At the same time, the research community now has unprecedented opportunities. Expanding in-situ networks, advances in remote sensing, and more complex and higher resolution Earth system and land surface models provide powerful tools to explore hydrological processes across scales. Yet, significant uncertainties remain and there have been concerns that advances in modelling and observational systems are not accompanied by advances in theory. Observational studies and global model simulations often yield divergent conclusions, revealing persistent knowledge gaps in how climate change, rising atmospheric CO2, and anthropogenic activities interact to reshape hydrological systems.
We invite submissions that address, but are not limited to, the following themes where we would like to hear about recent advances as well as current knowledge gaps:
1. Advanced ground- and space-based techniques and data-model fusion approaches for estimating hydrological variables (precipitation, evapotranspiration, streamflow, and water storage) and extremes (floods and droughts) from catchment to global scales.
2. Responses and feedbacks of hydrological processes and extremes to climate change and human activities.
3. Impacts of land use, land cover change, irrigation, and water withdrawals on streamflow regimes and hydrological extremes.
4. Projections of regional and global hydrological changes and extremes under near- and long-term climate scenarios.
5. Benchmarking hydrological and Earth system models against current observations, with particular attention to CO2 effects and human water use.
6. Hydrological processes and extremes in hotspot regions such as the Tibetan Plateau, the Arctic, the Amazon, and intensively irrigated areas.
The Yellow River Basin (YRB) is one of the world’s most densely populated regions that has suffered greatly from water resources shortage, serious soil erosion and high sediment loads, over-cultivation and ecological degradation in the past centuries. Since 1960, especially in the last 20 years, the middle reaches of the Yellow River have experienced large-scale soil and water conservation projects, including terraces, check dams and ecological restoration. The soil-water conservation (SWC) could have multi-faceted impacts on water, sediment and carbon-related processes. It’s crucial to assess the potential impacts of these human-induced transitions to provide guidance for the sustainable development in the YRB, as well as in other river basins around the world.
We first assess the multi-faceted impacts of SWC measures using a distributed ecohydrological model, which accounts for both the effects of hillslope SWC (HSWC) (i.e., terracing, afforestation, etc.) and river-network SWC (i.e., check dams) explicitly. During the study period (1961-2024), the YRB was characterized by a mismatch between the source areas of runoff and sediment. While the magnitude of runoff showed a pattern of initial decrease followed by a subsequent increase, soil erosion intensity exhibited an overall significant reduction throughout the period. Quantitatively, the erosion intensity decreased by 48.9% during 1981-2000 and 71.8% during 2001-2024 relative to the 1961-1980 baseline. An exponentially decreasing relationship between soil erosion intensity and the area of hillslope conservation measures was also found. Check dams along the river channel further intercepted approximately 4.52 billion m3 of sediment. The combined effects of these intervention measures caused the magnitude of sediment reduction to significantly exceed that of runoff volume.
We also investigate the water-sediment-carbon changes in response to intensive ecological restoration in the middle Yellow River basin. According to the results, ecological restoration promoted synergies between carbon sequestration and sediment control and led to improved water use efficiency (WUE). The actual Leaf Area Index and Gross Primary Productivity (GPP) showed improvements in region-averaged values by +0.56 m2 m-2 yr-1 (+7.4 %) and + 52 gC m-2 yr-1 (+10.9 %) compared to those under natural conditions. Furthermore, WUE changes indicated higher GPP gain per unit evapotranspiration. Meanwhile, trade-offs were also found when taking account of the water yield reduction. During 1982–2019, ecological restoration significantly increased actual evapotranspiration (+8.3 mm yr-1; +2.2 %) and decreased runoff (-7.6 mm yr-1; -12.7 %). Two indicators evaluating the cost-effectiveness of ecological restoration, i.e., carbon sequestration and sediment settlement at the cost of per unit runoff decline, remained positive with the average values of 6.12 kgC and 0.22 ton sediment load at the cost of per m3 water yield during 2000–2019, respectively. Nevertheless, both indicators showed downward trends, indicating decreasing marginal benefits brought by the ecological restoration measures which could have approached the optimal scale in the middle YRB. These results provide a scientific basis and quantitative indicators for sustainable water-carbon-sediment management.
How to cite: Yang, D.: Water-sediment-carbon effects of ecological conservation in the Yellow River Basin, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-2915, https://doi.org/10.5194/egusphere-egu26-2915, 2026.
Reservoir operation modules are essential for hydrological modeling in human-regulated catchments. This presentation is concentrated on testing the coupling of reservoir operation and rainfall-runoff processes under the framework of differentiable parameter learning (dPL). Specifically, the Community Water Model (CWatM)'s reservoir operation module is coupled with the Hydrologiska Byråns Vattenbalansavdelning (HBV) model; the differentiable fully coupled model (FCM) uses one long short-term memory (LSTM) network to calibrate all parameters using outflow; and the differentiable loosely coupled model (LCM) sets up two LSTM networks respectively for inflow and outflow. For comparison, the differentiable HBV is calibrated by outflow. The results of 77 reservoirs highlight that the dPL is effective in improving the efficiency of the conventional models. The median Kling-Gupta efficiency is improved from 0.53 for HBV to 0.59 for differentiable HBV, from 0.52 for FCM to 0.61 for differentiable FCM and from 0.54 for LCM to 0.60 for differentiable LCM. Zooming into the hydrological processes, it is found that the differentiable HBV fits reservoir outflow by underestimating recession coefficients and overestimating the baseflow index. The differentiable FCM fits the outflow but not the inflow since it tends to overestimate the maximum storage of the upper soil layer. The differentiable LCM fits both inflow and outflow with one LSTM estimating the parameters of HBV and the other LSTM estimating those of CWatM's reservoir operation module. For ungauged catchments, the differentiable LCM outperforms differentiable FCM in reproducing inflow and outflow. Overall, the dPL is effective in simulating the hydrological processes for human-regulated catchments.
How to cite: Zhao, T., Chen, Z., Zhang, B., and Li, Y.: Coupling Differentiable Modules of Reservoir Operation and Rainfall-Runoff Processes for Streamflow Simulation , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-2733, https://doi.org/10.5194/egusphere-egu26-2733, 2026.
Terrestrial ecosystems play a foundational role in modulating the global hydrological cycle and sequestering atmospheric CO2. Their functional integrity, however, is increasingly challenged by shifting environmental constraints on soil moisture and energy availability. While recent decades have seen significant advances in Earth system modeling and remote sensing, the precise mechanisms by which climate change alters vegetation sensitivity to water stress—and subsequently drives shifts in hydrological regimes—remain a subject of ongoing investigation. This study investigates long-term spatial and temporal transitions between energy-limited and water-limited regimes at a global scale from 1950 to 2025. To address the inherent uncertainties in multi-source datasets, we employ a robust collocation analysis that integrates remote sensing products with high-resolution reanalysis data. By applying a joint-solution methodology alongside an array of sensitivity experiments, we seek to disentangle the respective influences of climatic forcing and vegetative feedbacks on observed hydrological shifts. Our preliminary findings suggest a discernible historical transition in eco-hydrological dynamics. There is evidence of a contraction in energy-limited regions, potentially linked to increasing surface net radiation. Notably, the data indicates that areas experiencing a simultaneous increase in radiative demand and a decline in root-zone soil moisture are most susceptible to transitioning toward water limitation. Furthermore, our initial analysis points toward a strengthening coupling between vegetation transpiration and root-zone soil moisture, which may act as a critical feedback mechanism. These emerging results underscore the potentially pivotal role of evolving vegetation sensitivity to water stress in reshaping global ecosystem-water dynamics. By refining our understanding of these energy-water regime shifts, this work aims to contribute to more accurate benchmarking of Earth system models and provide insights into the resilience of regional hydrological systems under a changing climate.
How to cite: li, C.: Drivers of Global Shifts in Ecosystem Energy and Water Limitation: The Role of Evolving Vegetation Sensitivity to Water Stress, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-3286, https://doi.org/10.5194/egusphere-egu26-3286, 2026.
Climate change and anthropogenic activities have significantly altered the natural flow regimes, leading to hydrological instability and exacerbating extreme events in different parts of the world. In this study, long-term changes in the hydrological regime of the Chaliyar River Basin were evaluated using daily streamflow data from 1986 to 2022. For this purpose, the Indicators of Hydrologic Alteration (IHA), quantile-based environmental flow metrics, and Flow Duration Curves (FDCs) were employed to quantify flow patterns in the river basin. The range of variability approach indicated that 42.4% of the hydrologic parameters fall into the high-alteration category. The mean river discharge increased by 105% to 438% from January to May during the study period, indicating a pronounced shift in flow during the dry and pre-monsoon seasons. The analysis of low-flow characteristics revealed the strongest response with a 440% increase in the 1-day minimum discharge and a 448% rise in the Baseflow Index, signifying a substantial baseflow contribution. On the other hand, the flow regime became more unstable, as evidenced by a 155% increase in the frequency of high-flow pulses and a 193% increase in the frequency of low-flow pulses. The quantile analysis underpins this transition, indicating a significant increase in the normal low-flow discharge (Q90–Q95), whereas the intensity of the most substantial floods (Q1–Q10) remained relatively stable. These findings provide a quantitative roadmap for evidence-based river management, which can enable policymakers to address the challenges of intensified flow variability in the basin under changing environmental conditions.
How to cite: Bhardwaj, S. S. and Jha, M. K.: Quantifying Streamflow Alteration in a River Basin of South-Western India, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-3900, https://doi.org/10.5194/egusphere-egu26-3900, 2026.
