Global hydrology & change

Climate variations affect access to recreational opportunities through different mechanisms that influence the provisioning of water and ecosystem services. From 2000 to 2022, Northern and Central California experienced extreme climate fluctuations, with alternating multi‐year dry spells and wet years causing highly variable runoff from the Sierra Nevada into the Sacramento‐San Joaquin Delta. This study assesses how these hydroclimatic variations influence regional recreation by analyzing summer activities (boating, day use) alongside winter activities (birding, waterfowl hunting) at state parks and wildlife areas. The objective is to evaluate how monthly visitor volume and timing shift during drought versus wet conditions, distinguishing between activities directly or indirectly dependent on water resources. During summer droughts with critical low runoff, recreational activity declines sharply; boating drops by 65.8% and day use decreases by 13.8% at state park units. Conversely, winter droughts correlate with increased recreational engagement. Critical low runoff is associated with a 23.4% increase in birding observations and a 50.2% increase in waterfowl hunting takes. This winter increase occurs because restricted wetland resources concentrate waterfowl populations, making them more accessible to visitors. Activities directly dependent on water resources, including recreational fisheries, show the highest sensitivity to runoff fluctuations. We also find that visitor spending is highest during critical drought and wet runoff extremes, reflecting winter demand in drought years, and summer demand in the wettest years. These results highlight the vulnerability of water‐dependent recreation and its economic contributions as climate change accelerates the frequency and magnitude of extreme wet and dry cycles.

Reliable flood change projections under climate change are critical for water resources planning yet remain challenging due to uncertainty in climate‐informed statistical models. Conventional approaches relate flood distribution parameters to climate variables using log‐linear formulations, which can produce unrealistically large flood estimates. In this study, we develop a semi‐parametric statistical framework that constrains flood variability using a bounded logistic link function and estimates parameters from hydroclimatically analogous samples. Using 548 natural basins across the coterminous United States, we evaluate the proposed approach against log‐linear formulations from the entire sample. Model performances are assessed based on both the accuracy and sensitivity of flood quantiles to climate forcings. Results show that the proposed framework yields more stable and realistic flood quantile estimates, particularly under high precipitation conditions, and better preserves flood sensitivity. These findings highlight the value of bounded, locally informed statistical models for robust flood risk assessment under climate change.

Urbanization is often assumed to intensify precipitation and concentrate flood‐relevant extremes in city centers, yet clear observational evidence remains limited. Here, we analyze warm‐season sub‐daily precipitation during 2000–2024 across 20 urban agglomerations and 61 representative cities in China, partitioning built‐up areas into three urban‐intensity zones based on impervious surface fraction. Total precipitation generally increases more rapidly in urban areas than in nearby non‐urban reference regions, with about 75% of urban agglomerations showing faster urban wetting. However, this enhancement is primarily driven by light‐to‐moderate precipitation, for which urbanization effects strengthen systematically from weakly to highly urbanized zones. Quantitatively, urban‐intensity sensitivity is positive for non‐extreme precipitation, with urbanization effects increasing by about 0.1–0.7 mm yr −1 for each 10% increase in impervious surface fraction, but it becomes negative for extremes, decreasing on average by about 0.5–1.1 mm yr −1 . Accordingly, only 20%–25% of urban agglomerations show stronger amplification of extreme precipitation in highly urbanized zones, whereas roughly one‐third show weaker or negative urbanization effects in these zones. These patterns are consistent at the city scale, are more evident at hourly than daily resolution, and are strongest in humid regions. Our results show that stronger urbanization amplifies regional precipitation contrasts but does not systematically amplify precipitation extremes in urban cores, challenging a common assumption in urban flood‐risk assessment and emphasizing the need to distinguish general urban wetting from changes in short‐duration hazardous rainfall.

Many large river basins, such as the Yellow River Basin (YRB) of China, have undergone water governance transformations to resolve long‐standing and emerging challenges. However, how newly introduced water‐related laws and regulations structurally transform internal institutional dynamics—the functions and interactions of institutional elements (e.g., governance rules and actors) within the governance system—and ultimately contribute to improved governance performance remains poorly understood. This knowledge gap limits our ability to explain why some water governance reforms succeed while others fail. Therefore, this paper examines how new water‐related legislation alters governance rules, redefines institutional roles and interactions among basin actors, and drives changes in water governance mode and performance, taking the YRB as a case. To analyze institutional dynamics, we propose an approach that integrates Sankey diagrams, institutional grammar, the rule typology of the institutional analysis and development framework, and a set of rule‐based governance indicators. This approach can decompose, connect, and trace institutional effects generated by legislative variation. Five national‐level legislative documents that have fundamentally transformed the YRB's water governance over recent decades are analyzed. The basin's governance mode evolved from fragmented (1987–1997) to centralized uncoordinated (1998–2001) and ultimately centralized coordinated (2006 onward). The interplay of legislative documents, institutionalized areas (e.g., water allocation and flow regulation), governance rule types (e.g., action‐specific and target‐specific rules), power distribution (e.g., centralization and decentralization), and coordination mechanisms (e.g., prior consultation and joint operation), drives the evolution of water governance modes. These findings offer insights for institutional designs in large river basins worldwide.

