Water

15 pages, 1194 KB

Open AccessArticle

Micro-Nano Aeration Oxygenation Irrigation Has Increased Soil Nitrogen and Cotton Yield in Arid Areas

by Jiayue Wang, Qiqi Chai, Ze Wang, Yanbo Fu, Zhiguo Wang, Qingyong Bian, Junhui Cheng, Yupeng Zhao, Jinquan Zhu and Yanhong Wei

Water 2025, 17(18), 2778; https://doi.org/10.3390/w17182778 - 19 Sep 2025

Abstract

To explore the effects of micro-nano aeration and oxygenation irrigation on soil characteristics and cotton growth in cotton fields in arid areas, this study was conducted at the National Soil Quality Aksu Observation and Experiment Station in Baicheng County, Xinjiang. “Xinluzao 78” cotton [...] Read more.

To explore the effects of micro-nano aeration and oxygenation irrigation on soil characteristics and cotton growth in cotton fields in arid areas, this study was conducted at the National Soil Quality Aksu Observation and Experiment Station in Baicheng County, Xinjiang. “Xinluzao 78” cotton was used as the experimental material, and the soil column cultivation method was adopted. Four nitrogen concentration gradients (N0: 0 kg·hm⁻², NL: 112.5 kg·hm⁻², NM: 225 kg·hm⁻², and NH: 337.5 kg·hm⁻²) and two irrigation methods (micro-nano aeration and oxygenation irrigation Y: DO15 mg/L, conventional irrigation C: DO7.6 mg/L) were set up to systematically analyze the total nitrogen content of the soil, enzyme activity, microbial community structure, and the response characteristics of cotton growth and yield. The results show that aeration treatment significantly increases the total nitrogen content in the soil. The total nitrogen content in the 0–15 cm and 15–30 cm soil layers treated with YNM (aeration + local conventional nitrogen application rate) increased by 9.14% and 8.53%, respectively, compared with CNM. YNM treatment significantly increased the activities of soil urease, sucrase, and β-glucosidase, among which total nitrogen had the strongest correlation with the activity of β-glucosidase. Oxygenation significantly increased the richness of soil microorganisms. The Chao1 index of YNM-treated bacteria was 75.7% higher than that of CNM-treated bacteria. YNM treatment increased cotton yield by 26.73% compared with CNM treatment. Moreover, the number of bells formed per plant and the weight of the bells increased by 44.44% and 29.6%, respectively. In conclusion, micro-nano aeration and oxygenation irrigation effectively increase cotton yield. By optimizing the activities of soil enzymes and microorganisms, micro-nano aeration and oxygenation irrigation enhance the ability of cotton to utilize and transform nitrogen, and alleviate the impact of insufficient nitrogen utilization by cotton in arid areas. Full article

(This article belongs to the Special Issue Impact of Biochar Additions on Soil Hydraulic Properties)

15 pages, 1480 KB

Open AccessArticle

The Research on H₂O Adsorption Characteristics of Lunar Regolith Simulants: Implications for the Development and Utilization of Lunar Water Resources

by Yanan Zhang, Ziheng Liu, Rongji Li, Xinyu Huang, Jiannan Li, Ye Tian, Junyue Tang, Fei Su and Huaiyu He

Water 2025, 17(18), 2777; https://doi.org/10.3390/w17182777 - 19 Sep 2025

Abstract

This study prepared an adsorption-based water-containing lunar regolith simulant under low-temperature conditions to investigate H₂O behavior in simulated lunar environments. Experiments established that water binds to regolith particles via adsorption rather than existing in liquid/solid states, with critical initial pressure thresholds [...] Read more.

This study prepared an adsorption-based water-containing lunar regolith simulant under low-temperature conditions to investigate H₂O behavior in simulated lunar environments. Experiments established that water binds to regolith particles via adsorption rather than existing in liquid/solid states, with critical initial pressure thresholds identified at various temperatures to ensure pure adsorption conditions. Crucially, coexisting substances extend H₂O preservation to −100 °C, suggesting substantial water retention in lunar polar regolith even under extreme cold. Sublimation modeling further revealed phase transition boundaries, indicating water ice likely persists in both permanently shadowed regions and illuminated polar areas. These findings provide fundamental insights into: adsorption-driven enrichment/preservation mechanisms of lunar water, thermodynamic stability thresholds at ultralow temperatures, and water ice distribution patterns across lunar polar terrains. The data advance understanding of lunar water’s stability and extractability, offering critical scientific support for future in situ resource utilization and sustained lunar exploration. Full article

(This article belongs to the Section Hydrogeology)

31 pages, 2299 KB

Open AccessArticle

Spatiotemporal Cavitation Dynamics and Acoustic Responses of a Hydrofoil

by Ding Tian, Xin Xia, Yu Lu, Jianping Yuan and Qiaorui Si

Water 2025, 17(18), 2776; https://doi.org/10.3390/w17182776 - 19 Sep 2025

Abstract

This study aims to investigate the spatiotemporal evolution of cavitating flow and the associated acoustic responses around a NACA0015 hydrofoil. A coupled fluid–acoustic interaction model is developed by integrating a nonlinear cavitation model with vortex–sound coupling theory. Numerical simulations are conducted within a [...] Read more.

