Groundwater’s role in supporting ecosystems worldwide is rarely acknowledged. Groundwater-dependent ecosystems (GDEs), which depend on groundwater for some or all of their water needs, are diverse and include desert springs, mountain meadows and streams, coastal wetlands and forests. However, the location of these ecosystems worldwide has been largely unknown, hindering our ability to track impacts, establish protective policies, and implement conservation projects.

Here, leveraging Earth Observation datasets, random forest modelling, and multiple national and state-level GDE mapping initiatives, we map GDEs across global drylands at high resolution (1 arc-second, roughly 30 m pixels). We find GDEs present on over 8.3 million km² -- more than one-third of areas analyzed, including important biodiversity hotspots. GDEs are found to be more extensive and contiguous in pastoral landscapes with lower rates of groundwater depletion, suggesting that many GDEs are likely to have already been lost due to land and water use practices. Over half of GDEs exist within regions showing declining trends in regional groundwater storage, and only one-fifth of GDEs exist on protected lands or in jurisdictions with sustainable groundwater management policies, invoking a call to action to protect these vital ecosystems.

Cultural and socio-economic linkages with GDEs further underpin these protection needs. The Greater Sahel serves as a case study of these factors, where GDEs play an essential role in supporting biodiversity and rural livelihoods, and which we use as a basis to discuss other means for GDE protection in politically unstable regions. 

Our GDE map provides critical information for prioritizing and developing policies and protection mechanisms across various local, regional or international scales to safeguard these important ecosystems and the societies dependent on them. An interactive version of our global GDE and GDE probability maps are available at https://codefornature.projects.earthengine.app/view/global-gde. 

Reference

Rohde, M.M., Albano, C.M., Huggins, X. et al. Groundwater-dependent ecosystem map exposes global dryland protection needs. Nature 632, 101–107 (2024). https://doi.org/10.1038/s41586-024-07702-8 

How to cite: Huggins, X. and Rohde, M. M. and the Rohde et al. 2024 co-authors: Groundwater-dependent ecosystem map exposes global dryland protection needs, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-14986, https://doi.org/10.5194/egusphere-egu25-14986, 2025.

Terrestrial Water Storage (TWS) represents the total amount of water stored on land, which can be measured using satellite gravity missions like the Gravity Recovery and Climate Experiment mission (GRACE) and its Follow-On mission (GRACE-FO), as well as future gravity missions. Integrating TWS data into hydrological models through Data Assimilation (DA) frameworks has been shown to enhance TWS simulations by introducing long-term trends and adjusting seasonal variations. DA is often carried out using sequential ensemble-based methods such as the Ensemble Kalman Filter (EnKF), which is preferred for its straightforward implementation. The EnKF combines model predictions and observations in a Bayesian manner, i.e., weighting them based on their uncertainties. It then uses ensemble statistics to disaggregate the spatially coarse TWS increments into finer model grids and different vertical water storage components. Typically, EnKF-based TWS DA experiments use small ensemble sizes of 20-30 members to minimize computational demands. However, this can lead to spurious correlations that negatively impact the vertical and horizontal increment disaggregation, thus affecting model dynamics.

In this study, we aim to (i) understand how standard ensemble-statistics-driven disaggregation affects DA results, and (ii) propose an alternative filter that avoids using ensemble statistics in the disaggregation process. This new filter follows the design of sequential ensemble-based DA but introduces a new TWS disaggregation scheme, distributing the TWS increment according to the water content of each grid cell and vertical water storage component. We evaluate the performance of both filters by assimilating synthetic and real TWS observations from various regions worldwide. Our results indicate that both filters produce similar monthly TWS estimates that align well with the assimilated observations. However, the EnKF’s increment disaggregation leads to some issues, such as (i) discrepancies between DA results and ground truth for individual water storage component estimates (in the case of synthetic experiments) and (ii) a rapid divergence of model states from the updated state within a few daily timesteps after DA. These issues are particularly noticeable on a sub-monthly timescale but can also extend over several months in some periods and regions. The new filter proposed in this study mitigates these issues, resulting in more accurate estimates for individual components in synthetic experiments and a more natural model response to DA updates overall.

How to cite: Retegui-Schiettekatte, L., Schumacher, M., Yang, F., Madsen, H., and Forootan, E.: Terrestrial Water Storage Data Assimilation into large-scale hydrological models: a new sequential filter to mitigate errors of ensemble-based disaggregation schemes, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-10605, https://doi.org/10.5194/egusphere-egu25-10605, 2025.

