Water

28 pages, 46542 KB

Open AccessArticle

Process-Chain Analysis of Viscoplastic Landslide-Generated Impulse Waves from Initial Slide Conditions Through Water-Entry Dynamics to Wave Responses

by Zhenxia Yuan, Yadong Bian, Song Yin, Xian Liu, Xinchen Pan, Zhenzhu Meng and Bohan Cheng

Water 2026, 18(17), 2189; https://doi.org/10.3390/w18172189 (registering DOI) - 3 Sep 2026

Abstract

A key scientific challenge in landslide-generated impulse-wave research is to determine how initial slide conditions are translated into wave responses through the intermediate water-entry process. Existing data-driven studies generally focus on direct prediction or variable-importance ranking and therefore provide limited insight into the [...] Read more.

A key scientific challenge in landslide-generated impulse-wave research is to determine how initial slide conditions are translated into wave responses through the intermediate water-entry process. Existing data-driven studies generally focus on direct prediction or variable-importance ranking and therefore provide limited insight into the pathways linking slide release, water entry, and wave generation. To address this issue, a three-stage process-chain framework was developed using 290 laboratory test cases, integrating initial slide conditions, water-entry dynamics, and wave responses. Cross-stage correlation analysis, standardized path modeling, mediation-effect decomposition, cascade prediction, bootstrap resampling, and diagnostic analyses were jointly applied. The results show that relative effective entry mass M is strongly correlated with relative wave amplitude A and relative wave height H, with Spearman coefficients of 0.96 and 0.91, respectively, while the corresponding coefficients for the entry Froude number

F r

are 0.71 and 0.80. Within the present dataset and fitted model, mediation analysis indicates that the pathways through M have the largest modeled indirect contributions: the indirect effects of initial slide mass through M are 0.652 for A and 0.600 for H, whereas the corresponding effects of the Bingham number are

- 0.669

and

- 0.615

. The cascade framework further shows that the realized entry state can be systematically reconstructed from the initial conditions and used as an explicit intermediate layer for wave-response prediction. Prediction comparisons further show that the observed entry-state model is best interpreted as an explanatory or diagnostic benchmark because it uses measured entry-state variables, whereas the direct initial-condition and cascade models address prospective prediction from prescribed initial-condition information. The main advantage of the cascade framework therefore lies in its physical interpretability rather than a substantial gain in predictive accuracy. Within the present dataset and fitted model, the associations between the initial slide conditions and wave responses are expressed mainly through mass-related and inertia-related pathways involving the realized entry state. Full article

(This article belongs to the Section Hydraulics and Hydrodynamics)

25 pages, 2286 KB

Open AccessArticle

Tracing Water, Energy, and Pollution Pressures Driven by Chinese Household Food Demand Within the Water–Energy–Food Nexus

by Tianbo Fu, Jiawen Li and Zheng Wu

Water 2026, 18(17), 2188; https://doi.org/10.3390/w18172188 (registering DOI) - 3 Sep 2026

Abstract

Household food demand mobilizes water, energy, and pollutants throughout supply chains. Using China’s 2023 211-sector input-output table and environmental accounts, this study traces supply-chain water withdrawal, total energy consumption, and actual loads of chemical oxygen demand (COD), ammonia nitrogen (NH₃-N), total [...] Read more.

Household food demand mobilizes water, energy, and pollutants throughout supply chains. Using China’s 2023 211-sector input-output table and environmental accounts, this study traces supply-chain water withdrawal, total energy consumption, and actual loads of chemical oxygen demand (COD), ammonia nitrogen (NH₃-N), total nitrogen (TN), and total phosphorus (TP) across 21 food-related sectors, including catering services (restaurants and related commercial food service activities). Household food demand induced 257 km³ of water withdrawal, 11.7 EJ of energy consumption, and the following pollutant loads: COD (1430 × 10⁴ t), NH₃-N (20.9 × 10⁴ t), TN (129 × 10⁴ t), and TP (20.5 × 10⁴ t). Plant-based primary foods led water withdrawal and nutrient loads; animal-source primary foods generated 59.5% of COD; catering services had the largest energy share. Upstream sectors contributed 53.2% of water withdrawal, 75.6% of energy consumption, 45.1% of COD, and 52.2–52.3% of nutrient loads. Covering 80% of each burden required 12 origin–destination links for water withdrawal, 56 for energy consumption, 5 for COD, and 11 for each nutrient, indicating dispersed energy attribution. Grain, other agricultural products, and livestock and other animal products recurred as multi-pressure hotspots. A conditional 20% reduction in their direct intensities lowered water withdrawal by 15.7%, energy consumption by 2.6%, COD by 10.3%, and nutrient loads by 16.1–16.2%. These findings support targeted water and pollution measures alongside broader upstream energy-efficiency action. Full article

