This session highlights innovative and interdisciplinary approaches that strengthen resilience in water, health, and environmental systems. We emphasize the integration of hydroinformatics, numerical modelling, and emerging technologies, including artificial intelligence, remote sensing, Information and Communication Technologies (ICTs), and data assimilation—together with socio-economic and governance perspectives. Of particular interest are studies advancing attribution of hydrological and hydroclimatic extremes (floods, droughts, water quality degradation, and heatwaves) to climate change, and their cascading impacts on ecosystems and human health. Contributions are invited to demonstrate how new knowledge, innovative tools, and practices can enhance monitoring, forecasting, attribution, and decision-making to address these pressing challenges.
Key themes
• Advances in monitoring (low-cost sensors, Internet of Things), forecasting, and attribution of hydroclimatic extremes to climate change.
• Application of global datasets, field data and citizen science, and data assimilation methods for assessing climate-sensitive health risks.
• Integrated modeling frameworks to analyze compound impacts of climate variability, land use change, and ecosystem health.
• Cutting-edge hydroinformatics innovations, including physically-based model emulation, AI/ML-based decision support, and improved data assimilation for adaptive responses
• Socio-economic, policy, and governance innovations that complement technical solutions to enhance resilience in diverse contexts.
Climate change is reshaping the dynamics of waterborne pathogens, creating unprecedented challenges for public health, aquaculture, and environmental resilience. Among these, Vibrio species—emerging as sentinel organisms for climate-sensitive pathogens—illustrate the profound ecological shifts underway. Historically confined to warmer waters, vibrios are now expanding their range northward, driven by rising sea surface temperatures, altered salinity regimes, and changing ocean circulation patterns. This poleward migration is not merely an ecological curiosity; it poses tangible risks to human health through seafood consumption and recreational water exposure, and threatens aquaculture industries that sustain global food security.
Unlike conventional pathogens that can be controlled through eradication strategies, climate-sensitive pathogens such as vibrios cannot be eliminated from natural ecosystems. Their persistence and adaptability underscore the urgent need for predictive frameworks rather than reactive interventions. Here, we propose an innovative approach that leverages Earth observation systems to forecast pathogen dynamics under changing climatic conditions. Satellite-derived data on sea surface temperature, chlorophyll concentration, and salinity, combined with in-situ monitoring and advanced modeling, enable near-real-time risk assessments of pathogen proliferation. These predictive tools can inform early-warning systems, guiding public health advisories and aquaculture management before outbreaks occur.
Using vibrios as a model, we demonstrate how Earth observations can be integrated with ecological and epidemiological models to anticipate hotspots of pathogen emergence. Our analysis highlights the role of ocean warming and stratification in creating favorable conditions for vibrios, particularly in temperate regions previously considered low-risk. The northward expansion of vibrios into areas such as the North Atlantic and Baltic Sea exemplifies the cascading impacts of climate change on microbial ecology and human vulnerability. These shifts challenge traditional paradigms of disease control and demand a proactive, systems-based approach that links climate science, microbiology, and public health.
The implications extend beyond vibrios. Climate-sensitive pathogens—including enteric bacteria and viruses—are responding to the same environmental drivers, amplifying risks to water quality and food safety. By harnessing Earth observations, we can move from crisis response to anticipatory governance, building resilience in water, health, and environmental systems. This paradigm shift is critical for safeguarding communities and ecosystems in an era of accelerating climate change.
While eradication of climate-sensitive pathogens is unattainable, prediction is achievable—and essential. Earth observation technologies offer a powerful lens for understanding and forecasting pathogen behavior, enabling innovative solutions for resilient systems. The northward march of vibrios is a warning signal; our capacity to predict and prepare will determine whether it becomes a manageable challenge or a global health crisis.
How to cite: Jutla, A., Kumar, S., and Colwell, R.: Northward March of Climate-Sensitive Pathogens: Predicting the Unpredictable with Earth Observations , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-166, https://doi.org/10.5194/egusphere-egu26-166, 2026.
