Significance of the Water-Energy-Food Nexus on the Watershed Health Status - Sanaz Pourfallah Asadabadi; Seyed Hamidreza Sadeghi; Mehdi Vafakhah; Majid Delavar (Water Resources Management, 2025)

Estimation of the Spatiotemporal Variability of Surface soil Moisture Using Machine Learning Methods Integrating Satellite and Ground-based Soil Moisture and Environmental Data - Viktória Blanka-Végi; Zalán Tobak; György Sipos; Károly Barta; Brigitta Szabó; Boudewijn van Leeuwen (Water Resources Management, 2025)
Monitoring and quantifying the development of drought extremes is important to agriculture, water, and land management. For this, soil moisture (SM) is an effective indicator. However, currently, real-time monitoring and forecasting of SM is challenging. Thus, this study develops and tests a methodology based on machine learning methods that integrates ground-based data, Sentinel-1 satellite soil moisture (S1SSM) data, meteorological data, and relevant environmental parameters to improve the estimation of the spatiotemporal changes in SM. It also evaluates the relevance of the applied parameters and the applicability and limitations of S1SSM data in SM monitoring. Specifically, the performances of four machine learning methods (multiple linear regression, support vector machine regression, extreme gradient boosting, and a deep neural network) were evaluated in an area increasingly exposed to hydrological extremes. Overall, the extreme gradient boosting model provided the best result (R 2 = 0.92). In this case, the difference between the modeled and observed SM values at ground-based stations was below 3%, with only five stations reporting differences above 5%, indicating the effectiveness of this model for SM monitoring in larger areas. Additionally, the spatial pattern of the observed S1SSM values and the modeled values showed good agreement (with a difference below 10%) in the larger part (45.5%) of the area, while more than 20% difference occurred in 27.1% of the area, demonstrating the application potential of S1SSM data in areas with less heterogeneous land use. However, the results also suggest that the S1SSM data can be affected by land use and/or soil types.

Towards the Development of the European Inland Waterway Network – a Feasibility Assessment for the Odra River - Robert Banasiak; Dorota Dybkowska-Stefek; Stanisław Kostecki (Water Resources Management, 2025)

The Effect of Urbanization and Farmland to Forests Program on Surface Runoff - Jing Zhang; Zi-Jing Chen; Yong Ma; Qing Xie; Wen-jie Li; Hong-bo Du (Water Resources Management, 2025)

Advancing Water Quality Assessment and Monitoring with a Robust Stacked Ensemble Method - Manisha. S. Babu; S Sreelakshmi; Vinod Chandra. S. S; V. Sunitha; E. Shaji (Water Resources Management, 2025)

Spatio-Temporal Sensitivity Analysis for Flow and Sediment Load Modeling Using SWAT in a Heterogeneous Agricultural Watershed - Mansour Talebizadeh; Daniel N. Moriasi; Jean L. Steiner; Prasanna H. Gowda; Patrick J. Starks; Alan J. Verser (Water Resources Management, 2025)

Runoff Generation Signaled by Deviations from the Budyko Framework - Girum Getachew Demeke; Jr-Chuan Huang; Yi-Ying Chen (Water Resources Management, 2025)

Explainable Machine Learning to Analyze the Optimized Reverse Curve Geometry for flow over Ogee Spillways - Umank Mishra; Dipali Tiwari; Kamlesh Kumar Pandey; Abhishek Pagariya; Kaushal Kumar; Nitesh Gupta; Keval H. Jodhani; Upaka Rathnayake (Water Resources Management, 2025)

Water Quality Management using Federated Deep Learning in Developing Southeastern Asian Country - Bhagwan Das; Amr Adel; Tony Jan; M. D. Wahiduzzaman (Water Resources Management, 2025)

A Multi-Functional Genetic Algorithm-Neural Network Model for Predicting Suspended Sediment Loads - Haitham Abdulmohsin Afan; Wan Hanna Melini Wan Mohtar; Muammer Aksoy; Ali Najah Ahmed; Faidhalrahman Khaleel; Md Munir Hayet Khan; Ammar Hatem Kamel; Mohsen Sherif; Ahmed El-Shafie (Water Resources Management, 2025)

Development of a Fuzzy Indicator of Water Efficiency in Buildings - Rafael Flores; Enedir Ghisi (Water Resources Management, 2025)

Projecting Socio-Economic Exposure due to Future Hydro-Meteorological Extremes in Large Transboundary River Basin under Global Warming Targets - Rishi Gupta; Vinay Chembolu (Water Resources Management, 2025)

Application of F-HGAPSO Algorithm in Reservoir Flood Control Optimal Operation - Guangyun Cui; Zhen Qi; Huaqing Zhao; Ranhang Zhao; Haofang Wang; Jiaxing Zhao (Water Resources Management, 2025)

Risk Assessment of Flood Disaster in Cities Based on “Disaster-Pregnant, Disaster-Causing, Disaster-Forming and Disaster-Curing” - Xinjian Guan; Yuan Liu; Yu Meng; Hongfa Wang; Meng Liu (Water Resources Management, 2025)

A Novel Fuzzified Markov Chain Approach to Model Monthly River Discharge - Mohammad Mahdi Dorafshan; Mohammad Hossein Golmohammadi; Keyvan Asghari; Carlo De Michele (Water Resources Management, 2025)

Optimal Operation of Multi-reservoir System Utilizing DEA, AIDE Algorithm and Flood Control Assessment by MCDM Approach - Lalitesh Sinha; Sandeep M. Narulkar (Water Resources Management, 2025)

Assessing the Spatio-Temporal Evolution Mechanisms from Meteorological to Hydrological Drought in a Heavily Human-Influenced River Basin - Yuefen Zhang; Jianzhu Li; Ping Feng; Jiyang Tian (Water Resources Management, 2025)

Calibration and Uncertainty Analysis for Isovel Contours-based Stage-discharge Rating Curve by Sequential Uncertainty Fitting (SUFI-2) Method - Sajjad M. Vatanchi; Mahmoud F. Maghrebi (Water Resources Management, 2025)

Estimating Water Levels through Smartphone-Imaged Gauges: A Comparative Analysis of ANN, DL, and CNN Models - Celso Augusto Guimarães Santos; Mohammad Ali Ghorbani; Erfan Abdi; Utkarsh Patel; Siria Sadeddin (Water Resources Management, 2025)

Different Time-Increment Rainfall Prediction Models: a Machine Learning Approach Using Various Input Scenarios - Anas Rahimi; Noor Kh. Yashooa; Ali Najah Ahmed; Mohsen Sherif; Ahmed El-shafie (Water Resources Management, 2025)

Numerical Modelling of Groundwater Level and Salinity Evolution in a Low-Lying Coastal Area Under Intensive Agricultural Activity - Mattia Gaiolini; Nicolò Colombani; Valeria Chierici; Laura Montanari; Micòl Mastrocicco (Water Resources Management, 2025)

Evolution and Periodicity of Watershed-scale Runoff: Insight from the Spatiotemporal Variety in Intensive Anthropogenic Activities Region - Boyang Mao; Xihua Wang; Zejun Liu; Shunqing Jia; Cong Wang; Qinya Lv; Xuming Ji (Water Resources Management, 2025)

A Comparative Assessment of Machine Learning and Deep Learning Models for the Daily River Streamflow Forecasting - Malihe Danesh; Amin Gharehbaghi; Saeid Mehdizadeh; Amirhossein Danesh (Water Resources Management, 2025)

Machine Learning Approaches for Enhancing Energy Efficiency and Stability in Parallel Pumping Systems - Xingcheng Gan; Xiaobo Gong; Ji Pei; Giorgio Pavesi; Shouqi Yuan (Water Resources Management, 2025)

Automated Identification and Characterization of Compound Meander Loops - An-Bo Li; Xian-Li Xie; A-Xing Zhu (Water Resources Management, 2025)

Assessing the Impact of Rainfall Nowcasts on an Encoder-Decoder LSTM Model for Short-Term Flash Flood Prediction - Rim Mhedhbi; Marina G. Erechtchoukova (Water Resources Management, 2025)

Anthropogenic and Natural Impacts on Surface Water Quality: The Consequences and Challenges at the Nexus of Waste Management Facilities, Industrial Zones, and Protected Areas - Anna Podlasek; Eugeniusz Koda; Adriana Kwas; Magdalena Daria Vaverková; Aleksandra Jakimiuk (Water Resources Management, 2025)
The objective of this study was to evaluate the physicochemical characteristics of surface waters near an old landfill, to assess water quality using the Water Quality Index (WQI) and the Comprehensive Pollution Index (CPI), and to identify factors influencing surface water composition using multivariate statistical analysis. The analysis of temporal changes of 15 surface water parameters (pH, EC, Cl − , NH 4 + , SO 4 2− , BOD 5 , COD Cr , TOC, Zn, Cd, Pb, Cu, Cr, Hg, and PAH) was complemented by the investigation of surface water flow, ambient temperature, and precipitation. A twelve-year monitoring period was considered, focusing on monitoring sites located along a watercourse in the area of the Radiowo landfill (Poland). No significant correlations were found between precipitation and surface water quality parameters in the outflow direction. The results showed that the average WQI values ranged from 63.06 to 96.86, while the average CPI values ranged from 0.56 to 0.88, indicating good water quality or low pollution, respectively. The temperature had a greater influence on the physicochemical parameters than the precipitation, especially on the parameters of contamination by organic compound (correlations between temperature and BOD 5 , COD Cr , and TOC of 0.40, 0.50, and 0.38, respectively). Significant correlations were observed between EC, Cl − , NH 4 , BOD 5 , COD Cr , and TOC in the outflow direction of the landfill. In addition, surface water flow showed a significant moderate negative correlation with pH ( r = -0.44) and a moderate positive correlation with Cu concentration ( r = 0.47) downstream of the landfill. The analysis of surface water quality monitoring, coupled with the critical evaluation of global research, has made it possible to understand the variation in surface water quality and the factors that influence it. The study confirmed that given the complex nature of Municipal Solid Waste (MSW) landfills, understanding the dynamics and factors affecting water quality is essential for effective water protection and management and achieving the Sustainable Development Goals (SDGs).

