We invite contributions from across hydrological, atmospheric, and earth surface processes communities. In particular, we welcome abstracts that address advances in:
(i) understanding and predicting the present and future state of water resources worldwide and within large-scale systems;
(ii) the use of global earth observations and in-situ datasets for large-scale hydrology and data assimilation techniques for large-scale hydrological models;
(iii) the representation and evaluation of various components of the terrestrial water cycle fluxes and storages (e.g., soil moisture, snow, groundwater, lakes, floodplains, evaporation, river discharge) and atmospheric modelling;
(iv) providing syntheses that combine knowledge gained at smaller scales (e.g. catchments or hillslope) to increase our knowledge on process understanding needed for further development of large-scale hydrological models and to identify large-scale patterns and trends;
(vi) evaluating the effects of climate change, land-use change, and water-use change on global groundwater and implications of large-scale groundwater understanding on monitoring design, integrated water management, and large-scale water policies.
How to cite: Berghuijs, W., Carugati, S., Anand, M., Hrachowitz, M., Laaha, G., Campforts, B., and Hale, K.: Growing cold-season dominance of European streamflow, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-20180, https://doi.org/10.5194/egusphere-egu26-20180, 2026.
A key limitation in advancing ecohydrological understanding stems from the long-standing neglect of explicitly representing water velocities in models. Consequently, hydrological and water quality modelling often remains a grey box—capable of reproducing streamflow or solute dynamics, yet often for the wrong reasons. Stable water isotopes can bridge this knowledge gap, as their dynamics reflect integrated effects of transport and mixing along hydrological flow paths. Therefore, we developed EcoTWIN, a tracer-aided, fully distributed, process-based ecohydrological model that simultaneously tracks water, isotopes, and nitrogen fluxes. The model was applied to 3,821 European catchments at 5-km spatial and daily temporal resolution (1980–2024), and validated against discharge, in-stream isotope, and nitrate data from 1,218 sites, as well as remote sensing products and literature reports.
Through isotopic simulation, EcoTWIN provides novel insights into the velocities of the water cycle, complementing previous research focusing primarily on its celerity and magnitude. This allows a re-conceptualisation of the co-evolution of water and nitrogen cycles through the lens of water velocity. Under this water age framework, distinct hydrological–biogeochemical regimes were mapped along multiple geographic and hydroclimatic gradients across Europe, revealing how water velocity governs nitrogen retention versus export since 1980s. By quantifying the variability of transport (soil residence time) and reaction timescales (time required to remove nitrogen storage via denitrification and plant uptake), we identified four co-evolutionary schemes of water and nitrogen cycling over 1980-2024, which were dominated by the magnitudes of hydrological acceleration/deceleration: moderate hydrological shifts mitigated nitrogen leaching, whereas intense acceleration/deceleration of water cycling exacerbated soil nitrogen leaching/accumulation.
Projections towards 2100 further revealed an uncertain future of coupled water–nitrogen dynamics. Under low-emission scenario (SSP1-2.6), moderate hydrological shifts lengthened reaction times and enhanced biological uptake/denitrification, thus alleviating nitrogen leaching. In contrast, intensified droughts under high-emission scenario (SSP5-8.5) may trigger pronounced deceleration of water cycling in Eastern and Southern Europe, leading to moisture-driven suppression on nitrogen uptake and subsequent nitrogen accumulation. Such dual vulnerability of water quantity and quality is likely not confined to Europe but extends to Central and East Asia where water storage decline is ongoing and projected to intensify. This underscores the need to further extend water age frameworks to the global scale to better understand the coupled hydrological–biogeochemical resilience under climate change. Such insights can inform sustainable land management strategies to safeguard water quality and ecosystem resilience in a warming world.
How to cite: Wu, S., Soulsby, C., Zheng, Y., and Tetzlaff, D.: Re-conceptualising the continental-scale co-evolution of hydrological and nitrogen cycles under water age framework, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-861, https://doi.org/10.5194/egusphere-egu26-861, 2026.
The Asia–Pacific region encompasses hydrologically diverse and climate-vulnerable systems, where groundwater security is increasingly threatened by over-exploitation, climate change, urbanization, and salinization. This regional synthesis, developed under the IAEA Technical Cooperation Project RAS7040, integrates environmental isotopes (δ¹⁸O, δ²H, d-excess, ³H), hydrochemical indicators, and numerical modeling to delineate groundwater recharge processes, surface–groundwater interactions, and salinization mechanisms across contrasting hydroclimatic settings, including coastal, urban, alpine, riverine, and arid basins. Multi-country case studies from Lao PDR, Pakistan, Mongolia, China, Bangladesh, Indonesia, Vietnam, Australia, and the Philippines demonstrate the robustness of isotope-based diagnostics for resolving complex groundwater systems. Across the region, results consistently identify local meteoric precipitation as the dominant recharge source, while revealing pronounced contrasts in recharge timing, aquifer vulnerability, and salinity evolution governed by climate variability, land use, and geological framework. In densely populated coastal plains, such as Bangladesh, shallow aquifers exhibit active seawater intrusion, clearly traced by diagnostic Cl⁻–δ¹⁸O mixing relationships, whereas deeper confined aquifers commonly contain isolated paleo-salinity or remain largely protected from modern marine ingress. In high-altitude glacier-fed catchments (e.g., the Mingyong Basin, China), isotope-based hydrograph separation quantifies increasing seasonal meltwater contributions to river discharge, highlighting climate-driven shifts in runoff generation and long-term water storage. In Mongolia’s Kherlen River Basin, groundwater and surface water plot close to the Global Meteoric Water Line, indicating minimal evaporative modification prior to recharge. Strong seasonal contrasts in precipitation isotopes—from highly depleted winter values (δ¹⁸O ≈ −30‰) to enriched summer rainfall (δ¹⁸O ≈ −12‰)—demonstrate that groundwater recharge is dominated by warm-season precipitation, with clear isotopic evidence of river–groundwater exchange in alluvial reaches. In arid to semi-arid regions of Pakistan, stable isotopes are critical for quantifying evaporation losses, identifying recharge zones, and distinguishing irrigation return flow from natural recharge in intensively managed aquifer systems. In Australia, isotope (δ¹⁸O, δ²H, ³H) and hydrochemical investigations of the Thirlmere Lakes conclusively identify evaporation as the dominant mechanism driving lake-level decline, with a secondary, multi-decadal groundwater recharge component. Urban aquifers in major cities (e.g., Hyderabad, Metro Manila, Karachi) show heightened vulnerability to anthropogenic contamination and reduced recharge, diagnosed through isotopic enrichment patterns and complementary tracers such as nitrate isotopes. In the riverine systems of Lao PDR and Vietnam, isotopic apportionment clarifies Mekong and Red River connectivity with adjacent alluvial aquifers, providing essential insights for transboundary water management. When coupled with Bayesian mixing models and variable-density flow simulations, the integrated isotope–geochemical approach effectively differentiates modern seawater intrusion from relic salinity, quantifies river–aquifer interactions, and constrains recharge source elevations and catchment domains. This synthesis underscores the value of regional scientific coordination to harmonize methodologies, identify transboundary groundwater linkages, and upscale local findings. Overall, it demonstrates that isotope-based evidence is indispensable for science-informed policy, supporting managed aquifer recharge, regulation of abstraction, and early-warning systems for salinization and water-quality degradation, thereby advancing climate-resilient water governance across the Asia–Pacific region.
Key words: Isotope Hydrology, Groundwater Recharge, Seawater Intrusion, Aquifer Vulnerability, Water Resource Management, Asia–Pacific Region
How to cite: Moniruzzaman, M., Keesari, T., Hughes, C. E., Racadio, C. D., Sukhbaatar, C., Pan, D., Nhan, D. D., Dashdondog, G.-O., Mejus, L., Naing, M., Peterson, M. A., Mohd Hashim, M. M., Mujahid, N., Majumder, R. K., Nonthaxay, S., Dyah Larasati, T. R., Pang, Z., and Vital, M.: Integrated Isotope Hydrology for Assessing Water Resource Vulnerability Across the Asia–Pacific Region, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-11935, https://doi.org/10.5194/egusphere-egu26-11935, 2026.
Calibrating complex physically based hydrological models remains a major challenge due to the high computational demands of traditional optimization algorithms. Furthermore, if conducted, local calibration at several sites can lead to an uneven spatial distribution of parameters, imposing additional challenges when transferring parameter values from gauged to ungauged areas. As a result, simulations from complex models often rely on default parameter values that yield poor model performance. Recent studies have shown that machine learning emulators can speed up calibration and regionalization of model parameters while maintaining predictive accuracy similar to that of traditional optimization algorithms and improving the spatial distribution of parameters. However, most studies using emulators focus on streamflow, while there is a great opportunity to support improved process modelling using large datasets. Here, we focus on snow, a critical component of hydrological systems, and show how improved calibration of snow-related parameters could enhance the consistency of hydrologic model simulations. In this study, we assess whether emulators can (1) improve snow simulations across North America and (2) regionalize snow parameters across the continent. To this end, we use 770 snow stations located in Canada and the United States. We compare the performance of a conceptual model (FUSE: Framework for Understanding Structural Errors) and a physically based model (SUMMA: Structure for Unifying Multiple Modeling Alternatives), each calibrated using both traditional algorithms and emulator-based approaches. Our results show that snow simulations using SUMMA achieve performance comparable to that of FUSE with a fraction of the simulation runs usually required by optimization algorithms, suggesting that complex models can perform similarly to (calibrated) conceptual models. Further, when conducting local calibration, the use of large-sample emulators improves the smoothness of the spatial distribution of parameters, which, for parameter regionalization purposes, translates into smoother spatial distributions of parameter values in large geographic areas. This suggests that emulators can mitigate the effect of the highly irregular response surface during parameter calibration, thereby enhancing the robustness of simulations across large domains. Thus, this work offers new insights into the potential of emulators to enhance process-based modeling and snow representation across large, diverse regions.
