Search Results (6,785)

Intensive agriculture in semi-arid watersheds is considered a threat to global water security; however, the hydro-agronomic mechanisms that control diffuse pollution sources are often insufficiently characterized at the watershed scale. This study evaluates the hydrological response and nitrate leaching dynamics in the Upper Aguascalientes watershed by implementing the SWAT model, forced with meteorological data and calibrated using runoff derived from ERA5 reanalysis. Methodologically, the Potential Nitrate Leaching Risk Index (IRP_N) was formulated and coupled to the hydrological results. The comparative analysis shows that ERA captures the temporal dynamics of the HRUs, although it tends to significantly overestimate runoff volumes. The basin exhibits a marked scale-dependent duality, with the upper zone operating under a Hortonian regime, while the lower basin exhibits attenuation at the basin scale due to spatial integration and distributed storage processes. The $\left(IR{P}_N\right)$ analysis demonstrates a critical disconnect between fertilization rates (>1300 kg N·ha⁻¹) and crop absorption capacity, turning excess nitrogen into a rapid transport vector during runoff events. Finally, the results underscore the need to complement water management and infrastructure strategies with technical training programs and regulatory frameworks that promote modern agricultural practices aligned with the system’s retention capacity. Full article

Land use change is the most direct factor driving the supply and alteration of ecosystem services. This study employed the Dyna-CLUE tool to simulate future land use distributions under two scenarios—the Constrained Trend (CT) and Optimized Target-driven (OT) scenarios—based on land use data from 2010. Subsequently, their corresponding ecosystem service values (ESVs) were calculated, with the simulation outcomes revealing distinct land use layouts under each scenario. Under the CT scenario, grassland and urban areas expanded, whereas farmland and water bodies declined, reflecting a trend of urbanization at the expense of rural landscapes. In contrast, the OT scenario demonstrated a cessation of built-up land expansion, accompanied by marked increases in forest and water coverage, changes that facilitated the restoration of coastal watersheds, enhancing wetland provision and improving overall ESV. Consequently, per capita ESV increased substantially—from 1751 CNY in 2018 to 2356 CNY, matching the 2010 level—primarily due to the conversion of grasslands and farmlands into forests and wetlands. The OT scenario also improved the spatial distribution of ESVs, forming interconnected ecological zones around urban areas. The results underscore that policies restraining built-up expansion, promoting afforestation, and restoring wetlands can significantly improve ecosystem services and contribute to sustainability. Full article

The watershed boundaries in arid and semi-arid regions are critical zones where ecological vulnerability and socio-economic development are in severe conflict. The upper and middle reaches of the Yellow River basin are a typical example of this dilemma. Intensive land use and human developmental interventions in this region have severely disrupted the integrity and balance of the ecosystem. While spatially designated, networked conservation areas can effectively promote the integrity and balance of regional ecosystems, these areas may fail to capture dynamic changes in vulnerability. This study develops a “functional diagnosis-structural diagnosis-integrated optimization” framework. It integrates various scenarios to diagnose vulnerability under uncertainty and identifies bottlenecks in ecological networks. For functional diagnosis, the coupling of the sensitivity–resilience–pressure (SRP) model and the Ordered Weighted Averaging (OWA) algorithm accurately locates vulnerable areas within the regional ecosystem. In terms of structural diagnosis, the Morphological Spatial Pattern Analysis (MSPA), Minimum Cumulative Resistance model (MCR), and Circuit Theory are integrated to identify structural bottlenecks. The main findings of this study are as follows: (1) Functional Diagnosis: The coupling of SRP and OWA reveals the non-linear vulnerability responses to policy preferences and identifies areas that consistently exhibit functional vulnerability across different scenarios. (2) Structural Diagnosis: The circuit theory combined with MSPA and MCR analysis identifies 72 ecological pinch points. These bottlenecks represent the weakest structural nodes crucial for maintaining regional ecological robustness. (3) Coupled Delineation and Differentiated Restoration Strategies: High vulnerability areas identified by SRP and consistently vulnerable areas identified by OWA are combined to delineate four distinct ecological restoration units: Alpine Fragile Matrix Unit, Loess Hilly Soil Conservation Unit, Anthropogenic Pressure Pinch Point Unit, Key Structural Stepping Stone Unit. Differentiated ecological restoration strategies are proposed based on the varying sensitivity, resilience, and pressure characteristics of these units. The “functional-structural” coupled ecological vulnerability evaluation framework can precisely identify vulnerable areas. The delineated restoration units and their corresponding restoration strategies provide reference and supplementation for the protected areas system, offering transferable tools for enhancing regional ecological efficiency. Full article

