Search Results (5,536)

In the Ramis River basin of the Peruvian Andes, flood events have become increasingly frequent and intense due to climate variability. However, the basin has limited hydro-meteorological observation records, and its flood generation mechanisms are extremely complex. This situation not only hinders the accurate identification of flood-prone areas, but also limits the effective implementation of flood risk management measures. This study sets three core objectives: to assess flood sensitivity across the basin, identify the dominant factors that influence flood sensitivity, and verify the flood detection performance of multispectral indices. The study adopts two core methods. First, a multi-criteria framework that integrates the Analytic Hierarchy Process (AHP) and Geographic Information System (GIS) is used, incorporating seven flood-related environmental factors and one precipitation triggering variable. Second, the performance of four spectral indices—NDVI, NDWI, SAVI, and MSAVI2 is verified through Spearman correlation analysis, Moran’s I index, and the random forest algorithm. The study finds that landform and geology are the core factors controlling flood sensitivity, with weights of 0.35 and 0.23, respectively. Moderately flood-sensitive areas account for the largest share of the basin, reaching 66% and covering 236.54 km². The flood extent estimated by the spectral indices ranges from 36.73 km² to 101.87 km². Among these indices, NDVI has the strongest spatial correlation with flood-prone areas. The random forest model used in this study has an AUC of 0.6935 and an overall accuracy of 63.51%. The analytical framework proposed in this study is applicable to data-scarce Andean River basins, and the combined use of multispectral indices can provide support for flood risk management and decision-making in this region. Full article

Water erosion is a core issue that threatens the ecological integrity of the highland ecosystems in the Andes Mountains and the agricultural sustainability of the Ramis River basin. This study uses the Revised Universal Soil Loss Equation (RUSLE), which integrates five factors, rainfall erosivity (R), soil erodibility (K), topography (LS), cover and management (C), and support practices (P), to estimate the spatial distribution of potential water erosion rates in this basin. The results show that the very low and low erosion classes together cover 73.21% of the basin, while the high, very high, and extreme erosion classes account for 17.29% of the total area. Among these, the extreme erosion class, with an annual erosion volume exceeding 250 tons per hectare, covers 8.07% of the basin, equivalent to 1190.13 square kilometers. This extreme erosion is concentrated in steep headwater areas and five sub-basins including Cuenca Grande. Comparative model verification shows that the Ordinary Least Squares (OLS) model only identifies a positive correlation between slope gradient and potential soil loss, with an extremely low explanatory power (R² = 0.045). Its residuals exhibit significant spatial autocorrelation (Moran’s I = 0.204, p < 0.001). In contrast, the Geographically Weighted Regression (GWR) model greatly improves the model fit (R² = 0.359, RMSE = 148.288) and eliminates the spatial autocorrelation of residuals, proving that the slope-erosion relationship has spatial non-stationarity. Sensitivity analysis shows that the C factor has the highest sensitivity (0.980), followed by the LS factor (0.626). Based on these findings, this study proposes that cover and management measures such as vegetation restoration should be prioritized in high-risk headwater sub-basins. It should be noted that the values estimated in this study are potential soil loss amounts, rather than actually measured erosion values. Full article

This study proposes a surrogate model, Graph-Structured Physics-Informed Deep Operator Network (GS-PI-DeepONet), to simulate two-dimensional hydrodynamics in a tidal river network. The model was coupled with Graph Convolutional Networks (GCNs) for spatial feature extraction and Long Short-Term Memory (LSTM) network for temporal prediction, with 2D shallow-water equations (2D SWEs) embedded as physical constraints. To handle complex river network topologies, a mapping mechanism was proposed to transform discrete irregular boundaries into differentiable neural network constraints. A dynamic weighting strategy was developed to improve model training efficiency. GS-PI-DeepONet was applied to a river network within the Pearl River Basin in Zhuhai. Trained on high-fidelity Delft3D data, it achieved precise flow field reconstruction and millisecond-level extrapolation predictions, outperforming traditional data-driven models. The model can be a valuable tool for real-time hydrodynamic simulations and flood management strategies in tidal river networks. Full article

