Content:

ITS – Inter- and Transdisciplinary Sessions

ITS1.1/NP0.2 – Covid-19 pandemic: health, urban systems and geosciences

EGU21-2198 | vPICO presentations | ITS1.1/NP0.2 | Highlight

Remote data collection methods to inventory COVID-19 interventions in low-income urban settlements

Faith Taylor, Manshur Talib, Amos Wandera, Joseph Mulligan, Vera Bukachi, John Drummond, Bruce Malamud, and Mark Pelling

In this PICO, we outline methods used to inventory the spatial distribution and characteristics of COVID-19 response activities (‘interventions’) in Kibera (Nairobi, Kenya). About 1/8 of the World’s Population live in slums and informal settlements. For these people, COVID-19 has presented unique challenges for managing health and livelihoods within the constraints of high-density housing and poor-quality infrastructure. In addition, reliable spatial, demographic and health data is often limited for these areas. Between April and July 2020, using the Survey123 smartphone application, combined with social media searches and phone enumeration, we inventoried 270 individual COVID-19 interventions taking place in Kibera, an informal settlement of 2.67 km2 and an estimated 187,000 to 1 000,000 inhabitants. Results show a large variety in the type of intervention (58 unique types) and organiser (>88 individual organisers), with 39% of interventions led by small scale organisations such as local NGOs and community groups. We found an uneven spatial distribution of interventions within Kibera, with some already underserved neighbourhoods having less access to COVID-19 relief. Many interventions are clustered around the limited open spaces with good accessibility by road, highlighting the need for better coordination between organisers, and the importance of open space for resilience building. Using isochronal service area analysis, we find that 80% of structures are within a 9-minute round trip of a handwashing station. However, 64% of structures have a 24-54 minute round trip to female sanitary supplies, illustrating gender differences in the impact and recovery from COVID-19. Our data is available online in an interactive map dashboard. Our survey results illustrate that rather than being seen as vectors of disease, low income urban neighbourhoods are part of the solution for managing pandemics, and highlight the importance of infrastructure upgrading and planning to build resilience to a range of shocks and stresses.

How to cite: Taylor, F., Talib, M., Wandera, A., Mulligan, J., Bukachi, V., Drummond, J., Malamud, B., and Pelling, M.: Remote data collection methods to inventory COVID-19 interventions in low-income urban settlements, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-2198, https://doi.org/10.5194/egusphere-egu21-2198, 2021.

EGU21-9227 | vPICO presentations | ITS1.1/NP0.2 | Highlight

Intensity and frequency of extreme novel epidemics

Marco Marani, Gabriel Katul, William Pan, and Anthony Parolari

Human-natural processes that generate extreme events with large financial, social, and health consequences,  are inherently non-stationary due to ever-changing anthropogenic pressures and societal exposure. The issues posed by non-stationarity are recognized and addressed in Earth system science.  However, extensive epidemiological information remains fragmented and virtually unexplored from this perspective due to the lack of approaches to leverage observations of a heterogeneous past. To address this gap, we assembled a long historical record (1600-present) of infectious disease epidemics from the literature.  This new record enabled the development and applications of methods to quantify the time-varying probability of occurrence of extreme epidemic events. We define the intensity of epidemic events, the number of deaths/time/global population, and find that observations from several hundred years, covering almost four orders of magnitude of epidemic intensity, follow a probability distribution  with a slowly-decaying power-law tail (Generalized Pareto Distribution, asymptotic exponent = -0.705). To the contrary, the yearly number of epidemics is non-stationary, implying that conventional extreme value analyses are inappropriate.  We find that the rate of occurrence of extreme epidemics varies nine-fold over centennial time scales, from about 0.4 to 3.6 epidemics/year. As a result, yearly occurrence probabilities of extreme epidemics are far from constant:  The intensity computed for the most extreme event on record – the “Spanish Influenza” of 1918-1920 – has a probability of occurrence varying from 0.27 to 1.75 %/year in the time frame from 1600 to present. When optimistically assuming that 1 year is required to develop, produce, and begin distributing a vaccine/treatment for a new disease (e.g. the recent COVID-19 case), we estimate that the average recurrence time of a pandemic killing most of the global population is now less than 12,000 years.

How to cite: Marani, M., Katul, G., Pan, W., and Parolari, A.: Intensity and frequency of extreme novel epidemics, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-9227, https://doi.org/10.5194/egusphere-egu21-9227, 2021.

EGU21-12677 | vPICO presentations | ITS1.1/NP0.2

Vulnerability mapping to Covid-19 of the metropolitan area in central Tuscany 

Tiziana Pileggi, Simona Cioli, and Enrica Caporali

The COVID-19 pandemic has made urgent the need to improve the resilience of urban system from the effects of different hazards (natural, biological, technological and slow-onset climate change-related) through a multi hazard and multi sector approach that allows a more efficient use of resources and a holistic view of risk, including the interconnectedness across multiple hazards. According to Bangkok Principles for the implementation of the health aspects of the Sendai Framework for Disaster Risk Reduction 2015-2030, systematic integration of health aspects in Disaster Risk Reduction strategies is undelayable.

Building urban resilience means identifying vulnerabilities rapidly and adopting adequate actions to anticipate, resist and recover with the least amount of damage in front hazards impacts.

In this context, a synthetic index to measure vulnerability to COVID-19 is developed, by integrating different levels of information related to demographic characteristics, health profiles and access to resources, in order to identify any situations of fragility and predisposition to the spread of the epidemic, thus constituting a support element for the adoption of an efficient intervention strategy and for the management of any new epidemic waves. The integrated and multi-disciplinary approach that has been chosen allows, indeed, to take into account the complexity and multi-disciplinary nature of the concept of vulnerability. The following information are  analysed: demographic characteristics (population density, age, residence in welfare and prisons facilities); health profiles (presence of previous chronic diseases, such as cancer, diabetes, heart disease, lung disease, and particular lifestyles, such as smoking, alcohol consumption, poor diet) and characteristics of the local health infrastructure (number of beds, ratio of population to family doctor, number of health facilities in the area). To construct the vulnerability index, a Geographical Information System is setted up, through which the data are analysed, processed through normalisation, given the different availability and heterogeneity of the information, and combined. The resulting spatial data infrastructure allows us to rapidly identify situations of adversities and possible infrastructural deficiencies. 

The first prototypical result provides the implementation of an index of vulnerability to COVID19 and the related information support system, related to the metropolitan area in central Tuscany, in which there is a good availability of open data at different levels of geographical details and for which research on vulnerability to various types of risk is carried out and in progress.

How to cite: Pileggi, T., Cioli, S., and Caporali, E.: Vulnerability mapping to Covid-19 of the metropolitan area in central Tuscany , EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-12677, https://doi.org/10.5194/egusphere-egu21-12677, 2021.

EGU21-13488 | vPICO presentations | ITS1.1/NP0.2

Covid-19: What about Resilience and Scaling Dynamics?

Daniel Schertzer and Ioulia Tchiguirinskaia

The current Covid-19 pandemic has underlined the need to thoroughly revisit our conceptions of managing and developing urban systems and to make them resilient to epidemics. For  instance, it fundamentally questions the long-held goal of continually increasing human mobility. More generally, the definition of optimal Covid-19 mitigation strategies remains worldwide on the top of public health agendas, especially in the face of a second wave. However, the relevance of resilient strategies depends heavily on our understanding and our ability to model epidemic dynamics.

Epidemic models are phenomenologically based on the paradigm of a cascade of contacts that spreads infection. However, scaling -a fundamental characteristic that easily results from cascade models,- is not taken into account by conventional epidemic models.  The introduction of ad-hoc characteristic times and corresponding rates spuriously break their scale symmetry.

Here, we theoretically argue and empirically demonstrate that Covid-19 dynamics, during both growth and decline phases, is a cascade with a rather universal scale symmetry whose power-law statistics drastically differ from those of exponential processes. This implies slower but longer phases; which are furthermore linked by a fairly simple symmetry. The resulting variability across space-time scales is a major feature that requires alternative approaches with practical consequences for data analysis and modelling. We illustrate some of these consequences using the Johns Hopkins University Center for Systems Science and Engineering database. 

The obtained results explain biases of epidemic models and help to improve them. By virtue of their generality, these results pave the way for a renewed approach to epidemics, and more generally to growth phenomena, towards more resilient development and management of our urban systems.

How to cite: Schertzer, D. and Tchiguirinskaia, I.: Covid-19: What about Resilience and Scaling Dynamics?, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-13488, https://doi.org/10.5194/egusphere-egu21-13488, 2021.

EGU21-14008 | vPICO presentations | ITS1.1/NP0.2

Understanding CoVid-19’s chaotic dynamics

Imee Necesito, Donghyun Kim, Junhyeong Lee, Junghyun Eom, Deok-Woo Kim, and Hung Soo Kim

As we move towards the more critical age of technology and learning, understanding the underlying dynamics of events such as the unforeseen and unpredictable pandemics in the ecological system are deemed invaluable and important. In this paper, using acquired observations of daily cases of CoVid-19 in the US, UK and some parts of Asia, Recurrence Quantification Analysis (RQA) and the plots of state space were constructed. In this study, it was found that some countries have shown similar trends in RQA statistics as compared to classic chaotic attractors and functions while others have shown similar state space plots as that of the other countries. The authors believe that the data currently available worldwide does not allow reliable forecast because of the presence of untested asymptomatic cases, therefore construction of the evolution of the CoVid-19 cases signal in the absence of priori knowledge of other factors as well as analysing the RQA statistics can serve as a starting point as well as provide information for the appropriate prediction method for the prevalent CoVid-19 outbreaks.

How to cite: Necesito, I., Kim, D., Lee, J., Eom, J., Kim, D.-W., and Kim, H. S.: Understanding CoVid-19’s chaotic dynamics, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-14008, https://doi.org/10.5194/egusphere-egu21-14008, 2021.

EGU21-2615 | vPICO presentations | ITS1.1/NP0.2

Modelling the second wave of COVID-19 infections in France and Italy via a Stochastic SEIR model

Davide Faranda and Tommaso Alberti

COVID-19 has forced quarantine measures in several countries across the world. These measures have proven to be effective in significantly reducing the prevalence of the virus. To date, no effective treatment or vaccine is available. In the effort of preserving both public health as well as the economical and social textures, France and Italy governments have partially released lockdown measures. Here we extrapolate the long-term behavior of the epidemics in both countries using a Susceptible-Exposed-Infected-Recovered (SEIR) model where parameters are stochastically perturbed with a log-normal distribution to handle the uncertainty in the estimates of COVID-19 prevalence and to simulate the presence of super-spreaders. Our results suggest that uncertainties in both parameters and initial conditions rapidly propagate in the model and can result in different outcomes of the epidemics leading or not to a second wave of infections. Furthermore, the presence of super-spreaders adds instability to the dynamics, making the control of the epidemics more difficult. Using actual knowledge, asymptotic estimates of COVID-19 prevalence can fluctuate of order of ten millions units in both countries.

How to cite: Faranda, D. and Alberti, T.: Modelling the second wave of COVID-19 infections in France and Italy via a Stochastic SEIR model, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-2615, https://doi.org/10.5194/egusphere-egu21-2615, 2021.

EGU21-7187 | vPICO presentations | ITS1.1/NP0.2

Modelling buccopharyngeal droplet dispersion in an intensive care unit for Covid patients

Martin Ferrand, Mathieu Guingo, Christian Beauchêne, Maurice Mimoun, and Jean-Pierre Minier

Faced with the first Covid-19 epidemic wave in France, the hospital sector has been forced to considerably increase the number of intensive care beds. To meet this crucial need, some hospital structures have been adapted. This is the case with one of the intensive care sectors of the Burn Treatment Center (CTB) at Saint-Louis Hospital, which has intensive care rooms dedicated to treat burn patients. Beyond the provision and adaptation of these care structures to Covid patients, the hospital has currently an imperative need to progress on the understanding of the dispersion of buccopharyngeal droplets which constitute one of the risk vectors of airborne transmission and as a corollary of manual transmission.

As part of a partnership between CTB and the EDF Foundation, a CEREA research team provided the hospital with its aeraulics expertise which mainly relies on the digital modelling tool (CFD) code_saturne developed for more than 20 years by EDF-Research and Development. Numerical modelling in fluid mechanics makes it possible to accurately reproduce an architectural ensemble, to describe the air flows and what they carry, and thus to better understand where the risks of airborne contamination lie.

The objective of the study is to understand the dispersion of the buccopharyngeal droplets in the resuscitation room according to their sizes, identify the areas at risk of deposit, adapt the treatment protocols and optimise the level and the frequency of systematic bio-cleaning of surfaces exposed to deposit of oral-pharyngeal droplets. It should be noted that we are not directly dealing with the spread of the covid-19 virus but with one of the potential vehicles of oral-pharyngeal droplets.

The methodology consist of a parametric study of poly-dispersion of classes of particles. Each class correspond to a droplet diameter and contains one million of independent droplets for which a Generalized Langevin Model is solved to calculate the instantaneous fluid velocity seen from the particle, the particle velocity and its position. These particles are carried by a turbulent flow using the Reynolds Averaged Navier-Stokes approach, calculating only moments. The specific characteristics of this model allow dealing with poly-dispersed two-phase flow even for particles with very small diameters. The studied parameters are the angle of droplet ejection, the volume of humid air ejected and the time duration of this event and the air flowing activation of the room.

Expected conclusions are found: the largest particles sediment the fastest and close to the source, the finest droplets follow the streamlines to the air vents. In addition, non-intuitive areas of potential deposit are observed and a major impact of air conditioning on residence time is demonstrated.

How to cite: Ferrand, M., Guingo, M., Beauchêne, C., Mimoun, M., and Minier, J.-P.: Modelling buccopharyngeal droplet dispersion in an intensive care unit for Covid patients, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-7187, https://doi.org/10.5194/egusphere-egu21-7187, 2021.

EGU21-13112 | vPICO presentations | ITS1.1/NP0.2

Mobility modelling for simulation of spatial spread of infectious diseases

Krzysztof Knop, Kamil Smolak, Barbara Kasieczka, Witold Rohm, Tomasz Smolarczyk, and Marcin Zyga

The COVID-19 pandemic has highlighted the importance of public health policies and crisis management. The spread of diseases is a complex phenomenon with many time-dependent variables, which hampers an accurate prediction of epidemic evolution. Models of epidemic spread play an important role in guiding in designing public health policies, enabling hypothetical scenarios simulation and rapid analyses of ongoing epidemics.

Over the last century disease spread models evolved from deterministic compartmental models into complex metapopulation and agent-based simulations. Today’s solutions consider many factors, not limiting to the disease itself but also simulating socio-demographic structure and population flows. In the era of globalisation, human mobility became the major factor of rapid disease spread. Although current models consider international and regional travels, used mobility models are simplistic. This limits the accuracy and spatio-temporal resolution of these simulations, providing daily cases updates aggregated to large regions.

We propose an agent-based mobility model, offering a simulation of hourly temporal resolution depicting mobility with less than a few hundreds of meters spatial precision. Agent-based models allow each simulation agent to assign different characteristics, e.g. susceptibility to infection, mobility behaviour.

We integrate our mobility model with disease spread simulation, using an agent’s interaction to detect virus transmission. In every time step of the model, the interaction between the agents, their current state and localisation of interaction are used to determine the probability of infection. Social interactions in the context of the spread of the disease are a fundamental element influencing the temporal and spatial extent of the disease. An essential aspect of our model is the integration of the simulation environment with the points-of-interests (POIs), which represent the destination of the majority of non-home-work related activities. We validate the accuracy of mobility replication and present hypothetical scenarios of disease spread in one of the large European cities, presenting capabilities of our solution.

How to cite: Knop, K., Smolak, K., Kasieczka, B., Rohm, W., Smolarczyk, T., and Zyga, M.: Mobility modelling for simulation of spatial spread of infectious diseases, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-13112, https://doi.org/10.5194/egusphere-egu21-13112, 2021.

EGU21-13720 | vPICO presentations | ITS1.1/NP0.2

Epidemic compartmental models: Realizations for Covid-19 and the bearing of vaccination scenarios

Alin Andrei Carsteanu and Andreas Langousis

Our work is aimed at analyzing the intrinsic variability of epidemic compartmental models, including the main qualitative characteristics of the Covid-19 pandemic, such as a relatively long asymptomatic contagious incubation period and a time-limited immunity. Intrinsic variability is important in order to quantitatively distinguish it from extrinsic variation factors, such as variability of virulence, social behavior, weather and climate, or statistical interpretation of data. The influence of vaccination rates is also analyzed, in as far as different scenarios may avert or revert the existence of an asymptotic endemic equilibrium point, as well as contribute to the build-up of herd immunity.

How to cite: Carsteanu, A. A. and Langousis, A.: Epidemic compartmental models: Realizations for Covid-19 and the bearing of vaccination scenarios, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-13720, https://doi.org/10.5194/egusphere-egu21-13720, 2021.

EGU21-2742 | vPICO presentations | ITS1.1/NP0.2 | Highlight

On the uncertainty of real-time predictions of epidemic growths: A COVID-19 case study for China and Italy

Tommaso Alberti and Davide Faranda

While COVID-19 is rapidly propagating around the globe, the need for providing real-time forecasts of the epidemics pushes fits of dynamical and statistical models to available data beyond their capabilities. Here we focus on statistical predictions of COVID-19 infections performed by fitting asymptotic distributions to actual data. By taking as a case-study the epidemic evolution of total COVID-19 infections in Chinese provinces and Italian regions, we find that predictions are characterized by large uncertainties at the early stages of the epidemic growth. Those uncertainties significantly reduce after the epidemics peak is reached. Differences in the uncertainty of the forecasts at a regional level can be used to highlight the delay in the spread of the virus. Our results warn that long term extrapolation of epidemics counts must be handled with extreme care as they crucially depend not only on the quality of data, but also on the stage of the epidemics, due to the intrinsically non-linear nature of the underlying dynamics. These results suggest that real-time epidemiological projections should include wide uncertainty ranges and urge for the needs of compiling high-quality datasets of infections counts, including asymptomatic patients.

Alberti T. and Faranda D. (2020) On the uncertainty of real-time predictions of epidemic growths: A COVID-19 case study for China and Italy. Commun. Nonlin. Sci. Num. Sim., 90, 105372.

How to cite: Alberti, T. and Faranda, D.: On the uncertainty of real-time predictions of epidemic growths: A COVID-19 case study for China and Italy, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-2742, https://doi.org/10.5194/egusphere-egu21-2742, 2021.

EGU21-8955 | vPICO presentations | ITS1.1/NP0.2 | Highlight

Analysis of the potential drivers of seasonality in COVID-19 transmission dynamics in 409 locations across 26 countries 

Rachel Lowe, Ben Armstrong, Sam Abbott, Sophie Meakin, Kathleen O'Reilly, Rosa Von Borries, Rochelle Schneider, Dominic Roye, Masahiro Hashizume, Mathilde Pascal, Aurelio Tobias, Ana Maria Vicedo-Cabrera, Antonio Gasparrini, and Francesco Sera and the MCC Network & CMMID COVID-19 working group

More than a year since its emergence, there is conflicting evidence on the potential influence of weather conditions on SARS-CoV-2 transmission dynamics. We used a two-stage ecological modelling approach to estimate weather-dependent signatures in SARS-CoV-2 transmission in the early phase of the pandemic, using a dataset of 3 million COVID-19 cases reported until 31 May 2020, spanning 409 locations in 26 countries. We calculated the effective reproduction number (Re) over a location-specific early-phase time-window of 10-20 days, for which local transmission had been established but before non-pharmaceutical  interventions had become established as measured by the OxCGRT Government Response Index. We calculated mean levels of meteorological factors, including temperature and humidity observed in the same time window used to calculate Re.  Using a multilevel meta-regression approach, we modelled nonlinear effects of meteorological factors,  while accounting for government interventions and socio-demographic factors. A weak non-monotonic association between temperature, absolute humidity and Re was identified, with a decrease in Re of 0.087 (95% CI: 0.025; 0.148) between mean temperature of 10.2°C (maximum) and 20°C (minimum) and a decrease in Re of 0.061 (95% CI: 0.011; 0.111) between absolute humidity of 6.6 g/m3 (maximum) and 11 g/m3 (minimum). However, government interventions explained twice as much of the variation in Re compared meteorological factors. We find little evidence of meteorological conditions having influenced the early stages of local epidemics, and conclude that population behaviour and governmental intervention are more important drivers of transmission.

How to cite: Lowe, R., Armstrong, B., Abbott, S., Meakin, S., O'Reilly, K., Von Borries, R., Schneider, R., Roye, D., Hashizume, M., Pascal, M., Tobias, A., Vicedo-Cabrera, A. M., Gasparrini, A., and Sera, F. and the MCC Network & CMMID COVID-19 working group: Analysis of the potential drivers of seasonality in COVID-19 transmission dynamics in 409 locations across 26 countries , EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-8955, https://doi.org/10.5194/egusphere-egu21-8955, 2021.

EGU21-10284 | vPICO presentations | ITS1.1/NP0.2

A reflexion on the environmental effect on the transmission of COVID-19

Victor L Barradas and Monica Ballinas

This research is a general reflection of the possible transmission not only of COVID-19 but of any influenza disease depending on environmental parameters such as solar radiation, air humidity and air temperature (vapor pressure deficit), evoking the Penman-Monteith model regarding the evaporation of the water that constitutes the small water droplets (aerosols) that carry the virus. In this case the evapotranspiration demand of the atmosphere with which it can be deduced that the spread of the disease will be higher in those places with less evaporative demand, that is, high air humidity and / or low temperatures, and / or low radiation intensities, and vice versa. It can also be deduced that the hours of greatest potential contagion are the night hours, while those with the lowest risk are between 2:00 p.m. and 4:00 p.m. On the other hand, in those rooms with low temperatures the contagion would be more effective. So, considering that the drops produced by a sneeze, by speaking or breathing can go beyond two meters away, it is roughly explained that the use of face masks and keeping a safe minimum distance of two meters can limit transmission of viruses and / or infections. However, this practice is not entirely safe as the environment can play an important role. What is recommended to reduce the spread of these pathogens is to produce high evaporative demands: increasing solar radiation, and increasing air temperature and reducing air humidity, which is practice that can be effective in closed rooms.

How to cite: Barradas, V. L. and Ballinas, M.: A reflexion on the environmental effect on the transmission of COVID-19, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-10284, https://doi.org/10.5194/egusphere-egu21-10284, 2021.

EGU21-12733 | vPICO presentations | ITS1.1/NP0.2

Heat-related mortality in Portugal amplified during the COVID-19 pandemic

Pedro M. Sousa, Ricardo M. Trigo, Ana Russo, João L. Geirinhas, Ana Rodrigues, Susana Silva, and Ana Torres

The warmest July ever in Portugal was observed during 2020, leading to the highest number of total deaths in July months (10430) since consistent records became available in 2009. This record summed up to the very high death toll throughout the year, characterized by the COVID-19 pandemic. As a combined result of these factors, cumulated deaths during 2020 are also the largest in the records available since 2009 (123753), corresponding to an excess of ~12000 deaths (~11% above the baseline). COVID-19 was responsible for the largest fraction of anomalous mortality during the spring months (62% of the excess during March-May) and from autumn onwards (85% of the excess during October-December). However, during the warmer season, the direct impact of the pandemic decreased substantially (as in the rest of Europe) and other causes were the main trigger for the observed excessive mortality (~3500 versus 553 COVID-19 deaths). Prolonged hot spells, occurring between June 21 and August 7, triggered persistent mortality anomalies in the upper tertile (>310 deaths/day) reaching its peak in mid-July (+45% deaths/day). Two other shorter hot spells occurring outside summer months (May and September) also appear to have contributed to significant mortality anomalies.

July 2020 registered an overall temperature anomaly of +2.6ºC over continental Portugal, and a cumulated anomaly of +127ºC. The lethality rate associated to these cumulated anomalies (+14 deaths per cumulated ºC) was higher than that observed in recent relevant heat-related mortality episodes, even those with higher absolute temperature anomalies, such as in 2013 and 2018. Rates comparable to those observed in 2020 in Portugal are only found far back in tragic heatwaves like those experienced in June 1981 or August 2003. In fact, the 2003 European heatwaves triggered significant changes in public health policies, in order to minimize the mortality burden associated to hot spells, which resulted in lower lethality rates, until 2020. These results are further supported by a statistical model developed to estimate expected deaths due to cold/heat (calibrated for 2009-2019: r=0.84; ME=7%), estimating an amplification of at least 50% in heat-related deaths during 2020 compared to pre-pandemic years. We argue that the significant decrease observed in emergency admissions (ER) and disruption in health-care since the start of the pandemic helps explaining this amplification factor. A ~2/3 decrease in total ERs was observed at the peak of the COVID-19 crisis, never returning to normal pre-pandemic levels. Furthermore, in average cases classified as emergent and very urgent in triage remained below 80% of previous reference levels throughout the 2020 summer, particularly the latter.

The authors would like to acknowledge the financial support  FCT through project UIDB/50019/2020 – IDL.

How to cite: Sousa, P. M., Trigo, R. M., Russo, A., Geirinhas, J. L., Rodrigues, A., Silva, S., and Torres, A.: Heat-related mortality in Portugal amplified during the COVID-19 pandemic, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-12733, https://doi.org/10.5194/egusphere-egu21-12733, 2021.

EGU21-42 | vPICO presentations | ITS1.1/NP0.2 | Highlight

Modifying emissions data and projections to incorporate the effects of lockdown in climate modelling

Robin Lamboll, Piers Forster, Chris Jones, Ragnhild Skeie, Stephanie Fiedler, Bjørn Samset, and Joeri Rogelj

Lockdowns to avoid the spread of COVID-19 have created an unprecedented reduction in human emissions, however emissions estimates are typically only available after one or more years, making it hard to incorporate these reductions into emissions projections. In this talk we will outline how mobility data and power usage can nowcast country-and-sector emissions of various gases. In this way we show that the short-term impact of lockdown on emissions data is not expected to be significant for long-term temperature trends.

We will also outline how different recovery pathways can be made using basic longer-term emissions projections and how to construct detailed scenarios for non-CO2 emissions, using assumptions about the effects of lockdown on nationally determined contributions and a new software package called Silicone that can infill missing greenhouse gas emissions. Silicone allows the consistent incorporation of tradeoffs between emission species as modelled by IAMs, and as expressed in available greenhouse gas emission scenarios, to be applied to the proposed pathways. We will then show how to make these projections into the more detailed, gridded, CMIP-6 compatible emissions estimates that are required to run General Circulation Models (GCM).

How to cite: Lamboll, R., Forster, P., Jones, C., Skeie, R., Fiedler, S., Samset, B., and Rogelj, J.: Modifying emissions data and projections to incorporate the effects of lockdown in climate modelling, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-42, https://doi.org/10.5194/egusphere-egu21-42, 2021.

On 11 March 2020, the World Health Organization declared Covid19 a pandemic. Countries around the world rushed to declare various states of emergencies. Canada also implemented emergency measures to restrict the movements of people including the closure of borders, non-essential services, and schools and offices to slow the spread of Covid19. I used this opportunity to measure changes in seismic vibrations registered in Canada before, during, and after the lockdown due to the slowdown in transportation, economic, and construction activities. I analyzed continuous seismic data for 6 Canadian cities: Calgary and Edmonton (Alberta), Montreal (Quebec), Ottawa, and Toronto (Ontario), and Yellowknife (Northwest Territories). These cities represented the wide geographical spread of Canada. The source of data was seismic stations run by the Canadian National Seismograph Network (CNSN). Python and ObSpy libraries were used to convert raw data into probabilistic power spectral densities. The seismic vibrations in the PPSDs that fell between 4 Hz and 20 Hz were extracted and averaged for every two weeks period to determine the trend of seismic vibrations. The lockdown had an impact on seismic vibrations in almost all the cities I analyzed. The seismic vibrations decreased between 14% - 44% with the biggest decrease in Yellowknife in the Northwest Territories. In the 3 densely populated cities with a population of over 1 million - Toronto, Montreal, and Calgary, the vibrations dropped by over 30%.

To enable other students to undertake similar projects for their cities, I created a comprehensive online training module using Jupyter notebooks available on Github. Students can learn about seismic vibrations, how to obtain datasets, and analyze and interpret them using Python. They can share their findings with local policymakers so that they become aware of the effectiveness of the lockdown imposed and are better prepared for lockdowns in the future. When we make data and technology accessible, then lockdowns because of pandemics can be an opportunity for students to take up practical geoscience projects from home or virtual classrooms.

How to cite: Nath, A.: Using COVID19 as an Opportunity to Measure Seismic Silences and Bring Geoscience Projects to Students, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-1268, https://doi.org/10.5194/egusphere-egu21-1268, 2021.

EGU21-8385 | vPICO presentations | ITS1.1/NP0.2

The impact of the COVID-19 lockdown measures on the seismic monitoring in the Bucharest (Romania) metropolitan area

Bogdan Grecu, Alexandru Tiganescu, Natalia Poiata, Felix Borleanu, Raluca Dinescu, and Dragos Tataru

The lockdown measures taken to control and stop the spread of the novel coronavirus (COVID-19) in cities around the globe caused an unprecedented reduction of anthropic activities. The signature of this reduction, different from one place to another, has been captured by the seismic stations installed in the urban areas where lockdown restrictions have been implemented. Bucharest, the capital of Romania, was no exception from this phenomenon.

In this paper, we investigate the effect of the COVID-19 lockdown measures imposed by the Romanian authorities on the high-frequency ambient seismic noise (ASN) data recorded by the Bucharest Metropolitan Seismic Network (BMSN). BMSN consists of 26 stations of which 19 are equipped with strong motion sensors and 7 have both short-period velocity and accelerometer sensors. All the stations are continuously recording the ground motion and the data is sent in real-time to the data center of the National Institute for Earth Physics.         

The reduction of ASN was first observed at stations installed in educational units (kindergartens, schools) starting with 11th of March 2020, when the Romanian government decided to close the schools in Romania. For these stations, the largest reduction of ASN, up to 82%, was noticed in the 25-40 Hz frequency band. On 16th of March the state of emergency was imposed in Romania and a few days later, on 25th of March, the stay-at-home order was issued. These new restrictions caused substantial reduction in urban traffic and people’s mobility and reflected in significant reduction of ASN at almost all the other BMSN stations, located either free-field or in buildings. For these stations, we observed a decrease of the noise levels by as much as 66% in the 15-25 Hz frequency band. We also correlated the ambient seismic noise with other types of data that might be affected by human activity, such as the mobility data from Google and Apple, and we found good correlation between ASN in different frequency bands and various mobility data categories. Finally, we showed that the noise reduction due to lockdown measures improved the signal-to-noise ratio of the stations in the Bucharest area, allowing us to record smaller earthquakes which otherwise would not have been recorded.

How to cite: Grecu, B., Tiganescu, A., Poiata, N., Borleanu, F., Dinescu, R., and Tataru, D.: The impact of the COVID-19 lockdown measures on the seismic monitoring in the Bucharest (Romania) metropolitan area, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-8385, https://doi.org/10.5194/egusphere-egu21-8385, 2021.

EGU21-9346 | vPICO presentations | ITS1.1/NP0.2

Sanitary waste management under Covid-19 restrictions in Ecuador

Katerine Elizabeth Ponce Ochoa, Javier Rodrigo-Ilarri, and María-Elena Rodrigo-Clavero

Ecuador, with a population of approximately 17.08 million inhabitants, is one of the most COVID-19 affected countries in the world. On March 16th, 2020, a countrywide state of exception was declared by the national government, therefore applying measures to restrict mobility, suspension of working hours and closure of borders. This situation caused an increase in the massive demand for masks and gloves as the primary ways to preventing infection. These masks and gloves are single-used and discarded, causing an impact on the environment due to the time they take to decompose. In addition, syringes and other hospital may also become infectious waste.

 

Although hospitals may comply the regulations for the management and treatment of hazardous solid waste in Ecuador, the health emergency surprised all hospitals, clinics and health centers due to the increase in patients with coronavirus. This situation led to the establishment of new protocols for this type of waste and also for the management of corpses with COVID-19.

Health personnel are the ones that have been most affected during this time, so they have been working on the front line and have been the most exposed to contagion, increasing the use of disposable masks, gloves and gowns and contributing to the increase of waste from hospitals and health centers.

 

The objective of this study is to investigate and understand how the management of hospital waste has been developed in times of pandemic in the Ecuadorian Institute of Social Security (IESS) Manuel Ignacio Monteros in the city of Loja.

 

To carry out this study, information are taken from the records and databases generated in the IESS about the amount of hospital waste generated during the months of March to December 2020. Results are obtained making comparisons with the amount of hospital waste generated in the previous year 2019. The information was collected through surveys directed both to medical and administrative personnel who were in direct care of COVID-19 managing operations.

 

Results show that a considerable increase in the quantity and characteristics of hospital waste generated during the months of analysis was found. Hazardous hospital waste have been managed correctly as established by various protocols and agreements (Ministerial Agreement 0323) in full compliance with current legislation.

How to cite: Ponce Ochoa, K. E., Rodrigo-Ilarri, J., and Rodrigo-Clavero, M.-E.: Sanitary waste management under Covid-19 restrictions in Ecuador, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-9346, https://doi.org/10.5194/egusphere-egu21-9346, 2021.

EGU21-12035 | vPICO presentations | ITS1.1/NP0.2

Impacts of COVID-19 lockdown restrictions on urban NO2 and O3 level in Germany with consideration of meteorological impacts and seasonal variation

Vigneshkumar Balamurugan, Xiao Bi, Johannes Gensheimer, Jia Chen, Frank Keutsch, Shrutilipi Bhattacharjee, and Ankit Shekhar

In 2020, the entire world population has witnessed an unprecedented virus outbreak in terms of COVID-19, which led to restrictions in human activities across the world. Strict measures in Germany started on March-21, 2020 and ended on April-30, 2020, while more relaxed measures continued until July 2020. Vehicle traffic volume and industrial activities were drastically reduced, and, as a result, pollutant emission rates were expected to be reduced. Changes in atmospheric pollutant concentrations are an indicator for changes in emission rates although they are not directly proportional as concentrations are heavily influenced by meteorological conditions and as atmospheric photochemical reactions can be non-linear. Without accounting for the influence of meteorology and atmospheric photochemical reactions, a simple comparison of the lockdown period pollutant concentration values with pre-lockdown only to estimate emissions could be misleading. To normalize the effects of meteorological conditions and atmospheric chemical transformation and reactions, we adopted a method of comparing the predicted Business As Usual (BAU) NO2 and O3 concentrations, i.e., the expected value of NO2 and O3 concentration for 2020 meteorological conditions without lockdown restrictions, with the observed NO2 and O3 concentrations. BAU NO2 and O3 concentrations corresponding to 2020 meteorological conditions were predicted based on wind speed and sunshine duration (and season of the day) using the previous year NO2 and O3 concentrations as the references. Compared to BAU levels, big metropolitan cities in Germany show a decline in observed NO2 level (-24.5 to -37.7 %) in the strict lockdown period and rebound to the BAU level at the end of July 2020. In contrast, there is a marginal change in O3 level (+9.6 to -7.4 %). We anticipate that the imbalanced changes in precursors emission (decrease in NOX and increase in volatile organic compounds (VOCs) emission) are attributed to the marginal changes in observed O3 level compared to BAU level; decreased NOX would decrease the O3 concentration due to NOX-limited conditions, and increased VOCs would increase the O3 concentration. These results imply that the balanced emission control between the VOCs and NOX are required to limit the secondary pollutant (O3) formation.

How to cite: Balamurugan, V., Bi, X., Gensheimer, J., Chen, J., Keutsch, F., Bhattacharjee, S., and Shekhar, A.: Impacts of COVID-19 lockdown restrictions on urban NO2 and O3 level in Germany with consideration of meteorological impacts and seasonal variation, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-12035, https://doi.org/10.5194/egusphere-egu21-12035, 2021.

EGU21-15125 | vPICO presentations | ITS1.1/NP0.2

Investigating the effects of COVID-19 to crime rates through a geospatial approach: the case of New York, USA

Ioanna Tselka, Isidora Isis Demertzi, and George P. Petropoulos

Covid-19 pandemic has led to severe consequences to humanity worldwide. Yet, to our knowledge, little scientific evidence is available exploring the impact of the pandemic on criminality. Thus, it is imperative to examine their relationships spatially to obtain a better understanding of societal characteristics during the pandemic.

This study aims at demonstrating the use of geoinformation in analyzing the spatial patterns between crime properties and Covid-19 spread using as a case study New York City, USA, one of the largest metropolitan cities of the world. To address our objectives, geostatistical analysis and data visualization methods have been implemented in real-world crime data acquired from a web-GIS platform. Our analysis concerns two equal time periods before and after the lockdown implementation.

Results revealed some very interesting patterns spatially between the examined parameters and societal characteristics existing in the study region. The methodological framework presented underlined the added value of geoinformation as a robust and cost-effective approach in examining the impact of the pandemic to the society.

 

Keywords: Covid-19, pandemic, crime rates, geoinformation, New York

How to cite: Tselka, I., Demertzi, I. I., and Petropoulos, G. P.: Investigating the effects of COVID-19 to crime rates through a geospatial approach: the case of New York, USA, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-15125, https://doi.org/10.5194/egusphere-egu21-15125, 2021.

EGU21-16098 | vPICO presentations | ITS1.1/NP0.2

Assessing the effectiveness of an urban CO2 monitoring network through the COVID-19 lockdown natural experiment

Jinghui Lian, Thomas Lauvaux, Hervé Utard, Grégoire Broquet, François-Marie Bréon, Michel Ramonet, Olivier Laurent, Karina Cucchi, and Philippe Ciais

Quantitative monitoring of CO2 sources and sinks over cities is needed to support the urban adaptation and mitigation measures, but it is a challenging task. The Paris metropolitan area is a highly built-up and densely populated region in France. The two national COVID-19 forced confinements that are 1) effective on March 17th, with a duration of 55 days until May 11th, 2) effective on October 30th, with a duration of 46 days until December 15th provide an opportunity to assess the behaviour and robustness of the dedicated atmospheric inversion system for estimating the city-scale CO2 emissions.

In this study, the atmospheric Bayesian inversion approach that couples six in-situ continuous CO2 monitoring stations with the WRF-Chem transport model at 1-km horizontal resolutions has been used to quantify the impacts of lockdown on CO2 emissions for the Paris megacity. The prior emission estimate was from the Origins inventory, a near-real-time dataset of fossil fuel CO2 emissions by sector (transportation, residential, tertiary, industry and sink) at 1km² and hourly resolution recently developed by Origins.earth. Estimates of CO2 emissions were retrieved from the inversion by assimilating CO2 concentration gradients between upwind-downwind stations using a refined configuration of the existing Parisian inversion system developed by Bréon et al. (2015) and Staufer et al. (2016). A set of experiments was performed to assess the sensitivity of the posterior CO2 estimates to the changes in different inversion setups, including the selection of observations, prior flux uncertainties and error correlations. We also analyzed the potential contribution of the expanding CO2 monitoring network, in particular the two newly built urban stations in the city center since 2014, to the inverse modeling systems.

The optimized CO2 estimates show decreases of around 42% and 25% in anthropogenic CO2 emissions during the first and second lockdowns respectively when compared with the same period in past two years. Both lockdown emission reduction estimates from the inversion are consistent with recent estimates from activity data (resp. 37% and 19%), suggesting that our near-real time monitoring system is able to detect and quantify short-term variations at the whole-city level.

How to cite: Lian, J., Lauvaux, T., Utard, H., Broquet, G., Bréon, F.-M., Ramonet, M., Laurent, O., Cucchi, K., and Ciais, P.: Assessing the effectiveness of an urban CO2 monitoring network through the COVID-19 lockdown natural experiment, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-16098, https://doi.org/10.5194/egusphere-egu21-16098, 2021.

The COVID-19 pandemic has changed the way we work and live, and as of January 2020, the increase in cases and the initiation of the vaccine introduces even more uncertainty into the short-term future. With an increase in domestic responsibilities for many people, there is a heighted concern about the productivity of the Earth and space science research community, and especially the impact on student, early career researchers, and women. AGU's rich data has allowed us to investigate how the pandemic has affected our constituents, and in a poster presented at AGU 2020, we showed that submissions increased in 2020 with the same proportion of women submitting in 2020 and little monthly variation. Submissions from men and women in their 20s decreased in 2020 compared to 2019, while submissions from women in their 30s and 50s and men in their 40s increased.  We saw minor monthly fluctuations in submissions by the country-region of submitting author, with an increase in total and proportional submissions from China continuing from 2019. Additionally, our editors were concerned about the time the most affected scientists could devote to research and peer reviewing. This analysis seeks to update demographics of submitting authors with Q1 2021 data and introduce an analysis of the effect the pandemic had on our article peer reviewers. Preliminary analysis shows very little difference in the invite rates of women in 2020 compared to 2019 (+1%), and only a 0.4% decrease in women's accept to review rates in 2020 compared to 2019. We also only see slight monthly fluctuations in invite and review accept rates. Invitations to review by country of reviewer are proportionally similar in 2020 to those in 2019. This analysis will also investigate any changes in invited and agreed reviewer age to see how the pandemic may have influenced those likely to have research, teaching, and family commitments.

How to cite: Wooden, P. and Hanson, B.: Geoscience Authors and Reviewers during the COVID-19 Pandemic: Demographic Analysis of AGU's Authors and Peer Reviewers, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-1229, https://doi.org/10.5194/egusphere-egu21-1229, 2021.

EGU21-2408 | vPICO presentations | ITS1.1/NP0.2 | Highlight

Epidemics, climate change and natural hazards: Impacts and risk perceptions under COVID-19

Giuliano Di Baldassarre, Elena Mondino, and Elena Raffetti

Epidemics, climate change and natural hazards are increasingly affecting humankind and are plausibly re-shaping the way in which people perceive multiple risks. Here we integrate epidemiological, policy, climate and natural hazard data with the results of two waves of nationwide surveys in Italy and Sweden. These were conducted in two different phases of the COVID-19 pandemic corresponding to low (August 2020) and high (November 2020) levels of infection rates. We investigate the interplay between negative impacts and public perceptions of multiple hazards including epidemics, floods, droughts, wildfires, earthquakes, and climate change. Similarities and differences between Italy and Sweden allow us to investigate the role of policy, media coverage, and direct experience in explaining public perceptions of multiple hazards. The way in which people think about epidemics, for example, is expected to have been substantially influenced by the COVID-19 pandemic that has severely affected both countries, but to which the Italian and Swedish authorities responded differently. Indeed, we found that epidemics are perceived as less likely and more impactful in Italy compared to Sweden. In addition, when multiple hazards are considered, people are more worried about risks related to recently occurred events. This is in line with the cognitive process known as availability heuristic: individuals assess the risk associated with a given hazard based on how easily it comes to their mind. Furthermore, for the majority of hazards, we found that in both countries women and younger people are generally more concerned. These new insights about the interplay between multiple hazards and public perceptions can inform the development of sustainable policies to reduce disaster risk while promoting public health.

 

How to cite: Di Baldassarre, G., Mondino, E., and Raffetti, E.: Epidemics, climate change and natural hazards: Impacts and risk perceptions under COVID-19, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-2408, https://doi.org/10.5194/egusphere-egu21-2408, 2021.

EGU21-10438 | vPICO presentations | ITS1.1/NP0.2

Handwashing and water security in the context of a pandemic

David M. Hannah, Iseult Lynch, Feng Mao, Joshua D. Miller, Sera L. Young, and Stefan Krause

The COVID-19 pandemic is a wake-up call for water security issues. It makes us acutely aware how crucial access, and ability, for adequate hand hygiene are for reducing transmission risks of communicable diseases. An estimated 40% of households globally lack access to basic handwashing facilities. A recent cross-cultural study of household water insecurity experiences (HWISE) found that nearly one in four of 6,637 randomly sampled households across 23 sites in 20 low- and middle-income countries. Similar water, sanitation and hygiene problems impact on poorer families in high-income nations too.

We explore the challenge of hand hygiene in a changing water world and reflect on the importance of making rapid progress towards “ensure availability and sustainable management of water and sanitation for all” (UN Sustainable Development Goal 6). We contest that urgent action on water security is essential to better prepare societies for the future, including global health crises. Drawing on the latest evidence, we provide recommendations on how to increase handwashing, and improve human health and wellbeing more broadly, by reducing water insecurity. Across our world, policymakers must focus on: investment in water infrastructure, water independent alternatives, and behavioural change and knowledge promotion. Moreover, we must prioritise holistic, evidence-based solutions that address 3 facets of water (in)security: availability, quality & accessibility.

How to cite: Hannah, D. M., Lynch, I., Mao, F., Miller, J. D., Young, S. L., and Krause, S.: Handwashing and water security in the context of a pandemic, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-10438, https://doi.org/10.5194/egusphere-egu21-10438, 2021.

EGU21-13561 | vPICO presentations | ITS1.1/NP0.2 | Highlight

Environmental factors during COVID – 19 pandemic in Campinas, Brazil

Bruno Kabke Bainy and Ana Maria Heuminski de Ávila

During COVID – 19 pandemic, the main strategy to prevent virus dissemination adopted worldwide was the social distancing, in different degrees (ranging from simple recommendations to the population, to complete lockdown). In this context, many studies were performed around the world to assess the impacts of such measures on the environment, specially on air quality. The reported results almost unanimously pointed to a reduction in air contaminants, mainly as a response to vehicular traffic depletion and, at some level, to reduced human and industrial activities.  On March 24th, 2020, a partial lockdown was decreed in São Paulo state, Brazil, and since then it has undergone, back and forth, several stages of strictness according to contamination and hospitalization rates, being stricter whenever intensive care units (ICU) occupation increased. Our study aims to evaluate environmental aspects (air quality and meteorology) in Campinas city (São Paulo, Brazil), during the pandemic, from March 24th to December 31st, and compare it with the weeks prior to the social distancing and with the previous year. In addition to the environmental variables, the “social distancing index” (obtained by using mobile phone data to assess displacements) and medical data (hospital admissions and deaths) were employed to a preliminary analysis of  the influence of environmental factors on COVID – 19 evolution in the city.

How to cite: Kabke Bainy, B. and Heuminski de Ávila, A. M.: Environmental factors during COVID – 19 pandemic in Campinas, Brazil, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-13561, https://doi.org/10.5194/egusphere-egu21-13561, 2021.

EGU21-8622 | vPICO presentations | ITS1.1/NP0.2

Communicating ACTRIS science in times of COVID-19

Giulia Saponaro, Ariane Dubost, Eija Juurola, and Paolo Laj

The identification of the severe COVID-19 virus in December 2019 led the World Health Organization to declare a global pandemic by March 2020. Up till recently with the first available vaccines, the only prevention measures include strict social, travel, and working restrictions in a so-called lockdown period that lasted for several weeks (mid-March to the end of April 2020 for most of Europe). This abrupt change in social behavior is expected to impact local but also regional atmospheric composition, and the environmental impact is potentially of high interest to policy and decision-makers.

The Aerosol, Clouds, and Trace Gases Research Infrastructure (ACTRIS) is a pan-European research infrastructure producing high-quality data and information on short-lived atmospheric constituents and on the processes leading to the variability of these constituents in natural and controlled atmospheres. Realizing the crucial scientific value of ACTRIS observations of atmospheric composition changes across Europe, the ACTRIS community promptly actioned internal communication [AD1] thread to organize and set-up COVID-19 related activities.  Such reactive internal involvement of ACTRIS partners generated timely outcomes. In fact, during the lockdown period in spring 2020, most of the ACTRIS observational and exploratory platforms were operational providing continuous access to data on air quality and atmospheric composition and, as a tailored service arrangement, to reinvent ACTRIS simulation chambers for testing mask filtering efficiencies. [AD2] 

ACTRIS response to the COVID-19 pandemic showcases multiple benefits to policy- and decision-makers focused on environmental and societal impacts of COVID-19 and the closing down of several sectors of society (e.g. transport, industry, services). To boost the visibility of ACTRIS COVID-19 response at the European level, ACTRIS actively engaged and collaborated with the wider community of Research Infrastructures (ESFRI, ENVRI, and ERF-AISBL) in Europe to support joint activities for SARS-CoV-2.  The Open Science Session on COVID-19 during the ACTRIS week event brought together a broad audience and key-note speaker from major European agencies and organizations (ESA, ECMWF, FMI, ICOS). The online format of the event created the opportunity to open ACTRIS science to a broader public. At the national level, atmospheric scientists were interviewed on COVID impacts raising awareness on the work undertaken in the research infrastructure to the general public.

ACTRIS aims at establishing further engagement and direct communication with decision and policy-makers and, for that, envisage the implementation of ad-hoc efforts. This presentation will showcase the various efforts and success stories to capture society as well as policy- and decision-makers.

How to cite: Saponaro, G., Dubost, A., Juurola, E., and Laj, P.: Communicating ACTRIS science in times of COVID-19, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-8622, https://doi.org/10.5194/egusphere-egu21-8622, 2021.

EGU21-15801 | vPICO presentations | ITS1.1/NP0.2 | Highlight

How the COVID-19 pandemic is teaching us to tackle the climate crisis

Jan R. Baiker, Nadia Castro-Izaguirre, Christian Huggel, Simon Allen, Fabian Drenkhan, and Veruska Muccione

More than one year after its first appearance, COVID-19 has spread to almost all territories around the world –including more than 93 million confirmed infections and 2 million reported deaths. The real numbers are probably substantially higher as unreported cases remain particularly high in countries with weak state welfare and institutions. To date the COVID-19 pandemic has had a strong impact on social, cultural and economic life, stretching from physical isolation to the exacerbation of global famines, and to the largest global economic recession since the Great Depression in the 1930s. It is therefore important to analyse and monitor in detail how this pandemic is being approached and managed by the different governments and in their specific environmental and socio-cultural contexts. Given the slow-onset character of climate change in developing clearly visible effects on a short term, the respective actions to tackle multiple impacts on natural and social systems lack priority and are often delayed. Nonetheless, the climate crisis is considered to be a comparatively fundamental existential threat to humanity.

Based on an extensive literature review, here we analyse the interactions between the COVID-19 pandemic and the climate crisis as compound impacts, i.e. systemic risks that have to be taken into consideration in national emergency programs and in disaster risk management. Human populations with limited resources and capacities tend to be more vulnerable to such exceptional crisis, and as such COVID-19 is exacerbating existing inequalities at national, regional and global levels. Nevertheless, the national responses to the pandemic and their accuracy are not only related to resources and capacities; there are also important political and social factors at play. For instance, the pandemic spread has triggered migration from cities to rural areas and, as a consequence, could lead to higher social-ecological pressures and accelerated land-use change dynamics including e.g. deforestation, changes in water provision and wetland loss in the rural areas. In turn, these impacts would most likely exacerbate the climate crisis. However, some of these risks can be transformed into long-term opportunities, such as a growing implementation of Nature-based Solutions in order to increase the resilience of ecosystems, virtual solutions that reduce travel and emissions (changing working conditions), renovation and diversification of the tourism sector towards more sustainability, and an increase in uptake of sustainable solutions (e.g., car-free days, improved / less energy consuming material and food supply-chains, agroecological production, etc.).

As a “stress test” this pandemic outbreak and ongoing crisis has already taught us several important lessons that should be considered for dealing with the climate crisis. These include the need and opportunity to redesign social-ecological systems as a whole, aiming for transformational change as a globally coordinated and locally implemented effort at all socio-political levels, in the framework of actions based on the principles of the 2030 Agenda for Sustainable Development and the Paris Agreement on Climate Change.

How to cite: Baiker, J. R., Castro-Izaguirre, N., Huggel, C., Allen, S., Drenkhan, F., and Muccione, V.: How the COVID-19 pandemic is teaching us to tackle the climate crisis, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-15801, https://doi.org/10.5194/egusphere-egu21-15801, 2021.

ITS2.4/SSS2 – Bridging between Earth Science disciplines: Participatory Citizen Science and Open Science as a way to go

EGU21-10871 | vPICO presentations | ITS2.4/SSS2

Experiences from Recent Geo-Wiki Citizen Science Campaigns in the Creation and Sharing of New Reference Data Sets on Land Cover and Land Use 

Juan Carlos Laso Bayas, Linda See, Myroslava Lesiv, Martina Dürauer, Ivelina Georgieva, Dmitry Schepaschenko, Mathias Karner, Olga Danylo, Hedwig Bartl, Anto Subash, Santosh Karanam, Tobias Sturn, Ian McCallum, and Steffen Fritz

Geo-Wiki is an online platform for involving citizens in the visual interpretation of very high-resolution satellite imagery to collect reference data on land cover and land use. Instead of being an ongoing citizen science project, short intensive campaigns are organized in which citizens participate. The advantage of this approach is that large amounts of data are collected in a short amount of time with a clearly defined data collection target to reach. Participants can also schedule their time accordingly, with their past feedback indicating that this intensive approach was preferred. The reference data are then used in further scientific research to answer a range of questions such as: How much of the land’s surface is wild or impacted by humans?  What is the size of agricultural fields globally? The campaigns are organized as competitions with prizes that include Amazon vouchers and co-authorship on a scientific publication. The scientific publication is the mechanism by which the data are openly shared so that other researchers can use this reference data set in other applications. The publication is usually in the form of a data paper, which explains the campaign in detail along with the data set collected. The data are uploaded to a repository such as Pangaea, ZENODO or IIASA’s own data repository, DARE.  This approach from data collection, to opening up the data, to documentation via a scientific data paper also ensures transparency in the data collection process. There have been several Geo-Wiki citizen science campaigns that have been run over the last decade. Here we provide examples of experiences from five recent campaigns: (i) the Global Cropland mapping campaign to build a cropland validation data set; (ii) the Global Field Size campaign to characterize the size of agricultural fields around the world; (iii) the Human Impact on Forests campaign to produce the first global map of forest management; (iv) the Global Built-up Surface Validation campaign to collect data on built-up surfaces for validation of global built-up products such as the Global Human Settlement Layer (https://ghsl.jrc.ec.europa.eu/); and (v) the Drivers of Tropical Forest Loss campaign, which collected data on the main causes of deforestation in the tropics. In addition to outlining the campaign, the data sets collected and the sharing of the data online, we provide lessons learned from these campaigns, which have built upon experiences collected over the last decade. These include insights related to the quality and consistency of the classifications of the volunteers including different volunteer behaviors; best practices in creating control points for use in the gamification and quality assurance of the campaigns; different methods for training the volunteers in visual interpretation; difficulties in the interpretation of some features, which may need expert input instead as well as the inability of some features to be recognized from satellite imagery; and limitations in the approach regarding change detection due to temporal availability of open satellite imagery, among several others. 

How to cite: Laso Bayas, J. C., See, L., Lesiv, M., Dürauer, M., Georgieva, I., Schepaschenko, D., Karner, M., Danylo, O., Bartl, H., Subash, A., Karanam, S., Sturn, T., McCallum, I., and Fritz, S.: Experiences from Recent Geo-Wiki Citizen Science Campaigns in the Creation and Sharing of New Reference Data Sets on Land Cover and Land Use , EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-10871, https://doi.org/10.5194/egusphere-egu21-10871, 2021.

EGU21-9790 | vPICO presentations | ITS2.4/SSS2

The Home River Bioblitz: A World-Wide Collaboration Between Citizens to Show the Importance of Free-Flowing Rivers

Jessica Droujko, Carlos Velazco-Macías, David Faro, Jens Benöhr, Vera Knook, and Kara Lena Virik

One challenge in collaborating with citizen scientists is to keep them motivated to continuously collect data in the long-term. The Home River Bioblitz event overcomes this roadblock by engaging hundreds of citizens around the world in one single day. In general, a bioblitz is a communal citizen-science effort to record a wide variety of species at a specific location within a certain timeframe. This single-day commitment enables large-spatial resolution data to be collected. The Home River Bioblitz was created by the River Collective, National Geographic, Bestias del sur Salvaje, and iNaturalist as part of the citizen science program supported by the National Geographic Society. The first event took place on September 20th, 2020 on 43 rivers located in 24 countries around the world. Over 500 participants from five continents used the iNaturalist app to log 5245 observations and 1772 species of flora and fauna, with at least 14 species under IUCN status, contributing to the Global Biodiversity Information Facility repository. This method of low-temporal and high-spatial data collection is used to identify new species, IUCN red list species, local endemic species, and invasive species. Not only does this event engage citizen-scientists to contribute to biodiversity findings, but it also connects people to their local environments by having them zoom into details they normally pass by. By celebrating the diversity of rivers and meeting the people around them, we were able to bring communities closer to knowing the species of their local rivers and raise awareness about the importance of free-flowing and healthy rivers around the world. An online post-event was dedicated to sharing these local river species and the scientific impact of certain observations with the participants. This event also opens up the possibility to collect other types of short term, large-spatial data around river ecosystems. In the next edition of the Home River Bioblitz, we would like to encourage the participants to collect hydro-morphological and water quality data by using open-access and low-cost citizen science tools, such as the Discharge app and the Waterrangers kit. The Home River Bioblitz event will not only be used to engage and educate participants on their local rivers, but the biodiversity and potentially chemico-physical and hydro-morphological data that will be collected could serve to develop time-series to help assess temporal variations and stressors.

How to cite: Droujko, J., Velazco-Macías, C., Faro, D., Benöhr, J., Knook, V., and Virik, K. L.: The Home River Bioblitz: A World-Wide Collaboration Between Citizens to Show the Importance of Free-Flowing Rivers, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-9790, https://doi.org/10.5194/egusphere-egu21-9790, 2021.

Good environmental governance includes participatory, transparent and accountable decision-making. All sectors of society have an essential role in organizing climate action towards our shared future. Networking science into decision-making will allow us to build actionable resilience intelligence. Developed in 1992, Article 6 of the United Nations Framework Convention on Climate Change, Principle 10 of the Rio Convention, and the Article 12 of the 2015 Paris Agreement include specific mandates for public participation and engagement in climate actions. Governments have pledged, in international agreements, to broader public participation in environmental policy design processes facilitating access to information. Here we show how Latin-American countries are doing in regard to such responsibility by focusing on the reference to participatory processes and the inclusion in climate strategies of adequate instruments of participation in the contributions presented to the United Nations. This analysis provides a baseline from which we can ground truth and track progress of NDCs’ accelerating climate-smart future through stakeholder engagement. Our research shows there is a need for understanding and metrics for quality public participation and articulation of participatory processes

How to cite: Cintron Rodriguez, I. M.: Ensuring science-based climate action: Analysis of multi-stakeholder engagement in Nationally Determined Contributions, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-9016, https://doi.org/10.5194/egusphere-egu21-9016, 2021.

EGU21-10386 | vPICO presentations | ITS2.4/SSS2

Co-Designing Mobile Applications for Data Collection: A Comparative Evaluation of Co-Design Processes in the Project "Nachtlicht-BüHNE"

Friederike Klan, Christopher C.M. Kyba, Nona Schulte-Römer, Helga U. Kuechly, Jürgen Oberst, Anastasios Margonis, and Marius Hauenschild

Data collection via mobile software applications is playing an increasingly important role in Citizen Science projects. When developing such applications, it is important to consider both the requirements of the scientists interested in data collection and the needs of the citizen scientists who contribute data. Citizens and participating scientists therefore ideally work together when conceptualizing, designing, and testing such applications (co-design). In this way, both sides - scientists and citizens - can contribute their expectations, desires, knowledge, and engagement at an early stage, thereby improving the utility and acceptance of the resulting applications. How such a co-design process must and can be meaningfully designed depends very much on (1) the interests, skills and background knowledge of the project participants, (2) the complexity and type of the data collection methodology to be implemented, and (3) the time, financial and legal conditions under which the software is developed.

In our contribution, we address this point. We present two methodologies that enable the joint design and implementation of software applications for mobile data collection in citizen science projects. These represent quite different best practice approaches that emerged during the development of mobile applications on the topics of light pollution and meteor observation in our Citizen Science project Nachtlicht-BüHNE. We examine and compare the resulting methods with respect to their suitability for use under different conditions and thus provide future citizen science projects based on participatory developed mobile applications with decision support for the design of their co-design approach. We shed light on the two co-design methods with respect to the following criteria, among others: possible types of contributions by volunteers, requirements on expertise and knowledge of the contributors, flexibility of the method with respect to changing requirements, possibilities with respect to the design of complex data collection methods, costs incurred and time required for the implementation of the methodology.

How to cite: Klan, F., Kyba, C. C. M., Schulte-Römer, N., Kuechly, H. U., Oberst, J., Margonis, A., and Hauenschild, M.: Co-Designing Mobile Applications for Data Collection: A Comparative Evaluation of Co-Design Processes in the Project "Nachtlicht-BüHNE", EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-10386, https://doi.org/10.5194/egusphere-egu21-10386, 2021.

EGU21-8292 | vPICO presentations | ITS2.4/SSS2 | Highlight

Integrated Soil Health Assessment bridging Local Knowledge and Soil Science in Conservation Agriculture 

Thirze Hermans, Andrew Dougill, Stephen Whitfield, Caroline L. Peacock, Samuel Eze, and Christian Thierfelder

Climate change challenges across sub-Saharan Africa require more resilient food production systems. To improve agricultural resilience, the Climate Smart Agriculture (CSA) framework has been proposed including Conservation Agriculture (CA). CA has three key principles; 1) minimum soil disturbance, 2) crop residue cover, 3) crop diversification. Current soil health studies assessing CA’s impact have focused on 'scientific measurements', paying no attention to local knowledge. Local knowledge however influences farmers’ land decision making and their evaluation of CA. In this study, a participatory approach to evaluate CA’s soil health impacts is developed and implemented using farmers’ observations and soil measurements on farm trials in two Malawian communities. The on-farm trials compared conventional ridge and furrow systems (CP), with CA maize only (CAM) and CA maize-legume intercrop systems (CAML). This approach contextualizes the CA soil health outcomes and contributes to understanding how an integrated approach can explain farmer decision-making.

Based on a stepwise integrated soil assessment framework, firstly farmers’ soil health indicators were identified as crop performance, soil consistency, moisture content, erosion, colour and structure. These local indicators were consistent with conventional soil health indicators for quantitative measurements. Soil measurements and observations show that CA leads to soil structural change. Both soil moisture (Mwansambo: 7.54%-38.15% lower for CP; Lemu 1.57%-47.39% lower for CP) and infiltration improve under CA (Lemu CAM/CAML 0.15 cms-1, CP 0.09 cms-1; Mwansambo CP/CAM 0.14 cms-1, CAML 0.18 cms-1). Farmers perceive ridges as positive due to aeration, nutrient release and infiltration, which corresponds with higher exchangeable ammonium (Lemu CP 76.0 mgkg -1, CAM 49.4 mgkg -1, CAML 51.7 mgkg -1), and nitrate/nitrite (Mwansambo CP 200.7  mgkg -1, CAM 171.9 mgkg -1, CAML 103.3 mgkg -1). This perspective still contributes to the popularity of ridges, despite the higher yield and total nitrogen measurements under CA. The perceived carbon benefits of residues, and ridge advantages have encouraged farmers to bury residues in ridges.

This work shows that an integrated approach provides more nuanced and localized information about land management. The stepwise integrated soil assessment framework developed in this study can be used to understand the role of soil health in farmers’ land management decision-making. Thereby supporting a two-way learning process for scaling agricultural innovations and broadening the evidence base for sustainable agricultural innovations.

How to cite: Hermans, T., Dougill, A., Whitfield, S., Peacock, C. L., Eze, S., and Thierfelder, C.: Integrated Soil Health Assessment bridging Local Knowledge and Soil Science in Conservation Agriculture , EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-8292, https://doi.org/10.5194/egusphere-egu21-8292, 2021.

EGU21-12706 | vPICO presentations | ITS2.4/SSS2 | Highlight

Soils, Science and Community ActioN (SoilSCAN) to reduce land degradation in East Africa

Alex Taylor, Claire Kelly, Maarten Wynants, Aloyce Patrick, Francis Mkilema, Linus Munishi, Kelvin Mtei, Mona Nasseri, Patrick Ndakidemi, and Will Blake

East African farming communities face complex challenges regarding food and feed productivity. Primary production systems are under stress, nutritional choices are changing and the relationship between development and agriculture is undergoing profound transformation. In the face of severe threat of soil erosion, East African agro-pastoral systems are now at a tipping point and there has never been a greater urgency for evidence-led sustainable land management interventions to reverse degradation of natural resources that support food and water security. A key barrier, however, is a lack of high spatial resolution soil health data wherein collecting such information is beyond conventional research means. This research tests whether bridging this data gap can be achieved through a coordinated citizen science programme. Accessible and portable technology is currently available in the form of hand-held soil scanners that can enable farmers to become citizen scientists empowered to collect data to establish research data bases that support critical landscape decisions. The aim of the work was to test the potential for using soil scanners as a tool for mapping whole community soil health characteristics, using soil organic matter as an indicator, down to farm-scale; a resolution that is beyond that achievable in conventional research, with the ultimate objective to deliver information that empowers stakeholders to create a sustainable community landscape plan.

Key outcomes included:

(1) A training document for the usage of the soil scanner that includes a list of potential problems and their solutions. Moreover, a training session was organised in the Tanzanian partner institution to build capacity for the continuation of the project, wherein local researchers were trained in the application of the ‘Agrocares’ soil scanner to support continuing community engagement.

(2) Local farmers being provided an opportunity to circumvent traditional power and knowledge inequities. During the introductory meeting and field measurements, we noticed the development of locally-embedded scientific interests and skills that foster stronger community ownership and engagement in action research.

(3) A high resolution soil organic matter and nutrient status dataset in small-catchment and community setting. The citizen science data contributes to soil process and hydrological understanding of East African landscapes, which besides direct contribution to the scientific understanding, also supports co-design of effective management solutions to the soil erosion and land degradation challenges.

The inclusion of ‘big data’ digital data training and sharing platforms and has the potential to create more robust and better informed collective decision-making, as well as identifying key data gaps. Further it can expand the utility and applicability of existing techniques and data sets beyond the reach of conventional research. Challenges and opportunities for wider use of soil scanning technology by community groups are evaluated.

How to cite: Taylor, A., Kelly, C., Wynants, M., Patrick, A., Mkilema, F., Munishi, L., Mtei, K., Nasseri, M., Ndakidemi, P., and Blake, W.: Soils, Science and Community ActioN (SoilSCAN) to reduce land degradation in East Africa, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-12706, https://doi.org/10.5194/egusphere-egu21-12706, 2021.

EGU21-13068 | vPICO presentations | ITS2.4/SSS2

Combining citizen science and artificial intelligence to facilitate geology outreach and capture geodiversity: prospects from the RockNet project

Antoine Bouziat, Sylvain Desroziers, Mathieu Feraille, Jean-Claude Lecomte, Christophe Cornet, François Cokelaer, and Renaud Divies

Popularizing and disseminating a basic level of geological knowledge and understanding to the general public has become an important issue, either to valorize and protect our natural heritage, or to facilitate public engagement in environmental and energy debates. Emergent technologies and the increasing digitalization of our societies broaden the range of tools available to address this topic. In this talk, we focus on the prospects enabled by the combination of citizen science and Artificial Intelligence (AI), building on the birth of the RockNetTM project.

 

Inspired by the sucess of the Pl@ntNet project for botanical science outreach, RockNetTM aims at developping a mobile application, whose users can photograph rock samples and get a lithological classification from an AI algorithm. By doing so, a participative data base of rock images is progressively gathered and shared among all users. Meanwhile the most expert ones can correct the automated facies identification to gradually improve the AI capabilities. Then the resulting tool collectively produced becomes a possible support for geoscience outreach, relying on the citizens' curiosity for their immediate geological environment.

 

A first prototype, handling 12 different lithological classes, has already been developed and trained on several thousand pictures. From this practical experience, we illustrate the potential of this kind of technology and the numerous challenges to consider before a large-scale diffusion of the application.  

 

How to cite: Bouziat, A., Desroziers, S., Feraille, M., Lecomte, J.-C., Cornet, C., Cokelaer, F., and Divies, R.: Combining citizen science and artificial intelligence to facilitate geology outreach and capture geodiversity: prospects from the RockNet project, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-13068, https://doi.org/10.5194/egusphere-egu21-13068, 2021.

EGU21-11344 | vPICO presentations | ITS2.4/SSS2

Higher Education engagement in citizen enhanced open science: Between Humanities and Natural Sciences

Katerina Zourou and Mariana Ziku

EGU21-14995 | vPICO presentations | ITS2.4/SSS2 | Highlight

Participation of pupils in atmospheric measurements -- Potential for increasing climate change risk awareness and data availability for weather and climate research

Henning Rust, Bianca Wentzel, Thomas Kox, Jonas Lehmke, Christopher Böttcher, Andreas Trojand, Elisabeth Freundl, and Martin Göber

Voluntarily measuring atmospheric characteristics by citizens has a long tradition. Possibilities has been increasing in the last years with the rise of smart devices and the internet-of-things (IoT). Atmospheric measurements are also prototypical project examples within the Maker community. Maker projects (i.e. IoT-/technology-oriented projects) are popular means of strengthening interest in STEM subjects among pupils. In the frame of two projects, we use an IoT-based weather station to be assembled by pupils as a participatory vehicle to a) raise interest in and understanding of weather and climate, as well as weather forecasts, and b) obtain additional data to be used in scientific projects.  

In the project KARE-CS  (funding: German Ministry for Education and Research, BMBF), a lay weather network has been set up together with pupils in the Bavarian Oberland south of Munich in 2020 and 2021. The students' devices measure temperature, pressure, humidity, solar radiation and precipitation in their direct environment, data is visualized on their smartphones (or any device running a browser) and updated every few minutes. Pupils also report weather impacts such as observed damages or their own concernment about weather events. These data are evaluated in workshops involving the students, their teachers, local partners and scientists. Atmospheric as well as impact data is evaluated for further use in scientifc studies, such as within the mother project KARE (). KARE-CS focuses on upper secondary school students as participants and aim at a development of competences among teachers as multipliers and pupils, particularly in terms of climate change adaptation, understanding natural hazards and risks and in taking personal precautions.

A similar setup is used for supporting the measurement campaing FESSTVaL ( initiated for 2021 by the Hans-Ertel-Centre for Weather Research ( ). The pupils' network will consist of 100 instruments within and close to the campaign's main site. Additionally to the communication and education-oriented goals mentioned above, the resulting spatially and temporally high-resolution data is used for research on thunderstorm development and cold pool characteristics within the Hans-Ertel-Centre.

How to cite: Rust, H., Wentzel, B., Kox, T., Lehmke, J., Böttcher, C., Trojand, A., Freundl, E., and Göber, M.: Participation of pupils in atmospheric measurements -- Potential for increasing climate change risk awareness and data availability for weather and climate research, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-14995, https://doi.org/10.5194/egusphere-egu21-14995, 2021.

EGU21-9302 | vPICO presentations | ITS2.4/SSS2 | Highlight

The role of citizen science as a tool of public information in water quality management in the Brantas catchment, Indonesia

Reza Pramana, Schuyler Houser, Daru Rini, and Maurits Ertsen

Water quality in the rivers and tributaries of the Brantas catchment (about 12.000 km2) is deteriorating due to various reasons, including rapid economic development, insufficient domestic water treatment and waste management, and industrial pollution. Various parameters measured by agencies involved in water resource development and management and environmental management consistently demonstrate exceedance of the local water quality standards. Between the different agencies, water quality data are available – intermittently from 2009 until 2019 at 104 locations, but generally on a monthly basis. Still, opportunities to improve data availability are apparent, both to increase the amount and representability of the data sets. The opportunity to expand available data via citizen science is simultaneously an opportunity to provide education on water stewardship and empower citizens to participate in water quality management. We plan to involve people from eight communities living close to the river and researchers from two local universities in a citizen-science campaign. The community members would sample weekly at 10 locations, from upstream to downstream of the catchment. We will use probes and test strips to measure the temperature, electrical conductivity, pH, nitrate, phosphate, ammonia, iron, and dissolved oxygen. The results will potentially be combined with the data from government agencies to construct an integrated water quality data set to improve decision making and the quality of community engagement in water resource management.

How to cite: Pramana, R., Houser, S., Rini, D., and Ertsen, M.: The role of citizen science as a tool of public information in water quality management in the Brantas catchment, Indonesia, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-9302, https://doi.org/10.5194/egusphere-egu21-9302, 2021.

EGU21-14721 | vPICO presentations | ITS2.4/SSS2

Incorporating citizen science in open science: a case study of participatory rainfall measurements in the context of a Technical University

Sandra de Vries, Marit Bogert, Sabine Kunst, and Nicoleta Nastase

Citizen science has globally been recognized as a vital part of open science and as a way of doing research that enables new levels of science education and science communication. Due to its high levels of public participation, citizen science can be of great value in bringing society and science closer together. Universities across the world have acknowledged this value and aim to incorporate citizen science in their policies and daily practices as part of their open science practices.

The Delft University of Technology has set the goal to develop an open science program that includes citizen science. However, implementing and incorporating citizen science in an open science program is not a straightforward task and demands knowledge, understanding, and experience of the field as well as the practical implications. What should a university do to support the goals of various citizen science initiatives, within an open science context, and to assist and facilitate researchers to perform effective participatory science? To gain a deeper understanding of what a citizen science project entails within the context of a university, we performed a case-study implementing citizen science methods for hydrological research. The project, called Delft Measures Rain, was developed in collaboration with external partners and several internal departments and their staff, some already having experience with developing and coordinating citizen science projects. Citizens of Delft were encouraged to participate and work together with scientists from the Water Management department to investigate rainfall patterns within the city. In total, 95 citizens collaborated for two months to collect over 1900 individual rainfall measurements spread over the city and taken with home-made rain gauges.  We developed tailored recruitment strategies, data collection and validation tools, data visuals, and communication strategies. Overall, the project has delivered valuable results, including reliable rainfall data, involvement and enthusiasm of citizens, and valuable feedback from participants. Additionally, this project has led to more cooperation of relevant institutions and civil society organizations (CSO) across the city and between different departments within the university itself.

This case-study has showcased how various stakeholders (researchers, citizens, civil servants, CSO’s, etc.) can benefit from co-developed participatory research implementing citizen science and open science principles. With this case study, we were able to identify the benefits, drawbacks, and opportunities for all stakeholders involved. Furthermore, we identified key tools and facilitation needs to assist researchers within the university to perform effective participatory science. During the session, we would like to share our methods, successes, challenges, and lessons learned. This project shows that, with the right knowledge and tools, citizen science can deliver what it promises and be of great value to universities and open science in general. 

How to cite: de Vries, S., Bogert, M., Kunst, S., and Nastase, N.: Incorporating citizen science in open science: a case study of participatory rainfall measurements in the context of a Technical University, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-14721, https://doi.org/10.5194/egusphere-egu21-14721, 2021.

EGU21-2670 | vPICO presentations | ITS2.4/SSS2

The Dutch research infrastructure EPOS-NL: Access to Earth scientific research facilities and data 

Ronald Pijnenburg, Susanne Laumann, Richard Wessels, Geertje ter Maat, Lora Armstrong, Jarek Bieńkowski, Otto Lange, Reinoud Sleeman, Philip Vardon, David Bruhn, Auke Barnhoorn, André Niemeijer, Ernst Willingshofer, Oliver Plümper, Kees Wapenaar, Jeannot Trampert, and Martyn Drury

In response to the growing geo-societal challenges of our densely populated planet, current research frequently requires convergence of multiple research disciplines, and optimized use of openly available data, research facilities and funds. Such optimization is the main aim of many research infrastructures developing both at the national and international level. In the Netherlands, the European Plate Observing System – Netherlands (EPOS-NL) was formed, as the Dutch research infrastructure for solid Earth sciences. EPOS-NL aims to further develop world-class facilities for research into georesources and hazards, and to provide international access to these facilities and derived data. It is a partnership between Utrecht University, Delft University of Technology and the Royal Netherlands Meteorological Institute (KNMI) and is funded by the Dutch Research Council. EPOS-NL facilities include: 1) The Earth Simulation Lab at Utrecht University, 2) The Groningen gas field seismological network and the ORFEUS Data Center at KNMI, 3) The deep geothermal doublet (DAPwell), to be installed on the Delft university campus, and 4) A distributed facility for multi-scale imaging and tomography (MINT), shared between the Utrecht and Delft universities. EPOS-NL provides financial, technical and scientific support for access to these facilities. To get facility access, researchers can apply to a bi-annual call, with 2021 calls planned in Q1 and Q3. EPOS-NL further works with researchers, data centers and industry to provide access to essential data and models (e.g. pertaining to the seismogenic Groningen gas field) within the framework of the European infrastructure EPOS, conforming to FAIR (Findable, Accessible, Interoperable and Reusable) data principles. In that way, EPOS-NL contributes directly to a globally developing trend to make research facilities and data openly accessible to the international community. This supports cost-effective and multi-disciplinary research into the geo-societal challenges faced by our densely populated planet. See www.EPOS-NL.nl for more information.

How to cite: Pijnenburg, R., Laumann, S., Wessels, R., ter Maat, G., Armstrong, L., Bieńkowski, J., Lange, O., Sleeman, R., Vardon, P., Bruhn, D., Barnhoorn, A., Niemeijer, A., Willingshofer, E., Plümper, O., Wapenaar, K., Trampert, J., and Drury, M.: The Dutch research infrastructure EPOS-NL: Access to Earth scientific research facilities and data , EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-2670, https://doi.org/10.5194/egusphere-egu21-2670, 2021.

EGU21-13177 | vPICO presentations | ITS2.4/SSS2

Data Stewardship Practices for Earth Observation Transient and Optimized Analysis Platforms

Kaylin Bugbee, Rahul Ramachandran, Ge Peng, and Aaron Kaulfus

Access to valuable scientific research data is becoming increasingly more open, attracting a growing user community of scientists, decision makers and innovators. While these data are more openly available, accessibility continues to remain an issue due to the large volumes of complex, heterogeneous data that are available for analysis. This emerging accessibility issue is driving the development of specialized software stacks to instantiate new analysis platforms that enable users to quickly and efficiently work with large volumes of data. These platforms, typically found on the cloud or in a high performance computing environment, are optimized for large-scale data analysis. These platforms can be transient in nature, with a defined life span and a focus on improved capabilities as opposed to serving as an archive of record. 

 

While these transient, optimized platforms are not held to the same stewardship standards as a traditional archive, data must still be managed in a standardized and uniform manner throughout the platform. Valuable scientific research is conducted in these platforms, making these platforms subject to open science principles such as reproducibility and accessibility. In this presentation, we examine the differences between various data stewardship models and describe where transient optimized platforms fit within those models. We then describe in more detail a data and information governance framework for Earth Observation transient optimized analysis platforms. We will end our presentation by sharing our experiences of developing such a framework for the Multi-Mission Algorithm and Analysis Platform (MAAP).

How to cite: Bugbee, K., Ramachandran, R., Peng, G., and Kaulfus, A.: Data Stewardship Practices for Earth Observation Transient and Optimized Analysis Platforms, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-13177, https://doi.org/10.5194/egusphere-egu21-13177, 2021.

EGU21-12843 | vPICO presentations | ITS2.4/SSS2 | Highlight

Open Polar: a new freely search service of publications and research data of Polar Regions

Tamer Abu-Alam, Karl Magnus Nilsen, Obiajulu Odu, Leif Longva, and Per Pippin Aspaas

Research data plays a key role in monitoring and predicting any natural phenomena, including changes in the Polar Regions. The limited access to data restricts the ability of researchers to monitor, predict and model environmental changes and their socio-economic repercussions. In a recent survey of 113 major polar research institutions, we found out that an estimated 60% of the existing polar research data is unfindable through common search engines and can only be accessed through institutional webpages. In social science and indigenous knowledge, this findability gap is even higher, approximately 84% of the total existing data. This raises an awareness sign and the call for the need of the scientific community to collect information on the global output of research data and publications related to the Polar Regions and present it in a homogenous, seamless database.

In this contribution, we present a new, open access discovery service, Open Polar, with the purpose of rendering polar research more visible and retrievable to the research community as well as to the interested public, teachers, students and decision-makers. The new service is currently under construction and will be hosted by UiT The Arctic University of Norway in close collaboration with the Norwegian Polar Institute and other international partners. The beta version of the Open Polar was made available in February 2021. We welcome comments and suggestions from the scientific community to the beta version, while we plan to launch the stable production version of the service by summer 2021. The beta version of the service can already be tested at the URL: www.openpolar.no

How to cite: Abu-Alam, T., Nilsen, K. M., Odu, O., Longva, L., and Aspaas, P. P.: Open Polar: a new freely search service of publications and research data of Polar Regions, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-12843, https://doi.org/10.5194/egusphere-egu21-12843, 2021.

Monitoring Svalbard’s environment and cultural heritage through citizen science by expedition cruises

Michael K. Poulsen1, Lisbeth Iversen2, Ted Cheeseman3, Børge Damsgård4, Verena Meraldi5, Naja Elisabeth Mikkelsen6, Zdenka Sokolíčková7, Kai Sørensen8, Agnieszka Tatarek9, Penelope Wagner10, Stein Sandven2, and Finn Danielsen1

1NORDECO, 2NERSC, 3PCSC, 4UNIS, 5Hurtigruten, 6GEUS, 7University of Oslo, 8NIVA, 9IOPAN, 10MET Norway

Why expedition cruise monitoring is important for Svalbard. The Arctic environment  is changing fast, largely due to increasing temperatures and human activities. The continuous areas of wilderness and the cultural heritage sites in Svalbard need to be managed based on a solid understanding.

The natural environment of Svalbard is rich compared to other polar regions. Historical remains are plentiful. The Svalbard Environmental Protection Act aims at regulating hunting, fishing, industrial activities, mining, commerce and tourism. Expedition cruises regularly reach otherwise rarely visited places.

Steps taken to improve environmental monitoring. A workshop for enhancing the environmental monitoring efforts of expedition cruise ships was held in Longyearbyen in 2019, facilitated by the INTAROS project and the Association of Arctic Expedition Cruise Operators  (https://intaros.nersc.no/content/cruise-expedition-monitoring-workshop) with representatives of cruise operators, citizen science programs, local government and scientists. They agreed on a pilot assessment of monitoring programs during 2019.

Results show the importance of cruise ship observations. The provisional findings of the pilot assessment suggest thatexpedition cruises go almost everywhere around Svalbard and gather significant and relevant data on the environment, contributing for example to an improved understanding of thestatus and distribution of wildlife. Observations are often documented with photographs. More than 150 persons contributed observations during 2019 to eBird and Happywhale. iNaturalist, not part of the pilot assessment, also received many contributions. The pilot assessment was unable to establish a useful citizen science program for testing monitoring of cultural remains.

Conclusions relevant for monitoring and environmental management. Cruise ships collect environmental data that are valuable for the scientific community and for public decision-makers. The Governor of Svalbard isresponsible for environmental management in Svalbard. Data on the environment and on cultural remains from expedition cruises can be useful for the Governor’s office. Improved communication between citizen science programs and those responsible for environmental management decisions is likely to increase the quantity of relevant information that reaches public decision makers.

Recommendations for improving the use of cruise ship observations and monitoring.

  • 1) All cruise expedition ships should be equipped with tablets containing the apps for the same small selection of citizen scienceprograms so that they can easily upload records.
  • 2) Evaluation of data that can be created and how such data can contribute to monitoring programs, to ensure that data is made readily available in a form that is useful for institutions responsible for planning and improving environmental management.
  • 3) Clear lines of communication between citizen science program participants, citizen science program organizers, the scientific community and decision makers should be further developed.
  • 4) Developing expedition cruise monitoring is of high priority in Svalbard, but is also highly relevant to other polar regions.
  • 5) Further work is necessary to fully understand the feasibility and potential of coordinated expedition cruise operator based environmental observing in the Arctic.

How to cite: Poulsen, M.: Monitoring Svalbard’s environment and cultural heritage through citizen science by expedition cruises, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-15951, https://doi.org/10.5194/egusphere-egu21-15951, 2021.

EGU21-13203 | vPICO presentations | ITS2.4/SSS2

Modular designed Apps – an opportunity to standardize data collection methods and to encourage the reuse of software

Sina C. Truckenbrodt, Maximilian Enderling, Carsten Pathe, Erik Borg, Christiane C. Schmullius, and Friederike Klan

Data collection strategies vary among different citizen science projects. This complicates the intercomparability of parameter values acquired in different studies (e.g., methodological and scale issues) and results in variable data quality. This creates problems regarding the merging of different data sets and hampers the reuse of data from different projects. Modular designed applications for mobile devices (Apps) represent a framework that helps to foster the standardisation of data collection methods. While they encourage the reuse of the software, they provide enough flexibility for an adjustment in accordance with the research question(s) of interest.

The currently developed App “FieldMApp” offers such a framework running under Android and iOS. The related concept includes predefined frame functionalities, like settings for the user account and the user interface, and adaptable application-related functionalities. The latter comprise several modules that are categorized as sensor test, basic functionality, parameter collection and data quality collection modules. The interdependencies of these modules are documented in a wiki. This enables an individual and context-based selection of functionalities. The FieldMApp is based on open-source software libraries (Xamarin, Open Development Kit (ODK), SQLite, CoreCLR-NCalc, LusoV.YamarinUsbSerialForAndroid, Newtonsoft.Json, SharpZipLib) and will be published as open-source software. Hence, the existing catalogue of functionalities can be augmented in the future. The premise for such extensions is that modules are published together with smart, universally applicable data quality recording routines and a proper documentation in the wiki.

In this contribution, we present the concept and the structure of the FieldMApp and some current fields of application that are related to the cultivation of arable land, soil mapping, forest monitoring, and Earth Observation. The extension of the functionality catalogue is exemplified by the newly implemented speech recognition module. A related quality recording routine will be introduced. With this contribution we would like to encourage citizens and scientists to elicit which requirements such an App should fulfil from their point of view.

How to cite: Truckenbrodt, S. C., Enderling, M., Pathe, C., Borg, E., Schmullius, C. C., and Klan, F.: Modular designed Apps – an opportunity to standardize data collection methods and to encourage the reuse of software, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-13203, https://doi.org/10.5194/egusphere-egu21-13203, 2021.

EGU21-2770 | vPICO presentations | ITS2.4/SSS2

Nordic EPOS - A FAIR Nordic EPOS Data Hub

Annakaisa Korja, Kuvvet Atakan, Peter H. Voss, Michael Roth, Kristin Vogfjord, Elena Kozlovskaya, Eija I. Tanskanen, Niina Junno, and Nordic Epos Working Group

Nordic EPOS - A FAIR Nordic EPOS Data Hub – is a consortium of the Nordic geophysical observatories financed by NordForsk. It is delivering on-line data to European Plate Observing System’s Thematic Core Services (EPOS’s TCSs). Nordic EPOS consortium comprises of the Universities of Helsinki, Bergen, Uppsala, Oulu and GEUS and Icelandic Meteorological Office. Nordic EPOS enhances and stimulates the ongoing active Nordic interactions related to Solid Earth Research Infrastructures (RIs) in general and EPOS in particular. Nordic EPOS develops expertise and tools designed to integrate Nordic RI data and to enhance their accessibility and usefulness to the Nordic research community. Together we can address global challenges in Norden and with Nordic data.

The Nordic EPOS’s main tasks are to advance the usage of multi-disciplinary Solid Earth data sets on scientific and societal problem solving, increase the amount of open, shared homogenized data sets, and increase the scientific expertise in creating sustainable societies in Nordic countries and especially in the Arctic region. In addition to developing services better suited for Nordic interest for EPOS, Nordic EPOS will also try to bring forward Nordic research interest, such as research of Arctic areas in TCSs and EPOS-ERIC governance and scientific boards.

The Nordic EPOS is organized into Tasks and Activities. The project has six main infrastructure TASKs: I - Training in usage of EPOS-RI data and services; II - Nordic data integration and FAIRness; III - Nordic station management of seismological networks, IV - Induced seismicity, safe society; V - Ash and gas monitoring; and VI- Geomagnetic hazards. In addition, the project has one transversal TASK VII on Communication and dissemination. The activities within the TASKs are workshops, tutorials, demos and training sessions (virtual and on-site), and communication and dissemination of EPOS data and metadata information at local, national and international workshops, meetings, and conferences.

How to cite: Korja, A., Atakan, K., Voss, P. H., Roth, M., Vogfjord, K., Kozlovskaya, E., Tanskanen, E. I., Junno, N., and Working Group, N. E.: Nordic EPOS - A FAIR Nordic EPOS Data Hub, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-2770, https://doi.org/10.5194/egusphere-egu21-2770, 2021.

EGU21-13042 | vPICO presentations | ITS2.4/SSS2

V3Geo: A cloud-based platform for sharing virtual 3D models in geoscience

Simon Buckley, John Howell, Nicole Naumann, Conor Lewis, Kari Ringdal, Joris Vanbiervliet, Bowei Tong, Gail Maxwell, and Magda Chmielewska

V3Geo is a cloud-based repository for virtual 3D models in geoscience, allowing storage, searching tools and visualisation of 3D models typically acquired through photogrammetry (structure-from-motion), laser scanning or other laboratory-based 3D modelling methods. The platform has been developed to store and access 3D models at the range of scales and applications required by geoscientists – from microscopic, hand samples and fossils through to outcrop sections covering metres to tens of kilometres. A 3D web viewer efficiently streams the model data over the Internet connection, allowing 3D models to be explored interactively. A measurement tool makes it possible for user to measure simple dimensions, such as widths, thicknesses, fault throws and more. V3Geo differs from other services in that it allows very large models (consisting of multiple sections), is designed to include additional interpretations in future versions, and focuses specifically on geoscience through metadata and a classification schema.

The initial version of V3Geo was released in 2020 in reaction to the COVID-19 pandemic, with the aim of providing virtual tools in a time of cancelled field excursions, field-based courses and fieldwork. The repository has been accepting community contributions, based on a guideline for preparing and submitting high quality 3D datasets. Contributions are subject to a technical review to ensure underlying quality and reliability for scientific and professional usage. Model description pages give an overview of the datasets, with references, and datasets themselves are assigned Creative Commons licences. The 3D viewer can be embedded in webpages, making it easy to include V3Geo models in virtual teaching resources. V3Geo allows increased accessibility to field localities when travel or mobility is restricted, as well as providing the foundation for virtual field trips. The database currently includes around 200 virtual 3D models from around the world, and will continue to develop and grow, aiming to become a valuable resource for the geoscience community. Future updates will include tools to facilitate upload and technical review, interpretations and Digital Object Identifiers.

How to cite: Buckley, S., Howell, J., Naumann, N., Lewis, C., Ringdal, K., Vanbiervliet, J., Tong, B., Maxwell, G., and Chmielewska, M.: V3Geo: A cloud-based platform for sharing virtual 3D models in geoscience, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-13042, https://doi.org/10.5194/egusphere-egu21-13042, 2021.

EGU21-12872 | vPICO presentations | ITS2.4/SSS2

A compiled open-access geological map of Dronning Maud Land, Antarctica

Tamer Abu-Alam and Synnøve Elvevold

Geological mapping and investigation of the mountain chain in Dronning Maud Land (DML) has been carried out by a number of geologists from South Africa, Japan, India, Germany, Russia and Norway over the last 40-50 years. The produced geological maps of these teams are, for a large part, based on fairly old data which makes these maps inhomogeneous. The maps are at different scales, contain different levels of details, and the standards for classification of the rock units may also differ between the maps. This limits the ability to use these maps to draw an overview tectonic model of the evolution of Dronning Maud Land.

In this contribution, we present a newly compiled geological map and GIS database of the Dronning Maud Land. The map will be available soon as an open-access database, but the readers can test a test version of it at: https://geokart.npolar.no/Html5Viewer/index.html?viewer=Geology_DML. The geological importance of the Dronning Maud Land to understanding the evolution of the southern parts of the Gondwana supercontinent was the main motivation factor as the DML is considered as the missing link between the geology of South Africa, Australia and Indian subcontinent.

The new database covers the area between 20o W and 45o E and was compiled at a scale level of 1:250 000. However, the database provides another scale level of 1:5 000 000 to put the DML in the regional framework of the Gondwana. The geological map is descriptive based on the new topographic dataset of the Landsat 8. The project was based at the Norwegian Polar Institute from 2014 to 2018 and supported by a research grant from the Ministry of Foreign Affairs, Norway.

How to cite: Abu-Alam, T. and Elvevold, S.: A compiled open-access geological map of Dronning Maud Land, Antarctica, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-12872, https://doi.org/10.5194/egusphere-egu21-12872, 2021.

EGU21-2929 | vPICO presentations | ITS2.4/SSS2

Sentinel-1 InSAR data by LiCSAR system

Milan Lazecky, Yasser Maghsoudi Mehrani, Scott Watson, Yu Morishita, John Elliott, Andrew Hooper, and Tim Wright

Looking Into the Continents from Space with Synthetic Aperture Radar (LiCSAR) is a system built for large-scale interferometric processing of Sentinel-1 data. LiCSAR automatically produces geocoded wrapped and unwrapped interferograms combining every acquisition epoch with four preceding epochs, and complementary data (coherence, amplitude, line-of-sight unit vectors, digital elevation model, metadata, and atmospheric phase screen estimates by the Generic Atmospheric Correction Online Service, GACOS).

The LiCSAR products are generated in frame units where a standard frame covers ~220x250 km, at 0.001° resolution (WGS-84 coordinate system). Frames are continuously updated for tectonic and volcanic priority areas. In 2020, the LiCSAR system covered about 1,500 global frames in which we have processed over 89,000 Sentinel-1 acquisitions and generated over 300,000 interferograms. Among these, 470 frames cover 1,024 global volcanoes. We aim to cover the global seismic mask defined by the Committee on Earth Observation Satellites (CEOS), but focus initially on the Alpine-Himalayan belt and East African Rift.

We serve the products as open and freely accessible through our web portal: https://comet.nerc.ac.uk/comet-lics-portal and aim to provide them to shared infrastructures as the European Plate Observing System (EPOS). We also generate rapid response coseismic interferograms for earthquakes with moment magnitude (Mw)> 5.5  a few hours after the postseismic data become available, and we update frames covering active volcanoes twice per day.

Our products can be directly converted to displacement time series and velocities using  the LiCSBAS time series analysis software. We present solutions implemented in LiCSAR, and show several case studies that use LiCSAR and LiCSBAS products to measure tectonic and volcanic deformation.

How to cite: Lazecky, M., Maghsoudi Mehrani, Y., Watson, S., Morishita, Y., Elliott, J., Hooper, A., and Wright, T.: Sentinel-1 InSAR data by LiCSAR system, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-2929, https://doi.org/10.5194/egusphere-egu21-2929, 2021.

EGU21-7961 | vPICO presentations | ITS2.4/SSS2

Establishing a systematic regional scale identification of artificial ground in Catalan territory from geological perspective

Guillem Subiela, Jordi Peña, Fus Micheo, and Miquel Vilà

Anthropization is the transformation that human actions exert on the environment. Artificial interventions modify the morphology of the ground and affect physical and chemical properties of natural terrain. Therefore, providing information on the distribution of artificial ground throughout the territory is necessary for land management, development and sustainability. Despite the effects of anthropization, from a geological approach, the systematic characterization of anthropic ground on a regional scale is scarcely developed in Catalonia.

In the last decade, one of the lines of work of Institut Cartogràfic i Geològic de Catalunya (the Catalan geological survey organisation) has been the development of the project Geoanthropic map of Catalonia, which incorporate information of active geological processes and artificial ground. Up to now, the activity in this project has broadly consisted of publishing several map sheets of 1:25.000 scale from different areas of Catalonia (5.000 km2 from 32.108,2 km2). Recently, in the framework of this project, it is proposed to refocus with the purpose of ​​providing information on these two themes from all over the territory. In this process, in relation to artificial interventions, an analysis has been carried out to determine which anthropic terrains and related information can be obtained for its usefulness in a systematic way in the medium term.

In this analysis, firstly, the available reference information sources have been established from which information on anthropic lands in Catalonia can be extracted. Basically, these documents are topographic maps, geothematic maps, land use map, digital elevation models and other historical cartographic documents. Much of the existing information in these sources must be redirected to a more geological approach so that it can be used to address aspects related to geotechnics, natural hazards, soil pollution and other environmental concerns.

Secondly, based on data analysis, a series of certain anthropic lands have been evaluated which can be captured on a systematic identification at regional scale. Thereby, the following anthropogenic terrains have been established: built-up areas, agricultural areas, sealed ground, urban compacity, worked grounds (e.g., related to mineral excavations and transport infrastructures), engineered embankments, infilled excavations and other more singular anthropogenic deposits. Therefore, from a geological perspective, it will be feasible to identify and map these anthropic lands and provide this information throughout the Catalan territory in the medium term.

Bearing in mind all the above, the presentation will consist of this general analysis and the considerations that have been extracted regarding this. In addition, the preliminary results of the systematically characterized artificial ground will be shown.

How to cite: Subiela, G., Peña, J., Micheo, F., and Vilà, M.: Establishing a systematic regional scale identification of artificial ground in Catalan territory from geological perspective, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-7961, https://doi.org/10.5194/egusphere-egu21-7961, 2021.

ITS2.5/OS4.8 – Global plastic contamination: a journey towards scientifically informed policies and solutions

EGU21-274 | vPICO presentations | ITS2.5/OS4.8

Using machine learning techniques to predict beaching of marine debris on the Galapagos Islands

Stefanie Ypma, Mikael Kaandorp, Jen Jones, Andy Donnelly, and Erik van Sebille

The Galapagos Archipelago and the Galapagos Marine Reserve host one of the world’s most unique ecosystems. Although being a UNESCO world heritage site and being isolated from any dense population, over 8 tonnes of plastic are collected on the islands each year. To decrease the impact of plastic waste in the region, scientific evidence is needed on the sources and fate of the marine debris. Here, we will assess the skill of machine learning techniques to predict beaching events on these islands. In order to do so, we combine various hydrodynamic fields from ocean-, wave-, wind- and tide-models using the OceanParcels particle tracking framework to track virtual particles through the marine reserve. In addition, a beaching parameterization has been developed and implemented to quantify where and when virtual particles wash ashore. The results show that the particle pathways and beaching probabilities strongly depend on the dry and wet seasons characteristic for the Galapagos Islands. 

Therefore, it is expected that the beaching events can to some extent be predicted from the forecasts of currents, tides and waves - without performing a Lagrangian simulation. To test this hypothesis, PCA analysis and random forests are applied to a set of over 100 variables and their skill to explain the beaching variability given by the particle model is determined. In addition, the results are compared to a timeseries of observed beached litter on one of the Island of San Cristobal to apply the models in a realistic case study. This work, in combination with a growing observational data set, will form the basis of a predictive model that will support the Galapagos National Park in their efforts to free the Galapagos Archipelago from marine debris.

How to cite: Ypma, S., Kaandorp, M., Jones, J., Donnelly, A., and van Sebille, E.: Using machine learning techniques to predict beaching of marine debris on the Galapagos Islands, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-274, https://doi.org/10.5194/egusphere-egu21-274, 2021.

EGU21-280 | vPICO presentations | ITS2.5/OS4.8 | Highlight

Global modeled sinking characteristics of biofouled microplastic

Delphine Lobelle, Merel Kooi, Albert A. Koelmans, Charlotte Laufkotter, Cleo E. Jongedijk, Christian Kehl, and Erik van Sebille

Microplastic debris ending up at the sea surface has become a known major environmental issue. However, how microplastic particles move and when they sink in the ocean remains largely unknown. Here, we model microplastic subject to biofouling (algal growth on a substrate) to estimate sinking timescales and the time to reach the depth where particles stops sinking. We combine NEMO-MEDUSA 2.0 output, that represents hydrodynamic and biological properties of seawater, with a particle-tracking framework. Different sizes and densities of particles (for different types of plastic) are simulated, showing that the global distribution of sinking timescales is largely size-dependent as opposed to density-dependent. The smallest particles we simulate (0.1 μm) start sinking almost immediately around the globe and their trajectories produce the longest time to reach their first sinking depth (almost 40 days as a global median). In oligotrophic subtropical gyres with low algal concentrations, particles between 1 mm and 10 μm do not sink within the 90-day simulation time. This suggests that in addition to the comparatively well-known physical processes, biological processes might also contribute to the accumulation of floating plastic (of 1 mm to 10 μm) in subtropical gyres. Particles of 1 μm in the gyres start sinking largely due to vertical advection, whereas 0.1 μm particles sink both due to biofouling and advection. The qualitative impacts of seasonality on sinking timescales are small, however, localised sooner sinking due to spring algal blooms is seen. This study maps processes that affect the sinking of virtual microplastic globally, which could ultimately impact the ocean plastic budget.

How to cite: Lobelle, D., Kooi, M., Koelmans, A. A., Laufkotter, C., Jongedijk, C. E., Kehl, C., and van Sebille, E.: Global modeled sinking characteristics of biofouled microplastic, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-280, https://doi.org/10.5194/egusphere-egu21-280, 2021.

EGU21-697 | vPICO presentations | ITS2.5/OS4.8

Wind erosion controls on microplastics from soils: linking soil surface properties with microplastic flux

Annie Ockelford, Joanna Bullard, Cheryl McKenna Neuman, and Patrick O'Brien

Recent studies of soils in the Alps and Middle East indicate airborne transport of microplastics following wind erosion may be significant.  Where microplastics have been entrained by wind they show substantial enrichment ratios compared to mineral particle erosion.  Further, microplastic shape affects enrichment ratios with those for fibres greater than for microbeads which may reflect the lower density and asymmetric shape of microplastics compared to soil particles. This suggests that terrestrial to atmospheric transfer of microplastics could be a significant environmental transport pathway. However, currently we have very little understanding of how the properties, in particular the surface characteristics, of the sediment which they are being eroded from affects their entrainment potential.

This paper reports wind tunnel studies run to explore the impacts of soil surface characteristics on microplastic flux by wind erosion.  Experiments were performed in a boundary layer simulation wind tunnel with an open-loop suction design.  The tunnel has a working section of 12.5m x 0.7m x 0.76m and is housed in an environmental chamber which, for this study, was held constant at 20 oC and 20% RH. In experiments two types of low density microplastic (microbeads and fibres) were mixed into a poorly-sorted soil containing 13% organics.  The polyethylene microbeads had a size range of 212-250 microns and density of 1.2 g cm3 and the polyester fibres were 5000 microns long and 500-1000 microns in width with a density of 1.38 g cm3.  Microplastics were mixed into the sediment in concentrations ranging from 40-1040 mg kg-1. For each experiment, test surfaces were prepared by filling a 1.0m x 0.35m x  0.025m metal tray with the given mixture of test material which was lowered into the wind tunnel such that it was flush with the tunnel floor and levelled. The wind tunnel was then switched on and run with increasing wind speeds using 0.25 m s-1 increments until continuous saltation occurred.  Soil surface roughness was scanned prior to and after each experiment using a high resolution laser scanner (0.5mm resolution over the entire test section).  Transported soil and microplastic particles were captured in bulk using a 2 cm wide by 40 cm tall Guelph-Trent wedge trap that was positioned 2 m downwind of the test bed. 

Discussion concentrates on linking the changes in soil surface topography to the magnitude of microplastic flux where data shows that there is a correlation between the development of the soil surfaces and overall microplastic flux.  Specifically, soil surface roughness is seen as a significant control on microplastic flux where it has a greater overall effect on microplastic fibre flux as compared to the microplastic beads.  The outcome of this research is pertinent to developing understanding surrounding the likely controls and hence propensity of microplastics to be entrained from soil by wind erosion. 

How to cite: Ockelford, A., Bullard, J., McKenna Neuman, C., and O'Brien, P.: Wind erosion controls on microplastics from soils: linking soil surface properties with microplastic flux, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-697, https://doi.org/10.5194/egusphere-egu21-697, 2021.

EGU21-1022 | vPICO presentations | ITS2.5/OS4.8

Correlation of microplastic type and metal association: Croatian coast case study (Žirje Island)

Hana Fajković, Neven Cukrov, Željko Kwokal, Kristina Pikelj, Laura Huljek, Iva Kostanjšek, and Vlado Cuculić

The aim of the study was to determine the correlation of metals on floating marine litter and weathered microplastic samples from the pristine area. Sampled were collected from the accumulated material on the natural beach in Mala Stupica Cove (Žirje Island, Croatia) in June 2020. In addition to weathered microplastic, the concentrations of dissolved metals in the seawater, at the same location were determined. According to these measurements, the sampling site can be considered pristine, with Cd and Pb concentrations as background values and Zn and Cu as elements that have no toxic effect, based on the classification proposed by Bakke et al., (2010). The metals of interest due to their high toxicity were Zn, Cd, Pb, and Cu.

After sampling, the collected material was sieved through a metal sieve with a 4 mesh size, resulting in 4 subsamples (>4 mm; 4-2 mm; 2-1 mm; 1-0.250 mm). The type of plastic particles from subsample >4 mm was determined by FTIR spectroscopy performed on Bruker Tensor 27 in the region from 400-4000 cm-1. On such defined particles and in the seawater sample, trace metal concentrations were determined by the electrochemical method differential pulse anodic stripping voltammetry (DPASV) with standard addition method by Metrohm Autolab modular potentiostat/galvanostat Autolab PGSTAT204. A static mercury drop electrode (SMDE) was used as the working electrode.

Plastic particles were isolated from additional two fractions (2-1 mm and 1-0.250 mm) as bulk samples, but without polystyrene, and the metal concentration was also determined using the same method. Due to the particle size, the type of plastic was not determined. Additional analyzes of metal concentrations on a defined and isolated polystyrene particles (PS) from a subsample (4-2 mm) and (2-1 mm) were also performed.

By analogy with sediment particles, one would expect smaller microplastic particles to have higher metal concentrations due to their larger specific surface area, but this was not observed in this study. The metal concentration varied with the type of plastic, and from the observed results, plastics could be ranked according to their affinity for the analyzed metals, as follows: polystyrene (PS)>Polypropylene (PP)>Low-density polyethylene (LDPE). According to an average concentration of all analyzed samples defined as LDPE, Zn could be single out as an element with around 7-time higher affinity for LDPE than other elements (Cd, Pb, and Cu). For samples defined as PP, the highest affinity is observed for Pb, even 30 times higher than in LDPE, followed by Zn and Cu, while Cd has similar values as in LDPE.  For PS samples affinity of all elements is higher in comparison with the LDPE and PP, as follows: Pb>Cu> Zn>Cd, with a concentration of Pb 2.5 times higher than in PP and even 88 times higher than in LDPE.

 A general conclusion could be drawn, but the observed wide ranges indicate the need for additional research to determine the relationship between the degree and type of weathering with the associated metals.

This work has been fully supported by Croatian Science Foundation under the project lP-2019-04-5832.

How to cite: Fajković, H., Cukrov, N., Kwokal, Ž., Pikelj, K., Huljek, L., Kostanjšek, I., and Cuculić, V.: Correlation of microplastic type and metal association: Croatian coast case study (Žirje Island), EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-1022, https://doi.org/10.5194/egusphere-egu21-1022, 2021.

Microplastic particles (MPs) are found in marine ice in larger quantities than in seawater, indicating that the ice is an important link in the chain of spreading of this contaminant. Some studies indicate larger MPs abundance near the ice surface, while others did not find any consistent pattern in the vertical distribution of MPs within sea ice cores. We discuss physical mechanisms of incorporation of MPs in the ice and present the results of laboratory tests, underpinning our conclusions.

First, plastic hydrophobicity is shown to cause the effect of pushing the floating MPs further up of the newly-forming ice. This leads to a concentration of MPs at the ice surface in the laboratory, while in the field the particles at the surface may by covered by snow and become a part of the upper ice layer. Under open-air test conditions, the bubbles of foamed polystyrene (density 0.04 g/cm3), initially floating at the water surface, were gone by weak wind when the firm ice was formed.

Second, the difference between freshwater and marine ice is considered. Since fresh water has its temperature of the density maximum (Tmd=3.98 C) well above the freezing point (Tfr=0 C), the freshwater ice is formed when the water column is stably stratified for a relatively long period of cooling from the Tmd down to the Tfr. Under such steady conditions, even just slightly positively/negatively buoyant MPs have enough time to rise to the surface / to settle to the bottom. In contrast, the ice in the ocean freezes when thermal convection is at work, further enhanced by the brine release. Thus, strong convection beneath the forming marine ice keeps slightly positively/negatively buoyant MPs in suspension and maintains the contact between the MPs and the forming ice. Laboratory tests show both the difference between the solid-and-transparent freshwater ice and the layered, filled with brine marine ice, and the difference in the level of their contamination.

Lastly, it is demonstrated that MPs tend to be incorporated in the ice together with air bubbles and in-between the ice plates (in brine channels). This is most probably due t plastics’ hydrophobicity.

Investigations are supported by the Russian Science Foundation, grant No 19-17-00041.

How to cite: Chubarenko, I.: Physical processes behind interactions of microplastic particles with ice, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-1149, https://doi.org/10.5194/egusphere-egu21-1149, 2021.

EGU21-1171 | vPICO presentations | ITS2.5/OS4.8

Trace metals load on beached microplastics in the anthropogenically influenced estuarine environment - Croatian middle Adriatic

Vlado Cuculić, Hana Fajković, Željko Kwokal, and Renata Matekalo

Marine plastic litter can be a significant vector for ecotoxic trace metals into coastal areas. Eventually, it can be burried in sediment and in accumulated material on the beach with organic and inorganic material on its surface. In order to analyze the trace metal quantities (Cd, Cu, Pb and Zn) on different size particles in an anthropogenically affected environment, microplastics were sampled from the accumulated material on the Mala Martinska natural beach (Šibenik Bay, Croatia) in September 2019. The city of Šibenik and the Šibenik Bay are located in the lower part of the Krka River estuary (middle Adriatic). It is the main Croatian port for the phosphate ore import. Also, it was found earlier that Šibenik Bay was polluted by the ex-ferromanganese industry located in it, and the industrial slag spreading around the factory was the significant supply of trace metals in the Bay. The concentrations of dissolved and total metals in the surface seawater at the same location and at the reference point (coastal surface seawater at Jadrija, ~4 km SE from the sampling site) were determined in February and June 2020.

The collected material was sieved through a metal sieve with a 4 mesh size, resulting in 4 bulk (mixed microplastics) aliquots (> 4mm; 4-2 mm; 2-1 mm; 1-0.250 mm). From each of of the 4 bulk aliquots, subsamples of mixed plastics and polystyrene (PS) particles were isolated, resulting in 8 subsamples in total. The type of plastic particles (> 4mm; 4-2 mm and PS) was determined by FTIR spectroscopy performed on Bruker Tensor 27 in the region from 4000-400 cm-1. Trace metal concentrations on such defined particles and in seawater samples were determined using differential pulse anodic stripping voltammetry (DPASV) by Metrohm Autolab modular potentiostat/galvanostat Autolab PGSTAT204, connected with a three-electrode system Metrohm 663 VA STAND (Utrecht, The Netherlands). Working electrode used was static mercury drop electrode (SMDE).

In general, the amounts of trace metals associated with the plastic particles (Cd 0.02-0.35 µg/g; Pb 1.1-34.1 µg/g; Cu 1.7-32.9 µg/g and Zn 6-147 µg/g) were in the range of unpolluted and moderately affected sediments in the Adriatic Sea. The mass fractions of all tested trace metals increase with decreasing plastic particle size, probably due to the larger specific surface areas on the smaller particles. That was not the case for the plastic particles larger than 4 mm, both in mixed and PS samples, where the amounts of metal were higher compared to particles of 4-2 mm and 2-1 mm. Furthermore, all metals except cadmium showed a higher affinity for PS in comparison with mixed plastic samples of the same particle sizes (up to order of magnitude higher metal amounts), due to the PS highly developed specific surface area. In order to better understand the mechanism of association of trace metals with microplastics under different environmental conditions, further investigations are needed.

This work has been fully supported by Croatian Science Foundation under the project lP-2019-04-5832.

How to cite: Cuculić, V., Fajković, H., Kwokal, Ž., and Matekalo, R.: Trace metals load on beached microplastics in the anthropogenically influenced estuarine environment - Croatian middle Adriatic, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-1171, https://doi.org/10.5194/egusphere-egu21-1171, 2021.

EGU21-1203 | vPICO presentations | ITS2.5/OS4.8

Sinking microplastics in the water column: simulations in the Mediterranean Sea

Rebeca de la Fuente, Gábor Drótos, Emilio Hernández, Cristóbal López, and Erik van Sebille

We study the vertical dispersion and distribution of negatively buoyant rigid microplastics within a realistic circulation model of the Mediterranean sea. We first propose an equation describing their idealized dynamics. In that framework, we evaluate the importance of some relevant physical effects: inertia, Coriolis force, small-scale turbulence and variable seawater density, and bound the relative error of simplifying the dynamics to a constant sinking velocity added to a large-scale velocity field. We then calculate the amount and vertical distribution of microplastic particles on the water column of the open ocean if their release from the sea surface is continuous at rates compatible with observations in the Mediterranean. The vertical distribution is found to be almost uniform with depth for the majority of our parameter range. Transient distributions from flash releases reveal a non-Gaussian character of the dispersion and various diffusion laws, both normal and anomalous. The origin of these behaviors is explored in terms of horizontal and vertical flow organization.

How to cite: de la Fuente, R., Drótos, G., Hernández, E., López, C., and van Sebille, E.: Sinking microplastics in the water column: simulations in the Mediterranean Sea, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-1203, https://doi.org/10.5194/egusphere-egu21-1203, 2021.

The COVID-19 pandemic caused a massive use of disposable sanitary face masks. Based on data provided by Prata et al. (2020), we estimated that if only 0.1% of those masks are improperly discarded and enter the soil, approximately 361t of polypropylene (PP) will be monthly added to the soil, threatening the ecological balance of terrestrial systems, the health of wild animals and even humans. For a first evaluation of the environmental consequences of the mask littering during COVID-19, we compared the microbial degradability of 10 x 10 mm cuts of the single masks layers and the complete mask blended with topsoil from a Cambisol of the Sierra de Aznalcóllar, Southern Spain with natural soil organic matter (SOM) by measuring the CO2 release during a three-month decomposition experiment performed with a soil moisture of 75% of its maximal water holding capacity and at 25°C. In order to focus on biodegradation and to avoid abiotic impact of physical and chemical processes, the masks were not pretreated or exposed to UV-irradiation or natural daylight prior to decomposition. In addition, the incubation occurred in the dark. We identified an easily decomposable fraction with a mean residence time (MRTfast) of 2 to 3 days, releasing approximately 3 to 5% of the total mask carbon as CO2. Solid-state nuclear magnetic resonance (NMR) spectroscopy confirmed that all three layers of the mask were composed of PP without contributions of more than 2-3% of other additives. Microbial degradation resulted in a cut-off of terminal PP units as a main degradation mechanism. Assuming again that about 0.1% of the masks used during the COVID-19 crises may enter soil systems, we estimated that this fast pool may cause an additional CO2 emission of 41 to 68 t year-1. This corresponds to the globally averaged annual CO2-footprint of 10 to 17 persons (4 t year-1 person-1).  The slow turning fraction was mineralized with a rate constant of 0.05 to 0.14 year-1 corresponding to a MRTslow between 7 and 18 years. This is two to four times longer than that determined for the SOM pure reference soil but still lies in the range reported for humified SOM derived from other topsoils of the Sierra de Aznalcóllar. Our results allow us to confirm our hypothesis that in soil, microbes exist that can decompose PP, although their nature still has to be revealed in future attempts. Studies investigating the impact of pre-exposure to daylight and moisture on their degradability in soils are in process.

Prata, J.C., Silva, A.L.P., Walker, T.R., Duarte, A.C., Rocha-Santos, T., 2020. COVID-19 Pandemic Repercussions on the Use and Management of Plastics. Environ. Sci. Technol. 54, 7760–7765. https://doi.org/10.1021/acs.est.0c02178

 

How to cite: Knicker, H. and Velasco-Molina, M.: Biodegradability of single-use polypropylene-based face masks, littered during the COVID-19 pandemic – a first approach , EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-1293, https://doi.org/10.5194/egusphere-egu21-1293, 2021.

Initiation of motion, resuspension, transport, and accumulation of microplastic particles (MPs) at the sea bottom are prescribed by their physical properties – density, size, and shape, as it is known for natural sediment grains. However, from sedimentological approaches, not much can be said about the behavior of non-spherical particles at the bottom covered by another type of material. Thus, experimental disclosure of general features of the MPs transport and accumulation pattern should aid a lot further theoretical description of such a complex process.

Laboratory experiments on the MPs transport by the open-channel flow and their accumulation in regions with various bottom roughness were carried out in 10 m long and 0.33 m wide hydrodynamic flume. The bottom had 4 sections (ca. 2 m long each) with the roughness increasing downstream: smooth-bottom section, followed by the sections covered by natural calibrated coarse sand (particle diameter 1-1.5 mm), marine granules (3-4 mm), and small pebbles (1-2 cm). The upper sediment surface was carefully horizontally leveled. The set of MPs included 1d (flexible and rigid), 2d (square/round/elongated; flexible/rigid), and 3d (round/cubic) particles made of polystyrene, polyester, polyamide (nylon), and polyethylene terephthalat (material density ranging from 1.05 to 1.41 g/cm3). Principal sizes of MPs ranged from 0.5 mm (smaller than the smallest sediment grain) to 5 cm (larger than the largest sediment grain). At the beginning of the experiment, MPs were placed on the smooth bottom. Thereafter, the flow rate was increased step-by-step by small increments. At each step, after at least 5 min since the last particle movement, the coordinates of the particles in their (new) stationary positions were registered.

Although we did not aim to achieve a similarity between a laboratory experiment and natural conditions, the results of the present study can be useful for a qualitative interpretation of field observations and further theoretical efforts. The results show, that the initiation of motion of particular MPs is dependent both on MPs size and the sediment characteristics. The cumulative curve, integrating coordinates of all the kinds of MPs in their stationary locations at all the flow steps, indicates the potential for the existence of MP accumulation zones in the regions right after the change in the bottom roughness, at the side of coarser sediment.

Investigations are supported by the Russian Science Foundation, grant No 19-17-00041.

How to cite: Isachenko, I. and Chubarenko, I.: Different microplastics versus different bottom sediments: transport and accumulation pattern in the open-channel flow experiments, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-1791, https://doi.org/10.5194/egusphere-egu21-1791, 2021.

EGU21-2301 | vPICO presentations | ITS2.5/OS4.8

From rivers to retailers: using cross-sector stakeholder engagement to broaden dissemination and guide future research

Thomas Stanton, Paul Kay, Rachel Gomes, Matthew Johnson, and Jason Weeks

Concern for the fate and impacts of plastic waste has motivated cross-sector engagement with the environment and society’s impact on it. Though efforts to minimise plastic pollution should not be discouraged, it is important that such efforts do not exacerbate the environmental impacts associated with plastic alternatives; acknowledge that plastic per se is not the root of the plastic pollution problem; and recognise that environmentally conscious consumption is a privilege not currently afforded to all. Cross-sector communication and cooperation can maximise the impact of plastic pollution research and are vital tools in ensuring research can inform positive change. Here we report on the use of stakeholder engagement spanning UK industry, government, not-for-profit organisations and academia to share knowledge, motivations and priorities, in order to broaden research impact beyond academia.

Informed by our own work, microplastic researchers at the University of Nottingham hosted a cross-sector workshop to recognise evidence requirements, focus key questions, highlight misunderstandings and ultimately identify knowledge gaps across multiple sectors. This engagement identified key areas for improvement from the scientific community in order to better inform and engage decision makers. These included: a need for greater clarity from the scientific community as to the extent of the plastic pollution problem; communication of the implications of methodological inconsistencies in the science that informs industry; and the importance of placing the impacts of plastic pollution within the context of broader environmental quality for non-scientific stakeholders.

This workshop and engagement led to outputs that included: the writing of a policy brief; the writing of an opinion article on the topic of plastic pollution with authors from not-for profits, the wastewater industry and government organisations; and the public dissemination of these activities through press releases, articles for The Conversation, and their reproductions in UK news media. These outputs are designed to guide and inform individuals, industry, decision makers, and future research.

Concern for the problems posed by plastic pollution presents a generational opportunity for science to inform industries, governments and consumers, and enthuse their environmental action beyond plastic pollution. Our work highlights the value of considering, and where feasible engaging with, these stakeholders with environmental research from conception to dissemination.

How to cite: Stanton, T., Kay, P., Gomes, R., Johnson, M., and Weeks, J.: From rivers to retailers: using cross-sector stakeholder engagement to broaden dissemination and guide future research, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-2301, https://doi.org/10.5194/egusphere-egu21-2301, 2021.

EGU21-2361 | vPICO presentations | ITS2.5/OS4.8

Using line transect sampling to detect cetaceans and floating litter during vessel survey in western Black Sea

Romulus-Marian Paiu, Arda M. Tonay, Costin Timofte, Angelica Paiu, Mihaela Mirea Candea, Anca-Maria Gheorghe, Violeta Slabakova, Ayaka Amaha Ozturk, and Dumitru Murariu

                The quality of the Black Sea ecosystem is partly but importantly dependent on the survival and sustainability of the top predator populations. It is difficult to foresee all consequences for the regional biodiversity if cetaceans disappear as it had happened with the Mediterranean monk seals in the past. During 7 days, between 30 September and 7 October, 2019, a joint oceanographical survey was made with a multipurpose R/V Mare Nigrum in offshore as well as deep sea locations, within the Romanian (RO), Bulgarian (BG) and western Turkish (TK) national waters of the Black Sea in the frame of ANEMONE project. The total track line was around 700 nautical miles and the sampled area covered 9754,58 km2. Observations were made of cetaceans and floating litter, following line transect sampling method, with a single platform (2 observers, on the left and right of the vessel bridge) over 380.44 km of transects. A total of 54 cetacean sightings and 81 floating litter items were recorded. All the three species, short-beaked common dolphin (Delphinus delphis ssp. ponticus), Black Sea bottlenose dolphin (Tursiops truncatus ssp. ponticus), and Black Sea harbour porpoise (Phocoena phocoena ssp. relicta), were registered with a similar density (individuals/km2), 0.012 for RO sector and 0.013 for BG-TK sector. The number of debris varied between 1 and 24 items, reaching 5.26± 5.93 items on average. Among the transects, 53% contained less than 5 items and only 13% were with more than 10 items. Based on these results, the average density of floating macro-litter in BG waters was found 2.43 ± 2.4 items/km2, 1.73 ± 1.24 items/km2 in the RO waters and 2.43±2.17 items/km2 in TR waters. This study was the first to make a joint and continuous survey effort for both cetaceans and litter simultaneously in the Black Sea.

Key words:  Black Sea, cetaceans, marine litter, joint cruise, ANEMONE project.

How to cite: Paiu, R.-M., Tonay, A. M., Timofte, C., Paiu, A., Mirea Candea, M., Gheorghe, A.-M., Slabakova, V., Amaha Ozturk, A., and Murariu, D.: Using line transect sampling to detect cetaceans and floating litter during vessel survey in western Black Sea, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-2361, https://doi.org/10.5194/egusphere-egu21-2361, 2021.

EGU21-2418 | vPICO presentations | ITS2.5/OS4.8 | Highlight

Plastic pollution research in Indonesia: State of science and future research directions.

Paul Vriend, Hidayat Hidayat, Reza Cordova, Noir. P. Purba, Ansje Lohr, Nining Ningsih, Kirana Agustina, Semeidi Husrin, Devi D. Suryono, Inneke Hantoro, Budi Widianarko, Judith van Leeuwen, Bart Vermeulen, and Tim van Emmerik

Observational and modeling studies have suggested that Indonesia among the top plastic polluting countries globally. Data on the presence of plastic pollution are crucial to designing effective plastic reduction and mitigation strategies. Research quantifying plastic pollution in Indonesia has increased in recent years. However, most plastic research to date has been done with different goals, methods, and data formats. In this study, we present a meta-analysis of 85 studies published on plastic pollution in Indonesia to uncover gaps and biases in current research, and to use these insights to suggest ways to improve future research to fill these gaps. Research gaps and biases identified include a clear preference for marine research, and a bias towards certain environmental compartments within the marine, riverine, and terrestrial ecosystems, which are compartments that are easier to quantify such as riverbanks and beaches. Moreover, we identify polypropylene (PP) and polyethylene variants (HDPE, LDPE, PE) to be among the most frequently found polymers in both macro- and microplastic pollution, though polymer identification is lacking in most studies. Plastic research is mostly done on Java (57%). We recommend a shift in ecosystem focus of research towards the riverine and terrestrial environments, and a shift of focus of environmental compartments analyzed within these ecosystems. Moreover, we recommend an increase in spatial coverage across Indonesia of research, a larger focus on polymer characterization, and lastly, the harmonization of methods used to quantify plastic. With these changes, we envision future research that can aid with the design of effective reduction and mitigation strategies.

How to cite: Vriend, P., Hidayat, H., Cordova, R., Purba, N. P., Lohr, A., Ningsih, N., Agustina, K., Husrin, S., Suryono, D. D., Hantoro, I., Widianarko, B., van Leeuwen, J., Vermeulen, B., and van Emmerik, T.: Plastic pollution research in Indonesia: State of science and future research directions., EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-2418, https://doi.org/10.5194/egusphere-egu21-2418, 2021.

EGU21-4092 | vPICO presentations | ITS2.5/OS4.8

Global modelling of plastic beaching indicates coastlines and coastal waters as significant plastic reservoirs

Victor Onink, Cleo Jongedijk, Matthew Hoffman, Erik van Sebille, and Charlotte Laufkötter

The distribution of plastic in the ocean is poorly constrained, with the mass of floating plastic at the ocean surface being orders of magnitude smaller than estimated plastic inputs. Coastlines likely contain significant amounts of plastic, but inconsistent methodologies between beached plastic observations prevent determining the mass and distribution of globally beached plastic. We present Lagrangian model sensitivity experiments to estimate the beached fraction of marine plastic and to investigate the global distribution of beached plastic on coastlines.

We perform simulations where particles, representing masses of floating plastic, are inserted at the ocean coasts. The particles are then advected by surface currents (HYCOM/NCODA global reanalysis and surface Stokes drift from the WaveWatch III global reanalysis) for 5 years. Beaching is parametrized stochastically using exponentional probability. Here, we test the sensitivity to e-folding time scales between 1 and 100 days, applied when plastic is within the coastal zone, within 10km of the nearest coastline. Resuspension of beached plastic is parameterised exponentially with an e-folding timescale between 69 and 273 days. No other loss processes are implemented.

Between 39-95% of floating plastic mass is beached after 5 years, with the beached fraction depending on the ratio between the beaching and resuspension timescales. In all simulations, at least 77% of floating plastic mass is found either beached or within the coastal zone, indicating coastal regions are a significant reservoir of mismanaged terrestrial plastic. However, plastic entering the ocean from islands or near energetic boundary currents is more likely to reach the open ocean. The distribution of beached plastic is closely related to the input distribution, with the highest concentrations found in Southeast Asia and the Mediterranean.

Our results highlight coastlines and coastal waters as important reservoirs of marine plastic debris and indicate a need for greater understanding of plastic transport near and at the coastlines. Furthermore, improved representation of plastic beaching can help study marine plastic fragmentation, as mechanical stress during the transitions between coastlines and coastal waters and the increased UV exposure of beached plastic likely contribute to the fragmentation.

How to cite: Onink, V., Jongedijk, C., Hoffman, M., van Sebille, E., and Laufkötter, C.: Global modelling of plastic beaching indicates coastlines and coastal waters as significant plastic reservoirs, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-4092, https://doi.org/10.5194/egusphere-egu21-4092, 2021.

EGU21-4342 | vPICO presentations | ITS2.5/OS4.8

Modelling size distributions of marine plastics under the influence of continuous cascading fragmentation

Mikael Kaandorp, Henk Dijkstra, and Erik van Sebille

Field studies have shown that plastic fragments make up the majority of plastic pollution in the oceans in terms of abundance. How quickly environmental plastics fragment is not well understood, however. Here, we study this process by considering a model which captures continuous fragmentation of particles over time in a cascading fashion. With this cascading fragmentation model, we simulate particle size distributions (PSDs), specifying the abundance or mass of particles for different size classes.

 

The fragmentation model is coupled to an environmental box model, simulating the distributions of plastic particles in the ocean, coastal waters, and on the beach. Transport in the box model is based on a previous study regarding a previous study regarding sources and sinks of marine plastics in the Mediterranean Sea. We compare the modelled PSDs to available observations, and use the results to illustrate the effect of size-selective processes such as vertical mixing in the water column and resuspension of particles from the beach into coastal waters.

 

Using the coupled fragmentation and environmental box model, we quantify the role of fragmentation on the marine plastic mass budget. While fragmentation is a major source of (secondary) plastic particles in terms of abundance, it seems to have a minor effect on the total mass of particles larger than 0.1 mm. Future comparison to observed PSD data should allow us to understand size-selective plastic transport in the environment, and potentially inform us on plastic longevity.

How to cite: Kaandorp, M., Dijkstra, H., and van Sebille, E.: Modelling size distributions of marine plastics under the influence of continuous cascading fragmentation, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-4342, https://doi.org/10.5194/egusphere-egu21-4342, 2021.

EGU21-5026 | vPICO presentations | ITS2.5/OS4.8

Uncertainties on plastic concentration estimates at sea

Matthieu Mercier, Marie Poulain-Zarcos, Alexandra ter Halle, Marion Saint-Martin, and Florian Simatos

The large difference between the estimates of global plastic input in mass in the oceans (Jambeck et al., Science 347, 2015) and current global predictions from numerical models (van Sebille et al., Environ. Res. Lett. 10, 2015) or observations (Cózar et al., P. Natl. Acad. Sci., 111, 2014) is one of the most important issue regarding oceanic plastic litter. Yet, global predictions are based on observations, and uncertainties on the latter are rarely considered to provide error bounds on the former.

We discuss here the sources of uncertainties on plastic concentrations estimates (in number and mass), based on a recent model presented in (Poulain et al., Environ. Sci. Technol. 53, 2019). The two main sources of error are the plastic rise velocity and the model for the turbulent diffusivity, although they do not have the same importance. We validated the model with controlled laboratory experiments. Applying this model to global predictions provides us with more realistic encompassing values for the mass of plastic at sea, with a more important correction concerning small microplastics (with characteristic dimensions smaller than ~1mm).

How to cite: Mercier, M., Poulain-Zarcos, M., ter Halle, A., Saint-Martin, M., and Simatos, F.: Uncertainties on plastic concentration estimates at sea, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-5026, https://doi.org/10.5194/egusphere-egu21-5026, 2021.