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.

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.

In the context of increasing water stress and climate change, the assessment of changes in groundwater resources has become a major challenge for water decision-makers. As part of the EXPLORE2 project, this study aims to estimate changes in groundwater levels on French aquifers during the 21st century. We use the hydrogeological modelling platform AquiFR, forced by 36 regional climate projections from Eurocordex (CMIP5), bias-corrected according to a state-of-the-art method, based on three Representative Concentration Pathways (RCPs): RCP2.6, RCP4.5 and RCP8.5. To better reflect uncertainties, four storylines based on the RCP8.5 scenario have been selected to represent possible futures that illustrate the impacts of worst-case scenarios, to help decision-makers to adopt sustainable groundwater management policies. The future evolution of groundwater is assessed using the standardized piezometric level index, a normalized indicator of groundwater level that provides return periods based on the distribution value over a reference period – in this case 1975–2004. The analyses revealed significant variability between regional climate models. The predominant signal is an increase in groundwater levels, affecting most of the study area, particularly in northern France. This result is in contrast to previous studies in this area. Under the RCP8.5 (highest greenhouse gas emissions scenario), the evolution of the occurrence of current 10-year return period events shows a significant increase in the risk of high groundwater levels, primarily on the northern part of France, which is consistent with 3 out of the 4 storylines. This is accompanied by an increase in the 10-year low groundwater levels mostly observed in the southern part of the French aquifer systems represented by AquiFR. This pattern is shared by all four storylines. This finding underscores a discernible North-South distinction. The increase in the severity of events is a well-known consequence of climate change on surface hydrology, but is less prevalent in groundwater hydrology, which is characterised by a longer residence time.

Knowing water amounts destined for agricultural practices, essentially consisting of irrigation water use (IWU), is essential for water resources management. Although explicit and consistent information on IWU is generally lacking worldwide, remote sensing and modeling platforms offer valuable solutions for estimating it. The objective of this work is to perform an intercomparison between satellite‐based IWU products and modeling reconstructions over four main irrigation hotspots worldwide: the Contiguous United States (CONUS), the Ebro basin (Spain), India, and the Murray‐Darling basin (Australia). Remote‐sensing‐based IWU estimated through Soil‐Moisture‐Inversion (SM‐Inversion) and SM‐Delta approaches are considered. As modeled data, five Global Hydrological Models are evaluated: H08, PCR‐GLOBWB, WaterGAP, LPJmL, and QUAlloc. Reference irrigation volumes are exploited to assess which source is more reliable. Over CONUS and Murray‐Darling basin, lower discrepancies between data sources (satellite or modeling) are detected. Referring to spatially‐averaged IWU magnitudes, the comparison over CONUS provides ranging from 9.52 to 12.21 mm/month, between 3.24 and 7.06 mm/month, and Pearson correlation () from 0.77 to 0.90. The same intervals for the Murray‐Darling basin are 8.04–17.70 mm/month for , 4.81–12.63 mm/month for , and 0.42–0.85 for . Consequently, over such areas, similar performances in reproducing actual irrigation amounts by satellite estimates and modeling reconstructions, when available, are found. Over the Ebro basin, where the strongest discrepancies are found, satellite‐based data sets outperform model outputs in reproducing reference data. The lack of in situ observations for India prevents a clear assessment of the results, but insights on differences introduced by parametrizations are established.

Designing reservoir operation policies that can adapt to non‐stationary hydroclimatic conditions is a major challenge for water resources management under climate change. This study develops an adaptive dynamic operation policy (ADOP), in which Gaussian radial basis functions (RBFs) are coupled with a gated recurrent unit (GRU)‐based parameterization module that maps meteorological information to time‐varying RBF weights. Using the reservoir system in the Qingjiang River Basin, China, as a case study, ADOP is optimized using an expanded historical hydroclimatic ensemble within an evolutionary multi‐objective direct policy search (EMODPS) framework and is evaluated under both an independent historical test period and 12 GCM‐based climate scenarios. A static operation policy (SOP) and a scenario‐specific re‐optimized ideal operation policy (IOP) are used as benchmarks. Results show that ADOP outperforms SOP under both historical training and test conditions. Under future scenarios, ADOP achieves higher hypervolume than SOP in 10 of 12 scenarios, indicating the advantage of the time‐varying parameterization in out‐of‐sample conditions. Although both historically trained policies retain a performance gap relative to the re‐optimized IOP, ADOP partially mitigates this gap. The water‐level and release trajectories for representative years further demonstrate that the advantage of ADOP is associated with improved storage–release coordination. Moreover, a diagnostic comparison supports the value of the GRU‐based module, as adding meteorological inputs to a static RBF policy does not reproduce ADOP's gains. The proposed time‐varying policy provides a practical pathway for improving the adaptability of historically trained reservoir operation policies under uncertain future hydroclimatic conditions.