With time series ranging over more than twenty years, terrestrial water storage (TWS) variations observed by the Gravity Recovery and Climate Experiment (GRACE, 2002-2017) and GRACE-Follow-On (GRACE-FO, since 2018) missions are providing unique insights into hydrological dynamics, on a global scale. TWS encompasses changes in all water storage compartments, from soil moisture, surface water storage, snow and ice, to groundwater. GFZ operationally provides monthly TWS grids via the GravIS portal (Gravity Information Service, gravis.gfz.de).

Within the G3P project, GFZ recently released a global gravity-based product that includes both TWS variations and also groundwater storage (GWS) variations. GWS is calculated by subtracting the aggregated and filtered storage contributions of the other water storage components, from GRACE/-FO TWS. A challenge for both TWS and GWS is the separation of long-term trends (e.g., resulting from regional human activities and climate change) from stochastic variations as attributable to natural climate variability ("climate noise").

To address this challenge, we introduce an unsupervised trend analysis framework that uses power-law noise models to account for long-range memory in the hydrological time series under investigation. This approach requires minimal assumptions about the underlying processes and provides a robust method for separating long-term trends from stochastic variability. By addressing the limitations of existing methods, such as underestimated uncertainties and simplified noise representations, our framework allows for accurate quantification of trend magnitudes and of their significance. Firstly, this is confirmed for TWS, by comparing reported trends to our detected trends. Concerning the GWS product, we observe that anthropogenic depletion of groundwater is a primary driver of freshwater decline. Furthermore, we reveal previously unobserved trends, including increasing groundwater levels in parts of Africa and declining trends in central and eastern Europe. We also demonstrate how the presented method identifies potential false-positive trends, which enhances the reliability of trend detection. This scalable approach for trend analyses is currently extended to integrate uncertainties that arise from measurement system uncertainties, enhancing its applicability to other essential climate variables.

How to cite: Hohensinn, R., Gou, J., Meyer, U., Boergens, E., Soja, B., Güntner, A., Humphrey, V., Rast, M., and Dorigo, W.: Global water storage trends as observed from the GRACE/-FO G3P product, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-10755, https://doi.org/10.5194/egusphere-egu25-10755, 2025.

The impacts of natural climate variability and anthropogenic water use on our global water resources can be observed from space by dedicated satellite missions or simulated by global hydrological models. It is, however, difficult to quantify the relative contribution of fundamental drivers of terrestrial water storage (TWS) changes, e.g., due to a lack of data or processes in models and the limited vertical and spatial resolution of satellite data sets. Regions that are challenged by acute or constant water stress, as well as areas with increased flooding risk, would benefit from a better understanding and quantification of the main drivers of surface and sub-surface water storage changes.

In this study, we identify the main drivers of TWS changes due to natural and human-induced impacts under changing climate. We analyse almost two decades (2003-2021) of TWS changes simulated by the WaterGAP Global Hydrology Model (WGHM) and compare them to observations from the satellite gravity missions GRACE and GRACE-FO. The relative contribution of individual water storage components to TWS is calculated. At large-scale, their variations are found to correlate with natural processes, i.e. precipitation, evapotranspiration, and river outflow. In addition, the influence of human interventions on the water cycle are identified as episodic and long-term effects on the surface water and groundwater extraction. We analyse the largest river basins (>200.000km²) world-wide to identify regions under acute or chronic water stress.

How to cite: Klemmensen, S., Forootan, E., Nyenah, E., Döll, P., and Schumacher, M.: The impacts of climatic variations and human water use on global and regional terrestrial water storage changes, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-15623, https://doi.org/10.5194/egusphere-egu25-15623, 2025.

How to cite: Chauhan, T. A., Ghausi, S. A., Hildebrandt, A., and Kleidon, A.: Correcting overestimated potential evaporation from the Penman-Monteith equation during water-limited conditions, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-10825, https://doi.org/10.5194/egusphere-egu25-10825, 2025.

How to cite: Kholis, A., Kalbacher, T., Boeing, F., Cuntz, M., and Samaniego, L.: 1-D Richards equation or infiltration capacity approaches? A comparative assessment in mesoscale hydrologic modelling across 201 German basins, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-7161, https://doi.org/10.5194/egusphere-egu25-7161, 2025.

