Study on Joint Flood Control and Peak Shaving Optimization Operation of Reservoir Groups Based on Flood Operation Phase Division - Min Li; Xiaoye Han; Yonggao Yao; Wei Ding; Qianning Wang; Jianhua Luo; Feilin Zhu (Water Resources Management, 2025)

From Local to Regional: Deep Learning Models for Daily Water Discharge Forecasting in a Data-Scarce Basin and Engineered River - Nguyen Hao Quang; Nguyen Van An; Tran Quoc Viet (Water Resources Management, 2025)

Improving Streamflow Forecasting Efficiency Using Signal Decomposition Approaches - Dinesh Kumar Vishwakarma; Salim Heddam; Arpit Gaur; Ravindra Kumar Tiwari; Ozgur Kisi; Anurag Malik; Chetak Bishnoi; Abed Alataway; Ahmed Z. Dewidar; Mohamed A. Mattar (Water Resources Management, 2025)

Insights into Robustness as a Criterion to Support Cost-benefit Analysis of Supply-enhancing Investments Under Uncertainty Part I – Shaping the Uncertainty Environment - Claudio Arena; Marcella Cannarozzo; Giorgio Gullotti (Water Resources Management, 2025)

Towards More Efficient Urban Flood Assessment: Issue of Spatial Resolution in Urban Flood Hydrodynamic Modeling from Flood Exposure Perspective - Yun Xing; Dong Shao; Qigen Lin; Irfan Ullah; Junsheng Wang; Yuezhu Wang (Water Resources Management, 2025)

Adaptation of Urban Drainage Networks to Climate Change for Sustainable Mass Rapid Transit (MRT) Development - Rosidha Febriana; Jiun Hor Low; Izni Zahidi; Kai Siang Oh (Water Resources Management, 2025)
Mass Rapid Transit (MRT) systems lead to flood risks by altering land use and increasing impermeable surfaces. Existing drainage systems often fail to address the challenges of climate change, urban flooding, and sedimentation. This study evaluates the effectiveness of a sustainable drainage system (SuDS) designed for the UPM-Serdang MRT station in Malaysia, compared to pre- and post-MRT drainage systems. Hydrodynamic modelling was employed to simulate flood depth, extent, and sedimentation for several scenarios. The SuDS, integrating permeable pavement and bioretention facilities, achieved a flood risk score of 14%, significantly outperforming post-MRT (33%) and pre-MRT (54%) systems. It yielded a benefit–cost ratio of 3.03, surpassing the post-MRT (1.01) and pre-MRT (0.31) designs. The findings demonstrate how SuDS can mitigate floods up to a 200-year return period and reduce sedimentation. This study addresses the gap in MRT drainage research by introducing a novel framework that integrates hydrodynamic modeling, with engineering assessment and benefit–cost analysis to support sustainable, climate-resilient infrastructure design.

An Adaptive Rolling Runoff Forecasting Framework Based on Decomposition Methods - Linan Yu; Xu Wang; Jia Wang (Water Resources Management, 2025)

A Two-stage Stochastic Bi-level Model and a Decomposition Algorithm for the Valve Location Problem in Water Networks - F. Hooshmand; S. A. MirHassani; M. Jamalian (Water Resources Management, 2025)

MoSWIN: Monkey Search Optimization and SWIN-based Flood Classification Architecture - Vinay Dubey; Rahul Katarya (Water Resources Management, 2025)

Flood Inundation Mapping Using HEC-RAS 2D Modeling and Examining the Impact of Changes in the Model-Meshing Pixel Scale on the Final Output - Bosy A. El-Haddad; Ahmed M. Youssef; Zeinab Karimi; Hamid Reza Pourghasemi (Water Resources Management, 2025)

Artificial Intelligence Prediction of Water Quality of Complex Urban River Networks - Guohao Li; Qingqing Zhang; Hui Du; Xinbao Yun; Xue-yi You (Water Resources Management, 2025)

Statistical Analysis of Global Dam Accidents in the 21st Century: A Focus on Common Features and Causes - Reem Nasser; Dimitris Tzioutzios; Yiliu Liu (Water Resources Management, 2025)
Effective water resource management often involves constructing dams to provide numerous benefits, yet mismanagement of these structures can lead to serious consequences, including fatalities, infrastructure damage, and environmental harm. As climate change intensifies, the risk of dam failure is expected to rise. This study provides a comprehensive global analysis of dam failures in the 21st century, examining the characteristics, causes, and impacts of these incidents. A detailed dataset of 53 operational dam failures was compiled, categorizing each case based on parameters such as seasonality, dam age, height, capacity, and primary and secondary causes of failure. The findings of the analysis reveal that most failures occurred in intermediate, non-aging dams, with human-related factors representing the predominant causes. Statistical analysis of cases worldwide highlights poor management, design and construction flaws, and meteorological factors—largely influenced by climate change—as the leading contributors to dam failures. By presenting these findings, this study aims to inform policymakers and stakeholders of the critical need for proactive risk mitigation, guiding them in prioritizing actions to reduce dam-related risks. The results also highlight the need to revise safety standards and implement targeted measures to reduce the risk of dam failures effectively. Graphical Abstract

Performance of ERA5 Products in Capturing Pluvial and Fluvial Inundation: A Statistical-Cum-Hydrodynamic Framework for Resource-Constrained Large Flood-Prone Watersheds - Hrishikesh Singh; Mohit Prakash Mohanty (Water Resources Management, 2025)

Water Quality Monitoring Strategy for Sustainable Hydropower Use: SWOT-Based Framework for Identification of Key Drivers in Environmental Management of Small Hydropower Cascades - Yovana Todorova; Ivaylo Yotinov; Irina Schneider; Mihaela Kirilova; Nora Dinova; Stilyana Lincheva; Yana Topalova (Water Resources Management, 2025)

WQI-Based Algorithmic Decision-Making for Water Withdrawal Depth in Drinking Water Reservoirs - Haluk Bayram; Elif Soyer (Water Resources Management, 2025)
Determining optimum withdrawal depth for selective water withdrawal is crucial for managing the water quality of withdrawn water or reservoir. In this paper, we consider non-stratified periods, relatively less studied, and develop an algorithmic decision-making system based on water quality index. Our approach takes three inputs (depth profile data, current withdrawal depth, and weights in water quality index) and determines the water withdrawal depth. Finding the weights associated with these parameters in the index is formulated as an optimization problem in which the matching between our recommended depth and operator’s decision is maximized. Historical operator decisions over a 14-year period from a real water treatment plant were used to optimize these weights. We utilized surrogate optimization, genetic algorithm, pattern search, and brute-force solvers for this optimization problem. Brute-force solver achieved a 93.8% matching accuracy in predicting the water withdrawal depth. As the other three solvers require an initial solution, which can influence the quality of their results, the brute-force solver was selected as the most reliable approach. Our approach accurately predicts the decisions made by water treatment plant operators regarding water withdrawal from a reservoir with a multi-level intake structure during periods without thermal stratification.

Quasi-Global Soil Moisture Retrieval Using Spaceborne CYGNSS Data with the Stacking Fused Machine Learning Model - Yongchao Zhu; Jiangyang Li; Maorong Ge; Xiaochuan Qu; Tingye Tao; Shuiping Li; Fei Gao (Water Resources Management, 2025)

A Dynamic Time-varying Distributed Unit Hydrograph Considering the Heterogeneity of Runoff Generation - Bin Yi; Lu Chen; Binlin Yang; Zhiyuan Leng; Siming Li; Tao Xie (Water Resources Management, 2025)

Quantifying the Impact of Land Use/Land Cover Change Scenarios on Water-Related Ecosystem Services - Helen Aghsaei; Asef Darvishi; Maryam Yousefi; Michael Schirrmann; Nicola Fohrer; Paul Daniel Wagner (Water Resources Management, 2025)

Machine Learning-based Downscaling of GRACE Data to Enhance Assessment of Spatiotemporal Evolution of Coastal Plain Groundwater Storage - Chengcheng Wu; Chengpeng Lu; Jingya Hu; Bo Liu; Longcang Shu; Yong Zhang (Water Resources Management, 2025)

Long-term Simulation of Gross Primary Productivity and its Impact Evaluation Based on a Mechanism and Data Co-Driven Model - Xiaojing Zhang; A. Yinglan; Guoqiang Wang; Yuntao Wang; Min Shi; Jiping Yao; Qingqing Fang; Libo Wang; Guangwen Ma (Water Resources Management, 2025)

Reaction of Minimal Streamflow to Natural Factors in the Context of Climate Uncertainty - Lyazzat Birimbayeva; Lyazzat Makhmudova; Sayat Alimkulov; Aisulu Tursunova; Dimitris Tigkas; Nurlan Abayev; Aigerim Dostayeva; Zhomart Birimbayev; Oirat Alzhanov; María-Elena Rodrigo-Clavero; Javier Rodrigo-Ilarri (Water Resources Management, 2025)
This study analyzes long-term changes in minimal streamflow in the Zhaiyk-Caspian Water Management Basin (WMB), Western Kazakhstan, under climate variability. Using extensive hydrometeorological datasets (monthly discharge and daily meteorological records) from 1940 to 2021, the research assesses trends in low-flow characteristics across 18 hydrological posts. The analysis distinguishes two climatic periods: climate stabilization (pre-1973) and climate change (1974–2021). The methodology integrates hydrological and statistical analyses, including minimal monthly discharges, low-flow durations, soil freezing depths, and thaw frequencies. Findings reveal a widespread increase in winter low flows—up to 5.2 times—due to reduced frost depths and more frequent thaw events, enhancing groundwater contributions. Conversely, summer-autumn flows declined in several rivers, with drying trends linked to rising air temperatures and precipitation deficits. This study offers a novel, regionally adapted methodology for characterizing minimal streamflow under climate change, providing critical insights for hydrological drought assessment and water resource planning in arid and semi-arid environments.

Improving Bridge Safety: A Spider Monkey Optimization-based ANN Model for Scour Depth Prediction - Saad Sh. Sammen; Ata Amini; Kaywan Othman Ahmed; Tayeb Sadeghifar; Jagalingam Pushparaj; Sujay Raghavendra Naganna (Water Resources Management, 2025)
Hydraulic engineering research has long focused on understanding and predicting scour depth around bridge piers, a critical factor in maintaining structural integrity of bridges. This study delves into applying soft computing methods, specifically machine learning algorithms, to model and simulate local scour depth around simple piers. Leveraging a robust dataset compiled from various sources and utilizing five distinct models, including Artificial Neural Networks (ANN), Gradient Tree Boosting (GTB), and CatBoost Regression (CBR), the research aims to accurately predict pier scour depth and assess the impact of different variables on the estimation process. Additionally, to enhance estimation accuracy, the neural network weights were optimized using the Spider Monkey Optimization (SMO) and Particle Swarm Optimization (PSO) methods. Using mutual information (MI) as a feature selection method, the study reveals the critical role of specific features in enhancing the precision of scour depth predictions. Through a comprehensive analysis of model performance metrics, the study highlights the efficacy of the SMO-based ANN model for accurately predicting scour depth. Furthermore, through a detailed evaluation using the Taylor diagrams, the study provides an insightful comparison of the predictive capabilities of the hybrid machine learning models, shedding light on their respective errors and accuracy in estimating scour depth around bridge piers.

Comparative Trend Analysis of Suspended Sediment Concentration and Streamflow: A Multi-method Framework - Kiyoumars Roushangar; Arman Alirezazadeh Sadaghiani; Vahid Nourani (Water Resources Management, 2025)

Deep Neural Networks Hydrologic and Hydraulic Modeling in Flood Hazard Analysis - Alaa Hawamdeh; Anwar Tarawneh; Yousef Sharrab; Dimah Al-Fraihat (Water Resources Management, 2025)

The Application of Artificial Intelligence in Predicting the Effect of Gravel and Vegetation Cover on the Urban Runoff Volume Using Experimental Data - Hamidreza Ghazvinian; Hojat Karami (Water Resources Management, 2025)

A Novel Hybrid Model for Hourly Streamflow and Water Level Prediction from Radar Reflectivity Using Deep Learning Approaches - Thi-Linh Dinh; Dai-Khanh Phung; Hyun-Han Kwon; Deg-Hyo Bae (Water Resources Management, 2025)

Development and Evaluation of a Principal Component-Based Composite Drought Index Considering Temporal Lag Dependencies Among Indices - Tayeb Raziei; Morteza Miri; João Filipe Santos; Mehran Zand; Luis S. Pereira (Water Resources Management, 2025)

Application of the ResNet-Transformer Model for Runoff Prediction Based on Multi-source Data Fusion - Shibang Zhu; Zhaocai Wang; Wenting Zhang; Jingqi Yang (Water Resources Management, 2025)

Forecasting 21st Century Hydrological Challenges in Arid Regions: Climate-induced Shifts in Runoff and Evapotranspiration - Sajjad Hussain; Burhan Niyazi; Amro Elfeki; Milad Masoud (Water Resources Management, 2025)

An Integrated Framework for Pluvial Flooding and Population Vulnerability Assessment Based on agent-based Approach with Urbanization Forecasting at Urban Scale - Xinyi Shu; Chenlei Ye; Zongxue Xu (Water Resources Management, 2025)

Reconstructing Missing Streamflow Data in Mountainous, Snow-Dominated Regions Using Optimized Hybrid Machine Learning - Burak Çırağ; Reşat Acar; Okan Mert Katipoğlu; Mete Yağanoğlu; Selim Şengül (Water Resources Management, 2025)
Accurate streamflow estimation is pivotal for hydrological research and water resource management, particularly in mountainous, snow-dominated regions where missing data can significantly impair watershed modeling and water budget evaluations. This study presents an advanced hybrid machine learning framework to impute missing streamflow records at a strategically located observation station within a snow-abundant sub-basin of the Euphrates-Karasu Basin in Erzurum Province, Eastern Turkey. Leveraging daily meteorological and hydrological data collected over four decades (1983–2023), we developed and rigorously evaluated several predictive models using comprehensive performance metrics and rank-based analysis. In addition to conventional approaches-including a single Artificial Neural Network (ANN), CatBoost, and Deep Neural Network (DNN)-the study also compares innovative hybrid models in which ANN parameters were optimized using the Whale Optimization Algorithm (WOA) and the Hydrologic Cycle Algorithm (HCA). Notably, the hybrid models demonstrated superior predictive accuracy over traditional methods, thereby underscoring their potential to bridge data gaps in challenging terrains. The results affirm the efficacy of the proposed framework and its promise as a robust methodological foundation for future investigations in watershed modeling, drought assessment, and water budget analysis.

