequent severe weather incidents, necessitating their attention and adaptation (Fraga et al., 2020). Moreover, crop production in Morocco heavily relies on irrigation because rainfed cropping has limited productivity (Taheripour et al., 2020). The olive sector is of great importance in Morocco, and there is an urgent need to implement sustainable water management practices. This includes water-saving strategies such as regulated and sustained deficit irrigation (RDI and SDI) to sustain olive production and strengthen the sector's resilience to climate change and water scarcity. These strategies primarily differ in terms of their irrigation timing and the quantity of water applied (Ibba et al., 2023). This study aims to evaluate the effect of two deficit irrigation strategies on productive parameters of the Menara olive cultivar, to serve as a tool for operational irrigation water management and appraise the adaptive responses of this cultivar under conditions of induced drought stress. In pursuit of this aim, an experiment was carried out in an olive orchard over two consecutive years (2021 and 2022), comparing four treatments of regulated deficit irrigation (RDI): T1 (SP 100- NP 70% ETc), T2 (SP 100- NP 60% ETc), T3 (SP 80- NP 70% ETc), T4 (SP 80- NP 60% ETc) and two treatments of sustained deficit irrigation (SDI): T5 (70% ETc) and T6 (60% ETc), with fully irrigated trees T0 (100% ETc). The findings showed that controlled water stress, as applied through regulated deficit irrigation (RDI), did not exert a severe impact on the flowering traits and yield of the Menara olive cultivar. Notably, the RDI strategy, particularly under T4 treatment, allowed for the reduction of supplied water by 20% in sensitive periods (SP) flowering and from the beginning of oil synthesis to harvest and by 40% in the normal period (NP)during pit hardening, respectively, without compromising fruit yield. However, the SDI strategy, characterized by restricted water availability, which reduced total water application under T5 and T6 treatments by 30% and 40% throughout the entire season, led to a decline in the fruit yield by about 50% and resulted in the most significant drop in water productivity, ranging from 19% to 33% compared to the control T0. Furthermore, the findings underscored the adaptability of responses to water stress and elucidated the consequential impact of each irrigation strategy on the performance of Menara olive trees across successive years, particularly the importance of regulated deficit irrigation as a water management strategy and the need to consider its implication on flowering traits and crop yield over successive growing seasons to establish the enduring adaptability of this locally cultivated olive cultivar.

How to cite: Ibba, K., Er-Raki, S., Bouizgaren, A., and Hadria, R.: Sustainable Water Management for Menara Olive Cultivar: Unveiling the Potential of Regulated and Sustained Deficit Irrigation Strategies in Morocco, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-17808, https://doi.org/10.5194/egusphere-egu24-17808, 2024.

EGU24-17983 | ECS | Orals | HS2.1.5

Comparison of C-band radar and infrared thermal data for monitoring corn field in semi-arid area. 

Abdelhafid Elallaoui, Pierre-Louis Frison, Saïd Khabba, and Lionel Jarlan

In semi-arid Mediterranean regions, the scarcity and limitations of water resources pose major challenges. These invaluable resources are threatened by various factors such as climate change, population growth, urban expansion, and agricultural intensification. Specifically, agriculture, which consumes approximately 85% of the water in the semi-arid zone of the South Mediterranean region, directly contributes to the depletion of groundwater. To promote rational irrigation management, it becomes imperative to monitor the water status of crops. Remote sensing is a valuable technique allowing for monitoring crop fields in different parts of the electromagnetic spectrum giving complementary information about crop parameters. The main objective of this study is to assess the potential of radar and Infrared Thermal data for monitoring the water status of crops in semi-arid regions. In this context, a radar system was installed in Morocco, in the Chichaoua region, consisting of 6 C-band antennas mounted on a 20-meter tower. These antennas are directed towards a maize field. This system allowed for radar data acquisition in three different polarizations (VV, VH, HH) with a 15-minute time-step over the time period extending from September to December 2021. Additionally, the system is complemented by continuous acquisitions from a Thermal Infrared Radiometer (IRT) at 30-minute intervals. These data are further supplemented by in-situ measurements characterizing crop parameters (state of the cover, soil moisture, evapotranspiration and meteorological variables). The study initially focused on analyzing the diurnal cycle of radar temporal coherence. The results indicated that coherence was highly sensitive to wind-induced movements of scatterers, with minimal coherence when wind speed was highest in the late afternoon. Moreover, coherence was also responsive to vegetation activity, particularly its water content, as the morning coherence drop coincided with the onset of plant activity. Subsequently, the study examined the potential of the relative difference between surface vegetation temperature and air temperature to monitor the water status of crops. The results showed that during a period of imposed water stress, the amplitude of this difference increased. These results open perspectives for monitoring the water status of crops using radar and thermal observations with a high revisit frequency.

How to cite: Elallaoui, A., Frison, P.-L., Khabba, S., and Jarlan, L.: Comparison of C-band radar and infrared thermal data for monitoring corn field in semi-arid area., EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-17983, https://doi.org/10.5194/egusphere-egu24-17983, 2024.

EGU24-18201 | ECS | Orals | HS2.1.5

Analyzing Tree Degradation in the Haouz Plain through Remote Sensing: Assessing the Impact of Drought and Spatial Extent 

Youness Ablila, Abdelhakim Amazirh, Saïd Khabba, El Houssaine Bouras, Mohamed hakim Kharrou, Salah Er-Raki, and Abdelghani Chehbouni

Trees characterized by persistent foliage, like olive trees, serve as indispensable assets in arid and semi-arid regions, exemplified by the Haouz plain in central Morocco. The decline in water resources for irrigation, attributed to climate change and excessive underground water extraction, has led to significant degradation of tree orchards in recent years. Employing remote sensing data, we conducted a spatial analysis of tree degradation from 2013 to 2022 using the supervised classification method. Subsequently, a drying speed index (DS) was computed based on the Normalized Difference Vegetation Index (NDVI) derived from Landsat-8 data, specifically focusing on the identified trees. This DS was then correlated with the Standardized Precipitation Index (SPIn) to elucidate the connection between tree degradation and drought, as indicated by precipitation deficit. The findings reveal a discernible declining trend in trees, with an average decrease in NDVI by 0.02 between 2019 and 2022 compared to the reference period (2013-2019). This decline has impacted an extensive area of 37,550 hectares. Furthermore, the outcomes derived from the analysis of SPI profiles depict a prolonged period of dryness, particularly extreme drought in the past four years, characterized by SPI values consistently below -2. Notably, a high correlation coefficient (R) of -0.87 and -0.88 was observed between DS and SPI9 and SPI12 respectively, emphasizing the strong linkage between drying speed and the duration and intensity of drought. These findings emphasize the reliability of NDVI as an effective tool for precise classification of tree land cover. Additionally, they underscore the significant influence of drought on the degradation of trees in the Haouz plain.

How to cite: Ablila, Y., Amazirh, A., Khabba, S., Bouras, E. H., Kharrou, M. H., Er-Raki, S., and Chehbouni, A.: Analyzing Tree Degradation in the Haouz Plain through Remote Sensing: Assessing the Impact of Drought and Spatial Extent, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-18201, https://doi.org/10.5194/egusphere-egu24-18201, 2024.

EGU24-18295 | ECS | Posters on site | HS2.1.5

The relevance of Rossby wave breaking for precipitation in the world’s arid regions 

Andries Jan De Vries, Moshe Armon, Klaus Klingmüller, Raphael Portmann, Matthias Röthlisberger, and Daniela I.V. Domeisen

Precipitation-related extremes in drylands expose more than a third of the world population living in these regions to drought and flooding. While weather systems generating precipitation in humid low- and high-latitude regions are widely studied, our understanding of the atmospheric processes governing precipitation formation in arid regions remains fragmented at best. Regional studies have suggested a key role of the extratropical forcing for precipitation in arid regions. Here we quantify the contribution of Rossby wave breaking for precipitation formation in arid regions worldwide. We combine potential vorticity streamers and cutoffs identified from ERA5 as indicators of Rossby wave breaking and use four different precipitation products based on satellite-based estimates, station data, and reanalysis. Rossby wave breaking is significantly associated with up to 80% of annual precipitation and up to 90% of daily precipitation extremes in arid regions equatorward and downstream of the midlatitude storm tracks. The relevance of wave breaking for precipitation increases with increasing land aridity. Contributions of wave breaking to precipitation dominate in the poleward and westward portions of subtropical arid regions during the cool season. In these regions, climate projections for the future suggest a strong precipitation decline, while projections of precipitation extremes are highly uncertain due to the influence of the atmospheric circulation. Thus, our findings emphasize the importance of Rossby wave breaking as an atmospheric driver of precipitation in arid regions with large implications for understanding projections and constraining uncertainties of future precipitation changes in arid regions that are disproportionally at risk of freshwater shortages and flood hazards.

How to cite: De Vries, A. J., Armon, M., Klingmüller, K., Portmann, R., Röthlisberger, M., and Domeisen, D. I. V.: The relevance of Rossby wave breaking for precipitation in the world’s arid regions, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-18295, https://doi.org/10.5194/egusphere-egu24-18295, 2024.

EGU24-19012 | Orals | HS2.1.5

Decoupling the Influence of Climate Change and Natural Variability on the Middle Eastern Shamal Wind  

Hamza Kunhu Bangalth, Jerry Raj, Udaya Bhaskar Gunturu, and Georgiy Stenchikov

The Middle Eastern Shamal, a prominent north-northwesterly wind, plays a crucial role in the Arabian Peninsula's climate and environment. Originating from the interaction between a semipermanent anticyclone over northern Saudi Arabia and a cyclone over southern Iran, it influences regional climate. The Shamal is essential in transporting dust and pollutants from the Tigris-Euphrates to the Persian Gulf, affecting air quality, health, and travel. Its potential as a renewable energy source also highlights its importance for the region's future energy strategies.

However, understanding the time series of the Shamal wind is a complex task, owing to the intertwined influences of natural climate variability and human-induced climate change. While climate change is a critical factor, natural variability driven by internal climate modes like the Atlantic Multidecadal Oscillation (AMO), Pacific Decadal Oscillation (PDO), and North Atlantic Oscillation (NAO) also significantly influences these winds. These oscillations, operating over multidecadal scales, alongside the overarching trend of climate change, form a complex web affecting the regional climate. 

This study addresses the challenge of decoupling the impacts of climate change and natural climate variability on the Shamal wind. Our analysis employs Empirical Mode Decomposition (EMD), a relatively new approach that allows us to decouple the influence of various internal climate modes from that of anthropogenic climate change. This method surpasses traditional techniques by avoiding assumptions of linearity and stationarity. The study utilizes ERA5 reanalysis data to analyze summer and winter Shamal winds.

Preliminary findings indicate that internal climate modes like the AMO are equally significant as climate change in influencing Shamal wind in the past. This insight is crucial for more accurate projections and predictions of future Shamal wind behavior, benefiting the Middle East's environmental management, health, and renewable energy sectors.

How to cite: Bangalth, H. K., Raj, J., Gunturu, U. B., and Stenchikov, G.: Decoupling the Influence of Climate Change and Natural Variability on the Middle Eastern Shamal Wind , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-19012, https://doi.org/10.5194/egusphere-egu24-19012, 2024.

EGU24-19172 | Orals | HS2.1.5

Assessing the possibilities of Sentinel products for qualifying and quantifying soil water status of agricultural systems in southern France  

Claude Doussan, Urcel Kalenga Tshingomba, Nicolas Baghdadi, Fabrice Flamain, Arnaud Chapelet, Guillaume Pouget, and Dominique Courault

Water management poses a pervasive challenge in southern France, exacerbated by increasing summer droughts linked to global warming. Water use during spring and summer increases and gets more variable in term of quantity used for crops. Agricultural water use is highly influenced by the diversity in irrigation practices and technics (sprinkler irrigation, drip irrigation, flooding, etc.) ; and can lead to tensions among water users. It is thus essential to estimate field water use at basin scale, as well as crop water status, in order to further optimize water delivered for irrigation. Advances in remote sensing, particularly with Sentinel 1 (S1) and 2 (S2) data, facilitated the development of soil moisture products (SMP) with improved spatial and temporal resolution to characterize soil water in agricultural plots. These SMP products are accessible through the Theia French public platform and suitable for main crops, with NDVI below 0.75 or surfaces with moderate roughness. These specifications can be met for a variety of crop conditions in the south of France. Yet, the validity of the SMP products under various agricultural plot conditions, considering slope, orientation, roughness, and soil moisture, remains to be assessed over extended time periods. From another point of view, such SMP products do not presently apply to orchards plots, which are however, an essential but overlooked component of water use in irrigation and deserve further examination with S1 and S2 data. The objective of our study is twofold: (i) to test SMP products for field crops in different settings and among years, (ii) to preliminary test if S1 data, combined to S2 data, may be linked to soil moisture in orchard plots. Results reveal for (i) that differences can appear between SMP products and soil moisture in various monitored plots, primarily due to variability within farming systems. Beyond a specific slope and vegetation threshold, the correlation does not improve significantly. For (ii), in orchards plots, using a time smoothing of data, S1 VV-retrodiffusion data and NDVI from S2 seem to correlate with soil moisture measurements, with an RMSE < 0.05 cm3/cm3 and enable detection of irrigation events. This study shows that S1 and S2 data are valuable in estimating soil moisture of agricultural plots, giving however some limits in their use, and gives some hope in their further use for orchards water management.

How to cite: Doussan, C., Kalenga Tshingomba, U., Baghdadi, N., Flamain, F., Chapelet, A., Pouget, G., and Courault, D.: Assessing the possibilities of Sentinel products for qualifying and quantifying soil water status of agricultural systems in southern France , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-19172, https://doi.org/10.5194/egusphere-egu24-19172, 2024.

EGU24-19511 | Posters on site | HS2.1.5

OurMED PRIMA-funded Project: Sustainable Water Storage and Distribution in the Mediterranean 

Seifeddine Jomaa, Amir Rouhani, Maria Schade, J. Jaime Gómez-Hernández, Antonio Moya Diez, Maroua Oueslati, Anis Guelmami, George P. Karatzas, Emmanouil A Varouchakis, Maria Giovanna Tanda, Pier Paolo Roggero, Salvatore Manfreda, Nashat Hamidan, Yousra Madani, Patrícia Lourenço, Slaheddine Khlifi, Irem Daloglu Cetinkaya, Michael Rode, and Nadim K Copty

The Mediterranean Region is a unique mosaic of different cultures and climates that shape its peoples, natural environment, and species diversity. However, rapid population growth, urbanisation and increased anthropogenic pressures are threatening water quantity, quality, and related ecosystem services. Known as a climate change hotspot, the Mediterranean region is increasingly experiencing intensifying droughts, diminished river flows, and drier soils making water management even more challenging. This situation calls for an urgent need for water management to shift from a mono-sectoral water management approach based on trade-offs, to more balanced multisectoral management that considers the requirement of all stakeholders. This means that sustainable water management requires ensuring that water is stored and shared fairly across all sectors at the basin scale.

The research project OurMED (https://www.ourmed.eu/) is part of the Partnership for Research and Innovation in the Mediterranean Area (PRIMA) Programme supported by the European Union’s Horizon 2020 Research and Innovation Programme under Grant Agreement No 2222. The project was launched in June 2023 and will continue for three years with a grant of 4.4 million euros to develop a holistic water storage and distribution approach tightly integrated into ecosystem services at the river basin scale.

OurMED builds on the multidisciplinary skills of 15 consortium Partners and comprises universities, NGOs, research centres and SMEs from ten countries with complementary expertise in hydrology, hydrogeology, agronomy, climate change, social sciences, remote sensing, digital twins, ecology, and environmental sciences, among others, making it a truly interdisciplinary project. OurMED includes eight distinct demo sites, representing diverse water-related ecosystem properties of the Mediterranean landscape. These include the catchment areas of Bode (Germany), Agia (Crete, Greece), Konya (Turkey), Mujib (Jordan), Medjerda (Tunisia), Sebou (Morocco), Arborea (Sardinia, Italy), and Júcar (Spain). The Mediterranean basin, as a whole, is considered as an additional regional demo site to ensure replicability and reproducibility of proposed solutions at larger scales. 

OurMED vision combines not only technologically-advanced monitoring, smart modelling and optimization capabilities, but also provides data fusion and integrated digital twin technologies to make optimized solutions readily available for decision making. OurMED concept and its implementation to the different demo sites will be presented and discussed.

How to cite: Jomaa, S., Rouhani, A., Schade, M., Gómez-Hernández, J. J., Moya Diez, A., Oueslati, M., Guelmami, A., Karatzas, G. P., Varouchakis, E. A., Tanda, M. G., Roggero, P. P., Manfreda, S., Hamidan, N., Madani, Y., Lourenço, P., Khlifi, S., Daloglu Cetinkaya, I., Rode, M., and Copty, N. K.: OurMED PRIMA-funded Project: Sustainable Water Storage and Distribution in the Mediterranean, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-19511, https://doi.org/10.5194/egusphere-egu24-19511, 2024.

EGU24-20067 | ECS | Orals | HS2.1.5

Impact Of Ocean Layer Thickness on The Simulation Of African Easterly Waves in High-Resolution Coupled General Circulation Model Simulations 

Jerry Raj, Elsa Mohino Harris, Maria Belen Rodriguez de Fonseca, and Teresa Losada Doval

African easterly waves (AEWs) play a crucial role in the high-frequency variability of West African Monsoon (WAM) precipitation. AEWs are linked to more than 40% of the total Mesoscale Convective Systems (MCSs) in the region and these MCSs contribute approximately 80% of the total annual rainfall over the Sahel. Moreover, around 60% of all Atlantic hurricanes, including 80% of major hurricanes, have their genesis associated with AEWs. The simulation of AEWs poses challenges for General Circulation Models (GCMs), for instance, coarse-resolution models in CMIP5 cannot simulate distinct northern and southern AEW tracks. Additionally, accurately simulating rainfall over West Africa proves to be a challenge for these models due to the involvement of multiscale processes and the influence of complex topography and coastlines. 

The present study investigates the impact of ocean layer thickness on the simulation of African easterly waves (AEWs) using a high-resolution coupled General Circulation Model (GCM). The study employs high-resolution global simulations conducted using the climate model ICON as part of the next Generation Earth System Modeling Systems (nextGEMS) project. Two experiments, each spanning 30 years with a horizontal resolution of 10 km, are conducted. These experiments vary in terms of the thickness of the layers in the upper 20m of the ocean. In one experiment, the upper 20m ocean layers have a thickness of 2m, whereas in the other, it is 10m. The representation of two types of AEWs with periods of 3-5 days and 6-9 days are analyzed in the simulations. There is a notable disparity in the representation of African easterly waves (AEWs) between these two experiments. The simulation with thicker ocean layers exhibits less intense wave activity over the Sahel and equatorial Atlantic for 3-5 day AEWs which is evident in the eddy kinetic energy field. This corresponds to diminished convection and negative precipitation anomalies for 3-5 day AEWs compared to the 2m upper ocean layer thickness simulation. In the case of 6-9 day AEWs, the simulation with thicker ocean layers exhibits intensification of wave activity over northern West Africa.

How to cite: Raj, J., Mohino Harris, E., Rodriguez de Fonseca, M. B., and Losada Doval, T.: Impact Of Ocean Layer Thickness on The Simulation Of African Easterly Waves in High-Resolution Coupled General Circulation Model Simulations, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-20067, https://doi.org/10.5194/egusphere-egu24-20067, 2024.

EGU24-20356 | ECS | Posters on site | HS2.1.5

Seasonal Water Turbidity Dynamics in Arid Central Asia: A Case Study of Lake Balkhash, Kazakhstan, Under Changing Environmental Conditions 

Kanchan Mishra, Kathryn E. Fitzsimmons, and Bharat Choudhary

Lake Balkhash, one of the largest inland lakes in Central Asia, plays a pivotal role in providing water and ecosystem services to approximately 3 million people. However, like many water bodies in dryland regions worldwide, Lake Balkhash's hydrology has been significantly affected by climate change and land cover and land-use shifts driven by population growth and water-intensive economic activities. To manage these vital water resources effectively, monitoring the health of water bodies is essential for effective water resource management, security, and environmental conservation. Turbidity, a water quality indicator, measures the water clarity and represents a broader environmental change, allowing us to assess the water body's health and the extent of anthropogenic impact on the entire catchment. It is a measure of water clarity and serves as a crucial indicator of water health, as it represents the primary mechanism for transporting pollutants, algae, and suspended particles.

The present study investigates the temporal and spatial variability of turbidity in Lake Balkhash. We utilize the normalized difference turbidity index (NDTI) with Landsat satellite data spanning from 1991 to 2022 to map turbidity. We consider various climatic and anthropogenic factors, including precipitation, temperature, wind speed and direction, and water levels in and around the lake.

Our findings reveal an overall declining turbidity trend over interannual and seasonal timescales. The results provide a significant negative correlation between turbidity, temperature, and water levels at both temporal scales. However, no straightforward relationship emerges between turbidity and precipitation or wind variables. Specifically, during spring and summer, turbidity exhibits a strong association with temperature and water levels, while in the fall season, water levels are more closely correlated with turbidity. These results underscore the substantial impact of rising temperatures and fluctuations in water levels on the turbidity dynamics of Lake Balkhash. These findings highlight that the warming climate and alterations in lake hydrology pose significant risks to water quality, indicating that monitoring water health alone may not suffice to mitigate the impacts of climate change and human activities.  

How to cite: Mishra, K., Fitzsimmons, K. E., and Choudhary, B.: Seasonal Water Turbidity Dynamics in Arid Central Asia: A Case Study of Lake Balkhash, Kazakhstan, Under Changing Environmental Conditions, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-20356, https://doi.org/10.5194/egusphere-egu24-20356, 2024.

EGU24-20398 | Posters on site | HS2.1.5

Analysis of operational droughts in an alpine Mediterranean basin using a conjunctive use model of surface and groundwater resources 

Juan-de-Dios Gómez-Gómez, Antonio Collados-Lara, David Pulido-Velázquez, Leticia Baena-Ruiz, Jose-David Hidalgo-Hidalgo, Víctor Cruz-Gallegos, Patricia Jimeno-Sáez, Javier Senent-Aparicio, Fernando Delgado-Ramos, and Francisco Rueda-Valdivia

Extreme events, and particularly, droughts are a main concern in Mediterranean basins that will be increased in the future due to climate change (CC), according to the forecasting for the region made by researchers. A novel integrated approach is proposed to analyze operational droughts and their propagation in future CC scenarios at a basin scale. This approach has been applied to the Alto Genil basin (Granada, Spain), an alpine Mediterranean basin with the singularity of having an important snow component in its precipitation regime. The Standardized Precipitation Index (SPI) methodology has been applied to the variable Demand Satisfaction Index (DSI) at a monthly scale to evaluate operational droughts. A conjunctive use model of surface and groundwater resources developed with the code Aquatool has been used to obtain historical and future DSI monthly series. It is an integrated management model that includes all water demands, water resources (surface, groundwater, and their interaction), regulation and distribution infrastructures in the Alto Genil system. The Vega de Granada aquifer is a key element of the water supply system such for agricultural needs as for guarantee the urban supply to the city of Granada. Groundwater flow in this important aquifer has been simulated with a distributed approach defined by an eigenvalue model to integrate it in the management model, and in order to obtain a more detailed analysis of its future evolution. The proposed methodology consists of the sequential application of the following steps: (1) generation of future scenarios for the period 2071-2100 to obtain series of precipitation (P) and temperature (T); (2) application of a chain of models: a rainfall-runoff model (Témez) coupled with a snowmelt model to obtain runoff (Q) series in subbasins of Alto Genil basin, a crop water requirement model (Cropwat) to get agricultural demand series, and an integrated management model (Aquatool) to get historical and future series of DSI; and (3) analysis of operational droughts comparing historical and future series of the Standardized Demand Satisfaction Index (SDSI), which is the application of the SPI methodology to the variable DSI. A cluster analysis of variables P and Q has been made in order to define homogeneous hydroclimatic areas by aggregation of subbasins. It will allow us to perform an analyses of the heterogeneity in  the propagation of droughts.

Aknowledments: This research has been partially supported by the projects: STAGES-IPCC (TED2021-130744B-C21), SIGLO-PRO (PID2021-128021OB-I00), from the Spanish Ministry of Science, Innovation and Universities, RISRYEARTH (Recovery funds), and “Programa Investigo” (NextGenerationEU).

How to cite: Gómez-Gómez, J.-D., Collados-Lara, A., Pulido-Velázquez, D., Baena-Ruiz, L., Hidalgo-Hidalgo, J.-D., Cruz-Gallegos, V., Jimeno-Sáez, P., Senent-Aparicio, J., Delgado-Ramos, F., and Rueda-Valdivia, F.: Analysis of operational droughts in an alpine Mediterranean basin using a conjunctive use model of surface and groundwater resources, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-20398, https://doi.org/10.5194/egusphere-egu24-20398, 2024.

EGU24-20616 | Orals | HS2.1.5

Integrating Multi-Sensor and Multi-Platform Technologies for Enhanced Assessment of Spectral Indices and Phenological Dynamics in a Seasonal Tropical Dry Forest 

Magna Moura, Rodolfo Nobrega, Anne Verhoef, Josicleda Galvíncio, Rodrigo Miranda, Bruna Alberton, Desiree Marques, Cloves Santos, Bruno Nascimento, Maria Maraiza Pereira, and Patricia Morellato

The Seasonal Tropical Dry Forest (STDF) known as Caatinga occupies approx. 10% of the Brazilian territory. Its vegetation exhibits rapid phenological responses to rainfall resulting in corresponding increases in gross primary productivity and biomass production. Determining the timing of the start and end of the growing season is very important to ecosystem studies and to precisely quantify the carbon balance. Satellite-derived vegetation indices have been widely used to capture the vegetation dynamics in response to fluctuating environmental conditions. However, the spatial and temporal resolution of these indices cannot capture fine vegetation features and phenology metrics in a highly biodiverse and heterogeneous environment such as the Caatinga. On the other hand, phenocameras have been successfully used for this particular purpose for tropical and dry ecosystems. Complementarily, proximal spectral response sensors (SRS) have been used to allow computation of vegetation indices as phenology proxies. Due to their ability to capture high spatial resolution imagery, Unmanned Aerial Systems (UAS) or drones, can deliver an excellent spatial and a very good temporal resolution for diverse detailed vegetation studies. In this context, the objective of this study was to verify whether multi-sensor and multi-platform technologies provide an enhanced assessment of spectral indices and phenological dynamics of the Caatinga. The field campaign occurred in a pristine area of caatinga vegetation, located at the Legal Reserve of Caatinga, Embrapa Semi-Arid, Petrolina, Brazil. Indices for detecting phenology dynamics were obtained using multi-spectral cameras installed on unmanned aerial vehicles (UAV), field spectral response sensors (SRS), phenocameras (digital RGB cameras) and MODIS satellite data (visible and near infrared) from 2020 to 2023. Environmental driving data were measured via instrumentation installed on a flux tower. Standard statistical measures, including correlation coefficients were employed to verify the relationship observed on Normalized Difference Vegetation Index (NDVI), Photochemical Reflectance Index (PRI), and Green Chromatic Coordinate (Gcc) determined by different sensors and platforms. We observed a substantial and fast increase in Gcc, NDVI and PRI immediately after rainfall events. The sensitivity of NDVI and PRI to changes in vegetation can vary depending on factors such as vegetation greenness, overall plant health, and stress responses according to the environmental conditions of the study area. Particularly during the dry season, indices derived from higher spatial resolution sensors consistently showed lower NDVI values compared to those obtained from proximal spectral response sensors (SRS) and drones. Our observations indicate that the representation of vegetation captured by satellites and drones aligns well with the data obtained from phenocamera and proximal SRS platforms. The combination of high temporal resolution provided by SRS and phenocameras resulted in improved and more reliable indices that will be indispensable for evaluating the response of Caatinga vegetation to current and future conditions.

Funding: This study was supported by the São Paulo Research Foundation-FAPESP (grants ##2015/50488-5, #2019/11835-2; #2021/10639-5; #2022/07735-5), the Coordination for the Improvement of Higher Education Personnel - CAPES (Finance Code 001), the National Council for Scientific and Technological Development - CNPq (306563/2022-3).

How to cite: Moura, M., Nobrega, R., Verhoef, A., Galvíncio, J., Miranda, R., Alberton, B., Marques, D., Santos, C., Nascimento, B., Pereira, M. M., and Morellato, P.: Integrating Multi-Sensor and Multi-Platform Technologies for Enhanced Assessment of Spectral Indices and Phenological Dynamics in a Seasonal Tropical Dry Forest, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-20616, https://doi.org/10.5194/egusphere-egu24-20616, 2024.

EGU24-20999 | ECS | Orals | HS2.1.5

Soil and rock water dynamics in a semiarid karst savanna undergoing woody plant encroachment

Pedro Leite, Bradford Wilcox, Daniella Rempe, and Logan Schmidt

EGU24-58 | ECS | Orals | HS2.4.2

Unveiling Hydrological Dynamics in Data-Scarce Regions:A Comprehensive Integrated Approach 

Ayenew Desalegn Ayalew, Paul D. Wagner, Dejene Sahlu, and Nicola Fohrer

The hydrological system of Rift Valley Lakes in Ethiopia has recently experienced changes since the past two decades. Potential causes for these changes include anthropogenic, hydro-climatic and geological factors. The main objective of this study was to utilize an integrated methodology to gain a comprehensive understanding of the hydrological systems and potential driving factors within a complex and data-scarce region. To this end, we integrated a hydrologic model, change point analysis, indicators of hydrological alteration (IHA), and bathymetry survey to investigate hydrological dynamics and potential causes. A hydrologic model (SWAT+) was parameterized for the gauged watersheds and extended to the ungauged watersheds using multisite regionalization techniques. The SWAT+ model performed very good to satisfactory for daily streamflow in all watersheds with respect to the objective functions, Kling–Gupta efficiency (KGE), the Nash–Sutcliffe efficiency (NSE), Percent bias (PBIAS). The findings reveal notable changes of lake inflows and lake levels over the past two decades. Chamo Lake experienced an increase in area by 11.86 km², in depth by 4.4 m, and in volume by 7.8 x 108 m³. In contrast, Lake Abijata witnessed an extraordinary 68% decrease in area and a depth decrease of 1.6 m. During the impact period, the mean annual rainfall experienced a decrease of 6.5% and 2.7% over the Abijata Lake and the Chamo Lake, respectively. Actual evapotranspiration decreased by 2.9% in Abijata Lake but increased by up to 0.5% in Chamo Lake. Surface inflow to Abijata Lake decreased by 12.5%, while Lake Chamo experienced an 80.5% increase in surface inflow. Sediment depth in Chamo Lake also increased by 0.6 m. The results highlight that the changing hydrological regime in Chamo Lake is driven by increased surface runoff and sediment intrusion associated with anthropogenic influences. The hydrological regime of Abijata Lake is affected by water abstraction from feeding rivers and lakes for industrial and irrigation purposes. This integrated methodology provides a holistic understanding of complex data-scarce hydrological systems and potential driving factors in the Rift Valley Lakes in Ethiopia, which could have global applicability.

How to cite: Ayalew, A. D., Wagner, P. D., Sahlu, D., and Fohrer, N.: Unveiling Hydrological Dynamics in Data-Scarce Regions:A Comprehensive Integrated Approach, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-58, https://doi.org/10.5194/egusphere-egu24-58, 2024.

EGU24-95 | ECS | Orals | HS2.4.2

Flood prediction based on weather parameters using advanced machine learning-metaheuristic approaches 

Sandeep Samantaray, Abinash Sahoo, and Deba P Satapathy

Flood prediction has become more popular worldwide because of the devastating socioeconomic effects of this hazard and the predicted rise in its frequency in the near future. In India, public health, civil engineering, and agriculture are all greatly affected by flooding. Anything can be flooded, with levels ranging from a few inches to many feet. They may appear suddenly or develop gradually. The intensity and frequency of flooding will frequently increase due to human modifications to the environment. More frequent and severe weather occurrences could lead to more violent floods. Utilizing data-driven and machine-learning models to solve flow- and flood-related problems has lately gained traction as a subject of study. ML model shows two key advantages over traditional physically-based models controlled by differential equation systems. Firstly, without requiring a complete a priori understanding of the phenomenon, data-driven models are able to generate reasonably accurate predictions. The quantity, quality, and variety of data that are accessible all affect how accurate the model is. This feature shows that we can avoid the complexity of problems faced by physical-based models caused by the growing number of important components by learning from observational data. Second, data-driven flood models replace numerical integration of differential equations, which is an iterative process, with non-iterative procedures like forward propagation of neural networks.

We chose to study the floods in the Barak River basin (BRB), India, a high-elevation and quickly urbanized river basin that is prone to frequent flooding because of recent evidence of the impacts of regional climate change on the hydrological cycle. Using principal component analysis (PCA), the optimal inputs were found. Decision-makers in the hydrological field of research need accurate information regarding effective predictors. This study looks into the viability of using weather input data (rainfall, humidity, evapotranspiration, temperature) to predict monthly floods using a support vector machine customized with Manta-Ray foraging optimization (SVM-MRFO). The accuracy of SVM-MRFO was assessed by comparing it against SVM tuned by the Firefly algorithm, whale optimization algorithm, Salp swarm algorithm based on mean absolute errors (MAE), root mean square errors (RMSE), determination coefficient (R2), and Nash-Sutcliffe Efficiency (NSE). Implementing the FFA, WOA, SSA, and MRFO algorithms enhances the accuracy of the SVM.

The best performance metrics, NSE of 0.9914, RMSE of 0.0182, MAE of 0.0073, and BIAS of were obtained by the SVM model constructed using the MRFO training procedure, suggesting the model's potential for use in flood forecasting. The flood models in this study are significant since they were created using a mix of different inputs and AI algorithms. In conclusion, this study demonstrated the ability of AI algorithm-based models to forecast floods and produced a number of practical methods that the flood control departments of different states, regions, and nations might employ to estimate the likelihood of floods.

How to cite: Samantaray, S., Sahoo, A., and Satapathy, D. P.: Flood prediction based on weather parameters using advanced machine learning-metaheuristic approaches, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-95, https://doi.org/10.5194/egusphere-egu24-95, 2024.

Despite rising rainfall constraints on global climate-resilient agriculture, there is no clear consensus on the quantification of the wet season, leading to contentious issues in rainfall regime evolution and subsequent impacts on phenology and vegetation productivity. Hence, we conducted a comprehensive assessment of rainfall regimes between 1982 and 2020 by using a modified anomalous accumulation method on a daily scale at the pixel level. We observed divergent patterns of “wet areas becoming drier, and dry areas becoming wetter” with rainfall amount and rainy days increasing in dry regions, and decreasing in humid regions. The length of the wet season was extended in the dry regions and shortened in the wet regions, and the trends were linearly related on dryness. Simultaneously, as dryness increased, so did the length, number, and cumulative number of dry days. Concurrent increases in rainy days and dry spells indicated a seasonal rainfall regime trend toward more frequent extreme conditions in drier areas, which was not entirely consistent with a global intensification pattern of “dry getting drier and wet getting wetter”, implying increased potential risks of both floods and droughts in dry areas. For climate risk prediction, water resource allocation, and agricultural management, we advocate for a finer and more precise dynamic assessment of the wetting-drying pattern.

How to cite: Hu, Y.: Divergent patterns of rainfall regimes in dry and humid areas of China, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-126, https://doi.org/10.5194/egusphere-egu24-126, 2024.

Long-term variations in catchment evapotranspiration control water availability for human societies and freshwater ecosystems, with potential negative impacts particularly during low-flow conditions. Previous studies reported increases in water balance-derived evapotranspiration for parts of Central Europe, mostly between 1980s and 2010s. However, knowledge gaps still remain around (i) the extent of these increases in space and time, and (ii) uncertainties from the catchment water balance. Here we analyse trends in water balance-derived evapotranspiration for 461 German near-natural catchments, over multiple time windows in the last six decades. We constrain uncertainties through estimates of storage changes derived from recession analysis and the use of multiple precipitation products. Results show wide-spread, significant increases in catchment evapotranspiration during 1970s–2000s (for example, average regional trends of 3.2 mm year-2 with an uncertainty from precipitation of ±1 mm year-2 for the period 1970–2002). Yet, catchment evapotranspiration shows no significant changes or rather a tendency to the decrease after 2000s (-3.6±1.4 mm year-2 for Pre-Alpine catchments over 2000–2019). The directions of these variations are robust to the considered uncertainties and consistent with sparse in-situ data. We further discuss implications of these variations with respect to low-flow conditions.  This study offers a comprehensive synthesis on past variations in catchment evapotranspiration and their uncertainties, which is critical for a proper understanding of recent hydrological changes.   

How to cite: Bruno, G. and Duethmann, D.: Inter-decadal variations in water balance-derived catchment evapotranspiration in Central Europe and their uncertainties, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-1145, https://doi.org/10.5194/egusphere-egu24-1145, 2024.

EGU24-1420 | Orals | HS2.4.2 | Highlight

Storylines of climate variability for hydrological impact studies 

Theodore Shepherd

Physical climate storylines (physically-based unfoldings of past climate or weather events, or of plausible future events or pathways) are increasingly being used to represent the epistemic uncertainty in the forced response to climate change. But storylines can also be used to systematically explore the uncertainty space of climate variability, e.g. to construct plausible worst-case events. Their use in this latter context is perhaps less obvious since variability is generally considered to be an aleatoric rather than an epistemic uncertainty. However, for impact studies, variability is often hugely undersampled, which is a serious problem that storylines can help address. In this talk I will review the rationale behind the use of storylines, discuss some of the concerns and questions about storylines that continue to arise, and provide some examples of their use in this particular context and of how storyline and probabilistic representations of uncertainty can be usefully combined.

How to cite: Shepherd, T.: Storylines of climate variability for hydrological impact studies, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-1420, https://doi.org/10.5194/egusphere-egu24-1420, 2024.

EGU24-1538 | ECS | Posters on site | HS2.4.2

Small, flashy catchments response to variation in rainfall profile shape 

Alexandra Seawell

Flash flooding has the potential for severe consequences but is much less well understood or predictable than longer duration flooding. It is important to improve understanding of patterns of rainfall and behaviour of responding catchments in order to manage flash flooding effectively. One aspect of rainfall that could potentially affect flood hydrographs is the temporal shape of rainfall profiles.

Design flood estimation in the UK is principally based on the FSR /FEH/ReFH methodology, which uses a symmetrical centre-loaded profile for rainfall. However, recent research undertaken during Roberto Villalobos Herrera’s PhD is that front-loaded and back-loaded rainstorms occur just as frequently as centre-loaded. My PhD seeks to test how different rain profile shapes change the river flow hydrograph and flooding across the catchment.

My PhD concentrates on small catchments which have typically been less studied and because they are likely to be responsive to short, intense rainfall that can cause flash flooding. Hydrological modelling has been undertaken for 24 identified study catchments using ReFH2.3 software, which is the standard flood estimation design software in the UK. Results indicate that use of symmetrical profiles risks underestimating potential flood peaks compared to back-loaded storms. Meanwhile, time-to-peak is typically shorter for frontloaded storms indicating the hydrograph rises faster, but lagtime is shorter for back-loaded storms indicating the peak flow occurs more quickly after the peak rain. As well as modelled responses, I have also begun identifying and analysing observed hydrographs for selected study catchments to see if these show any pattern in their response to rainfall profile shapes.

