Journal of Water Resources Planning and Management

A novel methodology is proposed to detect and locate bursts in water pipe networks. Conventional detection methods include a cumulative sum (CUSUM) and a wavelet transform (WT). However, the CUSUM has a problem of slow response and the WT is sensitive to signal transitions. In this work, two algorithms are integrated to effectively detect sudden pressure changes of water pipe networks. A network node matrix is also proposed to systematically represent the candidate locations of bursts. The developed burst detection and location system is validated with real field data obtained from simulated bursts by opening hydrant valves for simple and complex pipe networks and the result shows better performance than previous works.

The optimal operation of reservoir systems to meet water demand is a complex and nonlinear problem. This paper applies the biogeography-based optimization (BBO) algorithm to solve reservoir operation problems. The BBO algorithm is first verified with the minimization of three mathematical benchmark functions (Sphere, Rosenbrock, and Bukin6). In addition, the BBO algorithm was applied to a single reservoir system and a four-reservoir system. The performance of the BBO algorithm was compared with that of the genetic algorithm (GA) in solving the three optimization problems. The results show that the BBO algorithm minimized the benchmark functions accurately, and outperformed the GA in this respect. In the case of the single-reservoir hydropower optimization problem the BBO reached a near-optimal solution. The values of the objective function averaged 1.228 and 1.746 with the BBO and GA, respectively. The global solution of this problem with the nonlinear programming method equals 1.213. In the four-reservoir system application the BBO converged to 99.94% of the optimal solution in its best-performing history, whereas the GA converged to 97.46% of the optimal solution. The results from the three test problems demonstrated the superior capacity of the BBO to optimize general mathematical problems and the operation of reservoir systems.

Replacement of deteriorated water pipes is a capital-intensive activity for utility companies. Replacement planning aims to minimize total costs while maintaining a satisfactory level of service and is usually conducted for individual pipes. Scheduling replacement in groups is seen to be a better method and has the potential to provide benefits such as the reduction of maintenance costs and service interruptions. However, developing group replacement schedules is a complex task and often beyond the ability of a human expert, especially when multiple or conflicting objectives need to be catered for, such as minimization of total costs and service interruptions. This paper describes the development of a novel replacement decision optimization model for group scheduling (RDOM-GS), which enables multiple group-scheduling criteria by integrating new cost functions, a service interruption model, and optimization algorithms into a unified procedure. An industry case study demonstrates that RDOM-GS can improve replacement planning significantly and reduce costs and service interruptions.

Meter tampering is an important problem for utilities (both power and water distribution) to address because they represent an important loss of income. The authors’ group at the Electronic Technology Department of the University of Seville worked with the Empresa Metropolitana de Abastecimiento y Saneamiento de Aguas de Sevilla (EMASESA Company, a water distribution company in Seville and one of the most important of the country) to develop a methodology consisting of three algorithms that makes it possible to jointly detect this type of manipulations among its customers. The algorithms were generated and programmed after a data mining process using the database of the company. In addition, these algorithms were supplemented with a study of the geographical information of the customer supply points to improve the results. To test the results, the Emasesa Company performed in situ inspections of the customers selected by the algorithms for several areas in the province of Seville. The success rate of the algorithms was approximately 9%, which is considered satisfactory owing to the nature of the problem, in which fraud is easy to hide and infrequently detected.

In this paper a method that determines the steady-state hydraulics of a water distribution system, the graph matrix partitioning algorithm (GMPA), is presented. This method extends the technique of separating the linear and nonlinear parts of the problem and using the more time-consuming nonlinear solver only on the nonlinear parts of the problem and faster linear techniques on the linear parts of the problem. The previously developed forest-core partitioning algorithm (FCPA) used this approach to separate the network graph’s external forest from its looped core but did not address the fact that within the core of a network graph there may be many internal trees—nodes in series—for which a more economical linear process can be used. This extension of the separation process can significantly reduce the dimension of the nonlinear problem that must be solved: GMPA applied to eight case study networks with between 900 and 20,000 pipes show reductions to between 5 and 55% of the core dimension (after FCPA). The separation of the problem into its nonlinear and linear parts involves no approximations, such as lumping or skeletonization, and the resulting solution is precisely the solution that would have been obtained by the slower technique of solving the entire network with a nonlinear solver. The new method is applied after the network has been separated into an external forest and core by the FCPA method. GMPA identifies all the nodes in the core that are in series (the internal forest) and then iterates alternately on the remaining core [the (nonlinear) global step] and the internal forest [the (linear) local step]. In this paper, it is formally shown that the smaller set of nonlinear equations in GMPA corresponds to the network equations of a particular topological subgraph of the original graph. Using algebraic manipulations, the size of the linearized system to be solved is reduced to the number of nodes in the core having degrees greater than two. For pipe models of real-world applications that are derived from geographic information system datasets, this can mean a dramatic reduction of the size of the nonlinear problem that has to be solved. The main contributions of the paper are (1) the derivation and presentation of formal proofs for the new method, and (2) demonstrating how significant the reduction in the dimension of the nonlinear problem can be for suitable networks. The method is illustrated by a simple example.

