Water

17 pages, 1388 KiB

Open AccessArticle

Invertebrate Assemblages in Some Saline and Soda Lakes of the Kulunda Steppe: First Regional Assessment and Ecological Implications

by Larisa Golovatyuk, Timur Kanapatskiy, Olga Samylina, Nikolay Pimenov, Larisa Nazarova and Anna Kallistova

Water 2025, 17(15), 2330; https://doi.org/10.3390/w17152330 - 5 Aug 2025

Abstract

The taxonomic composition and structure of invertebrate assemblages in five lakes from the Kulunda steppe, located in an arid region of southwestern Siberia (Russia), were studied. The lakes varied greatly in their total salinity (5 to 304 g L⁻¹) and carbonate [...] Read more.

The taxonomic composition and structure of invertebrate assemblages in five lakes from the Kulunda steppe, located in an arid region of southwestern Siberia (Russia), were studied. The lakes varied greatly in their total salinity (5 to 304 g L⁻¹) and carbonate alkalinity (0.03 to 4.03 mol-eq L⁻¹). The invertebrate fauna was characterized by low diversity. Only five taxa of macrozoobenthos and two taxa of planktonic invertebrates were identified. As water salinity increased, the taxonomic diversity of the studied lakes decreased, and at salinities > 276 g L⁻¹, monodominant assemblages were formed. The high numbers and biomass of aquatic organism provide a rich food supply for native and migratory waterfowl. The low taxonomic diversity of the invertebrate assemblages of the lakes makes them vulnerable to any negative external impact. The climate in the Kulunda steppe demonstrates a long-term aridization trend. If this continues in the future, then over time, this may lead to the gradual salinization of lakes and a further decrease in the taxonomic diversity of hydrobiological assemblages. This emphasizes the ecological importance of the studied territory and the necessity for its inclusion in the list of sites protected by the Ramsar Convention. Full article

(This article belongs to the Section Biodiversity and Functionality of Aquatic Ecosystems)

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21 pages, 3832 KiB

Open AccessArticle

Effects of Water Use Efficiency Combined with Advancements in Nitrogen and Soil Water Management for Sustainable Agriculture in the Loess Plateau, China

by Hafeez Noor, Fida Noor, Zhiqiang Gao, Majed Alotaibi and Mahmoud F. Seleiman

Water 2025, 17(15), 2329; https://doi.org/10.3390/w17152329 - 5 Aug 2025

Abstract

In China’s Loess Plateau, sustainable agricultural end products are affected by an insufficiency of water resources. Rising crop water use efficiency (WUE) through field management pattern improvement is a crucial plan of action to address this issue. However, there is no agreement among [...] Read more.

In China’s Loess Plateau, sustainable agricultural end products are affected by an insufficiency of water resources. Rising crop water use efficiency (WUE) through field management pattern improvement is a crucial plan of action to address this issue. However, there is no agreement among researchers on the most appropriate field management practices regarding WUE, which requires further integrated quantitative analysis. We conducted a meta-analysis by quantifying the effect of agricultural practices surrounding nitrogen (N) fertilizer management. The two experimental cultivars were Yunhan–20410 and Yunhan–618. The subplots included nitrogen 0 kg·ha⁻¹ (N0), 90 kg·ha⁻¹ (N90), 180 kg·ha⁻¹ (N180), 210 kg·ha⁻¹ (N210), and 240 kg·ha⁻¹ (N240). Our results show that higher N rates (up to N210) enhanced water consumption during the node-flowering and flowering-maturity time periods. YH–618 showed higher water use during the sowing–greening and node-flowering periods but decreased use during the greening-node and flowering-maturity periods compared to YH–20410. The N210 treatment under YH–618 maximized water use efficiency (WUE). Increased N rates (N180–N210) decreased covering temperatures (T_max, T_min, T_aver) during flowering, increasing the level of grain filling. Spike numbers rose with N application, with an off-peak at N210 for strong-gluten wheat. The 1000-grain weight was at first enhanced but decreased at the far end of N180–N210. YH–618 with N210 achieved a harvest index (HI) similar to that of YH–20410 with N180, while excessive N (N240) or water reduced the HI. Dry matter accumulation increased up to N210, resulting in earlier stabilization. Soil water consumption from wintering to jointing was strongly correlated with pre-flowering dry matter biological process and yield, while jointing–flowering water use was linked to post-flowering dry matter and spike numbers. Post-flowering dry matter accumulation was critical for yield, whereas spike numbers positively impacted yield but negatively affected 1000-grain weight. In conclusion, our results provide evidence for determining suitable integrated agricultural establishment strategies to ensure efficient water use and sustainable production in the Loess Plateau region. Full article