How to cite: Seawell, A.: Small, flashy catchments response to variation in rainfall profile shape, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-1538, https://doi.org/10.5194/egusphere-egu24-1538, 2024.

EGU24-1918 | ECS | Posters on site | HS2.4.2

Evaporation and hydrology of the Orinoco and Amazon basins modulated by the Atlantic 

Nicolas Duque-Gardeazabal, Andrew R. Friedman, and Stefan Brönnimann

El Niño/Southern Oscillation (ENSO) strongly impacts the hydroclimate of tropical South America. However, other ocean-atmospheric oscillations in the Atlantic also have teleconnections over the continent with the most extensive tropical rainforest; these oscillations influence hydroclimate extremes (i.e. droughts and floods). Our research focuses on the physical mechanisms that link the Atlantic Sea Surface Temperature conditions with the hydrological anomalies, i.e. soil moisture, streamflow and evaporation.

This research is grounded on the consistency of a multi-evidence approach between datasets. We use independent observations of land-surface and atmospheric variables whose robustness comes from gauges, physically consistent interpolations (i.e. reanalysis), simulations or satellite-based observations. The research focuses on the satellite era (1980-) to compare several datasets. Apart from the Amazon, other important basins such as the Orinoco, Magdalena and Tocantis have received little attention; hence, we also focused on them.

The Atlantic Meridional Mode (AMM) consists of cross-equatorial Sea Level Pressure anomalies that deflect climatological winds northward or southward. Hence, the seesaw of wind anomalies produces anomalous atmospheric transport, convergence and precipitation. When dividing the analysis by independent seasons, the results show changing impacts over different subbasins of the Orinoco and Amazon. On the other hand, the Atlantic El Niño/La Niña (Atl3) weakens or strengthens the trade winds from June to August, producing moisture convergence or divergence over the Guianas and eastern Orinoco.

The SST impact on evaporation is a complex consequence of the anomalous atmospheric circulation. The cascade of abnormal atmospheric circulation modifies not just the surface water but also the radiation availability, causing hydrological anomalies. The radiation anomalies combined with the soil moisture memory control the evaporation anomalies. This dynamic also depends on the season analysed.

How to cite: Duque-Gardeazabal, N., Friedman, A. R., and Brönnimann, S.: Evaporation and hydrology of the Orinoco and Amazon basins modulated by the Atlantic, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-1918, https://doi.org/10.5194/egusphere-egu24-1918, 2024.

EGU24-2089 | ECS | Orals | HS2.4.2

Groundwater level reconstruction using long-term climate reanalysis data and deep neural networks  

Sivarama Krishna Reddy Chidepudi, Nicolas Massei, Abderrahim Jardani, and Abel Henriot

Assessing long-term changes in groundwater is crucial for understanding the impacts of climate change on aquifers and for managing water resources. However, long-term groundwater level (GWL) records are often scarce, limiting understanding of historical trends and variability. In this study, we present a deep learning approach to reconstruct GWLs up to several decades back in time using recurrent-based neural networks with wavelet pre-processing and climate reanalysis data as inputs. GWLs are reconstructed using two different reanalysis datasets with distinct spatial resolutions (ERA5: 0.25◦ x 0.25◦ & ERA20C: 1◦ x 1◦) and monthly time resolution, and the performance of the simulations was evaluated.  Long term GWL timeseries are now available for northern France, corresponding to extended versions of observational timeseries back to the early 20th century. All three types of piezometric behaviors could be reconstructed reliably and consistently capture the multidecadal variability even at coarser resolutions, which is crucial for understanding long-term hydroclimatic trends and cycles. GWLs’multidecadal variability was consistent with the Atlantic multidecadal oscillation. From a synthetic experiment involving a modified long-term observational time series, we highlighted the need for longer training datasets for some low frequency signals. Nevertheless, our study demonstrated the potential of using DL models together with reanalysis data to extend GWL observations and improve our understanding of groundwater variability and climate interactions. 

How to cite: Chidepudi, S. K. R., Massei, N., Jardani, A., and Henriot, A.: Groundwater level reconstruction using long-term climate reanalysis data and deep neural networks , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-2089, https://doi.org/10.5194/egusphere-egu24-2089, 2024.

EGU24-3925 | ECS | Orals | HS2.4.2

Multi-decadal changes in root zone water storage capacity through vegetation adaptation to hydro-climatic variability 

Siyuan Wang, Markus Hrachowitz, and Gerrit Schoups

Climate change can considerably affect catchment-scale root zone storage capacity (Sumax) which may further influence the moisture exchange between land and atmosphere, as well as stream flow and biogeochemical processes in terrestrial hydrological systems. However, direct quantification of the evolution of Sumax over multi-decadal time periods at the catchment scale has so far been rare. As a consequence, it remains unclear how climate change affects Sumax (e.g., precipitation regime, canopy water demand) and how changes in Sumax may control the partitioning of water fluxes as well as the hydrological response at catchment scale. The objectives of this study in the upper Neckar river basin in Germany are therefore to provide an analysis of muti-decadal changes in Sumax that can be observed as a result of changing climatic conditions over the past century and how this has further affected hydrological dynamics. More specifically, we test the hypotheses that (1) Sumax significantly changes over multiple decades reflecting vegetation adaptation to climate variability, (2) changes in Sumax control water availability for evapotranspiration and thus multi-decadal deviations from long-term average positions in the Budyko framework, (3) a time-dynamic implementation of Sumax affects the hydrological response, which in return can improve the performance of a hydrological model.

We found that, indeed, a hydroclimatic condition considerably changed over time in the 1953 to 2022 study period, which was reflected by related fluctuations in the values of Sumax derived directly from observed water balance data These ΔSumax values varied by up to -20% in relatively wet decades to +20% in drier decades, which was very similar to ΔSumax obtained from calibration of a hydrological model (R2=0.95, p<0.05) in individual decades. However, evaporation estimated by the hydrological model using a long-term average Sumax for the study period was almost the same as that reproduced by the model when allowing dynamically changing root-zone storage capacities over multiple decades. In addition, no significant improvement in the reproduction of the hydrological response was observed when implementing a time-variant representation of decadally varying Sumax in the model compared with the implementation of a stationary Sumax irrespective of the hydroclimatic conditions in the individual decades.

Overall, this study provides quantitative evidence that Sumax significantly changes over multiple decades reflecting vegetation adaptation to climate variability. However, these changes are not responsible for deviations from the Budyko curves in different climatic conditions, in other words, the temporal evolution of Sumax in the study region is not a major control on the partitioning of water fluxes into evapotranspiration and drainage and does have therefore no significant effects on fundamental hydrological response characteristics of the upper Neckar catchment. This suggests that model predictions of future stream flows remain rather insensitive to uncertainties introduced by the use of time-invariant long-term average values of Sumax as model parameters.

How to cite: Wang, S., Hrachowitz, M., and Schoups, G.: Multi-decadal changes in root zone water storage capacity through vegetation adaptation to hydro-climatic variability, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-3925, https://doi.org/10.5194/egusphere-egu24-3925, 2024.

EGU24-6799 | ECS | Orals | HS2.4.2

Identification of extreme climatic events using SMOS 

Nitu Ojha, Yann Kerr, and Arnaud Mialon

Soil moisture (SM) is a crucial parameter in the hydrological cycle. SM wet and dry trends help to identify extreme weather events, with a rapid increase in SM suggesting heavy rainfall or flood events and a significant or prolonged decrease in SM representing drought events. SMOS and SMAP remote sensing satellites provide surface SM data globally. The surface SM is highly variable in terms of space and time. In contrast, root zone soil moisture (RZSM) is stable and retains long-term information, making it a better indicator of prolonged drought/wet conditions. In this context, SMOS RZSM is computed from the SMOS surface SM using a simple physical model to integrate surface SM information to a root zone. The study benefits from the availability of long-term series data of the SMOS RZSM on a global scale from 2010 to 2023 (approximately 14 years). Then, the SM index is developed using long-time series data of the SMOS RZSM for a better understanding of the distribution of wet and dry SM and its link to extreme events. The study primarily focuses on Australia and Europe. The results show that the developed SMOS SM index captures heavy rainfall/flood and drought conditions. The analysis determines the occurrence of floods due to La Niña and El Niño effects over Australia and the existence of drought in Europe due to the North Atlantic oscillation. This study can help to understand the interconnected factors that influence extreme climatic conditions, ranging from natural climatic phenomena to human-induced activities.

How to cite: Ojha, N., Kerr, Y., and Mialon, A.: Identification of extreme climatic events using SMOS, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-6799, https://doi.org/10.5194/egusphere-egu24-6799, 2024.

EGU24-7568 | Posters on site | HS2.4.2

Mapping UK Drought Teleconnections from Ocean to Land 

Amulya Chevuturi, Marilena Oltmanns, Maliko Tanguy, Ben Harvey, Cecilia Svensson, and Jamie Hannaford

Given the anticipated changes in future UK drought occurrences attributable to climate change, there is an imminent requirement for a thorough understanding of the underlying influences behind UK drought events, particularly the most extreme events. In this context, our study aims to understand the North Atlantic oceanic drivers responsible for drought events in the UK, subsequently tracing the teleconnection pathways that connect these drivers to meteorological and hydrological droughts within the region. We examine the teleconnection pathways associated with drought conditions by assessing the concurrent and lagged statistical links between the UK's standardized precipitation index (SPI) and standardized streamflow index (SSI) and two distinct North Atlantic Sea surface temperature (SST) patterns, which are associated with freshening events. Our findings reveal that these North Atlantic SST patterns exert varying influences on two distinct regions of the UK (northwest and southeast), each of which have distinct hydrometeorological characteristics. The identified SST patterns are linked to the dominant modes of SST variability in the North Atlantic, thereby contributing to the predictability of drought occurrences across seasonal to multi-annual timescales, including at some very long lead times. Our research therefore has significant potential in practical applications for quantifying and managing drought risk, and for advancing drought forecasting and early warning systems through the identification of novel, skilful predictors. Ultimately, our work endeavours to contribute to the progress of sustainable water resource management amidst the escalating drought risks in the UK.

How to cite: Chevuturi, A., Oltmanns, M., Tanguy, M., Harvey, B., Svensson, C., and Hannaford, J.: Mapping UK Drought Teleconnections from Ocean to Land, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-7568, https://doi.org/10.5194/egusphere-egu24-7568, 2024.

EGU24-7930 | Posters on site | HS2.4.2

An increasing role of subsurface processes in the water circulation in the High Arctic catchment – the results from Fuglebekken, SW Spitsbergen 

Marzena Osuch, Abhishek Alphonse, Nicole Hanselmann, and Tomasz Wawrzyniak

Changes in the depth of the active layer thickness (ALT) in Arctic and permafrost regions significantly impact the transformation of rainfall into runoff. Due to climate change, permafrost thawing and ALT alterations modify how water is transported and stored within catchments, affecting surface and subsurface hydrological processes. This study investigates the associations between temporal changes in active layer thickness, hydrological model parameters, and variations in catchment responses.

The study area covers the unglaciated catchment Fuglebekken, located near the Polish Polar Station Hornsund on Spitsbergen. For hydrological modelling, the conceptual rainfall-runoff HBV model was used. Model calibration and validation were carried out on runoff data within subperiods. A moving window approach (3-week duration) using data from the summer seasons 2014-2023 was applied to derive temporal variations of parameters. Model calibration, along with an evaluation of parametric uncertainty, was performed using the Shuffled Complex Evolution Metropolis algorithm.

A comprehensive investigation of the temporal variability of HBV model parameters demonstrated consistency in the results. The smallest parametric uncertainty and the largest temporal changes were estimated for the parameter KS representing a slow runoff reservoir. Temporal variability of the KS parameter is characterized by the presence of two maxima, the first maximum at the beginning of the ablation season (due to snowmelt and ice-rich permafrost thawing) and the second maximum in September (a result of high precipitation). The temporal variability of other parameters was smaller and usually within their parametric uncertainty.

In addition, the use of the HBV model allowed for the assessment of water storage in five conceptual reservoirs characterizing catchment processes. The outcomes highlighted large changes in slow runoff reservoir, demonstrating an increasing significance of subsurface processes in the water circulation in the High Arctic catchment. 

The study was supported by the Polish National Science Centre (grant no. 2020/38/E/ST10/00139).

How to cite: Osuch, M., Alphonse, A., Hanselmann, N., and Wawrzyniak, T.: An increasing role of subsurface processes in the water circulation in the High Arctic catchment – the results from Fuglebekken, SW Spitsbergen, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-7930, https://doi.org/10.5194/egusphere-egu24-7930, 2024.

Ice-induced winter flooding, intensified by sustained low temperatures, holds the potential for severe natural disasters, but is seldom explored probabilistically considering warming climate impacts. This study established both marginal and copula-based joint probability distributions of the upstream (QH) and downstream (QL) ice-induced floods in the Lower Yellow River, a hanging river above the ground, under four parametric scenarios (constant, time as covariates, mean air temperature as covariates, and accumulated negative air temperature as covariates), to compare historical and design flood regimes using six inference methods (UNI, OR, AND, KEN, SKEN, and COND) under air temperature changes. The results show that the Lognormal and Weibull marginal distribution models with accumulated negative air temperature as covariate parameters were optimal for QH and QL, respectively and the positive dependence between QH and QL was best described by the Gumbel-Hougaard copula. Impacts of increasing air temperature on flood downtrends and yearly change-points (1990 for QH and 1985 for QL) reduced both historical QH-QL flood magnitude combinations and projected return periods, thus denoting declining flood severities over time. Due to such flood downtrends, the most probable composition (MPC) values of 100-year design floods varied from the highest (1656 m3/s for QH and 1645 m3/s for QL using the OR method) to the lowest (624 m3/s for QH and 342m3/s for QL using the SKEN method). The average decreasing rates of MPC values before and after the discerned flood change-points were 17.4% for QH and 39.6% for QL. When conditioned on the occurrence of upstream QH having flood magnitudes less than 100-year design floods, large floods downstream exceeding a 50-year return period were inferred as improbable. This study can provide a paradigm of flood projections to meet diverse flood control objectives under changing climate.

How to cite: Li, L. and Xu, C.-Y.: Probabilistic projections of winter floods considering cumulative effect of air temperature changes, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-8371, https://doi.org/10.5194/egusphere-egu24-8371, 2024.

EGU24-10340 | ECS | Orals | HS2.4.2

The Impact of GRACE Data Assimilation on Water Storage Dynamics in CLM3.5 and CLM5 

Yorck Ewerdwalbesloh, Anne Springer, and Jürgen Kusche

The GRACE (Gravity Recovery And Climate Experiment) satellite mission as well as its successor GRACE Follow-On have monitored global and regional variability of total water storage (TWS) for the past two decades. Assimilating observations from these missions into hydrological models helps to improve modeled water storages and fluxes, to overcome deficits arising from simplifications or processes that are not considered in the model (e.g. unmodeled anthropogenic impacts), and to disaggregate GRACE observations temporally and spatially. Determining the optimal approach for assimilating these observations into hydrological models remains an ongoing area of research. The choice often depends on specific applications and the characteristics of the model itself.

In this study, we analyze the water storage dynamics of two versions of the Community Land Model (CLM) - versions 3.5 and 5 - within a GRACE data assimilation framework over a 12.5 km grid covering Europe. The analysis focuses on assessing (i) the skill of both models without data assimilation, (ii) the impact of GRACE data assimilation on the model performance and (iii) the distribution of assimilation increments to different storage compartments. We evaluate water storages and fluxes simulated by both models against independent observations such as discharge from river gauges and satellite derived soil moisture. The results offer valuable insights into the impact of advancements made in biophysical processes and the representation of the carbon cycle in CLM5. Furthermore, we discuss the effectiveness of GRACE data assimilation and its influence on the behavior of CLM3.5 and CLM5, analyzing whether the assimilation helps to address differences between the two model versions - particularly considering the advancements in CLM5 - which would underline the ability of GRACE data assimilation in mitigating model deficits.

How to cite: Ewerdwalbesloh, Y., Springer, A., and Kusche, J.: The Impact of GRACE Data Assimilation on Water Storage Dynamics in CLM3.5 and CLM5, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-10340, https://doi.org/10.5194/egusphere-egu24-10340, 2024.

EGU24-10459 | ECS | Posters on site | HS2.4.2

Past, Present, and Future Impacts of Climate Change and Variability on Flood Hazards in Sub-Saharan Africa 

Job Ekolu, Bastien Dieppois, Yves Tramblay, Jonathan Eden, Moussa Sidibe, Gabriele Villarini, Simon Moulds, Louise Slater, Stefania Grimaldi, Peter Salamon, Pierre Camberlin, Benjamin Pohl, Gil Mahé, and Marco van de Wiel

Sub-Saharan Africa (SSA) is strongly affected by flood hazards, which endanger human lives and disrupt economic stability. It is therefore critical to further understand the potential impact of climate change and variability on historical and future flood hazards in SSA. To do so, we first reconstructed a complete 65-yearlong daily streamflow, presenting over 600 stations distributed throughout SSA. Using this new dataset, we found that historical trends in flood frequency, duration, and intensity were strongly modulated by decadal to multidecadal variability. We then identified internal modes of climate variability in the Pacific and Indian Oceans as primary drivers of decadal variations in flood occurrence in southern and eastern Africa. Meanwhile, decadal sea-surface temperature anomalies (SSTa) over the eastern Mediterranean region and the North Atlantic were primarily driving decadal trends in floods occurring over western and central Africa. Using 12 climate model large ensembles from the Coupled Model Intercomparison Project Phases 5 and 6 (CMIP5 and 6), we also found such decadal variations in SSTa in the Mediterranean Atlantic, Pacific, and Indian oceans could modulate the occurrence of flood hazards by up to 50% in SSA during the 21st century. Finally, combining bias-corrected CMIP6 data and the open-source hydrological model LISFLOOD, we examine the potential impact of climate change on future trends affecting the intensity, frequency, and duration of floods in West Africa. This study therefore enabled us to compare for the first time the relative importance of climate change and climate variability on future changes affecting flood hazards in SSA.

How to cite: Ekolu, J., Dieppois, B., Tramblay, Y., Eden, J., Sidibe, M., Villarini, G., Moulds, S., Slater, L., Grimaldi, S., Salamon, P., Camberlin, P., Pohl, B., Mahé, G., and van de Wiel, M.: Past, Present, and Future Impacts of Climate Change and Variability on Flood Hazards in Sub-Saharan Africa, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-10459, https://doi.org/10.5194/egusphere-egu24-10459, 2024.

EGU24-10963 | ECS | Orals | HS2.4.2

Future changes in tropical vertical velocity variance and precipitation variability 

Zhenghe Xuan, Clarissa Kroll, and Robert Jnglin Wills

Understanding precipitation variability on subseasonal-to-decadal timescales is important because of its influence on regional water resources and hydrological extremes. The response of precipitation to global warming can be understood in terms of a superposition of thermodynamic and dynamic effects. The former has been studied on a range of timescales, including ENSO variability and precipitation extremes, and is strongly constrained by Clausius-Clapeyron scaling. Changes in dynamics, however, modulate the overall change significantly and represent an important source of uncertainty in projected changes of hydrological cycle variability.

Here, we investigate changes in the variance of vertical velocity in the tropics based on monthly outputs from the Community Earth System Model 2 Large Ensemble. We find a robust decrease in the tropical vertical velocity variance under the SSP3-7.0 scenario, even in periods where the underlying ENSO-related SST variance increases. This reduction in vertical velocity variance can be explained by the deepening of the troposphere, which increases the gross moist stability and thus the energetic demands for vertical motion. Finally, we investigate the influence of reduced vertical velocity variance on precipitation probability distribution and intensity.

How to cite: Xuan, Z., Kroll, C., and Jnglin Wills, R.: Future changes in tropical vertical velocity variance and precipitation variability, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-10963, https://doi.org/10.5194/egusphere-egu24-10963, 2024.

EGU24-11244 | ECS | Orals | HS2.4.2

Inter-annual and long-term variability in streamflow elasticity to precipitation reveal bias in estimates of hydrological sensitivity 

Bailey Anderson, Louise Slater, Jessica Rapson, Manuela Brunner, Simon Dadson, Jiabo Yin, and Marcus Buechel

Empirically derived sensitivities of streamflow to precipitation are often assumed to be temporally unchanging. This assumption may be unrealistic because changes in climate and storage are known to alter this relationship. We present a non-stationary regional regression approach which is functionally similar to typical elasticity estimation approaches. This is applied to 2967 catchments in the United States to estimate variability in interannual, and trends in long-term, streamflow elasticity to precipitation over a 39-year period. We show that interannual elasticity is highly variable in water-limited catchments, indicating that these are especially sensitive to year-to-year climate variability, as compared to other regions. Interannual elasticity is more often correlated with the one-year lagged standardized precipitation index than with temperature or in-phase standardized precipitation index, suggesting that antecedent soil moisture, groundwater storage, and precipitation seasonality influence streamflow sensitivity. Finally, statistically significant long-term trends in elasticity exist in some regions, but trend magnitude is generally small. These findings suggest that an assumption of stationarity in long-term average elasticity may still be appropriate at the regional scale, however, year-to-year variation in streamflow responsiveness to precipitation is often substantial.    

How to cite: Anderson, B., Slater, L., Rapson, J., Brunner, M., Dadson, S., Yin, J., and Buechel, M.: Inter-annual and long-term variability in streamflow elasticity to precipitation reveal bias in estimates of hydrological sensitivity, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-11244, https://doi.org/10.5194/egusphere-egu24-11244, 2024.

EGU24-12189 | ECS | Orals | HS2.4.2

Surface and groundwater drought impact on natural vegetation growth and drought recovery. 

Jorge Vega Briones, Steven De Jong, Wiebe Nijland, and Niko Wanders

Droughts' persistent impact and growing use of surface water and groundwater will likely exacerbate hydrological droughts. Variations in precipitation patterns worsen the effects in particular catchment regions as a result to climate change. The end result is less groundwater recharge and multi-year droughts that impact vegetation and rivers.

An essential factor to better understand the recovery in catchments affected by drought is to understand the interaction between water availability and vegetation dynamics. At the same time, the vegetation recovery in terms of growth and productivity can also be assessed with this framework. In this study, we focus on natural catchments of central Chile which have experienced drought and multi-year drought periods with severe impacts on surface water and groundwater.

We collected 250 tree ring samples of 5 species that are susceptible to droughts in central Chile in natural catchments, and used CAMELS-CL for statistical analysis. Cross correlation analysis between surface, groundwater and vegetation dynamics was performed for each catchment to quantify the interaction between these factors. To further determine the influence of drought events on vegetation, the compound NDVI correlation and SPEI at a catchment level were used. Finally, the drought termination framework was applied to understand the recovery response of surface, groundwater and vegetation.

Our analysis identifies the typical time lag between droughts in surface water, groundwater and  their impact on vegetation growth. This is done on an annual time scale as we are looking at multi-year events. We find that the typical response time varies throughout the country, depending on the local natural water availability. These findings highlight that the multi-year drought impact on vegetation and its recovery is not uniform and should be better understand in light of climate change and the global increase in multi-year drought events.

How to cite: Vega Briones, J., De Jong, S., Nijland, W., and Wanders, N.: Surface and groundwater drought impact on natural vegetation growth and drought recovery., EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-12189, https://doi.org/10.5194/egusphere-egu24-12189, 2024.

EGU24-14039 | ECS | Orals | HS2.4.2

Historical changes in the seasonality and timing of extreme precipitation events 

Gaby Gründemann, Enrico Zorzetto, Nick van de Giesen, and Ruud van der Ent
Global warming alters the hydrological cycle, influencing the seasonality and timing of extreme precipitation events. Understanding historical changes in the occurrence of extreme precipitation is important for assessing their effects. This study examines the timing and seasonality of extreme precipitation using 63 years of ERA5 data. By using relative entropy, we can assess changes in extreme daily precipitation occurrence on the global domain. Findings show notable regional differences. In the second half of the 20th century, Africa and Asia had high clustering of extreme precipitation events. Over 60 years, clustering intensified in Africa but became more spread out in Asia. North America and Australia, initially with less clustering, saw slight increases. Extreme precipitation events in extra-tropical land regions mainly occurred in summer, with minor shifts in timing. These results are important for improving risk management for hazards like flash floods and landslides and highlight the need for region-specific strategies in adapting to these changes.

How to cite: Gründemann, G., Zorzetto, E., van de Giesen, N., and van der Ent, R.: Historical changes in the seasonality and timing of extreme precipitation events, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-14039, https://doi.org/10.5194/egusphere-egu24-14039, 2024.

EGU24-14414 | ECS | Orals | HS2.4.2

Navigating Hydroclimatic Extremes: Understanding the Interplay of Climate Change and Variability 

Achala Singh, Priyank J. Sharma, and Ramesh S. V. Teegavarapu

Increased frequency of extreme and rare hydroclimatic events leading to substantial disruptions in hydrological patterns worldwide can be attributed to climate variability and change. The stationarity assumption routinely used for hydrologic design and water resources planning is no longer valid under an evolving climate. Conventional notions about hydrological stability are now challenged, considering the intricate connection between climate fluctuations and the rising prevalence of extreme weather events. High spatial and temporal variability of extreme events in tropical and semi-arid climatic regions pose challenges in assessing non-stationarity considering available data and understanding processing contributing to short and long-term changes in regional climate. This study proposes and evaluates a novel approach using nonparametric statistical tests to explore the presence of non-stationarity in hydroclimatic extremes for a tropical river basin. Further, changes in the return levels of hydroclimatic extremes under stationary and non-stationary conditions will be carried out using statistical modelling approaches. Using the proposed approach, the identification of pivotal climatic drivers, such as oceanic oscillations and atmospheric circulation patterns, and their roles in influencing hydroclimatic extremes is possible. Long-term observational data is assessed in this work to discern trends and patterns in frequency, intensity, and spatial distribution of extremes and their links to climate change and variability. The impact of shifting precipitation patterns, temperature extremes, and seasonal variations is evaluated. This research study helps to investigate the implications of climate-induced hydroclimatic extremes under diverse geographical and climatic settings. This research can help understand the impact of climate change in river basins driven by the shifts in precipitation, temperature patterns, and extremes and address water availability and management issues.

Keywords: Non-stationarity, Hydroclimatic extremes, Climatic drivers, Statistical modelling, Tropical River basin.

How to cite: Singh, A., Sharma, P. J., and Teegavarapu, R. S. V.: Navigating Hydroclimatic Extremes: Understanding the Interplay of Climate Change and Variability, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-14414, https://doi.org/10.5194/egusphere-egu24-14414, 2024.

EGU24-15120 | ECS | Orals | HS2.4.2

Groundwater storage trends in northern Italy as observed by GRACE, well measurements, and vertical land motion 

Grace Carlson, Christian Massari, Marco Rotiroti, Elisabetta Preziosi, Tullia Bonomi, Andrew Wilder, Susanna Werth, Destinee Whitaker, Tianxin Wang, Marianne Cowherd, and Manuela Girotto

Geodetic observations of the Earth’s gravitational and deformational response to changes in terrestrial water storage (∆TWS) have been essential measurements to identify regions experiencing long-term wetting and drying driven by a combination of climate and anthropogenic forces. The northern Italian Plains, home to a third of the country’s population and contributing more than half of the agricultural output, have experienced a dryer-than-normal two decades. Here, we investigate what impact these dry conditions have on the long-term groundwater storage (GWS) using observations of change in terrestrial water storage (∆TWS) from the Gravity Recovery and Climate Experiment (GRACE) and the second-generation follow-on (GRACE-FO) missions and in-situ groundwater level time series from 820 wells over the period of 2003-2022. We use a wavelet time-frequency analysis to deconstruct each signal into seasonal and long-term components and identify multi-year dry and wet epochs. We find two long periods of declining groundwater storage (2003-2007, 2015-2022), two short periods of groundwater recovery (2008-2009, 2012-2014), and one period of near-zero ∆GWS (2010-2011). We find a net volume loss of 12.0 km3 from 2003-2022. Further, we validate these ∆GWS trends and total volume loss estimates using a combination of in-situ groundwater level variations and vertical land motion observed at nearly 500 Global Navigation Satellite System (GNSS) stations. These stations show poroelastic deformation over aquifers related to groundwater storage changes and elastic loading deformation that is highly correlated with predicted elastic loading displacements from GRACE(-FO) ∆TWS outside of aquifer areas. To calculate groundwater storage from groundwater level, we estimate spatially- and depth-variable aquifer storage coefficients using a combination of lithologic information and co-located well and GNSS observations. By analyzing all three datasets in combination we can evaluate the impacts of multi-year dry- and wet- periods on groundwater resources, providing essential contextual information for future water management.

How to cite: Carlson, G., Massari, C., Rotiroti, M., Preziosi, E., Bonomi, T., Wilder, A., Werth, S., Whitaker, D., Wang, T., Cowherd, M., and Girotto, M.: Groundwater storage trends in northern Italy as observed by GRACE, well measurements, and vertical land motion, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-15120, https://doi.org/10.5194/egusphere-egu24-15120, 2024.

EGU24-15252 | ECS | Orals | HS2.4.2

Spatiotemporal Variability in Hydrological Drought Recovery Time Estimations from GRACE and GRACE-FO Data  

Çağatay Çakan, M. Tuğrul Yılmaz, Henryk Dobslaw, Fatih Evrendilek, Christoph Förste, E. Sinem Ince, and Ali L. Yağcı

This study aimed to explore the global spatiotemporal variability in hydrological drought recovery time (DRT) estimated using terrestrial water storage (TWS) and station-based precipitation data. TWS data were gathered from the Gravity Recovery and Climate Experiment (GRACE) between April 2002 and June 2017 and GRACE Follow-On (GRACE-FO) between June 2018 and September 2023. The GRACE and GRACE-FO mascon (RL06) solution were used. Precipitation data were obtained from the Global Precipitation Climatology Project (GPCP) monthly analysis product. DRT was derived from the following two approaches: (1) TWS data via storage deficit and (2) TWS and precipitation data via absolute required precipitation. Storage deficit was computed as the negative deviation of detrended TWS from climatological values. Absolute required precipitation to fill the storage deficit was estimated from the linear relationship between the cumulative detrended smoothed precipitation anomalies (cdPA) and detrended smoothed TWS anomalies (dTWSA). The end of hydrological drought was assumed as when TWS deviation turned positive for the first methodology and as when observed precipitation exceeded absolute required precipitation for the second one. Mean DRT values across continents were obtained for both the GRACE and GRACE-FO periods, and the temporal variability between these periods was explored across different continents. On average, DRT estimate was 29% higher during the GRACE period (11.2 months) than during the GRACE-FO period (8.6 months). The TWS-based method (11.5 months) yielded 38% higher DRT than did the TWS- and precipitation-based one (8.3 months). Overall, Australia exhibited the highest DRT estimate (averaging 11.3 months) among all continents for both methods, whereas Europe showed the lowest one (averaging 8.6 months), with a global average of 9.9 months. Analysis of the temporal consistency between DRT estimates from both methods revealed that 28% of estimates aligned during the GRACE period, increasing to 49% during the GRACE-FO period. In particular, the highest consistency (61%) was observed over Africa during GRACE-FO period, contrasting with the lowest consistency (17%) over Australia during the GRACE period. Overall, the consistency between the DRT estimates from the two methods increased from the GRACE period to the GRACE-FO period across all the continents by 18% to 40%, except for Europe, where consistency dropped by 3%. These findings provide insights not only into the potential of TWS data in globally estimating DRT with significant consistency but also into understanding the dynamics of global hydrological droughts, thus proving beneficial in devising management strategies for water resources.

How to cite: Çakan, Ç., Yılmaz, M. T., Dobslaw, H., Evrendilek, F., Förste, C., Ince, E. S., and Yağcı, A. L.: Spatiotemporal Variability in Hydrological Drought Recovery Time Estimations from GRACE and GRACE-FO Data , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-15252, https://doi.org/10.5194/egusphere-egu24-15252, 2024.

EGU24-19475 | ECS | Orals | HS2.4.2

Long-term changes in water resources: the challenge of disentangling water management, climate change, and natural variability 

Vincent Humphrey, Marius Egli, Johanna Wittholm, Laura Jensen, Sebastian Sippel, Annette Eicker, Gionata Ghiggi, and Reto Knutti

Every year, natural climate variability leads to droughts and floods which have significant impacts for ecosystems and societies. Water reservoirs like soil moisture, lakes, and groundwater act as natural buffers and balance these fluctuations by providing water supply during dry conditions and by storing water surplus after rain and snow events. Such natural fluctuations unfold over time scales that can reach several decades, making it challenging to assess the extent to which trends in water reservoirs observed over the recent past are caused by anthropogenic modifications. Such modifications can themselves be further partitioned into different terms. For instance, one can contrast the contribution of regional land and water management on the one hand, and the contribution of climate change on the other. Another frequent framework is to causally relate changes in water storage to individual changes in precipitation, evapotranspiration, and runoff.

In this contribution, we review the strengths and weaknesses of recent approaches used to causally attribute observed as well as projected changes in water availability. Ensembles of model simulations and factorial experiments typically represent a powerful way of assessing individual responses to drivers and developing a plausible and mechanistic understanding. However, contradictions also quickly emerge between global hydrological model simulations, which typically represent water reservoirs and water management more thoroughly, and Earth system (climate) model simulations, which include biogeochemical effects, like CO2 fertilization, that are typically neglected by hydrological models. We will show that these two incomplete modeling worlds can be reconciled with large-scale satellite observations in only a few regions, while very large uncertainties remain in other parts of the world and in particular over tropical areas.

How to cite: Humphrey, V., Egli, M., Wittholm, J., Jensen, L., Sippel, S., Eicker, A., Ghiggi, G., and Knutti, R.: Long-term changes in water resources: the challenge of disentangling water management, climate change, and natural variability, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-19475, https://doi.org/10.5194/egusphere-egu24-19475, 2024.

Several continental regions on Earth are getting wetter, while others are drying out not only in terms of precipitation but also measured by the increase or decrease in surface water, water stored in the soils, the plant root zone, and in groundwater. Drying and wetting as seen in terrestrial, space-geodetic and remote sensing data are generally ascribed to combined effects of global warming due to greenhouse gas forcing, natural variability, and anthropogenic modification of the water cycle. Existing climate models that account for these effects fail to explain observed patterns of hydrological change sufficiently. Contrary to common beliefs, observations also do not support a simple dry-gets-dryer and wet-gets-wetter logic. Instead, the observed trends, e.g. in precipitation, soil moisture, water storage, or flood discharge, differ considerably from a simplified logic.
The CRC 1502 DETECT, a collaborative research centre of the Universities of Bonn and Göttingen, the Geomar, the Research Centre Jülich and the German National Meteorological Service DWD, has been established by the German Research Foundation DFG with the objective of closing this gap of understanding. To better comprehend the origin of these patterns, DETECT  is developing a regional coupled modeling framework further that explains past observations as realistically as possible, accounts for potential drivers of change that may have been understudied in the past, and that can predict future changes. Our modelling framework is based on the TerrSysMP platform (i.e. the coupling of ICON/COSMO, CLM and ParFlow with/without data assimilation) and it ingests various conventional and new satellite and terrestrial data sets.
By applying this modelling framework to both historical and IPCC-type simulations, DETECT will test the hypothesis that humans – through several decades of land use change, and intensified water use and management – have caused persistent modifications in the coupled land and atmospheric water and energy cycles. It is hypothesized that (1) these human-induced modifications contribute considerably, compared to greenhouse gas (GHG) forcing and natural variability, to the observed trends in water storage at the regional scale, (2) land management and land and water use changes have modified the regional atmospheric circulation and related water transports and (3) these changes in the spatial patterns of the water balance have created and magnified imbalances that lead to excessive drying or wetting in more remote regions.
We test this hypothesis for the Euro-CORDEX region. In later phases, we evaluate the transferability of our approach for regions with different environmental conditions. We will develop evidence-based sustainability criteria for land and water use activities. The presentation will provide an overview on the central hypotheses and objectives of our research programme, the study logic and common approach, as well as anticipated results and contributions to the community. After two years, we highlight some first  findings.

How to cite: Siegismund, F. and Kusche, J.: Collaborative Research Centre 1502 DETECT: 'Regional Climate Change: Disentangling the Role of Land Use and Water Management', EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-20232, https://doi.org/10.5194/egusphere-egu24-20232, 2024.

EGU24-20247 | Posters on site | HS2.4.2

Towards a coupled km-scale Atmosphere-Land Reanalysis for Europe 

Bernd Schalge, Jane Roque Mamani, Olaf Stein, Stefan Poll, Klaus Görgen, Jan Keller, and Arianna Valmassoi

Modelling studies in hydrology depend on a good representation of forcing data, in particular precipitation, for a good process representation, especially at the catchment  or sub-catchment scale. Forcing data is often provided through reanalysis, that use observations to obtain model states with the smallest possible errors and biases. Here, we present a prototype convection-permitting reanalysis system using a coupled atmosphere-land model system utilizing ICON-eCLM for the EURO-CORDEX domain at a resolution of 3km. Due to the high resolution it is expected that in particular precipitation will be better represented than in existing reanalyses, leading to more realistic forcing data. We analyzed precipitation and other near-surface observables from preliminary model runs and evaluated them in comparison to other widely used reanalysis products such as ERA-5 as well as to output of an ICON standalone simulation to assess potential improvements of the new reanalysis. We show potential use cases of the new reanalysis and discuss limitations of this dataset, which are related to the currently short available time series.

How to cite: Schalge, B., Mamani, J. R., Stein, O., Poll, S., Görgen, K., Keller, J., and Valmassoi, A.: Towards a coupled km-scale Atmosphere-Land Reanalysis for Europe, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-20247, https://doi.org/10.5194/egusphere-egu24-20247, 2024.

EGU24-21583 | ECS | Posters on site | HS2.4.2 | Highlight

Influence of ENSO on extreme precipitation and peak river flow in the US 

Natalie Lord, Simbi Hatchard, Jorge Sebastian Moraga, Nans Addor, and Pete Uhe

Flooding in the US results in billions of dollars of losses every year. This is projected to increase further in many regions as the climate warms, due to a combination of more frequent and severe extreme rainfall events, with resulting impacts on flooding, and increased exposure as the population increases and development in flood-prone areas continues. Superimposed on this warming signal are the impacts of different internal cycles operating within the climate system on various timescales, such as El Niño Southern Oscillation (ENSO). These cycles may act to either exacerbate or reduce the severity of extreme precipitation and flooding, and on interannual timescales, ENSO is a dominant mode of variability. A better understanding of the influence of ENSO and other modes of variability on extreme precipitation and flooding, including under climate change, is important for a number of applications. These include climate change impact assessments, policy and decision-making, early warning systems for flooding and disaster response planning, and climate-related risk planning in the (re)insurance sector.

Here, we investigate the influence of ENSO on extreme precipitation and peak river flow in the US, under both historical and future climate conditions. For the historical period, we calculate annual maximum (AMAX) daily precipitation and flow, from the Multi-Source Weighted-Ensemble Precipitation (MSWEP) precipitation and USGS river gauge datasets, respectively. To assess whether positive, neutral, or negative phases of ENSO have a significant impact on extreme precipitation and flood magnitude, we calculate the correlation between AMAX and different ENSO phases. We use a number of different ENSO indices, including the Oceanic Niño Index (ONI) used operationally by NOAA, in order to test the sensitivity of these relationships to the method used to characterise ENSO.

