Employing Machine Learning Approaches for Predicting Pipeline Failures in Water Systems: A Survey of Challenges and Opportunities - Yasin Asadi (Water Resources Management, 2026)

Deep Learning-Based Multi-Step Ahead Streamflow Forecasting under RCP 4.5 and 8.5 Scenarios - Ayse Ozer; Afshin Shahbazi; Halit Apaydin (Water Resources Management, 2026)

Modeling and Design Optimization of Looped Water Distribution Networks using MS Excel: Developing the Open-Source X-WHAT Model - Marcus Nóbrega Gomes; Salma M. Elsherif; Igor Matheus Benites; Ahmad F. Taha; Marcio H. Giacomoni (Water Resources Management, 2026)
Cost-effective water distribution network (WDN) design is crucial for reliable urban water supply. This paper introduces the open-source Excel for Water system Hydraulic Analysis Tool (X-WHAT) for WDN simulation and preliminary design. Unlike traditional spreadsheet approaches based on Hardy-Cross iteration, X-WHAT uses Excel’s built-in Generalised Reduced Gradient (GRG) solver with a direct matrix formulation, requiring no mass-conservative initial estimates or iterative flow corrections. The mathematical framework covers network topology, mass and energy conservation, and a multi-component cost function including tank material, structural foundation, and pumping energy costs. X-WHAT supports educational and preliminary engineering applications without coding expertise, while enabling sensitivity analysis for demand, configuration, cost, and hydraulic-performance changes. Three case studies, validated against EPANET and published literature, demonstrate the tool’s effectiveness. A tentative class organisation and unit design following the Understanding by Design (UbD) approach are also proposed. Through optimization, X-WHAT achieved a cost reduction of 151,790 USD (9.8% of total network cost) compared to standard design in a network serving 44,416 people. Supplementary material provides a step-by-step guide.

Diagnosing the Roles of Meteorological Forcing, Storage Memory, and Network Connectivity in Cold-Region Streamflow Prediction - Adewale Ajao; Yeo Howe Lim (Water Resources Management, 2026)
Data-driven streamflow prediction models have achieved high predictive accuracy, but the relative contributions of meteorological forcing, hydrologic memory, and spatial watershed connectivity remain poorly understood, particularly in cold-region watersheds. This study proposes a six-tier diagnostic framework to quantify the contributions of meteorological forcing, storage memory, and network connectivity to streamflow prediction in the Goose River watershed. Six progressively more complex modelling tiers were developed, ranging from temporal deep learning and static machine-learning models to graph neural networks (GNNs) and spatio-temporal GNNs. Meteorological forcing dominated short-term discharge prediction, whereas static physiographic attributes alone showed limited predictive capability. Incorporating antecedent discharge memory produced the highest numerical performance (NSE = 0.989), while the spatio-temporal GNN achieved the most physically consistent representation by integrating dynamic forcing with upstream–downstream connectivity. These findings demonstrate a trade-off between statistical accuracy and hydrologic interpretability, showing that graph-based models provide greater physical realism and transferability despite slightly lower predictive accuracy than autoregressive approaches.

Water Efficiency Targets at the Basin Scale: Useful Guide or Policy Trap? - Alfonso Expósito; Carlos Gutiérrez-Martín; Fernando Delgado-Ramos; Julio Berbel (Water Resources Management, 2026)
Relevant EU water policy documents, such as the EU Blueprint for Water report (EC 2012) and the European Water Resilience Strategy (EC 2025) call for the development of basin-specific water-use efficiency targets, emphasising the importance of considering the broader impact of individual measures at the river basin level. This study contributes to the discussion on the suitability of basin-wide water-use efficiency targets to guide and inform water policy with the goal of achieving environmental objectives (as those set by the EU Water Framework Directive). The novelty of this study lies in the application of a hydro-economic model, calibrated with real data, to assess how local water-use efficiency improvements translate to basin-scale outcomes. The case of the Guadalquivir River Basin (southern Spain) shows that increases in on-farm efficiency do not necessarily generate basin-wide water savings. This is largely due to the reuse of return flows and the hydrological interdependence between upstream and downstream users, which limit the net reduction in water consumption at the basin level. Our study emphasizes the fact that targeting basin-wide water-use efficiency is a complex issue affected by multiple aspects (e.g., environmental, socio-economic, legal, technical, and political), which significantly limit its usefulness to make comparisons between river basins (or districts) and guide water policy.

Future Water-Deficit Risk and its Adaptive Strategy in Arid Agriculture Region: A Copula-Based Stochastic Optimization Framework - Zihan Wu; Fan Zhang; Xinying Wu; Jiachen Ji; Jingqiu Chen; Shanshan Guo (Water Resources Management, 2026)

Climate Change and Water Security in Semi-Arid Regions: Integrating Surface and Groundwater Responses - Mahin Khosravi; Khabat Khosravi; Mohammad Reza Yazdani; Shasha Han; Ali Asghar Zolfaghari; Javad Hatamiafkoueieh; Soroush Abolfathi (Water Resources Management, 2026)

A Hybrid Intelligent Framework for Risk Assessment in Water Sector Public-Private Partnership Projects Using Machine Learning - Salman Nazari-Shirkouhi; Ali Khorshidpour-Nobandegani; Hamzeh Amin-Tahmasbi; Ali Namazian (Water Resources Management, 2026)

Correction to: 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, 2026)

Integrating Long Short-Term Memory and Gaussian Process Regression for Enhanced Meteorological Drought Forecasting - Muhammad Ilyas; Rizwan Niaz; Luca Di Persio; Mohammed Alshahrani; Abdulkhaleq Q. A. Hassan; Mohammed M. A. Almazah (Water Resources Management, 2026)

Cross-Tributary Water Mass Signals and Seasonal Stratification Dynamics in a River-Type Reservoir - SooBin Yoon; ChangHyun Lee; Young Do Kim (Water Resources Management, 2026)

Climatic Variation Impacts on Water Resources Systems with and without Shared Socio-Economic Pathways Consideration – A Review - Lakshmi Raghu Nagendra Prasad Rentachintala (Water Resources Management, 2026)

Advancing River Water Level Prediction: A Comparative Machine Learning and Deep Learning Approach - Zohreh Sheikh Khozani; Monica Ionita (Water Resources Management, 2026)
River water level prediction plays an important role in effective planning and flood risk mitigation. In this study, four standalone machine learning (ML) models, M5Rules, Random Forest (RF), Sequential Minimal Optimization (SMO), and Long Short-Term Memory (LSTM), as well as a hybrid LSTM-RF model, were developed to predict weekly water levels of the Rhine River. The models were trained and tested using data collected between 2006 and 2024. Different scenarios with different input combinations were explored to improve the accuracy of the model. Statistical indicators were calculated to examine the reliability of the proposed scenarios and models. The results showed that the performance of the model increased in Scenario 4 with all input variables. Among the standalone models the M5Rule and SMO algorithms perform better with Nash-Sutcliffe Efficiency (NSE) of 0.78, in validation phase, followed by RF (NSE = 0.76) and LSTM (NSE = 0.72). In order to increase the model predictive power, the hybrid model LSTM-RF applied to the input variables of the best scenario and this hybrid model achieved a remarkable accuracy of NSE = 0.98 significantly outperforming standalone models. The findings of this research demonstrated the efficacy of the hybrid LSTM-RF model in capturing the changes in the water level in Rhine River.

Feature Selection and Data Decomposition-Driven Drought Forecasting: Designing Hybrid Deep Learning Models - Ayesha Parveen; Muhammad Azhar; Muhammad Gulistan; Mumtaz Ali; Aitazaz A. Farooque (Water Resources Management, 2026)

Performance Evaluation of Machine Learning Models (Random Forest and M5 Model Tree) for Runoff Simulation Under Climate Change and Reservoir Operation Scenarios - Shabnam Vakili; S. Morteza Mousavi (Water Resources Management, 2026)

Bridging the Nexus: Policy and Welfare Dimensions in Hydro-economic Models - Leon Bremer; Erik Ansink; Safa Baccour; Cian Twohig; Juan Pablo Henao; Pietro Sala; Adria Rubio Martin; Maria Vrachioli (Water Resources Management, 2026)
Europe’s water resources face growing pressures from climate change and competing demands. Addressing these pressures requires integrated approaches that capture interdependencies among sectors. This paper develops a unified hydro-economic modeling framework that embeds Water–Energy–Food–Ecosystem (WEFE) linkages directly into water policy analysis. Unlike existing approaches, the framework systematically combines hydrological processes, economic behavior, and policy instrument design, enabling analysis of cross-sectoral trade-offs and welfare outcomes. Its modular structure makes it suitable for both detailed numerical models and stylized applications. We illustrate its use with two European river basins, the Júcar in Spain and the Upper Main in Germany, showing how water trading and pricing policies can be evaluated within a nexus perspective. These cases demonstrate how the framework operationalizes nexus thinking and provides a transparent, policy-relevant basis for more coherent and resilient water governance.

A New Paradigm in Flood Management: Hydrological, Hydraulic, and Deep Learning Methods for Flood Routing - Metin Sarıgöl; Okan Mert Katipoğlu; Gaye Aktürk (Water Resources Management, 2026)

An Improved TOPSIS Model Based on Weighted Generalized Mahalanobis Distance for Multi-Attribute Decision-Making in Water Reservoir Operations - Serges Mendomo Meye; Paul Fabrice Nguema (Water Resources Management, 2026)

Comparison of Temperature-Based PET Methods and Their Impact on SPEI and RDI for Drought Assessment - Ihsan F. Hasan (Water Resources Management, 2026)

Cross-Sector Water Reallocation Through Tripartite Contracting: Institutional Design, Cost-Sharing, and Resilience Implications - Chia-Wen Wu; Frederick N.-F. Chou (Water Resources Management, 2026)

Field Investigation and EPANET-Based Assessment of Water Quality Variations in an Urban Water Distribution Network - Yunfeng Yang; Wenyu Jiang; Zihao Li; Jian Chen; Jiangyu Chen; Kaidi Wu; Jianxiong Kang; Fan Luo; Jian Hu; Wei Jiang; Yuanyao Ye (Water Resources Management, 2026)

Enhancing Water Availability in Mediterranean Basins: A Policy-Oriented Interpretation of Management Assumptions - Paola Bianucci; Álvaro Sordo-Ward; Luis Garrote (Water Resources Management, 2026)
Water availability is widely used to assess water resources and climate impacts, but its interpretation often depends on model assumptions. In regulated basins, it reflects political decisions (management objectives, demand representation, reliability criteria, and environmental constraints) more than intrinsic hydrological properties. This study offers a policy-oriented interpretation of water availability in Mediterranean basins using the WAAPA (Water Availability and Adaptation Policy Analysis) model. A coherent framework tests management hypotheses that include reservoir coordination, demand modulation, guarantee parameters, and the implementation of ecological flows. Results show that variations in management assumptions produce changes in water availability comparable in magnitude to those induced by climate change. By explicitly quantifying these relative contributions, the study demonstrates that water availability is a policy-dependent indicator, not an intrinsic hydrological property. These findings highlight the need for transparent definitions and consistent assumptions when using water availability metrics for planning climate change adaptation in highly regulated water-scarce regions.