Northeast China is an important industrial base and grain production region. Understanding terrestrial water storage (TWS) variations in Northeast China is crucial for the sustainable development of water resources and food security. TWS retrievals from the Gravity Recovery and Experiment (GRACE) and GRACE Follow-On (GRACE-FO) missions provide invaluable information to monitor TWS variations in the study domain. However, GRACE TWS retrievals have a coarse resolution in both space and time, which limits their application at finer scales. The study investigated the capability of ground-based Global Navigation Satellite System (GNSS) vertical displacement measurements to represent TWS variations in Northeast China with a finer spatial resolution. Afterward, TWS retrievals from GRACE and vertical displacements from GNSS will be assimilated into a land surface model to improve the hydrological process modeling in Northeast China. Preliminary results showed that after removing the non-hydrologic loading effects (e.g., non-tidal ocean loading, glacier isostatic adjustment, and thermal expansion) from GNSS data, the processed GNSS vertical displacement can reflect the seasonal and inter-annual variation of TWS in the study domain. However, the agreements of vertical displacements between GNSS and GRACE are inferior to findings in other regions, which may be explained by the weaker TWS variability, complex freeze-thaw process of ground, and extensive anthropogenic in this region. GRACE data assimilation (GRACE DA) in northeast China showed improved estimates of TWS and its constituent components, particularly across mountain regions. However, the degraded snow estimation from GRACE DA was also revealed. It is anticipated that the dual-assimilation of GRACE and GNSS data can take advantage of both data sets and benefit the estimates of snow, soil moisture, and groundwater in Northeast China.
How to cite: Yin, G., Hu, W., and Zhang, Z.: Toward Dual-Assimilation of Terrestrial Water Storage for Improving Land Hydrological Modeling in Northeast China, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-4151, https://doi.org/10.5194/egusphere-egu26-4151, 2026.
Current cascade-type models are the dominant approach for projecting future extreme floods, but they suffer from major limitations, including substantial bias, large uncertainty and coarse spatial resolution. To address these issues, we developed an observation-constrained framework that integrates flood estimates, based on historical data and regional flood frequency analysis, with changes in design floods from cascade-type model projections, enabling 1-km resolution projections across 28.2 million river pixels worldwide. Our analysis reveals that cascade-type models overestimate the historical 100-year flood by about 160% globally, while forcing-based corrections still exhibit considerable bias. Further, our observation-constrained approach reduces multi-model uncertainty by a median of 22.8% globally compared to cascade-type modeling. Under a high-emissions scenario, 83% of the global land mass shows increasing flood frequency. Globally, the historical 100-year flood is projected to have a median return period of about 36 years – more frequent than suggested by cascade-type model projections. Our results highlight the acceleration of flood risks, which may leave communities unprepared for intensifying climate impacts.
How to cite: xu, S., zhao, G., zhang, Y., wang, Q., ji, H., yu, J., and merz, B.: More Frequent Extreme Floods Revealed by Observation-Constrained Projections, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-6594, https://doi.org/10.5194/egusphere-egu26-6594, 2026.
Precipitation is the consequence of condensation within the atmosphere, which is intimately connected to the release of latent heat within the air. Latent heating creates buoyancy, that is, it accelerates air, while precipitation removes moisture from the atmosphere, that is, it dehumidifies air. Both of these basic aspects of precipitation involve physical work. Here, we use a thermodynamic systems approach to constrain this work and thereby precipitation and its response to global climate change. The central starting point is to view the release of latent heat as the fuel to drive a moist heat engine that generates the work to accelerate and dehumidify air. We then maximise the fraction of that work that goes into the generation of motion, consistent with previous, successful applications of the maximum power limit to the surface energy balance and poleward heat transport. This yields another constraint on the dynamics, which then provides temporal and spatial scales associated with precipitation, such as convective rainfall events and the Hadley circulation. We then show that this relatively simple, yet physical formulation can directly be used to understand precipitation changes found in observations and models, such as the intensification and shortening of convective rainfall events, decreases in cloud cover, deviations from Clausius-Clapeyron scaling in the scaling of extreme rainfall events, as well as the “wet-gets-wetter” hypothesis. These phenomena are directly consequences of the dynamics driven by condensational heating, and these become more powerful due to the simple fact that warmer air can hold more moisture, but also that moisture serves as the fuel for these dynamics. In addition to providing a simple, physical picture of these hydrological responses within the atmosphere, the approach also suggests potential shortcomings in climate models with respect to resolving these dynamics.
How to cite: Kleidon, A., Ghausi, S. A., and Chauhan, T. A.: How the maximum power limit constrains precipitation dynamics and their responses to global climate change, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-5043, https://doi.org/10.5194/egusphere-egu26-5043, 2026.
Large-scale ecological restoration is a critical strategy for combating land degradation, yet its hydrological consequences, particularly regarding evapotranspiration (ET) partitioning, remain uncertain due to complex spatial heterogeneity and non-linear vegetation-water feedbacks. To address this, we developed a novel "zonal and screened" attribution framework that integrates the Two-Source Energy Balance (TSEB) model with Bayesian Ridge Regression to disentangle the driving mechanisms of ET and its components (transpiration, Ec; soil evaporation, Es) on the Loess Plateau (2000–2020). Methodologically, this zoned framework significantly outperformed traditional global modeling, reducing the prediction error for Ec by a median of 24.3% in heterogeneous transition zones. Results indicate a fundamental shift in the regional water cycle: ET increased by 9.31 mm/yr, primarily driven by a surge in Ec (10.24 mm/yr). Crucially, the driving mechanisms exhibited distinct spatiotemporal divergence: while vegetation restoration dominated Ec in the hilly-gully regions (Zones A and B), climatic factors controlled the arid sandy areas (Zone C). Furthermore, we identified two universal non-linear regulation mechanisms: a "V-shaped" response for Es (shading vs. interception) and an "Inverted U-shaped" response for Ec (saturation effect). Specifically, the optimal Leaf Area Index (LAI) threshold for transpiration in Zone A shifted from 0.47 (2000–2010) to 0.42 (2011–2020), signaling intensified water stress. These findings challenge the "one-size-fits-all" greening policy and advocate for a paradigm shift towards "green-water synergy" management. We propose actionable strategies, including thinning dense plantations to maintain LAI near optimal thresholds and prioritizing water-saving agriculture in arid zones, to ensure the sustainability of ecological engineering in water-limited regions globally.
How to cite: Xu, Y. and Zuo, D.: Transition to transpiration-dominated evapotranspiration on the Loess Plateau: spatially divergent driving mechanism and threshold effect after two decades of reforestation, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-6396, https://doi.org/10.5194/egusphere-egu26-6396, 2026.
World-wide demands of drinking water, irrigation, livestock, domestic use, manufacturing and thermal power are satisfied by surface water and/or groundwater extraction. Although necessary for the well-being of humans and animals, as well as plant and energy production, climate change impacts on and overconsumption of our water resources lead to severe water scarcity that approximately half of the world’s population are regularly facing. Shifts in water storage patterns and water-related hazards can be observed from space by dedicated satellite missions or simulated by global hydrological models. However, quantifying the relative contribution of fundamental drivers of terrestrial water storage (TWS) changes is still a major scientific challenge, e.g., due to a lack of data or processes in models and the limited vertical and spatial resolution of satellite data sets.
Thus, in this study, we attempt to reveal the human water use impact on shifts of TWS patterns under changing climate. We compare two decades (2003-2023) of TWS changes simulated by the WaterGAP Global Hydrology Model (WGHM) while (a) disregarding and (b) considering surface water and groundwater extraction to isolate the human impact on the terrestrial water cycle. The identified patterns are compared to observations from the satellite gravity missions GRACE and GRACE-FO to better understand the individual contributions on current satellite-based continental wetting and drying trends. In addition, the relative contribution of individual water storage components to TWS is calculated, where groundwater overconsumption shows significant impacts on shifting TWS patterns. We present the largest river basins (>200.000 km2) world-wide and a country-based assessment to identify regions under acute or chronic water stress.
How to cite: Schumacher, M., Çakan, Ç., Klemmensen, S. G., Nyenah, E., Döll, P., and Forootan, E.: Model reveals human water use impact on shifts of terrestrial water storage patterns, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-6685, https://doi.org/10.5194/egusphere-egu26-6685, 2026.
Land available water has declined across many regions over recent decades, reflecting widespread aridification and increasing pressure on terrestrial systems. These changes are commonly attributed to shifts in precipitation, enhanced evaporative demand, and increasing soil moisture limitations under warming. However, the extent to which changes in large-scale atmospheric moisture transport contribute to declining continental water availability remains poorly understood.
To address this gap, we develop a four-decade (1979–2024) global dataset of atmospheric trajectories based on the Lagrangian transport model FLEXPART driven by ERA5 data, and evaluate the atmospheric moisture transport using a novel attribution framework. This framework allows us to link changes in the continental water balance (P–E) to shifts in moisture source–sink relationships, including continental evaporation recycling, defined as the fraction of land evaporation that returns as precipitation over land.
Our results show that the declining continental P–E is associated with a relative decrease of evaporation recycling and a relative increase in the export of land-evaporated moisture towards the oceans. These changes cannot be directly explained by changes in terrestrial evaporation. Instead, they respond to changes in large-scale circulation, together with reduced precipitation efficiency and longer atmospheric residence times.
These results demonstrate that thermodynamic and dynamical changes in atmospheric moisture transport provide a missing link between observed P–E declines and continental aridification, with important implications for future land water availability.
How to cite: Deman, V. M. H., Insua-Costa, D., and G. Miralles, D.: Changing moisture transport as a driver of continental drying, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-8321, https://doi.org/10.5194/egusphere-egu26-8321, 2026.
