Evaluating a Rainfall-Runoff-Inundation Model for Dam Inflow and Flood Inundation Forecasting in Extreme Rainfall Events - Arlen S. Alejandro; Lanie A. Alejo; Orlando F. Balderama; Jeoffrey Lloyd R. Bareng; Sameh A. Kantoush (Water Resources Management, 2026)

A Multi-Objective Optimization Model of Coupled Grey-Green Stormwater Infrastructure Based on PSO-NSGA-III: A Perspective Considering Drainage Pipeline Desilting Schemes - Zhaoyang Zhang; Hongyuan Fang; Danyang Di; Kairong Lin; Meixian Liu (Water Resources Management, 2026)

Streamflow Simulation Based on Pre-Processing-Based Hybrid Model: SSA-AR Model - Baydaa Abdul Kareem; Salah L. Zubaidi; Hussein Al-Bugharbee; Ali W. Alattabi; Mawada Abdellatif (Water Resources Management, 2026)
An accurate medium-term streamflow forecast is one of the significant functions for managing and planning water resources. Considering the characteristics of trend, periodicity, and stochasticity of streamflow into account. So, this research aims to develop a new strategy, including a singular spectrum analysis (SSA) technique and a linear autoregressive (AR) model to predict monthly Tigris River streamflow data with three scenarios. The first scenario applies the SSA to decompose the normalised and cleaned time series into different signals (i.e., trend, seasonal, stochastic, and noise), then reconstruct the signals without noise and use the AR model to simulate the new time series. The second scenario employs the AR model to forecast each signal of streamflow without noise separately. The simulated time series was obtained by summing each predicted signal. The third scenario uses the AR model to simulate the raw data. Based on several statistical tests, the comparative analysis reveals that the first and second scenarios were much more accurate than the third ones. The second scenario is the best, reaching RMSE = 0.1223 (m 3 /s) and MAE = 0.0913 (m 3 /s) in the testing phase. The novelty of this study lies in the comparative evaluation of three SSA-AR modelling scenarios and the finding that forecasting decomposed components individually leads to superior accuracy compared to conventional approaches. The results are of substantial significance to the Ministry of Water Resources in managing and planning freshwater resources amid growing water demand.

Investigation into the Management Protocols for “Reservoir-pumping Station” Irrigation Infrastructure during Characteristic Hydrological Years Amidst Variable Runoff Conditions - Cong Wei; Wenpeng Zhao; Jing Xu; Haiyu Liu; Zhicheng Zhong (Water Resources Management, 2026)

Spatio-Temporal Assessment of Drought Based on Multiple Datasets From Atmosphere-Surface-Subsurface Perspective - Peng. Yan; Wei. Wei; Liang. Zhou; JiPing. Wang; BinBin. Xie; JunJu. Zhou; Dibo. Chen (Water Resources Management, 2026)

Water Pricing Schemes: An Agent-Based Simulation - Michael Neugart; Vahideh Sotoudehmollashahi (Water Resources Management, 2026)
One way to address water scarcity could be to shift from flat or uniform rates to block tariffs. Empirical studies evaluating the effect of block rates on water conservation, however, surprisingly yield mixed results. To better understand the effects of block pricing on water conservation, we developed an agent-based model of a water market and calibrated it using German data. Our simulations show that water consumption increases compared to using uniform rates when a decreasing block rate is introduced and decreases when an increasing block rate is introduced. Moreover, block pricing has distributional consequences as measured by households’ water bills. We also evaluate the effect of pricing schemes on water consumption and households’ water bills under changing price elasticities, a shrinking population, and more hot days.

Analytical Model for Infiltration Process and Failure Time of Capillary Barrier Cover Under Heavy Rainfall: The GA-CBC Approach - Yifan Wang; Xiaokang Li; Yongkang Wu; Xu Li (Water Resources Management, 2026)

Examining the Applicability of Wavelet Packet Decomposition on Statistical Models for River Stage Forecasting - Adnan Bashir; Muhammad Ahmed Shehzad (Water Resources Management, 2026)

Data-Driven Water Governance: A Strategic Model to Develop Roadmap for National Water and Climate Data Infrastructure - Mahsa Ghotbizadeh; Mahdi Zarghami; Parviz Mohammadzadeh (Water Resources Management, 2026)

Determinants of BIM Adoption in Wastewater Treatment Plants: A Structural Equation Modeling Analysis - Anwar. Tarawneh (Water Resources Management, 2026)

Hydropower’s Role in Renewable Energy and Environmental Sustainability: From Blue to a Greener Future - Muneeb Sagheer; Alia Ashraf (Water Resources Management, 2026)

An Explainable AI Model for Anomaly Detection in Water Quality Data - Xurxo Rigueira; David Olivieri; Maria Araujo; Maria Pazo (Water Resources Management, 2026)
Detecting anomalies in water quality data is critical for ensuring safe and reliable water supplies. This study introduces an explainable, iterative, multitemporal machine learning algorithm based on decision trees to detect and characterize temporal anomalies. The model partitions time series into sliding windows at three temporal scales, capturing multivariate relationships across distinct chronological levels. Iterative training enables the classification of new data subsets, progressively refining the learned expert criteria and expanding the anomaly set. To improve interpretability, we developed the Visual Decision Paths Representation (VDPR) algorithm, which provides visual insights into variable importance, learned thresholds, and sample-level classification reasoning. Across all monitoring stations, the model achieved an average F1 score of 99% and an error rate below 1%. By comparing individual samples with the broader dataset, VDPR delivers deeper interpretability than state-of-the-art methods that focus fundamentally on variable importance while preserving strong predictive performance across varied environmental conditions and stations.

Higher Accurate Flash Floods and Debris Flows Simulation: A Hybrid Model Coupling HiPIMS and FLO-2D - Haibo Yang; Yitong Dong; Peng Xu; Xinyi Liu; Xiang Cui; Junhua Li; Qiuhua Liang; Fuxin Chai; Lan Feng; Xiaosong Shu (Water Resources Management, 2026)

Prediction of Water Consumption based on Randomly Distributed Embedding under Small-sample Conditions - Xiaojun Wang; Longxia Qian; Jianyun Zhang; Song Chen; Rui Zou; Weihua Peng (Water Resources Management, 2026)

Quantitative Assessment of Risk Evolution, Potential Vulnerability Thresholds, and Drought Resistance in Socioeconomic Drought Under a Changing Environment - Zeqian Zhao; Yimin Wang; Jianxia Chang; Aijun Guo; Zhiwen Peng (Water Resources Management, 2026)

Water Leakage Detection of in Urban Areas with a Holistic Approach Using the GPR Method - Zeynep Öğretmen Aydin; Hilal Alemdağ; Ali Erden Babacan (Water Resources Management, 2026)

Dynamic Simulation of Pedestrian Evacuation under Flood Conditions Using a Swarm Intelligence Approach - Bingyao Li; Jingming Hou; Dandan Cai; Fang Liu; Yongyong Ma; Qingshi Zhou (Water Resources Management, 2026)

Spatio-Temporal Attention in Federated Learning for Streamflow Forecasting - Aliyu Ashiru; Hongsong Chen; Suhail Ahmed Rajpar (Water Resources Management, 2026)

Deep Learning-Based Monthly Runoff Simulation in Changing Environments: Enhancing Accuracy by Reducing Data Redundancy - Shenghong Liu; Ji Liu; Shaokang Yang; Zhenxin Bao; Shan-e-hyder Soomro; Wenjuan Chang; Qingxia Lin; Qirui Zhang; Gantao Yang; Ruilin Geng; Haiyu Wang; Longteng Dai (Water Resources Management, 2026)

Impact of Climate Change on Potable Water Quality and its Remediation Using Artificial Intelligence: A Review - Mohit Singh; Shray Pathak; Athar Hussain (Water Resources Management, 2026)

Selection of an Appropriate Technique for the Estimation of Generalised Extreme Value Distribution Parameters in Extreme Rainfall Modelling - Iqbal Hossain; Anirban Khastagir; Mahmudul Haque; Monzur Alam Imteaz (Water Resources Management, 2026)
Application of the generalised extreme value (GEV) distribution in extreme event modelling requires the estimation of its three parameters (location, scale and shape). Although different techniques for estimating the parameters are available, the comparison of the available parameters’ estimation techniques is yet to be investigated rigorously for the determination of the probability of occurrence of extreme rainfall with higher degree of accuracy. This research presents the assessment of four parameters estimation techniques, maximum likelihood estimation (MLE), generalised maximum likelihood estimation (GMLE), Bayesian and L-moments. A key contribution of this research is the systematic evaluation of block length (in extreme rainfall extraction) sensitivity in GEV distribution parameters estimation and associated return levels which are evaluated consistently across several temporal time windows. The evaluation is accomplished within a regional-scale applying long-term rainfall recordings from 29 meteorological stations in Tasmania, Australia. The outcome of the study suggests that the method of L-moment method is more appropriate for fitting the GEV distribution to the time-series data with outliers. The selection of block length in extreme rainfall extraction has considerable influence on the magnitude of the GEV distribution parameters.

Non-stationary Drought Assessment: Coupling Complex Networks with Reconnaissance Drought Index for Drought Identification - Haris Vangelis; Konstantinos Spiliotis; Mike Spiliotis (Water Resources Management, 2026)
In this study, the annual and seasonal characteristics of meteorological drought are investigated using complex network theory. The analysis is based on the initial reconnaissance drought index (RDI), which is defined as the ratio of precipitation to potential evapotranspiration. This time series is subsequently transformed into a complex network through the visibility graph algorithm. The proposed framework is evaluated against the widely used standardized RDI, which assumes an underlying probability distribution based on the estimated mean and standard deviation. By examining the relationship between the standardized RDI and the topological properties of the resulting network, it is demonstrated that closeness centrality can serve as an effective indicator of drought intensity, while betweenness centrality provides useful information for detecting drought cyclicity and temporal transitions. A notable advantage of the proposed methodology is its robustness to non-stationary time series, whereas the standardized RDI is sensitive to deviations from stationarity.

A Hybrid Deep Learning Approach for Runoff Rolling Forecasting - Lingzi Wang; Rengui Jiang; Yong Zhao; Jiancang Xie; Fawen Li; Simin Wang; Ganggang Zuo (Water Resources Management, 2026)

Application of Hybrid Meta-Heuristic-Based Data‑Driven Models to Forecast Streamflow Drought Index - Redvan Ghasemlounia; Amin Gharehbaghi; Farshad Ahmadi (Water Resources Management, 2026)

An Optimized Long Short Term Based Modified Support Vector Regression for Water Evaporation Prediction - Jaydeep Narayan Kale; Sanjay Kumar Sharma (Water Resources Management, 2026)

A Cost-Effective Decision Support Model for Efficient Water Infrastructure Planning to Enhance Sustainability - Ayhan Köksüz; Zeynep Tuğçe Kalender (Water Resources Management, 2026)
In today’s world, understanding the role of effective water management is crucial for promoting sustainable development. It is essential to focus on strategies that balance resource conservation with the demands of growing communities. In this regard, this study focuses on developing a decision support system to enhance the planning and implementation of water infrastructure projects in rural areas, which often rely on international aid and partnerships. The proposed model aims to provide stakeholders with a clear understanding of the implications of various design and operational choices, offering an optimal balance. Main key contributions of the model include an adaptive approach to customizing rural water infrastructure planning based on local needs and conditions, accurate cost estimates that account for geographic and demographic factors, and the ability to compare different infrastructure scenarios to identify the most cost-effective and sustainable solutions. Additionally, the proposed model features an intuitive user interface designed for easy interaction by all stakeholders, including non-technical community members. This interface provided a 90% time savings in water infrastructure design and promoted a collaborative decision-making process by dynamically adjusting recommendations based on stakeholder input. By offering a standardized, easily understandable format for presenting project information, the model not only strengthens funding applications but also adds value to the existing literature, providing a valuable resource for future studies in the field of rural water infrastructure planning.

