Hydro Digest

Accurate and continuous streamflow monitoring can support effective water resource management, yet substantial gaps persist in national streamgage networks due to operational challenges and costs. Non‐contact, image‐based techniques such as Space‐Time Image Velocimetry (STIV) offer a safe, scalable and, cost‐effective alternative for measuring surface flow velocity in rivers, which allows for streamflow calculation, especially during hazardous flood events. Traditional STIV methods, however, often require manual parameter tuning and struggle with complex flow conditions. This study introduces Auto‐STIV, a novel deep learning framework that fully automates the estimation of both flow direction and velocity magnitude from video imagery. The proposed approach utilizes a two‐stage Convolutional Neural Network model. The first stage predicts the primary flow direction by analyzing a stack of Space‐Time Images (STIs) generated from a series of candidate orientations. The second stage estimates the velocity magnitude from the STI corresponding to the predicted direction. The model was trained on a comprehensive data set collected under diverse, controlled laboratory conditions and evaluated using real‐world footage from the Colorado River. Results demonstrate that Auto‐STIV outperforms the traditional autocorrelation‐based STIV method, exhibiting higher accuracy and greater robustness in the presence of challenging environmental factors such as variable lighting, low‐contrast surface textures, and complex flow patterns. This research underscores the potential of deep learning to enhance the capabilities of image‐based velocimetry, that may support more reliable, scalable, and automated streamflow monitoring systems.

Concurrent floods and concurrent droughts in nearby catchments pose challenges to risk assessment and water management. Climate change is affecting extremely high and low discharge, but the complex interplay between changes in individual catchments, and in the dependence across catchments, make it difficult to provide accurate assessments of the occurrence probabilities of concurrent extremes. In this work, we use a contemporary statistical deep learning model to capture concurrent river floods and droughts in four catchments in the Upper Danube basin, based on discharge simulated by a hydrological model driven with large ensemble climate model output. The statistical model is able to accurately capture the tail behavior of the simulated discharge, which we assess by making use of the large available sample. We subsequently use our statistical model to study changes in joint tail behavior of discharge over time, finding that both compound flooding and drought‐like conditions are becoming increasingly likely toward the end of the 21st century under a high‐emission scenario. In particular, our results highlight that changes in the dependence structure of extremes strongly contribute to the detected changes, an aspect that would be difficult to capture with traditional approaches. This work paves the way for highly flexible, general inference on compound extremes in hydrological applications, and demonstrates key advantages of using statistical deep learning in this setting.

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.

Over the last few years, significant progress has been made in the development of convection-permitting climate models (CPCMs), especially in improving precipitation estimates in regions with complex terrain. Recently, the South American Affinity Group (SAAG) developed a novel high-resolution dataset – hereafter referred to as the WRF-SAAG dataset – by dynamically downscaling the ERA5 reanalysis using the Weather Research and Forecasting (WRF) model over South America for the period 2000–2021. Here, we evaluate WRF-SAAG outputs across continental Chile and compare them with two widely used gridded meteorological products, CR2MET and RF-MEP, which combine reanalysis data with in situ measurements. In particular, we analyze spatial differences among climatological estimates from the three products, and we assess the quality of daily precipitation and daily maximum and minimum temperature simulations using observations from meteorological stations over the period April/2001–March/2018. We also compare the potential of these climate datasets for hydrological modeling using a conceptual, bucket-style rainfall-runoff model, evaluating the results against streamflow observations, satellite remote sensing (SRS) products, and a snow water equivalent (SWE) reanalysis product. We found that the detection accuracy varied within each Chilean macrozone, with worse performance in the Far North (17.5–26° S) and Patagonia (43.7–56° S). The evaluation of daily precipitation against station observations using Tang's Kling–Gupta efficiency (KGE T ) reveals that all datasets performed better in reproducing precipitation in rainy regions (median KGE T ≥ 0.65 in the Southern macrozone), while in arid areas such as the Near North during summer, the median KGE T was negative. The CR2MET product consistently provided the best performance for extreme precipitation and temperature, partly because it includes information from the stations used for evaluation. Finally, the application of the hydrological model shows that although WRF-SAAG simulations achieved runoff estimations comparable to the best observation-based gridded products (i.e., CR2MET or RF-MEP), with the best results in the southern macrozone they provided relatively lower performance in evapotranspiration (ET) and soil moisture estimates, as well as SWE simulations of competitive quality. More broadly, the results…

Hydrological extremes display long‐term trends and natural oscillations in response to climatic and socioeconomic factors. Knowing how and why these extremes are shifting is crucial for increasing water resilience in Europe. This work analyses trends in floods and droughts in European catchments between 1951 and 2020. Using the OS LISFLOOD hydrological model and non‐stationary extreme value analysis, we disentangle the effects of dynamics in climate, encompassing climate variability and climate change, land use, water abstraction and reservoirs on river flow extremes. Our results reveal spatial and temporal variations in magnitude of hydrological extremes. Land use changes have generally increased flood and drought hazards, while water demand primarily intensified droughts. Reservoirs, conversely, have decelerated the hydrological cycle. Our work delivers insights into the intricate connections between climate, society, and water in European river basins, enabling the development of effective strategies for enhancing resilience to extreme water events under global change.

Groundwater is one of the most valuable natural resources, yet it is increasingly under pressure from climate change and human activities. Shallow groundwater depth (GWD) is a key indicator for evaluating groundwater availability and flood hazards, yet the measurements are often too sparse to capture its variability both in time and space. In this study, we evaluated hybrid data‐driven machine learning (ML) models that combine the strengths of existing hydrological simulations with the predictive power of ML methods for improved GWD estimation in Denmark. The hybrid model reduced mean bias of hydrological simulations from 1.72 to 0.22 m and decreased the Root Mean Square Error by about 1.64 m at national scale. Furthermore, we demonstrated that increasing the availability of GWD observations over time and space enhances model performance. We also illustrated the flexibility and effectiveness of the hybrid approach for GWD estimation across scales, and results showed that the hybrid model developed with coarse spatial resolutions can be easily used for high‐resolution GWD estimation without significant decrease of accuracy. This study highlights the potential of hybrid modeling as a practical solution for improving groundwater quantification accuracy and shows avenues for higher‐resolution estimates.

The Amazon carbon sink is weakening as deforestation 1–5 , rising tree mortality 6–8 and climate change disrupt the climatic conditions that sustain a humid tropical forest 9–11 . Forest loss reduces precipitation, increases temperatures and lengthens the dry season, driving higher carbon emissions 1–4,11,12 . Climate stress is stronger in more deforested regions, rendering forests progressively more flammable 1,9,13 . Large-scale climate events, such as El Niño, further amplify regional climate impacts 9,14–17 . Here we present an analysis of 15 years of Amazonian carbon fluxes derived from atmospheric CO 2 measurements. We determine a carbon balance of 0.32 ± 0.08 PgC yr −1 , with fires acting as the dominant source (0.40 ± 0.07 PgC yr −1 ) and forest uptake becoming increasingly unable to offset emissions. Furthermore, we investigate key drivers of the carbon emissions of Amazonia and identify three distinct year groups. Group 1 (2011–2014, 2017 and 2018) represents the role of the Amazon in the global carbon balance (0.11 ± 0.08 PgC yr −1 ) during wetter years with low human activity, such as deforestation, biomass burning and degradation. Group 2 (2019–2022) represents Amazon carbon emissions (0.43 ± 0.13 PgC yr −1 ) from human activities (mainly deforestation and degradation) during years with low climatic stress. Group 3 (2010, 2015, 2016, 2023 and 2024) has the highest carbon emissions (0.49 ± 0.07 PgC yr −1 ) and comprises warm and dry years (high climate stress), such as El Niño and the warming of the Tropical North Atlantic Ocean, associated with biomass burning.

The French Alps, characterised by complex topography and diverse climatic influences, are highly vulnerable to extreme precipitation events, which can trigger floods and landslides with significant socio-economic impacts. While local stakeholders report a perceived increase in sub-daily extreme precipitation events, such a trend has not yet been statistically confirmed based on observations. In this study, we apply extreme value theory to long time series of in situ observations of daily and hourly precipitation to provide an updated climatology and trend analysis of precipitation extremes across the region. We focus on 20-year return levels to analyse rare, potentially high-impact events and show that precipitation extremes exhibit distinct spatial patterns with marked differences between daily and hourly timescales. By examining trends at monthly and seasonal levels and making use of an existing weather pattern classification, we gain a deeper understanding of the dynamics underlying these trends. For extremes of daily precipitation intensity, we observe strong positive trends in the southern French Alps and negative trends in the north, primarily driven by a stronger influence of southerly (Mediterranean) flows in autumn. The spatial patterns for hourly extremes show more spatially variable trends, and seem to be primarily influenced by local thermodynamic effects potentially linked to e.g. changing moisture availability or changes in surface albedo due to shorter snow cover persistence. This detailed analysis provides important insights for stakeholders who are working on prevention and protection from precipitation-related hazards, underscoring the need for adaptive strategies in response to changing precipitation patterns. It also highlights the importance of region-specific and timescale-specific approaches in future studies of precipitation extremes.

Extreme floods rank among the most destructive natural hazards, disrupting economic development, societal stability, and river ecosystems. Although macro‐scale flood risk disparities across poverty‐classified regions have drawn increasing attention, they remain critically underexplored in China, the world's most flood‐vulnerable country, where flood‐induced human displacement accounts for one‐third of the global total. Here, we develop a rigorously validated, macro‐scale framework to estimate flood‐induced economic losses across China and to quantify the disparity of these impacts across distinct poverty strata. We find that impoverished regions experience disproportionately severe relative impacts, with their flood losses escalating by at least 22% for each 0.5°C of global warming, outpacing non‐poor regions by up to seven percentage points. Furthermore, economic growth within projected flood‐prone areas markedly exacerbates these losses. Although enhancing flood‐defense standards can mitigate damages, maintaining historical loss levels requires ambitious defense standard upgrades of 80% (under 1.5°C of warming) and 95% (under 2.0°C), corresponding to an average 16‐percentage‐point improvement per 0.5°C of warming. Our findings reveal the accelerating flood risk and macro‐scale disparity faced by impoverished regions in China, a dynamic long overlooked, and challenge the adequacy of historical flood risk management strategies. As nations work toward the United Nations Sustainable Development Goals (SDGs), particularly SDG 10 (Reduced Inequalities), which intrinsically requires a foundational understanding of risk disparities, our study provides critical baseline evidence for national adaptation planning and offers globally transferable methodologies.

Process-based land surface models, such as the Noah-Multiparameterization (Noah-MP) model, are widely used for large-scale hydrologic simulations because of their flexibility in selecting multiple parameterization schemes. However, limited guidance on choosing appropriate configurations constrains their reliability in representing runoff and snowmelt dynamics across diverse land-cover and snow conditions. This study evaluates the default and four alternative parameterization schemes in the Noah-MP land surface model, including Runoff and Groundwater (RUN), Surface Exchange Coefficient for Heat (SFC), Frozen Soil Permeability (INF), and Snow/Soil Temperature Time Scheme (STC), across 50 Hydro-Climate Data Networks (HCDNs) in the Missouri River Basin. Model performance was evaluated using USGS streamflow observations and snow water equivalent (SWE) estimates from the University of Arizona dataset for 2014–2023. Results showed that the alternative schemes improved runoff simulation compared to the default scheme through different approaches representing key hydrological and thermodynamic processes. Specifically, the RUN, SFC, INF, and STC experiments improved the Kling–Gupta Efficiency (KGE) by 0.19, 0.38, 0.44, and 0.14, respectively in representative subbasins, through enhanced groundwater dynamics, reduced evapotranspiration bias, improved rapid runoff response, and more accurate SWE evolution. SWE evaluation further indicates that the STC experiment most effectively reduced SWE bias in snow-dominated subbasins, though improvements in SWE did not universally translate into better runoff simulation. These results highlight the importance of basin-specific parameterization schemes within Noah-MP to improve hydrological prediction and water management across diverse hydroclimatic regions. The findings further indicate optimal parameterization schemes for different climates, land cover, and snow regimes.

Atmospheric rivers (ARs) play a crucial role in the global hydrological cycle, yet how the intrinsic midlatitude atmospheric variability modulates their activity on subseasonal timescales remains poorly understood. Here, we show that AR frequency and landfall in both hemispheres vary strongly with the strength and phase of the Baroclinic Annular Mode (BAM). During its positive phase, BAM increases AR occurrence by up to ∼50% and AR‐driven precipitation extremes by up to ∼40% relative to climatology. Stronger and more frequent ARs during the positive phase of the BAM are primarily driven by enhanced potential‐to‐kinetic energy conversion, which favors AR growth and intensification, particularly in the storm‐track regions. This response is consistent with dynamically enhanced poleward moisture transport due to a more baroclinically unstable and stormier environment. The results underscore the importance of BAM‐related intrinsic midlatitude storm‐track variability in modulating global AR activity and its potential to improve extended‐range AR prediction on subseasonal timescales.

Groundwater depletion poses a challenge to water security, but the coarse spatial resolution of Gravity Recovery and Climate Experiment (GRACE) observations limits the characterization of groundwater variability at regional and local scales. Here, we present a 1 km monthly global groundwater storage anomaly (GWSA) dataset covering April 2002–December 2020. The dataset was generated using Singular Spectrum Analysis (SSA) for temporal gap filling, 19 high-resolution hydroclimatic and geophysical predictors, and aquifer-stratified machine-learning downscaling. Extreme Gradient Boosting (XGBoost) and deep neural networks (DNN) were evaluated as candidate models, and XGBoost was selected for dataset production based on internal, cross-scale, and in-situ evaluations. When aggregated to the original GRACE scale, the 1 km product agreed closely with the original GWSA ( R 2 = 0.972, RMSE = 2.10 cm). Independent evaluation used a global archive of 152 069 groundwater-monitoring wells and a 1° spatial-block calibration–validation design. Effective specific yield ( S yeff ) was estimated from calibration blocks and transferred to held-out blocks before comparison at a common 0.25° grid. For XGBoost, the held-out evaluation yielded R 2 = 0.538, RMSE = 0.378 cm yr −1 , and Bias = 0.106 cm yr −1 . The association between modeled and observation-derived trends remained similar under ± 30 % perturbations in S yeff , although amplitude-related metrics were more sensitive to the conversion coefficient. Standardized time-series comparisons and monthly evaluations of representative aquifers showed better agreement for long-term variability than for seasonal variability. Comparisons with published regional estimates showed close agreement in the North China Plain, weaker depletion magnitudes in northwestern India and California, and a trend-sign mismatch in the High Plains Aquifer during 2003–2013. Experiments using CSR, JPL, H08, and WaterGAP also showed that reconstruction performance and predictor importance depend on the target product. The dataset provides kilometre-scale estimates of groundwater-storage variability under the large-scale GRACE constraint. Local interpretation of the reconstructed spatial patterns requires additional groundwater-level, abstraction, and hydrogeological observations. The 1 km monthly global GWSA dataset is openly available from the National…

In situ measurements of riverine suspended sediment concentrations (SSC) are often sparse, limiting assessment of important patterns in space and changes in time. SSC measurements based on satellite data are a potential solution to this limitation, but training algorithms to estimate SSC from satellite data also requires substantial in situ data. This study presents new efforts to use Sentinel‐2 satellite data to infer SSC in large rivers (≥60 m width). Sentinel‐2 reflectance data has the benefit of a high relative frequency, with a nominal return period of 5 days, improving the likelihood that in situ SSC data was collected coincident with satellite overpass. We generated additional SSC training data using discharge—sediment flux rating curves and estimates derived from Landsat data using Dethier et al. (2020, https://doi.org/10.1029/2019jf005033 ) algorithms. The resulting algorithms were calibrated using 19,345 SSC observations, 23% of which were from this unconventional training data set. Despite the varied training base, these Sentinel‐2 algorithms can accurately predict suspended‐sediment concentrations (68% relative error). A case study on the heavily dammed Chattahoochee‐Apalachicola watershed accurately reproduced sparse in situ results and extended spatial and temporal coverage, highlighting the potential for improved understanding of river processes using Sentinel‐2‐based algorithms.

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.

Hydrological structural connectivity is key to regulating hillslope erosion, but its formation and interactions with runoff‐sediment dynamics during intermittent rainfall remain unclear. Through simulated intermittent rainfall (three consecutive rainfall events with a 1‐day interval between each, intensities: 30, 60, 90 mm hr −1 ) combined with overland‐flow hydraulics measurements and high‐resolution UAV photogrammetry, this study reveals how hydrological structural connectivity dynamically controls erosion. Runoff‐sediment responses showed strong intensity dependence. At 30 mm hr −1 the runoff coefficient rose continuously, whereas under higher intensities it increased and then stabilized. Soil loss rates rose steadily at low intensity, followed a “rise‐then‐fall” pattern at medium intensity, and declined at high intensity. Connectivity strengthened with rainfall progression, but its growth rate declined as intensity increased. The key structural connectivity index |RF| rose with intensity (36.71, 39.36, 41.52 under 30, 60, 90 mm hr −1 , respectively; ×10 −6 ), while its growth rate decreases significantly (18.58%, 9.96%, 1.02%), showing earlier stabilization under high intensity. |RF| exhibits significant positive correlations with surface runoff, hydrodynamic parameters, and key rill morphological parameters, indicating that connectivity influences erosion processes by regulating flow pathways and convergence efficiency. Under low rainfall intensity, erosion promotes connectivity development, whereas under high rainfall intensity, an early‐established highly connected network constrains further erosion. As rainfall intensity increased, the erosion system shifts gradually from an “erosion‐driven connectivity development” regime to a “connectivity‐controlled erosion regulation” regime. This study reveals the nonlinear responses of hillslope erosion from a connectivity perspective, offering a theoretical basis for mechanism understanding and targeted conservation strategies.

Deterministic model predictions can struggle to adequately capture extreme events such as floods and droughts, which are of particular importance in hydrology. This limitation arises because deterministic models collapse the conditional discharge distribution to a single point estimate. Probabilistic modeling provides a way to address this issue by explicitly representing uncertainty and assigning non-zero probabilities to all possible outcomes, including extreme events, thereby capturing the entire conditional discharge distribution. Motivated by this perspective, we examine whether probabilistic Long Short-Term Memory (LSTM) models improve the representation of extreme events in rainfall–runoff simulations across Switzerland. The differing degree of flexibility in the models leads to different degrees and distinct patterns of miscalibration in the predictive distributions. For the extreme discharges (top 0.05 % of the marginal discharge distribution), the deterministic LSTM systematically underestimates 97 % of these events, whereas 83 % of these fall within the probabilistic model's 99th percentile prediction interval. Given the rarity of these events, representing them as outcomes near the upper tail of the predictive distribution is both expected and statistically consistent. Building on the additional information provided by probabilistic runoff predictions, we further show how they can be translated into actionable flood warnings using reinforcement learning. To this end, we introduce a Flood Risk Communication Agent (FRiCA) that operates on probabilistic runoff predictions and learns risk-aware decision rules for issuing warnings of varying intensity. Results indicate that the FRiCA outperforms simple fixed heuristics, such as issuing warnings based on the deterministic LSTM predictions or a fixed high quantile (e.g., the 99th percentile). The learnt behavior demonstrates the potential of reinforcement learning as a decision layer for improved flood risk communication, and motivates further exploration of better reward design and policy network optimization for context-dependent decision policies that adapt to varying hydrological and societal contexts.

Tribal nations across the United States face disproportionate flood impacts yet remain significantly underrepresented in national‐scale assessments, creating a critical equity gap in climate adaptation. We address this deficiency through a climate‐to‐local‐inundation framework for the Santee Sioux Nation, Nebraska, by forcing 10‐m hydrodynamic simulations with a 42‐year paired historical and pseudo‐global‐warming CONUS404 runoff event set. Results show that projected changes in inundation frequency and flood depth are limited, with no evidence of widespread intensification of flood extremes. Instead, changes manifest as localized adjustments in floodplain activation and tributary connectivity. Pixel‐wise return‐period analysis shows small changes in flood depth and only minor deviations in the statistical structure of extremes. These findings demonstrate a transferable pathway for translating climate‐driven hydrologic forcing into locally resolved inundation information for under‐mapped Tribal lands.

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.

Flood peaks can develop and recede within a day, making them difficult to capture with satellites with multi‐day revisit intervals. Here, we exploit daily observations during the Calibration and Validation phase of the SWOT mission to examine a short‐lived flood in May 2023 along the Du Gouffre River in Canada. Despite channel widths below 100 m, SWOT tracked day‐to‐day changes in water surface elevation, capturing a rapid rise of approximately 5 m consistent in timing and magnitude with in situ measurements. We derived flood extent based on classification and confidence information. The results show partial spatial overlap with an independent post‐event flood hazard map, with differences primarily associated with reduced classification confidence in vegetated floodplains and narrow reaches. Some areas not identified as water may reflect erosion, sediment deposition, or post‐peak conditions. These results highlight the value of high‐frequency wide‐swath radar altimetry for monitoring transient floods.

Understanding changes in extreme precipitation is critical for enhancing climate resilience, particularly in regions such as West Africa, where communities are highly vulnerable to hydroclimatic extremes. This study examines the spatial and temporal variations in extreme precipitation across West Africa over 1998–2024 using high-resolution satellite data from NASA’s Integrated Multi-satellitE Retrievals for GPM (IMERG) V07B Final Run product. Extreme precipitation is analyzed using daily accumulated precipitation indices, including absolute and threshold-based indices. The Theil-Sen slope estimator and the Mann-Kendall test are applied to identify statistically significant trends at three significance levels. To assess the robustness of the detected trends, results from IMERG are compared with those from the Global Precipitation Climatology Project (GPCP) Version 3.3 Climate Data Record over the same period. The comparison shows generally similar spatial patterns and directions of change between the two datasets across West Africa. Results reveal distinct regional patterns of change. The Guineo-Congolian and Guinean regions, particularly Nigeria’s eastern coast, show significant declines in annual precipitation, along with significant decreases in the frequency and intensity of extreme events. In contrast, the western Sudanian and Sahelian regions, notably southern Mali and Burkina Faso, exhibit increasing trends in both total precipitation and extreme precipitation indices, indicating a shift toward wetter conditions driven by more frequent and intense extreme events. Furthermore, the contribution of extreme precipitation to annual totals varies across climatic zones, with smaller contribution from extreme days at lower latitudes. These findings highlight the importance of regionally tailored climate adaptation strategies for effective flood risk management and sustainable water resources.

Sub-daily quantitative precipitation forecasts (QPF) are a primary input to flood early warning, yet over Brazil they are hampered by convective, intermittent rainfall and a sparse observing network. We present a machine-learning post-processing system (V2.0C) producing 3-hourly QPF on a 0.25° grid by coupling a LightGBM precipitation-occurrence classifier with a Tweedie amount regressor and a dual-gate routing scheme with a physically guarded heavy-rain (≥5 mm) hard route. Its 71-dimensional feature set is dominated by GEFS ensemble statistics. The system is verified over two independent windows — April–May 2026 and the complete austral-summer convective season (October 2025– March 2026) — against the IMERG satellite product (areal) and the INMET automatic-station network (point). Against IMERG, V2.0C attains the highest correlation (0.324 and 0.286) and a heavy-rain CSI (≥10 mm) of 0.063–0.071, 3.3–4.4 times raw GFS, with stable month-to-month skill through the SACZ peak. A common mask analysis localizes the 60–65% ensemble-driven correlation gain to the southern third of the domain, where GEFS statistics are the only direct precipitation predictors available. Controls show that the guarded routing makes a high-recall heavy-rain expert safe to deploy: it adds 6–10% heavy-rain detection over a no-expert control, whereas unguarded use halves heavy-rain CSI. Gauge verification reproduces the heavy-rain advantage (CSI ≥10 mm an order of magnitude above raw GFS) and the wet bias, and the point–area correlation gap is explained by sub-grid representativeness. The detection-oriented bias is removable a posteriori: leakage-free quantile mapping eliminates it (bias +0.02 mm, MAE −69%) without reducing detection, supporting a dual-product strategy for flood-warning and hydrological applications.

The zenith total delay (ZTD) retrieved from 5G communication base stations represents a novel observation source for numerical weather prediction (NWP), leveraging the extensive global deployment of 5G infrastructure. In this study, the direct assimilation of 5G‐ZTD observations into a high‐resolution NWP system was accomplished for the first time. Two precipitation events were selected to evaluate the impact of 5G‐ZTD assimilation on short‐term precipitation forecasting. Assimilating 5G‐ZTD data corrected the dry bias in the initial water vapor field and improved precipitation forecasts in both cases, demonstrating its practical utility for short‐term heavy rainfall forecasts and operational potential given its low cost and extensive coverage.

We combine CONUS404 convection‐permitting climate simulations with the physics‐based WRF‐Hydro model to assess how climate warming may influence flood frequency and seasonality across the Midwestern United States. This framework captures interactions among precipitation, soil moisture, and snowpack that shape flood generation. Both simulated warming and historical records show a regional shift toward more extreme warm‐season, rainfall‐driven floods. This regional signal, however, does not translate uniformly to individual watersheds, where flood responses reflect not only warming but also the influence of subregional climate variability and sampling uncertainty. Local flood‐frequency changes thus remain highly uncertain despite the thermodynamic tendency toward stronger convective precipitation. Ensembles of convection‐permitting simulations are needed to constrain plausible local outcomes. These findings suggest that flood risk communication under climate warming should distinguish broad climate‐driven tendencies from uncertain local responses.

Plant‐available water (PAW) is a primary regulator of plant growth, ecosystem productivity, and drought resilience, yet its spatial heterogeneity and long‐term response to hydrothermal changes remain poorly quantified at continental scales. Here, we quantify historical (2005–2015) and projected (2030–2100) changes in growing‐season PAW across the continental United States (CONUS) by integrating site‐level soil moisture observations with environmental predictors and bias‐corrected downscaled Coupled Model Intercomparison Project phase six (CMIP6) climate projections. Root‐zone PAW (0–30 cm) was estimated from soil moisture content, field capacity, and wilting point, thereby capturing coupled soil and climatic controls on plant water availability. Ensemble CMIP6 projections under two Shared Socioeconomic Pathways (SSP): SSP2‐4.5 and SSP5‐8.5 indicate widespread declines in growing‐season PAW, driven primarily by increasing temperature and vapor‐pressure deficit that increase atmospheric evaporative demand, outpacing projected precipitation gains, with substantially stronger drying under SSP5‐8.5. Compared to baseline conditions, PAW declines across approximately 6.9%–19.8% of the CONUS, with the largest reductions concentrated in the Great Plains, western Corn Belt, and Intermountain West ecoregions. In contrast, 76.2%–84.6% of the region shows no significant change, while 4.0%–8.5% exhibits increase, primarily across northern forested regions. Collectively, these findings identify emerging hotspots of PAW limitation and highlight growing risks to crop production and rangeland resilience, emphasizing the need for region‐specific adaptation and water‐management strategies under future environmental conditions.

Climate‐driven changes in winter snow cover alter soil microclimate and hydrology, affecting soil organic carbon (SOC) dynamics, whereas the mechanisms by which long‐term deepened snow influences SOC sequestration across soil profiles remain unclear. Through a 7‐year snow manipulation experiment in a temperate grassland, we investigated how increased snow depth (ambient, 50, 70, 90 cm) affects SOC sequestration via microbial‐mediated pathways in topsoil and subsoil. Our results showed that long‐term increased snow significantly promoted total SOC accumulation by 6% (50 cm), 12% (70 cm), and 15% (90 cm) relative to ambient conditions across soil profile (0–70 cm). Increased snow did not increase topsoil SOC storage despite increased plant biomass, litter decomposition, and root exudates, as elevated microbial respiration offset C inputs and enhanced dissolved organic C (DOC) leaching to subsoil. In contrast, subsoil layer exhibited significant SOC accumulation, driven by improved microbial C‐use efficiency (by 11%–18%), enhanced fungal growth with only minor increases in respiratory C losses, and preferential accumulation of fungal necromass from fast‐growing Ascomycota taxa. The subsoil also effectively intercepted and processed DOC leached from topsoil, forming a vertical C transport‐stabilization continuum. Soil protein abundance (indicative of mineral‐associated C) increased significantly in both layers, confirming increased snow progressively promoted SOC stabilization. These findings reveal that topsoil maintains C equilibrium through compensating microbial processes, while subsoil acts as a C sink through fungal‐mediated pathways. These contrasting mechanisms highlight the need for depth‐resolved, microbial process‐based models to predict future C sequestration under altered winter precipitation.