Vegetation phenology has shifted globally over recent decades, yet its influence on streamflow seasonality remains insufficiently quantified. Here we present a global, catchment-scale assessment of streamflow seasonal responses to vegetation phenology changes using long‐term observations from ~3000 river basins spanning diverse hydroclimatic regimes. Our results reveal that where vegetation phenology advances, streamflow is systematically redistributed within the year, characterized by reduced spring and summer discharge and enhanced winter streamflow, reflecting increased growing-season water consumption by vegetation. In addition, streamflow timing responds coherently to phenological shifts, advancing in concert with earlier vegetation activity, with particularly pronounced responses in cold regions. In these regions, early-season streamflow timing exhibits a significant advancement driven jointly by earlier snowmelt and vegetation green-up, indicating a dual forcing that amplifies the sensitivity of runoff timing to phenological change in snow-affected catchments. Furthermore, phenological advancement is associated with a weakening of streamflow seasonality, primarily resulting from decreased summer flows and increased spring flows, thereby reducing intra-annual runoff variability.
How to cite: Han, J., Yang, Y., Guo, H., Tu, Z., Xiong, J., Guo, Y., and Li, C.: Global Streamflow Seasonality Responses to Vegetation Phenology Shifts Across Diverse Hydroclimatic Regimes, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-13191, https://doi.org/10.5194/egusphere-egu26-13191, 2026.
The response of terrestrial freshwater storage to anthropogenic climate forcing is a fundamental yet poorly constrained aspect of global hydrological change. Detection and attribution studies have identified human influence in several components of the hydrological cycle, including precipitation and runoff (e.g. Zhang et al., 2007; Marvel et al., 2019; Gudmundsson et al., 2021). However, attribution of observed changes in terrestrial water storage (TWS) has remained elusive due to the short length of observational records, substantial internal climate variability, and the confounding influence of direct human water management.
Here we use observations from NASA’s Gravity Recovery and Climate Experiment (GRACE), which provide a uniquely robust, spatially explicit measure of terrestrial water storage change, together with a formal detection and attribution framework (Santer et al., 2013) informed by simulations from the Coupled Model Intercomparison Project Phase 6 (CMIP6). We show that the observed GRACE TWS record contains a spatially coherent signal that exceeds the range of simulated unforced variability, strengthens over time, and is robust across alternative fingerprint constructions and GRACE processing choices.
The detected fingerprint is characterised by large-scale wetting and drying patterns broadly consistent with modelled responses to anthropogenic forcing across many regions. Regional deviations are primarily concentrated in intensively irrigated and groundwater-dependent areas, indicating the superimposed influence of direct human water use and remaining model limitations. Additional analyses using reanalysis products and observationally constrained climate model simulations provide complementary context for interpreting the emergence of this signal. Together, these results provide the first fingerprinting evidence of anthropogenically forced change in global terrestrial water storage and establish continental freshwater storage as a detectable and attributable component of the climate system.
References
Zhang, X. et al. Detection of human influence on twentieth-century precipitation trends. Nature 448, 461–465 (2007).
Marvel, K. et al. Twentieth-century hydroclimate changes consistent with human influence. Nature 569, 59–65 (2019).
Gudmundsson, L. et al. Globally observed trends in mean and extreme river flow attributed to climate change. Science 371, 1159–1162 (2021).
Santer, B. D. et al. Identifying human influences on atmospheric temperature. PNAS 110, 26–33 (2013).
How to cite: Fisch, C., Gudmundsson, L., Schumacher, D. L., and Seneviratne, S. I.: Detecting an externally forced signal in observed terrestrial water storage, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-14646, https://doi.org/10.5194/egusphere-egu26-14646, 2026.
Abstract
Understanding how atmospheric water vapor translates into precipitation is fundamental to assessing future changes in the hydrological cycle under climate change. While integrated water vapor (IWV) is a key precursor of precipitation, the temporal stability of their relationship and its evolution under different climate scenarios remain uncertain, particularly at continental scales. In this study, we investigate long-term changes in the statistical relationship between precipitation and water vapor across Europe using an ensemble of Coupled Model Intercomparison Project Phase 6 (CMIP6) climate model simulations. Historical and future projections under contrasting climate change scenarios were analyzed to examine how precipitation–vapor coupling evolves from the pre-industrial period through the end of the 21st century. To enable consistent comparison across regions and time periods, precipitation and IWV were transformed into standardized indices (Standardized precipitation index, Standardized integrated water vapor) based on probabilistic distributions. Correlation and cross-correlation analyses were then applied to quantify both the strength of coupling and the response times between precipitation and atmospheric water vapor. The results reveal pronounced regional differences in the precipitation–vapor relationship across Europe, with stronger coupling observed in parts of Western Europe compared to other regions. Under the Shared Socioeconomic Pathways 8.5, the precipitation–vapor relationship exhibits a tendency to weaken over time, suggesting a growing decoupling between precipitation and water vapor. In addition, precipitation responses were found to systematically lag changes in IWV by several weeks to months, highlighting the importance of considering temporal delays in hydroclimatic assessments. These findings provide new insights into the evolving dynamics of precipitation–vapor interactions under climate change and offer a basis for improving the interpretation of large-scale hydrological responses in climate model projections.
Keywords: Atmospheric Water Vapor, Precipitation, Precipitation-vapor Coupling, Climate Change, Cross-correlation, Europe
Acknowledgment
This research was supported by the National Research Foundation of Korea(NRF) grant funded by the Korea government (MSIT) (RS-2024-00334564 & RS-2021-NR060085), the Korea Environmental Industry & Technology Institute (KEITI) through Wetland Ecosystem Value Evaluation and Carbon Absorption Value Promotion Technology Development Project, funded by Korea Ministry of Environment (MOE) (RS-2022-KE002066), and Water Management Program for Drought, funded by the Korea Ministry of Climate, Energy and Environment (MCEE) (RS-2022-KE002032).
How to cite: Cha, H., Baik, J., Han, J., De Michele, C., Na, W., and Jun, C.: Evolving relationships between atmospheric water vapor and precipitation over Europe under climate change, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-15003, https://doi.org/10.5194/egusphere-egu26-15003, 2026.
Although Earth system models (ESMs) overestimate historical land surface warming, paradoxically, they also overestimate snow amounts in the Northern Hemisphere (NH). This apparent contradiction constitutes a snow-water resources paradox, but the underlying mechanisms of this paradox remain unclear. Combining ground-based datasets and ESMs, we found that the snow-water resources paradox can be explained by the overestimation of the frequency of light snow by ESMs. Using spatially-distributed emergent constraints, we show that this paradox persists in the mid- (2041–2060) and long-term (2081–2100) projections over more than half of the NH’s land surface, with the frequency of freezing days underestimated by 12%–19% and snow water equivalent (SWE) overestimated by 41%–47%. The constrained projections indicate that the raw ESMs overestimate future NH snowmelt runoff by 17%–24% (over 33%–35% of the NH’s land surface). This extensive and long-standing snow-water resources paradox poses a serious adaptation planning risk in that the degree of future snowmelt water available for agriculture, industry, ecosystems and domestic life may well be less than suggested by unadjusted ESM projections.
How to cite: Miao, C. and Chai, Y.: Overcoming the Northern Hemisphere snow-water resources paradox, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-15552, https://doi.org/10.5194/egusphere-egu26-15552, 2026.
The growing availability of reanalysis gridded products and in-situ observations offers new opportunities for multi-source data fusion in catchment-scale runoff forecasting. However, many existing multi-modal approaches decouple gridded feature extraction from sequence prediction, rely on simple concatenation that struggles with high-dimensional spatiotemporal information, and provide limited quantitative interpretation of the relative contribution of heterogeneous inputs. Here we present ReDF-Net, a task-oriented residual–attention framework for daily (t+1) runoff forecasting that explicitly fuses ERA5-Land surface soil moisture with in-situ observations. ReDF-Net employs a modified residual network to extract compact representations from sequences of daily gridded soil moisture fields, with the historical time window encoded as input channels. Learnable softmax attention weights are embedded within the residual blocks to adaptively reweight features during end-to-end training, ensuring that gridded feature learning is directly optimized by the forecasting loss. The attention-aggregated gridded representation is then fused with site-based predictors and fed into a time-series forecasting backbone for runoff prediction. Beyond predictive accuracy, we quantify global multi-source feature contributions using the converged attention weights and interpretability analysis, and contrast model behavior during flood and non-flood periods to facilitate process-consistent interpretation. The framework is evaluated at Yichang station in the Yangtze River basin and Lanzhou station in the Yellow River basin, and benchmarked against representative recurrent forecasting models. Results demonstrate that task-oriented fusion of ERA5-Land surface soil moisture and in-situ observations improves daily runoff forecasting skill while maintaining training stability, and provides transparent attribution of how different data sources support runoff prediction. The proposed approach offers an interpretable pathway for advanced data–model fusion of hydrological variables at the catchment scale.
How to cite: Yang, Z., Wang, D., Ye, X., and Yu, C.: ReDF-Net: task-oriented fusion of gridded and in-situ information for daily runoff forecasting, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-16864, https://doi.org/10.5194/egusphere-egu26-16864, 2026.