Investigation of The Permeability Characteristics of Sediment-trapping Slow-release Ecological Dams and Their Filter Media - Hu Sha; Yi Ma; Yaofa Wang; Xiaogang Li; Juan Li (Water Resources Management, 2026)

Unraveling the Complexities of Surface Water Quality: a Global Systematic Review of Drivers, Scales, and Analytical Methods - Yu Zhang; Huade Zhu; Jian Wang (Water Resources Management, 2026)

From Situation to Decision: A Transferable Graphical Decision-Support Framework for Water Allocation in Arid and Semiarid Reservoir Systems - Reginaldo Moura Brasil Neto; Antonio Rondinelly da Silva Pinheiro; Richarde Marques da Silva; Leandeson Pinheiro Santos de Araújo; Celso Augusto Guimarães Santos (Water Resources Management, 2026)

Daily Streamflow Forecasting using a VMD-Based Dual-Stream Temporal Convolutional Network with Gated Attention - Hongye Cao (Water Resources Management, 2026)

Flash Flood Forecasting Based-EF5 Model using Distinct Interpolation Methods: An Ensemble Framework - Abhisek Mishra; Abinash Sahoo; Sandeep Samantaray; Deba Prakash Satapathy; Sajjad Firas Abdulameer; Mayadah W. Falah; Zaher Mundher Yaseen (Water Resources Management, 2026)

Quantitative Analysis of Drought Uncertainties Using Copula Methods - Zohreh Pakdaman; Ommolbanin Bazrafshan; Sajad Jamshidi; Reza Alizade Noughabi (Water Resources Management, 2026)

Research on Multi Model Combination Prediction Strategy for Short term Runoff Prediction Based on Data Driven Approach - Zhongzheng He; Jiahao Lu; Yongqiang Wang; Chao Wang; Taixin Zhang; Jun Guo; Haimeng Guo; Hui Qin (Water Resources Management, 2026)

Prediction of Peak Discharge for Dam Overtopping Failure Based on Physical Experimental Analysis - Jeongmin Lee; Jungkyu Ahn; Insik Choi; Seongil Yeom; Chang Geun Song (Water Resources Management, 2026)

Integrating Climate Modelling, Downscaling, and Bias Correction for Drought Studies: Current Approaches and Future Directions - Chinthu Naresh; Aneesh Mathew (Water Resources Management, 2026)

Interpretable Daily Inflow Forecasting Model via Cross-Attention Hybrid Neural Networks Integrated with SHAP Analysis - Xiaoyan Wu; Hongye Zhao; Huaying Su; Huan Wang; Jiang Xiong; Shengli Liao (Water Resources Management, 2026)

Water Potability Classification by Using Deep Residual Networks with Sum-Product-Difference Feature Matrices - Sevilay Tufenkci; Baris Baykant Alagoz (Water Resources Management, 2026)

Innovative Integrated Sustainability Appraisal Approach for Irrigation Water Distribution Automation: A Combined MCDM-Based Food-Energy-Water Nexus - Dorsa Rahparast; S. Mehdy Hashemy Shahdany; Abbas Roozbahani (Water Resources Management, 2026)
This study presents a sustainability performance appraisal framework based on the Food-Energy-Water (FEW) nexus, for the modernizing of irrigation water distribution systems. Customized indicators were developed to construct an integrated Nexus index using multi-criteria decision-making methods, including AHP-Shannon Entropy and SWARA for indicator weighting, and SAW for the FEW-Nexus index. It assesses the impacts of modernization on water and energy conservation through two automated control systems: a decentralized Proportional-Integral (PI) system and a centralized Model Predictive Control (MPC) system. Two scenarios were analyzed: normal and water shortage (WS), which served as the boundary conditions for the hydraulic simulation models. The framework was applied to an irrigation district in Iran, delineated into 459 independent units in an 800-hectare grid for spatial assessment. FEW indicators and the integrated nexus index were analyzed per unit. Spatial analysis showed that automatic PI/MPC systems improved metrics in normal-WS scenarios, with surface water delivery increasing by 19 − 14%/14–28%. Surface Water productivity rose 21 − 2%/38 − 18%; economic productivity by 2.5-0%/47 − 10%; energy conservation improved 9–29%/21–39%; energy productivity increased 35 − 19%/47 − 27%; energy economic productivity slightly rose 8-0.2%/21 − 0.8%; and food economic productivity increased 6–51%/21 − 11%. Overall, the spatial analysis of the integrated FEW-Nexus index reveals a modest positive impact on water and energy conservation resulting from the automation of the main canal using PI and MPC systems.

OcisMILPNet: A Graph-Based MILP Approach for Sparse Operations in Open-Channel Irrigation Networks - Wentao Hou; Haorui Chen; Shaohui Zhang; Jing Fu; Juyan Mu; Boxiong Zhang; Meijian Bai; Yinong Li; Baozhong Zhang (Water Resources Management, 2026)

Climate-Change Based Analysis of the Water-Food-Energy Nexus with System-Dynamics - Sahar Baghban; Omid Bozorg-Haddad; Mohammad-Hosein Omid; Alireza Gohari; Hugo A. Loáiciga (Water Resources Management, 2026)

One River: Introducing an Integrated Framework and Paradigm for River-Livelihood-Governance Convergence - Matthew Chidozie Ogwu (Water Resources Management, 2026)
Rivers are foundational to ecological sustainability, cultural identity, and human well-being. Yet, existing governance models often treat them as fragmented hydrological systems, undermining the intricate interdependencies between ecosystems, livelihoods, and institutions. This paper introduces the One River framework—an integrative paradigm that reconceptualizes rivers as socio-ecological assemblages, advancing a convergence approach to water governance. Building on lessons from One Health , EcoHealth , and Planetary Health , the framework foregrounds four interlinked pillars: ecological integrity, livelihood interdependence, governance integration, and knowledge pluralism. Through critical synthesis and cross-regional case analyses—including the Volta, Amazon, Ganges, Mekong, Mississippi, and Whanganui rivers—the study identifies persistent shortcomings in dominant approaches such as Integrated Water Resources Management and River Basin Organizations, including technocratic bias, institutional fragmentation, and exclusion of Indigenous knowledge systems. The One River framework advocates for transdisciplinary collaboration, co-produced knowledge, legal innovations (e.g., river personhood), and adaptive governance attuned to local ecologies and socio-cultural realities. By embedding rivers within networks of relational, upstream–downstream, and human–nature interdependence, this paradigm offers a transformative roadmap for sustainable and just river futures. It calls for a shift in global water governance—from managing rivers as extractive units to stewarding them as living, relational systems central to equitable development and planetary resilience. This study contributes a structured and operationalizable analytical framework for river governance with demonstrated applicability across diverse socio-ecological contexts.

Reservoir Water Quality Inversion During Dredging Based on Sentinel-2 Imagery - Qingqing Tian; Zhikun Chen; Lei Guo; Jiyou Sun; Yadi Wang; Fei Wang (Water Resources Management, 2026)

CEEMDAN-Enhanced Bidirectional Temporal Convolution Network: A Novel Hybrid Model for Urban Surface Water Quality Prediction - Yongwen Guo; Hongming Chen; Juntao Huang (Water Resources Management, 2026)

A Fully Fuzzy Multi-Objective Linear Programming Model for Roof Gardening Using Trapezoidal Fuzzy Numbers - A.N. Revathi; Nachiappan Angappan (Water Resources Management, 2026)

A Fuzzy Analytic Hierarchy Process Framework for Estimating Potential Loss of Life in Dam-Failure Scenarios - Aditya Harikumar; Santosh G. Thampi (Water Resources Management, 2026)

Comparative Evaluation of Physics-Informed and Data-Driven Neural Networks for Reservoir Water Balance Simulation During Hydrological Extremes - S. Tharuka; L. Gunawardhana; B.C. Dissanayake; L. Rajapakse (Water Resources Management, 2026)

Evolution of Cooperative Water-Sharing Strategies Between Finite-Sized Riparian Country Coalitions in Transboundary Rivers Under Stochastic Perturbation - Ruicong Sun; Liang Yuan; Weijun He; Xia Wu; Dagmawi Mulugeta Degefu; Yang Kong; Thomas Stephen Ramsey (Water Resources Management, 2026)

Trends and Transitions in Hydrological Modelling: A Systematic Review of Model Approaches and Applications (2015–2025) - Tasmiyah Peerbhai; Michele Toucher; Feroza Morris-Kolawole; Shaeden Gokool (Water Resources Management, 2026)
Reliable streamflow modelling is critical for planning, allocation and sustainable resource management. In the era of rapid technological advancement, data sources for hydrological modelling are continuously evolving and hydrological models have never been more sophisticated. Satellite and reanalysis datasets have become increasingly popular, particularly in areas plagued with data scarcity. In this study, a bibliometric analysis was performed to consolidate progress, identify research gaps and guide future investigations for sustainable water resources management. Whilst physics-based models maintain a strong presence throughout the research period, there is a growing interest in data-driven and hybrid approaches. There are consistent gaps that were identified throughout the research period, such as issues of validation in ungauged basins, a lack of studies for regions such as sub-Saharan Africa, and the inconsistent treatment of model uncertainty. Despite these gaps, there is great potential in hybrid architectures to advance hydrological modelling in developing countries.

An Adaptive Water Resources Systems Management Framework for Mitigating Water Scarcity Under Runoff Change - Yibin Liang; Jianxia Chang; Jiang Li; Guanghui Wei; Aijun Guo; Yimin Wang; Zhehao Li; Dingrong Zhai; Yuexin Zhao (Water Resources Management, 2026)

Exploiting the Empirical Wavelet Transform and the Kullback-Leibler Entropies Divergence to Successfully Detect Leaks in Water Distribution Networks - Elvio Damonti; Giancarlo Bernasconi (Water Resources Management, 2026)
This study presents a highly sensitive methodology for the early detection of incipient leaks in Water Distribution Networks (WDNs), addressing the critical operational challenge where minute anomalies are obscured by severe background noise and complex, non-polynomial cyclical trends. To overcome the limitations of conventional filtering, we introduce a tailored Empirical Wavelet Transform (EWT) framework specifically engineered to suppress these nonlinear baseline dynamics while preserving weak anomaly signatures. Following this preprocessing, a novel dual-criteria detection scheme evaluates the detrended signals. This mechanism uniquely combines Kullback-Leibler (KL) entropies divergence, which captures subtle probabilistic shifts in signal distributions, with Root Mean Squared Error (RMSE) evaluation, which quantifies reconstruction deviations. The synergistic integration of these metrics enables highly reliable discrimination between normal operational variations and actual leak-induced anomalies. The efficacy of the proposed framework was rigorously validated across a comprehensive 500-scenario benchmark dataset based on the Hanoi WDN. The method successfully identified extremely small, developing leaks, achieving an exceptional F1-score of 96.17% and a sensitivity of 92.63% in distinguishing between leaky and leak-free scenarios. Crucially, the system maintained a 0.00% false alarm rate, significantly outperforming contemporary state-of-the-art deep learning and statistical baselines. By effectively eliminating false positives and capturing early-stage infrastructure failures, this automated detector provides precise, actionable decision support. The proposed methodology directly facilitates intervention prioritization, minimizes operational cost-risk trade-offs, and contributes significantly to sustainable water resource management and the reduction of urban water losses.