Currently, all natural environments, including the Arctic seas, are contaminated by microplastics (MP, plastic fragments less than 5 mm). Biogeochemical processes significantly affect the physical properties of MP, primarily its density due to biofouling.
The aim of this work is to develop a numerical model for assessing the fate of MP in the marine environment under the influence of natural biogeochemical cycles in the Arctic seas on the example of Oslofjord.
The biogeochemical model OxyDep (E. V. Yakushev et al., 2011) was used to reproduce the temporal variability of the phyto- and zooplankton, dissolved and particulate organic matter. The two-dimensional 2D benthic-pelagic transport model (2DBP), which considers the processes in the water column and bottom sediments together, is used as a hydrophysical model.
The separate module which describes the transformation of the MP under biogeochemical processes was developed. The biogeochemical and MP modules were coupled with the transport model using the Framework for Aquatic Biogeochemical Modeling (FABM) (Bruggeman & Bolding, 2014).
The results show, that there would be a decrease in the MP content in the surface layer in summer period due to the ingestion by zooplankton and its transfer to the sediments. Based on the obtained patterns, it is possible to predict zones of accumulation of MP for a specific water area, depending on the local ecosystem.

Funding: The reported study was funded by RFBR, project number 20-35-90056. This work was partly funded by the Norwegian Ministry of Climate and Environment project RUS-19/0001 “Establish regional capacity to measure and model the distribution and input of microplastics to the Barents Sea from rivers and currents (ESCIMO)” and the Russian Foundation for Basic Research, research project 19-55-80004.

How to cite: Berezina, A., Yakushev, E., and Ivanov, B.: Modeling the influence of biogeochemical and ecosystem processes on microplastic transport in the Arctic seas on the example of Oslofjord, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-6531, https://doi.org/10.5194/egusphere-egu21-6531, 2021.

EGU21-7376 | vPICO presentations | ITS2.5/OS4.8

Low density-microplastics detected in sheep faeces and soil: A case study from the intensive vegetable farming in Southeast Spain

Nicolas Beriot, Joost Peek, Raul Zornoza, Violette Geissen, and Esperanza Huerta Lwanga

One of the main sources of plastic pollution in agricultural fields is the plastic mulch used by farmers to improve crop production. The plastic mulch is often not removed completely from the fields after harvest. Over time, the plastic mulch that is left of the fields is broken down into smaller particles which are dispersed by the wind or runoff. In the Region of Murcia in Spain, plastic mulch is heavily used for intensive vegetable farming. After harvest, sheep are released into the fields to graze on the vegetable residues. The objective of the study was to assess the plastic contamination in agricultural soil in Spain and the ingestion of plastic by sheep. Therefore, three research questions were established: i) What is the plastic content in agricultural soils where plastic mulch is commonly used? ii) Do livestock ingest the microplastics found in the soil? iii) How much plastic could be transported by the livestock? To answer these questions, we sampled top soils (0–10 cm) from 6 vegetable fields and collected sheep faeces from 5 different herds. The microplastic content was measured using density separation and visual identification. We found ~2 × 103 particles∙kg−1 in the soil and ~103 particles∙kg−1 in the faeces. The data show that plastic particles were present in the soil and that livestock ingested them. After ingesting plastic from one field, the sheep can become a source of microplastic contamination as they graze on other farms or grasslands. The potential transport of microplastics due to a herd of 1000 sheep was estimated to be ~106 particles∙ha−1∙y−1. Further studies should focus on: assessing how much of the plastic found in faeces comes directly from plastic mulching, estimating the plastic degradation in the guts of sheep and understanding the potential effects of these plastic residues on the health of livestock.

How to cite: Beriot, N., Peek, J., Zornoza, R., Geissen, V., and Huerta Lwanga, E.: Low density-microplastics detected in sheep faeces and soil: A case study from the intensive vegetable farming in Southeast Spain, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-7376, https://doi.org/10.5194/egusphere-egu21-7376, 2021.

EGU21-8692 | vPICO presentations | ITS2.5/OS4.8

Polymer type and UV-treatment influence the first week of biofilm development in Caribbean sublittoral waters 

Maaike Goudriaan, Harry Witte, Sanne Vreugdenhil, Maartje Brouwer, and Helge Niemann

EGU21-10026 | vPICO presentations | ITS2.5/OS4.8 | Highlight

Model based assessment of drift and fate of marine micro plastics in the Baltic Sea

Jens Murawski, Jun She, and Vilnis Frishfelds

Marine micro plastic is a growing problem, because of its ability to accumulate in the environment. Reliable data of drift patterns and accumulation zones are required to estimate environmental impacts on natural protected areas, spawning areas and vulnerable habitats. H2020 project CLAIM (Cleaning Litter by developing and Applying Innovative Methods) uses model based assessments to improve the knowledge on marine pathways, sources and sinks of land emitted plastic pollution. The assessment follows a systematic approach, to derive reliable emission values for coastal sources, and to model drift and deposition pattern of micro plastics from multiple sources: car tyres, cosmetic products. A 3D modelling tool has been developed, that includes all relevant key processes, i.e. currents and wave induced transport, biofilm growth on the particle surface, sinking and sedimentation. Core engine is the HBM ocean circulation model, which has been set-up for the Baltic Sea in high resolution of 900m. Multi-years-studies (2013-2019) were performed to evaluate seasonal drift pattern and accumulation zones. Highest micro plastic concentrations were found in coastal waters, near major release locations, but transport related offshore pattern can be found as well. These follow the major pathways of deeper sea transport, but are controlled by the seasonal dynamic of biofilm growth and sinking. We introduce the model and all relevant key processes. Seasonal drift pattern are discusses in detail. Validation results in the Gulf of Riga and the Gulf of Finland provide an overview of the quality of the model to predict the distribution of micro plastics. The study includes the assessment of mitigation scenarios, of 30% micro plastic load reductions. The impacts on the ocean levels of micro plastic concentrations are studied in detail.  

 

 

How to cite: Murawski, J., She, J., and Frishfelds, V.: Model based assessment of drift and fate of marine micro plastics in the Baltic Sea, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-10026, https://doi.org/10.5194/egusphere-egu21-10026, 2021.

EGU21-10252 | vPICO presentations | ITS2.5/OS4.8

In situ microplastics ingestion by Antarctic marine benthic invertebrates

Jessica Hurley, Jorg Hardege, Katharina C. Wollenberg Valero, and Simon Morley

Microplastics have been recognised as persistent marine contaminants and mounting evidence supports their designation as anthropogenic stressors to marine organisms. Despite the remoteness of Antarctica, microplastics contamination has been reported in every marine environment investigated in this area to date. Due to ocean currents and frontal systems, microplastics may become entrapped within polar regions and increase bioavailibilty to inhabiting fauna. Antarctic marine benthic invertebrates represent a research priority due to their sensitivity to change as well as contribution to ecological functioning and food webs. The current study investigated microplastics ingestion by the epifaunal, carnivorous polychaete Barrukia cristata and the infaunal, filter-feeding bivalve, Laternula elliptica. Animals were collected by SCUBA adjacent to Rothera research station, Adelaide Island. After digestion in 10 % potassium hydroxide (KOH) followed by filtration, microplastics ingested by individual animals were separated. Microplastics were then counted and characterised by shape, colour, size and polymer type by Micro-Fourier transform Infrared spectroscopy. Polyethylene terephthalate (PET) was the most abundant polymer type, followed by polyacrylonitrile (PAN) and ethylene-vinyl acetate (EVA). Congruent to earlier reports, fibres were found to be the most abundant source of microplastics contamination. However, it must be highlighted that fragments were also recovered from the animals analysed. Results determined the current level of microplastics ingestion by two benthic marine invertebrates of different feeding strategies in coastal environments of the Antarctic Peninsula. These findings indicated the bioavailability of microplastics and highlighted the potential of trophic transfer throughout the Antarctic marine food web.

How to cite: Hurley, J., Hardege, J., Wollenberg Valero, K. C., and Morley, S.: In situ microplastics ingestion by Antarctic marine benthic invertebrates, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-10252, https://doi.org/10.5194/egusphere-egu21-10252, 2021.

EGU21-10579 | vPICO presentations | ITS2.5/OS4.8 | Highlight

Paddle surfing for science on microplastic pollution: a successful citizen science initiative

Anna Sanchez-Vidal, Oriol Uviedo, Sara Higueras, Maria Ballesteros, Xavier Curto, William P. de Haan, Elisabet Bonfill, Miquel Canals, Ingrid Canales, Antoni Calafat, Andrea Comaposada, Paula Del Río, Xavi Ferrer, Helena Fos, Galderic Lastras, Martí Llorente, Ferran Martínez, Martí Ramírez, and Gines Pedrero

Research on microplastics has rapidly expanded in recent years and has led to the discovery of vast amounts of microplastics floating offshore in all main oceanic gyres and including the Mediterranean Sea. However, there is a lack of information from a few meters from the coastline where the largest plastic mass flux is suspected to occur. The reason behind is the general use of manta trawls towed by boats or research vessels to obtain samples, which hinders nearshore sampling. We have designed a manta trawl to collect samples in the nearshore from any type of recreational sports floating gear like kayaks, sailboats, rowing boats, windsurf boards and others. Data generated is comparable to that obtained with traditional scientific equipment towed from boats. During one year, starting from October 2020, 12 social, environmental and sports associations along the NW Mediterranean coast are acquiring scientific samples in the nearshore within the frame of two citizen science monitoring projects lead by the Spanish delegation of the non-governmental organization Surfrider Foundation Europe and the University of Barcelona. The projects represent a paradigm shift in microplastic research, allowing to fill the gap in knowledge of this transition coastal area, and actively involving citizens in the generation of new monitoring data (http://surfingforscience.org/).

Our results reveal that densities of floating plastics in the nearshore along the NW Mediterranean coast are on average similar to those found offshore. However, we observe high variability due to meteorological and oceanographic conditions (i.e. the occurrence of eastern storms). We also observe that whereas floating microplastics dominate offshore, greater proportions of mesoplastics and macroplastics dominate at the nearshore waters, especially in between the breakwaters in Barcelona city. Indeed, the breakwaters, that protect Barcelona beaches against wave action and coastal erosion, behave as plastic traps. This is an indication of the importance of the nearshore as a source of plastic fragments to the open sea and calls for increased research in this area.

How to cite: Sanchez-Vidal, A., Uviedo, O., Higueras, S., Ballesteros, M., Curto, X., de Haan, W. P., Bonfill, E., Canals, M., Canales, I., Calafat, A., Comaposada, A., Del Río, P., Ferrer, X., Fos, H., Lastras, G., Llorente, M., Martínez, F., Ramírez, M., and Pedrero, G.: Paddle surfing for science on microplastic pollution: a successful citizen science initiative, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-10579, https://doi.org/10.5194/egusphere-egu21-10579, 2021.

EGU21-11182 | vPICO presentations | ITS2.5/OS4.8

Monitoring litter and microplastics in a highly polluted protected site of southern Spain: A research-based citizen science initiative

Alicia Mateos-Cárdenas, Patricio Peñalver-Duque, and David León-Muez

Plastic pollution research and awareness activities have increased exponentially over the last decade, however not all citizen science activities are run with a degree of control assurance. Also, not many research projects include collaborations beyond academia or have set goals for the dissemination of results to specific non-academic stakeholders. Here, our project involves a range of collaborators from different disciplines, from the Irish academic sector to Spanish environmental NGOs and citizen scientists. Also, the project is funded by the US-based NGO Sustainable Ocean Alliance (SOA). We selected the natural area of Maro-Cerro Gordo Cliffs (southern Spain) as our sampling site due of its special status under Natura 2000. Despite this protection, previous monitoring work in 2019 identified heavily plastic polluted sites due to intensive agriculture activities in the area. Therefore, this project was designed as a citizen science initiative with a focus on (1) clean up and characterisation of litter from selected terrestrial and aquatic sites, both freshwater and coastal, and (2) an analysis of microplastics in stream and coastal waters. The main objectives of the project are to characterise the presence of litter and microplastics while working closely with citizen scientists, raising awareness and informing local authorities about the issue.

 

First sampling activities were carried out in December 2020. A second field trip is organised for February 2021. Citizen scientists were previously trained and always worked together under the supervision of a team member. Litter was collected following transects and using tracking apps (eLitter and MARNOBA). A total of 43 items were collected from stream transects whereas 59 items were collected in beach transects. Remarkably, 74% of litter collected in streams were plastic items, 12% were other materials, 9% was paper or cardboard and 5% was metal. Whereas in beach transects, 51% of the litter collected was paper or cardboard, 25% plastic, 10% metal and 14% other materials. Regarding microplastic sampling, 200 L of stream water and 50000 L of coastal water samples were collected using a filtration unit with a 45 µm pore size. The volume of filtered coastal water was significantly higher as it was collected from three kayaks for 30 minutes. Microfibres and fragments have been detected at both sites. Sample processing and polymer analysis is currently ongoing using FTIR. All protocols follow strict QA/QC guidelines including clean conditions and airborne contamination procedures.

 

Results from this project will be submitted for peer-review and also shared in the form of mid-term and final reports among local stakeholders including local environmental managers and SOA. Also, citizen scientists will take part of a workshop aimed at informing the general public. Therefore, the findings from this project are directly used to raise awareness through citizen scientists and informing local and international non-profit stakeholders. More specifically, lessons learned will be presented at EGU in the form of successes and challenges for discussion. It is imperative that, when feasible, high quality environmental research is carried out between cross-disciplinary collaborators in order to gather sound data while raising awareness and discussing solutions.

How to cite: Mateos-Cárdenas, A., Peñalver-Duque, P., and León-Muez, D.: Monitoring litter and microplastics in a highly polluted protected site of southern Spain: A research-based citizen science initiative, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-11182, https://doi.org/10.5194/egusphere-egu21-11182, 2021.

EGU21-11667 | vPICO presentations | ITS2.5/OS4.8

 Microplastic in marine, nearshore waters of South Georgia: a study of background environmental levels of microplastic contamination

Jack Buckingham, Cath Waller, Claire Waluda, and Clara Manno

Microplastics are ubiquitous in the global ocean and have even been found in remote polar environments, including in Arctic snowfall and Antarctic subtidal sediments. Levels in some areas of the Southern Ocean have been shown to be 100,000 times higher than predictions.

This is the first comprehensive survey of microplastic in the nearshore waters of South Georgia, a sub-Antarctic South Atlantic island noted for its biodiversity. Microplastic has been previously documented in resident populations of higher predators. This is likely to originate from their food, but the degree to which their prey is exposed to microplastics from background environments has yet to be examined.

Surface water samples were collected from 12 sites at 1km intervals around the accessible shoreline of the Thatcher Peninsula, South Georgia, including adjacent to the outflow pipes of the research station, King Edward Point (KEP). Additionally, samples were taken directly from: (i) outflow pipes at KEP and Grytviken (a nearby whaling station, occupied in summer), in order to determine the level of local input from anthropogenic wastewater systems; (ii) Gull Lake, a freshwater system isolated from oceanographic influence; and (iii) directly from falling snow to evaluate the potential risk of atmospheric transfer of microplastics via precipitation. Preliminary results using FT-IR spectroscopy have confirmed over 24,000 suspected anthropogenic particles/fibres as being microplastic. Microplastics were present in every sample, from every site and range in size from 0.05-3mm.

Here we present the following results:

  • 1) the amount of microplastic in the background environment to which local biodiversity is exposed and;
  • 2) the similarity between the microplastic profiles of an anthropogenic point source and the local environment.

 

How to cite: Buckingham, J., Waller, C., Waluda, C., and Manno, C.:  Microplastic in marine, nearshore waters of South Georgia: a study of background environmental levels of microplastic contamination, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-11667, https://doi.org/10.5194/egusphere-egu21-11667, 2021.

EGU21-12012 | vPICO presentations | ITS2.5/OS4.8

Nanoplastics in the Dutch Wadden Sea

Dušan Materić, Rupert Holzinger, and Helge Niemann

Plastic pollution in the marine environment has been identified as a global problem;  different polymer types and sizes have been detected across all marine regions, from sea ice to the equator and the surface to the deep sea. Previous works show that smaller size classes of plastic debris are more abundant, e.g. fragments <100 µm account for 86% of all plastics pieces in the southern North Sea. However, the large unknown is to quantify the fraction of marine plastics debris below the size-detection limit of commonly used techniques (e.g. µFTIR spectroscopy, LOD >10 µm), such as ultrafine, nanometre-sized plastic particles - nanoplastics. In this work, we used a novel Thermal Desorption – Proton Transfer Reaction – Mass Spectrometry (TD-PTR-MS) method suitable for chemical detection and identification of plastics in the nm range and analysed samples from the Wadden Sea, Netherlands. We tested different sample preparation strategies including direct measurement of seawater and pre-concentration using a cascade filtration over quartz fibre filters of different average mesh sizes (>2.7, >1.2, >0.7, >0.3 µm).

Our results show the presence of Polystyrene (PS) and Polyethylene terephthalate (PET) in the fraction of small microplastics (e.g. <2.7 µm) and nanoplastics (<1 µm). The average mass concentration of our semiquantitative (highly conservative) measurements for PS nanoplastics was 0.8 µg/L indicating a substantial contribution of nanoplastics to the Wadden Sea’s total plastic’s budget. For example, considering the reported average of 27.2 microplastics in m3 of southern North Sea surface water, an average size of 100 µm, spherical shape and the density of 1 g/cm3 we calculate a tentative nanoplastics mass contribution of 38% compare to microplastics. Furthermore, we observed dynamic concentration changes of small microplastics and nanoplastics over time and water depth, and we are currently investigating if these are related to tidal currents, which are a strong forcing factor in the Wadden Sea.

How to cite: Materić, D., Holzinger, R., and Niemann, H.: Nanoplastics in the Dutch Wadden Sea, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-12012, https://doi.org/10.5194/egusphere-egu21-12012, 2021.

EGU21-12313 | vPICO presentations | ITS2.5/OS4.8

The effect of plastic leachates on respiration and foraging behaviour in hermit crabs

Victoria F. Scott, Jorg D. Hardege, and Katharina C. Wollenberg Valero

Plastic production has soared since the 1950s, with the last decade seeing an  increase of 43% from 250Mt (million tonnes) in 2009 to 368Mt in 2019. Plastics and their associated chemical congeners (variants of chemical structures) which enter the environment further exacerbate pollution within already contaminated ecosystems. In this study, we investigated the effect of plastic leachate on the common littoral marine hermit crab Pagurus bernhardus,  a species  at great risk from potential adverse effects of microplastics. The effects of  plastic additives released into the environment via microplastic leaching, and of contaminants adsorbed and accumulated onto the surface of microplastics on marine organisms is understudied. This study sought to (I) investigate whether plastic leachate has an effect on the respiration rate of hermit crabs and, (II) investigate whether plastic leachate has an effect on the foraging behaviour of hermit crabs. We found that within repeated measures design hermit crabs exposed to plastic leachate had different levels of oxygen consumption when compared to their control; with there being an increase or decrease dependent on the leachate type. This is potentially problematic due to high concentrations of microplastics along coastlines which may lead to impaired filtration within crustaceans resulting in lethality and reduced food intake.

How to cite: Scott, V. F., Hardege, J. D., and Wollenberg Valero, K. C.: The effect of plastic leachates on respiration and foraging behaviour in hermit crabs, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-12313, https://doi.org/10.5194/egusphere-egu21-12313, 2021.

EGU21-12473 | vPICO presentations | ITS2.5/OS4.8

The role of Stokes drift in the dispersal of North Atlantic surface marine debris

Sofia Bosi, Göran Broström, and Fabien Roquet

Understanding the physical mechanisms behind the transport and accumulation of floating objects in the ocean is crucial in order to efficiently tackle the issue of marine pollution. The main sinks of marine plastic are the coast and the bottom sediment. This study focuses on the former, investigating the timescales of dispersal from the ocean surface and onto coastal accumulation areas through a process called "beaching" in the presence of Stokes drift. Previous literature have found that the Stokes drift can reach the same magnitude as the Eulerian current speed and that it has a long-term effect on the trajectories of floating objects. Two virtual particle simulations are carried out and then compared, one with and one without Stokes drift, named SD and REF respectively. Eulerian velocity and Stokes drift data from global reanalysis datasets are used for particle advection. Particles in the SD model are found to beach at a yearly rate that is almost double the rate observed in the Eulerian model. The main coastal attractors are consistent with the direction of large-scale atmospheric circulation (Westerlies and Trade Winds). Long-term predictions carried out with the aid of adjacency matrices found that the concentration of particles in the subtropical accumulation zone after 100 years is 10 times lower in the presence of Stokes drift. The results confirm the need to accurately represent the Stokes drift in particle models attempting to predict the behaviour of marine debris, in order to avoid overestimation of its residence time in the ocean and guide policies towards prevention and removal more effectively.

How to cite: Bosi, S., Broström, G., and Roquet, F.: The role of Stokes drift in the dispersal of North Atlantic surface marine debris, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-12473, https://doi.org/10.5194/egusphere-egu21-12473, 2021.

Numerous studies have made the ubiquitous presence of plastic in the environment undeniable, and thus it no longer comes as a surprise when scientists monitor the accumulation of macroplastic litter and microplastic fragments in both urban and remote sites. The presence of plastic in the environment has sparked considerable discussion amongst scientists, regulators and the general public as to how industrialization and consumerism is shaping our world. Restrictions on the intentional use of primary microplastics, small solid polymer particles in applications ranging from agriculture to cosmetics, are under discussion globally, despite uncertain microplastic hazards and prioritization amongst options for action. In some instances, replacements are technically simple and easily justified, but in others substitutions may come with more uncertainty such as significant performance questions and monetary costs. Scientific impact assessment of primary microplastics compared to their alternatives relies on a number of factors including, but not limited to, microplastic harm, existence of replacement materials, and the quality, cost and hazards of alternate materials. Here we assess the scope, effectiveness and utility of microplastic regulations with specific emphasis on the new definitions proposed by ECHA for restriction of primary microplastics under REACH. To this end, we aim to 1) provide a systematic orientation of the polymer universe, to appreciate which (micro)plastic characteristics are relevant, measurable and enforceable, 2) cluster specific uses of solid plastic to highlight how primary microplastic can add to issues of environmental pollution and human health, 3) evaluate drivers leading to regulations and their potential for enforceability and impact and 4) suggest priority cases where regulations should be focused and precision increased to incentivize innovation of sustainable materials and promote environmental health and safety. Regulations need a precise focus and must be enforceable by measurements. Policy must carefully evaluate under which contexts microplastic use may be warranted and where incentives to replace certain microplastics can stimulate innovation of new, more competitive and environmentally conscious materials. 

How to cite: Mitrano, D. and Wohlleben, W.: Microplastic regulation should be more precise to incentivize both innovation and environmental safety, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-12732, https://doi.org/10.5194/egusphere-egu21-12732, 2021.

EGU21-13314 | vPICO presentations | ITS2.5/OS4.8

Real time interactions between soil microorganisms and microplastics at microscale

P. Micaela Mafla-Endara, Pelle Ohlsson, and Edith Hammer

Terrestrial ecosystems are under threat due to the continuous accumulation of plastics in soils. Particularly, microplastics have been proven to negatively affect the performance of soil macrofauna such as earthworms, as well as soil mesofauna including springtails and nematodes. Unfortunately, two big groups remain largely unexplored: the soil microfauna and microflora.

Recent studies have shown that soil microbial community composition can significantly vary depending on the concentration and type of plastic, favouring some groups and disfavouring others. To have a better understanding of these relationships, it is necessary to study them at relevant scale: the microscale.

Considering that in situ observations are hard to achieve due to the opacity of soil and ever-changing soil architecture, we used transparent micro-engineered chips to study interactions between microplastics and soil microorganisms live. We hypothesized that different concentrations of microplastics interfere with a natural microbial community in terms of 1. Soil microbial colonization/succession of the chips and 2. Soil microbial growth inside the chips’ pore space.

We fabricated chips containing different microstructures that simulate soil pore spaces. The chips were bonded to a glass slide and one side was opened to allow microbial colonization. Each chip was filled with a mix of liquid nutrient medium and 1.0 µm polystyrene microbeads at microplastic concentrations of 0.0, 0.006, 0.001 and 0.0005 mg/ml. The chip´s opening was inoculated with 5 g of soil and incubated in the laboratory at room temperature for one month. We documented the presence/absence and abundance of different soil microbial groups changing over time by using an inverted microscope.

Our preliminary study reveals that larger microorganisms are sensitive to the presence of microbeads 1.0 µm size. We found that all major soil microbial groups (fungi, bacteria, and protists) and nematodes colonized the chips. However, their abundance was affected by the presence of microplastics, irrespective of the concentration. Particularly protists and nematodes were lower in number during the first days of the exposure. The beads were clearly visibly taken up into the cells of the protists or the digestive tract of the nematodes.

We are now investigating what consequences the lower abundance of certain soil microbial groups have for the soil food web. As seen here, micro-engineered chips are useful tools to provide visual access at the scale where most cell-to-cell interactions occur.

How to cite: Mafla-Endara, P. M., Ohlsson, P., and Hammer, E.: Real time interactions between soil microorganisms and microplastics at microscale, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-13314, https://doi.org/10.5194/egusphere-egu21-13314, 2021.

The problem of contamination of the shore of the Sambian Peninsula with marine anthropogenic litter is pressing and requires detailed study since it has a detrimental effect on the touristic and recreational activity of the region. Observations show that the most volumetric marine litter wash-outs to the beach take place after certain storms and are associated with abundant spots ofbiota (primarily branched Furcellaria lumbricalis). Such spots contain litter of anthropogenic origin, such as glass, paper, etc., along with macro and micro plastics. In this paper, meteorological and hydrophysical data were collected and analyzed in order to determine the most significant factors causing the wash-outs of anthropogenic marine litter to the shore of Sambian Peninsula. Both in-situ observations and reanalysis datasets were used for the analysis. It was revealed that the wash-out to the shore occurs during the storm subsiding phase, and the determining factors are significant wave height, wind speed and current velocity during the preceding storm.

Investigations are supported by the Russian Science Foundation, grant No 19-17-00041 and IKBFU competitiveness improvement program for 2016-2020 (project 5-100).

How to cite: Fetisov, S. and Chubarenko, I.: Analysis of the influence of storms on massive marine litter wash-outs to the shore of the Sambian Peninsula, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-13603, https://doi.org/10.5194/egusphere-egu21-13603, 2021.

EGU21-13702 | vPICO presentations | ITS2.5/OS4.8

Fourier-Transform Infrared Spectroscopy of Environmentally Weathered Textile Fabrics for Enhanced Microplastic Identification

Shreyas Patankar, Ekaterina Vassilenko, Mathew Watkins, Anna Posacka, and Peter Ross

Microplastic pollution in oceans is among the global environmental concerns of our time. Emerging research on ocean environments indicates that microfibers, such as those originating from textiles, are some of the most commonly occurring type of microplastic contaminants. While Fourier-transform infrared spectroscopy (FTIR) is commonly used to identify and characterize pollutant samples obtained from the environment, this identification is challenging because infrared spectra of materials can be modified by exposure to the ocean, air, UV light, and other ambient conditions, in a process referred to as “weathering”. We report preliminary efforts in improving FTIR characterization of microplastics by building a library of infrared spectra of common textile fibers weathered under a selection of ambient conditions. Consumer textile materials including polyester, nylon, cotton, and other, were exposed to a selection of ambient conditions: ocean, air, and wastewater treatment stages, in a controlled weathering experiment. Infrared spectra were monitored for up to 52 weeks, with the resulting data illuminating on the environmental fate and longevity of synthetic and natural fibers. Spectral changes caused by weathering were found to depend strongly on both the composition of the material and the specific ambient conditions. This library of weathered material spectra is useful not only in easier identification of environmental microfibers, but also in helping us estimate the duration and manner of weathering that a given environmental microfiber may have experienced.

How to cite: Patankar, S., Vassilenko, E., Watkins, M., Posacka, A., and Ross, P.: Fourier-Transform Infrared Spectroscopy of Environmentally Weathered Textile Fabrics for Enhanced Microplastic Identification, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-13702, https://doi.org/10.5194/egusphere-egu21-13702, 2021.

EGU21-14369 | vPICO presentations | ITS2.5/OS4.8

Characterizing plastic debris accumulating in the North Pacific Garbage Patch

Matthias Egger, Wouter Jan Strietman, Ulphard Thoden van Velzen, Ingeborg Smeding-Zuurendonk, and Laurent Lebreton

Citizen science programs and tracking applications have been used in the collection of data on plastic debris in marine environments to determine its composition and sources. These programs, however, are mostly focused on debris collected from beach cleanups and coastal environments. Large plastic debris currently afloat at sea, which is a significant contributor to marine plastic pollution and a major source of beach litter, is less well-characterized.

Transported by currents, wind and waves, positively buoyant plastic objects eventually accumulate at the sea surface of subtropical oceanic gyres, forming the so-called ocean garbage patches. It is important to know where the debris that persists in the offshore gyres is entering the ocean, where it is produced and what practices (commercial, cultural, industrial) are contributing to the accumulation of these debris into the ocean garbage patches. This information coupled to data on how long and well the plastics persevere at the sea surface is necessary for creating effective and efficient mitigation strategies.

Here we provide a comprehensive assessment of plastic debris afloat in the North Pacific Garbage Patch (NPGP). Offshore debris collected by The Ocean Cleanup’s System 001b from the NPGP in 2019 was analyzed using the Litter-ID method, which applies an adapted and expended version of the OSPAR guideline for monitoring beach litter. Our results reveal new insights into the composition, origin and age of plastic debris accumulating at the ocean surface in the NPGP. The standardized methodology applied here further enables a first thorough comparison of plastic debris accumulating in offshore waters and coastal environments.

How to cite: Egger, M., Strietman, W. J., Thoden van Velzen, U., Smeding-Zuurendonk, I., and Lebreton, L.: Characterizing plastic debris accumulating in the North Pacific Garbage Patch, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-14369, https://doi.org/10.5194/egusphere-egu21-14369, 2021.

With a coastal population of nearly 150 million inhabitants, the influx of freshwater from densely populated river catchments and a contribution to 30% of the global shipping activity, the Mediterranean Sea has been recognized as one of the world most affected areas by marine litter. Moreover, the countries surrounding the region yearly attract about one third of the world tourism. Taken together, these pressures make this semi-enclosed sea an accumulation zone for marine litter. This high contamination goes hand to hand with a stream of adverse effects to marine ecosystems, public health or socio-economic costs. The beaches are one of the main land-based sources for litter to enter the oceans. The Mediterranean Sea is not an exception as during the summer, the beaches are a hotspot for leisure. This is particularly true for the Mediterranean islands, which due to their attractiveness will host a far greater population during the summer. In this study we evaluate the seasonal variation of marine litter as an effect of tourism on sandy beaches of Mediterranean islands and we assess the effectiveness of pilot actions in order to reduce the amount of marine litter.

147 surveys were conducted in 2017 during both the low and high touristic season. For each of the eight participating islands (Mallorca, Sicily, Rab, Malta, Crete, Mykonos, Rhodes and Cyprus), three different beaches were selected: a touristic beach, a beach mainly used by locals and a remote beach. For each beach, a periodic monitoring was performed on the same fixed 100m portion. Here, any item found was collected, characterized and properly disposed of. We included the mesoplastics (0.5 – 2.5cm), large microplastics (0.1 – 0.5cm) and pellets (raw plastic material). In 2019, a monitoring of 11 of the selected beaches was conducted following the implementation of pilot actions (mainly awareness campaigns). To test their effectiveness, the results are compared to those of 2017.

Our results show that tourism in Mediterranean island beaches is a main driver of marine litter generation. Popular beaches (touristic and locals) are clearly the most impacted sites. Every day, during the high touristic season peak (July-August), visitors will leave (i.e.: cigarette butts, drink can, etc.) or generate (i.e.: MePs and MPs) 950 – 1190 items on every 100m of beach. This amount falls to 60 items for the remote beaches. At the region scale, we estimated that during July-August, visitors could be responsible for the accumulation of about 47.5 106 ± 13.5 106 items/day on the beaches of the Mediterranean islands.

The awareness campaigns is an efficient tool to reduce the amount of litter generated by visitors on the beaches. We observed an average decrease of 52.5% of the accumulation of the items abandoned by the visitors after the implementation of the pilot actions. These encouraging results probably benefit from the growing attention of the public to the plastic pollution issue. However, this reduction has a price: the average cost of the pilot actions for the whole high season would be of 111.6 k€ per km of beach.

How to cite: Grelaud, M. and Ziveri, P.: The generation of marine litter in Mediterranean island beaches as an effect of tourism and its mitigation, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-14421, https://doi.org/10.5194/egusphere-egu21-14421, 2021.

EGU21-14442 | vPICO presentations | ITS2.5/OS4.8

Detection of microplastics in soil samples from the area of traffic route

Jagoda Worek, Anna Białas, and Katarzyna Styszko

At the end of the 1940s, mass production of plastics began. Since then, due to the very wide range of applications, a steady increase in their production has been observed. Anthropogenic activities have a significant impact on the natural environment. In this case, despite the knowledge of the problem, as early as the early 1970s, the harmful consequences continued to increase, and even if stopped immediately, their effects would last for centuries. In 2018, global production of plastics reached almost 360 million tonnes. The diverse use of plastics and low production costs mean that there are no other environmentally friendly alternatives that could replace them on a large scale. Therefore, it can be assumed that their production will continue to grow dynamically. The main hazard posed by the production of plastics is microplastic. These are plastic particles smaller than 5 mm. Research on microplastics in the environment is based mainly on diagnosing the problem in sea waters. Its concentration in soils is underestimated. The microparticles of plastics contained in the soil influence not only its structure or the ability to retain water, but also the organisms living in it. In the experiment, soil samples from the vicinity of a busy road in the city of Krakow, Poland, were examined. First, the samples were separated by density, and then the organic material was digested. The separated microplastics were analyzed both in terms of quantity and quality. Tests were carried out under the FTIR microscope, using the sensitive DRIFT method, and in the case of larger fragments, using ATR-FTIR. The results indicated the presence of a large fraction of microplastics, most often from tire abrasion.

How to cite: Worek, J., Białas, A., and Styszko, K.: Detection of microplastics in soil samples from the area of traffic route, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-14442, https://doi.org/10.5194/egusphere-egu21-14442, 2021.

EGU21-15053 | vPICO presentations | ITS2.5/OS4.8

Experimental assessment of settling velocity of pristine and biofouled microplastics 

Isabel Jalon-Rojas, Alicia Romero-Ramirez, Kelly Fauquembergue, Linda Rossignol, Benedicte Morin, and Jerome Cachot

Understanding and predicting the transport and fate of microplastics (MPs) in aquatic systems is a complex research challenge due to the simultaneous effect of different physical processes and the large variability in MPs dynamical properties. The dynamical behavior of MPs is further complicated by the development of biofilms and weathering processes. However, the effect of these processes on the dynamical properties of MPs is not fully understood. This study aims to evaluate the effect of the particle properties and biofilm on the settling velocity of microplastic sheets and fibers under laboratory conditions. The experiments focus on two types of particles (polyethylene sheets and polyester fibers), of nine sizes (between 1 and 5 mm), two degrees of biological colonization (new and aged during 3 months in the ocean) and three replicas of each type of particles. Density, size, and shape indices were first quantified. The settling velocity was then estimated by image analysis in a sedimentation column with salt- and freshwater. The dynamical behavior of the two types of particles was very different. Interestingly, the settling velocity of sheets increased with size up to a threshold in both salt- and freshwater, from which particle swinging and drag force increased, and settling velocity decreased. The effect of biofilm was also complex, increasing or decreasing the settling velocity of sheets as a result of the combined effect of the enhanced density and the biofilm distribution that influences the particle swinging. The settling velocity of fibers was independent of their length. Biofilms increased densities but their impact on settling velocity increase is less evident due to the high variability of this property for the same type of fiber. The relevance of theoretical drag models to predict the settling velocity of pristine and biofouled particles in salt- and freshwater will be also evaluated.

How to cite: Jalon-Rojas, I., Romero-Ramirez, A., Fauquembergue, K., Rossignol, L., Morin, B., and Cachot, J.: Experimental assessment of settling velocity of pristine and biofouled microplastics , EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-15053, https://doi.org/10.5194/egusphere-egu21-15053, 2021.

EGU21-15301 | vPICO presentations | ITS2.5/OS4.8

Laboratory and numerical investigation of the factors controlling the residence time of microplastics in the water column of thermally stratified lakes

Hassan Elagami, Pouyan Ahmadi, Sven Frei, Martin Obst, and Benjamin Gilfedder

Plastics are among the most widespread contaminants on Earth. They build up in fresh water bodies with high concentrations and migrate between different environmental compartments. In thermally stratified lakes, in summer, MPs pollutants can migrate between epilimnion, metalimnion and hypolimnion. This increases the probability of that microplastic will be filtered by filter feeders allowing MPs to migrate through different trophic levels. In this study, the transport of MPs in lake systems is presented through laboratory experiments as well as numerical modelling. The settling velocities of various biodegradable and non-biodegradable particles with various shapes and sizes were measured in the settling column under laminar conditions using particle image velocimetry (PIV). The particles used ranged between 150 to 2400 µm in diameter. The experimental results presented that shape, size and density of a particle are the key parameters controlling the sedimentation behavior of the particles. The measured settling velocities ranged between 0.4 to 50 mms-1. In parallel, the transport of the particles used in the laboratory experiments was simulated using CFD. The laboratory experiments and CFD have shown consistent results. Subsequently, the same MPs used in the first lab experiments were incubated in a pond at the University of Bayreuth for 6, 8 and 10 weeks. The formation of biofilm on the incubated particles was investigated using confocal laser scanning microscopy. Also, the effect of biofouling of microplastics on the physical properties and thus settling velocity was investigated experimentally. It was observed that biofilm-building organisms has only colonized few regions on the surface of MPs and the whole surface was not coated with biofilm as it was anticipated. In addition, no changes in shape, size and density of the incubated were detected. After 6, 8 and 10 weeks of incubation, no significant change in the settling velocity of the incubated particles was observed. The detected changes in the settling velocity ranged between ± 5 % which was considered as a measurement error. Finally, the residence time in suspension and the distribution of MPs throughout a virtual lake water column was simulated using a simplified model. The effect of turbulences and the temperature gradient on the settling velocity were considered during the simulations. The model presented that turbulences, water temperature and layer depth control the settling velocity and thus the residence time of the MPs.

How to cite: Elagami, H., Ahmadi, P., Frei, S., Obst, M., and Gilfedder, B.: Laboratory and numerical investigation of the factors controlling the residence time of microplastics in the water column of thermally stratified lakes, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-15301, https://doi.org/10.5194/egusphere-egu21-15301, 2021.

EGU21-15307 | vPICO presentations | ITS2.5/OS4.8

The use of expedition cruise ships and citizen science to bridge the gaps in plastic marine litter knowledge in remote areas. 

Verena Meraldi, Tudor Morgan, Kai Sørensen, and Bert van Bavel

Plastics and microplastics are regularly found in the marine environment around the world. Currently, the spatial and temporal dynamics of microplastics in remote areas, including polar regions, are poorly assessed and only limited long-term data is available on occurrence. Long-term data series are required to address changes in abundances of microplastics including variations in spatial and temporal distribution as well as to understand the influence of, for example, different seasons, changing weather or hydrological conditions. But there is very little data from remote regions of the world(1) including the Arctic and Antarctic.

One approach is to use ships of opportunity (www.norsoop.com) to collect data over replicated transects: these include research vessels as well as commercial vessels and expedition cruise ships. Advances in technology enable assessment of microplastic abundance at large spatial scale using existing infrastructure in addition to the collection of oceanographic meta-data. As part of the Hurtigruten – NIVA collaboration, a microplastic sampling module and a marine monitoring system (Ferry Box) was fitted on Hurtigruten’s Expedition vessel MS Roald Amundsen. The science center in this expedition ship, where single use plastic has been removed from all areas, provides a lab facility for preliminary plastic analysis and also a place for interaction with the passengers and engagement in citizen science. During the first year of operation, NIVA and Hurtigruten have collected microplastic samples in the Arctic and the Antarctic for long time periods. In addition, as part of a citizen science project, data and samples have been collected during beach clean-ups in remote areas and analysed on board using a handheld NIR smartphone scanner directly linked to a NIVA cloud database.

Average levels of microplastic within the Arctic (1.8-10 n/m3) and Antarctic (1.8-4.6) are still relatively low and consist mostly of fibres. The levels found in the Arctic study were comparable with the results from Lusher et al. 2015 and recent work in the Russian Arctic. Cellulose and cotton-based fibres dominate in the Antarctic samples and polyester is the dominant polymeric fibre. A citizen science project involving a beach clean-up and the subsequent analysis of the samples collected was performed on board MS Roald Amundsen in the Falkland/Malvinas Islands. The results showed large amounts of fishery related material including several polymer-based ropes and net pieces but also plastic utensils, food wrapping and plastic bottles.

 (1)        GESAMP (2016). Sources, fate and effects of microplastics in the marine environment: part two of a global assessment (Kershaw, P.J., and Rochman, C.M., eds). Rep. Stud. GESAMP No. 93, 220 p.

(2)         Lusher, A. L., Tirelli, V., O’Connor, I., and Officer, R. (2015). Microplastics in Arctic polar waters: the first reported values of particles in surface and sub-surface samples. Nature-scientific reports. 9 p.

(3)         Yakushev E., Gebruk A., Osadchiev A., Pakhomova S., Lusher A., Berezina A., van Bavel B., Vorozheikina E., Chernykh D., Kolbasova G., Razgon I., Semiletov I. Microplastics distribution in the Eurasian Arctic is affected by Atlantic waters and Siberian rivers. Communications Earth & Environment in press. DOI: 10.1038/s43247-021-00091-0

How to cite: Meraldi, V., Morgan, T., Sørensen, K., and van Bavel, B.: The use of expedition cruise ships and citizen science to bridge the gaps in plastic marine litter knowledge in remote areas. , EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-15307, https://doi.org/10.5194/egusphere-egu21-15307, 2021.

EGU21-15416 | vPICO presentations | ITS2.5/OS4.8

Marine Litter in Indonesia –  Tracking macro-plastic from river mouths with Argos buoys and modelling

Olivia Fauny, Marc Lucas, Claire Dufau, and Jean-Baptiste Voisin

The Indonesian archipelago is rated globally the second contributor to marine plastic litter pollution. This has driven the government in recent years to step up its efforts to combat plastic pollution, on land, in rivers and in the ocean. Indeed, although most of the plastic is disposed on land, lack of a systematic collection and processing network means that it often ends up rivers and ultimately into the seas. Heavy precipitation events during the Monsoon season exacerbate the problem by transporting massive amounts of plastic into rivers and hence into the coastal seas. Amongst the more recent initiative to combat the plastic litter issue, and with funding from the World Bankthe government of Indonesia has set up a program to track the movement of plastic through a hybrid observation & model approach and to determine the location of accumulation areas if any. The project deployed and tracked number of 20 Argos drifters over a year and set up a series of drift model simulation. As the project focuses on macro plastic, several types of macro-waste drifts have been modelized depending on their buoyancy by varying wind coefficient. Three river mouths were studied, located downstream from major populated areas. Results show that the dispersion and trajectory of particles vary depending on the source river, time of the year and meteoceanic conditions. For each river, accumulation areas were identified, concentring 38% to 90% of particles and all located on shore.  

How to cite: Fauny, O., Lucas, M., Dufau, C., and Voisin, J.-B.: Marine Litter in Indonesia –  Tracking macro-plastic from river mouths with Argos buoys and modelling, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-15416, https://doi.org/10.5194/egusphere-egu21-15416, 2021.

EGU21-15455 | vPICO presentations | ITS2.5/OS4.8

Sources of macro and micro-plastics contamination of agricultural fields in Rwanda

Nkeshimana Godeberthe, Jantiene Baartman, Michel Riksen, and Violette Geissen

Worldwide, there is intensive plastic waste accumulation in soil, agricultural fields, and water bodies. Focus has been on oceans and aquatic environments, but recently, plastic accumulation into terrestrial ecosystem is getting attention. In many sub-tropical countries plastic wastes are being buried or disposed in open landfills without proper environmental management. In Rwanda, despite efforts undertaken by the Government to control use of non-degradable plastic bags, plastic wastes dumped into open landfills continue to be redistributed within the landscape through soil erosion processes, which presents a risk of contamination of agricultural fields, water reservoirs and groundwater ecosystems. There is a strong lack of knowledge on possible pathways of (micro-) plastics into the terrestrial environment. This study identified and evaluated the use and source of plastic material in agricultural fields around landfills in three study sites in Rwanda: Kicukiro, Rwamagana, and Muhanga villages through survey  questionnaires. A total of 1,240 households (HHs) were surveyed. The Kicukiro landfill, near the capital, was established before 1994 and closed between 2011-2014, while the landfills of Muhanga and Rwamagana were established between 2006 and 2017. Results revealed that in rural areas (Muhanga and Rwamagana) most respondents do not use plastic bags (Muhanga 63% and Rwamagana 76.9%) compared to urban areas like Kicukiro where a high rate (64.3%) of respondents still use plastic bags, which were easily available from local (super)markets, according to 45.5% of the respondents. Most interviewees in all study sites ignore if the plastic materials that they are using are degradable or not. Results revealed also that the majority of respondents are aware of the impact of using plastic bags (Kicukiro: 77.6%; Muhanga: 60.5%; and Rwamagana: 62%), and they also confirmed that they would not use plastic bags even if the government would not punish people using these (Kicukiro: 78.8%; Muhanga: 74.8%; and Rwamagana: 81.8%).

Principal component analysis (PCA) was used to identify the most influential variables, and results revealed that respondents are aware of the impacts of using plastic bags on the environment with high significance. Furthermore, also a strong correlation was found between the study sites, and plastics wastes eroded during high rainfall events and causing environmental problems in surrounding areas located near the landfill. Results showed that the education level is correlated negatively to the use of plastics bags and the age of respondents. Environmental policies on plastic ban should be reinforced for improving the strategies of controlling plastic bags from neighboring countries to overcome the use of non-degradable plastic bags. There is a high need from the country to teach its population through differnt educational programs so that they can improve their level of knowledge and awareness and risks of using non degradable plastic bags. 

How to cite: Godeberthe, N., Baartman, J., Riksen, M., and Geissen, V.: Sources of macro and micro-plastics contamination of agricultural fields in Rwanda, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-15455, https://doi.org/10.5194/egusphere-egu21-15455, 2021.

EGU21-15851 | vPICO presentations | ITS2.5/OS4.8

Microplastics pollution in North and South Atlantic Ocean surface waters 

Svetlana Pakhomova and Evgeny Yakushev

Contamination of the World Ocean by synthetic non-biodegradable material has become a high profile environmental concern. Standardized sampling methods and methods of plastic identification should be developed so that results can be fed into international monitoring strategies to map plastic distribution worldwide. Here we present results of studies carried out on a transect between Tromsø and Svalbard and from Montevideo to Antarctica performed with the same sampling procedure onboard Norwegian and Russian ships in 08.2019 and 01.2020 respectively. Microplastic sampling was carried out using a filtering system. Water passed through the system and SPM was collected on a metal mesh screens. All potential plastic particles and fibers were checked for polymeric identification using a PerkinElmer Spotlight ATR-FTIR. The level of confirmed microplastics ranged from 0 to 1.9 items/m3 (0.7 items/m3 in average) on a transect Tromsø-Svalbard and from 0 to 2.5 items/m3 (0.4 items/m3 in average) on Montevideo-Antarctica transect. Both data sets were represented by 40% of fragments and 60% of fibers. Polyester was found as the main polymer type for both transects, 46% of microplastics. Other found polymer types were different in the North and South Atlantic Ocean waters. Nylon (polyamide) was the next most common polymer type in South Atlantic which was not found in Northern part. Difference was also observed in higher number of stations without any microplastics in South Atlantic.

This work was partly funded by the Norwegian Ministry of Climate and Environment project RUS-19/0001 “Establish regional capacity to measure and model the distribution and input of microplastics to the Barents Sea from rivers and currents (ESCIMO)” and the Russian Foundation for Basic Research, research projects 19-55-80004.

How to cite: Pakhomova, S. and Yakushev, E.: Microplastics pollution in North and South Atlantic Ocean surface waters , EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-15851, https://doi.org/10.5194/egusphere-egu21-15851, 2021.

EGU21-16515 | vPICO presentations | ITS2.5/OS4.8

Litter and Microplastics: Environmental monitoring in the Arctic

Jan Rene Larsen, Jennifer Provencher, and Eivind Farmen

While the Arctic Ecosystem is already stressed by the effects of the climate crisis, another threat is emerging: plastics. Plastic pollution has become an environmental issue of the highest concern world-wide, and the plastic pollution tide is also rising in the Arctic.

The pristine Arctic environments, far from most of the world’s major industrial areas, are becoming laden with plastic pollution. Microplastics have been found in Arctic snow, sea-ice, seawater, in sediments collected on the ocean floor, and on Arctic beaches. Larger pieces of plastic debris are also making their way into the food webs as whales, fish and birds can ingest them or get entangled in them. Climate change is expected to exacerbate the amount of debris in the Arctic, via melting sea-ice and increasing contributions from human activities.

The Artic Monitoring and Assessment Programme (AMAP) is a Working Group of the Arctic Council. AMAP has a mandate to monitor and assess the status and trends of contaminants in the Arctic. In the Spring of 2019, AMAP decided to step up its efforts on the plastic issue and established an Expert Group on microplastics and litter with experts from Artic Council States and Observer countries.

The Expert Group has developed a comprehensive monitoring plan and technical guidelines for monitoring microplastics and litter in the Arctic. It will be the first time that all parts of the Arctic ecosystem are examined for traces of this type of pollution. The Expert Group aims to:

  • Design a program for the monitoring of microplastics and litter in the Arctic environment.
  • Develop necessary guidelines supporting the monitoring program.
  • Formulate recommendations and identify areas where new research and development is necessary from an Arctic perspective.

How to cite: Larsen, J. R., Provencher, J., and Farmen, E.: Litter and Microplastics: Environmental monitoring in the Arctic, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-16515, https://doi.org/10.5194/egusphere-egu21-16515, 2021.

ITS2.6/BG9 – Solutions for sustainable agri-food systems under climate change and globalisation

EGU21-2358 | vPICO presentations | ITS2.6/BG9

Steep-slope cultivated landscapes: towards climate-resilient water resources management

Wendi Wang, Anton Pijl, and Paolo Tarolli

One of the most important tasks for humans in the 21st century is to ensure food security in the face of water scarcity. Steep-slope cultivated landscapes are widely distributed and feed more than 10% people in the world. They play an important role in Sustainable Development Goals (SDG) set in the United Nations Sustainable Development Summit which aimed to thoroughly solve the food problems in a sustainable way. In addition, more than 49 steep-slope cultivated landscapes are recognised and protected by UNESCO and FAO for their cultural and agronomic importance. It is necessary to find appropriate solutions towards climate-resilient water resources management and save more water for other ecosystems or human activities.

Climate change-induced drought and high intensity rainstorms are global challenges for water resources management in agricultural landscapes. Growing aridity and extreme rainfall are particularly exacerbating the problem of water scarcity in steep slope cultivated landscapes. Though a number of studies have shown the potential impact of climate change on agricultural systems, little is known about role of water resource management (water storages, water harvest, drainage systems, etc.) in the mitigation of these climate impacts to ensure sustainable farming in steep slope agricultural landscapes. The aim of our work is to analyse the threats and challenges of steep-slope agriculture due to climate change and provide examples of resilient water management and agricultural practices in these landscapes. In detail, the aims are to better understand and compare how shifting climatic zones particularly affect steep cultivated landscapes and to find a feasible way for water storage to sustain ecosystem service and agricultural cultivation on hillslope in long periods of drought. GIS-based techniques were employed to determine the global distribution of steep agricultural landscapes, and to quantify the fraction of these that are facing future aridity. Finally, key examples of best practices in sustainable water resource management around the world are discussed, providing a guideline for improving the resilience of steep cultivation systems in future climatic conditions.

How to cite: Wang, W., Pijl, A., and Tarolli, P.: Steep-slope cultivated landscapes: towards climate-resilient water resources management, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-2358, https://doi.org/10.5194/egusphere-egu21-2358, 2021.

EGU21-15252 | vPICO presentations | ITS2.6/BG9

Alternative Models of irrigation development in Ghana

Roshan Adhikari, Timothy Foster, Ralitza Dimova, Sarah Redicker, and Thomas Higginbottom

Expansion of irrigated agriculture is central to efforts to enhance food security, reduce rural poverty, and increase resilience to climate change across Sub-Saharan Africa (SSA). A broad variety of irrigation system typologies currently exist in SSA, ranging from ‘formal’ publically-financed surface water irrigation systems served by engineered infrastructure (e.g. dams and canals) to ‘informal’ farmer-led irrigation systems that receive little official support or recognition (e.g. private groundwater pumping and small-scale river diversions). Yet, at present, there is little objective or reliable information about the about the differences in agricultural productivity and livelihood outcomes resulting from these alternative approaches to irrigation developments in SSA, limiting capacity to design effectively new irrigation investments and evaluate reliably current and future trade-offs with other water uses (e.g. hydropower). Understanding the comparative performance of alternative existing approaches to irrigation development, along with the extent to which formal and informal systems complement or substitute one another, offers a valuable opportunity to generate new insights about best practice approaches for irrigation expansion. This paper seeks to address this challenge by exploring how alternative bio-physical, socio-economic, and institutional characteristics of irrigation developments influence welfare outcomes for smallholders.

Our analysis uses primary household panel data (n=646) collected in 2018 and 2019 from Upper East Ghana evaluate the characteristics of irrigation typologies and impacts on agricultural productivity. As a basis for our empirical analysis, we analyse the drivers of productivity differences across farmers in different irrigation typologies. We use descriptive statistics from the survey data to make inferences on heterogeneity of irrigation access across farmer groups. We then use a subset of the sample - limited to the main crops, paddy rice in the rainy season and pepper in the dry season- to analyse the differences in crop yields across farmer groups. To assess whether irrigation access and behaviour affects agricultural production and technical efficiency, we decompose the effects of agricultural inputs - including irrigation - and technical efficiency on crop yields.

Our preliminary findings demonstrate that farmers in formal irrigation schemes have higher yields, technical efficiency of agricultural production and lower costs compared to farmers in informal schemes across both growing seasons. We also find that farmers in formal schemes enjoy a broad range of benefits (for e.g., subsidised fertilizers and higher prices for their produce), which go beyond direct benefits from reliable and inexpensive water access. As a result, these farmers have lower agricultural costs, higher production and better welfare outcomes. These findings highlight the need for broadening public support for irrigators outside the government managed irrigation schemes, who are often neglected in official irrigation development narratives. Fertilizer subsidies, proper channels to sell agricultural output and proper maintenance of existing infrastructure outside formal schemes present opportunities to increase the efficiency and agricultural productivity of farmers in informal schemes.

How to cite: Adhikari, R., Foster, T., Dimova, R., Redicker, S., and Higginbottom, T.: Alternative Models of irrigation development in Ghana, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-15252, https://doi.org/10.5194/egusphere-egu21-15252, 2021.

EGU21-1659 | vPICO presentations | ITS2.6/BG9 | Highlight

Water sustainability of South African crop production under current and future climatic conditions

Sara Bonetti, Tafadzwanashe Mabhaudhi, Rob Slotow, and Carole Dalin

South Africa is a water scarce country, with 98% of available water resources already allocated. In addition, only 12% of the land is considered suitable for growing rainfed crops, making commercial agriculture production heavily dependent on irrigation. Current climate projections suggest that South Africa will experience increased frequency of drought events over the next century. This will have notable implications for food security, especially in rural communities that still depend on rainfed production for their livelihoods. In this work, we evaluate water sustainability for seventeen major crops produced in South Africa under current climatic and management conditions as well as under future climate scenarios. We map the spatial distribution of source- and crop-specific water use, and asses their sustainability in terms of water debt repayment time (i.e., the time needed to renew water resources used for annual crop production). We find high water debts in the Western and Eastern Cape regions, revealing unsustainable production due to irrigation in arid areas. Results from climate change scenarios suggest an intensification of such pressure on water resources and allow us to identify crop types and locations where production is likely to be more (or less) sustainable under future climatic conditions, a key step to informing land use planning decisions.

How to cite: Bonetti, S., Mabhaudhi, T., Slotow, R., and Dalin, C.: Water sustainability of South African crop production under current and future climatic conditions, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-1659, https://doi.org/10.5194/egusphere-egu21-1659, 2021.

EGU21-11580 | vPICO presentations | ITS2.6/BG9 | Highlight

Sustainable intensification of groundwater irrigation in the Eastern Indo-Gangetic Plains

Timothy Foster, Roshan Adhikari, Subash Adhikari, Scott Justice, Anton Urfels, and Timothy Krupnik

Groundwater irrigation has played a critical role in the Green Revolution in South Asia, helping to increase crop yields and improve livelihoods of millions of rural households. However, the spread of irrigation has not been homogeneous, with many farmers in the Eastern Indo-Gangetic Plains (EIGP – Nepal Terai and parts of eastern India) still lacking reliable and affordable irrigation access. As a result, agricultural productivity in the EIGP is some of the lowest found across South Asia, with many farmers trapped in chronic cycles of poverty and food insecurity.

A major focus of government and donor efforts to support intensification of groundwater irrigation in the EIGP has been the replacement of existing diesel-based pumping systems with alternative electric or solar powered pumping technologies. These technologies are viewed as being cheaper for to operate and less environmentally damaging due to their lower operational carbon emissions. However, scaling these technologies in practice has proved challenging due to their high upfront capital costs and the unique socio-technical constraints posed by farming systems in the EIGP (e.g., land fragmentation and poorly developed supply chains).

In response to these challenges, our research explores whether opportunities exist to make existing diesel pump systems more cost effective for farmers to support adaptation to climate change and reduce poverty. In particular, we seek to identify what factors lead to disparities in groundwater access costs for irrigation, how these disparities affect farmers’ water use behavior, and in turn how this impacts agricultural production outcomes. Our work draws on evidence from a recent survey of over 400 farmer households in the Nepal Terai, along with detailed in-situ testing and analysis of the fuel efficiency and cost-effectiveness of over 100 diesel pumpsets in the same region conducted between 2019-20.

Our results demonstrate that substantial variability exists in the costs of diesel pump irrigation in the EIGP and that higher costs of groundwater access are associated with lower levels of agricultural productivity and household income. Dependence on expensive pumpset rental markets, in particular amongst credit constrained households, is a major driver of the highest irrigation access costs. Additionally, many farmers also continue to operate and invest in pumpset models and designs that are significantly oversized for local hydrological conditions, resulting in fuel inefficiencies and excess costs that reduce the overall profitability of irrigation water use.

Our findings have important implications for national and regional policy debates about sustainable intensification of irrigated agriculture in the EIGP and other regions. We suggest that intensification of water use and improvements in agricultural productivity can be achieved in the near-term without need for radical technology changes. Targeted credit support, combined with data-driven advisories and improved supply chains for maintenance services and spare parts, could incentivize and enable adoption of low-cost fuel-efficient diesel pumpsets resulting in substantial reductions in costs of irrigation for many farmers. This would have positive near-term impacts on agricultural productivity and rural livelihoods, supporting adaptation to climate change and future transitions to alternative low-carbon irrigation technologies in the region.

How to cite: Foster, T., Adhikari, R., Adhikari, S., Justice, S., Urfels, A., and Krupnik, T.: Sustainable intensification of groundwater irrigation in the Eastern Indo-Gangetic Plains, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-11580, https://doi.org/10.5194/egusphere-egu21-11580, 2021.

EGU21-11234 | vPICO presentations | ITS2.6/BG9 | Highlight

Development of an integrated indicator to assess the multi-dimensional global environmental impact of crop production.

Mark jwaideh and Carole Dalin

EGU21-15932 | vPICO presentations | ITS2.6/BG9 | Highlight

A global deforestation footprint from production and consumption of primary goods 

Giorgio Vacchiano, Maria Giovanna Lahoz, Alessia Ventrice, and Marco Bagliani

Forests make over 30% of global land and perform functions of vital importance for the well-being of humans on Earth. Yet, forest cover is declining due to deforestation  that mainly affect tropical biomes, due to land use changes for agricultural, mining, and urban use to satisfy growing global demands. Globalization of markets and development have in fact raised the pressure on environmental resources by humans, and at least 30% of global deforestation is linked to the production of exported goods. 
We propose here a method to quantify the impact of global trade on forest cover, by assessing the deforestation embodied in the production, trade, and consumption of  forest-risk agricultural products and by-products. from 2000 to 2020. We provide the first estimate of a country-based deforestation footprint, an indicator of the pressure on forest cover by countries that consume goods produced on land previously occupied by forests.
This is a first attempt to systematically and critically address the issue taking into account responsibilities of both exporting and importing countries. Our methods and first assessment can support domestic and international policies aiming at reducing  deforestation through a correct assessment of a country's impact on global forests and their services. 

How to cite: Vacchiano, G., Lahoz, M. G., Ventrice, A., and Bagliani, M.: A global deforestation footprint from production and consumption of primary goods , EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-15932, https://doi.org/10.5194/egusphere-egu21-15932, 2021.

EGU21-12758 | vPICO presentations | ITS2.6/BG9 | Highlight

Future global crop production increases by adapting crop phenology to local climate change

Sara Minoli, Jonas Jägermeyr, Senthold Asseng, and Christoph Müller

Broad evidence is pointing at possible adverse impacts of climate change on crop yields. Due to scarce information about farming management practices, most global-scale studies, however, do not consider adaptation strategies.

Here we integrate models of farmers' decision making with crop biophysical modeling at the global scale to investigate how accounting for adaptation of crop phenology affects projections of future crop productivity under climate change. Farmers in each simulation unit are assumed to adapt crop growing periods by continuously selecting sowing dates and cultivars that match climatic conditions best. We compare counterfactual management scenarios, assuming crop calendars and cultivars to be either the same as in the reference climate – as often assumed in previous climate impact assessments – or adapted to future climate.

Based on crop model simulations, we find that the implementation of adapted growing periods can substantially increase (+15%) total crop production in 2080-2099 (RCP6.0). In general, summer crops are responsive to both sowing and harvest date adjustments, which result in overall longer growing periods and improved yields, compared to production systems without adaptation of growing periods. Winter wheat presents challenges in adapting to a warming climate and requires region-specific adjustments to pre and post winter conditions. We present a systematic evaluation of how local and climate-scenario specific adaptation strategies can enhance global crop productivity on current cropland. Our findings highlight the importance of further research on the readiness of required crop varieties.

How to cite: Minoli, S., Jägermeyr, J., Asseng, S., and Müller, C.: Future global crop production increases by adapting crop phenology to local climate change, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-12758, https://doi.org/10.5194/egusphere-egu21-12758, 2021.

EGU21-15233 | vPICO presentations | ITS2.6/BG9 | Highlight

Can an organically farmed world help to mitigate climate change through carbon sequestration?

Ulysse Gaudaré, Matthias Kuhnert, Pete Smith, Manuel Martin, Pietro Barbieri, Sylvain Pellerin, and Thomas Nesme

While the agricultural sector is responsible for 20-30% of global greenhouse gas emissions, agricultural lands may also represent an opportunity to mitigate climate change through soil carbon sequestration. In particular, organic farming is often presented as a way of farming that leads to increased soil carbon sequestration in croplands thanks to high soil carbon inputs, especially as animal manure (Skinner et al. 2013, Gattinger et al. 2012).

However, organic farming represents only ~1.4% of the global utilised agricultural area (UAA). In a world where organic farming would expand far above (e.g. up to 100% of the UAA), we expect stringent competition for fertilising materials and therefore, a reduction of organic yields beyond the current organic-to-conventional gap of ~20% (Seufert et al. 2012). Such yield reduction might impact the amount of carbon that returns to soil in form of crop roots and residues and, in fine, the soil organic carbon sequestration of organically managed croplands. The objective of the present study is to estimate to what extent soil carbon sequestration might be affected by organic farming expansion at the global scale.

To answer this question, we combined (i) the GOANIM model that estimates material and nutrient flows in the crop and livestock farming systems under different global scenarios of organic farming expansion and (ii) the RothC model that simulates soil carbon dynamics in agricultural soils. We combined those models with a series of global scenarios representing organic farming expansion together with a baseline simulating conventional – i.e. non-organic – farming systems and soil carbon inputs.

We found that organic farming expansion would negatively affect croplands’ SOC stocks at the global scale. We found a reduction of per-hectare soil carbon input in croplands of up to 40-60%. This is due to lower yields in an organic scenario because of nitrogen limitation (up to 60% lower than conventional), reducing the amount of crop residues returning to cropland. Another impact of lower yield is a reduction of feed availability and subsequently a reduction of animal population and manure spread to soil. This reduction of carbon input is lower if farming practices are adapted to foster biomass production and carbon inputs in soils (i.e. cover crops). Such results highlight the need of systemic approaches when estimating the mitigation potential of alternative farming systems.

 

References

Gattinger, A. et al. (2012) ‘Enhanced top soil carbon stocks under organic farming’, Proceedings of the National Academy of Sciences, 109(44), pp. 18226–18231. doi: 10.1073/pnas.1209429109.

Skinner, C. et al. (2014) ‘Greenhouse gas fluxes from agricultural soils under organic and non-organic management - A global meta-analysis’, Science of the Total Environment, 468–469, pp. 553–563. doi: 10.1016/j.scitotenv.2013.08.098.

Seufert, V., Ramankutty, N. and Foley, J. A. (2012) ‘Comparing the yields of organic and conventional agriculture’, Nature, 485(7397), pp. 229–232. doi: 10.1038/nature11069.

Connor, D. J. (2008) ‘Organic agriculture cannot feed the world’, Field Crops Research, 106(2), pp. 187–190. doi: 10.1016/j.fcr.2007.11.010.

How to cite: Gaudaré, U., Kuhnert, M., Smith, P., Martin, M., Barbieri, P., Pellerin, S., and Nesme, T.: Can an organically farmed world help to mitigate climate change through carbon sequestration?, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-15233, https://doi.org/10.5194/egusphere-egu21-15233, 2021.

EGU21-14699 | vPICO presentations | ITS2.6/BG9

Alternative crops for a changing climate in Switzerland

Malve Heinz, Olivia Romppainen-Martius, and Annelie Holzkämper

Rising temperatures, shifts in precipitation patterns and longer dry periods provoke a need for better adapted crops in Switzerland to maintain agricultural productivity in the long term. The aim of this work was to identify plants with a high climatic suitability in the future. A simple mechanistic model (ecocrop) was applied to determine suitability for different time periods under RCP scenarios 4.5 and 8.5. The model considers temperature and precipitation ranges. From a pool of 600 edible plants, 21 plants were identified that would benefit from progressing climate change in terms of average climatic yield potentials. In addition, these plants were found to have a high nutritional quality and could thus be seen as good candidate crops to expand the portfolio of cultivated crops in Switzerland in efforts to adapt to climate change and maintain or even increase food productivity in a future climate. The potentials of selected crops are discussed in terms of cultivation requirements, spatial suitability, and market potentials.

How to cite: Heinz, M., Romppainen-Martius, O., and Holzkämper, A.: Alternative crops for a changing climate in Switzerland, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-14699, https://doi.org/10.5194/egusphere-egu21-14699, 2021.

EGU21-14278 | vPICO presentations | ITS2.6/BG9

Determinant factors in olive oil accumulation for optimizing harvest time in a context of climate change

José M. Cabezas, Estrella Muñoz, Raúl De la Rosa, Lorenzo León, and Ignacio J. Lorite

Olive is a woody crop extended over 10 Mha around the world (FAOSTAT, 2019), being Spain the country with the largest area (2.7 Mha). Andalusia is located in the South of Spain, with 1.6 Mha cultivated with olive trees, most of them (around 90%) dedicated to olive oil production (MAPA, 2020). This region is characterized by a great diversity of weather conditions. This diversity greatly affects important agronomic parameters of olive as the pattern of oil accumulation. This influence is different depending on the cultivar considered. In addition, this pattern of oil accumulation is a key aspect since is the most relevant trait determining the optimal harvest time. For that reason, in the present study, the relative influence of cultivar and environment, and their interaction, have been evaluated for the full pattern of oil accumulation.

This study was carried out in four locations of Andalusia covering a wide range of weather conditions, and where olive trees are well established or under expansion: Antequera (Málaga), Córdoba, Úbeda (Jaén) and Gibraleón (Huelva). In 2008, five cultivars were planted in a randomized complete block design consisting in four blocks and four trees per elementary plot: Arbequina, Hojiblanca, Koroneiki, Picual and Sikitita-3 (a new registered cultivar from the olive breeding program developed by the University of Córdoba and IFAPA). The first two locations were monitored in 2018 and 2020 while the other two locations were monitored only during 2020 campaign. Fruits samples were collected periodically, starting 4 weeks after full bloom until the oil accumulation was finished. Then, in the laboratory, fruits’ oil content was measured by nuclear magnetic resonance.

Results show sigmoid patterns regarding fruit oil accumulation and dry basis along each campaign in all genotypes, locations and years. There were significant differences of maximum olive oil accumulation among genotypes, recording the genotype Sikitita-3 the maximum ones. Furthermore, a significant genotype-environment interaction was also found for these. These results have relevant consequences regarding the selection of the optimal harvest time, to accomplish a desired balance between maximum oil accumulation and quality indicators which require early harvest dates.

 

References:

FAOSTAT, 2019. Food and Agriculture Organization of the United Nations. FAOSTAT database available at http://www.fao.org/faostat/en/#data. Last accessed 12 January 2020.

MAPA, 2020. Ministry of Agriculture, Fisheries and Food. Survey of surfaces and crop yields 2020 available at https://www.mapa.gob.es/es/estadistica/temas/estadisticas-agrarias/agricultura/esyrce/. Last accessed 12 January 2020.

How to cite: Cabezas, J. M., Muñoz, E., De la Rosa, R., León, L., and Lorite, I. J.: Determinant factors in olive oil accumulation for optimizing harvest time in a context of climate change, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-14278, https://doi.org/10.5194/egusphere-egu21-14278, 2021.

EGU21-14900 | vPICO presentations | ITS2.6/BG9

A step forward toward the high-resolution assessment of virtual water flows at the company’s scale

Elena De Petrillo, Marta Tuninetti, and Francesco Laio

Through the international trade of agricultural goods, water resources that are physically used in the country of production are virtually transferred to the country of consumption. Food trade leads to a global redistribution of freshwater resources, thus shaping distant interdependencies among countries. Recent studies have shown how agricultural trade drives an outsourcing of environmental impacts pertaining to depletion and pollution of freshwater resources, and eutrophication of river bodies in distant producer countries. What is less clear is how the final consumer – being an individual, a company, or a community- impacts the water resources of producer countries at a subnational scale. Indeed, the variability of sub-national water footprint (WF in m3/tonne) due to climate, soil properties, irrigation practices, and fertilizer inputs is generally lost in trade analyses, as most trade data are only available at the country scale. The latest version of the Spatially Explicit Information on Production to Consumption Systems model  (SEI-PCS) by Trase provides detailed data on single trade flows (in tonne) along the crop supply chain: from local municipalities- to exporter companies- to importer companies – to the final consumer countries. These data allow us to capitalize on the high-resolution data of agricultural WF available in the literature, in order to quantify the sub-national virtual water flows behind food trade. As a first step, we assess the detailed soybean trade between Brazil and Italy. This assessment is relevant for water management because the global soybean flow reaching Italy may be traced back to 374 municipalities with heterogeneous agricultural practises and water use efficiency. Results show that the largest flow of virtual water from a Brazilian municipality to Italy -3.52e+07 m3 (3% of the total export flow)- comes from Sorriso in the State of Mato Grosso. Conversely, the highest flow of blue water -1.56e+05 m3- comes from Jaguarão, in the State of Rio Grande do Sul, located in the Brazilian Pampa. Further, the analysis at the company scale reveals that as many as 37 exporting companies can be identified exchanging to Italy;  Bianchini S.A is the largest virtual water trader (1.88 e+08 m3 of green water and 3,92 e+06 m3 of blue water), followed by COFCO (1,06 e+08 m3 of green water and 6.62 m3 of blue water)  and Cargill ( 6.96 e+07 m3 of green water and 2.80 e+02 m3 of blue water). By building the bipartite network of importing companies and municipalities originating the fluxes we are able to efficiently disaggregate the supply chains , providing novel tools to build sustainable water management strategies.

How to cite: De Petrillo, E., Tuninetti, M., and Laio, F.: A step forward toward the high-resolution assessment of virtual water flows at the company’s scale, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-14900, https://doi.org/10.5194/egusphere-egu21-14900, 2021.

EGU21-14664 | vPICO presentations | ITS2.6/BG9

Do trade agreements activate new links and increase flows? A data-driven analysis of the global cereal market.

Benedetta Falsetti, Luca Ridolfi, and Francesco Laio

Given the importance of food imports for food security and the role of exports in income generation, food trade is an indispensable component of most countries’ development strategies. Global and regional agreements set the rules for trade policies between countries. In this context, we investigate the impact of trade agreements on the trade network of agricultural products. We study whether the ratification of agricultural-oriented trade agreements has an influence on the topology of the cereal trade network (link establishment) and the variation of flows through existing links.

Our analysis differs from previous studies for three main reasons. Firstly, it is a data-driven analysis, based on a dataset that combines the trade agreement structure provided by the World Bank and cereal trade flow data from FAOSTAT. Secondly, the analysis focuses on a global scale, considering data for all countries where information is available. Finally, we carried out the analysis at the level of aggregated cereals, both from a monetary (US$) and diet-based (Kcal) perspective, over the period from 1993 to 2015. This time interval includes the most important recent reforms in the agricultural sector.

The results show that a new trade agreement between two countries increases the probability of activating a grain trade link by 7.3% in the year after the agreement is ratified. In the case where trade agreements are not considered, the probability of triggering a new link between two countries drops to 1.3%.

Regarding the volume of flows, we classify variations into three categories: flow decrease (negative variation of the flux), mild increase (<50% increase in the flow intensity), and sharp increase (>50% increase).

The results obtained, both in economic value (US$) and in quantitative variations (Kcal), show that the entry force of a trade agreement has two main effects: in flows covered by trade agreements, there is a significant increase in the percentage of flows experiencing a sharp increase, and a reduction of the percentage of flows experiencing a negative variation. 

We, therefore, provide here global-scale, data-based evidence. Previous results suggest that trade agreements are facilitators of the connections between different countries and, therefore, facilitators in terms of global food trade accessibility.  This work aims to be a first attempt to investigate the impacts of international agreements simultaneously on the topology of the agricultural product trade network, and on the increase of existing link flows. Our intention is to dedicate further analysis about which trade agreements perform better, increasing the traded volume, to explore the role of trade liberalization at a worldwide level.

 

How to cite: Falsetti, B., Ridolfi, L., and Laio, F.: Do trade agreements activate new links and increase flows? A data-driven analysis of the global cereal market., EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-14664, https://doi.org/10.5194/egusphere-egu21-14664, 2021.

EGU21-6748 | vPICO presentations | ITS2.6/BG9

Agricultural diversification and food security in low- and middle-income countries: Where is the evidence?

Katharina Waha, Francesco Accatino, Cecile Godde, Cyrille Rigolot, Jessica Bogard, Joao Pedro Domingues, Elisabetta Gotor, Guillaume Martin, Daniel Mason D'Croz, Francesco Tacconi, Mark van Wijk, and Mario Herrero

Diversity and diversification in agricultural systems are often presented in the literature as having multiple benefits such as enhancing resilience, increasing food production and decreasing risks in production systems and is often postulated to benefit food and nutrition security in low- and middle-income countries. Our study aims to provide an overview of the potential for agricultural diversification to improve food security status as reported in recently published research articles analysing the diversity-food security relationship. We consider results for different scales, from individual to global and for different food security dimensions: availability, access, stability and utilisation.

We carried out a literature review that includes exhaustive, comprehensive searching. We search for peer-reviewed publications in the Web of Science core collection (v.5.32) written in English, between 2010 and February 2020 on the association between diversity in agricultural systems and at least one dimension or measure of food security. From the original list of articles we exclude all publications that (1) focus on a study area outside a low- to middle income country; (2) do not include at least one metric of farm-, regional-, or global-level diversity as specified with the search terms; (3) do not explicitly measure at least one food security dimension, or (4) were exclusively focussed on describing drivers and trends in diversity or food security.

We find that a total number of 87 research articles assessed a total of 328 diversity-food security relationships using one or more statistical modelling approach. About half of them are positive (54%) and mostly refer to the diversity-food access relationship on the individual, household and farm scale as this was the food security dimension and spatial scale most analysed. Of all results for food access 60% were positive relationships and only 4% were negative relationships with the remainder having no or ambiguous relationships. Twenty-nine studies used household dietary diversity as a measure of food access and 10 studies used at least one food access indicator that is a validated proxy for nutrient adequacy. Positive relationships were more often reported for food availability (65%) than for food utilisation (33%) also because for food utilisation there are a lot of mixed findings for different measures of anthropometric and nutritional status. The most common spatial scale assessed was the household and farm scale (58%).

There is no food security dimension that primarily has a negative relationship with agricultural diversity but there is a considerable number of relationships that are found to be neutral or ambiguous. Diversity can be an important driver of food security, but the magnitude of the contribution depends on the  socio-economic and biophysical characteristics of the local farming system. We conclude that farmers mostly see diversification as a potential strategy to improve livelihoods, agricultural production and/or food and nutrition security where other strategies are more expensive but not as a desirable characteristic of the agricultural systems at all costs especially in the presence of other strategies that can achieve the same outcome.

How to cite: Waha, K., Accatino, F., Godde, C., Rigolot, C., Bogard, J., Domingues, J. P., Gotor, E., Martin, G., Mason D'Croz, D., Tacconi, F., van Wijk, M., and Herrero, M.: Agricultural diversification and food security in low- and middle-income countries: Where is the evidence?, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-6748, https://doi.org/10.5194/egusphere-egu21-6748, 2021.

EGU21-13736 | vPICO presentations | ITS2.6/BG9

Enhancing on-farm diversity: drivers and constraints. A review.

Francesco Tacconi, Katharina Waha, Jonathan Ojeda, and Peat Leith

Enhancing and maintaining on-farm diversity represent a potential strategy to improve farming systems sustainability, by reducing the pressure on the natural environment, alleviating farmers' risks and vulnerabilities, and increasing farms resilience. However, farms are complex systems and on-farm diversification, intended as the production of multiple crop, trees and/or livestock species, is not a panacea and it is driven or constrained by different factors and dynamics that vary across environmental, socio-economic and political contexts.

We argue that identifying indicators that reflect these drivers, constraints and contexts at farm scale is crucial to create favourable conditions for the farmers to increase on-farm diversity where doing so is likely to be beneficial. Therefore, the aim of this paper is to identify and clarify some of the patterns behind the process that lead farmers to adopt farm diversification strategies in order to understand where investments and interventions to support diversification are likely to be appropriate and effective, and how they should be targeted.

In this review, we analysed 97 articles, selected from the screening of 2,312 articles retrieved from Web of Science and Scopus, and published in English in peer-reviewed journals since 2010. Our selection criteria required that the articles focused on the analysis of drivers and constraints of agricultural diversification, intended as crops and/or livestock systems, agrobiodiversity and agroforestry systems, at farm and household scale.

From the selected studies, we identified and extracted a total of 239 different variables that were statistically assessed as potential drivers and constraints of farm diversity at farm scale. For each of the variables we counted the times they resulted as positive, negative and statistically significant, or not statistically significant. To present and discuss the results, we followed the Sustainable Rural Livelihood Framework, classifying the extracted variables as external (agroecological context, the political and institutional context, and exposure to environmental and market risks and shocks) and internal factors (human, economic/financial, socio-cultural and physical capitals), or other livelihood options (i.e. off-farm income).

Our findings show that the decision to maintain or increase on-farm diversification is a common strategy to cope with environmental and market risks, but that it is often alternative and negatively correlated to the adoption of off-farm livelihood. Overall, the drivers and constraints of diversification were highly context-dependent and contingent. For some relevant variables, such as farm size, household head's age, rainfall level and education, we also found some evidence of the presence of non-linear (e.g. inverted-U) relationships.

These results enforce the hypothesis of the complexity of land uses decision and the importance of understanding farms’ and farmers’ characteristics, and their local and wider context when it comes to design policies and research projects for sustainable rural development.

How to cite: Tacconi, F., Waha, K., Ojeda, J., and Leith, P.: Enhancing on-farm diversity: drivers and constraints. A review., EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-13736, https://doi.org/10.5194/egusphere-egu21-13736, 2021.

EGU21-86 | vPICO presentations | ITS2.6/BG9

Identifying key determinants of ecosystem health in the middle reaches of Yangtze River Urban Agglomerations, China

Fengjian Ge, Jiangfeng Li, Wanxu Chen, Shubing Ouyang, Peng Han, and Shipeng Ye
With the rapid development of urbanization in China, urban circles and urban agglomerations are gradually formed among different cities, which in turn has brought large pressure to the ecological environment. As an important monitoring index for evaluating the sustainable development of cities, quantified evaluation on the eosystem health is lacked for urban agglomerations. In this study, ecosystem health was assessed based on the framework of ecosystem vigor, organization, resilience, and services (VORS) in the Middle Reaches of the Yangtze River Urban Agglomerations (MRYRUA) in 2000, 2005, 2010, and 2015 with county as research units. Using GeoDetector to quantitatively analyze the impact of seven factors (including the proportion of construction land, forest land, and water, land use degree, population, average annual precipitation, and digital elevation model (DEM)) on ecosystem health in different periods. The results showed that: (1) There were significant differences in the spatial distribution of ecosystem health. The ecosystem health in the central area of Wuhan Metropolis, Changsha-Zhuzhou-Xiangtan City Group, and Poyang Lake City Group were significantly lower than the surrounding areas; (2) From the time scale, the research units of ordinary well level gradually develop to relatively well and well levels. The research units of relatively weak and weak level remain relatively stable. (3) Land use degree was the main factor affecting on ecosystem health. Moreover, there were interactions between different factors affecting. The impact of factors on ecosystem health were bi-enhanced or nonlinear enhanced. (4) The impacts of the proportion of construction land on ecosystem health had become greater over the time, and risen from fourth in 2000 to second in 2015. Therefore, a reasonable layout of urban land use planning has an important impact on the ecosystem health.

How to cite: Ge, F., Li, J., Chen, W., Ouyang, S., Han, P., and Ye, S.: Identifying key determinants of ecosystem health in the middle reaches of Yangtze River Urban Agglomerations, China, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-86, https://doi.org/10.5194/egusphere-egu21-86, 2021.

EGU21-5790 | vPICO presentations | ITS2.6/BG9

Current soybean feed consumption in Luxembourg and reduction capability

Stéphanie Zimmer, Sabine Keßler, Laura Leimbrock-Rosch, and Marita Hoffmann

Soybeans (Glycine max (L) Merr.) are an important protein source in animal feed. In Luxembourg, 100% of soybeans are imported and soybean feed consumption is unknown. This study aims to calculate the Luxembourgish soybean needs for 2018 for its predominant livestock (cattle, poultry, pigs) in conventional and organic agriculture, respectively, and to assess the reduction potential of soybeans.

Luxembourg has an agricultural area of 131,844 ha of which 51.4% is grassland and 47.3% is arable land. In 2018, 5.4% of the farms and 4.4% of the agricultural area were managed organically. Livestock data in 2018 indicates that 196,093 suckler and dairy cows are being raised in Luxembourg, whereof 4,050 are organic. Pigs add up to 91,745 (organic: 892) and poultry to 123,502 animals (organic: 31,318).

Soybean feed consumption was calculated per animal and year using two different approaches: SoyaMax is based on common feeding rations and SoyaMin represents a minimized soybean use in feeding rations. SoyaMin equals the potential for soybean reduction in Luxembourg. Based on the crude protein need of monogastric animals and ruminants, the consumption of soybean extraction meal is calculated for each animal category.

For rearing piglets, a SoyaMax of 46.2 kg is calculated and for fattening pigs SoyaMax is 99.4 kg (SoyaMin: 55.3 kg). For sows SoyaMax is 134.0 kg (SoyaMin: 68.5 kg). In organic pig production SoyaMax equals SoyaMin for all pig categories and is 56.0 kg.

For laying hens SoyaMax results in 10.2 kg (SoyaMin: 5.6 kg), whereas in organic agriculture SoyaMax is 9.3 kg (SoyaMin: 5.6 kg). Broilers are fed with a SoyaMax of 12.5 kg which also equals SoyaMin. In organic broiler production SoyaMax equals SoyaMin and is 6.9 kg.

SoyaMax for milk cows is based on different feed rations with various proportions of grass and maize silage, resulting soya extraction meal (SEM) for energy compensation and a protein surplus of 1.5 kg. SoyaMax in conventional agriculture is 287.0 kg (SoyaMin: 207.0 kg). In organic dairy production feeding in winter contains soybean, whereas feeding in summer is soybean-free. SoyaMax in organic production is 90.0 kg (SoyaMin: 66.0 kg). Both, conventional and organic suckler cows are not fed with soybean. For cattle less than one year SoyaMax is 49.0 kg (SoyaMin: 0 kg) and for male beef cattle between one and two years, SoyaMax is 219.0 kg (SoyaMin 33.0 kg). No soybean is fed to organic cattle under two years old, and the same is true for conventional and organic heifers and breeding bulls.

In 2018, the calculated national consumption was 27,453 t of SEM. Feeding rations of ruminants accounted for 69%, and organic agriculture accounted for 1.3% of total SEM. Based on SoyaMin, the consumption could be reduced to 15,886 t. Luxembourg has a high potential of using grassland for feeding of dairy cows. Regarding high self-sufficiency with farm-grown fodder, SoyaMin and the lower livestock density in organic compared to conventional agriculture, organic agriculture could act as a role model to lower soybean needs and reach a higher protein-autarky in Luxembourg.

How to cite: Zimmer, S., Keßler, S., Leimbrock-Rosch, L., and Hoffmann, M.: Current soybean feed consumption in Luxembourg and reduction capability, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-5790, https://doi.org/10.5194/egusphere-egu21-5790, 2021.

EGU21-14357 | vPICO presentations | ITS2.6/BG9

Salinity tolerance in Scolymus hispanicus L: preliminary findings from a soilless cultivation

Dimitris Papadimitriou, Ioannis Daliakopoulos, Thrassyvoulos Manios, and Dimitrios Savvas

Introducing edible salt-tolerant plant species to professional cultivation is a concept compatible with the need of improving the resilience of food systems to shocks and stresses, which is  required to tackle eminent global challenges, such as water scarcity and climate change (Cuevas et al., 2019). Hydroponic systems can contribute to substantial savings of water, nutrients, and space, while increasing yield and produce quality (Savvas and Gruda, 2018). In the current study, we examined the feasibility of cultivating the wild edible green Scolymus hispanicus L. under moderate levels of salinity in a soilless cultivation system. The experiment was installed in October 2019, in an unheated saddle roof double-span greenhouse, as a completely randomized block design with 4 treatments and 4 blocks per treatment (Papadimitriou et al., 2020). Treatments were formed by supplying a standard nutrient solution (NS) with four NaCl concentrations (0.5, 5.0, 10.0, and 15.0 mM), resulting in electrical conductivities of 2.2, 2.8, 3.2, and 3.8 dS m-1, respectively. Measurements of chlorophyll fluorescence (Fv/Fm) and relative chlorophyll levels (SPAD), which were performed to assess the photosynthetic capacity of leaves, did not indicate any significant differences between the non-salinized control (0.5 mM NaCl) and the salinity treatments (5.0, 10.0, and 15.0 mM NaCl), until 60 days after seedling transplanting (DAT). However, by 90 DAT, salinity levels of 10.0 and 15.0 mM significantly reduced leaf chlorophyll levels, as indicated by the SPAD indices, compared to 5.0 and 0.5 mM NaCl in the supplied NS. Moreover, by 90 DAT, the chlorophyll fluorescence (Fv/Fm) was significantly reduced at the salinity level of 15.0 mM compared to 0.5 and 5.0 mM. Nevertheless, no salinity treatment had a significant impact on leaf fresh weight, root fresh weight, rosette diameter, number of leaves and post-harvest storability in plants harvested 90 and 120 DAT, compared to the control. Based on these results, S. hispanicus L. exhibits a considerable resilience to moderate salinity and can be considered a promising candidate plant for introduction in hydroponic cropping systems.

Acknowledgements

The research work was supported by the Hellenic Foundation for Research and Innovation (HFRI) under the HFRI PhD Fellowship grant (Fellowship Number: 240).

References

Cuevas, J., Daliakopoulos, I.N., del Moral, F., Hueso, J.J., Tsanis, I.K., 2019. A Review of Soil-Improving Cropping Systems for Soil Salinization. Agronomy 9, 295. https://doi.org/10.3390/agronomy9060295

Papadimitriou, D., Kontaxakis, E., Daliakopoulos, I., Manios, T., Savvas, D., 2020. Effect of N:K Ratio and Electrical Conductivity of Nutrient Solution on Growth and Yield of Hydroponically Grown Golden Thistle (Scolymus hispanicus L.). Proceedings 30, 87.https://doi.org/10.3390/proceedings2019030087

Savvas, D., Gruda, N., 2018. Application of soilless culture technologies in the modern greenhouse industry - A review. Europ. J. Hort. Sci. 83, 280-293.

How to cite: Papadimitriou, D., Daliakopoulos, I., Manios, T., and Savvas, D.: Salinity tolerance in Scolymus hispanicus L: preliminary findings from a soilless cultivation, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-14357, https://doi.org/10.5194/egusphere-egu21-14357, 2021.

EGU21-9859 | vPICO presentations | ITS2.6/BG9

Influence of different nitrogen inhibitors on maize yield

Maria Heiling, Mahdi Shorafa, Rayehe Mirkhani, Elden Willems, Arsenio Toloza, Christian Resch, Lee Kheng Heng, and Gerd Dercon

Nitrogen (N) fertilizer management is challenging due to the many factors and have low N use efficiency (NUE). Heavy N losses from soil reduce plant yield and have negative impacts on the environment. Nitrogen processes inhibitors, such as urease and nitrification inhibitors (UI and NI), are chemical compounds which reduce urea hydrolysis and nitrification respectively. By coating ammonium based chemical fertilizers with N process inhibitors allows N to stay in a more stable form of ammonium (NH4+) thus minimising N losses as well as improving NUE and consequently enhancing crop yield.

A field experiment was established at the Soil and Water Management and Crop Nutrition Laboratory (SWMCNL) in Seibersdorf, Austria to determine the effect of different N fertilizers coated with N process inhibitors on maize yield in summer 2020. The field site is characterised by a moderately shallow Chernozem soil with significant gravel content. Three combinations of N fertilizer (urea or NPK) with N process inhibitors (UI and/or NI)) were tested and compared with a control treatment (without N fertilizer) and a urea application without any inhibitor. All treatments received 60 kg ha-1 P2O5 and 146 kg ha-1 K2O. The amount of N added to each treatment receiving N fertilizer was 120 kg N ha-1. The inhibitors used were (i) UI (2-NPT: N-(2-nitrophenyl) phosphoric acid triamide), (ii) NI-1 (MPA: N-[3(5)-methyl-1H-pyrazol-1-yl) methyl] acetamide), and (iii) NI-2 (DMPP: 3,4-dimethylpyrazole phosphate). DMPP, a nitrification inhibitor, was used in combination with NPK fertilizer. A randomized complete block design with four replications was used in this study. Treatments were: T1 (control treatment - without N fertilizer), T2 (Urea only), T3 (Urea + UI), T4 (Urea + UI + NI-1), and T5 (NPK + NI-2). Urea was applied through two split applications in the T2 treatment. In T3, T4, and T5 treatments, N fertilizers were applied only once. Supplemental irrigation was only applied in the early stages of growth, to ensure that the crop could establish. Harvest was carried out at 98 days after planting.

The yield data showed that different fertilizer treatments had a significant (p ≤ 0.01) effect on maize yield (dry matter production). There was no significant difference between treatments 4 and 5, which had the highest yield followed by treatments 2 and 3. The comparison between T2 and T3 showed that the application of a urease inhibitor avoids the need for a split application of urea, which decreases labour costs. Adding NI-1 (under T4) further increases the yield. Also, the package of NPK, a common choice by farmers in Austria, in combination with the nitrification inhibitor NI-2 showed equally good results as urea combined with two inhibitors. Based on the yield results, it can be concluded that N process inhibitors play a significant role in enhancing maize yields.

How to cite: Heiling, M., Shorafa, M., Mirkhani, R., Willems, E., Toloza, A., Resch, C., Heng, L. K., and Dercon, G.: Influence of different nitrogen inhibitors on maize yield, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-9859, https://doi.org/10.5194/egusphere-egu21-9859, 2021.

EGU21-9684 | vPICO presentations | ITS2.6/BG9

Influence of nitrification inhibitor (Nitrapyrin) on winter wheat yield 

Rayehe Mirkhani, Mohammad Sajad Ghavami, Elnaz Ahmadi, and Ebrahim Moghiseh

Nitrogen (N) is a crop nutrient that is commonly applied as fertilizer, however the dynamic nature of N and its propensity for loss from soil‐plant systems creates a unique and challenging environment for its efficient management. Nitrification inhibitors (NIs) are compounds that can reduce the bacterial oxidation of NH4+ to NO2 by inhibiting the activity of ammonia-oxidizing bacteria and maintaining a higher proportion of applied nitrogen in the soil by preventing nitrate loss from leaching and gaseous N losses from nitrification and denitrification. The organic compound 2-chloro-6-(tri-chloromethyl) pyridine, commonly known as nitrapyrin (NP), is such a nitrification inhibitor that is used in agriculture. The objective of this study was to investigate the effect of NI (NP) on winter wheat yield compared to farmers practice without NI at a given N rate and same number of N split applications.

A randomized complete block design in five replications was used in this study. Treatments were: T1 (control treatment - without urea), T2 (farmers practice - 300 kg urea/ha), and T3 (urea+NP - 300 kg urea/ha). Urea was applied in three split applications at tillering, stem elongation and booting stages in treatments T2 (farmers practice) and T3 (urea+NP). The average grain yield of winter wheat was 8.7 t ha-1 for the farmers practice (T2) and 9.1 t ha-1 for the urea+NP treatment (T3) at the same number of split fertilizer applications.

The crop yield data showed that urea applied with NP (T3) did increase only slightly grain yield, as compared to farmers practice (T2). The grain yield increase with NP was about 4%, however the statistical analysis showed that this increase due to the application of urea with NP was not significant. Further research is needed to investigate additional nitrification inhibitors and their effect on wheat production.

How to cite: Mirkhani, R., Ghavami, M. S., Ahmadi, E., and Moghiseh, E.: Influence of nitrification inhibitor (Nitrapyrin) on winter wheat yield , EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-9684, https://doi.org/10.5194/egusphere-egu21-9684, 2021.

ITS2.7/ESSI2 – Detecting and Monitoring Plastic Pollution in Rivers, Lakes, and Oceans.

EGU21-4017 | vPICO presentations | ITS2.7/ESSI2

Plastic plants: Long-term monitoring of macroplastic entrapment by water hyacinths in the Saigon river 

Louise Schreyers, Tim van Emmerik, Thanh-Khiet L. Bui, Lauren Biermann, Dung Le Quang, Niels Janssens, Emily Strady, Nguyen Hong Quan, Dung Duc Tran, and Martine van der Ploeg

Our recent field-based study undertaken at the Saigon river, Vietnam, shows that water hyacinths are responsible for entraining and transporting a majority of floating macroplastic litter. These invasive, free-floating water plants can form patches of several meters in length and width and tend to aggregate large amounts of plastic litter. Over the course of a six-week study, we demonstrated that 78% of the floating macroplastic observed were carried downstream accumulated within these floating plant patches.

The strong seasonality of water hyacinths, coupled with the temporal variability in macroplastic flux, calls for a longer monitoring effort. To this end, a one-year monitoring campaign is currently being undertaken at the Saigon river, which will apply satellite imagery, drone, camera imagery analysis and visual counting from bridges. Combined, these methods can help to characterize the contribution of hyacinths to macroplastic transport and accumulation at different temporal (from hours/days to weeks/months) and spatial (from sample sites to the river system) scales.

We evaluate the selected monitoring techniques, and present the preliminary results of this large-scale monitoring effort. We provide the first scientific overview of the contribution of water hyacinths in plastic transport relative to the total plastic transport, and its spatiotemporal variability. In addition, we assess the monitoring techniques used and provide suggestions for similar long-term monitoring strategies.

How to cite: Schreyers, L., van Emmerik, T., L. Bui, T.-K., Biermann, L., Le Quang, D., Janssens, N., Strady, E., Hong Quan, N., Duc Tran, D., and van der Ploeg, M.: Plastic plants: Long-term monitoring of macroplastic entrapment by water hyacinths in the Saigon river , EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-4017, https://doi.org/10.5194/egusphere-egu21-4017, 2021.

EGU21-9156 | vPICO presentations | ITS2.7/ESSI2

Towards a coastal marine litter observatory with combination of drone imagery, artificial intelligence, and citizen science

Konstantinos Topouzelis, Apostolos Papakonstantinou, Marios Batsaris, Ioannis Moutzouris, Spyros Spondylidis, and Argyris Moustakas

The presence of plastic litters in the coastal zone has been recognized as a significant problem. It can dramatically affect flora and fauna and lead to severe economic impacts on coastal communities, tourism and fishing industries. Traditional beach litter reports include individual transects on the beach, reporting on the litter's presence through a dedicated measuring protocol. In the new era of drone imagery, a new integrated coastal marine litter observatory is proposed. This observatory is based on aerial images acquired through citizen science using low cost self-owned drones and the automatic identification of litter accumulation zones through computer vision. The methodology consists of four steps: i) a dedicated protocol for acquiring drone imagery from non-experienced citizens using commercial drones, ii) image pre-processing (image tiling and geo-enrichment) and crowdsourced annotation, iii) data classification to litter and no litter though an artificial intelligence classification approach and iv) marine litter density maps creation and reporting. The resulted density maps currently are produced calculating the tiles containing litter at areas of hundred square meters on the beach and the entire process requires some minutes to run once the aerial data is uploaded online. The density maps automatically are reported to a spatial data infrastructure, ideal for time series analysis. Classification accuracy calculated against manual identification of 77.6%. The coastal marine litter observatory presents several benefits against traditional reporting methods, i.e. improved measurement of the policies against plastic pollution, validating marine litter transportation models, monitoring the SDG Indicator 14.1.1, and most important, guiding the cleaning efforts towards areas with a significant amount of litter.

How to cite: Topouzelis, K., Papakonstantinou, A., Batsaris, M., Moutzouris, I., Spondylidis, S., and Moustakas, A.: Towards a coastal marine litter observatory with combination of drone imagery, artificial intelligence, and citizen science, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-9156, https://doi.org/10.5194/egusphere-egu21-9156, 2021.

EGU21-10730 | vPICO presentations | ITS2.7/ESSI2

I spy with my hyperspectral eye: unique reflectance database of plastics and riverbank-harvested litter

Paolo Tasseron, Tim van Emmerik, Joseph Peller, Louise Schreyers, and Lauren Biermann

Airborne and spaceborne remote sensing (RS) collecting hyperspectral imagery provides a promising way forward for detection and monitoring of riverine and marine plastic pollution. However, a major challenge in the application of RS techniques is the lack of fundamental understanding of spectral signatures of floating plastic debris at multiple scales. Recent work emphasised the case for open-access hyperspectral reflectance reference libraries of commonly used polymer items. In this paper, we present a high-resolution hyperspectral image database of a unique mix of (i) 40 virgin macroplastic items, (ii) organic material of plant leaves, tree leaves and riparian vegetation, and (iii) 50 items of riverbank-harvested macrolitter including plastics and other anthropogenic debris. We used a double camera setup that covered the VIS-SWIR range from 400-1700 nm in a dark room experiment with controlled illumination. The cameras scanned the samples floating in water and captured high-resolution images in 336 spectral bands. From these images we identified diagnostic absorption features for different materials, item categories, and plastic polymers. The identification was done by applying a linear discriminant analysis to the spectra, allowing the creation of combined band indices distinguishing between the different item types and polymer categories. We present reflectance spectra of all items in our image dataset, complemented by easy-to-interpret visual representations of derived indices. We demonstrate the importance of high-resolution reference reflectance libraries, to (i) further optimise existing remote sensing monitoring techniques, and (ii) contribute towards the development of future plastic monitoring and classification missions.

How to cite: Tasseron, P., van Emmerik, T., Peller, J., Schreyers, L., and Biermann, L.: I spy with my hyperspectral eye: unique reflectance database of plastics and riverbank-harvested litter, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-10730, https://doi.org/10.5194/egusphere-egu21-10730, 2021.

EGU21-12085 | vPICO presentations | ITS2.7/ESSI2

Monitoring plastic accumulation in water hyacinths using remote sensing

Niels Janssens, Lauren Biermann, Louise Schreyers, Martin Herold, and Tim van Emmerik

While efforts to quantify plastic waste accumulation in the marine environment are rapidly increasing, the data on plastic transport in rivers are relatively scarce. Rivers are a major source of plastic waste into the oceans and understanding seasonal dynamics of macroplastic transport is necessary to develop effective mitigation measures. Macroplastic transport in rivers varies significantly throughout the year. Research shows that in the case of the Saigon river, Vietnam, these plastic transport fluxes are mainly correlated to the amount of organic debris (mostly water hyacinths). Since large water hyacinths patches can be monitored from space, this gives the opportunity for large scale monitoring using freely available remote sensing products. Remote sensing products, such as Sentinel-2, can be applied to areas where water hyacinths occur and plastic emissions are estimated to be high. In this study, we present a first method to detect and monitor water hyacinths using optical remote sensing. This was done by developing an algorithm to automatically detect and quantify water hyacinth coverage for a large section of the Saigon river in Vietnam, for the year 2018. Spectral signatures of water,  infrastructure in the river, and water hyacinths were used to classify the water hyacinths coverage and dynamics using a Naive Bayes algorithm. Water hyacinths were promisingly identified with 95% accuracy by the Naive Bayes classifier. The comparison between the seasonal dynamics of classified water hyacinth and seasonal dynamics of the field measurements resulted in an overall Pearson correlation of 0.72. The comparison we attempted between seasonal dynamics of plastics from satellite and field measurements yielded a Pearson correlation of 0.48. With the next field campaign collecting in-situ data matched to satellite overpasses, we aim to improve this. In conclusion, we were able to successfully map seasonal dynamics of water hyacinth in an automated way using Sentinel-2 data. Our study provides the first step in exploring the possibilities of mapping water hyacinth from satellite as a proxy for river plastics.

How to cite: Janssens, N., Biermann, L., Schreyers, L., Herold, M., and van Emmerik, T.: Monitoring plastic accumulation in water hyacinths using remote sensing, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-12085, https://doi.org/10.5194/egusphere-egu21-12085, 2021.

EGU21-12168 | vPICO presentations | ITS2.7/ESSI2

On the use of drones to detect and map marine macro-litter on the North Atlantic Portuguese beach-dune systems: the experiences of UAS4Litter project

Umberto Andriolo, Gil Gonçalves, Filipa Bessa, Paula Sobral, Luis Pinto, Diogo Duarte, Angela Fontán-Bouzas, and Luisa Gonçalves

Unmanned Aerial Systems (UAS, aka drones) are being used to map marine macro-litter on the coast. Within the UAS4Litter project, the application of UAS has been applied on three sandy beach-dune systems on the wave-dominated North Atlantic Portuguese coast. Several technical solutions have been tested in terms of drone mapping performance, manual image screening and marine litter map analysis. The conceptualization and implementation of a multidisciplinary framework allowed to improve and making more efficient the mapping of marine litter items with UAS on coastal environment. 

The location of major marine litter loads within the monitored areas were found associated to beach slope and water level dynamics on the beach profiles. Moreover, the abundance of marine pollution was related to the geographical location and level of urbanization of the study sites. The testing of machine learning techniques underlined that automated technique returned reliable abundance map of marine litter, while manual image screening was required for a detailed categorization of the items. 

As marine litter pollution on coastal dunes has received limited scientific attention when compared with sandy shores, a novel non-intrusive UAS-based marine litter survey have been also applied to quantify the level of contamination on coastal dunes. The results showed the influence of the different dune plant communities in trapping distinct type of marine litter, and the role played by wind and overwash events in defining the items pathways through the dune blowouts. 

The experiences on the Portuguese coast show that UAS allows an integrated approach for marine litter mapping, beach morphodynamic and nearshore hydrodynamic, setting the ground for marine litter dynamic modelling on the shore. Besides, UAS can give a new impulse to coastal dune litter monitoring, where the long residence time of marine debris threat the bio-ecological equilibrium of these ecosystems.

How to cite: Andriolo, U., Gonçalves, G., Bessa, F., Sobral, P., Pinto, L., Duarte, D., Fontán-Bouzas, A., and Gonçalves, L.: On the use of drones to detect and map marine macro-litter on the North Atlantic Portuguese beach-dune systems: the experiences of UAS4Litter project, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-12168, https://doi.org/10.5194/egusphere-egu21-12168, 2021.

EGU21-12272 | vPICO presentations | ITS2.7/ESSI2

Detecting plastic items in the water column using an Acoustic Doppler Current Profiler (ADCP)

Frans Buschman and Sophie Broere

An Acoustic Doppler Current Profiler (ADCP) is commonly used to monitor flow velocity. An accurate method to obtain discharge in a river or a channel is to mount an ADCP to a boat and sail transects across the channel. Additionally, these surveys may also be used to obtain the amount of plastic items in the water column. The transport of plastic items suspended in the water column may be substantial and is more challenging to monitor than the transport of floating items. We carried out a feasibility test in a harbour of a river. We deployed the ADCP horizontally at 1.0 m depth and released plastic items (and similarly shaped organic items for comparison) 5 times at 1.0, 3.0 and 5.0 m from the ADCP. We compared the signal strength in a 5 s period after release with the background signal strength.

The item was steady within the detection volume for the majority of the 5 s periods. Three out of five plastic items had signal strengths a least 5 dB higher than the background strength (at several distances). We conclude that at least these items were detected. The similarly shaped organic items generally had a lower signal strength. Although the response of each item as a function of orientation, distance along and across the beam should be investigated further, the feasibility study shows the potential to additionally determine the amount of plastic items in the water column from ADCP observations.  

How to cite: Buschman, F. and Broere, S.: Detecting plastic items in the water column using an Acoustic Doppler Current Profiler (ADCP), EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-12272, https://doi.org/10.5194/egusphere-egu21-12272, 2021.

EGU21-14802 | vPICO presentations | ITS2.7/ESSI2

BEWATS: BEACH WASTE TRACKING SYSTEM USING SATELLITE, UAV’s and MARINE DYNAMICS MODELS. 

Omjyoti Dutta, Beatriz Revilla-Romero, Adrian Sanz-Díaz, Fernando Martin-Rodriguez, Orentino Mojon-Ojea, Ana Mancho, Guillermo García-Sánchez, and Gerard Margarit Martín

Marine litter is a growing problem that advances parallel to economic and industrial development and seriously affects ecosystems. One of the most abundant pollutants are plastics. The BEWATS project focuses on innovative tools for remote marine litter control and management through satellite and UAV’s. The areas of study are currently at the Vigo coast in Galicia (North-West of Spain). In this area, there are many high natural value beaches including Nature Reserve and part of a National Park. These beaches are receiving an increasing amount of marine litter, mainly plastic, helped by strong currents in the area. Every few months, these beaches are clean and the collected litter information tracked. In this context, the BEWATS project concentrates on tracking the possible path through which marine litter reaches the area of interest. In this presentation, we will discuss how this is achieved by data fusion from UAV imagery, marine dynamics model simulations and Earth-observation satellite data (Sentinel-2). To detect possible marine litter, we have developed a novel synthetic data-based approach to marine litter detection using Sentinel-2 images and machine learning techniques. Within this approach, one can classify and quantify according to pixel-level litter fraction present. We have validated our approach with existing open-sourced available datasets.  

The BEWATS project is led by Vigo University, which provides UAV’s imagery, and the Spanish Research Council (CSIC) provides marine dynamics models for tracking waste routes and delineation of waste concentration zones. In this context, GMV provides Earth observation based solution of detecting marine litter. BEWATS is founded by the Biodiversity Foundation of the Spanish Ministry for the Ecological Transition and the Demographic Challenge.

How to cite: Dutta, O., Revilla-Romero, B., Sanz-Díaz, A., Martin-Rodriguez, F., Mojon-Ojea, O., Mancho, A., García-Sánchez, G., and Margarit Martín, G.: BEWATS: BEACH WASTE TRACKING SYSTEM USING SATELLITE, UAV’s and MARINE DYNAMICS MODELS. , EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-14802, https://doi.org/10.5194/egusphere-egu21-14802, 2021.

EGU21-15150 | vPICO presentations | ITS2.7/ESSI2

Microplastics in sediments of Santa Teresa water reservoir (Salamanca, Spain): methodology and sources. 

Juan Morales, Juan Gómez Barreiro, Aroa Marcos Pascual, and Santos Barrios Sánchez

Plastics stand out for being cheap, lightweight, easily moulded and highly endurance materials, hence used in a wide range of applications all around the planet.  One of the reasons of the amazing durability is that common plastics are not biodegradable, being able to remain intact several hundreds of years on the environment. However, an important phenomenon occurring on plastics is the degradation, which can be due to the UV radiation, either mechanical or biological activity, temperature degradation, or even hydrolysis processes, making the plastic materials weaker and fragile, fractioning the plastics into smaller and smaller pieces.
Because of their low density, plastic fragments can be affected by long distance transport on the water column. In addition, some plastics fractions could be incorporated into the sediments, which, in the long-term, could act as a secondary source of plastics. Due to the presence of chemicals, either as additives or sorbed contaminants on their surfaces, plastic materials have become a global environmental concern and need to be evaluated.
Despite the great amount of research done in marine water transport and debris of plastic, freshwater environments remain less known.  Microplastics (MP) have been observed in both sediments and water samples of lakes and rivers. Water reservoirs are critical sites in terms of water supply management and have to be monitored for MP at different scales both in water and sediments. There exist different sources of microplastics in continental waters like urban runoff, sewage sludge or agricultural wastes. In this sense, wastewater treatment plants have been identified as one of the main sources for the release of plastics into freshwater and terrestrial environments which may lead to further concern.
Here, we study the distribution of microplastics in sediments found in the Santa Teresa water reservoir, in Salamanca (Spain), in the area close to Guijuelo town. The aim of this work is to optimize a methodology to study the influence of the outputs from wastewater treatment plant and to evaluate how plastics distribution in sediments around the reservoir is related to the plant. Our work also deals with the seasonality by analysing spring and fall sediments for differences on microplastic densities and composition, along different stations downstream Tormes River. MP morphological analyses was use to categorize particles from different grain-size fractions. Raman microspectroscopy was used to characterize microplastics, while optical microscopy allowed us quantifying microplastics of each sediment sample and grain-size fraction.
Our results show that there is positive correlation between microplastics density in sediments and the proximity to the plant. Most of the microplastics found in sediments are related to fibers potentially from industrial, urban and agricultural origins, most likely coming from the wastewater treatment plant.

How to cite: Morales, J., Gómez Barreiro, J., Marcos Pascual, A., and Barrios Sánchez, S.: Microplastics in sediments of Santa Teresa water reservoir (Salamanca, Spain): methodology and sources. , EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-15150, https://doi.org/10.5194/egusphere-egu21-15150, 2021.

EGU21-15204 | vPICO presentations | ITS2.7/ESSI2

Vessel-based optical data and artificial intelligence for sampling mega-plastic concentrations on the high seas

Robin de Vries, Matthias Egger, Thomas Mani, and Laurent Lebreton

Remote sensing of marine debris has seen recent successes in coastal regions. However, these approaches focus on the detection of large accumulations of marine debris, often mixed with organic waste and related to events. Individual large plastic items (macroplastics, > 50cm) in remote marine environments are a substantial part of the marine debris surface mass budget, yet  remain poorly quantified.

Current knowledge on the accumulation of macroplastic debris at the ocean surface is mostly limited due to methodological constraints. Macroplastics are typically too large for collection by neuston trawls. Furthermore, the relatively small sea surface area typically investigated during offshore research expeditions often is too small to account for the low areal concentrations of macroplastics. Given the importance of macroplastic in the global ocean plastic mass balance, quantitative information on the spatiotemporal distribution of macroplastics afloat in the surface ocean are urgently needed.

By now, our location-enabled action camera's on-board vessels of opportunity have recorded a vast amount of optical data from the North Pacific and North Atlantic Ocean (approximately 1 million images). By selection and labelling of occurrences of debris in images, we have trained an object detection and localization algorithm. We use the camera’s intrinsic parameters to estimate relevant sampling parameters, such as size and distance of each object detected. An overview of numerical concentrations is generated by combining the object detection solution with bulk processing of the optical data. The first results are promising and well-comparable to sampling methods applicable to smaller debris size classes, such as surface neuston nets.

How to cite: de Vries, R., Egger, M., Mani, T., and Lebreton, L.: Vessel-based optical data and artificial intelligence for sampling mega-plastic concentrations on the high seas, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-15204, https://doi.org/10.5194/egusphere-egu21-15204, 2021.

EGU21-15243 | vPICO presentations | ITS2.7/ESSI2

Detecting and Classifying Marine Plastic Debris from high-resolution multispectral satellite data

Aikaterini Kikaki, Ioannis Kakogeorgiou, Paraskevi Mikeli, Dionysios E. Raitsos, and Konstantinos Karantzalos

Plastic debris in the global ocean is considered an essential issue with severe implications for human health and marine ecosystems. Remote sensing is a useful tool for detecting and identifying marine pollution; however, there are still few studies and benchmark datasets for developing monitoring solutions for marine plastic debris detection from high-resolution satellite data.

Here, we present an annotated plastic debris dataset from different geographical regions, seasons, and years, including annotations for sea surface features (e.g., foam), objects (e.g., ship) and floating macroalgae species such as Sargassum. Our dataset is based on high-resolution multispectral satellite observations collected mainly for the period 2014-2020 over the Gulf of Honduras (Caribbean Sea). Over this region, large plastic debris masses and Sargassum macroalgae blooms have been frequently reported, suggesting that it is an ideal region to examine satellite sensors' effectiveness in plastic debris identification, as well as monitoring along with sea surface circulation and meteorological data.

We also present a set of machine learning classification frameworks for marine debris detection on high-resolution satellite imagery, comparing qualitatively and quantitatively their overall performance. The new algorithms were validated against different regions that have been reported as major plastic polluted areas, as well as their performance was compared to well-established FAI and new promising FDI. This benchmark study can trigger more research and developement efforts towards the systematic detection and monitoring of marine plastic pollution.

How to cite: Kikaki, A., Kakogeorgiou, I., Mikeli, P., Raitsos, D. E., and Karantzalos, K.: Detecting and Classifying Marine Plastic Debris from high-resolution multispectral satellite data, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-15243, https://doi.org/10.5194/egusphere-egu21-15243, 2021.

EGU21-15275 | vPICO presentations | ITS2.7/ESSI2

Mapping Windrows as Proxies for Marine Litter Monitoring from Space (WASP)

Manuel Arias, Romain Sumerot, James Delaney, Fatimatou Coulibaly, Andres Cozar, Stefano Aliani, Giuseppe Suaria, Theodora Papadopoulou, and Paolo Corradi

WASP (Windrows AS Proxies) is a data processor, developed in the frame of the European Space Agency (ESA) OSIP Campaign, exploiting Copernicus Sentinel-2 L1C images to detect and catalogue the presence of filaments of floating marine debris with high probability of containing man-made litter. WASP takes advantage of the prototype EO data processor developed in the frame of ESA project  “Earth Observation (EO) Track for Marine Litter (ML) in the Mediterranean Sea” that successfully proved for first time that Copernicus Sentinel-2 data can detect the presence of marine litter accumulations as proxies of plastic litter content.

WASP puts significant effort in masking unneeded data that has been source of false-positive detections, including sun glint and clouds. Also, a new spectral analysis technique has been employed to identify the most promising Copernicus Sentinel-2 bands to be used in the detection of such filaments, which has also led to the construction of a novel spectral index WASP Spectral Index (WSI). This index enables the detection of filaments of floating debris.

The images processed using WSI are transformed into binary masks to be analysed by a deterministic object classifier, which looks at the geometry and shapes of the detections to identify ML windrows within them and separate them from background noise and/or false positives. This enables automatic processing and classification of the images, which makes possible to generate regional and/or local databases of remote-sensed floating debris, which can be exploited by means of geostatistics to support research and monitoring of marine litter in the environment.

These implementations are also supported with the introduction of advanced super-resolution techniques that are downscaling the spatial resolution of the bands to 10m, well beyond the simple interpolation, yielding better quality on the results.

In a preliminary assessment, the implemented proposed algorithm has proven to be successful in identifying windrows even when those are too thin to be visible in True Colour images by the naked eye. Nevertheless, some drawbacks/limitations have been found, principally associated to residual limitations when removing bad data, and with the special case of the problematic wave glint, well known in the Sentinel-2 data but of difficult solution.

Once the entire Sentinel-2 archive over the Mediterranean Sea is processed and following an in-depth analysis, a database of the identified proxies, including spatial and temporal patterns will be created over this initial region. The final EO product will be a map of on sub-mesoscale marine debris concentrations in the Mediterranean Sea based on Copernicus Sentinel-2. The product will consist on a census of these structures for each processed tile for the Mediterranean Sea, with potential for global scalability. Scientific research, cleaning activities and policy making on marine litter are only a few of the activities that could benefit from such a product.

This activity collaborates on the “Remote Sensing of Marine Litter and Debris” IOCCG taskforce.

How to cite: Arias, M., Sumerot, R., Delaney, J., Coulibaly, F., Cozar, A., Aliani, S., Suaria, G., Papadopoulou, T., and Corradi, P.: Mapping Windrows as Proxies for Marine Litter Monitoring from Space (WASP), EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-15275, https://doi.org/10.5194/egusphere-egu21-15275, 2021.

EGU21-16384 | vPICO presentations | ITS2.7/ESSI2

Satellite Detection of Ghost Nets and Plastic Debris in Pacific Atolls

Yangrong Ling, Lauren Biermann, Mark Manuel, Ellen Ramirez, Austin Coates, Megan Gallagher, and Davida Streett

Since 2014, the NOAA Satellite Analysis Branch has used high resolution optical satellite imagery in an effort to detect ghost nets (derelict fishing gear) and other large plastic debris in the Pacific Ocean and its atolls in support of clean-up efforts (by the NOAA Pacific Islands Fisheries Science Center, Ocean Voyages Institute, etc.). Until recently, reliable detection has proven challenging. With the application of Worldview imagery matched to in situ information on known net locations, we have been able to extract spectral signatures of floating plastics and use these to detect and identify other instances of plastic debris. Using ENVI’s Spectral Angle Mapper (SAM) target detection method, a number of likely locations of nets/plastics in the Pearl and Hermes atoll in the Northwestern Hawaiian Islands (NWHI) were highlighted. The resulting locations of the 41 debris detections were strikingly similar to the distributions along the coast reported in surveys, and are consistent with those that would be expected due to the seasonal ocean currents. This satellite imagery analysis procedure will be repeated shortly before the next NWHI clean-up effort, which will better enable us to support the removal of ghost nets and other marine plastics, and also assess the accuracy and rapid reproducibility of the technique.

How to cite: Ling, Y., Biermann, L., Manuel, M., Ramirez, E., Coates, A., Gallagher, M., and Streett, D.: Satellite Detection of Ghost Nets and Plastic Debris in Pacific Atolls, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-16384, https://doi.org/10.5194/egusphere-egu21-16384, 2021.

ITS2.9/SSS3 – Land degradation in savanna environments - assessments, dynamics and implications

EGU21-1041 | vPICO presentations | ITS2.9/SSS3

Crust formation on sandy savanna cropland soils and their potential to reduce dust emissions

Heleen Vos, Wolfgang Fister, Frank Eckardt, Anthony Palmer, and Nikolaus Kuhn

After the conversion to cropland, dust emissions can lead to the degradation of agricultural soil. There are also offsite effects of dust emission due to the impact of dust on climate, human health, and global biogeochemistry. The sandy croplands in the Free State of South Africa have been identified by Eckardt et al. (2020) as one of the main dust sources in South Africa. The Free State is a semi-arid province that is dominated by grassland plains and 31% of the land is utilized for agriculture. The emission of dust from sandy Luvisols and Arenosols, which are typically used for crop farming, is mainly controlled by the cropping cycle. In general, the fields are left bare from at least July until December. When the fields have low surface roughness and stubble cover, the presence of physical soil crusts could be one of the main factors protecting the surface against wind erosion. Crusts can form before or during the growing season, before the vegetation cover is too extensive and protects the soil from raindrop impact. The aim of this study was to investigate the occurrence and strength of physical soil crusts on cropland soils in the Free State, to identify the rainfall required to form a stable crust, and to test their impact on dust emissions. Crust strength was measured using a fall cone penetrometer and a torvane, while laboratory rainfall simulations were used to form experimental crusts. Dust emissions from non-crusted and crusted soils were measured and compared with a Portable In-Situ Wind Erosion Laboratory (PI-SWERL).

Our results show that crusts with sufficient strength to limit dust emissions form on bare Arenosols and Luvisols in the field, illustrating their potential impact on dust emissions. The laboratory rainfall simulations showed that stable crusts could be formed on these soils by 15 mm of rainfall, which is a common amount for single events during the rainy season in the Free State. The PI-SWERL experiments illustrated that the PM10 emission flux of such crusted soils is between 0.14% and 0.26% of that of a non-crusted Luvisol and Arenosol, respectively. The presence of loose sand on the crust acts as an abrader and can increase the emissions up to 4% and 8 % of the non-crusted dust flux. Overall, our study shows that crusts in the field are potentially strong enough to protect the soil surfaces against wind erosion during a phase of the cropping cycle when the soil surface in not protected by plants. These conclusions are not limited to the converted grasslands in the Free State. This indicates that applying farming techniques on croplands that protect crusts or enhance crust formation could be considered as soil management approach to minimize dust emission from dryland sandy soils.

How to cite: Vos, H., Fister, W., Eckardt, F., Palmer, A., and Kuhn, N.: Crust formation on sandy savanna cropland soils and their potential to reduce dust emissions, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-1041, https://doi.org/10.5194/egusphere-egu21-1041, 2021.

EGU21-5747 | vPICO presentations | ITS2.9/SSS3

Semi-automated detection of gully slivers from a Digital Surface Model in rough agricultural terrain

George Olivier, Marco van de Wiel, and Willem de Clercq

Gully erosion is regarded as one of the worst land degradation processes in the world. Rapid identification of the location of gully features is urgently required, to aid in recognizing regions where gully erosion is prominent. Manual digitizing of gully features is both time consuming and prone to bias. Generating semi-automated or automated workflows to detect gully erosion allows quick and unbiased mapping of gully features over large extents.

In the Sandspruit catchment, South Africa, contour banks with a combined length of approximately 25000km have been constructed to mitigate soil erosion. Gullies are now mostly confined to narrow slivers in the natural vegetation, fynbos and Renosterveld, between agricultural fields. The morphological similarity and proximity of contour banks and gullies in this region provides a good test site to evaluate whether a semi-automated detection workflow could map gullies in complex, rough agricultural terrain.

Here, a Digital Surface Model (DSM) with a spatial resolution of 2m was used to test a semi-automated detection workflow in a Geographical Information System (GIS) environment. Two main building blocks were generated from the DSM: 1) a normalized DSM, created by subtracting a convolved mean DSM with a designated filter size from the original DSM, and 2) local slope generated from the normalized DSM. Subsequently, using expert knowledge, mapped gully polygons were refined and smoothed, by threshold determination, masking features not related to drainage, and pixel-based growing and shrinking. The semi-automated workflow was completed for two different spatial resolutions: 1) the native 2m-resolution and 2) a 0.5m-resolution DSM, upsampled without producing artificial values from interpolation methods. A GeoEye-1 image with a spatial resolution of 0.5m was included at the backend of the workflow as an additional step, to test whether gully mapping from using terrain attributes only, could be improved upon.     

Gully detection from terrain attributes only, achieved an overall accuracy of 0.68 (0.5m DSM) and 0.74 (2m DSM) with kappa values ranging from 0.36 (0.5m DSM) and 0.35 (2m DSM). The upsampled 0.5m DSM performed worse than the native 2m DSM due to increased noise detection. Although reasonable performance was obtained from the 2m DSM, issues encountered include: 1) vegetation that caused some inaccuracies in gully boundary delineation and discontinuities along gully channels and 2) false positive detection of contour banks. The addition of the GeoEye-1 image increased overall accuracy to 0.79 and kappa value to 0.5, mostly because of the elimination of false positives in agricultural fields.

The accuracy statistics indicate that the semi-automated detection workflow developed here shows promise as a tool to detect gully erosion on a catchment scale. Furthermore, due to the workflow being built upon the distinct morphology of gully features, it could be transferable to other regions that are dissimilar to the Sandspruit catchment. The transferability of the workflow should be tested in future, in addition to how accuracy would be affected if the DSM were substituted with a Digital Terrain Model (DTM) of similar spatial resolution.

How to cite: Olivier, G., van de Wiel, M., and de Clercq, W.: Semi-automated detection of gully slivers from a Digital Surface Model in rough agricultural terrain, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-5747, https://doi.org/10.5194/egusphere-egu21-5747, 2021.

EGU21-16468 | vPICO presentations | ITS2.9/SSS3 | Highlight

Incorporating vegetation trends to the MEDALUS-ESA approach for assessing environmental sensitivity at the national scale: the case of Kenya

Elias Symeonakis, Eva Arnau-Rosalén, Antony Wandera, Thomas Higginbottom, and Bradley Cain

Land degradation is one of the main causes of loss of productivity and ecosystem services worldwide. According to the United Nations Convention to Combat Desertification (UNCCD), sub-Saharan Africa is on a path to experiencing some of the strongest increases in pressures on land and land-based resources than any other continent. Assessing the sensitivity of sub-Saharan African countries to land degradation is, therefore, important for identifying areas of concern, setting a baseline for national land degradation neutrality targets, and for the prioritisation of mitigation measures. The widely used MEDALUS-ESA framework is employed here to assess the sensitivity of Kenya to land degradation using the year 2010 as a baseline. We modify the MEDALUS-ESA approach by adding two important variables that are closely linked with observed land degradation in Kenya: soil erosion and livestock density. Altogether, 16 indicators are estimated from existing global-to-national-scale land cover, vegetation (MCD12Q1, MOD44B), soil (ISRIC African SoilGrids), elevation (SRTM), population and livestock density data, divided into 4 main environmental quality indices (vegetation, soil, climate and management). In order to address the dynamic nature of the land degradation process, we incorporate two additional vegetation indicators: the statistically significant (p≤ 0.05) trend over the last three decades in the Normalised Difference Vegetation Index (NDVI) and the Rain Use Efficiency (RUE; estimated using the GIMMS3g dense NDVI dense time-series and precipitation from CHIRPS). Our results show that ~40% of the country is in critical and ~48% in fragile condition, with respect to environmental sensitivity. Our approach is successful in identifying areas of known long-term degradation, for example the rangelands South and East of Nairobi (e.g. Machacos and Kitengela) and the parts of the northern rangelands (e.g. Yamicha and eastern parts of Isiolo District). It is also successful in mapping the areas of least concern, including some of the major protected areas(e.g. Tsavo National Parks, Meru National Park and the Masai Mara National Reserve) and forested areas (Mt Kenya and the Aberdares). Our modification of the MEDALUS-ESA is an important tool that can be employed at the national scale using free and open-access data to assess environmental sensitivity and assist in the UNCCD efforts to successfully define land degradation neutrality targets.

How to cite: Symeonakis, E., Arnau-Rosalén, E., Wandera, A., Higginbottom, T., and Cain, B.: Incorporating vegetation trends to the MEDALUS-ESA approach for assessing environmental sensitivity at the national scale: the case of Kenya, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-16468, https://doi.org/10.5194/egusphere-egu21-16468, 2021.

EGU21-10052 | vPICO presentations | ITS2.9/SSS3

Analyzing trends of savannah degradation in Tanzania using Google Earth Engine and INLA 

Joris Wiethase, Rob Critchlow, and Colin Beale

Semiarid rangelands have been identified as at high risk of degradation as a result of changing socio-ecological conditions. Tanzanian savannahs are typical and some areas have become degraded in recent years, while other areas maintain resilience. To track pathways to degradation, we developed a workflow to create annual maps of degradation for all of Tanzania, at a high spatial (30m) and temporal (30+ years) resolution, as a function of bare ground and invasive plant cover. Making use of the freely available Google Earth Engine (GEE) computing platform, we created annual composites of Landsat remote sensing data. Using GEE machine learning algorithms, trained with data from extensive field surveys conducted in 2016, we predicted degradation scores for all of Tanzania from the Landsat composites. Our models produced significant correlations at the pixel level between test predictions and observations, rather better for the bare ground component of degradation than the invasive plants cover (bare ground r = 0.7, invasive plant cover r = 0.44). The resulting map provides an unprecedented data source for degradation in terms of extent and spatial resolution for the region. Through a novel data analysis approach using Integrated Nested Laplace Approximations (INLA), we show that degradation correlates with rainfall, human population and livestock density, as well as different management strategies. This study showcases the potential of GEE for analysing savannah degradation over large geographical areas, whilst highlighting the usefulness of INLA for this type of analysis.

How to cite: Wiethase, J., Critchlow, R., and Beale, C.: Analyzing trends of savannah degradation in Tanzania using Google Earth Engine and INLA , EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-10052, https://doi.org/10.5194/egusphere-egu21-10052, 2021.

EGU21-12808 | vPICO presentations | ITS2.9/SSS3

South African Land Degradation Monitor (SALDI) – An overview of recent advancements

Jussi Baade, Christiane Schmullius, Marcel Urban, Harald Kunstmann, Patrick Laux, Zhenyu Zhang, Christoph Glotzbach, Ursula Gessner, Andreas Hirner, Pawel Kluter, Insa Otte, Ilse Aucamp, George Chirima, Mohammed Abd Elbasit, Theunis Morgenthal, Izak Smit, Tercia Strydom, Jay J. Le Roux, Graham von Maltitz, and Thandi Msibe

For many decades the problem of land degradation has been an issue in South Africa. This is mainly due to the high variability of the mostly semi-arid climatic conditions providing a challenging environmental setting. Strong population growth and resulting socio-economic pressure on land resources aggravate the situation. Thus, reaching a number of Sustainable Development Goals (SDGs), like achieving food security (#2), access to clean water (#6), and the sustainable use of terrestrial (#15) and marine (#14) resources represents a challenge.

