Subsurface hydrology

In fractured media with rough fissure skins, ignoring the fissure skin effect limits the accuracy of parameter inversion. In karst aquifers, groundwater storage and movement are typically governed by dual‐porosity models, where the fissure skin plays a key role in water exchange between fissures and the rock matrix. This study develops a radial CRT model incorporating the fissure skin effect based on a quasi‐steady state approach, deriving an analytical solution in the Laplace domain and obtaining a real‐time domain solution via Stehfest method. The improved model deepens the understanding of drawdown responses in fractured aquifers. The ratio of fissure skin storage coefficient ( α ) and exchange coefficient ( β ) jointly control the shape of the drawdown curve. An increase in α enhances the storage capacity of system, significantly reducing drawdown in the later stages. When β > 1, the fissure skin strengthens the hydraulic connection between the matrix and fissures, causing the early‐stage drawdown curve to shift downward, while β < 1 exhibits a typical flow resistance effect. Validation through a constant‐rate pumping test (CRT) in a mining area of Inner Mongolia demonstrates that the new model provides a better fit than the classical dual‐porosity model. Automatic inversion further confirms improved transition‐stage fitting with stable parameter estimates, providing a more reliable basis for characterizing fissure–matrix transition zones in karst aquifers.

Rapid infiltration influences rainfall partitioning, subsurface water movement, and freshwater availability, yet its large‐scale spatial organization remains poorly constrained by limited observations and the lack of scalable approaches for synthesizing infiltration measurements. Here, we identify rapid infiltration signatures from a global database using a dual‐mechanism modeling framework. Rapid infiltration signatures occurred in 26.6% of 4,992 valid curves. Occurrence varied among measurement methods, while rapid and non‐rapid signatures frequently coexisted within closely matched local contexts. At identical recorded locations, 74.7% of qualifying groups contained both rapid and non‐rapid signatures; for rapid‐classified curves, the fitted rapid component accounted for a median 41.2% of cumulative infiltration. Across 83 sites, rapid‐signature odds decreased by approximately 26% per 10° increase in absolute latitude. These findings provide a large‐scale observational basis for investigating rapid infiltration and highlight the importance of measurement context and independent physical evidence for improving its representation in hydrological and Earth system models.

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

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.

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

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.

The internal recharge dynamics of karst aquifers are difficult to resolve with traditional outlet‐based indicators, owing to their heterogeneous structure and nonlinear response. To address this, we propose a non‐invasive anomaly detection framework based on a Temporal Convolutional AutoEncoder (TCAE), which integrates high‐frequency electrical conductivity (EC) and temperature ( T ) signals to characterize recharge mechanisms. The study was conducted in a karst basin in Guilin, China, using 15‐min EC, T , and rainfall data from three spring types: conduit‐dominated, fracture‐matrix mixed, and shallow‐fracture controlled. The model accurately reconstructed non‐event EC across sites ( R 2 = 0.980–0.998) and quantified rainfall‐triggered anomalies using peak (AE peak ) and cumulative (AE AUC ) anomaly energy. AE peak showed strong internal consistency with event‐scale EC change ( r = 0.897–0.923), while AE AUC was significantly correlated with cumulative outlet‐discharge response ( r = 0.590–0.857). Mixed‐effects analysis of Ca 2+ and HCO 3 − concentrations, together with environmental isotope evidence from δ 13 C DIC , 222 Rn, and δ 18 O, provided complementary support for the physical interpretation of AE‐based recharge‐type classification. Recharge events were classified into four types based on the maximum event‐scale changes in EC and T : cold dilution, warm dilution, cold concentration, and warm concentration. Kruskal–Wallis test, and K ‐means clustering revealed distinct patterns across spring types: rapid recharge in conduit systems, buffered responses in fracture‐matrix systems, and threshold activation in shallow fracture systems. This study presents the first application of TCAE in karst hydrology. The dual‐parameter EC–T framework offers a real‐time, high‐resolution, and low‐cost tool for characterizing dynamic recharge, supporting sustainable management and early warning in karst aquifers.

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.

