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.
Accurate estimation of potential evaporation is crucial for water resource management and trends in continental aridity. Potential evaporation is widely estimated using the Penman-Monteith (P-M) equation which has two main terms: the radiative forcing term, and the atmospheric dryness term, also called the aerodynamic component, which depends on the vapor pressure deficit (VPD). However, the aerodynamic component of the P-M equation overestimates potential evaporation in the presence of water limitation, exceeding the limits imposed by surface energy balance. This inconsistency arises because the high VPD in arid regions does not represent entirely the conditions of the idealized wet surface — an important underlying assumption for potential evaporation. Here we show that changes in VPD are mainly caused by changes in the diurnal temperature range (DTR). For a given radiative forcing, soil water limitation amplifies the DTR through a reduction in the latent heat flux, this enhances the VPD, and therefore the aerodynamic component, yet without enhancing energy availability. We quantify this overestimation using FLUXNET observations and ERA-5 reanalysis data in combination with a thermodynamically-constrained surface energy balance approach. We find that soil water limitation amplifies DTR by up to 20 K, which increases VPD by up to 25 hPa. This additional VPD generates very high potential evaporation estimates in the aerodynamic component that exceed the available energy at the surface by over 100 W/m². When we remove the imprints of water limitation from DTR and VPD, the P-M equation leads to reduced potential evaporation rates approaching consistency with the surface energy balance. These results have significant implications for quantifying continental aridity and its changes with global warming.
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.
Soil moisture (SM) infiltration is crucial in hydrological modeling, as it significantly influences runoff, groundwater recharge, and evapotranspiration. This study compares two widely used approaches for modeling SM infiltration in mesoscale hydrology: the one-dimensional Richards equation (1-D RE), which controls vertical flux exchange but is complex and nonlinear, and the infiltration capacity (IC) scheme, which is simpler and only allows downward movement of SM. The challenge in implementing the RE lies in determining effective parameters at the targeted resolution (typically several hundred to thousands of meters) and ensuring computational efficiency. This is because the RE is inherently nonlinear and was developed for much finer scales than those used in typical simulations. As a result, RE-based land surface models (LSMs) have often underperformed compared to those using the IC scheme.
To address this challenge, an experiment was conducted using the mesoscale Hydrologic Model (mHM) equipped with Multiscale Parameter Regionalization (MPR) to parameterize both the RE and IC approaches, keeping everything else equal (Kholis et al. 2024). To improve computational efficiency, Ross’s fast numerical solution was employed, utilizing the Kirchhoff transform to linearize the RE via matric flux potential (MFP). The RE parameterization involved the use of three distinct pedo-transfer functions (PTFs): Cosby for mHM-RE1, Campbell for mHM-RE2, and Rawls & Brakensiek for mHM-RE3. These model parameters were estimated across randomly selected basins in Germany and subsequently validated with streamflow data across 201 basins at multiple resolutions, as well as with soil moisture observations from 46 sites (0-25 cm depth) and 42 sites (25–60 cm and 0–60 cm depths).
The results demonstrate that mHM-IC and all mHM-RE variants perform comparably well in predicting streamflow. The application of MPR facilitates the transferability of PTF parameters across different scales and areas. Due to its two-way flow mechanism, the mHM-RE variants show better predictability of SM, especially in deeper soil layers. However, for large catchment areas, these variants can be up to six times slower than that with IC. Although the IC approach can sometimes lead to saturation in deeper soil layers, it still provides good predictability for SM anomalies. Importantly, the choice of PTF is critical for the performance of RE models, as parameterization discrepancies, such as overestimated saturated hydraulic conductivity (Ks) and porosity (θs) in mHM-RE2, can lead to overpredicted SM values, even when streamflow simulations are accurate. This study highlights the potential of mHM-RE for generating transferable parameters and achieving reliable streamflow and SM simulations, provided that appropriate PTFs are carefully selected to minimize parameterization errors. We conclude that the poor performance of RE-based land surface models with respect to streamflow prediction is likely due to deficient parameterization or the use of an inefficient RE solver.
