Water

17 pages, 3512 KB

Open AccessArticle

Statistical Evaluation of Observed Precipitation from INMET Meteorological Stations and MERGE Estimates in the Eastern Amazon

by Priscila da S. Batista, Júlio T. da Silva, Ana Carla dos S. Gomes, Jéssica A. de J. Corrêa, Gabriel Brito Costa, Antônio Marcos D. de Andrade, Carlos T. S. Dias, Leila S. S. Lisboa and Lucietta Guerreiro Martorano

Water 2026, 18(8), 898; https://doi.org/10.3390/w18080898 - 9 Apr 2026

Abstract

Accurate precipitation data are essential for understanding hydrological processes and supporting environmental and water resource management in the Amazon, where observational networks remain sparse and spatially uneven. This study evaluates the performance of the MERGE (Merge of Satellite and Gauge Precipitation Data) dataset, [...] Read more.

Accurate precipitation data are essential for understanding hydrological processes and supporting environmental and water resource management in the Amazon, where observational networks remain sparse and spatially uneven. This study evaluates the performance of the MERGE (Merge of Satellite and Gauge Precipitation Data) dataset, developed by CPTEC/INPE, in representing rainfall variability in the Eastern Amazon. Daily precipitation data from five INMET meteorological stations were compared with MERGE estimates over a 20-year period (1998–2017) using a multi-metric statistical framework, including correlation, regression, error metrics, efficiency indices, and clustering analysis. The results indicate strong agreement between observed and estimated precipitation, with Pearson correlation coefficients ranging from 0.94 to 0.99 and Nash–Sutcliffe efficiency values between 0.87 and 0.97. Regression analyses show coefficients of determination between 0.89 and 0.98, indicating that MERGE effectively reproduces the magnitude and temporal variability of precipitation. Monthly and interannual analyses confirm consistent representation of seasonal patterns and rainfall dynamics across the evaluated stations. The boxplot analysis reveals that MERGE accurately captures the overall distribution of precipitation but tends to underestimate higher precipitation values, particularly during months associated with intense rainfall. This behavior reflects limitations in representing localized convective events and spatial variability. Overall, the results demonstrate that MERGE provides a reliable representation of precipitation variability in the Eastern Amazon and represents a valuable dataset for hydroclimatic analyses in regions with limited observational coverage. Full article

(This article belongs to the Special Issue Database Integration and Conceptual Frameworks in Hydrological Research)

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26 pages, 7514 KB

Open AccessArticle

Meltwater Contribution and Mass Balance of the Juncal Norte Glacier During an Extreme Drought Year in the Dry Andes of Central Chile

by Antonio Bellisario, Jason Janke and Sam Ng

Water 2026, 18(8), 897; https://doi.org/10.3390/w18080897 - 9 Apr 2026

Abstract

The Juncal Norte Glacier (33°00′ S, 70°06′ W) is in the Dry Andes of central Chile within the Juncal Basin, a headwater watershed of the Aconcagua River, a semi-arid region experiencing an ongoing megadrought since 2010 and a 37% reduction in streamflow relative [...] Read more.

The Juncal Norte Glacier (33°00′ S, 70°06′ W) is in the Dry Andes of central Chile within the Juncal Basin, a headwater watershed of the Aconcagua River, a semi-arid region experiencing an ongoing megadrought since 2010 and a 37% reduction in streamflow relative to pre-1990 baselines. This study provides the first glacier-specific annual melt and runoff estimate for Juncal Norte during mature megadrought conditions. Mass balance was estimated using a temperature index (positive degree day, PDD) model calibrated with automatic weather station (AWS) air temperature data and glacier hypsometry, assuming limited snow accumulation given that 2018–2019 precipitation and snow water equivalent (SWE) were extremely low relative to the long-term mean. Basin runoff was evaluated using a closure method comparing proglacial sub-basin-integrated discharge with modeled glacier melt volumes. Modeled glacier melt for 2018–2019 was equivalent to approximately 30% of observed annual discharge at the proglacial sub-basin, a disproportionate contribution given the glacier covers only 2.7% of the total basin area. The lower ablation zone (2900–4000 m), comprising 30% of glacier area, produced 90% of total melt volume. A + 1 °C temperature perturbation increased glacier-wide melt by 21.4%, confirming high climatic sensitivity. These results underscore the glacier’s critical but increasingly vulnerable buffering role for downstream water availability in the Dry Andes. Full article

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

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32 pages, 3764 KB

Open AccessArticle

Assessment of Compound Hydrological–Thermal Extremes over Indian River Systems

by Jaya Bharat Reddy Buchupalle, Satish Kumar Mummidivarapu, Shaik Rehana, Shahid Latif and Taha B. M. J. Ouarda

Water 2026, 18(8), 896; https://doi.org/10.3390/w18080896 - 9 Apr 2026

Abstract

River water quality assessment has traditionally been conducted using univariate or threshold-based approaches; however, the exploration of extremes assessment under bivariate water quality variables has been limited by many studies. Understanding the compound extremes of low river discharge (Q) and elevated river water [...] Read more.