Global methane emissions from freshwater offsets 25% of terrestrial greenhouse gas sink (Bastviken et al., 2011). Global rivers contribute roughly the same as lakes to this emission (Stanely et al., 2016; Rocher-Ros et al., 2023). Global warming is set to exacerbate this further as warmer water leads to lower levels of dissolved O₂, reduced CO₂ capture, and increase in methane production and eutrophication. Understanding the thermal inflow from rivers to lakes and reservoirs is, therefore, essential to monitor (and forecast) the exceedance of emission critical values.

Large-scale hyper-resolution modeling allows for locally relevant analyses, a feature recently achieved for hydrological modeling at continental-scale and global-scale (Hoch et al,. 2023; van Jaarsveld et al., 2025). While global river and lake temperature models exist (van Vliet et al., 2011; Wanders et al., 2019), hyper-resolution modeling of river thermal content at the global scale is yet to be demonstrated.

Here, we analyze the changes in thermal inflows to surface water bodies (lakes and reservoirs) globally at 1 km based on the river temperature routing module implemented within the mesoscale hydrological model (mHM, https://mhm-ufz.org). Surface water temperature in rivers is modeled by balancing the heat exchange between the atmosphere and river water while accounting for energy sources from the sub-surface systems. The experimental setup is based on Shrestha et al. (under review), where 62 domains cover the global land-surface and the simulation period is set to 1961-2020. The setup includes major global lakes and reservoirs enlisted in the HydroLAKES and GRanD v1.3, respectively, the process representation of which follows Shrestha et al. (2024).

We have evaluated the model at 5,000 streamflow observations from the Global Runoff Data Centre and 500 river temperature observations from Global Environment Monitoring System. We have also carried out sensitivity analysis of water temperature simulations to the surface albedo, where space-time varying albedo is expected to result in a closer match to the observations, than with a constant albedo, besides analyzing the trends and clusters of water temperature and derived indicators. For reproducibility, the experiment backend is powered by ecFlow, the workflow management tool developed by ECMWF. Our modeling framework on the analysis of water and energy inflows, covering surface water systems – lakes and reservoirs – globally forms a basis for timely warning of critical events with high thermal inflows, and such systems could see far-reaching applications, e.g., insurance underwriting for the fisheries industry. 

How to cite: Shrestha, P. K., Kumar, R., Mueller, S., Thober, S., Attinger, S., and Samaniego, L.: Source or Sink? Thermal inflow to global reservoirs and lakes at 1 km, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-13249, https://doi.org/10.5194/egusphere-egu25-13249, 2025.

Global hydrological models (GHMs) have significantly advanced in process representation and spatial resolution over the past four decades. These advancements include the inclusion of reservoirs. However, significant needs and opportunities remain to improve these models, particularly for better representing human-environment interactions and reducing model uncertainties by improved integration of model output observations.

As research questions and GHMs become more complex, maintaining and further developing an existing model code in an efficient manner becomes increasingly challenging. Similar to other complex research software, GHMs are developed by scientists with limited software development training, time, and funding, and thus lack the software quality that is required for a sustainable research software. This includes non-modular design, poor variable naming, suboptimal comment density, and a lack of testing frameworks. The sustainability of GHMs could be significantly enhanced by reprogramming them using modern best practices.

While global models such as HydroPy and CLASSIC (a global land surface model) have been reprogrammed, publications on the reprogrammed software focus on evaluating model performance. Details in the reprogramming process, from project management to final software release, are missing. Here, we present the process of reprogramming the GHM WaterGAP to a sustainable research software. This involves rewriting WaterGAP from scratch, introducing improvements such as modular architecture, Python programming, version control, open-source licensing, consistent variable naming, comprehensive documentation, and testing, while maintaining good computational performance. We evaluate the reprogrammed WaterGAP code against software sustainability criteria and FAIR4RS principles.

Reprogramming with best practices requires effort but makes the resulting software easier to use, maintain, modify, and extend. With reprogramming WaterGAP, we aim to facilitate joint code development across multiple locations and by various developer groups, thus establishing a community GHM that is easily understood, used and enhanced by novice users.

How to cite: Nyenah, E., Döll, P., Flörke, M., Mühlenbruch, L., Nissen, L., and Reinecke, R.: The Process and Value of Reprogramming a Legacy Global Hydrological Model, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-9720, https://doi.org/10.5194/egusphere-egu25-9720, 2025.

