River-connected lakes are characterized by complex hydrological regimes, where hydrodynamic conditions serve as key physical drivers of aquatic ecosystem evolution and eutrophication. However, traditional water-balance methods struggle to accurately quantify water exchange under strong seasonal water-level fluctuations and the backwater effect of the Yangtze River, resulting in significant gaps in understanding lake hydrodynamic features and their seasonal eutrophication response patterns. Taking Dongting Lake as an example, this study employed a two-dimensional hydrodynamic model coupled with the advection–dispersion equation of a conservative tracer to simulate the spatiotemporal patterns of flow velocity and water turnover time during the dry season, rising-water season, wet season, and receding-water season using observed hydrological data from 2017 to 2025. Field sampling data and structural equation modeling were further used to identify the pathways through which hydrodynamic conditions affect lake trophic status. Flow velocity and water turnover time exhibited significant spatiotemporal heterogeneity: water turnover time was generally within 10 d in main flood channels but exceeded 60 d in stagnant floodplain areas and local topographic depressions. Seasonally, it was shortest in the wet season due to enhanced hydrological connectivity, yet longest in the dry season because of weakened hydraulic connection. The effects of hydrodynamics on trophic status were strongly season-dependent: during the rising-water season, hydrodynamics inhibited nutrient accumulation through dilution and flushing; during the wet season, strong runoff promoted external nutrient input; and during the dry season, hydrodynamics mainly affected trophic status by modifying physical habitat conditions for algal growth. These findings reveal the hydrological and hydrodynamic mechanisms regulating eutrophication in typical river-connected lakes, providing direct scientific support for hydrological regulation optimization, zonal eutrophication prevention and control, and water environmental carrying capacity assessment in Dongting Lake and similar systems, enabling lake managers to formulate differentiated pollution control strategies based on the hydrodynamic–trophic status response relationships across different hydrological seasons.
Existing DNAPL groundwater source inversion approaches are confronted with prominent bottlenecks: shallow surrogate models often fail to capture strong nonlinear multiphase flow relationships, traditional heuristic optimizers suffer from premature convergence, and ill-posed equifinality further degrades inversion reliability, together with prohibitive computational costs from repeated multiphase numerical simulation. Taking a typical chemical-contaminated site in Northeast China as the research object, this study establishes a multiphase flow numerical model that fully reproduces the migration and transformation mechanisms of chlorobenzene-based DNAPLs after systematic generalization of the site’s geological and hydrogeological conditions. To drastically cut the computational burden incurred during iterative inversion, high-quality datasets are generated via parameter sensitivity analysis and Latin hypercube sampling, based on which a deep convolutional neural network (DCNN)-driven high-fidelity surrogate model is constructed and embedded into the optimization framework as an equality constraint. A separated nonlinear programming model is formulated to independently quantify pollution source characteristics and hydrogeological parameters, with the objective of minimizing the residual error between field-measured and numerically simulated contaminant concentrations. A hybrid homotopy-particle swarm optimization (HH-PSO) algorithm is further proposed to address the limitations of conventional optimizers, including strong dependence on initial guesses and susceptibility to local optima. On this basis, a closed-loop feedback iteration scheme is developed, where source identification and parameter calibration are implemented alternately with bidirectional constraints and progressive correction to continuously refine and stabilize inversion outputs. This work presents distinct innovations in the methodology, algorithm, and practical application of DNAPL groundwater source inversion. Results from synthetic benchmark cases and on-site field applications demonstrate that the DCNN surrogate model achieves far higher fitting accuracy than shallow learning approaches (e.g., Kriging and support vector regression), with the coefficient of determination R2 exceeding 0.99. After the feedback correction iteration procedure, the average relative error for retrieved source locations, release histories, and hydrogeological parameters drops to 3.72%, and the overall computational efficiency is elevated by approximately 99.84%. The integrated simulation–optimization inversion framework proposed in this work integrates monitoring signal denoising, multiphase numerical simulation, deep learning surrogate modeling, hybrid intelligent optimization, and feedback iterative correction. This integrated system effectively resolves core technical bottlenecks in DNAPL groundwater source inversion, such as nonlinear ill-posedness, equifinality induced by mutual interference between source terms and aquifer parameters, prohibitive computational costs of multiphase simulations, and premature convergence of traditional optimization algorithms. The established framework can serve as a robust theoretical foundation and technical tool for rapid, precise source tracing, pollution liability confirmation, and remediation design at complex contaminated sites.
