Headwater streams are biodiversity-rich yet highly vulnerable ecosystems, especially in Mediterranean mountain regions where hydrological regimes are increasingly altered by irrigation and climate change. We assessed the effects of flow reduction by traditional irrigation weirs on benthic macroinvertebrate communities in five headwaters within the National–Natural Park of Sierra Nevada. We sampled upstream (reference) and downstream (flow-impacted) sites of the weirs in spring and autumn. We also quantified stream physicochemical characteristics and macroinvertebrates, and evaluated changes in their community structure. Flow reduction downstream of the abstraction points was severe (76–98
Accurate hourly precipitation fields are needed for flash-flood simulation in mountainous basins, but sparse gauges and satellite-retrieval bias remain major constraints. This study develops a retrospective CBAG workflow in which a convolutional neural network–bidirectional long short-term memory–attention model (CBA) corrects IMERG-elevation patches and Geographical Discrepancy Analysis Kriging (GDAK) interpolates the remaining fitting-gauge residuals. The workflow was tested in the Shentan River Basin with three fitting gauges and four independent spatial test gauges. Across the Early, Late and Final IMERG products, CBAG reduced root mean square error to 1.79–1.89 mm and achieved correlation coefficients of 0.75–0.78 at the independent spatial test gauges, while reducing mean absolute error and relative bias compared with the uncorrected products. CBAG-Final was then routed through the Hydrologic Engineering Center–Hydrologic Modeling System (HEC-HMS); under forcing-specific calibration, mean Nash–Sutcliffe efficiency was 0.95 for five calibration events and 0.84 for three validation events. A 2,000-member-per-event HEC-HMS ensemble was used as an uncertainty diagnostic. Event-block bootstrap confidence intervals for mean NSE were 0.930–0.966 for calibration and 0.750–0.900 for validation. The central 90
The accelerating deterioration of aging water distribution networks poses critical economic, environmental, public health challenges worldwide, with approximately 3 million kilometers of pipelines surpassing expected lifespans and leakage accounting for 70
Effective water resource management during climate change is increasingly dependent on accurate streamflow forecasting. This study explores the potential of advanced deep learning methods, specifically Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) models, for long-term, multistep ahead, streamflow forecasting in two sub-basins of the Sakarya Basin in Türkiye. By integrating high-resolution CMIP6-based climate projections under RCP 4.5 and RCP 8.5 scenarios, spatial interpolation techniques, and diverse meteorological and land use datasets, the research investigates streamflow dynamics until 2099. Approximately 40,000 alternative configurations, including hyperparameter adjustments, model architecture modifications, and training strategies, were tested to optimise model performance. The results demonstrate that deep learning models, particularly when enriched with spatially interpolated inputs and land cover variability, significantly enhance forecasting accuracy. This approach not only provides valuable projections for future water availability but also informs sustainable water management strategies in climate-sensitive regions. In terms of model performance, CNN generally outperformed LSTM in both sub-basins, achieving Kling-Gupta Efficiency (KGE) values ranging between 0.80 and 0.85 during the testing phase, indicating strong predictive capability. Regarding future projections, streamflow in the E12A033 sub-basin is expected to decline by approximately 37
Cost-effective water distribution network (WDN) design is crucial for reliable urban water supply. This paper introduces the open-source Excel for Water system Hydraulic Analysis Tool (X-WHAT) for WDN simulation and preliminary design. Unlike traditional spreadsheet approaches based on Hardy-Cross iteration, X-WHAT uses Excel’s built-in Generalised Reduced Gradient (GRG) solver with a direct matrix formulation, requiring no mass-conservative initial estimates or iterative flow corrections. The mathematical framework covers network topology, mass and energy conservation, and a multi-component cost function including tank material, structural foundation, and pumping energy costs. X-WHAT supports educational and preliminary engineering applications without coding expertise, while enabling sensitivity analysis for demand, configuration, cost, and hydraulic-performance changes. Three case studies, validated against EPANET and published literature, demonstrate the tool’s effectiveness. A tentative class organisation and unit design following the Understanding by Design (UbD) approach are also proposed. Through optimization, X-WHAT achieved a cost reduction of 151,790 USD (9.8