The Role of Metaheuristic Algorithms in Tuning Extreme Learning Machine Model for Lake Water Level Modeling - Gebre Gelete; Zaher Mundher Yaseen; Hüseyin Gökçekuş (Water Resources Management, 2026)
Lake water level (WL) is an important indicator of the lake’s physical condition, and its variation may significantly influence ecosystem stability and water supply security. Hence, precise estimation of lake WL is important for environmental protection and sustainable water resource management. Consequently, the current study introduced a novel hybridized extreme learning machine (ELM) with four optimization algorithms, namely grey wolf optimizer (GWO-ELM), marine predator algorithm (MPA-ELM), mountain gazelle optimizer (MGO-ELM), and whale optimization algorithm (WOA-ELM). The performance of the developed hybrid models was compared with the standalone ELM and multilinear regression (MLR) using different statistical metrics such as coefficient of determination (R 2 ), Root mean square error (RMSE), Nash Sutcliffe Efficiency (NSE), and mean absolute error (MAE). The results demonstrated that the hybrid models outperformed the standalone models. During the testing phase, the best prediction results were obtained using the MGO-ELM model at Lake Hawassa (R 2 = 0.997, RMSE = 0.022 m, MAE = 0.008 m, and NSE = 0.996), Langano (R 2 = 0.987, RMSE = 0.056 m, MAE = 0.022 m, and NSE = 0.986), Abiyata ((R 2 = 0.999, RMSE = 0.02, MAE = 0.012 m, and NSE = 0.998) and Ziway (R 2 = 0.995, RMSE = 0.036 m, MAE = 0.021 m, and NSE = 0.994). The MGO-ELM model enhanced the estimation performance in comparison with ELM and MLR models by 70.322%-89.848% and 44.578%-82.609%, respectively, in terms of testing set RMSE. Generally, the developed hybrid ELM models can be used as a practical computational method for sustainable water resource management in the Ethiopian Rift Valley Lakes.

Improving Regional Drought Monitoring Through Auxiliary Data Integration and Statistical Process Control - Muhammad Arshad; Alina Mukhtar; Nasrullah Khan; Zulfiqar Ali; Miklas Scholz (Water Resources Management, 2026)

Quantifying Complexities in Hydrological Indicators and Classification of Water Resources through Information Theory - Shekhar Singh; Anil Kumar (Water Resources Management, 2026)

Secure Water Quality Monitoring using Blockchain-Smart Contract and IoT in 6G Smart Cities - Qaisar Abbas; Riyad Almakki; Mubarak Albathan; Waeal J. Obidallah; Zeyad Alshaikh (Water Resources Management, 2026)

Distributive Assessment of the Groundwater Sustainability Level Using the GMS Model and Aggregating the Developed Indicators based on the Cloud Model-based and Evidential Reasoning Approach - Mahmoud Mohammad Rezapour Tabari; Saeed Toghyani (Water Resources Management, 2026)

Scaling Approaches for Manning’s Roughness Coefficient in Reduced-Scale Hydrodynamic River Simulations With Delft3D - Amira S. Farag; Mohamed B. Ezzat; M. M. Ibrahim (Water Resources Management, 2026)
Despite extensive theoretical work on Manning’s roughness coefficient (n) in hydraulic literature, its numerical validity across multiple scales in two-dimensional depth-averaged (2DH) frameworks remains poorly quantified. This study addresses this gap by developing a systematic multi-scale calibration protocol applied to a complex reach of a large alluvial river. The novelty lies in the simultaneous evaluation of three geometric ratios (1:10, 1:20, and 1:30) within a single modeling environment to derive an empirically grounded scaling relationship. Results demonstrate that prototype roughness is not directly transferable: unscaled parameters yield errors 25 to 50 times greater than optimal configurations. A non-linear power-law relationship is identified between the scale ratio and the required roughness reduction, achieving 98% mean agreement with classical Froude–Manning theory. Crucially, this research identifies the 1:20 scale as the optimal threshold for minimizing scale-induced numerical distortions. By quantifying the slight deviation from theoretical exponents caused by the numerical “absorption” of three-dimensional turbulent structures into two-dimensional parameters, this work provides a physics-based predictive tool that eliminates subjective trial-and-error calibration in downscaled river simulations.

Evaluation and Prediction of Drought Dynamic Using the Convolutional and Bidirectional Long Short-Term Memory Frameworks - Abdol Rassoul Zarei (Water Resources Management, 2026)

Dynamic Variations of Agricultural Drought and its Response to Meteorological Drought Based on a Three-Dimensional Spatiotemporal Clustering Perspective - Fei Wang; Hexin Lai; Ruyi Men; Zipeng Wang; Yanbin Li; Yunzhong Jiang; Kai Feng; Wenxian Guo; Qingqing Tian; Yanping Qu (Water Resources Management, 2026)

A LightGBM–AHP Integrated Framework for Robust Water Quality Index Evaluation - Paike Ma; Wenle Chen; Juncai Huang; Wei Liu; Chongchong Zhu (Water Resources Management, 2026)

Spatiotemporal Optimization Method for Watershed Management Practice Scenarios Within a Simulation–Optimization Framework - Tong Wu; Liang-Jun Zhu; Shen Shen; Cheng-Zhi Qin; A-Xing Zhu (Water Resources Management, 2026)

Evaluating the Performance of Automatic Detection for Urban Flood Levels Using Different Deep Learning Approaches - Zhonglin Zhao; Baohong Lu; Shuo Zhang; Daoli Wang; Jiaquan Wan; Ranyu Liu; Huang Li; Zhensong Wang (Water Resources Management, 2026)

Rapid Generation of Urban Flood Susceptibility Maps Using Machine Learning Under Different Rainfall Scenarios - Chenchen Zhao; Chao Gao; Caihong Hu; Silong Zhang; Yehai Tang; Chengshuai Liu; Wenzhong Li; Xiongpeng Tang; Shuaifeng Lu (Water Resources Management, 2026)

Runoff Prediction Using State-Cross-Attention LSTM Model with Comparisons to Transformer - Hanlin Yin; Xinyan Song; Gangpei Qin; Qirui Zheng; Yanning Zhang (Water Resources Management, 2026)

Deep Learning-Based Estimation of Seepage Loss in Lined Irrigation Canals - Mohamed Kamel Elshaarawy (Water Resources Management, 2026)
Accurate estimation of seepage loss from lined irrigation canals is essential for improving irrigation efficiency and water sustainability. This study evaluates and compares five deep learning (DL) models, namely a deep neural network (DNN), convolutional neural network (CNN), recurrent neural network (RNN), long short-term memory network (LSTM), and bidirectional long short-term memory network (BiLSTM), for predicting the dimensionless seepage-loss ratio ( q / ky ) from four dimensionless geometric and hydraulic variables. The main contribution of the work is a controlled benchmark of these DL architectures on a lined-canal numerical dataset using identical training/testing partitions, Grid Search Cross-Validation (GSCV), and a multi-criteria validation framework based on error indices, regression error characteristic (REC) curves, Taylor diagrams, uncertainty analysis, and generalization criteria. The dataset comprised 600 numerical cases and was divided into training (70%) and testing (30%) subsets. Results showed that all models achieved high predictive accuracy; however, the CNN provided the most balanced overall performance. In testing, the CNN yielded the best overall testing performance, with (R² = 0.996), the lowest RMSE (0.225), and the lowest testing uncertainty ( U 95 =0.6245), while preserving close agreement with the observed mean and standard deviation. Although the DNN and RNN produced competitive correlation statistics, their testing errors and/or uncertainties were slightly higher. The LSTM and BiLSTM showed larger dispersion and less stable generalization. These findings demonstrate that the CNN was the most reliable model for this dataset when accuracy, uncertainty, and generalization were considered jointly rather than on the basis of R² alone. The proposed framework offers a practical tool for rapid seepage assessment in lined-canal design and rehabilitation.

Assessing the Performance of the Periodic Autoregressive Fractionally Integrated Moving Average and Periodically Correlated Models to Predict Periodic Drought - Abdol Rassoul Zarei; Mohammad Reza Mahmoudi (Water Resources Management, 2026)

Green Operational Strategy and Cost Sharing Mechanism for the Inter-Basin Water Transfer Project: A Differential Game-Theoretical Study from the Supply Chain Perspective - Zhisong Chen; Danli Chen; Rong Yu; Andrea Appolloni; Huimin Wang (Water Resources Management, 2026)

Optimizing Crop Yield Prediction and Suitability using Reinforcement Learning and Context Aware Multi-Channel Networks in IoT-Enabled Smart Agriculture - J. Jencewin; M. R. Geetha (Water Resources Management, 2026)

Machine Learning-Based Prediction of Hazard Intensity Indicators for Cascading Dam Failures Under Different Breach Combinations - Xinyi Ma; Xiangyi Ding; Meirong Jia; Xiaojie Tang; Zhongchen Lv (Water Resources Management, 2026)

How Increasingly Frequent Drought-Flood Abrupt Alternation Events Affect Reservoir Operation? - Yifan Huang; Jianyu Fu; Xuejin Tan; Zhihong Deng; Haibo Peng; Wenzhi Zhang; Bingjun Liu (Water Resources Management, 2026)

Cost-Effective Urban Runoff Management Using Hybrid Simulation-Optimization of Low-Impact Development (LID) - Ramtin Moeini; Seyed Hossein Hosseini; Amir Mohamad Mandegar (Water Resources Management, 2026)

An Improved Dynamic Parameterization Scheme for the Evapotranspiration Complementation Theory Model - Yanyui Dai; Fan Lu; Kui Zhu; Chenyuanyu Liu; Shiyuan Tian; Kangdi Yu (Water Resources Management, 2026)

Urban Waterlogging Prediction Based on Time Lag Correlation Analysis and Multi-Model Coupling - Xiaohui Lei; Dongkun Liu; Yan Long; Haocheng Huang (Water Resources Management, 2026)

Evaluating Cost Efficiency in Water Utilities: a Latent Class Stochastic Frontier Approach to Address Technological Heterogeneity - Alexandros Maziotis; Maria Molinos-Senante (Water Resources Management, 2026)
Improving the efficiency of water utilities is a key objective for regulators and policymakers, particularly in capital-intensive sectors such as water services. In this context, cost efficiency refers to the ability of water companies to provide a given level of service at the minimum possible cost. This study evaluates the cost efficiency of English and Welsh water utilities over the period 1991–2019 while accounting for technological heterogeneity across companies. To address this issue, a latent class stochastic frontier model is applied, allowing firms with similar technological characteristics to be grouped into different classes. The empirical analysis is based on a sample of English and Welsh water utilities from 1991 to 2019. The results identify three distinct groups of water utilities with average cost efficiency scores of 0.981, 0.983 and 0.965. Overall, the estimated efficiency levels are relatively high and remain stable over time, with limited potential for additional cost savings. The results also highlight differences in returns to scale and cost drivers across the identified classes of utilities.