Understanding streamflow–sediment relationships in large rivers is essential for effective ecosystem conservation and sediment management. However, the multi-scale regimes of streamflow–sediment load in the Yellow River, shaped by the combined influences of climate variability and intensive human activities, remain inadequately understood. This study investigates streamflow–sediment coupling patterns at annual, monthly, and flood-event scales using long-term observations (1950s–2022) from six mainstem stations along the Yellow River. The results reveal pronounced spatiotemporal variability in streamflow–sediment relationships and hysteresis behaviors along the river mainstem. At the annual scale, streamflow and sediment load generally exhibit a linear relationship. At both the monthly and annual maximum flood-event scales, power-law sediment rating curves effectively describe the relationships between discharge (Q) and sediment concentration (SC), expressed as SC = aQb. Notably, the goodness of fit (R2) of the annual and monthly streamflow–sediment relationships exhibits a declining trend over time, indicating increasing nonstationarity. Despite this overall weakening, a robust linear coupling between ln(a) and b persists in the sediment rating curves at both monthly and flood-event scales, with a remarkably consistent regression slope (approximately −0.14) across different periods. In addition, pronounced hysteresis patterns are observed at both intra-annual and flood-event scales. These hysteresis loops evolve from simple clockwise forms, indicative of sediment supply limitation, toward more complex figure-eight patterns, reflecting enhanced sediment resuspension and altered sediment delivery dynamics. Overall, the results highlight the scale-dependent and dynamically evolving nature of sediment transport processes in the Yellow River, underscoring the need for adaptive, multi-scale sediment management strategies under changing climatic and anthropogenic influences.
How to cite: Yin, S. and Gao, G.: Multi-Scale Streamflow–Sediment Relationships and Regime Shifts in the Yellow River Mainstream, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-15778, https://doi.org/10.5194/egusphere-egu26-15778, 2026.
In intensely irrigated regions, groundwater levels are strongly influenced by crop water consumption and human activities. Although machine learning methods have proven effective for groundwater prediction, few studies have explicitly incorporated irrigation water use as a key predictor. In this study, we developed a groundwater prediction framework that integrates precise agricultural water consumption using machine learning techniques, with Shijiazhuang in China as the study area. Crop water consumption was simulated by coupling multi-source data with the AquaCrop model. A Random Forest-based groundwater prediction framework was then constructed to explore the effects of irrigation water consumption on groundwater level changes from 2018 to 2024. Sentinel-2A remote sensing imagery was employed to extract regional crop cultivation patterns, achieving an Overall Accuracy (OA) greater than 0.94 and a Kappa coefficient exceeding 0.89. Subsequently, AquaCrop model was applied to simulate crop yields and water consumption, with simulated yields showing strong agreement with official statistics, thus enabling the generation of a long-term time series of crop water consumption. Building on these results, the simulated irrigation water consumption was incorporated into the groundwater level prediction model. The performance of models using different predictor combinations was compared. Results show that including water consumption significantly enhanced the predictive accuracy, reducing root mean square error (RMSE) by 6.34%, 4.17%, and 3.97% in the three groups, and reducing the average error by 9.09%, 19.33% and 21.37% respectively. Spatially, model errors were also notably reduced. Overall, this study demonstrates that integrating irrigation water consumption into a groundwater model can effectively quantify the response of groundwater levels to climatic and anthropogenic factors, providing a scientific basis for groundwater resource management and ecological restoration.
How to cite: Zhang, J. and Yang, J.: Enhancing Groundwater Level Predictions by Integrating Precise Agricultural Water Consumption with Machine Learning Algorithms, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-21736, https://doi.org/10.5194/egusphere-egu26-21736, 2026.
Quantifying and visualizing how natural landscapes regulate floods remains a challenge for continental- and national-scale modelling. Few frameworks exist that incorporate both hydrological processes and ecosystem functions to identify where landscapes provide natural flood protection, and even fewer are applicable to regions where cryospheric processes strongly influence hydrology. Using Canada as the study area, this work presents an emerging national-scale modelling framework designed to quantify and map flood-regulating ecosystem services (FRES) using open-source global hydrological datasets.
The presented workflow utilizes sub-basin delineations from HydroBASINS and associated attribute layers from HydroATLAS to parameterize surface and subsurface hydrological characteristics. First, important indicators such as vegetation cover, land use, slope, and soil properties are synthesized into a comprehensive indicator to represent landscape capacity for runoff attenuation. Then, to improve applicability to cold environments, additional variables representing snow, glaciation, and permafrost are incorporated to reflect cryospheric controls on flood-regulating processes.
To explicitly link ecosystem functions to hydrological processes, an eco-hydrological model adapted from global hydrography concepts is being implemented. This routing approach traces upstream contributions of flood-regulating capacity through connected river networks, allowing downstream flood risks to be evaluated against upstream landscape properties. The resulting maps can identify sub-basin hotspots where natural landscapes are expected to provide disproportionate flood protection benefits. These outputs also facilitate multi-scale analysis of FRES through the aggregation of FRES indicators from sub-basin to national extents by utilizing the hierarchically nested sub-basin structure of HydroBASINS.
The proposed approach aims to provide a scalable and tractable workflow that bridges hydrological reasoning with ecosystem service assessment at the national and continental scale. Ultimately, this framework will serve as a foundation for directly including natural flood protection into large-scale water management, conservation planning, and climate adaptation strategies across Canada and other cold-region contexts.
How to cite: Ruth, D., Ouellet Dallaire, C., and Schuster, R.: A national-scale framework for mapping flood-regulating ecosystem services in cold and cryosphere-influenced regions, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-2604, https://doi.org/10.5194/egusphere-egu26-2604, 2026.
Evapotranspiration (ET) is experiencing profound shifts in response to elevated CO2 (eCO2), with critical implications for hydrological cycles and ecosystems. It is widely recognized that CO2 enrichment modulates ET components through three distinct pathways: radiative forcing (intensifying the greenhouse effect), physiological effects (regulating stomatal conductance), and structural effects (enhancing vegetation productivity and leaf area index). However, these changes in the coupled biosphere-atmosphere processes and their interactions, which potentially compensate for or amplify one another, make them difficult to disentangle and assess individually. Consequently, there is little consensus on the individual and combined effects of eCO2 on ET through radiative climate change, plant physiological changes and structural changes, not to mention the variability among vegetation types.
In this work, we establish a "bottom-up" attribution framework based on counterfactual sensitivity experiments, and employed an optimized Shuttleworth-Wallace dual-source model to decouple the specific impacts of eCO2 on plant transpiration, soil evaporation, and precipitation interception. The research primarily addresses two pivotal questions: (1) What are the isolated and the combined effects of eCO2 on ET across different propagation pathways? (2) How do these effects vary across different vegetation types?
Preliminary results indicate that the negative physiological effects and positive radiative effects largely offset each other, with the absolute magnitude of their individual contributions far exceeding the positive structural effects. The ET response exhibits significant inter-biome heterogeneity, and physiological effects dominate the response magnitude across all vegetation types, with the exception of croplands and deciduous broadleaf forests. These findings suggest that further increases in CO2 concentrations may intensify physiological regulation to a threshold that triggers a regime shift in ET from an increasing to a decreasing trend. These findings allows us to project the impact of futureCO2 concentrations on the interaction processes of water between the biosphere and atmosphere.
How to cite: Zhao, J., Sha, Z., Sun, S., Jiang, S., and J. Winkler, A.: Disentangle the individual effects of eCO2 on ET across vegetation types, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-17825, https://doi.org/10.5194/egusphere-egu26-17825, 2026.
WSIMOD is an integrated water modelling framework that enables the representation of flow and water quality interactions across scales, sectors, and processes within complex water systems. Due to its integrated and flexible nature, WSIMOD allows for hydrological processes, infrastructure, land use, and management decisions to be jointly simulated. Recent developments have significantly expanded its analytical capabilities, including bespoke modelling of wetlands, improved representation of groundwater dynamics, and integration with optimisation-based approaches, strengthening its relevance for applied water management and policy analysis.
Despite these advances, a key barrier to wider adoption remains the substantial effort required to preprocess heterogeneous datasets and configure integrated water system models for specific catchments. This limits model reuse, slows uptake by practitioners, and constrains comparative analysis for regional and national applications. To address these challenges, we present WSIMODanywhere, an automated pipeline for data preprocessing, model generation, and initial parameterisation for any catchment or sub-catchment in England. The model can be accessed via an interactive web interface, free of charge for research, allowing users to select a geographic area and obtain a ready-to-use WSIMOD configuration created using openly available, pre-processed datasets. We present baseline historic simulations of WSIMODanywhere from regional to national scale, and discuss model performance when using a baseline, uncalibrated and data-driven configuration of WSIMOD, in which parameters are estimated solely from publicly available data without site-specific calibration. We then outline a pipeline for further model improvement through regional fine-tuning and additional data integration and discuss potential applications of a national-scale integrated water system model.
How to cite: Mijic, A., Rico Carranza, E., Maes Prior, R., Tang, J., Zong, W., and Dobson, B.: WSIMODanywhere: Ready-to-use integrated water system modelling anywhere in England from open data, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-18821, https://doi.org/10.5194/egusphere-egu26-18821, 2026.
Hydrological systems are undergoing significant changes due to the combined effects of climate change, land surface alterations, and increasing human pressures. As a result of these influences, streamflow regimes show variations across temporal and spatial scales. Understanding the spatial and temporal variability of streamflow regimes and how they are affected by climate, anthropogenic factors and catchment characteristics are crucial for improving water resource management and for addressing challenges in ungauged basins.
This study presents a catchment classification framework based on the analysis of streamflow regime variability across Türkiye and applies a multi-method framework using observed discharge records obtained from 214 gauging stations. Streamflow regimes were identified using two different approaches and for this purpose the Agglomerative Hierarchical Clustering algorithm was utilized. The first approach is Functional Data Clustering with B-spline representations of monthly streamflow records. This approach enables the identification of streamflow regimes through hydrograph shape. The second approach utilizes Hydrologic Index-Based Clustering and uses a set of monthly flow indices. The resulting regime classifications obtained from both methodologies were compared to evaluate consistency, reliability, and hydrological interpretability of detected streamflow regimes across Türkiye.