Timely precipitation information is essential for resilient water resources management disaster risk reduction, and climate adaptation, particularly in mountainous and data-scarce regions. While Satellite Precipitation Products (SPPs) such as IMERG Early Run (IMERG-ER) offer valuable spatial and temporal coverage, their latency of more than 4 hours limits their use for real-time applications, including flash flood early warning and operational decision-making. This study presents a hydroinformatics-based solution to bridge this critical latency gap by combining deep learning with near-time geostationary satellite observations. We developed a U-Net convolutional neural network driven by GOES-16 infrared imagery to emulate IMERG-ER precipitation fields with a latency of only minutes. The framework is applied to the Jubones river basin (3,340 km²) in the tropical Andes of Ecuador, a region characterized by complex topography and limited ground observations. The model was trained using five years (2019–2023) of GOES-16 data and evaluated across 15 spectral input configurations. Results show that a combination of water vapor (6.2, 6.9, 7.3 µm) and longwave infrared bands (8.4, 11.2 µm) yielded the best performance, effectively capturing atmospheric moisture dynamics and cloud-top characteristics. The proposed approach successfully reduced precipitation data latency from 4 hours to approximately 11 minutes. Model evaluation yielded an RMSE of 0.46 mm/h, a Pearson correlation of 0.60, and a Critical Success Index of 0.53. While performance decreased for high-intensity precipitation due to data imbalance, the model performed robustly for low-intensity precipitation (<3 mm/h), which accounts for 97% of events in the study area and is critical for hydrological monitoring and water management. Overall, the results demonstrate how integrating deep learning with geostationary satellite data can enhance near-real-time precipitation monitoring, supporting climate resilience, early warning systems, and operational hydrology in vulnerable and data-limited regions.
How to cite: Velez, M., Muñoz, P., Samaniego, E., Merizalde, M. J., and Célleri, R.: Bridging the Data Latency Gap for Real-Time Precipitation Monitoring: A U-Net CNN Approach Using GOES-16 in the Tropical Andes, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-2253, https://doi.org/10.5194/egusphere-egu26-2253, 2026.
Satellite-based water accounting is increasingly used to estimate agricultural water demand in regions facing growing pressure from climate variability, land-use change, and limited availability of in situ observations. However, most operational applications rely on spatially aggregated satellite products that implicitly assume homogeneous conditions within basins or irrigation districts, thereby overlooking the spatiotemporal structure of hydrometeorological variability and associated measurement errors. The implications of this simplification for agricultural water accounting outcomes remain insufficiently quantified.
This study evaluates how agricultural water accounting results differ when spatiotemporal variability is explicitly represented, compared to conventional approaches that apply satellite products without detailed spatial and temporal reconstruction. A comparative framework is developed and applied to the Actopan River Basin in Veracruz, Mexico, which supplies Irrigation District 035 La Antigua, a region of high agricultural relevance dominated by sugarcane cultivation. Satellite-derived precipitation and reference evapotranspiration products for the period 2018–2024 are analyzed under two contrasting methodologies: (i) a baseline approach using non-interpolated satellite data, and (ii) a high-resolution approach incorporating spatiotemporal interpolation and error characterization.
Results show that neglecting spatial and temporal variability leads to systematic differences in estimated water balance components (P–ET), with implications for the magnitude, timing, and spatial distribution of agricultural water demand. Incorporating spatiotemporal structure enables identification of localized deviations that are masked under aggregated representations and provides a more realistic basis for accounting of crop water use. The analysis further demonstrates how systematic spatial and temporal discrepancies can be characterized and learned to improve consistency in water accounting calculations.
The proposed framework highlights the importance of scale-aware methodologies in satellite-based agricultural water accounting and is transferable to data-scarce basins where decision-making increasingly depends on remotely sensed information.
How to cite: Anell Parra, X., Corzo Perez, G. A., and Ontiveros Capurata, R. E.: Assessing the Impact of Spatiotemporal Representation on Agricultural Water Accounting Using Satellite Data, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-4109, https://doi.org/10.5194/egusphere-egu26-4109, 2026.
