Search Results (4,192)

This study presents the first systematic evaluation of the capability of the Surface Water and Ocean Topography (SWOT) satellite Level-2 High Rate Pixel Cloud (L2_HR_PIXC) product for retrieving topography in reservoir drawdown zones under varying terrain conditions in arid and semi-arid regions. Three representative reservoirs in southern Xinjiang, China—characterized by plain, canyon, and pocket-shaped canyon morphologies—were selected to establish a terrain-dependent validation framework. A novel multi-feature clustering strategy integrating elevation and radar backscatter coefficients was explored to reduce the misclassification of wet mudflats as water pixels in the PIXC product, aiming to improve DEM accuracy in reservoir drawdown zones. Based on this framework, multi-cycle SWOT-derived digital elevation models (DEMs) were generated and quantitatively evaluated against high-resolution unmanned aerial vehicle (UAV) Light Detection and Ranging (LiDAR) DEMs. Results demonstrate a strong terrain dependency in SWOT-derived elevation accuracy. In low-relief environments, sub-meter accuracy is achieved, with the root mean square error (RMSE) below 0.25 m, confirming the suitability of SWOT for high-precision monitoring. However, errors increase significantly in steep and complex terrains, reaching up to ±6 m, primarily due to interferometric decorrelation, geometric distortion, and slope-induced biases. Despite these limitations, multi-temporal observations exhibit generally similar spatial error patterns across terrains, indicating reasonable repeatability under the tested conditions. This study reveals the performance boundaries of SWOT-derived DEMs in dynamic land–water transition zones and provides a robust methodological framework for improving DEM extraction in similar environments. The findings contribute to advancing the application of SWOT data in hydrological monitoring and geomorphological analysis at regional scales. Full article

Frequent flooding and potential levee breaches pose severe threats to life safety and economic development in the Qujiang River Basin, highlighting the need for integrated risk assessments to improve flood management strategies. This study developed a flood risk assessment framework that combines hydrological design, 1D/2D hydrodynamic models and flood impact analysis. Design flood hydrographs for 10-, 20-, 50-, and 100-year return periods were generated using the instantaneous unit hydrograph method, and breach scenarios were incorporated to evaluate extreme failure conditions. The results indicate that inundation extent, depth, and duration increase significantly with return period, with the 100-year flood producing a maximum depth of 10.04 m and an inundation duration of up to 70 h. Levee breach simulations reveal that the Lingshangang breach results in rapid but short inundation, whereas the Qujiang breach results in prolonged deep flooding depths, posing severe risks to critical infrastructure and densely populated areas. Socio-economic impact assessments demonstrate substantial losses under extreme flood scenarios. These findings provide valuable insights for targeted flood risk mitigation, emergency evacuation planning, and resilient land use management in vulnerable river basins. Full article

Gravity Recovery and Climate Experiment (GRACE) satellites primarily monitor changes in land water storage, including groundwater, soil moisture, lake and river surface water, and canopy and snow water. However, its coarse spatial resolution of 0.25 degrees limits its ability to observe smaller basins. To assess aquifer depletion and evaluate a long-term water resource management framework, GRACE data are crucial. It remains rare for GRACE-focused studies to be conducted in great depth. A comprehensive review of 80 articles published between 2011 and 2025 was conducted using the Scopus and Web of Science databases. These articles focused on downscaling GRACE data using machine learning (ML) methods. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) reporting guidelines were used in this review. This study highlights the attributes of ML models, the input variables used, the evaluation metrics, and the output resolution. Based on the analysis of the articles, random forest (RF) methods were used in the majority of the papers. Gradient boosting (GB), artificial neural networks (ANN), support vector machines (SVM), support vector regression (SVR), and long short-term memory (LSTM) were the most widely used ML methods. As input variables, rainfall (Pr), soil moisture (SM), and runoff (Qs) are essential. In 2011, there were very few journal articles; since 2021, the number has increased. The number of published studies from China was the highest (24), followed by the USA (12) and Iran (9). A total of 38 journals published reviewed articles. In terms of articles, Remote Sensing generates 19%, Journal of Hydrology has 10%, and Journal of Hydrology: Regional Studies has 8%. The paper also discusses limitations, challenges, recommendations, and potential future directions for improving the accuracy of the GWS change prediction model. Full article