Urban Surface-Water Segmentation from Multi-Source UAV Data using SLI-UNet and Morphological Refinement - Yangdong Li; Tian Dong; Qinghong Mao; Guohua Cai; Bohan Ding (Water Resources Management, 2026)

Exploring the Impact of Hyperparameter Tuning on the Accuracy of Machine Learning-based Flood Susceptibility Mapping - Seyed Vahid Razavi-Termeh; Abolghasem Sadeghi-Niaraki; Ali Pourzangbar; Ozgur Kisi; Jamil Hussain; Soo-Mi Choi (Water Resources Management, 2026)

A Causality-Driven Dynamical Framework for Multi-Step-Ahead Daily Streamflow Forecasting in Data-Limited Scenarios - Longxia Qian; Qingli Chen; Yong Zhao; Suzhen Dang; Haoshan Wei; Guoqiang Tang (Water Resources Management, 2026)

Threshold of Regional Gray Water Footprint Based on CART Decision Tree - Yan Feng; Fang Li; Wu Hongiang (Water Resources Management, 2026)

Explainable AI for Water Quality Intelligence in Sacred Waters: Insights from the World’s Largest Water Dip Gathering - Mantena Sireesha; Abdul Gaffar Sheik; Ameer Khan Patan; Upaka Rathnayake (Water Resources Management, 2026)

Characteristics of Water and Sediment Transport in a Channel-type Reservoir Affected by Upstream Dam Construction and Multi-phase Reservoir Operation - Hanqing Zhao; Weijie Wang; Saiyu Yuan; Chengxiao Zhang; Shi Ren; Yanfang Zhao; Mingliang Zhao (Water Resources Management, 2026)

Review of risk analysis and management practices for urban water utilities’ sustainability and resilience - Muhammad Fahim Aslam; Farhad Jazaei; Peyman Babakhani; Brian Waldron; Rouzbeh Nazari (Water Resources Management, 2026)
Urban water utilities (UWU) face escalating challenges from rapid urbanization, climate change, aging infrastructure, and institutional fragmentation. This review synthesizes evidence from 62 peer-reviewed studies to critically examine how risk analysis and management are conceptualized and applied in UWU across diverse geographies and economic settings. Through thematic dimensions on geospatial factors, including climate, terrain, population density, Gross Domestic Product (GDP), and water governance structures, along with institutional data, the study employs descriptive patterns, statistical analysis, and cross-regional comparison to explore the distribution and drivers of water risks in UWU. Email correspondence with corresponding authors was conducted to evaluate the implementation status of proposed recommendations and the translation of research findings into practice. The risks are categorized into two types: natural and anthropogenic. The latter includes operational, health and safety, financial, and socio-political risks. The paper evaluates six major risk analysis methodological categories, which vary in terms of data requirements and suitability for different types of risk. These approaches provide valuable interpretations, but many applications represent one issue, often neglecting cascading effects and dynamic interdependencies between risk domains. The review identifies critical research and practice gaps and emphasizes the need for integrated and adaptive risk governance that bridges academic knowledge and utility practice to enhance the resilience of UWU.

Managing Basin Vulnerability Under Uncertainty: A Confidence-Based Framework for Small-Sample Water Resources Decisions - Fatima Zahra Kherazi; Dongying Sun; Charafa El Rhadiouini; Jan Muhammad Sohu; Sonia Najam Shaikh; Sanam Soomro (Water Resources Management, 2026)

Introducing Rolling Window Innovative Trend Analysis (RW-ITA): A New Method for Identifying Hidden Trends in Hydro-Meteorological Variables - Musa Esit; Ibrahim Halil Deger; Mehmet Ishak Yuce; Islam Yasa; Ergun Akbas (Water Resources Management, 2026)

Rapid Identification of Flood Inundation Areas and Dominant Drivers in Compound Floods using Explainable Machine Learning - Jiqiang Xie; Bing Yu; Heng Lyu; Shengnan Fu; Chen Yang; Chi Zhang (Water Resources Management, 2026)

A Weight-Optimized Federated Learning Framework for Leak Detection in Water Distribution Networks - Juan Li; Weijia Feng; Xiaofeng Tang (Water Resources Management, 2026)

Enhancing Water Resource Management in Data-Scarce Regions using the WA+ Framework - Dorothea Deus; George Venance Lugomela (Water Resources Management, 2026)

Economic Potential Analysis of Rainwater Harvesting System in Public Buildings with Introduction of Non-Potable Water Tariffs - Huayue Xie; Weilun Chen; Qinfeng Zhao; Tian Wang; Jiawei Gao; Mengyuan Zhou; Yingxiao Zhang; Qingqing Liang; Weijun Gao (Water Resources Management, 2026)

A Comprehensive Study of Component Importance-Based Approaches for Optimal Post-Earthquake Repair Sequences of Water Distribution Systems - Fei Wang; Yuwei Liu; Zhijie Liu; Yuanchun Kong; Jiayi Zhou (Water Resources Management, 2026)

Water Supply Under Conditions of Scarcity and Willingness-to-Pay to Maintain Service Performance - Bénédicte Rulleau; Lina Essafi (Water Resources Management, 2026)

Modeling Drivers and GLOF Hazards of Glacial Lakes in the Central Asian Mountains Using Geodetector and XGBoost - Nan Wang; Jianghua Zheng; Xurui Mao; Wanqiang Han; Chuqiao Han; Tao Zhong (Water Resources Management, 2026)

CGD-FloodNet: A Robust Hybrid Model for Flood Probability Estimation Using Environmental Parameters - Vinay Dubey; Rahul Katarya (Water Resources Management, 2026)

A Sensitivity Analysis Framework for Pre-design Green Infrastructure Prioritization - Dustin Krise; Wonmin Sohn; Jun-Hyun Kim (Water Resources Management, 2026)
Urbanization and climate change have increased the frequency and severity of stormwater-related challenges, necessitating more effective approaches for selecting green infrastructure (GI) during the planning and design process. Although numerous studies have evaluated the environmental performance of individual GI practices, comparatively limited attention has been devoted to translating performance assessments into practical guidance for selecting and prioritizing GI practices before implementation. To address this gap, this research developed a pre-design GI prioritization framework by integrating local sensitivity analyses with three deterministic performance models. The framework quantified runoff mitigation, ecosystem-service benefits, and 20-year life-cycle costs to establish hierarchical rankings of GI practices under alternative decision-making scenarios. The framework was applied to a 266-ha redevelopment scenario in downtown Lansing, Michigan, USA, and subsequently evaluated through post-design performance prediction. Results consistently identified rain gardens, street planters, infiltration basins, wetlands, and vegetation filter strips as the highest-priority practices under the balanced environmental-economic scenario. Implementation of the resulting GI plan reduced annual runoff volume by 52%, decreased pollutant loading by 9%–15%, and generated substantial carbon sequestration benefits. The study demonstrates how sensitivity-analysis results derived from multiple performance models can be synthesized into a practical decision-support framework for pre-design GI prioritization, providing a transferable method for informing GI planning and resource-allocation decisions.

Gradient Light Spectrum Optimization based Deep Long Short-Term Memory for Water Quality Prediction - Dhanalakshmi Lanka; Jaya Kumari D; Ramakrishnaiah N (Water Resources Management, 2026)

A Shared Vision Model-Based Framework for Assessing Drought Coping Capacity in a Multi-Infrastructure Basin - Joohyung Lee; Young-Oh Kim (Water Resources Management, 2026)
This study develops a system-dynamics-based Shared Vision Model (SVM), a core tool of Shared Vision Planning, for the Yeong–Seom Basin in South Korea, which experienced consecutive extreme droughts in 2022–2023, and applies it to quantify drought coping capacity at the medium-scale watershed level. The SVM integrates multipurpose dams, agricultural reservoirs, intake facilities, and streamflow gauging stations while reproducing operational rules such as water-supply adjustment standards and coordinated drought operations. Validation against 2022–2023 observations showed high reproducibility for most dams (R² = 0.93–0.99; NSE = 0.80–0.99). Although Boseonggang and Sueo Dams initially showed low performance, accuracy improved markedly after incorporating additional releases associated with coordinated drought operations, with corresponding improvements at downstream gauging stations confirming basin-wide flow continuity. Drought coping capacity was evaluated using 30 annual inflow ensemble scenarios derived from a 1995–2024 meteorological ensemble. Frequency–Duration–Magnitude indicators were calculated by drought stage and aggregated into a dimensionless coping-capacity index. The 2005 climate-outlook scenario produced the most severe basin-wide impacts overall, although other years were selected for specific watersheds and indicators. Under this scenario, supplier-based and consumer-based coping capacities differed systematically, especially where supply and demand locations were spatially separated. A ± 20% threshold sensitivity analysis showed that absolute DCC values varied with threshold assumptions, but the representative worst-case scenario, major vulnerable watersheds, and supplier–consumer contrasts remained broadly consistent. Countermeasure simulations demonstrated downstream protection effects, including improvement in the lower Seomjin watershed, supporting collaborative, scenario-based drought-risk evaluation using shared indicators and a common basin model.

Machine Learning Reveals Source–Sink Regulation by Green Infrastructure Morphology Across Scales and Rainfall Scenarios - Hangyuan Fan; Fan Yang; Suwen Xiong (Water Resources Management, 2026)

Multi-step Daily Reservoir Inflow Prediction using Temporal Conventional Network Model Integrating time-Varying Filter-Based empirical mode Decomposition and Holt-Winters Algorithms - Atefeh Sadat Hosseini; Sultan K. Salamah; Ahmed Alkhayyat; Anupam Yadav; Abhinav Kumar; Marwan Kheimi; Sandeep Singh; KDV Prasad (Water Resources Management, 2026)

Reframing Reclaimed Wastewater as a Resource: Socio-Ecological and Economic Benefits of Managed Reuse in an Arid Agricultural System - Hamidreza Bana Bafrouei; Saeed Alimohammadi; Hossein Azhdari (Water Resources Management, 2026)

A Systematic Review of Water Quality Indexing Approaches - Deborshee Sinha; Mayur Shirish Jain (Water Resources Management, 2026)

An Optimal Water Resources Allocation Method Based On Spatial Equilibrium Theory for Coordinating Water and Socio-economic Development in Megacities - Qingyuan Li; Yicong Dai; Tiesheng Guan; Xin Yin (Water Resources Management, 2026)

Analysing Spatiotemporal Complexity of Precipitation Using Sample Entropy and Wavelet Spectrum - Hakan Tongal (Water Resources Management, 2026)
Analysing precipitation dynamics is vital to improve water resource management practices and socio-economic stability, particularly in regions susceptible to climate change. This study examines the spatiotemporal variability in precipitation dynamics and complexity within the Antalya Basin, a region significant to Türkiye’s socio-economic framework, over the period 1970–2017. Utilizing daily precipitation data from eight meteorological stations, changes in precipitation complexity were analyzed through sample entropy, wavelet spectra, change-point analyses (Pettitt, Buishand, Standard Normal Homogeneity, Pruned Exact Linear Time, and Wild Binary Segmentation), and trend analyses. The findings revealed significant shifts in precipitation periodicity, frequency, and complexity, highlighting the evolving hydrological characteristics of the basin. The results indicated that precipitation complexity, particularly in the northern and western basin, exhibited distinct patterns compared to those in the eastern and southern regions, with sample entropy values approximately 1.5 times greater in the north. Periodic fluctuations and increased irregularity in precipitation dynamics were observed, particularly from 1999 to 2017. Such changes have the potential to disrupt water resource planning and exacerbate risks such as floods and droughts. The findings could be useful for decision-makers in water resources management and for mitigating the impacts of climate change in one of the country’s highly sensitive basins.

