Water

27 pages, 5357 KB

Open AccessReview

From Sources to Environmental Risks: Research Progress on Per- and Polyfluoroalkyl Substances (PFASs) in River and Lake Environments

by Zhanqi Zhou, Fuwen Deng, Jiayang Nie, He Li, Xia Jiang, Shuhang Wang and Yunyan Guo

Water 2025, 17(21), 3061; https://doi.org/10.3390/w17213061 (registering DOI) - 25 Oct 2025

Abstract

Per- and polyfluoroalkyl substances (PFASs) have attracted global attention due to their persistence and biological toxicity, becoming critical emerging contaminants in river and lake environments worldwide. Building upon existing studies, this work aims to comprehensively understand the pollution patterns, environmental behaviors, and potential [...] Read more.

Per- and polyfluoroalkyl substances (PFASs) have attracted global attention due to their persistence and biological toxicity, becoming critical emerging contaminants in river and lake environments worldwide. Building upon existing studies, this work aims to comprehensively understand the pollution patterns, environmental behaviors, and potential risks of PFASs in freshwater systems, thereby providing scientific evidence and technical support for precise pollution control, risk prevention, and the protection of aquatic ecosystems and human health. Based on publications from 2002 to 2025 indexed in the Web of Science (WoS), bibliometric analysis was used to explore the temporal evolution and research hotspots of PFASs, and to systematically review their input pathways, pollution characteristics, environmental behaviors, influencing factors, and ecological and health risks in river and lake environments. Results show that PFAS inputs originate from both direct and indirect pathways. Direct emissions mainly stem from industrial production, consumer product use, and waste disposal, while indirect emissions arise from precursor transformation, secondary releases from wastewater treatment plants (WWTPs), and long-range atmospheric transport (LRAT). Affected by source distribution, physicochemical properties, and environmental conditions, PFASs display pronounced spatial variability among environmental media. Their partitioning, degradation, and migration are jointly controlled by molecular properties, aquatic physicochemical conditions, and interactions with dissolved organic matter (DOM). Current risk assessments indicate that PFASs generally pose low risks in non-industrial areas, yet elevated ecological and health risks persist in industrial clusters and regions with intensive aqueous film-forming foam (AFFF) use. Quantitative evaluation of mixture toxicity and chronic low-dose exposure risks remains insufficient and warrants further investigation. This study reveals the complex, dynamic environmental behaviors of PFASs in river and lake systems. Considering the interactions between PFASs and coexisting components, future research should emphasize mechanisms, key influencing factors, and synergistic control strategies under multi-media co-pollution. Developing quantitative risk assessment frameworks capable of characterizing integrated mixture toxicity will provide a scientific basis for the precise identification and effective management of PFAS pollution in aquatic environments. Full article

(This article belongs to the Special Issue Pollution Process and Microbial Responses in Aquatic Environment)

18 pages, 2548 KB

Open AccessArticle

Flood-Induced Agricultural Damage Assessment: A Case Study of Pakistan

by Abid Nazir, Awais Ahmad, Mohsin Ramzan, Hammad Gilani, Muhammad Mobeen, Shahid Tarer and Niall P. Hanan

Water 2025, 17(21), 3060; https://doi.org/10.3390/w17213060 (registering DOI) - 25 Oct 2025

Abstract

Climate variability and extreme weather events, particularly flooding, pose growing threats to agricultural productivity worldwide, including in Pakistan. Traditional crop damage assessments during flood events have relied on field surveys, which are often time-intensive and spatially limited. Recent advancements in remote sensing technologies [...] Read more.

Climate variability and extreme weather events, particularly flooding, pose growing threats to agricultural productivity worldwide, including in Pakistan. Traditional crop damage assessments during flood events have relied on field surveys, which are often time-intensive and spatially limited. Recent advancements in remote sensing technologies now allow for rapid and large-scale estimation of flood-induced agricultural damage. This study assesses agricultural damage from two recent extreme flood events in Pakistan, integrating crop condition and flood intensity metrics. We present remote sensing-based case studies that employ an interdisciplinary approach, using Moderate Resolution Imaging Spectroradiometer (MODIS), Sentinel-1, and Sentinel-2 imagery along with crop data. Our results show that flood timing, crop stage, and inundation duration were the most influential factors in determining crop loss. We determined that Northern Sindh province and areas along the Indus River and its tributaries are highly vulnerable to flooding, resulting in extensive damage to infrastructure, crops, and loss of lives during flood events in 2010 and 2022, followed by Punjab, Balochistan, and Khyber Pakhtunkhwa. Remote sensing-derived damage estimates were closely aligned with post-event ground reports, validating the approach. Full article

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

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24 pages, 9449 KB

Open AccessArticle

Assessing the Hydraulic Parameters of an Open Channel Spillway Through Numerical and Experimental Approaches

by Elaheh Motahari Moghadam, Ali Saeidi, Javier Patarroyo, Alain Rouleau and Meghdad Payan

Water 2025, 17(21), 3059; https://doi.org/10.3390/w17213059 (registering DOI) - 25 Oct 2025

Abstract

The effective design and operation of hydraulic structures, particularly open channel spillways, are crucial for water resource management and flood risk reduction in dams. A clear understanding of flow properties, such as velocity fluctuations and discharge, across various depths is essential for optimizing [...] Read more.