Drought and Water Quality in a Semi-arid Area: Effects in Livestock Production, Agriculture and Use Urban - Germán Santacruz de León; José Alfredo Ramos-Leal; Janete Morán Ramírez; Oscar Guadalupe Almanza-Tovar (Water Resources Management, 2025)

Efficient Roof Selection in Rainwater Harvesting: Hybrid Multi-criteria and Experimental Approach - Muhammed Nimet Hamidi; Shayan Shitreh; Ali Izzet Cengiz; Kadir Ozcelik; Bahriye Eryildiz-Yesir; Ömer Ekmekcioğlu; Oguzhan Murat Halat; Mehmet Cuneyd Demirel; Berk Canberk; Ismail Koyuncu; Onur Isik; Huseyin Guven; et al. (Water Resources Management, 2025)

Scenario Neutral Based Multilateral Decision-Making Framework for Drought Management - Jiheun Kim; Yeonju Kim; Dong Hyeok Park; Tae-Woong Kim; Seung Beom Seo (Water Resources Management, 2025)

Long-term Water Management Model in the Presence of Extraordinary Events - Annamaria De Vincenzo; Bruno Molino; Antonio Jacopo Molino; Antonio de Nigris; Luigi Ambrosone (Water Resources Management, 2025)

Development of Geomorphological Unit Hydrograph (GUH) for Ungauged Basins - Aimilia-Panagiota Theochari; Evangelos Baltas (Water Resources Management, 2025)

Calibration of Linear Muskingum Model Coefficients and Coefficient Parameters Using Grey Wolf and Particle Swarm Optimization - Kemal Saplıoğlu (Water Resources Management, 2025)

Mathematical Model and Application of Overtopping Breach of Cascade earth-rock dams Considering Flood peak Enhancement Effect - Wen Qiu; Yanlong Li; Wei Zhao; Qiang Zhang; Lifeng Wen; Ye Zhang; Ting Wang (Water Resources Management, 2025)

Enhancing Sustainable Flood Risk Management Through a Novel Fuzzy Multi-Stakeholder Framework - Mustafa Utku Yilmaz (Water Resources Management, 2025)

How Planning Shared Infrastructure could Support Agriculture and Desalinated Irrigation - Babak Zolghadr-Asli; Neil McIntyre; Slobodan Djordjevic; Raziyeh Farmani; Liliana Pagliero (Water Resources Management, 2025)
The high price of desalination is often considered one of the primary obstacles to making desalinated water a viable option for irrigated agriculture. Relatively little attention has been given to how strategic planning of regional water supplies might contribute to addressing this issue, particularly in leveraging investment in desalination by wealthier sectors to support agricultural communities. The Copiapó valley in Chile has been selected as a case study to investigate this. The study illustrates using modelling how the expansion of the mining and associated investment in desalination can reduce the economic and environmental costs of supplying desalinated water to irrigators. It was observed that the reduction of costs is affected by the degree of expansion in the mining sector, the degree of expansion in the irrigated agriculture sector, and the energy market. With potential expansions, if they invest strategically in creating an optimal water supply network, the marginal costs of producing desalinated water can be reduced to between 1.09 USD/m³ and 2.53 USD/m³ depending on the energy price. This represents a reduction of 58–3%, respectively, compared to the average cost of producing desalinated water under baseline conditions where irrigated desalination is not supported. Results show that foresightful integrated water planning, which accounts for planned expansion in water demand, can result in more realistic costs of irrigating with desalinated water - although subsidy remains a likely requirement. Such integrated water planning would help bridge the gap between the price of desalinated water for irrigation practices. Furthermore, as demonstrated in this study, these practices can minimize the environmental impact of establishing a water conveyance network.

An Integrated Framework for Optimal Allocation of Land and Water Resources in an Agricultural Dominant Basin - Eswar Sai Buri; Venkata Reddy Keesara; K. N. Loukika; Venkataramana Sridhar (Water Resources Management, 2025)
The water deficit is one of the primary challenges faced by developing countries, stemming from several factors such as limited water resources, population growth, and climate change. Optimal allocation of water resources represents a comprehensive strategy for water resource management, acknowledging the intricate connections between water systems and their repercussions on the environment, society, and economy. It serves as a means of integrating diverse elements of development plans into a cohesive approach for land and water planning and management. In the current study, we undertook the optimal allocation of land and water resources across different sectors for the water years 2016-17, 2017-18, and 2018-19. The study area chosen was the Munneru basin, situated in the lower section of the Krishna River Basin in India. This basin is predominantly agricultural, covering 63.17% of the area, and was selected to validate the proposed framework concept. Within the study area, we identified six distinct water-demanding sectors and calculated their sectoral water demands at a basin level. To assess water availability in the basin, we conducted hydrological modeling employing the Soil and Water Assessment Tool (SWAT). Furthermore, we determined the crop water requirements for various crops using CROPWAT. For the optimal allocation of water resources, we applied the Non-dominated Sorting Genetic Algorithm-II (NSGA – II) optimization model, considering two different objectives that account for social and economic aspects. To identify superior solutions from the Pareto front, we employed the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) and Compromising Programming (CP) methods. Through this methodology, we achieved maximum utilization of water and land resources and maximized returns from the agricultural sector. Following the optimal allocation of land and water, we observed an average annual increase of 3.61% in agricultural sector returns. These outcomes demonstrated a substantial enhancement in the water use efficiency across all pertinent water use sectors. As a result, decision-makers may contemplate the implementation of this framework in large-scale regions, with potential expansion to encompass a national sustainable development strategy at the country level.

Water Management Optimization in Agriculture: a Digital Model Development - Luca Preite; Federico Solari; Giuseppe Vignali (Water Resources Management, 2025)

Urban Flood Depth Prediction and Visualization Based on the XGBoost-SHAP Model - Yuan Liu; Hongfa Wang; Xinjian Guan; Yu Meng; Hongshi Xu (Water Resources Management, 2025)

Optimization of Extreme Learning Machine with Metaheuristic Algorithms for Modelling Water Quality Parameters of Tamburawa Water Treatment Plant in Nigeria - Sani I. Abba; Quoc Bao Pham; Anurag Malik; Romulus Costache; Muhammad Sani Gaya; Jazuli Abdullahi; Sagiru Mati; A. G. Usman; Gaurav Saini (Water Resources Management, 2025)

Impact of Wing-wall on Scour Development in Box Culverts: Experimental Insights and Predictive Equations - Masoud Ghodsian; Mohammad Mohajeri (Water Resources Management, 2025)

Characteristics and Prediction of Reservoir Water Quality under the Rainfall-Runoff Impact by Long Short-Term Memory Based Encoder-Decoder Model - Xiaodan Sheng; Yulan Tang; Shupeng Yue; Xu Yang; Yating He (Water Resources Management, 2025)

Unveiling Precipitation Trend Characteristics in Changing Poorly-gauged Regions: Leveraging Alternative Raster Sources - Milad Nouri (Water Resources Management, 2025)

Modernizing Rural Water Governance: A Systematic Review of Transitions, Dynamics, Influential Factors, and Challenges - Humayun Khan; Hadiqa Anum; Zeng Yan; Qi Chunjie (Water Resources Management, 2025)

Reference Crop Evapotranspiration Prediction Based on Gated Recurrent Unit with Quantum Inspired Multi-head Self-attention Mechanism - Zehai Gao; Dongzhe Yang; Baojun Li; Zijun Gao; Chengcheng Li (Water Resources Management, 2025)

Resilience to Climate Change in Agricultural Water-Scarce Areas: The Major Obstacles and Adaptive Strategies - Atefeh Ahmadi; Marzieh Keshavarz; Farid Ejlali (Water Resources Management, 2025)

Distributive Assessment of Vehicle Damage in Flash Floods Caused by Unplanned Urban Development Using PCSWMM - Reyhaneh Golmohammadi; Alireza Shokoohi (Water Resources Management, 2025)

Predictive Performance of Ensemble Learning Boosting Techniques in Daily Streamflow Simulation - Divya Chandran; N. R. Chithra (Water Resources Management, 2025)

A Novel Approach for Predicting peak flow from Breached Dam: Coupling Monte Carlo Simulation, Hydrodynamic Model, and an Interpretable XGBoost Model - Ali El Bilali; Abdeslam Taleb (Water Resources Management, 2025)

Flood Mitigation and Water Resource Preservation: Hydrodynamic and SWMM Simulations of nature-based Solutions under Climate Change - Maelaynayn El Baida; Mimoun Chourak; Farid Boushaba (Water Resources Management, 2025)

Water Resources Quality Indicators Monitoring by Nonlinear Programming and Simulated Annealing Optimization with Ensemble Learning Approaches - Mojtaba Poursaeid; Amir Hossein Poursaeed; Saeid Shabanlou (Water Resources Management, 2025)

An Inventory Comparison of Harvesting Rainwater from Roofs and Stormwater from Pervious Pavements - Igor Catão Martins Vaz; Enedir Ghisi; Liseane Padilha Thives (Water Resources Management, 2025)

Green Roofs Hydrological Performance and Contribution to Urban Stormwater Management - Ana Mafalda Mendes; Cristina M. Monteiro; Cristina Santos (Water Resources Management, 2025)

A Novel Optimized Vibration-based Energy Harvester for Leak Detection in Wall-mounted Water Pipelines - Muhammad Faizan Mysorewala; Mati-Ur-Rasool Ashraf Virk; Mujahid Naqeebuddin Syed; Abdul-Wahid Saif; Lahouari Cheded; Saifullah Shafiq; Karim Sattar (Water Resources Management, 2025)