Freshwater fishes remain inadequately considered in the planning of river protected areas (RPAs), a concern in eco‐fragile areas such as arid regions. However, range shifts of fishes driven by climate‐related hydrological change, which affect spatial prioritization of habitats, have not been sufficiently considered, challenging the effectiveness of RPAs. By integrating the Maximum Entropy model (MaxEnt) with the Soil and Water Assessment Tool (SWAT), we assessed how climatic and hydrological variables shape the habitat range dynamics of the native fish Gymnodiptychus dybowskii ( G. dybowskii ) in the arid Kaidu River Basin of Xinjiang, northwest China, over the period 1980–2100, using five general circulation models and three CMIP6 climate scenarios (SSP1‐2.6, SSP3‐7.0, and SSP5‐8.5). Our findings indicated that the mean difference in monthly flow is the most influential hydrological variable, accounting for 60.4% of the relative contribution to habitat suitability for G. dybowskii , followed by temperature seasonality. This highlights the sensitivity of this species to hydrological variability in arid regions. Moreover, highly suitable habitats are projected to shift toward higher elevations, rising from a historical mean of 1835 m to 2198–2519 m in the future. The projected upward shift is predominantly influenced by the synergistic interactions among hydrological fluctuations, climate seasonality, and climatic characteristics of the dry season. This interaction results in the emergence of high‐altitude regions as potential future climatic refugia. Existing habitat conservation plans inadequately cover these potential refugial habitats, possibly threatening future fish population persistence. This study provides an important basis for developing adaptive biodiversity conservation strategies under climate change.

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.

The Indo-Gangetic Basin (IGB) is a global hotspot for groundwater overexploitation. Previous studies have shown that groundwater levels initially rose due to enhanced recharge following the construction of irrigation canals, but subsequently declined as agricultural, municipal, and industrial abstractions intensified. However, the relative impacts of separate recharge and abstraction components (precipitation, canal leakage infiltration, irrigation return flow, and irrigation, municipal and industrial abstraction), remain unclear, as do the effects on groundwater-surface water interactions and environmental flows. This study therefore aims to quantify spatio-temporal changes in groundwater recharge and abstraction components over the past two centuries and assesses how these changes have impacted groundwater–surface water interactions in the Upper Ganges–Yamuna interfluve in northern India. Groundwater model simulations indicate that canal water infiltration following canal construction after 1830 increased groundwater recharge and groundwater discharge to local rivers. Although canals continue to provide significant recharge, rapid rise in groundwater abstraction since the 1970s has lowered groundwater tables and reduced groundwater discharge to surface waters. Currently irrigation accounts for roughly 85 % of the abstractions, with municipal (15 %) and industrial (< 1 %) uses accounting for much smaller shares. The model simulations indicate that around 2000 local rivers likely shifted from draining to infiltrating conditions. This change in groundwater–surface water exchange may affect environmental flows and increase the vulnerability of groundwater to contamination where surface waters are polluted. Although both the Yamuna and the Ganges show reduced groundwater exfiltration, they are not (yet) infiltrating.

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.

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.

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

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.

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.

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.

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.

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.

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.

Urban land subsidence driven by groundwater extraction is widespread, but the processes governing how groundwater recovery translates into spatially variable surface uplift remain poorly understood. We use satellite geodesy and multi-decadal groundwater observations to measure vertical land motion in Greater Osaka, Japan, where aquifers are intersected by a dense network of crustal faults. We observe widespread surface uplift at rates up to 12 mm/year, organized into distinct domains separated by sharp boundaries. Uplift rates vary abruptly across domain boundaries, which coincide with mapped faults. Where no faults are mapped, abrupt spatial changes in uplift rate suggest the presence of previously unrecognized buried faults. Uplift rates are positively correlated with groundwater-level rise in wells screened at depths up to 600 m, while deformation modelling indicates dominant uplift sources are shallower than 500 m. Our analysis suggests crustal faults act as hydraulic barriers to lateral groundwater flow, localising vertical land motion during aquifer recovery, with implications for groundwater management, infrastructure stability, and land-use planning in urban basins worldwide.

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.

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.

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.

With nearly two‐thirds of global long rivers regulated by dams, the development of cascade reservoirs profoundly alters riparian ecohydrology, yet isolating their net ecological effects from global warming remains a critical challenge. Integrating the difference in differences (DID) method, structural equation modeling (SEM), and an interpretable machine learning framework with multi‐source remote sensing data (2001–2020), this study decouples the impacts of cascade reservoirs on riparian vegetation phenology within the transboundary Lancang‐Mekong River. While climate warming generally advances the start of the growing season (SOS) and delays the end of the growing season (EOS) across the Lancang River Basin, cascade reservoirs reshape this trajectory within a 5 km buffer along the dammed reaches. After isolating the climatic background, reservoir regulation counteracts the advancing SOS with a relative delay of 6.3 days, while further delaying EOS by 2.5 days (driven primarily by the 2–5 km band). This seasonally asymmetric intervention stems from localized hydrothermal alterations. In spring, the reservoir‐induced asymmetric warming reduces chilling accumulation, elevating the forcing temperature threshold and tending to desensitize vegetation to warming. Conversely, in autumn, while reservoirs alleviate moisture deficits, their thermal inertia introduces elevated nighttime thermal stress, shifting the dominant EOS limiting factor from moisture availability to temperature constraints. By uncovering these microclimatic mechanisms, this study provides a vital scientific basis for sustainable hydropower and adaptive ecosystem management in regulated alpine river systems.

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.

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.

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.