Robust Monthly Lake Water Level Forecasting Using an Ensemble of Singular Spectrum Analysis Based Deep Learning and Categorical Boosting Models - Okan Mert Katipoğlu; Veysi Kartal; Türker Tuğrul; Veysel Süleyman Yavuz; Sema Ariman (Water Resources Management, 2026)
This study developed an innovative hybrid modelling approach to predict one-month-ahead water levels (LWL (T + 1)) for two major lakes in Türkiye (Eber and Eğirdir), aiming to model complex dynamics of hydrological systems and optimize sustainable water resources management. In the proposed methodological framework, Singular Spectrum Analysis (SSA) was employed to decompose hydrometeorological time series into sub-components, while Mutual Information (MI) and Partial Autocorrelation Function (PACF) techniques were utilized to determine optimal input variables. The hybrid model architecture, incorporating Deep Neural Networks (DNN), Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), and CatBoost algorithms, was optimized using 70% of the data for training and 30% for testing, and its performance was evaluated through deterministic and probabilistic metrics. The analyses revealed that SSA-based data preprocessing significantly improved the performance of prediction models, and depending on lake characteristics, CNN-based models demonstrated superior performance for Lake Eber, while DNN architecture excelled for Lake Eğirdir. The developed hybrid approach presents an original methodology that can be integrated into decision support systems for water resources management, contributing to the development of sustainable water management strategies and adaptability to similar hydrological systems.

Optimized Desiccant-Driven Atmospheric Water Harvesting: Machine Learning and Evolutionary Algorithms for Sustainable Water Resource Management - Seyed Amir Ahghar; Elshan Soltani; AmirHamzeh Farajollahi (Water Resources Management, 2026)

The Impact of Designed Rainstorms on River Flood Evolution Process Based on Dynamic Active Grid Hydrodynamic Model - Donglai Li; Jiahao Lv; Jingming Hou; Tian Wang; Wei Zhou; Xinxin Pan; Yu Tong; Qingshi Zhou; Xinghua Wang; Guangzhao Chen; Liping Ma (Water Resources Management, 2026)

Human Dimensions of Water Vulnerability Under Climate Change: A Systematic Review - Alex Y. Lo; Rukuh Setiadi (Water Resources Management, 2026)

Water Security in Developing Economies: Leveraging Citizen Science for Near-Real-Time Groundwater Assessment - Junias Adusei-Gyamfi; Afia Sarpong A. Gyebi; Owusu Abebrese; Gideon A. Appiah-Yeboah; Sampson Marfo; Emmanuel O. Owiredu; Azanu David; Seifu Tilahun (Water Resources Management, 2026)
As climate change and human-induced pollution make surface water sources increasingly unreliable, reliance on groundwater is steadily growing, underscoring the critical importance of effective groundwater monitoring. However, in most developing contexts, the conventional real-time monitoring approach is impractical due to resource constraints or the inaccessibility of remote locations. To overcome these challenges, a citizen science (CS) approach was used as a complement to traditional scientific methods to collect high-frequency data on key hydro-chemical parameters. Over 19 months, local participants measured eight physicochemical parameters and performed periodic samplings to analyze 15 heavy metals in 9 observation wells. Four wells had depths of 0–4 m, indicating their vulnerability to anthropogenic pollution and climatic variation. Although the sodium absorption ratio was medium (12.16), the salinity hazard was high (806.91 µS/cm), indicating a moderate to high risk of soil structure degradation when the high-saline wells are used for irrigation. Although the CS approach is not yet entirely scientifically robust, the consistency of the data in this study confirmed that, with comprehensive training protocols and continuous oversight, CS serves as a reliable and invaluable complement to traditional research for large-scale data collection and environmental monitoring.

Dynamic Recession Mechanisms Identification and Resilience Assessment Framework of Inundation in Typical Comprehensive Urban Areas - Bingxue Li; Jingming Hou; Xia Zhou; Donglai Li; Guangzhao Chen; Chenchen Fan; Jiahao Lv; Jing Jing; Yanhong Wang; Xinxin Pan; Yuhan Wang; Qingshi Zhou (Water Resources Management, 2026)

Bridging Data Gaps in Water Resources Planning: Comparative Regionalization for Ungauged Basins - R. K. Jaiswal; R. V. Galkate; S. Mandloi; S. Indwar; Pushpanjali Kumari (Water Resources Management, 2026)

Comprehensive Analysis of Water Contamination in IoT-Enabled Aquaculture Systems Using Advanced Quantum Optical Convolutional Neural Network Techniques for Enhanced Environmental Monitoring - S Janarthanan; S Shanthini Devi; M Monica Dhana Ranjini; Raghul G (Water Resources Management, 2026)

Hydrodynamics and Sediment Transport Capacity of Vegetated Rivers Covered by Multiple Plants Species: A Reach-Scale Analysis - Giuseppe Francesco Cesare Lama (Water Resources Management, 2026)

Sustainable Flood Management through Traditional Nature-Based Flood Detention Infrastructure: A Hydrological Assessment - Radhakrishna Govindarajulu Ramalingam; Ravikumar Govindasamy; Sudharsanan Rajagopalan (Water Resources Management, 2026)

Fast and Accurate Flood Inundation Dynamics Modeling and Prediction Based on Spatial Reconstruction and ConvLSTM - Shuang Zhu; Qiaolong Chen; Jun Guo; Xiangang Luo; Yi Liu; Yida Li; Hui Qin (Water Resources Management, 2026)

Sustainable Energy Dissipation in Perforated Stepped Weirs: A Flow-Regime-Based Experimental Study - Nashwan K. Alomari; Ahmed Y. Mohammed; Thaer Hashem (Water Resources Management, 2026)

Long-Term Multi-Parameter Monitoring of Subsurface Fluids: Advances, Applications, and Future Prospects - Sihang Han; Yifei Xing; Guangzheng Jiang; Chuanzhi Liao; Shu Hu; Zhenyu Li; Bohan Zeng (Water Resources Management, 2026)

Uncertainty Quantification in Climate Forecasting: How Bayesian Structural Time Series Models Excel Over Traditional and Data-Driven Techniques - Achal Lama; Soumik Ray; Pradip Kumar Sahu; K. N. Singh; Bishal Gurung; Bikramjeet Ghose; Pradeep Mishra; Tufleuddin Biswas (Water Resources Management, 2026)

Beyond Per Capita: Superlinear Scaling and Industrial Decoupling in Urban Water Metabolism - Mariana Brüning-González; Fernando Arenas-Araya; Jorge Amaya; Camila Alvarez-Garreton; Horacio Samaniego (Water Resources Management, 2026)

Comparative Assessment of Transformer and Conventional Deep Learning Models for Dam Inflow Prediction - Chakir Achahboun; Mohamed Chikhaoui; Mustapha Naimi; Mostafa Bellafkih; Oumayma Bassairate (Water Resources Management, 2026)

From Real to Laboratory Scale: Simulation and Scaling Methodology for a New Benchmark Water Distribution Network Model - Giacomo Ferrarese; Abazar Fathi; Stefano Malavasi (Water Resources Management, 2026)
Optimisation of water distribution networks (WDNs) for leakage reduction and energy recovery has been extensively investigated through numerical simulations. However, a significant gap remains in the physical validation of these methods because of the lack of scalable laboratory benchmarks. This study addresses this limitation by developing a systematic scaling methodology to translate the Jowitt and Xu benchmark network into a controllable numerical implementation laboratory-scale model, E-NET. The methodology uses a flow-scale factor of 4.33, derived through recursive pipe-diameter adjustment to preserve hydraulic similarity, specifically similarity in head loss and Reynolds number, using commercially available HDPE pipes. To manage leakage, pressure-reducing valves (PRVs) were implemented using a genetic algorithm (GA) to optimise both valve locations and pressure settings under different load conditions. The optimisation followed a pressure-dependent leakage model with a minimum service pressure constraint. Quantitative results showed close agreement between the Jowitt and Xu and the E-NET model, with RMSE values of 0.38 m for head loss. The GA successfully identified broadly consistent optimal PRV locations in both networks, particularly under low-demand conditions. The leakage discrepancies were generally small across the tested valve configurations. This work provides a numerically validated basis for a laboratory benchmark platform and represents a first step towards bridging the gap between theoretical optimisation and physical implementation in WDN pressure management.

Enhancing Daily Runoff Prediction Accuracy with the Coupled KAN-LSTM Model and a Dynamic Postprocessing Module - Chongxun Mo; Tao Feng; Keke Huang; Zhiwei Yan; Zihan Lin; Haiyang Wang; Jiameng Xu; Yuyi Zhong; Gang Tang; Yi Huang (Water Resources Management, 2026)

Fully Automated Risk and Hazard Assessment for Decision-support (FARHAD): A Cloud-Native Machine-Learning Framework for Worst-Case Scenario Flood-Risk Mapping via Weakline Analysis - Afshin Fouladi Semnan; Mohammad Javad Ostad Mirza Tehrani (Water Resources Management, 2026)

Managing Small and Medium-Sized Drinking Water Systems: Water-Energy-Carbon-Based Resilience Assessment - Michelle Sofia Sarmiento Barrios; Husnain Haider; Manjot Kaur; Gyan Chhipi-Shrestha; Kasun Hewage; Rehan Sadiq (Water Resources Management, 2026)

Enhancing Rainfall-Runoff Simulation in Data-Scarce Watersheds: Integration of Satellite Observations, Physically-Based Hydrological Modeling, and Machine Learning - Khabat Khosravi; Aitazaz Farooque; Shasha Han; David M. Hannah (Water Resources Management, 2026)

Enhancing Water Resource Sustainability: A Stochastic Optimization Framework for Desalination Plant Activation and Allocation - Mengru Wang; Jie Leng; Caiyun Lu (Water Resources Management, 2026)

Optimization of Open Channel Cross Section by Considering Seismic Slope Stability Through WSAR Algorithm - Tulin Cetin; Mustafa Erkan Turan; Mumin Emre Senol (Water Resources Management, 2026)
This study proposes a methodology for the design of trapezoidal cross-sections of open channels with minimum construction cost subject to uniform flow and stability constraints. The proposed methodology integrates the application of the Weighted Superposition Attraction–Repulsion (WSAR) method for finding economically optimized geometric parameters with iterative search methods that calculate the seismic stability factor of the slope. Compared to existing research, which mainly used the Fellenius method and did not consider seismic loading adequately, seismic loading is used explicitly in this research through consideration of the Fellenius, Bishop, and Janbu methods simultaneously. The WSAR algorithm is applied for the optimum design of open channel sections for the first time in literature. The results prove that most economical channel designs are obtained from the Janbu method in all cases. In non-seismic conditions, cost savings varied from 2.78% to 3.29% for varied discharge values. Under seismic conditions, this advantage is more significant. In the case of seismic conditions, K h = 0.01, the Janbu approach accounted for cost savings of up to 12.13% for Q = 100 m³/s and up to 9.16% for Q = 500 m³/s. In the case of seismic conditions, K h = 0.02, these savings amounted to 50.91% and 25.82% for Q = 100 m³/s and Q = 500 m³/s, respectively.