Streamflow intermittence, i.e. the days without streamflow, was predicted with a high spatial resolution for the whole of Europe by downscaling the output of a global hydrological modeling and using the deriving monthly time series for computing predictors, together with other predictors, in a random forest model that simulates the number of no-flow days (Döll et al. 2024(. Development of the data-driven random forest model required a large amount of daily streamflow observations. Now, the challenge is to learn from this modeling work for simulating streamflow intermittence on continents with fewer daily streamflow observations such as South America. What is the quality of simulated streamflow intermittence in South America if we apply the random forest model trained for Europe for South America, i.e. running the model with predictors specific to South America?

We focused on three main aspects: 1) evaluating the similarity of predictor values in the training continent Europe and the application continent South America, 2) conducting sensitivity analysis for the number of observations and 3) utilizing different explainable AI methods. For the first point, we performed two analyses: 1) examining the probability distribution function of 23 predictors across both continents and 2) applying the area of applicability (AOA) analysis for the period 1981-2019. The AOA indicates where the predictor values in South America fall within the range of values that were used to develop the RF model trained on European data. This analysis helps identify areas where the model's predictions are likely to be most reliable, based on the similarity of environmental conditions to those in the training data.

We also analyzed the sensitivity of simulated streamflow intermittence to the number of gauge-months with observed no-flow days by 1) building several different models, each trained on a randomly selected subset of European gauging stations (i.e., 50% of the total), including all monthly values for these gauging stations and 2) evaluating the performance of these models on the remaining gauging stations not used in training and 3) comparing the resulting continent-wide streamflow intermittence patterns across Europe to assess consistency and variability in predictions. Finally, we leveraged various explainable AI methods to analyze the influence of each predictor on the results of the RF model. This analysis helps identify potential biases and understand how models perform across different geographical contexts. Without explainable AI, there is a risk of failing to meet the specific needs of different regions, undermining the model’s effectiveness and reliability when applied across diverse geographical areas.

How to cite: Abbasi, M. and Döll, P.: Cross-continental application of a random forest model for streamflow intermittence from data-rich to data-poor regions  , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-5646, https://doi.org/10.5194/egusphere-egu25-5646, 2025.

The EU-Hydro 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. It is a hydrographic reference dataset part of the Copernicus Land Monitoring Service (CLMS) portfolio, implemented by the European Environment Agency (EEA). The first version of EU-Hydro was developed in 2012, with subsequent updates aimed at improving data accuracy and network topology. EU-Hydro has been widely used for hydrographic mapping applications, among them serving as input to several CLMS productions. However, its use for hydrological modelling remained limited due to inconsistencies and shortcomings in data structure, resolution, and quality. To offer a state-of-the-art next generation of EU-Hydro, it is currently being updated to produce an improved and upgraded version of this unique European reference dataset. Highlighting the importance of water mapping and modelling, the new version of EU-Hydro (EU-Hydro 2.0) shall tackle the requirements of a modern reference product within the pan-European hydrological domain, serving various use cases, 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 database will build upon a latest generation Digital Elevation Model (DEM) to provide highly detailed and high-quality topographic input data: 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 upstream and downstream that flow in and out of the EEA38 +UK area (EU27 + European Free Trade Association (EFTA) + Western Balkans + Turkey + UK). The production of EU-Hydro 2.0 will involve the best possible ancillary data of hydrography, land cover, and infrastructure to allow seamless integration into the DEM editing process, as well as VHR satellite data for quality control 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) maps, 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). All layers will be interrelated, scalable and logically consistent. The approach aims at transparency and automation to-the-extent-possible, supported by manual corrections where needed to increase quality and meet user requirements. This will ensure efficient and reproducible data processing and facilitate further updates of EU-Hydro into the future.

How to cite: Lehner, B., Moser, L., Roth, A., Mortel, G. M., Lindmayer, A., Warmedinger, L., Grill, G., Wegscheider, S., Keller, C., Ram, S., Wetzel, A., Kampouraki, M., Huber, M., Rubio Iglesias, J. M., and Przystawaska, J.: Introducing EU-Hydro 2.0: A Copernicus high-resolutionhydrographic product across Europe based on latest generationelevation and ancillary data, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-21410, https://doi.org/10.5194/egusphere-egu25-21410, 2025.

