Water

17 pages, 7770 KB

Open AccessArticle

Long-Term Runoff Prediction Using Large-Scale Climatic Indices and Machine Learning Model in Wudongde and Three Gorges Reservoirs

by Feng Ma, Xiaoshan Sun and Zihang Han

Water 2025, 17(20), 2942; https://doi.org/10.3390/w17202942 (registering DOI) - 12 Oct 2025

Abstract

Reliable long-term runoff prediction for Wudongde and Three Gorges reservoirs, two major reservoirs in the upper Yangtze River basin, is crucial for optimal operation of cascade reservoirs and hydropower generation planning. This study develops a data-driven model that integrates large-scale climate factors with [...] Read more.

Reliable long-term runoff prediction for Wudongde and Three Gorges reservoirs, two major reservoirs in the upper Yangtze River basin, is crucial for optimal operation of cascade reservoirs and hydropower generation planning. This study develops a data-driven model that integrates large-scale climate factors with a Gated Recurrent Unit (GRU) neural network to enhance runoff forecasting at lead times of 7–18 months. Key climate predictors were systematically selected using correlation analysis and stepwise regression before being fed into the GRU model. Evaluation results demonstrate that the proposed model can skillfully predict the variability and magnitude of reservoir inflow. For Wudongde Reservoir, the model achieved a mean correlation coefficient (CC) of 0.71 and Kling–Gupta Efficiency (KGE) of 0.57 during the training period, and values of 0.69 and 0.53 respectively during the testing period. For Three Gorges Reservoir, the CC was 0.67 (training) and 0.66 (testing), and the KGE was 0.52 and 0.49 respectively. The model exhibited robust forecasting capabilities across a range of lead times but showed distinct seasonal variations, with superior performance in summer and winter compared to transitional months (April and October). This framework provides a valuable tool for long-term runoff forecasting by effectively linking large-scale climate signals to local hydrological responses. Full article

(This article belongs to the Section Hydrology)

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22 pages, 1401 KB

Open AccessArticle

Techno-Economic Assessment of Microalgae-Based Biofertilizer Production from Municipal Wastewater Using Scenedesmus sp.

by Alejandro Pérez Mesa, Paula Andrea Céspedes Grattz, Juan José Vidal Vargas, Luis Alberto Ríos and David Ocampo Echeverri

Water 2025, 17(20), 2941; https://doi.org/10.3390/w17202941 (registering DOI) - 12 Oct 2025

Abstract

This research determines the techno-economic feasibility of valorizing as biofertilizer the nitrogen (N) and the phosphorus (P) from a municipal wastewater effluent using the microalgae Scenedesmus sp., contributing to phosphorus recycling, resource optimization, and diminishing eutrophication by capturing 74% of N, 97% of [...] Read more.

This research determines the techno-economic feasibility of valorizing as biofertilizer the nitrogen (N) and the phosphorus (P) from a municipal wastewater effluent using the microalgae Scenedesmus sp., contributing to phosphorus recycling, resource optimization, and diminishing eutrophication by capturing 74% of N, 97% of P, and 41% of chemical oxygen demand in effluents. The inoculum was conditioned in 20 L photobioreactors by weekly harvesting and refilling at room temperature (25 °C day, 12 °C night) with a 12:12 photoperiod and 4 L/min atmospheric air bubbling. The improved operational conditions were obtained using a Box–Behnken experimental design, establishing that 70% wastewater concentration (vol./vol.), 4.5% nutrient addition, and 3 days’ harvesting time were the best conditions. The estimated biomass production was 176 tons/year, and this represents a maximum net present value of 1.5 MUSD for a 6.8 Ha plant, capturing 10% of municipal wastewater effluent, which serves 64000 inhabitants. The representative operational costs (OPEX) were 32% for utilities, 30% labor costs, and 25% for raw materials, and the required capital expenditures (CAPEX) were 11 MUSD and are related to photobioreactors (64%) and land (21%). The findings demonstrate the potential of microalgae-based systems as a feasible and profitable approach to wastewater valorization, while also highlighting the need for scale-up validation and integration with existing treatment infrastructures, where land requirements and photobioreactor installation will be relevant for financial feasibility. Full article

(This article belongs to the Special Issue Algae-Based Technology for Wastewater Treatment)

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27 pages, 1875 KB

Open AccessArticle

Assessing Portuguese Public Health Literacy on Legionella Infections: Risk Perception, Prevention, and Public Health Impact

by Susana Dias, Maria Margarida Passanha, Margarida Figueiredo and Henrique Vicente

Water 2025, 17(20), 2940; https://doi.org/10.3390/w17202940 (registering DOI) - 12 Oct 2025

Abstract

Legionella is an environmental bacterium capable of causing severe respiratory infections, with outbreaks posing significant public health challenges in developed countries. Understanding public awareness of Legionella transmission, risk perception, and preventive behaviors is crucial for reducing exposure and guiding health education strategies. This [...] Read more.