Achieving integrated water resources management at all levels, as called for by Sustainable Development Goal (SDG) target 6.5, requires assessment tools that operate at the local administrative scale. However, watershed sustainability assessments are mostly conducted at the whole-basin or provincial scale, which masks the spatial disparities that matter for local water management. This study develops a sub-district-scale Watershed Sustainability Index (WSI) for the Nong Han Basin, Thailand, by integrating the HELP framework (Hydrology, Environment, Life, and Policy) with the Pressure–State–Response structure, a calibrated QSWAT hydrological model, and spatial analysis in a geographic information system, covering 25 sub-districts. The results show that the basin has a moderate-to-high level of sustainability, with a mean WSI of 0.620: 18 sub-districts are classified as high and 7 as moderate, and none fall into the low category. The Life and Hydrology dimensions are the strongest, whereas the Policy dimension is the limiting factor in most sub-districts. This limitation arises from a low Response component (0.19) rather than from a lack of institutional capacity, as confirmed by the finding that sub-districts with low and high policy scores differ only in the Policy dimension. The apparently uniform aggregate index, combined with the high disparity among dimensional scores, confirms the value of diagnosis at the sub-district scale. The proposed framework translates the assessment results into spatial prioritization, an agency-linked decision matrix, and an intervention typology, thereby supporting evidence-based water management by local administrative organizations. Full article

Floodplains sustain fisheries, water supply, and climate regulation, and their services depend on the water regime—the extent, depth, frequency, duration, and connectivity of inundation—which dams, drought, and land-use change are altering. Remote sensing and GIS can supply evidence for managing these systems sustainably, yet the methods remain dispersed, and their fit to management needs has not been assessed. Following the PRISMA Extension for Scoping Reviews (PRISMA-ScR) and a publicly posted protocol, we retrieved peer-reviewed studies from Dimensions and OpenAlex (2004–2026), searched on 13 July 2026 and updated on 14 August 2026, and screened them in two stages with two reviewers. We charted data by study area, sensors, methods, variables, and drivers, then mapped them onto the decisions and Sustainable Development Goal targets they inform. Of 137 studies, 53% appeared since 2021; 2026 is only partially covered. Inundation extent dominates (83%), mapped mainly with Landsat (36%) and radar, whereas water level (28%), connectivity (21%), inundation frequency (14%), storage (13%), hydroperiod (12%), and depth (8%) remain scarce, as does evidence from data-scarce transboundary basins, including Central Asia. The attributes most needed for environmental-flow, allocation, and restoration decisions thus appear to be the least observed—a decision–observation mismatch that shapes monitoring priorities. Full article

This study investigates the impact of critical raw material mining and processing on surface water quality within a representative catchment area, using the Luda Yana River Basin in Southern Bulgaria as a case study. Water quality was evaluated using the Canadian Council of Ministers of the Environment Water Quality Index (CCME WQI), based on data collected from five monitoring stations. The analysis focused on key heavy metals, including Cu, Zn, Pb, Cd, Fe, Mn, Ni, and As, reflecting the influence of both active and historical mining activities in the region. Index values were calculated for the period 2008–2024 and revealed considerable temporal variability and pronounced spatial differences in water quality along the river course. This study provides one of the first long-term integrated assessments of heavy-metal-related water quality in a mining-impacted river basin in Bulgaria using the CCME WQI framework and offers new evidence on the cumulative effects of historical and ongoing mining activities on surface waters. The calculated CCME WQI values ranged from very low levels indicating poor conditions to moderate values corresponding to marginal and, occasionally, fair conditions. Overall, the predominant water quality categories were “poor” and “marginal.” The results demonstrate that the waters of the studied river basin remain below the thresholds for “fair” physicochemical status as defined by the European Water Framework Directive (2000/60/EC) and the corresponding Bulgarian legislation, including Regulation No. H-4/2012 on surface water characterization and the 2010 Ordinance on environmental quality standards for priority substances and certain pollutants. The findings highlight the persistent anthropogenic pressure exerted on the river system and emphasize the need for improved water management strategies. The study further underlines the importance of integrating environmental protection measures into the exploitation of critical raw materials in order to balance economic development with the sustainable management of water resources. Full article