A Novel Framework for Urban Water System Prediction and Regulation Based on LSTM Neural Networks - Minghan Yang; Shang Gao; Chao Wang; Min Cai; Lina Zhang; Hao Wang; Yongwei Shao (Water Resources Management, 2026)

Water Saving Behaviors in Agriculture within the Framework of Norm Activation Model - Serhan Candemi̇r; Hasan Gökhan Doğan; Ayşen Edi̇rneli̇gi̇l; Zeki Bayramoğlu; Emel Mi̇rza; Ali Gani̇yusufoğlu (Water Resources Management, 2026)
Today, sustainable water use has become a critical policy area, as dynamics such as increasing global water demand, water scarcity, and climate change directly affect agricultural production. In this study, the role of psychological and social factors in explaining water-saving behaviors in agricultural production was examined based on the Norm Activation Model (NAM); the model was extended with the environmental concern (EC) variable. Within the scope of the field study carried out in Konya province, data were collected from 270 farmers. Structural equation modeling results showed that personal norms exert a strong positive effect on water-saving behaviors (β = 0.430 in the original model; β = 0.341 in the extended model). The original NAM explained 19.5% of the variance in personal norms, while the extended NAM increased this explanatory power to 22.0%, corresponding to an improvement of approximately 2.5 percentage points. Environmental concern directly affected both personal norms (β = 0.118) and water-saving behaviors (β = 0.234). Guilt emotions contributed positively to water-saving behaviors (β = 0.085), while pride did not show a statistically significant direct effect. The findings indicate that psychological and environmental drivers play a decisive role in shaping farmers’ water-saving behaviors. The study suggests that environmental sensitivity and normative approaches should be integrated into interventions aimed at increasing the sustainability of water management policies and agricultural irrigation practices

Satellite-Informed Transformer Model for Enhanced Coastal Water Quality Modelling - Mehvish Bilal; Zaher Mundher Yaseen; Syed Masiur Rahman (Water Resources Management, 2026)

A Strategic Study on Water Resources Management in Islands to Balance Water Supply, Usage, and Environmental Impact from the Perspective of Environmental Responsibility - Jie Leng; Caiyun Lu (Water Resources Management, 2026)

Comparison Study of ANFIS and RSM Models for Predicting of Water Production by Capacitive Deionization Method - H. Etezadi; P. Zarafshan; M. Khashehchi; E. Pipelzadeh; G. Fang; K. Salari (Water Resources Management, 2026)

SWMM Parameter Calibration Method for Urban Subcatchments Based on Spatial Heterogeneity - Jingsi Li; Tian Wang; Guangxue Luan; Shan Li; Wei Zhou; Guangzhao Chen (Water Resources Management, 2026)

Coordinating Flood Control and Stormwater Harvesting Through Optimization of In-pipe Storage Capacity Use in Urban Stormwater Drainage System - Yunchong Liu; Zi Hui (Water Resources Management, 2026)

Machine learning and optimization applied to the assessment of flood hazard magnification by reservoir operation - Manizhe Zarei; Omid Bozorg-Haddad; Hugo A. Loáiciga (Water Resources Management, 2026)

A Novel Spectral Decomposition Framework for TimesNet: Integrating DCT and Wavelet Transforms for Hydrological Forecasting - Kübra Küllahcı; Elif Kartal; Abdüsselam Altunkaynak (Water Resources Management, 2026)
This study presents methodological enhancements to the TimesNet architecture for deep learning–based streamflow forecasting. To this end, alternative spectral decomposition techniques—namely the Discrete Cosine Transform (DCT) and the Discrete Wavelet Transform (DWT)—were integrated into the TimesNet framework in place of the conventional Fast Fourier Transform (FFT). The primary objective of this modification is to improve feature representation and learning efficiency when modeling complex hydrological time series. In addition, LSTM and Naive Persistence models were included as baseline approaches to position TimesNet relative to commonly used temporal forecasting methods. The proposed models were applied to daily discharge data obtained from three key hydrometric stations—Chilia, Sulina, and Sfântu Gheorghe—located along the main distributary branches of the Danube River prior to its discharge into the Black Sea. Model performance was evaluated not only using station-specific univariate time series but also using aggregated discharge time series constructed by combining flow records from all three stations, representing the total freshwater inflow to the Black Sea. Forecasting accuracy was quantitatively assessed using standard hydrological performance metrics, including Mean Squared Error (MSE), Mean Absolute Error (MAE), and Nash–Sutcliffe Efficiency (NSE). The results demonstrate that the DCT-TimesNet model consistently outperforms both the original TimesNet architecture and the wavelet-enhanced variant across all test scenarios. DCT-TimesNet-based models were also found to more effectively capture both short-term fluctuations and long-term temporal dependencies compared to the LSTM and Naive Persistence benchmarks. Furthermore, station-level modeling yielded slightly higher accuracy than aggregated time series forecasting.

A Contract Period Optimizing Method for Water Saving Management Contract Project in Public Institutions Considering Social Benefits - Wei Li; Xiaona Li; Xiaosheng Wang (Water Resources Management, 2026)

Sustainable Urban River Network: Integrating the River Ecological Sensitivity Index - Haibo Yang; Fan Wu; Tianji Dong; Fei Chen; Zheng Duan; Lan Feng; Longfei Hou (Water Resources Management, 2026)

Multi-objective Optimization of Low Impact Development (LID) Facilities Spatial Layout for Urban Water Management - Xiaolan Chen; Zhengjie Fang; Jun Liu; Hong Zhou; Yikai Chai; Haotian Feng (Water Resources Management, 2026)

Multi-Objective Relationship Analysis of Cascade Reservoirs Based on Set Pair Analysis and Cloud Model - Jian Wang; Zhangling Xiao; Yude Zhu; Zhongmin Liang; Yan Xing; Xinyue Liu; Zongmo Yang (Water Resources Management, 2026)

Quantile estimation in frequency analysis of hydrologic variables for the generalized Pareto distribution: An approach based on specially formulated pivotal quantities - Weiqiang Zheng; Shuguang Liu; Zhengzheng Zhou (Water Resources Management, 2026)

Benchmarking Success: A Framework for Fidelity Assessment in Local Councils’ Rainwater Harvesting Policy Adherence - Hafizah Mohd Latif (Water Resources Management, 2026)
Implementation fidelity has been widely assessed in health and policy research, however, its application within the built environment remains conceptually fragmented and inconsistently operationalised. The aim of this paper is to unveil the relationship between regulatory provisions, implementation strategies and fidelity in the context of rainwater harvesting enforcement at the local government level. A comprehensive framework for assessing rainwater harvesting policy adherence across contexts is developed by identifying and synthesising patterns from implementation science literature, prior built environment research and insights derived from two empirical studies in Malaysia. The analysis reveals an outer-setting regulatory layer and two interrelated inner-setting tiers that translate policy inputs into fidelity outcomes: Tier 1 comprises organisational-level implementation strategies (interventions and organisational factors), while Tier 2 represents the individual-level enactment mechanism in which employee characteristics position frontline actors as interpretive mediators whose judgements directly influence adherence. Considered together, these layers demonstrate that implementation fidelity is shaped by the interaction between regulatory design, organisational coherence and the interpretive practices of employees responsible for enforcement, through which policy intent is translated, mediated and at times diluted in practice. The framework further shows that fidelity gaps emerge not solely from resource constraints, but from regulatory ambiguity, fragmented organisational support and discretionary judgement, challenging assumptions that clearer by-laws or additional resources alone can secure consistent enforcement. The framework, therefore, provides a coherent basis for assessing policy adherence. By consolidating key implementation concepts into a structured, consensus-oriented taxonomy, the framework reduces terminological ambiguity and strengthens conceptual clarity for future fidelity research in the built environment.

Optimization of Dynamic-Staged Drought-Resistance Operation Strategy for Reservoirs - Jie Li; Zhao Liu; Qin-nan Mao; Jin-xia Zhang; Ya-jian Wei; Si-qi Shen (Water Resources Management, 2026)

A Sustainable Approach for Energy Recovery in Branched Irrigation Networks Using an Integrated Optimization Model - Gökhan Ayna; Hayrullah Ağaçcıoğlu (Water Resources Management, 2026)

A Hybrid Computational and Machine Learning Framework for Flooding Risk Assessment in Urban Rail Transit Under Extreme Weather Scenarios - Yan Gao; Yuchao Jiang; Quan Yuan; Xiaohan Li; Le Sun; Ketian Sun (Water Resources Management, 2026)

Understanding Patterns and Drivers of Changing Sectoral Economic Water Use in Scotland - Maria Clemens; Scott McGrane; Christopher J. White (Water Resources Management, 2026)
Understanding economic water use remains challenging due to limited data availability and confidentiality constraints that restrict the robust assessment of sectoral patterns. This study presents the first publicly available national dataset for Scotland that integrates abstraction and network-supplied water volumes across 81 economic sectors, enabling consistent sectoral water-use assessment and ranking of major contributors to national demand. An integrated, diagnostic analytical approach was applied to capture both short- and long-term water-use behaviour. Short-term analysis captures monthly water-use dynamics, including recurring seasonal patterns and responses to extreme events, while long-term results describe sectoral developments and the drivers shaping them. Sectoral profiles combining these observations with qualitative evidence reveal three dominant driver types: hydro-climatic, economic-structural, and behavioural-operational. These profiles were used to derive indicative sectoral estimates for 2025, reflecting heterogeneous and evolving influences over time. Gross Value Added (GVA) provides contextual information but has only limited explanatory power, underscoring the need for sector-specific interpretation. Differentiated approaches therefore provide a more realistic basis for water-use assessment than uniform assumptions, particularly where demand is heterogeneous. The study delivers a sector-level overview of economic water use in Scotland, supporting more transparent monitoring and more robust interpretation of future demand. The dataset further offers insights into water-intensive activities common across many economies. This driver-based classification offers transferable analytical logic for regions with fragmented water-use data and evolving economic structures, supporting more targeted monitoring and realistic demand assessment.

A Novel Approach for Predicting Friction Factor in Steep Mountain Channels: An Investigation into Machine Learning Methodologies for Complex Phenomena - Ajaz Ahmad Mir; Mahesh Patel (Water Resources Management, 2026)

Deep Learning for Sustainable Water Futures: SAITS and Transformer Models to Reconstruct and Forecast Precipitation in Data-scarce Catchments - Nizar Troudi; Mounira Zammouri; Fadoua Hamzaoui-Azaza; Nadhir Al-Ansari (Water Resources Management, 2026)

A Hybrid Deep Learning Framework Integrating Multi-Strategy Optimization and Error Correction for Ensemble Monthly Runoff Prediction - Wen-chuan Wang; Miao Gu; Ming-wei Ma; Hong-fei Zang; Qiang Ma; Dong-mei Xu (Water Resources Management, 2026)

A Novel GIS-Based Multi-Criteria Framework for Climate-Induced Water Scarcity Assessment - Sena Ödensoy; Basak Guven; Zeynep Akdoğan (Water Resources Management, 2026)
Global climate change, driven by cumulative anthropogenic activities, has resulted in observable extremes, including floods caused by intense precipitation and droughts or wildfires triggered by heatwaves. Research indicates a high level of confidence regarding the future impacts of climate change on water resources, with the Mediterranean region identified as particularly vulnerable to severe drought risk. This study develops a comprehensive framework for mapping climate change-induced water scarcity risk by integrating climatic, geographical, socio-economic and infrastructural drivers. The Büyük Menderes Basin was selected as the study area due to its strategic location and the intensity of agricultural activities. Multi-Criteria Decision Analysis, combined with Geographical Information Systems and fuzzy membership methods, was applied to identify spatial variations in water scarcity risk projected for 2050. Three climate scenarios based on Shared Socioeconomic Pathways were considered: SSP1-1.9, SSP3-2.6 and SSP5-8.5. Results indicate that water scarcity risk increases progressively from SSP1-1.9 to SSP5-8.5. While infrastructural drivers did not dominate the overall risk assessment, they exerted a substantial influence on basin-wide water scarcity, underscoring the importance of integrating infrastructural conditions into water risk models. This study presents a novel, adaptable framework that combines climatic, socio-economic and infrastructural parameters, offering a comprehensive approach for assessing water scarcity in river basins. The proposed approach provides a valuable tool for guiding water resource planning and policy-making under future climate scenarios.