Investigating the effect of vegetation and impervious surface exchange on the rising limb of the runoff hydrograph under simulated rainfall intensities in laboratory experiments - Rakesh K. Verma; Ashwini B. Mirajkar (Water Resources Management, 2026)

Hydraulic Optimization of an Urban Water Supply Network Using Evolutionary Design Techniques - Rushikesh R. Yennawar (Water Resources Management, 2026)

Nighttime Urban Waterlogging Detection and Segmentation Using Vision-Based Edge Intelligence - Kun Li; Ran Pan; Ying Zang; Wenjun Hu; QingShan Liu; Zhifeng Hu (Water Resources Management, 2026)

Pipe Failure Risk Analysis of Water Distribution Network Using Analytic Hierarchy Process and Geographical Information System - Ajitha T T; Viji R (Water Resources Management, 2026)

The Cost of Bad Timing: How Phenology and Frequency Determine Agricultural Flood Risk - Shokhrukh-Mirzo Jalilov; Robert Maltsbarger; Haluk Gedikoglu (Water Resources Management, 2026)
Accurate valuation of flood risk is fundamental to efficient resource allocation, insurance pricing, and public investment in agriculture. Standard economic models, which often link asset damage directly to hazard magnitude, fail to capture the unique vulnerability of agricultural capital—where the value of standing crops is contingent upon phenological stage. This study develops a capital valuation framework to deconstruct the economic burden of flooding on cropland, demonstrating that the timing of a flood is a primary determinant of financial loss, often outweighing the role of physical flood magnitude. We model agricultural flood risk as expected annual damage (EAD) to crop capital, integrating hydrologic frequency analysis for a U.S. Midwest County with a phenologically-explicit damage function. This function disaggregates risk into two components: a Flood Hazard Index (FHI), quantifying flood intensity and duration across return periods, and a Flood Susceptibility Index (FSI), representing the time-sensitive depreciation rate of crop capital at different growth stages. Probability-weighted losses are summed across all flood scenarios to derive total EAD. Results reveal that the distribution of losses is heavily skewed toward high-probability, low-severity events. The 2-year and 25-year floods collectively account for approximately 45% of total EAD, despite extreme (≥ 100-year) events generating substantially larger per-event losses. Frequent floods impose the highest economic cost not because of peak discharge, but because their high likelihood of coinciding with phenologically sensitive periods is compounded by longer inundation durations—a “double liability” where occurrence probability and capital impairment duration are simultaneously maximized. Conversely, low-probability, high-severity events tend to occur outside the growing season, leaving lower-value capital exposed. These findings invert conventional risk models and carry significant implications for crop insurance design, flood mitigation investment, and agricultural capital management under climate volatility.

A Framework of Multi-Objective Reservoir Operation Model Construction Based On Competitive Feature Recognition - Rong Tang; Minglei Ren; Yuntao Wang; Xiaoli Zhang (Water Resources Management, 2026)

Integrating Urban Resilience into Human–Environmental Systems Enhances Adaptive Urban Flood Management - Mingyang Liu; Hongwu Zhang; Xiangzhou Xu; Zongping Ren; Wendi Wang; Zhanbin Li (Water Resources Management, 2026)

Spatiotemporal Dynamics of Flash Drought and Ecosystem Sensitivity in Coastal Zones: Implications for Agricultural Water Resources Management - Chuan-hao Yang; Xin Li; Cui-fang Zhang; Jin Zhang; Qi-peng Zhang; Qian Wang (Water Resources Management, 2026)

A Hydrological–hydraulic Modeling Framework for 2D Simulations of Street-level Urban Flooding - Samuel Almeida Dutra Júnior; Frederico Carlos Martins de Menezes Filho; Abderraman Róger Amorim Brandão; José Eduardo Alamy Filho (Water Resources Management, 2026)
The reduction of permeable areas due to from urban expansion, coupled with inadequate maintenance and the undersizing of drainage systems, has exacerbated urban flooding by increasing surface runoff. This study proposes an integrated framework that combines a VBA-based hydrological model with two-dimensional HEC-RAS simulations supported by a high-resolution digital terrain model explicitly representing curbs and gutters. Using a 2 h design storm with a 5- year return period, the model generated lot- and catchment-scale hydrographs and simulated flood dynamics at 1-s resolution. Results showed that enforcing a ≥ 20% permeable-area quota in highly impervious lots produced negligible reductions in flood extent and depth. In contrast, strategically placed stormwater inlets, especially upstream and on both sides of streets, substantially reduced water depths and curbed overtopping. The findings indicate that permeability-based policies alone are insufficient to mitigate flooding in consolidated urban areas, whereas the proposed framework provides a reliable tool to diagnoses of critical hotspots and evaluations structural interventions.

Implementing Machine Learning Algorithms for Pipe Failure Analysis in Water Distribution Systems - I. Ethem Karadirek; Omur Tosun; Asli Nur Rizvanoglu; Alper Ozcan; Tugba Akdeniz (Water Resources Management, 2026)
Pipe failures in water distribution systems occur due to various physical and environmental factors, and their analysis is essential for improving water loss control and asset management strategies. This study provides an innovative application of machine learning techniques to analyze pipe failures in a pilot study area (PSA) in Antalya, Türkiye, where failure data were recorded over a seven-year period. The factors considered in this study include pipe material, age, diameter, and length. Eight machine learning techniques, including Naïve Bayes, artificial neural networks, logistic regression, XGBoost, LightGBM, CatBoost, random forest, and support vector classification, were applied for pipe failure analysis. According to the findings, XGBoost achieved the highest prediction accuracy with an Area Under Curve (AUC) value of 0.99. The results further identified pipe length as the most influential factor in pipe failures, while PVC pipes were found to be the most prone to failure in the PSA. This comprehensive analysis not only provides valuable insights into pipe failure analysis but also offers an effective tool for optimal management of water distribution systems. Future studies should incorporate additional factors, such as the number of service connections and traffic load, to enhance failure probability assessments and inform optimal pipe replacement strategies.

Integrating Metaheuristic Optimization with Stochastic Gradient Boosting for Groundwater Potential Prediction in Data-Scarce Arid Environments - Rachid Mohamed Mouhoumed; Eyyup Ensar Başakın; Ibrahim Souleiman Abdallah; Cemre Sude Atan; Ömer Ekmekcioğlu (Water Resources Management, 2026)
In arid and semi-arid regions, the limited availability of surface water resources makes groundwater a critical source for domestic, agricultural, and industrial use. However, in Djibouti, unplanned and excessive exploitation of groundwater has led to aquifer depletion and seawater intrusion. Therefore, accurate and reliable prediction of groundwater potential represents an urgent need for sustainable water management. In this study, a comprehensive dataset comprising 14 hydrogeological, topographic, climatic, and remote sensing-based variables was developed to model groundwater potential. To predict groundwater potential, the Stochastic Gradient Boosting (SGB) algorithm was applied, while hyperparameter optimization was carried out using three different metaheuristic approaches, namely Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Gravitational Search Algorithm (GSA). Model performance was evaluated using accuracy, precision, recall, F1-score, and ROC/AUC metrics. Results demonstrated that the GSA-SGB model exhibited the most balanced performance on the test set, with 78.6% precision, 73.6% recall, and an F1-score of 0.749. Additionally, the PSO-SGB model achieved the highest classification accuracy (0.8621), while runtime analysis revealed that PSO was the fastest (195 s) and GA the slowest (627 s). ROC analysis indicated that the highest AUC value (0.813) belonged to the GSA-SGB model. SHapley Additive exPlanations analysis further revealed that Normalized Difference Vegetation Index and the sediment transport index were the most influential factors, with low slope and high precipitation conditions significantly enhancing groundwater potential. Overall, the findings highlight that the integration of machine learning with metaheuristic optimization techniques provides a powerful decision-support framework for predicting groundwater potential in Djibouti.

A Hybrid Linear and Nonlinear Hierarchical Reconciliation Method for Consistent Multi-timescale Precipitation Forecasting - Mengjie Yang; Zhen Zhu; Xinguang He (Water Resources Management, 2026)

Analysis of the factors affecting the reservoir paradox of drought disasters in mega-cities - Jieyu Li; Yuanjian Wang; Hang Li; Gang Liu; Donglin Li; Fei Yang; Qiang Wang (Water Resources Management, 2026)

Improving Water Quality Evaluation Using an Optimized Multi-Output Support Vector Machine - Jingjing Xia; Yi Zhou; Ziwei Yang; Jin Zeng (Water Resources Management, 2026)

Machine Learning and Deep Learning for Snowmelt Prediction: Long-Term Trends and Implications for Water Resource Management - Dawood Ahmad; Muhammad Haseeb (Water Resources Management, 2026)

A Generalized Determination Framework for Variable-Parameter Nonlinear Muskingum Models with Lateral Flow - Zhiming Liu; Li Mo; Jianzhong Zhou (Water Resources Management, 2026)

Development of a Novel Non-Stationary Daily Hydrological Drought Index - Chenlu Yu; Dong Wang; Vijay P. Singh; Pengcheng Xu; Along Zhang; Xiaoyu Ye; Xiankui Zeng; Jianguo Jiang; Handan He; Jichun Wu (Water Resources Management, 2026)

Modeling Strategy-Driven Socio-Hydrologic Dynamics in Uncertain Watershed Water Resource Systems Using a Bi-level Multiagent Framework - Mengxiang Zhang; Ting Fong May Chui (Water Resources Management, 2026)
Climate change and rapid urbanization intensify uncertainties in watershed water resource systems, challenging the effectiveness of integrated green infrastructure (GI) and water resource management strategies. This study investigates the socio-hydrologic dynamics of uncertain watershed systems under various water allocation schemes in the arid Colorado River Lower Basin, where GIs are implemented to enhance local water availability through rainwater harvesting. In this study, a bi-level multiagent framework (BL-MAUS) is developed that integrates fuzzy random variables with a bi-level multiagent architecture to model these complex interactions, integrating agent-based models that simulate the decision-making of watershed and urban water managers with hydrologic models that capture both city- and watershed-scale dynamics under social and hydrologic uncertainties. This coupled system enables the prediction of uncertain watershed behavior by modeling multi-agent interactions driven by specific water allocation schemes and GI implementation. The results show that a Water Trading Scheme (WTS) effectively minimizes water use costs but is highly sensitive to hydrologic uncertainties. In contrast, Water Quota (WQS) and Water Tariff schemes (WTaS) are found to improve the equity of water allocation. The implementation of GIs mitigates the adverse impacts of social uncertainty on costs and equity, notably reducing costs in WTS and WQS and enhancing equity in WTaS, although their overall impact remains limited—an outcome attributable to high construction costs, land constraints in dense urban areas, and the inherently local scale of GI benefits, though their effectiveness is strongly policy-dependent. This framework provides a robust tool for assessing watershed water resource systems under uncertainty, offering valuable governance insights for arid regions and advancing adaptive water management strategies globally.