(This article belongs to the Special Issue Advanced Perspectives on the Water–Energy–Food Nexus)

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29 pages, 2167 KB

Open AccessReview

Explainable Artificial Intelligence in Water Research: Methods, Applications, Insights, and Future Directions

by Yingren Deng, Yanni Cao and Jianyong Wu

Water 2026, 18(17), 2187; https://doi.org/10.3390/w18172187 (registering DOI) - 3 Sep 2026

Abstract

Artificial intelligence (AI) is increasingly used in water research. However, many AI models, particularly complex machine learning models, often generate outcomes that are difficult for humans to interpret. Explainable artificial intelligence (XAI) has been developed to address these challenges by providing transparent and [...] Read more.

Artificial intelligence (AI) is increasingly used in water research. However, many AI models, particularly complex machine learning models, often generate outcomes that are difficult for humans to interpret. Explainable artificial intelligence (XAI) has been developed to address these challenges by providing transparent and human-interpretable explanations of model behavior and predictions. We conducted a structured narrative review using predefined searches of Web of Science Core Collection and Scopus to synthesize empirical XAI applications across six water-research domains: hydrological processes, water quality and pollution, groundwater systems, urban water systems, climate–water interactions, and water and wastewater treatment. The review covers feature-importance methods, SHapley Additive exPlanations (SHAP), Local Interpretable Model-agnostic Explanations (LIME), partial dependence plots (PDPs), individual conditional expectation (ICE) plots, accumulated local effects (ALE) plots, counterfactual explanations, and deep-learning attribution methods. Building on previous reviews and perspectives focused on particular water domains or methodological priorities, we provide a cross-domain synthesis of XAI spanning natural and engineered water systems, with emphasis on method selection, model and data compatibility, explanation reliability, and operational implementation. These capabilities, however, must be interpreted with appropriate caution because XAI explanations remain conditional on the data, fitted model, and explanation method, and therefore should not be treated as evidence of causal mechanisms or environmental controls. Recognizing these limitations, we provide practical guidance for selecting and evaluating XAI methods and outline priorities for developing reliable, scalable, and operationally useful AI systems for water research and management. Full article

(This article belongs to the Special Issue Advanced Data Analytics for Water Quality and Public Health)

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23 pages, 17791 KB

Open AccessArticle

Effects of Hydrological and Hydrodynamic Processes on Water Eutrophication in Typical River-Connected Lakes: Dongting Lake, China

by Zheheng Yan and Jialei Zhang

Water 2026, 18(17), 2186; https://doi.org/10.3390/w18172186 (registering DOI) - 3 Sep 2026

Abstract

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 [...] Read more.

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. Full article

(This article belongs to the Special Issue Impact of Environmental Factors on Aquatic Ecosystem, 2nd Edition)

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21 pages, 12439 KB

Open AccessArticle

Inversion of Groundwater DNAPL Pollution Source Based on DCNN Surrogate Model and Hybrid Homotopy-PSO with Feedback Iteration

by Jiayuan Guo, Tiansheng Miao, Guanghua Li and Han Wang

Water 2026, 18(17), 2185; https://doi.org/10.3390/w18172185 - 3 Sep 2026

Abstract

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 [...] Read more.