The integration of Geographic Information Systems (GISs) with hydrologic science has evolved over seven decades from manual catchment delineation and output visualization to AI-native spatial water intelligence, reshaping how the water cycle is observed, modeled, and managed. This review explores that evolution, from the progressively tightening coupling between GIS software and hydrologic models to an AI-assisted future in which the line between these two fields blurs and eventually dissolves completely. The evolution of GISs in hydrology is traced through four eras, stratified as: (1) the formalization of governing equations and digital terrain representations (1950–1985); (2) the initial GIS–model coupling era and the rise in watershed simulation (1985–2000); (3) open source and the start of the open data deluge (2000–2015); and (4) machine learning and cloud-native computing (2015–present). A four-level vision for the role of artificial intelligence in the next generation of spatial hydrology is then articulated, from AI-assisted GIS operation to spatially aware AI water intelligence that reasons directly over geospatial data without requiring a traditional GIS or simulation software as an intermediary. Broader limitations and challenges are also discussed. Full article

Transboundary river basins face water stress exacerbated by data scarcity and political instability, and most allocation models require ideal conditions that ordinarily do not exist. This study operationalizes Water Diplomacy Theory (WDT) for data-scarce, conflict-prone basins through quantifiable allocation rules—a critical gap as 310 transboundary basins worldwide face similar challenges. We address: (1) How can a four-stage allocation framework reduce basin-wide water stress under varying Institutional Capacity (IC), Data Transparency (DT), and Stakeholder Inclusion (SI)? (2) What treaty provisions achieve bindingness under upstream-downstream power asymmetries? (3) How does this framework advance beyond existing models in equity, efficiency, and adaptive capacity? We synthesize Water Diplomacy Theory with Hydro-political Security Complex Theory to construct a novel four-stage framework: initial allocation with ecological floors, conditional reallocation triggers, interannual water banking, and satellite-verified compliance. Drawing on 14 treaty precedents and 30-year hydrological data for the Salween River, we embed these rules in an open-source water banking model. Results demonstrate that increasing IC from low to high reduces basin-wide water stress by 34% (±7%, 95% IC) under drought conditions. Stakeholder Inclusion decreases allocation conflicts by 52%. Water banking outperforms priority rules by 23% across climate scenarios. Cooperation becomes self-enforcing when IC exceeds 0.55. The novelty and contribution to existing literature our study provides are: (1) first operationalization of hybrid commons-to-treaty transition with 85.7% empirically grounded clauses; (2) evidence that binding cooperative treaty design is achievable in weak-state contexts through institutional design; and (3) a portable template for data-scarce conflict-affected basins. Full article

Building height is a key indicator of vertical urbanization and urban morphological complexity, yet accurately mapping building height at fine spatial resolution and large spatial scales remains challenging. This study proposes an attention-based deep learning framework (ABHNet) for building height estimation at a 10 m spatial resolution by integrating multi-source remote sensing data and socioeconomic information. The model jointly exploits Sentinel-1 synthetic aperture radar data, Sentinel-2 multispectral imagery, and point of interest (POI) data. The proposed framework is evaluated in Shanghai, a megacity with dense and vertically complex urban structures, using Baidu Maps-derived building height data as reference information. The results demonstrate that the proposed method achieves accurate building height estimation, with a root mean squared error (RMSE) of 3.81 m and a mean absolute error (MAE) of 0.96 m for 2023, and an RMSE of 3.30 m and an MAE of 0.78 m for 2019, indicating robust performance across different time periods. Also, this model is applied in two other cities (Changzhou and Guiyang) and the results indicate good performance. In addition, the expandability of the framework is examined by incorporating higher-resolution ZY-3 imagery, for which the spatial resolution was increased to 2.5 m, highlighting the potential extension of the model to heterogeneous data sources. Overall, this study demonstrates the effectiveness of attention-based deep learning and multimodal data fusion for large-scale and fine-resolution building height estimation using open-source data. Full article