Baseflow constitutes over half of streamflow in mountainous headwaters of the western United States (US), making accurate quantification critical for water resource management. However, traditional baseflow separation methods are often arbitrary and ambiguous, complicating validation. Here, we developed an integrated hydrologic model coupling surface and subsurface flow to quantify baseflow in the Coal Creek watershed, a snow‐dominated catchment in Colorado, US. A model‐agnostic approach was implemented to estimate baseflow from simulated hydrologic exchange fluxes and these estimates were used as a reference to evaluate four common baseflow separation methods: the Pettyjohn and Henning (PH) graphical, United Kingdom Institute of Hydrology (UKIH) graphical, Eckhardt digital filter, and conductance mass balance (CMB) methods. Both the UKIH and Eckhardt methods performed relatively well, with modified Kling‐Gupta Efficiency values of 0.73 and 0.68 and Nash‐Sutcliffe Efficiency values of 0.59 and 0.71, respectively. However, the UKIH method underperformed during average and dry years, when stream hydrographs exhibit unimodal peaks typical of snow‐dominated catchments. The PH and CMB methods showed poor performance overall: the PH method substantially overestimated baseflow (average baseflow index (BFI) of 85%) owing to its short moving window, while the CMB method underestimated it (average BFI of 38%), potentially due to the exclusion of interflow contributions, compared to a numerically derived BFI of 58%. These findings highlight the utility of integrated hydrologic models as a reference for evaluating baseflow separation methods, particularly in snow‐dominated catchments where graphical and tracer‐based approaches may misrepresent baseflow contributions.

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

Hydropeaking indicates the sudden and frequent fluctuation of river stage caused by the operation of hydropower plants. This river alteration affects numerous physical, chemical, biological, and ecohydrological processes in surface water bodies. The impact of hydropeaking on groundwater hydraulics has been investigated only recently through modeling and experimental studies at multiple spatial scales. This study presents new hydrological and geochemical surface water and groundwater data providing the first evidence of the impact of hydropeaking on groundwater hydrogeochemistry. To interpret these observations, we propose a conceptual model where the rapid vertical oscillation of the groundwater table acts as a pressure‐driven “geochemical pump” impacting redox conditions in the aquifer. This work has important implications for better understanding of groundwater biogeochemistry as well as for nutrient and carbon cycles.

Groundwater is a critical source for vegetation in drylands, yet the quantification of vegetation dependence on groundwater (VDG) remains underexplored. Understanding this dependence is essential for unraveling vegetation‐groundwater interactions and informing sustainable water resources management in drylands. This study constructed a vegetation response curve to depth to groundwater (DTG) by analyzing the relationship between DTG and the Normalized Difference Vegetation Index (NDVI) at the pixel scale. A new method was developed to quantify VDG from the fitted NDVI–DTG response curve, where VDG was defined as the rate of NDVI change per unit change in normalized DTG at the inflection point of the NDVI–DTG response curve. VDG values were obtained for nine representative arid, semi‐arid, and semi‐humid regions in northern China (with an annual precipitation gradient of 200–800 mm). VDG shows pronounced spatial variation across the nine regions, with mean values decreasing from approximately 1.28 in the driest region to approximately 0.24 in the wettest region along the precipitation gradient. A strong negative correlation between annual precipitation (P) and VDG was identified (VDG = 2.08e −0.0037P ), highlighting precipitation as a dominant regulator of VDG across regions. The research provides new empirical evidence for a deeper understanding of the effects of groundwater on vegetation, and also provides strong scientific support for groundwater management and ecological restoration with various annual precipitation amounts.

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

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

Groundwater–surface water exchange is a key hydrological process that governs river water quality and aquatic ecosystems. However, the quantification of such exchange remains uncertain, especially within river island aquifers where exchange processes are highly heterogeneous. In this study, direct seepage meter measurements and indirect Darcy‐based estimates of exchange fluxes were compared under consistent spatiotemporal conditions within the aquifer of Jungsa Island, a river island in the lower West Nakdong River, South Korea. At the downstream low‐permeability site (SP‐3), Darcy‐based exchange fluxes ranged from −2.14 × 10 −5 to 1.15 × 10 −5 m/hr. Owing to these small magnitudes, net intrusion and discharge were not distinguishable. However, seepage meter measurements consistently indicated intrusion of surface water into the island aquifer, with fluxes ranging from −8.46 × 10 −4 to −1.94 × 10 −4 m/hr. At the upstream high‐permeability site (SP‐6), seepage meter measurements captured both transient groundwater discharge during short measurement periods and sustained surface water intrusion during longer measurements. Darcy‐based estimates at this site consistently indicated intrusion, indicating that both methods showed the same flow direction during longer measurements. Consequently, Darcy‐based approaches may underestimate exchange fluxes under low‐permeability conditions. These findings suggest that short‐term monitoring is favorable for capturing immediate, transient flow dynamics, whereas extended continuous measurements help buffer short‐term fluctuations to provide a more representative estimate of daily integrated exchange. Ultimately, by establishing a methodological framework that accounts for localized riverbed heterogeneity, this study provides practical guidelines for optimizing field investigation strategies in complex riparian environments.