We also assess the impacts of ENSO on projected future changes in AMAX precipitation, using climate model data from the Community Earth System Model Large Ensemble Project Phase 2 (CESM2-LENS). For this, we calculate the relative change in AMAX daily precipitation for positive, neutral, and negative phases of ENSO, to determine how projected extreme precipitation changes differ between the phases, and how this varies spatially across the US.

How to cite: Lord, N., Hatchard, S., Moraga, J. S., Addor, N., and Uhe, P.: Influence of ENSO on extreme precipitation and peak river flow in the US, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-21583, https://doi.org/10.5194/egusphere-egu24-21583, 2024.

EGU24-466 | ECS | Orals | HS8.2.14

Combination of Multiple Investigation Methods to Reveal the Recharge Area of a Karst Aquifer 

Suleyman Selim Calli, Mehmet Celik, and Zehra Semra Karakas

Karst aquifers are heterogeneous groundwater systems having both diffuse and concentrated recharge mechanisms. Since their complex recharge, storage, and discharge characteristics, the groundwater divide is generally different from the topographical catchment borders. As a result, karst hydrogeologists are using different methods to obtain more certain recharge areas. Tracer tests are very important and preferred tools to obtain the groundwater recharge areas. An ideal tracer must be detectable in very low concentrations, conservative along the pathways, and cost-effective. In this manner, mineralogical analysis of the suspended particles would be a very good alternative to the isotopic, biochemical, and dye tracers due to the easy collection and cost-efficient analysis methods. In the present study, we collected rock samples from approximately 10 locations surrounding the potential recharge area of the karst aquifer covering all lithological units surrounding the study area. Then, we collected sediment samples at the discharge outlet of the karst spring and suspended particles by filtering the water samples. We analyzed both the sediments and rock samples by the petrographic thin-sections, XRD whole rock, and XRD-clay fraction analysis to compare the minerals between the rock and sediment samples. We obtained Eocene-aged Planktonic Foraminiferal fossils in the spring sediments (in the thin sections), which perfectly fit the Eocene-aged limestone formation in the study area. By overlapping the lithological outcrop of the formation with the isotope-derived recharge elevation, we obtained the locations of two major dolines in the study area. As the final step, we validated our results by conducting dye-tracer tests from these points, and we recovered the tracer dye from the karst springs.

How to cite: Calli, S. S., Celik, M., and Karakas, Z. S.: Combination of Multiple Investigation Methods to Reveal the Recharge Area of a Karst Aquifer, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-466, https://doi.org/10.5194/egusphere-egu24-466, 2024.

EGU24-602 | ECS | Orals | HS8.2.14

Using contrasting tracers to characterize groundwater dynamics under a prolonged drought in the lowland catchments in the North German Plain 

Zhengtao Ying, Doerthe Tetzlaff, Jonas Freymueller, Jean-Christophe Comte, Tobias Goldhammer, Axel Schmidt, and Chris Soulsby

Groundwater, as the key strategic reserve in times of drought, is sensitive to climate change, especially unconfined, shallow aquifers. Frequent and prolonged drought provides an urgent impetus to improve understanding of groundwater dynamics and its residence times in drought-sensitive areas where water and food security are threatened. The Demnitzer Mill Creek catchment is a long-term environmental observatory typical lowland of North German Plain where streams are dominated by groundwater, however its groundwater recharge and dynamics remain poorly constrained. We applied water table observations, isotopic (δ18O, δ2H, 3H), hydrogeochemical, and geophysical investigations to characterize the spatial and temporal patterns of groundwater recharge in a shallow, unconfined aquifer system. Long-term groundwater levels showed a declining trend since 2011, which accelerated after 2018 resulting in increasingly intermittent seasonal streamflow. Geophysical surveys and groundwater monitoring indicated that shallow water tables (typically <3 m deep) in low to moderate permeability surficial deposits are generally recharged during winter, leading to higher groundwater – surface water connectivity in riparian alluvial aquifers, which is the first order control on streamflow generation. This was supported by similar geochemical characteristics of groundwater and streamflow. Water stable isotopes indicated a high damping in groundwater with a bias towards winter precipitation and direct recharge. Although 3H dating showed that the age of shallow groundwater was young (~5 years) and generally similar to streamflow, estimates had high uncertainty and some deeper groundwater was free of 3H. Such multiple approaches help understand changes in groundwater recharge and dynamics during droughts and contribute to the development of sustainable land and water management strategies for groundwater systems that are sensitive to climate change.

How to cite: Ying, Z., Tetzlaff, D., Freymueller, J., Comte, J.-C., Goldhammer, T., Schmidt, A., and Soulsby, C.: Using contrasting tracers to characterize groundwater dynamics under a prolonged drought in the lowland catchments in the North German Plain, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-602, https://doi.org/10.5194/egusphere-egu24-602, 2024.

EGU24-1565 | ECS | Posters on site | HS8.2.14

Advancing in the estimation of effective recharge and its propagation in karst aquifers by combining soil moisture observations and the natural responses of springs. An example from Southern Spain. 

Alejandro Carrasco Martín, Matías Mudarra Martínez, Beatriz De la Torre Martínez, Andreas Hartmann, and Bartolomé Andreo Navarro

Improving our comprehension of infiltration processes in karst systems is crucial for a better adaptation to the global change regarding water resources availability and management. In this work, the effective recharge under different meteorological conditions and its transfer along the vertically distributed compartments of a geologically complex karst aquifer in southern Spain have been evaluated. Continuous records of soil moisture and temperature values (at 5 and 10 cm depth and the soil-rock transition -average depth of 28 cm-) have been combined with hourly hydrodynamic and hydrothermal responses recorded at two springs with a marked influence of the unsaturated zone (UZ) and the saturated zone (SZ), respectively.

Most rainfalls generate soil moisture signal in the shallowest probes. However, a mean increase of soil water content of 10.5% in summer (from background values of 2.5%) and 6.1% in autumn-winter (from 9.6%) at the soil-rock interface were needed to produce hydrodynamic responses in the system: first in the spring related to the UZ, with a time delay of 4-9 hours after moisture peaks, and then (14-18 hours) in the spring draining the SZ, but only during autumn-winter recharge events. In addition, recharge caused decreases (up to 0.9°C) in the temperature of the water drained by the first spring, while lagged rises (up to 0.6°C) occurred in the second outlet.

Transmission of the input signal would be favoured by stronger karstification, but the presence of inter-bedded detrital formations in the lithological sequence of the aquifer (partially confined in the SE border) filter and buffer groundwater flows before being drained by the spring related to the SZ. These findings will help to assess thresholds for effective infiltration and to predict groundwater recharge in karst aquifers under different climate change scenarios.

How to cite: Carrasco Martín, A., Mudarra Martínez, M., De la Torre Martínez, B., Hartmann, A., and Andreo Navarro, B.: Advancing in the estimation of effective recharge and its propagation in karst aquifers by combining soil moisture observations and the natural responses of springs. An example from Southern Spain., EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-1565, https://doi.org/10.5194/egusphere-egu24-1565, 2024.

In the epikarst zone of carbonate areas, numerous fractures have different sizes, shapes, and filling materials. Determining the fractures' horizontal hydraulic conductivity (Kh) simply using slug tests is challenging due to variable flow states (e.g., steady and unsteady). In this study, we characterized fracture features of apertures and soil fillings in terms of 260 fractures of 25 borehole logs at five sites in a karst area of southwest China. The Bouwer and Rice (B & R) solution and a numerical model were used to determine Kh based on the best fitting of observed water head in 105 slug tests. The results comparatively show that Kh from the B & R solution is significantly underestimated. For numerical modeling, the non-linear flow expressed by the Dupuit and Forchheimer equation can improve the water head fitting when the Reynolds number (Re) > 17.27. The optimized Kh ranges 0.014 – 2673 m/d. The mean value of Kh is about 100 times the median value, suggesting that epikarst flow might be controlled by a limited number of larger fractures. Expectedly, Kh exponentially increases with d, but three is a turning point for the fracture aperture d around 10 mm, Kh abruptly decreases due to soil filling. The hydraulic permeability in the naturally full-filling fractures resembles the soil matrix. In contrast, the partial-filling fractures can create preferential pathway with a high Kh around the soil-rock interfaces, allowing preferential flow in fractures. These results fundamentally improve our understanding of water infiltration, retention, and availability for plant uses. 

How to cite: Liu, X.: Estimating fracture characteristics and hydraulic conductivity from slug tests in epikarst of southwest China, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-2436, https://doi.org/10.5194/egusphere-egu24-2436, 2024.

EGU24-2828 | ECS | Orals | HS8.2.14

An ancient river disappears in a Mediterranean karst land: the old history of Cana River (Apulia, Southern Italy)  

Vito Cofano, Umberto Samuele D'Ettorre, Isabella Serena Liso, Domenico Capolongo, and Mario Parise

Apulia is one of the most interesting karst lands in the Mediterranean area, hosting a variety of distinctive surficial and underground landforms. Among these, polje, a wide and flat depression of tectono-karstic origin, represents one of the most typical epigean landforms in karst. The “Canale di Pirro” polje, located in the central part of Apulia (SE Italy), is the largest in the region (Pisano et al., 2020), bounded on both sides by tectonically-controlled ridges, with an overall length of some 12km and a remarkable underground system of caves, among which there is the deepest of Apulia, where the water table is reached at -264 m from the ground (Parise & Benedetto, 2018). As a karst land, within the polje the water rapidly infiltrates into the ground, making difficult its accumulation at the surface, with the exception of the period of heavy rainfall, when wide sectors of Canale di Pirro become temporary lakes which require several hours to days to be absorbed underground. In ancient documents and maps, with particular regard to historical cartography, the Canale di Pirro polje was drawn as being crossed by a long river, nowadays missing, called Cana (from this river, it seems that the same toponym of the polje took its name). The first written testimonies concern in particular a parchment dating back from the twelfth century; the more recent document we found, still showing the presence of the river, instead, is an ancient map of the nineteenth century. Considering the time span in which Cana River is still represented in historical writings and maps, it is possible to identify its existence between 1195 and 1840, and to hypothesize a presumed coincidance with the Little Ice Age, a climate interval characterized by a long cooling period, especially in the northern hemisphere. In this work, we present a series of historical documents about the existence of the Cana River, collected through literature research, in order to evaluate all the possible causes that led to the river disappearance over the centuries.

References

Parise M. & Benedetto L. (2018). Surface landforms and speleological investigation for a better understanding of karst hydrogeological processes: a history of research in southeastern Italy. In: Parise M., Gabrovsek F., Kaufmann G. & Ravbar N. (Eds.), Advances in Karst Research: Theory, Fieldwork and Applications. Geological Society, London, Special Publications, 466, p. 137-153, https://doi.org/10.1144/SP466.25.

Pisano, L., Zumpano, V., Liso, I. S., & Parise, M. (2020). Geomorphological and structural characterization of the ‘Canale di Pirro’ polje, Apulia (Southern Italy). Journal of Maps16(2), 479-487, https://doi.org/10.1080/17445647.2020.1778550.

How to cite: Cofano, V., D'Ettorre, U. S., Liso, I. S., Capolongo, D., and Parise, M.: An ancient river disappears in a Mediterranean karst land: the old history of Cana River (Apulia, Southern Italy) , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-2828, https://doi.org/10.5194/egusphere-egu24-2828, 2024.

EGU24-3029 | ECS | Orals | HS8.2.14

Application of anomalous transport modeling for karst aquifer discharge response to rainfall 

Dan Elhanati, Simon Frank, Nadine Goeppert, and Brian Berkowitz

Discharge in many karst aquifers exhibits distinctive long tails during recession that follow recharge events, a phenomenon often associated with the intricate flow paths that develop due to the underground structure of karst systems. This complexity poses a unique task from the perspective of modeling the flow and discharge patterns. In this study, we propose a novel approach to address long tail discharge during base-flow conditions, by adapting the continuous time random walk (CTRW) framework, known as a robust tool for modeling the long-tailed behavior observed in breakthrough curves of chemical species during transport, under diverse flow conditions. By establishing a theoretical analogy between partially saturated karst flow and chemical transport, we develop and implement a particle tracking (CTRW-PT) model that provides robust fits of three years of data from the Disnergschroef high alpine study site in the Austrian Alps, underscoring the predominance of slow diffusive flow over the rapid conduit flow. The agreement between measured and simulated data not only validates the proposed analogy between partially saturated karst flow and chemical transport but also highlights the utility of the CTRW-PT model, offering valuable insights and enhanced modeling capabilities for future research in this complex field.

How to cite: Elhanati, D., Frank, S., Goeppert, N., and Berkowitz, B.: Application of anomalous transport modeling for karst aquifer discharge response to rainfall, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-3029, https://doi.org/10.5194/egusphere-egu24-3029, 2024.

EGU24-3143 | ECS | Posters on site | HS8.2.14

Geomorphological and hydrogeological features of submerged coastal sinkholes in the Apulian karst 

Michele Onorato, Raffaele Onorato, Isabella Serena Liso, Sergio Orsini, Pino Palmisano, Mario Parise, and Luca Zini

Low coastal karst is often characterized by widespread presence of sinkholes flooded by mixing of fresh and salt water. Such a mixture creates peculiar environments and ecosystems, at the same time predisposing the areas to possible hazards, in the form of formation of new sinkholes, or enlargement and coalescence of the existing ones through failures at their rims. This is definitely the situation for the south-western coast of Salento (Apulia, southern Italy), where the local karst setting is dominated at the surface by presence of flooded sinkholes, and by bays and inlets of circular shape along the coast. These latter are typically the result of coalescing processes of original individual sinkholes, which outer rim is eventually broken by the action of sea waves. Such a situation characterizes actually many other sites in the region, not only limited to the Ionian side but also involving the Adriatic coastine of Apulia, to the east (Liso & Parise, 2023).

In the coastal stretch extending from Torre Castiglione to Palude del Capitano, we have started a variety of activities, with further more on the way: among these, mapping of the sinkholes and interpretation of their mechanisms of formation, both along the coast and inland; identification of the main structural lineations, and of the likely control they exert on sinkhole development and evolution; monitoring of the physico-chemical parameters of the waters, with particular focus on those where upwelling of sulphureous waters has been observed; evaluation of the dissolution rate of carbonate rocks within the submerged areas; assessment of the sinkhole hazard, also in relation to the widespread presence of tourist sites, highly frequented during the summer season. Comprehension of the main flowpath of groundwater, from the inland areas toward the coast, is one of the main goals of our research, which is part of a wider project addressed also to evaluate the biological aspects in these peculiar, high biodiversity, ecosystems.

 

References

 

Liso I.S. & Parise M., 2023, Sinkhole development at the freshwater-saltwater interface in Apulia (southern Italy). In: Land L., Kromhout C. & Suter S. (Eds.), Proceedings of the 17th Multidisciplinary Conference on Sinkholes and the Engineering and Environmental Impacts of Karst, Tampa (Florida, USA), 27-31 March 2023, NCKRI Symposium no. 9, p. 229-238.

Parise M., Palmisano P. & Onorato R., 2017, Contributo alla conoscenza dei fenomeni carsici di collasso in zone costiere del Salento Jonico (Puglia): la Spunnulata della Pajara. Thalassia Salentina, n. 39, p. 99-121. 

How to cite: Onorato, M., Onorato, R., Liso, I. S., Orsini, S., Palmisano, P., Parise, M., and Zini, L.: Geomorphological and hydrogeological features of submerged coastal sinkholes in the Apulian karst, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-3143, https://doi.org/10.5194/egusphere-egu24-3143, 2024.

EGU24-4793 | Orals | HS8.2.14

Changes in the residence time of a spring in a tectonic active zone of central Mexico 

Betsabe Atalia Sierra Garcia, Selene Olea-Olea, and Priscila Medina Ortega

The residence time of a spring located in central Mexico has been affected by seismic activity. The region is influenced by the interaction of five tectonic plates - Cocos, North American, Pacific, Rivera, and Caribbean - with convergent, divergent, and transform boundaries, leading to frequent earthquakes.

The spring, known as the name “Agua Hedionda”, has therapeutic properties due to sulfate concentrations exceeding 1 g/L that contributes significantly to the local economy. However, the earthquake of magnitude 7.1 in 2017 had a substantial impact, particularly on the flow quantity and sulfate concentrations, evidencing the vulnerability of the spring and, consequently, the community's economy.

To comprehend the vulnerability and changes in the spring, data of tracers (O-18, H-2, H-3, C-14), major ions and flow measurements were collected in 2022.Then, these data were compared with pre- and post-earthquake information.

Tracers facilitated the estimation of residence time for water reaching the spring, indicating a regional flow after the earthquake and an intermediate flow before and currently. The chemical and isotopic data suggest a mixing of flows.

Tectonic movements imply that the spring received water with a longer residence time compared to its original state. The combined analysis of these data in tectonically active areas offers valuable insights into changes in residence times, thereby understanding variations and the vulnerability of groundwater resources.

How to cite: Sierra Garcia, B. A., Olea-Olea, S., and Medina Ortega, P.: Changes in the residence time of a spring in a tectonic active zone of central Mexico, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-4793, https://doi.org/10.5194/egusphere-egu24-4793, 2024.

EGU24-5158 | ECS | Orals | HS8.2.14

Flux tracking of groundwater via integrated modelling for abstraction management 

Leyang Liu, Marco Bianchi, Christopher Jackson, and Ana Mijic

In systems where surface water and groundwater interact, management of the water resource often involves conflicting objectives between water supply and baseflow maintenance. Balancing such objectives requires understanding of the role of groundwater in integrated water systems to inform the design of an efficient strategy to minimise abstraction impacts. This study first develops a reduced-complexity, processed-based groundwater model within the water systems integration modelling framework (WSIMOD). This model is applied to the Lea catchment, UK, as a case study and evaluated against monitored groundwater level and river flow data. A flux tracking approach is developed to reveal the origins of both river baseflow at a critical assessment point and abstracted groundwater across the systems. The insights obtained are used to design two strategies for groundwater abstraction reduction. Results show that the model has good performance in simulating the groundwater and river flow dynamics. Three aquifer bodies that contribute the most to the river baseflow in the dry season at the assessment point are identified; contributions being 17%, 15%, and 5%. The spatial distribution of abstracted groundwater originating from these aquifer bodies is illustrated. Compared to the default equal-ratio reduction, the strategy prioritising abstraction reduction in these three aquifer bodies increases a similar amount of baseflow (13%) by reducing much less abstraction (23%). The other strategy, which further decreases abstraction in the adjacent aquifer bodies, increases more baseflow (16%) with a similar abstraction reduction (30%). Both strategies can more efficiently improve the baseflow. The flux tracking approach can be further implemented to trace water from other origins, including runoff, stormwater, and wastewater, to enable coordinated management for better systems-level performance.

How to cite: Liu, L., Bianchi, M., Jackson, C., and Mijic, A.: Flux tracking of groundwater via integrated modelling for abstraction management, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5158, https://doi.org/10.5194/egusphere-egu24-5158, 2024.

EGU24-6096 | Posters on site | HS8.2.14

Investigating the hydrological behaviour of a shallow karst aquifer flooding intermittently: the Pivka Karst Aquifer (Slovenia) 

Cyril Mayaud, Blaž Kogovšek, Metka Petrič, Nataša Ravbar, Matej Blatnik, and Franci Gabrovšek

The Pivka Karst Aquifer is a shallow karst aquifer located under the Upper Pivka Valley, about 40 km SW from Ljubljana (Slovenia). This aquifer is connected to the larger Javorniki Karst Aquifer that borders the Upper Pivka Valley on the NE. While the geometry of the conduit system in the Pivka Karst Aquifer is practically unknown, the geometry of the Javorniki Karst Aquifer is better characterized. Under low water conditions, water from the Pivka Karst Aquifer drains through the Javorniki Karst Aquifer towards the Unica and Malenščica Springs in the N, which are the terminal outlets of the region. Under high-water situations, the regional groundwater level rises up to 45 m, and the regional flow direction is modified. The Pivka Karst Aquifer receives water from the Javorniki Karst Aquifer which provides in the meantime autogenic water to the Unica and Malenščica Springs. The rise of water level in the Pivka Karst Aquifer result in the appearance of 17 intermittent lakes in the Upper Pivka Valley. This work aims establishing a conceptual hydrological model of the Pivka Karst Aquifer to better understand its interaction with the Javorniki Karst Aquifer. To do so, a network of automatic stations recording water level, specific electrical conductivity and water temperature at a 30 min interval has been progressively established in the Upper Pivka Valley since 2020. The four years dataset were analysed with data collected in the water active caves of the Javorniki Karst Aquifer and at the Unica and Malenščica Springs. The interpretation of water level records suggest that the Javorniki Karst Aquifer is a large recharge contributor of the Pivka Karst Aquifer, which act as an overflow of the whole system. However, the southern and western parts of the Pivka Karst Aquifer are also recharged locally. Such finding is supported by the analysis of specific electric conductivity data, which suggests the existence of several preferential flow paths in the Pivka Karst Aquifer that activate during flooding.

How to cite: Mayaud, C., Kogovšek, B., Petrič, M., Ravbar, N., Blatnik, M., and Gabrovšek, F.: Investigating the hydrological behaviour of a shallow karst aquifer flooding intermittently: the Pivka Karst Aquifer (Slovenia), EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-6096, https://doi.org/10.5194/egusphere-egu24-6096, 2024.

EGU24-6376 | ECS | Orals | HS8.2.14 | Highlight

Understanding groundwater chemistry and residence times of two thermal springs in east-central Mexico. 

Lorena Ramírez González, Oscar Escolero, Selene Olea-Olea, and Priscila Medina-Ortega

Thermal springs are natural discharge points that can offer valuable information on groundwater circulation. The use of tracers to determine residence times can help us understand complex hydrogeochemical processes despite limited data availability.

The present work aims to determine groundwater chemistry composition of two thermal springs located in east-central Mexico as well as understand some of the processes that may impact residence time estimation.

Tritium and carbon-14 tracers indicated a significant component of pre-modern water. Major ions data collected showed both springs have concentrations of HCO3- greater than 1,000 mg/l and temperatures around 41 °C. Saturation indices showed water-rock interaction with geological formations present in the area, such as limestone sequence ‘El Doctor’, that could influence groundwater residence time. Isotope data (δ18O) was used to determine a recharge elevation ranging from 2900 to 3000 meters above sea level. Additionally, SiO2 geothermometers were also applied to quantify circulation depth and reservoir temperature.

Analysis of hydrochemical composition, residence times, and any other information obtained from tracers, such as tritium and C-14, allows us to gain a better understanding of how groundwater systems work, along with a more accurate interpretation of results.

How to cite: Ramírez González, L., Escolero, O., Olea-Olea, S., and Medina-Ortega, P.: Understanding groundwater chemistry and residence times of two thermal springs in east-central Mexico., EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-6376, https://doi.org/10.5194/egusphere-egu24-6376, 2024.

EGU24-7450 | ECS | Posters on site | HS8.2.14

Spatial variability of lacustrine groundwater discharge at basin scale 

Xiaoliang Sun, Yao Du, and Yiqun Gan

Lacustrine groundwater discharge (LGD) is a crucial component of water balance in lakes. However, research on the spatial variability of LGD on a large basin scale is scarce, and the factors controlling this variability are not well understood. In this study, we examined various lakes located throughout the CYRB using multiple tracers and field surveys to determine the occurrence of LGD. We employed a 222Rn mass balance model to determine LGD rates in various lakes within the central Yangtze River basin (CYRB). Additionally, we identified the factors controlling the spatial variability of the LGD rates using correlation analysis and a multiple linear regression model. Our findings revealed that while the 222Rn concentration in groundwater (6082.27 ± 3860.16 Bq/m3) was within the global average, the concentration in lake water (306.97 ± 239.45 Bq/m3) was relatively high, indicating a stronger LGD in the CYRB. The stable isotopes, 222Rn concentration, and the groundwater seepage and springs, collectively confirm the occurrence of LGD. The LGD rates in lakes within the CYRB area exhibited significant spatial variability, ranging from 13.76 to 83.96 mm/d, with larger LGD rates found at the interior of the basin than at the edges. Hydrological connectivity, location within basin, and lake water depth collectively control the LGD rate, with each contributing 53.95%, 22.90%, and 23.16%, respectively. This study not only enriches our understanding of LGD, serving as a reference for global research on LGD, but also provides theoretical guidance for local water resource management.

How to cite: Sun, X., Du, Y., and Gan, Y.: Spatial variability of lacustrine groundwater discharge at basin scale, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-7450, https://doi.org/10.5194/egusphere-egu24-7450, 2024.

Bedding plane partitions are an important geological medium to guide cave passages during the early stages of karstification in limestone formations. However, how stress load affects karst genesis processes along the large rough fractures remains poorly understood. Here, we develop a novel coupled hydro-mechanical-chemical (HMC) model to improve the understanding of this complicated process. This model considers a two-way mechanical-chemical coupling where dissolution perturbs the contact-stress distribution, in return impacting the fracture dissolutional enlargement. A non-linear correlation between the local fracture stiffness and contact stress is further incorporated. We study a two-dimensional horizontal fracture surface embedded in a three-dimensional rock block subjected to vertical stress loading. Simulation results show that dissolution causes local stress reduction (mechanical weakening), simultaneously accompanied by stress concentration at its fringe. The competition between dissolution-induced aperture enlargement and compaction-induced closure significantly retards the dissolution evolution. Without mechanical effect, linear dissolution fingering exhibits. As the applied stress increases, the secondary karstic conduits become more pronounced and a ramiform dissolution fingering featuring branching and winding is induced. Our results also provide important implications for understanding other engineering applications such as geothermal development and carbonate acidification.

How to cite: Jiang, C., Wang, X., and Jourde, H.: Stress-induced ramiform karstic conduits along a bedding plane: insights from a coupled hydro-mechanical-chemical (HMC) model, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-9657, https://doi.org/10.5194/egusphere-egu24-9657, 2024.

EGU24-9697 | ECS | Orals | HS8.2.14

Optimizing Water Storage in a Mediterranean Karst Aquifer: A Comprehensive Vadose and Phreatic Modeling Approach 

Lysander Bresinsky, Jannes Kordilla, Yakov Livshitz, and Martin Sauter

This study focuses on the role of karst aquifers in the Mediterranean Basin as a buffered storage of freshwater, especially considering the anticipated increase in drought periods due to climate change. Climate change underscores the need for innovative groundwater management approaches to maximize the storage capacity of these aquifers. This study emphasizes the importance of enhancing aquifer recharge during normal or high rainfall to mitigate the impacts of droughts. Notably, many karst aquifers in this region, which developed extensively during the lower base levels of the Messinian Salinity Crisis, exhibit a dual-domain flow pattern. This pattern consists of a slower flow through the rock matrix and a faster flow through conduits. Despite the rapid drainage of these mature karst systems, some, particularly those in the Mediterranean, are limited in their outflow to the sea by marine clay deposits, as highlighted by Bakalowicz (2015, Environmental Earth Sciences). These systems have shown a significant capacity for storage over several years.

In our study, we applied dual-permeability flow modeling to evaluate the storage potential of the Western Mountain Aquifer in Israel and the West Bank. The model utilizes the volume-averaged Richards' equation and integrates a term to account for the characteristic preferential infiltration in karst aquifers, even under nearly dry conditions. The model includes phreatic and vadose zone flows to comprehensively assess the storage capacities of the aquifer comprehensively. The results indicate that despite its advanced karst development, the Western Mountain Aquifer possesses a notable long-term storage capability. This is attributed to its extensive vadose zone and the restricted outflow, which is constrained by surrounding and overlying low-permeability formations (such as the Talme-Yafe, Negba, Daliya, and Menuha Formations, composed mainly of chalk and marl). The study explores various infiltration sites for managed aquifer recharge and considers current and future climatic conditions based on the RCP4.5 climate change scenario.

How to cite: Bresinsky, L., Kordilla, J., Livshitz, Y., and Sauter, M.: Optimizing Water Storage in a Mediterranean Karst Aquifer: A Comprehensive Vadose and Phreatic Modeling Approach, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-9697, https://doi.org/10.5194/egusphere-egu24-9697, 2024.

EGU24-11063 | ECS | Orals | HS8.2.14

Understanding the impacts of human wastewater effluent pollution on karst springs using chemical contamination fingerprinting techniques 

Luka Vucinic, David O'Connell, Donata Dubber, Patrice Behan, Quentin Crowley, Catherine Coxon, and Laurence Gill

Groundwater from karst aquifers is a major source of drinking water worldwide. These complex aquifer systems are exceptionally vulnerable to pollution and may be impacted by multiple contamination sources. Consequently, water contaminated with pollutants, such as microbial and chemical, from different sources can reach water sources used for human supplies (i.e. karst springs, boreholes, and wells that are being used for domestic purposes and/or irrigation).

In rural and suburban areas, human wastewater effluent (from on-site domestic wastewater treatment systems - DWTSs) and agricultural sources are generally considered among the most significant threats to groundwater quality. This is particularly of concern in Ireland given that more than one third of the population (>500,000 homes) use DWTSs. However, significant knowledge gaps exist with respect to linking contaminants with the origins of pollution and quantifying different pollution impacts on groundwater quality in karst environments.

The domestic wastewater is primarily discharged from toilets, washing machines, showers, and dishwashers, therefore, a wide range of contaminants (including source-specific contaminants) eventually reach the environment even after on-site wastewater treatment processes. We evaluated a range of chemical contamination fingerprinting techniques in terms of their ability to determine human wastewater pollution impacts on karst aquifers. Springs provide appropriate natural locations for monitoring pollutant concentrations in karst aquifer systems as they provide an integrated picture of contaminant transport through a karst conduit network, compared to wells and boreholes which are not necessarily directly connected to the most transmissive parts of the aquifer. Hence, nine separate karst springs in the West of Ireland (of varying catchment sizes) were studied and monitored over a 14-month period.

The results demonstrate how fluorescent whitening compounds (FWCs; well-known indicators of human contamination since their origin is mostly from laundry detergents), microplastic particles, and faecal sterols and stanols can be used together to cover different detectability chances, and provide useful information about DWTSs pollution impacts on karst springs. This study also provides an important benchmark for microplastic contamination in low-lying karst aquifer systems. Furthermore, a link between changes in FWCs signals and microplastic concentration changes in karst groundwater has been found, which indicates that the majority of microplastic particles originated from human wastewater sources. Unsurprisingly, the highest detection rates of FWCs and high concentrations of microplastic particles were found in karst catchments with very high densities of DWTSs and high percentages of DWTSs in the catchment that are within 200 m of at least one karst feature (such as swallow hole), indicating a direct pathway into the underlying aquifer. Moreover, the results suggest that while total sterol content in collected groundwater samples was generally low, faecal sterols and stanols can still be used as chemical faecal markers at karst springs.

How to cite: Vucinic, L., O'Connell, D., Dubber, D., Behan, P., Crowley, Q., Coxon, C., and Gill, L.: Understanding the impacts of human wastewater effluent pollution on karst springs using chemical contamination fingerprinting techniques, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-11063, https://doi.org/10.5194/egusphere-egu24-11063, 2024.

EGU24-11841 | Posters on site | HS8.2.14

Karst geomorphology, hydrogeology and caves in the areas of the Prespa Lakes, at the Albania-Macedonia-Greece border 

Mario Parise, Viacheslav Andreychouk, Isabella Serena Liso, Antonio Trocino, and Romeo Eftimi

The lakes of Prespa and Ohrid represent a very important hydrogeological system shared between Albania, FYR of Macedonia and Greece, and are the largest tectonic lakes in Europe. Prespa Lake is about 150 m higher in elevation than Ohrid, and the twos are separated by high mountains (Mali Thate, 2,287 m, and Galičica Mt., 2,262 m a.s.l.), built up during the Pliocene-Quaternary tectonic events. These mountains mainly consist of Upper Triassic–Lower Jurassic limestones, with wide graben to the E (Prespa) and the W (Ohrid). Pliocene clays, sandstones, and conglomerates fill most of the lakes. In 2002, an artificial tracer experiment physically demonstrated the underground connection between them (Amataj et al., 2007).

In the past, periodical oscillations of the level at Prespa Lake were in the 1-3 m range. After middle 1980’s, a steady decrease of water level has been recorded, producing serious disturbance to its ecological balance. Shape of Lake Prespa is quite irregular: the narrow sandy isthmus Gladno Polje separates it into Macro and Micro Prespa. In the recent past, Micro Prespa was a gulf of Macro Prespa, but then, due to erosion and sedimentation processes, the isthmus has been formed and the lakes separated (Popovska & Bonacci, 2007).

In this contribution we illustrate the main karst geomorphological characters, also providing updated information on its hydrogeology. In Galičica Mt. the most important surface karst forms are the Petrinska Plateau, a 20 km2 feature developed at elevation of 1500 m a.s.l., and the Samari blind valley, about 7 km long, in the NE part at about 1300–1400 m a.s.l. At least 12 high elevation caves have been documented, the longest being Samoska Dupka with length of 279 m. Numerous small caves are also situated along the Prespa Lake coastline near the villages of Stenie and Gollomboc; the longest is Treni cave (315 m long) at the W point of MicroPrespa Lake (Trocino et al., 2010). The Zaver swallow hole is situated at the Prespa W border, near Mala Gorica, with an extensive karst cave just uphill. Other smaller swallow holes are near Gollomboc; about in the same area, several caves of limited size (up to some tens of meters) are present, too. All these elements are important to describe the Prespa Lakes area as a sector of potential interest for further karstological studies, addressed to a better comprehension of the karst phases that interested this trans-boundary sector.

 

References

Amataj S. et al., 2007, Tracer methods used to verify the hypothesis of Cvijic about the underground connection between Prespa and Ohrid lake. Environ. Geol. 51 (5), 749-753.

Eftimi R., Stevanovic Z. & Stojov V., 2021, Hydrogeology of Mali Thate–Galičica karst massif related to the catastrophic decrease of the level of Lake Prespa. Environ. Earth Sci. 80, 708.

Popovska & Bonacci O., 2007, Basic data on the hydrology of Lakes Ohrid and Prespa. Hydrol. Proc. 21, 658-664.

Trocino A., Parise M. & Rizzi A., 2010, Ricerche speleologiche in Albania: primi dati sulle cavità nei pressi del lago di Prespa. XII Reg. Meeting Speleology “Spelaion 07”, 246-259.

How to cite: Parise, M., Andreychouk, V., Liso, I. S., Trocino, A., and Eftimi, R.: Karst geomorphology, hydrogeology and caves in the areas of the Prespa Lakes, at the Albania-Macedonia-Greece border, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-11841, https://doi.org/10.5194/egusphere-egu24-11841, 2024.

EGU24-12200 | Orals | HS8.2.14

Data-driven approaches to infer transit time distributions from high-resolution tracer data 

Paolo Benettin, Quentin Duchemin, Maria Grazia Zanoni, Andrea Rinaldo, and James Kirchner

Catchment transit times are often inferred by assuming a transit time distribution (TTD) or a SAS function and calibrating their parameters against measured tracer data. In the presence of high-resolution tracer data, machine learning tools may offer a promising avenue for advancing TTD estimation by leveraging data-driven approaches, integrating diverse data sources, and improving accuracy, scalability, and adaptability. Here, we lump together ideas coming from Large Languages Models, survival analysis and sum of squares techniques to introduce a novel data-driven model for estimating TTDs. Our model is influenced by SAS-based approaches; however, unlike previous studies, we avoid imposing strong parametric assumptions on the SAS function. We showcase the performance of our model against a benchmark of eight virtual datasets that differ in precipitation amounts, seasonality and runoff flashiness. We find that machine learning methods may effectively predict solute concentration in streamflow yet struggle to accurately estimate the true TTDs. However, when the appropriate inductive bias is incorporated, numerous key aspects of TTDs, such as the young water fraction and the average TTDs, can be estimated robustly. We also identify settings where the estimation task is more challenging for our model. This analysis, based on reproducible virtual benchmarks, provides a first overview of machine learning capabilities in estimating TTDs and inspires future TTD model inter-comparisons.

How to cite: Benettin, P., Duchemin, Q., Zanoni, M. G., Rinaldo, A., and Kirchner, J.: Data-driven approaches to infer transit time distributions from high-resolution tracer data, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-12200, https://doi.org/10.5194/egusphere-egu24-12200, 2024.

EGU24-12814 | Posters on site | HS8.2.14

Helium-isotope data and groundwater ages of 700 shallow and deep groundwater sites in and around the Austrian Eastern Alps  

Martin Kralik, Heike Brielmann, Franko Humer, and Jürgen Sültenfuß

Groundwater ages provide valuable insights for water managers and users, helping them understand the timeframe required for water quality improvement measures to become effective and the timeframe in which groundwater bodies are renewed. To estimate groundwater ages in important shallow Austrian aquifers more than 700 tritium/helium-3 analyses and some tracer gas (CFC, SF6) investigations were conducted within the national groundwater monitoring and additional research projects. Noble gases 3He, He and 20Ne were measured at the Institute of Environmental Physics (IUP), University of Bremen, Germany and some at the Isotope Hydrology Section of the IAEA in Vienna, Austria. Groundwater ages vary across Austria and within groundwater bodies due to hydrogeological heterogeneities and depending on gradients of precipitation amounts and recharge rates. They range generally between 0 – 25 yrs. Tritium/helium-3 analyses are an essential tool for groundwater age estimation and the respective piston flow model ages of the shallow aquifers are mostly in the range of 0 – 15 years. However, the missing correlation with the sampling depth indicate partly an internal mixing in the observation wells due to large screen lengths.

The existent of elevated 4He-concentrations in aquifers with low background U and Th-content are good indicators of the admixture of old groundwater or just increased 4He-fluxes. The 4He concentrations range from air-equilibrium up to 1.6E-03 (cm3 STP /kg).  The 3He/4He- ratio decreases down to 8.0E-08. Preliminary studies of increased 4He-data with major tectonic fault zones indicate a positive correlation. Clear indications of the admixture of mantle helium were discovered at the end of Eastern Alps toward the western border of the Pannonian Basin.

 

[1]        Kralik, M., Humer, F., Fank, J., Harum, T., Klammler, D., Gooddy, D., Sültenfuß, J., Gerber, C., Purtschert, R. (2014): Using 18O/2H, 3H/3He, 85Kr and CFCs to determine mean residence times and water origin in the Grazer and Leibnitzer Feld groundwater bodies (Austria). Applied Geochemistry, 50 (2014), 150-163 http://dx.doi.org/10.1016/j.apgeochem.2014.04.001

[2]        Bundesministerium für Land- und Forstwirtschaft, Regionen und Wasserwirtschaft, Grundwasseralter 2009-2021, Wien (2022).

 (https://info.bml.gv.at/themen/wasser/wasserqualitaet/grundwasser/grundwasseralter2019-2021.html)

How to cite: Kralik, M., Brielmann, H., Humer, F., and Sültenfuß, J.: Helium-isotope data and groundwater ages of 700 shallow and deep groundwater sites in and around the Austrian Eastern Alps , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-12814, https://doi.org/10.5194/egusphere-egu24-12814, 2024.

