EGU General Assembly 2024  

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.

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.

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.

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.

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.

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.

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 = (e^Tpe*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.

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.

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 95^th 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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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 90^th percentile (RI_0.9), with RT and RI_0.9 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 RI_0.9 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  RI_0.9) 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.

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 W_MAX (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+W_MAX/(3.1+5.2 · W_MAX/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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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 Z_H, 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 Z_H larger than 40 dBZ at and above -10℃ layer, and a criterion of correlation coefficient ρ_HV 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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

On the night of November 1^st, 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.

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.

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.

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.

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.

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.

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.

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 24^th, 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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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 15^th 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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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 (90^th percentile of daily discharge) and the Q_max (maximum annual discharge) parameters.

Initial results from the EURO-CORDEX initiative predict a 5-15% reduction in Q₉₀ and Q_max in the summer half year, and a 5-30% increase in Q₉₀ and Q_max 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 Q_max 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.

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.

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.

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.

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.

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.

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.

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.

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 19^th 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.

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.

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.

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.

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.

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.

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.

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.

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.

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 0.5^o 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.

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 20^th 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.

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.

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.

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.

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.

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 (R_c) are shown to be ranging between 0.6 to 1.2 (median 0.7). These values are clearly higher than expected in continental climate (R_c ~ 0.20-0.51) and the latest 100-year return flood observed in 2016 (with R_c ~ 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 R_c 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.

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.

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.

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.

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.

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.

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.

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.