(This article belongs to the Special Issue Soil–Water Interaction and Management)

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19 pages, 4451 KiB

Open AccessArticle

Assessment of the Payments for Watershed Services Policy from a Perspective of Ecosystem Services: A Case Study of the Liaohe River Basin, China

by Manman Guo, Xu Lu and Qing Ma

Water 2025, 17(15), 2328; https://doi.org/10.3390/w17152328 - 5 Aug 2025

Abstract

Payments for Watershed services (PWSs) have been emerging as a critical tool for environmental governance in watershed, yet their comparative effectiveness across implementation models has remained poorly understood. Based on a comparative analysis of Eco-Compensation (EC) and Payments for Ecosystem Services (PESs) frameworks, [...] Read more.

Payments for Watershed services (PWSs) have been emerging as a critical tool for environmental governance in watershed, yet their comparative effectiveness across implementation models has remained poorly understood. Based on a comparative analysis of Eco-Compensation (EC) and Payments for Ecosystem Services (PESs) frameworks, examining both theoretical foundations and implementation practices, this study aims to quantitatively assess and compare the effectiveness of two dominant PWSs models—the EC-like model (Phase I: October 2008–April 2017) and the PESs-like model (Phase II: 2017–December 2021). Using the Liaohe River in China as a case study, utilizing ecosystem service value (ESV) as an indicator and employing the corrected unit-value transfer method, we compare the effectiveness of different PWSs models from October 2008 to December 2021. The results reveal the following: (1) Policy Efficiency: The PESs-like model demonstrated significantly greater effectiveness than the EC-like model, with annual average increases in ESV of 3.23 billion CNY (491 million USD) and 1.79 billion CNY (272 million USD). (2) Functional Drivers: Water regulation (45.1% of total ESV growth) and climate regulation (24.3%) were dominant services, with PESs-like interventions enhancing multifunctionality. (3) Stakeholder Impact: In the PESs-like model, the cities implementing inter-county direct payment showed higher growth efficiency than those without it. The operational efficiency of PWSs increases with the number of participating stakeholders, which explains why the PESs-like model demonstrates higher effectiveness than the EC-like model. Our findings offer empirical evidence and actionable policy implications for designing effective PWSs models across global watershed ecosystems. Full article

(This article belongs to the Special Issue NBS for Watershed Management: From Ecological Health Assessment to Ecosystem Restoration)

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19 pages, 30180 KiB

Open AccessArticle

Evaluating Distributed Hydrologic Modeling to Assess Coastal Highway Vulnerability to High Water Tables

by Bruno Jose de Oliveira Sousa, Luiz M. Morgado and Jose G. Vasconcelos

Water 2025, 17(15), 2327; https://doi.org/10.3390/w17152327 - 5 Aug 2025

Abstract

Due to increased precipitation intensity and sea-level rise, low-lying coastal roads are increasingly vulnerable to subbase saturation. Widely applied lumped hydrological approaches cannot accurately represent time and space-varying groundwater levels in some highly conductive coastal soils, calling for more sophisticated tools. This study [...] Read more.

Due to increased precipitation intensity and sea-level rise, low-lying coastal roads are increasingly vulnerable to subbase saturation. Widely applied lumped hydrological approaches cannot accurately represent time and space-varying groundwater levels in some highly conductive coastal soils, calling for more sophisticated tools. This study assesses the suitability of the Gridded Surface Subsurface Hydrologic Analysis model (GSSHA) for representing hydrological processes and groundwater dynamics in a unique coastal roadway setting in Alabama. A high-resolution model was developed to assess a 2 km road segment and was calibrated for hydraulic conductivity and aquifer bottom levels using observed groundwater level (GWL) data. The model configuration included a fixed groundwater tidal boundary representing Mobile Bay, a refined land cover classification, and an extreme precipitation event simulation representing Hurricane Sally. Results indicated good agreement between modeled and observed groundwater levels, particularly during short-duration high-intensity events, with NSE values reaching up to 0.83. However, the absence of dynamic tidal forcing limited its ability to replicate certain fine-scale groundwater fluctuations. During the Hurricane Sally simulation, over two-thirds of the segment remained saturated for over 6 h, and some locations exceeded 48 h of pavement saturation. The findings underscore the importance of incorporating shallow groundwater processes in hydrologic modeling for coastal roads. This replicable modeling framework may assist DOTs in identifying critical roadway segments to improve drainage infrastructure in order to increase resiliency. Full article

