Surface hydrology

Nearly half of Earth’s seasonal freshwater storage variability occurs in human‐managed reservoirs, resulting in large areas of reservoir‐bed that are episodically inundated or subaerially exposed. Here we use hyperspectral data from NASA’s Earth Surface Mineral Dust Source Investigation (EMIT) mission to map calcite mineral assemblages across exposed surfaces in two large U.S. reservoirs, Lake Mead and Lake Powell. Results show distinct patterns in the occurrence of carbonate, clay, and mica minerals. Reservoir‐deposited sediments exhibited significantly higher calcite proportions than adjacent, lithologically similar, non‐inundated terrestrial surfaces. These differences likely reflect hydrologically‐driven calcite precipitation, wherein warm, clear, productive reservoir waters drive calcium carbonate out of solution. We also detect reductions in calcite proportions in areas of the shoreline disturbed by human recreation. EMIT's imaging capabilities enable rapid, high‐resolution mineralogical inventories across vast areas where field sampling is infeasible. We find EMIT is a scalable tool for monitoring geochemical changes associated with large water level fluctuations.

The Surface Water and Ocean Topography (SWOT) satellite mission introduces unprecedented 2-D radar interferometry to inland hydrology, aiming to evaluate water bodies down to 0.0625 km 2 . Standard operational products, such as LakeSP, are designed for global applicability but may exhibit suboptimal design choices for particular hydrological regimes. To address this, a customized processing workflow utilizing low-level SWOT L2 HR PIXC vD data was evaluated using 37 small Danish lakes (0.25 to 40 km 2 ) against daily in-situ gauge data from July 2023 to May 2026. The workflow achieves a median root-mean-square error (RMSE) of 5.57 cm and a Pearson correlation coefficient (PCC) of 0.91, similar to LakeSP. Crucially, this approach retains a median of 125 valid timestamps per lake compared to 60 timestamps preserved by the filtered LakeSP product, effectively doubling the temporal resolution and enabling the capture of higher-frequency hydrological variations. Additionally, we find indications that SWOT's PIXC data contains time-varying residual roll-errors over Danish lakes that affect ca 18 % of all passes with more than 10 observed lakes covering Denmark.

The instantaneous unit hydrograph (IUH) synthetically describes how effective rainfall is transformed into discharge at the catchment outlet. According to classical geomorphological theories, the IUH is regarded as a time‐invariant probability density function of watershed response times, an interpretation that implicitly postulates a fixed configuration of the channel network. However, river networks dynamically expand and contract, leading to systematic temporal variations in response times that challenge the foundations of standard IUH approaches. Here, we develop a generalized unit hydrograph theory that explicitly accounts for time‐varying river networks. The proposed framework introduces a dynamic formulation of the convolution relationship linking effective rainfall and streamflow, applicable when active network length varies during and across events. In this case, the rainfall–runoff kernel can be decomposed into two components: the classical IUH contribution evaluated on the instantaneous network configuration and an additional dynamic term capturing the effects of temporal changes in flowing stream length. Numerical applications show that, across the explored configurations, network dynamics reshape the hydrological response of catchments and the associated flow‐duration curves, in particular by increasing flood peaks and slowing down recessions. This pattern is confirmed by a proof‐of‐concept application to a monitored rainfall–runoff event in the Valfredda catchment (northeastern Italy), which achieved reasonably good performance and illustrates the potential of the proposed framework in real‐world settings. The proposed framework bridges classical unit hydrograph theory and empirical evidence on channel network dynamics, providing a new pathway for representing catchment functioning in hydrological models.

Energy and water availability are essential controls on terrestrial ecosystem functions such as evapotranspiration. Accurately identifying the dominant regime of water or energy limitation on evapotranspiration is crucial for understanding ecosystem responses to climate change. Recent studies using Earth System Model (ESM) projections suggest widespread shifts from energy‐ to water‐limited conditions under global warming. We demonstrate that incorporating a thermodynamically appropriate energy indicator fundamentally changes this projection. Surface energy availability for evapotranspiration and related ecosystem functions is primarily determined by net radiation ( R n ) rather than downwelling shortwave radiation or air temperature. Using this R n ‐based ecosystem limitation framework, we find no significant net increase in projected water limitation on either evapotranspiration or transpiration toward the end of the century. Idealized ESM CO 2 experiments attribute this pattern to CO 2 ‐induced physiological effects on vegetation—primarily stomatal regulation and enhanced water‐use efficiency—that largely dampen the increased water limitation from enhanced radiative forcing under greenhouse warming. Key physiological variables such as net primary production and leaf area index also show coherent and mechanistically interpretable responses to shifts in R n ‐based water–energy limitation regimes in transitional zones. These findings are consistent with patterns reported in other ecohydrologically based studies and are supported by empirical evidence of reduced vegetation sensitivity to dry conditions under elevated CO 2 . Our study bridges ecological and physical theories to improve ecosystem water‐energy limitation analysis and provides a clear mechanistic understanding of future ecosystem dynamics.

Wetlands play a crucial role in maintaining the global ecosystem balance, and determining the inundation dynamics is of great significance for understanding wetland ecosystems. Hydrodynamic models are effective tools for mapping the inundation dynamics; however, they are affected by uncertainties arising from environmental changes, including anthropogenic pressures and land cover modifications, especially in large wetlands. To overcome hydrodynamic model limitations, this study integrated a hydrodynamic module, a remote sensing module, and a deep learning fusion module to develop an efficient approach for mapping the inundation dynamics. This approach was applied in the Poyang Lake Wetland in China, a vital conservation wetland in the Ramsar Convention. The effectiveness of this approach was verified by comparing the simulated inundation dynamics of the hydrodynamic model during the dry season. We found that the performance of the hydrodynamic model was relatively poor. In particular, the performance in the sub‐lake area was significantly inferior than the other zone, with an overall accuracy (OA, dimensionless, ranging from 0 to 1) of only 0.63–0.66. The proposed approach in this study exhibited better performance, especially in the sub‐lake area, with an OA increasing to 0.82–0.92. Furthermore, the hydrodynamic model tended to overestimate the inundation time, with the median overestimated time reaching 42–50 days. Our study provides a transferable framework that can improve the accuracy of wetland hydrological monitoring under changing environment, contribute to maintaining the ecological integrity and stability.

Intensifying wet extremes under continued warming threaten water security and ecosystem functioning by perturbing land–atmosphere water fluxes. Yet, the watershed‐scale controls on how wet‐event intensity and duration translate into changes in fractional evapotranspiration (fET) remain poorly constrained at global scales. Here, using monthly wet climate extreme indices (CEIs) and fET across ∼986 global watersheds, we quantify CEI–fET coupling and attribute its spatial variability to watershed properties using Random Forest models and SHapley Additive exPlanations (SHAP). Across event types, soil organic carbon (SOC) and groundwater table depth (GWT) consistently structure coupling regimes, indicating a robust role for soil state and subsurface storage. However, the dominant texture axis differs by extreme type: intensity‐based extremes are most strongly organized by SOC–GWT–clay regimes, consistent with pulse retention and buffering, whereas duration‐based extremes are organized by SOC–GWT–silt regimes and permafrost‐related constraints, consistent with persistence‐driven drainage and saturation sensitivity. Supported SHAP‐transition analysis identifies data‐constrained regime boundaries. Lagged correlations further reveal multi‐timescale behavior: coupling varies at short lags but tends to shift toward negative associations at longer lags, especially for duration‐based extremes. In a permafrost‐limited subset, vegetation state and topography become more prominent predictors, highlighting how cold‐region constraints alter which attributes dominate. Together, these results provide a globally consistent diagnostic framework for identifying watershed regimes where intensifying wet extremes are more likely to yield sustained evapotranspiration responses versus reduced fET sensitivity. By quantifying regime thresholds, the study offers a diagnostic framework that may inform targeted mitigation strategies and policy frameworks, such as IPCC adaptation guidelines.

Wildfires are increasingly reshaping global terrestrial ecosystems, yet their impacts on the fundamental coupling between vegetation and the hydrological cycle remain poorly understood. Here, we present a global-scale assessment of how wildfire disturbances alter the sensitivity of surface runoff to vegetation dynamics using multidecadal satellite observations and reanalysis datasets. We developed a spatially explicit framework to quantify pre- and postfire runoff-vegetation sensitivity across climatic gradients and fire regimes. We show a widespread and systematic weakening of vegetation control on runoff after wildfires, indicating a fundamental decoupling of hydrology-vegetation interactions. This attenuation is notably modulated by aridity and fire intensity, with the most pronounced sensitivity losses occurring in fragile drylands. These findings expose a critical blind spot in traditional hydrological models that overlook disturbance-driven vegetation shifts. Our study provides a mechanistic understanding of how compound climate-fire perturbations accelerate terrestrial water flux instability, suggesting that increasing fire frequency will exacerbate ecosystem vulnerability and threaten water security in a warming world.

Vapor pressure deficit (VPD) has been widely recognized as a key driver of uncertainty in future global evapotranspiration ( E ) trends. Accurately characterizing the spatiotemporal dynamics of VPD and elucidating its influence on terrestrial E are critical for understanding land–atmosphere water and energy feedback processes under climate warming. However, previous studies have largely focused on the physiological regulation of plant transpiration ( E t ) at the leaf or canopy scale, leaving the quantitative understanding of VPD– E interactions at the global scale incomplete. By integrating multi-source remote sensing products and reanalysis datasets, this study quantitatively evaluates the role of VPD in regulating global terrestrial E variability. The main findings are as follows: (1) Approximately 60.7 % of global land area exhibits positive apparent sensitivity of E to VPD, with a global mean sensitivity of 293.3±62.3 mm hPa −1 , and stronger responses observed in warm and humid regions. (2) VPD is positively correlated with E t overall, particularly in mid-to-high latitudes of the Northern Hemisphere, whereas its correlation with bare soil evaporation ( E b ) is weaker and tends to be negative in water-limited regions. (3) The VPD– E relationship exhibits a pronounced aridity-dependent threshold, which decreases progressively from arid (1.90 kPa) and semi-arid (1.46 kPa) regions to semi-humid (0.49 kPa) and humid (0.47 kPa) regions. Further path analysis reveals that VPD regulates E through interconnected pathways involving atmospheric demand, soil moisture, vegetation physiology, and energy supply, with an overall negative net effect on E . This study clarifies the dominant pathways and regional heterogeneity of VPD– E interactions at the global scale, providing quantitative constraints for predicting the response of the terrestrial water cycle to increasing atmospheric aridity.

We present a robust, global method to reconcile absolute water surface elevations (WSE) from the Surface Water and Ocean Topography (SWOT) mission with a large‐scale hydrodynamic model by iteratively correcting river bathymetry. After removing sensor and representativeness bias, we update riverbed elevations under DEM and min‐depth feasibility constraints and interpolate corrections along reaches. Applied worldwide at 0.05° resolution over 2023–2025, the approach reduces the global median absolute WSE bias (3.10 m → 0.18 m), preserving the same global median correlation (0.38). A modest ∼4% increase in the standard deviation ratio (1.07 → 1.11) indicates slight variance inflation. Independent evaluation over 2015–2025 at 16,746 Hydroweb sites and 2123 GRDC gauges, restricted to reaches with updated beds, confirms substantially reduced bias, negligible change in both correlation and variance, and no significant change in global median discharge skill. When combined with models, this bias‐aware bathymetry will enable a refined numerical representation of floods globally.

The expansion of impervious surfaces (IS) substantially modifies urban water cycles and local climate regimes by suppressing evapotranspiration (ET), thereby affecting the sustainability of human settlements. However, most existing studies have emphasized the effects of IS quantity or coverage, while it remains unclear how different spatial patterns of urban expansion—dispersed versus compact—differentially regulate ET loss, as well as the extent of their respective impacts. This study integrates machine learning, interpretable artificial intelligence, and landscape pattern analysis to disentangle, with 256 cities in China as the research area, for the first time, the role of IS spatial expansion mode in regulating ET. Our results reveal that the suppressive effect of IS on ET weakens progressively from humid to arid regions and that its contribution to ET reduction follows a logarithmic growth pattern (all R 2 > 0.6), intensifying most rapidly during initial urbanization. This early‐stage sensitivity makes the initial choice of spatial expansion modes critical. Our causal analysis confirms that leap‐frogging expansion—characterized by high dispersion and low connectivity—exerts the strongest per‐unit‐area suppression, reducing ET by 93.1 ± 48.4% more than compact, interior‐filling expansion. In humid coastal megacities, each 10% increase in impervious surface percentage under this dispersed pattern reduces ET by 28.2 mm/yr. These findings underscore the critical role played by the spatial pattern of urban expansion in hydroclimatic regulation and provide a quantitative basis for designing climate‐resilient cities that sustain both development and ecological functions.