Enhancing Snow Detection through Deep Learning: Evaluating CNN Performance Against Machine Learning and Unsupervised Classification Methods - Mustafa Hayri Kesikoglu (Water Resources Management, 2025)
Snow cover is a crucial natural water resource that provides water storage. Monitoring snow cover is essential for sustainable water resource management. Accurate and efficient snow cover mapping is vital for water resource planning, flood control, and climate impact assessments. Remote sensing provides a cost-effective approach for monitoring snow cover dynamics, but classification accuracy remains a challenge. This study introduces a deep learning-based 1D convolutional neural network (1D CNN) approach for improved snow cover classification in mountainous regions utilizing Landsat 5 and 8 satellite images. The effectiveness of 1D CNN was compared with unsupervised classification (UC) methods (Normalized Difference Snow Index (NDSI), band ratio-based Red/Short wave infrared (R/SWIR), and Near infrared/Short wave infrared (NIR/SWIR)) and supervised machine learning classification methods (support vector machines (SVM) and classification and regression trees (CART)) in this research. The study was performed on Erciyes Mountain, Turkey, across four different years. Classifier performances were assessed using various quantitative (precision, recall, F-score, overall accuracy, kappa) and qualitative (mean squared error, Pearson correlation coefficient, structural similarity index measure, peak signal-to-noise ratio) evaluation metrics. Pairwise statistical analysis using McNemar’s test revealed significant differences between 1D CNN and the other classifiers. The findings demonstrated that the 1D CNN approach outperformed all other methods in achieving the highest accuracy in snow cover classification. These results indicate the potential of deep learning for enhancing snow cover assessment and supporting more effective water resource management strategies and policy-making.

Spatio-Temporal Variability of Hydrological Drought and Trends: Implementation of Classical and Innovative Approaches - Ibrahim Halil Deger; Mehmet Ishak Yuce; Musa Esit (Water Resources Management, 2025)
Drought is known as natural hazard having negative potential effects on water resources, economy, sustainability and energy. Hydrological drought is defined as deficiency of surface or subsurface water. Drought is insidious and potentially harmful to the environment and socio-economy, which motivates researchers to monitor and develop new strategies in order to manage possible negative effects. In this research, a detailed hydrological drought monitoring study considering spatio-temporal variability has been performed by employing Streamflow Drought Index (SDI) and mean monthly streamflow records of 36 stations for the standard time scales of 1-, 3-, 6-, 9- and 12-months in the Euphrates Basin which covers the $$\:\sim$$ 16% of Turkey is one of the most important basins in the country in care of water resources availability and hosting many production sectors and cultural heritage. Spatial distribution of drought and wet categories have been analyzed by using Inverse Distance Weighting (IDW) approach. Trends in the time scales are observed by Mann-Kendall Test, Spearman’s Rho Test, Wilcoxon Test, Innovative Trend Significance Test and Sen’s Slope Estimator. Graphical Innovative Trend Analysis are employed to examine temporal trends in drought and wet periods. Results have indicated that the mild drought and wet periods are the most repetitive categories based on percentage of occurrences. Different parts of the basin seem to have experienced droughts on the different standard time scales. Decreasing trends are noted to be widespread across the basin. The findings of this research are expected to contribute a better understanding of hydrological drought dynamics in Euphrates Basin that plays critical role in water resources and the sustainability of life in the region.

Hydrological Assessment of Uttarakhand Using the Budyko Framework and Geospatial Analysis: Insights for Sustainable Water Resource Management - Milan Samanta; Saurabh Kumar Gupta; Shruti Kanga; Suraj Kumar Singh; Pankaj Kumar; Gowhar Meraj; Tarig Ali; Mohamed Yehia Abouleish (Water Resources Management, 2025)

A Novel Approach for Water Resources Management to Accurate River Flow Prediction: Utilization of Linear Programming and Weighted Ensemble Learning - Mojtaba Poursaeid (Water Resources Management, 2025)

Evaluating Rainfall-Runoff Generation Mechanisms of Deep Learning Models Using a Process-Based Rainfall-Runoff Model - Toan D. Duong; Vinh Ngoc Tran; Tam V. Nguyen (Water Resources Management, 2025)

Improving the Accuracy of Groundwater Level Forecasting by Coupling Ensemble Machine Learning Model and Coronavirus Herd Immunity Optimizer - Ahmed M. Saqr; Veysi Kartal; Erkan Karakoyun; Mahmoud E. Abd-Elmaboud (Water Resources Management, 2025)
Groundwater levels are under severe pressure globally due to over-extraction, pollution, and climate change necessitating continuous monitoring for sustainable aquifer management. This study introduces a novel ensemble machine learning (En) model that integrates shallow and deep machine learning (ML) models, optimized through the coronavirus herd immunity optimizer (CHIO), for accurate groundwater level forecasting. This En model was applied to the Ergene River Basin, Türkiye, a region facing severe groundwater depletion and contamination due to intensive agricultural and industrial activities. Groundwater level data spanning 1966 to 2023 on a weekly basis from four wells were used, split into 70% for training and 30% for testing under short- and long-term scenarios. Using the partial autocorrelation function and gamma test the best lag numbers were determined for input data, reflecting aquifer heterogeneity. Score analysis, supported by statistical metrics such as the coefficient of determination (R²) and root mean square error (RMSE), was employed alongside visual aids to assess the developed En model performance. Results demonstrated that deep ML models outperformed shallow ML models achieving R² ~ 0.99 and RMSE ~ 0.5 m. The developed En model outperformed all individual ML models, with score values exceeding 200, and its predictions closely aligned with measured water levels during both testing phases. The findings underscored the developed En model’s contribution to achieving sustainable development goals (SDGs) by enhancing water-use efficiency and addressing environmental, economic, and social sustainability challenges. The proposed approach offers a reliable and adaptable solution for groundwater level forecasting, applicable to other aquifers worldwide. Graphical Abstract

Influence of Long-term Irrigation with Reclaimed Water on the Soil Quality of Different Land Use Types - Zhuo Tian; Dongli She; Hongde Wang; Xiaoqin Sun; Kai Fang; Chao Qiu; Yu Li (Water Resources Management, 2025)

Defining the Most Appropriate Water Network Management Plan with Different Optimization Algorithms for Sustainable Water Management - Salih Yilmaz; Abdullah Ateş; Mahmut Firat; Hüseyin Cinal (Water Resources Management, 2025)
Leakages, operational difficulties, operating and investment costs are the main problems faced by utilities. The aim of this study is to define the most appropriate network management plan with Ant Lion Optimizer (ALO), Mountaineering Team-Based Optimization (MTBO), and Grey Wolf Optimizer (GWO) optimization algorithms. A novel optimization model is developed and tested with field data by considering the current status, economic and technical capacity of the utilities. This model can determine the optimal values ​​of key components in water loss management (WLM) based on current status. The economic level of losses for System I were calculated as 17.10% (ALO), 19.17% (MTBO), and 19.26% (GWO). The ideal level of losses for System II was defined as 17.23% (ALO), 19.50% (MTBO), and 16.83% (GWO). The economic level of losses for System III were determined as 20.89% (ALO), 23.91% (MTBO), and 24.27% (GWO). The most appropriate management plans and methods are generated to achieve the ideal loss levels for each system by the optimization model. It is thought that this model will make a significant contribution to decision makers in terms of WLM.

An Integrated Model for Water Resource Allocation, Distribution and Management: A Genetic Algorithm-Based Optimisation Model - Shama Parween; Rajan Chandra Sinha (Water Resources Management, 2025)

Deriving Future Rainfall Depth-Duration-Frequency Curves from Hourly Regional Climate Projections and Simple Scaling in Sicily - Gaetano Buonacera; Nunziarita Palazzolo; Antonino Cancelliere; David J. Peres (Water Resources Management, 2025)
Climate change is increasingly recognized as a major driver of extreme rainfall intensification worldwide. Quantifying its effects on extreme rainfall, expressed through depth-duration-frequency curves (DDFs), is crucial for designing flood protection infrastructures. In this study, we propose a methodology for deriving future DDFs under RCP 4.5 and RCP 8.5 scenarios for the near (2021–2050), middle (2041–2070), and far future (2071–2100). Hourly climate projections at the regional scale from three EURO-CORDEX CMIP5 models are used to derive annual maxima series (AMS) at durations of 1, 3, 6, 12, and 24 h for historical and future periods, enabling the computation of change factors by comparing the two. A simple scaling assumption is used for the derivation of DDFs. These change factors are applied to observed precipitation averages and the moments of the observed dimensionless rainfall depth series, which are then used for parameter estimation of AMS probability distributions according to the method of moments. The approach is tested at four locations in Sicily spanning distinct climatic subregions. Results indicate an overall increase in future DDFs, particularly under RCP 4.5 and in the far future, with changes ranging from approximately 30–70% relative to current conditions, despite significant climate model uncertainty implying variations of up to 80 mm across models. These increases imply that a 50-year DDF in the future climate corresponds to a return period exceeding 100 years in the control period, reaching up to 300 years in some scenarios. Despite the uncertainty, these findings further underscore the importance of integrating future climate scenarios into flood protection design.

Enhancing Rainfall-Runoff Simulation in Data-Poor Watersheds: Integrating Remote Sensing and Hybrid Decomposition for Hydrologic Modelling - Mostafa Riazi; Sayed M Bateni; Changhyun Jun; Aitazaz Ahsan Farooque; Khabat Khosravi; Soroush Abolfathi (Water Resources Management, 2025)

Evaluating the Impact of the Spatial Resolution of Digital Elevation Models on Flood Modelling - Ekundayo Abayomi Adesina; Joseph Olayemi Odumosu; Oluibukun Gbenga Ajayi; Ahmed Musa; Ifeanyi Chukwudi Onuigbo; Adeolu Richard Adesiji (Water Resources Management, 2025)

Reconstructing Reservoir Water Level-Area-Storage Volume Curve Using Multi-source Satellite Imagery and Intelligent Classification Algorithms - Xu Gui; Qiumei Ma; Jiqing Li; Zheng Duan; Lihua Xiong; Chong-Yu Xu (Water Resources Management, 2025)

Urban Flood Inundation Area Detection using YOLOv8 Model - Fengchang Xue; Yannian Cheng; Yufang Shen; Jianfei Chen; Jiaquan Wan (Water Resources Management, 2025)

The Effect of Lining Hydraulic Properties on the Efficiency and Cost of Irrigation Canal Reconstruction - Martina Zeleňáková; Aybek Arifjanov; Hany F. Abd-Elhamid; Marcela Bindzárová Gergeľová; Alisher Fatxulloyev; Davronjon Allayorov (Water Resources Management, 2025)
Irrigation canals play an important role in the economic development of Uzbekistan which is located in arid zone and shared user of transboundary water resources. Almost 75% of the irrigation network in Uzbekistan is unlined that subject to some natural processes such as vegetation, erosion, sedimentation, and seepage. The efficiency of the irrigation networks in Uzbekistan is about 63%. Lining of canals is the major protection measure against water scarcity for Uzbekistan. This study aims to investigate how the hydraulic properties of different lining materials such as geomembrane, concrete, asphalt, sand mixed bentonite and stone pitching affect the rehabilitation cost of canals. In this study, field data and hydrodynamic model HEC-RAC are applied for analyzing the use of different lining materials on the efficiency of the canal and then the costs of rehabilitation were compared. To apply the hydrodynamic model HEC-RAS, the flow and bathymetric parameters were measured by employing the acoustic Doppler current profiler system for a reach of length 1.0 km in the Tashkent magistral canal. The obtained R 2 between modeled and measured data equals to 0.86. The use of different lining materials such as geomembrane, concrete, asphalt, sand mixed bentonite and stone pitching affect the channel geometry with different ranges from 21.69 to 34.41 per running meter (RMT). With different cross-sectional values, rehabilitation of the first reach of Toshkent magistral canal costs about $ 260, 688, 536, 286, 210 thousand respectively. For the point of lifetime, geomembrane then concrete, and stone pitching showed the minimum annual coverage value of $ 32, 46, 10 thousand per year, respectively. Stone pitching with the longest durability is the most efficient technique to improve water-use efficiency of Toshkent magistral canal. This study could help the policy makers to select the best material for lining based on the lifetime, cost and durability. Moreover, the efficiency of the irrigation networks will increase that play important role in water resources management.