Comparison of Calibration Strategies for Daily Streamflow Simulations in Semi-Arid Basins - Khaoula Ait Naceur; El Mahdi El Khalki; Abdessamad Hadri; Oumar Jaffar; Luca Brocca; Mohamed Elmehdi Saidi; Yves Tramblay; Abdelghani Chehbouni (Water Resources Management, 2025)

Shear Layer Development and Fully Developed Flows in Compound Channels - João N. Fernandes; João B. Leal; António H. Cardoso (Water Resources Management, 2025)

Study on Ensemble Calibration of Flood Forecasting Based on Response Curve of Rainfall Dynamic System and LSTM - Lu Tian; Qiying Yu; Zhichao Li; Chengshuai Liu; Wenzhong Li; Chen Shi; Caihong Hu (Water Resources Management, 2025)

Numerical Simulation of Dam-Break Flood Routing in Pumped Storage Power Stations with Multi-Conditions and Disaster Impact Analysis - Baojun Guan; Jingming Hou; Jiahao Lv; Donglai Li; Guangzhao Chen; Yuan Fang; Lei Shi (Water Resources Management, 2025)

A Novel and Efficient Mathematical Programming Approach for the Optimal Design of Rainwater Drainage Networks - Ana Paula Sene; Jose A. Caballero; Mauro A. S. S. Ravagnani (Water Resources Management, 2025)

Simulation-based Optimization of Water Resource Systems: A Review of Limitations and Challenges - Shewandagn Lemma Tekle; Brunella Bonaccorso; Mohamed Naim (Water Resources Management, 2025)

A Comprehensive Assessment of Water Loss and Driving Forces for the Middle Route of the South-to-North Water Diversion Project from Humanistic Perspective - Jinyu Xiao; Quansheng Ge; Ming Hu; Huijuan Cui (Water Resources Management, 2025)

Numerical Study of the Water Surface Movement on the Breaching Process of Natural Dams - Alibek Issakhov; Zhansaya Rakhymzhanova; Aizhan Abylkassymova (Water Resources Management, 2025)

Assessment of the Influence of a Multilevel Protective Column on the Spread of Mud-stone Admixture During a Dam Break - Alibek Issakhov; Aliya Borsykbaev; Aizhan Abylkassymova (Water Resources Management, 2025)

Sustainable Groundwater Management through Collaborative Local Scale Monitoring - L. Reading; L. B. Gurieff; S. Catania (Water Resources Management, 2025)

A Meta-Analysis of Water Institutions and Their Performance: Implications for Water Resource Management - Alfred Tunyire Apio; Djiby Racine Thiam; Ariel Dinar (Water Resources Management, 2025)
This paper uses meta-regression analysis to investigate the empirical literature on the performance of water institutions. This pioneering study synthesizes and quantifies the overall water institution-performance effect using data extracted from 23 original studies. A bivariate meta-regression and funnel asymmetric test are estimated and conducted, respectively, and for our multivariate meta-regressions (MMRs), four different fixed and random effects weighted least square models are estimated. Both bivariate and MMRs confirm the presence of a publication selection bias that favors the positive impact of water institutions on performance. Once this bias is corrected, evidence of a genuine empirical effect of water institutions on the performance is evident. Also, the variations in this literature are attributable to differences in the way these studies capture water institutions, the variables used to capture performance, and the estimation strategies, among others. Primary studies that use the water law, water policy, and/or some aspects of these to capture performance tend to report a greater impact of water institutions on performance. This paper contributes not only to improving the quality of research, reporting, review, and publication in the water space but also provides further insights for additional development, facilitation, and strengthening of water laws, policies, and administration to lower transaction costs in the water space.

Watersheds Governance Optimizing Water-Energy-Food Nexus Approach Across Water Users and Watershed Scales - Icaro Yuri Pereira Dias; Lira Luz Benites Lazaro; Marcela Procopio Burian de Castro; Roberto Novaes Dagios; Virginia Grace Barros (Water Resources Management, 2025)

LSTM Model-Based Rapid Prediction Method of Urban Inundation with Rainfall Time Series - Xinxin Pan; Jingming Hou; Xujun Gao; Guangzhao Chen; Donglai Li; Muhammad Imran; Xinyi Li; Nan Yang; Menghua Ma; Xiaoping Zhou (Water Resources Management, 2025)

Assessment of Climate Change Impact on Flood Hazard Zones - Tomasz Dysarz; Paweł Marcinkowski; Joanna Wicher-Dysarz; Mikołaj Piniewski; Dorota Mirosław-Świątek; Zbigniew W. Kundzewicz (Water Resources Management, 2025)
There have been many destructive pluvial and fluvial floods in Poland and the projection of increasing flood hazards in the future is a reason of considerable concern. The maps of river hazard zones are changing over time, and understanding these changes is of primary importance for flood risk reduction and climate change adaptation. This article aims to assess the impact of climate change on the spatial extent and depth classes of flood hazard zones for a selected reach of the River Warta in the western part of Poland. To this end, we integrated the Soil & Water Assessment Tool (SWAT) hydrological model of the Warta River Basin with the 1D hydraulic model HEC-RAS of the selected reach. The climate change effect was quantified based on the coupled model simulations forced with bias-corrected projections from the EURO-CORDEX project. Flood hazard maps were developed for two townships along the River Warta (Oborniki and Wronki), three greenhouse gas concentration scenarios (one for the baseline scenario in the reference period, 1971–2000; one for RCP 4.5 and one for RCP 8.5, for the time horizon 2021–2050) and for three return periods (10-, 100- and 500-year floods). Based on the ensemble mean, the increase in the flooded area projected in the future is more pronounced for RCP8.5 than for RCP4.5. This unique combination of software and data enabled the transformation of climate change impact into the land surface part of the hydrological cycle and assessment of changes in flood hazard and opens the way to assess the potential increases in the economic losses in the future.

Rainwater Harvest with Desert-Sand Bricks: An Adaptive Water Resources Management Strategy for Climate Change - Xiangzhou Xu; Mingyang Liu; Hang Gao; Peiqing Xiao; Yu Zhang (Water Resources Management, 2025)

Dual-Diameter Drip Laterals Laid on Flat Fields: Modelling and Measurements - Giorgio Baiamonte; Samuel Palermo (Water Resources Management, 2025)

An Application of Hybrid Bagging-Boosting Decision Trees Ensemble Model for Riverine Flood Susceptibility Mapping and Regional Risk Delineation - Javeria Sarwar; Saud Ahmed Khan; Muhammad Azmat; Faridoon Khan (Water Resources Management, 2025)

Evaluation of Sentinel-1 Satellite-based Soil Moisture Products for Runoff Modelling with Karst Formation Characteristics - Hanggar Ganara Mawandha; Afinafghani Duta Pratama; M. Ramadhan Al Ghifari; Nasywa Hanin Hanifah; Issiami Nursafa; Prieskarinda Lestari; Satoru Oishi (Water Resources Management, 2025)

Optimizing District Metered Areas and Associated Pressure Reducing Valves and Pumping Stations for Effective and Efficient Management of Water Distribution Network - Hongyan Li; Wentao Shi; Zifeng Chang; Feng Zhang; Jianguo Cui; Xulong Lu; Libo Mao (Water Resources Management, 2025)

Determining the Economic Benefit of Watershed Conservation for Urban Water Supply Based on Scarcity rent for Water and Consumer Surplus - Rossano Belladona; Guilherme Fernandes Marques; Tiago De Vargas (Water Resources Management, 2025)

A Review of Climate Change Impacts on Irrigation Water Demand and Supply - A Detailed Analysis of Trends, Evolution, and Future Research Directions - Ransford Opoku Darko; Flavio Odoi-Yorke; Agnes Abeley Abbey; Emmanuel Afutu; Joshua Danso Owusu-Sekyere; Livingstone Kobina Sam-Amoah; Lawerence Acheampong (Water Resources Management, 2025)

Machine Learning-Based Multifaceted Analysis Framework for Comparing and Selecting Water Quality Indices - Dana Simian; Marin-Eusebiu Șerban; Alina Bărbulescu (Water Resources Management, 2025)
Water quality is essential to the population’s well-being, water resources management, and environmental development strategies. In this article, we propose a framework based on machine learning (ML) techniques for enhancing the assessment of water quality based on water quality indices (WQIs). It consists of three algorithms that could serve as a foundation for automating the evaluation of any resource based on indices and can operate locally or globally. Local-level algorithms assist in selecting suitable WQIs tailored to specific water sources and quality requirements, while global-level algorithm evaluates WQI robustness across diverse water sources. We also provide a warning system to mitigate differences in water quality evaluation using WQIs and a valuable tool (based on the features’ importance) for selecting ML models that prioritize the water parameters’ significance. The framework’s design draws upon conclusions from a case study involving the forecast and comparison of two WQIs for the Brahmaputra River. Any other data series, WQIs, and water parameters can be employed.

A Conceptual Decision-Making Framework for River Ecological Flow Based on a Dynamic Balance Between the Supply and Demand of Stakeholders - Lei Liu; Xingqi Luo; Bo Cheng; Huaien Li; Wenjuan Cheng; Xijun Wu (Water Resources Management, 2025)

Advancing Reservoir Water Level Predictions: Evaluating Conventional, Ensemble and Integrated Swarm Machine Learning Approaches - Issam Rehamnia; Amin Mahdavi-Meymand (Water Resources Management, 2025)
Accurate estimation of reservoir water level fluctuation ( WLF ) is crucial for effective dam operation and environmental management. In this study, seven machine learning (ML) models, including conventional, integrated swarm, and ensemble learning methods, were employed to estimate daily reservoir WLF . The models comprise multi-linear regression (MLR), shallow neural network (SNN), deep neural network (DNN), support vector regression (SVR) integrated with homonuclear molecules optimization (HMO) and particle swarm optimization (PSO) meta-heuristic algorithms, classification and regression tree (CART), and random forest (RF). These models were trained and evaluated using in situ data from three embankment dams in Algeria: the Kramis dam, the Bougous dam, and the Fontaine Gazelles dam. Performance evaluation was conducted using statistical indices, scatter plots, violin plots, and Taylor diagrams. The results revealed superior prediction accuracy for the Fontaine Gazelles dam compared to Kramis and Bougous dams. Particularly, the RF, DNN, and SVR-HMO models exhibited consistent and excellent predictive performance for WLF at the Fontaine Gazelles dam with RMSE values of 0.502 m, 0.536 m, and 0.57 m, respectively. The RF model demonstrates remarkable accuracy across all three case studies. This can be attributed to the ensemble structure of RF, as evidenced by the results. This study underscores the significance of considering factors such as seepage flow intensity in understanding WLF variability. Furthermore, the proposed ML models offer promising capabilities in WLF prediction, highlighting their potential utility in enhancing reservoir management practices and addressing the limitations of traditional regression models. Keys words. Embankment dam, Water level fluctuations, Seepage, Artificial neural network, meta-heuristic algorithm.