Hydrological models are crucial for evaluating the water cycle, offering decision makers vital insights into floods, droughts, and water resource management, while enabling scenario analysis under different natural and anthropogenic conditions. One example is the open-source hydrological model OS-LISFLOOD that is used to generate flood forecasts and drought indicators for the European and Global Flood Awareness Systems (EFAS & GloFAS) as well as the European and Global Drought Observatories (EDO & GDO) of the Copernicus Emergency Management Service (CEMS).

OS-LISFLOOD is a distributed, physically based rainfall-runoff model. Being used in an operational setting, the hydrological model and its European and global model domain set-up benefit from regular upgrades. In its current operational version, the global model set-up (GloFAS v4.x) uses a spatial resolution of 3 arcminutes (~5.4 km) and a daily time step, whereas the European model set-up (EFAS v5.x) uses a spatial resolution of 1 arcminute (~1.8 km) and a 6-hourly time step. Both set-ups are used to provide a hydrological reanalysis as well as hydrological predictions.

A specific feature of the European and global model set-up of OS LISFLOOD is that not only the model and associated tools for pre-/post-processing, calibration, etc. are open-source, but also the required input and calibrated parameter maps are freely accessible. This allows users to benefit from the latest developments and, more importantly, it enables a wider community in contributing to further extending and improving the model and its set-up.

In this presentation we describe the next major evolution of OS LISFLOOD and its set-up for the European (EFAS v6.x) and global domain (GloFAS v5.x). The main foreseen changes can be grouped into three categories: 1.) model input; 2.) model improvements; and 3.) calibration and regionalization.

The main changes in the model input concern the meteorological forcings. For the European domain, the meteorological forcings benefit from an increased number of meteorological observations, improved quality control, and a modified interpolation method. In the global model domain, enhancements include a correction of spurious rainfall and a modified downscaling of ERA-5 meteorological variables. Furthermore, changes in the surface fields related to soil properties, lakes and reservoirs as well as water demand for anthropogenic use integrating the latest available datasets have been included. Hydrological model advancements focus on river routing, in particular for mild sloping rivers, and a modified reservoir routine. Furthermore, the model state initialization has been enhanced and a new modelling routine called transmission loss which accounts for streamflow leakage has been added. For model calibration and regionalization, it is foreseen to increase the number of calibration stations, improve the overall performance of the objective function along the whole flow duration curve, add more hydrological performance statistics, and to utilize the power of deep learning during the regionalization of model parameters.

The improved model, its new set-up as well as the hydrological model reanalysis and predictions will be freely available. Its release as part of the operational EFAS, GloFAS, EDO, and GDO of CEMS is foreseen during 2025. 

How to cite: Salamon, P. and the team of co-authors: Improving hydrological modelling and prediction at the European and Global scale, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-8642, https://doi.org/10.5194/egusphere-egu25-8642, 2025.

There is increasing recognition that providing robust assessments of future water resource availability and water-related risks requires the use of the right models in the right places. Traditionally, selecting or developing an appropriate model for a given basin was possible based on thorough understanding of the dominant hydrologic processes in the basin under consideration. On national, continental, and global scales however, the commonly used method so far has been a “one model fits all” approach. This is in part due to the lack of a comprehensive overview of how dominant hydrologic processes vary across large geographical domains.

Here we introduce a community-driven synthesis effort to address this large-scale hydrologic challenge, focusing on North America as a test case. Over the past half year, we have convened multiple virtual workshops and organized several in-person opportunities to bring together water science experts working in various regions across the continent. The workshops covered five key parts of the continent (the densely populated East and West coasts, the center region used for agriculture, the northern regions that are particularly vulnerable to climate change, and the tropical islands). Invited speakers shared their knowledge, experience, and expertise around the dominant hydrologic processes and existing modeling efforts in these regions. These were followed by structured discussion among the workshop attendees, as well as during dedicated further interactions later, to divide the continent into a manageable number of hydrologic landscapes and to define representative perceptual models of hydrologic behavior for the various parts of each larger region. Here we present an overview of these resulting perceptual models and invite further discussion. Ultimately, these perceptual models can be mapped onto computational models, modules and individual equations, and so support a theory-based large-scale effort to develop the most appropriate hydrologic model for any location in the wider North American domain. The methodology used is not unique to the North American context and similar approaches could be used elsewhere where large-scale synthesis of hydrologic process understanding is desired.