To understand spatial and temporal variability in streamflow regimes, regime classification framework was first applied to the full observation period (1997–2015) to characterize long-term regime behavior and then subsequently to overlapping five-year sub-periods derived using a moving-window approach. This approach enabled the detection of potential streamflow regime shifts over time across Türkiye. The identified regime types were then correlated with an integrated dataset of climate indices, catchment attributes, land cover, soil type, and geology to investigate the controls on streamflow regime variability. Finally, a Random Forest Classification framework was used to assess the relative importance of multiple drivers and to enable the prediction of streamflow regimes in ungauged basins.
The results reveal that streamflow regimes have spatially distinct patterns and temporal variability across Türkiye. The findings also emphasize the critical role of elevation and precipitation seasonality in this variability. The consistency between the two classification approaches further supports the reliability of the identified regimes. Overall, the integrated framework combining streamflow regime classification, detection of regime shifts, and data-driven approach provides a basis for understanding streamflow variability and its dominant controls across hydrologically diverse and ungauged basins.
How to cite: Varli, D. and Yilmaz, K. K.: From Streamflow Regime Identification to Regime Prediction in Ungauged Basins: Catchment Classification Across Türkiye, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-5921, https://doi.org/10.5194/egusphere-egu26-5921, 2026.
Frozen ground, including permafrost and seasonally frozen ground (SFG), is a critical element of the cryosphere that strongly regulates hydrological processes in cold regions. It has been debated whether frozen ground degradation will make landscapes more, or less, sensitive to precipitation inputs; either outcome has profound implications for water resources and climate resilience. Using a data-driven approach based solely on observations, we quantify four decades of changes in event-scale runoff responses to daily precipitation in the source region of the Yellow River on the northeastern Tibetan Plateau. We apply Ensemble Rainfall–Runoff Analysis (ERRA), which infers hydrologic impulse responses directly from precipitation–runoff data without relying on model assumptions. This enables the assessment of nonlinear, nonstationary, and spatially heterogeneous hydrologic behavior across different frozen ground types and precipitation intensities. Results show that, relative to 1979–1998, the permafrost zone during 1999–2018 experienced a 47% reduction in peak runoff response per unit precipitation and a 32% decrease in the 25-day runoff coefficient, while the SFG region showed no substantial changes. The weakened runoff response in the permafrost zone, particularly under high-intensity precipitation (>10 mm d⁻¹), likely reflects enhanced infiltration and subsurface storage associated with thaw-induced deepening of the active layer. These findings highlight the power of data-driven approaches in detecting hydrological regime shifts and provide critical insights for drought mitigation and flood risk assessment in permafrost-affected regions.
How to cite: Tu, Z., Wang, T., Han, J., Seybold, H., Liu, S., Culha, C., Yang, Y., and Kirchner, J.: Declining runoff sensitivity to precipitation following permafrost degradation: Insights from event-scale runoff response in the Yellow River source region, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-4688, https://doi.org/10.5194/egusphere-egu26-4688, 2026.
A refined understanding of how hydrological processes act as key drivers of terrestrial carbon dynamics is essential for improving assessments of atmospheric CO2 fluxes and strengthening climate change mitigation strategies. This study characterizes the spatiotemporal dynamics of net primary productivity (NPP), a fundamental indicator of terrestrial CO2 sequestration capacity, across South Korea from 2004 to 2019 using the Carnegie–Ames–Stanford Approach (CASA) model, with particular emphasis on hydrological drivers. The CASA framework was implemented by integrating Moderate Resolution Imaging Spectroradiometer (MODIS) remote sensing products with meteorological variables, enabling consistent estimation of vegetation productivity across diverse land-cover types. Pronounced increases in NPP were observed in deciduous broadleaf forests and croplands, whereas urban areas exhibited declining trends, reflecting contrasting trajectories in ecosystem productivity linked to land use patterns.
To quantify the roles of individual hydrological drivers, we evaluated the seasonal contributions of precipitation, temperature, and groundwater storage (GWS). Precipitation and temperature inputs were derived from ground-based meteorological station observations and spatially interpolated using the Barnes objective analysis method to generate continuous spatiotemporal datasets. GWS was derived from satellite observations, combined with machine learning model to capture spatiotemporal variability in subsurface water availability. Elevated temperatures and increased GWS during spring and autumn—corresponding to the major growing season—served as strong positive drivers of vegetation productivity, while summer NPP was predominantly influenced by precipitation variability. Beyond the widely recognized roles of temperature and precipitation, the analysis underscores groundwater as a critical and previously underappreciated driver of spatiotemporal NPP variability.
By clarifying the contribution of groundwater to terrestrial CO2 uptake, the findings provide essential guidance for enhancing carbon-flux monitoring strategies and for managing ecosystems that rely substantially on subsurface water resources. More broadly, the results highlight the importance of incorporating groundwater dynamics and the full suite of hydrological drivers into assessments of carbon cycle processes and into comprehensive climate adaptation and mitigation frameworks.
(Acknowledgments) This work was supported by a National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIT) (RS-2022-NR072257). This work was also supported by the Management Technology for Groundwater Dams in Water Supply Vulnerable Areas Program of the Korea Environmental Industry & Technology Institute (KEITI), funded by the Ministry of Environment (MOE) (RS-2025-01842973).
How to cite: Seo, J. Y. and Lee, S.-I.: Spatiotemporal variability analysis of remote sensing–derived net primary productivity and its hydrological drivers , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-2020, https://doi.org/10.5194/egusphere-egu26-2020, 2026.
Study Region: The Hailar River Basin in northeastern China, the Xijiang River Basin in southern China, and the Dongjiang River Basin in southeastern China.
Study Focus: Changes in runoff timing under concurrent climate change and large-scale afforestation are still poorly understood in typical catchments. This study analyses four decades of daily streamflow together with reanalysis climate data and satellite-derived forest cover in the three basins, all affected by major afforestation projects. Center timing, flood timing and spring flood timing indices are derived using circular statistics, and generalized additive models are applied to quantify nonlinear relationships between timing indices and hydroclimatic and vegetation variables and to separate the relative contributions of climate variability and forest expansion.
New Hydrological Insights for the Region: Runoff time has shifted significantly under the combined influence of climate change and forest expansion, with changes in flood timing being more pronounced than shifts in mean runoff timing. Afforestation delays flood timing much more strongly in the semi-arid basin than in the humid, whereas in humid regions earlier and more concentrated precipitation can still advance floods. Changes in precipitation regime and antecedent soil moisture emerge as primary controls on center timing and spring flood timing. These findings highlight that afforestation policies must be tailored to local hydroclimatic context, while implementing it more cautiously in water-scarce basins to balance flood mitigation against water availability.
How to cite: Zhou, X.: Contrasting runoff response times regulated by vegetation and climate changes in typical dry and wet basins, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-4486, https://doi.org/10.5194/egusphere-egu26-4486, 2026.
Accurate soil moisture (SM) forecasting is essential for hydrological and agricultural applications, particularly in the face of non-stationary climate conditions. This study developed a two-tiered modeling framework that combined a rolling forecast mechanism with non-stationary vine copula-based quantile regression models to improve SM forecast accuracy. Non-stationarity in hydrometeorological variables was assessed using the Mann-Kendall trend test, revealing statistically significant trends in over 97% of Yunnan Province, China. Then, a non-stationary vine copula model was constructed by embedding time-varying covariates into both marginal distributions and inter-variable dependence structures, enabling the model to dynamically capture both marginal and structural non-stationarity. Three temporal forecasting strategies—prediction, forecasting, and rolling forecast—were implemented to evaluate model adaptability and robustness. Model performance was benchmarked against three AI-based models (eXtreme Gradient Boosting (XGB), Random Forest (RF), and Long Short-Term Memory (LSTM)) and traditional quantile regression approaches. A suite of evaluation metrics was employed, including deterministic scores (e.g., Kling-Gupta Efficiency, KGE), probabilistic accuracy (e.g., coverage ratio, CR), and extreme-value diagnostics (e.g., total quantile error, TQE). Results demonstrated that non-stationary vine copula models outperformed stationary ones, achieving KGE values exceeding 0.90 in most regions, with structural (49.63%) and marginal (42.17%) non-stationarities contributing most to accuracy improvement. Among the methods, the rolling forecast with a sliding time window of 50 years emerged as the most reliable method, effectively mitigating "fake precision" by addressing biases from future information. Furthermore, the proposed model successfully identified and characterized agricultural droughts, including their frequency, duration, and severity. Taking the 2009~2010 winter-spring drought in Yunnan Province as an example, the model accurately captured its spatiotemporal evolution, demonstrating its potential in agricultural risk management and drought mitigation. Overall, this study highlights the necessity of incorporating non-stationarity in SM forecasting and presents a robust, interpretable, and operationally feasible framework for supporting drought preparedness and agricultural decision-making under climate uncertainty.
How to cite: Yu, C. and Wang, D.: A dynamic soil moisture forecast framework considering non-stationary margins and structures, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-2418, https://doi.org/10.5194/egusphere-egu26-2418, 2026.
Hydrological systems are undergoing rapid change under increasing greenhouse gas concentrations, yet substantial uncertainty remains regarding how precipitation is partitioned into blue (runoff) and green (transpiration) water flows across regions (blue–green water partitioning). Global assessments can reveal dominant large-scale signals, but they may mask regional differences in controlling mechanisms that are critical for climate-impact interpretation and climate-service applications. Here, we investigate regional controls on blue-green water partitioning using Coupled Model Intercomparison Project Phase 6 (CMIP6) simulations.