Characterization of Partial Dam-Break Waves: Effects of Upstream and Downstream Water Levels - Yanshun Liu; Xiao Zhang; Yuxue Sun; Hao Yu; Chuanyu Sun; Zihan Li; Xianghui Li (Water Resources Management, 2025)

Supporting Environmental Decision-Making in Urban Water and Wastewater Systems: Proposal for an Environmental Performance Index - Mirtes Tatiane Neisse Boldrin; Anderson Cristiano Neisse; Klebber Teodomiro Martins Formiga (Water Resources Management, 2025)

Distributed Nonconvex Optimization for Control of Water Networks with Time-coupling Constraints - Bradley Jenks; Aly-Joy Ulusoy; Filippo Pecci; Ivan Stoianov (Water Resources Management, 2025)
In this paper, we present a new control model for optimizing pressure and water quality operations in water distribution networks. Our formulation imposes a set of time-coupling constraints to manage temporal pressure variations, which are exacerbated by the transition between pressure and water quality controls. The resulting optimization problem is a nonconvex, nonlinear program with nonseparable structure across time steps. This problem proves challenging for state-of-the-art nonlinear solvers, often precluding their direct use for near real-time control in large-scale networks. To overcome this computational burden, we investigate a distributed optimization approach based on the alternating direction method of multipliers (ADMM). In particular, we implement and evaluate two algorithms: a standard ADMM scheme and a two-level variant that provides theoretical convergence guarantees for our nonconvex problem. We use a benchmarking water network and a large-scale operational network in the UK for our numerical experiments. The results demonstrate good convergence behavior across all problem instances for the two-level algorithm, whereas the standard ADMM approach struggles to converge in some instances. With an appropriately tuned penalty parameter, however, both distributed algorithms yield good quality solutions and computational times compatible with near real-time (e.g. hourly) control requirements for large-scale water networks.

Risk Assessment as a Tool to Improve Water Resource Management - Yelizaveta Chernysh; Lada Stejskalová; Přemysl Soldán; Foon Yin Lai; Uzair Akbar Khan; Cecilia Stålsby Lundborg; Laure Giambérini; Laetitia Minguez; M. Concepción Monte; Angeles Blanco; Maksym Skydanenko; Hynek Roubík (Water Resources Management, 2025)

Exploration of Equity Cooperation Concepts in Water Resource Management - Pan Liu; Yang Zheng; Yukun Yang; Hao Wang; Xuefeng Sang; Siqi Zhang (Water Resources Management, 2025)

Glacial Lake Outburst Flood (GLOF) Hazard and Risk Management Strategies: A Global Overview - Rayees Ahmed (Water Resources Management, 2025)

Assessing Water Resource Vulnerability in an Agricultural Basin for Climate Change Adaptation - Chen Cao; Miaomiao Ying (Water Resources Management, 2025)

Suspended Load Estimation in Data Scarce Rivers - Nikolaos Efthimiou (Water Resources Management, 2025)
Sediment rating curves (SRCs) are tools of satisfactory reliability in the attempt to describe the sediment regime in catchments with limited or poor-quality records. The study valorised the most suitable SRC development method for the estimation of the coarse suspended sediment load at the outlet of nine Mediterranean sub-watersheds. Four established grouping techniques were assessed, to minimize the uncertainty of the results, namely simple rating curve, different ratings for the dry and wet season of the year, hydrographic classification, and broken line interpolation, at three major Greek rivers (Aliakmon, Acheloos – upper route, Arachthos). The methods’ performance was benchmarked against sediment discharge field records, utilizing statistical measures and graphical analyses. The necessary observations were conducted by the Greek Public Power Corporation. The results were site/station dependent, and no methodology emerged as universally accepted. The analysis designated that the simple rating curve performs best at the cross-sections Moni Ilarion, Moni Prodromou, and Arta bridge, the different ratings for the dry and wet season of the year at Grevena bridge and Gogo bridge, the hydrographic classification at Velventos and Plaka bridge, and the broken line interpolation at Avlaki dam and Tsimovo bridge. In this regard, the study advocates the use of multiple SRC methods. Despite its limitations, the method merits a rather simple and cost-effective generation of a (continuous, detailed, sufficiently accurate) synthetic suspended sediment discharge timeseries, with high interpolating, extrapolating and reproducibility potential. The success of the application could benefit, among others, water quality restoration and dam management operations.

Near Real-time Leak Location by Inverse Analysis Integrating Measurement Uncertainty - Bruno Ferreira; Nelson Carriço; Dídia Covas (Water Resources Management, 2025)
This paper presents a novel model-based method for near real-time pipe burst location in water distribution networks by integrating measurement uncertainty into inverse analysis. The method accounts for expected errors between measured and computed values, providing a pipe burst location area whose size varies according to the expected error level and the burst size. The proposed method is demonstrated and compared with the traditional inverse approach using a real case study with artificial bursts of different sizes and with different pressure signal noise levels. The performance of both methods is also assessed and discussed considering the effect of seasonal water demands. The traditional inverse analysis fails to accurately locate the pipe burst events, and depending on the expected error level and pipe burst size, the obtained locations may be significantly further away from the real burst location. Conversely, the proposed method does not point to the exact burst location but provides an approximated area in which step-testing can be carried out to pinpoint the exact burst location; the size of this area can be larger or smaller depending on the burst flow rate and signal uncertainty.

Influence of Water Transfer between River Basins on the Operation of Water Systems in Semi-arid Regions - Francisco Soares Roque; Joana Darc Freire de Medeiros (Water Resources Management, 2025)

A Hybrid Method for Designing Sustainable River Monitoring Networks Using Fuzzy Logic Site Selection and Genetic Algorithm Optimization - Farhad Salimian; Reza Ghiassi (Water Resources Management, 2025)

Statistical Post-Processing for Precipitation Forecast Through Deep Learning Coupling Large-Scale and Local-Scale Spatiotemporal Information - Tuantuan Zhang; Zhongmin Liang; Chenglin Bi; Jun Wang; Yiming Hu; Binquan Li (Water Resources Management, 2025)

Multi-Objective Modeling Framework for Environmental Flow Optimization in a River-Reservoir System Using Histogram Comparison Approach for Estimation of Hydrologic Alteration - Ruby Jose; Srinivasan K (Water Resources Management, 2025)

Rainfall Analysis using FUCOM Weighted Logarithmic Distance Measure Based on Probabilistic Dual Hesitant Preference Values - Rohit; Kamal Kumar; Reeta Bhardwaj; Gagandeep Kaur (Water Resources Management, 2025)

Coupling SWAT and LSTM for Improving Daily Streamflow Simulation in a Humid and Semi-humid River Basin - Ziyi Mei; Tao Peng; Lu Chen; Vijay P. Singh; Bin Yi; Zhiyuan Leng; Xiaoxue Gan; Tao Xie (Water Resources Management, 2025)

A Comparative Study of Machine Learning Models for Daily and Weekly Rainfall Forecasting - Vijendra Kumar; Naresh Kedam; Ozgur Kisi; Saleh Alsulamy; Khaled Mohamed Khedher; Mohamed Abdelaziz Salem (Water Resources Management, 2025)
Accurate rainfall forecasting is crucial for various sectors across diverse geographical regions, including Uttarakhand, Uttar Pradesh, Haryana, Punjab, Himachal Pradesh, Madhya Pradesh, Rajasthan, and the Union Territory of Delhi. This study addresses the need for precise rainfall predictions by bridging the gap between localized meteorological data and broader regional influences. It explores how rainfall patterns in neighboring states affect Delhi's precipitation, aiming to improve forecasting accuracy. Historical rainfall data from neighboring states over four decades (1980–2021) were collected and analyzed. The study employs a dual-model approach: a daily model for immediate rainfall triggers and a weekly model for longer-term trends. Several machine learning algorithms, including CatBoost, XGBoost, ElasticNet, Lasso, LGBM, Random Forest, Multilayer Perceptron, Ridge, Stochastic Gradient Descent, and Linear Regression, were used in the modeling process. These models were rigorously assessed based on performance metrics from training, validation, and testing datasets. For daily rainfall forecasting, CatBoost, XGBoost, and Random Forest emerged as top performers, showcasing exceptional accuracy and pattern-capturing capabilities. In weekly rainfall forecasting, XGBoost consistently achieved near-perfect accuracy with an R 2 value of 0.99, with Random Forest and CatBoost also demonstrating strong performance. The study provides valuable insights into how climate patterns in neighboring states influence Delhi's weather, leading to more reliable and timely rainfall predictions.