In South Africa, land degradation has been linked to the terms veld degradation and soil degradation and has been addressed by numerous measures over the past decades. However, there is still uncertainty on the extent of human induced land degradation as compared to periodic climate induced land surface property changes. In cooperation with South African institutions and stakeholders the overarching goal of SALDi is to implement novel, adaptive, and sustainable tools for assessing land degradation in multi-use landscapes. Building upon the state of the art in land degradation assessments, the project aims to advance current methodologies by innovatively incorporating inter-annual and seasonal variability in a spatially explicit approach. SALDi takes advantage of the emerging availability of high spatio-temporal resolution Earth observation data (e.g. Copernicus Sentinels, DLR TanDEM-X, NASA/USGS Landsat), growing sources of in-situ data and advancements in modelling approaches.

SALDi focusses on six study sites representing a major climate gradient from the (humid) winter-rainfall region in the SW across the (semi-arid) year-round rainfall to the (very humid) summer-rainfall region in the NE. The sites cover also different geological conditions and different agricultural practices. These include commercial, rain-fed and irrigated cropland, free-range cattle and sheep farming as well as communal and subsistence farming. Protected areas within our study regions represent benchmark sites, providing a foundation for baseline trend scenarios, against which climate-driven ecosystem-service dynamics of multi-used landscapes (cropland, rangeland, forests) will be evaluated.

The aim of this presentation is to provide an overview of recent activities and advancements in the three thematic fields addressed by the project:

i) to develop an automated system for high temporal frequency (bi-weekly) and spatial resolution (10 to 30 m) change detection monitoring of ecosystem service dynamics,

ii) to develop, adapt and apply a Regional Earth System Model (RESM) to South Africa and investigate the feedbacks between land surface properties and the regional climate,

iii) to advance current soil degradation process assessment tools for soil erosion.

A number of additional SALDi team member presentations will provide detailed information on current developments.

How to cite: Baade, J., Schmullius, C., Urban, M., Kunstmann, H., Laux, P., Zhang, Z., Glotzbach, C., Gessner, U., Hirner, A., Kluter, P., Otte, I., Aucamp, I., Chirima, G., Abd Elbasit, M., Morgenthal, T., Smit, I., Strydom, T., Le Roux, J. J., von Maltitz, G., and Msibe, T.: South African Land Degradation Monitor (SALDI) – An overview of recent advancements, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-12808, https://doi.org/10.5194/egusphere-egu21-12808, 2021.

EGU21-9418 | vPICO presentations | ITS2.9/SSS3

Evaluating the role of soil physical properties on simulated land-atmosphere interactions over South Africa using coupled atmosphere-hydrological modeling

Zhenyu Zhang, Patrick Laux, Joël Arnault, Jianhui Wei, Jussi Baade, Marcel Urban, and Harald Kunstmann

Land degradation with its direct impact on vegetation, surface soil layers and land surface albedo, has great relevance with the climate system. Assessing the climatic and ecological effects induced by land degradation requires a precise understanding of the interaction between the land surface and atmosphere. In coupled land-atmosphere modeling, the low boundary conditions impact the thermal and hydraulic exchanges at the land surface, therefore regulates the overlying atmosphere by land-atmosphere feedback processes. However, those land-atmosphere interactions are not convincingly represented in coupled land-atmosphere modeling applications. It is partly due to an approximate representation of hydrological processes in land surface modeling. Another source of uncertainties relates to the generalization of soil physical properties in the modeling system. This study focuses on the role of the prescribed physical properties of soil in high-resolution land surface-atmosphere simulations over South Africa. The model used here is the hydrologically-enhanced Weather Research and Forecasting (WRF-Hydro) model. Four commonly used global soil datasets obtained from UN Food and Agriculture Organization (FAO) soil database, Harmonized World Soil Database (HWSD), Global Soil Dataset for Earth System Model (GSDE), and SoilGrids dataset, are incorporated within the WRF-Hydro experiments for investigating the impact of soil information on land-atmosphere interactions. The simulation results of near-surface temperature, skin temperature, and surface energy fluxes are presented and compared to observational-based reference dataset. It is found that simulated soil moisture is largely influenced by soil texture features, which affects its feedback to the atmosphere.

How to cite: Zhang, Z., Laux, P., Arnault, J., Wei, J., Baade, J., Urban, M., and Kunstmann, H.: Evaluating the role of soil physical properties on simulated land-atmosphere interactions over South Africa using coupled atmosphere-hydrological modeling, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-9418, https://doi.org/10.5194/egusphere-egu21-9418, 2021.

EGU21-15198 | vPICO presentations | ITS2.9/SSS3

Earth Observation Strategies for degradation monitoring in South Africa with Sentinels – Results from the SPACES 2 SALDi-project 

Christiane Schmullius, Marcel Urban, Kai Heckel, Hilma Sevelia Nghiyalwa, Andreas Hirner, Ursula Gessner, Abel Ramoelo, Izak Smit, Tercia Strydom, George Chirima, Theunis Morgenthal, Gregor Feig, Nosiseko Mashiyi, Andiswa Mlisa, and Jussi Baade

The project ‘South African Land Degradation Monitor (SALDi)’ contributes to the German-South African Science Program SPACES by addressing the dynamics and functioning of multi-use landscapes with respect to land use, land cover change, water fluxes, and implications for habitats and ecosystem services. Particularly, SALDi aims: i) to develop an automated system for high temporal (bi-weekly) and spatial resolution (10 to 30 m) change detection monitoring of ecosystem service dynamics, ii) to develop, adapt and apply a Regional Earth System Model (RESM) to South Africa and investigate the feedbacks between land surface properties and the regional climate, iii) to advance current soil degradation process assessment tools as a limiting factor for ecosystem services. Protected areas (SANParks and other) within our six study regions represent benchmark sites, providing a foundation for baseline trend scenarios, against which climate-driven ecosystem service dynamics of multi-used landscape (cropland, rangeland, forests) are evaluated. Our study regions follow a climatic SW-NE transect: 1-Overberg, 2-Kai !Garib/Augrabies Falls, 3-Sol Plaatje/Kimberley, 4-Mantsopa/Ladybrand, 5-Bojanala Platinum/Pilanesberg, 6-Ehlanzeni /Mpumalanga.

We are utilizing Sentinel-1A/B C-Band VV/VH-SAR time series with a 10 m resolution. The revisit time is 12 days on average for South Africa. Pre-processing is done using pyroSAR, a Python framework for large-scale SAR-processing providing processing utilities in ESA’s Sentinel Application Platform (SNAP) as well as GAMMA Remote Sensing software. The first two analytical approaches for the evaluation of the Sentinel-1 time series to detect surface changes, are based on the recognition of irregularities in the radar backscatter or coherence dynamics. Sentinel-2A/B data were pre-processed to L2A and used to calculate a wide range of vegetation indices (e.g. NDVI, EVI, SAVI, REIP) using DLR’s Sen2Cor-processor. The time frame starts with the first Sentinel-1 and -2 acquisitions and continues. The analysis-ready data, that is, harmonized, standardized, interoperable, radiometrically and geometrically consistent data, is being ingested in the SALDi Data Cube. Algorithms and models for developing products such as land degradation indicators are being developed using Jypiter notebooks. SANSA in collaboration with SARAO (South African Radio Astronomy Observatory), is developing the open data cube Digital Earth South Africa (DESA) based on SPOT data. Other datasets from different sensors will be ingested at a later stage. SALDi’s Data Cube will be open access to make it available to the wider scientific community, and also for teaching and training purposes. The application/use of the individual development stages should be possible on the fly for the partners in South Africa. The SASSCAL platform shall be used for distribution of the finalised SALDi Data Cube.

This presentation demonstrates results from hyper-temporal Sentinel-1 and -2 timeseries concerning woody cover mapping and breakpoint analyses of the complex savanna systems, invasive slangbos (Seriphium plumosum) bush encroachment in grassland areas and regional soil moisture retrievals. Validation has been performed by cross-comparisons with VHR airborne DMC surface products, field trips and permanently installed soil moisture networks and interaction with local South African stakeholders.

 

How to cite: Schmullius, C., Urban, M., Heckel, K., Nghiyalwa, H. S., Hirner, A., Gessner, U., Ramoelo, A., Smit, I., Strydom, T., Chirima, G., Morgenthal, T., Feig, G., Mashiyi, N., Mlisa, A., and Baade, J.: Earth Observation Strategies for degradation monitoring in South Africa with Sentinels – Results from the SPACES 2 SALDi-project , EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-15198, https://doi.org/10.5194/egusphere-egu21-15198, 2021.

EGU21-14349 | vPICO presentations | ITS2.9/SSS3 | Highlight

Development of earth observation data cubes for monitoring land degradation processes in South Africa

Insa Otte, Nosiseko Mashiyi, Pawel Kluter, Steven Hill, Andreas Hirner, Jonas Eberle, Marcel Urban, Andiswa Mlisa, Mahlatse Kganyago, Maximilian Schwinger, Ursula Gessner, Christiane Schmullius, and Jussi Baade

Global biodiversity and ecosystem services are under high pressure of human impact. Although avoiding, reducing and reversing the impacts of human activities on ecosystems should be an urgent priority, the loss of biodiversity continues. One of the main drivers of biodiversity loss is land use change and land degradation. In South Africa land degradation has a long history and is of great concern. The SPACES II project SALDi (South African Land Degradation Monitor) aims for developing new, adaptive and sustainable tools for assessing land degradation by addressing the dynamics and functioning of multi-use landscapes with respect to land use change and ecosystem services. SPACES II is a German-South African “Science Partnerships for the Adaptation to Complex Earth System Processes”. Within SALDi ready-to-use earth observation (EO) data cubes are developed. EO data cubes are useful and effective tools using earth observations to deliver decision-ready products. By accessing, storing and processing of remote sensing products and time-series in data cubes, the efficient monitoring of land degradation can therefore be enabled. The SALDi data cubes from optical and radar satellite data include all necessary pre-processing steps and are generated to monitor vegetation dynamics of five years for six focus areas. Intra- and interannual variability in both, a high spatial and temporal resolution will be accounted to monitor land degradation. Therefore, spatial high resolution earth observation data from 2016 to 2021 from Sentinel-1 (C-Band radar) and Sentinel-2 (multispectral) will be integrated in the SALDi data cube for six research areas of 100 x 100 km. Additionally, a number of vegetation indices will be implemented to account for explicit land degradation and vegetation monitoring. Spatially explicit query tools will enable users of the system to focus on specific areas, like hydrological catchments or blocks of fields.

How to cite: Otte, I., Mashiyi, N., Kluter, P., Hill, S., Hirner, A., Eberle, J., Urban, M., Mlisa, A., Kganyago, M., Schwinger, M., Gessner, U., Schmullius, C., and Baade, J.: Development of earth observation data cubes for monitoring land degradation processes in South Africa, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-14349, https://doi.org/10.5194/egusphere-egu21-14349, 2021.

EGU21-3004 | vPICO presentations | ITS2.9/SSS3

Using Sentinel-1 and Sentinel-2 Time Series for Slangbos Encroachment Mapping in the Free State Province, South Africa

Marcel Urban, Konstantin Schellenberg, Theunis Morgenthal, Clèmence Dubois, Andreas Hirner, Ursula Gessner, Zhenyu Zhang, Buster Mogonong, Jussi Baade, and Christiane Schmullius

Increasing woody cover and overgrazing in semi-arid ecosystems are known to be major factors driving land degradation. During the last decades woody cover encroachment has increased over large areas in southern Africa inducing environmental, land cover as well as land use changes. 

The goal of this study is to synergistically combine SAR (Sentinel-1) and optical (Sentinel-2) earth observation information to monitor the slangbos encroachment on arable land in the Free State province, South Africa, between 2015 and 2020. Both, optical and radar satellite data are sensitive to different land surface and vegetation properties caused by sensor specific scattering or reflection mechanisms they rely on. 

This study focuses on mapping the slangbos aka bankrupt bush (Seriphium plumosum) encroachment in a selected test region in the Free State province of South Africa. Though being indigenous to South Africa, the slangbos has been documented to be the main encroacher on the grassvelds (South African grassland biomes) and thrive in poorly maintained cultivated lands. The shrub reaches a height and diameter of up to 0.6 m and the root system reaches a depth of up to 1.8 m. Slangbos has small light green leaves unpalatable to grazers due to their high oil content and is better adapted to long dry periods compared to grass communities.

We used the random forest approach to predict slangbos encroachment for each individual crop year between 2015 and 2020. Training data were based on expert knowledge and field information from the Department of Agriculture, Forestry and Fisheries (DAFF). Several input variables have been tested according to their model performance, e.g. backscatter, backscatter ratio, interferometric coherence as well as optical indices (e.g. NDVI (Normalized Difference Vegetation Index), SAVI (Soil Adjusted Vegetation Index), EVI (Enhanced Vegetation Index), etc.). We found that the Sentinel-1 VH backscatter (vertical–horizontal/cross-polarization) and the Sentinel-2 SAVI time series information have the highest importance for the random forest classifier among all input parameters. The estimation of the model accuracy was accomplished via spatial-cross validation and resulted in an overall accuracy of above 80 % for each time step, with the slangbos class being close to or above 90 %. 

Currently we are developing a prototype application to be tested in cooperation with local stakeholders to bring this approach to the farmers level. Once field work in southern Africa is possible again, further ground truthing and interaction with farmers will be carried out.

How to cite: Urban, M., Schellenberg, K., Morgenthal, T., Dubois, C., Hirner, A., Gessner, U., Zhang, Z., Mogonong, B., Baade, J., and Schmullius, C.: Using Sentinel-1 and Sentinel-2 Time Series for Slangbos Encroachment Mapping in the Free State Province, South Africa, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-3004, https://doi.org/10.5194/egusphere-egu21-3004, 2021.

EGU21-15912 | vPICO presentations | ITS2.9/SSS3

Understanding the feedback of landuse practices and vegetation change in a Namibian savannah - a model assessment 

Katja Irob, Britta Tietjen, Niels Blaum, Ben Strohbach, and Angelina Kanduvarisa

Changing climatic conditions and unsustainable management strategies associated with biodiversity loss are perceived as major threats to Namibian savannahs. In the past, land-use in Namibia is dominated by livestock-farming as one of the major economic products. However, high grazing pressure led to degrading pastures in many regions in the country. In response, more farmers have recently shifted their land-use strategy from livestock to wildlife-based management, with so far unclear consequences for ecosystem dynamics. 
In this study, the ecohydrological, spatially explicit savanna model EcoHyD (Tietjen et al. 2009, 2010; Lohmann et al. 2012, Guo et al. 2016) was used to assess the impact of different land-use strategies on plant composition and ecosystem properties. The aim was to systematically evaluate the impact of different land-use strategies in terms of animal types and densities on the diversity of major plant groups (shrubs, perennial and annual grasses) and on several ecosystem processes. The results allow for identifying sustainable landuse strategies that avoid degradation and that lead to long-term provision of ecosystem services and economic income. 
We identified typical different functional plant types (PFTs) of the study region and parameterized the model to reflect the local environmental dynamics of the private game reserve Etosha Heights in Namibia. Afterwards, we run the model and assessed the composition and cover of our simulated PFTs, as well as water availability dependent on the land-use scenario. The results are in line with our expectations: they show that total plant cover increases with decreasing stocking rate and that cover and biodiversity are generally higher in browsing scenarios. In addition, we could explore, which PFTs of a given plant group are best adapted to grazing or browsing animals in a certain density. We could also show that perennial grasses benefitted more than shrubs from lower stocking rates. This benefit led to an improved soil water availability to plants, since less water was lost by overland flow, implying also a lower erosion risk. As the model has been applied to a variety of environmental settings regarding climatic conditions but also soil properties, we are confident that this study can serve as  blueprint to assess shifts in land-use also in other savannah systems. 

How to cite: Irob, K., Tietjen, B., Blaum, N., Strohbach, B., and Kanduvarisa, A.: Understanding the feedback of landuse practices and vegetation change in a Namibian savannah - a model assessment , EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-15912, https://doi.org/10.5194/egusphere-egu21-15912, 2021.

EGU21-15264 | vPICO presentations | ITS2.9/SSS3

Land degradation in savanna environments - assessments, dynamics and implications

Jussi Baade, Jay J. Le Roux, Theunis Morgenthal, and Hilma Sevelia Nghiyalwa

Land degradation is a human-induced process deteriorating ecosystem functioning and services including soil fertility or biological productivity and, usually, it is accompanied by a loss of biodiversity. Land degradation causes on-site and off-site damages like a profound change or removal of vegetation cover and soil erosion on one hand as well as flooding of receiving streams and siltation of reservoirs one the other hand. Thus, land degradation poses a threat to a number of Sustainable Development Goals (SDG) including foremost sustainable life on land and under water, the provision of clean water and eventually the eradication of poverty and hunger on Earth.

Often, land cover change is a valid indicator of land degradation providing the opportunity to take advantage of the increasing geometrically and temporally high-resolution remote sensing capabilities to identify and monitor land degradation. However, especially in semi-arid regions like savanna environments, globally driven inter-annual and decadal climate variations cause as well profound land cover dynamics which might be mistaken for land degradation.

Assessing and combating land degradation has already a long scientific, socio-economic and political history. Based on this, the aim of this session is to explore the wide range of methodological approaches to assess land degradation, its dynamics over all spatial and temporal scales as well as the implications for society and the interaction with the different spheres of the Earth including the anthroposphere, atmosphere, biosphere, hydrosphere and pedosphere. Contributions to this session can be based on field work, remote sensing approaches or modelling exercises, they can also focus on specific physical and socio-economic aspects of land degradation like land management, land cover change or soil erosion or discuss land degradation in a broader societal context. The aim of this contribution is to provide a concise overview of the thematic framework, current activities, research questions and advancements.

How to cite: Baade, J., Le Roux, J. J., Morgenthal, T., and Nghiyalwa, H. S.: Land degradation in savanna environments - assessments, dynamics and implications, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-15264, https://doi.org/10.5194/egusphere-egu21-15264, 2021.

ITS2.11/AS4.12 – Pan-Eurasian EXperiment (PEEX) – Observation, Modelling and Assessment in the Arctic-Boreal Domain

EGU21-10618 | vPICO presentations | ITS2.11/AS4.12 | Highlight

Overview: Recent advances on the understanding of the Northern Eurasian environments and of the urban air quality in China – Pan-Eurasian Experiment (PEEX) program perspective

Hanna Lappalainen, Tuukka Petaja, Timo Vihma, Jouni Raisanen, Aleksander Baklanov, Sergey Chalov, Igor Ezau, Ekaterina Ezhova, Matti Lepparanta, Dimitry Pozdnyakov, Jukka Pumpanen, Yubao Qiu, Aijun Ding, Huadong Guo, Valery Bondur, Nikolay Kasimov, Sergey Zilitinkevich, Veli-Matti Kerminen, and Markku Kulmala

Pan-Eurasian Experiment (PEEX) Programme (www.atm.helsinki.fi/peex) is an asset for PEEX to have high international visibility, to attract further research collaboration and to upscale its scientific impact in various arenas. The PEEX research focus is on the northern high latitudes environments and on the transport and transformation of air pollution in China (Kulmala et al. 2015, Lappalainen et al. 2014; 2015; 2016; 2018, Vihma et al. 2019, Alekseychik et al. 2019, Kasimov et al. 2018). In 2019 PEEX started comprehensive analysis on the first results over last five years attained from the PEEX geographical domain.  The aim of the analysis is to study the state-of-the-art research outcome versus the PEEX large-scale research questions addressed by the Science Plan (Lappalainen et al. 2015). Lappalainen et al. 2021 (submitted) introduces recent observations and results from the Russian Arctic, Northern Eurasian boreal forests (Siberia) and peatlands and on the mega cities in China. We frame our analysis against research themes introduced in the the PEEX Science Plan (2015). Although the scientific knowledge in these regions has increased, there are still gaps in our understanding of large-scale climate-Earth surface interactions and feedbacks. This arises from limitations in research infrastructures and integrative data analyses, hindering a comprehensive system analysis. The fast-changing environment and ecosystem changes driven by climate change, socio-economic activities like the China Silk Road Initiative, and the global trends like urbanization further complicate such analyses. We recognize new topics with an increasing importance in the near future, such as enhancing biological sequestration capacity of greenhouse gases into forests and soils to mitigate the climate change and the socio-economic development to tackle air quality issues.

 

 

How to cite: Lappalainen, H., Petaja, T., Vihma, T., Raisanen, J., Baklanov, A., Chalov, S., Ezau, I., Ezhova, E., Lepparanta, M., Pozdnyakov, D., Pumpanen, J., Qiu, Y., Ding, A., Guo, H., Bondur, V., Kasimov, N., Zilitinkevich, S., Kerminen, V.-M., and Kulmala, M.: Overview: Recent advances on the understanding of the Northern Eurasian environments and of the urban air quality in China – Pan-Eurasian Experiment (PEEX) program perspective, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-10618, https://doi.org/10.5194/egusphere-egu21-10618, 2021.

EGU21-7901 | vPICO presentations | ITS2.11/AS4.12

Water uptake of subpollen aerosol particles: hygroscopic growth, CCN activation, and liquid-liquid phase separation

Eugene Mikhailov, Mira Pöhlker, Kathrin Reinmuth-Selzle, Sergey Vlasenko, Christopher Pöhlker, Olga Ivanova, and Ulrich Pöschl

Pollen grains emitted from vegetation can release subpollen particles (SPP) that contribute to the fine fraction of atmospheric aerosols and may act as cloud condensation nuclei (CCN), ice nuclei (IN), or aeroallergens. Here, we investigate and characterize the hygroscopic growth and CCN activation of birch, pine, and rapeseed SPP. A high humidity tandem differential mobility analyzer (HHTDMA) was used to measure particle restructuring and water uptake over a wide range of relative humidity (RH) from 2 % to 99.5 %, and a continuous flow CCN counter was used for size-resolved measurements of CCN activation at supersaturations (S) in the range of 0.2 % to 1.2 %. For both subsaturated and supersaturated conditions, effective hygroscopicity parameters к , were obtained by Köhler model calculations. Gravimetric and chemical analyses, electron microscopy, and dynamic light scattering measurements were performed to characterize further properties of SPP from aqueous pollen extracts such as chemical composition (starch, proteins, DNA, and inorganic ions) and the hydrodynamic size distribution of water-insoluble material. All investigated SPP samples exhibited a sharp increase of water uptake and k above ~95 % RH, suggesting a liquid-liquid phase separation (LLPS). The HHTDMA measurements at RH> 95% enable closure between the CCN activation at water vapor supersaturation and hygroscopic growth at subsaturated conditions, which is often not achieved when HTDMA measurements are performed at lower RH where the water uptake and effective hygroscopicity may be limited by the effects of LLPS. Such effects may be important not only for closure between hygroscopic growth and CCN activation but also for the chemical reactivity, allergenic potential, and related health effects of SPP.

This research has been supported by the Russian Science Foundation (grant no. 18-10 17-00076) and Max Planck Society.

How to cite: Mikhailov, E., Pöhlker, M., Reinmuth-Selzle, K., Vlasenko, S., Pöhlker, C., Ivanova, O., and Pöschl, U.: Water uptake of subpollen aerosol particles: hygroscopic growth, CCN activation, and liquid-liquid phase separation, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-7901, https://doi.org/10.5194/egusphere-egu21-7901, 2021.

EGU21-6302 | vPICO presentations | ITS2.11/AS4.12

Сlimate-active aerosol components in the Siberian Arctic, by data from new-developed research aerosol station on island Bely 

Olga Popovicheva, Vasilii Kobelev, Marina Chichaeva, Nikolai Kasimov, and Antony Hansen

Black carbon is a short - living climate forcer, it plays a significant role especially in the Arctic environment due to heating the atmosphere and changing the radiation balance while depositing on snow and ice. Analysis of black carbon (BC) in the Arctic atmosphere shows a contribution of anthropogenic combustion of fossil fuels and natural wildfires to the Arctic atmosphere chemistry as well as of the main characteristics of Arctic aerosol pollution. Presently, assessments of the environment and climate change in the Siberian Arctic are strongly complicated by an existing lack of knowledge about emission sources, quantity, and composition of the aerosol pollution defining the impacts on an Arctic ecosystem.

Research aerosol station is firstly installed on island Bely located in Kara sea, Siberian Arctic. It takes place on the pathway of air mass from the Northern Siberia region of high anthropogenic and gas flaring activity to the Arctic. Presently, assessments of the environment and climate change in this region are strongly complicated by an existing lack of knowledge about emission sources, quantity and composition of the aerosol pollution defining the impacts on an Arctic ecosystem. Aethalometer and aerosol sampling system is continuously operated on the aerosol station in order to analyze black carbon and chemical characteristics including ionic and elemental composition. Annual BC trend obtained from august 2019 to September 2020 shows the typical Arctic aerosol tendency of a seasonal variability, disturbed by episodes of large-scale emission transportation.

Unprecedented high BC is observed in September 2020 at the research aerosol station on the island Bely. The BC concentrations early in September were exceeded 20 times the arctic background. They are found to be even higher than the highest arctic haze concentrations observed in December 2019.   Monthly averaged black carbon concentration in September 2020 exceeded 3 times that one in previous summer months. Such strong event is a result of large-scale air mass transportation from Eurasian continent in the period of strong wildfires in western Siberia, namely in Krasnoyarsk Kray and Yakutia, where around one million hectares of forest were burned out in August 2020. 

Basic researches of aerosol characteristics as a tracer of anthropogenic emissions are supported by Russian Fond for Basic Research, project №18-60084.

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How to cite: Popovicheva, O., Kobelev, V., Chichaeva, M., Kasimov, N., and Hansen, A.: Сlimate-active aerosol components in the Siberian Arctic, by data from new-developed research aerosol station on island Bely , EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-6302, https://doi.org/10.5194/egusphere-egu21-6302, 2021.

EGU21-6909 | vPICO presentations | ITS2.11/AS4.12

Vertical distribution of aerosol particles over the Russian Arctic derived from in-situ aircraft measurements: the September 2020 campaign

Mikhail Yu. Arshinov, Boris Belan, Denis Davydov, Artem Kozlov, and Alexandr Fofonov

The Arctic is warming much faster than other regions of the globe. In 2020, temperature anomalies in the Russian Arctic reached unprecedented high levels. The atmospheric composition in this key region still remains insufficiently studied that makes difficult predicting future climate change.

In September 2020, an extensive aircraft campaign was conducted to document the tropospheric composition over the Russian Arctic. The Optik Tu-134 research aircraft was equipped with instruments to carry out in-situ measurements of trace gases and aerosols, as well as with a lidar for profiling of aerosol backscatter. The aircraft flew over a vast area from Arkhangelsk to Anadyr. Six measurement flights with changing altitudes from 0.2 to 9.0 m were conducted over the waters of the Barents, Kara, Laptev, East Siberian, Chukchi, and Bering Seas. The weather was unusually warm for this period of the year, surface air temperatures were above 0°C through the campaign.

Here, we present the results of in-situ measurements of the vertical distribution of aerosol number concentrations in a wide range of sizes. A modified diffusional particle sizer (DPS) consisted of the Novosibirsk-type eight-stage screen diffusion battery connected to the TSI condensation particle counter Model 3756 was used to determine the number size distribution of particles between 0.003 mm and 0.2 mm (20 size bins). Distribution of particles in the size range from 0.25 µm to 32 µm (31 size bins) was measured by means of the Grimm aerosol spectrometer Model 1.109.

The flights over Barents and Kara Seas were predominantly performed under clear sky or partly cloudy weather conditions. Number size distributions were wide representing particles of almost all aerosol fractions. When flying in the upper troposphere with a constant altitude over these seas, some cases of enhanced concentrations of nucleation and Aitken mode particles comparable to ones in the lower troposphere were recorded, suggesting in situ new particle formation was likely to be taking place via gas-to-particle conversion aloft.

East of the Kara Sea, flights were conducted under mostly cloudy conditions resulting in a lower median aerosol number concentration and narrower size distributions.

This work was supported by the Russian Foundation for Basic Research (Grant No. 19-05-50024).

How to cite: Arshinov, M. Yu., Belan, B., Davydov, D., Kozlov, A., and Fofonov, A.: Vertical distribution of aerosol particles over the Russian Arctic derived from in-situ aircraft measurements: the September 2020 campaign, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-6909, https://doi.org/10.5194/egusphere-egu21-6909, 2021.

EGU21-6892 | vPICO presentations | ITS2.11/AS4.12

Vertical distribution of trace gases and aerosols over the Russian Arctic in September 2020

Boris D. Belan, Pavel Antokhin, Olga Antokhina, Victoriya Arshinova, Mikhail Arshinov, Sergey Belan, Denis Davydov, Georgii Ivlev, Artem Kozlov, Alexandr Kozlov, Olesya Okhlopkova, Tatyana Rasskazchikova, Denis Savkin, Alexandr Safatov, Denis Simonenkov, Gennadii Tolmachev, and Alexandr Fofonov

In 2020, a unique experiment, which had ever been implemented either in the former USSR or in modern-day Russia, was carried out in the Russian Arctic by means of the Optik Tu-134 aircraft laboratory operated by IAO SB RAS. The airborne measurement campaign was conducted on September 4-17 over all seas and coastal regions of the Russian sector of the Arctic, including northern part of the Bering Sea.

During the flights, in situ measurements of CO, CO2, CH4, NO, NO2, SO2, O3, aerosols, and black carbon (BC) were performed. Air samples were taken to determine organic and inorganic compounds and biological material in aerosol particles. A remote sensing of the water turbidity in the upper sea layers was conducted by means of the LOZA-2 lidar that allowed a concentration of plankton to be derived there. Spectral characteristics of the water and underlying coastal surfaces were measured using a spectroradiometer.

The primary analysis of the obtained data showed that concentrations of CO, NO, NO2, SO2, O3, aerosols, and BC during the experiment were low that is typical for background regions. CO2 mixing ratios in the lowest part of the troposphere above seas were lower than aloft. As compared with coastal areas, concentration of methane over all the seas of the Arctic sector and the Bering Sea was higher.

We would like to acknowledge our colleagues from the following organizations for their assistance in organizing and conducting this campaign, and in particular, Laboratoire des sciences du climat et de l'environnement and Laboratoire atmosphères, milieux, observations spatiales (France); Finnish Meteorological Institute and Institute for Atmospheric and Earth System Research, University of Helsinki (Finland); Center for Global Environmental Research at the National Institute for Environmental Studies (Japan); the National Oceanic and Atmospheric Administration, US Department of Commerce (USA); Max-Planck-Institute for Biochemistry (Germany); and University of Reading (UK).

How to cite: Belan, B. D., Antokhin, P., Antokhina, O., Arshinova, V., Arshinov, M., Belan, S., Davydov, D., Ivlev, G., Kozlov, A., Kozlov, A., Okhlopkova, O., Rasskazchikova, T., Savkin, D., Safatov, A., Simonenkov, D., Tolmachev, G., and Fofonov, A.: Vertical distribution of trace gases and aerosols over the Russian Arctic in September 2020, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-6892, https://doi.org/10.5194/egusphere-egu21-6892, 2021.

EGU21-16490 | vPICO presentations | ITS2.11/AS4.12

Satellite-based analysis of CO and Fires in the Arctic

Tomi Karppinen, Anu-Maija Sundström, Hannakaisa Lindqvist, and Johanna Tamminen

Climate change is proceeding fastest in the Arctic region. While human-induced emissions of long-lived greenhouse gases are the main driving factor of global warming, short-lived climate forcers or pollutants emitted from the forest fires are also playing an important role, especially in the Arctic. Forest fire emissions also affect local air quality and photochemical processes in the atmosphere. For example, CO contributes to the formation of tropospheric ozone and affects the abundance of greenhouse gases such as methane and CO2.

During past years Arctic summers have been warmer and drier elevating the risk for extensive forest fire episodes. Satellite observations show, that during the past three summers (2018-2020) fire detections in Arctic, especially in Arctic Siberia have increased considerably, affecting also local emissions of CO. This work focuses on studying CO concentration and its variation at high latitudes and in the Arctic using satellite and ground-based observations. Satellite observations of CO from TROPOMI are analyzed for the 2018-2020 (NH) summer months. To assess the satellite retrieved columns the satellite measurements are compared to ground-based remote sensing measurements at Sodankylä. Also, ground-based in-situ measurements are used to see how the total column changes mirror the ground level concentrations. The fire characteristics are analyzed using observations from MODIS instruments onboard Aqua and Terra. Fire effects on seasonal cycle and interannual variability of CO concentrations at Arctic high latitudes are analyzed.

How to cite: Karppinen, T., Sundström, A.-M., Lindqvist, H., and Tamminen, J.: Satellite-based analysis of CO and Fires in the Arctic, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-16490, https://doi.org/10.5194/egusphere-egu21-16490, 2021.

EGU21-13124 | vPICO presentations | ITS2.11/AS4.12

Fire activity and its influence on Aerosol Optical Depth and Green-House Gases over PEEX area for the last two decades

Larisa Sogacheva, Anu-Maija Sundström, Timo H. Virtanen, Antti Arola, Tuukka Petäjä, Hanna K. Lappalainen, and Markku Kulmala

The Pan-Eurasian Experiment Program (PEEX) is an interdisciplinary scientific program bringing together ground-based in situ and remote sensing observations, satellite measurements and modeling tools aiming to improve the understanding of land-water-atmosphere interactions, feedback mechanisms and their effects on the ecosystem, climate and society in northern Eurasia, Russia and China. In a view of the large area covering thousands of kilometres, large gaps will remain where no or little ground-based observational information will be available. The gap can partly be filled by satellite remote sensing of relevant parameters as regards atmospheric composition.

Biomass burning is a violent source of atmospheric pollutants. Fires and corresponding emissions to the atmosphere dramatically change the atmospheric composition in case of long-lasting fire events, which might cover extended areas. In the burned areas, CO2 exchange, as well as emissions of different compounds are getting to higher levels, which might contribute to climate change by changing the radiative budget through the aerosol-cloud interaction and cloud formation. In the boreal forest, after CO2, CO and CH4, the largest emission factors for individual species were formaldehyde, followed by methanol and NO2 (Simpson et al., ACP, 2011). The emitted long-life components, e.g., black carbon, might further be transported to the distant areas and measured at the surface far from the burned areas.

In the boreal forest region, fires are very common, very large and produce a lot of smoke. Boreal areas  have been burning regularly for thousands of years and is adapted to fires, which happen most often between May and October. In boreal ecosystems, future increases in air temperature may lengthen the fire season and increase the probability of fires, leading some to hypothesize a positive feedback between warming, fire activity, carbon loss, and future climate change (Kasischke et al., 2000). 

 During the last few decades, several burning episodes have been observed over PEEX area by satellites (as fire counts), specifically over Siberia and central Russia. The following information available from satellites will be utilized to reveal a connection between Fire activity and atmospheric composition for the period 2002-2020 over the PEEX area:

  • - Fire count, FRP and burned areas from MODIS
  • - Absorbing Aerosol Index (AAI), multi-instrument (GOME-2, OMI, TOMS) product
  • - CO from MOPPIT
  • - HCHO and NO2 from OMI

Monthly temperature and humidity fields from ERA5 re-analysis will be also utilized to reveal if a connection exist between climate variables and occurrence and intensity of the forest fires.

Kasischke, B. J. Stocks: Fire, Climate Change, and Carbon Cycling in the Boreal Forest. M. M. Cadwellet al.,Eds., Ecological Studies (Springer, New York, 2000)

Simpson, I. J., Akagi, S. K., Barletta, B., Blake, N. J., Choi, Y., Diskin, G. S., Fried, A., Fuelberg, H. E., Meinardi, S., Rowland, F. S., Vay, S. A., Weinheimer, A. J., Wennberg, P. O., Wiebring, P., Wisthaler, A., Yang, M., Yokelson, R. J., and Blake, D. R.: Boreal forest fire emissions in fresh Canadian smoke plumes: C1-C10 volatile organic compounds (VOCs), CO2, CO, NO2, NO, HCN and CH3CN, Atmos. Chem. Phys., 11, 6445–6463, https://doi.org/10.5194/acp-11-6445-2011, 2011.

 

How to cite: Sogacheva, L., Sundström, A.-M., Virtanen, T. H., Arola, A., Petäjä, T., Lappalainen, H. K., and Kulmala, M.: Fire activity and its influence on Aerosol Optical Depth and Green-House Gases over PEEX area for the last two decades, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-13124, https://doi.org/10.5194/egusphere-egu21-13124, 2021.

EGU21-1477 | vPICO presentations | ITS2.11/AS4.12

Estimation of the tropospheric and stratospheric CO2 content by ground-based IR technique

Yury Timofeyev, Georgy Nerobelov, Anatolii Poberovskii, and Nikolai Filippov

Ground-based spectroscopic international measurement systems TCCON and NDACC are important for regular obtaining the data on atmospheric gas composition. A great part of such data is derived as the total content of the gases and as an averaged mixing ratio for the dry atmosphere as, for example, XCO2. On the other hand, the measurements of solar IR radiation spectra with high spectral resolution carry within them some amount of information on the vertical structure of the content of some gases. The method of estimation of CO2 content in the troposphere and stratosphere was described in a study [Timofeyev Yu.M., Nerobelov G.M., Poberovskii A.V., Filippov N.N. Evaluation of CO2 content in troposphere and stratosphere by ground-based IR method.  “Izvestiya, Atmospheric and Oceanic Physics”. 2021, Nо.2]. In our work we present the analysis of the inaccuracies of the suggested approach using different spectral windows. Also, we demonstrate the comparison between CO2 tropospheric and stratospheric content obtained by the suggested approach using ground-based measurements of IR spectra with high resolution in Peterhof (2009-2019), by Copernicus Atmosphere Monitoring Service (CAMS) and by satellite measurements of XCO2 in the troposphere and stratosphere using ACE instrument.

How to cite: Timofeyev, Y., Nerobelov, G., Poberovskii, A., and Filippov, N.: Estimation of the tropospheric and stratospheric CO2 content by ground-based IR technique, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-1477, https://doi.org/10.5194/egusphere-egu21-1477, 2021.

EGU21-4815 | vPICO presentations | ITS2.11/AS4.12

Abiotic drivers of CO2 fluxes in the transition from the winter-to-spring in coniferous forest and bog in central Siberia

Sung-Bin Park

EGU21-9773 | vPICO presentations | ITS2.11/AS4.12

Accurate continuous observations of carbon dioxide and methane dry mole fractions in the arctic atmosphere near the Dikson settlement, Siberia

Alexey Panov, Anatoly Prokushkin, Jošt Lavrič, Karl Kübler, Mikhail Korets, Anastasiya Urban, Nikita Sidenko, Galina Zrazhevskaya, Mikhail Bondar, and Martin Heimann

Measurements of the atmospheric sources and sinks of carbon dioxide (CO2) and methane (CH4) in the pan-Arctic domain are extremely sparse that limits our knowledge of carbon cycling over this dramatically sensitive environment and making predictions about a fate of carbon conserved in currently frozen ground. Especially critical are the gaps in the arctic latitudes of Siberia, covered by the vast permafrost underlain tundra, where only few continuous atmospheric observation stations are currently operational.

We present the first two years of accurate continuous observations of atmospheric CO2 and CH4 dry mole fractions at the new atmospheric carbon observation station located near the Dikson settlement (73.33° N, 80.34° E) on the seashore of the western part of the Taimyr Peninsula in Siberia. Data quality control of trace gas measurements is achieved by regular calibrations against WMO-traceable reference gases from pressurized dry air tanks filled at the Max Planck Institute for Biogeochemistry (Jena, Germany). Associated meteorological variables permit evaluation of the climate variability of the local environment and provide a background for screening and interpreting the greenhouse gases (GHG) data records. Here we summarize the scientific rationale of the new site, give technical details of the instrumental setup, analyse the local environments and present CO2 and CH4 fluctuations in the arctic atmosphere. Along with the temporal variability of GHG’s, we provide an overview of the angular distribution of detected GHG signals in the region and their input to the atmospheric fluctuations on the measurement site. Observation records deal with the daytime mixed layer and may be considered as representative throughout the vast area (~500–1000 km), and cover the period from September 2018 to September 2020.

The reported study was funded by Russian Foundation for Basic Research, Krasnoyarsk Territory and Krasnoyarsk Regional Fund of Science, project number 20-45-242908, RFBR project 18-05-60203 and by the Max Planck Society (Germany)

How to cite: Panov, A., Prokushkin, A., Lavrič, J., Kübler, K., Korets, M., Urban, A., Sidenko, N., Zrazhevskaya, G., Bondar, M., and Heimann, M.: Accurate continuous observations of carbon dioxide and methane dry mole fractions in the arctic atmosphere near the Dikson settlement, Siberia, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-9773, https://doi.org/10.5194/egusphere-egu21-9773, 2021.

Wildfires remain among the most challenging problems in Ukraine. Each year numerous cases of open burning contribute to huge carbon emissions and turn into forest fires. Using the Global Fire Emissions Database (GFED4), there were studied an average burned fraction in Ukraine, which equals of about 0.2-0.3. 90% of wildfires appeared on agricultural lands. The total contribution to carbon emissions is 0.2-1.0 g·m2·month-1 with the increasing trend of about 1-2 g·m2·month-1 per decade. There are three periods with the highest carbon emissions: April, July-August and September-October. While a summer maximum is corresponding to unfavorable temperature and moisture regimes, the main reason of wildfires in spring and autumn is the agricultural open burning. Based on the Sentinel-5P data, it was found that wildfires significantly change the seasonality of carbon monoxide (CO) variations. If maximal CO content is mainly observed in winter at the end of the heating season, in Ukraine the highest CO values continue to exist in April until the open burning stops and the resulting forest fires are extinguished. Wildfires caused the CO content increase to 4.0–5.0 mol·m-2 which is comparable to the most polluted Ukrainian industrial cities. As a result, air quality deterioration observed at the distances more than 200 km from the burned areas. Using the Enviro-HIRLAM simulations, there were estimated black carbon (BC) distribution, which showed elevated content within the lowest 3-km layer. BC content reaches 600 ppbm near the active fires, 150 ppbm at the distance up to 100 km and 30 ppbm at the distance of about 200-500 km.

How to cite: Savenets, M. and Pysarenko, L.: The impact of wildfires in Ukraine on carbon flux and air quality changes by carbon-containing compounds, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-329, https://doi.org/10.5194/egusphere-egu21-329, 2021.

EGU21-681 | vPICO presentations | ITS2.11/AS4.12 | Highlight

A local climate perspective from Arctic towns

Igor Esau and the SERUS team

Across the Arctic, human settlements are challenged by rapid climate change and a broad range of environmental transformations. Some of them, such as Barrow (Utqiagvik, Alaska), must relocate; others, such as Norilsk (Russia), must restructure and rebuild. This presentation reports on local climate anomalies in 118 circum-Arctic cities and towns. For several key towns, a nexus review of the environmental consequences of the local warm anomalies is detailed. Longyearbyen (Svalbard), Apatity and Nadym (Russia) are in focus. For instance, Longyearbyen – the European “gate” to the Arctic – experiences one of the stongest climate change. The surface air temperature here has increased by almost 10oC over the last 100 years with more than 100 consecutive months being warmer than normal. Snowfall increases threatening with hazardous slab snow avalanches. The last extreme heat wave (July, 2020) showed temperatures up to +21oC and massive flooding in the coal mine.  This study synthesizes observational evidence of the climate change in the town from a local perspective. We relate meteorological conditions with sustainability issues. The study looks at local climate diversity and its role for society and economy of the settlement.

How to cite: Esau, I. and the SERUS team: A local climate perspective from Arctic towns, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-681, https://doi.org/10.5194/egusphere-egu21-681, 2021.

EGU21-5168 | vPICO presentations | ITS2.11/AS4.12 | Highlight

Spring-summer 2020 aerosol pollution in Moscow metropolitan area

Nikolay Kasimov, Olga Popovicheva, Dmitry Vlasov, Marina Chichaeva, and Anastasia Larionova

Reduction of urban emissions following the response to COVID-19 pandemic has provided the unique possibility for assessment of the aerosol pollution in the metropolitan area with the highest population density in Russia. According to observation data obtained from the aerosol research station of Meteorological Observatory of Lomonosov Moscow State University, the strict control measures and social lockdowns initiated in spring 2020 in Moscow megacity have had a notable decreasing of PM2.5, black carbon (BC), and PM10-bound potentially toxic elements (PTEs) concentrations. The average concentration of PM2.5 and BC has decreased by 42% and 75%, respectively, in comparison to the following period of economical restoration in summer 2020. A city traffic decrease led to a gentle dynamics of a BC diurnal trend due to a reduced energy load in the morning hours. Changes in the enterprises operating regime affected the redistribution of emissions intensities from working days to weekends. During the period of recovery of economic activity in the summer of 2020, the emission intensity has increased and the direction of BC sources has changed. Furthermore, these factors resulted in substantial increase in the pollution levels for the most of PTEs during the period of economical restoration. For instance, Ba, Sn, K, Cu, Bi, B, Mo, As, Sb, and Pb concentrations emitted from vehicles and industrial sources were increased by 42–167%. Levels of PTEs originated from construction and demolition processes (Sr, Mg, and Ca by 175%, 21%, and 19%, respectively), road dust and soil particles resuspension (Zr, P, Mn, and Fe, by 76%, 51%, 49% and 46%, respectively) also experienced the significant growth. Real-time measurements of short-term changes in the atmosphere aerosol pollution with a rapid extreme fall and subsequent restoration of economic activity allows a better understanding of the processes taking place in the system of economy-society-environment of large cities.

How to cite: Kasimov, N., Popovicheva, O., Vlasov, D., Chichaeva, M., and Larionova, A.: Spring-summer 2020 aerosol pollution in Moscow metropolitan area, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-5168, https://doi.org/10.5194/egusphere-egu21-5168, 2021.

EGU21-4160 | vPICO presentations | ITS2.11/AS4.12

Сhanges in air quality and aerosol pollution in Moscow megacity and its direct and indirect impact on radiative and meteorological properties of the atmosphere due to COVID-19 pandemic lockdown in spring 2020 according to modelling and measurements.

Nataly Chubarova, Elizaveta Androsova, Alexander Kirsanov, Alexei Poliukhov, Ekaterina Zhdanova, Marina Shatunova, Julia Khlestova, Bernhard Vogel, Heike Vogel, and Gdaliy Rivin

Atmospheric aerosol has a noticeable effect on the microphysical and optical properties of the atmosphere, solar radiation, temperature and humidity conditions, thereby determining the quality of the forecast of important meteorological elements and affecting the regional climate and the dynamics of geochemical processes. Using the results of the spring AeroRadCity experiment at the MSU Meteorological Observatory in 2018-2019, and numerical calculations on the base of modern COSMO and COSMO-ART mesoscale models using Russian (-Ru) configurations we determined the level and main features of urban air/aerosol pollution, and assessed its magnitude and its impact on the radiative and meteorological characteristics of the atmosphere in typical conditions (Chubarova et al., 2020). In the context of the coronavirus pandemic in 2020, especially during the period of lockdown in the spring, there was a significant decrease in emissions of pollutants in many countries, including Russia. The aim of this study is to show the consequences of decrease in emissions of pollutants on the air quality and on urban aerosol pollution. A special attention is paid to the division between the effects of meteorological factors and the influence of pollution emission on aerosol and gas concentration. The effects of the air pollution decrease on solar radiation and air temperature during this period have been analyzed using COSMO-Ru-ART model.  For a more detailed study of the observed spatial aerosol distribution on solar radiation and air temperature, we have developed a methodology of the implementation of the satellite aerosol optical thickness (AOT) data in the COSMO-Ru model. Using this approach we evaluated the radiative and temperature effects observed due to aerosol in typical conditions during the spring of 2018-2019 and during the period of lockdown in the spring of 2020 under various meteorological conditions. To do this, the satellite AOT data from the MAIAC/MODIS algorithm and aerosol measurements from Cimel sun photometers data were used for characterising the urban aerosol in typical and lockdown conditions. We also discuss the aerosol indirect effects on cloud properties using an experimental scheme of COSMO-Ru model and their influence on solar radiation and surface temperature during this period. The aerosol study has been partially supported by the RSF grant number 18-17-00149; the analysis of gas species has been partially funded by the megagrant number 2020-220-08-5835.

Reference:

Chubarova N.Ye., Ye.Yu. Zhdanova., Ye.Ye. Androsova, A.A. Kirsanov, M.V. Shatunova, Yu.O. Khlestova, Ye.V. Volpert, A.A. Poliukhov, I.D. Eremina, D.V. Vlasov, O.B. Popovicheva, A.S. Ivanov, Ye.V. Gorbarenko, Ye.I. Nezval, D.V. Blinov, G.S. Rivin. The aerosol urban pollution and its effects on weather, regional climate and geochemical processes: Monograph / Edited by N.Ye. Chubarova – Moscow, MAKS Press, 2020. 339 pp.  ISBN 978-5-317-06464-8

How to cite: Chubarova, N., Androsova, E., Kirsanov, A., Poliukhov, A., Zhdanova, E., Shatunova, M., Khlestova, J., Vogel, B., Vogel, H., and Rivin, G.: Сhanges in air quality and aerosol pollution in Moscow megacity and its direct and indirect impact on radiative and meteorological properties of the atmosphere due to COVID-19 pandemic lockdown in spring 2020 according to modelling and measurements., EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-4160, https://doi.org/10.5194/egusphere-egu21-4160, 2021.

EGU21-1497 | vPICO presentations | ITS2.11/AS4.12

Validation of the capability of WRF-Chem model and CAMS to simulate near surface atmospheric CO2 mixing ratio for the territory of Saint-Petersburg

Georgy Nerobelov, Yury Timofeyev, Sergei Smyshlyaev, Stefani Foka, Ivan Mammarella, and Yana Virolainen

The growing content of greenhouse gases (GHGs) influences the radiation balance of the planet causing the rise of air temperature in lower atmosphere. This circumstance triggers researchers to create and develop the new methods of estimation of anthropogenic CO2 emissions. One of such method is top-down estimation which is based on measurements and chemical transport modelling. Since the errors of the top-down approach depend on quality of the modelled data it requires validation by complex observations. In current study we investigated the performance of regional numerical weather prediction and chemistry transport model WRF-Chem and CAMS service in simulating spatio-temporal variation of near surface atmospheric CO2 mixing ratio in March and April 2019 for the Saint-Petersburg area (Russia). To validate the modelled data, we used local observations obtained on Peterhof (St. Petersburg) station. The analysis demonstrates that WRF-Chem model can adequate simulate the transport of CO2 in near-surface layer with spatial resolution of 3 km. Average difference and correlation coefficient are in range 0.8-1.6% and 0.55-0.72 respectively. It was found that the WRF-Chem modelled data where biogenic and anthropogenic fluxes were considered fit the observation data worse than the WRF-Chem simulation where only anthropogenic emissions were used. It can be linked to the errors of the biogenic flux calculation. However, to prove that investigations for two contrast periods (in summer and winter) are needed. Despite the rude spatial resolution of the CAMS data (approximately 200x400 km) we found that in general the trend of surface atmospheric CO2 mixing ratio in March and April 2019 for the Saint-Petersburg area from the CAMS dataset fits the observations.

How to cite: Nerobelov, G., Timofeyev, Y., Smyshlyaev, S., Foka, S., Mammarella, I., and Virolainen, Y.: Validation of the capability of WRF-Chem model and CAMS to simulate near surface atmospheric CO2 mixing ratio for the territory of Saint-Petersburg, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-1497, https://doi.org/10.5194/egusphere-egu21-1497, 2021.

EGU21-13613 | vPICO presentations | ITS2.11/AS4.12

Numerical experiments on sensitivity of local meteorology vs. land-cover changes in the Arctic through seamless Enviro-HIRLAM modelling

Alexander Mahura, Roman Nuterman, Alexander Baklanov, Sergej Zilitinkevich, and Markku Kulmala

In the recent decade, the Arctic as a whole is subject to amplified warming and well documented changes in the Arctic ecosystems, and especially, these changes are became more and more pronounced over territories of the Russian Arctic.

In this research, to study atmosphere-land-sea surfaces interactions, and in particular, heat-moisture exchange/ regime between these surfaces and for better understanding and forecasting of local meteorology in the Arctic, the seamless modelling approach was tested and applied. The Enviro-HIRLAM (Environment HIgh Resolution Limited Area Model) is an online integrated meteorology – atmospheric composition multi-scales and -processes modeling system. This model was adapted for a region of interest located in the Russian Arctic covering the inland, seashore and adjacent seas territories with the Yamal Peninsula in the center of the domain. Two short-term periods during summer (in July) and winter (in January) were chosen.

The performed model runs include changes in vegetation and land-cover as well as taking into account direct  and indirect aerosol effects (for summer), which is needed to estimate interactions and feedbacks between meteorology – atmospheric composition – land cover changes. In this study, the model was run in a downscaling chain with 5 and 1+ km horizontal resolutions. The meteorological and aerosols/ gases initial and boundary conditions required were extracted from ECMWF. The model output includes both 3D meteorology and atmospheric composition (with focus on aerosols in this study) in the surface, boundary layer and free troposphere.

The analysis of variabilities on a diurnal cycle (for key selected meteorological parameters such as air temperature, relative humidity, wind characteristics, boundary layer height, latent and sensible heat fluxes) due to changes in vegetation and land-cover was performed for selected warm and cold periods and will be presented.

How to cite: Mahura, A., Nuterman, R., Baklanov, A., Zilitinkevich, S., and Kulmala, M.: Numerical experiments on sensitivity of local meteorology vs. land-cover changes in the Arctic through seamless Enviro-HIRLAM modelling, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-13613, https://doi.org/10.5194/egusphere-egu21-13613, 2021.

EGU21-6197 | vPICO presentations | ITS2.11/AS4.12

Detecting climate change in Ukraine: trends, prediction and extreme events

Anna Bohushenko, Sergiy Stepanenko, and Inna Khomenko

In this study the trends and variations in 25 extreme temperature and precipitation indices
defined by ETCCDI, are examined using trend method, probability distribution analysis and
spatial statistics for periods of 71 to 137 years for 16 stations evenly distributed in Ukraine. Data
on the indices were obtained from www.ecad.eu.
Since 1981, temperature has increased by about 1ºC in all stations in question relative to the
period of 1945-1980. Analysis of the temperature indices indicates that during the 20th and the
beginning of the 21th century there is significant warming which is particularly pronounced in
annual mean and annual maximum temperatures. Occurrence of more summer days, warm days
and tropical nights and warm spell duration reached the record highest level, and conversely
occurrence of frost and ice days, cold days and cold spell duration fall to a record low for the last
three decades in the most of study territory.
Since 1981, precipitation amount has grown by 30-50 mm relative to the period of 1945-1980 for
the most of Ukrainian territory, except Uzhhorod and Uman where precipitation amount has
remained the same. For Ukraine average, an increase in maximum daily and maximum 5 days
precipitation amount, the maximum number of consecutive wet days, heavy and very heavy
precipitation days, and a decrease in the maximum number of consecutive dry days are observed
for the last three decades.
The analysis of the spatial distribution of trend of precipitation and temperature indices showed
that there are large differences between regions of Ukraine, and coherence of spatial distribution
of trends of various indices is low.
Spectral analysis and harmonic regression techniques were used to derive simulated and
predicted (2019-2050) values of annual precipitation and annual mean temperature and four
indices such as maximum value of daily maximum temperature, minimum value of daily
minimum temperature, the highest 1-day precipitation amount and maximum number of
consecutive dry days for some stations such as Kerch (the Crimean Peninsula), Kyiv (situated in
north-central Ukraine along the Dnieper River), Lubny (Dnieper Lowland), Lviv and Shepetivka
(Podillia Upland), Uzhhorod (Transcarpathia), Uman (Dnieper Upland).
Annual mean temperature and maximum value of daily maximum temperature were predicted to
increase by 0.33°C per decade in the period of 2019-2050 with respect to 1981-2018, while
minimum value of daily minimum temperature was predicted to grow slightly faster (by 0.43-
0.63ºC per decade).
Precipitation was predicted to increase for the stations in question by 20-66 mm up to 2050
relative to 1981-2018 and conversely maximum number of consecutive dry days will slightly
decline except Lubny where increase in an aridity index was predicted. In the next three decades
changes in maximum daily precipitation will be various: in Shepetivka and Kyiv such
precipitation will be decreased and in other stations increasement in such precipitation will be up
to 6 mm till 2050 with respect to 1981-2018.

How to cite: Bohushenko, A., Stepanenko, S., and Khomenko, I.: Detecting climate change in Ukraine: trends, prediction and extreme events, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-6197, https://doi.org/10.5194/egusphere-egu21-6197, 2021.

EGU21-13821 | vPICO presentations | ITS2.11/AS4.12

Projections of regional climate change in Ukraine based on multi-model ensembles of Euro-CORDEX

Svitlana Krakovska, Vira Balabukh, Anastasia Chyhareva, Larysa Pysarenko, Iryna Trofimova, and Tetiana Shpytal

Climate change is one of the major challenges for future development in every country including Ukraine where actual warming already has impacted many sectors, population, and ecosystems. Recently, the International Initiative of Coordinated Downscaling Experiment for Europe (Euro-CORDEX) has provided RCM data for 0.1o grid. This detailed RCM projection dataset is an excellent basis for estimation of exposure and vulnerability to climate change of different objects and for updating projections for a new National Communication of Ukraine to UNFCCC as well as for Strategy of Ecological Safety and Adaptation to Climate Change in Ukraine.

The study is focused on the estimation of the essential and special climatic characteristics and their changes in the near future (2021-2040) as well as to the middle (2041-2050) and end (2081-2100) of the century over the base period 1991-2010 for three scenarios: RCP2.6, RCP4.5, and RCP8.5. We used bias-adjusted RCM data for daily maximum, mean, and minimum temperature and precipitation provided via ESGF web-portal. We applied a multi-model ensemble approach with further bias-correction by delta-method for multi-year monthly values of the essential characteristics as well as calculated climatic indices using a gridded observational dataset of E-Obs v.20.0e. Ensembles for RCP4.5 and RCP8.5 consisted of 34 RCMs while for RCP2.6 only data of 3 RCMs were available. That is why RCP2.6 is only indicative, while the other two scenarios results have a high confidence level and quartiles and percentiles of the ensemble range are estimated.

More consistent temporally and spatially results were obtained for temperature projections. Increases relative to the baseline were in the range of 0.5-1.5ºC for all the RCPs with a bit higher warming in the North of the country in 2021-2040. In 2041-2060, the increases were 1.0-2.0ºC under RCP2.6 and 1.5-2.5ºC under RCP8.5, with RCP4.5 in between. By the end of the century 2081-2100 the differences between scenarios became much pronounced: from 1-2ºC for RCP2.6 to 4-6ºC for RCP8.5.

Precipitation changes are much complex with high variability across the seasons and the territory. In winter precipitation tends to increase relative to the baseline in most of the country for all the RCPs. In early spring (March) there is a relative decline in the near-future period, especially in RCP2.6 and RCP8.5 but not in RCP4.5. In later periods the decline becomes less and in the higher RCPs, there is a relative increase. Later spring rainfall changes show a decline in RCP2.6 but an increase for the other RCPs. The summer months show a relative decline with all the higher RCPs getting drier over time. In the fall relative changes are mixed, with declines in some months and increases in others.

Based on these two essential climatic characteristics other important indices were calculated and analyzed: length of vegetation season, tropical nights, summer days, water deficit, aridity/humidity index, etc.

Obtained projections of climatic characteristics were(will be) used for further agriculture, forest, and human health impact assessments, that will be the basis for the development of adaptation measures to climate change in the frames of the National Adaptation Plan of Ukraine.  

How to cite: Krakovska, S., Balabukh, V., Chyhareva, A., Pysarenko, L., Trofimova, I., and Shpytal, T.: Projections of regional climate change in Ukraine based on multi-model ensembles of Euro-CORDEX, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-13821, https://doi.org/10.5194/egusphere-egu21-13821, 2021.

EGU21-3556 | vPICO presentations | ITS2.11/AS4.12

The atmosphere circulation movements in the matching with space weather parameters variations

Olga Stupishina and Elena Golovina

The work presents some investigation results of the Space Weather state during the macrosynoptic processes movements in North Atlantic and Eurasia extratropical latitudes. The types of these processes, as it is known, were defined by A. F. Vangengeim as atmosphere circulation types: E-type (east transport in the troposphere which matches with stable anticyclone above the continent), W-type (west transport), and C-type (meridional transport).

The investigation time interval: 1.01.2007 – 1.01.2014. That corresponds to: the Solar Activity (SA) 23 cycle fall branch, the SA minimum, the rise branch of the 24 SA cycle, the maximum of 24 SA cycle.

From the investigation we have found out the different periods of the circulation types conservation:  (5-7) days which corresponds to the Natural Synoptic Period (NSP) in Europe region – in our study we have registered 95 NSP cases - it occurs 45% of all discovered periods); (7-10) days – 27% (58 cases), and the Long Period (LP) which endured more than 10 days - 28% (59 cases).

Here we compare the space weather state at the beginnings of NSP and LP.

We have investigated the matching of LP-circulation with registered Long-live Pressure Systems (LPS) on different terrestrial latitude locations - Saint-Petersburg (59o57‘N, 30o19‘E) and Tambov (52o43‘N, 41o27‘E).

Space Weather parameters were: global  variations of SA parameters; daily characteristics of the SA flare component in various bands of the electromagnetic spectrum; variations of Interplanetary Space characteristics in Earth vicinity; variations of daily statistics of Geomagnetic Field characteristics.

Results: (1) The modes of LP-circulation distributions are in the SA maximum and on the SA rise branch (37% and 36% of all LP cases respectively). (2) LP- E-type occurs 56% of all LP. (3) NSP- W-type occurs 48% of all NSP. (4) Most frequent LP- E- type placed on the SA rise branch (24% of all LP). (5) The opening and final moments of LP-circulations was not the same for those moments of LPS on different terrestrial latitude locations but 50% of Saint-Petersburg LPS and 81% of Tambov LPS were intersecting with the time intervals of LP-circulations. (6) All Saint-Petersburg anticyclonic LPS and 82% of them in Tambov area have registered with the E-type of atmosphere circulation. (7) The behaviour of the whole Space Weather parameters complex is specific for LP and differs from it for NSP of different circulation types. (8) The days of the maximal difference of abovementioned complexes were discovered in the folder epoch’s interval – that shows the good forecast perspective. (9) The concrete Space Weather parameters which difference the moments of LP-beginnings from NSP-beginnings are listed in the work.

Results may be useful for the understanding of the solar-terrestrial connections and can create the base for the forecast of atmosphere response to the space impact.

How to cite: Stupishina, O. and Golovina, E.: The atmosphere circulation movements in the matching with space weather parameters variations, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-3556, https://doi.org/10.5194/egusphere-egu21-3556, 2021.

EGU21-14353 | vPICO presentations | ITS2.11/AS4.12

Formation and growth of aerosol particles in boreal forest of Siberia

Anastasiia Demakova, Olga Garmash, Ekaterina Ezhova, Mikhail Arshinov, Denis Davydov, Boris Belan, Steffen Noe, Kaupo Komsaare, Marko Vana, Heikki Junninen, Federico Bianchi, Lubna Dada, Tuukka Petäjä, Veli-Matti Kerminen, and Markku Kulmala

New Particle Formation (NPF) is a process in which a large number of particles is formed in the atmosphere via gas-to-particle conversion. Previous research shows the important role of formation of new particles for atmosphere, clouds and climate (Kerminen, V.-M. et al. 2018).

              There exist measurements from different parts of the world which show that NPF is happening worldwide (Kerminen, V.-M. et al. 2018). Measurements at SMEAR II station in Hyytiälä, Finland (Hari P. and Kulmala M., 2005), show that NPF is a common process in Finland’s boreal forests. However, measurements at Zotto station in Siberia, Russia, show that NPF events are very rare in that area (Wiedensohler A. et al., 2018). Measurements in Siberian boreal forests are sparse. We have conducted new measurements at Fonovaya station near Tomsk (Siberia, Russia) using Neutral cluster Air Ion Spectrometer (NAIS), Particle Size Magnifier (PSM), Differential Mobility Particle Sizer (DMPS) and the Atmospheric Pressure interface Time-Of-Flight mass spectrometer (APi-TOF). Those instruments measure aerosol particle number size distribution (NAIS, DMPS), ion number size distribution (NAIS), size distribution of small particles (PSM) and chemical composition of aerosol particles (APi-TOF). The novelty of this work is that such complex measurements have not been done in Siberia before.

              Here we report the first results of our research on NPF phenomenon in Siberian boreal forest. We present detailed statistics of NPF events, as well as formation rates (J) and growth rates (GR) of aerosol particles. The results from Fonovaya station are compared with those from SMEAR II station and from SMEAR Estonia station in Järvselja, Estonia.

               

 

 

Literature

  • [1] Kerminen V.-M. et al. “Atmospheric new particle formation and growth: review of field observations”. In: Environmental Research Letters 10 (2018), p. 103003.
  • [2] Wiedensohler A. et al. “Infrequent new particle formation over the remote boreal forest of Siberia”. In: Atmospheric Environment 200 (2019), pp. 167–169.
  • [3] Dada L. et al. “Long-term analysis of clear-sky new particle formation events and nonevents in Hyytiälä”. In: Atmospheric Chemistry and Physics 10 (2017), pp. 6227–6241.

 

How to cite: Demakova, A., Garmash, O., Ezhova, E., Arshinov, M., Davydov, D., Belan, B., Noe, S., Komsaare, K., Vana, M., Junninen, H., Bianchi, F., Dada, L., Petäjä, T., Kerminen, V.-M., and Kulmala, M.: Formation and growth of aerosol particles in boreal forest of Siberia, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-14353, https://doi.org/10.5194/egusphere-egu21-14353, 2021.

EGU21-14824 | vPICO presentations | ITS2.11/AS4.12 | Highlight

Integrative and Comprehensive Understanding on Polar Environments (iCUPE) – concept, results and outlook

Tuukka Petäjä and the iCUPE team

The world is changing. The polar regions are critical component in the Earth system and influenced by on-going megatrends, such as globalization and demographical changes. The extensive use of Arctic natural resources will have effects on regional pollutant concentrations in the Arctic. We set up the ERA-PLANET Strand 4 project “iCUPE – integrative and Comprehensive Understanding on Polar Environments” to provide novel insights and observational data on global grand challenges with a polar focus. We deploy an integrated approach with in-situ observations, satellite remote sensing and multi-scale modeling to synthesize data from a suite of comprehensive long-term measurements, intensive campaigns, and satellites. This enabled us to deliver novel data and indicators descriptive of the polar environment. The iCUPE framework includes thematic state-of-the-art research and the provision of novel data in atmospheric pollution, local sources and transboundary transport, characterization of arctic surfaces and their changes, an assessment of the concentrations and impacts of heavy metals and persistent organic pollutants and their cycling, the quantification of emissions from natural resource extraction, and the validation and optimization of satellite Earth observation data streams. Here we summarize the project results and provide novel insights into continuation of the work. 

How to cite: Petäjä, T. and the iCUPE team: Integrative and Comprehensive Understanding on Polar Environments (iCUPE) – concept, results and outlook, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-14824, https://doi.org/10.5194/egusphere-egu21-14824, 2021.

EGU21-8077 | vPICO presentations | ITS2.11/AS4.12

Heterogeneous response of ecosystem productivity to anthropogenic modifications in different bioclimatic locations in northern high latitudes.

Victoria Miles and Igore Ezau

EGU21-3090 | vPICO presentations | ITS2.11/AS4.12

Multiyear Dynamics of Remotely Mapped Characteristics of Ecosystems in Northern Eurasia 

Victor Gornyy, Andrei Kiselev, Sergei Kritsuk, Iscander Latypov, and Andrei Tronin

The climate change of the last decades has to be reflected in the ecosystems dynamics. By investigating the ecosystem dynamics one can get the information about climate change. One of the most suitable source of information about ecosystem dynamics is remote sensing satellite data. We used EOS multispectral images, gravimetric data of the GRACE satellite, and AURA satellite contents of sulfur dioxide data for the period of the last ~20 years. Daily and 8 days composites of different quantitative characteristics, reduced to the spatial resolution 1x1 km, were retrieved from standard products and raw data: - the daily averaged land surface temperature; - the duration of vegetation (the period of year when a land surface temperature is higher than +10oC); - the Enhanced Vegetation Index (EVI); - the effective water layer thickness (EWLT) according satellite gravimentry); - the concentration of sulphur dioxide in atmosphere. The speed of each characteristics change was estimated and mapped by using linear regression. As the result, the regular chain of isometric domains of land surface temperature rising and decreasing was noticed from the West edge to the East edge of Northern Eurasia. The presence of this chain was the reason to express hypothesis about more complex structure of the modern atmospheric circulation and interaction between the Ferrel and the Polar cells. Additionally we have noticed that domain of the land surface temperature growth at the Northern part of West Siberia coincides with decreasing of EWLT. We interpreted this phenomena as result of permafrost degradation.

How to cite: Gornyy, V., Kiselev, A., Kritsuk, S., Latypov, I., and Tronin, A.: Multiyear Dynamics of Remotely Mapped Characteristics of Ecosystems in Northern Eurasia , EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-3090, https://doi.org/10.5194/egusphere-egu21-3090, 2021.

EGU21-10654 | vPICO presentations | ITS2.11/AS4.12

Inhomogeneous surface of West Siberian peatland diagnosed by skin temperature distribution

Irina Repina, Victor Stepanenko, Alexander Varentsov, Arseniy Artamonov, and Kuksova Natalia

Skin temperature (Ts) plays a central role in shaping the land surface energy balance and is also widely available from remote sensing for model evaluation and data assimilation. Both offline land models and land–atmosphere coupled models still have difficulty in realistically simulating or predicting Ts. In the case of an inhomogeneous surface, under the same atmospheric conditions, there are patches of different skin temperature and different daily variability. This observational study reports variations of surface fluxes (turbulent, radiative, and soil heat) and ancillary atmospheric/surface/soil data based on in-situ measurements conducted at Mukhrino field station located in the middle taiga zone of the West Siberian Lowland. To measure the surface temperature, we used an infrared camera (TIR, ~8–14 mkm wavelength range) based on an unmanned aerial vehicle. This UAV-based system provides high-resolution multi-sensors data acquisition. It also provides maximal flexibility for data collection at low cost with minimal atmospheric influence, minimal site disturbance, flexibility in measurement planning, and ease of access to study sites (e.g., peatlands) in contrast with traditional data collection methods. e demonstrate that the temperature of the boggy surface has significant variability: depending on the time of day, temperature contrasts can reach more than 10 degrees, which is associated with different surface moisture and albedo. A technique has been developed for restoring the surface albedo from the data of IR measurements. Ground measurements have shown that the variations of temperature and humidity across the subsurface layer can be very large. Furthermore, these variations are directly related to the concept of a difference between the roughness length for momentum versus that for heat. Information about the ratio of z0/z0h is necessary in order to be able to use surface skin temperature from satellite remote sensing for the computation of surface fluxes. The relationship between the difference in skin temperature and soil contact temperature with the heat balance, especially with sensible heat fluxes and heat flux through the soil, is considered. The parametrizations obtained in this work can be used in Earth System models to represent wetland ecosystems.

The work was supported by RFBR grant 18-05-60126, by the Moscow Center for Fundamental and Applied Mathematics and within the grant of the Tyumen region Government in accordance with the Program of the World-Class West Siberian Interregional Scientific and Educational Center (National Project "Nauka").

How to cite: Repina, I., Stepanenko, V., Varentsov, A., Artamonov, A., and Natalia, K.: Inhomogeneous surface of West Siberian peatland diagnosed by skin temperature distribution, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-10654, https://doi.org/10.5194/egusphere-egu21-10654, 2021.

EGU21-11428 | vPICO presentations | ITS2.11/AS4.12

The role of fires for tundra-forest transition in northwest Siberia

Ekaterina Ezhova, Oleg Sizov, Petr Tsymbarovich, Andrey Soromotin, Nikolay Prihod'ko, Tuukka Petäjä, Sergey Zilitinkevich, Markku Kulmala, Jaana Bäck, and Kajar Köster

Transition of arctic vegetation from tundra to shrubs and forest is an important process influencing global carbon budget. Transition is predicted due to warming and prolongation of the growing season but observations show that it is slower than expected. Fires are disturbances that could trigger a shift of biomes.

We studied the transition of dry tundra to forest and woodland in northwest Siberia for burned and background sites within the time interval of 60 years. We used meteorological data to estimate potential shifts in vegetation based on a bioclimatic model. To investigate fire and vegetation dynamics, we used historical and modern satellite imagery (Corona KH-4b, Landsat-5,7,8, Resurs-P, SPOT-6,7). We performed comparative analysis of vegetation using high-resolution satellite data from different years.

The growing season length increased by 20 days and the mean temperature of the growing season increased by 1°C making climatic conditions suitable for trees. We showed that ca 40% of the total study area experienced fires at least once during the last 60 years. Within this period, shift from dry tundra to tree-dominated vegetation occurred in 6-15% of the area in the non-disturbed sites compared to 40-85% of the area in the burned sites.

How to cite: Ezhova, E., Sizov, O., Tsymbarovich, P., Soromotin, A., Prihod'ko, N., Petäjä, T., Zilitinkevich, S., Kulmala, M., Bäck, J., and Köster, K.: The role of fires for tundra-forest transition in northwest Siberia, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-11428, https://doi.org/10.5194/egusphere-egu21-11428, 2021.

The impact of temperate forests on climate still has open questions about their quantitative effect on radiative and thermal properties of the territory. The study addresses some of these questions and the analysis is based on the data from the Land-Use Model Intercomparison Project (LUMIP), which is the part of Coupled Model Intercomparison Project Phase 6 (CMIP6). The main aim of CMIP is to study climate on different periods of time from the past to the future with help of observations and Earth System Models (ESM).

LUMIP belongs to historical experiments and implies gradual deforestation with linear trend up to 1% all over the world during 50 years in pre-industrial period (1850-1899) and next 30 years with no change in forest cover. The goal of this experiment is to reveal the contribution of forest cover reduction on climate characteristics under quasi-constant anthropogenic forcing. This experiment was based on ESM simulations and the dataset of 8 ESM was retrieved for calculations of different climatic characteristics for the territory of Ukraine. These models have different spatial resolution, the initial and the final forest cover in grid cells respectively. Therefore, we analysed ESMs one-by-one and summarised the results over latitudinal zones. To analyse radiative regime we used monthly data of downwelling and upwelling shortwave radiation, which affect thermal regime estimated via surface and 2-m air temperature changes as well as mean daily and annual ranges. Anomalies of each characteristics were obtained over the base averages of the first 20 years of deforestation (1850-1869), which were further smoothed using the 5-year running mean.

It is known that the forest cover influences the ratio of surface downwelling and upwelling shortwave radiation, particularly, via albedo. We found the highest changes in albedo in winter season, most probably due to the presence of snow cover. Increase of albedo is well correlated with deforestation and the maximal rate of 18%/50 years was found in the Carpathians in winter. There were much less changes in warm season with rates up to 2%/50 years due to small difference between values of forest (~3-10%) with grass (~10-30%) than snow albedo (~40-90%).

These changes in radiative properties cause shifts in temperature regime with moderate and strong negative correlations between albedo and both surface and air temperatures. Higher albedo in winter season caused the decrease of mean monthly surface temperature up to -0.4℃/10 years in winter and -0.3℃/10 years in warm season. Values of changes of mean monthly air temperature corresponded to surface temperature changes and they were -0.4℃/10 years in winter and -0.2℃/10 years in warm season. Based on mean maximum and minimum monthly temperatures we found that deforestation also affected mean daily air temperature range only in winter with tendency up to 0.1–0.3℃/ 10 years. Meanwhile the models showed controversial results for annual air temperature range. One of the essential research outcomes we found that the impact of gradual deforestation on the thermal regime was shifted on approximately 20 years and diminished after stopping land cover change.

How to cite: Pysarenko, L. and Krakovska, S.: Impact of deforestation on surface radiative properties and temperature characteristics in Ukraine based on LUMIP CMIP6 datasets, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-7726, https://doi.org/10.5194/egusphere-egu21-7726, 2021.

EGU21-8838 | vPICO presentations | ITS2.11/AS4.12

Thermal regime of soil active layer at the Bolshevik Island (Archipelago Severnaya Zemlya) during 2016 – 2020 years

Alexander Makshtas, Petr Bogorodski, and Ilya Jozhikov

Investigations of active soil layer on the Research station “Ice Base Cape Baranova’’ had been started in February 2016 after installation on the meteorological site sensors of Finnish Meteorological Institute: thermochain with IKES PT00 temperature sensors at depths of 20, 40, 60, 80 and 100 cm, soil heat flux sensor HFP, and two ThetaProbe type ML3 soil moisture sensors. Based on the results of measurements annual cycle of soil temperature changes was revealed with amplitudes 10 - 15 ° C less than the amplitudes of surface air layer temperature (Ta) and especially the temperature of the soil upper surface (Tsrad), in great degree determined by short-wave radiation heating and long-wave radiation cooling. Approximation by linear fittings shows average rates of increase Ta - 0.4°C/year, Тsrad - 0.3°C/year, and temperatures of active soil layer - 0.2°C/year.

The data on thermal regime of active soil layer and characteristics of energy exchange in atmospheric surface layer make it possible to draw the conclusion about the reason for the abnormally warm state of the upper meter soil layer in summer 2020, despite in March during the whole period under study active soil layer was the warmest in 2017. Comparison in temperatures of the underlying surface and characteristics of surface heat balance during period under study showed that in 2020 the temperature of the soil surface at the end of May for a short time reached the temperature of snow melting. It is happened 25 days earlier than in 2017 as well as other years and led to radical decrease in surface albedo, sharp increase of heat flux to the underlying surface, and increased duration of active soil layer heating.

Additionally, permafrost thawing studies using a manual contact method were carried out on the special site, organized according to CALM standards. These studies showed significant variety of soil active layer thicknesses in the relatively small area (~0.12 km2), which indicates significant spatial variability of microrelief, structure and thermophysical properties of soil, as well as vegetation, typical for Arctic desert. Calculations carried out with version of the well-known thermodynamic Leibenzon model for various parameterizations of vegetation and soil properties partly described peculiarities of spatial variability of observed thawing depths.

How to cite: Makshtas, A., Bogorodski, P., and Jozhikov, I.: Thermal regime of soil active layer at the Bolshevik Island (Archipelago Severnaya Zemlya) during 2016 – 2020 years, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-8838, https://doi.org/10.5194/egusphere-egu21-8838, 2021.

EGU21-6451 | vPICO presentations | ITS2.11/AS4.12 | Highlight

Estimation of emission from organic soils

Sergiy Stepanenko, Anatoliy Polevoy, and Alexander Mykytiuk

Dynamic modeling of the processes of transformation of soil organic matter is part of a more complex problem - modeling the processes of soil formation and functioning of soils, and the development of the entire soil system. It is important tool for studying the functioning and predicting changes in the soil system, quantifying the role of the soil cover in the balance of greenhouse gases in the atmosphere and in the processes of climate change

The PEAT-GHG-Model (furthermore – PEAT-GHG-MODEL), based on further development of ROTHC-model (Coleman, Jenkinson, 2008) for mineral soil and ECOSSE model (Smith, Gottschalk et al., 2010) for organic soils.

 The PEAT-GHG-MODEL evaluates of CO2, CH4, N2O fluxes values at organic soils and soil carbon deposition for non-forest types of land cover. The model utilize data from existing weather stations, published soil data, and data generated by remote sensing of land cover. The model evaluates of CO2, CH4, N2O fluxes values at organic soils and soil carbon deposition, including at peatlands, retrospectively for targeted period or back in time with available space images library. The model can evaluates of CO2, CH4, N2O fluxes values at organic soils and soil carbon deposition for future period based on meteorological input data generated by climate change scenarios and land cover data generated by relevant habitats (land cover) change scenarios. The PEAT-GHG-MODEL estimates of CO2, CH4, N2O fluxes from organic soils and soil carbon deposition for non-forest types of land cover. The model input data generates by existing weather stations, remote sensing of land cover and published soils data. The model estimates of GHG emissions from organic soils, including peatlands, retrospectively for targeted period or back in time with available space images library. The model can simulates of GHG emissions for future period based on meteorological input data generated by climate change scenarios and land cover data generated by relevant habitats change scenarios. The model generates georeferenced data. Minimum land surface area, which can be evaluates by model, equal of size of one pixel of land cover images, used for remote sensing of land cover, it can be 1 m2 or less. Due to high resolution, the model estimates highly variable in space CO2, CH4, N2O fluxes with high accuracy. Maximum land surface area is not limited. The model generates data on decade and/or annual bases. Article presents the model’ verification results. The model verified in 2017 by independent, from the model authors, verification team in frame of “CLIMA EAST: conservation and sustainable use of peatlands” project (UNDP-Ukraine). Direct field measurement data for two peatlands used for model verification, including one site drained, and another one is under natural hydrological conditions.  The cumulative annual of CH4 and CO2 emission presented in Table.

The model calculations were compared with the experimental data obtained for peat soils in the western Polesie of Belarus. The cumulative annual of CH4 and CO2 emission presented in Table.

Table. Cumulative annual of CH4 and CO2 emissions according to field measurements and assessment of PEAT-GHG-MODEL

 

 

How to cite: Stepanenko, S., Polevoy, A., and Mykytiuk, A.: Estimation of emission from organic soils, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-6451, https://doi.org/10.5194/egusphere-egu21-6451, 2021.

EGU21-15069 | vPICO presentations | ITS2.11/AS4.12

Geochemical sensitivity of lacustrine ecosystems of Yamal Peninsula (Russian Arctic) to climate change

Irina Fedorova, Roman Zdorovennov, Valeriy Kadutskiy, Grigorii Fedorov, Elena Shestakova, Galina Zdorovennova, Alina Guzeva, Mariya Chernyshova, Antonina Chetverova, Larisa Frolova, and Gulnara Nigamatzyanova

Yamal Peninsula is one of the significant region which terrestrial and aquatic landscapes are sensitive to the climate change. Geochemical processes in lakes can show impact of climate variability on hydrochemical and biological specific, trophic and ecological status. During 2012-2013 and 2018-2020 several Yamal lakes were observed during the summer field investigations. Water samples and sediment cores were taken and analyzed. Distribution of hydrochemical data is wide and cover Yamal coastal zone and central part of the peninsula including several anthropogenic chanced ecosystems. Sediment cores were taken in river terraces of Yuribey, Erkuta, Pysedeiyakha rivers, marine terraces of central Yamal (Neitinskie Lakes), and small core from Beliy island (North part of Yamal). Main ions and trace elements in lakes will be presented in a report as well as TOC/TC, grain-size, dating and paleoecological description of sediments. In order to reconstruct recent environmental and ecological changes half-core MSCL logging (physical properties, 0.5 cm spacing) and half-core XRF scanning (chemical composition, 0.1 cm spacing) have been applied for cores from Neitinskie Lakes (central part of Yamal). The first results of scanning and the statistic will be presented in the report. Comparison of aquatic ecosystem geochemistry for different parts of Yamal peninsula allow to explain the climate impact in different landscapes. Studies supported by RFBR 18-05-60291.  MSCL logging and XRF scanning have been done in Shirshov Institute of Oceanology of RAS by Geotek MSCL-XYZ instrument using.

How to cite: Fedorova, I., Zdorovennov, R., Kadutskiy, V., Fedorov, G., Shestakova, E., Zdorovennova, G., Guzeva, A., Chernyshova, M., Chetverova, A., Frolova, L., and Nigamatzyanova, G.: Geochemical sensitivity of lacustrine ecosystems of Yamal Peninsula (Russian Arctic) to climate change, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-15069, https://doi.org/10.5194/egusphere-egu21-15069, 2021.

EGU21-7399 | vPICO presentations | ITS2.11/AS4.12

Distribution assessment of climate-induced changes of primary production in the Barents Sea ecosystem

Sergey Berdnikov, Vera Sorokina, and Valerii Kulygin

Changes in the Arctic environment in recent decades may result in favourable conditions for the increase of biological production. However, there are not many well-documented climate-related shifts in plankton, fish and benthic communities in the Arctic Ocean marine ecosystems, and there is significant uncertainty about the present and future productivity values. Researchers often estimate (using forecasts, etc.) how some key stocks may respond to future climatic changes to assess the prospects of fisheries.
In our study, applying the Ecopath multi-species balance production model, we estimated the distribution of climate-induced primary productivity increase, along the food web in the Barents Sea ecosystem. Assessment was made for two periods (“cold” (1970-1990) and “warm” (1991-2016)) and three regions - the Southern Barents Sea and the adjacent areas of the Norwegian Sea, the Svalbard Archipelago region, and the Northern Barents Sea. For each identified area, the food web has differences in both the structure and quantitative indices (for example, in abundance and biomass) of different trophic groups in different periods, in particular, during the increased ice coverage and relative warming.
We propose a new approach to assess food rations for the Ecopath model. It allows to consider more flexibly the change in the ecosystem food structure, associated with changes in biomasses (stocks) and the appearance of new species in the studied area due to environmental fluctuations related to marine climate warming. Based on the simulation results, we made conclusions concerning the observed and probable changes, related to the primary productivity increase, in the considered ecosystems of the three identified Barents Sea regions.
An integral indicator of the mean trophic level reflects climate-induced changes in the Barents Sea ecosystem. It remained almost unchanged in the southern region but increased for the Northern region and the Svalbard region. This is due to the fact that new species appeared in the structure of food webs of these regions and/or the existed species' biomass (stocks) changed during the warm period when compared to the cold one.
А generalized indicator of biological diversity is an additional evidence of climate-induced changes in the primary production. During the warm period, the Shannon Biodiversity Index for the Northern and the Svalbard regions increases, while it decreases in the Southern region mainly because the biomass of the main trophic groups (cod, herring) increases.
The commercial fishing increase in the Northern and the Svalbard Archipelago regions is likely to be expected. However, there is a possibility that there will be increased stratification between the upper cold and less salty water masses formed by melt ice and the Atlantic water below, which becomes cooler and denser. This can lead to the decrease in the nutrients content of the productive zone and prevent the positive effects of the warm water inflow.

How to cite: Berdnikov, S., Sorokina, V., and Kulygin, V.: Distribution assessment of climate-induced changes of primary production in the Barents Sea ecosystem, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-7399, https://doi.org/10.5194/egusphere-egu21-7399, 2021.

A thermistor-string-based Snow and Ice Mass Balance Apparatus (SIMBA) was deployed in an Arctic lake Orajärvi in northern Finland (67.36°N, 26.83°E) during winter seasons 2011/2012 - 2019/2020. The snow depth and ice thickness (total and separately for congelation ice and granular ice) were retrieved from SIMBA temperature measurements. The average maximum ice thickness was 72 cm with a standard deviation of 10 cm. The interannual variability of lake ice composition was large. In the past 3 ice seasons, the granular ice dominated the total ice thickness. For example, granular ice accounted 80% of the total ice thickness in May 2020. A high-resolution thermodynamic snow/ice model was applied to simulate ice mass balance, with special attention to the lake ice composition. Local weather station data and ECMWF reanalysis products were used as model forcing.

 

The increase of granular ice formation is a result of more snow precipitation during the ice season, increased variability of seasonal air temperature, and a warming trend. The observed snow thickness on land showed a high correlation with snow-ice thickness on top of lake ice. The relationships between the ratio of snow-ice to total ice thickness and the large-scale atmospheric circulation indexes were investigated. Precipitation and, consequently, snow ice thickness on Lake Orajärvi correlated with the phase of the Pacific Decadal Oscillation, which is in line with previous results for precipitation and ice conditions in northern Finland, but an eventual causal teleconnection still requires further studies.

 

How to cite: Cheng, B., Cheng, Y., Vihma, T., and Zheng, F.: Inter-annual variations and large-scale atmospheric forcing on ice thickness and composition during the last decade in an Arctic lake , EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-14840, https://doi.org/10.5194/egusphere-egu21-14840, 2021.

EGU21-3840 | vPICO presentations | ITS2.11/AS4.12 | Highlight

A means-corrected estimate for the Arctic sea-ice volume in 1990–2019

Petteri Uotila, Joula Siponen, Eero Rinne, and Steffen Tietsche

Decadal changes in sea-ice thickness are one of the most visible signs of climate variability and change. To gain a comprehensive understanding of mechanisms involved, long time series, preferably with good uncertainty estimates, are needed. Importantly, the development of accurate predictions of sea ice in the Arctic requires good observational products. To assist this, a new sea-ice thickness product by ESA Climate Change Initiative (CCI) is compared to a set of five ocean reanalysis (ECCO-V4r4, GLORYS12V1, ORAS5 and PIOMAS).

The CCI product is based on two satellite altimetry missions, CryoSat-2 and ENVISAT, which are combined to the longest continuous satellite altimetry time series of Arctic-wide sea-ice thickness, 2002–2017. The CCI product performs well in the validation of the reanalyses: overall root-mean-square difference (RMSD) between monthly sea-ice thickness from CCI and the reanalyses ranges from 0.4–1.2 m. The differences are a sum of reanalysis biases, such as incorrect physics or forcing, as well as uncertainties in satellite altimetry, such as the snow climatology used in the thickness retrieval.

The CCI and reanalysis basin-scale sea-ice volumes have a good match in terms of year-to-year variability and long-term trends but rather different monthly mean climatologies. These findings provide a rationale to construct a multi-decadal sea-ice volume time series for the Arctic Ocean and its sub-basins from 1990–2019 by adjusting the ocean reanalyses ensemble toward CCI observations. Such a time series, including its uncertainty estimate, provides new insights to the evolution of the Arctic sea-ice volume during the past 30 years.

How to cite: Uotila, P., Siponen, J., Rinne, E., and Tietsche, S.: A means-corrected estimate for the Arctic sea-ice volume in 1990–2019, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-3840, https://doi.org/10.5194/egusphere-egu21-3840, 2021.

EGU21-15155 | vPICO presentations | ITS2.11/AS4.12

Lake ice phenology changes in the northern hemisphere

Yubao Qiu, Xingxing Wang, Matti Leppäranta, Bin Cheng, and Yixiao Zhang

Lake-ice phenology is an essential indicator of climate change impact for different regions (Livingstone, 1997; Duguay, 2010), which helps understand the regional characters of synchrony and asynchrony. The observation of lake ice phenology includes ground observation and remote sensing inversion. Although some lakes have been observed for hundreds of years, due to the limitations of the observation station and the experience of the observers, ground observations cannot obtain the lake ice phenology of the entire lake. Remote sensing has been used for the past 40 years, in particular, has provided data covering the high mountain and high latitude regions, where the environment is harsh and ground observations are lacking. Remote sensing also provides a unified data source and monitoring standard, and the possibility of monitoring changes in lake ice in different regions and making comparisons between them. The existing remote sensing retrieval products mainly cover North America and Europe, and data for Eurasia is lacking (Crétaux et al., 2020).

Based on the passive microwave, the lake ice phenology of 522 lakes in the northern hemisphere during 1978-2020 was obtained, including Freeze-Up Start (FUS), Freeze-Up End (FUE), Break-Up Start (BUS), Break-Up End (BUE), and Ice Cover Duration (ICD). The ICD is the duration from the FUS to the BUE, which can directly reflect the ice cover condition. At latitudes north of 60°N, the average of ICD is approximately 8-9 months in North America and 5-6 months in Eurasia. Limited by the spatial resolution of the passive microwave, lake ice monitoring is mainly in Northern Europe. Therefore, the average of ICD over Eurasia is shorter, while the ICD is more than 6 months for most lakes in Russia. After 2000, the ICD has shown a shrinking trend, except northeastern North America (southeast of the Hudson Bay) and the northern Tibetan Plateau. The reasons for the extension of ice cover duration need to be analyzed with parameters, such as temperature, the lake area, and lake depth, in the two regions.

How to cite: Qiu, Y., Wang, X., Leppäranta, M., Cheng, B., and Zhang, Y.: Lake ice phenology changes in the northern hemisphere, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-15155, https://doi.org/10.5194/egusphere-egu21-15155, 2021.

EGU21-3346 | vPICO presentations | ITS2.11/AS4.12

Mercury background monitoring in the Lake Baikal region

Fidel Pankratov, Alexander Mahura, Valentin Popov, and Vladimir Masloboev

During 2013-2015 the gaseous elemental mercury (thereafter, mercury) measurements were carried out at two weather monitoring stations/sites (Listvyanka – from 25 July until 19 November 2013 and Tankhoj – from 27 July 2014 until 11 January 2015). The mercury analyzer Lumex RA-915AM was used for measurements. Although in the Northern Hemisphere the minimal average mercury concentration is about 1.5 ng m-3, the obtained results indicated that in the southern part of the Lake Baikal the lowest average concentration was about 1.18 ng m-3. Thus, the natural reserve territory of the Lake Baikal can be used as the main region to the background level of especially clean areas for monitoring heavy metals and persistent organic pollutants.

For the Listvyanka measurements, the mercury analyzer was installed at 20 m distance from a shore of the lake. During July-November 2013, the average concentration value was about 1.41±0.37 ng m-3 (with max - 4.81, min - 0.16). For the July-August period, the maximum variance distribution was estimates as 0.62 ng m-3. For the August-November period, the variance did not exceed the value of 0.38 ng m-3. Mercury from the atmosphere is deposited on the underlying surface, and with increasing intensity of total solar radiation the re-emission of mercury occurred resulting in increased concentrations of mercury at Listvyanka. A rather low mercury values were recorded during October-November 2013. Analysis of atmospheric transport during summer showed, that main sources of pollution are situated to the west of the lake, and it is a relatively larger area in the southwestern sector, and therefore, it is complex to identify exact locations of such pollution sources.

For the Tankhoj site, in July 2014 the mercury analyzer was installed at about 100 m distance from a shore of the lake. It was for the first time, when the mercury monitoring was conducted for such long-term period of time in the Lake Baikal region. Note, that short-term measurements of mercury do not provide full understanding of the background level mercury and are insufficient to study dynamics. Analysis of obtained time-series showed that summer is characterized by a high variability of mercury (max - 2.86, min - 0.27, with an average 1.19±0.27 ng m-3). In particular, in July an average value of 1.18 ng m-3 (max - 2.68, min - 0.43) was obtained, which corresponds to concentrations observed in the Northern Hemisphere. In August the average value of 1.22 ng m-3 (max - 2.86, min - 0.27) was obtained.

Moreover, obtained results showed that location of the Tankhoj monitoring site can be used for long-term background monitoring of mercury.

How to cite: Pankratov, F., Mahura, A., Popov, V., and Masloboev, V.: Mercury background monitoring in the Lake Baikal region, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-3346, https://doi.org/10.5194/egusphere-egu21-3346, 2021.

EGU21-9669 | vPICO presentations | ITS2.11/AS4.12

Social consequences of climate change in the Arctic towns

Elena Klyuchnikova, Larisa Riabova, and Vladimir Masloboev

Climate change in the Arctic is noticeable and affecting the well-being of the population. The health and emotional state, food and water availability, livelihoods are on the threat. The towns are particularly sensitive to climate change. Their population and infrastructure density is exceptionally high, and temperature fluctuations, as well as extreme weather events, have an exceptionally strong impact on air and water quality, health and other components of human well-being. At the same time, urban communities in the Arctic, especially in industrial development zones, represent a little-studied area in this case.

The report presents the interdisciplinary study results concerning the climate change consequences for the population of Russian Arctic industrial developed areas. The study carried out in Murmansk Region which is a highly industrial and highly urbanized region that is completely included in the Arctic zone of the Russian Federation. Qualitative methods were used; in-depth (more than 50 questions) interviews were conducted with residents of several towns in the region. The study showed corresponds between the subjective perceptions of climate change by urban residents of the Murmansk Region with objective data on meteorological parameters changes. The surveyed urban residents feel changes in health and environmental management practices, and many respondents associate these changes with climate fluctuations. Such a phenomenon as the destruction of infrastructure (residential, public and industrial buildings, roads, energy infrastructure) due to climate change has not been identified. Concerns have been raised about the potential impact of climate warming on the ability to have a decent job due to reduced employment in some industries (such as energy).

The results obtained contribute to a better understanding of the social consequences of climate change in the Russian Arctic. This is important for adaptation actions development.

 

How to cite: Klyuchnikova, E., Riabova, L., and Masloboev, V.: Social consequences of climate change in the Arctic towns, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-9669, https://doi.org/10.5194/egusphere-egu21-9669, 2021.

EGU21-14631 | vPICO presentations | ITS2.11/AS4.12

Hack the Arctic: transforming data into solutions as a community  

Stephany Buenrostro Mazon, Magdalena Brus, Katri Ahlgren, Alexander Mahura, Hanna K. Lappalainen, and Markku Kulmala

A recurring question among research projects is how to optimize the use data that already exists and to identify its stakeholder’s needs, particularly in effort to bring services to a wider community outside academia. We propose a hackathon to allow the collaboration between civil, educational, business and governmental actors to address environmental challenges with the use of environment scientific data from international projects.

Hack the Arctic is co-organized by the Institute for Atmospheric and Earth System Research (INAR)/University of Helsinki, the Integrated Carbon Observation System Research Infrastructure (ICOS-ERIC) Headoffice, and the Environmental Research Infrastructures (ENVRI) Community. The hackathon event aims to enhance the usage and impact of environmental research data by and for society. The 48 hr event will gather multi-disciplinary teams through a public call to make use of existing environmental data from a network of research projects to develop services addressing the needs of different end-users. The participating teams will be mentored by researchers and data scientist in the use of the data. A panel of judges comprising of science mentors, innovation specialists and government sector actors will assess the implementation of the final pilot products at the end of the event.

We present Hack the Arctic as an up-and-coming alternative to expand the usage and visibility of research data and to make it widely accessible to a broader (nonacademic) audience by offering mentorship from data and scientific experts under one roof.

How to cite: Buenrostro Mazon, S., Brus, M., Ahlgren, K., Mahura, A., Lappalainen, H. K., and Kulmala, M.: Hack the Arctic: transforming data into solutions as a community  , EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-14631, https://doi.org/10.5194/egusphere-egu21-14631, 2021.

EGU21-12840 | vPICO presentations | ITS2.11/AS4.12

Science education for doctoral students: MODEST approach and experience

Katja Anniina Lauri, Alexander Mahura, Sini Karppinen, Irina Obukhova, Tatiana Kalganova, Marek Frankowicz, and Inga Skendere

MODEST (Modernization of Doctoral Education in Science and Improvement Teaching Methodologies) is a capacity building project funded by the Erasmus+ programme.

The project is coordinated by the University of Latvia. There are three other EU partners (from Finland, Poland and the United Kingdom) and a total of ten partners from three partner countries (Russia, Belarus and Armenia).

The project aims to improve the structure and content of doctoral education and the internal capacities of services that manage doctoral studies in accordance with the modern European practices, to facilitate a successful adherence with Bologna process reforms and its instruments, to improve and increase the quality of international and national mobility of doctoral students of Armenia, Belarus and Russia, and to establish a sustainable professional network providing the use of participatory approaches and ICT-based methodologies.

During the past year, almost one hundred members of academic and administrative personnel as well as doctoral students have contributed to creating a total of 14 new courses mainly in transferable skills: Research methodology and research design; Project writing, project management, and funding sources; International research writing and presentation skills; Research ethics, Intellectual property rights and personal data protection; 3I - Interdisciplinarity, interculturality, internationalization in research; Organization of doctoral training; Educational/constructive alignment, design and implementation of courses for doctoral studies; Digital literacy; Data analysis and expert systems; Virtual environment; Commercialization of research, managerial skills; Personal development; Complexity; and Sustainable development and global challenges of 21st century. Each course has specific target group(s) such as PhD students, university teachers, doctoral programme managers, or administrative staff.

Summary of each developed course – aims, learning outcomes, content (including course blocks on lectures, seminars, homeworks, etc.), planned learning activities and teaching methods, assessment methods and criteria, and other relevant – will be presented. The developed courses will be an integral part of the Doctoral Training Centers for PhD students to be established in the MODEST partner universities in Armenia, Belarus and Russia.

The MODEST project serves as a great example of transfer of good practices in higher education, especially on doctoral level, but it has also created new connections for educational and scientific collaboration. From the PEEX perspective, MODEST is an important initiative strengthening connections between European universities and institutions in Russia, Belarus and Armenia. The project will continue until 2022. More detailed information is available at www.emodest.eu.

How to cite: Lauri, K. A., Mahura, A., Karppinen, S., Obukhova, I., Kalganova, T., Frankowicz, M., and Skendere, I.: Science education for doctoral students: MODEST approach and experience, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-12840, https://doi.org/10.5194/egusphere-egu21-12840, 2021.

ITS2.14/HS12.2 – Nature-Based Solutions for Global Environmental Challenges and SDG nexus research

EGU21-1563 | vPICO presentations | ITS2.14/HS12.2 | Highlight

Promoting strategic Nature-based Solutions (NBS) by understanding ecosystem services as a driving factor for urban growth

Haozhi Pan, Jessica Page, Zahra Kalantari, and Stephan Barthel

Nature-based solutions (NBS) can be used in improving and protecting ecosystem services (ES), in order to address urban challenges. However, current urban planning approaches have not efficiently integrated NBS into planning to better manage urban land use. This paper examines the interactions between human and natural systems resulting in urban ES and land use and cover change (LUCC) and presents a social-ecological model for LUCC and ES that can help introduce NBS in urban planning. In the model, spatial variations in ES are treated as both drivers and consequences of human decision-making in commercial and residential location choices that drive LUCC. Stockholm County, Sweden, is used as a case study, with a social-ecological LUCC model on 30x30m grid. The results show that ES accessibility drives urban residential and commercial development, with the presence of non-linearity. Areas around existing urban centers show high ES accessibility and high development probabilities, while smaller population centers in large areas enjoy high ES accessibility and low urban development probabilities. Based on the model results, we propose place-specific NBS strategies to deal with the heterogeneous spatial relationship between ES and urban development probabilities.

How to cite: Pan, H., Page, J., Kalantari, Z., and Barthel, S.: Promoting strategic Nature-based Solutions (NBS) by understanding ecosystem services as a driving factor for urban growth, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-1563, https://doi.org/10.5194/egusphere-egu21-1563, 2021.

EGU21-1042 | vPICO presentations | ITS2.14/HS12.2

Environmental and socio-economic factors influencing the use of urban parks in Coimbra (Portugal)

Luis Valença Pinto, Carla Sofia Ferreira, and Paulo Pereira

Urban green spaces (UGS) are considered by the United Nations a fundamental component to achieve some of the United Nations sustainable development goals (SDGs), namely good health and wellbeing (Goal 3) and sustainable cities and communities (Goal 11). Urban parks, a type of UGS, provide a large and diverse number of regulating, provisioning, and cultural Ecosystem Services (ES), particularly relevant to face emerging challenges driven by increasing population and climate change. Furthermore, the cultural ecosystem services (CES) provided by urban parks can have a positive impact on human health and wellbeing. This study aims to identify the most relevant environmental and socio-demographic factors influencing the use of different urban parks in the city of Coimbra, Portugal. Five parks with different biophysical characteristics (e.g. park size, location within the city, tree coverage, available sport and social facilities) were selected for the study. Data were collected through personal interviews which took place in August 2020, performed on working days and weekend days. The activity performed by respondents was recorded, as well as its relevance for the user (in a 5-point Likert scale) and the associated perceived value of its benefits, regarding physical and emotional wellbeing and social interactions. Several motivation options were assessed, as well as the user perception of a set of possible disservices. Socio-demographic data were collected, including age, gender, education level, average monthly income, visitation frequency, mean of transportation to the park, and distance traveled to reach the park. Activities performed by respondents were aggregated into three groups of cultural ecosystem services: Physical interactions, Aesthetical and experiential interactions, and Social interactions. The results showed that physical interactions (e.g. walking, running, biking) dominate CES use identified in all the parks. A factor analysis was performed to investigate the association between the different variables. Perceived physical and emotional wellbeing benefits were always associated with the relevance of the activity attributed by the users, which is common to all the parks. Differences between parks emerge regarding both socio-demographic and motivation variables. Tranquility of space and landscape beauty form detached groups of variables in three of the five parks, with two of them with similar size and including the presence of a water element. Age group, average monthly income, and frequency of visits tend to be associated in three of the parks. Such is also the case of transport type and distance to park, which form clear groups in two of the parks. As for perceived disservices, no relevant limitations were considered by the users, with plagues (e.g. mosquitoes) and dangerous animals being the only ones registering average concerns (the latter associated with dogs without a leash). Findings can help UGS managers to better understand users’ needs and expectations, with potentially positive implications for UGS design and management.

How to cite: Valença Pinto, L., Ferreira, C. S., and Pereira, P.: Environmental and socio-economic factors influencing the use of urban parks in Coimbra (Portugal), EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-1042, https://doi.org/10.5194/egusphere-egu21-1042, 2021.

EGU21-2035 | vPICO presentations | ITS2.14/HS12.2

Nature-Based Solutions to face climate change adaptation and mitigation in Mediterranean watersheds

Itxaso Ruiz, João Pompeu, and María José Sanz

Rural areas of the Mediterranean watersheds face great environmental challenges, where climate change impacts the water cycle, the soil, and biodiversity, which are often priority issues for adaptation. These, have been aggravated by historical land management practices trends. In this context, we propose Nature Based Solutions (NBS) in the form of Sustainable Land Management (SLM) actions at the watershed scale to achieve climate change adaptation and mitigation while promoting other ecosystem services.

SLM actions are local adaptation practices that promote sustainable rural development. Thus, we seek the combination of several actions to achieve regional (watershed scale) more integrated approaches. With this study, we aim at proving that NBS, and thus SLM, is a successful tool for alleviating climate change impacts (i.e. water scarcity, enhanced erosion, biodiversity decline) while promoting the role of land in mitigation and enhancing biodiversity in the rural Mediterranean areas.

For this, we propose a novel conceptualization of SLM actions that moves from their local application and evaluation to the regional more systemic approaches through their combination. Results show synergies in the atmosphere, biosphere, and hydrosphere, allow for the upscaling of SLM through systemic approaches and point at direct contributions to several Sustainable Development Goals.

How to cite: Ruiz, I., Pompeu, J., and Sanz, M. J.: Nature-Based Solutions to face climate change adaptation and mitigation in Mediterranean watersheds, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-2035, https://doi.org/10.5194/egusphere-egu21-2035, 2021.

Research indicates that spatial differentiation of crop yields and soil properties are largely influenced by agricultural practices and the nature of the soil itself. The aim of this study was to examine the spatial relationship between cereal (wheat and oats ) yields and soil properties related to the application of soil-improving cropping systems (SICS). Four-year experiment (2017-2020) was carried out on low productive sandy soil with application of following SICS: S1 – control; S2 – liming; S3 – green manure/cover crops including lupine, phacelia, serradella; S4 – manure and S5 – manure, liming and cover crops together. Effect of the SICS was evaluated using classical statistics, Bland-Altman analysis and geostatistical methods. Mathematical functions, fitted to the experimental cross- and semivariograms were used for mapping the yields (grain and straw) by ordinary cokriging. The grain yields in years with normal rainfall increased by 2% for S2, 10% for S3, 46% for S4, 47% for S5 compared to control (S1) 2789 kg/ha and in dry years were lower (respectively for S2-S5 by 16.3, 10.6, 2.8, 9.9% compared to control 1567 kg/ha. The range of spatial dependence for the yields in direct semi-variograms varied was 50–100 m and > 100 m in cross-semivariograms using textural fractions as secondary variables. The spatial relationships were stronger between yield and soil texture and properties were much stronger with texture and cation exchange capacity than with pH and organic carbon content. Using cokriging for interpolation (mapping) allowed the delineation of zones of lower and higher cereal yields including areas of the SICS application. Higher cereal yield and lower spatial variability in the areas of SICS compared to control soil were observed in the years with normal rainfall. Analysis of the Bland-Altman including limits of agreement enabled to quantify the effect of particular SICS on cereal yield vs. control reference. Different effect of particular SICS on the cereal yield was observed in the years with scarce and good rainfall amount and distribution during growing season. The greatest variation of the cereal yield was observed in manure amended soil (S4) and it was lower and similar in the areas of remaining SICS (S2-S5). The results will help to to select most effective SICS for localized improving crop productivity and adaptation to global warming.

Acknowledgements.The study was funded by HORIZON 2020, European Commission, Programme H2020-SFS-2015-2: SoilCare for profitable and sustainable crop production in Europe, project No. 677407 (SoilCare, 2016-2021).

How to cite: Lipiec, J. and Usowicz, B.: Spatial distribution of cereal yields related to the application of soil-improving cropping systems (SICS), EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-2109, https://doi.org/10.5194/egusphere-egu21-2109, 2021.

EGU21-3124 | vPICO presentations | ITS2.14/HS12.2

The socio-ecologic aspects of nature-based solutions for coastal flooding mitigation

Miguel Inácio, Donalda Karnauskaitė, Katažyna Mikša, Marius Kalinauskas, Eduardo Gomes, and Paulo Pereira

Coastal flooding has been historically mitigated through engineered artificial (grey) infrastructures such as breakwaters, dikes, and sea walls. However, these structures have a pervasive long-term impact on coastal ecosystems (e.g. sediment transport disruption), and require constant maintenance, and have little resilience to climate change (e.g. hurricanes, sea-level rise) related events.  Grey infrastructures failed to mitigate the effects of coastal floods, and the damages were significantly less in areas where healthy coastal ecosystems were present. This highlighted the role and contribution of coastal habitats to mitigate coastal floods and adapt to new conditions. The inefficiency of grey infrastructure to mitigate the impact of extreme events and following ecosystem-based management led to the development of the Nature-Based Solutions (NBS) concept. In the context of coastal flooding mitigation, to reduce the effects of storm surges, wave action, and erosion, NSB can be designed using (1) natural solutions (e.g., the creation of marine protected areas), (2) soft engineering and ecological restoration practices (e.g., mangrove plantation), and (3) hybrid solutions, which integrates natural and grey infrastructures (e.g. artificial reefs). NBS integrate multiple international environmental agendas, for their capacity to provide multiple co-benefits (e.g. recreation, fisheries). NBS are also key for supporting other agendas and global objectives: the Sustainable Development Goals (e.g. SDG14), Green/Blue economy, coastal resilient and climate-adapted coastal communities, biodiversity targets of the Convention for Biological Diversity and Circular Economy.

 


“Lithuanian National Ecosystem Services Assessment and Mapping (LINESAM)” No. 09.3.3-LMT-K-712-01-0104 is funded by the European Social Fund according to the activity “Improvement of researchers’ qualification by implementing world-class R&D projects” of Measure No. 09.3.3-LMT-K-712.

How to cite: Inácio, M., Karnauskaitė, D., Mikša, K., Kalinauskas, M., Gomes, E., and Pereira, P.: The socio-ecologic aspects of nature-based solutions for coastal flooding mitigation, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-3124, https://doi.org/10.5194/egusphere-egu21-3124, 2021.

EGU21-3530 | vPICO presentations | ITS2.14/HS12.2

Impact of different sub-tropical trees on outdoor thermal comfort in an Indian city – A microclimatic modelling approach

Sana Javaid, Kameswara Yashaswini Sista, and Stephan Pauleit

Indians cities are facing incessant urbanization with lack of adequate green spaces exposing inhabitants to heat stress and increased mortality. Reduction of heat stress or optimization of outdoor thermal comfort (OTC) has been recognized as one of the multiple benefits of urban green infrastructure across different climatic zones. However, there is dearth of such studies in humid-subtropical (Cwa) context, especially India. ‘Urban trees’ are most preferred vegetation type concerning OTC, whereas, ‘parks, streets and gardens’ are most preferred urban green settings in a residential neighbourhood, as indicated by social survey results of another part of this study. But role of urban trees in enhancing OTC in different urban settings remains underexplored. In particular, it needs to be better understood how different morphological characteristics of trees influence their thermal benefits. Hence, we investigated nine sub-tropical tree species in these urban settings of a typical residential neighbourhood in the mid-sized, humid-subtropical city of Dehradun in north India. A sizeable world population inhabits humid-subtropical climates and almost 1/3rd of Indians reside in mid-size cities, making this study widely relevant.

We used a modelling approach enabling comparison of different trees in similar urban settings which is not possible through on-ground studies. 70 tree species were identified through field surveys and further filtered based on frequency, canopy density, morphology and growth habit. Finally, nine species were selected, three for each urban setting and modelled using Albero, a plugin of the 3D microclimatic simulation software, ENVI-met. Parameters such as tree height, trunk height, canopy shape and density, leaf area density, root spread and diameter etc. were considered for tree modelling. Modelling was validated using the field measurements and indicated a high correlation of 90%. Total nine scenarios were created using ENVI-met for each tree species in the respective urban setting maintaining canopy cover area. Their performance was evaluated by air temperature, relative humidity and mean radiant temperature at 15:00 and 19:00 hours of a peak summer day (2nd July 2019). Thermal comfort was also evaluated using PET (Physiologically Equivalent Temperature) between 9:00-20:00 hours. 

Our results indicate that Mangifera Indica, Azadirachta Indica and Alstonia Scholaris perform best on an average for all parameters in gardens, park and streets respectively. These three trees had dense canopy i.e. high leaf area density (LAD) values and an average tree height between 11-15m. It should be noted that we did not have trees bigger than 15m on our site so results need to be further verified for taller trees. It can, however, be inferred that LAD value and tree height influenced cooling benefits more than trunk height or canopy shape in all urban settings. These results will be used to explore most suitable plantation arrangement in these urban settings. We acknowledge limitation of tree modelling using a software, however, forthcoming ENVI-Met 2021 release will enable detailed tree modelling and further improvise the study. Our results can be used in green space planning in humid subtropical climatic zones with similar urban settings or for further exploration of role of urban tree species. 

How to cite: Javaid, S., Sista, K. Y., and Pauleit, S.: Impact of different sub-tropical trees on outdoor thermal comfort in an Indian city – A microclimatic modelling approach, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-3530, https://doi.org/10.5194/egusphere-egu21-3530, 2021.

This study investigates using a survey how disciplinary scholars perceive Nature Based Solutions (NBS) and how they differ in their NBS implementation approach at the local level. Respondents participated in the 2020-2021 , a ten-week course (online from Dec. 3, 2020, to Jan. 26, 2021) with a focus on Disaster Risk Reduction and Water Security. Supported by the United Nations Environmental Program and the Partnership for Environment and Disaster Risk Reduction (PEDRR), a global alliance of UN agencies, NGOs, and institutes, the Winter School Program is delivered via a partnership model between the University of Massachusetts Amherst's School of Public Policy and Department of Economics, McMaster University, and the United Nations University. Aiming to build young professionals' capacity on NBS framing and application potential, the Program focuses on the delivery of conceptual and empirical information on ecosystem-based climate adaptation and disaster risk reduction. The Program represents a knowledge hub and an opportunity to network with scholars, international experts, and practitioners. 40 graduate students from numerous disciplines (e.g. economics, public policy, international affairs, geosciences, engineering, chemistry and physics) have been selected to attend the Program and have participated in a survey to assess how disciplinary scholars perceive NBS and to explore differences in strategies and priorities while implementing NBS within communities. The results of the survey offer lessons about opportunities and possible challenges of interdisciplinary collaborations when implementing NBS.

How to cite: Vicarelli, M. and Nagabhatla, N.: Differences in Nature Based Solutions perception and implementation strategies across academic disciplines, an empirical analysis, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-3767, https://doi.org/10.5194/egusphere-egu21-3767, 2021.

EGU21-3908 | vPICO presentations | ITS2.14/HS12.2

Nature Based Solutions for Ecosystem Restoration in Southern Europe

Marijana Kapovic Solomun, Carla Ferreira, Ratko Ristić, Zahra Kalantari, and Omid Rahmati

The southern Europe has been recognized as a “hot spot” of ongoing climate change in Europe, being particularly vulnerable to natural disasters in the recent decade. Southern Europe suffers from frequent and disastrous floods, drought and wildfires which foster land degradation while certainly threatening ecosystems in a changing climate. Measures for ecosystem restoration and climate change mitigation are of utmost importance particularly for agricultural and forestry ecosystems. The urgent action to combat climate change impacts calls for measures e.g. by implementing nature-based solutions (NBS) in key sectors to achieve ecosystem restoration and land degradation neutrality, and thus assure relevant ecosystem services to society and human wellbeing. Various approaches can be used to apply NBS, in different fields but practical implementation of NBS needs participatory involvement, institutional and human resources capacity building, requiring local communities and vulnerable groups inclusion. Ecosystem restoration and climate change mitigation achieved by multiple functions of NBS also contribute to the implementation of UN 2030 Agenda for Sustainable Development Goals and Land Degradation Neutrality targets, and lead to enhanced development of circular economy. This research investigates NBS as an opportunity for ecosystem restoration in southern Europe, aiming to provide a comprehensive overview of main obstacles and opportunities for the regional specific conditions.

Key words: ecosystem restoration, southern Europe, climate change, land degradation

How to cite: Kapovic Solomun, M., Ferreira, C., Ristić, R., Kalantari, Z., and Rahmati, O.: Nature Based Solutions for Ecosystem Restoration in Southern Europe, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-3908, https://doi.org/10.5194/egusphere-egu21-3908, 2021.

EGU21-5586 | vPICO presentations | ITS2.14/HS12.2

Nature-based solutions to mitigate the risk of shallow landslides: a global analysis

Udo Nehren and Teresa Arce Mojica

Landslides claim many lives and cause high financial losses in mountainous regions around the world every year. Especially in high mountain regions, the landslide risk is likely to increase further in the coming years due to the thawing of permafrost soils and the associated activation of slope dynamics. However, a higher landslide risk is also expected regionally in tropical and subtropical mountainous regions, namely where an increase in extreme weather events is projected and at the same time, there is a higher socio-ecological vulnerability and exposure due to population growth, land use pressure and other factors.

To mitigate the risk to people and their assets, various hard and soft infrastructure measures are available. Especially in the European Alps, the concept of protection forests (German: Schutzwälder) in combination with hard infrastructure has been used for years as an ecosystem-based or hybrid measure. Based on a systematic global literature review (275 papers), we investigated which Nature-based Solutions (NbS) to mitigate the risk of shallow landslides are in place worldwide, in which countries and regions they were implemented, and which approaches under the NbS umbrella concept were applied (e.g. Green Infrastructure, Ecological Engineering, Eco-DRR, etc.). 

As a result of this comprehensive analysis, we present a portfolio of measures to mitigate the risk of shallow landslides that are being applied in various (eco)regions and cultural contexts around the world and discuss the success of these measures as well as potential risks, uncertainties, and failures. We also emphasize the need for further research particularly on the effectiveness of ecosystem-based landslide risk reduction in different mountain ecosystems, as well as the cost-effectiveness of NbS compared to hard infrastructure. We conclude that despite a successful implementation in the Alps and few other mountain regions, the protection forest concept has hardly been applied so far, especially in the Global South. In addition, we emphasize the particular challenge of establishing protection forests due to the rapid climatic and ecological changes and related geomorphological process dynamics in mountain regions in the course of global climate change.

How to cite: Nehren, U. and Arce Mojica, T.: Nature-based solutions to mitigate the risk of shallow landslides: a global analysis, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-5586, https://doi.org/10.5194/egusphere-egu21-5586, 2021.

EGU21-6487 | vPICO presentations | ITS2.14/HS12.2

Implementation and upscaling of nature-based solution in protected areas and pathways to providing human well-being and biodiversity benefits

Snežana Štrbac, Milica Kašanin-Grubin, Gorica Veselinović, Gordana Gajica, Sanja Stojadinović, Aleksandra Šajnović, and Duška Dimović

Human activities have changed ecosystems and today ≈ 60% of the world’s ecosystems are already degraded. These changes have caused growing environmental costs, including biodiversity loss and land degradation, which in turn has resulted in many economic, social and cultural losses. Protected areas (PAs) are the key tool in biodiversity conservation, moreover they may help to maintain water supplies and food security, strengthen climate resilience and improve human health and well-being. International Union for Conservation of Nature (IUCN) defined PA as „a clearly defined geographical space, recognized, dedicated, and managed, through legal or other effective means, to achieve the long-term conservation of nature with associated ecosystem services (ES) and cultural values”. Such areas represent Earth systems in which influence of human interactions with preserved ecosystems are readily evident. The coverage of PA is a widely used indicator of sustainable development, because the loss of biodiversity is recognized as one of the most serious global environmental threats. The “Big Five” threats to global biodiversity are fragmentation, habitat loss, overexploitation of natural resources, pollution, and the spread of invasive alien species. New interventions for governing nature are captured by the umbrella of nature-based solutions (NBS) in the European Union (EU) policy context. NBS can offer accessible, sustainable, and feasible benefits via a range of areas affecting public health and social well-being. According to IUCN NBS are defined as “actions to protect, sustainably manage, and restore natural or modified ecosystems, that address societal challenges effectively and adaptively, simultaneously providing human well-being and biodiversity benefits”. NBS address these societal challenges over the delivery of ES. The main objective of this study is to use the effect of NBS to enhance the sustainability of management of the PAs that would have environmental, social and economic benefits. The methodology includes determination of heavy metals in soils and needles of Picea alba, and quantification and qualification of PAs benefits based on Protected Areas Benefits Assessment Tool + (PA-BAT+) in six sites: Zlatibor, Golija, Tara, Đerdap, Stara planina, and Fruška gora. Zlatibor, Golija, and Stara planina are protected as a Nature Park – protected areas of international, national, i.e., exceptional importance Category I (first) in accordance with the Law on Nature Protection ("Off. Gazette of RS", No. 36/2009, 88/2010 , 91/2010 and 14/2016). By the decision of the UNESCO commission within the MAB program in 2001, Golija was declared as Biosphere Reserve ”Golija - Studenica”. Tara, Đerdap, and Fruška gora are protected as National Parks – protected area of international, national, i.e., exceptional importance Category I (first) in accordance with the Law on National Parks ("Off. Gazette of RS", No. 39/1993, 44/1993-correction, 53/1993, 67/1993, 48/1994, 101/2005 and 36/2009). According to categorization of the IUCN Zlatibor, Golija, and Stara planina are classified in Category V, while Tara, Đerdap, and Fruška gora are classified in Category II. Based on heavy metals content in soils and needles, different interventions in managed ecosystems are proposed.

How to cite: Štrbac, S., Kašanin-Grubin, M., Veselinović, G., Gajica, G., Stojadinović, S., Šajnović, A., and Dimović, D.: Implementation and upscaling of nature-based solution in protected areas and pathways to providing human well-being and biodiversity benefits, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-6487, https://doi.org/10.5194/egusphere-egu21-6487, 2021.

EGU21-6973 | vPICO presentations | ITS2.14/HS12.2

Nature Based Solutions applied to road infrastructure in Nepal, a vehicle for development.

Marta Vicarelli, Karen Sudmeier-Rieux, Dhiroj Koirala, and Sanjay Devkota

This article describes research undertaken in the Panchase Region of Western Nepal as part of the “Ecosystems Protecting Infrastructure and Communities” (EPIC) project 2012-2017, where three community-led bio-engineering demonstration sites were established along roadsides.  The topic of Nature Based Solutions (NBS) and Eco-DRR/CCA is explored adopting interdisciplinary research methods, spanning both social and physical sciences, and citizen science alongside state-of-the art high resolution erosion monitoring and remote sensing. We examine the nexus between infrastructure design (traditional roads vs green roads) and landslides. Investigations included a watershed study of land use changes over time and erosion rates associated with road construction in the Phewa Lake Watershed (Kaski district, Western Nepal), an analysis of the effectiveness of vegetation in reducing erosion rates using LIDAR and drone measurements, and a cost-benefit analysis of conventional “grey” versus bio-engineered roads, or “eco-safe roads”.

Results of the watershed study indicate a trend from erosion due to open grazing thirty years ago to increased erosion by new roads; Land IDAR measurements show that vegetation has been effective in reducing erosion rates. The cost benefit analysis (CBA) explores the net benefit of grey vs eco-safe roads using different time horizons and precipitation distributions associated to monsoonal activity and climate change trends. The CBA results demonstrate that initial costs in installing the bio-engineered eco-safe road are higher than for the “grey” road, however the bio-engineered road rapidly becomes more cost-effective, especially when factoring in avoided damages and multiple co-benefits to the population. Findings from this work have led to policy recommendations promoting and upscaling a more sustainable approach to bio-engineering for rural road construction in Nepal as well as methodological recommendations for replicating and up-scaling similar studies elsewhere.

How to cite: Vicarelli, M., Sudmeier-Rieux, K., Koirala, D., and Devkota, S.: Nature Based Solutions applied to road infrastructure in Nepal, a vehicle for development., EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-6973, https://doi.org/10.5194/egusphere-egu21-6973, 2021.

EGU21-7103 | vPICO presentations | ITS2.14/HS12.2

Promote Nature-Based Solutions to adapt the environment to climate change – The LIFE ARTISAN project

Pierre-Antoine Versini, Daniel Schertzer, and Mathilde Loury

Nature-Based Solutions (NBS) appear as some relevant alternatives to mitigate the consequences of climate change. For this reason, they are promoted for the implementation of the national plan for adaptation to climate change (PNACC) in France, in line with the Paris Agreement, the strategy of the European Union for adaptation to climate change and the French national strategy for biodiversity.

Nevertheless, this ambitious goal of democratizing NBS poses some institutional and technical challenges because many obstacles remain to their implementation. Overcoming these shortcomings is the objective of the LIFE integrated project called ARTISAN (Achieving Resiliency by Triggering Implementation of nature-based Solutions for climate Adaptation at a National scale). Coordinated by the French Biodiversity Office (OFB), its consortium regroups several local authorities, technical, research and education institutes.

For this purpose, ARTISAN is creating a framework promoting the implementation of NBS by improving scientific and technical knowledge about them, then by developing and disseminating relevant tools for project leaders (for the design, sizing, implementation and evaluation of ecosystem performance).

To demonstrate that NBS can respond to a diversity of climatic, ecological and institutional contexts, 10 pilot sites will be monitored in metropolitan and overseas France. The concerned issues are for example the reduction of urban heat island by the de-waterproofing of the public space, the limitation of the impact of cyclonic episodes on the urbanized coastline overseas by promoting the restoration of the mangrove, and the decrease of agricultural water stress during the low flow period by the hydromorphological restoration of wetlands. These pilot sites will serve to develop, improve and validate operational tools, methods and trainings devoted to practitioners.

How to cite: Versini, P.-A., Schertzer, D., and Loury, M.: Promote Nature-Based Solutions to adapt the environment to climate change – The LIFE ARTISAN project, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-7103, https://doi.org/10.5194/egusphere-egu21-7103, 2021.

EGU21-7306 | vPICO presentations | ITS2.14/HS12.2

Evaluation and analysis of riparian vegetation through satellite images

Xana Alvarez, Paula Rivas, Carolina Acuña-Alonso, and Enrique Valero

One of the main goals of the EU political agenda, supported by the green agenda, and one of the Sustainable Development Goals (SDGs) is the conservation, restoration and sustainable use of terrestrial and inland freshwater ecosystems and their services. Within these ecosystems is the riparian vegetation, an essential element in river ecosystems that influences the processes related to the surface and underground flow, modifying the temperature and humidity, it also functions as a filtering tool for the water. The riparian vegetation has been degraded as a cause of changes in land use, or the increase in population. In order to guarantee the biodiversity of ecosystems, as well as guarantee water security, it is necessary to explore environmental governance solutions. In this sense, new technologies can be useful tools that facilitate their characterization. For this reason, the feasibility of using satellite images has been evaluated to characterize the degradation of the riparian vegetation, facilitating decision-making by the administration. In this way, the improvement of riparian vegetation can be promoted, as a nature-based solution (NBs) with multiple environmental, social and economic benefits. Nowadays, there are multiples indices for determining the quality of riparian vegetation but all of them involve a high time, technical and economic effort. The implementation of solutions based on satellite images will improve and facilitate these actions. For this purpose, the images from the WorldView 2 satellite were analysed. The treating these images through geographic information systems, a scale is obtained that adapts to existing indices. With these new methods it would no longer be necessary to visit all the sample points, thus reducing the time to obtain results. The verification of the data obtained through the mapping of images (Riparian Strip Quality Index) was compared with data taken in the field (QBR index), obtaining a value of 92% of truthfulness, and a Kappa coefficient of 0.88 (very good). In other words, a methodology with high concordance with the data collected in situ was obtained. The application of this index through satellite images will facilitate the environmental governance of multiple ecosystems. Providing tools to implement best practices allowing an improvement of the NBs. In this way, biodiversity will be improved, and water quality will be improved, guaranteeing or improving water security, contributing to the achievement of the SDGs.

How to cite: Alvarez, X., Rivas, P., Acuña-Alonso, C., and Valero, E.: Evaluation and analysis of riparian vegetation through satellite images, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-7306, https://doi.org/10.5194/egusphere-egu21-7306, 2021.

EGU21-8902 | vPICO presentations | ITS2.14/HS12.2

Nature Based Solutions:  Reporting and analyzing insights from Europe

Mario Al Sayah, Pierre-Antoine Versini, and Daniel Schertzer

The challenges imposed by climate change and urbanization require a paradigm yet holistic shift that considers the trade-off between ecosystemic conservation, social needs and economic growth. By concomitantly providing socioeconomic and environmental benefits, Nature Based Solutions (NBS) according to the European Commission (EC) present viable, resource-efficient and adaptable tools for ensuring the above-mentioned transition. Accordingly, NBS are high on European and French priority agendas, and are believed to be the way forward. The abundant scientific literature on NBS solidifies their potential through the various advantages they present. Evidently, NBS are win-win resolutions to environmental challenges (climate change, natural risks, food and water security), they support greener economies, conserve biodiversity, promote sustainability, support adaptive capacities, and reduce natural/socioeconomic sensitivities. In spite of their potential, NBS are faced by many obstacles. Conceptual obstacles include contested definitions of NBS, reduced reporting on uncertainties, and overlaps with sister notions that make the NBS concept somewhat vague. Systemic challenges include governance barriers, public willingness to adopt NBS and stakeholder participation (acceptance, perspectives and engagement). Implementation challenges encompass limited funds or budgets, difficulties of upscaling what works and maintaining-monitoring progress. Accounting for the above-mentioned elements, this study will use France as a micro scale and the European continent as a macro scale, to provide a local and regional inventory of NBS’ potential and limits. First, an in-depth bibliographic analysis and text mining techniques are carried out for providing detailed science-based evidence on the performance of NBS. For the national scale, peer-reviewed literature from the Scopus database and official UN bodies or international organizations reports are used. For the European scale, deliverables of several Horizon 2020 projects serve the purpose. Subsequently, an analysis of stakeholder profiles, categories, and participation for mapping NBS actors in both contexts will follow. By combining theoretical investigations and stakeholder analysis, a holistic representation of the NBS framework is ensured. The logic behind this approach is to draw up scientific and technical evidence on NBS to mainstream their integration into development projects. Accordingly, the objective of this research work falls under one of the several actions of the Life ARTISAN project, action A1: reporting on obstacles and levers for Nature Based Adaptation Solutions. Under this scope, the project ARTISAN standing for “Achieving Resiliency by Triggering Implementation of nature-based Solutions for climate Adaptation at a National scale” aims to achieve the plans set in France’s second national climate change adaptation plan by leveraging NBS. Beyond the national scale, by capitalizing on past experiences and grouping dispersed findings, this study will provide deeper insights on NBS, and will allow a prioritization of research and knowledge building needs.