References:
Kholis, A. N., Kalbacher, T., Rakovec, O., Boeing, F., Cuntz, M., & Samaniego, L. E. (2024). Evaluating Richards equation and infiltration capacity approaches in mesoscale hydrologic modelling. Authorea Preprints. https://doi.org/10.22541/essoar.173532490.04454195/v1
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.
Global warming has resulted in a continuous rise in sea levels, posing significant challenges to coastal and island ecosystems. While atoll islands have largely avoided widespread erosion in recent decades, the majority of existing studies has concentrated on atolls within the Pacific and Indian Oceans, leaving the broader study of global islands insufficiently addressed. Notably, the melting of glaciers and snow in boreal regions is a major contributor to sea level rise, heightening erosion risks for islands in northern latitudes.
To bridge this research gap, the study adopted a comprehensive approach by encompassing islands across all five climatic zones. Its primary objective is to assess the Changes in morphology and ecological structure and of small and medium-sized islands over the past 40 years. Leveraging data from Landsat 5, 7, 8 and 9 satellites, the study employed image normalization techniques to enhance the identification of smaller islands and applied support vector machines (SVM) to classify normalized difference spectral vector (NDSV) images. This methodology enabled the detailed analysis of shifts in area, shape and ecological community composition, while also investigating the underlying factors driving these changes.
Recognizing the diverse climatic dynamics across temperate zones, the study also incorporated a region- and size-specific evaluation framework to improve the accuracy of erosion pattern predictions for atoll islands. The findings reveal that islands with larger populations and close to the mainland demonstrate greater resilience to erosion, largely due to the benefits associated with artificial reinforcement. The study highlights that land area changes are predominantly influenced by human activities, particularly in the Maldives and the South China Sea. Furthermore, alterations in shallow reef ecosystems emerge as a critical driver of island size variability.
Finally, the research explored the relationship between island size and erosion, emphasizing the significant proportion of smaller islands among those experiencing changes in area and shape. These insights provide a nuanced understanding of the interplay between anthropogenic and ecological factors shaping island dynamics in the context of rising sea levels.
How to cite: niu, L.: Global-scale changes in the area of atoll islands, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-1325, https://doi.org/10.5194/egusphere-egu25-1325, 2025.
Europe was plagued by severe drought events over the first decades of the 21st century, most notably in the years 2003, 2015, and 2018. Meteorological drought conditions affected large parts of the continent, which in turn led to widespread streamflow deficits that affected many rivers simultaneously. Understanding such spatio-temporal patterns of streamflow droughts is important for drought relief, but few studies have investigated in detail how such spatio-temporal drought characteristics change in space and time. In this study, we set up the PCR-GLOBWB global hydrological model over Europe at a hyper-resolution of 30 arcsec (approx. 1km) and ran it for the period 1980-2019. We use the resulting model simulations to analyse how spatio-temporal characteristics of streamflow droughts vary between different river basins in Europe. Furthermore, we apply trend analyses to understand how such spatial characteristics of droughts change over time. Our preliminary results suggest distinct patterns in spatio-temporal characteristics of streamflow droughts, which vary over the continent and are affected by changing hydro-meteorological conditions.
How to cite: Janzing, J., Wanders, N., and Brunner, M. I.: Spatio-temporal characteristics of streamflow drought change over Europe, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-2474, https://doi.org/10.5194/egusphere-egu25-2474, 2025.