An Interpretable Hybrid TCN-BiLSTM Model for Reference Evapotranspiration Prediction - Zehai Gao; Xiaojun Zhang; Zijun Gao; Beibei Zhou; Yuwei Liu; Dingmin Liu; Xiaotao Ma (Water Resources Management, 2025)

Effect of Hydro-geomorphological Environments on Surface Water Areas Extraction - Yuan-Yuan Jia; Huan-Feng Duan; Xu-Feng Yan (Water Resources Management, 2025)

Evaluating the Long-Term Performance and Economic Benefits of SUDS Using Continuous Simulation - Valentina Cerda; Osheen; Jorge Gironás (Water Resources Management, 2025)

Modelling & Assessment of the Impact of Water Network Construction on Mitigating Regional Water Supply–Demand Conflicts: Strategies Approaches for Water Resources Planning and Management - Can Cui; Zengchuan Dong; Yalei Han; Li Ren; Xinkui Wang; Shuyang Si (Water Resources Management, 2025)

SWAT+ Model Enhanced with Dynamic Phenology Remote Sensing and High-Precision Precipitation Data for Water Resource Vulnerability Assessment in Semi-Arid Regions - Qingyang Zhang; Junhu Wu; Weibin Liu; Tianle Wang; Yichao Wang (Water Resources Management, 2025)

A Systematic Review of Flood Damage Assessment: Insight for the Data-Scarce Regions - Noor Suraya Romali; Sumiliana Sulong; Akiyuki Kawasaki (Water Resources Management, 2025)

An Innovative Regional Frequency Analysis Approach for Robust Extreme Precipitation Assessment in Data-rich and Climatically Diverse Regions - Mehdi Mahbod; Azade Ebrahimiat; Mahmood Mahmoodi-Eshkaftaki; Mohammad Rafie Rafiee (Water Resources Management, 2025)

Enhancing Monthly Streamflow Prediction with LSTM-P and ANN-P Models using Statistical Feature-Based Penalty Factors - Zifan Xu; Hao Zheng; Hong Zhang; Xuguang Wang; Xinzhe Xu; Peng Liu; Suzhen Feng; Jinwen Wang (Water Resources Management, 2025)

QoS-Aware and Energy-Optimized Cooperative Opportunistic Routing Protocol for Underwater Wireless Sensor Networks in Water Resource Management - Sabeen Arain; Intesab Hussain Sadhayo; Sarang Karim (Water Resources Management, 2025)

Harvesting of Condensate Water from Air Conditioners in Large Institutions as a Sustainable Resource - Amr Elbrashy; Kambiz Vafai; Abdullah Elshennawy; Manar Ayman; Ahmed Elgebaly; Maher Rashad (Water Resources Management, 2025)
The presented study aligns with global sustainability goals by integrating water harvesting with energy-efficient transactions. Infrastructure and energy in buildings are worth attention to tackle water scarcity and energy consumption management. This research highlights the dual benefits of enhancing water sustainability while optimizing energy use in air conditioning (AC) systems. The methodology includes investigating the potential of harnessing condensate water produced by a network of 113 AC units and a surface area of 3000 m 2 . All data have been collected and evaluated at the faculty of engineering at Horus University in New Damietta, Egypt. The greatest cumulative amount of water produced for one day was 51.24 L in June, 51.56 L in July, 55.65 L in August, and 53.32 L in September. By multiplying the average amount by the number of AC units in the building, the daily water production records 5977.7 L/day, approximately 6 m 3 . Although this number of air conditioners consumes 236 kW to operate, the operating cost is not considered since water is a byproduct. Therefore, the main cost can be represented by maintenance, renewal, and water treatment when used for drinking. Also, using this water in other applications without treatment, such as irrigation, cleaning, practical experiments, and engineering applications, provides a great return on investment. Graphical Abstract

Spatiotemporal Variation of the Water Resources Carrying Capacity Based on the PS-DR-DP Model - Shiwei Zhang; Tao Liao; Guohua Fang; Huayu Zhong; Maosen Ju; Dasheng Zhang (Water Resources Management, 2025)

A Self-Adaptive Population-Based Hybrid Optimisation Technique for Multireservoir Benchmark Problems - K. B. Baladaniya; P. L. Patel; P. V. Timbadiya (Water Resources Management, 2025)

Transboundary River Basin Eco-Compensation with Reward-Punishment Mechanisms: a Differential Game Perspective - Zuliang Lu; Mingsong Li; Lu Xing; Junman Li (Water Resources Management, 2025)

Study on SWMM Calibration and Optimizing the Layout of LID Based on Intellgent Algorithm: A Case in Campus - Kehan Liu; Jiake Li; Jiayu Gao; Xiaoqing Chi; Chunbo Jiang (Water Resources Management, 2025)

Probabilistic Models for Optimal Rainwater Harvesting Tank Sizing: a Comparison with Traditional Approaches - Maria Gloria Di Chiano; Mariana Marchioni; Gianfranco Becciu (Water Resources Management, 2025)
With growing water scarcity and increasing variability in rainfall patterns due to climate change, along with the pressures of urbanization and population growth, the adoption of non-conventional water resources such as rainwater harvesting and water reuse has become a crucial sustainable strategy. Traditional approaches to the design of rainwater tanks, based on the demand volume in a predefined dry period, don’t take into account properly the risk of failure. Two probabilistic methods are then proposed, to address this issue. The first method is a modification of the conventional “demand-side” approach, by the use of a probabilistic estimation of the inter-event time. The second one, more reliable and more complex, takes into account in a parametric way the full stochastic rainfall process, allowing to consider also the pre-filling possibility due to consecutive storm events. Complexity in this second method is managed in order to develop direct relationships for practical applications. Although enough simple, these methods improve the design of Rainwater Harvesting Systems (RWHs), allowing to apply cost-benefit analysis procedures. Methods are compared and evaluated through the application to a case study in Milan, Italy.

Mitigation Effects of Integrated Green-Grey-Blue-Control Systems on Urban Flooding in Plain Regions - Xiaolan Chen; Zhengjie Fang; Jun Liu; Yikai Chai; Haijun Yu; Hong Zhou; Yan Gao (Water Resources Management, 2025)

Water Resources Management in Coastal Areas Under Climate Change and Irrigation and Fertilization Scenarios - Aikaterini Lyra; Athanasios Loukas; Pantelis Sidiropoulos (Water Resources Management, 2025)
The study aims to analyze the effects of climate change, irrigation and nitrogen fertilization practices and water reserve strategies, on water resources, aimed at nitrogen use efficiency, groundwater nitrate pollution, groundwater budget, and seawater intrusion. Intensive groundwater abstraction for irrigation and nitrogen fertilization has led to a substantial water deficit, rising nitrate pollution, and seawater intrusion in the Almyros aquifer system. The analysis employs an Integrated Modelling System (IMS) to simulate coastal water resources, incorporating models for surface hydrology (UTHBAL), reservoir operations (UTHRL), groundwater hydrology (MODFLOW), nitrate leaching/crop growth (REPIC), nitrate pollution (MT3DMS), and seawater intrusion (SEAWAT) to capture the complex interactions between climate, hydrology, and agricultural practices. Climate projections for Representative Concentration Pathway 8.5 (RCP 8.5) from the Med-CORDEX (Mediterranean Coordinated Regional Downscaling Experiment) database for precipitation and temperature are bias-corrected with Empirical Quantile Mapping and used to estimate the effects of climate change. Irrigation scenarios, including base irrigation and deficit irrigation, as well as reduced nitrogen fertilization, are evaluated to determine their effects on water resources management and sustainability. The study’s results highlight a significant decline in water availability across climate models, with reduced runoff and groundwater recharge projected for the Almyros Basin. Increasing nitrate concentrations and chloride levels suggest worsening water quality, posing risks of seawater intrusion and nutrient pollution. The Nitrogen Use Efficiency index (NUE) improves under reduced nitrogen fertilization, supporting more efficient nutrient use and reducing excess nitrogen losses. The findings highlight critical water quality and quantity challenges, aligning mainly with Sustainable Development Goal 6, and following SDGs 9, 12, and 13 by promoting efficient resource use, pollution reduction, and climate resilience, guiding adaptation strategies for mitigating water scarcity under climate change.

Optimal Allocation and Evaluation of Regional Water Resources Integrated with Rural Revitalization Strategy - Qinghong Yang; Tao Bai; Dong Liu; Xiange Sun (Water Resources Management, 2025)

Real-Time Waterlogging Monitoring on Urban Roads Using Edge Computing - Zheng Sheng; Fan Chen; QiCheng Liu; BaoHua Gao; JiaJun Zhang; Kang Zhao; QingShan Liu; Ying Zang (Water Resources Management, 2025)

Improved Flood Forecasting using Numerical Weather Prediction Meteorological Forecasts with Integrated Rainfall-Runoff and Hydrodynamic Model - Archana Ramchandra Mohite; Amina Khatun; Chandranath Chatterjee; Rajendra Singh; Rakesh Kumar (Water Resources Management, 2025)

Spatio-Temporal Drought Assessment by Using the Innovative Probability of Drought Severity (PDS) Method - Ahmet Faruk Ipek; Ali Altas; Ugur Serencam; Ismail Dabanli (Water Resources Management, 2025)
This study examines drought variability across Türkiye from 1960 to 2020, utilizing data from 224 precipitation stations and the Standardized Precipitation Index (SPI) at three temporal scales, SPI-3, SPI-6, and SPI-12, to assess meteorological, agricultural, and hydrological droughts. Temporal analysis reveals two distinct periods, 1960–1990 and 1990–2020, both characterized by increasing drought frequency, particularly in the severe and extreme categories. While drought indices such as SPI, provides time series results, the technical limitations of simultaneously illustrating these time series on a map required the development of an innovative method. To address this challenge, the Probability of Drought Severity (PDS) method was introduced, enabling the spatial representation of derived outputs from the obtained time series. Spatial analysis highlights considerable variability in drought distribution, with shifting drought-prone areas complicating the definition of consistent regional boundaries. The results show higher drought probabilities in Southeastern Anatolia and the Mediterranean regions. Additionally, the study observes increased spatial heterogeneity under normal conditions over the last decade, with greater diversity in map values. Temporal evaluation of the data reveals that the probability of extreme droughts has risen significantly in 2010–2020 phase, reaching unprecedented levels (3,91%, 4,11%, and 4,43%) for SPI-3, SPI-6, and SPI-12, respectively.

Deployment Optimization of Pressure Sensors in Pipeline Network Via Fusing Shortest Path Planning and Monte Carlo Tree Search - Yupei Yan; Longyu Chen; Jinyu Ma; Jian Li; Zhoumo Zeng; Xinjing Huang (Water Resources Management, 2025)

Optimal Design of Water Transmission Pipelines Using a Metaheuristic Algorithm and Geoprocessing Techniques - Armando Dias Duarte; José Almir Cirilo; Rochele Sheila Vasconcelos; Abdeladhim Tahimi; Francisco Jácome Sarmento; Saulo de Tarso Marques Bezerra (Water Resources Management, 2025)

Comprehensive Assessment of Dam Failure Risk Consequences Based on TOPSIS-WRSR Coupled Model - Qiaogang Yin; Yanlong Li; Lifeng Wen; Ye Zhang; Ruiqing Li (Water Resources Management, 2025)

Deciphering the Variations in the Generalized Extreme Value Distribution Parameters in the Non-stationary Flood Frequency Analysis - Meera G. Mohan; Adarsh S (Water Resources Management, 2025)

Expert System for Updating the Rainfall Intensity Duration Frequency Curve Under Climate Change and Non-stationarity Over India - M. S. VishnuPriya; V. Agilan (Water Resources Management, 2025)

Can Time and Cost Efficiency Be Enhanced for GWQI Prediction Utilizing Machine Learning Modeling? - Farhan `Ammar Fardush Sham; Ahmed El-Shafie; Wan Zurina Binti Wan Jaafar; S. Adarsh; Mohsen Sherif; Ali Najah Ahmed (Water Resources Management, 2025)

Evaluation and Application of the PT-JPL Physical Model Optimized with XGBoost Algorithm in Latent Heat Flux Estimation - Lizheng Wang; Jinling Kong; Qiutong Zhang; Lixin Dong; Yanling Zhong (Water Resources Management, 2025)

The Impact of Temporal Rainfall Pattern Uncertainties on Water Quantity and Sediment Transportation Results of an Integrated Flood Simulator - Christos Ouzounis; Vasilis Bellos (Water Resources Management, 2025)
Meteorological dynamics are characterized by significant non-linearities and therefore, we cannot predict the long-term rainfall behavior. Hence, either the design of a new hydraulic work or the analysis of a current hydraulic system is performed using numerical models and simulating the impact of a synthetic storm at the ground, for a specific return period which is derived using the Intensity-Duration-Frequency curves of an area. However, the methodology used to produce synthetic storms, as well as the parameters incorporated in each methodology, are a source of uncertainty at the model output. In this work we investigate these uncertainties assuming water quantity metrics, such as the inundated area and water depths, and water quality metrics, such as the impact of sediment motion on the landscape transformation. To achieve this analysis, we use the well-known hydraulic software HEC-RAS, and specifically the Rain-on-Grid approach in a small catchment located in Xanthi (Greece). First, we perform a Morris-based Sensitivity analysis for the parameters of the Alternating Block Method (ABM), which is used to produce a synthetic storm, to screen the most influential parameters to the model output. Then, we perform an interval analysis for several rainfall disaggregation methodologies, to define the uncertainty band at the model output. It is found that the synthetic storm pattern and the parameters used for rainfall disaggregation create significant uncertainties. Moreover, it is found that the variables with quantitative vs. the corresponding with qualitative nature have different dynamics in terms of uncertainties produced by the model.