Streamflow Simulation and Interpretability Analysis in Multi-Climatic Basins Using Physics-Based and Data-Driven Hybrid Models - Jijun Chen; Jiali Guo; Jiahui Gu; Kunteng Zhang (Water Resources Management, 2026)

Leveraging Transfer Learning for Improved Tidal Level Forecasting - Yuan Liu; Chao Wang; Tongbi Tu (Water Resources Management, 2026)

Analysis of Sustainable Relationship Between Industrial Structure and Water Utilization Structure in Social Change Based on the Quantitative Model - Honglin Xiao; Jinping Zhang; Yuhao Wang (Water Resources Management, 2026)

Water Scarcity Risk Transmission of the Global Mining and Quarrying Sector - Chenglong Wang; Chenyang Shuai; Xi Chen; Bu Zhao; Jingran Sun (Water Resources Management, 2026)

Blending-ensemble LSTM-SVR Modeling for Streamflow Prediction with Limited Data Series Based on Tree-structured Parzen Estimator Bayesian Optimization - Jiyu Zhang; Yufeng Wang; Liangjie Zhao; Bing Yan; Shiqin Xu; Zhe Yang; La Zhuo (Water Resources Management, 2026)

A hybrid VMD–BiLSTM–XGBoost approach for multi-scale drought forecasting in urbanizing monsoon transition zones - Zhe Zhu; Weihua Lin; Fujiang Liu; Yan Guo; Bo Li (Water Resources Management, 2026)

Exploring Accuracy and Uncertainty in Watershed-Scale Estimation of Actual Evapotranspiration: Comparing Conceptual Budyko Framework and Machine Learning Methods - Mohsen Nasseri; Yousef Kanani-Sadat; Hassan Naghavi; Mohammad Salimi (Water Resources Management, 2026)

Variable Rate Irrigation Through Digital Agriculture for Sustainable Water Management: A Meta Review on Current Challenges and Future Directions - Nauman Yaqoob; Aitazaz A. Farooque; Syed Hamid Hussain Shah; Farhat Abbas; Muhammad Hassan (Water Resources Management, 2026)
Efficient agricultural water management is essential due to increasing pressure on global water resources. To support environmentally sustainable farming water management, a detailed review of existing research on data-driven irrigation systems is conducted. The review paper reviews present advancements, challenges, and future directions based on extensive peer-reviewed studies on variable-rate irrigation (VRI), machine learning and artificial intelligence (AI), deep learning techniques, smart sensing technologies, state-of-the-art control systems, and coupled frameworks. To ensure transparency and reproducibility, studies were selected through a systematic search of major research databases (such as Dimensions AI, Web of Science, and Scopus) using key keywords. The inclusion criteria required that studies be peer-reviewed, published in English between 2000 and 2023, and specifically address precision irrigation techniques, control strategies, or implementation methods relevant to VRI. Conference proceedings and high-impact journal articles were prioritized. This review focuses on three central components of precision irrigation: (1) development of prescription maps, (2) innovative control strategies, and (3) realistic implementation of VRI systems. To improve the efficiency and flexibility of modern irrigation systems, the paper identifies emerging knowledge gaps and proposes innovative pathways to address them. The study describes a new methodology through a case study on developing irrigation prescription maps. Precision irrigation, especially when supported by VRI and state-of-the-art AI technologies, has shown considerable potential to improve water productivity. This approach assists researchers, practitioners, and stakeholders in designing, comparing, and executing sustainable irrigation strategies.

Exploration on Coupling Machine Learning with Hydrological Model to Enhance Runoff Simulation - Yinmao Zhao; Ningpeng Dong; Chao Ma; Hao Wang (Water Resources Management, 2026)

Roads in Rainstorms: A Systematic Optimization Framework for Overloaded Road Drainage Sections - Buyun Fang; Jinjin Gu (Water Resources Management, 2026)

DMA Optimization of Water Distribution Networks Using Multi-Objective Genetic-Particle Swarm Optimization - Haiyan Yang; Haoruo Lu; Chunlong Xiao; Senmiao Gi; Peng Wang; Junhong Zhang (Water Resources Management, 2026)

GIS-Based Assessment of Urbanization Impacts on River Network Sustainability - Aleksandar Valjarević (Water Resources Management, 2026)

Evaluating Design Peak Flow Estimation Methods - Soheyl Moradi; Andrea Petroselli; Matej Vojtek (Water Resources Management, 2026)
This study evaluates four hydrological approaches, in particular the Rational method, Fuller’s formula, EBA4SUB, and the STORAGE + COSMO4SUB (S + C) model, across five sub-basins of the Gidra River (Slovakia) for return periods (T) of 2–1000 years. In the gauged Píla sub-basin, all methods deviated from official design flows (6–44.5 m³/s), with empirical approaches showing systematic overestimation (e.g., up to 35 m³/s vs. 6 m³/s at T = 2 years), while Fuller’s formula reached up to threefold overestimation at long return periods. The S + C model provided closer agreement, generally within a factor of two. In ungauged basins, peak flows ranged from near 0 to 479 m³/s, with inter-method variability (CV at T = 1000 years) decreasing from 0.65 in small basins to 0.55 in larger ones. Empirical methods displayed largely linear scaling with basin area, which may oversimplify underlying hydrological processes. More physically-based approaches, such as S + C, offer internally consistent results, but require careful calibration and validation.

Quantitative Research on the Interaction Relationship between Water and Land Resources Based on the Binary Water Cycle - Xi Guo; Lewei Yu; Mengmeng Jiang; Weijing Wang (Water Resources Management, 2026)

Dynamic Prediction of Slope Stability in Sponge Roads of Mountainous Cities Using BP Neural Network and Its Engineering Application - Wensheng Tang; Hongliang Zhang; Haiyuan Ma; Zhiyu Shao; Dafu Shen; Yuejian Wang (Water Resources Management, 2026)

An Adaptive Reservoir Operation Model for Stochastic Demand-Inflow Planning - Hossein Yousefi; Saeed Alimohammadi; Ali Moridi (Water Resources Management, 2026)

Environmental Impact Assessment of Hydropower: A Systematic Review of Common Criteria, Dynamic Nature of Criteria, and Emerging Properties - Elham Soleimanian; Nima Zafarmomen (Water Resources Management, 2026)
Hydropower has emerged as a critical component of clean energy initiatives worldwide, providing substantial contributions to electricity generation and grid stability. However, the construction and operation of hydropower dams also pose significant environmental and socio-economic challenges, including habitat fragmentation, water quality fluctuations, and community displacement. Environmental Impact Assessment (EIA) offers a structured framework to evaluate these potential impacts and inform mitigation strategies. Yet existing EIAs often overlook two pivotal dimensions: the dynamic nature of certain criteria and the emergent properties of hydropower as a complex system. This paper addresses these gaps through a systematic review of scholarly articles published between 2000 and 2024, examining current practices in hydropower EIAs and identifying overlooked criteria. Our findings indicate that standard EIAs tend to prioritize static parameters while underrepresenting temporal variability in key biophysical and socio-economic processes (e.g., water temperature, sediment transport, and socio-economic change). Moreover, EIAs typically pay limited attention to emergent system-level outcomes, including changes in local microclimate and community cohesion, which may develop nonlinearly and with substantial uncertainty after dam commissioning. We proposed an analytical framework that classifies EIA criteria into static and dynamic, alongside recommendations for integrating emergent complexities. This includes adaptive management practices, real-time monitoring to capture evolving conditions, and greater stakeholder engagement to address social implications. By explicitly accounting for temporal dynamics and emergent effects in hydropower systems, the proposed EIA framework strengthens impact assessment by improving the characterization of risks and trade-offs, thereby informing more sustainable and equitable hydropower development.

Catchment’s Resilience to Flood Generation Mechanism: The Role of Soil Moisture - Pankaj Dey; Narendra Kumar Goel; Mohd Talha (Water Resources Management, 2026)

A Framework for Selection of a Hydrological Model-SAWT: A Review - G. Praveen Kumar; G. S. Dwarakish (Water Resources Management, 2026)

Seismic-based Estimation of River Discharge in a Low-flow Tributary: Field Evidence from Riverside Monitoring - Liang Feng; Xiaoyue Zhou; Weijie Yu; Shizhe Zhang; Bin Xie; Wei Xu; Xing Huang; Veronica Pazzi; Emanuele Intrieri (Water Resources Management, 2026)

Hydrological Effects Assessment of Double-Layer Permeable Pavement by Physical Experimental Basis - Pingping Luo; Yubin Zhang; Shiwen Zhu; Yixuan Zhang; Madhab Rijal; Peiyao Yan; Maochuan Hu (Water Resources Management, 2026)

A Decision Support Framework for Catchment-Scale Nature-Based Flood Solutions Using Multi-Objective Particle Swarm Optimization - Mahdi Sedighkia; Roslyn Prinsley; Barry Croke (Water Resources Management, 2026)
Designing Nature-based Solutions for flood mitigation in catchments can benefit from integrated decision-support frameworks (DSFs) that analyse trade-offs effectively. This study presents a framework for evaluating catchment-scale solutions, particularly reforestation, for flood risk reduction. It combines a semi-distributed hydrological model, flood frequency analysis (FFA) using the Log-Pearson Type III distribution, and regression models within a multi-objective particle swarm optimization (MOPSO) approach. The framework leverages total annual average flood damage (TAAD), implementation costs, and carbon credits (CCU) in a Pareto front analysis. The proposed framework was applied to the Bremer catchment in Australia, a region vulnerable to downstream flood impacts. Case study results showed that even during extreme flood events—such as the 100-year flood (1% annual exceedance probability)—afforestation has a meaningful impact on flood damage reduction. Specifically, scenarios with significant forest restoration or preservation demonstrated peak discharge reductions exceeding 10%. A maximum afforestation scenario across the catchment can reduce Total Annual Average Damage by approximately 12%. As an example of trade-off analysis, under 60% afforestation of the catchment area with an initial investment of $400 million, the DSF estimates $3 million in reduced TAAD, while the total annual benefit—including TAAD reduction and CCU revenue—is around $10 million annually. Despite challenges and limitations, including uncertainties in hydrological modelling and simplified treatment of the spatial distribution of afforestation, the framework offers a solid foundation for sustainable and resilient decision-making regarding catchment-scale nature-based flood mitigation strategies. Future research should focus on expanding the framework to capture broader social and ecological co-benefits.

Research on the Dynamic Interval of Ecological Compensation in the River Basin - Boning Zhao; Chang Feng; Liu Yang (Water Resources Management, 2026)

Near-Real-Time Statistical Analysis and Visualization of Streamflow from a Deep-Learning Rainfall-Runoff Model - Toan D. Duong; Vinh Ngoc Tran; Tam V. Nguyen (Water Resources Management, 2026)

Exploring Interactions between the Water-Energy-Food Nexus and the Economic System Using A Panel Vector Autoregressive (PVAR) Model - Daohan Huang; Zijun Wu; Guijun Li; Yulong Li (Water Resources Management, 2026)

Enhancing Forecasting of Water Release Volumes from Dams: A Comparative Study of Supervised Learning Models - Mehdi Amiour; Toufik Bouziane; Noureddine Daif; Djamel Bengora (Water Resources Management, 2026)

Hybrid Approaches in Water Quality Monitoring: A Study of the Integration of Machine Learning and Classical Analytical Methods - Christine Saab; Gérard-Philippe Zéhil (Water Resources Management, 2026)

Strategies for Implementing Deep Learning Techniques for Rainfall-Runoff Modeling in a River Having Sparse Data - Divya Chandran; N.R Chithra (Water Resources Management, 2026)

Multi-Objective Variance-Bias Tradeoff Optimization of ANN by Hybridizing with Multi-Objective GA for Water Discharge Estimation - Arvind Yadav; Ashwini Kumar Pradhan; Manish Kumar; Priyaranjan Sharma; Sunil Kr. Maakar; Shivangi Singh; Umamaheswararao Batta; Devendra Joshi (Water Resources Management, 2026)

Improving Short-term Forecasts of Soil Moisture and Evapotranspiration with Multi-task Learning and Transformer-based Feature Processing - Yuguang Yan; Gan Li; Qingliang Li; Xiao Chen; Jinlong Zhu (Water Resources Management, 2026)