This study aims to investigate the spatiotemporal evolution of cavitating flow and the associated acoustic responses around a NACA0015 hydrofoil. A coupled fluid–acoustic interaction model is developed by integrating a nonlinear cavitation model with vortex–sound coupling theory. Numerical simulations are conducted within a computational domain established for the hydrofoil to capture the interactions between cavitation dynamics and acoustic radiation. The results indicate that the temporal variations in cavity evolution and pressure fluctuations agree well with experimental observations. The simulations predict a dominant pressure fluctuation frequency of 30.15 Hz, consistent with the cavitation shedding frequency, revealing that the evolution of leading-edge vortex structures governs the periodic variations in the lift-to-drag ratio. Cavitation significantly modifies the development of vortex structures, with vortex stretching effects mainly concentrated near cavitation regions. The dilation–contraction term is closely associated with cavity formation, while the pressure–torque tilting term predominantly affects cloud cavitation collapse. Dynamic mode decomposition (DMD) shows that the coherent structures of the leading modes exhibit morphological similarity to multiscale cavitation and vortex structures. Furthermore, hydrofoil cavitation noise consists mainly of loading noise and cavitation-induced pulsating radiation noise, with surface acoustic sources concentrated in cloud cavitation shedding regions. The dominant frequency of cavitation-induced radiation noise is highly consistent with experimental measurements. Full article

(This article belongs to the Special Issue Advances in Hydrodynamics for Pumping Systems: Modeling, Optimization, and Applications)

28 pages, 5027 KB

Open AccessArticle

Daily Runoff Prediction Method Based on Secondary Decomposition and the GTO-Informer-GRU Model

by Haixin Yu, Yi Ma, Aijun Hu, Yifan Wang, Hai Tian, Luping Dong and Wenjie Zhu

Water 2025, 17(18), 2775; https://doi.org/10.3390/w17182775 - 19 Sep 2025

Abstract

Hydrological runoff prediction serves as the core technological foundation for water resource management and flood/drought mitigation. However, the nonlinear, non-stationary, and multi-temporal scale characteristics of runoff data result in insufficient accuracy of traditional prediction methods. To address the challenges of single decomposition methods’ [...] Read more.

Hydrological runoff prediction serves as the core technological foundation for water resource management and flood/drought mitigation. However, the nonlinear, non-stationary, and multi-temporal scale characteristics of runoff data result in insufficient accuracy of traditional prediction methods. To address the challenges of single decomposition methods’ inability to effectively separate multi-scale components and single deep learning models’ limitations in capturing long-range dependencies or extracting local features, this study proposes an Informer-GRU runoff prediction model based on STL-CEEMDAN secondary decomposition and Gorilla Troops Optimizer (GTO). The model extracts trend, seasonal, and residual components through STL decomposition, then performs fine decomposition of the residual components using CEEMDAN to achieve effective separation of multi-scale features. By combining Informer’s ProbSparse attention mechanism with GRU’s temporal memory capability, the model captures both global dependencies and local features. GTO is introduced to optimize model architecture and training hyperparameters, while a multi-objective loss function is designed to ensure the physical reasonableness of predictions. Using daily runoff data from the Liyuan Basin in Yunnan Province (2015–2023) as a case study, the results show that the model achieves a coefficient of determination (R²) and Nash-Sutcliffe efficiency coefficient (NSE) of 0.9469 on the test set, with a Kling-Gupta efficiency coefficient (KGE) of 0.9582, significantly outperforming comparison models such as LSTM, GRU, and Transformer. Ablation experiments demonstrate that components such as STL-CEEMDAN secondary decomposition and GTO optimization enhance model performance by 31.72% compared to the baseline. SHAP analysis reveals that seasonal components and local precipitation station data are the core driving factors for prediction. This study demonstrates exceptional performance in practical applications within the Liyuan Basin, providing valuable insights for water resource management and prediction research in this region. Full article

(This article belongs to the Special Issue Application of Big Data and Machine Learning in Hydrological Forecasting and Water Resource Management)

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29 pages, 17828 KB

Open AccessArticle

GIS-Based Multi-Criteria Assessment of Managed Aquifer Recharge (MAR) Zones Using the Analytic Hierarchy Process (AHP) Method in Southern Kazakhstan

by Zhuldyzbek Onglassynov, Ronny Berndtsson, Valentina Rakhimova, Timur Rakhimov, Abai Jabassov, Issa Rakhmetov, Mira Muratova and Kamshat Tussupova

Water 2025, 17(18), 2774; https://doi.org/10.3390/w17182774 - 19 Sep 2025

Abstract

Southern Kazakhstan, particularly the Zhambyl Region, is facing increasing groundwater stress due to climate change, degradation of irrigation infrastructure, and unsustainable water use. Despite substantial renewable groundwater reserves (8.33 km³/year), irrigation still relies on ephemeral surface flow. This study delineates priority [...] Read more.