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. Full article

(This article belongs to the Special Issue Sustainable Water Resource Management Using Cutting-Edge Technologies)

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21 pages, 12886 KB

Open AccessArticle

Forecasting Dam Storage Volume Using a Hybrid RNN Model Empowered by Tunable Q-Factor Wavelet Transform and Metaheuristic Optimization

by Turker Tugrul

Water 2026, 18(17), 2184; https://doi.org/10.3390/w18172184 - 3 Sep 2026

Abstract

It is universally acknowledged that water is essential for the survival of humanity. Therefore, the effective utilization and sustainability of water resources are of paramount importance. Driven by this necessity, this study develops predictive models for the Çubuk2 Dam, which supplies drinking water [...] Read more.

It is universally acknowledged that water is essential for the survival of humanity. Therefore, the effective utilization and sustainability of water resources are of paramount importance. Driven by this necessity, this study develops predictive models for the Çubuk2 Dam, which supplies drinking water to Ankara, using historical data on temperature, precipitation, humidity, reservoir volume, and water level. The models were constructed using deep learning, optimization, and wavelet decomposition techniques, which have garnered significant attention from researchers in recent years. Specifically, Recurrent Neural Network (RNN), Random Forest (RF), Particle Swarm Optimization (PSO), and Tunable Q-Factor Wavelet Transform (TQW) methods were utilized. RNN was employed both as a standalone model and hybridized as RNNRF and RNNPSO, with TQW applied to all models to enhance predictive performance. Furthermore, ten different input scenario structures were established using Mutual Information (MI). To evaluate model performance, the Correlation Coefficient (R), Nash–Sutcliffe Efficiency (NSE), Kling–Gupta Efficiency (KGE), Performance Index (PI), and Root Mean Square Error (RMSE) metrics were adopted. The results demonstrated that the hybrid models yielded highly effective outcomes and that Mutual Information successfully identified optimal model input structures. Full article

(This article belongs to the Special Issue New Techniques for Hydrologic Modelling and Forecasting)

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12 pages, 1870 KB

Open AccessArticle

Regional Patterns of Dissolved Organic Carbon in Lakes and Reservoirs Across Four Major Climate Regions of China

by Xingkui Tao, Na Li, Siyu Zhang, Hailin Qin, Wenyu Chu, Ningning Wang, Quanliang Jiang and Shuaidong Li

Water 2026, 18(17), 2183; https://doi.org/10.3390/w18172183 - 3 Sep 2026

Abstract

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 [...] Read more.

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. Full article

(This article belongs to the Section Water and Climate Change)

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24 pages, 9389 KB

Open AccessArticle

Source Discrimination of Mine Water Inrush Based on UV–Vis Spectroscopy and Dual-Optimized CNN Model: A Case Study of the Baode Mine

by Longqiang Zhang, Jinkai Yan, Kai Liu, Donglin Dong, Yaoyao Zhang, Shouchuan Zhang, Luyao Wang and Xinrui Yue

Water 2026, 18(17), 2182; https://doi.org/10.3390/w18172182 - 3 Sep 2026

Abstract

Mine water hazards are one of the main factors limiting the safe and efficient extraction of coal resources in China. The rapid and accurate identification of the source of water inrushes is central to the prevention and control of mine water hazards. This [...] Read more.

Mine water hazards are one of the main factors limiting the safe and efficient extraction of coal resources in China. The rapid and accurate identification of the source of water inrushes is central to the prevention and control of mine water hazards. This study aims to address the issues of complex pre-treatment procedures and low accuracy in identifying mixed water samples associated with traditional methods for determining the source of mine water inrushes. This study proposes an intelligent model for identifying the source of mine water inrushes that integrates ultraviolet–visible spectrophotometry (UV–Vis) with convolutional neural networks (CNNs). This model utilizes convolutional neural networks to automatically extract features from and classify spectral images of water sources associated with mine water inrushes, eliminating the cumbersome pre-processing steps involved in traditional spectral source analysis and significantly improving the efficiency of water source identification. The CNN is used to classify and identify the mine water spectral images measured by UV–Vis. The model training results showed that the accuracy of the model was 98.89%, 95.93%, and 90.67% for the training sets of single water samples, mixed water samples, and overall water samples, respectively. The test results indicated that the model was accurate for all the water samples in the test set, and the UV-CNN model had better classification recognition ability for complex mixed water samples compared with the water chemistry-based kernel function principal component analysis and support vector machine (KPCA-SVM) discrimination model. When the characteristics of water samples are relatively similar, the discriminative advantage of the model becomes more prominent. This model has the features of fast convergence speed, short computing time, high discriminative accuracy and stability, and can provide an efficient technical method for the rapid and accurate identification of the water source of mine water inrush. Full article