EGU24-13483 | ECS | Posters on site | HS8.2.14

Groundwater travel time distribution in the subsurface of a high energy beach – a multi tracer approach 

Rena Meyer, Janek Greskowiak, Anja Reckhardt, Stephan Seibert, Jürgen Sültenfuß, and Gudrun Massmann

In beach aquifers two water bodies, relatively old terrestrial freshwater and young oceanic saltwater mix, biogeochemical reactions change the solute composition of the water and groundwater discharge modifies element net fluxes to the ocean. Residence times are baseline information for the biogeochemical interpretation and help to understand groundwater flow and transport regimes. In the present study we used environmental tracers, i.e. apparent tritium-helium (3H/He) ages, temperatures and silica (Si) concentrations to derive groundwater ages and travel times in the subsurface along a cross-shore transect at the high energy beach aquifer on Spiekeroog, a barrier island in North-Western Germany. Recent generic modelling studies suggested that in beach aquifers under high energy conditions, characterized by high waves and tidal amplitudes as well as seasonal storm floods, flow and transport patterns in space and time are highly variable. As a consequence, the typical salinity and age stratification is distorted as compared to the classical stable concept of water bodies in beach aquifers derived from more embayed sites. To advance the understanding of such highly dynamic systems we obtained two sets of apparent 3H/He ages one year apart at three permanently installed multilevel wells each filtered in four depths (6, 12, 18, 24 m bgs), located at the dune base, near the mean high water line and near the mean low water line respectively. At the same locations, data loggers continuously recorded groundwater temperatures and were used to calculate travel times. In addition, Si was measured in samples taken every six weeks over one year. The results show relatively young apparent 3H/He ages in all samples, ranging from weeks to approximately 18 years. The water was youngest in the shallow part and near the high water line and ages increased with depth and towards the low water line and dune base. Interestingly, 3H/He ages vary significantly at some locations in the two data sets. Temperature derived travel times, representing the young water component (from the North Sea), overall agree well with the mixed apparent 3H/He ages. Si accumulating with time shows a similar trend. In the next steps, the results will help to constrain site specific groundwater modelling and support the interpretation of geochemical data and underlying processes in order to finally better understand the functioning  of high energy beach systems.

How to cite: Meyer, R., Greskowiak, J., Reckhardt, A., Seibert, S., Sültenfuß, J., and Massmann, G.: Groundwater travel time distribution in the subsurface of a high energy beach – a multi tracer approach, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-13483, https://doi.org/10.5194/egusphere-egu24-13483, 2024.

Karst landscapes develop distinct surface landforms intricately connected to a more complex subsurface drainage system due to the highly soluble nature of its bedrock. Because of this, surface processes can more directly affect the groundwater system through conduits such as caves and sinkholes. Due to high hydraulic conductivity, aside from surface and groundwater, the soil produced from weathering and erosion of karst is also affected. Samcheok, found in southeastern Gangwon Province, is an example of an area that is underlain by limestone-bearing formations, allowing the formation of karst. In this study, the patterns in the hydrochemical characteristics of surface water and the land use of areas adjacent to the streams in Samcheok karst were explored through geospatial analyses. Additionally, recent land use change in the area was also investigated. Surface waters from four streams in Samcheok were analyzed: Osipcheon River, Yeosam Stream, Sohan Stream, and Gyogok River. Results show that hydrochemical parameters in northeast Samcheok karst are mostly varied and to an extent dependent on the stream where the samples were taken from more than the sampling distance from the coast. Usual patterns for pH and dissolved oxygen in terms of salinity were not observed. Concentrations of cations and anions mostly varied between the two sampling seasons (winter and spring for February and April 2020 samples, respectively) and were also varied in terms of linear correlation for concentration vs. distance to stream outlet graphs. High linear correlation was observed for spring samples from Gyogok River for the following ions: Ca2+ (R2 = 0.976), Mg2+ (R2 = 0.9321), SO42- (R2 = 0.879), and HCO3- (R2 = 0.955). More than 50% of the area adjacent to streams is classified as “other bare land”. Between 2019 and 2020, there was an increase in the total land area for coniferous forests and a decrease in mixed forest and undeveloped arable field. Research on geospatial patterns for hydrochemical parameters and land use change in environments susceptible to pollution such as karst areas are useful for land use planning and erosion studies. This research was funded by the Basic Science Research Program through the National Research Foundation of Korea funded by the Ministry of Education (Nos. 2019R1I1A2A01057002, and 2019R1A6A1A03033167) and the Korea Ministry of Environment as "The SS (Surface Soil conservation and management)” project (No. 2019002820004).

How to cite: Lumongsod, R. M. and Kim*, H.: Geospatial patterns in surface water parameters and recent land use change in the karst of Samcheok, South Korea, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-13787, https://doi.org/10.5194/egusphere-egu24-13787, 2024.

The study of hydrogeochemical processes in Karst Critical Zone (KCZ) is of great significance for scientific understanding of their internal evolutionary environment and structural characteristics. Karst groundwater is the main information carrier after water-rock interaction. Quantitative analysis of its hydrochemical characteristics and causes is an effective means to reveal the medium environment and hydrodynamic conditions of aquifer system in KCZ. In this paper, three typical karst aquifer systems in the KCZ of central Yunnan Plateau are taken as the research objects. Through field sampling and laboratory testing of karst springs exposed by different aquifer systems, mathematical statistics analysis, hydrochemical diagram, ion ratio coefficient and hydrogeochemical simulation are comprehensively used to deeply analyze the characteristics of hydrochemical components, genesis and aquifer medium of karst groundwater in each aquifer system, and the internal relationship and law between water cycle and hydrochemistry in the key belt are discussed. The results show that : (1)  HCO3 and Ca2+ are the highest and stable ion components in regional karst groundwater, and Mg2+ is the key factor to control the alienation of hydrochemical types in each aquifer system ; (2)  The rock weathering and mineral dissolution of carbonate rocks are the main causes of the chemical composition characteristics of karst water in each aquifer system, and the karst groundwater dissolution on the aquifer of Huaning aquifer system is still occurring. The alternation of cation adsorption and the weathering and dissolution of silicate rocks are the main sources of Na+ and K+ in regional karst groundwater. (3)  The development intensity of regional karst, the exposed condition of karst aquifer and the lithology and connectivity of aquifer medium jointly shape the groundwater chemical characteristics of different aquifer systems in the KCZ of central Yunnan Plateau.

How to cite: Xu, M. and Huang, J.: Hydrochemical characteristics and genesis analysis of typical aquifer system in Karst Critical Zone of central Yunnan Platea, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-14478, https://doi.org/10.5194/egusphere-egu24-14478, 2024.

The characteristics of groundwater flow and solute transport in karst aquifers differ considerably from those in intergranular and fissured aquifers. To understand how they function, appropriately adapted hydrogeological research techniques and analyses are required. In this study, a binary karst aquifer in the recharge area of the Malenščica and Unica springs, which covers an area of about 820 km2 in southwestern Slovenia, was selected as the study area. A monitoring network was set up to obtain data on precipitation and discharge at the two springs, two sinking streams and two water-active caves in their catchment over a period of two hydrological years. First, a classical approach of correlation and spectral analysis of these time series data was applied to determine and compare the flow characteristics and storage capacity of selected springs and their recharge areas. The allogenic and autogenic recharges were considered separately as input functions and the results of the analysis were compared. Although these widely used methods provided a good characterization of the studied karst system, the interpretation can be ambiguous due to the interference of the two input components. To avoid this problem, an innovative method of partial cross-correlation analysis was used, which has previously only been applied to separate the influence of different precipitation stations in karst. Here, its application was extended to the evaluation of the influence of allogenic recharge. By controlling the input parameters precipitation and discharge of one of the sinking streams, it was possible to determine the contribution of the other sinking stream to the observed spring. The differences in the recharge characteristics of the Unica and Malenščica springs were revealed, and the ability of this innovative approach to provide additional insights into the functioning of binary karst aquifers was confirmed.

 

Key words: karst aquifer, autogenic and allogenic recharge, time series analysis, partial cross-correlation, Slovenia.

How to cite: Kogovšek, B., Jemcov, I., and Petrič, M.: Distinguishing between different sources of recharge in a complex binary karst aquifer: a case study of the Unica springs (SW Slovenia), EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-14951, https://doi.org/10.5194/egusphere-egu24-14951, 2024.

EGU24-15310 | ECS | Posters on site | HS8.2.14 | Highlight

Assessing tritium and persistent organic micropollutants as tracers for investigating surface and groundwater interaction in a large river system (Moselle River)  

Jessica Landgraf, Liza-Marie Beckers, Sabrina Quanz, Dirk Radny, and Axel Schmidt

Understanding the couplings of surface-groundwater interaction as well as their environmental impact is crucial for sustainable water management. However, water fluxes vary depending on external factors like water levels or heavy rain events and may alter the quantity and quality of surface and groundwater. As direct measurements of the ongoing mixing processes are challenging, various tracers are utilized to estimate water fluxes and transit times. Tritium as an environmental radioactive tracer introduced into the environment via nuclear bomb tests in the late 1950s has widely been used for water flux and transit time analyses. However, the tritium concentrations in surface waters in most regions declined to background concentrations due to the nuclear decay of tritium. Therefore, scientists are searching for alternatives like stable water isotopes or other chemical tracers to investigate surface-groundwater fluxes. Persistent organic micropollutants emitted into surface waters might present suitable alternative tracers.

The Moselle River has its source in the southern Vosges mountains and flows through France, along Luxembourg and through western Germany. The river contains high tritium concentrations (up to 50 Bq/l) induced by the French nuclear power plant Cattenom. Hence, tritium concentrations of the Moselle River surface water surpass the naturally abundant tritium concentrations ( ~1 Bq/l) found in groundwater reservoirs adjacent to the river. The German part of the Moselle River was monitored in 2020 to 2022 with monthly to quarterly intervals. Two spatially distributed sampling campaigns along the German Moselle River as well as continuous monthly investigations of a barrage site at Lehmen roughly 20 km upstream of Koblenz were conducted. The analysis of the water samples involves on-site parameters, cations, anions, metals, dissolved organic carbon, stable water isotopes, radon-222, tritium, and organic trace substances like pharmaceuticals. The study found significant surface-groundwater interaction at Lehmen. Thus, we evaluated correlations between tritium and detected organic micropollutants at this site. So far, seven organic micropollutants including the corrosion inhibitor benzotriazole and its derivative 5-methyl-1H-benzotriazole as well as the pharmaceuticals carbamazepine, lamotrigine, tramadol, candesartan and olmesartan were selected for this investigation. These pollutants enter the environment via wastewater release.

In this study, we explored the capability of tritium and persistent organic micropollutants tracers to reflect surface-groundwater interaction. So far, we compared the suitability of different organic micropollutants to reflect the observed water fluxes and transit time estimations with estimated results from tritium. Furthermore, we discuss the possible utility of benzotriazole or other organic compounds for future investigations of surface-groundwater-interaction.

How to cite: Landgraf, J., Beckers, L.-M., Quanz, S., Radny, D., and Schmidt, A.: Assessing tritium and persistent organic micropollutants as tracers for investigating surface and groundwater interaction in a large river system (Moselle River) , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-15310, https://doi.org/10.5194/egusphere-egu24-15310, 2024.

EGU24-18008 | Orals | HS8.2.14 | Highlight

Characterisation of drainage dynamics of karst landscapes over Europe 

Tunde Olarinoye, Nane Weber, Tom Gleeson, Vera Marx, Yan Liu, and Andreas Hartmann

Karst aquifers play a crucial role as water sources globally, with several European countries relying significantly on them for their water supply. Managing these aquifers is challenging due to their subsurface hydraulic heterogeneity. Hydrological modeling has proven valuable, offering insights into the hydraulic dynamics and management of karst water resources. However, characterizing karst drainage attributes at large catchment and regional scales remains challenging, hindering the incorporation of spatial heterogeneity and complexity in large-scale models and leading to unrealistic estimations in karst regions. This study addresses the issue by providing the first regional estimation of karst drainage attribute across Europe, this attribute is herein called Karstification Index (KI). Leveraging a newly developed automated karst spring recession analysis tool, and extensive climatic and physiographic datasets, we applied a regression-based regionalization model to estimate slow and quick flow parameters in karstic landscapes. By estimating KI as the ratio of quick to slow flow parameters, we were able to identify sub-regions with higher and lower degrees of karstification. Our findings highlight the significance of drainage density metrics, particularly in combination with specific climate signals, as predictors of KI. The regionalization model demonstrated high performance, validated by high R2 values, especially in well-gauged European catchments. Encouraged by these results, the analysis is being extended to a global scale, marking the first attempt to estimate karstic drainage attributes globally. We believe that this large-scale parameterization of karstification will enhance regional and global karst water resource management. By improving the parameterization and consideration of karst processes in large-scale hydrological models, our research contributes to a more accurate understanding of karst aquifers on a global scale.

How to cite: Olarinoye, T., Weber, N., Gleeson, T., Marx, V., Liu, Y., and Hartmann, A.: Characterisation of drainage dynamics of karst landscapes over Europe, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-18008, https://doi.org/10.5194/egusphere-egu24-18008, 2024.

EGU24-18110 | ECS | Posters on site | HS8.2.14

Monitoring of microfiber pollutants in karst environments 

Valentina Balestra, Adriano Fiorucci, Paola Marini, and Rossana Bellopede

Recent studies highlighted a preoccupant pollutant which impact natural environments: the microfibres. The term “anthropogenic microfibres” (MFs) includes fibres <5 mm in length of any composition (natural, regenerated and synthetic) derived from larger primary textiles manufactured for human use. MFs have been detected in different environments, as well as in human and animal organs, and adverse effects on animal health have been studied. Not-synthetic MFs have been often considered microplastics because of their colours, and because a lot of them are extruded and processed industrially. However, natural and regenerated fibres are a source of carbon for organisms, and are generally considered biodegradable. However, despite the general consensus on the reduced dangerousness of the not-synthetic fibres in the environment, little is known about their degradation in ecosystems. Their potential faster degradation could release toxic compounds into the environment, such as resins, dyes, and flame retardants. In addition, natural and regenerated textiles release more fibres than synthetic ones during laundering. All these factors may explain a long-term accumulation of MFs in the environment over time.

The Classical Karst Region represents important habitats characterized by the presence of dissolution feature in carbonate rock such as caves and sinkholes, which connect surface and subterranean environments. The Classical Karst waters played an important role for the development of this region: thanks to the high water quality, this area has been heavily exploited and was strongly altered by human activities, which irreversibly modified the hydrology of the system.

In this preliminary study we collected and investigated several water and submerged sediment samples in different caves and springs of the Classical Karst Region. MFs from 5 to 0.1 mm were counted and characterized by size, color and shape via visual identification under a microscope, with and without UV light. Spectroscopic analyses were carried out on 10% particles.

MFs were found in all samples, highlighting MF pollution in surface and subterranean habitats in the karst system. The 81% in water and 74% in submerged sediments were natural and regenerated fibres, while only 13% and 10% respectively were synthetics. The size distribution of collected MFs indicated that big MFs (1-5 mm) are less abundant (<22%). More than 80% of fibres were fluorescent under UV light. Of the fluorescent MFs, 91% were transparent; non-fluorescent MFs were mainly black and blue. Of the synthetic fibres, samples contained especially polyesters and copolymers.

Our results improve knowledge on micro pollutants in aquatic and karst environments, laying the foundations for future research. MF pollution monitoring in karst areas must become a priority for species protection, habitat conservation, and waters management, improving analyses on a larger number of aquatic environments, taking into account the ecological connections between surface and subterranean habitats.

How to cite: Balestra, V., Fiorucci, A., Marini, P., and Bellopede, R.: Monitoring of microfiber pollutants in karst environments, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-18110, https://doi.org/10.5194/egusphere-egu24-18110, 2024.

EGU24-18189 | ECS | Orals | HS8.2.14

Use of dissolved gases as tracers to study the impacts of floods and river works on Surface water – Groundwater interactions. 

Théo Blanc, Friederike Currle, Morgan Peel, Matthias S. Brennwald, Yama Tomonaga, Oliver S. Schilling, Daniel Hunkeler, Rolf Kipfer, and Philip Brunner

Alluvial aquifers have a significant potential for pumping large quantities of groundwater, essential for meeting drinking water needs. Abstracted water typically consists of a mix of regional groundwater and freshly infiltrated river water. A good understanding of surface water – groundwater interactions in these types of systems is required for managing both qualitative and quantitative aspects of riverbank filtration or river renaturations. Tracers are important tools in these contexts, as they provide crucial information on travel times and mixing ratios.

We present data from a comprehensive multi-tracer approach obtained in a field experiment conducted in a pre-alpine valley in central Switzerland. Over several months, river works were undertaken in an infiltrating river in the proximity of an important field of groundwater wells used for drinking water production. We investigated the impact of these river modifications on surface water - groundwater dynamics by monitoring the natural concentrations of (noble) gases with multiple potable mass spectrometers (miniRuedi, Gasometrix) and radon detectors (Rad7, Durridge). Additionally, we injected different noble gas species as artificial tracers both in the river and in groundwater and gained valuable insights into the evolving dynamics of the system.

The combination of these different tracers provided insights that could not have been obtained by a single tracer. Our results demonstrate that during and immediately after restoration works the infiltration of river water increases temporarily and provide insights about the time it takes for a riverbed to recover after restoration works and for river-groundwater interactions to reach a new dynamic equilibrium.

How to cite: Blanc, T., Currle, F., Peel, M., Brennwald, M. S., Tomonaga, Y., Schilling, O. S., Hunkeler, D., Kipfer, R., and Brunner, P.: Use of dissolved gases as tracers to study the impacts of floods and river works on Surface water – Groundwater interactions., EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-18189, https://doi.org/10.5194/egusphere-egu24-18189, 2024.

EGU24-19597 | ECS | Orals | HS8.2.14

Simulation of Carbonated Fault Zones Hydrodynamics and Transport Considering Parametric and Predictive Uncertainty 

Boura Aurelie, Cousquer Yohann, Clauzon Victor, Valois Rémi, and Leonardi Véronique

Hydrodynamic understanding of karstic aquifers is a real challenge due to the complexity of their internal structures. However, their societal significance lies in the substantial quantity of groundwater resources they embody. Among these complexities, faults partially control the organization of flows in these systems. The nature of this control can either facilitate rapid flow transfer or act as a barrier, impacting both groundwater quality and quantity. Understanding the behavior of fault zone features is crucial for efficiently management of karstic aquifer resources. However, there is a lack of studies that estimate and simulate flow within fault zones. In this study we estimate the hydraulic properties of the fault zone within carbonate karstic aquifers for flow and transport forecasting purposes based on cross-hole pumping tests simulation and inversion. The flow and transport are modeled using MODFLOW6 and MODPATH7. The inverse modeling approach is based on the Gauss-Levenberg-Marquardt Algorithm (GLMA) and the Iterative Ensemble Smoother (IES) integrated into the PEST++ code. Initially, we applied and validated the approach on a synthetic fault zone and subsequently on a real case studies within karstic carbonate aquifers of interest (Lez aquifer, Montpellier (France)). The inverse modeling approach has proven efficient in exploring hydrodynamic properties and then obtained both flow and transport forecasts with a satisfactory level of uncertainty. These works contribute to a better understanding of the hydrodynamic aspects of fault zones in carbonate environments through an innovative approach specific to its application. This study offers a reproducible method to understand and quantify hydrodynamics in aquifer in general and carbonated aquifer fault zones in particular. This improvement enhances the management strategies for groundwater resources in carbonated aquifers.

How to cite: Aurelie, B., Yohann, C., Victor, C., Rémi, V., and Véronique, L.: Simulation of Carbonated Fault Zones Hydrodynamics and Transport Considering Parametric and Predictive Uncertainty, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-19597, https://doi.org/10.5194/egusphere-egu24-19597, 2024.

EGU24-20334 | ECS | Orals | HS8.2.14

Conclusions from an IAEA Meeting on the sample preparation and measurement of radio sulfur in natural water samples 

Stephen Kamau, El Mostafa Amghar, Richard Bibby, Lorenzo Copia, Laura Coulson, Sandra Damatto, Astrid Harjung, Juergen Kopitz, Martin Kralik, Bradley McGuire, Michael Schubert, and Stefan Terzer-Wassmuth

Research on groundwater residence times is essential for evaluating groundwater abstraction rates and aquifer vulnerabilities, and hence, for sustainable water resources management. Naturally occurring radionuclides are suitable tools for related investigations. While the applicability of several long-lived radionuclides for the investigation of long-term processes has been demonstrated frequently, tracer-based approaches for studying residence times of less than one year have not been fully exploited. That is due to the rather small number of applicable radionuclides that show adequately short half-lives. A promising approach for investigating sub-yearly residence times applies radioactive Sulphur (35S). Radio-Sulphur is naturally produced by high-energy cosmic radiation in the upper atmosphere from where it is transferred with precipitation to the groundwater. As soon as the meteoric water enters the subsurface its 35S activity concentration decreases with an 87.4-day half-life. This makes 35S suitable for investigating sub-yearly groundwater residence times. However, the low 35S activities in natural waters require sulphate pre-concentration for 35S detection by means of liquid scintillation counting. This is done by sulphate extraction from large water samples with anion-exchange resins or/and precipitation as BaSO4. The resulting samples are usually associated with background interferences and quenching. The presented experiments aim at (i) optimizing the sample preparation procedure by simplifying the pre-concentration of sulphate to make it applicable for field sampling and at (ii) reducing quench and background during measurement. We will discuss the different sample preparation methods and lessons learned for the detection and quantification of 35S pre-concentrated from natural water samples that contain a wide range of SO42− concentrations.

How to cite: Kamau, S., Mostafa Amghar, E., Bibby, R., Copia, L., Coulson, L., Damatto, S., Harjung, A., Kopitz, J., Kralik, M., McGuire, B., Schubert, M., and Terzer-Wassmuth, S.: Conclusions from an IAEA Meeting on the sample preparation and measurement of radio sulfur in natural water samples, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-20334, https://doi.org/10.5194/egusphere-egu24-20334, 2024.

During flood events, due to extreme hydraulic loading in recharge areas of aquifers, groundwater flow dynamics can change, causing a risk of pathogens being flushed into aquifers used for drinking water supplies. Extreme flood events, as they are increasingly experienced with climate change, have potential to cause impacts not seen before, and drinking water sources that were free of pathogen contamination in the past may become contaminated in the future.

As an example, in the Heretaunga Plains, Hawkes Bay, contaminated water from heavy rain inundated paddocks entered an unconfined part of the aquifer and drinking water wells in it, resulting in >6260 cases of illness including 42 hospitalizations, and Campylobacter infection contributed to at least four deaths.

Most of the c. 30 drinking water wells in the Heretaunga Plains, including those supplying the cities of Hastings and Napier, are, however, in the confined part of the aquifer and these were not affected by pathogen contamination. But will more extreme flood events, predicted with climate change, eventually also compromise drinking water sources in the confined aquifer which were deemed safe in the past? Wells in the confined aquifer have shown indications of changing groundwater flow dynamics, for example variable water age, and changing hydrochemistry after flood events, which might be associated with younger water, bearing the risk of pathogen intrusion.

On 13 and 14 February, 2023, Cyclone Gabrielle lashed Hawke’s Bay, with record rainfalls causing rivers to burst their banks causing a death toll of 11. To improve understanding of the impact of the extreme hydraulic loading on the aquifers through such events, specifically changes to the water flow dynamics with potential for new, previously unrecognised contaminant pathways and associated risks for drinking-water supply wells, we measured age-tracers in selected wells again, two months after Cyclone Gabrielle. Comparing the results of this survey with age-tracer data from just three months prior to the cyclone provided an opportunity to test how extreme events like
Cyclone Gabrielle change groundwater flow dynamics in confined aquifers.

On 12 and 13 April 2023 we re-sampled for age tracers a selection of drinking-water supply wells in partnership with Hastings District Council and Napier City Council, and of private and monitoring
wells in the central and marginal confined parts of the aquifer system in partnership with
Hawkes Bay Regional Council.

The data indicate that groundwater ages in these wells have not changed significantly because of Cyclone Gabrielle. The wells that showed slight changes in age-tracer concentrations consistently showed older water after Cyclone Gabrielle. Other wells, despite showing no detectable changes in age-tracer concentrations, contained water that was more evolved after the cyclone, indicated by decreased dissolved oxygen and elevated methane, ammonia, and phosphorous concentrations.

These observations all point toward older (probably deeper) groundwater having been
pushed into the active groundwater flow paths by the increased hydraulic loading. With no younger water detected in the investigated wells following Cyclone Gabrielle, there is no indication of increased risk of pathogen contamination in the confined aquifer system following extreme flood events.

How to cite: Morgenstern, U.: Did Cyclone Gabrielle increase the risk of pathogen contamination for drinking water supply wells in the confined aquifer of the Heretaunga Plains, New Zealand?, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-20783, https://doi.org/10.5194/egusphere-egu24-20783, 2024.

EGU24-772 | ECS | Orals | HS7.8

Exploring Precipitation Intensity-Duration-Area-Frequency Patterns using Weather Radar Data  

Talia Rosin, Efrat Morin, and Francesco Marra

Extreme precipitation is the main trigger of hazardous phenomena such as floods and flash-floods, that pose a serious threat to human beings and livelihood worldwide. Extreme precipitation is highly variable in both space and time, thus understanding and managing the related risks necessitates improved knowledge of their probability at different spatial-temporal scales.

We employ the simplified metastatistical extreme value (SMEV) framework, a novel non-asymptotic framework, to estimate extreme return levels (up to 100 years) at multiple temporal (10 min–24 h) and, for the first time, spatial (0.25 km2–500 km2) scales using weather radar precipitation estimates. The SMEV framework reduces uncertainties and enables the use of relatively short archives typical of weather radar data (12 years in this case).

Focusing on the eastern Mediterranean - a region characterised by sharp climatic gradients and susceptibility to flash floods - we derive at-site intensity-duration-area-frequency relations at various scales. Comparison with extreme return levels derived from daily rain gauge data over areas with dense gauge networks yields comparable results, demonstrating that radar precipitation data can provide important information for the understanding of extreme precipitation climatology.

We then examine the climatological differences in extreme precipitation emerging from coastal, mountainous, and desert regions at different spatial and temporal scales. Three key findings emerge:

  • At the pixel scale, precipitation and duration exhibit simple scaling, but this relationship breaks down with increasing area - this has significance for temporal downscaling.
  • Precipitation intensity is dissimilar for different area sizes at short durations but becomes increasingly similar at long durations - thus areal reduction factors may be unnecessary when computing precipitation for long durations.
  • The reverse orographic effect causes increased precipitation for multihour events and decreased precipitation for hourly and sub-hourly durations; however, this phenomenon decreases over larger areas.

How to cite: Rosin, T., Morin, E., and Marra, F.: Exploring Precipitation Intensity-Duration-Area-Frequency Patterns using Weather Radar Data , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-772, https://doi.org/10.5194/egusphere-egu24-772, 2024.

EGU24-1126 | ECS | Posters on site | HS7.8

Intercomparison of Different Automatic Threshold Selection Methods in Modelling Precipitation Extremes via Peak Over Threshold Model 

Sree Anusha Ganapathiraju and Maheswaran Rathinasamy

The peak-over-threshold (POT) model is the most extensively used for regional precipitation frequency analysis (RPFA) for estimating extreme precipitation events (EPEs). Yet, choosing proper threshold values is critical and challenging while estimating rainfall quantiles for the Indian subcontinent due to the diverse climatic conditions and physical barriers. This study investigates and compares various threshold methodologies, including graphical, analytical, and multiple threshold methods (MTM) for identifying EPEs. These extracted extreme events with high thresholds followed the Generalized Pareto distribution (GPD), whose shape and scale parameters remain constant and increase linearly with increased threshold values. Therefore, the POT-GPD model was employed in the current work, and the parameters were estimated using L-moments to explore and quantify the heavy tail behavior. In addition, the uncertainty associated with the quantiles was also evaluated using nonparametric bootstrapping techniques and later understanding the spatial variability of the GPD parameters from various methods. The effectiveness of the models is assessed on daily gridded precipitation datasets for the Indian region and validated using synthetic datasets generated through Monte Carlo simulations. Results reveal the importance of combining the MTM and analytical threshold methods for identifying a range of critical thresholds to overcome the subjectivity of graphical methods and quantify the uncertainty. These findings contribute to developing region-specific thresholds, highlighting the importance of modifying thresholds to the regional characteristics rather than relying on a fixed percentile for characterizing the EPEs. The proposed approach is essential for assessing the increasing intensity and frequency of precipitation extremes associated with climate change while allowing for more focused mitigation actions and disaster risk reduction.

How to cite: Ganapathiraju, S. A. and Rathinasamy, M.: Intercomparison of Different Automatic Threshold Selection Methods in Modelling Precipitation Extremes via Peak Over Threshold Model, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-1126, https://doi.org/10.5194/egusphere-egu24-1126, 2024.

EGU24-1997 | ECS | Orals | HS7.8 | Highlight

Spatial coherences of flood-generating processes in Europe and their impact on flood statistics 

Svenja Fischer and Andreas Schumann

Flood events in Europe are caused by different generating mechanisms that lead to events with different peaks, volumes and hydrographs. Understanding such mechanisms is crucial not only for deterministic or stochastic modelling of floods, but also for practical purposes such as hydrological planning and design estimation. In this study, driving mechanisms of floods are analysed and the associated catchment and atmospheric attributes controlling these flood types are identified through a classification and regression tree approach. In addition, the role of flood types in flood statistics is analysed using type-based flood statistics. It is shown which flood types dominate the more frequent floods and which flood types are most frequently associated with extreme floods. Ordinary and extraordinary floods are identified by a Likelihood-Ratio test and tested for a significant difference in the frequency distribution of flood types. Our results show that the flood types vary regionally in Europe. In the Alpine region, heavy rainfall floods are responsible for the most extreme flood events, while in the northern parts of Europe flood events caused by snowmelt lead to the largest peaks. This is reflected in the flood statistics in the type-specific distributions, which have a different tail heaviness. These findings provide information to identify the most crucial circumstances in which floods become extreme and on the flood event itself.

How to cite: Fischer, S. and Schumann, A.: Spatial coherences of flood-generating processes in Europe and their impact on flood statistics, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-1997, https://doi.org/10.5194/egusphere-egu24-1997, 2024.

EGU24-2195 | ECS | Posters on site | HS7.8

Examining Daily Snow Depths at the Catchment Scale in Canada Using CMIP6 

Hebatallah Abdelmoaty, Simon Papalexiou, Abhishek Gaur, and Yannis Markonis

The lack of reliable data on daily snow depth (SD) is a significant challenge for studying water systems, ecology, and resources. Climate models present a potential solution for generating daily SD data, but the literature has not thoroughly explored how accurately they simulate this data. This study investigates the capabilities of CMIP6 climate models to replicate daily SD characteristics in eleven major Canadian catchments. The results depict that CMIP6 simulations overestimate the average SD values by a median of 57.7% (6.9 cm). In the Arctic and Pacific regions, this overestimation becomes particularly pronounced. However, the simulations align more closely with observations in smaller catchments with homogenous land characteristics. This finding suggests a shortcoming in how these models simulate different land types within the grid. Additionally, the models appear to overestimate the snow cover duration, with a median underestimation of 30.5 days. This overestimation could be due to the models failing to accurately account for the rates at which snow accumulates and melts away. However, the models perform relatively well when predicting extreme SD conditions. This study carries valuable implications for refining the outputs of climate models and effectively utilizing them in impact studies.

How to cite: Abdelmoaty, H., Papalexiou, S., Gaur, A., and Markonis, Y.: Examining Daily Snow Depths at the Catchment Scale in Canada Using CMIP6, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-2195, https://doi.org/10.5194/egusphere-egu24-2195, 2024.

EGU24-2908 | Posters on site | HS7.8

Modeling drought effects on rainfed crop yields using probabilistic and machine learning approaches 

Clement Sohoulande and Prakash Khedun

Drought is a major hazard with significant impacts on agriculture, water resource availability, and terrestrial ecosystems. Under climate change drought events are expected to increase in frequency, severity, duration, and propagation with consequent impacts on crop yields. Given these circumstances, a thorough understanding of drought is needed to increase societal preparedness to drought effects on food production particularly in regions where agriculture is dominantly rainfed. Unfortunately, drought events remain very unpredictable suggesting the need to enhance the understanding of drought effects on rainfed crops. Hence, this study aims to examine the relationships between drought characteristics and rainfed crop yields. Particularly, the study uses probabilistic and machine learning (i.e., random forest) approaches to investigate the influence of standardized precipitation and evapotranspiration index (SPEI) severity and duration on the yield of corn, cotton, peanuts, and soybeans in the southeast region of the United State (US). County wise analyses were conducted for three contiguous southeastern States including North Carolina, South Carolina, and Georgia. Preliminary results outlined different performances depending on the approach, the counties, and the crops. Highly performing approaches could be considered for modeling drought effect on crops at county, State, or regional levels.

How to cite: Sohoulande, C. and Khedun, P.: Modeling drought effects on rainfed crop yields using probabilistic and machine learning approaches, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-2908, https://doi.org/10.5194/egusphere-egu24-2908, 2024.

EGU24-2918 | Orals | HS7.8 | Highlight

Spatio-temporal clustering of storm surges along the global coastline 

Thomas Wahl, Alejandra Enriquez, and Ariadna Martin

When storm surges often affect the same coastline stretches simultaneously (i.e., they cluster in space, leading to spatial compounding) or if they occur in close succession (i.e., they cluster in time, leading to temporal compounding), the impacts are often greatly amplified. Hurricanes Irma and Maria in 2017 in the eastern Caribbean and Hurricanes Ian and Nicole in Florida were recent reminders how back-to-back storm surges affecting long coastline stretches can cripple economies and societies which are still in recovery mode. This can be a significant burden for the (re-)insurance industry and government budgets, as has been shown for the case of river floods (Jongman et al., 2014). Despite many examples where spatial or temporal compounding effects worsened coastal flooding impacts, developing appropriate tools to incorporate such events into present-day and future coastal flood impact assessments and hazard mitigation planning is still at its infancy. This presentation will showcase a novel algorithm to identify independent storm surge events and preliminary results from applying it to a global tide gauge data set to detect hotspots of temporal storm surge clusters at different time scales and different levels of extremeness. Results from identifying spatial storm surge footprints along the global coast and associated non-stationarity (for selected coastline stretches) will also be presented. The latter will be linked to large-scale weather patterns causing shifts in the spatial footprints at seasonal to decadal time scales. The results can inform the development of flexible statistical models capable of capturing both spatial and temporal dependences to overcome existing limitations in flood risk assessments where this is typically ignored.

How to cite: Wahl, T., Enriquez, A., and Martin, A.: Spatio-temporal clustering of storm surges along the global coastline, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-2918, https://doi.org/10.5194/egusphere-egu24-2918, 2024.

EGU24-5024 | ECS | Posters on site | HS7.8

Time Varying Copula based formulations for Flood Risk Assessment of two Tropical basins of Kerala, India 

Adarsh Sankaran, Meera G Mohan, Ananya Raj, and Anagha Shaji

Flood frequency analysis is a challenging but essential hydrologic problem for design of control structures and water resources management. The design flood estimates based on traditional stationary assumption may lead to inaccurate estimation of flood risk because of non-stationarity and the compounding impacts of several drivers in a dynamic environment. Copulas are a useful and adaptable technique for determining the multivariate joint dependency amongst flood variables. This study employed time-varying copula models to investigate the nonstationary dependence structures between two highly correlated flood variables, such as flood peak and flood volume, in order to determine the joint and conditional return periods of the flood events revealed by the 2018 Great Kerala floods. The proposed approach is executed for two potential locations of high flood risk namely, Periyar river basin and Greater Pamba river basin of Kerala, India. The Archimedean copula (Clayton, Frank and Gumbel) parameters were estimated using Maximum likelihood estimation and the optimal copula selection was made using Akaike Information Criterion. The non-stationary joint return time was found to be shorter than the stationary joint return period, suggesting that the extreme flood occurrences happened more frequently in the non-stationary bivariate study. Thus, it can be demonstrated that the extreme flood episodes are underestimated by stationary bivariate flood frequency analysis. The validation of results by comparing the flood magnitude of Neeleswaram station for 2018 flood quantile ascertained the necessity of non-stationary flood risk estimation. The study advocates the conduct of multivariate frequency analysis over the univariate analysis for the risk assessment of hydrological extremes. The results demonstrate that the long-term decision-making methods need to be updated to account for the oddities of the nonstationary climate. This study rendered flood risk assessment indicators as well as a risk-based design approach for hydraulic infrastructures in a non-stationary environment, which is crucial for climate change adaption and water security management.

How to cite: Sankaran, A., Mohan, M. G., Raj, A., and Shaji, A.: Time Varying Copula based formulations for Flood Risk Assessment of two Tropical basins of Kerala, India, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5024, https://doi.org/10.5194/egusphere-egu24-5024, 2024.

EGU24-5234 | ECS | Posters on site | HS7.8

Metereological Drought in the western Po river basin: trends and characteristics from 1958 to 2023 

Emanuele Mombrini, Stefania Tamea, Alberto Viglione, and Roberto Revelli

Since the start of the 21st century, greater focus has been put on drought and its wide range of environmental and socioeconomic effects, particularly in the context of climate change. This is especially true for the North-western region of Italy, comprising the Piedmont and Aosta valley, which have been affected in recent years by droughts that have had acute effects on water resources and water security in all sectors, including agriculture, energy and domestic use. The region also belongs to the Mediterranean hot-spot, characterized by faster than global average warming rates and higher vulnerability to their effects. Therefore, characterizing the observed changes and trends in drought conditions is of particular significance. To this end, 60 years of precipitation and temperature data from the North West Italy Optimum Interpolation data set are used to calculate the drought indices SPI (Standardized Precipitation Index) and SPEI (Standardized Precipitation Evapotranspiration Index) at a shorter (3-month) and at a longer (12-month) time scale. First, trend analysis on precipitation and temperature is performed, finding limited areas with significant precipitation decrease and, conversely, a general temperature increase over the region, with higher values found in the higher elevation areas. Changes in meteorological drought are then evaluated, both in terms of drought indices trends and in terms of changes in the characteristics of drought periods, on both a local and regional scale. A relation between the altitude of the area and the observed changes is highlighted, with significant differences between the plain and mountainous portion of the region. The differences are mainly related to the observed trends, with the low-altitude part of the region displaying a tendency towards dryer conditions not in common with the mountainous area. Significantly, no trend is found at a region-wide level but is instead found when considering homogeneous areas defined by terrain ruggedness. Furthermore, changes in the number of drought episodes and in their severity, duration and intensity are found to be correlated with terrain ruggedness at all time scales.

How to cite: Mombrini, E., Tamea, S., Viglione, A., and Revelli, R.: Metereological Drought in the western Po river basin: trends and characteristics from 1958 to 2023, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5234, https://doi.org/10.5194/egusphere-egu24-5234, 2024.

EGU24-5685 | ECS | Posters on site | HS7.8

Exploring Multivariate Return Periods: Enhancing Accuracy in Hydrological Analysis for Flood Prediction  

Diego Armando Urrea Méndez, Dina V. Gómez, and Manuel Del Jesus Peñil

The assessment of multivariate return periods determines how frequently different variables co-occur within a specific region. Recent studies have used two- and three-dimensional copulas for this assessment. G. Salvadori et al., (2011) introduced an approach based on Archimedean copulas and the Kendall measure. Gräler et al., (2013) calculated the trivariate return period using Vine copulas and Kendall distribution functions, incorporating annual maximum peak discharge, volume, and duration. Tosunoglu et al., (2020) applied three-dimensional Archimedean, Elliptical, and Vine copulas to study flood characteristics. These methodologies enhance the accuracy of extreme events risk measurement, emphasizing the importance of understanding tail dependence and the appropriate selection of copulas.