How to cite: Vásquez, N., Eythorsson, D., Casson, D., Aguirre, I., Thébault, C., Knoben, W., Hatami, S., Han, F., and Clark, M.: Snow Matters: Emulator-Driven Calibration Across a Large Sample of Snow Stations, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-14960, https://doi.org/10.5194/egusphere-egu26-14960, 2026.
Hydrological modelling plays a crucial role in Earth System Models at many scales, affecting results in numerical weather predictions through surface fluxes and storages. Data assimilation is used in weather models to ascertain accurate initial conditions for weather forecasts based on observations. At the land surface, the land data assimilation system (LDAS) exploits observational data to update selected fields of the state vector, such as soil moisture or snow cover. In turn, soil moisture and snow cover affect the terrestrial water cycle through the partitioning of runoff components, and consequently streamflow.
Whereas LDAS configuration choices are usually driven by improvements in meteorology, here we aim to investigate how they impact streamflow. To this end, several LDAS configuration are used to run ECMWF’s Land Surface Modelling System (ecLand), which generates grid-wise runoff routed as streamflow in rivers by the Catchment-based Macro-scale Floodplain (CaMa-Flood) hydrological model. Two experiments, one with snow analysis and the other with soil moisture analysis, are compared against the baseline without any data assimilation over 1990-2023 using meteorological forcing from ERA5. In addition, a baseline experiment without any data assimilation is run. Therefore, differences in the model output of these experiments can be attributed to the data assimilation procedure. Snow cover analysis uses ESA-CCI data from 1990 to 2010, and IMS snow cover data from 2010 to 2023. The soil moisture analysis assimilates ERS-SCAT products from 1992 to 2006 and ASCAT soil moisture products from 2007 to 2023, as well as gridded SYNOP observations of 2-m temperature and relative humidity.
Streamflow is the main diagnostic variable to quantify the impact of LDAS on river hydrology, as observational data is available. A filtering procedure was applied to the Global Runoff Data Centre (GRDC) dataset to ensure sufficient observations to represent local climatology: at least 25 daily values per month, for seasonal representation, and a minimum of 19 years over the 33-year period. Monthly climatologies of simulated discharge and surface fluxes are calculated to identify catchment-scale patterns. Surface fluxes, such as evapotranspiration and (sub-)surface runoff, and streamflow output of the respective experiments are compared for 342 gauged catchments, across 209 distinct river systems in the northern hemisphere.
The Kling–Gupta Efficiency and its components are computed for each catchment, allowing the assessment of bias, correlation, and variability separately. Assessments show a widespread impact of LDAS configuration on the hydrological skill of ecLand-Cama-Flood system: for 48% of the stations the baseline experiment has a higher hydrological skill, while 20% of the stations benefit from snow cover analysis and 32% from soil moisture analysis. Then, to assess hydrological processes across horizontal and vertical spatial scales, performance of data assimilation is analysed as function of catchment characteristics such as upstream drainage area, orography, and spatial variability of orography.
This study pinpoints catchments where river hydrology benefits or is negatively impacted by land data assimilation and directly supports further development of ecLand.
How to cite: Covella, F., Denissen, J. M. C., Rüdiger, C., Fairbairn, D., and Hendricks-Franssen, H.-J.: A catchment-scale analysis of the impact of land data assimilation on surface fluxes and river discharge with a land surface model, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-12671, https://doi.org/10.5194/egusphere-egu26-12671, 2026.
Extreme events such as droughts and floods can have severe socioeconomic and ecological impacts, particularly in transboundary basins like the Danube - the most international river basin in the world. Accurate water monitoring in this region is essential for effective water management and risk mitigation. Hydrological models play a key role in simulating these processes. However, their accuracy is often limited by structural uncertainties, parameterization errors, and imperfect forcing data. Data assimilation (DA) frameworks have proven effective in reducing these uncertainties, especially for terrestrial water storage (TWS) and its individual components. In this study, we assimilate satellite-derived TWS anomalies from the Gravity Recovery and Climate Experiment (GRACE) and its successor GRACE-FO into the WaterGAP Global Hydrology Model (WGHM) for the highly regulated Danube River Basin. Our results indicate that assimilating GRACE/-FO data into WGHM leads to notable improvements in water storage representation across the entire basin, reducing model uncertainties and aligning simulations more closely with independent observations. For example, high correlations of around 0.90 are observed for both the groundwater and soil water components after DA (0.82 and 0.93 for open loop run, respectively) indicating accurate representation of drying and wetting patterns. Although DA does not significantly improve streamflow simulations, they still exhibit reasonable Nash-Sutcliffe Efficiency (NSE) values of around 0.50. These findings highlight the potential of satellite-based DA frameworks to strengthen large-scale hydrological modeling and to support sustainable water resource management in transboundary basins.
How to cite: Çakan, Ç., Forootan, E., Nyenah, E., Dӧll, P., and Schumacher, M.: Advancing Large-Scale Water Cycle Understanding in the Danube River Basin by GRACE/-FO Data Assimilation, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-6682, https://doi.org/10.5194/egusphere-egu26-6682, 2026.
Anthropogenic activities, such as water withdrawals and inter-basin transfers, cause significant spatial redistribution of river runoff along channel networks. However, this process is poorly constrained in large-scale hydrological models (LHMs) traditionally calibrated against streamflow time series only at basin outlets. To address this gap, we employ a novel Relative Centroid Index (RCI), a normalized metric quantifying the upstream/downstream shift of the runoff "center of mass" within a basin, which serves as a novel metric to evaluate how well models capture such spatial footprints. We first calculate benchmark RCI values (RCI_gauge) at over 200 globally distributed basins with sufficient gauge density. We then evaluate the capability of four major model-based global river discharge products to replicate these observed RCI patterns at gauge locations. They involve GRADES, its enhanced versions GRFR and GRADES-hydroDL, and GRDR which adds a river width data assimilation module. Furthermore, we explore the potential of remote sensing (Landsat, SWOT) to provide complementary spatial distribution information, despite potential biases in absolute magnitude. Preliminary analysis suggests systematic biases in model-simulated RCI particularly in highly human regulated basins, while remote sensing shows promise in capturing relative spatial patterns. This work provides a new framework to diagnose spatial inaccuracies in LHMs and highlights the value of multi-source observations for improving the representation of human-altered hydrological processes.
How to cite: Lin, P., Yin, Z., Yamazaki, D., Slater, L., Lin, H., and Zhang, F.: Assessing Spatial Redistribution of River Runoff Using a Relative Centroid Index (RCI): Insights from Multi-Model and Remote Sensing Data Comparisons, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-16162, https://doi.org/10.5194/egusphere-egu26-16162, 2026.
Groundwater, Earth’s largest source of liquid freshwater, sustains ecosystems and provides freshwater supply for billions of people worldwide. Increasing reliance on groundwater resources, together with climate-driven changes in recharge, places growing pressure on aquifers, contributing to depletion and threatening both ecosystem integrity and socio-economic stability. Global assessments of groundwater stress remain largely based on hydroclimatic indicators, offering limited insight into how physical pressures translate into societal consequences, or why similar levels of groundwater stress can lead to markedly different outcomes across regions. Here, we advance a global approach to groundwater risk assessment that moves beyond stress metrics by jointly considering physical groundwater pressures, the presence of human populations and groundwater-dependent assets, and the societal capacity to cope with and respond to stress. Applied globally at high spatial resolution for the early 21st century, this approach enables a systematic exploration of how spatial patterns of groundwater risk evolves when societal conditions are explicitly taken into account. Rather than focusing on single indicators, the analysis highlights pronounced spatial heterogeneity in risk patterns and demonstrates how societal conditions can amplify or dampen the severity of groundwater-related impacts, even under comparable levels of physical stress. We will identify regions where groundwater risk is most sensitive to changes in societal capacity, as well as priority areas where targeted investments in governance, infrastructure, and social resilience could most effectively reduce future groundwater risk under rising water demand and climate change.
How to cite: Nazari, S., Reinecke, R., and Moosdorf, N.: Reconceptualizing global groundwater risk beyond stress metrics, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-5500, https://doi.org/10.5194/egusphere-egu26-5500, 2026.
