Urban water distribution systems face growing challenges from energy inefficiencies caused by hydraulic anomalies, such as pipe aging, bursts, demand variability, and suboptimal pump and valve operations. This review systematically evaluates current technologies for energy-efficient management of WDNs under such conditions, structured around both basic and applied technologies. Basic technologies include real-time monitoring, data acquisition, and hydraulic modeling with CFD simulation. Applied technologies focus on demand forecasting, pressure management for energy optimization, and leakage anomaly detection. Case studies demonstrate the practical value of these approaches. Despite recent advances, challenges persist in data interoperability, real-time optimization complexity, scalability, and forecasting uncertainty. Future research should emphasize adaptive AI algorithms, integration of digital twin platforms with control systems, hybrid optimization frameworks, and renewable energy recovery technologies. This review provides a comprehensive foundation for the development of intelligent, energy-efficient, and resilient urban water distribution systems through integrated, data-driven management strategies.

This paper addresses the critical aspect of water conservation in public buildings within the context of sustainable urban water resources management. While conventional approaches rely on pricing controls and water‐saving appliances, this research emphasizes the key consideration of psychological factors influencing users' willingness to conserve water. Through a survey involving 1,206 volunteers, an expanded theory of planned behavior model is constructed and analyzed to evaluate the impact of water‐saving attitudes, subjective norms, self‐efficacy, and perceived behavioral control on individuals' water‐saving behavior. Intervention experiments conducted in three types of public buildings equipped with smart water meters unveil the nuanced dynamics of real‐time water‐saving behavior and its responsiveness to external interventions. Findings underscore the collective influence of subjective norms, water‐saving attitudes, self‐efficacy, and perceived behavioral control on individuals' intentions and behaviors. Noteworthy is the observed time lag and diminishing impact of external interventions, where economic, feedback, and subjective norms interventions prove more effective. This study not only contributes a theoretical framework but also provides practical insights, emphasizing the need for consistent and targeted external interventions. Practitioners, decision‐makers, and stakeholders are urged to recognize the profound impact of users' psychological factors on public water‐saving behavior and strategically employ interventions for sustained positive outcomes.

This paper addresses the critical aspect of water conservation in public buildings within the context of sustainable urban water resources management. While conventional approaches rely on pricing controls and water-saving appliances, this research emphasizes the key consideration of psychological factors influencing users' willingness to conserve water. Through a survey involving 1,206 volunteers, an expanded theory of planned behavior model is constructed and analyzed to evaluate the impact of water-saving attitudes, subjective norms, self-efficacy, and perceived behavioral control on individuals' water-saving behavior. Intervention experiments conducted in three types of public buildings equipped with smart water meters unveil the nuanced dynamics of real-time water-saving behavior and its responsiveness to external interventions. Findings underscore the collective influence of subjective norms, water-saving attitudes, self-efficacy, and perceived behavioral control on individuals' intentions and behaviors. Noteworthy is the observed time lag and diminishing impact of external interventions, where economic, feedback, and subjective norms interventions prove more effective. This study not only contributes a theoretical framework but also provides practical insights, emphasizing the need for consistent and targeted external interventions. Practitioners, decision-makers, and stakeholders are urged to recognize the profound impact of users' psychological factors on public water-saving behavior and strategically employ interventions for sustained positive outcomes.

Schematic of the study framework showing flow decomposition and discharge testing for customer-side leakage detection in DMAs. Customer-side leakage, a component of background leakage in water distribution systems, is commonly characterized through inlet flow signal decomposition and mitigated through pressure management. However, decomposition-based methods often lack physical grounding, and the direct identification and spatial localization of such subtle leakages remain insufficiently explored. This study proposes an integrated framework that combines signal decomposition with controlled field testing to enable precise detection of customer-side leakage within district metered areas. High-resolution smart meter data support refined hydraulic simulation and flow signal analysis. Controlled discharge experiments are conducted to induce representative leakage conditions, from which characteristic leakage signatures can be extracted from inlet flow series using variational mode decomposition with experimental support. The decomposed components are compared with consumption signals from all individual household meters via dynamic time warping to quantify similarity and locate leakage sources. The proposed framework is applied to a real district metered area in Danzhou, China, where minor customer-side leakages at household meter connections are successfully identified and verified through field inspection. The results demonstrate that integrating data-driven mode decomposition with experimental calibration provides a robust and transferable approach for proactive customer-side leakage management.