Southern Kazakhstan, particularly the Zhambyl Region, is facing increasing groundwater stress due to climate change, degradation of irrigation infrastructure, and unsustainable water use. Despite substantial renewable groundwater reserves (8.33 km³/year), irrigation still relies on ephemeral surface flow. This study delineates priority zones for Managed Aquifer Recharge (MAR) using a GIS-based Multi-Criteria Decision Analysis framework integrated with the Analytic Hierarchy Process (AHP). Nine hydrogeological criteria were incorporated: shallow aquifer depth, groundwater salinity, precipitation, terrain slope, soil texture, land use/land cover, Normalized Difference Vegetation Index (NDVI), drainage density, and lineament density. Each parameter was normalized to a five-class suitability scale and weighted through expert-informed pairwise comparisons. The MAR suitability map identifies about 19% of the region (27,060 km²) as highly favorable for implementation. Field investigations at eleven groundwater sites in 2024 corroborate model results, providing aquifer depth, quality, and infiltration data. The most suitable areas are concentrated on Quaternary alluvial–proluvial fans near the Kyrgyz Alatau foothills and the Talas-Assa interfluve. Three hydrostratigraphic settings were identified: unconfined alluvial aquifers, Neogene–Quaternary unconsolidated sediments, and fractured Carboniferous carbonates. Recommended MAR methods include infiltration galleries, check dams, and injection wells. The proposed approach, validated through consistency analysis (Consistency Ratio ≤ 0.1), demonstrates the applicability of integrated geospatial and field methods for site-specific MAR planning. Strategic MAR deployment could restore productivity to 37,500 ha of degraded irrigated lands and improve groundwater resilience. These findings provide a practical framework for policymakers and water management authorities to optimize groundwater use and enhance agricultural sustainability under changing climatic conditions. Full article

(This article belongs to the Section Water Use and Scarcity)

27 pages, 11366 KB

Open AccessArticle

Evaluating Infiltration Methods for the Assessment of Flooding in Urban Areas

by Paola Bianucci, Javier Fernández-Fidalgo, Kay Khaing Kyaw, Enrique Soriano and Luis Mediero

Water 2025, 17(18), 2773; https://doi.org/10.3390/w17182773 - 19 Sep 2025

Abstract

Urban flooding caused by short and high-intensity rainfall events presents increasing challenges for cities, threatening infrastructure, public safety and economic activity. Accurately representing infiltration processes in hydrodynamic models is critical, as oversimplifying infiltration can lead to significant errors in predicted flood extents and [...] Read more.

Urban flooding caused by short and high-intensity rainfall events presents increasing challenges for cities, threatening infrastructure, public safety and economic activity. Accurately representing infiltration processes in hydrodynamic models is critical, as oversimplifying infiltration can lead to significant errors in predicted flood extents and water depths. This study systematically compares two widely used infiltration models—Green-Ampt and Curve Number—implemented within two leading 2D hydraulic models, HEC-RAS and IBER, to assess their influence on urban flood predictions. Simulations were conducted for 26 rainfall events, including both observed and synthetic hyetographs, across two urban neighbourhoods in Pamplona metropolitan area, Spain. Model performance was evaluated using root mean square error, mean absolute error and confusion matrix-derived metrics such as precision, accuracy, specificity, sensitivity and negative predictive value. Results indicate that the choice of infiltration method significantly affects both water depths and inundation extents: while Green-Ampt yields more conservative water depth estimates, Curve Number tends to underestimate flood extents. The comparison between the two hydraulic models has shown that IBER simulates broader flood extents and lower water depth errors compared to HEC-RAS. The findings highlight the importance of selecting appropriate infiltration methods and hydraulic models for reliable urban flood risk assessment, as well as providing guidance for model selection in urban inundation studies. Full article

(This article belongs to the Special Issue Urban Flood Frequency Analysis and Risk Assessment, 2nd Edition)

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19 pages, 2594 KB

Open AccessArticle

Inorganic Constituents in Shale Gas Wastewater: Full-Scale Fate and Regulatory Implications

by Yunyan Ni, Ye Zhang, Chun Meng, Limiao Yao, Jianli Sui, Jinchuan Zhang, Quan Zheng, Mingxuan Di and Jianping Chen

Water 2025, 17(18), 2772; https://doi.org/10.3390/w17182772 - 19 Sep 2025

Abstract

Shale gas wastewater from hydraulic fracturing poses significant environmental risks due to its high salinity and complex inorganic composition. This study investigates the behavior of major and trace inorganic constituents across a full-scale treatment train in the Sichuan Basin, China. Despite multi-stage processes [...] Read more.

Shale gas wastewater from hydraulic fracturing poses significant environmental risks due to its high salinity and complex inorganic composition. This study investigates the behavior of major and trace inorganic constituents across a full-scale treatment train in the Sichuan Basin, China. Despite multi-stage processes including equalization, flocculation, flotation, biological reactors, membrane filtration, and clarification, key inorganic species such as Cl, Na, Br, Sr, Li, and B remained largely persistent in the final effluent with values of 13,760, 8811, 70, 95.9, 26.6, and 60.2 mg/L, respectively. Geochemical tracers including Br/Cl (average: 0.0022 mM/mM), Na/Br (average: 125 mg/mg), and Sr/Ca (average: 0.15 mM/mM) ratios, combined with halide endmember mixing models, revealed that salinity primarily originated from highly evaporated formation brines, with limited evidence for halite dissolution or external contamination. Elevated Sr (average: 89.3 mg/L) and Ca (average: 274 mg/L) levels relative to Mg (average: 32 mg/L) suggest significant water–rock interaction. Environmental risk assessments showed that concentrations of several elements in treated effluent greatly exceeded national and international discharge or reuse standards. These findings underscore the limitations of conventional treatment technologies and highlight the urgent need for advanced processes and regulatory frameworks that address the unique challenges of high-TDS (total dissolved solids) unconventional wastewater. Full article