How to cite: Knoben, W., Fan, Y., Garousi-Nejad, I., Masterman, J., McMillan, H., Read, J., van Werkhoven, K., and Clark, M.: Towards a synthesis of perceptual models of dominant hydrologic processes across North America, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-7228, https://doi.org/10.5194/egusphere-egu25-7228, 2025.

Climate conditions and human impact influence surface water variability. Extreme events like river flooding alter interactions between land surfaces and groundwater, affecting sediment and nutrient exchange, ecosystems, and land-atmosphere feedback. Modeling these interactions is challenging due to uncertainties in inputs like floodplain topography, channel morphology, and river flow parameterizations, which impact water and energy balances. Coarse-resolution land surface models (LSMs) (over 10 km) struggle to accurately represent surface water dynamics because they cannot capture complex topography while still accounting for local and regional hydroclimatological dynamics.

This study uses the HydroBlocks Land Surface Modeling framework to address these challenges by resolving land-surface interactions at finer spatial resolutions (~90 m). Through a hierarchical multivariate tiling structure, HydroBlocks overcomes the limitations of coarser models and better represents small-scale heterogeneity. A two-way coupling scheme allows for horizontal water redistribution through the kinematic wave equation.

Water levels are highly sensitive to local factors like channel bathymetry, riverbed slope, and floodplain inundation. Validating water level dynamics requires extensive observations. The launch of the Surface Water and Ocean Topography (SWOT) mission in December 2022 provides high-resolution (~100 m) water surface elevation observations, offering a unique opportunity to study flooding dynamics and improve its representation in LSMs.

This study aims to enhance understanding of water surface dynamics by comparing SWOT observations with HydroBlocks simulations. This integrated approach provides insights into localized and broader trends in water surface elevation, enabling the identification of groundwater signatures and climatological influences. By validating HydroBlocks against SWOT data and conducting sensitivity analyses, the study aims to improve understanding of processes controlling flooding dynamics and better inform the structure of LSMs and spatially distributed validation strategies.

The study examines the Connecticut River watershed, covering 29,200 square kilometers across six northeastern U.S. states, with elevations ranging from sea level to over 1,200 m. Seasonal variations in precipitation and snowmelt create a complex hydrological system, making it suitable for our model validation.

How to cite: Guyumus, D. and Chaney, N.:  Assessing the Performance of Land Surface Models in Representing Flood Dynamics: A HydroBlocks-SWOT Approach in the Connecticut River Basin, US, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-14409, https://doi.org/10.5194/egusphere-egu25-14409, 2025.

Assessment of Environmental flow (E-flow) plays a vital role for achieving sustainable water resources management. This study intends to develop suitable methodologies for assessing E-flows in the middle reaches of the Ganga River. The methodology includes developing a hydrological model, hydrological alteration analysis, stream health assessment, and E-flow estimation using flow health indicators. First, a hydrological model has been simulated, calibrated and validated in northern and southern tributaries of Ganga River (namely Kosi, Gandak and others) of the study region. The outcomes of the model reveals that the monthly discharge for ungauged basins is promising and aids in completing the water balance analysis for the entire reach. Second, hydrological alternation analysis is carried out using Indicators of Hydrological Alternation tool in the tributaries and the main river. The analysis indicates that no significant changes occurred in the river and its tributaries' flow for the last four decades. However, the water balance flow chart shows notable variations in the interaction patterns between surface water and groundwater. Third, stream health conditions of the river are analysed by using Flow Health tool. The results represent the natural flow variation of the stream for the analysis period. Since the deviation between natural and observed flows reflects the stream's health condition, a detailed stream health analysis is carried out by considering various combinations. The analysis reveals that stream health and its temporal variation of upstream are good with the less temporal variation. At major tributary level, the stream health and its temporal variations are deteriorating from 1970s. At the main Ganga reach level river health and its temporal variations does not show any declining trend. Finally, E-flows are estimated in two methods; computing ten percent Mean Annual Flow and computation of monthly E-flows using Flow Health. The outcome of the study demonstrates, Flow Health tool can be applied for estimation of the variations in the monthly E-flows in the case of rivers like Ganga.

How to cite: Laveti, N. V. S. and Dutta, S.: Assessment of Environmental Flows and River Health in the Middle Regions of Ganga River and its Tributaries, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-16858, https://doi.org/10.5194/egusphere-egu25-16858, 2025.