River water quality assessment has traditionally been conducted using univariate or threshold-based approaches; however, the exploration of extremes assessment under bivariate water quality variables has been limited by many studies. Understanding the compound extremes of low river discharge (Q) and elevated river water temperatures (RWTs) resulting from climatic variability is essential for effective water quality management and protection of the river. This study investigates the joint behaviour of RWTs and Q in six Indian rivers: Kaveri, Mahi, Sabarmati, Vardha, Bhadra, and Yamuna. The Weibull-3P and Generalised Extreme Value (GEV-3P) distributions best fit for Q and RWTs, respectively. The adequacy of eighteen different parametric copula classes was evaluated. The Gaussian copula provided the best fit for the Vardha River, the Frank copula for Bhadra, and the BB8 copula for the Yamuna River. The evaluation of joint return periods (RPs) and conditional distributions has identified notable spatial variability in compound hydrological and thermal extreme hazards. The semi-arid Vardha River showed the shortest RPs for simultaneous low Q and high RWTs, indicating a greater likelihood of combined extremes. Conversely, the monsoon-fed Bhadra River displayed moderate hazard levels, while the Himalayan-fed Yamuna River had the longest joint RPs and the lowest conditional probabilities. This suggests that simultaneous extreme drought and heat events are less likely in the Yamuna basin, although significant risks remain for less severe thresholds. Full article

(This article belongs to the Special Issue Hydroclimate Risk Assessment and Management: Data, Models and Remote Sensing Approaches)

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22 pages, 3072 KB

Open AccessReview

Organic Grafting of Clay Minerals with Organo-Alkoxides, Silanes and Amines: Structure–Performance Relationships and Implications for Water Treatment

by Marzhan S. Kalmakhanova, Aizhan M. Serikbayeva, Nursulu K. Sarypbekova, Karashash B. Adikhodzhayeva, Nazgul S. Murzakasymova, Seitzhan A. Orynbayev and Helder T. Gomes

Water 2026, 18(8), 895; https://doi.org/10.3390/w18080895 - 9 Apr 2026

Abstract

This review provides a comprehensive analysis of organically grafted clay minerals modified with organo-alkoxides, silanes, and amine-based compounds for water treatment applications. Emphasis is placed on per- and polyfluoroalkyl substances (PFAS) as representative emerging contaminants due to their persistence and environmental relevance. The [...] Read more.

This review provides a comprehensive analysis of organically grafted clay minerals modified with organo-alkoxides, silanes, and amine-based compounds for water treatment applications. Emphasis is placed on per- and polyfluoroalkyl substances (PFAS) as representative emerging contaminants due to their persistence and environmental relevance. The review systematically examines synthesis strategies, surface functionalization mechanisms, and structure–performance relationships governing adsorption behavior. The analysis demonstrates that adsorption performance is controlled by the interplay between grafting chemistry, surface accessibility, and environmental conditions rather than adsorption capacity alone. While ion-exchange organoclays exhibit high adsorption capacities under controlled conditions, covalently grafted and polymer-modified systems provide superior stability and resistance to leaching. However, discrepancies in experimental conditions across studies limit direct comparison of reported adsorption capacities. The review identifies key challenges related to regeneration efficiency, environmental safety, and scalability, and highlights that long-term stability and compatibility with realistic water matrices are decisive factors for practical implementation. Future research should focus on standardized testing protocols, pilot-scale validation, and comprehensive environmental risk assessment. Full article

(This article belongs to the Section Wastewater Treatment and Reuse)

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28 pages, 4371 KB

Open AccessArticle

Hydrological Stability and Sensitivity Analysis of the Cahaba River Basin: A Combined Review and Simulation Study

by Pooja Preetha, Brian Tyrrell and Autumn Moore

Water 2026, 18(8), 894; https://doi.org/10.3390/w18080894 - 8 Apr 2026

Abstract

A continuous integration framework and methodology for hydrological modeling is proposed that integrates model sensitivity analysis with real-time sensor tasking to prioritize data collection in regions and periods of high hydrological variability and drive model refinement. The Cahaba River Watershed in central Alabama [...] Read more.