Overtopping Breach of Cascade Earth-Rock Dams – State of the Art Review - Wen Qiu; Yanlong Li; Lifeng Wen; Ye Zhang; Ting Wang; Qiang Zhang; Haotian Chen (Water Resources Management, 2026)

Sustainable Desalination through Brine Mining: Turning an Environmental Challenge into an Economic Opportunity - Mohsen Abdesharif E.; Leila Ebrahimi; Elham Ebrahimi Sarindizaj; Davood Reza Arab (Water Resources Management, 2026)

Evaluating Seasonal Variability in Rainfall Forecast Accuracy Using Comparative Regression Modelling Approaches - Shravankumar S. M.; Vartika Paliwal; Bukke Lavanya (Water Resources Management, 2026)

Advances in AI-Driven Surface Water Quality Assessment: A Comprehensive Review of Machine Learning and Emerging Technologies - Ly Quang Thien; Christine Joy M. Pacilan; Luu Tang Phuc Khang; Nguyen Xuan Tong (Water Resources Management, 2026)

Nature-Based Solutions for Sustainable Flood Management: A Comprehensive Analysis - Nadir Murtaza; Aïssa Rezzoug; Ghufran Ahmed Pasha; Omar Eid Almutairi; Zeeshan Akbar (Water Resources Management, 2026)

Progress of Research on the Earth–Rock Dam Failure Mechanism and Numerical Simulation - Jiaheng Sui; Jie Ren; Yanan Lei; Jihui Shi; Shenghao Nan (Water Resources Management, 2026)

A Novel Statistical Weighting Approach for Multiple GCMs Ensembles to Enhance Drought Projections - Farman Ali; Jing-Cheng Han; Dong Su; Mahrukh Yousaf; Zulfiqar Ali; Shafeeq Ur Rahman; Yuefei Huang (Water Resources Management, 2026)

Aridity Index-Integrated First-Order Planar Regression Framework for Refining Curve Number in a Medium-Scale Watershed - Prashant Prashant; Surendra Kumar Mishra; Anil Kumar Lohani (Water Resources Management, 2026)

Impacts of WRF-based Precipitation Quality on Urban Flood Simulation Accuracy - Shuhong Xu; Qiongfang Li; Zhengmo Zhou; Yiqun Sun; Xingye Han; Qihui Chen; Yao Du; Zihao Liu (Water Resources Management, 2026)

Hesitant Intuitionistic Fuzzy Based Multiobjective Optimization for Water Resources Systems - Sangita V. Pawar; Premlal Patel; Ashwini B. Mirajkar (Water Resources Management, 2026)

From Floods to Social Risks: Human-Centered Design for Safer Water Infrastructure - Brian M. Crookston; Daniel Valero (Water Resources Management, 2026)
Critical water infrastructure is designed to satisfy performance criteria and also withstand natural disasters such as extreme flood scenarios. Safety in this regard has improved drastically over the last century, yet hundreds of casualties occur each year at hydraulic structures from public interactions that include all types of routine social behaviors and recreational activities. For example, in the UK and US such drownings in one year have resulted in as many casualties as deaths due to dam failures in the last 200 years. Herein we explore UK and US official and crowdsourced data to expose a risk perception gap and a neglected class of infrastructure risk. At hydraulic structures, fatal activities across all human ages ranged from accidental to intentional interactions. Examples include walking along a scenic canal, visiting a park, or more intrusive behaviors such as fishing from a dam, canoeing, or sliding down a spillway. Some new activities may be inspired by social media. Contrary to common expectations, the routine interactions with hidden risks are weighty contributors to fatalities and data indicates that alcohol did not play a significant role in these accidents (< 13.5%, UK). Furthermore, only 15% of drownings occurred during swimming activities (UK). Also, public safety measures were generally found to be non-existent or inadequate (US), despite repeated injuries or fatalities at certain structures. Many of these public safety hazards would be readily solved at the design stage, but retrofits that consider local social behaviors can be viable for both newer and legacy infrastructure. Although some risk reduction efforts, including awareness campaigns, are underway for US low-head dams, most other hazards remain invisible, —with walking/running by waterways amounting to 37% of these casualties (UK)—; which contrasts with the complete lack of risk perception by the general population. For these water risks –concealed in plain sight –, several generally applicable solution strategies are proposed herein, hierarchical to their effectiveness and involving interdisciplinary engineering, operation, and governance.

An Efficient Nonlinear Muskingum Method with Time-Variable Parameters for Flood Routing - Ali Raeisi Isa-Abadi; Fahimeh Sartip; Saleh Mehdizadeh (Water Resources Management, 2026)

Characteristics of Water Resources Variability and Comprehensive Water Security Assessment in the Chaobai River Basin of China - Lei Fan; Yi Wang; Fangfang Luan; Jianxin Xia; Junwei Ding (Water Resources Management, 2026)

Predicting Land Use-Driven Water Quality Changes in Arid Inland River Basins Using a PLUS-BP Neural Network Coupling Model - Yangan Ren; Yaqun Zhang; Chengcheng Wen; Tingting Shang (Water Resources Management, 2026)

Investigation of Soil Moisture and surface Roughness using Physical Models and Neural Networks in Dried Wetland Areas - Mina Baghlaneyane; Hossein Mohammad Asgari (Water Resources Management, 2026)

Towards Precision Agriculture: IOT-Enabled Intelligent Irrigation Using Hyper-Node Knowledge Relational Botox Graph Attention Network - K. S. Balamurugan; Sundresan Perumal; R. Mahendran; S. N. Manoharan (Water Resources Management, 2026)

Forecasting Minimum Night Flow in Water Distribution Networks with Feature-Selected Neural Networks - Shirzad Iranmehr; Ali Moosavi; Siamak Kazemzadeh Hannani (Water Resources Management, 2026)

Performance-Based Design of Extensive Green Roofs for Stormwater Retention and Drought Resilience: An Experimental–HYDRUS-1D Modeling Study - Jianzhao Ding; Mingjie Jiang; Liang Xiao; Libin Guo; Guoxiong Mei (Water Resources Management, 2026)

Integrating Ensemble Machine Learning with Meta-Learning and Averaging Synchronisations for Flood Susceptibility Modelling - Vincent E. Nwazelibe; Johnbosco C. Egbueri; Mubarak Abdulkarim; Johnson C. Agbasi (Water Resources Management, 2026)

Climate Variability and Surface Water Disconnections in Semi-Arid Urban Cities - Manas Kumar Jha; Vaishali Sahu; Ashutosh Kainthola; Dilip Kumar Markandey (Water Resources Management, 2026)

Reviewing the Braiding Index –New Scientific Braiding Index Proposed - Dipankar Paul (Water Resources Management, 2026)

A New Peak-alignment Statistical Design Storm Method with Return Period - Xingpo Liu; Keer Wei (Water Resources Management, 2026)

Evaluating Climatic Trends through Mann-Kendall and Sen Slope Analysis of CMIP6 Model Downscaled Future Projections - Ankit Balvanshi; Jayakumar K.V.; V. R. Desai (Water Resources Management, 2026)

Development of New Fuzzy Multi-objective Optimization Algorithm (F-MOICA3D) for Quantitative-Qualitative Sustainable Operation of Water Resources Based on 3D Response Space - Sadaf Mavali; Arash Azari (Water Resources Management, 2026)

A Novel Deep Learning Ensemble Framework for Riverbed Topography Reconstruction in 2D Hydrodynamic Simulations - Qiang Liu; Jiaxin Zheng; Chuanxing Zheng; Fengjiao Zhao; Lei Yu; Feng Ling; Zhixiang Da; Jijian Lian (Water Resources Management, 2026)

Multi-Scale Collaborative Regulation Technology of Water Transfer Projects - Tian Gan; Yunzhong Jiang; Chao Wang (Water Resources Management, 2026)

Optimal Multipurpose Reservoir Operation using Many-objectives Particle Swarm Optimization - Xiong Feng; Sadra Salmani; Saeed Bagheri; Mojtaba Shourian (Water Resources Management, 2026)

Investigation of the Effects of Internal Structure and Material Properties on the Hydraulic Performance of Rectangular Gabion Weirs: Experimental and Numerical Study - Hamidreza Abbaszadeh; Reza Tarinejad; Kiyoumars Roushangar; Rasoul Daneshfaraz (Water Resources Management, 2026)

Comparison of Stochastic Dynamic Programming and Sequential Decision-Dependent Operational Rules - Mina Khosravi; Abbas Afshar; Paolo Perona; David Andrew Barry; Amir Molajou (Water Resources Management, 2026)

Runoff Simulation and Prediction with Water Conservation Capacity Assessment in a Cold Region Watershed - Yunying Wang; Zongxing Li; Juan Gui; Xiaoying Liu; Lanping Si (Water Resources Management, 2026)

A Ride in Semi-arid: Evaluation of Remote Sensing-based Indices for the Detection and Monitoring of Surface Water Bodies Under Dry and Wet Periods - Eduardo Gonçalves Patriota; Guillaume Francis Bertrand; Alexandro Medeiros Silva; Natalia Maria Mendes da Silva; Khalil Grisi Velôso Mendes; José Welton Gonçalo de Sousa; Richarde Marques da Silva; Cristiano das Neves Almeida; Cinthia Maria de Abreu Claudino; Carolyne Wanessa Lins de Andrade Farias; Jorge Flávio C. B. C. Silva; Victor Hugo Rabelo Coelho (Water Resources Management, 2026)

Solving Fuzzy Matrix Games with Triangular Type-2 Intuitionistic Fuzzy Numbers for Water Management Problem - Tina Verma (Water Resources Management, 2026)

A Hybrid Approach Combining Phase Space Reconstruction with Random Forest, KNN, and LSTM for Reducing Prediction Lag in Lake Water Level Forecasting - Bo Yuan (Water Resources Management, 2026)