Evaluating the Impact of Transformation and Upgrading on the Green Efficiency of Industrial Water: Evidence from Sectoral Performance - Abderhim Ötkür; Qiangqiang Rong; Wencong Yue; Jianyu Zhang; Yulei Xie; Meirong Su (Water Resources Management, 2025)

Bayesian Inference for the Urban Drainage Models Uncertainty Quantification Based on Heteroscedastic Residual Assumption - Tingchao Yu; Shaosong Wei; Zhihong Long; Tuqiao Zhang; Shipeng Chu (Water Resources Management, 2025)

Three-step Merging of Daily Multi-satellite Rainfall Estimates Based on Probability Density Function Matching and Dynamic Bayesian Model Averaging - Yunyao Chen; Binquan Li; Maihuan Zhao; Tuantuan Zhang; Zhijun Wu; Xindai An (Water Resources Management, 2025)

Assessment of a Tailings Dam Breach by Experimental, Numerical, and Gene-Expression Programming Model - Arian Eghbali; Mehdi Soltanabadi; Mitra Javan; Omid Mohseni (Water Resources Management, 2025)

Water Quality Index and Sanitary Inspection as Tools to Assess Water Quality in Supply Systems - André V. C. Ribeiro; Guilherme V. Oliveira; Maicon de C. Machado; Paulo R. G. Barrocas; Jaime L. M. Oliveira (Water Resources Management, 2025)

Valley Dam Optimization Framework to Reduce Water-induced Conflict in Migratory Communities - Felix Obunguta; Kiyoyuki Kaito; Kiyoshi Kobayashi (Water Resources Management, 2025)
Effective management of valley dams, crucial for sustaining water resources for migratory communities, necessitates strategic planning to harmonize maintenance with intermittent usage patterns to reduce water-induced conflict risk. This paper presents a valley dam optimization framework aimed at preserving dam integrity while efficiently allocating scarce water resources. A gravity model was employed to quantify water-related migration and to identify high risk conflict areas. The proposed framework enables evaluation of dam improvement works and water supply effects including migrant flow that creates clash points. An example application in Karamoja-Turkana in East Africa provided water management insights for migratory populations. The model application results showed that it is optimal and more beneficial to prioritize dam management based on water resources rather than population as the former had lower costs and achieved zero migrant flow (stationarity) earlier, significantly reducing conflict risk. Collaboration with migratory communities in dam planning and monitoring is encouraged to optimize incomplete dam usage and water provision, promoting sustainability and community resilience.

Uncertainty Analysis of Machine Learning Methods To Estimate Snow Water Equivalent Using Meteorological and Remote Sensing Data - Mohammad Reza Goodarzi; Ali Barzkar; Maryam Sabaghzadeh; Meysam ghanbari; Nasrin Fathollahzadeh Attar (Water Resources Management, 2025)

Evaluating the Impact of High Source Variability and Extreme Contributing Sources on Sediment Fingerprinting Models - Borja Latorre; Ivan Lizaga; Leticia Gaspar; Ana Navas (Water Resources Management, 2025)
Sediment fingerprinting is a powerful tool used in drainage basin analysis to identify and quantify sediment sources, crucial for effective water management strategies. However, methodological debates persist regarding the influence of tracer type, tracer selection, and source dominance on fingerprinting model accuracy. This study introduces a novel linear variability propagation analysis (LVP method) to address and quantify potential bias in fingerprinting model outcomes, particularly when dealing with dominant or non-contributing sources and high source variability. We compare the results from two different models, Frequentist and Bayesian, to assess these effects using two datasets: the first one which was synthetically generated, and the other, obtained from a published laboratory study. Both datasets consisted of virtual mixtures. In such a way, uncertainties related to physical processes were eliminated, leaving only those which were introduced by mathematical or statistical methods. The comparison between theoretical and estimated apportionments from the synthetic dataset reveals systematic discrepancies in the results of both models when dominant or non-contributing sources coexist with high source variability. We analytically demonstrated that these deviations arise from the classical variability analysis used in both models. The proposed LVP method provides a means to quantify and mitigate these biases, offering a significant advancement for field fingerprinting studies where direct comparison with theoretical apportionments is not feasible. The laboratory dataset further validates these findings, revealing systematic deviations when non-contributing or dominant sources are present. Increasing the number of sources from 2 to 4 further enhanced the discrepancies that were observed.

Optimizing the LID Facility Layout Considering the Impacts of the Terrain Slope and Rainfall Frequency on the Implementation Cost - Faiza Chikhi; Chuancheng Li; Yu Jiang; Xilin Zhou (Water Resources Management, 2025)

Extra Dimensions to the Calibration of Hargreaves-Samani Equation Under Data-Scarce Environment - Ammar Ahmed Musa; Nadir Ahmed Elagib (Water Resources Management, 2025)
Evapotranspiration estimates are paramount for understanding climatology and better water management, especially in regions notorious for recurrent droughts, high evapotranspiration losses and basins with overspilling. This study adds new dimensions to the adjustment of the Hargreaves-Samani model (HS) against the standard FAO Penman-Monteith method for estimating reference evapotranspiration. The original coefficient (C = 0.0023) and the overall exponent (E = 1.0) in HS are calibrated and validated while splitting and exchanging of odd and even years’ datasets. Sudan and South Sudan are selected as a case of least studied and data-scarce countries though encompassing the largest part of the Nile basin with diverse hydroclimate zones. Implications of the proposed dimensions for the results and their usage in water management are discussed. Data splitting in the present manner reveals variation in the results between the different datasets, depending on the geographical location and the associated climate as well as the season. Thus, data splitting avoids bias towards certain mode of climate in a changing world and subsequent misinterpretation. Both C and E increase linearly with latitude from the dry sub-humid to the hyper-arid zone. The resulting latitude dependence offers interpolation and extrapolation of the constants across this large yet understudied region. Least calibration characterized the wettest months whereas largest calibration distinguished the transitional months towards the dry/cool season. Adjusting C is more suitable for the hyper-arid and semi-arid zones as well as for the hot and wet seasons. Calibrating E suits better the arid and dry sub-humid zones in addition to the dry/cool season. The present results (mis)match results reported in the literature for similar climate zones, thus opening venues for further studies elsewhere.

Interpretable Machine Learning for Predicting and Optimizing Pressure Extremes in Pipeline Water Hammer Effects Based on the Method of Characteristics - Yu Zhou; Yuhang Wang; Yujia Zhang; Wuyi Wan (Water Resources Management, 2025)

Dual-modality Encoder-decoder Framework for Urban Real-time Rainfall-runoff Prediction - Yuan Tian; Ruonan Cui; Wenlong Fu; Zhenyu Ao; Qi Han; Yi Xiang; Xinyu Lei (Water Resources Management, 2025)

Comprehensive Evaluation of Satellite-Based Rainfall Measurements Through Rain Gauge Validation Using Advanced Statistical Regression and Machine Learning Models by Using Python - K V Sumith (Water Resources Management, 2025)

Laboratory-Validated Model of Air Discharge at User Connections under Intermittent Water Supply - Marco Ferrante; Francesco Casinini; David Meyer (Water Resources Management, 2025)
The cyclic nature of intermittent supply subjects the network to frequent (partial) draining and filling. During filling, water displaces air in the pipes, and much of that air is discharged through user connections and water meters, causing over-reading and reliability issues. Important laboratory experiments have measured air discharge through meters during filling, but have not yet quantified or modeled the role of air compression. This paper revisits and augments these pipe-filing experiments, proposing, calibrating and interpreting a numerical model for the compressible flow of air during pipe filling. The rapid filling of a distribution-scale (1500 L) pipe compressed the air by 1.3 -2.5 fold, broke two-thirds of the tested water meters, and caused water meters to register 33-63% of the volumetric air flow as if it were water (500-1000 L). This partial registration was primarily caused by compression. Our results quantitatively relate air discharge to water meter over-registration, emphasizing the importance of gradual filling in intermittent systems. For meter protection, we recommend limiting filling speeds to less than the nominal flow rate of all downstream water meters.

A Pioneering DelugeNet Model with Optimization for Enhanced Urban Flood Detection and Analysis - G. Vasumathi; R. Vani (Water Resources Management, 2025)

Projecting Depth-Duration-Frequency Curves for Future Climate: a Case Study in the Mediterranean Area - Dario Treppiedi; Antonio Francipane; Leonardo Valerio Noto (Water Resources Management, 2025)
Depth-Duration-Frequency (DDF) curves are an essential tool in hydrological planning and risk management. However, the assumption of stationarity that is traditionally embedded in their derivation, is increasingly questioned by the impacts of climate change. This study focuses on adapting and projecting DDF curves for Sicily (Italy), which is experiencing an intensification of rainfall extremes, particularly for shorter durations. The proposed framework adapts the most up-to-date regional frequency analysis for the island by using an adaptation factor that incorporates the thermodynamic relationship between extreme precipitation and temperature, as well as future climate projections for temperature from an ensemble of regional climate models under the worst-case scenario. By the end of the century, the design rainfall estimates may require to be increased up to 50%, especially for hourly durations, to account for climate change effects. The results also highlight a strong spatial variability in the precipitation quantiles, with higher values observed in specific areas such as the north-eastern part of the island, which is characterized by small catchments and particularly prone to flash floods. Finally, this study provides a simple but still physical-based approach to updating DDF curves, that can be useful for engineers and practitioners, enhancing international efforts to mitigate climate change impacts through improved hydrological planning.

Adaptive Calibration of Nodal Demands of Water Distribution System by Extended Kalman Filter and Prior Information of Demand Pattern - Benwei Hou; Yanning Li; Yuchen Wu; Shan Wu (Water Resources Management, 2025)

A Pipeline Leakage Detection Technique Using Ground Penetrating Radar Polarization Method - Ran Wang; Fan Cui; Guoqi Dong; Xiaoxue Zhao; Qi Cheng; Guixin Zhang; Mengli Zhang (Water Resources Management, 2025)

Transferred Long Short-Term Memory Network for River Flow Forecasting in Data-Scarce Basins - Zaichao Xie; Wei Xu; Bing Zhu; Shiming Yin; Yi Yang; Xiaojie Li; Sufan Wang (Water Resources Management, 2025)

Numerical Simulation of Dynamic Process of Dam-break Flood and its Impact on Downstream Dam Surface - Shubing Dai; Yifan Wang; Jiaqi Guo; Ruihao Song; Zhaolin Shi; Shuya Yang; Zhe Zhang; Kuandi Zhang; Hansheng Liu; Sheng Jin (Water Resources Management, 2025)

Desilting Efficiency Assessment Under Four Hydraulic Sediment Prevention Operations - Fong-Zuo Lee; Yu-Yun Du (Water Resources Management, 2025)

Incorporating Catchment Attributes Grouping into Model Parameter Regionalization To Enhance Root Zone Soil Moisture Estimation - Hongxia Li; Yuting Zhao; Yongliang Qi; Yanjia Jiang; Elizabeth W. Boyer; Carlos R. Mello; Li Guo (Water Resources Management, 2025)

Assessing the Runoff Response to Vegetation Cover and Climate Change in a Typical Forested Headwater Watershed - Ge Zhang; Jiacong Xue; Wenting Liu; Yuntao Wang; Guoqiang Wang; Baolin Xue (Water Resources Management, 2025)

Framework for the Statistical Characterization of Supplied Water Age in Water Distribution Networks - Sarai Díaz; Javier González (Water Resources Management, 2025)
Water age or time of residence is frequently adopted as surrogate indicator for water quality in water distribution networks. High water ages, which are expected in terminal branches and meshed areas with low renovation rates, are associated with different physical, chemical and/or biological issues. However, the identification of critical areas is complex because it depends on the system design and associated demands, which are stochastic in nature. This work aims to develop a methodological framework to statistically characterize water age in water distribution networks. A detailed analysis of residence times is here performed at a parametric terminal branch inspired in a real case study. Water age statistical properties (mean and variance) are computed in this work from Monte Carlo simulations and compared for different network sizes, types of nodes and demand allocation strategies. Moreover, volumetric probabilistic curves for the Supplied Water AGE (SWAGE plots) are here defined and generated at each node. The practical interest of SWAGE plots (which provide the ratio of supplied water volumes with age above different threshold values) for the identification of critical areas justifies the necessity of defining a systematic framework for the STATistical characterization of Water AGE (STATWAGE). The potential and difficulties associated with the real implementation of STATWAGE are here discussed.