We analyse blue-green water partitioning in historical simulations and two contrasting future scenarios (SSP1-2.6 and SSP3-7.0), quantified from monthly runoff, transpiration, and precipitation, and evaluated in multi-decadal means. To ensure interpretability at typical CMIP6 spatial resolution, we focus on selected large-scale IPCC AR6 reference regions and use multi-model ensemble statistics to characterise spread and agreement. We further assess differences across individual Earth system models to contextualise ensemble behaviour and sources of uncertainty.
To identify the main factors shaping regional changes in blue-green water partitioning, we apply statistical learning methods that relate partitioning changes to candidate controls representing precipitation characteristics, atmospheric demand, soil moisture conditions, and vegetation functioning. We use an ensemble-based framework with interpretability diagnostics to assess the relative importance of these controls across regions and scenarios. This regional perspective aims to complement global analyses by highlighting where and why controlling factors differ across hydroclimatic regimes, providing decision-relevant context for ecosystem-relevant green-water changes and runoff-relevant blue-water availability in a changing climate.
How to cite: Heselschwerdt, S. P., Dengri, A., and Greve, P.: Regional controls on blue-green water partitioning under climate change in CMIP6, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-17191, https://doi.org/10.5194/egusphere-egu26-17191, 2026.
Posters on site: Thu, 7 May, 14:00–15:45 | Hall A
Land biosphere models which simulate interactive carbon and vegetation dynamics, are critical for understanding and predicting the water and carbon cycles. However, significant discrepancies exist among models regarding simulated variables, raising questions about the accuracy of the underlying process representations. In this study, we use land biosphere models from the TRENDY (Trends and Drivers of Terrestrial Sources and Sinks of Carbon Dioxide) project to understand the discrepancies in evaporative fluxes, including canopy evaporation, transpiration, evapotranspiration, and soil evaporation. Particularly, we try to understand how we can identify differences in process representation that cause these discrepancies. Initial results suggest that models differ from each other and significantly overestimate or underestimate the sensitivity of fluxes compared to data products such as GLEAM, FLUXCOM-X-BASE, and PML. These findings indicate a critical gap in how current models parametrize the coupling between vegetation structure and evaporative fluxes, which may explain part of the uncertainty in future projections.
How to cite: Jarajapu, D. C., Wei, H., Zhang, Y., and Wagener, T.: Understanding discrepancies in simulated evaporative fluxes across the land biosphere models, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-11120, https://doi.org/10.5194/egusphere-egu26-11120, 2026.
Water within the terrestrial environment is fundamentally interconnected. A single unit of precipitation follows multiple pathways: infiltrating soils, being taken up by vegetation, evaporating, undergoing freeze-thaw cycles, percolating into groundwater, flowing as surface and subsurface runoff, and being withdrawn or artificially recharged by human activities. However, quantitative understanding of the magnitudes of these flux fractions, as well as their temporal, regional, and global-scale variability, remains limited.
To address this gap, we develop a standard relationship matrix framework to represent flux exchanges—such as rainfall, evapotranspiration, and runoff—within the terrestrial water cycle. This framework incorporates two derived matrices termed “contribution” and “delivery”, which together characterize transfer rates and efficiencies among key subsystems: the atmosphere, oceans, pedosphere, and anthroposphere. These matrices help identify principal “donor” and “receiver” compartments within the terrestrial water system, revealing the dominate pathway of both blue and green water. Furthermore, we re-examine the terrestrial water cycle from a network-based perspective. By constructing water-cycle networks at global and regional scales—treating subsystems as nodes and fluxes as links—we apply a suite of network analysis methods to quantify key structural features. Metrics such as node strength, closeness, betweenness, and clustering are used to identify critical nodes, pivotal flux pathways, and structurally dominant subnetworks, thereby revealing the central subsystems and major flux routes in the water cycle.
Through these approaches, we systematically identify primary water flux pathways across regions with differing climate and land-use types, analyze their temporal dynamics, and thereby elucidate the principal factors shaping regional and global terrestrial water cycle patterns. Notably, the partitioning of surface streamflow to groundwater differs across regions, contributing to distinct terrestrial water network configurations. The influence of groundwater dynamics warrants further consideration in future studies.
How to cite: Mo, R., Zarei, M., and Destouni, G.: Understanding the Hydrological Interconnections: A Network and Relationship Matrix Approach, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-3911, https://doi.org/10.5194/egusphere-egu26-3911, 2026.
Accurate quantification of global water-cycle components remains a major challenge in Earth system science. In this study, we refine historical and future estimates of global river flow by applying an emergent constraint (EC) approach, which integrates outputs from 26 Earth System Models (ESMs) with observed streamflow from 50 large river basins (covering 26.9% of global land area). For the historical period (1980–2014), we estimate global river flow at 39.1±5.4×103 km3 yr-1 and a river flow-to-precipitation ratio of 0.35±0.03 , both lower than previous assessments. Land evapotranspiration is estimated at 73.4±6.2×103 km3 yr-1. Under future climate change, the EC-constrained projection indicates a global river flow increase of ,7.8±5.5 mm yr-1 K-1, which is 9.3% lower than the ESM ensemble mean and reduces inter-model uncertainty by 66%. Our results highlight a systematic overestimation of river flow increases in current ESMs, underscoring the importance of incorporating observational constraints and human impacts to improve the reliability of hydrological projections. This study provides a benchmark for global water-cycle partitioning and supports more accurate water resource planning under climate change.
This work is supported by the National Natural Science Foundation of China (Grant No. 42330506 and 42361144709), the Talent Program of the Ministry of Science and Technology of China, and the PIFI outstanding international team project by the Chinese Academy of Sciences.
How to cite: Zhang, Y., Blöschl, G., Wei, H., Kong, D., Ma, N., Wagener, T., Tian, J., Xia, J., Li, C., Wang, L., Chiew, F. H. S., Leung, L. R., Liu, X., Zheng, H., Zhang, X., and Liu, C.: Refining global water cycle components under a changing climate, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-4378, https://doi.org/10.5194/egusphere-egu26-4378, 2026.
Reliable long-term estimates of terrestrial evapotranspiration (ET) and gross primary production (GPP) are fundamental for understanding ecohydrological responses to climate change, yet remain challenged by satellite sensor transitions and forcing inconsistencies. Here, we build upon the diagnostic Penman–Monteith–Leuning (PML) model to introduce PML-V2.2, an extended 45-year ET and GPP dataset (1980–2024). By bridging multiple satellite epochs, PML-V2.2 provides a globally consistent record that supports both high-resolution near-present monitoring and robust long-term ecohydrological attribution.
Driven by bias-corrected MSWEP precipitation and MSWX meteorological forcing, PML-V2.2 leverages a multi-sensor simulation and consolidation framework to provide an extended data record. The dataset comprises three complementary products: (1) PML-V2.2a, an 8-day 500-m MODIS-based product (2000–2024) optimized for near-present monitoring; (2) PML-V2.2b, a half-month 0.1° AVHRR-based record (1980–2020) for long-term climate attribution; and (3) PML-V2.2c, a consolidated half-month 0.1° product (1980–2024) ensuring 45-year temporal continuity. The model was calibrated using 208 eddy-covariance flux sites with a refined parameterization that explicitly distinguishes irrigated from rainfed croplands, reducing agricultural biases in ET and GPP by ~7.5% and ~15%, respectively. Cross-validation against flux observations demonstrates robust performance across various plant functional types (with most NSE values > 0.60 and absolute bias < 5%), while water-balance validation across 152 large river basins yields excellent agreement (NSE = 0.89–0.91).
Globally, mean annual ET and GPP over 1980–2024 are estimated at 65.6 103 km3 yr−1 and 147.0 PgC yr−1, respectively. Both exhibit significant (p < 0.05) increasing trends, with ET rising by 0.015 103 km3 yr−2 and GPP by 0.338 PgC yr−2, indicating enhanced ecosystem productivity and water-use efficiency, partially moderated by CO2-induced physiological water savings. By providing an internally consistent, observation-constrained long-term record, PML-V2.2 offers a robust foundation for global ecohydrological studies, including drought impacts, carbon–water coupling, and model benchmarking under a changing climate.
How to cite: Xu, Z. and Zhang, Y.: PML-V2.2: Extended global terrestrial evapotranspiration and gross primary production dataset from 1980 to near present, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-9536, https://doi.org/10.5194/egusphere-egu26-9536, 2026.
Vegetation greening is significantly altering hydrological cycles in arid regions. However, current research often emphasizes direct hydrological effects, largely neglecting the underlying policy-driven mechanisms. By integrating GRACE/FO, MODIS LAI, and ISIMIP3a data (2003–2024), we reveal distinct hydrological responses across the Mongolian Plateau. Results show that Inner Mongolia, China experienced 2.73×104 km2 grassland restoration and 5.64×104 km2 cropland expansion, compared to 1.92×104 km2 and 1.25×104 km2 in Mongolia. Despite a synchronous precipitation increase (3.87 and 2.45 mm/yr), TWS trends diverged sharply: severe depletion in Inner Mongolia (-2.11 km³/yr) versus a marginal decline in Mongolia (-0.25 km³/yr). Deriving groundwater anomalies via water balance residuals confirms that groundwater depletion in Inner Mongolia (-2.73 km³/yr) is the primary driver of the TWS decline. Notably, ISIMIP3a natural simulations maintain a coupled TWS-precipitation increase, contrasting with the stark decoupling observed in reality; this divergence identifies policy-driven irrigation as the primary cause. We urge integrating anthropogenic processes into models and adopting adaptive policies to enhance regional resilience.
How to cite: Li, J., Zhang, Y., and Xu, Z.: Unintended Groundwater Depletion in Inner Mongolia Driven by Grazing Ban-Induced Irrigation Expansion, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-16073, https://doi.org/10.5194/egusphere-egu26-16073, 2026.