Legionella is an environmental bacterium capable of causing severe respiratory infections, with outbreaks posing significant public health challenges in developed countries. Understanding public awareness of Legionella transmission, risk perception, and preventive behaviors is crucial for reducing exposure and guiding health education strategies. This study aimed to evaluate the Portuguese population’s knowledge of Legionella infections and their readiness to adopt preventive measures. A structured questionnaire was developed and administered to 239 participants aged 18–76 years across Portugal, collecting socio-demographic data and assessing literacy through statements organized into domains related to Legionella risk, control measures, and public health impact. The results indicate that participants possess moderate to high awareness of Legionella severity, transmission routes, and preventive strategies, yet gaps remain in understanding key risk factors, optimal water system maintenance, and the influence of temperature on bacterial growth. Age, educational attainment, and occupational status were associated with differences in self-assessed literacy levels. Artificial neural network models were applied to classify literacy levels, achieving a near 90% accuracy and demonstrating higher confidence in low and moderate categories. These findings provide insights for designing tailored educational programs, improving public health communication, and enhancing preventive behaviors to reduce Legionella infection risks. Full article

(This article belongs to the Special Issue Exposure, Ecological Effects and Risk Assessment of Emerging Contaminants in Water Environment)

22 pages, 2695 KB

Open AccessArticle

Modeling Total Alkalinity in Aquatic Ecosystems by Decision Trees: Anticipation of pH Stability and Identification of Main Contributors

by Hichem Tahraoui, Rachida Bouallouche, Kamilia Madi, Oumnia Rayane Benkouachi, Reguia Boudraa, Hadjar Belkacemi, Sabrina Lekmine, Hamza Moussa, Nabil Touzout, Mohammad Shamsul Ola, Zakaria Triki, Meriem Zamouche, Mohammed Kebir, Noureddine Nasrallah, Amine Aymen Assadi, Yacine Benguerba, Jie Zhang and Abdeltif Amrane

Water 2025, 17(20), 2939; https://doi.org/10.3390/w17202939 (registering DOI) - 12 Oct 2025

Abstract

Total alkalinity (TAC) plays a pivotal role in buffering acid–base fluctuations and maintaining pH stability in aquatic ecosystems. This study presents a data-driven approach to model TAC using decision tree regression, applied to a comprehensive dataset of 454 water samples collected in diverse [...] Read more.

Total alkalinity (TAC) plays a pivotal role in buffering acid–base fluctuations and maintaining pH stability in aquatic ecosystems. This study presents a data-driven approach to model TAC using decision tree regression, applied to a comprehensive dataset of 454 water samples collected in diverse aquatic environments of the Médéa region, Algeria. Twenty physicochemical parameters, including concentrations of bicarbonates, hardness, major ions, and trace elements, were analyzed as input features. The decision tree algorithm was optimized using the Dragonfly metaheuristic algorithm coupled with 5-fold cross-validation. The optimized model (DT_DA) demonstrated exceptional predictive performance, with a correlation coefficient R of 0.9999, and low prediction errors (RMSE = 0.3957, MAE = 0.3572, and MAPE = 0.4531). External validation on an independent dataset of 68 samples confirmed the model’s robustness (R = 0.9999; RMSE = 0.4223; MAE = 0.3871, and MAPE = 0.4931). The tree structure revealed that total hardness (threshold: 78.5 °F) and bicarbonate concentration (threshold: 421.68 mg/L) were the most influential variables in TAC determination. The model offers not only accurate predictions but also interpretable decision rules, allowing the identification of critical physicochemical thresholds that govern alkalinity. These findings provide a valuable tool for anticipating pH instability and guiding water quality management and protection strategies in freshwater ecosystems. Full article

(This article belongs to the Section New Sensors, New Technologies and Machine Learning in Water Sciences)

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21 pages, 5915 KB

Open AccessArticle

A Machine Learning Approach to Predicting the Turbidity from Filters in a Water Treatment Plant

by Joseph Kwarko-Kyei, Hoese Michel Tornyeviadzi and Razak Seidu

Water 2025, 17(20), 2938; https://doi.org/10.3390/w17202938 (registering DOI) - 12 Oct 2025

Abstract

Rapid sand filtration is a critical step in the water treatment process, as its effectiveness directly impacts the supply of safe drinking water. However, optimising filtration processes in water treatment plants (WTPs) presents a significant challenge due to the varying operational parameters and [...] Read more.

Rapid sand filtration is a critical step in the water treatment process, as its effectiveness directly impacts the supply of safe drinking water. However, optimising filtration processes in water treatment plants (WTPs) presents a significant challenge due to the varying operational parameters and conditions. This study applies explainable machine learning to enhance insights into predicting direct filtration operations at the Ålesund WTP in Norway. Three baseline models (Multiple Linear Regression, Support Vector Regression, and K-Nearest Neighbour (KNN)) and three ensemble models (Random Forest (RF), Extra Trees (ET), and XGBoost) were optimised using the GridSearchCV algorithm and implemented on seven filter units to predict their filtered water turbidity. The results indicate that ML models can reliably predict filtered water turbidity in WTPs, with Extra Trees models achieving the highest predictive performance (R² = 0.92). ET, RF, and KNN ranked as the three top-performing models using Alternative Technique for Order of Preference by Similarity to Ideal Solution (A-TOPSIS) ranking for the suite of algorithms used. The feature importance analysis ranked the filter runtime, flow rate, and bed level. SHAP interpretation of the best model provided actionable insights, revealing how operational adjustments during the ripening stage can help mitigate filter breakthroughs. These findings offer valuable guidance for plant operators and highlight the benefits of explainable machine learning in water quality management. Full article

