Search Results (1,423)

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

Multi-site water quality sequences are jointly affected by seasonal periodicity, meteorological disturbances, and inter-site differences in the Jianghuai Watershed region. Conventional quality prediction models struggle to simultaneously achieve multi-scale feature extraction, spatial heterogeneity characterization, and prediction uncertainty expression. This study used daily-scale monitoring data on dissolved oxygen (DO), pH, and ammonia nitrogen (NH3N) from 32 monitoring stations within the region in 2025 and proposed the FT-TransONet (Fourier-enhanced Temporal Transformer Operator Network) multi-site probabilistic water quality prediction model. Within a Transformer framework, the model employed a FourierTime learnable frequency-domain filtering module, a GeoBias (Geographic Bias) attention bias mechanism, and a multi-residual ensemble strategy composed of a multilayer perceptron (MLP), a gated recurrent unit (GRU), and a temporal convolutional network (TCN) combined with a mass conservation constraint, thereby achieving both point and interval prediction of key water quality indicators. The results showed that FT-TransONet achieved the lowest Macro_RMSE among all compared methods on the multi-site water quality prediction task. At a prediction horizon of three days, its Macro_RMSE reached 0.2255, which was 21.89% lower than that of the long short-term memory network and 5.57% lower than that of the strongest baseline MC-Dropout. For the three individual indicators, the model attained coefficients of determination of 0.9135, 0.9338, and 0.8660 for dissolved oxygen, pH, and ammonia nitrogen, with corresponding root-mean-square errors of 0.5145, 0.1098, and 0.0523, confirming its potential to characterize the temporal variation in the main water quality indicators. Under multi-step prediction, the error grew gently, with the Macro_RMSE rising only from 0.2255 to 0.2384 as the horizon extended from three to seven days, and the ablation experiments, together with the probabilistic prediction results, further supported the effectiveness of the proposed structural design. Validated on 32 water quality monitoring stations in the Jianghuai Watershed, the method improved multi-site prediction accuracy while accounting for stability and uncertainty quantification, providing a preliminary reference for regional water quality early warning and management. Full article

Rock bolts are the primary ground support mechanism in underground mining. Yet verification of their installation is rarely captured in a spatially precise, retrievable form, leaving operators without an auditable as-built record for regulatory review or post-incident reconstruction. This paper presents an automated rock bolt detection process that closes this documentation gap using dense point clouds from an underground hard rock mine acquired by terrestrial laser scan. The method computes per-point ambient occlusion (AO) on closure plane-sealed chambers using a PCV implementation of the ShadeVIS principle, forms candidates from a multi-scale protrusion field, and segments them by prominence watershed before classifying each candidate with PCA-based geometric descriptors, without machine learning or training data. Installation perpendicularity is applied as a per-detection confidence cue, and detections are reported in confidence tiers that concentrate human review on the ambiguous minority. Validated against a database of 1447 bolts across 20 walls in two areas of an underground mine, the system achieved an overall recall of 83.7%, with human review completing the inventory to 100%. The physics-based design transfers across bolt types and mine geometries through parameter re-tuning rather than retraining, addressing the core limitation of deep learning methods, which require site-specific labelled datasets. Full article

Contemporary approaches to urban water management increasingly rely on concepts such as Nature-Based Solutions (NBSs), Green Infrastructure, and Blue-Green Infrastructure. Although these frameworks have gained broad acceptance, their typological character provides limited guidance for project-oriented decision-making, as they primarily describe infrastructure types rather than their functions within integrated hydrological systems. To address this gap, this study proposes a complementary functional-operational framework for interpreting ecological infrastructures in urban flood mitigation. Employing a qualitative comparative case study methodology, we analysed four diverse international models—the Dutch Water Squares (Rotterdam), Tokyo’s underground flood control system, Copenhagen’s Cloudburst Management Plan, and Singapore’s ABC Waters Programme—to examine the systemic interaction between grey, green, and blue infrastructures at different watershed scales. The results indicate that flood mitigation effectiveness depends less on the predominance of a single infrastructure type and more on the functional coupling among them. Specifically, three primary functions were identified: rapid conveyance (grey infrastructure), infiltration and thermal regulation (green infrastructure), and dynamic storage and biodiversity support (blue infrastructure). Despite the contextual limitations and varying scales of the selected cases, blue infrastructure universally emerges as a systemic buffer that enhances urban resilience by regulating excess volumetric flows. Ultimately, the proposed framework introduces an actionable interpretative layer that complements existing typological classifications, providing planners and urban designers with a robust, scalable basis for implementing integrated ecological infrastructures. Full article