Freshwater resources are fundamental to supporting humanity, and measures of water scarcity have been critical for identifying where water requirements and water availability are imbalanced. Existing water scarcity metrics typically account for blue water withdrawals (i.e., from surface-/groundwater), while the contribution of green water (i.e., soil moisture) and water quality – dimensions with important implications for multiple societal sectors – to water scarcity remain unclear. Here we introduce the concept of multidimensional water scarcity that explicitly assesses all three of these dimensions of water scarcity and evaluates their individual and combined effects. We find that 22-26% of the global land area and 58-64% of the global population are exposed to some form of water scarcity annually, with multidimensional (i.e., blue, green, and quality) water scarcity particularly high in India, China, and Pakistan. Examining seasonal water scarcity, we estimate that 5.9 billion people (or 80% of the world’s population in 2015) were exposed to at least one dimension of water scarcity for at least one month per year and that 1-in-10 people (10%) were exposed to multidimensional water scarcity at least one month per year. Our findings demonstrate that the challenges of water scarcity are far more widespread than previously understood. As such, our assessment provides a more holistic view of global water scarcity issues and points to previously overlooked scarcity where action needs to bring human pressure on freshwater resources into balance with water quantity and quality.
How to cite: Liu, W. and Fu, Z.: Global overlooked multidimensional water scarcity, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-9907, https://doi.org/10.5194/egusphere-egu26-9907, 2026.
The new EU-Hydro 2.0 production is tackling the requirements of a modern hydrographic reference product within the pan-European hydrological domain, uniting the requirements of both the hydrographic mapping and the hydrological monitoring communities. The EU-Hydro 2.0 dataset is part of the Copernicus Land Monitoring Service (CLMS) portfolio, implemented by the European Environment Agency (EEA). The current EU-Hydro v1.3 dataset offers detailed information on the geographical distribution and spatial characteristics of water resources throughout Europe, such as river networks, surface water bodies and watersheds. However, its use for hydrological modelling remained limited due to shortcomings in data structure, resolution, and quality. The new version of EU-Hydro (EU-Hydro 2.0) builds on new, advanced hydrological conditioning algorithms that improve hydrological representation and consistency of current methods. As a result, it supports various use cases beyond mapping, including hydrological modelling and prediction as well as environmental assessments related to river connectivity and the evaluation of anthropogenic impacts, all with the goal to strengthen water resilience across Europe.
The EU-Hydro 2.0 production is underway and planned to be made available towards the end of 2026. It builds upon a latest generation Digital Elevation Model (DEM): the Copernicus DEM, a pan-European DEM available at 10m resolution, based on the TanDEM-X mission, supported by the Copernicus DEM at 30m resolution for catchments that flow in and out of the EEA38+UK area. The production of EU-Hydro 2.0 involves best-suitable and most recent ancillary data of hydrography, land cover, and infrastructure, as well as VHR satellite data for quality control, editing (e.g., river corrections, coastline mapping) and validation. The product suite consists of eight main layers: The three main raster products are the hydrologically conditioned DEM (Hydro-DEM), the flow direction (Hydro-DIR) and the flow accumulation (Hydro-ACC) products, supported by additional raster layers for expert hydrological use. The five vector products are the river network (Hydro-NET), water bodies (Hydro-WBO), basins and sub-watersheds (Hydro-BAS), a product on artificial hydrographic structures (Hydro-ART) and a coastline (Hydro-COAST). In addition, a cartographically enhanced river product in focus regions (Carto-NET) is being produced. All layers are interrelated, scalable and logically consistent.
A ramp-up phase was carried out between 2024-2025 in six test sites of different hydrological and terrain characteristics (full Po catchment, N-Sweden, S-Spain, Slovenia/Croatia, Central Türkiye, and boundary area Romania/Moldova). An independent validation of the different layers in the test sites, considering geometry, topology, attribution and complex interrelations between different layers was performed, in order to evaluate fitness for potential mapping and modelling use cases. The validation utilized national/regional reference data and very high resolution (VHR) image interpretation and concluded that every layer is generally performing well, with some layers exceeding expectations whereas for other layers some issues on harmonization remain to be addressed. The approach aims at transparency and automation to the extent possible, supported by manual corrections where needed to increase quality and meet user requirements. By applying efficient and reproducible data processing, further updates of EU-Hydro into the future can be facilitated.
How to cite: Moser, L., Lehner, B., Roth, A., Huch, J., Mortel, G. M., Huber, M., Sanz, V., Grill, G., Sogno, P., Wetzel, A., Hora, T., Bachofer, F., Ram, S., Prajapati, M., Stern, G., Luksikova, J., Stephanie, W., Ruiz, I., Keller, C., Rovira, S., Ortigosa, N., D'Amico, Q., Iorio, V., and Rubio Iglesias, J. M.: The new EU-Hydro 2.0 production: A Copernicus high-resolution hydrography dataset across Europe based on latest generation elevation and ancillary data, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-18560, https://doi.org/10.5194/egusphere-egu26-18560, 2026.
The systematic wetting and drying of river channels exert a fundamental control on hydrological connectivity and biogeochemical functioning of watersheds. Quantifying the proportion of river networks that cease to flow seasonally or episodically is therefore essential, yet remains highly uncertain due to sparse observations and persistent underrepresentation of small streams in large-scale analyses. In this contribution, we integrate global-scale hydrological simulations with detailed field-based evidence from experimental catchments spanning diverse climatic regions to derive revised estimates of non-perennial stream occurrence worldwide. Our findings show that non-perennial streams are far more prevalent than previously recognized, both regionally and globally. When headwater streams are comprehensively accounted for, non-perennial reaches account for more than 70% of the global river network length, with upper estimates approaching 78%. Even in comparatively humid regions, such as Italy and the eastern United States, non-perennial streams represent over half of the total network length. Our analysis further demonstrates that the dominance of small upland channels allows wetting–drying dynamics to propagate their influence well beyond headwaters, leaving relevant signatures that persist even at the scale of large basins. These results highlight the need to systematically incorporate channel intermittency into large-scale hydrological models and assessments, with important implications for water resources evaluation, ecosystem functioning, and river management under ongoing climatic and environmental change.
How to cite: Durighetto, N., Barone, F., and Botter, G.: Small streams, large impacts: headwaters control the non-perennial fraction of the global river network, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-9422, https://doi.org/10.5194/egusphere-egu26-9422, 2026.
Global streamflow modelling is crucial, as it underlies our capacity to forecast riverine floods able to devastate infrastructure and ecosystems, and adversely affect human lives. To that end, the hydrodynamic Catchment-based Macro-scale Floodplain model (CaMa-Flood) has been included in ECMWF’s Land Surface Modelling System (ecLand), and consequently in the Integrated Forecasting System (IFS). Precipitation, which is partitioned into infiltration and runoff by ecLand’s land surface processes, is eventually converted into streamflow through CaMa-Flood. This means streamflow carries an imprint of both meteorology and land surface processes. This is particularly relevant, as runoff is not available as an observation, while streamflow is, making the latter a key variable for the aggregated evaluation of modelled land surface processes. As this analysis is done under the auspices of the Destination Earth project, it presents the additional possibility to evaluate land surface processes across all operational spatial scales up to the km-scale and temporal scales from daily to hourly. For example, through running daily CaMa-Flood simulations driven with runoff forcing from the control ensemble member and from the Continuous-Extremes Digital Twin (C-EDT), the land surface’s hydrological processes can be evaluated at the spatial resolutions of ~9km and ~4.4km, respectively. Comparing results from these daily simulations with streamflow observations, we found that the C-EDT generates insufficient surface runoff in orographic regions. This stems from the sub-grid runoff parameterization in ecLand, which generates less surface runoff at higher resolutions for the same amount of precipitation, and is therefore not scale-adaptive.
Beyond providing hydrological simulations, CaMa-Flood is used in this study as a diagnostic tool for hydrological processes to guide future development of ecLand. More specifically, we have implemented scale-specific orographic parameters in the model’s runoff-generating algorithm, aiming to provide consistent orographic surface runoff generation across spatial scales. Runoff partitioning is important for flood extremes on timescales of a few days, because it directly modulates the magnitude of the flood peak. In addition, it affects the soil moisture, and consequently sub-surface runoff and streamflow on timescales of months to years. Therefore, the efficacy of these adaptations is tested with both long-term land surface experiments with ecLand/CaMa-Flood and fully-coupled 5-day meteorological forecasts with IFS/CaMa-Flood at spatial resolutions of ~29km, ~9km and ~4.4km. For the forecasts on shorter time scales, we assess the flood peak magnitude, timing and durations errors. For the long-term integrations from 1990 – 2025, streamflow time series allow a robust evaluation of the simulations against the observations, and its components as a measure of goodness-of-fit. Moving beyond the KGE, a cross-spectral analysis is applied to evaluate the time signature of the hydrological processes undelrying streamflow and occurring at different time scales, which is especially useful considering the partitioning between surface (fast) and sub-surface (slow) runoff. Through addressing these scale-dependent issues, ample surface runoff generation is ensured, allowing the river hydrology simulated by CaMa-Flood to benefit fully from running meteorology and the land surface at the km-scale.
How to cite: Denissen, J., Balsamo, G., Arduini, G., Zsoter, E., Wortmann, M., Tanguy, M., Gascon, E., Mazzetti, C., Prudhomme, C., Morrison, O., Dueben, P., Sandu, I., Vanniere, B., and Rüdiger, C.: Rivers in Earth System Modelling: CaMa-Flood supporting land surface model development at the km-scale, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-11312, https://doi.org/10.5194/egusphere-egu26-11312, 2026.