Floods are among the most frequent and destructive natural disasters, yet future flood risk assessments have largely relied on precipitation indices, overlooking runoff depth—a variable that more directly captures flood hazard under diverse regional hydrological conditions. Moreover, the interactions among risk components (hazard, exposure, vulnerability) remain underexplored. Here we integrate 14 multidimensional sub‐indicators, including both precipitation‐ and runoff‐based indices, to assess global rainstorm flood risk from 1991 to 2100 under four SSP scenarios (SSP1‐2.6, SSP2‐4.5, SSP3‐7.0, SSP5‐8.5). Comparative experiments reveal that omitting runoff depth systematically overestimates high‐risk areas and brings forward the time at which risk projections begin to diverge across scenarios. Global risk increases persistently, with SSP5‐8.5 showing the highest rise by 2100, and high‐risk regions are concentrated in the Americas, Africa, South Asia, East Asia, and Southeast Asia. Exposure dominates risk, while hazard and vulnerability play regionally distinct modulating roles. Alarmingly, by 2100, half of the global population (up to 6 billion under SSP3‐7.0) and half of global GDP (under SSP5‐8.5) will face the highest risk level, with South and East Asia accounting for half of that population (or GDP) due to dense populations and infrastructure. This study advances flood risk assessment by reconciling hydrological complexity with socioeconomic pathways and highlights that mitigation strategies must prioritize emission reductions and the relocation of people and assets from flood‐prone areas.

Groundwater plays a critical role in sustaining the water supply in the Colorado River Basin (CRB), a region experiencing persistent drought, population growth, and increasing water demand. Using GRACE/GRACE‐FO satellite observations combined with hydrological data sets, we analyze basin‐scale variations and long‐term trends in terrestrial and groundwater storage across the CRB from water year 2004–2023. We further apply a joint framework integrating copula‐based statistical modeling with statistical inference to quantify probabilistic relationships among storage‐based drought indicators and to characterize groundwater–surface water interactions. Results reveal sustained basin‐wide groundwater depletion over the past two decades, with accelerated losses since 2019 and during the water year 2020–2022 drought. Notably, during this recent drought period, groundwater losses were larger in the typically more resilient Upper CRB (17.5 ± 3.5 km 3 ) than in the Lower CRB (9.4 ± 6.5 km 3 ), indicating increasing vulnerability across both subbasins. Copula‐based analysis shows strong coupling between surface‐water deficits and groundwater drought probability in the Upper CRB, whereas parts of the Lower CRB exhibit weaker statistical dependence, consistent with groundwater extraction operating partly independently of surface water availability under chronically limited supply conditions. These findings provide a basin‐wide observational synthesis of groundwater–surface water dynamics and demonstrate the value of satellite‐based monitoring for diagnosing hydroclimatic stress in large managed river basins. The results highlight emerging shifts in groundwater vulnerability across the CRB and underscore the need for integrated water‐management strategies that jointly consider climatic pressures and sustained anthropogenic demand.

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.

Flood depth estimation from social media images provides timely and cost‐effective observations, offering significant potential for flood monitoring and emergency response. Existing image‐based methods typically rely on humans, vehicles, or traffic signs as scale references, however, such objects are frequently absent in real flood scenes and often yield unreliable scale information. In contrast, urban 3D models offer consistent and accurate metric references across large spatial extents. In this paper, we propose a flood depth estimation method based on urban 3D models and develop an Object‐level Coarse‐to‐Fine Matching Model that transfers metric scale from 3D models to social media images. The results demonstrate that our model significantly outperforms the representative 2D3D‐MATR on an urban public benchmark, increasing the inlier ratio by approximately 30% and improving the registration recall by about 16%. Building upon the matching capability, our 3D‐based flood depth estimation method generalizes across diverse flood scenarios and achieves improved accuracy, with a Mean Absolute Error (MAE) of 0.164 m and a Root Mean Square Error (RMSE) of 0.212 m. By transforming discrete social media data into spatially explicit, high‐accuracy flood depth maps, our method not only provides reliable support for rapid flood assessment and emergency decision‐making during events but also offers valuable data for calibrating hydraulic models post‐disaster.

The TV-SAT signals received by the ground antennas of Satellite Microwave Links (SMLs) can be opportunistically used for detecting and quantifying precipitation. Consequently, SMLs have the potential to serve as low-cost rainfall sensors complementing conventional instruments. However, a major challenge for opportunistic sensors, including both SMLs and their terrestrial counterpart – Commercial Microwave Links (CMLs) – is related to data ownership and accessibility constraints, possibly hindering progress in the development of processing tools and validation studies. This limitation highlights the importance of openly accessible datasets. While CML open datasets are already available, there are no large-scale SML datasets in public repositories. To address this gap, we introduce the OpenSat4Weather dataset, a comprehensive and openly accessible collection of measurements from 215 SML sensors deployed in Southern France, over a five-month period from August to December 2022. The dataset is accessible at https://doi.org/10.5281/zenodo.16530166 (Nebuloni et al., 2025b). OpenSat4Weather also includes concurrent conventional measurements, namely, 6 min rainfall depths from 113 operational rain gauges, and radar-based estimates of rainfall intensity along each SML path. The radar data were derived from the gauge-adjusted weather radar product PANTHERE provided by Météo-France. Additionally, the dataset stores ERA5 reanalysis data of the 0 °C isotherm height for rain height estimation, which are essential for accurately converting the received signal level into rainfall intensity. In this paper, we detail the OpenSat4Weather data preparation process and draw statistics of data availability. We then provide a descriptive analysis of the dataset, including rainfall characteristics, derived from rain gauges, statistical properties of the SML received signal levels, and a comparison between SML and radar data. In addition, we illustrate examples of disturbances and anomalous patterns observed in raw SML signals. Finally, we provide some practical guidelines for processing and analyzing SML data. Our ultimate goal is to promote open research initiatives aimed at accelerating the development of SML-based applications. Enhancing rainfall monitoring by opportunistic sensors has the potential to provide substantial benefits, particularly in regions where conventional…

The influence of reservoirs on the water cycle introduces significant uncertainty for hydrologic prediction. The representation of reservoirs in hydrologic models ideally must be accurate, interpretable, and transferable across sites. Recent studies have highlighted the potential for data‐driven methods, including long short‐term memory (LSTM) networks, to accurately capture reservoir releases. However, the performance of LSTM models of reservoir releases has not yet been diagnosed on a large‐sample data set to understand whether their accuracy is physically justified. This study evaluates the ability of LSTMs to represent reservoir release policies across the continental U.S., leveraging the recently developed ResOpsUS data set. In particular, we focus on whether LSTMs can infer a latent storage representation without being provided observed storage as input. We find that in many cases the LSTM succeeds in encoding physically interpretable storage dynamics in its internal states, despite measurement errors in the data. However, the model accuracy is only weakly related to the strength of the learned storage representation; instead, it is log‐linearly related to the degree of regulation. In addition, LSTMs struggle to generalize storage dynamics and learned policies in space with pooled training. This study contributes to the growing literature on interpreting deep learning models of human‐hydrologic systems.

Physics‐informed neural networks (PINNs) provide a promising framework for river hydrodynamic simulation and parameter inversion under sparse observations. However, spatial heterogeneity in channel geometry and roughness causes strong variations in flood‐wave propagation and parameter sensitivity, challenging local conservation and joint state‐parameter estimation. This study proposes a Characteristic‐aligned Control‐volume Physics‐Informed Neural Network for Roughness Estimation (CC‐PINN‐RE). It constructs dynamic diamond‐shaped space‐time control volumes from local characteristic speeds of the Saint‐Venant equations, enforces mass and momentum conservation in integral form, and combines adaptive control‐volume management with local‐sensitivity‐weighted roughness regularization. The method is evaluated on three one‐dimensional channel cases of increasing complexity against strong‐form PINN‐RE and fixed‐control‐volume cvPINN‐RE. CC‐PINN‐RE achieves the lowest errors in eight of nine relative metrics for water depth, discharge, and roughness. In Case 3, where channel width, bed slope, and roughness vary spatially, it reduces the corresponding errors by 57.0%, 82.2%, and 57.3% relative to PINN‐RE and reduces the maximum absolute error in peak water depth by 69.7%. Ablation experiments with three random seeds support the contributions of the three proposed components. Observation‐deployment experiments show that increasing the number of monitoring stations is generally more effective than increasing temporal sampling frequency at individual stations. Under 0%–2% observational noise in Case 3, the model remains stable. These results indicate that CC‐PINN‐RE provides a physically consistent framework for integrating characteristic propagation, local conservation, and parameter inversion, offering a useful basis for joint hydrodynamic reconstruction and spatially varying roughness estimation in heterogeneous river channels.

The evaluation of hydrological models is essential for both model selection and reliability assessment. However, comparing predictions with observations alone is insufficient to fully characterize model behavior. This limitation is particularly relevant to deep learning (DL) models because of their complex internal structures. In risk‐averse operational settings, water managers require models whose reliability and failure modes have been carefully evaluated because unstable predictions can put critical infrastructure at risk. Motivated by the need to assess model reliability before operational deployment, we introduce and explore adversarial robustness analysis for hydrological modeling and evaluate whether small, targeted perturbations to meteorological forcings induce substantial changes in simulated discharge. We compare a physical‐conceptual model (HBV) with a deep learning‐based model (LSTM) across 1,347 German catchments using perturbations of varying magnitudes generated by the fast gradient sign method (FGSM). We find that FGSM perturbations generally reduce Kling–Gupta efficiency and increase mean squared error. However, widespread catastrophic failures under the specific FGSM perturbations are not observed, and LSTM generally exhibits less performance degradation than HBV. In the examined catchments, changes in the predicted hydrographs and time‐averaged internal model states often vary approximately linearly with perturbation magnitude, providing a compact summary of how errors grow under such perturbations. Similar patterns are observed along random perturbation directions in selected catchments, suggesting that, in those catchments, small input changes produce approximately proportional changes in model output. Overall, these findings characterize local predictive stability under controlled stress tests and motivate future studies of model robustness in real‐world operational settings.

Water within the root zone regulates water, energy, and carbon exchanges at the land‐atmosphere interface, yet the dynamically active portion of subsurface water that ecosystems mobilize for evapotranspiration (ET) remains unclear, because it represents only part of the total subsurface storage. To address this gap, we develop a catchment‐scale, ecosystem‐oriented hybrid machine learning model that integrates a parsimonious ecohydrological structure with data‐driven flexibility to infer time‐varying active root zone storage (a S rz ) as a latent state variable. Without prescribing rooting depth or soil layers, the model learns adaptive water partitioning from multi‐source observations of ET, runoff, snow, and terrestrial water storage, enabling diagnosis of both dynamic a S rz and its realized capacity. Applied across hundreds of U.S. catchments over the period 2001–2020, the framework reveals coherent spatial and hydroclimatic patterns, with a S rz peaking in transitional regions where atmospheric demand and water supply are balanced. The realized capacity reflects how belowground water access varies with long‐term hydroclimatic and substrate constraints. We further show that defining storage as a realized quantity through system dynamics improves stability and interpretability under varying observational and structural conditions. Overall, this study demonstrates how hybrid modeling can be used to diagnose ecosystem‐accessible water storage as a latent state, providing a physically consistent and observation‐informed pathway to quantify belowground processes that remain inaccessible to direct measurement and control key land‐atmosphere interactions.

Urban flood monitoring demands timely, reliable decisions under urgent and often unseen conditions. However, faults in sensor networks can undermine response, leading to human and economic losses. To address these issues, we propose a Nowcasting Physics‐Informed (NPI) model for fault detection in streamflow sensors, with a 100‐min memory window at 2‐min resolution. A Long Short‐Term Memory (LSTM) consumes fused features from measured signals and Storm Water Management Model simulator outputs to produce end‐of‐window fault probabilities. To ensure operational reliability, a lightweight per‐sensor eXtreme Gradient Boosting (XGBoost) stage calibrates the LSTM score using window‐local statistics of the measurement. In this study, we investigate whether and to what extent fusing a physical model with sensor data improves fault detection, and whether per‐sensor threshold calibration matters. Our results indicated yes. The Area under the precision–recall curve (AUC‐PR) was improved by 3.7%–12.5% over the physics‐informed model compared to baseline non‐physics‐informed learning. In addition, post‐hoc calibration improved AUC‐PR by an additional 0.2%–2.3%. Across five cross‐validation folds, F1‐score (F1) increased from 0.75 for the non‐physics LSTM baseline to 0.83 with calibration, and further to 0.88 for the physics informed model. Adding post‐hoc calibration to the physics informed model yielded the best result at 0.90. The NPI is causal and auditable, exporting calibrated probabilities and binary alerts for direct operator use without retraining the LSTM, and offers a practical template for warning systems and training strategies that remain robust to unseen conditions.

Global warming is increasing the frequency and intensity of extreme droughts, threatening terrestrial ecosystem structure and function. However, existing indices often fail to provide a comprehensive representation of the water cycle, and high-resolution datasets for characterizing vegetation responses to drought remain limited. To address this gap, we first developed a monthly Vertical Composite Drought Index (VCDI) by integrating precipitation, surface runoff, soil moisture, and groundwater storage anomalies. This index summarizes multidimensional water constraints across the “atmosphere-surface-subsurface” continuum and captures cumulative effects of drought across multiple timescales. Furthermore, VCDI, land surface temperature, and vegetation greenness were combined to produce a monthly Vegetation Drought Resistance Index (VDRI) dataset for China at 250 m resolution from 2000 to 2022. Multi-source evaluations indicate that VDRI provides spatially detailed and temporally consistent information on vegetation conditions under concurrent hydroclimatic and thermal stress. The datasets support ecosystem assessment, ecological drought monitoring, wildfire risk analysis, and benchmarking for drought-impact studies and related ecological models.

Global warming has intensified extreme precipitation, a trend projected to continue as warming persists. However, how extreme precipitation characteristics respond to global warming mitigation strategies remains poorly understood. This study examines East Asian summer extreme precipitation responses under a net-zero anthropogenic carbon dioxide (CO 2 ) emission scenario. Despite substantial cooling over East Asia following atmospheric CO 2 concentration decline, the intensity of extreme precipitation events continues to increase for about 30 years and does not subsequently weaken. This counterintuitive result arises mainly from intensified upward motion during extreme precipitation events, driven by enhanced baroclinicity and strengthening of the upper-tropospheric jet, which together overcompensate the cooling effect. These are essentially due to the hysteresis in the Atlantic (i.e., Atlantic Meridional Overturning Circulation slowdown), Southern (i.e., sustained warming), and Pacific (i.e., El Niño–like warming) Oceans. Our findings highlight that achieving net-zero anthropogenic CO 2 emissions is insufficient to mitigate intensifying extreme rainfall events in the densely populated East Asian countries.

Water bodies such as lakes and reservoirs can play a crucial role in reducing estimated flood peaks, at both daily and hourly time resolution. While the effect of water bodies on flood peaks has been demonstrated in the past at different time resolutions, it remains unclear how water bodies affect the ratio between estimated daily and hourly quantiles, that is, to which degree their attenuation effect is scale dependent. Here, we analyse how water bodies influence this ratio using two approaches: (1) by comparing estimated daily and hourly flood peak quantiles upstream and downstream of reservoirs of four local case studies, and (2) by comparing daily and hourly flood peak quantiles across a large sample of catchments in Switzerland with various degrees of water-body influence. Our results show that reservoirs dampen hourly peak discharge much more strongly than daily peak discharge, which leads to similar daily and hourly flood peaks downstream of reservoirs. Specifically, our case study analysis highlights that (sub-)hourly peak discharge is attenuated by up to 70 % downstream of reservoirs during flood events with a 10-year return period. We also find that the attenuation effect is particularly pronounced in catchments that are heavily influenced by water bodies, i.e. those catchments where more than 60 % of the area contributes to water body inflow. This implies that daily flood peaks are a good proxy for hourly peaks in strongly regulated catchments in contrast to natural ones. We conclude that considering water body influence on flood peaks is crucial to understand the similarity between daily and hourly flood peaks downstream of water bodies. This suggests that water body influence should also be considered in large-sample studies for which the exact influence may be unknown, and quantified using suitable metrics such as the fraction of the total catchment above the water body.

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.

Catchment hydrologic function describes how meteorological conditions are transformed into streamflow. Whether this transformation is stable or varies from event to event, and what controls that variability, remains unknown at the global scale, limiting confidence in flood-risk and water-security assessments. Here, we analyze about 1.7 million runoff-producing events across 4838 catchments on six continents. We classify each event by its water-input type (rain, snowmelt, or rain-on-snow) and by its antecedent soil moisture (dry, moderate, or wet). These two descriptors together define the event’s hydro-meteorological conditions. We summarize each catchment’s dominant hydro-meteorological event type in the dormant and growing seasons separately, and find a wide diversity of dominant types across the global catchment population. We then quantify, for each catchment, the extent of event-to-event time-variance in the precipitation–streamflow transfer function that represents catchment hydrologic function. Time-variant catchment hydrologic function is pervasive: 95.6% of catchments in the dormant season and 99.0% in the growing season show moderate to strong event-to-event variation, and even catchments experiencing highly similar hydro-meteorological conditions across events remain functionally time-variant. Within-catchment spatial heterogeneity in hydro-meteorological conditions modulates this variation. Greater time-variance in hydrologic function is associated with greater event-to-event variation in a catchment’s runoff response.

Droughts and dry periods represent a novel and emerging threat to agriculture in historically water rich, temperate regions, such as the Northeast United States. Water governance institutions to manage water quantity in these regions are underdeveloped in the context of current needs. In this study, we examine the state of Maine as a case study in drought institutions in a historically water‐rich region. Specifically, we review the institutional landscape for drought resilience and resistance for Maine agriculture and identify the gaps that may be hindering agricultural adaptation. Through focus groups with farmers and agricultural advisors, we find: (a) There are few quasi‐governmental organizations and programs dedicated to agricultural drought‐adaptation; rather farmers rely upon mostly federal, state, and non‐governmental regional programs and policies that are non‐drought specific in nature; and (b) there is a need for articulated and persistent coordination between federal‐state‐local organizations and programs, to ensure vertical and horizontal integration and complementarity. In the absence of drought‐specific institutions that coordinate across scales, there is the potential for ineffective drought adaptation and maladaptation in Maine and other temperate regions. This could result in wasted public and private resources, ineffective use of natural resources, and inequitable or otherwise socially undesirable outcomes. Our research adds to the growing body of evidence showing that, even in temperate and historically water‐rich regions, drought is a threat to agricultural viability. Here, we outline the need for institutional coordination around drought‐adaptation to facilitate strategic program development.

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.

Hydrological extremes are projected to intensify under climate change, yet their implications for water scarcity remain poorly understood. We use a multi‐model ensemble to assess how changes in hydrological low and high extremes will constrain renewable water for human use across 520 major river basins worldwide. We evaluate trends in two metrics of water availability: (a) annual lowest monthly runoff, and (b) annual accessible runoff, a conceptual indicator defined as total runoff excluding extreme high flows typically released rapidly for flood control. More than 40% of river basins are projected to experience declines in both metrics during 2015–2100. Mediterranean and several scattered basins are identified as hotspots where runoff reductions compound existing water scarcity. Scenario comparison shows that low emission mitigates, though do not fully eliminate, these declines, highlighting hydrological co‐benefits of climate mitigation. The novelty lies in explicitly incorporating hydrological extremes into water availability assessment.

Tropical forests are an important component of the global carbon cycle. Increasingly frequent droughts and heatwaves are undermining their resilience, with far‐reaching impacts on local livelihoods and on regional and global hydrological cycles. El Niño events, recurring climate patterns that lead to dry and hot conditions across multiple tropical regions, have always influenced these dynamics, but the 2023–2024 El Niño produced extreme droughts and heatwaves that exceeded the typical El Niño footprint. Here, we place the 2023–2024 El Niño in both past (2000–2024) and future (2025–2100) climatic contexts across pantropical forests. We assess its climatic magnitude using ensembles of reanalyses, satellite climate products, and Earth System Models. Climate anomalies over 2000–2024 show that three quarters of pantropical forests experienced, on average, 6 months of compound droughts and heatwaves during 2023–2024. Based on future climate projections, most tropical forest regions will reach high probabilities of experiencing 2023–2024‐like temperatures on an annual basis, while the probability of similar droughts is expected to increase mostly in Amazonian forests. These results indicate that the 2023–2024 El Niño may have presented a preview of future tropical forest conditions, which can only be prevented by rapid and drastic reductions in global greenhouse gas emissions.

This study presents an integrated framework for estimating Probable Maximum Precipitation (PMP)‐equivalent return levels under climate change, addressing two critical challenges: sampling uncertainty for rare events and the validation of bias correction under different global warming scenarios. Leveraging large regional climate model ensembles, the framework combines spatial transposition, based on L‐moment homogeneity, with ensemble pooling to expand the effective sample size to nearly 10,000 independent annual maxima per location. Results show that spatial transposition significantly reduces uncertainty in rare‐event return levels, narrowing 95% confidence intervals by over 60% for the 10,000‐year event. Improvements were most notable in the upper tail, where empirical quantiles closely matched Generalized Extreme Value fits due to increased parameter stability from the expanded sample size. Subsequently, three bias correction techniques, CDF‐transform (CDFt), Quantile Delta Mapping (QDM), and Quantile Mapping (QM), were applied to CanRCM4‐LE and evaluated against CRCM5‐LE using a pseudo‐observation framework that allowed assessment under different warming scenarios. CDFt consistently provided the best agreement with distributional properties and empirical quantiles, followed by QDM. Using the best‐performing bias correction method applied to CRCM5‐LE with ERA5 reanalysis serving as the observational reference, PMP‐equivalent return levels at grid cells corresponding to 2,730 dam locations across Northeastern North America, show spatially coherent increases, with median 100,000‐year return levels rising by ∼57% under +1.5°C and ∼77% under +3.0°C warming. Broader spatial patterns mirror this intensification, underscoring the thermodynamic drivers of future extremes. The framework enables robust, reproducible PMP estimation and supports adaptive design strategies for critical infrastructure under climate uncertainty.

Raindrop‐induced soil surface sealing alters infiltration, runoff, and sediment detachment, yet most erosion models fail to capture its dynamic impacts on interrill processes at event scales. Conceptually informed by the process‐based approach of Water Erosion Prediction Project, we develop a modeling framework that couples seal formation, two‐layer infiltration, kinematic wave overland flow, and interrill erosion. The framework integrates slope gradient and rainfall intensity into the temporal evolution of interrill erodibility, enabling explicit representation of sealing dynamics. Laboratory data of Assouline and Ben‐Hur (2006, https://doi.org/10.1016/j.catena.2006.02.005 ) covering rainfall intensities of 24–60 mm hr −1 and slopes of 5%–25%, were used for evaluation. The model reproduced observed sediment loads with normalized RMSE of 14%–20% and Nash‐Sutcliffe efficiencies of 0.70–0.89, outperforming the version in which the progressive sealing effect is omitted. Results highlight two competing sealing effects: reduced interrill erodibility suppresses detachment, while increased rainfall excess enhances transport capacity. Their relative importance depends on slope: increasing rainfall excess dominates erosion on gentle slopes, whereas interrill erodibility reduction controls erosion on steep slopes. The model also reveals dynamic regime shifts from transport‐limited to transitional or detachment‐limited during sealing. Comparisons show that neglecting seal formation can substantially underestimate sediment loads, whereas assuming instantaneous sealing overestimates erosion by 37%–71% on gentle slopes and underestimates it by 2%–7% on steep slopes. By resolving slope‐ and rainfall‐dependent sealing dynamics, the proposed model provides a physically based framework for event‐scale interrill erosion prediction, improving the reliability of rainfall‐runoff‐erosion assessments in erosion‐prone landscapes.

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.

Recent advances have introduced diffusion models for probabilistic streamflow forecasting, demonstrating strong early flood‐warning skill. However, current implementations rely on Long Short‐Term Memory (LSTM) backbones and discrete‐time diffusion formulations. This leaves unresolved whether more advanced sequence‐modeling architectures and continuous‐time diffusion formulations can improve probabilistic streamflow forecasting. In this study, we developed HydroDiffusion, a diffusion‐based probabilistic forecasting framework integrating a decoder‐only state space model (SSM) backbone with a continuous‐time score‐based diffusion formulation. HydroDiffusion is evaluated across 531 watersheds in the contiguous United States (CONUS) in the CAMELS data set. We benchmark HydroDiffusion against two diffusion baselines with LSTM backbones, as well as the recently proposed Diffusion‐based Runoff Model (DRUM). Results show that HydroDiffusion achieves the strongest nowcast accuracy when driven by observed meteorological forcings. Moreover, HydroDiffusion delivers higher deterministic and probabilistic forecast skill than its LSTM‐based counterpart in operational forecasting. Attribution analysis further reveals that the SSM backbone excels in humid, densely vegetated, storage‐dominated watersheds with strong hydrologic memory, while its advantage becomes limited in arid regions and hydrologically complex systems such as karst and prairie pothole basins. These results establish HydroDiffusion as an effective generative modeling framework for medium‐range streamflow forecasting, providing both a new modeling benchmark and a foundation for future research on probabilistic hydrologic prediction at continental scales.

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.

Estimation of surface soil moisture (SSM) is fundamental for hydrological monitoring and agricultural management, particularly in drought-prone regions such as central–northeastern Argentina. Data assimilation provides a robust approach for estimating SSM by integrating land surface models with remote sensing data. Satellite missions such as SMAP and SMOS deliver global SSM products; however, these observations often exhibit systematic biases arising from instrument noise, indirect measurements, and temporal and spatial heterogeneities. Since standard data assimilation techniques assume unbiased observations, bias correction is required to ensure optimal assimilation. This work evaluates off-line observation bias-correction methods within a land data assimilation framework based on the Noah-MP land surface model and an ensemble Kalman filter, focusing on the 2022 dry season in the endorheic Pampas region. During dry conditions, SSM probability density functions differ significantly among soil textures within the bin. To address sampling errors in cumulative distribution function (CDF) matching, we propose an approach in which climatological statistics are computed using homogeneous soil texture pixels, and a 45-day moving temporal window is applied to smooth CDF evolution. The proposed method is compared with standard CDF matching and normal deviate scaling. Results indicate that improved statistical sampling in CDF matching produces a measurable impact on SSM estimates, particularly during dry periods. It shows better agreement with independent in-situ soil measurements than standard CDF matching, provided that the soil texture classifications at the in-situ site and in the model grid cell are consistent.

One of the most important skills of hydrological models is to simulate timing and magnitude of flood events. Long Short-Term Memory (LSTM) networks are currently among the most successful models for streamflow and flood prediction over large regions. In snow-influenced catchments, which typically comprise a minority in large-scale studies, floods are generated by two distinctly different processes, snowmelt and rainfall. The applicability of hydrological models in such regions is therefore dependent on their ability to represent both types of floods. Nevertheless, flood evaluations of LSTM taking different flood-generating processes into account are currently lacking. This study fills this gap by evaluating the ability of LSTM to model flood peak characteristics separately for snowmelt and rainfall generated floods. The trained LSTM model successfully simulated streamflow time series across the 103 evaluated catchments, with average NSE of 0.84 and average KGE of 0.86 over the unseen evaluation period. LSTM exhibited better performance in the majority of the catchments in terms of flood peak timing and magnitude for both rainfall and snowmelt generated floods when compared to the operational hydrological model in the region (HBV) used as a benchmark. LSTM had a 27 pp higher percentage of correctly simulated peak days for rainfall generated floods as compared to snowmelt generated floods, similar to what was found for HBV (29 pp). LSTM's ability to simulate flood peak magnitudes was similar for the different flood types, with percent errors within 20 % for close to half of the events. False alarm rate and probability of detection results were similar for snowmelt and rainfall generated events, both slightly better than those of mixed events. LSTM outperformed HBV in 70 %–91 % of the catchments in terms of flood peak timing and magnitude for snowmelt and rainfall generated floods. The largest improvements in flood peak magnitudes were found for rainfall generated events, in particular for catchments where HBV exhibited high (>40 % ) absolute errors. Overall, our findings bring confidence that LSTM can improve hydrological services in regions subject to both snowmelt and rainfall generated floods.

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.

Accurate representation of raindrop size distribution (DSD) is crucial for understanding precipitation microphysics and improving quantitative precipitation forecasting. Conventional methods in numerical models often prescribe the DSD shape parameter as a constant or derive it from empirical relationships based solely on disdrometer observations, limiting their ability to capture DSD variability driven by dynamic, thermodynamic, and microphysical processes. This study develops a machine learning (ML) framework integrating disdrometer observations with environmental atmospheric variables to retrieve DSD parameters. Trained on data from 11 stations in Shanghai during 2019–2021, three ML models outperform conventional methods, effectively reducing biases and capturing observed DSD variability. They remain robust in independent heavy‐rainfall and tropical cyclone validations during 2022–2025, providing reliable DSD and rain rate estimates. Feature analysis and ablation experiments further underscore the critical role of environmental factors in improving retrieval accuracy, offering a data‐driven pathway to improve DSD parameterizations in numerical models.

