Search Results (2,947)

Meticulous segmentation of medical images requires obtaining both local and global spatial detailed information. The conventional U-Net model excels at local spatial feature extraction through residual convolutional blocks but struggles to capture global features. To resolve this issue, we propose the vision transformer U-NeT (ViTUNet) model framework, which combines the self-attention mechanism of the vision transformer (ViT) to capture global information while maintaining the extraction of local features via U-NeT. This proposed architecture introduces vision transformers to the existing residual convolution blocks in the U-Net encoder path, thereby capturing both local and global features. The decoder path then rebuilds this information into high-quality segmentation maps with accurately highlighted boundaries/edges. This model is utilized to segment carious lesions in bitewing dental radiographs. These images are pre-processed using augmentation, morphological operations, and segmentation to identify the boundaries/edges of the regions of interest (caries/cavity). The proposed method is evaluated on an augmented dataset containing 3000 image–watershed mask pairs. It was trained on 2400 training images and tested on 600 testing images. The experimental results exemplified significant improvements in segmentation performance, achieving $98.45\%$ validation accuracy, $97.88\%$ validation Dice coefficient, and $95.87\%$ validation intersection over union (IoU) metric scores. These results are superior compared to other conventional and state-of-the-art U-NeT models, thus highlighting the impact of transformer-based hybrid architectures in improving medical image segmentation tasks. Full article

Groundwater sustains human activity in arid crystalline terrains where surface water is scarce and hydrogeological data are limited. However, most groundwater potential mapping approaches depend on deterministic weighting methods without quantifying model variability. This study describes an uncertainty-aware Remote Sensing and Geographic Information Systems (RS–GIS) framework to delineate groundwater potential zones in the Wadi Arab Watershed, Northeastern Sudan. Nine thematic factors—geology and lithology, rainfall, slope, drainage density, lineament density, soil, land use/land cover, topographic wetness index, and height above nearest drainage—were integrated using the Analytical Hierarchy Process (AHP), with acceptable consistency (Consistency Ratio (CR) < 0.1). To address subjectivity in weights, a Dirichlet-based Monte Carlo simulation (500 iterations) was implemented to perturb AHP weights whilst preserving compositional constraints. The resulting Groundwater Potential Index (GWPI) classified 32.69% of the watershed as high to very high potential, primarily associated with alluvial deposits and fractured crystalline rocks. Model validation using Receiver Operating Characteristic (ROC) analysis yielded an Area Under the Curve (AUC) of 0.704, indicating acceptable predictive performance. Uncertainty assessment showed low spatial variability (mean standard deviation (SD) = 0.215) and stable exceedance probabilities, verifying the robustness of predicted high-potential zones. The proposed probabilistic AHP framework augments decision reliability and provides a transferable, cost-effective tool for groundwater planning in data-limited arid basement environments. Full article

Sediment accumulation in reservoirs represents a critical challenge for sustainable water resources management in semi-arid regions. In Jordan, accelerated sedimentation threatens the operational capacity of major dams, including the King Talal Dam (KTD), which serves as a key water resource in the Amman–Zarqa Basin (AZB). This study assesses sediment yield and erosion risk at the catchment scale using the Soil and Water Assessment Tool (SWAT) integrated with the Modified Universal Soil Loss Equation (MUSLE). The AZB was subdivided into 31 sub-basins and 586 Hydrological Response Units (HRUs) based on land use, soil characteristics, topography, and slope. The model was calibrated for the period 1993–2002 and validated for 2003–2012 using hydrological and sediment observations from 17 monitoring stations. Long-term simulations covering more than two decades were conducted to quantify spatial and temporal sediment yield patterns across the basin. Results indicate a mean annual sediment yield of 2.79 t ha⁻¹ yr⁻¹, corresponding to approximately 0.59 MCM yr⁻¹ of sediment inflow to the reservoir. These estimates closely agree with bathymetric survey results reported by the Jordan Valley Authority, which indicate sedimentation rates of 2.59 t ha⁻¹ yr⁻¹ (0.55 MCM yr⁻¹). Overall, the model demonstrates strong agreement between observed and simulated sediment loads, confirming its reliability for sediment dynamics assessment. The findings are relevant to Sustainable Development Goals (SDGs) 6 (clean water and sanitation) and 15 (life on land) by informing sustainable watershed and soil erosion management practices. Full article

