Search Results (1,527)

Understanding drought and water availability requires integrating multiple components of the hydrological cycle. Satellite observations enable consistent monitoring of water storage, groundwater variability, and water budget components at continental scales. This study synthesises results from several satellite-based analyses to examine hydrological signals across Europe within the Köppen–Geiger climate zones. Indicators were analysed jointly, including the Combined Climatologic Deviation Index (CCDI), Water Budget (WB), Water Storage Deficit Index (WSDI), and Groundwater Drought Index (GDI). The comparison of these indices reveals consistent spatial and temporal patterns of water deficit across Europe, with the strongest drying signals observed in temperate and Mediterranean regions. In contrast, northern climatic zones show higher retention capacity. The integrated approach highlights relationships among groundwater variability, water storage anomalies, climate anomalies, and water budget dynamics, providing a broader perspective on hydrological responses to climate variability. The results demonstrate the value of multi-indicator satellite analysis for large-scale drought monitoring and water resource assessment. Full article

Microplastic (MP) contamination has become a widespread environmental concern in coastal and freshwater wetlands, ecosystems that play a crucial role in hydrological regulation, nutrient cycling, and biodiversity conservation. Despite their ecological importance, research on MPs in wetlands remains fragmented and comparatively underexplored. This study presents a comprehensive bibliometric and visualization analysis of global research on MPs in coastal wetlands. A total of 17,523 publications were retrieved from the Web of Science Core Collection (2002–2025) using predefined search strings and screening criteria. Analytical tools, including VOSviewer version 1.6.20, were employed to examine co-authorship networks, country contributions, and keyword co-occurrence patterns. The results indicate a significant increase in MP-related publications after 2016, with China, the United States, and India emerging as leading contributors. However, wetland-specific studies constitute only a small fraction compared to marine-focused MP research, highlighting a substantial research gap. Key research themes identified include MP sources, transport pathways, sediment–water interactions, and ecotoxicological impacts. Additionally, there is growing attention to remediation approaches, particularly those involving TiO₂, ZnO, Fe₃O₄, and graphene derivatives, employing photocatalytic, magnetic, and adsorptive mechanisms. Overall, the findings underscore the limited focus on wetland ecosystems in MP research and emphasize the urgent need for integrated research efforts and management strategies to address MP contamination in these vulnerable ecosystems. Full article

Accurate estimation of global terrestrial evapotranspiration (ET) is fundamental for understanding the Earth’s water and energy cycles, yet existing multi-source ET products inevitably contain uncertainties that require spatially explicit characterization for optimal data merging or data assimilation. While Quadruple Collocation Analysis (QCA) offers a robust and reference-free approach to quantify uncertainties, its reliability in the ET discipline remains underexplored, and algorithmic non-convergence frequently results in substantial spatial data gaps. To address these limitations, this study evaluated the accuracy of the QCA method using validation errors derived from high-quality FLUXNET sites (N = 55). Moreover, we employed a Random Forest (RF) framework that is driven by 17 environmental variables to generate spatially seamless error maps for four mainstream ET products, i.e., ERA5, GLDAS, GLEAM, and MERRA2, from 2000 to 2020. Results demonstrate that QCA-based errors strongly correlated with ground-based errors as Pearson’s correlation coefficient was >0.3 for all four ET products. Furthermore, the RF model successfully reconstructed the spatial gaps in QCA errors, achieving an exceptionally low mean prediction error of approximately 0.03 mm/day. Based on these seamless maps, the global mean ET error is estimated at roughly 0.3 mm/day, with pronounced high-error clusters emerging in regions such as central Canada and northern Argentina driven by underlying land cover heterogeneity. Ultimately, this seamless gap-filling redefined the global map of product with the lowest estimated collocation error. ERA5 emerged as the superior choice across approximately 45% of the land surface (predominantly in the tropics and mid-to-high latitudes). Meanwhile, before algorithmic gap-filling, GLEAM was optimal across approximately 28% of the valid land pixels; after spatial gap-filling, it proved most effective across approximately 30% of the globe, particularly within arid deserts and glaciated regions. Our work provides useful geographic guidance for optimizing multi-source data merging and land data assimilation frameworks in future global hydrological studies. Full article