(This article belongs to the Special Issue Application of Artificial Intelligence (AI) in Water Quality Monitoring)

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17 pages, 709 KB

Open AccessReview

Behaviors of Highway Culverts Subjected to Flooding: A Comprehensive Review

by Omer Zeyrek, Fei Wang and Jun Xu

Water 2025, 17(20), 2937; https://doi.org/10.3390/w17202937 (registering DOI) - 12 Oct 2025

Abstract

Highway culverts are essential components of transportation infrastructure, designed to convey water beneath highways and protect embankments from flooding. However, extreme flood events often impose hydraulic loads, overtopping, and debris accumulation that can trigger erosion, scour, blockage, and in severe cases, catastrophic washout. [...] Read more.

Highway culverts are essential components of transportation infrastructure, designed to convey water beneath highways and protect embankments from flooding. However, extreme flood events often impose hydraulic loads, overtopping, and debris accumulation that can trigger erosion, scour, blockage, and in severe cases, catastrophic washout. This paper presents a comprehensive review of highway culvert behavior under flooding conditions, integrating insights from hydraulics, geotechnical engineering, and structural performance. The review is organized around four themes: (1) types of flooding and their interactions with culverts; (2) hydraulic performance during flood events; (3) common failure modes, including scour, debris blockage, and structural instability; and (4) mitigation strategies to enhance resilience. Advances in hydraulic modeling, including 1D, 2D, 3D, and CFD approaches, are summarized, with attention to their accuracy, applicability limits, and validation needs. Representative experimental, numerical, and empirical studies are grouped by common properties to highlight key findings and constraints. Finally, emerging research opportunities are discussed, including the need for quantitative relationships between culvert geometry and flood intensity, methods to assess structural capacity loss during flooding, and the integration of artificial intelligence and computer vision for rapid post-flood inspection. This synthesis establishes a foundation for more robust evaluation, design, and maintenance strategies, supporting the long-term resilience of highway culverts in an era of increasingly frequent and severe floods. Full article

(This article belongs to the Special Issue Analysis and Simulation of Urban Floods)

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21 pages, 5810 KB

Open AccessArticle

Investigating Seasonal Water Quality Dynamics in Humid, Subtropical Louisiana Facultative Waste Stabilization Ponds

by Mason Marcantel, Mahathir Bappy and Michael Hayes

Water 2025, 17(20), 2936; https://doi.org/10.3390/w17202936 (registering DOI) - 11 Oct 2025

Abstract

Waste stabilization ponds (WSPs) in humid, subtropical climates rely on stable temperatures and mechanical aeration to promote microbial activity. These critical infrastructures can lack operational resources to ensure efficient treatment, which can impact downstream communities. This study aims to use remote water quality [...] Read more.

Waste stabilization ponds (WSPs) in humid, subtropical climates rely on stable temperatures and mechanical aeration to promote microbial activity. These critical infrastructures can lack operational resources to ensure efficient treatment, which can impact downstream communities. This study aims to use remote water quality sensor data to establish trends in a yearly dataset and correlate various water quality parameters for simplistic identification of pond health. A facultative WSP was monitored in two stages: the primary settling over a period of 14 months to evaluate partially treated water, and the secondary treatment pond for a period of 11 months to monitor final stage water quality parameters. A statistical analysis was performed on the measured parameters (dissolved oxygen, temperature, conductivity, pH, turbidity, nitrate, and ammonium) to establish a comprehensive yearly, seasonal, and monthly dataset to show fluctuations in water parameter correlations. Standard relationships in dissolved oxygen, conductivity, pH, and temperature were traced during the seasonal fluctuations, which provided insight into nitrogen processing by microbial communities. During this study, the summer period showed the most variability, specifically a deviation in the dissolved oxygen and temperature relationship from a yearly moderate negative correlation (−0.593) to a moderate positive correlation (0.459), indicating a direct relationship. The secondary treatment pond data showed more nitrogen species correlation, which can indicate final cycling during seasonal transitions. Understanding pond dynamics can lead to impactful, proactive operational decisions to address pond imbalance or chemical dosing for final treatment. By establishing parameter correlations, facilities with WSPs can strategically integrate sensor networks for real-time pond health and treatment efficiency monitoring during seasonal fluctuations. Full article

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

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22 pages, 3652 KB

Open AccessArticle

Research on Optimal Water Resource Allocation in Inland River Basins Based on Spatiotemporal Evolution Characteristics of Blue and Green Water—Taking the Taolai River Basin of the Heihezi Water System as an Example

by Jiahui Zhang, Xinjian Fan, Xinghai Wang, Lirong Wang, Jiafang Wei and Yuhan Xiao

Water 2025, 17(20), 2935; https://doi.org/10.3390/w17202935 (registering DOI) - 11 Oct 2025

Abstract

Water demand has increased due to population growth and rapid socioeconomic development, creating conflicts between human activities and water resources and having a substantial impact on the balance between blue and green water supplies. Existing study lacks a spatial perspective to examine the [...] Read more.