Extended Intuitionistic Fuzzy PROMETHEE II Group Decision Making for Mediterranean Basin Management - Thomas Bakas; Christopher Papadopoulos; Dionissis Latinopoulos; Ifigenia Kagalou; Mike Spiliotis (Water Resources Management, 2025)

Interactions and Integrated Optimization between Secondary Water Supply Devices and Drinking Water Distribution Systems: a Review - Yue Cao; Ying Zhang; Yifan Zhao; Chenchen Wang; Zhiguang Niu (Water Resources Management, 2025)

A Comparative Analysis of Machine Learning Models for Simulating, Classifying, and Assessment River Inflow - Ali Najah Ahmed; Nguyen Van Thieu; Kai Lun Chong; Yuk Feng Huang; Ahmed El-Shafie (Water Resources Management, 2025)

Integrating Geographic Information Systems and Multi-Criteria Decision Analysis for Evaluating Artificial Groundwater Recharge - Zahra Eghbali Lord; Ali Rasoulzadeh; Armin Abedi; Sharare Alikhani; Jesús Fernández-Gálvez (Water Resources Management, 2025)
The excessive exploitation of groundwater has led to a significant decline in water levels in recent years, emphasizing the need for sustainable water resource management strategies. Artificial groundwater recharge has emerged as an effective solution to address this challenge. This study integrates Geographic Information System (GIS) and Multi-Criteria Decision Analysis (MCDA) techniques to identify suitable areas for artificial groundwater recharge in the Ardabil plain, located in northwest Iran. Key parameters, including geology, slope, unsaturated zone thickness, soil texture, specific yield, drainage density, and land use, were analyzed. These parameters were weighted using three methodologies: Analytic Network Process (ANP), Analytic Hierarchy Process (AHP), and Fuzzy Analytic Hierarchy Process (FAHP). The final suitability map was developed by overlaying and combining the weighted information layers. The analysis revealed that 53.3%, 6%, and 42% of the plain area were classified as “very good” for artificial recharge according to the AHP, FAHP, and ANP methods, respectively. The southern part of the plain was consistently identified as a suitable area across all methods, characterized by pasture lands with young alluvial sediments, a deep unsaturated zone, gentle slopes, low drainage density, and high specific yield. To evaluate the performance of these methods, the results were cross-validated against natural recharge estimates, considering factors influencing water level fluctuations and recharge rates. Among the methods, ANP demonstrated the highest consistency with natural recharge estimates, making it the preferred approach.

A Review of Evapotranspiration Estimation Models: Advances and Future Development - Yayong Xue; Zhenshan Zhang; Xuliang Li; Haibin Liang; Lichang Yin (Water Resources Management, 2025)

Optimising from the Ground-Up: Spatio-temporal Leakage Modelling for Optimal Pressure Management in Water Distribution Systems - Natalia Persefoni Chela; Georgios Moraitis; Christos Makropoulos (Water Resources Management, 2025)

Effect of the Spatial Resolution of Digital Elevation Models on the Performance of Nash, Rosso, and Clark Instantaneous Unit Hydrograph Models - Fariba Esmaeili; Mehdi Vafakhah; Changhyun Jun; Sayed M. Bateni; Vahid Moosavi (Water Resources Management, 2025)

Soft Computing Techniques for Rainfall-Runoff Modeling and Analysis in River Basin - Pradeep Kumar Mishra; Rashmi Dwivedi (Water Resources Management, 2025)

Ensemble Framework for Multi-scale Runoff Interval Forecasting using Weight Combination and Reconstruction Strategy - Xi Yang; Min Qin; Zhihua Zhu; Zhihe Chen (Water Resources Management, 2025)

A Calculation Method for Flood Control Benefits of River Treatment Projects Considering the Uncertainty of Flood Peak and Flood Frequency - Yadong Zhang; Siwei Wang; Wei Ge; Zongkun Li; Haodong Li; Weibing Du; Hui Wang; Yongzhi Wang (Water Resources Management, 2025)

Annual Peak Runoff Forecasting Using Two-Stage Input Variable Selection-Aided k-Nearest-Neighbors Ensemble - Wei Sun; Decheng Zeng; Shu Chen; Miaomiao Ren; Yutong Xie (Water Resources Management, 2025)

Evaluating Discharge Coefficient of Rectangular Sharp Crested Weirs Using Machine Learning Models - Mrinmoy Dhar; Sanjog Chhetri Sapkota; Prasenjit Saha; Sameer Arora (Water Resources Management, 2025)

StocHastIc Early Leakage Detection System (SHIELDS) for Water Distribution Networks - Jose-Luis Molina; Carmen Patino-Alonso; Xi Wan; Raziyeh Farmani (Water Resources Management, 2025)
Effective leak detection in water distribution networks is essential to minimize water loss, reduce maintenance costs, and ensure sustainable resource management. This study introduces the Stochastic Early Leakage Detection System (SHIELDS), an innovative methodology employing Bayesian Networks (BNs) for probabilistic leak identification and localization. SHIELDS models a 10-node hydraulic network, selected for its balance between computational efficiency and system complexity, with nodes representing critical points where key variables (pressure, flow rate, velocity, elevation, head loss, and demand) are monitored at five-minute intervals over 31 days. A baseline dataset representing standard operational conditions was established. Synthetic leaks were then introduced at random points to analyze variations in flow, pressure, and velocity as leak indicators. SHIELDS accurately detected leaks, identifying a flow reduction of 13.13 l/s at minute 120. Under maximum demand, lower system pressure reduced leak volumes, with a flow reduction of 17.37 l/s, reflecting the pressure-dependent nature of leaks. The system also captured how leak effects propagate downstream, impacting unaffected nodes. By integrating spatial and temporal dimensions, SHIELDS supports real-time leak detection and proactive strategies. This approach advances artificial intelligence and machine learning applications in water management, providing a robust framework for efficient leak detection and mitigation.

A New Integrated Water Quality Evaluation Approach Combined with Different Evaluation Methods Based on Weighted Membership Degree - Won-Chol Yang; Jae-Bok Ri; Myong-Song Om; Jin-Sim Kim; Un-Ha Kim; Wi-Song Ri; Sun-Hak Sok (Water Resources Management, 2025)

Quantification of the Available Information of Hydrological Datasets: Stabilizing the Estimator of Multivariate Mutual Information - Evangelos Findanis; Athanasios Loukas (Water Resources Management, 2025)
The Available Information contained in a hydrological dataset represents the meaningful information per timestep that enters a rainfall-runoff model. To estimate this quantity, Independent Component Analysis (ICA) must be performed to transform the original observations into statistically independent signals. However, ICA algorithms may detect many sets of nearly independent signals suitable for this transformation, posing the question of which set is the optimal one. In the present paper, it is proposed that the components of the optimal set must share the least total pairwise mutual information among all sets since the mutual information of a pair of components is a measure of their statistical independence. This novel approach of estimating the available information of a dataset is applied to five basins in Thessaly, Greece and it is compared to the standard approach of taking the average value occurring from multiple ICA runs. It is illustrated that discarding ICA solutions with high total pairwise mutual information shared between their components stabilizes the estimator of Available Information and increases its precision. This comes with the cost of additional computation time since multiple evaluations of bivariate mutual information are needed.

Reliability Analysis of Water Distribution System using Benchmark Table - Suja S. Nair; P. L. Meyyappan (Water Resources Management, 2025)

Employing Artificial Intelligence to Improve the Accuracy of Hydraulic Jump Length Predictions in Water Engineering - Manal Gad; Hanaa Salem Marie; Ghada M. Abozaid (Water Resources Management, 2025)

Transforming Complex Water Quality Monitoring Data into Water Quality Indices - Nashwa A. Shaaban; David K. Stevens (Water Resources Management, 2025)
Unplanned urbanization and economic development can deteriorate water quality (WQ) and alter its beneficial usage. Continuous monitoring of biotic and abiotic parameters describing the WQ is essential to track changes and classify water resources to protect public health. Various invest significant effort, money, and time in monitoring programs. Using data from those sources is challenging due to the large number of observations, and inconsistencies in sampling time, date, stations, and gaps. This study aims to design different water quality index (WQI) models to provide policymakers, stakeholders, and water managers with a more comprehensive assessment by converting complex datasets from over 10 years, processed with the statistical software R, into consistent data sets. These datasets are then transformed into small principal components. WQ datasets of lakes and reservoirs in the western USA were chosen as case studies. The strategy of data processing is explained, and the results organized as a descriptive summary of the 12,000 observations for 31 parameters are discussed. Outputs of principal component analysis (PCA) are used to create relative and absolute WQI models for water irrigation usage and protecting cold- and warm-water species of game fish. Weighted arithmetic water quality indices are applied, and the relation between different models is examined.

Bayesian Stochastic Rainfall Generator (BSRG): Intelligent and Digital tool for Rainfall Forecasting through Bayesian Causal Modelling - Jose-Luis Molina; Carmen Patino-Alonso; Fernando Espejo (Water Resources Management, 2025)
Rainfall is probably the most uncertain natural variable. This research is mainly aimed to design an intelligent data-driven tool called Bayesian Stochastic Rainfall Generator (BSRG) for improving the rainfall forecast. There are several previous stochastic generators for rainfall data generation. However, there is not any designed through a Bayesian stochastic approach. The utilization of rainfall data from the Muñogalindo rain gauge in Ávila, Spain, is discussed in this study, offering insights into the region’s Mediterranean continental climate. The innovative methodology known as Hyetoclust, serves as the foundation for the subsequent development of the BSRG. Findings reveal that cluster-specific characteristics significantly influence rainfall patterns, expressed as probabilistic distributions, under both normal and extreme regimes. In normal conditions, Cluster 3 exhibits the most sensitive behaviour of Rainfall Intensity to the optimization (Max and Min) of Event Duration compared to Clusters 1 and 2. This highlights the heterogeneous nature of rainfall patterns and emphasizes the necessity of considering cluster-specific traits in modelling and forecasting. Conversely, under extreme rainfall conditions, clusters exhibit varied responses. Clusters 1 and 3 tend to have similar effectively impacts under optimization (Max and Min) scenarios, while Cluster 2 displays a more complex behavior. In general terms, cluster 2 is the least sensitive to a minimization of time duration. This emphasizes the nuanced nature, expressed as probabilistic distributions, of hydrological responses and reinforces the importance of cluster-specific analysis in optimizing strategies during extreme regimes.

From Meteorological to Hydrological Drought: A Focus on the Characterization of Hydrological Drought with Prediction Uncertainty - Frank Joseph Wambura (Water Resources Management, 2025)

Multimodal Deep Learning for Two-Year ENSO Forecast - Mohammad Naisipour; Iraj Saeedpanah; Arash Adib (Water Resources Management, 2025)

Resiliency through Applied Research into Emerging Constituents - Gregor R. Posadas; Joshua C. Baker; Abigail S. Ryan; Sondra M. Miller (Water Resources Management, 2025)
Emerging constituents (ECs), which are referred to as contaminants of emerging concern (CECs) or emerging contaminants, include pharmaceuticals and personal care products (PPCPs). ECs represent a growing challenge in wastewater treatment due to their persistence, environmental impacts, and potential health risks. This paper synthesizes findings from research focused on the behavior and fate of ECs within the Lander Street Water Renewal Facility (LSWRF). This research explores EC removal trends across treatment processes, including physical settling, biological treatment, and UV disinfection, highlighting varying efficiencies dependent on constituent characteristics. Key observations include significant reductions for some ECs, sequestering and release behaviors, and persistent concentrations of others. Temporal and spatial EC concentration variations within LSWRF underscore the influence of seasonality and facility-specific factors on treatment outcomes. The synthesis integrates these findings into a unified framework for understanding EC dynamics in water renewal facilities, emphasizing the need for resilient and adaptive infrastructure to mitigate their environmental and health impacts. This study provides critical insights to guide future research, policy development, and the design of sustainable water treatment systems.

Enhancing Water Level Prediction Using Ensemble Machine Learning Models: A Comparative Analysis - Saleh Alsulamy; Vijendra Kumar; Ozgur Kisi; Naresh Kedam; Namal Rathnayake (Water Resources Management, 2025)

Prioritization Blue-Green Infrastructure Strategies: Developing a Hybrid Quantitative Approach Model for Sustainable Urban Water Management - Sara Demir; Merve Dilman Gokkaya; Nazlı Deniz Ersoz (Water Resources Management, 2025)
Blue-Green Infrastructure (BGI) is critical in protecting natural and cultural landscapes, managing urban water resources, and mitigating agricultural, urban, and industrial pressures. BGI aims to implement nature-based solutions across various scales, from planning to design, to reduce surface runoff and enable rainwater collection and reuse. This study focuses on Ayvali Creek in Bursa, a significant urban stream impacted by agricultural, residential, and industrial activities, along with inadequate technical infrastructure, degrading water quality and the natural landscape. This study aims to identify BGI solutions suitable for Ayvali Creek and formulate planning and design strategies for sustainable and effective rainfall management. The methodology of this study included a SWOT (Strengths-Weaknesses-Opportunities-Threats) analysis for identifying the current conditions of the study area and a multi-criteria decision-making (MCDM) method known as the best and worst method (BWM) conducted with experts and stakeholders to rank BGI strategies according to their priority. Among the main criteria and sub-criteria, the “water/soil” (0.41) and “land-use/land-cover” (0.32) criteria, which contribute to the overarching ecological protection criterion (0.71), emerged as the highest priority strategies for effective BGI solutions. This finding underscores the paramount importance of ecological protection for Ayvali Creek. The methods and results of this quantitative research can serve as a framework for BGI, offering insight into successful strategies for sustainable water resource and rainfall management, not only for the region under study but also for other developing countries facing similar challenges with their water resources.