Forest disturbance dynamics play a crucial role in terrestrial carbon sequestration and regional climate regulation. However, the coupled recovery processes of forest structure and function remain insufficiently understood. Here, we rigorously identified 930 forest recovery sites across China, each paired with undisturbed control sites. These sites represent large‐scale, single‐event disturbances (e.g., deforestation, wildfires) with varying life‐history strategies. Using random forest with partial dependence plot analysis and partial least squares structural equation modeling, we analyzed the temporal response dynamics of structural (canopy cover) and functional (▵evapotranspiration (ET)) recovery in coniferous and broadleaf forests. Our results demonstrate that ΔET recovers significantly faster than canopy cover in both forest types, though this difference is more pronounced in coniferous forest than broadleaf forest. Spatial analysis revealed evident heterogeneity in these recovery patterns. Specifically, disturbed coniferous forest sites showed ΔET recovery in 6.8 ± 2.5 years versus 15.2 ± 1.6 years for canopy cover, while broadleaf forest sites required 8.2 ± 2.9 and 13.1 ± 1.7 years, respectively. This comprehensive analysis establishes a clear asynchrony between canopy cover and hydrological function recovery mechanisms. Leaf area index drives initial ET recovery and maintains ET influence even after canopy cover effects stabilize, independent of climatic and environmental factors. These findings reveal fundamental asynchronous recovery mechanisms between structural and functional indicators, further validated by GEDI data, providing critical theoretical support for region‐specific forest restoration strategies.

Although geothermal energy is a low‐carbon resource, some powerplants can coproduce and release , which is then subject to emissions monitoring and sometimes financial penalty. Accurate attribution of these emissions is difficult because exploitation of the reservoir alters hydrological flows that in turn modify surface fluxes. Hence, emissions metered through the powerplant do not represent the true anthropogenic impact. This study develops a lumped‐parameter model of a geothermal reservoir that tracks pressure evolution, dissolved and emissions through the powerplant and the Earth's surface. Closed‐form analytical solutions are obtained for reservoir degassing, outflow reversal, delayed reinjection returns and mineralization reactions, and these are calibrated against emissions data from the Rotokawa and Ngā Tamariki geothermal fields in New Zealand. The interplay of characteristic timescales for pressure equilibration, loss, reservoir residence, breakthrough delay and mineralization are shown to control whether a field's emissions decline, stabilize or rebound over decades. Calibrated models indicate that plant‐metered emissions at Rotokawa overcount the true anthropogenic impact by 17–45 kt , because production suppresses surface emissions that are not credited to the operator. storage in geothermal reservoirs is shown to be fundamentally different from conventional carbon capture and storage, as it is a flow‐through system in which dissolved only resides temporarily. Long‐term sequestration requires either mineralization or reinjection at sufficient distance to delay returns. Field‐specific models and repeat flux surveys are needed to constrain these effects and support emissions accounting based on true anthropogenic impact.

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.

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

Conventional water allocation models focus on reducing water shortage, maximizing economic benefits, and controlling environmental impacts, but rarely incorporate the coordinated evolution of multidimensional water systems. To address this gap, an integrated prediction‐simulation‐optimization framework was developed for 62 prefecture‐level cities in the Yellow River Basin, embedding multidimensional water‐system order directly into the optimization objective. Sectoral water use was estimated using the quota method, water availability was predicted with machine learning, and land‐use change was simulated using the PLUS model. Based on synergistic theory and information entropy, order degree was quantified across water resources, water environment, and water ecology subsystems, and the improved model was solved using NSGA‐II with entropy‐weighted TOPSIS. Water‐demand uncertainty was represented using sector‐specific truncated normal distributions and evaluated through Monte Carlo simulation with robust solution screening. Support vector machine achieved the best prediction performance, with 98% of cities meeting the accuracy threshold. Land‐use simulation yielded an average Kappa coefficient of 0.82; the most intense transitions during 2023–2030 occurred among grassland, cropland, and forestland. By 2030, both models project persistent water shortages in several cities. The improved model introduces total entropy‐increase violation as a fourth objective, thereby penalizing city‐level deterioration in water‐system order while maintaining balanced performance under conventional objectives. Robust solutions further reduce expected and worst‐case shortages under uncertainty, although basin‐scale deficits remain. This study moves water‐system order from post‐evaluation to an explicit optimization objective, providing decision support for coordinated water allocation under deterministic and uncertain conditions.

Amazonia is highly sensitive to climate extremes, yet we lack an Amazon-wide assessment that examines the spatial-temporal changes in these extreme conditions. We address this within seasons and across the hydrological year, using a quantiles approach that integrates the frequency and magnitude of climate extremes and by linking changes in temperature with evapotranspiration. We show that high temperature extremes and temperature-linked measures of water deficit are both changing at a much faster rate than central trends. The 95 th percentile of maximum temperatures in the driest period increased by 0.49 °C per decade (dec -1 ) compared to 0.21°C dec -1 for the central trend of mean temperatures for the year. Crucially, over 700,000 square kilometres in central-north Amazonia have experienced increases in extreme dry season temperatures ≥ 0.75 °C dec -1 ( ≥ 3.22 °C over 43 years). Adaptation measures for these rapid changes in climate extremes must include preventing the deforestation and disturbances that amplify risks.