In multivariate analysis of compound extreme events, addressing the dependence structure in the tails of the variables of interest becomes essential. If the selected copula fails to accurately capture this extreme dependence, the estimation of extreme values may be significantly affected by uncertainty (Hangshing & Dabral, 2018). Therefore, conducting a comprehensive assessment of the copula model fit to the data is crucial, with a particular focus on tail dependence (Serinaldi, 2015). This process guides the choice of the most suitable copula family to model these compound extreme events.

We propose a two-part methodology: (I) In this phase, we focus on comparing various multivariate models that address the entirety of uncertainty. This involves analyzing different models and copula structures. The main objective is to evaluate how goodness of fit and tail dependence impact the calculation of design events, where, in some cases, underestimation may occur.

(II) In a subsequent stage, we formulate a more robust approach that encompasses the study, evaluation, and implementation of various statistical and machine learning techniques. The focus is on using the results obtained in the previous stage to develop flood models. These models enable us to compare multivariate approaches in terms of their performance in flood prediction and other associated impacts.

The study results highlight the importance of diversifying approaches in the hydrological analysis of precipitation-conditioned design events. It was found that the use of a multivariate approach provides more accurate estimations of precipitation compared to the univariate method. The careful choice of the multivariate model is crucial, as Gaussian models underestimate extreme events, while extreme vine copula models yield more tightly fitted results. This advancement benefits engineering by reducing uncertainty in design processes and providing a more precise approximation of climate impacts, with the potential to enhance territorial management.

References

Salvadori, C. De Michele, & F. Durante., 2011. On the return period and design in a multivariate framework. Hydrol. Earth Syst. Sci., 15(11), 3293–3305.

Gräler, B., Berg, M. J. van den, Vandenberghe, S., Petroselli, A., Grimaldi, S., De Baets, B. & Verhoest, N. E. C., 2013. Multivariate return periods in hydrology: a critical and practical review focusing on synthetic design hydrograph estimation. Hydrol. Earth Syst. Sci., 17(4), 1281–1296.

Hangshing, L. & Dabral, P. P., 2018. Multivariate Frequency Analysis of Meteorological Drought Using Copula. Water Resour Manage, 32(5), 1741–1758.

Serinaldi, F., 2015. Dismissing return periods! Stoch Environ Res Risk Assess, 29(4), 1179–1189.

How to cite: Urrea Méndez, D. A., V. Gómez, D., and Del Jesus Peñil, M.: Exploring Multivariate Return Periods: Enhancing Accuracy in Hydrological Analysis for Flood Prediction , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5685, https://doi.org/10.5194/egusphere-egu24-5685, 2024.

EGU24-6170 | Posters on site | HS7.8

On the Vulnerability of NATO Installations to Climate Variability and Change: A System-Level Perspective 

Gabriele Villarini, Sandro Carniel, Taereem Kim, Hanbeen Kim, Aniello Russo, Wenchang Yang, Gabriel Vecchi, and Thomas Wahl

This task focuses on the understanding of the spatial connections among 91 NATO installations subject to hydroclimatological extremes, including precipitation, surface temperature, and wet bulb temperature, under both historical and future conditions. It allows a system-level view of the vulnerabilities of NATO installations to climate change and the associated extremes. We first perform statistical bias correction and evaluate how well global climate models (GCMs) part of the Sixth Phase of the Coupled Model Intercomparison Project (CMIP6) are able to reproduce the historical trends. Based on these analyses, we select a subset of well-performing models, which we use to examine how the spatial dependence in climate extremes is projected to change. In particular, we consider two future periods (Mid-of-Century: 2015-2048; End-of-Century: 2067-2100) with respect to the 1981-2014 period, under four shared socioeconomic pathway scenarios (SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5).

We show that temperature-based extremes are correlated in space and have a large footprint, impacting more than one base at once. When we focus on precipitation extremes, we find that their spatial correlation is much weaker, with a much smaller chance of impacting more than one installation. Moreover, GCMs can reproduce these observed behaviors. In analyzing the future projections of these hydroclimatic extremes, we show that the spatial correlation in temperature-based extremes across NATO installations is projected to increase, especially toward the end of the 21st century and for higher emission scenarios. These results highlight the current and future susceptibility of the NATO installations to climate extremes in light of climate change when viewed through a system-level perspective.

How to cite: Villarini, G., Carniel, S., Kim, T., Kim, H., Russo, A., Yang, W., Vecchi, G., and Wahl, T.: On the Vulnerability of NATO Installations to Climate Variability and Change: A System-Level Perspective, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-6170, https://doi.org/10.5194/egusphere-egu24-6170, 2024.

EGU24-6265 | ECS | Orals | HS7.8

Understanding Compound Flooding hazard in Estuaries: Insights and Implications 

Dina Vanessa Gomez Rave, Diego Armando Urrea Mendez, and Manuel Del Jesus Peñil

Estuaries are highly prone to compound flooding. These areas often face flooding prompted by fluvial discharge, coastal water levels, wind and pluvial (rainfall) conditions (Moftakhari et al. 2017). Flooding drivers, even if they are not extreme individually, can combine and generate extreme local impacts. Nevertheless, their dependence and co-occurrence are often ignored, leading to misinterpretation of flooding risk. 

In this regard, assessing multivariate extremes requires understanding their stochastic structure and interconnections. Sensitivity relies on modeling properties like tail dependence strength and symmetry (Hua and Joe 2011). Copulas enable the study of tail dependency, providing insights into the relative strength between the extremes (De Luca et al, 2023). Once these primary dependencies and interconnected relationships are appropriately captured and modelled, the next step involves translating them into potential impacts (Zscheischler 2020). Therefore, defining hazard scenarios establishes the connection between the dependence structure of multiple drivers and the associated impacts. 

The critical level or return period used in risk analysis and infrastructure design inherently represents a hazard scenario. It can be seen as upper sets encompassing all occurrences deemed hazardous, potentially leading to impacts and damages based on certain criteria. This definition implies a connection with the upper tails of variables, which depicts specific dangerous conditions. In contrast to univariate analysis, where critical events are defined by surpassing a specific threshold, the multivariate hazard scenario lacks a singular definition (Bernardi et al. 2018). Moreover, in an n-dimensional framework, this set collects all 'dangerous' values based on suitable criteria and consequently defines the (n-1) iso-hyper-surface that generates the 'dangerous region', known as the 'critical layer' (Salvadori et al, 2011). In higher dimensions, this critical layer possesses more of a mathematical than a graphical definition, entailing theoretical and computational challenges.

This study aims to robustly characterize compound flooding in estuaries, employing high-dimensional analysis alongside multivariate statistical techniques and computational optimizations. Using a 100-year return level, critical events that compose the iso-hypersurface (critical layer) are identified. These design events capture variability, enabling the incorporation of uncertainty involved in predicting these dynamics.


References

Bernardi, M., Durante, F., Jaworski, P., Petrella, L., & Salvadori, G. (2018). Conditional risk based on multivariate hazard scenarios. Stochastic Environmental Research and Risk Assessment, 32, 203-211.
De Luca, G., Ruscone, M. N., & Amati, V. (2023). The use of conditional copula for studying the influence of economic sectors. Expert Systems with Applications, 120582.
Hua, L., & Joe, H. (2011). Tail order and intermediate tail dependence of multivariate copulas. Journal of Multivariate Analysis, 102(10), 1454-1471.
Moftakhari, H. R., Salvadori, G., AghaKouchak, A., Sanders, B. F., & Matthew, R. A. (2017). Compounding effects of sea level rise and fluvial flooding. Proceedings of the National Academy of Sciences, 114(37), 9785-9790.
Salvadori, G., De Michele, C., & Durante, F. (2011). On the return period and design in a multivariate framework. Hydrology and Earth System Sciences, 15(11), 3293-3305.
Zscheischler, J., Van Den Hurk, B., Ward, P. J., & Westra, S. (2020). Multivariate extremes and compound events. In Climate extremes and their implications for impact and risk assessment (pp. 59-76). Elsevier.

 

How to cite: Gomez Rave, D. V., Urrea Mendez, D. A., and Del Jesus Peñil, M.: Understanding Compound Flooding hazard in Estuaries: Insights and Implications, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-6265, https://doi.org/10.5194/egusphere-egu24-6265, 2024.

EGU24-6701 | ECS | Posters on site | HS7.8

Detecting the heaviness of daily rainfall probability distributions in Europe through an expeditious method, the Obesity Index 

Flavia Marconi, Benedetta Moccia, Elena Ridolfi, Fabio Russo, and Francesco Napolitano

Extreme precipitation events have a significant impact on hydraulic infrastructure design and risk management. The World Climate Research Programme (WCRP) Grand Challenges highlights the need for further investigation in analyzing and modeling weather and climate extremes due to their substantial effects. These rare meteorological occurrences represent the upper tail of the probability distribution, which can be effectively defined as heavy or light. The Obesity Index (OB) represents a user-friendly, non-parametric, empirical method capable of quantitatively assessing the heaviness of probability distribution tails directly from the original dataset, without the need to extract only extreme values. Our assessment of OB involves two distinct gridded datasets: one specific to Italy (SCIA) and another covering the entire Europe (E-OBS). The analysis shows a robust correlation between OB and L-moment ratios (L-variation, L-skewness, L-kurtosis), along with the Coefficient of Variation (CV). It is interesting to note that the tail heaviness in a specific region may vary depending on the dataset employed. For instance, OB indicates a prevalence of heavy tails across Italy or lighter tails in specific areas of the peninsula when employing SCIA or E-OBS dataset, respectively. This divergence could be attributed to an excessive smoothing of rainfall observations during the interpolation procedures used in generating E-OBS dataset. Thus, our findings reinforce the thesis of using light-tail probability distributions with caution when addressing rainfall extremes.

How to cite: Marconi, F., Moccia, B., Ridolfi, E., Russo, F., and Napolitano, F.: Detecting the heaviness of daily rainfall probability distributions in Europe through an expeditious method, the Obesity Index, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-6701, https://doi.org/10.5194/egusphere-egu24-6701, 2024.

EGU24-7433 | Orals | HS7.8

Flash Flood Hazard: A Counterfactual Analysis for Germany 

Paul Voit and Maik Heistermann

In response to heavy rainfall, flash floods can arise from rapid runoff concentration in the landscape, presenting significant damage potential due to high flow velocities and minimal lead times. Flash floods are among the most destructive natural hazards. Managing their risks usually necessitates the application of extreme value statistics. However, the small temporal and spatial scale of flash floods poses a challenge, as the requisite data for statistical methods is often unavailable or incomplete.  Furthermore, the effects of climate change may compromise the robustness of extreme value statistics.

To enhance our understanding of flash flood hazards in Germany, we present a novel "counterfactual" scenario analysis. This approach considers alternative ways of how events could have unfolded. To identify worst-case scenarios is particularly interesting for risk assessment. Accordingly, we assumed that historical rainfall events could have happened anywhere else in Germany: What would have happened if a particular rainfall event occurred in a different area? Would it result in a flash flood?

To address these questions, we created a catalog of extreme rainfall events for the years 2001-2022 from radar rainfall estimates. Because flash flood triggering rainfall is often embedded in precipitation fields of larger spatio-temporal extent, we used the cross-scale weather extremity index (xWEI) to identify and rate the events. We then shifted the ten most extreme events systematically across Germany and modeled the peak discharge for every shifted realization (counterfactual peaks), thus creating close to a billion runoff datasets. This approach preserves the spatio-temporal event structure that significantly influences the overlapping scales of runoff processes and hence the hazard. Results are provided to users via an interactive web interface.

Our results reveal that, on average, the worst case counterfactual peaks would exceed the maximum original peak by a factor 5.3. Furthermore, it shows that not every event is equally likely to trigger high runoff peaks, even when rated similarly extreme. Our study might help to expand the view on what could happen in case certain extreme events occurred elsewhere, help to identify flash flood prone areas, and thereby reduce the element of surprise in disaster risk management. The proposed method is transferable and could be a valuable asset, especially in data-scarce regions.

How to cite: Voit, P. and Heistermann, M.: Flash Flood Hazard: A Counterfactual Analysis for Germany, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-7433, https://doi.org/10.5194/egusphere-egu24-7433, 2024.

EGU24-10268 | ECS | Posters on site | HS7.8

Changing spatio-temporal characteristics of extreme rainfall events under climate change using high resolution climate projections  

Laura Devitt, Gemma Coxon, Jeffrey Neal, Leanne Archer, Paul Bates, and Elizabeth Kendon

Extreme precipitation is projected to intensify and occur more frequently under climate change. However, the effect of global warming on the spatial and temporal structure of extreme rainfall events at the local scale is uncertain. In the UK, the current method for estimating changes in flood hazard under climate change involves applying a simple multiplicative uplift to spatially uniform catchment rainfall. This approach neglects spatio-temporal characteristics of rainfall, which are known to be important for flood hazards. The UCKP Local Convection Permitting Model (CPM) has for the first time provided the capacity to assess these characteristics of rainfall at the local scale. Here, we use an ensemble of 2.2km hourly convection-permitting transient projections from UKCP Local to identify changes in the spatial and temporal characteristics of precipitation extremes over 100-years (1981-2080) across the UK. The analysis uses an ‘event-based’ approach, exploring seasonal changes in the peak intensity, total rainfall, and duration of events, but also changes in the spatial extent and temporal clustering of events through time. We identify ~13000 extreme rainfall events across the UK over the 100-year period. Event peaks are identified using a seasonal and time-varying threshold (99th percentile) on hourly rainfall rates, and event start and stop times are extracted using a lower threshold (20th percentile). We identify seasonal differences in how spatial extents of rainfall extremes will change, with winter and spring events growing, but summer and autumn events reducing in areal coverage. We also identify changes in the sub-seasonal timing of rainfall extremes, with events becoming more clustered, particularly during the winter months. Understanding changes in the spatial and temporal characteristics of rainfall events is critical as they may compound with increases in rainfall intensity, exacerbating the impacts of flooding.

How to cite: Devitt, L., Coxon, G., Neal, J., Archer, L., Bates, P., and Kendon, E.: Changing spatio-temporal characteristics of extreme rainfall events under climate change using high resolution climate projections , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-10268, https://doi.org/10.5194/egusphere-egu24-10268, 2024.

EGU24-11587 | ECS | Posters on site | HS7.8

Climatology and moisture sources of heavy rainfall in the Andes of southern Ecuador 

Diego Urdiales-Flores, Gregoire Mariethoz, Rolando Célleri, and Nadav Peleg

Mountains cover approximately one-quarter of the total land surface on the planet, and a significant fraction of the world’s population lives in their vicinity. Orography critically affects weather processes at all scales and, in connection with factors such as land-cover heterogeneity and mesoscale atmospheric process, is responsible for high spatial variability in mountain weather, such as the Tropical Andes. Due to this high complexity, monitoring the atmosphere in the Ecuadorian Andes has remained a challenge due to the lack of high spatio-temporal resolution operational observing systems. We studied heavy rainfall associated with floods to identify the main rain types and their sources of moisture based on non-stationary rainfall-similarity indices and Lagrangian approaches. We analyzed five years of data collected from a high space-time resolution (5 min and 500 m) X-band weather radar that was located at 4450 m a.s.l in the Tropical Andes of southern Ecuador. To identify the origin and trajectories of water vapor masses, we used the NOAA meteorological database (GDAS, global data assimilation system, at 0.5° resolution). Our analysis shows that the heavy rainfall in the region can be divided into five rainfall types: two spatially-clustered rain types (convective) and three spatially-homogenous rain types (stratiform). We found that air masses typing as convective reach the study area preferentially from the eastern flank of the Andes through the Amazon basin (~ 70% of all events). We also compared discharge data with rain types and discussed the type and source of rainfall potentially responsible for triggering flash floods in the Andes of southern Ecuador.

How to cite: Urdiales-Flores, D., Mariethoz, G., Célleri, R., and Peleg, N.: Climatology and moisture sources of heavy rainfall in the Andes of southern Ecuador, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-11587, https://doi.org/10.5194/egusphere-egu24-11587, 2024.

EGU24-13301 | ECS | Posters on site | HS7.8

A Global Analysis of Daily Streamflow Data to Unravel the Heaviness of Flood Distribution 

Masoud Zaerpour, Simon Michael Papalexiou, and Alain Pietroniro

Hydroclimatic extremes, such as floods, present complex challenges in risk assessment due to their spatial and temporal compounding nature. This research aims to improve our understanding and modelling capabilities by investigating the complex interactions among record length, flow regime, and upper tail of floods. Our study resolves conflicting results in prior studies by utilizing a quasi-global peak-over-threshold (POT) analysis of flood with the Generalized Pareto (GP) distribution. Based on an analysis of 4,482 streamflow series over six different regime types with record lengths ranging from 30 to 213 years, our results show a strong relationship between the GP shape parameter and record length. The results show that the variance of the shape parameter of GP distribution diminishes with record length, and it eventually converges to a single value depending on the flow regime. We show that the shape parameter of snow-dominated streams is the lowest, whereas intermittent streams have the highest. Our research reveals regime-specific patterns in the impact of hydroclimatic and catchment controls on flood tails, underscoring the necessity of regime-specific strategies for flood risk management. Identifying catchments that are more likely to experience extreme flooding provides useful information for determining which mitigation measures to prioritize.

How to cite: Zaerpour, M., Papalexiou, S. M., and Pietroniro, A.: A Global Analysis of Daily Streamflow Data to Unravel the Heaviness of Flood Distribution, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-13301, https://doi.org/10.5194/egusphere-egu24-13301, 2024.

EGU24-14054 | ECS | Posters on site | HS7.8 | Highlight

Climate Change both Increases and Decreases Winter Snowmelt across North America 

Shadi Hatami, Masoud Zaerpour, Jan Adamowski, and Simon Michael Papalexiou

Snowmelt is a vital source of freshwater for a large proportion of North America’s population. Sudden snowmelt can also lead to various extreme events and environmental hazards, such as floods in the cold season and droughts in the upcoming warmer months. However, this natural water resource is at risk due to climate change and variability. Temperature and precipitation are significant climatic controllers that regulate snowmelt dynamics. Warmer temperatures can affect snowmelt extremes, persistence, and distribution, while changing precipitation alters the available snow budget and, consequently, the snowmelt amount. Yet, the precise role of the compound changes in temperature and precipitation under changing climate on future snowmelt dynamics is unknown. To address this knowledge gap, we use observation-driven data and future projections to quantify the response of winter snowmelt to changes in temperature and precipitation across North America (United States and Canada). Our analysis of far-future (2091-2100) changes reveals a significant increase (> 60%) in winter (November-March) snowmelt in northern latitudes, while it declined (by up to< 38%) in southern latitudes. Higher temperatures proved to be the primary driver of the increased snowmelt, whereas decreased snowfall modulated the declines in snowmelt, with variability seen across the study domain. Our findings suggest that the probability of an increase in winter snowmelt is high under the warmer and wetter climatic conditions prevailing in northern regions. In contrast, winter snowmelt across southern latitudes is likely to decline. These findings have significant implications for freshwater availability in the future in the affected areas. 

How to cite: Hatami, S., Zaerpour, M., Adamowski, J., and Papalexiou, S. M.: Climate Change both Increases and Decreases Winter Snowmelt across North America, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-14054, https://doi.org/10.5194/egusphere-egu24-14054, 2024.

EGU24-14140 | ECS | Posters virtual | HS7.8

Unfolding Multivariate Drought Risk in Large River Basins accounting Onset Seasonality and Event Magnitude 

Aparna Raut and Poulomi Ganguli

The frequency and severity of droughts are expected to increase in the warming climate. Understanding mutually interacting drought properties, such as their severity (deficit volume) and time to onset, is crucial for managing reservoir operations and low flows. Previous studies have performed bivariate drought frequency analysis considering drought severity and duration across different climate regions. However, little is known about the role of drought seasonality in shaping drought severity. This study aims to investigate the dependence between onset time (i.e., directional occurrence date) and deficit volume and evaluate the impact of drought seasonality on the deficit volume distributions in disparate climate regions across the global tropics. Leveraging streamflow observations from representative catchments in the northern and southern hemispheres and considering the nonlinear dependence strengths between onset time and deficit volume, we implemented a multivariate drought frequency model that yields a conditional probability of drought severity given the timing of peak drought intensity. We consider multiple univariate probability functions for modelling drought deficit volume, whereas drought onset time is modelled using von Mises distribution. Further, the joint dependence between drought onset and deficit volume is modeled using a bivariate Archimedean class of copulas. First, we show temporal variations of exceedance probabilities of drought deficit volume and their seasonal clustering behavior during dry/wet phases and then explore any possible shift in the risk of peak drought intensity based on its seasonality. Finally, employing a flexible multivariate probabilistic tool, we demonstrate different scenarios of drought characteristics combinations and a seasonality-informed drought probability model, aiding understanding complex processes of drought propagations across disparate climate regimes and assessing possible climatic shifts to drought frequency.

How to cite: Raut, A. and Ganguli, P.: Unfolding Multivariate Drought Risk in Large River Basins accounting Onset Seasonality and Event Magnitude, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-14140, https://doi.org/10.5194/egusphere-egu24-14140, 2024.

EGU24-14570 | ECS | Posters on site | HS7.8

Regional Flood Risk Assessment Using Ensemble of GCMs 

Kiran Kezhkepurath Gangadhara, Sarath Muraleedharan, Raja Bharath, and Martin Kadlec

Flood risk assessment is generally carried out at a basin scale by developing hydrologic and hydraulic models with an objective to arrive at hazard/inundation maps for the river segments/reaches. A hydrologic model is used to derive a flood hydrograph corresponding to a specific return period and this is routed through the flood plain of the study area with the aid of a hydraulic model to obtain water surface elevations and inundation extents. This approach is best suited to represent a flood event at a river segment accurately, but the risks associated with floods need to be analyzed on a regional scale for effective flood risk management. As the size of the region increases beyond one basin, this approach fails to realistically represent the flood events across different river segments and basins. The simultaneous occurrences of different return periods on different river segments cannot be captured by this approach. The traditional approach to model these simultaneous occurrences is by using the streamflow records to arrive at spatially correlated stochastic simulations of streamflow. One issue that compounds this problem is the data scarcity in certain regions to accurately estimate the return periods of floods at distinct locations.

To address these issues, Impact Forecasting, Aon’s catastrophe model development team, has undertaken a study to simulate stochastic flood events in the Southeast Asian region by considering Malaysia as a case study. The approach involves (i) downscaling of precipitation and temperature data from an ensemble of Global Circulation Models (GCMs), (ii) calibration of a grid-based Rainfall-Runoff (RR) model using available historical data of meteorological variables and streamflow, (iii) providing the downscaled precipitation and temperature data as input to the calibrated RR model to simulate streamflow across the study area and (iv) identifying flood events from the simulated streamflow to extract an exhaustive set of realistic flood events in the region. The approach involves the use of meteorological data of 15,000 years from 7 different GCMs, downscaled to 10 km grids from 100 km resolution. This enables capturing a broader spectrum of potential climate conditions and therefore generating a wide range of possible flood events without relying significantly on in-situ data. The proposed methodology considers the uncertainty inherent in climate models, providing a robust framework for assessing flood risk and results in a more reliable and realistic representation of stochastic flood events over a region. The approach presents a physically based alternative to the commonly used statistical approaches based on extreme value theory and could be a valuable tool for policymakers, researchers, and practitioners in making informed decisions in the face of evolving climate conditions.

How to cite: Kezhkepurath Gangadhara, K., Muraleedharan, S., Bharath, R., and Kadlec, M.: Regional Flood Risk Assessment Using Ensemble of GCMs, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-14570, https://doi.org/10.5194/egusphere-egu24-14570, 2024.

EGU24-16999 | ECS | Orals | HS7.8

Spatio-Temporal Dynamics of Extreme Precipitation and Dry Spells in Alpine Catchments under Changing Climatic Conditions 

Tatjana Milojevic, Christian R. Steger, and Michael Lehning

Heavy and extreme precipitation and drought events are expected to increase in frequency and intensity as a result of climate change. Investigating the projected evolution of these events in terms of their spatio-temporal dynamics is important for understanding if certain regions are more susceptible to negative impacts of the changes in extremes. The spatio-temporal dynamics of extremes in complex terrain, such as in the Swiss Alps, is of particular interest as the same event might impact nearby catchments in different ways. Using climate model data at a horizontal resolution of 2.2km, dynamically downscaled with the regional climate model COSMO for the emission scenario RCP8.5, we explore projected extreme precipitation and dry spells for the end of the 21st century (2090-2099) relative to present conditions. We apply connected component labelling (CCL) to define precipitation clusters and identify the spatio-temporal changes in extreme precipitation events in alpine catchments of the southern Swiss Alps. In addition, we investigate changes between present and possible future drought conditions. The main aim is to determine if certain watersheds in the southern Alps are expected to experience different vulnerabilities to climate change-driven extreme precipitation and drought events and if the propensity to a certain type of extreme varies between different catchments. Preliminary results indicate that, relative to present-day conditions, the total amount of precipitation tends to decrease in the future scenario with increasing temperature across multiple sites. Initial assessment of the CCL results indicates that a higher overall number of extreme precipitation clusters may be found in the future summer season relative to present conditions, with weaker differences for the remaining seasons. We also expect to find shifts in the spatial range and duration of the precipitation clusters and dry spells between the present and end of century conditions.

How to cite: Milojevic, T., Steger, C. R., and Lehning, M.: Spatio-Temporal Dynamics of Extreme Precipitation and Dry Spells in Alpine Catchments under Changing Climatic Conditions, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-16999, https://doi.org/10.5194/egusphere-egu24-16999, 2024.

EGU24-17547 | Orals | HS7.8 | Highlight

The space-time representation of extraordinary rainfall events 

Salvatore Manfreda

Extraordinary events are rarely observable in a single rainfall gauge, and this make extremely challenging the correct prediction of their arrivals. However, it may be possible to develop a more robust approach by employing a space-time modelling scheme that is able to capture the spatial dynamics of such phenomena. Therefore, a space-time Poisson model of rainfall cells with circular shape and random depth has been exploited for the first time to interpret the behaviour of this family of extraordinary events. This category of events that may be connected to larger meteorological phenomena not necessarily connected with local heterogeneity of the landscape. Following the identification of the observed extraordinary event across southern Italy, six zones with significantly different dynamics in terms of the frequency of such extremes were identified. Subsequently, a simple mathematical representation was adopted to calibrate the model parameters, leading to an estimate of regional probability distributions defined on the space-time occurrences of extraordinary events over homogeneous zones. The approach allows to overcome the limitations posed by point observations allowed the definition of a probability distribution that pertains to an entire area rather than just a point. The obtained quantiles of rainfall estimated seems to align well with the upper bound of the probability distribution of the annual maxima observed over the areas of interests.

Keywords: Rainfall statistics, Space-time Poisson models; Extraordinary events.

How to cite: Manfreda, S.: The space-time representation of extraordinary rainfall events, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-17547, https://doi.org/10.5194/egusphere-egu24-17547, 2024.

EGU24-19682 | ECS | Orals | HS7.8

Informativeness of teleconnections in local and regional frequency analysis of rainfall extremes 

Andrea Magnini and Attilio Castellarin

Numerous studies have established strong long-range relationships (teleconnections) between global climatic indexes and precipitation across diverse geographical regions worldwide. Typically, these investigations focus on the number of wet days or cumulative rainfall over specific seasons or the entire year, while only a few explicitly explore the informative value of teleconnections in describing the frequency regime of sub-daily rainfall annual maxima. Furthermore, most studies analyze the correlation between rainfall characteristics and teleconnection index values at individual gauge stations within the same season and without considering any time lag. 

Our study provides a comprehensive assessment of the potential and informative content of teleconnections for representing and modeling the frequency regime of rainfall extremes, addressing the limitations mentioned above. Our dataset comprises annual maximum series (AMS) of sub-daily rainfall depth recorded between 1921 and 2022 at approximately 2300 rain gauges spanning a large and climatically diverse region in Northern Italy. Based on a comprehensive literature review, we selected six global climate indexes and evaluated their correlation with time series of gridded regional L-moments, statistical measures characterizing the distribution of sub-daily rainfall extremes. In analyzing the spatial patterns of gridded L-moments, we considered time aggregation intervals (durations) ranging from 1 to 24 hours, discretization of the study region with tile sizes (resolutions) up to 100 km, and time lags in teleconnections up to 30 years. Our results reveal significant spatial patterns in the teleconnections, with the Western Mediterranean Oscillation Index exhibiting stronger relationships. The robustness of these spatial patterns is confirmed by their limited sensitivity to the chosen grid resolution and time lag, likely arising from the utilization of time series of spatially smoothed statistics of AMSs (gridded L-moments) rather than raw annual sequences of rainfall maxima. Consequently, our research suggests promising pathways for climate-informed local and regional frequency analysis of rainfall extremes. 

How to cite: Magnini, A. and Castellarin, A.: Informativeness of teleconnections in local and regional frequency analysis of rainfall extremes, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-19682, https://doi.org/10.5194/egusphere-egu24-19682, 2024.

EGU24-20390 | ECS,ECS | Orals | HS7.8

Evaluating Compound Risks of Heatwaves and Droughts on Crop Yield and Food Security in Morocco 

Bouchra Zellou, El Houcine Bergou, and Nabil El Moçayd

In an era marked by climate change, heatwaves and droughts have increasingly begun to co-occur within a single growing season, significantly impacting crop yields in key agricultural regions globally. Against this backdrop, the current study is dedicated to quantitatively evaluating the effects of these combined hot-dry episodes on agricultural productivity in Morocco, a country where such climatic extremes pose a significant threat to food security and economic stability. Utilizing high-resolution gridded precipitation and temperature data that closely aligns with 29 ground station observations, we calculate the Standardized Precipitation Index (SPI) and the Standardized Temperature Index (STI) across Moroccan arable regions in the agricultural season (September-May) during 1981-2018. Employing a vine-copula conditional probability model, the study explores the complex interactions between drought and heatwaves and their joint impact on vegetation, as indicated by the Normalized Difference Vegetation Index (NDVI). The focus is on identifying the conditional probability of vegetation loss under multiple compound dry-hot episodes. The findings highlight that the combined effects of droughts and heatwaves can have catastrophic consequences for crop yields, especially during the growth season. This underscores the critical need to assess their compound impact on agricultural productivity, rather than examining each factor separately. This study provides a robust understanding of compound hot-dry events and their impacts on crop yields, highlighting the emerging need for comprehensive adaptation strategies that bolster agricultural resilience and support sustainable productivity in the face of evolving climatic challenges.

Keywords: Compound, drought, heat waves, NDVI, vine-copula, conditional probability.

How to cite: Zellou, B., Bergou, E. H., and El Moçayd, N.: Evaluating Compound Risks of Heatwaves and Droughts on Crop Yield and Food Security in Morocco, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-20390, https://doi.org/10.5194/egusphere-egu24-20390, 2024.

This study aims to identify key characteristics of rainfall events, such as critical duration, extent, and severity (i.e., return period), to unveil potential dependencies with the resulting impact scenarios. Severity diagrams, introduced by Ramos et al. (2005) serve here as a straightforward tool, providing a synthetic visualization of storm severity while accounting for the complexity associated with rainfall spatial variability and duration. The method emphasizes the coexistence of extreme and ordinary (non-extreme) rainfall intensities. In contrast, the conventional approach of assigning a single return period to an event obscures a significant portion of storm complexity by overlooking spatial variability. Maximum mean areal precipitations observed over different areas during the storm event are evaluated. Subsequently, maximum equivalent point rainfalls are derived using ARF (Areal Reduction Factor) estimation, and their return period values deduced from the IDF (Intensity Duration Frequency) curves. The return periods are eventually mapped as a function of area and duration of rainfall accumulation. Several damaging storm events observed in the Calabria region (south Italy) over the last 20 years have been selected as illustrative examples for the analysis.

How to cite: Biondi, D. and Bloise, S.: Characterization of Extreme Rainfall Events Severity in Calabria: Exploring Spatial-Temporal Variability through Severity Diagrams, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-20687, https://doi.org/10.5194/egusphere-egu24-20687, 2024.

Nature depends on the inherent unpredictability of randomness, a significant force influencing hydrometeorological processes. While physics provides sophisticated models, understanding the variability within randomness is crucial for evaluating environmental risks. Despite the availability of numerous stochastic models tailored to specific statistical properties, identifying essential features for accurate simulations across time, space, and scales remains a challenge. This presentation outlines the progress in CoSMoS, a user-friendly stochastic modeling framework that advances from basic scenarios to complex multisite and space-time simulations. The underlying philosophy of this framework is to faithfully replicate the probabilities describing the occurrences of magnitudes and correlations in space and time. CoSMoS excels in generating time series for various hydroclimatic variables and simulating intricate space-time phenomena, as demonstrated by its effectiveness in replicating storms, cyclones, and air mass collisions. This showcases its versatility in capturing complex behaviors across different scales.

References

  • Papalexiou, S. M., Serinaldi, F., & Clark, M. P. (2023). Large-Domain Multisite Precipitation Generation: Operational Blueprint and Demonstration for 1,000 Sites. Water Resources Research, 59(3), e2022WR034094. https://doi.org/10.1029/2022WR034094
  • Papalexiou, S. M. (2022). Rainfall Generation Revisited: Introducing CoSMoS-2s and Advancing Copula-Based Intermittent Time Series Modeling. Water Resources Research, 58(6), e2021WR031641. https://doi.org/10.1029/2021WR031641
  • Papalexiou, S. M., Serinaldi, F., & Porcu, E. (2021). Advancing Space-Time Simulation of Random Fields: From Storms to Cyclones and Beyond. Water Resources Research, 57(8), e2020WR029466. https://doi.org/10.1029/2020WR029466
  • Papalexiou, S. M., & Serinaldi, F. (2020). Random Fields Simplified: Preserving Marginal Distributions, Correlations, and Intermittency, With Applications From Rainfall to Humidity. Water Resources Research, 56(2), e2019WR026331. https://doi.org/10.1029/2019WR026331
  • Papalexiou, S. M. (2018). Unified theory for stochastic modelling of hydroclimatic processes: Preserving marginal distributions, correlation structures, and intermittency. Advances in Water Resources, 115, 234–252. https://doi.org/10.1016/j.advwatres.2018.02.013
  • Papalexiou, S. M., Markonis, Y., Lombardo, F., AghaKouchak, A., & Foufoula‐Georgiou, E. (2018). Precise Temporal Disaggregation Preserving Marginals and Correlations (DiPMaC) for Stationary and Nonstationary Processes. Water Resources Research, 54(10), 7435–7458. https://doi.org/10.1029/2018WR022726

How to cite: Papalexiou, S. M.: Simulating Nature’s randomness with CoSMoS - A Versatile Stochastic Modeling Framework for Hydrometeorological Phenomena, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-21666, https://doi.org/10.5194/egusphere-egu24-21666, 2024.

EGU24-599 | ECS | Posters on site | AS1.22

Langragian analysis of the extreme-windstorm dynamics associated to post-tropical cyclone Leslie landfall in Portugal 

Miguel Lima, Luana C. Santos, Rita M. Cardoso, Pedro M. M. Soares, and Ricardo M. Trigo

Windstorms in Europe are responsible for more than half of the economic loss associated with natural disasters. In October 2018 a post-tropical cyclone, formerly Hurricane Leslie, made landfall in continental Portugal. This event was characterized by very intense winds, with a gust record-hitting value of 176 km/h registered near Figueira da Foz, a coastal city located in the center of the country. The main factors causing this event of extreme winds were likely a “cold-conveyor belt jet” or a “jet sting”, roughly 12 hours after losing its main tropical characteristics. Despite the strong impact associated with this windstorm there are still few studies modeling this kind of dynamics, and here we present a simulation and thorough analysis of the rare dynamics linked with this post-tropical cyclone affecting western Europe.

The WRF-ARW model, version 4.4.1, was used to numerically model Leslie as it transitioned from a hurricane to post-tropical cyclone. Three one-way nested domains were used with a large (5 km), medium (1 km), and lower (200m) resolution, with 68 hybrid levels (15 m - 20 hPa). The larger domain covers the Iberian Peninsula and a large portion of the Atlantic Ocean nearby, while the inner ones are focussed in the central and northern sectors of continental Portugal - the most affected areas. Initial and boundary conditions were retrieved from the GFS operational analysis at 0.25º spacing, in 6-hour intervals. Due to the difficulties modeling this cyclone, nudging was used in the outer domain to ensure that the cyclone would make landfall as close as possible to the real location.

Several state-of-the-art thermodynamics-based diagnostics were used to analyze in-depth the midlatitude cyclone dynamics observed in the recently transitioned cyclone Leslie. Midlatitude cyclone-related dynamics were identified in the simulation, leading to the extreme winds in the most impacted region. The set of final simulated data reveals a close resemblance to the real event, with parameterized wind gusts presenting a lower intensity around 140 km/h, but the largest values impacting approximately the same region of center Portugal. A Langragian approach was also used to study particle trajectories and evaluate the atmospheric circulation leading to the extreme winds showing vertical downdrafts up to 4 m/s. This study highlights the catastrophic potential a post-tropical cyclone such as Leslie has and, while at the end of their life-time with presumably less intensity, storms of this type should not be disregarded for warnings and need to be considered in general evaluations of midlatitude storm impacts.

Acknowledgements: This work was funded by the Portuguese Fundação para a Ciência e a Tecnologia (FCT) I.P./MCTES through national funds (PIDDAC) – UIDB/50019/2020. M. M. Lima was supported through the PhD MIT Portugal MPP2030-FCT programme grant PRT/BD/154680/2023. L. C. Santos is supported by the EarthSystems Doctoral School, at University of Lisbon, supported by Portuguese Fundação para a Ciência e a Tecnologia (FCT) project UIDP/50019/2020-2023, University of Lisbon.

How to cite: Lima, M., C. Santos, L., M. Cardoso, R., M. M. Soares, P., and M. Trigo, R.: Langragian analysis of the extreme-windstorm dynamics associated to post-tropical cyclone Leslie landfall in Portugal, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-599, https://doi.org/10.5194/egusphere-egu24-599, 2024.

EGU24-1418 | ECS | Posters on site | AS1.22 | Highlight

Windstorm losses in Europe - What to gain from damage datasets 

Julia Moemken, Gabriele Messori, and Joaquim G. Pinto

Windstorms are among the most impacting natural hazards affecting Western and Central Europe. Information on the associated impacts and losses are essential for risk assessment and the development of adaptation and mitigation strategies. In this study, we compare reported and estimated windstorm losses from five datasets belonging to three categories: Indices combining meteorological and insurance aspects, natural hazard databases, and loss reports from insurance companies. We analyse the similarities and differences between the datasets in terms of reported events, the number of storms per dataset and the ranking of specific storm events for the period October 1999 to March 2022 across 21 European countries.