Drought assessment is an extremely complex task that relies on high-quality datasets, posing a major challenge for scientists and water managers. Here, we present the Global Multi-Index Drought (GMID), a multiscalar high-resolution dataset that includes five standardised drought indices: the Standardised Precipitation Index (SPI), the Evaporative Drought Demand Index (EDDI), the Standardised Precipitation Evapotranspiration Index (SPEI), the Standardised Evapotranspiration Deficit Index (SEDI), and the Standardised Vapor Pressure Deficit Index (SVPDI). This dataset is available globally at multiple time scales (1- to 12-, 18-, 24-, 48-month) for the period 1980–2024, with high spatial (0.1°) and temporal (weekly and monthly) resolution. The GMID dataset contains two versions of the drought indices; one generated using a traditional single-distribution approach, and another using a new flexible-distribution approach allowing the selection of probability distributions providing the best-fitting normalised series in each case. GMID adds significant value to previous drought data products through its high resolution and flexible multi-index nature, representing a comprehensive and robust long-term dataset for drought (and flash drought) assessment at the global scale.

SWOT river discharge products rely on the SWOT River Database (SWORD), designed mainly for single‐thread channels. We assess how well SWORD represents ∼65,000 river bifurcations from the Global River Topology (GRIT) network. SWORD geographically intersects both downstream branches at 88.2% of bifurcations, but only 2.0% of these have distinct reach IDs per branch. At structurally usable bifurcations, summed branch discharge exceeds the upstream value by a median factor of 2.1 in every algorithm tested, and the closure error is independent of branch width asymmetry. A width‐based redistribution ( Q ∝ W 1.52 ) enforces closure by construction; at the Athabasca–Embarras bifurcation, when anchored to the upstream gauge discharge and using Landsat‐derived branch widths, the redistribution matches the gauged Embarras flow fraction ( Q EM / Q upstream ) within 0.004 whereas SoS V3 underestimates it by 0.089. Improved multi‐channel topology in SWORD‐like frameworks remains the longer‐term path to branch‐resolved discharge.

Ecohydrological models are increasingly applied in regional scenario‐based studies, yet their cross‐resolution application remains constrained by high computational costs of fine‐resolution simulations and by structural inconsistencies introduced during scale transfer. We propose a Structure–Function Coherent Coarsening (SFCC) framework that improves the preservation of hydrological structure and functional heterogeneity during model input coarsening. Using the VELMA model in 24 subbasins of the Salish Sea Basin, USA, we evaluate coarsening strategies for three input types: (a) DEMs coarsened with a Hydro‐Aware approach that better preserves drainage topology; (b) land cover and soil type data sets coarsened with function‐preserving methods ( Auto‐Weight and Auto‐Reassign ) that retain small but process‐dominant classes; and (c) initial conditions coarsened with Hydro‐Aware , Landcover‐Aware , and Soil‐Aware strategies to improve temporal stability. Results show that the Hydro‐Aware method better preserves watershed morphology and yields more consistent runoff and nitrate loss predictions across resolutions than mean‐based coarsening. For categorical inputs, the function‐preserving methods can reduce the dominant‐class bias of majority aggregation, particularly in basins where small, high‐impact patches drive nitrogen export. Long‐term simulations show that hydrological variables stabilize quickly, whereas biogeochemical variables stabilize more slowly. In both cases, differences caused by coarsening decrease over time and eventually level off. This suggests that preserving both structural consistency and functional heterogeneity can help maintain stable model behavior over time. By operating directly at the data‐input level, SFCC improves integration of multi‐source data sets while retaining high‐resolution information. It supports cross‐scale simulation, calibration, and model integration, and provides a scalable foundation for regional scenario analysis.

Root zone water storage capacity () sets the lower limit on the water available to plants and is critical for modeling terrestrial water and carbon fluxes. Due to subsurface heterogeneity in soils, roots, and geology, in‐situ measurements of are infeasible at regional to global scales. Instead, is often estimated using a water balance approach—calculating the difference between precipitation and evapotranspiration to approximate water deficits. This method can be applied using diverse data sets, including those from eddy covariance, satellite remote sensing, reanalysis, and land surface model outputs. However, each data product contains its own uncertainties, and the influence of data set choice on estimates has not been quantified. In this study, we assess the uncertainty in estimates across 90 FLUXNET sites in the continental United States with at least 5 years of data, comparing results from six different precipitation and evapotranspiration data sets. We find substantial variation in estimates across data sets, sites, and land cover types, with particularly high uncertainty for values exceeding 500 mm. Much of this uncertainty can be attributed to differences among evapotranspiration products rather than random errors. Our analysis shows that single data set approaches to quantifying underestimate the variability that emerges when multiple data sets are considered, especially in the Western US. These results underscore the need to account for data‐driven uncertainty when estimating subsurface water storage and provide a foundation for improving model parameterizations of ecosystem drought response and land‐atmosphere interactions.

Amazon droughts have historically been driven by precipitation deficits, but recent escalating drought events highlight a regime shift toward “hot droughts.” By decomposing historical drought drivers (1979–2024), we show that rising evaporative demand (represented by potential evapotranspiration, PET) now acts as a primary driver of drought intensification. While rainfall deficits remain the dominant contributor to the record‐breaking 2024 drought, escalating atmospheric thirst almost doubled its severity. Rising PET also expands the total drought footprint, disproportionately amplifying exceptional (D4) droughts and nearly tripling their affected area in 2024 compared to precipitation deficits alone. This PET increase is forced comparably by background warming and increased shortwave radiation; neutralizing either factor during 2024 reduces D4 drought coverage by 25%–33%. Linked to observationally confirmed declines in dry‐season cloud cover, these compounding thermodynamic and radiative feedbacks are driving a shift toward increasingly extreme hot droughts across the Amazon.

Improving our understanding of snow–groundwater connectivity remains a key challenge in high-elevation mountain environments. This calls for a multidisciplinary and multimethod research framework that integrates different types of field observations, including the collection of water samples from diverse sources for stable isotope analysis. However, in remote alpine areas, the limited frequency of sampling hinders the generation of robust, data-driven insights into ecohydrological processes. Therefore, accurately modelling water movement and stable isotope transport through soil, vegetation, and groundwater recharge is essential for advancing our understanding of the hydrological functioning of high-altitude ecosystems. In this work, we combine a recently introduced snow isotope model with the HYDRUS-1D model to simulate water fluxes and isotope transport within the soil–plant–atmosphere continuum of a high-elevation mountain grassland located in the Aosta Valley, north-western Italy, over the period November 2017–February 2023. We use this modelling framework to: investigate the seasonal origin of two key water fluxes, namely transpiration and deep drainage (the latter assumed to contribute to groundwater recharge) clarify how seasonal water inputs and root water uptake patterns contribute to ecohydrological separation. The results demonstrate the effectiveness of the proposed modelling framework in accurately simulating volumetric water content (mean absolute error, MAE ≈ 0.03 cm 3 cm −3 at 10, 20, and 40 cm), actual evapotranspiration (MAE = 0.62 mm d −1 ), soil (MAE = 1.6 ‰, 3.6 ‰, 3.7 ‰ at 10, 20, and 40 cm) and xylem (MAE = 1.85 ‰) isotope content at the study site. Based on the model outputs, a separation between the water used by plants and the water contributing to deep drainage is evident during the 2018–2020 growing seasons, when median Seasonal Origin Index (SOI) values of transpiration and deep drainage are positive and negative, respectively. Here, positive and negative SOI values indicate an overrepresentation of summer- and winter-derived water in the considered flux. This separation weakens in 2021, when drainage water exhibits a slightly positive median SOI value. Over intense snowmelt periods, meltwater (winter water) rapidly drains through the lower soil layers, whereas rainfall (summer water), which predominantly occurs after the…

Bedload transport widely occurs in natural environments and encompasses flow regimes from saltation to sheet‐flow, with markedly different fluid‐particle interaction characteristics. Understanding these mechanisms requires a numerical tool capable of capturing both the turbulence and fluid‐particle interactions, for which challenges exist due to the constraints of grid resolution on the coupling accuracy. Here, we propose a semi‐resolved LES‐DEM framework to overcome such limitations in the conventional volume‐averaged CFD‐DEM paradigm. A feedback‐controlled body‐force term is also proposed to maintain a prescribed discharge under periodic boundary conditions in turbulent open‐channel flow simulations. Three benchmarks are performed to examine the accuracy of the proposed framework, including clear‐water turbulent channel flow and bedload transport in both saltation and sheet‐flow regimes. The present method is demonstrated to avoid the conventional grid‐size limitation and thus allows the fluid field to be resolved on sufficiently fine grids while preserving accurate fluid‐particle coupling. We further explore the micromechanics and layerwise flow characteristics underlying the transition from the saltation to sheet‐flow regimes and identify the thickness of the transport layers as a function of the Shields number. This numerical framework offers a unified and reliable approach for simulating sediment transport across various flow regimes, providing a solid foundation for micromechanical investigations and the development of the continuum model.

Drought is one of the most severe and complex natural hazards causing changes in water balance and impeding crucial water‐dependent ecosystem services. Over the last 25 years, multiple severe droughts linked to El Niño events have affected the Zambian and Zimbabwean regions. As a result, Lake Kariba, world's largest artificial reservoir by volume, saw historically low water levels and power shortages from reduced hydropower generation. This study employs a novel integrated approach to investigate the effects of varying water levels on the spatio‐temporal dynamics of phytoplankton blooms across the lake, combining remote sensing, in situ measurements, and a three‐dimensional hydrodynamic model (Delft3D). Satellite Earth Observations of lake water level, surface water temperature, and Chlorophyll‐a concentration are used, and spatially resolved water transparency is estimated from lake water leaving reflectance. The Delft3D model is calibrated and validated against in situ and satellite data, including Rainfall Estimates from Rain Gauge and Satellite Observations (CHIRPS), and in situ discharge data, while assimilating maps of water transparency to simulate surface thermal stratification accurately. Results show pronounced spatial and seasonal heterogeneity in transparency, thermal structure and phytoplankton dynamics across the three basins. Phytoplankton phenology varies from short, pulse‐like blooms in the most sensitive riverine upstream basin to longer, more coherent bloom seasons in downstream transitional and lacustrine basins. Bloom hotspots occur near shorelines and tributary mouths during windy and wet seasons, following basin‐scale circulation patterns and possibly nutrient inflows. Our integrated approach captures Lake Kariba's heterogeneity and identifies vulnerable areas, highlighting its importance for effective water resource management.