Eucalyptus plantations are expanding rapidly in southern China, delivering economic benefits but also posing ecological risks, which creates a pressing need for precise, municipal-scale monitoring. Mapping eucalyptus with sub-meter resolution imagery, however, is confronted by two main challenges: (1) the pronounced multi-scale heterogeneity of fragmented stands, and (2) the difficulty in achieving precise boundary delineation due to shadowed and complex canopy edges. To address these, this study makes two primary contributions. First, we present the Eucalyptus Semantic Segmentation Dataset (ESSD)—a high-quality, pixel-level annotated dataset that includes geographic coordinates to support reproducible research. Second, we propose SDCNet, a task-specific deep learning network optimized for eucalyptus mapping. SDCNet incorporates a redesigned SD-ASPP module that leverages Deep Over-parameterized Convolution (DO-Conv) to capture multi-scale features, alongside a novel Coordinated Self-Attention Mechanism (CSAM) to enhance the accuracy of canopy boundary detection. Ablation studies confirm the effectiveness of each component. In benchmark tests against seven state-of-the-art semantic segmentation models, SDCNet achieves superior performance, obtaining a per-class Intersection over Union (IoU) of 88.83% and an F1-score of 93.81% for eucalyptus—an improvement of +2.24% in IoU and +1.71% in F1-score over the strongest baseline. Applied to Nanning City, SDCNet produces the first 0.3 m resolution eucalyptus distribution map for the region. This map reveals a critical finding: within the watershed of the Xiyunjiang Reservoir—Nanning’s primary drinking water source—eucalyptus plantations cover more than 50% of the forested area. This result provides the first quantitative, high-resolution evidence of potential hydrological risk at a municipal scale. Our work establishes an integrated framework that bridges advanced remote sensing with actionable forest governance, offering scientifically grounded support for ecological risk assessment and sustainable land-use policy. Full article

The Juncal Norte Glacier (33°00′ S, 70°06′ W) is in the Dry Andes of central Chile within the Juncal Basin, a headwater watershed of the Aconcagua River, a semi-arid region experiencing an ongoing megadrought since 2010 and a 37% reduction in streamflow relative to pre-1990 baselines. This study provides the first glacier-specific annual melt and runoff estimate for Juncal Norte during mature megadrought conditions. Mass balance was estimated using a temperature index (positive degree day, PDD) model calibrated with automatic weather station (AWS) air temperature data and glacier hypsometry, assuming limited snow accumulation given that 2018–2019 precipitation and snow water equivalent (SWE) were extremely low relative to the long-term mean. Basin runoff was evaluated using a closure method comparing proglacial sub-basin-integrated discharge with modeled glacier melt volumes. Modeled glacier melt for 2018–2019 was equivalent to approximately 30% of observed annual discharge at the proglacial sub-basin, a disproportionate contribution given the glacier covers only 2.7% of the total basin area. The lower ablation zone (2900–4000 m), comprising 30% of glacier area, produced 90% of total melt volume. A + 1 °C temperature perturbation increased glacier-wide melt by 21.4%, confirming high climatic sensitivity. These results underscore the glacier’s critical but increasingly vulnerable buffering role for downstream water availability in the Dry Andes. Full article

A continuous integration framework and methodology for hydrological modeling is proposed that integrates model sensitivity analysis with real-time sensor tasking to prioritize data collection in regions and periods of high hydrological variability and drive model refinement. The Cahaba River Watershed in central Alabama serves as a case study to develop this approach. To this end, a benchmark Soil and Water Assessment Tool (SWAT) model (30 m DEM) was refined with high-resolution spatial datasets in QGIS, including 1 m DEMs, NLCD land cover, and SSURGO soil data. The refined model significantly enhanced subbasin delineation, increasing granularity from 8 to 99 subbasins, thereby improving representation of slope, runoff, and storage variability across heterogeneous landscapes. Sensitivity analyses were performed to evaluate the influence of DEM resolution and curve number (CN) perturbations on hydrologic responses, including retention, flow partitioning, and dominant flow direction. High-resolution DEMs (≤5 m) captured microtopographic features that strongly affect infiltration and surface runoff, while coarser DEMs (≥20 m) systematically underestimated retention and smoothed hydrologic gradients. The higher-resolution DEMs can be used to selectively improve the model at certain hotspots/areas of higher sensitivity. Localized flow simulations demonstrated that fine-scale terrain data substantially improve model realism, with up to 58% greater retention captured in 10 m DEMs compared to 30 m DEMs. The results confirm that aligning sensor placement and model refinement with spatially explicit sensitivity zones enhances both predictive accuracy and computational efficiency. The proposed continuous integration approach provides a scalable pathway for coupling high-resolution modeling with adaptive sensing in watershed management and supports future integration of real-time data assimilation for continuous model improvement. Full article