(This article belongs to the Topic Natural Hazards and Disaster Risks Reduction, 2nd Edition)

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13 pages, 2022 KiB

Open AccessArticle

A Practical Method for Ecological Flow Calculation to Support Integrated Ecological Functions of the Lower Yellow River, China

by Xinyuan Chen, Lixin Zhang and Lei Tang

Water 2025, 17(15), 2326; https://doi.org/10.3390/w17152326 - 5 Aug 2025

Abstract

The lower Yellow River is characterized by low water discharge and a high sediment load, resulting in a fragile aquatic ecosystem. It is important to develop a reasonable method of ecological flow calculation that can be applied to the water-scarce rivers like the [...] Read more.

The lower Yellow River is characterized by low water discharge and a high sediment load, resulting in a fragile aquatic ecosystem. It is important to develop a reasonable method of ecological flow calculation that can be applied to the water-scarce rivers like the Yellow River. In this paper, we selected the Huayuankou hydrological station in the lower Yellow River as our study site and assessed the ecological flow using several methodologies including the monthly frequency calculation method, the sediment transportation method, the habitat simulation method, and the improved annual distribution method. Based on the seasonal applicability of the four methods across months of the year, we established an ecological flow calculation method that considers the integrated ecological functions of the lower Yellow River. In this method, ecological flow in the lower Yellow River during the dry season (November to March) can be determined by using the improved annual distribution method, ecological flow in the fish spawning period (April to June) can be calculated using the habitat simulation method, and the ecological flow during the flood season (July to October) can be calculated using the sediment transportation method. The optimal ecological flow regime for the Huayuankou section was determined using the established method. The ecological flow regimes derived in our study ranged from 310 m³/s to 1532 m³/s. However, we also observed that the ecological flow has a relatively low assurance rate during the flood season in the lower Yellow River, with the assurance rate not exceeding 63%. This highlights the fact that more attention should be given in reservoir regulations to facilitating sediment transport downstream. Full article

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

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25 pages, 6730 KiB

Open AccessArticle

Decentralized Coupled Grey–Green Infrastructure for Resilient and Cost-Effective Stormwater Management in a Historic Chinese District

by Yongqi Liu, Ziheng Xiong, Mo Wang, Menghan Zhang, Rana Muhammad Adnan, Weicong Fu, Chuanhao Sun and Soon Keat Tan

Water 2025, 17(15), 2325; https://doi.org/10.3390/w17152325 - 5 Aug 2025

Abstract

Coupled grey and green infrastructure (CGGI) offers a promising pathway toward sustainable stormwater management in historic urban environments. This study compares CGGI and conventional grey infrastructure (GREI)-only strategies across four degrees of layout centralization (0%, 33.3%, 66.7%, and 100%) in the Quanzhou West [...] Read more.

Coupled grey and green infrastructure (CGGI) offers a promising pathway toward sustainable stormwater management in historic urban environments. This study compares CGGI and conventional grey infrastructure (GREI)-only strategies across four degrees of layout centralization (0%, 33.3%, 66.7%, and 100%) in the Quanzhou West Street Historic Reserve, China. Using a multi-objective optimization framework integrating SWMM simulations, life-cycle cost (LCC) modeling, and resilience metrics, we found that the decentralized CGGI layouts reduced the total LCC by up to 29.6% and required 60.7% less green infrastructure (GI) area than centralized schemes. Under nine extreme rainfall scenarios, the GREI-only systems showed slightly higher technical resilience (Tech-R: max 99.6%) than CGGI (Tech-R: max 99.1%). However, the CGGI systems outperformed GREI in operational resilience (Oper-R), reducing overflow volume by up to 22.6% under 50% network failure. These findings demonstrate that decentralized CGGI provides a more resilient and cost-effective drainage solution, well-suited for heritage districts with spatial and cultural constraints. Full article

(This article belongs to the Special Issue Sustainable Water Treatment Systems: Green Infrastructure and Bioremediation)

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22 pages, 2029 KiB

Open AccessArticle

A Deep Reinforcement Learning Framework for Cascade Reservoir Operations Under Runoff Uncertainty

by Jing Xu, Jiabin Qiao, Qianli Sun and Keyan Shen

Water 2025, 17(15), 2324; https://doi.org/10.3390/w17152324 - 5 Aug 2025

Abstract

Effective management of cascade reservoir systems is essential for balancing hydropower generation, flood control, and ecological sustainability, especially under increasingly uncertain runoff conditions driven by climate change. Traditional optimization methods, while widely used, often struggle with high dimensionality and fail to adequately address [...] Read more.