Water demand has increased due to population growth and rapid socioeconomic development, creating conflicts between human activities and water resources and having a substantial impact on the balance between blue and green water supplies. Existing study lacks a spatial perspective to examine the inherent relationship between blue and green water supply and demand, particularly in terms of geographical differentiation characteristics and rational allocation of blue and green water supply–demand balance in inland river basins. Using the Taolai River Basin as a case study, this research uses the distributed hydrological model SWAT from a blue–green water resources viewpoint to simulate the spatiotemporal distribution features of blue and green water resources at the sub-basin scale from 2002 to 2021. The supply and demand balance relationship of blue and green water resources within the basin was investigated, an assessment index system for water resource security was developed, and the realizable potential of blue water resources was quantified using various indicators. The findings show that during the study period, the average annual green water resources in the Taolai River Basin were 1.95 times greater than blue water resources, making green water the most abundant component of regional water resources. Spatially, both blue and green water resources showed considerable latitudinal zonality, with a declining tendency from south to north and very consistent distribution patterns. Blue water resources showed high geographic variability, with a safety index more than one, suggesting that supply–demand imbalances were most concentrated in the upper and intermediate ranges of the irrigated region, as well as the desert zone, where safety levels were relatively low. In contrast, green water resources had a safety score ranging from 0.7 to 1.0, indicating great overall safety and negligible regional variability. During the research period, the average annual theoretical transferable blue water resources were 4.06 × 108 m³, based on cross-regional water resource allocation potential analysis. This reveals tremendous potential for enhancing regional water resource allocation, hence providing substantial support for effective water consumption within the Taolai River Basin and regional economic growth. In conclusion, the assessment method developed in this work provides a solid foundation for improving water resource allocation and sustainable management in river basins. It provides technical assistance in the construction of water network systems in inland river basins, which is critical in establishing reasonable water resource distribution across various areas within these basins. Full article

(This article belongs to the Special Issue Application of Hydrological Modelling to Water Resources Management)

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17 pages, 5561 KB

Open AccessArticle

Swimming Pools in Water Scarce Regions: A Real or Exaggerated Water Problem? Case Studies from Southern Greece

by G.-Fivos Sargentis, Emma Palamarczuk and Theano Iliopoulou

Water 2025, 17(20), 2934; https://doi.org/10.3390/w17202934 (registering DOI) - 11 Oct 2025

Abstract

Swimming pools, symbols of luxury in tourism-driven Greece, raise concerns about water consumption in water-scarce regions. This study assesses their hydrological impact in two regions of Southern Greece, West Mani (Peloponnese) and Naxos Island (Cyclades), within the water–energy–food nexus framework, evaluating the resulting [...] Read more.

Swimming pools, symbols of luxury in tourism-driven Greece, raise concerns about water consumption in water-scarce regions. This study assesses their hydrological impact in two regions of Southern Greece, West Mani (Peloponnese) and Naxos Island (Cyclades), within the water–energy–food nexus framework, evaluating the resulting trade-offs. Using satellite imagery, we identified 354 pools in West Mani (11,738 m²) and 556 in Naxos (26,825 m²). Two operational scenarios were evaluated: complete seasonal emptying and refilling (Scenario 1) and one-third annual water renewal (Scenario 2). Annual water use ranged from 39,000 to 51,000 m³ in West Mani and 98,000 to 124,000 m³ in Naxos—equivalent to the needs of 625–2769 and 1549–6790 people in West Mani and Naxos, respectively. In Naxos, this volume could alternatively irrigate 27–40 hectares of potatoes, producing food for 700–1500 people. Energy requirements, particularly where desalination is used, further increase the burden, with Naxos pools requiring 384–846 MWh annually. Although swimming pools are highly visible water consumers, their overall contribution to water scarcity is modest compared to household and agricultural uses. Their visibility, however, amplifies public concern. Rainwater harvesting, requiring collection areas 10–24 times larger than pool surface areas, especially in residential and hotel settings, could make pools largely self-sufficient. Integrating such measures into water management and tourism policy can help balance luxury amenities with resource conservation in water-scarce Mediterranean regions. Full article

(This article belongs to the Special Issue Challenges and Innovations in Resilience of the Water-Energy-Food Nexus)

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22 pages, 8224 KB

Open AccessArticle

Hypothesis-Driven Conceptual Model for Groundwater–Surface Water Interaction at Aguieira Dam Reservoir (Central Portugal) Based on Principal Component Analysis and Hierarchical Clustering

by Gustavo Luís, Alcides Pereira and Luís Neves

Water 2025, 17(20), 2933; https://doi.org/10.3390/w17202933 (registering DOI) - 11 Oct 2025

Abstract

The interaction between groundwater and surface water can be significant in lakes or irrigation channels, as well as in large dam reservoirs or along portions of them. To evaluate this interaction at a sampling location directly controlled by a large dam equipped with [...] Read more.