Early-season winter wheat mapping is crucial for agricultural management and food security, but reliable identification remains challenging under weak spectral conditions during early growth stages. To address this challenge, this study developed a CNN–LSTM with a lag-aware gated fusion model (CL-LGFM) for winter wheat mapping in the Chaohu Basin, China, using a reconstructed 5-day Sentinel-2 NDVI time series and precipitation data from the Global Precipitation Measurement (GPM) mission. The model employs a dual-branch architecture to jointly learn vegetation and precipitation features and introduces a lag-aware dynamic gated fusion module to capture the delayed response of vegetation to precipitation and enhance multi-source feature fusion. The results show that the proposed method achieved reliable early-season winter wheat mapping (OA ≥ 0.90, Kappa ≥ 0.80) on 26 January, at least 10 days earlier than traditional methods, including SVM, RF, DTW, and TCN, using the same reconstructed 5-day NDVI time series. Optimal performance uses a 7 × 7 patch size and 30-day precipitation window. Full article

Improving the understanding of hydrological drought mechanisms is paramount for drought resistance and early warning in a changing environment. The Upper Yellow River Basin (UYRB), the primary water-producing region of the Yellow River Basin, experiences hydrological droughts that are jointly influenced by climate variability and human activities. This study systematically investigated the spatiotemporal evolution of hydrological droughts in the UYRB and elucidated their underlying mechanisms from the perspectives of sub-basin contributions, reservoir regulation, and large-scale climate drivers. We found an increasing trend in yearly drought severity from 1956 to 2010 under the natural scenario, but large reservoirs significantly reduced the severity. The headwater region, Tao River Basin, and interval region 2 were identified as the key areas in drought formation of the whole UYRB. Reservoirs generally increased monthly drought intensity in summer and autumn but decreased drought intensity in spring and winter. For drought events lasting for a longer time, reservoirs interrupted their continuity, which reduced the average severity and duration but exaggerated peak intensity, especially for extreme events. The impacts of different reservoirs on drought variations showed distinct differences due to different operation regulations. Cascade reservoir regulation weakened and altered the relationships between hydrological drought and climate indices, including PDO, AMO, NAO, and ENSO, at the 8–16 and 32–64 month timescales. This finding indicates that the effects of large-reservoir regulation should be removed using naturalized streamflow when identifying teleconnection drivers of hydrological drought and conducting drought early warning. These findings provide new insights into the mechanisms governing hydrological drought under the combined influences of climate change and reservoir regulation and offer a scientific basis for drought early warning, reservoir operation, and integrated water resources management. Full article

This study investigates the influence of rainfall variability on runoff generation in the Upper Ing River Basin and inflow to the Mae Tum Reservoir in northern Thailand using the physically based Soil and Water Assessment Tool (SWAT) version 2012. In small mountainous watersheds, water supply reliability for irrigation and domestic use—particularly for unmonitored royal initiated projects like the Mae Tum Reservoir—has become a critical concern due to shifting climatic extremes. A SWAT model was developed using detailed spatial data on topography, land use, and soil characteristics together with long-term daily climate and streamflow records. The model performance at Station I.17 was evaluated through calibration and validation using the R², Nash–Sutcliffe Efficiency (NSE), and percent bias indices. Rainfall regimes were classified into dry, normal, and wet years based on the mean and standard deviation of 25-year gauge records to drive scenario simulations. The calibrated model reproduced seasonal runoff patterns satisfactorily (monthly NSE up to 0.685 and R² up to 0.712). The simulations demonstrated the strong sensitivity of both the runoff at Station I.17 and reservoir inflow to interannual rainfall differences, with the annual runoff ranging from 71.5 to 379.7 million m³ and the annual inflow to Mae Tum Reservoir ranging from 28.84 to 48.33 million m³. These findings demonstrate that physically based spatial modeling can effectively replace traditional empirical operating rules, providing a highly transferable framework for runoff forecasting, reservoir inflow assessment, and climate responsive water resources planning in data-scarce tropical mountainous basins. Full article