This research investigates the relationships between the parameters of the GR4J hydrological model and a set of morphometric descriptors, climatic indices, land-cover characteristics, and soil properties across the Caquetá River Basin (Colombia). Twelve limnimetric–limnographic gauges with consistent records for the period 2001–2022 were selected for model calibration and validation. The corresponding sub-watersheds were delineated and characterized in terms of geomorphometry, vegetation cover, and soil permeability. According to that, the morphometric assessment focused on estimating key geomorphometric parameters, while land-cover descriptions utilized NDVI data. Soil type identification was based on the average approximate permeability across each analyzed sub-watershed. Model calibration was performed using the Differential Evolution Markov Chain (DE-MC) algorithm with 8000 simulations, forced by CHIRPS satellite precipitation and ERA5 potential evaporation data. Relationships between GR4J parameters and watershed attributes were assessed using Spearman’s rank correlation and curve-fitting analyses. The results reveal strong and consistent relationships between GR4J parameters (X1–X4) and key morphometric variables, including basin perimeter, circularity ratio, main channel length, and channel slope. Coefficients of determination ranged from 0.80 to 0.98, highlighting the potential for parameter regionalization based on physiographic and environmental descriptors. Full article

Lubricating oil (LO) is manufactured in various formulations for different applications. The inappropriate disposal of petroleum hydrocarbons can increase soil contamination, promoting deleterious environmental and human health impacts. More specifically, following prolonged exposure, LO contaminants are known to have carcinogenic and neurotoxic effects in humans. Bioremediation provides an effective and attractive strategy to expedite the clean-up processes of LO contaminants. We isolated and identified environmentally adapted strains of Pseudomonas aeruginosa, Pseudomonas putida, and Proteus vulgaris from Houston watershed bayou soils. Interestingly, all three exhibited increased resistance, vis-a-vis surrogate strains, to various antibiotic challenges (of chloramphenicol, tetracycline, kanamycin, penicillin, streptomycin, etc.) and increased biofilm formation ranging from 1.6 to 6.7-fold. In fact, all three environmental strains were significantly better at producing enhanced biofilm formation in the presence of spent LO rather than clean LO as well as outproducing biofilm made by the surrogate strains. Finally, the environmental isolates P. aeruginosa, P. putida, and P. vulgaris demonstrated an enhanced ability to sequester clean (2-, 2.5- and 1.14-fold) and spent (1.4-, 1.5, and 1.2-fold) LO when compared to their commercially acquired surrogate reference strains. Our three environmentally isolated organisms from Houston watershed soils appeared to be environmentally adapted to tolerate LO exposures. In the presence of LOs, all three environmentally isolated strains exhibited enhanced growth, enhanced biofilm production, and improved bioaccumulation of LOs relative to commercial reference strains. Taken together, environmentally adapted organisms can promote the bioremediation of contaminants threatening our environment and, potentially, human health. Full article

Identifying the specific sources of heavy metal accumulation in intensive agricultural landscapes is essential for ensuring soil sustainability and food security. In this study, we independently carried out a high-density regional geochemical survey and high-resolution field sampling in the Yangtze–Huaihe Watershed, Eastern China, and used the original sample dataset to distinguish between geogenic backgrounds and anthropogenic enrichments. By employing the APCS-MLR model, four distinct pollution sources were quantitatively identified: natural pedogenesis, agricultural activities, traffic emissions, and industrial inputs. Results demonstrated that while most heavy metal concentrations remained below national safety thresholds, Cd and Hg exhibited significant topsoil enrichment, signaling potential ecological risks. Source apportionment revealed that natural sources primarily controlled As, Cr, Ni, and Pb, with the contribution ranging from 41% to 70%. In contrast, traffic emissions (e.g., tire wear and fuel combustion) emerged as the dominant source for Cd (68%), Zn (55%), and Cu (34%), while industrial activities accounted for a substantial 89% of Hg accumulation via atmospheric deposition. Notably, despite the region’s intensive cultivation, agricultural practices played a surprisingly minor role in heavy metal accumulation. These findings highlight that the accumulations from traffic and industry now account for approximately 50% of the total heavy metal load in the region. Our results underscore the critical importance of high-resolution spatial data for precise source identification and suggest that implementing vegetative buffer zones and stricter industrial emission controls are imperative to mitigate further soil degradation in similar agricultural watersheds. Full article