Effective management of cascade reservoir systems is essential for balancing hydropower generation, flood control, and ecological sustainability, especially under increasingly uncertain runoff conditions driven by climate change. Traditional optimization methods, while widely used, often struggle with high dimensionality and fail to adequately address inflow variability. This study introduces a novel deep reinforcement learning (DRL) framework that tightly couples probabilistic runoff forecasting with adaptive reservoir scheduling. We integrate a Long Short-Term Memory (LSTM) neural network to model runoff uncertainty and generate probabilistic inflow forecasts, which are then embedded into a Proximal Policy Optimization (PPO) algorithm via Monte Carlo sampling. This unified forecast–optimize architecture allows for dynamic policy adjustment in response to stochastic hydrological conditions. A case study on China’s Xiluodu–Xiangjiaba cascade system demonstrates that the proposed LSTM-PPO framework achieves superior performance compared to traditional baselines, notably improving power output, storage utilization, and spillage reduction. The results highlight the method’s robustness and scalability, suggesting strong potential for supporting resilient water–energy nexus management under complex environmental uncertainty. Full article

(This article belongs to the Section Hydrology)

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24 pages, 3518 KiB

Open AccessArticle

Assessing Community Perception, Preparedness, and Adaptation to Urban Flood Risks in Malaysia

by Maniyammai Kumaresen, Fang Yenn Teo, Anurita Selvarajoo, Subarna Sivapalan and Roger A. Falconer

Water 2025, 17(15), 2323; https://doi.org/10.3390/w17152323 - 5 Aug 2025

Abstract

Urban flooding has significantly impacted the livelihoods of households and communities worldwide. It highlights the urgency of focusing on both flood preparedness and adaptation strategies to understand the community’s perception and adaptive capacity. This study investigates the levels of risk perception, flood preparedness, [...] Read more.

Urban flooding has significantly impacted the livelihoods of households and communities worldwide. It highlights the urgency of focusing on both flood preparedness and adaptation strategies to understand the community’s perception and adaptive capacity. This study investigates the levels of risk perception, flood preparedness, and adaptive capacity, while also exploring the inter-relationships among these factors within the context of urban flooding in Malaysia. A quantitative approach was employed, involving a structured questionnaire administered to residents in flood-prone urban areas across Greater Kuala Lumpur, Malaysia. A total of 212 responses were analysed using descriptive statistics, categorical index classification, and Spearman correlation analysis. The findings indicate that residents generally reported high levels of risk perception and preparedness, although adaptive capacity exhibited greater variability, with a mean score of 3.97 (SD = 0.64). Positive associations were found among risk perception, flood preparedness, and adaptive capacity. This study contributes to the existing knowledge by providing evidence on community resilience and highlighting key factors that can guide flood management policies and encourage adaptive planning at the community level. Full article

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

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5 pages, 162 KiB

Open AccessEditorial

The Multiple Challenges Faced by Coastal and Marine Governance

by Camilo M. Botero, Daniel O. Suman and Celene B. Milanes

Water 2025, 17(15), 2322; https://doi.org/10.3390/w17152322 - 5 Aug 2025

Abstract

Coastal and marine areas are only partially integrated into governance across different geographical scales, with persistent fragmentation and sectoral approaches remaining common challenges [...] Full article

(This article belongs to the Special Issue Coastal and Marine Governance and Protection)

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Graphical abstract

6 pages, 180 KiB

Open AccessEditorial

Aquatic Ecosystems: Biodiversity and Conservation

by Marcos Gomes Nogueira and Douglas Donald Kane

Water 2025, 17(15), 2321; https://doi.org/10.3390/w17152321 - 5 Aug 2025

Abstract

The structure and functioning of marine and inland water ecosystems are highly dependent on living organisms [...] Full article

(This article belongs to the Special Issue Aquatic Ecosystems: Biodiversity and Conservation)

18 pages, 2365 KiB

Open AccessArticle

Integrated Environmental–Economic Assessment of CO₂ Storage in Chinese Saline Formations

by Wentao Zhao, Zhe Jiang, Tieya Jing, Jian Zhang, Zhan Yang, Xiang Li, Juan Zhou, Jingchao Zhao and Shuhui Zhang

Water 2025, 17(15), 2320; https://doi.org/10.3390/w17152320 - 4 Aug 2025

Abstract

This study develops an integrated environmental–economic assessment framework to evaluate the life cycle environmental impacts and economic costs of CO₂ geological storage and produced water treatment in saline formations in China. Using a case study of a saline aquifer carbon storage project [...] Read more.