A total of 94 individual windstorms were documented. Only 11 of them were reported in all five datasets, while the large majority (roughly 60%) was solely recorded in single datasets. Results show that the total number of storms is different in the various datasets, although for the meteorological indices such number is fixed a priori. Additionally, the datasets often disagree on the storm frequency per winter season. Moreover, the ranking of storms based on reported/estimated losses varies in the datasets. However, these differences are reduced when the ranking is calculated relative to storm events that are common in the various datasets. The results generally hold for losses aggregated at European and at country level.

Overall, the datasets provide different views on windstorm impacts. Thus, to avoid misleading conclusions, we use no dataset as “ground truth” but treat all of them as equal. We suggest that these different views can be used to test which features are relevant for calibrating windstorm models in specific regions. Furthermore, it could enable users to assign an uncertainty range to windstorm losses. We conclude that a combination of different datasets is crucial to obtain a representative picture of windstorm associated impacts.

How to cite: Moemken, J., Messori, G., and Pinto, J. G.: Windstorm losses in Europe - What to gain from damage datasets, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-1418, https://doi.org/10.5194/egusphere-egu24-1418, 2024.

EGU24-1553 | ECS | Posters on site | AS1.22

An Investigation into the Role of the Ocean for Seasonal Predictability of European Windstorms 

Kelvin S. Ng and Gregor C. Leckebusch

Extreme extra-tropical cyclones and related windstorms are the most dangerous and costly meteorological hazards in Europe. The latest state-of-the-art seasonal forecast suites show now usable forecast skill for basic parameters like mean temperature or precipitation for mid-latitude Europe on lead times of up to 4 months (Nov-Feb). One avenue for skilful prediction of extremes is the now-proven forecast skill for large-scale climate modes, as these directly influence extreme windstorms. Improved ability to simulate successfully the relevant large-scale climate patterns like e.g., the North-Atlantic Oscillation, the East-Atlantic pattern, and/or the Scandinavian pattern opens up a prominent route to progress the forecast skill for extreme storms.

Nevertheless, recent publications have shown that even in the current model suites, the existing skill for forecasting the frequency or intensity of windstorm tail events, is not fully explained by those dominant large-scale variability patterns. Furthermore, studies revealed a potential connectivity of storm count predictions to stratospheric sudden warming events and also highlighting the influence of atmosphere-ocean coupling. Recent developments in the forecast skill of the upper-ocean heat content and the role of re-emerging temperature anomalies for the European winter climate allow to explore another pathway with potentially predictive power, the role of ocean-atmosphere interaction. Ocean-atmosphere interaction caused e.g., by the NAO have been increasingly recognised but have not been systematically linked to the ability to predict extreme severe windstorms on a seasonal time scale. In this presentation, we will present preliminary results of the role of ocean on the predictability of European windstorms.

How to cite: Ng, K. S. and Leckebusch, G. C.: An Investigation into the Role of the Ocean for Seasonal Predictability of European Windstorms, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-1553, https://doi.org/10.5194/egusphere-egu24-1553, 2024.

EGU24-1736 | ECS | Posters on site | AS1.22 | Highlight

Intra-seasonal variability of temporal clustering of European winter windstorms 

Sophie Feltz, Gregor C. Leckebusch, Kelvin S. Ng, and Tim Kruschke

Severe European winter windstorms are one of the most damaging natural hazards and thus a major threat to societies. Clustered European winter windstorms, storms that occur in quick succession over a specific period of time over a fixed location, can result in amplified structural and environmental damage and accumulated losses. Yet, variability of storm clustering on intra-seasonal timescales has not been fully investigated. We analyse winters (DJF) for the period 1981-2016 from ERA5 reanalysis, where tracks and storm impact footprints are identified through the impact-oriented wind-based tracking algorithm WiTRACK.  

We quantify the magnitude of clustering using the widely employed dispersion statistic as used in Mailier et al. (2006). The spatial distribution of clustering on 45- and 30-day timescales as well as the time development of clustering on even shorter 30-, 20-, 15- and 11-days reference periods are investigated. Thus, in a seamless approach from seasonal to synoptic clustering. Results from both windstorm clustering of tracks and the storm footprints will be presented. Preliminary findings suggest an increase in clustering occurrence in the later half of the winter season on 45- and 30-day timescales.  On shorter timescales (<30 days), depending on location, distinct periods of increased clustering e.g., in the middle and the end of the season can be identified.

How to cite: Feltz, S., Leckebusch, G. C., Ng, K. S., and Kruschke, T.: Intra-seasonal variability of temporal clustering of European winter windstorms, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-1736, https://doi.org/10.5194/egusphere-egu24-1736, 2024.

EGU24-1974 | ECS | Posters virtual | AS1.22

Shifting of Western Disturbances winter precipitation over Western Himalayas 

Pooja Pooja and Ashok Priyadarshan Dimri

The Indian Himalayan region receives an enormous amount of precipitation due to synoptic weather systems known as Western Disturbances (WDs). WDs are east-ward propagating systems embedded in the Subtropical Westerly Jetstream (SWJ). The main objective of this study is to investigate the change in magnitude and dynamics of WDs precipitation over the western Himalayan region. In this study, different observational datasets (IMD, AHRODITE, GPCP, GPCC, and ERA5) were selected to compare and assess the magnitude of WDs precipitation for the period 1987–2020 during the winters (DJF: December, January, and February). Further, to examine the structure of WDs precipitation at the pressure level of 200hPa, ERA5 Reanalysis datasets having a similar resolution of 25 km with the gridded dataset of the Indian Meteorological Department (IMD) are used for the analysis. WDs moisture sources from the Arabian Sea are assessed at 23 pressure levels (1000–200 hPa) for further understanding of WDs dynamics. Our study shows the daily shifting of WDs precipitation towards February during the winters and an intriguing decrease in daily WDs precipitation in recent years. During the study, we found that WDs precipitation contributed a significant amount of precipitation (~80%) over the Western Himalayan region of the Indian subcontinent. Using Theil-Sen method, trend analysis was performed, showing a decreased trend of WDs precipitation in recent years The present findings indicate that WDs have changed their precipitation characteristics and dynamics due to climate change. The number of active WDs days is decreasing. Our results show there is enough moisture present over the Bay of Bengal region other than WDs which helps in sustaining and replenishing glaciers over the Indian Himalayan region.

How to cite: Pooja, P. and Dimri, A. P.: Shifting of Western Disturbances winter precipitation over Western Himalayas, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-1974, https://doi.org/10.5194/egusphere-egu24-1974, 2024.

EGU24-3651 | ECS | Orals | AS1.22

Air-Sea Flux Influences on Extratropical Cyclone and Atmospheric River Mesoscale Development and Upstream Temporal Clustering 

Juan Crespo, Catherine Naud, Rosa Luna-Niño, James Booth, and Derek Posselt

Latent and sensible heat fluxes (LHF and SHF, respectively) within the marine boundary layer are believed to play a significant role in the genesis and evolution of Extratropical Cyclones (ETCs) and Atmospheric Rivers (ARs, often associated with ETCs in the midlatitudes). However, consistent observations of air-sea interactions with in-situ observatories are limited in both time and space, and traditional polar orbiting satellites may miss large swaths in the lower midlatitudes due to their orbits, leading to daily gaps in coverage where the most robust fluxes often occur and change rapidly. Satellite missions like CYGNSS (Cyclone Global Navigation Satellite System) have filled in data gaps by providing improved observations over the lower midlatitudes of air-sea interactions. These improved observations of air-sea processes, coupled with observations of cloud and precipitation structure within ETCs and ARs from other satellites, like GPM and MODIS, can help one begin to link the correlations between surface heat fluxes to changes of the mesoscale features within these synoptic-scale systems. Previous studies have shown the correlation of observed surface heat fluxes to precipitation and cloud thickness increases along the frontal regions. Still, they have only looked at the connections between ETCs and ARs when LHF and SHF were at their strongest or the peak intensity of the system, not during its early formation (or just before formation) when they may be at their strongest. 

Additionally, recent studies have examined through idealized models how surface heat fluxes within an ETC can impact the development of ETCs and ARs upstream of the primary cyclone and lead to multiple ETCs in succession, often called a family or temporal clustering of ETCs and ARs. This clustering can lead to significant and excessive precipitation over parts of the globe, such as the United States West Coast in early 2023, with successive ARs over one month. Improved observations of real-world conditions can help us better understand the interplay within these systems. This presentation will highlight the role air-sea interactions may have during the genesis and early evolution of ETCs and ARs, the correlations to cloud and precipitation structure changes, the upstream impacts, and setting the groundwork that will be able to show that air-sea interactions directly impact the development of these systems.

How to cite: Crespo, J., Naud, C., Luna-Niño, R., Booth, J., and Posselt, D.: Air-Sea Flux Influences on Extratropical Cyclone and Atmospheric River Mesoscale Development and Upstream Temporal Clustering, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-3651, https://doi.org/10.5194/egusphere-egu24-3651, 2024.

EGU24-3675 | Orals | AS1.22 | Highlight

Poleward intensification of midlatitude extreme winds under warmer climate 

Emanuele Silvio Gentile, Ming Zhao, and Kevin Hodges

In this work, we investigate the global impact of midlatitude cyclones on the geographical distribution and intensity of near-surface extreme wind speeds in a warmer climate. We use  state-of-the-art high-resolution general circulation models developed by the Geophysical Fluid Dynamics Laboratory. Results indicate a clear poleward shift of extreme wind speeds, driven by the associated shift in midlatitude storm tracks, and attributed to global warming and associated changes in general circulations. The total number of midlatitude cyclones decreases by roughly 4%, but the proportion of cyclone-associated extreme wind speed events increases by 10% in a warmer climate. Notably, the research has identified Northwestern Europe, the British Isles, and the West Coast of North America as hot spots with the greatest socio-economic impacts from increased cyclone-associated extreme winds. In addition, we also use the GFDL ultra-high resolution global storm resolving model to study cyclone-associated extreme winds.

How to cite: Gentile, E. S., Zhao, M., and Hodges, K.: Poleward intensification of midlatitude extreme winds under warmer climate, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-3675, https://doi.org/10.5194/egusphere-egu24-3675, 2024.

EGU24-3750 | ECS | Orals | AS1.22

Perturbation Energetics of the December 2022 Bomb Cyclone over North America 

Emerson DeLarme, Jianping Li, Hongyuan Zhao, Yuan Liu, and Ruipeng Sun

Bomb cyclones over land are an understudied phenomenon. As such, there are open questions about the underlying physical processes, for example, why do bomb cyclones stop deepening. Atmospheric energetics is a prevalent approach to solve such problems, however the commonly used method of Available Potential Energy is not valid at local scales. Therefore, this study aims to provide further insight into the life cycle of bomb cyclones, specifically over land, by conducting a case study of the bomb cyclone that occurred over North America at the end of December 2022, focusing on the energetics using the Perturbation Potential Energy (PPE) framework. Hourly ERA5 reanalysis data provides the improved time resolution needed to study the evolution of such a rapidly developing system. PPE analysis of the evolution of this bomb cyclone reveals a possible stop signal to the positive feedback loop associated with explosive deepening. Further research is needed to clarify the mechanics associated with this thermodynamic signal.

How to cite: DeLarme, E., Li, J., Zhao, H., Liu, Y., and Sun, R.: Perturbation Energetics of the December 2022 Bomb Cyclone over North America, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-3750, https://doi.org/10.5194/egusphere-egu24-3750, 2024.

EGU24-5188 | ECS | Posters on site | AS1.22

A climatology of Mediterranean cyclones and compound weather extremes 

Alice Portal, Olivia Martius, Shira Raveh-Rubin, and Jennifer L Catto

Mediterranean cyclones are the main driver of surface weather extremes in the Mediterranean region. In this work we establish a new procedure for the attribution of different types of meteorological extremes to Mediterranean cyclones, where we also distinguish the presence of different airflows (warm conveyor belts, dry intrusions) and fronts composing the structure of a cyclone. We apply the procedure to a dataset of rain-wind and wave-wind compound extremes extracted from ERA5 reanalysis in a recent climatological period, and show that the majority of weather compounds occurring in the Mediterranean area is indeed linked to the presence of a nearby cyclone. The association of compound rain-wind events with Mediterranean cyclones locally surpasses an 80% level, while interesting differences between transition seasons and winter are detected. Winter cyclones - generally stronger, larger and distinctively baroclinic - are associated with a higher compound density. The de-construction of the cyclone in airflows and fronts evidences a strong association of rain-wind compounds with regions of warm conveyor belt ascent, and of wave-wind compounds with regions of dry intrusion outflow.

How to cite: Portal, A., Martius, O., Raveh-Rubin, S., and Catto, J. L.: A climatology of Mediterranean cyclones and compound weather extremes, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5188, https://doi.org/10.5194/egusphere-egu24-5188, 2024.

EGU24-6370 | ECS | Posters on site | AS1.22

A catastrophe model for Windstorm in Italy: developing a stochastic windstorm event set adjusted with open-access reanalysis datasets 

Lorenzo Aiazzi, Simone Persiano, Michele Bottazzi, Glauco Gallotti, Antonio Petruccelli, Farid Ait-Chaalal, and Giovanni Leoncini

Windstorms are one of the most destructive natural disasters in Europe, causing considerable human and economic impacts, ranging from fatalities and injuries to damage to agriculture, infrastructures, and properties. The European Commission’s Joint Research Centre (JRC) estimates annual losses of 5 €-billion for the European Union and United Kingdom (Spinoni et al., 2020). While in these areas there is not high confidence on the projected changes in windstorm intensity and frequency due to climate change (Ranasinghe et al., 2021), damages resulting from windstorms will most likely increase in the future due to the appreciation of asset values (Spinoni et al., 2020).

Although Italy is one of the most affected European countries, with annual absolute losses estimated above 0.5 €-billion (Spinoni et al., 2020), windstorm is still considered to be a secondary peril. However, severe windstorm events in the last few years (e.g., Storm Vaia in October 2018) have raised an increasing interest of the Italian insurance industry in understanding and modelling this peril.

In this context, we aim at developing a catastrophe model that quantifies the financial impacts of windstorms on the insurance market in Italy. To this aim, here we perform the calibration of a stochastic windstorm event set for the hazard component of the model. Uncalibrated footprints are obtained from simulation outputs of global and regional numerical models. Then, historical event footprints are extracted from open-access reanalysis datasets (e.g., ERA5, CERRA) and used to correct the climatology of the stochastic set and to adjust the wind-speeds of its individual events. This analysis is expected to be preparatory for the development of a comprehensive catastrophe model that combines wind hazard with exposure and vulnerability to assess windstorm-related financial losses in Italy.

 

References:

Ranasinghe, R., et al., 2021: Climate Change Information for Regional Impact and for Risk Assessment. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [Masson-Delmotte, V., et al., (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 1767–1926, doi: 10.1017/9781009157896.014.

Spinoni, J., et al., 2020: Global warming and windstorm impacts in the EU, EUR 29960 EN, Publications Office of the European Union, Luxembourg, 2020, ISBN 978-92-76-12955-4, doi:10.2760/039014. JRC118595.

How to cite: Aiazzi, L., Persiano, S., Bottazzi, M., Gallotti, G., Petruccelli, A., Ait-Chaalal, F., and Leoncini, G.: A catastrophe model for Windstorm in Italy: developing a stochastic windstorm event set adjusted with open-access reanalysis datasets, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-6370, https://doi.org/10.5194/egusphere-egu24-6370, 2024.

EGU24-7310 | ECS | Orals | AS1.22

Unlocking the dynamics of extreme wind speeds of North Atlantic storms 

Jun-Hyeok Son, Christian L.E. Franzke, and Seok-Woo Son

North Atlantic extra-tropical storms are some of the most severe weather systems, causing enormous economic damages and threatening human lives. In general, these storms are characterized by strong cyclonic convergent surface winds, upward vertical flow, and precipitation. In specific confined areas inside the storm where downward flows occur with clear sky, extreme surface wind speeds are observed. Such a horizontal variation of vertical wind direction and surface wind speed can cause severe and damaging impacts; however, the underlying key dynamics are not understood. Here we show the dynamical and thermodynamical linkage between the horizontal wind impinging on the frontal surface at the lower troposphere, downward flow, and very intense surface wind speeds inside the storm. The anti-clockwise cyclonic wind into the cold frontal area is mainly responsible for generating the downward flow, which transports the high-altitude horizontal momentum to the surface layer causing intense surface wind speeds. About half of North Atlantic storms accompany the downward wind, and that downward flow is more frequently observed in the southern and western part of the storm center. Overall results illuminated in this paper have a far-reaching impact in multiple ways to enhance forecasting skills for devastating weather events associated with extra-tropical storms.

How to cite: Son, J.-H., Franzke, C. L. E., and Son, S.-W.: Unlocking the dynamics of extreme wind speeds of North Atlantic storms, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-7310, https://doi.org/10.5194/egusphere-egu24-7310, 2024.

EGU24-7801 | ECS | Posters on site | AS1.22

Environmental Characteristics Associated with the Tropical Transition of Mediterranean Cyclones 

Lisa Bernini, Leone Cavicchia, Fabien Desbiolles, Antonio Parodi, Claudia Pasquero, and Enrico Scoccimarro

Using tracks from a reference dataset (Flaounas et al., 2023), cyclones in the Mediterranean Sea have been classified based on thermal winds (Hart, 2003). This classification allowed us to explore the major differences between extra-tropical cyclones with a cold inner core and tropical-like cyclones with a
deep inner warm core. For that purpose, the time evolution along the cyclones’ lifetime of different environmental characteristics taken from the ERA5 reanalysis has been studied. Warm-core cyclones are characterized by higher surface wind speeds, larger air-sea fluxes, and more intense precipitations. In comparison to cold-core cyclones, their development is favored by low wind shear and high moisture levels in the mid-troposphere. Different proxies also attest the major importance of the convective process in the establishment of the warm core. Finally, their dissipation seems to be driven by an abrupt decrease in the mid-level moisture content. This decrease is possibly related to the occlusion phase of the cyclone, and not to a limitation of moisture supply at the surface due to landfall.

How to cite: Bernini, L., Cavicchia, L., Desbiolles, F., Parodi, A., Pasquero, C., and Scoccimarro, E.: Environmental Characteristics Associated with the Tropical Transition of Mediterranean Cyclones, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-7801, https://doi.org/10.5194/egusphere-egu24-7801, 2024.

EGU24-8101 | ECS | Posters on site | AS1.22

Past and future Mediterranean cyclone characteristics using a regional climate model 

Onno Doensen, Martina Messmer, Woon Mi Kim, and Christoph Raible

Extratropical cyclones play a dominant role in the Mediterranean. They are important for local water supplies, but they can also cause severe damage due to heavy winds, extreme precipitation and coastal floods. Over the last decades, a decrease in the number of extratropical cyclones in the Mediterranean has been observed. Climate models suggest that this decreasing trend will continue in the future under global warming, leading to fewer storms and dryer conditions over the region compared to the present. However, it is much less clear how extreme cyclones in the Mediterranean will respond to climate change. Our previous study, based on a simulation from the Community Earth System Model (CESM) covering the last 3500 years, indicates that extreme cyclones show a distinct centennial variability in frequency, cyclone-related precipitation and wind speed. In addition, we found a weak relation between atmospheric circulation modes and varying cyclone characteristics across different regions in the Mediterranean. However, the coarse horizontal resolution of CESM (2.0°×2.5°) is not very well suited to resolve the mesoscale cyclones that often occur in the Mediterranean. For this study, we downscaled the CESM simulation for the period 1821–2100 (RCP8.5 scenario from 2005 onwards) to a horizontal grid resolution of 20 km using the Weather Research and Forecasting (WRF) model. The WRF simulation can resolve the cyclone characteristics in the Mediterranean more accurately than CESM. Additionally, the WRF simulation is able to reproduce the complexity of cyclone-related wind speed and precipitation in a much more detailed way. Preliminary results show a strong decrease in cyclone frequency as a result of global warming. However, this trend is much less clear for extreme cyclones with respect to wind speed and precipitation. Using the long downscaled WRF simulation, we intend to identify characteristics in CESM that lead to extreme wind and precipitation in the downscaled simulation. Additionally, we will investigate the most extreme wind and precipitation events in the Mediterranean to understand what processes are better captured at smaller scales than in the global model.

How to cite: Doensen, O., Messmer, M., Kim, W. M., and Raible, C.: Past and future Mediterranean cyclone characteristics using a regional climate model, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-8101, https://doi.org/10.5194/egusphere-egu24-8101, 2024.

EGU24-10642 | ECS | Orals | AS1.22

Synoptic perspective on the conversion and maintenance of local available potential energy in extratropical cyclones 

Marc Federer, Lukas Papritz, Michael Sprenger, Christian M. Grams, and Marta Wenta

The global atmospheric circulation is maintained by the conversion of available potential energy (APE) into kinetic energy. At midlatitudes, this conversion occurs to a large extent in extratropical cyclones through baroclinic instability. Although kinetic energy is easily defined locally, APE is typically defined as a global integral. Therefore, local APE conversion is not well understood.

Here, we investigate local APE conversion within the North Atlantic storm track using ERA5 reanalysis data. We utilize a recently introduced formulation of APE, which is exact and defined locally for individual air parcels. First, we explore APE conversion during a period of rapid cyclogenesis, which we then extend to a climatology of extratropical cyclones.

Our results indicate that the synoptic upper-level flow determines the distribution of high APE values, which are primarily located in the high-latitude upper troposphere. We show that APE is converted locally into kinetic energy by descending air parcels within the ageostrophic circulation, for example, induced by a jet streak upstream of an extratropical cyclone. The local APE originates not only from advection from the polar, upper-tropospheric APE reservoir, but also from local generation by vertical motion. In fact, the net baroclinic conversion of APE to kinetic energy is the result of much larger positive and negative local contributions. Thus, the global Lorenz energy cycle is more complex on synoptic scales. In addition, we show that surface heat fluxes resulting from air-sea interactions and latent heat release act as diabatic sinks for APE. However, the effect of surface heat fluxes is small compared to the conversion of APE to kinetic energy, as little APE is located in the mid-latitude lower troposphere.

In summary, the study shows that the local APE perspective allows the energetics of North Atlantic extratropical cyclones to be better understood in terms of local APE advection as well as adiabatic (ascent and descent) and diabatic effects.

How to cite: Federer, M., Papritz, L., Sprenger, M., Grams, C. M., and Wenta, M.: Synoptic perspective on the conversion and maintenance of local available potential energy in extratropical cyclones, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-10642, https://doi.org/10.5194/egusphere-egu24-10642, 2024.

EGU24-11704 | Posters on site | AS1.22

Climate change signature on Euro-Mediterranean lee cyclogenesis 

Lorenzo Sangelantoni, Stefano Tibaldi, Leone Cavicchia, Daniele Peano, and Enrico Scoccimarro

This study explores whether and why a warmer climate induces alterations in climatological statistics and the underlying physical features of lee cyclogenesis in the Euro-Mediterranean region.

The investigation focuses on a specific cyclogenesis type, wherein orography (the Alps), influences the spatial structure and growth rate of the cyclone.

This regional scale phenomenon is inspected within the framework of a general weakening and poleward shift of the mid-latitude jet. This large-scale signal, despite being evident in zonal-averaged results from the majority of climate models, remains subject to considerable uncertainty when specific regions and seasons are considered. This uncertainty stems from the intricate interplay and delicate equilibrium among numerous competing mechanisms.

The analysis focuses on historical and future trends during the cold semesters across the Euro-Mediterranean region. The historical period is examined using ERA5 reanalysis spanning from 1940 to the present, supplemented by a higher-resolution regional reanalysis product (COSMO-REA6) at approximately 6 km resolution, covering the period 1995-2019 over the Euro-CORDEX (EUR11) domain. State-of-the-art high-resolution climate models are employed to assess historical reproducibility and future trends through an ensemble of global climate models from the HighResMIP initiative.

Methodologically, two distinct approaches are pursued. Firstly, changes in statistical properties of lee cyclogenesis are examined, along with composites of precipitation and wind extremes footprint, utilizing two tracking algorithms: TempestExtremes (Ullrich et al., 2021) and TRACK (Hodges, 1994). These algorithms differ in their identification/tracking variables, i.e., mean sea level pressure and 850hPa relative vorticity, respectively. Secondly, an empirical orthogonal function (EOF) analysis is employed to evaluate whether dominant spatial patterns of relevant variables (e.g., mean sea level pressure and 500hPa geopotential height) associated with cyclogenesis undergo significant changes across different time segments.

This investigation is conducted as a spin-off of the Copernicus-ECMWF-funded contract C3S2_413 - Enhanced Operational Windstorm Service. The findings aim to enhance our understanding of the complex dynamics of Euro-Mediterranean lee cyclogenesis in the context of a changing climate, providing further insights for climate science and operational windstorm services.

How to cite: Sangelantoni, L., Tibaldi, S., Cavicchia, L., Peano, D., and Scoccimarro, E.: Climate change signature on Euro-Mediterranean lee cyclogenesis, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-11704, https://doi.org/10.5194/egusphere-egu24-11704, 2024.

EGU24-13459 | ECS | Orals | AS1.22

Assessing high-resolution global climate models in simulating subtropical cyclones over the southeast coast of Brazil 

Andressa Andrade Cardoso and Rosmeri Porfírio da Rocha

Subtropical cyclones when occurring close to the coast can be very dangerous for human activities bringing high amounts of precipitation, intense winds and gusts in the coastal cities. This can lead to natural hazards and risks, such as floods, inundations, and even deaths. Over the southeast coast of Brazil, seven subtropical cyclones occur on average each year, with higher frequency in austral summer and autumn.  However, there are still few studies focusing on its global models climatology and future projections. It is crucial to evaluate how accurate are the global climate models of the new HighResMIP-CMIP6 dataset, with fine horizontal high-resolution, in representing subtropical cyclones in the historical period. Thus, this study assesses the classification of the subtropical cyclones based on two reanalyses (ERA5 and ERA-Interim) to evaluate the fine-resolution HighResMIP-CMIP6 datasets.  First, we tracked all cyclones over the South Atlantic Ocean applying an automatic scheme using relative vorticity at 925 hPa. Then, the vertical structure of the cyclones are accessed by calculating three parameters (symmetry, thermal wind at low and upper levels) from the cyclone phase space approach. Finally, we classified subtropical features using an automatic scheme based on a pre-establish threshold. In general, the approach is able for classifying subtropical cyclones providing realistic climatology. Overall, for the total of cyclones, ERA5, ERAInterim and HighResMIP-CMIP6 reproduce similar areas of great cyclogenetic activity over the eastern coast of South America.  In terms of frequency, it is greater in ERA5 than ERAInterim, for both total and subtropical cyclones, while a similar behavior is noted in relation to the seasonal frequency. HighResMIP-CMIP6 tends to overestimate the total of cyclones in subtropical latitudes, impacting directly the frequency of the subtropical ones. 

How to cite: Andrade Cardoso, A. and Porfírio da Rocha, R.: Assessing high-resolution global climate models in simulating subtropical cyclones over the southeast coast of Brazil, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-13459, https://doi.org/10.5194/egusphere-egu24-13459, 2024.

EGU24-14305 | Posters on site | AS1.22

Towards AI-enhanced prediction of Mediterranean cyclones 

Leone Cavicchia, Enrico Scoccimarro, and Silvio Gualdi

Intense cyclones form frequently in the Mediterranean region, with the potential to cause damage to life and property when they hit highly populated coastal areas. Cyclone impacts are caused by the associated strong winds, flash flooding and storm surge. The social and economic impacts are not limited to the Mediterranean area, as cyclones forming in the region can affect Central Europe. While the skill of weather models to forecast such events has dramatically improved over the last decade, the seasonal predictability of Mediterranean cyclones lags behind due to the limitations on horizontal resolution in probabilistic forecasts requiring a large ensemble of simulationss. Improving the climate prediction at a seasonal scale of those extreme events would be of great benefit for society, enabling better disaster risk management and reducing the economic losses they cause. A better prediction of climate extremes would also directly benefit a number of economic sectors such as the insurance and re-insurance industry.

The ambition of the CYCLOPS project is to use Artificial Intelligence techniques to enhance the prediction skills of Mediterranean cyclones in a state-of-the-art Seasonal Prediction System. Here we present initial results making use of AI to link those extreme events to their large-scale driver. The training of different machine learning models is performed using ERA5 reanalysis data. The assessment of model skill is evaluated on the C3S operational seasonal forecast in hindcast mode. The performance of machine learning models of varying complexity (e.g. random forest, artificial neural networks) is evaluated.

How to cite: Cavicchia, L., Scoccimarro, E., and Gualdi, S.: Towards AI-enhanced prediction of Mediterranean cyclones, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-14305, https://doi.org/10.5194/egusphere-egu24-14305, 2024.

EGU24-14720 | ECS | Posters on site | AS1.22

Mergers as the Maintenance Mechanism of Cutoff Lows: A Case Study over Europe in July 2021 

Koryu Yamamoto, Keita Iga, and Akira Yamazaki

A cutoff low that covered Central Europe in the middle of July 2021 brought heavy rainfall and severe flooding, resulting in more than 200 fatalities. This low was formed by a trough on 11 July and merged with another cutoff low around 12–13 July. Analysis of the energy budget and potential vorticity suggests that the main cutoff low was maintained through the merger with another cutoff low; this was the dominant contributor to maintenance of the main cutoff low around 12–13 July. The results of Lagrangian trajectory analyses support this conclusion. Analysis of diabatic PV modification during the merger indicates that radiation acts mainly to enhance the potential vorticity of the parcels when they move from another cutoff low into the main cutoff low, especially in the upper layer (~ 350 K). However, that effect is not pronounced in the lower layer (~ 330 K). These results demonstrate that cutoff lows can be maintained through the merger with another cutoff low and underline the need to consider diabatic processes when investigating mergers.

How to cite: Yamamoto, K., Iga, K., and Yamazaki, A.: Mergers as the Maintenance Mechanism of Cutoff Lows: A Case Study over Europe in July 2021, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-14720, https://doi.org/10.5194/egusphere-egu24-14720, 2024.

EGU24-16593 | ECS | Posters on site | AS1.22

High-impact storms during the extended winters of 2018–2021 in the Iberian Peninsula 

Ana C. R. Gonçalves, Raquel Nieto, and Margarida L. R. Liberato

During the extended winter period from December 2017 to April 2021, the Iberian Peninsula (IP) was impacted by several high-impact storms characterized by intense precipitation and/or strong winds. This study provides a detailed assessment of the events, including synoptic conditions, large-scale dynamics associated with the storms, and a climatological analysis aimed at improving public understanding and preventing natural disasters. The analysis of the cyclones’ variability indicates that their maximum intensity varies between 955 hPa and 985 hPa, with a duration of two to four days, and the most frequent occurrence (eight events) was in January. At the peak of maximum intensity, the composite anomaly patterns showed lower mean sea level pressure (MSLP) values (−21.6 hPa), higher water vapor values (327.6 kg m−1s−1), and wind speed at 250 hPa exceeding 29.6 m s−1 the mean values. Additionally, there were high anomaly values of equivalent potential temperature (θe) of 19.1 °C at 850 hPa, sea surface temperature (SST) anomaly values of −1 °C, and negative anomaly values of surface latent heat flux (QE) (−150 W m−2) close to the IP. During the days impacted by the storms, the recorded values surpassed the 98th percentile in a significant percentage of days for daily accumulated precipitation (34%), instantaneous wind gusts (46%), wind speed at 10 m (47%), and concurrent events of wind/instantaneous wind gusts and precipitation (26% and 29%, respectively). These findings allow us to describe their meteorological consequences on the IP, particularly the effects resulting from intense precipitation such as floods, and strong winds associated with various destructive impacts. Finally, clear, real-time, and predictive information about weather systems and their impacts is crucial for the public to understand and enable effective responses to mitigate these natural hazards damage.

Keywords: extreme events; extratropical cyclones; explosive development cyclones; winter storms; Iberian Peninsula.

 

Acknowledgments

This work was funded by the Portuguese Fundação para a Ciência e a Tecnologia (FCT) I.P./MCTES through national funds (PIDDAC)–UIDB/50019/2020 (https://doi.org/10.54499/UIDB/50019/2020), UIDP/50019/2020 (https://doi.org/10.54499/UIDP/50019/2020) and LA/P/0068/2020 (https://doi.org/10.54499/LA/P/0068/2020), and project WEx-Atlantic (PTDC/CTAMET/29233/2017, LISBOA-01-0145-FEDER-029233, NORTE-01-0145-FEDER-029233). FCT is also providing for Ana Gonçalves doctoral grant (2021.04927.BD). The EPhysLab group was also funded by Xunta de Galicia, Consellería de Cultura, Educación e Universidade, under project ED431C 2021/44 “Programa de Consolidación e Estructuración de Unidades de Investigación Competitivas.

 

 References

Gonçalves, A.C.R.; Nieto, R.; Liberato, M.L.R. Synoptic and Dynamical Characteristics of High-Impact Storms Affecting the Iberian Peninsula during the 2018–2021 Extended Winters. Atmosphere 2023, 14, 1353. https://doi.org/10.3390/atmos14091353

How to cite: C. R. Gonçalves, A., Nieto, R., and L. R. Liberato, M.: High-impact storms during the extended winters of 2018–2021 in the Iberian Peninsula, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-16593, https://doi.org/10.5194/egusphere-egu24-16593, 2024.

EGU24-16634 | ECS | Posters on site | AS1.22

Drivers of Cold Frontal Hourly Extreme Precipitation: A Climatological Study 

Armin Schaffer, Tobias Lichtenegger, Douglas Maraun, Heimo Truhetz, and Albert Ossó

Understanding the processes driving extreme precipitation is paramount to socioeconomic interest. In the mid-latitudes extreme precipitation events are strongly associated with cold fronts. By exploring drivers across a wide range of scales, this study aims to improve our understanding of processes influencing frontal precipitation. Past research predominately focused on detailed studies of individual frontal extreme events. Here we present the first climatological study of frontal characteristics and their impact on precipitation.
Using hourly resolved ERA5 data, cold fronts are detected using the equivalent potential temperature gradient, and associated conditions from the synoptic to the meso-scale are identified. Further, seasonal and regional dependencies are explored. Quantile regression models are employed to find the strongest drivers of frontal precipitation and to quantify these relationships. Additionally, composite analysis are used to study the synoptic conditions and meso-scale structure of extreme events.
Findings reveal that humidity close to the frontal boundary, convergence of different scales and the low level jet speed contribute most to formation of extreme precipitation events. Interestingly, we discovered that stronger fronts, characterized by a significant change in humidity, do not always lead to a higher chance of extreme precipitation. This is evident in the weak correlation between the humidity gradient and frontal precipitation, in contrast with the relationship observed for the temperature gradient.
The findings of this study improve our understanding of cold frontal processes. Additionally, they provide the foundation to evaluate model performance and climate change projections. 

How to cite: Schaffer, A., Lichtenegger, T., Maraun, D., Truhetz, H., and Ossó, A.: Drivers of Cold Frontal Hourly Extreme Precipitation: A Climatological Study, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-16634, https://doi.org/10.5194/egusphere-egu24-16634, 2024.

EGU24-17127 | ECS | Orals | AS1.22

A Cold Frontal Life Cycle Climatology and Front-Cyclone Relationships over the North Atlantic and Europe during Winter 

Tobias Lichtenegger, Armin Schaffer, Douglas Maraun, Albert Osso Castillon, and Heimo Truhetz

Atmospheric fronts and cyclones play an important role in day-to-day weather variability, especially in the mid-latitudes and during the winter season. Severe rainfall and windstorm events are often associated with the passage of a front or a cyclone. While there are many studies of individual fronts and climatologies based on objectively detected fronts, there is no comprehensive study considering the whole frontal life cycle over time. Therefore, a front and cyclone tracking algorithm, based on overlapping features at consecutive time steps, is used together with an improved front detection method to detect and track cold fronts and cyclones over the North Atlantic and Europe in the extended winter season (October - March) in the ERA5 reanalysis dataset. Several life cycle characteristics, e.g. the duration, velocity, frontogenesis and -lysis regions as well as dynamic and thermodynamic frontal parameters are defined to investigate the frontal life cycle and the conditions and processes in the frontal region. Fronts are linked to their parent cyclone to study relationships between frontal and cyclonic properties. The study confirms that fronts are mostly formed over the western and central North Atlantic and travelling along the main storm track into the European continent. During positive phases of the North Atlantic Oscillation, fronts are travelling faster and further and are associated with stronger precipitation and surface wind speeds over their whole life cycle. Stronger cyclones are related to stronger dynamics in the frontal region.

How to cite: Lichtenegger, T., Schaffer, A., Maraun, D., Osso Castillon, A., and Truhetz, H.: A Cold Frontal Life Cycle Climatology and Front-Cyclone Relationships over the North Atlantic and Europe during Winter, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-17127, https://doi.org/10.5194/egusphere-egu24-17127, 2024.

EGU24-18002 | ECS | Orals | AS1.22

Power outages in windstorms: the influence of rainfall preconditioning, wind direction and season 

Colin Manning, Sean Wilkinson, Hayley Fowler, Elizabeth Kendon, and Sarah Dunn

Windstorms are the main cause of large power outages in the UK. Faults to electricity distribution networks during windstorms are predominantly a result of windthrow, the uprooting or breakage of trees by winds that then fall on assets such as overhead lines. The impact of strong winds on windthrow is influenced by a several conditions: trees uproot more easily in saturated soils, they are more vulnerable to strong winds from unusual directions, and they are more susceptible to strong winds in the growing season when their leaves catch the wind. Despite this, risk assessments of impacts, such as power outages, during windstorms generally focus on wind intensity alone. Here, we quantify the influence of contributing variables of windthrow including antecedent rainfall, wind direction of the maximum wind gust, and the season a windstorm occurs in. We demonstrate that including them in a logistic regression model alongside wind speed can improve the predictive skill of the number of electricity faults during windstorms compared to a reference model that only includes wind speed. The analysis uses fault data from the National Fault and Interruption Scheme (NaFIRs) database during the period 2006-2018 in four regions in the UK: South Wales, Southwest England, East Midlands, and West Midlands. Meteorological data is provided by ERA5. Each variable is shown to modulate the impact of strong winds and improve predictive skill, though with some regional variability. The probability of a high fault numbers in a windstorm with winds exceeding 25 m/s can be doubled following high rainfall accumulations and five times higher when strong winds come from a direction that deviates more than 40 degrees south or west from the prevailing south-westerly direction. Furthermore, this probability is doubled in summer months compared to winter. These results can help improve impact forecasting during windstorms and highlight the importance of including these variables in historical and future risk assessments of assets vulnerable to windthrow. Ignoring such contributions may lead to misrepresentation of risk and potential maladaptation, particularly for electricity distribution networks that will undergo a huge transformation as we reduce our dependence on greenhouse gases in the future.

How to cite: Manning, C., Wilkinson, S., Fowler, H., Kendon, E., and Dunn, S.: Power outages in windstorms: the influence of rainfall preconditioning, wind direction and season, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-18002, https://doi.org/10.5194/egusphere-egu24-18002, 2024.