Great Britain's variable maritime climate has until relatively recently limited the utility of seasonal hydrological forecasts. The latest generations of seasonal atmospheric forecasting systems have created new opportunities to improve flow forecasting across Great Britain, such as for the UK Hydrological Outlook. Here, we combine newly-developed high-resolution rainfall forecasts derived from historical weather analogues (HWA) conditioned on large-scale circulation patterns with a monthly-resolution national-scale hydrological model to produce seasonal and monthly forecasts. This forecasting scheme is evaluated using hindcasts from 1993–2017. We show that the skill of the rainfall forecasts is comparable to that achieved at coarser resolutions by other models. However, increasing the spatial resolution has degraded some of the skill found by previous authors producing HWA forecasts at lower resolutions. The river flow forecasts perform better than rainfall, with much of the improvement attributable to the skill provided by hydrological initial conditions. Both rainfall and river flow forecasts are most skilful in winter and perform adequately in spring and autumn. We show that in responsive catchments, primarily found in the northwest, rainfall forecasts contribute most of the skill in the river flow predictions but are much less important in regions with long hydrological memory. We thus indicate where flow forecasts would benefit most from improved rainfall forecasts or from better hydrological modelling. The introduction of these high-resolution rainfall forecasts now enables hydrological forecasting at unprecedented levels of detail across Great Britain and demonstrates a forecasting approach that may be similarly beneficial elsewhere in the world.

Water allocation has long been framed as a general optimization problem in which users compete for a finite resource, leading much of the existing literature to adopt a top‐down perspective on how water should be distributed among users. However, in practice, water withdrawals may also be influenced by physical access and operational arrangements, such as direct abstraction from river channels through intake facilities for agricultural and industrial use. In such systems, disparities may arise between institutional allocation principles and effective access to water during shortage conditions, potentially creating asymmetric impacts among upstream, midstream, and downstream stakeholders. This study quantifies these impact asymmetries across river locations and evaluates stage‐based intake restriction strategies designed to mitigate inequitable outcomes during droughts. The proposed framework is applied to the Yeongsan River Basin in southwestern Korea through computational experiments to demonstrate how drought‐stage‐based intake regulations can improve both net profit and equity among stakeholders. A game‐theoretic approach is further employed to assess the potential for compensation across Pareto‐optimal solutions by examining whether the additional gains generated under cooperation are sufficient to offset stakeholder losses relative to the non‐cooperative baseline. The analysis is extended to projected scenarios of intensified climatic and socio‐economic droughts to assess the robustness of the proposed policies under future stress conditions. Through this approach, the study introduces a generalized framework for identifying water allocation policies that reconcile efficiency, equity, and sustainability under institutional and hydrologic constraints.

Inter‐basin groundwater flow (IGF) is a critical process that links material and energy exchange between basins. Based on the source and flow direction of groundwater, IGF can be separated into export IGF and import IGF, which respectively denote the lateral outflow and inflow of groundwater from and into a basin. However, traditional hydrological modeling based on net IGF obscures the bidirectional exchange processes between basins. Furthermore, export and import IGF are difficult to measure directly, and existing methods to estimate them (e.g., hydrochemical tracing and groundwater modeling) are often costly and data‐demanding. Therefore, this study innovatively proposes a method that integrates the Long Short‐Term Memory (LSTM) with the ranked Storage Age Selection (rSAS) approach to partition export and import IGF. This method does not rely on detailed hydrogeological characterization and regional groundwater model. The feasibility of the proposed method was evaluated through a case study in the Lower Hafren basin, demonstrating its ability to quantify chloride transport processes within a unified water‐age balance framework, allowing a constrained estimation of unobserved inter‐basin groundwater flows. The results indicate that export and import IGF exhibit significantly different statistical characteristics compared to the net IGF. At the monthly timescale, the magnitudes of export IGF and import IGF are over two orders of magnitude greater than that of net IGF. And net IGF conceals the true seasonal fluctuation patterns inherent to export and import IGF. Furthermore, the uncertainties in the IGF partitioning were quantified and controlled through Bayesian uncertainty analysis and the LM‐CMA‐ES algorithm.

Evaluating water availability in catchments with widespread anthropogenic influences demands a joint analysis of natural hydrological processes and anthropogenic influences. Achieving this through hydrological modeling requires proper representation of relevant processes. Beyond the application of a single model set‐up, we assess the impact of different model representations of irrigation and subsurface flow dynamics by implementing three irrigation calendars based on remote sensing data sets and two subsurface flow model concepts in the distributed hydrological model wflow_sbm applied to the Hindon River Basin, part of the larger Ganges River Basin in India, featuring intensive irrigation, groundwater abstractions, and inter‐basin water transfer. A comprehensive inter‐comparison of the model output reveals the spatio‐temporal variability in simulated water balance partitioning. Irrigation calendar has a strong dynamic influence across the monsoon and non‐monsoon seasons, producing differences of 9–43 mm/year in river discharge and 32–147 mm/year in evapotranspiration. The impact of the model concept of subsurface flow on the magnitude of the simulated fluxes (differences of 81–90 mm/year in river discharge and 30–37 mm/year in evapotranspiration) is mostly consistent across the seasons but varies spatially, having the strongest influence in areas limited in groundwater storage and near the river. Model evaluation based on observed river discharge (KGE = 0.62–0.7) and groundwater level changes (MAE = 1.6–2.0 m) indicates varying performances. The study highlights the necessity of comprehensive assessment of water balance components and reduction of uncertainties in model estimates when utilizing remote sensing data sets to constrain irrigation timing and increasing the complexity of groundwater process model representation.

Using satellite‐derived dust optical depth (DOD), reanalysis meteorology, and a global topsoil flash drought (TSFD) inventory for 2003–2022, we find that TSFD events are associated with increased dust loading at the global scale. During TSFD events, 44.7% of land pixels exhibited positive DOD anomalies. The net global DOD anomaly increased significantly over the study period; however, this trend cannot be attributed solely to TSFD‐related effects, as changes in background environmental conditions may also have contributed. Compound TSFD–strong dust (TSFD_SD) events occurred most frequently under sparse vegetation and moderately elevated wind conditions. SHapley Additive exPlanation (SHAP) analysis showed that DOD variability during TSFD events was more strongly associated with soil moisture, background aridity, and vegetation state than with wind speed. These relationships are consistent with a mechanism in which rapid topsoil drying and vegetation stress increase surface erodibility and may enhance dust loading, although the observational analyses do not establish causality.

Flood frequency analysis (FFA) forms the basis of many hydrological assessments and practical design calculations. Traditionally, FFA is based on the assumptions of stationary distributions and homogeneous samples. These assumptions are questionable in light of climate change and the wide variety of hydrological processes that lead to flooding. Furthermore, these assumptions can obscure important insights, including process‐specific changes. In this study, we develop a process‐aware, non‐stationary mixture model that combines type‐specific distributions with time‐varying parameters. An application to more than 600 catchments across Europe shows that the various flood types change differently over time. Heavy rainfall floods at many locations in Europe (e.g., the Alps and UK) are increasing significantly, whereas snowmelt floods are decreasing. In conventional models, these findings are obscured by the combined analysis of opposing trends, and trends are often not recognised. This causes design quantiles, for example, for a 100‐year return period, to be underestimated by about 15% on average in many regions of Europe when non‐stationary, type‐specific distributions are not used. Our results highlight the importance of incorporating process understanding into FFA to improve design‐flood estimation and our understanding of changing flood‐generating processes.

Large-scale hydrological models like CTRIP and MGB are essential for simulating river dynamics and supporting large-scale climate studies. Their accuracy can be significantly improved through satellite data assimilation. This study leverages the stand-alone value of 20 years of ESA Climate Change Initiative (CCI) high-resolution discharge and water surface elevation (WSE) products (2000–2020) for improving large-scale hydrological simulations through data assimilation. To evaluate the added-value of these produtcs across contrasting modelling and hydrological contexts, we assimilate altimetry-derived discharge, multispectral-imagery-derived discharge, and WSE anomalies into two existing ensemble Kalman Filter frameworks: HyDAS in CTRIP, a global-scale physically based and uncalibrated river-routing model, and HYFAA in MGB, a calibrated semi-distributed regional hydrological model. The experiments are conducted over the Niger and Congo basins, which differ in hydrological variability, river-network structure, wetland influence, and CCI product availability. Across the experiments, discharge assimilation generally outperformed WSE anomaly assimilation because discharge is directly represented in the routing models, providing a more direct and physically consistent correction, while WSE requires consistency between observed and simulated rating curves to be converted into effective discharge corrections. In the Niger basin, where the seasonal signal and station coverage better constrain the main river dynamics, assimilating altimetry-derived discharge led to the strongest improvement in MGB, increasing the median Nash-Sutcliffe Efficiency (NSE) to 0.83 and the correlation coefficient to 0.94. WSE anomaly assimilation was beneficial in specific cases, particularly when the baseline simulation was poor and when observed and simulated rating curves were well aligned. Temporal data density in discharge assimilation emerged as a key driver of performance gains. Assimilating high-frequency discharge data from multispectral imagery significantly reduced bias, from 1.2 to near 1 in MGB, and from 2.3 to 1.78 in CTRIP (median values), supporting hydrological assessments related to long-term variability. Furthermore, the higher temporal resolution allowed for better capture of flow variability, with Kling-Gupta Efficiency γ approaching 1.0 in MGB, which is relevant for…

Accurate streamflow prediction is critical for effective water resource management, and recent advances in deep learning and multi‐basin data sets have significantly improved model skill. Global deep‐learning models, trained on thousands of catchments have demonstrated strong generalization across diverse climates and continents. However, these models rely solely on globally available meteorological inputs, potentially overlooking the value of nationally developed data sets with enhanced regional accuracy. In this study, we assess the benefit of using Canada's high‐resolution national reanalysis data set (CaSR) in long short‐term memory (LSTM) models for streamflow prediction. We compare model performance using different meteorological forcings, global (ERA5‐Land), continental (Daymet), national (CaSR), and a combined “Complete” model, across over 1,000 Canadian stream gauges. Model performance is evaluated using the Nash–Sutcliffe Efficiency (NSE), Kling‐Gupta Efficiency (KGE) and related metrics. We also benchmark our best‐performing model against Google's global LSTM streamflow model at 412 Canadian gauges. Results show that national‐scale deep‐learning models using CaSR outperforms deep‐learning models using only global or continental inputs. The Complete model achieved the highest performance, with a median KGE 0.886−exceeding the 0.839 achieved by Google's global model within Canada. These findings suggest that nationally trained models using a wide array of inputs, including national meteorological data sets, can outperform state‐of‐the‐art global deep‐learning models that do not use such data when applied within their region of origin. For countries with dense observational networks and distinct hydrological regimes, investing in national data sets and models can enhance deep‐learning model prediction accuracy, leading to improved resilience to climate extremes and local water management decisions.

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…

Increasing droughts are exacerbating the imbalance between water supply and demand, complicating efforts to secure reliable water and food supplies for key sectors. However, limited understanding of how water use responds to drought constrains effective risk management and climate adaptation. Using monthly water use and meteorological data across mainland China from 1981 to 2020, we developed an event‐scale framework integrating time series decomposition, deep learning, and event coincidence analysis to quantify sector‐specific responses to meteorological droughts. Droughts impacted a relatively small portion of land (14.9% of China mainland) but accounted for a disproportionately large share of water use (47.4%). Northern basins were particularly sensitive, with water use rising by 5.67% (4.49%–6.37%) during drought events. Irrigation emerged as the primary driver of water use changes, with demand increasing as drought intensity rises. Projected intensification of droughts suggests that by 2100, more than 10% of affected regions could face high water scarcity risks under SSP1‐2.6, which may increase to 25.5% under SSP5‐8.5. These findings highlight pronounced spatial disparities in drought responses and emphasize the need to incorporate water use dynamics into climate risk assessments.

Streamflow reanalysis is vital for water resources management and climate impact assessment; however, the extent to which it is affected by precipitation forcing errors remains poorly understood. Focusing on the Global Flood Awareness System driven by the European Centre for Medium-Range Weather Forecasts Reanalysis v5 (GloFAS-ERA5), this paper details how streamflow reanalysis errors respond to precipitation errors. Specifically, the root mean square errors (RMSEs) are calculated by hydrological year for reanalysis products across 671 catchments in the Catchment Attributes and Meteorology for Large-sample Studies (CAMELS) dataset; and by combining catchment-specific linear regression with global panel regression, the effects of precipitation errors on streamflow errors are quantified. The results demonstrate an improved performance from GloFAS-ERA5 v2.1 to v4.0, with the median RMSE decreasing from 2.16 to 1.81 mm. For GloFAS-ERA5 v4.0, the panel regression estimates an average increase of 0.51 mm in streamflow RMSE for each 1 mm increase in precipitation RMSE across the 671 catchments. In the meantime, the corresponding catchment-specific increase of streamflow RMSE reaches up to 2.5 mm in humid catchments but remains below 0.7 mm in arid catchments. These divergent responses suggest that streamflow errors may be more directly associated with precipitation errors under the saturation-excess conditions while soil moisture deficits can dampen their effects. Furthermore, incorporating interaction terms into panel regression increases the coefficient of determination ( R 2 ) from 0.16 to 0.36, indicating these responses are modulated by catchment heterogeneity. This modulation is further presented by targeted case studies, indicating that temperature controls the storage and release of snow water, thereby dampening and delaying the responses of streamflow errors to precipitation errors in snow-dominated catchments. These findings provide a valuable diagnostic method and practical guidance for applications of global streamflow reanalysis to complex, heterogeneous catchments.

In May 2019, unprecedented extreme rainfall over the central United States (US) facilitated severe flooding along the Arkansas River. The well‐documented relationship between atmospheric rivers (ARs) and central US heavy precipitation motivated investigation into AR relevance to this event and its predictability on extended timescales. AR conditions coinciding with the extreme precipitation indicate ARs contributed to the Arkansas River flooding, while poisson weighted forecast probabilities of SHiELD ensemble hindcasts show both the ARs and extreme precipitation were predictable at 2 week leads. Comparison of precursory atmospheric conditions between the top and bottom five performing ensemble members reveal the top performers developed a Rossby wave train over the North Pacific, which promoted AR‐favorable conditions downstream and facilitated better forecasts of precipitation than the bottom performers. Results suggest cross‐Pacific Rossby wave trains can provide a forecast of opportunity for extended predictability of ARs and extreme precipitation over the central US.

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…

Historical peatland drainage resulted in surface subsidence and reduced water storage capacity (WSC), a process that can be exacerbated by climate change. We assessed the effects of rewetting on WSC using mesocosm experiments in climate chambers, field measurements, and Interferometric Synthetic Aperture Radar (InSAR) analysis. Results showed that rewetting significantly increased WSC under both present and future RCP 4.5 climates, with stronger effects where Sphagnum moss recovery may restore surface porosity. Rewetting enhances WSC under shallow antecedent water levels (WLs) and high precipitation, despite reduced vertical storage, due to living Sphagnum moss and porous near‐surface peat. Field validation confirmed increased WSC adjacent to the original ditches. In addition, InSAR analysis detected accelerated peat uplift post‐rewetting compared with pristine peatland conditions. These results demonstrate that rewetting effectively restores the flood control function of drained boreal peatlands, strengthens their resilience under climate change, and supports ecosystem recovery and climate adaptation strategies.

The resilience of human society to multiple climate disasters is crucial for supporting sustainable development progress and promoting human well‐being in an era of climate change. Yet little research has attempted to assess global spatial‐explicit capacity of communities to deal with compound extreme climate disasters simultaneously, and the relationship between resilience to different climate extremes remains unknown. Here we presented a high‐resolution assessment of community resilience to compound climate extremes simultaneously by developing the Community‐Climate Resilience Index (CCRI) using the multisource satellite remote sensing data sets, the geocoded disaster records and fuzzy logic model. We further explored interrelationships among resilience to major climate extremes, that is, resilience to floods (Resilience flood ), resilience to extreme temperature (Resilience ET ) and resilience to droughts (Resilience drought ). The results indicated western Europe showed higher CCRI while South America, Africa and Oceania showed lower CCRI. Urban areas showed higher CCRI but lower scaling effects comparing with rural areas with lower CCRI level and higher scaling effects. Most of the regions showed positive associations among resilience to different climate extremes, but there were also negative associations between resilience in some areas, especially located in suburban and rural Sub‐Saharan Africa. Our analysis suggested leveraging integrated and coordinated risk management for resilience capacity building to multiple climate extremes in vulnerable areas and on a local and global scale.

Streamflow estimates derived from stage–discharge rating curves (HQRC) inadequately represent the spatiotemporal complexity of unsteady gradually varied flows (UGVF) and are commonly reported without uncertainty, encouraging their treatment as deterministic. These limitations are increasingly problematic as data users demand defensible confidence in streamflow estimates for cross‐agency comparisons and for scientific applications. One of the dominant yet poorly quantified sources of HQRC error is hysteresis arising from ubiquitous unsteady and nonuniform flow conditions characteristic of UGVFs. Hydrometric agencies attempt to mitigate these effects through using alternative approaches such as HQRC corrections or use of stand‐alone multivariable methods that augment stage with velocity or free‐surface slope measurements. However, evaluating the effectiveness of these alternatives remains challenging, as HQRC corrections rely on simplified flood‐wave representations, while multivariable methods retain the semi‐empirical underpinnings of traditional HQRC formulations. To address this evaluation gap, we introduce a hysteresis‐centric performance metric that quantifies departures between bivariate relationships obtained using HQRC correction and multivariable methods and those derived from conventional HQRC ratings. The metric integrates three complementary indicators: (a) flow deviation magnitude, (b) hydrograph peak phasing, and (c) cumulative volumetric differentiation, collectively capturing the hysteresis severity, variable peak timing, and integrated impacts of hysteresis on discharge estimates. The framework is applied to three U.S. Geological Survey gaging stations spanning diverse hydro‐geomorphic settings. Results provide practical guidance for evaluating and selecting alternative monitoring methods in relation to dominant flood‐wave behavior (kinematic vs. non‐kinematic) and are synthesized into a structured decision‐making workflow for monitoring‐system selection under UGVF conditions.

Seasonal snow is a critical component of the water cycle in the Southern Andes of Chile and Argentina. Quantitative assessments of snow accumulation remain constrained by the scarcity, heterogeneity, and inconsistency of in situ observations, resulting in substantial uncertainties in mountain hydrological modeling. To address this gap, we compiled and quality-controlled snow depth observations to produce a consistent daily dataset from 81 stations between 21 and 54° S covering 2010–2024. Our quality-control procedure involved data compilation and preprocessing, followed by harmonization of snow depth observations, primarily by correcting the ground reference level relative to the soil surface during snow-free periods and removing anomalous spikes and observations outside physically plausible ranges. This process improved data reliability, increasing the Physical Consistency Index (PCI) from 87 % to 95 % at selected stations in the Maipo River Basin, while reducing the median number of days with data across stations by 23 % (from 1392 to 1074 d). The snow depth data availability increased from one station in 2010 to 57 stations in 2024, largely driven by the expansion of the monitoring network operated by the General Directorate of Water (DGA), Chile. However, this expansion remains spatially uneven across the Andean zones. The Mediterranean Andes have the largest number of stations (39), as well as the largest number of stations with highly complete records with 17 stations reaching 80 %–100 % data coverage, compared with only nine stations each in the Arid and Wet Andes, highlighting persistent spatial and temporal gaps. Using this newly quality-controlled dataset, we find that snow depth generally increases from the Arid to the Wet Andes in association with increasing precipitation, whereas its relationship with elevation is not consistently positive. The snow depth-elevation relationship is nonlinear in the Arid and Mediterranean Andes, with maximum observed snow depths at 4300 m a.s.l. in the Elqui River Basin and 3300 m a.s.l. in the Maipo River Basin. In contrast, the available observations indicate a positive snow depth-elevation relationship in the Wet Andes (Maule–Itata River Basin). However, these relationships should be interpreted in the context of the specific spatial and temporal conditions represented by the available observations. This…

Atmospheric rivers (ARs) are key drivers of extreme precipitation, and their future changes indicate major climate risks. Historical variations of ARs in Asia exhibited dynamic suppressions and thermodynamic enhancements under climate change, yet critical gaps remain in understanding the future impacts of these competing mechanisms. Based on a framework integrating the largest ensemble of multiple high-resolution global climate models for AR projection with multiple AR detection configurations, the projected changes reveal a dynamic inhibition of near-future ARs dominating lower latitudes under warming, while thermodynamic enhancement dominates densely populated subtropical and midlatitude regions. This pattern relates to the anomalously steered moisture transport by the westward-extended subtropical high. Consequently, population exposure to AR-induced extreme precipitation is projected to increase across 58% of terrestrial AR-active zones, particularly in urban regions within northern China, India, and Bangladesh (+24–59%). These findings underscore the urgency of implementing adaptation strategies against AR hazards in these regions during the coming decades.

Recent advances in satellite-based soil moisture observations present a promising opportunity to monitor irrigation dynamics from space. This study presents an approach for identifying irrigation signals that are periodic (i.e., similar year-over-year) using Soil Moisture Active Passive (SMAP) products, with feasibility demonstrated in Central Valley, California. The approach leverages two SMAP products: the Level 3 Enhanced retrieval product, which provides direct observations that inherently capture irrigation, and the Level 4 data assimilation product, which is only weakly sensitive to periodic irrigation signals owing to the design of the Level 4 algorithm. First, the systematic differences between the two products within individual non-irrigated grid cells in the Central Valley are demonstrated to be consistent across the cropping and non-cropping seasons. Therefore, during the cropping season the soil moisture differences between the two products can be corrected for systematic differences that are unrelated to irrigation, and the remaining differences can be attributed to periodic irrigation. The estimated irrigation signal is validated through comparisons with two benchmarks; results show reasonable spatial Pearson correlation coefficients of 0.66 and 0.50, respectively. Our findings demonstrate the potential of using SMAP products to extract periodic irrigation signals in regions that have limited precipitation during the cropping season. In regions characterized by frequent precipitation events during the cropping season, our method struggles to separate irrigation signals from precipitation.

Peatlands play a crucial role in regional water balance and carbon dynamics but are often degraded due to drainage and agricultural use. In Germany, many drained peatlands have shifted from carbon sinks to CO 2 sources. Rewetting these ecosystems is therefore essential to restore their ecological functions and mitigate greenhouse gas emissions. However, effective rewetting requires a detailed understanding of peatland hydrology and its response to climatic and management conditions. To address this need, this study employs a fully coupled surface–subsurface hydrological model (HydroGeoSphere) to analyze the complex hydrological functioning of a typical degraded fen peatland site (11.6 ha) in Brandenburg, Germany. The model-based quantification of hydrological fluxes is basis for assessing peatland vulnerability to climate variability and land use while providing a hydrological baseline for future evaluation of rewetting strategies. The studied peatland is connected to a regional aquifer and intensively drained by a system of ditches. Simulations used daily meteorological inputs and detailed field measurements from 2015 to 2023. Evapotranspiration (ET) was parameterized using field-measured vegetation dynamics (seasonal leaf area index and management schedules), while measured ditch water levels served as hydraulic boundary conditions. The site was spatially divided into different management units with distinct vegetation parameters. The peat profile was represented by two layers (a 0.3 m highly degraded surface peat overlying a 0.7 m less degraded layer) overlying sand (aquifer) and till (aquifer base). The model was evaluated from different angles against eddy covariance ET and groundwater table dynamics during a calibration period (2016–2020) and a validation period (2021–2023) using a multi-metric approach. The model successfully reproduced seasonal water-table fluctuations and ditch–peatland interactions, including ET-driven hydraulic gradient dynamics between summer and winter. Simulated ET closely matched eddy covariance measurements, with RMSE values of 64 mm yr −1 , 10.2 mm month −1 , and 1.01 mm d −1 , and showed only minor biases during dry conditions, while over the year seasonal dynamics of ET were also well captured by the model. The model reproduced groundwater variations with sufficient accuracy, achieving KGE values of 0.80–0.85, NSE of…

Groundwater plays a crucial role in maintaining baseflow in rivers and ensuring water supply, particularly in alpine regions where the freeze–thaw (FT) cycle exerts a strong influence on hydrological processes. However, a systematic understanding is still lacking regarding how FT processes affect the composition of groundwater recharge sources and the transitions among recharge pathways. This study takes the Qinghai Lake basin (QLB) as a case study and combines water isotope and hydrometeorological data to quantify the dynamic characteristics of groundwater recharge sources and pathways during the FT periods. The study found that soil water (57.0 %–76.3 %) was the dominant source of groundwater recharge during the FT periods, followed by rainfall (13.8 %–26.1 %) and snowmelt (7.9 %–22.0 %). The thawing process enhances the vertical connectivity of the soil profile, facilitating the recharge of groundwater from snowmelt and the 60–90 cm soil layer. Furthermore, the lc-excess value of groundwater gradually shifts from values closer to soil water to those closer to precipitation, indicating that piston flow gradually weakens during the process of groundwater recharge by soil water, while preferential flow intensifies, resulting in a pattern where piston flow and preferential flow coexist. Spatially, in the middle and upper regions dominated by permafrost, groundwater is primarily recharged by water from the 0–60 cm soil layer traveling along longer hydrological pathways, whereas in the downstream regions of the basin dominated by seasonal frozen ground, groundwater is primarily recharged by rapid infiltration from the 30–90 cm soil layer. Our research demonstrates that in alpine permafrost regions, freeze–thaw processes regulate water storage and transport, thereby further influencing the recharge sources and pathways of shallow groundwater.

Coastal El Niño (COA) events follow two evolution pathways, remaining in the far eastern Pacific or expanding into basin‐wide El Niño events. Analyzing 55 CMIP6 models, we show that the two pathways are associated with distinct central and eastern Pacific ocean‐atmosphere conditions, and the simulated frequency of each type is linked to tropical Pacific mean‐state biases (zonal for standalone, meridional for spreading) while inversely related to ENSO simulation skill. Only 16% of models reproduce observed COA frequency. Models overproducing COA events do so because of mean‐state biases that make coastal warming too easy to trigger, amplified by an overly sensitive local atmospheric response. Spreading events are systematically under‐produced, but their occurrence increases in models with more realistic meridional precipitation structure and, among the better‐performing models, more efficient wind‐thermocline coupling. These findings identify physical constraints governing COA evolution and the key model biases limiting the fidelity of COA simulation in CMIP6.

Synchronous flooding across distant regions can amplify socioeconomic impacts. As the hydrological cycle intensifies, flood synchronization appears to be expanding, yet predictive understanding beyond gauged basins remains limited. We analyze the global synchronization of annual peak runoff (a proxy for flooding) using gridded data sets and complex network approach, and evaluate 13 CMIP6 (Coupled Model Intercomparison Project Phase 6) models in reproducing spatial synchronization patterns. CMIP6 models reproduce major synchronization hotspots but systematically overestimate connectivity and local clustering (in 90% and 85% regions, respectively) while underestimating average link length (in 70% of regions), yielding overly connected networks disproportionately dominated by short‐range synchronization. We show that exaggerated temporal dependence driven by overly concentrated and shifted peak timing is the primary source of this structural bias. These emergent errors suggest a potential overestimation of spatially compound flood risk and highlight deficiencies of climate models in reproducing global hydrological seasonality, particularly in snow‐dominated regions.

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.

Flood‐Regulating Ecosystem Services (FRES) are widely used to assess the capacity of ecosystems in retaining water during a storm and mitigating hydrological extremes. Agriculture can play a role in the regulation of flood hazard, but the role of specific crop types in influencing flood regulation in small agricultural catchments remains largely unexplored. Indeed, FRES are typically evaluated by comparing the runoff generated in current conditions and a barren land scenario, treating agricultural land use uniformly in space (without distinguishing between crops) and time (without considering that agriculture has a seasonality). This study presents an approach to assess the seasonality of crop‐specific FLOod Regulating Agro‐Ecosystem Services (FLORAES). The proposed approach is based on coupling two fully distributed hydrological models: WATNEEDS, an agro‐hydrological model that estimates daily soil moisture based on agricultural water demand, and MOBIDIC, which uses the soil moisture conditions derived from WATNEEDS as the initial state to simulate flood hazards during extreme storm events. As a proof of concept for the methodology, we analyzed the upper part of the Ombrone Grossetano river basin (Italy), where agriculture plays a huge role in the local economy. Our results reveal that, under identical climatic conditions, different crops generate distinct initial soil moisture conditions, which can affect flood peak discharges. FLORAES don't contribute to flood mitigation in winter, whereas their contribution at the end of the growing season of each crop is not negligible, reaching the 20% of the peak discharge depending on the crop and the river basin size.