The effective design and operation of hydraulic structures, particularly open channel spillways, are crucial for water resource management and flood risk reduction in dams. A clear understanding of flow properties, such as velocity fluctuations and discharge, across various depths is essential for optimizing performance. In this study, experimental analysis and numerical simulation using FLOW-3D were combined to investigate the hydraulic parameters of a scaled model of the Romaine IV spillway located in Quebec, Canada. Measurements focused on flow properties, including velocity fluctuations at various discharge rates in specific flow depths, at selected points along the spillway. The numerical model was assessed by reproducing experimental geometry, initial water levels, and boundary conditions, and through sensitivity analyses to ensure accurate flow representation. Comparisons of flow rates of 180, 240, and 340 L/s showed that while simulations with the renormalized group (RNG) turbulence model reliably predicted average velocities, they underestimated maximum values and overestimated minimum values, especially at higher discharges. The results highlight the difficulty of accurately capturing velocity extremes in turbulent flows and the need for further model refinement. This was evident from the 60% discrepancy in minimum velocities observed at the channel center. Despite these discrepancies, the study advances our understanding of spillway performance and identifies avenues to improve the accuracy of numerical modeling in hydraulic engineering. Full article

(This article belongs to the Special Issue Hydrodynamics Science Experiments and Simulations, 2nd Edition)

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21 pages, 1713 KB

Open AccessArticle

Hydrochemical Characteristics of Shallow Groundwater and Analysis of Vegetation Water Sources in the Ulan Buh Desert

by Xiaomeng Li, Jie Zhou, Wenhui Zhou, Lei Mao, Changyu Wang, Yi Hao and Peng Bian

Water 2025, 17(21), 3058; https://doi.org/10.3390/w17213058 (registering DOI) - 24 Oct 2025

Abstract

The Ulan Buh Desert represents a quintessential desert ecosystem in the arid northwest of China. As the key factor to maintain the stability of ecosystem, the chemical characteristics of groundwater and its water relationship with vegetation need to be further studied. Through field [...] Read more.

The Ulan Buh Desert represents a quintessential desert ecosystem in the arid northwest of China. As the key factor to maintain the stability of ecosystem, the chemical characteristics of groundwater and its water relationship with vegetation need to be further studied. Through field sampling, hydrochemical analysis, hydrogen and oxygen isotope testing and the Bayesian mixing model (MixSIAR), this study systematically analyzed the chemical characteristics of groundwater, spatial distribution and vegetation water sources in the study area. The results show that the groundwater is predominantly of the Cl⁻–SO₄²⁻ type, with total dissolved solids (TDS) ranging from 0.34 to 9.56 g/L (mean: 2.03 g/L), indicating medium to high salinity and significant spatial heterogeneity. These characteristics are jointly controlled by rock weathering, evaporative concentration, and ion exchange. Soil water isotopes exhibited vertical differentiation: the surface layer (0–20 cm) was significantly affected by evaporative fractionation (δD: −72‰ to −45‰; δ¹⁸O: −9.3‰ to −6.2‰), while deep soil water (60–80 cm) showed isotopic enrichment (δD: −29‰ to −58‰; δ¹⁸O: −6.8‰ to 0.9‰), closely matching groundwater isotopic signatures. Vegetation water use strategies demonstrated depth stratification: shallow-rooted plants such as Reaumuria soongorica and Kalidium foliatum relied primarily on shallow soil water (0–20 cm, >30% contribution), whereas deep-rooted plants such as Nitraria tangutorum and Ammopiptanthus mongolicus predominantly extracted water from the 40–80 cm soil layer (>30% contribution), with no direct dependence on groundwater. Full article

(This article belongs to the Special Issue Advances in Groundwater Resource Development: Innovative Methods and Technologies)

24 pages, 2070 KB

Open AccessArticle

Enhanced Time Series–Physics Model Approach for Dam Discharge Impacts on River Levels: Seomjin River, South Korea

by Chunggil Jung, Darae Kim, Gayeong Lee and Jongyoon Park

Water 2025, 17(21), 3057; https://doi.org/10.3390/w17213057 (registering DOI) - 24 Oct 2025

Abstract

In dam operations, sudden discharges during extreme rainfall events can pose severe flood risks to downstream communities. This study developed a dam discharge-based river water level forecasting model using a data-driven deep learning approach, long short-term memory (LSTM). To enhance predictive performance, physics-based [...] Read more.