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

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

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

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

For analysing karst hydrogeological systems, observations of karst springs and cave drips are considered indispensable. In addition to hydrometric observations, knowing the oxygen and hydrogen stable isotope ratios has improved the understanding of vadose zone and aquifer dynamics, likewise supporting system characterisation and modelling. However, limited accessibility and high costs of the analysis of stable isotopes in karst aquifers have hindered progress in karst research and impeded the accurate understanding of karst processes especially when it comes to comparative or large-scale studies. In this study, we present our workflow to compile the WoKaS-Iso database, the first extensive collection of time series data for Oxygen-18 and Deuterium isotopes in karst springs and cave drip water from diverse sources, encompassing publications, theses, reports, online archives, and collaborative initiatives worldwide. The database incorporates data sourced from 241 springs and 74 caves, of which 73 caves contain 365 individual drip sites and one cave contains input data only. In total, the database comprises 1007 time series, including 389 spring time series and 618 cave drip water time series. The compiled isotope records span the period from 1967 to 2024, with the strongest temporal coverage during the 2010s and continuing coverage into the early 2020s. Of these sites, 148 springs and 64 caves contain both δ 18 O and δ 2 H records, while 93 springs and 9 caves contain only one isotope tracer. These datasets provide coverage across significant karst regions globally, spanning China, the USA, Europe, the Middle East, and Australia. Within datasets, 79 % for springs and 68 % for cave drip water exhibit resolutions finer than monthly intervals. In addition, by integrating isotopic records with ancillary environmental variables including spring discharge, cave drip rate, precipitation, and rainwater isotopes, the database offers a more comprehensive perspective on hydrological behaviours in karst aquifers, hence advancing hydrogeological characterisation and modelling. The WoKaS-Iso database not only deepens the understanding of the complex systems but also promotes sustainable water resource management as well as the potential to foster collaborative research. The database can be accessed at: https://doi.org/10.25532/OPARA-909 (Zang, 2025).

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.

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

Plants can access water stored in aquifers to sustain their activity, releasing moisture into the atmosphere—a critical survival mechanism during drought. Understanding the role of groundwater in regulating photosynthesis is thus key for modelling land-atmosphere interactions. However, the contribution of groundwater to spatial variation in mean annual photosynthesis remains poorly quantified, particularly relative to well-known factors like climatic aridity. Here, we use satellite observations of solar-induced fluorescence together with model estimates of water table depth and aridity to investigate the relative role of groundwater and aridity in determining the spatial pattern of photosynthesis in the contiguous United States. Using causality-guided explainable machine learning, we show that the relative importance of groundwater accounts for 48 to 101% of the effect attributed to aridity in modulating forest photosynthesis across the contiguous United States. The relative importance of groundwater compared to aridity remains substantial in savannahs and shrublands (30-58%), grasslands (22-42%), and croplands (15-32%). Our findings reflect the role of water tables in regulating the water available in the rooting zone, with an effect comparable in magnitude to climatic aridity.

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

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

Near‐surface groundwater dynamics (NSGD) regulate water residence time and downstream water resources in mountain watersheds, yet whether a single extreme rainfall event can reorganize near‐surface storage remains unclear. Here, we examine typhoon‐driven NSGD using ambient seismic noise from four stations, together with hydrological and meteorological records. Single‐station cross‐component correlations and the stretching method were applied to estimate seismic velocity changes ( dυ / υ ) at 4–8 Hz. Following the typhoon, has spatial variability, ranging from ∼10 days upstream to at least 3 months downstream. We attribute this delayed recovery to thicker colluvium and fractured bedrock that enhance subsurface water retention. Concurrent decreases in dυ / υ and increases in groundwater levels indicate transient vertical coupling between shallow and deeper systems. Continued post‐typhoon dυ / υ decreases suggest delayed lateral inflow from upstream, whereas later low‐intensity rainfall responses indicate subsequent decoupling. Our results show that a single typhoon prolonged watershed scale NSGD with insight for water residence time in mountainous regions.