Watershed Ecological Compensation (WEC) is a vital tool for water environmental governance, yet existing research often focuses on either upstream or downstream regions in isolation, lacking a systematic assessment of basin-wide aggregate effects. Taking China’s Xin’an River Basin as a case study, this paper investigates the impacts of cross-provincial WEC on pollutant emissions, economic performance, and green economic efficiency. Theoretical analysis based on a social welfare maximization framework indicates that WEC helps reduce emissions and enhance green economic efficiency, though its impact on economic output is conditional. Using the Synthetic Control Method (SCM) for empirical analysis, the results show that the policy significantly reduced industrial COD emissions by an average of 111 t/10⁸ m³ per year and notably improved green economic efficiency, with industrial COD emissions per unit of GDP decreasing by 3.5 t per 100 million RMB annually. However, no significant impact on overall basin-wide economic development was observed. Robustness tests using Synthetic Difference-in-Differences (SDID) and staggered DID models further confirm the reliability of these findings. This study provides theoretical and empirical support for the policy effectiveness of WEC in pollution control and green development. Full article

Characterizing surface runoff and the transport process of non-point source pollutants (NSPs) carried by this runoff is crucial for identifying key source areas, estimating pollution loads entering water bodies, and implementing pollution control, which is particularly important in regions dominated by smallholder farming in China. Currently, most of the commonly used NSP models originated from international countries and have shortcomings such as high data requirements, high generalization degrees, and requiring the calibration of numerous parameters in the application process. Therefore, a distributed non-point source pollution model based on the time-varying gain and stormwater runoff response was constructed, designed for application at the watershed scale. This study describes the construction of the model, introducing its principles and structure through three key modules: a rainfall–runoff module, a soil erosion module, and a pollutant migration and transformation module. The proposed model was used to simulate the rainfall–runoff, soil erosion, and nutrient migration and transformation processes at different spatiotemporal scales. Although it achieved the best performance at the monthly and annual scales, its simulation results at the daily and hourly scales still met the relevant requirements, with relative errors within 20% and Nash–Sutcliffe Efficiency (NSE) coefficients of approximately 0.7. The annual sediment delivery ratios for the Yangliu Small Watershed and the basin above the Ankang section in 2022 were determined to be 0.445 and 0.36, respectively. The pollutant processes corresponding to different runoff events in the Yangliu Small Watershed were simulated, and the average NSE for total nitrogen (TN), ammonia nitrogen (NH₃-N), nitrate nitrogen (NO₃-N), total phosphorus (TP), and soluble reactive phosphorus (SRP) were determined to be 0.69, 0.74, 0.79, 0.71, and 0.71, respectively. For the basin above the Ankang section, the NSE coefficients for the simulation of NH₃-N and TP pollutant processes were 0.78 and 0.83, respectively. The model demonstrated robust applicability across various spatial (ranging from small to large watersheds) and temporal (hourly−daily−monthly−annual) scales, and exhibited stability across different basins in a semi-humid region of China. The model is characterized by a parsimonious parameter set, ease of calibration, and strong spatiotemporal versatility, thus providing an efficient and reliable tool for non-point source pollution simulation. Full article

The snow module of the HYDROTEL (version 2.8.x-078-00-4.1.15.5551) hydrological model was modified to incorporate a multilayer structure composed of ice and air layers within the snowpack, as well as to account for the impact of freezing rain on snow cover. This study examines whether this enhanced physical representation of snow processes improves the accuracy of streamflow simulations. The analysis was conducted across ten watersheds in Quebec, Canada. The multilayer snow model consistently improved low-flow simulations during both calibration and validation periods and enhanced the representation of the falling limb during the calibration period. However, the monolayer snow model performs slightly better during the rising limb of the freshet season for the calibration phase. In addition, the multilayer configuration reduced the bias of the cumulative freshet volumes and annual maximum freshet discharge. Overall, the multilayer snow model achieved comparable performance to the monolayer model for high-flow simulations while outperforming it for low-flow conditions, leading to a more accurate representation of freshet volumes and falling limb dynamics. Full article