In dam operations, sudden discharges during extreme rainfall events can pose severe flood risks to downstream communities. This study developed a dam discharge-based river water level forecasting model using a data-driven deep learning approach, long short-term memory (LSTM). To enhance predictive performance, physics-based HEC-RAS simulation outputs, including extreme events, were incorporated as additional inputs. The Seomjin River Basin in South Korea, which recently experienced severe flooding, was selected as the study area. Hydrological data from 2010 to 2023 were utilized, with 2023 reserved for model testing. Forecasts were generated for four lead times (3, 6, 12, and 24 h), consistent with the operational flood forecasting system of the Ministry of Environment, South Korea. Using only observed data, the model achieved high accuracy at upstream sites, such as Imsil-gun (Iljung-ri, R² = 0.92, RMSE = 0.27 m) and Gokseong (Geumgok Bridge, R² = 0.91, RMSE = 0.35 m), for a 6-h lead time. However, performance was lower at Gurye-gun (Songjeong-ri, R² = 0.72, RMSE = 1.48 m) due to the complex influence of two dams. Incorporating enhanced inputs significantly improved predictions at Gurye-gun (R² = 0.91, RMSE = 1.17 m at 3 h). Overall, models using only observed data performed better at upstream sites, while enhanced inputs were more effective in downstream or multi-dam regions. The 6-h lead time yielded the highest overall accuracy, highlighting the potential of this approach to improve real-time dam operations and flood risk management. Full article

(This article belongs to the Special Issue Advances in Machine Learning and Artificial Intelligence Technologies for Hydrological Processes and Hydrologic Disasters)

19 pages, 8637 KB

Open AccessArticle

The Shrinkage of Lakes on the Semi-Arid Inner Mongolian Plateau Is Still Serious

by Juan Bai, Yue Zhuo, Naichen Xing, Fuping Gan, Yi Guo, Baikun Yan, Yichi Zhang and Ruoyi Li

Water 2025, 17(21), 3056; https://doi.org/10.3390/w17213056 (registering DOI) - 24 Oct 2025

Abstract

In the Inner Mongolia Plateau Lake Zone (IMP), situated in China’s semi-arid region, its lake water storage change plays a critical role in wetland ecosystem conservation and regional water security through its lake water storage dynamics. To investigate long-term lake water storage (LWS) [...] Read more.

In the Inner Mongolia Plateau Lake Zone (IMP), situated in China’s semi-arid region, its lake water storage change plays a critical role in wetland ecosystem conservation and regional water security through its lake water storage dynamics. To investigate long-term lake water storage (LWS) changes, this study proposes a novel lake monitoring framework that reconstructs historical lake level time series and estimates water level variations in lakes without altimetry data. Using multi-source satellite data, we quantified LWS variations (2000–2021) across 109 lakes (≥5 km²) on the IMP and examined their spatiotemporal patterns. Our results reveal a net decline of 1.21 Gt in total LWS over the past two decades, averaging 0.06 Gt/yr. A distinct shift occurred around 2012: LWS decreased by 10.82 Gt from 2000 to 2012 but increased by 9.61 Gt from 2013 to 2021. Spatially, significant LWS reductions were concentrated in the central and eastern IMP, resulting from intensive water diversion and groundwater exploitation. In contrast, increases were observed mainly in the western and southern regions, driven by enhanced precipitation and reduced aridity. The findings improve understanding of lake dynamics in semi-arid China over the last two decades and offer technical guidance for sustainable water resource management. Full article

(This article belongs to the Special Issue Remote Sensing of Spatial-Temporal Variation in Surface Water)

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20 pages, 8623 KB

Open AccessArticle

Revitalization of Trakošćan Lake—Preliminary Analyses of the Sediment with the Possibility of Its Reuse in the Environment

by Saša Zavrtnik, Dijana Oskoruš, Sanja Kapelj and Jelena Loborec

Water 2025, 17(21), 3055; https://doi.org/10.3390/w17213055 (registering DOI) - 24 Oct 2025

Abstract

Trakošćan Lake is an artificial lake created in the mid-19th century for aesthetic and economic purposes. The area around the lake has been protected as park forest. Recently, the lake has become the most famous example of eutrophication in Croatia, as by 2022, [...] Read more.

Trakošćan Lake is an artificial lake created in the mid-19th century for aesthetic and economic purposes. The area around the lake has been protected as park forest. Recently, the lake has become the most famous example of eutrophication in Croatia, as by 2022, a significant amount of sediment had accumulated in it. Therefore, the lake was drained that same year, followed by mechanical removal of the sediment. The total amount of sediment removed was 204,000 m³. After the removal work, a particularly important question arose of what to do with such a large amount of sediment. The objective of this research was to gain specific insight into the chemical composition of the sediment with the aim of its possible use in agricultural production for increasing the quality of arable land. A comprehensive qualitative geochemical and agrochemical analysis of the sediment composition was carried out for the first time, including indicators of the pH value, amount of organic matter and carbon, total nitrogen, available phosphorus and potassium, amount of carbonates, and the presence of metals, metalloids, and non-metals, of which As, Cd, Hg, and Pb are toxic. Electrochemical, spectrophotometric, and titration methods were used, along with three atomic absorption spectrometry techniques. The results of the analyses were interpreted in comparison with the natural substrate, as well as with the current regulations for agricultural land in the Republic of Croatia. According to this, sediment is not harmful for the environment. Full article

(This article belongs to the Section Water Erosion and Sediment Transport)

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18 pages, 1891 KB

Open AccessArticle

Plants Decrease Network Complexity and Increase Environmental Stability of Microbial Communities, Shifting the Dominant Environmental Controls from Carbon-Related Factors to pH in Newly Formed Wetlands

by Yijing Wang, Junyu Dong, Xiaoke Liu, Changchao Li, Yongkang Zhao, Yan Wang and Jian Liu

Water 2025, 17(21), 3054; https://doi.org/10.3390/w17213054 (registering DOI) - 24 Oct 2025

Abstract

Soil microorganisms are crucial regulators of wetland ecological functions and are significantly influenced by plants. However, the ecological patterns underlying soil microbial responses to plants during wetland restoration remain poorly understood. Soil samples from sections with and without plants in each wetland were [...] Read more.