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 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).

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.

Reservoir hypsometric relationships are crucial in determining water storage changes and modeling hydrological processes. Previous studies have demonstrated that uncertainties in reservoir surface area observations are a primary source of error in satellite‐derived storage estimates. However, little research has examined how variability in hyposometric data sets influences storage estimation accuracy. In addition to evaluating the impacts of hypsometric data set variability on storage estimation, this study introduced a new, simple hypsometric satellite and geometry‐based (SAGE) approach. SAGE combines remote sensing and geometry‐based methods to generate a global hypsometric data set for 7,250 reservoirs worldwide using the Global Reservoir and Dam (GRanD) database, and its performance is compared with existing data sets. The SAGE‐based results were validated against in situ Elevation‐Volume (E‐V), Area‐Volume (A‐V) relationships and storage dynamics from 132 reservoirs in the United States and Vietnam. Validation results showed strong performance, with median Normalized Root Mean Square Error (NRMSE) of 9.2% for A‐V curves and 12.4% for E‐V curves, compared to higher median NRMSE values from other global data sets, exceeding 20% for E‐V curves and ranging from 12.5% to 32.3% for A‐V curves. Reservoir storage dynamics were then estimated by integrating satellite‐derived surface areas from the Global Reservoir Surface Area Data set (GRSAD) with the SAGE‐derived AEV curves. The SAGE‐based approach significantly improved storage estimates, reducing errors by over 20% compared to other global AEV products, based on in situ storage data from 109 reservoirs in the United States and Vietnam. Overall, this study improved the accuracy of hypsometric relationships and storage dynamics, producing a more robust product for global hydrological applications.

Mixed plant ecosystems (MPEs) play a crucial role in climate‐smart ecosystems by optimizing resource use and enhancing sustainability. However, ongoing climate change may exacerbate hydrologic niche overlap in MPEs by altering water availability, which could enhance interspecific water competition and undermine the sustainability of these systems. Despite its importance, the variation and dominant factors influencing hydrologic niche overlap remain understudied in MPEs. Here, we synthesized 2,152 observations from 65 studies worldwide which analyzed hydrologic niche overlap using stable water isotope techniques, covering various plant mixtures, including woody‐herbaceous, woody‐woody, and herbaceous‐herbaceous combinations. Our results show that herbaceous plants primarily rely on shallow soil water (0–40 cm), while woody plants in drylands seasonally switch between shallow and deep soil water sources. As a result, woody‐herbaceous systems exhibit the lowest niche overlap index (NOI), while herbaceous‐herbaceous systems show the highest. Given these rooting patterns, soil moisture emerges as the primary factor regulating NOI across all systems. Specifically, in woody‐herbaceous systems, NOI increases with both shallow and deep soil water content, whereas in woody‐woody and herbaceous‐herbaceous systems, NOI decreases as available soil moisture increases. This study reveals the impact of plant mixture on water use, deepens the understanding of hydrologic niche differentiation, and emphasizes the importance of designing MPEs to enhance their adaptability and sustainability.

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.

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.

Crop yields integrate contemporaneous weather and lagged vegetation and subsurface water states that reflect eco‐hydrological memory. However, these controls are observed only during the satellite era, creating a temporal mismatch between multi‐decadal crop‐yield records and limiting data‐driven models ability to learn antecedent vegetation and groundwater effects. We developed an interpretable spatiotemporal framework to reconstruct historical crop yields responses and assess future climate‐scenario impacts in Northeast China (NEC). Central to this framework is the reconstruction module (ReGen‐Net), which extends vegetation Normalized Difference Vegetation Index and groundwater storage‐anomaly memory back to 1981, bridging satellite period observations with four decades of yield records. Model validation used a temporal hold‐out design to prevent leakage and emulate forecasting conditions. The final GATAE‐TFT model achieved test R 2 values of 0.857, 0.777, 0.798, and 0.837 for maize, rice, soybean, and wheat, respectively, and was used for spatially informed yield assessment and future projections. YieldTransformer was used as a temporal baseline, whereas GATAE‐TFT served as the final spatially informed model for scenario‐based projections. Bias‐adjusted Inter‐Sectoral Impact Model Intercomparison Project projections from 2017 to 2060, indicate gradual declines in maize, rice, and wheat yields, while soybean yields remain stable under both SSP1‐2.6 and SSP5‐8.5. SHAP‐based attribution identifies vegetation greenness and hydro‐climatic memory as the most influential predictors across the crops, with crop‐specific sensitivities to drought indices and groundwater anomalies. These climate‐conditioned yield trajectories provide a physically grounded and interpretable benchmark for assessing water‐stress risks to crop production in NEC, China's primary grain‐producing region and a critical node in national food security under climate change.