In contrast to the wealth of information on evaporation dynamics from placid water surfaces such as lakes and reservoirs, estimating water evaporation from turbulent surfaces of streams remains a challenge. Evidence suggests a considerable change in evaporation from flowing surfaces relative to placid surfaces with local modifiers such as chemical, physical and biological processes that alter the energy budget and water temperature. While the studies on evaporation from wavy surfaces of oceans offer valuable insights, significant differences in hydrodynamics and heat exchange processes distinguish evaporation in oceans from that in rivers. Here we experimentally investigate how water flow characteristics (velocity and turbulence) and atmospheric boundary conditions (wind and radiation) affect evaporation rates and temperature dynamics in a flume. A closed flume (7.6 m length, 0.31 m width, and 0.5 m depth) is used to impose different boundary conditions over a test section of the flume (length of 1.5 m) while other parts of the flume are covered to reduce evaporative losses. Our preliminary findings show significant enhancement in evaporation rates, reaching 2-5 times that of placid water surfaces, driven by increases in surface velocity and turbulence characteristics. Furthermore, we observe that radiative and aerodynamic factors contribute nonlinearly to evaporation enhancement and affect temperature distribution in the water body. The study offers novel insights into evaporation from wavy and turbulent flowing water surfaces for better prediction of evaporation from riverine networks across flow regimes and climatic conditions.
How to cite: Hou, L., Aminzadeh, M., Or, D., Patzke, J., Fröhle, P., and Shokri, N.: Evaporation dynamics from flowing water surfaces, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-2716, https://doi.org/10.5194/egusphere-egu25-2716, 2025.
Accurate quantification of evapotranspiration (ET) is essential for a better understanding of hydrological processes and the interactions between hydrological, climatic, and vegetation systems. Existing global ET products have significant differences in describing regional ET and its trends in China. Limited by the short coverage period of observational data such as runoff, previous studies have rarely evaluated the performance of ET products in reproducing long-term ET and its trends. In addition, studies evaluating ET products based on the water balance method often use single input data, which increases the uncertainty of the water balance method to a certain extent. In order to better understand the applicability of commonly used global land surface ET products in China's watersheds, we evaluated the performance of eight ET products (i.e., 6 widely used global ET products: ERA5L, GLDAS, MODIS, FLUXCOM, GLEAM, PMLV2, and 1 newly released ET product with two different resolution datasets: CAMELE) in simulating monthly and annual ET and their ability to describe long-term trends in ET, using the multi-source input water balance method in 133 small basins in China. The results show that all products overestimate basin ET, whether on monthly or annual scale, with GLEAM and PMLV2 performing best with an RMSE of less than 50mm/month and the overall deviation less than 50% in most basins, followed by MODIS, and CAMELE's two resolution products having the most overall overestimation. Remote sensing-based evapotranspiration products GLEAM, PMLV2, and MODIS generally have better accuracy, followed by GLDAS. All products have greater uncertainty mainly in the small basins of the Southeastern Rivers and Pearl River Basin with the highest RMSE, while perform better in the upper source areas of the Yangtze River Basin, the Yellow River Basin, the Hai River Basin and the Songliao Basin. The average ET over all basins shows an increasing trend which is 1.49mm/year2 from 1980 to 2016 and 1.08mm/year2 from 1980 to 2014. All four long-series ET products (i.e., ERA5L, GLDAS, GLEAM, CAMELE-0.25°) capture this trend, but GLEAM and GLDAS overestimate the trend of ET, and the other products underestimate the corresponding trend. ET-WB mainly experiences two stages of change, gradually decreasing from 1980 to 2001, and then begin to rise during 2002-2016. All long-series products capture this change process. All products underestimate the increasing trend of ET in most basins and cannot describe the spatial distribution of ET trend well. The interannual variation of ET-WB has greater fluctuations, and all products underestimate the Cv of ET-WB. In contrast, PMLV2 performs best, followed by GLDAS, GELAM, and MODIS, while FLUXCOM performs worst, followed by CAMELE-0.25°, ERA5L, and CAMELE-0.1°. This latest assessment helps to understand the status and development of current land surface ET datasets and provides guidance for selecting appropriate ET products for use in specific regions within China and its interior.