How to cite: Al Sayah, M., Versini, P.-A., and Schertzer, D.: Nature Based Solutions:  Reporting and analyzing insights from Europe, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-8902, https://doi.org/10.5194/egusphere-egu21-8902, 2021.

Urbanization and climate change affect the balance of coastal ecosystems, determining impacts on social, economic and environmental dimensions of their waterfronts. Coastal cities use the criticalities deriving by flood phenomena as an opportunity to renew the models for mitigating environmental impacts. Italian coastal cities are examples of waterfronts widely impacted by floods, such as Venice. These waterfronts are characterized by consolidated ecosystems with a dominant identity, thus reinterpreting their flooding mitigation models can be useful in addressing the risk of flood disasters. This paper presents and discusses the flood mitigation strategy implemented in Venice, based on transforming and integrating advanced technology with nature-based solutions, as well as requirements and community needs. The advantages and limitations for protecting local communities and the environment with this aproach, its cost-effectiveness and its contribution to enhance resilience are also discussed.
Venice integrates an anti-flooding technological solution called Electromechanical experimental module (MOSE), with its historical lagoon ecosystem, as part of the UNESCO Management Plan "Venice and its Lagoon''. MOSE is a system of independent mobile sluice gates, hinged at the bottom and actuated by the floating variation integrated with nature-based coastal reinforcement practices based on environmental elements in complementary operation with the technological solution. The natural and morphological restoration of the lagoon, in fact, represents the first part of an integrated plan for the protection and sanitation of the coastal habitat. This solution indicates the ways in which the needs expressed by the inhabitants can affect the solutions already implemented in place by the technicians and the administration, determining new criteria and tools for mitigation factors such as tangible compatibility and intangible adaptivity.
In order to integrate a non-human (nature and technology) and human (actors) factors, the operation of the technological solution is based on an Actor-Network Theory (ANT) approach. On the one hand, the research acts in the multi-scalar horizon analyzing the actions governed by multidimensional approaches in order to strengthen coastal relational systems. On the other hand, it studies the experimental solutions, reflecting on the need to rethink the nature based solutions in a way that it integrates the socio-ecological interactions associated to vulnerable systems. An Ecosystem-Based Mitigation Model for coastal cities investigates climate mitigation solutions to support decision-making. The model includes the socio-economic and environmental requirements, deriving from the community needs examined, in order to improve the carrying capacity of an ecosystem by considering a sustainable vision. The example of Venice can be used in addressing the risk of flooding in other coastal cities.  


Keyword: nature-based solutions, coastal ecosystems, flood mitigation, anti-flooding technology.

How to cite: Ciampa, F.: An Ecosystem-Based Mitigation Model for vulnerable settlement systems: the experience of Venice (Italy) in addressing the risk of flood disasters, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-9206, https://doi.org/10.5194/egusphere-egu21-9206, 2021.

To meet CO2 reduction targets, the UK aims to plant c1.5 billion trees by 2050. Gaps within thousands of miles of hedgerows across the country are potentially suitable planting sites, but the extent of gaps and suitability for replanting are currently unknown. Maximising the potential growth of hedgerows however appears to receive relatively little attention compared with wide area tree planting. Hedgerow gaps present the opportunity for tree planting, contributing towards the annual tree-planting goals and net-zero CO2 plan as part of Defra’s 25-year objectives (HM Government, 2018), without requiring extensive land change.

Our ambitions of fostering a greener society and meeting net zero goals is heavily reliant on ensuring that children and youth are engaged with environmental concerns and have the right skills and knowledge for future careers. This project has been engaging with youth organisations to enhance their environmental and digital knowledge, whilst combining their input with state-of-the-art artificial-intelligence approaches. The open dataset created with public contributions will inform planting decisions whilst educating young people and citizens. The aligned education programme will provide resources detailing how new planting will drawdown CO2, reduce flood risk and increase biodiversity availability, ultimately fostering the participants as agents of change in addressing the climate crisis. 

Citizens will be trained in hedgerow surveying techniques, with focus on both remote sensing/geographic information systems applications (GIS) and field surveying - enabling contributions from home (during COVID) as well as encouraging outdoor activity and learning. Through a series of surveys and tasks, citizens are able to utilise a smartphone device (or similar) to contribute new data into an open survey on hedgerow characteristics, simple field experimental measurements and images/videos, all whilst utilising the GPS built into the device. The objectives of the project are two-fold: first, data collected by citizens will be used to refine an existing deep learning model trained to identify hedgerow gaps from high-resolution earth observation imagery. Second, to encourage citizens to learn about and take ownership of their local environment, contributing to the fostering of a nation of climate champions.

How to cite: Parsons, K. and Wolstenholme, J.: Mapping hedgerow gaps and fostering positive environmental behaviours through a combination of citizen scientists and artificial intelligence, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-9453, https://doi.org/10.5194/egusphere-egu21-9453, 2021.

EGU21-9480 | vPICO presentations | ITS2.14/HS12.2

A review of the potential of nature-based solutions (NBS) to address the challenges of the water-energy-food nexus (WEF Nexus) in the coming decades

Pedro N Carvalho, David Finger, Fabio Masi, Giulia Cipolletta, Hasan Volkan Oral, Attila Tóth, Martin Regelsberger, and Alfonso Exposito

The water, energy, and food security nexus (WEF Nexus) is the interlinkage between water security, energy security, and food security. An increasing world population is projected to increase energy and food requirements, which will increase the need for freshwater drastically in the coming decades. Projected climate change impacts will aggravate water availability, especially in urban areas. Nature-based solutions (NBS) have proven to generate multiple benefits that defuse the expected merging tensions within the WEF Nexus. This paper outlines the theories, provides examples, and discusses the potential of NBS to address the future WEF Nexus. For this purpose we reviewed recent papers on the theories of WE, WF, EF, and WEF Nexus, we described and summarized 19 representative real-life case studies, and we identified the knowledge gap within the theory and the case studies. We provide quantitative potentials and qualitative benefits for NBS described in the literature over the past decades. Our review demonstrated the impressive potential of NBS to address the projected challenges within the WEF Nexus. The study concludes by recommending NBS for specific WEF Nexus challenges and highlighting the need for decision-makers to consider the implementation of NBS in urban planings.

How to cite: Carvalho, P. N., Finger, D., Masi, F., Cipolletta, G., Oral, H. V., Tóth, A., Regelsberger, M., and Exposito, A.: A review of the potential of nature-based solutions (NBS) to address the challenges of the water-energy-food nexus (WEF Nexus) in the coming decades, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-9480, https://doi.org/10.5194/egusphere-egu21-9480, 2021.

EGU21-9512 | vPICO presentations | ITS2.14/HS12.2

Potential agricultural soil carbon sequestration across Europe: a reality check

Leonor Rodrigues, Brieuc Hardy, Bruno Huyghebaert, and Jens Leifeld

To meet the Paris Agreement goal of limiting average global warming to less than 1.5°C above pre-industrial temperatures, European Union (EU) aims to reduce by 40% its domestic greenhouse gas (GHG) emissions by 2030 and in the longer term to become the world’s first climate-neutral economy by 2050 (“Green Deal”). Today, 10% of the European GHG emissions derive directly from agriculture, and measures to decrease or compensate these emissions are required for achieving climatic goals. The role of soils in the global carbon cycle and the importance of reducing GHG emissions from agriculture has been increasingly acknowledged (IPCC, 2018, EEA report 2019). The “4 per 1000” initiative (4p1000) has become a prominent model for mitigating climate change and securing food security through an annual increase in soil organic carbon (SOC) stocks by 0.4 %, or 4‰ per year, in the first 0-40 cm of soil. However, the feasibility of the 4p1000 scenario and more generally the capacity of European countries to implement soil carbon sequestration (SCS) measures are highly uncertain.

As part of the EJP Soil project, we collected country-specific informationonon on the available knowledge and data of achievable carbon sequestration in mineral agricultural soils (cropland and grassland) across Europe, under various farming systems and pedo-climatic conditions. With this bottom-up approach, we provide a reality check on weather European countries are on track in relation to GHG reductions targets and the “4p1000” initiative. First results showed that the availability of datasets on SCS is heterogeneous across Europe. While northern Europe and central Europe is relatively well studied, references are lacking for parts of Southern, Southeaster and Western Europe. Further, this stocktake highlighted that the current country-based knowledge and engagement is still poor; very few countries have an idea on their national-wide achievable SCS potential. Nevertheless, national SCS potentials that were estimated for 13 countries support the view that SCS can contribute significantly to climate mitigation, covering from 1 to 28, 5 % of the domestic GHG emissions from the agricultural sector, which underpins the importance of further investigations.

How to cite: Rodrigues, L., Hardy, B., Huyghebaert, B., and Leifeld, J.: Potential agricultural soil carbon sequestration across Europe: a reality check, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-9512, https://doi.org/10.5194/egusphere-egu21-9512, 2021.

EGU21-9923 | vPICO presentations | ITS2.14/HS12.2

Collaborative aspects of nature-based solutions: strategies, plans, programs, policies, projects

Cristina-Gabriela Mitincu, Ioan-Cristian iojă, Constantina-Alina Hossu, Mihai-Răzvan Niță, and Andreea Niță

The integrated approach of sustainable development refers to addressing complex challenges by combining knowledge from various environmental and planning fields. Thus, nature-based solutions (NbS) are a category of new tools that can help cities increase their resilience and sustainability. They represent those actions inspired, supported or copied from nature, which have a high potential to be energy efficient and to use natural resources, as well as promote multi-functionality and connectivity between green infrastructure and built-up areas. To achieve their purpose NbS have to be developed and managed in collaborative ways. The strategies, plans, programs, policies and projects developed at European and international level have led to the consolidation, at least from a theoretical perspective, of the significance and role of NbS in urban areas. Thus, this study aims to identify the way these documents are directed towards sustainability and innovative solutions (such as NbS), with emphasis on the collaborative approaches for NbS. Our preliminary results indicate that most of the international and European documents specify that the economic development needs to be achieved in close connection with increasing urban sustainability, based on sustainable investments such as green infrastructures or NbS. Furthermore, under the guidance of these documents, the international institutions, research experts and decision makers seek collaboration with city representatives in order to integrate the benefits generated by such sustainable investments. Among the analyzed documents, the new 2030 Urban Agenda and its Sustainable Development Goals, reveal the need for participatory approaches to reach consensus about sustainable development. Other important international and European documents directed towards sustainability and NbS are Urban Water Agenda 2030, New Urban Agenda – Habitat III or 2030 Agenda for Sustainable Development. So, NbS represent a support in the efficient use of resources in order to promote urban development in concordance with the economic growth, participatory planning and guvernance, environmental policy, social cohesion and justice, public health and quality of life, environment protection.

How to cite: Mitincu, C.-G., iojă, I.-C., Hossu, C.-A., Niță, M.-R., and Niță, A.: Collaborative aspects of nature-based solutions: strategies, plans, programs, policies, projects, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-9923, https://doi.org/10.5194/egusphere-egu21-9923, 2021.

EGU21-10928 | vPICO presentations | ITS2.14/HS12.2

NewLife4Drylands: remote sensing - oriented nature-based solutions towards a new life for drylands

Paolo Mazzetti and the NewLife4Drylands Consortium

According to the European Environment Agency (2008), the areas of Southern, Central and Eastern Europe showed “very high” or “high” sensitivity to desertification. One of the main drivers of desertification is climate change, affecting particularly the Mediterranean regions. Drought intensity and frequency are expected to increase with global warming in southwestern parts of Europe, whereas an opposite trend is projected for north-eastern Europe. 
Nature-Based Solutions (NBS) can represent an effective approach for the implementation of drought impact mitigation measures at local level. On one hand, increased availability of satellite imagery and constant development of analytical techniques are stepping up monitoring processes at various spatial and temporal scales. On the other hand, short-term monitoring systems can be applied immediately after the restoration implementation but it is essential to evaluate the biophysical status of the restored areas at mid-term and long terms after the implementation.

NewLife4Drylands is a LIFE Preparatory Project co-funded by the European Union under the LIFE programme. It started on January 2021 and it will end in June 2023.
NewLife4Drylands deals with the specific need set by the “Life-Environment” subprogram “Restoration of desertified land through nature-based solutions” to contrast the soil degradation leading to desertification by using NBS. NewLife4Drylands focuses on developing a protocol based on remote sensing techniques for the identification of a framework for achieving land degradation neutrality (LDN), combating desertification and for a mid and long-term monitoring of restoration interventions on desertified lands. The protocol will be an instrument for a clear, specific and costless assessment of the restoration process useful for further decision-making concerning restoration interventions. 

Six European areas (in Greece, Spain and Italy) affected by land degradation and desertification which either have NBS and restoration activities ongoing - implemented in the context of other LIFE+ or existing projects - or are candidate for restoration have been selected. 
Free high resolution time-series data from Landsat and the Copernicus Sentinel satellites at high temporal repetitiveness (every 16 or 5 days, respectively) combined with high spatial resolution (30 or up to 10 meters, respectively) will be investigated for monitoring processes at various spatial and temporal scales. The new hyperspectral satellite PRISMA data from the Italian Space Agency will be considered for information integration. The availability of ground reference data will be essential for calibration and validation of satellite imagery analysis.
NewLife4Drylands will select a set of well-known indicators, such as  spectral indices used as proxies for monitoring vegetation, water content, drought degree, primary production. Moreover SDG’s sub indicator 15.3.1 (Proportion of land that is degraded over total land area) will be implemented at local scale. 
Based on such indicators, NewLife4Drylands will define a monitoring model and a protocol able to connect NBS and remote sensing indicators which will provide a guide for the identification of specific measures of restoration of drylands to be used as a support in decision making for adaptive management of restoration actions in drylands, improving ecosystems services provision and related economic issues, including local resources to mobilize and new green jobs. 

How to cite: Mazzetti, P. and the NewLife4Drylands Consortium: NewLife4Drylands: remote sensing - oriented nature-based solutions towards a new life for drylands, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-10928, https://doi.org/10.5194/egusphere-egu21-10928, 2021.

The global water sector is changing and it is in need of more evidence-based responses of emerging global, regional, national and local challenges. Communities are seeking interventions which achieve multiple benefits and outcomes such as: improved quality of water bodies, reduced greenhouse emissions, reliably delivered water for human use but also some that are rather urgent like: flood-risk management. In order to take into account the environmental, technological, economic, institutional and cultural characteristics of river basins, we need to move from current management regimes towards more adaptive regimes with the use of Nature-based solutions (NbS) instead of traditional 'grey' engineering approaches. Quite a vast amount of tools have been developed throughout the years for achieving this transition. This paper identifies the challenges and opportunities that water professionals face when using these tools in the process of planning NbS. An online tailor-made approach, based on a modified nominal group technique (NGT) and Multi-criteria analysus (MCA) was developed and applied. The NGT-based assessment of these tools consists of two rounds during which participants were asked to reflect first individually, and then collectively about the prerequisites and implications of these tools in the process of planning NbS. The participants are water professionals from the European project Co-Adapt. Here we presented one approach where new scientific methods and practical tools are developed for participatory assessment and implementation of adaptive water management.

How to cite: Bogatinoska, B., Lansu, A., Floor, J., Huitema, D., and Dekker, S.: Tooling in engaging stakeholders in adaptive water management with Nature-based solutions: reflections from an online NGT approach through the perspectives of the water professionals, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-11040, https://doi.org/10.5194/egusphere-egu21-11040, 2021.

EGU21-11073 | vPICO presentations | ITS2.14/HS12.2

Nature-based solutions for flood mitigation on private land – human rights perspective 

Katazyna Bogdzevic and Marius Kalinauskas

Nature-based solutions (NBS) for flood mitigation lately are becoming more and more popular. However, comparing to traditional grey infrastructure, NBS require more land, often – privately owned. This is why the question of implementation of NBS on private land needs to be addressed more thoroughly. There are different ways how to implement the NBS on private land. Those ways can be divided into "sticks", "carrots" and "sermons". The last two refer to "soft" measures, like financial incentives, payments for ecosystem services, knowledge sharing, and partnership for NBS. Whereas, "sticks" refer to coercive measures, which imply "a command-and-control strategy" and any behaviours contradicting "sticks" can be considered unlawful. In other words, "sticks" are the measures that restrict land-use or even deprive the owners of their land. Expropriation, land-use restrictions, and pre-emption rights are the best-known examples of "sticks". The land-owners have little room for manoeuvre if the state decides to apply "sticks". However, the powers of the state are also limited. One of such limitations derive from international law, to be precise – from provisions related to human rights protection. Article 1 of the Protocol No. 1 to the European Convention on Human Rights grants protection for the property rights and prohibits the authorities to deprive owners of their possession unless the public interest justifies it. The state can also control the use of property only if this is required by general interest. The European Court of Human Rights in its case-law for several times addressed the issue of property restrictions and expropriation due to implementation of environmental laws and land-use planning laws. The Court elaborated on such issues as the notion of "public interest", proportionality and lawfulness of measures adopted by the state. Those considerations can also be relevant for the implementation of NBS on private land. 

How to cite: Bogdzevic, K. and Kalinauskas, M.: Nature-based solutions for flood mitigation on private land – human rights perspective , EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-11073, https://doi.org/10.5194/egusphere-egu21-11073, 2021.

EGU21-11086 | vPICO presentations | ITS2.14/HS12.2

Human in nature: two cases on social factors nested in the implementation of Nature-based Solutions

Wei Weng, Luís Costa, Matthias Lüdeke, Delphine Zemp, Sue-Ching Jou, Fernando Jaramillo, and Mei-Yi Liu

Nature-based Solutions (NbS), inspired or supported by nature, aim to address societal challenges in a fast-changing environment via an integrated and sustainable approach. Effective implementation of such intervention certainly requires compliance with specific societal configurations in different geographies. Here two cases of NbS to hydrological disaster risks are used to demonstrate the relevance of social barriers and opportunities for the full function of NbS.

Firstly, we introduce a novel large-scale NbS designed for reducing water scarcity in the Bolivian city of Santa Cruz de la Sierra. In this case, strategic reforestation was planned to bring rainfall to a downwind city taking advantage of atmospheric moisture pathways. In the process of co-designing reforestation sites, experiences from failed reforestation projects have improved the site selection originally based solely on the scientific evidence of the moisture pathways. Social barriers to implementation include underground economic activities and pressures for local food production. The latter factor also implies a trade-off between the fulfilments of different sustainable development goals.

Secondly, a case of landscape-scale NbS that aims to mitigate flood risk from typhoons in Taiwan will be discussed. It consists of a flood diversion framework that directs excess runoff to local farmlands following Typhoon storms. The concept of payment for ecosystem services has been employed to increase the willingness of farmers and landowners to participate in this framework. Institution of compensation for agricultural loss established from previous meteorological disasters has paved the way for implementation. A combination of subsidies and agricultural loss compensation has offered an opportunity for the new intervention to take place in the rice-cropping landscape, while the effect of this ongoing framework will be further documented.

These two cases show that the inertia from existing policy/institutional schemes and the lessons from past unsuccessful experiences provide an opportunity to identify and overcome social barriers to the implementation of innovative NbS.

How to cite: Weng, W., Costa, L., Lüdeke, M., Zemp, D., Jou, S.-C., Jaramillo, F., and Liu, M.-Y.: Human in nature: two cases on social factors nested in the implementation of Nature-based Solutions, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-11086, https://doi.org/10.5194/egusphere-egu21-11086, 2021.

EGU21-11162 | vPICO presentations | ITS2.14/HS12.2

Experiences of a nature-based urban development on the example of a Hungarian city

Máté Katona and Adrienn Horváth

Kaposvár is a developing middle-size city in the middle of South-western Hungary surrounded by mountains and forested areas. Due to its location and natural surroundings, the city strives to achieve close-to-nature urban development. Sustainable urban management, increasing green space, promoting carbon neutrality, adaptation to the challenges of climate change, reducing emissions are the main aims of the development. For support, city-wide investigations began last year to make further suggestions for future direction based on measurements and experience. A foundational survey was conducted to characterize the conditions of urban soils and urban plantations; thus, it would have a proper space in the city’s climate strategy and settlement development concept in the future. Soil properties (artefacts, pH, texture, CaCO3) and trace metal concentrations (Pb, Cu, Zn, Ni, Cr, Sn, Cd, Co) were measured as well. Compared to the actual condition of the natural environment, the soil-changing effect of the city became visible. For changed soil conditions, trees should be chosen that are well adapted to this changing environment. The city is currently afforesting its area; however, it wants to increase the number of trees planted in the future. This is especially justified in areas where there are many overgrown, old, diseased trees in its hundred-year-old parks and tree lines. Choosing the right tree species is not only an aesthetic consideration, but it can also affect the condition of the soils in the environment. We also considered the effects of heavy metals pollution on vegetation important, so we took samples from the trees leaves in many parts of the city and measured the total metal content they absorbed. To comparison, the nearby soil test points showed a correlation between leaves pollution levels in several cases. Based on the results, the heavy metal uptake capacity of the different tree species became comparable. It can be used effectively in the selection of tree species for future afforestation, so that afforestation can also play a role in soil protection and city climate maintenance in the future.

How to cite: Katona, M. and Horváth, A.: Experiences of a nature-based urban development on the example of a Hungarian city, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-11162, https://doi.org/10.5194/egusphere-egu21-11162, 2021.

EGU21-11172 | vPICO presentations | ITS2.14/HS12.2

Performance of two small experimental bioretention cells during the first year of operation

Petra Hečková, Michal Sněhota, Vojtěch Bareš, and David Stránský

Due to increasing urbanization, bioretention cells are becoming an increasingly popular solution for stormwater management. The data on long term performance bioretention are still sparse. The aim of this study was to set-up two experimental bioretention cells designed for long-term monitoring and to evaluate the rainfall-runoff characteristics, assess the development of properties of the biofilter, and dynamics of plant growth during the first growing season.

Two identical experimental bioretention cells were established. The first collects water from the roof and the second is supplied from the tank for simulating artificial rainfall. The 30 cm thick biofilter soil mixture is composed of 50% sand, 30% compost, and 20% topsoil. Bioretention cells are isolated from the surrounding soil by a waterproof membrane. Both bioretention cells are instrumented by an identical system of sensors. Four time-domain reflectometry probes monitor soil water contents 20 cm below the surface. Five tensiometers record the water potential in a biofilter. The amount of a discharge from each bioretention cell is determined by a tipping bucket flowmeter. A ponding depth is recorded by an ultrasonic sensor.

Rainfall-runoff episodes were evaluated for the period from 18.6. 2018 to the 22.11.2018. 17 episodes were evaluated for bioretention cell with the inflow of stormwater from the roof. Six ponding experiments were done in the bioretention cell with an artificial supply. Rainfall depth, maximal rainfall intensity, episode duration, runoff coefficient, and maximal peak outflow rate from both bioretention cells were determined for each episode. The effective saturated hydraulic conductivity was determined using Darcy’s law under the assumption of one-dimensional, vertical flow. The estimation method was verified by simulating two-dimensional variably saturated flow using HYDRUS-2D. Outflow water quality was measured from one bioretention cell during ponding experiments.

The runoff coefficient for the entire period of the growing season was 0.72, while the peak outflow reduction for individual rainfall events ranged between 75% to 95% for the bioretention cell connected to the roof. The runoff coefficient determined from artificial ponding events was 0.86 for the event started in the partially saturated biofilter, while it was nearly 1.0 for all subsequent artificial ponding events. The peak flow reduction ranged from 19% to 30%. The saturated hydraulic conductivity of biofilter with a natural rainfall supply ranged between 1.6·10-6 to 8.6∙10-6 m·s-1, which is significantly less than hydraulic conductivity 1.3∙10-4 m·s-1 measured in the laboratory on packed samples. Perennials Aster, Hemerocallis and Molinia have shown good growth and adaptation to conditions in bioretention cells. In the case of the current experiment, the gravel mulch layer has proven to be an effective barrier to reducing evaporation. The values of total suspended solids and turbidity were highly correlated and generally high, especially at the beginning of outflow in artificial ponding experiments. The value of electrical conductivity reached up to 2200 µS·cm-1, this may be due to the higher compost content in the soil. The monitoring of bioretention cells continues in order to record long term changes in the performance of the bioretention cells.

How to cite: Hečková, P., Sněhota, M., Bareš, V., and Stránský, D.: Performance of two small experimental bioretention cells during the first year of operation, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-11172, https://doi.org/10.5194/egusphere-egu21-11172, 2021.

EGU21-11975 | vPICO presentations | ITS2.14/HS12.2

Natural infrastructure interventions and their effect on soil erosion mitigation in the Andes

Armando Molina, Veerle Vanacker, Miluska Rosas-Barturen, Boris Ochoa-Tocachi, Vivien Bonnesoeur, Francisco Román-Dañobeytia, and Wouter Buytaert

The Andes region is prone to soil erosion because of its steep topographic relief, high spatio-temporal variability in precipitation and heterogeneity in lithological strength. Soil erosion by water is affecting natural and anthropogenic environments through its impacts on water quality and availability, loss of soil nutrients, flood risk, sedimentation in rivers and streams, and damage to civil infrastructure. Sustainable land and water management, referred here as natural infrastructure interventions, aims to avoid, reduce and reverse soil erosion and can provide multiple benefits for the environment, population and livelihoods. In this study, we present a systematic review of peer-reviewed and grey literature involving more than 120 local case-studies from the Andes. Three major categories of natural infrastructure interventions were considered: protective vegetation, soil and water conservation measures, and adaptation measures that regulate the flow and transport of water. The analysis was designed to answer the following research questions: (1) Which soil erosion indicators allow us to assess the effectiveness of natural infrastructure interventions across the Andean range? (2) What is the overall impact of implementing natural infrastructure interventions for on-site and off-site erosion mitigation?

The systematic review shows that the effectiveness of protective vegetation on soil erosion mitigation is the most commonly studied characteristic, accounting for more than half of the empirical studies. From the suite of physical, chemical and biological indicators that were commonly used in soil erosion research, our review identified two indicators to be particularly suitable for the analyses of the effectiveness of natural infrastructure interventions: soil organic carbon (SOC) of the topsoil, and soil loss rates at plot scale. The implementation of soil and water conservation measures in areas under traditional agriculture had positive effects on SOC (1.28 to 1.29 times higher SOC than in agricultural land). Soil loss rates were 54% lower when implementing SWC than on cropland. When implementing SWC in rangeland, the data indicated an increase in soil loss rate by 1.54 times. Untreated degraded land is reported to have significantly higher soil loss and specific sediment yield compared to cropland.

The results of this systematic review allows to assess the overall effectiveness of commonly used natural infrastructure interventions, which can guide policy and decision making in the Andes. Similarly, the review identified critical gaps in knowledge that must be attended by more comprehensive research to consider the high spatiotemporal variability of the Andes region.

How to cite: Molina, A., Vanacker, V., Rosas-Barturen, M., Ochoa-Tocachi, B., Bonnesoeur, V., Román-Dañobeytia, F., and Buytaert, W.: Natural infrastructure interventions and their effect on soil erosion mitigation in the Andes, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-11975, https://doi.org/10.5194/egusphere-egu21-11975, 2021.

EGU21-12749 | vPICO presentations | ITS2.14/HS12.2

Local example for the compensation of negative global environmental phenomena

Pál Balázs, Imre Berki, and Adrienn Horváth

The main negative global phenomena are climate change, biodiversity loss and biological invasions. Attaining the 2050 climate neutrality target is of great importance in agriculture and forestry. Land use is a significant factor in carbon sequestration from the atmosphere (carbon sink) and can be employed to potentially store carbon for decades. Land use can also contribute to climate change adaptation against aridification, preserve biodiversity, and reduce CO2 and NOx emissions. In addition, growing global environmental problems impact the entire world, which compels society to live with changed circumstances. Nevertheless, negative processes do not affect all territories equally. Some areas are more vulnerable and sensitive to changes, while others are more flexible and demonstrate higher resilience against negative changes. Nature compensates negative global environmental phenomena and people can contribute to this process. This compensation is hard in semi-arid and arid regions of the world, however, in humid regions it needs less effort.

Őrség - one of the southwestern landscape of the Carpathian basin - is a typical example of a humid-mesic climate. Due to its unique ecological, economic, and social characteristics, Őrség shows higher resistance against global changes. The humid-mesic climate and the acid soil with low fertility promote the forest succession on abandoned arable lands and pastures. Due to the warming and the anthropogenic CO2 and NOx forest areas show accelerating growth. High forest coverage (62%), extensive land management, high humidity, high proportion of nature close areas, unique landscape structure, and soft tourism all manifest themselves in higher stability against negative changes. Under these specific site conditions, reviving capacity of forests is relatively high: uncultivated lands quickly become forests without human intervention. Therefore, the best line of action would be to support this natural afforestation process with tree species that are less climate-sensitive and more drought-tolerant. The increasing proportion of forests parallel with the decreasing proportion of uncultivated land reduces the possibility of the invasion of alien plant species. The afforestation process of rural areas is highly supported by the present Hungarian policy. 

Our research aims to enhance the observation that rural landscapes provide great examples for sustainability. These areas have not only remained viable, they also safeguard our future.

How to cite: Balázs, P., Berki, I., and Horváth, A.: Local example for the compensation of negative global environmental phenomena, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-12749, https://doi.org/10.5194/egusphere-egu21-12749, 2021.

EGU21-13906 | vPICO presentations | ITS2.14/HS12.2

Using Deep Machine Learning to Understand Green Space Popularity, Activities, Functionality, and Design Implications: a comparison of Chicago and Stockholm

Si Chen and Brian Deal

EGU21-14833 | vPICO presentations | ITS2.14/HS12.2

Combination of constructed wetland and extensive green roof that uses growing media with added recycled materials

Marek Petreje, Michal Snehota, Tomas Chorazy, Michal Novotny, Barbora Rybova, and Petra Hečkova

As implementation of green roofs can require a large amount of natural resources, such as water and natural components of growing media, the green roof system that uses principles of circular economy was developed and tested. The objective of the study was to verify the performance of the novel concept of combination of constructed wetland and extensive green roof irrigated with pre-treated grey water. Furthermore, the growing medium of the extensive part of the roof contains fractions of recycled crushed brick and pyrolyzed sewage sludge (biochar). In order to design and select a suitable growing medium, 16 variants of substrates were prepared and tested for water holding capacity and water retention curves. Two small test beds were built to test the viability of the novel green roof concept. In order to assess the effect of pyrolyzed sewage sludge, only one experimental bed contained this material (9.5 vol. %), whereas the crushed brick was part of both substrates (37.5 vol. %). The concept of the constructed wetland-extensive green roof was assessed on the basis of water balance measurements, laboratory analyses of water samples taken from various parts of the experimental beds, temperature and water content measurements along the experimental bed´s layers height. Physical properties of the designed substrates such as maximum water capacity, bulk density, grain size, and pH were determined.

After the first six months of performance, the concept of the constructed wetland-extensive green roof seems to be viable. There are relatively low concentrations of nutrients (phosphorus and nitrogen) in the leachate from test beds, namely because the irrigation provides the water directly to the drainage layer, and nutrient-rich substrate enriched with biochar isn't leached by irrigation water. Concentrations of nutrients increase only in response to precipitation. The constructed wetland part of the system proven a high potential to reduce the concentration of the nutrient in pre‑treated grey water.

The vegetation formed by Sedum spp. carpets is prospering well on both test beds. Nutrients from biochar are apparently available for the vegetation. Therefore, the vegetation on the bed with biochar amended substrate shows more vigorous growth and higher evapotranspiration. Substrates amended with recycled materials developed in the study had comparable properties (maximum water capacity, bulk density, pH) with commercial substrates. The monitoring of test beds continues in order to understand better the processes affecting water quantity and quality in long-term perspective.

How to cite: Petreje, M., Snehota, M., Chorazy, T., Novotny, M., Rybova, B., and Hečkova, P.: Combination of constructed wetland and extensive green roof that uses growing media with added recycled materials, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-14833, https://doi.org/10.5194/egusphere-egu21-14833, 2021.

EGU21-15279 | vPICO presentations | ITS2.14/HS12.2

Intra-soil milling for long-term efficient land use prospects 

Saglara Mandzhieva, Vladimir Chernenko, Valery Kalinitchenko, Alexey Glinushkin, Alexey Zavalin, Marina Burachevskaya, Tatiana Minkina, Svetlana Sushkova, Ljudmila Ilyina, Daniil Kozlov, and Alexander Kashcheev

Current land-use policy needs for innovative soil processing technologies. We carried out a long-term field experiment on the Kastanozem in following options: moldboard plowing to a depth of 22 cm; chiselling to a depth of 35 cm; three-tier PTN–40 plowing to a depth of 40–45 cm; PMS–70 intra-soil milling of the 20–45 cm layer. Moldboard, chisel and three-tier plowing does not improve soil aggregate system. 20–45 cm soil layer milling by PMS–70 provides the formation of the 1–3 mm aggregates. 30–40 years after PMS–70 processing, the soil profile structure remained fine aggregate. Soil organic matter and dissolved organic matter content, as well as the soil productivity, were higher after PMS–70. New intra-soil milling machine PMS–260 was developed. The moldboard plowing did not change the natural soil profile vertical morphological differentiation. The soil loosening effect was short-term after soil chiselling. After the three-tier PTN-40 plowing, a large part of humus horizon material strews down the soil profile between the chaotically spread large structural blocks of illuvial and transitional horizons. After PMS–70 processing, the content of 1–3 mm size aggregate particle fraction in the illuvial horizon was triple compared to the three-tier PTN–40 plowing. The soil desalination was intensive after PMS–70. The absorbed Na+ content in solonetz was about 18–20% of soil cation exchange capacity (CEC) in the moldboard option. The same was after the chiselling. The CEC Na+ content was of 14–16% after the PTN–40. The CEC Na+ content was of 10–12% after the PMS–70. The SOM content in the 0­–20 cm soil layer was 2.0%, in the 20-40 cm layer of 1.3%; the DOM content was 0.03% and 0.02% respectively in moldboard plowing option. The SOM and DOM content increased slightly in a period 3–4 years after chiselling. The SOM content was 2.2% in the 0­–20 cm, and 1.4% in the 20–40 cm; the DOM content was 0.04% and 0.03% respectively after the PTN–40. The SOM content increased to 3.3% in the 0–20 cm soil layer, and to 2.1% in the 20–40 cm layer; the DOM content increase was 0.05% and 0.04% respectively after the PMS–70. In the moldboard option, the rhizosphere developed only in the upper soil layer of 0–20 cm. The rhizosphere spreads through the soil crevices after chilling. The conditions of rhizosphere development were better in the local comfort zones of the soil profile after three-tier PTN–40 plowing. The rhizosphere developed well and uniformly both in the upper 0–20 cm and in the 20-45 cm layer after intra-soil milling by PMS–70. Improved plant growing conditions provide higher plant resistivity to pathogens.  The technology life cycle profitability: moldboard 21.5%, chiseling 6.9%, three-tier 15.6%, intra-soil milling 45.6%. The new design of intra-soil milling machine provides five times less traction resistance; 80% increased reliability, halving energy costs. Intra-soil milling provides long-term land-use prospects.

The research was supported by the RFBR, project no. 18-29-25071, and by the President of the Russian Federation, no. MK-2244.2020.5.

How to cite: Mandzhieva, S., Chernenko, V., Kalinitchenko, V., Glinushkin, A., Zavalin, A., Burachevskaya, M., Minkina, T., Sushkova, S., Ilyina, L., Kozlov, D., and Kashcheev, A.: Intra-soil milling for long-term efficient land use prospects , EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-15279, https://doi.org/10.5194/egusphere-egu21-15279, 2021.

Wetlands as large-scale nature-based solutions (NBS) provide multiple ecosystem services of local, regional, and global importance. Knowledge concerning location and vulnerability of wetlands, specifically in the Arctic, is vital to understand and assess the current status and future potential changes in the Arctic. Using available high-resolution wetland databases together with datasets on soil wetness and soil types, we created the first high-resolution map with full coverage of Arctic wetlands. Arctic wetlands' vulnerability is assessed for the years 2050, 2075, and 2100 by utilizing datasets of permafrost extent and projected mean annual average temperature from HadGEM2-ES climate model outputs for three change scenarios (RCP2.6, 4.5, and 8.5). With approximately 25% of Arctic landmass covered with wetlands and 99% being in permafrost areas, Arctic wetlands are highly vulnerable to changes in all scenarios, apart from RCP2.6 where wetlands remain largely stable. Climate change threatens Arctic wetlands and can impact wetland functions and services. These changes can adversely affect the multiple services this sort of NBS can provide in terms of great social, economic, and environmental benefits to human beings. Consequently, negative changes in Arctic wetland ecosystems can escalate land-use conflicts resulting from natural capital exploitation when new areas become more accessible for use. Limiting changes to Arctic wetlands can help maintain their ecosystem services and limit societal challenges arising from thawing permafrost wetlands, especially for indigenous populations dependent on their ecosystem services. This study highlights areas subject to changes and provides useful information to better plan for a sustainable and social-ecological resilient Arctic.

Keywords: Arctic wetlands, permafrost thaw, regime shift vulnerability, climate projection

How to cite: Kåresdotter, E. and Kalantari, Z.: Vulnerability and Importance of Arctic Wetlands as large-scale nature-based solutions for Sustainability in a Changing Climate, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-3148, https://doi.org/10.5194/egusphere-egu21-3148, 2021.

Globally, urban areas contribute significantly to the emissions of the greenhouse gases (GHGs) which are leading to anthropogenic climate change. To achieve long-term sustainable development goals, urban regions will need to grow and develop in such a way that they can both provide a good quality of life for all of their inhabitants, and also reduce and offset their GHG emissions to reach and maintain net-zero GHG emissions.

This work aims to further our understanding of the impact of urban form and growth on GHG emissions, to identify ways in which nature-based solutions (NBS) can be integrated into urban planning to help cities reach net zero emissions while continuing to grow sustainably. We will conduct a high-resolution (1x1km) spatial accounting and mapping of GHG emissions from selected urban anthropogenic activities (residential, commercial, transportation) for Stockholm, Sweden which includes those factors relevant to and impacted by urban form (such as density, land use pattern transportation networks, green spaces) to allow for the analysis of different types of city spatial patterns and planning decisions and their implications in GHG emissions. The results will be further expanded to cities across the European Union (EU) for comparison. Conclusions will be drawn about where and how NBS interventions should be used most effectively to reduce urban GHG emissions and facilitate sustainable city growth in the future.

Keywords: Sustainable cities; Land-use; Greenhouse Gas Emissions; Nature-based Solutions

How to cite: Page, J., Pan, H., and Kalantari, Z.: Assessing impacts of urban form on GHG emission with high-resolution spatial grids to implement nature-based solutions for carbon neutral cities, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-10793, https://doi.org/10.5194/egusphere-egu21-10793, 2021.

EGU21-6898 | vPICO presentations | ITS2.14/HS12.2

Pilot Study of Methods to Support Stakeholder Prioritization of Transboundary Aquifers for Investigation along the US-Mexico Border

James Callegary, Anne-Marie Matherne, Sandra Owen-Joyce, Elia Tapia Villaseñor, Amy Rosebrough, Ismael Minjárez Sosa, Gilbert Anaya, Floyd Gray, Sharon Megdal, and Rogelio Monreal

Four US and six Mexican border states share significant interconnections in terms of trade, jobs, energy production, manufacturing, and natural resources such as water. The border states have a mutual interest in characterizing groundwater resources shared between the US and Mexico, a task made difficult by scarcity of information. To address this challenge, a number of US and Mexican federal agencies and universities via the Transboundary Aquifer Assessment Program (TAAP) have come together to jointly study the shared groundwater resources of the border region, and to develop the information needed by cities, states, industries and local communities to support decision making and land management.

Investigations of four binational aquifers selected in the first phase of TAAP are in progress. Carrying out these investigations has created a cohesive binational multi-institutional team of social and physical scientists and established relationships with a broad network of stakeholders. Completed products relevant to the present work include: (1) analysis of the availability and integration potential of binational data sets, (2) aquifer assessments including a review of US-Mexico aquifer classifications (3) development of water-balance models, (4) analysis of aquifer vulnerability to contamination, and (5) a set of protocols and agreements that address the specific physical, legal, cultural, and institutional setting of the US-Mexico border.

Additional aquifers along the border (estimates of the total range from of 8 to 38) could be investigated, but there are questions as to how to define them, which to choose, and what types of studies are needed. To help answer these questions, we developed a pilot project to investigate and develop methods and tools to assist decision makers and land managers in prioritizing additional aquifers for investigation along the US-Mexico border. First is an approach for rapid assessment of additional aquifers using existing data, published literature, and simple analytical tools including conceptual hydrogeologic model development and precipitation-groundwater lag-correlation analysis. Second, a groundwater modeling platform was developed for use by stakeholders for both learning and planning. Third, in preparation for stakeholder ranking of aquifers for investigation, we conducted a review of multicriteria decision analysis (MCDA) as applied to coupled human-natural resource systems and a review of real-world examples of aquifer prioritization schemes used by governmental entities. Finally, an assessment of uncertainty with respect to knowledge about and trajectory of the coupled human-biophysical system was carried out to aid in stakeholder discussions of prioritization criteria and weighting schemes. These results and tools can be used to support prioritization of any set of aquifers. However, some are specifically designed to address transboundary aquifers and will be used to inform binational discussions regarding prioritization of future aquifer investigations along the US-Mexico border.

How to cite: Callegary, J., Matherne, A.-M., Owen-Joyce, S., Tapia Villaseñor, E., Rosebrough, A., Minjárez Sosa, I., Anaya, G., Gray, F., Megdal, S., and Monreal, R.: Pilot Study of Methods to Support Stakeholder Prioritization of Transboundary Aquifers for Investigation along the US-Mexico Border, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-6898, https://doi.org/10.5194/egusphere-egu21-6898, 2021.

EGU21-10130 | vPICO presentations | ITS2.14/HS12.2

Impacts and solutions associated with glacier-driven river toxicity in the Cordillera Blanca, Peru

Moya Macdonald, Jemma Wadham, Fiorella La Matta Romero, Jon Hawkings, Bram Willems, and Raul Loayza-Muro

Approximately 70% of the world’s tropical glaciers are found in Peru, with 40% of these in the Cordillera Blanca (CB). Here, glaciers are an important source of meltwater to downstream people (~0.25 million) and ecosystems, supporting 40% of streamflow in the dry season. However, the CB has experienced high levels of glacier retreat and mass loss in recent decades, which has influenced the quantity and quality of water supply. During this time, some meltwater-fed rivers have become ‘toxic’, characterised by low pH and high metal concentrations. This toxicity has been linked to exposure of sulphide- and metal-rich rock types as glaciers retreat, and has implications for clean water supply (SDG 6), subsistence farming (contributing to SDG1 and 2), and freshwater biodiversity (SDG 15). Here, we present a comprehensive spatial analysis of water quality in the CB to understand the key drivers of worsening water quality and to predict which catchments may be vulnerable in the future. We sampled 18 glacierised catchments in the CB for geochemical and biological parameters during the dry and wet seasons. River pH ranged from 2.5 to 8.3, with two catchments highly acidic (~pH 2.5-3.8). The concentrations of several riverine metal species (including manganese, nickel, copper and a suite of rare-earth elements) were strongly negatively correlated with pH in the catchments. Additionally, most of the 40 metals analysed in rivers with low pH were present in a truly dissolved phase (>90% of 0.45 µm filtered concentrations were <0.02 µm), indicating high potential bioavailability and biotoxicity. Indeed, shifts in community composition of benthic macroinvertebrates indicated a replacement of sensitive benthic macroinvertebrate taxa (Limnephilidae, Hyaleliidae) in pristine rivers by more tolerant taxa (Chironomidae) in acidic rivers. We suggest that metal leaching and altitude may be important factors influencing diversity, richness and abundance of benthic macroinvertebrate communities. Here, we synthesize data on water quality and glacier retreat, offer predictions of future river toxicity and introduce a novel citizen-science, green-infrastructure initiative being developed to combat water quality degradation in the region.

How to cite: Macdonald, M., Wadham, J., La Matta Romero, F., Hawkings, J., Willems, B., and Loayza-Muro, R.: Impacts and solutions associated with glacier-driven river toxicity in the Cordillera Blanca, Peru, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-10130, https://doi.org/10.5194/egusphere-egu21-10130, 2021.

Hydrological and geomorphological processes within the land-river interface (LRI) can be directly linked to several Sustainable Development Goals (SDG). The transfer of water and material along the LRI provides a range ecosystem services that support environmental, economic and social needs. However, the LRI is also very dynamic from a hydrologic and geomorphic perspective. Benefits can turn into hazards and vice versa, depending on natural and human-induced variations in flow and associated geomorphic activity. This study aimed to identify these critical areas by (i) quantifying the natural and human controlled variation in hydrology and geomorphology, and (ii) mapping associated SDG-related opportunities and trade-offs. The upper reaches of the Himalayan Beas River (India) were used as a case study, where the LRI is characterised by three main sections: (i) a free-flowing confined upper valley, (ii) a heavily regulated confined middle valley, and (iii) and a valley with wide floodplains flowing into the Pong Reservoir. Remote sensing imagery from Sentinel-2 (ESA) (2016-2019) were used to quantify the monthly spatial recurrence of river channels and gravel bars. In addition, data was collected on human and natural infrastructure within the catchment (including road network, urban areas, cropland, national parks, etc.). Combination of both datasets indicated that hydrological and river geomorphological processes in the upper part are the most spatially and temporally variable, leading to fertile soils (SDG 1,2), but also the highest risk of flooding in urban areas and cropland (SDG 11, 13) . The middle part is characterised by stable river channels (i.e. no lateral movement) due to the presence of two dams and confines valleys, leading to limited interaction with the surrounding land, except for the provision of water (SDG 6) and a higher risk of landslides (SDG 1,11). Finally, the lower part is again more dynamic in terms of geomorphological processes, with wide gravel bars and side channels. These dynamics allow larger urban areas and cropland to develop (SDG 1, 11), but also exposes cropland to flooding and erosion (SDG 2, 6). By quantifying the spatiotemporal dimension of hydrological and geomorphological processes and how these relate to LRI characteristics, this study provides a dynamic baseline to identify opportunities and trade-offs in optimising the role of the LRI in driving sustainable development.

How to cite: Vercruysse, K. and Grabowski, B.: Quantifying how hydrological and geomorphical dynamics in the land-river interface create opportunities and trade-offs for sustainable development, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-4693, https://doi.org/10.5194/egusphere-egu21-4693, 2021.

Bioenergy with carbon capture and storage (BECCS) plays a critical role in many stringent scenarios targeting the 2°C goal. Although irrigation is considered a promising way to enhance BECCS potential while reducing the land requirement, it is still unknown where and to what extent it can enhance the global BECCS potential in view of sustainable water use. Based on integrated hydrological simulations, we found that sustainable irrigation without intervention in water usage for other sectors and refrain from exploiting nonrenewable water sources enhanced BECCS potential by only 5–6% (much smaller than 60–71% for unlimited irrigation) above the rainfed potential by the end of this century. Nonetheless, it adds limited additional water withdrawal (166–298 km3 yr-1, corresponding to only 4–7% of the current total withdrawal) compared to that with unlimited irrigation (1392–3929 km3 yr-1, corresponding to 35–98% of the current total withdrawal).

How to cite: Ai, Z. and Hanasaki, N.: How would irrigation enhance the global BECCS potential in view of sustainable water use?, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-8596, https://doi.org/10.5194/egusphere-egu21-8596, 2021.

EGU21-2891 | vPICO presentations | ITS2.14/HS12.2

The GCRF Living Deltas Hub: using water quality monitoring and lived experiences to achieve the UN Sustainable Development Goals 

Lucy R. Roberts, Heather L. Moorhouse, Oanh Truong, Phong Nguyen Thanh, Suzanne McGowan, Virginia N. Panizzo, Phillip Barker, Nga T. Do, Feisal M.F. Rahman, Jorge Salgado, Tuhin Ghosh, Sourav Das, Mashfiquas Salehin, Ahmed Ishtiaque Amin Chowdhury, Andrew C.G. Henderson, and Andrew R.G. Large

The Living Deltas Hub is a UKRI GCRF-funded community investigating the environmental, societal, and natural-cultural heritage of three South and Southeast Asian mega-deltas; the Ganges-Brahmaputra-Meghna delta spanning India and Bangladesh, and the Mekong Delta and Red River Delta of Vietnam. Globally, deltas occupy only 1% of the total land area, but support the livelihoods of ~500 million citizens. As a consequence of growing human populations and intensified anthropogenic activity these deltas face multiple challenges, such as eustatic sea level rise, land subsidence, saline intrusion, unsustainable extraction of natural resources, habitat loss, pollution, and are currently on a trajectory towards collapse. The waterscape of the deltas place SDG 6 (clean water and sanitation) at the heart of sustainable development. Thus, the Hub aims to quantify and assess human impacts on the water quality of major river channels, canals, and ponds by establishing catchment-wide water quality monitoring supplemented by historical data, biomonitoring networks, community science projects (including water quality and participatory GIS) and local knowledge of water quality. This will result in improved understanding of the impacts of the multi-functionality of water sources in Asian mega-deltas from basic domestic use (bathing and drinking water) up to industrial scale aquaculture, and can lead to the success of SDG 6 (clean water), SDG 3 (good health and wellbeing), SDG 2 (zero hunger – here, through sustainable aquaculture), and SDG 14 (life below water). In addition, the combined methodology of water quality monitoring and understanding lived experiences can be used to identify the concerns of local communities, identify inequalities in the access to safe water (working towards SDG 10 reduced inequalities) and understand the female experience (working towards SDG 5 gender inequality). Using a literature review of pond water quality and use in the delta regions and data from household surveys conducted in three regions of the Mekong Delta (Ben Tre, An Giang and Can Tho), we will use ponds as a case study to demonstrate how this approach can be used to improve understanding of community access to safe water. 

 

How to cite: Roberts, L. R., Moorhouse, H. L., Truong, O., Nguyen Thanh, P., McGowan, S., Panizzo, V. N., Barker, P., Do, N. T., Rahman, F. M. F., Salgado, J., Ghosh, T., Das, S., Salehin, M., Amin Chowdhury, A. I., Henderson, A. C. G., and Large, A. R. G.: The GCRF Living Deltas Hub: using water quality monitoring and lived experiences to achieve the UN Sustainable Development Goals , EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-2891, https://doi.org/10.5194/egusphere-egu21-2891, 2021.

ITS2.15/CL3.1.7 – Bringing together climate scientists and impact modellers to build knowledge to effectively deal with climate change

EGU21-13386 | vPICO presentations | ITS2.15/CL3.1.7

Building bridges between disciplines: A generalized mathematical framework for climate change impact assessment

Anneli Guthke, Amin E. Bakhshipour, Felipe P.J. de Barros, Holger Class, James E. Daniell, Ulrich Dittmer, Markus Friedrich, Jannik Haas, Cordula Kropp, Bruno Merz, Sergey Oladyshkin, Andreas Schäfer, Michael Sinsbeck, Daniel Straub, Kristina Terheiden, Silke Wieprecht, and Wolfgang Nowak

Climate change impact and risk assessment is per definition a highly interdisciplinary task. Collaboration across disciplines is, unfortunately, often complicated by different perspectives, approaches, and terminology. To help building bridges, we propose a generalized mathematical framework for impact and risk assessment.

In an unprecedented community effort, we have derived a generally applicable risk equation for spatially-distributed and dynamic systems. We start off with a general framing and then refine individual parts of the equation as much as needed. We will show how the individual terms of our unified risk equation explicitly relate to concepts of frequency, intensity, duration, exposure, vulnerability and asset worth. The rigorous mathematical treatment allows investigating the importance of risk factors and serves as a basis for risk management and reduction. Yet, the actual quantification of risk is not our primary goal – rather, the proposed framework forces us to be very precise in definitions and terminology. Thereby, it effectively improves communication and collaboration across disciplines. Indeed, we even learn greatly in cases where we identify limitations that seem to spoil such a mathematically rigorous treatment.

We have successfully applied the framework to various disciplines of civil and environmental engineering, such as flood risk assessment, seismic risk assessment and reliability analysis of critical infrastructure. Users of the equation praise the structured common ground for discussion and highly recommend at least hypothetically applying this framework to gain a more unified understanding of the problem at hand. In this presentation, we discuss the potential of our proposed framework for risk assessment under climate change. Our transparent and rigorous approach is ideally suited to inform stakeholders and policymakers. Further, we are confident that our approach will serve as a catalyst for interdisciplinary advances toward effective adaptation and mitigation strategies.

How to cite: Guthke, A., Bakhshipour, A. E., de Barros, F. P. J., Class, H., Daniell, J. E., Dittmer, U., Friedrich, M., Haas, J., Kropp, C., Merz, B., Oladyshkin, S., Schäfer, A., Sinsbeck, M., Straub, D., Terheiden, K., Wieprecht, S., and Nowak, W.: Building bridges between disciplines: A generalized mathematical framework for climate change impact assessment, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-13386, https://doi.org/10.5194/egusphere-egu21-13386, 2021.

EGU21-3413 | vPICO presentations | ITS2.15/CL3.1.7

A protocol for the evaluation of climate change impact models

Thorsten Wagener and Francesca Pianosi

Understanding the implications of climate change for our environment and subsequent services and disservices for nature and society is a key science challenge of our days. Simulation model chains that link the causality of climate-meteorology-hydrology-impact in some way or another are rapidly being developed and increasingly applied to understand the implications of future climate change projections. We discuss in our contribution the urgent need to simultaneously develop protocols to evaluate such models and their adequacy to ensure that scientific rigour is upheld in such analyses. We believe that such an evaluation protocol should consist of at least 3 evaluation stages to ensure a model is justified and its limitations are understood. These are: [1] Establishing an impact model as an adequate representation of our current understanding of the underlying system. [2] Establishing an impact model as an adequate model for the task at hand. [3] Establishing that dominant processes are adequately depicted to enable the assessment of intervention strategies. We argue that it is important to distinguish these stages because achieving stage 1 does not guarantee stage 2, while both stages 1 and 2 can potentially be achieved without ensuring stage 3. Different approaches to implement each of these stages exist and they range in rigour from simple (possibly simplistic) to complex (and therefore demanding). In our contribution we will use different impact modelling examples to discuss the current state of impact model evaluation, the limitations of current strategies and methods, and define additional development needs to obtain the scientific rigour we believe is needed for credible and robust impact assessment.

How to cite: Wagener, T. and Pianosi, F.: A protocol for the evaluation of climate change impact models, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-3413, https://doi.org/10.5194/egusphere-egu21-3413, 2021.

EGU21-12137 | vPICO presentations | ITS2.15/CL3.1.7

NUKLEUS - User-relevant and applicable kilometer-scale climate information for Germany

Kevin Sieck, Bente Tiedje, Hendrik Feldmann, and Joaquim Pinto

Given the current developments in climate science it becomes more a more feasible to provide climate information at the kilometer-scale from convection-permitting climate simulations. This progress will enable many users to directly feed high-resolution climate information into their impact-models for climate impact studies at the local scale. Examples include urban heat stress at street level or the design of drainage systems for future precipitation extremes. Within the RegIKlim (Regional information for action on climate change) consortium, the NUKLEUS (Actionable local climate information for Germany) project will not only provide climate information at the local scale, but also to co-develop interfaces between climate and impact models, in order to fulfil the needs of the impact modelling community as good as possible. Within the RegIKlim consortium, the impact modelling community is organised in six “model regions” across Germany, which cover a wide range of geographical and socio-economic conditions.

For the NUKLEUS project, the baseline will be the latest generation of EURO-CORDEX downscaled CMIP6 simulations, which will be further refined to roughly 3 km horizontal resolution and 30-year time-slices for Germany with convection-permitting climate models (ICON CLM, COSMO-CLM, REMO-NH) and statistical-dynamical downscaling approaches. A detailed analysis on the performance of the multi-model mini-ensemble is planned to assess the quality of the provided data. At the interface to the users, we will follow three different approaches to provide usable climate information at the kilometer-scale. One is to provide easy-access to data and post-processing opportunities using the FREVA system. FREVA offers various access-levels from shell to web-based, which serves different levels of user-expertise. In addition, it provides a transparent way of post-processing data by workflow sharing mechanisms. The second one is to develop appropriate additional downscaling methods for the “last mile” where needed. For this “last mile”, we will apply dynamical and statistical methods such as urban climate models and/or weather generators. With the third approach we explicitly aim at integrating a collected user-feedback into the regional modelling systems used within NUKLEUS. Specifically, we intend to identify and incorporate data processing that is best done during the simulation permanently into the models. Examples are wind speeds at rotor heights of windmills or high frequency precipitation sums. NUKLEUS is a contribution to the German research program RegIKlim funded by the Federal Ministry of Education and Research (BMBF).

How to cite: Sieck, K., Tiedje, B., Feldmann, H., and Pinto, J.: NUKLEUS - User-relevant and applicable kilometer-scale climate information for Germany, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-12137, https://doi.org/10.5194/egusphere-egu21-12137, 2021.

Biodiversity includes any type of living variation, from the ecosystem level to genetic variation within organisms. The greatest threats to biodiversity is climate change, destruction of habitats and other human activities. High-altitude mountain regions are pristine environments, with historically small impacts from air pollution, but at risk of being disproportionately impacted by climate change. We focus on three mountainous regions: the Scandinavian Mountains, the Guadarrama Mountains in Spain, and the Pyrenees in France, Andorra and Spain. We study the impact of drivers of change of biodiversity such as future climate change, increased incidences of wild fires, emissions from new shipping routes in the Arctic as ice sheets are melting, human impacts on land use and management practices (such as reindeer grazing) and air pollution.

We simulate future climate change using WRF and a convective permitting climate model, HARMONIE-Climate, with a spatial resolution of 3km. The high resolution strongly improves the representation of precipitation compared to coarser scale simulations (Lind et al., 2020). We use these simulations to develop future scenarios of air pollution load, using two well established chemistry transport models (MATCH and CHIMERE; Marécal et al., 2015). These climate and air pollution scenarios are subsequently used, together with management scenarios, to develop scenarios for biodiversity and ecosystem services. These scenarios are developed applying a process-based dynamic vegetation and biogeochemistry model, LPJ-GUESS (Smith et al., 2014). 

The scenarios, representing mid-21st century, will be made available through a web-based planning tool, where local stakeholders in each region can explore the project results to understand how scenarios of climate change, air pollution and policy development will affect these ecosystems. Local stakeholders are involved throughout the project, such as reindeer herder communities, regional county boards and national authorities, and in a time of changing climate and a global pandemic we have learned the necessity for flexibility in such interactions.

 

References

Lind et al. 2020., Climate Dynamics 55, 1893-1912.

Marécal et al., 2015. Geosci. Mod. Dev. 8, 2777-2813.

Smith et al. 2014 Biogeosciences 11, 2027-2054.

How to cite: Andersson, C. and the BioDiv-Support research team: BioDiv-Support: scenario-based decision support tool for policy planning and adaptation to future challenges in biodiversity and ecosystem services , EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-14412, https://doi.org/10.5194/egusphere-egu21-14412, 2021.

EGU21-1032 | vPICO presentations | ITS2.15/CL3.1.7

AFRICAP - The impact of climate change on soil erosion in Tanzania and Malawi in a convection-permitting model

Sarah Chapman, Cathryn Birch, Marcelo Galdos, Edward Pope, Jemma Davie, Catherine Bradshaw, Samuel Eze, and John Marsham

East Africa has high rates of soil erosion which negatively impact agricultural yields. Climate projections suggest that rainfall intensity will increase in East Africa, which may increase soil erosion. Soil erosion estimates require information on rainfall erosivity, which is calculated using sub-daily storm characteristics that are known to be biased in traditional parameterized convection climate models. Convection-permitting climate models, which are run at higher resolution to negate the need for convection parameterisation, generally better represent rainfall intensity and frequency. We use a novel convection-permitting pan-Africa regional climate model (CP4A) to estimate rainfall erosivity in Tanzania and Malawi, and compare it to its parameterized counterpart (P25), to determine if there is a benefit to using convection permitting climate models to look at rainfall erosivity. We use 8-year historical and end-of-century RCP8.5 simulations to examine the impact of climate change on rainfall erosivity. We then apply the Revised Universal Soil Loss Equation (RUSLE), using the rainfall erosivity estimates from CP4A and P25, to calculate soil erosion in Tanzania and Malawi. The distribution of rainfall intensity and duration was closer to the TRMM rainfall observations in the convection permitting model than in the parameterized model before and after bias-correction. We found that rainfall erosivity was lower in the parameterized convection model than in CP4A due to differences in storm characteristics, even after bias-correction. These results suggest that parameterized convection regional and global climate models might under-estimate rainfall erosivity, and the associated soil erosion. We found high values of present day erosion associated with mountainous regions in Tanzania and Malawi in CP4A. Under climate change, areas at high risk of soil erosion expanded due to increases in rainfall intensity in CP4A. The levels of soil erosion were high enough to negatively impact on agricultural yields.  Soil management was less effective in the future at reducing soil erosion risk than in the present day, and more extensive soil management may be required in the future to manage soil erosion and reduce the negative impacts of soil erosion on agriculture.

How to cite: Chapman, S., Birch, C., Galdos, M., Pope, E., Davie, J., Bradshaw, C., Eze, S., and Marsham, J.: AFRICAP - The impact of climate change on soil erosion in Tanzania and Malawi in a convection-permitting model, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-1032, https://doi.org/10.5194/egusphere-egu21-1032, 2021.

EGU21-3112 | vPICO presentations | ITS2.15/CL3.1.7

Climate change impacts on viticulture in Italy

Laura Massano, Giorgia Fosser, and Marco Gaetani

In Italy the wine industry is an economic asset representing the 8% of the annual turnover of the Food & Beverage sector, according to Unicredit Industry Book 2019. Viticulture is strongly influenced by weather and climate, and winegrowers in Europe have already experienced the impact of climate change in terms of more frequent drought periods, warmer and longer growing seasons and an increased frequency of weather extremes. These changes impact on both yield production and wine quality.

Our study aims to understand the impact of climate change on wine production, to estimate the risks associated with climate factors and to suggest appropriate adaptation measurement. The weather variables that most influence grape growth are: temperature, precipitation and evapotranspiration. Starting for these variables we calculate a range of bioclimatic indices, selected following the International Organisation of Vine and Wine Guidelines (OIV), and correlate these with wine productivity data. According to the values of different indices it is possible to determine the more suitable areas for wine production, where we expect higher productivity, although the climate is not the only factor influencing yield.

Using the convection-permitting models (CPMs – 2.2 horizontal resolution) we investigate how the bioclimatic indices changed in the last 20 years, and the impact of this change on grapes productivity. We look at possible climate trends and at the variation in the frequency distribution of extreme weather events. The CPMs are likely the best available option for this kind of impact studies since they allow a better representation of surface and orography field, explicitly resolve deep convection and show an improved representation of extremes events. In our study, we compare CPMs with regional climate models (RCMs – 12 km horizontal resolution) to evaluate the possible added value of high resolution models for impact studies. To compare models' output to observation the same analysis it carried out using E-OBS dataset.

Through our impact study, we aim to provide a tool that winegrower and stakeholders involved in the wine business can use to make their activities more sustainable and more resilient to climate change.

How to cite: Massano, L., Fosser, G., and Gaetani, M.: Climate change impacts on viticulture in Italy, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-3112, https://doi.org/10.5194/egusphere-egu21-3112, 2021.

EGU21-542 | vPICO presentations | ITS2.15/CL3.1.7

Different approaches of finding European climate analogue regions for the Steigerwald forest (Germany) in the future

Katrin Ziegler, Felix Pohl, Felix Pollinger, and Heiko Paeth

Adapting the impacts of climate change is a great challenge. To facilitate forest adaptation long-term and forward-looking decisions must be made today since they have to be valid for several decades. Therefore, fundamental knowledge of the future climate and of tree species which are more resilient to the future climate than trees growing in the forests today is necessary.

To give local foresters a basis for their decisions, we use the so-called analogue region method. With this method we aim to find regions in Europe which currently have the same climate as it is projected in a specific reference region for different future scenarios. For the projections, the model runs of the regional climate model REMO are used. As an example of finding analogue regions, we selected the forest region Steigerwald in North Bavaria. We use different climatic and forest specific indices and data preparation methods to test the influence of varying indices and methods on the resulting regions. After identifying the respective analogue regions, we analyze which tree species are growing currently in these regions by using the EU-Forest data set.

How to cite: Ziegler, K., Pohl, F., Pollinger, F., and Paeth, H.: Different approaches of finding European climate analogue regions for the Steigerwald forest (Germany) in the future, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-542, https://doi.org/10.5194/egusphere-egu21-542, 2021.

EGU21-726 | vPICO presentations | ITS2.15/CL3.1.7

Providing future UK heavy precipitation guidance for water management stakeholders using a convection-permitting climate model ensemble and a spatial extreme statistical model

Steven Chan, Elizabeth Kendon, Benjamin Youngman, Giorgia Fosser, Christopher Short, Hayley Fowler, Simon Tucker, and Murray Dale

The UK Climate Projections (UKCP) provide the latest information on future climate change expected in the UK. The latest UKCP products include the first UK national climate scenarios at a resolution consistent with weather forecasting. In particular, they include projections from a 12-member 2.2km convection permitting climate model (CPM) ensemble, called UKCP Local (2.2km), released by the UK Met Office in September 2019. A key added value of CPMs is their improved representation of precipitation extremes, and as such the UKCP Local ensemble is particularly useful for water management stakeholders (water utilities and flood risk management professionals) for future adaptation in waste water and flood risk management. A key metric of interest is future increases (“uplifts”) of precipitation return levels. However, diagnosing precipitation return levels for such high-resolution model simulations is difficult due to their spatial-temporal variability and correlation. Here, we adapt an Exeter University-developed spatial extreme statistical model which incorporates the spatial-temporal variability and correlation of precipitation extreme, and apply it to daily and hourly precipitation data from the UKCP Local Ensemble for both the present-day and future RCP8.5 simulations. This allows us to provide robust estimates of uplifts for high return levels across all of the UK for months and seasons of interest.

How to cite: Chan, S., Kendon, E., Youngman, B., Fosser, G., Short, C., Fowler, H., Tucker, S., and Dale, M.: Providing future UK heavy precipitation guidance for water management stakeholders using a convection-permitting climate model ensemble and a spatial extreme statistical model, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-726, https://doi.org/10.5194/egusphere-egu21-726, 2021.

EGU21-8309 | vPICO presentations | ITS2.15/CL3.1.7

Applying a storyline approach to explore the impact of future Atmospheric River induced floods in western Norway 

Trine Jahr Hegdahl, Kolbjørn Engeland, Malte Müller, and Jana Sillman

Atmospheric rivers (AR) are responsible for the most extreme precipitation events causing devastating landslides and floods in western Norway. In this study an event-based storyline approach is used to compare the flood impact of extreme AR events in a warmer climate to those of the current climate.  The four most extreme precipitation events were selected from 30 years of present and future climate simulations from the high-resolution global climate model, the EC-Earth model. For each of the four events, EC-Earth was rerun creating 10 perturbed realizations. A regional convective permitting weather prediction model, AROME-MetCoOp, was used to further downscale the events, and thereafter the operational Norwegian flood-forecasting model was used to estimate the flood levels for 37 catchments in western Norway. The magnitude and the spatial impact were analyzed, and different hydrological initial conditions, which affect the total flooding, were analyzed.

The results show that more catchments were affected with larger floods in the future climate events compared to the current climate events. In addition, the combination of multiple realizations of meteorological forcing and different hydrological initial conditions, for example soil saturation and snow storage, were important for the estimation of the maximum flood level. The meteorological forcing had the highest overall effect on flood magnitude; however, varying and depending on event and catchment. Finally, operational flood warning levels were used to visualize the difference between future and current climate flood events. Applying a setup similar to the one used operationally and relating the future events to known current events associated with ARs, enables a common reference and ease communication with end-users and decision makers.

How to cite: Hegdahl, T. J., Engeland, K., Müller, M., and Sillman, J.: Applying a storyline approach to explore the impact of future Atmospheric River induced floods in western Norway , EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-8309, https://doi.org/10.5194/egusphere-egu21-8309, 2021.

EGU21-1566 | vPICO presentations | ITS2.15/CL3.1.7

Global assessment of flood impact on emergency service provision to vulnerable populations under climate change 

Sarah Johnson, Robert Wilby, Dapeng Yu, and Tom Matthews

Flooding is a major global hazard that accounts for one-third of all reported natural disasters and over 500,000 fatalities since 1980. Globally, vulnerable populations (very young, elderly, medical special needs individuals, etc.) are disproportionately affected by flooding and predominantly encompass the majority of flood associated injuries and fatalities. This is caused by their low self-reliance, weak political voice and insufficient inclusion into climate adaptation and emergency response plans.

Vulnerable individuals are largely reliant on Ambulance and Fire & Rescue Services due to flood induced injuries, exacerbated medical conditions, and requiring evacuative assistance. These services are primary emergency responders to flooding that provide rescue and relief efforts. However, during flood events, the demand for Ambulance and Fire & Rescue Service often exceeds the potential capacity and limits service provision, whilst flooded road networks and short emergency responder-timeframes decrease accessibility, service area and population coverage.

Therefore, an important step towards resolving these social inequalities and emergency responder strains from flooding is to understand the geographic, spatial, temporal, and demographic distributions of vulnerability. This will be undertaken by identifying vulnerability ‘hotspots’ of global populations in terms of emergency service provision during times of flooding of various magnitude under climate change.

The research will use Big Geographical and Climate Data and a ‘hotspot’ approach to investigate how the global extent and distribution of flood hazards and vulnerable population hotspots vary spatially and temporally, based on differing global fluvial and coastal flooding (at 10-year and 100-year return periods), and present and future flood conditions (present-day and 2050, under RCP 4.5 and RCP8.5 climate scenarios). Network Analysis modelling will be used to investigate the impact of this on Ambulance and Fire & Rescue accessibility from service stations to vulnerable populations based on restrictions of road network inundation and emergency response-times (8-, 15-, and 60- minutes). Finally, comparisons will be made to highlight how vulnerability and emergency service accessibility compares demographically between different vulnerable population groups.

It is expected that there will be significant geographical and temporal differences in social vulnerability and emergency service provision between countries and regions globally. Although to what extent is currently unknown. Ultimately, the framework of this research may provide real-world applications for informing strategic planning of emergency response operations and resolving social inequalities to flood hazards. These applications could include the production of more detailed flood hazard and evacuation maps that highlight vulnerability hotspots, the prioritisation of vulnerable population groups in emergency response plans to minimise geographic and population disparities of flood injuries and fatalities, and the allocation of emergency service hubs in regions of high-vulnerability but low-emergency response provision.

How to cite: Johnson, S., Wilby, R., Yu, D., and Matthews, T.: Global assessment of flood impact on emergency service provision to vulnerable populations under climate change , EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-1566, https://doi.org/10.5194/egusphere-egu21-1566, 2021.

EGU21-506 | vPICO presentations | ITS2.15/CL3.1.7

Evaluation of the impacts of climate change and land-use dynamics on water resources: The case of the Lobo River watershed: Central-Western Côte d'Ivoire

Berenger Koffi, Zilé Alex Kouadio, Affoué Berthe Yao, Kouakou Hervé Kouassi, Martin Sanchez Angulo, and Kouakou Lazare Kouassi

EGU21-12992 | vPICO presentations | ITS2.15/CL3.1.7

Rainfall-runoff modelling of volcanic islands for future risk assessment (and beyond)

Albrecht Weerts, Frederiek Spera Weiland, and Marjanne Zander

Rainfall-runoff modelling of volcanic islands for future risk assessment (and beyond)

Volcanic islands are often densely populated and attract large numbers of tourists each year. Climate change may alter weather related risks like floods on these islands. They have often complex orography that influences the precipitation patterns. For impact assessments therefore, this complexity calls for detailed modelling. As part as part of the H2020 European Climate Prediction System project (https://www.eucp-project.eu/) we aim investigating the usage of the new generation convection-permitting regional climate models (Ban et al., accepted for publication 2021) for future flood risk and water security assessments.

Here we focus on the Lesser Antilles and La Reunion that are part of EU’s outermost regions. We have setup distributed hydrological wflow_sbm models at ~1km2 resolution for each island following the approaches of Imhoff et al (2020) and Eilander et al. (2021). Validation of these models is difficult because of the poor quality of available precipitation data sources and limited discharge observations records if available at all. Still, for La Reunion, we show that wflow_sbm performs well once driven with high resolution gridded rainfall (provided by MeteoFrance). CHIRPS rainfall (Funk et al., 2014) shows potential in some seasons but leads to significant underestimation of flow for other seasons. Similar behavior is obtained for rivers on Guadeloupe and Martinique islands in the Lesser Antilles (where high-quality gridded rainfall data is lacking). To further validate the approach/models for the Lesser Antilles, we also setup a wflow_sbm model for the whole of the Dominican Republic (including Haiti) and compared the wflow_sbm model against available discharge observations using both ERA5, CHIRPS and MSWEP2.0 as rainfall sources.

Next step in the project will be to force the wflow_sbm model for La Reunion with future climate projections obtained with AROME. For the Lesser Antilles, we will force the wflow_sbm models using pseudo global warming scenarios.

Besides the intended use for flood risk (incl. operational forecasting) and water resources management, these high-resolution hydrological models and climate scenarios may be helpful in exploring future changes to river water salinization, inflows of sediment and nutrient/pollutants to nearby coastal waters and coral reefs.

 

Ban, N., E. Brisson, C. Caillaud, E. Coppola, E. Pichelli, S. Sobolowski, …, M.J. Zander (2021): “The first multi-model ensemble of regional climate simulations at the kilometer-scale resolution, Part I: Evaluation of precipitation”, manuscript accepted for publication in Climate Dynamics.

Eilander, D., van Verseveld, W., Yamazaki, D., Weerts, A., Winsemius, H. C., and Ward, P. J.: A hydrography upscaling method for scale invariant parametrization of distributed hydrological models, Hydrol. Earth Syst. Sci. Discuss. [preprint], https://doi.org/10.5194/hess-2020-582, in review, 2020

Funk, C.C., Peterson, P.J., Landsfeld, M.F., Pedreros, D.H., Verdin, J.P., Rowland, J.D., Romero, B.E., Husak, G.J., Michaelsen, J.C., and Verdin, A.P., 2014, A quasi-global precipitation time series for drought monitoring: U.S. Geological Survey Data Series 832, 4 p. http://pubs.usgs.gov/ds/832/

Imhoff, R.O., W. van Verseveld, B. van Osnabrugge, A.H. Weerts, 2020. “Scaling point-scale pedotransfer functions parameter estimates for seamless large-domain high-resolution distributed hydrological modelling: An example for the Rhine river.” Water Resources Research, 56. Doi: 10.1029/2019WR026807

 

How to cite: Weerts, A., Spera Weiland, F., and Zander, M.: Rainfall-runoff modelling of volcanic islands for future risk assessment (and beyond), EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-12992, https://doi.org/10.5194/egusphere-egu21-12992, 2021.

EGU21-13201 | vPICO presentations | ITS2.15/CL3.1.7

The impact of using bias-corrected precipitation in estimating extreme discharge: A study over the Italian Territory

Matilde García-Valdecasas Ojeda, Erika Coppola, Fabio Di Sante, Francesca Raffaele, Rita Nogherotto, and Filippo Giorgi

A common way to study the impact of climate change on water resources is through hydrological models fed by precipitation from global or regional climate models (GCMs and RCMs, respectively). However, precipitation from climate models is usually affected by systematical biases that may produce inadequate streamflow estimations. For this reason, users find it necessary to apply some bias-corrected technique to reduce errors in precipitation before its use in hydrological simulations. Among the different methods, quantile mapping (QM) is a widely used method as it has shown satisfactory results for historical conditions.

In recent years, several studies have investigated the QM method, with a focus on mean precipitations. However, it remains quite uncertain how bias-corrected precipitation modifies river discharges, particularly the extreme discharges on a sub-daily timescale. In this framework, this study aims to quantify differences between simulated river discharges using corrected and uncorrected precipitation to feed a hydrological model in the context of flood hazard assessment in Italy.

To adequately estimate flood events, high spatiotemporal resolution data are required. Therefore, sub-daily precipitation outputs from the ICTP RegCM Regional Climate Model driven by the HadGEM2-ES model at 12 km were contemplated in this study. Precipitation outputs for the period 1976-2100 were bias-corrected concerning the observations from GRIPHO, which is a high-resolution observational product. Then, bias-corrected and uncorrected precipitations were used to feed the CETEMPS Hydrological Model (CHyM) completing thus, a set of hydrological simulations covering the entire Italian Territory, in both present-day and future conditions. Analyses focused on the comparison between simulated and observed discharges for present-day conditions, but also on the comparison between corrected and uncorrected values ​​in the future.

The results of this study could provide valuable information on whether the use of the QM method is appropriate for studying extreme discharges on a sub-daily scale, an essential issue for assessing the impacts of climate change on extreme hydrological events.

Keywords: flood hazard assessment, quantile delta mapping, CHyM model, RegCM model, Italy

Acknowledgments: The research reported in this work was supported by OGS and CINECA under HPC-TRES program award number 2020-02.

How to cite: García-Valdecasas Ojeda, M., Coppola, E., Di Sante, F., Raffaele, F., Nogherotto, R., and Giorgi, F.: The impact of using bias-corrected precipitation in estimating extreme discharge: A study over the Italian Territory, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-13201, https://doi.org/10.5194/egusphere-egu21-13201, 2021.

EGU21-5568 | vPICO presentations | ITS2.15/CL3.1.7

Risk Assessment of Climate Change Impacts on Urban Discharge Fraction and Eutrophication in Large European River Networks

Soohyun Yang, Olaf Büttner, Rohini Kumar, Stefano Basso, and Dietrich Borchardt

Climate change impacts on natural environments and human-built landscapes have been extensively studied from the meteorological, hydrological, agricultural, and urban point of views. Embracing the inevitability of climate change, there is a need for investigating and establishing adaptation strategies to changing climate conditions in order to protect essential resources for the survival of humans and ecosystems. Especially for surface water resources, water quality in rivers is a sensitive aspect which might be affected by the impact of climate change on hydrological regimes along river networks.

In fact, with a grand target of achieving Good-Ecological-Status for all European surface water bodies, the implementation of the EU Water Framework Directive since year 2000 has facilitated remarkable reductions of point-source nutrient loads discharged from municipal wastewater treatment plants (WWTPs) into rivers. Nevertheless, satisfying the environmental regulations at the emission-pipe-end of individual WWTPs has not guaranteed a perfect resolution of river water quality problems (e.g., eutrophication) at the scale of entire river basins. This likely occurred because decisions concerning WWTPs size and location were mainly influenced by the scale and location of residential areas and driven by efficiency purposes. That is, the hydrological, biogeochemical, and ecological characteristics of river water bodies receiving the WWTPs emissions were less likely to be considered. Climate-change-driven shifts of hydrological regimes in rivers could exacerbate the current situation and accelerate the water quality degradation caused by the urban emissions.

To tackle this issue, this study aims to decipher the interplays between WWTPs discharges and hydrological regimes of the receiving river water bodies, and to assess water quality risks due to WWTPs emissions under climate-change-induced alteration of hydrologic regimes, by using systematic and general tools at the scale of entire river networks (e.g., combined dimensions of stream-orders and WWTP-sizes). To this end, we synthesize the EU-scale reliable dataset for river networks and WWTPs and the simulation results of the mesoscale hydrologic model under a climate change scenario. We focus on nutrient concentrations (NH4-N, total P) and urban discharge fraction from WWTPs (i.e., the fraction of treated wastewater in river flows), performing the risk assessments for three large European river basins. Our diagnostic results at the river-network-scale could assist river basin managers and stakeholders to select WWTPs to be preferentially managed for minimizing water quality risks in the future under climate change. The presented concept here for the specific components is generally applicable to assess environmental risks and guide strategic management options for other pollutants in urban emissions (e.g., microplastics and pharmaceuticals).

How to cite: Yang, S., Büttner, O., Kumar, R., Basso, S., and Borchardt, D.: Risk Assessment of Climate Change Impacts on Urban Discharge Fraction and Eutrophication in Large European River Networks, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-5568, https://doi.org/10.5194/egusphere-egu21-5568, 2021.

EGU21-13365 | vPICO presentations | ITS2.15/CL3.1.7

Understanding climate-related risks to infrastructure in Chinese cities, Climate Risk Assessment of Infrastructure Tool

Maria Sunyer, Louise Parry, Oliver Pritchard, Harriet Obrien, Astrid Kagan, Laura Frost, and Ben Smith

Climate resilient infrastructure is essential for the safety, wellbeing, sustainability and economic prosperity of cities. An understanding of current and future climate risks is an essential consideration for the planning, design, delivery and management of new and existing resilient infrastructure systems. While there is a growing number of tools which focus on assessing specific components of climate risk there is a need for tools which help bridge the gap between climate science, resilience practitioners, infrastructure owners and policy makers.

The Climate Risk Infrastructure Assessment Tool developed within the Climate Science for Service Partnership China (CSSP China) aims to help planners and policy-makers understand how climate change may impact a city’s infrastructure systems. CSSP China seeks to bring together climate practitioners in China and the UK, and to forge links between climate scientists and industry practitioners to develop practical tools that translate the science into valuable insights for policymaking, planning and design. The development of this tools builds on earlier work carried out with the Shanghai Met Service and the British Embassy in Beijing to develop a qualitative tool to guide the assessment of climate risks for infrastructure.

The tool guides the user through a semi-quantitative climate risk assessment for a section of an infrastructure system. At present it uses ensemble data from global climate models from the Coupled Model Intercomparison Project Phase 5 (CMIP5) to estimate and visualise future climate change projections helping cities understand the current and future likelihood of weather events. The tool then enables cities to assess the overall impact of severe weather on infrastructure by determining its vulnerability and criticality. Risk is estimated as a combination of event likelihood and impact. For key risks, guidance on implementing appropriate adaptation measures is provided to support planners and policy-makers to consider what action is needed.

How to cite: Sunyer, M., Parry, L., Pritchard, O., Obrien, H., Kagan, A., Frost, L., and Smith, B.: Understanding climate-related risks to infrastructure in Chinese cities, Climate Risk Assessment of Infrastructure Tool, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-13365, https://doi.org/10.5194/egusphere-egu21-13365, 2021.

EGU21-2030 | vPICO presentations | ITS2.15/CL3.1.7

Probabilistic projection of the regional climate as the basis for the development of adaptation programs in the economy of Russia

Yuliya Rudakova, Igor Shkolnik, Elena Khlebnikova, and Vladimir Kattsov

The prospects of using the probabilistic regional climate projection technique for adaptation to climate change in the territory of Russia are considered. The analysis focuses on future changes in the climatic indicators of the thermal regime and humidification which play a significant role in the evaluation of the reliability of the functioning of construction and technical systems as well as transport and energy infrastructure.

The analysis is based on the output of the 50-member ensemble of high-resolution climate projections using an RCM developed at the Main Geophysical Observatory (MGO). The RCM grid has a horizontal resolution of 25 km across Russia. Modeling projections have been recently used to assess the impacts of regional climate change on hydropower facilities (Shkolnik et al., 2018).

Numerical experiments are carried out from different (random) initial conditions for the baseline 1990-1999 and future periods 2050-2059 and 2090-2099 using the IPCC RCP8.5 scenario (Kattsov et al., 2020). The boundary conditions on the ocean surface are derived from the output of the five CMIP5 models. For each ocean state trajectory, ten experiments from the different initial conditions are conducted. Lateral boundary conditions for the RCM ensemble are provided by MGO AGCM under an identical experimental setup.

To study the future impacts of the thermal regime, several universal indicators are used, particularly, the annual and seasonal extremes of temperature for a given averaging period as well as the characteristics of intra-annual periods with the temperature above/below the thresholds. The thresholds ​​are selected to meet the needs of construction, land transport, and the energy sector. Besides, the indicators of the precipitation regime are considered (seasonal maxima of daily amounts and characteristics of dry/wet periods).

Along with obtaining median ensemble estimates of changes in mean values, an analysis of future changes in the indicators in the probabilistic aspect is conducted. Using the temperature of the hottest 30-day period and the maximum duration of the dry period, the regional features of their projected changes are demonstrated accounting for the contribution of internal climatic variability. In agreement with observations, significant differences in the changes between the European part of Russia and certain regions of its Asian part are revealed.

The study is supported by the Russian Science Foundation (grant 16-17-00063).

References

Kattsov V., E. Khlebnikova, I. Shkolnik, and Yu. Rudakova: Probabilistic Regional Climate Projecting as a Basis for the Development of Adaptation Programs for the Economy of the Russian Federation. Russian Meteorology and Hydrology, 2020, Vol. 45, No. 5, pp. 330–338. Allerton Press, Inc., 2020.

Shkolnik, I., Pavlova, T., Efimov, S. et al. Future changes in peak river flows across northern Eurasia as inferred from an ensemble of regional climate projections under the IPCC RCP8.5 scenario. Clim Dyn 50215–230 (2018). https://doi.org/10.1007/s00382-017-3600

How to cite: Rudakova, Y., Shkolnik, I., Khlebnikova, E., and Kattsov, V.: Probabilistic projection of the regional climate as the basis for the development of adaptation programs in the economy of Russia, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-2030, https://doi.org/10.5194/egusphere-egu21-2030, 2021.

EGU21-1237 | vPICO presentations | ITS2.15/CL3.1.7

Projected changes in days with zero-crossings for Norway

Irene Brox Nilsen, Inger Hanssen-Bauer, Ole Einar Tveito, and Wai Kwok Wong

This presentation describes projected changes in the number of days with zero-crossings (DZCs) for Norway, that is, a day where the maximum temperature exceeds 0 °C and the minimum temperature drops below 0 °C, as an example of how the Norwegian Centre for Climate Services disseminates climate information to various user groups. Changes in DZCs have been requested by several user groups in Norway, for instance by agriculture and the transport sector. 
A cold bias was detected in the regional climate model ensemble for Norway (here: EURO-CORDEX), which highlighted the need for bias-adjusting temperature fields before analyses. This is important for any index that is dependent on a fixed temperature threshold, not only the given index DZCs.
Gridded projections of changes in DZCs were produced for the period 2071–2100 relative to 1971–2000 under RCP4.5 and RCP8.5, at a 1 × 1 km resolution. The projections have been made publicly available at the Norwegian Centre for Climate Services' website https://klimaservicesenter.no. Results show that in regions and seasons that are mild, the number of DZCs is thus projected to decrease. This decrease was found for lowland regions in spring and coastal regions in winter. In regions and seasons that are cold, the number of DZCs is projected to give more frequent crossings of the 0 °C threshold. This increase was found for inland regions in winter and the northernmost county, Finnmark, in spring. Thus, more frequent icing of the snowpack is expected in Finnmark. This information can be used by the transport sector (e.g. winter road maintenance) and agriculture (e.g. reindeer herders) in the relevant regions. The Norwegian Centre for Climate Services disseminates information through fact-sheets, web-based maps and downloadable files.

How to cite: Nilsen, I. B., Hanssen-Bauer, I., Tveito, O. E., and Wong, W. K.: Projected changes in days with zero-crossings for Norway, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-1237, https://doi.org/10.5194/egusphere-egu21-1237, 2021.

Convection-permitting regional climate model simulations may serve as driving data for crop and dynamic vegetation models. It is thus possible to generate physically consistent scenarios for the future-concerning effects of climate change on crop yields and pollinators. Here, we performed convection-permitting hindcast simulations with the regional climate model COSMO5.0-CLM16 (CCLM) from 1980 to 2015 with a spin-up starting at 1979. The model was driven with hourly ERA5 data, which is the latest climate reanalysis product by ECMWF and directly downscaled to 3 km horizontal resolution over central Europe. The land-use classes are described by ECOCLIMAP, and the soil type and depth by HWSD. The evaluation is carried out in terms of temperature, precipitation, and extreme weather indices, comparing CCLM output with the gridded observational dataset HYRAS from the German Weather Service. While CCLM inherits a warm/cold and dry/wet summer/winter bias found in its parent model, it reproduces the main features of the present climate of the study domain, including the distribution, the seasonal mean climate patterns, and probability density distributions. The bias for precipitation ranges between ±20 % and the bias for temperature between ±1 °C compared to the observations over most of the regions. This is in the range of the bias between observational data. Furthermore, the model catches extreme weather events related to droughts, floods, heat/cold waves, and agriculture-specific events. The results highlight the possibility to directly downscale ERA5 data with regional climate models avoiding the multiple nesting approach and high computational costs. This study adds confidence to convection-permitting climate simulations of future changes in agricultural extreme events.

How to cite: Zhang, H. and Tölle, M.: Evaluation of agricultural-related extreme events in hindcast COSMO-CLM simulations over Central Europe , EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-9507, https://doi.org/10.5194/egusphere-egu21-9507, 2021.

EGU21-6886 | vPICO presentations | ITS2.15/CL3.1.7

Rainfall Characteristics from Convection-Permitting Downscaling over the Western Maritime Continent

Venkatraman Prasanna, Sandeep Sahany, Aurel F. Moise, Xin Rong Chua, Gerald Lim, Muhammad E. Hassim, and Chen Chen

Long-term convection-permitting dynamical downscaling has been carried out over the western Maritime Continent, using the Singapore Variable Resolution Regional Climate Model (SINGV-RCM) at 8km and 2km spatial resolutions. The SINGV-RCM is forced with ERA-5 reanalyses data for a 36-year period (1979-2014) at 8km resolution over Southeast Asia (79E-160E;16S-24N) with regular update of the sea surface temperature at 6-hr interval; further, this 8km domain simulation is used for forcing a smaller domain over the western Maritime continent at a resolution of 2km (93E-110E;7.2S-9.9N) for a 20-year period (1995-2014). Rainfall characteristics including the diurnal cycle and extremes from the two simulations evaluated against satellite retrievals, and the added value from dynamical downscaling will be presented.

How to cite: Prasanna, V., Sahany, S., Moise, A. F., Chua, X. R., Lim, G., Hassim, M. E., and Chen, C.: Rainfall Characteristics from Convection-Permitting Downscaling over the Western Maritime Continent, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-6886, https://doi.org/10.5194/egusphere-egu21-6886, 2021.

EGU21-14599 | vPICO presentations | ITS2.15/CL3.1.7

Visual representation of CORDEX climate data by using QGIS

Kalamkas Yessimkhanova and Mátyás Gede

The majority of studies are dedicated to the analysis of climate change and climate models with no regard for data visualization part. Therefore, this research is aimed at highlighting challenges, with an emphasis on spatial referencing that can occur while visualizing CORDEX data. CORDEX data are stored in NetCDF file format, and sometimes georeferencing may be misconceived in QGIS software. For this reason, two techniques of georeferencing data are examined in this work. The first way of data georeferencing is re-projecting coordinates from original projection to an interpolated latitude/longitude grid. The second way is re-encrypting initial data file so that QGIS is able to interpret projection information. Preference of using QGIS explained by two reasons: it is open source GIS application and it has expanded visualization toolkit.

In addition, there are a great deal of climate models based on CORDEX data for some regions whereas there is a lack of climate projections for particular areas. In this regard, carrying out analysis for the region of Kazakhstan is beneficial. Outcomes of this research may stimulate spreading local climate models for Kazakhstan territory. Results are represented in the form of maps of Kazakhstan illustrating temperature change over 21st century time period.

How to cite: Yessimkhanova, K. and Gede, M.: Visual representation of CORDEX climate data by using QGIS, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-14599, https://doi.org/10.5194/egusphere-egu21-14599, 2021.

ITS2.16/CL3.2.19 – Economics and Econometrics of Climate Change: evaluating the drivers, socio-economic and development impacts, and policies of climate change

EGU21-559 | vPICO presentations | ITS2.16/CL3.2.19

A State Space Representation of a Two-Component Energy Balance Model

Jingying Lykke, Eric Hillebrand, and Mikkel Bennedsen

Energy Balance models (EBMs) condense the complicated processes underlying temperature change into a single equation that describes the disequilibrium between absorbed radiation and emitted radiation, where the relation between temperature change and radiative forcing is established. The two-component EBM divides the climate into a mixed shallow ocean/atmosphere layer and a deep ocean layer, thereby accommodating the heat exchange between these two layers. However, the predominant nature of non-stationarity in the observations of climate variables poses challenges for standard statistical inference.

This study maps the two-component EBM into a versatile linear state space system (named EBM-SS model) of temperatures in the mixed layer and in the deep ocean layer with radiative forcing. This EBM-SS model allows for the modeling of non-stationarity and time-varying behaviors, the incorporation of multiple alternative variables for one object of interest, and the handling of missing observations. It opens up the possibility to couple with other frameworks to identify the drivers underlying the temperature evolution while maintaining consistency with physical theory. We decompose the latent state of radiative forcing, which is exogenous in this system, into a smooth component and a rough component. The smooth component is modeled as a random walk process with drift to represent the deterministic and stochastic trends of radiative forcing, while the rough component captures the transitory episodes in forcing following major volcanic eruptions.

We conduct an empirical analysis on data series at the global level from the period 1955 -- 2019, where the maximum likelihood estimates of the physical parameters are obtained via outputs from the Kalman Filter. We employ proxy variable for the temperature in the deep ocean layer, which is an integral quantity of the ocean temperature and represents the heat storage in the ocean.

How to cite: Lykke, J., Hillebrand, E., and Bennedsen, M.: A State Space Representation of a Two-Component Energy Balance Model, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-559, https://doi.org/10.5194/egusphere-egu21-559, 2021.

EGU21-826 | vPICO presentations | ITS2.16/CL3.2.19

A Statistical Model of the Global Carbon Budget

Eric Hillebrand, Mikkel Bennedsen, and Siem Jan Koopman

We propose a dynamic statistical model of the Global Carbon Budget (GCB) as represented in the annual data set made available by the Global Carbon Project (Friedlingsstein et al., 2019, Earth System Science Data 11, 1783--1838), covering the sample period 1959--2018. The model connects four main objects of interest: atmospheric CO2 concentrations, anthropogenic CO2 emissions, the absorption of CO2 by the terrestrial biosphere (land sink) and by the ocean and marine biosphere (ocean sink).  The model captures the global carbon budget equation, which states that emissions not absorbed by either land or ocean sinks must remain in the atmosphere and constitute a flow to the stock of atmospheric concentrations. Emissions depend on global economic activity as measured by World gross domestic product (GDP), and sink activity depends on the level of atmospheric concentrations and the Southern Oscillation Index (SOI). We use the model to determine the time series dynamics of atmospheric concentrations, to assess parameter uncertainty, to compute key variables such as the airborne fraction and sink rate, to forecast the GCB components from forecasts of World-GDP and SOI, and to conduct scenario analysis based on different possible future paths of World-GDP.

How to cite: Hillebrand, E., Bennedsen, M., and Koopman, S. J.: A Statistical Model of the Global Carbon Budget, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-826, https://doi.org/10.5194/egusphere-egu21-826, 2021.

EGU21-8020 | vPICO presentations | ITS2.16/CL3.2.19

High-Dimensional Granger Causality for Climatic Attribution

Luca Margaritella, Marina Friedrich, and Stephan Smeekes

We use the framework of Granger-causality testing in high-dimensional vector autoregressive models (VARs) to disentangle and interpret the complex causal chains linking radiative forcings and global as well as hemispheric temperatures. By allowing for high dimensionality in the model we can enrich the information set with all relevant natural and anthropogenic forcing variables to obtain reliable causal relations. These variables have mostly been investigated in an aggregated form or in separate models in the previous literature. An additional advantage of our framework is that it allows to ignore the order of integration of the variables and to directly estimate the VAR in levels, therefore avoiding accumulating biases coming from unit-root and cointegration tests. This is of particular appeal for climate time series which are often argued to contain specific stochastic trends as well as yielding long memory. We are thus able to display the causal networks linking radiative forcings to global and hemispheric temperatures but also to causally connect radiative forcings among themselves, therefore allowing for a careful reconstruction of a timeline of causal effects among forcings. The robustness of our proposed procedure makes it an important tool for policy evaluation in tackling global climate change.

How to cite: Margaritella, L., Friedrich, M., and Smeekes, S.: High-Dimensional Granger Causality for Climatic Attribution, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-8020, https://doi.org/10.5194/egusphere-egu21-8020, 2021.

The following paper analyses monthly trends for CO2 emissions from energy consumption for 31 European countries, four primary fuels (i.e., Crude Oil, Natural Gas, Hard Coal, Lignite) and three secondary fuels (i.e., Gas/Diesel Oil, LPG, Naphta, Petroleum Coke) from 2008 to 2019. Carbon dioxide emission has been estimated following the Reference Approach in the 2006 IPCC Guidelines for National Greenhouse Gasses Inventories. Country-specific (e.g. Tier 2) coefficient were retrieved from the IPCC Emission Factor Database and the UN Common Reporting Framework. Data on fuel consumption (e.g., Gross Inland Deliveries) were taken from the Eurostat database. This paper will fill some knowledge gap analysing monthly trends of carbon dioxide emissions for major EU Countries. As the progressive phase-out of carbon is taking place pretty much in all Europe, Crude Oil exerted the largest amount of carbon dioxide emissions in the period considered. Analysis of selected countries unveiled several clusters within the EU in terms of major source of emissions. As final step, the paper has endeavoured the task of fitting a model for monthly CO2 forecasting. The whole series presents two structural breaks and can be explained by an autoregressive model of the first order. Indeed, further speculations on a more appropriate fit and more fuels in the estimation, is demanded to other works.

How to cite: Quatrosi, M.: Analysis of monthly CO2 emission trends for major EU Countries: a time series approach, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-1132, https://doi.org/10.5194/egusphere-egu21-1132, 2021.

EGU21-2016 | vPICO presentations | ITS2.16/CL3.2.19 | Highlight

Investors’ Climate Sentiment and Financial Markets

Caterina Santi

We propose a measure of investors’ climate sentiment by performing sentiment analysis on StockTwits posts on climate change and global warming. We find that investors’ climate sentiment generates a mispricing in the Emission-minus-Clean (EMC) portfolio (Choi et al., 2020), the portfolio that invests in emission stocks and goes short on clean stocks. Specifically, when investors share a positive attitude towards climate change, they tend to overvalue the negative externalities produced by emission stocks. Moreover, we show that carbon prices are a successful incentive to reduce CO2 emissions. Finally, our model can predict the price of the EMC portfolio also for long-term horizons.

How to cite: Santi, C.: Investors’ Climate Sentiment and Financial Markets, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-2016, https://doi.org/10.5194/egusphere-egu21-2016, 2021.

EGU21-7840 | vPICO presentations | ITS2.16/CL3.2.19

Climate change and air conditioning adoption: the role of remittances

Filippo Pavanello and Teresa Randazzo

Do remittances improve how households adapt to global warming? We explore this question exploiting a nationally-representative household data from Mexico - a country that experiences a large flow of remittances. Mexican households respond to excess heat by purchasing air conditioning and remittances can be used to adopt and use cooling devices that contribute to maintaining thermal comfort at home. Our results show that recipient households have a higher probability to adopt air conditioning at home with important implication on electricity consumption. The effect is even larger for those households living in high-temperature areas showing an important role of remittances in the climate adaptation process.

How to cite: Pavanello, F. and Randazzo, T.: Climate change and air conditioning adoption: the role of remittances, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-7840, https://doi.org/10.5194/egusphere-egu21-7840, 2021.

EGU21-7923 | vPICO presentations | ITS2.16/CL3.2.19

Implications of extreme temperatures and socio-economic development on power markets’ peak demand across the world

Francesco Colelli, Enrica De Cian, Malcolm Mistry, and Irene Mammi

Relevance: Extreme temperature events, both heatwaves and cold spells, can put pressure on power systems’ reliability by pushing power demand to record highs. Within the literature assessing the impacts of climate change on the energy sector,  gathering new evidence on the drivers of peak load is a pressing issue for multiple reasons. First, peaks in power load must be accommodated by exceptional ramp-up requirements of power generating units, so that in the future adapting to climate change may involve the construction of plentiful under-utilized peak generation plants, putting pressures on the decarbonization goals and increasing stranded assets risks. Furthermore, peak load shocks induced by extreme temperatures can coincide with reduced transmission and distribution capacity, further challenging the operation of electricity grids [1].

Both the empirical and modeling literature assessing the impacts of climate change on the energy sector have generally focused on aggregated electricity demand rather than on its peaks. Few available empirical studies  investigate how extreme events can affect peak demand focus on industrialized countries and estimate reduced-form models, that hold adaptation, economic growth, technology, and current infrastructure constant [2,3]. Our paper aims to fill this gap by identifying if and how climatic and socio-economic drivers can affect the magnitude of the peak load response to extreme weather events.

Methods: We assess these interrelated dynamics by exploiting high-frequency power demand data collected from load balancing authorities. Specifically, we assemble a novel dataset spanning for the last two decades across more than 100 power markets, comprising both countries (European Member States, Asian and African countries) and large sub-national regions (power markets in Japan, Australia and Russia and Federal States or Provinces in the US, Canada, Brazil and India). The dataset includes: i) daily peak and total load; ii) daily population-weighted exposure to weather from 3 hourly near surface temperature data at 0.25 degrees gridded resolution; iii) quarterly and yearly regional statistics and indicators on demography, economy, education and innovation. We investigate how daily peak load responds to extreme temperatures by adopting a suite of time-series and panel econometric methods that fully exploit the high-frequency and sub-national disaggregation of our dataset.

Results: Utilizing the innovative methodological framework proposed, we: i) identify how peak load responds to temperature extremes in different regions; ii) test if and how such response can be modulated by regional climatic and socio-economic characteristics; iii) derive cost implications due to the amplification of peak demand deriving from future increases in the intensity and frequency of extreme events.

References:

[1] Yalew, S. G., van Vliet, M. T., Gernaat, D. E., Ludwig, F., Miara, A., Park, C., ... & Van Vuuren, D. P. (2020). Nature Energy, 5(10), 794-802.

[2] Auffhammer, M., Baylis, P., & Hausman, C. H. (2017). PNAS, 114(8), 1886-1891.

[3] Wenz, L., Levermann, A., & Auffhammer, M. (2017). PNAS, 114(38), E7910-E7918.

How to cite: Colelli, F., De Cian, E., Mistry, M., and Mammi, I.: Implications of extreme temperatures and socio-economic development on power markets’ peak demand across the world, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-7923, https://doi.org/10.5194/egusphere-egu21-7923, 2021.

EGU21-9973 | vPICO presentations | ITS2.16/CL3.2.19

Day-to-day temperature variability reduces economic growth

Maximilian Kotz, Leonie Wenz, Annika Stechemesser, Matthias Kalkuhl, and Anders Levermann

Elevated annual average temperature has been found to impact macro-economic growth. However, various fundamental elements of the economy are affected by deviations of daily temperature from seasonal expectations which are not well reflected in annual averages. Here we show that increases in seasonally adjusted day-to-day temperature variability reduce macro-economic growth independent of and in addition to changes in annual average temperature. Combining observed day-to-day temperature variability with subnational economic data for 1,537 regions worldwide over 40 years in fixed-effects panel models, we find that an extra degree of variability results in a five percentage-point reduction in regional growth rates on average. The impact of day-to-day variability is modulated by seasonal temperature difference and income, resulting in highest vulnerability in low-latitude, low-income regions (12 percentage-point reduction). These findings illuminate a new, global-impact channel in the climate–economy relationship that demands a more comprehensive assessment in both climate and integrated assessment models.

How to cite: Kotz, M., Wenz, L., Stechemesser, A., Kalkuhl, M., and Levermann, A.: Day-to-day temperature variability reduces economic growth, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-9973, https://doi.org/10.5194/egusphere-egu21-9973, 2021.

EGU21-13189 | vPICO presentations | ITS2.16/CL3.2.19

From Micro-level Weather Shocks to Macroeconomic Impacts

Anthony Harding

Weather matters at the local level. Microeconomic climate econometric analyses find evidence that weather has localized effects on labor supply, agricultural yields, mortality rates, and other socio-economic measures. However, macroeconomic analyses at the national level find no evidence that weather affects macroeconomic aggregates, such as GDP or aggregate productivity in the US and other developed economies. These results present a seeming contradiction. In this paper, I develop a general equilibrium theoretical model of an economy with localized weather shocks to bridge the gap between microeconomic and macroeconomic studies. The theoretical model provides a simple, modular framework for aggregating weather shock impacts. I apply the findings to an empirical setting in the US, a prime example of the contradictory findings. I first estimate the microeconomic impacts of weather on labor productivity growth across county-industry pairs in the US from 2002 to 2017. I then apply these to construct annual estimates of the impact of weather shocks across the economy on US GDP according to the theoretical framework. I construct confidence intervals using the estimated microeconomic impact uncertainty. Across the sample years, I find no evidence that the annual impacts are distinct from $0. I then deconstruct the aggregate impacts, again following the theoretical framework, to examine what generates this no-effect result. I find consistent evidence of statistically significant but heterogeneous effects across a majority of counties and industries. For example, within a given year, over two-thirds of counties are consistently and significantly impacted by their local weather. This effect is positive for some counties and negative for others. I show that it is the aggregation of these heterogeneous impacts across the spatial distribution and industrial composition of the economy that masks the impact of weather. This finding highlights the importance of understanding micro-level economic impacts and changes in the composition of economic activity for projections of future macroeconomic climate change impacts.

How to cite: Harding, A.: From Micro-level Weather Shocks to Macroeconomic Impacts, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-13189, https://doi.org/10.5194/egusphere-egu21-13189, 2021.

EGU21-11981 | vPICO presentations | ITS2.16/CL3.2.19

Impact of climate change on the future of tourism areas in the Canary Islands

Judit Carrillo, Albano González, Juan C. Pérez, Francisco J. Expósito, and Juan P. Díaz

Tourism is an essential sector of the economy of the Canary Islands. Tourism Climate Index (TCI) and Holiday Climate Index (HCI) are good indicators of environmental conditions for leisure activities. Regional climate model (RCM) has been addressed to analyze the impact of climate change on the indices of tourist areas. The initial and boundary conditions for future scenarios are prescribed through three CMIP5 models (GFDL, IPSL and MIROC)  surface and lateral boundary conditions within the Meteorological Research and Forecast (WRF), with a high resolution, 3x3 km. Two time periods (2030 – 2059, and 2070-2099) and two Representative Concentration Pathways (RCPs 4.5 and 8.5) are considered. Tourism indicators are projected to improve significantly during the winter and shoulder seasons, but will worsen in the summer months, including October, in the southeast, which is where hotels are currently located.

How to cite: Carrillo, J., González, A., Pérez, J. C., Expósito, F. J., and Díaz, J. P.: Impact of climate change on the future of tourism areas in the Canary Islands, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-11981, https://doi.org/10.5194/egusphere-egu21-11981, 2021.

EGU21-15833 | vPICO presentations | ITS2.16/CL3.2.19

Promoting Sustainable Housing with Fundamental Shift beyond Net-Zero and Green Building

Biswadeep Bharali, Basanta Rajbanshi, Tashi Yangzom, Himal Dahal, Muden Rai, and Bhuwan Sewa

Net positive buildings can be the solution to slow down climate change. Old buildings and minimum code buildings only strive for structural protection, but they do not play a part in climate change mitigation solutions. In this study, we try to demonstrate net positive buildings' contribution in reducing global greenhouse gas emissions by taking the Guwahati region (India) as a study area. First, we developed a north-facing 3-B-H-K residential building plan with a two-car garage using the most commonly used construction materials in the region as a base case scenario. The weather data (like Temperature, Relative Humidity, and Airspeed) for 2020 is collected. With these inputs, the annual total energy consumption for the present climatic condition is simulated using the Ecotect tool. Then three different scenarios (modification of walls, modification of roofs, and floor modification) were created. The energy interpretation for the overall modified case was done and compared with the base case scenario. The result indicates that the total annual energy consumption for the overall modified case was reduced by 70% as compared to the base case model. The remaining 30% of the energy usage was supplied by renewable energy sources using photovoltaic cells to make net energy consumption zero.  These findings suggest that the old building can be renovated and modified to act as a mitigation solution to climate change.

How to cite: Bharali, B., Rajbanshi, B., Yangzom, T., Dahal, H., Rai, M., and Sewa, B.: Promoting Sustainable Housing with Fundamental Shift beyond Net-Zero and Green Building, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-15833, https://doi.org/10.5194/egusphere-egu21-15833, 2021.

EGU21-13698 | vPICO presentations | ITS2.16/CL3.2.19

An Agent-Based Approach for International Environmental Negotiations

Paolo Gazzotti, Andrea Castelletti, and Massimo Tavoni


International Environmental Agreements (IEAs) on greenhouse-gases (GHG) emissions reductions have demonstrated to be extremely hard to achieve. Even after the Paris Agreement, global cooperation still may be cursed by free-riding threats and the risk of withdrawals, discouraging countries from increasing their voluntary commitment. 

Several studies have already addressed the problem of agreement stability, self-enforcing strategies and coalition formation. Most of them are supported by models grounded on game theory, which account for participation rationales and address research questions about coalition-formation and optimal transfer incentives. However, diplomacy on climate change is a considerably complex problem, not exhaustively tractable by any game-theoretical framework, as it combines several deep international issues. Historical disappointments (i.e., the COP15 in Copenhagen, 2009) as well as encouraging achievements (i.e., the Paris Agreement in 2015) have also demonstrated the importance of negotiation and interaction rules in facilitating common ground for cooperation. 

Here we present an attempt to reproduce and investigate IEAs on GHGs mitigation though an Agent-Based negotiating framework. It follows a bottom-up approach, based on the insights of complex systems theory,  by modelling the behaviour of each region-representative negotiator. Single agents generate and update their mitigation proposals accounting for personal multi-objective evaluations over potential upcoming scenarios informed by Integrated Assessment Models projections, reactions to other participants proposals, and private negotiation strategies. Few and simple interaction rules, shared as common-knowledge, regulate the negotiation process and guarantee termination and agreement, although not imposing any minimum participation level. Several negotiations follow one another on regular time intervals, allowing all participants to rediscuss and modify their commitment.

Preliminary results point out the importance of agents multi-objective evaluations, as the potential co-benefit estimated may foster personal participation and satisfaction from the agreement achieved. The high flexibility provided by this Agent-Based approach allows to easily vary and test several implementations and settings, searching for the best conditions to obtain cooperation as emerging behaviour in a complex yet realistic dynamic. 

How to cite: Gazzotti, P., Castelletti, A., and Tavoni, M.: An Agent-Based Approach for International Environmental Negotiations, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-13698, https://doi.org/10.5194/egusphere-egu21-13698, 2021.

EGU21-4688 | vPICO presentations | ITS2.16/CL3.2.19 | Highlight

A strategy for achieving net-zero emissions by 2050

David Hendry and Jennifer Castle

To achieve greenhouse gas (GHG) emissions targets of net zero requires an integrated strategy to remove all fossil fuel and other GHG emitters, less natural absorption and carbon capture and storage (CCS), possibly combined with atmospheric CO2 extraction. Clean electricity generation is achievable with known technologies, but storage is essential for when renewables cannot generate power. Small modular nuclear reactors (SMRs) could help with background supply, but storage can be facilitated by decarbonizing the transport sector then using electric vehicles plugged into an intelligent vehicle-to-grid network also helping balance electricity flows. Batteries alone seem inadequate for this, so we propose supplying electric vehicles with supercapacitors using graphene-based nanotubes (GNTs) which can charge and discharge rapidly, offset by reducing costs in vehicle manufacture from eliminating catalytic convertors. GNTs could supply trains in place of diesel-electric, and are very light so help developments in electric aircraft. By ensuring continuity of renewables electricity supply, capacity can expand. This could sustain methane pyrolosis or electrolysis production of hydrogen gas when electricity demand is low, for fuel cells and to replace households’ methane use while liquid hydrogen offers a high heat source for industry. New buildings must be constructed as net zero.

Renewables electricity is fully price competitive, especially given free storage from GNT vehicles; graphene prices are falling and there may be `Moore’s laws’ for nanotube manufacture and SMRs. Hydrogen is a more expensive fuel than methane, but its production at `off-peak’ could be cost saving by sustaining 100% continuous renewables’ generation. All these developments interact and should maintain employment in new industries with real per-capita growth, while retrofitting vehicles and housing. Relevant skills already exist, from off-shoring, manufacturing and supply, through making electric engines. Taxing non-recyclable and high-carbon content products (as with plastic bags) would incentivise alternatives. The usual tools of carbon pricing, cap and trade, research support, prizes for great ideas etc., remain available.

Methane, nitrous oxide and CO2 emissions are by-products of modern food production. Ruminant emissions can be reduced by dietary changes, and nitrous oxide by reducing nitrogen fertiliser use, replacing some by basalt dust that also absorbs CO2. Animal dietary changes could be cost saving with lower feed input, as their methane production wastes energy; and mineral rich basalt dust is far cheaper than artificial fertilisers. Crop production efficiency can be greatly improved, benefitting the environment and reducing cropland, along with vertical and underground farms. Aquaculture (including seaweed production) could be greatly improved, noting that off-shore wind farms also act as marine reserves. Human dietary changes to eating less mammal meat are feasible. Pandemic responses confirm rapid adjustment is feasible.

The analysis is illustrated by the UK because it created the Industrial Revolution leading to the GHG problem; its Climate Change Act  of 2008 has markedly reduced its emissions at little aggregate cost; and we have modelled its performance in economic and climate terms.

How to cite: Hendry, D. and Castle, J.: A strategy for achieving net-zero emissions by 2050, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-4688, https://doi.org/10.5194/egusphere-egu21-4688, 2021.

EGU21-10961 | vPICO presentations | ITS2.16/CL3.2.19 | Highlight

Using an emulator to apply a Carbon Takeback Obligation alongside demand-side carbon pricing in Integrated Assessment Models

Stuart Jenkins, Eli Mitchell-Larson, Matthew Ives, Stuart Haszeldine, and Myles Allen

Integrated Assessment Model (IAM) design philosophy currently focuses on demand-side global carbon pricing as the principal policy tool to drive mitigation. However, ambitious mitigation scenarios produced with these IAMs rely heavily on the availability of carbon capture and storage (CCS) technologies in the mid-century, at the scale of billions of tonnes. If integrated assessment continues to employ demand-side policies exclusively we risk a gap forming between the requirements of economically-optimal mitigation trajectories in these IAMs and the reality of developed CCS capacity.

If CCS capacity fails to keep up with the ambition of mitigation policy, carbon prices could rise well above the cost of direct air capture as markets aim to drive residual emissions down. To avoid this, scenarios could include both supply and demand-side policies in tandem, where supply-side policies are targeted to increase CCS capacity to appropriate levels.

One such supply-side policy option is a Carbon Takeback Obligation (CTBO), where suppliers of fossil carbon are required to recapture and store an increasing fraction of the carbon in their products. This ‘stored fraction’ would be increased from near zero at present, up to 100% at the time of net-zero. By applying such a policy suppliers of fossil carbon products are forced to take responsibility for decarbonising their own products and provide the drive to develop the CCS capacity necessary to achieve net-zero emissions in the mid-century. In theory, if a CTBO was enforced globally the costs associated with the production of one tonne of CO2 would be capped around the price for the capture, transport and storage of diffuse, mobile, or otherwise hard-to-abate CO2 emission sources (i.e. the cost of direct air capture).

Here, we discuss the implementation of a global CTBO. Using an Integrated Assessment Model emulator, tuned to existing IAM carbon price/abatement rate relationships, we explore the total policy cost of applying a CTBO globally to achieve net-zero by 2050. Using the emulator we harmonise the combined CTBO and demand-side carbon price policies, and show how a SSP2-26 level of ambition can be achieved using these policies with a similar total policy cost. Further, we explore what additional near-term carbon prices can be included to achieve SSP2-19 level policy ambition. These results suggest there are significant benefits to defining climate policy around measures targeting suppliers of fossil carbon, including for long-term planning, implementation and governance of the policy, and overall cost. For further insight, and to provide a greater variety of policy options feeding into IPCC’s WG3, we argue IAMs should look to include CTBO-like policies in future scenario design.

How to cite: Jenkins, S., Mitchell-Larson, E., Ives, M., Haszeldine, S., and Allen, M.: Using an emulator to apply a Carbon Takeback Obligation alongside demand-side carbon pricing in Integrated Assessment Models, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-10961, https://doi.org/10.5194/egusphere-egu21-10961, 2021.

EGU21-11500 | vPICO presentations | ITS2.16/CL3.2.19

Improving the decision-making in DICE: self-adaptive climate policies to handle explicit uncertainty and adaptation modelling

Angelo Carlino, Massimo Tavoni, and Andrea Castelletti

DICE (Dynamic Integrated Climate Economy) and other cost-benefit integrated assessment models are used to study the economically optimal climate policy or to evaluate economic performance of alternative policies, such as 2°C compliant emission trajectories.

Recently, DICE has been updated to provide economically optimal climate policies keeping global warming in line with the Paris Agreement. Yet, explicit uncertainty and adaptation modelling are still overlooked. Introducing these components requires a transition from the traditional perfect-foresight static decision-making framework to a dynamic one, able to change strategy in order to react to the realization of uncertainties.

In this work, starting from the updates proposed by Hansel et al. (2020), we present an updated DICE model that: i) explicitly represents adaptation in the form of temporary and long-term adaptation investment; ii) explicitly describes stochastic, parametric and structural uncertainty over the physical and socio-economic components of the model including adaptation efficiency and climate damages specification; iii) leverages self-adaptive control policies to implement a more realistic decision-making scheme that allows to adjust climate policy after that new information arises.

Results show that the self-adaptive policies allow for a reduction in the discrepancy between economically optimal climate policy and the 2°C temperature target set with the Paris Agreement, which resurfaces when introducing adaptation, also in presence of uncertainty. When using self-adaptive policies, average adaptation costs remain low and, thanks to the ability to modulate adaptation choices depending on the scenario eventually unfolding, also climate damages are maintained at a low level. As a result, more economic resources are made available for mitigation in the short-term resulting in a reduced temperature increase in 2100 for a same level of welfare.

How to cite: Carlino, A., Tavoni, M., and Castelletti, A.: Improving the decision-making in DICE: self-adaptive climate policies to handle explicit uncertainty and adaptation modelling, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-11500, https://doi.org/10.5194/egusphere-egu21-11500, 2021.

ITS2.18/CL3.2.16 – The Importance of Being Global – Globally coordinated Research Infrastructures to support the UN system

EGU21-16541 | vPICO presentations | ITS2.18/CL3.2.16

GERI – The emerging Global Ecosystem Research Infrastructure

Jaana Bäck, Werner Kutsch, and Michael Mirtl

Ecosystem Research Infrastructures around the world have been designed, constructed, and are now operational as a distributed effort. The common goal is to address research questions that require long-term ecosystem observations and other service components at national to continental scales, which cannot be tackled in the framework of single and time limited projects.  By design, these Research Infrastructures capture data and provide a wider range of services including access to data and well instrumented research sites. The coevolution of supporting infrastructures and ecological sciences has developed into new science disciplines such as macrosystems ecology, whereby large-scale and multi-decadal-scale ecological processes are being explored. 

Governments, decision-makers, researchers and the public have all recognized that the global economy, quality of life, and the environment are intrinsically intertwined and that ecosystem services ultimately depend on resilient ecological processes. These have been altered and threatened by various components of Global Change, e.g. land degradation, global warming and species loss. These threats are the unintended result of increasing anthropogenic activities and have the potential to change the fundamental trajectory of mankind.  This creates a unique challenge never before faced by society or science—how best to provide a sustainable economic future while understanding and globally managing a changing environment and human health upon which it relies.

The increasing number of Research Infrastructures around the globe now provides a unique and historical opportunity to respond to this challenge. Six major ecosystem Research Infrastructures (SAEON/South Africa, TERN/Australia, CERN/China, NEON/USA, ICOS/Europe, eLTER/Europe) have started federating to tackle the programmatic work needed for concerted operation and the provisioning of interoperable data and services. This Global Ecosystem Research Infrastructure (GERI) will be presented with a focus on the involved programmatic challenges and the GERI science rationale.

How to cite: Bäck, J., Kutsch, W., and Mirtl, M.: GERI – The emerging Global Ecosystem Research Infrastructure, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-16541, https://doi.org/10.5194/egusphere-egu21-16541, 2021.

EGU21-9945 | vPICO presentations | ITS2.18/CL3.2.16 | Highlight

Geomojis – a Global Symbology for Communicating Geosciences

Claire Shires, Friederike Spitzl-Dupic, Michaël Grégoire, Dana Martin, and Benjamin van Wyk de Vries

Communicating geosciences across linguistic and cultural borders is becoming increasingly important in our globalised world, and it is as important to have a globally coherent communication as it is to have global research infrastructure. In the context of geological hazards and the geological environment, we need a clear system that enables specialists and others to communicate effectively with each other. By using symbols and pictograms to represent geohazards, we can communicate these hazards clearly and efficiently. Certain hazard symbols are already in use across the globe, such as those for chemical or environmental hazards. In this project, we focus on the geological environment and geohazards, and much of the work is done within a UNESCO Geoscience Programme project 'Geoheritage for Resilience', using geoheritage sites as sites for communication and testing. The geological pictograms, or ‘geomojis’, bridge the gap between simple symbols and words, crossing language borders by representing concepts that we have identified as particularly important for understanding geohazards and risk. Our geomojis are based on the Global Framework for Geology (see Global and Planetary Change, 2018 - https://digitalcommons.mtu.edu/michigantech-p/427), also introduced during the IUGG centenary at UNESCO. This shows the context where they fit in the Earth system. We invite feedback on the geomojis that we have created, to consolidate geoscience knowledge and create a basic standardised set of symbols for all geological hazards. This standardisation of geohazard symbols could improve communication not only between specialists and non-specialists, but between geologists themselves. The global framework and geomojis will help us to think outside the box of our specialist environment. The geohazard pictograms can be used for geoscience communication in all forms, from hazard and risk publications to signage at geological sites. They can be adapted and modified for the local context and needs, while providing a central, and global, base for comparison. We plan to use the geomojis to accompany a multilingual glossary on geological hazard and risk terminology, a project that we hope will help international geoscience communication.

How to cite: Shires, C., Spitzl-Dupic, F., Grégoire, M., Martin, D., and van Wyk de Vries, B.: Geomojis – a Global Symbology for Communicating Geosciences, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-9945, https://doi.org/10.5194/egusphere-egu21-9945, 2021.

EGU21-10337 | vPICO presentations | ITS2.18/CL3.2.16

Global Terrestrial Network of Water Resources Observation Infrastructures

Stephan Dietrich, Valentin Aich, Wouter Dorigo, Thomas Recknagel, Harald Koethe, and Simon Egglestone

Life on earth is closely linked to the availability of water and its variability. However, global change means that the demands placed on water resources are constantly increasing. According to the conclusions of the IPCC's 5th Assessment Report, it is likely that human activities have influenced the global water cycle since 1960. Satellite-based remote sensing of water-related parameters and operational data-assimilation services are becoming increasingly important to assess changes of the global water cycle as part of the essential climate variables (gcos.wmo.int). However, particularly over land or in the deep ocean where space-borne monitoring is not possible, in-situ data provide long-term records of changes in the various components of the hydrological cycle.

Global data centres, often operating under the auspices of UN agencies, collect and harmonise water data worldwide and make the global data sets available to the public again. Most of these relevant Global Data Centres are members of the Global Terrestrial Network of Hydrology (GTN-H) that operates under auspices of WMO and the Terrestrial observation Panel of Climate (TOPC) of the Global Climate Observing System GCOS. GTN-H links existing networks and systems for integrated observations of the global water cycle. The network was established in 2001 as a „network of networks“ to support a range of climate and water resource objectives, building on existing networks and data centres, and producing value-added products through enhanced communications and shared development. Since 2017 the GTN-H coordination is held by the International Centre for Water Resources and Global change (ICWRGC, operating under auspices of the UNESCO) aiming for a data and knowledge transfer between data providers, scientists and decision makers as well as between the different institutional bodies on UN-level inter alia the WMO, UNESCO, FAO, UNEP or GCOS.

We will demonstrate the state-of-the art of the global in-situ terrestrial water resources monitoring and draw a picture of a global water observation architecture.
As a major outcome we will share the most recent evaluation of global water storage and water cycle fluxes. Here, we assess the relevant land, atmosphere, and ocean water storage and the fluxes between them, including anthropogenic water use. Based on the assessment, we discuss gaps in existing observation systems and formulate guidelines for future water cycle observation strategies.

How to cite: Dietrich, S., Aich, V., Dorigo, W., Recknagel, T., Koethe, H., and Egglestone, S.: Global Terrestrial Network of Water Resources Observation Infrastructures, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-10337, https://doi.org/10.5194/egusphere-egu21-10337, 2021.

EGU21-10877 | vPICO presentations | ITS2.18/CL3.2.16

Developing Global Coordination of Solid Earth Research Infrastructures in Support of the United Nations Sustainable Development Goals. 

Lesley Wyborn, Tim Rawling, Simon Cox, Ben Evans, Simon Hodson, Jens Klump, and Steven McEachern

AuScope is Australia’s National Geoscience Research Infrastructure Program. As outlined in is 2020-2030 10-year Strategy1, AuScope seeks to provide a world-class research physical and digital infrastructure to help tackle Australia's key geoscience challenges, in particular, food and water sustainability, minerals and energy security, and mitigating impact from geohazards. These challenges tie in directly with the following United Nations (UN) Sustainable Development Goals (SDGs): SDG#6 (Clean Water and Sanitation); SDG#7 (Affordable and Clean Energy); SDG#8 (Decent Work and Economic Growth); SDG#9 (Industry, Innovation and Infrastructure); SDG#13 (Climate Action) and SDG#15 (Life on Land). 

 

The SDGs were set in 2015 by the UN General Assembly to be achieved by the year 2030. If the global research sector is to support achieving them, is a rethink required? Current practices tend to focus on building infrastructures in domain and/or national/regional and/or sector (research, government, private) and/or institutional/network silos. These are not necessarily enabling global interoperability, reuse and open sharing of data. For example, AuScope is building high-quality geoscience research data and software infrastructures that are at the heart of positioning Australia to meet these SDG challenges. Equivalent geoscience research infrastructures are also being built internationally (EPOS (Europe); EarthScope, EarthCube (USA)) and AuScope is looking for ways to interoperate more effectively with these.

 

Within the international geoscience community some interoperable networks are in place to enable global collaborations that share data and software (e.g., Earth System Grid Federation (ESGF), which develops software infrastructure for the management, dissemination, and analysis of model output and observational climate data; the Federation of Digital Seismograph Networks (FDSN) enables members to coordinate station siting and provide free and open data). However, these are the exceptions rather than the rule. 


None of the SDGs depend exclusively on geoscience data: all require integration with data from other domains, particularly from the social sciences and humanities. Some initiatives trying to assist data combination between the social sciences and the physical or environmental sciences are emerging (e.g., the Data Documentation Initiative - Cross Domain Integration (DDI-CDI)2; the CODATA/ISC Decadal programme on “Making data work for cross-domain grand challenges”3) , but traditional organizational and funding arrangements do not usually facilitate this. While there are exemplars of how to achieve integration of global domain and cross-domain research infrastructures and data sharing frameworks, we urgently need to leverage these to develop a roadmap that enables global integration of data and research infrastructures, both within the geosciences and beyond, to ensure sustainable production of data, products and services that support the realisation of the UN SDGs by 2030. In doing so, potentially the main tension will be to ensure that in enabling the broader, global transdisciplinary goals of the SDGs that deeper domain science is not compromised, scarce expertise is not misdirected, and that infrastructure developments within the domains are not unduly hampered.

1https://www.auscope.org.au/news-features/strategy-and-investment-plan-launch 

2https://ddi-alliance.atlassian.net/wiki/spaces/DDI4/pages/860815393/DDI+Cross+Domain+Integration+DDI-CDI+Review 

3https://codata.org/initiatives/strategic-programme/decadal-programme/

How to cite: Wyborn, L., Rawling, T., Cox, S., Evans, B., Hodson, S., Klump, J., and McEachern, S.: Developing Global Coordination of Solid Earth Research Infrastructures in Support of the United Nations Sustainable Development Goals. , EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-10877, https://doi.org/10.5194/egusphere-egu21-10877, 2021.

EGU21-15099 | vPICO presentations | ITS2.18/CL3.2.16

ICNet Global: Infrastructure and Climate Networks of Networks

Anne Stoner, Jennifer Jacobs, Jo Sias, Gordon Airey, and Katharine Hayhoe

Climate change is already impacting the performance and integrity of transportation infrastructure around the world and is anticipated to have serious ramifications for infrastructure safety, environmental sustainability, economic vitality, mobility and system reliability. These impacts will disproportionately affect vulnerable populations and urban locations as well as compromising the resilience of the larger interconnected physical, cyber, and social infrastructure networks. For this reason, increasing the resilience of transportation infrastructure to current and future weather and climate extremes is a global priority.

The complexity of this challenge requires a convergence approach to foster collaboration and innovation among technically and socially diverse researchers and practitioners. The multi-institutional ICNet Global Network of Networks unites domestic and international research and practice networks to facilitate integrated engineering, climate science, and policy research to advance the development of resilient transportation infrastructure and systems. ICNet Globalcollaborators represent networks based in Korea, Europe, United Kingdom, and the United States and link researchers at the forefront of scientific, engineering, and policy research frontiers, drawing expertise from many disciplines and nations to share and enhance best practices for transportation resilience.

ICNet Global’s long-term mission is to prepare the world’s existing and future transportation infrastructure for a changing climate. To that end, we are working to: (1) build a network of existing research networks who are tackling the challenges climate change poses to transportation infrastructure; (2) establish a common base-level knowledge, capacity, and vision to support the convergence of novel and diverse ideas, approaches, and technologies for creating climate resilient transportation infrastructure; and (3) grow the next generation of critical and diverse thinkers with the expertise to address and solve climate-related infrastructure challenges. Although just one year into our work, and dispite challenges represented by COVID-19, we have surveyed over 100 potential members worldwide to learn about fields of interest and held five productive virtual workshops to discuss current research, how to encorporate climate change information into engineering education, and how practitioners are currently including climate information into planning and design. In this presentation we highlight our goals and recent accomplishments while laying out future plans and inviting interested researchers and practitioners to join us.