A continuous integration framework and methodology for hydrological modeling is proposed that integrates model sensitivity analysis with real-time sensor tasking to prioritize data collection in regions and periods of high hydrological variability and drive model refinement. The Cahaba River Watershed in central Alabama serves as a case study to develop this approach. To this end, a benchmark Soil and Water Assessment Tool (SWAT) model (30 m DEM) was refined with high-resolution spatial datasets in QGIS, including 1 m DEMs, NLCD land cover, and SSURGO soil data. The refined model significantly enhanced subbasin delineation, increasing granularity from 8 to 99 subbasins, thereby improving representation of slope, runoff, and storage variability across heterogeneous landscapes. Sensitivity analyses were performed to evaluate the influence of DEM resolution and curve number (CN) perturbations on hydrologic responses, including retention, flow partitioning, and dominant flow direction. High-resolution DEMs (≤5 m) captured microtopographic features that strongly affect infiltration and surface runoff, while coarser DEMs (≥20 m) systematically underestimated retention and smoothed hydrologic gradients. The higher-resolution DEMs can be used to selectively improve the model at certain hotspots/areas of higher sensitivity. Localized flow simulations demonstrated that fine-scale terrain data substantially improve model realism, with up to 58% greater retention captured in 10 m DEMs compared to 30 m DEMs. The results confirm that aligning sensor placement and model refinement with spatially explicit sensitivity zones enhances both predictive accuracy and computational efficiency. The proposed continuous integration approach provides a scalable pathway for coupling high-resolution modeling with adaptive sensing in watershed management and supports future integration of real-time data assimilation for continuous model improvement. Full article

(This article belongs to the Special Issue The Impact of Climate Change and Land Use on Water Resources—an Issue of Environmental Global Safety, 2nd Edition)

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38 pages, 6396 KB

Open AccessArticle

Nonlinear Motion Analysis of Floating Bodies in Waves Using the MPS Method

by Xianglong Fu, Di Ren, Jun Soo Park, Xiangxi Han, Junlong Su, Zhanbin Meng and Kunpeng Chen

Water 2026, 18(8), 893; https://doi.org/10.3390/w18080893 - 8 Apr 2026

Abstract

This paper develops a two-dimensional fully Lagrangian meshless fluid–structure interaction solver by integrating the Moving Particle Semi-implicit (MPS) method with continuum mechanics to investigate the nonlinear interaction between waves and floating bodies. The stability and accuracy of the proposed model are validated through [...] Read more.

This paper develops a two-dimensional fully Lagrangian meshless fluid–structure interaction solver by integrating the Moving Particle Semi-implicit (MPS) method with continuum mechanics to investigate the nonlinear interaction between waves and floating bodies. The stability and accuracy of the proposed model are validated through several benchmark cases. Furthermore, the solver is employed to analyze the dynamic response of a flat plate floating body in waves. The numerically generated waves exhibit a minimum error of approximately −0.5% and a period consistent with theoretical values, maintaining a smooth and continuous free surface. Due to the inherent limitations of the two-dimensional wave-floating body simulation, the Root Mean Square Error (RMSE) of the interaction results ranges from 5.4% to 15.2%. These findings indicate that the proposed method provides a valuable reference for the design and analysis of floating structures in ocean engineering. Full article

(This article belongs to the Section Hydraulics and Hydrodynamics)

42 pages, 10164 KB

Open AccessArticle

Construction and Application of Distributed Non-Point Source Pollution Model in Watersheds Based on Time-Varying Gain and Stormwater Runoff Response at the Watershed Scale

by Gairui Hao, Kangbin Li and Jiake Li

Water 2026, 18(8), 892; https://doi.org/10.3390/w18080892 - 8 Apr 2026

Abstract

Characterizing surface runoff and the transport process of non-point source pollutants (NSPs) carried by this runoff is crucial for identifying key source areas, estimating pollution loads entering water bodies, and implementing pollution control, which is particularly important in regions dominated by smallholder farming [...] Read more.

Characterizing surface runoff and the transport process of non-point source pollutants (NSPs) carried by this runoff is crucial for identifying key source areas, estimating pollution loads entering water bodies, and implementing pollution control, which is particularly important in regions dominated by smallholder farming in China. Currently, most of the commonly used NSP models originated from international countries and have shortcomings such as high data requirements, high generalization degrees, and requiring the calibration of numerous parameters in the application process. Therefore, a distributed non-point source pollution model based on the time-varying gain and stormwater runoff response was constructed, designed for application at the watershed scale. This study describes the construction of the model, introducing its principles and structure through three key modules: a rainfall–runoff module, a soil erosion module, and a pollutant migration and transformation module. The proposed model was used to simulate the rainfall–runoff, soil erosion, and nutrient migration and transformation processes at different spatiotemporal scales. Although it achieved the best performance at the monthly and annual scales, its simulation results at the daily and hourly scales still met the relevant requirements, with relative errors within 20% and Nash–Sutcliffe Efficiency (NSE) coefficients of approximately 0.7. The annual sediment delivery ratios for the Yangliu Small Watershed and the basin above the Ankang section in 2022 were determined to be 0.445 and 0.36, respectively. The pollutant processes corresponding to different runoff events in the Yangliu Small Watershed were simulated, and the average NSE for total nitrogen (TN), ammonia nitrogen (NH₃-N), nitrate nitrogen (NO₃-N), total phosphorus (TP), and soluble reactive phosphorus (SRP) were determined to be 0.69, 0.74, 0.79, 0.71, and 0.71, respectively. For the basin above the Ankang section, the NSE coefficients for the simulation of NH₃-N and TP pollutant processes were 0.78 and 0.83, respectively. The model demonstrated robust applicability across various spatial (ranging from small to large watersheds) and temporal (hourly−daily−monthly−annual) scales, and exhibited stability across different basins in a semi-humid region of China. The model is characterized by a parsimonious parameter set, ease of calibration, and strong spatiotemporal versatility, thus providing an efficient and reliable tool for non-point source pollution simulation. Full article