Improving water resource use efficiency (WRUE) is essential for achieving sustainable water management under increasing socioeconomic and environmental pressures. This study investigates the spatiotemporal evolution, associated factors, and spatial transition characteristics of WRUE across 11 provincial-level administrative regions in the Yangtze River Basin during 2010–2024. An integrated framework combining the super-efficiency SBM-window DEA model, Malmquist–Luenberger index, GeoDetector, and conventional and spatial Markov chain models was developed to characterize efficiency dynamics, productivity changes, explanatory factors, and state-transition pathways. The results showed that WRUE exhibited an overall fluctuating upward trend with a clear spatial gradient of lower reaches > middle reaches > upper reaches. The mean ML index was 1.004, indicating that technological change (TC) was the main contributor to productivity improvement. Urbanization rate, water use per CNY 10,000 of GDP, industrial water-use share, and primary-industry share exhibited relatively high explanatory power, and their interactions enhanced explanatory power. Markov analysis revealed strong persistence in WRUE states, while transition probabilities differed across spatial neighborhood conditions. Assuming stable transition probabilities, the high-efficiency state would reach a steady-state probability of 0.8155. These findings provide insights for differentiated water resource management and coordinated regional development.
Dissolved organic carbon (DOC) is a climate-sensitive component of carbon cycling in inland waters, but consistent regional comparisons of its seasonal and interannual patterns remain limited across China. Here, we conducted a secondary analysis of a published monthly DOC dataset for 60 selected lake and reservoir series (15 per climate region) spanning China’s subtropical monsoon, temperate monsoon, temperate continental, and plateau mountainous regions during 2000–2023. The source dataset was generated using random forest models constrained by 1326 DOC observations from 83 lake and reservoir stations, and with watershed-scale climate, soil, and anthropogenic variables used as predictors. Across the four regions, the long-term mean DOC concentrations were 9.78, 14.10, 16.12, and 15.14 mg L−1, respectively. Seasonal medians showed spring–summer enrichment in the two monsoon regions, nearly equal spring and summer values in the temperate continental region, and an autumn maximum in the plateau mountainous region. Interannual variability was greatest in the subtropical monsoon region (CV = 3.18%), whereas the plateau mountainous region had the lowest variability (CV = 0.95%). Mann–Kendall analysis identified a significant decline only in the temperate continental region (Z = −2.51, p = 0.012). These results provide a climate–region synthesis of model-derived DOC patterns in Chinese inland waters. Because climate variables contributed to the original random forest predictions, the present study interprets regional contrasts descriptively rather than as independent causal evidence of climatic controls.
This study provides a systematic review of mathematical programming models applied to agricultural water management, with a focus on their relevance for policy design addressing water scarcity and agricultural pollution. Following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) methodology, 42 peer-reviewed studies were selected from an initial sample of 438 records and analysed using a multi-dimensional framework covering research context, economic objectives and policy orientation, and model features. The analysis reveals a pronounced geographical and thematic segmentation: water scarcity studies are concentrated in Asia, while water quality studies are predominantly European and regulatory-driven, with limited mutual influence. While deterministic optimisation approaches remain prevalent, models increasingly integrate biophysical processes through coupling with agro-hydrological components. However, policy applicability is often constrained by the limited representation of farmers’ behavioural responses, trade-offs between model complexity and usability, and difficulties in transferring results across institutional contexts. Emerging policy instruments remain limited in the sample. Progress in this field depends less on technical elaboration within existing frameworks and more on integration across disciplinary approaches. A suitable pathway would be the development of models that treat water availability and quality as jointly determined outcomes and embed institutional design within the optimisation framework.