Future climate change projections are characterised by uncertainties associated with Global Climate Models (GCMs) and emission scenario (SSPs). Different GCMs and SSPs represent key climate processes differently, yielding divergent projections rather than a single “best” future. In turn, this propagates into decision uncertainty for long-term water-resources management and planning. Climate model uncertainty analysis therefore provides a structured framework to identify, quantify, decompose, and communicate these uncertainties in water resource modelling. This helps bound plausible futures by emphasizing ranges of outcomes rather than single point estimates. This study develops an integrated framework that leverages unsupervised machine learning to characterize and quantify climate-model uncertainty for long-term water-resources management and planning. The framework integrates ranking, clustering, and scenario-discovery methods. We analyze outputs from 24 climate models from the Coupled Model Intercomparison Project Phase 6 (CMIP6) alongside observed reanalysis from the Princeton dataset. Monthly precipitation and temperature are evaluated across multiple locations within the basin to account for spatial heterogeneity. Model ranking was performed by evaluating each climate model against the observed reanalysis dataset. Performance was assessed using mean bias and percent bias, along with metrics capturing seasonality, spatial patterns, and interannual variability for basin-scale monthly temperature and precipitation. For each GCM, engineered features describing annual and seasonal change were then used for clustering. Unsupervised grouping was followed by classification based on Bayes decision theory. Within each cluster, a representative medoid was identified by minimizing the sum of Euclidean distances to all other members, yielding the most central model in that group. Cluster labels Low, Normal, and High projection were assigned by computing the percent change in simulated mean streamflow from the hydrological simulations for each climate model. Results indicate that the representative medoids are GISS-E2-1-G (Low projection), CanESM5 (Normal projection), and EC-Earth3 (Wet projection). The remaining GCMs are then probabilistically assigned to clusters with reference to these central medoids. The framework is demonstrated for the Blue Nile Basin to support long-term water-resources planning under climate uncertainty. This study extends the application of unsupervised machine learning for characterizing and quantifying climate-model uncertainty, with the objective of resilient water resource planning across multiple, dynamically evolving future possibilities.
How to cite: Sahlu, S. B., Corzo, G., Gold, D., Newton, C., and Zevenbergen, C.: Unsupervised Machine Learning to Quantify Climate-Model Uncertainty for Resilient Water Resources Planning, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-10167, https://doi.org/10.5194/egusphere-egu26-10167, 2026.
Climate extremes such as heatwaves, river floods, droughts, crop failures, including aspects of wildfires and tropical cyclones, are increasingly attributable to anthropogenic climate change. Yet how this translates into unprecedented levels of extreme event exposure in one’s lifetime remains unclear. Here we show that, neglecting adaptation, many of today’s youth will experience unprecedented exposure to extremes during their lifetimes. For the events above, the share of people facing unprecedented lifetime exposure is projected to at least double from 1960 to 2020 birth cohorts under current mitigation policies aligned with a global warming pathway reaching 2.7 °C above pre-industrial temperatures by 2100. In a 1.5 °C pathway, ∼50% of people born in 2020 will experience unprecedented lifetime exposure to heatwaves. If global warming reaches 3.5 °C by 2100, this rises 30 to ∼90% of this birth cohort. For the same cohort and warming pathway, ∼30% will live with unprecedented exposure to crop failures and ∼10% to river floods. Further, under current policies, two indicators of vulnerability show that the most vulnerable experience significantly more unprecedented exposure to heatwaves than the least vulnerable. Our results call for sustained greenhouse gas emissions reductions to lower the burden of climate change on young generations
How to cite: Thiery, W., Grant, L., Vanderkelen, I., Gudmundsson, L., Fischer, E., and Seneviratne, S. I.: Will you live an unprecedented life?, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-13966, https://doi.org/10.5194/egusphere-egu26-13966, 2026.
Flood intensification due to climate variability and urbanization necessitates advanced forecasting tools, particularly in regions undergoing rapid transformation where drainage infrastructure data is often scarce. This study presents a methodological framework for flood forecasting in the Ribeirão Anhumas watershed (Campinas, Brazil), specifically applied to the International Hub for Sustainable Development (HIDS–Unicamp). As an innovation district currently under implementation, HIDS represents a unique opportunity to integrate predictive modeling into early-stage urban planning.