Concurrent high river water temperature (RWT), low dissolved oxygen (DO), and low river flow (RF) can degrade water quality and aquatic habitat, but their joint probability is rarely quantified in a fully trivariate framework. This study develops a semiparametric trivariate drawable-vine (D-vine) copula model that uses Gaussian kernel density estimation (GKDE) margins with parametric pair-copulas to estimate compound thermal–oxygen–low-flow hazards. The framework provides conditional exceedance probabilities and AND- and OR-joint return periods (RPs) for monthly synchronized RWT–RF–DO states. The analysis uses 117 synchronized monthly triplets from the Tungabhadra and 152 from the Yamuna. Kendall’s $\tau$ values for RWT–RF and RF–DO are $-0.12$ and $+0.12$, respectively, at the Tungabhadra, compared with $+0.24$ and $-0.24$ at the Yamuna, indicating contrasting basin-specific dependence pathways. Candidate D-vine orderings are evaluated through permutation-based centred-variable selection and compared with a minimum-spanning-tree heuristic. The final selection uses information criteria, scoring rules, and calibration diagnostics. The selected structure is RF-centred for the Tungabhadra and DO-centred for the Yamuna. Bootstrap analysis supports the stability of the principal dependence structure but shows higher uncertainty in some conditional tail components. For an RWT threshold of $30°\mathrm{C}$, conditioned on DO and RF below their fifth percentiles, the fitted exceedance probability is approximately 0.90 at the Tungabhadra and 0.99 at the Yamuna. Within the available records, the Yamuna shows higher fitted co-occurrence probabilities and generally shorter trivariate AND-joint recurrence intervals than the Tungabhadra. The findings support risk-based screening of warm, low-flow, oxygen-stressed periods while emphasizing the need for local recalibration of margins, dependence structures, and management thresholds. Full article

Integrated watershed sustainability assessments are still incipient in Ecuador, where scarce and heterogeneous data are limiting factors. This study introduces the Watershed Sustainability Index (WSI) to the Chibunga Basin in Chimborazo, Ecuador, marking its first application in this region. A key aspect of this study is the development of a clear and reproducible method for applying the index, particularly in inter-Andean basins where data are limited. The four dimensions—Hydrology, Environment, Life, and Policy—were assessed through the Pressure–State–Response (PSR) framework by combining secondary statistics, field sampling, GIS land-use analysis, and a two-round Delphi consultation with ten experts to operationalize the institutional (Policy) component. The basin scored 0.38 (low sustainability), with a critical Hydrology dimension (0.08), poor Policy dimension (0.33), moderate–low Life dimension (0.42), and moderate Environment dimension (0.67). The central finding goes beyond these scores: the operationalized PSR framework pinpointed where the management cycle breaks down in practice. Systematically null response scores reveal that unsustainability stems from the failure to translate an existing regulatory framework into formal institutional action, rather than from regulatory absence or physical water scarcity alone, further constrained by unfavorable socioeconomic conditions. The procedure turns the WSI from a scoring tool into a diagnostic one, providing a replicable reference for data-scarce Andean basins. Full article

This study proposes an innovative framework that combines hydroclimatic and agro-environmental predictors, including nitrate concentration and NDVI, with climatic factors such as Rainfall, temperature, and evapotranspiration to forecast piezometric level variations in the Saïss Basin, Morocco. Eight Machine Learning (ML) models were benchmarked, and feature importance was assessed using Shapley Additive exPlanations (SHAP) to ensure model transparency and interpretability. In parallel, the PELT algorithm was applied to detect structural change points, while Sen’s slope estimator and the Mann–Kendall test were used to quantify long-term trends. The Extra Trees (ET) model achieved the best performance (R² = 0.92), with Rainfall emerging as the most influential predictor, followed by nitrate concentration, confirming the added value of hydrochemical indicators for groundwater forecasting. Change-point analysis revealed significant declines during the 1980s and 1990s, followed by lower-amplitude fluctuations since the late 2000s. Projections toward 2050 suggest partial stabilization in the central part of the basin under favorable recharge conditions, whereas persistent declines are expected to continue in peripheral areas subjected to sustained groundwater abstraction pressure. These findings provide a robust and transferable decision-support tool for the sustainable management of groundwater resources in semi-arid agricultural area. Full article