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

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

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

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

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

Soil-Vegetation-Atmosphere Transfer (SVAT) models are essential tools for simulating and underploting the dynamic interactions governing water balance components within forest ecosystems. These models are widely employed to predict hydrological responses to environmental change, including the impacts of shifting meteorological conditions on forested landscapes. Despite their usefulness, the reliability of SVAT models is frequently compromised by uncertainties arising from incomplete or imprecise input data. These limitations often result in model assumptions that may lead to over- or underestimation of critical water balance components such as groundwater recharge. In order to improve the accuracy of SVAT models, observed soil moisture data are integrated to enhance parameterization processes by aligning simulated outputs with measured values. However, uncertainties remain regarding the selection of representative soil moisture profiles for calibration and the extent of measurements necessary to robustly characterize a forest plot. To address these challenges, the present study explores the spatial variability of soil moisture across two forested plots with contrasting soil and vegetation conditions by the deployment of an extensive network of soil moisture probes in 11 profiles per plot. The influence of soil moisture variability on the adjustment of model input parameters during the calibration process and its subsequent impact on the computation of groundwater recharge is evaluated. The findings reveal that soil moisture variability at the plot characterized by a heterogeneous soil was greater, both horizontally and in depth, throughout the study period. These patterns of variability are also mirrored in the different parameter sets obtained from the calibration of the LWF Brook90 model, based on the recorded soil moisture time series in each of the 11 profiles per plot. The most significant variation is observed in the infiltration and hydraulic soil parameters, whereby this is more pronounced at the plot with heterogeneous soil structure. When examining the groundwater recharge rates calculated using the 30 best-performing parameter sets for each of the 11 profiles, both plots exhibited comparable temporal patterns but substantial differences in total groundwater recharge volumes across profiles. The results also suggest that model-inherent…

Despite the widespread use of physically based groundwater models, their deployment at the catchment scale remains challenging and time-consuming. HydroModPy was developed to address this gap by enabling automated and streamlined multi-site development of hydrogeological models at the catchment scale. This open-source Python toolbox facilitates the construction, execution, calibration, and analysis of unconfined shallow groundwater models. The current version integrates well-established geospatial tools, such as Whitebox Tools, and hydrogeological libraries with FloPy-driven MODFLOW-NWT simulations, along with optional particle-tracking and solute transport modules (MODPATH and MT3DMS), to provide a fully scriptable, end-to-end workflow. Automation is achieved through dedicated functions and classes capable of performing watershed delineation from digital elevation models, preparing spatial and temporal recharge forcings, generating computational meshes and vertical discretization schemes, assigning model parameters, and running simulations in steady-state or transient modes. The overall framework supports systematic and reproducible calibration routines that leverage subsurface data such as groundwater head measurements, as well as surface observations – including stream network maps and stream intermittency patterns – to constrain model estimates of aquifer hydraulic properties. Model outputs and provenance metadata are exported in standard geospatial formats to ensure interoperability and alignment with FAIR data principles. Built-in visualization tools and integration with Jupyter Notebooks support interactive exploration, teaching applications, and fully reproducible analyses. In this technical note, we present the HydroModPy architecture and its core functionalities, demonstrate model deployment across various hydrogeological contexts, and discuss ongoing and planned developments for future versions of this collaborative tool. The codebase is modular and extensible, making it suitable for adoption by a broad user community. Planned enhancements include tighter coupling with land-surface or ecohydrological models adding new numerical solvers, integrating advanced calibration and uncertainty quantification algorithms, and improving user interfaces to facilitate application in various environmental settings. HydroModPy contributes to improving the…

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

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

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.

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

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

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

Understanding water fluxes and mixing dynamics in the unsaturated zone is crucial for ecohydrological studies. Yet, observations of soil moisture and soil water isotopes remain limited in spatial extent and temporal resolution, restricting modeling studies on the critical zone. A data‐driven Artificial Intelligence (AI) approach was applied in a first attempt to simulate daily soil moisture and soil water isotopes (δ 2 H, δ 18 O) across soil profiles of a mixed land use catchment using parsimonious climate and vegetation predictors. A sequential model combined a Long Short‐Term Memory network for soil moisture and a Random Forest model for soil water isotope simulation, using LSTM‐predicted soil moisture as input. Training used ca. 2 years of daily soil moisture and one year of monthly soil water isotopes from different depths (0–100 cm) under seven land uses in the Demnitzer Millcreek catchment (66 km 2 ), NE Germany. The AI model captured seasonal and depth‐dependent hydrological patterns, outperforming a process‐based model previously applied in the catchment. Soil moisture simulations showed Kling‐Gupta Efficiency of 0.75–0.92 (RMSE: 1.81%–4.50%), while isotopes simulations achieved 0.80–0.88 (RMSE: 4.3–5.8‰ for δ 2 H, 0.6–0.9‰ for δ 18 O). However, the performance of AI model depended on the study period and catchment, showing overestimation in drier test periods and underestimation in wetter validation periods. Also, in this evapotranspiration‐dominated site, the model relied heavily on indirect climate predictors (e.g., relative humidity), derived from direct drivers (e.g., precipitation), reducing performance in short‐term hydrological responses. Our sequential model has potential as stand‐alone simulations or as a surrogate for process‐based models.