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

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29 pages, 1929 KB

Open AccessArticle

Watershed Ecological Compensation and Transboundary Water Governance: Impacts on Pollution Abatement and Green Economic Efficiency in the Xin’an River Basin, China

by Guang Yang, Chenxu Cui, Yu Li and Hui Wang

Water 2026, 18(8), 891; https://doi.org/10.3390/w18080891 - 8 Apr 2026

Abstract

Watershed Ecological Compensation (WEC) is a vital tool for water environmental governance, yet existing research often focuses on either upstream or downstream regions in isolation, lacking a systematic assessment of basin-wide aggregate effects. Taking China’s Xin’an River Basin as a case study, this [...] Read more.

Watershed Ecological Compensation (WEC) is a vital tool for water environmental governance, yet existing research often focuses on either upstream or downstream regions in isolation, lacking a systematic assessment of basin-wide aggregate effects. Taking China’s Xin’an River Basin as a case study, this paper investigates the impacts of cross-provincial WEC on pollutant emissions, economic performance, and green economic efficiency. Theoretical analysis based on a social welfare maximization framework indicates that WEC helps reduce emissions and enhance green economic efficiency, though its impact on economic output is conditional. Using the Synthetic Control Method (SCM) for empirical analysis, the results show that the policy significantly reduced industrial COD emissions by an average of 111 t/10⁸ m³ per year and notably improved green economic efficiency, with industrial COD emissions per unit of GDP decreasing by 3.5 t per 100 million RMB annually. However, no significant impact on overall basin-wide economic development was observed. Robustness tests using Synthetic Difference-in-Differences (SDID) and staggered DID models further confirm the reliability of these findings. This study provides theoretical and empirical support for the policy effectiveness of WEC in pollution control and green development. Full article

(This article belongs to the Special Issue Using Economics for Sustainability, Effective Management, and Conflict Resolution in Water)

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25 pages, 16852 KB

Open AccessArticle

The Impact of Noise on Machine Learning-Based Lake Ice Detection on Lake Śniardwy Using Sentinel-1 SAR Data

by Augustyn Crane and Mariusz Sojka

Water 2026, 18(8), 890; https://doi.org/10.3390/w18080890 - 8 Apr 2026

Abstract

Lake ice monitoring is critical for assessing climate change, but in-situ observations are often limited. Sentinel-1 Synthetic Aperture Radar (SAR) data is a strong method for ice detection because it is not restricted by cloud cover and it is readily available. However, SAR-based [...] Read more.

Lake ice monitoring is critical for assessing climate change, but in-situ observations are often limited. Sentinel-1 Synthetic Aperture Radar (SAR) data is a strong method for ice detection because it is not restricted by cloud cover and it is readily available. However, SAR-based classification can be affected by atmospheric and surface-related noise. This study examines the impact of noise on machine learning-based lake ice detection over Lake Śniardwy, Poland, using Sentinel-1 Vertical-Vertical (VV) and Vertical-Horizontal (VH) backscatter data. Binary logistic regression models were trained on scenes with strong class separability between ice and water and then validated on separate low- and high-noise datasets. The models achieved high accuracy under low-noise scenes, reaching up to 96.9%, but performed poorly on high-noise scenes. The results show that wind-related surface roughness and associated atmospheric conditions can significantly reduce classification reliability. Comparison with backscatter from a nearby coniferous forest confirmed that the main disturbances were concentrated over the lake surface. The study highlights the importance of careful scene selection and noise assessment in SAR-based lake ice classification. Full article

(This article belongs to the Special Issue Advances and Challenges in the Lake, River, and Sea Ice Sciences and Engineering)

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18 pages, 11149 KB

Open AccessArticle

LRES-YOLO: Target Detection Algorithm for Landslides on Reservoir Embankment Slopes

by Xiaohua Xu, Xuecai Bao, Zhongxi Wang, Haijing Wang and Xin Wen

Water 2026, 18(8), 889; https://doi.org/10.3390/w18080889 - 8 Apr 2026

Abstract

To address the urgent need for enhancing landslide risk monitoring in reservoir embankment slopes, a core component of water conservancy projects, this paper proposes the LRES-YOLO algorithm for real-time landslide detection on reservoir embankments. In LRES-YOLO, we first integrate coordinate attention into basic [...] Read more.