The methodology addresses data scarcity by integrating physical modeling with machine learning. We have established a simulation environment using PCSWMM to replicate hydrological behavior under distinct infrastructure scenarios. These simulations, driven by high-resolution precipitation (10-min) and geospatial data (1 m DTM), generate the necessary synthetic training data for regions where sensor networks are yet to be deployed. The proposed architecture is designed to perform a binary classification of flood occurrence (Flood/No Flood), utilizing a multi-model approach: Recurrent Neural Networks (RNNs) for temporal dynamics, Convolutional Neural Networks (CNNs) for spatial patterns, and Graph Neural Networks (GNNs) to explicitly model the hydrological connectivity of the watershed.
In this contribution, we present the complete data processing pipeline and the defined model architecture. The study focuses on evaluating the comparative performance of these architectures using classification metrics (accuracy, precision, recall, F1-score, and ROC curve). Furthermore, to ensure the model is transparent for decision-makers, we outline the application of Explainable AI (XAI) techniques, specifically SHAP and LIME. These are intended to identify the contribution of input variables to flood predictions, bridging the gap between "black-box" deep learning and interpretable hydrological processes. The final results aim to demonstrate how hybrid modeling can support the strengthening of early warning systems and resilience strategies in developing urban territories.
How to cite: Pinheiro e Silva, A. E., de Araújo Figueiredo, L. F., Corzo Perez, G. A., and Dalfré Filho, J. G.: Hybrid Approach for Flood Forecasting in Urban Innovation Districts (HIDS–Unicamp): Integrating PCSWMM, Neural Networks, and Explainable Artificial Intelligence, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-11084, https://doi.org/10.5194/egusphere-egu26-11084, 2026.
Extreme urban floods increasingly threaten pedestrians, yet hazard assessments often emphasise peak inundation maps and overlook the duration and overlap of instability conditions that drive real-life exposure and operational decisions. We evaluate the spatiotemporal evolution of pedestrian hazard during a 3-h, 100-year design storm simulated with a coupled rainfall–flood (1D–2D) model for Cúcuta, Colombia (130.5 ha). Every 5 min, gridded flow velocity (V) and water depth (h) were extracted and translated into four hazard levels using widely adopted pedestrian stability indicators (V, h, and V·h). We quantify (i) the fraction of wet area in each hazard class through time (normalised by the instantaneous wet area and by the event’s maximum wet footprint) and (ii) persistence (time above thresholds per cell/sector) and simultaneity (co-occurrence of medium-high/high classes among the three indicators).
The wet footprint expands rapidly and peaks at ~60 min before draining incompletely. Velocity shows an impulsive response, with high-V corridors emerging near the rising limb and collapsing shortly after the peak, while hazardous depths persist longer and concentrate in low-drainage sectors. The combined indicator V·h delineates a critical hazard window (~40–120 min), when threshold exceedance and indicator overlap are maximised, identifying recurrent hotspots and the time intervals most relevant for pedestrian management. The proposed curve-plus-persistence framework complements peak hazard mapping by providing quantitative criteria to prioritise interventions and define operational time windows for closures and warning measures.
How to cite: Contreras Martínez, M. A., Corzo Perez, G. A., and Santos Granados, G. R.: Spatiotemporal dynamics of pedestrian flood hazard in urban areas: beyond peak inundation maps, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-14563, https://doi.org/10.5194/egusphere-egu26-14563, 2026.