Application of Transformer Hybrid Model in Runoff Process Prediction Based on Frequency Decomposition and Physical Correction of Multi-Source Data - Gan Miao; Wenzhong Li; Chengshuai Liu; Caihong Hu; Xin Dong; Haojie Li; Yi Zhou; Zhili Zhu (Water Resources Management, 2026)

Effect of Climate Variability and Land-Use/Land-Cover Change on Water Yield in a Water Deficit Basin - Jonathan Bautista Nájera; Juan M. Esquivel Martínez; Miguel Angel Gómez-Albores; Alfredo Méndez Bahena; Sergio A. Salgado Souto; Vania Jiménez-Lobato (Water Resources Management, 2026)

Methodology for Determining Runoff Coefficients Based on Rainfall Depth and Sensitivity Analysis of Influencing Factors - Hao Yu; Fei Han; Wei Zhang (Water Resources Management, 2026)

Optimized Relevance Vector Machine using Improved Manta-ray Foraging Algorithm for Groundwater Level Modeling - Sandeep Samantaray; Abinash Sahoo; Haydar Abdulameer Marhoon; Mohammad Saleh Al-Suwaiyan; Zaher Mundher Yaseen (Water Resources Management, 2026)

Assessing Whole-of-Society Water Sustainability Efficiency Toward SDG 6 - Yuan Zhuang; Haiwei Zhou; Yung-ho Chiu; Lina Zhang; Changgao Cheng; Yufei Han (Water Resources Management, 2026)

Assessing Hydrological Responses to Land Use Type Transition in Watershed with Reservoir Operation Using SWAT Model - Hiyaw Hatiya Ware; Il-Moon Chung; Ju-Young Shin; Myoung-Jin Um (Water Resources Management, 2026)

The Integrated Machine Learning Model Driven by Multi-source Data Improves the Accuracy in Lake Water Quality Prediction - Jiale Li; Hua Wang; Yanqing Deng; Xiaomao Wu; Yichuan Zeng (Water Resources Management, 2026)

Dynamic Game-Theoretic Modeling and Optimization of Conjunctive Water Use Management - Yahya Saleh; Ülkü Gürler; Emre Berk (Water Resources Management, 2026)

Random Elites-Mean Hybrid (REMH) Metaheuristic Algorithm for Optimising Water Resource Management Under Real-World Constraints - K. B. Baladaniya; P. L. Patel; P. V. Timbadiya (Water Resources Management, 2026)

Dynamic Equivalent Drainage Method for Urban Flood Modeling: A Rainfall-adaptive Fusion Approach - Ming Wu; Yuqin Gao; Yaya Cheng; Xiao Chen; Jingang Zhang; Lin Yi (Water Resources Management, 2026)

Complexity-Guided Modeling for Daily Pan Evaporation Prediction Using Compact LSTM Models and Nature-Inspired Optimization - Abdullah A. Alsumaiei (Water Resources Management, 2026)

Integrating the Indexes of Energy and Food Security into the Measurement of Water-Energy-Food Nexus Efficiency Towards SDGs - Lina Zhang; Chenyu Sun; Liyin Zhang; Yung-ho Chiu; Haolin Yang; Qinghua Pang; Zhen Shi (Water Resources Management, 2026)

A Comparative Study of Maximum Entropy Based and Traditional Copula Models for Joint Simulation of Bivariate Flood - Kuang Wang; Fan Li (Water Resources Management, 2026)

A Method for Calculating the Shortest Time Path for the Evacuation and Transfer of People at Risk of Flooding - Wei Ge; Heqiang Sun; Yutie Jiao; Laihong Jing; Yadong Zhang; Meimei Wu; Te Wang; Hua Zhang; Jianyou Wang; Yupan Qin; Fuheng Ma; Pieter van Gelder (Water Resources Management, 2026)

Empirical Formulation for Time of Concentration Based on Watershed Morphometry Using the Diffusion Wave Equation (DWE) Model - Mohamad Bagus Ansori; Umboro Lasminto; Anak Agung Gde Kartika; Satria Damarnegara (Water Resources Management, 2026)

Design and Implementation of a Novel Smart Agriculture’s Irrigation System Integrating IoT Technologies - Anissa Ghomrassi; Sihem Amara; Monia Chaabane; Hatem Mhiri; Chokri Ben Salah (Water Resources Management, 2026)

Research on Dynamic Control of Flood Control Level for Jinxia Reservoir Group Considering Risks - Min Li; Yue Sun; Rui Feng; Wei Ding; Qianning Wang; Jinnan Zhang; Yong Peng; Feilin Zhu (Water Resources Management, 2026)

A Validated Hybrid Modelling Framework for Climate-Resilient Flood Risk Management in Data-Scarce Semi-Arid Catchments - Maryam Jan Ahmadi; Seyedbasir Saidi; Leila Khajenoori (Water Resources Management, 2026)
Flood risk in semi-arid regions is intensifying due to climate change; however, reliable prediction remains a fundamental challenge in catchments with severe hydrometric data scarcity (station density < 0.2 per 1000 km²). To address this gap, we developed and validated a novel hybrid modeling framework that integrates a physically based hydrological model (Hydrologic Engineering Center - Hydrologic Modeling System, HEC-HMS) with interpretable, Bayesian-optimised machine learning techniques (Random Forest, eXtreme Gradient Boosting , XGBoost ). The SHapley Additive exPlanations (SHAP) method was employed to interpret and identify the key drivers of flooding. SHAP analysis revealed that antecedent precipitation and soil permeability are the primary controlling factors in flood occurrence. The validated framework demonstrates robust performance, achieving excellent accuracy in flood event classification (Precision-Recall- Area Under the Curve (PR-AUC) = 0.98) and reducing false alarm rates by 87.5% compared to the standalone physical model. This study provides a practical, validated tool that enhances prediction reliability and delivers actionable insights for climate-resilient flood risk management in data-scarce environments.

Hydraulic Behavior and Dissipation of Energy in Stepped Spillways with Gabion Steps - Rasoul Daneshfaraz; Amir Ghaderi; Hojjat Sadeghi; Hossein Mohammadnezhad; John Patrick Abraham (Water Resources Management, 2026)

Detection of Urban Road Waterlogging Using Deep Learning Methods - Yuncan Gao; Ran Pan; Guoqiang Li; Zhifeng Hu; Wenjun Hu; Qingshan Liu; Ying Zang (Water Resources Management, 2026)

On the Fundamental Limits of Pressure-Based Leak Detection under Pressure-Driven Demand Conditions - Ali Maleki; Mohammadreza Alizadeh Tataki Afshar; Amin Nejat (Water Resources Management, 2026)

A Physics-guided Unsupervised Learning Framework for High-impact Heavy Rainfall Prediction in Data-sparse Environments - K. Karthick; S K Aruna; S. Krishnan; S. Ravivarman; R. Manikandan (Water Resources Management, 2026)

Integrating Machine Learning and Geospatial Analysis for Flood Hazard, Vulnerability, and Risk Assessment in Odisha, India - Manoranjan Mishra; Rajkumar Guria; Richarde Marques da Silva; Somanath Sethi; Minati Mishra; Mansour Almazroui; Rabin Chakrabortty; Celso Augusto Guimarães Santos (Water Resources Management, 2026)

Evaluation of Deep Learning and Physically Based Models for Urban Streamflow Estimation Under Historical and Future Climate Conditions - Harshanth Balacumaresan; Monzur Alam Imteaz; Iqbal Hossain; Md Abdul Aziz; Tanveer Choudhury (Water Resources Management, 2026)
Accurate streamflow estimation in urban catchments remains challenging under accelerating hydroclimatic variability and urbanisation. Conventional models such as Runoff-Routing-Burroughs (RORB), widely adopted in Australian practice, often struggle to represent the non-linear rainfall-runoff dynamics, governing urban flood responses. This study develops and evaluates a Deep-Learning-Neural-Network (DLNN) framework for streamflow estimation in the highly urbanised Gardiners Creek catchment, situated in southeastern Melbourne, using a dual-simulation-approach, integrating event-based and continuous-modelling. Historical rainfall and streamflow data (1989–2021) were utilised for calibration and validation against the RORB model, while future rainfall inputs were derived from dynamically downscaled ACCESSS1-0-CCAM projections under RCP4.5 and RCP8.5 scenarios from the Victorian-Climate-Projections dataset. Results demonstrate the DLNN’s improved overall predictive accuracy and adaptability (R²: 79.2–95.3%, NSE/KGE: 0.83–0.95, VE < 10%) relative to RORB (R²: 72.3–91.8%, NSE/KGE: 0.68–0.89, VE: −17.13% to − 13.20%). The DLNN-model effectively captured short-term flood peaks and long-term runoff variability, maintaining stability across diverse hydroclimatic regimes. Future projections indicate increased short-duration peak flows under RCP8.5, highlighting heightened flash-flood risks and limitations of current design frameworks. These findings establish DLNN as a robust, parsimonious, and climate-adaptive alternative for supporting flood prediction and resilient water resource management.

Application of the IGWO Algorithm for Flood Control in Reservoir Groups in Optimal Operation - Hai-tao Chen; Shu-min Li; Xiao-qi Guo; Yuan-yuan Liu; Wen Guo (Water Resources Management, 2026)

An Improved Fuzzy Multi-objective Nonlinear Programming Model for Irrigation Water Resources Optimization Allocation Under Uncertainties - Chongfeng Ren; Linghui Yu; Yu Zhang; Zhishuai Xie (Water Resources Management, 2026)

Detecting Undocumented Shifts in Seasonal Precipitation: An MDL-Driven Periodic Autoregressive Segmenter with Fourier Compression and Genetic Search - Youssef Saliba; Alina Bărbulescu (Water Resources Management, 2026)
This study proposes a modeling framework based on piecewise constant regression with a Periodic Autoregressive (PAR) remainder. Model complexity is controlled using Rissanen’s Minimum Description Length (MDL) criterion, while Fourier-based compression reduces dimensionality without compromising likelihood, improving computational efficiency. A multi-island Genetic Algorithm (GA) jointly estimates changepoint (CP) configurations and seasonal order. Monte Carlo (MC) experiments under Dobrogea conditions show reliable detection of all three change points when shifts exceed 1.5 innovation standard deviations. Applied to Medgidia precipitation (1965–2019), the method identifies an early shift while retaining a parsimonious AR(1) structure. By separating true climatic shifts from noise, the framework provides a robust basis for water resource assessment and infrastructure planning.