Against the backdrop of climate change and compounded by human activities, increasing water scarcity has triggered a series of drought disasters, which have already severely impacted both ecological environments and socioeconomic production. The SWAT model, recognized for its strong portability and superior spatial heterogeneity, has gained widespread acceptance in fields such as hydrology and environmental science, and is extensively applied in hydrological simulation studies across large-scale river basins. Hydrological models of the study area can be constructed in the SWAT model to simulate changes in hydrological variables by conducting spatial discretization, parameter specification, and boundary condition definition. Standardized drought index can effectively reflect the spatiotemporal variations in drought disasters, holding significant importance for clarifying and predicting drought characteristics. This study took the Luanhe River Basin as the research area, constructed a watershed hydrological model based on SWAT, and projected changes in the basin’s hydrological processes for the period 2030–2060. Based on the model’s projected data, we calculated drought indices and extracted drought events for the basin. The results indicate the following: (1) During the simulation period, only 30% of the years in the Luanhe River basin had annual runoff above the long-term average, with a range of 228.18 mm. The range of mean annual runoff across sub-basins was 173.32 mm. Drought and uneven water resource allocation over both spatial and temporal scales coexisted, and this issue is expected to intensify under future climate warming and drying. (2) The mid-reaches of the Luanhe River are more prone to drought compared to the upper reaches for its higher water demand. However, due to a stronger capacity for ecological restoration, droughts there are mostly of low intensity in the mid-reaches. In contrast, the upper reaches experience more periods classified as severe or extreme drought, and the drought events encountered are generally more intense than those in the mid-reaches. (3) The method proposed in this study can screen extreme drought events based on outliers in the characteristic values of drought events. Taking the simulation from this study as an illustration, anomalies in drought event characteristic values suggest a potential basin-scale, prolonged extreme drought event in the Luanhe River Basin from June 2038 to July 2042. Proactive drought prevention policies should be formulated for this period. The findings of this study provide guiding significance and practical value for drought assessment, risk management, and policy application in the Luanhe River Basin. This study methodologically combines hydrological model predictions with drought event responses, providing a novel method for predicting basin-scale drought conditions and issuing early warnings for extreme drought events. Full article

Understanding how urbanization modifies stream thermal regimes is essential for assessing freshwater ecosystem responses to climate variability and land-use and land-cover (LULC) change. This study investigated air temperature (AT)–water temperature (WT) relationships at annual, monthly, and diurnal timescales in two adjacent, relatively flat watersheds with contrasting urbanization levels in North Central Texas: the urbanized Doe Branch and less urbanized Little Elm Creek during 2012–2021. A single harmonic analysis was applied to characterize annual and diurnal thermal patterns, including mean temperature, amplitude, and phase, while statistical analyses were used to evaluate seasonal and daily thermal variability and peak timing. At the annual scale, AT and WT metrics were strongly correlated at both sites (r = 0.85–0.94, p < 0.01) indicating that atmospheric conditions were the dominant control of annual stream temperature variability. Annual mean WT increased with AT, suggesting strong air–water thermal coupling and the potential for warmer stream temperatures under future climate warming. However, Doe Branch exhibited higher annual mean WTs, delayed seasonal peak WTs, and reduced annual temperature ranges compared with Little Elm Creek, reflecting the influence of urban watershed characteristics on seasonal thermal responses. At the diurnal scale, daily mean WT remained strongly coupled with daily mean AT, whereas daily temperature range and peak timing showed weaker relationships with AT. The greater variability in peak WT timing at Doe Branch suggests that short-term stream thermal dynamics were influenced by additional watershed characteristics beyond atmospheric forcing alone. Full article