Storm-driven turbidity is a major water-quality concern in urban watersheds, reflecting the mobilization and transport of fine sediment during runoff events. This study examines how seasonal storm characteristics influence turbidity and associated sediment transport responses in the Middle Fork of Beargrass Creek, Louisville, Kentucky, over a two-year period. Forty-one erosive storm events were identified and characterized using high-resolution rainfall data to capture storm magnitude and structure. Study objectives were to: (1) quantify event-scale turbidity responses to erosive storms, (2) compare upstream and downstream turbidity behavior to assess spatial variability, (3) evaluate seasonal variation in these relationships, and (4) assess implications for sediment-focused best management practice (BMP) design. Event-based regression models related downstream turbidity to lagged upstream turbidity and downstream erosivity. Turbidity ratios and turbidity–discharge hysteresis characterized spatial and temporal sediment transport dynamics. Results showed that winter and spring storms exhibited longer durations, stronger upstream–downstream turbidity coupling, and more stable lag relationships, indicating integrated sediment transport. Short-duration, high-intensity summer storms produced elevated turbidity ratios, pronounced clockwise hysteresis, and greater model sensitivity, consistent with localized sediment mobilization. Findings support seasonally adaptive BMP strategies, with volume-reduction approaches most effective during winter–spring and source control measures critical during summer-fall. Full article

The spatial assessment of soil erosion drivers provides essential information for prioritizing soil conservation areas. This study aims to compare the performance of the Ordinary Least Squares (OLS) regression model and the Geographically Weighted Regression (GWR) model in explaining and analyzing the spatial variations of soil erosion in the Qara-Su watershed (Ardabil Province, Iran) and identifying the relative roles of the driving factors affecting erosion. To determine the relative importance of factors influencing soil erosion in the Qara-Su watershed, potential soil erosion (A) data and RUSLE model factors, including R, K, LS, C, and P, were collected at 13,845 points within the watershed. Initially, general relationships between erosion and contributing factors were examined using the OLS regression model. Subsequently, to analyze the spatial variability of relationships and identify the relative importance of factors at different locations within the watershed, the GWR model with an adaptive kernel and optimal bandwidth selection based on AICc was employed. The performance of the OLS and GWR models was compared based on fit indices such as R² and Akaike Information Criterion corrected (AICc), and the relative importance of erosion factors was determined based on the mean local GWR coefficients. Results from the RUSLE model indicated an average annual soil erosion of approximately 7.64 tons per hectare, suggesting that the watershed falls into the moderate erosion risk category. According to the GWR model, significant improvements in explaining variations and reducing errors were observed, with higher R² and adjusted R² values (0.62 vs. 0.50) and lower AICc values (3687 vs. 97,848) compared to the OLS model. The local GWR coefficients confirmed spatial non-stationarity and revealed that LS (topography) has the highest importance in mountainous areas. The C factor showed a stronger protective effect in agricultural land-use areas. These results provide a basis for developing targeted strategies to mitigate and manage erosion drivers with higher relative importance and facilitate a better understanding of the causes and mechanisms of soil erosion across the watershed. Full article

This study systematically evaluates the regulatory effects of multi-reservoir water diversion on ecological risk thresholds in the upper Yangtze River. Taking multiple reservoirs in the upper basin as the research object, a system dynamics model was developed to simulate reservoir operation, water level regulation, ecological water diversion, and diversion capacity enhancement. Key indicators included upstream ecological risk thresholds, ecohydrological risk levels, habitat ecological risk levels, and water environment ecological risk levels. Five scenarios were designed: S0 (baseline), S1 (enhanced ecological compensation), S2 (industrial coordination and optimization), S3 (economic synergy promotion), and S4 (comprehensive regulation and optimization). These scenarios were used to assess the combined effects of different diversion strategies on ecological risk control. Results indicate the following: (1) All scenarios reduce ecological risks to some extent, but the degree of effectiveness differs. (2) The overall ranking is S4 > S1 > S3 > S2 > S0, demonstrating that comprehensive regulation optimization is most effective in mitigating ecohydrological risks, improving habitat quality, and enhancing water environment security. (3) S1 is particularly effective in reducing ecohydrological risks and is suitable as an emergency safeguard during dry seasons, though less effective than S4 in habitat and water quality improvements. (4) S3 supports economic–ecological synergy but remains less effective than S1 and S4. (5) S2 primarily enhances industrial–ecological coordination with limited contribution to overall risk control. (6) S0 yields minimal improvement under existing operational conditions, failing to meet ecosystem safety thresholds. Overall, the findings highlight that in multi-reservoir joint diversion contexts, a composite strategy centered on comprehensive regulation optimization, supplemented by ecological compensation and economic synergy, should be prioritized to achieve systematic ecological risk reduction and ensure long-term watershed ecological security. Full article