Runoff on the Chinese Loess Plateau has declined substantially over recent decades, but the relative roles of climate change and non-climatic disturbance remain debated. Here, we provide a robust regional attribution of runoff reduction across 14 major catchments during 1961–2009 by integrating seven Budyko-based climate elasticity methods with long-term hydro-meteorological analysis and change-point detection. Across the region, runoff and runoff coefficients decreased markedly, while evapotranspiration and leaf area index increased, indicating a widespread reduction in catchment water yield. Runoff showed consistently greater sensitivity to precipitation than to potential evapotranspiration, highlighting precipitation as the primary climatic control on runoff variability. However, the Budyko-based climatic component explained only part of the observed runoff decline, and the residual component not explained by annual precipitation and potential evapotranspiration was large in many catchments, with estimated contributions generally exceeding 50% and reaching more than 80% in several basins. Independent evidence, including vegetation greening, the expansion of ecological engineering measures, and increasing anthropogenic water demand, suggests that this residual was at least partly associated with human disturbance, although other non-Budyko climatic and hydrological processes may also contribute. These results indicate that annual precipitation and potential evapotranspiration alone cannot explain runoff decline across much of the Loess Plateau and underscore the need to jointly consider climatic forcing, land surface alteration, and direct human water use in regional water management. Full article

Riverine detritus is a key nutritional resource for benthic consumers, yet its biochemical quality fluctuates rapidly and is poorly captured by bulk indicators such as elemental analysis. To improve assessment sensitivity, we compared two analytical approaches targeting organic nitrogen. We refined a fluorimetric assay for primary amines using o-phthalaldehyde (OPA), identifying 2 M KCl as an optimal extraction medium that maximizes recovery while minimizing matrix interference. In parallel, we optimized capillary electrophoresis with contactless conductivity detection (CE-C4D) for free amino acid determination using 0.4 M ammonium carbonate. Applied to detritus from multiple river sites and seasons, both methods showed that primary amines and amino acids vary by an order of magnitude more than total nitrogen and exhibit patterns not detectable by elemental analysis, with consistent temporal trends across catchments. Primary amine-based measurements therefore provide a more sensitive and ecologically relevant assessment of detrital nutritional quality than bulk nitrogen metrics. The OPA assay is well suited for routine monitoring due to its simplicity and robustness, whereas CE-C4D enables detailed compositional profiling where amino acid speciation is required. Overall, detrital quality reflects both intrinsic properties and recent hydrological conditions, underscoring the importance of antecedent discharge and precipitation dynamics in its interpretation. Full article

Water ecosystem service flows (WESFs) help address spatial mismatches in water resources and support basin resilience. However, their dynamic evolution and nonlinear drivers under climate change and intensive human activities remain poorly understood. This study evaluates the spatiotemporal dynamics of WESFs in the Yangtze River Basin (YRB) from 2005 to 2022 by integrating dynamic flow analysis with mechanism interpretation. We developed an integrated framework coupling SWAT hydrological simulations with a proxy-based spatial allocation approach for social water demand. Using the Water Stress Index (WSI) and river topology, dynamic inter-regional WESFs were simulated. Furthermore, an interpretable machine learning approach was employed to identify the nonlinear effects of multiple driving factors. Results reveal a persistent supply–demand mismatch: supply exhibited a northwest–southeast gradient (averaging 567.21 mm annually), while demand concentrated in mid-lower plains and urban corridors. The flow network, which accounts for accumulated upstream inflow, demonstrated a stable “upstream supply, mid-reach transmission, and downstream benefit” pattern, highlighting downstream reliance on upstream inputs. Driving analysis identified land surface and vegetation as the largest associated driver category, while climate–hydrology and human activity were not cleanly separable. Climate provided the hydro-climatic conditions for redistribution. Nonlinear responses and blue–green interactions were also identified, informing transboundary ecological compensation and regional water-resilience management. Full article