How to cite: Fu, X. and Yang, H.: Assessments of long-term means and trends of eight evapotranspiration products over China based on water balance method, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-3012, https://doi.org/10.5194/egusphere-egu25-3012, 2025.
Stream water temperature (SWT) is fundamental to studies in water quality and ecology, with effects ranging from drinking water treatment chemistry to lotic species metabolism. Assessment of current and future SWT at large scales requires data at high spatial and temporal resolution, which can be supported by modeled datasets at considerably higher spatial resolution and extent than is feasible with monitoring networks. While several models support moderate- to high-resolution SWT estimation and prediction for unmonitored streams over large domains and global SWT modeling has been conducted at 10 km/daily resolution, no high-resolution (1 km/daily) dataset exists for the contiguous United States (CONUS) or other subcontinental domains. In addition, current high-resolution models are not optimized for gridded processing over large regions and are thus computationally impractical for high-density model runs over subcontinental domains. We address this limitation by enhancing an existing remote-sensing, ungauged, high-resolution SWT model, TempEst 2 (“temperature estimation, version 2”), to support computationally efficient analyses, including data retrieval and processing, over large blocks of input pixels. TempEst 2 is particularly suited to this optimization because the model only uses data near the point of interest, allowing a direct mapping from input to output grids without the need to process entire watersheds. Using the optimized model TempEst 2-FAST (“fast analysis in space and time”), we present progress on a 1 km/daily resolution SWT dataset over the CONUS (~8 million km2).
TempEst 2 has a median CONUS validation RMSE of 2.0 C, NSE of 0.91, and bias of 0.10%, within the typical performance range of regional to global ungauged daily SWT models (RMSE ~ 1.8-3.2 C). While TempEst 2 is trained on the United States Geological Survey SWT gauge network, it uses globally-available satellite-based or gridded data (e.g., land surface temperature, humidity) for prediction, supporting straightforward application outside the CONUS given a suitable local gauge network for training and validation. TempEst 2 is also relatively robust to spatial gaps in gauge network coverage and to overall sparse gauge networks, maintaining reasonable accuracy (approximately a 20% performance penalty) with just 100 gauges across the CONUS (~13 per million km2). Within the CONUS, the model shows consistent performance across a range of geographic and climate conditions, though there is some performance penalty in extrapolating to high-elevation (> 3000 m) sites (a small proportion of streams). Building on that robust performance and efficient data generation, the CONUS-wide gridded dataset we are developing provides readily-available data for large-domain analyses at far higher resolution than previously possible, with millions of prediction points over ~9,000 days (2001-2024). The availability of high-resolution SWT estimates over the CONUS enables rapid assessment of stream thermal conditions that would otherwise require extensive local fieldwork or modeling efforts. We anticipate that the dataset could be particularly useful for detailed assessments of ecological conditions or regulatory compliance over large regions.
How to cite: Philippus, D., Corona, C. R., and Hogue, T. S.: A Remote Sensing-Based Daily Stream Water Temperature Model for Gridded, High-Resolution Predictions at Subcontinental Scales, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-4506, https://doi.org/10.5194/egusphere-egu25-4506, 2025.
Increasing a model's spatio-temporal resolution and extent has scientific and operational implications. First, hydrological processes
operating at a smaller spatio-temporal scale may need to be incorporated in the model's description, increasing its computational load. Second,
the data storage requirements of the model will increase. Third, an increase in model size (operations and data) will result in an increase
in memory and runtime requirements. Here, we focus on this latter implication.
To allow models that increase in size to execute they must be able to use additional hardware efficiently: they must scale with hardware. We
have ported two existing hydrological models, PCR-GLOBWB (Sutanudjaja et al. 2018) and PyCatch (Lana-Renault et al. 2013), to the LUE modelling
framework (LUE contributors. 2024), and are conducting scalability experiments to assess how well the models are capable of using
additional hardware.