Watershed streamflow is often the focus of hydrological model calibration and evaluation, despite other potential objectives, including water quality management, flood protection, or agricultural management. When hydrological models are calibrated on streamflow, intermediate processes such as those affecting soil-moisture are not necessarily well represented. This research evaluated whether calibration using downscaled and bias-corrected satellite soil-moisture improves prediction of field-scale soil-moisture relative to conventional streamflow-based calibration. In this work, downscaled satellite soil-moisture and streamflow data are used to calibrate a soil and water assessment tool – variable source area model initialized using a terrain informed process to create hydrologic response units. In-situ soil-moisture measurements at 25 locations across a 4.2-ha mixed-grass pasture located in southwestern Virginia were used to estimate field-scale average soil-moisture variability for model evaluation. Leveraging downscaled satellite soil-moisture data substantially improved estimation of temporal soil-moisture variability without affecting the model streamflow performance. The multi-objective calibration using streamflow and satellite soil-moisture improved soil-moisture performance while maintaining streamflow performance comparable to streamflow-only calibration. These results demonstrate the potential for satellite soil-moisture–informed calibration to improve internal hydrologic state estimation in small, saturation excess watersheds. Furthermore, these results highlight the importance of coupling statistical performance gains with evaluation of hydrologic realism when extending such approaches to broader modeling applications.

Floods are among the most damaging disasters worldwide, necessitating high-resolution modeling of where terrain and environmental conditions predispose societies to flooding. While geospatial data are invaluable for flood susceptibility mapping, comprehensive and globally consistent datasets remain scarce. We present the Global Flood Susceptibility Map (GFSM v1), a globally harmonized 30 m flood susceptibility dataset produced by incorporating multi-source flood conditioning factors accounting for topographic, hydrological, meteorological, and anthropogenic characteristics within a machine learning framework. A gradient-boosted tree model was trained on ~17,000 curated grid tiles with ~30.45 million training samples, using a global flood inundation dataset as training labels and nine conditioning factors (elevation, slope, aspect, wetness index, vegetation index, height above nearest drainage, water/roads proximity, and rainfall frequency). The model was deployed based on geopolitical and climate strata (units n = 192) to generate global pixel-level susceptibility. Rigorous cross-validation indicates strong model predictive accuracy (median area-under-curve ~0.95), and the output captures localized flood-prone regions worldwide. GFSM v1 offers a global open-source, high-resolution resource to support informed flood risk management, particularly in data-sparse regions.

Drought is a highly consequential natural disaster that may likely increase in both severity and extent across the conterminous United States (CONUS). The mechanisms affecting the propagation of drought from the atmosphere to streamflow are complex and interactive, making the prediction of streamflow drought difficult with current modeling approaches. Machine learning is an emerging tool in the field of hydrology that may be well-suited to prediction of streamflow drought across large and topographically diverse areas. Here, we train and analyze 3198 random forest models at U.S. Geological Survey streamgages to understand common meteorological drivers of streamflow drought and to define physiographic characteristics of basins sensitive to these drivers. We also develop a novel dynamic regionalization approach using donor gages to predict daily streamflow drought at pseudo-ungaged locations. Our results show that teleconnections, temperature, evaporative demand, and snow–water equivalent are important drivers of streamflow drought in the West, Southwest, and Northern Rocky Mountains (Northern Rockies) regions of the United States, and precipitation and soil moisture are primary drivers of streamflow drought in the Northeast, Southeast, and the Northwest regions. Prediction using dynamic regionalization shows comparable performance to at-site models. This method may be applicable to other hydrologic prediction problems requiring transferability to ungaged locations.

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).

The Rio Grande is the main source of surface water supply for south‐central Colorado and much of New Mexico, with obligations via interstate and international treaties to Texas and Mexico. Recent drought conditions exacerbated by a warming climate have increased demand for this limited water supply. Like other snowmelt‐dominated watersheds in the western US, this water resource is vulnerable to declining snowpack and earlier melt‐off, and the drought conditions over the past two decades have challenged the management of this river system. An examination of key hydroclimatic variables shows that in spite of warm, dry conditions and decreasing snowpack, the Rio Grande headwaters show no significant trend in annual flow, and runoff efficiency shows a positive trend over recent decades. Given this unexpected result, it is important to understand underlying causes of the apparent increase in runoff efficiency to anticipate changes in Rio Grande water supply in the future. Two possible factors are hypothesized: the shift in timing of runoff and the impacts of widespread high‐elevation tree mortality due to a spruce beetle epidemic, and both may be playing a role. The increase in runoff efficiency may be short‐lived, however, and could already be declining. The Rio Grande headwaters exemplifies the convergence of conditions –including natural climate variability, forest disturbance, and anthropogenic climate change– which may be affecting runoff efficiency in other critical headwaters.

Rivers draining continuous permafrost landscapes on the North Slope of Alaska span from the mountains to the coast, integrating gradients in vegetation, soil organic matter, and hydrologic flow. We investigated how these landscape transitions influence dissolved organic carbon (DOC) concentrations, radiocarbon ages, and export along the river continuum over seasonal timeframes in northeastern Alaska. Water samples were collected from the Canning River and its tributaries from the spring freshet through late summer via multiple campaigns during 2011–2024. A simulation from the Permafrost Water Balance Model (PWBM) estimated terrain‐specific contributions to freshwater and DOC export. River DOC concentrations peaked during the spring and declined in summer, with episodic increases linked to summer rainfall. Summer river DOC concentrations from the Brooks Range remained low (0.41–0.64 mg C L −1 ) but increased downstream (0.5–3.0 mg C L −1 ), becoming younger from the mountains (805 yBP) to the river delta (525 yBP) as flow from DOC‐enriched streams draining the foothills (6–18 mg C L −1 ; Modern yBP) entered the Canning. The PWBM simulation revealed that while the mountains dominate the Canning's annual (90%) and spring‐summer (88%–93%) freshwater export, the foothills disproportionately influence its annual (56%) and spring‐summer (44%–58%) DOC export. DOC yields from the coastal plain were also high but had little influence due to its small contributing area. Our findings highlight the outsized role of the foothills in modulating river‐integrated DOC export to coastal ecosystems. Considering the impact of these landscapes on carbon source and runoff will aid in anticipating future DOC fluxes from Arctic rivers.

Achieving high prediction accuracy while maintaining low‐complexity and interpretability has been a critical challenge in streamflow forecasting. To address this issue, this study proposes an improved reservoir computing (RC) framework that simultaneously supports structural inference and dynamic forecasting of streamflow‐meteorology networks. The framework operates in two stages: first, a cross‐validation‐based interpretable dynamic weight inference algorithm is designed to uncover the relational structures within the streamflow–meteorology network; subsequently, the inferred structural information is integrated into an architecture embedded with catchment similarity to predict future streamflow states. This process guides the internal dynamics of the framework toward representations that align with the data‐driven dependency relationships between streamflow and meteorological variables, thereby enhancing the interpretability of the predictions. We partition a subset of catchments from CAMELS data set into eight groups and conduct a series of experiments to evaluate the proposed framework. Results demonstrate that the proposed method achieves an average improvement of 38.87% in Nash–Sutcliffe Efficiency (NSE) and 38.21% in peak flow prediction accuracy compared to benchmark models and effectively mitigates the temporal lag and instability commonly observed in deep learning approaches, exhibiting superior adaptability and robustness in handling spatial heterogeneity under diverse hydrological conditions. With fewer than 10% of the trainable parameters of benchmark models and a runtime 2 to 3 times faster, the framework enables rapid inference, adaptation, and modeling in cross‐catchment scenarios within complex hydrological dynamical systems, thus holding considerable promise for practical applications.

Exploring the future evolution of runoff within reservoir drainage areas and its dominant drivers provides insights for future reservoir management. We combine a georeferenced reservoir data set with historical runoff simulations and future projections from an ensemble of Global Hydrological Models (GHMs) to investigate the long‐term runoff evolution across 913 drainage areas of large reservoirs in China. To reduce uncertainties in the GHM ensemble, we adopt an ensemble best approach, which outperforms the ensemble mean and median. Using this approach, we incorporate observational constraints to project three aspects of runoff‐related variations: trends in inter‐annual magnitude, inter‐annual variability, and intra‐annual variability. A total of 337–438 reservoirs are projected to exhibit decreasing inter‐annual magnitude, weakening inter‐annual variability, and enhancing intra‐annual variability during the historical period, while increasing magnitude and enhancing variability under future scenarios. These three aspects of runoff‐related variations do not occur synchronously within the same reservoir groups, highlighting the need to consider each reservoir individually when developing effective management strategies. Attribution analyses indicate that anthropogenic climate change predominates over natural climate variability and human water and land management, serving as the dominant driver of runoff‐related variations. Although the ensemble best approach performs well at large spatial scales, uncertainties remain at the individual reservoir level, as relative errors exceed ±50% for 90–259 reservoirs in drylands and cold regions. To promote the reliability of future projections, we advocate continued efforts to improve GHM mathematical structures and parameterization schemes, alongside the integration of additional in situ observations for model calibration.

In alpine permafrost basins, dry‐season runoff largely depends on subpermafrost groundwater (BPG, deep groundwater), yet cryohydrogeological conditions are complicated by time‐varying hydrothermal regimes. Combined with the storage and regulation effects of permafrost on runoff, hydrograph‐based estimates of the fractional contribution of BPG to streamflow ( f BPG ) are limited in validity and accuracy, and f BPG has not been fully quantified—particularly intra‐annually. Here we leverage ensemble end‐member mixing analysis (EMMA) and extend it to a non‐negative inequality‐constrained least squares (LSEI) framework suited to sparse‐sample estimation with incomplete end members. Using the covariance structure of δ 18 O time series in precipitation, stream water, glacier and snow meltwater, and suprapermafrost groundwater, the approach resolves the contribution of the unknown end member, that is, BPG. The δ 18 O series undergo temporal harmonization, state‐space smoothing, and collinearity screening prior to regression. Applied to the Three‐River headwaters Region—a representative mid‐latitude permafrost region—the method estimated ablation‐season f BPG of 0.10 ± 0.02 to 0.17 ± 0.06, about 0.05 lower than the annual frozen‐season flow fraction Q frozen / Q annual , consistent with additional shoulder‐season recharge. Long‐term (1961–2020) discharge records show slightly increasing Q frozen / Q annual , highlighting a gradually strengthening slow‐reservoir role. The inferred BPG δ 18 O provided an isotopic baseline: winter stream water δ 18 O converged to it, diverged during ablation, and re‐approached after events. Although ablation‐season f BPG was modest, BPG sustained dry‐season baseflow, underscoring the need to track f BPG for future water‐security and climate‐adaptation planning in degrading permafrost regions.

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.

Ground‐based Global Navigation Satellite System (GNSS) vertical displacements are increasingly used to infer terrestrial water storage (TWS) variations, but their applicability remains uncertain where weak hydrological signals coexist with non‐hydrological deformation processes. This study evaluates the potential of GNSS‐derived vertical displacements to capture TWS variability in Northeast China (NEC), a representative cold region characterized by weak hydrologic variability, a sparse GNSS network, and widespread freeze‐thaw processes. Vertical displacements from GNSS, GRACE, and the Noah land surface model during 2012–2023 were compared. At the regional scale, network‐mean GNSS displacements calculated using all examined stations were moderately correlated with GRACE‐ and Noah‐based estimates ( R = 0.61 and 0.68, respectively). At the station scale, GNSS captured long‐term and interannual TWS variations at most stations, but its seasonal cycle differed markedly from GRACE and Noah. Agreement between GNSS and GRACE degraded substantially in winter ( R = 0.30), possibly due in part to non‐elastic deformation associated with freeze‐thaw processes. Regional‐average snow water equivalent was weakly correlated with station‐mean vertical displacements from GNSS ( R = 0.10), GRACE ( R = 0.28), and Noah ( R = −0.21), while correlations between TWS anomalies and vertical displacements were −0.60, −0.88, and −0.91, respectively. These results suggest that snow mass alone does not dominate seasonal TWS variability in NEC. Overall, GNSS vertical displacements capture regional and interannual TWS variations in cold regions, highlighting their potential for large‐scale hydrological monitoring. However, possible non‐elastic deformation associated with freeze‐thaw processes requires further investigation to improve GNSS‐based hydrological applications in complex cold‐region environments.

Accurate estimation of snowpack dynamics is essential for predicting snowmelt‐driven hydrological processes in mountainous regions. Although data assimilation techniques can integrate remote sensing observations into snow models to reduce uncertainties, their operational implementation remains constrained by sparse and irregular coverage of satellite data, complexity of mountainous terrain, and the computational demands of high‐dimensional assimilation. In this study, an Observation‐Masked Localized Ensemble Kalman Filter (OML‐EnKF) framework was developed to restrict analysis updates to observed regions while preserving localized spatial covariance among nearby observations. This framework assimilated daily Copernicus snow cover fraction (SCF) observations into the Wflow_sbm distributed hydrological model across the Rhône River basin, France, for 2017–2019. We compared its performance against an open‐loop run, a direct insertion scheme, and a one‐dimensional EnKF. Results demonstrate that incorporating a snow module in the Wflow_sbm model improved seasonal discharge patterns. The OML‐EnKF produced spatially coherent snow water equivalent patterns consistent with topography and improved snow depth estimates in mid‐elevation regions. Both EnKF schemes refined SCF and improved discharge predictions across multiple gauged stations. Furthermore, temporal shifts in snowpack accumulation and ablation modulated runoff timing, with increased snowmelt contributions during winter (October–March) and diminished discharge in late spring (April–June). Sensitivity analyses showed that a moderate localization radius (0.025–0.1, approximately 2.8–11.1 km) effectively captured spatial correlations within the OML‐EnKF, whereas excessively restrictive radius values degraded performance. These findings show that the OML‐EnKF enhances snowpack characterization and discharge prediction in the Rhône River basin, offering a promising tool for water resource management in snow‐dominated basins.

Accurate estimation of terrestrial water storage (TWS) variations is important for understanding climate change and managing water resources. Satellite gravimetry has been a unique tool for this purpose, but has inherently low spatial resolution. Dense networks of the Global Navigation Satellite System (GNSS) receivers may provide a higher resolution alternative by observing crustal deformation due to changing water mass loads. However, GNSS observations contain noise and other residual signals from various sources that are unrelated to elastic loading, which is troublesome in TWS applications. In this study, we estimated monthly TWS changes on a 0.25° grid in California from January 2019 to December 2023 using GNSS data, after developing a method to separate elastic loading signals through a Combined Empirical Orthogonal Function (CEOF) analysis with Gravity Recovery and Climate Experiment Follow‐On (GRACE‐FO). The corrected GNSS data were used to estimate TWS change via a regularized linear inversion, revealing clear seasonal variations and long‐term decline from January 2019 to October 2022, followed by an abrupt increase associated with atmospheric river events from October 2022 to March 2023. GNSS‐based estimates are validated with in situ lake water elevation data, and provide additional spatial detail compared to GRACE‐FO mass concentration solutions and hydrological model estimates. These results improve the potential of GNSS‐based TWS estimates in regional hydrological analyses, water resource management, and climate change assessment.

Dynamic imbalances in model initialization severely constrain the predictability of the rapid intensification (RI) and abrupt track recurvature of tropical cyclones (TCs). In this study, the effectiveness of an incremental analysis update (IAU)‐based four‐dimensional variational (4DVar) data assimilation scheme in forecasting Typhoons Doksuri and Khanun in 2023 was investigated. By reducing spin‐up impact, the mean track errors obtained with the IAU scheme stabilized below 60 km, and the intensity forecast errors were consistently reduced compared to the control experiment. These improvements can be mechanistically attributed to a superior representation of the fractured subtropical high and the internal asymmetric dynamic structure of TCs, which resulted in critical steering flow correction. Consequently, this dynamical enhancement yielded a 60% relative improvement in forecast skill for heavy rainfall (≥50 mm) at the 72‐hr lead time, indicating the potential benefit of improved TC initialization for heavy precipitation prediction.

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.

Atmospheric Rivers (ARs) often co‐occur with other weather systems, including fronts, low‐pressure systems (LPSs), and mesoscale convective systems (MCSs), which can amplify precipitation and flood risk. While extreme rainfall is frequently linked to multiple interacting features, how these systems co‐occur with AR intensity scales and regions remains unclear. Using ERA5‐derived feature masks and satellite data, we quantify spatiotemporal overlaps between ARs and other systems. AR + Front combinations dominate across much of the conterminous U.S. In higher AR scales (4–5), more complex MCS‐related combinations become increasingly common, particularly in DJF and MAM and along the West Coast, while limited LPS‐related co‐occurrences appear mainly in JJA and SON and are confined to regions such as the Gulf of Mexico and offshore Pacific genesis regions. In contrast, AR‐only occurrences dominate in some inland western regions. Extreme precipitation exhibits seasonal variation, with distinct co‐occurring systems contributing across seasons.

Stationary precipitation measurements are frequently affected by undercatch errors, which are particularly pronounced in cold and alpine regions with strong winds. Since gridded precipitation products used in land surface modeling are often derived from spatial interpolation of meteorological station data, these measurement errors propagate directly into gridded datasets. In this study, we train monthly Generalized Additive Models (GAMs) using undercatch corrected station observations, with geographical exposure and terrain elevation as predictors, achieving R 2 values above 0.76 in Leave-One-Out Cross-Validation. We apply these models to generate monthly undercatch correction factors for Austria and – combined with an exposed terrain penalty – use them to adjust existing station-based gridded precipitation products. We validate the undercatch correction using the conceptual rainfall-runoff model COSERO across Austria and in two high-alpine reservoir catchments: Kölnbrein and Schlegeis. Our results demonstrate that retrospectively corrected precipitation reduces runoff simulation biases across Austria, especially in catchments above 1500 m elevation, and closes the water balance in both alpine study regions where uncorrected data showed runoff deficits exceeding 20 %. Biases in snow depth simulations – assessed using the physically-based snowpack model Alpine3D and validated against stereo-satellite observations – decrease from a median difference of −0.87 to +0.15 m. Additionally, undercatch-corrected precipitation enables more realistic simulations of snow covered area during the melting season and long-term glacier volume changes. The proposed method shows promising results in both alpine case study catchments and across Austria, highlighting the importance of accounting for undercatch errors in high-alpine terrain and indicating the need for further research into their magnitude at high elevations.

In a new application of GRACE and GRACE‐FO satellite observations, we estimate basin‐scale net groundwater recharge rates for the combined Sacramento, San Joaquin, and Tulare basins encompassing California's Central Valley aquifer. Net recharge is calculated as the temporal derivative of groundwater storage anomalies and compared with precipitation and snow water equivalent over a fixed study area. Net positive recharge is rare, occurring in only six water years since 2004, and is consistently associated with years of above‐average Sierra Nevada snowpack. Across the observational record, net positive recharge is observed only in years when snow water equivalent exceeds approximately 21 km 3 (17 MAF; ∼121% of the 20‐year mean value), with the largest recharge event coinciding with the exceptionally wet winter of water year 2023. These results suggest that above‐average snowpack conditions, rather than rainfall alone, are an important enabling condition for basin‐scale groundwater recharge under a warming climate.

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 decoupling coefficient (Ω), a dimensionless metric that quantifies the balance between stomatal conductance ( G s ) and aerodynamic conductance ( G a ) in regulating evapotranspiration (ET), is key to understanding vegetation‐atmosphere interactions, which are fundamental to ecohydrological cycles and ecosystem functioning. However, the spatiotemporal patterns of Ω across different plant functional types (PFTs) and climatic gradients remain poorly understood, particularly in heterogeneous land surfaces under climate change. Here, we quantified Ω at 52 AmeriFlux sites across four PFTs in the contiguous U.S. (CONUS) from 2001 to 2024 and developed a high‐resolution Ω modeling framework that integrates eXtreme Gradient Boosting (XGBoost) with satellite observations and reanalysis data to produce spatially explicit 8‐day Ω maps at 0.1° resolution. This framework reveals a significant increasing trend in continental‐mean Ω (1.0 × 10 −3 yr −1 , p < 0.05), indicating progressively weaker vegetation‐atmosphere coupling, whereby ET becomes relatively insensitive to G s and increasingly constrained by available energy. We also found that Ω in water‐limited regions show highly volatile and asymmetric responses to drought, shifting toward stronger coupling in dry periods and toward stronger decoupling in wet periods, whereas Ω in energy‐limited regions remain strongly buffered with a stable coupling state. Our study reveals the spatiotemporal variability of Ω in CONUS, shedding light on the pivotal role of vegetation in modulating water fluxes under shifting climatic regimes and drought stress.

In the WRR 50th Anniversary Special Collection, Lettenmaier et al. (2015, https://doi.org/10.1002/2015WR017616 ) provided a timely account of how satellite remote sensing evolved from a niche capability in hydrology into a mainstream driver of hydrologic discovery, as the community shifted from imagery toward validated Earth Observation (EO) products and open‐access data systems. In this Commentary for the WRR 60th Anniversary Special Collection, we revisit and extend that synthesis and argue that the next step is not just more EO data, but a more accountable remote sensing hydrology. The challenge is to integrate remote sensing more directly into hydrologic science questions in ways that ground networks alone cannot provide consistently across scales. We center this Commentary on a core question: How can EO be used, credibly and consistently, to quantify where water is stored, how it moves, and how the hydrologic cycle is changing, in ways that improve hydrologic understanding and decision value beyond what ground networks alone can provide? We summarize key advances since 2015 that sharpen water‐cycle observability, expand analysis‐ready data systems and computation, and accelerate hybrid inference, while highlighting persistent challenges that continue to limit credible hydrologic inference near thresholds and extremes. We conclude with a science‐driven agenda focused on closing observability gaps, improving record coherence, advancing uncertainty‐aware inference, strengthening end‐to‐end traceability from measurements to societal impacts, and sustaining climate‐quality EO records for hydrologic science in a changing climate, while recognizing that these advances will depend on continuity of observing capability and credible follow‐on mission strategies.

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.

Climate change is intensifying the global water cycle, with extreme precipitation events increasing in frequency and intensity at the global scale. While trends in daily precipitation extremes are well-documented, sub-daily extremes – critical for flash flood risk – remain poorly characterized, due to limited long-term sub-daily observations. Convection-permitting models explicitly resolve deep convection and therefore offer the potential for a substantially improved representation of convective processes and short-duration precipitation extremes. This study evaluates the ability of the convection-permitting regional climate model AROME (2.5 km resolution, 1959–2022), forced by ERA5 reanalysis, to reproduce precipitation extremes and their trends, at daily and hourly scales, using a dense network of Météo-France stations. Using extreme value theory (GEV modeling), we analyze trends in 10-year return levels for both daily (1959–2022) and hourly (1990–2022) extremes. At the daily scale, AROME reproduces observed positive trends in southeastern France, consistent with previous studies. Hourly trends are more heterogeneous and less robust, with high spatial variability and low model-observation correlation. Overall, the results demonstrate the value of the explicit-convection model for studying extreme precipitation, while also highlighting its limitations for convective extremes.

The availability of large-sample hydrometeorological datasets, now widespread across many regions worldwide, has changed hydrological catchment modelling. Assessing model performance is an essential component of any modelling exercise, and an important question is how to interpret performance measure values. Performances of uncalibrated bucket-type models vary significantly across regions and can reach NSE values of 0.8 or higher, particularly in humid or snow-dominated catchments. This implies that using a fixed value for a performance measure to judge model performance, as sometimes suggested in the literature, is inappropriate. Instead, one should consider that, given local hydroclimatic conditions and the quality of the available data, the performance we should expect from any model in a particular catchment can vary widely. At the same time, a perfect fit (NSE value of 1) is usually impossible to achieve due to errors and uncertainties in the model and data. Therefore, it is helpful to compare model performances to lower and upper benchmarks. The purpose of this study was two-fold. First, we examined how to compute lower bounds, including determining appropriate ensemble sizes, assessing the effects of parameter ranges, deciding whether to use random or regional parameter sets, and evaluating how best to aggregate the ensemble of simulations. We also examined the relationships between lower and upper benchmarks and catchment characteristics. Secondly, we utilised these findings to compute both lower and upper benchmarks for many of the existing large sample datasets. By providing these values to the modelling community, we aim to facilitate the broader use of lower and upper benchmarks in large-sample hydrological modelling studies. We argue that these values are valuable as they provide a basis for evaluating model performance across the various large-sample datasets. This will allow assessment of model performance, considering what one could and should expect for a particular catchment.

Groundwater drought reflects the combined influence of meteorological forcing, aquifer memory, hydraulic connectivity, and anthropogenic modification. This study develops a hydrogeologically informed statistical framework that integrates temporal groundwater-drought characteristics with spatial indicators of Exposure, Pressure, and environmental Sensitivity. Standardized Precipitation and Groundwater Level Indices were analysed at 3-, 6-, and 12-month aggregation scales for 16 piezometers grouped into shallow unconfined, intermediate confined, and deep confined aquifer systems. The shallow unconfined aquifer showed the strongest SPI–SGI association, whereas the deep confined aquifer exhibited weaker correlations, longer drought persistence, and no observed recovery within the available record. These contrasts are consistent with differences in aquifer memory and hydraulic conditions, but conclusions concerning the deep system remain exploratory because it is represented by only two monitoring wells and may also be affected by sustained anthropogenic pressure. The Drought Impact Potential Index (DIPI) combines piezometer-level SGI-12 drought-response metrics with proxy indicators of groundwater abstraction, mining influence, groundwater-dependent ecosystems, and groundwater quality. Weighted and equally weighted formulations produced broadly similar regional patterns, although leave-one-out cross-validation indicated substantial interpolation uncertainty. The resulting maps should therefore be interpreted as exploratory screening products rather than predictive vulnerability maps. The E − P contrast describes only the relative mapped magnitudes of Exposure and Pressure and does not provide causal attribution of climatic and anthropogenic controls.

Snow droughts, defined as periods with below-normal Snow Water Equivalent, have recently received substantial attention as an emerging hazard in a warming world, but their impacts are still poorly understood. Here, we shed light on these impacts across the socio-ecohydrologic spectrum, by leveraging heterogeneous data sources from 38 catchments in Italy: 13 years of snow and runoff data, remote-sensing and in-situ measurements of Gross Primary Production, an inventory of emergency water restrictions obtained via a web-scraping tool and direct consultation of national to local regulations, and a survey among 113 mountain huts. We found that the majority of snow droughts in our sample were warm-dry (53 %), that is, the combination of higher-than-usual temperatures and low precipitation, followed by warm-wet snow droughts (22 %). These events resulted in the snow season being shortened by as much as one month across all elevations, more melt-out events compared to non-snow-drought years, approximately −50 % summer runoff, and a decline in runoff ratio. Notably, growing-season Gross Primary Production of vegetation after a snow drought was up to 10 % higher than after a non-snow-drought winter, particularly above 1500 m, which ground-based data suggest was due to an earlier meltout of snow leading to an earlier-than-usual greening date. By focusing on the recent 2022 and 2023 snow droughts, we also found that water-supply restrictions were issued at all elevations, but particularly across foothills rather than floodplain regions. Meanwhile, about 70 % of hut managers at elevations above 2000 m reported water-supply impacts, with 28 % of them even reporting earlier closing dates. Overall, snow droughts emerge as a deeply multi-sectorial and multi-elevation risk, interconnecting the cryosphere with hydrology, ecology, and society.

Drought is increasingly reshaping terrestrial ecosystems under climate change, yet how aridity modulates the climatic sensitivities of satellite‐derived green‐up rate (GR) and senescence rate (SR) in natural ecosystems remains poorly understood at the global scale. Here, we combine two decades of satellite observations and climate data sets to identify the primary climatic drivers (temperature, precipitation, solar radiation, soil moisture, and vapor pressure deficit) of maximum GR and SR across 764 global ecoregions and to quantify their sensitivities to interannual variability in climate‐forcing accumulation rates of these drivers. We reveal contrasting temperature‐response patterns along the aridity gradient in arid versus humid ecoregions. In arid ecoregions, decreasing aridity increases the GR sensitivity to temperature accumulation rates during the green‐up phase, whereas in humid ecoregions this sensitivity weakens and can even become negative as conditions become wetter. Similar, though asymmetric, those sensitivity reversal patterns are observed for SR. These findings demonstrate that aridity fundamentally reshapes vegetation‐climate interactions, with important implications for the stability of ecosystem carbon uptake under increasing hydroclimatic variability. By identifying critical transitions in climate sensitivities across aridity gradients, this study provides new insights for improving predictions of ecosystem responses to intensifying drought and for assessing risks to carbon sequestration under future climate change.