The interaction between groundwater and surface water can be significant in lakes or irrigation channels, as well as in large dam reservoirs or along portions of them. To evaluate this interaction at a sampling location directly controlled by a large dam equipped with reversible pump-turbines, data from Rn-222 and physicochemical parameters at specific depths and times were obtained and studied using Principal Component Analysis and Hierarchical Clustering. Dimension 1 explains 45.3% of the total variability in the original data, which can be interpreted as the result of external factors related to seasonal variability (e.g., temperature, turbulent flow, and precipitation), while Dimension 2 explains up to 31.2% and can be interpreted as the variability related to groundwater inputs. Five hierarchical clusters based on these dimensions were considered and were related to the temporal variability observed in the water column throughout the year, as well as the depth relationships observed between successive surveys. A hypothesis-driven conceptual piston-like effect model is proposed for groundwater–surface water interactions, considering the identified relationships between variables, including higher Rn-222 concentrations in surface water after heavy rain. According to this simplified conceptual model, water infiltrates in a weathered granitic recharging area; during heavy rain, it is forced through the fracture systems of a lesser-weathered granite. Thus, an overall increase in pressure over the hydrological system forces the older radon-enriched water to discharge into the Mondego River. This work highlights the importance of exploratory techniques such as PCA and Hierarchical Clustering, in addition to underlying knowledge of the geological setting, for the proposal of simplified conceptual models that help in the management of important reservoirs. This work also demonstrates the utility of Rn-222 as a simple tracer of groundwater discharge into surface water. Full article

(This article belongs to the Section Hydrogeology)

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28 pages, 3979 KB

Open AccessReview

Beyond Deterministic Forecasts: A Scoping Review of Probabilistic Uncertainty Quantification in Short-to-Seasonal Hydrological Prediction

by David De León Pérez, Sergio Salazar-Galán and Félix Francés

Water 2025, 17(20), 2932; https://doi.org/10.3390/w17202932 (registering DOI) - 11 Oct 2025

Abstract

This Scoping Review methodically synthesizes methodological trends in predictive uncertainty (PU) quantification for short-to-seasonal hydrological modeling-based forecasting. The analysis encompasses 572 studies from 2017 to 2024, with the objective of addressing the central question: What are the emerging trends, best practices, and gaps [...] Read more.

This Scoping Review methodically synthesizes methodological trends in predictive uncertainty (PU) quantification for short-to-seasonal hydrological modeling-based forecasting. The analysis encompasses 572 studies from 2017 to 2024, with the objective of addressing the central question: What are the emerging trends, best practices, and gaps in this field? In accordance with the six-stage protocol that is aligned with PRISMA-ScR standards, 92 studies were selected for in-depth evaluation. The results of the study indicate the presence of three predominant patterns: (1) exponential growth in the applications of machine learning and artificial intelligence; (2) geographic concentration in Chinese, North American, and European watersheds; and (3) persistent operational barriers, particularly in data-scarce tropical regions with limited flood and streamflow forecasting validation. Hybrid statistical-AI modeling frameworks have been shown to enhance forecast accuracy and PU quantification; however, these frameworks are encumbered by constraints in computational demands and interpretability, with inadequate validation for extreme events highlighting critical gaps. The review emphasizes standardized metrics, broader validation, and adaptive postprocessing to enhance applicability, advocating robust frameworks integrating meteorological input to hydrological output postprocessing for minimizing uncertainty chains and supporting water management. This study provides an updated field mapping, identifies knowledge gaps, and prioritizes research for the operational integration of advanced PU quantification. Full article

(This article belongs to the Section Hydrology)

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Graphical abstract

48 pages, 5238 KB

Open AccessArticle

Chemodynamics of Mercury (Hg) in a Southern Reservoir Lake (Cane Creek Lake, Cookeville, TN, USA): II—Estimation of the Hg Water/Air Exchange Coefficient Using the Two-Thin Film Model and Field-Measured Data of Hg Water/Air Exchange and Dissolved Gaseous Hg

by Hong Zhang, Lesta S. Fletcher and William C. Crocker

Water 2025, 17(20), 2931; https://doi.org/10.3390/w17202931 - 10 Oct 2025

Abstract

This paper reports a novel effort to estimate and evaluate the coefficients of Hg transfer across the water/air interface in lakes such as Cane Creek Lake (CCL, Cookeville, TN, USA). This was accomplished by calculating the coefficients (k_w) using the [...] Read more.

This paper reports a novel effort to estimate and evaluate the coefficients of Hg transfer across the water/air interface in lakes such as Cane Creek Lake (CCL, Cookeville, TN, USA). This was accomplished by calculating the coefficients (k_w) using the Two-Thin Film (TTF) Model for Hg transfer together with the field-measured data of Hg emission flux (F), dissolved gaseous mercury concentration (DGM), air Hg concentration (C_a), and water temperature for Henry’s coefficient (K_H) obtained from a separate field study at the CCL. The daily mean k_w values range from 0.045 to 0.21 m h⁻¹, with the min. at 0.0025–0.14 and the max. at 0.079–0.41 m h⁻¹, generally higher for the summer, and from 0.0092 to 0.15, with the min. at 0.0032–0.033 and the max. at 0.017–0.31 m h⁻¹, generally lower for the fall and winter, exhibiting an apparent seasonal trend. The highest k_w values occur in August (mean: 0.21, max.: 0.41 m h⁻¹). Our k_w results add to and enrich the aquatic interfacial Hg transfer coefficient database and provide an alternative avenue to evaluate and select the coefficients for the TTF Model’s application. The k_w results are of value in gaining insights into the Hg transfer actually occurring across the water/air interface under environmental influences (e.g., wind/wave, solar radiation). Our k_w results do not show a clear, consistent correlation of k_w with wind/wave effect, nor sunlight effect, in spite of some correlations in sporadic cases. Generally, the k_w values do not exbibit the trends prescribed by the model sensitivity study. The comparisons of our k_w results with those obtained using wind-based transfer models (the Liss/Merlivat Model, the Wanninkhof Model, and the modified linear model) show that they depart from each other. The findings of this study indicate that the TTF Model has limitations and weaknesses. One major assumption of the TTF Model is the equilibrium of the Hg distribution between the air and water films across the water/air interface. The predominant oversaturation of DGM shown by our DGM data evidently challenges this assumption. This study suggests that aquatic interfacial Hg transfer is considerably more complicated, involving a group of factors, more than just wind and wave. Full article