Robust Control of Water Distribution Networks Under Drought Stress: A Multi-Agent Curriculum Learning Approach - Vidhyalakshmi Amarnath (Water Resources Management, 2026)

Environment-Aware Mamba State-Space Model for Multivariate Water Quality Prediction - Xianbao Tan; Yulong Bai (Water Resources Management, 2026)

A New Stepwise Regression Based Multi-Model Ensemble for Accurate Assessment of Future Drought - Abdul Baseer; Mahrukh Yousaf; Zulfiqar Ali; Zongxue Xu (Water Resources Management, 2026)

Error-Decomposition Machine Learning for IMERG Precipitation Correction in Karst Regions - Fuwan Gan; Xinsong Zhang; Xiang Diao; Xianci Zhong; Yang Gao (Water Resources Management, 2026)

Reconstructing Hydrological Drought Using SWAT, Deep Learning Models, and SSI - Vahid Nourani; Habibeh Abbasi; Arman Hosseinpour Salehi; Mohammad Taghi Aalami; Mohammad Bejani; Hadi Pourali (Water Resources Management, 2026)

Integrating Seasonal-Trend Decomposition and Multi-Head Self-Attention for Feature Fusion with Bidirectional Long Short-Term Memory in Reference Evapotranspiration Prediction - Zehai Gao; Zijun Gao; Chao Sun; Xiaojun Zhang; Siyuan Cao; Yuwei Liu (Water Resources Management, 2026)

Simplified Approach to Assess Supplied Water Age Variance in Branched Water Distribution Networks - Sarai Díaz; Javier González (Water Resources Management, 2026)
Residence time (or water age) is often used as a surrogate indicator of water quality in water distribution networks. Water age depends on the system design and associated demands, which are stochastic in nature, so the identification of critical areas (areas prone to high water ages) must be addressed from a probabilistic perspective. A systematic approach for the STATistical characterization of Water AGE (STATWAGE) has been recently presented in the literature. It has proved that Bottom-Up (BU) demand allocation strategies, which acknowledge the pulsating nature of water demands, are associated with greater water age variance than more conventional Top-Down (TD) approaches, which rely on average consumption patterns. The aim of this paper is (i) to assess if there are any laws of behavior that can explain the difference in variance between STATWAGE-BU and STATWAGE-TD by relying on temporal variability levels, and (ii) if such behavioral laws exist, to propose a systematic approach to simplify the estimation of this additional variance and thereby complement STATWAGE-TD results. This paper demonstrates that these differences are related to the short-term variance of network flows, and a new complement (Short-Term Incremental effect over Water AGE Variance, STIWAGEV) is here presented to consider their effect. The STATWAGE-TD + STIWAGEV approach is a potentially meaningful contribution for effectively quantifying the statistical properties of water age without having to rely on complex BU models and/or high-resolution data. Therefore, this framework constitutes the first step towards developing a systematic methodology for accurately and affordably identifying critical areas.

Multi-Criteria Sorting Model for Water Resource Allocation in Drought Regions - Gabriel de Oliveira Castro; Danielle Costa Morais (Water Resources Management, 2026)
Drought is a recurring natural hazard that severely reduces the availability of water in semi-arid regions. Water scarcity intensifies social inequalities, undermines agricultural productivity, and increases public health risks. Allocating resources strategically mitigates these effects by targeting critical areas to maximize the benefits of interventions. This study proposes a group multi-criteria sorting model for allocating water resources during drought. This study seeks to assist decision-makers (DMs) in prioritizing municipalities using a fair, transparent framework. The model integrates the PROMETHEE-ROC method with partial-information procedures to define parameters. Three government DMs evaluated eight water, sanitation, environmental, and socio-economic criteria. Twenty-three municipalities in Pernambuco, Brazil, were sorted into categories of High, Moderate, and Low criticality. The findings indicate three municipalities (13%) require immediate intervention, fifteen (65%) require moderate attention, and five (22%) have lower criticality. Sensitivity analysis confirms stable sorting in more than 60% of simulations. Thus, the model enhances decision-making efficiency, minimizes conflicts, and provides a structured tool to prioritize action plans based on real-world conditions.

AI-driven Poverty Risk Mapping and Causal Assessment of Piped Water Supply in Rural Region: A Policy Simulation Framework Aligned with SDG 1 - Kezia Saini; Priyam Nath Bhowmik (Water Resources Management, 2026)

Multi-objective Reservoir Operation Scheme Considering Hydrologic Alteration and Ecological Water Demand - Heyu Wang; Shenglian Guo; Jing Tian; Sirui Zhong (Water Resources Management, 2026)

Bi-objective Optimization of Water Distribution Networks - Gustavo H. B. Cassiolato; Mauro A. S. S. Ravagnani (Water Resources Management, 2026)
Water Distribution Networks (WDNs) are vital in urban and industrial contexts, with high costs driven by pipe diameters and pumping needs. This paper presents a bi-objective MINLP optimization model based on disjunctive programming, accounting for unknown flow directions and minimizing both total costs (installation, operation, and pumping) and CO 2 emissions from pipe manufacturing and system operation. Implemented in GAMS and tested across three case studies, the model applies the $$\:ε\:$$ -restricted method to balance economic efficiency and environmental sustainability, underscoring the importance of accounting for environmental impacts in WDN planning.

A Hybrid Jaya Algorithm for Design Optimisation of Large-Scale Water Distribution Networks - Chemseddine Kouloughli; Abdelmoutia Telli; Mohamed Nazih Omri (Water Resources Management, 2026)

Pipe Failure Prediction in Water Distribution Networks Using Interpretable Machine Learning under Imbalanced and Recurrent Failure Conditions - Homayoun Motiee; Mohsen Sattari (Water Resources Management, 2026)

A Framework to Define Optimal Fallowing Programs - Shokoufeh Mirzaei; Anna Maria Garcia (Water Resources Management, 2026)

Accessibility Evaluation of Pan-Basin Water Network Based on Space Syntax and Water Intake Resistance - Fangning Kong; Fawen Li (Water Resources Management, 2026)

An Understanding of Hydrometeorological Trends: A Multi-Method Trend Assessment - Veysi Kartal; Abdullah Muratoglu (Water Resources Management, 2026)
Climate change and anthropogenic pressures significantly affect Mediterranean water resources, necessitating robust trend analysis methods for effective water management. This study presents a comprehensive assessment of hydrometeorological trends in Türkiye’s largest freshwater lake system, the Beysehir Basin (1981–2016), using a multi-method analytical framework. Traditional approaches (Mann–Kendall test and Sen’s slope estimator) were integrated with advanced techniques (Innovative Trend Analysis, Innovative Polygon Trend Analysis, IV-ITA, and Star-IPTA) to evaluate trends in precipitation, temperature, streamflow, lake water level, and groundwater level. The results reveal statistically significant warming trends at both stations, particularly during the summer months. The strongest increase was observed in summer at Çumra (τ = 0.565, p < 0.0001) and Beyşehir (τ = 0.470, p < 0.0001), with additional significant increases in spring, autumn, and annual temperature series ( p < 0.05); However, winter trends were generally not significant. Precipitation trends exhibited considerable spatial variability at the Beyşehir station, September precipitation showed a significant increasing trend under the Yue and Wang (2004) approach (τ = 0.231, p = 0.007), while spring precipitation at Çumra demonstrated a significant decreasing trend (τ = −0.205, p < 0.05). Annual precipitation trends were not consistently significant across stations. Lake water levels displayed consistent declining trends across multiple temporal scales, and groundwater levels indicated systematic depletion (Sen’s slope: 0.382–0.435 m/yr; Mann–Kendall τ: 0.778–0.86; ITA: 0.459–0.511). A comparative evaluation demonstrated that the innovative methods provided enhanced sensitivity in detecting subtle hydrometeorological changes compared with traditional approaches. Regular polygon patterns in temperature and groundwater analyses contrasted with more irregular precipitation patterns, reflecting distinct environmental response mechanisms. Overall, these findings highlight the increasing vulnerability to water scarcity in the Beyşehir Basin and underscore the urgent need for adaptive water management strategies in Mediterranean watershed systems. The proposed methodological framework provides a replicable template for comprehensive trend assessment in regions experiencing similar climatic and anthropogenic pressures.

Multi-Feature and Multi-Step Monthly Rainfall Forecasting with the Hybrid VMD-LSTM-CATNet Model in Diverse Climatic Regions - Man-Hong Fan; Xiao-Ying Ma; Shi-Qi Xu; Rui-Lin Fu; Zhong-Xing Tao (Water Resources Management, 2026)

Revealing the Relationship between Rainfall Extremes, Atmospheric Changes and Potential Flooding using Wavelet and Extreme Value Models - Sribas Kanji; Subhasish Das (Water Resources Management, 2026)

Assessing River Ecosystem Resilience in a Vulnerable River: Macroinvertebrate Community Dynamics as Drivers of Conservation and Water Resource Management - Francis O. Arimoro; Tetengi K. Ndana; Eunice O. Ikayaja; Stella A. Mgbemena; Unique N. Keke; Augustine O. Edegbene (Water Resources Management, 2026)

On the Integration of the River Chief System and Peasant Households’ Participation from the Perspective of Watershed Water Environment Governance - Mingqing Xu (Water Resources Management, 2026)

Comparative Analysis of Artificial Neural Networks, Support Vector Machines, and Gated Recurrent Units for Dam Alert Prediction - Nisha C.M; N. Thangarasu (Water Resources Management, 2026)

Outsiders and Effective Collective Action in Irrigation Organizations: Do Illegal Withdrawals Harm Internal Cooperation? - Carlos Bopp; Carlos Chávez; Cristian Jordán (Water Resources Management, 2026)

Multi-Target Water Demand Forecasting with Graph Neural Networks: A Comparative Study - Yacan Man; Xiao Zhou; Rui Yuan; Kuizu Su; Shuming Liu (Water Resources Management, 2026)

Advancing Smart Water Loss and Oil Spillage Management: a Comprehensive Review of Remote Sensing and GIS Application - Ndivhuwo Ramovha; Makhutsisa Charlotte Mokoatle; Phoka Rathebe; Thandiwe Sithole (Water Resources Management, 2026)
Water infrastructure and hydrocarbon production systems are increasingly exposed to ageing networks, rapid urbanization, and climate variability, which collectively exacerbate non-revenue water losses and oil spill incidents with severe environmental and socioeconomic consequences. This systematic review synthesizes empirical evidence on the application of remote sensing and Geographic Information Systems (GIS) for monitoring, detecting, and managing water loss and oil spills across diverse hydrological, industrial, and coastal settings. The review examines the performance of satellite, airborne, uncrewed aerial vehicle, and in-situ sensor platforms, in combination with machine learning and advanced image processing, for leak localization, spill delineation, and change detection. It examines the role of GIS-based spatial analytics in integrating hydraulic, infrastructural, environmental, and socioeconomic datasets to support risk mapping, early warning systems, and decision-making support. The findings demonstrate that integrated RS–GIS approaches significantly enhance detection accuracy, spatial coverage, and temporal responsiveness compared to conventional monitoring methods, while also revealing persistent challenges related to multi-sensor data fusion, algorithm transferability, ground validation, and operational continuity under adverse environmental conditions. The review highlights the significant potential of RS–GIS frameworks to enhance evidence-based planning and proactive management of water and oil infrastructure, particularly in data-scarce and resource-constrained contexts.