(This article belongs to the Section Hydrogeology)

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24 pages, 9579 KB

Open AccessArticle

Spatiotemporal Evolution, Associated Factors, and Spatial Transition of Water Resource Use Efficiency in the Yangtze River Basin

by Xiaodong Huang, Jingqi You, Dong Wang, Haokun Fang and Wenkai Liu

Water 2026, 18(17), 2181; https://doi.org/10.3390/w18172181 - 3 Sep 2026

Abstract

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 [...] Read more.

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. Full article

(This article belongs to the Section Water Resources Management, Policy and Governance)

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34 pages, 4203 KB

Open AccessReview

Remote Sensing of Surface Soil Moisture: A Comprehensive Review of Retrieval Methods and Satellite Products

by Xingrui Zeng, Xiaoman Qi, Junhuan Peng, Yuebin Wang, Liqiang Zhang, Xiaotong Qi, Yongze Song, Linlin Xu, Zhen Wang, Zhouzheng Gao, Xiaolong Wu, Chuangao Xie, Xu Li, Xinwei Jiang, Pengcheng Hu and Jiazhi Tang

Water 2026, 18(17), 2180; https://doi.org/10.3390/w18172180 - 3 Sep 2026

Abstract

Soil moisture (SM) is a key parameter in surface water and energy cycles, playing a vital role in agriculture, hydrology, and atmospheric science. Remote sensing (RS) has been widely applied in SM retrieval, yet most existing reviews focus on individual data sources or [...] Read more.

Soil moisture (SM) is a key parameter in surface water and energy cycles, playing a vital role in agriculture, hydrology, and atmospheric science. Remote sensing (RS) has been widely applied in SM retrieval, yet most existing reviews focus on individual data sources or specific methodologies, lacking a systematic pathway from retrieval principles to product applications, which poses a barrier for early-stage researchers. To address this gap, this study provides a comprehensive review of commonly used SM retrieval models based on different RS data sources, including optical, thermal infrared, active microwave, passive microwave, and multi-source approaches. Their underlying principles, assumptions, advantages, and limitations are critically examined. In parallel, key specifications of major satellite-based SM products, such as spatial-temporal resolution, coverage, and data accessibility, are summarized and compared. By integrating methodological explanations with product characterization, this review serves as a practical reference for researchers entering the field, facilitating informed decisions in SM retrieval applications. Full article

(This article belongs to the Section Soil and Water)

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17 pages, 6852 KB

Open AccessArticle

Molecular Characterization of Water-Column and Sedimentary Dissolved Organic Matter in Coal Mining Subsidence Areas

by Xiaoli Kai, Han Song, Liangmin Gao, Jing Xu, Fengjie Li, Yanjun Liu, Leilei Luan, Mengting Zhao and Qi Liu

Water 2026, 18(17), 2179; https://doi.org/10.3390/w18172179 - 3 Sep 2026

Abstract

Dissolved organic matter (DOM) in the water column and sediments in coal mining subsidence areas considerably affects the carbon cycle and ecological environment. This study analyzed the spectral characteristics, material composition, and molecular transformation of DOM in the water column and sediments of [...] Read more.