EGU24-19896 | ECS | Posters on site | AS1.22

Climate change's influence on Cut-off Lows in the future 

Aditya N. Mishra, Douglas Maraun, Reinhard Schiemann, Kevin Hodges, and Giuseppe Zappa

Cut-off Lows (COLs) are mid-latitude storms that are detached from the main westerly flow. They tend to propagate slower than other mid-latitude storms and are often harbingers of heavy and persistent rainfall. COLs have long been subject to thorough studies that have examined the physical structure and climatology across both hemispheres, however, their assessment in models is relatively low. In fact, there is no study on future changes in COLs in models. In this study, we analyze the cut-off lows in the northern hemisphere in the historic and future time slices in the CMIP6 dataset to study the frequency, duration, and intensity of the cut-off lows alongside the changes in velocity. Results show that the COL season, which is currently mostly limited to summer, extends into spring over Europe, North America, and Asia. This rise in activity in spring is more pronounced for COLs that are long-lasting and also have higher intensity maxima, i.e., the most impactful ones. Moreover, COL propagation velocity for persistent systems is due to slow down over North America in the summer. Slow-moving COLs are known to cause heavy localized rainfall. Through this study, we fill the information gap on the first insights of projected future changes in COLs by using TRACK to detect and trace COLs in the SSP5-8.5 projections of the CMIP6 ensemble.

 

How to cite: Mishra, A. N., Maraun, D., Schiemann, R., Hodges, K., and Zappa, G.: Climate change's influence on Cut-off Lows in the future, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-19896, https://doi.org/10.5194/egusphere-egu24-19896, 2024.

EGU24-20374 | ECS | Orals | AS1.22

How do winter-time extratropical cyclones change in the future over South Africa? 

Sandeep Chinta, Adam Schlosser, Xiang Gao, and Kevin Hodges

Extratropical cyclones (ETCs) in South Africa usually occur during the winter (June to August), specifically influencing the Western Cape, causing extreme rain and strong winds. We investigate future changes in these winter-time ETCs using the simulations from three CORDEX-CORE Africa models. Each of these models was driven by three Coupled Model Intercomparison Project phase 5 (CMIP5) General Circulation Models (GCMs), resulting in nine sets of simulations. The simulations are from 1970-2100, with scenarios starting from 2006. We identified the cyclone tracks using the Hodges tracking algorithm, which used 6-hourly relative vorticity data at 850 hPa level. We chose a 20-year historical period from 1986 to 2005 for comparison with a future period of the same length from 2080 to 2099, focusing on the Representative Concentration Pathway (RCP) 8.5 scenario for the future projections. We observed a projected decrease in the number of ETCs in the future. The average track distance and duration are also projected to reduce. These reductions are statistically significant. We explored the future changes in the ETC-associated rainfall, which is also projected to be reduced in the future. We are currently looking at extending our analysis with the high-resolution 4 km gridded Climate Predictions for Africa (CP4A) data and see how our earlier results compare with the high-resolution data.

How to cite: Chinta, S., Schlosser, A., Gao, X., and Hodges, K.: How do winter-time extratropical cyclones change in the future over South Africa?, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-20374, https://doi.org/10.5194/egusphere-egu24-20374, 2024.

EGU24-3548 | Posters on site | AS1.2

Improving the Completion of Weather Radar Missing Data with Deep Learning 

Aofan Gong, Haonan Chen, and Guangheng Ni

Weather radars commonly suffer from the data-missing problem that limits their data quality and applications. Traditional methods for the completion of weather radar missing data, which are based on radar physics and statistics, have shown defects in various aspects. Several deep learning (DL) models have been designed and applied to weather radar completion tasks but have been limited by low accuracy. This study proposes a dilated and self-attentional UNet (DSA-UNet) model to improve the completion of weather radar missing data. The model is trained and evaluated on a radar dataset built with random sector masking from the Yizhuang radar observations during the warm seasons from 2017 to 2019, which is further analyzed with two cases from the dataset. The performance of the DSA-UNet model is compared to two traditional statistical methods and a DL model. The evaluation methods consist of three quantitative metrics and three diagrams. The results show that the DL models can produce less biased and more accurate radar reflectivity values for data-missing areas than traditional statistical methods. Compared to the other DL model, the DSA-UNet model can not only produce a completion closer to the observation, especially for extreme values, but also improve the detection and reconstruction of local-scale radar echo patterns. Our study provides an effective solution for improving the completion of weather radar missing data, which is indispensable in radar quantitative applications.

How to cite: Gong, A., Chen, H., and Ni, G.: Improving the Completion of Weather Radar Missing Data with Deep Learning, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-3548, https://doi.org/10.5194/egusphere-egu24-3548, 2024.

EGU24-5373 | ECS | Orals | AS1.2 | Highlight

Convective environments in AI-models - What have AI-models learned about atmospheric profiles? 

Monika Feldmann, Louis Poulain-Auzeau, Milton Gomez, Tom Beucler, and Olivia Martius
The recently released suite of AI-based medium-range forecast models can produce multi-day forecasts within seconds, with a skill on par with the IFS model of ECMWF. Traditional model evaluation predominantly targets global scores on single levels. Specific prediction tasks, such as severe convective environments, require much more precision on a local scale and with the correct vertical gradients in between levels. With a focus on the North American and European convective season of 2020, we assess the performance of Panguweather, Graphcast and Fourcastnet for convective available potential energy (CAPE) and storm relative helicity (SRH) at lead times of up to 7 days.
Looking at the example of a US tornado outbreak on April 12 and 13, 2020, all models predict elevated CAPE and SRH values multiple days in advance. The spatial structures in the AI-models are smoothed in comparison to IFS and the reanalysis ERA5. The models show differing biases in the prediction of CAPE values, with Graphcast capturing the value distribution the most accurately and Fourcastnet showing a consistent underestimation.
By advancing the assessment of large AI-models towards process-based evaluations we lay the foundation for hazard-driven applications of AI-weather-forecasts.

How to cite: Feldmann, M., Poulain-Auzeau, L., Gomez, M., Beucler, T., and Martius, O.: Convective environments in AI-models - What have AI-models learned about atmospheric profiles?, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5373, https://doi.org/10.5194/egusphere-egu24-5373, 2024.

EGU24-5571 | ECS | Orals | AS1.2

SHADECast: Enhancing solar energy integration through probabilistic regional forecasts 

Alberto Carpentieri, Doris Folini, Jussi Leinonen, and Angela Meyer

Surface solar irradiance (SSI) is a pivotal component in addressing climate change. As an abundant and non-fossil energy source, it is harnessed through photovoltaic (PV) energy production. As the contribution of PV to total energy production grows, the stability of the power grid faces challenges due to the volatile nature of solar energy, predominantly influenced by stochastic cloud dynamics. To address this challenge, there is a need for accurate, uncertainty-aware, near real-time, and regional-scale SSI forecasts with forecast horizons ranging from minutes to a few hours.

Existing state-of-the-art SSI nowcasting methods only partially meet these requirements. In our study, we introduce SHADECast [1], a deep generative diffusion model designed for probabilistic nowcasting of cloudiness fields. SHADECast is uniquely structured, incorporating deterministic aspects of cloud evolution to guide the probabilistic ensemble forecast, relying only on previous satellite images. Our model showcases significant advancements in forecast quality, reliability, and accuracy across various weather scenarios.

Through comprehensive evaluations, SHADECast demonstrates superior performance, surpassing the state of the art by 15% in the continuous ranked probability score (CRPS) over diverse regions up to 512 km × 512 km, extending the state-of-the-art forecast horizon by 30 minutes. The conditioning of ensemble generation on deterministic forecasts further enhances reliability and performance by more than 7% on CRPS.

SHADECast forecasts equip grid operators and energy traders with essential insights for informed decision-making, thereby guaranteeing grid stability and facilitating the smooth integration of regionally distributed PV energy sources. Our research contributes to the advancement of sustainable energy practices and underscores the significance of accurate probabilistic nowcasting for effective solar power grid management.

 

References

[1] Carpentieri A. et al., 2023, Extending intraday solar forecast horizons with deep generative models. Preprint at ArXiv. https://arxiv.org/abs/2312.11966 

How to cite: Carpentieri, A., Folini, D., Leinonen, J., and Meyer, A.: SHADECast: Enhancing solar energy integration through probabilistic regional forecasts, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5571, https://doi.org/10.5194/egusphere-egu24-5571, 2024.

EGU24-5849 | ECS | Posters on site | AS1.2

Towards seamless rainfall and flood forecasting in the Netherlands: improvements to and validation of blending in pysteps 

Ruben Imhoff, Michiel Van Ginderachter, Klaas-Jan van Heeringen, Mees Radema, Simon De Kock, Ricardo Reinoso-Rondinel, and Lesley De Cruz

Flood early warning in fast responding catchments challenges our forecasting systems. It requires frequently updated, accurate and high-resolution rainfall forecasts to provide timely warning of rainfall amounts that will reach a catchment in the coming hours. The Netherlands is a typical example, with polder systems below sea level, a high level of urbanization and catchments with short response times. The need for better short-term rainfall forecasts is clearly present, but this is generally not feasible with numerical weather prediction (NWP) models alone. Hence, an alternative rainfall forecasting method is desirable for the first few hours into the future.

Rainfall nowcasting can provide this alternative but quickly loses skill after the first few hours. A promising way forward is a seamless forecasting system, which tries to optimally combine rainfall products from nowcasting and NWP. In this study, we applied the STEPS blending method to combine rainfall forecasts from ensemble radar nowcasts with those from the Harmonie-AROME configuration of the ACCORD NWP model in the Netherlands. This blending method is part of the open-source nowcasting initiative pysteps. To make blending possible in an operational setup, including the needs of involved water authorities, we made several adjustments to the blending implementation in pysteps, for instance:

  • We reduced the computational time by using a faster preprocessing and advection scheme.
  • We improved the noise initialization (needed for generating ensemble members) to allow for stable forecasts, also when one or both product(s) contain(s) no rain.
  • We enabled a dynamic disaggregation of the 1-hour resolution NWP forecasts to match the temporal resolution of the radar nowcast.

We operationalized the updated blending framework in the flood forecasting platforms of the involved water authorities. Given a forecast duration of 12 hours for the blended forecast and a 10-minute time step, average computation times are 3.4 minutes for a deterministic run and 12.3 minutes for an ensemble forecast with 10 members on a 4-core machine. Preprocessing takes approximately 10 minutes and only needs to occur when a new NWP forecast is issued. We tested the implementation for an entire, rainy summer month (July 15 to August 15, 2023) and analyzed the results over the entire domain. The results demonstrate that the blending method effectively combines radar nowcasts with NWP forecasts. Depending on the statistical score considered (such as RMSE and critical success index), the blending method performs either better or on par with the best-performing individual product (radar nowcast or NWP). A consistent finding is that the blending closely tracks the nowcast quality during the initial 1 to 2 hours of the forecast (in this study, the nowcast had lower errors than NWP during the first 2 – 2.5 hours), after which it gradually transitions into the NWP forecast. At longer lead times, the seamless product retains local precipitation structures and extremes better than the NWP product. It does this by leveraging information from the radar nowcast and the stochastic perturbations. Based on these results, a seamless forecasting approach can be regarded as an improvement for the involved water authorities.

How to cite: Imhoff, R., Van Ginderachter, M., van Heeringen, K.-J., Radema, M., De Kock, S., Reinoso-Rondinel, R., and De Cruz, L.: Towards seamless rainfall and flood forecasting in the Netherlands: improvements to and validation of blending in pysteps, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5849, https://doi.org/10.5194/egusphere-egu24-5849, 2024.

EGU24-5909 | ECS | Posters on site | AS1.2

Impact of Spatial Density of Automatic Weather Station Data on Assimilation Effectiveness in WRF-3DVar Model 

Zeyu Qiao, Bu Li, Aofan Gong, and Guangheng Ni

Implementing the 3-Dimensional Variational (3DVar) data assimilation technique using high-density automatic weather station (AWS) observations substantially improves the precipitation simulation and forecast capabilities in the Weather Research and Forecasting (WRF) model. Given the impact of spatial distribution and quantity of observation data on assimilation effectiveness, there is a growing need to assimilate the most efficient amount of observation data to improve the precipitation forecast accuracy, especially in the context of the proliferation of data from diverse sources. This study investigates the impacts of spatial density of assimilated data on enhancing model predictions, focusing on a squall line event in Beijing on 2 August 2017 which has approximately 2400 AWSs in the simulation domain. Seven experiment groups assimilating varying proportions of AWS data (3.125, 6.25, 12.5, 25, 50, 75, and 100 percent of total AWSs) were conducted, comprising 10 experiments per group. The results were then compared with the experiment without data assimilation (CTRL) and the observations. Results show that while the WRF model roughly captured the evolution of this event, it overestimated the precipitation amount with significant deviations in precipitation locations. A general positive correlation was observed between the spatial density of assimilated data and the enhancement in model performance. However, there is a notable threshold beyond which additional data ceases to enhance forecast accuracy. The model performs best when the ratio of the number of assimilated AWSs to the model simulated area reaches 1/40 km-2. Moreover, significant variations in improvement effects across experiments within the same group indicate the substantial impact of spatial distribution of assimilated AWSs on forecast outcomes. This study provides a reference for devising more efficient and cost-effective data assimilation strategies in numerical weather prediction.

How to cite: Qiao, Z., Li, B., Gong, A., and Ni, G.: Impact of Spatial Density of Automatic Weather Station Data on Assimilation Effectiveness in WRF-3DVar Model, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5909, https://doi.org/10.5194/egusphere-egu24-5909, 2024.

EGU24-6155 | ECS | Orals | AS1.2

Enhanced Foundation Model through Efficient Finetuning for Extended-Range Weather Prediction 

Shan Zhao, Zhitong Xiong, and Xiao Xiang Zhu

Weather forecasting is a vital topic in meteorological analysis, agriculture planning, disaster management, etc. The accuracy of forecasts varies with the prediction horizon, spanning from nowcasting to long-range forecasts. The extended range forecast, which predicts weather conditions beyond two weeks to months ahead, is particularly challenging. This difficulty arises from the inherent variability in weather systems, where minor disturbances in the initial condition can lead to significantly divergent future trajectories.

Numerical Weather Prediction (NWP) has been the predominant approach in this field. Recently, deep learning (DL) techniques have emerged as a promising alternative, achieving performance comparable to NWP [1, 2]. However, their lack of embedded physical knowledge often limits their acceptance within the research community. To enhance the trustworthiness of DL-based weather forecasts, we explore a transformer-based framework which considers complex geospatial-temporal (4D) processes and interactions. Specifically, we select the Pangu model [3] with a 24-hour lead time as the initial framework. To extend the prediction horizon to two weeks ahead, we employ a low-rank adaptation for model finetuning, which saves computation resources by reducing the number of parameters to only 1.1% of the original model. Besides, we incorporate multiple oceanic and atmospheric indices to capture a broad spectrum of global teleconnections, aiding in the selection of important features.

Our contributions are threefold: first, we provide an operational framework for foundation models, improving their applicability in versatile tasks by enabling training rather than limiting them to inference stages. Second, we demonstrate how to leverage these models with limited resources effectively and contribute to the development of green AI. Last, our method improves performance in extended-range weather forecasting, offering enhanced prediction skills, physical consistency, and finer spatial granularity. Our methodology achieved reduced RMSE on T2M, Z500, and T850 for 0.13, 139.2, and 0.52, respectively, compared to IFS. In the future, we plan to explore other settings, such as predicting precipitation and extreme temperatures.

REFERENCES
[1] Nguyen, Tung, et al. "ClimaX: A foundation model for weather and climate." arXiv preprint arXiv:2301.10343 (2023).
[2] Lam, Remi, et al. "Learning skillful medium-range global weather forecasting." Science (2023): eadi2336.
[3] Bi, Kaifeng, et al. "Accurate medium-range global weather forecasting with 3D neural networks." Nature 619.7970 (2023): 533-538.

How to cite: Zhao, S., Xiong, Z., and Zhu, X. X.: Enhanced Foundation Model through Efficient Finetuning for Extended-Range Weather Prediction, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-6155, https://doi.org/10.5194/egusphere-egu24-6155, 2024.

EGU24-6545 | Posters on site | AS1.2

Improving precipitation nowcasting using deep generative models: a case-study and experiences in R2O  

Kirien Whan, Charlotte Cambier van Nooten, Maurice Schmeits, Jasper Wijnands, Koert Schreurs, and Yuliya Shapovalova

Precipitation nowcasting is essential for weather-dependent decision-making. The combination of radar data and deep learning methods has opened new avenues for research. Deep learning approaches have demonstrated equal or better performance than optical flow methods for low-intensity precipitation, but nowcasting high-intensity events remains a challenge. We use radar data from the Royal Netherlands Meteorological Institute (KNMI) and explore various extensions of deep learning architectures (i.e. loss function, additional inputs) to improve nowcasting of heavy precipitation intensities. Our model outperforms other state-of-the-art models and benchmarks and is skilful at nowcasting precipitation for high rainfall intensities, up to 60-min lead time. 

Transferring research to operations is difficult for many meteorological institutes, particularly for new applications that use AI/ML methods. We discuss some of these challenges that KNMI is facing in this domain. 

How to cite: Whan, K., Cambier van Nooten, C., Schmeits, M., Wijnands, J., Schreurs, K., and Shapovalova, Y.: Improving precipitation nowcasting using deep generative models: a case-study and experiences in R2O , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-6545, https://doi.org/10.5194/egusphere-egu24-6545, 2024.

EGU24-6856 | Posters on site | AS1.2

Very Short-Range Precipitation Forecast in Korea Meteorological Administration 

ho yong lee, Jongseong Kim, Joohyung Son, and Seong-Jin Kim

Korea Meteorological Administration (KMA) has been providing the public with an hourly precipitation forecast updated every 10 minutes for the next 6 hours since 2015. This forecasts, named as the Very Short-Range Forecast (VSRF), differs from other longer forecasts ? such as short-range and medium-range forecasts issued by forecasters. The VSRF is automatically generated by a system based on two different models: MAPLE (McGill Algorithm for Precipitation nowcasting by Lagrangian Extrapolation) and KLAPS (Korea Local Analysis and Prediction System). 

MAPLE, based on Variational Echo Tracking (VET) from radar observations, has an intrinsic disadvantage: its performance decreases rapidly. On the other hand, numerical weather prediction systems like KLAPS are not initially as effective as MAPLE due to model balancing factors such as spin-up, but they maintain initial skill for a slightly longer period. Therefore, to provide the best predictions to the public, it is necessary to merge the two models properly. KMA conducted tests to determine the optimal way to utilize both models and established weights for each model based on their performance and precipitation tendencies. According to a 4-year evaluation, MAPLE outperforms for up to 2 hours, while KLAPS performs better after 4 hours. Consequently, the two models were merged with a hyperbolic tangent weight applied between 2 and 4 hours, and we named it as the best guidance. 

The best guidance was verified against precipitation observed by 720 raingauges over South Korea during the summer seasons from 2020 to 2023. It demonstrated better skill compared to both MAPLE and KLAPS. The average threat scores, with a rain intensity threshold of 0.5 mm/h throughout the forecast period, were 0.40 for the best guidance, 0.38 for MAPLE, and 0.35 for KLAPS.

The best guidance depends on both MAPLE and KLAPS. Therefore, KMA is actively working to improve the performance of each model. Additionally, a very short-range model based on AI is currently under development and running in semi-operations.

How to cite: lee, H. Y., Kim, J., Son, J., and Kim, S.-J.: Very Short-Range Precipitation Forecast in Korea Meteorological Administration, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-6856, https://doi.org/10.5194/egusphere-egu24-6856, 2024.

EGU24-6873 | Posters on site | AS1.2

Does more frequent Very Short-Range Forecast provide more useful information? 

Joohyung Son, Jongseong Kim, and Seongjin Kim

The Very Short-Range Forecast (VSRF) for precipitation from the Korea Meteorological Administration (KMA) is released every 10 minutes, providing forecasts for the next 6 hours at 10-minute intervals. However, when the forecast is provided to the public, it is updated at 10-minute interval, but only provides up to 6 hours at every hour. Consequently, from the public's perspective, forecasts for specific times may change every 10 minutes. While this allows users to access the latest updates, it also poses a challenge in terms of reduced reliability due to constantly changing predictions.

This study aims to assess the prediction performance and variability between forecasts released at 10-minute intervals and those at 1-hour intervals. We evaluated with the Very Short-Range Forecast numerical model KLAPS in VSRF and seek to determine which approach offers more valuable information from the public's standpoint. The assessment focuses on two distinct types of precipitation. The first involves convective showers, which sporadically appear over short durations, driven by atmospheric instability during the Korean Peninsula's summer. The second relates to systematic precipitation associated with a frontal boundary accompanying a medium-scale low-pressure system. For convective showers, the 1-hour interval exhibits better performance and continuity, particularly as the forecast time extends. In the case of systematic precipitation, the 1-hour interval remains superior, though the skill is not as prominent as with convective showers. This highlights that an abundance of information doesn't always equate to high-quality information.

How to cite: Son, J., Kim, J., and Kim, S.: Does more frequent Very Short-Range Forecast provide more useful information?, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-6873, https://doi.org/10.5194/egusphere-egu24-6873, 2024.

EGU24-7086 | Posters on site | AS1.2

Development of stadium-specific numerical forecast guidance for weather forecast for the 2024 Gangwon Winter Youth Olympic Games 

Yeon-Hee Kim, Eunju Cho, Sungbin Jang, Junsu Kim, Hyejeong Bok, and Seungbum Kim

The 2024 Gangwon Winter Youth Olympic Games (GANGWON 2024) will be held in the province of Gangwon in the Republic of Korea from January 19 to February 1, 2024, which already hosted the Olympic Winter Games PyeongChang 2018. In order to successfully host these first Winter YOG to be held in Asia, which will be held for the first time in Asia, it is necessary to provide customized weather information for decision-making in game operation and support in establishing game strategies for athletes and their teams. Accordingly, the Korea Meteorological Administration develops point-specific numerical forecast guidance for major stadiums and provides it to the field to support successful hosting of YOG and improvement of performance. Numerical forecast guidance is the final data delivered to consumers or forecasters as post-processed numerical model data that has been corrected by applying altitude correction and statistical methods to produce highly accurate forecasts. For a total of 13 forecast elements (temperature, minimum/maximum temperature, humidity, wind direction/speed, precipitation, new snow cover, sky conditions, precipitation probability, precipitation type), we developed user-customized numerical forecast guidance specialized for competition points  (Gangneung Olympic Park, Pyeongchang Alpensia Venue, Biathlon Center, Olympic Sliding Center departure/arrival, Wellyhilli departure/arrival, High1 departure/arrival). Through the process of Perfect Prognostic Method (PPM), Model Output Statistics (MOS), optimization, and optimal merging, the systematic errors inherent in the numerical model are removed, and the optimal data (BEST) with improved forecasting performance is provided as customized numerical forecast guidance specific to stadium locations.  In the prediction performance evaluation for the period of December 2023, the accuracy (improvement rate) compared to the average of available models was temperature 1.49℃ (18%), humidity 12% (25%), wind speed 1.87m/s (33%), and visibility 12.8km (17%).

How to cite: Kim, Y.-H., Cho, E., Jang, S., Kim, J., Bok, H., and Kim, S.: Development of stadium-specific numerical forecast guidance for weather forecast for the 2024 Gangwon Winter Youth Olympic Games, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-7086, https://doi.org/10.5194/egusphere-egu24-7086, 2024.

EGU24-7091 | Posters on site | AS1.2

The Development of precipitation model modifed with ECMWF IFS and XGBoost and its performance verification 

Eunju Cho, Yeon-Hee Kim, Seungbum Kim, and Young Cheol Kwon

This study was conducted to develop a modified precipitation model for its amount and existence by combining machine learning method, Extreme Gradient Boosting(XGBoost), with ECMWF IFS(Integrated forecasting system) and, finally, estimate the related performance.

According to the analysis of regional precipitation characteristic, prior to its development, the ratio of precipitation existence was various on a basis of a forecast’s district and its season. These different patterns on each district makes it necessary to develop the regional and seasonal model respectively.

And, the first attempt at the machine learning showed the importance of each feature as input-variables, as a result of which cloud physics-related features, for example large-area precipitation, total precipitation, visibility and what not, proved so significant. However, the insufficient amount of these feature’s data seemed to result in overfitting. And therefore, the feasible features, except for cloud physics-related things, of IFS data were used. In addition, auxiliary features and their gradient for every lead-time were calculated and added: relative vorticity, divergence, equivalent potential temperature, main 6 patterns for Korean summer and so on. The number of features amounted to around 144 with which for the 9-year training set, 2013~2021, based learning to be conducted regionally, followed by using validation-set of 2022.

As a result of validation for precipitation existence and its amount up to 135 hours ahead on the 10 regions at 00UTC in summer of 2022, Critical Success Index(CSI) was more improved by 10.3% than before. Accuracy(ACC) for each lead-time rose by 6% and its fluctuation also decreased. And the correction by this machine learning alleviated the overfitting trend of precipitation forecast amount produced by the original model, and improved correlation and linearity between observation and forecast. In particular, while the machine learning prevailed over the original model up to 100 hours ahead, from then on, both of them showed similar performance or that of the former was downward slightly. If the above-mentioned cloud physics features are used to further sharpen machine learning technique, its performance should be enhanced more and more.

How to cite: Cho, E., Kim, Y.-H., Kim, S., and Kwon, Y. C.: The Development of precipitation model modifed with ECMWF IFS and XGBoost and its performance verification, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-7091, https://doi.org/10.5194/egusphere-egu24-7091, 2024.

EGU24-7291 | ECS | Posters on site | AS1.2

Improvements in fog predictions via a modified reconstruction of moisture distribution using the Weather Research and Forecasting(WRF) model 

Eunji Kim, Soon-Young Park, Jung-Woo You, and Soon-Hwan Lee

Since fog is an important weather phenomenon affecting the traffic safety, accurate fog forecasting should be attained to minimize meteorological disasters. Most fog forecasts determine only the presence or absence of fog based on less visibility than 1 km, which is known as the visibility diagnostic method. During this process, fog could be predicted by the visibility calculated in the numerical weather prediction (NWP) model using the cloud liquid water content (LWC) near the surface. In this study, we investigated to increase the accuracy of fog forecast by optimizing the reconstruction of moisture distribution method, which can simulate the intensity of fog as well as the presence or absence of fog. The performances of the fog simulations were examined by modifying the relative humidity threshold at a height of 2 m and the stability parameters which affect turbulence and also one of the important criteria for fog occurrence. When we applied the optimize parameters to fog prediction in the winter seasons, the probability of detection (POD) has been increased significantly from 0.21 to 0.54. These improvements were attributed to the corrected relative humidity threshold and the stability parameters. Although the false alarm rate (FAR) remained almost unchanged, the critical success index (CSI) has been improved slightly lesser than those of the POD. When we analyzed the life cycle of fog, it takes time for the NWP model to simulate water droplets in the fog-developing stage. Therefore, the accuracy of the fog simulation is intimately related to the reconstruction of moisture distribution. The NWP model, however, showed a better performance in the process of fog dissipation than the reconstruction of moisture distribution method that was sensitive to temperature and turbulence. In conclusion, the reconstruction of moisture distribution led to a considerable improvement of the fog prediction in the generation and development stage since we used the optimized humidity threshold. It is also expected that accurate fog prediction could be achieved in the future by considering the aerosol effects, which is another importance factor for the fog generation.

How to cite: Kim, E., Park, S.-Y., You, J.-W., and Lee, S.-H.: Improvements in fog predictions via a modified reconstruction of moisture distribution using the Weather Research and Forecasting(WRF) model, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-7291, https://doi.org/10.5194/egusphere-egu24-7291, 2024.

EGU24-7536 | Orals | AS1.2

Nowcasting with Transformer-based Models using Multi-Source Data  

Çağlar Küçük, Apostolos Giannakos, Stefan Schneider, and Alexander Jann

Rapid advancements in data-driven weather prediction have shown notable success, particularly in nowcasting, where forecast lead times span just a few hours. Transformer-based models, in particular, have proven effective in learning spatiotemporal connections of varying scales by leveraging the attention mechanism with efficient space-time patching of data. This offers potential improvements over traditional nowcasting techniques, enabling early detection of convective activity and reducing computational costs. 

In this presentation, we demonstrate the effectiveness of a modified Earthformer model, a space-time Transformer framework, in addressing two specific nowcasting challenges. First, we introduce a nowcasting model that predicts ground-based 2D radar mosaics up to 2-hour lead time with 5-minute temporal resolution, using geostationary satellite data from the preceding two hours. Trained on a benchmark dataset sampled across the United States, our model exhibits robust performance against various impactful weather events with distinctive features. Through permutation tests, we interpret the model to understand the effects of input channels and input data length. We found that the infrared channel centered at 10.3 µm contains skillful information for all weather conditions, while, interestingly, satellite-based lightning data is the most skilled at predicting severe weather events in short lead times. Both findings align with existing literature, enhancing confidence in our model and guiding better usage of satellite data for nowcasting. Moreover, we found the model is sensitive to input data length in predicting severe weather events, suggesting early detection of convective activity by the model in rapidly growing fields. 

Second, we present the initial attempts to develop a multi-source precipitation nowcasting model for Austria, tailored to predict impactful events with convective activities. This model integrates satellite- and ground-based observations with analysis and numerical weather prediction data to predict precipitation up to 2-hour lead time with 5-minute temporal resolution.  

We conclude by discussing the broad spectrum of applications for such models, ranging from enhancing operational nowcasting systems to providing synthetic data to data-scarce regions, and the challenges therein.

How to cite: Küçük, Ç., Giannakos, A., Schneider, S., and Jann, A.: Nowcasting with Transformer-based Models using Multi-Source Data , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-7536, https://doi.org/10.5194/egusphere-egu24-7536, 2024.

EGU24-7753 | ECS | Orals | AS1.2

On the usefulness of considering the run-to-run variability for an ensemble prediction system 

Hugo Marchal, François Bouttier, and Olivier Nuissier

The run-to-run variability of numerical weather prediction systems is at the heart of forecasters' concerns, especially in the decision-making process when high-stakes events are considered. Indeed, forecasts that are brutally changing from one run to another may be difficult to handle and can lose credibility. This is all the more true nowadays, as many meteorological centres have adopted the strategy of increasing runs frequency, some reaching hourly frequencies. However, this aspect has received little attention in the literature, and the link with predictability has barely been explored.

In this study, run-to-run variability is investigated through 24h-accumulated precipitations forecasted by AROME-EPS, Météo-France's high resolution ensemble, which is refreshed 4 times a day. Focusing on the probability of some (warning) thresholds being exceeded, results suggest that how forecasts evolve over successive runs can be used to improve their skill, especially reliability. Various possible aspects of run sequence have been studied, from trends to rapid increases or decreases in event probability at short lags, also called "sneaks" or "phantoms", as well as the persistence of a non-zero probability through successive runs. The added value provided by blending successive runs, known as lagging, is also discussed.

How to cite: Marchal, H., Bouttier, F., and Nuissier, O.: On the usefulness of considering the run-to-run variability for an ensemble prediction system, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-7753, https://doi.org/10.5194/egusphere-egu24-7753, 2024.

EGU24-8449 | ECS | Orals | AS1.2

Radiation fog nowcasting with XGBoost using station and satellite data 

Michaela Schütz, Jörg Bendix, and Boris Thies

The research project “FOrecasting radiation foG by combining station and satellite data using Machine Learning (FOG-ML)” represents a comprehensive effort to advance radiation fog prediction using machine learning (ML) techniques, with focus on the XGBoost algorithm. The nowcasting period is up to four hours into the future.

The initial phase of the project involved developing a robust classification-based model that could accurately forecast the occurrence of radiation fog, a challenging meteorological phenomenon. Radiation fog is particularly difficult to predict because it depends on a complex interplay of factors such as ground cooling, humidity, and minimal cloud cover. It often forms rapidly and in local areas. This required careful analysis of the chronological order of the data and consideration of autocorrelation to increase the effectiveness of model training.

Building upon this foundation, the next two phases concentrated on improving the model’s forecasting performance for visibility classes (step 2) and for absolute visibility values (step 3). The main focus was then on a nowcasting period of up to two hours. This nowcasting period is critical in fog prediction as it directly impacts transportation planning and safety. The use of ground-level observations in step 2 and integration of satellite data in step 3 provided a rich dataset that allowed for more nuanced model training and validation.

In the latest phase of research, satellite data has been incorporated to further refine the prediction model, especially regarding the fog formation and dissipation. Satellite imagery provides additional variables of atmospheric data that are not readily available from ground-based observations. This integration aims to address one of the inherent limitations in fog forecasting methods, particularly in areas where ground-based observations are sparse.

Throughout the different stages, the project emphasized the need for thorough data processing and validation. This included the implementation of cross-validation techniques to assess the generalizability of the models and the use of various metrics to gauge their predictive power. This has also included the incorporation of trend information, which has proven to be crucial for forecasting with XGBoost. Our research has also shown that not only the overall performance, but also the performance of the transitions (fog formation and resolution) should be analyzed to get a complete picture of the model performance. This finding was consistent throughout the entire study, regardless of classification-based forecast or regression-based forecast.

We have been able to significantly improve the performance of our nowcasting model with each step. We will be presenting the key findings and latest results from this research at EGU24.

All results from step 1 can be found in “Current Training and Validation Weaknesses in Classification-Based Radiation Fog Nowcast Using Machine Learning Algorithms” from Vorndran et al. 2022. All results from step 2 can be found in “Improving classification-based nowcasting of radiation fog with machine learning based on filtered and preprocessed temporal data” from Schütz et al. 2023.

How to cite: Schütz, M., Bendix, J., and Thies, B.: Radiation fog nowcasting with XGBoost using station and satellite data, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-8449, https://doi.org/10.5194/egusphere-egu24-8449, 2024.

EGU24-9528 | ECS | Orals | AS1.2

Ensemble forecast post-processing based on neural networks and normalizing flows 

Peter Mlakar, Janko Merše, and Jana Faganeli Pucer

Ensemble weather forecast post-processing can generate more reliable probabilistic weather forecasts compared to the raw ensemble. Often, the post-processing method models the future weather probability distribution in terms of a pre-specified distribution family, which can limit their expressive power. To combat these issues, we propose a novel, neural network-based approach, which produces forecasts for multiple lead times jointly, using a single model to post-process forecasts at each station of interest. We use normalizing flows as parametric models to relax the distributional assumption, offering additional modeling flexibility.We evaluate our method for the task of temperature post-processing on the EUPPBench benchmark dataset. We show that our approach exhibits state-of-the-art performance on the benchmark, improving upon other well-performing entries. Additionally, we analyze the performance of different parametric distribution models in conjunction with our parameter regression neural network, to better understand the contribution of normalizing flows in the post-processing context. Finally, we provide a possible explanation as to why our method performs well, exploring per-lead time input importance.

How to cite: Mlakar, P., Merše, J., and Faganeli Pucer, J.: Ensemble forecast post-processing based on neural networks and normalizing flows, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-9528, https://doi.org/10.5194/egusphere-egu24-9528, 2024.

EGU24-9659 | Posters on site | AS1.2

Application Research of Multi-source New Detection Data in Snow Depth Prediction for Beijing Winter Olympics 

Jia Du, Bo Yu, Yi Dai, Sang Li, Luyang Xu, Jiaolan Fu, Lin Li, and Hao Jing

According to the demand of the Winter Olympic Organizing Committee for snow depth prediction, the application of multi-source new data in snow depth was studied based on densely artificial snow-depth measurement, microscopic snowflake shape observation and PARSIVEL data. The specific conclusions are as follows: (1) Most of the Snow-Liquid-Ratio(SLR) in Beijing competition zone was between 0.69 and 1.43 (unit: cm/mm, the same below), while that in Yanqing zone was between 0.53 and 1.17. But 7.5% of the SLRs in Yanqing zone exceeded 3.5, which all occurred in the same period of the key service time of 2022 Beijing Winter Olympics, making it more difficult to predict new snow depth. (2) The higher the SLR, the lower the daily minimum surface temperature and lowest air temperature.  Plate or column ice crystals, rimed snowflakes, and dendritic snowflakes were observed, whose corresponding SLRs increased. The average falling speed of particles falling below 2m/s can be used as an indicator of phase transfer. (3) The vertical distributions of temperature and humidity with SLR <1 or >2 were summarized. It was found that when the cloud area coincided with the dendritic growth zone with height close to Yanqing zone, the SLR would be more than 2, higher than that of Beijing zone. (4) A weather concept model generating large SLR was extracted. Snow in Beijing is often accompanied by easterly winds in boundary layer, which is easy to form a wet and ascending layer in the lower troposphere due to the blocking of western mountain. In the late winter season, helped by the temperature’s profile, it tends to produce unrimed dendritic snowflakes, leading to a great SLR.

How to cite: Du, J., Yu, B., Dai, Y., Li, S., Xu, L., Fu, J., Li, L., and Jing, H.: Application Research of Multi-source New Detection Data in Snow Depth Prediction for Beijing Winter Olympics, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-9659, https://doi.org/10.5194/egusphere-egu24-9659, 2024.

EGU24-9935 | ECS | Posters on site | AS1.2

Machine and Deep Learning algorithms to improve weather forecasts over a complex orography Mediterranean region 

Luca Furnari, Umair Yousuf, Alessio De Rango, Donato D'Ambrosio, Giuseppe Mendicino, and Alfonso Senatore

The rapid development of artificial intelligence algorithms has generated considerable interest in the scientific community. The number of scientific articles relating to applying these algorithms for weather forecasting has increased dramatically in the last few years. In addition, the recent operational launch of products such as GraphCast has put this area of research even more in the spotlight. This work uses different Machine Learning and Deep Learning algorithms, namely ANN (Artificial Neural Network), RF (Random Forest) and GNN (Graph Neural Network), with the aim to improve the short-term (1-day lead time) forecasts provided by a physically-based forecasting system. Specifically, the CeSMMA laboratory, since January 2020, has been producing daily forecasts accessible via the https://cesmma.unical.it/cwfv2/ webpage related to a large portion of southern Italy. The NWP (Numerical Weather Prediction) system is based on the WRF (Weather Research and Forecasting) model, with boundary and initial conditions provided by the GFS (Global Forecasting System) model. The AI algorithms post-process the NWP output, applying correction factors achieved by a two-year training considering the observations of the dense regional monitoring network composed of ca. 150 rain gauges.

The results show that the AI is able to improve daily rainfall forecasts compared to ground-based observations. Specifically, the ANN reduces the average MSE (Mean Square Error) by approximately 29% and the RF by 21% with respect to the WRF forecast for the whole study area (about 15’000 km2). Moreover, the GNN applied to a smaller area (considering only 22 rain gauges) further reduces the MSE by 35% during the heaviest rainfall months.

In addition to improving the performance of the forecast, the AI-based post-processing provides reasonable precipitation spatial patterns, reproducing the main physical phenomena such as the orographic enhancement since it is not a surrogate model and benefits from the original physically-based forecasts.

 

Acknowledgements. This work was funded by the Next Generation EU - Italian NRRP, Mission 4, Component 2, Investment 1.5, call for the creation and strengthening of ‘Innovation Ecosystems’, building ‘Territorial R&D Leaders’ (Directorial Decree n. 2021/3277) - project Tech4You - Technologies for climate change adaptation and quality of life improvement, n. ECS0000009. This work reflects only the authors’ views and opinions, neither the Ministry for University and Research nor the European Commission can be considered responsible for them.

How to cite: Furnari, L., Yousuf, U., De Rango, A., D'Ambrosio, D., Mendicino, G., and Senatore, A.: Machine and Deep Learning algorithms to improve weather forecasts over a complex orography Mediterranean region, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-9935, https://doi.org/10.5194/egusphere-egu24-9935, 2024.