The cascading effects of meteorological droughts on river flows and water quality in mountainous regions are poorly understood. Using a data-driven framework, we studied drought impacts on streamflow, water temperature (WT), specific conductance (SC) in the Upper Colorado River Basin (1998-2022). Severe meteorological droughts in 2001-2002, 2012, 2018, 2020-2021 reduced annual streamflows by 15%, increased WT by 9% and SC by 5% relative to normal adjacent years. Declines were greatest in spring and summer, with reduced peak flows. Streamflow and SC recovered until 2018, after which successive droughts prolonged impacts, whereas WT effects were transient. Catchments with higher freshwater withdrawals, deciduous forest cover, air temperatures, residence times, finer soils had greater streamflow declines, while higher subsurface flows and infiltration enabled resilience. WT increases were associated with higher elevation, slopes, precipitation and lower agriculture, shrub cover. Open water and reservoirs mitigated SC increases. Thus, catchment attributes influence drought vulnerability in mountainous regions.

Climate extremes are increasingly disrupting livelihoods and shaping patterns of human mobility. Their longer-term and compounding relationships with migration remain understudied, particularly at the subnational scale. In this study, by analysing global subnational net migration data, we show that associations between climate extremes and migration vary by hazard type, timing, consecutive events, income level and pre-existing migration context. Floods are associated with decreased net migration across contexts, with effects lasting several years and appearing stronger in lower-income areas. Droughts, by contrast, show associations with net migration primarily in higher-income settings, with effects generally short-term. Tropical cyclones correspond to decreased net migration primarily in lower-income areas, with effects lasting over several years. For compound exposure, droughts show the clearest patterns, with prolonged exposure linked to reduced migration inflows in population-attracting areas. These results highlight how climate extremes interact with local socioeconomic contexts to shape mobility.

The construction and operation of reservoirs have fundamentally modified the natural hydrograph globally. However, complex seasonal operating patterns remain opaque due to data scarcity, limiting the accuracy of hydrological modeling and water management. Here we show that the Surface Water and Ocean Topography satellite overcomes these challenges by providing full‐coverage water surface elevation measurements for a basin‐wide network of small reservoirs. Applying a two‐step framework to 305 reservoirs (>0.5 km 2 ) in the Ganjiang River Basin (Yangtze), we found that single‐cycle regulation (33.4%) and dual‐cycle regulation (36.1%) patterns are dominant. Coupled with TimesNet deep learning, we predicted reservoir primary functions with an 86.0% accuracy and successfully disentangled multi‐purpose facilities. Our methodology characterizes reservoir regimes in data‐scarce basins, offering high‐resolution insights critical for understanding anthropogenic impacts on the global water cycle and supporting effective management.

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…

Rainfall during the snow season plays an increasingly important role in flood risk as climate warms and extreme events become more frequent. However, a given sized rain-on-snow (ROS) event can yield outcomes ranging from flooding to no runoff, depending partly on the snowpack's antecedent cold content and capillary retention forces. Here, we analyze the seasonal evolution of the snowpack's physical state over a 72-year period to assess long-term changes in its capacity to buffer runoff from liquid water input. We use ERA-5 Land data to force Alpine3D, a distributed snowpack model that tracks the layer-by-layer development of heat, mass, and structural framework of the snowpack throughout the snow season. We test our approach in a large Columbia River headwaters basin in NW Montana, USA. We evaluate cold content and total capillary retention of the snowpack to determine long term trends in Liquid Water Buffering Capacity (LW bc ) as it evolves throughout the snow season. The LW bc of the snowpack exhibited robust long-term declines across all elevation bands, that is driven by reduced SWE along with reduced cold content in late November through late January across all elevation bands, despite high intra- and interannual variability. The largest declines occurred during the Spring period, trending downward across the historical period by 43 % to 80 % depending on the elevation band. The core five weeks of mid-winter showed no trending change of LW bc , and in fact demonstrated an increase in cold content over the 72 years. Our findings demonstrate that changes in the snowpack's ability to buffer runoff, including dependencies on local basin factors related to snowpack seasonality and elevation, are a key component of evolving ROS risk.

Differentiation in water use strategies is essential for maintaining the function and resilience of dryland ecosystems. Under prolonged drought, dominant shrubs self‐organize into two typical spatial configurations: scattered with separated individuals and clumped with clustered individuals. How the coordination between root water uptake and leaf physiological traits drives water use strategies of self‐organized shrubs for adapting to drought remains poorly understood. To address this gap, soil moisture, morphological and root traits, leaf‐level physiological traits, and stable isotope ( δ 2 H, δ 18 O, and δ 13 C) of scattered and clumped Vitex negundo were observed during 2022–2024 in the semi‐arid Loess Plateau. The scattered shrubs primarily utilized middle and deep soil water (62.3 ± 7.8%), associated with isolated canopies that promote infiltration into deep soil layers. In contrast, clumped shrubs relied on shallow and middle soil water (82.0 ± 6.5%), linked to aggregated canopies and shallow roots. Scattered shrubs showed tight stomatal regulation with high midday leaf water potential and intrinsic water use efficiency (iWUE) during dry season, whereas clumped shrubs exhibited relaxed stomatal regulation, with declines in midday leaf water potential and iWUE. In rainy season, scattered shrubs constrained gas exchange, while clumped shrubs enhanced stomatal conductance and photosynthesis. These findings indicate that scattered shrubs adopt an active deep‐water acquisition with conservative water utilization strategy, enhancing drought resistance, while clumped shrubs exhibit an opportunistic shallow‐water acquisition with acquisitive water utilization strategy, improving water capture efficiency for drought adaptation. This study reveals the ecohydrological processes of self‐organized vegetation in drylands and provides basis for vegetation allocation in ecological restoration.

The absence of a definitive follow‐on strategy for the Soil Moisture Active Passive (SMAP) mission, which has already surpassed its planned mission lifetime, presents a significant challenge for the hydrological science community. GNSS‐Reflectometry (GNSS‐R), which exploits reflected satellite navigation signals, has emerged as a rapidly growing alternative for soil moisture retrieval, increasingly positioned as a low‐cost path to global observation. The two approaches, however, differ fundamentally in how they sense the surface, and the hydrological science community should weigh these differences carefully before decisions are made that could compromise the continuity of this essential climate variable's data record. In this study, we compared soil moisture products from these two systems across seven perspectives: (a) observation characteristics, (b) spatial resolution, (c) coverage and frequency of observations, (d) vulnerability to radio frequency interference, (e) complementarity potential, (f) performance, and (g) cost considerations. Our assessment suggests that GNSS‐R is better understood as a complement to SMAP than as a replacement. A GNSS‐R constellation can deliver sub‐daily observations, and lower upfront costs, capabilities that genuinely extend what single‐platform missions can offer. At the same time, GNSS‐R retrievals exhibit variable spatial sampling and canopy attenuation, and the highest‐performing products are typically trained, calibrated, or benchmarked against SMAP (an implicit cost that the low‐cost framing of GNSS‐R does not fully capture). Taken together, these findings underscore the need for sustained investment in both SMAP‐class L‐band missions and the expanding GNSS‐R constellation, each contributing complementarity strengths to a resilient global soil moisture observing system.

Understanding how meteorological drought propagates to hydrological drought under natural conditions is essential for improving drought early warning, yet the roles of precipitation frequency and intensity in regulating this process remain poorly understood. Here, we quantified drought propagation time (DPT) and drought response rate (DRR) across mainland China under natural conditions, and further assessed the spatial effects of precipitation intensity and frequency using geographically weighted regression (GWR) and future climate projections. The results show that: (a) DRR declines significantly with aridity, with 22.9% of the study area showing DRR values of ≤0.2, indicating weaker meteorological–hydrological coupling in drylands. (b) GWR results indicate that meteorological controls on DPT are dominated by precipitation, while the effects of precipitation intensity and frequency exhibit strong spatial heterogeneity: total precipitation metrics fluctuate around zero across aridity classes, whereas light‐precipitation intensity and heavy‐precipitation frequency are more strongly associated with DPT in drylands, indicating that drought propagation depends more on precipitation partitioning than on bulk precipitation alone. (c) Under future climate scenarios (2030–2100; SSP2‐4.5 and SSP5‐8.5), future changes in drought propagation are driven less by shifts in mean precipitation intensity than by increasing uncertainty in precipitation‐frequency effects, especially in arid and hyper‐arid regions. These findings highlight the critical role of precipitation partitioning in regulating drought propagation and establish a natural‐condition baseline for drought early warning and climate adaptation in water‐limited regions.

The flood reduction potential of individual reservoirs within a large river network continuum remains poorly understood due to the complex interplay between reservoir characteristics and network properties. Here we investigate whether a collection of relatively small reservoirs can play a significant role in mediating downstream floods and assess how that role may be influenced by reservoir network properties compared to traditionally known reservoir characteristics. Our unique contribution was the simulation of downstream flood inundation maps alongside peak flows for each of the 81 major reservoirs (6 × 10 4 to 8 × 10 9 m 3 ) across 15,000 river reach segments, integrated into a process‐based hydrologic model covering a 415,000 km 2 region in the Texas Gulf Coast, United States. The three key takeaways from our study are as follows. (a) Smaller reservoirs can substantially reduce downstream flooding, suggesting that inclusion of large reservoirs—the traditional approach in flood risk management studies—may present only a partial picture. (b) Flood reduction by smaller reservoirs is more effective upstream, although this phenomenon may be linked with aridity and overall water availability. (c) While reservoir size matters, it is not the primary factor determining its downstream flood reduction potential; the influence of network properties, such as catchment area, the count of upstream reservoirs, the cumulative maximum storage capacity of upstream reservoirs, and Stream Order (i.e., location), is equally and often more important. The broader impact of our findings goes beyond just floods, providing foundational insights for addressing emerging challenges such as aging dams and river connectivity.

Sub-daily climate data are increasingly important for climate-impact assessments because many processes, such as heat stress, hydrological extremes, land–surface energy balance, and renewable-energy production, respond non-linearly to intra-day variability. Daily data miss short-duration events and obscure sub-daily inter-variable interactions, creating biases in impact estimates. To address these limitations and provide consistent forcing across sectors, we generated a global hourly climate dataset by temporally disaggregating the Inter-Sectoral Impact Model Intercomparison Project Phase 3 (ISIMIP3) daily climate archives using the Temporal Disaggregation Tool (Teddy). The approach uses analogue-based hourly profiles from the bias-corrected WFDE5 (WATCH Forcing Data methodology applied to ERA5) reanalysis, preserves daily mass and energy, and maintains temporal coherence between variables. We illustrate the utility of the hourly data with four applications using the MPI Earth System Model (MPI-ESM) under ScenarioMIP pathway SSP3–7.0: (1) the fraction of wet hours, revealing rainfall intermittency not captured by daily wet-day metrics; (2) the number of hours with dangerous heat-index values, capturing joint diurnal cycles of temperature and humidity; (3) hours with wind speeds suitable for onshore wind-power generation; and (4) photovoltaic power potential calculated from radiation, temperature, and wind speed at hourly resolution. We discuss the benefits of preserving inter-variable timing, along with limitations such as reduced spatial coherence at sub-daily scales and potential constraints under strong climate-change signals. The resulting hourly ISIMIP3 dataset provides a harmonized foundation for more realistic sub-daily climate-impact modeling across sectors. The dataset is openly available through the ISIMIP repository under the following DOIs: https://doi.org/10.48364/ISIMIP.736682 (Bechtold et al., 2026a) and https://doi.org/10.48364/ISIMIP.170328 (Bechtold et al., 2026b).

We present the globally available dataset of Line-of-sight Gravity Differences (LGD) as a new data product to fill the long-standing gap of investigating sub-monthly surface mass change from the satellite gravimetry along-track perspective. The dataset is generated directly from Gravity Recovery and Climate Experiment Follow-on (GRACE-FO) Level-1B intersatellite ranging observations, positioned conceptually between Level-1B measurements and Level-2 monthly gravity field solutions. Provided as along-track time series, LGD delivers instantaneous gravity change at satellite altitude, thus ensuring the fidelity of measurements by avoiding the complex preprocessing, regularization, and filtering applied to monthly products. The instantaneous LGD measurements enable monitoring of sub-monthly terrestrial water storage variability and provide accurate information on timing and magnitude of water mass changes. GRACE-FO's ground track allows LGD retrievals every 5–6 d in most low and mid latitude regions, while the revisit interval becomes shorter toward higher latitudes. Our LGD data products also contain climatology in the form of along-track gravity variations. We demonstrate its potential through case studies, including along-track diagnosis of flash drought evolution in the southeastern United States and the characterization of sub-monthly hydrological extremes in global hotspots. Results show that the LGD dataset has the potential to resolve the critical timing (e.g., onset and peak) of hydrological extremes, which is unattainable from monthly products. The LGD dataset is available at https://doi.org/10.5281/zenodo.17766177 (Tang et al., 2025a), which is expected to pave the way for advances in sub-monthly hydrological monitoring and can serve as a unique constraint for hydrological and geophysical models, opening new opportunities for Earth system sciences.

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…

Precise seasonal prediction of groundwater nitrate concentrations in intensive agricultural areas faces challenges such as data sparsity, strong spatiotemporal heterogeneity, and complex hydro-biogeochemical processes. To address these issues, this study proposes an integrated prediction framework combining hybrid quantum-classical machine learning, advanced virtual sample generation (t-SNE-GMM-KNN), and remote sensing foundation model semantic embedding (AEF). Modeling was conducted across the 2022–2023 normal, dry, and wet seasons in Xiong'an New Area. Hydrochemical types were dominated by Ca-Mg-HCO3-, controlled by mineral dissolution and evaporation. Nitrate concentrations were highest in the dry season (mean 42.93 mg L −1 ), driven by evaporative concentration. Spatially, high-value zones shifted: southeast (normal), central (dry), and northwest (wet). MixSIAR modeling based on isotopes indicated domestic sewage and livestock manure (74.1 %) as dominant sources. The t-SNE-GMM-KNN strategy mitigated small-sample bias while preserving nonlinear structure. When virtual samples were augmented to 10-fold, the Random Forest R 2 in the dry season increased from 0.284 to > 0.85. Furthermore, a hybrid quantum-classical Random Forest exhibited superior robustness for data sparsity, achieving peak performance in the normal season ( R 2 = 0.962, RMSE = 5.73 mg L −1 ). Additionally, using only AEF embeddings achieved screening-level accuracy ( R 2 up to 0.860), providing a feasible rapid survey scheme for extensive unmonitored regions, where field sampling is impractical, whereas the field-parameter-based model serves primarily to elucidate hydrochemical driving mechanisms and enables rapid on-site nitrate estimation using portable water quality meters. Correlation analysis identified TDS and EC as persistent top predictors ( r > 0.8). This comprehensive framework offers a robust solution for seasonal nitrate prediction by distinguishing between mechanistic analysis, field-operational estimation, and large-scale screening and sustainable water management.

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.

Irrigation alters the terrestrial water cycle, yet its spatial distribution and temporal variability remain poorly constrained because existing data sets often rely on indirect proxies or inventories rather than observations tied to land‐surface water‐balance dynamics. Here, we introduce a wavelet‐based method to detect irrigation from spectral differences between modeled and satellite‐observed soil‐moisture time series, implemented using Noah–MP simulations and Soil Moisture and Ocean Salinity (SMOS) observations. The method, applied across the contiguous United States, exploits differences in seasonal to sub‐seasonal soil‐moisture variability between irrigation‐off model simulations and satellite observations, particularly in the 6–18‐month band, enabling irrigation detection. The resulting soil‐moisture‐based irrigation classification complements high‐resolution optical irrigation data sets, while reflecting the coarse‐scale information contained in SMOS soil moisture. When incorporated into Noah–MP simulations, it improves the distribution of simulated irrigation water use. This framework supports scalable irrigation detection from satellite soil moisture and improved irrigation representation in Earth system models.

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.

Interferometric Synthetic Aperture Radar (InSAR) offers a potential pathway for measuring seasonal snow water equivalent (SWE) and snow depth at spatial and temporal scales needed for basin‐scale hydrology. First proposed for seasonal snow more than 25 years ago, InSAR uses repeat‐pass radar phase differences to estimate changes in SWE or depth caused by microwave propagation through snow. This review synthesizes InSAR‐based snow monitoring from early theoretical and satellite demonstrations through recent tower, airborne, and spaceborne studies. We summarize the retrieval physics, evaluate published validation results, compare performance across wavelengths and snow environments, and identify barriers to operational implementation. The literature shows that InSAR can provide high‐resolution, spatially distributed information on snow accumulation and ablation, especially under dry‐snow conditions when temporal coherence is maintained and snow phase can be separated from atmospheric, vegetation, soil‐moisture, and other non‐snow contributions. Longer wavelengths, particularly L‐band, help maintain coherence, reduce phase‐wrapping ambiguity, and support retrievals in some vegetated environments, making the NASA–ISRO Synthetic Aperture Radar (NISAR) mission a timely opportunity for basin‐scale testing. Remaining challenges include correcting ionospheric and tropospheric delays; defining stable reference phases; identifying, correcting, or flagging coherence loss and phase‐unwrapping errors; accounting for forest‐canopy interactions; collecting spatially distributed validation data; and scaling site demonstrations to regional or global products. Operational use will require algorithmic maturation, robust validation, and integration with other remote sensing and modeling approaches. If these challenges are addressed, InSAR‐capable satellites could become an important component of an integrated global snow observation suite.

Data‐driven rainfall–runoff models have advanced rapidly, yet the majority of large‐scale applications still rely on lumped inputs that smooth out spatial variability in precipitation, temperature, and landscape properties. This simplification can introduce substantial biases in flood peaks, hydrograph timing, and water balance estimates. Here, we develop a deep learning framework that ingests spatially distributed (gridded) meteorological forcings and is evaluated across 921 catchments in North America using the CAMELS‐SPAT data set. We compare two strategies for fusing spatial and temporal information, namely a spatial‐to‐temporal (S2T) architecture and a temporal‐to‐spatial (T2S) architecture, and assess the added value of gridded catchment attributes and finer spatial resolutions. The T2S approach consistently achieves the best performance, increasing median Nash–Sutcliffe Efficiency (NSE) from 0.60 (lumped baseline) to 0.65 and Kling–Gupta Efficiency (KGE) from 0.63 to 0.69, with NSE improvements observed in 75.4% of catchments. Performance gains are consistent across catchment sizes and aridity regimes, with enhanced skill in low‐flow, high‐flow, and peak timing representations. Integrated Gradients (IG) analysis shows that the model learns physically coherent spatial behavior, highlighting grid cells near the outlet and identifying dominant meteorological drivers of peak runoff events. These results demonstrate the practical value of spatially distributed forcings for data‐driven hydrologic modeling and outline a pathway toward interpretable, large‐scale streamflow prediction.

Groundwater‐dependent ecosystems (GDEs) provide critical ecosystem services in arid and semiarid regions and help sustain ecosystems during droughts in humid areas. Accurate mapping of GDEs is essential for improving water resource management and ecosystem conservation. The distribution of GDEs remains largely unknown across contiguous China. In this study, remote sensing data, soil data, and modeled groundwater depth (GWD) data are integrated to map GDEs across both arid and humid regions of contiguous China. The mapping framework utilizes long‐term remote sensing data combined with drought‐related climatic data to characterize vegetation responses under selected hydroclimatic conditions. These conditions are selected to highlight the differences in vegetation responses between GDEs and non‐GDEs. By integrating vegetation temporal dynamics with hydroclimatic indicators, this approach enables systematic mapping of GDEs. The results indicate that high‐, moderate‐, and low‐potential GDEs account for 1.6%, 7.7%, and 22.1% of the mapped area, respectively. Assuming a vegetation rooting depth within 10 m, the GDE map shows a commission error of 13.6% against observed GWD. The reliability of the GDE map is further supported by vegetation responses to drought and the characteristic topographic settings of GDEs.

Tropical cyclone (TC)‐induced extreme precipitation (TCEP) poses heavy risks, yet its seasonal predictability remains limited by insufficient understanding of cross‐scale climatic drivers. Here, we elucidate a stratosphere‐to‐stratosphere coupling pathway where the quasi‐biennial oscillation (QBO) modulates TC activity and subsequent TCEP over the southeastern coast of China, with the second boreal summer intraseasonal oscillation mode (BSISO2) acting as a critical mediator. The westerly QBO phase reshapes the spatial structure of BSISO2 during its Phases 4–6, creating favorable dynamical conditions and moisture convergence that nearly double TCEP over the southeastern China coast relative to the easterly QBO phase. Statistical fitting results further show that incorporating the QBO signal substantially improves seasonal TCEP predictability beyond conventional El Niño‐Southern Oscillation predictors. These findings establish the QBO as a coherent stratospheric driver for TCEP, providing a mechanistic basis for enhanced seasonal TCEP prediction and regional disaster preparedness.

Soil moisture is an essential climate variable that controls the exchange of water and energy between land and atmosphere, directly impacting land surface feedbacks. Satellite‐derived soil moisture provides valuable large‐scale information, but long‐term applications are limited by sensor lifespans, retrieval noise, and systematic biases. Earth system reanalyses rely on data assimilation to merge these observations with model dynamics. The mentioned limitations of the observations can degrade assimilation performance by propagating artifacts into model states. Here, we present a European daily soil moisture reanalysis for 2003–2023 that explicitly addresses these limitations by pre‐processing satellite observations prior to assimilation. We assimilate a newly developed Deep Learning (DL)‐enhanced AMSR‐E/2 soil moisture data set that provides a seamless daily record from 2003 to 2023 by reducing retrieval artifacts while preserving spatiotemporal consistency. Residual systematic biases are mitigated through climatological bias correction, and observation uncertainty is characterized using spatially distributed uncertainty estimates derived from Triple Collocation Analysis. The processed observations are subsequently assimilated into the encore Community Land Model (eCLM). This study evaluates whether DL‐enhanced observations improve reanalysis skill when assimilated into a continental‐scale ensemble land surface DA system. Evaluation against independent in situ measurements shows that assimilation of the DL‐enhanced AMSR‐E/2 reduces PBIAS from 28.68% to 3.28% and RMSE from 0.100 to 0.082 relative to open‐loop simulation, and outperforms assimilation of the original AMSR‐E/2 (PBIAS 15.13%, RMSE 0.094 ). Representing observation uncertainty as a spatially distributed field rather than a uniform value further improved temporal correlation at 66% of validation stations in a dedicated 3‐year experiment (2016–2018). Improvements are consistent across most in situ networks, with the largest gains observed in semi‐arid and transitional climate zones. At the same time, seasonal benefits are most pronounced during spring, autumn, and summer, where model dry‐down and vegetation dynamics are better represented. These improvements result in better preservation of hydrological signals and more physically consistent soil moisture dynamics. Our results demonstrate that…

Fire danger can escalate rapidly when vegetation dries over days to weeks. Flash droughts, characterised by rapid root-zone soil moisture depletion under high evaporative demand, may accelerate wildfire spread. Here, we quantify relationships between flash droughts and wildfire spread dynamics across land-cover types in the conterminous United States, using a model-derived root-zone soil-moisture flash-drought metric and contrast these with conventional drought conditions defined by persistently low soil moisture without rapid onset. We find that fires following flash droughts exhibit faster early spread than fires not associated with flash droughts, with model estimates indicating about 1.3–1.8-fold faster early burning across major flammable land covers. Grasslands and croplands show the strongest acceleration, followed by open shrublands, woody savannas, savannas and deciduous broadleaf forests, consistent with faster drying and greater fuel continuity in fine-fuel ecosystems. In contrast, conventional drought conditions produce weaker and less consistent changes in fire spread. Our results demonstrate that rapid soil-moisture drawdown—rather than background dryness—appears to provide a more direct pathway linking drought to accelerated wildfire spread. We recommend that monitoring declines in root-zone soil moisture in the days to weeks preceding ignition could improve early warning of fire risk in ecosystems prone to rapid drying.

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.

Accurately simulating overland flow in vegetated landscapes remains a challenge in hydrological modeling due to the complex interactions between vegetation, surface roughness, and soil infiltration. This study evaluates multiple methods for estimating Manning's roughness coefficient and explores the influence of vegetation on infiltration parameters, namely saturated hydraulic conductivity ( K sat ) and wetting front suction (Psi), using the OpenLISEM model. Based on 132 artificial rainfall experiments across 22 sites in southwest Germany, the model was calibrated and validated against observed runoff data, incorporating both depth-independent and depth-dependent roughness formulations. Incorporating water depth-dependent roughness into the model can improve its performance in simulating overland flow. Beyond roughness effects, vegetation was shown to significantly alter soil hydraulic properties, particularly saturated hydraulic conductivity ( K sat ). Paired site comparisons revealed that increased vegetation cover corresponded with higher infiltration capacities, emphasizing vegetation's role not only in surface resistance but also in enhancing subsurface water fluxes. The findings demonstrate that models must account for both surface and subsurface impacts of vegetation to improve runoff predictions.

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.

We evaluate the performance of the Super-Fast INundation of CoastS (SFINCS) hydrodynamic model for simulating riverine floods, combined with a fully automated open-source data preprocessing pipeline. To do this, we assessed the simulated extent of 499 historic flood events against the satellite derived flood extents using the Critical Success Index (CSI) as a performance metric. We utilised simulated discharges from the Global Flood Awareness System (GloFAS) hydrological model and found that SFINCS performance improved with upstream basin size, with a global mean CSI of 0.42 for basins with large upstream area (>1000 km 2 ) and a CSI of 0.29 for basins with small upstream area (<50 km 2 ). Our results illustrate the importance of accurate discharge data input to flood hazard simulations. When the (globally simulated) GloFAS data is replaced with observed discharge data for ten events in the US, the CSI improved from 0.39 to 0.67. These results suggest that global hydrological model performance limits the accuracy of the flood hazard simulations. Our findings also showed a significant improvement in the CSI (from 0.37 to 0.57) when changing to a higher-resolution elevation input by contrasting a ∼1 m digital elevation model (DEM; 3DEP) with our default ∼30 m global DEM (FABDEM) in six US events. Sensitivity analysis of bathymetric calculations revealed a systematic underestimation of the default 2-year return period estimated by GloFAS discharge, likely driven by underrepresentation of annual block maxima, which resulted in underestimated channel dimensions. All of these factors resulted in a loss of detail, which impacted model performance, especially in smaller headwater rivers. We recommend to improve the estimation of bathymetry, for instance by employing the “gradually varying solver” method or using data from the SWOT mission. Furthermore, incorporating additional validation data which ideally includes flood depth measurements can largely enhance our understanding of the model performance.

The simulation of streamflow is a complex task due to its intricate formation process. Existing single models struggle to accurately capture the stochastic, non-stationary, and nonlinear dynamics of basin streamflow in changing environments. This study combined process-driven hydrological mechanism models with data-driven deep learning models, considering various factors like hydrology, meteorology, environment, and the interconnected effects of upstream and downstream rivers, to develop an interpretable hybrid streamflow simulation model. The study collected multiple external variables to better understand the hydrologic system complexity and used the Maximum Information Coefficient (MIC) to analyze their relationship with streamflow. Subsequently, the Xinanjiang (XAJ) model with physical mechanisms was employed, alongside the TCN-GRU model integrating Temporal Convolutional Network (TCN) and Gated Recurrent Unit (GRU), for separate streamflow simulations. Furthermore, a robust integration method was adopted, realizing nonlinear ensemble through Random Forest (RF), thus establishing the hybrid XAJ-TCN-GRU model. This model exhibits promising results in simulating streamflow in four different basins in China, achieving high Nash-Sutcliffe Efficiency (NSE) values with 0.991, 0.971, 0.984, and 0.986 for the Wuding River, Chu River, Jianxi River, and Qingyi River respectively. In terms of streamflow simulation, flood simulating, and interval simulation, this model outperforms other benchmark models. Additionally, the study quantified the contributions of each hydro-meteorological variable to the long-term streamflow trend using mean absolute SHAP values (SHAPABS), Feature Importance (FI), and Permutation Feature Importance (PFI), thereby enhancing the model's external interpretability. The results of this study are of significant importance for optimizing water resource management and mitigating flood disasters.

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.

The resistance and resilience of vegetation determine its ability to maintain ecosystem functional stability under flash drought stress. However, a research framework that quantifies event‐scale response characteristics of vegetation resistance, resilience, and vulnerability remains lacking. To fill this gap, we developed a high‐temporal‐resolution framework to assess the multi‐dimensional attributes of vegetation response to flash droughts, quantifying the spatiotemporal response characteristics of vegetation resistance, resilience, and vulnerability in China from 1982 to 2019. Our analysis shows that, over the past four decades, vegetation vulnerability to flash droughts in China's monsoon regions has increased by 0.019 per year. On one hand, this is primarily due to the significantly higher average decline rate of vegetation ecosystem resistance (0.074 per year) under flash drought stress, compared to the average increase rate of its resilience (0.010 per year). On the other hand, the trade‐off relationship between vegetation ecosystem resistance and resilience has shifted, particularly in the middle and lower Yangtze River Basin. In both monsoon and non‐monsoon regions, explainable machine learning models indicate that temperature is the most significant stress factor influencing vegetation resistance and recovery processes during flash drought events, with SHAP values contributing between 20% and 50% to the overall model explanation. These findings highlight the urgent need to focus on the impact of increasingly frequent flash droughts on the stability of terrestrial ecosystem carbon sink functions in the context of current global warming.