In arid inland watersheds, the compounding impacts of climate change and intensive human activities have severely altered hydrological regimes and accelerated landscape degradation. However, conventional spatial planning often overlooks the critical coupling between subsurface hydrological processes and surface landscape dynamics. Taking the Manas River Watershed in northwestern China as a representative case, this research investigates the multi-scale dynamics of landscape patterns and their underlying spatial determinants. Integrating multi-period land-use data (2000–2020), landscape metrics, and the GeoDetector model, we diverge from conventional uniform buffer approaches by redefining riparian boundaries utilizing four distinct River–Groundwater Transformation (RGT) patterns. This methodological shift reveals critical eco-hydrological heterogeneities previously masked by fixed-width approaches. Our multi-scale analyses demonstrate that watershed-level landscapes exhibited a trajectory of declining diversity, transient recovery, and ultimately, intensified fragmentation, while riparian patches concurrently expanded and became increasingly homogenized. GeoDetector assessments indicate a fundamental shift in driving forces: early-stage variations were constrained by natural factors, whereas post-2010 dynamics became overwhelmingly dominated by socio-economic determinants, particularly agricultural expansion and GDP growth. Crucially, our RGT-coupled spatial analysis reveals a strong spatial association between agricultural sprawl and landscape risk hotspots concentrated within groundwater overflow zones—a pattern consistent with, but not directly demonstrating, disrupted vertical hydrological connectivity. Direct verification of subsurface mechanisms would require continuous piezometric monitoring beyond the scope of this study. Consequently, rather than generic zoning, we propose a multi-scale “hydro-spatial” governance framework featuring targeted interventions. By establishing strict agricultural redlines in vulnerable overflow zones and implementing eco-hydrological restoration tailored to specific RGT regimes, this paradigm delivers robust methodological insights for advancing precision spatial planning in fragile arid ecosystems. Full article

Dam constructions alter the river flow, leading to a cascade of physical, chemical, and biological changes in the ecosystem’s structure and function. This study presents a systematic framework for assessing the impact of these built structures on adjacent surface water bodies. The approach integrates mandatory long-term monitoring data with a multivariate statistical approach (Partial Least Squares Discriminant Analysis, PLS-DA) to provide a robust assessment of fourteen of Bulgaria’s major and significant reservoirs’ influence on nearby rivers and streams. Datasets for studied reservoirs include basic physicochemical parameters, and for 8 out of 14 dams—potentially toxic elements (PTEs). To assess the influence of each reservoir on the river, two sampling locations were selected per dam: upstream (U) and downstream (D). Results for the water quality parameters, identified as significant discriminators in each PLS-DA model, are presented. A clear upstream dominance was observed for Pchelina, Saedinenie, and Ticha, a strong downstream pattern was observed for Dospat and Yovkovtsi, and a mixed spatial pattern for the remaining dams. The hierarchical clustering revealed three groups of parameters studied. The first cluster (EC, NO₂⁻, NO₃⁻, TN) likely reflects diffuse inputs. The second cluster (TP, PO₄³⁻) describes the relationship between total and dissolved phosphorus fractions. The third cluster (pH, NH₄⁺, DO, BOD) highlights organic matter decomposition and oxygen dynamics. The results highlight that reservoir impacts are governed by the interplay of hydrological conditions, catchment characteristics, and in-reservoir biogeochemical processes, leading to distinct functional behaviours such as retention, transformation, or release of substances. Full article

Drought exhibits a complex coupling response to regional meteorological factors, hydrological characteristics, land cover, and large-scale teleconnection climate indices, while their direct and indirect influences on multi-scale meteorological and hydrological droughts remain insufficiently understood, particularly in karst basins. This study investigated drought dynamics in China’s Yujiang River Basin using an integrated framework combining run theory, drought propagation analysis, and the partial least squares–structural equation model (PLS-SEM). We analyzed the 1-, 3-, 6-, and 12-month standardized precipitation index (SPI) and standardized streamflow index (SSI) at four hydrological stations during 1984–2014, together with meteorological factors, land cover indices, large-scale climate indices, areal precipitation, and naturalized streamflow. The results show that precipitation and streamflow exhibited slight declining tendencies with marked seasonal variability, and that drought durations of all severity levels generally decreased with increasing time scales. At the same time scale, SSI was more stable than SPI, and both indices tended to become more stable as the time scale increased. SPI-3 and SSI-1 were identified as the optimal time scales for monitoring meteorological and hydrological drought, respectively, providing a practical basis for drought identification and early warning in karst basins. Hydrological drought lagged meteorological drought by 1–3 months, indicating a measurable propagation time that is valuable for improving drought preparedness and water resources regulation. PLS-SEM further revealed that precipitation and streamflow were the dominant direct drivers of drought development, while land cover exerted a persistent negative effect, and climate-related factors mainly influenced drought indirectly. These findings enhance the understanding of drought propagation and multi-factor coupling mechanisms in karst basins and provide scientific support for regional drought monitoring and water resources management. Full article