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

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17 pages, 2930 KB

Open AccessArticle

Phosphorus Loss Risk in the Ju River Basin, China, Under Urbanization and Climate Change: Insights from the Hydrological Simulation Program—FORTRAN (HSPF) Model

by Chaozhong Deng, Qian Xiang, Qinxue Xiong, Shunyao Jiang, Fuli Xu, Liman Li, Jianqiang Zhu and Yuan Zhou

Water 2025, 17(18), 2771; https://doi.org/10.3390/w17182771 - 19 Sep 2025

Abstract

Despite increasing concerns over recurrent phosphorus (P) pollution, the Ju River—a small tributary of the Yangtze River—has received limited scientific attention. To correct this, the present study integrates field-based observations with the Hydrological Simulation Program—FORTRAN (HSPF) model to comprehensively assess the conjunct effects [...] Read more.

Despite increasing concerns over recurrent phosphorus (P) pollution, the Ju River—a small tributary of the Yangtze River—has received limited scientific attention. To correct this, the present study integrates field-based observations with the Hydrological Simulation Program—FORTRAN (HSPF) model to comprehensively assess the conjunct effects of urban expansion and changing precipitation patterns on watershed hydrology and phosphorus dynamics at the small-catchment scale. A total of five urban expansion scenarios and three precipitation enhancement scenarios were simulated to capture both seasonal and event-driven variations in daily discharge and total phosphorus (TP) concentrations. The model was calibrated and validated using in situ water quality data, ensuring high reliability of the simulations. The results indicate that agricultural non-point sources are the primary contributor to total phosphorus (TP) loads. During the overlapping period of intensive farming and heavy rainfall (June–July), TP concentrations more than doubled compared to other months, with these two months accounting for over 70% of the annual TP load. Urban expansion significantly amplified hydrological extremes, increasing peak discharge by up to 224% under extreme rainfall, thereby intensifying flood risks. Although increased precipitation diluted TP concentrations, it simultaneously accelerated overall phosphorus export. This study offers a novel modeling–monitoring framework tailored for small watersheds and provides critical insights into how land use transitions and climate change jointly reshape nutrient cycling. The findings support the development of targeted, scenario-based strategies to mitigate eutrophication risks in vulnerable river systems. Full article

(This article belongs to the Topic Water-Soil Pollution Control and Environmental Management)

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18 pages, 5619 KB

Open AccessArticle

Composition and Abundance Distribution of Filamentous Bacteria During the Variable- and Low-Temperature Operation Periods of Wastewater Treatment Plants

by Xiaoling Wang, Lu Niu, Wenbo Pan, Xu Zhang and Hai Lu

Water 2025, 17(18), 2770; https://doi.org/10.3390/w17182770 - 18 Sep 2025

Abstract

Activated sludge microorganisms in sewage treatment plants are crucial for controlling water pollution and protecting public health and the ecological environment. Activated sludge must have biodegradation, easy sedimentation, and separation functions. Filamentous bacteria play an essential role in floc formation and structure. However, [...] Read more.

Activated sludge microorganisms in sewage treatment plants are crucial for controlling water pollution and protecting public health and the ecological environment. Activated sludge must have biodegradation, easy sedimentation, and separation functions. Filamentous bacteria play an essential role in floc formation and structure. However, low temperature, low load and low dissolved oxygen (DO) will destroy the balance between beneficial structural action and harmful overgrowth. In this study, the high-throughput sequencing (HTS) dataset of 16s rRNA gene sequence V3–V4 amplicons from 30 activated sludge samples from the Chuanhu Sewage Treatment Plant in Changchun was analyzed to investigate the abundance distribution of filamentous bacteria and further determine the main operating parameters and environmental factors. The experimental results showed that the filamentous bacterial community accounted for a large part of the entire microbial community, with the total filamentous bacterial percentage in each sample ranging from 7.32% to 56.81%, with large fluctuations in abundance and consistent with the SVI value. Although most of them were in flocs, they occasionally caused sedimentation problems when the water temperature was low. With 14 species of filamentous bacteria detected, the population structure of filamentous bacteria in the thermophilic, variable-temperature and low-temperature periods was universal and specific. The groups with a detection frequency of 100%, high abundance, and significant fluctuations in distribution were Microthrix parvicella and Nostocoida limicola I. The Pearson correlation analysis showed that the total abundance of filamentous bacteria and the fluctuation distribution of dominant filamentous bacteria abundance were significantly correlated with water temperature, sludge load, sludge age, and mixed liquid suspended solids (MLSS). Full article

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

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30 pages, 16884 KB

Open AccessArticle

Evaluating the Long-Term Effectiveness of Marsh Terracing for Conservation with Integrated Geospatial and Wetland Simulation Modeling

by Nick Carpenter, Laura Costadone and Thomas R. Allen

Water 2025, 17(18), 2769; https://doi.org/10.3390/w17182769 - 18 Sep 2025

Abstract

Coastal marshes provide essential ecosystem services, yet they are vulnerable to anthropogenic stressors and climate change, particularly sea level rise (SLR). Restoration approaches like marsh terracing have emerged as nature-based strategies to enhance resilience and reduce habitat loss. This study applies the Sea [...] Read more.