Explicit Stochastic Procedure for Developing Reservoir Storage-Yield-Reliability-Vulnerability Relationships - José Eduardo Santos Araújo; Alcigeimes B. Celeste (Water Resources Management, 2025)

Cleanformer: A Confident Learning Based ERP Label Denoising Framework for Public Attitude Assessment to Recycled Water - Yong Peng; Shuxiang Lin; Jiachen Niu; Hanliang Fu; Chaojie Fan (Water Resources Management, 2025)

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 Method for Calculating the Design Volume of the Initial Rainwater Storage tank - Xingpo Liu; Keer Wei (Water Resources Management, 2025)

Study on Flood Control Operation of Parallel Reservoir Groups Considering the Difference of Solution Order - Qiong Wu; Zhiqiang Jiang; Zongye Chang; Suiling Wang (Water Resources Management, 2025)

Sub-watershed Prioritization Based on Watershed Hydrological Security Using Different Multi-Criteria Decision-Making Methods - Mohammad Tavosi; Mehdi Vafakhah; Seyed Hamidreza Sadeghi; Sayed M. Bateni (Water Resources Management, 2025)

Application of Machine Learning Models for Short-term Drought Analysis Based on Streamflow Drought Index - Majid Niazkar; Reza Piraei; Mohammad Zakwan (Water Resources Management, 2025)

Study on Runoff Simulation with Multi-source Precipitation Information Fusion Based on Multi-model Ensemble - Runxi Li; Chengshuai Liu; Yehai Tang; Chaojie Niu; Yang Fan; Qingyuan Luo; Caihong Hu (Water Resources Management, 2024)

Improving Sub-daily Runoff Forecast Based on the Multi-objective Optimized Extreme Learning Machine for Reservoir Operation - Wenhao Jia; Mufeng Chen; Hongyi Yao; Yixu Wang; Sen Wang; Xiaokuan Ni (Water Resources Management, 2024)

Optimal Operation of Reservoirs Considering Water Quantity and Quality Aspects: A Systematic State-of-the-Art Review - Mahta Nazari; Reza Kerachian (Water Resources Management, 2024)

Climate Resilient Cities: A Review of Blue-Green Solutions Worldwide - Kornelia Przestrzelska; Katarzyna Wartalska; Weronika Rosińska; Jakub Jurasz; Bartosz Kaźmierczak (Water Resources Management, 2024)
Progressing climate change significantly affects the quality of life in cities. The related changes in the rainfall structure lead to an increased frequency of extreme hydrological phenomena such as droughts and floods. The increasing intensity of rainfall is a particular threat to urbanised areas, where due to the high degree of area sealing, an increase in runoff volumes and peak flow rate is observed. Storm water drainage systems are often not adapted to accommodate increased rainwater flows, which leads to the so-called urban floods. On the other hand, prolonged periods without precipitation may, in turn, lead to periodic water shortages. Cities must implement a climate change adaptation strategy. One of the solutions for rational rainwater management is the implementation of Blue-Green Infrastructure. These facilities are a modern solution supporting the sewage networks operation, water retention, and improving the quality of life in cities. The article presents the literature review on the current state of knowledge about Blue-Green Infrastructure and the implementation stage of these solutions in the world. Solutions that are newly introduced, not necessarily newly invented, in many places around the world are described. The literature review was limited to the period 2018–2023 to indicate the most modern and up-to-date strategies.

Potential Amplifiers of Hydrological Drought: A Deep Dive into the Seasonal Variations of New, Young, and Old Water Fractions in the Stream Discharge of a Pre-Alpine Catchment - Simon Hoeg (Water Resources Management, 2024)

Automatic Detection of Water Consumption Temporal Patterns in a Residential Area in Northen Italy - Elena Cristiano; Pietro Biddau; Andrea Delogu; Martina Gandolfi; Roberto Deidda; Francesco Viola (Water Resources Management, 2024)
One of the main challenges for city development is to ensure a sustainable water resource management for the water supply system. A clear identification of the urban water consumption patterns supports policy and decision makers in managing the water resources, satisfying the total demand and, at the same time, reducing losses and identifying potential leakages or other issues in the distribution network. High resolution smart meters have widely shown to be an efficient tool to measure in-pipe water consumption. The collected data can be used to identify water demand patterns at different temporal and spatial scales, reaching the end-uses level. Water consumption patterns at building level can be influenced by multiple factors, such as socio-demographic aspects, seasonality, and house characteristics. The presence of a garden that requires summer irrigation strongly alters the daily consumption pattern. In this framework, we present an innovative approach to automatically detect the presence of garden irrigation, identifying daily average water consumption patterns with and without it. The proposed methodology was tested in a residential area in Northen Italy, where 23 smart meters recorded data at 1-minute resolution for two years. Results show very good performances in distinguishing between days with and without garden irrigation. The derived average normalized water consumption patterns for both scenarios can help decision makers and water managers to regulate the pressure regimes in the distribution network correctly.

New Hybrid Optimization Approaches for the Optimal Management of Surface Water Resources Systems - Saeid Akbarifard; Mohammad Zounemat-Kermani (Water Resources Management, 2024)

Multimodal Fusion of Optimized GRU–LSTM with Self-Attention Layer for Hydrological Time Series Forecasting - Huseyin Cagan Kilinc; Sina Apak; Furkan Ozkan; Mahmut Esad Ergin; Adem Yurtsever (Water Resources Management, 2024)

Improving Short-term Daily Streamflow Forecasting Using an Autoencoder Based CNN-LSTM Model - Umar Muhammad Mustapha Kumshe; Zakariya Muhammad Abdulhamid; Baba Ahmad Mala; Tasiu Muazu; Abdullahi Uwaisu Muhammad; Ousmane Sangary; Abdoul Fatakhou Ba; Sani Tijjani; Jibril Muhammad Adam; Mosaad Ali Hussein Ali; Aliyu Uthman Bello; Muhammad Muhammad Bala (Water Resources Management, 2024)

Characterizing Shifts in Major Land Use Types and the Response of Water Yield in a Catchment with Widespread Peaty Wetlands - Wenhang Su; Hailong Wang; Liming Gao (Water Resources Management, 2024)

Enhancing Decision-Making in Sustainable Urban Drainage System Optimization: A Novel Framework for Sparse Pareto-Fronts - Omid Seyedashraf (Water Resources Management, 2024)

Evaluation of Optimized Multi-Model Ensembles for Extreme Precipitation Projection Considering Various Objective Functions - Seung Taek Chae; Eun-Sung Chung; Dongkyun Kim (Water Resources Management, 2024)

Optimizing Water Conservation Strategies and Blockchain Integration in High-Water Consumption Industries Across Diverse Power Structures: A Differential Game Approach - Jie Leng; Xin Qi; Daoqing Hao (Water Resources Management, 2024)

The Use of Attention-Enhanced CNN-LSTM Models for Multi-Indicator and Time-Series Predictions of Surface Water Quality - Minhao Zhang; Zhiyu Zhang; Xuan Wang; Zhenliang Liao; Lijin Wang (Water Resources Management, 2024)

Assessing the Consumption-based Water Use of Global Construction Sectors and its Impact to the Local Water Shortage - Wei Huang; Chenyang Shuai; Pengchen Xiang; Xi Chen; Bu Zhao; Jingran Sun (Water Resources Management, 2024)

A Comparative Analysis of Good Water Governance in Iran’s Water-Poor Basins - Samira Nabiafjadi; Maryam Sharifzadeh; Hossein Shabanali Fami; Mostafa Ahmadvand (Water Resources Management, 2024)

Optimization of Irrigation Programming for Different Water Allocation Strategies at Network Level: Method and Application - Murat Kilic; Emrah Özçakal (Water Resources Management, 2024)
Optimum irrigation programming at network level is important not only for maximum yield and benefit from the system but also for sustainable use of constrained resources. The subject of this investigation is to devise a method which enables optimum water allocation in irrigation networks, to apply it to a real system, and to analyze the results. In the first step, the irrigation district was divided into the most suitable water allocation zones considering the hydraulic properties of the canals. Next, alternative system rotation periods were defined in accordance with the properties of the research area, the hydraulic infrastructure of the irrigation network and the crop pattern. In this process, the model was run for five different water allocation strategies. The model was applied to the command area of Sarıkız Irrigation Association in the Ahmetli Regulator Right Bank Irrigation System in the Gediz Basin. Therefore, irrigation programs were prepared for the crop pattern, which receives water from 45 tertiary canals of the Y9 secondary. The irrigation time allocated to each tertiary canal and the amount of irrigation water were evaluated, together with the water shortage levels which occurred in these tertiary canals. The results indicated that the model defined the optimum system rotation period, the borders and the sizes of the most suitable water allocation zones, and the most suitable irrigation programs under the prevailing conditions. The proposed model can provide an insight for decision makers as a decision support tool.

A Systematic Literature Review on Classification Machine Learning for Urban Flood Hazard Mapping - Maelaynayn El baida; Mohamed Hosni; Farid Boushaba; Mimoun Chourak (Water Resources Management, 2024)

Construction of Ecological Water Resources Management Leadership Model Based on Multi-Agent Collaboration - Meng Meng (Water Resources Management, 2024)

Assessment of Different Methods for Estimation of Missing Rainfall Data - Tuğçe Hırca; Gökçen Eryılmaz Türkkan (Water Resources Management, 2024)
Missing data is a common problem encountered in various fields, including clinical research, environmental sciences and hydrology. In order to obtain reliable results from the analysis, the data inventory must be completed. This paper presents a methodology for addressing the missing data problem by examining the missing data structure and missing data techniques. Simulated datasets were created by considering the number of missing data, missing data pattern and missing data mechanism of real datasets containing missing values, which are often overlooked in hydrology. Considering the missing data pattern, the most commonly used methods for missing data analysis in hydrology and other fields were applied to the created simulated datasets. Simple imputation techniques and expectation maximization (EM) were implemented in SPSS software and machine learning techniques such as k-nearest neighbor (kNN), together with the hot-deck were implemented in the Python programming language. In the performance evaluation based on error metrics, it is concluded that the EM method is the most suitable completion method. Homogeneity analyses were performed in the Mathematica programming language to identify possible changes and inconsistencies in the completed rainfall dataset. Homogeneity analyses revealed that most of the completed rainfall datasets are homogeneous at class 1 level, consistent and reliable and do not show systematic changes in time.