Global hydrological models represent the terrestrial water cycle across the globe and help to study the impacts of climate change on water stress and flood risks. They are generally based on the coupling of a land surface model and a river routing model (RRM). RRMs were first created to close the water budget at the global scale in climate studies. They convey the runoff generated by land surface models to the sea by propagating the water through the river network. In the climate model community, for example in the CMIP6 exercise, most models use a very simplified routing scheme such as the kinematic wave, or no routing scheme at all. With the increase of computing capacities and observational global datasets, there is a recent and general tendency to increase the spatial resolution of models. As a consequence, some processes that can usually be neglected at coarser resolutions (such as backwater effects or overbank flows) have to be accounted for. More complex RRMs have been developed based on simplifications of the Saint-Venant equations (e.g., the local inertia approximation). They allow to more realistically represent the flow dynamics in rivers, and are then better suited to higher resolutions. In parallel, numerical methods like the Preissmann scheme are used in the hydraulic community to solve the full Saint-Venant equations for river reach to catchment scale applications. Yet, such methods are not adapted to global scale simulations due to their high computing demand. With the increase of computing capacities, the hydraulic community is also evolving towards larger scale modelling. Both communities tend to get closer, and there is a scientific debate on the best approach to improve process based hydraulic models.

The CTRIP model (CNRM version of the Total Runoff Integrated Pathways) is the RRM developed at CNRM (Météo France). Currently, CTRIP simulates the propagation of river discharges using the kinematic approximation of the Saint-Venant equations. Our study aims to complexify the routing scheme of CTRIP and try to investigate at which optimal complexity river dynamics should be simulated over various basins. As a first step, the complete Saint-Venant equations are integrated by a Crank Nicolson scheme with a Gauss Seidel iterative method in CTRIP. This scheme runs over the globe with a reasonable computing time. It is then comparatively analysed with the Saint-Venant equations integrated with a Preissmann scheme with a double sweep method, over an idealized test channel. Then, simplified models can be derived from the complete model of Saint-Venant, by neglecting terms of the momentum equation. Different wave types can be obtained: dynamic (with or without advection), gravity, diffusive or kinematic waves. We analyse over France at 1/12° resolution the governing conditions of those wave types (slope and friction bed, wave period) and the order of magnitude of the Saint-Venant terms over the domain. The complete model is compared to the simplified models in term of stability, physical realism, and computing time, and then evaluated against discharge observations.

How to cite: Peronnet, E., Decharme, B., and Munier, S.: Investigation of the optimal complexity to simulate flow dynamics in a global river routing model., EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-11074, https://doi.org/10.5194/egusphere-egu25-11074, 2025.

Forest restoration and hydropower production play a key role in mitigating climate change. However, they both depend on water availability, and influence the regional water distribution. It is therefore essential to understand how forest restoration affects hydropower potential under current and future climates. Here, we investigate these interactions on a global scale through an interdisciplinary approach using the Budyko framework. The analysis draws on cutting-edge datasets, including potential tree cover change, high-resolution climatic variables, and moisture recycling data. We assess the impact of regional and global afforestation scenarios on hydropower potential across current climate and future change scenarios (SSP1-2.6 and SSP3-7.0). While regional restoration generally results in a net negative impact on water availability (−13.4 mm yr⁻¹), global restoration helps mitigate this effect (−6.9 mm yr⁻¹). Similarly, global restoration yields more positive and fewer negative effects on hydropower potential compared to regional restoration. Future climate projections suggest a net positive impact on hydropower potential, though with more pronounced positive and negative effects at the dam catchment scale.  We stress that it is essential to consider the interaction between forest restoration and climate impacts on renewable energy systems, for the effective prioritization of forest restoration plans. Future research should focus on process-based models that better capture seasonal climate variability and account for feedback effects of restoration on moisture recycling. Furthermore, these models should integrate actual reservoir operations to accurately represent hydropower production within natural and physical constraints.

How to cite: M. Ali, A., Hoek van Dijke, A. J., Roebroek, C. T. J., van Emmerik, T., Scheffer, P. J. J. P., and Teuling, R.: Global hydropower potential affected by interplay between forest restoration and climate change, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-18996, https://doi.org/10.5194/egusphere-egu25-18996, 2025.