Surrogate Prediction of Urban Waterlogging for Emergency Warning: Deep Learning Comparison Based on Hydrodynamic Simulation-Generated Samples - Jian Chen; Yangyang Tian; Luyao Zhang; Haizhou Wang (Water Resources Management, 2026)

Numerical Simulation of Transient Flow Characteristics During the Water Filling Process in Undulating Pipelines Under Complex Terrain Conditions - Xi Shi; Tiantian Huang; Hui Chen; Hu Tao; Yuanshun Huo (Water Resources Management, 2026)

WLPC-LBM: a Mesoscopic Model for Efficient One-Dimensional Unsteady River Flow Simulation - Dehao Zou; Jian Wu; Yong Peng; Jiaxuan Li (Water Resources Management, 2026)

Flood Risk Assessment and Management Strategies under Extreme Frequency Events - Xianqi Zhang; Yimeng Xiao; Jie Zhu; Wanhui Cheng; Yuehan Zhang (Water Resources Management, 2026)

A Study on the Mountainous Merging Precipitation Model and Effective Covariates - Mengya Xing; Simin Qu; Peng Jiang; Xing Chen; Wen Ji (Water Resources Management, 2026)

Evolution of Water Resources Allocation in Watersheds Based on the Three-Way Decisions Theory - Zuliang Lu; Zhuran Xiang; Zhihui Cao; Lu Xing; Mingsong Li; Junman Li (Water Resources Management, 2026)

Simulation-Optimization Approach Based on the Meta-Model in the Optimal Operation of a Single Reservoir Using the Development of the FOA-SL Surrogate Model - Seyedeh Zahra Hassani; Parisa-Sadat Ashofteh (Water Resources Management, 2026)

Identifying Water Source Locations on a Data-Limited Small Island Using GIS and the Factor Index Method - Uma Seeboonruang; Witthawin Horpeancharoen; Uba Sirikaew; Pinit Tanachaichoksirikun; Virun Chulkaivalsucharit (Water Resources Management, 2026)

An Integrated Physics-Informed Multi-Perturbation Framework for Scalable and Interpretable Leakage Detection in Water Distribution Networks - Feiyu Duan; Tianwei Mu; Bo Zhou; Baokuan Ning; Haoqiang Tan (Water Resources Management, 2026)

Bargaining for Water: An Integrated Nash and Agent-Based Simulation for Sustainable Residential Water Demand Management in the Global South - Shahmir Janjua; Duc-Anh An-Vo; Kathryn Reardon-Smith; Shahbaz Mushtaq (Water Resources Management, 2026)
Karachi, Pakistan’s largest city, faces serious challenges in the fair and equitable distribution of water among its seven administrative divisions. To address the growing scarcity of water, fairly and equitably, this study develops an integrated Nash equilibrium and agent-based simulation for sustainable residential demand management. The approach is divided into two parts. In the first part, we present an agent-based model (ABM) developed in NetLogo, which simulates water allocation dynamics across the seven administrative divisions of the city. The model incorporates both demand-driven (e.g., water-saving behaviour, theft, and adaptive demand reduction) and supply-driven (e.g., infrastructure expansion) scenarios to evaluate system responses under varying scarcity conditions. It implements a proportional allocation strategy while simulating government interventions when water stress exceeds a certain threshold. In the second part, the Nash Bargaining Solution (NBS) is applied to address situations where supply falls short of demand in selected scenarios characterized by extreme water shortages. The scenarios, representing extreme cases of supply-demand gap, and NBS are used to reallocate the water between the seven districts fairly and efficiently. The inclusion of NBS, which is a game-theoretic approach, ensures that agents (in this case, the city divisions or districts) are allocated water based on negotiated outcomes that reflect their vulnerabilities and needs, resulting in greater fairness and equity of access to scarce water resources. We demonstrate how an agent-based model can be coupled with the cooperative bargaining solution to offer insights onto adaptive water governance, supporting equitable water distribution in heavily populated water-stressed urban cities.

Integrating GIS and Neural Networks for Sustainable Water Harvesting Site Selection - Marwa Elshebli; Yousef Sharrab (Water Resources Management, 2026)

Unveiling the Effects of Landscape Metrics on Regionalization of Runoff Parameters - Hüseyin Akay (Water Resources Management, 2026)
Landscape Metrics (LMs) have an important role in linking spatial configuration with runoff to characterize hydrological response of a watershed. However, the effects of LMs on runoff parameters have not been investigated quantitatively, and predictive accuracy has not been examined in detail yet. To address this gap, runoff parameters are regionalized by LMs using simple linear and log-linear regressions, Catchment Similarity Approach (CSA), and a sound method the Ordered Weighted Averaging (OWA). Curve number method and Nash’s instantaneous unit hydrograph concept are used for lumped hydrological modelling at Kocaırmak and Darıören stream gauging stations located in Türkiye. In the watershed, 25 sub-basins are delineated, LMs are extracted, hydrological parameters are computed and regionalized by LMs. Prediction results are assessed by Nash–Sutcliffe Efficiency coefficient (E NS ), Peak Error (PE), Peak–Weighted Root Mean Square Error (PW–RMSE). E NS , PE, and PW–RMSE are computed using the extracted runoff parameters as 0.943, 0.792, 2.20%, 0.84%, 68.30 and 9.10 for Kocaırmak and Darıören, respectively. Regression-based methods reveal that number of patches, landscape shape index, area-weighted patch area, mesh and various statistics of shape indexes estimate more enhanced predictions than the extracted runoff parameters. CSA contributes to estimate satisfactory runoff hydrographs. Outcomes from regression-based methods and CSA provide satisfactory predictions by collaboration of sound decision strategy coefficients and threshold values of catchments similarity index in the OWA method. The methodology adapted in this study may initiate attempts encouragingly to link LMs with hydrological parameters to develop sustainable land use strategies by controlling runoff parameters.

A Novel Runoff Network Synchronous Prediction Model Based on Distribution Similarity Analyzing and Sparse Matrix Mapping - Ling Liu; Jujie Wang; Jianping Li; Jun Hao; Yinhong Yao (Water Resources Management, 2026)

Influence of Multi-source Precipitation Forcing on Deep Learning-based Runoff Prediction: A Multi-basin Comparative and Explainable Analysis - Ye Tian; Shunan Xiang; Weili Tan; Guoqing Wang; Bingrong Zhou (Water Resources Management, 2026)

The Methodological Evolution of the Water-Food-Energy Nexus Index in Agriculture: A Systematic Review - Nisar Muhammad; Rashid Farooq; Khan Zaib Jadoon; Ghufran Ahmed Pasha (Water Resources Management, 2026)
The Water-Food-Energy (WFE) nexus has become a central framework for addressing resource sustainability in agriculture, yet its operationalization increasingly relies on composite indices whose methodological foundations remain fragmented. This study presents the first systematic and critical review of the Water-Food-Energy Nexus Index (WFENI) applied in agricultural systems. Following a rigorous PRISMA-guided protocol, we analyzed 21 peer-reviewed studies to examine indicator selection, index construction, weighting schemes, and validation practices. The review reveals a clear methodological evolution from early resource-efficiency metrics toward expanded frameworks that integrate environmental, climate, and emission dimensions. While normalization approaches are largely standardized, substantial divergence exists in weighting and aggregation, marking a gradual shift from equal and expert-based weights toward data-driven and optimization-based methods. However, a critical “robustness gap” persists: the vast majority of studies lack formal sensitivity or uncertainty analysis, raising serious concerns about reliability. This review systematically maps these methodological trends and outlines a strategic agenda for future research, advocating for the institutionalization of robustness testing, dynamic modeling, and expanded system boundaries to enhance the rigor and policy relevance of agricultural sustainability assessments.

Urban Runoff Simulation Using HEC-RAS and the Modified Rational Method - Stavros S. Bagiouk; Aikaterini E. Agiou; Konstantinos L. Katsifarakis (Water Resources Management, 2026)
In this paper, we deal with simulation of surface rain-runoff in urban areas, in particular densely built ones, where impermeable surfaces prevail and prevent rainwater infiltration to the ground. Our aim is to check whether simple computational methods and freely available computational tools can adequately describe rain-runoff along streets. The basic idea is that streets can be simulated as open channels and adjacent neighbourhoods as hydrological basins, feeding them. Then, the Modified Rational method can be combined with HEC-RAS to calculate water depths at selected street cross-sections. To test the procedure, we have selected an area of the historical centre of Thessaloniki, Greece, which is densely built. Two streets have been considered as the main open channels. Using detailed topographical maps and findings of in situ survey, we delineated the respective drainage areas. Special care was needed in street junctions, to decide on inflows and outflows. Moreover, to highlight the importance of using low environmental impact techniques in the urban fabric, application of rain gardens in suitable locations was proposed, with the aim of reducing the runoff volume and, consequently, local inundation problems during the peaks of extreme rain events. Finally, we have compared the calculated water depths with those obtained using the Manning formula for open channel flows. Our results show that the free hydraulic model HEC-RAS, although developed for simulation of flows in natural watercourses, can be used successfully in the simulation of stormwater runoff at the urban street scale and for planning local flood alleviation measures.

When Water Flows, Resilience Grows: Alleviating Water Shortage Builds a Shock-Resistant Urban Economy - Wei Dai; Zhongxin Ni; Enping Yu (Water Resources Management, 2026)

A Stochastic Dynamic Rubinstein Bargaining Framework for Transregional Water Resources Allocation Considering Inflow Forecasting Errors - Jisi Fu; Tong Ding; Yong Zheng; Bin Xu; Lianghui Li; Feng Yan; Zhongzheng He (Water Resources Management, 2026)

Influence of Distance Metrics Under Different Temporal Units on Flood Season Staging with the Fisher Optimal Segmentation Method - Jiahui Lin; Jun Li (Water Resources Management, 2026)

Enhancing Runoff Prediction through Feature Engineering and Cluster-Specific Modeling - Hamid Saadatfar; AmirHossein Eshghi; MohammadErfan ShuridehBakht (Water Resources Management, 2026)

Hydrological Assessment of Climate Change Impacts on Streamflow Using Downscaled CMIP6 GCM Ensemble - Halah Kadhim Tayyeh; Ruqayah Mohammed (Water Resources Management, 2026)

Improving Monthly Streamflow Prediction with ICEEMDAN-SVR Hybrid Model Through Combination Range of Parameters Optimization - Qiucen Guo; Xuehua Zhao; Yuhang Zhao; Zhijing Ren; Huifang Wang (Water Resources Management, 2026)

Flood Risk Assessment Using Multitemporal Satellite Data and Explainable Artificial Intelligence (XAI): Analyzing Susceptibility and Driving Factors - Pouya Mahmoudnia; Jalal Karami (Water Resources Management, 2026)

Rising with Intelligence: A Systematic Review of AI Transforming Smart Flood Management - Deepika Mahajan; Sandeep Sharma; Komal Saini (Water Resources Management, 2026)

A Study on the Water Price Adjustment Strategy to Promote Urban Residents' Water Conservation — From the Perspective of a Three-Party Evolutionary Game - Jingru Liang; Yanrong Wang; Bin Yue; Huijie Ma (Water Resources Management, 2026)

Beyond Borders: Tracking Water and Carbon Footprints of Global Transport Modes - Chenglong Wang; Xuewei Guo; Chenyang Shuai; Xi Chen; Wei Huang; Lin Gan; Jingran Sun; Bu Zhao (Water Resources Management, 2026)

An Optimization Framework for Pressure Monitoring in Water Distribution Networks: Coupling Krill Herd Algorithm with Monte Carlo-Based Uncertainty Analysis - Peng Feng; Shen Dong; Hang Li; Mou Lv (Water Resources Management, 2026)