How to cite: Stoner, A., Jacobs, J., Sias, J., Airey, G., and Hayhoe, K.: ICNet Global: Infrastructure and Climate Networks of Networks, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-15099, https://doi.org/10.5194/egusphere-egu21-15099, 2021.

EGU21-9607 | vPICO presentations | ITS2.18/CL3.2.16

Measuring countries' progress in sustainable development through network theory 

Carla Sciarra, Guido Chiarotti, Luca Ridolfi, and Francesco Laio

The Agenda 2030 of sustainable development introduced in 2015 by the United Nations is a call for action to address the major challenges the world faces [1]. To tackle these challenges, the Agenda defines the 17 Sustainable Development Goals (SDGs), which have been conceived with respect to five pillars (planet, people, prosperity, peace and partnership), thus creating synergies and trade-offs among the Goals. The Agenda also addresses the need for more targeted policy implementations, totaling 169 targets across the Goals. Moreover, indicators have been defined to measure progresses in each target, and so, Goal.

To create aggregated scores of such countries’ performance indicators is a recurrent and crucial issue within the SDGs framework, where several methodologies have been proposed to create a ranking of countries which can provide insights about the fulfillment of all of the Agenda’s objectives and principles (see, e.g., Sachs et. al. [2] and Biggeri et al. [3]). In light of the complex nature of the Agenda (as pointed out by LeBlanc [4]), we argue that the use of multidisciplinary tools is essential to help shed light on how to address efforts in global sustainable development. In particular, network theory can be used to create several aggregated scores that can actually account for the complex nature of the Agenda, the synergies and trade-offs among the Goals and, no less, of the role of countries toward the achievement of SDGs.

In this work, we recast the data concerning the performances of countries in each Goal’s indicators as the incidence matrix of a bipartite system constituted of two sets: countries and Goals, connected by the performances of countries within each Goal. We exemplify our framework using the data taken from the 2020 SDG Index and Dashboard by Sachs et al. [2]. We show that, framed within network science, the SDG Index coincides with measuring the degree centrality of countries within this bipartite system and that such measure neglects the heterogeneity of countries in tackling the Goals and their responsibilities at the global scale. More informative centrality measures, and so, aggregated scores, can be obtained by the adoption of the economic complexity theory, in particular, the GENEPY framework [5]. The GENEPY rationale defines a data-driven weighting scheme in which relative countries’ performances of all SDGs are considered to define a more comprehensive ranking of countries.

References:

[1] Transforming our world: the 2030 Agenda for Sustainable Development. Division for Sustainable Development Goals: New York, NY, USA, 2015.

[2] Sachs, J., et al. . 2020. The Sustainable Development Goals and COVID-19. Sustainable Development Report 2020. Cambridge: Cambridge University Press.

[3] Biggeri, M., et al. (2019). Tracking the SDGs in an ‘integrated’ manner: A proposal for a new index to capture synergies and trade-offs between and within goals. World Development, 122, 628-647.

[4] Le Blanc, D. (2015). Towards integration at last? The sustainable development goals as a network of targets. Sustainable Development, 23(3), 176-187.

[5] Sciarra, C., et al. (2020). Reconciling contrasting views on economic complexity. Nature Communications, 11(1), 1-10.

How to cite: Sciarra, C., Chiarotti, G., Ridolfi, L., and Laio, F.: Measuring countries' progress in sustainable development through network theory , EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-9607, https://doi.org/10.5194/egusphere-egu21-9607, 2021.

EGU21-15251 | vPICO presentations | ITS2.18/CL3.2.16

From climate models to informing policy decisions: the end-to-end importance of an effective research infrastructure

Fanny Adloff, Bryan Lawrence, Sylvie Joussaume, Michael Lautenschlager, Janette Bessembinder, Joachim Biercamp, Antonio Cofiño, Alessandro D’Anca, Uwe Fladrich, Adrian Hines, Martin Juckes, Rémi Kazeroni, Philip Kershaw, Stephan Kindermann, Paola Nassisi, Christian Pagé, Kim Serradell, and Sophie Valcke

The last few decades have seen a range of advances in climate science and consequential policy initiatives at both national and international levels. These advances have been built on the back of progress in modelling and in part been enabled by the global data sharing initiative - the Earth System Grid Federation (ESGF) - which has underpinned recent phases of the World Climate Research Programme's Coupled Model Intercomparison Projects.  

The ESGF itself consists of data nodes deployed by individual modelling centres and a backbone of software development and services delivered by a few core institutions. Within Europe, along with some shared development of model components, these core ESGF software development and services are coordinated by the European Network on Earth System Modelling (ENES) and supported by the H2020 IS-ENES Phase 3 research infrastructure project.  

We provide an historical overview on advances in policy-relevant science, such as the Intergovernmental Panel for Climate Change (IPCC), that have been enabled by long-term underpinning development and funding of the ENES and ESGF infrastructure. We illustrate the recent shift of research funding from physical science objectives alone towards funding services to society (and the necessary underpinning research). We stress the potential dangers of underfunding research infrastructures that need to be simultaneously flexible and reliable enough to serve both ongoing basic research and the growing societal objectives, as emphasised by the development of climate services such as Copernicus Climate Change Service. We conclude by presenting some steps towards sustaining such research infrastructure in the context of the ENES and the possible futures of climate science.

How to cite: Adloff, F., Lawrence, B., Joussaume, S., Lautenschlager, M., Bessembinder, J., Biercamp, J., Cofiño, A., D’Anca, A., Fladrich, U., Hines, A., Juckes, M., Kazeroni, R., Kershaw, P., Kindermann, S., Nassisi, P., Pagé, C., Serradell, K., and Valcke, S.: From climate models to informing policy decisions: the end-to-end importance of an effective research infrastructure, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-15251, https://doi.org/10.5194/egusphere-egu21-15251, 2021.

EGU21-16086 | vPICO presentations | ITS2.18/CL3.2.16

Forecasting Forced Migration by Coupling an Agent-based Simulation Approach with Weather Data

Diana Suleimenova, Alireza Jahani, Hamid Arabnejad, and Derek Groen

There are nearly 80 million people forcibly displaced worldwide, of which 26 million are refugees and 45 million are internally displaced people (IDPs) (UNHCR, 2020). It is difficult to foresee and accurately forecast forced migration trends due to the severity and instability of conflicts or crises. However, it is possible to capture relevant aspects of this complex phenomenon and propose an approach forecasting future migration trends. Hence, we present an agent-based modelling approach, namely FLEE, that predicts the distribution of incoming refugees from a conflict origin to neighbouring countries (Suleimenova et al., 2017). Our aim is to assist governments, organisations and NGOs to efficiently allocate humanitarian resources, manage crises and save lives.

To construct a forced migration model, we obtain relevant data from three sources: the United Nations High Commissioner for Refugees (UNHCR, https://data2.unhcr.org) providing the number of forcibly displaced people in the conflict, the camp locations in neighbouring countries and their population capacities; the Armed Conflict Location and Event Data Project (ACLED, https://acled-data.com) for conflict locations and dates of battles; and the OpenStreetMaps platform (https://openstreetmap.org) to geospatially interconnect camp and conflict locations with other major settlements that reside en-route between these locations. Consequently, we simulate the constructed model using the FLEE code (https://github.com/djgroen/flee-release) and obtain the distribution of incoming forced displacement across destination camps. We were able to reproduce key trends in refugee counts found in the UNHCR data across Burundi, Central African Republic and Mali (Suleimenova et al., 2017), as well as investigated the impact of policy decisions, such as camp and border closures, in the South Sudan conflict (Suleimenova and Groen, 2020).

In our recent collaboration with Save the Children, we focus on an ongoing conflict in Ethiopia’s Tigray region and forecast IDP numbers within the region and refugee arrival counts in Sudan. We found that the number of arrivals in Sudan seem to depend strongly on whether the conflict will erupt in the east or in the west of Tigray. This seems to be a larger factor than the actual intensity of the conflict.

Moreover, our modelling approach allows us to investigate possible effects of weather conditions on forcibly displaced people by coupling FLEE with precipitation data, seasonal flood and river discharge levels. The purpose of coupling with the European Centre for Medium-Range Weather Forecasts (ECMWF) data is to identify the effect of weather conditions on the behaviour and movement speed of forced migrants.

The overall strategy is the static coupling of weather data where we have analysed 40 years of precipitation data for South Sudan to identify the precipitation range (minimum and maximum levels) as triggers which by the agents’ movement speed changes accordingly. Besides, we have used daily river discharge data from Global flood forecasting system (GloFAS) to explore the threshold for closing the link considering values of river discharge for return periods of 2, 5 and 20 years. Currently, we only use a simple rule with one threshold to define the river distance for a given link, which we aim to investigate further.

References
1. UNHCR (2020). Figures at a Glance, Available at: https://www.unhcr.org/figures-at-a-glance.html.
2. Suleimenova D., Bell D. and Groen D. (2017) “A generalized simulation development approach for predicting refugee destinations”. Scientific Reports 7:13377. (https://doi.org/10.1038/s41598-017-13828-9).
3. Suleimenova D. and Groen D. (2020) “How policy decisions affect refugee journeys in SouthSudan: A study using automated ensemble simulations”. Journal of Artificial Societies and Social Simulation 23(1)2, pp. 1-17. (https://doi.org/10.18564/jasss.4193).

How to cite: Suleimenova, D., Jahani, A., Arabnejad, H., and Groen, D.: Forecasting Forced Migration by Coupling an Agent-based Simulation Approach with Weather Data, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-16086, https://doi.org/10.5194/egusphere-egu21-16086, 2021.

EGU21-16236 | vPICO presentations | ITS2.18/CL3.2.16

EMODnet Chemistry new and consolidated large scale cooperation actions for 2020 and beyond

Alessandra Giorgetti, Chiara Altobelli, François Galgani, Georg Hanke, Neil Holdsworth, Hans Mose Jensen, Dominique Obaton, Maria Eugenia Molina Jack, Elena Partescano, Benjamin Pfeil, Sylvie Pouliquen, Dick Schaap, and Matteo Vinci

EMODnet Chemistry is one of the seven thematic portals of EMODnet (European Marine Observation and Data Network), the long-term initiative aiming to ensure that European marine data are findable, accessible, interoperable and re-usable. EMODnet was launched by DG MARE in 2009 as the pillar of the Blue Growth strategy, Marine Knowledge 2020.

Eutrophication (e.g. nutrients, oxygen and chlorophyll), contaminants (e.g. hydrocarbons, pesticides, heavy metals, antifoulants) and marine litter (e.g. beach litter, seafloor litter and floating micro litter) are the main categories of quality assured marine data sets and data products made available through the EMODnet Chemistry portal.

45 marine research and monitoring institutes and oceanographic data management experts from 30 countries comprise the EMODnet Chemistry network, including National Oceanographic Data Centres (NODC), National Environmental Monitoring Agencies and Marine Research Institutes actively involved in managing, processing and providing access to data sets from European marine waters and global oceans.

During 2020 EMODnet Chemistry consolidated fundamental international collaborations and upgraded cooperation actions on the European and global level to share and harmonize data, knowledge and services, following decision-makers’ needs to implement EU directives, such as MSFD, MSPD, INSPIRE directive, and the Agenda 2030 Sustainable Development Goals of the United Nations

Main EMODnet Chemistry 2020 transnational cooperation actions are:

  • The MSFD Technical Group on Marine Litter used the EMODnet Chemistry Marine Litter Database to compute the EU beach litter quantitative Baselines and Threshold values.
  • The European Environment Agency confirmed the use of EMODnet Chemistry data for three environmental state indicators relating to eutrophication and contaminants.
  • Mercator Ocean International and EMODnet Chemistry set up the first joint portfolio of products in support of the MSFD implementation. The two partners are also exploring opportunities to support the aquaculture sector.
  • EMODnet -Chemistry and the In Situ Thematic Assembly Centre of the Copernicus Marine Environment Monitoring Service (CMEMS INSTAC) collaborated with ENVRI Marine European Research Infrastructures (Euro-Argo, EMSO, ICOS, Lifewatch and SeaDataNet) to enhance FAIRness of in situ data.
  • Mercator Ocean international, UNDESA, SULITEST NGO and EMODnet Chemistry have been creating an awareness questionnaire to raise awareness on the Goal 14 of the UN Agenda 2030 for Sustainable Development.
  • The EU asked EMODnet Chemistry to share its experience at the G20 workshop on harmonized monitoring and data compilation of marine plastic litter organized by the Ministry of the Environment, Japan.
  • The international Oxygen data portal and Ocean Acidification data portal received contributions from EMODnet Chemistry and CMEMS in situ TAC for their implementation.
  • The National Marine Data and Information Service of China collaborates with EMODnet to strengthen international ocean data through the EMOD-PACE project.

How to cite: Giorgetti, A., Altobelli, C., Galgani, F., Hanke, G., Holdsworth, N., Jensen, H. M., Obaton, D., Molina Jack, M. E., Partescano, E., Pfeil, B., Pouliquen, S., Schaap, D., and Vinci, M.: EMODnet Chemistry new and consolidated large scale cooperation actions for 2020 and beyond, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-16236, https://doi.org/10.5194/egusphere-egu21-16236, 2021.

EGU21-13106 | vPICO presentations | ITS2.18/CL3.2.16

The Integrated Ocean Carbon Observing System

Richard Sanders and Andrew Watson

The Oceans have taken up 20-25% of the carbon dioxide released to the atmosphere by human activities, in the process slowing the rate of climate change and giving us more time to adapt to and mitigate the effects of global warming. However this ‘sink’ has not been stable over the recent past and there is therefore a need to measure it in near real time with higher confidence than currently possible so that appropriate policy measures can be developed and implemented in response to any change. We have a wide array of tools including satellites, ship based and autonomous (gliders, moored, floats and surface vehicles) measuring systems which together with the associated data infrastructure can demonstrably come together to deliver this vision. These have largely been developed under short-term funding streams and, as a consequence do not currently deliver the robust, near real time, sustainable estimate of ocean C uptake that we believe is necessary to support international climate negotiations and the development of adaptation/mitigation strategies. We are currently developing a blueprint for the ‘Integrated Ocean Carbon Observing System’ which we believe will be as necessary for reliably forecasting climate over the next 5-10 years as meteorological observations currently are for forecasting weather over the next 5-10 days. In this contribution we will describe the key elements of this blueprint and outline a timeline for assembling them together to deliver an annual near realtime databased estimate of ocean carbon uptake to the annual COP in support of international climate negotiations.

How to cite: Sanders, R. and Watson, A.: The Integrated Ocean Carbon Observing System, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-13106, https://doi.org/10.5194/egusphere-egu21-13106, 2021.

ITS3.1/NP0.1 – Tipping Points in the Earth System

EGU21-4961 | vPICO presentations | ITS3.1/NP0.1

A global decadal mode in a high-end climate model and in observations: Any connection to the solar cycle?

Michael Ghil, Yizhak Feliks, and Justin Small

The present work addresses two persistent quandaries of the climate sciences: (i) the existence of global oscillatory modes in the coupled ocean–atmosphere system; and (ii) solar effects on this coupled system. Interannual oscillatory modes, atmospheric and oceanic, are present in several large regions of the globe. We examine here interannual-to-decadal variability over the entire globe in the Community Earth System Model (CESM) and in the NCEP-NCAR reanalysis, and apply multichannel singular spectrum analysis (MSSA) to these two datasets.

In the fully coupled CESM1.1 model, with its resolution of 0.1 × 0.1 degrees in the ocean and 0.25 × 0.25 degrees in the atmosphere, the fields analyzed are surface temperatures, sea level pressures and  the 200-hPa geopotential. The simulation is 100-yr long and the last 66 yr are used in the analysis. The two statistically significant periodicities in this IPCC-class model are 11 and 3.4 yr.

In the reanalysis, the fields of sea level pressure and of 200-hPa geopotential are analyzed at its resolution of 2.5 × 2.5 degrees over the 68-yr interval 1949–2016. Oscillations with periods of 12 and 3.6 yr are found to be statistically significant in this dataset. The spatio-temporal patterns  of the oscillations in the two datasets are quite similar. The spatial pattern of these  global oscillations over the North Pacific and North Atlantic resemble the Pacific Decadal Oscillation and the interannual variability found in the western North Atlantic, respectively.

The two global modes, with their 11–12-yr and 3.4–3.6-yr periodicities, are quite robust, suggesting potential contributions of both to predictability at 1–3-yr horizons. On the other hand, the CESM run has no year-to-year changes in the prescribed insolation, excluding any role of the solar cycle in the model’s 11-yr mode. The solar cycle is present, however, in the reanalysis, since it is present in nature and hence it does affect the observations. We speculate, therefore, that regional oscillations — with their distinct near-periodicities and spatial patterns — are synchronized  over the globe, thus yielding both the global oscillatory modes found in CESM. In nature, the decadal mode could be further synchronized with the solar cycle, but that does not seem to be the case, given the slight difference in period — 12 yr for the reanalysis and 11 yr for the solar cycle, which makes them drift in and out of phase.

The work’s tentative conclusion is, therefore: (i) yes, there are global oscillatory modes in the climate system, especially a decadal mode; but (ii) no, this mode has little or nothing to do with the solar cycle.

How to cite: Ghil, M., Feliks, Y., and Small, J.: A global decadal mode in a high-end climate model and in observations: Any connection to the solar cycle?, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-4961, https://doi.org/10.5194/egusphere-egu21-4961, 2021.

EGU21-239 | vPICO presentations | ITS3.1/NP0.1

Stochastic modelling of stratospheric temperature

Mari Eggen, Kristina Rognlien Dahl, Sven Peter Näsholm, and Steffen Mæland

A stochastic model for daily-spatial mean stratospheric temperature over a given area is suggested. The model is a sum of a deterministic seasonality function and a Lévy driven vectorial Ornstein-Uhlenbeck process, which is a mean-reverting stochastic process. More specifically, the model is an order 4 continuous-time autoregressive (CAR(4)) process, derived from data analysis suggesting an order 4 autoregressive (AR(4)) process to model the deseasonalized stochastic temperature data empirically. In this analysis, temperature data as represented in ECMWF re-analysis model products are considered. The residuals of the AR(4) process turn out to be normal inverse Gaussian distributed random variables scaled with a time dependent volatility function. In general, it is possible to show that the discrete time AR(p) process is closely related to CAR(p) processes, its continuous counterpart. An equivalent effort is made in deriving a dual stochastic model for stratospheric temperature, in the sense that the year is divided into summer and winter seasons. However, this seems to further complicate the modelling, rather than obtaining a simplified analytic framework. A stochastic characterization of the stratospheric temperature representation in model products, such as the model proposed in this paper, can be used in geophysical analyses to improve our understanding of stratospheric dynamics. In particular, such characterizations of stratospheric temperature may be a step towards greater insight in modelling and prediction of large-scale middle atmospheric events like sudden stratospheric warmings. Through stratosphere-troposphere coupling, this is important in the work towards an extended predictability of long-term tropospheric weather forecasting.

How to cite: Eggen, M., Rognlien Dahl, K., Näsholm, S. P., and Mæland, S.: Stochastic modelling of stratospheric temperature, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-239, https://doi.org/10.5194/egusphere-egu21-239, 2021.

Forced-dissipative beta-plane turbulence in a single-layer shallow-water fluid has been widely considered as a simplified model of planetary turbulence as it exhibits turbulence self-organization into large-scale structures such as robust zonal jets and strong vortices. In this study we perform a series of numerical simulations to analyze the characteristics of the emerging structures as a function of the planetary vorticity gradient and the deformation radius. We report four regimes that appear as the energy input rate ε of the random stirring that supports turbulence in the flow increases. A homogeneous turbulent regime for low values of ε, a regime in which large scale Rossby waves form abruptly when ε passes a critical value, a regime in which robust zonal jets coexist with weaker Rossby waves when ε passes a second critical value and a regime of strong materially coherent propagating vortices for large values of ε. The wave regime which is not predicted by standard cascade theories of turbulence anisotropization and the vortex regime are studied thoroughly. Wavenumber-frequency spectra analysis shows that the Rossby waves in the second regime remain phase coherent over long times. The coherent vortices are identified using the Lagrangian Averaged Deviation (LAVD) method. The statistics of the vortices (lifetime, radius, strength and speed) are reported as a function of the large scale parameters. We find that the strong vortices propagate zonally with a phase speed that is equal or larger than the long Rossby wave speed and advect the background turbulence leading to a non-dispersive line in the wavenumber-frequency spectra.

How to cite: Bakas, N.: Waves, jets and vortices: Regime transitions in shallow water turbulence, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-1025, https://doi.org/10.5194/egusphere-egu21-1025, 2021.

EGU21-14427 | vPICO presentations | ITS3.1/NP0.1

Time-scale dependence of climate-carbon cycle feedbacks for weak perturbations in CMIP5 models

Guilherme Torres Mendonça, Julia Pongratz, and Christian Reick

The increase in atmospheric CO2 driven by anthropogenic emissions is the main radiative forcing causing climate change. But this increase is not only a result from emissions, but also from changes in the global carbon cycle. These changes arise from feedbacks between climate and the carbon cycle that drive CO2 into or out of the atmosphere in addition to the emissions, thereby either accelerating or buffering climate change. Therefore, understanding the contribution of these feedbacks to the global response of the carbon cycle is crucial in advancing climate research. Currently, this contribution is quantified by the α-β-γ framework (Friedlingstein et al., 2003). But this quantification is only valid for a particular perturbation scenario and time period. In contrast, a recently proposed generalization (Rubino et al., 2016) of this framework for weak perturbations quantifies this contribution for all scenarios and at different time scales. 

Thereby, this generalization provides a systematic framework to investigate the response of the global carbon cycle in terms of the climate-carbon cycle feedbacks. In the present work we employ this framework to study these feedbacks and the airborne fraction in different CMIP5 models. We demonstrate (1) that this generalization of the α-β-γ framework consistently describes the linear dynamics of the carbon cycle in the MPI-ESM; and (2) how by this framework the climate-carbon cycle feedbacks and airborne fraction are quantified at different time scales in CMIP5 models. Our analysis shows that, independently of the perturbation scenario, (1) the net climate-carbon cycle feedback is negative at all time scales; (2) the airborne fraction generally decreases for increasing time scales; and (3) the land biogeochemical feedback dominates the model spread in the airborne fraction at all time scales. This last result therefore emphasizes the need to improve our understanding of this particular feedback.

References:

P. Friedlingstein, J.-L. Dufresne, P. Cox, and P. Rayner. How positive is the feedback between climate change and the carbon cycle? Tellus B, 55(2):692–700, 2003.

M. Rubino, D. Etheridge, C. Trudinger, C. Allison, P. Rayner, I. Enting, R. Mulvaney, L. Steele, R. Langenfelds, W. Sturges, et al. Low atmospheric CO2 levels during the Little Ice Age due to cooling-induced terrestrial uptake. Nature Geoscience, 9(9):691–694, 2016.

How to cite: Torres Mendonça, G., Pongratz, J., and Reick, C.: Time-scale dependence of climate-carbon cycle feedbacks for weak perturbations in CMIP5 models, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-14427, https://doi.org/10.5194/egusphere-egu21-14427, 2021.

EGU21-10231 | vPICO presentations | ITS3.1/NP0.1

Causal discovery in climate research: Overview and recent progress

Jakob Runge and Andreas Gerhardus

Discovering causal dependencies from observational time series datasets is a major problem in better understanding the complex dynamical system Earth. Recent methodological advances have addressed major challenges such as high-dimensionality and nonlinearity (PCMCI, Runge et al. Sci. Adv. 2019), as well as instantaneous causal links (PCMCI+, Runge UAI, 2020) and hidden variables (LPCMCI, Gerhardus and Runge, 2020), but many more remain. In this presentation I will give an overview of challenges and methods and present a recent approach, Ensemble-PCMCI, to analyze ensembles of climate time series. An example for this are initialized ensemble forecasts. Since the individual samples can then be created from several time series instead of different time steps from a single time series, such cases allow to relax the assumption of stationarity and hence to analyze whether and how the underlying causal relationships change over time. We compare Ensemble-PCMCI to other methods and discuss preliminary applications.

Runge et al., Detecting and quantifying causal associations in large nonlinear time series datasets, Science Advances eeaau4996 (2019).

Runge, J. Discovering contemporaneous and lagged causal relations in autocorrelated nonlinear time series datasets. Proceedings of the 36th Conference on Uncertainty in Artificial Intelligence, UAI 2020, Toronto, Canada, 2019, AUAI Press, 2020

Gerhardus, A. & Runge, J. High-recall causal discovery for autocorrelated time series with latent confounders. Advances in Neural Information Processing Systems, 2020, 33

How to cite: Runge, J. and Gerhardus, A.: Causal discovery in climate research: Overview and recent progress, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-10231, https://doi.org/10.5194/egusphere-egu21-10231, 2021.

EGU21-12462 | vPICO presentations | ITS3.1/NP0.1

Persistent Homology, Regimes and Climate Data

Kristian Strommen, Nina Otter, Matthew Chantry, and Joshua Dorrington

The concept of weather or climate 'regimes' have been studied since the 70s, to a large extent because of the possibility they offer of truncating complicated dynamics to vastly simpler, Markovian, dynamics. Despite their attraction, detecting them in data is often problematic, and a unified definition remains nebulous. We argue that the crucial common feature across different dynamical systems with regimes is the non-trivial topology of the underlying phase space. Such non-trivial topology can be detected in a robust and explicit manner using persistent homology, a powerful new tool to compute topological invariants in arbitrary datasets. We show some state of the art examples of the application of persistent homology to various non-linear dynamical systems, including real-world climate data, and show how these techniques can shed light on questions such as how many regimes there really are in e.g. the Euro-Atlantic sector. Future directions are also discussed.

How to cite: Strommen, K., Otter, N., Chantry, M., and Dorrington, J.: Persistent Homology, Regimes and Climate Data, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-12462, https://doi.org/10.5194/egusphere-egu21-12462, 2021.

EGU21-5457 | vPICO presentations | ITS3.1/NP0.1

Unstable Periodic Orbits Sampling and Its Applications to Climate Models

Chiara Cecilia Maiocchi and Valerio Lucarini

Climate can be interpreted as a complex, high dimensional non-equilibrium stationary system characterised by multiple time and space scales spanning various orders of magnitude. Statistical mechanics and dynamical system theory have been key mathematical frameworks in the study of the climate system. In particular, unstable periodic orbits (UPOs) have been proven to provide relevant insight in the understanding of its statistical properties. In a recent paper Lucarini and Gritsun [1] provided an alternative approach for understanding the properties of the atmosphere.

In general, UPOs decomposition plays a relevant role in the study of chaotic dynamical systems. In fact, UPOs densely populate the attractor of a chaotic system, and can therefore be thought as building blocks to construct the dynamic of the system itself. Since they are dense in the attractor, it is always possible to find a UPO arbitrarily near to a chaotic trajectory: the trajectory will remain close to the UPO, but it will never follow it indefinitely, because of its instability. Loosely speaking, a chaotic trajectory is repelled between neighbourhoods of different UPOs and can thus be approximated in terms of these periodic orbits. The statistical properties of the system can then be reconstructed from the full set of periodic orbits in this fashion.

The numerical study of UPOs thus represents a relevant problem and an interesting research topic for Climate Science and chaotic dynamical systems in general. In this presentation we address the problem of sampling UPOs for the paradigmatic Lorenz-63 model. First, we present results regarding the measure of the system, thus its statistical properties, using UPOs theory, namely with the trace formulas. Second, we introduce a more innovative approach, considering UPOs as global states of the system. We approximate the exact dynamics by a suitable Markov chain process, describing how the system hops on different UPOs, and we compare the two different approaches.  

[1] V. Lucarini and A. Gritsun, “A new mathematical framework for atmospheric blocking events,” Climate Dynamics, vol. 54, pp. 575–598, Jan 2020.

How to cite: Maiocchi, C. C. and Lucarini, V.: Unstable Periodic Orbits Sampling and Its Applications to Climate Models, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-5457, https://doi.org/10.5194/egusphere-egu21-5457, 2021.

EGU21-4643 | vPICO presentations | ITS3.1/NP0.1

Multi-variate factor separation of numerical simulations

Dan Lunt, Deepak Chandan, Gavin Schmidt, Jonty Rougier, and George Lunt

Factor separation is widely used in the analysis of numerical simulations.  It allows changes in properties of a system to be attributed to changes in multiple variables associated with that system.  There are many possible factor separation methods; here we discuss three previously-proposed methods that have been applied in the field of climate modelling: the linear factor separation, the Stein and Alpert (1993) factor separation, and the Lunt et al (2012) factor separation.  We show that, when more than two variables are being considered, none of these three methods possess all four properties of 'uniqueness', 'symmetry', 'completeness', and 'purity'.  Here, we extend each of these methods so that they do possess these properties for any number of variables, resulting in three factor separation methods -- the 'linear-sum' , the 'shared-interaction', and the 'scaled-total'.  We show that the linear-sum method and the shared-interaction method reduce to be identical in the case of four or fewer variables, and we conjecture that this holds for any number of variables.  We present the results of the factor separations in the context of studies that used the previously-proposed methods.  This reveals that only the linear-sum/shared-interaction factor separation method possesses a fifth property -- `boundedness', and as such we recommend the use of this method in applications for which these properties are desirable.   The work described here is in review in Geoscientific Model Development - see https://gmd.copernicus.org/preprints/gmd-2020-69 .

How to cite: Lunt, D., Chandan, D., Schmidt, G., Rougier, J., and Lunt, G.: Multi-variate factor separation of numerical simulations, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-4643, https://doi.org/10.5194/egusphere-egu21-4643, 2021.

The analysis of nonlinear and nonstationary processes is, in general, a challenging task.
One way to tackle it is to first decompose the signal into simpler components and then analyze them separately. This is the idea behind the Empirical Mode Decomposition (EMD) method, published originally in 1998. EMD had a big impact in many filed of research as testified by the more than 15300 citations (based on Scopus). However, the mathematical properties of EMD and its generalizations, like the Ensemble EMD, are still under investigation. For this reason an alternative technique, called Iterative Filtering (IF), was proposed in 2009.

In this talk we introduce the IF method and present new insights in its mathematical properties. In particular, we show its robustness to noise, its ability to avoid mode mixing, and its speed up in what is called the Fast Iterative Filtering (FIF).
Both IF and FIF have been extened to handle multivariate and multidimensional data sets, outperforming, in terms of computational time, any alternative method proposed so far in the literature for the decomposition of nonstationary signals.

This is a joint work with H. Zhou (Georgia Tech).

How to cite: Cicone, A. and Zhou, H.: Fast Iterative Filtering: a new, fast and robust decomposition method for nonlinear and nonstationary processes, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-3303, https://doi.org/10.5194/egusphere-egu21-3303, 2021.

EGU21-6114 | vPICO presentations | ITS3.1/NP0.1

Consistent Modelling of Non-Equilibrium Thermodynamic Processes in the Atmosphere

Paul Bowen and John Thuburn

Approximations in the moist thermodynamics of atmospheric/weather models are often inconsistent. Different parts of numerical models may handle the thermodynamics in different ways, or the approximations may disagree with the laws of thermodynamics. In order to address these problems we may derive all relevant thermodynamic quantities from a defined thermodynamic potential; approximations are then instead made to the potential itself --- this guarantees self-consistency. This concept is viable for vapor and liquid water mixtures in a moist atmospheric system using the Gibbs function but on extension to include the ice phase an ambiguity exists at the triple-point. In order to resolve this the energy function must be used instead; constrained maximisation methods may be used on the entropy in order to solve the system equilibrium state. Once this is done however, a further extension is necessary for atmospheric systems. In the Earth's atmosphere many important non-equilibrium processes take place; for example, freezing of super-cooled water, evaporation, and precipitation. To fully capture these processes the equilibrium method must be reformulated to involve finite rates of approach towards equilibrium. This may be done using various principles of non-equilibrium thermodynamics, principally Onsager reciprocal relations. A numerical scheme may then be implemented which models the competing finite processes in a moist thermodynamic system.

How to cite: Bowen, P. and Thuburn, J.: Consistent Modelling of Non-Equilibrium Thermodynamic Processes in the Atmosphere, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-6114, https://doi.org/10.5194/egusphere-egu21-6114, 2021.

Earth’s long-term carbonate-silicate cycle is continuously perturbed by processes of mountain building and erosion. Mountain uplift near convergent plate boundaries causes steep slopes, which in turn imply high rates of continental erosion. Erosion rates ultimately affect the weatherability and thereby the regulation of Earth’s climate. Using a simple 1D-model that includes the outlines processes, I investigate the resulting climate oscillations over timescales from thousands to millions of years. With a simple model of the long-term carbon cycle that includes biological enhancement of weathering and marine biogenic calcite precipitation, I study the role of Earth’s biosphere in damping these oscillations [1]. I show that both mechanisms play a role: Biological enhancement of weathering damps oscillations mainly on timescales > 1 Ma and marine calcification mainly on shorter timescales. Altogether, the results indicate that Earth’s biosphere contributes to a stable climate over a wide range of timescales.

In the context of anthropogenic emissions, a dramatic elevation in the atmospheric CO2 and related temperature is known to damage Earth’s biosphere [2] and may even trigger runaway processes [3]. The results presented here indicate that a damaged biosphere may furthermore cause the Earth system to react more sensitive to oscillations from geological forcing and may also affect climate recovery.

References:

[1] Höning 2020, Geochem. Geophys. Geosyst. 21(9), e2020GC009105
[2] Sully et al. 2019, Nat. Comm. 10, 1264
[3] Lenton 2013, Annu. Rev. Environ. Resour. 38, 1-29

How to cite: Höning, D.: Climate Oscillations from Mountain Uplift and Erosion are Damped by Bioactivity, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-13627, https://doi.org/10.5194/egusphere-egu21-13627, 2021.

Landslides are common in the mid-hill region of Nepal where the terrain slopes are steep and consist of fragile geo-morphology. In Nepal, the casual and triggering factors of the landslides are respectively the underlying geology, intense rainfall and unplanned construction of rural roads is highly recognized, which is however less known and limited in study. Establishment of rainfall threshold for landslides at the watershed landscape is data driven, which is scared in the context of Nepal. The only available long term daily rainfall and sparsely available historical landslides date has been used to develop the rainfall threshold model for the two watersheds in central and western mid-hill regions respectively the Sindhukhola and Sotkhola in Bagmati and Karnali Provinces of Nepal. The watersheds are located in two distinct hydro-climatic regions in terms of rainfall amount and intensity. Historical daily (monsoonal) rainfall data of over four decades (1970-2016) were analyzed available from the Department of Hydrology and Meteorology (DHM)/Government of Nepal and five days’ antecedent rain was calculated. With the limitedly available temporal landslides data, correlation was examined among the 5-days antecedent rain (mm/5days) and daily rainfall (mm/day) portrayed the rainfall threshold (RT) model (Sindhukhola=180-1.07RT5adt and Sotkhola = 110-0.83*RT5adt). Utilizing the five days’ antecedent rain fitted into the model, results the threshold rainfall. Deducting the five days’ antecedent rains to the RT described the threshold exceedance (R) for the landslides. The model can be plotted in simple spreadsheet (landslides date in Y-axis and threshold exceedance R in X-axis) to visualize the changes in the threshold exceedance over time, whenever the threshold exceedance progressively and rapidly increased and crossed the threshold line and reached to the positive (> 0) zone, the plots allows for the landslides warning notice. In case of the threshold exceedance is further increased there is likely to have landslides in the watersheds. The model was validated with the 35 dated landslides recorded in monsoon 2015 in Sotkhola watershed. The result indicated that the model preserves 72% coefficient of determination (R2) where there were landslides in the watershed during 2015 monsoon. Due to the simplicity and at the data scarce situation, the model was found to be useful to forecast the landslides during the monsoon season in the region. The model; however, can be improved for better performance whenever the higher resolution time-series landslides data and automated weather stations are available in the watersheds. Linking this model to the proper landslide susceptibility map, and the real time rainfall data through mobile communication techniques, landslide early warning system can be established.

KEYWORDS: landslide, rainfall threshold, data-scare, antecedent rainfall

References:

Aleotti, P. (2004). A warning system for rainfall-induced shallow failures. Engineering Geology, 73(3-4), 247-265.

Jaiswal, P. and van Westen, C.J., 2009. Estimating temporal probability for landslide initiation along transportation routes based on rainfall thresholds. Geomorphology, 112(1-2): 96-105.

Acknowledgement: Comprehensive Disaster Risk Management Programme – UNDP in Nepal for the opportunity to conduct this research.

How to cite: Devkota, S., Kc, D., Jaboyedoff, M., and Acharya, G.: Development of Rainfall threshold model for the watershed/sub-watershed landscape at data scarce situation – a case study of mid-hill region, Nepal., EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-4797, https://doi.org/10.5194/egusphere-egu21-4797, 2021.

EGU21-1036 | vPICO presentations | ITS3.1/NP0.1

Extratropical low-frequency variability with ENSO forcing: a reduced-order coupled model study  

Stéphane Vannitsem, Jonathan Demaeyer, and Michael Ghil

The impact of the El Niño-Southern Oscillation (ENSO) on the extratropics is investigated in an idealized, reduced-order model that has a tropical and an extratropical module. Unidirectional forcing is used to mimic the atmospheric bridge between the tropics and the extratropics. The variability of the coupled ocean--atmosphere extratropical module is then investigated through the analysis of its pullback attractors (PBA). This analysis focuses on two ENSO-type forcings generated by the tropical module, one periodic and one aperiodic.

 

For a substantial range of coupling parameters, multiple chaotic PBAs are found to coexist for the same set of parameter values. Different types of extratropical low-frequency variability are associated with each PBA over the parameter ranges explored. For periodic ENSO forcing, the coexisting PBAs are nonlinearly stable, while for the chaotic forcing, they are unstable and certain extratropical perturbations induce transitions between the PBAs. These distinct stability properties may have profound consequences for extratropical climate predictions, provided they are confirmed by studies using comprehensive climate models. Thus, for instance, ensemble averaging may no longer be a valid approach to isolate the low-frequency variability signal.

How to cite: Vannitsem, S., Demaeyer, J., and Ghil, M.: Extratropical low-frequency variability with ENSO forcing: a reduced-order coupled model study  , EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-1036, https://doi.org/10.5194/egusphere-egu21-1036, 2021.

EGU21-1996 | vPICO presentations | ITS3.1/NP0.1

Is 2020 super Meiyu a result of changing annual cycle of East Asian monsoon?

Mengqian Lu, Mengxin Pan, Lun Dai, and Tat Fan Cheng

2020 was exceedingly difficult for humans. As the world was experienced surge waves of COVID19, East Asia was also facing a one in a century, record-breaking flood,  as the result of a super 47-day Meiyu/Baiu stage of East Asian summer monsoon. As East Asian monsoons (EAM) follow a yearly cyclical pattern, we wonder which stage(s) were collateral damages of the extended Meiyu. Was it an early termination of the anomalous dry Spring, or was it a delayed northward propagation of the rain belt, i.e. late Mid-summer? The hypothesis stems from our recent finding (Dai et al., 2020) that the duration of the Spring stage is informative for the onset of Meiyu, while the duration of Meiyu is negatively correlated with that of Mid-summer, i.e., the longer the Meiyu, the shorter the Mid-summer. To verify this, we first positioned the 2020 pre-Meiyu, Meiyu, Mid-summer stages in the 40-year climatology annual cycle (Dai et al., 2020). Although neither the onset nor the termination was beyond the 40-year variance, Meiyu indeed hastened to arrive but postponed its departure. Rain belt stalled over the Yangtze river basin and southern Japan since mid-June; until the end of July, a planetary-scale anomalous high pressure band was in place encompassing the Arabian sea and north Pacific. It hindered the South Asian monsoonal flow to the South China Sea, curbing the northward propagation of the rain belt with assistance by both southeast-ward shift of South Asian High and lower level high pressure system persistent over the northern China. With these observations, we put forward a framework of ocean-atmosphere coupled mechanisms that traces back to the summer in 2019, and reveal the climate teleconnection and circulation systems that pave the road to the 2020 super Meiyu. With this study, we address the question of whether the 2020 super Meiyu was a “black swan” or a manifestation of ongoing systematic changes of the EAM annual cycle?

 

 

 

Reference

Dai, L., Cheng, T. F., & Lu, M. (2020). Define East Asian monsoon annual cycle via a self‐organizing map‐based approach. Geophysical Research Letters, 47. e2020GL089542. https://doi.org/10.1029/2020GL089542

How to cite: Lu, M., Pan, M., Dai, L., and Cheng, T. F.: Is 2020 super Meiyu a result of changing annual cycle of East Asian monsoon?, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-1996, https://doi.org/10.5194/egusphere-egu21-1996, 2021.

EGU21-1556 | vPICO presentations | ITS3.1/NP0.1

Carbon cycle response to Dansgaard-Oeschger events in a freely oscillating LGM setup of the Community Earth System Model.

Markus Jochum, Zanna Chase, Roman Nutermn, Joel Pedro, Sune Rasmussen, and Guido Vettoretti

We use a LGM setup of the CESM with marine and terrestrial biogeochemistry. This free-running  set-up (i.e., no freshwater hosing) exhibts Dansgaard-Oeschger events and Antarctic Isotope Maxima with time-lags and amplitudes that are consistent with paleo reconstructions. The CO2 signal associated DO events is also consistent with reconstructions: a 10 ppm/kyr increase during stadials, with the increase continuing some 400 years after Antarctica has started to cool again. An analysis of the modelled air-sea/land carbon fluxes reveals that some 3ppm of the stadial increase are due to shifting rain and temperature patterns that reduce growth of land vegetation. This adjustment is largely concluded after 3 centuries. The remainder of the signal is due to reduced ocean uptake. It turns out that reduced subduction of carbon in the Southern Ocean is mostly compensated by reduced upwelling in the equatorial oceans. Thus, as found in previous studies, much of the extra carbon is due to reduced uptake in the North Atlantic, partly directly due to reduced deep convection, and partly due to a reduced biological productivity because much of the North Atlantic nutrients are supplied by the AMOC. A big surprise is the emergence of the North Pacific as a major contributor to the changes in the air-fluxes of carbon. It is the reorganization of its wind-driven circulation that explains why global net-outgassing of carbon continues long after the interstadial has begun.

How to cite: Jochum, M., Chase, Z., Nutermn, R., Pedro, J., Rasmussen, S., and Vettoretti, G.: Carbon cycle response to Dansgaard-Oeschger events in a freely oscillating LGM setup of the Community Earth System Model., EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-1556, https://doi.org/10.5194/egusphere-egu21-1556, 2021.

EGU21-2286 | vPICO presentations | ITS3.1/NP0.1

Loss of Amazon rainforest resilience since the early 2000s

Chris Boulton, Timothy Lenton, and Niklas Boers

The resilience of the Amazon rainforest to both climate and land use change is of critical importance for biodiversity, regional climate, and the global carbon cycle. Some models project future climate-driven Amazon rainforest dieback (Cox et al. 2000) and others argue that land-use and climate change have already pushed the Amazon close to a tipping point of rainforest dieback and transition to savanna (Lovejoy & Nobre 2018, 2019). But competing effects between rising temperatures, changing precipitation patterns, and CO2 fertilization, make the future of the Amazon uncertain. An alternative approach is to look for direct observational signals of changing rainforest resilience from timeseries analysis - here of remotely-sensed vegetation optical depth (VOD) (Moesinger et al. 2018), which correlates well with changes in broadleaf tree fraction coverage. Our results indicate that the Amazon rainforest has been losing resilience since the early 2000s, with statistical characteristics evolving consistently with critical slowing down on the way to a bifurcation-induced transition. Specifically, changes in lag-1 autocorrelation of VOD show that resilience is lost faster in regions with less mean annual rainfall. Parts of the rainforest that are closer to human activity are also losing resilience more quickly. Given observed increases in dry-season length, and expanding areas of land use change, the loss of Amazon rainforest resilience is likely to continue. Our results provide direct empirical evidence that the Amazon rainforest is losing stability, risking a sudden dieback that would have profound implications for biodiversity, carbon storage and climate change.

 

References

Cox, P. M., Betts, R. A., Jones, C. D., Spall, S. A. & Totterdell, I. J. Acceleration of global warming due to carbon-cycle feedbacks in a coupled climate model. Nature 408, 184-187, doi:10.1038/35041539 (2000).

Lovejoy, T. E. & Nobre, C. Amazon Tipping Point. Science Advances 4, eaat2340, doi:10.1126/sciadv.aat2340 (2018).

Lovejoy, T. E. & Nobre, C. Amazon tipping point: Last chance for action. Science Advances 5, eaba2949, doi:10.1126/sciadv.aba2949 (2019).

Moesinger, L. et al. The global long-term microwave Vegetation Optical Depth Climate Archive (VODCA). Earth System Science Data 12, 177-196, doi:10.5194/essd-12-177-2020 (2020).

 

This work was funded by the Volkswagen foundation and the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 820970.

How to cite: Boulton, C., Lenton, T., and Boers, N.: Loss of Amazon rainforest resilience since the early 2000s, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-2286, https://doi.org/10.5194/egusphere-egu21-2286, 2021.

EGU21-2379 | vPICO presentations | ITS3.1/NP0.1

Identifying the drivers of stochastic Dansgaard–Oeschger scale variability in a GCM

Edward Armstrong, Paul Valdes, and Kenji Izumi

The driver of the Dansgaard-Oeschger (DO) events remains uncertain, in part because many models do not show similar behaviour of a climate system tipped into a DO oscillatory state. Here we present results from glacial simulations of the HadCM3 GCM that show stochastic DO-scale variability. This is driven by variations in AMOC strength in response to North Atlantic salinity fluctuations. This represents a salt oscillator, driven by the salinity gradient between the subtropical gyre and Nordic seas. We give a mechanistic explanation of the feedbacks that drive this oscillator, particular the interplay between surface fluxes and advection. We identify that the key trigger that pushes the system into this oscillatory mode is the height of the North American ice sheet, which alters atmospheric and subsequently ocean circulation patterns. Our results highlight that glacial background conditions and ice sheet height act to push the system past a tipping point and into an oscillatory state on a timescale comparable to the DO events.

How to cite: Armstrong, E., Valdes, P., and Izumi, K.: Identifying the drivers of stochastic Dansgaard–Oeschger scale variability in a GCM, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-2379, https://doi.org/10.5194/egusphere-egu21-2379, 2021.

A quasi-geostrophic, low-order model of the wind-driven ocean circulation is used to illustrate tipping points induced by time-dependent forcing in excitable chaotic systems. When the wind stress amplitude G is constant in time, our model has a bifurcation from low-amplitude oscillations to high-amplitude relaxation oscillations (ROs) at a wind intensity value Gc. In the presence of time-dependent wind stress, the corresponding tipping point time ttp is defined as the time at which ROs arise. Numerical experiments are carried out using ensemble simulations in the presence of different drift rates of monotonically increasing forcing. Additional experiments include small periodic perturbations of such forcing. The results indicate substantial sensitivity of ttp and G(ttp) Rate-induced tipping, coexisting pullback attractors and total independence from initial states are found for subsets of parameter space. Besides, nonlinear resonance occurs in the presence of periodic perturbations for periods comparable to the RO time scale. The small periodic perturbation can be thought of as the seasonal-to-interannual variability in the wind stress, while the monotonically increasing component stands for the effect of amplification in the midlatitude winds due to anthropogenic warming.

How to cite: Pierini, S. and Ghil, M.: Tipping points induced by parameter drift in an excitable low-order model of the wind-driven ocean circulation, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-2462, https://doi.org/10.5194/egusphere-egu21-2462, 2021.

EGU21-2520 | vPICO presentations | ITS3.1/NP0.1

Early-Warning Signals For Cenozoic Climate Transitions

Georg Klinghammer and Christopher Böttner

Paleoclimatic records document large-scale shifts in the Earth’s climate history. Among other possibilities, these transitions might have been caused by bifurcations in the leading dynamical modes. Such bifurcation-induced critical transitions are typically preceded by characteristic early-warning signals (EWS), for example in terms of rising standard deviation and lag-one autocorrelation. These EWS are caused by the phenomenon of critical slowing down (CSD) in response to a widening of the underlying basin of attraction as the bifurcation is approached. The presence of EWS prior to an observed transition therefore provides evidence that the transition is caused by a bifurcation. We reveal significant EWS prior to several critical transitions within a paleoclimate record spanning the Cenozoic Era, i.e., the last 67M years. We employed the CENOzoic Global Reference benthic foraminifer carbon and oxygen Isotope Dataset (CENOGRID), comprising two time series of isotope variations of δ18O and δ13C. The standard deviation and lag-one autocorrelation are estimated in sliding windows for both records, to reveal whether CSD occurs ahead of the major abrupt transitions in these records. Specifically, we detect significant EWS for five out of nine previously identified transitions in at least one of the two available records. EWS are recognized for significant increases in both CSD indicators prior to the transition. Our results hence suggest that at least five major climate transitions of the last 67 Ma were triggered by bifurcations in leading modes of variability, indicating bifurcations have likely played a key role in the deep-time evolution of the Earth's climate system.

How to cite: Klinghammer, G. and Böttner, C.: Early-Warning Signals For Cenozoic Climate Transitions, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-2520, https://doi.org/10.5194/egusphere-egu21-2520, 2021.

EGU21-4264 | vPICO presentations | ITS3.1/NP0.1

The Structure of Millennial Scale Glacial Climate Variability

Guido Vettoretti, Peter Ditlevsen, Markus Jochum, and Sune Rasmussen

The Dansgaard-Oeschger (D-O) oscillation recorded in isotopic analyses of Greenland ice cores is a climate oscillation with millennial scale variability alternating between cold stadial climate and warm interstadial climate states. Using a series of long comprehensive climate model integrations of the glacial climate system under different levels of radiative forcing, we formulate a simple heuristic model to emulate the D-O oscillation. We demonstrate that the D-O oscillation has properties that are consistent with an internal unforced oscillation as well as displaying interesting behaviour that is consistent with noise induced transitions. Therefore, the D-O oscillation is more aptly characterized as a stochastic oscillator with stadial and interstadial durations that are more dependent upon a control parameter and internal climate variability rather than an intrinsic characteristic timescale.

How to cite: Vettoretti, G., Ditlevsen, P., Jochum, M., and Rasmussen, S.: The Structure of Millennial Scale Glacial Climate Variability, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-4264, https://doi.org/10.5194/egusphere-egu21-4264, 2021.

EGU21-740 | vPICO presentations | ITS3.1/NP0.1

Early Methane Mitigation Critical to Preserving Arctic Summer Sea Ice

Tianyi Sun, Ilissa Ocko, and Steven Hamburg

Methane mitigation is a key component of limiting the extent of global warming. However, little is known about how methane mitigation can benefit other critical aspects of the climate system, such as tipping elements. This study explores how reducing methane emissions can avert an approaching and concerning climate event: the loss of Arctic summer sea ice. We show that early deployment of feasible methane mitigation measures is essential to delaying and potentially even avoiding the loss of Arctic summer sea ice. Whether or not the sea ice is preserved beyond this century will ultimately depend on the level of concomitant carbon dioxide mitigation, but it is clear that sea ice will be at risk in the absence of methane mitigation. This analysis provides further justification of the value of early methane mitigation and supports the need to consider climate benefits beyond temperature when evaluating mitigation pathways.

How to cite: Sun, T., Ocko, I., and Hamburg, S.: Early Methane Mitigation Critical to Preserving Arctic Summer Sea Ice, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-740, https://doi.org/10.5194/egusphere-egu21-740, 2021.

EGU21-5536 | vPICO presentations | ITS3.1/NP0.1

Predictability of tipping points with rate-dependent effects

Johannes Lohmann and Peter Ditlevsen

Due to non-linearities in the dynamics of crucial elements in the climate system, Earth’s safe operating space is limited. Beyond a certain level of a control parameter, such as the atmospheric Greenhouse gas concentration, qualitative regime shifts in one or more sub-systems may take place. Additionally, theoretical studies suggest that abrupt, irreversible change can happen already prior to the crossing of a critical threshold in a control parameter.

In these so-called rate-induced transitions, the effective parameter level to induce tipping depends on the rate of change, or more generally the precise trajectory of the changing control parameter. Here we show rate-induced tipping points of the overturning circulation in a global ocean model. Due to the chaotic dynamics of the system, whether there will be tipping or not depends both on the rate and initial conditions in a very sensitive, non-smooth way. This raises questions about whether the safe operating space is still well-defined, and whether an approach of its boundary can be predicted.

For tipping points associated with slow passages across a bifurcation, generic early-warning signals have been developed for these purposes. Due to the necessarily fast parameter changes involved in rate-induced tipping, early-warning is more challenging. In many cases the tipping involves a saddle escape, which results in a delay of the actual transition and can be exploited for early-warning. Here this is demonstrated in the context of low-dimensional models. While due to the sensitivity of the dynamics around the saddle one might not be able to predict with certainty whether and when the system will tip, the indicators presented here may allow issuing a warning as the system gets close to tipping.

How to cite: Lohmann, J. and Ditlevsen, P.: Predictability of tipping points with rate-dependent effects, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-5536, https://doi.org/10.5194/egusphere-egu21-5536, 2021.

EGU21-5918 | vPICO presentations | ITS3.1/NP0.1

Re-thinking noise-induced tipping

Julian Newman and Peter Ashwin

When modelling potential tipping elements of the earth system, one conventionally distinguishes "bifurcation-induced" and "noise-induced" tipping. The former occurs when an internal system parameter slowly crosses a critical threshold and external noise is negligible. The latter arises from forcing by noise well before a critical threshold for the internal dynamics is reached. The former comes with early warning signals, due to "critical slowing down" in the internal dynamics; but the latter occurs randomly without warning. However, these descriptions typically assume that the noise is Gaussian white noise, which arises as a limit of fast-timescale chaotic driving. We will instead consider, through a simple discrete-time prototype, finite-timescale bounded chaotic driving; this is a more suitable description of the subgrid forcing of turbulent geophysical fluid dynamics than uncorrelated noise. We will see that the phenomenon previously known as "noise-induced tipping" now corresponds to a deterministic bifurcation-induced tipping of the joint dynamics of the tipping element and the driving. Although "critical slowing down" does not occur in this bifurcation, early warning and near-exact prediction of the tipping event may still be possible. We also discuss the phenomenon of "noise-induced" prevention or delay of a tipping event, which cannot occur under conventional memoryless noise.

How to cite: Newman, J. and Ashwin, P.: Re-thinking noise-induced tipping, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-5918, https://doi.org/10.5194/egusphere-egu21-5918, 2021.

EGU21-7807 | vPICO presentations | ITS3.1/NP0.1

A statistical model for dating uncertainties in Greenland ice core records

Eirik Myrvoll-Nilsen, Niklas boers, Martin Rypdal, and Keno Riechers

Most layer-counting based paleoclimate proxy records have non-negligible uncertainties that arise from both the proxy measurement and the dating processes. Proper knowledge of the dating uncertainties in paleoclimatic ice core records is important for a rigorous propagation to further analyses; for example for identification and dating of stadial-interstadial transitions during glacial intervals, for model-data comparisons in general, or to provide a complete uncertainty quantification of early warning signals. We develop a statistical model that incorporates the dating uncertainties of the Greenland Ice Core Chronology 2005 (GICC05), which includes the uncertainty associated with layer counting. We express the number of layers per depth interval as the sum of a structural component that represents both underlying physical processes and biases in layer counting, described by a linear regression model, and a noise component that represents the internal variation of the underlying physical processes, as well as residual counting errors. We find the residual components to be described well by a Gaussian white noise process that appear to be largely uncorrelated, allowing us to represent the dating uncertainties using a multivariate Gaussian process. This means that we can easily produce simulations as well as incorporate tie-points from other proxy records to match the GICC05 time scale to other chronologies. Moreover, this multivariate Gaussian process exhibits Markov properties which grants a substantial gain in computational efficiency.

How to cite: Myrvoll-Nilsen, E., boers, N., Rypdal, M., and Riechers, K.: A statistical model for dating uncertainties in Greenland ice core records, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-7807, https://doi.org/10.5194/egusphere-egu21-7807, 2021.

EGU21-7857 | vPICO presentations | ITS3.1/NP0.1

Abrupt transitions in past climates: How reliable are they and what do they mean?

Denis-Didier Rousseau, Witold Bagniewski, and Michael Ghil

Early evidence of abrupt transitions in Camp Century and Dye 3 Greenland ice cores (Dansgaard et al. 1982) has recently been reinforced by the identification of additional abrupt transitions in the NGRIP ice core (Rasmussen et al. 2014). These additional events correspond to changes of either short duration or amplitude of d18O that visual or statistical inspections do not necessarily validate. Abrupt transitions have been described for marine (Bond et al. 1992) and continental (Wang et al. 2001) records as well, and they provide a broader spatial perspective. Finally, abrupt transitions have also been documented over much deeper timescales (Zachos et al., 2001, Hodell & Channel, 2016, Westerhold et al. 2020). In spite of the variable time resolution of all these records, the abrupt transitions seem to reflect the individual impact of external forcing, of internal climate variability, or a combination of the two on Earth’s climate system. To illustrate this, we have analyzed 4 reference datasets with timescales ranging from one glacial cycle — i.e., the last 130,000 years — to the last 70 Ma. We show patterns that repeat within a single glacial cycle and seem to be related to internal variability, along with patterns associated with longer time periods and possibly related to external forcing; such forcing may arise from changes in either Earth’s orbit or its dynamics. This study is supported by the H2020-funded Tipping Points in the Earth System (TiPES) project.

How to cite: Rousseau, D.-D., Bagniewski, W., and Ghil, M.: Abrupt transitions in past climates: How reliable are they and what do they mean?, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-7857, https://doi.org/10.5194/egusphere-egu21-7857, 2021.

EGU21-8059 | vPICO presentations | ITS3.1/NP0.1

Dynamical Landscape and Multistability of a Climate Model

Georgios Margazoglou, Valerio Lucarini, Tobias Grafke, and Alessandro Laio

We apply two independent data analysis methodologies to locate stable climate states in an intermediate complexity climate model and analyze their interplay. First, drawing from the theory of quasipotentials, and viewing the state space as an energy landscape with valleys and mountain ridges, we infer the relative likelihood of the identified multistable climate states, and investigate the most likely transition trajectories as well as the expected transition times between them. Second, harnessing techniques from data science, specifically manifold learning, we characterize  the data landscape of the simulation output to find climate states and basin boundaries within a fully agnostic and unsupervised framework. Both approaches show remarkable agreement, and reveal, apart from the well known warm and snowball earth states, a third intermediate stable state in one of the two climate models we consider. The combination of our approaches allows to identify how the negative feedback of ocean heat transport and entropy production via the hydrological cycle drastically change the topography of the dynamical landscape of Earth's climate.

How to cite: Margazoglou, G., Lucarini, V., Grafke, T., and Laio, A.: Dynamical Landscape and Multistability of a Climate Model, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-8059, https://doi.org/10.5194/egusphere-egu21-8059, 2021.

EGU21-8503 | vPICO presentations | ITS3.1/NP0.1

Dansgaard-Oeschger Tipping Events (TEs): Towards determining if IPCC-relevant models represent these TEs.

Louise Sime, Irene Malmierca Vallet, and Paul Valdes

Dansgaard-Oeschger (DO) events are abrupt, large climate swings that punctuated the last glacial period. There is uncertainty whether current IPCC-relevant models can effectively represent the processes that cause DO events. This has implications for whether these models are also capable of simulating future TEs,  and more in general, for the delivery of accurate climate change projections. Here we present progress on possible pathways to a DO Paleoclimate Modelling Intercomparison Project (PMIP) protocol. This is broad interest to the climate community since (1), there is currently no PMIP common guidance to investigate DO events, (2) it could help carry out simulations in Earth system models under a common framework, and (3) it will help guide a more methodical search for DO events in current models. A protocol could help investigate cold-period TEs through a range of insolation-, freshwater-, green-house-gas-, and Northern Hemisphere ice sheet-related forcings, as well as evaluating the possibility of spontaneous TEs. MIS3 was a period of noticeable millennial-scale climate variability, characterised by the most regular incidence of DO events (Schulz et al., 1999). Although most abrupt DO events happened during MIS3, only few studies investigate TEs in coupled general circulation models under MIS 3 conditions (e.g., Kawamura et al., 2017; Zhang and Prange, 2020). Here, we therefore suggest that the MIS3 period could be the focus of such a DO-event focussed modelling protocol. Experiments performed under MIS 3 boundary conditions may help (1) explore variability under intermediate glacial conditions, (2) better understand the mechanisms behind millennial-scale TEs, (3) look for spontaneous DO-type oscillations, and (4) help answer the question: “are models too stable?”.

How to cite: Sime, L., Malmierca Vallet, I., and Valdes, P.: Dansgaard-Oeschger Tipping Events (TEs): Towards determining if IPCC-relevant models represent these TEs., EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-8503, https://doi.org/10.5194/egusphere-egu21-8503, 2021.

The Amazon rainforest is widely recognised as a potential tipping element in the Earth's climate system. While several studies suggest a sudden dieback of the rainforest ecosystem after partial deforestation [e.g., 1, 2], there is still a lack of understanding where to search for early-warning signals that might precede such a dieback. In this work we employ a non-linear model of the moisture transport across the Amazon Basin to propose several statistical and physical early warning signals for a critical transition in the coupled dynamics of the Amazon rainforest and the atmospheric circulation of the South American monsoon. 

Widespread deforestation and its effects on evapotranspiration and radiation have been shown to potentially trigger a collapse of the positive feedback related to latent heat release over the rainforest [3], resulting in substantially reduced rainfall amounts. The model includes a nonlinear contribution representing the acceleration of low-level moisture flow caused by condensational latent heating.  

Guided by our modelling results, we associate characteristic changes in the hydrological cycle as well as statistical indicators in observed data with deforestation-induced circulation changes that are consistent with the identified early-warning signals. Our findings indicate that in response to deforestation, the coupled atmosphere-vegetation system is destabilising and that further deforestation could trigger a transition of the Amazon rainforest to a savanna state. 

[1] Nobre, C. A., & Borma, L. D. S. (2009). “Tipping points” for the Amazon forest. Current Opinion in Environmental Sustainability. https://doi.org/10.1016/j.cosust.2009.07.003

[2] Hirota, M., Holmgren, M., Van Nes, E. H., & Scheffer, M. (2011). Global resilience of tropical forest and savanna to critical transitions. Science, 334(6053), 232–235. https://doi.org/10.1126/science.1210657

[3] Boers, N., Marwan, N., Barbosa, H. M. J., & Kurths, J. (2017). A deforestation-induced tipping point for the South American monsoon system. Scientific Reports, 7. https://doi.org/10.1038/srep41489

How to cite: Bochow, N.: Early-Warning Signals for a Critical Transition in the Coupled Amazon Rainforest-South American Monsoon System, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-2023, https://doi.org/10.5194/egusphere-egu21-2023, 2021.

EGU21-9433 | vPICO presentations | ITS3.1/NP0.1

Global reorganization of atmospheric circulation during Dansgaard-Oschger cycles

Jens Fohlmeister, Natasha Sekhon, Andrea Columbu, Kira Rehfeld, Louise Sime, Cristina Veige-Pires, Norbert Marwan, and Niklas Boers

Ice core records from Greenland provide evidence for multiple abrupt warming events recurring at millennial time scales during the last glacial interval. Although climate transitions strongly resembling these Dansgaard-Oeschger (DO) transitions have been identified in several speleothem records, our understanding of the climate and ecosystem impacts of the Greenland warming events in lower latitudes remains incomplete.

Here, we investigate the influence of DO transitions on the global atmospheric circulation pattern. We comprehensively analyse d18O changes during DO transitions in a globally distributed dataset of speleothems (SISALv2; Comas-Bru et al., 2020). Speleothem d18O signals mostly reflect changes in precipitation amount and moisture source. Thereby this proxy allows us to infer spatially resolved changes in global atmospheric dynamics that are characteristically linked to DO transitions. We confirm the previously proposed shift of the Intertropical Convergence Zone towards more northerly positions. In addition, we find evidence for a similar northward shift of the westerly winds of the Northern Hemisphere. Furthermore, we identify a decreasing trend in the transition amplitudes with increasing distances from the North Atlantic region. This confirms previous suggestions of this region being the core and origin of these past abrupt climate changes.

 

References:

Comas-Bru et al., 2020, Earth System Science Data 12, 2579–2606

 

How to cite: Fohlmeister, J., Sekhon, N., Columbu, A., Rehfeld, K., Sime, L., Veige-Pires, C., Marwan, N., and Boers, N.: Global reorganization of atmospheric circulation during Dansgaard-Oschger cycles, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-9433, https://doi.org/10.5194/egusphere-egu21-9433, 2021.

EGU21-9699 | vPICO presentations | ITS3.1/NP0.1

Prediction of Dansgaard-Oeschger events using machine learning

Nuno Moniz and Susana Barbosa

The Dansgaard-Oeschger (DO) events are one of the most striking examples of abrupt climate change in the Earth's history, representing temperature oscillations of about 8 to 16 degrees Celsius within a few decades. DO events have been studied extensively in paleoclimatic records, particularly in ice core proxies. Examples include the Greenland NGRIP record of oxygen isotopic composition.
This work addresses the anticipation of DO events using machine learning algorithms. We consider the NGRIP time series from 20 to 60 kyr b2k with the GICC05 timescale and 20-year temporal resolution. Forecasting horizons range from 0 (nowcasting) to 400 years. We adopt three different machine learning algorithms (random forests, support vector machines, and logistic regression) in training windows of 5 kyr. We perform validation on subsequent test windows of 5 kyr, based on timestamps of previous DO events' classification in Greenland by Rasmussen et al. (2014). We perform experiments with both sliding and growing windows.
Results show that predictions on sliding windows are better overall, indicating that modelling is affected by non-stationary characteristics of the time series. The three algorithms' predictive performance is similar, with a slightly better performance of random forest models for shorter forecast horizons. The prediction models' predictive capability decreases as the forecasting horizon grows more extensive but remains reasonable up to 120 years. Model performance deprecation is mostly related to imprecision in accurately determining the start and end time of events and identifying some periods as DO events when such is not valid.

How to cite: Moniz, N. and Barbosa, S.: Prediction of Dansgaard-Oeschger events using machine learning, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-9699, https://doi.org/10.5194/egusphere-egu21-9699, 2021.

EGU21-9733 | vPICO presentations | ITS3.1/NP0.1

Glaciation cycle models and their pullback attractors

Keno Riechers, Niklas Boers, Michael Ghil, and Takahito Mitsui

The Pleistocene climate was dominated by alternating retreat and regrowth of massive ice sheets accompanied by large variations in the global mean temperature and sea level. Partial agreement between the power spectra of global ice volume proxies and high-latitude summer insolation provides evidence that quasi-periodic changes in the earth’s orbital configuration affect the timing of glaciations and deglaciations. It remains, however, a topic of active debate whether the main cause of glacial cycles is an internal self-sustained oscillation of the climate system that merely phased locked, more or less, to orbital forcing or whether glacial cycles could not exist at all in the absence of orbital forcing. Furthermore, it is unclear whether past ice volume records should be regarded as the result of a purely deterministic process or as a randomly selected trajectory of a stochastic process. To study plausible paths of the earth’s climate system given the orbital forcing, we compute the pullback attractors of several conceptual Pleistocene models. The results are confronted with the power spectra, as well as the time series of proxy records and conclusions will be drawn about the role of internal vs. forced variability and the possible contribution of stochastic processes to the mix of causes. We argue, moreover, that the explanatory power of either a deterministically chaotic or a dynamic-stochastic model cannot be assessed by comparing the model output to observations in the time domain alone.

How to cite: Riechers, K., Boers, N., Ghil, M., and Mitsui, T.: Glaciation cycle models and their pullback attractors, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-9733, https://doi.org/10.5194/egusphere-egu21-9733, 2021.

EGU21-9902 | vPICO presentations | ITS3.1/NP0.1

Testing robustness of co-existing climate states

Charline Ragon, Valerio Lembo, Valerio Lucarini, Christian Vérard, Jérôme Kasparian, and Maura Brunetti

The climate can be regarded as a stationary non-equilibrium statistical system (Gallavotti 2006): a continuous and spatially inhomogeneous input of solar energy enters at the top-of-atmosphere and compensates the action of non-conservative forces, mainly occurring at small scales, to give rise to a statistically steady state (or attractor) for the whole climate.

Depending on the initial conditions and the range of forcing, all other parameters being the same, some climate models have the property to settle down on different attractors. Multi-stability reflects how energy, water mass and entropy can be re-distributed in multiple ways among the climate components, such as the atmosphere, the ocean or the ice, through a different balance between nonlinear mechanisms.

Starting from a configuration where competing climate attractors occur under the same forcing, we have explored their robustness performing two kinds of numerical experiment. First, we have investigated the impact of frictional heating on the overall energy balance and we have shown that such contribution, generally neglected in the atmospheric component of climate models, has crucial consequences on conservation properties: it improves the energy imbalance at top-of-atmosphere, typically non negligible in coarse simulations (Wild et al. 2020), strengthens the hydrological cycle, mitigates the mechanical work associated to atmospheric circulation intensity and reduces the heat transport peaks in the ocean. Second, we have compared two bulk formulas for the cloud albedo, one where it is constant everywhere and the other where it increases with latitude, as implemented in the new version of the atmospheric module SPEEDY in order to improve comparisons with observational data (Kucharski 2013). We have checked that this new parameterization does not affect energy and water-mass imbalances, while reduces global temperature and water-mass transport on the attractor, giving rise to a larger conversion of heat into mechanical work in the atmosphere.

In order to perform such studies, we have run the climate model MITgcm on coupled aquaplanets at 2.8 horizontal resolution until steady states are reached (Brunetti el al. 2019) and we have applied the Thermodynamic Diagnostic Tool (TheDiaTo, Lembo et al. 2019).

 

References:

Brunetti, Kasparian, Vérard, Climate Dynamics 53, 6293 (2019)

Gallavotti, Math. Phys. 3, 530 (2006)

Kucharski et al., Bulletin of the American Meteorological Society 94, 25 (2013)

Lembo, Lunkeit, Lucarini, Geoscientific Model Development 12, 3805 (2019)

Wild, Climate Dynamics 55, 553 (2020)

How to cite: Ragon, C., Lembo, V., Lucarini, V., Vérard, C., Kasparian, J., and Brunetti, M.: Testing robustness of co-existing climate states, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-9902, https://doi.org/10.5194/egusphere-egu21-9902, 2021.

EGU21-9968 | vPICO presentations | ITS3.1/NP0.1

PaleoJump database for research on rapid climate transitions

Witold Bagniewski, Denis-Didier Rousseau, and Michael Ghil

Tipping points (TPs) in the Earth system have been studied with growing interest and concern in recent years due to the potential risk of anthropogenic forcing causing abrupt, and possibly irreversible, climate transitions. Paleoclimate records are essential for identifying TPs in the Earth’s past and to properly understand the climate system’s underlying bifurcation mechanisms. Due to their varying quality, resolution, and dating methods, it is often necessary to select the records that give the best representation of past climate. Furthermore, as paleoclimate records vary in their origin, time spans, and periodicities, an objective, automated methodology is crucial for identifying and comparing TPs.

To reach this goal, here we present the PaleoJump database of carefully selected, high-resolution records originating in ice, marine sediments, speleothems, loess, and lake sediments. These records, which include tipping elements, cover long time intervals and represent a global distribution from all continents and ocean basins. For every record, a transition detection methodology based on an augmented Kolmogorov-Smirnov test is applied to identify abrupt transitions. The PaleoJump database highlights these automatically detected transitions for every record together with other essential information, including location, temporal scale and resolution, as well as temporal plots; it therefore represents a valuable resource for researchers investigating TPs in past climates. This study is supported by the H2020-funded TiPES project.

How to cite: Bagniewski, W., Rousseau, D.-D., and Ghil, M.: PaleoJump database for research on rapid climate transitions, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-9968, https://doi.org/10.5194/egusphere-egu21-9968, 2021.

EGU21-12302 | vPICO presentations | ITS3.1/NP0.1

The future of ski resorts in the Swiss Alps: using DMDU to identify tipping points

Saeid Ashraf Vaghefi, Veruska Muccione, Kees C.H. van Ginkel, and Marjolijn Haasnoot

The future of ski resorts in the Swiss Alps is highly uncertain. Being dependent on snow cover conditions, winter sport tourism is highly susceptible to changes in temperature and precipitation. With the observed warming of the European Alps being well above global average warming, snow cover in Switzerland is projected to shrink at a rapid pace. Climate uncertainty originates from greenhouse gas emission trajectories (RCPs) and differences between climate models. Beyond climate uncertainty, the snow conditions are strongly subject to intra-annual variability. Series of unfavorable years have already led to the financial collapse of several low-altitude ski resorts. Such abrupt collapses with a large impact on the regional economy can be referred to as climate change induced socio-economic tipping points. To some degree, tipping points may be avoided by adaptation measures such as artificial snowmaking, although these measures are also subject to physical and economical constraints. In this study, we use a variety of exploratory modeling techniques to identify tipping points in a coupled physical-economic model applied to six representative ski resorts in the Swiss Alps. New high-resolution climate projections (CH2018) are used to represent climate uncertainty. To improve the coverage of the uncertainty space and accounting for the intra-annual variability of the climate models, a resampling technique was used to produce new climate realizations. A snow process model is used to simulate daily snow-cover in each of the ski resorts. The likelihood of survival of each resort is evaluated from the number of days with good snow conditions for skiing compared to the minimum thresholds obtained from the literature. Economically, the good snow days are translated into the total profit of ski resorts per season of operation. Multiple unfavorable years of total profit may lead to a tipping point. We use scenario discovery to identify the conditions under which these tipping points occur, and reflect on their implications for the future of snow tourism in the Swiss Alps.

How to cite: Ashraf Vaghefi, S., Muccione, V., van Ginkel, K. C. H., and Haasnoot, M.: The future of ski resorts in the Swiss Alps: using DMDU to identify tipping points, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-12302, https://doi.org/10.5194/egusphere-egu21-12302, 2021.

EGU21-12477 | vPICO presentations | ITS3.1/NP0.1

Ancient warming pushed wetlands across biogeochemical tipping points

Richard Pancost, David Naafs, Gordon Inglis, and Vittoria Lauretano

Ancient peat deposits provide valuable and complementary insight into the biogeochemical response of wetlands to climate perturbations, including potential tipping points in such systems. The combination of temperature (GDGTs) and hydrology (leaf wax hydrogen isotopic compositions) proxies with qualitative proxies for methanogenesis (archaeal lipid abundances) and methanotrophy (bacterial lipid carbon isotopic compositions) has revealed dramatic perturbations to the carbon cycle during transient warming events, including the Palaeocene Eocene Thermal Maximum.  Bacterially-derived hopanes in at least two PETM-spanning lignite sequences record negative carbon isotope excursions of near-unprecedented magnitude in response to rapid global warming.  The warming – either directly or indirectly – clearly caused a fundamental reorganisation of the carbon cycle in those ancient wetlands. Intriguingly however, these excursions persist for a far shorter duration than the PETM warming. Similarly, hopane δ13C values in lignites of the Early Eocene Climate Optimum, the warmth of which was reached more gradually, are similar to those of today. This suggests that these unusually isoptopically light hopanoids represent a transient disruption to the methane cycle associated with a climate perturbation rather than an equilibrium response to warmer temperatures.  Such an interpretation is consistent with Deglacial and Holocene peat-derived records, in which hopane δ13C values exhibit large responses to transient drying events and modest responses to longer-term change. Such findings could have implications for future climate change feedbacks, with the wetland methane cycle being particularly sensitive to the rate of climatic change.

How to cite: Pancost, R., Naafs, D., Inglis, G., and Lauretano, V.: Ancient warming pushed wetlands across biogeochemical tipping points, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-12477, https://doi.org/10.5194/egusphere-egu21-12477, 2021.

EGU21-14691 | vPICO presentations | ITS3.1/NP0.1

A simple model to assess the long term fate of the AMOC

Richard Wood

Studies with global climate models over the past 25 years have shown a range of long-term responses of the Atlantic Meridional Overturning Circulation (AMOC), in response to scenarios in which greenhouse gases are increased then eventually stabilised at some value, possibly after temporarily overshooting that value. AMOC responses include stabilisation at weaker than the pre-industrial level, rapid recovery following a period of quasi-steady weak circulation, overshooting to stronger than pre-industrial strength, or tipping to a quasi-permanent weak state. While many of these studies have gained insight into the mechanisms behind their individual model behaviour, no overarching understanding exists of what determines how a particular model will respond.   

We present a simple AMOC model suitable to characterise the different possible long-term responses, and use it as a (partially) unifying framework to show how the different behaviours can arise from a competition between thermal and haline feedbacks. The results are relevant to defining ‘safe mitigation pathways’ that avoid or reduce the risk of AMOC tipping or potentially dangerous overshoots.

How to cite: Wood, R.: A simple model to assess the long term fate of the AMOC, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-14691, https://doi.org/10.5194/egusphere-egu21-14691, 2021.

EGU21-16401 | vPICO presentations | ITS3.1/NP0.1

Bivalves indicate that the North Atlantic was under stress before the onset of the Little Ice Age

Beatriz Arellano Nava, Paul R. Halloran, Chris A. Boulton, and Timothy M. Lenton

The last millennium was characterised by a cooling from the Medieval Warm Period into the Little Ice Age. While strong volcanic eruptions could have triggered the onset of the Little Ice Age by reducing solar irradiance, modelling experiments suggest that it was amplified and maintained by sea ice-ocean feedbacks, including a potential abrupt weakening of the subpolar gyre. The weakening of negative feedbacks that maintain a system in a stable state, prior to an abrupt transition, can be detected as an increase in temporal autocorrelation and variability. Here we use an annually-resolved and absolutely dated shell-derived record from the North Icelandic Shelf that spans the last millennium, to detect such a loss of resilience in the marine environment leading up to the Little Ice Age transition. We find a significant increase in autocorrelation and variance in bivalve growth increments and oxygen isotopes before the transition, providing evidence consistent with loss of stability in the marine environment. This supports the idea that internal feedbacks played an important role in the Little Ice Age onset.

How to cite: Arellano Nava, B., Halloran, P. R., Boulton, C. A., and Lenton, T. M.: Bivalves indicate that the North Atlantic was under stress before the onset of the Little Ice Age, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-16401, https://doi.org/10.5194/egusphere-egu21-16401, 2021.

EGU21-16427 | vPICO presentations | ITS3.1/NP0.1 | Highlight

Henri Poincare’s legacy for tipping points

Richard Blaustein

The science of Earth system and climate tipping points has evolved and matured as a disciplined approach to understanding anthropogenic and non-anthropogenic stresses on the Earth’s subsystems in the 21st century. However, tipping points is strongly interlinked with the science of bifurcations and dynamical systems, which received a seminal and resonant illumination by the great French mathematician Henri Poincare (1854-1912). Thus, quite a few historically minded tipping point scientists mention Poincare as the seminal, path-setting thinker for tipping point understandings.

Moreover, Poincare’s bifurcation and dynamical systems-pertinent science is also linked to his seminal role in chaos theory, which illuminates today’s understanding of climate stochasticity. Poincare famously said, "A very small cause which escapes us determines a considerable effect that we cannot see; so, we say this effect is random," which provided grounding for the chaos notion of critical sensitivity to initial conditions. Since Poincare, great strides in abrupt change understanding as linked to chaos (and within an examination of turbulence) have taken place in the science that informs tipping points, such as with the work of Ed Lorenz and David Ruelle. Additionally, the Russian mathematicians (e.g., Andronov and Arnold) have contributed greatly with the refining of differential equations for bifurcation understandings that Poincare began.  

This EGU presentation is a history of science presentation on Henri Poincare's commencement of bifurcation, dynamical system and chaos understandings as presented by a journalist who has done both interviews and general historical research. The presentation sets key points in Poincare’s biography and pertinent career and sketches the legacy of this Poincare focus up from Henri Poincare through Russian bifurcation scientists, catastrophe theorist Rene Thom, and ultimately Lorenz and current bifurcation theorists, such as Michael Ghil and Valerio Lucarini. It offers light on the ancestry of one of the most important examinations of the Anthropocene, climate change tipping points.  

How to cite: Blaustein, R.: Henri Poincare’s legacy for tipping points, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-16427, https://doi.org/10.5194/egusphere-egu21-16427, 2021.

ITS3.2/BG7 – Climate extremes, biosphere and society: impacts, cascades, feedbacks, and resilience

As has been made acutely clear in recent years, many natural and human systems are particularly prone to the co-occurrence of extremes like severe heat, heavy rainfall, storm-surge flooding, severe drought, and extreme wildfire conditions. The co-occurrence of these conditions, both simultaneously (or in rapid succession) in a given location or in different parts of the world, is critical for a broad suite of climate-sensitive concerns, including agricultural markets, food security, poverty vulnerability, supply chains, weather-related insurance and reinsurance, and disaster preparedness and recovery - particularly when those conditions are sufficiently extreme to fall outside of historical experience. This seminar will summarize recent work quantifying changes in the frequency of unprecedented events without consideration for joint probability probability, and then present a framework for quantifying the spatial and temporal co-occurrence of climate stresses in a nonstationary climate. This framework shows that, globally, anthropogenic climate forcing has doubled the joint probability of years that are both warm and dry in the same location (relative to the 1961–1990 baseline). In addition, the joint probability that key crop and pasture regions simultaneously experience severely warm conditions in conjunction with dry years has also increased, including high statistical confidence that human influence has increased the probability of previously unprecedented co-occurring combinations. The potential for this methodology to lend insight for other sectors that are accustomed to deploying resources based on historical probabilities, such as wildfire risk management, will also be discussed.

How to cite: Diffenbaugh, N.: Multidimensional risk in a nonstationary climate: changes in joint probability of extreme conditions in space and time, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-1836, https://doi.org/10.5194/egusphere-egu21-1836, 2021.

EGU21-5115 | vPICO presentations | ITS3.2/BG7

Pathways to a sustainable, inclusive and healthy food system

Franziska Gaupp

Currently, the global food system is the single largest threat to people and planet. Food is the leading cause behind transgressing five of the nine planetary boundaries. It is a major source of carbon emissions, as well as the single largest contributor to global deforestation, overuse of fresh water and eutrophication of our aquatic ecosystems. And while agriculture has been a major engine of poverty reduction, agricultural activities are unable to deliver a decent livelihood for an estimated 80 percent of those living in extreme poverty. The projected increase in frequency and severity of climate extreme events is posing additional threats to the global food system.

A transformation towards a more inclusive, sustainable and health-promoting food system is urgently needed. This presentation will introduce the newly established Food Systems Economics Commission (FSEC) that provides detailed and robust evidence assessing the implications of the policy and investment decisions needed to foster a food system transformation. It integrates global modelling tools such as integrated assessment modelling and innovative applications of agent-based modelling with political economy considerations.  It investigates the hidden costs of our current food system, explores transitions pathways towards a new food and land use economy and suggests key policy instruments to foster the transformation towards a sustainable, inclusive, healthy and resilient food system.

How to cite: Gaupp, F.: Pathways to a sustainable, inclusive and healthy food system, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-5115, https://doi.org/10.5194/egusphere-egu21-5115, 2021.

EGU21-10715 | vPICO presentations | ITS3.2/BG7

Unravelling socio-hydrological processes behind cascading drought-to-flood disasters

Anne Van Loon, Alessia Matanó, Giuliano di Baldassarre, Rosie Day, Margaret Garcia, Melanie Rohse, Marleen de Ruiter, Johanna Koehler, and Philip Ward

Future climate projections show a strengthening of the hydrological cycle with more droughts and floods expected in many regions of the world. This means a higher likelihood of cascading drought-to-flood disasters such as the Millennium Drought – Brisbane flooding in Australia or the California drought – Oroville spillway collapse in the US. Droughts allow ample time for impacts and adaptation, which influence hazard, exposure, and vulnerability of a subsequent flood. When we treat the flood risk as independent from the drought this might lead to large underestimations of future risk.

Here, we present the PerfectSTORM project (‘STOrylines of futuRe extreMes’). In this project we will study drought-to-flood events to provide the understanding needed to prevent major disasters in the future. We will use a mixed-methods approach based on a combination of qualitative and quantitative storylines of past and future drought-to-flood risk in case studies and extrapolation of this rich case study information to the global scale. Qualitative storylines will be collected with narrative interviews and mental simulation workshops and will be analysed to develop timelines and causal loop diagrams. Quantitative storylines will be developed from timeseries of hydrological and social data that will be analysed to distinguish interrelated drivers and modelled with system dynamics modelling. These storylines will then be combined in an iterative way using innovative data visualisation as a basis for co-creating management solutions.

To generalise our case study understanding, a range of global datasets will be analysed to find global types and hotspots of drought-to-flood events. This information will be combined with the system dynamics model developed in the case studies and a global multi-dimensional possibility space will be developed. This will allow us to explore positive pathways for future management of drought-to-flood events in different parts of the world. The PerfectSTORM project will provide in-depth understanding of the hydrosocial feedbacks and dynamic vulnerability of cascading hazards.

How to cite: Van Loon, A., Matanó, A., di Baldassarre, G., Day, R., Garcia, M., Rohse, M., de Ruiter, M., Koehler, J., and Ward, P.: Unravelling socio-hydrological processes behind cascading drought-to-flood disasters, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-10715, https://doi.org/10.5194/egusphere-egu21-10715, 2021.

EGU21-711 | vPICO presentations | ITS3.2/BG7

Analysis of floodplain forest sensitivity to drought

Natalia Kowalska, Ladislav Šigut, Marko Stojanović, Milan Fischer, Ina Kyselova, and Marian Pavelka

Floodplain forests are very complex, productive ecosystems, capable of storing huge amounts of soil carbon. With the increasing occurrence of extreme events, they are today among the most threatened ecosystems. Our study’s main goal was to assess the productivity of a floodplain forest located at Lanžhot in the Czech Republic from two perspectives: carbon uptake (using an eddy covariance method) and stem radius variations (using dendrometers). We aimed to determine which conditions allow for high ecosystem production and what role drought plays in reducing such production potential. Additionally, we were interested to determine the relative soil water content threshold indicating the onset and duration of this event. We hypothesized that summer drought in 2018 had the most significant negative effects on the overall annual carbon and water budgets. In contrast with our original hypothesis, we found that an exceptionally warm spring in 2018 caused a positive gross primary production (GPP) and evapotranspiration (ET) anomaly that consequently led in 2018 to the highest seasonal total GPP and ET from all of the investigated years (2015–2018). The results showed ring-porous species to be the most drought resistant. Relative soil water content threshold of approximately 0.45 was determined as indicating the onset of drought stress.

How to cite: Kowalska, N., Šigut, L., Stojanović, M., Fischer, M., Kyselova, I., and Pavelka, M.: Analysis of floodplain forest sensitivity to drought, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-711, https://doi.org/10.5194/egusphere-egu21-711, 2021.

EGU21-2166 | vPICO presentations | ITS3.2/BG7

Climate change-related risks and adaptation measures in South and Central America during the 21st century

Isabel Hagen, Christian Huggel, Laura Ramajo Gallardo, Jean P. Ometto, Noemí Chacón, and Edwin J. Castellanos

The consequences of climate change in South and Central America are already widespread and take on many forms. Albeit there is an increasing number of studies focusing on specific climate change-related risks in the region, a synthesis of risks for the 21st century, together with current and future adaptation options is lacking. This study synthesizes major climate related risks in South and Central America, while also looking at implications for adaptation measures and (un)avoidable loss and damage. A review of over 100 peer-reviewed articles published since 2013 was completed to examine the current and projected state of the risks. We identify eight key risks in South and Central America that have the potential to become severe with climate change during the 21st century. The criteria for a severe risk relate to the number of people potentially affected, the severity of the negative effects of the risk, the importance of the affected systems, and the irreversibility versus potential to reduce the risk. The risks are analysed in relation to different climate scenarios, and changes in associated hazards, exposure, and/or vulnerability. The risks include 1: risk of food insecurity due to repeated and/or extreme drought conditions; 2: risk to life and infrastructure due to floods and landslides; 3: risk of water insecurity in Central America and the Andes region; 4: systemic risks of surpassing infrastructure and public service system capacities due to cascading impacts of storms, floods and epidemics; 5: risk of severe health effects due to increasing epidemics (in particular vector-borne diseases); 6: risk of large-scale ecological transformation of the Amazon forest; 7: risk to coral reef ecosystems due to coral bleaching in Central America; 8: risk to coastal socio-ecological systems due to sea level rise, intensification of upwelling and ocean acidification. In addition, we focus on already implemented and possible adaptation measures for each of the risks. Subsequently, we draw conclusions of the potential losses and damages caused by each risk. Our assessment of risks in the Central and South America region show that several risks have the potential to become severe already in the near future. The extent of the severity is driven by the specific region’s exposure, vulnerability and adaptation capacity. Adaptation capacity is in turn dependent on physical as well as socio-economic systems. Inequalities, corruption, and poor communication between decision makers, stakeholders and the scientific community together with a lack of available data can critically limit adaptation options. Still, many adaptation options are available, and efforts to thoroughly research further adaptation measures should be of highest priority. This will undoubtedly save both lives and severe economic damage as South and Central America face the consequences of climate change.

How to cite: Hagen, I., Huggel, C., Ramajo Gallardo, L., Ometto, J. P., Chacón, N., and Castellanos, E. J.: Climate change-related risks and adaptation measures in South and Central America during the 21st century, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-2166, https://doi.org/10.5194/egusphere-egu21-2166, 2021.

EGU21-2208 | vPICO presentations | ITS3.2/BG7

Rise and fall of projected vegetation primary production resilience to climate variability

Matteo Zampieri, Bruna Grizzetti, and Andrea Toreti

The SDGs recognize the importance of ensuring conservation, restoration and sustainable use of terrestrial ecosystems and their services and strengthening the resilience and adaptive capacity to climate-related hazards. Vegetation primary production is the main function of terrestrial ecosystems providing food and other services to society. Agricultural production is a main source of employment, livelihood and income for a large portion of population, especially in developing countries.

Anticipating the changes in vegetation primary production resilience – the plant capacity to cope with disturbances and shocks including such as those related to climate variability and extremes – is therefore critical to understand and project ecosystems’ responses to global change and the impacts on the related ecosystem services, to support mitigation actions, and to define proper adaptation plans. However, the estimation of resilience is not straightforward.

Here, we applied a recently proposed resilience metrics – the annual production resilience indicator (Zampieri et al. 2019, 2020) – to quantify the changes in vegetation gross primary production (GPP) resilience computed from a large ensemble of state-of-the-art CMIP6 Earth System Model (ESM) simulations.

Our results indicate that climate change mitigation is necessary to significantly reduce the risk of losing terrestrial ecosystems production resilience. In the ‘Sustainability (Taking the Green Road)’ and ‘Middle of the Road’ scenarios considered here (ssp126 and ssp245), the areas where vegetation shows increasing GPP resilience (mainly boreal, African and Indian monsoon regions) are wider than the areas with decreasing resilience. The situation drastically reverses in the ’Fossil-fuel Development (Taking the Highway)’ scenario (ssp585), mostly because the increase of GPP interannual variability outbalances the mean GPP increase due to the CO2 fertilization effect in this high greenhouse gases’ emission scenario. 

To raise social awareness, identify adaptation plans, but especially to stimulate mitigation efforts, we analyse and discuss the gains and losses of vegetation GPP resilience for each World country. Among the larger countries, Brazil is exposed to the highest risk of losing vegetation GPP resilience, especially in the higher emission scenario.

This study explores the linkages between future climate, associated changes in resilience of global vegetation gross primary production, and the mitigation pathways that society can undertake to conserve and restore ecosystems and their services, on which human well-being depends.

How to cite: Zampieri, M., Grizzetti, B., and Toreti, A.: Rise and fall of projected vegetation primary production resilience to climate variability, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-2208, https://doi.org/10.5194/egusphere-egu21-2208, 2021.

EGU21-2341 | vPICO presentations | ITS3.2/BG7

From climate change perception to bottom-up adaptation initiatives: a case study from banana producers of Upper Huallaga basin, Peru

Livia Serrao, Lorenzo Giovannini, Luz Elita Balcazar Terrones, Hugo Alfredo Huamaní Yupanqui, and Dino Zardi

Climatic characteristics and weather events have always conditioned the success of a harvest. Climate change and the associated increase in intense weather phenomena in recent years are making it clearer than ever that agriculture is among the sectors most at risk. Although problems in agriculture are found all over the world, the most vulnerable contexts are those where agriculture is low-tech and rainfed. Here, adaptation strategies are even more urgent to secure the food production. Assuming that the awareness of climate change is the basis for the adoption of adaptation and mitigation strategies, it is interesting to correlate the degree of perception of local inhabitants with their willingness to adopt bottom-up initiatives.

The current study focuses on banana producers’ perceptions of climate change in a tropical valley, and the initiatives that farmers adopt to cope with recent intense weather events. The banana plant (Musa Musacae) grows in tropical climates with annual rainfall around 2000 mm and average temperatures around 27°C. The species’ threadlike root system and the weak pseudostem make it particularly vulnerable to wind gusts, which, at speeds higher than 15 m/s, can bend and knock over entire plantations. The increased frequency of convective thunderstorms observed in connection with climate change has made downburst phenomena more frequent and caused greater crop loss.

The aim of the present work is to estimate the correlation between banana producers’ perceptions of climate change and their bottom-up initiatives for adaptation. To achieve this goal, the case study of the Upper Huallaga valley, which is located in the Peruvian Amazon region as shown in Figure 1, is analysed. The work was carried out at two levels: (i) we interviewed 73 banana producers in the valley, (ii) we estimated the alterations and trends in temperature and precipitation recorded by the only three available meteorological stations within the valley. Finally, we compared the two databases to evaluate if the perception of the population was confirmed by the data. Most of the surveyed population observed an increase in temperature, consistent with the results of the data analysis, and an increase in precipitation, which was not consistent with observations as these showed a cyclic variation without a clear trend. With regards to the adaptation measures, it was observed that, although a clear majority of the sample surveyed (around 82%) agreed with the existence of climate change, only 46% of them had taken any initiative to counteract adverse events in some way. However, it is important to note that the strategies implemented were all devised and implemented by the farmers themselves. Funding and coordinating the dissemination of these adaptation practices by the local authority through a rural development plan could certainly strengthen the population’s effort.

Figure 1, On the left side: the Upper Huallaga basin. On the right side: the study area

How to cite: Serrao, L., Giovannini, L., Balcazar Terrones, L. E., Huamaní Yupanqui, H. A., and Zardi, D.: From climate change perception to bottom-up adaptation initiatives: a case study from banana producers of Upper Huallaga basin, Peru, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-2341, https://doi.org/10.5194/egusphere-egu21-2341, 2021.

EGU21-5127 | vPICO presentations | ITS3.2/BG7

Characterizing hydro-meteorological extremes from a societal perspective

Ekaterina Bogdanovich, Lars Guenther, Markus Reichstein, Georg Ruhrmann, and René Orth

Extreme hydro-meteorological events often affect the economy, social life, health, and well-being. One indicator for the impact of extreme events on society is the concurrently increased societal attention. Such increases can help to measure and understand the vulnerability of the society to extreme events, and to evaluate the relevance of an event, which is important for disaster research and risk management. In this study, we analyzed and characterized hydro-meteorological extreme events from a societal impact perspective. In particular, we investigated the impact of heat waves on societal attention in European countries with contrasting climate (Germany, Spain, and Sweden) using Google trends data during 2010–2019. Thus, we seek to answer two general research questions: (i) how and when do extreme events trigger societal attention, (ii) are there temperature thresholds at which societal attention increases? 

To describe heat waves, we used maximum, minimum, average, and apparent temperature, aggregated to a weekly time scale. We analyzed the relationship between temperature and societal attention using piecewise regression to identify potential temperature-related thresholds in societal attention. The threshold is determined as the breaking point between two linear models fitted to data. We determined the corresponding goodness of fit by computing R2 for each temperature variable. The variable with the highest R2 is considered as the most influential one.

The overall relationship between temperature and Google attention to heat waves is significant in all countries and reveals clear temperature thresholds. The variable with the highest explanatory power is the weekly average of the daily maximum temperatures, which accounts for 71% of google attention in Germany, 51 % in Sweden, and 38 % in Spain. For Germany, similar results are found with media attention. In Sweden, with its colder climate, a lower temperature threshold is identified, indicating higher heat vulnerability. No significant impact of temperatures from the previous weeks is found. While further work is needed to improve the understanding of the attention-heat coupling, the demonstrated significant societal attention response to heat waves offers the opportunity to characterize heat waves from an impact perspective using the identified temperature variables, time scales, and thresholds.

How to cite: Bogdanovich, E., Guenther, L., Reichstein, M., Ruhrmann, G., and Orth, R.: Characterizing hydro-meteorological extremes from a societal perspective, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-5127, https://doi.org/10.5194/egusphere-egu21-5127, 2021.

EGU21-5414 | vPICO presentations | ITS3.2/BG7

The effect of differing drought-heat signatures on terrestrial carbon dynamics and vegetation composition

Elisabeth Tschumi, Sebastian Lienert, Karin van der Wiel, Fortunat Joos, and Jakob Zscheischler

Droughts and heatwaves have large impacts on the terrestrial carbon cycle. They lead to reductions in gross and net carbon uptake or anomalous carbon emissions by the vegetation to the atmosphere because of responses such as stomatal closure, hydraulic failure and vegetation mortality. The impacts are particularly strong when drought and heat occur at the same time. Climate model simulations diverge in their occurrence frequency of compound hot and dry events, and so far it is unclear how these differences affect carbon dynamics. Furthermore, it is unknown whether a higher frequency of droughts and heatwaves leads to long-term changes in carbon dynamics, and how such an increase might affect vegetation composition.

To study the immediate and long-term effects of varying signatures of droughts and heatwaves on carbon dynamics and vegetation composition, we employ the state-of-the-art dynamic global vegetation model LPX-Bern (v1.4) under different drought-heat scenarios. We constructed six 100-yr long scenarios with different drought-heat signatures: a “control”, “no extremes”, “no compound extremes”, “heat only”, “drought only”, and “compound drought and heat” scenario. This was done by sampling daily climate variables from a 2000-year stationary simulation of a General Circulation Model (EC-Earth) for present-day climate conditions. Such a sampling ensures physically-consistent co-variability between climate variables in the climate forcing.

The scenarios differ little in their mean climate conditions (global mean land temperature differences of around 0.3°C and global mean land precipitation differences smaller than 7%), but vary strongly in the occurrence frequency of extremes such as droughts, heatwaves, and compound drought and heatwaves (up to five times more compound extremes in the “hotdry “scenario than in the “control”), allowing us to study the effects of the extremes on vegetation. Combined hot and dry extremes reduce all tree types and promotes grassland, while only hot extremes favours trees, especially in higher latitudes. No extremes are preferred by all tree types in LPX. Net Ecosystem Production (NEP) is expected to increase in most regions for the “noextremes” scenario, while the “hotdry” scenario is likely to reduce NEP.

Our results provide a better understanding of the links between hot and dry conditions and vegetation dynamics as well as carbon dynamics. These analyses may help to reduce uncertainties in carbon cycle projections, which is important for constraining carbon cycle-climate feedbacks. The presented scenarios can be used for a variety of purposes such as studying the effects of differing drought-heat signatures on crop yield or the occurrence of fire besides others.

How to cite: Tschumi, E., Lienert, S., van der Wiel, K., Joos, F., and Zscheischler, J.: The effect of differing drought-heat signatures on terrestrial carbon dynamics and vegetation composition, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-5414, https://doi.org/10.5194/egusphere-egu21-5414, 2021.

EGU21-6162 | vPICO presentations | ITS3.2/BG7

Analysis of cumulative climate risks and associated impact cascades in Switzerland

Raphael Neukom, Nadine Salzmann, Christian Huggel, Veruska Muccione, Sabine Kleppek, and Roland Hohmann

A recent study on ‘climate-related risks and opportunities’ of the Swiss Federal Office for the Environment (FOEN) identified knowledge gaps and related missing planning tools for risks with low probability of occurrence but potentially very severe impacts for society and/or the environment. Such risks refer in particular to risks triggered by cumulating meteorological/climatic extremes events, which (i) exacerbate through process cascades or (ii) return within shorter time intervals than expected.

To respond to these knowledge gaps and ‘blind spots’ in climate risks, a collaborative effort including academic and government institutions at different administrative levels is undertaken in order to explore and analyse the potential of such large cumulative, complex risks and to suggest actions needed to manage them in Switzerland. The project is based on two case studies, which are developed in consultation with stakeholders from science, policy and practice at the national and sub-national level.

The case studies analyse risks triggered by meteorological events based on projected and recently published Swiss Climate Scenarios CH2018, considering rare but plausible scenarios where such triggering events cumulate and/or occur in combinations.

The first case study focuses on mountain systems in the southern Swiss Alps, with a potential reduction of the protective capacity of forests caused by extreme drought and heat, and subsequent increase of risks due to multiple natural hazards (fires, snow avalanches, landslides). A semi-quantitative analysis based on expert surveys allows us to estimate the probability of different levels of loss of the protective function caused by the given meteorological trigger event. In a parallel bottom-up approach we perform the analysis with an impacts-perspective and estimate the ecological and climatological thresholds that lead to a partial or complete loss of protective function. Results from the two methods are qualitatively compatible, but the bottom-up approach tends to show a higher risk of damage compared to the more ‘classical’ top-down analysis for similar meteorological events.

The second case study focuses on cascading impacts in relation with recurrent large-scale drought and heat events on urban systems and their vulnerable elements. We draw potential process cascades across various socio-economic systems for the urban area of Basel based on a systematic analysis of potentially relevant precedent information from selected past cases worldwide.

Our study is expected to provide important information concerning highly vulnerable systems and elements, their protection, and tipping points towards severe risk amplification. Moreover, we point to feasible risk management approaches and suggest transformative adaptation measures.

How to cite: Neukom, R., Salzmann, N., Huggel, C., Muccione, V., Kleppek, S., and Hohmann, R.: Analysis of cumulative climate risks and associated impact cascades in Switzerland, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-6162, https://doi.org/10.5194/egusphere-egu21-6162, 2021.

EGU21-7481 | vPICO presentations | ITS3.2/BG7

Detailed analysis of extreme heatwaves in Serbia, South-East Europe

Biljana Basarin, Tin Lukić, and Tanja Micić Ponjiger

A detailed analysis of extreme heatwave events in Serbia from the biometeorological point of view is presented in this study.  For this purpose, the newly developed Heat Wave Magnitude Index daily (HWMId), was used on Physiologically equivalent temperature (PET) for Serbia. A series of daily maximum air temperature, relative humidity, the wind was used to calculate PET for the investigated period 1979–2019. HWMId is defined as the maximum magnitude of the heatwaves in a year. Here, the heatwave is characterized as 3 consecutive days with maximum PET above the daily threshold for the reference period 1981–2010. The analysis revealed that during the investigated period the most intensive heat waves occurred in 2007, 2012 and 2015. HWMId values for 2007 were in the range of 8 to 23 indicating extreme heat stress, while for the other two events the values were not as high. Hourly temperatures revealed that the PET values during the day were as high as 55°C. Thus, the mitigation and adaptation to extreme temperature events are of vital importance for humans and their everyday activities. Future investigation should be oriented towards a way to deal with the oppressive heat. Additionally, more research is needed in order to explain and predict these catastrophic events. The main focus of future activities will be on determining the physical causes which lead to the occurrence of extreme heatwaves.

Keywords: Heat Wave Magnitude Index daily, Physiologically equivalent temperature, Serbia, heat waves

Acknowledgment: This research is supported by EXtremeClimTwin project funded from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 952384

How to cite: Basarin, B., Lukić, T., and Micić Ponjiger, T.: Detailed analysis of extreme heatwaves in Serbia, South-East Europe, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-7481, https://doi.org/10.5194/egusphere-egu21-7481, 2021.

EGU21-8427 | vPICO presentations | ITS3.2/BG7

Climate variability controlled the development of the pre-Viking society during the Late Antiquity in Southeastern Norway

Manon Bajard, Eirik Ballo, Helge I. Høeg, Jostein Bakke, Eivind Støren, Kjetil Loftsgarden, Frode Iversen, William M. Hagopian, Anne H. Jahren, Henrik H. Svensen, and Kirstin Krüger

Understanding how agricultural societies were impacted and adapted to past climate variations is critical to face to contemporary climate change and guaranty the food security (#SDG2 Zero Hunger). However, linking climate and change in the behaviour of a population are difficult to evidence. Here, we studied the climate variations of the period between 200 and 1300 CE and its impact on the pre-Viking and Viking societies in Southeastern Norway, including the adaptation and resilience of the agricultural management. This period includes, between 300 and 800 CE, one of the coldest period of the last 2000 years. We used a retrospective approach combining a multi-proxy analysis of lake sediments, including geochemical and palynological analyses, to reconstruct past changes in temperature and agricultural practices during the period 200-1300 CE. We associated variations in Ca/Ti ratio as a result of change in lake productivity with the temperature. The periods 200-300 and 800-1300 CE were warmer than the period between 300 and 800 CE, which is known as the “Dark Ages Cold Period” in the Northern Hemisphere. During this colder period, phases dominated by grazing activities (280-420 CE, 480-580 CE, 700-780 CE) alternated with phases dominated by the cultivation of cereals and hemp (before 280 CE, 420-480 CE, 580-700 CE, and after 800 CE). The alternation of these phases is synchronous of temperature changes. Cold periods are associated to livestock farming, and warmer periods to crop farming. This result suggests that when temperature no longer allowed crop farming, the food production specialized in animal breeding. The result of a Principal Component Analysis show a succession of phases of crisis, adaptation and resilience of the socio-environmental system. The Viking Age (800-1000 CE) started with an increase in temperature and corresponds to the warmest period between 200 and 1300 CE, allowing a larger development of the agriculture practices and society. Our results prove that the pre-Viking society adapted their agricultural practices to the climate variability of the Late Antiquity and that the Vikings expanded with climate warming.

How to cite: Bajard, M., Ballo, E., Høeg, H. I., Bakke, J., Støren, E., Loftsgarden, K., Iversen, F., Hagopian, W. M., Jahren, A. H., Svensen, H. H., and Krüger, K.: Climate variability controlled the development of the pre-Viking society during the Late Antiquity in Southeastern Norway, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-8427, https://doi.org/10.5194/egusphere-egu21-8427, 2021.

EGU21-8641 | vPICO presentations | ITS3.2/BG7

A conceptual framework of drought legacies in grasslands

Lena M. Müller and Michael Bahn

EGU21-16489 | vPICO presentations | ITS3.2/BG7

The relationship between temperature and digital hate – strong increase of racist tweets outside of climate comfort zone in Europe

Annika Stechemesser, Leonie Wenz, Maximilian Kotz, and Anders Levermann

Temperature has been identified as a potential cause for human conflict. Conflict poses a fundamental obstacle to Sustainable Development Goal 16 which acknowledges the importance of building peace, justice and strong institutions for people around the world. Today, conflict is no longer limited to the physical space. The increasing digitalization of all areas of everyday life reinforces the impact of cyber racism, cyber discrimination and online hate. It disproportionally affects groups with an already increased risk of marginalization such as women, lgbtq+ youth or people of color, causing affected persons to feel unsafe in digital spaces and limiting their access to online services. Twitter is one of the biggest social media platforms with more than 300 million active users around the world. We provide evidence that the amount of racist content posted to Twitter is non-linearly influenced by temperature. Exploiting the linguistic plurality of Europe, we investigate the relationship between daily maximum temperature and racist or xenophobic content online using a fixed-effects panel-regression approach for countries spanning multiple European climatic zones. Racist tweets are lowest between daily temperatures of 8°C to 17°C whereas ambient temperatures warmer or colder are associated with steep, non-linear increases. Within the next 30 years, temperatures are projected to shift with new heat extremes being reached. To quantify the potential impact on cyber hate, the number of days outside this range, weighted by the identified temperature-racist-tweet response curve is projected to increase across Europe. Results suggest, that future warming and more extreme temperatures could aggravate xenophobia and racism online, further hindering the achievement of SDG 16 and posing a challenge for future human well-being.  

How to cite: Stechemesser, A., Wenz, L., Kotz, M., and Levermann, A.: The relationship between temperature and digital hate – strong increase of racist tweets outside of climate comfort zone in Europe, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-16489, https://doi.org/10.5194/egusphere-egu21-16489, 2021.

EGU21-8736 | vPICO presentations | ITS3.2/BG7

Low-likelihood high-warming storylines for extremes 

Erich Fischer, Clemens Schwingshackl, and Jana Sillmann

Recent IPCC focused their assessment of changes in climate extremes primarily on the likely range and on mapping them as multi-model means. Recently, it has been argued that focusing primarily on the likely range potentially ignores changes in the physical climate system that are unlikely to occur but are associated with the highest risks for human and ecological systems. This is particularly the case for extremes where impacts often non-linearly depend on changes in hazards and where uncertainties are typically large both due to model response uncertainty and internal variability. Low-likelihood high-warming storylines have been proposed as a powerful tool to assess and communicate the risk associated with such future climates. However, storylines that are consistent across variables and spatial patterns is challenging.

Here, we introduce and compare different approaches for creating low-likelihood high-warming storylines for extremes based on CMIP6 models, and discuss their strengths and limitations for temperature extremes, heavy rainfall and droughts. We demonstrate that all approaches yield storylines in which changes in hot extremes, extreme rainfall and droughts strongly exceed the multi-model mean over large parts of the globe. This suggests that a focus on the likely range may indeed substantially underestimate the risk associated with changes in extremes.

We further demonstrate that the choice of the storyline approach needs to be informed by the purpose of the assessment. Pattern-scaling based storyline approaches are simple and easy to communicate and provide a reasonable first guess for extremes that are closely related to temperature changes. However, they often lead to implausible global patterns and violate physical consistency across regions and different variables. Particularly for wet and dry extremes, the models showing the largest global warming often do not show the greatest changes in extremes. Other more complex approaches have the advantage of generating storylines of globally coherent patterns of changes in extremes. Such approaches allow assessing physically consistent and spatially coherent global low-likelihood high-warming storylines of regional extremes that are suited for global risk assessment and resilience building across different sectors.

How to cite: Fischer, E., Schwingshackl, C., and Sillmann, J.: Low-likelihood high-warming storylines for extremes , EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-8736, https://doi.org/10.5194/egusphere-egu21-8736, 2021.

Abstract:

Introduction and theoretical background: The increase in extreme events as a result of climate change has serious consequences for the world (Bevacqua, Yu, & Zhang, 2018; Clark et al., 1998), with higher impacts on Andean communities, which are more vulnerable to its effects due to the scarce resources they have to cope with its effects. The study on local risk perception, as a strategy that allows people to be more aware of the hazard and therefore be more willing to deal with the eventuality of the hazard (Lopez and Marvan, 2018). Our study analyses experience with extreme events: severe storms, avalanches, droughts and floods. Furthermore, we analyze how experiences with extreme weather can be related to risk perception, communication, and adaptive behaviours.

Methods: After a thorough pilot. We selected two interviewers, from the same community. To comply with COVID-19 health protocols, the questionnaire was implemented online. All questions were presented in a closed format. The total number of participants (N=200) belonged to the Phinaya community located at the bottom of the Quelccaya glacier (5650 mamsl). All gave their consent to participate voluntarily in the study.

Results: 86% indicated having experienced drought or water shortage in the last 5 years between 1 and more than 3 times, 14% did not. Then 59% indicated that they had experienced storms between 1 and more than 3 times in the last 5 years, 41% indicated that they had not experienced any. Regarding floods, 21.5% indicated that they had experienced them, while 78.5% had not. 34.9 % indicated that they had experienced avalanches. 97.5% said they were very concerned about climate change. 82% received information on storms, 90% received information on droughts, 82% received information on floods, 51% received information on avalanches. There is a relationship between people who have had experiences with severe storms and those who have experienced landslides and avalanches. Regarding the perception of risk, we found differences between men and women. No clear relationship was identified between risk perception and extreme events. It is observed that communications about droughts influenced negatively on risk perception, the other extreme events did not show significant relations. Finally, with respect to adaptation behaviours, we found a positive relationship between experiences with storms, and perceptions of risk of climate change, greater perception of risk, greater willingness to develop adaptive behaviours.

Conclusions: Most people have been exposed to more than one type of extreme events such as droughts and storms. This study contributes to a better understanding of the relationships between public perception of climate change in Andean communities and corroborates the important role of communication and adaptive behaviors in the context of risk perceptions.

How to cite: Monge-Rodríguez, F. S. and Alvarado- Yepez, A.: Extreme events, risk perception, communication, and adaptation in the context of climate change: the case of an Andean community in Peru, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-9380, https://doi.org/10.5194/egusphere-egu21-9380, 2021.

EGU21-10242 | vPICO presentations | ITS3.2/BG7

Forcing climate variability has large impacts on terrestrial carbon storage in a dynamic global vegetation model

Andreas Krause, Katharina Küpfer, and Anja Rammig

Terrestrial carbon storage is largely driven by prevailing climate conditions. However, ecosystems are not only affected by mean climate conditions but also by day-to-day climate variability, which is projected to increase in the future. Here we explore the effects of low vs. high climate variability on global terrestrial carbon storage in the dynamic global vegetation model LPJ-GUESS. Low variability corresponds to linear interpolation between monthly means while high variability corresponds to daily means. We conduct three factorial simulations: one driven by low variability for temperature, radiation, and precipitation; one with low temperature and radiation variability but high precipitation variability; and one with high variability for all climatic drivers. All three options are commonly used in existing LPJ-GUESS studies but have so far not been compared to each other in terms of carbon cycle impacts. Surprisingly, the low variability simulation results in the smallest terrestrial carbon stocks globally (1963 Gt C), while low temperature/radiation variability but high precipitation variability simulates the largest carbon storage (2171 Gt C). Differences are most pronounced in high latitudes and deviations from the global trend also occur in some regions. Exploring the underlying processes, we find that differences in carbon stocks are largely driven by differences in ecosystem productivity. In LPJ-GUESS, high precipitation variability increases nitrogen availability via enhanced nitrogen mineralisation and reduced leaching, thereby promoting plant growth. In contrast, high temperature variability decreases productivity as the optimum temperature range for photosynthesis is often exceeded in temperate and boreal regions. Differences in fire mortality and soil water availability across simulations seem to be less important. Our results suggest that future changes in climate variability could impact ecosystem carbon storage via subtle effects on photosynthesis and coupled carbon-nutrient cycling. They also imply that ecosystem modellers need to be aware that changing the temporal resolution of the input climate (e.g. from monthly to daily means) may substantially affect their simulation results.

How to cite: Krause, A., Küpfer, K., and Rammig, A.: Forcing climate variability has large impacts on terrestrial carbon storage in a dynamic global vegetation model, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-10242, https://doi.org/10.5194/egusphere-egu21-10242, 2021.

EGU21-11716 | vPICO presentations | ITS3.2/BG7

Winter wheat and maize under varying soil moisture: from leaf to canopy

Thuy Huu Nguyen, Matthias Langensiepen, Thomas Gaiser, Heidi Webber, Hella Ahrends, Hubert Hueging, and Frank Ewert

Drought is one of the most detrimental factors limiting crop growth and production of important staple crops such as winter wheat and maize. For both crops, stomatal regulation and change of canopy structure responses to water stress can be observed. A substantial range of stomatal behavior in regulating water loss was recently reported while the crop growth and morphological responses to drought stress depend on the intensity and duration of the imposed stress. Insights into the responses from leaf to the canopy are important for crop modeling and soil-vegetation-atmosphere models (SVAT). Stomatal responses and effects of soil water deficit on the dynamic change of canopy photosynthesis and transpiration, and seasonal crop growth of winter wheat and maize are investigated based on data collected from field-grown conditions with varying soil moisture treatments (sheltered, rainfed, irrigated) in 2016, 2017, and 2018. A reduction of leaf net photosynthesis (An), stomatal conductance (Gs), transpiration (E), and leaf water potential (LWP) was observed in the sheltered plot as compared to the rainfed and irrigated plots in winter wheat in 2016, indicating anisohydric stomatal responses. Maize showed seasonal isohydric behaviour with the minimum LWP from -1.5 to -2 MPa in 2017 and -2 to -2.7 MPa in the extremely hot and dry year in 2018. Crop growth (biomass, leaf area index, and yield) was substantially reduced under drought conditions, particularly for maize in 2018. Leaf water use efficiency (An/E) and crop WUE (total dry biomass/canopy transpiration) were not significantly different among treatments in both crops. The reduction of tiller number (in winter wheat) and leaf-rolling and plant size (in maize) resulted in a reduction of canopy transpiration, assimilation rate, and thus biomass. The seasonal isohydry in maize and the seasonal variability of LWP in winter wheat suggest a possibility to use the same critical LWP thresholds for maize and wheat to simulate the stomatal control in process-based crop and SVAT models. The canopy response such as dynamically reducing leaf area under water stress adds complexity in simulating gas exchange and crop growth rate that needs adequate consideration in the current modeling approaches.

How to cite: Nguyen, T. H., Langensiepen, M., Gaiser, T., Webber, H., Ahrends, H., Hueging, H., and Ewert, F.: Winter wheat and maize under varying soil moisture: from leaf to canopy, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-11716, https://doi.org/10.5194/egusphere-egu21-11716, 2021.

EGU21-12267 | vPICO presentations | ITS3.2/BG7

The kids aren't alright

Wim Thiery, Stefan Lange, Joeri Rogelj, Carl-Friedrich Schleussner, Lukas Gudmundsson, Sonia I. Seneviratne, Katja Frieler, Kerry Emanuel, Tobias Geiger, David N. Bresch, Fang Zhao, Sven N. Willner, Matthias Büchner, and Jan Volkholz and the ISIMIP modelling team

People are being affected by climate change around the globe today at around 1°C of warming above pre-industrial levels. Current policies towards climate mitigation would result in about twice as much warming over the next 80 years, roughly the lifetime of a today's newborn. Here we quantify the stronger climate change burden that will fall on younger generations by introducing a novel analysis framework that expresses impacts as a function of how they are experienced along the course of a person's life. Combining projections of population, temperature, and 15 impact models encompassing droughts, heatwaves, tropical cyclones, crop failure, floods, and wildfires, we show that, under current climate pledges, newborns in 2020 are projected to experience 2-13 times more extreme events during their life than a person born in 1960, with substantial variations across regions. Limiting warming to 1.5°C consistently reduces that burden, while still leaving younger generations with unavoidable impacts that are unmatched by the impacts experienced by older generations. Our results provide a quantified scientific basis to understand the position from which younger generations challenge the present shortfall of adequate climate action.

How to cite: Thiery, W., Lange, S., Rogelj, J., Schleussner, C.-F., Gudmundsson, L., Seneviratne, S. I., Frieler, K., Emanuel, K., Geiger, T., Bresch, D. N., Zhao, F., Willner, S. N., Büchner, M., and Volkholz, J. and the ISIMIP modelling team: The kids aren't alright, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-12267, https://doi.org/10.5194/egusphere-egu21-12267, 2021.

This keynote presentation introduces the sources, methods, and major findings of the History of Climate and Society (HCS), a recently-coined field that uncovers the past influences of climate change on human history. It begins by offering a brief history of the field, from the eighteenth century through the present. It then describes how HCS scholars “reconstruct” past climate changes by combining what they call the “archives of nature” – paleoclimatic proxy sources such as tree rings, ice cores, or marine sediments – with the texts, stories, and ruins that constitute the “archives of society.” Next, it explains how HCS scholars in different disciplines have used distinct statistical and qualitative methods, and distinct causal frameworks, to identify the influence of climate change in the archives of society. It explores how HCS scholars conceptualize the vulnerability and resilience of past societies by introducing some telling case studies, and explaining how those case studies have grown more complex as HCS matured as a field. It then emphasizes the enduring challenges faced by HCS scholars and how, in recent months, they have been identified and are beginning to be addressed. Finally, it describes how HCS has informed climate change policy and public discourse, before offering some key lessons that policymakers can learn from the field.

How to cite: Degroot, D.: The Impacts of Climate Change on Societies: What Can We Learn from the Past?, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-99, https://doi.org/10.5194/egusphere-egu21-99, 2021.

EGU21-15524 | vPICO presentations | ITS3.2/BG7

Identifying meteorological drivers of extreme impacts: an application to simulated crop yields

Jakob Zscheischler, Johannes Vogel, Pauline Rivoire, Cristina Deidda, Leila Rahimi, Christoph Sauter, Elisabeth Tschumi, Karin van der Wiel, and Tianyi Zhang

Compound weather events may lead to extreme impacts that can affect many aspects of society including agriculture. Identifying the underlying mechanisms that cause extreme impacts, such as crop failure, is of crucial importance to improve their understanding and forecasting. In this study we investigate whether key meteorological drivers of extreme impacts can be identified using Least Absolute Shrinkage and Selection Operator (Lasso) in a model environment, a method that allows for automated variable selection and is able to handle collinearity between variables. As an example of an extreme impact, we investigate crop failure using annual wheat yield as simulated by the APSIM crop model driven by 1600 years of daily weather data from a global climate model (EC-Earth) under present-day conditions for the Northern Hemisphere. We then apply Lasso logistic regression to determine which weather conditions during the growing season lead to crop failure.

We obtain good model performance in Central Europe and the eastern half of the United States, while crop failure years in regions in Asia and the western half of the United States are less accurately predicted. Model performance correlates strongly with annual mean and variability of crop yields, that is, model performance is highest in regions with relatively large annual crop yield mean and variability. Overall, for nearly all grid points the inclusion of temperature, precipitation and vapour pressure deficit is key to predict crop failure. In addition, meteorological predictors during all seasons are required for a good prediction. These results illustrate the omnipresence of compounding effects of both meteorological drivers and different periods of the growing season for creating crop failure events. Especially vapour pressure deficit and climate extreme indicators such as diurnal temperature range and the number of frost days are selected by the statistical model as relevant predictors for crop failure at most grid points, underlining their overarching relevance.

We conclude that the Lasso regression model is a useful tool to automatically detect compound drivers of extreme impacts, and could be applied to other weather impacts such as wildfires or floods. As the detected relationships are of purely correlative nature, more detailed analyses are required to establish the causal structure between drivers and impacts.

How to cite: Zscheischler, J., Vogel, J., Rivoire, P., Deidda, C., Rahimi, L., Sauter, C., Tschumi, E., van der Wiel, K., and Zhang, T.: Identifying meteorological drivers of extreme impacts: an application to simulated crop yields, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-15524, https://doi.org/10.5194/egusphere-egu21-15524, 2021.

EGU21-12529 | vPICO presentations | ITS3.2/BG7

Economic ripple resonance from consecutive weather extremes amplifies consumption losses

Kilian Kuhla, Sven Norman Willner, Christian Otto, Tobias Geiger, and Anders Levemann

Weather extremes such as heat waves, tropical cyclones, and river floods are likely to intensify with increasing global mean temperature. In a globally connected supply and trade network such extreme weather events cause economic shocks that may interfere with each other potentially amplifying their overall economic impact.

Here we analyze the economic resonance of consecutive extreme events, that is the overlapping of economic response dynamics of more than one extreme event category both spatially and temporally. In our analysis we focus on the event categories heat stress, river floods, and tropical cyclones. We simulate, via an agent-based anomaly model with more than 7,000 economic agents and 1.8 million connections, the regional (direct) and inter-regional (indirect via supply chains) economic losses and gains for each extreme event category individually as well as for their concurrent occurrence for the next two decades (2020-2039). Thus we compare the outcome of the sum of the three single simulations to the outcome of the concurrent simulation. We show that the global welfare losses due to concurrent weather extremes are increased by more than 18% due to market effects compared to the summation of the losses of each single event category. Overall, this economic resonance yields a non-linearly enhanced price effect, which leads to a stronger economic impact. As well as a highly heterogeneous distribution of the amplification of regional welfare losses among countries.

Our analysis is based on the climate models of the CMIP5 ensemble which have been bias-corrected within the ISIMIP2b project towards an observation-based data set using a trend-preserving method. From these we use RCP2.6 and 6.0 for future climate projections. Thus we compute for each of the three extreme weather event category regional, and sectoral production failure on a daily time scale. Our agent-based dynamic economic loss-propagation model Acclimate then uses these local production failures to compute the immediate response dynamics within the global supply chain as well as the subsequent trade adjustments. The Acclimate model thereby depicts a highly interconnected network of firms and consumers, which maximize their profits by choosing the optimal production level and corresponding upstream demand as well as the optimal distribution of this demand among its suppliers; transport and storage inventories act as buffers for supply shocks. The model accounts for local price changes; supply and demand mismatches are resolved explicitly over time.

Our results suggest that economic impacts of weather extremes are larger than can be derived from conventional single event analysis. Consequently the societal cost of climate change are likely to be underestimated in studies focusing on single extreme categories.

How to cite: Kuhla, K., Willner, S. N., Otto, C., Geiger, T., and Levemann, A.: Economic ripple resonance from consecutive weather extremes amplifies consumption losses, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-12529, https://doi.org/10.5194/egusphere-egu21-12529, 2021.

EGU21-13157 | vPICO presentations | ITS3.2/BG7

Climate Services for eXtremes: Bi-directional knowledge transfer for developing adaptation strategies in agriculture and forestry on the example of the 2018-2020 summer drought in Germany.

Karsten Haustein, Diana Rechid, Florian Knutzen, Markus Groth, Paul Averbeck, Oliver Froer, Lukas Hey, and Hermann Jungkunst

The main goal of Climate Services for eXtremes (which in is an integral part of the ClimXtreme framework) is to advance our understanding of the intensity as well as the spatio-temporal distribution of extreme weather and climate events, but tailored to the needs of stakeholders in the agricultural and forestry sector. The project is designed to optimise the communication between scientists and decision makers and thus to maximise the mutual benefit with regard to climate adaption. The scientists involved learn from the interview partners what climate information is actually required on the ground to facilitate the development of adaptation strategies, whereas the sector experts gain insights into the capabilities and limits of state-of-the-art climate information.

In order to increase the efficiency of the knowledge transfer between scientists and stakeholders, we introduce a process-chain based approach: (i) the sector-specific identification of the characteristics of extreme weather conditions in close cooperation with partners from forestry and agriculture, (ii) the analysis of past and future weather and climate extremes with various statistical techniques, (iii) the investigation of the effects of these extremes by means of forest and agricultural case studies, and (iv) the development of possible needs-based adaptation strategies to future climatic conditions and extreme events based on this information.

The extended summer drought in Germany during the warm seasons 2018 to 2020 is the perfect testbed for the approach, given the wide-ranging consequences this multi-year event had especially on the forestry sector. The event will be analysed from a probabilistic point of view, i.e. what is the return time and what were the causal factors from an atmospheric dynamic and teleconnection point of view. There is also potential to investigate the role of climate change in terms of altered risks. With this information, we can offer initial guidance for the project partners as to what they have to prepare for. But crucially, the interview feedback will help guide our ultimate research strategy. It will be a function of spatial scale, indices of interest as well as scope and complexity of the data and services our partners require. The new insights will serve as a basis to investigate such extreme drought events under potential future climate conditions. 

How to cite: Haustein, K., Rechid, D., Knutzen, F., Groth, M., Averbeck, P., Froer, O., Hey, L., and Jungkunst, H.: Climate Services for eXtremes: Bi-directional knowledge transfer for developing adaptation strategies in agriculture and forestry on the example of the 2018-2020 summer drought in Germany., EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-13157, https://doi.org/10.5194/egusphere-egu21-13157, 2021.

EGU21-13263 | vPICO presentations | ITS3.2/BG7

A framework for analysing cross-border climate change impacts, responses and their propagation

Timothy R. Carter, Magnus Benzie, Emanuele Campiglio, Henrik Carlsen, Stefan Fronzek, Mikael Hildén, Christopher Reyer, and Chris West

Most studies of climate change impacts, adaptation and vulnerability confine their attention to impacts and responses within the same geographical region. However, cross-border climate change impacts that occur remotely from the location of their initial impact can severely disrupt societies and livelihoods (Benzie et al., 2019; Carter et al., under review). In this paper we present a conceptual framework and accompanying terminology for describing and analysing such cross-border impacts. The conceptual framework distinguishes an initial impact that is caused by a climate trigger within a specific region. Downstream consequences of that impact propagate through an impact transmission system while adaptation responses to deal with the impact are propagated through a response transmission system.

The framework recognises and classifies differences in the types of climate trigger, categories of cross-border impacts, scales and dynamics of impact transmission, targets and dynamics of responses and the socio-economic and environmental context. We will demonstrate how the framework can be applied using  historical examples of cross-border impacts (e.g. the severe 2011 floods that affected industrial production in Thailand, propagating through the global economy) as well as prospective cases (e.g. multiple cross-border risks and opportunities presented by Arctic sea ice decline).

We argue that the framework provides a simple, but flexible, structure to describe and analyse cross-border climate impacts and their consequences. It offers a foundation for consistent comparisons of different patterns of cross-border impacts in different sectors and geographies. It also aids understanding of adaptation strategies and their potential consequences. In particular, with systematic application of the framework it is possible to highlight gaps in our existing understanding of system dynamics, or gain new insights into particular leverage points within the system. These can be targeted in order to find ways of building resilience to climate change in the region of origin, along the impact transmission system and in the recipient region exposed to the propagated risk.

Acknowledgement

This work is being undertaken as part of the European Commission Horizon 2020-funded project CASCADES (Cascading climate risks: Towards adaptive and resilient European Societies).

References

Benzie M, Carter TR, Carlsen H, Taylor R (2019) Cross-border climate change impacts: implications for the European Union. Regional Environmental Change 19: 763-776, https://doi.org/10.1007/s10113-018-1436-1.

Carter TR, Benzie M, Campiglio E, Carlsen H, Fronzek S, Hildén M, Reyer CPO, West C (in review) A conceptual framework for cross-border impacts of climate change.

How to cite: Carter, T. R., Benzie, M., Campiglio, E., Carlsen, H., Fronzek, S., Hildén, M., Reyer, C., and West, C.: A framework for analysing cross-border climate change impacts, responses and their propagation, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-13263, https://doi.org/10.5194/egusphere-egu21-13263, 2021.