Enhancing Leakage Control in Aging Networks: A Hybrid Game Theory-TOPSIS Approach for Non-invasive Pressure Management - Hongyan Li; Zifeng Chang; Wentao Shi; Feng Zhang; Jingkai Hao (Water Resources Management, 2026)

Enhancing Operational Efficiency in Water Canals Using Sequential Fuzzy Reinforcement Learning - Kazem Shahverdi; Reyhaneh Loni; Icen Yoosefdoost; Ronny Berndtsson; Mitra Kaviani; Arash Yoosefdoost (Water Resources Management, 2026)

Uncertainty Reduction in Near Real-time Satellite Precipitation Estimates by Integrating Soil Moisture and Potential Evapotranspiration Using a Machine Learning Approach - Susantha Wanniarachchi; Ranjan Sarukkalige; H. A. Prasantha Hapuarachchi; Pattiyage I.A. Gomes; Upaka Rathnayake (Water Resources Management, 2026)
Near-real-time (NRT) satellite precipitation data inherits complex and random errors due to various reasons. The primary objective of this research is to utilize satellite-based precipitation data for hydrological modelling in ungauged areas. The novelty of this study lies in the development of a hybrid stacking-based machine learning framework that integrates hydrologically meaningful predictors: root-zone soil moisture, potential evapotranspiration (PET), and their time-lagged representations to reduce uncertainty in near-real-time satellite precipitation (GSMaP-NRT). Unlike conventional bias-correction approaches that rely primarily on statistical adjustment between satellite and gauge rainfall, this study incorporates physically relevant catchment-state variables to improve predictive skill, with a focus on the Ovens River basin in Australia. A calibrated GR4H hydrological model was used to simulate the runoff of the catchment. Six objective functions were used to evaluate the performance of the approach. The results demonstrate that stacking machine learning algorithms reduces the Mean Absolute Error of GSMaP-NRT satellite precipitation data by 36% and the corresponding modelled streamflow error by 44% for lower precipitation events (< 2 mm/hour). All six objective functions achieved optimal performances within the low precipitation events. However, RMSE remained high for intermediate and heavy precipitation events. The model-estimated major streamflow peaks for the years 2010 and 2016, based on gauged precipitation and ML-corrected satellite precipitation, are 41% and 48% lower than the observed streamflow peaks, respectively. The reasons were the inability of the GR4H model to capture the perfect initial conditions and the x 4 time parameter during the calibration process.

Modelling the Impact of Climate Change on the Reservoir Filling Rates of Dams Used for Drinking Water Supply Through Artificial Neural Networks - Furkan Demirbaş; Emine Elmaslar Özbaş; Mehmet Can Sarıkap; Mirac Nur Ciner; Hüseyin Yurtseven; Hüseyin Kurtuluş Özcan (Water Resources Management, 2026)
Climate change has increasingly altered global precipitation patterns and hydrological cycles, posing significant risks to the storage performance and operational reliability of dams. Changes in rainfall regimes, prolonged droughts, and rising evapotranspiration rates directly affect reservoir inflows, making accurate prediction of these variations essential for sustainable water supply management. This study aims to model the effects of climate change on the storage levels of drinking water supply dams located in Ankara, Istanbul, and Izmir using various Artificial Neural Network (ANN) techniques. Among the ANN structures evaluated, dynamic time series, curve fitting, and feedforward distributed time delay networks demonstrated limited performance on the dataset; however, the feedforward backpropagation network demonstrated successful performance. This network type was optimized for the dataset through various modifications to parameters such as training, adaptive learning, performance, and transfer functions, as well as the number of layers and neurons. The findings were supported by regression analyses conducted for dams supplying drinking water. The differences between expected and observed precipitation amounts illustrate the impacts of climate change and water losses in the reservoirs. The models developed in the study exhibited R² values ranging between 0.85 and 0.99, with the highest value calculated as 0.994. The results quantitatively demonstrate the divergence between projected and observed precipitation on an individual dam basis, thereby elucidating the susceptibility of drinking water reservoirs to the impacts of climate change. In this context, the study offers robust, data-driven insights that can inform evidence-based revisions of water management and adaptation strategies for major metropolitan regions.

Digital Expansion: Pathways for a Digital Ecology to Empower Water Resource Carrying Capacity - Dongming Gu; Xiaofei Hu; Meiling Chen; Yunjie Xie (Water Resources Management, 2026)

Multi-dimensional Assessment of the Water-Food Nexus in a Semi-Arid Watershed - Beyza Özel; Yasemin Demir; Emre Alp (Water Resources Management, 2026)
The availability of water is crucial for ensuring global food security, particularly in regions most affected by climate change. Understanding the trade-offs and synergies between the availability of water and the food sector is crucial for ensuring water and food security. This study employs a holistic approach to aid decision-makers in the water and food sectors for developing sustainable strategies in Upper Sakarya Watershed located in a semi-arid region dominated by agricultural activities. Several agricultural water management scenarios are assessed based on various criteria: i) water budget at the basin scale, ii) economic feasibility, and iii) stakeholder opinions. The WEAP model was used to evaluate the hydrological analysis of management scenarios. In addition, socioeconomic analyses and stakeholder perspectives on the management scenarios are incorporated into the integrated investigation of the water-food nexus. The most effective scenario in terms of water consumption relative to the baseline scenario can result in a reduction of 60 million m 3 of irrigation water per year. According to the economic analysis, significant contributions to the farmer’s income by €5.4–13.5 per m 3 of water use compared to reference scenarios can be achieved by the most feasible scenarios. The most effective scenarios show parallel trends between water conservation and positive economic contribution. Moreover, stakeholders anticipated that the implementation and sustainability of the most effective scenarios can be vulnerable to technical, practical, and political constraints. This holistic approach demonstrated the need for a multi-dimensional assessment of the water-food nexus that incorporates the perspective of stakeholders. Graphical Abstract

A high-order Model-free Dynamic Framework for Accurate Daily Streamflow Prediction - Jiaying Zhang; Shiqian Tang; Longxia Qian; Mei Hong; Yong Zhao; Linlin Fan (Water Resources Management, 2026)

Optimization of Water-saving Performance under Green Finance: a Multi-perspective Game-theoretic Approach - Jie Zhang; Ruolan Sun; Yufei Hu (Water Resources Management, 2026)

Predicting LULC Changes and Assessing their Impact on Surface Runoff with Machine Learning and Remote Sensing Data - Abdelkader Riche; Ammar Drias; Riccardo Ricci; Boularbah Souissi; Farid Melgani (Water Resources Management, 2026)

Divergence or Convergence? A Comparison of InVEST and SWAT in Simulating Water Conservation Patterns and Drivers - Li Ma; Jianling Yang; Yingjuan Han; Zhenhua Di; Xueyi Zhang; Yazhen Wu; Liping Ma; Xuemei Yang (Water Resources Management, 2026)

A Global Assessment of Climate Change and Anthropogenic Effects on Changes in Streamflow - Ruting Yang; Xiongpeng Tang; Silong Zhang; Chao Gao; Yanli Liu; Shuaifeng Lu; Fubo Zhao; Bikui Zhao (Water Resources Management, 2026)

Advancing Physical Realism in Hydrological Modelling: Selection and Integration — A Review and Synthesis - Kunyang Wang; Shin-ichi Onodera; Mitsuyo Saito; Yu War Nang (Water Resources Management, 2026)
Hydrological models play a key role in understanding, predicting, and managing anthropogenically altered hydro-environments. They support assessment of water quantity and quality, simulation of water and material transport, prediction of risks and disasters, and decision support for management. However, fragmented applications and limited interoperability constrain their utility. Following PRISMA 2020 guidelines, we systematically searched the Web of Science Core Collection and selected 30 representative hydrological models and 186 peer-reviewed studies for evidence synthesis (case-specific details provided in Supplementary Appendices). We classify these models by structural and functional characteristics and propose a practitioner-focused, decision-oriented synthesis framework (with practical criteria) that maps model capabilities to fit-for-purpose research and management tasks. Pathways for model integration, enhancement of physical realism, and applications to extreme events and ungauged basins are outlined. The framework highlights persistent challenges, including uncertainty in key hydrological and environmental processes, prediction of compound extremes, and reliable simulation in ungauged settings. We further outline pathways for ungauged and data-scarce settings via physics-guided machine learning and multi-source remote sensing. Process-level constraints, physically informed pre-parameterization, and transparent uncertainty treatment are emphasized to strengthen physical realism. The concept of “hydrological limits” is positioned as a policy-relevant lens to quantify thresholds and feedback within a planetary-boundaries context. This review advances adaptive water resources management in increasingly complex water environments.

On the Choice of Optimal Reservoir Operating Rules in a Changing Climate for the Sustainable Management of Drinking Water Sources - Mattia Neri; Elena Toth (Water Resources Management, 2026)
The effect of climate change on water availability is a growing concern for decision-makers involved in resource management, as well as within the hydrological and hydraulic engineering communities. To ensure the resilience of water supply systems, it is essential to adapt the sustainable and integrated management of drinking water sources to the expected changes in the magnitude and occurrence of hydrological droughts. This paper introduces a methodological framework for the sustainable management of water resources in a coastal region of Northern Italy under climatic change, aimed at modelling a multi-basin water supply reservoir, which represents the primary and most sustainable source of the system. The approach involves the multi-objective optimization of its withdrawal rules under both historical and future meteorological forcings, aiming to maximize reservoir production while meeting the demand during water scarcity conditions. The reconstruction of the historical operation of the dam controls and the estimates of the reservoir minimum demand requirements further support the implementation of the methodology. Utilizing a Parameterization-Simulation-Optimization approach and a wide range of climate models, the findings offer valuable insights to support decision-makers. Specifically, the research quantifies future expected relative changes in optimal operating rules based on different management policies. It also outlines the corresponding expected patterns of withdrawal volumes and potential water system failures throughout the century. This information allows to effectively assess the risk associated with future water scarcity and aids in developing long-term resource management strategies, thereby mitigating stress on less sustainable water sources.

Evaluating the Accuracy of Machine Learning Models with Short-Term Lag Inputs for Univariate Daily Streamflow time Series Forecasting - Saad Ashfaq; Safia Waseem; Muhammad Tufail; Aqil Tariq (Water Resources Management, 2026)

Machine Learning–Based Characterization of Groundwater Recharge in Semi-Arid Drylands - Shadi Askari Azghandi; Ehsan Behnamtalab (Water Resources Management, 2026)

Simulation and Rapid Prediction of Water Quantity and Quality Processes Based on Numerical Models and Deep Learning - Qingyuan Guo; Jingming Hou; Tian Wang; Xinxin Pan; Guangxue Luan; Rongbing Zhang (Water Resources Management, 2026)

Exploring Monthly, Seasonal and Annual Spatio-Temporal Variability of Temperature and Precipitation Series by Classical and Innovative Techniques - Ibrahim Halil Deger (Water Resources Management, 2026)

A Study on Rapid Dynamic Flood Forecasting in Small Watersheds Using a GNN-Transformer Approach Integrated with Spatial Physical Information - Xinxin Pan; Jingming Hou; Donglai Li; Yanhong Wang; Xingyi Li; Jiantao Sun; Chenchen Fan; Yongping Yang (Water Resources Management, 2026)

A Novel Integration of Decision Tree and K-Means for Enhanced Monthly Rainfall Forecasting and Variable Importance Ranking - Seyed Hassan Mirhashemi; Mohammad Reza Karimi (Water Resources Management, 2026)

Improving Daily Precipitation Estimates through Machine Learning-Based Downscaling, Precipitation Event Classification, and Categorical Merging - Zhenzhen Yue; Lihua Xiong; Chenguang Xiang (Water Resources Management, 2026)

Sinking City : The Future of Bangkok Amid Climate Change and Rising Sea Levels - Seree Supharatid; Sawitree Rojpratak (Water Resources Management, 2026)

WD-HRNN-BiGRU: A Novel Spatiotemporal Sequence Multi-Value Prediction Model - Zhongrong Zhang; Junyan Sun; Yulin Shen; Kang Lin; Yijia Liu; Jisheng Li (Water Resources Management, 2026)

Rapid Prediction of Compound Flood Based on Hydrological-Hydrodynamic Model and Convolution Neural Network - Kui Xu; Yizhuang Tian; Lingling Bin; Chengguang Lai; Weichao Yang (Water Resources Management, 2026)