Green stormwater infrastructure (GSI) systems such as bioretention basins are frequently used in urban settings to reduce the amount of stormwater runoff entering combined sewer systems, thus protecting downstream waterbodies. Retaining stormwater in GSI allows it to infiltrate into the soil or return to the atmosphere via evapotranspiration (ET). While infiltration rates can be quantified with reasonable accuracy, methods of quantifying ET typically rely on models designed for homogeneous landcover like agricultural fields; the high spatial variation in factors including vegetation, light, and soil moisture renders estimates of ET from bioretention basins highly uncertain. To assess the influence of such variation on basin-scale ET and evaluate means of correcting for it, we quantified ET for a bioretention basin in Philadelphia, USA using three approaches: (1) an empirically-based model that incorporated chamber flux measurements of ET and accounted for heterogeneity in plant size, light conditions, and microtopography, (2) an empirical estimate of ET based on changes in soil moisture at a single location, and (3) a set of six conventional ET models that did not account for spatial heterogeneity. We further evaluated three methods of adjusting conventionally-modeled ET estimates to better align with those from the modeling approach based on chamber flux data. Our empirically-based model indicated that basin-scale, daily ET ranged from 0–6 mm d −1 , with temporal variation depending on weather conditions and time of year. A sensitivity analysis demonstrated that the spatial composition of plant height and shade strongly influenced basin-scale estimates. The soil moisture-based method found daily values to range from 0–4 mm d −1 , which more closely matched empirically based estimates for the sensor location than basin-scale estimates. Most conventional models overpredicted ET on average, though three models (Granger-Gray, Hargreaves-Samani, and Matt-Shuttleworth) were less sensitive to variation in atmospheric conditions and thus overpredicted ET at the low to middle part of the range but underpredicted ET at the upper end of the range. The muted response to atmospheric conditions limited the ability of additive or multiplicative adjustments (i.e., landscape coefficients) to improve agreement. In contrast, additive and multiplicative adjustments, as well…

River discharge estimation (RDE) methods are traditionally prone to high costs, vulnerability to extreme events, and are constrained by real‐time data availability. Current remote sensing‐based RDE approaches mainly extract basic river features such as width and water surface elevation, but rarely exploit the full spectral information from satellite imagery. Most studies also lack proper uncertainty assessment, which is essential for reliable water management decisions. This study proposes a machine learning (ML) approach for RDE that overcomes traditional limitations due to geometry, shadows and vegetated banks. Data are analyzed from 12 United States Geological Survey (USGS) hydrometric stations, spanning four river size classes: stream, small, medium, and large rivers. For each station, mean spectral band values are extracted from multiple polygons along the river channel and paired with temporally coincident in situ discharge measurements. This integrated data set is used to train five ML models—Extreme Gradient Boosting (XGBoost), Multilayer Perceptron (MLP), and three Bayesian Neural Networks (BNNs)—establishing a robust estimation framework that quantifies uncertainty. Models are trained and tested on 2016–2023 data, then evaluated on 2024–2025 observations to assess temporal transferability. Test (evaluation) results show strong performance with Kling‐Gupta Efficiency (KGE) ranging from 0.60 (0.35) for streams to 0.96 (0.92) for medium and large rivers. The approach maintains accuracy under both normal and extreme flow conditions and is robust to shadowed areas and complex channel geometries. By integrating satellite observations with ML, the proposed method enables real‐time RDE essential for streamflow forecasting, water allocation, and reservoir operation.

Rainfall-runoff events are widely used in hydrological applications, from flood estimation and flood forecasting, to understanding catchment responses in a climate/anthropogenic affected world. The majority of methods used to identify rainfall-runoff events are statistical in nature, relying on subjective, user-defined “rules” (i.e., parameters) that define a rainfall-runoff event. Since no ground-truth information is available to confirm the exact beginning and end of rainfall-runoff events, there is noticeable inconsistency (i.e. uncertainty) within the results. In this study, we propose the Robust Variance-based Event Identification Method (RVEIM), a new parsimonious rainfall-runoff event identification method which uses fewer parameters and better mimics the natural runoff generation process; decreasing the uncertainty in rainfall-runoff event identification. RVEIM detects runoff events by focusing on changes of streamflow variance and pairs to the corresponding rainfall event(s) simultaneously. RVEIM was compared to two benchmarking event identification methods in 8 representative catchments in Australia. A sensitivity analysis was performed using a comprehensive set of plausible event identification parameter values for all methods. Results revealed that the variation of rainfall-runoff events characteristics – including annual number, length, and the mean of paired runoff events – showed limited uncertainty from the RVEIM (standard deviation within ±15 % of the mean across 8 representative Australian catchments). In contrast, the two benchmarking methods exhibited substantially higher uncertainties (43 % to 93 %). Using RVEIM, we present the first comprehensive summary of rainfall-runoff event characteristics across 467 Australian catchments. The distribution of event-scale runoff coefficients for individual catchments shows a strong climate gradient. Such systematic shifts in the distribution of runoff coefficient across climate regions indicate that climate variables play an important role in catchment response and point to potential contrasts in dominating runoff generation mechanisms across climate zones.

Long-term monitoring of global river discharge has been hindered by the uneven distribution of gauging stations and limited data accessibility, a challenge that is particularly acute in China. Although China contains one of the world's densest river networks, high-frequency in situ discharge observations remain largely unavailable in the international public domain. To address this gap, we compiled daily discharge records from 1196 gauges across China, comprising approximately 2.33 ×10 6 observations – 39 times as many gauges as are currently available for the region in the Global Runoff Data Centre (GRDC). Leveraging this unprecedented collection of in situ discharge records, along with river width time series derived from Landsat and Sentinel-2 imagery and gauge-specific hydraulic geometry relationships, we reconstructed and extended daily river discharge observations for 310 gauges from 1990 to 2024, resulting in the China Daily River Discharge Records (CDR 2 ) dataset. Compared with existing global satellite-derived discharge products, CDR 2 expands the number of available gauges in China by more than fivefold while delivering substantially improved performance, achieving a median Kling–Gupta efficiency of 0.66 during validation. Moreover, uncertainties propagated from the fitted hydraulic geometry parameters remain low, with a median relative uncertainty of only 11.89 % in the reconstructed discharge estimates. Sensitivity analyses further indicate that discharge estimation accuracy increases markedly with greater river width variability and stronger hydraulic sensitivity. Trend analysis reveals that nearly 65 % of gauges exhibit declining discharge over 1990–2024, with a median relative trend of −0.21 % yr −1 , most pronounced in the Haihe, Liaohe, Yellow River, and middle Yangtze River basins. As the most extensive satellite-derived, gauge-constrained river discharge dataset currently available for China at daily resolution, CDR 2 bridges a critical geographic gap in global river monitoring and provides a valuable benchmark for future discharge estimation, hydrological research, water resources management, and the calibration and evaluation of satellite missions. The CDR 2 dataset is publicly available on Zenodo at https://doi.org/10.5281/zenodo.22231453 (Wang and Li, 2026).

Monitoring continental runoff is important for the global water cycle. Here, we update downstream monthly streamflow for the world’s 936 largest rivers to 2024 using multiple data sources for quantifying variations and long-term changes in continental discharge from 1950 to 2024. The update added new data for 697 rivers and extended the record length for 512 rivers. Still, many large rivers in South and East Asia, Africa, and some in South America have short records, and stations with observations have decreased since 1978. Streamflow in the world’s major rivers exhibits large interannual-to-decadal variations that are correlated with basin-mean precipitation, with insignificant long-term trends since 1948 for most of the rivers. Out of the 200 rivers with the largest discharges, only 71 (35.5%) show statistically significant streamflow trends during 1948–2024, with 34 being positive and 37 being negative. Global continental discharge decreased slightly from 1949 to 1993 and then rebounded to levels slightly above the 1950–90 mean in recent years. The streamflow-inferred runoff trends during 1950–2024 show coherent spatial patterns that are broadly consistent with observed precipitation trends, both showing increases over the central and Northeast United States, northern Europe and most of Russia, Argentina and Uruguay, and northwestern Australia, but decreases over most of Africa, East and South Asia, eastern Australia, the western United States, western and eastern Canada, and parts of Brazil. Both internal climate variability (especially from the tropical Pacific) and externally forced changes [e.g., due to carbon dioxide (CO 2 ) increases] likely contributed to these regional trends in precipitation and runoff, with below (above)-normal land precipitation and runoff during warm (cold) phases of the tropical Pacific sea surface temperatures.

Riparian landscapes support biodiverse and productive ecosystems that in turn provide valuable services like water filtration and flood attenuation. Extensive anthropogenic modifications to riparian environments have altered natural water connectivity patterns important for ecosystem health, leading to an increased effort to understand and restore natural floodplain connectivity. It is well known that secondary floodplain channels facilitate exchange at the river‐floodplain boundary with increasing discharge, but comparatively little is known about connectivity of floodplain waterbodies that are productivity hotspots in riparian ecosystems. In this study we investigate patterns of floodplain and floodplain waterbody connectivity in the coastal meandering Mission River, TX, USA. We use a modeling approach to examine water movement and connectivity, and find that water exchange generally increases with discharge, while the number of active floodplain connections is greatest at moderate flows. We also observe that the number of effective connections, a measure of connection prominence, is maximized at moderate discharges and is typically one‐third to half the number of active connections, indicating that few connections dominate river‐floodplain exchange. Counterintuitively, we find that exchange at the river‐floodplain interface is most uneven at the highest discharges, despite complete bank inundation. We interpret a process‐based transition in connectivity from being topographically controlled at lower discharges to hydraulically controlled at high discharges, and identify a spatial association to this transition. Finally, we consider that exchange and exposure time in each floodplain region can describe nutrient processing potential in a framework that reflects complexity inherent in natural floodplains.

Bedforms in lowland sand‐bed rivers, which contribute substantially to total flow resistance, are known to adapt to changing flow conditions. While the growth of subaqueous dunes with increasing bedload transport and their wash‐out at higher transport stages are well documented in laboratory experiments, their possible evanescence in lowland rivers characterized by very low Froude numbers remains poorly observed, as do the effects on flood propagation. In this study, a 2D depth‐averaged fixed‐bed hydrodynamic model is enhanced to account for bedform evolution and applied in a real river setting to analyze conveyance shifts caused by dune evanescence during floods. Bed resistance is dynamically updated based on local flow conditions using different roughness predictors for dune dimensions and complemented by additional dissipative components. The model is applied to the lower 200 km reach of the Po River (Italy), where traditional fixed‐resistance approaches misrepresent the stage‐discharge rating curve, overestimating peak water levels by more than 2 m (~25% of the water depth). The results confirm a marked resistance reduction at flood stage due to dune wash‐out into upper‐stage plane bed, occurring at very low Froude numbers (∼0.2) and initiating at approximately 60% of the maximum flow capacity. Enhanced with the Van Rijn roughness predictor, the model enables accurate water level predictions under all flow conditions. In real river settings, flow‐bedform interactions are complicated by irregular morphology and mutual feedback mechanisms. This study demonstrates that an appropriately tuned roughness predictor may substantially improve flood prediction and flow capacity assessment in lowland alluvial rivers.

Accurate discharge estimation in hydroelectric canals is essential for turbine efficiency monitoring and operational decision‐making, yet conventional current‐meter methods provide no rigorous uncertainty quantification for the integrated discharge quantity. This study proposes a hierarchical geostatistical framework for inference on an integrated hydraulic quantity—namely canal discharge—that treats it as an integrated spatial random field and propagates uncertainty at two complementary levels: (a) integration kriging, which provides optimal discharge estimates and a preliminary uncertainty measure grounded in sampling geometry and variogram structure, enabling sampling‐design optimization; and (b) geostatistical conditional simulation, which generates complete probability distributions for discharge conditioned on all observed values, yielding confidence intervals for risk‐based decisions. The framework is applied to 36 velocity surveys at the Kootenay Canal (British Columbia, Canada; discharges 38–109 /s) over two complementary measurement configurations: a sparsely sampled section (∼294 points) and a densely sampled section (∼1,217 points). Three key results emerge. First, integration kriging delivers discharge estimates whose coefficient of variation reaches in the densely sampled section and in the sparsely sampled one, comparable to or below the operational repeatability of the current‐meter method (0.4%–0.6%). Second, the gap between the kriging‐based and simulation‐based coefficients of variation narrows as sampling density increases. Third, the framework yields sampling‐design curves that quantify the trade‐off between measurement density and discharge uncertainty, providing direct operational guidance. These results establish a principled, operationally applicable foundation for uncertainty‐aware discharge estimation in hydroelectric canals.

Despite widespread recognition that lithological heterogeneity helps shape river networks, a widely applicable topographic diagnostic for whether and where lithological contrasts drive drainage reorganization is still lacking. Here, we introduce a drainage-network metric, the rock proportion–contributing area order (RP-CAO) relationship, and further show that responses diverge with drainage scale: small, low-discharge channels divert around resistant rocks, whereas large, high-discharge rivers largely maintain their course and incise through them. We capture this divergence by tracking rock-type enrichment along the network as a function of contributing area. Across landscape evolution simulations and natural river networks, the enrichment-scale relation forms a robust V-shaped pattern that quantifies these end members. Applied to networks along the eastern Tibetan Plateau, it isolates where lithology most strongly localizes rerouting and divide mobility despite strong spatial variability in tectonic and climatic influences. Requiring only topography and lithological maps, the framework enables comparable tests of drainage reorganization driven by spatially discrete lithological contrasts across mountain belts worldwide.