Soil microorganisms are crucial regulators of wetland ecological functions and are significantly influenced by plants. However, the ecological patterns underlying soil microbial responses to plants during wetland restoration remain poorly understood. Soil samples from sections with and without plants in each wetland were collected to investigate the impact of plants on soil microbial communities using high-throughput absolute quantification sequencing and analysis of soil physicochemical properties. Results showed that environmental drivers exerted stronger effects on microbial communities in areas without plants. Soil microbial networks in areas without plants were more complex and stable, while plants enhanced the contribution of stochastic processes to microbial community assembly. In areas with plants, pH was the most important environmental driver of soil microbial community variations, while organic carbon was the primary driver in areas without plants. Moreover, bacteria exhibited higher sensitivity than fungi to the same environmental variation in both areas with and without plants. In summary, our findings elucidate the responses of soil microbial ecological patterns to plants in newly formed wetlands, while emphasizing that the major environmental drivers of soil microbial communities are influenced by plants. This study provides important implications for enhancing wetland restoration efficiency. Full article

(This article belongs to the Section Soil and Water)

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15 pages, 913 KB

Open AccessArticle

The Impact of China’s Targeted Poverty Alleviation Policy on Water Resource Utilization Pressure and Allocation in Arid Regions: A Case Study of Hotan Prefecture, Xinjiang

by Jin-Wei Huo, Fu-Qiang Xia, Rong-Qian Lu, Dan-Ni Lu, De-Gang Yang and Yang Chen

Water 2025, 17(21), 3053; https://doi.org/10.3390/w17213053 (registering DOI) - 24 Oct 2025

Abstract

Targeted poverty alleviation is a major national initiative in China. The Hotan region, located within the four prefectures of Southern Xinjiang, is one of the 14 contiguous poverty-stricken areas in China as well as a quintessential inland arid zone. Water scarcity is the [...] Read more.

Targeted poverty alleviation is a major national initiative in China. The Hotan region, located within the four prefectures of Southern Xinjiang, is one of the 14 contiguous poverty-stricken areas in China as well as a quintessential inland arid zone. Water scarcity is the primary constraint on development in the Hotan region and a major bottleneck for Northwest China as a whole. However, previous assessments of the effectiveness of poverty alleviation measures have primarily focused on industrial growth itself, lacking an analysis of the adaptability between key regional resource elements and industrial poverty alleviation measures. The core of promoting targeted poverty alleviation in arid regions is properly managing the relationships within the “industry–water resources” system and achieving a balance between resource use, environmental capacity, and economic development. Focusing on the coordinated development of industry and water resources, this study evaluates the spatio-temporal evolution of the industry–water resource relationships in the Hotan region after the implementation of the targeted poverty alleviation policy with the aim of measuring the sustainability of industrial poverty alleviation outcomes in this arid region. The results indicate the following: (1) The targeted poverty alleviation policy has reduced industrial water consumption. Following the policy’s implementation, industrial water consumption decreased by 541 million m³, driven by improvements in water use intensity and shifts in the industrial structure. The primary contributor to this reduction was enhanced water use efficiency within the primary sector. (2) The policy exacerbated the misallocation of water resources relative to industrial output across the region. The Gini coefficient for water resources versus GDP across Hotan’s eight counties and cities rose from 0.26 to 0.32, indicating a shift from a ‘relatively balanced’ to a ‘moderately imbalanced’ allocation. Therefore, achieving sustainable poverty alleviation in this arid region necessitates enhanced coordination between industrial development and water resources. Full article

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

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25 pages, 1582 KB

Open AccessReview

A Review on Climate Change Impacts on Freshwater Systems and Ecosystem Resilience

by Dewasis Dahal, Nishan Bhattarai, Abinash Silwal, Sujan Shrestha, Binisha Shrestha, Bishal Poudel and Ajay Kalra

Water 2025, 17(21), 3052; https://doi.org/10.3390/w17213052 (registering DOI) - 24 Oct 2025

Abstract

Climate change is fundamentally transforming global water systems, affecting the availability, quality, and ecological dynamics of water resources. This review synthesizes current scientific understanding of climate change impacts on hydrological systems, with a focus on freshwater ecosystems, and regional water availability. Rising global [...] Read more.