To address the urgent need for enhancing landslide risk monitoring in reservoir embankment slopes, a core component of water conservancy projects, this paper proposes the LRES-YOLO algorithm for real-time landslide detection on reservoir embankments. In LRES-YOLO, we first integrate coordinate attention into basic feature extraction convolutional blocks to form the CACBS attention module, which enhances the model’s ability to identify and locate landslide targets in complex reservoir terrain, overcoming positional information insensitivity in deep networks. Second, we add novel downsampling DP modules and ELAN-W modules to the backbone network, improving feature recognition efficiency for embankment slopes with diverse hydrological and topographical interference. Third, we optimize the feature fusion network with targeted concatenation and pooling operations, balancing semantic information enhancement with computational load reduction to mitigate overfitting in variable reservoir environments. Finally, we adopt Focal Loss and EIoU Loss to accelerate training convergence and strengthen target feature representation for small or obscured landslides on embankments. Experimental results show that LRES-YOLO outperforms traditional algorithms in detecting landslides across diverse reservoir embankment scenarios: it achieves an average improvement of 8.4 percentage points in mean mAP over the best-performing baseline across five independent trials, a detection speed of 8.2 ms per image, and memory usage of 139 MB. This lightweight design makes it suitable for edge computing devices, providing robust technical support for intelligent monitoring systems in water conservancy projects. Full article

(This article belongs to the Special Issue AI, Machine Learning and Digital Twin Applications in Water, 2nd Edition)

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5 pages, 166 KB

Open AccessEditorial

Geotechnic and Geostructure Modelling for Landslides: Prediction and Control

by Xiaoshuang Li and Qihang Li

Water 2026, 18(8), 888; https://doi.org/10.3390/w18080888 - 8 Apr 2026

Abstract

Landslides, as one of the most frequent and highly destructive geological disasters worldwide, pose a continuous and severe threat to human society [...] Full article

(This article belongs to the Special Issue Geotechnic and Geostructure Modelling for Landslides: Prediction and Control)

20 pages, 8935 KB

Open AccessArticle

Impact of Spatiotemporal Characteristics of Microbial Communities in Typical Wastewater Treatment Processes on Treatment Efficiency

by Jia Liu, Lingfei Zhang, Jie Guo, Bernard Lassimo Diawara, Shuai Yang, Hong Shen, Wangyang Chen and Yulin Tang

Water 2026, 18(8), 887; https://doi.org/10.3390/w18080887 - 8 Apr 2026

Abstract

The performance of biological wastewater treatment processes directly impacts water resource recycling and ecological safety. This year-long study compared full-scale wastewater treatment plants (WWTPs) using either the anaerobic/anoxic/aerobic (AAO) or modified Bardenpho process. By integrating water quality analysis with 16S rRNA sequencing, we [...] Read more.

The performance of biological wastewater treatment processes directly impacts water resource recycling and ecological safety. This year-long study compared full-scale wastewater treatment plants (WWTPs) using either the anaerobic/anoxic/aerobic (AAO) or modified Bardenpho process. By integrating water quality analysis with 16S rRNA sequencing, we examined how process type, influent quality, and seasonal factors affect microbial communities and treatment performance. Systems with high chemical oxygen demand (COD) and biochemical oxygen demand (BOD)/COD influent exhibited the best pollutant removal performance, with average nitrogen and phosphorus concentrations in the effluent as low as 7.0 mg/L and 0.1 mg/L, respectively. Optimizing a 1:9 influent distribution ratio between the pre-anoxic and first anoxic zones in the modified Bardenpho process increased total nitrogen (TN) removal efficiency by an average of 14 percentage points compared to the AAO process. Additionally, the modified Bardenpho process identified 1100 bacterial genera, indicating a more complex and stable community. Influent water quality had the most significant impact on microbial communities and treatment efficiency, followed by seasonal factors and process type. This study provides theoretical and data support for the optimization of wastewater treatment processes and seasonal regulations. Full article

(This article belongs to the Section Wastewater Treatment and Reuse)

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28 pages, 457 KB

Open AccessReview

Heavy Metals Burden in Drinking Water: Global Patterns, Sources, and Public Health Implications

by Joshua O. Olowoyo, Olasunkanmi O. Olaiya, Omuferen-Oke L. Oharisi, Johnson A. Olusola, Unathi A. Tshoni and Oluwaseun M. Oladeji

Water 2026, 18(8), 886; https://doi.org/10.3390/w18080886 - 8 Apr 2026

Abstract

Heavy metal contamination in drinking water remains a pervasive global challenge with significant consequences for environmental quality and human health. This review synthesizes findings from recent studies examining heavy metal concentrations in different sources of drinking water, including municipal tap water, groundwater, surface [...] Read more.