Considerable progress has been made in forecasting the likelihood, magnitude, and mobility of post-wildfire flash floods and debris flows; however, we lack systematic observations of the physical and financial effects of these hazards, which can be used to close gaps between scientific understanding of the hazard and risk reduction. Here, we introduce a 147-year (1879–2025) inventory of damage and losses associated with post-fire flows in the United States. These observations enable an assessment of the geographic convergence and frequency of damaging post-fire flow events, as well as their multi-asset damage profiles and loss distributions. We find that the long-term (1879–2025) mean annual frequency of damaging flow events is greater than one and that recent (2016–2025) fatality rates and levels of monetary loss appear higher compared to historical periods. Our study provides an empirical baseline that can be used for long-term hazard planning amid global pressures compounded by wildland-urban interface expansion, increased wildfire activity, and rainfall intensification.

Pollution and deposition of sediments in rivers and streams are critical environmental, ecological, navigational, and recreational concerns. While several well‐known watershed models such as SWAT (Soil and Water Assessment Tool) and HSPF (Hydrological Simulation Program‐FORTRAN) are applied to predict sediment concentrations and loads in rivers and streams, their application requires the acquisition of a large amount of climatic, GIS, land use, and watershed input data, which is very time‐consuming and cost‐prohibitive. This study developed a copula‐based regression method to predict sediment concentrations and loads in rivers using streamflow data, which is difficult to achieve using traditional methods. Predicted sediment concentrations from the method were verified and validated by field measurements from three US Geological Survey (USGS) gauge stations across the US, as well as by statistical metrics. Prerequisites, advantages, uncertainties, and limitations of the method were presented and discussed. Results revealed that sediment loads were not proportional to watershed drainage area, indicating that land use and anthropogenic activities also play an important role in sediment load. Overall, no significant increasing or decreasing trends of annual sediment loads were found at study sites in New York and Florida over the 11‐year period from 2011 to 2021. This study suggests that the copula‐based regression approach, which is time‐saving and cost‐effective compared to the traditional watershed models, is a promising alternative for predicting sediment concentrations and loads using streamflow data when a good dependence (or correlation) exists between the sediment content and streamflow after copula transformation.

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.

Seasonal snowfall is declining globally, yet the relative roles of precipitation variability versus warming‐induced phase transitions remain poorly quantified. We develop a physically based attribution framework that decomposes snowfall trends (1983–2023) into hydrological (precipitation‐driven) and thermodynamic (phase‐driven) components, constrained by a wet‐bulb‐temperature‐based phase relationship. Across most seasonal snow regions, snowfall decline is primarily driven by thermodynamic phase shifts rather than changes in precipitation amount. Regions near the rain–snow transition exhibit the strongest sensitivity to warming, consistent with systematic snowline retreat. Results are robust across independent data sets, including ERA5 reanalysis and the satellite‐based GPCP v3.3 products. These findings indicate that precipitation‐phase transitions exert pivotal control on contemporary snowfall loss, suggesting that projected precipitation increases are unlikely to offset warming‐driven reductions in snowfall. This has critical implications for future hydrological regimes and cryosphere‐climate feedbacks.

In the past decade, the Amazon has experienced multiple severe droughts, raising critical questions about how vegetation stress can be detected using remote sensing indicators such as Solar‐Induced Chlorophyll Fluorescence (SIF). In this study, we compare two anomaly detection approaches applied to TROPOSIF data. First a classical pixel wise z‐score method, and second a 3D Convolutional Autoencoder (CAE) that learns spatiotemporal SIF patterns. We evaluate both methods against two drought products, the global Standardised Precipitation‐Evapotranspiration Index (SPEI) metric and Brazil's Monitor de Secas (MSB). Our results show that the CAE identifies broader, more persistent anomalies than the z‐score method and aligns more closely with drought patterns detected by SPEI and MSB, particularly during the peak dry period in September 2024 and the months of delayed recovery that followed. These findings highlight how methodological choices shape the interpretation of vegetation stress and why anomaly detection methodologies should be carefully selected.

Storm surges severely salinize coastal aquifers, yet post‐storm subsurface hydrodynamics remain poorly understood. We investigated surge inundation in unconfined tidal aquifers, revealing that subsurface flow recovers much faster than salinity distributions. Immediately post‐storm, submarine groundwater discharge rapidly increases to greatly exceed the pre‐storm level under extreme surge scenarios. Concurrently, downward surge infiltration creates a transient hydraulic barrier, forcing the saltwater wedge to temporarily retreat seaward, depending on surge height. Furthermore, we reveal the upper saline plume overshoot effect, as infiltrated storm water continues to migrate downward and seaward. This overshoot is exacerbated in micro‐tidal, low‐permeability aquifers. Understanding these asynchronous responses is critical for predicting groundwater quality and managing coastal resources under intensifying storm events.

Wildfires fundamentally alter snow accumulation and melt dynamics by modifying forest canopy structure, snow albedo, and snowpack physical properties. Despite the growing body of work on fire impacts on snow, few studies have used physically based models to simulate post‐fire changes, and none have examined the global parameter sensitivities that govern these dynamics. In this study, we apply the Structure for Unifying Multiple Modeling Alternatives in a high elevation Colorado watershed to identify and evaluate the model parameters that influence post‐fire snowpack evolution. We conduct a global sensitivity analysis, then sample sensitive parameters to identify behavioral parameter sets with respect to airborne LiDAR‐based snow depth in burned and unburned forests. Among behavioral parameter sets, we identify 4 parameters that differ significantly in burned versus unburned forests. These include the albedo decay rate (45% faster in burn than unburned forest), maximum canopy interception for rain (60% lower), minimum temperature for snow unloading (3.9°C warmer), and snow surface roughness (902% higher). Model simulations using behavioral parameter sets for burned and unburned conditions indicate 17% lower peak absolute cold content on average and 17% lower peak snow‐water equivalent (SWE) in burned conditions on average, with differences in depth, SWE, and cold content increasing throughout the snow season. These findings highlight the combined influence of canopy loss and accelerated albedo decay on snow energy balance and reveal additional post‐fire snow processes that are not typically represented in existing modeling frameworks. This study therefore provides a process‐level foundation for improving the simulated representation of post‐fire snow dynamics.

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.

Hydrologic models applied in snow‐dominated basins are typically evaluated by comparing simulated outputs, such as snow water equivalent and streamflow, with observations. However, this approach may overlook the energy balance aspect of snow modeling. Snowmelt initiation and rate are primarily determined by the net energy balance, which varies across snow models due to diverse model inputs, parameterizations, and layering structures. Here, we discuss how decisions regarding model parameters must be made in conjunction with energy budget considerations and model layer structure to model snowmelt effectively, and highlight the importance of incorporating multiple validation data sources, including snow surface temperature. We use the two‐layer Distributed Hydrology Soil Vegetation Model in California's upper Tuolumne River watershed as a case study and explore the influence of model top layer thickness (TLT) on simulating snowmelt, focusing on its significant effects on modeled snow surface temperature ( T ss ), energy propagation through the snowpack, and the timing of snowmelt. Additionally, we examine the interplay between model forcing and parameters, specifically incoming longwave radiation and albedo, to reveal how variations in these factors interact with TLT and shape snowmelt simulations. Our results reveal that TLT has a significant impact on diurnal variations in T ss , snowpack energy distribution, and the overall timing of snowmelt. Lower albedo enhances melting rates more strongly in the thinner upper layers, and biases in incoming longwave radiation can either amplify or mitigate these effects. Snow surface temperature was a key variable to investigate, and we recommend considering it in snowmelt investigations.

Snow plays a critical role in cold-region environments, from climate regulation to water storage and from spring freshet to ecosystem services. However, quantification of snow accumulation and redistribution across spatio-temporal scales is challenging, owing to non-uniform snowfall, topographic attributes, wind redistribution, and interception and sublimation by vegetation. Manual snow surveys are often conducted to improve understanding of snow processes across scales and to support societal needs (e.g., seasonal water supply projections, flood forecasting, reservoir filling, etc.). Nevertheless, manual snow surveys are time-consuming and expensive, which limits their temporal frequency and are rarely conducted in remote locations. This manuscript presents snow data from manual snow surveys conducted from 2009 to 2024 across four small watersheds in high-latitude boreal plains of northern Alberta in Canada. The dataset includes various snow properties, including depth, density and snow water equivalent across different watersheds and land cover types (i.e., Open, Flat Low Lying, Coniferous and Mixed Deciduous). The dataset is expected to be useful in understanding snow processes in high-latitude boreal plains, to help calibrate snow models and to validate airborne lidar or satellite-derived snow depth measurements.

Rapid Arctic warming plays a crucial role in changes of vegetation, hydrology and permafrost, substantially affecting the riverine carbon and nitrogen dynamics; however, their long‐term trajectories and dominant controls remain uncertain. Here, by combining observations and model simulations in six major Arctic rivers, we estimate the changes and drivers of riverine carbon and nitrogen exports from 2001 to 2020. The dissolved and particular organic carbon (DOC, POC) fluxes show minor declines, NO 3 − , NH 4 + and particulate organic nitrogen (PON) fluxes remarkedly decrease, while total dissolved nitrogen (TDN) has a slight increase. Seasonally, DOC and PON peak in spring and summer, while inorganic nitrogen dominates in winter. Attribution analysis demonstrates that riverine organic carbon exports are primarily controlled by hydrology with the discharge exerting more than 50% control on DOC and POC fluxes, whereas nitrogen fluxes are co‐regulated by interactions among permafrost, vegetation and climate. Specifically, NH 4 + flux is mainly affected by vegetation (35.0%) and permafrost thaw (34.8%), NO 3 − flux has the most important influencing factors of climate (38.6%) and discharge (38.3%), and PON flux suffers from the effects of hydrology (30.8%) and vegetation (19.1%). These findings highlight the divergent drivers for riverine carbon and nitrogen exports, providing critical insights into the Arctic land‐ocean biogeochemical cycle.

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.

Hydrological impact assessments under climate change commonly rely on conventional modeling chains where climate projections are bias-corrected before being used in hydrological simulations. While this improves agreement with historical observations, it can introduce methodological uncertainties, reduce the diversity of climate ensembles, and smooth out extreme events. Asynchronous methods have been proposed as an alternative, allowing hydrological models to be calibrated directly with raw climate model outputs. However, fully asynchronous methods often fail to capture the timing of key hydrological processes, especially in snow-affected regions. This study introduces and evaluates a semi-asynchronous calibration on monthly observed discharge distributions to address these limitations. Using the physically based WaSiM model, we compare the semi-asynchronous, fully asynchronous, and conventional methods across ten snow-influenced catchments in southern Quebec, Canada, under historical and future climate conditions. The results show that while the fully asynchronous and semi-asynchronous methods perform well in preserving streamflow distributions and high-flow extremes, only the semi-asynchronous method succeeds in restoring the seasonal timing of key processes such as snowmelt and low flows. The semi-asynchronous method notably reduces intermodel variability in streamflow and snow water equivalent compared to the fully asynchronous approach. It also exhibits seasonal dynamics that closely align with observations and the conventional method, despite relying on uncorrected climate inputs. In contrast, the fully asynchronous method shows signs of desynchronization, with unrealistic snowmelt timing and elevated variability across projections. The conventional method, while more stable in the historical period, exhibits an increase in intermodel variability under future conditions, likely due to divergent magnitudes of projected change across climate models. Compared to the conventional method, which benefits from stable and consistent simulations but tends to dampen extremes through bias correction, the semi-asynchronous approach offers a compelling alternative. It strikes a different balance between realism, ensemble diversity, and the ability to represent extreme flood events, making it particularly valuable for future-oriented climate impact assessments.…

Precipitation is widely perceived to drive river flow (discharge), yet many rivers have experienced declining flow despite increasing precipitation, or vice versa. Here we quantify the prevalence of such diverging trends and identify their drivers. Using the Caravan data set and Theil–Sen regression, we analyzed long‐term trends in precipitation and river discharge at 10,179 sites worldwide (1951–2020). River discharge declined at 48% of sites, while precipitation declined at 52%. Diverging trends occurred at 27% of sites, 40% of which showed declining discharge despite increasing precipitation; their magnitude increased with aridity. A random forest classification model identified the frequency of low (or high) precipitation days and runoff ratio as the most influential drivers of strong divergence, highlighting the importance of precipitation extremes beyond the extensively discussed role of evapotranspiration. These findings reveal widespread decoupling of precipitation and river flow, highlight heightened water risks in drylands, and underscore the need for sustained river discharge monitoring, as precipitation trends alone are insufficient to predict changes in water availability.

Mesoscale convective systems (MCSs) are powerful contributors to extreme precipitation, often resulting in flash floods. However, the extent to which these systems are influenced by natural modes of climate variability is still not fully understood. In this study, we found that the occurrence of extreme precipitation from MCSs nearly doubles during the intense positive phase of the Baroclinic Annular Mode (BAM). The positive phase of BAM slows down the zonal translation speed of extratropical cyclone (ETC) trajectories, creating a favorable dynamical environment for extreme MCS precipitation. Furthermore, we discovered that when ETCs and MCSs coincide, they account for more than 90% of extreme precipitation during springtime in the U.S. These multiscale interactions between the planetary‐scale BAM, synoptic‐scale extratropical cyclones, and MCSs manifest as significant regional impacts on extreme precipitation.

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.

Siberian wildfires, a major disturbance to ecosystems and carbon cycles, have intensified in recent decades, yet long‐term changes in fire seasonality remain unclear. Here, using a refined fire‐season framework, we identify a pronounced shift in fire season characteristics over eastern Siberia during 2002–2023, marked by substantial lengthening of the fire season and increasingly clustered occurrence of large fires. The fire season extends primarily through delayed termination of 1.48 days/yr into late autumn, associated with the growing persistence of extremely large September fires and more wildfire ignitions in October. We further find that enhanced fire‐weather conditions are the main driver, arising from accumulated drought associated with precipitation deficits in the preceding August–September period. These deficits coincide with persistent high‐pressure anomalies linked to declining Arctic sea ice and subpolar North Atlantic cooling, underscoring the importance of understanding mechanisms and improving early warning of antecedent hydroclimate in shaping high‐latitude fire‐season dynamics.

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.

Accurate simulation of snowmelt runoff (SMR) is critical for water resource management. However, despite the abundance of global hydrological models, little is known about their SMR performance. This study presents a comprehensive evaluation of SMR across 15 state-of-the-art large-scale models and runoff products by focusing on their biases in first-order indices, i.e., the total volume ( Q sum ), peak flow ( Q max ), and centroid timing (CTQ) of runoff in the snowmelt period. Then by introducing 1455 snow-dominated basins with diverse topography and vegetation complexities, we further proposed a novel model robustness metric to test how different models perform under increasing basin complexity, thereby allowing for a quantification on how they adapt to complex environmental conditions. Our results reveal that (1) most models exhibit underestimated Q sum and Q max and predict CTQ too early. These biases are particularly pronounced in regions such as the western United States, northern Europe, and northeastern China. (2) Model biases systematically increase with basin complexity, with CTQ exhibiting strong sensitivity to mean elevation and topographic variability, while Q sum and Q max being shaped more by mean elevation and the diversity of vegetation types in the basin. (3) The robustness assessment further shows that observation-constrained runoff products exhibit the most outstanding performance (i.e., low biases and strong adaptability to stern conditions), followed by the hydrological and land surface models. Notably, while global hydrological models generally exhibit stronger robustness in simulating Q sum and Q max , land surface models show a clear advantage in simulating CTQ, highlighting their structural strength in capturing melt timing rather than runoff magnitude. This study provides a large-sample benchmark for SMR evaluation and complements existing model assessment approaches by examining model performance across basin complexity gradients, offering useful insights for future model development and uncertainty reduction.

Compound drought and heat extremes (CDHEs) under global warming pose significant threats to terrestrial ecosystems. However, the concurrent impacts of heat events and diverse drought types, as well as 3D (longitude, latitude, and time) vegetation recovery patterns, remain poorly understood. Here, we defined compound integrated drought-heat extremes through coupling heat extremes with diverse droughts and assessed basin-scale 3D vegetation recovery under varying hazards. The severity of CDHEs increased globally during 1982-2019, with the affected area expanding by 10 million km². Drought was a primary driver of vegetation loss, leading to longer vegetation recovery periods than either CDHEs or heat events, with approximately 44% of recovery events lasting longer than 10 months. In equatorial and high-latitude basins, CDHEs drove prolonged recovery events, though patterns varied among vegetation types. Spatially, vegetation loss generally kept pace with droughts, with extensive loss dominating low-latitude basins. This was less pronounced for CDHEs, which exhibited limited migration distances and could promote vegetation growth in some regions, including Europe and Central Asia. SHAP analysis revealed that temperature was a key factor shaping vegetation responses to CDHEs. Our study advances understanding of multivariate CDHEs, drought and heat impacts on vegetation responses, informing ecosystem management under climate change.

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…

Streamflow prediction is essential for water resources management and flood mitigation but remains challenging in large basins due to spatial heterogeneity. Machine learning (ML) models have been widely applied to streamflow prediction, typically relying on explicit routing modules, whereas the potential of pure ML models remains underexplored. This study presents a purely ML‐based model (Convolutional Entity‐Aware Long Short‐Term Memory) for fully distributed streamflow prediction. Spatial cross‐validation across 50 gauges in the Amazon Basin yields a median Nash‐Sutcliffe Efficiency of 0.73 and an R 2 of 0.84, comparable to or exceeding benchmark models with routing modules. The model reduces annual peak flow bias (APFB = 0.12) and accurately reproduced spatial autocorrelation patterns (Pearson's r > 0.6, Moran's I > 0.8, and Geary's C < 0.2), indicating spatially consistent grid‐scale discharge predictions. These findings demonstrate the potential of a pure data‐driven ML model for fully distributed streamflow prediction in ungauged basins while ensuring spatial consistency.

As part of NASA’s Global Precipitation Measurement (GPM) ground validation program, a multi-year winter field study in Storrs, Connecticut brought a unique opportunity to study the long-lasting problem of precipitation phase determination within retrieval algorithms. This study evaluates the phase algorithms of NASA’s Integrated Multi-satellitE Retrievals for GPM (IMERG) dataset, the flagship GPM global precipitation product, and NOAA’s Multi-Radar Multi-Sensor (MRMS) dataset, which is frequently used to validate IMERG precipitation rates over the continental United States. The study selected ten long-lasting phase transition events where rain and snow were recorded for more than one hour with the presence of mixed precipitation and focused on two diverse events. Using the PARSIVEL disdrometer phase as a reference, phase transition occurs at around wet-bulb temperature of 0.5˚C, which agrees with prior studies. IMERG and MRMS phase algorithms performed better when using High Resolution Rapid Refresh (HRRR) and European Center for Medium-Range Weather Forecast Reanalysis (ERA5) temperatures than Modern-Era Retrospective Analysis for Research and Applications reanalysis (MERRA2) temperatures. Overall, IMERG Version 07 performed better than its predecessor Version 06. This study also evaluated HRRR deterministic and probabilistic phase algorithms, which performed better than HRRR temperature-based MRMS and IMERG algorithms, respectively.

Compound heatwave-extreme precipitation (CHWEP) events, characterized by the rapid succession of heatwaves and extreme precipitation, pose escalating compound and cascading hazards. Yet, global-scale comparisons of their spatiotemporal evolution relative to single extremes remain limited. This study systematically examines the changes in CHWEP and corresponding single extremes from 1980 to 2100 using climate observations and projections under SSP (Shared Socioeconomic Pathway) 2-4.5 and SSP5-8.5 scenarios. We find that CHWEP exhibit higher frequency, stronger precipitation, and longer heatwave duration in mid-to-high latitudes of the Northern Hemisphere, while tropical CHWEP feature more intense heatwaves than single heatwave events. These spatial contrasts persist in future projections. Under both scenarios, CHWEP and single extreme metrics intensify globally by 2056–2100, with post-heatwave precipitation exceeding that of single precipitation extremes, particularly under SSP5-8.5, highlighting sensitivity to greenhouse forcing. Critically, the co-occurrence is non-random, indicating an emerging physical linkage. In the tropics, the likelihood of extreme precipitation following heatwaves increases markedly. Our findings demonstrate that CHWEPs are evolving into a distinct, intensifying hazard class, necessitating their integration into climate resilience, early warning, and adaptation frameworks.

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.

Large‐sample deep learning runoff models rely primarily on aggregated catchment attributes, which may obscure informative spatial patterns. Using 531 watersheds in the contiguous United States, we develop a hierarchical deep learning framework that represents static watershed properties from watershed‐averaged raster aggregation to convolutional neural network encodings at fixed‐grid and adaptive multi‐patch scales, thereby providing a controlled test of whether increasingly explicit spatial encoding improves temporal generalization and spatial transferability to ungauged watersheds. Watershed‐averaged rasters perform comparably to curated attribute tables and show a slight advantage under the prediction‐in‐ungauged‐basins benchmark. Incorporating intermediate spatial structure via fixed‐grid encoding provides the clearest temporal‐split benefit relative to watershed‐averaged aggregation, with 26.2% of watersheds showing uncertainty‐supported Nash–Sutcliffe efficiency improvement and 6.0% showing uncertainty‐supported degradation, although this advantage weakens under spatial holdout. Under spatial holdout, diagnostic analyses indicate that fixed‐grid encoding tends to outperform watershed‐averaged raster aggregation in higher‐elevation, steeper watersheds. The adaptive multi‐patch level further reveals that finer spatial representation does not necessarily provide consistent gains, highlighting a trade‐off between spatial detail and computational demand. Overall, this framework provides practical guidance for selecting raster‐based spatial representations in large‐sample deep learning runoff prediction.

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.

Understanding how groundwater–surface water interactions shape streamflow variability is critical for diagnosing low flow behavior and prediction bias in continental scale hydrologic models. We present a process informed framework that links observed baseflow (BF) dynamics, watershed attributes, and National Water Model (NWM) performance across the contiguous United States. Using daily observed streamflow from 797 reference quality streamgages, we developed monthly baseflow index (BFI) signatures using a streamgage specific, calibrated digital filter. Hierarchical clustering of these signatures identified seven distinct BFI regimes capturing regional and seasonal variability. We evaluated NWM v3.0 retrospective streamflow performance within each regime using multiple hydrograph and flow duration curve‐based metrics. Model skill varied systematically across regimes: mixed flow systems were simulated most accurately, while predominantly BF dominated and quickflow dominated regimes exhibited substantially poorer performance. Across nearly all regimes, the NWM underestimated observed BFI magnitude and frequently failed to reproduce seasonal BF patterns, indicating systematic biases in simulated low flow contributions. To relate these regimes to potential process controls, we trained a Random Forest classifier using static watershed attributes and applied Shapley Additive Explanations to identify features most strongly associated with each regime. Results highlight regionally varying influences, including the dominant role of snow fraction and seasonal runoff timing in snow dominated basins and the importance of evapotranspiration and aridity in quickflow dominated systems. Collectively, these findings demonstrate how hydrologic signatures combined with interpretable machine learning can diagnose regime specific model biases and generate process‐based hypotheses about limitations in large scale hydrologic prediction systems.

Hydrologic models are often calibrated using streamflow alone, but increasing availability of in situ and satellite-based observations provide numerous opportunities to constrain model outputs and improve process representation. However, as new observation data emerges, it is often unclear whether calibration with additional data would inform or misinform streamflow prediction. Here, we carry out a multi-observational sensitivity and uncertainty analysis using the U.S. Geological Survey's National Hydrologic Model (NHM) in four headwater catchments in the Upper Colorado River Basin. We use seven different observational data products that pertain to discharge, snow water equivalent, snow-covered area, soil moisture, and evapotranspiration. Informative model parameters are identified using the Morris screening method across all data sets, followed by parameter estimation and streamflow performance assessment using a Latin Hypercube Sample Monte-Carlo filtering approach. Results show that an increased number of informative parameters are determined through the screening process with the use of observation data representing terms beyond streamflow, and that forcing corrections and rain-snow partitioning parameters are particularly impactful to the model fit to observations. Multi-objective Monte Carlo filtering reduces the number of behavioral parameter sets, and estimated parameter values can depend strongly on the observation data criteria. Evapotranspiration is informative for streamflow prediction across all catchments included in this study, but snow and soil moisture datasets are only informative in some. These results provide new insight into the variable value of alternative observation data for streamflow prediction and highlight challenges related to model/observation scale mismatches, compensating errors, and misinformative data.

While excess rainfall is associated with mosquito-borne disease because it supports mosquito breeding, drought may also counterintuitively increase disease transmission by altering mosquito and host behavior. This phenomenon is important to understand because climate change is projected to increase both extreme rainfall and drought. In this study, we investigated the extent to which seasonally driven mosquito and primate behavior drove the first yellow fever virus (YFV) epidemic in an urban area in Brazil in nearly a century, coinciding with an equally rare drought, and assessed the role of interventions in ending the outbreak. We hypothesized that drought-like conditions played a key role in the outbreak dynamics by driving the forest mosquitoes and nonhuman primates toward the city in search of water and that the mosquitoes were biting more frequently to avoid desiccation. A dynamical YFV model supports these hypotheses, showing that both behavioral changes were needed to explain the outbreak timing and incidence. Furthermore, a combination of vector control, conservation measures, and vaccination contributed to ending the outbreak, with the strongest effects from vaccination. Together, these results suggest that drought, likely to become more frequent in this region in the coming decades, can considerably influence mosquito-borne disease transmission and that sustained control will require multiple interventions.

Flowing stream networks expand and contract in response to dynamic groundwater levels. Field studies generally associate greater flowing network length ( L ) with higher streamflow ( Q ), but this neglects potential hysteresis caused by nonequilibrium groundwater flow after rain and snowmelt. Using a new version of the Distributed Hydrology Soil Vegetation Model (DHSVM), we predict that groundwater hysteresis may decouple L from Q across large (> 100 %) variations in Q . Groundwater hysteresis contributes to the spatial reconfiguration of active flowpaths and changes to hillslope-riparian hydrological connectivity, which can manifest as a network length scaling anomaly relative to the best-fit power law. In a 27 km 2 snowy volcanic watershed, seasonal anomalies in measured stream ionic concentration indicate an outsized contribution from longer subsurface flowpaths during recession, supporting our L - Q hysteresis hypothesis and refining our model calibration. The model can reproduce observed stream network elasticity (from field surveys), and the predicted network length anomaly mirrors seasonal anomalies in measured stream ionic concentration (r=-0.92), suggesting that the model can capture seasonal changes in the spatial configuration of groundwater convergence and streamflow generation. A warmer climate is expected to cause a partial transition from snow to rain resulting in flashier streamflow, but our simulations predict that seasonal groundwater hysteresis would dampen storm-scale stream network elasticity, thereby significantly increasing L - Q hysteresis on daily to monthly timescales ( p < 0.01). Conceptual models of stream networks should consider the potential effects of groundwater hysteresis, especially in a changing environment. More broadly, our investigation highlights how spatially distributed process-based hydrological modeling can reveal emergent hydrological behaviors that are not necessarily apparent from sparse field data.

Differentiable hydrologic models that use machine learning to infer parameters for process-based models show promise for both prediction and inference. However, these models are often developed with time-varying parameters, despite evidence that such flexibility can undermine physical consistency and yield only marginal predictive improvements over simpler static approaches. In this study, we revisit the comparison between static and dynamic differentiable models across 531 CAMELS-US basins, evaluating key architectural choices: (1) neural network type (multi-layer perceptron (MLP) vs. long short-term memory network (LSTM)); (2) process model configuration (single- versus ensemble-parameter estimation); and (3) comprehensive versus alternative input feature sets. Using the Hydrologiska Byråns Vattenbalansavdelning (HBV) conceptual model, we find that although ensemble parameterizations improve performance relative to single-parameter configurations, they also alter the conclusions about the relative value of network architecture: LSTMs outperform MLPs in single-parameter configurations, but static, MLP-based ensembles achieve performance comparable to dynamic, LSTM-based ensembles despite their simpler structure. Additionally, we find that LSTM-estimated parameters rarely exhibit meaningful temporal variability despite their time-varying inputs, and when they do, this temporal variability may reflect hydrologic model equifinality rather than process dynamics. We further show that models using only latitude and longitude as static inputs achieve spatial generalization comparable to models using comprehensive feature sets describing climate, topography, geology, soils, and land cover. Similarly, temporal generalization is retained even when comprehensive features are replaced with physically meaningless values. These results raise the possibility that static inputs may function less as direct representations of physical basin processes and more as spatial proxies for generalization in space or as site identifiers when generalizing in time. Overall, our results support reduced complexity in differentiable hydrologic modeling to provide greater transparency while retaining predictive performance.