Climate change is fundamentally transforming global water systems, affecting the availability, quality, and ecological dynamics of water resources. This review synthesizes current scientific understanding of climate change impacts on hydrological systems, with a focus on freshwater ecosystems, and regional water availability. Rising global temperatures are disrupting thermal regimes in rivers, lakes, and ponds; intensifying the frequency and severity of extreme weather events; and altering precipitation and snowmelt patterns. These changes place mounting stress on aquatic ecosystems, threaten water security, and challenge conventional water management practices. The paper also identifies key vulnerabilities across diverse geographic regions and evaluates adaptation strategies such as integrated water resource management (IWRM), the water, energy and food (WEF) nexus, ecosystem-based approaches (EbA), the role of advanced technology and infrastructure enhancements. By adopting these strategies, stakeholders can strengthen the resilience of water systems and safeguard critical resources for both ecosystems and human well-being. Full article

(This article belongs to the Special Issue Water Management and Geohazard Mitigation in a Changing Climate)

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19 pages, 5541 KB

Open AccessArticle

Hybrid LSTM-ARIMA Model for Improving Multi-Step Inflow Forecasting in a Reservoir

by Angela Neagoe, Eliza-Isabela Tică, Liana-Ioana Vuță, Otilia Nedelcu, Gabriela-Elena Dumitran and Bogdan Popa

Water 2025, 17(21), 3051; https://doi.org/10.3390/w17213051 (registering DOI) - 24 Oct 2025

Abstract

In the hydropower sector, accurate estimation of short-term reservoir inflows is an essential element to ensure efficient and safe management of water resources. Short-term forecasting supports the optimization of energy production, prevention of uncontrolled water discharges, planning of equipment maintenance, and adaption of [...] Read more.

In the hydropower sector, accurate estimation of short-term reservoir inflows is an essential element to ensure efficient and safe management of water resources. Short-term forecasting supports the optimization of energy production, prevention of uncontrolled water discharges, planning of equipment maintenance, and adaption of operational strategies. In the absence of data on topography, vegetation, and basin characteristics (required in distributed or semi-distributed models), data-driven approaches can serve as effective alternatives for inflow prediction. This study proposes a novel hybrid approach that reverses the conventional LSTM (Long Short-Term Memory)—ARIMA (Autoregressive Integrated Moving Average) sequence: LSTM is first used to capture nonlinear hydrological patterns, followed by ARIMA to model residual linear trends.The model was calibrated using daily inflow data in the Izvorul Muntelui–Bicaz reservoir in Romania from 2012 to 2020, tested for prediction on the day ahead in a repetitive loop of 365 days corresponding to 2021 and further evaluated through multiple seven-day forecasts randomly selected to cover all 12 months of 2021. For the tested period, the proposed model significantly outperforms the standalone LSTM, increasing the R² from 0.93 to 0.96 and reducing RMSE from 9.74 m³/s to 6.94 m³/s for one-day-ahead forecasting. For multistep forecasting (84 values, randomly selected, 7 per month), the model improves R² from 0.75 to 0.89 and lowers RMSE from 18.56 m³/s to 12.74 m³/s. Thus, the hybrid model offers notable improvements in multi-step forecasting by capturing both seasonal patterns and nonlinear variations in hydrological data. The approach offers a replicable data-driven solution for inflow prediction in reservoirs with limited physical data. Full article

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

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24 pages, 2539 KB

Open AccessArticle

From Indices to Algorithms: A Hybrid Framework of Water Quality Assessment Using WQI and Machine Learning Under WHO and FAO Standards

by Senem Güneş Şen

Water 2025, 17(21), 3050; https://doi.org/10.3390/w17213050 (registering DOI) - 24 Oct 2025

Abstract

Assessing water quality is essential for the sustainable use of freshwater resources, especially under increasing climatic and agricultural pressures. Small irrigation ponds are particularly sensitive to pollution due to their limited buffering capacity. This study evaluates the water quality of the Taşçılar and [...] Read more.

Assessing water quality is essential for the sustainable use of freshwater resources, especially under increasing climatic and agricultural pressures. Small irrigation ponds are particularly sensitive to pollution due to their limited buffering capacity. This study evaluates the water quality of the Taşçılar and Yumurtacılar ponds in Kastamonu, Türkiye, by combining conventional Water Quality Indices (WQI) with machine-learning-based interpretation. Physicochemical parameters were measured monthly for one year, and water quality was classified according to WHO and FAO thresholds using the CCME-WQI and weighted arithmetic methods. The integrated approach identified significant differences among standards and highlighted the parameters most responsible for water quality degradation. Machine-learning models improved the interpretation of these indices and supported consistent classification across datasets. The findings emphasize that coupling index-based and data-driven methods can enhance routine monitoring and provide actionable insights for sustainable irrigation-water management, thereby contributing to achieving the SDGs 6, 13, and 15. Full article

(This article belongs to the Special Issue Water Modeling Using Combined Machine Learning and Fieldwork Investigation)

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22 pages, 4399 KB

Open AccessArticle

Coupled Model Validation and Characterization on Rainfall-Driven Runoff and Non-Point Source Pollution Processes in an Urban Watershed System

by Hantao Wang, Genyu Yuan, Yang Ping, Peng Wei, Fangze Shang, Wei Luo, Zhiqiang Hou, Kairong Lin, Zhenzhou Zhang and Cuijie Feng

Water 2025, 17(21), 3049; https://doi.org/10.3390/w17213049 - 24 Oct 2025

Abstract

Rainfall-driven non-point source (NPS) pollution has become a critical issue for water environment management in urban watershed systems. However, single-model use is limited to fully represent the intricate processes of rainfall-correlated NPS pollution generation and dispersion for effective decision-making. This study develops a [...] Read more.