PCR-GLOBWB simulates hydrology and water resources at a global scale. PyCatch simulates hydrological processes at the catchment scale. Both
models currently use the PCRaster modelling framework (PCRaster contributers. 2024), rasters to represent spatially varying model state,
and discrete time steps for simulating changes in model state over time. PCR-GLOBWB supports being run at continental and global scale at 5
arc-minute spatial resolution, using daily time steps. The PCR-GLOBWB research team aims to support 1km spatial resolution, and even 100m
resolution and hourly time steps after that. PyCatch supports being run at catchment scale at 10m spatial resolution, using hourly time steps.
Its research team aims to support regional scale runs at 5m spatial resolution.
PCRaster supports executing models using a single CPU core. LUE is a successor of PCRaster, capable of using all CPU cores in multiple
computers. It is implemented in C++ and makes use of the HPX standard library for concurrency and parallelism (Kaiser et al. 2024). For model
developers LUE provides language APIs for multiple programming languages, like C, C++, Java, and Python. Currently, the Python API is
ready to be used.
We have developed the lue.pcraster Python sub-package which allows PCR-GLOBWB and PyCatch to be executed with LUE, without having to change
the model code. In our presentation we will show more about LUE and highlight the first results of scalability experiments we are currently
performing for both models. These experiments characterize how well LUE is able to execute models faster by using additional hardware, and how
well LUE is able to use additional hardware to execute models with larger datasets.
References
Kaiser et al. 2024. "STEllAR-GROUP/hpx: HPX: The C++ Standards Library for Parallelism and Concurrency." https://doi.org/10.5281/zenodo.598202
Lana-Renault et al. 2013. "PyCatch: Component Based Hydrological Catchment Modelling." Cuadernos de Investigación Geográfica 39 (2): 315--333. https://doi.org/10.18172/cig.1993
LUE contributors. 2024. "LUE Scientific Database and Environmental Modelling Framework." https://doi.org/10.5281/zenodo.5535686
PCRaster contributers. 2024. "The PCRaster Environmental Modelling Framework." https://pcraster.computationalgeography.org
Sutanudjaja et al. 2018. "PCR-GLOBWB 2: A 5 Arcmin Global Hydrological and Water Resources Model." Geoscientific Model Development 11 (6): 2429--53. https://doi.org/10.5194/gmd-11-2429-2018
How to cite: de Jong, K., Schmitz, O., Sutanudjaja, E., Nussbaum, M., Scheffer, P., and Karssenberg, D.: The LUE modelling framework for scalable hydrological models, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-9124, https://doi.org/10.5194/egusphere-egu25-9124, 2025.
In recent decades, many regions across the globe have experienced severe natural disasters, such as floods and droughts, resulting in substantial loss of life, economic damage, infrastructure destruction, and public health crises. In 2022 alone, the worldwide economic cost of natural disasters totaled 313 billion U.S. dollars. The frequency and intensity of these extreme events are crucial factors for assessing river systems and developing effective flood risk management strategies for both present and future scenarios. Large-scale hydrological modeling tools are indispensable for addressing a range of water-related challenges, fostering sustainable development, and adapting to the impacts of climate change. A variety of advanced hydrological models have been developed for flood mapping, forecasting, and operational use. Beyond traditional in-situ data, remote sensing technologies offer valuable datasets for monitoring and analyzing river systems on a global scale. These data present new opportunities to improve hydrological forecasting and integrate into large-scale models, highlighting the importance of collaborative efforts across agencies and organizations. This presentation will explore key tools, systems, and applications resulting from such collaborations, while identifying critical gaps and areas that require further development within the hydrological modeling community.
How to cite: Tavakoly, A. A., Geheran, M., Roland, V., Memarsadeghi, N., and Sisco, A.: Advancements in Large-Scale Hydrology Simulation Technologies, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-10858, https://doi.org/10.5194/egusphere-egu25-10858, 2025.