Surface Water and Ocean Topography (SWOT) provides water surface elevation (WSE) that can constrain river hydraulics through data assimilation (DA), but basin‐scale impacts of assimilating real observations remain uncertain under heterogeneous availability and regulation. We assimilate node‐level SWOT WSE into a large‐scale hydrodynamic model over the Ohio River Basin using an ensemble Kalman filter and test river‐network‐aware localization. Assimilation improves discharge skill at most of the 66 USGS gages, including sites without direct SWOT overpasses, with ∼60% showing higher KGE and ~3% degrading. These improvements suggest that SWOT WSE assimilation extends beyond direct observations, with localization adding benefit. Impacts are governed by transferable constraints, including quality control (QC), residual observation uncertainty, and river‐network context. Comparison with SWOT‐derived discharge at 27 overlap sites provides complementary context and supports using WSE as a direct hydrodynamic constraint. These results show that SWOT WSE assimilation improves discharge estimates under real‐world constraints, supporting water management in data‐sparse and transboundary basins globally.

Plants growing in highly permeable karst critical zones are exposed to shallow soils and rapid hydrological turnover. The surface organic layer is recognized as a key ecohydrological interface in these environments, yet its direct contribution to plant water uptake has rarely been quantified. Using δ 2 H and δ 18 O stable isotopes, this study quantified the relative contributions of soil water, surface organic layer water, and shallow epikarst water to plant water use in a karst critical zone. The results reveal that shallow soil water dominated plant uptake (43%–67%). Notably, the thin surface organic layer contributed more than 10%, with the highest proportion observed in herbs (15%), followed by shrubs (11%) and trees (10%). In contrast, epikarst water, traditionally considered an important reservoir, contributed <10%. Scenario analysis further revealed that excluding surface organic layer water as a distinct source systematically increased estimated shallow‐soil water contributions by approximately 10%–15% in herbaceous species, highlighting the risk of misattribution in isotope‐based studies. This indicates that even a thin surface organic layer can represent a functionally relevant water source in highly permeable karst systems. Our study provides the first systematic quantitative assessment of surface organic layer water uptake across multiple plant functional types in a highly permeable critical zone. These findings underscore the ecological significance of surface organic layer‐mediated water pathways. Explicitly incorporating surface organic layers into ecohydrological models and Earth system simulations can improve predictions of vegetation‐water interactions under changing climate and land cover.

In contrast to the wealth of information on evaporation dynamics from placid water surfaces such as lakes and reservoirs, estimating water evaporation from turbulent surfaces of streams remains a challenge. Evidence suggests considerable differences in evaporation rates from flowing water bodies relative to placid surfaces. Here, we experimentally investigate how flow characteristics (velocity and turbulence) and boundary conditions (wind and radiation) regulate evaporation from flowing water surfaces by using a controlled laboratory flume to isolate key hydrodynamic–atmospheric interactions relevant to streams and rivers. We used a closed flume (7.6 m length, 0.31 m width, and 0.5 m depth) imposing different boundary conditions over the test section of 1.5 m in length while other parts of the flume were covered to prevent evaporative losses. Our results demonstrate that the effect of turbulence on evaporation depends on the temperature profile of the water body and redistribution of radiative heat with turbulent mixing highlighting that evaporation emerges from a nonlinear coupling between hydrodynamic mixing and energy availability. Under solar radiation, increased water flow and turbulence can reduce evaporation by suppressing the formation of a warm surface layer and redistributing absorbed heat in depth of the water column. Without shortwave radiation flux, however, enhanced surface velocity and turbulent mixing may cause significant enhancement in evaporation rates up to 2–5 times that of placid water surfaces. Numerical simulations indicate that enhanced surface turbulence and wave activity increase interfacial roughness and shear, regulating the vapor boundary layer and producing spatially heterogeneous and intermittent vapor fluxes. The study offers novel insights into evaporation from wavy and turbulent flowing water surfaces for a better understanding of evaporation from riverine networks across flow regimes and climatic conditions.

The lack of reliable, spatiotemporally-completed and millennium-long drought dataset for China has limited a full-field and accurate understanding of drought variability over the past millennium. Here, we present a millennium-long gridded dataset of summer (June-August) Standardized Precipitation Evapotranspiration Index (SPEI) for China at 1.875° spatial resolution and annual temporal resolution. This dataset was generated using the paleoclimate data assimilation (PDA) approach by assimilating an expanded proxy network comprising 186 tree rings, 69 historical documents, three stalagmites and two ice cores from China and adjacent regions - addressing the limitations of previous gridded drought reconstructions that lacked proxies in eastern China and had sparsely-distributed proxies in northwestern China. Validation indicated that this dataset exhibits substantially-improved reliability compared to model-only calculations and previous PDA-based reconstruction. As a significantly-improved, millennium-long gridded SPEI dataset for China, this product provides essential support for elucidating the spatiotemporal evolution of drought variability over the past millennium, contextualizing current droughts within that period and evaluating climate model performance in simulating long-term drought events.

Latent heat flux ( L E ) provides a direct representation of terrestrial evapotranspiration (ET) and plays a critical role in hydrological cycle studies, land surface model development, and the evaluation of remotely sensed evapotranspiration products. Although flux observations based on the eddy covariance technique are widely regarded as essential benchmark data for evapotranspiration estimation, existing ChinaFlux observations are generally limited by short observation periods and extensive data gaps, which substantially constrain their applicability in long-term change analyses and multi-scale studies. To address these limitations, we developed a gap-filling and temporal prolongation framework specifically designed for half-hourly L E and established a continuous ground-based benchmark dataset covering China for the period 2000–2024 based on observations from 50 ChinaFlux sites. The framework is built upon an automated machine learning approach (AutoML) and integrates ERA5-Land reanalysis data with MODIS vegetation indices, enabling accurate gap-filling within observation periods and reliable prolongation beyond observation intervals. Comprehensive evaluations demonstrate that the AutoML framework achieves high accuracy at the half-hourly scale across different gap-length scenarios, with an overall correlation coefficient (CC) of 0.862 and a root mean square error (RMSE) of 33.75 W m −2 , and it substantially outperforms conventional methods under long-gap conditions (i.e., 7 and 30 d) introduced in the artificial gap experiments. The forward and backward prolongation results show high consistency (CC values of 0.902 and 0.896, respectively) and exhibit robust temporal stability under varying training data lengths. Multi-timescale validations further indicate that the prolonged L E data reasonably reproduce diurnal variations, seasonal cycles, and interannual variability from half-hourly to daily and monthly scales. Comparisons with ChinaFlux observations under strict quality control (half-hourly L E observations with QA/QC = 0) reveal good consistency across different temporal scales, underlying surface types, and climate zones. Interpretability analysis based on Shapley Additive Explanations (SHAP) indicates that energy supply consistently dominates L E variability, while vegetation state and water availability modulate their relative importance…

Water yield (WY) is a key indicator of water availability and the mechanisms regulating WY have become increasingly complex with ongoing global changes in climate and vegetation. Existing studies remain confined to the relative contribution and interacting pathways of multiple drivers, providing limited insights into heterogeneity across different regions. This study conducted a global spatiotemporal analysis of WY using multi‐source remote sensing and meteorological data from 1982 to 2020, integrating the Random Forest model and Structural Equation Model to quantify the relative contributions and pathways of climate and vegetation changes to WY at global and regional scales (arid and humid regions). The results show: (a) WY increased at the global scale and in arid regions but decreased in humid regions; (b) Climate change contributed more to WY at the global scale (relative contribution ratio, R = 55.11%) and in humid regions ( R = 62.00%), whereas vegetation change contributed relatively more in arid regions ( R = 64.49%); (c) Globally, the Palmer Drought Severity Index (PDSI), vapor pressure deficit, and wind speed exerted positive effects on WY. Downward surface shortwave radiation and Leaf Area Index (LAI) had negative effects. Notably, the positive effect of PDSI weakened in arid regions but strengthened in humid regions, whereas the negative effect of LAI reversed. These findings highlight the nonlinear and regional responses of WY and provide a scientific basis for ecological restoration and water resource management.

Copernicus Land Monitoring Service (CLMS) produces biogeophysical maps of the global land surface. The CLMS portfolio so far did not include actual evapotranspiration (ETa), despite it being a direct link between the energy, water and carbon cycles and its importance for global food security, efficient water resources management and weather forecasting. However, a global CLMS ETa product was recently developed and entered operational production at the end of 2025. It consists of water flux (ETa, evaporation and transpiration) and energy flux (latent and sensible heat) sub-products, has a spatial resolution of 300 m, dekadal (10 d mean) temporal resolution for water fluxes and daily temporal resolution for instantaneous energy fluxes, is produced in near-real-time, and (like all other CLMS products) is distributed under free and open data policy. It is based mainly on Copernicus input data with primary satellite imagery coming from the observations of OLCI and SLSTR sensors on board of Sentinel-3 satellites. Such product fills a gap in previously existing global and operational ETa products, thus satisfying a wide range of potential users' needs. In this paper, we describe, evaluate and justify the various design choices taken during the development of the ETa product, ranging from cloud masking and gap-filling, through derivation of biophysical traits, radiation components and weather forcings to spatial sharpening of the land surface temperature observations. Those data were then used to drive two ETa models: TSEB-PT and ETLook. A prototype implementation of the ETa processing chain was used to produce ETa data (using both models individually and their ensemble) across a globally representative range of climatic zones and plant functional types, which was validated against measurements from 206 Eddy Covariance flux tower sites. The ensemble resulted in the all-site root mean squared error (RMSE) of 0.87 mm d −1 (relative RMSE of 47 %, site range: 0.31–1.96 mm d −1 ), bias of 0.01 mm d −1 (relative bias of 0.6 %, site range: −1.39–1.50 mm d −1 ) and correlation coefficient of 0.81 (site mean of 0.77), which compares well with WaPOR global ETa dataset and therefore supports the operational production of global, near-real time, Copernicus-based 300 m ETa maps. Finally, we propose a number of potential evolutions of the CLMS ETa product, including enlarging…

Groundwater resources represent Germany's most important source of freshwater but they are increasingly under pressure. Climate change, societal developments, and rising abstraction rates are impacting subsurface storage in ways that are currently difficult to predict, affecting both the quantity and quality of groundwater. To ensure sustainable groundwater management, it is crucial to evaluate the intrinsic and spatially variable vulnerability of groundwater systems, especially to prepare for the effects of hydrological extremes. In this context, the groundwater response time, generally defined as the timescale over which a groundwater system responds or adjusts to changes in external or internal conditions and derived here as the characteristic timescale of the aquifer's low-pass filtering behavior, serves as a valuable indicator for vulnerability assessments. Unlike traditional methods, we propose estimating response times through spectral analysis of groundwater level data. Time series from around 200 selected observation wells across Bavaria in Southern Germany were processed and transformed into the spectral domain. Corresponding recharge time series were extracted from high-resolution hydrological model outputs. By integrating these data with hydrogeomorphic information, we fitted a semi-analytical model to the groundwater level spectra to obtain aquifer response times. The semi-analytical solution for the spectral domain accurately reproduced the majority of observed groundwater level spectra. Half of the estimated response times fall between 30 and 100 d. Significant correlation were found between the response time and the depth of the groundwater table. Groundwater systems exhibiting longer response times are interpreted as more resilient to drought conditions and therefore potentially better suited for groundwater abstraction than aquifers with shorter response times.