South America and Sub-Saharan Africa host some of the world’s most dynamic and societally critical terrestrial water cycles, yet their hydro-climatic responses to ongoing climate warming remain incompletely understood. Here, we assess spatial patterns, temporal trends, and hydrological extremes of precipitation, runoff, evapotranspiration, and water storage change across 222 hydrological catchments (95 in South America and 127 in Sub-Saharan Africa) over the period 1980–2010, based on and comparing four widely used global datasets.
Across both regions, the datasets robustly indicate widespread warming over the study period. For the mean water fluxes of precipitation, runoff, and evapotranspiration, most datasets show weak and often statistically insignificant trends. Among the datasets, ERA5 implications emerge as systematically anomalous, yielding strongly divergent results of persistent water-storage depletion driven by physically unrealistic evapotranspiration behavior.
Analyses of hydrological drought- and flood-related water-flux extremes reveal both shared and contrasting regional signals. In South America, wet-season high-flux extremes increase in magnitude in the Amazon Basin, indicating heightened flood risk, while the magnitudes of dry-season low-flux extremes decrease across much of the continent, indicating increasing drought risk, particularly in the La Plata Basin. In Sub-Saharan Africa, the highest 5% of monthly precipitation and runoff extremes intensify in the wettest season, whereas severe drought hazards, characterized by zero precipitation and runoff, persist in the driest season. Regional variability is pronounced, with catchments in Namibia showing wetting trends, yet still experiencing severe drought extremes.
Together, these results underscore the multi-faceted and region-specific nature of terrestrial hydro-climatic change in the Global South. They highlight the importance of comparative multi-dataset analyses combined with integrated water-balance diagnostics across many catchments to improve process understanding, distinguish physically plausible long-term change signals and extreme short-term variations, and more robustly assess changing flood and drought risks in a warming climate.
How to cite: Destouni, G. and Zarei, M.: Hydro-climatic Variability, Change, and Extremes Across South America and Sub-Saharan Africa: Insights from Multi-Catchment, Multi-Dataset Analyses, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-17633, https://doi.org/10.5194/egusphere-egu26-17633, 2026.
While critical to humans and ecosystems, groundwater accessibility is threatened by climate change, which alters groundwater recharge and can lead to water table decline. However, quantifying groundwater response to climatic changes remains highly uncertain. Modeling efforts are constrained by the oversimplification of processes, a lack of data for calibration and validation, and an uncertainty regarding processes and state variables. Improving our understanding of hydrological systems requires better quantification of the relationship between individual system inputs (climate signals) and outputs (groundwater table). However, this task is challenging, as groundwater is an integral part of the Earth system, where complex feedback mechanisms and multiple interacting factors and drivers (confounders) complicate the investigation of single processes. Topography, for example, is a core driver of groundwater flow and determines water table depth as well as where recharge and discharge areas develop. Geological properties like permeability and porosity strongly influence groundwater flow, response time, and storage capacity. Infiltration from surface waters is often the main source of groundwater recharge in drylands and may blur the direct influence of precipitation. Consequently, the nature and strength of the climate-groundwater connection are likely to vary across different environmental contexts. In particular, the subsurface's damping effect complicates a climate-groundwater analysis. Damping is described as the delay and smoothing of an output signal (i.e., water table) in a system compared to the input signal (i.e., precipitation). This effect is not only dependent on static subsurface characteristics but also nonstationary and nonlinear. Finding a relationship between precipitation and groundwater time series with classical correlation analysis remains, therefore, often unsuccessful. Here, we propose analyzing in situ groundwater time series and other groundwater-associated variables using statistical methods that account for confounders and damping. We compare the performance and feasibility of methods like (1) partial cross-correlation, (2) deconvolution, (3) clustering of pulse-response functions (a byproduct from deconvolution), (4) clustering functional relationships between climate variables and groundwater, and (5) causal interference methods (i.e., PCMCI–CMI). We give an overview of the advantages and disadvantages of every tested method. We aim to provide clarity in a landscape of numerous available methods and to offer practical guidance for holistic analyses that encounter similar challenges.
How to cite: Bäthge, A. and Reinecke, R.: Quantifying the impact of climate drivers on groundwater dynamics in diverse environmental settings and heterogeneous time series data – a method comparison, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-7389, https://doi.org/10.5194/egusphere-egu26-7389, 2026.
Streamflow, a key measure of water availability, follows a prominent seasonal cycle characterized by its amplitude and phase representing the range between high- and low-flow and their timing. This natural rhythm has profound implications for both ecosystems and human societies. However, evidence for whether anthropogenic warming has altered the timing of water availability remains limited to specific regions. Here, we synthesize a global large-sample hydrology dataset and use the centroid timing of mass of streamflow as a robust metric to quantify changes in the phase of seasonal cycle of streamflow. We find that approximately 20% of gauging stations show statistically significant timing shifts. By integrating multiple gridded runoff products derived from observation-based reconstruction, reanalysis and land surface models, we further identify a globally coherent yet contrasting change pattern. Importantly, this pattern is reproduced only by climate model simulations that include anthropogenic climate forcing, providing evidence for a detectable fingerprint on the timing of global water availability.
How to cite: Ren, Y., Tang, Q., and Zhao, G.: Globally observed changes in the timing of water availability attributed to climate change, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-8584, https://doi.org/10.5194/egusphere-egu26-8584, 2026.
Water resource optimization allocation is essential for maintaining ecological balance, promoting economic development, and ensuring social stability within river basins. However, many existing studies treated ecological water requirements as static baselines in allocation models, failing to address the dynamic utilization of water resources efficiently under varying hydrological conditions. In the Tao’er River Basin, a typical semiarid river basin, the combined effects of climate change and human activities have led to a severe imbalance between water supply and demand, particularly in regard to the competition between ecological water replenishment for the Xianghai Wetland, agricultural irrigation, and social water use. There is an urgent need for optimized water resource allocation to ensure the coordinated and sustainable development of these sectors. In this study, we developed the WEP-L model, a coupled natural–societal system, using various ecohydrological monitoring data to analyze the water supply‒demand balance under different hydrological conditions. We proposed an allocation plan that considers ecological marginal benefits, agricultural needs, and water use guarantees: 1.04 × 108 m3 for ecological supplementation and 5.16 × 108 m3 for agricultural water in the flat year, adjusted to 0.94 × 108 m3 and 3.60 × 108 m3 in the dry year, respectively. Furthermore, by incorporating reservoir water supply rules into the WEP-L model, we simulated the feasibility of intra-annual water allocation. The proposed allocation scheme concentrates ecological water in spring during flat years and distributes it at a ratio of 80% in spring and 20% in autumn during dry years. This study emphasizes the dynamic adjustment of ecological water resources on the basis of environmental conditions and societal needs, aiming to maximize ecological and social benefits. It provides scientific support for optimizing ecological water allocation and offers practical guidance for wetland conservation, agricultural water management, and water resource policy-making.
How to cite: Yan, Y.: Collaborative allocation of water resources considering ecological marginal benefits in a semiarid and cold region, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-9107, https://doi.org/10.5194/egusphere-egu26-9107, 2026.
Large reservoirs have expanded rapidly worldwide over recent decades, substantially altering surface water distribution and land–atmosphere interactions. While reservoir-induced temperature effects have been documented at local and regional scales, their global characteristics and controlling factors remain poorly constrained. Using a dataset of 348 reservoirs and spatially consistent reanalysis data constrained by in situ observations (2001-2020), with elevation effects explicitly corrected, we quantified near-surface air temperature differences between reservoir-adjacent areas and surrounding reference regions. About 67% of reservoirs are associated with local cooling, particularly in arid and continental regions. Reservoirs reduce daytime extreme temperature by 0.14 °C on average and increase nighttime extreme temperature by 0.04 °C, showing a general pattern of daytime cooling and nighttime warming and narrowing the diurnal temperature range. Notably, the mitigating effect on extreme high temperatures has strengthened significantly over the 20-year period. Attribution analysis using mixed-effects modeling indicates that reservoir-induced thermal responses are primarily regulated by water body characteristics (area, capacity, regulation, and shape) and modulated by regional climate. These findings provide an observation-constrained global characterization of reservoir-induced temperature effects and highlight the role of large reservoirs in modifying land-atmosphere thermal interactions across diverse climatic settings.
How to cite: Wang, Y., Liu, P., Xu, H., and Liu, W.: Global Assessment of Reservoir Impacts on Near-Surface Temperature, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-9130, https://doi.org/10.5194/egusphere-egu26-9130, 2026.
The expansion of cropland has been demonstrated to be a significant disturbance to the environment, capable of altering hydrological processes, often resulting in modified streamflow characteristics and sediment fluxes. South America, especially Brazil, has undergone a significant - nearly double - increase in cropland since 2000, alongside temporally fluctuating land use and land cover (LULC) changes. However, the hydrological consequences of these changes continue to be a subject of debate in this region.
Here, we investigate the reciprocal effects of climate and LULC alterations on streamflow by conducting trend analysis and analyzing long-term time series data of streamflow and precipitation (i.e., 38 years of data provided by the National Water Agency (ANA) of Brazil) over 78 catchments. Additionally, we use the WaterSed model to simulate runoff and soil erosion in response to specific sequences of rainfall events occurring in the selected catchments.