This study develops an integrated environmental–economic assessment framework to evaluate the life cycle environmental impacts and economic costs of CO₂ geological storage and produced water treatment in saline formations in China. Using a case study of a saline aquifer carbon storage project in the Ordos Basin, eight full-chain carbon capture, utilization, and storage (CCUS) scenarios were analyzed. The results indicate that environmental and cost performance are primarily influenced by technology choices across carbon capture, transport, and storage stages. The scenario employing potassium carbonate-based capture, pipeline transport, and brine reinjection after a reverse osmosis treatment (S5) achieved the most balanced outcome. Breakeven analyses under three carbon price projection models revealed that carbon price trajectories critically affect project viability, with a steadily rising carbon price enabling earlier profitability. By decoupling CCUS from power systems and focusing on unit CO₂ removal, this study provides a transparent and transferable framework to support cross-sectoral deployment. The findings offer valuable insights for policymakers aiming to design effective CCUS support mechanisms under future carbon neutrality targets. Full article

(This article belongs to the Special Issue Mine Water Treatment, Utilization and Storage Technology)

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18 pages, 1259 KiB

Open AccessArticle

Artificial Neural Network-Based Prediction of Clogging Duration to Support Backwashing Requirement in a Horizontal Roughing Filter: Enhancing Maintenance Efficiency

by Sphesihle Mtsweni, Babatunde Femi Bakare and Sudesh Rathilal

Water 2025, 17(15), 2319; https://doi.org/10.3390/w17152319 - 4 Aug 2025

Abstract

While horizontal roughing filters (HRFs) remain widely acclaimed for their exceptional efficiency in water treatment, especially in developing countries, they are inherently susceptible to clogging, which necessitates timely maintenance interventions. Conventional methods for managing clogging in HRFs typically involve evaluating filter head loss [...] Read more.

While horizontal roughing filters (HRFs) remain widely acclaimed for their exceptional efficiency in water treatment, especially in developing countries, they are inherently susceptible to clogging, which necessitates timely maintenance interventions. Conventional methods for managing clogging in HRFs typically involve evaluating filter head loss coefficients against established water quality standards. This study utilizes artificial neural network (ANN) for the prediction of clogging duration and effluent turbidity in HRF equipment. The ANN was configured with two outputs, the clogging duration and effluent turbidity, which were predicted concurrently. Effluent turbidity was modeled to enhance the network’s learning process and improve the accuracy of clogging prediction. The network steps of the iterative training process of ANN used different types of input parameters, such as influent turbidity, filtration rate, pH, conductivity, and effluent turbidity. The training, in addition, optimized network parameters such as learning rate, momentum, and calibration of neurons in the hidden layer. The quantities of the dataset accounted for up to 70% for training and 30% for testing and validation. The optimized structure of ANN configured in a 4-8-2 topology and trained using the Levenberg–Marquardt (LM) algorithm achieved a mean square error (MSE) of less than 0.001 and R-coefficients exceeding 0.999 across training, validation, testing, and the entire dataset. This ANN surpassed models of scaled conjugate gradient (SCG) and obtained a percentage of average absolute deviation (%AAD) of 9.5. This optimal structure of ANN proved to be a robust tool for tracking the filter clogging duration in HRF equipment. This approach supports proactive maintenance and operational planning in HRFs, including data-driven scheduling of backwashing based on predicted clogging trends. Full article

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

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23 pages, 4325 KiB

Open AccessArticle

Groundwater Level Estimation Using Improved Transformer Model: A Case Study of the Yellow River Basin

by Tianming Zhou, Chun Fu, Yezhong Liu and Libin Xiang

Water 2025, 17(15), 2318; https://doi.org/10.3390/w17152318 - 4 Aug 2025

Abstract

Accurate estimation of groundwater levels in river basins is essential for effective water resource planning. Innovations in deep learning and artificial intelligence (AI) have been introduced into this field to enhance the accuracy of long-term groundwater level estimation. This study employs the Transformer [...] Read more.