The Surface Water and Ocean Topography (SWOT) satellite mission offers unprecedented estimates of global river discharge. This study provides a first continental‐scale assessment of SWOT Consensus discharge performance across South America and compares it with a large‐scale hydrodynamic model and in situ observations. Consensus generally exhibits higher skill with a median Kling‐Gupta Efficiency (0.43) across the 569 benchmarked reaches. Performance varies systematically with physiographic settings. Random Forest and Spearman correlation analyses identify slope and distance to outlet as the top‐ranked controls among the variables tested, though no single variable explains performance, reflecting its multi‐dimensional nature. Consensus discharge inherits systematic bias from its machine learning prior, and substantial unexplained variability highlights that further work is needed to fully diagnose low‐skill reaches.

Plant transpiration ( E t ), the dominant element of evapotranspiration, plays an important role in hydrological, ecological, and climate processes. However, attributing projected future E t change to elevated atmospheric carbon dioxide concentration ([CO 2 ]), air temperature ( T a ), and leaf area index (LAI) remains challenging because these drivers exert opposing and interacting influences. Here, we develop a quantitative first‐order attribution framework that combines grid‐cell‐level sensitivities diagnosed from CMIP6 single‐forcing experiments with projected driver changes under Shared Socioeconomic Pathways (SSPs). Our results show that simulated future global E t shifts from +10.42 ± 11.84 mm yr −1 (+3.9%) under SSP1‐2.6 to −14.33 ± 29.91 mm yr −1 (−5.4%) under SSP5‐8.5, largely because the diagnosed negative physiological contribution of elevated [CO 2 ] increasingly exceeds the positive contributions associated with increasing T a and LAI under stronger forcing. Spatially, the [CO 2 ]‐related reduction is widespread, whereas the T a ‐related contribution is more pronounced in humid regions and the LAI‐related contribution is larger in arid and semi‐arid regions. Inter‐model spread in the attributed contributions arises substantially from differences in diagnosed sensitivities of Δ E t to changes in [CO 2 ], T a , and LAI, with the LAI‐related term showing the largest spread among the three first‐order terms considered. These results provide a consistent quantitative basis for interpreting how elevated [CO 2 ], warming, and vegetation dynamics jointly shape projected E t and highlight the need to better represent plant physiological responses, vegetation structural dynamics, and land‐atmosphere coupling in Earth System Models.

Forest disturbance effects on peak flows have been debated for decades. Paired‐catchment analysis (PCA) has been criticized in relation to the long‐term stability of pre‐harvest regressions and the inability of chronological pairing to characterize changes in event frequency. Non‐stationary frequency analysis (NFA) avoids some concerns with PCA. However, a fundamental problem is that the concept of return period is ambiguous under non‐stationary conditions. This study re‐examined peak flow response to forestry at Camp Creek, a snow‐dominated 34‐km 2 catchment in British Columbia, using five approaches: (a) chronologically paired PCA using adjacent Greata Creek as a control; (b) frequency‐paired PCA using maintenance‐of‐variance regression to fit the pre‐harvest relationship; (c) time‐series regression to relate Camp Creek annual maximum flows to annual peak snowpack water equivalent (SWE) and equivalent clearcut area (ECA); (d) NFA using SWE and ECA as covariates; (e) the design‐life‐level (DLL) approach. While all approaches reveal aspects of forestry effects on peak flows, only DLL provides a conceptually meaningful probabilistic approach to risk analysis under non‐stationary forest cover. However, the DLL‐based approach involves the application of NFA using ECA as a covariate. ECA may not provide a robust indicator of the effects of forest disturbance on peak flows in snow‐dominated catchments, particularly for situations in which logging occurs progressively over extended periods of time or where disturbance also occurs due to fire, insect infestation or disease. Further research should focus on developing alternative indices of forestry effects on peak flow, and on testing process‐based models using rigorous evaluation procedures.

Previous studies have suggested that the linear superposition principle fails for thin‐layer overland flows, yet direct experimental evidence remains scarce. This work adopts high‐resolution particle image velocimetry (HR‐PIV) to measure the 2D flow fields of grain resistance and form resistance in thin‐layer overland flows. Experiments cover four bed roughnesses (grain resistance), three cylinder sizes (form resistance), and five flow discharges. Key results: (a) Streamwise velocity vertical profiles fit the Keulegan log law well ( R 2 > 0.88); the cross‐sectional mean flow velocity drops with larger roughness and cylinder diameter. (b) Flow resistance coefficient rises with roughness and cylinder size but falls with Reynolds number. (c) The wilcoxon test reveals that the summed increments of individual grain and form resistance are significantly lower than the measured total resistance increment ( p < 0.001), disproving linear superposition; the discrepancy enlarges as flow depth declines. A dimensionless product model ( R 2 = 0.86) is proposed and yields reliable predictions. (d) Coexisting resistances produce far larger high Reynolds stress regions and Reynolds stress than their separate superposition; the linear fitting slope of measured versus superimposed Reynolds stress increments equals only 0.701, demonstrating that linear superposition underestimates micro‐turbulence and explaining the mechanism of additional resistance. This study provides direct hydrodynamic evidence to revisit resistance superposition for overland flow and supports theoretical calculations in soil and water conservation.

Tile drainage is widely adopted in the U.S. Midwest to manage excess soil water and sustain agricultural productivity, yet its impact on key water budget components—particularly evapotranspiration (ET)—remains insufficiently understood under varying drainage designs and climate conditions. We enhanced the PFLOTRAN model by developing an ET module and validated it against HYDRUS simulations. Using the calibrated model, we analyzed tile drainage effects on hydrological dynamics at an Iowa corn site during a critical vegetative‐stage period and assessed the influence of tile depth and spacing. Simulations showed that tile drainage produced distinct spatiotemporal soil moisture patterns, with topsoil moisture aligned with the pipe layout. Compared to an undrained scenario, the tile drainage system at the experimental site increased ET by approximately 16% and significantly altered surface–subsurface water partitioning. Design experiments further showed that compared to the baseline depth (1.2 m), shallower drains (0.76 m) reduced tile flow by about 78%, decreased ET by approximately 10%, and substantially increased surface runoff. In contrast, tile spacing has a limited impact on ET (<2%) across the tested range (10.8–27 m) and exerts a secondary influence relative to tile depth, with consistently smaller variations across other water budget components. Using these findings, we proposed a framework to evaluate drainage design using ET deficit (Potential ET–ET) and economic cost, identifying 1.2 m depth and ≥18 m spacing as optimal at the study site. These findings contribute to a site‐adaptable digital twin framework for tile drainage design, offering a tool to optimize water management.

Saline lakes are hypersensitive to changes in their water balance and therefore show amplified responses to climatic and land-use changes in their catchment. Despite often dramatic ecological impacts, saline lakes rank low on policy agendas as they are assumed to support few ecosystem services and low levels of biodiversity. Here, we challenge this view and evaluate ecosystem services and threatened species in 85 saline lakes distributed across the globe. We show that saline lakes support, additionally to threatened aquatic biota, a diverse range of red-listed terrestrial species that contribute together with a large beta diversity to their conservation value. Further, our results highlight that saline lakes provide a number of culturally and economically important ecosystem services but several of them are ‘hidden’ and difficult to quantify. We conclude our analysis with best-practice recommendations for sustainable management of saline lakes. Their local adaptation and implementation will be key for safeguarding biodiversity and ecosystem services of these valuable and highly sensitive ecosystems.

Poyang Lake, China's largest floodplain lake, plays a crucial role in regional hydrology by seasonally regulating water to mitigate floods and sustain extensive wetland ecosystems. However, complex hydrodynamics and significant spatial hydraulic gradients often invalidate the traditional “flat‐water” assumption, making reliable estimation of the lake's true seasonal buffering capacity a persistent challenge. To address this, we developed a SWOT‐based Floodplain Lake wAter volume and Storage cHange (SWOT‐FLASH) framework. By integrating two‐dimensional water surface elevations from the SWOT mission with reconstructed full‐lake bathymetry, this method explicitly resolves spatial surface gradients to estimate a massive seasonal buffering capacity of 22.71 ± 1.96 km 3 for Poyang Lake, accounting for 95.10% of the maximum lake water storage recorded from July 2023 to July 2025. Beyond revealing the lake's critical regional flood‐mitigation role, this framework introduces a physically realistic tool for quantifying lake water storage dynamics in complex river‐floodplain lake systems worldwide.

Significant changes in flow‐sediment dynamics and channel evolution have occurred in the Lower Yellow River owing to upstream damming. Notably, the post‐dam stage has witnessed an unexpected increase in movable bed roughness and more frequent dune development, driven by reduced sediment load, coarsened bed material, and altered hydraulic conditions. However, existing formulas for movable bed roughness, mostly calibrated using pre‐dam or flume data, fail to capture these new trends. A new formula is developed to calculate movable bed roughness, considering the effects of flow condition and bedforms, based on a new criterion for flow regime partition. The spatiotemporal variations in bedforms and the contributions of different factors were discussed. Results indicate that: (a) Froude number and relative water depth are key factors of movable bed roughness. The formula shows high accuracy, with the determination coefficients under different flow regimes larger than 0.70. (b) Lower flow regime is more likely to develop after the reservoir operation, especially in the braided reach under low discharges. The frequency of lower flow regime (embodied with ripples and dunes) under the low discharge generally increased by 1.3 times during the post‐dam stage. But there is a lag in the peak frequency of lower flow regime between the braided and transitional reach. (c) Although a slight increase existed in the contributions of relative water depth to movable bed roughness during the post‐dam stage, the flow condition, was still a dominant factor, with the contributions of Froude number exceeding 60% during both pre‐ and post‐ dam stages.

Vapor pressure deficit (VPD), a key indicator of atmospheric dryness, has markedly increased across most global land areas due to progressive warming. Although site‐scale studies show that increasing VPD may enhance plant transpiration (Tran) and influence water and energy fluxes between land and atmosphere, this relationship has not been thoroughly investigated on the global scale. Here, we apply a binning approach to quantify the contribution of VPD to global Tran by ruling out the effect of soil moisture. To this aim, we integrate satellite‐based observations, reanalysis data sets, and simulations from sixteen Coupled Model Intercomparison Project Phase 6 (CMIP6) models into a coherent framework. VPD shows a broadly positive influence on Tran over most parts of the global land consistently across all data sets. Exceptions occur in tropical rainforests where reanalysis data and CMIP6 model outputs suggest a negative VPD‐Tran interplay. Attribution analysis suggests that increasing leaf area index reduces the positive effect of VPD on Tran in reanalysis data sets and CMIP6 models compared to satellite‐based observations. This discrepancy may reflect differences in the representation of the effective canopy‐level response to high atmospheric water demand, potentially involving stomatal regulation, plant hydraulics, and vegetation structure. Such differences may contribute to an underestimated transpiration response to VPD. These findings highlight the need to better represent physiological processes, hydraulic processes, and vegetation structural characteristics in process‐based models to capture vegetation responses to increasing water stress.

The rainforests of South America and Africa are considerably reliant on rainfall supplied by evaporation from the rainforests themselves through moisture recycling. Thus, rainforest loss in these areas can reduce evaporation as well as subsequent rainfall in the wider forested region. During dry periods, a dampened water cycle may potentially aggravate rainforest water stress. However, few studies have investigated dry‐period moisture recycling statistics within rainforests over longer records and consistently compared the rainforest moisture recycling in both continents. Here, we analyze dry‐season anomalies of moisture recycling for mean‐years and dry‐years, in the South‐American (Amazon) and African (Congo) rainforests over the years 1980–2023. We find that dry‐season rainfall reliance on rainforest moisture increases slightly, but statistically significantly, by 5% (from a mean annual of 21% to 22%) in the Amazon and up to 12% (from 24% to 27%) in the Congo. At the same time, a lower fraction of rainforest evaporation stays within the rainforest during the dry season: 21% less from the Amazon (from 41% to 32%) and 22% less from the Congo rainforest (from 32% to 25%), which further decreases with increasing dryness. In response, areas downwind of the rainforests gain a higher dependency on rainforest moisture during dry seasons, amplified during dry years. For example, rainforest‐sourced rainfall in Ecuador increases from annually to in the dry season. The findings highlight the critical role of rainforests in maintaining rainfall during periods when water is already scarce.