Climate change and unsustainable water management are intensifying agricultural drought in arid regions, threatening food security and socio-ecological sustainability. But existing frameworks have certain limitations in capturing the compound probability of supply-demand gaps and stochastic dependencies among hydrological variables under climate scenarios. Moreover, they struggle to reconcile multi-level and multi-decision-maker conflicts transmitted through the water-energy-food nexus system. To address this gap, this study proposes an integrated assessment framework that couples Copula-based joint probability analysis of future water supply-demand deficits with a Bi-Level Stochastic Multi-Objective Programming (BLSMOP) model embedded within the water-energy-food (WEF) nexus. The framework was applied to China’s Heihe River Basin. Results indicate that: (1) the future climate will become warmer and more humid, with increasing probability of normal-dry conditions and significantly higher agricultural drought risk; (2) the BLSMOP model effectively handles complexities such as conflicting objectives, decision-level interdependencies, and hydrological randomness in optimizing water allocation under future scenarios; and (3) compared with the 2014 baseline year, the optimized schemes improve water-use efficiency, economic benefits, and crop yields by 3.24–6.31
With drought years in recent years, water infrastructure projects have gained significant importance. Public–private partnerships (PPPs) offer a way to help address budget limitations of the government by tapping the private sector for financing of needed infrastructure. In this study, the risk factors in water transmission and distribution projects based on PPP were evaluated. The risks were initially identified from literature review followed by a three-round Delphi process, and categorized into six types: managerial risk, environmental-social risk, financial risk, technical risk, operational risk and political-economic risk. The 130 experts were asked to fill in a structured questionnaire based on three dimensions, namely severity, likelihood of occurrence and detectability. A total of 96 questionnaires were completed, giving valid answers. The linguistic data were then converted into fuzzy data and a dataset based on a three-dimensional (3D) risk matrix was subsequently created. Finally, three machine learning models: adaptive neuro-fuzzy inference system (ANFIS), support vector machine (SVM) and multilayer perceptron (MLP) were employed for risk evaluation. Numerical results showed that the ANFIS method was more effective than the two other methods in terms of the error rate and risk correlation. The framework gives governments and PPP project managers a complete picture of the risks present in a project, helping them to plan and make decisions.
Seasonal stratification in river type reservoirs controls vertical water quality structure, but it is difficult to assess where only routine monitoring data are available. This is particularly relevant to Paldang Reservoir, where multiple tributaries, short residence time, rainfall, and dam operation interact to produce variable mixing. This study applied a Relative Difference (RD) framework to paired upper and lower layer observations of water temperature, electrical conductivity (EC), and total organic carbon (TOC). A reference period (2013 to 2014) and a recent period (2020 to 2023) were compared across stations PD1 to PD4. RD values were interpreted with discharge, precipitation, station depth, and tributary setting rather than as independent hydrodynamic evidence. RD captured seasonal vertical heterogeneity at deeper stations (PD2 and PD4), whereas shallow stations dominated by inflow effects (PD1 and PD3) were more frequently mixed or irregular. EC based and TOC based RD often showed similar seasonal tendencies at deeper stations, but local divergences indicated that ionic water mass signals and organic matter dynamics do not always respond synchronously. The novelty of this study is a multistation RD framework applied across two monitoring periods for screening stratification related water quality variability in a river type reservoir using routine data. The results identify periods and locations where vertical heterogeneity may affect management, while showing that direct density, velocity, tracer, or high frequency profile data are required when density current mechanisms are inferred.
Hydrologic models aid interpretation of climate-water relationships. Current studies emphasize common problems in models: parameter assessment, scale, model validation, climatic scenario development, and datasets. Research needs comprise physical interpretation of hydrologic processes, parameter measurement methods, quantitative actions of uncertainty, enhanced climatic scenario development methodologies, detailed data sets, and modular modelling tools. Solutions to these aspects would enhance models’ capability to assess climatic variation effects. Thus, as there is need, the present study reviews various aspects of climatic variation impact on water resources systems with and without shared socio-economic pathways consideration and suggests future study needs in the subject.