A paradigm shift is taking place in the way urban rainwater drainage is thought about, with the understanding that conventional drainage structures, known as gray infrastructures, end up collecting large volumes of water on impermeable surfaces, causing problems downstream. It is therefore necessary to consider systems that favor the interception and infiltration of water into the soil so that surface runoff is treated at the point where it is generated. Such systems are known as Nature-based Solutions (NbS), which comprise a set of structures that simulate natural drainage processes. These must be carefully designed and positioned to act at points in the urban landscape that, if impermeable, would favor water accumulation. This study consists of using Machine Learning (ML) techniques to identify the NbS layout that provides the best flood protection in an area of the city of Campinas, Brazil. To this end, a model in PCSWMM software is used, which will involve the implementation of eight NbS: bio-retention cell, infiltration trench, permeable pavement, rain barrel, vegetative swale, rain garden, green roof, and rooftop disconnection. Different rainfall scenarios are simulated to assess surface runoff generation in each subcatchment. The same volume of precipitation is considered, which is temporally and spatially distributed differently in each rainfall scenario, allowing the identification of differences in the floods generated. Using a database derived from the simulation results, Artificial Neural Networks (ANN) are applied to create a predictive model of surface runoff generated in a given rainfall event. An analysis of the variability of runoff in the different subcatchments is then performed, identifying how much the source of flow generation varies spatially when the rainfall configuration is modified. The NbS are then dimensioned with the help of the rainfall configuration most likely to cause flooding. The hydrological model is simulated several times, varying the positioning and quantity of NbS throughout the subcatchments, in order to generate data that, when applied to ANN, identifies the implementation scenario that best combats flooding in the studied area. The NbS are allocated so that each scenario generates the same implementation cost according to the Brazilian price benchmark, making each scenario have the same intensity of NbS allocation. The study presents a new methodology for sizing sustainable solutions, showing how much the use of ML techniques can assist in the design process of rainwater drainage for new developments. The study area considered is the Campinas International Hub for Sustainable Development, which hosts universities and research institutions. It will be expanded over the next few years, and the implementation of NbS on site will serve as a living laboratory for students who, on a daily basis, will be able to see in practice how sustainable solutions contribute to flood control.
How to cite: de Araújo Figueirêdo, L. F., Pinheiro e Silva, A. E., Corzo Perez, G. A., and Dalfré Filho, J. G.: Use of Machine Learning techniques to identify scenarios for implementing Nature-based Solutions that best prevent flooding, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-15341, https://doi.org/10.5194/egusphere-egu26-15341, 2026.
Changing climatic conditions have led to hydroclimatic extremes, posing significant risks to water availability, agricultural productivity, and food security in climate-sensitive regions. The Brahmaputra River basin, situated in northeastern India, largely within the state of Assam, is particularly vulnerable to climate change, as rain-fed rice cultivation in this area is highly dependent on the monsoon.This study assesses historical and projected climate-yield relationships at the district level in Assam using a machine learning framework.
The present analysis utilizes hourly ECMWF ERA5 surface-level data (AgERA5), which includes agrometeorological variables such as 2 m temperature, total precipitation, and reference evapotranspiration. Agricultural drought stress has been evaluated using the Standardized Precipitation–Evapotranspiration Index (SPEI), sourced from the global SPEI database. The Expert Team on Climate Change Detection and Indices (ETCCDI) indices were employed to evaluate climate extremes, including various temperature indices (annual maximum and minimum of daily maximum and minimum temperatures: TXX, TXN, TNX, TNN), diurnal temperature range (DTR), and precipitation extremes (maximum 1-day and 5-day precipitation amounts: RX1day, RX5day).
These indices were temporally correlated with district-level rice yield data and spatially aggregated across the Upper, Middle, and Lower Brahmaputra Basin regions. Long Short-Term Memory (LSTM) neural networks were applied to capture the nonlinear and temporal relationships between agrometeorological variables, climate extremes, and rice yield variability. To account for model uncertainty, multi-model ensemble spreads from CMIP6 projections under SSP2-4.5 and SSP5-8.5 scenarios were utilized.
The study's findings indicate a warming trend throughout Assam, coupled with increasing evapotranspiration demand and declining SPEI values, signifying heightened moisture stress during the rice-growing season. Yield variability is more significantly influenced by nighttime temperature extremes (TNX and TNN) and reductions in diurnal temperature range than by midday heat extremes. Multi-day extreme rainfall events (RX5day) negatively affect yields in flood-prone areas of the Upper and Middle Brahmaputra Basin and display mixed effects in regions with comparatively limited moisture; overall, precipitation extremes show substantial spatial variability. Scenario-based projections reveal greater yield volatility and an increased risk of yield decline under SSP5-8.5 compared to SSP2-4.5. This research framework provides a scalable and practical decision-support tool to enhance early warning systems for agro-meteorological variability, support climate-resilient agricultural planning, and inform evidence-based policy development.
How to cite: Shukla, S. and Corzo, G.: Machine Learning–Based Attribution of Hydroclimatic Extremes and Agricultural Yield Risk in the Brahmaputra Basin, Assam, India under CMIP6 Scenarios, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-17392, https://doi.org/10.5194/egusphere-egu26-17392, 2026.