Human activities such as intensive groundwater abstraction and mine dewatering can profoundly disrupt the natural hydrological functioning of karst aquifers. The Transdanubian karst aquifer in Hungary represents one of Central Europe’s most prominent examples, where decades of coal-mine dewatering lowered groundwater levels by more than 40 m and fundamentally altered the natural recharge–discharge regime. Understanding and forecasting recovery in such complex karst systems remain challenging because of heterogeneous conduit–fracture networks, strong climate sensitivity, incomplete monitoring records, and uncertainty in long-term predictions. This study presents an integrated end-to-end machine learning framework for groundwater characterization and climate-constrained probabilistic forecasting. Monthly groundwater-level records (1970–2026) from five monitoring wells were first reconstructed using a hybrid Moving Average–Random Forest gap-filling approach, achieving high reconstruction accuracy (R2 = 0.87–0.98). Self-Organizing Maps subsequently identified four hydrogeological states representing the dewatering, transition, recovery, and near-equilibrium phases, while inter-well weight-plane correlations (>0.95) confirmed strong basin-scale hydraulic connectivity. A Bootstrapped Random Forest model forced by bias-corrected COSMO-CLM precipitation projections under the SSP2-4.5 climate scenario generated probabilistic groundwater forecasts through 2030, achieving high predictive performance (NSE > 0.80; RMSE = 0.10–0.35 m). Forecast results indicate that the basin as a whole is approaching hydraulic equilibrium by 2030, with distinct well-specific trajectories including mild steady decline and near-stable water level. The proposed framework provides a robust and transferable methodology for groundwater characterization and long-term forecasting in complex karst and fractured aquifer systems under changing climatic conditions.
Halogenated ketones (HKs) and aldehydes (HAs) are prevalent emerging disinfection by-products (DBPs) threatening drinking water safety. Although pre-oxidation and coagulation are widely applied in drinking water treatment, critical knowledge gaps remain regarding the precursor-specific effects of KMnO4 and O3 pre-oxidation on chlorinated ketone/aldehyde formation potential, as well as the interaction between coagulation optimization and DBP yields. This study systematically investigates the effects of KMnO4/O3 pre-oxidation and optimized coagulation on the formation potential (FP) of 1,1-dichloroacetone (1,1-DCP), 1,1,1-trichloroacetone (1,1,1-TCP), and chloral hydrate (CH) from four typical precursors (fulvic acid, citric acid, L-threonine, L-asparagine). Batch pre-oxidation and chlorination experiments were performed; three-dimensional excitation–emission matrix (3D-EEM) fluorescence spectroscopy was used to characterize organic-precursor structural changes, while gas chromatography–mass spectrometry (GC-MS) was adopted to quantify target DBPs. Results show that KMnO4 and O3 pre-oxidation significantly promote (p < 0.05) DBP formation from fulvic acid but exert precursor-specific effects on small-molecule precursors: KMnO4 enhances CKs while inhibiting CH from amino acids, and O3 suppresses all three DBPs from small-molecule precursors. Coagulation pretreatment weakly inhibits DBP formation (reduction rate < 20%) by removing partial precursors. This study clarifies the mechanism of process-driven DBP modulation and provides a theoretical basis for optimizing water treatment processes to control chlorinated ketone/aldehyde DBPs.
Water governance in the Colombian Amazon is shaped by institutional fragmentation, the marginalization of local and ancestral knowledge, and a disconnect between water conservation and territorial development. This article applies a social multi-criteria evaluation (NAIADE) and a territorially situated prospective analysis to two contrasting Amazonian municipalities, Puerto Caicedo (Putumayo) and Puerto Nariño (Amazonas), to examine how strategic actors evaluate alternative water governance scenarios. Working from participatory workshops, documentary analysis, and an actor characterization, we elicited actor judgments on five governance scenarios across 10 criteria and analyzed them with this method, reporting the two preorders and their intersection rather than a single synthetic index. Contrary to the expectation that the more socially cohesive, predominantly indigenous municipality would converge on a distinct preferred scenario, both municipalities exhibit weak to absent discrimination among scenarios: Puerto Caicedo shows complete indifference across all five alternatives, and Puerto Nariño does not distinguish among continuity, active community participation, and interinstitutional coordination, while clearly rejecting external dependence and purely technological solutions. Coalition structures are similar in both sites, reaching complete fusion at comparable similarity levels. We interpret this shared non-differentiation as an expression of generalized institutional fragility under post-normal conditions. The article contributes a transparent and reproducible NAIADE-to-prospective workflow, comparative evidence that contrasting structural social capital endowments do not by themselves produce divergent deliberative outcomes here, and a diagnosis-to-instrument design logic for fragile contexts.