Evaporation is a fundamental process in the global water cycle, playing a critical role in regulating climate, sustaining ecosystems, and managing water resources. Despite its importance, accurately estimating evaporation on a global scale remains a significant challenge due to its spatial and temporal variability and the scarcity of direct ground-based observations, especially in water-limited regions. Satellite observations of key land surface processes offer a potential solution to these challenges, providing consistent and high-resolution observations that can enhance model-based evaporation estimates.
In this study, we assimilate observations from the Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow-On (GRACE-FO) into the Global Land Evaporation Amsterdam Model (GLEAM). GRACE provides measurements of terrestrial water storage changes by detecting variations in Earth's gravity field, offering critical insights into large-scale hydrological processes that are otherwise difficult to observe. GLEAM, a widely used model for land evaporation, integrates meteorological data, vegetation dynamics, and satellite-based soil moisture to provide comprehensive estimates of evaporation through the computation of its main components (interception loss, bare soil evaporation, and transpiration). GLEAM4 is able to represent groundwater-sourced transpiration, making it suitable for improvements via GRACE data assimilation.
More specifically, we investigate the impact of assimilating GRACE data into GLEAM4 and compare its performance across three data assimilation scenarios: (1) when only GRACE data are assimilated, (2) when only ESA-CCI soil moisture data are assimilated, and (3) when both GRACE and ESA-CCI soil moisture data are assimilated. This comparative analysis evaluates the ability of GLEAM to incorporate complementary remote sensing products to better capture evaporation-related processes, thus reducing uncertainties and improving accuracy in global evaporation estimates.
Our findings reveal that the integration of both GRACE and soil moisture data can marginally but consistently improve the model’s ability to represent the spatial and temporal variability of evaporation, particularly in water-limited regions, where accurate evaporation estimates are the most needed. This study highlights the potential of combining satellite-based datasets synergistically to address challenges in global evaporation estimation. By advancing the understanding of evaporation dynamics, these results contribute to improved hydrological and climatic assessments and water resource management in the context of climate change.
How to cite: Haghdoost, S., Koppa, A., M. Baez-Villanueva, O., Bonte, O., Lievens, H., Rouholahnejad Freund, E., E. C. Verhoest, N., and G. Miralles, D.: Combined Data Assimilation of Satellite-Based Total Water Storage and Soil Moisture Data to Improve Global Evaporation Estimation, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-11294, https://doi.org/10.5194/egusphere-egu25-11294, 2025.
Floodplains, a type of wetland regularly flooded by large rivers, are important hydrological objects to document and understand. They are places where the hydrological risk can be highly damageable, and where the high frequency of saturated soil moisture conditions due to flooding sustains an important biodiversity, provides important ecosystem services for human communities and regulates hydrological flows and exchanges between the land surface and the atmosphere.
Despite this importance, floodplain dynamics are difficult to represent in large-scale hydrologic models because of the control that small-scale topography imposes on water flow and storage. Some coarse resolution large-scale models use simplified representations of floodplain dynamics at the subgrid scale. In these cases, the relationship between water height, water storage and flooded area is parameterized. It should be noted that this approach does not always capture the complex relationships between floodplains and other hydrologic processes. On the other hand, the use of finer scale integrated hydrologic models could explicitly represent the complex relationship between rivers, aquifers and floodplains, but at an burdensome computational cost.
Here, we propose a methodology to represent floodplains in the integrated hydrological model CLM-PARFLOW, at a relatively low computational cost that allows its use in large-scale and high-resolution implementations even with a kinematic wave approach for surface flows. We 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 connecting river grids with neighboring floodplain grids when the water level is high enough to flood. The computational cost is low, as it uses the current capabilities of PARFLOW to represent horizontal subsurface flow at high resolution. We apply this representation to the Ouemé River basin in Benin (47000km²), at a resolution of 1 km, to test and optimize the parameters controlling the anisotropic layer.