Rainfall-driven non-point source (NPS) pollution has become a critical issue for water environment management in urban watershed systems. However, single-model use is limited to fully represent the intricate processes of rainfall-correlated NPS pollution generation and dispersion for effective decision-making. This study develops a novel cross-scale, multi-factor coupled model framework to characterize hydrologic and NPS pollution responses to different rainfall events in Shenzhen, China, a representative worldwide metropolis facing challenges from rapid urbanization. The calibrated and validated coupled model achieved remarkable agreements with observed hydrologic (Nash–Sutcliffe efficiency, NSE > 0.81) and water quality (NSE > 0.85) data in different rainfall events and demonstrated high-resolution dynamic changes in flow and pollutant transfer within the studied watershed. In an individual rainfall event, heterogeneous spatial distributions of discharge and pollutant loads were found, highly correlated with land use types. The temporal change pattern and risk of flooding and NPS pollution differed significantly with rainfall intensity, and the increase in the pollutants (mean 322% and 596%, respectively) was much larger than the discharge (207% and 302%, respectively) under intense rainfall conditions. Based on these findings, a decision-support framework was established, featuring land-use-driven spatial prioritization of industrial hotspots, rainfall-intensity-stratified management protocols with event-triggered operational rules, and integrated source-pathway-receiving end intervention strategies. The validated model framework provides quantitative guidance for optimizing infrastructure design parameters, establishing performance-based regulatory standards, and enabling real-time operational decision-making in urban watershed management. Full article

(This article belongs to the Special Issue Urban Water Pollution Control: Theory and Technology, 2nd Edition)

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26 pages, 652 KB

Open AccessReview

Coagulation–Sedimentation in Water and Wastewater Treatment: Removal of Pesticides, Pharmaceuticals, PFAS, Microplastics, and Natural Organic Matter

by Ewelina Łukasiewicz

Water 2025, 17(21), 3048; https://doi.org/10.3390/w17213048 - 24 Oct 2025

Abstract

Coagulation–sedimentation remains a widely used process in drinking and wastewater treatment, yet its performance for emerging contaminants requires further evaluation. This review summarizes recent advances in conventional and novel coagulant systems for the removal of pesticides, pharmaceuticals, per- and polyfluoroalkyl substances (PFAS), natural [...] Read more.

Coagulation–sedimentation remains a widely used process in drinking and wastewater treatment, yet its performance for emerging contaminants requires further evaluation. This review summarizes recent advances in conventional and novel coagulant systems for the removal of pesticides, pharmaceuticals, per- and polyfluoroalkyl substances (PFAS), natural organic matter (NOM), and micro- and nanoplastics (MNPs). The efficiency of conventional aluminum- and iron-based coagulants typically ranges from 30–90% for NOM and pesticides, 10–60% for pharmaceuticals, <20% for PFAS, and up to 95% for microplastics. Modified and hybrid materials, including titanium-based and bio-derived coagulants, demonstrate superior performance through combined mechanisms of charge neutralization, adsorption, and complexation. The zeta potential of particles was identified as a key factor in optimizing MNP removal. The ability of iron and titanium to form complexes with organic ligands significantly influences the removal of organic pollutants and metal–organic interactions in water matrices. While most research remains at the laboratory scale, promising developments in hybrid and electrocoagulation systems indicate potential for field-scale application. The review highlights that coagulation is best applied as a pretreatment step in integrated systems, enhancing subsequent adsorption, oxidation, or membrane processes. Future studies should focus on large-scale validation, energy efficiency, and the recovery of metal oxides (e.g., TiO₂) from residual sludge to improve sustainability. Full article

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

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25 pages, 2395 KB

Open AccessArticle

Eco-Tourism and Biodiversity Conservation in Aquaculture Lagoons: The Role of Operator Philosophy and Low-Vibration Pontoon Boats

by Po-Jen Chen, Chun-Han Shih, Yu-Chi Sung and Tang-Chung Kan

Water 2025, 17(21), 3047; https://doi.org/10.3390/w17213047 - 23 Oct 2025

Abstract

Aquaculture lagoons must reconcile visitor access with biodiversity protection. This study integrates results of a large survey of the attitudes of tour operators with field observations of fish populations to test whether operator choices can align tourism and conservation. Using data from 801 [...] Read more.

Aquaculture lagoons must reconcile visitor access with biodiversity protection. This study integrates results of a large survey of the attitudes of tour operators with field observations of fish populations to test whether operator choices can align tourism and conservation. Using data from 801 guided-tour participants in Taiwan’s Cigu Lagoon, a sequential experience hierarchy was validated whereby environmental knowledge enhanced attitudes, strengthened perceived guide professionalism, induced flow, and ultimately increased conservation intention (R² = 0.523). Experiential service quality exerted stronger effects than functional quality (β = 0.287 vs. 0.156; both p < 0.001). Parallel underwater monitoring indicated that electric, low-vibration motors were associated with richer fish assemblages and larger fish body sizes; fish abundance is 61% higher and mean body length 38% greater, with community composition differing significantly by motor type (PERMANOVA, p < 0.001). Together, these results link training and technology adoption to measurable ecological gains and pro-conservation motivation, indicating that electrified propulsion and interpretive practice are mutually reinforcing levers for biodiversity-positive tourism. The framework offers directly actionable criteria—motor choice, guide development, and safety/facility context—for transitioning small-scale fisheries and recreation toward low-impact marine experiences. Full article

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

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18 pages, 2705 KB

Open AccessArticle

Real-Time Risk Rate Quantification Model and Early Warning Method for Earth–Rock Dams Under Sudden Changes in Reservoir Water Levels

by Xiang Luo, Fuheng Ma, Wei Ye, Benxing Lou, Qiang Li and Hanman Li

Water 2025, 17(21), 3046; https://doi.org/10.3390/w17213046 - 23 Oct 2025

Abstract

Under the influence of global climate change, extreme weather events have become more frequent, and earth and rockfill dams often encounter unconventional working conditions such as sudden changes in reservoir water levels during operation. These abrupt changes are characterized by their strong suddenness [...] Read more.