Heavy metal contamination in drinking water remains a pervasive global challenge with significant consequences for environmental quality and human health. This review synthesizes findings from recent studies examining heavy metal concentrations in different sources of drinking water, including municipal tap water, groundwater, surface water, and bottled/sachet water across various geographical regions. The study used a systematic review of studies published from 2015 to 2024. The result showed a variation in the concentrations of heavy metals from all the sources, with tap water generally exhibiting lower heavy metal levels. Pb, Fe, Mn, and other metals persist in different sources and from many regions with levels above the permissible limits recommended by the World Health Organization (WHO) in some instances, which were sometimes linked to aging distribution systems and other pollution sources. Bottled and sachet water, commonly regarded as safer alternatives, also showed some levels of heavy metals such as Pb, Cd, and Cr, reflecting inconsistent packaging or production oversight. Surface waters display variability with heavy metals pollution, driven by industrial discharge, mining activities, agricultural runoff, and urban wastewater inputs. Groundwater sources, although naturally shielded, frequently contained elevated concentrations of As, Hg, and Ni due to both geological and anthropogenic factors. Pb concentrations were below detection limit in some of the published papers; however, the values reported in this study ranged from ND to 260.0 µg/L (tap water), ND to 0.259 mg/L (surface water), ND to 0.791 mg/L (groundwater), and ND to 123.15 µg/L (bottled water). Arsenic (As) concentrations ranged from ND to 692 µg/L from different sources, with the highest concentration from groundwater. Collectively, these patterns underscore the need for strengthened monitoring frameworks, improved water treatment technologies, and integrated pollution-prevention strategies. Addressing heavy metal contamination in drinking water requires coordinated policy approach and continuous monitoring to reduce human exposure and safeguard global public health. Full article

(This article belongs to the Special Issue New Technologies to Ensure Safe Drinking Water)

34 pages, 5480 KB

Open AccessArticle

Metaheuristic Optimization of Treated Sewage Wastewater Quality Parameters with Natural Coagulants

by Joseph K. Bwapwa and Jean G. Mukuna

Water 2026, 18(8), 885; https://doi.org/10.3390/w18080885 - 8 Apr 2026

Abstract

This study presents a comprehensive multi-objective optimization of sewage wastewater treatment using bio-based coagulants, guided by the Grey Wolf Optimizer (GWO) and its multi-objective variant (MOGWO). Experimental coagulation data, employing Citrullus lanatus and Cucumis melo as natural coagulants, were modeled using multivariate regression [...] Read more.

This study presents a comprehensive multi-objective optimization of sewage wastewater treatment using bio-based coagulants, guided by the Grey Wolf Optimizer (GWO) and its multi-objective variant (MOGWO). Experimental coagulation data, employing Citrullus lanatus and Cucumis melo as natural coagulants, were modeled using multivariate regression techniques, yielding high coefficients of determination (R² > 0.95) across key water quality parameters. The optimization process targeted maximal reductions in turbidity, total suspended solids (TSS), biochemical oxygen demand (BOD), and chemical oxygen demand (COD) through strategic manipulation of pH and coagulant dosage. The single-objective GWO achieved significant outcomes, including a 96.68% turbidity reduction at pH 5 and 50 mg/L dosage. The MOGWO algorithm identified Pareto-optimal solutions, such as a 94.2% turbidity reduction at pH 5 and 72 mg/L dosage, and a balanced BOD reduction of 52.7% at pH 7. The predictive models indicated that optimal treatment conditions could reduce chemical usage by up to 90% compared to conventional coagulants, resulting in potential cost savings of up to 30%. Moreover, the algorithms demonstrated rapid convergence, averaging 200 iterations, highlighting their computational efficiency and robustness. These findings illustrate that integrating bio-based coagulants with advanced optimization techniques can achieve high treatment efficiency while reducing chemical inputs, thus directly supporting environmental sustainability by minimizing sludge and secondary pollution. In this situation, the wastewater treatment plant will focus on resource-recovery systems with less or no waste at the end of the treatment process. This approach aligns with circular economy principles by promoting eco-friendly, cost-effective wastewater treatment solutions suitable for resource-limited settings. The study offers a forward-looking pathway for environmentally responsible wastewater management practices that significantly reduce chemical dependency and contribute to pollution mitigation efforts. Full article