Europe’s water resources face growing pressures from climate change and competing demands. Addressing these pressures requires integrated approaches that capture interdependencies among sectors. This paper develops a unified hydro-economic modeling framework that embeds Water–Energy–Food–Ecosystem (WEFE) linkages directly into water policy analysis. Unlike existing approaches, the framework systematically combines hydrological processes, economic behavior, and policy instrument design, enabling analysis of cross-sectoral trade-offs and welfare outcomes. Its modular structure makes it suitable for both detailed numerical models and stylized applications. We illustrate its use with two European river basins, the Júcar in Spain and the Upper Main in Germany, showing how water trading and pricing policies can be evaluated within a nexus perspective. These cases demonstrate how the framework operationalizes nexus thinking and provides a transparent, policy-relevant basis for more coherent and resilient water governance.
Rainfall–runoff (R–R) modeling is essential for effective water resources management, particularly under changing climatic conditions and increasing anthropogenic pressures on hydrological systems. This study evaluates the capability of machine learning models for runoff simulation in the Kharroud River basin, Iran, under climate variability and reservoir operation scenarios. Two machine learning approaches, Random Forest (RF) and the M5 Model Tree (M5), were applied using monthly hydro-meteorological data from 1984 to 2025. Precipitation (Pt−1 and Pt−2), two-month cumulative precipitation (Cum2 = Pt−1 + Pt−2), mean temperature, evaporation, and lagged runoff variables (Qt−1 and Qt−2) were used as input predictors. The dataset was divided into 70
Drought is one of the most destructive natural hazards, threatening agriculture, water resources, food security, and regional economies. This study develops a hybrid deep learning framework for multi-timescale drought forecasting in three hydro-climatically contrasting regions of Queensland, Australia: Darling Downs, Rockhampton, and Townsville. Eighteen hydro-meteorological variables were used to compute the Standardized Precipitation Evapotranspiration Index (SPEI) at 1-, 3-, 6-, and 12-month time scales. To improve data quality, Multivariate Variational Mode Decomposition (MVMD) and De-Mixing Multivariate Variational Mode Decomposition (DMVMD) were combined with LASSO feature selection before training eight deep learning models: BiGRU, BiCNN, BiLSTM, CNN-LSTM, CNN-BiGRU, TCN, PINN, and LNN. The proposed framework consistently improved forecasting accuracy across all regions and time scales, with BiGRU demonstrating the most consistent performance among the evaluated models. For example, under the proposed DMVMD framework, the BiGRU model achieved NSE = 0.9757, KGE = 0.7923, WI = 0.9936, MAE = 0.1192, RMSE = 0.1500, and R² = 0.9757 for SPEI-12 in the Darling Downs, while similarly high accuracy was obtained in Rockhampton and Townsville. The results demonstrate that integrating multivariate signal decomposition with feature selection substantially enhances drought forecasting and provides a reliable framework for drought early-warning and climate-resilient water resources management.
Flood routing is essential for water resources management and flood mitigation, yet traditional methods often struggle to represent complex hydraulic processes and rapidly changing flow conditions. In this study, hydrological models (Muskingum and SCS), hydraulic methods (Kinematic Wave, Muskingum–Cunge, and Dynamic Wave), and deep learning (DL) algorithms (Autoencoder, Deep Neural Network (DNN), Gated Recurrent Unit (GRU), Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), and Recurrent Neural Network (RNN)) were applied to predict flood routing. Model performance was evaluated using several statistical indicators, including RMSE, AIC, MAE, KGE, NSE, R², Pbias, and MBE. The results showed that the Autoencoder model provided the best predictive performance (RMSE: 0.30, MAE: 0.17, NSE: 0.97, R²: 0.99), while the RNN model produced the weakest results. Other DL models, particularly DNN, CNN, and LSTM, also demonstrated strong predictive capability. Among the hydraulic approaches, the Kinematic Wave method yielded the most accurate results, whereas the SCS model showed the best performance among hydrological routing methods. Overall, the findings indicate that DL models generally outperform classical routing approaches in flood prediction, with the Autoencoder architecture emerging as the most effective model under the conditions of the present study.