EGU21-14282 | vPICO presentations | ITS3.2/BG7

Accuracy and completeness of a near real-time citizen science-based multi-disaster inventory in the Rwenzori Mountains, Uganda

John Sekajugo, Grace R Kagoro, Liesbet Jacobs, Clovis Kabaseke, Esther Namara, Olivier Dewitte, and Matthieu Kervyn

Accurate and complete inventory of natural hazard occurrence and their level of impact is a key first step to risk assessment, but it remains a challenge, especially for high frequency low impact events that rarely makes it to the news media. This challenge is even greater in rural areas of developing countries such as Uganda, where limited IT facilities prevent dissemination of information through social media. Here we report on a citizen-science initiative to monitor small-scale disasters (landslides and floods) occurring in the Rwenzori Mountains. A network of citizen (geo-)observers was established in February 2017 to collect temporally explicit geo-referenced information on eight different hazards and their impact using smartphone technology. Since then, over 500 hazard occurrences have been reported. However, such dataset needs to be assessed for its accuracy and potential biases before being used for scientific analysis. In this study, we evaluate the accuracy and completeness of the geo-observer-based disaster reports. First, systematic errors are reduced by peer reviewing the reports and implementing automatic tests to assess potential errors in detection and biases. Then, we compare the geo-observer-based records with two independent inventories collected through systematic field mapping and  satellite imagery mapping, focusing on landslide and flood events for the period between May 2019 and May 2020.  Results show over 95% of the geo-observer reports validated in the field were correctly identified and recorded less than 5 days after the occurrence (60% true positives, 1% false positives and 39% false negatives). For the satellite imagery mapping, 29% were true positives, 43% false positives and 28% false negatives. Geo-observers provide near real time disaster information on the location and level of impact, something difficult to achieve with systematic field and satellite imagery mapping. Depending on the topography of the area and the weather conditions, it can take several days to weeks before a cloud-free satellite image of a place can be obtained. The false negatives in the Geo-observer data are due to the tendency to report mainly occurrences along roads and rural foot paths since such occurrences are easily seen and accessed. Isolated small and inaccessible landslides are often not seen or reported to the Geo-observers. While satellite imagery mapping provides an opportunity to record disaster occurrences even in extremely inaccessible places, small landslides are often missed while shallow ones can easily be confused with freshly cleared vegetation for crop planting. Citizen science-based disaster reporting therefore not only provide the spatial occurrence of disasters but also the temporal and weather-related information, necessary for disaster risk analysis.

How to cite: Sekajugo, J., Kagoro, G. R., Jacobs, L., Kabaseke, C., Namara, E., Dewitte, O., and Kervyn, M.: Accuracy and completeness of a near real-time citizen science-based multi-disaster inventory in the Rwenzori Mountains, Uganda, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-14282, https://doi.org/10.5194/egusphere-egu21-14282, 2021.

Vegetation greening in the recent three decades significantly alters the carbon and water cycles over China. The response of terrestrial ecosystem productivity to flash droughts could be influenced by vegetation conditions and characteristics of flash droughts. However, it is still unclear that how the sensitivity of vegetation to flash drought varies with increasing leaf area index (LAI) across China. We use a land surface model and multiple satellite LAI products to assess the response of gross primary productivity (GPP) to flash droughts. Evapotranspiration is increased with increasing LAI and soil moisture is correspondingly decreased. Thus, the frequency, duration, and severity of flash droughts are all intensified from a water-budget perspective. The increasing LAI is contributed to the enhanced terrestrial carbon sink through increasing water use efficiency (WUE). The resistance and resilience of GPP to flash drought are also enhanced due to the increased LAI across various climates and vegetation types. These results refine the sensitivity of GPP to flash droughts in greening China and constrain the prognostic models to simulate the response of vegetation to droughts in changing environments.

How to cite: Zhang, M. and Yuan, X.: Impacts of vegetation greenness on the sensitivity of terrestrial ecosystem productivity to flash drought in China, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-14462, https://doi.org/10.5194/egusphere-egu21-14462, 2021.

EGU21-14766 | vPICO presentations | ITS3.2/BG7

Assessment of precipitation extremes in CMIP6 decadal hindcasts over India

Jayshri Patel, Gnanaseelan Chellappan, Anant Parekh, and jasti Chowdhary

A skillful decadal precipitation prediction (DPP) is valuable for sustainable development, which currently face many challenges.Deriving reliable information from DPP is still a challenge because of the difficulties linked with precipitation predictions and coarse spatial resolution by General Circulation Models (GCMs) not able to be in a straight line appropriate for impact assessment.This study examines the decadal hindcast simulations of precipitation extreme over seven sub regions of India from different ocean-atmosphere coupled models from the Coupled Model Intercomparison Project(CMIP6) by applying quantile mapping approach.Each decadal hindcast consists of predictions for a 10-year period from the initial climate states of 1961 to 2014/2018 and the assessment of skill is carried out lead-wise from 1 to 10 for different season and different regions over India (both raw and bias corrected). The potential skill of precipitation extreme is examined in terms of  extreme precipitation index (EPIs) i.e.cumulative wet days (CWD), cumulative dry days (CDD), precipitation events between P1020(10 and 20 mm),P20P40(20 and 40 mm), PG40(>40 mm) and  annual maximum 1 & 5 day precipitation (Rx1day and Rx5day). The promising results revealed that the skills of DPPs are enhanced after the bias adjustment and the data product can be used as a key input for impacts assessments in the region.

 

How to cite: Patel, J., Chellappan, G., Parekh, A., and Chowdhary, J.: Assessment of precipitation extremes in CMIP6 decadal hindcasts over India, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-14766, https://doi.org/10.5194/egusphere-egu21-14766, 2021.

EGU21-16466 | vPICO presentations | ITS3.2/BG7

Emergence of multisectoral impacts of the global warming during the 21st century.

Audrey Brouillet and Benjamin Sultan

The current observed global warming is projected to intensify by the end of the 21st century. According to simulations of the climate system and its impacts on populations, previous studies show significant projected impacts on four main sectors: water, health, energy and agriculture. Concurrent analyses have also focused on the time of emergence (ToE) of future climate modifications to assess when new climate regimes will emerge from a prior reference. Here we propose to investigate the timing and the emergence of global warming impacts on populations over three main vulnerable regions: Western Africa (WAF), Eastern Africa (EAF) and South-eastern Asia (SEA). We propose to analyse multi-sectoral impacts that may affect human being by accounting for (but not limited to) 6 fields: crop failure, water scarcity, health, droughts, floods, and heatwaves. The ISIMIP2b protocol (phase 2b of the Intersectoral Impact Model Intercomparison Project), which provides simulated impacts from 1 to 8 sectoral impact models and four CMIP5 (5th phase of the Coupled Model Intercomparison Project) climate models, is used in this study.

              Preliminary results under the RCP8.5 future climate scenario show a strong acceleration of the decrease of the annual maize yields before 2048 in WAF and EAF according to the CLM45 impact model, suggesting a significant emergence at this time. No particular fluctuation from the long-term trend is shown in SEA. CMIP5 climate forcing (i.e. GFDL-ESM2M, HadGEM2-ES, IPSL-CM5A-LR and MIROC5) responses in maize yields exhibit larger uncertainties over EAF than over WAF and SEA. Drought metrics such as the annual number of consecutive dry days (i.e. daily precipitations < 1mm) and the annual number of periods with more than 5 consecutive dry days show an acceleration of their increases around 2052 in WAF with large climate forcing uncertainties, but no significant emergence over EAF and SEA. Flood metrics from the ORCHIDEE impact model simulations do not exhibit particular fluctuation nor acceleration of the change during the 21st century in the three regions. The next step of our study is to quantify the ToE of the significant fluctuations compared to the long-term trends of the different metrics that cover every impact sectors. The Kolmogorov-Smirnov test (‘KS-test’) method will be applied as the statistical approach to quantify the ToE independently from the signal shape. Impact models uncertainties will also be quantified compared to the climate model uncertainties, in order to assess whether impact or climate modelings is the main driver of the total uncertainties when studying the emergence of the impacts of global warming.

How to cite: Brouillet, A. and Sultan, B.: Emergence of multisectoral impacts of the global warming during the 21st century., EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-16466, https://doi.org/10.5194/egusphere-egu21-16466, 2021.

EGU21-15012 | vPICO presentations | ITS3.2/BG7

SMDRM - Social Media for Disaster Risk Management

Valerio Lorini, Peter Salamon, and Carlos Castillo

Social media has been described as a form of distributed cognition, a mechanism for understanding a situation using information spread across many minds. The interactions among people in social media are a form of collective intelligence, as they allow people to make sense of a developing event collectively. Social media users can contribute to creating a "sensor" for citizen-generated data that modelling or monitoring systems can assimilate during a crisis. Gaining situational awareness in a disaster is critical and time-sensitive. Social media presents the possibilities of a growing data source to help improve response in the early hours and days of a crisis. However, social media platforms may not provide the functionality of summarising the information that is useful for crisis responders.
SMDRM is a software platform that streamlines the processing of text and images extracted from Twitter in near real-time during a specific event. The data is collected using a combination of keywords and locations based on daily forecasts from the early warnings systems of the Copernicus Emergency Management Service such as EFAS, GloFAS and EFFIS (emergency.copernicus.eu) or triggered manually in case of earthquakes or not-forecasted events. Text is automatically "annotated" using a binary multilingual classifier trained on 12 languages and extended with multilingual embeddings. Simultaneously, a multi-class convolutional neural network labels relevant images for floods, storms, earthquakes and fires. The information that doesn't embed coordinates is geolocated in a two-step algorithm where location candidates are first selected using a multilingual named-entity recognition tool and then searched on available gazetteers. The last step of the SMDRM data processing is the aggregation of relevant information in spatial (administrative areas) and temporal (daily) units. Social media activity about an event can finally be distributed as a data map and visualised on a map server and made available to users.
SMDRM could offer timely information useful for reducing the hazard models' uncertainty and providing added-value information such as reports or descriptions of the situation on the ground or in the vicinity. Other stakeholders, such as research groups could access new data to complement the ones extracted from traditional sensors or earth observation. 
The platform can adapt to cope with the varying workload as it uses scalable software containers. If the number of tweets is higher during an impactful event, the platform can use more containers to annotate them. SMDR code, together with the tens of thousands of annotated social media messages used for training its models, will be released as an open-source platform whose modules can be adapted to serve other research projects. We describe the platform's architecture and implementation details, and two use cases where images and text were used as a use-case to test the system's modules.

How to cite: Lorini, V., Salamon, P., and Castillo, C.: SMDRM - Social Media for Disaster Risk Management, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-15012, https://doi.org/10.5194/egusphere-egu21-15012, 2021.

EGU21-6211 | vPICO presentations | ITS3.2/BG7

3D modeling of UK Coastline using social media data for landslide mapping and monitoring

Sewedo Todowede and Irene Manzella

The ubiquitousness of social media has created a valuable and massive amount of data relating to real-live events which are being explored to investigate a wide range of phenomena including, disaster monitoring, health surveillance, user sentiments, etc. For events such as landslide, which mostly occur in remote and localized areas, social media provide an opportunity to expand landslide mapping beyond the current approach.

Traditional sources of landslide events are via media reports, scientific articles, or aerial photography, thus, limiting landslide mapping to only areas where such resources exist. This is followed by repeated in-situ measurements with equipment such as LIDAR to create a 3D geomorphological model of the landslide. At locations with poor accessibility or hazardous conditions, mobilizing personnel and equipment to the site is often impossible. The financial implication of repeated field operations also means landslide monitoring programs are prioritized. These deficiencies have hampered the generation of a robust landslide inventory which is the crucial tool for understanding past landslides and developing an effective system for managing future landslides.

This study demonstrates the application of social media analytics for the identification and modeling of landslides along the South West coast of the UK. From the analysis of over 100,000 tweets, 23 landslide events reported by Twitter users are identified. Five (5) of these events have not been previously reported. Also, drone videos obtained from Twitter and YouTube were processed using Structure from Motion-Multiview Stereo (SFM-MVS) photogrammetry techniques to create a 3D model of landslides at five (5) selected locations.

Analysis of the 3D model created at one of the locations shows that an estimated 1480 m3 of earth material was removed from the landslide due to the impact of Storm Dennis and Storm Ciara events of the 8th – 9th and 15th -16th of February 2020 respectively, while an estimated 295 m3 was retained at the base of the landslide, possibly an effect of the landslide control/stabilization installation.

The result from this study shows the potential of social media to expand landslide coverage in the UK and to provide a high-resolution 3D model at minimum cost. These data can be used to monitor landslide evolution and to assess their hazard.

How to cite: Todowede, S. and Manzella, I.: 3D modeling of UK Coastline using social media data for landslide mapping and monitoring, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-6211, https://doi.org/10.5194/egusphere-egu21-6211, 2021.

EGU21-12717 | vPICO presentations | ITS3.2/BG7

Social media, diversity and vulnerability: their role in a disaster

Olga Nardini, Sara Bonati, Stefano Morelli, and Veronica Pazzi

Very few research studies have been dedicated to understanding the role of social media, diversity and vulnerability during a highly impacting event for a society. Social media are very important nowadays as a way to be in "connection to" and "link between" individuals. Thanks to technological support it is possible to create new virtual and real social relationships and networks and to be always up to date about what happen in the world. The role that virtual space plays "reducing distances", connecting people and places and facilitating the provision of support to people in need, has been receiving increasing interest in disaster studies in last years. In particular, connectivity has assumed an increasing role in relation to the diffusion of means to reach people and places in virtual mode. Furthermore, the use of social media as a means of providing information on disasters and risks could help to reduce exposure in disasters. However, several knowledge gaps are still opened, and in particular which are the potential repercussions of a high connected disaster management process on vulnerability? How can the weight of diversity change into the virtual space? The premise is that not everyone has the same possibility of accessing social media (e.g. to be informed, to know what is happening and to link with rescuers). The difficulty of accessing social media can make people invisible into the disaster management process with the risk that someone could be left behind. Thus, this presentation aims to discuss the challenges that derive from an increasing use of social platform in providing and receiving information during disasters. A second relevant point, that this presentation aims to discuss, is linked to the way citizens perceive communication platforms and how the flow of information significantly impacts on the interpretation and on the management of risk. Conclusions of this work suggest that communication should take into account the risk perception models by the public and therefore the peculiarities of each vulnerable group, to provide "targeted" communications in relation to the cultural context with the aim of reducing vulnerability growing up citizens’ awareness and knowledge. This presentation is the result of the work provided as part of the EU H2020 founded project LINKS (http://links-project.eu). 

How to cite: Nardini, O., Bonati, S., Morelli, S., and Pazzi, V.: Social media, diversity and vulnerability: their role in a disaster, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-12717, https://doi.org/10.5194/egusphere-egu21-12717, 2021.

EGU21-15361 | vPICO presentations | ITS3.2/BG7

Developing Efficient Web Crawler for Effective Disaster Management

Lakshmi S Gopal, Rekha Prabha, Divya Pullarkatt, and Maneesha Vinodini Ramesh

The exponential escalation of disaster loss in our country has led to the awareness that disaster risks are presumably increasing. In the past few years, numerous hazards have been reported in India which has caused severe casualties, infrastructural, agricultural and economic damages. Over the years, researchers have scrutinized social media data for disaster management as it has the advantage of being available in real time and stays relevant in hazard response. But, the authenticity of social media data has been questioned particularly in a disaster management scenario where false information cannot be afforded. Collection of credible disaster statistics during or after a hazard occurrence is a demanding task. Web documents such as a news report are credible when compared to social media data and hence, the proposed work aims in developing a web crawler which is a software that's capable of indexing legitimate news websites from the world wide web which contains news articles related to hazards. The articles are extracted by incorporating the technique of data scraping which includes the use of a developed hazard ontology. The ontology contains hazard relevant keywords at multiple granularities. The developed crawler is able to prioritise websites based on its contents which makes the data collection more accurate. The collected data is  analyzed and structured as it may assist in administering hazard emergencies during a hazard, preparedness before a hazard occurrence and other post disaster activities efficiently. The proposed work also focuses on local media as it may provide news reports from regional locations which might not be reported in the mainstream media.  News articles are written in natural languages and hence structuring them into a statistical form involves natural language processing methodologies. The proposed work mainly focuses on semantic information extraction from news articles to extract statistical data related to the hazard, its impacts and loss.  News illustrations often include less newsworthy content such as advertisements and past studies of the hazard location. Hence, a supervised learning based text classification is performed to classify newsworthy content from the articles and approximately 70% accuracy has been achieved.

How to cite: S Gopal, L., Prabha, R., Pullarkatt, D., and Vinodini Ramesh, M.: Developing Efficient Web Crawler for Effective Disaster Management, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-15361, https://doi.org/10.5194/egusphere-egu21-15361, 2021.

EGU21-1873 | vPICO presentations | ITS3.2/BG7

Assessment of Community Internet Intensity (CII) in Sakhalin Island

Alexey Konovalov, Yuliya Stepnova, and Andrey Stepnov

Sakhalin Island is a region with high rate of seismic activity. Tens of felt earthquakes occur within the studied area every year. Rapid macroseismic observation through the web questionaries, social networks etc. gives reliable information about ground shaking intensities and today is processed by major seismological agencies (Bossu et al., 2018; Quitoriano and Wald, 2020).

The recent development of the methodology began with the web-based macroseismic observations following Dengler and Deweey (1998) and Wald et al. (1999). Widespread global use of Community Internet Intensity (CII) was routinely applied by the U.S. Geological Survey (USGS) through the USGS DYFI questionaries. Over 5 millions felt reports were collected during last 15 years (Quitoriano and Wald, 2020).

During last 5 years the methodology was tested in Sakhalin Island (Konovalov et al., 2018). For the collection of felt reports we used regional internet resource (https://eqalert.ru/#/). The DYFI USGS questionnaires translated into Russian were used for processing the macroseismic information. The felt reports of the respondents from each settlement were transformed to the Community Weighted Sum (CWS) which takes into account various indicators of ground shaking: human sensations, position of objects, visible damages of the building. The CII was calculated using the equation (Wald et al., 1999):

CII = 3.4 ln (CWS) – 4.38.

The obtained values were rounded to the first number after the comma. In general CII should be similar to the MM intensity.

During the period from 2016 to 2020 we have got about 400 felt reports. Most of the responses came in the first minutes after the origin time of seismic event. Data with only one report or incorrectly submitted questionnaires were excluded in further calculations. The small number of the felt reports may be explained by low population density of the central and northern districts of Sakhalin Island. Finally we have found correlation between the CII and PGA (cm/s/s) which is given by the equation:

CII = 2.5 log (PGA) + 2.32.

It is suggested that given approach can be used as a robust tool for express analysis of ground shaking. It is also a good way to involve the population and bring them closer to understanding the scientific process in the era of the growth of computer technology and social networks.

How to cite: Konovalov, A., Stepnova, Y., and Stepnov, A.: Assessment of Community Internet Intensity (CII) in Sakhalin Island, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-1873, https://doi.org/10.5194/egusphere-egu21-1873, 2021.

EGU21-8816 | vPICO presentations | ITS3.2/BG7

Assessment of Flood Early Warning Systems with Social Media

Thierry Hohmann, Judit Lienert, Jafet Andersson, Darcy Molnar, Peter Molnar, and Martijn Kuller

Introduction

Flood early warning systems (FEWS) can reduce casualties and economic losses (UNEP, 2012). The EC Horizon 2020 project FANFAR (www.fanfar.eu) aims to co-develop a FEWS in West Africa together with stakeholders, predicting streamflow and return period threshold exceedance (Andersson et al., 2020). A Multi-Criteria Decision Analysis (MCDA) indicated, that stakeholders find information accuracy especially important, among a broad set of fundamental objectives (Lienert et al., 2020). Social media have the potential to support accuracy assessment by detecting flood events (Lorini et al., 2019; de Bruijn et al., 2019) due to their large spatial coverage (Restrepo-Estrada et al., 2018). We investigated the potential of social media to assess FANFAR forecast accuracy.

 

Research Approach

FANFAR forecasts are based on HYPE, which is a semi-distributed land-cover and sub-catchment based hydrological model (Arheimer et al., 2020). We lumped the forecasted flood risk (FFR) on a country scale and compared it to flood events detected on Twitter, using an algorithm (FEDA) developed by de Bruijn et al. (2019). FEDA detects flood-related tweet bursts based on regionally and temporally adjusted thresholds (de Bruijn et al., 2019). We compared FEDA detected events with floods from the disaster database EM-DAT (https://www.emdat.be/), to find if tweets indicate flooding. We also compared FEDA to the lumped FFR to identify false positives (FP), false negatives (FN), and true positives (TP), from which we deduced the probability of detection (POD) and false alarm rate (FAR). We further calculated the correlation of single flood-related tweets with the lumped FFR and investigated seasonality, lag, and the influence of rainfall.

 

Findings

The detailed findings are described in Hohmann (2021). FEDA (i.e., tweets) and EM-DAT events (i.e., floods) mostly occurred in the same period. However, FEDA detected shorter and more frequent events than EM-DAT. In the Upper Niger, PODFEDA and FARFEDA (deduced from FEDA) were of similar order of magnitude as the PODS and FARS (deduced from streamflow) but were different in the Lower Niger region. This suggests that tweets can be employed additionally to e.g. streamflow timeseries as a complementary way to evaluate accuracy. Correlation analysis between single flood-related tweets and the lumped FFR showed no relationship. We also did not find a systematic influence of seasonality or a lagged response between tweets and FFR. The correlation coefficients between tweets and rainfall ranged from 0.1-0.9, but were mostly non-significant. This suggests that a performance assessment based on single tweets is not (yet) adequate. Also, since FEDA does not differentiate between pluvial and fluvial floods, it is less suited to assess the accuracy of FANFAR. Our findings suggest the need for inclusion of other factors into the performance assessment of FEWSs, such as regional thresholds to identify TP, FP, and FN. Also, rainfall causing pluvial flooding must be considered. Finally, our approach is limited to Twitter. Further research should assess the potential of e.g. Facebook to be included in FEWS performance assessment. The question whether social media, FEWSs, or EM-DAT are correct remains, and is in our opinion best addressed by employing multiple data sources.

How to cite: Hohmann, T., Lienert, J., Andersson, J., Molnar, D., Molnar, P., and Kuller, M.: Assessment of Flood Early Warning Systems with Social Media, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-8816, https://doi.org/10.5194/egusphere-egu21-8816, 2021.

EGU21-12935 | vPICO presentations | ITS3.2/BG7

An integrated approach for investigating flood risk perception in urban areas: some hints from the city of Brindisi (southern Italy)

Stefania Santoro, Vincenzo Totaro, Ruggiero Lovreglio, Domenico Camarda, Vito Iacobellis, and Umberto Fratino

Increased availability of social media and crowdsourced data is becoming a precious source of information in Disaster Risk Management, heralding a new era where the policy makers adapt their strategies to the potential of these new technologies.  This is also happening in the field of Flood Risk Management, where the aid of new technologies can provide important support for disaster risk reduction. On the one hand, they play an important role in the collection, monitoring and data analysis of physical flood processes. On the other hand, they foster the involvement of citizens threatened by the flood risk situation, creating shared knowledge and collaboration and becoming tools to educate and empower citizens' behavior, increasing community resilience.

Evidence shows that community response to flood risk is associated with the social context in which a specific flood occurs. A wide range of sociodemographic characteristics, but also the psychological factor of risk perception, have been identified as factors influencing citizens’ response, contributing in increasing or decreasing the effects of flooding on the environment.

In this study a coupled approach that combine Crowdsourced retrieved data and information from newspaper media is proposed and applied to the urban territory of the city of Brindisi (southern Italy), subject to multiple sources of flood risk, in order to demonstrate potential advantages arising from the implementation of such built analysis.

Crowdsourcing data based on e-survey allowed the collection of social flood data in order to explore how citizens living in the urban area of Brindisi perceive flood risk and assess their preparedness for protective measures. Specifically, the degree of citizen risk perception has been investigated through factors influencing risk perception subdivided into three categories: world view, media influence and social value and trust; the degree of citizens’ preparedness knowledge has been investigated asking citizens to select the recognized Flood Protection Strategies from the set of alternatives in the Civil Protection Behavioral Guide.

Integration of available data about previous floods with a newspaper-based research of historical floods allowed to detect a tendence of Brindisi urban territory to be subject to floods that can be reconducted mainly to pluvial and fluvial type. Journal reports provided precious details not only on affected streets and neighborhoods, but also on type and dynamics of damages. Results of surveys showed how this flood phenomenology is perceived by population, providing an important integration of the information available from current flood maps. Measurement of emergency measures knowledge revealed to be an effective source of information for an a priori modelling of reliable flooding scenarios.Results emerging from proposed approach can constitute a precious support for emergency managers and local Authorities, because of its ability in capture heterogeneities in flood phenomenology and population preparedness. Emergency planning phase can be therefore enriched with elements that contribute to the definition of risk potential situations and therefore make the response and recovery phase more effective.

How to cite: Santoro, S., Totaro, V., Lovreglio, R., Camarda, D., Iacobellis, V., and Fratino, U.: An integrated approach for investigating flood risk perception in urban areas: some hints from the city of Brindisi (southern Italy), EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-12935, https://doi.org/10.5194/egusphere-egu21-12935, 2021.

EGU21-2732 | vPICO presentations | ITS3.2/BG7

How to deliver information on induced seismicity to the authorities and general public? 

Niina Junno, Pia Bäcklund, Johanna Tuomisaari, Kati Oinonen, Toni Veikkolainen, Annakaisa Korja, and Seismic Risk Working Group

Alternative, carbon-free energy sources are essential to regulate the global climate crisis. Geothermal energy – i.e., heat harvested by geothermal systems by drilling geothermal wells to circulate water in a fractured hot rock mass at the depth of 1-7 km – has a huge potential as an environmentally friendly carbon-free energy source. One of the drawbacks is that geothermal systems can induce small-magnitude earthquakes that pose seismic risk to critical sensitive infrastructure. SEISMIC RISK - Mitigation of induced seismic risk in urban environments -project focuses on how to evaluate, mitigate and communicate seismic hazard and risk in an urban environment. Some of the associated challenges are the unclear regulatory, administrative and policy processes and unclear roles of the different actors. Another problem concerns defining what constitutes relevant information and how it should be disseminated to the public.

One part of the project is to carry out interviews of stakeholders (energy companies, municipalities and state authorities) on, how they perceive the current situation. These will give information on 1) the extent to which different actors have a common understanding of the situation and potential risks, 2) who should be responsible for coordinating risk management, and 3) how citizens should be informed of potential risks and should they be able to participate in location decisions of such geothermal power plants. Another part of the project is focusing on, how social media can better be used for rapid communication of induced seismic events and for the gathering of observations. Currently social media (Twitter) is already used for rapid notification of seismic events to the public. Gathering of macroseismic observations is handled online.

How to cite: Junno, N., Bäcklund, P., Tuomisaari, J., Oinonen, K., Veikkolainen, T., Korja, A., and Working Group, S. R.: How to deliver information on induced seismicity to the authorities and general public? , EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-2732, https://doi.org/10.5194/egusphere-egu21-2732, 2021.

ITS3.5/NH3 – Learning from the past? The role of extreme events and natural hazards in the human past

EGU21-8123 | vPICO presentations | ITS3.5/NH3

Investigating the impact of Pleistocene climate change on early humans in the southern Kalahari

Jayne Wilkins, Robyn Pickering, Jessica von der Meden, Luke Gliganic, Kyle S Brown, Irene Esteban, Wendy Khumalo, Precious Chiwara, Yonatan Sahle, Kelly Kirsten, and Benjamin J Schoville

Homo sapiens exhibit extreme behavioural plasticity, mediated by culture and technology, that permits us to adapt rapidly to new environments and situations. Understanding the role that past climate change played in selecting for Homo sapiens’ adaptability is a key question in human evolution research. The arid and semi-arid Kalahari Basin in southern Africa is an ideal region for addressing this question because fossil, genetic, and archaeological evidence supports an early origin for Homo sapiens in southern Africa. The growing archaeological record of the Kalahari Basin reveals that significant behavioural innovations accumulated in the region over the course of the Middle and Late Pleistocene, including ochre use, hafted hunting weapons, fishing, and figurative paintings. Here, we report the results of interdisciplinary investigations at two locales in the southern Kalahari; Ga-Mohana Hill and Witberg 1. The archaeological and palaeoenvironmental record (based on U-Th dating of tufas) at Ga-Mohana Hill reveals that site occupation correlated with a previous period of increased effective precipitation ~110-100 ka, and preliminary results suggest a more complicated relationship between occupation and precipitation after that time. At Witberg 1, Middle Stone Age archaeology is associated with the shoreline of a previously unidentified palaeolake. Current investigations are focused on dating the Witberg deposits, analysing the lithic technology, and generating palaeoenvironmental archives using phytoliths and diatoms. Collectively, this research provides a rare opportunity to evaluate Middle Stone Age occupation across a changing landscape from both stratified rockshelters and sealed open-air sites, to explore the complex interactions between past climate change and early human behaviors, and to better understand the origins of Homo sapiens extreme adaptability.

How to cite: Wilkins, J., Pickering, R., von der Meden, J., Gliganic, L., Brown, K. S., Esteban, I., Khumalo, W., Chiwara, P., Sahle, Y., Kirsten, K., and Schoville, B. J.: Investigating the impact of Pleistocene climate change on early humans in the southern Kalahari, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-8123, https://doi.org/10.5194/egusphere-egu21-8123, 2021.

EGU21-2844 | vPICO presentations | ITS3.5/NH3

Climate change, not human population growth, correlates with late Quaternary megafauna declines in North America

Mathew Stewart, Christopher Carleton, and Huw Groucutt

The late Quaternary saw the extinction of a great number of the world’s megafauna (those animals >44 kg), an event unprecedented in 65 million-years of mammalian evolution. Extinctions were notably severe in North America where 37 genera (~80%) of megafauna disappeared by around the late Pleistocene/Holocene boundary (~11.7 thousand-years-ago, or ka). Scholars have typically attributed these extinctions to overhunting by rapidly expanding human populations (i.e., overkill), climate change, or some combination of the two. Testing human- and climate-driven extinctions hypotheses in North America, however, has proven difficult given the apparent concurrency of human arrival in the Americas—more specifically, the emergence of Clovis culture (~13.2–12.9 ka)—and terminal Pleistocene climate changes such as the abrupt warming of the Bølling-Allerød interstadial (B-A; ~14.7–12.9 ka) or near-glacial conditions of the Younger-Dryas stadial (YD; 12.9–11.7 ka). Testing these hypotheses will, therefore, require the analysis of through-time relationships between climate change and megafauna and human population dynamics. To do so, many researchers have used summed probability density functions (SPDFs) as a proxy for through-time fluctuations in human and megafauna population sizes. SPDFs, however, conflate process variation with the chronological uncertainty inherent in radiocarbon dates. Recently, a new Bayesian regression technique was developed that overcomes this problem—Radiocarbon-dated Event-Count (REC) modelling. Using the largest available dataset of megafauna and human radiocarbon dates, we employed REC models to test whether declines in North American megafauna species could be best explained by climate change (temperature), increases in human population densities, or both. On the one hand, we reasoned that if human overhunting drove megafauna extinctions, there would be a negative correlation between human and megafauna population densities. On the other hand, if climate change drove megafauna extinctions, there would be a correlation between our temperature proxy (i.e., the North Greenland Ice Core Project [NGRIP] δ18O record) and megafauna population densities. We found no correlation between our human and megafauna population proxies and, therefore, no support for simple models of overkill. While our findings do not preclude humans from having had an impact—for example, by interrupting megafauna subpopulation connectivity or performing a coup de grâce on already impoverished megafauna—they do suggest that growing populations of “big-game” hunters were not the primary driving force behind megafauna extinctions. We did, however, consistently find a significant, positive correlation between temperature and megafauna population densities. Put simply, decreases in temperature correlated with declines in North American megafauna. The timing of megafauna population declines and extinctions suggest that the unique conditions of the YD—i.e., abrupt cooling, increased seasonality and CO2, and major vegetation changes—played a key role in the North American megafauna extinction event.

How to cite: Stewart, M., Carleton, C., and Groucutt, H.: Climate change, not human population growth, correlates with late Quaternary megafauna declines in North America, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-2844, https://doi.org/10.5194/egusphere-egu21-2844, 2021.

The purpose of this paper is to look at the prehistoric human settlement patterns in the northern Great Basin of the United States
in light of a variety of climate proxies.  The intent is to look at the response of Great Basin hunter-gatherers in response to extreme climatic events.  
Focus will be on two US Geological Survey designated hydrographic basins: the Black Rock Basin and 
the Truckee Basin.  The Black Rock Basin contains the Quinn River which originates in the Montanna Mountains and terminates into a seasonal lake
on the Black Rock playa.  The Truckee Basin contains the Truckee River which flows from Lake Tahoe in the Sierra Nevada range 
to the terminal Pyramid Lake.  

Radiocarbon dates from excavated archaeological sites in the two basins are used as a demographic and settlement proxy.  Climate proxies
from the two basins include: oxygen isotope data from Pyramid Lake, pollen cores from Mud Meadows spring and Summit Lake, and tree ring 
data from the Jackson Mountains. 

Both basins see initial human settlement during the Younger Dryas period, with a growth in population/settlements through 8000 BP. After
approximately 7800 BP, there is a paucity of dated sites until approximately 4000 BP.  Whether this is due to the 8.2 kya BP climatic event and/or
the Mount Mazama volcanic eruption, is uncertain.  Oxygen isotope data from Pyramid Lake does indicate a period of hyper-aridity throughout the
northern Great Basin between ca. 8-4 kya BP.  The aridity declines after 4 kya based on the oxygen isotope data, and settlement in the 
two basins increases.  With the onset of the Late Holocene Drought, ca. 2500 BP, population/settlement declines are seen except around 
major lakes, north of 42N latitude, and elevations above 2000m. After 2000 BP, population/settlement increases throughout both basins.  Notable
increases of population/settlement occurs in the Late Antique Little Ice Age and continues throughout the Medieval Climatic Anomaly (MCA). Environmental proxy data indicates the MCA was a period of extreme aridity in the northern Great Basin. Despite ameorilating conditions in both basins after the MCA and in the Little Ice Age, population/settlement declines after circa 700 BP.    

How to cite: Hall, M.: Climatic Extremes and Human Resilience: An Examination of Two Hydrographic Basins in the Great Basin (northern Nevada, USA), EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-13370, https://doi.org/10.5194/egusphere-egu21-13370, 2021.

EGU21-10760 | vPICO presentations | ITS3.5/NH3

The 4.2 ka event and the end of the ‘Temple Period’ in Malta

Huw S. Groucutt

The compact size of the semi-isolated Maltese archipelago and its relatively challenging environmental conditions, with limited soil cover and variable precipitation averaging around 600 mm a year, mean that the area offers an important case study of human-environment interactions. Following an initial phase of Neolithic settlement, the ‘Temple Period’ in Malta began from around 5.8 ka and within a few hundred years the spectacular ‘temples’ which characterize the period and are among the oldest buildings in the world began to be constructed. After over a thousand years this long-lived culture came to a seemingly abrupt end at ca. 4.4 to 4.2 ka, and was followed by Bronze Age societies with radically different material culture, funerary behaviour, and architecture. Various ideas concerning the reasons for the end of the Temple Period have been expressed. These range from climate change, to invasion, to social conflict resulting from the development of a powerful ‘priesthood’. Here, the idea that the end of the Temple Period was caused by aridity induced by the 4.2 ka event is tested. The 4.2 ka event is a classic example of an abrupt climate episode, and while it has been linked with several examples of significant societal change, such as the end of the Old Kingdom in Egypt, its details and relevance have been debated. To evaluate the Maltese example, archaeological data is fused with an understanding of the geology and palaeoenvironment of Malta, as well as consideration of the wider regional situation at this time in terms of demography and material culture, as well as the possible role of factors such as disease epidemics. The Maltese example forms a fascinating case study for understanding issues such as chronological uncertainty, disentangling cause and effect when several different processes are involved, and the role of abrupt environmental change in impacting human societies. Ultimately, it is suggested that the 4.2 ka event played a significant role in the end of the Temple Period, but this has to be understood within the specific geological and societal circumstances of the Maltese islands.

How to cite: Groucutt, H. S.: The 4.2 ka event and the end of the ‘Temple Period’ in Malta, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-10760, https://doi.org/10.5194/egusphere-egu21-10760, 2021.

The Atlantic Forest is a major biogeographic zone of Brazil, encompassing biodiverse evergreen, semi‐deciduous, and Araucaria forests. It is presently home to millions of people, and, consequently, has experienced high levels of defaunation/deforestation through fragmentation and habitat loss in recent years. A growing archaeological and palaeoecological consensus indicates growing anthropic influences on forest distribution during the pre-Columbian period, hand-in-hand with land use intensification and increasing social complexity over time. 

Against this backdrop, this paper expands upon recent palaeodemographic work in South America to evaluate the role of long-term (centennial-scale) hydroclimatic oscillations (and the antiphasing thereof) in the Atlantic Forest domain as a potential "push factor" engendering human-driven forest expansion. It will synthesise archaeological, palaeoclimatological, and palaeoecological records, evaluate data quality, and identify areas for expansive future research. 

How to cite: Riris, P. and Gregorio de Souza, J.: Pre-Columbian palaeodemography in the Atlantic Forest (Brazil): evaluating the role and influence of extreme hydroclimatic oscillations, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-16059, https://doi.org/10.5194/egusphere-egu21-16059, 2021.

EGU21-7563 | vPICO presentations | ITS3.5/NH3

Super-volcanic eruptions and impacts on hominin evolution

James Cole and Rob Hosfield

The impact of super volcanic eruptions (Volcanic Explosivity Index 7-8+) on human evolution is a topic that has invited much debate and controversy (Ambrose 1998, Petraglia et al. 2007, 2012; Clarkson et al., 2020), and has typically focused on the impacts on human populations within the last 100-200kya (e.g. Groucutt 2020). What is less well understood is whether there is any clear evidence to show how super-volcanic eruptions, and their subsequent impacts on paleo-environments and climates, may have influenced hominin evolution over the last c. 5mya. Previous studies using first and last hominin appearance dates have suggested that orbitally-induced climatic cycles (eccentricity, obliquity and precession) may play a role in hominin speciation events, but that only obliquity shows any significant relationship with extinction events (Grove 2012a). Firth and Cole (2015) subsequently suggested that selected super-eruptions may have acted as critical enhancers to particular orbital forcing events.

 

This paper revisits the Firth and Cole (2015) study and presents a comparison of super volcanic eruptions against first and last hominin appearance dates; orbitally induced climatic cycles; global temperature (measured using the LR04 Benthic Stack – Lisiecki and Raymo 2005); and broad technological behavioural changes in order to assess to what extent such eruptions may have impacted, either directly or indirectly, on human evolution at different temporal and geographic scales. Such large eruptive events certainly do seem to disrupt climatic conditions for significant periods of time at a generational level (Harris 2008). Where data is fine grained enough, volcanic activity also seems to impact on human population dispersals, through push and pull factors, and drive changes in the behavioural record (e.g. Groucutt 2020). However, at the broad evolutionary scale, volcanic eruptions do not seem to lead to a significant turnover of hominin species (at least in regard to the resolution of the data currently available). Therefore, we suggest that future work should seek to bring these two perspectives of scale together to better understand super volcanoes in terms of the complex interplay of changing local conditions and their impacts on the broader global picture of human evolution.

 

 

References:

Ambrose, S.H., 1998. Late Pleistocene human population bottlenecks, volcanic winter, and differentiation of modern humans. Journal of Human Evolution. 34, 623–651.

Clarkson, C. et al. 2020. Human occupation of northern India spans the Toba super-eruption ~74,000 years ago. Nature Communications 11: 961.

Firth C.R. and Cole J. 2015: A review of super-volcano eruptions and their impact on hominin evolution. INQUA XIX Congress: Japan, July.

Groucutt, H. 2020. Volcanism and human prehistory in Arabia. Journal of Volcanology and Geothermal Research 402: 107003.

Harris, B. 2008. The potential impact of super-volcanic eruptions on the Earth’s atmosphere. Weather 63 (8): 221 – 225.

Petraglia, M.D., et al.,  2007. Middle Paleolithic assemblages from the Indian subcontinent before and after the Toba super-eruption. Science 317, 114–116.

Petraglia, M.D., Korisettar, R., Pal, J.N., 2012. The Toba volcanic super-eruption of 74,000 years ago: climate change, environments, and evolving humans. Quaternary International. 258, 1–4.

How to cite: Cole, J. and Hosfield, R.: Super-volcanic eruptions and impacts on hominin evolution, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-7563, https://doi.org/10.5194/egusphere-egu21-7563, 2021.

EGU21-13283 | vPICO presentations | ITS3.5/NH3

Two extreme events near the Allerød-Younger Dryas transition: A story read from Bohemian Forest lake sediments (Central Europe)

Daniel Vondrák, Gunther Kletetschka, Eva Svecova, Jolana Hruba, Richard Štorc, Tomas Hrstka, Marco Heurich, Willem Oscar van der Knaap, and Evzen Stuchlik

Near 12,850 cal. yr. BP, the Younger Dryas cooling (YD) abruptly reversed the warming trend from the last glacial to the present interglacial at high northern latitudes. Subsequent YD-onset-related changes, including hydroclimate shifts, affected ecosystems and human societies worldwide. The main YD trigger – e.g., a massive meltwater input into the North Atlantic Ocean, volcanic gas aerosols from the cataclysmic Laacher See (LS) eruption in the Volcanic Eifel, Germany, or an extraterrestrial body impact or airburst – remains widely debated and unclear. We have obtained lake sediment cores from three sites located in the Bohemian Forest Mts., Czechia-Germany-Austria border area (distance of 450–470 km from the LS volcanic crater). The characteristic LS tephra glass shards were documented in all three cores using X-ray fluorescence scanning, magnetic susceptibility measurements, and direct observation by scanning electron microscopy, and their concentrations were quantified by a TESCAN Integrated Mineral Analyzer (TIMA). Our geochemical results show the closest match with the so-called MLST-B phreatomagmatic phase of the LS eruption. Moreover, a significant amount of LS-(crypto)tephra-related phosphorus (up to 0.15%), often the limiting nutrient in both terrestrial and freshwater ecosystems, was found in the sediments. The discovery of the LS volcanic ash in the Bohemian Forest points to a wider distribution of this (crypto)tephra than has been known so far (evident transport also in the eastern direction). It opens up new potential for tephrochronologically supported research of Late-glacial sediments in eastern Central Europe and exploring the role of the event in human prehistory. In addition to the LS cryptotephra, we observed magnetically extracted iron-rich microspherules with signs of high-temperature melting and quenching in all studied sediment cores. Their maxima (3–36 objects per 1 g of dry sediment) were situated 2.2–3.1 cm above peaks in the LS tephra shard concentrations. Such exotic objects were reported from numerous sites on several continents where more impact-related proxies were documented by proponents of the YD impact hypothesis. Based on this evidence, we hypothesize that the Allerød-Younger Dryas transition in Central Europe was likely affected by more than one extreme event. The LS eruption was followed by an event during which the iron-rich microspherules were formed. The ongoing study is supported by the Czech Grant Foundation (20-08294S – PROGRESS).

How to cite: Vondrák, D., Kletetschka, G., Svecova, E., Hruba, J., Štorc, R., Hrstka, T., Heurich, M., van der Knaap, W. O., and Stuchlik, E.: Two extreme events near the Allerød-Younger Dryas transition: A story read from Bohemian Forest lake sediments (Central Europe), EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-13283, https://doi.org/10.5194/egusphere-egu21-13283, 2021.

Approximately 13ka BP, the Laacher See volcano (East Eifel volcanic field, Rhenish Shield) erupted cataclysmically1. The details of this eruption as well as its impact on climate, environments and human in the near and far fields have been intensely researched offering rich data for designing Realistic Disaster Scenarios that consider, specifically, the potential consequences of renewed volcanic activity in the Eifel and, more generally, the consequences of similar extreme events/natural hazards on societies in Europe2. In this paper, I review the available evidence relating to the Late Pleistocene eruption with particular focus on (i) new climate modelling3, (ii) the impacts of the tephra-fall on ecosystem services4,5 and (iii) the disruption to contemporaneous forager migration and communication networks6,7. Building on this, I reflect on how this evidence has recently fed into a special museum exhibition that places a Laacher See-type eruption in the year 2100 (https://www.moesgaardmuseum.dk/en/exhibitions/after-the-apocalypse/). Combing principles of evidence-based climate communication8–10, Realistic Disaster Scenario thinking11,12 and state-of-the-art exhibition design, the exhibition addresses likely impacts on economy, travel/communication networks, politics and culture within the context of Anthropocene warming as projected by the IPCC scenarios.

 

 

References:

How to cite: Riede, F.: Apocalypse then! Apocalypse now? Using the Laacher See eruption (13ka BP) for Realistic Disaster Scenario design, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-14745, https://doi.org/10.5194/egusphere-egu21-14745, 2021.

EGU21-3460 | vPICO presentations | ITS3.5/NH3

Samalas and the Fall of the Mongol Empire:  A volcanic eruption’s influence on the dissolution of history’s largest contiguous empire

Zoltán Kern, Stephen Pow, Zsolt Pinke, and László Ferenczi

Climate responses to major tropical volcanic eruptions bring about complex social effects with lasting historical consequences. Based on several historical episodes, we establish an argument that the weather-altering eruption of Samalas (1257), which shifted the Asian monsoon and caused global weather anomalies, may have played a significant role in the breakup of the Mongol Empire. The empire’s end came soon after the largest eruption of the Common Era, and its political situation devolved into open warfare between claimants to the throne. While this has previously been described in the historiography as a purely political series of events guided by individual actors’ motivations, the state’s collapse occurred in fact amidst a series of epidemics, droughts, famines, and erratic weather which can be plausibly tied to aftereffects of the eruption. 

 

Focusing on a few case studies, textual sources mention a fatal epidemic in southwestern China in 1259 which suddenly ended the life of Möngke Khan, the last ruler of the unified Mongol Empire. Based on terminology and descriptions of the epidemic, records of cholera across the larger region, and an ostensible relationship between other historical mega eruptions and ensuing pandemics, we argue that 1259 may have seen a cholera outbreak. Secondly, we note that the hydroclimatic aftereffect of extreme drought over Mongolia and Eastern China, peaking in 1259-60, weakened cavalry forces based in Inner Asia and the Mongolian Plateau. The drought and resultant famine had major historical consequences by influencing the outcome of the civil war (1259–1264) fought between Möngke Khan’s surviving brothers for control of the empire. Mongolia lost its undisputed central position as the state fragmented into at least six independent khanates, marking the end of the unified Mongol Empire. While political events and human decisions played major roles in developments, and societal responses could ameliorate the Samalas eruption’s impact, we argue that ignoring it leaves out an important element of our understanding of these events of global historical significance. The work of the researchers is presently being prepared for publication.

How to cite: Kern, Z., Pow, S., Pinke, Z., and Ferenczi, L.: Samalas and the Fall of the Mongol Empire:  A volcanic eruption’s influence on the dissolution of history’s largest contiguous empire, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-3460, https://doi.org/10.5194/egusphere-egu21-3460, 2021.

EGU21-12270 | vPICO presentations | ITS3.5/NH3

Large volcanic eruptions as a natural hazard: The impact of the 536/540 CE double event on the atmospheric circulation, surface climate, vegetation and society in Scandinavia

Evelien van Dijk, Ingar Mørkestøl Gundersen, Manon Bajard, Helge Høeg, Kjetil Løftsgård, Frode Iversen, Claudia Timmreck, Johann Jungclaus, and Kirstin Krüger

Large volcanic eruptions that reach the stratosphere cool the surface climate and impact the atmospheric circulation, feeding back on the local climate. The mid-6th century is an outstanding period in climate history that featured an extreme cold period, including one of the coldest decades in the past 2000 years. It was triggered by the 536/540 CE volcanic double event, creating the strongest decadal volcanic forcing in the last two millennia. During this period societal changes are recorded around the world, like the Great Migration period and the outbreak of the Justinian Plague. However, not a lot is known about the causal relationships between global cooling and societal change. Less is known also, about the impact of the large-scale atmospheric circulation on the regional climate, vegetation and society in Scandinavia after this volcanic double event. Here we aim to improve this understanding by combining global climate and regional growing-degree-day (GGD) modeling with climate proxies and archaeological records from Southeastern Norway.

We use PMIP4 past2k runs and the MPI-ESM ensemble simulation of the 6th/7th century (520-680 CE), to analyze the atmospheric circulation, surface climate and vegetation changes as a response to the volcanic double event of 536/540 CE, over Scandinavia, specifically Southeastern Norway. Thereby we focus on the response of the major circulation patterns that influence the climate over Northern Europe: the positive and negative North Atlantic Oscillation, the Scandinavian blocking and the Atlantic ridge. The results of the GDD model, driven with the MPI-ESM model input, are compared to local pollen and climate records and archaeological data (e.g. grave density and settlement records) to shed more light on the local climate, vegetation and society impact. This comparison allows us to better understand how a natural hazard influenced local areas and climate records in Southeastern Norway. This study is part of the VIKINGS project, which focuses on the impact of volcanic eruptions on climate, environment and society in Norway/ Scandinavia.

How to cite: van Dijk, E., Gundersen, I. M., Bajard, M., Høeg, H., Løftsgård, K., Iversen, F., Timmreck, C., Jungclaus, J., and Krüger, K.: Large volcanic eruptions as a natural hazard: The impact of the 536/540 CE double event on the atmospheric circulation, surface climate, vegetation and society in Scandinavia, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-12270, https://doi.org/10.5194/egusphere-egu21-12270, 2021.

Around 8200 years ago, the Storegga tsunami, caused by a massive submarine landslide off the coast of Central Norway, struck the coasts of west Norway, Scotland and Doggerland. This event is well known from wide ranging geological and palaeobotanical work undertaken over the last 30 years. What has been less explored, however, is the potential social impact that this natural freak event had on the Mesolithic hunter-gatherer societies living on the coasts and shores of the North Sea. What happened in the tsunami’s aftermath? It has been widely assumed to have been a disaster – but was it? What constituted a disaster in the Mesolithic? In the Mesolithic, people were hunter-gatherer-fishers, they lived by, off, and with the sea. Settlement sites in West Norway were concentrated along the outer coast. People lived on the shores of islands and headlands, or along resource rich tidal currents. Eastern Scottish Mesolithic sites are also found on contemporary coasts, while the coasts of central Doggerland have long since become submerged. What happened to groups in these landscapes on the day the sea became a monster and in the years that followed? In this paper, we will outline a newly started project that will investigate the social impact of the tsunami in areas of the North Sea that have distinctive Mesolithic histories. These coastal inhabitants had, for millennia, developed their own traditions to engage with and learn how to exploit and keep safe from the sea. What can we learn about Mesolithic societies by investigating how communities handled the forces of a tsunami? Responses identified in the archaeological material and environmental archives can potentially inform us of social structures, institutions or ways of living that made the existing societies resilient or vulnerable.

How to cite: Nyland, A., Warren, G., and Walker, J.: When the sea become a monster? The social impact of the Storegga tsunami, 8200 BP, on the Mesolithic of northern Europe, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-2008, https://doi.org/10.5194/egusphere-egu21-2008, 2021.

Paleogeographic data give grounds to assert that at the end of the Valdai Ice Age, transgressions of the Caspian Sea took place, and the sea level during these periods exceeded the current one by tens of meters. The physical mechanisms, climatic or others, that could have caused such an extreme sea level rise have not yet been established. At the same time, in the modern Volga basin, traces of very large ancient river channels are widespread, which could have been formed by ancient rivers with the water flow 2-3 times larger than the modern rivers. Thus, the hypotheses of the extreme rise in the Caspian Sea level can be reduced to considering possible sources of the increase in the flow of the ancient rivers. However, the question of possible sources of such a significant river flow remains open. At the end of the Paleocene - beginning of the Holocene, precipitation over the Caspian Sea catchment was not higher than now, the contribution of melted glacial waters in the Late Glacial Era was also insignificant.  Hypotheses about significant changes in the catchment area of the Caspian Sea during those times are not confirmed by paleogeographic data either. In our work, we test the hypothesis that the river inflow into the ancient Caspian Sea could significantly exceed the current inflow due to the spread of post-glacial permafrost over the sea catchment area, which contributed to a decrease in runoff losses due to infiltration into frozen soils.

The physical validity of the above hypothesis was tested using numerical experiments with a hydrological model of the Volga River basin, developed on the basis of the ECOMAG modeling platform. Assuming that the climatic conditions in the modern Volga basin area during the Late Glacial Era were close to the current conditions, numerical experiments were carried out to simulate deep freezing of soil throughout the entire territory of the modern Volga basin area. It is shown that under permafrost conditions, the Volga River runoff increases by 15-20% and does not provide a twofold rise in water inflow into the sea, estimated from paleogeographic data. At the same time, the experiments have shown that such extreme inflow of water into the Caspian Sea could be formed under the conditions of deep freezing of soils and in the absence of seasonal thawing of the frozen catchment area, i.e. at a colder climate than the modern one.

How to cite: Gelfan, A. and Kalugin, A.: Permafrost in the Caspian Sea basin in the Late Glacier Era as a possible trigger of the sea transgression: checking the hypothesis using a hydrological model, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-3812, https://doi.org/10.5194/egusphere-egu21-3812, 2021.

EGU21-5317 | vPICO presentations | ITS3.5/NH3

The kingdom of Tonga devastated by a megatsunami in the mid-15th century

Franck Lavigne, Julie Morin, Wassmer Patrick, Weller Olivier, Kula Taaniela, Ana V. Maea, Karim Kelfoun, Fatima Mokadem, Raphael Paris, Mukhamad N. Malawani, Faral Audrey, Mhammed Benbakkar, Ségolène Saulnier-Copard, Céline M. Vidal, Tu’I’ahai Tu’I’afitu, Gomez Christopher, and Fuka Kitekei’aho

The pre-colonial history of Tonga and West Polynesia still suffers from major gaps because its reconstruction is essentially based on legends left by oral tradition, and by archaeological evidence somehow difficult to interpret. By the fourteenth century, the powerful Tu'i Tonga kingdom united the islands of the Tongan archipelago under a centralised authority and, according to tradition, extended its influence to neighbouring island groups in the Central Pacific. However, some periods of deep crisis were identified, e.g. in the mid- 15th century, marked by an abrupt cessation of inter-archipelago migration on the deep seas in the Pacific, significant cultural changes, and a decrease in accessible natural resources. The origins of these disturbances are still debated, and are usually assigned to internal political problems or loss of external influence vis-à-vis neighboring states. However, the hypothesis of a major natural disaster was never suggested up to now.

Drawing on a body of new evidence from sedimentary signatures and radiocarbon dating of charcoal and marine bioclasts, geomorphology, and sedimentology, in support of previously published archaeological data, we argue that the Tu’i Tonga kingdom was severely impacted by a megatsunami in the mid-15th century. We also discuss the likely sources of this event, which happened in an isolated region of the world before the European maritime “great discoveries”. This tsunami could be the source of vivid local myths that strongly suggest that a giant wave covered almost the entire island of Tongatapu at one time.

How to cite: Lavigne, F., Morin, J., Patrick, W., Olivier, W., Taaniela, K., Maea, A. V., Kelfoun, K., Mokadem, F., Paris, R., Malawani, M. N., Audrey, F., Benbakkar, M., Saulnier-Copard, S., Vidal, C. M., Tu’I’afitu, T., Christopher, G., and Kitekei’aho, F.: The kingdom of Tonga devastated by a megatsunami in the mid-15th century, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-5317, https://doi.org/10.5194/egusphere-egu21-5317, 2021.

EGU21-4720 | vPICO presentations | ITS3.5/NH3

 Geo-historical analysis of flood impacts in a large Alpine catchment (Arve River, French Alps, 1850 – 2015).

Eva Boisson, Bruno Wilhelm, Emmanuel Garnier, Alain Mélo, Sandrine Anquetin, and Isabelle Ruin

In France, flooding is the most common and damaging natural hazard. Due to global warming, it is expected to globally exacerbate, and it could be even more pronounced in the European Alps that warm at a rate twice as high in the Northern Hemisphere. The Alps are densely populated, increasing exposure and vulnerability to flood hazard. To approach long-term evolutions of past flood occurrence and related socio-economic impacts in relation to changes in the flood risk components (i.e. hazard, exposure and vulnerability), the study of historical records is highly relevant.

To this aim we build and analyze the newly constituted database of Historical Impacts of Floods in the Arve Valley (HIFAVa), located in French Northern Alps and starting in 1850. The database reports flood occurrences and impacts in a well-documented Alpine catchment that encompasses both a hydrological and societal diversity.

We analyze past impacts in regard to their characteristics and evolution in both time and space. Our results show an increasing occurrence of impacts from 1920 onwards, which is more likely related to indirect source effects and/or increasing exposure of goods and people rather than hydrological changes. The analysis reveals that small mountain streams and particularly glacial streams caused more impacts (67%) than the main river. While increase in heavy rainfall and ice melt are expected to enhance flood hazard in small Alpine catchments, this finding calls to pay a particular attention to flood risk assessment and management in small catchments.

How to cite: Boisson, E., Wilhelm, B., Garnier, E., Mélo, A., Anquetin, S., and Ruin, I.:  Geo-historical analysis of flood impacts in a large Alpine catchment (Arve River, French Alps, 1850 – 2015)., EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-4720, https://doi.org/10.5194/egusphere-egu21-4720, 2021.

EGU21-14567 | vPICO presentations | ITS3.5/NH3

A multidisciplinary approach to improve and share the understanding of landslide hazard in mountain environments: the PIURO 1618 disaster

Enrico Pigazzi, Tiziana Apuani, Riccardo Bersezio, Corrado Camera, Alessandro Comunian, Maurizio Lualdi, and Andrea Morcioni

Large landslides have affected the geomorphological evolution of most Alpine territories. Some catastrophic events also had a huge impact on the economic and cultural development of human societies. In the Bregaglia Valley and in nearby territories, evidences of settlements date back to the Roman age. In these areas, human activities always coexisted with the natural evolution of the valley, which has been characterized by recurrent natural events such as floods and landslides. Among these, the 1618 Piuro landslide was the one with the strongest impact, remaining impressed on the collective imagination and artistic representations. It erased an entire village and its 1000-2000 inhabitants few km East of Chiavenna, and it is still remembered as one of the worst tragedies in the history of the region. Understanding the evolutionary dynamics of such a geomorphologically active landscape, taking notes from the ancient or recent past, plays a central role in risk assessment and mitigation. In Piuro, such dynamics were investigated through a multidisciplinary approach, starting from the historical and archaeological analyses of the event and involving: (i) the geological/geomorphological characterization of the Last Glacial Maximum, to present palimpsest landscape of the valley through the realization of thematic maps, (ii) the stratigraphic interpretation of new boreholes crossing the landslide deposits and the deeper intra-mountain sedimentary valley fill, (iii) the realization of topographic, petrographic, geophysical (HVSR and MASW) and geo-mechanical surveys. In addition, the implementation of numerical models is on the way, to check different hypotheses on the predisposing factors, triggers, timing and evolution of the 1618 Piuro landslide. To increase the awareness of natural hazards in mountain settings and to promote a risk and resilience culture, all these acquired knowledge will be disseminated and shared with citizen, authorities and scientists in the frame of the Interreg project A.M.AL.PI.18. The fulfilment of a transboundary (Italian-Swiss) geo-cultural path will link other sites of historical and geological relevance through the territories of Bregaglia, Valchiavenna, Moesa and Ticino. Showing and telling the history of catastrophic landslides and their impacts on the involved communities, it will contribute to enhance the perception of beauty and the awareness of geo-hazard. The dissemination of knowledge and awareness is one main goal towards risk mitigation.

The present work was co-funded through the EU, Regional Development European Fund, by Italian State, Helvetic Confederation and Cantons under the Interreg V-A IT-CH 2014-2020 Cooperation Program - A.M.AL.PI.2018 “Alpi in Movimento, Movimento nelle Alpi. Piuro 1618-2018", ID 594274 – Axis 2 “Cultural and natural enhancement”.

How to cite: Pigazzi, E., Apuani, T., Bersezio, R., Camera, C., Comunian, A., Lualdi, M., and Morcioni, A.: A multidisciplinary approach to improve and share the understanding of landslide hazard in mountain environments: the PIURO 1618 disaster, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-14567, https://doi.org/10.5194/egusphere-egu21-14567, 2021.

To a large extent, the temporal definition of an extreme event depends on the context and the level of analysis that we are able to deploy. It should be massive and concentrated compared to the challenges a system is facing on everyday basis, it should provide a shock, and it should require major efforts to absorb its impacts. On historical timescales, extreme events happen over hours, days, months, at the longest, years. Compared to the process through which environmental archives develop, these are very short timescales, possibly with no chance of being recorded in the sediments. However, if we consider that an extreme event should have massive impacts, and these should be last for longer than the event itself, there is a good chance we could actually observe the environmental change associated with the extreme event in the sediments.

In my talk, I will look at two plague pandemics – the first, 6th-8th c. AD, and the second, 14th-18th c. AD – and their initial outbreaks (known as the Justinianic Plague and the Black Death) in order to see their reflection in the sediments throughout Europe and the Mediterranean, primarily in the pollen data. As I will demonstrate, in some cases the impact was minimal, barely visible, while in others it was indeed massive. This will bring me back to the definition of the extreme event: is it possible to have an extreme event that did not have any impact? Can the same event – the spread of a new pathogen, in our case – become extreme in one social-geographical context and not in another?

How to cite: Izdebski, A.: Extreme events at historical time-scales: are they visible in the paleoenvironmental records?, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-9195, https://doi.org/10.5194/egusphere-egu21-9195, 2021.

ITS3.6/GMPV2 – Volcanic Plumes: Insights into Volcanic Emissions and their Impacts on the Environment and Health

EGU21-15239 | vPICO presentations | ITS3.6/GMPV2

A spectra classification methodology of infrared hyperspectral images to reach near real-time SO2 emission flux estimation of Mount Etna plume

Charlotte Segonne, Nathalie Huret, Sébastien Payan, and Mathieu Gouhier

Monitoring active volcanoes activity passes through the detection of fluctuations in degassing levels which may reflect changes in the magma supply rate and help inform a short-term forecast of on-going eruptions. Infrared hyperspectral imagers, which is an imaging technology still little used for volcanoes monitoring, have been deployed for various field campaigns on active volcanoes recently. For example, the Hyper-Cam LWIR (LongWave InfraRed) ranging between 850-1300 cm-1 (7.7 - 11.8 µm) with a spectral resolution up to 0.25 cm-1, provided high spectral resolution images from ground-based measurements of the Mount Etna (Sicily, Italy) plume during IMAGETNA campaign in June 2015. Processing the raw data and retrieving the infrared spectra with the LATMOS (Laboratoire Atmosphères Milieux Observations Spatiales) Atmospheric Retrieval Algorithm (LARA), a robust and a complete radiative transfer model, require a calculation time of ~7 days per image.

One of the main ways of risk mitigation effects of explosive eruptions is to get a fast and accurate quantification of SO2 fluxes emitted by volcanoes. In this context, using the dataset acquired during IMAGETNA campaign at Mount Etna, a spectra classification methodology has been developed to drastically decrease the calculation time and reach near real-time retrievals of SO2 slant column densities. The methodology is based on a network built on two layers of information from the extraction of spectral features in the O3 and SO2 emission bands. A training dataset of five SO2 slant column densities images retrieved with the time-consuming pixel-by-pixel retrieval method allowed the creation of a library. The spectra classification makes it possible to process each hyperspectral image in less than 40 seconds. It opens the possibility to infer near real-time estimation of SO2 emission fluxes from IR hyperspectral imager measurements.

How to cite: Segonne, C., Huret, N., Payan, S., and Gouhier, M.: A spectra classification methodology of infrared hyperspectral images to reach near real-time SO2 emission flux estimation of Mount Etna plume, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-15239, https://doi.org/10.5194/egusphere-egu21-15239, 2021.

EGU21-9794 | vPICO presentations | ITS3.6/GMPV2

Quantifying gas, ash and aerosols in volcanic plumes using emission OP-FTIR measurements

Jean-François Smekens, Tamsin Mather, and Mike Burton

Monitoring of volcanic emissions (gas, ash and aerosols) is crucial to our understanding of eruption mechanisms, as well as to developing mitigation strategies during volcanic eruptions. Ultraviolet (UV) spectrometers and cameras are now ubiquitous monitoring tools at most volcano observatories for quantifying sulphur dioxide (SO2) emissions. However, because they rely on scattered UV light as a source of radiation, their use is limited to daytime only, and measurement windows are often further restricted by unfavourable weather conditions. On the other end of the spectrum, Open Path Fourier Transform Infrared (OP-FTIR) instruments can be used to measure the concentrations of a series of volcanic gases, and they allow for night-time operation. However, the retrieval methods rely on the presence of a strong source of IR radiation in the background - either natural (lava flow, crater rim, the sun) or artificial – restricting their use to very specific observation geometries and a narrow range of eruptive conditions. Here we present a new approach to derive quantities of SO2, ash and aerosols from measurements of a drifting volcanic plume. Using the atmosphere as a background, we measured self-emitted IR radiation from plumes at Stromboli volcano (Italy) capturing both passive degassing and ash-rich explosive plumes. We use an iterative approach with a forward radiative transfer model (the Reference Forward Model – RFM) to quantify concentrations of sulphur dioxide (SO2), aerosols and ash in the line of sight of the spectrometer. This new method could significantly enhance the scientific return from OP-FTIR instruments at volcano observatories, ultimately expanding their deployment as part of permanent scanning networks (an alternative to DOAS instruments) to provide continuous data on the emissions of gas, ash and aerosols. 

How to cite: Smekens, J.-F., Mather, T., and Burton, M.: Quantifying gas, ash and aerosols in volcanic plumes using emission OP-FTIR measurements, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-9794, https://doi.org/10.5194/egusphere-egu21-9794, 2021.

EGU21-3107 | vPICO presentations | ITS3.6/GMPV2

Extremely fast retrieval of volcanic SO2 layer heights from UV satellite data using inverse learning machines

Pascal Hedelt, MariLiza Koukouli, Konstantinos Michaelidis, Taylor Isabelle, Dimitris Balis, Don Grainger, Dmitry Efremenko, and Diego Loyola

Precise knowledge of the location and height of the volcanic sulfur dioxide (SO2) plume is essential for accurate determination of SO2 emitted by volcanic eruptions, however so far not available in operational near-real time UV satellite retrievals. The FP_ILM algorithm (Full-Physics Inverse Learning Machine) enables for the first time to extract the SO2 layer height information in a matter of seconds for current UV satellites and is thus applicable in NRT environments.

The FP_ILM combines a principal component analysis (PCA) and a neural network approach (NN) to extract the information about the volcanic SO2 layer height from high-resolution UV satellite backscatter measurements. So far, UV based SO2 layer height retrieval algorithms were very time-consuming and therefore not suitable for near-real-time applications like aviation control, although the SO2 LH is essential for accurate determination of SO2 emitted by volcanic eruptions.

In this presentation, we will present the latest FP_ILM algorithm improvements and show results of recent volcanic eruptions.

The SO2 layer height product for Sentinel-5p/TROPOMI is developed in the framework of the SO2 Layer Height (S5P+I: SO2 LH) project, which is part of ESA Sentinel-5p+ Innovation project (S5P+I). The S5P+I project aims to develop novel scientific and operational products to exploit the potential of the S5P/TROPOMI capabilities. The S5P+I: SO2 LH project is dedicated to the generation of an SO2 LH product and its extensive verification with collocated ground- and space-born measurements.

How to cite: Hedelt, P., Koukouli, M., Michaelidis, K., Isabelle, T., Balis, D., Grainger, D., Efremenko, D., and Loyola, D.: Extremely fast retrieval of volcanic SO2 layer heights from UV satellite data using inverse learning machines, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-3107, https://doi.org/10.5194/egusphere-egu21-3107, 2021.

EGU21-16345 | vPICO presentations | ITS3.6/GMPV2

Towards operational use of satellite SO2 measurements in a volcano observatory

Matthieu Epiard and Simon Carn

Along with monitoring of seismic activity and ground deformation, the measurement of volcanic gas emissions and composition plays a key role in the surveillance of active volcanoes and the mitigation of volcanic hazards. Volcanic gas emissions also potentially impact the environment, human health and climate, providing further motivation for study. Currently, volcano observatories typically employ ground-based or airborne techniques to monitor volcanic gas emissions, mainly sulfur dioxide (SO2) fluxes and its ratios over other species (e.g., CO2, H2S). However, in recent years there have been significant breakthroughs in satellite observations of passive volcanic SO2 emissions, including high-resolution ultraviolet (UV) measurements from the Tropospheric Monitoring Instrument (TROPOMI) on the Sentinel-5 Precursor (S5P) satellite, and the development of long-term records of volcanic SO2 degassing from the Ozone Monitoring Instrument (OMI) on NASA’s Aura satellite. Satellite measurements offer some advantages over traditional gas monitoring techniques, e.g., synoptic coverage of large regions, relative immunity to variations in wind direction, and ability to map the spatial extent and dispersion of volcanic SO2 plumes with applications for health hazard mitigation. Although these satellite datasets are potentially valuable for active volcano monitoring and as a supplement to other gas monitoring techniques, significant barriers remain to their use at many volcano observatories, particularly in low-income countries. Notably, the increasing volume of satellite datasets (NASA’s database is bigger than 3 petabytes) and the demands of data processing represent challenges to their operational use at observatories with limited internet connectivity or computational capacity. Here, we present an ongoing effort to develop open-source Python software to access and process SO2 data directly through NASA’s Earthdata portal Application Processing Interface (API), in order to streamline the satellite SO2 data processing workflow for a volcano observatory. By allowing server-side satellite data subsetting around the volcano of interest, this API greatly reduces the processing burden and only requires an internet connection to the NASA server hosting the required datasets (including S5P/TROPOMI, Aura/OMI and many others). We present some examples of software output and potential applications. Our current goal is to deploy and test the software for operational use in a volcano observatory.  

How to cite: Epiard, M. and Carn, S.: Towards operational use of satellite SO2 measurements in a volcano observatory, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-16345, https://doi.org/10.5194/egusphere-egu21-16345, 2021.

EGU21-11828 | vPICO presentations | ITS3.6/GMPV2

Observations of plumes from the 2019 Raikoke eruption with the Infrared Atmospheric Sounding Interferometer (IASI)

Isabelle A. Taylor, Roy G. Grainger, and Tamsin A. Mather

Raikoke, a remote volcano in the Kuril Islands, erupted on the 21st June 2019. The eruption injected significant quantities of SO2 into the atmosphere along with volcanic ash. These plumes have been studied with tools developed for the Infrared Atmospheric Sounding Interferometer (IASI) by the Earth Observation Data Group (EODG) at the University of Oxford. IASI is a hyperspectral sensor onboard of three meteorological satellites (Metop A, B and C). Each instrument obtains near global coverage twice a day and has a spectral range which includes sensitivity to both SO2 and ash: making them useful for studying the Raikoke plumes. A fast linear SO2 retrieval was first applied to flag pixels with elevated amounts of SO2. With this tool it was possible to follow the Raikoke plume as it circulated the northern hemisphere above 30 degrees, with parts of the plume still visible around 2 months after the eruption took place. Next an iterative SO2 retrieval was used to quantify the amount and height of the SO2 in each pixel. In the first few days after the eruption took place, very high column amounts are recorded, in some cases exceeding 600 DU. Using this retrieval, a preliminary estimate of 1.6 Tg was obtained for the total amount of SO2 emitted (measured on the 23rd of June). Height information from this technique shows that there were probably multiple injection heights during the eruption and that SO2 was emitted into both the troposphere and stratosphere. The tropospheric plume remains visible for just a few days after the eruption, while the stratospheric portion of the plume persists for several weeks.

How to cite: Taylor, I. A., Grainger, R. G., and Mather, T. A.: Observations of plumes from the 2019 Raikoke eruption with the Infrared Atmospheric Sounding Interferometer (IASI), EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-11828, https://doi.org/10.5194/egusphere-egu21-11828, 2021.

EGU21-4889 | vPICO presentations | ITS3.6/GMPV2

Modelling gas dispersal phenomena at La Soufrière volcano (Guadeloupe, Lesser Antilles)

Silvia Massaro, Fabio Dioguardi, Laura Sandri, Giancarlo Tamburello, Jacopo Selva, Sèverine Moune, David Jessop, Roberto Moretti, Jean-Christophe Komorowski, and Antonio Costa

In recent decades, reliable computational models have significantly advanced, and now represent a valuable tool to make quantitative and testable predictions, supporting gas dispersal forecasting and hazard assessments for public safety. In this study, we carried out a number of tests aimed to validate the modelling of gas dispersal at La Soufrière de Guadeloupe volcano (Lesser Antilles), which has shown quasi-permanent degassing of low-temperature hydrothermal nature since its last magmatic eruption in 1530 AD. In particular, we focused on the distribution of CO2 and H2S discharged from the three main present-day fumarolic sources at the summit, using the MultiGAS measurements of continuous gas concentrations collected during March-April 2017. We implemented the open-source Eulerian code DISGAS-2.0 for passive gas dispersion coupled with the mass consistent Diagnostic Wind Model (DWM), using wind measurements and atmospheric stability information from a local meteorological station and the ECMWF-ERA5 reanalysis data. We found that model outputs are highly dependent on the resolution of the topographic data, which affect mainly the reliability of DWM meteorological fields, especially on and around the steep dome. Our results satisfactory reproduce the observed data, indicating the potential usefulness of DISGAS-2.0 as a tool for quantifying gas hazard and reproducing the fumarolic degassing and at La Soufrière de Guadeloupe.

 

How to cite: Massaro, S., Dioguardi, F., Sandri, L., Tamburello, G., Selva, J., Moune, S., Jessop, D., Moretti, R., Komorowski, J.-C., and Costa, A.: Modelling gas dispersal phenomena at La Soufrière volcano (Guadeloupe, Lesser Antilles), EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-4889, https://doi.org/10.5194/egusphere-egu21-4889, 2021.

EGU21-7916 | vPICO presentations | ITS3.6/GMPV2

Calculating the Height and the Position of Volcanic Cloud SO2 With a Lagrangian Trajectory Tool 

Nick Gorkavyi, Nickolay Krotkov, Can Li, Leslie Lait, Simon Carn, Peter Colarco, Nikita Fedkin, Matthew DeLand, Mark Schoeberl, Alexander Vasilkov, and Joanna Joiner

We have developed a new trajectory tool to reconstruct the altitude and the position of SO2 in a volcanic plume. Starting with 2D map of satellite observed SO2, known volcano location, and reanalysis wind fields from the NASA Goddard Earth Observing System (GEOS) model, the Goddard trajectory tool allows us to estimate the altitude and concentration of SO2 in the volcanic plume at time of observation. We used this tool for the June 21, 2019 Mt. Raikoke eruption and the June 15, 1991 Mt. Pinatubo event. We used SO2 data from the Ozone Mapping and Profiler Suite/Nadir Mapper (OMPS/NM) onboard the NASA-NOAA Suomi satellite and obtained a distribution of SO2 altitudes between 1 and 19 kilometers in different parts of the Raikoke SO2 clouds, with the highest SO2 concentration between 11 and 16 km, in good agreement with data from independent SO2 layer height retrievals from the Ozone Monitoring Instrument (OMI) aboard the NASA Aura spacecraft; the Tropospheric Monitoring Instrument (TROPOMI) onboard the European Copernicus Sentinel 5 precursor satellite and Infrared Atmospheric Sounding Interferometer (IASI) on the European Space Agency's (ESA) MetOp series of a polar orbiting satellites. We then applied this method to the Pinatubo eruption using SO2 column measurements from the NASA Total Ozone Mapping Spectrometer (TOMS) and using wind fields from the National Centers for Environmental Prediction Reanalysis version 2. We found that the southern part of the Pinatubo plume is located in the troposphere, and the northern part is in the stratosphere.

How to cite: Gorkavyi, N., Krotkov, N., Li, C., Lait, L., Carn, S., Colarco, P., Fedkin, N., DeLand, M., Schoeberl, M., Vasilkov, A., and Joiner, J.: Calculating the Height and the Position of Volcanic Cloud SO2 With a Lagrangian Trajectory Tool , EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-7916, https://doi.org/10.5194/egusphere-egu21-7916, 2021.

EGU21-9606 | vPICO presentations | ITS3.6/GMPV2

A new simulation tool for automatic dilute and dense gas dispersion modelling 

Fabio Dioguardi, Silvia Massaro, Giovanni Chiodini, Antonio Costa, Arnau Folch, Giovanni Macedonio, Laura Sandri, and Jacopo Selva

The emission of volcanic gases can occur both during volcanic eruptions and in quiescent stages of the volcanic activity. This process can affect the air quality in the areas downwind; in fact, many gas species can be a threat to human health and even life at concentrations and doses above species-specific thresholds. Gas emissions can be of different types, the two main categories being dilute passive degassing and heavy gas flow. The former occurs when the gas concentration is low and/or temperature is high, hence its density is lower than the atmospheric density; the latter takes place when the gas density is higher than the atmosphere and the gas accumulates onto the ground and may flow as a gravity current more or less affected by the wind. Examples of the first and second types of emissions are fumaroles and limnic explosions, respectively.

Numerical modelling is one of the approaches used to quantify the hazard related to these processes. Ideally, for hazard quantification purposes numerous simulations originating from varying the most important input parameters (e.g. wind field, emission rates, etc.) in their range of uncertainty should be carried out. The whole process of gas dispersion modelling is time consuming, since it starts with the assessment of the wind field with an ad-hoc meteorological model, proceeds with the actual gas dispersion simulation and concludes with the post-processing stage. In order to simplify the whole workflow with the final aim to manage numerous simultaneous simulations for hazard assessment applications, we created APVGDM (Automatic Probabilistic Volcanic Gas Dispersion Modelling), a simulation tool made of a collection of Python scripts. APVGDM is interfaced with two dispersion models that can be selected by the user depending on the application of interest: a dilute (DISGAS) and a dense gas (TWODEE) dispersion model. The post-processing script is capable of building Empirical Cumulative Distribution Functions (ECDF) of the gas concentrations combining the outputs of multiple simulations; the ECDF can be interrogated by the user to produce outputs at the desired exceedance probability. Here we present APVGDM and some application examples showing the wide range of options that the tool offers.

How to cite: Dioguardi, F., Massaro, S., Chiodini, G., Costa, A., Folch, A., Macedonio, G., Sandri, L., and Selva, J.: A new simulation tool for automatic dilute and dense gas dispersion modelling , EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-9606, https://doi.org/10.5194/egusphere-egu21-9606, 2021.

EGU21-12058 | vPICO presentations | ITS3.6/GMPV2

The Effect of using a New Parameterization of Nucleation in the WRF-Chem model on the Cluster Formation Rate and Particle Number Concentration in a Passive Volcanic Plume

Somayeh Arghavani, Clémence Rose, Sandra Banson, Céline Planche, and Karine Sellegri

Volcanic eruption is one of the main natural sources of atmospheric particles. In particular, evidence of New Particle Formation (NPF) from volcanic emission is reported in previous studies (Boulon et al., 2011; Sahyoun et al., 2019), which also suggests an essential role of sulfuric acid in this process. In addition, Rose et al. (2019) highlighted a significant contribution of the particles formed in the volcanic plume of the piton de la Fournaise to the budget of potential CCN at the Maïdo observatory, located ~40 km from the vent of the volcano. Therefore, it is predicted that the number and size of the cloud droplets, cloud growing and precipitation processes might be affected by the frequency of occurrence and characteristics of volcanically induced NPF in both local and regional scales, because volcanic plumes can extend far from the vent and even lower heights under the influence of the wind field and atmospheric dispersion. 

Following the study of Planche et al. (2020), the effect of using the New Parameterization of Nucleation (NPN) derived from the measurements performed in the passive volcanic emission plume of Etna (37.748˚ N, 14.99˚ E; Italy) (Sahyoun et al., 2019) in the WRF-Chem model (Weather Research and Forecasting Model coupled with Chemistry) is assessed, with a specific focus on the cluster formation rate and particle number concentration including CCN. In particular, results obtained with the NPN are compared to the predictions obtained with the default model settings, and further with observations.

In the next step, the resulting aerosol fields will be used to further evaluate the influence of the newly formed and grown particles on cloud formation and properties in a 3D cloud-scale model using a detailed microphysics scheme (DESCAM; Flossmann and Wobrock, 2010; Planche et al. 2010; 2014) . 

How to cite: Arghavani, S., Rose, C., Banson, S., Planche, C., and Sellegri, K.: The Effect of using a New Parameterization of Nucleation in the WRF-Chem model on the Cluster Formation Rate and Particle Number Concentration in a Passive Volcanic Plume, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-12058, https://doi.org/10.5194/egusphere-egu21-12058, 2021.

EGU21-8761 | vPICO presentations | ITS3.6/GMPV2

Trade-offs between deep (magmatic) and shallow (hydrological) forcings on volcanic unrest at La Soufrière de Guadeloupe (Lesser Antilles)

David Jessop, Roberto Moretti, Séverine Moune, and Vincent Robert

Fumarolic gas composition and temperature record deep processes that generate and transfer heat and mass towards the surface.  These processes are a result of the emplacement, degassing and cooling of magma and the overturning of the above hydrothermal system.  A reasonable expectation, and too often an unproved assumption, is that fumarole temperatures and the deep heat sources vary on similar timescales.  Yet signals from deep and shallow processes have vastly different temporal variations.  This indicates that signals arising from deep activity may be masked or modified by intervening hydrothermal processes, such as fluid-groundrock reactions in which secondary minerals play a major role.  Clearly, this complicates the interpretation of the signals such as the joint variation of fumarole vent temperature and geochemical ratios in terms of what is occurring at depth.  So what do the differences between the timescales governing deep and shallow processes tell us about the intervening transport mechanisms?

At the volcanic dome of La Soufrière de Guadeloupe, the Observatoire Volcanologique et Sismologique de la Guadeloupe has performed weekly-to-monthly in-situ vent gas sampling over many years.  These analyses reliably track several geochemical species ratios over time, which provide important information about the evolution of deep processes.  Vent temperature is measured as part of the in-situ sampling, giving a long time series of these measurements.  Here, we look to exploit the temporal variations in these data to establish the common processes, and also to determine why these signals differ.  By fitting sinusoids to the gas-ratio time series we find that several of the deep signals are strongly sinusoidal.  For example, the He/CH4 and CO2/CH4 ratios, which involve conservative components and mark the injection of deep and hot magmatic fluids, oscillate on a timescale close to 3 years. We also analyse the frequency content of the temperature measurements since 2011 and find that such long signals are not seen.  This may be due to internal buffering by the hydrothermal system, but other external forcings are also present.  From these data we build up a more informed model of the heat-and-mass supply chain from depth to the surface.  This will potentially allow us to predict future unrest (e.g. thermal crises, seismic swarms), and distinguish between sources of unrest.

How to cite: Jessop, D., Moretti, R., Moune, S., and Robert, V.: Trade-offs between deep (magmatic) and shallow (hydrological) forcings on volcanic unrest at La Soufrière de Guadeloupe (Lesser Antilles), EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-8761, https://doi.org/10.5194/egusphere-egu21-8761, 2021.

EGU21-5684 | vPICO presentations | ITS3.6/GMPV2

Near crater observations of gas and aerosols variability at Mount Etna during the EPL-RADIO and EPL-REFLECT measurement campaigns.

Suzanne Crumeyrolle, Marion Ranaivombola, Tjarda Roberts, Chiara Giorio, Giusseppe Salerno, Salvatore Giammanco, Alessandro Laspina, Alcide Disarra, Letizia Spampinato, and Pasquale sellitto

During the EPL (Etna Plume Lab) campaigns occurring in 2017 (EPL-RADIO) and 2019 (EPL-REFLECT),  gas  and aerosol measurements were performed  at Mount Etna (Sicily, Italy) to better assess the role of volcanic aerosols on both regional climate system and local health hazard. Gas related to volcanic emissions (such as SO2, H2S and others) were measured with low cost sensors (Alphasense) and HCl/SO2 ratio was validated in comparison to FTIR measurements. Aerosol physical and chemical properties were measured using low-cost Optical Particle Counters (OPCN2 from Alphasense) and filter measurements dedicated to organic acids, inorganic ions, soluble metals and total metals. During the EPL-REFLECT campaign, in-situ measurements were performed during: 1) the hike up, 2) a 2-hours period in the close vicinity of the Bocca Nuova crater, 3) the hike down and 4) in Milo (city on the flank of the Etna). Moreover, few OPCs were left unattended at the Bocca Nuova crater for two full days. 

 

Gas abundances at the crater-rim ranged from a few to 10’s ppmv SO2, with correlation to PM. The analysis of the 2 days measurements highlights a clear diurnal variation of aerosol size distributions. Indeed, at sunrise the total number and mass concentration is rapidly increasing from 15mg/m3 to 125mg/m3 in less than 2 hours. The variation of PM1/PM10 ratio shows a constant trend throughout the day except during a short period of time associated with high wind speeds. These results suggest that most aerosols are emitted through degassing and conversion of precursor gases to particles.

Moreover, analysis of aerosol samples collected on filters showed a change in metal solubility from the samples collected at the crater and the samples collected after atmospheric transport in Milo. This may indicate that the volcanic plume underwent processing in the aqueous phase during transport.

How to cite: Crumeyrolle, S., Ranaivombola, M., Roberts, T., Giorio, C., Salerno, G., Giammanco, S., Laspina, A., Disarra, A., Spampinato, L., and sellitto, P.: Near crater observations of gas and aerosols variability at Mount Etna during the EPL-RADIO and EPL-REFLECT measurement campaigns., EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-5684, https://doi.org/10.5194/egusphere-egu21-5684, 2021.

EGU21-10664 | vPICO presentations | ITS3.6/GMPV2

Three-dimensional distribution of Mount Etna’s emissions during the EPL-REFLECT campaign in July 2019

Alessia Sannino, Antonella Boselli, Giuseppe Leto, Simona Scollo, Ricardo Zanmar Sanchez, Salvatore Amoruso, Letizia Spampinato, and Pasquale Sellitto

Mount Etna (Italy) is the most high-impact volcanoes on Mediterranean scale mainly due to its eruptive activity and continuous passive degassing, and the inherent large amount of effluents released into the atmosphere. Mount Etna’s emission mainly originate from the summit craters at an altitude of about 3300 m, feeding frequently volcanic gases and aerosols into the free troposphere. Consequently, their effects on the atmosphere and regional climate system span over relatively long spatiotemporal scales.

In order to better understand the role that Mount Etna’s emissions play on the atmospheric composition and radiative balance in the Mediterranean area, multidisciplinary and multi-scale studies have been carried out since a few years within the different phases of the EtnaPlumeLab (EPL) research cluster. A part of the EPL effort is based on dedicated field campaigns, that aim at the characterization of volcanic sources emissions and nears-source plume dispersion and evolution.

In this work, we investigate the three-dimensional (3D) distribution of the volcanic aerosols from Mount Etna observed during the most recent EPL field campaign, named EPL-REFLECT (near-source estimations of Radiative EFfects of voLcanic aErosols for Climate and air quality sTudies) carried out within the Transnational Access component of the EUROVOLC project. This field campaign completes the previous EPL-RADIO (Radioactive Aerosols and other source parameters for better atmospheric Dispersion and Impact estimatiOns) campaign. Here we discuss the observations of a multiparametric LiDAR system AMPLE. The LiDAR is equipped with a fast scanning, double depolarization (at 532 and 355 nm) and high repetition laser source (1kHz), which is an essential point to derive time series of 3D-resolved aerosol properties near Etna. During the 8-12th of July 2019 period, day/night LiDAR measurements were performed by AMPLE from the astronomical observatory of the INAF-Catania in the location of Serra la Nave at 1725 m a.s.l., pointing towards the summit of Mount Etna. In particular, on the July 11th, the scan was performed with time-steps of 15 minutes at different angles from the top of the volcano to the zenith. These acquisitions highlight the atmospheric evolution of two layers related to two distinct degassing episodes. A comparative analysis with wind speed information and the integration with complementary photometric ground measurements have further constrained this 3D characterization and the evolution of these layers, including those outside the LiDAR field of view.

How to cite: Sannino, A., Boselli, A., Leto, G., Scollo, S., Sanchez, R. Z., Amoruso, S., Spampinato, L., and Sellitto, P.: Three-dimensional distribution of Mount Etna’s emissions during the EPL-REFLECT campaign in July 2019, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-10664, https://doi.org/10.5194/egusphere-egu21-10664, 2021.

EGU21-15527 | vPICO presentations | ITS3.6/GMPV2

Reconstruction of Dynamically Evolving Volcanic Ash Clouds from Simulated Satellite Imagery

Tom Etchells, Lucy Berthoud, Kieran Wood, Andrew Calway, and Matt Watson

Large volcanic eruptions can pose significant hazards over a range of domains. One such hazard is volcanic ash becoming suspended in the atmosphere. This can lead to significant risks to aviation, with the potential to cause severe or critical damage to jet engines. As such, the effective measurement and forecasting of ash contaminated airspace is of vital importance. Forecasts are generally produced using volcanic ash atmospheric transportation and dispersion models (ATDMs). Among the inputs to these models are eruption source parameters such as cloud-top height and cloud volume. One method of providing estimates of these source parameters, and to aid in characterising the size, shape, and distribution of a volcanic plume, is the reconstruction of the outer hull of the plume using multi-angle imagery.

When considering platforms for generating this imagery, satellites provide a range of advantages. These include the potential for global coverage, the wide range of viewing angles during an orbital pass, and being safely removed from any potential volcanic hazards. This method of plume reconstruction has been previously demonstrated by the authors using simulated satellite imagery of a model volcanic plume. However, the simple model plume used during this previous work was static and did not evolve with time, an assumption that is not realistic.

This presentation builds on the previous work and assess the efficacy of satellite imagery-based plume reconstruction under conditions closer to real-world, namely with a plume that is evolving with time. The time evolving plume model is produced via a Blender particle simulation. The images required for reconstruction are then generated at multiple user-determined time intervals and locations. A Space Carving reconstruction method is then applied to the imagery to generate the reconstructed plume. Performance and reconstruction accuracies are investigated by comparison of the reconstructed plume with the ‘ground-truth’ simulation model. The impacts of a range of variables on the reconstruction performance are investigated, including plume size, imager properties, satellite orbit, and the use of additional satellites. The accuracy of the Blender plume simulation is compared with more mature plume simulations such as the University of Bristol PlumeRise model. These comparison models were not themselves used for the reconstruction process due to issues with the generation of accurate imagery.

The improved simulation environment presented in this work further demonstrates the efficacy of a satellite-based reconstruction process for the measurement and forecasting of volcanic ash, potentially leading to improvements in hazard monitoring and aviation safety.

How to cite: Etchells, T., Berthoud, L., Wood, K., Calway, A., and Watson, M.: Reconstruction of Dynamically Evolving Volcanic Ash Clouds from Simulated Satellite Imagery, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-15527, https://doi.org/10.5194/egusphere-egu21-15527, 2021.

Episodes of large igneous province (LIP) volcanism punctuate Earth history. LIPs are anomalous geologically rapid large‐volume accumulations of igneous rock on the Earth’s surface and in the shallow crust. Periods of LIP emplacement are often temporally associated with times of profound environmental and climatic change throughout Earth history, particularly during the last 300 million years. The fluxes of gas and particles emitted during LIP volcanism are key candidates for triggering these Earth system responses. Understanding these events, their feedbacks and impacts on the Earth system requires collaboration between the fields of volcanology, atmospheric science, ocean chemistry, sedimentology and palaeobiology amongst other fields. This presentation will explore how evidence of the environmental impacts of LIP volcanism and the processes leading to these effects is best combined often from disparate sources including: (1) temporal associations between the dates or proxies of LIPs and evidence of environmental change captured in the geological record; (2) historical records or monitoring studies of the effects of large‐scale recent volcanic activity such as the Laki eruption in 1783–1784 CE and its deposits; and (3) scaling up from observations and measurements of the environmental impacts of present‐day volcanism such as the 2014–2015 Holuhraun eruption and the 2018 Lower East Rift Zone eruption at Kīlauea, Hawai‘i. Recent progress in each of these areas sets the scene for future advances in our understanding of these profoundly important events in Earth’s history.

How to cite: Mather, T., Schmidt, A., and Percival, L.: Insights into the environmental impacts of large igneous province volcanism from volcanology, atmospheric modelling and sedimentary archives, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-3418, https://doi.org/10.5194/egusphere-egu21-3418, 2021.

EGU21-2167 | vPICO presentations | ITS3.6/GMPV2

Impact of the Christmas 2018 Mount Etna Eruption on the Regional Air Quality

Claire Lamotte, Jonathan Guth, Virginie Marécal, Giuseppe Salerno, Nicolas Theys, Hughes Brenot, Stefano Corradini, and Mickaël Bacles

Volcanic eruptions are events that can eject several tons of material into the atmosphere. Among these emissions, sulfur dioxide is the main sulfurous volcanic gas. It can form sulfate aerosols that are harmful to health or, being highly soluble, it can condense in water particles and form acid rain. Thus, volcanic eruptions can have an environmental impact on a regional scale.

The Mediterranean region is very interesting from this point of view because it is a densely populated region with a strong anthropogenic activity, therefore polluted, in which Mount Etna is also located. Mount Etna is the largest passive SO2 emitter in Europe, but it can also sporadically produce strong eruptive events. It is then likely that the additional input of sulfur compounds into the atmosphere by volcanic emissions may have effects on the regional atmospheric sulfur composition.

We are particularly investigating the eruption of Mount Etna on December 24, 2018 [Corradini et al, 2020]. This eruption took place along a 2 km long breach on the side of the volcano, thus at a lower altitude than its main crater. About 100 kt of SO2 and 35 kt of ash were released in total, between December 24 and 30. With the exception of the 24th, the quantities of ash were always lower than the SO2.

The availability of the TROPOMI SO2column estimates, at fine spatial resolution (7 km x 3.5 km at nadir) and associated averaging kernels, during this eruptive period made it also an excellent case study. It allows us to follow the evolution of SO2 in the volcanic plume over several days.

Using the CNRM MOCAGE chemistry-transport model (CTM), we aim to quantify the impact of this volcanic eruption on atmospheric composition, sulfur deposition and air quality at the regional scale. The comparison of the model with the TROPOMI observation data allows us to assess the ability of the model to properly represent the plume. In spite of a particular meteorological situation, leading to a complex plume transport, MOCAGE shows a good agreement with TROPOMI observations. Thus, from the MOCAGE simulation, we can evaluate the impact of the eruption on the regional concentrations of SO2 and sulfate aerosols, but also analyse the quantities of dry and wet deposition, and compare it to surface measurement stations.

How to cite: Lamotte, C., Guth, J., Marécal, V., Salerno, G., Theys, N., Brenot, H., Corradini, S., and Bacles, M.: Impact of the Christmas 2018 Mount Etna Eruption on the Regional Air Quality, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-2167, https://doi.org/10.5194/egusphere-egu21-2167, 2021.

EGU21-12380 | vPICO presentations | ITS3.6/GMPV2

The observation of different aerosols types in the Mount Etna environment and their relative and mutual impacts on local radiative balance

Pasquale Sellitto, Giuseppe Salerno, Simona Scollo, Alcide Giorgio di Sarra, Antonella Boselli, Giuseppe Leto, Ricardo Zanmar Sanchez, Alessia Sannino, Tommaso Caltabiano, Salvatore Giammanco, Francesco Monteleone, Giandomenico Pace, Chiara Giorio, Suzanne Crumeyrolle, and Bernard Legras

The EPL-RADIO (Etna Plume Lab - Radioactive Aerosols and other source parameters for better atmospheric Dispersion and Impact estimatiOns) and EPL-REFLECT (near-source estimations of Radiative EFfects of voLcanic aErosols for Climate and air quality sTudies) projects, funded by the EC Horizon2020 ENVRIplus and EUROVOLC Transnational Access to European Observatories programmes, aim to advance the understanding of Mount Etna as a persistent source of atmospheric aerosols and its impact on the  radiative budget at proximal to regional spatial scales. Research was tackled by carrying out three campaigns in the summers of 2016, 2017 and 2019 to observe the volcanic plume produced by passive degassing, proximally and distally from the summit craters, using a wide array of remote sensing and in situ instruments. Diverse data are collected to explore the link of inner degassing mechanisms to the characterisation of near-source aerosol physicochemical properties and subsequent impacts on the atmosphere, environment and regional climate system.

The results of the three campaigns have shown that the volcanic plume emitted by Mount Etna often mixes with aerosols of different origins generating a complex layered pattern. Frequent mineral dust transport events were observed by both LiDAR observations located at Serra La Nave (~7 km south-west from summit craters) and at a medium-term radiometric station, equipped with a Multi-Filter Rotating Shadowband Radiometer (MFRSR), and other instruments located at Milo (~10 km eastwards from the craters). LiDAR observations also allowed to study the coexistence of volcanic aerosols and biomass burning particles from local to more distal smoke plumes transports (like for the well-documented large fires from continental southern Italy in July 2017). In situ filter and optical particles counter measurements confirmed the presence of dust at Milo. The interaction/mixing among volcanic, wildfire, and dust aerosols occurs in an overall dynamical regime which appears to be dominated by sea breeze, which is strengthened by the presence of the dark volcanic lava flanks. Photolysis process also possibly play a role in determining the daily evolution of the aerosol plume.

The sources of these different aerosol types are studied in detail using Lagrangian trajectories and meteorological data. Off-line radiative transfer calculations, using EPL-RADIO/REFLECT observations as input data, are used to estimate the relative radiative impact of the different aerosol types with respect to the background passive-degassing aerosols coming from Mount Etna.

How to cite: Sellitto, P., Salerno, G., Scollo, S., di Sarra, A. G., Boselli, A., Leto, G., Zanmar Sanchez, R., Sannino, A., Caltabiano, T., Giammanco, S., Monteleone, F., Pace, G., Giorio, C., Crumeyrolle, S., and Legras, B.: The observation of different aerosols types in the Mount Etna environment and their relative and mutual impacts on local radiative balance, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-12380, https://doi.org/10.5194/egusphere-egu21-12380, 2021.

ITS3.7/ERE1.6 – Navigating the Anthropocene: Human agency in global society-environment interaction assessments and modelling approaches

EGU21-16 | vPICO presentations | ITS3.7/ERE1.6

Nexus approaches to foster sustainable resource use: relations between stocks and flows of materials, services, and practices

Helmut Haberl, Martin Schmid, Willi Haas, Dominik Wiedenhofer, Henrike Rau, and Verena Winiwarter

Societies use material and energy resources to build up, maintain and utilize long-lasting structures such as buildings, infrastructures or machinery, and in the process release huge amounts of wastes and emissions. While in 1900 less than a quarter of all material use served to build up new material stocks, this fraction is now ~60% globally. Nexus approaches provide useful heuristics for interdisciplinary analyses of (un)sustainable resource use and the potentials and limitations of societal agency for interventions. Such a nexus can be conceptualized between different resources (e.g. land, materials, energy, or water), between biophysical stocks and flows involved in social metabolism, and the services and contributions to human well-being they provide. The novel concept of a stock-flow-service nexus explicitly recognizes the diverse and potentially conflicting purposes of resource use (e.g. products, services), thereby enriching concepts of “eco-efficiency”. At the same time, its applicability is in some contexts reduced by its dependence on the valuation of services, which has been subject to controversy and debate. Focusing on relationships between stocks, flows and practices, e.g. linkages between the routines of everyday life and the consumption of resources such as materials and energy, the complementary approach of a “stock-flow-practice” nexus avoids some of these challenges. Building on prominent theories of practice, especially those that have gained traction in consumption research, it offers a new conceptual basis for engaging with human agency and its implications for resource use. Both nexus approaches emphasize the key role of patterns of material stocks (e.g., settlement patterns, transport or production infrastructures, machinery) in shaping the (un)sustainability of resource use and the importance of services- and practice-oriented efforts to reshape these patterns when aiming to tackle the present sustainability crisis. In this presentation, we discuss how these two complementary nexus approaches can serve as heuristic models for interdisciplinary sustainability research, sketch the different conceptual and empirical research directions each of these two approaches inspires, and reflect on their importance for conceptualizing agency.

How to cite: Haberl, H., Schmid, M., Haas, W., Wiedenhofer, D., Rau, H., and Winiwarter, V.: Nexus approaches to foster sustainable resource use: relations between stocks and flows of materials, services, and practices, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-16, https://doi.org/10.5194/egusphere-egu21-16, 2021.

EGU21-324 | vPICO presentations | ITS3.7/ERE1.6

Unpacking the role of agency in the emergence of differentiated environmental institutions: the case of the territorial classification of native forests in the Argentinian Chaco

Michele Graziano Ceddia, Dimitris Christopoulos, Sara Frey, Carla Inguaggiato, Walter Mioni, Rodrigo Montani, Maurice Tschopp, and Elena Zepharovich

The Gran Chaco represents an important habitat that is undergoing significant changes, as a result of the expansion of the agricultural frontier, with a range of negative social and environmental consequences. Such a change is the result of a “bad transition” from an extensive/subsistence agricultural system towards a capital-intensive one, to which corresponds a completely different level of anthropization. The largest part of the Gran Chaco is located in Argentina. Partially as a response to the rapid loss of natural habitat in the region, Argentina passed a federal forest law in 2007. The law requires the different provinces to introduce a set of implementing regulations and adopt a territorial classification of native forests (TCNF), denoting different conservation values (high, medium and low). Although referring to the same federal law, the TCNFs developed by the various provinces in the Argentinian Chaco ecoregion differ significantly. We first develop a theoretical framework, which combines historical materialism with the theory of socio-ecological systems, to explain the emergence of institutional configurations. Through this framework, we hypothesise that the heterogeneity in the TCNFs results from the combination of contextual factors (i.e., differences in the physical environment among the provinces), material/economic conditions (i.e., production processes, social relationships and reproduction processes) and different forms of agency. We test the hypothesis by developing thick case-studies on the various Argentinian provinces in the Gran Chaco region via qualitative comparative analysis. The results allow determining which configurations co-occur with certain outcomes in terms of TCNFs. The results shed light on the process of emergence of differentiated environmental institutions in the region from the interactions of different conditions, contexts and forms of agency. This knowledge, in turn, could be extremely useful in navigating the Anthropocene while promoting a “good transition” towards sustainability.

How to cite: Ceddia, M. G., Christopoulos, D., Frey, S., Inguaggiato, C., Mioni, W., Montani, R., Tschopp, M., and Zepharovich, E.: Unpacking the role of agency in the emergence of differentiated environmental institutions: the case of the territorial classification of native forests in the Argentinian Chaco, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-324, https://doi.org/10.5194/egusphere-egu21-324, 2021.

EGU21-874 | vPICO presentations | ITS3.7/ERE1.6

Estimated climate impact of the end of agriculture as the primary food production system

Andrew H. MacDougall, Joeri Rogelj, and Patrick Withey

Global agriculture is the second largest contributor to anthropogenic climate change after the burning of fossil fuels. However the potential to mitigate the agricultural contribution is limited by the imperative to supply food for the global population. Advances in microbial biomass cultivation technology have recently opened a pathway to growing substantial amounts of food for humans or livestock, by fuelling microbial growth with hydrogen produced from electrolysis powered by renewable energy. This method of food production would use a small fraction of the land presently used for agriculture. Here we investigate the potential climate change impacts of the end of agriculture as the primary human food production system. We find that microbial biomass cultivation technology has both the potential to exacerbate climate change by outcompeting economic decarbonization for renewable energy and the potential to mitigate climate change if deployed following economic decarbonization. A duality which originates from the contrast between the reversibility of agricultural driven climate change and the irreversibility of fossil-fuel CO2 driven climate change. The range of reduced warming from the replacement of agriculture ranges from -0.22 [-0.29 to -0.04]oC for Shared Socioeconomic Pathway (SSP) 1-1.9 to -0.85 [-0.99 to -0.39]oC for SSP4-6.0. For limited temperature target overshoot scenarios, replacement of agriculture could thus eliminate or reduce the need for active atmospheric CO2 removal to achieve the necessary peak and decline in global warming. Given current societal barriers to switching to a microbial-based diet, deep near-term emissions reductions in CO2 and agricultural emissions remain necessary steps to keep warming within the bounds set by the Paris Agreement.

How to cite: MacDougall, A. H., Rogelj, J., and Withey, P.: Estimated climate impact of the end of agriculture as the primary food production system, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-874, https://doi.org/10.5194/egusphere-egu21-874, 2021.

EGU21-1347 | vPICO presentations | ITS3.7/ERE1.6 | Highlight

No time to tax! Can a gradually increasing carbon tax really provide a cost-efficient green transition?

Claudia Wieners, Francesco Lamperti, Andrea Roventini, and Roberto Buizza

It is widely assumed in climate economics that a uniform, gradually increasing carbon tax mirroring the “social cost of carbon” (SCC) leads to cost-optimal emission reduction. The underlying idea is that emitters switch to carbon-saving technologies as soon as the tax becomes so high that this switch saves money. If we let the tax equal the SCC, i.e. the extra damage caused by emitting one extra ton of CO2, then everybody who can save carbon at a lower price than the damage caused by this emission will do so, whereas those for whom the emission-saving costs more than the associated damage will not. Models like Nordhaus’ famous DICE model find a roughly exponentially increasing SCC, corresponding to an exponentially increasing carbon tax.

We implemented such an exponentially increasing carbon tax in a simple agent-based model, the Dystopian Schumpeter-Keynes (DSK) model. Agent-based models dispense with restrictive perfect rationality and market equilibrium assumptions and are able to describe non-equilibrium dynamics and tipping as emergent properties of collective behaviour. They are not yet widely used in climate economics.

The DSK model contains two types of firms which manufacture machines or a consumption good, respectively, using labour and energy (electricity and/or fuel). Electricity is provided by a monopolist using green (carbon neutral) or brown (fuel-based) plants.

In DSK, the DICE-based carbon tax is far from satisfying as climate policy. In the first ≈30 years, the tax is too low to trigger a green transition in the electricity sector. When green plants finally do become competitive, it still takes decades until the transition is completed, because power plants have a long lifetime and are replaced only gradually. Higher taxes can speed up the process somewhat, but even modest increases in the transition rate require big increases in the tax (and hence considerable side effects on the economy, including unemployment). The exponentially increasing carbon tax is thus low at the beginning of the transition, where higher taxes would be most needed, but becomes high (with associated side effects) at later stages, when the transition is already gaining momentum by positive feedbacks, most notably innovation reducing the price of green plants. It would be preferable to implement a constant (or even slightly decreasing) tax which is sufficiently high from the beginning.

Apart from green electricity, decarbonisation also requires fuel-using firms to switch to electricity. However, the carbon tax does not incentivise this switch initially, as the tax increases not only fuel price, but also electricity price. Again, the switch takes time, while rapid decarbonisation requires a swift start of electrification. One way around it is to levy a higher carbon tax on manufacturing firms than on the electricity sector (to make electricity use more attractive); alternatively, one could simply impose regulations.

Our results suggest that a carbon tax should not be gradually increasing and uniform, but high from the beginning and sector-dependent. We also find that tax-free policies, such as green subsidies or regulations, can bring about a green transition with possibly less side effects.

How to cite: Wieners, C., Lamperti, F., Roventini, A., and Buizza, R.: No time to tax! Can a gradually increasing carbon tax really provide a cost-efficient green transition?, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-1347, https://doi.org/10.5194/egusphere-egu21-1347, 2021.

EGU21-2337 | vPICO presentations | ITS3.7/ERE1.6

Process taxonomies and copan:CORE modelling framework for studying human-Earth system interaction dynamics in the Anthropocene

Jonathan F. Donges, Jobst Heitzig, Wolfgang Lucht, Wolfram Barfuss, Sarah E. Cornell, Johannes Kassel, Tim Kittel, Jakob J. Kolb, Till Kolster, Steven J. Lade, Finn Müller-Hansen, Ilona M. Otto, Maja Schlüter, Marc Wiedermann, and Kilian B. Zimmerer

Analysis of Earth system dynamics in the Anthropocene requires explicitly taking into account the increasing magnitude of processes operating in human societies, their cultures, economies and technosphere and their growing feedback entanglement with those in the physical, chemical and biological systems of the planet. However, current state-of-the-art Earth system models do not represent dynamic human societies and their feedback interactions with the biogeophysical Earth system and macroeconomic integrated assessment models typically do so only with limited scope. This paper (i) proposes design principles for constructing world–Earth models (WEMs) for Earth system analysis of the Anthropocene, i.e., models of social (world)–ecological (Earth) coevolution on up to planetary scales, and (ii) presents the copan:CORE open simulation modeling framework for developing, composing and analyzing such WEMs based on the proposed principles. The framework provides a modular structure to flexibly construct and study WEMs. These can contain biophysical (e.g., carbon cycle dynamics), socio-metabolic or economic (e.g., economic growth or energy system changes), and sociocultural processes (e.g., voting on climate policies or changing social norms) and their feedback interactions, and they are based on elementary entity types, e.g., grid cells and social systems. Thereby, copan:CORE enables the epistemic flexibility needed for contributions towards Earth system analysis of the Anthropocene given the large diversity of competing theories and methodologies used for describing socio-metabolic or economic and sociocultural processes in the Earth system by various fields and schools of thought. To illustrate the capabilities of the framework, we present an exemplary and highly stylized WEM implemented in copan:CORE that illustrates how endogenizing sociocultural processes and feedbacks such as voting on climate policies based on socially learned environmental awareness could fundamentally change macroscopic model outcomes.

References

Donges, J.F. et al.: Taxonomies for structuring models for World-Earth system analysis of the Anthropocene: subsystems, their interactions and social-ecological feedback loops, Earth Syst. Dynam. Disc., in review (2021), DOI: 10.5194/esd-2018-27.

Donges, J. F. and Heitzig,et al..: Earth system modeling with endogenous and dynamic human societies: the copan:CORE open World–Earth modeling framework, Earth Syst. Dynam., 11, 395–413, 2020.

How to cite: Donges, J. F., Heitzig, J., Lucht, W., Barfuss, W., Cornell, S. E., Kassel, J., Kittel, T., Kolb, J. J., Kolster, T., Lade, S. J., Müller-Hansen, F., Otto, I. M., Schlüter, M., Wiedermann, M., and Zimmerer, K. B.: Process taxonomies and copan:CORE modelling framework for studying human-Earth system interaction dynamics in the Anthropocene, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-2337, https://doi.org/10.5194/egusphere-egu21-2337, 2021.

The Earth’s population of seven billion consume varying amounts of planetary resources with varying impacts on the environment.  We combine the analytical tools offered by the socio-ecological metabolism and class theory and propose a novel social stratification theory to identify the differences and hot spots in individual resource and energy use. The theory is applied to German society and we use per capita greenhouse gas emissions as a proxy for resource and energy use. We use socio-metabolic profiles of individuals from an economic, social and cultural perspective to investigate resource intensive lifestyles. The results show large disparities and inequalities in emission patterns in German society. For example, the greenhouse gas emissions in the lowest and highest emission classes can differ by a magnitude of ten. Income, education, age, gender and regional differences (FRG vs. GDR) result in distinct emission profiles. Class differentiation is also noted as economic, cultural and social factors influence individual carbon footprints. We also analyze the role of digital technologies, regarding resource and energy consumption, as a proxy for cultural capital. Highlighting inequalities within societies is a step towards downscaling carbon emission reduction targets that are key to avoid transgressing climate change planetary boundary. We discuss the results in the context of climate policy implications as well as behavioral changes that are needed to meet climate policy objectives.

How to cite: Schuster, A. and Otto, I. M.: Socio-metabolic class conflicts in the Anthropocene: Developing a novel class theory based on German population data, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-2880, https://doi.org/10.5194/egusphere-egu21-2880, 2021.

EGU21-3546 | vPICO presentations | ITS3.7/ERE1.6

Operationalizing Human Agency in World-Earth System Models

Ilona M. Otto

The role of humans significantly altering natural systems is undisputed and human influence has been the dominant cause of global warming since the mid-20th century. If components of the Earth System are pushed beyond critical states, further large-scale impacts on human and ecological systems are likely. However, In the Anthropocene, humans are also seen as a force able to facilitate a global sustainability transformation. In other words, the individual becomes a focal point and, applying its inherent human agency, understood as the ability to shape life circumstances, opens up an analytical level incorporating choices and future action plans. The paper systematically reviews approaches that are relevant for operationalizing human agency in global human-environmental interaction models. The key aspects include representing inequalities in the resource and energy that are used to support lifestyles of different social groups, developing a hierarchical networked representation of social structure layers and rules for agent operation at the different levels, and finally building feedback and learning mechanisms that are used by agents to respond to changing environmental conditions and the behaviour of other agents.

How to cite: Otto, I. M.: Operationalizing Human Agency in World-Earth System Models, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-3546, https://doi.org/10.5194/egusphere-egu21-3546, 2021.

EGU21-8137 | vPICO presentations | ITS3.7/ERE1.6 | Highlight

Living Well Within Planetary Limits: is it possible? And what will it take?

Julia Steinberger

This talk will report on the multiple research streams resulting from the Living Well Within Limits project. The Living Well Within Limits project investigates the energy requirements of well-being, from quantitative, participatory and provisioning systems perspectives. In this presentation, I will communicate individual and cross-cutting findings from the project, and their implications for the engineering research community. In particular, I will share our most recent results on global energy footprint inequality, implications of redistribution, as well as modelling the minimum energy demand that would provide decent living standards for everyone on earth by 2050. I will show that achieving low-carbon well-being, both from the beneficiary (“consumer”) and supply-chain (producer) sides, involves strong distributional and political elements. Simply researching this area from a technical, social or economic lens is insufficient to draw out the reasons for poor outcomes and most promising avenues for positive change. I thus argue for the active engagement of the research community.

How to cite: Steinberger, J.: Living Well Within Planetary Limits: is it possible? And what will it take?, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-8137, https://doi.org/10.5194/egusphere-egu21-8137, 2021.

EGU21-9161 | vPICO presentations | ITS3.7/ERE1.6

Social tipping processes for sustainability: An analytical framework

Ricarda Winkelmann, Jonathan F. Donges, E. Keith Smith, Manjana Milkoreit, Christina Eder, Jobst Heitzig, Alexia Katsanidou, Marc Wiedermann, Nico Wunderling, and Timothy M. Lenton

Societal transformations are necessary to address critical global challenges, such as mitigation of anthropogenic climate change and reaching UN sustainable development goals. Recently, social tipping processes have received increased attention, as they present a form of social change whereby a small change can shift a sensitive social system into a qualitatively different state due to strongly self-amplifying (mathematically positive) feedback mechanisms. Social tipping processes have been suggested as key drivers of sustainability transitions emerging in the fields of technological and energy systems, political mobilization, financial markets and sociocultural norms and behaviors.

Drawing from expert elicitation and comprehensive literature review, we develop a framework to identify and characterize social tipping processes critical to facilitating rapid social transformations. We find that social tipping processes are distinguishable from those of already more widely studied climate and ecological tipping dynamics. In particular, we identify human agency, social-institutional network structures, different spatial and temporal scales and increased complexity as key distinctive features underlying social tipping processes. Building on these characteristics, we propose a formal definition for social tipping processes and filtering criteria for those processes that could be decisive for future trajectories to global sustainability in the Anthropocene. We illustrate this definition with the European political system as an example of potential social tipping processes, highlighting the potential role of the FridaysForFuture movement. Accordingly, this analytical framework for social tipping processes can be utilized to illuminate mechanisms for necessary transformative climate change mitigation policies and actions. 

How to cite: Winkelmann, R., Donges, J. F., Smith, E. K., Milkoreit, M., Eder, C., Heitzig, J., Katsanidou, A., Wiedermann, M., Wunderling, N., and Lenton, T. M.: Social tipping processes for sustainability: An analytical framework, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-9161, https://doi.org/10.5194/egusphere-egu21-9161, 2021.

The breakdowns of big empires have attracted the attention of historians for generations. Massive processes of social and political change, taking place over several generations and often several continents, they constantly invite new questions and ideas, and thus have often been studied in incredible detail. If we already know so much about them, is there potentially a lesson to be learnt for our times? Can we look at the historical examples to understand how the process of social collapse takes place, what are its key determinants and how – if at all – human agency can steer this process, with different human actors achieving their goals despite the overall unpredictability and decomposition of the existing structures.

In my talk, I will look at one of the best-studied instances of social collapse – that of the Roman Empire, or, more precisely, the Eastern part of it, which took place in the 7th c. AD – in order to look at the moments when human agency was able to steer the process of disintegration/collapse. I will focus on the different interventions of the imperial government (the emperor and the central administrative and military apparatus), the ideological innovations, elite transformation and other processes, which created a smaller, yet surprisingly resilient, social-economic system. Moreover, while environmental factors, such as climate or disease – as things stand now – do not seem to have been the primary causes of the collapse, a profound environmental change was taking place in parallel to the social transformation, underpinning the new emerging system in terms of its resource base. Overall, the seventh-century collapse of the Eastern Roman Empire could be seen as a successful transformation, largely due to human agency, or, more specifically, thanks to fortunate interventions and innovations of different human actors.

How to cite: Izdebski, A.: The place for agency in social collapse: the case of the Eastern Roman Empire in the 7th c. AD, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-9192, https://doi.org/10.5194/egusphere-egu21-9192, 2021.

EGU21-9658 | vPICO presentations | ITS3.7/ERE1.6

Household final energy footprints in Nepal, Vietnam and Zambia: composition, inequality and links to well-being

Marta Baltruszewicz, Julia Steinberger, Diana Ivanova, Lina Brand-Correa, Jouni Paavola, and Anne Owen

The link between energy use, social and environmental well-being is at the root of critical synergies between clean and affordable energy (SDG7) and other SDGs. Household-level quantitative energy analyses enable better understanding regarding interconnections between the level and composition of energy use, and SDG achievement. This study examines the household-level energy footprints in Nepal, Vietnam, and Zambia. We calculate the footprints using multi-regional input-output (MRIO) with energy extensions based on International Energy Agency (IEA) data. We propose an original perspective on the links between household final energy use and well-being, measured through access to safe water, health, education, sustenance, and modern fuels. In all three countries, households with high well-being show much lower housing energy use, due to a transition from inefficient
biomass-based traditional fuels to efficient modern fuels, such as gas and electricity. We find that households achieving wellbeing have 60-80% lower energy footprint of residential fuel use compared to average across the countries. We observe that collective provisioning systems in form of access to health centres, public transport, markets, and garbage disposal and characteristics linked to having solid shelter, access to sanitation, and minimum floor area are more important for the attainment of wellbeing than changes in income or total energy consumption. This is an important finding,  contradicting the narrative that basic wellbeing outcomes require increased income and individual consumption of energy. Substantial synergies exist between the achievement of well-being at a low level of energy use and other SDGs linked to poverty reduction (encompassed in SDG1), health (SDG3), sanitation (SDG6), gender equality (SDG5), climate action and reduced deforestation (SDG 13 and SDG15) and inequalities (SDG10). 

How to cite: Baltruszewicz, M., Steinberger, J., Ivanova, D., Brand-Correa, L., Paavola, J., and Owen, A.: Household final energy footprints in Nepal, Vietnam and Zambia: composition, inequality and links to well-being, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-9658, https://doi.org/10.5194/egusphere-egu21-9658, 2021.

EGU21-13703 | vPICO presentations | ITS3.7/ERE1.6

Socio-economic conditions for satisfying human needs at low energy use: an international analysis of provisioning factors

Jefim Vogel, Julia K. Steinberger, Daniel W. O'Neill, William F. Lamb, and Jaya Krishnakumar

Meeting human needs at low levels of energy use is fundamental for avoiding catastrophic climate change and securing the well-being of all people. In the current international political-economic regime, no country does so.

Here, we assess which socio-economic conditions might enable societies to satisfy human needs at sustainable levels of energy use, and thus reconcile human well-being with ambitious climate mitigation. Applying a novel analytical framework and a novel regression-based moderation approach to data from 106 countries, we analyse how the relationship between energy use and six dimensions of human need satisfaction varies with a wide range of socio-economic factors relevant to the provisioning of goods and services (‘provisioning factors’).

We find that higher achievements in provisioning factors such as income equality, public service quality, democracy and electricity access are associated with greater need satisfaction and lower energy dependence of need satisfaction. Conversely, higher levels of economic growth and extractivism are associated with lower need satisfaction and greater energy dependence of need satisfaction. Our analysis suggests that countries with beneficial configurations of key provisioning factors are much more likely to reach high levels of need satisfaction at low(er) levels of energy use. Based on our statistical models, countries with highly beneficial configurations of several key provisioning factors could likely achieve sufficient need satisfaction within levels of energy use found compatible with limiting global warming to 1.5 °C without negative emissions technologies. Achieving this would be very unlikely for countries with detrimental provisioning configurations.

Improvements in relevant provisioning factors may thus be crucial for ending human deprivation in currently underproviding countries without exacerbating climate and ecological crises, and for tackling the ecological overshoot of currently needs-satisfying countries without compromising sufficient need satisfaction. However, as key pillars of the suggested changes in provisioning run contrary to the dominant political-economic regime, a broader political-economic transformation may be required to organise provisioning for the satisfaction of human needs within sustainable levels of energy use.

Our findings have important implications for climate mitigation, poverty eradication, development discourses, and efforts towards Sustainable Development Goals and socio-ecological transformation.

How to cite: Vogel, J., Steinberger, J. K., O'Neill, D. W., Lamb, W. F., and Krishnakumar, J.: Socio-economic conditions for satisfying human needs at low energy use: an international analysis of provisioning factors, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-13703, https://doi.org/10.5194/egusphere-egu21-13703, 2021.

EGU21-14484 | vPICO presentations | ITS3.7/ERE1.6

Artificial radionuclides, mercury, lead, and oil components in sediment cores as markers of the Anthropocene Epoch

Yury Fedorov, Andrey Kuznetsov, Irinageo Dotsenko, and Anna Mikhailenko

The majority of researches of the Working group on the ‘Anthropocene’ of the International Commission on Stratigraphy (ICS) voted for the recognition of the Anthropocene as a formal chrono-stratigraphic unit characterized by profound alterations of several conditions and processes on Earth by human impact. It is also proposed to place its beginning and the end of the Holocene epoch in the mid-20th century, coinciding with the launch of nuclear weapon tests [1]. In contemporary sediment cores of the Sea of ​​Azov, the Don and the Kuban rivers, we will distinguish a "layer of anthropogenic impact", meaning the layer containing considerable quantities of technogenic material and (or) pollutants [2]. To reveal the chronology of its formation, its thickness, and boundaries, it is proposed to use the results of layer-by-layer determining of the Cs-137 and Am-241 specific activities, as well as the content of oil components, lead and mercury in the bottom sediments of the water bodies. The upper Cs-137 peak formed due to the Chernobyl accident and sometimes the lower Cs-137 and Am-241 peaks related to the global radioactive fallout in the 1950s and 1960s have been detected [3]. The decrease of mercury, lead, and oil components concentrations from the upper to the lower parts of sediment cores has also been observed. The results of analysis of technogenic radionuclides and priority pollutants distribution have proved that since the 1950s and 1960s in the bottom sediments of the Sea of ​​Azov and water bodies of its basin the “layer of anthropogenic impact" has been being formed. Its thickness varies from 20 to 50 cm and may even exceed 50 cm in areas characterized by high sedimentation rates. It has been found out that in the mid-20th century the ecosystem of the Sea of ​​Azov began to suffer from intense anthropogenic pressure, which reached its maximum in the 1970s and 1980s. It is proposed to consider the studied pollutants (technogenic radionuclides, mercury, lead, and oil components) as a possible set of priority markers of the Anthropocene epoch. The Holocene - Anthropocene boundary should be placed at the base of the identified “layer of anthropogenic impact”.

 

The research was supported by the Russian Foundation for Basic Research, project no. 19-05-50097.

 

Bibliography

[1] Working Group on the ‘Anthropocene’. Results of binding vote by AWG. http://quaternary.stratigraphy.org/working-groups/anthropocene/ (last accessed 17 January 2021).

[2] Kuznetsov A.N., Fedorov Yu.A., and Yaroslavtsev V.M. (2018) Technogenic and natural radionuclides in the bottom sediments of the Sea of Azov: regularities of distribution and application to the study of pollutants accumulation chronology. IOP Conference Series: Earth and Environmental Science 107, 012063.

[3] Fedorov Yu.A., Kuznetsov A.N., and Trofimov M.E. (2008) Sedimentation rates in the Sea of Azov inferred from Cs-137 and Am-241 specific activity. Doklady Earth Sciences, vol. 423, no. 1, pp. 1333-1334.

How to cite: Fedorov, Y., Kuznetsov, A., Dotsenko, I., and Mikhailenko, A.: Artificial radionuclides, mercury, lead, and oil components in sediment cores as markers of the Anthropocene Epoch, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-14484, https://doi.org/10.5194/egusphere-egu21-14484, 2021.

EGU21-15137 | vPICO presentations | ITS3.7/ERE1.6 | Highlight

Shifting the patterns of agency: identifying some requirements for transformative change

Daniel Hausknost

I propose an analytical distinction between four different modes of human agency: decision, choice, solution and routine. These modes are distinct through their respective combination of two basic criteria: one is the question, if the options the agentic mode is dealing with are commensurable or incommensurable; the other concerns the question whether the agentic mode is eliminating or retaining options. That way, the four modes of agency do very different things to the reality they are applied to. I suggest that purposive interventions into socio-ecological reality follow patterns that are typical to the respective political-economic order they are part of. For example, contemporary liberal democratic orders tend to favor combinations of solution (typically: technological innovation) and choice (typically: individual market behavior), while avoiding decisions (the collectively binding selection between incommensurable options) for their disruptive potential. At the same time, the establishing of new niche routines in terms of more sustainable social practices is encouraged or at least tolerated. I argue that the resulting agentic regime of liberal democratic orders (i.e. their constitutive pattern of agency) is only very weakly transformative, as it shuns decisions and individualizes the selection of incommensurable options (e.g. ‘ideological’ choices between different production standards, forms of mobility or models of infrastructure). It tends to institute an agentic regime resulting in ‘evolutionary’ patterns of change (the combination of technological variation and market selection) rather than opting for willful, political and (therefore) conflictive forms of change.

A purposive steering of human-environment interactions under time pressure towards radical system transformation, however, requires a different type of agentic regime: a new combination of agentic modes with a much stronger weighting of (collective) decisions. Indeed, the paper argues, any purposively ‘transformative’ agentic regime would have to institute patterns of agency that combine solutions, choices, routines and decisions in a novel, and much more disruptive, way. For example, solutions (technological innovations) and new routines (social innovations) that are proven to have a highly transformative impact when rolled out would need to be subject to collective decisions rather than individual choice. The result would be a new pattern of agency leading to a different rhythm and pattern of change, but also to new political-economic challenges and conflicts. Therefore, shifting the patterns of agency is at the same time a necessity and a massive institutional and political challenge for complex societies.

The paper concludes by outlining some suggestions as to how the proposed distinction of agentic modes can be operationalized for empirical investigations into the transformative capacities of human agency in different political-economic settings. 

How to cite: Hausknost, D.: Shifting the patterns of agency: identifying some requirements for transformative change, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-15137, https://doi.org/10.5194/egusphere-egu21-15137, 2021.

EGU21-15823 | vPICO presentations | ITS3.7/ERE1.6

Dynamics of collective action to conserve a large common-pool resource

Andrew Ringsmuth, David Andersson, Sigrid Bratsberg, and Astrid de Wijn

A pressing challenge in the Anthropocene is sustainable and just management of large-scale common-pool resources (CPRs) including the atmosphere, biodiversity and public services. This poses a difficult collective action problem because such resources may not show signs that usage restraint is needed until tragedy is almost inevitable. To solve this problem, a sufficient level of cooperation with a pro-conservation behavioural norm must be achieved, within the prevailing sociopolitical environment, in time for the action taken to be effective. In this work, we investigate the transient, nonequilibrium dynamics of behavioural change in an agent-based model on structured networks that are also exposed to a global external influence. Our model combines elements of rational choice theory with psychology-based opinion dynamics to reflect that individuals who promote collective action to conserve a large CPR are rationally motivated and also face psychosocial constraints. We find that social polarisation emerges naturally, even without assuming bounded confidence, but that for rationally motivated agents, it is temporary. The speed of convergence to a final consensus is controlled by the rate at which the polarised clusters are dissolved. This depends strongly on the combination of external influences and the network topology. Both high connectivity and a favourable environment are needed to rapidly obtain final consensus. Our findings expand the evidence that designing systems to encourage constructive engagement between disagreeing groups could be a powerful promoter of large-scale collective action.

How to cite: Ringsmuth, A., Andersson, D., Bratsberg, S., and de Wijn, A.: Dynamics of collective action to conserve a large common-pool resource, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-15823, https://doi.org/10.5194/egusphere-egu21-15823, 2021.

ITS3.8/SSS1 – Geochemistry and human health: fundamentals and approaches towards improvement of risk assessments and practical recommendations

EGU21-8735 | vPICO presentations | ITS3.8/SSS1

Living Matter as a Self-Organizing System

Vyacheslav Korzh

Biological evolution proceeded under the sign of the liberation of developing organisms from the power of random phenomena in the external environment. At a certain stage in the evolution of living matter - the totality of all living organisms, it became possible to basically implement liberation from the instability of the external environment. Back in the eighteenth century J.-B. Lamarck argued and tried to prove that all substances located on the surface of the globe and forming its crust were formed due to the activity of living organisms. V.I. Vernadsky wrote: “On the earth's surface there is no chemical force that is more permanently acting, and therefore more powerful in its ultimate consequences than organisms taken as a whole” [1]. We find convincing proofs of the formation of a biogeochemical environment by living matter in accordance with their needs in the work of V.V. Kovalsky's [2].

We have studied the dynamics of the global process of transfer of chemical elements in the ocean-atmosphere-continent-ocean system. Living matter is an active participant in this process. As a result of metabolic processes, living matter constantly creates and constantly maintains an increased concentration of trace elements in its environment. The biocenosis of the hydrosphere initiates  increasing of the soluble forms of microelements in its habitat. The terrestrial biocenosis acts in the opposite direction [3]. The nonlinear laws of the processes of redistribution of average elemental compositions in the biosphere between liquid and solid phases (hydrosphere-lithosphere system) have been established. We have established a universal constant of nonlinearity of these processes in the biosphere (equal to 0.7) [3].

Human activity makes irreversible changes in the dynamics of the biosphere, and at the present stage of development of a technogenic civilization, the scale of human expansion into natural processes is such that they begin to destroy the biosphere as an integral ecosystem. The impending global ecological catastrophe requires development of fundamentally new strategies in scientific  activities that ensure harmonious coexistence of man and nature. We are developing the concept of the harmonious integrity of the biosphere (the concept of biosphere homeostasis). The stability of biogeochemical and other processes on the Earth's surface is completely determined by the coordinated, purposeful activity of living matter as an integral system [3]. The universal constant of nonlinearity of the processes of formation of the elemental composition of the biosphere (equal to 0.7) established by us should be accepted as an ecological standard, violation of which is unacceptable.