Understanding which soil properties control water retention at different soil moisture states remains critical for predicting soil responses to management and environmental change. Traditional pedotransfer functions predict parameters of equations like van Genuchten's model, but parameter correlations create ill‐posed inverse problems that propagate errors. Fundamentally, parameter‐based approaches cannot reveal which properties matter most at different water potentials or how texture‐structure interactions vary across the moisture spectrum. This study presents Hierarchical Attention‐Based Pedotransfer Function (HABIT) to address these limitations through two objectives: (a) achieve superior prediction accuracy through direct soil moisture prediction that circumvents parameter correlation errors, and (b) quantify property importance and interactions across soil moisture regimes through interpretable attention mechanisms. The model employs attention architectures that dynamically weight soil properties based on water potential, enforces physical constraints including monotonicity, and accommodates incomplete soil characterization through hierarchical training. HABIT is evaluated against traditional approaches using comprehensive international soil databases spanning global textural and structural variability. Attention weight analysis reveals moisture‐dependent property interactions, including unequal bidirectional texture‐structure interactions where texture exerts stronger influence on structural interpretation than the reverse, organic carbon's dual structural and sorptive roles, and saturated hydraulic conductivity's role as a statistical surrogate encoding pore network information through correlations with unmeasured properties. These patterns generate testable predictions while explaining how direct prediction with adaptive property weighting captures soil‐specific retention behaviors that parametric forms miss. HABIT demonstrates that properly designed machine learning architectures serve as both prediction tools and hypothesis‐generating frameworks for quantitative analysis of soil property interactions.

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

Saltwater extraction is a key strategy of the Salt Interception Schemes (SIS) in Australia's Murray River Basin, aimed at intercepting saline groundwater loads to the river and adjacent floodplains. Despite their widespread application, a general analytical framework is lacking to evaluate how pumping configuration and regional boundary conditions control riparian freshwater lenses, or to predict the maximum saltwater extraction rate that avoids capturing river‐derived fresh groundwater. Here we derive sharp‐interface analytical solutions that quantify pumping‐induced expansion of riparian freshwater lenses and the maximum saltwater extraction rate. The solutions cover four representative regional scenarios: a semi‐infinite flux‐controlled aquifer (S1), a finite‐width flux‐controlled aquifer (S2), a finite‐length head‐controlled aquifer (S3) and a rectangular head‐controlled aquifer (S4). The analytical solutions are validated against numerical simulations, showing good agreement in lens shape and maximum saltwater extraction rates. Sensitivity analysis indicates that regional boundary conditions primarily control the feasible pumping strategy. The finite‐length head‐controlled system (S3) consistently supports the highest saltwater extraction rate, whereas the finite‐width flux‐controlled system (S2) is the most restrictive. The rectangular scenario (S4) can perform either better or worse than the semi‐infinite scenario (S1), depending on aquifer geometry and extraction well location. These solutions provide a fast and transparent tool for designing and optimizing SIS in saline riparian aquifers.

Conventional constant‐rate sampling methods are inefficient in low‐permeability, low‐storativity formations, where water from casing storage persistently biases collected samples. The High‐Stress Low‐Flow (HSLF) approach addresses this limitation by switching from a high to a low pumping rate, using the drawdown transition to create a sampling window in which downward movement and mixing of casing storage are minimized. In this study, we investigated the purging process of the HSLF approach through controlled experiments in a physical well model, measuring transient drawdown and solute mixing with pressure transducers and the light‐attenuation (LA) technique. A well storage‐mixing model for bounded confined aquifers was developed to interpret the laboratory results and scale them to field conditions. Results show that when the switch occurs later and the pumping‐rate ratio is sufficiently high, a sampling window forms. In bounded aquifers within the tank, the window length approaches a steady‐state limit, whereas in large field‐scale aquifers it increases linearly with later switch times. Comparisons between the constant‐rate and two‐stage purging methods confirm that, with an optimal switch time and pumping‐rate ratio, the HSLF approach effectively minimizes casing storage effect, accelerates mixing, and substantially reduces purge volume and time across all sampling scenarios. Observed drawdown and mixing behaviors are generally consistent with model predictions, and analytical extension of the aquifer domain suggests the potential applicability of the HSLF approach at the field scale. Nonetheless, a comprehensive field study is required to fully assess and validate its performance under complex hydrogeologic conditions.