Background: Hospitals are recognized as important point sources of antimicrobials; however, their contributions to antimicrobial contamination at the watershed scale remain poorly understood. Methods: Seasonal monitoring was conducted between December 2024 and May 2026 at two hospitals, a wastewater treatment plant (WWTP), and receiving river waters in the Yodo River Basin, Japan. Seventeen antimicrobials were quantified, and concentration profiles and mass fluxes were integrated to evaluate source contributions. Results: Ampicillin, levofloxacin, azithromycin, clarithromycin, and vancomycin were the predominant antimicrobials. Hospital wastewater was identified as the dominant source of ampicillin and vancomycin, whereas levofloxacin and clarithromycin were largely influenced by diffuse watershed inputs. Several antimicrobials persisted through wastewater treatment and were continuously detected in WWTP effluent and downstream river waters. Conclusions: This watershed-scale assessment demonstrates that antimicrobial contamination is driven by both hospital-derived point sources and broader watershed inputs. These findings provide a scientific basis for integrated hospital wastewater and WWTP management to mitigate environmental antimicrobial resistance (AMR) within the One Health framework. Full article

Accurately understanding the scale dependence of complex interactions among ecosystem services and their driving mechanisms is of great significance for regional ecosystem management. However, existing research has insufficiently addressed the scale sensitivity of ecosystem services interactions and their underlying driving mechanisms, particularly in ecologically critical mountainous regions. This study focused on the Qinling–Daba Mountains in Shaanxi Province and examined three key water-related ecosystem services. The InVEST model was employed to assess water yield, water purification, and soil conservation services across three spatial scales—5 km × 5 km grid, sub-watershed, and county—over the period 2002–2022. Pearson correlation analysis and geographically weighted regression were applied to quantify trade-off and synergies relationships at each scale, and the Geodetector method was used to systematically analyze their multi-scale driving mechanisms. The main findings were as follows: (1) All three ecosystem services exhibited significant spatiotemporal heterogeneity across scales. Water yield showed an overall increasing trend, rising from 230.25 mm in 2002 to 333.64 mm in 2022; water purification declined persistently, decreasing from 0.38 kg/ha to 0.30 kg/ha; and soil conservation increased substantially, from 78.59 t/ha to 99.85 t/ha, with the spatial patterns of each service varying markedly across scales. (2) Ecosystem services interactions demonstrated strong temporal stability but significant spatial scale dependence. The synergies between water yield and water purification weakened monotonically with increasing scale, whereas both the intensity of synergies between water yield and soil conservation and the trade-offs intensity between water purification and soil conservation strengthened progressively with coarsening scale. (3) Both the number and the explanatory power of significant driving factors and their interactions increased systematically with coarsening scale. Land use factors (FLP, CLP) were dominant at grid scale, while the importance of climatic factors (PRE) and socioeconomic factors (GDP, POP) increased progressively from the sub-watershed to the county scale. Interaction types underwent three-stage scale-dependent transitions: two-factor enhancement and nonlinear enhancement coexisted at the grid scale; nonlinear enhancement became overwhelmingly dominant at the sub-watershed scale; and enhancement and weakening types coexisted at the county scale, producing a heterogeneous interaction structure. These findings reveal that the direction, intensity, and driving mechanisms of ecosystem services interactions are inherently scale-dependent. These conclusions methodologically demonstrate the necessity of multi-scale analytical frameworks and provide important scientific foundations for developing scale-matched, spatially differentiated ecological management strategies. Full article

Soil organic carbon (SOC) is a critical indicator for soil fertility and ecosystem stability, yet its spatial distribution in slope croplands remains poorly understood at the small watershed scale. This study compared SOC content and its associated factors in two small watersheds in the hilly black soil region of Northeast China that share similar topographic and climatic conditions but differ in the adoption of slope cropland conservation measures. Surface soil samples (0–20 cm) were collected across both watersheds using a 400 m × 400 m grid, and a random forest model was used to identify the association of each factor with SOC content. The SOC content in forests was higher than that in cropland. The SOC content in cropland with soil and water conservation measures was 51.84% higher than that in cropland without such measures. SOC tended to be lower on steeper slopes. For slopes of 5~8°, cropland with conservation measures had SOC 76.98% higher than cropland without measures. Random forest analysis showed that the relative importance of SOC predictors varied among different management types. The contribution of total nitrogen (TN) and slope was higher on cropland without conservation measures, while the importance of soil pH and slope was relatively higher in cropland with measures. These findings suggest avenues for research to optimize conservation measures to improve soil quality in black soil regions. Full article