(This article belongs to the Section Wastewater Treatment and Reuse)

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21 pages, 2682 KB

Open AccessArticle

Monolayer or Multilayer Snow Model: Implications for the HYDROTEL Hydrological Model for Flow Modeling

by Julien Augas, Alain N. Rousseau and Etienne Foulon

Water 2026, 18(7), 884; https://doi.org/10.3390/w18070884 - 7 Apr 2026

Abstract

The snow module of the HYDROTEL (version 2.8.x-078-00-4.1.15.5551) hydrological model was modified to incorporate a multilayer structure composed of ice and air layers within the snowpack, as well as to account for the impact of freezing rain on snow cover. This study examines [...] Read more.

The snow module of the HYDROTEL (version 2.8.x-078-00-4.1.15.5551) hydrological model was modified to incorporate a multilayer structure composed of ice and air layers within the snowpack, as well as to account for the impact of freezing rain on snow cover. This study examines whether this enhanced physical representation of snow processes improves the accuracy of streamflow simulations. The analysis was conducted across ten watersheds in Quebec, Canada. The multilayer snow model consistently improved low-flow simulations during both calibration and validation periods and enhanced the representation of the falling limb during the calibration period. However, the monolayer snow model performs slightly better during the rising limb of the freshet season for the calibration phase. In addition, the multilayer configuration reduced the bias of the cumulative freshet volumes and annual maximum freshet discharge. Overall, the multilayer snow model achieved comparable performance to the monolayer model for high-flow simulations while outperforming it for low-flow conditions, leading to a more accurate representation of freshet volumes and falling limb dynamics. Full article

(This article belongs to the Section Hydrology)

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21 pages, 5619 KB

Open AccessArticle

Influence of Riparian Vegetation on River Morphodynamics: A Numerical Modeling Framework

by Ricardo Gutiérrez, Alejandro Mendoza and Moisés Berezowsky

Water 2026, 18(7), 883; https://doi.org/10.3390/w18070883 - 7 Apr 2026

Abstract

Riparian vegetation plays an important role in the morphological evolution of rivers; here, an alternative numerical methodology for modeling river morphodynamics influenced by vegetation is presented. The approach integrates a vegetation growth and flow-resistance submodule coupled with the TELEMAC–MASCARET system. Vegetation is represented [...] Read more.

Riparian vegetation plays an important role in the morphological evolution of rivers; here, an alternative numerical methodology for modeling river morphodynamics influenced by vegetation is presented. The approach integrates a vegetation growth and flow-resistance submodule coupled with the TELEMAC–MASCARET system. Vegetation is represented at the patch scale, and its hydraulic effect is incorporated through an additional drag force in the momentum equation, while stem obstruction is accounted for using the porosity formulation in TELEMAC-2D. Vegetation dynamics consider water depth variability, interspecific competition, and nutrient availability. The model is applied to a braided river reach in southeastern Mexico. The results indicate that riparian vegetation promotes more organized flow paths, enhances bar development, and plays a significant role in modulating bar stability. These findings highlight the importance of explicitly representing flow–sediment–vegetation feedback in river hydro-morphological modeling. Full article

(This article belongs to the Section Hydraulics and Hydrodynamics)

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34 pages, 8819 KB

Open AccessArticle

Mitigating Overfitting and Physical Inconsistency in Flood Susceptibility Mapping: A Physics-Constrained Evolutionary Machine Learning Framework for Ungauged Alpine Basins

by Chuanjie Yan, Lingling Wu, Peng Huang, Jiajia Yue, Haowen Li, Chun Zhou, Congxiang Fan, Yinan Guo and Li Zhou

Water 2026, 18(7), 882; https://doi.org/10.3390/w18070882 - 7 Apr 2026

Abstract

Flood susceptibility mapping in high-altitude ungauged basins faces a structural dichotomy: physically based models often suffer from systematic biases due to uncertain satellite precipitation, whereas data-driven models are prone to overfitting and lack physical consistency in data-scarce regions. To resolve this, this study [...] Read more.