Economic Instruments in Water Resource Management: A Critical Review of Water Pricing and Hydro-Economic Models - Iago de Carvalho Mello; Marina Batalini de Macedo; Daniel Thá (Water Resources Management, 2026)

Generalized Reciprocal Based Tversky Indexive Support Vector Extreme Boost Classification for Water Quality Prediction Analysis - Raghuveer Narsing; Saikumar Chary Konnoju (Water Resources Management, 2026)

Structural Trend and Variability Identification (STVI) Method - Zekai Şen (Water Resources Management, 2026)

Urban Pluvial Flood Maximum Water Depth Prediction Using a Transformer-CNN Hybrid Deep Learning Model with Data Augmentation - Ziqi Liu; Dejun Zhu; Jiaming Luo; Danxun Li (Water Resources Management, 2026)

Spatiotemporal Variability of Runoff Coefficients and Rainfall–Runoff Responses in a Mountainous Basin Using Integrated Hydrological Analogy and SCS–CN Models - Zhang Jing; Yang Lu Ping; Chen Zi-jing; Peng Shi-tao; Xing Bing (Water Resources Management, 2026)

Development and Evaluation of the Multi-Objective Slime Mould Algorithm (Mosma) in the Optimal Operation of the Urban Water Distribution Network - Parisa-Sadat Ashofteh; Maryam Nejadi (Water Resources Management, 2026)

Blockchain-Enabled Water Management and Crop Optimization in Arid Regions: A Sustainable Path to Food and Energy Security – The Australian Case - Rıdvan Karacan; Eda Yalçın Kayacan (Water Resources Management, 2026)
This study examines the short-run effects of blockchain-enabled irrigation management in the Murray–Darling Basin (MDB), one of Australia’s most water-stressed agricultural regions. Although interest in digital water technologies has accelerated, empirical evidence on their real-world effectiveness remains scarce, leaving a substantial gap in the literature. To help address this gap, we implement a simulation-assisted difference-in-differences (DID) framework that integrates annual agricultural data (2014–2023) with a counterfactual control series generated from pre-intervention dynamics. Within this framework, the adoption of blockchain-enabled irrigation in 2019 is treated as a hypothetical intervention year created through simulation rather than an observed policy change. The results reveal a significant post-2019 decline in agricultural water use alongside a measurable increase in energy consumption attributable to blockchain-assisted monitoring. No statistically significant short-term effect is detected for crop yield, suggesting that productivity responses may require longer behavioural or agronomic adjustment periods that are not fully captured by annual observations. Overall, the findings underscore both the efficiency gains and the energy trade-offs associated with blockchain-supported irrigation systems. This study offers one of the earliest empirical assessments of blockchain applications in agricultural water management and provides policy-relevant insights for arid and semi-arid regions seeking to modernize irrigation governance.

Impacts of Climate Change and Demographic Growth on Future Water Supply and Demand Gap in a River Basin - Mridusmita Debnath; Ashok Mishra (Water Resources Management, 2026)

A Multifaceted Evaluation System for the Effect of Interconnected River–lake System Based On Emergy Theory - Cuimei Lv; Wenzhao Zong; Minhua Ling; Qianqian Cao; Huali Liao (Water Resources Management, 2026)

Coupling Coordination between Urbanization and Water Ecosystem Services in Central Asia and its Driving Mechanisms - Shoufeng Wang; Jia He; Yuxuan Zhou (Water Resources Management, 2026)

Comparative Analysis of Deep Learning and Machine Learning Models for Evapotranspiration Prediction in Semi-Arid Regions: Statistical Model Evaluation with a Paired t-Test and Bootstrap Resampling - K. V. Sumith; Bhavya S (Water Resources Management, 2026)

Waterlogging Simulation Model Based on Bidirectional Coupling Between Runoff Production and Confluence in Plain Areas - Zhen Qi; Huaqing Zhao; Ranhang Zhao; Xingju Wang; Yuehua Xu (Water Resources Management, 2026)

Enhancing Multi-Step Ahead Daily Runoff Prediction via HydMoE Model with Local-Global Hybrid Attention - Peilin Yang; Daoyi Chen (Water Resources Management, 2026)

Analysis of Seasonal Propagation Dynamics and the Potential Driving Factors from Meteorological to Soil Moisture Drought - Meng Dai; Ping Feng; Jianzhu Li; Renjie Tao (Water Resources Management, 2026)

A River Hydrodynamic Surrogate Model Using Nonlinear Autoregressive with Exogenous Inputs Fourier Neural Operator - Youkun Li; Junqiang Lin; Di Zhang; Boran Zhu; Qidong Peng; Yicheng Wang; Tiantian Jin (Water Resources Management, 2026)

Predicted Streamflow Sensitivity to Climate Change Using TOPMODEL with CLIGEN Weather Generator in a Data-Sparse Medium-Sized Mediterranean Watershed - Pece V. Gorsevski (Water Resources Management, 2026)
Climate change (CC) significantly affects streamflow decline, especially in semi-arid climates. The Mediterranean region is expected to face severe effects, threatening future water resources. This research aims to create a framework for assessing future streamflow sensitivity in data-sparse regions due to CC. It analyzes historical trends and predicts future conditions based on temperature increases of 1.5° and 3.0 °C. The historical trends of decreasing streamflow were confirmed by the Mann-Kendall statistic and Theil-Sen median trend estimator. The predictive future conditions framework employs the Climate Generator (CLIGEN) to generate 30 years of daily climate data for the TOPography-based hydrological model (TOPMODEL), which was assessed for uncertainty using generalized likelihood uncertainty estimation (GLUE) method. With a Nash-Sutcliffe efficiency (NSE) of 0.73 and uncertainty bounds ranging from − 52.7% to 35.4%, the best-fit for the calibration underestimated the observed streamflow by 16%. With an NSE of 0.61 and bounds ranging from − 46.5% to 18.4%, it overestimated by 12% during validation. The inferred future streamflow indicated a decline of 32% and 50% with temperature increases of 1.5° and 3.0 °C, respectively. The summer was the most notable, which projected streamflow shifts with increasing trends that could be triggered by changes in the timing of spatiotemporal precipitation patterns and increased evapotranspiration. The findings also indicated that precipitation and temperature have different effects, with temperature having a greater effect on streamflow decrease.

A Fuzzy Ensemble Framework for Multi-Criteria Water Resources Management Under Uncertainty - Xi Yang; Chao Tan; Zhoujie Wang; Min Qin; Zhihe Chen (Water Resources Management, 2026)

Projecting Future TOC in Data-Scarce Agricultural Reservoirs: a WGAN-Enhanced Framework Revealing the Importance of Dynamic Variability - Christian Joseph Siose; Jeongho Han; Kyung-Sook Choi; Byoung Han Choi; Kyoung Jae Lim (Water Resources Management, 2026)

Rural and Urban Dietary Water Footprints and Self-Sufficiencies in a Water-Abundant and Populous Region - Chenjian Yan; Meng Li; Dong Wang; La Zhuo (Water Resources Management, 2026)

Water Demand Forecasting Based on Multi-Rainfall Gauging Stations using Stand-Alone Soft Computing Techniques with Improved Novel Hybrid Paradigms - Tarek Merabtene; Abdullahi G. Usman; Berna Uzun; Dilber Uzun Ozsahin (Water Resources Management, 2026)

A Comparative Analysis of Groundwater Suitability for Drinking Purpose using Water Quality Index and GIS - Sachin L. Suryawanshi; Pradeep Kumar Singh; Mahesh Kothari; Manjeet Singh; Kamal Kishore Yadav; Trilok Gupta (Water Resources Management, 2026)

Comparative Simulation of Hillslope Runoff Using Two Infiltration Equations - Hwansuk Kim; Kristine Joy B. Mallari; Anya Catherine C. Arguelles; Yonca Cavus; M. Levent Kavvas; Hafzullah Aksoy; Jaeyoung Yoon (Water Resources Management, 2026)
Infiltration is one of the major hydrologic processes affecting the generation of overland flow. Accordingly, different infiltration equations have been developed, applied for water loss calculations, and compared mostly at the watershed scale or the point scale. However, their inter-comparison has not been made in the context of runoff simulation at the hillslope scale that includes microtopographic features such as rills and interrills. Here, we simulate overland flow by using the Horton and Green-Ampt equations to compare their performance in calculating runoff over a hillslope that consist of rills and interrills, freshly cut along Highway 299 in Northern California, USA. We find the better performance of the Green-Ampt equation than the Horton equation when coupled with the overland flow model. This is thought to be due to the ability of the Green-Ampt equation to account for antecedent moisture conditions. In addition, overland flow is found to be more sensitive to infiltration parameters than to the friction factor of the hillslope surface. The proposed overland flow model coupled with the Green-Ampt infiltration equation looks promising for runoff simulation over a rilled hillslope.

Stacking Ensemble Learning for Daily Potential Evapotranspiration using Limited Climate Data - Dong Zhang; Yangyu Deng; Yakun Liu; Di Zhang (Water Resources Management, 2026)

Evaluating Effect of Reservoir Operation Policy on Agricultural Water Consumption in Climate Change Condition Using a Socio-Hydrological Model - Saeed Heidari; Mojtaba Shourian; Maryam Javansalehi; Saeed Lotfi (Water Resources Management, 2026)

Extending the Usability Boundary of Low-Quality Data in Environmental Modelling: a Quality-Quantity Trade-off Framework - Tian-yi Cai; Fiallos-Salguero Manuel; Soon-Thiam Khu (Water Resources Management, 2026)

A Methodology for Selecting Climate Models Considering Precipitation and Temperature for Modeling Runoff - Keke Huang; Chongxun Mo; Na Li; Gang Tang; Yi Huang; Lingling Tang; Yugong Li (Water Resources Management, 2026)

A New Water Demand Prediction Approach using Adjustable Self-Feedback Gain Elman Neural Networks Coupled with Enhanced Sparrow Search Algorithm - Liying Wang; Qiang Ge; Zhenxing Zhang; Shixiang Gu; Nima Khodadadi; Cheng-I Hsieh; Weiguo Zhao (Water Resources Management, 2026)

Advancing Flood Susceptibility Mapping with Explainable AI: A Novel Application of Accumulated Local Effects (ALE) - Abdulwaheed Tella; Quoc Bao Pham; Izni Zahidi; Chow Ming Fai; Karim Sherif Mostafa Hassan Ibrahim (Water Resources Management, 2026)
Flood susceptibility mapping is essential for mitigating urban flood risks, yet balancing model interpretability and predictive accuracy remains a challenge in environmental modelling. While ensemble machine learning models offer strong predictive performance, their complexity often limits interpretability. This study introduces the application of Accumulated Local Effects (ALE), an explainable AI method, to enhance transparency in flood susceptibility mapping. ALE was integrated with logistic regression (LR), random forest (RF), and eXtreme Gradient Boosting (XGBoost) to interpret variable contributions while maintaining predictive performance. Ensemble models outperformed logistic regression, achieving 94% accuracy and an AUC of 0.98, and effectively captured non-linear relationships, including rainfall thresholds and the influence of urban infrastructure. Key flood drivers identified across models included annual rainfall, distance to river, elevation, slope, and Normalised Difference Vegetation Index (NDVI). ALE further revealed nuanced local effects and threshold responses in variables, patterns that were oversimplified in the linear structure of logistic regression. Validated against historical flood records, susceptibility maps clearly delineate high-risk zones suitable for targeted mitigation. This study advances flood risk modelling by combining interpretability and accuracy and provides a scalable, explainable framework for urban planning and early warning systems in rapidly developing regions.