Accurate estimation of groundwater levels in river basins is essential for effective water resource planning. Innovations in deep learning and artificial intelligence (AI) have been introduced into this field to enhance the accuracy of long-term groundwater level estimation. This study employs the Transformer deep learning model to estimate groundwater levels, with a benchmark comparison against the long short-term memory (LSTM) model. These models were applied to estimate groundwater levels in the Yellow River Basin, where approximately 1100 monitoring wells are located. Monthly average groundwater level data from the period 2018–2023 were collected from these wells. The two models were used to estimate groundwater levels for the period 2003–2017 by incorporating remote sensing information. The Transformer model was enhanced to simultaneously capture features from both historical temporal data and surrounding spatial data, while automatically enhancing key features, effectively improving estimation accuracy and robustness. At the basin-averaged scale, the enhanced Transformer model outperformed the LSTM model: R² increased by approximately 17.5%, while RMSE and MAE decreased by approximately 12.4% and 10.9%, respectively. The proportion of poorly predicted samples decreased by an average of approximately 12.1%. The estimation model established in this study contributes to improving the quantitative analysis capability of long-term groundwater level variations in the Yellow River Basin. This could be helpful for water resource development planning in this densely populated region and likely has broad applicability in other river basins. Full article

(This article belongs to the Special Issue Machine Learning Applications in the Water Domain)

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30 pages, 4529 KiB

Open AccessArticle

Rainwater Harvesting Site Assessment Using Geospatial Technologies in a Semi-Arid Region: Toward Water Sustainability

by Ban AL- Hasani, Mawada Abdellatif, Iacopo Carnacina, Clare Harris, Bashar F. Maaroof and Salah L. Zubaidi

Water 2025, 17(15), 2317; https://doi.org/10.3390/w17152317 - 4 Aug 2025

Abstract

Rainwater harvesting for sustainable agriculture (RWHSA) offers a viable and eco-friendly strategy to alleviate water scarcity in semi-arid regions, particularly for agricultural use. This study aims to identify optimal sites for implementing RWH systems in northern Iraq to enhance water availability and promote [...] Read more.

Rainwater harvesting for sustainable agriculture (RWHSA) offers a viable and eco-friendly strategy to alleviate water scarcity in semi-arid regions, particularly for agricultural use. This study aims to identify optimal sites for implementing RWH systems in northern Iraq to enhance water availability and promote sustainable farming practices. An integrated geospatial approach was adopted, combining Remote Sensing (RS), Geographic Information Systems (GIS), and Multi-Criteria Decision Analysis (MCDA). Key thematic layers, including soil type, land use/land cover, slope, and drainage density were processed in a GIS environment to model runoff potential. The Soil Conservation Service Curve Number (SCS-CN) method was used to estimate surface runoff. Criteria were weighted using the Analytical Hierarchy Process (AHP), enabling a structured and consistent evaluation of site suitability. The resulting suitability map classifies the region into four categories: very high suitability (10.2%), high (26.6%), moderate (40.4%), and low (22.8%). The integration of RS, GIS, AHP, and MCDA proved effective for strategic RWH site selection, supporting cost-efficient, sustainable, and data-driven agricultural planning in water-stressed environments. Full article

(This article belongs to the Special Issue Advancements in Remote Sensing and UAV Technologies for Water Management in Climate-Smart Agriculture Systems)

29 pages, 14336 KiB

Open AccessArticle

Geospatial Mudflow Risk Modeling: Integration of MCDA and RAMMS

by Ainur Mussina, Assel Abdullayeva, Victor Blagovechshenskiy, Sandugash Ranova, Zhixiong Zeng, Aidana Kamalbekova and Ulzhan Aldabergen

Water 2025, 17(15), 2316; https://doi.org/10.3390/w17152316 - 4 Aug 2025

Abstract

This article presents a comprehensive assessment of mudflow risk in the Talgar River basin through the application of Multi-Criteria Decision Analysis (MCDA) methods and numerical modeling using the Rapid Mass Movement Simulation (RAMMS) environment. The first part of the study involves a spatial [...] Read more.