In the context of climate change, the hydrological processes and water resource system vulnerabilities in inland river basins of arid regions are intensifying. Understanding their evolutionary patterns and driving mechanisms is crucial for sustainable water resource management, agricultural development, and the protection of ecological security. This study focuses on the Kaidu River Basin, systematically analyzing the temporal and spatial variations in hydrological cycle elements in the basin from 1998 to 2023 based on multi-source precipitation data, the SWAT hydrological model, and the glacier degree-day model. The study also identifies the main driving factors using a geographic detector. The results show that the SWAT model performs well (calibration period R² and NSE ≥ 0.75, validation period R² and NSE of 0.75 and 0.70, respectively), indicating reliable simulation results. The surface water resources and the contribution of glacier meltwater to runoff in the basin both show a fluctuating downward trend, while potential evapotranspiration increases. The contribution of glacier meltwater during the ablation season decreased from 69.86% in 2014–2016 to 45.01% in 2017–2021. The hydrological processes exhibit a spatial pattern of “mountain areas generating runoff, non-mountain areas consuming water”. The geographic detector results indicate that precipitation is the decisive factor for the spatial differentiation of hydrological processes (influence degree q = 56.9%), with temperature, potential evapotranspiration, and altitude playing important synergistic roles. Moreover, the explanatory power of multi-factor interactions is much greater than that of individual factors. The findings of this study provide a scientific basis for the optimized allocation of watershed water resources, efficient agricultural irrigation, and the sustainable development of oasis ecosystems under changing environmental conditions, thereby supporting the goals of water security and sustainable development in inland river basins of arid regions. Full article

A vital region for China’s water resource storage and ecological balance maintenance, the Yangtze River Basin is strategically significant for maintaining regional water security and promoting long-term social and economic development. Precipitation is the main driver of the hydrological cycle. In order to address current problems with the basin’s ecological environment and water supplies, comprehensive analyses of multi-source precipitation data are necessary. They provide an essential scientific basis for evaluating the sustainability of water resources in the Yangtze River Basin in the context of climate change. Most existing precipitation fusion studies utilize only a limited number of datasets and do not fully consider the independence among different data sources, which leads to less-than-ideal fusion accuracy and assessment metrics. This paper employs the Triple Collocation (TC) method to evaluate and fuse multiple precipitation datasets over a 19-year period from 2003 to 2021, with the aim of enhancing precipitation accuracy in the Yangtze River Basin. The Multi-Source Weighted-Ensemble Precipitation (MSWEP) precipitation data were found to have the highest accuracy among seven datasets, with a Correlation Coefficient (CC), Relative Bias (Rbias), and Root Mean Square Error (RMSE) of 0.907, −0.027, and 25.930 mm, respectively. The “MSWEP–PERSIANN–NOAH (MPN)” fusion was shown to be the best using the Multiplicative Triple Collocation (MTC) method in conjunction with cross-error analysis. Compared to MSWEP alone, it improved CC by 0.8% and decreased RMSE by 3.8%, with matching spatial-grid CC and RMSE improvements of 1.2% and 1.8%, respectively. Further spatiotemporal analysis of the fused data increase detection capabilities for short-term flood and waterlogging occurrences and provide better knowledge of basin water-resource status. Full article

The Rimac River Basin supplies drinking water to more than ten million people in Lima, Peru, yet its hydrological regulation capacity is increasingly constrained by land degradation, with over 35% of the basin lacking vegetation cover. Nature-based solutions implemented through conservation and restoration of natural ecosystem offer a potential complement to grey infrastructure, although their basin-scale hydrological benefits remain scantily quantified. This study proposes an inverse assessment framework that uses future degraded states as hydrological benchmarks to quantify redistributed water as a proxy for the volumetric benefits that conservation or restoration could potentially provide. Degraded Andean shrubland and grasslands were identified and prioritized using the RIOS investment assessment tool, resulting in three degradation portfolios (2826; 6566; and 10,720 ha) for the 2011–2016 period. Their hydrological responses were then simulated using the SWAT model, with a focus on dry-season dynamics. The model achieved a Kling Gupta Efficiency of 46.9% and a seasonally targeted Nash–Sutcliffe efficiency of 70% during the dry season, ensuring that despite the basin anthropization, the low flow dynamics key for water security are reliably represented. Water availability indicators and flow-duration curve metrics were applied to evaluate changes in hydrological regulation. Results show that all portfolios increased dry-season streamflow relative to baseline conditions, with the largest portfolio producing a 2.39% increase, equivalent to approximately 4 hm³ during the critical June–August period. These findings indicate that degradation alters flow redistribution within the basin water cycle and suggest that conservation or restoration may reverse these effects. The intermediate and large portfolios provided the most informative benchmarks, supporting spatially explicit decision making for basin-scale water regulation. Full article