Simulation and Evaluation of Collapsible Risk of Low Impact Development Rainwater System in Collapsible Loess Area - Jing Jing; Jingming Hou; Zhanpeng Pan; Tian Wang; Xinyi Li; Guangzhao Chen; Xueliang Sun; Changhui Duan (Water Resources Management, 2024)

Aggregation-Decomposition Coupling Drawdown Rule and Progressive Optimal Algorithm for Optimization of Large-Scale Reservoirs - Jiahui Sun; Chao Wang; Hao Wang; Yunke Xiao; Xiaohui Lei; Zhongzheng He; Peibing Song; Pengyu Jin (Water Resources Management, 2024)

Xinanjiang-Based Interval Forecasting Model for Daily Streamflow Considering Climate Change Impacts - Hao Ke; Wenzhuo Wang; Zengchuan Dong; Benyou Jia; Ziqin Zheng; Shujun Wu (Water Resources Management, 2024)

An Improved Coupled Hydrologic-Hydrodynamic Model for Urban Flood Simulations Under Varied Scenarios - Siwei Cheng; Mingxiang Yang; Chenglin Li; Houlei Xu; Changli Chen; Dewei Shu; Yunzhong Jiang; Yunpeng Gui; Ningpeng Dong (Water Resources Management, 2024)

Estimating U.S. Subnational Freshwater Withdrawals by Water Use Category from 1995 to 2021 - Sarah Rehkamp; James Chandler Zachary (Water Resources Management, 2024)

Investigating the Role of Driving Variables on ETo Variability and “Evapotranspiration Paradox” Across the Indian Subcontinent Under Historic and Future Climate Change - Femin C. Varghese; Subhasis Mitra (Water Resources Management, 2024)

Water–Energy–Land Reallocation and Multiobjective Optimization in the Agricultural System - Jue Wang; Keyi Ju; Xiaozhuo Wei (Water Resources Management, 2024)

Desalination Powered by Renewables: A Challenge and an AI Opportunity - Tawfiq Chekifi; Amine Benmoussa; Moustafa Boukraa (Water Resources Management, 2024)

Monthly Runoff forecasting using A Climate‑driven Model Based on Two-stage Decomposition and Optimized Support Vector Regression - Zhuo Jia; Yuhao Peng; Qin Li; Rui Xiao; Xue Chen; Zhijin Cheng (Water Resources Management, 2024)

WEI + as an Indicator of Water Scarcity and Ecological Demand Issue - Dejan Dimkić; Zoran Simić (Water Resources Management, 2024)

Relating the Drought Precipitation Percentiles Index to the Standardized Precipitation Index (SPI) under Influence of Aridity and Timescale - Tayeb Raziei; Luis Santos Pereira (Water Resources Management, 2024)

Suitable Ecological Water Demand for Wetlands Restored to Different Historical Periods in a Latitude area and their Response to Changing Environments - Haipeng Xu; Dequan Zhang; Yao Wu; Peng Qi; Xiaofeng Wang (Water Resources Management, 2024)

Monthly Precipitation Prediction Based on the CEEMDAN-BMA Model - Youyi Zhao; Shangxue Luo; Jiafang Cai; Zhao Li; Meiling Zhang (Water Resources Management, 2024)

Quantifying the Irrigation Requirements for Major Crops Under the Influence of Climate Change in a Semi-Arid Region - Ankur Sharma; Prajakta Prabhakar Surkar; Ruchi Khare; Mahendra Kumar Choudhary; Vishnu Prasad (Water Resources Management, 2024)

The Application and Potential of Multi-Objective Optimization Algorithms in Decision-Making for LID Facilities Layout - Yuanyuan Xie; Haiyan Wang; Kaiyi Wang; Xiaoyu Ge; Xin Ying (Water Resources Management, 2024)

Combining Prediction Models and Dimensionality Reduction Technology for Water Resources Management Under Incomplete Information and Dynamic Change - Xi Yang; Zhihe Chen (Water Resources Management, 2024)

A New Hybrid Weighted Regional Drought Index to Improve Regional Drought Assessment - Alina Mukhtar; Aamina Batool; Zulfiqar Ali; Sadia Qamar; Saba Riaz; Saad Sh. Sammen (Water Resources Management, 2024)

Prediction of Monthly Flow Regimes Using the Distance-Based Method Nested with Model Swapping - Muhammad Uzair Qamar; Cuauhtémoc Tonatiuh Vidrio-Sahagún; Jianxun He; Usama Tariq; Akbar Ali (Water Resources Management, 2024)

Sustainable Water Resources Management through Disaggregated Multi-Region Virtual Water Flow and Interaction Analysis - Boyue Zheng; Lirong Liu; Guohe Huang; Brian Baetz; Mengyu Zhai; Kaiqiang Zhang; Chen Lu (Water Resources Management, 2024)
Virtual water transfer is considered as an important pathway to alleviate water shortage in arid regions due to limited water resources and unbalanced distribution. It is essential to explore the hidden mechanism of virtual water transfers among multiple regions to support future water resources allocation and management. In this study, a Disaggregated Multi-Region Virtual Water Flow and Interaction (DrWIn) model is developed to facilitate the analysis of virtual water inflow, outflow, transfer balance, and the related interactions. In addition, a factorial analysis is integrated to quantify the impacts of industrial water consumption and their interactive effects. A special case study of China is conducted to illustrate the applicability and superiority of the DrWIn model. It is found that interaction effects of any two industries are negative, indicating that high freshwater consumption in two industries is not the best choice. The obtained results provide a solid scientific basis for identifying the key industries and regions across a multi-region study system and supporting water resources utilization management in the future.

Statistical Analysis of Rainfall Intensity Frequency Considering Rainfall Time in the Diurnal Cycle - Xingpo Liu; Chenchen Jia (Water Resources Management, 2024)

Correction to: A Machine Learning-Based Probabilistic Approach for Irrigation Scheduling - Shivendra Srivastava; Nishant Kumar; Arindam Malakar; Sruti Das Choudhury; Chittaranjan Ray; Tirthankar Roy (Water Resources Management, 2024)

A New Flexible and Widely Software to Achieve Sustainable Agriculture based on Water-Energy-Food Nexus - Rezvan Khosravi; Arash Azari; Ali Bafkar (Water Resources Management, 2024)

A Framework for Quantifying Stormwater Control Measures’ Hydrologic Performance with Analytical Stochastic Models - Jiachang Wang; Jun Wang; Shengle Cao; Chuanqi Li; Shouhong Zhang; Yiping Guo (Water Resources Management, 2024)

Flood Susceptibility Mapping Using Information Fusion Paradigm Integrated with Decision Trees - Hüseyin Akay (Water Resources Management, 2024)
Accurate estimation of flood-damaged zones in a watershed is prominent in guiding a framework for developing sustainable strategies. For these purposes, several flood conditioning factor values at flooded and non-flooded points are extracted, and those points are analyzed using decision tree algorithms and eight novel information fusion techniques to get more reliable flood susceptibility mapping. The belief function values of flood susceptibility values at leaf nodes of the tree are fused by several techniques named Dempster-Shafer (DS), Fuzzy Gamma Overlay (FGO), Hesitant Fuzzy Weighted Averaging (HFWA), Hesitant Fuzzy Weighted Geometric (HFWG), Hesitant Fuzzy Weighted Ordered Averaging (HFWOA), HFWOG, Closeness coefficient (C c ) using Euclidean and Manhattan distances. The flood susceptibility values are extracted from the generated maps and are validated by receiver operating characteristics (ROC) curve parameters, and the seed cell area index (SCAI) of classified flood levels. The area under ROC (AUROC) values of training process are 0.997 for DS, HFWA, HFWOA, and C c -Euclidean, 0.996 for C c -Manhattan, 0.995 for FGO and 0.994 for HFWG and HFWOG. The AUROC values of the testing process are 0.951 for DS, HFWA, HFWOA, C c -Euclidean, and C c -Manhattan, 0.945 for FGO, 0.943 for HFWG, and 0.941 for HFWOG. True Skill Statistics values are 0.962 and 0.870 for training and testing processes. Although these techniques present excellent performance, the SCAI values versus flood susceptibility classes are fitted to assess the prediction capabilities of the techniques further. HFWA and HFWOG have the first- and second-best performances on the estimations. Hence, information fusion paradigm can be employed to combine flood conditioning factors based on a robust classification method to get reliable predictions of flood potential levels and utilize them for land use and construction planning and management.