Coastal marshes provide essential ecosystem services, yet they are vulnerable to anthropogenic stressors and climate change, particularly sea level rise (SLR). Restoration approaches like marsh terracing have emerged as nature-based strategies to enhance resilience and reduce habitat loss. This study applies the Sea Level Affecting Marshes Model (SLAMM) to assess the potential of marsh terraces to mitigate future losses, while also examining the model’s limitations, including its assumptions and capacity to reflect complex marsh processes. A geospatial approach was used to generate 3D representations of terraces through morphostatic modeling within digital elevation models (DEMs). Under a no-restoration scenario, SLAMM projections show that all marshes analyzed are at risk of total loss by 2100. In contrast, scenarios including terracing demonstrate a delay in net marsh loss, extending the persistence of key marsh habitats by approximately a decade. Although marsh degradation remains likely under high SLR conditions, the results underscore the utility of marsh terraces in prolonging habitat stability. Additionally, the study demonstrates the feasibility of integrating restoration features like terraces into DEMs and wetland models. Despite SLAMM’s simplified erosion and accretion assumptions, the model yields important insights into restoration effectiveness and long-term marsh dynamics, informing more adaptive, forward-looking coastal management strategies. Full article

(This article belongs to the Special Issue New Insights into Sea Level Dynamics and Coastal Erosion)

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19 pages, 1545 KB

Open AccessArticle

Study on an Evaluation Model for Regional Water Resource Stress Based on Water Scarcity Footprint

by Lu Qiao, Xue Bai, Yan Bai, Jialin Liu, Lingsi Kong and Lan Zhang

Water 2025, 17(18), 2768; https://doi.org/10.3390/w17182768 - 18 Sep 2025

Abstract

Under the multiple pressures of intensifying global climate change disruption and rapid economic growth, China has become one of the countries facing the most serious water scarcity problems. Based on the ISO 14046 standard and the framework of water scarcity footprint theory, this [...] Read more.

Under the multiple pressures of intensifying global climate change disruption and rapid economic growth, China has become one of the countries facing the most serious water scarcity problems. Based on the ISO 14046 standard and the framework of water scarcity footprint theory, this study will break through the static limitations and lack of dimensions of traditional characteristic factors (i.e., water stress) and construct a water stress evaluation index system that combines nature, economy, and society. The results indicate that in recent years, regional water stress in China has exhibited significant spatiotemporal variations and spatial clustering, primarily driven by composite factors, with an overall decreasing trend. Among them, Shanghai is the highest-pressure area and Shaanxi is the lowest-pressure area, which is mainly due to the spatial projection of the coupling effect of multi-dimensional factors. In addition, the obstacle degree analysis method shows that indicators such as the utilization rate of water resource development constitute cross-regional constraints. To this end, all regions should make efforts to regulate and control the water use structure, introduce water-saving technologies, and strengthen water-saving publicity according to their needs. Therefore, this study not only provides a scientific basis for in-depth understanding of the distribution law and influencing mechanism of water stress but also provides an important reference for the rational allocation and sustainable use of water resources by upgrading the characteristic factors to system control signals. Full article

(This article belongs to the Section Water Use and Scarcity)

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16 pages, 671 KB

Open AccessArticle

Prevalence and Resistance Patterns of Campylobacter spp. and Arcobacter spp. in Portuguese Water Bodies

by Igor Venâncio, Inês Martins, Rodrigo M. Martins, Mónica Oleastro and Susana Ferreira

Water 2025, 17(18), 2767; https://doi.org/10.3390/w17182767 - 18 Sep 2025

Abstract

Campylobacter spp. and Arcobacter spp. are recognized etiological agents of gastroenteritis worldwide. While poultry is their best-known reservoir, human exposure can also occur via environmental pathways, particularly through contaminated water sources, which play a significant role in their transmission dynamics. In addition to [...] Read more.

Campylobacter spp. and Arcobacter spp. are recognized etiological agents of gastroenteritis worldwide. While poultry is their best-known reservoir, human exposure can also occur via environmental pathways, particularly through contaminated water sources, which play a significant role in their transmission dynamics. In addition to their pathogenicity and widespread environmental prevalence, increasing antibiotic resistance has contributed to the global emergence of multidrug-resistant strains, hindering effective treatment. Here, the distribution and antibiotic resistance potential of Campylobacter spp. and Arcobacter spp. isolates collected from water bodies in Portugal were investigated. Water samples were collected from rivers, their tributaries, and springs, at 25 sites over a six-month period. Campylobacter spp. were isolated from 13.3% of the samples, whereas Arcobacter spp. were detected in 57.6% of the samples. Of the 27 isolated Campylobacter isolates, 44.0% were resistant to at least one antibiotic, while only one strain exhibited a multidrug-resistant (MDR) phenotype. In contrast, 98.9% of the 177 Arcobacter isolates were resistant to at least one antibiotic, with 15.8% classified as MDR. These findings contribute to the surveillance of Campylobacter spp. and Arcobacter spp., highlighting the critical role of aquatic environments in their epidemiology and supporting the need to incorporate waterborne transmission pathways into integrated surveillance and control strategies within the One Health framework. Full article