Sustainable development of regional water resources requires objective classification of lake systems according to dominant hydrochemical processes. The aim of the study was to develop a data-driven hydrochemical typology of natural lakes in Polissya based on the Self-Organizing Map (SOM) method to identify functionally distinct water quality regimes and justify management decisions within the basin approach. The study covered nine lakes of different genesis and trophic status. Key water quality indicators were analyzed: total nitrogen (TN), biochemical and chemical oxygen demand (BOD₅, COD), suspended solids (TSS), iron (Fe), and total dissolved solids (TDS). Descriptive statistics, correlation analysis, and neural network SOM modeling with subsequent clustering were applied. The results revealed strong positive correlations between TN, BOD₅, COD, and TSS, indicating joint control by biogenic and organic processes, while TDS showed negative correlations with organic indicators, reflecting mineralization control. SOM classification allowed us to identify three hydrochemical clusters: background systems with low anthropogenic load; organically enriched lakes with intense biogeochemical cycling; and mineralization-controlled water bodies dominated by geogenic factors. It has been established that spatial features of land use and morphometric characteristics (depth, type of feeding, hydrological connectivity) determine the sensitivity of lakes to external loads and their location. Full article

Urban districts are increasingly exposed to overlapping heat stress and stormwater loads driven by warming trends, more intense rainfall, and continued growth of impervious surfaces. Pavements occupy a large share of the public right-of-way, so their material and structural design offers a scalable pathway for urban climate adaptation. Yet the literature on porous asphalt remains fragmented, with hydrological performance often assessed using infiltration or permeability metrics in isolation, while thermal studies frequently report surface cooling without consistently tracking the governing water budget or its persistence. To reconcile these disconnected strands, this review synthesizes a conceptual hydro-thermal balance framework in which runoff mitigation and heat moderation are treated as a coupled problem controlled by storage, drainage pathways, and evaporative demand. Within this framing, cooling is primarily water-limited: permeability enables wetting and redistribution, but the magnitude and duration of temperature reduction depend on how much water is retained near the surface and how long it remains available for evaporation, rather than on permeability alone. The review integrates the current understanding of mixture structure and pore connectivity, permeability–storage behavior, moisture availability and evaporation, and the operational factors that govern performance persistence. Laboratory and field evaluation approaches are summarized alongside modeling methods used to interpret coupled hydro-thermal responses under different climates. Practical constraints—including clogging, maintenance requirements, and durability risks under repeated moisture–temperature cycling—are discussed as mechanisms that can progressively suppress both infiltration and water availability, undermining long-term function without performance-based specifications and life-cycle planning. Finally, design and policy implications are outlined for integrating porous asphalt into coordinated heat-and-stormwater strategies, and research priorities are identified to advance standardization, long-term monitoring, and coupled hydro-thermal–mechanical assessment. Full article

Hydrodynamically triggered landslides remain a major concern in reservoir regions, where the mechanisms controlling displacement evolution are still not fully understood and the multi-scale deformation responses induced by individual hydrodynamic factors remain difficult to quantify. To address these issues, this study establishes a TSD-TET composite framework by integrating time-series signal decomposition with deep learning for multi-scale displacement prediction and the mechanism-oriented interpretation of hydrodynamically triggered landslides. The monitored displacement sequence is first decomposed into physically interpretable components, including trend, periodic, and random terms. Each component is subsequently predicted using deep temporal learning models to capture different deformation characteristics at multiple temporal scales. Meanwhile, key hydrodynamic driving factors, including rainfall, reservoir water level, and groundwater level, are decomposed within the same framework to examine their statistical associations with different displacement components. The proposed approach is applied to the Donglingxin landslide located in the Sanbanxi Hydropower Station reservoir area. Results show that the model achieves high prediction accuracy under both long-term forecasting horizons and limited-sample conditions, with a cumulative displacement coefficient of determination reaching R² = 0.945. Mechanism analysis further indicates that trend deformation is mainly controlled by geological structure and gravitational loading, periodic deformation is strongly modulated by hydrological cycles associated with reservoir water level fluctuations, and random deformation is more likely to reflect short-term disturbances and transient hydrodynamic forcing. These findings provide new insights into the deformation mechanisms of hydrodynamically triggered landslides and offer a promising technical pathway for improving displacement prediction, monitoring, and early warning of reservoir-induced landslide hazards. Full article