Dissolved organic matter (DOM) in the water column and sediments in coal mining subsidence areas considerably affects the carbon cycle and ecological environment. This study analyzed the spectral characteristics, material composition, and molecular transformation of DOM in the water column and sediments of a coal mining subsidence area in Huaibei, China, using ultraviolet–visible absorption spectroscopy, three-dimensional fluorescence spectroscopy combined with the parallel factor analysis (PARAFAC) model, and Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR MS). The DOM concentration (11.99 ± 4.73 mg/L) and the degree of DOM humification in the sediment were higher than those (9.54 ± 0.68 mg/L) in the water column. The PARAFAC model revealed three chemical components: fulvic acid–like, protein-like, and humus-like substances. DOM in the water column is primarily derived from endogenous inputs, characterized by low humification and high biological activity. In contrast, DOM in the sediments primarily originates from external inputs. FT-ICR MS analysis revealed that lignin-like (50.80–67.02%), protein-like (13.18–18.38%), and lipid-like (5.74–10.39%) components are the main DOM constituents in the water column and sediments, with lignin content in the water column (66.16%) being higher than that in the sediments (55.09%). Paired mass difference network analysis identified redox reactions as the predominant reactions in the water column and sediments, converting aldehydes or carbonyl compounds into acids or alcohols. This study elucidates the composition and transformation characteristics of DOM in the water column and sediments in coal mining subsidence areas, providing a scientific foundation for the protection and management of aquatic ecosystems. Full article

(This article belongs to the Section Water Quality and Contamination)

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27 pages, 14244 KB

Open AccessArticle

Evolution of Unsteady Internal Flow and Rotordynamic Characteristics of Siphon Vertical Axial-Flow Pump During Start-Up

by Yadong Zhu, Yingyan Zhao, Zhuangzhuang Sun, Zhongshen Zhou, Weixuan Jiao and Yang Yang

Water 2026, 18(17), 2178; https://doi.org/10.3390/w18172178 - 3 Sep 2026

Abstract

The start-up process of a siphon vertical axial-flow pump is accompanied by rapid internal-flow reconstruction, transient hydraulic loading and unsteady rotor response, which directly affect the operational stability of the pump system. In this study, the unsteady internal flow evolution and rotordynamic characteristics [...] Read more.

The start-up process of a siphon vertical axial-flow pump is accompanied by rapid internal-flow reconstruction, transient hydraulic loading and unsteady rotor response, which directly affect the operational stability of the pump system. In this study, the unsteady internal flow evolution and rotordynamic characteristics of a siphon vertical axial-flow pump during start-up were investigated using a transient numerical method with dynamic rotational-speed updating. The instantaneous impeller speed was solved based on a torque-balance equation considering motor driving torque, hydraulic resistance torque and rotor inertia, and the angular-velocity boundary condition of the rotating domain was updated at each time step through a custom UDF routine. The numerical model was validated against model-test data, and good agreement was obtained for both pump head and efficiency. Based on the validated model, the flow-angle distribution, vortex stretching term, blade-surface pressure, rotor mechanical response, radial-force time–frequency characteristics and blade-loading variation were analyzed. The results show that the internal flow in the main pump section evolves from a strongly unsteady swirling state to an axially dominated quasi-steady state. In the early stage, obvious pre-swirl, local backflow and strong vortex stretching occur near the impeller inlet, blade-tip clearance and impeller–guide-vane interaction region. With increasing rotational speed and flow rate, the disordered vortical structures are gradually suppressed, and the internal flow becomes more organized. The rotor response exhibits clear stage-dependent characteristics, and the radial force is more sensitive to local flow instability than the axial force and torque. Continuous wavelet transform and variational mode decomposition further indicate that the radial-force signal is dominated by low-frequency transient excitation in the early stage, while medium- and high-frequency modulation components appear in the later stage. This study reveals the coupling mechanism between transient internal-flow evolution and rotor dynamic response during pump start-up, providing guidance for improving the start-up stability of siphon vertical axial-flow pump systems. Full article

(This article belongs to the Section Hydraulics and Hydrodynamics)

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27 pages, 6911 KB

Open AccessArticle

An End-to-End Machine Learning Framework for Groundwater Level Characterization and Climate-Constrained Probabilistic Forecasting in a Complex Karst Aquifer

by Péter Szűcs, Norbert P. Szabó, Géza Hajnal, Judit Barbara Nagy and Musaab A. A. Mohammed

Water 2026, 18(17), 2177; https://doi.org/10.3390/w18172177 - 3 Sep 2026

Abstract

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 [...] Read more.