(This article belongs to the Special Issue Aquatic Chemodynamics of Environmental Inorganic Pollutants: Transformation and Transfer Within and Across Various Environmental Compartments)

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22 pages, 9503 KB

Open AccessArticle

Analysis of Annual Maximum Ice-Influenced and Open-Water Levels at Select Hydrometric Stations on Canadian Rivers

by Yonas Dibike, Laurent de Rham, Spyros Beltaos, Daniel L. Peters and Barrie Bonsal

Water 2025, 17(20), 2930; https://doi.org/10.3390/w17202930 - 10 Oct 2025

Abstract

River ice is a common feature in most Canadian rivers and streams during the cold season. River channel hydraulics under ice conditions may cause higher water levels at a relatively lower discharge compared to the open-water flood events. Elevated water levels resulting from [...] Read more.

River ice is a common feature in most Canadian rivers and streams during the cold season. River channel hydraulics under ice conditions may cause higher water levels at a relatively lower discharge compared to the open-water flood events. Elevated water levels resulting from river ice processes throughout fall freeze-over, mid-winter, and spring break-up are important hydrologic events with diverse morphological, ecological, and socio-economic impacts. This study analyzes the timing of maximum water levels (occurring during freeze-over, spring break-up, and open-water periods) and the typology of maximum ice-related events (at freeze-over, mid-winter, and spring break-up) using data from the Canadian River Ice Database. The study also compares annual maximum water levels during the river ice and open-water periods at selected hydrometric stations from 1966 to 2015, divided into two 25-year windows: 1966–1990 and 1991–2015. A return period classification method was applied to define ice-influenced, open-water, and mixed-regime conditions. The results indicate that the majority of ice-influenced maximum water levels occurred during spring break-up (~79% in 1966–1990 and ~69% in 1991–2015), followed by fall freeze-up (~13% and ~23%) and mid-winter break-up (~8% and ~7%) for the two periods, respectively. Among 15 stations analyzed for 1966–1990 and 42 stations for 1991–2015, the proportion of annual maximum water levels dominated by open-water conditions increased from 47% to 55%, while ice-dominated events decreased from 13% to 12%, and mixed-regime events dropped from 40% to 33%. However, a focused comparison of eight common stations revealed minimal change in the distribution of water level-generating events between the two periods. The findings offer valuable insights into the spatial distribution of maximum water level-generating mechanisms across Canada. Full article

(This article belongs to the Special Issue Hydroclimatic Changes in the Cold Regions)

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21 pages, 1526 KB

Open AccessArticle

BIM Lightweight Technology in Water Conservancy Engineering Operation and Maintenance: Improvement of the QEM Algorithm and Construction of the Evaluation System

by Zhengjie Zhan, Zihao Tang, Lihong He and Junzhi Ding

Water 2025, 17(20), 2929; https://doi.org/10.3390/w17202929 - 10 Oct 2025

Abstract

In recent years, with continuous technological advances, BIM technology has gradually expanded from the traditional construction industry into the field of hydraulic engineering. Since BIM models, which span the entire project lifecycle, contain substantial amounts of data and the operation and maintenance phase [...] Read more.

In recent years, with continuous technological advances, BIM technology has gradually expanded from the traditional construction industry into the field of hydraulic engineering. Since BIM models, which span the entire project lifecycle, contain substantial amounts of data and the operation and maintenance phase accounts for the majority of this lifecycle, higher computational demands are imposed. Consequently, the lightweighting of BIM models has become imperative. In this study, an improved Quadric Error Metric (QEM) algorithm was applied to simplify the geometric data of the constructed BIM model. The research investigates whether the lightweight model can reduce the computational requirements during its application in the operation and management of hydraulic engineering, thereby enhancing its general applicability. Furthermore, a fuzzy comprehensive evaluation model was established to assess the effectiveness of the lightweighting process. The experimental results indicate that the optimized model occupies significantly less memory space. Additionally, model loading time and rendering CPU usage were substantially improved. The lightweight effect was evaluated as excellent based on the fuzzy comprehensive evaluation. Full article

(This article belongs to the Topic Hydraulic Engineering and Modelling)

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23 pages, 5645 KB

Open AccessArticle

Analysis and Optimization of Coagulation Efficiency for Brackish Water Reverse Osmosis Brine Based on Ensemble Approach

by Dayoung Wi, Sangho Lee, Seoyeon Lee, Song Lee, Juyoung Lee and Yongjun Choi

Water 2025, 17(20), 2928; https://doi.org/10.3390/w17202928 - 10 Oct 2025

Abstract

Reuse of wastewater through brackish water reverse osmosis presents a major challenge due to the generation of brine, which contains organic and inorganic compounds to be removed. This study focuses on analyzing and optimizing coagulation conditions for brackish reverse osmosis brine treatment by [...] Read more.