This article presents a comprehensive assessment of mudflow risk in the Talgar River basin through the application of Multi-Criteria Decision Analysis (MCDA) methods and numerical modeling using the Rapid Mass Movement Simulation (RAMMS) environment. The first part of the study involves a spatial assessment of mudflow hazard and susceptibility using GIS technologies and MCDA. The key condition for evaluating mudflow hazard is the identification of factors influencing the formation of mudflows. The susceptibility assessment was based on viewing the area as an object of spatial and functional analysis, enabling determination of its susceptibility to mudflow impacts across geomorphological zones: initiation, transformation, and accumulation. Relevant criteria were selected for analysis, each assigned weights based on expert judgment and the Analytic Hierarchy Process (AHP). The results include maps of potential mudflow hazard and susceptibility, showing areas of hazard occurrence and risk impact zones within the Talgar River basin. According to the mudflow hazard map, more than 50% of the basin area is classified as having a moderate hazard level, while 28.4% is subject to high hazard, and only 1.8% falls under the very high hazard category. The remaining areas are categorized as very low (4.1%) and low (14.7%) hazard zones. In terms of susceptibility to mudflows, 40.1% of the territory is exposed to a high level of susceptibility, 35.6% to a moderate level, and 5.5% to a very high level. The remaining areas are classified as very low (1.8%) and low (15.6%) susceptibility zones. The predictive performance was evaluated through Receiver Operating Characteristic (ROC) curves, and the Area Under the Curve (AUC) value of the mudflow hazard assessment is 0.86, which indicates good adaptability and relatively high accuracy, while the AUC value for assessing the susceptibility of the territory is 0.71, which means that the accuracy of assessing the susceptibility of territories to mudflows is within the acceptable level of model accuracy. To refine the spatial risk assessment, mudflow modeling was conducted under three scenarios of glacial-moraine lake outburst using the RAMMS model. For each scenario, key flow parameters—height and velocity—were identified, forming the basis for classification of zones by impact intensity. The integration of MCDA and RAMMS results produced a final mudflow risk map reflecting both the likelihood of occurrence and the extent of potential damage. The presented approach demonstrates the effectiveness of combining GIS analysis, MCDA, and physically-based modeling for comprehensive natural hazard assessment and can be applied to other mountainous regions with high mudflow activity. Full article

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

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17 pages, 3208 KiB

Open AccessArticle

The Spatiotemporal Evolution Characteristics of the Water Use Structure in Shandong Province, Northern China, Based on the Gini Coefficient

by Caihong Liu, Mingyuan Fan, Yongfeng Yang, Kairan Wang and Haijiao Liu

Water 2025, 17(15), 2315; https://doi.org/10.3390/w17152315 - 4 Aug 2025

Abstract

The spatiotemporal evolution of the regional water use structure holds significant theoretical value for optimizing regional water resource allocation, adjusting industrial structures, and achieving sustainable water resource development. Shandong Province, located at the lowest reach of the Yellow River Basin in China, is [...] Read more.

The spatiotemporal evolution of the regional water use structure holds significant theoretical value for optimizing regional water resource allocation, adjusting industrial structures, and achieving sustainable water resource development. Shandong Province, located at the lowest reach of the Yellow River Basin in China, is a major economic, agricultural, and populous province, as well as a region with one of the most prominent water supply–demand imbalances in the country. As a result, exploring how water use patterns change over time and space in this region has become crucial. Using analytical methods like the Lorenz curve, Gini coefficient, cluster analysis, and spatial statistics, we examine shifts in Shandong’s water use structure from 2001 to 2023. We find that while agriculture remained the largest water consumer over this period, industrial, household, and ecological water use steadily increased, signaling a move toward more balanced resource distribution. Across Shandong’s 16 regions (cities), the water use patterns varied considerably, particularly in terms of agriculture, industry, and ecological needs. Among these, agricultural, industrial, and domestic water use were distributed relatively evenly, whereas ecological water use showed greater regional disparities. These results may have the potential to guide policymakers in refining water allocation strategies, improving industrial planning, and boosting the water use efficiency in Shandong and the country ore broadly. Full article

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

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9 pages, 281 KiB

Open AccessArticle

Decolourisation of a Mixture of Dyes from Different Classes Using a Bioreactor with Immobilised Pleurotus ostreatus Mycelium

by Wioletta Przystaś

Water 2025, 17(15), 2314; https://doi.org/10.3390/w17152314 - 4 Aug 2025

Abstract

Dyes are widely used in various industries, but their removal from wastewater remains a challenge due to their resistance to biodegradation. While substantial research exists regarding the removal of individual dyes, there is much less about the removal of their mixtures. The aim [...] Read more.