Heavy-metal contamination in alpine agricultural watersheds reflects interacting geological, environmental, and anthropogenic controls. In this study, arsenic (As), copper (Cu), lead (Pb), zinc (Zn), and chromium (Cr) were investigated in 213 farmland soil and sediment samples collected from the Nyangqu River Basin during 2019–2021 and 2024–2025. Pollution status and ecological risks were evaluated using the Nemerow Integrated Pollution Index (Pn) and Håkanson Potential Ecological Risk Index (RI), while potential factors associated with spatial variation were explored using GeoDetector and an explainable machine-learning framework integrating XGBoost, SHAP, and LIME. Mean As, Cu, Zn, and Cr concentrations exceeded Tibetan soil background values, whereas mean Pb remained below background. Farmland soils exhibited higher concentrations of As, Pb, and Zn than sediments, whereas Cu displayed comparable levels between the two media. Among the investigated metals, As showed persistent enrichment, whereas Cr exhibited the greatest spatial variability and strongest local anomalies. Overall, slight pollution dominated the study area (69.0%; median Pn = 1.73), although several hotspots increased the mean Pn to 2.10, indicating moderate pollution at the regional scale. After applying coefficient-adjusted thresholds (23/133), the ecological risk index (RI) ranged from 16.45 to 54.24 (mean = 32.70), with 5.6%, 93.9%, and 0.5% of samples categorized as low, moderate, and considerable risk, respectively, and no samples exhibiting high risk. The associated environmental factors showed element-specific patterns, involving soil physicochemical conditions, geological background, and localized anthropogenic indicators. Notably, factor combinations generally showed greater explanatory power than individual covariates, suggesting stronger joint statistical associations with heavy-metal spatial differentiation. Full article

Accurate rainfall input is critical for reliable flood simulation, particularly in semi-arid watersheds with pronounced spatiotemporal precipitation heterogeneity. This study developed a radar–gauge rainfall fusion framework to improve HEC-HMS (Hydrologic Engineering Center–Hydrologic Modeling System) model performance in the Liulin experimental watershed, Xingtai City, Hebei Province. Radar quantitative precipitation estimation (QPE) was generated via a dynamically optimized Z-I relationship, then fused with gauge observations using three methods—Geographical Differential Analysis (GDA), Conditional Merging (CM), and Random Forest (RF). The fused products drove a calibrated HEC-HMS model, evaluated over five representative flood events. All three methods corrected radar QPE underestimation. Under independent cross-validation, GDA and CM achieved comparable point-scale accuracy (CC ≈ 0.81, RMSE ≈ 5.7 mm), while RF showed lower generalization (CC ≈ 0.48, RMSE ≈ 8.7 mm) due to overfitting. In flood simulations, GDA performed most robustly, followed by RF and CM, all surpassing single-source inputs. Notably, CM’s higher statistical accuracy did not translate into better flood performance, indicating that optimal statistical fidelity does not guarantee optimal hydrological results. Peak discharge deviations persisted for short-duration intense storms and long-duration uneven rainfall events. This study confirms that radar–gauge fusion enhances rainfall input quality and provides a reliable approach for improving flood forecasting. Full article

Accurate reservoir inflow forecasting is essential for sustainable watershed management and low-carbon hydropower operation. Traditional fixed-weight ensemble models lack adaptability under non-stationary hydrological conditions, limiting their reliability in reservoir operation. This study proposes a Context-Aware Localized Weighting Ensemble (CALWE) framework for reservoir inflow forecasting. The framework constructs a predictive response space from heterogeneous model outputs, enabling context identification based on similarities in model prediction behaviors. A localized weighting strategy is then employed to adaptively determine model contributions across contexts. The framework was evaluated using daily reservoir inflow data from Xiaowan Hydropower Station in the Lancang River Basin and monthly inflow data from Xiluodu Hydropower Station in the Jinsha River Basin. Results demonstrate that CALWE outperforms individual models and conventional ensemble approaches in both cases. Compared with the best-performing individual benchmark model for each basin (i.e., SVR for Xiaowan and XGBoost for Xiluodu), CALWE achieved a relative RMSE reduction of 4.93% and an absolute NSE improvement of 0.007 for daily inflow forecasting at Xiaowan, while achieving a relative RMSE reduction of 5.32% and an absolute NSE improvement of 0.027 for monthly inflow forecasting at Xiluodu. SHAP analysis revealed scale-dependent feature contributions, with daily forecasts dominated by antecedent inflow information and monthly forecasts influenced by meteorological, land surface, and hydrological factors. These findings demonstrate that CALWE captures context-dependent inflow responses while providing interpretable insights into model predictions, thereby supporting sustainable watershed management and reservoir operation. Full article