Soil preferential flow plays a crucial role in governing hydrological cycles and soil moisture distribution in mountain forests. This makes it essential for understanding subsurface water movement and for guiding hillslope hydrological management. In this study, soil preferential flow, soil properties, and root characteristics across three slope positions on a Larix gmelinii var. principis-rupprechtii (Mayr) Pilger (larch) plantation hillslope in the Liupan Mountains were systematically observed to reveal the spatial patterns and formation mechanisms of hillslope soil preferential flow. The results showed that soil preferential flow development followed a distinct spatial pattern across the slope positions, with the mid-slope exhibiting the most developed preferential flow characteristics. The comprehensive preferential flow index further quantified this spatial variation, ranking the slope positions as mid-slope > upper slope > lower slope. Different soil structural properties exerted varying influences on preferential flow. Macropore-related properties (low bulk density and high porosity and saturated conductivity) promoted most preferential flow, whereas aggregate-related properties (high organic matter and water-stable aggregates) suppressed it. The influence of root characteristics on preferential flow was also dual. Root length density generally promoted preferential flow (e.g., DC, LI, and UniFr), whereas root surface area density primarily exerted an inhibitory effect (e.g., LI, UniFr, and total stained area TotStAr). This study clarifies how slope position modulates preferential flow through soil and root characteristics, offering insights for slope-specific hydrological understanding and targeted soil and water conservation practices. Full article

Climate change and rapid urbanization are intensifying urban pluvial flooding and threatening sustainable urban development. This study proposes a three-stage, four-dimensional framework (TSFD-UPFR) to assess urban pluvial flood resilience across resistance, response, and recovery phases that integrate natural, infrastructural, social, and economic dimensions. Using a representative urban catchment affected by a typical extreme rainfall event, we couple hydrological–hydrodynamic simulations with multi-source remote sensing and socio-economic indicators at a 100 m grid resolution to enable spatially explicit assessment. The results indicate moderate overall resilience with pronounced spatial heterogeneity. Resistance is primarily constrained by drainage capacity and impervious surfaces, response is shaped by road connectivity and public service accessibility, and recovery is determined by essential facility restoration and economic support. Low-resilience clusters are concentrated in dense built-up areas and transport hubs, revealing structural weaknesses in adaptive capacity. By linking flood processes with socio-economic recovery dynamics, the framework captures cross-stage interactions within urban systems. The findings support climate-adaptive planning, targeted infrastructure investment, and resilience-oriented governance, contributing to sustainable and equitable urban transformation in megacities facing intensifying extreme rainfall. Full article

Hybrid hydro–solar microgrids offer a practical electrification option for remote and weak-grid communities by combining run-of-river hydropower with photovoltaic generation. However, their performance depends strongly on coordinated decisions across three layers: (i) system sizing and architecture, (ii) turbine selection and rating under variable river flow, and (iii) operational energy dispatch under time-varying solar resource and demand. This paper develops an optimization-driven planning framework for a run-of-river hydro–PV microgrid that co-optimizes component capacities and turbine-related design choices while enforcing time-series operational feasibility. Physics-based component models translate river discharge into hydroelectric output via turbine efficiency characteristics and operating limits, and compute PV generation and storage trajectories under dispatch and state-of-charge constraints. The planning problem is formulated as a multi-objective optimization that quantifies trade-offs among life-cycle cost, supply reliability (e.g., unmet-load metrics), and sustainability indicators (e.g., diesel-free operation or emissions when backup generation is present). A Pareto-optimal set of designs is obtained using a population-based multi-objective algorithm, and representative knee-point (balanced) solutions are selected to illustrate how turbine choice and dispatch strategy interact with seasonal hydrology and solar variability. The proposed approach supports transparent and robust design decisions for hybrid hydro–solar microgrids. Full article