Reliable climate data are essential for sustainable water management systems, especially under the challenges posed by climate change. In data-scarce regions, reanalysis products such as ERA5 can support flood and drought risk assessment and water security analysis. However, raw reanalysis precipitation is systematically biased relative to local observations and can distort hydrological indicators; bias correction is therefore needed. This study tests five bias correction techniques (Linear Scaling, Empirical Quantile Mapping, Quantile Mapping Spline Bias Correction, Mean Bias Subtraction, and Simple Linear Regression) on ERA5 precipitation data for Georgia, using classical and sliding window approaches at daily and monthly scales. Results show the importance of selecting the most appropriate method according to data availability and study objectives. The sliding window approach improved performance, especially at the daily scale, and distribution-based methods proved most effective in data-scarce regions. Full article

Sustainable groundwater management in regions subjected to intensive agricultural pressure requires reliable simulation tools capable of anticipating the impacts of climate change. However, in overexploited multilayer aquifers such as Tierra del Vino, locally calibrated predictive tools capable of quantifying climate-driven piezometric decline remain scarce. This study develops a numerical groundwater flow model using MODFLOW for the Tierra del Vino aquifer (Spain), a multilayer detrital system currently characterized by a critical quantitative status. Agricultural irrigation accounts for approximately 94% of total groundwater withdrawals, making it the dominant anthropogenic pressure on the system. The model was manually calibrated through more than 500 iterations, achieving a consistent representation of groundwater dynamics. Statistical evaluation based on groundwater level data from 34 piezometric monitoring points distributed across the aquifer yielded a good fit (NSE = 0.816; R = 0.928), supporting the suitability of the model for scenario analysis. Under the RCP 8.5 climate scenario, aquifer recharge could decrease by 31.75%, resulting in a significant piezometric decline within the system. At the representative well selected for the farm-scale agricultural impact analysis, this decline reaches 3.33 m and is used to evaluate its effect on pumping energy costs. The implementation of management measures proposed by the water authority reduces this decline to 1.84 m, although overexploitation conditions persist. These results indicate that current administrative restrictions are insufficient on their own and that future management should adjust abstraction rights to projected recharge conditions, maintaining the exploitation index below 0.8 to reduce the risk of long-term overexploitation. In this context, aquifer resilience is interpreted as the capacity of the groundwater system to respond to the combined pressures of climate change and agricultural abstraction while maintaining its hydrological functioning. Full article

Rain-on-snow (ROS) is a hydrometeorological phenomenon in which liquid precipitation falls onto an existing snowpack, augmenting runoff through the combined effects of rainfall and accelerated snowmelt. Anthropogenic climate change is progressively shifting the rain-to-snow partitioning of precipitation and altering land-surface conditions across mid- to high-latitude mountainous regions, thereby heightening flood potential. Most previous work, however, has addressed ROS at regional scales and over historical periods; hemispheric-scale assessments of future ROS dynamics and their implications for flood hazard and societal exposure remain scarce. Here we apply 10 bias-corrected CMIP6 models together with ERA5-Land reanalysis data to project changes in ROS days across the Northern Hemisphere under four Shared Socioeconomic Pathway (SSP) scenarios. ROS days are coupled with flood frequency analysis to quantify changes in ROS flood occurrence, and gridded population and Gross Domestic Product (GDP) data are integrated to evaluate future population-socioeconomic exposure. Under low-to-medium emission scenarios, ROS days increase substantially over historical hotspots, whereas under high-emission scenarios they decline at mid- to high latitudes yet expand into previously unaffected high-latitude and inland cold regions. ROS flood days respond nonlinearly to ROS frequency because progressive snow water equivalent loss limits runoff generation, causing ROS floods to decrease in some mountainous areas even as ROS events become more frequent. Population-socioeconomic exposure exhibits a corresponding polarization: it declines in mid-latitude regions where snow cover is disappearing but rises sharply at high latitudes, with high-emission pathways accelerating the northward migration of disaster risk. These findings bridge critical gaps in large-scale ROS climatology and shed light on future changes in ROS-induced hydrological extremes. Besides, the findings facilitate the creation of regionally focused adaptation strategies and provide useful references for integrating climate model projections with remote sensing observations to improve future monitoring and risk assessment of ROS-related floods. Full article