Reported values of average annual peak snow water equivalent (SWE) in the mountains of the western United States differ by more than a factor of five, from not enough water to fill Lake Mead, the largest reservoir in the region, to enough water to fill Lake Mead five times. These products are not created equal. Many vastly underestimate snow water storage, and guidelines regarding which products are more accurate are crucial for using these products in any scientific analysis. Here, we review existing products and compare them to each other and to SWE derived from 26 aerial LiDAR acquisitions in Colorado, California, and Washington mountains of the western U.S. Products that match well with observations include (a) resolution of 9 km or finer, (b) reliance on observations of either fractional snow‐covered area or in situ SWE measurements, and (c) a well‐calibrated model framework that includes both quality precipitation estimates and accurate snowmelt. Many of the energy balance models reviewed had too much melt, likely from too much incoming radiation. Over all three regions, for the 14 flights that occurred close to peak SWE (March–April), the Western U.S. SWE Reanalysis (WUS‐SR) is least biased in reproducing near‐peak SWE (bias of 1.6%) and also best represented the spatial distribution of SWE (smallest mean absolute error). SNODAS and both of the University of Arizona products provide reasonable estimates (biases of 7.8%–10.2%). At the basin scale, ERA5‐Land also has small bias (−17.5%), but due to its coarser resolution, less accurate spatial representations.

Global warming is widely expected to intensify the hydrological cycle. However, observational evidence for accelerated evapotranspiration (ET), a key terrestrial water cycle and energy balance component, remains ambiguous. Here, we constrained diverse datasets using basin-scale ET estimates covering 47% of global land to reveal a hidden asymmetry. We found a small global change in ET [−0.06 ± 0.44 mm year −2 (millimeters per year squared), P > 0.10] from 2000 to 2022, resulting from strongly opposing hemispheric trends. The Northern Hemisphere shows noticeable ET increases (0.91 ± 0.46 mm year −2 , P < 0.05), primarily driven by vegetation greening, whereas the Southern Hemisphere exhibits strong declines (−2.63 ± 0.46 mm year −2 , P < 0.05) due to precipitation deficits. Our observation-constrained projections indicate that this asymmetry is likely to persist through 2050. The small global ET trend masks a marked compensation between northern greening and southern drying, suggesting that current models may overestimate homogenized water cycle intensification. Our study provides profound implications for global food security, carbon sequestration, drought risks, and regional climate adaptation.

Rice, a principal global staple crop, is increasingly threatened by flooding under climate change, placing rice‐dependent economies at risk. Yet the capacity of human activities to mitigate inundation impacts on rice remains insufficiently quantified. Here we develop an integrated agro‐hydrological framework that links climate forcing, hydrological regulation, and farmer decision‐making from a systems perspective. We apply the framework to the rapidly changing Lancang‐Mekong River Basin, focusing on Tonle Sap Lake, where flood‐pulse dynamics strongly shape rice production. Relative to the baseline period (1980–2014), with mean annual inundation‐induced rice losses of US$78 million, average losses decline in the near future (2021–2060) across all SSP scenarios, mainly because inundated area and rice exposure decrease. In the far future (2061–2100), losses increase relative to the near future; they remain below the baseline under SSP1‐2.6 and SSP2‐4.5 but slightly exceed the baseline under SSP5‐8.5. Extreme losses remain possible, with the maximum projected loss reaching US$160 million under far‐future SSP5‐8.5. Future losses are controlled not only by inundation extent and duration, but also by the timing of inundation relative to rice phenology. Losses shift from the reproductive and maturity stages in the baseline period toward the vegetative and reproductive stages in future periods, reflecting earlier flood recession and delayed planting. Reservoir operation substantially reduces inundation‐induced losses, particularly during the reproductive and maturity stages, while planting date adjustment provides additional benefits, especially in the far future. This framework supports climate‐resilient assessment of coupled water‐agriculture systems and adaptation pathways in flood‐dependent rice regions.

We present a 225-year (1799–2024) homogenized daily water level series for the Vistula River in Warsaw, comprising 82 453 observations. The construction of this consistent dataset required adjustments for changes in gauge location, shifts in gauge zero, differences in historical measurement units, and calendar discrepancies between the Julian and Gregorian systems. A small number of missing measurements was completed using the Long Short-Term Memory (LSTM) method, applied to measurements taken at the Warsaw gauge and parallel measurements from downstream water level gauge stations along the Vistula. The resulting dataset offers a robust foundation for long-term hydrological, climatic, and socio-environmental research. The dataset is openly available at Zenodo repository: https://doi.org/10.5281/zenodo.16919654 (Sobechowicz et al., 2025a).

Plants often exhibit a midday depression in water use (i.e., transpiration), reflecting a constraint on their ability to sustain maximum water transport, which may occur at the cost of reduced photosynthesis. Eddy covariance observations and geostationary satellites cannot quantify this widespread phenomenon globally while resolving fine-scale spatial variability. Using evapotranspiration measurements from the ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) and machine learning, we quantify the global distribution of midday depression in evapotranspiration. Midday depression primarily occurs during peak-growing seasons in temperate zones and dry periods in the tropics, with a morning shift of the peak evapotranspiration time by > 0.5 h. Droughts and heatwaves intensify midday depression, with peak evapotranspiration time shifting earlier, typically by 0.5 to 1.5 h and approaching 2 h during local extremes. By contrast, the coarse resolution upscaled flux products and hourly meteorological reanalysis datasets cannot capture this phenomenon. Midday depression of evapotranspiration detected using ECOSTRESS and machine learning is strongly associated with plant water stress, and helps us better understand how plants use water during drought and heatwaves.

Evaporative fraction (EF), defined as the ratio of latent heat flux to the sum of latent and sensible heat fluxes, is a key metric of surface energy partitioning and an indicator of plant water stress. To investigate the mechanisms underlying EF dynamics, we developed an explainable machine learning (ML) model based on a Long Short-Term Memory (LSTM) architecture that explicitly incorporates memory effects. The model was trained using data from 90 eddy-covariance sites across diverse plant functional types (PFTs) from the ICOS, AmeriFlux, and FLUXNET2015 Tier 1 datasets. Using only routinely available meteorological inputs (e.g., precipitation, incoming shortwave radiation, air temperature, and vapor pressure deficit) together with static site attributes (e.g., PFT and soil properties), the model accurately captured EF dynamics, particularly during post-rainfall pulses and soil moisture dry-down events. Across all test periods, ensemble mean predictions showed good agreement with observations, with a median site-level NSE of 0.63 across sites spanning broad climate and ecosystem gradients. Explainable ML analyses identified precipitation and vapor pressure deficit as the dominant drivers of EF in woody savanna, savanna, open shrubland, and grassland ecosystems, whereas air temperature emerged as the dominant factor in deciduous broadleaf, evergreen needleleaf, and mixed forests. Expected Gradients further revealed substantial variation in memory effect contributions across PFTs, with evergreen broadleaf forests and savannas showing stronger influences from antecedent conditions than grasslands. These memory effects were closely associated with rooting depth, soil water-holding capacity, and plant water-use strategies, which together regulate drought-response timescales. The learned memory patterns also suggest potential for inferring information related to rooting-zone water storage capacity and plant water stress from surface observations. Overall, our findings underscore the critical role of memory effects in EF prediction and highlight their relevance for anticipating vegetation water stress under increasing drought frequency and intensity.

Given the insurmountable challenge of measuring all rivers in situ, global river models serve as the foundation of freshwater knowledge past, present, and future. We adopt an inductive empirical framework based on Surface Water and Ocean Topography (SWOT) satellite measurements to assess these models. After controlling for SWOT data quality, we examine 68,347 individual river reaches representing ∼38% of global discharge. We find river models currently struggle in areas of heavy economic development, multi‐channel rivers, arid areas, and many Arctic rivers. After controlling for these expected errors, we find better skill as rivers get wider and that parts of Siberia and China are particularly difficult to model. We also find large variability and spatial heterogeneity to model performance, resisting oversimplification. Our results suggest that leveraging SWOT observations within river models will improve them, but river models must adapt their structure to represent realistic hydraulics to do so.

Vegetation restoration (VR) can affect ecohydrological processes in water‐limited regions. Paired numerical experiments with and without VR are commonly used to quantify these effects. However, existing studies often rely on static root schemes that cannot capture plant root adjustments under water stress and assume fixed vegetation states in the no‐restoration case, even though climate variability alone can drive vegetation change. To address these limitations, this study applies the Noah‐MP model with a prognostic dynamic vegetation scheme and a dynamic root module to the Chinese Loess Plateau—one of the world's largest VR hotspots. This approach improves the paired numerical experiments' ability to isolate VR effects from climate variability. Relative to the static scheme, the dynamic root configuration improved simulated evapotranspiration (ET), gross primary production (GPP), and leaf area index, with performance gains of 43.8%, 51.5%, and 59.5%, respectively, and the largest improvements occurring in dry years. Moreover, VR increased GPP and ET by 40.4% and 3.9%, and reshaped ET partitioning: transpiration ( T ) increased by 40.4%, while soil evaporation decreased by 19.9%, elevating T /ET by 41.5% and water‐use efficiency by 38.2%. Streamflow declined by 33.9%, driven primarily by reductions in baseflow, which accounted for 60.5% of the total decrease. Land‐use conversions from grassland or cropland to forest produced the most pronounced ecohydrological responses. Overall, VR enhanced carbon uptake but also increased water consumption, highlighting trade‐offs between ecological benefits and water availability. These findings underscore the importance of representing dynamic root processes when evaluating restoration impacts and supporting sustainable restoration planning.

Runoff responds differently to rainfall events depending on antecedent wetness conditions and catchment properties. One major characteristic of the response is the runoff event timescale, herein defined as the ratio of event volume to peak runoff rate. Since the event timescale essentially reflects the event runoff shape (e.g., prolonged or flashy), analyzing this metric and its main drivers can potentially help identify fast and slow runoff generation mechanisms in a catchment. Yet, large‐sample studies assessing the spatiotemporal variability of the event timescale and its controls in different climatic regions remain scarce. Here, we quantify how hydro‐meteorological event conditions and physical catchment attributes influence runoff event timescales in catchments with contrasting climates. We conducted an event‐based analysis in 388 snow‐free catchments in the contiguous US, separately for wet and dry climate types based on aridity. We demonstrate that spatiotemporal drivers of the event timescale significantly differ between wet and dry groups. In wet catchments, the temporal variability of the timescale was primarily determined by rainfall event characteristics, particularly rainfall event duration. While rainfall characteristics were important in the dry group as well, antecedent soil moisture was more important in predicting the timescale in dry than in wet catchments. The catchment‐median timescale was influenced by topography, soil and climate attributes. Physical attributes such as catchment area were mostly important in wet catchments, whereas mean potential evapotranspiration had the strongest influence on the median timescale in dry catchments. Our results imply that the potential controls on event timescales vary with catchment climate type.

Recession analysis is a powerful tool for determining the hydraulic properties of a riparian aquifer. The basis of the method relies on analytical solutions of the nonlinear Boussinesq equation in horizontal aquifers. In the present work, the nonlinear Boussinesq equation for groundwater flow in sloping aquifers is tackled analytically via a refinement of its linear solution in a perturbation series framework. The analytical solution is composed of three parts: the representative depth , the linear solution, and the correction term that is derived from the perturbation expansion. It can handle arbitrary initial water table profile, time‐varying stream level at the downstream end, as well as non‐uniform and non‐steady recharge and seepage distributions. The solution is evaluated for specific drainage conditions to obtain explicit equations for the outflow discharge rates and volume for both finite and semi‐infinite aquifers. The approximate solutions reproduce the original nonlinear behavior with high accuracy due to the introduction and proper quantification of the depth parameter . The linear semi‐infinite solution is used as the basis for introducing a recession flow model and formulating a surrogate model that replicates the original nonlinear behavior. An additional model that is based on the quasi‐steady approach is also presented and constitutes a special case of the surrogate model. Explicit equations relating the discharge rate to the outflow volume are obtained for easy implementation in recession flow analysis. Application of the recession models to practical cases demonstrate their effectiveness in the estimation of the basin‐scale hydraulic conductivity and drainable porosity.