Multi-objective Reservoir Flood Control Optimization uing a Real-time Adaptive Hybrid CNN-MCTS Framework with Spatiotemporal Feature Extraction - Xiong Cheng; Jingwen Sun; Miriam Aczel; Lei Wang; Zhengfeng Bao; Matin Mir; Longwen Liu (Water Resources Management, 2026)

Real-Time Areal Rainfall-Based Spatiotemporal Deep Learning Model (CNN–LSTM) for Urban Drainage Discharge Prediction - Hyunjung Kim; Donghwi Jung (Water Resources Management, 2026)

Optimizing Rainfall Prediction in Agriculture with Deep Learning: a Hybrid LSTM and Deep Stacked Autoencoder Network for Big Data - Vinoth R; Gangadevi G; C. G. Balaji; Cypto J (Water Resources Management, 2026)

Water Quality Reliability Analysis of Water Distribution Networks By Considering Parameter Uncertainties in Operational Period Under Critical Condition - Amin Mohammadikaleibar; Mehdi Dini; Vahid Nourani; Ali Pourzangbar; Saeed Hashemi (Water Resources Management, 2026)

A Holistic Assessment of Climate Change, Land Use, and Water Governance Through Complex Water Systems - Carolina Stager Quaggio; Lucas Vituri Santarosa; Philip Moffatt; Marcelo Donadelli Sacchi; Bruno Valdambrini; Amanda Rodrigues Soares; Jan Boll; Didier Gastmans (Water Resources Management, 2026)

Enhanced Streamflow Prediction Using a Hybrid CNN-LSTM Model: Integrating Climate-Driven Spatio-Temporal Features in a Deep Learning Framework - Shadman Shahariar; Hasan Zobeyer; Nasreen Jahan; Mostafa Ali (Water Resources Management, 2026)

Multi-objective Optimization of Sponge City Storage Tank Considering Inlet and Outlet Water Conditions-based on NSGA-II Algorithm - Wang Xiaojuan; Ma Yingqiang; Li Hao; Qin Jie; Xiao Yifei; Yan Yaping; Zhang Tingting; Gao Suxia; Li Jiapeng; Liu Jianlin; Cao Lianbao; Li Wei; et al. (Water Resources Management, 2026)

A Case-Based Reasoning and Knowledge Graph-Driven Decision Support Model for Emergency Risk Management in Hydraulic Engineering Safety - Jiayuan Guo; Yijie Bian; Ming Li; Yuanwen Zhang; Jianbo Du; Jiarui Wang (Water Resources Management, 2026)

Crisis and Cooperation Under Water Resource Depletion: an Evolutionary Game Framework Involving Agriculture, Mining, and Government - Zhaofa Sun; Junhua Guo (Water Resources Management, 2026)

From Planning to Control: an Intelligent System for the Optimisation of Drinking Water Resources - Carlos Calatayud Asensi; Jose Vicente Berná Martínez; Lucía Arnau Muñoz; David Saavedra Pastor (Water Resources Management, 2026)
The optimised operation of drinking water supply infrastructures is a crucial objective for companies and cities, facing the complexity of multiple conflicting objectives and factors such as climate change and demand fluctuations. This paper presents an intelligent and predictive control system designed for the efficient and safe management of these resources. The proposed system combines a multi-agent model with Artificial Intelligence algorithms. It uses the KNN algorithm for consumption prediction, highlighting its high accuracy (average relative error of 6%) and its ability to provide interpretable results that reflect human expert knowledge, avoiding the “black box” behaviour of other AIs. An essential component is the simulation tool developed. This allows the configuration, testing, validation and adjustment of the control system and its hyperparameters in a safe environment, preventing damage to critical infrastructure and optimising investments. The solution has been successfully validated in two real drinking water infrastructures in south-eastern Spain (serving 5,000 and 90,000-200,000 inhabitants). The results demonstrate a tangible improvement in pressure stabilisation, maintenance of optimal water levels (reducing unnecessary storage and the formation of by-products) and minimisation of pump starts/stops, which translates into energy savings and a longer service life for the equipment. In addition, this approach is adaptable to various infrastructure configurations.

Screening the Future Security of Groundwater-based Water Supply on the Scale of a Regional Water Supplier - Frederick Höckh; Michael Finkel; Andreas Morhard; Olaf A. Cirpka (Water Resources Management, 2026)
Climate change alters the hydrological cycle, which may impair the replenishment of aquifers. Water suppliers need tools to examine whether available regional groundwater resources will sustainably meet the demands under future conditions. We present a screening framework that can be easily and quickly implemented - thanks to low model and data requirements. It uses a new indicator, the groundwater use index ( $$GWUI_N$$ ), relating the water demand of a month to the groundwater recharge in the catchment averaged over N preceding months. The framework was applied to a region in southwest Germany to assess possible changes of regional water resources by 2070 for a diversity of climate projections. We identified the main drivers of water demand and projected possible future development paths. Future groundwater recharge was estimated by means of a distributed soil-water-budget model informed by climate data of respective projections. Historical data were analyzed to determine appropriate averaging periods N for evaluating the $$GWUI_N$$ . Our assessment reveals that water supply is not secure. Despite the rather humid climate in the region, the absence of any irrigation agriculture, and a substantial share of water demand being covered by a long-distance water supplier, results for climate projections that agree with observed historical precipitation of the past 25 years indicate future demand-to-availability ratios that signal water stress for the supplier.

Application of GA-BPNN in Predicting the Roughness Coefficient of Gravity Drainage Pipelines with Sediment Beds - Bin Sun; Lianghan Hu; Wei Zheng; Chunyi Zhuang; Wenjuan Jin (Water Resources Management, 2026)

FloodWatch: An Automatic Machine Learning Tool for Flood Forecasting and Segmentation using Weather Data and Images - Tasnim Ullah Shakib; Tariq Hasan Rizu; Ellora Yasi; Nusrat Sharmin; Rubyeat Islam (Water Resources Management, 2026)

Physics-Guided Deep Learning with Bayesian Optimization for Enhanced River Streamflow Prediction - D. Suresh; M. Sahaya Sheela; Pamarthi Sunitha; S. Gopalakrishnan (Water Resources Management, 2026)

FlDepth: A New Method for Estimating Fluvial and Pluvial Flood Depths from Near Real-Time Satellite-Derived Inundation Map and Topography - Anjaneyulu Akkimi; Srinivasulu Jella; Radha Manasa Peesapati; Amanpreet Singh; Durga Rao KHV; Prakash Chauhan (Water Resources Management, 2026)

Urban Flood Vulnerability, Economic Loss, Functionality Loss, and Restoration Modeling in Built Environment: Probabilistic Model Formulation and Application in a Mixed Formal and Informal Setting - Vishnu Prasad Pandey; Pradhumna Joshi; Rabindra Adhikari; Bhesh Raj Thapa; Sabin Dangol; Ashish Devkota; Aayush Gautam; Giovanni Forte; Dipendra Gautam (Water Resources Management, 2026)
Flooding is well recognized as one of the most destructive natural disasters, often leading to heavy losses of lives, property, and economy. Flood impacts are often presented in terms of flood vulnerability/fragility functions considering univariate intensity measures. However, flooding duration also plays an instrumental role in physical damage, functionality loss, and economic losses. This prompts the development of multivariate flood vulnerability and loss models; however, such models are not available in the existing literature to the best of our knowledge. To address this gap, we develop multivariate and univariate flood vulnerability models, economic loss models, and restoration models for Reinforced Concrete (RC), masonry, steel, and semi-permanent buildings. Similar models are also developed for agricultural land using actual damage/loss data. Bivariate models considering inundation depth and inundation duration are developed using response surface method, whereas several univariate vulnerability and loss models are also presented. The models are interpreted together with the outcomes of two-dimensional flood hazard analysis to outline and exemplify probabilistic flooding scenarios and likely consequences. The sum of observations highlights that although physical damage can be limited in building structures, considerable functionality and economic losses will be inevitable due to urban flooding.

Optimized Configuration of Urban Low Impact Development Based on MIKE + and Life Cycle Cost Analysis - Yanbin Li; Hongxing Li; Yuhang Han; Tongxuan Huang; Qian Wang; Xin Wang (Water Resources Management, 2026)

A Quantitative Comparison of the Distributed Liuxihe Model’s Performance in Simulating Different Flood Types - Renjie Zheng; Ji Li; Zhiqiang Xia; Chenrun Liu; Yuechen Li (Water Resources Management, 2026)

Assessment of Annual Rainfall Patterns Using a Clustering-based Comparative Framework - Mohamad Basel Al Sawaf (Water Resources Management, 2026)
Recent decades have seen a dramatic increase in destructive rainfall events, leading to concerns regarding the uniqueness and severity of these events. The literature does not provide an accepted definition for “patterns” for assessing rainfall severity. Hence, the main challenge lies in utilizing flexible patterns that can effectively reflect variations within annual rainfall datasets and suitable for diverse seasons and climate contexts. This study aimed to address the severity of annual rainfall records and investigate how they vary between lenient and intense years in Wajima City, Japan. For this, a clustering-based framework was proposed. The framework comprises two key approaches: (i) pentad precipitation integrated with hierarchical clustering, and (ii) information-complexity analysis integrated with partitional clustering. Pentad analysis could provide a comprehensive image of key patterns at sub-monthly scales. As a result, it was found that heavy-rainfall incidents were intense mainly during the autumn rain front. Importantly, this study offered an innovative application of information and complexity analysis, which may provide a new perspective for the assessment of hydrological systems and diverse environmental systems. The findings demonstrated that information and complexity metrics can be used as deductive indicators to capture hidden patterns in annual rainfall incidents, such as the frequency and persistence of major rainfall events. As the mean information gain increased, the number of days that exceeded the daily rainfall threshold increased, and vice versa. In addition, a higher effective complexity was associated with more prolonged rain incidents. Overall, the findings provide an important reference for rainfall assessments.

A Thematic Examination of System Dynamics in River Science Research and an Agenda for Future Investigations - Kazem Sadeghian; Mehdi Vafakhah; Seyed Hamidreza Sadeghi; Vahid Moosavi; Brahim Amaziane (Water Resources Management, 2026)

Hydro-morphologic Variations Induced by Bandal-like Structures for River Bank Protection - Riham Ezzeldin; Ahmed M. Saqr; Mahmoud E. Abd-Elmaboud (Water Resources Management, 2026)
River training protects alluvial riverbanks from erosion and flood damage worldwide. This approach is crucial in sediment-laden river systems, such as the Ganges-Brahmaputra-Meghna delta and the Mekong River basin in Southeast Asia. Researchers are investigating bandal-like structures as a sustainable riverbank protection alternative. These hybrid spur dikes combine upper impermeable sections for flow deflection with lower permeable sections for sediment passage, enhancing both bank stability and navigation sustainability. This study assesses the efficacy of bandals by laboratory experiments and numerical simulations using the computational fluid dynamics (CFD) framework FLOW-3D Hydro. Laboratory experiments were conducted in clear water conditions to determine the maximum relative scour depth around these structures. Three types of bandals were used, revealing different permeability and bandal size ratios for a range of Froude numbers from 0.13 to 0.36. An impermeable spur dike model was used for comparison purposes. The study confirmed that bandal-like structures with a permeability ratio of 0.625 and a bandal size ratio of 0.75 yielded superior results, achieving 51.6% reduction in local scour around the structure compared to impermeable spurs. Furthermore, significant silt deposition was observed downstream of the bandal, in contrast to conventional spurs, which significantly reduces bank erosion. A novel optimization-based empirical equation was developed to predict the relative maximum scour depth with a correlation coefficient R 2 of 0.9791. The results indicated that bandal-like structures can effectively function for riverbank protection and the sustainability of river management and navigation, supporting the United Nations Sustainable Development Goals (SDGs).

Spatial Vulnerability of Surface Water: A Sensitivity Analysis Approach - Pantea Kazemi Veisari; Hamid Motieyan; Farhad Qaderi (Water Resources Management, 2026)

Multi-Criteria Decision Analysis of Water–Energy Strategies for Sustainability - Zhenzhen Song; M. Mehdi Shafieezadeh (Water Resources Management, 2026)