A Comparative Approach to Understand the Performance of CMIP6 Models for Maximum Temperature near Tropic of Cancer Using Multiple Machine Learning Ensembles - Gaurav Patel; Subhasish Das; Rajib Das (Water Resources Management, 2025)

Forecasting and Simulation of Urban Floods Based on the Integration of RegCM and Stormwater Management Models - Mokhtar Karami; Rasoul Sarvestan; Manfred Kleidorfer (Water Resources Management, 2025)

Determination of Water Surface Area Thresholds for Terminal Lakes in Arid Regions: Balancing Ecological Security and Water use Efficiency - Yun Zhang; Hongbo Ling; Junjie Yan; Yanlei Zhang; Xiaolin Qin (Water Resources Management, 2025)

Effect of Artificial Aeration on Evaporation Inhibition under Floating Balls Cover - GuoChen Hao; KeBin Shi; KeWu Han (Water Resources Management, 2025)

Techno-economic Efficiency of Water Utilities: a Peer-Evaluation Estimation - Manuel Mocholi-Arce; Ramon Sala-Garrido; Alexandros Maziotis; Maria Molinos-Senante (Water Resources Management, 2025)
Water utilities provide water and sanitation services in monopolistic conditions. Hence, assessing their performance through benchmarking is crucial for proper regulation. This research addresses the limitations of self-evaluation Data Envelopment Analysis (DEA) models commonly used for benchmarking water utilities’ efficiency. Given that these models often lead to overestimated efficiency scores, our study introduces a cross-efficiency analysis framework integrating both self and peer-evaluation perspectives. This innovative approach, applied to a representative sample of Chilean water utilities, uniquely considers unplanned water supply interruptions and sewerage blockages as undesirable outputs, emphasizing service continuity. Average techno-economic efficiency scores based on self-evaluation, and peer-evaluation were 0.681 and 0.388, respectively. Hence, significant techno-economic efficiency overestimations in self-evaluation scores are evident, with implications for regulatory challenges and potential service quality compromises. The data also highlights a considerable opportunity for improvement in water and sanitation continuity in Chilean water utilities. The findings not only shine a spotlight on the inherent biases of prevalent benchmarking techniques but also highlight a substantial avenue for bolstering water and sanitation service continuity within water utilities.

Estimation of R-Vine Copula Parameters in Multivariate Flood Frequency Analysis Using Arithmetic Optimization Algorithm and Comparing the Performance with Genetic Algorithm - Mahsa Boustani; Saeed Farzin; Sayed-Farhad Mousavi (Water Resources Management, 2025)

Green Infrastructure and Urban Flooding: A Survey of Prevailing Issues and Current Modelling Approaches - Liaqat Ali; Shirley Gato-Trinidad; Monzur Imteaz (Water Resources Management, 2025)
Green Infrastructure (GI) is gaining attention in urban flood management for its ability to reduce peak flow and volume while providing multiple urban and environmental benefits. Climate change and urbanization intensify GI development needs but also amplify design, management, and performance challenges for local governments and stakeholders. Understanding the complex challenges of implementing GI is essential for advancing sustainable urban water management in line with the Sustainable Development Goals (SDGs), particularly SDG 6 (Clean Water and Sanitation), SDG 11 (Sustainable Cities and Communities), and SDG 13 (Climate Action). Previous studies have often focused on specific components of GI systems, but this research provides the first comprehensive work covering it from pre-design to post-installation—of GI implementation in metropolitan Melbourne. Through a targeted survey of local councils and water consultancies, our study bridges the gap between theoretical design and practical application. By incorporating practitioners’ real-world experiences alongside technical modelling challenges, we reveal how factors like limited real-time data and regional parameterization can affect GI performance. Our approach uniquely examines interlinked obstacles, from financial constraints to maintenance protocols, giving decision-makers valuable insights to enhance GI project outcomes. This research addresses critical knowledge gaps, such as the need for community engagement and tailored technical expertise, providing actionable solutions for improving GI planning, implementation, and sustainability. Our findings offer a practical framework for future GI initiatives, helping practitioners pre-emptively tackle challenges, optimize resource use, and establish a resilient urban infrastructure that supports clean water access, sustainable urban growth, and climate adaptation.

Characterisation of Groundwater Drought Using Distributed Modelling, Standardised Indices, and Principal Component Analysis - V. Christelis; M. M. Mansour; C. R. Jackson (Water Resources Management, 2025)
A modelling framework was developed to characterise groundwater drought at a catchment scale in the absence of adequate observational records. The framework was used to characterise historical groundwater drought events for a Chalk aquifer in southern England over the period 1971–2004 during which three major drought events occurred. A numerical groundwater model was used to simulate the groundwater level fluctuations driven by historical time-variable and spatially non-uniform recharge inputs. The standardised groundwater level index (SGI) was applied to the simulated groundwater levels to evaluate the spatial pattern of groundwater drought and of their severity and duration. A dimensionality reduction method, namely principal component analysis (PCA), was applied to the SGI dataset and to the standardised precipitation index (SPI) to further explore the spatio-temporal drought characteristics. The analysis showed inconsistency in the spatial distribution of the duration and severity among the three studied events. PCA indicated that the SPI was not a good predictor of groundwater drought during the extreme European heatwave of 2003 whereas the proposed modelling framework correctly identified the resilience of the groundwater system to that event and in line with observations. Furthermore, significant differences were observed between the spatial patterns obtained from SPI and SGI datasets highlighting the important role that hydrological and hydrogeological features of a catchment have in groundwater drought development.

Nowcasting Floods in Detailed Scales Considering the Uncertainties Associated with impact-based Practical Applications - Vasilis Bellos; Carmelina Costanzo; John Kalogiros; Reza Ahmadian; Evangelos Rozos; Pierfranco Costabile (Water Resources Management, 2025)

Deciphering the Sustainable Stormwater Management Strategies for Urban Areas: a Review - Shivani Yadav; Saurav Ambastha; Harsh Pipil; Anil Kumar Haritash; Krishna R. Reddy (Water Resources Management, 2025)

Correction to: A Conceptual Design of Water Circulation in an Urban Environment: Towards Eco-solidarity with Economic and Social Values - Mohammad Karamouz; Sahar Kolahdouz (Water Resources Management, 2025)

Survey of Wildfire Effects on the Peak Flow Characteristics - Farshad Jalili Pirani; Paulin Coulibaly (Water Resources Management, 2025)

A Hybrid-fidelity Modeling Approach for Evaluating Flooding Risk in Large Urban Infrastructures - Xuan Wang; Jiaxin Yan; Yong Liu; Zhipeng Liu; Shixiang Kuang; Jinsuo Lu (Water Resources Management, 2025)

The Layout Optimization of low-impact Development Facilities is Based on a Multi-Objective Genetic Algorithm and the SWMM Model - Xiaoyue Wang; Wenzhuo Sun; Yumeng Lan; Xiaoyu Ge (Water Resources Management, 2025)

Risk Assessment of Campus Waterlogging and Evacuation Route Planning - Jing Feng; Yu Tian; Zhaolong Xie; Hao Wang (Water Resources Management, 2025)

Influence of Geomorphological Parameters on Flash Flood Susceptibility Analyzed using a Coupled Approach of HEC-HMS Model and Logistic Regression - Zhenyue Han; Fawen Li; Chengshuai Liu; Xueli Zhang; Caihong Hu (Water Resources Management, 2025)

Water Quality Prediction Method Coupling Mechanism Model and Machine Learning for Water Diversion Projects with a Lack of Data - Xiaochen Yang; Kai Liu; Xiaobo Liu; Fei Dong; Aiping Huang; Bing Ma; Yang Lei; Zhi Jiang (Water Resources Management, 2025)

Improving Flood Control Optimal Operation of River-Type Cascade Reservoirs through Coupling with 1D Hydrodynamic Model - Lishuang Yao; Yang Peng; Xianliang Yu; Zhihong Zhang; Shiqi Luo (Water Resources Management, 2025)

Spaceborne GNSS-R for Sensing Soil Moisture Using CYGNSS Considering Land Cover Type - Shengjia Song; Yongchao Zhu; Xiaochuan Qu; Tingye Tao (Water Resources Management, 2025)

Proposal of Groundwater Governance Indexes: Application to Nine Spanish Aquifers - E. Custodio; M. C. Cabrera; D. Sánchez-García; A. de la Hera-Portillo; J. L. García-Aróstegui; J. Jódar; S. Martos-Rosillo; L. O. Puga; E. Queralt (Water Resources Management, 2025)
Groundwater management and governance have properties related to the long renewal time, evapo-concentration processes in the soil and interaction with the rock, and dispersion, mixing and degradation of possible contaminants. Groundwater is part of the hydrologic balance, with specific properties and different behaviours from that of surface water, although there are interconnected. These differences also appear in other aspects related to economy, society, norms and administration, and to water ethics and morality. These differences are not always taken into account by water managers and decision makers. A methodology is being used to evaluate quantitatively groundwater management and sound governance. A score of themes are classified into aspects and categories, including hydrogeological knowledge, administration and socio-economics aspects. Three indexes are presented, the most developed of them correcting reserve depletion, unavoidable discharges and legal restrictions. The proposed indexes were tested in nine pilot areas in Spain, representing different circumstances, scales and environmental conditions. The discussion refers to aquifers and aquifer systems, considering the corresponding groundwater bodies and their quantitative and chemical status. It is not possible to condense the reality into one or a few figures. Comparisons have to be considered cautiously as a broad guide, with possible notable deviations, especially considering the groundwater average renewal time. There is not a well-defined correlation between groundwater management - governance evaluation and groundwater body’s status, but they have some common aspects that are relevant to improve groundwater management and governance.

Quantifying the Impacts of Climate Change and Human Interventions on Flow Alterations in a Tropical River - Doan Van Binh; Binh Quang Nguyen; Thi-Thu-Ha Nguyen; Xuan-Hien Le; Luc Anh Tuan; Manh-Hung Le; Sameh A. Kantoush; Tam V. Nguyen; Vuong Nguyen Dinh; Nguyen Thanh Luan; Menna Farag Ahmed; Tetsuya Sumi (Water Resources Management, 2025)

MTV19ANet: A Multi-tier Visual Geometry Group 19 with Attention Network-Based Streamflow Prediction System - Shashank A; Geetha P; Jyothish Lal G; Sankaran Rajendran (Water Resources Management, 2025)

Investigation of Hydraulic Characteristics of Stepped Labyrinth Weirs under Fixed Crest Length - Mehmet Cihan Aydin; Ali Emre Ulu; Ercan Işık (Water Resources Management, 2025)
Labyrinth weirs can pass more flow than conventional weirs with fixed openings for the same headwater, thanks to their increased crest length. However, nappe interference and local submergence in downstream cycles reduce discharge efficiency. This study experimentally and numerically investigated stepped-in-plan labyrinth weirs, designed to minimize these issues. Four stepped weirs and a diagonal linear weir were tested under the same crest length and free overflow conditions, and compared with conventional labyrinth weirs. The results showed that, for constant crest length and head, discharge efficiency decreases with more steps, mainly due to flow separation and turbulence on the weir walls, unlike traditional labyrinth weirs.

Enhancing the Stability of Hydrological Modelling through Multivariable Calibration Schemes Using the Satellite-Based Soil Moisture and Evapotranspiration - Shashi Bhushan Kumar; Ashok Mishra (Water Resources Management, 2025)

Coupled SWAT, Stationary Wavelet Transform, and Interpretable Machine Learning to Improve Watershed Streamflow Simulation - Chengqing Ren; Jianxia Chang; Xuebin Wang; Chen Niu; Liyuan Wang; Junhao Zhang (Water Resources Management, 2025)

Data-Driven Insights into Climate Change Effects on Groundwater Levels Using Machine Learning - Xinyong Lu; Zimo Wang; Menghao Zhao; Songzhe Peng; Song Geng; Hamzeh Ghorbani (Water Resources Management, 2025)

Enhancing Reservoir Operational Modelling with Satellite Altimetry-Derived Water Level Data - Li Tang; Xiaohui Sun; Shuyuan Xu (Water Resources Management, 2025)

A Three-stage Cooperative Game Model for Water Resource Allocation Under Scarcity Using Bankruptcy Rules, Nash Bargaining Solution and TOPSIS - Shahmir Janjua; Duc-Anh An-Vo; Kathryn Reardon-Smith; Shahbaz Mushtaq (Water Resources Management, 2025)
The global water security situation is deteriorating due to unequal distribution of water resources and changing climate, leading to increased conflicts in many regions. This article proposes and develops a three-stage collaborative water resource allocation model and applies this to the Indus River basin in Pakistan, where water resources are shared by four provinces (agents): Punjab, Sindh, Balochistan, and Khyber Pakhtunkhwa (KPK). The model uses bankruptcy rules, Nash bargaining theory, and TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) to allocate water resources. The model considers various factors, such as water risk and water satisfaction to achieve the best possible (most equitable and acceptable) outcome. Water allocation was conducted under three scenarios of ‘median’, ‘maximum’ and ‘low’ river flows. In the first stage of water allocation, the positive and negative ideal solutions were defined for all agents (in this case, provinces). These initial ideal solutions provided a baseline for the negotiation process. In the second stage, water allocation ratios of the four provinces Punjab, Sindh, Balochistan and KPK, using the Nash bargaining solution, under the median flows were 57.61%, 29.91%, 6.24% and 6.24%. In the third stage, water allocation ratios demonstrated the reduction in the allocation for those provinces facing high risks and having high satisfaction rates. The final allocations under the median flow conditions for the four provinces were 54.92%, 28.95%, 8.50% and 7.63%, respectively. The developed three-stage water allocation model considers the multi-dimensional attributes of water resources and is expected to support the cooperation of water agents, enabling collective bargaining and group negotiation and improving the acceptability and stability of allocations.