Obtaining accurate large-scale estimates of top-soil water content is a grand challenge in land-surface modelling [1]. Soil moisture (SM) is a key climate variable for understanding changes in the terrestrial water cycle [2], monitoring drought evolution [3], predicting drought severity [4], and improving flood forecasting by constraining antecedent wetness [5]. Predicting SM in extreme climates (paramo or permafrost) is further complicated by the coupling of soil water flow and heat transport. Decades of research have been invested in this subject, yet a scalable and transferable solution has not emerged.
Evidence from controlled multi-model experiments with harmonised forcings, geodata, and initial conditions (e.g., ESA https://4dhydro.eu/) suggests that epistemic uncertainty in simulated SM is dominated by model structure, soil parameterisations, and the scaling of soil properties. The spread can be substantial; Wang et al. noted that "differences in model-predicted soil moisture can be quite large" [6]. A common narrative is that Richards-equation-based (RE) land-surface models are impractical at the kilometre scale: their effective parameters are difficult to infer, transfer across scales is often unsuccessful [7], and calibration against control variables such as streamflow is considered computationally intractable. Simpler infiltration-capacity (IC) schemes or conceptual models, while readily calibrated against streamflow, are often assumed to yield poorer SM dynamics.
We revisit these assumptions by embedding a fast RE solver—the SLI module as implemented in CABLE [8,13]—into the mesoscale Hydrologic Model (mHM) and parameterising it with Multiscale Parameter Regionalization (MPR) [9]. MPR uses pedo-transfer relationships, high resolution physiographic datasets, and upscaling operators to derive effective, scale-consistent soil hydraulic parameters, while mHM provides the distributed water-balance and streamflow (Q)-based calibration framework. This design targets transferability across basins and resolutions. Using the results of Kholis et al. [10], we show that, when implemented in mHM, RE and IC yield similar streamflow performance under consistent calibration, while their SM states diverge. RE- and IC-based simulations agree on SM anomalies, but differ in volumetric water content, with discrepancies increasing with soil depth. The SLI module adds a thermal diffusion equation to mHM-RE, enabling joint tests of SM and soil temperature (Ts). We evaluate across German sites using station-based soil moisture and soil temperature observations and report mean daily performance of KGE(Q) = 0.89, KGE(SM) = 0.40 and KGE(Ts) = 0.90. In addition, we will present a first cold-region application using the 20-year Bayelva permafrost record (1998–2017) from Spitsbergen [11].
We conclude that (1) MPR enables practical parameterisation and scale transfer of RE across locations, (2) an RE+MPR SM module can be optimised without sacrificing streamflow skill, and (3) the mHM-RE infrastructure enables consistent multi-variable evaluation (SM and Ts), including EO-based benchmarking where available. Next steps include benchmarking against CryoGrid[12] and CABLE[13], extending evaluation to alpine and paramo observatories to probe combined hydro-thermal realism, and developing a long-term global SM reconstruction to advance state-of-the-art drought monitoring [2].
References:
[1] 10.1029/2010WR010090
[2] 10.1126/science.adw5851
[3] 10.1088/1748-9326/11/7/074002
[4] 10.1029/2021EF002394
[5] 10.1038/s41467-024-48065-y
[6] 10.1175/2008JCLI2586.1
[7] 10.22541/essoar.174982768.80043676/v1
[8] 10.1016/j.jhydrol.2010.05.029
[9] 10.1029/2008WR007327
[10] 10.1029/2024WR039625
[11] 10.5194/essd-10-355-2018
[12] 10.5194/gmd-16-2607-2023
[13] 10.1002/2017MS001100
How to cite: Samaniego, L., Kholis, A., Shrestha, P. K., Modiri, E., and Boike, J.: MPR-enabled hydro-thermal soil physics in mHM: scaling and transferability tests, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-6188, https://doi.org/10.5194/egusphere-egu26-6188, 2026.
Almost one-quarter of the global population resides on floodplains, although they cover only about 6.6% of the Earth’s land surface. Floodplains play a central role in regulating river discharge in large lowland river systems, yet their seasonal hydrological function is still poorly represented in large-scale hydrological models. In such systems, river–floodplain interactions control the attenuation, timing, and seasonal persistence of high flows during the wet season. Neglecting these effects leads to simulated hydrographs with unrealistically sharp flood peaks and a limited representation of sustained elevated flows throughout the wet season.
Here, we present the development and evaluation of a floodplain module integrated into the mesoscale hydrologic model (mHM) [1]. Each river reach is represented as a coupled river–floodplain system in which water exceeding the bankfull channel capacity is temporarily stored in an adjacent floodplain compartment. Floodplain storage capacity is derived from high-resolution topographic information using the Height Above Nearest Drainage (HAND) concept, yielding reach-specific height–area–volume relationships. At each routing time step, the available water volume is partitioned between the channel and the floodplain assuming a uniform water level shared by both compartments within the reach. Only the channel volume is routed downstream using the selected routing scheme (e.g. Muskingum–Cunge) in the multiscale Routing Model (mRM) module of mHM [2]. This module is also scalable, as it is based on the Subgrid Catchment Conservation (SCC) concept [3], enabling the simulation of river–floodplain interactions at different routing resolutions while ensuring hydrological connectivity and preserving subgrid-scale catchment contributions.
Simulations for the Ucayali River Basin (Upper Amazon, Peru) show that accounting for floodplain storage attenuates and delays flood peaks and enhances wet-season persistence of high flows. Overall, the results indicate that the proposed module improves the representation of wet-season discharge dynamics in the Ucayali River Basin and is transferable to other floodplain-dominated catchments.
Keywords
floodplains; river routing; large-scale hydrology; mHM; HAND; Amazon Basin
References
[1] Samaniego, L., Kumar, R., & Attinger, S. (2010). Multiscale parameter regionalization of a grid-based hydrologic model at the mesoscale. Water Resources Research, 46(5), 1–25. https://doi.org/10.1029/2008WR007327
[2] Thober, S., Cuntz, M., Klebling, M., Kumar, R., Mai, J., & Samaniego, L. (2019). The multiscale Routing Model mRM v1.0: Simple river routing at resolutions from 1 to 50 km. Geoscientific Model Development, 12, 2501–2521. https://doi.org/10.5194/gmd-12-2501-2019
[3] Shrestha, P. K., Samaniego, L., Rakovec, O., Kumar, R., & Thober, S. (2025). A novel stream network upscaling scheme for accurate local streamflow simulations in gridded global hydrological models. Water Resources Research, 61, e2024WR038183. https://doi.org/10.1029/2024WR038183
How to cite: Fernandez-Palomino, C. A., Thober, S., Müller, S., Lüdke, V. S., Shrestha, P. K., and Samaniego-Eguiguren, L.: Representing river–floodplain interactions in large lowland basins: development and evaluation of a floodplain module within mHM, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-8333, https://doi.org/10.5194/egusphere-egu26-8333, 2026.
Global water models are valuable tools for predicting river flood hazard in data‑scarce regions and under future climate scenarios. Their ability to produce spatially coherent projections means that their results can be used broadly for global or large‑scale scientific analysis and policy planning. However, the complexity of these models, together with the large volume of data they generate, creates challenges for evaluating how well they represent key processes. At long‑term climatic timescales, global water models show marked differences in the controlling processes for water‑balance components, as previous studies revealed. At event timescales, accurate representation of hydro‑meteorological dynamics requires consideration of multiple flood drivers, such as precipitation, soil moisture, or snowmelt.
In this analysis we compare simulations from six global water models (CWatM, H08, LPJmL, JULES‑W2, MIROC, and WaterGAP2) that were run within the international model‑comparison framework ISIMIP3a. We evaluate event drivers at the spatial scale of individual model cells (0.5°). We define high‑flow events as annual runoff maxima that exceed the 2‑year flood threshold. We classify potential drivers (short extreme rainfall, long extreme rainfall, soil moisture and snowmelt proxies). Drivers can be identified either individually or in combination with others (e.g., snowmelt + rainfall, soil moisture + rainfall, etc.).
We find that, in some models, extreme rainfall (short rain or short + long rain) often dominates high‑flow events, while other models show more influence from combinations of drivers such as snow or soil moisture. Except in snow‑dominated regions, all models share one feature: short extreme rainfall, either alone or combined with other factors, is part of the dominant flood driver almost everywhere. This has potentially significant consequences for future estimates of flood frequency under changing conditions. Still, the importance of antecedent soil moisture in flood generation remains ambiguous among the models, which contrasts with current process understanding and observation‑based analyses. This and other results demonstrate that process‑based model intercomparison provides valuable guidance for model development.
How to cite: Stein, L., Kularathne, N., Reinecke, R., Tarasova, L., Müller Schmied, H., Burek, P., Gosling, S. N., Grillakis, M., Koutroulis, A., Hanasaki, N., Ostberg, S., Satoh, Y., and Wagener, T.: Using river flood event drivers for model-intercomparison – a process-based analysis of global water models, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-6578, https://doi.org/10.5194/egusphere-egu26-6578, 2026.