(This article belongs to the Special Issue Evaluation of Microbiological Indicators for Water and Wastewater Treatment and Reuse)

21 pages, 4834 KB

Open AccessArticle

A Displacement Monitoring Model for High-Arch Dams Based on SHAP-Driven Ensemble Learning Optimized by the Gray Wolf Algorithm

by Shasha Li, Kai Jiang, Shunqun Yang, Zuxiu Lan, Yining Qi and Huaizhi Su

Water 2025, 17(18), 2766; https://doi.org/10.3390/w17182766 - 18 Sep 2025

Abstract

Displacement monitoring data is essential for assessing the structural safety of high-arch dams. Existing models, predominantly based on single-model architectures, often lack the ability to effectively integrate multiple algorithms, leading to limited predictive performance and poor interpretability. This study proposes an ensemble learning [...] Read more.

Displacement monitoring data is essential for assessing the structural safety of high-arch dams. Existing models, predominantly based on single-model architectures, often lack the ability to effectively integrate multiple algorithms, leading to limited predictive performance and poor interpretability. This study proposes an ensemble learning framework for dam displacement prediction, combining Hydraulic–Seasonal–Temporal model (HST), Random Forest (RF), and Bidirectional Gated Recurrent Unit (BiGRU) models as base learners. A stacking strategy is employed to enhance predictive accuracy, and the Grey Wolf Optimizer (GWO) is used for hyperparameter optimization. To improve model transparency, the Shapley Additive Explanations (SHAP) algorithm is applied for interpretability analysis. Extensive experiments demonstrate that the proposed ensemble model outperforms individual models, achieving a Root Mean Squared Error (RMSE) of 0.2241 and a Coefficient of Determination (R²) of 0.9993 on the test set. The SHAP analysis further elucidates the contribution of key variables, providing valuable insights into the displacement prediction process and offering a robust technical foundation for arch dam safety monitoring and early risk warning. Full article

(This article belongs to the Special Issue Hydraulic Engineering Applications of Artificial Intelligence, Deep Learning, and Digital Twin Technology)

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26 pages, 4713 KB

Open AccessArticle

Impacts of the Degree of Heterogeneity on Design Flood Estimates: Region of Influence vs. Fixed Region Approaches

by Ali Ahmed, Mohammad A. Morshed, Sadia T. Mim, Ridwan S. M. H. Rafi, Zaved Khan, Rajib Maity and Ataur Rahman

Water 2025, 17(18), 2765; https://doi.org/10.3390/w17182765 - 18 Sep 2025

Abstract

In regional flood frequency analysis (RFFA), the formation of homogeneous regions is commonly regarded as a necessary condition for reliable regional flood estimation. However, achieving true homogeneity is often challenging in practice. This study investigates the formation of homogeneous regions by applying two [...] Read more.

In regional flood frequency analysis (RFFA), the formation of homogeneous regions is commonly regarded as a necessary condition for reliable regional flood estimation. However, achieving true homogeneity is often challenging in practice. This study investigates the formation of homogeneous regions by applying two region delineation approaches—fixed regions and the region-of-influence (ROI) method—accompanied by the widely used heterogeneity measure (H₁) proposed by Hosking and Wallis. The analysis utilizes data from 201 stream gauging stations across southeast Australia, evaluating a total of 1211 candidate regions. The computed H₁-statistics range from 13 to 30 for fixed regions and from 6 to 30 for ROI-based regions, indicating a consistently high level of heterogeneity across the study area. This suggests that the assumption of homogeneity may not be realistic for many parts of southeast Australia. Moreover, regression equations developed for regional flood estimation yield absolute median relative errors between 29% and 56%, with a median of 39% across return periods from 2 to 100 years. These findings underscore the limitations of relying solely on homogeneity in regional flood modelling and highlight the need for more flexible and robust approaches in RFFA. The outcomes of this research have significant implications for improving flood estimation practices and are expected to contribute to future enhancements of the Australian Rainfall and Runoff (ARR) national guidelines. Full article

(This article belongs to the Special Issue Urban Flood Mitigation and Sustainable Stormwater Management—2nd Edition)

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18 pages, 2624 KB

Open AccessArticle

Comparative Assessment of Different Electrode Combinations for Phosphate Removal from Onsite Wastewater via Electrocoagulation

by Arif Reza, Xiumei Jian, Fanjian Zeng and Xinwei Mao

Water 2025, 17(18), 2764; https://doi.org/10.3390/w17182764 - 18 Sep 2025

Abstract

Phosphorus (P) discharge from onsite wastewater treatment systems (OWTSs) poses a significant threat to water quality, contributing to eutrophication in nutrient-sensitive aquatic environments. In treated effluents, P predominantly exists as orthophosphate (PO₄³⁻), a highly bioavailable and reactive form that requires [...] Read more.