As the largest inland river basin in China’s extremely arid region, the stability of the groundwater–vegetatifon system in the Tarim River Basin is crucial for the consolidation of the ecological security barrier in the northwest. To reveal the evolution law of groundwater storage in the watershed from 2003 to 2024 and its response mechanism to vegetation dynamics, this study is based on GRACE gravity satellite, GLDAS land surface assimilation and MODIS remote sensing data. The Theil Sen trend analysis, Hurst index, spatiotemporal Granger causality test, and standardized multiple linear regression model are integrated to systematically analyze the spatiotemporal heterogeneity, future evolution trend, and multi-driving factor contribution pattern of groundwater storage (GWSA) in the watershed. The results showed that: (1) During the study period, the GWSA of the watershed showed a significant downward trend, with a rate of −3.5 mm/a, and experienced a spatial redistribution process of “comprehensive loss local recovery southern compensation northern loss”. The northern and peripheral regions faced new depletion risks. (2) The vegetation condition continues to improve, and the VCI gradually rises from the low to medium range, but the spatial heterogeneity increases synchronously; there is a significant spatial positive correlation between VCI and GWSA, with only a strong lag driving effect in the southwestern region (F > 40). The explanatory power of vegetation factors for groundwater in other regions is limited. (3) Future trend predictions show that over 70% of the region will continue in the direction of historical changes, and the continuous loss trend in the north is difficult to reverse. (4) There is significant spatial differentiation in the contribution rate of driving factors: vegetation conditions (VCI) are the dominant factor, controlling 57.53% of the watershed edge and eastern region; precipitation and temperature dominate the central region (24.94%) and southwestern desert areas (17.53%), respectively. The research results can provide scientific basis for differentiated ecological water delivery and refined management of water resources in the Tarim River Basin.
A membrane brine concentrator (MBC) can reduce the concentrate volume entering thermal processes for zero liquid discharge (ZLD). Previous LSRRO-based studies have largely focused on high-salinity brines using modified or specifically selected low-salt-rejection membranes. This study examined the extent to which water recovery could be increased in wastewater reclamation using conventional nanofiltration (NF) modules in MBC processes. Two brackish water reverse osmosis (BWRO) modules and two NF modules were tested in 2000–40,000 mg/L NaCl. NE4040-90 provided the best balance between salt-concentrating performance and required pressure. An NF module model was developed using experimentally estimated water permeability, salt permeability, and mass-transfer coefficient. It reproduced permeate concentration and feed pressure with normalized root-mean-square errors of 5.73% and 1.20%, respectively. The developed NF module model was then iteratively coupled with the upstream BWRO simulation to evaluate an integrated two-stage BWRO and three-stage MBC process. Compared with conventional BWRO, the integrated system increased overall recovery from 81.0% to 95.9%, reduced concentrate flow from 32 to 7 m3/h, predicted a final concentrate concentration of 51,396 mg/L, and maintained permeate concentration at 34 mg/L while remaining below the 41.4 bar pressure limit. The reduced concentrate load lowered total specific energy consumption from 4.5 to 1.6 kWh/m3 of wastewater feed under the adopted ZLD assumptions. Conventional NF modules therefore provide a practical option for high-recovery wastewater reclamation toward ZLD.
Torrential flows, a broad category of rapid hydrogeomorphic processes that in the Colombian Andes includes debris flows, mudflows, and hyperconcentrated flows, pose a major hazard in tropical mountain regions. This study used two complementary binary classification models to examine geomorphometric conditioning and antecedent rainfall triggering of torrential flow occurrence. A 12.5 m ALOS PALSAR DEM and 42 years of daily rainfall data (1981–2023) from IDEAM rain gauges and CHIRPS v2 were analyzed in a GIS-based regional framework. Antecedent rainfall variables were aggregated at watershed scale using zonal statistics. The conditioning dataset comprised 642 watersheds (321 with documented events and 321 controls). Gradient boosting ranked first in the preliminary grouped holdout comparison, whereas the uncalibrated random forest achieved the highest mean score under spatial leave-one-province-out validation and was selected as the final conditioning model (mean ROC-AUC = 0.747 ± 0.052). Basin scale and relief were the leading morphometric associations. In the rainfall trigger model, previous day IDEAM mean rainfall and previous day IDEAM maximum rainfall were the two leading permutation importance predictors, followed by monthly CHIRPS rainfall; the 90-day IDEAM maximum accumulation ranked fourth. This ordering indicates that immediate rainfall dominated the fitted model, while longer antecedent wetness retained a secondary contribution. The results support watershed prioritization and regional hazard assessment; because operational rainfall thresholds were not derived, they should not be treated as a ready-to-use early-warning model.