The Regionalization of parameters remains a major challenge for large-scale hydrological modeling, especially in regions with limited data where direct calibration against streamflow observations is not feasible. In this study, we compare two data-driven regionalization frameworks to a classical approach based on the physical and climatic proximity of catchments. The first framework relies on a modified Kling–Gupta efficiency (‘KGE) emulator coupled with a distributed evolutionary algorithm in Python (DEAP)-based evolutionary calibration framework. A deep neural network (DNN) and a random forest (RF) are trained using the "the most recent calibration dataset of the Copernicus Emergency Management Service Global Flood Awareness System (CEMS GloFASv5), which includes over 5000 catchments spanning a wide range of hydroclimatic, physiographic and land use conditions. Static catchment attributes and long-term climatic descriptors serve as predictors, and the target variable is the modified KGE obtained from an extensive parameter history generated by DEAP-based evolutionary calibration of the hydrological model OS LISFLOOD. To promote robust generalization across climates, we split the dataset into training, validation, and testing subsets using a climate-stratified sampling strategy that preserves key indicators, such as aridity, mean precipitation, and precipitation seasonality. Once trained, the emulator is embedded within an evolutionary algorithm to identify parameter sets that maximize the emulated ‘KGE for target catchments, thereby avoiding repeated hydrological simulations. The second framework uses a surrogate modeling approach that combines an LSTM-based emulator of OS LISFLOOD with a reinforcement learning-driven regionalization strategy. The surrogate model is trained to reproduce OS LISFLOOD's dynamic behavior, while a separate LSTM agent explores the parameter space and proposes parameter sets iteratively. This exploration is guided by a reward function based on a ‘KGE, which is computed from the surrogate model outputs. This enables efficient parameter optimization without the need for direct hydrological simulations. The transfer and optimization of parameters are governed by implicitly learned similarities in a latent feature space. Both data-driven approaches are evaluated by comparing the modified KGE achieved by OS LISFLOOD simulations using the inferred parameters with that achieved by the conventional regionalization method relying on explicit physical or geographical distance metrics. Preliminary results suggest that both data-driven methods reproduce large-scale spatial patterns of model performance and yield KGE values comparable to those obtained with the classical approach. While the current results are similar to existing methodologies, they suggest that emulator-based optimization and surrogate modeling are viable alternatives for large-scale regionalization with potential for further refinement.
How to cite: Salamon, P., Chalifour, O., Russo, C., Grimaldi, S., and Taccari, M. L.: A Comparison of Data-Driven Regionalization Frameworks for Large-Scale Hydrological Modelling, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-17335, https://doi.org/10.5194/egusphere-egu26-17335, 2026.
Global freshwater systems are critically threatened by environmental change and over-exploitation, stressing the need for novel, transformative solutions. As social-hydrological systems are diverse and complex, water-related risks and decision-making needs are often strongly embedded in a locally specific context. Therefore, such solutions need to be informed by solid scientific evidence while remaining tailored to local realities, knowledge, and practices. However, the current generation of global water system models struggles to produce evidence that is accurate, tailored, and actionable at the local scale.
Here we outline an approach to support local knowledge co-production and its integration with existing and emerging data sources in global water system models. We focus on three knowledge sources that are currently underrepresented in global modelling approaches: non-statutory monitoring, citizen observations, and local knowledge.
We show how data science methods such as semantic data models, distributed workflows, and machine learning can be leveraged to develop novel knowledge integration pipelines. These pipelines explicitly represent data provenance and track epistemic and aleatoric uncertainties across heterogeneous data sources. When combined with flexible modelling frameworks, this approach provides a blueprint for next generation simulation systems that bridge global modelling and local decision-making. Such systems enable the identification, prioritization, and targeted reduction of local knowledge gaps, thereby enhancing the relevance and legitimacy of global water assessments for regional and community-level action.
How to cite: Buytaert, W., Tilahun, S., Paschalis, A., Bonetti, S., Vishwakarma, B., Crespo, P., Drenkhan, F., Agyei-Mensah, S., Ochoa-Tocachi, B., Mijic, A., Arcucci, R., Moseley, B., and Howard, B.: Unlocking local knowledge production for global water systems analysis , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-14743, https://doi.org/10.5194/egusphere-egu26-14743, 2026.
Human alterations to the hydrologic cycle, such as groundwater pumping, surface water diversions, and interbasin transfers, are often the dominant drivers of water availability and stress in the Anthropocene. However, large-scale hydrological models (LSHMs) have historically struggled to represent these anthropogenic fluxes with sufficient granularity, largely due to a scarcity of standardized, open-access data. Consequently, models often rely on coarse estimates or static coefficients that mask critical spatial and temporal heterogeneity.
In this presentation, I will provide an overview of a new suite of high-resolution, open-access datasets that describe water infrastructure and use across the United States at an unprecedented scale. I will highlight three foundational data products: (1) a comprehensive inventory of interbasin water transfers (IBTs) characterizing over 600 projects and their conveyance volumes; (2) the United States Groundwater Well Database (USGWD), which standardizes attributes for over 14.2 million wells to map subsurface infrastructure and aquifer access; and (3) the United States Water Withdrawals Database (USWWD), providing user-level historical time series for nearly 190,000 unique water users across all economic sectors. Collectively, these datasets offer a new empirical basis for parameterizing and validating the "human" components of LSHMs. I will discuss the implications of these data for reducing model uncertainty, specifically in closing local water budgets and characterizing the complex spatial connectivity introduced by infrastructure.
Finally, I will outline current initiatives to expand this data-intensive framework globally. As the field moves from data scarcity to data abundance, the modeling community plays a critical role in shaping how these data are structured and utilized. I will conclude by discussing how the large-scale hydrology community can contribute to and benefit from these emerging global data products to better predict the present and future state of water resources in a changing environment.
How to cite: Marston, L.: High-Resolution Data of Human-Water Systems to Advance Large-Scale Hydrologic Modeling, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-8565, https://doi.org/10.5194/egusphere-egu26-8565, 2026.
We present the Geographical, Environmental and Behavioural (GEB) socio-hydrological model at the pan-European scale. The model is agent-based, representing the interactions between (local) hydrology and human water management for hundreds of millions of people across Europe. For this, GEB couples an agent-based adaptation model, a fully distributed hydrological model (originally forked from CWatM), and a hydrodynamic model (SFINCS). Within GEB, people can dynamically respond to their environment, and the decisions that agents make also can affect the environment. For example, farmer agents can change their crops and adopt other measures such as wells in response to droughts, which in turn affects ground- and surface water in the hydrological model. Household agents can adapt to changes in flood risk and respond to flood events by wet- or dry-proofing their house. All adaptation decisions consider heterogeneity in the agent population, such as differences in age and education level. The model architecture allows for a fully automated setup of the model. To initialize the model, and to allow for parallel computing, river basins are automatically clustered based on size and proximity, after which those clusters are run in parallel using an efficient Snakemake workflow. The new hydrological model in GEB simulates hydrological fluxes on an hourly timestep. To validate the model, we compare simulated discharge with discharge observations from the Global Runoff Data Centre (GRDC) dataset, focussing on high flows during flood events. Subsequently, the skill scores (e.g. Kling–Gupta efficiency, KGE) are compared to state-of-the-art hydrological models such as LISFLOOD. GEB is currently used for a wide range of applications, such as (but not limited to) assessments of drought and flood risk, extreme weather impacts (e.g. hail), multi-risk, household adaptation measures, nature-based solutions and early warning. The model is open source and can be accessed via https://github.com/GEB-model/GEB.
How to cite: Busker, T., de Bruijn, J., Kalthof, M., de Moel, H., Bril, V., Sadana, T., Oliveira, R., Tierolf, L., Carral, C., Pontman, R., Kiesel, J., van Amelsvoort, L., Botzen, W., and Aerts, J.: GEB: a socio-hydrological model for risk management on a European scale, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-20604, https://doi.org/10.5194/egusphere-egu26-20604, 2026.
Climate change is fundamentally altering the spatial and temporal dynamics of the hydrological cycle, with profound implications for water security, ecosystem stability, and regional climate resilience. Building on the framework of Thakur et al. (2025), which evaluated how PET-method choices influence historical hydrological trends, we extend the framework to future conditions using CMIP6-based ISIMIP3b projections. We assess changes in precipitation (P), runoff (Q), total water storage (TWS), and actual evapotranspiration (AET) at 1°C, 2°C, and 3°C global warming levels across 553 European catchments. Using 165 mesoscale Hydrologic Model (mHM) simulations per catchment (five GCMs, three SSPs, 11 PET methods, we introduce a framework to detect emerging hydrological cycle patterns based on trend combinations. quantify projection agreement using the Data Concurrency Index (DCI) and characterize uncertainty with two complementary metrics that capture variability (ψ) and temporal inconsistency (χ) for each warming level and hydrological variable.
Our findings show a marked expansion in the spatial extent of negative trends in P, Q, and TWS with increasing warming, while AET trends are positive in over 96% of catchments. More than two-thirds of catchments follow clear wetting (W1) or drying (D1) hydrological cycle patterns, with D1 becoming increasingly dominant at higher warming levels. PET methods offer consistent directional agreement, but GCMs contribute the most disagreement, particularly for Q and TWS. Even with these coherent signals, uncertainty (ψ and χ) remains substantial and increases with warming. Although the PET contribution increases, it consistently remains below that from GCMs and SSPs.