First results show an improvement of horizontal flows between rivers and floodplain areas, especially during wet and high river discharge seasons, and a better representation of hydroclimate variables like ET in these areas. This methodology will further be applied to improve an existing 1 km² PARLFOW simulation over the West Africa domain (3.5 Mkm²), an area with large scale floodplain areas and intermittent endoreic ponds where the coupling between wetlands, rivers and aquifers control low-water levels in the dry seasons, and induce preferential recharge parthways, and where agriculture and pastoralism feed millions of people in West Africa.
How to cite: Arboleda Obando, P. F., Cohard, J.-M., Hector, B., and Pellarin, T.: An explicit representation of river-floodplains relationships in the integrated hydrological - land surface model CLM-PARFLOW, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-11296, https://doi.org/10.5194/egusphere-egu25-11296, 2025.
As the third-largest country in the world by land area, with highly diverse climatic regions and major river basins, China serves as a critical case study for examining large-scale hydrology under climate change. However, few studies have comprehensively investigated future runoff variability and extreme events across the entire region at high spatial resolution. This study analyses future runoff changes across China at 0.25-degree resolution under medium (SSP245) and high (SSP585) emission scenarios, using the Joint UK Land Environment Simulator (JULES), which has been calibrated and validated for simulating hydrological processes in China. The Global Climate Models (GCMs) are downscaled and bias-corrected using the bias-correction and spatial disaggregation (BCSD) method to drive the model. The results highlight significant regional imbalances in annual runoff, with an increase of 41.45 mm/decade in the Southeast basin under SSP585, compared to 7.30 mm/decade at the national scale. Seasonal patterns reveal contrasting trends, including wetter summers and drier winters in the south, while the northwest is expected to experience the opposite pattern. Projected changes indicate a rise in extreme high runoff in over 56% of regions, particularly in the south, and increased extreme low runoff in over 40% of China, notably in the central Yangtze River basin. Both extreme high and low runoff are projected to intensify in the far future, with SSP585 indicating more severe impacts. This study identifies spatial disparities and trends critical for sustainable water resource management and targeted adaptation strategies in response to climate change.
How to cite: Gao, D., Marthews, T. R., and Fu, G.: High-Resolution Simulation of Future Runoff Variability and Extremes Across China, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-14382, https://doi.org/10.5194/egusphere-egu25-14382, 2025.
Understanding streamflow extremes is essential for effective water resource management and disaster risk reduction. This study uses the Global Runoff Data Centre (GRDC) database, comprising daily discharge records from stations worldwide, to calculate return periods for streamflow extremes using L-moments and bootstrapping techniques. Bootstrapping techniques were employed to enhance the robustness of return period estimates by quantifying uncertainties and providing confidence intervals, ensuring more reliable insights into streamflow extremes across diverse hydrological contexts, particularly when there are numerous stations with limited or incomplete observations.
In this study, we analyze the multi-decadal changes and trends in streamflow extremes for different climatic zones across the globe. The temporal trends indicate potential shifts in return periods, suggesting the possible influence of climate variability. Regional anomalies highlight localized hydrological phenomena, emphasizing the importance of spatially explicit analyses.
The results have broad implications for flood and drought risk assessment, water resource planning, and climate adaptation strategies. By providing a global perspective on hydrological extremes, this study contributes to an improved understanding of streamflow variability and offers critical benchmarks for future hydrological modeling and climate impact assessments.
How to cite: Kolaparambil, F. J. and Bout, B. V. D.: Global Analysis of Streamflow Return Periods Using GRDC Data and Bootstrapping Techniques, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-14689, https://doi.org/10.5194/egusphere-egu25-14689, 2025.