References.

How to cite: Korzh, V.: Living Matter as a Self-Organizing System, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-8735, https://doi.org/10.5194/egusphere-egu21-8735, 2021.

EGU21-11120 | vPICO presentations | ITS3.8/SSS1

Theoretical and practical aspects in determining the current geochemical status of the Crimean peninsula and anthropogenic risk to public health

Elena Evstafeva, Svetlana Tymchenko, Anna Bogdanova, Olga Zalata, Yuliia Boyarinceva, Olga Moskovchuk, Irina Evstafeva, Alexandra Slusarenko, Anna Makarova, and Elena Yaseneva

The implementation of basic principles of medical and ecological monitoring programs in Crimea previously reported in EGU proceedings consists of determining the content of a wide range of toxic, essential and rare earth elements in various biological substrates: soil, plants, water, human body. Biosubstrates are sampled in different locations with contrast natural and anthropogenic conditions: urbanized-rural, industrial-agricultural, natural resources. Lichens and poplar leaves are used as indicators of environmental contamination, particularly atmospheric pollution; liquid precipitation is used as an indicator showing the negative impact of air pollution on ecosystems; hair is used as an indicator of the total body intake of chemical elements. The update of databases, on some of the territories (Simferopol, Sevastopol, geographical regions with different soil characteristics, etc.) with regard to some of the elements (mercury, lead, cadmium, selenium, etc.) at this stage allowed to determine their biogeochemical status in conditions of intensive growth of anthropogenic load in recent years, and to compare it with the elemental status of the humans living in this territory. The databases for other types of territories continue to be extended, the relationship between morbidity to estimate of the environmental burden of disease for environmentally determined diseases (neurodegenerative, endocrine, respiratory, etc.) and chemical load on the territories, based on USEtox model; the functional state of target systems (nervous, immune, cardiovascular) and level of chemical elements in the human body and the overall elemental imbalance, is established. This has provided us with a degree of understanding on how the degree of population and individual health risk could be determined.

Mercury analysis was funded by RFBR according to the research project № 18-29-24212\19 entitled “Development of neutralization of mercury-containing waste without heating and the formation of wastewater”, 2018–2021 years; elemental composition was possible to determine due to RFBR project № 18-45-920042\20 entitled “Bioecological monitoring of heavy metals at board of Black Sea of Crimea”, 2018–2020 years. Physiological part of research was possible to accomplish due to funds by the V.I. Vernadsky Crimean Federal University (Project No VG2019/15, АААА-А20-120012090158-7).

How to cite: Evstafeva, E., Tymchenko, S., Bogdanova, A., Zalata, O., Boyarinceva, Y., Moskovchuk, O., Evstafeva, I., Slusarenko, A., Makarova, A., and Yaseneva, E.: Theoretical and practical aspects in determining the current geochemical status of the Crimean peninsula and anthropogenic risk to public health, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-11120, https://doi.org/10.5194/egusphere-egu21-11120, 2021.

EGU21-14516 | vPICO presentations | ITS3.8/SSS1

Biogeosystem Technique methodology as a new chemical soil-biological engineering foundation for the safe expanded technological development in the Noosphere

Vladislav Zinchenko, Elena Grishina, Valery Kalinitchenko, Alexey Glinushkin, Valery Kudeyarov, Sergey Gudkov, Alexander Savostyanov, Tatiana Minkina, Vladimir Ilyin, and Saglara Mandzhieva

Nature of soil as a main Earth’s biogeochemical reactor is dramatically underestimated. This is the inappropriate result of outdated technologies of the current industrial stage of development.

There are attempts to hide the technological drawbacks under the veil of different modern terms. This, unfortunately, does not change the essence of the current aggravating conflict between biosphere and technology. This is a reluctance to abandon the nature-imitation approach to technology, including environmental, chemical, agrarian technology. The development potential of the biosphere is used now not on the full scale. There is a need now for heuristically qualified intuition to understand the nature of the niche for environmental soil engineering technology strategic development. This approach will ease the current contradiction between biosphere and technology. The global environmental challenge is a transcendental (not a direct imitation of nature) Biogeosystem Technique (BGT*) technological platform of the Noosphere.  BGT* is capable to promote a promising niche for the environmental business development and nature-similar technological management.

One time BGT* based intra-soil milling of the 20–50 cm layer provides soil stable fine multilevel aggregate system, soil biome function for up to 40 years. The BGT* based intra-soil pulse continuous-discrete watering solves the world water scarcity problem. Water consumption is 5–20 times less compared to standard irrigation. The soil solution matrix potential range is from −0.2 MPa to −0.4 MPa, plant stomatal apparatus operates in the regulation mode. Water and nutrient efficacy is high. Intra-soil recycling of the municipal, industrial, waste and gasification byproduct in the soil layer of 20–50 cm in the course of this layer milling provides safety of the environment and plant nutrition. The yield is higher for 50–80 % compared to standard technology.

BGT* gives the new transcendental prospect to stabilize the Earth’s biosphere and climate system. The possibilities to achieve a goal are as follows: soil compaction overcoming; freshwater saving and high-level soil solution equilibria control; environmentally safe waste recycling; high biogeochemical barrier for heavy metal; of atmosphere N fixation in photosynthesis; soil organic matter synthesis, better function of humic substances, polymicrobial biofilms, and plant stimulants; plant resistance to phytopathogen, phytopathological, medical and veterinary environmental safety.

BGT*chemical soil-biological engineering intensifies the nutrient turnover and fertilizers return, decreases pesticides and nutrients off-target transport. This ensures higher yield and biofuel, higher efficacy of the technology, soil-biological reversible C sequestration, productive biosphere spreading, abundance, and safety; adaptation to climate change. BGT* provides the higher recreational potential of the biosphere. BGT* implementation requires technological and regulatory breakthrough for soil-chemical technology development niche expansion non-contradicting to Nature. BGT* is a promising sphere for worldwide Noosphere ventures.

The research was financially supported by the RFBR, project no. 18-29-25071, and the Ministry of Science and Higher Education of Russia, project no. 0852-2020-0029.

How to cite: Zinchenko, V., Grishina, E., Kalinitchenko, V., Glinushkin, A., Kudeyarov, V., Gudkov, S., Savostyanov, A., Minkina, T., Ilyin, V., and Mandzhieva, S.: Biogeosystem Technique methodology as a new chemical soil-biological engineering foundation for the safe expanded technological development in the Noosphere, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-14516, https://doi.org/10.5194/egusphere-egu21-14516, 2021.

EGU21-9264 | vPICO presentations | ITS3.8/SSS1

Assessment of the Risk of Thyroid Cancer in Rural Settlements in the Areas of the Bryansk Region (Russia) Affected by the Chernobyl NPP Accident

Vladimir Baranchukov, Elena Korobova, Alexander Silenok, and Irina Kurnosova

Thyroid cancer is one of the most important medical problems. The disease often occurs in regions that have been exposed to radiation and where there is insufficient iodine in nature. Adequate intake of iodine is necessary for the functioning of the thyroid gland and the development of the mammalian fetus. Thus, in 1990, a special International Council for Combating Iodine Deficiency Disorders was established at WHO. Since 1990, the incidence of thyroid cancer in the world has increased by 169% (Dang et al., 2020). It can be assumed that such an increase is associated with early detection of the disease. However, in countries with high human development index (HDI), where the detection rate of the disease is 4-5 times higher than in low HDI countries, this indicator does not correlate with mortality from thyroid cancer. In our opinion, this is because the food sources must be considered. As local foods to the diet varies significantly between urban and rural areas, it is important to compare cases of thyroid cancer in them. For example, in rural areas of the United States, the overall incidence of thyroid cancer is 14% lower than in cities (McDow et al., 2020).

For the Bryansk region (the most affected by the Chernobyl accident in Russia) data on thyroid cancer also show a difference: in 27 regional centers (67% of the population), an estimate of the thyroid cancer incidence is 20.9 per 100 000 people per year (period from 1990 to 2019), while for other localities the rate is much lower (16.3). However, mortality from thyroid cancer in rural areas is 46% higher than in urban areas (0.89 and 0.60, respectively). Using a specialized GIS developed to study natural and man-made geochemical factors responsible for the spread of endemic diseases, we zoned the territory according to evaluated risk (Baranchukov et al., 2019).

Assessment of the risk of thyroid cancer turned out to be more effective for rural settlements (excluding the most contaminated area, where special measures were taken): the correlation between the calculated total natural and man-made risk and the incidence of thyroid cancer was significant and higher in rural areas (r=0.54, p=0.05, n=25) than in the main urban areas (r=0.27; p=0.17). The result of the study shows that the prevalence of thyroid cancer is associated, first of all, not with the level of diagnosis, but with the structure of nutrition, which ensures the entry of elements into the human body.

This study was funded by RFBR and BRFBR, project #20-55-00012.

References

Deng Y et al. Global Burden of Thyroid Cancer From 1990 to 2017. JAMA Netw Open. 2020;3(6):e208759. Published 2020 Jun 1. doi:10.1001/jamanetworkopen.2020.8759

McDow AD, et al. Impact of Rurality on National Trends in Thyroid Cancer Incidence and Long-Term Survival. J Rural Health. 2020 Jun;36(3):326-333. doi: 10.1111/jrh.12374. Epub 2019 May 17. PMID: 31099945

Baranchukov V et al. Application of Geoinformation Technologies for minimization of thyroid gland diseases in the impact areas of the radioiodine fallout, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-9000, https://doi.org/10.5194/egusphere-egu2020-9000, 2020

How to cite: Baranchukov, V., Korobova, E., Silenok, A., and Kurnosova, I.: Assessment of the Risk of Thyroid Cancer in Rural Settlements in the Areas of the Bryansk Region (Russia) Affected by the Chernobyl NPP Accident, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-9264, https://doi.org/10.5194/egusphere-egu21-9264, 2021.

EGU21-9348 | vPICO presentations | ITS3.8/SSS1

The critical parameters of the human health impact calculation            

Natalia Baranovskaya, Alexandra Belyanovskaya, Bertrand Laratte, and Elena Ageeva

There are many LCA methods and models (e.g. CML 1992, Eco-Indicator 95, IMPACT 2002+, TRACI, USEtox, etc.), used to characterize environmental impacts. Only four LCIA methods include spatial dimension at different geographical levels: Impact World+, LC-IMPACT, EDIP 2003 and USEtox (Bratec et al., 2019). Among these, three (Impact World+, EDIP 2003 and USEtox) include a human health impact category: human toxicity. The USEtox model, recommended by the European Commission, has already proved its efficiency for the coupling of environmental and geochemical studies. The Characterization factors of the USEtox describe environmental fate (FF) of the chemicals, their non- and carcinogenic effect (EF), direct and the indirect exposure (XF). All these factors vary depends on the applicable area. However, despite all advantages of the model, its geographical customization is rather generic. This paper presents the utilization of the already published case study (Belyanovskaya et al., 2019: 2020) with the indirect human exposure factor modification. The investigation present the modified biotransfer factor of the metals (Cr, Zn, Sb, As, Ba) of the meat product calculated specifically for different location inside the area “Central Asia”. The paper extends already published results with local data of the city of Vladivostok (Russia).

Acknowledgement

The statistical data processing is supported by State program RF «Science». Project FSWW-0022-2020.

The impact assessment with the USEtox model is supported by the RSF grant (№ 20-64-47021).

How to cite: Baranovskaya, N., Belyanovskaya, A., Laratte, B., and Ageeva, E.: The critical parameters of the human health impact calculation            , EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-9348, https://doi.org/10.5194/egusphere-egu21-9348, 2021.

EGU21-12725 | vPICO presentations | ITS3.8/SSS1

Theoretical approaches to revealing and treating endemic diseases of geochemical origin 

Elena Korobova and Sergey Romanov

Now it is obvious that animals and humans suffer from both deficit and excess of trace elements due to a systematic imbalance of chemical elements in modern diets. Although the physiological role of many elements is not completely studied, some part of them is treated as toxic. We consider that this definition is fallacious and propose a spatial dose-dependent approach accounting of optimal element concentrations for better identification and treatment of diseases of geochemical origin.

Basing on the fact that the living organisms have met with a wide variety of geochemical conditions in the process of evolution, we suggest that the ability to survive in case of fast changing chemical conditions has been fixed in the genetic code of existing species by preservation of the so-called “dormant” genes. We assume that the ability to adapt quickly could have been realized due to the activity of “dormant” genes, which are inherent as active only in a small part of individuals, because in extremely changing conditions it can give rise to a new generation. In our opinion, experiments of Prat with campion species [1], those of Bradshaw with sorrel and plantain [2], of Letunova with soil microorganisms [3], of Krivolutsky with lower vertebrates [4] along with recent publication on the genetically determined rapid adaptation of Colorado beetles to many pesticides [5] earnestly confirms this idea. As this mechanism protects species stability, we presume that it is a common feature of members of biocenosis. Therefore, all species of biogeocenoses should represent a specific collection of individuums (morphs) with “active dormant” genes thereby acquiring high ability of not only survival of populations in extremal conditions but also occupation of new ecological niches. We consider such a defence mechanism to be rational, since it allows a quick leveling reaction of species to a certain type of extremal impact.

This additional theoretical hypothesis seems to be productive from the point of view of solving practical problems, since it allows more rational directing of genetic research and analyzing the risk of endemic diseases.

References

1. S. Prat, 1934. Die erblichkeit der resistenz gegen Kupfer, Ber. Dtsch. bot. Ges 1 (102), 65–67.
2. A.D. Bradshaw, 1952. Populations of Agrostis tenuis resistant to lead and zinc poisoning, Nature 169, 28.
3. S.V. Letunova & V.V. Koval’skii, 1978. Geochemical Ecology of Microorganisms. Nauka, Moscow.
4. D.A. Krivolutskii, 1983. Radioecology of Communities of Land Animals. Energoatomizdat, Moscow.
5. K. Brevik, E.M. Bueno, S. McKay, S.D. Schoville, Y.H. Chen, 2020. Insecticide exposure affects intergenerational patterns of DNA methylation in the Colorado potato beetle, Leptinotarsa decemlineata. Evolutionary Applications, DOI: 10.1111/eva.13153.

How to cite: Korobova, E. and Romanov, S.: Theoretical approaches to revealing and treating endemic diseases of geochemical origin , EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-12725, https://doi.org/10.5194/egusphere-egu21-12725, 2021.

EGU21-5641 | vPICO presentations | ITS3.8/SSS1 | Highlight

Influences of air substances and meteorological conditions on human health

Stephanie Koller, Elke Hertig, Christa Meisinger, and Markus Wehler

Influences of air substances and meteorological conditions on human health

For a long time it has been known that exceptionally strong and long-lasting heat waves have negative health effects on the population, which is expressed in an intensification of existing diseases and over-mortality of certain risk groups (Kampa, Castanas 2008). Often associated with heat are stagnant airflow conditions that cause a large increase in the concentration of certain air substances (Ebi, McGregor 2008). Many of these air substances have a strong adverse effect on the human organism (Kampa, Castanas 2008).

The aim of the project is to investigate the actual hazard potential air pollution- and climatological variables by quantifying the effects on human health of increased exposure to air constituents and temperature extremes. Different multivariate statistical methods such as correlation analysis, regression models and random forests, extreme value analysis and individual case studies are used.

As a medical data basis for this purpose, the emergency department data of the University Hospital Augsburg are regarded. In addition to the diagnosis, supplementary information such as age, gender, place of residence and pre-existing conditions of the patients are used. Among the air constituents, the focus is on ozone, nitrogen dioxide and particulate matter. In the meteorological part, the focus is primarily on temperature, which is not only a direct burden but, as in the case of ozone, also has a decisive influence on the formation of ground-level ozone. However, a large number of other meteorological parameters such as precipitation, relative humidity and wind speed as well as the synoptic situation also play a major role in the formation, decomposition process and the distribution of pollutants (Ebi, McGregor 2008).

The first major question to answer is whether air pollution and meteorological stress situations are visible in the emergency department data. Further in-depth questions are which factors have the greatest negative impact, what is the most common environment-related disease, which weather conditions carry a higher than average risk and what are the health risks of climate change.

Ideally, the analysis may also provide a short-term forecast from which to derive whether or not there will be an above or below average number of visits to the emergency department.

The project is funded by the German Federal Foundation for Environment (DBU) and the German Research Foundation (DFG) - project number 408057478.

Literature:

Nuvolone D., Petri D., Voller F. (2017): The effects of ozone on human health. doi: 10.1007/s11356-017-9239-3.

Requia W., Adams M., Arain A., Papatheodorou S., Koutrakis P., Mahmoud M. (2018): Global Association of Air Pollution and Cardiorespiratory Diseases: A Systematic Review, Meta-Analysis, and Investigation of Modifier Variables. doi: 10.2105/AJPH.2017.303839

Xing Y., Xu Y., Shi M., Lian Y. (2016): The impact of PM2.5 on the human respiratory system. doi: 10.3978/j.issn.2072-1439.2016.01.19

 

How to cite: Koller, S., Hertig, E., Meisinger, C., and Wehler, M.: Influences of air substances and meteorological conditions on human health, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-5641, https://doi.org/10.5194/egusphere-egu21-5641, 2021.

EGU21-15724 | vPICO presentations | ITS3.8/SSS1 | Highlight

Environmental and human health risks associated with soil potentially toxic element exposure around the largest coal fired power plant in Southern Russia

Tatiana Minkina, Elizaveta Konstantinova, Saglara Mandzhieva, Tatiana Bauer, Dina Nevidomskaya, Yuri Fedorov, Valery Kalinitchenko, and Alexey Glinushkin

The combustion of solid fuel at power plants pollutes adjacent areas with potentially toxic elements (PTEs), which increases risks to public health in the vicinity of these facilities. With an installed electric capacity of 2258 MW, the Novocherkassk Power Plant (NPP) is the top electric energy producer in Southern Russia. This facility is located in the vicinity of one of the largest cities in the Rostov Region, Novocherkassk, with a population of 168,035 people. Among the major cities in the region, Novocherkassk is characterized by the maximum level of atmospheric pollution. The study was conducted at 12 monitoring sites laid along the radii emanating from the NPP chimneys. The sites were located in the near-source influence zone (up to 3 km) in various directions and at greater distances (from 3 to 20 km) downwind. In this study, various indicators of environmental quality (geoaccumulation index (Igeo), pollution index (PI), and Nemerow pollution index (NPI)) as well as human health risk model (US EPA 1989) were applied to identify spatial distribution and to evaluate risks of seven PTEs (Cr, Mn, Ni, Cu, Zn, Cd, and Pb) in soils. The results demonstrate the relationship between the features of atmospheric circulation and PTE content in soils within the NPP impact zone. The main pattern in spatial distribution of soil pollution is a decrease in the concentrations of PTEs with distance from the source. The influence of NPP can be traced out to approximately 7 km downwind. The total content of PTEs in the soils slightly exceed the Clarke values for the upper continental crust, as well as the world average concentrations of these elements in soils (up to two times). Moderate to high pollution by Cd and Pb, according to the Igeo, is characteristic of the soils at a distance of up to 3 km to the NPP. The PI also demonstrates higher pollution estimates relative to distance from the source; soils in impact zone of NPP are characterized by low or no pollution by Cr, Mn, Ni, Cu, and Zn, and moderate pollution by Cd and Pb. Soils located in the leeward zone are moderately polluted with Ni and Zn and very strongly to strongly polluted with Cd and Pb. According to the NPI values, pollution decreases from heavy in the area immediately downwind of the source to slight for most of the territory under consideration. The risks of noncarcinogenic effects on children are assessed as low, their occurrence is attributed to the intake of Mn, Ni, and Pb, while for adults there was no significant general toxic risk associated with the intake. The total carcinogenic risk to human health slightly exceeds the permissible standard for soils in close vicinity of the enterprise due to the potential intake of Ni, Cd, and Pb.

The reported study was funded by RFBR, project number 19-05-50097 and Grant of President of Russian Federation, no. МК-6137.2021.1.5.

How to cite: Minkina, T., Konstantinova, E., Mandzhieva, S., Bauer, T., Nevidomskaya, D., Fedorov, Y., Kalinitchenko, V., and Glinushkin, A.: Environmental and human health risks associated with soil potentially toxic element exposure around the largest coal fired power plant in Southern Russia, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-15724, https://doi.org/10.5194/egusphere-egu21-15724, 2021.

EGU21-2710 | vPICO presentations | ITS3.8/SSS1

Risk assessment mapping of diseases caused by excess heavy metals in river water

Tatiana Fashchevskaia and Yury Motovilov

A medico-ecological research method is proposed based on the coupled spatial analysis of zones of excessive pollution of river waters with heavy metals (HM) in the basin of the Nizhnekamskoe Reservoir (catchment area of 186 000 km2) and data on the health status of the local population.

For the spatial analysis of the heavy metal cycle in the river basin (on its surface, in soil, ground and river waters), a physically based ECOMAG-HM model with a daily time step resolution was developed. The model consists of two main blocks: a hydrological submodel of runoff formation and a hydrochemical submodel of migration and transformation of HM in the river basin [1]. The model was calibrated and verified on the basis of long-term hydrometeorological and hydrochemical observations data at 34 hydrochemical monitoring sites. Maps of simulated mean annual HM concentrations in river water were constructed and areas with significant levels of HM contamination (copper, zinc, manganese) were identified, including catchment areas not covered by hydrochemical monitoring.

The population in the study region has notably higher morbidity rate in priority class diseases (of digestive system, urogenital system, blood and hemopoietic organs, as well as disorders related to immunity mechanism) than the average level in Russia. Occurrence of these diseases is mostly determined by the state of the environment and, even more, by the quality of drinking water and consumed biological products (fish). To analyze the influence of the river water contaminated with heavy metals on the health of the population the statistical data on general morbidity in the region had been previously analyzed separately for two age group: adult population and children under 14. The most relevant research object is child morbidity. Children permanently live in the area without being directly exposed to hazardous work conditions and have relatively healthy lifestyle which excludes the influence of additional harmful factors (overeating, smoking, alcohol consumption) that increase the risk of many diseases development.

The coupled spatial analysis of the population morbidity and the river water contamination maps shows that zones with high and excessive population morbidity rates are located mainly within the highlighted areas with increased concentration of HM in the river water. However, it does not seem possible at this point to separate the effects of man-made impact of air, contaminated with toxic emissions, water and locally produced food on the health of the population. Therefore, to obtain more accurate results within the next stage it is planned to conduct spatial statistical analysis of morbidity risk in separate groups of diseases, mostly determined by health effect of heavy metal water contamination.

1. Motovilov Yu.G., Fashchevskaya T.B., 2019. Simulation of spatially-distributed copper pollution in a large river basin using the ECOMAG-HM model. Hydrological Sciences Journal, 64 (6), 739-756. DOI: 10.1080/02626667.2019.1596273

 

This study was carried out under Governmental Assignment to the Water Problems Institute, Russian Academy of Sciences (subject no. 0147-2019-0001)

How to cite: Fashchevskaia, T. and Motovilov, Y.: Risk assessment mapping of diseases caused by excess heavy metals in river water, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-2710, https://doi.org/10.5194/egusphere-egu21-2710, 2021.

EGU21-15744 | vPICO presentations | ITS3.8/SSS1

Potentially toxic elements in floodplain soils the Don River basin of Southern Russia

Ilia Lobzenko, Dina Nevidomskaya, Elizaveta Konstantinova, Tatiana Minkina, Tatiana Bauer, Inna Zamulina, Victor Chaplygin, Svetlana Sushkova, and Ivan Savin

Large rivers and their deltaic parts and adjacent coastal zones are subjected to strong anthropogenic influence and are often considered as hotspots of environmental pollution. The Don River basin is a highly urbanized area with developed agriculture and industry which negatively affect water quality, aquatic ecosystems and soils. The main objectives of the proposed research were to determine the levels of potentially toxic elements (PHEs) in soils of various aquatic landscapes of the study area, as well as to reveal the relationships between the content of exchangeable PTEs and the physical-chemical properties of floodplain soils.

Depending on the soil-landscape and hydrological conditions and taking into account the intensity of anthropogenic influence, the following zones were identified: the lower Don floodplain from the Tsimlyansk Reservoir to the source of the Mertvy Donets River, Don Delta, the coastal zone of the Taganrog Bay, the mouths of small rivers flowing into the bay, and Taganrog city, an industrial port center on the northern coast of the bay.

The floodplain and coastal landscapes of the study area are dominated by Fluvisols. Solonchaks, Arenosols and Haplic Chernozems which are background soils of the region are less common. Soil samples were collected in summer 2020 from the surface soil horizon (0–20 cm deep). The particle size analysis was conducted using the pipette method; the total organic carbon content in the soils was determined using the dichromate oxidation; the pH was measured by potentiometry in the supernatant suspension of soil and water in a ratio of 1:2.5. The total concentrations of Cr, Mn, Ni, Cu, Zn, Cd, and Pb were determined by X-ray fluorescence analysis using a Spectroscan MAX-GV spectrometer (Spectron, Russia), and the content of exchangeable forms extracted from the soil by NH4Ac buffer solution with pH 4.8 and soil/solution ratio of 1:10 for 18 h was determined by atomic absorption spectrophotometry.

The obtained results showed that soils of the Lower Don and Taganrog Bay coastal zone are rather contrasted in terms of properties and metal contents, which indicates the variability of landscapes, natural and anthropogenic processes in the studied systems. High CV values for Pb, Zn, Cd and Cr indirectly indicate strong anthropogenic influence on these environments. The results of PCA analysis showed that there are two association of metals in terms of geochemical behaviour and sources. The first one included Cr-Zn-Pb-Cd, the elements of anthropogenic origin, the second Mn, Ni, and Cu, which are probably of mixed origin. The obtained results showed that the coastal zone is a diverse and complex system subjected to anthropogenic activities, which is pronounced in the enrichment of aquatic soils with a number of metals and higher proportions of exchangeable forms from different types of sources.

This work was funded by the Russian Science Foundation, grant no. 20-14-00317.

How to cite: Lobzenko, I., Nevidomskaya, D., Konstantinova, E., Minkina, T., Bauer, T., Zamulina, I., Chaplygin, V., Sushkova, S., and Savin, I.: Potentially toxic elements in floodplain soils the Don River basin of Southern Russia, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-15744, https://doi.org/10.5194/egusphere-egu21-15744, 2021.

Chemical composition of drinking water samples (n=515 in total) collected in rural settlements of Bryansk region (Russia) in the period from 2007 to 2020 was analyzed to reveal its relation to the origin of water-bearing rocks and spatial distribution of endemic diseases of thyroid gland among local population. To identify the health effects of drinking water, spatial correlation between concentrations of iodine, selenium and other elements in particular settlements and medical information on the prevalence of endemic thyroid diseases among the local residents was assessed. Sampling was carried out in rural settlements distributed within all the 27 districts of the Bryansk region from different available sources fed by aquifers in hydrogeological structures of different composition and age. Water samples were analyzed for major cationogenic elements (Ca, Mg, Sr, K, Na, Mn, Zn, Fe, Al, Si) and anions (HCO32-, Cl-, F-, SO42-, NO32-, PO42-)as well as for I- and Se using ICP-AES, potentiometry, photometry and spectrofluorimetry.

The results confirmed a low supply of water samples with iodine (Me= 5.96 μg/L, variation range 0.06 - 41.2 μg/L) and selenium (Me= 0.18 μg/L, variation 0.001 - 6.21 μg/L). The concentration levels of iron (64% of examined districts), manganese (36% of examined districts) and strontium (8% of examined districts) appeared to be inconsistent with hygienic standards. Tendencies of relationship between water iodine concentration and the incidence of thyroid diseases caused by iodine deficiency among teenagers aged 8 to 12 have been found. The same trend was found when comparing geochemical data with iodine content in renal excretion in this age group.

The presented study demonstrates the applicability of spatial data analysis in identifying and visualizing the relationship between the manifestation of endemic thyroid diseases and the geochemical characteristics of the environment, which is necessary for assessing the quality of local diets and their correction.

The study was partly supported by Russian Foundation for Basic Research and Belorussian Republican Foundation for Basic Research (project number 20-55-00012).

 

How to cite: Kolmykova, L., Korobova, E., Baranchukov, V., and Kurnosova, I.: Peculiarities of chemical composition of drinking water in rural settlements of Bryansk region and evaluation of its health effect (contribution to endemic thyroid diseases), EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-8786, https://doi.org/10.5194/egusphere-egu21-8786, 2021.

EGU21-13686 | vPICO presentations | ITS3.8/SSS1 | Highlight

Geological occurrence, mineralogical character and preliminary risk assessment of carcinogenic erionite in New Zealand

Janki Patel, Martin Brook, Dario Di Giuseppe, Valentina Scognamiglio, and Alessandro F. Gualtieri

Erionite is a naturally-occurring zeolite mineral that has emerged as a well-known health hazard over the last few decades. Human exposure to erionite fibers has been unequivocally linked to malignant mesothelioma, a disease also associated with inhalation of airborne asbestos. Indeed, erionite is now classified by the International Agency for Research on Cancer (IARC) as a Group 1 carcinogen (i.e., carcinogenic to humans), but it appears to be more toxic than asbestos. Since volcaniclastic rocks containing erionite are widely present in New Zealand, there is a concern over potential health issues following inhalation of dust particles in particular areas.  Indeed, New Zealand is one of a number of high-income countries with elevated incidence of malignant mesothelioma (2.6 per 100,000), and this has traditionally been thought to be a result of occupational exposure to airborne asbestos fibers. However, recent cases of malignant mesothelioma have emerged without a known link to asbestos exposure, and in 2015, the New Zealand Government acknowledged that erionite was a more potent carcinogen than asbestos. Despite this, there are no established occupational exposure limits for erionite in New Zealand or globally. We are currently using a multi-methodological approach, based upon field investigation, morphological characterization, scanning electron microscopy (SEM)/energy-dispersive spectroscopy (EDS), Transmission Electron Microscopy (TEM), and X-ray powder diffraction (XRPD) to analyse erionite from sites around New Zealand. Preliminary results are reported here, including erionite from Miocene tuff in Auckland. The erionite appears to be erionite-K. From the dimensional analysis, 45.6% of minerals satisfied the requirements for a respirable airborne fibre (length, L ≥ 5 μm, a diameter, w ≤ 3 μm, and L/w value ≥ 3:1). The presence of this mineral is of concern for risk to human health, especially considering the land development in the Auckland region and the quarries and mining-related activities that are operating in the zeolite host rocks elsewhere in New Zealand. Thus, there is a need for a detailed risk assessment in parts of the country indicative of potential hazard. Further assessments of erionite species, quantification of the potentially respirable airborne fibers, and targeted epidemiological surveillance are planned.

How to cite: Patel, J., Brook, M., Di Giuseppe, D., Scognamiglio, V., and Gualtieri, A. F.: Geological occurrence, mineralogical character and preliminary risk assessment of carcinogenic erionite in New Zealand, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-13686, https://doi.org/10.5194/egusphere-egu21-13686, 2021.

Soils are the main component in biogeocenoses. More than 30 types and subtypes of soils are represented in the territory of the Perm region (Russia); however, in the oil-producing areas, the main share of soils is Albicluvisols/Retisols, Calcaric Leptosols, Luvic Phaeozems, Greyzamic Phaeozems and Folic Fluvisols. These soils cover a large area of the Perm region and are the basis for the existence of typical coniferous-deciduous forests and forest-steppe biogeocenoses. In this regard, the question of the impact of oil pollution of these soils on plants, animals and humans is relevant.

The ecological safety of microbocenosis, phytocenosis, zoocenosis and humans depends on the state of the soil. Soil contamination, including pollution by oil and refined products, negatively affects the state of all components of the cenosis.

The aim of the present study was to investigate the relationship between soil type and the response of test organisms of various organismic levels to oil pollution, to determine the most sensitive criterion for assessing the safe concentration of oil in each type of soil.

As a result of the environmental assessment, it was possible to identify patterns between the type of oil-contaminated soil and the state of test organisms of various organismic levels. The use of direct experimental methods, as well as software products, made it possible to assess the impact of oil pollution in various types of soils on microbial and plant test organisms, to assess the impact on aquatic organisms.

How to cite: Buzmakov, S.:  Environmental standards for the concentration of hydrocarbons in soils, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-342, https://doi.org/10.5194/egusphere-egu21-342, 2021.

Cities are the key centers of technogenesis, which leads to environmental pollution. The state of the soil cover reflects the long-term anthropogenic impact as a result of urbanization processes. In the urban environment, the priority pollutants are potentially toxic elements (PTEs) and polycyclic aromatic hydrocarbons (PAHs), since they are not only an environmental hazard, but also a risk factor for the public health. Tyumen city, with a population of 807,300 people, is a large transport and trade center in Western Siberia, Russia, with a developed service sector, construction and manufacturing industries. The aim of the study is to evaluate possible carcinogenic and noncarcinogenic risks related to PTEs and PAHs in urban topsoils of Tyumen, as representative of urban environment in Western Siberia.

Topsoil samples (0-10 cm) were collected according to the regular grid at 241 sampling points. The total content of V, Cr, Co, Ni, Cu, Zn, As, Sr, and Pb was determined using X-Ray fluorescence spectrometry. Content of twelve priority PAHs was measured using high-performance liquid chromatograph Agilent 1260 Infinity. Human health risk assessment was based on the US EPA model (1989). The noncarcinogenic risk for different age groups of the population, expressed as a hazard quotient (HQ), was evaluated by comparing the average daily dose of pollutant (ADD) with a reference dose (RfD). Carcinogenic risk (CR) reflects the probability of developing cancer in an individual throughout their life, taking into account the lifetime average daily dose of a pollutant (LADD) and carcinogen slope factor (SF). Values of RfD and SF were based on toxicological data (U. S. EPA 1997, 2004, 2020; ATSDR 2020; OEHHA 2020). The combined effects were assessed using the total hazard index (THI) and the total carcinogenic risk (TCR).

Noncarcinogenic risks were more likely caused by intake of V, Co, As, Pb, Ni and Cu. For both children and adults, the risk associated with the oral intake of pollutants was the greatest. For children, significant risks arose from exposure to V, Co, As and Pb (HQ> 1). The THI values for children varied from 0.78 to 7.25, on average 2.72, for adults - from 0.08 to 0.79, on average 0.27. Most of the territory was characterized by a medium non-carcinogenic risk for children and a low risk for adults.

Significant CR was associated with long-term exposure to Co, As, Pb and benzo[a]pyrene. The TCR values under the combined effect of PTEs and PAHs ranged from 1.2 × 10-5 to 2.2 × 10-4, on average 6.9 × 10-5. In general, the level of carcinogenic risk in the city was assessed as low. Medium carcinogenic risk was established in the soils of impact zones of enterprises for the production and disposal of batteries, CHPP-1 and some large transport hubs. An extensive zone of increased carcinogenic risk was established in the residential area of the central part of the city.

The research was funded by RFBR and Tyumen Region, project no. 20-45-720003, and by Ministry of Science and Higher Education of the Russian Federation, no. 0852-2020-0029.

How to cite: Konstantinova, E., Minkina, T., and Konstantinov, A.: Human health risk assessment of potentially toxic elements (PTEs) and polycyclic aromatic hydrocarbons (PAHs) in urban topsoils of Tyumen city, Russia, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-9203, https://doi.org/10.5194/egusphere-egu21-9203, 2021.

EGU21-10612 | vPICO presentations | ITS3.8/SSS1

Intra-soil phosphogypsum recycling for environmental safety, higher soil sustainability and productivity

Valery Kalinitchenko, Alexey Glinushkin, Tatiana Minkina, Saglara Mandzhieva, Svetlana Sushkova, Ljudmila Il’ina, Dmitry Makarenkov, Tatiana Kovaleva, and Ilya Lobzenko

Amelioration and remediation technology was developed comprises dispersed application and mixing of the phosphogypsum into the soil layer 20–45 cm with the intra-soil milling. The phosphogypsum doses 0, 10, 20, and 40 t ha−1 were studied in the model experiment focusing on the intra-soil passivation of Cd contained in phosphogypsum, environmental remediation, and amelioration of the Haplic Chernozem of South-European facies (Russia). The soil total and water-soluble Cd form content depend on geographical location, the ionic composition of soil solution, and soil genesis. The mean total Cd content in soils of South Russia is about 1 mg kg1 SDW. The mobility of Cd in the soil solution, as well as its penetration into the plants, depends on the content of carbonates, pH, ionic composition of the soil solution. The mathematical chemical-thermodynamic model and program ION–3 developed for the quantitative characterization of Cd thermodynamic forms in soil solution. The forms of ion in soil solution were calculated accounting the soil solution calcium-carbonate equilibrium, ionic strength,  and association of ion pairs СаСО30; CaSO40, MgCO30, MgSO40, CaHCO3+, MgHCO3+, NaCO3, NaSO4, CaOH+, MgOH+. The coefficient of microelement association kas was proposed for the calculation of the equilibrium concentration of microelement ion or heavy metal (HM) in soil solution. According to calculations, a Cd2+ ion mostly bounded to associates CdOH+, partly to associates CdCO30 and CdHCO3+. The Cd kas was 1.24 units in the control option and decreased to 0.95 units at a phosphogypsum dose 40 t ha−1. The calculated ratio of “active [Cd2+] to total Cd” reduced from 33.5% in control option to 28.0% in the option of a phosphogypsum dose 40 t ha−1.  According to calculation, the biogeochemical barrier for penetration of HMs from soil to plant roots was high after application of phosphogypsum. The standard soil environmental limitations for the content of Cd in soil overestimate the real toxicity of Cd. Re-evaluation of the current TENORM and other environmental limitations become possible. The new decision for intra-soil milling and simultaneous application of phosphogypsum was developed the chemical soil engineering technology to decide simultaneously the tasks of soil contamination decrease, soil amelioration and soil remediation. The technology based on the transcendental Biogeosystem Technique (BGT*) methodology provides environmentally safe phosphogypsum application to soil. The BGT* management of ecosphere provides health and productivity. Indirect transcendental nature-similarity of technology provides the new niche of developing capabilities addressing environmental safety concerns of ecosphere management. The technology ensures geophysical, chemical, physicochemical structural and architectural prerequisites for the stable soil evolution, environmentally safe waste recycling, the healthy soil microbiome and phytopathogen suppression, high-quality soil biological production, and human health.

The research was financially supported by the RFBR, project no. 18-29-25071, and the Ministry of Science and Higher Education of Russia, project no. 0852-2020-0029.

How to cite: Kalinitchenko, V., Glinushkin, A., Minkina, T., Mandzhieva, S., Sushkova, S., Il’ina, L., Makarenkov, D., Kovaleva, T., and Lobzenko, I.: Intra-soil phosphogypsum recycling for environmental safety, higher soil sustainability and productivity, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-10612, https://doi.org/10.5194/egusphere-egu21-10612, 2021.

EGU21-11892 | vPICO presentations | ITS3.8/SSS1 | Highlight

Particulate bound mercury pollution in the Central Italian Herbarium (Firenze, Italy)

Francesco Ciani, Laura Chiarantini, Pilario Costagliola, and Valentina Rimondi

The attention devoted to air quality is particularly important in workplaces, such as museums, where the health of visitors and workers must couple with the safeguard of collections. This especially holds for herbaria where, until the middle of the last century, the collections were protected using a solution of mercury dichloride (HgCl2) to prevent cryptogamic or animal infestations. The decomposition of HgCl2 causes the Hg reduction through a reaction pathway that is still poorly known, and the consequent release of Hg0 in the indoor atmosphere. Besides Hg0, Hg in air exists also as bound to particulate (PBM). In the museums’ atmosphere, this fraction may represent a non-negligible proportion of total atmospheric Hg and should be monitored.

This study aims to characterize the PBM in the Central Italian Herbarium of Firenze (University of Firenze, Italy), one of the largest herbaria worldwide. Here recent studies proved high levels of Hg0.

PBM sampling has been carried twice (2018 and 2020 soon after the lockdown period caused by the Covid-19), collecting the dust on a SEM-EDS stub from different surfaces (furniture, wall cornice, sample cabinet). Samples were roughly divided according to their deposition time between old (OD), almost-new (AD) and new dust (ND). The samples were analyzed using SEM-EDS to characterize the dimension and the chemical speciation of Hg particulate.

Hg-particles were detected in all the three types of dust collected in both the years: the mean dimension is 0.80±0.01 µm (3σ). The highest number of Hg-particles has been always reached in the AD, i.e. the dust collected directly on the packages containing herbarium specimens, with a strong increase in the 2020 sample. Additionally, the EDS microanalysis revealed that Hg-particles are now mainly associated with S (sometimes with O), suggesting the presence of sulphate or sulphide.

The above evidences show that PBM constitutes a fraction of Hg pollution in the Herbarium that cannot be ignored. The number of particles strongly increased in a period of low attendance of the Herbarium rooms and consequently cleaning, due to the COVID-19 pandemic: despite this, almost all are still classifiable as fine particulate (i.e. ECD< 2.5 µm) particularly harmful for human health. The presence of sulphate/sulphide indicated the change of Hg speciation with time and its reaction with S and O. These compounds, although less bioavailable than Hg0, still represent a risk for both herbarium workers and visitors.

The results of the present study offer preliminary information on the abatement system to be installed in the museum halls, which should be supplied with filters to retain very fine particles (< 1 µm).

How to cite: Ciani, F., Chiarantini, L., Costagliola, P., and Rimondi, V.: Particulate bound mercury pollution in the Central Italian Herbarium (Firenze, Italy), EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-11892, https://doi.org/10.5194/egusphere-egu21-11892, 2021.

EGU21-12293 | vPICO presentations | ITS3.8/SSS1

A study of iodine concentration in soils and grasses of pastures of Bryansk and Gomel regions affected by the Chernobyl accident

Viсtor Berezkin, Elena Korobova, Sergey Romanov, and Vladimir Baranchykov

Bryansk (Russia) and Gomel (Belarus) regions were among the areas most exposed to the so-called "radioiodine impact" (131I) that occurred as a result of the Chernobyl accident in 1986. Medical examination of different groups of local population after the accident revealed a pronounced increase in thyroid cancer among children which was associated with not only 131I fallout but also with a deficiency of natural iodine in these areas [1].

The aim of the research was to study iodine in vegetation and soils of different grasslands adjacent to rural settlements in the Bryansk and Gomel regions and used by local residents for grazing. The basic original data was iodine concentration in soil and plant samples collected in the affected areas of Russia and Belarus in 44 test sites.

Soil samples were collected from upper layers 0-5, 5-10 and 10-20 cm deep with the help of the soil auger, averaged vegetation samples were taken from the plots 25x25 or 50x50 cm depending upon the vegetation density. Iodine determination was performed with the help of kinetic rhodanide-nitrite technique.

The results showed insignificant difference of iodine concentration in soil samples taken from various depth and considerable variation in the content of iodine in the upper soil layer (0-5 cm) both in the Bryansk region (0.24-1.36 mg/kg, n=29) and in the Gomel region (0.23 - 5.27 mg/kg, n=15), depending upon soil type and texture.

In the Bryansk region, the highest average iodine content was observed in gray soils characteristic for its central part (mean value 0.85±0.12 mg/kg in the top 5-cm layer). In the Gomel region, the highest iodine content was observed in meliorated peat-bog soils (5.27 mg/kg, mean value equaled to 1.02±0.42 mg/kg in the top 5-cm layer).

The iodine content in the pasture vegetation ranged from 23 µg/kg to 271 µg/kg dw. Both median and mean value of iodine concentration in vegetation of upland meadows (autonomous landscapes) were significantly lower than those in lowland meadows (57 and 113 µg/kg; 67±10 and 125±16 µg/kg correspondingly).

Accounting of considerable soil ingestion by cows we hypothesize that grazing in lowland pastures with the highest stable iodine content in soils and domination of hydrophyte plant species causes higher 127I consumption by cattle and transfer to milk. In case of contamination of the area by 131I this can lead to a relatively lower 131I transfer to milk, other conditions being equal.

The study was carried out with partial financial support by RFBR grant No. 16-55-00205 and RFBR and BRFBR, project No. 20-55-00012.

References

1. Cardis et al. 2005. Risk of thyroid cancer after exposure to 131I in childhood //Journal of National Cancer Institute, vol. 97, No 10, Pp. 724 – 732.

 

How to cite: Berezkin, V., Korobova, E., Romanov, S., and Baranchykov, V.: A study of iodine concentration in soils and grasses of pastures of Bryansk and Gomel regions affected by the Chernobyl accident, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-12293, https://doi.org/10.5194/egusphere-egu21-12293, 2021.

ITS3.10/ERE1.7 – Geochemical and isotopic methodologies for traceability and food security

After the release of high levels of radioactivity into the environment, one of the main concern relates the contamination foodstuffs. In some exposure scenarios the transfer of radionuclides through the food chain to consumers represents a major contribution to human dose. Therefore an accurate estimation of radionuclide activity concentrations in agricultural products is crucial to evaluate the ingestion dose to the population consuming locally produced food. There are many mechanisms contributing to the radioacive contamination of agricultural products as interception, retention, absorption and translocation, due to mechanisms as deposition to the exposed plant surfaces, and/or root uptake. In the last decades several efforts have been spent in developing mathematical models to predict the potential transfers of radionuclides in plants and their concentration in the edible parts. Nevertheless the relative significance of each pathway depends on a large amount of variables and parameters that increase the complexity of the models, moreover the lack of expermental data, often limit the possibility to make any meaningful results. The main aspect that make difficult to predict the uptake of radionuclides by plants is the dynamic nature of the contamination scenarios due primarly to the the growing of plants. Nevertheless, there are some factors that can be considered as ‘static’ for each specific geographic area, and each specific radionuclide, as the soil characteristics, the type of crop, and the behavour of some radionuclides in the environment. In the framework of a preliminary safety assessment of a radioactive release scenario, these factors could be taken as reference indicators of the potential impact on the local human food chain radioactive contamination. In this work we focus on the analysis of the scientific literature pertaining to all experimntal studies in radionuclide plant uptake, from 2000 to 2020. The aims of this analysis is to collect set of some characteristics allowing to classify, in a macroscopic scale, specific reference indicators that most contribute to the radioactive contamination of agricultural products in different geographyc areas.

How to cite: Ferrucci, B. and Telloli, C.: Reference indicators enabling preliminary evaluations of the impact of radioactive releases on agricultural products, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-474, https://doi.org/10.5194/egusphere-egu21-474, 2021.

EGU21-4554 | vPICO presentations | ITS3.10/ERE1.7 | Highlight

Hazelnut products traceability through Isotope Ratio Mass Spectrometry approach

Giuseppe Sammarco, Mattia Rossi, Michele Suman, Daniele Cavanna, Chiara Dall'Asta, and Paola Iacumin

The geographical origin of hazelnuts products is nowadays a relevant aspect of high-quality food characterization. Isotope Ratio Mass Spectrometry (IRMS) could play a key role in origin discrimination. The present study aims to assess the geographical provenience of Italian roasted hazelnuts and paste of hazelnuts, by analysing relative isotopic ratios of carbon, nitrogen, and oxygen, through Elemental Analyzer – and Thermal Conversion – Isotope Ratio Mass Spectrometry. Method development is performed by evaluating test samples repeatability, considering 15 replicates measurements on the same day, reproducibility, considering 30 replicates measurements on two different days, and robustness, considering 30 replicates measurements, varying mass parameter. Preliminary outcomes highlight reproducible and robust results, having acceptable standard deviation values (from 0.07 to 0.3). One-way ANOVA test demonstrates a significant statistical difference between Italian and Georgian hazelnut test samples (ca. 1 δ of difference). A Design of Experiment, for training and validation sets building, is prepared, taking into account factors as harvesting year, variety, processing, and percentage of the peel. A total of n=30 processed hazelnuts lots, from Italy, Turkey, Georgia, and Azerbaijan, are going to be analysed for origin evaluation. Despite further analysis are still in progress, this strategy could potentially be the election method for a lot of food chain traceability since food isotopic abundances reflect ground and climate-related features, typical of precise locations. Moreover, this approach consists of limited or even inexistent sample preparation and provides for high sensitivity.

How to cite: Sammarco, G., Rossi, M., Suman, M., Cavanna, D., Dall'Asta, C., and Iacumin, P.: Hazelnut products traceability through Isotope Ratio Mass Spectrometry approach, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-4554, https://doi.org/10.5194/egusphere-egu21-4554, 2021.

EGU21-1610 | vPICO presentations | ITS3.10/ERE1.7 | Highlight

Not essential elements as tracers of geographical provenience of Sorrento PGI lemon juices  

Luigi Ruggiero, Maria Chiara Fontanella, Carmine Amalfitano, Gian Maria Beone, and Paola Adamo

The mineral composition of agri-food products is useful to define their provenance for fraud protection. The potential of mineral composition to define the geographical provenance of high-value PGI agri-food products was explored in order to protect them from fraud. The Sorrento lemon (Citrus limon (L.) Burm. f. cv. Ovale di Sorrento), is known for its characteristic cultivation on terraces in the Sorrento peninsula and Capri island of Campania region (South Italy). In this environment, the peculiar soil and climatic features and the traditional cultivation on terraces have contributed not only to high-quality lemon productions but also to protect the landscape. The geographical conformation of the territory leads to different microclimates and habitats even at a very small scale. In this work, the multielement fingerprinting (essential and not essential elements) is proposed for discrimination lemon juices of six different cultivars (Femminello Ovale di Sorrento, Femminello Zagara Bianca, Femminello Siracusano 2KR, Femminello Sfusato Amalfitano, Femminello Adamo, and Femminello Cerza), grown in the PGI area of Sorrento lemon and in other two Campania region areas (no-PGI), according to the cultivars and their geographical origin on regional territory scale in two years (2018 and 2019). The explorative analysis by PCA on the mineral profile of the lemon juices showed natural grouping according to provenance at the expense of different cultivars. This suggests that the juice mineral composition depends slightly on cultivars, but strongly on the features of the cultivation environments. The applied discriminant model S-LDA, according to territorial provenance of lemon juices, showed 97.73% correct classification, 98.48% accuracy, and 93.83% external validation, and Mo, Ba, Rb, Mg, Co, Ca, Fe and Sr as discriminant elements. However, the annual variation of discriminant elements regarding many nutrients, the correlation of lemon juices/soil of some not essential elements (Ba, Rb, and Sr) which also discriminate juices and soils according to areas in both years, suggested the use of not essential elements as stable indicators of lemon juice provenance. In support of this suggestion, we applied S-QDA, more stringent than S-LDA, on only the determined, not essential elements (Ti, Co, Rb, Ba, and Sr). The results were discrimination of lemon juices according to provenance by all not essential elements, with 87.50% correct classification and 83.95% validation, despite the low number of variables. An increasing number of not essential elements is expected to improve the discrimination models. 

How to cite: Ruggiero, L., Fontanella, M. C., Amalfitano, C., Beone, G. M., and Adamo, P.: Not essential elements as tracers of geographical provenience of Sorrento PGI lemon juices  , EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-1610, https://doi.org/10.5194/egusphere-egu21-1610, 2021.

EGU21-7480 | vPICO presentations | ITS3.10/ERE1.7 | Highlight

Traceability of extra virgin olive oil: geochemical and environmental fingerprints revealed by ICP-MS-QQQ analysis

Chiara Telloli, Silvia Tagliavini, Fabrizio Passarini, Antonietta Rizzo, and Stefano Salvi

Studies on food traceability are of great importance nowadays, involving high demanding processes to cover all food chain steps. Extra virgin olive oil is a typical product that has a strong linkage with the Mediterranean area, and its origin protection is continuously improved both by European Regulations about its quality policy and by the development of analytical techniques increasingly appropriate. Simultaneous multi-element approach like Inductive Coupled Plasma Mass Spectrometry (ICP-MS) makes possible the representation of EVO oil’s mineral composition. Involving a ICP-MS Triple Quadrupoles (ICP-MS-QQQ) it becomes even more a powerful tool for interference-free quantitative analysis of trace and ultra-trace elements. This study aims at elaborating a method to better determine mineral composition of this matrix and at validating the method used to determine its reliability. EVO oil’s fingerprint shows its predominant elements and it points out its possible contamination with toxic elements.

How to cite: Telloli, C., Tagliavini, S., Passarini, F., Rizzo, A., and Salvi, S.: Traceability of extra virgin olive oil: geochemical and environmental fingerprints revealed by ICP-MS-QQQ analysis, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-7480, https://doi.org/10.5194/egusphere-egu21-7480, 2021.

EGU21-9914 | vPICO presentations | ITS3.10/ERE1.7

Influence of zeolitite foliar coating on photosynthesis, VOC emission and quality of extra virgin olive oil

Lucia Morrone, Luisa Neri, Osvaldo Facini, Giulio Galamini, Valeria Medoro, and Annalisa Rotondi

Olive fruit fly (Bactrocera oleae) is the most dangerous pest of olive fruits and strongly impairs both quality and quantity of the resulting olive oil. Organic farms have few tools against this pest and are constantly looking for effective and sustainable products; furthermore, for conventional farms the recent ban on dimethoate use in EU, made the defence from B. oleae very difficult. In this context the use of zeolitites, applied as particle films, began to take hold.

Since particle film covers the leaves, the organs responsible for gas exchange, a study on the plant responses to zeolite foliar coating was carried out by measuring photosynthetic rates from July to October (harvest) in two orchards located at San Lazzaro di Savena and Montiano in the Emilia Romagna region (Italy), respectively under organic and conventional farming.

Plant response to foliar treatment was also evaluated by measuring oil quantity in olives fruits. The layer of particle film covering leaves and fruits reduces the attractiveness of visual cues and prevents insects from recognizing and finding the plant parts on which they lay eggs: volatile organic compounds (VOC) emitted from both leaves and olives could act as oviposition promoters and were determined as well. Finally, chemical and sensory analyses on the resulting olive oils were performed. In the San Lazzaro orchard the tested treatments were: natural zeolitite (NZ), natural zeolitite enriched with ammonium (EZ) and Spyntor Fly® (SF), a protein bait based on spinosad for the control of B. oleae. In the Montiano orchard the treatments tested were: Dimethoate (DM), an organophosphate insecticide, natural zeolite with a reduced dose of dimethoate (ZN-DM) and negative control (Test).

Photosynthetic activity of plants treated with EZ was higher than the other two treatments in all dates, while no differences in photosynthetic rate were found between SF and NZ. In the Montiano orchard a slight reduction in photosynthetic rate was found only on the last two dates. The analyses of the VOC emitted by leaves and fruits allowed to identify respectively 35 and 31 different chemical compounds, belonging mainly to the chemical classes aldehydes, alkanes and alcohol, ketones, esters, ethers and terpenes. Chemical and sensory characteristics of oils were influenced by the incidence of olive fruit fly rather than foliar treatment with zeolite. In the Montiano orchard, subjected to a severe B. oleae attack, the effectiveness of the zeolite against the pest was observed, and the oil from untreated plants showed higher chemical parameters associated with secondary oxidation phenomena. In the San Lazzaro orchard, where  a weak B. oleae attack occurred, sensory differences were recorded between treated (NZ and EZ) and untreated plants. According to the results of this study, the use of zeolite film cover on olive tree canopy do not negatively influence the plant physiology and represents a useful tool against olive fruit fly.

How to cite: Morrone, L., Neri, L., Facini, O., Galamini, G., Medoro, V., and Rotondi, A.: Influence of zeolitite foliar coating on photosynthesis, VOC emission and quality of extra virgin olive oil, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-9914, https://doi.org/10.5194/egusphere-egu21-9914, 2021.

EGU21-10007 | vPICO presentations | ITS3.10/ERE1.7

Effect of different foliar particle films (kaolin and zeolitite) on chemical and sensory properties of olive oil

Annalisa Rotondi, Gianpaolo Bertazza, Barbara Faccini, Giacomo Ferretti, and Lucia Morrone

During the growing season, the use of foliar treatments based on fine grained geomaterial to reduce the negative impact of environmental stresses and protect the olives from insect pests is a well-known approach; however, while kaolin powders have been widely employed, zeolitite-based materials are much less known and exploited.

The aim of this study is to assess the effect of the two different treatments (zeolitite and kaolin) on the chemical and sensory qualities of the oils produced.

The study was carried out during two consecutive crop seasons in a 15 year old commercial olive orchard (Olea europaea), cv Correggiolo, located on the Appennine hills near Bologna (Italy). Foliar treatments were distributed during summer, until olive harvest. Ripening index, weight, oil and water content were measured on olive fruits. Olive productions were transformed in oils using a low scale continuous mill, quality parameters (free acidity, peroxide numbers, K232, K270, total phenols, fatty acids) were evaluated according to the official methods described in Regulation EC 2568/91 and subsequent amendments. Phenolic compounds, vitamins and pigments were determined by HPLC-DAD. Sensory analysis was performed by the panel of Agency for Agrofood Sector Services of Marche region (ASSAM), a fully-trained analytical taste panel recognized by the International Olive Oil Council (IOC) of Madrid, Spain, and by the Italian Ministry for Agriculture, Food, and Forestry Policy.

Olives treated with zeolite showed higher oil contents with respect to the other treatments. Oils produced by plants treated with zeolite particle film exhibited higher contents of total phenols, tyrosol, oleuropein and secoiridoids than to the oils produced by other treatments. Oils produced from olives treated with kaolin had sensory profiles characterized by sweet notes ascribable to ripe fruits, the tasters perceived notes of berries that are not typical of the Correggiolo cultivar.

The sensory taint test revealed a statistically significant difference between oils produced from olives treated with kaolin and the control, whereas no difference emerged between oils obtained from olives treated with zeolite and the control.

Particle film can influence some physiological plant parameters (photosynthesis, transpiration, water use efficiency) and, by consequence, it has also an influence on olive and oil quality. Olive plants treated with zeolite produced oils with higher antioxidant endowment, while oils produced from plants treated with kaolin were characterized by lower phenolic contents. Moreover, the kaolin treatment significantly affected the organoleptic properties of oils.

How to cite: Rotondi, A., Bertazza, G., Faccini, B., Ferretti, G., and Morrone, L.: Effect of different foliar particle films (kaolin and zeolitite) on chemical and sensory properties of olive oil, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-10007, https://doi.org/10.5194/egusphere-egu21-10007, 2021.

EGU21-16291 | vPICO presentations | ITS3.10/ERE1.7

Use of gamma spectroscopy for characterization and traceability of beans

Alberto Ubaldini, Antonietta Rizzo, Barbara Ferrucci, Chiara Telloli, and Giuseppe Ottaviano

The ϒ-ray spectroscopy is the quantitative study of the ϒ spectra of and finds applications in a very large number of fields, from the astrophysics to the geochemistry. The radionuclides are instable isotopes because of an excess of nuclear energy that must be released, leading to the formation of more stable nuclides. One of the possible releasing mechanisms is the emission of gamma radiation. A spectrum is characteristic, in terms of energies and intensities, of the nuclides present and allows to determine their quantity and the nature of sample under investigation. This offers the possibility of obtaining specific information, which can be acquired only with difficulty or even not at all by other techniques.

This is also true in the field of food characterization and their traceability.

The traceability in the food industry has become a fundamental request for the modern society. It consists in the ability of tracing any food, feed or substance used for consumption, through all stages of production, processing and distribution. For this reason, it is essential to provide transparency and safety to consumers who are demanding high quality products, with good nutritional characteristics. In the same moment, it is also important for producers, because it ensures certification and accreditation of their products. Traceability is indeed a way for ensuring that all food products are safe.

In order to achieve this goal, it is necessary to use specific experimental techniques, sometime developing innovative solutions. In this paper, an application of the ϒ spectroscopy to the food traceability is presented.

The gamma-emitting radionuclides can be used as markers for establishing correlations between soil and plants. Actually, a plant cannot have a much different amount of radioisotopes and a different isotopic composition than the soil in which it grows. This can make possible to trace a product and ascertain the place where it was produced.

A study of the γ characterization of some Italian bean (Phaseolus vulgaris) varieties with different geographic origins, using a portable AMETEK ORTEC High Purity Germanium (HPGe) Radiation Detector, is presented.

Beans are suitable for this study because they are rich in potassium, which exists in nature with a relatively high abundance of its radioactive isotope 40K. Its content in different parts of the plant, such as seeds, pods, leaves, has been measured, along with the presence of other radioisotopes. This has also allowed us to establish correlations between this element and macro elements, such as carbon and nitrogen, measured by elementary combustion.

It was possible to verify the relationship between the concentration in the seeds and in the soil. Attention was also paid to the content of other radioisotopes, especially those of alkaline metals such as cesium. Due to their chemical nature, they can mimic the biological behavior of potassium and be absorbed. This may suggest further use of the cultivation of beans or leguminous plants as a possible method of bioremediation for polluted soils, because they can accumulate some specific contaminants. In principle it is also possible to recognize radioactivity arising from natural and anthropogenic origin in  the soil.

How to cite: Ubaldini, A., Rizzo, A., Ferrucci, B., Telloli, C., and Ottaviano, G.: Use of gamma spectroscopy for characterization and traceability of beans, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-16291, https://doi.org/10.5194/egusphere-egu21-16291, 2021.

EGU21-15266 | vPICO presentations | ITS3.10/ERE1.7

Relation among geochemical elements in soil and red chicory as a tool for geographical origin identification.

Elena Marrocchino, Serena Di Sarcina, Carlo Ragazzi, and Carmela Vaccaro

The identification of the geographical origin of food products is important for both consumers and producers to ensure quality and avoid label falsifications. Determination and authentication of the geographical origin of food products throughout scientific research have become recently relevant in investigations against frauds for consumer protection. Advances in methods and analytical techniques led to an increase in the application of fingerprinting analysis of foods for identification of geographical origin. Since in organic material the inorganic component is more stable than the organic one, several studies examined trace elements, suggesting the potential application for determination of geographical origin. Moreover, the studies on territoriality are based on the hypothesis that chemical elements detected in plants and in their products reflect those contained in the soil and, within these studies, the geographical features of the production area, such as the soil type and the climate, are considered relevant factors affecting the specific designation, so an accurate determination of geographical origin would be necessary to guarantee the quality and territoriality of the products.

In this light, two varieties of red chicory from the southern Po Delta area have been characterized together with the soil. The two inspected red chicory varieties (long-leaves and round-leaves) are cultivated in a well-defined area in the southern part of Po Delta, in an area sited around Massenzatica (Municipality of Mesola, Province of Ferrara, NE of Italy). Sampling was undertaken between October and December 2020 and samples were collected from a randomized field. Together with the red chicory also roots and soils have been collected in order to analyze each part and correlate the geochemical data obtained using ICP-MS and XRF techniques.

Purpose of this study is to establish a method to identify the geographical origin and the results confirm that some major and trace elements could be used as geochemical markers according to the geological areas. These elements, therefore, could be useful to establish geochemical fingerprints for testing the origin of this product and create a protected designation of origin label.

How to cite: Marrocchino, E., Di Sarcina, S., Ragazzi, C., and Vaccaro, C.: Relation among geochemical elements in soil and red chicory as a tool for geographical origin identification., EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-15266, https://doi.org/10.5194/egusphere-egu21-15266, 2021.

The present contribution reports the results from a geochemical and statistical study aimed to identify in the Rare Earth Elements (REEs) absorption a good fingerprinting marks for determining the territoriality and the provenance of Vitis vinifera L. into two different geological contexts in Sicily: the volcanic district of Mount Etna and the carbonate platform of the Hyblean Plateau (Sicily, southern Italy). Our aim was to: i) define if the REEs  distribution in plants may reflect the composition of the provenance soil under similar climate conditions; ii) highlight differences, if any, in REE absorption within the various parts of the plants; and  iii) propose, for selected cultivar of Vitis vinifera L., a REE fingerprint in the Etna Volcano and Hyblean carbonate soils as well as to recognize characteristic REEs pattern.

To this aim, REE content has been determined by ICP-MS investigation in the soils and in the selected grapevine varieties for all the following parts: leaves, seeds, juice, skin and berries. Geochemical data have also been approached by a multivariate statistical analysis of the Principal Component Analysis (PCA), together with the Linear Discriminant Analysis (LDA).

The work permitted to highlight various REE distribution within the various parts of the plant and assessed as each grape variety presents a characteristic geochemical pattern in the absorption of REEs in relationship with the geochemical features of the type of soil on which the grapes grew.

How to cite: Punturo, R., Vaccaro, C., and D'Antone, C.: Rare Earth Elements distribution in grapevine varieties grown on different types of soils: examples from Mount Etna and from the Hyblean Plateau (Sicily, Italy), EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-10158, https://doi.org/10.5194/egusphere-egu21-10158, 2021.

EGU21-11922 | vPICO presentations | ITS3.10/ERE1.7

Geographical traceability in grapes: possible applications of trace and ultratrace elements

Carmela Vaccaro, Fabio Alessandro Faccia, Luigi Sansone, and Elena Marrocchino

In the last decades the demand for information and criteria, suitable for connecting products to their production regions, is becoming more urgent in order to protect the qualitative high-level productions by forgery. Wine is one of the products that could benefit of a scientific system of analysis able to define its production area. Features of the association between wine and territory are not only related to pedological but also to geographical aspects. Currently, several studies to define markers, such as isotopic ratios of O, C, and N, able to identify types of wine has been carried out, but they are not suitable to univocally define a specific type of wine in particular due to the high variability of some factors (temperature, age of the vineyard, period of such us isotopic…). Several samples of soils and grapes have been collected within the narrow area, characterized by quite heterogeneous lithologies, of the Euganei Hills area (NE of Italy) in order to identify possible markers typical of the growing area. The concentration of 25 elements (Na, Mg, Al, K, Ca, V, Cr, Mn, Fe, Co, Ni, Cu, Zn, Ga, Rb, Sr, Y, Zr, Nb, Ba, La, Ce, Nd, Pb, Th) have been determined on grapes by using ICP-MS and on soils by using XRF techniques. Moreover, grapes have been further refined and separated in two different fractions (one residual solid fraction and one liquid fraction). The concentration of Pr, Sm, Eu, Gd, Tb, Dy, Ho, Er, Tm, Yb and Lu have been determined on both these fractions in order to implement and complete the distribution pattern of REEs in the samples. Areas with geochemically different soils have been identified and in each one of these areas have been collected grapes of Cabernet Franc and Cabernet Sauvignon.  Moreover, in most areas, several cultivars have been collected in order to better understand how biological variables could affect the assimilation of chemical elements from soils. Chemical composition of the grapes’ inorganic fraction seems more influenced by soils than by cultivar type. In fact, REEs distribution patterns tend to differ more considering the same cultivar grown in areas with different pedological features.

How to cite: Vaccaro, C., Faccia, F. A., Sansone, L., and Marrocchino, E.: Geographical traceability in grapes: possible applications of trace and ultratrace elements, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-11922, https://doi.org/10.5194/egusphere-egu21-11922, 2021.

EGU21-8808 | vPICO presentations | ITS3.10/ERE1.7

What the Fish: Tracing geographical origin by Stable Isotope, Multielement profile and NIR spectroscopy

Nidhi Dalal, Antonio G. Caporale, and Paola Adamo

Commercialization of seafood industry has led to better accessibility of seafood around the globe and is an important part of global food chain to ensure food requirements worldwide. It forms one of the most complex international food chains and this makes it particularly vulnerable to fraud. In Europe, species substitution and origin mislabelling are the most common frauds faced by the seafood industry. Europe imports over 75% of its seafood with demand for it rising every year, which further increases chances of fraud and make authentication of seafood difficult. Owing to this complex global scenario, traceability of seafood becomes even more important to protect consumer’s rights and ensure safety in food systems. Origin mislabelling includes concealment of geographical origin of illegally harvested fish species whereas species substitution includes replacement of low-value species for a more expensive one for economic gain. Fish growing in different regions have different composition of fatty acids, elemental and isotopic compounds depending on their surroundings. Same differences occur between different species of fish living in the same region due to their varying feeding habits. These traits are used to identify origin mislabelling and species substitution. Several techniques have been employed to identify fish frauds such as DNA based methods, immunological assays, spectroscopic methods, stable isotopes, trace element analysis, fish microbiome analysis, etc. Multielement and stable isotope analyses and NIR spectroscopy are reliable analytical techniques providing useful information and thus accurate chemometric-based traceability models. Multielement profile can also allow to assess the fish nutritional quality and possible presence of contaminants. Stable isotope analysis of elements such as carbon, oxygen, nitrogen and strontium enables to discriminate fish provenance, natural vs feed-based diet, frozen vs fresh fish. NIR is a non-destructive and cost-effective analytical tool. A combined use of these methodologies to identify the fish fraud can strengthen the traceability models, minimising the occurrence of possible prediction errors.

In this context, SUREFISH* PRIMA project aims at deploying innovative solutions to achieve unequivocal traceability of Mediterranean fish products, preventing possible frauds. It gathers 13 partners from Italy, Spain, Tunisia, Egypt, Lebanon and 4 pilot sites fishing/growing and processing the following fish species: anchovy (Engraulis encrasicolus), sardine (Sardina pilchardus), bluefin tuna (Thunnus thynnus), tilapia (Tilapia spp.) and grouper (Epinephelus itajara). In the framework of WP3, we will develop and harmonise multi-element, isotope and NIR based analytical methodologies to trace the provenance of these Mediterranean fish species. Basically, we will analyse fresh or thawed fish meat and additional samples such as fish bones and otoliths, aquaculture feeds and sea or fresh waters. The findings will be gathered in a database useful for comparison with data from literature and other FAO fishing areas.

 

* SUREFISH PRIMA project: Fostering Mediterranean fish ensuring traceability and authenticity, https://surefish.eu/. PRIMA Call 2019 Section 1 - Agro-food Value Chain 2019, Topic 1.3.1.

How to cite: Dalal, N., Caporale, A. G., and Adamo, P.: What the Fish: Tracing geographical origin by Stable Isotope, Multielement profile and NIR spectroscopy, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-8808, https://doi.org/10.5194/egusphere-egu21-8808, 2021.

ITS3.12/AS2.10 – Atmosphere – Cryosphere interaction with focus on transport, deposition and effects of dust, black carbon, and other aerosols

EGU21-15939 | vPICO presentations | ITS3.12/AS2.10 | Highlight

Ice nucleation by glaciogenic dust and cloud climate feedbacks

Alberto Sanchez-Marroquin, Olafur Arnalds, Kelly J. Baustian-Dorsi, Jo Browse, Pavla Dagsson-Waldhauserova, Alexander D. Harrison, Elena C. Maters, Kirsty J. Pringle, Jesus Vergara-Temprado, Ian T. Burke, Jim B. McQuaid, Ken S. Carslaw, and Ben J. Murray

Although most of the dust present in the atmosphere originates from low-latitude arid deserts, it has been increasingly recognised that there are significant sources of High-Latitude Dust (HLD) in locations such as Iceland, Greenland, North American Arctic or North Eurasia [1]. The emission, transport and deposition of HLD can interact with the atmosphere, cryosphere and the marine ecosystem in several ways. Particularly, HLD has the potential to act as significant source of atmospheric Ice-Nucleating Particles (INP), competing with other sources such as dust and other INP types from lower-latitude arid sources [2, 3]. INPs are the fraction of aerosol particles that can trigger ice-formation in supercooled water droplets, that otherwise would remain unfrozen until temperatures of about -36 oC.

Ice formation initiated by the presence of INPs dramatically affects the amount of solar radiation reflected by clouds containing both liquid water and ice, known as mixed-phase clouds. However, ice-related processes in mixed-phase clouds such as the INP concentration are commonly oversimplified in most climate models, which leads to large discrepancies in the amount of water and ice that the models simulate at mid- to high-latitudes [4]. These present-day divergences in simulated mixed-phase clouds lead to a large uncertainty in the cloud climate feedback. This feedback is associated to the fact that mid- to high-latitude mixed-phase clouds dampen a part of the of the global temperature rise associated with greenhouse gases [5] [6].

Here we will explain the importance of understanding the chemical and ice-nucleating properties of HLD, as well as how it is emitted, transported and deposited for the cloud climate feedback. We will present new results from aircraft studies of the ice nucleating ability of HLD as well as modelling work which shows that this dust can be transported to altitudes and regions where it has the potential to influence mixed-phase clouds and climate.

How to cite: Sanchez-Marroquin, A., Arnalds, O., Baustian-Dorsi, K. J., Browse, J., Dagsson-Waldhauserova, P., Harrison, A. D., Maters, E. C., Pringle, K. J., Vergara-Temprado, J., Burke, I. T., McQuaid, J. B., Carslaw, K. S., and Murray, B. J.: Ice nucleation by glaciogenic dust and cloud climate feedbacks, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-15939, https://doi.org/10.5194/egusphere-egu21-15939, 2021.

Aerosol transport processes in the Southern Hemisphere (SH) have been the center of renewed attention in the last two decades because of a number of major geophysical events such as volcanic eruptions (Chile and Argentina), biomass burning (Australia and Chile) and dust storms (Australia and Argentina).

While volcanic and fire activity in the SH have been the focus of several studies, there is a dearth of satellite assessments of dust activity. The lack of such analysis impairs the understanding of biological processes in the Southern Ocean and of the provenance of dust found in snow at the surface of East Antarctica.

This presentation will show an analysis of time series of Aerosol Optical Depths over the Patagonia desert in South America. Data from two aerosol algorithms (Dark Target and Deep Blue) will be jointly analyzed to establish a timeline of dust activity in the region. Also, dust proxies from both algorithms will be compared with ground-based observations of visibility at different airports in the area. Once an understanding of frequency and time evolution of the dust activity is achieved, first estimations of ocean-going dust fluxes will be derived.

How to cite: Gassó, S., Gupta, P., Ginoux, P., and Levy, R.: Preliminary results of the first assessment of 20 years of dust activity in the Patagonia desert (South America) with aerosol products from the MODIS sensors, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-7805, https://doi.org/10.5194/egusphere-egu21-7805, 2021.

EGU21-11113 | vPICO presentations | ITS3.12/AS2.10 | Highlight

Saharan dust episodes and giant quartz particles in Iceland

György Varga, Pavla Dagsson-Walhauserová, Fruzsina Gresina, and Agusta Helgadottir

Saharan dust has an impact on the atmospheric environment and sedimentary units in distant regions. Although Iceland is located within one of the main atmospheric dust pathways moving towards the Arctic, no evidence of Saharan dust deposition has been provided to date for the region. Here we present the results of fourteen Saharan dust episodes, which were identified in Iceland between 2008 to 2020. Aerosol optical depth data of Terra MODIS, HYSPLIT backward trajectories and numerical simulations of Barcelona Supercomputing Center were used in this work to identify the dust episodes. 
Grain size and shape of the Saharan mineral material deposited in Iceland during two severe deposition events were investigated in detail. Icelandic dust samples from the most active local dust sources were compared with samples of deposited mineral dust from these two severe Saharan dust events to determine their granulometric (complex grain size and shape parameters) and mineralogical characteristics. An automated static optical image analysis technique was applied to thousands of individual particles, and was completed by Raman spectroscopy to identify external quartz particles. 
Saharan dust episodes were associated with enhanced meridional atmospheric flow patterns driven by unusual meandering jets. Strong southerly winds were able to carry large Saharan quartz particles (> 100 µm) towards Iceland. Our results confirm the atmospheric pathways of Saharan dust towards the Arctic, and identify new pathways of giant Saharan dust particles in the study region, including the first evidence of their deposition in Iceland as previously predicted by models.
The support of the National Research, Development, and Innovation Office (projects NKFIH KH130337 and K120620 (for G. Varga)), Czech Science Foundation (project No. 20-06168Y (for P. Dagsson-Waldhauserova)), and COST inDust Action are gratefully acknowledged.

How to cite: Varga, G., Dagsson-Walhauserová, P., Gresina, F., and Helgadottir, A.: Saharan dust episodes and giant quartz particles in Iceland, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-11113, https://doi.org/10.5194/egusphere-egu21-11113, 2021.

Carbonaceous matter, including organic carbon (OC) and black carbon (BC), is an important climate forcing agent and contributes to glacier retreat in the Himalayas and the Tibetan Plateau (HTP). The HTP - the so-called “Third Pole” – contains the most extensive glacial area outside of the polar regions. Considerable research on carbonaceous matter in the HTP has been conducted, although this research has been challenging due to the complex terrain and strong spatiotemporal heterogeneity of carbonaceous matter in the HTP. A comprehensive investigation of published atmospheric and snow data for HTP carbonaceous matter concentration, deposition and light absorption is presented, including how these factors vary with time and other parameters. Carbonaceous matter concentrations in the atmosphere and glaciers of the HTP are found to be low. Analysis of water-insoluable organic carbon and BC from snowpits reveals that concentrations of OC and BC in the atmosphere and glacier samples in arid regions of the HTP may be overestimated due to contributions from inorganic carbon in mineral dust. Due to the remote nature of the HTP, carbonaceous matter found in the HTP has generally been transported from outside the HTP (e.g., South Asia), although local HTP emissions may also be important at some sites. This study provides essential data and a synthesis of current thinking for studies on atmospheric transport modeling and radiative forcing of carbonaceous matter in the HTP.

How to cite: Li, C., Yan, F., and kang, S.: Carbonaceous Matter in the Atmosphere and Glaciers of the Himalayas and the Tibetan Plateau: An Investigative Review, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-1687, https://doi.org/10.5194/egusphere-egu21-1687, 2021.

EGU21-9020 | vPICO presentations | ITS3.12/AS2.10

Impact of CMIP6 biomass burning emissions on Arctic sea ice loss

Patricia DeRepentigny, Alexandra Jahn, Marika Holland, John Fasullo, Jean-François Lamarque, Cécile Hannay, Michael Mills, David Bailey, Simone Tilmes, and Andrew Barrett

The use of the Global Fire Emissions Database (GFED) from 1997-2014 to create the CMIP6 historical biomass burning (BB) forcing allows for a more accurate representation of BB emissions in climate models, but also results in an unrealistic increase in their inter-annual variability compared to pre- and post-GFED years, especially in the Northern Hemisphere mid-latitudes. We find that this new BB forcing affects the simulated Arctic sea ice loss in several CMIP6 models, bringing them into better agreement with the observed sea ice decline by leading to enhanced sea ice loss in the early 21st century. This suggests that BB emissions may have played a role in the acceleration of the observed early 21st century Arctic sea ice loss.

Using the Community Earth System Model version 2 (CESM2), we conduct sensitivity experiments in which we use BB emissions with a fixed annual cycle over the GFED period, to remove the inter-annual variability between 40-70°N. These experiments show that the strong acceleration in sea ice decline since the late 1990s simulated by the CESM2 is caused by enhanced Arctic warming driven by the increased variability in BB emissions over the GFED period. We also find that about half of the increase in sea ice sensitivity to CO2 and global mean surface temperature in the CESM2 compared to its CMIP5 counterpart, the CESM1, can be attributed to the change in BB emissions from CMIP5 to CMIP6, which suggests that the previously found improvement in sea ice sensitivity in CMIP6 models may in part be due to this new BB forcing and not only to changes in model physics. Overall, the results from this analysis highlight the influence of mid-latitude BB emissions on Arctic sea ice and provide new insights into the potential of a forced contribution to the observed accelerated early 21st century Arctic sea ice loss. Furthermore, this work highlights the importance of avoiding temporal discontinuities in prescribed aerosol forcing datasets as well as the need to better understand inter-model contrasts within the CMIP6 archive related to sensitivity to BB emissions.

How to cite: DeRepentigny, P., Jahn, A., Holland, M., Fasullo, J., Lamarque, J.-F., Hannay, C., Mills, M., Bailey, D., Tilmes, S., and Barrett, A.: Impact of CMIP6 biomass burning emissions on Arctic sea ice loss, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-9020, https://doi.org/10.5194/egusphere-egu21-9020, 2021.

EGU21-8202 | vPICO presentations | ITS3.12/AS2.10

Modern atmospheric monitoring using pollen analysis of ice cores: a case study from the Elbrus Western Plateau, Caucasus, Russia

Vlada Batalova, Vladimir Mikhalenko, Stanislav Kutuzov, Lyudmila Shumilovskikh, and Karim Shukurov

The report highlights the results of first ice-core palynology studies from the Elbrus Western Plateau. The title of the highest point in Europe and the geographical location of Elbrus determine the diversity of natural conditions and, as a result, palynological spectra, which act as markers of seasonal vegetation, climate dynamics, fires and anthropogenic activities in the Mediterranean, southern European Russia, the Middle East, and North Africa.

The 24-m ice core from the Elbrus Western Plateau collected in 2017 (5115 m a.s.l., 43о20′53,9′′ N, 42о25′36′′ E) covers the period 2012-2017. Pollen analysis revealed a significant number of biological markers contained in the ice core, including pollen and spores, fungi, algae, testate amoebae, feather barbules, microcharcoal, and black carbon.

The obtained results show that taxonomic diversity and concentration of biomarkers in the ice core were determined by the seasons of the year and their inherent convective flows. Pollen assemblages are characterized by predominance of native Caucasian plant species. Among them pollen values of Picea forming the high-altitude forest belt in the Western Caucasus significantly exceed pollen frequency of Pinus growing near the upper timber line on Elbrus Mt in the Central Caucasus that suggests a westerlies of air masses and transfer of microparticles. A high abundance of non-pollen palynomorphs in pollen assemblages demonstrates a high potential for studying of human impact on mountain ecosystems. The first pollen data from the ice core evidences a promising resource of the high-altitude temperate glaciers as a flexible tool for atmospheric monitoring of microparticle transfer and fixing its seasonality and biotic relationships.

This work was supported by the Russian Science Foundation, project № 17-17-01270.

How to cite: Batalova, V., Mikhalenko, V., Kutuzov, S., Shumilovskikh, L., and Shukurov, K.: Modern atmospheric monitoring using pollen analysis of ice cores: a case study from the Elbrus Western Plateau, Caucasus, Russia, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-8202, https://doi.org/10.5194/egusphere-egu21-8202, 2021.

EGU21-8334 | vPICO presentations | ITS3.12/AS2.10

Modeling large dust deposition events to alpine snow and their impacts: the role of model resolution

Foteini Baladima, Jennie Thomas, Marie Dumont, Didier Voisin, Clementine Junquas, Rajesh Kumar, Louis Marelle, Jean-Christophe Raut, Christophe Lavaysse, Francois Tuzet, and Romain Biron
Mineral dust and black carbon (BC) constitute the most important aerosols present in the atmosphere and cryosphere and have well known potential effects on regional and global climate. Upon their deposition they can impact snow albedo, snowpack evolution and timing of snow-melt. However, capturing BC and dust deposition events in mountain regions is currently a challenge due to the complexity of aerosol-cloud interactions and the specifics of mountain meteorological systems, which are difficult to represent in large scale models. Here, we use a case study of dust deposition, between 30 March and 5 April 2018, when a significant dust deposition event was observed within the seasonal snowpack at the Col du Lautaret in the French Alps. This comes in addition to the background BC deposition that occurred during the same period. Specifically, we investigate the role of model resolution in capturing both mountain meteorology, precipitation, and the resulting model predicted dust and BC deposition. For this, the meteorological-chemical model WRF-Chem is used with three nested domains including the primary dust emissions region in Africa (low resolution domain), a second domain that includes Europe, and a third high resolution domain over the Alps. We compare WRF-Chem predicted aerosol and meteorological properties (at different model resolution) with in-situ, remote sensing, and reanalysis products to validate the model and quantify the added value of high resolution modelling within the Alps. We conclude that predicted mountain meteorology including precipitation is significantly better when using the high resolution configuration (3 x 3 km horizontal resolution domain). Additionally, this improved meteorology predicted by the model has significant impacts on predicted dust deposition and BC. The better representation of the mountain meteorology when the resolution becomes finer leads to improved model predicted dust and BC deposition to alpine snow. Implications for this, including improved resolution within models that consider the full aerosol lifecycle in the atmosphere and in snow covered mountain regions is discussed.

How to cite: Baladima, F., Thomas, J., Dumont, M., Voisin, D., Junquas, C., Kumar, R., Marelle, L., Raut, J.-C., Lavaysse, C., Tuzet, F., and Biron, R.: Modeling large dust deposition events to alpine snow and their impacts: the role of model resolution, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-8334, https://doi.org/10.5194/egusphere-egu21-8334, 2021.

EGU21-14524 | vPICO presentations | ITS3.12/AS2.10

Identifying source regions for airborne particles in East Antarctica, Dronning Maud Land, using backward trajectory modelling

Karen De Causmaecker, Andy Delcloo, and Alexander Mangold

Atmospheric composition plays an important role in present and near-future climate change. Airborne particles can serve as cloud condensation and ice nuclei and have therefore a strong influence on cloud formation and thus also on precipitation. This is in particular of interest in Antarctica, since precipitation is the only source of mass gain to the Antarctic ice sheet, which is expected to become the dominant contributor to global sea level rise in the 21st century. A detailed insight into the transport pathways and distribution of airborne particles is therefore essential.
 
At the Belgian Antarctic research station Princess Elisabeth in Dronning Maud Land, East Antarctica, aerosol particles and their characteristics are measured. Atmospheric particles have been collected on filters during the last three austral summers for organic and inorganic chemical analysis by high-volume sampling. In addition, the atmospheric particle number concentration, size distribution and optical particle properties have been measured since 2010.

The geographical source regions of airborne particles in Dronning Maud Land remain however to a large extent unknown. In this work, we investigate the climatology of the particle properties with respect to their source regions. To that end, we use the FLEXTRA model to calculate 10-day 3D backward trajectories over the past 10 years. We apply a non-hierarchical cluster method to identify and classify the dominant source regions.

How to cite: De Causmaecker, K., Delcloo, A., and Mangold, A.: Identifying source regions for airborne particles in East Antarctica, Dronning Maud Land, using backward trajectory modelling, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-14524, https://doi.org/10.5194/egusphere-egu21-14524, 2021.

EGU21-10657 | vPICO presentations | ITS3.12/AS2.10 | Highlight

Modulation of the snow cover changes in the French Alps and the Pyrenees by the deposition of light absorbing particles over the last 40 years 

Marion Réveillet, Marie Dumont, Simon Gascoin, Matthieu Lafaysse, Pierre Nabat, Aurelien Ribes, Rafife Nheili, Francois Tuzet, Martin Menegoz, and Paul Ginoux

By darkening the snow surface, mineral dust and black carbon (BC) deposition accelerate snowmelt and triggers numerous feedbacks. Assessments of their long-term impact at the regional scale are still largely missing despite the environmental and socio-economic implications of snow cover changes. Using detailed snowpack simulations, we show that dust and BC deposition advance snowmelt by 17 days on average in the French Alps and the Pyrenees over the 1979-2018 period, with major implications for water availability and ground temperature. The effect of BC compared to dust is generally prevailing except in the Southern Pyrenees more exposed to Saharan dust events. We also quantify a contribution of BC and dust deposition up to 30% to the variance of the snow melt-out date. Lastly, we demonstrate that the decrease in BC deposition since the 80's alleviated the impact of current warming on snow cover decline. Therefore, this study highlights the importance of accounting for the inter-annual fluctuations in light absorbing particles deposition to improve the accuracy of snow cover reanalyses and climate projections.

How to cite: Réveillet, M., Dumont, M., Gascoin, S., Lafaysse, M., Nabat, P., Ribes, A., Nheili, R., Tuzet, F., Menegoz, M., and Ginoux, P.: Modulation of the snow cover changes in the French Alps and the Pyrenees by the deposition of light absorbing particles over the last 40 years , EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-10657, https://doi.org/10.5194/egusphere-egu21-10657, 2021.

EGU21-15763 | vPICO presentations | ITS3.12/AS2.10

The trace elements in the snow cover of the 2018-2019 season in the Novy Urengoy region (Arctic part of Western Siberia)

Dmitriy Belyanin, Yuliya Vosel, Kseniya Mezina, Mikhail Melgunov, and Vladimir Dobretsov

Revealing background concentrations of chemical elements and radionuclides in the surface components of the environment exposed to constant atmospheric processes is the first step towards detecting areas with their abnormally high concentrations of natural and man-made character. In this work, we present the results of studying the content of trace elements and microparticles in the snow cover accumulated during the 2018-2019 winter season in the Novy Urengoy region. Samples were taken along the roads using a rare sampling grid over the entire depth of the snow cover. In laboratory conditions, after the snow melted, the solution was filtered. The results of mass spectrometric measurements of the trace element concentrations in the filtrates show that their composition is homogeneous and does not vary slightly at the sampling points. Evaluation of the prevailing directions of backward air-mass trajectories was computed using the HYSPLIT model.

This work was supported by the Russian Science Foundation grant (project No 18-77-10039). Analytical studies were carried out at the Center for multi-elemental and isotope research SB RAS.

How to cite: Belyanin, D., Vosel, Y., Mezina, K., Melgunov, M., and Dobretsov, V.: The trace elements in the snow cover of the 2018-2019 season in the Novy Urengoy region (Arctic part of Western Siberia), EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-15763, https://doi.org/10.5194/egusphere-egu21-15763, 2021.

EGU21-482 | vPICO presentations | ITS3.12/AS2.10 | Highlight

Mountain-valley circulation in central Chile: consequences for Black Carbon deposition over glaciers in wintertime and summertime

Rémy Lapere, Sylvain Mailler, Laurent Menut, and Nicolás Huneeus

The configuration of the Santiago basin, Chile (33.5°S 70.65°W) is quite unique in that it combines very strong emissions of urban anthropogenic pollutants with the steep topography of the coastal and Andes cordilleras surrounding the Metropolitan area. Interactions between atmospheric pollution and mountain meteorology are therefore exacerbated, and the potential for black carbon (BC) deposition on glaciers is strong. Based on chemistry-transport modeling with WRF-CHIMERE, we investigate (i) the pathways leading to deposition of BC from Santiago up to Andean glaciers in wintertime and (ii) the differences in magnitude and time dynamics of such deposition between wintertime and summertime.

Ice and snow in the central Andes contain significant amounts of BC often attributed to emissions from Santiago. However, given the usually stable conditions in wintertime and the height of the obstacle to overcome for urban air masses (Santiago is 500 m a.s.l., summits are above 4000 m a.s.l.) the pathways for such deposition are not straightforward. We find that, for a typical winter month, up to 40% of BC dry deposition on snow- or ice-covered areas in the central Andes directly downwind from the Metropolitan area can indeed be attributed to emissions from Santiago. The adjacent network of canyons plays a key role in this export: for the case of the Maipo canyon, polluted urban air masses follow gentle slopes upward in the afternoon, consistently with mountain-valley circulation, before being vertically exported when reaching the tip of the main canyon. Statistical analysis shows that zonal wind speed in the urban area and vertical diffusion deep into the canyon account for most of the variance in BC deposition.

In summertime, more intense convection takes place, and mountain-valley circulation is seldom perturbed by cloud cover, resulting in a greater export potential. Accordingly, summertime dry deposition of BC on glaciers occurs on a regular basis with equivalent amounts each day, contrarily to a more chaotic time series in wintertime. The contribution of wet deposition in winter (nonexistent in summer) exacerbates this irregularity. However, as a consequence of weaker emissions, average monthly dry deposition of BC over the central Andes glaciers (29°S to 38°S) is found to be less than half in summertime (135 µg/m2) compared to wintertime (320 µg/m2). Given the lesser role played by wood burning for residential heating in summertime, emissions from Santiago through traffic and industry dominate the signal leading to 55% of dry deposition, while it accounts for only 14% in wintertime, at the regional scale, due to more scattered sources.

How to cite: Lapere, R., Mailler, S., Menut, L., and Huneeus, N.: Mountain-valley circulation in central Chile: consequences for Black Carbon deposition over glaciers in wintertime and summertime, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-482, https://doi.org/10.5194/egusphere-egu21-482, 2021.

EGU21-8515 | vPICO presentations | ITS3.12/AS2.10

Black carbon concentration in the central Himalayas: impact on glacier melt and potential source contribution

Chaman Gul, Parth Sarathi Mahapatra, Shichang Kang, Praveen Kumar Singh, Xiaokang Wu, Cenlin He, Rajesh Kumar, Mukesh Rai, Yangyang Xu, and Siva Praveen Puppala

This study discusses year-long (October 2016–September 2017) observations of atmospheric black carbon (BC) mass concentration, its source and sector contributions using a chemical transport model at a high-altitude (28°12'49.21"N, 85°36'33.77"E, 4900 masl) site located near the Yala Glacier in the central Himalayas, Nepal. During a field campaign, fresh snow samples were collected from the surface of the Yala Glacier in May 2017, which were analysed for BC and water-insoluble organic carbon mass concentration in order to estimate the scavenging ratio and surface albedo reduction. The maximum BC mass concentration in the ambient atmosphere (0.73 μg m-3) was recorded in the pre-monsoon season. The BC and water-insoluble organic carbon analysed from the snow samples were in the range of 96–542 ng g-1 and 152–827 ng g-1, respectively. The source apportionment study using the absorption Ångström exponent from in situ observations indicated approximately 44% contribution of BC from biomass-burning sources and the remainder from fossil-fuel sources during the entire study period. The source contribution study, using model data sets, indicated ~14% contribution of BC from open-burning and ~77% from anthropogenic sources during the study period. Our analysis of regional contributions of BC indicated that the highest contribution was from both Nepal and India combined, followed by China, while the rest was distributed among the nearby countries. The surface snow albedo reduction, estimated by an online model – Snow, Ice, and Aerosol Radiation – was in the range of 0.8–3.8% during the pre-monsoon season. The glacier melt analysis suggested that BC contributed to approximately 28% of the total melting in the pre-monsoon season. 

How to cite: Gul, C., Sarathi Mahapatra, P., Kang, S., Kumar Singh, P., Wu, X., He, C., Kumar, R., Rai, M., Xu, Y., and Puppala, S. P.: Black carbon concentration in the central Himalayas: impact on glacier melt and potential source contribution, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-8515, https://doi.org/10.5194/egusphere-egu21-8515, 2021.

EGU21-5362 | vPICO presentations | ITS3.12/AS2.10

Seasonal variations in black and organic carbon wet and dry deposition rates at SMEAR III station (60oN), Finland

Outi Meinander, Enna Heikkinen, Jonas Svensson, Minna Aurela, Aki Virkkula, Mika Vestenius, and Antti Hyvärinen

Black carbon (BC) and organic carbon (OC, including brown carbon BrC) aerosols in the atmosphere, and their wet and dry deposition, are important for their climatic and cryospheric effects. Seemingly small amounts of BC in snow, of the order of 10–100 parts per billion by mass (ppb), have been shown to decrease its albedo by 1–5 %. Due to the albedo-feedback mechanism, surface darkening accelerates snow and ice melt. In snow, the temporal variability of light absorbing aerosols, such as BC, depends both on atmospheric and cryospheric processes, mostly on sources and atmospheric transport, and dry and wet deposition processes, as well as post-depositional snow processes.

We started a new research activity on BC and OC wet and dry deposition at Helsinki Kumpula SMEAR III station (60°12 N, 24°57 E, Station for Measuring Ecosystem-Atmosphere Relations, https://www.atm.helsinki.fi/SMEAR/index.php/smear-iii). The work included winter, spring, summer and autumn deposition samples during January 2019 - June 2020 (sampling is currently on hold). In winter, wet deposition consisted of snowfall and rainwater samples. Dry deposition samples were separately collected in 2020. For sample collection, a custom-made device, including a heating-system, was applied. The samples were analyzed using the OCEC analyzer of the Finnish Meteorological Institute’s aerosol laboratory, Helsinki, Finland. The special features in our deposition data are: 

  • seasonal BC, OC, and TC (total carbon, the sum of BC and OC) deposition data for an urban background station at 60 oN
  • precipitation received as either water or snow  
  • dry deposition samples included (only in 2020)
  • data as wet and dry deposition rates [concentration/time/area]
  • simultaneous atmospheric measurements of the SMEAR III station

Since our deposition samples are collected manually, the data are non-continuous, yet they allow us to provide deposition rates. Such data can be utilized in various modeling approaches including, for example, climate and long-range transport and deposition modeling. According to our knowledge, these data are the first BC (determined as elemental carbon, EC), OC and TC wet and dry deposition data to represent Finland. Our sampling location, north of 60 deg. N, can be useful for other high-latitude studies and Arctic assessments, too.

Acknowledgements. We gratefully acknowledge support from the Academy of Finland NABCEA-project of Novel Assessment of Black Carbon in the Eurasian Arctic (no. 296302) and the Academy of Finland Flagship funding (grant no. 337552).

How to cite: Meinander, O., Heikkinen, E., Svensson, J., Aurela, M., Virkkula, A., Vestenius, M., and Hyvärinen, A.: Seasonal variations in black and organic carbon wet and dry deposition rates at SMEAR III station (60oN), Finland, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-5362, https://doi.org/10.5194/egusphere-egu21-5362, 2021.

EGU21-11972 | vPICO presentations | ITS3.12/AS2.10

Implications of aerosol-induced snow darkening on regional hydroclimate over the Himalayas

Vijayakumar Sivadasan Nair, Usha Keshav Hasyagar, and Surendran Nair Suresh Babu

The snow-covered mountains of Himalayas are known to play a crucial role in the hydrology of South Asia and are known as the “Asian water tower”. Despite the high elevations, the transport of anthropogenic aerosols from south Asia and desert dust from west Asia plays a significant role in directly and indirectly perturbing the radiation balance and hydrological cycle over the region. Absorbing aerosols like black carbon (BC) and dust deposited on the snow surface reduces the albedo of the Himalayan snow significantly (snow darkening or snow albedo effect). Using a Regional Climate Model (RegCM-4.6.0) coupled with SNow, ICe and Aerosol Radiation (SNICAR) module, the implications of aerosol-induced snow darkening on the regional hydroclimate of the Himalayas are investigated in this study. The aerosols deposited on snow shows a distinct regional heterogeneity. The albedo reduction due to aerosols shows a west to east gradient during pre-monsoon season and this results in the positive radiative effect of about 29 Wm-2, 17 Wm-2 and 5 Wm-2 over western, central and eastern Himalayas respectively. The reduction in the snow albedo also results in the sign reversal of the aerosol direct radiative effect i.e., from warming to cooling at the top of the atmosphere during pre-monsoon season. The excess solar energy trapped at the surface due to snow darkening warms the surface (0.66-1.9 K) and thus decreases the snow cover extent significantly. This results in the reduction of the number of snow-covered days by more than a month over the western Himalayas and about 10 – 15 days over the central Himalayas. The early snowmelt due to aerosol-induced snow darkening results in the increase of runoff throughout the melting season. Therefore, the present study highlights the heterogeneous response of aerosol induced snow albedo feedbacks over the Himalayan region and its impact on the snowpack and hydrology, which has further implications on the freshwater availability over the region.

How to cite: Nair, V. S., Keshav Hasyagar, U., and Babu, S. N. S.: Implications of aerosol-induced snow darkening on regional hydroclimate over the Himalayas, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-11972, https://doi.org/10.5194/egusphere-egu21-11972, 2021.

EGU21-12080 | vPICO presentations | ITS3.12/AS2.10

The distribution and behavior of mercury in snow

Anna Mikhailenko, Yury Fedorov, Tatiana Minkina, Leonid Dmitrik, Irina Dotsenko, Daria Solodko, and Viktoria Chepurnaya

With atmospheric precipitation to 28% of mercury (from their total input into this basin) is transported to the Azov Sea via precipitation [1,2]. There is an increasing tendency in the mercury concentrations in rain and snow sampled in the cities of the Rostov Region, compared to precipitation over the sea and its coast. The maximum mercury concentrations in the hydrometeors were found in the cities in autumn and winter. It is due to its penetration into the troposphere as a result of the rapidly increasing dust amounts and gas emissions sourced by combustion of coal, fuel oil, and gas during the heating season. The mercury concentrations in the hydrometeors are higher in stale snow than in just-fallen snow. It is suggested that stale snow is a depositing material absorbing mercury from the troposphere, where it accumulates due to activity of various enterprises with pollutant emissions. This statement is confirmed indirectly by the fact that the Donbass coals are characterized by high mercury concentrations [1]. Another mechanism could be mercury re-distribution during the compaction of snow cover and its interaction with soil. In the course of the winter expeditions, a clear snow stratification was registered: just-fallen powder and stale crystallized grey snow with a large amount of mineral and organic material. In stale snow, the dissolved and suspended form of mercury migration prevailed over its content in freshly fallen snow. The mercury content in hydrometeors was influenced by such factors as wind activity and the amount of atmospheric precipitation. On the one hand, when wind activity increases, the atmosphere surface layers in the cities are cleared from technological substances, and the input of soil particles increases during dust storms. There is intensive mercury leaching from the atmosphere during torrential rains. It leads to a sharp decrease in its atmospheric concentrations. On the other hand, there is an increase in the mercury content in the rainfall after a dry period under calm weather conditions.

The work was carried out with the financial support of the RF President grant No. MK-1862.2020.5., RFBR projects No. 19-05-50097.

Литература

  • [1]. Fedorov Yu. A., Mikhailenko V., Dmitrik L. Y., Dotsenko I. V., Solodko D. F., Chepurnaya V. I. Mercury and iron in precipitation of the Azov Sea basin// Limnology and Freshwater Biology, 2020,№1,pp. pp.838-839.
  • [2]. Klenkin A. A., Korpakova I. G., Pavlenko L. F., Temerdashev Z. A. Ecosystem of the Sea of Azov: anthropogenic pollution. Krasnodar: "Enlightenment-SOUTH", 2007. – 324p.

How to cite: Mikhailenko, A., Fedorov, Y., Minkina, T., Dmitrik, L., Dotsenko, I., Solodko, D., and Chepurnaya, V.: The distribution and behavior of mercury in snow, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-12080, https://doi.org/10.5194/egusphere-egu21-12080, 2021.

EGU21-13306 | vPICO presentations | ITS3.12/AS2.10

Long-range transport of Icelandic dust towards Europe and Arctic 

Pavla Dagsson-Waldhauserova, Nathalie Burdova, Slobodan Nickovic, Bojan Cvetkovic, Olafur Arnalds, Beatrice Moroni, Dragana Djordjevic, and Darius Ceburnis

High Latitude Dust (HLD) contributes 5% to the global dust budget and active HLD sources cover > 500,000 km2. In Iceland, desert areas cover about 44,000 km2, but the hyperactive dust hot spots of area < 1,000 km2 are the most dust productive sources. For example Hagavatn dust source of area about 10 km2 is captured on satellite images to produce visible dust plumes exceeding distance of > 700 km.

Recent studies have shown that Icelandic dust travelled about 2,000 km to Svalbard (Moroni et al., 2018) and about 3,500 km to Balkan Peninsula (Djordjevic et al., 2019). It estimated that about 7% of Icelandic dust can reach the high Arctic (N>80°). Previous study on Icelandic dust travelling about 1,300 km to Ireland (Ovadnevaite et al., 2009) serves as a case study to identify additional dust events arriving to Mace Head, Ireland in 2018-2020. In situ dust concentrations in Iceland, remote sensing and dust forecasts based on atmospheric-dust model DREAM (Dust REgional Atmospheric Model, https://sds-was.aemet.es/forecast-products/dust-forecasts/icelandic-dust-forecast) are used for this study.        

Reference:

Đorđević D., et al. 2019. Can Volcanic Dust Suspended From Surface Soil and Deserts of Iceland Be Transferred to Central Balkan Similarly to African Dust (Sahara)? Frontiers in Earth Sciences 7, 142-154.

Moroni B., et al. 2018. Mineralogical and chemical records of Icelandic dust sources upon Ny-Ålesund (Svalbard Islands). Frontiers in Earth Science  6, 187-219.

Ovadnevaite J., Ceburnis D., et al. 2009. Volcanic sulphate and arctic dust plumes over the North Atlantic Ocean. Atmospheric Environment 43, 4968-4974.

Additional studies on Icelandic dust: https://icedustblog.wordpress.com/publications/

How to cite: Dagsson-Waldhauserova, P., Burdova, N., Nickovic, S., Cvetkovic, B., Arnalds, O., Moroni, B., Djordjevic, D., and Ceburnis, D.: Long-range transport of Icelandic dust towards Europe and Arctic , EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-13306, https://doi.org/10.5194/egusphere-egu21-13306, 2021.

ITS4.2/PS4.4 – Machine Learning in Planetary Sciences and Heliophysics

EGU21-12960 | vPICO presentations | ITS4.2/PS4.4

Mapping Surface Winds on Mars from the Global Distribution of Barchan Dunes Employing an Instance Segmentation Neural Network

Lior Rubanenko, Mathieu G.A. Lapotre, Joseph Schull, Sebastian Perez-Lopez, Lori K. Fenton, and Ryan C. Ewing

The surface of Mars is riddled with dunes that form by accumulating sand particles that are carried by the wind. Since dune geometry and orientation adjust in response to prevailing wind conditions, the morphometrics of dunes can reveal information about the winds that formed them.

Previous studies inferred the prevailing local wind direction from the orientation of dunes by manually analyzing spacecraft imagery. However, building a global map remained challenging, as manual detection of individual dunes over the entire Martian surface is impractical. Here, we employ Mask R-CNN, a state-of-the-art instance segmentation neural network, to detect and analyze isolated barchan dunes on a global scale.

We prepared a training dataset by extracting Mars Context Camera (CTX) scenes of dune fields from a global CTX mosaic, as identified in the global dune-fields catalog. Images were cropped and standardized to a resolution of 832x832 pixels, and labeled using Labelbox’s online instance segmentation platform. Image augmentation and weight decay were employed to prevent overfitting during training. By inspecting 100 sample images from the validation database, we find that the network correctly identified ~86% of the isolated dunes, falsely identifying one feature as a barchan dune in a single image.

After dune outlines are detected, they are automatically analyzed to extract the dominant-wind and net sand-flux directions using traditional computer vision techniques. We expect our future surface-wind dataset to serve as a constraint for atmospheric global circulation models to help predict weather events for upcoming in situ mission as well as shed new light on the recent climate history of Mars.

How to cite: Rubanenko, L., Lapotre, M. G. A., Schull, J., Perez-Lopez, S., Fenton, L. K., and Ewing, R. C.: Mapping Surface Winds on Mars from the Global Distribution of Barchan Dunes Employing an Instance Segmentation Neural Network, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-12960, https://doi.org/10.5194/egusphere-egu21-12960, 2021.

EGU21-9188 | vPICO presentations | ITS4.2/PS4.4

Generative Adversarial Networks for automatic detection of mounds in Digital Terrain Models (Mars Arabia Terra)

Sahib Julka, Michael Granitzer, Barbara De Toffoli, Luca Penasa, Riccardo Pozzobon, and Ute Amerstorfer

Mounds are positive relief features that can be ascribed to a variety of phenomena; they can be related to monogenic edifices due to spring or mud volcanism, rootless cones on top of lava flows, pingos and so on. In the case of sedimentary or spring case of mud extrusion, these mounds can be widespread regionally and/or contained in large complex craters, often in populations of several hundreds or thousands . Previous work on detection of such mounds in the Mars Arabia Terra involved exploiting morphometric parameters and mapping them onto Digital Terrain Models . In this work, we take a step further and develop more general methods to automatically detect them without explicitly defining the topographical features. We achieve this by using a generative framework trained in an adversarial fashion to produce realistic mappings with only a small number of training samples. Further, we introduce a terrain simulator based on this framework that learns the terrain simulation parameters, and allows us to induce domain specific knowledge automatically into the network.  Our key results indicate that learning latent representations based on simulations can offer improvements in detection accuracy, while making it more robust to changing terrain scenarios.



How to cite: Julka, S., Granitzer, M., De Toffoli, B., Penasa, L., Pozzobon, R., and Amerstorfer, U.: Generative Adversarial Networks for automatic detection of mounds in Digital Terrain Models (Mars Arabia Terra), EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-9188, https://doi.org/10.5194/egusphere-egu21-9188, 2021.

EGU21-14672 | vPICO presentations | ITS4.2/PS4.4

Cloud Catalog from Mars Orbiter Laser Altimeter / Mars Global Surveyor Data Using Machine Learning Algorithms

Vincent Caillé, Anni Määttänen, Aymeric Spiga, Lola Falletti, and Gregory A. Neumann

In the development of Mars climate models, modeling clouds is an important challenge, and especially for CO2 clouds. This is due to the complexity of the atmospheric processes involved that may imply rethinking microphysical theories, but also to the scarcity of observations. In the late 90’s, Mars Orbiter Laser Altimeter was one of the instruments aboard the Mars Global Surveyor spacecraft. Its first goal was to build a precise map of Mars’ topography through laser altimetry but its sensitivity allowed for cloud observations as well . Thus, previous studies (Neumann & al. 2003 Ivanov & Muhlemann 2001) have shown that some laser returns were cloud signatures coming from the atmosphere. However, at that time, the huge amount of data was analysed using simple distinction criteria.

We use K-means clustering algorithms to computationally analyse MOLA data. In order to optimise the method, we first determine the best observed parameters to distinguish the different kinds of returns (surface, noise and clouds). The best number of clusters is determined using three independent methods : elbow, silhouette score and gap statistics. The method is tested on a restricted sample (10 % of the dataset) and then applied to the full raw dataset. Once that cloud cluster identified, we can plot spatial and temporal distributions of the cloud returns and compare them with previous results.

As mentioned by Neumann & al. (2003), the product of surface reflectivity and two-way transmissivity of the atmosphere appears as the best parameter discriminating between surface and cloud returns. A unique number of clusters (6) is identified by all three optimisation methods. Among those clusters, one clearly identifies cloud returns, while others represent noise and surface returns. Our methods allows us to identify more clouds than previous studies. Our cloud distribution remains coherent with the ones given in previous studies, showing the viability of our method. We will present a catalog of cloud returns coming from MOLA data. We are now working to separate different kinds of clouds within these returns (absorptive and reflective clouds, CO2 / water clouds, dust …) using machine learning algorithms and a recent MOLA surface reflectivity map (Heavens & al. 2016).

How to cite: Caillé, V., Määttänen, A., Spiga, A., Falletti, L., and Neumann, G. A.: Cloud Catalog from Mars Orbiter Laser Altimeter / Mars Global Surveyor Data Using Machine Learning Algorithms, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-14672, https://doi.org/10.5194/egusphere-egu21-14672, 2021.

EGU21-1490 | vPICO presentations | ITS4.2/PS4.4

Multi-Channel Coronal Hole Detection with Convolutional Neural Networks

Robert Jarolim, Astrid Veronig, Stefan Hofmeister, Stephan Heinemann, Manuela Temmer, Tatiana Podladchikova, and Karin Dissauer

Being the source region of fast solar wind streams, coronal holes are one of the key components which impact space weather. The precise detection of the coronal hole boundary is an important criterion for forecasting and solar wind modeling, but also challenges our current understanding of the magnetic structure of the Sun. We use deep-learning to provide new methods for the detection of coronal holes, based on the multi-band EUV filtergrams and LOS magnetogram from the AIA and HMI instruments onboard the Solar Dynamics Observatory. The proposed neural network is capable to simultaneously identify full-disk correlations as well as small-scale structures and efficiently combines the multi-channel information into a single detection. From the comparison with an independent manually curated test set, the model provides a more stable extraction of coronal holes than the samples considered for training. Our method operates in real-time and provides reliable coronal hole extractions throughout the solar cycle, without any additional adjustments. We further investigate the importance of the individual channels and show that our neural network can identify coronal holes solely from magnetic field data.

How to cite: Jarolim, R., Veronig, A., Hofmeister, S., Heinemann, S., Temmer, M., Podladchikova, T., and Dissauer, K.: Multi-Channel Coronal Hole Detection with Convolutional Neural Networks, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-1490, https://doi.org/10.5194/egusphere-egu21-1490, 2021.

EGU21-10954 | vPICO presentations | ITS4.2/PS4.4

An Unsupervised Machine Learning Pipeline to study the shape of  solar WINQSEs  

Ujjaini Alam, Shabbir Bawaji, Surajit Mondal, and Divya Oberoi

The perplexing mystery of what maintains the solar coronal temperature at about a million K, while the visible disc of the Sun is only at 5800 K, has been a long standing problem in solar physics. A recent study by Mondal et al. (2020, ApJ, 895, L39)  has provided the first evidence for the presence of numerous ubiquitous impulsive emissions at low radio frequencies from the quiet sun regions, which could hold the key to solving this mystery. These Weak Impulsive Narrowband Quiet Sun Emissions (WINQSEs) occur at rates of about five hundred events per minute, and their strength is only a few percent of the background steady emission. Based on earlier work with events of larger flux densities and theoretical considerations, WINQSEs are expected to be compact in the image plane. To characterise the spatial structure of WINQSEs, we have developed a pipeline based on an unsupervised machine learning approach. We first identify the boundaries of the radio sun using edge detection techniques, and detect peaks within the solar boundary. Density-Based Spatial Clustering of Application with Noise (DBSCAN), an unsupervised machine learning algorithm, is used to classify the peaks as isolated or clustered. It is also used to find the optimal hyper-parameters for peak-fitting. The peaks are then fit with Gaussian models, and statistical and heuristic filtering criteria are used to obtain robust fits for a subset of these WINQSEs . We find that the vast majority of WINQSEs can be described by well behaved compact Gaussians. By its very design, this approach is focused on morphological characterisation of these weak features and is better suited for identifying them than earlier attempts. We present here our first results of the observed distributions of intensities, sizes and axial ratios of the Gaussian models for WINQSEs arrived at from analysis of multiple independent datasets.

How to cite: Alam, U., Bawaji, S., Mondal, S., and Oberoi, D.: An Unsupervised Machine Learning Pipeline to study the shape of  solar WINQSEs  , EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-10954, https://doi.org/10.5194/egusphere-egu21-10954, 2021.

EGU21-12716 | vPICO presentations | ITS4.2/PS4.4

Unsupervised Solar Wind Classification Using Wavelet Variational Autoencoders and Self-Organazing Maps

Jorge Amaya, Sara Jamal, and Giovanni Lapenta

Last year we published an automatic method for the automatic classification of the solar wind [1]. We showed that data transformation and unsupervised clustering can be used to classify observations made by the ACE spacecraft. Two data transformation techniques were used: Kernel Principal Component Analysis (KPCA) and Auto-encoder Neural Networks. After data transformation three clustering techniques were tested: k-means, Bayesian Gaussian Mixtures (BGM), and Self-Organizing Maps (SOM). Although the results were very positive we ran into a few difficulties: a) the data from the ACE mission contains a very small population of observations originated at high latitude coronal holes, b) the measured features contain a high degree of intercorrelation, c) the data distribution is compact in the feature space, and d) the final algorithm produces a single categorical class for a single point in time.


In this work we present an improvement of the model that redresses some of the limitations above. We are still making use of the two main features of our previous work, i.e. the data transformation using auto-encoders and the unsupervised classification using SOM. But in the present work: a) we include the analysis of Ulysses data with observations of the solar wind originated at high latitudes; b) we perform a Factor Analysis to reduce the number of features used as inputs; c) we transform windows of time of the multi-variate time series (instead of instantaneous observations) into scalograms using wavelet transformations; d) we apply the variational version of the auto-encoder [2] to parametrize the scalograms; f) we finally use the SOM to automatically classify the windows of time in different categories.


This method can be adapted to the classification of observations from the Parker Solar Probe and Solar Orbiter missions.


The work presented in this abstract has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No. 754304 (DEEP-EST, www.deep-projects.eu), and from the European Union's Horizon 2020 research and innovation programme under grant agreement No 776262 (AIDA, www.aida-space.eu).


[1] Amaya, Jorge, Romain Dupuis, Maria Elena Innocenti, and Giovanni Lapenta. "Visualizing and Interpreting Unsupervised Solar Wind Classifications." arXiv preprint arXiv:2004.13430 (2020).

[2] Kingma, Diederik P., and Max Welling. "Auto-encoding variational bayes." arXiv preprint arXiv:1312.6114 (2013).

How to cite: Amaya, J., Jamal, S., and Lapenta, G.: Unsupervised Solar Wind Classification Using Wavelet Variational Autoencoders and Self-Organazing Maps, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-12716, https://doi.org/10.5194/egusphere-egu21-12716, 2021.

EGU21-1601 | vPICO presentations | ITS4.2/PS4.4

Automatic Detection and Classification of ICMEs in Solar Wind Data

Hannah Ruedisser, Andreas Windisch, Ute V. Amerstorfer, Tanja Amerstorfer, Christian Moestl, and Rachel L. Bailey

Interplanetary coronal mass ejections (ICMEs) are one of the main drivers for space weather disturbances. In the past, different machine learning approaches have been used to automatically detect events in existing time series resulting from solar wind in situ data. However, classification, early detection and ultimately forecasting still remain challenges when facing the large amount of data from different instruments. We attempt to further enhance existing convolutional neural network (CNN) models through extending their possibilities to process data from multiple spacecraft and to include a post processing step commonly used in the area of computer vision. Additionally, we make an effort to extend the previously binary classification problem to a multiclass classification, to also include corotating interaction regions (CIRs) into the range of detectable phenomena. Ultimately, we aspire to explore the suitability of several other methods used in time series forecasting, in order to pave the way for the elaboration of an early warning system.

How to cite: Ruedisser, H., Windisch, A., Amerstorfer, U. V., Amerstorfer, T., Moestl, C., and Bailey, R. L.: Automatic Detection and Classification of ICMEs in Solar Wind Data, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-1601, https://doi.org/10.5194/egusphere-egu21-1601, 2021.

EGU21-9624 | vPICO presentations | ITS4.2/PS4.4

Solar wind propagation delay predictions between L1 and Earth based on machine learning

Carsten Baumann and Aoife E. McCloskey

GNSS positioning errors, spacecraft operations failures and power outages potentially originate from space weather in general and the solar wind interaction with the geomagnetic field in particular. Depending on the solar wind speed, information from L1 solar wind monitor spacecraft only give a lead time to take safety measures between 20 and 90 minutes.  This very short lead time requires end users to have the most reliable warnings when potential impacts will actually occur. In this study we present a machine learning algorithm that is suitable to predict the solar wind propagation delay between Lagrangian point L1 and the Earth.  This work introduces the proposed algorithm and investigates its operational applicability to a realtime scenario.

The propagation delay is measured from interplanetary shocks passing the Advanced Composition Explorer (ACE) first and their sudden commencements within the magnetosphere later, as recorded by ground-based magnetometers. Overall 380 interplanetary shocks with data ranging from 1998 to 2018 builds up the database that is used to train the machine learning model. We investigate two different feature sets. The training of one machine learning model DSCOVR real time solar wind (RTSW) like data which contains all three components of solar wind speed is used. For the other machine learning model ACE RTSW like data which only provide bulk solar wind speed will be used for training. Both feature sets also contain the position of the spacecrafts. The performance assessment of the machine learning model is examined on the basis of a 10-fold cross-validation. The major advantage of the machine learning approach is its simplicity when it comes to its application. After training, values for the different features have to be fed into the algorithms only and the evaluation of the propagation delay can be continuous.

Both machine learning models will be validated against a simple convective solar wind propagation delay model as it is also used in operational space weather centers. For this purpose time periods will be investigated where L1 spacecraft and Earth satellites just outside the magnetosphere probe the same features of the interplanetary magnetic field. This method allows a detailed validation of the solar wind propagation delay apart from the technique that relies on interplanetary shocks.

How to cite: Baumann, C. and McCloskey, A. E.: Solar wind propagation delay predictions between L1 and Earth based on machine learning, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-9624, https://doi.org/10.5194/egusphere-egu21-9624, 2021.

EGU21-12703 | vPICO presentations | ITS4.2/PS4.4

Automatic detection of magnetopause and bow shock crossing signatures in MESSENGER magnetometer data using Convolutional Neural Networks.

Alexander Lavrukhin, David Parunakian, Dmitry Nevskiy, Sahib Julka, Michael Granitzer, Andreas Windisch, Christian Möstl, Martin A. Reiss, Rachel L. Bailey, and Ute Amerstorfer

During its 2011-2015 lifetime the MESSENGER spacecraft completed more than 4000 orbits around Mercury, producing vast amounts of information regarding the planetary magnetic field and magnetospheric processes. During each orbit the spacecraft left and re-entered the Hermean magnetosphere, giving us information about more than 8000 crossings of the bow shock and the magnetopause of Mercury's magnetosphere. The information obtained from the magnetometer data offers the possibility to study in depth the structures of the bow shock and magnetopause current sheets and their shapes. In this work, we take a step in this direction by automatically detecting the crossings of bow-shock and magnetopause. To this end, we propose a five-class problem and train a Convolutional Neural Network based classifier using the magnetometer data. Our key experimental results indicate that an average precision and recall of at least 87% and 96% can be achieved on the bow hock and magnetopause crossings by using only a small subset of the data. We also model the average three-dimensional shape of these boundaries depending on the external interplanetary magnetic field . Furthermore, we attempt to clarify the dependence of the two boundary locations on the heliocentric distance of Mercury and on the solar activity cycle phase. This work may be of particular interest for future Mercury research related to the BepiColombo spacecraft mission, which will enter Mercury’s orbit around December 2025.

How to cite: Lavrukhin, A., Parunakian, D., Nevskiy, D., Julka, S., Granitzer, M., Windisch, A., Möstl, C., Reiss, M. A., Bailey, R. L., and Amerstorfer, U.: Automatic detection of magnetopause and bow shock crossing signatures in MESSENGER magnetometer data using Convolutional Neural Networks., EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-12703, https://doi.org/10.5194/egusphere-egu21-12703, 2021.

EGU21-2661 | vPICO presentations | ITS4.2/PS4.4

Unsupervised classification of Mercury’S Visible–Near-Infrared MASCS/MESSENGER reflectance spectra for automated surface mapping.

Mario D'Amore, Jörn Helbert, Maturilli Alessandro, and Varatharajan Indhu

The surface of Mercury has been mapped in the 400–1145 nm wavelength range by the Mercury Atmospheric and Surface Composition Spectrometer (MASCS) instrument during orbital observations by the MErcury Surface, Space ENvironment, GEochemistry, and Ranging (MESSENGER) spacecraft.

Under the hypothesis that surface compositional information can be efficiently derived from spectral reflectance measurements with the use of machine learning techniques, we have conducted unsupervised hierarchical clustering analyses to identify and characterize spectral units from MASCS observations.

We apply our analysis on the latest MESENGER data delivery to PDS including the new spectral photometric correction , finding result consistent with our previous analysis based on our custom photometric effect removal.

The input is a global hyperspectral data cube image of normalized MASCS visible (VIS) detector spectra, from the first Earth year of the orbital mission. Data coverage varies from region to region, but global maps at 1 degree/pixel can be obtained with a high signal-to-noise ratio (SNR). The resultant hyperspectral map was then visually inspected to search for anomalies that originated mainly in regions of low coverage or from high levels of spectral variation within a single pixel.

Our approach consist of several steps:
1.  Data cleaning step: remove data artifact.
2.  Independent Component Analysis (ICA): features compression and undelyng signal demixing.
3.  Manifold learning : embedding of data in a low dimensional space via UMAP.
4.  Hierarchical clustering : creation of spectrally similar partition and projection on the surface with comparison to existing human generated classifications.

We  found the existence of two large and spectrally distinct regions, which we call the polar spectral unit (PSU) and the equatorial spectral unit (ESU).
The spatial extent of the polar unit in the northern hemisphere generally correlates well with that of the northern volcanic plains.
Further analysis indicates the presence of smaller sub-units that lie near the boundaries of these large regions and may be transitional areas of intermediate spectral characters.

How to cite: D'Amore, M., Helbert, J., Alessandro, M., and Indhu, V.: Unsupervised classification of Mercury’S Visible–Near-Infrared MASCS/MESSENGER reflectance spectra for automated surface mapping., EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-2661, https://doi.org/10.5194/egusphere-egu21-2661, 2021.

EGU21-8404 | vPICO presentations | ITS4.2/PS4.4

A Machine Learning technique for ULF wave classification in Swarm magnetic field measurements

Alexandra Antonopoulou, George Balasis, Constantinos Papadimitriou, Zoe Boutsi, Omiros Giannakis, Konstantinos Koutroumbas, and Athanasios Rontogiannis

Ultra-low frequency (ULF) magnetospheric plasma waves play a key role in the dynamics of the Earth’s magnetosphere and, therefore, their importance in Space Weather studies is indisputable. Magnetic field measurements from recent multi-satellite missions are currently advancing our knowledge on the physics of ULF waves. In particular, Swarm satellites have contributed to the expansion of data availability in the topside ionosphere, stimulating much recent progress in this area. Coupled with the new successful developments in artificial intelligence, we are now able to use more robust approaches for automated ULF wave identification and classification. The goal of this effort is to use a machine learning technique to classify ULF wave events using magnetic field data from Swarm. We construct a Convolutional Neural Network that takes as input the wavelet power spectra of the Earth’s magnetic field variations per track, as measured by each one of the three Swarm satellites, aiming to classify ULF wave events in four categories: Pc3 wave events, background noise, false positives, and plasma instabilities. Our primary experiments show promising results, yielding successful identification of 90% accuracy. We are currently working on producing larger datasets, by analyzing Swarm data from mid-2014 onwards, when the final constellation was formed.

How to cite: Antonopoulou, A., Balasis, G., Papadimitriou, C., Boutsi, Z., Giannakis, O., Koutroumbas, K., and Rontogiannis, A.: A Machine Learning technique for ULF wave classification in Swarm magnetic field measurements, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-8404, https://doi.org/10.5194/egusphere-egu21-8404, 2021.

EGU21-10477 | vPICO presentations | ITS4.2/PS4.4

Automatic Extraction of the Dispersion Coefficients of Lightning Whistler Waves Observed By SCM Boarded On ZH-1 Satellite

Jing Yuan, Le Zhou, Qiao Wang, Dehe Yang, Zeren Zima, Ying Han, Zijie Wang, Xuhui Shen, and Lei Hu

Lightning whistler waves, as an important tool for geospace exploration, can be found from the vast amount of electromagnetic satellite data. In recent years, with the development of computer vision and deep learning technologies, some advanced  algorithms have been developed to automatically identify lightning whistler waves from the massive archived data of electromagnetic satellites. However, these algorithms fail to automatically extract the dispersion coefficients of lightning whistlers(DCW). Since the DCW are depended on the propagation path of lightning and geospace environments, it is extremely important for further geospace exploration.

We proposed an algorithm that can automatically extract the dispersion coefficients of lightning whistler: (1) using two seconds time window on the SCM VLF data from the ZH-1 satellite to obtain segmented data; (2) generating time-frequency profile (TFP) of the segmented waveform by performing a band-pass filter and the short-time Fourier transform with a 94% overlap; (3) annotating the ground truth of the whistler with the rectangular boxes on the each time-frequency image to construct the training dataset; (4) building the YOLOV3 deep neural network and setting the training parameters; (5) inputting the training dataset to the YOLOV3 to train the whistler recognition model; (6) detecting the whistler from the unknown time-frequency image to extract the whistler area with the rectangle box as a sub-image; (7) conducting the BM3D algorithm to denoise the sub-image; (8) employing an adaptive threshold segmentation algorithm on the denoised sub-image to obtain the binary image which represents the whistler trace with the black pixel and other area with white pixel. (9) removing isolated points in the binary image with the open operation in morphology; (10) extracting lightning whistler trajectory region using connected domain analysis; (11) converting the trajectory coordinates from (t-f) to (f-0.5-t); (12) taking into account the Eckersley formula, which depicts the relationship between the scattering coefficient and the time frequency, we use the least-squares method on the converted trajectory coordinates to fit a straight line and obtain the slope of the line as the dispersion coefficient.

In order to evaluate the effectiveness of the proposed algorithm, we construct two dataset: a simulation set and an observational dataset. The simulation set is composed of 1000 lightning whistler trajectories, which are generated according to the Eckersley formula. The observational dataset containing 1000 actual single-trace lightning whistler, are generated by collecting the data from the SCM VLF from the ZH-1 satellite. The experiment results show that: the mean-square error on the simulation set is below 2.8x 10-4; The mean-square error on the observational dataset is below 2.1054x10-3.

Keywords: ZH-1 Satellite, SCM, Lightning Whistler, YOLOV3, Dispersion Coefficients

How to cite: Yuan, J., Zhou, L., Wang, Q., Yang, D., Zima, Z., Han, Y., Wang, Z., Shen, X., and Hu, L.: Automatic Extraction of the Dispersion Coefficients of Lightning Whistler Waves Observed By SCM Boarded On ZH-1 Satellite, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-10477, https://doi.org/10.5194/egusphere-egu21-10477, 2021.

EGU21-11024 | vPICO presentations | ITS4.2/PS4.4

Automatic Recognition of the Lighting Whistler waves from the Wave Data of SCM Boarded on ZH-1 satellite

Jing Yuan, Zijie Wang, Dehe Yang, Qiao Wang, Zeren Zima, Ying Han, Le Zhou, Xuhui Shen, and Qihang Guo

Lightning whistlers, found frequently in electromagnetic satellite observation, are the important tool to study electromagnetic environment of the earth space. With the increasing data from electromagnetic satellites, a considerable amount of time and human efforts are needed to detect lightning whistlers from these tremendous data. In recent years, algorithms for lightning whistlers automatic detection have been conducted. However, these methods can only work in the time-frequency profile (image) of the electromagnetic satellites data with two major limitations: vast storage memory for the time-frequency profile (image) and expensive computation for employing the methods to detect automatically the whistler from the time-frequency profile. These limitations hinder the methods work efficiently on ZH-1 satellite. To overcome the limitations and realize the real-time whistler detection automatically on board satellite, we propose a novel algorithm for detecting lightning whistler from the original observed data without transforming it to the time-frequency profile (image).

The motivation is that the frequency of lightning whistler is in the audio frequency range. It encourages us to utilize the speech recognition techniques to recognize the whistler in the original data \of SCM VLF Boarded on ZH-1. Firstly, we averagely move a 0.16 seconds window on the original data to obtain the patch data as the audio clip. Secondly, we extract the Mel-frequency cepstral coefficients (MFCCs) of the patch data as a type of cepstral representation of the audio clip. Thirdly, the MFCCs are input to the Long Short-Term Memory (LSTM) recurrent neutral networks to classification. To evaluate the proposed method, we construct the dataset composed of 10000 segments of SCM wave data observed from ZH-1 satellite(5000 segments which involving whistler and 5000 segments without any whistler). The proposed method can achieve 84% accuracy, 87% in recall, 85.6% in F1score.Furthermore, it can save more than 126.7MB and 0.82 seconds compared to the method employing the YOLOv3 neutral network for detecting whistler on each time-frequency profile.