Water governance faces growing challenges from climate change, pollution, and increasing demand, rendering policy evolution a critical research focus. This study analyzes the evolutionary characteristics of China’s national water resources governance policies from 1988 to 2025 through an integrated quantitative textual analysis. Based on 154 authoritative policy documents, the study employs Latent Dirichlet Allocation topic modeling, semantic network analysis, and a tripartite policy instrument coding scheme (command-and-control, market-based, and public participation instruments). The results reveal three key findings: a significant shift in policy attention from early administrative control toward system-oriented governance emphasizing watershed/ecological protection, conservation, and technology; a persistently imbalanced instrument mix with command-and-control tools remaining dominant, despite gradual diversification after 2000; and a three-stage evolutionary trajectory from administrative framework building (1988–1999), through comprehensive management and diversification (2000–2015), to collaborative innovation and basin/ecology integration (2016–2025). This study contributes a long-term empirical perspective on water policy evolution in an emerging economy, demonstrates an integrated textual-analytic approach, and provides actionable insights for optimizing policy mixes through strengthened incentive compatibility, substantive participation mechanisms, and coherent governance-aligned instrument portfolios. Full article

Flood events represent a major natural threat, and identifying the key factors contributing to flood occurrence has gained considerable attention in 2010 and 2022 in the Swat River, Pakistan. In this study, Google Earth Engine was utilized to extract flood-related indices for the Mohmand Dam catchment, Pakistan. Different types of datasets were used to calculate fourteen influencing parameters. These indices were processed and normalized in ArcMap 10.8 and Python to enhance their visual and analytical representation. Two multi-criteria analyses with AHP, FAHP, and five machine learning models, including logistic regression, K-nearest neighbors, random forest, support vector machine, and multi-layer perception, were applied to determine the relative importance of each parameter and produce a flood susceptibility map. The results indicate that rainfall, LULC, and soil texture are the most influential factors, each contributing 11.11% to flood susceptibility. The random forest approach demonstrated stronger predictive performance than the AHP and FAHP techniques. The flood susceptibility map reveals that approximately 31.67% (4320.40 km²) of the study area falls under high flood risk. This methodology provides valuable support for planners, policymakers, hydrologists, and disaster management authorities in developing effective flood mitigation, watershed management, and resilience strategies. Full article

This study investigates the influence of parametric uncertainty in the van Genuchten–Mualem (VGM) model on hydrological simulations and proposes a deterministic, soil-focused calibration strategy within the MOHID-Land model. The approach was applied to the Pedro do Rio watershed to quantify the impact of VGM parameters, typically estimated via pedotransfer functions, on streamflow performance and to reduce uncertainty through targeted calibration. A one-at-a-time sensitivity analysis using the 95% Prediction Uncertainty (95PPU) metric identified the saturated water content (θ_s) and pore-size distribution (n) as the most influential parameters. Calibration scenarios adjusting these parameters, especially Scenario S45 (+30% θ_s, +20% n), significantly improved model performance, increasing the Nash–Sutcliffe Efficiency (NSE) from 0.20 to 0.66 on a daily scale and to 0.80 on a monthly scale during the validation period. Subsequent hydrodynamic refinements raised the daily NSE to 0.72, while monthly performance remained unchanged. The results underscore that soil parameter uncertainty plays a central role in long-term water balance representation, while hydrodynamic parameters primarily influence short-term dynamics in steep, responsive basins. Overall, the proposed strategy provides a computationally efficient alternative to fully automatic calibration methods, delivering robust performance while maintaining physical consistency, particularly in data-scarce environments. Full article