Reuse of wastewater through brackish water reverse osmosis presents a major challenge due to the generation of brine, which contains organic and inorganic compounds to be removed. This study focuses on analyzing and optimizing coagulation conditions for brackish reverse osmosis brine treatment by evaluating pollutant removal efficiencies under various scenarios and leveraging advanced modeling techniques. Jar tests were performed using polyaluminum chloride and ferric chloride, evaluating the removal of total organic carbon, turbidity, UV₅₂₄, and phosphorus. Models were developed using response surface methodology, support vector machines, and random forest. Although the same data sets were used, the characteristics of these models were found to be different: Response surface methodology delivered high-fidelity, smooth response surfaces (R² > 0.92), support vector machine pinpointed sharp threshold regions, and random forest defined robust operating plateaus. By overlaying model-specific optimum contours, the consensus regions were identified for reliable removal across total organic carbon, turbidity, and phosphate. This ensemble strategy enhanced predictive reliability and provided a comprehensive decision-support tool for multi-objective optimization. The findings underscore the potential of ensemble-based modeling to improve the design and control of brackish reverse osmosis brine treatment processes, offering a data-driven pathway for addressing one of the most critical bottlenecks in wastewater reuse systems. Full article

(This article belongs to the Topic Membrane Separation Technology Research)

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22 pages, 1205 KB

Open AccessArticle

Evaluating the Effects of Irrigation Water Quality and Compost Amendment on Soil Health and Crop Productivity

by Subanky Suvendran, Miguel F. Acevedo, Breana Smithers, Stephanie J. Walker and Pei Xu

Water 2025, 17(20), 2927; https://doi.org/10.3390/w17202927 - 10 Oct 2025

Abstract

Brackish water is becoming an increasingly important resource for agricultural irrigation due to limited freshwater availability; however, concerns persist regarding its potential to degrade soil quality and reduce crop yields. This study evaluated the combined effects of irrigation water quality (brackish water, electrical [...] Read more.

Brackish water is becoming an increasingly important resource for agricultural irrigation due to limited freshwater availability; however, concerns persist regarding its potential to degrade soil quality and reduce crop yields. This study evaluated the combined effects of irrigation water quality (brackish water, electrical conductivity (EC) of 2958 µS/cm; agricultural water, EC 796 µS/cm), soil type (agricultural soil and reclaimed desert soil), and compost treatments (no compost, mulch compost, Johnson-Su compost, and mulch compost incorporation) on soil health and chili pepper (Capsicum annuum) growth under greenhouse conditions. Compost amendments significantly improved plant height by 58–213%, root length by 35–166%, and wet biomass by 154–1400% compared to control treatments. Agricultural water maintained lower soil EC (0.553–0.870 mS/cm) than brackish water (0.751–1.104 mS/cm), while Johnson-Su compost most effectively reduced salinity impact on plant growth. Leached water analysis showed higher Na⁺, Cl⁻, and SO₄²⁻ mobility under brackish irrigation, with compost treatments enhancing nutrient retention and soil moisture by buffering salinity stress with carboxylic group and cation exchange capacity. Johnson-Su compost incorporation consistently mitigated the negative effects of brackish irrigation by reducing sodium accumulation, improving chloride mobility, and enhancing soil nitrogen dynamics. These results highlight that combining high-quality irrigation water and biologically active composts improves soil health and plant productivity, while brackish water use requires soil amendments to mitigate salinity risks. Full article

(This article belongs to the Special Issue Soil Water Use and Irrigation Management)

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19 pages, 6255 KB

Open AccessArticle

Data–Physics-Driven Multi-Point Hybrid Deformation Monitoring Model Based on Bayesian Optimization Algorithm–Light Gradient-Boosting Machine

by Lei Song and Yating Hu

Water 2025, 17(20), 2926; https://doi.org/10.3390/w17202926 - 10 Oct 2025

Abstract

Single-point deformation monitoring models fail to reflect the structural integrity of the concrete gravity dams, and traditional regression methods also have shortcomings in capturing complex nonlinear relationships among variables. To solve these problems, this paper develops a data–physics-driven multi-point hybrid deformation monitoring model [...] Read more.