Managing large-scale hydraulic projects involves complex trade-offs between flood control, power supply, and ecological sustainability. Although the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) is widely used for multi-attribute decision-making, the traditional Euclidean distance formulation fails to account for correlation and multi-collinearity among water resource index attributes. To resolve this limitation, this paper introduces an improved TOPSIS model that replaces Euclidean distance with a weighted generalized Mahalanobis distance metric. This mathematical enhancement captures the underlying coupling relationships between inter-dependent criteria, eliminating parameter redundancy. The proposed model was implemented to evaluate and optimize operational scheduling schemes for a real reservoir project. The multi-attribute evaluation matrix was systematically solved, and the resulting alternative rankings were validated against field datasets. The comparative results demonstrate that the improved model successfully mitigates the ranking inversion flaws inherent to classical distance metrics, delivering a more stable and objective decision-making framework. This unified algorithmic approach provides reservoir managers and engineering consultants with a high-fidelity, intelligent optimization tool to maximize project value, ensure structural safety, and balance economic-ecological constraints in active hydrological systems. An equilibrium model balances period, cost, and quality for multi-mode reservoir construction. IBBPSO algorithm successfully converges on 14 non-dominated Pareto-optimal project schemes. MATLAB criteria correlation matrix identifies heavy dependencies between duration, cost, and quality. Weighted generalized Mahalanobis distance replaces Euclidean TOPSIS to eliminate collinearity. Sensitivity analysis of preference index λ validates ranking stability and model rationality.
The Standardized Precipitation Evapotranspiration Index (SPEI) and the Reconnaissance Drought Index (RDI) are valuable meteorological drought indices, as both incorporate precipitation and potential evapotranspiration (PET). However, the SPEI and RDI require testing of PET method selection before they can be adopted. This study presents a comparative analysis of SPEI and RDI calculated utilizing different PET methods for five stations over 45 years across multiple timescales (1, 3, 6, 9, and 12 months). Three temperature-based PET methods, Thornthwaite, Blaney-Criddle, and Hargreaves, were evaluated against the Penman-Monteith (PM) method. Statistical and graphical analyses were employed to assess the suitability and performance of SPEI and RDI under arid and semi-arid conditions. The results indicated that SPEIBC and RDIBC showed the closest agreement with the PM method across most stations and timescales. The findings offer insight into choosing the suitable temperature-based PET method, which may serve as a practical alternative to the Penman-Monteith model for supporting reliable drought assessment in data-scarce arid and semi-arid regions, such as northern Iraq.
Urban water distribution networks are critical for ensuring drinking water safety; however, water quality may deteriorate during transportation due to pipeline aging, corrosion and hydraulic conditions. This study investigated seasonal variations in water quality within an urban water supply network in Jianghan District, Wuhan, China, based on field monitoring and hydraulic simulation. Turbidity, residual chlorine, pH, temperature and total iron concentration were measured during spring and summer. The results showed that the overall water quality remained stable, while localized deterioration occurred at specific locations. Turbidity exceeded the drinking water standard at 15.7–16.7
Cross-sector water reallocations in non-market systems are typically governed through negotiated agreements rather than price-based trading. Such arrangements create coordination challenges among agricultural, municipal, and industrial users whose priorities differ and whose entitlements are administratively defined. This study develops a game-theoretic framework to examine the institutional evolution of a tripartite water-transfer contract that reallocates conserved irrigation water to high-tech manufacturing. Drawing on a coordination game with asymmetric preferences and a Nash bargaining formulation, the analysis explains how stakeholders adjust contractual rules to balance compensation for irrigation savings, the municipal utility’s cost exposure, and industry’s demand for predictable allocation and pricing. The revised contractual elements—dynamic municipal quotas and a refund mechanism that returns part of the cost savings to industrial users—constitute cooperative solutions that realign incentives while maintaining public control over water. These mechanisms also reduce dependence on energy-intensive reclaimed or desalinated water, establishing a lower-energy sourcing pathway in which industrial reliability is achieved through efficiency rather than new supply development. More broadly, the proposed framework demonstrates how institutional design can enhance equity, institutional resilience, and intersectoral cooperation in regions where water remains publicly owned and allocated through administrative rules rather than markets.