Hydrologic processes are well understood in many locations worldwide and this understanding is commonly encoded as perceptual models of hydrologic behavior. Currently lacking is a large-scale synthesis of this understanding: it is difficult to accurately describe the relation between the drivers of hydrologic behaviors and the resulting hydrologic processes for a given point in space. As large-sample and large-domain modeling is increasingly used, knowledge of the relationship between drivers and processes is crucial to inform modeling decisions, such as the choice of process parametrizations and spatial discretization. Therefore, there is a need to investigate the relationship between hydrologic drivers and processes for large geographical domains. Here, we report progress on a detailed analysis of the connection between hydrologic processes and drivers.
Previous studies have investigated the relationship between hydrologic signatures and drivers, identifying climate attributes as the dominant driver in most locations. However, these previous studies did not find clear results for the importance of additional drivers and/or did not focus on a clear connection to hydrologic processes. We investigate the importance of additional drivers, such as land use, subsurface properties, and topography, and their relationship with hydrologic processes in different hydrologic landscapes. These landscapes are derived from a large community-driven initiative and are intended to provide a high-level division of the North American continent into smaller regions that should have distinct hydrologic behavior. For this purpose, we use large sample datasets for the United States and Canada, which help systemize the importance of drivers in time and space and the processes they influence.
We evaluate the inter and intra-region variations in signatures and drivers using various statistical analysis methods. Preliminary results confirm that (i) these hydrologic landscapes capture meaningful differences in dominant processes and (ii) the statistical analyses often highlight the most influential drivers within each region and their resulting processes. We will use the gained knowledge to adjust model structures to improve process representation across the continent.
How to cite: Wagener, P., Knoben, W. J. M., and Clark, M. P.: Evaluating Hydrologic Processes and Their Drivers For a Large Geographical Domain, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-16742, https://doi.org/10.5194/egusphere-egu25-16742, 2025.
Numerous gridded precipitation (P) datasets have been developed to address a variety of needs and challenges. However, selecting the most suitable and reliable dataset remains a challenge for users. We conducted the most comprehensive global evaluation to date of gridded (sub-)daily $P$ datasets using hydrological modeling. A total of 23 datasets, derived from satellite, model, gauge sources, or their combinations thereof, were assessed. To evaluate their performance, we calibrated the conceptual hydrological model HBV against observed daily streamflow for 16,295 catchments (each <10,000~km2) worldwide, using each P dataset as input. The Kling-Gupta Efficiency (KGE) was used as the performance metric and the calibration score served as a proxy for P dataset performance. Overall, MSWEP V2.8 demonstrated the highest performance (median KGE of 0.75), highlighting the value of merging P estimates from diverse data sources and applying daily gauge corrections. Among the purely satellite-based P datasets, the soil moisture- and microwave-based GPM+SM2RAIN dataset performed best (median KGE of 0.60), while the JRA-3Q reanalysis ranked highest among the purely model-based datasets (median KGE of 0.67), outperforming the widely used ERA5 reanalysis (median KGE of 0.59). Performance varied across Köppen-Geiger climate zones, with the best results in polar (E) regions (median KGE of 0.74 across datasets) and the lowest in arid (B) regions (median KGE of 0.33 across datasets). We further examined the spatial relationships between catchment attributes and KGE scores, identifying potential evaporation, air temperature, solid P fraction, and latitude as the strongest predictors of performance. Our analysis revealed significant regional differences in dataset performance and heterogeneity in P error characteristics, underscoring the critical importance of careful dataset selection for water resource management, hazard assessment, agricultural planning, and environmental monitoring.
How to cite: Abbas, A., Yang, Y., Pan, M., Tramblay, Y., Shen, C., Ji, H., Gebrechorkos, S. H., Pappenberger, F., Pyo, J., Feng, D., Huffman, G., Nguyen, P., Massari, C., Brocca, L., Jackson, T., and Beck, H. E.: Comprehensive Global Assessment of 23 Gridded PrecipitationDatasets Across 16,295 Catchments Using Hydrological Modeling, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-19338, https://doi.org/10.5194/egusphere-egu25-19338, 2025.