Phosphorus (P) discharge from onsite wastewater treatment systems (OWTSs) poses a significant threat to water quality, contributing to eutrophication in nutrient-sensitive aquatic environments. In treated effluents, P predominantly exists as orthophosphate (PO₄³⁻), a highly bioavailable and reactive form that requires targeted removal. This study evaluates the performance of electrocoagulation (EC) as a polishing step for PO₄³⁻ removal from OWTS effluents using 12 anode/cathode combinations comprising aluminum (Al), iron (Fe), magnesium (Mg), and stainless steel (SS). Key operational parameters, including treatment time, mixing speed, current density, pH, and initial PO₄³⁻ concentration, were systematically investigated when synthetic denitrified effluent (20 mg P/L) was treated. Based on the performance, the four most effective electrode combinations (Al/Al, Al/Mg, Fe/Al, and Mg/Mg), along with a commercial benchmark (Fe/Fe), were further tested under extended hydraulic retention times (up to 48 h) in both synthetic and real (denitrified) wastewater. To date, none of the studies have systematically evaluated all possible anode/cathode combinations involving multiple electrode materials under uniform operational conditions. The Al/Al and Mg/Mg EC systems achieved rapid and high PO₄³⁻ removal efficiencies (>95%), while Mg-based systems demonstrated sustained performance over prolonged treatment durations, especially in real wastewater. Bimetallic pairs such as Al/Mg and Fe/Al exhibited synergistic effects through enhanced coagulant generation and pH stabilization. The results indicated that PO₄³⁻ removal efficiency was strongly influenced by electrode material selection, hydrodynamic conditions, and wastewater compositions, underscoring the need to design EC systems based on site-specific water quality conditions in OWTSs. Full article

(This article belongs to the Special Issue Application of Electrochemical Technologies in Wastewater Treatment)

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22 pages, 6378 KB

Open AccessArticle

LU-Net: Lightweight U-Shaped Network for Water Body Extraction of Remote Sensing Images

by Chengzhi Deng, Ruqiang He, Zhaoming Wu, Xiaowei Sun and Shengqian Wang

Water 2025, 17(18), 2763; https://doi.org/10.3390/w17182763 - 18 Sep 2025

Abstract

Deep learning-based water body extraction methods generally focus on maximizing accuracy while neglecting inference speed, which can make them challenging to apply in real-time applications. To address this problem, this paper proposes a lightweight u-shaped network (LU-Net), which improves inference speed while maintaining [...] Read more.

Deep learning-based water body extraction methods generally focus on maximizing accuracy while neglecting inference speed, which can make them challenging to apply in real-time applications. To address this problem, this paper proposes a lightweight u-shaped network (LU-Net), which improves inference speed while maintaining comparable accuracy. To reduce inference latency, a lightweight decoder block (LDB) is designed, which employs a depthwise separable convolution structure to accelerate the decoding process. To enhance accuracy, a lightweight convolutional block attention module (LCBAM) is designed, which effectively captures water-specific spectral and spatial characteristics through a dual-attention mechanism. To improve multi-scale water boundary extraction, a structurally re-parameterized multi-scale fusion prediction module (SRMFPM) is designed, which integrates multi-scale water boundary information through convolutions of different sizes. Comparative experiments are conducted on the GID and LoveDA datasets, with model performance assessed using the MIoU metric and inference latency. The results demonstrate that LU-Net achieves the lowest GPU latency of 3.1 MS and the second-lowest CPU latency of 36 MS in the experiments. On the GID, LU-Net achieves the MIoU of 91.36%, outperforming other tested methods. On the LoveDA datasets, LU-Net achieves the second-highest MIoU of 86.32% among the evaluated models, which is 0.08% lower than the top-performing CGNet. Considering both latency and MIoU, LU-Net demonstrates commendable efficiency on the GID and LoveDA datasets across all compared networks. Full article

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

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28 pages, 11202 KB

Open AccessArticle

Enhancing Streamflow Modeling in Data-Scarce Catchments with Similarity-Guided Source Selection and Transfer Learning

by Yuxuan Gao, Rupal Mandania, Jun Ma, Jack Chen and Wuyi Zhuang

Water 2025, 17(18), 2762; https://doi.org/10.3390/w17182762 - 18 Sep 2025

Abstract

Accurate streamflow modeling in data-scarce catchments remains a significant challenge due to the limited availability of historical records. Transfer Learning (TL), increasingly applied in hydrology, leverages knowledge from data-rich catchments (sources) to enhance predictions in data-scarce catchments (targets), providing new possibilities of hydrological [...] Read more.

Accurate streamflow modeling in data-scarce catchments remains a significant challenge due to the limited availability of historical records. Transfer Learning (TL), increasingly applied in hydrology, leverages knowledge from data-rich catchments (sources) to enhance predictions in data-scarce catchments (targets), providing new possibilities of hydrological predictions. Most existing TL approaches pre-train models on large-scale meteoro-hydrological datasets and show good generalizability across multiple target catchments. However, for a specific target catchment, it remains unclear which source catchments contribute most effectively to the accurate prediction. Including many irrelevant sources may even degrade model performance. In this study, we investigated how source catchment selection affects TL performance by employing similarity-guided strategies based on three key factors, i.e., spatial distance, physical attributes, and flow regime characteristics. Using the CAMELS-GB dataset, we conducted comparative experiments by pre-training the networks with different ranked groups of the source catchments and fine-tuning them on three target catchments representing distinct hydrological environments. The results showed that carefully selected small subsets (fewer than 40, or even as few as 10) of highly similar catchments can achieve comparable or better TL performance than using all 668 available source catchments. All three target catchments yielded better NSE results from source catchments with closer spatial proximity and more consistent flow regimes. The TL performance of physical attribute similarity-based selection varied depending on the attribute combinations, with those related to land cover, climate, and soil properties leading to superior performance. These findings highlight the importance of similarity-guided source selection in hydrological TL. In addition, they demonstrate ways to reduce computational costs while improving modeling accuracy in data-scarce regions. Full article