In this work, iron–phosphorus-based composite biochar (FPBC) was prepared by modification with the leachate of spent LiFePO₄ batteries. The effects of solution pH, dosage, adsorption time, initial concentration, and temperature on the adsorption performance of FPBC were investigated by batch adsorption experiments with Pb(II) and Sb(III) as the target pollutants, and the adsorption mechanism was explored using SEM, BET, XPS, FTIR and XRD characterization. The results indicated that as the initial pH of the solution increased, the removal efficiency of FPBC for Pb(II) gradually increased, while the removal efficiency for Sb(III) remained largely unchanged. The removal of Pb(II) and Sb(III) by FPBC fitted the pseudo-second-order kinetic model and the three-step intraparticle diffusion model, indicating that their removal was primarily controlled by chemical adsorption. Isothermal adsorption studies revealed that FPBC adsorption of Pb(II) better fitted the Langmuir and D-R models, suggesting a monolayer-dominated adsorption process. In contrast, adsorption of Sb(III) fitted the Langmuir, Freundlich, and Temkin models, suggesting a combination of monolayer and multilayer adsorption characteristics. The maximum adsorption capacities of FPBC for Pb(II) and Sb(III) were 312.54 mg·g⁻¹ and 219.20 mg·g⁻¹ at 30 °C, which were approximately 12.85 and 3.37 times those of commercial corn stalk biochar (BC). Thermodynamic analysis confirmed that the removal of Pb(II) and Sb(III) by FPBC was a spontaneous and endothermic process. In addition, FPBC demonstrated strong selective adsorption of Pb(II) in the binary co-adsorption system of Pb(II) and Sb(III). Mechanism studies indicated that Pb(II) removal primarily occurred through co-precipitation, complexation, ion exchange, and electrostatic adsorption, while Sb(III) was mainly adsorbed by FPBC via redox reactions and complexation. Therefore, this work not only provides a low-cost, high-performance adsorbent for the remediation of water contaminated with Pb(II) and Sb(III), but also opens up new avenues for the resource recovery of the leachate of spent LiFePO₄ batteries. Full article

Investigating the influence of landscape evolution on river eutrophication is critical for optimizing spatial patterns to improve water quality. Machine learning (ML) models can capture the complex relationship between landscape metrics and water quality, but their black-box property restricts the interpretability of the underlying mechanisms and makes it difficult to forecast future trends in water quality. To address this, we developed a novel framework that, for the first time, couples an interpretable ML model with the Patch-generating Land Use Simulation (PLUS) model for eutrophication index (EI) prediction. This approach elucidates the response of river eutrophication to landscape dynamics and forecasts future river EI trends. The random forest regression (RFR) model outperformed other algorithms in quantifying these relationships (R² = 0.934 for training, 0.711 for testing). SHAP analysis revealed that landscape metrics contributed 81.78% to the river EI, far exceeding climate factors (18.22%). Consequently, landscape evolution emerged as the dominant explanatory factor. Scenario simulations indicated that while the ecological protection (EP) scenario effectively mitigates river eutrophication, the urban development (UD) scenario significantly exacerbates it. Specifically, under the UD scenario, the average EI in urban sub-watersheds is projected to reach 60.78 by 2040, approaching heavy eutrophic levels. Our findings inform spatial optimization strategies for river eutrophication management and facilitate the design of targeted, localized water ecological protection policies in subtropical monsoonal basins. Full article

Eutrophication is a pervasive issue in Mediterranean reservoirs, where external nutrient inputs and internal sediment releases interact to impair water quality and ecological stability. This study assessed the trophic condition of the artificial lake Cuga in Sardinia (Italy), mainly used for irrigation and providing potable water, by integrating watershed nutrient load estimates, inter-lake transfers, and internal phosphorus release. Field campaigns between July 2022 and May 2023 provided bi-monthly measurements of physical, chemical, and biological parameters, complemented by GIS-based land cover analysis and export coefficient modeling to quantify spatial nutrient sources. Additional phosphorus inputs from water transfers with a nearby reservoir were calculated, while internal sediment release was estimated using a calibrated mass balance model. Results revealed high nutrient concentrations, with mean total phosphorus of 128 mg P m⁻³, chlorophyll a averaging 9.9 mg m⁻³, and Secchi depth below 1 m, classifying the reservoir as eutrophic to hypertrophic under OECD and Carlson indices. Spatial loads were dominated by agricultural areas, while inter-lake transfers and internal sediment release contributed substantially to the overall phosphorus budget. The predictive Vollenweider model closely matched the observed conditions, confirming the robustness of the combined approach. Maintaining good ecological status in Mediterranean reservoirs is essential for safeguarding human well-being, as eutrophication degrades drinking-water quality, increases treatment costs, and can promote toxin-producing algal blooms with direct implications for public health. These findings highlight the need for integrated management strategies addressing both external and internal nutrient sources to mitigate eutrophication in Mediterranean reservoirs, which affects the ecosystem functioning and the related human needs and well-being. Full article