Understanding how land change affects water yield is essential for sustainable land management and water security. However, previous studies often rely on a single model or spatial scale, limiting their ability to capture fine-scale land-surface heterogeneity and basin-integrated hydrological processes; consequently, cross-scale consistency in water-yield patterns and drivers remains unresolved. This study developed a dual-spatial-scale framework for the Upper Yangtze River Basin. The Patch-generating Land-Use Simulation (PLUS) model projected land patterns for 2030, the Integrated Valuation of Ecosystem Services and Tradeoffs Annual Water Yield (InVEST-AWY) model mapped grid-scale water yield, the Soil and Water Assessment Tool (SWAT) model simulated sub-basin hydrological responses, and Geodetector identified dominant drivers. Their integration links local land transitions with basin-scale hydrological consequences under consistent scenarios, thereby overcoming the limitations of single-model assessments. Results showed that high water-yield areas were concentrated in the eastern and central basin, while the two scales exhibited similar spatial patterns but locally different magnitudes and trends. Built-up land under urban development was 7.96% greater than under ecological protection. Climatic variability increased runoff by 2115.33 m³/s under ecological protection, while precipitation dominated water-yield variation, with explanatory powers of 0.81 and 0.67 at the grid and sub-basin scales, respectively. These findings advance understanding of scale-dependent responses and support land optimization and watershed planning. Full article

The intention of this study is to compare invasion risk and economic loss for selected fish species in both different watersheds and countries on a global scale. The potential risk of invasion for four different species was assessed by the prediction models developed from machine learning algorithms and joint dataset comprising species occurrence records and relevant environmental information. Combining invasion risk and unit economic costs derived from a comprehensive invasion cost database also enabled us to calculate the economic loss of invaders in three example countries. Results showed that the multi-layer perceptron algorithm, which was selected as the best model out of them, successfully identified areas of very-high-to-no risk for four selected fishes. The potential risk of invasion was relatively high for Perccottus glenii and low for Salmo trutta in the Republic of Korea, as compared to that of Japan and Australia. In addition, both invasion risk and unit economic costs were found to be largely responsible for the economic loss of selected fish species, specifically the species Petromyzon marinus in Australia. We expect that the methodology proposed in this study can be used to address the potential risk of invasion for non-native species along with other similar models such as species distribution models, and can be used to recommend affordable management options for controlling their populations. Full article

Accurately depicting the spatiotemporal evolution patterns and driving mechanisms of soil drought is of great significance for regional agricultural drought warning and adaptive management of water resources. There are still shortcomings in the existing research in terms of indicator applicability, mutation detection and trend persistence collaborative diagnosis, as well as the quantification of multi-scale meteorological driving factors. In response to the above issues, this study constructs the Standardized Soil Moisture Index (SSMI) based on the principle of soil moisture supply and demand balance, and comprehensively uses BFAST structure mutation detection, autocorrelation correction Mann–Kendall (MMK) trend test, Hurst persistence analysis, and cross-wavelet transform methods to systematically analyze soil drought in the Haihe River Basin (HRB) from 2000 to 2022. Using the FLDAS reanalysis dataset and multi-source meteorological observation data, this study revealed the stage changes, seasonal evolution characteristics, and dominant meteorological driving factors of soil drought in the watershed. Key findings include: (1) the most significant structural breakpoint occurred in May 2005 (confidence interval: March–November 2005); (2) spring exhibited the strongest drying trend (mean Z_s = −0.51), while autumn showed the strongest anti-persistence (mean Hurst = 0.41), making it the most vulnerable season for future soil moisture state shifts; (3) evapotranspiration was the dominant meteorological driver, with the highest significant coherence area percentage (SCAP), followed by air humidity, soil moisture, soil temperature, air temperature, and precipitation in descending order of influence. Full article