(This article belongs to the Special Issue Computer Modelling Techniques in Environmental Hydraulics and Water Resource Engineering)

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20 pages, 2066 KB

Open AccessArticle

Oxidic Substrate with Variable Charge Surface Chemically Modified for Copper Ion Adsorption from Aqueous Solutions

by José G. Prato, Fernando Millán, Iván Ríos, Marin Senila, Erika Andrea Levei, Luisa Carolina González and Enju Wang

Water 2025, 17(18), 2761; https://doi.org/10.3390/w17182761 - 18 Sep 2025

Abstract

The presence of toxic elements in drinking water poses important risks to human health. Among the diverse methodologies available to remove these elements from water, adsorption methods are among the most effective; however, many adsorbent materials are either costly, not widely available, or [...] Read more.

The presence of toxic elements in drinking water poses important risks to human health. Among the diverse methodologies available to remove these elements from water, adsorption methods are among the most effective; however, many adsorbent materials are either costly, not widely available, or difficult to handle. This work focuses on the application of a new natural geologic material, named “V” material, to prepare an adsorbent substrate applied to water treatment, using its adsorption properties to remove metallic species from aqueous media. The geologic material is a thermally and mechanically resistant material, composed basically of quartz, iron and aluminum oxides, with amphoteric properties. A granular medium or substrate was prepared via thermal treatment using three granulometric fractions of the material: the smaller fraction, less than 250 μm, named the fine fraction, VFF; from 250 μm to 425 μm, named the medium fraction, VMF; and from 425 μm to 1200 μm, named the gross fraction, VGF. The experiments were carried out on both alkaline-treated and non-treated substrates, named activated and non-activated substrates, respectively. The BET and external surface, as well as the pore volume, increased significantly after the calcination process. The adsorption isotherms pointed to a strong interaction between metallic ions and activated substrates, in contrast to the non-activated substrate, which showed much less affinity. This type of isotherm is associated with specific adsorption, where the adsorption occurs chemically between Cu²⁺ ions and the substrate surface, basically composed of amphoteric metallic oxides. The adsorption data fit fairly well to the Freundlich and Langmuir models, where the K values are higher for activated substrates. According to the Freundlich K values, the copper adsorptions on the activated substrates were higher: 5.0395, 3.9814 and 4.2165 mg/g, compared with 0.3622, 1.8843 and 0.4544 mg/g on non-activated substrates. The pH measurements showed the production of 0.56 and 0.10 μmol H⁺ during the adsorption reaction on the activated substrate, following the theoretical model for the chemisorption of transitional metals on amphoteric oxides. These results show the potential applicability of this kind of substrate in retaining transitional metals from polluted drinkable water at low cost. It is environmentally friendly, non-toxic, and available for rural media and mining-impacted regions. Full article

(This article belongs to the Special Issue Advanced Technologies in Water and Wastewater Treatment)

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20 pages, 3989 KB

Open AccessArticle

A²DSC-Net: A Network Based on Multi-Branch Dilated and Dynamic Snake Convolutions for Water Body Extraction

by Shuai Zhang, Chao Zhang, Qichao Zhao, Junjie Ma and Pengpeng Zhang

Water 2025, 17(18), 2760; https://doi.org/10.3390/w17182760 - 18 Sep 2025

Abstract

The accurate and efficient acquisition of the spatiotemporal distribution of surface water is of vital importance for water resource utilization, flood monitoring, and environmental protection. However, deep learning models often suffer from two major limitations when applied to high-resolution remote sensing imagery: the [...] Read more.

The accurate and efficient acquisition of the spatiotemporal distribution of surface water is of vital importance for water resource utilization, flood monitoring, and environmental protection. However, deep learning models often suffer from two major limitations when applied to high-resolution remote sensing imagery: the loss of small water body features due to encoder scale differences, and reduced boundary accuracy for narrow water bodies in complex backgrounds. To address these challenges, we introduce the A²DSC-Net, which offers two key innovations. First, a multi-branch dilated convolution (MBDC) module is designed to capture contextual information across multiple spatial scales, thereby enhancing the recognition of small water bodies. Second, a Dynamic Snake Convolution module is introduced to adaptively extract local features and integrate global spatial cues, significantly improving the delineation accuracy of narrow water bodies under complex background conditions. Ablation and comparative experiments were conducted under identical settings using the LandCover.ai and Gaofen Image Dataset (GID). The results show that A²DSC-Net achieves an average precision of 96.34%, average recall of 96.19%, average IoU of 92.8%, and average F1-score of 96.26%, outperforming classical segmentation models such as U-Net, DeepLabv3+, DANet, and PSPNet. These findings demonstrate that A²DSC-Net provides an effective and reliable solution for water body extraction from high-resolution remote sensing imagery. Full article

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

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