Exploring Runoff Response to Simulated Rainfall: A Study of the Rising Limb of a Hydrograph on Sandy Slopes - Radha S. Mohril; Avinash D. Vasudeo (Water Resources Management, 2025)

A Conceptual Design of Water Circulation in an Urban Environment: Towards Eco-solidarity with Economic and Social Values - Mohammad Karamouz; Sahar Kolahdouz (Water Resources Management, 2025)

CEEMDAN-BILSTM-ANN and SVM Models: Two Robust Predictive Models for Predicting River flow - Elham Ghanbari-Adivi; Mohammad Ehteram (Water Resources Management, 2025)

Using Machine Learning to Predict Suspended Sediment Transport under Climate Change - Nejc Bezak; Klaudija Lebar; Yun Bai; Simon Rusjan (Water Resources Management, 2025)
Sediment transport, an important element of the erosion‒sedimentation cycle, can be very high during extreme flood events and can cause hydromorphological changes within river networks. Therefore, improved sediment transport predictions are needed to establish sediment management at the catchment scale. A machine learning model (i.e., XGBoost) and a sediment rating curve method were tested for predicting the suspended sediment load in the Sora River catchment in Slovenia. The evaluation of the models based on the historical data for 2016–2021 revealed that XGBoost outperformed the sediment rating curve model and resulted in a lower bias (i.e., approximately 15%). The XGBoost model was used to predict future suspended sediment load dynamics. Three representative concentration pathway (RCP) scenarios (RCP2.6, RCP4.5, and RCP8.5) and several climate change models were used. The rainfall–runoff model was set up, calibrated, validated and applied to simulate future daily discharge data, as this was the required input for the XGBoost and sediment rating curve models. The simulation results indicate that suspended sediment load is expected to increase in the future in the range 15–20% under both the RCP4.5 and RCP8.5 scenarios. Additionally, the number of days with a suspended sediment concentration (SSC) greater than 25 mg/l, which is often used an indicator of inadequate water quality, is expected to increase by 2–4%, whereas some models indicate an increase of up to 8%. Erosion and sediment management mitigation measures need to be applied in the future to ensure adequate water quality and good ecological status of the river.

Smart Irrigation for Coriander Plant: Saving Water with AI and IoT - Abhirup Paria; Arindam Giri; Subrata Dutta; Sarmistha Neogy (Water Resources Management, 2025)

Experimental Study of Hydrodynamics of Inbank and Overbank Flows in Meandering Compound Channel - Hosna Shafaei; Kazem Esmaili; AliAsghar Beheshti (Water Resources Management, 2025)

Reconstructing Long-Term Daily Streamflow Data at the Discontinuous Monitoring Station in the Ungauged Transboundary Basin Using Machine Learning - Vinh Ngoc Tran; Hanh Duc Nguyen; Hai Van Khuong; Huy Ba Dao; Quan Huu Minh Le; Chi Que Nguyen; Giang Tien Nguyen (Water Resources Management, 2025)

Advanced Framework for Predicting Rainfall-Runoff: Comparative Evaluation of AI Models for Enhanced Forecasting Accuracy - Hadi Sanikhani; Mohammad Reza Nikpour; Fatemeh Jamshidi (Water Resources Management, 2025)

Multi-Step Forecasting of Meteorological Time Series Using CNN-LSTM with Decomposition Methods - Eluã Ramos Coutinho; Jonni G. F. Madeira; Dérick G. F. Borges; Marcus V. Springer; Elizabeth M. de Oliveira; Alvaro L. G. A. Coutinho (Water Resources Management, 2025)

Improve Precipitation Zoning Accuracy by Applying Ensemble Learning Models Based on Interpolation and Data Mining Integration - Khalil Ghorbani; Meysam Salarijazi; Laleh Rezaei Ghaleh; Esmaeil Valizadeh (Water Resources Management, 2025)

Life Cycle Water Footprint Analysis and Future Prediction Based on LSTM for Arundo donax in Sustainable Aviation Fuel Production - Zongwei Zhang; Xinyuan Wang; Haonan Liu; Wenjie Li (Water Resources Management, 2025)

Developing a Risk Management Framework for Agricultural Water Systems Using Fuzzy Dynamic Bayesian Networks and Decision-Making Models - Atiyeh Bozorgi; Abbas Roozbahani; Seied Mehdy Hashemy Shahdany; Rouzbeh Abbassi (Water Resources Management, 2025)
Given the various natural and human-caused hazards that threaten the agricultural water distribution process from the main source to farms, establishing a framework to analyze these risks is crucial. This study aims to develop an intelligent risk management framework to help stakeholders devise long-term and sustainable solutions for managing agricultural water systems. First, we developed a Fuzzy Dynamic Bayesian Network (FDBN) model for multi-hazard risk assessment, taking into account the temporal causal interactions between parameters and incorporating fuzzy theory. Next, we defined several risk management scenarios across structural, non-structural, automated control, and integrated methods. These scenarios were implemented in the FDBN model to mitigate the risks associated with the system. Various economic, social, environmental, and technical criteria were considered, and scenarios were ranked using the WASPAS, TOPSIS, and MultiMoora methods. The Copeland approach was used to combine the ranking results. The results showed that automated scenarios, specifically Model Predictive Control (MPC) and Proportional-Integral (PI) controllers, could reduce the system's risk by 11.4% and 9.8%, respectively, and were ranked the highest. The findings of this study and the proposed framework can assist operators in the sustainable planning and management of water systems in light of anticipated threats.

A Smart Pipe Length Splitting Method for Cost Minimization of a Pressurized Irrigation Network - N. Samarinas; A. Gravani; C. Evangelides; A. Loukas (Water Resources Management, 2025)

Short-Term Water Level Prediction for Long-Distance Water Diversion Projects Using Data-Driven Methods with Multi-Scale Attention Mechanism - Xinyong Xu; Zixuan Zhu; Xiaonan Chen; Bo Wang; Xianliang Liu; Li Jiang; Zhenbao Wang; Yuxian Qiu; Laike Yang; Xu Qi; Hao Lu (Water Resources Management, 2025)

A Review of IUWM Approach to Address Urban Water Challenges Faced by a Developing Country - Snigdha Sarita Mohapatra; Meenakshi Arora; Wenyan Wu; Manoj Kumar Tiwari (Water Resources Management, 2025)

Water Sector 5.0: Harnessing Digital Transformation Opportunities and Mitigating Risks - Mahi Chaudhary; Shivam Pandey (Water Resources Management, 2025)

Reallocating Shared Groundwater Resources Using a Participatory Two- level Weighted Bankruptcy Framework - Mohammad Ali Tolouei Virani; Reza Javidi Sabbaghian; Bardia Roghani; Ehsan Bahrami Jovein; Mohammad Fereshtehpour (Water Resources Management, 2025)

Anthropogenic and Hydroclimatic Drivers of Livelihood Vulnerability in Wetland Communities: A Geospatial and Pragmatic Assessment - Kashif Imdad; Mehebub Sahana; Ouseen Gautam; Archana Chaudhary; Shubhi Misra; Sudha Dwivedi; Rayees Ahmed (Water Resources Management, 2025)
Over the last few decades, the degradation of wetlands has significantly contributed to the decline in rural sustainability and the exacerbation of livelihood losses, particularly pronounced among wetland communities in developing nations. This paper employs a pragmatic approach and geospatial modelling to scrutinise the vulnerabilities in the livelihoods of these wetland communities. The primary focus of the study is to comprehensively analyse the ramifications of climate variability, natural disasters, alterations in wetland hydrology, diminishing fish diversity, sanitation concerns, anthropogenic pressures, and infrastructural development on the livelihoods of these communities. Along with a field observation-based pragmatic approach, geospatial techniques, Fragstat modelling, and water quality analysis, methods were employed in a mixed methods analysis. A Multi Criteria Decision Making Approach (MCDMA) was adopted to quantify the final composite rank of the Livelihood Vulnerability Index (LVI). In our mixed methods approach, we utilised qualitative interventions such as focus group discussions (FGD) to understand the complex issue of livelihood vulnerability of wetland communities. Our analysis revealed that the vulnerability of wetland communities has been increasing due to anthropogenic pressures. To maintain the stability of the livelihoods of wetland communities, all indicators studied need to be optimised or supported by various state and non-state actors to reduce vulnerability. Graphical Abstract

A Comprehensive Framework for Water Affordability Analysis - T. S. Fagundes; R. C. Marques; T. F. Malheiros (Water Resources Management, 2025)
Water and sanitation affordability has emerged as a significant concern in recent years due to economic crises and the increasing reliance on tariffs to finance public services. Governments have often depended on inadequate affordability analyses, if any, to identify constraints faced by vulnerable families in accessing water supply and sanitation services. This paper proposes a comprehensive affordability analysis framework, considering six key dimensions: coverage rates, conventional affordability ratio, community poverty conditions, affordability ratio for poorer households, access to water social programs, and the burden of connection fees. The framework was applied to Brazil, focusing on its state-owned providers, which serve more than 70% of the population. The results demonstrate that the proposed framework offers a simple yet robust tool for decision-makers globally, with the flexibility to adapt to various contexts. The case study revealed that the Brazil’s conventional affordability ratio masks affordability issues faced by poorer families, along with low access to social tariffs, high incidence of poverty, and low coverage in several areas. These findings provide critical insights for stakeholders, enabling the development of targeted public policies and the design of appropriate subsidy mechanisms.

A Novel Rapid Flood Inundation Method Based on GIS Combined with Hydrodynamics in Coastal and Riverine Regions - Zhuo Zhang; Zhiyao Song; Haocheng Wang; Songshan Yue; Di Hu; Fei Guo (Water Resources Management, 2025)

Integrated Runoff Forecasting Model with Mode Decomposition and Metaheuristic-optimized Bidirectional Gated Recurrent Unit - Zhong-kai Feng; Wen-jie Liu; Zheng-yang Tang; Bao-fei Feng; Guo-liang Ji; Yin-shan Xu; Wen-jing Niu (Water Resources Management, 2025)

The Role of Channel Modification in Flood Mitigation: A Study Using HEC-RAS - Anurag Yadav; Raj Mohan Singh (Water Resources Management, 2025)

A Methodology to Improving the Performance of MOAHA Optimization Algorithm using Chaos Theory; Principle and Application in Optimal Reservoir Operation - Zahra khoramipoor; Saeed Farzin (Water Resources Management, 2025)

Analysis of Flooding Under Extreme Conditions with Factors Interactions Using Hybrid Machine Learning - Yanfen Geng; Xinyu Hu; Xiao Huang; Peng Liu (Water Resources Management, 2025)

Estimation of Monthly Water Table Depth Anomalies Based on the Integration of GRACE and ERA5-Land with Large-Scale Simulations Using Random Forest and LSTM Networks - Leandro Avila; Alban de Lavenne; Maria-Helena Ramos; Stefan Kollet (Water Resources Management, 2025)
Increasing pressure on groundwater resources, exacerbated by climate change, highlights the need to develop advanced methods for monitoring groundwater storage and levels. While numerical and physics-based models are widely used to analyze the spatial and temporal dynamics of groundwater levels, they require extensive input data and can be computationally expensive for high-resolution and large-scale simulations. In contrast, remote sensing products such as the Gravity Recovery and Climate Experiment (GRACE) provide global-scale information on total water storage anomalies. However, due to its coarse spatial resolution (0.25 $$^{° }$$ ), GRACE data cannot be used directly to assess groundwater conditions at local and regional scales. In order to obtain local groundwater levels that can be quickly accessed by stakeholders to monitor and define appropriate groundwater management, this study implements a methodology based on data-driven models to estimate monthly water table depth anomalies (wtda), integrating simulations from the Terrestrial Systems Modeling Platform (TSMP) with GRACE and reanalysis ERA5-Land datasets. Considering the spatial resolution of current TSMP simulations (TSMP-G2A - 0.11 degrees), we tested and compared multiple Random Forest (RF) and LSTM networks at the pixel scale over the Seine River Basin, combining different hydrological and climatological variables with GRACE as input features. For each data-driven approach, we selected the model that best represents the temporal pattern of the wtda during the test period and compared the results with the original TSMP simulation, as well as in-situ groundwater observations. The results indicate that both RF and LSTM networks can well reproduce the temporal patterns of groundwater levels across the Seine Basin obtained by the TSMP simulations, with average Pearson correlations of 0.65 and average KGE of 0.6, respectively. A comparison with multiple groundwater wells allowed us to identify the regions where the applied models are more reliable for representing wtda over the Seine River Basin. In general, the proposed models show good agreement with in-situ observations, independent of the groundwater well depth. However, we found significant differences between observed and simulated water table depths in the downstream regions of the Seine River Basin, where coastal systems and the presence of karst in the chalk might influence groundwater levels and the performance of the adopted models, respectively. The proposed methods provide end users with an extremely lightweight reconstruction and prediction tool for wtda at the pixel level, including reliability estimates, which is easy to implement in an ad hoc fashion in any evaluation and groundwater management workflow.