Reference:
Thakur, V., Markonis, Y., Kumar, R., Thomson, J. R., Vargas Godoy, M. R., Hanel, M., and Rakovec, O.: Unveiling the impact of potential evapotranspiration method selection on trends in hydrological cycle components across Europe, Hydrol. Earth Syst. Sci., 29, 4395–4416, https://doi.org/10.5194/hess-29-4395-2025, 2025
How to cite: Thakur, V., Markonis, Y., Papalexiou, S. M., and Rakovec, O.: Continental-scale assessment of hydrological cycle across Europe under anthropogenic warming, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-862, https://doi.org/10.5194/egusphere-egu26-862, 2026.
Both groundwater quality and quantity must be considered to meet the demand for safe drinking water for humans, irrigation water for agriculture, process water for industry, and to sustain ecosystem health. While groundwater quality processes at the catchment or aquifer scale have been studied intensively, such studies are lacking at the regional-to-continental and especially global scale. This gap may be the missing link to better assess long-term effects that may develop into creeping catastrophes, as well as the impacts of climate change and the associated intensification of hydrological extremes on groundwater quality. We hypothesize that the key to understanding groundwater quality processes lies in a multi-scale approach that represents interactions between groundwater and surface water, including coastal systems. With multi-scale, we refer to the necessity of understanding processes at their respective temporal (e.g., long-lasting legacy effects vs. extreme events) and spatial (pore scale, catchment vs. large or even global scale) scales and their connectiveness in which spatially small and temporally short impacts might emerge into future impacts that are spatially large and temporally long. To develop this multi-scale understanding, we require an inventory of dominant processes across temporal and spatial scales, informed by local-scale knowledge that already exists. Perceptual models can be effectively used not only to represent expert knowledge graphically but also to identify knowledge gaps and clearly communicate the assumptions embedded in our current understanding of dominant processes. This poster outlines the DFG-funded TRAILS (Towards a multi-scale understanding of gRoundwater quAlity InterLinkageS) network's efforts to gain this multi-scale understanding of groundwater. It presents our initial efforts towards a collective perceptual model, informed by existing literature, that identifies the dominant processes affecting groundwater quality across multiple temporal and spatial scales. Our goal is to use this knowledge to apply existing knowledge in new contexts and to gain new multi-scale understanding towards developing new approaches, such as large-scale modeling tools.
How to cite: Brakebusch, I. and Reinecke, R. and the TRAILS: Advancing our understanding of water quality requires a multi-scale approach to groundwater-surface water connections, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-2789, https://doi.org/10.5194/egusphere-egu26-2789, 2026.
Groundwater is a vital natural resource, providing the primary source of freshwater for billions of people across the globe and supporting a diverse range of groundwater-dependent ecosystems. Groundwater drought, which is defined as a prolonged period of below-normal groundwater levels, threatens our reliance on this important resource. This can lead to a wide variety of detrimental impacts on society, the environment and the economy. Despite this, our understanding of groundwater drought has historically been restricted. This can be attributed to the poor availability of in-situ groundwater data, as well as limitations in our understanding of how effectively existing models are able to simulate groundwater drought. By combining newly available in-situ observations from nearly 2,000 wells, provided by the International Groundwater Resources Assessment Centre (IGRAC), with a continental-scale model over Europe, this study addresses this knowledge gap. In addition to characterising groundwater drought, this study evaluates the capability of the model to propagate meteorological droughts to groundwater across the continent, using in-situ observations for validation.
How to cite: Lenthall, A., Rahman, S., and Fung, F.: Propagation of meteorological drought to groundwater in Europe, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-4903, https://doi.org/10.5194/egusphere-egu26-4903, 2026.
Evapotranspiration (ET) plays a central role in the terrestrial water cycle by coupling water, energy, and carbon exchanges between land and atmosphere. A Recent intercomparison of global ET products (Thomson and Markonis, 2024) revealed substantial uncertainties in estimated ET trends, including strong product dependence in magnitude, spatial patterns, statistical significance, and even trend direction.
Here, we extend this work by introducing a topology framework that categorizes ET products based on their trend signatures. We processed and harmonized 14 global ET products—derived from reanalysis, remote sensing, synthesis approaches, and land surface models—onto a common 0.25° × 0.25° grid for the period 2000–2019. ET trends and associated significance were estimated using a block-bootstrapped Theil–Sen estimator at the grid scale and across meaningful spatial groupings, including IPCC reference regions, biomes, land-cover classes, Köppen–Geiger climate zones, elevation classes, and evaporation quantiles.
Using this catalogue of recent ET trends and trend indices, such as the dataset concurrence index (DCI), we construct product-specific topologies by ranking the area fraction associated with characteristic behaviors including positive and negative signal boosters, and several forms of opposition.
Globally, we find that “top negative signal boosters” are also “top outliers”. This means that top outliers are products that produce significant negative trends where all other significant trends are positive. This is caused by a majority of products producing positive trends. However, “top positive signal boosters” tend to be “top signal opposers”. These products have significant positive trends where the majority of products have nonsignificant trends. Both tendencies are true for a range of p-value thresholds. As a result, apparent large-scale ET trend signals are often driven by a limited number of products rather than by broad inter-product agreement.
These topologies transform complex multi-product trend information into intuitive categories, enabling systematic identification of product-specific uncertainties and agreement patterns in large-scale ET trend assessments. This framework provides a new basis for categorizing ET products supporting interpretation of large-scale ET changes and data selection.
Thomson, J. and Markonis, Y.: Multi-source analysis of recent changes in global terrestrial evapotranspiration, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-917, https://doi.org/10.5194/egusphere-egu24-917, 2024.
How to cite: Thomson, J. R., Markonis, Y., Dutta, R., Fatichi, S., Hanel, M., Koppa, A., Maca, P., Vargas Godoy, M. R., and Paschalis, A.: Multi-source comparison of recent terrestrial evapotranspiration trends: Introducing a topology framework., EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-6151, https://doi.org/10.5194/egusphere-egu26-6151, 2026.
Long-term catchment water balance is generally described as precipitation (P) equaling the sum of runoff (Q) and evaporation (E). While P and Q are reliably observed, accurately observing or estimating E remains a great challenge. The generalized complementary relationship (GCR), as the latest development of the complementary principle, addresses this by describing land-atmosphere interactions through various functions, including sigmoid (denoted as H18), polynomial (B15), exponential (G21), and power (S2, S1, and S0), offering a promising approach for long-term watershed evaporation estimation. Evaluating the performance of these different functions is key to enhance estimation accuracy and support better hydrological modeling at the catchment scale. The modeling performance and parameters of six typical GCR functions are investigated in global 2112 catchments. Results indicated that all non-linear GCR functions can well estimate multi-year average evaporation, with a determination coefficient (R2) of 0.93 ± 0.06. Performance and parameters exhibit obvious spatial variability, which depend on catchment attributes to a certain extent. Specifically, model performance demonstrates higher linear correlation with net radiation, water vapor pressure deficit, and normalized difference vegetation index (NDVI); whereas parameters are more strongly linked to aridity index (AI), NDVI. All six GCR-based functions perform well in catchments with moderate humidity by properly calibrating shape and complementary parameters, but some (i.e., H18, G21, S0, and S1) have limitations or become inapplicable under extremely wet or dry conditions.
How to cite: Lei, X., Cheng, L., Zhang, L., Fu, C., Wang, S., and Liu, P.: Performance of Non-Linear Evaporation Complementary Relationships: A Global Basin-Scale Intercomparison, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-6094, https://doi.org/10.5194/egusphere-egu26-6094, 2026.
Climate change affects different hydrological drought characteristics, including their spatial extent. This drought property is crucial for water management, as drought size can limit the effectiveness of drought mitigation strategies such as water transfers. As droughts propagate through the hydrological cycle, hydrological drought extent is influenced by meteorological factors such as precipitation and land-surface conditions such as soil moisture and snow cover. Each of these components responds differently to climate change and it remains unclear how their combined changes influence hydrological drought extent in the future.
Here, we study the influence of climate change on spatial extent in droughts around the European Alps. We use climate projections from a single-model initial-condition large ensemble (SMILE) to run the PCR-GLOBWB model (at 1km resolution) over 4 major Alpine river basins (Danube, Rhine, Rhone, and Po rivers) until 2100. Using the resulting simulations, we study trends in drought extent, specifically focusing on the different components of the hydrological system such as meteorological, soil moisture and hydrological droughts.
Our results indicate that trends in drought extent vary depending on the hydro-climatological characteristics of the Alpine basins considered. Furthermore, we highlight that trends in meteorological drought extent do not translate directly to trends in the extent of other hydrological components due to land surface processes. These findings can contribute to a better understanding of drought extent evolution, which can inform future water management decisions and lead to more robust drought mitigation strategies.
How to cite: Janzing, J., Wanders, N., Astagneau, P., and Brunner, M.: Hydrological drought extents in a warming world in large Alpine river basins , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-7393, https://doi.org/10.5194/egusphere-egu26-7393, 2026.
Global water systems are under increasing pressure from climate change, hydrological extremes, and competing demands on limited freshwater resources. Robust, interoperable data and data sharing infrastructures are essential to advance scientific understanding, support operational forecasting, and inform effective water management and climate adaptation strategies. The Reference Observatory of Basins for INternational hydrological climate change detection (ROBIN) and the Global Hydrological Status and Outlook System (HydroSOS) represent complementary international efforts to unify and leverage hydrological data for science and service delivery.