Alpine streams are increasingly exposed to multiple disturbances, including extreme flood events, expected to become more frequent under climate change, and the expansion of small hydropower plants (SHPs). Understanding the resilience of aquatic communities and the ability of biomonitoring tools to detect disturbance-driven changes is essential for ecological assessment. We conducted an eleven-year monitoring programme (2014–2024) in the Corsaglia Stream (Northwestern Italy), comprising 16 sampling campaigns before, during, and after two flood events and SHP construction. Macroinvertebrate and fish communities were analysed using taxonomic, functional, and temporal beta-diversity metrics. Macroinvertebrates were assessed using the nationally standardised STAR_ICMi biomonitoring index and the recently developed Flow-T index. Macroinvertebrate assemblages showed high resilience, recovering taxonomic richness while maintaining “Good ecological status” despite severe flood-induced collapse. Recovery followed a nestedness-to-turnover trajectory, indicating recolonization from refugia rather than community replacement. Flow-T detected transient functional changes not captured by STAR_ICMi. In contrast, fish communities exhibited persistent structural changes, with reduced abundance, marked shifts in species composition, and slower recovery of native salmonids and European bullhead (Cottus gobio) under combined flood and hydropower impacts. These findings show that integrating taxonomic, functional, and temporal approaches improves ecological assessment beyond single-index biomonitoring approaches such as STAR_ICMi alone.
Slope stability in vegetated hillslopes depends on groundwater, geometry, external loading, soil properties, and root reinforcement. This study evaluated a low-plasticity clayey silt (CL–ML) from Loja, Ecuador, under bare soil, Eucalyptus, Pine, Vetiver, and Kikuyu covers. Direct shear tests on root-containing specimens provided equivalent Mohr–Coulomb parameters for limit-equilibrium and PLAXIS 2D strength-reduction analyses considering three slope geometries and three loading conditions: elevated groundwater, groundwater plus an 8 kN m−2 surcharge, and groundwater, surcharge, and pseudo-static loading. The resulting 90 factor-of-safety values were assessed using scenario-based factorial ANOVA and MANOVA. Geometry and loading explained 41.4% and 35.0% of total variance, respectively, whereas numerical method explained 1.1%. Limit-equilibrium factors of safety were 5.4% higher on average than PLAXIS 2D values. The vegetation-cover main effect was small and marginal (η2 = 1.5%, p = 0.051), while significant cover–loading and cover–method interactions indicated a scenario- and method-dependent response. Under pseudo-static loading, Vetiver and Kikuyu showed the highest PLAXIS 2D factors of safety, whereas LEM results remained narrowly grouped, precluding a general ranking of vegetation effectiveness. These findings support scenario-specific evaluation of equivalent root reinforcement in slope-stabilization design.
While traditional mine hydrogeology focuses on preventing water inrush to protect underground workings, this study establishes a novel evaluation framework aimed at protecting valuable, high-temperature geothermal water resources during deep coal extraction in coal–water dual-resource mines. We selected seven key indicator factors to quantify the disturbance intensity of mining to the overlying geothermal reservoir aquifer, and applied fuzzy variable set theory to evaluate the disturbance level and delineate disturbance zones to investigate the Tongzhe Coalfield area in the eastern Henan Plain of China. The results show that zones of stronger disturbance associated with the extraction of the main coal seam are concentrated in the northern, northeastern, and southwestern parts of the study area. Area statistics indicate that high disturbance zones account for 5.0% of the study area, relatively high disturbance zones for 30.6%, moderate disturbance zones for 32.6%, relatively low disturbance zones for 22.1%, and low disturbance zones for 9.7%. In total, zones at moderate disturbance or above represent 68.2%, suggesting that appropriate mining methods are required during extraction of the main coal seam to mitigate mining induced disturbance to the roof geothermal aquifer. These results provide a scientific basis for selecting mining practices that protect water resources in coal–water dual-resource mining areas.