Water availability is widely used to assess water resources and climate impacts, but its interpretation often depends on model assumptions. In regulated basins, it reflects political decisions (management objectives, demand representation, reliability criteria, and environmental constraints) more than intrinsic hydrological properties. This study offers a policy-oriented interpretation of water availability in Mediterranean basins using the WAAPA (Water Availability and Adaptation Policy Analysis) model. A coherent framework tests management hypotheses that include reservoir coordination, demand modulation, guarantee parameters, and the implementation of ecological flows. Results show that variations in management assumptions produce changes in water availability comparable in magnitude to those induced by climate change. By explicitly quantifying these relative contributions, the study demonstrates that water availability is a policy-dependent indicator, not an intrinsic hydrological property. These findings highlight the need for transparent definitions and consistent assumptions when using water availability metrics for planning climate change adaptation in highly regulated water-scarce regions.
The sediment-trapping slow-release eco-dam has been increasingly employed to intercept sediment, release purified water, detent flood and peak flow. To investigate its permeability mechanism, this study employed geotextile-wrapped filter media to form four types of filter cells: (a) fine sand, (b) medium sand, (c) coarse sand, and (d) layered composite. Considering hydraulic head (seepage pressure), dam height (consolidation pressure), and sediment concentration, an orthogonal experimental design was implemented to determine the horizontal permeability coefficient. Results demonstrated that the fine sand filter cell exhibited the lowest permeability across all test conditions, which was significantly lower by one order of magnitude compared to other filter cell configurations. Silt concentration was a key influencing factor for both fine sand and coarse sand, exhibiting a significant negative correlation with the horizontal permeability coefficient. For medium sand filter media, seepage pressure served as the primary driving factor. The order of influencing factors for layered composite filter media is: Sediment Concentration > Consolidation pressure > Seepage pressure. Under conditions of seepage pressure, consolidation pressure, and Sediment Concentration, the influence of a single factor on the horizontal permeability coefficient of different filter units consistently follows the order: coarse sand > medium sand > layered composite > fine sand.These findings establish a scientific foundation for material screening in ecological dam engineering and advance the mechanistic understanding of permeability processes.
Surface water quality is threatened by a variety of variables including natural and human factors. Although extensive studies have been conducted to identify the driving factors of surface water quality, an integrated synthesis that compares previous findings across different study areas, water quality parameters, driving factors, spatial scales, and quantitative analysis methods is still lacking. In this review, we synthesize 229 recent articles on driving factors of surface water quality, providing a comprehensive analysis of surface water quality research across multiple dimensions. The synthesis reveals several notable trends in current research on surface water quality. The reviewed studies reveal that previous research has mainly focused on the watershed, lake, reservoir, sea, and river network, among which the watershed occurred frequently in previous studies as a relatively closed system. Furthermore, the major driving factors affecting surface water quality include land use types, land use pattern, topography, soil, rainfall, climate change, hydrological connectivity, policies and management measures, wildfire, and human activities. In addition, numerous quantitative analysis methods are employed to explore the effects of these driving factors and their interrelationships. This study also highlights the future prospects of using a tired monitoring framework and sediment core analysis to provide real-time, high resolution, and historic data. More attention should be paid to the significant effects of cascading events and interactions among the driving factors. This review provides a broader understanding of the mechanisms controlling surface water quality and supports decision-makers in developing effective measures to maintain surface water quality.