Global streamflow, a critical resource for ecosystems, agriculture, and human activities, is influenced by various factors, including rising atmospheric CO₂ (eCO₂). Through direct regulation of vegetation physiology and structure, eCO₂ can either increase or decrease streamflow. However, despite a 21% rise in CO₂ over the past 40 years, its impact on streamflow remains unclear and subject to ongoing debate. This study evaluates the effects of eCO₂ on global streamflow from 1981 to 2020, focusing on its direct regulation of vegetation. Using a dataset of 1,116 unimpacted catchments, we find that precipitation is the dominant driver of streamflow changes, accounting for over 70% of observed variability. In contrast, eCO₂ exhibits a negligible influence, with its median contribution approaching zero across catchments. At the global scale, attribution analyses conducted via the regularized optimal fingerprinting method for 14 global ecological model simulations confirm that climate change predominantly explains streamflow trends. No significant evidence supports attributing these changes to eCO₂ or land-use change. Observation-constrained models further enhance the robustness of these findings by reducing uncertainties inherent in global ecological models. These results highlight the limited role of vegetation responses to eCO₂ in driving global streamflow changes, underscoring the primacy of climate variability. This improved understanding of hydrological responses to rising CO₂ is vital for refining future water resource management and adaptation strategies under changing climatic conditions.

How to cite: Zhang, Y. and Wei, H.: Limited contribution of recent elevated CO2 to global streamflow changes, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-2799, https://doi.org/10.5194/egusphere-egu25-2799, 2025.

Large-ensemble global river flow projections are crucial for assessing future changes in extremes of river discharge in light of internal climate variability, model uncertainty, and anthropogenic climate change. However, river discharge simulations from global hydrology models often consider only a limited number of climate projections, while global climate models usually focus on grid-cell level runoff only.

To bridge this gap, we present a new global river discharge dataset covering 250 years derived by routing runoff from 20 global climate models from CMIP6 along the river network. Specifically, we consider daily runoff from both the historical CMIP6 experiment (1850–2014) as well as the most extreme future scenario (SSP5-8.5, 2015–2099). Routing is computed at a 0.1 degree horizontal resolution using the multi-scale routing model mRM, which implements the kinematic wave equation and is adaptable to a wide range of spatial scales. For the validation of the new dataset, we compare distributional properties of annual maximum (1-day) and annual minimum (7-day) river flow to observations at almost 2000 GRDC-Caravan gauge stations. To this end, we use mean squared error (MSE) decomposition to additionally examine the contribution of different error sources. We find that the squared bias is the most important MSE component at each gauge station for both annual extreme statistics while the shape of the distribution is simulated more accurately. Building on these validated river discharge simulations, we project changes in high, low, and mean flows and evaluate the agreement between the 20 ensemble members. This way, the robustness and range of the projections can be estimated considering uncertainties from both global climate models as well as internal climate variability.

How to cite: Seubert, P., Thober, S., Schumacher, D. L., Seneviratne, S. I., and Gudmundsson, L.: Global river discharge projections from 250 years of routed runoff from 20 CMIP6 climate models, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-11544, https://doi.org/10.5194/egusphere-egu25-11544, 2025.

Flash floods present significant challenges for monitoring and forecasting due to their rapid onset. While extreme rainfall events, a primary driver of flooding, are becoming both more intense and frequent, yet there remains no consensus on whether floods will exhibit similar trends in a warming climate. Here we assess flash flood risk development over four decades across 741 basins globally using the flashiness index. Our findings reveal a shift towards flashier floods in over one-third of basins, predominantly concentrated in the mid-northern latitudes. We identify basins characterized by rising flood peaks and those marked by shorter onset times. By examining these types, we attribute the changes in basins with increasing flood peaks to increased extreme rainfall, whereas the causes in basins with shortening onset times are more complex involving multiple drivers. Regions with rising flash flood magnitudes are likely to require flood defense infrastructure capable of withstanding more severe flood events, while regions with shortening onset times may face challenges in implementing short-term early warning systems. By identifying regions prone to flash floods and highlighting global hotspots, the study offers valuable insights for policymakers to design effective flood management strategies. These findings underscore the urgency of implementing region-specific strategies to adapt to flashier floods in a warmer future.

How to cite: Zhou, F., Wang, S., Slater, L., Lin, P., AghaKouchak, A., Yang, H., and Tang, S.: One-third of global basins facing flashier floods in a warming climate, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-2384, https://doi.org/10.5194/egusphere-egu25-2384, 2025.