Under the influence of global climate change, extreme weather events have become more frequent, and earth and rockfill dams often encounter unconventional working conditions such as sudden changes in reservoir water levels during operation. These abrupt changes are characterized by their strong suddenness and rapid rate of change, which can be challenging for traditional numerical analysis methods due to slow modeling and time-consuming calculations, presenting certain limitations. Therefore, an approach has been developed that integrates seepage monitoring data into the failure probability analysis and early warning methods for earth and rockfill dams. Based on the model’s prediction results, dynamic safety warning indicators for the effect of single measurement points on earth and rockfill dams under sudden reservoir water level changes have been quantitatively designed. A risk probability function reflecting the relationship between the residuals of seepage monitoring effects and the risk rate has been constructed to calculate the risk rate of single measurement points for dam seepage effects. By employing the Copula function, which considers the differences and correlations in monitoring effect amounts across different parts of the dam, the single-point seepage risk rates are elevated to a multi-point seepage risk rate analysis. This enables the quantification of the overall seepage risk rate of dams under sudden reservoir water level changes. Case study results show that the safety model has high prediction accuracy. The joint risk rate of the dam based on the Copula function can simultaneously consider spatial correlations and individual differences among multiple measurement points, effectively reducing the interference of randomness in the calculation of single-point risk rates. This method successfully achieves the dynamic transformation of actual seepage effect measurements into risk rates, providing a theoretical basis and technical support for the operational management and safety monitoring of earth and rockfill dams during emergency events. Full article

(This article belongs to the Special Issue Intelligent Safety Diagnosis and Reinforcement of Water-Related Buildings)

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23 pages, 2997 KB

Open AccessArticle

Improving a Prediction Model for Tunnel Water Inflow Estimation Using LSTM and Bayesian Optimization

by Zhen Huang, Zishuai Yang, Yun Wu, Lijian Ma, Tao Sun, Zhenpeng Wang, Kui Zhao, Xiaojun Zhang, Haigang Li and Yu Zheng

Water 2025, 17(21), 3045; https://doi.org/10.3390/w17213045 - 23 Oct 2025

Abstract

Water inrush and mud burst disasters pose severe challenges to the safe and efficient construction of underground engineering. Water inflow prediction is closely related to drainage design, disaster prevention and control, and the safety of the surrounding ecological environment. Thus, assessing the water [...] Read more.

Water inrush and mud burst disasters pose severe challenges to the safe and efficient construction of underground engineering. Water inflow prediction is closely related to drainage design, disaster prevention and control, and the safety of the surrounding ecological environment. Thus, assessing the water inflow accurately is of importance. This study proposes a Bayesian Optimization-Long Short-Term Memory (BOA-LSTM) recurrent neural network for predicting tunnel water inflow. The model is based on four input parameters, namely tunnel depth (H), groundwater level (h), rock quality designation (RQD), and water-richness (W), with water inflow (WI) as the single-output variable. The model first processes and analyzes the data, quantitatively characterizing the correlations between input parameters. The tunnel water inflow is predicted using the long short-term memory (LSTM) recurrent neural network, and the Bayesian optimization algorithm (BOA) is employed to select the hyperparameters of the LSTM, primarily including the number of hidden layer units, initial learning rate, and L2 regularization coefficient. The modeling process incorporates a five-fold cross-validation strategy for dataset partitioning, which effectively mitigates overfitting risks and enhances the model’s generalization capability. After a comprehensive comparison among a series of machine learning models, including a long short-term memory recurrent neural network (LSTM), random forest (RF), back propagation neural network (BP), extreme learning machine (ELM), radial basis function neural network (RBFNN), least squares support vector machine (LIBSVM), and convolutional neural network (CNN), BOA-LSTM performed excellently. The proposed BOA-LSTM model substantially surpasses the standard LSTM and other comparative models in tunnel water inflow prediction, demonstrating superior performance in both accuracy and generalization. Hence, it provides a reference basis for tunnel engineering water inflow prediction. Full article

(This article belongs to the Special Issue Advances in Hydro-Thermal–Mechanical Coupling Geotechnical Engineering)

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18 pages, 650 KB

Open AccessArticle

The Influence of Sewage on the Quantitative and Functional Diversity of Nematode Communities in Constructed Wetlands (VFCW): Analysis of Trophic Relationships Using Canonical Methods

by Magdalena Bagińska, Tomasz Warężak, Wacław Romaniuk, Dawid Kozacki, Zbigniew Skibko, Andrzej Borusiewcz and Jarosław Dąbrowski

Water 2025, 17(21), 3044; https://doi.org/10.3390/w17213044 - 23 Oct 2025

Abstract

Given the increasing demand for water and the need to reduce energy consumption, modern wastewater treatment systems should be characterised by high pollutant removal efficiency while consuming low resources. Hydrophytic wastewater treatment plants with vertical flow through a soil-plant bed (VFCW) are one [...] Read more.