High-density polyethylene (HDPE) geomembranes (GMs) in landfill liners experience UV exposure during installation. While tensile strength deterioration after UV aging is known, changes in interfacial shear properties are rarely reported. This study investigates the evolution of interfacial shear behavior at the GM/sand interface by subjecting GM specimens to varying durations of indoor UV aging followed by direct shear tests. Underlying mechanisms were explored through tensile strength, melt flow index, crystallinity, and oxidation induction time (OIT) measurements. Results show that displacement required to reach peak shear strength for smooth geomembrane (GMS)/sand interface decreased with aging time (49.0–70.1% reduction), while no clear trend emerged for textured geomembrane (GMX)/sand interface. Following 80 days of UV exposure, the GMS/sand interfacial shear strength declined, with the peak friction angle dropping 20.6% from 26.2° to 20.8°. For the GMX/sand interface, the peak friction angle dropped to its lowest value of 31.2° after 40 days of exposure (from 34.3°), and then exhibited an increase with further UV aging. The large displacement shear strength followed a trend similar to that of the peak strength. Among the other tested indicators, the variation pattern of OIT with UV exposure exhibited the best correlation with the GMS/sand interface shear strength. Full article

Surface water–groundwater interactions within oasis–desert ecotones of arid regions play a pivotal role in sustaining regional water security and ecological stability. Taking the Niya River Basin in Xinjiang, Northwest China, as a representative inland watershed, this study systematically elucidates the mechanisms and seasonal dynamics of surface water–groundwater coupling under the combined influences of natural processes and anthropogenic activities. A total of 68 surface water and groundwater samples were collected during the dry, normal, and wet hydrological periods. Integrated hydrochemical characterization, mineral saturation index analysis, and stable isotope (δ²H and δ¹⁸O) mass balance modeling were employed to quantify recharge contributions and unravel hydrogeochemical evolution pathways. Results indicate that the waters in the study area are predominantly brackish to saline, with consistent dominant ionic assemblages (SO₄²⁻ and Na⁺) across all hydrological periods, highlighting evaporite dissolution as the primary control on solute composition. Hydrochemical evolution is jointly regulated by evaporation concentration, water–rock interactions, and cation exchange processes. Surface water chemistry reflects the combined effects of silicate weathering and evaporite dissolution, whereas groundwater chemistry is mainly governed by evaporite dissolution coupled with pronounced cation exchange. Stable isotope signatures reveal substantial secondary evaporation of regional precipitation prior to recharge. Frequent bidirectional recharge between surface water and groundwater was observed, exhibiting distinct seasonal transitions. During the dry period, groundwater provides significant baseflow support to surface water (48.6% in the oasis zone and 54.3% in the desert zone). In the normal period, recharge direction reverses, with surface water becoming the dominant source of groundwater recharge (99.0% in the oasis zone and 76.6% in the desert zone). In the wet period, spatial heterogeneity becomes evident: surface water continues to dominate groundwater recharge in the oasis zone (92.7%), whereas groundwater recharge to surface water prevails in the desert zone (50.5%). This study identifies a seasonally dynamic “discharge–infiltration–zonal regulation” bidirectional recharge pattern in arid inland river systems. The findings advance the mechanistic understanding of hydrological connectivity reconstruction within oasis–desert ecotones and provide a scientific basis for optimized regional water resource allocation and groundwater salinization risk mitigation. Full article

Sediment yield remains a major challenge in erosion-prone watersheds because it affects reservoir capacity, water quality, hydraulic infrastructure, and ecological stability. Although numerous studies have examined sediment yield estimation and sediment control, these topics are often treated separately, limiting the development of integrated watershed management strategies. Unlike many existing sediment yield review papers that focus primarily on predictive models, erosion processes, or management measures in isolation, this study provides an integrated synthesis of sediment yield estimation methods and sediment control strategies within a single watershed management framework for erosion-prone environments. The review covers empirical models, traditional sampling, physically based models, and emerging data-driven tools such as artificial intelligence, machine learning, remote sensing, and sensor-based monitoring, alongside structural, vegetative, and adaptive sediment control measures. The reviewed literature indicates three major trends: increasing integration of GIS and remote sensing with conventional models, wider use of process-based models for scenario analysis, and rapid growth of AI-based methods for real-time and nonlinear prediction. The findings further show that no single estimation or control strategy is universally applicable; performance depends strongly on watershed scale, sediment connectivity, land use, climatic regime, and data availability. Overall, the review highlights the need for integrated, adaptive, and site-specific sediment management frameworks that combine predictive modeling, monitoring technologies, and practical control interventions to improve long-term watershed resilience. Full article