Snowmelt runoff onset timing represents a critical hydrological parameter, particularly in mountainous regions where seasonal snow serves as a natural reservoir for downstream water resources. Despite this importance, high-resolution observations of snowmelt runoff onset across complex terrain are limited, due to challenges from sparse in situ monitoring networks, intermittent optical remote sensing data, and coarse passive microwave remote sensing data. To address this gap, we prepared a global snowmelt runoff onset timing dataset (https://doi.org/10.5281/zenodo.16953614, Gagliano et al., 2026) for the 10-year period spanning 2015–2024, with 80 m spatial resolution and 9.5 d average temporal resolution. We created this dataset by identifying backscatter minima empirically associated with runoff onset in a time series of Sentinel-1 C-band SAR images, with detection constrained by a custom MODIS-derived snow phenology dataset. We evaluated our dataset using in situ snow pillow estimates of runoff onset from 1116 automated weather stations across the Western United States, British Columbia, Norway, and Nepal, finding a median timing difference of −2.0 d and a median absolute deviation of 10.0 d. The local agreement between our runoff onset estimates and snow pillow runoff onset estimates varies with site-specific variables like forest cover fraction, SWE, and dataset temporal resolution. We characterized these dependencies to provide empirically-derived thresholds for quality filtering as well as guidance for interpretation and use of our products. The dataset includes global annual runoff onset products for each water year, annual local temporal resolution products for each water year, and 10-year composites of median runoff onset, median absolute deviation, and local temporal resolution. This unique combination of high spatial resolution, global coverage, and decade-long temporal coverage provides unprecedented detail for the study of snowmelt runoff onset across snow-covered regions. Our snowmelt runoff onset dataset enables an improved understanding of mountain hydrological processes and informs water resource management in snow-dominated watersheds.

Soil moisture dynamics in vertical profiles result from multiple interacting processes, generating complex temporal behavior and intricate connectivity patterns. However, accurately simulating multi‐depth soil moisture while diagnosing time‐varying vertical connectivity remains challenging. This study develops a Long Short‐Term Memory model with time‐varying spatial attention (LSTM + ATT) to predict multi‐depth soil moisture and quantify vertical hydrological connectivity via attention weights. Evaluated across 19 USCRN (U.S. Climate Reference Network) stations, LSTM + ATT demonstrated superior robustness compared to independent (Ind‐LSTM) and joint (Joint‐LSTM) baselines. While performance was nearly identical at shallow depths (5–20 cm), LSTM + ATT excelled in deeper layers, achieving median NSE values of 0.91 (50 cm) and 0.93 (100 cm), outperforming Ind‐LSTM (0.86–0.89) and Joint‐LSTM (0.90–0.91) while eliminating catastrophic failures (NSE < 0). Learned attention weights revealed physically consistent connectivity patterns, weakening as vertical distance increased. Weight dynamics were strongly linked to soil moisture: influence from upper layers correlated positively ( r : 0.20 to 0.50) with moisture content, while influence from lower layers (50 and 100 cm) correlated negatively ( r : −0.76 to −0.18). During preferential flow, weights reorganized to enhance connectivity with upper layers (increasing 30%–100%) at the expense of self‐influence (decreasing 10%–30%), highlighting distinct hydraulic signatures of preferential pathways. Unlike the implicit relationships in Joint‐LSTM, this dynamic attention framework provides a novel quantitative diagnostic for time‐varying connectivity, effectively bridging the gap between predictive accuracy and process interpretability in subsurface hydrology.

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.

Hot droughts are intensifying under anthropogenic warming, yet whether extreme heat systematically alters the triggering of abrupt drought‐to‐pluvial transitions and their downstream impacts remains poorly understood. Here, we identify global drought‐to‐pluvial abrupt transition (DPAT) events and isolate the influence of antecedent thermal conditions by comparing hot‐drought‐triggered DPATs (DH‐DPATs) with transitions preceded by droughts without concurrent heat. We find that hot drought related transitions display broader hotspots and substantially higher occurrence, with frequency increasing from 66.7% to 316.7% across the interquartile range and a higher conditional transition probability (global mean: 44.89% vs. 38.95%). They also transition faster, sustain longer post drought heavy rainfall, and produce stronger rainfall intensity. Mechanistically, although both event types are associated with enhanced moisture convergence, antecedent heat imposes an additional thermodynamic preconditioning characterized by deeper boundary layers and higher convective instability, making comparable moisture recovery more likely to trigger deep convection. These heat‐mediated transitions amplify ecosystem carbon losses, intensifying reductions in gross primary productivity and solar‐induced chlorophyll fluorescence by about 43.7% and 26.8%, respectively, with the strongest relative impacts in croplands. They also contribute to increasing population and managed‐land exposure. Under future warming, DPATs are projected to shift further toward hot‐drought‐dominated pathways, with increases in both the contribution and transition likelihood of DH‐DPATs. These results highlight a thermodynamic pathway through which hot droughts increase the likelihood and impacts of abrupt drought‐to‐pluvial transitions worldwide.

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 use of deep learning models in hydrology is becoming an ever more prevalent application in operational flood forecasting. Such operational systems face performance degradation when transitioning from high quality reanalysis to meteorological forecast data with lower accuracy. This study investigates training strategies and Long Short-Term Memory network architectures to mitigate meteorological forecast-induced bias in maximum daily discharge predictions using the Extended LamaH- CE dataset and a subset of 451 basins. We systematically evaluated cross-domain generalization, transfer learning approaches, Encoder–Decoder LSTMs, Sequential Forecast LSTMs, and the role of input embeddings and integrating past discharge observations. The results show that domain shifts between reanalysis and forecast data lead to substantial skill loss, with median Nash–Sutcliffe Efficiency decreasing from 0.58 to 0.33. Among the tested strategies, the Sequential Forecast LSTM demonstrated the most stable improvements, achieving a median NSE of 0.63. Integrating recent discharge observations further enhanced performance, raising median NSE to 0.71 and surpassing even the reanalysis-driven baseline. In contrast, integrating archived forecasts or using more complex input embeddings did not yield consistent benefits and in some cases degraded model stability. Basin-level analysis reveals that forecast skill improvements compared to our baseline are not uniformly distributed across catchment types: the largest gains are concentrated in arid and precipitation-limited catchments, while alpine and snow-dominated catchments, despite experiencing the largest meteorological domain shift, show smaller improvements. This is likely because the LSTM cell state retains strong seasonal signals and thereby compensates for forecast input bias through its long-term memory mechanism, particularly in alpine and snow-dominated catchments where strong seasonal cycles dominate the hydrological response. These findings highlight the value of training strategies that allow models to directly learn bias correction during forecast transitions, emphasize the operational potential of combining sequential processing with near real-time discharge observations and identify physiographic catchment characteristics as key modulators of forecast skill improvement across diverse hydroclimatic settings.

Predicting changes in urban pluvial flood hazards under climate warming is crucial for risk mitigation and disaster management. A key challenge in simulating future urban flood hazards is the scarcity of high-resolution rainfall projections, particularly at the sub-daily and kilometer scales required for hydrodynamic modeling. We present a cascading process-informed framework that requires minimal observed climatic data, enabling scenario analysis even in data-scarce cities. This framework consists of a distribution‐based spatial quantile mapping (DSQM) method to morph observed rainfall fields conditioned on temperature changes, a stochastic storm transposition (SST) method to account for the spatial variability of urban rainfall, and a rain‐on‐grid hydrodynamic model (AUTOSHED) for efficient simulation of urban pluvial floods at high spatio-temporal resolution. The framework allows the generation of stochastic rainfall fields under different rainfall return levels and regional warming levels. It supports the quantification of changes in future urban flood statistics with detailed hazard maps of inundation depth, duration, and flow velocity. We select the metropolitan area of Beijing (300 km 2 ) as a case study area and utilize gridded hourly and 1 km rainfall data to simulate flood evolution at 5 min and 5 m resolution under regional warming levels of 1, 3, and 5 °C relative to the period 1998–2019. Our results show that with rising temperatures, regional storms tend to become more intense but smaller in spatial extent, which may in turn drive increased local flood depth, accelerated flow velocity, and deeper inundation, collectively elevating pluvial flood risk. Specifically, mean rainfall intensity increases by 6 %, 11 %, and 20 % (respectively with the warming levels), peak flood depth exhibits a nonlinear increase of 4 %, 7 %, and 8 %, due to the complex interactions of reduced storm area, increased storm intensities, and rainfall spatial variability. The proposed DSQM-SST-AUTOSHED framework offers a data-driven, physically grounded, and efficient approach to assess urban flood risk under regional warming. It only requires observed rainfall fields and temperature datasets, which are readily accessible from public sources, making the approach easily extendable to other cities.

Deep learning hydrological models rely on static catchment attributes to characterize basin heterogeneity, but these traditional descriptors suffer from regional inconsistency and limited temporal representativeness. This study evaluates geospatial foundation model embeddings (from AlphaEarth and StefaLand) as transferable catchment descriptors for rainfall‐runoff modeling. Temporal validation across 531 CAMELS basins showed that deep learning models trained on AlphaEarth embeddings alone achieved comparable performance to those trained on traditional attributes (median Nash‐Sutcliffe Efficiency (NSE) 0.779 vs. 0.781). In a global prediction in ungauged basins (PUB) scenario with 3,434 basins, combining embeddings with traditional attributes improved median NSE from 0.683 to 0.720. A dual‐path gated model revealed structured spatiotemporal reliance patterns: models preferentially utilized embeddings during snowmelt transitions and in humid basins, with limited reliance in arid catchments. Independent validation with StefaLand embeddings from a land‐surface foundation model yielded consistent temporal validation results, supporting the broader use of learned embeddings as transferable catchment descriptors.

This study investigates the application of Physics‐Informed Neural Operators (PINOs) for solving the two‐dimensional shallow water equations (2D SWE) in the context of flood modeling. Unlike Physics‐Informed Neural Networks (PINNs), which require retraining for each new initial or boundary condition (BC), PINOs learn the solution operator itself, enabling fast inference across a range of conditions without retraining. PINNs have moreover been shown to struggle with complex bed topography due to the difficulty of satisfying the well‐balanced property (Tian et al., 2025, https://doi.org/10.1029/2025wr040052 ; Dazzi, 2024, https://doi.org/10.1029/2023wr036589 ), a limitation that PINOs can partially mitigate by incorporating data alongside the physics loss. We evaluate the method on experiments of increasing physical complexity: a radial dam break, constant BCs with and without friction, time‐dependent BCs, and a real‐world test case. The results show that PINOs capture key flood dynamics, particularly water depth, while reducing inference time by up to two orders of magnitude compared to numerical solvers. Relative test errors for water depth ranged from 0.3% for the radial dam break to 10.9% for cases with bottom topography, 7.3% with friction, and 9.0% under time‐dependent BCs. For the real‐world case, an additional data loss term yielded a water depth error of 25.8%. Larger errors were consistently observed for velocity components. The findings establish PINOs as a promising complement to traditional numerical solvers, offering a balance between computational efficiency and solution accuracy. Future work will focus on improving accuracy and extending the framework toward real‐world flood forecasting applications.

Satellite-based precipitation products (SPPs) face persistent challenges over mountains due to complex orographic processes. This study uses high-resolution radar-gauge precipitation data and environmental reanalysis parameters to evaluate IMERG V07B Final Run (IM-F) performance across six mountain ranges (U.S. West Coast, Rockies, East Coast; French Alps, Central Massif, Pyrenees). We condition the analysis on environmental clusters defined by orographic upward motion, moisture flux convergence, and stability, to address two questions: (1) To what extent do environmental parameters explain the distribution and extremes of radar-observed precipitation in each mountain range? (2) How much of the variation in IM-F retrieval error across these mountain ranges is explained by environmental parameters? Results show that environmental conditions strongly control precipitation distributions – for instance, convective windward regimes yield the highest median and 99th-percentile rain rates, whereas stable, stratiform conditions produce much lower rainfall totals. Conditional validation reveals that a substantial portion of IM-F error is attributable to these environmental factors: precipitation detection skill and bias vary systematically with cluster conditions. Cluster 1 (unstable upslope flow) produces frequent heavy precipitation that IM-F underestimates when cloud ice is minimal, whereas Cluster 4 (stable, downslope conditions) is associated with light stratiform drizzle that IM-F often misses. These findings expose how orographic lift, moisture convergence, and stability interact to shape both precipitation extremes and satellite retrieval biases. This is the first study to systematically dissect IM-F error by environmental regimes across multiple global mountain ranges, providing novel insight into physical drivers of SPP errors. Building on these insights, we propose adaptive algorithm enhancements – for example, incorporating orographic uplift and moisture convergence indicators – to improve mountainous precipitation estimation. Our results underscore a new process-informed pathway for refining satellite QPE in complex terrain.

Rainfall variability in West Africa, driven by the West African Monsoon, poses significant challenges to agricultural productivity and livelihoods. In this context, understanding root-zone soil moisture (RZSM) dynamics is crucial since it serves as the primary water source for crops. While surface soil moisture (SSM) has been widely studied, research on RZSM remains limited. This study investigates RZSM dynamics across West Africa from 2003 to 2019 using multiple satellite-derived and model-based datasets, including ESA CCI v0.81, GLWS2.0, WaterGAP, CLM5.0, and in-situ observations. Results indicate that ESA CCI exhibits the strongest temporal and spatial alignment with ground measurements, whereas CLM5.0 and GLWS2.0 effectively capture latitudinal soil moisture (SM) gradients associated with climatic zones. A novel application of an analytical solution to Richards' equation was employed to translate surface moisture signals to deeper soil layers, demonstrating GLWS2.0's superior ability to reproduce seasonal patterns at various depths, notably in Benin and Niger. Despite challenges posed by sparse in-situ data and vegetation-induced signal attenuation, the study highlights the significant benefits of GRACE/-FO data assimilation in enhancing model accuracy. The proposed depth-projection methodology improves the vertical representation of SM, offering new insights into the dynamics of surface and subsurface water storage. These findings have important implications for agricultural forecasting, sustainable water resource management, and climate adaptation strategies in regions where accurate SM data are essential for resilience planning.

Intensifying soil drought poses a significant threat to maize yields in China. However, the compounding effects of soil drought and salinity remain poorly understood. Here, we analyze maize yield trends in China from 1982 to 2016 and show that an increase in the total number of drought days is associated with significant yield declines. We find that the co‐occurrence of drought and soil salinity (CODS) amplifies maize yield‐loss risk, with the probability of yield loss exceeding 20% and remaining higher than under drought alone. Compared with drought‐only conditions, CODS increases the additional yield‐loss risk by approximately five percentage points. These findings are further supported by analyses in the North China Plain and the Songnen Plain, where irrigated areas exhibit more negative yield anomalies than rain‐fed areas under comparable drought conditions, suggesting that irrigation practices may be associated with increased soil salinity and thereby reduce the effectiveness of drought mitigation. This study underscores the urgent need for integrated soil and water management strategies that simultaneously address drought resilience and salinity control to safeguard China's maize production.

Socio‐hydrology models explore interactions between human and hydrological systems, enabling the analysis of dynamic feedbacks between society and flooding. Simplified conceptual models of socio‐hydrology often treat the social or human domain as single homogenous entities, failing to account for disparities among the communities that make up a city, and thus underestimating the true impacts of flooding on vulnerable populations. We propose a disaggregated socio‐hydrology model that incorporates a Resilience domain to capture differences among communities within a single city. Using empirical socioeconomic data from United States metropolitan areas, we inform the model's representation of inequality and its influence on community‐level adaptive capacities. With the further addition of a disaster relief component, our approach captures community‐specific recovery trajectories, linking economic inequalities to urban flood resilience. Results show that a community's adaptive capacity is shaped by a city's overall growth rate and the degree of economic disparities between communities. We find that improving a city's economic growth rate can unintentionally widen disparities in community adaptive capacity, consistent with the Kuznets curve hypothesis. However, simulations show that equitable resource allocation reduces recovery gaps without hindering overall city growth. Our results emphasize the necessity of incorporating socioeconomic heterogeneity into socio‐hydrological models to inform equitable disaster response policies. By grounding resilience in empirical economic data, this study advances socio‐hydrology modeling to provide more actionable insights for disaster response and community‐based risk reduction strategies.

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…

Traditional static designs of urban stormwater storage systems are increasingly inadequate for addressing extreme rainfalls. Recent advances highlight optimizing both spatial layout and real‐time control (RTC) of storage facilities, yet most of these decisions are made sequentially, overlooking how layout constraints can affect RTC efficiency and attain additional benefits from spatial‐temporal coordination. This study developed a synergistic optimization framework for coordinating stormwater storage system designs with their operational intelligence. This framework integrates a predictive target flow allocation‐based global control method with the optimization of storage capacity distribution, producing combined solutions of layout designs and control settings. Our methodology was applied to an urban catchment in Shenzhen, China, to evaluate the flood control performance under design and monitored heavy storms. Results show that the global RTC method substantially reduces flood peaks and runoff volumes under critical heavy storms. Compared with fixed‐layout or sequential optimization scenarios, integrating global RTC with synergistic optimization achieves the highest performance improvement over static control, with peak flow reduction rates increased by 11%∼32%. Moreover, the synergistic optimization identifies a subset of strategically located, high‐capacity control nodes contributing more than 98% of the system‐wide benefits, thus avoiding unnecessary RTC retrofitting elsewhere. These findings demonstrate the practical value of the synergistic optimization framework in guiding adaptive and efficient urban stormwater management.

An important challenge with Machine Learning (ML) is its transferability; that is, whether an ML model trained on one set of data can be applied to a second set of data without requiring full retraining of the model. Transfer Learning (TL) addresses this challenge by transferring knowledge learned in the source domain (the data it was trained on) to the target domain (a second set of data that is statistically different but related, which the model was not trained on). This study investigates the use of TL for street‐scale nuisance flood forecasting by exploring whether an ML model trained for one set of streets can effectively forecast flooding for another set of streets in the same city. TL is explored using a Long Short‐Term Memory (LSTM) model trained on data for the flood‐prone streets of Norfolk City, Virginia. The results show that full‐weight retraining proved most effective and minimal retraining of only the output layer was insufficient. The advantage of TL was most pronounced when target data was limited, meaning data collected at the new water depth sensor location included generally less than 18 flood events. As the target data increased beyond 18 flood events, the benefit of TL diminished relative to training an ML model directly on the local flood events. These findings can assist cities as they implement street‐scale flood sensing systems to create accurate forecasts for new sensing locations that do not yet have sufficient data records to train a local ML model.

Abrupt snowmelt, triggered by rain-on-snow events or “snow-eater heat waves,” can cause flooding, initiate or accelerate snow drought, and affect water availability. However, the characteristics (e.g., area, duration, and frequency), impacts, and trends of snow-eater heat waves have received little attention. To address this gap, we developed a method to identify snow-eater heat waves and estimate their melt potential using 20th Century Reanalysis version 3 air temperature data, the TempestExtremes algorithm, and an operational snowmelt model (SNOW-17) across 1850–2015. Melt season snow-eater heat waves typically last 3 to 5 days, with three to five events, doubling snowmelt rates. Seven of 11 spring superfloods are shown to coincide with snow-eater heat waves. Since the 1850s, snow-eater heat waves have increased in area and frequency, decreased in duration, and shifted earlier in the melt season. Incorporating snow-eater heat-wave impacts into SNOW-17 enhances extreme melt estimates, improving water management support tools.

Tropical regions may experience periodic extreme precipitation and suffer from associated periodic deluges in a warmer climate. Recent studies conducted small-domain (around 100 kilometers by 100 kilometers) atmospheric model simulations and found that precipitation transitions from a steady state to a periodic oscillation state in a hothouse climate when the sea surface temperature reaches 320 to 325 kelvin. Here, we conduct global-scale atmospheric model simulations with varying complexity. We find that tropical precipitation in convective regions already transitions to a O (10-day) periodic oscillation state with a O ( 100 mm day − 1 ) amplitude at 305 to 310 kelvin, about 15 kelvin lower than previously reported and within reach in a century under a high–carbon emission scenario. Using a low-order analytical model, we attribute the onset of the periodic extreme precipitation to the intensification of convectively coupled waves that were not considered in the previous small-domain simulations. Our mechanism highlights the importance of the interaction between convection and atmospheric wave dynamics for climate change.

The Gravity Recovery and Climate Experiment (GRACE) and its Follow-On (GRACE-FO) missions provide observations of terrestrial water storage (TWS) dynamics from regional to global scales. However, their coarse spatio-temporal resolution limits the applicability in many hydrological studies. A joint collaboration between the National Aeronautics and Space Administration (NASA) and the European Space Agency (ESA), known as the Mass Change And Geosciences International Constellation (MAGIC), aims to launch a new paired mission, i.e., GRACE-C and NGGM (Next Generation Gravity Mission), to improve the monitoring of extreme events such as floods and droughts. The primary objective of this study is to examine the impact of the expected improvement in the spatial-temporal resolution and accuracy of NGGM and MAGIC on precipitation estimation by developing multiple synthetic experiments over Europe. The study employed the well-known SM2RAIN algorithm to estimate the precipitation accumulated between two consecutive TWS measurements. The total amount of water in the soil from the fifth generation ECMWF reanalysis for the land (ERA5L) is used as a proxy of TWS over the period 2003–2012, while ERA5L precipitation serves as a reference dataset. The results showed that SM2RAIN exhibited satisfactory performance at a daily temporal resolution, with a mean correlation coefficient of 0.86. Good agreement was obtained across most of Europe except in some areas of northern Italy, northeastern Europe (Estonia, Latvia) and some coastal regions. Synthetic experiments were developed by degrading the temporal resolution (5–10, 15, and 30 d) and by adding Gaussian errors to TWS data ranging from 1 to 40 mm. The results showed robust performance under moderate temporal degradation (5–10 d) and low measurement errors (< 5 mm), with correlations generally exceeding 0.75. However, performance deteriorates significantly when errors exceed 10–20 mm, with correlations dropping below 0.40 for an error of 42 mm. These findings identify critical design thresholds for NGGM/MAGIC missions, demonstrating that achieving a measurement accuracy better than 5–10 mm is crucial for reliable precipitation estimation across diverse European hydroclimatic conditions.

Extreme drought and heat are driving forest die‐off globally, yet regional‐scale mapping of subsurface water storage capacity, governing forest vulnerability, remains a major challenge. Here, we demonstrate that airborne gamma‐ray spectrometry (GRS) of potassium ( 40 K) decay provides a powerful proxy for subsurface constraints on water availability. In southwestern Australia, we found that spatial patterns of forest die‐off during an extreme 2023–2024 drought‐heat event were strongly predicted by surficial 40 K concentrations. While GRS measures the upper ∼30 cm of soil, plot‐scale electrical resistivity tomography suggests that elevated 40 K signals shallow granite or gneiss bedrock, to depths reaching several tens of meters due to leaching of mobile elements, remnant bedrock minerals, erosional thinning of regolith and biological nutrient recycling from K‐rich saprolite. Airborne GRS can map forest vulnerability across highly leached landscapes, which cover one‐third of Earth's ice‐free land surface. This enables targeted adaptive management of forests for future drought‐heat intensification.

Flood risk is correlated in space and time, challenging insurance systems that rely on diversification across assets. Financial instruments governing flood coverage are typically structured as 1–5‐year contracts, exposing portfolios to climate‐driven risk at interannual‐to‐decadal scales. Yet existing tools address climate risk either through seasonal forecasts extending only months or multidecadal projections misaligned with fiscal horizons, leaving a critical gap in actionable flood risk simulation. We introduce a multisite flood simulation framework combining attention‐based analog retrieval with stochastic generation of multivariate flood frequency, intensity, and duration sequences. Applied to over 100 sites in the Mississippi River Basin, the model produces spatiotemporally coherent flood portfolios conditioned on interannual climate variability. Explainable AI attribution paired with wavelet analysis links simulated clustering to large‐scale climate drivers, yielding physically interpretable flood clusters for portfolio‐scale loss simulation. The framework provides plausible, out‐of‐sample flood risk catalogs for interannual‐to‐decadal insurance risk assessment and financial planning.

Accurate, timely, and actionable flood forecasting has potential for reducing flood risk but remains challenging due to uncertainties in meteorological forecasts, poor hydrological observations, and increasing forecast errors with lead time. While Long Short‐Term Memory (LSTM) models have surpassed conventional hydrological forecasting models, they often struggle to capture the timing and magnitude of extreme floods due to long‐range dependencies and static feature importance. Here, we propose a sequential LSTM with Multi‐Head Attention (SeqLSTM‐MHA) to improve 1–3 day ahead flood forecasts, explicitly using historical hydrometeorological and upstream hydrological observations. SeqLSTM‐MHA attained an NSE of 0.85–0.81 for water level and 0.74–0.71 for streamflow corresponding to t +1 to t +3 day lead, outperforming benchmark models (traditional LSTMs, bidirectional LSTM, CatBoost, and multiple linear regression). The ablation analysis shows that notable improvements at longer lead times ( t +2 and t +3) arise from the interaction of sequential forecasting, hierarchical upstream representation, and attention‐based temporal weighting rather than merely increased model capacity. Our model demonstrates strong skill in forecasting extremes (NSE >0.6) and correctly captures more than 80% of peak streamflow during the extreme events exceeding the 95th percentile threshold. Time‐resolved attention analysis shows that recent rainfall primarily drives immediate responses, while upstream flow propagation becomes more important at longer lead times. Overall, our findings demonstrate the potential of attention‐based LSTM for operational flood forecasting, particularly for high‐impact events.

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.

Climate change is causing the magnitudes of extreme sub-daily precipitation events to increase. The ability to predict changes to these precipitation extremes is crucial for disaster preparedness. The TENAX model was proposed to predict return levels of sub-daily extreme precipitation under climate change based on the projected temperature shifts. It combines a Weibull distribution with an exponential temperature dependence in the scale parameter, accounting for the Clausius–Clapeyron relation, with an explicit representation of the temperatures during precipitation events. The Weibull distribution's shape parameter could also have a temperature dependence, which would mean that the tail heaviness changes with temperature. This implies that the rarest events may increase at faster rates. However, implementing this dependence increases the number of parameters to be estimated, affecting the model's accuracy. Here, we use hourly data from thousands of rain gauges in Germany, Japan, the UK, and the USA to assess the dependence of the Weibull shape parameter on temperature, exploring how it should be implemented in the TENAX model. We find that there is a significant dependence in many stations and that the magnitude and sign of the dependence have regional patterns. In the majority of stations, the sign is negative, implying that rarer events intensify with temperature at a higher rate. However, Monte Carlo simulations show that including this dependence without careful consideration may lead to overestimation of precipitation return levels and increase the model uncertainty. The dependence should therefore be introduced with caution, in the context of surrounding stations.

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.

Characterizing storms that generate heavy precipitation across different timescales is essential for parameterizing and assessing stochastic weather generators, evaluating climate models, enhancing risk management, and understanding climate change impacts. This study develops a detailed climatology of precipitation and temperature time series for storms producing heavy rainfall at sub‐hourly to daily scales across the Greater Alpine Region. Using a high‐resolution data set from ∼400 climate stations with 15–44 years of records for which a storm typology is available, we identify independent storm events and describe them through multiple characteristics, including peak intensity, lifetime, depth, temporal structure, internal variability, and thermal and geographical attributes. At each climate station, we focus on storms producing the largest 10% of the precipitation intensities at durations from 10 min to 24 hr. Results show that storm characteristics vary systematically with both duration and geography. At sub‐hourly scales, the highest intensities are typically generated by short‐lived storms with front‐loaded temporal profiles, high internal variability, and warmer conditions, especially in lowland regions, and exhibit evident reverse elevation‐intensity relationship. At daily scales, the highest intensities are associated with storms of greater depth, more symmetric temporal profiles, and smaller pre‐to‐within‐storm temperature differences, particularly in the inner Alps. At multi‐hour durations, distinct spatial patterns highlight the varying dominance of convective versus stratiform precipitation mechanisms. This climatology provides new insights into storm behavior in complex terrain and can inform precipitation modeling, climate impact assessments, and hydro‐geomorphological studies.

Although objective functions (OFs) are widely discussed in the literature, many modelling studies still default to a few common metrics, without much consideration of their relative strengths and weaknesses. This paper systematically investigates the impact of OF choice on the representation of various streamflow characteristics across 47 conceptual models and 10 hydro-climatically diverse catchments selected from the CARAVAN dataset. We use eight different OFs for calibration, including the Kling–Gupta efficiency (KGE), Nash–Sutcliffe efficiency (NSE), a variant of each that uses a streamflow transformation, and four more recently proposed metrics. We further account for parameter uncertainty by retaining the top 500 parameter sets for each combination of model, basin and OF. We evaluate the representation of 15 hydrological signatures that capture a relevant selection of streamflow characteristics to determine generalisable strengths and weaknesses of individual OFs across different models and hydroclimatic conditions. A random forest importance analysis showed that OF choice is often more influential than model structure choice in determining signature representation, with catchment characteristics remaining the dominant control. While certain signatures, particularly those related to flow variability, are relatively insensitive to OF choice, other signatures exhibit large performance shifts across different OFs. As other studies have already noted, no single OF performs best across all signatures. Our consistent multi-model and multi-catchment experiment now provides generalisable guidance on which OF best reproduces which aspect of streamflow behaviour. This helps to better select objective functions most appropriate for a specific modelling purpose and provides insights into which metric combinations can cover a broader range of flow characteristics.