ROBIN, coordinated by the UK Centre for Ecology & Hydrology, addresses critical gaps in global streamflow observations by integrating long-term, near-natural catchment data to form a global Reference Hydrometric Network. This open-access dataset, comprising >3000 of quality-controlled streamflow records from near-natural catchments spanning diverse climates and geographies, is shared under common standards and protocols to enable global-scale climate change trend detection, model evaluation, and large-scale hydrological research. The initiative also fosters long-term collaboration among researchers and institutions, promoting shared infrastructure, code libraries, and harmonised metadata to support international scientific agendas. ROBIN is aligning its data-sharing processes with the GRDC, ensuring interoperability and complementarity between datasets, and strengthening the long-term international hydrological data legacy. Through collaboration with the FRIEND-Water and EUROFRIEND networks, ROBIN is increasing its emphasis on social hydrology, enabling analyses that better capture human–water interactions, vulnerability, and adaptation alongside climate-driven hydrological change.
The World Meteorological Organization’s HydroSOS builds operational capacity for standardised hydrological status assessments and sub-seasonal to seasonal outlooks across spatial scales. HydroSOS enhances national and regional water information systems by linking local observations, forecasts, and global services to produce consistent products for water resources management, disaster risk reduction, and climate resilience. Developed and supported by the UKCEH, the HydroSOS portal provides open access to these standardised “change from normal” assessments, enabling transparent comparison of hydrological conditions across countries and regions. It emphasises the value of consistent and standardised hydrological information among National Meteorological and Hydrological Services, contributing to united global water information frameworks and decision support tools.
Together, ROBIN and HydroSOS exemplify synergistic efforts to overcome current and historical fragmentation in hydrological data and services. By promoting open data practices, harmonised standards, and shared technical capacity, these initiatives enable a more integrated global hydrological data ecosystem that supports science, policy, and operational services in the face of evolving environmental challenges and a need to improve resilience to extreme hydrological events both now and in the future.
How to cite: Facer-Childs, K., Barker, L., Pawar, S., Turner, S., Hannaford, J., Dixon, H., Jenkins, A., Mishra, S., Silva Vara, L. R., Schwab, M., Korhonen, J., Otieno, W., and Berod, D.: Strengthening global hydrological data sharing and capacity for climate science and water services , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-7645, https://doi.org/10.5194/egusphere-egu26-7645, 2026.
River discharge is an Essential Climate Variable (Global Climate Observing System, 2022) which has been included in the ESA Climate Change Initiative (CCI) program since 2023, through a precursor project (https://climate.esa.int/en/projects/river-discharge/, CCI RD). Currently, no satellite instrument can directly measure river discharge, this project is a proof-of-concept and aims at estimating river discharge using Earth Observation and ancillary data. The objective is to deliver consistent discharge estimates from 2002 to 2025, at 50 locations across 18 selected river basins around the world. The CCI RD extends in situ discharge time series, using four robust methods: (1) estimating discharge through a rating curve approach using ancillary in situ discharge data and multiple satellite radar altimeter long time series of water surface elevation, (2) exploiting near-infrared (NIR) multispectral data, to compute the dry/wet pixel reflectance ratio linked to the river flow variations, (3) computing discharge from river width derived from optical images and ancillary discharge data using nonparametric stochastic quantile mapping approach, and (4) combination of these 3 approaches. These products are already available online: https://catalogue.ceda.ac.uk/uuid/dbba9cfe8d104648b19e39f4c2da1a27/. This presentation provides a general overview of the generated discharge products, their validation against ground observations, and their assimilation into basin-scale hydrology models . Furthermore, we will presents preliminary climate assessment based on these products.
How to cite: Biancamaria, S. and the CCI River Discharge precursor project: ESA CCI River Discharge precursor project: current status and challenges, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-9613, https://doi.org/10.5194/egusphere-egu26-9613, 2026.
Abstract: The role of turbulence in heat and mass exchange across flowing water surfaces remains poorly understood. Insights from oceanic wavy surfaces offer useful analogies [1], yet, fundamental differences in directional hydrodynamics and near-surface turbulence limit their direct applicability to flowing rivers and streams. Evidence suggests that intermittent turbulence-interfacial interactions observed from rapid IR imagery of flowing surfaces regulate evaporation rates. A laboratory flume with adjustable bottom roughness and flow configurations was used to generate distinct turbulent and mixing regimes [2]. Synchronized high-speed infrared surface thermography and vertically resolved micro-thermocouple measurements captured the transient evolution of the thermal skin layer and near-surface eddy diffusion characteristics. Preliminary results from shallow-water flows indicate that increased turbulence intensity, reflected in surface thermal fluctuations enhance evaporation rates relative to placid surfaces under similar conditions. Surface renewal theory was employed to quantify the contribution of turbulent renewal events on exchange rates across contrasting flow regimes. Results provide new insights into turbulence-driven interfacial processes and offer a mechanistic basis for improving representations of evaporation dynamics across variable flow conditions in riverine systems.
Reference
[1] Gerbi, G. P., Trowbridge, J. H., Terray, E. A., Plueddemann, A. J., & Kukulka, T. (2009). Observations of turbulence in the ocean surface boundary layer: Energetics and transport. Journal of Physical Oceanography, 39(5), 1077-1096.
[2] Hou, L., Aminzadeh, M., Or, D., Patzke, J., Fröhle, P., & Shokri, N. (2025). Evaporation dynamics from flowing water surfaces (No. EGU25-2716). Copernicus Meetings.
How to cite: Hou, L., Aminzadeh, M., Or, D., Patzke, J., Fröhle, P., and Shokri, N.: How Turbulence Regulates Evaporation from Flowing Water Surfaces, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-14399, https://doi.org/10.5194/egusphere-egu26-14399, 2026.
We investigate non-stationarity in streamflow regimes across Brazil through a large-scale assessment of trends in annual maximum, mean, and minimum discharges under climate variability and change. The analysis is based on daily streamflow records from 515 Brazilian catchments with at least 30 years of continuous observations (1980–2010), obtained from the Catchment Attributes for Brazil (CABra) dataset. For each catchment, annual maximum discharge (Qmax), annual mean discharge (Qmean), and annual minimum 7-day average discharge (Q7,min) were derived using the hydrological year. The primary objective was to identify coherent spatial patterns of hydrological change across major hydrographic regions and to determine which components of the flow regime are most sensitive to non-stationary signals. With this design, we aim to address two research questions: (i) Do non-stationary signals exhibit distinct spatial patterns across Brazil’s diverse hydroclimatic regions? (ii) Which streamflow metrics (high, mean, or low flows) are most sensitive to long-term changes? We adopt a regionalized assessment approach, applying the non-parametric Mann–Kendall test and Sen’s slope estimator to quantify the significance and magnitude of trends. The findings reveal a marked spatial dichotomy and strong metric-dependent sensitivity to non-stationarity. A pervasive decline in minimum flows (Q7,min) is observed across central and northeastern Brazil, indicating a systematic loss of catchment buffering capacity and baseflow resilience. Specifically, the São Francisco basin emerges as the most critically affected region, where 86.3% of catchments exhibit significant reductions in Q7,min and 45.2% show decreasing trends in Qmean. Similarly pronounced declines in low flows were identified in the Parnaíba (70%), East Atlantic (>60%), and Tocantins–Araguaia (54%) basins. Conversely, the Amazon basin displays an intensification of the regional hydrological cycle, with approximately 20–27% of catchments showing increasing trends across all flow metrics ( Qmax, Qmean, and Q7,min). Outside the Amazon, trends in Qmax remain largely stable, suggesting that changes in extreme high flows are less widespread than those affecting low-flow conditions. Overall, our results demonstrate that minimum streamflows are the most sensitive indicators of non-stationarity in Brazilian hydrology. The severe depletion of baseflows—particularly in the São Francisco basin—poses significant risks to water security, hydropower generation, and the viability of large-scale interbasin water transfer projects. The study underscores the limitations of the stationarity assumption in traditional water management and emphasizes the urgent need for region-specific adaptation strategies to manage the increasing vulnerability to hydrological droughts.
How to cite: Andrade, J. M. D., Ribeiro Neto, A., and Nóbrega, R.: Large-Scale Evidence of Non-stationarity in Brazilian Streamflows Across Hydrographic Regions , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-15264, https://doi.org/10.5194/egusphere-egu26-15264, 2026.
Advances in hydrology science are opening new opportunities for using hydrology models as part of larger information systems in order to produce hydrologic reanalysis datasets. These products or services are especially important in areas with limited field-based information. These have to be consistent in terms of available data and in terms of water budget. In this regard, models that integrate surface and groundwater hydrology at fine resolution, based on physical principles, such as the PARFLOW-CLM model, offer advantages as they are fed by measurable parameters, they can be run at large scale, and provide results at high spatial and temporal resolution. Finally these models are able to be upgraded with missing or poorly represented processes.
One example of this continuous improvement is the representation of floodplains: these wetlands correspond to areas that are regularly flooded by large rivers and maintain a complex relationship between surface water, groundwater, and land surface fluxes. Furthermore, due to the flooding conditions, floodplains present highly damageable hydrological risk and high-frequency saturated soil conditions, which favour a significant hotspot of biodiversity, sustain key ecosystem services for human communities, and regulate hydrological flows. But floodplain dynamics are difficult to represent in large-scale hydrologic models because of the control that small-scale topography exerts on water flow and storage. Furthermore in the case of a fine resolution, the simulation must involve the use of explicit relationships and physics-based equations with a high computational cost.