Dyes are widely used in various industries, but their removal from wastewater remains a challenge due to their resistance to biodegradation. While substantial research exists regarding the removal of individual dyes, there is much less about the removal of their mixtures. The aim of the research was to determine the possibility of using the immobilised mycelium of Pleurotus ostreatus strains to remove three-component mixtures of dyes from different classes. Efficiency of the removal in the continuously aerated reactor was similar to that obtained in a periodically aerated reactor and was over 90% at the end of each cycle. Despite the addition of subsequent portions of dyes, no increase in the toxicity of post-process samples was observed, and even a decrease in zootoxicity was noticed. The results of the study therefore indicate that an immobilised biomass can be used to remove the dyes, without the need to constantly inject air into the reactor. Full article

(This article belongs to the Special Issue Monitoring and Degradation of Drugs and Organic Pollutant in Wastewater)

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28 pages, 3909 KiB

Open AccessArticle

Exploring How Climate Change Scenarios Shape the Future of Alboran Sea Fisheries

by Isabella Uzategui, Susana Garcia-Tiscar and Paloma Alcorlo

Water 2025, 17(15), 2313; https://doi.org/10.3390/w17152313 - 4 Aug 2025

Abstract

Climate change is disrupting marine ecosystems, necessitating a deeper understanding of environmental and fishing-related impacts on exploited species. This study examines the effects of physical factors (temperature, thermal anomalies, salinity, seabed conditions), biogeochemical elements (pH, oxygen levels, nutrients, primary production), and fishing pressure [...] Read more.

Climate change is disrupting marine ecosystems, necessitating a deeper understanding of environmental and fishing-related impacts on exploited species. This study examines the effects of physical factors (temperature, thermal anomalies, salinity, seabed conditions), biogeochemical elements (pH, oxygen levels, nutrients, primary production), and fishing pressure on the biomass of commercially important species in the Alboran Sea from 1999 to 2022. Data were sourced from the Copernicus observational program, focusing on the geographical sub-area 1 (GSA-1) zone across three depth ranges. Generalized Additive Models were applied for analysis. Rising temperatures and seasonal anomalies have largely negative effects, disrupting species’ physiological balance. Changes in water quality, including improved nutrient and oxygen concentrations, have yielded complex ecological responses. Fishing indices highlight the vulnerability of small pelagic fish to climate change and overfishing, underscoring their economic and ecological significance. These findings stress the urgent need for ecosystem-based management strategies that integrate climate change impacts to ensure sustainable marine resource management. Full article

(This article belongs to the Special Issue Impact of Climate Change on Marine Ecosystems)

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28 pages, 1185 KiB

Open AccessReview

A Review of Water Quality Forecasting Models for Freshwater Lentic Ecosystems

by Jovheiry Christopher García-Guerrero, José M. Álvarez-Alvarado, Roberto Valentín Carrillo-Serrano, Viviana Palos-Barba and Juvenal Rodríguez-Reséndiz

Water 2025, 17(15), 2312; https://doi.org/10.3390/w17152312 - 4 Aug 2025

Abstract

Water quality (WQ) monitoring is critical for Mexico and the world due to water pollution and scarcity problems in recent years. In this article, a systematic review was conducted considering only forecasting models focused on lentic freshwater bodies (to specialize the analysis of [...] Read more.

Water quality (WQ) monitoring is critical for Mexico and the world due to water pollution and scarcity problems in recent years. In this article, a systematic review was conducted considering only forecasting models focused on lentic freshwater bodies (to specialize the analysis of variables, problems, considerations, etc.) from 2019 to 2025 (to ensure the inclusion of the most relevant and new studies). This review analyzes 52 articles focused on the monitoring place, predictors, forecasted variables, configuration of each forecasting model, results with or without multiple forecast horizons, monitoring conditions, forecasting horizon, data availability, and model replicability. Our review shows that the main models documented used to predict WQ are based on machine learning (where RFs are the most used), AI (where ANNs are the most used and LSTM-based architectures are the most implemented), and statistical methods (where MLR is the most used). The principal forecasted WQ variables are Chl-α, DO, and TP. In comparison, the most used predictors are TP, temperature, and Chl-α. Furthermore, only 10 articles have made their databases available, and nine articles share the configuration of their models. Future research should investigate the real impact of data (quantity and inputs) variation in forecasting values for multiple forecast horizons. Full article

(This article belongs to the Special Issue Intelligent Water Management: Machine Learning, Remote Sensing, Data Analytics, Predictive Modeling, and the Path to Sustainability)

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