Small streams are highly sensitive to variations in discharge, a sensitivity predicted to increase in future climate scenarios, impacting ecological health of streams and water management practices. Prolonged low-flow conditions alter groundwater-surface water (GW-SW) exchange patterns, leading to extended losing phases and a reduced duration of gaining periods. This study examines the relationship between hydrological turnover (HT) and stream-stage-groundwater-level hysteresis patterns under various system states in a third-order tributary of the River Mosel in Trier, Germany, using high-resolution stream-stage and groundwater-level data (GW1, GW2) together with complementary chemical observations collected over two years. Our results reveal distinct seasonal dynamics in GW-SW exchange. Counterclockwise hysteresis, prevalent during summer and drought conditions, coincides with conditions indicative of hyporheic zone expansion and bank storage, potentially affecting flow paths and redox dynamics. We established a strong correlation between HT and hysteresis characteristics, identifying the h -Index as a valuable diagnostic tool for tracking seasonal changes in GW-SW connectivity, storage and hyporheic zone behaviour based on hydraulic preconditions. The h -Index quantifies the direction and magnitude of hysteresis based on the integrated difference between rising and falling limbs, thereby capturing time lags and asymmetries in GW–SW interactions. In the context of climate change, drought conditions alter exchange dynamics. Thus, the hyporheic zone plays a vital role in solute cycling and GW-SW connectivity. The h -Index, combined with chemical and hydrological monitoring, provides a robust framework for understanding these dynamics in small stream ecosystems.

Whether surface and deeper water storage trends move in the same direction underpins assessments of freshwater availability. Theory predicts vertical coherence, yet it is untested globally and questioned under climate extremes and human management. Using depth‐sensitive satellite observations spanning surface soil moisture (SSM) (Soil Moisture Active Passive) and total terrestrial water storage (GRACE/FO) for 2015–2025, we assess the vertical organization of multi‐year land water change across ∼78 million km 2 of land. We find that vertical coherence dominates, with same‐sign surface and total storage trends across >70% of the analyzed area. Satellite Normalized Difference Vegetation Index reinforces this pattern, aligning with coherent trends and tracking deeper storage where surface and total trends diverge. Divergence is spatially clustered and linked to repeated hydroclimatic extremes or intensive groundwater abstraction. These results provide the first global observational test of vertical coherence in land water storage change, showing it remains dominant but can be weakened by depth‐selective climatic and anthropogenic pressures.

Accurate hydrological prediction in alpine regions remains challenging due to complex cryospheric processes and limited observational records. Traditional hydrological models are often subject to structural uncertainty, whereas purely data‐driven deep learning (DL) models may lack physical interpretability under non‐stationary climate conditions. Here, we present a distributed differentiable framework for alpine basins that combines bottom‐up physical constraints with top‐down global learning to test frozen‐ground structural hypotheses while mitigating the effects of local data scarcity. Specifically, we developed differentiable Coupled Routing and Excess STorage model (dCREST), a distributed differentiable physics‐informed model that enables the joint diagnosis of model structure, parameter dynamics, and spatial transferability. Using the Headwater Area of the Yellow River (HAYR) as a test bed, we investigated the hydrological role of frozen‐ground processes and the value of global pretraining for runoff simulation in data‐scarce alpine regions. Results indicate that conventional single‐layer soil structures are unable to adequately capture permafrost‐controlled hydrological dynamics. In contrast, an explicit dual‐layer soil structure coupled with dynamic cryospheric parameterizations substantially improves the simulation of seasonal runoff partitioning and long‐term hydroclimatic responses. Furthermore, pre‐training on globally distributed catchments enables dCREST to learn transferable rainfall‐runoff relationships, enhancing modeling performance despite limited local observations. This transfer learning strategy increases the average Nash–Sutcliffe efficiency (NSE) from 0.35 in the current operational system to 0.63. Our findings demonstrate that differentiable hydrological modeling provides a powerful framework for simultaneously diagnosing model structural deficiencies and improving runoff prediction in data‐scarce alpine basins through physically constrained transfer learning.

The increased frequency and intensity of hydrological extremes, including drought, due to anthropogenic climate change will drive the need for enhanced water supply resilience, even in water-rich countries. Previous studies have shown that small reservoirs have considerable potential for expanding water supply for various purposes, including when repurposed from flood-only reservoirs for both flood and drought protection. However, whether these repurposed reservoirs retain the same flood protection ability when operating under forecasts is still unclear, as reservoir operation under forecasts has primarily been researched in the context of large reservoirs. In this study, we investigated potential operating rules under forecasts for 30 small-to-midsize (less than 5 million m 3 ) flood reservoirs to (a) determine if the uncertainty introduced by forecasts degrades the performance of repurposed reservoirs so significantly as to render the concept unusable, (b) identify patterns in the relationship between forecast accuracy and optimal reservoir performance, and (c) identify patterns in optimal reservoir operation rules, under the constraint that flood protection should not be compromised. Performance is determined by the modelled ability to either supplement streamflow to avoid low flows or to provide water for irrigation purposes in the area of the reservoir. 1000 combinations of three operation parameters – the warning threshold at which flood pre-release begins, the rate at which water is released from the reservoir for flood pre-release, and the inflow at which the reservoir begins storing water – were sampled to optimize for both maintenance of flood protection (viability) and benefit for the reservoir's additional uses. While some reservoirs indeed were no longer beneficial when optimized to operate under forecasts, many still maintained benefits above 40 %, with a couple even surpassing their performance under perfect knowledge. Comparing changes in benefit from the perfect knowledge operation to forecast accuracy indicated that high rates of hits, false alarms and misses, and misses (HFM) could explain the largest decreases in performance, while other forecast accuracy metrics were less impactful. However, even if HFM were low but nonzero, a poorly-timed false alarm could drain a reservoir's storage before a spike in demand, causing a noticeable loss…

The Amazon's arc of deforestation is a hotspot of rapid land‐cover change. Using 20 years of MODIS imagery combined with cloud property retrievals, CERES radiative fluxes, and ERA5 data, we show that deforestation consistently enhances low‐level cloudiness over non‐forest relative to nearby forest during the Amazon dry season. Non‐forest‐preference cloud scenes overwhelm those of forest‐ or no‐preference, with MODIS imagery often revealing clouds strikingly echoing the underlying land cover pattern. While the sign of this cloud response is robust, its magnitude is state‐dependent, varies with the background atmospheric state. Within the non‐forest‐preference cloud scenes, two distinct cloud regimes emerge: a warmer and drier regime, where higher sensible heat and lower latent heat produce sparse, shallow clouds with low cloud fraction (CF), liquid water path (LWP), and optical thickness (COT); and a cooler and moister “deepening” regime, where enhanced latent heat supports clustered, deeper clouds with higher CF, LWP, COT, greater precipitation, and stronger net cooling at the top‐of‐atmosphere (TOA). Our findings reveal that while deforestation triggers an enhancement in cloudiness, the nature of this response is tightly coupled to the background climate conditions, with implications for both the radiative energy budget and the hydrological cycle. As the Amazon trends toward a warmer and drier climate, a shift in cloud regime prevalence could diminish the TOA cooling and precipitation currently provided by the deepening cloud regime.

Permafrost thermal state, representing the stored “cold energy” in the ground, is crucial for assessing permafrost changes. However, the conventional metric, mean annual ground temperature (MAGT), has inherent limitations because it overlooks the thermodynamic contribution of ground ice. To address this, we developed an alternative metric based on enthalpy change (Δ H ′), which integrates ground temperature and ice content. Using a process‐based model, we designed controlled warming experiments to evaluate the performance of the two metrics. The results show that permafrost traditionally classified as “unstable” (MAGT between −0.5 and 0°C) can persist for 30–100 years under an air warming rate of 0.4°C per decade, largely due to variations in ground ice content. Compared with MAGT, Δ H ′ provides a more uniform scale and higher discernibility in indicating potential permafrost duration, and is therefore more suitable for representing permafrost thermal state. Furthermore, using high‐resolution gridded data sets, we mapped permafrost thermal state across the Qinghai‐Tibet Plateau (QTP) and quantified the spatial discrepancy between the two metrics. The results indicate that permafrost on the western QTP is less stable than suggested by MAGT, whereas permafrost in the southeast and around lakes in the endorheic basins is more stable than MAGT estimates. Explainable machine‐learning analyses reveal that precipitation, soil coarse fraction, and solar radiation are the primary factors controlling the spatial heterogeneity of this discrepancy. This study highlights the dominant role of ground ice in regulating permafrost thermal state and calls for greater attention to ground ice in future permafrost projections.

India has one of the largest networks of large dams in the world, where reservoirs play a crucial role in regulating monsoon‐driven water availability for irrigation, hydropower, flood control, and water supply. However, long‐term daily reservoir storage observations are available only from the year 2000 onwards. In this study, we reconstruct long‐term daily live storage for major reservoirs across India by integrating climate variables, hydrological model simulations, and machine learning approaches. We used model‐simulated reservoir storage as a baseline predictor and applied Random Forest (RF) and XGBoost (XGB) with meteorological and hydrological variables, substantially improving accuracy compared to the raw simulations. The trained models over the observation period were then applied to the pre‐2000 period to reconstruct reservoir storage. Using the reconstructed data, we examined long‐term changes in reservoir storage variability and the timing of peak variability in reservoir storage during both the summer monsoon (June–September) filling and dry‐season release periods. We show that despite increasing reservoir capacity, normalized storage shows a moderate long‐term decline, reflecting an increase in water withdrawals. Pre‐monsoon storage exhibits larger and more spatially variable trends than post‐monsoon storage, while the frequency of low‐storage conditions has declined and high‐storage states have become more common. Peak storage variability typically occurs during late July‐mid August and around January, corresponding to peak monsoon inflows and irrigation demand. These findings provide insights into evolving reservoir storage dynamics and their implications for water resources management under changing hydroclimatic conditions in India.

Airborne electromagnetic (AEM) surveys offer rapid, cost‐effective subsurface imaging, yet converting their electrical resistivity (ER) models into physically meaningful hydraulic property fields for groundwater models remains a challenge. We develop and demonstrate a data‐driven workflow for an unconsolidated sedimentary aquifer system in Scott Valley, northern California, USA, that incorporates AEM ER data with borehole logs to build and calibrate a geologically heterogeneous groundwater‐surface water model. ER and texture observations are first combined through consensus clustering into five meta‐texture classes; a probabilistic ER‐texture transform then converts ER data to cell‐scale texture probabilities. These probabilities and borehole data are combined using Texture2Par to create a three‐dimensional texture model, which is translated to grid‐scale hydraulic conductivity and storage via power‐law averaging. During calibration, we parameterized the ER‐texture distributions, essentially allowing parameter estimation to adjust the estimated textures along the AEM flight lines. The texture‐based groundwater‐surface water model attains the same high goodness‐of‐fit as the previous zonal calibration (Fort Jones streamflow Nash‐Sutcliffe efficiency = 0.84; groundwater heads r 2 = 0.98), with more geologically plausible heterogeneity and improved simulation of groundwater‐driven seasonally low streamflow. The proposed ER‐to‐texture workflow provides an adaptable workflow for embedding AEM information into basin‐scale groundwater models.

Compound events, arising from the combinations of multiple interacting underlying physical drivers, have gained increasing attention over the past decade with a wide range of applications, including floods, droughts, and wildfires. This growing body of research has advanced both the conceptual understanding and statistical modeling of compound events, often emphasizing the role of dependence between underlying drivers. Building on this foundation, we recognize the need to further describe hydrological and climate phenomena not only through the overall tendency of contributing physical drivers to interact but also by considering the individual combinations of drivers leading to compound events, regardless of whether these drivers are statistically dependent. Here, we introduce the concepts of structural and transient compoundness as system properties of compound events. Structural compoundness quantifies the overall contribution of each driver to the generative mechanism of the compound variable, while transient compoundness captures the role of each driver in the individual realization of the compound variable. This will ultimately improve risk assessment and mitigation strategies. Indeed, by applying these concepts to two types of compound events, river discharge at confluences and hot and dry events, we show that structural and transient compoundness can inform modeling choices and enhance system understanding, with both the dependence and magnitude of the drivers playing critical roles in shaping compound events. Incorporating structural and transient compoundness enhances scientific modeling and, at the same time, improves risk assessment, guiding adaptation strategies for persistent (structural) and temporary (transient) risks.

Dryland tree survival critically depends on deep soil water (DSW), particularly during dry spells. However, DSW is often limited and can be exhausted during periods of active tree growth, as recharge from precipitation is minimal. The eco‐physiological responses of trees to compound droughts (i.e., combined deep soil and atmospheric droughts) remain poorly understood, particularly the variation of sensitivity to atmospheric drought under deep soil desiccation. This study addresses this gap through a three‐year field experiment (2019–2021) on China's Loess Plateau, a global hotspot for DSW depletion due to extensive afforestation. We found that deep soil drying below 200 cm significantly suppressed daily sap flux density ( J s) by 8%–13%, transpiration rate by 24%–36%, and canopy conductance by 25%–34%, in both normal and dry years. Importantly, deep soil drying heightened the sensitivity of trees to vapor pressure deficit (VPD), as demonstrated by two key physiological responses. First, partitioned trees (i.e., those with physically isolated deep and shallow root systems) exhibited reduced hydraulic buffering capacity, with hysteresis analysis revealing a 16% decrease in hysteresis area compared to controls, indicating stronger J s–VPD coupling. Second, partitioned trees initiated stomatal closure at lower VPD thresholds (1.0–1.4 kPa) compared to controls (1.3–2.8 kPa). These findings underscore the role of deep soil water as ecological insurance against compound droughts, and highlight that its depletion exacerbates tree vulnerability to such stresses. Our results may provide insights into the effect of compound atmospheric and soil drought on the evolution of managed ecosystems.

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…

Traditional methods for assessing levee projects focus solely on single‐year economic data, evaluating only the reduction in flood‐related economic losses while neglecting the benefits of saving lives and the dynamic changes in the economy and population over the project's design life. This study introduces a dynamic evaluation method for flood control and waterlogging drainage benefits of levee projects, incorporating economic and demographic changes. The total benefits are categorized into economic and life‐saving components. Separate hydrodynamic models for flood routing in river channels and rainfall‐waterlogging models in the basin are established. Through comparative analysis (with and without projects), the number of lives saved and the economic benefits generated by flood control and waterlogging drainage can be estimated. The Value of Statistical Life (VSL) is used to monetize life benefits, and calculation methods for flood control and waterlogging drainage benefits that considers both economic and life benefits are proposed. Then, a dynamic model incorporating regional GDP growth rates and population change coefficients is constructed, which can quantify the above‐mentioned benefits over the project's lifespan. A case study demonstrates that annual benefits increase from CNY 10.6 million in the base year to CNY 34.4 million by the end of the design life, with flood control benefits accounting for 59.1% and drainage benefits for 40.9%. It provides a comprehensive tool to assess long‐term project value, supporting investment decisions and improving benefit assessment systems for disaster risk reduction in levee projects.

Soil freezing is a major cold region process that influences hydrological response of northern catchments, in particular during winter rainfall and snowmelt events. In land surface models, frozen soil infiltration is difficult to represent because soil structure and hydropedological processes vary at scales finer than the model grid. This is particularly true in operational modeling, where physical process integration must balance performance improvements against computational efficiency and complexity. In this study, we propose a new configuration of the Soil, Vegetation, and Snow (SVS) model used within the operational prediction systems of Environment and Climate Change Canada (ECCC) that enhances frozen soil infiltration by reducing both surface runoff and sub-surface lateral flow. We assessed the effects of this new configuration (Fr-Inf) on streamflow simulations at more than 580 hydrometric stations located in the Great-Lakes and Saint-Lawrence domain over a five-year period. Fr-Inf significantly improves the Kling-Gupta Efficiency (KGE) compared to the default soil freezing configuration (Fr; ΔKGE = 0.28) but slightly underperforms the configuration of SVS without frozen soil (noFr; ΔKGE = −0.07). Strong degradations relative to the no freezing configuration (ΔKGE < −0.5) are observed only at 4 stations with Fr-Inf (< 1 %) as opposed to 172 stations under the Fr configuration (33 %), highlighting the robustness of the approach. To ensure that the proposed change is also acceptable in the context of operational numerical weather prediction, an evaluation of its impact on soil freezing depth as well as screen-level temperature and dew point temperature predictions is performed against in-situ observations. These results support the potential operational implementation of soil freezing at ECCC for numerical weather and streamflow prediction.

In mountainous regions orographic effects create strong horizontal gradients of various rainfall statistics such as the frequency of occurrence, the distribution of intensity and the structure of spatial correlation. However, most statistical models of daily rainfall assume spatial stationarity (i.e., the spatial homogeneity of rainfall statistics) and are therefore not well suited for studying the highly non-homogeneous characteristics of orographic rainfall. To overcome this limitation, we design a non-stationary trans-Gaussian geostatistical model for the analysis of daily rainfall fields over complex topography. This framework infers rainfall statistics from sparse rain gauge observations, simulates realistic rainfall fields after calibration and stochastically interpolates rain gauge observations to create rainfall maps. The performance of the model is assessed with data from the Island of Hawai`i where extreme spatial gradients in rainfall are observed. Results demonstrate that the non-stationary trans-Gaussian model can skillfully reproduce orographic rainfall statistics as well as their variations in space.

South and Southeast Asia is a major global hub for paddy rice cultivation, with the highest rice cropping intensity worldwide owing to its favorable hydrothermal conditions. The region has also experienced considerable spatiotemporal changes driven by climate change and anthropogenic activities. However, the absence of spatially explicit long-term datasets on paddy rice distribution and cropping intensity hinders effective agricultural and environmental management. This gap is particularly critical in the 21st century, as changes in climate, water resources, and food trade patterns increasingly reshape regional rice production systems. Using all available Landsat and Sentinel-2 archives, we refined a phenology-based algorithm to generate 30 m multi-year composite rice distribution and cropping-intensity products across South and Southeast Asia for four nominal reference years: 1995, 2005, 2015, and 2024. The algorithm addresses the challenge of detecting rice cropping intensity from long-term satellite time series and comprises three core steps: (1) identifying pixel-level rice phenological peaks using an enhanced peak detection method, thereby defining potential transplanting windows and minimizing monsoon-induced cloud and precipitation interference; (2) detecting paddy flooding signals and delineating rice cultivation areas based on phenological rules derived from the relationship between the Land Surface Water Index (LSWI) and Enhanced Vegetation Index (EVI); and (3) determining rice cropping intensity according to the number of valid crop peaks and associated flooding signals detected within the corresponding composite-period time series. The resulting maps were validated using 23 396 samples derived from a field photo library, visual interpretation of Sentinel-1/2 satellite imagery, and a sample migration algorithm. Across the four periods, the maps achieved overall accuracies ranging from 83.74 % to 87.60 %. In addition, the resulting products were compared with existing regional and period-specific rice datasets (e.g., NESEA-Rice10 and Open-SEA-Rice-10) for further evaluation. The comparisons demonstrated that the refined approach achieved higher accuracy and robustness in mapping both rice distribution and cropping intensity. When compared with official FAO statistics for South and mainland Southeast Asian countries, the derived maps yielded R 2…

Water labeled with stable isotopes provides a conservative tracer, being neither produced nor consumed, for water flowpaths within soils and root systems. We added a strong, evenly distributed 2 HHO label to one m 2 of soil surface and continuously monitored its passage downward into the soil and upward into the stems of surrounding trees, with the objective of illuminating spatiotemporal lateral root water uptake and overlap. The study was conducted during the historic drought of 2018 in a mature Scots pine ( Pinus sylvestris ) forest growing on sandy soil in northern Sweden. Continuous in situ isotopic measurements of tree xylem water evidenced root system overlap of six trees within the labeled square meter. This result is consistent with previous estimates from labelled nutrient uptake measurements at this site. However, label uptake differed sharply among trees, even within the same radius; 90 % of the label was taken up by one of the two trees closest to the labelled plot. Horizontal transport rates in tree roots averaged 0.17±0.05 m d −1 , meaning that the arrival of label pulse in tree stems was delayed by 6–33 d from first tree to last. Root water uptake by trees appeared restricted to the upper 60 cm of mineral soil, even at the peak of the drought; the volume-weighted average depth of uptake was 30.6±1.2 cm throughout the study period. Label intensity of the mineral soil weakened throughout the drought, consistent with the notion that the label was being dispersed or diluted into pre-event water. Our data provide a daily and three-dimensional description of the passage of a labeled water pulse, highlighting the heterogeneity in horizontal water distribution by roots and the uneven partitioning of label among individual trees in a boreal forest.

Deep learning-based rainfall-runoff models, in particular long short-term memory networks (LSTM), have been shown to outperform traditional hydrological models at various tasks, both when used as purely data-driven models and when combined with process-based models in a hybrid setting. These tasks include predictions in ungauged basins (PUB) and regions (PUR), tasks which have traditionally been challenging for conceptual hydrological models. While the spatial generalizability of deep learning-based models has received a lot of attention, it is less clear how they generalize to unseen and warmer climate conditions, i.e. how suitable these models are for hydrological climate impact studies. To address this research gap, we assess the ability of three types of models including (1) fully data-driven (LSTMs), (2) conceptual (Hydrologiska Byråns Vattenbalansavdelning; HBV), and (3) hybrid (differentiable hydrologic models) to simulate streamflow under conditions warmer than those used to train the models by running a differential split sample test. That is, we trained the models using data from the historical period 1960–1990 and evaluated them on data of this period as well as of the warmer period 2000–2023. We find that the LSTM is the most accurate model overall (median NSE: 0.74–0.79), followed by the hybrid (median NSE: 0.68–0.71) and HBV model (median NSE: 0.53–0.56). However, the LSTM shows a stronger change in performance between the warm and cold period (NSE: −0.05 to −0.04) compared to the hybrid (NSE: −0.03–0) and HBV model (NSE: −0.03 to −0.02). When comparing observed with simulated streamflow signals, we find a higher sensitivity of the LSTM to changes in meteorological forcings (mainly temperature change) for mean flows (MQ: −8.5 % to −12.6 %) and high flows (Q99: −7.0 % to −13.4 %) than in observations (MQ: −4.6 % to −9.4 %, Q99: −3.6 % to −9.7 %). In contrast, the hybrid and HBV model show good agreement with observed change signals. These findings indicate that the LSTM, while being the most accurate overall, has a lower generalizability to warmer conditions than the other models. However, we also find that the models' capability to generalize can differ between catchment type. While all models generalize better in rainfall-dominated catchments, they all show larger but similar reductions in performance in snow-dominated catchments. This…

Seasonal snow strongly influences groundwater recharge in mountain aquifers, yet its role in mid-altitude karst systems under climate warming remains poorly quantified. We investigated the Dévoluy karst aquifer (Southern French Alps) to assess how snow controls recharge and how spring discharge may respond to rising temperatures. Using the KarstMod platform, we developed a rainfall-snow-discharge model incorporating a degree-day snow routine to partition precipitation between rainfall and snow, and simulate the snowmelt. The model was calibrated and validated over four contrasting years (two low-snow, one high-snow, and one very high-snow year). Results show that accounting for snow processes is essential to reproduce the observed discharge dynamics, highlighting the dominant role of snow accumulation and melting in controlling both flow timing and magnitude in karst environments. We then tested the sensitivity of the karst spring discharge to temperature perturbations. Under +2 and +4 °C warming scenarios, simulated winter flows increase while snowmelt peaks occur earlier, resulting in earlier and more severe summer low-flow periods. August discharge decreases by 26 % to 42 %, respectively, compared to present conditions. These findings demonstrate the critical role of seasonal snow in regulating recharge in mid-altitude karst aquifers and highlight that ongoing warming will substantially reduce summer water availability in mountain regions.

Amount of liquid water has been continuously grown in coastal Antarctica. It accumulated in the hydrological system including rivers and lakes existing both on ice surface and inside it. This complex hydrological system is poorly understood and it is challenging without comprehensive measurements of components of hydrological balance. This study focused on lake evaporation which were measured directly on two lakes located in the Schirmacher Oasis (70° S, Antarctica) with the eddy-covariance (EC) technique. The experiment lasted during two austral summers (December–February) in 2017–2018 and 2019–2020. The lakes were warmer than ambient air during whole summers, and they became ice free for the period of 3–6 weeks. The measurements showed that summertime evaporation varied from 0.3 to 5.0 mm d −1 , and depending on the ice cover presence, the average evaporation ranged from 1.5±0.1 mm d −1 in December to 3.0±0.2 mm d −1 in January–February. The EC measurements were used as a reference for evaluating uncertainties of five empirical formulas and bulk-aerodynamic method implementing various turbulent transfer coefficients. The bulk-aerodynamic method with locally adjusted turbulent transfer coefficients shows the most accurate estimates of daily rates of the lake evaporation (of 6 %–8 %). The empirical formulas underestimated the seasonal rate of lake evaporation by 27 %–73 %. Our results indicate that wind speed is the primary factor that controls summertime evaporation over the lakes. The largest day-to-day variations in evaporation were associated with changes in the wind speed.

Data drives our understanding of hydrological processes, supports model development, and enables anticipatory water management. This contribution introduces EARLS: European Aggregated Reconstructions for Large-sample Studies. EARLS offers daily streamflow reconstructions for more than 10 000 basins in Europe including uncertainty estimates, covering the period from 1953 to 2023. The reconstruction is derived from a single Long Short-Term Memory (LSTM) based rainfall–runoff model trained on more than 5000 basins. LSTMs represent the state of the art in rainfall–runoff modeling and are well suited to provide predictions in ungauged basins. We evaluate the quality of the reconstruction through quantitative evaluation on two held-out sets of basins and by conducting a qualitative assessment that compares EARLS-based peak flows and flood timing to previous large-scale hydrological studies. EARLS represents a new generation of datasets that harness the capabilities of Deep Learning to obtain accurate and high-resolution data. EARLS is available at https://doi.org/10.5281/zenodo.13864842 (Klotz et al., 2026b).

Extreme precipitation events can cause long‐lasting changes to river systems, altering flood hazards beyond the initial disaster. We investigate how Typhoon Morakot (2009) impacted flood inundation patterns across four river catchments in southern Taiwan. Morakot triggered over 17,000 landslides, delivering large volumes of sediment to steep mountain rivers, reducing channel conveyance. Using aerial imagery and Structure from Motion photogrammetry, we constructed pre‐ and post‐event digital elevation models to quantify morphological changes. We simulated flood inundation using HEC‐RAS 2D across a range of discharges. Results show that sediment‐driven channel infilling increased inundation area by up to 44%. Areas with minimal sediment supply exhibited thalweg erosion, vegetation regrowth, and reduced inundation extent. These findings underscore the importance of including geomorphic change in flood hazard assessments, as landscape responses to extreme events can amplify, prolong, or reduce flooding hazards for years after the event.

Atmospheric rivers (ARs) are long, narrow corridors of enhanced water vapor transport that play an important role in transporting moisture from lower latitudes toward the extratropical and polar regions. On one hand, ARs can cause precipitation, end drought, accumulate snowpacks, and support ecosystems and society. On the other hand, ARs also represent a type of hazard and are responsible for economic losses, such as by extreme precipitation and winds, rain on existing snowpacks, or causing debris flows and landslides. Given the importance of ARs, this paper proposes a multi-method fusion algorithm for more objectively identifying ARs on a global scale. The proposed algorithm, based on the vertically integrated water vapor transport (IVT), integrates advanced strategies from multiple existing algorithms and introduces a dual-axis test method to enhance the stability of AR identification. Using IVT data from ERA5, a global AR database is constructed at a 1° × 1° horizontal resolution and a six-hourly temporal resolution for 1940–2024, and is publicly available at https://doi.org/10.5281/zenodo.18051602 (Chen and Rao, 2025). Comparative evaluation against established AR databases reveals strong agreement in mid-latitude ocean basins where ARs are most active. The usefulness of the new AR database is also demonstrated by examining the role of ARs in two extreme events in the recent past: atypical AR activity during the East Asian Meiyu rainfalls in late June 2018, and rare AR activity during the Australian Black Summer in late January 2020. The results show that the new AR database helps to reduce the uncertainty in AR identification and to better understand extreme events and their variations in time and space.