Given the increasing demand for water and the need to reduce energy consumption, modern wastewater treatment systems should be characterised by high pollutant removal efficiency while consuming low resources. Hydrophytic wastewater treatment plants with vertical flow through a soil-plant bed (VFCW) are one solution that meets these requirements. The efficiency of these systems largely depends on the biological activity of the bed, of which free-living soil nematodes are an important component. The study presented in this paper aimed to assess the relationship between the quality of domestic wastewater flowing into VFCW beds and the abundance and trophic structure of soil nematode communities. The analysis was carried out on two real-world sites, where VFCW beds were the third stage of the plant bed system. Both treatment plants received only domestic wastewater. Statistical analysis showed no significant differences (p > 0.05) in the physicochemical composition of the wastewater flowing into the two treatment plants, indicating homogeneous system feed conditions. Nevertheless, canonical correspondence analysis (CCA) showed that the relationships between effluent parameters and the abundance of individual nematode trophic groups differed in each bed, suggesting the influence of local environmental and biocenotic conditions. In particular, bacterivorous nematodes—key to bed function—were shown to be sensitive to different sets of variables at the two sites despite similar effluent composition. These results confirm that the rhizosphere—a zone of intense interactions between plant roots, microorganisms, and soil microfauna—plays a critical role in shaping the biological activity of the bed. Nematodes, particularly bacterivorous nematodes, support the mineralisation of organic matter and nutrient cycling, resulting in increased efficiency of treatment processes. The stability of the total nematode abundance, irrespective of inflow conditions, demonstrates the bed biocenosis high ecological resilience to external disturbances. The study’s results highlight the importance of an ecosystem approach in designing and managing nature-based solutions (NBS) treatment plants, which can be a sustainable component of sustainable water and wastewater management. Full article

(This article belongs to the Special Issue Rural Wastewater Treatment by Nature-Based Solutions)

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34 pages, 23946 KB

Open AccessArticle

Estimation of Groundwater Recharge in the Volcanic Aquifers in a Tropical Climate, Southwestern Ethiopia: Insights from Water Table Fluctuation and Chloride Mass Balance Methods

by Adisu Befekadu Kebede, Fayera Gudu Tufa, Wagari Mosisa Kitessa, Beekan Gurmessa Gudeta, Seifu Kebede Debela, Alemu Yenehun, Fekadu Fufa Feyessa, Thomas Hermans and Kristine Walraevens

Water 2025, 17(21), 3043; https://doi.org/10.3390/w17213043 - 23 Oct 2025

Abstract

The sustainable use and management of groundwater resources is a challenging issue due to population growth and climate change. Accurate quantification of groundwater recharge is a basic requirement for effective groundwater resource management, yet it is still lacking in many areas around the [...] Read more.

The sustainable use and management of groundwater resources is a challenging issue due to population growth and climate change. Accurate quantification of groundwater recharge is a basic requirement for effective groundwater resource management, yet it is still lacking in many areas around the world. The study was designed to estimate recharge to groundwater from natural rainfall in the Gilgel Gibe and Dhidhessa catchments in southwestern Ethiopia, employing the water table fluctuation (WTF) and chloride mass balance (CMB) techniques. These methods are being applied for the first time in the study area and have not previously been used in these catchments. Given the region’s data scarcity, a community-based data collection program was implemented and supplemented with additional field measurements and secondary data sources. Groundwater level, spring discharge, and rainfall were monitored over the 2022/2023 hydrological year. Groundwater level fluctuations were found to be influenced by topography and rainfall patterns, reaching 8.2 m in amplitude in the upstream part of the catchments. Chloride concentrations were determined in groundwater samples collected from hand-dug wells and springs, and rainwater was also collected. Rainwater exhibited a mean chloride concentration of 2.46 mg/L, while groundwater chloride concentrations ranged from 3 mg/L to 36.99 mg/L. The estimated recharge rates varied spatially, ranging from 170 to 850 mm/year using the CMB method (11% to 55% of annual rainfall, mean recharge rate of 454 mm/year) and from 76 to 796 mm/year using the WTF method (4% to 43% of annual rainfall, mean recharge rate of 439 mm/year). Notably, recharge estimates were lowest downstream in the lowland areas and highest upstream in the highland regions. Rainfall amount, local lithology, and topography were identified as major influences on groundwater recharge across the study area. Both CMB and WTF methods were deemed applicable in the volcanic aquifers, provided that all the respective assumptions are followed. This study significantly contributes to the groundwater dataset for the region, in addition to recharge estimation and the research conclusions, emphasizing the importance of long-term monitoring and time series analysis of chloride data to reduce uncertainties. The work serves as a valuable reference for researchers, policymakers, and regional water resource managers. Full article

(This article belongs to the Section Hydrogeology)

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