A zone where these difficulties are clearly depicted is the Continental West Africa (CONWA). The CONWA domain covers an area of 3.5 million km² that contains some of the world's largest floodplains, such as the inland Niger River delta. It also covers other smaller intermittent endorheic ponds, with not measured data, and where the combination of wetlands, rivers and aquifers controls both low water levels in dry seasons, downstream high water levels, and induces preferential recharge pathways.
In these perspectives PARFLOW-CLM is implemented in the CONWA domain at 1 km² resolution using the ERA-5 reanalysis, IMERG precipitation dataset, and Copernicus Leaf Area Index data. The methodology representing floodplains prescribe an anisotropic layer near the surface in areas that are “regularly flooded” to allow up-slope flows driven by water head gradient. This anisotropic layer is defined by a depth and a tensor factor affecting horizontal permeability, and allows river grids to connect to neighboring floodplain grids when the water level is high enough to flood them.
We will focus our analysis in disentangle and evaluate the effects of floodplains in three main variables: river discharge seasonality, evapotranspiration, and groundwater storage. These results are important to improve the representation of key hydrologic elements in large-scale hydrology models and Earth system models, and constitute a step toward creating a multi source hydrologic reanalysis system.
How to cite: Arboleda-Obando, P. F., Cohard, J.-M., Zoppis, A., Basile, H., and Pellarin, T.: Improving river-floodplain relationship in PARFLOW-CLM in continental West Africa as an early step to build an multi source hydrologic reanalysis, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-20566, https://doi.org/10.5194/egusphere-egu26-20566, 2026.
Large-scale hydrological models simulating river dynamics for climate studies face limitations from structural, forcing, and process uncertainties. Data assimilation, by integrating observations, mitigates these limitations. In this context, satellite remote sensing (radar altimetry, multispectral sensors) offers long-term spatially distributed hydrological data, compensating for declining in situ monitoring networks.
This study builds on the ESA Climate Change Initiative (CCI) River Discharge precursor project, which develops long-term global satellite-derived discharge and water surface elevation (WSE) datasets for climate applications. A first phase of the project (during year 2024) evaluated the assimilation of either CCI discharge or WSE products into regional to global hydrological models (CTRIP and previously calibrated version of MGB). This evaluation highlighted the added value of discharge products in terms of information content, temporal sampling, and uncertainty characteristics (Sadki et al., 2024, HESS Discuss., https://doi.org/10.5194/hess-2024-328). Building on these results, the present work advances multi-source data assimilation strategies using improved and newly developed CCI products.
Ensemble Kalman filter experiments, conducted with CTRIP-HyDAS and MGB-HYFAA assimilation systems over the Niger and Congo basins, assess the contribution of increased spatial and temporal sampling and the joint assimilation of discharge and WSE observations. Early results highlight the key role of increased temporal density in correcting model biases and internal variability, while revealing the complementary effects of combining spatially dense WSE observations with hydrologically consistent discharge information.
Overall, this work provides new insights into robust multi-observation data assimilation strategies for large-scale hydrological modeling in a climate studies context.
How to cite: Sadki, M., Verma, K., Pedinotti, V., Munier, S., Noual, G., Larnicol, G., Biancamaria, S., Paris, A., Gal, L., Mourot, P., and Albergel, C.: Advancing Large-Scale Hydrological Modeling for Climate Studies through Multi-Source Assimilation of ESA CCI Products , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-17030, https://doi.org/10.5194/egusphere-egu26-17030, 2026.
Rice-based agricultural systems account for the largest share of global agricultural water use and are a major source of methane emissions, yet their hydrological dynamics remain among the least constrained water fluxes in hydrological, land-surface, and greenhouse gas models. This is largely due to difficulty of cloud cover in optical data use in monsoon regions and a lack of validation data for natural and irrigation related soil moisture dynamics due to insufficient ground truthing data and poor resulting satellite product quality.
The implications are becoming more acute due to climate change as major rice-growing regions in India are shifting their practices and adapting to new realities by either decreasing or increasing water use. These further increases the uncertainties in already coarse irrigation and soil moisture products that currently drive models. With rice being both severely affected by climate change and methane emissions from water management in rice fields a key mitigation opportunity – these uncertainties propagate into hydrological and emission assessment globally and locally. Fortunately, recent advances in remote sensing such as AI-driven embeddings such as a AlphaEarth, new satellites such as the NISAR L-Band, and continuously increasing computational power and deep learning, promise rapid improvements in filling this crucial data gap – but to materialize these promises, ground truthing benchmark datasets will be required to adequately validate and compare these new approaches.
Here, we present our Rice Water Benchmark (RIWA) dataset that were are currently developing across India, Cambodia, the Philippines and Vietnam with multiple colleagues and partners. The dataset contains sub-weekly soil moisture and water level readings from more than 300 rice fields across multiple seasons that capture spatial and temporal heterogeneity of soil water status. We further present initial results for how these datasets can be used to evaluate different remote sensing approaches for predicting soil moisture and water management in rice fields – that can also be applied to other crops – and how this matters for hydrological and methane modelling applications.
Besides, we discusses challenges for data quality that include consistency, deployment of low-cost devices, spatial representativeness and the need for auxiliary data such as irrigation events timings from regular phone surveys. By developing harmonized and transparent global datasets for water use in agriculture will be crucial to fully utilize the promise of advances in remote sensing, digital hydrology and digital agriculture and the use of AI for global cereal systems, of which rice provides an important stress test due to its complex water management regimes.
How to cite: Urfels, A., Deb, P., Sankar, H. N., and Arenas-Calle, L.: Rice systems as a stress test for hydrological and methane modelling: Developing rice water (RIWA) benchmark dataset for remote sensing of soil moisture and water levels in rice fields across Asia, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-15574, https://doi.org/10.5194/egusphere-egu26-15574, 2026.
Drylands are confronting dual challenges of intensifying drought and insufficient water vapor supply, which threaten regional ecosystem stability and food security. As a critical moisture source, terrestrial evapotranspiration (ET) may buffer such risks by enhancing regional water vapor cycling; however, the role of ET increase induced by vegetation restoration in supplying water vapor to downwind drylands remains poorly understood. This study investigates the drought-mitigating effect of ET increase driven by vegetation restoration (initiated by the Grain-for-Green Program since 2001) on China’s Loess Plateau, focusing on its impact on downwind dryland regions. Results indicate that since 2001, vegetation restoration on the Loess Plateau has substantially increased annual mean ET. Simulations using a Lagrangian-trajectory-based PyTraject method show an expanded water vapor transport contribution range, with the northeast direction as the primary pathway, indicating a notable increase in water vapor supply. By integrating a Copula model with extreme water vapor deficit scenario analyses, we identified key convergence zones where water vapor export from the Loess Plateau significantly alleviates drought severity in downwind dryland areas. Further analysis reveals that regions more strongly influenced by this water vapor transport exhibit lower actual drought occurrence probabilities—particularly in May–June, when ET increase from vegetation restoration can reduce the probability of severe drought in downwind dryland regions by up to 7%. This study demonstrates that under vegetation restoration, the Loess Plateau plays a stable and sustained regulatory role in supplying water vapor to downwind drylands, thereby enhancing drought resilience and supporting ecosystem stability and food security in these regions.
How to cite: Wang, C. and Yang, T.: Loess Plateau Vegetation Restoration Enhances Water Vapor Transport to Mitigate Drought in Downwind Drylands, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-15613, https://doi.org/10.5194/egusphere-egu26-15613, 2026.
The wetness conditions of a river basin, besides rainfall characteristics, are important factors for the amount of runoff that is generated, eventually leading to a flood event. Satellite gravimetry with GRACE and its successor mission GRACE Follow-On (GRACE-FO) allows for retrieving terrestrial water storage (TWS) anomalies by measuring temporal variations of the Earth’s gravity field.
In this study, we use TWS anomalies derived from daily GRACE/GRACE-FO data downscaled to a 50 km global resolution. This opens the possibility of estimating the wetness conditions before and during flood events. We collect a set of historical flood events at the global scale from multiple sources, including Dartmouth Flood Observatory (DFO). To complement our analysis, we use additional datasets, such as the GRDC global river discharge database. Within these datasets, flood events are identified as periods where hydrometeorological time series (e.g., river discharge, cumulative precipitation, or soil moisture) exceed certain thresholds. During flood events, we check whether exceptionally high storage or discharge anomalies can be observed in the GRACE-based high-resolution TWS. The evaluation is based on correlation analysis of the temporal event dynamics to assess the consistency in the timing and magnitude of peaks. Furthermore, we assess which regional scales (down to <100,000 km²), flood types, and hydro-climatological zones yield the most prominent signals. We expect stronger TWS anomalies for rainfall-driven floods and flood events of larger magnitude. The results can contribute to improving global flood monitoring and flood early warning systems.
How to cite: Guo, Y., Güntner, A., Latinovic, M., and Merz, B.: Flood monitoring with high-resolution TWS data from satellite gravimetry, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-4978, https://doi.org/10.5194/egusphere-egu26-4978, 2026.