Flood susceptibility mapping in high-altitude ungauged basins faces a structural dichotomy: physically based models often suffer from systematic biases due to uncertain satellite precipitation, whereas data-driven models are prone to overfitting and lack physical consistency in data-scarce regions. To resolve this, this study proposes a Physically constrained Particle Swarm Optimization–Random Forest (P-PDRF) framework, validated in the Lhasa River Basin. The core innovation lies in coupling a hydrological model with statistical learning by utilizing the maximum daily runoff depth as a “Relative Hydraulic Intensity Index.” This approach leverages the topological correctness of physical simulations to circumvent absolute forcing errors. Furthermore, a Physiographically Constrained Negative Sampling (PCNS) strategy and a PSO-optimized “Shallow Tree” configuration are introduced to enforce structural regularization against stochastic noise. Empirical results demonstrate that P-PDRF achieves superior generalization (AUC = 0.942), significantly outperforming standard Random Forest, Support Vector Machine, and Analytic Hierarchy Process models. Ablation studies confirm that the dynamic index outweighs the static Topographic Wetness Index in feature importance, effectively correcting topographic artifacts where static models misclassify arid depressions as high-risk zones. This study offers a scalable Physics-Informed Machine Learning solution for the global “Prediction in Ungauged Basins” initiative. Full article

(This article belongs to the Special Issue Urban Flood Risk Assessment and Management)

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19 pages, 2241 KB

Open AccessArticle

Multi-Objective Optimization and Adaptive Control for Frequency Regulation of Hydropower Units Under Variable Operating Conditions

by Dong Liu, Chen Li, Yanbo Xue, Xiaoqiang Tan and Xiaoyuan Zhang

Water 2026, 18(7), 881; https://doi.org/10.3390/w18070881 - 7 Apr 2026

Abstract

As a key part of the new power system, hydropower units (HPUs) are capable of maintaining the stability of system frequency through the flexible conversion of operating conditions. Fixed control parameters are generally adopted by existing HPU governors, which cannot meet the requirements [...] Read more.

As a key part of the new power system, hydropower units (HPUs) are capable of maintaining the stability of system frequency through the flexible conversion of operating conditions. Fixed control parameters are generally adopted by existing HPU governors, which cannot meet the requirements of variable operating conditions, and the flexibility of hydropower regulation is thus restricted. Therefore, an adaptive optimal control strategy for units in frequency regulation mode is proposed for a large hydropower station in this paper. Firstly, a segmented linearized mathematical model for HPU frequency regulation is established. On this basis, objective functions under frequency and load perturbation are constructed. Control parameters under each operating condition are optimized via an improved multi-objective particle swarm optimization based on the objective functions. The nonlinear relationship between optimal control parameters and operating conditions is fitted to obtain the adaptive adjustment strategy. Comparative verification with the fixed-parameter strategy shows that the proposed strategy improves comprehensive performance (frequency adjustment and recovery time) under 48 operating conditions. The improvement rate exceeds 50% under large opening conditions, with an overall average of 51.01%, fully proving its superiority. Full article

(This article belongs to the Special Issue Research Status of Operation and Management of Hydropower Station, 2nd Edition)

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23 pages, 8119 KB

Open AccessArticle

A Detailed Simulation of Overtopping-Induced Breach Processes and Breach Evolution in Non-Cohesive Earth Dams

by Shengyao Mei, Yu Li, Jianjun Xu, Qiming Zhong, Yibo Shan and Lingchun Chen

Water 2026, 18(7), 880; https://doi.org/10.3390/w18070880 - 7 Apr 2026

Abstract

Non-cohesive earth dams are widely distributed in natural and semi-engineering scenarios, and overtopping-induced breaches are their most catastrophic failure mode. Accurate prediction of the overtopping failure process and breach evolution is critical for risk assessment, emergency management, and dam design optimization. In this [...] Read more.

Non-cohesive earth dams are widely distributed in natural and semi-engineering scenarios, and overtopping-induced breaches are their most catastrophic failure mode. Accurate prediction of the overtopping failure process and breach evolution is critical for risk assessment, emergency management, and dam design optimization. In this study, an improved 3D numerical method is developed to simulate the coupled hydrodynamic–erosion–breach evolution processes of non-cohesive earth dams. The model based on the finite volume method integrates three core modules: a hydrodynamic module based on the Reynolds-Averaged Navier–Stokes equations with the Volume of Fluid method for free surface tracking, a dam material erosion module considering particle entrainment and transport mechanisms of non-cohesive soils, and a breach development module coupling erosion and gravitational collapse. To validate the model, two levels of verification are conducted: first, a classic benchmark dam break case is employed to confirm the feasibility of the hydrodynamic and breach evolution algorithms; second, published flume experimental data of non-cohesive earth dam overtopping failures are adopted to evaluate the model accuracy in predicting breach hydrographs and spatiotemporal evolution of breach geometry. The results demonstrate that the proposed model accurately reproduces the key characteristics of overtopping failure with high fidelity. The predicted breach flow rates and flow depths are in excellent agreement with experimental observations, with relative errors less than 5% for both peak discharge and time to peak. Consequently, this study provides a reliable numerical tool for detailed simulation of non-cohesive earth dam breaches and offers scientific support for emergency management. Full article

(This article belongs to the Special Issue Numerical Modeling of Hydrodynamics and Sediment Transport)

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