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.

Land reclamation has been widely implemented on oceanic islands to alleviate land scarcity. Previous studies of groundwater pumping in reclaimed island aquifers have primarily considered symmetric reclamation and fixed well placement. Here, we extend this framework to an asymmetric strip island aquifer with single‐sided low‐permeability reclamation and variable well placement. We derive analytical solutions for the critical pumping rate, at which the seawater upconing interface just reaches the well bottom, and for the remaining freshwater storage under critical pumping (“residual freshwater storage”). The analytical solutions are validated against laboratory sand‐tank experiments and variable‐density numerical simulations, showing consistent agreement. Sensitivity analyses reveal that low‐permeability reclamation does not universally enhance the critical pumping rate. Instead, the effects of reclamation width and hydraulic conductivity depend strongly on well location, such that a wider and less permeable land reclamation zone may either increase or decrease the critical pumping rate. This non‐monotonic behavior can be mechanistically interpreted by the reclamation‐induced migration of groundwater divides and associated changes in capture zones. In this single‐sided reclamation setting, the optimal pumping location generally does not coincide with the pre‐pumping groundwater divide, contrasting the behavior of homogeneous, symmetric island aquifers. Low‐permeability reclamation increases pre‐pumping freshwater storage, but residual storage under critical pumping may be either greater or smaller than that of a homogeneous island aquifer, highlighting that enhancing freshwater storage and maximizing critical pumping capacity are not equivalent objectives. These results provide a mechanistic framework for evaluating reclamation design, pumping limits and well placement in reclaimed island aquifers.

Land feedbacks regulate energy flux partitioning and hydroclimatic extremes, yet their global characterization remains constrained by global‐scale robust flux estimates. Here, we introduce a new observation‐driven metric that circumvents multi‐variate flux dependencies by characterizing land feedback states using satellite‐derived soil moisture variability as the only dynamically varying input. Results demonstrate that climatic gradients fundamentally shape the seasonal evolution of three land feedback regimes. Weak coupling dominates arid regions throughout the year, whereas decoupling prevails in humid climates during wet seasons. Dynamically interacting states peak in semi‐arid to sub‐humid regions during pre‐ and post‐rainfall seasons when soil moisture intermittently crosses critical threshold. Analysis using energy flux‐based coupling diagnostics further shows that dynamically coupled states exhibit systematically stronger and more coherent water‐energy coupling, particularly across transitional climates. These findings confirm that the dynamic state is not merely a classification artifact but represents a physically distinct regime with robust and measurable coupling strength.

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.

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

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

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

Drywells are vadose‐zone wells constructed for infiltration and recharge, typically positioned far above the water table. They provide for efficient infiltration, with high infiltration rates per land area, because the infiltration process is below the land surface. The drywell infiltration process is highly transient, with extremely high infiltration rates initially when the surrounding medium is dry, and monotonically declining rates as it wets and the hydraulic gradient decreases. The process is governed by the infiltration rate, the drywell geometry (depth, radius, casing), and the hydraulic properties of the adjacent subsurface media. This study develops a model for characterizing the drywell infiltration capacity (DIC)—the maximum infiltration rate at any time—as a unique function relating infiltration rate to cumulative infiltration. The model depends solely on the drywell's geometry and the hydraulic properties of the medium. An analytical solution is derived with the Green‐Ampt approach to describe the transient nature of infiltration and to provide a direct method to delineate the DIC function. Numerical experiments are conducted for a range of scenarios and properties, which confirm that, while the filling time of the drywell itself varies among infiltration rates, all cases converge to a single characteristic DIC curve once the well is full. Each drywell configuration, with its unique properties and subsurface settings, exhibits its characteristic DIC function. The proposed model and methodology provides a parsimonious framework for infiltration planning and real‐time management by enabling prediction of infiltration rates directly from cumulative infiltrated volume, offering a practical and robust approach for stormwater infiltration and groundwater‐recharge applications.

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.