A considerable and statistically significant increase has been observed in soybean croplands following a shift from other temporary crops (such as maize, wheat, oats, etc.), which was also associated with a statistically significant reduction of 34% in the catchment’s runoff coefficient. In contrast, we found that the annual trends related to rainfall and streamflow were statistically insignificant. A 27 % increase in water demand is also interpreted as an important proxy that is linked to the expansion of soybean croplands, supporting a decrease in the runoff coefficient. Consequently, we may primarily attribute these alterations to LULC changes. These outcomes will be used to calibrate a soil erosion model (WaterSed) to understand the impact of potential LULC changes on water and sediment fluxes in the future.
How to cite: Avcioglu, A., Vandromme, R., Grangeon, T., Gomes Minella, J. P., Evrard, O., Tassano, M., Scariot, N., Rippel, E., Peixoto de Barros, C. A., and Cerdan, O.: Land use change-driven streamflow fluctuations and implications for soil erosion modeling in Southern Brazil, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-9707, https://doi.org/10.5194/egusphere-egu26-9707, 2026.
Global flood hazard models are central to assessing flood risk, informing adaptation planning, and interpreting the impacts of climate change on hydrological extremes. However, these models rely on structural assumptions that introduce substantial but poorly quantified uncertainty. One such assumption is that bankfull discharge corresponds to a fixed two-year return period, effectively prescribing a uniform river channel conveyance capacity globally and shaping discharge-inundation relationships across flood magnitudes.
Here, we quantify how uncertainty in river channel conveyance propagates through global flood hazard and population exposure estimates and assess its magnitude relative to climate-driver changes in discharge. Using global bankfull discharge estimates from a geomorphological-hydrological modelling framework, we derive change factors that adjust discharge-inundation relationships within a global flood hazard model. This enables adjusted estimates of flood hazard and exposure that reflect regional channel-floodplain interactions rather than a uniform global assumption.
Accounting for bankfull variability leads to systematic and spatially coherent changes in global flood exposure. Sub-Saharan Africa shows a robust net increase in exposure across return periods, including a 1.4 million increase (11%) for the 20-year flood. In contrast, several large river basins exhibit net reductions in exposure, such as a 7.5 million decrease across the Ganges-Brahmaputra basin. However. This basin-scale signal masks substantial internal variability, with Bangladesh seeing a net increase in exposure of 10% for the 20-year flood.
We compare these effects with climate-driven changes in flood hazard and assess how the uncertainty in river channel conveyance compares with the magnitude of projected climate signals. These results highlight how structural uncertainties in global flood hazard models cascade into risk assessments, with important implications for interpreting present day flood exposure and future climate impacts.
How to cite: Devitt, L., Neal, J., Nicholas, A., Darby, S., Gasparotto, A., Liu, Y., Aggarwal, E., Cloke, H., Slater, L., Leyland, J., and Parsons, D.: Variability in river channel conveyance reshapes global flood hazard and exposure, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-9962, https://doi.org/10.5194/egusphere-egu26-9962, 2026.
The Rhine river basin has increasingly faced severe summer droughts, resulting in critically low water availability for different water users. Rising temperatures combined with reduced precipitation have also intensified irrigation areas during summer (Purnamasari et al., 2025a). Concurrently, land use and land cover changes, population growth, and economic development are likely to reshape future water consumption patterns. These climatic and socioeconomic drivers present major challenges for sustainable water resources management in the Rhine basin. To address this, we developed a methodology to quantify future water demand in the Rhine basin by integrating climate projections from the KNMI’23 scenarios with socioeconomic development pathways. The socioeconomic development pathways are build upon previous work by Purnamasari et al. (2025b).Our approach combines projected changes in climate variables with land use and land cover dynamics, population growth, and economic trends to estimate evolving water use patterns across different sectors. By linking spatially explicit water demand scenarios with a distributed hydrological model, we capture spatiotemporal changes in hydrological processes under multiple plausible futures. The results highlight potential future hotspots of water deficit, supporting informed decision-making on water allocation, drought mitigation, and long-term water resources planning in the Rhine basin under climate and socioeconomic change. The approach is readily transferable to other European river basins and beyond. This work is part of the ongoing Horizon Europe project Stars4Water.
Purnamasari, D., Teuling, A. J., and Weerts, A. H.: Identifying irrigated areas using land surface temperature and hydrological modelling: Application to Rhine basin, Hydrology and Earth System Sciences, https://doi.org/10.5194/hess-29-1483-2025, 2025.
Purnamasari, D., van Verseveld, W. J., Buitink, J., Sperna Weiland, F. C., Dalmijn, B., Teuling, R., and Weerts, A. H.: Improving realism of high-resolution hydrological modeling with anthropogenic water use: A study on the Rhine Basin, ESS Open Archive, 2025.
How to cite: Purnamasari, D., Weiland, F. S., Teuling, A., and Weerts, A.: Quantifying future water demand in the Rhine river basin under climate and socioeconomic change, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-16143, https://doi.org/10.5194/egusphere-egu26-16143, 2026.
River flow regimes across Europe are increasingly influenced by climate change in addition to direct human interventions. Rising air temperatures, shifts in the spatiotemporal patterns of precipitation, and changes in terrestrial and snow water storage are expected to modify hydrological processes across European river basins, with direct implications for water availability and the frequency and magnitude of hydrological extremes. Alterations of flow regimes represent a further direct consequence of climate change. Such changes can have profound consequences for riverine ecosystems, as aquatic species composition and biodiversity have evolved under relatively stable natural flow conditions and are sensitive to climate-driven hydrological change. This study assesses climate change induced shifts in natural river flow regimes at the continental scale using the Soil and Water Integrated Model (SWIM). The model is calibrated against observed discharge from the Global Runoff Data Centre (GRDC) and remotely sensed evapotranspiration from MODIS using multi-objective optimization, with meteorological forcing from the E-OBS dataset. Future projections are driven by bias-adjusted climate forcing from ISIMIP3b, based on an ensemble of ten global climate models and three socioeconomic pathways (SSP1-2.6, SSP3-7.0, and SSP5-8.5). Hydrological alterations are quantified using the Indicators of Hydrologic Alteration (IHA) framework and Range of Variability Analysis (RVA), capturing changes in the magnitude, timing, frequency, and duration of high- and low-flow events. To explore potential ecological implications, changes in selected IHA metrics are linked to the Shannon diversity index using a previously derived empirical relationship, allowing estimation of biodiversity responses at hydrological stations lacking ecological observations. Natural flow conditions support higher ecological diversity and provide a reference for assessing climate induced deviations. Results are analyzed at fine spatial resolution across major European river basins, enabling the identification of sub-basins experiencing particularly strong hydrological alterations. The study aims to provide a comprehensive assessment of future flow regime shifts across Europe and to improve understanding of their potential hydrological and ecological consequences under climate change.
How to cite: Joseph, J. and Hattermann, F.: Future Shifts in European River Flow Regimes under Climate Change, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-18119, https://doi.org/10.5194/egusphere-egu26-18119, 2026.
Evaporation and runoff are the major surface water fluxes. Together they determine water availability on land, which ecosystems and humans rely on. The mechanisms driving trends in evaporation and runoff in the context of global change are not fully understood, which is also reflected in a significant uncertainty of projections of these water fluxes across Earth system models. To better understand trends in surface water fluxes and model discrepancies, we examine whether i) trends in total precipitation or ii) changes in the partitioning of precipitation to evaporation and runoff primarily control those changes. While changes in the partitioning are partly related to trends in total precipitation, additional factors such as land cover change and altered precipitation variability act as important contributors.
We analyze the trends in surface water fluxes across the 21st century utilizing data from several Earth system models. We compute differences in the mean daily partitioning of precipitation as well as mean daily precipitation between the first and last 25 years of the century. We find that changes in runoff are primarily driven by changes in precipitation partitioning, while changes in evaporation are dominated by changes in precipitation amount. However, for both surface water fluxes the spatial pattern of the relative importance of total precipitation versus precipitation partitioning varies among models. Our results highlight the importance of considering both changes in precipitation amount as well as changes in partitioning when investigating long-term trends in surface water fluxes.
Additionally, we will compare Earth system model simulations with observation-based data to test the robustness of our conclusions. Understanding the drivers of trends in surface water fluxes can inform related process-based modelling. This enables more accurate projections of the terrestrial water cycle as a basis for more targeted long-term regional water management.
How to cite: Kroll, J., Stephan, R., Ruiz-Vásquez, M., and Orth, R.: Attribution of trends in modeled and observed surface water fluxes, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-19048, https://doi.org/10.5194/egusphere-egu26-19048, 2026.
Posters virtual: Wed, 6 May, 14:00–18:00 | vPoster spot A
EGU26-16329 | ECS | Posters virtual | VPS9
Dynamics of Sectoral Water Demand and Future Water Stress Hotspots in Indian River BasinsWed, 06 May, 15:00–15:03 (CEST) vPoster spot A
Terrestrial water availability in India is increasingly affected by both climate variability and human activities such as groundwater extraction, reservoir operations, and the expansion of irrigation. However, the observed trends in these factors and their future changes at the river basin level are still not well quantified, making it difficult to plan effective water management strategies. In this study, we assess the individual and combined impacts of climate change and human interventions on the water budgets of major Indian river basins using an ensemble framework that includes the Community Water Model (CWatM) hydrological models. We specifically analyze the changes in sectoral water demands, including agricultural, domestic, and industrial, analyzing their historical progression and projected changes from 1951 to 2100. Based on IMD datasets and CMIP6 scenarios, we identify key regions likely to face water stress in the future and estimate uncertainties in water availability. These findings support the development of sustainable water management plans in response to evolving sectoral trends and climate-related challenges across the Indian subcontinent.
How to cite: Kushwaha, A. P. and Mishra, V.: Dynamics of Sectoral Water Demand and Future Water Stress Hotspots in Indian River Basins, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-16329, https://doi.org/10.5194/egusphere-egu26-16329, 2026.