Understanding crop water use is essential for improving agricultural water management and ensuring sustainable food production, especially in regions with limited water resources. Evapotranspiration (ET) is a key component of the hydrological cycle, directly influencing irrigation planning and crop productivity. However, accurately estimating ET at local scales remains a challenge due to the limitations of conventional measurement methods and the difficulty of integrating high-resolution remote sensing data. This study investigates the estimation of terrestrial evapotranspiration (ET) in a sugarcane cultivation area located in the northern coastal region of Paraíba, Brazil, using meteorological data and aerial images acquired by an Unmanned Aerial Vehicle (UAV). We adapted the PT-JPL model to estimate ET at the local scale, using thermal and multispectral imagery obtained from UAVs. Data validation was performed using surface energy balance measurements obtained from a micrometeorological tower, thereby enabling comparison of estimated and observed ET values. The results demonstrated strong correlations between modeled predictions and field measurements of net radiation (R² = 0.85), with performance metrics indicating moderate reliability for local-scale simulated ET when compared to flux-tower-based ET (R² = 0.48; RMSE ≈ 0.045 mm/30 min). This research highlights the potential of integrating UAV-based remote sensing with the PT-JPL model to improve understanding of crop water use, support irrigation management, and contribute to sustainable agricultural practices. Full article

Snow plays a significant role in the global energy balance, climate change, hydrological cycles, and other areas. However, traditional surface observation methods are limited in capturing the spatiotemporal dynamics of snow. This paper systematically reviews Machine Learning algorithms applicable to snow cover recognition. It highlights traditional Machine Learning methods such as Support Vector Machines and Random Forests, as well as more semantically oriented Deep Learning methods, including CNNs, attention mechanisms, and Transformers. These methods have shown robust performance in the domain of snow identification. Lastly, the paper discusses the strengths and weaknesses of different approaches and suggests directions for future research. Through this paper, readers will gain a comprehensive understanding of Machine Learning-based snow recognition algorithms and how these algorithms can be leveraged better. Full article

Against the backdrop of accelerated terrestrial hydrological cycling and the increasing concurrence of drought-heatwave compound extremes under global warming, regional land-atmosphere coupling has emerged as a central mechanism shaping climate feedbacks and trajectories of ecosystem carbon uptake. However, prior studies spanning climatic regimes, observational scales, and data sources have often yielded contradictory conclusions. Here, we challenge these fragmented perspectives by constructing an integrated LST-SM-ET-GPP chain that jointly represents land surface temperature, soil moisture, evapotranspiration, and gross primary productivity, thereby linking water availability, surface energy balance, and plant physiological processes within a unified framework. We synthesize a conceptual diagnostic roadmap for interpreting land-atmosphere coupling across observations and models. When ecosystems operate in humid, energy-limited environments, radiative and advective controls should be prioritized to diagnose system forcing. By contrast, as the system becomes water-depleted, attribution must shift to a nonlinear regime transition framework governed by a critical soil moisture threshold. This threshold mechanism implies that, once the system enters the moisture-limited regime, even modest declines in soil moisture can trigger a rapid weakening of evaporative cooling, substantially amplifying LST anomalies and strongly suppressing GPP. The competitive regulation of stomatal conductance by atmospheric demand (vapor pressure deficit, VPD) and terrestrial supply (rootzone soil moisture) further explains why the “dominant” controlling factor can dynamically reverse across hydrothermal states, timescales, and stages of extreme-event evolution. Notably, the steady-state coupling assumption may break down under flux “flooring” during extreme drought, or when structural buffering such as deep root water uptake is present, delineating strict applicability bounds for existing diagnostic frameworks. Finally, current assessments remain constrained by multiple uncertainties, particularly the lack of ET partitioning constraints, representativeness biases arising from clear-sky observations and sampling-depth limitations, and systematic errors in Earth system model simulations during the warm season. Full article