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

Within subterranean estuaries (STEs), Fe 2+ from freshwater is oxidized by O 2 (aq) from seawater to form Fe precipitates and low‐permeability zones (“Fe curtains”). Tidal fluctuations generate an upper saline plume (USP) above the saltwater wedge (SW), enhancing freshwater–seawater mixing and associated chemical reactions. However, existing studies did not resolve how tidal forcing influences Fe curtain formation or how the resulting precipitates alter groundwater flow. Here, TOUGHREACT was employed to numerically investigate abiotic Fe precipitation and its consequent impacts on groundwater flow in STEs under tidal forcing. The simulation results indicate that Fe oxidation and precipitation predominantly occur within the USP and SW mixing zones. Fe accumulation within the USP is approximately twice that within the SW, especially near the high and low tidal marks, due to the faster tide‐driven seawater circulation. The resulting Fe precipitates reduce sediment porosity and permeability, locally decrease porewater velocity, and alter porewater flow patterns and the freshwater–seawater interface. The horizontal and vertical porewater velocities are reduced by about 20%–40% and 40%–80%, and ∼20% and ∼40% within the USP and SW Fe‐precipitated zone, respectively. Variations in freshwater flux and tidal amplitude regulate Fe precipitation primarily by modifying the configuration of the freshwater–seawater interface, whereas the Fe 2+ and O 2 (aq) concentrations impact Fe precipitation mainly by the supply of reactants. These insights improve understanding of abiotic Fe precipitation and its hydrological implications under tidal forcing in STEs.

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

Over the years, methods for measuring groundwater‐surface water (GW‐SW) interaction have improved significantly. The use of heat to quantify GW‐SW interaction has evolved with technical advancements. Recently, the use of temperature probes embedded with compact self‐recording temperature loggers has increased for measuring GW‐SW fluxes. However, these loggers are often expensive, making it difficult for researchers to use, especially for the dense networks. Eventually, this can be a hinderence for research in resource‐constrained scenarios. This work presents an affordable instrument (TemPro), designed for monitoring temperature at discrete depths. While the base TemPro system has a fixed initial cost ($89), the marginal cost of adding each additional sensor is approximately $3.48, whereas the total cost of commercially available logger‐based systems increases linearly with each additional sensor. The instrument was tested in the field and validated against one of the widely used sensors for environmental applications. The instrument was tested for consistency, agreement and reliability using different statistical metrics (NSE, R 2 , RMSE, MAE and ICC). Furthermore, we have presented a case study where GW‐SW fluxes were estimated using both the reference instrument and the new temperature probe. The estimated fluxes closely matched those from the reference sensor, with a very low MRE (<2.5%). Therefore, this study demonstrates the potential to enhance GW‐SW monitoring in a spatially robust and cost‐effective manner using the developed instrument.

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

The changing Arctic climate alters the dynamics of melting and freezing in the ground. A methodology has been developed to assess small magnitude, ice-induced earthquakes, called frost quakes, by estimating thermal stresses in the soil in Oulu, Finland. Information on temporally and spatially varying soil properties, such as soil temperature and soil ice content, is required to calculate thermal stress. Developing this methodology further on a larger scale, over the whole country, is challenging due to a lack of in-situ measurements of those parameters with high spatial and temporal coverage. However, they can be simulated using land surface models, one of which is HydroBlocks. Previously, HydroBlocks has been applied in the contiguous United States. The goal of this paper was to configure the model in subarctic and arctic Finland. However, another challenge with large scale LSMs is that they often use coarse global soil datasets, and their simplified soil presentations can introduce errors in the modelling results. For that reason, using local soil data helps to achieve a more accurate representation of soil and improves the model performance. Hence, in this paper, the HydroBlocks model was run first by using coarser SoilGrids soil data and second, by using local soil data provided by the Geological Survey of Finland (GTK). HydroBlocks' ability to produce accurate snow accumulation and melt approximations, as well as estimate soil temperature and soil water content at different depths in Finland, has not been evaluated before. The snow model (snow water equivalent) and the modeled soil temperatures and soil water contents were compared with observational data to evaluate the model performance. For six observational snow water equivalent stations, with GTK's soil data the average RMSE and KGE were 29 mm and 0.34, respectively. The worst KGE was −0.30, and the best was 0.90. For the three observational soil stations, the soil temperature had an average RMSE and KGE of 1.3 °C and 0.7, respectively. The worst KGE was 0.22, and the best was 0.86. For the soil water content, using local soil data provided by the GTK significantly improved the modelling. The average RMSE and KGE were first, 0.15 vol/vol and −3.5, and after calibration, they were reduced to 0.03 vol/vol and increased to 0.09, respectively. For the calibrated model, the worst KGE was −0.8, and the…