Computer-vision-based monitoring of aquatic organisms and benthic targets requires detectors that remain reliable across variable water bodies and acquisition conditions. Underwater imagery is affected by wavelength-dependent attenuation, scattering, turbidity, illumination interference, and color cast, which can degrade models trained in source environments when they are deployed in unseen monitoring conditions. Long-duration observation with autonomous or edge platforms also requires favorable accuracy–efficiency characteristics. This study presents MOMO (Multi-Representation Optical Monitoring of Objects), an RGB–pseudo-event spiking detector for robust ecological visual monitoring. Here, “multi-representation” refers to two complementary representations of the same optical input: the RGB appearance view and the frame-derived pseudo-event structural view; it does not denote independently acquired sensing modalities. The method combines RGB appearance with a frame-derived pseudo-event structural representation in a quantized spiking neural network. A training-free generator converts each RGB frame into a three-channel pseudo-event image through grayscale projection, Prewitt gradients, polarity separation, and sample-wise normalization, Under a spatially constant positive affine photometric transformation, the representation remains unchanged apart from discretization and numerical stabilization. A dual-branch spiking backbone extracts RGB semantic features and pseudo-event structural features, which are fused by multi-scale spiking cross-attention. Under the standard unseen-domain evaluation protocol of S-UODAC, MOMO achieves 57.2% mAP@0.50, outperforming the strongest evaluated SNN baseline by 3.5 percentage points. Across the haze-like, color-cast, and illumination-interference subsets of RUOD, the mean mAP@[0.5:0.95] is 43.7%, with stronger performance under color and illumination variation. Ablation studies and feature analyses indicate that both the pseudo-event representation and spike-compatible fusion improve target–background discrimination under cross-domain water-condition shifts. The theoretical neural-network inference energy is estimated at 24.21 mJ per frame from arithmetic operations under a representative 45 nm model.
The environmental and economic impacts of nitrate leaching from agricultural soils highlight the need for accurate prediction to support effective nitrogen management. This study evaluated six machine learning (ML) models—Multiple Linear Regression, Elastic Net, K-Nearest Neighbors, Decision Trees, Extra Trees, and Multi-Layer Perceptron—using 2993 field observations to predict nitrate leaching. The models incorporated crop sequence, manure and fertilizer inputs, soil, and percolation and are benchmarked against the empirical NLES5 model used in Danish nitrogen regulation. Four ML models achieved higher predictive accuracy than NLES5 when evaluated on independent test data, with the Extra Trees model achieving the best performance (R2 = 0.63, RMSE = 23.3 kg N ha−1), exceeding NLES5 (R2 = 0.39, RMSE = 29.8 kg N ha−1). Model interpretability analyses identified winter percolation, winter vegetation cover, and soil type as key drivers of nitrate leaching. The Extra Trees model was further evaluated using scenario analyses of long-term leaching trends, marginal responses to spring-applied mineral nitrogen, and spatial patterns within the Bolbro Bæk catchment. Findings highlight the potential of ML models to improve nitrate leaching predictions in ungauged areas. Future research could incorporate additional variables, including crop yield and tillage practices, to enhance model accuracy and support sustainable agricultural practices that maintain productivity while reducing nitrate leaching.
Free chlorine is commonly applied at treatment works as a disinfectant residual but decays throughout the water supply system. Chlorine decay consists of a bulk and a wall decay component. The bulk component depends on water composition (i.e., it does not vary across the network) and is often characterized at the entrance to the system by fitting a first-order decay model based on bottle test data. The bulk decay coefficient (kb) is known to vary widely depending on the water source characteristics and temperature. Recent studies have shown that the uncertainty of kb can be significant (>15%), and its quantification is essential to avoid misinterpreting imprecise kb values. This work aims to explore how bottle test sampling conditions affect the uncertainty of kb through State Estimation (SE) and Uncertainty Assessment (UA) techniques. First, a sensitivity analysis is presented to explore the effect of the number of measurement replications and sampling times. Then, an experimental comparison is carried out to highlight the relevance of kb uncertainty and assess the effect of different types of chlorination (laboratory vs. onsite chlorination). This two-stage approach is essential for deriving a set of practical recommendations for the appropriate design of bottle tests, controlling (i.e., limiting) the associated kb uncertainty for each application. This is key to better understanding and modeling free chlorine residual bulk decay and, thus, chlorine dynamics, through water supply systems.