Intensively managed irrigation districts in arid regions pose major challenges for land surface and hydrological modeling due to strong anthropogenic disturbances and highly nonlinear soil–vegetation–atmosphere interactions. The Hetao Irrigation District (HID), one of the largest irrigated areas in northern China, exemplifies such complexities, where conventional land surface models often struggle to realistically represent soil moisture （SM） dynamics and evapotranspiration (ET) processes. In this study, we improve the performance of the Noah Land Surface Model with Multi-Parameterization options (Noah-MP) by integrating global sensitivity analysis and parameter optimization. The model was driven by long-term meteorological forcing, and dominant parameters related to soil hydraulic properties and vegetation phenology were identified as key controls on simulated soil moisture and ET. These parameters were subsequently optimized using the Shuffled Complex Evolution (SCE-UA) algorithm, jointly constrained by in-situ observations and remotely sensed SM and ET products. The calibrated model shows a consistent improvement in reproducing observed soil moisture dynamics and better captures the seasonal variability of ET associated with irrigation practices. In particular, the optimized parameter set enhances the representation of irrigation-induced soil wetting and crop growth cycles, leading to more realistic land–atmosphere exchange processes. This study highlights the importance of multi-source observational constraints and parameter sensitivity-informed calibration for land surface modeling in human-dominated environments. The proposed framework provides a transferable approach for improving hydrological simulations in heavily managed arid irrigation districts.
How to cite: Li, Q., Zhao, H., Duan, H., and Corzo Perez, G. A.: Improving Soil Moisture and Evapotranspiration Simulations in an Intensively Irrigated Arid Region Using Noah-MP, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-22011, https://doi.org/10.5194/egusphere-egu26-22011, 2026.
Posters virtual: Thu, 7 May, 14:00–18:00 | vPoster spot A
EGU26-245 | ECS | Posters virtual | VPS10
Surface Water Dynamics under Changing Climate: Integrating Multi-Sensor Satellite Observations (1999–2025) across the Falkland IslandsThu, 07 May, 14:03–14:06 (CEST) vPoster spot A
A major threat to Falkland Islands (FI) biodiversity and livelihoods is a drying climate. As warming continues, FI water security is a growing concern, but we lack baseline data to inform mitigation and adaptation. This study applies an innovative remote sensing approach to monitor long-term surface water variability across the Falkland Islands, aiming to support climate-resilient water management. Using the Google Earth Engine (GEE) platform, historical dynamics from 1999–2021 were derived from the Global Surface Water (GSW) Explorer dataset, while recent trends (2021–2025) were assessed using Harmonized Sentinel-2 MSI Level-2A imagery. Together, these enable the first continuous, multi-decadal assessment of pond, wetland, and lake dynamics across East Falkland, West Falkland, and Lafonia. Preliminary results show a relative decline in surface water extent across East Falkland, West Falkland, and Lafonia from 1999 to 2021. More recent Sentinel-2 observations reveal regionally distinct trends from 2021 to 2025: East Falkland remains relatively stable, West Falkland shows a modest increase, and Lafonia exhibits a pronounced rise with strong seasonal variability. These results align with limited ground observations from the water level monitoring site, where satellite-derived surface water area strongly correlates with recorded maximum water levels, confirming the hydrological consistency of the satellite data. Despite limited ground validation, this proof-of-concept highlights the capability of cloud-based remote sensing tools to monitor hydrological variability at regional scale. This approach illustrates how open-access Earth observation data and hydroinformatics tools can aid early detection of climate-driven water changes and strengthen water management in data-scarce areas. It also establishes a basis for future studies linking satellite data with peatland hydrology and ecosystem resilience.
Keywords: Climate Change Impacts, Surface Water Variability, Remote Sensing, Sentinel-2, Google Earth Engine
How to cite: Ko, N. T., Baylis, A., Davies, G. M., Barlow, D., and Evans, C.: Surface Water Dynamics under Changing Climate: Integrating Multi-Sensor Satellite Observations (1999–2025) across the Falkland Islands, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-245, https://doi.org/10.5194/egusphere-egu26-245, 2026.