On May 17, 2019, a rare severe convective weather occurred in Beijing, accompanied by local heavy rainstorm, hail, thunderstorm and gale. This severe convective weather occurred significantly earlier than normal years, bringing great challenge to the forecast. Using multiple observation data and radar four-dimensional variational assimilation products to analyze the triggering and development evolution of this severe convection. Under the conditions of no obvious weather scale system and local high potential unstable energy, the eastward advancement of the sea breeze front was the main factor triggering strong convection. As the northwest wind in the air increasing, the environmental conditions became stronger vertical wind shear, which was beneficial for the storm to maintain for a longer period of time. The supercell was the main cause of the convective weather. During the development of storms, they split into two parts and moved counterclockwise. The southern echo gradually weakened as it moved northward, while the northern echo moved southward, strengthening and developing into a super cell accompanied by a mesocyclone. The significant fluctuations in the height of the 0 ℃ layer within a small range resulted in different melting rates of hail during its descent, leading to the formation of spiky hail.

How to cite: Yu, B.: Analysis of a rare severe convective weather event in spring in Beijing of China, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-10809, https://doi.org/10.5194/egusphere-egu24-10809, 2024.

EGU24-12420 | ECS | Orals | AS1.2

Can convection permitting forecasts solve the tropical African precipitation forecasting problem? 

Felix Rein, Andreas H. Fink, Tilmann Gneiting, Philippe Peyrille, James Warner, and Peter Knippertz

Forecasting precipitation over Africa, the largest landmass in the tropics, has been a long standing problem. The unique conditions of the West African monsoon result in large and long lasting mesoscale convective systems. Global numerical weather prediction (NWP) models have gridsizes in the 10s of kilometers, particular when run in ensemble mode, leaving convection to be parameterized. This often results in precipitation being forecast on too large scales, in the wrong places, and with too weak intensity, ultimately leading to little to no skill in tropical Africa.


It has been argued that convection permitting (CP) NWP forecasts would cure some of the problems described above but those have only recently become feasible in an operational setting, although ensembles are still deemed to be too expensive. Here, we systematically compare regional deterministic CP and global ensemble forecasts in the region over multiple rainy seasons for the first time. We analyze CP forecasts from AROME and Met Office Tropical African Model, and seven global ensemble forecasts from the TIGGE archive, both individually and as a multi-model ensemble. In order to create an uncertainty estimate, we create neighborhood ensembles from CP forecasts at surrounding grid points, which allows for a fair comparison to the ensembles and a probabilistic climatology. Considering both precipitation occurrence and amount, we use the Brier score (BS) and the continuous ranked probability score (CRPS), along with their decompositions in discrimination, miscalibration and uncertainty, for evaluation.


Using neighborhood methods, deterministic forecasts are turned into probabilistic forecasts, allowing a fair comparison with ensembles. All numerical forecasts benefit from Neighborhoods, improving their BS and CRPS in terms of both miscalibration and discrimination. We find all individual forecasts to have skill over most of tropical Africa, with some ensemble models lacking skill in some regions and the multi model showing the most overall skill. The CP forecasts TAM and AROME outperform non-CP forecasts mainly in the region of the little dry Season and the Soud. However, large areas of low skill in terms of CRPS remain and even with high resolution, numerical models still struggle to predict precipitation in tropical Africa. 

How to cite: Rein, F., Fink, A. H., Gneiting, T., Peyrille, P., Warner, J., and Knippertz, P.: Can convection permitting forecasts solve the tropical African precipitation forecasting problem?, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-12420, https://doi.org/10.5194/egusphere-egu24-12420, 2024.

EGU24-12855 | Posters on site | AS1.2

Project IMA: Lessons Learned from Building the Belgian Operational Seamless Ensemble Prediction System 

Lesley De Cruz, Michiel Van Ginderachter, Maarten Reyniers, Alex Deckmyn, Idir Dehmous, Simon De Kock, Wout Dewettinck, Ruben Imhoff, Esteban Montandon, and Ricardo Reinoso-Rondinel

 

In recent years, several national meteorological services (NMSs) have invested considerable resources in the development of a seamless prediction system: rapidly updating forecasts that integrate the latest observations, covering timescales from minutes to days or longer ahead (e.g. DWD's SINFONY; FMI's ULJAS, MetOffice's IMPROVER and Geosphere Austria's SAPHIR) [1]. This move was motivated mainly by rising expectations from end users such as hydrological services, local authorities, the renewable energy sector and the general public. The development of seamless prediction systems was made possible thanks to the increasing availability of high-resolution observations, continuing advances in numerical weather prediction (NWP) models, nowcasting algorithms, and improved strategies to combine multiple information sources optimally. Moreover, the rise of AI/ML techniques in forecasting and nowcasting can further reduce the computational cost to generate frequently updating seamless operational forecast products.

 

We present the journey of building the Belgian seamless prediction system at the Royal Meteorological Institute of Belgium, with the working title "Project IMA". IMA uses both the deterministic INCA-BE and the probabilistic pysteps-BE systems to combine nowcasts with the ALARO and AROME configurations of the ACCORD NWP model. In the lessons learned along the way, we focus on what is often omitted, moving from research to operations, and integrating what we learn from operations back into research. We discuss the benefits of integrating new developments within the free and open-source software (FOSS) pysteps [2]. Our experience shows that using and contributing to FOSS not only leads to more transparency and reproducible, open science; it also enhances international collaboration and can benefit other users, including developing countries, bringing us a step closer to the ambitious goal of Early Warnings for All by 2027 [3].

 

References

 

[1] Bojinski, Stephan, et al. "Towards nowcasting in Europe in 2030." Meteorological Applications 30.4 (2023): e2124.

[2] Imhoff, Ruben O., et al. "Scale‐dependent blending of ensemble rainfall nowcasts and numerical weather prediction in the open‐source pysteps library." Quarterly Journal of the Royal Meteorological Society 149.753 (2023): 1335-1364.

[3] WMO, "Early warnings for all: Executive action plan 2023-2027", 8 Nov 2022,  https://www.preventionweb.net/quick/75125.

How to cite: De Cruz, L., Van Ginderachter, M., Reyniers, M., Deckmyn, A., Dehmous, I., De Kock, S., Dewettinck, W., Imhoff, R., Montandon, E., and Reinoso-Rondinel, R.: Project IMA: Lessons Learned from Building the Belgian Operational Seamless Ensemble Prediction System, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-12855, https://doi.org/10.5194/egusphere-egu24-12855, 2024.

Moderate to heavy rain produced by slantwise ascent of moist air above the cold high is prevalent in cold season in East China. The slantwise ascent is usually characterized by a southwest moist flow aroused by the so-called southern branch trough of 500hPa level to the south of the Qinghai-Tibet Plateau, while the cold high is usually formed by cold air damming, which is familiar to weather forecasters due to topographic feature of East China. The routine short-range forecast skill for this kind of precipitation of weather forecasters is usually limited by model performance. Through large sample model verification, our study indicates that, for the rainfall produced by southwesterly moist flow ascending above the cold high, the ECMWF model always underestimates the rainfall amount on the northeastern part of the rainfall belt, which could be taken as a systematic bias of the state-of-the-art global model. Our case studies indicate that the underestimation of rainfall amount is related to the weaker slant ascent of moist southwest flow forecast by ECMWF model than observation or reanalysis. The southwest flow above the northeastern flow induced by the cold high forms strong wind shear and warm-moist advection, which favors the occurrence of conditional symmetric instability producing strong slantwise ascent not well reflected by global model.

How to cite: Hu, N. and Fu, J.: Investigating Model Forecast Bias for Rainfall Produced by Slantwise Ascent above Cold High, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-13809, https://doi.org/10.5194/egusphere-egu24-13809, 2024.

EGU24-13853 | Orals | AS1.2 | Highlight

A Research Agenda for the Evaluation of AI-Based Weather Forecasting Models 

Imme Ebert-Uphoff, Jebb Q. Stewart, and Jacob T. Radford and the CIRA-NOAA team

Over the past few years purely AI-driven global weather forecasting models have emerged that show increasingly impressive skill, raising the question whether AI models might soon compete with NWP models for selected forecasting tasks. At this point these AI-based models are still in the proof-of-concept stage and not ready to be used for operational forecasting, but entirely new AI-models emerge every 2-3 months, with rapidly increasing abilities. Furthermore, many of these models are orders of magnitude faster than NWP models and can run on modest computational resources enabling repeatable on-demand forecasts competitive with NWP. The low computational cost enables the creation of very large ensembles, which better represent the tails of the forecast distribution, which, if an ensemble is well calibrated, allows for better forecasting of rare and extreme events.

However, these AI-based weather forecasting models have not yet been rigorously tested by the meteorological community, and their utility to operational forecasters is unknown. In this presentation we propose several studies to address the above issues, grouped into two central foci:

(1) Nature of AI models: AI-based models have very different characteristics from NWP models. Thus, in addition to applying evaluation procedures developed for NWP models, we need to develop procedures that test for AI-specific weaknesses. For example, NWP models and their physics backbone guarantee certain properties - such as dynamic coupling between fields - that AI-based models are not required to uphold. Developing suitable tests is based on a fundamental understanding of the AI-based models.

(2) Forecaster Perspective: Evaluation of weather forecasting models should be performed with respect to particular applications of weather forecasts, and it is critical to have research meteorologists and operational forecasters involved in the evaluation process. Our initial evaluation of AI-based models in CIRA weather briefings revealed that these models have characteristics that make interpretation of their forecasts fundamentally different from the physics-based NWP model predictions meteorologists are familiar with. For example, the increasing “blurriness” of AI-based predictions with longer lead times is not a reflection of weaker atmospheric circulations, but rather a reflection of uncertainty. Evaluations aimed at specific meteorological phenomena and atmospheric processes will allow the community to make informed decisions in the future regarding in what environments and for which applications AI-based weather forecasting models may be safe and beneficial to use.

In summary, AI-based weather forecasts have different characteristics from familiar dynamically-based forecasts, and it is thus important to have a robust research plan to evaluate many different characteristics of the models in order to provide guidelines to operational forecasters and feedback to model developers. In this abstract we propose a number of characteristics to evaluate, present results we already obtained, and suggest a research plan for future work.

How to cite: Ebert-Uphoff, I., Stewart, J. Q., and Radford, J. T. and the CIRA-NOAA team: A Research Agenda for the Evaluation of AI-Based Weather Forecasting Models, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-13853, https://doi.org/10.5194/egusphere-egu24-13853, 2024.

Large-eddy simulations of an idealized tropical cyclone (TC) were conducted as benchmarks to provide statistical information about subgrid convective clouds at a convection-permitting resolution over a TC convection system in different stages. The focus was on the vertical and spatial distributions of the subgrid cloud and associated mass flux that need to be parameterized in convection-permitting models. Results showed that the characteristics of the subgrid clouds varied significantly in various parts of the TC convection system. Statistical analysis revealed that the subgrid clouds were mainly located in the lower troposphere and exhibited shallow vertical extents of less than 4 km. The subgrid clouds were also classified into various cloud regimes according to the maximum mass flux height. Local subgrid clouds differed in mass-flux profile shape and magnitude at various regimes in the TC convection system.

How to cite: Zhang, X. and Bao, J.-W.: Statistics of the Subgrid Cloud of an Idealized Tropical Cyclone at Convection-Permitting Resolution, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-14232, https://doi.org/10.5194/egusphere-egu24-14232, 2024.

EGU24-14541 | Posters on site | AS1.2

Status and Plan of Standard Verification System for the NWP model in Korea Meteorological Administration 

Sora Park, Hyeja Park, Haejin Lee, Saem Song, Jong-Chul Ha, and Young Cheol Kwon

The Korea Meteorological Administration (KMA) has established and operated a standard verification system of the operational NWP models to evaluate the predictive performance of NWP model and compare them with other NWP models operated by domestic and foreign organization. This secures the objectivity of the verification results by applying the verification standards (WMO-No.485) presented by World Meteorological Organization (WMO), and being able to compare the performance with the numerical forecasting models of other institutions under the same conditions. The NWP models to be verified is a global, a regional, very short-range, and an ensemble prediction system and verification against analyses and observations are performed twice a day (00 UTC, 12 UTC). In addition to standard verification, precipitation, typhoon and various verification indexes (CBS index, KMA index, jumpiness index) are verified and used to evaluate the utilization of NWP models. The Korea Integrated Model (KIM), which is developed for Korea’s own NWP model, has been in operation since April 2020. Since the start of operation, the RMSE of 500hPa geopotential height (in Northern Hemisphere) has decreased every year, showing that forecast performance is improving. In addition, it can be seen that the 72-hour prediction accuracy for 12-hour accumulated precipitation (1.0 mm or more) in the Korean Peninsula area (75 ASOS stations) is also improving. As such, this study intends to discuss the predictive performance of the numerical forecast model based on the standard verification system and plans to improve the verification system in the future. 

How to cite: Park, S., Park, H., Lee, H., Song, S., Ha, J.-C., and Kwon, Y. C.: Status and Plan of Standard Verification System for the NWP model in Korea Meteorological Administration, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-14541, https://doi.org/10.5194/egusphere-egu24-14541, 2024.

EGU24-15431 | ECS | Orals | AS1.2 | Highlight

Nowcasting of extreme precipitation events: performance assessment of Generative Deep Learning methods 

Gabriele Franch, Elena Tomasi, Rishabh Umesh Wanjari, and Marco Cristoforetti

Radar-based precipitation nowcasting is one of the most prominent applications of deep learning (DL) in weather forecasting. The accurate forecast of extreme precipitation events remains a significant challenge for deep learning models, primarily due to their complex dynamics and the scarcity of data on such events. In this work we present the application of the latest state-of-the-art generative architectures for radar-based nowcasting, focusing on extreme event forecasting performance. We analyze a declination for the nowcasting task of all the three main current architectural approaches for generative modeling, namely: Generative Adversarial Networks (DGMRs), Latent Diffusion (LDCast), and our novel proposed Transformer architecture (GPTCast). These models are trained on a comprehensive 1-km scale, 5-minute timestep radar precipitation dataset that integrates multiple radar data sources from the US, Germany, the UK, and France. To ensure a robust evaluation and to test the generalization ability of the models, we concentrate on a collection of out-of-domain extreme precipitation events over the Italian peninsula extracted from the last 5 years. This focus allows us to assess the improvements these techniques offer compared to extrapolation methods, evaluating continuous (MSE, MAE) and categorical scores (CSI, POD, FAR), ensemble reliability, uncertainty quantification, and warning lead time. Finally, we analyze the computational requirements of these new techniques and highlight the caveats that must be considered when operational usage of these methods is envisaged. 

How to cite: Franch, G., Tomasi, E., Wanjari, R. U., and Cristoforetti, M.: Nowcasting of extreme precipitation events: performance assessment of Generative Deep Learning methods, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-15431, https://doi.org/10.5194/egusphere-egu24-15431, 2024.

EGU24-16617 | Posters on site | AS1.2

Forecasting extreme events with the crossing-point forecast  

Zied Ben Bouallegue

The crossing-point forecast (CPF) is a new type of ensemble-based forecast developed at the European Centre for Medium-Range Weather Forecasts. The crossing point refers to the intersection between the cumulative probability distribution of a forecast and the cumulative probability distribution of a model climatology. Originally, the CPF has emerged as a consistent forecast with the diagonal score, a weighted version of the continuous ranked probability score targeting high-impact events. Ranging between 0 and 1, the CPF can serve as an index for high-impact weather and thus directly be compared with the well-established extreme forecast index. The CPF is also interpretable in terms of a return period and conveys a sense of a “probabilistic worst-case scenario”.  Using a recent example of an extreme event affecting Europe, we illustrate and discuss the performance and specificities of this new type of forecast for extreme weather forecasting.

Ben Bouallegue, Z (2023).  Seamless prediction of high-impact weather events: a comparison of actionable forecasts. arXiv:2312.01673

How to cite: Ben Bouallegue, Z.: Forecasting extreme events with the crossing-point forecast , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-16617, https://doi.org/10.5194/egusphere-egu24-16617, 2024.

EGU24-17158 | Orals | AS1.2 | Highlight

AIFS – ECMWF’s Data-Driven Probabilistic Forecasting  

Zied Ben Bouallegue, Mihai Alexe, Matthew Chantry, Mariana Clare, Jesper Dramsch, Simon Lang, Christian Lessig, Linus Magnusson, Ana Prieto Nemesio, Florian Pinault, Baudouin Raoult, and Steffen Tietsche

In just two years, the idea of an operational data-driven system for medium-range weather forecasting has been transformed from dream to very real possibility. This has occurred through a series of publications from innovators, which have rapidly improved deterministic forecast skill. Our own evaluation confirms that these forecasts have comparable deterministic skill to NWP models across a range of variables. However, on medium-range timescales probabilistic forecasting, typically achieved through ensembles, is key for providing actionable insights to users. ECMWF is building on top of these recent works to develop a probabilistic forecasting system, AIFS. We will showcase results from our progress towards this system and outline our roadmap to operationalisation.

How to cite: Ben Bouallegue, Z., Alexe, M., Chantry, M., Clare, M., Dramsch, J., Lang, S., Lessig, C., Magnusson, L., Prieto Nemesio, A., Pinault, F., Raoult, B., and Tietsche, S.: AIFS – ECMWF’s Data-Driven Probabilistic Forecasting , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-17158, https://doi.org/10.5194/egusphere-egu24-17158, 2024.

The increasing integration of renewable energy resources to the national grids necessitates
accurate prediction of power generation from those sources in terms of secure operation of
electricity grid system and energy trading. Electricity generation of renewable energy power
plants such as wind and solar are inherently affected by weather conditions. The wind condition
particularly is affected by surface characteristics such as orography and vegetation, therefore it is
the one of the near surface atmospheric variables having the strongest local variability. The high-
resolution Numerical Weather Prediction (NWP) models are utilized to take the local conditions
into account. WRF model is the one of the most common NWP models having been widely
investigated by various researchers. On the other hand, The Model for Prediction Across Scales
(MPAS) is a relatively new NWP model utilizing non-uniform mesh structures, developed by the
National Center for Environmental Predictions (NCEP). However, there are limited studies in the
literature which compare the prediction performance of WRF and MPAS model in terms of
surface wind speed. This study evaluates the prediction accuracy of near surface wind of two
downscaled NWP models namely, WRF-ARW and MPAS. Both models are configured with
almost identical physics suites and initialized with 3 hourly 00-UTC initialization of Global
Forecast System (GFS) data. The model outputs are obtained at 10 minutes interval for 48 hours
horizon. Hourly averaged model results are compared with observations from 104 on-site
meteorological stations located in Turkiye having different complexity in terms of correlation
coefficient and RMSE.

How to cite: Yalcin, R. D., Yilmaz, M. T., and Yucel, İ.: Evaluation of the Impact of Uniform and Non-Uniform Resolution Implementations in Numerical Weather Prediction Models over the Accuracy of Short-Term Wind Prediction, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-17339, https://doi.org/10.5194/egusphere-egu24-17339, 2024.

EGU24-18548 | ECS | Posters on site | AS1.2

Enhancing Regional NWP Model with GNSS Zenith Total Delay Assimilation: A WRF and WRFDA 3D-Var Approach in the Greater Region of Luxembourg 

Haseeb Ur Rehman, Felix Norman Teferle, Addissu Hunegnaw, Guy Schumann, Florian Zus, and Rohith Muraleedharan Thundathil

Compared to alluvial floods, flash or pluvial floods are difficult to predict because they result from intense and brief periods of extreme precipitation. Luxembourg has a history of being impacted by floods, with notable occurrences in January 2011, May 2016, December 2017, January 2018, February 2019, and February 2020. However, July 2021 stands out as the most severe flood year on record in the region. In this study we are aiming to develop, a high-resolution numerical weather prediction (NWP) model for effective local heavy rainfall prediction in a nowcasting scenario and provide real time for flood simulation. The modeling relies on the Weather Research and Forecasting (WRF) model, which incorporates local Global Navigation Satellite System (GNSS) data assimilation and local precipitation observations to simulate small-scale, high-intensity convective precipitation.

As part of this, we will also test run the LISFlood flood model in an operational inundation forecast mode, meaning that the flood model will be run with the WRF precipitation forecasts as inputs.

The WRF model was configured for the Greater Region, utilizing a horizontal grid resolution of 12 km and incorporating high-resolution static datasets. Meteorological data i.e. July 13 -14 2021, from the Global Forecast System (GFS) were employed in the model setup as initial boundary condition. Zenith Total Delay (ZTD) data collected from various GNSS stations (112) across Germany and Luxembourg were assimilated into the model. Additionally, observational datasets including Surface Synoptic Observations (SYNOP), Upper Air Data, Radiosonde measurements (TEMP), and Tropospheric Airborne Meteorological Data Reporting (TAMDAR) were assimilated. Following this integration, an sensitivity analysis of various meteorological parameters such as precipitation, surface temperature (T2), and relative humidity was performed.

 

Keywords: NWP, WRF, Flash flood, LISFlood, Weather forecast, High-Resolution, GNSS, ZTD

How to cite: Rehman, H. U., Teferle, F. N., Hunegnaw, A., Schumann, G., Zus, F., and Muraleedharan Thundathil, R.: Enhancing Regional NWP Model with GNSS Zenith Total Delay Assimilation: A WRF and WRFDA 3D-Var Approach in the Greater Region of Luxembourg, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-18548, https://doi.org/10.5194/egusphere-egu24-18548, 2024.

EGU24-18938 | Posters on site | AS1.2

Forecasting tropical high-impact rainfall events using a hybrid statistical dynamical technique based on equatorial waves 

Samantha Ferrett, Gabriel Wolf, John Methven, Tom Frame, Christopher Holloway, Oscar Martinez-Alvarado, and Steve Woolnough

Recent work within the WCSSP FORSEA project and its successor FORWARDS has demonstrated that a hybrid statistical-dynamical forecasting technique combining model ensemble forecasts of equatorial waves with climatological rainfall statistics conditioned on wave phase and amplitude can provide additional skill in predicting high impact weather. The underlying rationale for the technique is twofold. Firstly that high impact rainfall events in the tropics are commonly associated with presence of equatorial waves; and secondly that while global models can adequately predict the evolution of dynamical structure of equatorial waves on time-scales of several days they do not predict the relationship between waves and rainfall well. In tests using the Met Office Global and Regional Forecasting System (MOGREPS) the hybrid forecast is found to outperform model rainfall forecasts from both the global and regional convection permitting versions of MOGREPS, however a weighted blend of the MOGREPS forecasts and the hybrid forecast was found to have the highest skill and further improvements in the method may be obtained by taking into consideration the effects of wave-superposition and interaction. To ascertain whether forecasts can be further improved by better predictions of wave amplitude and phase we compare to hypothetical best-case hybrid forecast computed using wave amplitudes and phases taken from reanalysis. This best-case scenario indicates that errors in forecasting all wave types diminish the hybrid forecast's skill, with the most significant reduction observed for Kelvin waves, suggesting that a significant improvement in the prediction of the propagation of equatorial waves would have a significant impact on rainfall prediction in the tropics. 

How to cite: Ferrett, S., Wolf, G., Methven, J., Frame, T., Holloway, C., Martinez-Alvarado, O., and Woolnough, S.: Forecasting tropical high-impact rainfall events using a hybrid statistical dynamical technique based on equatorial waves, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-18938, https://doi.org/10.5194/egusphere-egu24-18938, 2024.

EGU24-19257 | ECS | Orals | AS1.2

Dynamic Locally Binned Density Loss 

Jan Prosi, Sebastian Otte, and Martin V. Butz

In the field of precipitation nowcasting recent deep learning models now outperform traditional approaches such as optical flow [1,2]. Despite their principled effectiveness, these models and their respective training setups suffer from particular shortcomings.  For instance, they often rely on pixel-wise losses, which lead to blurred predictions by which the model expresses its uncertainty [2]. Additionally, these losses can negatively impact training dynamics by overly penalizing small spatial or temporal discrepancies between predictions and actual observations, i.e., the double penalty problem [3]. Generative methods such as discriminative losses or diffusion models do not suffer from the blurring effect as much [1, 4]. However, training these methods is complicated because training success is highly sensitive to the network architecture as well as to the learning setup and its parameterization [5].

Previous research has shown that spatial verification methods such as the fractions skill score offer an easy-to-implement alternative to solve the problem of pixel-wise losses [6, 7]. However, the fact that each pixel within the neighborhood of a spatial kernel is weighted equally poses a limiting factor to their performance and potential. Inspired by theories of cognitive modeling and in relation to the fractions skill score loss, we introduce a dynamic locally binned density (DLBD) loss: Forecasting target is not the actual precipitation in a grid cell but a target distribution, which encodes the density of binned precipitation values in a locally weighted area of grid cells. The loss is then determined via the cross-entropy of the predicted and the target distribution. We show that our novel prediction loss avoids the double penalty problem.  It thus diminishes the negative impact of small spatial offsets. Moreover, it enables the learning model to gradually shift focus towards progressively more accurate predictions.

We achieve best performance by simultaneously training on multiple concurrent forecasting targets that cover different local extents. We schedule the weighting of the loss terms such that the focus shifts from larger to smaller neighborhoods over the course of training. This way, the DL model first learns density dynamics and basic precipitation shifts. Later, it focuses on minimizing small spatial deviations, tuning into the local dynamics towards the end of training.  Our DLBD loss is easy-to-implement and shows great performance improvements.  We thus believe that DLBD losses can also be used by other forecasting architectures where the current forecasting loss precludes smooth loss landscapes.

 


1: Leinonen et al. 2023: Latent diffusion models for generative precipitation nowcasting with accurate uncertainty quantification
2: Espeholt et al. 2022: Deep learning for twelve hour precipitation forecasts
3: Grilleland et al. 2009: Intercomparison of spatial forecast verification methods.
4: Ravuri et al. 2021: Skilful precipitation nowcasting using deep generative models of radar
5: Mescheder et al. 2018: Which training methods for GANs do actually converge?
6: Roberts et al. 2008: Scale-selective verification of rainfall accumulations from high resolution forecasts of convective events.
7: Lagerquist et al. 2022: Can we integrate spatial verification methods into neural-network loss functions for atmospheric science?

How to cite: Prosi, J., Otte, S., and Butz, M. V.: Dynamic Locally Binned Density Loss, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-19257, https://doi.org/10.5194/egusphere-egu24-19257, 2024.

EGU24-19321 | ECS | Orals | AS1.2

Viability of satellite derived irradiance data for ML-based nowcasts 

Pascal Gfäller, Irene Schicker, and Petrina Papazek

Photovoltaic (PV) power production is increasingly becoming a central pillar in the shift to renewable power sources. The use of solar irradiance has great potential, as it is practically limitless and globally provides magnitudes more energy to the Earth than currently or foreseeable required. Solar irradiance as a power source does, however come with certain downsides. Besides the effects of seasonality and day-night-cycles on its usable potential, it´s broad use suffers mostly from uncertainty through its volatility. The actual extent of solar irradiance at the surface of the Earth is strongly influenced by a variety of atmospheric phenomena, most prominently clouds and atmospheric turbidity. The forecasting of near-future solar irradiance can thereby be beneficial in the estimation of PV power production in itself and with the goal of maintaining a stable equilibrium in electrical grids.

To achieve nowcasts on a larger grid scope, forecasting of solar irradiance from satellite data can substitute forecasting of power output for individual sites. Satellite data, in contrast to ground-based data sources or NWP model estimates, is less reliant on the proper workings of a wide range of externalities. General-purpose spatiotemporal neural networks can be adapted to this task and provide predictions within a very short timeframe, with no requirement of HPC-infrastructure. A sparse model relying on a single satellite-based data source has less points of failure that could affect its forecasting performance and can be very efficient, but this sparsity could also reduce the achievable predictive accuracy. Benefits of smaller and simpler forecasting pipelines therefore may need to be balanced with requirements in terms of accuracy.

To gather more meaningful and reliable results, a variety of spatiotemporal neural networks is implemented and tested to provide a more meaningful foundation. The models were selected and evaluated with respect to their different architectural patterns and designs, to get a notion of architectures beneficial to this task and achieve a more generalizable argument concerning the use satellite data as the sole basis of solar irradiance nowcasting.

In an attempt of improving the viability of satellite-based nowcasting a commonly occurring flaw in near-real-time satellite data sources, missing or skipped frames, solutions to mitigate issues in operational nowcasting are considered. In place of ad-hoc preprocessing such as interpolation of missing data frames, an attempt to condition the models to missing frames is made.

How to cite: Gfäller, P., Schicker, I., and Papazek, P.: Viability of satellite derived irradiance data for ML-based nowcasts, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-19321, https://doi.org/10.5194/egusphere-egu24-19321, 2024.

EGU24-19377 | ECS | Posters on site | AS1.2

Advancing Spatiotemporal Rainfall Nowcasting through Deep Learning Techniques 

Ahmed Abdelhalim, Miguel Rico-Ramirez, Weiru Liu, and Dawei Han

For weather forecasters and hydrologists, predicting rainfall in the short term – minutes to a few hours – is crucial for a range of applications. While traditional nowcasting methods excel in operational settings, they face limitations in predicting convective storm formation and high-intensity events. Enter deep learning, a powerful tool transforming numerous fields. Convolutional neural networks, in particular, have shown promise in improving nowcasting accuracy. These networks can learn complex patterns and relationships within data, like the intricate tapestry of rainfall variations observed in historical radar sequences. However, capturing long-term dependencies in this data remains a challenge, resulting in fuzzy nowcasts and underestimating high-intensity events. This study proposes a novel deep learning model that goes beyond simple extrapolation, effectively capturing both the spatial correlations and temporal dependencies within rainfall data. Our hybrid convolutional neural network architecture tackles this challenge through three key components: Decoder & Encoder: These modules focus on unraveling the intricate spatial patterns of rainfall and a temporal Module to learn the subtle long-term evolutions and interactions between rain cells over time. By capturing these temporal dependencies, the model can produce more accurate forecasts. To evaluate the model performance, it is compared against both deep learning and optical flow baselines. This presentation will introduce the model and provide a summary of its performance in spatiotemporal rainfall nowcasting.

Keywords: deep learning; spatiotemporal encoding, rainfall nowcasting; radar; optical flow

How to cite: Abdelhalim, A., Rico-Ramirez, M., Liu, W., and Han, D.: Advancing Spatiotemporal Rainfall Nowcasting through Deep Learning Techniques, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-19377, https://doi.org/10.5194/egusphere-egu24-19377, 2024.

EGU24-19699 | ECS | Posters on site | AS1.2

Evaluation of seamless forecasts for severe weather warnings  

Verena Bessenbacher, Jonas Bhend, Lea Beusch, Daniele Nerini, Colombe Siegenthaler, Christoph Spirig, and Lionel Moret

At MeteoSwiss, NWP and ML-based models are run operationally on a daily basis to provide weather forecasts and weather warnings for the general public. These forecasts come from various models that differ in lead times, initialization frequency, spatial resolution, and extents. We aim at combining those sources into a probabilistic, gridded weather forecast that is seamless in space and time. Creating a seamless forecast needs careful post-processing so as not to introduce cut-offs or unphysical behavior at the seams between the model runs. This includes using multiple forecast sources and forecast initializations (called lagged ensembles) and combining these using comprehensive blending methods. 

The first minimal viable product of a seamless forecast is currently being produced at MeteoSwiss, and will soon be available to the forecasters in real time. 

We evaluate the merit of these forecasts in terms of warning thresholds for rain and wind gusts. To do so, we compare reforecasts and observations from ground stations as well as rain radar observations from a set of past severe weather events over Switzerland. We benchmark the seamless forecast with individual forecast sources and post-processed products to evaluate the added value of seamlessly combining different forecast sources into one blended product. We furthermore plan to compare different methods for blending between sources soon.

How to cite: Bessenbacher, V., Bhend, J., Beusch, L., Nerini, D., Siegenthaler, C., Spirig, C., and Moret, L.: Evaluation of seamless forecasts for severe weather warnings , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-19699, https://doi.org/10.5194/egusphere-egu24-19699, 2024.

Integrating the hybrid and multiscale analyses and the parallel computation is necessary for current data assimilation schemes. A local data assimilation method, Local DA, is designed to fulfill these needs. This algorithm follows the grid-independent framework of the local ensemble transform Kalman filter (LETKF) and is more flexible in hybrid analysis than the LETKF. Local DA employs an explicitly computed background error correlation matrix of model variables mapped to observed grid points/columns. This matrix allows Local DA to calculate static covariance with a preset correlation function. It also allows using the conjugate gradient (CG) method to solve the cost function and allows performing localization in model space, observation space, or both spaces (double-space localization). The Local DA performance is evaluated with a simulated multiscale observation network that includes sounding, wind profiler, precipitable water vapor, and radar observations. In the presence of a small-size time-lagged ensemble, Local DA can produce a small analysis error by combining multiscale hybrid covariance and double-space localization. The multiscale covariance is computed using error samples decomposed into several scales and independently assigning the localization radius for each scale. Multiscale covariance is conducive to error reduction, especially at a small scale. The results further indicate that applying the CG method for each local analysis does not result in a discontinuity issue. The wall clock time of Local DA implemented in parallel is halved as the number of cores doubles, indicating a reasonable parallel computational efficiency of Local DA.

How to cite: Wang, S. and Qiao, X.: A Local Data Assimilation Method (Local DA v1.0) and its Application in a Simulated Typhoon Case, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-21770, https://doi.org/10.5194/egusphere-egu24-21770, 2024.

EGU24-21772 | Orals | AS1.2

Calibration of Convective-scale Hourly Precipitation Based on the Frequency-Matching Method 

Xiaoshi Qiao, Shizhang Wang, and Mingjian Zeng

Calibration of convective-scale hourly precipitation based on the frequency-matching method was carried on using CMPASS observation and CMA-MESO 3km forecast data. The character of hourly precipitation bias was studied.The effect of frequency-matching method (FMM) on the bias correction of CMA-MESO 3km hourly precipitation forecasts was analyzed. In the bias characteristic analysis, the differences in precipitation intensity in different regions of the country and the differences in precipitation in different months were considered. The whole country was divided into 7 sub-regions for monthly analysis. In the bias correction based on the frequency-matching method, the daily variations of precipitation bias and the impact of increasing and decreasing precipitation values on the corrected precipitation scores were analyzed. The results show that CMA-MESO 3km forecasts have a wet bias in light rainfall in the cold season, while a dry bias dominates in moderate to heavy rainfall. In the warm season, except for the Tibet region, the hourly precipitation forecast bias of CMA-MESO 3km shows significant daily variations, with more precipitation in the afternoon and less at night and in the morning, especially for heavy rainfall. Therefore, whether to consider the daily variations of precipitation bias in the use of FMM correction mainly reflects in the summer, especially at night and in the morning. Considering the daily variations of precipitation bias is beneficial to improving the forecast skills (TS scores) for nighttime and morning in the summer. Further analysis shows that the positive contribution of FMM correction to forecast scores mainly comes from the increase in frequency adjustment, especially for heavy rainfall. However, for light rainfall with wet bias, FMM often results in negative contribution. Therefore, FMM has a significant improvement effect on heavy rainfall in winter and nighttime rainfall in summer. The reason for this result is that the hit rate of CMA-MESO hourly precipitation forecast is low, and the false alarm rate is generally high, especially for heavy rainfall. In this case, the increased precipitation significantly increases the hit rate, while the false alarm rate increases to a lesser extent, thereby improving the precipitation scores.

How to cite: Qiao, X., Wang, S., and Zeng, M.: Calibration of Convective-scale Hourly Precipitation Based on the Frequency-Matching Method, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-21772, https://doi.org/10.5194/egusphere-egu24-21772, 2024.

NH2 – Volcanic Hazards

EGU24-1402 | ECS | Posters on site | NH2.1

Global Sensitivity Analysis of tephra models for forecasting 

Emmy Scott, Melody Whitehead, Stuart Mead, Mark Bebbington, and Jonathan Procter

Accurate forecasts are needed to help mitigate the risks of volcanic hazards to society. Current approaches use probabilistic estimates based on sparse data, supplemented with expert judgment, to describe likely future eruption characteristics. These probabilistic eruption characteristics then inform input parameters required by hazard models.

This process requires a lot of simulations with varying input parameters to constrain uncertainty around a future eruption’s hazard characteristics. It is also computationally intensive, and the outputs may quantify, but do not reduce eruption uncertainty. As hazard models become increasingly more complex, so do the number of input parameters that need to be estimated, thus increasing the number of sources of uncertainty. As input parameters used for volcanic hazard models are fundamentally uncertain before (and often also after) an eruption, how do they affect the accuracy and utility of forecasts made using these models?

This research explores the input space of volcanic hazard models to understand the interactions between model complexity and robustness of hazard model forecasts. We use the exemplar of volcanic ash distribution models Tephra2 and Fall3D at Mt. Taranaki, Aotearoa-New Zealand (30-50% chance of eruption in the next 50 years). Sampling strategies for Tephra2 and Fall3D were developed to ensure that the input parameter space was fully covered and represent real-world values – both through independent and dependent sampling of parameters. For example, plume height is dependent on the amount of mass ejected during an eruption. A Global Sensitivity Analysis is presented here to investigate the input parameters that significantly influence model output variance. This exploration is conducted through the statistical assessment of Sobol’ indices and eFAST (extended Fourier Amplitude Sensitivity Tests) to discern the key parameters that contribute to variations in the model’s outputs. The results also shed light on which inputs are vital to robust short-term and real-time hazard forecasting, and ultimately require better understanding/quantification before an event.

How to cite: Scott, E., Whitehead, M., Mead, S., Bebbington, M., and Procter, J.: Global Sensitivity Analysis of tephra models for forecasting, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-1402, https://doi.org/10.5194/egusphere-egu24-1402, 2024.

EGU24-1455 | ECS | Orals | NH2.1

Dam-break of shear-thickening suspensions: A new perspective for crystal-rich lava flows and lahars - 

Alexis Bougouin, Henri Lhuissier, Yoel Forterre, and Bloen Metzger

The physical mechanism governing the peculiar behavior of discontinuous shear-thickening suspensions, i.e. a change from liquid-like to solid-like state with increasing applied stress, has recently been described as a frictional transition occuring at the grain scale above a critical shear stress [1, 2, 3]. This understanding offers a new view for describing complex grain-fluid flows, and especially crystal-rich lava flows, which can shear-thicken [4], or lahars, whose heterogeneities affect local friction [5].

In this context, we performed macroscopic and local measurements to characterize dam-break laboratory experiments of model shear-thickening suspensions, for which the volume and the solid fraction of the suspension are varied. Below a critical stress related to the gravitational pressure gradient, the suspension flow is close to that of a viscous Newtonian liquid, i.e. strongly decelerating and with a thickness decreasing progressively from the release side to the spreading front [6]. By contrast, above this critical stress, the front velocity becomes constant, i.e. independent of flow height, and the layer thickness is close to be uniform (Figure 1). We interpret this remarquable behavior by the formation of a highly-dissipative (shear-thickened) flow structure at the front, which keeps the suspension upstream essentially stress-free with a low (frictionless) viscosity. We model the front and provide scaling laws for its velocity in good agreement with experimental observations. These results offer a new perspective for the interpretation and modeling of heavily particle-laden geophysical flows, such as crystal-rich lava flows and lahars, which could also be essentially controlled by their highly-dissipative front.