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. Full article

(This article belongs to the Section Hydrogeology)

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26 pages, 2808 KB

Open AccessArticle

Evolution of the Deep Channels in the Hechangzhou Anabranching Reach Under the Regulation of Submerged Dikes

by Qing Luo, Hao Zhu, Maomei Wang, Jinping Ling, Yehemin Gao and Xiaosong Wang

Water 2026, 18(17), 2176; https://doi.org/10.3390/w18172176 - 3 Sep 2026

Abstract

The Hechangzhou Anabranching Reach is a typical tidal anabranching segment of the Yangtze River with intense riverbed evolution driven by altered water-sediment regimes and channel regulation. Submerged dikes stabilize river regimes and navigation yet trigger differential local clear-water scour; most prior studies only [...] Read more.

The Hechangzhou Anabranching Reach is a typical tidal anabranching segment of the Yangtze River with intense riverbed evolution driven by altered water-sediment regimes and channel regulation. Submerged dikes stabilize river regimes and navigation yet trigger differential local clear-water scour; most prior studies only adopt short-term bathymetric data and lack long-term quantitative analysis across diverse hydrological scenarios, so the synergistic geomorphic mechanism under the control of the submerged dike group remains unclear. This study integrates the 2002–2025 flow division ratio series and multi-period bathymetric data from 2018 to 2026 to quantify spatiotemporal deep-channel adjustments, scour-deposition patterns and cross-sectional deformation of the two anabranches, and evaluate morphological effects of river training structures and relevant potential scour risks. The submerged dike group regulates diversion patterns and keeps the left anabranch’s flow division ratio below 70%. The right anabranch deep channel undergoes persistent vertical incision and reverses its historical deposition trend, while the left anabranch features spatially uneven scour: intense scour zones migrate within the SD1–SD2 reach, and persistent landward erosion occurs between SD2 and SD3. The reach’s polarized evolution of intensified scour and attenuated deposition arises from the joint control of the submerged dike group, bank protection and reduced basin sediment supply. This study reveals the spatially differentiated riverbed evolution driven by engineering-induced hydrodynamic redistribution, offering scientific support for refined waterway management and scour prevention in analogous tidal anabranching rivers. Full article

(This article belongs to the Special Issue River Dynamics: Flow and Sediment Transport)

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16 pages, 2382 KB

Open AccessArticle

Melatonin Mitigates the Impacts of Recurrent Water Deficit in Robusta Coffee Plants by Modulating Photosynthesis and Biomass Partitioning

by Cristhiane Tatagiba Franco Brandão, Matheus Vieira dos Santos, Vinicius de Souza Oliveira, Ana Júlia Câmara Jeveaux-Machado, Fernando Gomes Hoste, Edlaine Lacerda Araújo, Thayanne Rangel Ferreira, Janyne Soares Braga Pires, Simone Alves Fernandes, Johnatan Jair de Paula Marchiori, Carla da Silva Dias, José Altino Machado Filho, Lúcio de Oliveira Arantes and Sara Dousseau-Arantes

Water 2026, 18(17), 2175; https://doi.org/10.3390/w18172175 - 3 Sep 2026

Abstract

Melatonin (N-acetyl-5-methoxytryptamine) is a regulatory molecule with potential to increase plant tolerance to water stress by modulating stomatal function, redox balance, and photosynthetic efficiency. However, its physiological effects depend on dose, species, and stress intensity, and studies on Coffea canephora under recurrent drought [...] Read more.

Melatonin (N-acetyl-5-methoxytryptamine) is a regulatory molecule with potential to increase plant tolerance to water stress by modulating stomatal function, redox balance, and photosynthetic efficiency. However, its physiological effects depend on dose, species, and stress intensity, and studies on Coffea canephora under recurrent drought are scarce. This study evaluated the effects of exogenous melatonin (0, 100, 200, 300, and 400 µM) on young plants of conilon coffee genotype 02 (Clone V12) subjected to three consecutive cycles of water deficit and rehydration. The experiment was conducted in a greenhouse using a randomized block design, with evaluations of gas exchange, water potential, and biomass allocation. Melatonin improved physiological recovery after rehydration, particularly at 100 and 300 µM during the second stress cycle, when photosynthesis reached values similar to the irrigated control. The 400 µM treatment maintained root dry mass close to the control under prolonged deficit, suggesting preferential biomass allocation to the root system. Overall, melatonin effects were more pronounced during recovery than stress, indicating a possible priming action. These results highlight the potential of melatonin as a phytoprotective agent in C. canephora, although responses depend on dose and stress cycle. Full article

(This article belongs to the Section Water, Agriculture and Aquaculture)

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29 pages, 733 KB

Open AccessSystematic Review

Mathematical Programming Models for Agricultural Water: A Systematic Review

by Elisa Belfiore and Davide Viaggi

Water 2026, 18(17), 2174; https://doi.org/10.3390/w18172174 - 3 Sep 2026

Abstract

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 [...] Read more.