Improving Drought Prediction Accuracy: A Hybrid EEMD and Support Vector Machine Approach with Standardized Precipitation Index - Reza Rezaiy; Ani Shabri (Water Resources Management, 2024)

Multi-Objective Ecological Operation of Large-Scale Reservoir-Gate System Coupled with Vegetation Priority Irrigation in Arid Regions - Dong Liu; Tao Bai; Mingjiang Deng; Jia Xu; Xiaoting Wei (Water Resources Management, 2024)

Comparison of Hydrological Modeling, Artificial Neural Networks and Multi-Criteria Decision Making Approaches for Determining Flood Source Areas - Erfan Mahmoodi; Mahmood Azari; Mohammad Taghi Dastorani; Aryan Salvati (Water Resources Management, 2024)

Morphological Dynamics of Mid-Channel Bars under Controlled River Regime - Balai Chandra Das (Water Resources Management, 2024)

Sustainability Assessment and Multi-angle Diagnosis of Regional Water Resources-Social Economy-Ecological Environment System Using a Novel TODIMSort with MS Clustering Algorithm - Yan Tu; Nengxin Wang; Linqi Cheng; Liyi Liu (Water Resources Management, 2024)

Floodplain Lake Water Level Prediction with Strong River-Lake Interaction Using the Ensemble Learning LightGBM - Min Gan; Xijun Lai; Yan Guo; Yongping Chen; Shunqi Pan; Yinghao Zhang (Water Resources Management, 2024)

Inconsistent Monthly Runoff Prediction Models Using Mutation Tests and Machine Learning - Miaomiao Ren; Wei Sun; Shu Chen; Decheng Zeng; Yutong Xie (Water Resources Management, 2024)

Multi-Step-Ahead Rainfall-Runoff Modeling: Decision Tree-Based Clustering for Hybrid Wavelet Neural- Networks Modeling - Amir Molajou; Vahid Nourani; Ali Davanlou Tajbakhsh; Hossein Akbari Variani; Mina Khosravi (Water Resources Management, 2024)

GIS-Based Methods for Identifying River Networks Types and Changing River Basins - Aleksandar Valjarević (Water Resources Management, 2024)

Achieving Sustainable Goals Using an Effective Budget-Allocation Multicriteria Mives Model: Case Study of a Spanish Water Utility Company - Núria Roigé; Francesc Pardo-Bosch; Pablo Pujadas (Water Resources Management, 2024)
Within the global sustainability agenda, companies must align their operations with specific Sustainable Goals (SGs) and develop corresponding budgeting strategies. This paper presents a robust multicriteria decision-making model tailored for a Spanish utility company specializing in water services. The model ensures that financial allocations reflect the company's vision and its interpretation of sustainable development within its service areas. This approach aims to promote objectivity, traceability, and sustainability in decision-making, as a critical component in achieving SGs. The article emphasizes the importance of aligning water companies' budget allocations with SGs, highlighting key indicators for consideration. It provides a practical illustration of how the company's financial allocations were synchronized with SGs using a multicriteria decision-making model. A case study is included, involving a budget allocation of 40 million euros compared across four scenarios with the historical average of budgets from the Past Three Investment Plans (P3IP). The results reveal significant variations in budget allocation across different infrastructure categories, demonstrating the system's robustness, such as a 70% variation in production infrastructures, 27% in transportation, 25% in distribution, and 40% in the remaining areas, depending on the scenario.

Agricultural Irrigation Water Requirement and Its Response to Climatic Factors Based on Remote Sensing and Single Crop Coefficient Method - Jiaxin Sun; Liwen Chen; Peng Qi; Guangxin Zhang (Water Resources Management, 2024)

A New Data-Driven Model to Predict Monthly Runoff at Watershed Scale: Insights from Deep Learning Method Applied in Data-Driven Model - Shunqing Jia; Xihua Wang; Y. Jun Xu; Zejun Liu; Boyang Mao (Water Resources Management, 2024)

Flood and Non-Flood Image Classification using Deep Ensemble Learning - Ellora Yasi; Tasnim Ullah Shakib; Nusrat Sharmin; Tariq Hasan Rizu (Water Resources Management, 2024)

Water Quality Monitoring and Assessment for Efficient Water Resource Management through Internet of Things and Machine Learning Approaches for Agricultural Irrigation - Mushtaque Ahmed Rahu; Muhammad Mujtaba Shaikh; Sarang Karim; Sarfaraz Ahmed Soomro; Deedar Hussain; Sayed Mazhar Ali (Water Resources Management, 2024)

Classification of Sub-Watersheds with Respect to Flooding Susceptibility in a Tropical River Basin Using Multi Criteria Approach Based on VIKOR - Ajith G. Nair; K. Sunil Kumar; Sonu V. Sabu (Water Resources Management, 2024)

Assessing the Catastrophic Environmental Impacts on Dam Breach Using Remote Sensing and Google Earth Engine - Rasha M. Abou Samra; R. R. Ali; Bijay Halder; Zaher Mundher Yaseen (Water Resources Management, 2024)

Improved Streamflow Simulation by Assimilating In Situ Soil Moisture in Lumped and Distributed Approaches of a Hydrological Model in a Headwater Catchment - Hongxia Li; Yuanyuan Huang; Yongliang Qi; Yanjia Jiang; Xuan Tang; Elizabeth W. Boyer; Carlos R. Mello; Ping Lan; Li Guo (Water Resources Management, 2024)

From Pressure to Water Consumption: Exploiting High-Resolution Pressure Data to Investigate the End Uses of Water - Valentina Marsili; Filippo Mazzoni; Stefano Alvisi; Marco Franchini (Water Resources Management, 2024)
In the era of digital transformation of water distribution networks, an increasingly important role is played by smart metering technologies, which allow detailed characterization of water consumption up to the end-use (i.e., domestic-fixture) level. To this end, smart flow meters make the collection of water-consumption data at high temporal resolution possible, but their installation can be unfeasible due to technical and economic limitations. As an alternative to the traditional flow-measurement-based methods for end-use characterization, a pragmatic method to obtain information about end-use water consumption exclusively based on pressure data is proposed in this study. In particular, a dual-phase methodology is developed, exploiting (i) pressure data collected at two sections of the user’s inlet pipeline and (ii) the pressure-flowrate relationship to discriminate between internal and external water-use events and estimate the household water-consumption time series, which is then subjected to individual-event analysis. The results obtained on a real case study undergone to 1-s resolution pressure monitoring over about one month and a half confirm the method’s effectiveness in obtaining the flowrate time series with an average error of about 2.3% and successfully identifying water-consumption events along with their features. Graphical Abstract

Optimization of Deficit Irrigation Water Usage for Maximisation of Jute Fibre Yield Using the Soil-water-crop Model in a Sub-tropical Climate - Debarati Datta; Arvind Kumar Singh; Girindrani Dutta; Nurnabi Meherul Alam; Dhananjay Barman; Ranjan Kumar Naik; Sourav Ghosh; Gouranga Kar (Water Resources Management, 2024)

Improved Multi-objective Butterfly Optimization Algorithm and its Application in Cascade Reservoirs Optimal Operation Considering Ecological Flow - Zhangling Xiao; Mingjin Zhang; Zhongmin Liang; Jian Wang; Yude Zhu; Binquan Li; Yiming Hu; Jun Wang; Xiaolei Jiang (Water Resources Management, 2024)

A Novel Hybrid Algorithm Based on Beluga Whale Optimization and Harris Hawks Optimization for Optimizing Multi-Reservoir Operation - Xiaohui Shen; Yonggang Wu; Lingxi Li; Peng He; Tongxin Zhang (Water Resources Management, 2024)

Optimal Exploitation of Water Resources by Using New Multi-Objective Reptile Search Algorithm (MORSA) - Sufia Bajelani; Saeid Shabanlou; Fariborz Yosefvand; Mohammad Ali Izadbakhsh; Ahmad Rajabi (Water Resources Management, 2024)

Appraisal of Urban Waterlogging and Extent Damage Situation after the Devastating Flood - Shan-e-hyder Soomro; Muhammad Waseem Boota; Xiaotao Shi; Gul-e-Zehra Soomro; Yinghai Li; Muhammad Tayyab; Caihong Hu; Chengshuai Liu; Yuanyang Wang; Junaid Abdul Wahid; Mairaj Hyder Alias Aamir Soomro; Jiali Guo; et al. (Water Resources Management, 2024)

Beyond Counting Zeroes: Using Entropy-Based Hydrologic Signatures and Classification for Streamflow Intermittency Assessment - Ioannis Niadas; Christos Makropoulos (Water Resources Management, 2024)

Integrating Machine Learning Models with Comprehensive Data Strategies and Optimization Techniques to Enhance Flood Prediction Accuracy: A Review - Adisa Hammed Akinsoji; Bashir Adelodun; Qudus Adeyi; Rahmon Abiodun Salau; Golden Odey; Kyung Sook Choi (Water Resources Management, 2024)

Review of Metaheuristic Methodologies for Leakage Reduction and Energy Saving in Water Distribution Networks - G. Ferrarese; D. Medoukali; D. Mirauda; S. Malavasi (Water Resources Management, 2024)
Metaheuristic methods have emerged as powerful tools for solving complex optimization problems in various domains, including the sustainability of water distribution systems. They provide efficient and effective solutions by mimicking natural processes and searching for the optimal option within a large solution space. Despite the existence of these methods in the water distribution field for several years, a direct comparison between the various proposed solutions often proves challenging, due to the different parameter definitions used by the authors. The present review presents the solutions proposed by a total of 36 research papers taken from the Web of Science, Scopus and Google Scholar databases focusing on the application of metaheuristic methods for leakage reduction and energy saving in water distribution networks. The review is intended to facilitate comparative analysis among the solutions proposed by authors concerning key aspects of the optimization process. These aspects include the definition of the algorithm, the specification of the objective function, and the strategies employed for reducing the search space. The characteristics of the networks used as case studies by the reviewed papers are also presented to allow the reader to evaluate the applicability of the solutions to specific networks.