Vegetation restoration on the Loess Plateau has raised an important question of whether enhanced land–atmosphere coupling can alleviate regional water scarcity in this water-limited environment. In this study, we integrate the WAM-2layers atmospheric moisture-tracking model with a random forest–enhanced Budyko framework and introduced a vegetation-weighted leaf area index (LAIw) to examine vegetation-associated precipitation recycling and its hydrological effects during the peak growing season (July–August during 2000–2022). Results show that precipitation is dominated by terrestrial moisture sources (86.5 %), while oceanic contributions account for 13.5 %. Internal moisture recycling contributes 14.4 % of total precipitation but exhibits a trend of −0.073 mm yr−1. At the regional mean scale, vegetation-associated recycled precipitation has a limited effect on precipitation, evapotranspiration, and surface water availability (0.059 %, 0.02 %, and 0.107 %, respectively), reflecting strong compensation between positive and negative vegetation effects and the limited fraction of internal recycling. However, a clear regime shift emerges along the LAIw gradient, from weakly positive effects under low vegetation density to increasingly negative effects under high vegetation density. These findings suggest that under water-limited conditions, enhanced vegetation–atmosphere coupling does not necessarily increase surface water availability, but instead reflects a constrained redistribution of water within a monsoon–continental transition system characterized by nonlinear vegetation density–dependent regime shifts.

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.

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.

In the western US, limited water supply and high demand--accentuated by anthropogenic climate change--have resulted in overallocated water resources and rapidly depleting groundwater. Simultaneously, legal and policy mechanisms are being pursued regarding the contributions of large emitting entities (“Carbon Majors”) to such impacts. Here, we quantify impacts on western US water resources attributable to the Carbon Majors, and compare results with a simpler-to-use (‘proportional’) approach. Anthropogenic climate change has driven large decreases in water supply (snowpack and streamflow) and increases in water demand (irrigation demand), with roughly half attributable to the Carbon Majors. These changes have led to a widening seasonal gap between water supply and demand, and with Carbon Majors emissions accounting for ~1/3 of observed, climatically-driven groundwater loss in the Central Valley during 2003-2024. Though both approaches yield similar results, consistent over- and underestimations are present in each variable, suggesting caution is needed when using the proportional approach.

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.

This study connects the dots between global extent of severe water scarcity (SWS) events and spikes in food price. The authors start with three staple crops (rice, wheat, and maize) using a empirical but crop specific characterization of severe water scarcity occurrence, which is defined based on the area of land cultivated with each studied crop that on a given year is under SWS conditions. Water scarcity is estimated based on 1, 3, and 12 months standardized precipitation evapotranspiration index during crop specific sensitive periods (SPs). SPs coincide with the 4 months prior to harvest. A SWS‐wheat price relationship model was developed for period 2001–2021 and tested for 1986–2000 and 2022–2024 with future projections calculated under climate change using climate model simulations from CMIP5 and CMIP6 up to 2100. The results show a marked increase in wheat prices driven by increasing SWS area, which is in turn a function of greenhouse gas emissions. The results indicate that the meanglobal mean temperature increase by 3°C (compared to 1951–1980) could lead to the tripling of the mean global wheat price compared to 2010 (after deflating).

Hydropower plants will operate under new conditions as water regimes shift, reservoir operating rules evolve, and the grid requires flexible balancing of wind and solar. Hydropower plant turbine systems are traditionally designed to operate at the highest efficiency within narrow ranges of flow and reservoir levels while deviations from design conditions can degrade HP efficiency and trigger failures, especially in aging infrastructure. Turbine replacements typically occur every 35 to 50 years, offering a rare opportunity to modernize turbine designs by adjusting turbine head and discharge capacity to fit new conditions, rather than replicating legacy designs. The central challenge is to determine when to replace instead of extending lifetime of existing fleet, which system signals indicate the need for redesign, and which configurations will deliver the most robust gains. This study introduces HyTUNE (Hydropower Turbine Upgrade and Next‐generation Planning), a dynamic decision‐support tool that integrates basin hydrology, plant hydraulics, and adaptive optimization to guide turbine replacement timing and configuration. HyTUNE shifts planning from reactive, end‐of‐life replacement toward proactive, information‐driven strategies. The framework learns from evolving system states, identifies threshold conditions where adjustments in head or capacity improve performance, and prescribes phased interventions that balance energy production, reliability, and cost under uncertainty. Application to the Hoover Hydropower Plant demonstrates that HyTUNE’s adaptive policies consistently outperformed conventional reactive strategies across diverse hydrologic futures. HyTUNE is of considerable practical relevance as hydropower systems worldwide confront the dual challenge of modernization and adaptation to future uncertainty and demand growth.

Anthropogenic warming is projected to enhance future drought risk in the northeastern United States, yet the region has experienced precipitation increases. Here, we investigate how seasonal Northeast drought has changed over the last century in response to hydrologic intensification. To assess drought from 1901 to 2022, we use the Palmer drought severity index calculated with Global Historical Climatology Network station data. We find that Northeast drought across all months is 59% less frequent and 19% less intense over the last 40 years relative to 1901–83. Warm-season drought frequency and intensity decreased by 56% and 16%, respectively. These results are spatially consistent throughout the Northeast. The variance in monthly dry and wet events has not significantly changed, suggesting that the Northeast hydroclimate is not more volatile than in the past. We explore the potential drivers of evolving drought by rerunning our analysis with changes in temperature and precipitation removed, finding that reduced drought is attributable to precipitation increases, which to date have outpaced warming-driven evapotranspiration increases. Collectively, our results show a marked reduction in seasonal drought since 1984 in contrast with the enhanced regional drought risk projected across generations of climate model ensembles under anthropogenic forcing. However, these findings do not preclude the possibility of historical increases in drought at subseasonal time scales or enhanced future drought. Our results inform current and future Northeast water management decisions, as well as efforts to prioritize climate change hazards.