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. Full article

(This article belongs to the Section Water, Agriculture and Aquaculture)

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29 pages, 7721 KB

Open AccessArticle

Hydrology and Bloom Shapes Dissolved and Particulate Organic Carbon and DOC Fluxes in Danjiangkou Reservoir

by Yuling Huang, Huan Li, Huihuang Luo and Lei Cheng

Water 2026, 18(17), 2173; https://doi.org/10.3390/w18172173 - 2 Sep 2026

Abstract

Reservoirs are increasingly recognized as active components of the global carbon cycle, acting as both sinks and sources of organic carbon while providing essential drinking-water services. Yet, the spatiotemporal behavior of organic carbon fractions and their coupled controls by hydrological extremes, algal blooms, [...] Read more.

Reservoirs are increasingly recognized as active components of the global carbon cycle, acting as both sinks and sources of organic carbon while providing essential drinking-water services. Yet, the spatiotemporal behavior of organic carbon fractions and their coupled controls by hydrological extremes, algal blooms, and reservoir operations remain poorly resolved in large, regulated water-supply reservoirs. Here, we quantified the distribution, drivers, and transport balance of organic carbon in Danjiangkou Reservoir (China) through monthly monitoring from March to December 2024 across the main reservoir, downstream reach, and major tributaries. Dissolved organic carbon (DOC), particulate organic carbon (POC), and total organic carbon (TOC) were measured together with key water-quality parameters and hydrological indicators to identify the dominant controls. Across the reservoir–tributary network, DOC, POC, and TOC ranged from 1.03 to 13.17 mg/L, 0.03 to 45.40 mg/L, and 1.34 to 48.57 mg/L, respectively, with tributaries generating the largest extremes. Reservoir waters exhibited substantially lower concentrations than tributaries and showed pronounced seasonal heterogeneity, with DOC generally higher in autumn than in summer and spring. Event-scale analyses revealed two distinct regimes: precipitation-driven runoff primarily intensified POC and TOC through terrestrial particle inputs, whereas bloom periods increased DOC and, under severe conditions, elevated TOC. POC and TOC were positively correlated with inflow, indicating that inflow dynamics govern the delivery of particulate carbon. The annual DOC transport flux was about 61 kt, with seasonal alternation between sink and source months, indicating net interception of upstream DOC under specific hydrological and operational conditions. These findings clarify how tributary forcing and reservoir regulation jointly govern carbon routing in large drinking-water reservoirs, with implications for carbon budgeting and protection of source water. Full article

(This article belongs to the Special Issue Advances in River Ecology Research)

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20 pages, 1151 KB

Open AccessArticle

Evolution Characteristics and Driving Factors of Net Anthropogenic Nitrogen and Phosphorus Inputs in the Typical Plateau Basins in the Upper Yangtze River Basin

by Fangxin Xu, Hai Lu, Yuxi Ying, Xiang Li, Zhengyang Duan, Yongtao Xu, Qifa Sun and Sheng Wang

Water 2026, 18(17), 2172; https://doi.org/10.3390/w18172172 - 2 Sep 2026

Abstract

Most existing studies on anthropogenic N and P inputs in the Yangtze River Basin focus on the middle-lower main stem and large lake regions, while long-term fine-scale assessments of small and medium plateau tributaries in the upper reaches remain limited. This study was [...] Read more.