Single-point deformation monitoring models fail to reflect the structural integrity of the concrete gravity dams, and traditional regression methods also have shortcomings in capturing complex nonlinear relationships among variables. To solve these problems, this paper develops a data–physics-driven multi-point hybrid deformation monitoring model based on Bayesian Optimization Algorithm–Light Gradient-Boosting Machine (BOA-LightGBM). Building upon conventional single-point models, spatial coordinates are incorporated as explanatory variables to derive a multi-point deformation monitoring model that accounts for spatial correlations. Subsequently, the finite element method (FEM) is employed to simulate the hydrostatic component at each monitoring point under actual reservoir water levels. Finally, a hybrid model is constructed by integrating the derived mathematical expression, simulated hydrostatic components, and the BOA-LightGBM algorithm. A case study demonstrates that the proposed model effectively incorporates spatial deformation characteristics within dam sections and achieves satisfactory fitting and prediction accuracy compared to traditional single-point monitoring models. With further refinement and extension, the proposed modeling theory and methodology presented in this study can also provide valuable references for safety monitoring of other hydrostatic structures. Full article

(This article belongs to the Special Issue Application of Artificial Intelligence in Hydraulic Engineering, 2nd Edition)

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16 pages, 9032 KB

Open AccessArticle

Spatiotemporal Evolution, Transition, and Ecological Impacts of Flash and Slowly Evolving Droughts in the Dongjiang River Basin, China

by Qiang Huang, Liao Ouyang, Zimiao Wang and Jiayao Lin

Water 2025, 17(20), 2925; https://doi.org/10.3390/w17202925 - 10 Oct 2025

Abstract

Based on 0.1° × 0.1° soil moisture reanalysis data from 1950 to 2024, combined with remote sensing ecological products such as Enhanced Vegetation Index (EVI) and gross primary productivity (GPP), this study systematically investigates the spatiotemporal evolution, transition process, and ecological responses of [...] Read more.

Based on 0.1° × 0.1° soil moisture reanalysis data from 1950 to 2024, combined with remote sensing ecological products such as Enhanced Vegetation Index (EVI) and gross primary productivity (GPP), this study systematically investigates the spatiotemporal evolution, transition process, and ecological responses of flash droughts and slowly evolving droughts (including seasonal and cross-seasonal droughts) in the Dongjiang River Basin of China. The results indicate the following: (1) The average occurrence frequencies of flash droughts, seasonal droughts, and cross-seasonal droughts within the basin were 4.1%, 7.8%, and 8.4%, respectively. (2) The vast majority of flash droughts (approximately 90.1%) further developed into longer-lasting, slowly evolving droughts, indicating that flash droughts serve as a critical precursor to persistent drought events. Moreover, winter was identified as the key season for the occurrence of flash droughts and their transition to slowly evolving droughts. (3) In terms of ecological response, droughts significantly suppressed vegetation growth, but ecosystem resilience exhibited notable differences: although flash droughts caused relatively mild initial suppression, they were accompanied by a severe lack of ecosystem resilience; in contrast, cross-seasonal droughts, despite inducing stronger suppression, were met with higher ecosystem resilience. This study underscores the importance of the early monitoring and warning of flash droughts, and the findings provide a scientific basis for drought risk management in humid basins. Full article

(This article belongs to the Section Hydrology)

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19 pages, 8788 KB

Open AccessArticle

Source Analysis of Groundwater Chemical Components in the Middle Reaches of the Dawen River Based on Unsupervised Machine Learning and PMF Source Analysis

by Xinqi Wang, Zhenhua Zhao, Hongyan An, Lin Han, Mingming Li, Zihao Wang, Xinfeng Wang and Zheming Shi

Water 2025, 17(20), 2924; https://doi.org/10.3390/w17202924 - 10 Oct 2025

Abstract

Groundwater chemical composition often exhibits complex characteristics under the combined influence of anthropogenic activities and natural geological conditions. Accurately distinguishing between human-derived and naturally occurring constituents is crucial for formulating effective pollution control strategies and ensuring sustainable groundwater resource management. However, conventional hydrogeochemical [...] Read more.

Groundwater chemical composition often exhibits complex characteristics under the combined influence of anthropogenic activities and natural geological conditions. Accurately distinguishing between human-derived and naturally occurring constituents is crucial for formulating effective pollution control strategies and ensuring sustainable groundwater resource management. However, conventional hydrogeochemical analytical methods often face challenges in quantitatively differentiating these overlapping influences. In this study, 66 groundwater samples were collected from the midstream section of the Dawen River Basin, an area subject to significant anthropogenic pressure. An integrated approach combining hydrogeochemical analysis, Self-Organizing Map (SOM) clustering, and Positive Matrix Factorization (PMF) receptor modeling was employed to identify sources of chemical constituents and quantify the proportional contributions of various factors. The results indicate that: (1) The predominant groundwater types in the study area were Cl·SO₄·Ca. (2) SOM clustering classified the groundwater samples into five distinct groups, each reflecting a dominant influence: (i) natural geological processes—samples distributed within the central geological mining area; (ii) agricultural activities—samples located in intensively cultivated zones along both banks of the Dawen River; (iii) hydrogeochemical evolution—samples concentrated in areas with impermeable surfaces on the eastern and western sides of the study region; (iv) mining operations—samples predominantly found in industrial zones at the periphery; (v) domestic wastewater discharge—samples scattered relatively uniformly throughout the area. (3) PMF results demonstrated that natural geological conditions constituted the largest contribution (29.0%), followed by agricultural activities (26.8%), consistent with the region’s extensive farming practices. Additional contributions arose from water–rock interactions (23.9%), mining operations (13.6%), and domestic wastewater (6.7%). This study establishes a methodological framework for quantitatively assessing natural and anthropogenic impacts on groundwater quality, thereby providing a scientific basis for the development of protection measures and sustainable management strategies for regional groundwater resources. Full article

(This article belongs to the Section Hydrogeology)

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