Study of Rainfall Occurrence Process by Markov Chain Models and Decision Tree-based Ensemble and Boosting Techniques - Dwijaraj Paul Chowdhury; Deep Roy; Ujjwal Saha (Water Resources Management, 2025)

Projection of Mean Annual and Maximum 24-h Precipitation under Future Climatic Scenarios in Semi-Arid Regions - Pezhman Allahbakhshian-Farsani; Mehdi Vafakhah; Hadi Khosravi-Farsani; Elke Hertig (Water Resources Management, 2025)

Development of a Multi-objective Optimal Operation Model of a Dam using Meteorological Ensemble Forecasts for Flood Control - Mitra Tanhapour; Jaber Soltani; Hadi Shakibian; Bahram Malekmohammadi; Kamila Hlavcova; Silvia Kohnova (Water Resources Management, 2025)
Reservoir operation in flood conditions on hourly time scales poses significant difficulties due to the inherent uncertainty of inflow forecasts. Hence, incorporating ensemble flood forecasts into real-time reservoir operation optimization has rarely been investigated to effectively address uncertainties in inflow forecasts for flood control, let alone its added value compared to no-forecast operating scheme in multi-objective reservoir operating system. This research first investigates the potential of sequential Long Short-Term Memory networks to forecast one-day-ahead ensemble inflow floods based on numerical weather prediction data. Next, a new framework has been developed to incorporate ensemble inflow forecasts into multi-objective reservoir operation optimization using grid search-based genetic algorithm. Finally, we implemented our proposed ensemble-based operating method on the Dez dam basin in Iran and compared it with the benchmark no-forecast operating approach. The results revealed that the Long Short-Term Memory model reasonably performed well to propagate uncertainty in short-term ensemble inflow forecasts. In addition, the ensemble-based operating method could reduce release peaks and increase reservoir storage more than that of a deterministic (no-forecast) operating approach. Our findings demonstrated that the applicability of a skillful ensemble-based scheme in reservoir flood control operations could be effective in decreasing flood damage and water shortage.

Groundwater Level Prediction by Wavelet Deep Learning with Smart Pumping Data - Tsai-Ning Weng; Ray-Shyan Wu; Tzu-Han Weng; Yi-Ho Chen; Tsai-Chun Lai; Jui-Yun Hsieh; Chu-Chun Hsu; Yuan-Chien Lin (Water Resources Management, 2025)

Establishing a Strategic Blueprint for the Design and Evaluation of Flood Control Infrastructure in Extensive Watersheds - Panagiotis Dimas; Georgia-Konstantina Sakki; Panagiotis Kossieris; Ioannis Tsoukalas; Andreas Efstratiadis; Christos Makropoulos; Nikos Mamassis; Katia Pipilli (Water Resources Management, 2025)

Multi-objective Optimization of Urban Water Allocation Considering Recycled Water - Siwei Chen; Yue-Ping Xu; Yuxue Guo; Xinting Yu (Water Resources Management, 2025)

Compound Hydrological Forecasting Model by Long Short-term Memory Network Coupled with Adaptive Mode Decomposition and Evolutionary Algorithm - Zhong-kai Feng; Wen-jie Liu; Wen-jing Niu; Tao Yang; Wen-chuan Wang; Sen Wang (Water Resources Management, 2025)

Comparison of Multilayer Perceptron with an Optimal Activation Function and Long Short-Term Memory for Rainfall-Runoff Simulations and Ungauged Catchment Runoff Prediction - Mun-Ju Shin; Yong Jung (Water Resources Management, 2025)

AIoT-Driven Leak Detection in Real Water Networks Using Hydrophones - Beenish Bakhtawar; Ali Fares; Tarek Zayed (Water Resources Management, 2025)
Acoustic sensing technology is a familiar approach to detect leakage in urban water networks. Critical issues like false alarms, difficult leak locations, missed leaks, unknown site conditions, and high repair costs are still prevalent. The situation warrants developing a more sophisticated and efficient leak detection approach in real water networks. Hydrophone based acoustic technology has a strong promise for high precision detection of leaks. However, AIoT approach using hydroacoustic data for real water leak detection are rarely reported. The current study, therefore, proposes an integrated signal analysis and machine learning-based ensemble model for leak detection using a hydrophone-based smart IoT system. The results show that the most significant features are peak frequency and maximum amplitude. Random forest is the most robust classifier for cost effective long-term monitoring, and the proposed voting ensemble classifies leaks and no leaks with high accuracy on both unseen data and new sites. Specifically, proposed models have very few alarms and missed leaks are reported, a significant problem in models developed using accelerometers and noise loggers. The study shows a significant contribution to the domain of leak detection for real urban water networks.

Modeling Water Age and Chlorine Reduction Effects on Water Quality in the Distribution Network of the Lower Usuma Dam Using EPANET - Begmyrat Kulmedov; Lucy Akaiku Achobe (Water Resources Management, 2025)

Land use Planning and Water Resources at Local Scale: Opportunities and Challenges towards an Integrated Management Strategy - Anabella Montico; Mariana I. Zilio; Paula A. Zapperi (Water Resources Management, 2025)

A Hybrid Gradient Boosting Model for Predicting Longitudinal Dispersion Coefficient in Natural Rivers - Yangyu Deng; Yakun Liu; Di Zhang; Ze Cao (Water Resources Management, 2025)

Emergency Response to Urban Flooding: An Assessment of Mitigation Performance and Cost-Effectiveness in Sponge City Construction - Zhiwen Zheng; Xianqi Zhang; Wenbao Qiao; Ruichao Zhao (Water Resources Management, 2025)

OSE-WQI: An Optimized Stacked Ensemble Classifier to aid Water Quality Assessment - Sakshi Khullar; Nanhay Singh; Yogita Thareja (Water Resources Management, 2025)

Correction to: Enhancing Stormwater Management through Hydromodification Measures and Low Impact Development Strategies in Urban Areas: A Neighborhood-Scale Study - Ardeshir Hassankhan; Mahnaz Ghaeini-Hessaroeyeh; Ehsan Fadaei-Kermani (Water Resources Management, 2025)

A Novel, Ecology-Inclusive, Hybrid Framework for Rainwater Harvesting Site Selection in Arid and Semi-Arid Regions - Safaa Ahmed; Mike Jesson; Soroosh Sharifi (Water Resources Management, 2025)
The water crisis is a critical issue, particularly in arid and semi-arid regions where rainfall is limited. Rainwater harvesting systems have been introduced in many locations to capture what rainfall does occur, but selection of the optimum site is vital to ensure efficient capture and storage. Over the past few decades, a range of frameworks for ranking proposed rainwater harvesting sites on the basis of site suitability have been suggested. The goal of this study was to develop a robust methodology to extend these frameworks, which consider biophysical and socio-economic criteria only, to include ecological criteria in the site selection process. This is essential for ensuring environmental protection, maintaining biodiversity, water quality improvement, climate resilience, regulatory compliance and sustainability of the system. In this paper, the inter-relationships of ecological criteria are shown to be complex, with “independent” criteria affecting “mediator” criteria which then directly impact ecological standards, i.e. the “dependent” criteria such as number of aquatic organisms. It is shown how a robust combination of data analysis and expert opinion can be applied to determine relative weightings of the different ecological criteria, using temperature and light as examples of key independent criteria. The developed hybrid framework is applied to a case study of site selection in Erbil Province in Iraq, where both climate change and human actions have seriously reduced water supplies in the past twenty years, showing that inclusion of these ecological criteria changes the ranking of the sites compared to ranking without ecological considerations.

Water Infiltration from Flash Floods, and its Disappearance into Alluvial Sediments in Semi-arid Regions - Gholamreza Kamali; Ali Torabi Haghighi (Water Resources Management, 2025)

Modelling Water Management using SWAT+ : Application of Reservoirs Release Tables and the New Water Allocation Module in a Highly Managed River Basin - Alejandro Sánchez-Gómez; Jeffrey G. Arnold; Katrin Bieger; Natalja Čerkasova; Nancy B. Sammons; Silvia Martínez-Pérez; Eugenio Molina-Navarro (Water Resources Management, 2025)
Incorporating the simulation of water management actions in hydrological models is paramount to enhance their reliability and usefulness. SWAT + (Soil and Water Assessment Tool) includes novelties in this aspect compared to its previous versions: the decision tables and the water allocation module provide enhanced capabilities for configuring management actions. Despite their potential, these features have not yet been applied due to their novelty. This study pioneers the use of SWAT + ’s new features applied in the Upper Tagus River Basin, a densely populated and highly regulated catchment. Irrigation, reservoir management, and various kinds of water transfers were incorporated in a SWAT + model of this basin. The implementation of management actions and their impact on the model performance were evaluated. The model accurately reproduced water demand for irrigation and water transfers, capturing both the demand volume and timing. The water sources were configured to meet most of the demand, with 73% of irrigation and 90% of water transfer requirements being satisfied. Release decision tables were configured for 31 reservoirs, resulting in an accurate simulation of outflow and storage in many of them. Incorporating management actions improved the streamflow simulation at the basin outlet, both considering the hydrograph and performance metrics (e.g., PBIAS was reduced by more than 50%). Some potential improvements in the model configuration and in the code were identified and will be addressed in future studies. This work provides a comprehensive guide to SWAT + ’s new features and the methodology employed, making it valuable for anyone working with the model.

Multi-criteria Assessment of Potential Check Dam Location for Sustainable Development in Urban River Basins of the Eastern Mediterranean - Hazem Ghassan Abdo; Sahar Mohammed Richi; Mohammed J. Alshayeb; Pankaj Prasad; Taorui Zeng; Bijay Halder; Maged Muteb Alharbi; Hoang Thi Hang; Jasem A. Albanai (Water Resources Management, 2025)

The Changing Landscape of Water Resources Planning in England - Ali Leonard; Jaime Amezaga; Richard Blackwell; Elizabeth Lewis; Chris Kilsby (Water Resources Management, 2025)
Water resources planning in England has undergone a significant transformation from a fragmented, piecemeal approach to a more strategic, multi-scale framework. This shift is a response to the pressing need for increased resilience in the face of climate change, population growth, and environmental pressures. Recognising the limitations of existing planning frameworks established during privatisation, new national, regional, company, and sub-regional frameworks have emerged to address gaps and enhance strategic planning efforts. Understanding the critical pathway dependencies, opportunities, and constraints allows reforms to be designed and implemented with a better chance of success. Several key features characterise water resources planning in England. Firstly, the systems are inherently complex and fragmented, requiring tailored approaches rather than one-size-fits-all solutions. Secondly, planning operates within a neoliberal framework emphasising economic efficiency. Thirdly, subjective concepts like risk, uncertainty, and value are managed through technical quantitative methods which can pose challenges for transparency. Fourthly, while legislation often operates in silos, there is a growing demand for more integrated planning approaches. Funding and regulatory powers play crucial roles in water resources planning. Access to capital is influenced by the institutional environment and broader economic and political factors, with government and regulators ultimately holding power over the framework. Companies, driven by the profit motive, are responsible for detailed planning and delivery, regulated by standards and reputational incentives. Public participation is framed as consumer engagement. Aligning incentives for public good with financial rewards and ensuring effective regulation are vital for the framework’s success.

Comparative Assessment of Artificial Neural Networks (ANNs), Long Short Term Memory Network (LSTM) and Hydrologic Engineering Centre-Hydrologic Modelling System (HEC-HMS) for Runoff Modelling - Aparna M. Deulkar; Pradnya R. Dixit; Shreenivas N. Londhe; Rakesh K. Jain (Water Resources Management, 2025)

Review and Intercomparison of Machine Learning Applications for Short-term Flood Forecasting - Muhammad Asif; Monique M. Kuglitsch; Ivanka Pelivan; Raffaele Albano (Water Resources Management, 2025)
Among natural hazards, floods pose the greatest threat to lives and livelihoods. To reduce flood impacts, short-term flood forecasting can contribute to early warnings that provide communities with time to react. This manuscript explores how machine learning (ML) can support short-term flood forecasting. Using two methods [strengths, weaknesses, opportunities, and threats (SWOT) and comparative performance analysis] for different forecast lead times (1–6, 6–12, 12–24, and 24–48 h), we evaluate the performance of machine learning models in 94 journal papers from 2001 to 2023. SWOT reveals that the best short-term flood forecasting was produced by hybrid, random forest (RF), long short-term memory (LSTM), artificial neural network (ANN), and adaptive neuro-fuzzy inference system (ANFIS) approaches. The comparative performance analysis, meanwhile, favors convolutional neural network, ANFIS, multilayer perceptron, k-nearest neighbors algorithm (KNN), hybrid, LSTM, ANN, and support vector machine (SVM) at 1–6 h; hybrid, ANFIS, ANN, and LSTM at 6–12 h; SVM, hybrid, and RF at 12–24 h; and hybrid and RF at 24–48 h. In general, hybrid approaches consistently perform well across all lead times. Trends such as hybridization, model selection, input data selection, and decomposition seem to improve the accuracy of models. Furthermore, effective stand-alone ML models such as ANN, SVM, RF, genetic algorithm, KNN, and LSTM, provide better outcomes through hybridization with other ML models. By including different machine learning models and parameters such as environmental, socio-economical, and climatic parameters, the hybrid system can produce more accurate flood forecasting, making it more effective for early warning operational purposes.

Urban Flood Hazard Assessment Based on Machine Learning Model - Guoyi Li; Weiwei Shao; Xin Su; Yong Li; Yi Zhang; Tianxu Song (Water Resources Management, 2025)

Navigating the Challenges of Rainfall Variability: Precipitation Forecasting using Coalesce Model - Suraj Kumar Bhagat (Water Resources Management, 2025)