Flood monitoring in dry tropical basins, such as the Tumbes River (Peru), faces critical challenges due to persistent cloud cover that restricts the operability of optical sensors during extreme events, coupled with the operational gap between satellite products and conventional hydrological monitoring. To overcome these limitations, this research developed a comprehensive methodological framework in Google Earth Engine that unifies automated image thresholding and Sentinel-1 SAR time series analysis for flood detection and the estimation of early warning thresholds. The Bmax Otsu and Edge Otsu algorithms were evaluated, previously calibrated using high-resolution imagery (PlanetScope) as reference data, topographically constrained by the HAND (Height Above the Nearest Drainage) model, and validated against established change detection algorithms. The analysis of seven hydrological events between 2017 and 2024 confirmed the statistical superiority of Bmax Otsu; although both methods achieved high overall accuracy (Bmax 95.8% versus Edge 95.7%), Bmax Otsu outperformed Edge Otsu in spatial consistency (Kappa 66.1% vs. 63.7%; IoU 45.6% vs. 45.0%). Based on this, a time series analysis was applied to discriminate permanent water bodies and isolate flood dynamics. Subsequently, the functional discharge–impact response was evaluated by linking the instantaneous flood extent captured by the SAR overpasses to their corresponding peak discharges. Validated against official INDECI damage reports, it was determined that significant impacts begin at an activation threshold of 743.49 m³/s (151 flooded ha, 157 affected inhabitants) and scale linearly up to extreme peak events of 1629.02 m³/s, compromising 1234 agricultural ha and 749 inhabitants. This methodology provides a validated, low-cost tool to translate SAR observations into critical thresholds for early warning systems in data-scarce regions. Full article

The rivers in the middle and lower reaches of the Yangtze River are important wintering and migration stopovers for waterbirds. The hydrological characteristics of rivers directly affect the habitats of overwintering waterbirds and thus lead to changes in the diversity of overwintering waterbirds. The construction of artificial low-head dams has altered the natural hydrological processes of rivers, and therefore, investigating their influence on the composition of wintering waterbird communities is of great significance for the conservation and management of waterbirds. This study was carried out in the Xin’anjiang River Basin from October 2021 to March 2022, with 11 low-head dams selected as the research sites. Utilizing the sampling method, it investigated the species and abundance of wintering waterbirds in both the catchment and tailwater zones of these dams. Subsequently, the diversity of overwintering waterbirds in the two aforementioned zones was calculated, and their inter-zonal differences were analyzed and compared. The results of the study indicate that there are significant differences between the catchment area and the tailwater area of the “ZSJC” Dam (Z = 1.945, p = 0.001), whereas no significant disparities are observed in the species count and abundance of wintering waterbirds using that particular area between the catchment and tailwater areas of other dams. Compared with the catchment areas, the tailwater areas of the dams exhibit a more concentrated and abundant distribution of overwintering waterbirds, while the distribution of overwintering waterbirds in the catchment areas is more uniform than that in the tailwater areas. The 11 dams under study all demonstrated spatial turnover advantages, suggesting that catchment areas and tailwater areas make comparable contributions to β diversity. Bivariate correlation analysis in SPSS detected a significant correlation between dam vertical length and β diversity. In summary, low-head dam construction significantly affects the alpha diversity, beta diversity, abundance, and community composition of wintering waterbirds by modifying hydrological conditions and habitat structure in the Xin’an River Basin. This study provides a scientific basis for waterbird protection and low-head dam management. Full article

Accurate extraction of mountaintop areas from grid digital elevation models (DEMs) is essential for terrain analysis, geomorphological research, hydrological modeling, natural disaster monitoring, and emergency communication site selection. However, existing deep-learning-based methods often suffer from inadequate representation of local details and limited global contextual awareness, leading to blurred boundaries and reduced segmentation accuracy in complex mountainous terrains. To address these limitations, this study proposes a dual-encoder and global-context-enhanced TransUNet framework, named DEGC-TransUNet, for automated mountaintop delineation. The architecture integrates a convolutional encoder to capture fine-grained local terrain features and a MaxViT-based encoder to model multi-scale global context by encoding low-dimensional topographic attributes such as slope and curvature. A dedicated feature fusion module harmonizes complementary representations from both encoding paths, while a BiFormer-based strategy is introduced at the bottleneck to strengthen long-range dependencies and enhance convergence. The experimental results demonstrate that DEGC-TransUNet significantly outperforms baseline models such as TransUNet, DE-TransUNet, and GC-TransUNet, with relative improvements of 19.8% in Intersection over Union (IoU), 10.4% in overall accuracy (ACC), and 10.9% in F1-score. These findings provide a robust solution for mountaintop extraction, with significant potential in analyzing geomorphological evolution, simulating soil erosion, modeling species distribution in “sky island” ecosystems, and optimizing strategic placements for communication base stations and wind energy infrastructures. Full article