The 7th Rio declaration principle emphasizes global collaboration to protect and restore ecosystem health. While dryland ecosystem health (EH) remains critical to global sustainability agendas, its long‐term regional dynamics remain poorly constrained across the Asian drylands (AD), where land management decisions increasingly shape ecological outcomes. Here, we develop an integrated regional framework that combines vegetation dynamics, soil moisture, land use, and landscape spatial configuration to assess EH trajectories across AD from 2000 to 2020. We found that EH declined across AD until 2012 and partially recovered after 2013. Specifically, areas with poor EH increased by 2.1% (58 Mha), while healthy ecosystems declined by 0.08% and accounted for only 2% (7.9 Mha) of AD by 2020. These regional trends concealed pronounced spatial contrasts, with continued degradation across much of Central Asia and improvement in parts of Dryland East Asia. These divergent trends reflect interacting human land‐use and climate pressures. Land‐use change affected nearly 4.9% (42.87 Mha) of AD, largely driven by increasing pressure on rangelands and cropland expansion, and reductions in inland water and urbanization. Climatic conditions further contributed to these drivers, as post‐2013 warming coincided with increased soil moisture stress in Central Asia, while higher precipitation supported ecohealth recovery in East Asia drylands. These findings show that recent improvements in AD remain limited in extent and unevenly distributed, emphasizing that without region‐specific strategies integrating sustainable water resources management, rangeland stewardship, and climate adaptation, poor ecosystem health is likely to continue expanding across large portions of the Asian drylands.

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.

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.

We introduce OpenMesh, a publicly available dataset of wireless signal measurements from the NYC Mesh, a community-run communication network in New York City (NYC). While originally designed for affordable internet access, these links can be used opportunistically for high-resolution weather monitoring in diverse settings, including dense urban areas, providing 1 min sampling of signal strength measurements with dense spatial coverage across the study region. Spanning eight months of measurements (November 2023 to June 2024), the dataset comprises 103 wireless links in Lower Manhattan and Brooklyn, operating in three primary frequency ranges: 5–6 GHz (C-band), 24 GHz (K-band), and 58–70 GHz (V-band), part of the millimeter-wave (mmWave) spectrum. Our analysis incorporates meteorological records from two primary sources: (1) 37 Weather Underground (WUnderground) personal weather stations (PWS) across Manhattan and Brooklyn with 5 and 15 min sampling, and (2) three Automated Surface Observing System (ASOS) stations. The study period saw total rainfall of approximately 900 mm and included diverse weather events, from intense rain to snowstorms in winter 2023–2024. These events were captured by the NYC Mesh wireless network, with intense rainfall causing attenuation up to 30 dB and frequent outages, especially on high-frequency V-band links. This highlights the trade-off of using high-frequency bands in wireless networks: while precipitation might cause severe channel disruptions, this greater sensitivity simultaneously makes them effective opportunistic weather sensors. The OpenMesh dataset is available at https://doi.org/10.5281/zenodo.15287692 (Jacoby et al., 2025b). By publishing the dataset and demonstrating its usability, we aim to encourage further research that leverages wireless networks for real-time urban weather sensing.

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

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.

Future oil palm expansion could substantially alter tropical carbon and water cycles, but its regional impacts remain unclear. Using CLM5‐palm, we simulate large‐scale oil palm expansion from 2023 to 2100 in Southeast Asia, Africa and the Amazon. Expansion increases total vegetation carbon by 9.7%, decreases soil carbon by 5.9% and raises net ecosystem production more than fivefold, reflecting oil palm's high productivity. Vegetation carbon responses depend strongly on previous land use, with forest‐to‐oil palm conversion causing the largest losses and cropland conversions yielding modest gains. Hydrologically, expansion enhances evapotranspiration (+2.5%), reducing surface water availability (−3.3%), runoff (−3.3%) and soil moisture (−3.7%). Land‐surface temperature responses reflect opposing biogeophysical (−0.54°C) and biogeochemical (+0.18°C) effects, with regional contrasts: cooling in Africa (−0.37°C), warming in the Amazon (+0.23°C), and near offsets in Southeast Asia. These results highlight strong regional differences and climate‐dependent trade‐offs, with important implications for sustainable land‐use planning under continued warming.

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.

Atmospheric aridity is widely projected to intensify under climate warming, driven by rapidly increasing atmospheric water demand, as reflected in potential evaporation (PE). Yet global trends in precipitation, runoff, and vegetation productivity reveal a contrasting pattern of enhanced water fluxes and widespread greening, known as the “aridity paradox.” Here we show that this apparent contradiction stems from a conceptual flaw in conventional PE formulations under a changing climate, which treat vapor pressure deficit (VPD) as an external driver, despite its strong regulation by land–atmosphere coupling. We address this issue by revising the PE equation that embeds VPD as a dynamic feedback, thereby restoring the physical coupling between energy and moisture fluxes. This new formulation projects significantly weaker increases in PE and better matches observed hydroecological trends, indicating that global aridity remains relatively stable in a warmer world. Our work establishes a conceptual basis for resolving the aridity paradox and calls for a systematic reevaluation of PE‐based indices in climate change projections and drought risk assessments.