Sustainable river basin management requires an integrated approach that considers interactions among hydrological resources, socioeconomic development, and ecological protection. Existing studies have mainly focused on evaluating system development and coupling coordination, with limited attention to optimization-based regulation. This study conducts optimal operation of coupling coordination for the integrated system considering water resources, socioeconomic, and ecological environment. Initially, an indicator framework integrating multiple dimensions is developed to characterize system performance, followed by the application of the coupling coordination degree model to assess subsystem evolution and overall coordination. The obstacle degree model is further employed to diagnose dominant limiting factors and determine adjustable variables with regulatory potential. Finally, an optimization model is formulated to maximize the coupling coordination degree under constraints related to total water consumption, green ecological development, and regional feasibility, and NSGA-II is applied to obtain optimal regulation schemes. The results showed that the coupling coordination degree increased from 0.72 to 0.83 (+15.2%). Under the proposed operation strategy, the coupling coordination degree was further improved across all seven league-level cities, with average increases of 0.007–0.012 during the operation period. Optimization further improved coordination in all cities, demonstrating the effectiveness of the proposed framework for sustainable basin management. The proposed framework supports coordinated development and sustainable resource management in the Yellow River Basin.
Seawater reverse osmosis (SWRO) desalination is electricity-intensive, making its environmental performance highly dependent on the power supply. This study couples site-specific energy-system modelling with life cycle assessment to examine how renewable integration changes both total impacts and life-cycle hotspots. A modelled SWRO plant with a specific electricity consumption of 3.4 kWh m−3, derived from a process model for Mediterranean feedwater at 48% recovery with energy recovery devices, was assessed in Gela and Trapani, two grid-connected coastal sites with contrasting wind resources, and Lipari, a non-interconnected island with carbon-intensive backup generation. Grid-only, photovoltaic (PV)-grid, wind-grid, and PV-wind-grid configurations were modelled in HOMER Pro without storage and with excess electricity limited to 13%, then evaluated in SimaPro using Environmental Footprint 3.1. Hybrid configurations supplied 46.7%, 64.4%, and 53.3% renewable electricity in Gela, Trapani, and Lipari, reducing climate-change impacts by 30%, 42%, and 45%, respectively. Renewable integration also lowered fossil resource use, whereas PV-containing scenarios increased land and mineral/metal resource use. As electricity-related impacts declined, chemical consumption became the main non-energy hotspot, particularly for ecotoxicity, freshwater and eutrophication. Environmental performance therefore depends not only on renewable penetration, but also on technology choice and the residual electricity supply. Comprehensive system boundaries are essential when planning lower-carbon desalination for coastal and island water security.
Dongting Lake is one of the most important floodplain lakes in China and plays a critical role in regional flood regulation, ecological conservation, and water-resource management. Understanding long-term variations in lake water storage is therefore essential for evaluating hydrological responses to climate change and human activities. In this study, a hydrodynamic-simulation-based storage estimation framework was developed to quantify wet-season water storage variations in Dongting Lake during 1990–2022. The proposed estimation equation showed strong agreement with hydrodynamic-simulation-derived water storage, with a coefficient of determination (R2) of 0.972 and a root mean square error (RMSE) of 0.86 billion m3. Results indicate that both annual peak water storage and mean wet-season water storage exhibited significant declining trends over the study period. Mann–Kendall analysis revealed an evident decrease in peak storage after 2008 and a major hydrological transition around 2003, corresponding to the initial operation of the Three Gorges Dam (TGD). Comparative analysis showed that the average annual peak storage decreased from 17.27 billion m3 during 1990–2002 to 14.41 billion m3 during 2003–2022, while mean wet-season storage decreased from 8.16 billion m3 to 7.23 billion m3. Reduced inflow from both the Yangtze River and the Four Rivers was identified as the dominant factor controlling the decline in water storage. Although lakebed erosion increased the potential storage volume associated with bathymetric evolution by approximately 0.95 billion m3, its contribution was substantially smaller than the reduction in inflow runoff. Although long-term storage decreased, Dongting Lake retained substantial flood-regulation capacity, with only limited changes observed in flood-retention performance during major flood events. The findings contribute to a better understanding of long-term hydrological changes in Dongting Lake and provide useful information for flood control and water-resource management in the middle Yangtze River basin.