Most existing studies on anthropogenic N and P inputs in the Yangtze River Basin focus on the middle-lower main stem and large lake regions, while long-term fine-scale assessments of small and medium plateau tributaries in the upper reaches remain limited. This study was based on the Net Anthropogenic Nitrogen and Phosphorus Input (NANI) & (NAPI) models to systematically quantify total anthropogenic N/P inputs and their sources, and to characterize spatiotemporal dynamics. The random forest model was employed to disentangle and quantify key driving factors; additionally, the multiple linear regression (MLR) model was employed to construct prediction equations, and key-factor scenarios were set to predict N and P input evolution trends under reduced fertilizer application and population change. Results indicate that: (1) from 2009 to 2023, the mean annual NANI and NAPI in the study area were (8741.97 ± 1715.37) kg·km⁻²·yr⁻¹ and (2016.01 ± 610.68) kg·km⁻²·yr⁻¹, respectively, exhibiting an overall “first increase, then decrease” trend. Their spatial distribution patterns were highly coupled. The spatial distribution was highly heterogeneous, with high values concentrated in the central dam regions and low values in mountainous areas. (2) Across all sub-basins, food and feed N input and fertilizer application were the primary sources of NANI, accounting for 50.87% and 43.94% of the total on average, respectively, while food and feed P input and fertilizer application dominated NAPI with average shares of 31.31% and 68.69%. (3) Population size, fertilizer application intensity, and livestock/poultry breeding volume were the primary drivers of regional NANI and NAPI. (4) Scenario prediction results show that fertilizer reduction significantly reduces N and P inputs, whereas population growth does not directly elevate N and P loads. Overall, N and P inputs in the Longchuan River Basin are dominated by human activities, with spatiotemporal variations closely linked to agricultural activity intensity and population distribution. These findings provide scientific support for targeted precise zonal N/P control in the Longchuan River Basin, and also offer a reference for non-point source pollution control research and management policy-making in similar plateau basins of the upper Yangtze River. Full article

(This article belongs to the Special Issue Sources, Migration and Variation Characteristics of Pollutants in Aquatic Environments)

31 pages, 9909 KB

Open AccessArticle

Objective Sub-Domain Selection Substantially Improves Sensitivity of Spatial Distribution Models of Heterogeneous Groundwater Arsenic Hazard in Bihar, India

by Mohammad Mehrabi, Yang Han, Laura A. Richards, Ajmal Roshan, Maiko Sakamoto and David A. Polya

Water 2026, 18(17), 2171; https://doi.org/10.3390/w18172171 - 2 Sep 2026

Abstract

Spatial heterogeneity of data is often a crucial obstacle to the optimal implementation of geospatial machine learning models, especially in larger study areas. In this research, a novel framework is proposed to systematically partition a heterogeneous area (Bihar, India) into more homogeneous sub-domains [...] Read more.

Spatial heterogeneity of data is often a crucial obstacle to the optimal implementation of geospatial machine learning models, especially in larger study areas. In this research, a novel framework is proposed to systematically partition a heterogeneous area (Bihar, India) into more homogeneous sub-domains (SDs) in order to more accurately predict the spatial distribution of groundwater arsenic hazard. Fed by expert-selected key predictive variables (PVs), the algorithm performs local feature importance analysis to cluster the data points accordingly, followed by an automatic demarcation that results in generating SDs that are statistically significantly separated and more homogeneous than the full domain. Accuracy assessment of the used random forest model showed that while Sub-domained and Full-domain Models could both exhibit a high accuracy (e.g., respective AUCs (area under the curve) of 0.95 and 0.93), a Sub-domained Model achieved about 30% higher Sensitivity (0.85 vs. 0.56) while maintaining a high Specificity (0.93). Shapley Additive Explanations (SHAP) was also applied to interpret the predictive model and to explore modelled As-PV relationships. The consistency of the model comparison results was also demonstrated by internal and external cross-validations. The findings confirm that building geospatial models upon heavily heterogeneous data can lead to hidden loss of accuracy metrics, such as Sensitivity, the underestimation of which may be of more practical importance than Specificity. The proposed sub-domaining framework was demonstrated here to be an effective partial solution for this specific application. It is noted, however, that improvements in Sensitivity may be dependent upon several project-specific parameters, including the set of PVs selected and the degree of heterogeneity of the study area with respect to the target variable and processes controlling the distribution of its value. Full article

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