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

Tidal influences are rarely considered in parameter estimation procedures for coastal groundwater flow models. Yet, considering tides adds to the already high computational burden associated with such models due to the presence of density‐dependent flow. Our objective was to present a parameter estimation procedure that includes tidal overheight in coastal groundwater flow models and to analyze the appropriateness of the chosen model, process setup, and its simplifications. As an example, the procedure was applied to a model for the barrier island Norderney (Germany). The focus of the model was to replicate seasonal groundwater recharge dynamics and the influence of storm surges on the freshwater lens as well as the freshwater/saltwater interface. We applied an iterative‐ensemble smoother (PESTPP‐IES) for history matching with a model chain of a constant‐density freshwater‐lens model and a density‐dependent submodel around the island's water works. An adequate match between measured and simulated observations was obtained from history matching. Model verification was performed using a density‐dependent model of the entire island. Additional verification runs focusing on the transient dynamics of a single boundary condition each (i.e., the sea level or recharge) were compared to deconvoluted signal components of monitored, high‐resolution hydraulic head time series. This revealed that seasonal recharge patterns and the magnitude of storm surge influences were replicated well. However, the simulated hydraulic heads and interface depth underestimated observed data. We attribute this to the scarce information available regarding salinity observations and a possibly too pronounced focus on an accurate representation of tidal influences in the modeling setup.

Karst aquifers are notoriously heterogeneous, making it challenging to characterize recharge dynamics using traditional, static indicators. Percolating (drip) water records, when analyzed at high temporal resolution, offer insights into flow mechanisms in karst vadose zones. This study utilized karst caves to observe drip rates and identify recharge behaviors. An integrated time series analysis was used combining fast Fourier transform (FFT), cross wavelet transform (XWT), change point analysis (CPA), and clustering. This study analyzes one year of daily drip water and rainfall data from 46 monitoring sites across six karst regions in southeastern Australia. Five major types of recharge emerged from this analysis, including mixed flow dominated by soil (8.7%) or epikarst (37.0%), fracture‐dominated flow (17.4%), storage‐controlled flow (13.0%), and threshold‐controlled flow (23.9%). Across regions, recharge behavior varies with climate, lithology and karst maturity, reflecting contrasting storage and flow conditions. These categories capture temporal patterns, rainfall‐drip synchrony, and abrupt shifts in recharge behavior across diverse karst environments. Notably, the FFT method highlighted overall periodicity of drip counts, while XWT and CPA revealed non‐linear responses and flow paths transitions, often linked to rainfall thresholds and seasonal shifts. These results demonstrate the complexity and heterogeneity of karst systems, where similar rainfall amounts can produce significantly different recharge behaviors depending on site‐specific flow paths and storage dynamics. This framework enables interpretation of drip data sets and provides a tool for karst hydrological research, with direct implications for water resource management under shifting climate regimes.

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

Accurate field-scale near-surface soil moisture is essential for infiltration, runoff generation, land-atmosphere exchange, and agricultural water management. Drone-based low-frequency (L-band) radiometry offers a promising intermediate scale between in situ measurements and satellite observations, but retrieval remains ill-posed because brightness temperature depends jointly on soil dielectric properties, vegetation attenuation, surface temperature, and sub-footprint heterogeneity. This study develops an uncertainty-aware Bayesian retrieval framework that integrates unmanned aerial vehicle (UAV) dual-polarized L-band brightness temperature with red-green-blue (RGB) and thermal infrared (TIR) information through footprint-consistent priors. Optical fraction vegetation cover, thermal state, and texture descriptors are used to constrain vegetation optical depth ( τ ) and its uncertainty at the scale of the radiometric footprint. The method was evaluated over heterogeneous cropland in Pengzhou, China, using independent calibration (4 scenes, about 1.3 ha) and validation datasets (13 scenes, about 12.6 ha). The proposed approach reduced root mean square error (RMSE) from about 0.057 to about 0.024 m 3 m −3 and largely eliminated the systematic dry bias of the conventional τ – ω inversion. The uncertainty diagnostics showed that footprint-scale texture was more strongly associated with τ -related uncertainty than with posterior soil-moisture variance or absolute retrieval error. Overall, the results indicate that physically informed multi-source priors can improve both accuracy metrics and interpretability for field-scale hydrological soil-moisture observation.

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

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

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