 

Key words: ZH-1 satellite, SCM,lightning whistler, MFCC, LSTM

How to cite: Yuan, J., Wang, Z., Yang, D., Wang, Q., Zima, Z., Han, Y., Zhou, L., Shen, X., and Guo, Q.: Automatic Recognition of the Lighting Whistler waves from the Wave Data of SCM Boarded on ZH-1 satellite, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-11024, https://doi.org/10.5194/egusphere-egu21-11024, 2021.

EGU21-12344 | vPICO presentations | ITS4.2/PS4.4

Atmospherically driven ground motion at InSight: a machine learning perspective

Alexander E. Stott, Raphael F. Garcia, Baptiste Pinot, Naomi Murdoch, David Mimoun, Aymeric Spiga, Donald Banfield, Sara Navarro, Luis Mora-Sotomayor, Constantinos Charalambous, William T. Pike, Philippe Lognonné, and Anna Horleston

The NASA InSight lander is a geophysical and meteorological observatory operating on Mars for over a Martian year/two Earth years. Continuous records of seismic, pressure, wind and temperature data over this period have led to significant breakthroughs in determining the planet's structure and climate. With such a wealth of data now received, machine learning offers a nascent tool to extract further information. 

The seismic data is extremely correlated to the atmospheric conditions. Discerning the coupling between the atmosphere and ground motion is of significant interest and this work aims to predict the ground motion generated by wind and pressure forcing using machine learning techniques. From this prediction we can untangle the various contributions to ground motion, determine atmospheric/ground properties, analyse/discriminate marsquakes and potentially decorrelate waveforms to remove the atmospheric contribution. While a physical model for this atmospheric forcing is desirable, machine learning approaches the problem from an alternative view point where mathematical and algorithmic tools add the necessary complexity for fitting the data. In this way, we may be able to capture detailed variation and inform further modelling efforts.

We will detail the initial application of machine learning for predicting the ground motion from the atmospheric data inputs of wind speed, wind direction, pressure and temperature. First though, we will describe the issues that need to be tackled to obtain a good prediction using the InSight data. To illustrate some of some of these problems, consider that glitches are known to occur in the seismic data. They offer a way to detect overfitting as they should not in general be predicted from atmospheric forcing. However, a subset of the glitches are correlated to temperature on top of the fact they are only visible during quiet enough periods, as are marsquakes. Therefore, they are not a normally distributed source of noise or uncorrelated from the input atmospheric data, breaking typical assumptions used for regression. A similar issue is presented by the changing weather conditions throughout a Martian sol, where the time series distribution varies. As a result, prior information on the instrumentation and data qualities is essential for applying the machine learning methods and interpretation of the results.

We demonstrate the specifics of the InSight data with respect to 1) how a curve fitting problem can be constructed, 2) the necessary degrees of freedom of the problem, 3) consideration of non-stationary/heteroscedastic errors and 4) the optimisation and machine learning method applied. Current results will be presented from the implementation of random forests and gaussian processes. These results demonstrate a good performance so far for capturing the global variation and we will offer perspectives on how these results can be used and improved.

How to cite: Stott, A. E., Garcia, R. F., Pinot, B., Murdoch, N., Mimoun, D., Spiga, A., Banfield, D., Navarro, S., Mora-Sotomayor, L., Charalambous, C., Pike, W. T., Lognonné, P., and Horleston, A.: Atmospherically driven ground motion at InSight: a machine learning perspective, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-12344, https://doi.org/10.5194/egusphere-egu21-12344, 2021.

EGU21-14749 | vPICO presentations | ITS4.2/PS4.4

Error identification in orbital laser altimeter data by machine learning

Oliver Stenzel, Robin Thor, and Martin Hilchenbach

Orbital Laser altimeters deliver a plethora of data that is used to map planetary surfaces [1] and to understand interiors of solar system bodies [2]. Accuracy and precision of laser altimetry measurements depend on the knowledge of spacecraft position and pointing and on the instrument. Both are important for the retrieval of tidal parameters. In order to assess the quality of the altimeter retrievals, we are training and implementing an artificial neural network (ANN) to identify and exclude scans from analysis which yield erroneous data. The implementation is based on the PyTorch framework [3]. We are presenting our results for the MESSENGER Mercury Laser Altimeter (MLA) data set [4], but also in view of future analysis of the BepiColombo Laser Altimeter (BELA) data, which will arrive in orbit around Mercury in 2025 on board the Mercury Planetary Orbiter [5,6]. We further explore conventional methods of error identification and compare these with the machine learning results. Short periods of large residuals or large variation of residuals are identified and used to detect erroneous measurements. Furthermore, long-period systematics, such as those caused by slow variations in instrument pointing, can be modelled by including additional parameters.
[1] Zuber, Maria T., David E. Smith, Roger J. Phillips, Sean C. Solomon, Gregory A. Neumann, Steven A. Hauck, Stanton J. Peale, et al. ‘Topography of the Northern Hemisphere of Mercury from MESSENGER Laser Altimetry’. Science 336, no. 6078 (13 April 2012): 217–20. https://doi.org/10.1126/science.1218805.
[2] Thor, Robin N., Reinald Kallenbach, Ulrich R. Christensen, Philipp Gläser, Alexander Stark, Gregor Steinbrügge, and Jürgen Oberst. ‘Determination of the Lunar Body Tide from Global Laser Altimetry Data’. Journal of Geodesy 95, no. 1 (23 December 2020): 4. https://doi.org/10.1007/s00190-020-01455-8.
[3] Paszke, Adam, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, et al. ‘PyTorch: An Imperative Style, High-Performance Deep Learning Library’. Advances in Neural Information Processing Systems 32 (2019): 8026–37.
[4] Cavanaugh, John F., James C. Smith, Xiaoli Sun, Arlin E. Bartels, Luis Ramos-Izquierdo, Danny J. Krebs, Jan F. McGarry, et al. ‘The Mercury Laser Altimeter Instrument for the MESSENGER Mission’. Space Science Reviews 131, no. 1 (1 August 2007): 451–79. https://doi.org/10.1007/s11214-007-9273-4.
[5] Thomas, N., T. Spohn, J. -P. Barriot, W. Benz, G. Beutler, U. Christensen, V. Dehant, et al. ‘The BepiColombo Laser Altimeter (BELA): Concept and Baseline Design’. Planetary and Space Science 55, no. 10 (1 July 2007): 1398–1413. https://doi.org/10.1016/j.pss.2007.03.003.
[6] Benkhoff, Johannes, Jan van Casteren, Hajime Hayakawa, Masaki Fujimoto, Harri Laakso, Mauro Novara, Paolo Ferri, Helen R. Middleton, and Ruth Ziethe. ‘BepiColombo—Comprehensive Exploration of Mercury: Mission Overview and Science Goals’. Planetary and Space Science, Comprehensive Science Investigations of Mercury: The scientific goals of the joint ESA/JAXA mission BepiColombo, 58, no. 1 (1 January 2010): 2–20. https://doi.org/10.1016/j.pss.2009.09.020.

How to cite: Stenzel, O., Thor, R., and Hilchenbach, M.: Error identification in orbital laser altimeter data by machine learning, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-14749, https://doi.org/10.5194/egusphere-egu21-14749, 2021.

ITS4.3/NH1 – Data Science and Machine Learning for Geohazard

EGU21-1304 | vPICO presentations | ITS4.3/NH1

3D point cloud-based assessment of detailed building damage through a combination of machine learning, crowdsourcing and earthquake engineering

Vivien Zahs, Benjamin Herfort, Julia Kohns, Tahira Ullah, Katharina Anders, Lothar Stempniewski, Alexander Zipf, and Bernhard Höfle

Timely and reliable information on earthquake-induced building damage plays a critical role for the effective planning of rescue and remediation actions. Automatic damage assessment based on the analysis of 3D point cloud (e.g. from photogrammetry or LiDAR) or georeferenced image data can provide fast and objective information on the damage situation within few hours. So far, studies are often limited to the distinction of only two damage classes (e.g. damaged or not damaged) and to information provided by 2D image data. Beyond-binary assessment of multiple grades of damage is challenging, e.g. due to the variety of damage characteristics and the limited transferability of trained algorithms to unseen data and other geographic regions. The detailed damage assessment based on full 3D information is, however, required to enable efficient use and distribution of resources and for evaluation of structural stability of buildings. Further, the identification of slightly damaged buildings is essential to estimate the vulnerability for severe damage in potential aftershock events.

In our work, we propose an interdisciplinary approach for timely and reliable assessment of multiple building-specific damage grades (0-5) from post- (and pre-) event UAV point clouds and images with high resolution (centimeter point spacing or pixel size). We combine expert knowledge of earthquake engineers with fully automatic damage classification and human visual interpretation from web-based crowdsourcing. While automatic approaches enable an objective and fast analysis of large 3D data, the ability of humans to visually interpret details in the data can be used as (1) validation of the automatic classification and (2) alternative method where the automatic approach showed high levels of uncertainty.

We develop a damage catalogue that categorizes typical geometric and radiometric damage patterns for each damage grade. Therein, we consider influences of building material and region-specific building design on damage characteristics. Moreover, damage patterns include observations of previous earthquakes to ensure practical applicability. The catalogue serves as decision basis for the automatic classification of building-specific damage using machine learning, on the one hand. On the other hand, the catalogue is used to design quick and easy single damage mapping tasks that can be solved by volunteers within seconds (Micro-Mapping, Herfort et al. 2018). A further novelty of our approach consists in the combination of strengths of machine learning approaches for point cloud-based damage classification and visual interpretation by human contributors through Micro-Mapping tasks. The optimal combination of operation and weighted fusion of both methods is thereby dependent on event-specific conditions (e.g. data availability and quality, temporal constraints, spatial scale, extent of damage). 

By considering observations from previous earthquakes and influences of building design and structure on potential damage characteristics, our approach shall be applicable to events in different geographic regions. By the combination of automated and crowdsourcing methods, reliable and detailed damage information at the scale of large cities shall be provided within a few days. 

 

References

Herfort, B., Höfle, B. & Klonner, C. (2018): 3D micro-mapping: Towards assessing the quality of crowdsourcing to support 3D point cloud analysis. ISPRS Journal of Photogrammetry and Remote Sensing. Vol. 137, pp. 73-83.

How to cite: Zahs, V., Herfort, B., Kohns, J., Ullah, T., Anders, K., Stempniewski, L., Zipf, A., and Höfle, B.: 3D point cloud-based assessment of detailed building damage through a combination of machine learning, crowdsourcing and earthquake engineering, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-1304, https://doi.org/10.5194/egusphere-egu21-1304, 2021.

EGU21-2496 | vPICO presentations | ITS4.3/NH1

Machine learning classifiers for detecting and classifying major explosions and paroxysms at Stromboli volcano (Italy) using radar and optical satellite imagery

Ciro Del Negro, Claudia Corradino, Eleonora Amato, Federica Torrisi, and Sonia Calvari

The persistent explosive activity of Stromboli is characterized by several hundred of moderate-intensity events per day. These explosions eject pyroclastic fragments to the height of some tens of meters, which fall a short distance from the summit vents. Occasionally, major explosions eject pyroclastic material to more than a few hundred meters high, which can fall outside the crater terrace on the area visited by tourists. The frequency of these phenomena is variable, with an average of 2 events per year. Paroxysms, violent explosions that produce eruptive columns more than 3 km high and are often associated with pyroclastic flows, can also occur at Stromboli. Ballistic blocks associated with these explosions can reach up to 4 m in diameter and fall on the hinabited areas. Paroxysms are rare (5 events in the last 20 years) and their occurrence frequency varies over time. Nevertheless, major explosions and paroxysms represent the main danger to visitors and inhabitants of the Stromboli Island. Here, we propose a novel approach to detect and classify the type of explosive activity occurring on Stromboli volcano by combining radar and optical satellite imagery with machine learning algorithms. In particular, we considered the plume height, the summit area temperature, and the area affected by large ballistic projectiles as the discriminant factors to distinguish between ordinary activity, major explosions and paroxysms. These factors are retrieved from both radar (Sentinel-1-GRD) and multi-spectral (Landsat-MSI and TIR) satellite images and fed to a machine learning classifier. A retrospective analysis is conducted investigating the main explosive events that have occurred since 1983. This algorithm is based on the in the Google Earth Engine (GEE), which is a cloud computing platform for environmental data analysis from local to planetary scales, with fast access and processing of satellite data from different missions.

How to cite: Del Negro, C., Corradino, C., Amato, E., Torrisi, F., and Calvari, S.: Machine learning classifiers for detecting and classifying major explosions and paroxysms at Stromboli volcano (Italy) using radar and optical satellite imagery, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-2496, https://doi.org/10.5194/egusphere-egu21-2496, 2021.

EGU21-2788 | vPICO presentations | ITS4.3/NH1

Hierarchical exploration of single station seismic data with unsupervised learning

René Steinmann, Leonard Seydoux, and Michel Campillo

Seismic datasets contain an enormous amount of information and a large variety of signals with different origins. We usually observe signatures of earthquakes, volcanic and non-volcanic tremors, rockfalls, road and air traffic, atmospheric perturbations and many other acoustic emissions. More and more seismic sensors are deployed worldwide and record the seismic wavefield in a continuous fashion, generating massive volumes of data that cannot be analyzed manually in decent times. Therefore, identifying classes of signals in seismic data with automatic strategies is a crucial stage towards the understanding of the underlying physics of geological objects. For that reason seismologists have developed different tools to detect and classify certain types of signals. Recently, machine learning gained much attention due to its ability to recognize patterns. While supervised learning is a great tool for detecting and classifying signals within already-known classes, it cannot be used to infer new classes of signals, and can be strongly biased by the labels we impose. We here propose to overcome this limitation with unsupervised learning. In this study, we present a new way to explore single-station continuous seismic data with a dendrogram produced by agglomerative clustering. Our method is motivated by the idea that labels in a seismic data set follow a hierarchical order with different levels of details. For example earthquakes belong to the larger class of stationary signals and can be also divided into subclasses with different focal mechanism or magnitudes. We first use a scattering network (a convolutional neural network that makes use of wavelet filers) in order to extract a multi-scale representation of the continuous seismic waveforms. We then select the most meaningful features by means of independent component analysis, and apply an agglomerative clustering on this representation. We finally explore the dendrogram in a systematic way in order to explore the different signal classes revealed by the strategy. We illustrate our method on seismic data continuously recorded in the vicinity of the North-Anatolian fault, in Turkey. During this time period, a seismic crisis with more than 200 micro-earthquakes occurred, together with many other anthropogenic and meteorological events. By exploring the classes revealed by the dendrogram with a posteriori signal features (occurrence, within-class correlations, etc.) we show that the strategy is capable of retrieving the seismic crisis as well as signals related to anthropogenic and meteorogical activities.

How to cite: Steinmann, R., Seydoux, L., and Campillo, M.: Hierarchical exploration of single station seismic data with unsupervised learning, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-2788, https://doi.org/10.5194/egusphere-egu21-2788, 2021.

EGU21-3524 | vPICO presentations | ITS4.3/NH1

Tremor Waveform Denoising and Automatic Location with Neural Network Interpretation

Claudia Hulbert, Romain Jolivet, Blandine Gardonio, Paul Johnson, Christopher Ren, and Bertrand Rouet-Leduc

Active faults release tectonic stress imposed by plate motion through a spectrum of slip modes, from slow, aseismic slip, to dynamic, seismic events. Slow earthquakes are often associated with tectonic tremor, non-impulsive signals that can easily be buried in seismic noise and go undetected. 

We present a new methodology aimed at improving the detection and location of tremors hidden within seismic noise. After detecting tremors with a classic convolutional neural network, we rely on neural network attribution to extract core tremor signatures. By identifying and extracting tremor characteristics, in particular in the frequency domain, the attribution analysis allows us to uncover structure in the data and denoise input waveforms. In particular, we show that these cleaned signals correspond to a waveform traveling in the Earth's crust and mantle at wavespeeds consistent with local estimates. We then use these cleaned waveforms to locate tremors with standard array-based techniques. 

We apply this method to the Cascadia subduction zone. We analyze a slow slip event that occurred in 2018 below the southern end of the Vancouver Island, Canada, where we identify tremor patches consistent with existing catalogs. Having validated our new methodology in a well-studied area, we further apply it to various tectonic contexts and discuss the implications of tremor occurrences in the scope of exploring the interplay between seismic and aseismic slip.

How to cite: Hulbert, C., Jolivet, R., Gardonio, B., Johnson, P., Ren, C., and Rouet-Leduc, B.: Tremor Waveform Denoising and Automatic Location with Neural Network Interpretation, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-3524, https://doi.org/10.5194/egusphere-egu21-3524, 2021.

EGU21-3755 | vPICO presentations | ITS4.3/NH1

Optimization of the time series surface deformation analysis using machine learning algorithms on the interferogram simulation data

Muhammad Fulki Fadhillah, SeulKi Lee, and Chang-Wook Lee

Time-series InSAR techniques, such as Stanford Method for Persistent Scatterers (StaMPS) are commonly used to measure time-series surface deformation. This study presents a novel approach of optimized time series deformation analysis based on a support vector regression (SVR) algorithm and optimization Hot-Spot Analysis on persistent scatterers (PS). To examine the performances of the optimized process in time-series, we generated a synthetic interferogram using a Mogi model equation to construct a simulated surface deformation phase. Topography errors simulated orbital error and atmospheric error phases have been added to synthetic interferogram construction. All the synthetic interferogram based on Sentinel-1 SAR Image acquisition dates over Seoul, Korea. An SVR algorithm was used to find an optimum measurement point and reduce error points in time-series analysis. Then, the OHSA approach was implemented on the optimum measurement point through the analysis of Getis-Ord Gi* statistics. As the result, the optimization measurement point indicates refined results in the mean velocity deformation map and time-series graph. In addition, the detection accuracy can be improved by more than 10% with synthetic data. Then, the correlation coefficient between the optimization result and the deformation model shows a good correlation (> 0.8). Also, the standard deviation of time-series results can be reduced by more than 7% after optimizing the process. The proposed method is useful to detect a low deformation rate and can be implemented for several deformation cases.   

How to cite: Fadhillah, M. F., Lee, S., and Lee, C.-W.: Optimization of the time series surface deformation analysis using machine learning algorithms on the interferogram simulation data, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-3755, https://doi.org/10.5194/egusphere-egu21-3755, 2021.

EGU21-4718 | vPICO presentations | ITS4.3/NH1

Insights into deep learning for earthquake magnitude and location estimation

Jannes Münchmeyer, Dino Bindi, Ulf Leser, and Frederik Tilmann

The estimation of earthquake source parameters, in particular magnitude and location, in real time is one of the key tasks for earthquake early warning and rapid response. In recent years, several publications introduced deep learning approaches for these fast assessment tasks. Deep learning is well suited for these tasks, as it can work directly on waveforms and can learn features and their relation from data.

A drawback of deep learning models is their lack of interpretability, i.e., it is usually unknown what reasoning the network uses. Due to this issue, it is also hard to estimate how the model will handle new data whose properties differ in some aspects from the training set, for example earthquakes in previously seismically quite regions. The discussions of previous studies usually focused on the average performance of models and did not consider this point in any detail.

Here we analyze a deep learning model for real time magnitude and location estimation through targeted experiments and a qualitative error analysis. We conduct our analysis on three large scale regional data sets from regions with diverse seismotectonic settings and network properties: Italy and Japan with dense networks (station spacing down to 10 km) of strong motion sensors, and North Chile with a sparser network (station spacing around 40 km) of broadband stations.

We obtained several key insights. First, the deep learning model does not seem to follow the classical approaches for magnitude and location estimation. For magnitude, one would classically expect the model to estimate attenuation, but the network rather seems to focus its attention on the spectral composition of the waveforms. For location, one would expect a triangulation approach, but our experiments instead show indications of a fingerprinting approach. Second, we can pinpoint the effect of training data size on model performance. For example, a four times larger training set reduces average errors for both magnitude and location prediction by more than half, and reduces the required time for real time assessment by a factor of four. Third, the model fails for events with few similar training examples. For magnitude, this means that the largest events are systematically underestimated. For location, events in regions with few events in the training set tend to get mislocated to regions with more training events. These characteristics can have severe consequences in downstream tasks like early warning and need to be taken into account for future model development and evaluation.

How to cite: Münchmeyer, J., Bindi, D., Leser, U., and Tilmann, F.: Insights into deep learning for earthquake magnitude and location estimation, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-4718, https://doi.org/10.5194/egusphere-egu21-4718, 2021.

EGU21-7603 | vPICO presentations | ITS4.3/NH1

Identification of Infrasound Regimes at Mount Etna using Pattern Recognition Techniques

Felix Eckel, Horst Langer, and Mariangela Sciotto

Mount Etna, Europe’s largest and most active volcano is situated close to the Metropolitan area of Catania with about 1 Million inhabitants. Continuous monitoring has therefore been carried out for decades. Among the various disciplines infrasound recordings play an important role in this context. Explosive activity near or above ground as well as shallow tremor processes are easier to identify with airborne sound waves than with seismic waves that are significantly scattered and refracted in the volcanic edifice. However, infrasound signals are often affected by noise, especially by wind noise in the summit area.

At Mount Etna five summit craters are currently known with fluctuating levels of activity. This leads to a wide variety of infrasound signal patterns interfered by changing noise levels. Manual distinction of noisy data from real volcanogenic signals brings along a considerable effort and requires expert knowledge. We therefore apply unsupervised pattern recognition techniques for this task. Extracting features from the amplitude spectrum we are able to distinguish different infrasound regimes with Self-Organizing maps (SOMs). SOMs allow to color-code the results for an intuitive interpretation and evidence the presence of transitional activity regimes. We define a reference data set from multiple months of infrasound waveforms to include as many activity regimes as possible to train the SOM. This enables a straight forward interpretation of new data.

How to cite: Eckel, F., Langer, H., and Sciotto, M.: Identification of Infrasound Regimes at Mount Etna using Pattern Recognition Techniques, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-7603, https://doi.org/10.5194/egusphere-egu21-7603, 2021.

EGU21-9670 | vPICO presentations | ITS4.3/NH1

Machine Learning Classification of Cohen's Class Time-Frequency Representations of Non-Stationary Signals: Effects on Earthquake Detection

Marko Njirjak, Erik Otović, Dario Jozinović, Jonatan Lerga, Goran Mauša, Alberto Michelini, and Ivan Štajduhar

The analysis of non-stationary signals is often performed on raw waveform data or on Fourier transformations of those data, i.e., spectrograms. However, the possibility of alternative time-frequency representations being more informative than spectrograms or the original data remains unstudied. In this study, we tested if alternative time-frequency representations could be more informative for machine learning classification of seismic signals. This hypothesis was assessed by training three well-established convolutional neural networks, using nine different time-frequency representations, to classify seismic waveforms as earthquake or noise. The results were compared to the base model, which was trained on the raw waveform data. The signals used in the experiment were seismogram instances from the LEN-DB seismological dataset (Magrini et al. 2020). The results demonstrate that Pseudo Wigner-Ville and Wigner-Ville time-frequency representations yield significantly better results than the base model, while Margenau-Hill performs significantly worse (P < .01). Interestingly, the spectrogram, which is often used in non-stationary signal analysis, did not yield statistically significant improvements. This research could have a notable impact in the field of seismology because the data that were previously hidden in the seismic noise are now classified more accurately. Moreover, the results might suggest that alternative time-frequency representations could be used in other fields which use non-stationary time series to extract more valuable information from the original data. The potential fields encompass different fields of geophysics, speech recognition, EEG and ECG signals, gravitational waves and so on. This, however, requires further research.

How to cite: Njirjak, M., Otović, E., Jozinović, D., Lerga, J., Mauša, G., Michelini, A., and Štajduhar, I.: Machine Learning Classification of Cohen's Class Time-Frequency Representations of Non-Stationary Signals: Effects on Earthquake Detection, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-9670, https://doi.org/10.5194/egusphere-egu21-9670, 2021.

EGU21-12142 | vPICO presentations | ITS4.3/NH1

Intra-domain and cross-domain transfer learning for time series

Erik Otović, Marko Njirjak, Dario Jozinović, Goran Mauša, Alberto Michelini, and Ivan Štajduhar

In this study, we compared the performance of machine learning models trained using transfer learning and those that were trained from scratch - on time series data. Four machine learning models were used for the experiment. Two models were taken from the field of seismology, and the other two are general-purpose models for working with time series data. The accuracy of selected models was systematically observed and analyzed when switching within the same domain of application (seismology), as well as between mutually different domains of application (seismology, speech, medicine, finance). In seismology, we used two databases of local earthquakes (one in counts, and the other with the instrument response removed) and a database of global earthquakes for predicting earthquake magnitude; other datasets targeted classifying spoken words (speech), predicting stock prices (finance) and classifying muscle movement from EMG signals (medicine).
In practice, it is very demanding and sometimes impossible to collect datasets of tagged data large enough to successfully train a machine learning model. Therefore, in our experiment, we use reduced data sets of 1,500 and 9,000 data instances to mimic such conditions. Using the same scaled-down datasets, we trained two sets of machine learning models: those that used transfer learning for training and those that were trained from scratch. We compared the performances between pairs of models in order to draw conclusions about the utility of transfer learning. In order to confirm the validity of the obtained results, we repeated the experiments several times and applied statistical tests to confirm the significance of the results. The study shows when, within the set experimental framework, the transfer of knowledge brought improvements in terms of model accuracy and in terms of model convergence rate.

Our results show that it is possible to achieve better performance and faster convergence by transferring knowledge from the domain of global earthquakes to the domain of local earthquakes; sometimes also vice versa. However, improvements in seismology can sometimes also be achieved by transferring knowledge from medical and audio domains. The results show that the transfer of knowledge between other domains brought even more significant improvements, compared to those within the field of seismology. For example, it has been shown that models in the field of sound recognition have achieved much better performance compared to classical models and that the domain of sound recognition is very compatible with knowledge from other domains. We came to similar conclusions for the domains of medicine and finance. Ultimately, the paper offers suggestions when transfer learning is useful, and the explanations offered can provide a good starting point for knowledge transfer using time series data.

How to cite: Otović, E., Njirjak, M., Jozinović, D., Mauša, G., Michelini, A., and Štajduhar, I.: Intra-domain and cross-domain transfer learning for time series, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-12142, https://doi.org/10.5194/egusphere-egu21-12142, 2021.

The International Telecommunication Union (ITU), World Meteorological Organization (WMO), and United Nations Environment Programme (UNEP) have recently partnered to establish the Focus Group on Artificial Intelligence for Natural Disaster Management (FG-AI4NDM). FG-AI4NDM is exploring the potential of AI-based algorithms to support data collection and handling, to improve modeling (i.e., reconstructions, forecasts, and projections) across spatiotemporal scales through extracting complex patterns (and gaining insights) from a growing volume of geospatial data, and to provide effective communication. To achieve these objectives, FG-AI4NDM is building an interdisciplinary, multi-stakeholder, and international community to explore specific natural disaster use cases. Special effort is made to support participation from low- and mid-income countries and those countries shown to be particularly impacted by these types of events. Here, we will explore: what is an ITU focus group, what are the objectives and planned deliverables of FG-AI4NDM, what progress has been made since its inception, and how members of the geoscience community can become involved.

How to cite: Kuglitsch, M.: ITU/WMO/UNEP Focus Group on AI for Natural Disaster Management: Introduction and call for participation, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-12791, https://doi.org/10.5194/egusphere-egu21-12791, 2021.

Natural disasters ravage the world's cities, valleys, and shores on a monthly basis. Having precise and efficient mechanisms for assessing infrastructure damage is essential to channel resources and minimize the loss of life. Using a dataset that includes labeled pre- and post- disaster satellite imagery, the xBD dataset, we train multiple convolutional neural networks to assess building damage on a per-building basis. In order to investigate how to best classify building damage, we present a highly interpretable deep-learning methodology that seeks to explicitly convey the most useful information required to train an accurate classification model. We also delve into which loss functions best optimize these models. Our findings include that ordinal-cross entropy loss is the most optimal loss function to use and that including the type of disaster that caused the damage in combination with a pre- and post-disaster image best predicts the level of damage caused. We also make progress in the realm of qualitative representations of which parts of the images that the model is using to predict damage levels, through gradient class-activation maps. Our research seeks to computationally contribute to aiding in this ongoing and growing humanitarian crisis, heightened by climate change. Specifically, it advances the study of more interpretable machine learning models, which were lacking in previous literature and are important for the understanding of not only research scientists but also operators of such technologies in underserved regions.

How to cite: Chen, T.: Interpretability in Convolutional Neural Networks for Building Damage Classification in Satellite Imagery, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-13873, https://doi.org/10.5194/egusphere-egu21-13873, 2021.

EGU21-14091 | vPICO presentations | ITS4.3/NH1

Individual Sick Fir Tree (Abies mariesii) Identification in Insect Infested Forests by Means of UAV Images and Deep Learning 

Ha Trang Nguyen, Maximo Larry Lopez Caceres, Koma Moritake, Sarah Kentsch, Hase Shu, and Yago Diez

ITS4.4/AS4.1 – Machine learning for Earth system modelling

EGU21-16087 | vPICO presentations | ITS4.4/AS4.1 | Highlight

Artificial intelligence reconstructs missing climate information

Christopher Kadow, David Hall, and Uwe Ulbrich

Historical temperature measurements are the basis of global climate datasets like HadCRUT4. This dataset contains many missing values, particularly for periods before the mid-twentieth century, although recent years are also incomplete. Here we demonstrate that artificial intelligence can skilfully fill these observational gaps when combined with numerical climate model data. We show that recently developed image inpainting techniques perform accurate monthly reconstructions via transfer learning using either 20CR (Twentieth-Century Reanalysis) or the CMIP5 (Coupled Model Intercomparison Project Phase 5) experiments. The resulting global annual mean temperature time series exhibit high Pearson correlation coefficients (≥0.9941) and low root mean squared errors (≤0.0547 °C) as compared with the original data. These techniques also provide advantages relative to state-of-the-art kriging interpolation and principal component analysis-based infilling. When applied to HadCRUT4, our method restores a missing spatial pattern of the documented El Niño from July 1877. With respect to the global mean temperature time series, a HadCRUT4 reconstruction by our method points to a cooler nineteenth century, a less apparent hiatus in the twenty-first century, an even warmer 2016 being the warmest year on record and a stronger global trend between 1850 and 2018 relative to previous estimates. We propose image inpainting as an approach to reconstruct missing climate information and thereby reduce uncertainties and biases in climate records.

From:

Kadow, C., Hall, D.M. & Ulbrich, U. Artificial intelligence reconstructs missing climate information. Nature Geoscience 13, 408–413 (2020). https://doi.org/10.1038/s41561-020-0582-5

The presentation will tell from the journey of changing an image AI to a climate research application.

How to cite: Kadow, C., Hall, D., and Ulbrich, U.: Artificial intelligence reconstructs missing climate information, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-16087, https://doi.org/10.5194/egusphere-egu21-16087, 2021.

EGU21-12253 | vPICO presentations | ITS4.4/AS4.1

Deep learning-based downscaling of seasonal forecasts over the Iberian Peninsula

Carlos Alberto Gómez-Gonzalez, Lluís Palma Garcia, Llorenç Lledó, Raul Marcos, Nube Gonzalez-Reviriego, Giulia Carella, and Albert Soret Miravet

Seasonal climate predictions can forecast the climate variability up to several months ahead and support a wide range of societal activities. The coarse spatial resolution of seasonal forecasts needs to be refined to the regional/local scale for specific applications. Statistical downscaling aims at learning empirical links between the large-scale and local-scale climate, i.e., a mapping from a low-resolution gridded variable to a higher-resolution grid.

Statistical downscaling of gridded climate variables is a task closely related to that of super-resolution in computer vision, and unsurprisingly, several deep learning-based approaches have been explored by the climate community in recent years. In this study, we downscale the SEAS5 ECMWF seasonal forecast of temperature over the Iberian Peninsula using deep convolutional networks in supervised and generative adversarial training frameworks. Additionally, we apply the traditional analog method for statistical downscaling, which assumes that similar atmospheric configurations (e.g., the predictors) lead to similar meteorological outcomes in a K-Nearest Neighbors fashion. 

The deep learning-based algorithms are trained on the UERRA gridded temperature  dataset and several ERA5 reanalysis predictor variables. Finally, we evaluate the accuracy of our deep learning-based downscaling of SEAS5 temperature and compare it to the analog downscaling and a bicubic interpolation, as the simplest baseline method.

How to cite: Gómez-Gonzalez, C. A., Palma Garcia, L., Lledó, L., Marcos, R., Gonzalez-Reviriego, N., Carella, G., and Soret Miravet, A.: Deep learning-based downscaling of seasonal forecasts over the Iberian Peninsula, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-12253, https://doi.org/10.5194/egusphere-egu21-12253, 2021.

EGU21-2046 | vPICO presentations | ITS4.4/AS4.1

Oceanographic data reconstruction using machine learning techniques

Hrvoje Kalinić, Zvonimir Bilokapić, and Frano Matić

In certain measurement endeavours spatial resolution of the data is restricted, while in others data have poor temporal resolution. Typical example of these scenarios come from geoscience where measurement stations are fixed and scattered sparsely in space which results in poor spatial resolution of acquired data. Thus, we ask if it is possible to use a portion of data as a proxy to estimate the rest of the data using different machine learning techniques. In this study, four supervised machine learning methods are trained on the wind data from the Adriatic Sea and used to reconstruct the missing data. The vector wind data components at 10m height are taken from ERA5 reanalysis model in range from 1981 to 2017 and sampled every 6 hours. Data taken from the northern part of the Adriatic Sea was used to estimate the wind at the southern part of Adriatic. The machine learning models utilized for this task were linear regression, K-nearest neighbours, decision trees and a neural network. As a measure of quality of reconstruction the difference between the true and estimated values of wind data in the southern part of Adriatic was used. The result shows that all four models reconstruct the data few hundred kilometres away with average amplitude error below 1m/s. Linear regression, K-nearest neighbours, decision trees and a neural network show average amplitude reconstruction error of 0.52, 0.91, 0.76 and 0.73, and standard deviation of 1.00, 1.42, 1.23 and 1.17, respectively. This work has been supported by Croatian Science Foundation under the project UIP-2019-04-1737.

How to cite: Kalinić, H., Bilokapić, Z., and Matić, F.: Oceanographic data reconstruction using machine learning techniques, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-2046, https://doi.org/10.5194/egusphere-egu21-2046, 2021.

In large eddy simulations (LES), the subgrid-scale effects are modeled by physics-based or data-driven methods. This work develops a convolutional neural network (CNN) to model the subgrid-scale effects of a two-dimensional turbulent flow. The model is able to capture both the inter-scale forward energy transfer and backscatter in both a priori and a posteriori analyses. The LES-CNN model outperforms the physics-based eddy-viscosity models and the previous proposed local artificial neural network (ANN) models in both short-term prediction and long-term statistics. Transfer learning is implemented to generalize the method for turbulence modeling at higher Reynolds numbers. Encoder-decoder network architecture is proposed to generalize the model to a higher computational grid resolution.

How to cite: Guan, Y., Chattopadhyay, A., Subel, A., and Hassanzadeh, P.: Stable and accurate a posteriori LES of 2D turbulence with convolutional neural networks: Backscatter analysis and generalization via transfer learning, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-402, https://doi.org/10.5194/egusphere-egu21-402, 2021.

EGU21-5507 | vPICO presentations | ITS4.4/AS4.1

Machine-Learned Preconditioners for Linear Solvers in Geophysical Fluid Flows

Jan Ackmann, Peter Düben, Tim Palmer, and Piotr Smolarkiewicz

Semi-implicit grid-point models for the atmosphere and the ocean require linear solvers that are working efficiently on modern supercomputers. The huge advantage of the semi-implicit time-stepping approach is that it enables large model time-steps. This however comes at the cost of having to solve a computationally demanding linear problem each model time-step to obtain an update to the model’s pressure/fluid-thickness field. In this study, we investigate whether machine learning approaches can be used to increase the efficiency of the linear solver.

Our machine learning approach aims at replacing a key component of the linear solver—the preconditioner. In the preconditioner an approximate matrix inversion is performed whose quality largely defines the linear solver’s performance. Embedding the machine-learning method within the framework of a linear solver circumvents potential robustness issues that machine learning approaches are often criticized for, as the linear solver ensures that a sufficient, pre-set level of accuracy is reached. The approach does not require prior availability of a conventional preconditioner and is highly flexible regarding complexity and machine learning design choices.

Several machine learning methods of different complexity from simple linear regression to deep feed-forward neural networks are used to learn the optimal preconditioner for a shallow-water model with semi-implicit time-stepping. The shallow-water model is specifically designed to be conceptually similar to more complex atmosphere models. The machine-learning preconditioner is competitive with a conventional preconditioner and provides good results even if it is used outside of the dynamical range of the training dataset.

How to cite: Ackmann, J., Düben, P., Palmer, T., and Smolarkiewicz, P.: Machine-Learned Preconditioners for Linear Solvers in Geophysical Fluid Flows, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-5507, https://doi.org/10.5194/egusphere-egu21-5507, 2021.

EGU21-1754 | vPICO presentations | ITS4.4/AS4.1

Seasonal prediction of Indian Summer Monsoon onset with machine learning

Takahito Mitsui and Niklas Boers

The prediction of the onset date of the Indian Summer Monsoon (ISM) is crucial for effective agricultural planning and water resource management on the Indian subcontinent, with more than one billion inhabitants. Existing approaches focus on extended-range to subseasonal forecasts, i.e., provide skillful predictions of the ISM onset date at horizons of 10 to 60 days. Here we propose a method for ISM onset prediction and show that it has high forecast skill at longer, seasonal time scales. The method is based on recurrent neural networks and allows for ensemble forecasts to quantify uncertainties. Our approach outperforms state-of-the-art numerical weather prediction models at comparable or longer lead times. To our knowledge, there is no statistical forecasting approach at comparable, seasonal time scales. Our results suggest that predictability of the ISM onset emerges earlier than previously assumed.

How to cite: Mitsui, T. and Boers, N.: Seasonal prediction of Indian Summer Monsoon onset with machine learning, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-1754, https://doi.org/10.5194/egusphere-egu21-1754, 2021.

EGU21-2175 | vPICO presentations | ITS4.4/AS4.1

Deep Learning for Climate Model Output Statistics

Michael Steininger, Daniel Abel, Katrin Ziegler, Anna Krause, Heiko Paeth, and Andreas Hotho

Climate models are an important tool for the assessment of prospective climate change effects but they suffer from systematic and representation errors, especially for precipitation. Model output statistics (MOS) reduce these errors by fitting the model output to observational data with machine learning. In this work, we explore the feasibility and potential of deep learning with convolutional neural networks (CNNs) for MOS. We propose the CNN architecture ConvMOS specifically designed for reducing errors in climate model outputs and apply it to the climate model REMO. Our results show a considerable reduction of errors and mostly improved performance compared to three commonly used MOS approaches.

How to cite: Steininger, M., Abel, D., Ziegler, K., Krause, A., Paeth, H., and Hotho, A.: Deep Learning for Climate Model Output Statistics, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-2175, https://doi.org/10.5194/egusphere-egu21-2175, 2021.

EGU21-4457 | vPICO presentations | ITS4.4/AS4.1

Gaussian Process Regression – A tool for improved climate index reconstructions

Marlene Klockmann and Eduardo Zorita

We present a flexible non-linear framework of Gaussian Process Regression (GPR) for the reconstruction of past climate indexes such as the Atlantic Multidecadal Variability (AMV). These reconstructions are needed because the historical observation period is too short to provide a long-term perspective on climate variability. Climate indexes can be reconstructed from proxy data (e.g. tree rings) with the help of statistical models. Previous reconstructions of climate indexes mostly used some form of linear regression methods, which are known to underestimate the true amplitude of variability and perform poorly if noisy input data is used.

We implement the machine-learning method GPR for climate index reconstruction with the goal of preserving the amplitude of past climate variability. To test the framework in a controlled environment, we create pseudo-proxies from a coupled climate model simulation of the past 2000 years. In our test environment, the GPR strongly improves the reconstruction of the AMV with respect to a multi-linear Principal Component Regression. The amplitude of reconstructed variability is very close to the true variability even if non-climatic noise is added to the pseudo-proxies. In addition, the framework can directly take into account known proxy uncertainties and fit data-sets with a variable number of records in time. Thus, the GPR framework seems to be a highly suitable tool for robust and improved climate index reconstructions.

How to cite: Klockmann, M. and Zorita, E.: Gaussian Process Regression – A tool for improved climate index reconstructions, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-4457, https://doi.org/10.5194/egusphere-egu21-4457, 2021.

EGU21-8262 | vPICO presentations | ITS4.4/AS4.1

Neural Partial Differential Equations for Simple Climate Models 

Maximilian Gelbrecht, Niklas Boers, and Jürgen Kurths

When predicting complex systems such as parts of the Earth system, one typically relies on differential equations which can often be incomplete, missing unknown influences or higher order effects. By augmenting the equations with artificial neural networks we can compensate these deficiencies. The resulting hybrid models are also known as universal differential equations. We show that this can be used to predict the dynamics of high-dimensional chaotic partial differential equations, such as the ones describing atmospheric dynamics, even when only short and incomplete training data are available. In a first step towards a hybrid atmospheric model, simplified, conceptual atmospheric models are used in synthetic examples where parts of the governing equations are replaced with artificial neural networks. The forecast horizon for these high dimensional systems is typically much larger than the training dataset, showcasing the large potential of the approach. 

How to cite: Gelbrecht, M., Boers, N., and Kurths, J.: Neural Partial Differential Equations for Simple Climate Models , EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-8262, https://doi.org/10.5194/egusphere-egu21-8262, 2021.

EGU21-8357 | vPICO presentations | ITS4.4/AS4.1

Adjusting spatial dependence of climate model outputs with Cycle-Consistent Adversarial Networks 

Bastien François, Soulivanh Thao, and Mathieu Vrac

Climate model outputs are commonly corrected using statistical univariate bias correction methods. Most of the time, those 1d-corrections do not modify the ranks of the time series to be corrected. This implies that biases in the spatial or inter-variable dependences of the simulated variables are not adjusted. Hence, over the last few years, some multivariate bias correction (MBC) methods have been developed to account for inter-variable structures, inter-site ones, or both. As proof-of-concept, we propose to adapt  a computer vision technique used for Image-to-Image translation tasks (CycleGAN) for the adjustment of spatial dependence structures of climate model projections. The proposed algorithm, named MBC-CycleGAN, aims to transfer simulated maps (seen as images) with inappropriate spatial dependence structure from climate model outputs to more realistic images with spatial properties similar to the observed ones. For evaluation purposes, the method is applied to adjust maps of temperature and precipitation from climate simulations through two cross-validation approaches. The first one is designed to assess two different post-processing schemes (Perfect Prognosis and Model Output Statistics). The second one assesses the influence of non-stationary properties of climate simulations on the performance of MBC-CycleGAN to adjust spatial dependences. Results are compared against a popular univariate bias correction method, a "quantile-mapping" method, which ignores inter-site dependencies in the correction procedure, and two state-of-the-art multivariate bias correction algorithms aiming to adjust spatial correlation structure. In comparison with these alternatives, the MBC-CycleGAN algorithm reasonably corrects spatial correlations of climate simulations for both temperature and precipitation, encouraging further research on the improvement of this approach for multivariate bias correction of climate model projections.

How to cite: François, B., Thao, S., and Vrac, M.: Adjusting spatial dependence of climate model outputs with Cycle-Consistent Adversarial Networks , EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-8357, https://doi.org/10.5194/egusphere-egu21-8357, 2021.

EGU21-9141 | vPICO presentations | ITS4.4/AS4.1

Graph Deep Learning for Long Range Forecasting

Salva Rühling Cachay, Emma Erickson, Arthur Fender C. Bucker, Ernest Pokropek, Willa Potosnak, Salomey Osei, and Björn Lütjens

Deep learning-based models have been recently shown to be competitive with, or even outperform, state-of-the-art long range forecasting models, such as for projecting the El Niño-Southern Oscillation (ENSO). However, current deep learning models are based on convolutional neural networks which are difficult to interpret and can fail to model large-scale dependencies, such as teleconnections, that are particularly important for long range projections. Hence, we propose to explicitly model large-scale dependencies with Graph Neural Networks (GNN) to enhance explainability and improve the predictive skill of long lead time forecasts.

In preliminary experiments focusing on ENSO, our GNN model outperforms previous state-of-the-art machine learning based systems for forecasts up to 6 months ahead. The explicit modeling of information flow via edges makes our model more explainable, and it is indeed shown to learn a sensible graph structure from scratch that correlates with the ENSO anomaly pattern for a given number of lead months.

 

How to cite: Rühling Cachay, S., Erickson, E., Fender C. Bucker, A., Pokropek, E., Potosnak, W., Osei, S., and Lütjens, B.: Graph Deep Learning for Long Range Forecasting, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-9141, https://doi.org/10.5194/egusphere-egu21-9141, 2021.

EGU21-11905 | vPICO presentations | ITS4.4/AS4.1

Fusing model ensembles and observations together with Bayesian neural networks

Matt Amos, Ushnish Sengupta, Scott Hosking, and Paul Young

To fuse together output from ensembles of climate models with observations, we have developed a custom Bayesian neural network that produces more accurate and uncertainty aware projections.

Ensembles of physical models are typically used to increase the accuracy of projections and quantify projective uncertainties. However, few methods for combining ensemble output consider differing model performance or similarity between models. Current weighting strategies that do, typically assume model weights are invariant in time and space though this is rarely the case in models.

Our Bayesian neural network infers spatiotemporally varying model weights, bias and uncertainty to capture that some regions or seasons are better simulated in certain models. The Bayesian neural network learns how to optimally combine multiple models in order to replicate observations and can also be used to infill gaps in historic observations. In regions of sparse observations, it infers from both the surrounding data and similar physical conditions. Although we are using a typically black box technique, the attribution of model weights and bias maintains interpretability.

We demonstrate the utility of the Bayesian neural network by using it to combine multiple chemistry climate models to produce continuous historic predictions of the total ozone column (1980-2010) and projections of total ozone column for the 21st century, both with principled uncertainty estimates. Rigorous validation shows that our Bayesian neural network predictions outperform standard methods of assimilating models.

How to cite: Amos, M., Sengupta, U., Hosking, S., and Young, P.: Fusing model ensembles and observations together with Bayesian neural networks, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-11905, https://doi.org/10.5194/egusphere-egu21-11905, 2021.

EGU21-12541 | vPICO presentations | ITS4.4/AS4.1

The Frontiers of Deep Learning for Earth System Modelling 

David Hall

This talk gives an overview of cutting-edge artificial intelligence applications and techniques for the earth-system sciences. We survey the most important recent contributions in areas including extreme weather, physics emulation, nowcasting, medium-range forecasting, uncertainty quantification, bias-correction, generative adversarial networks, data in-painting, network-HPC coupling, physics-informed neural nets, and geoengineering, amongst others. Then, we describe recent AI breakthroughs that have the potential to be of greatest benefit to the geosciences. We also discuss major open challenges in AI for science and their potential solutions. This talk is a living document, in that it is updated frequently, in order to accurately relect this rapidly changing field.

How to cite: Hall, D.: The Frontiers of Deep Learning for Earth System Modelling , EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-12541, https://doi.org/10.5194/egusphere-egu21-12541, 2021.

EGU21-1158 | vPICO presentations | ITS4.4/AS4.1

Using Machine Learning to reduce uncertainty in historical ocean temperature measurements

Stephen Haddad, Rachel Killick, Matt Palmer, and Mark Webb

Historical ocean temperature measurements are important in studying climate change due to the high proportion of heat absorbed by the ocean. These measurements come from a variety of sources, including Expendable Bathythermographs (XBTs), which are an important source of such data. Their measurements need bias corrections which are dependent on the type of XBT used, but poor metadata collection practices mean the type is often missing, increasing the measurement uncertainty and thus the uncertainty of the downstream dataset. 

 

This talk will describe efforts to fill in missing instrument type metadata using machine learning techniques so better bias corrections can be applied and the uncertainty in ocean temperature datasets reduced. I will describe the challenge arising from the nature of the dataset in applying standard ML techniques to the problem. I will also describe how we have used this project to explore the benefits of different platforms for ML and what open reproducible science looks like for Machine Learning projects.

How to cite: Haddad, S., Killick, R., Palmer, M., and Webb, M.: Using Machine Learning to reduce uncertainty in historical ocean temperature measurements, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-1158, https://doi.org/10.5194/egusphere-egu21-1158, 2021.

EGU21-10045 | vPICO presentations | ITS4.4/AS4.1

Estimating the air-sea gas transfer velocity from a statistical reconstruction of ocean turbulence observations

Giulia Carella, Leonie Esters, Martí Galí Tàpias, Carlos Gomez Gonzalez, and Raffaele Bernardello

Although the air-sea gas transfer velocity k is usually parameterized with wind speed, the so-called small-eddy model suggests a relationship between k and the ocean surface turbulence in the form of the dissipation rate of turbulent kinetic energy ε. However, available observations of ε from oceanographic cruises are spatially and temporally sparse. In this study, we use a Gaussian Process (GP) model to investigate the relationship between the observed profiles of ε and co-located atmospheric and oceanic fields from the ERA5 reanalysis. The model is then used to construct monthly maps of ε and to estimate the climatological air-sea gas transfer velocity from existing parametrizations. As an independent  validation,  the same model is also trained on EC-Earth3 outputs with the objective of reproducing the temporal and spatial patterns of turbulence kinetic energy as simulated by EC-Earth3. The ability to predict ε is instrumental to achieve better estimates of air-sea gas exchange that take into account multiple sources of upper ocean turbulence beyond wind stress.

How to cite: Carella, G., Esters, L., Galí Tàpias, M., Gomez Gonzalez, C., and Bernardello, R.: Estimating the air-sea gas transfer velocity from a statistical reconstruction of ocean turbulence observations, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-10045, https://doi.org/10.5194/egusphere-egu21-10045, 2021.

EGU21-11356 | vPICO presentations | ITS4.4/AS4.1

Using new observations and Machine Learning to improve organic sinking processes in the PlankTOM global ocean biogeochemical model 

Anna Denvil-Sommer, Corinne Le Quéré, Erik Buitenhuis, Lionel Guidi, and Jean-Olivier Irisson

A lot of effort has been put in the representation of surface ecosystem processes in global carbon cycle models, in particular through the grouping of organisms into Plankton Functional Types (PFTs) which have specific influences on the carbon cycle. In contrast, the transfer of ecosystem dynamics into carbon export to the deep ocean has received much less attention, so that changes in the representation of the PFTs do not necessarily translate into changes in sinking of particulate matter. Models constrain the air-sea CO2 flux by drawing down carbon into the ocean interior. This export flux is five times as large as the CO2 emitted to the atmosphere by human activities. When carbon is transported from the surface to intermediate and deep ocean, more CO2 can be absorbed at the surface. Therefore, even small variability in sinking organic carbon fluxes can have a large impact on air-sea CO2 fluxes, and on the amount of CO2 emissions that remain in the atmosphere.

In this work we focus on the representation of organic matter sinking in global biogeochemical models, using the PlankTOM model in its latest version representing 12 PFTs. We develop and test a methodology that will enable the systematic use of new observations to constrain sinking processes in the model. The approach is based on a Neural Network (NN) and is applied to the PlankTOM model output to test its ability to reconstruction small and large particulate organic carbon with a limited number of observations. We test the information content of geographical variables (location, depth, time of year), physical conditions (temperature, mixing depth, nutrients), and ecosystem information (CHL a, PFTs). These predictors are used in the NN to test their influence on the model-generation of organic particles and the robustness of the results. We show preliminary results using the NN approach with real plankton and particle size distribution observations from the Underwater Vision Profiler (UVP) and plankton diversity data from Tara Oceans expeditions and discuss limitations.

How to cite: Denvil-Sommer, A., Le Quéré, C., Buitenhuis, E., Guidi, L., and Irisson, J.-O.: Using new observations and Machine Learning to improve organic sinking processes in the PlankTOM global ocean biogeochemical model , EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-11356, https://doi.org/10.5194/egusphere-egu21-11356, 2021.

EGU21-11974 | vPICO presentations | ITS4.4/AS4.1

Deep learning approach to reconstruct satellite ocean color time series in the global ocean

Joana Roussillon, Ronan Fablet, Lucas Drumetz, Thomas Gorgues, and Elodie Martinez

Phytoplankton plays a key role in the carbon cycle and constitutes the basis of the marine food web. Its seasonal and interannual cycles are relatively well-known on a global scale thanks to continuous ocean color satellite observations acquired since 1997. The satellite-derived chlorophyll-a concentrations (Chl-a, a proxy of phytoplankton biomass) time series are still too short to investigate phytoplankton biomass low-frequency variability. However, it is a vital prerequisite before being able to confidently detect anthropogenic signals, as natural decadal variability can accentuate, weaken or even mask out any anthropogenic trends. Machine learning appears as a promising tool to reconstruct Chl-a past signals (including periods before satellite Chl-a era), and deep learning models seem particularly relevant to explore the spatial and/or temporal structure of the data.

Here, different neural network architectures have been tested on a 18-year satellite and re-analysis dataset to infer Chl-a from physical predictors. Their ability to reconstruct spatial and temporal (seasonal and interannual) variations on a global scale will be presented. Convolutional neural networks (CNN) better capture Chl-a spatial fields than models that do not account for the structure of the data, such as multi-layer perceptrons (MLPs). We also assess how the selection of training period may affect the reconstruction performance. This is a necessary step before being able to reconstruct any past Chl-a multi-decadal time series with confidence, which is the ultimate goal of this work.

Our study also addresses the carbon footprint associated with the use of GPU resources when training the CNN. GPUs are energy intensive, and their use in geosciences is expected to grow fast. Systematically reporting the computational energy costs in the geoscience community studies would provide an overview of models energy-efficiency on different kinds of datasets and may encourage actions to reduce consumption when possible.

How to cite: Roussillon, J., Fablet, R., Drumetz, L., Gorgues, T., and Martinez, E.: Deep learning approach to reconstruct satellite ocean color time series in the global ocean, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-11974, https://doi.org/10.5194/egusphere-egu21-11974, 2021.

EGU21-15128 | vPICO presentations | ITS4.4/AS4.1

Reconstructing Sea Surface Dynamics Using a Linear Koopman Kalman Filter

Said Ouala, Ronan Fablet, Ananda Pascual Pascual, Bertrand Chapron, Fabrice Collard, and Lucile Gaultier

Spatio-temporal interpolation applications are important in the context of ocean surface modeling. Current state-of-the-art techniques typically rely either on optimal interpolation or on model-based approaches which explicitly exploit a dynamical model. While the optimal interpolation suffers from smoothing issues making it unreliable in retrieving fine-scale variability, the selection and parametrization of a dynamical model, when considering model-based data assimilation strategies, remains a complex issue since several trade-offs between the model's complexity and its applicability in sea surface data assimilation need to be carefully addressed. For these reasons, deriving new data assimilation architectures that can perfectly exploit the observations and the current advances in signal processing, modeling and artificial intelligence is crucial.

In this work, we explore new advances in data-driven data assimilation to exploit the classical Kalman filter in the interpolation of spatio-temporal fields. The proposed algorithm is written in an end-to-end differentiable setting in order to allow for the learning of the linear dynamical model from a data assimilation cost. Furthermore, the linear model is formulated on a space of observables, rather than the space of observations, which allows for perfect replication of non-linear dynamics when considering periodic and quasi-periodic limit sets and providing a decent (short-term) forecast of chaotic ones. One of the main advantages of the proposed architecture is its simplicity since it utilises a linear representation coupled with a Kalman filter. Interestingly, our experiments show that exploiting such a linear representation leads to better data assimilation when compared to non-linear filtering techniques, on numerous applications, including the sea level anomaly reconstruction from satellite remote sensing observations.

How to cite: Ouala, S., Fablet, R., Pascual, A. P., Chapron, B., Collard, F., and Gaultier, L.: Reconstructing Sea Surface Dynamics Using a Linear Koopman Kalman Filter, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-15128, https://doi.org/10.5194/egusphere-egu21-15128, 2021.

Earth Observation (EO) satellites are drawing considerable attention in areas of water resource management, given their potential to provide unprecedented information on the condition of aquatic ecosystems. Despite ocean colours long history; water quality parameter retrievals from shallow and inland waters remains a complex undertaking. Consistent, cross-mission retrievals of the primary optical parameters using state-of-the-art algorithms are limited by the added optical complexity of these waters. Less work has acknowledged their non- or weakly optical parameter counterparts. These can be more informative than their vivid counterparts, their potential covariance would be regionally specific. Here, we introduce a multi-input, multi-output Mixture Density Network (MDN), that largely outperforms existing algorithms when applied across different bio-optical regimes in shallow and inland water bodies. The model is trained and validated using a sizeable historical database in excess of 1,000,000 samples across 38 optical and non-optical parameters, spanning 20 years across 500 surface waters in Scotland. The single network learns to predict concurrently Chlorophyll-a, Colour, Turbidity, pH, Calcium, Total Phosphorous, Total Organic Carbon, Temperature, Dissolved Oxygen and Suspended Solids from real Landsat 7, Landsat 8, and Sentinel 2 spectra. The MDN is found to fully preserve the covariances of the optical and non-optical parameters, while known one-to-many mappings within the non-optical parameters are retained. Initial performance evaluations suggest significant improvements in Chl-a retrievals from existing state-of-the-art algorithms. MDNs characteristically provide a means of quantifying the noise variance around a prediction for a given input, now pertaining to real data under a wide range of atmospheric conditions. We find this to be informative for example in detecting outlier pixels such as clouds, and may similarly be used to guide or inform future work in academic or industrial contexts. 

How to cite: Harding, J.: Unified, high resolution water quality retrievals from Earth Observation satellites, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-16181, https://doi.org/10.5194/egusphere-egu21-16181, 2021.

EGU21-7596 | vPICO presentations | ITS4.4/AS4.1 | Highlight

Global fine resolution mapping of ozone metrics through explainable machine learning

Clara Betancourt, Scarlet Stadtler, Timo Stomberg, Ann-Kathrin Edrich, Ankit Patnala, Ribana Roscher, Julia Kowalski, and Martin G. Schultz

Through the availability of multi-year ground based ozone observations on a global scale, substantial geospatial meta data, and high performance computing capacities, it is now possible to use machine learning for a global data-driven ozone assessment. In this presentation, we will show a novel, completely data-driven approach to map tropospheric ozone globally.

Our goal is to interpolate ozone metrics and aggregated statistics from the database of the Tropospheric Ozone Assessment Report (TOAR) onto a global 0.1° x 0.1° resolution grid.  It is challenging to interpolate ozone, a toxic greenhouse gas because its formation depends on many interconnected environmental factors on small scales. We conduct the interpolation with various machine learning methods trained on aggregated hourly ozone data from five years at more than 5500 locations worldwide. We use several geospatial datasets as training inputs to provide proxy input for environmental factors controlling ozone formation, such as precursor emissions and climate. The resulting maps contain different ozone metrics, i.e. statistical aggregations which are widely used to assess air pollution impacts on health, vegetation, and climate.

The key aspects of this contribution are twofold: First, we apply explainable machine learning methods to the data-driven ozone assessment. Second, we discuss dominant uncertainties relevant to the ozone mapping and quantify their impact whenever possible. Our methods include a thorough a-priori uncertainty estimation of the various data and methods, assessment of scientific consistency, finding critical model parameters, using ensemble methods, and performing error modeling.

Our work aims to increase the reliability and integrity of the derived ozone maps through the provision of scientific robustness to a data-centric machine learning task. This study hence represents a blueprint for how to formulate an environmental machine learning task scientifically, gather the necessary data, and develop a data-driven workflow that focuses on optimizing transparency and applicability of its product to maximize its scientific knowledge return.

How to cite: Betancourt, C., Stadtler, S., Stomberg, T., Edrich, A.-K., Patnala, A., Roscher, R., Kowalski, J., and Schultz, M. G.: Global fine resolution mapping of ozone metrics through explainable machine learning, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-7596, https://doi.org/10.5194/egusphere-egu21-7596, 2021.

EGU21-678 | vPICO presentations | ITS4.4/AS4.1

Inferring precipitation from atmospheric general circulation model variables

Philipp Hess and Niklas Boers

The accurate prediction of precipitation, in particular of extremes, remains a challenge for numerical weather prediction (NWP) models. A large source of error are subgrid-scale parameterizations of processes that play a crucial role in the complex, multi-scale dynamics of precipitation, but are not explicitly resolved in the model formulation. Recent progress in purely data-driven deep learning for regional precipitation nowcasting [1] and global medium-range forecasting [2] tasks has shown competitive results to traditional NWP models.
Here we follow a hybrid approach, in which explicitly resolved atmospheric variables are forecast in time by a general circulation model (GCM) ensemble and then mapped to precipitation using a deep convolutional autoencoder. A frequency-based weighting of the loss function is introduced to improve the learning with regard to extreme values.
Our method is validated against a state-of-the-art GCM ensemble using three-hourly high resolution data. The results show an improved representation of extreme precipitation frequencies, as well as comparable error and correlation statistics.
   

[1] C.K. Sønderby et al. "MetNet: A Neural Weather Model for Precipitation Forecasting." arXiv preprint arXiv:2003.12140 (2020). 
[2] S. Rasp and N. Thuerey "Purely data-driven medium-range weather forecasting achieves comparable skill to physical models at similar resolution." arXiv preprint arXiv:2008.08626 (2020).

How to cite: Hess, P. and Boers, N.: Inferring precipitation from atmospheric general circulation model variables, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-678, https://doi.org/10.5194/egusphere-egu21-678, 2021.

Wind energy is widely used in renewable energy systems but the randomness and the intermittence of the wind make its accurate prediction difficult. This study develops an advanced and reliable model for multi-step wind variability prediction using long short-term memory (LSTM) network based on deep learning neural network (DLNN). A 20 Hz Ultrasonic anemometer was positioned in northern France (LOG site) to measure the random wind variability for the duration of thirty-four days. Real-time turbulence kinetic energy is computed from the measured wind velocity components, and multi-resolution features of wind velocity and turbulent kinetic energy are used as input for the prediction model. These multi-resolution features of wind variability are extracted using one-dimensional discrete wavelet transformation. The proposed DLNN is framed to implement multi-step prediction ranging from 10 min to 48 h. For velocity prediction, the root mean square error, mean absolute error and mean absolute percentage error are 0.047 m/s, 0.19 m/s, and 11.3% respectively. These error values indicate a good reliability of the proposed DLNN for predicting wind variability. We found that the present model performs well for mid-long-term (6-24h) wind velocity prediction. The model is also good for the long-term (24-48h) turbulence kinetic energy prediction.

How to cite: Roy, S.: Multi-step wind variability prediction based on deep learning neural network, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-1487, https://doi.org/10.5194/egusphere-egu21-1487, 2021.

Precise and reliable information on the tropospheric temperature and water vapour profiles plays a key role in weather and climate studies. Among the sensors supporting the atmosphere's observation, one can distinguish the Global Navigation Satellite System Radio Occultation (RO) technique, which provides accurate and high-quality meteorological profiles of temperature, pressure and water vapour. However, external knowledge about temperature is essential to estimate other physical atmospheric parameters. Hence, to overcome the constraint of the need of a priori temperature profile for each RO event, I trained and evaluated 4 different machine learning models comprising Artificial Neural Network (ANN) and Random Forest regression algorithms, where no auxiliary meteorological data is needed. To develop the models, I employed almost 7000 RO profiles between October 2019 and June 2020 over the part of the western North Pacific in Taiwan's vicinity (110-130° E; 10-30° N). Input vectors consisted of bending angle or refractivity profiles from the Formosa Satellite‐7/Constellation Observing System for Meteorology, Ionosphere, and Climate-2 mission together with the month, hour, and latitude of the RO event. Whilst temperature, pressure and water vapour profiles derived from the modern ERA5 reanalysis and interpolated to the RO location served as the models' targets. Evaluation on the testing data set revealed a good agreement between all model outputs and ERA5 targets. Slightly better statistics were noted for ANN and refractivity inputs, however, these differences can be considered as negligible. Root mean square error (RMSE) did not exceed 2 K for the temperature, 1.5 hPa for pressure, and reached slightly more than 2.5 hPa for water vapour below 2 km altitude. Additional validation with 56 colocated radiosonde observations and operational one-dimensional variational product confirms these findings with vertically averaged RMSE of around 1.3 K, 1.0 hPa and 0.5 hPa for the temperature, pressure and water vapour, respectively.

How to cite: Lasota, E.: New machine learning approaches for tropospheric profiling based on COSMIC-2 data over Taiwan, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-2577, https://doi.org/10.5194/egusphere-egu21-2577, 2021.

EGU21-3342 | vPICO presentations | ITS4.4/AS4.1

Machine Learning Emulation of 3D Cloud Radiative Effects

David Meyer, Robin J. Hogan, Peter D. Dueben, and Shannon L. Mason

The treatment of cloud structure in radiation schemes used in operational numerical weather prediction and climate models is often greatly simplified to make them computationally affordable. Here, we propose to correct the current operational scheme ecRad – as used for operational predictions at the European Centre for Medium-Range Weather Forecasts – for 3D cloud radiative effects using computationally cheap neural networks. The 3D cloud radiative effects are learned as the difference between ecRad’s fast Tripleclouds solver that neglects 3D cloud radiative effects, and its SPeedy Algorithm for Radiative TrAnsfer through CloUd Sides (SPARTACUS) solver that includes them but increases the cost of the entire radiation scheme. We find that the emulator increases the overall accuracy for both longwave and shortwave with a negligible impact on the model’s runtime performance.

How to cite: Meyer, D., Hogan, R. J., Dueben, P. D., and Mason, S. L.: Machine Learning Emulation of 3D Cloud Radiative Effects, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-3342, https://doi.org/10.5194/egusphere-egu21-3342, 2021.

EGU21-4570 | vPICO presentations | ITS4.4/AS4.1

AI for Fast Atmospheric Chemistry

Frauke Albrecht, Felix Stiehler, Björn-Martin Sinnhuber, Stefan Versick, and Tobias Weigel

In coupled global circulation models, chemical interaction between atmospheric trace gases is modelled through dedicated atmospheric chemistry submodels. As these components tend to be computationally expensive, one is often faced with the situation to either run the models with chemistry in relatively coarse resolution, or to ignore atmospheric chemistry altogether. Here an alternative approach is presented in order to overcome the high computational costs while attaining comparable quality of results. A fully connected neural network is used to make predictions of chemical tendencies. As input data of the neural network serve chemical mixing ratios, temperature, pressure, the ozone column and the solar zenith angle, all resulting from the global numerical atmosphere-chemistry model EMAC. The time period considered is 3 month, divided in time steps of consecutive 11 hours. In total, 181 time steps are analysed, from which the first 128 are used as training data, the following 26 as validation data and the last 27 are kept for final testing. The EMAC model produces results of 110 chemicals at a horizontal grid of 160x320 and 90 vertical levels. In our preliminary approach, only 6 of these chemicals - which correspond to the chemicals describing the Chapman mechanism and the nitrogen oxides - are predicted and the analysis area is restricted to the stratosphere. Further, chemicals that are zero at 95% or more of the data points have been deleted from the input data. The results of the neural network represent the spatial patterns of the climate model data very well and are in the same order of magnitude. Spatial correlations depend on the chemical and the vertical level, but are in general >0.95 at levels where the considered variable is present. However, errors are increasing during the validation period, which is probably due to trends in the analysed data. This work presents a proof of concept that neural networks are able to predict atmospheric chemistry tendencies. Left for future work is a detailed hyperparameter tuning in order to optimize the model and the extension to longer time periods to overcome modelling problems due to seasonal trends in the data. 

How to cite: Albrecht, F., Stiehler, F., Sinnhuber, B.-M., Versick, S., and Weigel, T.: AI for Fast Atmospheric Chemistry, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-4570, https://doi.org/10.5194/egusphere-egu21-4570, 2021.

EGU21-5826 | vPICO presentations | ITS4.4/AS4.1

Use of Machine Learning algorithms in evaluating the WRF model parameter sensitivity for the simulation of tropical cyclones

Baki Harish, Sandeep Chinta, Chakravarthy Balaji, and Balaji Srinivasan

The Indian subcontinent is prone to tropical cyclones that originate in the Indian Ocean and cause widespread destruction to life and property. Accurate prediction of cyclone track, landfall, wind, and precipitation are critical in minimizing damage. The Weather Research and Forecast (WRF) model is widely used to predict tropical cyclones. The accuracy of the model prediction depends on initial conditions, physics schemes, and model parameters. The parameter values are selected empirically by scheme developers using the trial and error method, implying that the parameter values are sensitive to climatological conditions and regions. The number of tunable parameters in the WRF model is about several hundred, and calibrating all of them is highly impossible since it requires thousands of simulations. Therefore, sensitivity analysis is critical to screen out the parameters that significantly impact the meteorological variables. The Sobol’ sensitivity analysis method is used to identify the sensitive WRF model parameters. As this method requires a considerable amount of samples to evaluate the sensitivity adequately, machine learning algorithms are used to construct surrogate models trained using a limited number of samples. They could help generate a vast number of required pseudo-samples. Five machine learning algorithms, namely, Gaussian Process Regression (GPR), Support Vector Machine, Regression Tree, Random Forest, and K-Nearest Neighbor, are considered in this study. Ten-fold cross-validation is used to evaluate the surrogate models constructed using the five algorithms and identify the robust surrogate model among them. The samples generated from this surrogate model are then used by the Sobol’ method to evaluate the WRF model parameter sensitivity.

How to cite: Harish, B., Chinta, S., Balaji, C., and Srinivasan, B.: Use of Machine Learning algorithms in evaluating the WRF model parameter sensitivity for the simulation of tropical cyclones, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-5826, https://doi.org/10.5194/egusphere-egu21-5826, 2021.

EGU21-6338 | vPICO presentations | ITS4.4/AS4.1

Improving Summer Precipitation Prediction in China Using Deep Learning

Weixin Jin and Yong Luo

Summer precipitation in China exhibits considerable spatial-temporal variation with direct social and economic impact. Yet seasonal prediction remains a long-standing challenge. The dynamical models even with a 1-month lead still shows limited forecast skill over China in summer. The present study focuses on applying deep learning to summer precipitation prediction in China. We train a convolutional neural network (CNN) on seasonal retrospective forecast from forecast centres in several European countries, and subsequently use transfer learning on reanalysis and observational data of 160 stations over China. The Pearson’s correlation coefficient (PCC) and the root mean square error (RMSE) are used to evaluate the performance of precipitation forecasts. The results demonstrate that deep learning approach produces skillful forecast better than those of current state-of-the-art dynamical forecast systems and traditional statistical methods in downscaling, with PCC increasing by 0.1–0.3, at 1–3 months leads. Moreover, experiments show that the data-driven model is capable to learn the complex relationship of input atmospheric state variables from reanalysis data and precipitation from station observations, with PCC of about 0.69. Image-Occlusion technique are also performed to determine variables and  spatial features of the general circulation in the Northern Hemisphere which contribute maximally to the spatial distribution of summer precipitation in China through the automatic feature representation learning, and help evaluate the weakness of dynamic models, in order to gain a better understanding of the factors that limit the capability to seasonal prediction. It suggests that deep learning is a powerful tool suitable for both seasonal prediction and for dynamical model assessment.

How to cite: Jin, W. and Luo, Y.: Improving Summer Precipitation Prediction in China Using Deep Learning, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-6338, https://doi.org/10.5194/egusphere-egu21-6338, 2021.

EGU21-7678 | vPICO presentations | ITS4.4/AS4.1

Machine learning emulation of gravity wave drag in numerical weather forecasting

Matthew Chantry, Sam Hatfield, Peter Duben, Inna Polichtchouk, and Tim Palmer

We assess the value of machine learning as an accelerator for a kernel of an operational weather forecasting system, specifically the parameterisation of non-orographic gravity wave drag. Emulators of this scheme can be trained that produce stable and accurate results up to seasonal forecasting timescales. By training on an increased complexity version of the parameterisation scheme we build emulators that produce more accurate forecasts than the existing parameterisation scheme. Leveraging the differentiability of neural networks we generate tangent linear and adjoint versions of our parameterisation, key components in 4D-var data-assimilation. We test our tangent linear and adjoint codes within an operational-like 4D-var setup and find no degradation in skill vs hand-written tangent-linear and adjoint codes.

How to cite: Chantry, M., Hatfield, S., Duben, P., Polichtchouk, I., and Palmer, T.: Machine learning emulation of gravity wave drag in numerical weather forecasting, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-7678, https://doi.org/10.5194/egusphere-egu21-7678, 2021.

EGU21-9333 | vPICO presentations | ITS4.4/AS4.1

Machine Learning Parameterization of Mature Tropical Cyclone Boundary Layer

Le-Yi Wang and Zhe-Min Tan

Tropical cyclone (TC) is among the most destructive weather phenomena on the earth, whose structure and intensity are strongly modulated by TC boundary layer. Mesoscale model used for TC research and prediction must rely on boundary layer parameterization due to low spacial resolution. These boundary layer schemes are mostly developed on field experiments under moderate wind speed. They often underestimate the influence of shear-driven rolls and turbulences. When applied under extreme condition like TC boundary layer, significant bias will be unavoidable. In this study, a novel machine learning model—one dimensional convolutional neural network (1D-CNN)—is proposed to tackle the TC boundary layer parameterization dilemma. The 1D-CNN saves about half of the learnable parameters and accomplishes a steady improvement compared to fully-connected neural network. TC large eddy simulation outputs are used as training data of 1D-CNN, which shows strong skewness in calculated turbulent fluxes. The data skewness problem is alleviated in order to reduce 1D-CNN model bias. It is shown in an offline TC boundary layer test that our proposed model, the 1D-CNN, performs significantly better than popular schemes now utilized in TC simulations. Model performance across different scales is essential to final application. It is found that the high resolution data contains the information of low resolution data but not vise versa. The model performance on the extreme data is key to final performance on the whole dataset. Training the model on the highest resolution non-extreme data plus extreme data of different resolutions can secure the robust performance across different scales.

How to cite: Wang, L.-Y. and Tan, Z.-M.: Machine Learning Parameterization of Mature Tropical Cyclone Boundary Layer, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-9333, https://doi.org/10.5194/egusphere-egu21-9333, 2021.

EGU21-9910 | vPICO presentations | ITS4.4/AS4.1

A deep learning LSTM forecasting approach for renewable energy systems

Petrina Papazek and Irene Schicker

In this study, we address point-forecasting using a deep learning LSTM-approach for renewable energy systems with focus on the short- to medium-range. Hourly resolution (medium-range) as well as 10-minute resolution (nowcasting) are the anticipated forecasting frequency. The forecasting approach is applied to: (i) wind speed at 10 meters height (observation sites), (ii) wind speed at hub-height of wind turbines, and (iii) solar power forecasts for selected solar power plants.

As input to the proposed method numerical weather prediction (NWP) data, gridded observations (analysis and/or reanalysis), and point data are used. The data of studied test-cases is extracted from the Austrian TAWES system (Teilautomatische Wetterstationen, meteorological observations in 10-minute intervals),  SCADA data of wind farms, solar power output of a solar power plant, INCA's (Integrated Nowcasting through Comprehensive Analysis) gridded observation fields, reanalysis fields from Merra2 and Era5-land, as well as, NWP data from the ECMWF IFS (European Center for Medium-Range Weather Forecast’s Integrated Forecasting System). These data-sources embrace very different temporal and spatial semantics, thus, careful pre-processing was carried out. Four daily runs over the course of one year for 12 synoptic sites + 38 wind turbines + 1 solar power plant test locations are conducted.

The advantage of an LSTM architecture is that it includes recurrent steps in the ANN and, thus, is useful especially for time-series, such as meteorological observations or NWP forecasts. So far, comparatively few attempts have been made to integrate time-series with different semantics of a sensor network and physical models in one LSTM. We tackle this issue by conserving the time-steps of the delayed NWP along with their difference to recently observed time-series and, additionally, separate them into forecasting-intervals (e.g., of 3 to 12 subsequent forecasting hours being shortest in nowcasting). This enables us to employ a sequence-to-sequence LSTM based artificial neural network (ANN). The benefit of a sequence-to-sequence setup is to match an input- and output time-series in each sample, thereby, learning complex temporal relationships. To fully use the advantage of the diverse data a tailored pre- and post-processing of these heterogenous data sources in the renewable energy applications is needed.

The ANN’s results yield, in general, high forecast-skills, indicating a successful learning based on the used training data. Different combinations of inputs and processing-steps were investigated. It is shown that combining various data sources and implement an adequate pre- and post-processing yields the most promising results in the case studies (e.g.: a heuristic to estimate produced power based on the meteorological parameters and prediction of the offset to NWPs tailored to the studied location). Results are compared to traditional forecast methods and statistical methods such as a random forest and multiple-linear-regression.

How to cite: Papazek, P. and Schicker, I.: A deep learning LSTM forecasting approach for renewable energy systems, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-9910, https://doi.org/10.5194/egusphere-egu21-9910, 2021.

Aerosols sourced from combustion such as black carbon (BC) are important short-lived climate forcers whose direct radiative forcing and atmospheric lifetime depend on their morphology. These aerosols are typically fractal aggregates consisting of ~20-80 nm spheres. This complex morphology makes modeling their optical properties difficult, contributing to uncertainty in both their direct and indirect climate effects. Accurate and fast calculations of BC optical properties are needed for remote sensing inversions and for radiative forcing calculations in atmospheric models, but current methods to accurately calculate the optical properties of these aerosols such as the multi-sphere T-matrix method or generalized multiple-particle Mie Theory are computationally expensive and must be compiled in extensive data-bases off-line and then used as a look-up table. Recent advances in machine learning approaches have applied the graph convolutional neural network (GCN) to various physical science applications, demonstrating skill in generalizing beyond initial training data by exploiting and learning internal properties and interactions inherent to the larger system. Here we demonstrate for the first time that a GCN trained to predict the optical properties of numerically-generated BC fractal aggregates can accurately generalize to arbitrarily shaped aerosol particles, even over much larger aggregates than in the training dataset, providing a fast and accurate method to calculate aerosol optical properties in atmospheric models and for observational retrievals. This approach could be integrated into atmospheric models or remote sensing inversions to more realistically predict the physical properties of arbitrarily-shaped aerosol and cloud particles. In addition, GCN’s can be used to gain physical intuition on the relationship between large-scale properties (here of the radiative properties of aerosols) and small-scale interactions (here of the spheres’ positions and their interactions).

How to cite: Lamb, K. D. and Gentine, P.: Predicting the Optical Properties of Arbitrarily Shaped Black Carbon Aerosols with Graph Neural Networks, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-10035, https://doi.org/10.5194/egusphere-egu21-10035, 2021.

EGU21-11105 | vPICO presentations | ITS4.4/AS4.1

Improving Regional Rainfall Forecasts using Convolutional-Neural Networks

Andrew Barnes, Nick McCullen, and Thomas Rodding Kjeldsen

Traditional weather forecasting approaches utilize numerous numerical simulations and empirical models to produce a gridded estimate of rainfall, the cells of which often span multiple regions and struggle to capture extreme events. The approach presented here combines the power of modern meteorological forecasts from the ECMWF C3S seasonal forecasts service with convolutional neural networks (CNNs) to improve the forecasting of total monthly regional rainfall in the UK. The CNN is trained using mean sea-level pressure and 2m air temperature forecasts from the ECMWF C3S service using three lead-times: one month, three months and six months. The training is supervised using the equivalent true rainfall data provided by the CEH-GEAR (Centre for Ecology and Hydrology, gridded estimates of areal rainfall). The resulting predictions are then compared with the total monthly regional rainfall values calculated from the precipitation forecasts provided by the ECMWF C3S service. The results of this comparison show the new CNN model out-performs the ECMWF model  across all three leadtimes. This performance is calculated using the root-mean square error between the predicted rainfall values for each region and the true values calculated from the CEH-GEAR dataset. The largest gap is found at a one month leadtime where the CNN model scores a root-mean square error (RMSE) 13% lower than the ECMWF model (RMSEs: 46.5 and 53.4 respectively), the smallest gap is found at a six month leadtime where the CNN scores an RMSE only 2.2% lower than the ECMWF model (RMSEs: 48.5 and 49.6 respectively). However, these differences are exacerbated at the extremes with the CNN producing errors 26% lower than the ECMWF model at a one-month leadtime, 19% lower at a three-month leadtime and 3% at a six-month leadtime. These results are then extended to show how the CNN made the predictions and by comparing the attribution patterns of North West and South East England we are able to show a reliance on both the mean sea-level pressure to the west of the UK and the 2m air temperature to the south west of the UK and over the European continent.

How to cite: Barnes, A., McCullen, N., and Kjeldsen, T. R.: Improving Regional Rainfall Forecasts using Convolutional-Neural Networks, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-11105, https://doi.org/10.5194/egusphere-egu21-11105, 2021.

EGU21-11448 | vPICO presentations | ITS4.4/AS4.1

WeatherBench Probability: Medium-range weather forecasts with probabilistic machine learning methods.

Sagar Garg, Stephan Rasp, and Nils Thuerey

Because the atmosphere is inherently chaotic, probabilistic weather forecasts are crucial to provide reliable information. In this work, we present an extension to the WeatherBench, a benchmark dataset for medium-range, data-driven weather prediction, which was originally designed for deterministic forecasts. We add a set of commonly used probabilistic verification metrics: the spread-skill ratio, the continuous ranked probability score (CRPS) and rank histograms. Further, we compute baseline scores from the operational IFS ensemble forecast. 

Then, we compare three different methods of creating probabilistic neural network forecasts: first, using Monte-Carlo dropout during inference with a range of dropout rates; second, parametric forecasts, which optimize for the CRPS; and third, categorical forecasts, in which the probability of occurrence for specific bins is predicted. We show that plain Monto-Carlo dropout does not provide enough spread. The parametric and categorical networks, on the other hand, provide reliable forecasts, with the categorical method being more versatile.

How to cite: Garg, S., Rasp, S., and Thuerey, N.: WeatherBench Probability: Medium-range weather forecasts with probabilistic machine learning methods., EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-11448, https://doi.org/10.5194/egusphere-egu21-11448, 2021.

EGU21-12146 | vPICO presentations | ITS4.4/AS4.1

Representing chemical history for ozone time-series predictions - a method development study for deep learning models

Felix Kleinert, Lukas H. Leufen, Aurelia Lupascu, Tim Butler, and Martin G. Schultz

Machine learning techniques like deep learning gained enormous momentum in recent years. This was mainly caused by the success story of the main drivers like image and speech recognition, video prediction and autonomous driving, to name just a few.
Air pollutant forecasting models are an example, where earth system scientists start picking up deep learning models to enhance the forecast quality of time series. Almost all previous air pollution forecasts with machine learning rely solely on analysing temporal features in the observed time series of the target compound(s) and additional variables describing precursor concentrations and meteorological conditions. These studies, therefore, neglect the "chemical history" of air masses, i.e. the fact that air pollutant concentrations at a given observation site are a result of emission and sink processes, mixing and chemical transformations along the transport pathways of air.
This study develops a concept of how such factors can be represented in the recently published deep learning model IntelliO3. The concept is demonstrated with numerical model data from the WRF-Chem model because the gridded model data provides an internally consistent dataset with complete spatial coverage and no missing values.
Furthermore, using model data allows for attributing changes of the forecasting performance to specific conceptual aspects. For example, we use the 8 wind sectors (N, NE, E, SE, etc.) and circles with predefined radii around our target locations to aggregate meteorological and chemical data from the intersections. Afterwards, we feed this aggregated data into a deep neural network while using the ozone concentration of the central point's next timesteps as targets. By analysing the change of forecast quality when moving from 4-dimensional (x, y, z, t) to 3-dimensional (x, y, t or r, φ, t) sectors and thinning out the underlying model data, we can deliver first estimates of expected performance gains or losses when applying our concept to station based surface observations in future studies.

How to cite: Kleinert, F., Leufen, L. H., Lupascu, A., Butler, T., and Schultz, M. G.: Representing chemical history for ozone time-series predictions - a method development study for deep learning models, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-12146, https://doi.org/10.5194/egusphere-egu21-12146, 2021.

EGU21-12525 | vPICO presentations | ITS4.4/AS4.1

Application of Machine Learning for the operational Cloud Product of the Copernicus Satellite Sensors Sentinel-4 (S4) and TROPOMI / Sentinel-5 Precursor (S5P)

Fabian Romahn, Victor Molina Garcia, Athina Argyrouli, Ronny Lutz, and Diego Loyola

The increasing amount of Earth observation data provided by the Copernicus Satellite Sensors, the already operational Sentinel-5 Precursor (S5P) and the upcoming Sentinel-4 (S4), that has to be processed within strict near real time (NRT) requirements demands the use of new approaches to cope with this challenge.

In order to solve the inverse problems that arise in atmospheric remote sensing, usually complex radiative transfer models (RTMs) are used. These are very accurate, however also computationally very expensive and therefore often not feasible in combination with the time requirements of operational products. With the recent significant breakthroughs in machine learning, easier application through better software and more powerful hardware, the methods of this field have become very interesting as a way to improve the classical remote sensing algorithms.

In this presentation we show a general approach in order to replace the RTM of an inversion algorithm with an artificial neural network (ANN) with sufficient accuracy while at the same time increasing the performance by several orders of magnitude. The several steps, sampling and scaling of the training data, the selection of the ANN architecture and the training itself, is explained in detail. This is then demonstrated at the example of the ROCINN (Retrieval of cloud information using neural networks) algorithm for the operational cloud product of S5P. It is then shown how this approach can also be easily applied to the upcoming S4 mission and how the current algorithm for S5P can be improved by replacing or adding new physical models (e.g. for ice-clouds) in the form of ANNs.

The procedure has been continuously developed and evaluated over time and the most important results, in terms of sampling, architecture selection, activation functions and training parameters, are presented.

Finally, the huge performance benefits of using an ANN instead of the original RTM also allow for improvements in the inversion algorithm. Several ideas regarding this, e.g. global optimization techniques, are also shown.

How to cite: Romahn, F., Molina Garcia, V., Argyrouli, A., Lutz, R., and Loyola, D.: Application of Machine Learning for the operational Cloud Product of the Copernicus Satellite Sensors Sentinel-4 (S4) and TROPOMI / Sentinel-5 Precursor (S5P), EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-12525, https://doi.org/10.5194/egusphere-egu21-12525, 2021.

EGU21-211 | vPICO presentations | ITS4.4/AS4.1

KI:STE Project − AI Strategy for Earth System Data

Scarlet Stadtler, Julia Kowalski, Markus Abel, Ribana Roscher, Susanne Crewell, Benedikt Gräler, Stefan Kollet, and Martin Schultz

Artificial intelligence (AI) methods currently experience rapid development and are also used more and more frequently in environmental and Earth system sciences. To date however, this is often done in the context of isolated rather than systematic solutions. In particular, for researchers there is often a discrepancy between the requirements of a solid and technically sound environmental data analysis and the availability of modern AI methods such as deep learning. Their systematic use is not yet established in environmental and Earth system sciences.

The recently started KI:STE project bridges this gap with a dedicated strategy that combines both, the development of AI applications and a strong training and network concept, thereby covering  different relevant aspects of environmental and Earth system research. It creates the technical prerequisites to make high-performance AI applications on environmental data portable for future users and to establish environmental AI as a key technology. 

Specifically, within KI:STE an AI-platform is envisioned which unifies machine learning (ML) workflows designed to study five core Earth system topics: cloud variability, hydrology, earth surface processes, vegetation health and air quality. All of them are strongly coupled and will profit from ML, e.g. to extend locally available information into global maps, or the track the interplay of spatio-temporal variability on different scales along process cascades. Besides being already connected across disciplines in the classical sense, KI:STE aims to furthermore bridge between these different topics by jointly addressing cutting edge ML research questions beyond pure algorithmic approaches. In particular, we will put emphasize on an explainable AI approach, which itself is a yet to be explored highly relevant topic within the Earth system sciences. It has the potential to connect the interdisciplinary work on yet another level.

KI:STE will also launch an e-learning platform in order to support the usage of the AI-platform as well as to communicate the knowledge to adequately use ML techniques within the different Earth system science domains.

How to cite: Stadtler, S., Kowalski, J., Abel, M., Roscher, R., Crewell, S., Gräler, B., Kollet, S., and Schultz, M.: KI:STE Project − AI Strategy for Earth System Data, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-211, https://doi.org/10.5194/egusphere-egu21-211, 2021.

EGU21-13307 | vPICO presentations | ITS4.4/AS4.1

Exploring microphysical properties of marine boundary layer clouds through network analysis

Lucile Ricard, Athanasios Nenes, Jakob Runge, and Paraskevi Georgakaki

Aerosol-cloud interactions remain the largest uncertainty in assessments of anthropogenic climate forcing, while the complexity of these interactions require methods that enable abstractions and simplifications that allow their improved treatment in climate models. Marine boundary layer clouds are an important component of the climate system as their large albedo and spatial coverage strongly affect the planetary radiative balance. High resolution simulations of clouds provide an unprecedented understanding of the structure and behavior of these clouds in the marine atmosphere, but the amount of data is often too large and complex to be useful in climate simulations. Data reduction and inference methods provide a way that to reduce the complexity and dimensionality of datasets generated from high-resolution Large Eddy Simulations.

In this study we use network analysis, (the δ-Maps method) to study the complex interaction between liquid water, droplet number and vertical velocity in Large Eddy Simulations of Marine Boundary Layer clouds. δ-Maps identifies domains that are spatially contiguous and possibly overlapping and characterizes their connections and temporal interactions. The objective is to better understand microphysical properties of marine boundary layer clouds, and how they are impacted by the variability in aerosols. Here we will capture the dynamical structure of the cloud fields predicted by the MIMICA Large Eddy Simulation (LES) model. The networks inferred from the different simulation fields are compared between them (intra-comparisons) using perturbations in initial conditions and aerosol, using a set of four metrics. The networks are then evaluated for their differences, quantifying how much variability is inherent in the LES simulations versus the robust changes induced by the aerosol fields. 

How to cite: Ricard, L., Nenes, A., Runge, J., and Georgakaki, P.: Exploring microphysical properties of marine boundary layer clouds through network analysis, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-13307, https://doi.org/10.5194/egusphere-egu21-13307, 2021.

EGU21-13334 | vPICO presentations | ITS4.4/AS4.1

Deep Learning for Three-Dimensional Volumetric Recovery of Cloud Fields

Yael Sde-Chen, Yoav Y. Schechner, Vadim Holodovsky, and Eshkol Eytan

Clouds are a key factor in Earth's energy budget and thus significantly affect climate and weather predictions. These effects are dominated by shallow warm clouds (shown by Sherwood et al., 2014, Zelinka et al., 2020) which tend to be small and heterogenous. Therefore, remote sensing of clouds and three-dimensional (3D) volumetric reconstruction of their internal properties are of significant importance.

Recovery of the volumetric information of the clouds relies on 3D radiative transfer, that models 3D multiple scattering. This model is complex and nonlinear. Thus, inverting the model poses a major challenge and typically requires using a simplification. A common relaxation assumes that clouds are horizontally uniform and infinitely broad, leading to one-dimensional modeling. However, generally this assumption is invalid since clouds are naturally highly heterogeneous. A novel alternative is to perform cloud retrieval by developing tools of 3D scattering tomography. Then, multiple satellite images of the clouds are acquired from different points of view. For example, simultaneous multi-view radiometric images of clouds are proposed by the CloudCT project, funded by the ERC. Unfortunately, 3D scattering tomography require high computational resources. This results, in practice, in slow run times and prevents large scale analysis. Moreover, existing scattering tomography is based on iterative optimization, which is sensitive to initialization.

In this work we introduce a deep neural network for 3D volumetric reconstruction of clouds. In recent years, supervised learning using deep neural networks has led to remarkable results in various fields ranging from computer vision to medical imaging. However, these deep learning techniques have not been extensively studied in the context of volumetric atmospheric science and specifically cloud research.

We present a convolutional neural network (CNN) whose architecture is inspired by the physical nature of clouds. Due to the lack of real-world datasets, we train the network in a supervised manner using a physics-based simulator that generates realistic volumetric cloud fields. In addition, we propose a hybrid approach, which combines the proposed neural network with an iterative physics-based optimization technique.

We demonstrate the recovery performance of our proposed method in cloud fields. In a single cloud-scale, our resulting quality is comparable to state-of-the-art methods, while run time improves by orders of magnitude. In contrast to existing physics-based methods, our network offers scalability, which enables the reconstruction of wider cloud fields. Finally, we show that the hybrid approach leads to improved retrieval in a fast process.

How to cite: Sde-Chen, Y., Schechner, Y. Y., Holodovsky, V., and Eytan, E.: Deep Learning for Three-Dimensional Volumetric Recovery of Cloud Fields, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-13334, https://doi.org/10.5194/egusphere-egu21-13334, 2021.

EGU21-14485 | vPICO presentations | ITS4.4/AS4.1

Supervised and unsupervised machine-learning for automated quality control of environmental sensor data

Julius Polz, Lennart Schmidt, Luca Glawion, Maximilian Graf, Christian Werner, Christian Chwala, Hannes Mollenhauer, Corinna Rebmann, Harald Kunstmann, and Jan Bumberger

We can observe a global decrease of well maintained weather stations by meteorological services and governmental institutes. At the same time, environmental sensor data is increasing through the use of opportunistic or remote sensing approaches. Overall, the trend for environmental sensor networks is strongly going towards automated routines, especially for quality-control (QC) to provide usable data in near real-time. A common QC scenario is that data is being flagged manually using expert knowledge and visual inspection by humans. To reduce this tedious process and to enable near-real time data provision, machine-learning (ML) algorithms exhibit a high potential as they can be designed to imitate the experts actions. 

Here we address these three common challenges when applying ML for QC: 1) Robustness to missing values in the input data. 2) Availability of training data, i.e. manual quality flags that mark erroneous data points. And 3) Generalization of the model regarding non-stationary behavior of one  experimental system or changes in the experimental setup when applied to a different study area. We approach the QC problem and the related issues both as a supervised and an unsupervised learning problem using deep neural networks on the one hand and dimensionality reduction combined with clustering algorithms on the other.

We compare the different ML algorithms on two time-series datasets to test their applicability across scales and domains. One dataset consists of signal levels of 4000 commercial microwave links distributed all over Germany that can be used to monitor precipitation. The second dataset contains time-series of soil moisture and temperature from 120 sensors deployed at a small-scale measurement plot at the TERENO site “Hohes Holz”.

First results show that supervised ML provides an optimized performance for QC for an experimental system not subject to change and at the cost of a laborious preparation of the training data. The unsupervised approach is also able to separate valid from erroneous data at reasonable accuracy. However, it provides the additional benefit that it does not require manual flags and can thus be retrained more easily in case the system is subject to significant changes. 

In this presentation, we discuss the performance, advantages and drawbacks of the proposed ML routines to tackle the aforementioned challenges. Thus, we aim to provide a starting point for researchers in the promising field of ML application for automated QC of environmental sensor data.

How to cite: Polz, J., Schmidt, L., Glawion, L., Graf, M., Werner, C., Chwala, C., Mollenhauer, H., Rebmann, C., Kunstmann, H., and Bumberger, J.: Supervised and unsupervised machine-learning for automated quality control of environmental sensor data, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-14485, https://doi.org/10.5194/egusphere-egu21-14485, 2021.

EGU21-14819 | vPICO presentations | ITS4.4/AS4.1

Constraining uncertainty in projected precipitation over land with Causal Discovery

Kevin Debeire, Veronika Eyring, Peer Nowack, and Jakob Runge

EGU21-10507 | vPICO presentations | ITS4.4/AS4.1 | Highlight

Accelerating Climate Model Computation by Neural Networks: A Comparative Study

Maha Mdini, Takemasa Miyoshi, and Shigenori Otsuka

In the era of modern science, scientists have developed numerical models to predict and understand the weather and ocean phenomena based on fluid dynamics. While these models have shown high accuracy at kilometer scales, they are operated with massive computer resources because of their computational complexity.  In recent years, new approaches to solve these models based on machine learning have been put forward. The results suggested that it be possible to reduce the computational complexity by Neural Networks (NNs) instead of classical numerical simulations. In this project, we aim to shed light upon different ways to accelerating physical models using NNs. We test two approaches: Data-Driven Statistical Model (DDSM) and Hybrid Physical-Statistical Model (HPSM) and compare their performance to the classical Process-Driven Physical Model (PDPM). DDSM emulates the physical model by a NN. The HPSM, also known as super-resolution, uses a low-resolution version of the physical model and maps its outputs to the original high-resolution domain via a NN. To evaluate these two methods, we measured their accuracy and their computation time. Our results of idealized experiments with a quasi-geostrophic model [SO3] show that HPSM reduces the computation time by a factor of 3 and it is capable to predict the output of the physical model at high accuracy up to 9.25 days. The DDSM, however, reduces the computation time by a factor of 4 and can predict the physical model output with an acceptable accuracy only within 2 days. These first results are promising and imply the possibility of bringing complex physical models into real time systems with lower-cost computer resources in the future.

How to cite: Mdini, M., Miyoshi, T., and Otsuka, S.: Accelerating Climate Model Computation by Neural Networks: A Comparative Study, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-10507, https://doi.org/10.5194/egusphere-egu21-10507, 2021.

Near real-time groundwater table depth measurements are scarce over Europe, leading to challenges in monitoring groundwater resources at the continental scale. In this study, we leveraged knowledge learned from simulation results by Long Short-Term Memory (LSTM) networks to estimate monthly groundwater table depth anomaly (wtda) data over Europe. The LSTM networks were trained, validated, and tested at individual pixels on anomaly data derived from daily integrated hydrologic simulation results over Europe from 1996 to 2016, with a spatial resolution of 0.11° (Furusho-Percot et al., 2019), to predict monthly wtda based on monthly precipitation anomalies (pra) and soil moisture anomalies (θa). Without additional training, we directly fed the networks with averaged monthly pra and θa data from 1996 to 2016 obtained from commonly available observational datasets and reanalysis products, and compared the network outputs with available borehole in situ measured wtda. The LSTM network estimates show good agreement with the in situ observations, resulting in Pearson correlation coefficients of regional averaged wtda data in seven PRUDENCE regions ranging from 42% to 76%, which are ~ 10% higher than the original simulation results except for the Iberian Peninsula. Our study demonstrates the potential of LSTM networks to transfer knowledge from simulation to reality for the estimation of wtda over Europe. The proposed method can be used to provide spatiotemporally continuous information at large spatial scales in case of sparse ground-based observations, which is common for groundwater table depth measurements. Moreover, the results highlight the advantage of combining physically-based models with machine learning techniques in data processing.

 

Reference:

Furusho-Percot, C., Goergen, K., Hartick, C., Kulkarni, K., Keune, J. and Kollet, S. (2019). Pan-European groundwater to atmosphere terrestrial systems climatology from a physically consistent simulation. Scientific Data, 6(1).

How to cite: Ma, Y., Montzka, C., Bayat, B., and Kollet, S.: Knowledge transfer from simulation to reality via Long Short-Term Memory networks:  Estimating groundwater table depth anomalies over Europe, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-590, https://doi.org/10.5194/egusphere-egu21-590, 2021.

EGU21-647 | vPICO presentations | ITS4.4/AS4.1

A toy model to investigate stability of AI-based dynamical systems

Blanka Balogh, David Saint-Martin, and Aurélien Ribes

The development of atmospheric parameterizations based on neural networks is often hampered by numerical instability issues. Previous attempts to replicate these issues in a toy model have proven ineffective. We introduce a new toy model for atmospheric dynamics, which consists in an extension of the Lorenz'63 model to a higher dimension. While neural networks trained on a single orbit can easily reproduce the dynamics of the Lorenz'63 model, they fail to reproduce the dynamics of the new toy model, leading to unstable trajectories. Instabilities become more frequent as the dimension of the new model increases, but are found to occur even in very low dimension. Training the neural network on a different learning sample, based on Latin Hypercube Sampling, solves the instability issue. Our results suggest that the design of the learning sample can significantly influence the stability of dynamical systems driven by neural networks.

How to cite: Balogh, B., Saint-Martin, D., and Ribes, A.: A toy model to investigate stability of AI-based dynamical systems, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-647, https://doi.org/10.5194/egusphere-egu21-647, 2021.

EGU21-783 | vPICO presentations | ITS4.4/AS4.1

Self-validating deep learning of continental hydrology through satellite gravimetry and altimetry

Christopher Irrgang, Jan Saynisch-Wagner, Robert Dill, Eva Boergens, and Maik Thomas

Space-borne observations of terrestrial water storage (TWS) are an essential ingredient for understanding the Earth's global water cycle, its susceptibility to climate change, and for risk assessments of ecosystems, agriculture, and water management. However, the complex distribution of water masses in rivers, lakes, or groundwater basins remains elusive in coarse-resolution gravimetry observations. We combine machine learning, numerical modeling, and satellite altimetry to build and train a downscaling neural network that recovers simulated TWS from synthetic space-borne gravity observations. The neural network is designed to adapt and validate its training progress by considering independent satellite altimetry records. We show that the neural network can accurately derive TWS anomalies in 2019 after being trained over the years 2003 to 2018. Specifically for validated regions in the Amazonas, we highlight that the neural network can outperform the numerical hydrology model used in the network training.

https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2020GL089258

How to cite: Irrgang, C., Saynisch-Wagner, J., Dill, R., Boergens, E., and Thomas, M.: Self-validating deep learning of continental hydrology through satellite gravimetry and altimetry, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-783, https://doi.org/10.5194/egusphere-egu21-783, 2021.

EGU21-2401 | vPICO presentations | ITS4.4/AS4.1

The SWAG solution for probabilistic predictions with a single neural network

Yann Haddad, Michaël Defferrard, and Gionata Ghiggi

Ensemble predictions are essential to characterize the forecast uncertainty and the likelihood of an event to occur. Stochasticity in predictions comes from data and model uncertainty. In deep learning (DL), data uncertainty can be approached by training an ensemble of DL models on data subsets or by performing data augmentations (e.g., random or singular value decomposition (SVD) perturbations). Model uncertainty is typically addressed by training a DL model multiple times from different weight initializations (DeepEnsemble) or by training sub-networks by dropping weights (Dropout). Dropout is cheap but less effective, while DeepEnsemble is computationally expensive.

We propose instead to tackle model uncertainty with SWAG (Maddox et al., 2019), a method to learn stochastic weights—the sampling of which allows to draw hundreds of forecast realizations at a fraction of the cost required by DeepEnsemble. In the context of data-driven weather forecasting, we demonstrate that the SWAG ensemble has i) better deterministic skills than a single DL model trained in the usual way, and ii) approaches deterministic and probabilistic skills of DeepEnsemble at a fraction of the cost. Finally, multiSWAG (SWAG applied on top of DeepEnsemble models) provides a trade-off between computational cost, model diversity, and performance.

We believe that the method we present will become a common tool to generate large ensembles at a fraction of the current cost. Additionally, the possibility of sampling DL models allows the design of data-driven/emulated stochastic model components and sub-grid parameterizations.

Reference

Maddox W.J, Garipov T., Izmailov P., Vetrov D., Wilson A. G., 2019: A Simple Baseline for Bayesian Uncertainty in Deep Learning. arXiv:1902.02476

How to cite: Haddad, Y., Defferrard, M., and Ghiggi, G.: The SWAG solution for probabilistic predictions with a single neural network, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-2401, https://doi.org/10.5194/egusphere-egu21-2401, 2021.

EGU21-2681 | vPICO presentations | ITS4.4/AS4.1

Deep Learning on the sphere for weather/climate applications

Michaël Defferrard, Wentao Feng, Natalie Bolón Brun, Icíar Lloréns Jover, and Gionata Ghiggi

Deep Learning (DL) has the potential to revolutionize numerical weather predictions (NWP) and climate simulations by improving model components and reducing computing time, which could then be used to increase the resolution or the number of simulations. Unfortunately, major progress has been hindered by difficulties in interfacing DL with conventional models because of i) programming language barriers, ii) difficulties in reaching stable online coupling with models, and iii) the inability to exploit the horizontal spatial information as classical convolutional neural networks can’t be used on spherical unstructured grids.

We present a solution to perform spatial convolutions directly on the unstructured grids of NWP models. Our convolution and pooling operations work on any pixelization of the sphere (e.g., Gauss-Legendre, icosahedral, cubed-sphere) provided a mesh or the pixel’s locations. Moreover, our solution allows mixing data from different grids and scales linearly with the number of pixels, allowing it to ingest millions of inputs from 3D spherical fields.

We show that a proper treatment of the spherical topology and geometry of the Earth (as opposed to a projection to the plane, cylinder, or cube) i) yields geometric constraints that provide generalization guarantees (i.e., the learned function does not depend on its localization on the Earth), and ii) induces prior biases that facilitate learning. We demonstrate that doing so improves prediction performance at no computational overhead for data-driven weather forecasting. We trained autoregressive ResUNets on five spherical samplings, covering those adopted by the major meteorological centers.

We believe that the proposed solution can find immediate use for post-processing (e.g., bias correction and downscaling), model error corrections, linear solvers pre-conditioning, model components emulation, sub-grid parameterizations, and many more applications. To that end, we provide open-source and easy-to-use code accompanied by tutorials.

How to cite: Defferrard, M., Feng, W., Bolón Brun, N., Lloréns Jover, I., and Ghiggi, G.: Deep Learning on the sphere for weather/climate applications, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-2681, https://doi.org/10.5194/egusphere-egu21-2681, 2021.

EGU21-4007 | vPICO presentations | ITS4.4/AS4.1

Using machine learning to correct model error in data assimilation and forecast applications

Alban Farchi, Patrick Laloyaux, Massimo Bonavita, and Marc Bocquet

Recent developments in machine learning (ML) have demonstrated impressive skills in reproducing complex spatiotemporal processes. However, contrary to data assimilation (DA), the underlying assumption behind ML methods is that the system is fully observed and without noise, which is rarely the case in numerical weather prediction. In order to circumvent this issue, it is possible to embed the ML problem into a DA formalism characterised by a cost function similar to that of the weak-constraint 4D-Var (Bocquet et al., 2019; Bocquet et al., 2020). In practice ML and DA are combined to solve the problem: DA is used to estimate the state of the system while ML is used to estimate the full model. 

In realistic systems, the model dynamics can be very complex and it may not be possible to reconstruct it from scratch. An alternative could be to learn the model error of an already existent model using the same approach combining DA and ML. In this presentation, we test the feasibility of this method using a quasi geostrophic (QG) model. After a brief description of the QG model model, we introduce a realistic model error to be learnt. We then asses the potential of ML methods to reconstruct this model error, first with perfect (full and noiseless) observation and then with sparse and noisy observations. We show in either case to what extent the trained ML models correct the mid-term forecasts. Finally, we show how the trained ML models can be used in a DA system and to what extent they correct the analysis.

Bocquet, M., Brajard, J., Carrassi, A., and Bertino, L.: Data assimilation as a learning tool to infer ordinary differential equation representations of dynamical models, Nonlin. Processes Geophys., 26, 143–162, 2019

Bocquet, M., Brajard, J., Carrassi, A., and Bertino, L.: Bayesian inference of chaotic dynamics by merging data assimilation, machine learning and expectation-maximization, Foundations of Data Science, 2 (1), 55-80, 2020

Farchi, A., Laloyaux, P., Bonavita, M., and Bocquet, M.: Using machine learning to correct model error in data assimilation and forecast applications, arxiv:2010.12605, submitted. 

How to cite: Farchi, A., Laloyaux, P., Bonavita, M., and Bocquet, M.: Using machine learning to correct model error in data assimilation and forecast applications, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-4007, https://doi.org/10.5194/egusphere-egu21-4007, 2021.

The water cycle connects many essential parts of the environment and is a key process supporting life on Earth. Amid climate change impacts and competing water consumptions from a growing population, there is a need for better management of this scarce resource. Yet, water management is complex. As a resource, water exists under various forms, from water droplets in the atmosphere to embodied water in consumer products. Its flows and existence transcend national and geographic borders; its management, however, are limited by boundaries. To date, machine learning has shown potentials in applications across domains, from showing skills in game plays to improving efficiencies and operation of real-life processes. The system-of-systems perspective has emerged in many fields as an attempt to capture the complexity arising from individual components. Within a system, the interactions and interdependencies across components can produce unintended consequences. Moreover, their effects that are not explainable just from studying a component on its own. Its concept intertwines with Complexity Science, and points to Wicked Problems, solutions of which are difficult to find and achieve. Climate change itself has been recognised as a ‘Super Wicked’ problem, for which deadlines are approaching but for which there are no clear solutions. Yet, there is often a lack of understanding of the interactions and dependencies, even from a physical modelling perspective. A comprehensive approach to capturing these interactions is through physical modelling of water processes, such as hydraulics and hydrological modelling. The structure and data pipelines of such an approach, nevertheless, is static and does not evolve unless reconfigured by model experts. 

We propose that a form of machine learning, Deep Reinforcement Learning, can be used to better capture the complex whole system interactions of components in the water cycle and assist in their management. This approach capitalises the rapid advances of Machine Learning in environmental applications and differ to traditional optimisation techniques in that it provides distributed learning, consistent models for components that can evolve to connect and continuously adapt to the operating environment. This is key in capturing the changes brought about by climate change and the subsequent environmental and human change in response.

1. Reinforcement Learning for improving process modelling to produce a spectrum of fully physical models for hybrid physical-neural networks to full Deep Learning models that can mimic the natural processes of interest, such as streamflows or rainfall-runoff. An example case study could be a hydrological model of a river catchment and its upstream-downstream dam operation. The components in this case can be individual reservoir models, neural network-based emulators, or differential equation models.

2. Reinforcement Learning for holistic modelling of physical processes in water managemen to capture the whole system. Since each component is modelled as a full or hybrid physical-neural network model, the components could be integrated to provide a whole system approach. Within this, Reinforcement Learning can act as the constructor or go beyond this to provide solutions for targeted problems.


 

How to cite: Hoang, L.: Reinforcement Learning for a system-of-systems approach in water management, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-7484, https://doi.org/10.5194/egusphere-egu21-7484, 2021.

EGU21-8279 | vPICO presentations | ITS4.4/AS4.1

Comparing machine learning metamodels of different scale for pasture nitrogen response rate prediction

Christos Pylianidis, Val Snow, Hiske Overweg, and Ioannis N. Athanasiadis

In this work we compare the performance of machine learning metamodels of different scale for the prediction of pasture grass nitrogen response rate using a case study across different locations in New Zealand. We first used a range of soil, plant and management parameters known to affect grass growth and/or nitrogen response. These generated a complete factorial that enabled us to run virtual nitrogen response rate experiments, using the APSIM simulation model, in eight locations around the country. We included 40 years of weather data to capture the effect of weather variability on response rate. This created a large database with which to train machine learning models. We created local, regional, and nation-wide models using Random Forest and tested them on known and unknown locations. To evaluate the models, we first calculated the RMSE, MAE and R2 and then determined if the distributions of the predictions were statistically different using the Mann-Whitney U test. Finally, we explore the generalizability of the models using the error metrics and the results of the statistical test.

How to cite: Pylianidis, C., Snow, V., Overweg, H., and Athanasiadis, I. N.: Comparing machine learning metamodels of different scale for pasture nitrogen response rate prediction, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-8279, https://doi.org/10.5194/egusphere-egu21-8279, 2021.

EGU21-8746 | vPICO presentations | ITS4.4/AS4.1

Application of machine learning as a gap-filling tool for satellite land surface temperature

Isaac Newton Buo, Valentina Sagris, and Jaak Jaagus

The frequency of heatwave events has increased in recent decades because of global warming. Satellite observed Land Surface Temperature (LST) is a widely used parameter for assessing heatwaves. It provides a wide spatial coverage compared to surface air temperature measured at weather stations. However, LST quality is limited by cloud contamination. Because heatwaves have a limited temporal frame, having a full and cloud-free complement of LST for that period is necessary.  We explore gap filling of LST using other spatial features like land cover, elevation and vegetation indices in a machine learning approach. We use a seamless open and free daily vegetation index  product which is paramount to the success of our study.  We create a Random Forest model that provides a ranking of features relevant for predicting LST. Our model is used in filling gaps in Moderate Resolution Imaging Spectroradiometer (MODIS) over three heat wave periods in different summers in Estonia. We compare the output of our model to an established spatiotemporal gap filling algorithm and with in-situ measured temperature to validate the predictive capability of our model. Our findings validate machine learning as a suitable tool for filling gaps in satellite LST and very useful when short time frames are of interest. In addition, we acknowledge that while time is an important factor in predicting LST, additional information on vegetation can improve the predictions of a model.

How to cite: Buo, I. N., Sagris, V., and Jaagus, J.: Application of machine learning as a gap-filling tool for satellite land surface temperature, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-8746, https://doi.org/10.5194/egusphere-egu21-8746, 2021.

EGU21-12814 | vPICO presentations | ITS4.4/AS4.1

Nowcasting heavy precipitation over the Netherlands using a 13-year radar archive: a machine learning approach

Eva van der Kooij, Marc Schleiss, Riccardo Taormina, Francesco Fioranelli, Dorien Lugt, Mattijn van Hoek, Hidde Leijnse, and Aart Overeem

Accurate short-term forecasts, also known as nowcasts, of heavy precipitation are desirable for creating early warning systems for extreme weather and its consequences, e.g. urban flooding. In this research, we explore the use of machine learning for short-term prediction of heavy rainfall showers in the Netherlands.

We assess the performance of a recurrent, convolutional neural network (TrajGRU) with lead times of 0 to 2 hours. The network is trained on a 13-year archive of radar images with 5-min temporal and 1-km spatial resolution from the precipitation radars of the Royal Netherlands Meteorological Institute (KNMI). We aim to train the model to predict the formation and dissipation of dynamic, heavy, localized rain events, a task for which traditional Lagrangian nowcasting methods still come up short.

We report on different ways to optimize predictive performance for heavy rainfall intensities through several experiments. The large dataset available provides many possible configurations for training. To focus on heavy rainfall intensities, we use different subsets of this dataset through using different conditions for event selection and varying the ratio of light and heavy precipitation events present in the training data set and change the loss function used to train the model.

To assess the performance of the model, we compare our method to current state-of-the-art Lagrangian nowcasting system from the pySTEPS library, like S-PROG, a deterministic approximation of an ensemble mean forecast. The results of the experiments are used to discuss the pros and cons of machine-learning based methods for precipitation nowcasting and possible ways to further increase performance.

How to cite: van der Kooij, E., Schleiss, M., Taormina, R., Fioranelli, F., Lugt, D., van Hoek, M., Leijnse, H., and Overeem, A.: Nowcasting heavy precipitation over the Netherlands using a 13-year radar archive: a machine learning approach, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-12814, https://doi.org/10.5194/egusphere-egu21-12814, 2021.

EGU21-13409 | vPICO presentations | ITS4.4/AS4.1

Learning Soil Freeze Characteristic Curves with Universal Differential Equations

Brian Groenke, Moritz Langer, Guillermo Gallego, and Julia Boike

Permafrost thaw is considered one of the major climate feedback processes and is currently a significant source of uncertainty in predicting future climate states. Coverage of in-situ meteorological and land-surface observations is sparse throughout the Arctic, making it difficult to track the large-scale evolution of the Arctic surface and subsurface energy balance. Furthermore, permafrost thaw is a highly non-linear process with its own feedback mechanisms such as thermokarst and thermo-erosion. Land surface models, therefore, play an important role in our ability to understand how permafrost responds to the changing climate. There is also a need to quantify freeze-thaw cycling and the incomplete freezing of soil at depth (talik formation). One of the key difficulties in modeling the Arctic subsurface is the complexity of the thermal regime during phase change under freezing or thawing conditions. Modeling heat conduction with phase change accurately requires estimation of the soil freeze characteristic curve (SFCC) which governs the change in soil liquid water content with respect to temperature and depends on the soil physical characteristics (texture). In this work, we propose a method for replacing existing brute-force approximations of the SFCC in the CryoGrid 3 permafrost model with universal differential equations, i.e. differential equations that include one or more terms represented by a universal approximator (e.g. a neural network). The approximator is thus tasked with inferring a suitable SFCC from available soil temperature, moisture, and texture data. We also explore how remote sensing data might be used with universal approximators to extrapolate soil freezing characteristics where in-situ observations are not available.

How to cite: Groenke, B., Langer, M., Gallego, G., and Boike, J.: Learning Soil Freeze Characteristic Curves with Universal Differential Equations, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-13409, https://doi.org/10.5194/egusphere-egu21-13409, 2021.

EGU21-13729 | vPICO presentations | ITS4.4/AS4.1

Towards machine learning for the estimation of wildfire risk from weather and sociological data

Jonas Pilot, Thanh Binh Bui, and Niklas Boers

Estimating the probability of a wildfire occurring at a specific location on a given day comes with the challenge that it not only depends to a high degree on weather conditions and soil moisture, but also on the presence of an ignition source [1]. A commonly used index to assess wildfire risks is the Canadian Fire Weather Index [2], which does, however, not model the presence of an ignition source. 

We develop a machine learning model which discriminates between (1) the probability of a wildfire occurring given an ignition source, and (2) the probability of an ignition source being present, and inferences both. We first demonstrate the performance of our approach by estimating these probabilities on simulated data. With these simulations, we also assess the robustness of our model to machine learning-related challenges that arise with wildfire data, such as extreme class imbalance and label uncertainty. We then show the performance of our model trained on satellite-derived global wildfire occurrences between 2001 and 2017. The dataset FireTracks, which includes a comprehensive record of wildfire occurrences [3], is used as ground truth. Input features include weather data (ERA5 [4]) and population densities (GPW4 [5]). Finally we compare wildfire risk ratings computed with the Canadian Fire Weather Index to the probabilities estimated by our model.

References
[1] K. Rao et al., SAR-enhanced mapping of live fuel moisture content, Remote Sensing of Environment, 2020. 
[2] R. D. Field et al., Development of a Global Fire Weather Database. Natural Hazards and Earth System Sciences, 2015. 
[3] D. Traxl, FireTracks Scientific Dataset, 2021. (https://github.com/dominiktraxl/firetracks) 
[4] H. Hersbach et al., ERA5 hourly data on single levels from 1979 to present, Copernicus Climate Change Service (C3S) Climate Data Store (CDS), 2018. 
[5] Center for International Earth Science Information Network - CIESIN - Columbia University, Gridded Population of the World, Version 4 (GPWv4): Population Density, NASA Socioeconomic Data and Applications Center (SEDAC), 2016.

How to cite: Pilot, J., Bui, T. B., and Boers, N.: Towards machine learning for the estimation of wildfire risk from weather and sociological data, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-13729, https://doi.org/10.5194/egusphere-egu21-13729, 2021.

EGU21-14183 | vPICO presentations | ITS4.4/AS4.1

Machine learning to estimate surface roughness from satellite images

Abhilash Singh and Kumar Gaurav

Soil surface attributes (mainly surface roughness and soil moisture) play a critical role in land-atmosphere interaction and have several applications in agriculture, hydrology, meteorology, and climate change studies. This study explores the potential of different machine learning algorithms (Support Vector Regression (SVR), Gaussian Process Regression (GPR), Generalised Regression Neural Network (GRNN), Binary Decision Tree (BDT), Bragging Ensemble Learning, and Boosting Ensemble Learning) to estimate the surface soil roughness from Synthetic Aperture Radar (SAR) and optical satellite images in an alluvial megafan of the Kosi River in northern India. In a field campaign during 10-21 December 2019, we measured the surface soil roughness at 78 different locations using a mechanical pin-meter. The average value of the in-situ surface roughness is 1.8 cm. Further, at these locations, we extract the multiple features (backscattering coefficients, incidence angle, Normalised Difference Vegetation Index, and surface elevation) from Sentinel-1 A/B, LANDSAT-8 and SRTM data. We then trained and evaluated (in 60:40 ratio) the performance of all the regression-based machine learning techniques. 

We found that SVR method performs exceptionally well over other methods with (R= 0.74, RMSE=0.16 cm, and MSE=0.025 cm2). To ensure a fair selection of machine learning techniques, we have calculated some additional criteria that include Akaike’s Information Criterion (AIC), corrected AIC and Bayesian Information Criterion (BIC). On comparing, we observed that SVR exhibits the lowest values of AIC, corrected AIC and BIC amongst all other methods, indicating best goodness-of-fit.

How to cite: Singh, A. and Gaurav, K.: Machine learning to estimate surface roughness from satellite images, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-14183, https://doi.org/10.5194/egusphere-egu21-14183, 2021.

EGU21-14386 | vPICO presentations | ITS4.4/AS4.1

Analysing Temporal Effects on Classification of SAR and Optical Images

Ahmet Batuhan Polat, Ozgun Akcay, and Fusun Balik Sanli

Obtaining high accuracy in land cover classification is a non-trivial problem in geosciences for monitoring urban and rural areas. In this study, different classification algorithms were tested with different types of data, and besides the effects of seasonal changes on these classification algorithms and the evaluation of the data used are investigated. In addition, the effect of increasing classification training samples on classification accuracy has been revealed as a result of the study. Sentinel-1 Synthetic Aperture Radar (SAR) images and Sentinel-2 multispectral optical images were used as datasets. Object-based approach was used for the classification of various fused image combinations. The classification algorithms Support Vector Machines (SVM), Random Forest (RF) and K-Nearest Neighborhood (kNN) methods were used for this process. In addition, Normalized Difference Vegetation Index (NDVI) was examined separately to define the exact contribution to the classification accuracy.  As a result, the overall accuracies were compared by classifying the fused data generated by combining optical and SAR images. It has been determined that the increase in the number of training samples improve the classification accuracy. Moreover, it was determined that the object-based classification obtained from single SAR imagery produced the lowest classification accuracy among the used different dataset combinations in this study. In addition, it has been shown that NDVI data does not increase the accuracy of the classification in the winter season as the trees shed their leaves due to climate conditions.

How to cite: Polat, A. B., Akcay, O., and Balik Sanli, F.: Analysing Temporal Effects on Classification of SAR and Optical Images, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-14386, https://doi.org/10.5194/egusphere-egu21-14386, 2021.

EGU21-14548 | vPICO presentations | ITS4.4/AS4.1

Using high‐resolution aerial imagery and deep learning to detect tree spatio-temporal dynamics at the treeline

Mirela Beloiu, Dimitris Poursanidis, Samuel Hoffmann, Nektarios Chrysoulakis, and Carl Beierkuhnlein

Recent advances in deep learning techniques for object detection and the availability of high-resolution images facilitate the analysis of both temporal and spatial vegetation patterns in remote areas. High-resolution satellite imagery has been used successfully to detect trees in small areas with homogeneous rather than heterogeneous forests, in which single tree species have a strong contrast compared to their neighbors and landscape. However, no research to date has detected trees at the treeline in the remote and complex heterogeneous landscape of Greece using deep learning methods. We integrated high-resolution aerial images, climate data, and topographical characteristics to study the treeline dynamic over 70 years in the Samaria National Park on the Mediterranean island of Crete, Greece. We combined mapping techniques with deep learning approaches to detect and analyze spatio-temporal dynamics in treeline position and tree density. We use visual image interpretation to detect single trees on high-resolution aerial imagery from 1945, 2008, and 2015. Using the RGB aerial images from 2008 and 2015 we test a Convolution Neural Networks (CNN)-object detection approach (SSD) and a CNN-based segmentation technique (U-Net). Based on the mapping and deep learning approach, we have not detected a shift in treeline elevation over the last 70 years, despite warming, although tree density has increased. However, we show that CNN approach accurately detects and maps tree position and density at the treeline. We also reveal that the treeline elevation on Crete varies with topography. Treeline elevation decreases from the southern to the northern study sites. We explain these differences between study sites by the long-term interaction between topographical characteristics and meteorological factors. The study highlights the feasibility of using deep learning and high-resolution imagery as a promising technique for monitoring forests in remote areas.

How to cite: Beloiu, M., Poursanidis, D., Hoffmann, S., Chrysoulakis, N., and Beierkuhnlein, C.: Using high‐resolution aerial imagery and deep learning to detect tree spatio-temporal dynamics at the treeline, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-14548, https://doi.org/10.5194/egusphere-egu21-14548, 2021.

The trying of automatic creation of the Kola Peninsula geomorphological map [after Grave M.K. et al., 1971] at the morphogenetic legend’s principle was provided based on the "random forest" classification technique. As input data a several geomorphometric variables were used only (the basic variables – elevation, slope angle, curvatures etc., and the relatively rare variables including spectral terrain variables – result of the decomposition of digital elevation model into 2D Fourier series). On the training data covering only 1.3 % study area with known labels for one of thirteen probable landform types, it were carried out the reconstruction of geomorphological boundaries and the automatic creation of the geomorphological map. The accuracy of resulting map was 81 % (area’s share with the correct classification result – the same landform type that expertly way defined). This result gives increasing of accuracy over “zero accuracy” (random guessing) more than x10. In general, a large visual similarity between the expertly created geomorphological map and the one created automatically based on the known typological affiliation of the landforms of a small part of the territory is also noticeable. Mistakenly recognized affiliation to one or another genetic type of landform in 19% of tries is rather not a problem, but a good opportunity to improve the predictive power of the model by targeted search of representative morphometric variables. We emphasize - the obtained accuracy of the model is achieved only when using variables extracted entirely from the DEM and calculated fully automatically. The use of data from tectonic and surficial geology maps, maps of quaternary deposits and other data sources can significantly improve the accuracy of the classification and bring it to the level of confident use of the model in practical work. As a by-product of landform classification by the random forest method - the characteristics that are most representative of the prediction of the genetic types of the landforms of the Kola Peninsula have been identified. Almost all of them turned out to be relatively rarely used focal geomorphometric variables. More standard and familiar parameters - slopes, aspect, curvatures are not characterized by significant representativeness. The predictive power of the model was considerably increased by using the spectral characteristics of the relief (parameters of periodicity of the elevation field, calculated by the sliding window method of two-dimensional discrete Fourier transform). The obtained results, we think, convince us that the possibilities of morphometric indicators alone in general geomorphological mapping are underestimated.

The study was supported by the Russian Science Foundation (project No. 19-77-10036).

How to cite: Kharchenko, S.: Automated recognition of the landforms origin for the Kola Peninsula based on morphometric variables, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-15564, https://doi.org/10.5194/egusphere-egu21-15564, 2021.

In the recent years, prediction modelling techniques have been widely used for modelling groundwater arsenic contamination. Determining the accuracy, performance and suitability of these different algorithms such as univariate regression (UR), fuzzy model, adaptive fuzzy regression (AFR), logistic regression (LR), adaptive neuro-fuzzy inference system (ANFIS), and hybrid random forest (HRF) models still remains a challenging task. The spatial data which are available at different scales with different cell sizes. In the current study we have tried to optimize the spatial resolution for best performance of the model selecting the best spatial resolution by testing various predictive algorithms. The model’s performance was evaluated based of the values of determination coefficient (R2), mean absolute percentage error (MAPE) and root mean square error (RMSE). The outcomes of the study indicate that using 100m × 100m spatial resolution gives best performance in most of the models. The results also state HRF model performs the best than the commonly used ANFIS and LR models.

How to cite: Bindal, S.: Mapping arsenic vulnerability at different spatial scales using statistical and machine learning models , EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-15964, https://doi.org/10.5194/egusphere-egu21-15964, 2021.

EGU21-16355 | vPICO presentations | ITS4.4/AS4.1

Applying neural network for identification of land surface model parameters

Ruslan Chernyshev, Mikhail Krinitskiy, and Viktor Stepanenko

This work is devoted to development of neural networks for identification of partial differential equations (PDE) solved in the land surface scheme of INM RAS Earth System model (ESM). Atmospheric and climate models are in the top of the most demanding for supercomputing resources among research applications. Spatial resolution and a multitude of physical parameterizations used in ESMs continuously increase. Most of parameters are still poorly constrained, many of them cannot be measured directly. To optimize model calibration time, using neural networks looks a promising approach. Neural networks are already in wide use in satellite imaginary (Su Jeong Lee, et al, 2015; Krinitskiy M. et al, 2018) and for calibrating parameters of land surface models (Yohei Sawada el al, 2019). Neural networks have demonstrated high efficiency in solving conventional problems of mathematical physics (Lucie P. Aarts el al, 2001; Raissi M. et al, 2020). 

We develop a neural networks for optimizing parameters of nonlinear soil heat and moisture transport equation set. For developing we used Python3 based programming tools implemented on GPUs and Ascend platform, provided by Huawei. Because of using hybrid approach combining neural network and classical thermodynamic equations, the major purpose was finding the way to correctly calculate backpropagation gradient of error function, because model trains and is being validated on the same temperature data, while model output is heat equation parameter, which is typically not known. Neural network model has been runtime trained using reference thermodynamic model calculation with prescribed parameters, every next thermodynamic model step has been used for fitting the neural network until it reaches the loss function tolerance.

Literature:

1.     Aarts, L.P., van der Veer, P. “Neural Network Method for Solving Partial Differential Equations”. Neural Processing Letters 14, 261–271 (2001). https://doi.org/10.1023/A:1012784129883

2.     Raissi, M., P. Perdikaris and G. Karniadakis. “Physics Informed Deep Learning (Part I): Data-driven Solutions of Nonlinear Partial Differential Equations.” ArXiv abs/1711.10561 (2017): n. pag.

3.     Lee, S.J., Ahn, MH. & Lee, Y. Application of an artificial neural network for a direct estimation of atmospheric instability from a next-generation imager. Adv. Atmos. Sci. 33, 221–232 (2016). https://doi.org/10.1007/s00376-015-5084-9

4.     Krinitskiy M, Verezemskaya P, Grashchenkov K, Tilinina N, Gulev S, Lazzara M. Deep Convolutional Neural Networks Capabilities for Binary Classification of Polar Mesocyclones in Satellite Mosaics. Atmosphere. 2018; 9(11):426.

5.     Sawada, Y.. “Machine learning accelerates parameter optimization and uncertainty assessment of a land surface model.” ArXiv abs/1909.04196 (2019): n. pag.

6.     Shufen Pan et al. Evaluation of global terrestrial evapotranspiration using state-of-the-art approaches in remote sensing, machine learning and land surface modeling. Hydrol. Earth Syst. Sci., 24, 1485–1509 (2020)

7.     Chaney, Nathaniel & Herman, Jonathan & Ek, M. & Wood, Eric. (2016). Deriving Global Parameter Estimates for the Noah Land Surface Model using FLUXNET and Machine Learning: Improving Noah LSM Parameters. Journal of Geophysical Research: Atmospheres. 121. 10.1002/2016JD024821.

 

 

How to cite: Chernyshev, R., Krinitskiy, M., and Stepanenko, V.: Applying neural network for identification of land surface model parameters, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-16355, https://doi.org/10.5194/egusphere-egu21-16355, 2021.

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