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.

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.

BY Record-breaking heatwaves disrupt global water, energy and food systems, yet the co-occurrence of atmospheric and riverine events remains largely unexplored. Here we analyse 796 river basins in the USA and Central Europe to characterize such co-occurring atmospheric–riverine compound heatwaves. Combining water quality observations with a deep learning model, we find that compound heatwaves have increased by about 0.40 events per decade since the 1980s. This corresponds to a rise from roughly 0.76 events per year in the early period to about three times that frequency today, meaning their occurrence has effectively tripled over the past four decades. This trend coincides with the rapid intensification of riverine heatwaves, which have increased in frequency (114%), duration (148%) and intensity (95%) between 1981–1990 and 2010–2019, far outpacing changes in atmospheric heatwaves. Compound occurrences are primarily controlled by climatic (59.2%), topographic (22.4%) and hydrological (18.3%) factors, with amplified trends in high-elevation (>3,000 m) mountain rivers (+128% per decade). Compared with isolated riverine heatwaves, compound heatwaves drive an additional 16% rise in water temperature and a further 2.9% decline in dissolved oxygen. Under a high-emissions scenario, 98.5% of riverine heatwaves will coincide with atmospheric heatwaves by 2100. These findings highlight the escalating threats of compound heatwaves to freshwater ecosystems and the need to incorporate their dynamics into future water risk assessments.

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…

Accurately understanding and predicting the evolution process of meteorological elements in the Luanhe River basin is of vital importance for ensuring water security in the Luanhe River basin. Based on the observation data from meteorological and hydrological stations, this study analyzed the spatiotemporal evolution characteristics of key meteorological elements such as temperature and precipitation in the Luanhe River basin. By using machine learning and CMIP6 data, combined with statistical downscaling and error-correction techniques, it predicted the trends of temperature and precipitation in the basin from 2025 to 2049. The results show that the annual average temperature, precipitation, and runoff in the Luanhe River basin have shown a fluctuating upward trend from 1980 to 2023. The average temperature rises by 0.33°C every 10 years, and the average precipitation increases by 23.73 mm every 10 years. In the future, with the increase of greenhouse gas emission scenarios, both the annual average temperature and the annual cumulative precipitation in the Luanhe River basin will show an upward trend. Under the Shared Socioeconomic Pathway 5-8.5 (SSP585) scenario, the predicted annual average temperature is 10.15°C and the annual cumulative precipitation is 712.06 mm. The complementary use of machine learning and CMIP6 provides mutually supportive evidence for interpreting future climate change tendencies. Significance Statement This study aims to assess future tendencies of temperature and precipitation in the Luanhe River basin, a critical water source for the Beijing–Tianjin–Hebei region. Our key finding is that both temperature and rainfall in the basin will continue to rise until 2049, with a temperature increase of up to 0.33°C and a precipitation increase of up to 23.73 mm decade −1 . These changes matter because they directly impact regional water security, flood risks, and ecological health. Our complementary approach, using both climate models and machine learning, provides additional evidence for interpreting climate impacts, which can help policymakers design better water management and climate adaptation strategies for this and similar watersheds.

Historical droughts and projected increases in drought risk have created an impetus for irrigation development across much of sub-Saharan Africa, including Malawi. While irrigation can boost incomes and enhance food security, interactions between drought risk and irrigation planning are insufficiently explored, heightening the risks of expensive irrigation infrastructure and competition for water resources. We use a suite of bias-corrected outputs of 18 global climate models coupled with a Soil and Water Assessment Tool (SWAT) model to examine the dynamics of water availability and irrigation water demand (IWD) in five river basins over Malawi, southern Africa. We find that strong coupling between precipitation and streamflow creates conditions suitable for quick propagation of drought signals typified by short lags between meteorological and hydrological droughts. Consequently, episodic spikes in IWD often coincide with periods of diminished streamflow, thus reducing water supply for irrigation when IWD is most pronounced. Mean IWD is projected to increase across the five river basins (ranging from 20% to 50% for the low-mitigation scenario). The frequency of IWD spikes is also projected to increase. Meteorological–hydrological drought lags are projected to become shorter, increasing the prospect of IWD spikes coinciding with hydrological droughts more often. Having grounded the study on extremes, our results highlight other dimensions of climate risks for irrigation aside changing average climatic conditions. Such information is critical in supporting decisions around sustainable and resilient irrigation landscapes, but prevailing uncertainties demand robust decision-making approaches and model improvements. Significance Statement This study sought to develop a picture of the interaction between climate risk and irrigation development in Malawi. Our findings reveal that climate change and variability create conditions of isolated incidents of amplified irrigation water demand that coincide with periods of water scarcity. Irrigation water demand is consistently projected to increase across the twenty-first century, with the risk of decreasing water supply imposing considerable limits to irrigation development. While views that irrigation presents a key route to navigating climate-related challenges—often centered around droughts—underlie the rationale…