Experimental Study on the Hydraulic Performance of Porous Broad-Crested Weirs with Sloping Crests - Mehrdad Doustkam; Mostafa Rahmanshahi; Manoochehr Fathi-Moghadam; Alireza Keramat; Huan-Feng Duan (Water Resources Management, 2024)

Flood Hazard Assessment Using Hydrodynamic Modeling Under Severity-Frequency Based Changing Flood Regime - Prachi Pratyasha Jena; Chandranath Chatterjee; Rakesh Kumar; Amina Khatun (Water Resources Management, 2024)

Evaluating the Impact of Digital Elevation Models on Urban Flood Modeling: A Comprehensive Analysis of Flood Inundation, Hazard Mapping, and Damage Estimation - Zanko Zandsalimi; Sajjad Feizabadi; Jafar Yazdi; Seyed Ali Akbar Salehi Neyshabouri (Water Resources Management, 2024)

Evaluating Building Sustainability Rating Systems: Standards and Methodologies for Energy-Water Based Assessment Criteria - Yousif Mohammed; Gasim Hayder; Sivadass Thiruchelvam; Hamidi Abdul Aziz (Water Resources Management, 2024)

Improving the Reliability of Compound Channel Discharge Prediction Using Machine Learning Techniques and Resampling Methods - Seyed Morteza Seyedian; Ozgur Kisi; Abbas Parsaie; Mojtaba Kashani (Water Resources Management, 2024)

A Quantile Regression Approach to the Heterogeneity in Price Elasticity of Domestic Water Demand - Mónica Maldonado-Devis; Vicent Almenar-Llongo (Water Resources Management, 2024)

Boosting Progressive Optimality Algorithm Performance in Optimizing Complex Large-Scale Multi-Reservoir System Operations by Using Discrepant Optimization Windows and Disturbance-Response Strategy - Nan Xu; Jia Chen (Water Resources Management, 2024)

Impact of Pressure on the Deterioration of Drinking Water Distribution Networks - Amira Rjaibi; Sophie Duchesne (Water Resources Management, 2024)

Live-Bed Scour Depth Modelling Around the Bridge Pier Using ANN-PSO, ANFIS, MARS, and M5Tree - Anubhav Baranwal; Bhabani Shankar Das (Water Resources Management, 2024)

Analysis of the Likelihood Function and Cutoff Threshold in the GLUE Procedure for Calibration of the Resistance Parameters of Mountain Rivers - Sebastián Cedillo; Esteban Sánchez-Cordero; Paola Duque-Sarango; Luis Timbe; Jaime Veintimilla-Reyes; Esteban Samaniego; Andrés Alvarado (Water Resources Management, 2024)

How to Manage the Naturel Resources Pricing in the Sustainability Context: Application to Drinking Water - Bouchrika Ali; Issaoui Fakhri; Hamid Abdelkader Bachouche (Water Resources Management, 2024)

Enhanced Methods for Evaluating Water-inrush Risk from Underlying Aquifers: Incorporating Dynamic Weight Theory and Uncertainty Analysis Model - Ning Li; Wenfeng Du (Water Resources Management, 2024)

Real-Time Urban Flood Depth Mapping: Convolutional Neural Networks for Pluvial and Fluvial Flood Emulation - Maelaynayn El baida; Farid Boushaba; Mimoun Chourak; Mohamed Hosni (Water Resources Management, 2024)

The Potential of RainWater Harvesting Systems in Europe – Current State of Art and Future Perspectives - Katarzyna Wartalska; Martyna Grzegorzek; Maciej Bełcik; Marcin Wdowikowski; Agnieszka Kolanek; Elżbieta Niemierka; Piotr Jadwiszczak; Bartosz Kaźmierczak (Water Resources Management, 2024)
Water scarcity and climate change led to changes in water management, especially in urban areas. RainWater Harvesting (RWH) is a promising technique that allows the collection and reuse of rainwater, as well as protecting sewage systems from overload. This article reviews the current state of RWH in Europe, including advantages, implementation, potential efficiency, usage requirements, quality, and treatment processes. The main findings include the importance of RWH as a sustainable water management technique, the historical background and renewed interest in RWH systems in recent years, the positive impact of RWH on reducing energy consumption and greenhouse gas emissions, the versatility of rainwater usage, and the potential cost savings and benefits in various regions. RWH systems are gaining popularity in Europe, particularly in Germany, Austria, and Switzerland. Climate change and precipitation patterns affect rainwater availability and quality. RWH can be used for various purposes, including drinking, but requires proper purification for health safety. It is also being implemented in new locations like airports and large buildings. RWH systems have a high potential to overcome undesired results of climate change. Among that, numerous aspects still need to be considered in the future that allow the application of RWH systems on a larger scale.

Urban HydroConnect: Harnessing SWMM for Dynamic Waterway Management Assessment - Xianqi Zhang; Peng Chen; Shengnan Dai; Yonghua Han; Xing Chang (Water Resources Management, 2024)

Moving Trend Analysis Methodology for Hydro-meteorology Time Series Dynamic Assessment - Zekâi Şen (Water Resources Management, 2024)
In the last 30 years, there are many publications in the literature due to global warming and climate change impacts exhibiting non-stationary behaviors in hydro-meteorology time series records especially in the forms of increasing or decreasing trends. The conventional trend analyzes cover the entire recording time with a single straight-line trend and slope. These methods do not provide information about up and down partial moving trends evolution at shorter durations along the entire record length. This paper proposes a dynamic methodology for identifying such evolutionary finite duration moving trend method (MTM) identifications and interpretations. The purpose of choosing MTM was to investigate the dynamic partial trend evolution over the recording period so that dry (decreasing trend) and wet (increasing trend) segments could be objectively identified and these trends could assist in water resources management in the study area. The moving trend analysis is like the classical moving average methodology with one important digression that instead of arithmetic averages and their horizontal line representations, a series of finite duration successive increasing and decreasing trends are identified over a given hydro-meteorology time series record. In general, partial moving trends of 10-year, 20-year, 30-year and 40-year occur above or below the overall trend and thus provide practical insight into the dynamic trend pattern with important implications. The moving trend methodology is applied to annual records of Danube River discharges, New Jersey state wise temperatures and precipitation time series from the City of Istanbul.

The Potential use of Reclaimed Water for Irrigation Purposes: Is it Overestimated? - Alfonso Expósito; Esther Díaz-Cano; Julio Berbel (Water Resources Management, 2024)
The use of reclaimed water is expected to increase in the coming years, mainly in water-scarce areas. In the European Union (EU), an increase in the use of reclaimed water is expected to play a significant role within the European circular economy strategy and climate change adaptation policies with the aim to enhance overall sustainability of water resource management. While several institutions have offered estimations of the potential of reclaimed water reuse in the EU context, these estimations tend to overestimate potential reuse volumes since they fail to fully consider the following important issues: (a) the role of return flows in basins where cascade reuse is crucial in maintaining downstream uses (including ecological flows); (b) the availability of abundant (and cheaper) conventional resources; and (c) the economic productivity of water as an indicator of users’ willingness to pay for reclaimed water. This study focuses on the Spanish case since this is currently the EU member state with the highest potential for reclaimed water reuse. Findings show that previous estimations of reusable water volumes in Spain may have overestimated potential volumes. The proposed analysis can be extrapolated to other EU regions, where realistic estimations of the potential of reclaimed water might be much needed.

Enhanced Daily Reference Evapotranspiration Estimation Using Optimized Hybrid Support Vector Regression Models - Stephen Luo Sheng Yong; Jing Lin Ng; Yuk Feng Huang; Chun Kit Ang; Norashikin Ahmad Kamal; Majid Mirzaei; Ali Najah Ahmed (Water Resources Management, 2024)

Statistical Modeling of Indus River Outflow at Tarbela Dam using Generalized Gumbel Type 2 Distribution - Kahkashan Ateeq; Tahira Bano Qasim; Wajeeha Kiran (Water Resources Management, 2024)

Bayesian Framework for Uncertainty Quantification and Bias Correction of Projected Streamflow in Climate Change Impact Assessment - Jose George; P. Athira (Water Resources Management, 2024)

Comparative Prioritization of Sub-Watersheds in Flood Generation Using Co-Management Best-Worst Method and Game Theory Algorithm - Ali Nasiri Khiavi; Seyed Hamidreza Sadeghi; Mehdi Vafakhah (Water Resources Management, 2024)

Water Pricing and Quotas: A Quantitative Analysis from a Private and Social Perspective - Ángela Valle-García; Carlos Gutiérrez-Martín; Nazaret M. Montilla-López (Water Resources Management, 2024)
The current situation of structural water scarcity due to the rise in demand, the reduction in supply as a consequence of climate change, increasingly frequent drought periods, and overall quantitative pressure on water resources creates a need for economic instruments to reduce the amount of water used, especially in the agricultural sector. Thus, water pricing and allocation quotas (proportional reduction of allocations) may be suitable tools to reduce demand or allocate scarce water resources. For a comparative analysis of the performance of these two measures, a Positive Mathematical Programming model has been developed, using the Guadalquivir River Basin as a case study. Additionally, the analysis takes into account the revenue generated from water pricing and the marginal cost of public funds. The results indicate that, from the farmer’s perspective, quotas result in smaller losses than water pricing. However, when considering water pricing along with the revenue generated from this measure, this mechanism would be more beneficial for society as a whole, since the taxes collected could be used for other purposes, albeit with efficiency losses measured by the marginal cost of public funds and the excess burden of taxation.

The Frequency Distribution Estimation Method of Excess Rainfall Intensity of Double Thresholds (ERId) for Combined Sewer Overflow - Xingpo Liu; Liang Shen; Wenke Zang; Yuwen Zhou (Water Resources Management, 2024)

Public Satisfaction with Water Quality Under the Implementation of Water Quality Monitor Standard System - Wenwen Wu; Zilin Wei; Lifeng Wu (Water Resources Management, 2024)

Novel Perspectives on Environmental Dynamic Resilience: Adapting Urban Water Systems to a Changing Climate - Milan Stojković; Ana Mijić; Barnaby Dobson; Dušan Marjanović; Brankica Majkić-Dursun (Water Resources Management, 2024)

A Novel Method for Estimating Time of Concentration in Ungauged Catchments - Abazar Fathi; Masih Zolghadr (Water Resources Management, 2024)

Enhancing Urban Drainage Infrastructure Through Implementation of Low Impact Development Techniques - Osheen; Mitthan Lal Kansal; Deepak Singh Bisht (Water Resources Management, 2024)

A Hydrodynamic Model and Data-Driven Evolutionary Multi-Objective Optimization Algorithm Based Optimal Operation Method for Multi-barrage Flood Control - Xuan Li; Xiaoping Zhou; Jingming Hou; Yuan Liu; Shuhong Xue; Huan Ma; Bowen Su (Water Resources Management, 2024)

Hybrid Approach for Streamflow Prediction: LASSO-Hampel Filter Integration with Support Vector Machines, Artificial Neural Networks, and Autoregressive Distributed Lag Models - Maha Shabbir; Sohail Chand; Farhat Iqbal; Ozgur Kisi (Water Resources Management, 2024)