Runoff threshold behavior is widely reported in event-based hydrological studies, but its interpretation and cross-catchment variability remain unresolved because threshold metrics, values, and process interpretations vary among studies, climates and landscape settings. This study synthesizes reported storm-runoff thresholds from 138 experimental catchments worldwide, as well as reported dominant runoff mechanisms, documented wetness-dependent mechanism transitions, and soil-geology-hydrogeology associations. Across the reviewed literature, threshold-like responses were identified using rainfall metrics (e.g., event rainfall amount and rainfall intensity), hydrological-state metrics (e.g., antecedent or within-event soil moisture, storage, and groundwater level), and composite rainfall–state indicators. Hydrological-state and composite indicators were reported more frequently than rainfall-only metrics. Subsurface- and saturation-related mechanisms were most frequently reported, particularly among studies in humid catchments. Among the catchments with explicitly documented event-scale transitions in runoff generation mechanisms, shifts from surface-dominated responses toward saturation-, subsurface-, or shallow-groundwater-influenced responses were more frequently reported as catchment wetness increased within the reported transition subset, although reverse and context-dependent pathways are hydrologically possible. Co-occurrence analysis indicates that reported mechanisms are associated with soil-depth, texture, permeability, lithology, and hydrogeological descriptors, which we interpret as structural contexts that condition state-dependent functional connectivity. Together, the synthesis supports a connectivity-based framework in which rainfall forcing interacts with catchment state and structural constraints to activate or connect runoff pathways.

The White Nile from Lake Victoria through Lakes Kyoga and Albert to the Sudd wetlands forms a complex connected lake-river-wetland system where flood propagation, storage, and attenuation remain poorly quantified. Following unprecedented and persistent flooding across South Sudan in 2022, this study quantified system-scale flood-wave transit time and examined how long it takes a flood wave to travel from Lake Victoria to the Sudd and how upstream storage and connectivity shape multi-year flood behaviour. Using daily lake levels, discharge, CHIRPS rainfall, and MODIS-derived inundation for 2002–2024, we tracked sequential flood peaks through the Victoria–Kyoga–Albert–Sudd cascade and mapped monthly wetland dynamics across five South Sudan sub-catchments. Flood-wave tracking showed a mean system transit time of approximately 17 months (16.84±1.95 months; range 13.0–20.9 months), substantially longer than the commonly inferred four-to-five-month timescale based on seasonal peak alignment. Segmental analysis revealed rapid transmission from Victoria to Kyoga (mean 4.2 months) but strong attenuation through the Albert–Sudd reach (mean 9.3 months), consistent with extensive floodplain storage and backwater control. Correlations between Lake Victoria peaks and downstream wetland extents strengthened markedly after 2019, with r 2 exceeding 0.8 at 9–13-month lags, confirming strong hydraulic coupling and long system memory. These statistical lags complement, but do not represent, physical flood-wave transit times. The 2019–2024 high-water regime was not a series of isolated rainfall events but a multi-year propagation of excess storage initiated by the 2019 positive Indian Ocean Dipole anomaly and consecutive rainfall seasons. Lake Victoria reached exceptional peak levels of 1136.48 m a.s.l. in 2020, 1136.50 m a.s.l. in 2021, and 1136.66 m a.s.l. in 2024, each exceeding the historical 1964 maximum (1136.42 m a.s.l.). Over the same period, the Sudd Wetland exceeded its previous MODIS-era maximum extent (81 496 km 2 in 2016) in every year from 2019 to 2024, reaching annual maxima of 120 680, 111 684, 111 480, 163 475, 122 292, and 116 359 km 2 , respectively. Flood-persistence mapping shows a shift from rainfall-driven activation in the eastern Sudd (Baro-Akobbo-Sobat–White Nile) in 2019–2020 to sustained, inflow-driven inundation across the central and western Sudd…

Groundwater is widely regarded as a critical buffer that sustains evapotranspiration (ET) and streamflow under hydrologic stress. However, whether this buffering capacity persists under sustained increases in ET demand remains unclear. Here we test whether sustained increases in ET demand can reorganize subsurface connectivity and undermine effective groundwater buffering, using controlled hillslope simulations with integrated hydrologic modeling and particle tracking. Under baseline semi-arid forcing, ET and outflow exhibit coexisting young and older age components. Following late-summer groundwater drawdown, intermediate-age flow paths weaken, eventually producing a temporary age gap that separates shallow and deep sources. Outflow responds more abruptly than ET due to hydraulic disconnection at the outlet. Warming and vegetation greening amplify this intrinsic seasonal age-segregation pattern. Intermediate-age contributions collapse earlier and recover more slowly, amplifying the polarization between shallow and deep water pools and further suppressing older groundwater inputs. Outflow becomes increasingly dominated by very young water, indicating strengthened groundwater–surface decoupling. These results suggest that sustained hydrologic stress structurally reduces effective groundwater connectivity, weakening subsurface buffering and shortening hydrologic memory. This shift persists across parameter perturbations. As a consequence, water cycling shifts toward shallower and faster pathways. Progressive groundwater decoupling therefore represents not merely a change in source depth, but a structural transition toward a more rapidly recycled and potentially less predictable mode of terrestrial water cycling under sustained increases in terrestrial water use.

As the atmosphere warms, atmospheric evaporative demand is expected to increase across many high-latitude ecosystems, while the duration of seasonal snow cover is projected to change. In Norway, a typically moisture-rich region, improved understanding of the controls on evaporation is needed to assess how these changes may affect ecosystem hydrology. In this study, we used year-round evaporation estimates from four eddy-covariance wetland sites in Norway to quantify evaporation and identify its main controls. To estimate monthly, seasonal, and annual evaporation, eddy-covariance data were gap-filled using a random forest model. The sites cover a latitudinal gradient from 60 to 78° N, a precipitation gradient from 218 to 968 mm per year and a gradient in mean temperature from −3.9 to 2.7 °C. To identify evaporation controls, we performed a factor analysis on observed time series in the snow-free and snow-covered season, separately. In addition, we compared the observed evaporation with the results of a Penman-Monteith model. We found that ecosystem evaporation was mainly controlled by atmospheric evaporative demand, both in the snow-free and the snow-covered season, whereas soil moisture likely never decreased to a level where it restricted evaporation. However, the sensitivity of the Bowen ratio to the vapour pressure deficit varied between sites, showing a decrease in the Bowen ratio beyond a vapour pressure deficit of 1 kPa at sites with a larger cover of open water and non-vascular vegetation compared to a site with a higher cover of vascular plants. Annual evaporation ranged from 80 to 208 mm, equivalent to 9 % to 30 % of the total precipitation. In the warm season, evaporation was typically around 50 % of the seasonal precipitation, reaching a maximum of 72 %. Sites and years with long-duration snow-cover had lower annual evaporation. Compared to other northern latitude sites in the FLUXNET2015 data set, evaporation was lower than expected from the mean temperature of the warm season. Our results show that evaporation is an important part of the northern latitude water balance, especially during the warm season and in regions with low precipitation. Furthermore, our results indicate that earlier snow-cover melt-out and increased vapour pressure deficit have the potential to increase annual evaporation.

Bankfull discharge, the maximum flow a river can convey before spilling over its banks, is central to modelling flood risk and understanding river-channel evolution. Global flood inundation models assume a 2-year return period for bankfull conditions, but this assumption remains untested globally. Here we use observations and machine learning to estimate bankfull discharge at ~1-km resolution along a recently developed global river network. We show that the widely used 2-year discharge reasonably approximates bankfull-flow magnitude, but bankfull return periods vary substantially within and across climate zones. Bankfull conditions occur more frequently in tropical and temperate regions (median return periods of 1.5 and 1.8 years; interquartile ranges of 2.5 and 3.2 years, respectively) and less frequently in cold and arid regions (2.8 and 4.3 years; interquartile ranges of 4.8 and 6.0 years). This variation across climate zones provides a basis for improving the representation of channel capacity in global flood models.

Offline Land Surface Models (LSMs) are essential for a wide range of applications, including water resource management and agricultural planning. A critical variable in these models is evapotranspiration, but its value is easily biased in irrigated areas. In fact, irrigation fundamentally alters local atmospheric conditions – cooling and humidifying the air and reducing wind speeds – factors that contribute to reducing evapotranspiration rates. This phenomenon is called “atmospheric feedback”, but is often missing or poorly represented in offline LSMs because most of the atmospheric forcings used, such as reanalyses and climate model outputs, overlook the atmospheric effect of irrigation. This leads to a tendency for offline LSMs to overestimate evapotranspiration rates over irrigated areas. In this study, the atmospheric effects of irrigation are quantified using data from the Land surface Interactions with the Atmosphere over the Iberian Semi-arid Environment (LIAISE) project field campaign. The various surface processes that influence the dynamics of evapotranspiration in response to the atmospheric feedback are then systematically investigated. The results confirm the importance of considering the atmospheric feedback in the Interactions Soil Biosphere Atmosphere (ISBA) LSM over irrigated areas in many configurations. For well irrigated crops, the average overestimation of evapotranspiration is about 25 %. Conversely, for water-stressed crops, this overestimation is negligible because of the delay in stomatal closure caused by the atmospheric feedback mechanisms, providing a compensatory effect which mitigates the overestimation. These findings highlight the need for improved representation of irrigation-related atmospheric feedback in the atmospheric forcings used as upper boundary conditions in LSMs to improve the accuracy of evapotranspiration estimates in agricultural or hydrological contexts.

Wetland restoration under the EU Nature Restoration Law prioritises the recovery of surface water connectivity, yet the ways in which water moves and connects across European wetlands remains poorly understood, hindering effective restoration and monitoring. To address this, we analysed ~50 European Ramsar wetlands between 2015 and 2024 using Sentinel-1 satellite imagery and a set of connectivity metrics that capture the spatiotemporal reorganisation of surface water bodies. We show that wetlands with similar inundated areas exhibited very different connectivity patterns, demonstrating that surface water area alone cannot adequately represent wetland hydrological behaviour. Connectivity stability, measured from the repeatability of annual inundation patterns and the timing of connectivity changes, exhibited a strong latitudinal gradient, with northern peatland-dominated wetlands showing the most predictable behaviour. Higher connectivity stability was associated with later snowmelt ( r = 0.57-0.70, p < 0.05) and fewer annual growing degree days ( r = –0.46 to −0.55, p < 0.05). These findings establish a pan-European baseline of surface water connectivity and support hydrologically informed wetland monitoring, restoration, and management.

Agricultural weather data are fundamental for the accurate estimate of evapotranspiration (ET), irrigation scheduling, and water-use accounting. In particular, reference ET provides a standardized atmospheric demand for water loss from a hypothetical well-watered grass (ETo) or alfalfa (ETr); however, weather stations may not adequately represent such climatic conditions. Weather data commonly contain errors from poor siting, sensor drift, and network management deficiencies. No standardized dataset exists over the contiguous United States (CONUS). Systematic errors affect ETo/ETr calculations and derived products. Notably, satellite-based platforms like OpenET require agricultural weather data to bias correct gridded reference ET to interpolate between satellite overpasses. CONUS-AgWeather is a benchmark dataset of daily agricultural weather data (precipitation, solar radiation, air temperature, humidity, wind speed, ETo, ETr) from 793 stations. This dataset contains 4,191,808 days (11,484 station-years, 1981–2020) and was produced through standardized and systematic quality control procedures and open-source software packages for time series inspection, outlier detection, corrections, and ETo/ETr calculations. CONUS-AgWeather is intended primarily to support OpenET in the Western U.S. but has broader applications.

