Journal of Water Resources Planning and Management

Forum papers are thought-provoking opinion pieces or essays founded in fact, sometimes containing speculation, on a civil engineering topic of general interest and relevance to the readership of the journal. The views expressed in this Forum article do not necessarily reflect the views of ASCE or the Editorial Board of the journal.

Leakage detection is one of the important aspects of water distribution management. Water companies are exploring alternative approaches to detect leaks in a timely manner with high accuracy to reduce water losses and minimize environmental and economic consequences. In this article, a literature review is presented to develop a step-by-step analytic framework for the leakage detection process based on flow and pressure data collected from water distribution networks. The main steps of the data analytic for leakage detection are: setting up the goals, data collection, preparing the gathered data, analyzing the prepared data, and method evaluation. The issues of concern for each step of the proposed leakage detection framework are analyzed and discussed. The smart sensor-based leakage detection methods can be categorized as data-driven methods and model-based methods. Data-driven methods can be further categorized as statistical process control-based methods, prediction-classification methods, and clustering methods. Hydraulic model-based methods can be further categorized as calibration-based methods, sensitivity analysis, and classifier-based methods. The advantages and disadvantages of each method are discussed, and suggestions for future research are provided. This review represents a new perspective on the subject from five aspects: (1) most of the leakage detection methods are focused on burst detection, and different types of leakage should be considered in future research, (2) it is important to consider data uncertainties, and more robust real-time leakage detection methods should be developed, (3) it is important to consider hydraulic model uncertainties, (4) unrealistic assumptions should be addressed in future research, and (5) spatial relations between sensors could provide more information and should be considered.

This study developed an ecological-water-demand-based interval chance–constrained system eco-compensation model (EW-ICCEC) for eco-compensation planning and ecological restoration. Interval chance–constrained programming, the Soil and Water Assessment Tool, ensurance of ecological water demand, and inexact modeling are integrated in a general framework for eco-compensation. The EW-ICCEC model was applied to a case of China’s Daguhe watershed. Results show that the ecological-water-demand-based system eco-compensation mechanism can achieve maximum system benefits and minimum system loss in medium- and long-term eco-compensation planning. The contributions of the study include that (1) a systematic watershed ecological compensation model is established; and (2) the optimal mechanism and a scheme of the eco-compensation bill and land-use transformation are obtained for the planning of eco-compensation.

An early contamination warning system with deployed water quality sensors is often used to enhance the safety of a water distribution system (WDS). While algorithms have been developed to select an optimal water quality sensor placement strategy (WQSPS) for WDSs, many of them do not account for the influences caused by future uncertainties, such as sensor failures and system changes (e.g., demand variations and configuration/expansion changes in the WDS). To this end, this paper proposes a comprehensive framework to evaluate the robustness of WQSPSs to these possible uncertainties. This is achieved by considering five different performance objectives of WQSPSs as well as possible future demand and typology variations of WDSs under a wide range of sensor failure scenarios. More specifically, an optimization problem is formulated to evaluate the robustness of the WQSPSs, in which an evolutionary-based optimization approach coupled with an efficient data-archive method is used to solve this optimization problem. The framework is demonstrated on two real-world WDSs in China. The results show that: (1) the WQSPS’s robustness can be highly dependent on the performance objectives considered, implying that an appropriate objective needs to be carefully selected for each case driven by practical needs, (2) the WDS’s demand and configuration changes can have a significant influence on the WQSPS’s robustness, in which the solution with more sensors in or close to the affected area is likely to better cope with these system changes, and (3) the proposed framework enables critical sensors to be identified, which can then be targeted for prioritizing maintenance actions.

Rapid and accurate detection/localization of leakage is important to preventing severe consequences in water distribution systems, such as loss of water resources and sinkholes. This study presents a novel two-phased approach to leakage detection and localization as a part of the ‘Battle of Leakage Detection and Isolation Methods’ (BattLeDIM). During Phase 1, data classification, data feature scaling, and data verification were performed, where individual leakage events were detected using the K-mean clustering algorithm. In Phase 2, the location of the leakage was identified using a trial-and-error optimization procedure. During the process, an emitter coefficient is assigned to each node, and the node that has the minimum error between the supervisory control and data acquisition (SCADA) measurements and the simulation results is determined as the leakage location. The results show that there were 23 real leakage events, none of which were detected or localized. Finally, ideas for real leak detection are provided, and a direction to overcome the limitations of existing leak detection techniques is presented.

Hydrologic variability can cause large swings in hydropower generation, inducing significant volatility in power sales. Dry years often result in low revenues that can threaten a hydropower supplier’s ability to meet its fixed costs, leading to budget shortfalls, lower credit ratings, higher interest rates, and, ultimately, higher rates. This is particularly true for suppliers in hydropower-dominated regions, such as the Bonneville power administration (BPA). The BPA strategy for managing its hydrologic financial risk is multilayered, involving cash reserves, a line of credit, and tariff adjustments. Yet, compared to its long-term energy contracts and debt service, BPA’s risk assessment is conducted on a short-term basis, thereby neglecting medium- and long-term temporal dynamics impacting their financial risk. This paper focuses on (1) evaluating BPA’s hydrologic financial risk; and (2) testing the effectiveness of BPA’s existing risk management strategy. Results suggest that BPA’s financial risk will grow substantially over the next 20 years as its risk management tools become increasingly inadequate, providing cautionary lessons for organizations in similarly hydrodominated systems despite the current use of common risk management tools.