Surface deformation induced by underground coal mining is characterized by strong nonlinearity and spatial heterogeneity, which complicates early warning and ecological assessment. While InSAR-driven data assimilation models and remote sensing-based ecological indices are widely used for long-term monitoring, three fundamental limitations remain unresolved: (1) severe spatial imbalance in deformation samples biases data-driven models toward mean-reverting predictions, (2) recursive multi-step forecasting accumulates errors, leading to instability in long-horizon extrapolation, and (3) in ecological monitoring, vegetation resilience further induces a multi-year observation lag, resulting in a “pseudo-stable” bias in optical indicators. To address these issues, this study proposes an unified framework integrating multi-step deformation prediction and ecological time-lag analysis. Taking the Datong Coalfield as the study area, we utilized 231 Sentinel-1A images from March 2017 to December 2024 for SBAS-InSAR deformation inversion. A spatial stratified sampling strategy is used to extract 5894 representative points. A 24-step backward and 15-step forward windows were reconstructed to systematically compare six predictive models. Simultaneously, the Remote Sensing Ecological Index (RSEI) derived from Landsat data is used for cross-lagged analysis. The results demonstrate that: (1) The maximum deformation rate reached −276.75 mm/year, with cumulative subsidence exceeding −2000 mm. (2) At 3-step short-term forecasting, all models proved robust, with LSTM performing best (RMSE = 5.78 mm). At 15-step extreme extrapolation, however, traditional recursive models diverged significantly (Kalman, RMSE = 45.70 mm), whereas N-BEATS maintained stability and effectively mitigated temporal error cascades with an RMSE of 17.98 mm. (3) The core collapse zone exhibited concurrent ecological degradation (Lag 0), while the marginal basin presented a hidden degradation period of one to two years. It provides reliable scientific support for precise tracking and proactive safety management in complex mining areas. Full article

Soil erosion is a major environmental issue that threatens watershed ecological security and the sustainable use of land resources. Quantifying its spatiotemporal variability and associated environmental controls is important for sustainable land use planning and watershed management. To characterize the spatiotemporal variation in CSLE-simulated soil erosion and the relative explanatory contributions of environmental variables in the Qingyi River Basin, this study integrated rainfall, soil type, digital elevation model, land use, and vegetation coverage data for six observation years from 2000 to 2025 with GIS spatial analysis and the Chinese Soil Loss Equation (CSLE). Geodetector and CatBoost–SHAP were further applied to evaluate the explanatory contributions and interaction patterns of the selected environmental variables on the simulated erosion results. The results showed the following: (1) Woodland and cropland dominated the land use structure of the basin, while construction land increased from 90.95 km² to 164.21 km². Land use patterns differed markedly between the upstream and downstream areas, with woodland and grassland dominating the upstream area and cropland and construction land accounting for higher proportions in the downstream area. (2) Across the six observation years, the mean soil erosion modulus ranged from 135.74 to 378.39 t·km⁻²·a⁻¹, indicating generally low erosion levels, with the highest value occurring in 2015 and the lowest in 2025. (3) Soil erosion intensity was mainly characterized by slight and mild erosion, which together accounted for more than 95% of the basin area, whereas areas of moderate erosion and above were mainly concentrated in downstream mountainous areas and along both sides of river valleys. (4) The explanatory analysis showed that elevation, land use, and vegetation coverage made relatively high contributions to the spatial variability in the CSLE-simulated erosion results. Topographic and vegetation-related variables showed higher explanatory contributions in the upstream area, whereas land use showed a higher contribution in the downstream area. These findings provide a quantitative basis for soil erosion monitoring, the identification of priority areas for soil and water conservation, sustainable land use optimization, and region-specific watershed management in the Qingyi River Basin. Full article

Balancing terrestrial carbon-storage conservation with land development is a central sustainability challenge in transboundary river basins, yet the consequences of alternative land-use pathways in the Chinese portion of the Tumen River Basin remain insufficiently understood. Using land-use datasets from 1990, 2000, 2010, and 2020, the InVEST and PLUS models were employed to quantify historical carbon-storage change and projected land-use and carbon-storage outcomes under three scenarios for 2050. Separately, an XGBoost–SHAP framework was applied to the 2020 spatial data to interpret the spatial heterogeneity of carbon storage and identify nonlinear thresholds. Forest remained the dominant land-use type between 1990 and 2020, whereas the area of Built-up expanded by 78.7%. Over the same period, total carbon storage decreased from 153.07 × 10⁸ t to 151.03 × 10⁸ t, following a decline–recovery–decline trajectory. Among the three 2050 pathways, only the Ecological protection scenario produced a net increase in carbon storage relative to 2020 (+3.82 × 10⁶ t), whereas the Urban development scenario resulted in the largest loss (−1.65 × 10⁸ t). For the 2020 spatial pattern, the XGBoost–SHAP analysis identified NDVI and slope as the leading explanatory variables, with model-derived thresholds for slope (6.18°), precipitation (666.98 mm), NDVI (0.92), elevation (442.65 m), GDP (CNY 8725), and distance to railways (10,379.04 m). These results therefore support a spatially differentiated land-use strategy that prioritizes forest-patch continuity and restricts the replacement of carbon-dense land by Built-up within the basin. Full article