The carbon budget of floodplain lakes is regulated by natural hydrological processes, yet the role of human interventions, including cascade river‐lake hydraulic projects, remains unclear. We evaluated the combined effects of the Three Gorges Dam (TGD) located on the Yangtze River and the proposed lake‐specific hydraulic projects (LHPs) on the carbon budgets of the respective Dongting and Poyang Lakes downstream of the TGD. As a result of TGD's operation, Dongting Lake saw a weakened carbon sink, with carbon dioxide (CO 2 ) uptake reduced by 6% and methane (CH 4 ) emissions increasing by 4%. The opposite is true for the downstream Poyang Lake, with CO 2 uptake increasing by 8% and CH 4 emissions decreasing by 1%. This divergence stems from the lake‐specific vegetation composition and TGD's regulation strategy. The Phragmites in Dongting Lake is sensitive to hydrological regulation during the pre‐flooding seasons, while the Carex in Poyang Lake is sensitive to hydrological regulation during the post‐flooding seasons. In contrast, the operation of LHPs will uniformly reduce the carbon sink due to elevated lake water levels, leading to a sharp decline in CO 2 uptake in Poyang Lake (36.98 gC m −2 yr −1 ) and Dongting Lake (11.87 gC m −2 yr −1 ). A cascade operation of these projects will increase the global warming potential (GWP) by 0.88 Tg CO 2 eq yr −1 . Our findings underscore the necessity of integrating ecological consequences into water resources management to reconcile water security with climate stability goals.

Drought is a dominant factor influencing terrestrial ecosystem water-use efficiency (WUE). However, the coupling relationship between WUE and drought remains insufficiently understood. Currently, the coupling relationship is primarily assessed using correlation coefficients or linear regression slopes. However, the optimal drought timescale at which WUE responds to drought has largely been overlooked. Therefore, this study investigated the spatiotemporal patterns of the WUE – meteorological drought coupling relationship across global terrestrial ecosystems from 1982 to 2018 with satellite- derived and model-simulated WUE, together with the Standardized Precipitation-Evapotranspiration Index (SPEI), and explored the potential causal mechanisms. Within the framework of WUE-SPEI coupling, the maximum correlation coefficient between WUE and SPEI represents the sensitivity of WUE to meteorological drought ( R max ), whereas the corresponding optimal drought timescale represents its resistance time ( T opt ). The results indicated that the sensitivity of WUE to meteorological drought decreased at a rate of −0.0003 yr −1 ( p < 0.01), while the resistance time increased at a rate of 0.0155 month yr −1 ( p < 0.01), indicating a weakening of the coupling between WUE and meteorological drought. Attribution analysis indicated that CO 2 fertilization was the primary factor contributing to the weakening of the coupling relationship. Surface soil moisture was the most critical hydrometeorological driver, exhibiting nearly opposite effects and significant threshold effects on R max and T opt . Peter & Clark Momentary Conditional Independence (PCMCI+) algorithm was further employed to construct a causality diagnosis framework for identifying the relationships between WUE-drought coupling and temperature, precipitation, radiation, wind speed, vapor pressure deficit, and surface and root-zone soil moisture. The results showed that the decrease in the R max had direct negative causal effects on precipitation, temperature, and radiation. In contrast, the increase in the T opt was primarily driven by a negative causal effect of radiation. This study highlights the weakened coupling between WUE and meteorological drought, suggesting that vegetation's carbon-water trade-off is evolving toward drought adaptation, which is crucial for understanding the adaptive strategies of…

Ecohydrological water isotope studies often rely on destructive sampling of soils and plants. Recently, techniques for collecting equilibrated water vapor were developed as alternative. Here, we present a systematic evaluation of water vapor sampling methods with the goal of identifying how key parameters influence δ 18 O and δ 2 H values. In controlled laboratory experiments we first tested the isotopic stability of three container types: 250 mL infusion glass bottles, 1 L FlexFoil sample bags, and 500 mL Aluminum-zip bags. The most suitable container was subsequently tested for different storage times (6 h to 7 d), storage temperatures (4 to 40 °C) and sampling flow rates (35 to 125 mL min −1 ). The 250 mL infusion glass bottles showed the best performance, with deviations to reference standards of ±0.5 ‰ for δ 18 O and ±1 ‰ for δ 2 H followed by FlexFoil sample bags (deviations of ±1 ‰ for δ 18 O and −3 ‰ to +2 ‰ for δ 2 H). Aluminum bags showed the largest deviations (−2.5 ‰ to −3 ‰ for δ 18 O and −12 ‰ to −25 ‰ for δ 2 H). Further extensive testing of the most suitable container type – the infusion glass bottles – revealed that the Mean Absolute Error (MAE) for δ 18 O remained stable under all tested conditions (<0.6 ‰). For δ 2 H, best results were obtained using flow rates of 100–125 mL min −1 , and storage times of ≤1 d under ambient conditions (20–25 °C) (MAE =±0.5 for δ 18 O and ±1.5 ‰ for δ 2 H); hence, our recommendation is using these settings. Although vapor sampling cannot match the analytical precision of conventional methods, it offers a practical, inexpensive framework. The protocol enables reliable water vapor isotope measurements and is especially advantageous in remote areas or locations with limited infrastructure. Therefore, it has great potential for investigating ecohydrological processes in a customizable spatiotemporal resolution without the need of repeated destructive sampling or full in situ setups.

Baseflow represents streamflow sustained between rainfall events and is fundamental to water security, ecosystem resilience, and drought management. However, widely used digital filters such as the Lyne–Hollick (LH) algorithm, when applied with a fixed, uncalibrated parameter, can substantially underestimate baseflow and obscure non‐stationary streamflow responses. Changes in climate and catchment conditions alter streamflow behavior and recession dynamics, yet many baseflow separation methods rely on fixed parameters, motivating the need for adaptive approaches. This study applied AutoVL, an automated self‐calibrating baseflow‐separation algorithm, based upon LH, to 467 Australian catchments spanning diverse hydroclimatic regimes over 1951–2021. AutoVL reconstructs streamflow components using a variable‐leak signal formulation coupled with a statistical recession model that iteratively estimates governing parameters from the discharge record. Variations in AutoVL‐derived flow diagnostics were evaluated relative to LH to assess systematic differences in flow partitioning across catchments. Compared with the conventional LH configuration ( = 0.925 with three passes), AutoVL consistently identified larger baseflow contributions, often nearly doubling Baseflow Index (BFI) where LH yielded BFI < 0.5. Hydrological variability was examined using clustering of interannual streamflow as a similarity construct to group catchments with comparable temporal behavior. These clusters delineated distinct streamflow regimes, from stable perennial systems to highly variable flood–drought responses and shifts in runoff efficiency. Systematic variation in AutoVL parameters across these regimes demonstrated the method's capacity to adapt to differing streamflow dynamics. Together, these results provide a framework for interpreting spatial and temporal variability in streamflow behavior and highlight the need for adaptive approaches under changing conditions.

Evapotranspiration (ET) is a critical component of the water cycle, influencing climate, agriculture, and water resource management. However, most satellite-derived ET products are limited to daily or coarser temporal resolutions, despite the strong diurnal variability of ET processes. Existing satellite-based ET retrievals are largely restricted to daytime conditions, when nighttime ET is a small but often non-trivial flux. In this study, we introduce the Advanced Baseline Imager Live Imaging of Vegetated Ecosystems ET (ALIVE ET ), a near real-time, 5 min ET estimation framework, leveraging geostationary satellite observations from the GOES-R Advanced Baseline Imager (ABI) and machine learning models under both clear and cloudy conditions. We test Gradient Boosting Regression (GBR) and Long Short-Term Memory (LSTM) models to assess their ability to estimate ET variations across the diurnal cycle. GBR captures daytime ET with an R 2 of 0.74 (normalized RMSE of 0.91) while maintaining low computational cost. For nighttime ET, LSTM models trained on time-series observations perform better, achieving an R 2 of 0.24 (nRMSE of 1.29) by leveraging temporal dependencies in land surface temperature (LST) and past ABI observations. Comparisons against daily ET estimates from the physically-based ALEXI remote sensing model demonstrates good agreement but opportunities for improvement. This study demonstrates the potential of integrating machine learning with geostationary remote sensing to advance high-temporal-resolution ET estimation.

Groundwater plays a critical role in land‐atmosphere interactions by regulating soil moisture and surface energy fluxes, yet its contribution to land‐surface processes and heat extremes remains poorly quantified globally. Here, we combine observation‐based aquifer records with gridded groundwater table depth (GWD) model outputs (2000–2015) to assess how GWD annual fluctuations covary with soil moisture, evaporation, soil temperature, and heatwave frequency across 1,658 aquifers worldwide. Soil temperature shows the most frequently dominant correlation with GWD among statistically significant, robust relationships (37% of aquifers), followed by evaporation (28%), heatwave frequency (22%), and soil moisture (13%). Aquifers with declining GWD suggest different coupling patterns compared to stable aquifers, with more negative relationships between GWD and evaporation and positive associations between GWD and soil temperature. Our results highlight the importance of incorporating groundwater dynamics into assessments of climate extremes and land‐surface processes.

Urbanization and climate change profoundly impact hydrological processes and the associated water budget, challenging sustainable water management. In this study, we use a distributed mechanistic ecohydrological model adapted for urban environments to quantify changes in the hydrological budget components in response to urbanization, as well as projected future climate conditions and additional land cover changes. The city of Singapore is selected as the study site, given the high complexity of its water management system that integrates elements highly relevant to similar tropical urban settings, where the presented framework can be applicable. Results reveal that, compared to a pristine landscape in Singapore, urbanization has increased sensible heat flux by ∼5% and decreased latent heat flux by ∼11%. This is accompanied by a ∼32% reduction in total transpiration and a ∼16% increase in runoff. Climate change scenarios with higher temperature and elevated atmospheric CO 2 levels show minimal impact on total evapotranspiration; therefore, runoff is very much dependent on the potential changes in future precipitation. Notably, the response of vegetation is shown to be relevant for long‐term urban hydrology, with higher CO 2 concentrations projected in the future significantly reducing the simulated stomatal conductance, which results in decreased transpiration rates broadly compensated by increased evaporation. By quantifying how urbanization and climate change can jointly impact hydrological budget components, this study demonstrates the significance of urban ecohydrology for a proper assessment of water resources, which is highly relevant to Water Sensitive Urban Design, in Singapore as well other cities.

The Penman–Monteith–Leuning (PML) model is a widely recognized diagnostic framework for estimating coupled terrestrial evapotranspiration (ET) and gross primary production (GPP). To address the critical need for high-fidelity, long-term, and near-present eco-hydrological records, we developed the PML-V2.2 dataset, spanning from 1982 to 2025. Driven by observation-constrained Multi-Source Weighted-Ensemble Precipitation (MSWEP) and Multi-Source Weather (MSWX) meteorological variables, the dataset comprises three complementary products: (1) PML-V2.2a, an 8 d 500 m MODIS/VIIRS satellite-based product (2000–2024 and 2012–2025) optimized for near-present monitoring (updated annually); (2) PML-V2.2b, a half-month 0.1° AVHRR-based product (1982–2020) anchoring long-term climate attribution; and (3) PML-V2.2c, a consolidated half-month 0.1° record integrating the above products for seamless 44-year continuity (1982–2025). Our methodological framework features an expanded bottom-up calibration using 208 flux sites (∼ 1400 site-years) across various plant functional types (PFTs) and a refined parameterization that explicitly distinguishes between irrigated and rainfed croplands. This distinction effectively mitigated systematic biases in agricultural regions, reducing ET and GPP estimation errors by 8.7 % and 16.2 %, respectively. Performance evaluation reveals high accuracy across PFTs (cross-validation Nash-Sutcliffe Efficiency, NSE > 0.60, absolute bias < 5 %), while top-down water-balance validation across 56 large river basins during 1982–2016 and 152 basins during 2003–2020 confirms high reliability (NSE: 0.89–0.91) as compared with other products. The MODIS- and VIIRS-based PML-V2.2a datasets are internally consistent, and exhibit high agreement with PML-V2.2b during their overlapping period (NSE = 0.90 and 0.79 for annual ET and GPP anomalies), ensuring a seamless transition across satellite epochs. Based on the consolidated PML-V2.2c dataset, global terrestrial ET and GPP during 1982–2025 are estimated at 65.8 × 10 3 km 3 yr −1 (with 58.2 % from transpiration) and 143.4 PgC yr −1 , respectively. Long-term analysis reveals significant ( p < 0.05) increasing trends in GPP (0.343 PgC yr −2 ) and ET (0.019 × 10 3 km 3 yr −2 ) during 1982–2025, where vegetation greening impact on ET is partially offset by physiological water saving under rising atmospheric CO 2…