This paper describes the development and application of a reservoir management decision support system for evaluating floodplain benefits and socioeconomic trade-offs of reservoir management alternatives in the Connecticut River watershed. The decision support system is composed of a reservoir system simulation model, an ecological model, and two river hydraulics models. The reservoir model simulated current operations at 73 reservoirs and flows at locations of interest in the Connecticut River watershed. Regulated flows from the reservoir model were compared with unregulated flows, both statistically and spatially, for a suite of environmental flow metrics based on inundation patterns related to floodplain vegetation communities. Analyses demonstrate use of the decision support system and show how its use illuminates (1) trends in existing hydrologic alteration for the Connecticut River mainstem and one of its tributaries, the Farmington River, and (2) management scenarios that might have ecological benefits for floodplain plant communities. The decision support system was used to test two management scenarios to assess potential floodplain benefits and associated trade-offs in hydropower generation and flood risk. The process described shows the usefulness of large-scale reservoir management decision support systems that incorporate environmental considerations in assisting with watershed planning and environmental flow implementation.

About 95% of the water supply systems in Canada are being operated by small-to-medium-sized water utilities (SMWU) serving populations less than 50,000. The representation of these utilities in National Water and Wastewater Benchmarking Initiative (NWWBI) is almost negligible due to less awareness, issues related to data availability, and financial constraints. In the present work, an inter-utility performance benchmarking model (IU-PBM) is developed, which entails consideration of 47 performance indicators (PIs) for different functional components of SMWU, such as water resources and environment, personnel, physical assets, quality of service, water quality and public health, and financial aspects. For performance benchmarking, 47 (linear, exponential, logarithmic, and polynomial) transformation functions have been established to translate the calculated PIs into performance levels between 10 and 100, which is based on literature, NWWBI reports, and expert judgment. The weights are estimated using a specific method from the literature for ranking of PIs by different water utilities in the Okanagan basin, British Columbia, Canada, and opinions of experts working in water infrastructure management. Finally, performance indices have been established by aggregating the transformed performance levels using the technique for order preference by similarity to ideal solution (TOPSIS) (i.e., based on the concept of relative closeness to the most desirable performance and remoteness from the least desirable performance). The IU-PBM results presented in the form of a web diagram demonstrate the utility’s performance to the top management for pragmatic decision making. The proposed model has also been implemented for two SMWU operating in Okanagan Basin to evaluate its practicality.

Previous studies have naturally related water distribution network performance to the ability to deliver sufficient pressure and flow. The present paper emphasizes that performance also depends on the efficiency of delivering these requirements. Accordingly, an efficiency-based performance index is proposed. It is the geometric average of four performance metrics: reliability, vulnerability, resilience, and connectivity. These are themselves based on the energy efficiency, hydraulic capacity, and structural ability of the system to deliver water under a range of conditions. The metrics are applied to two example networks and variations of these, enabling the assessment of their relevance, their sensitivity to system changes, and permitting a comparison to existing metrics. Variations represent different redundancy increasing strategies, recognized for improving performance. The proposed performance index generally follows a similar trend as the previous indices, increasing with network pressure. Nevertheless, it varies differently and penalizes networks with unnecessarily high pressures. Because the index is based on energy and demand efficiency metrics, it automatically complies with the energy and mass balances of the network. Moreover, the new metric is easily interpreted and can be applied to various systems, whether complex or involving multiple scenarios.

Connectivity is important to the structure and function of systems. In water infrastructure systems, connections between components determine flow patterns and operational flexibility. Network theory can provide tools to assess and view connectivity in water resource networks that augment current methods in water resource systems analysis. This paper presents an analysis of connectivity and resilience in the network of California’s water resources infrastructure using network theory visualization and metrics. Applying these techniques to a link-node network used in a statewide hydroeconomic model of water management in California, the analysis shows how such metrics are useful to describe aspects of connectivity and identify important system components. Both the entire system and the San Francisco Bay Area subsystem are analyzed using measures of centrality, spacing, and linkage. The analysis also assesses the effects of network degradation by removing selected components, revealing complex relationships between connectivity, efficiency, and central dominance. The results demonstrate tools to understand network structure in California’s water system. Finally, insights are presented from the analysis in the context of the broader literature on resilience in engineered and environmental systems.

Comprehensive studies examining the driving forces of water use and evaluating their historical and future effects are paramount for cities concerned about water shortages. Conventional studies using input-output-structural decomposition analysis (IO-SDA) identify aggregated factors (technology, industrial structure, and production scale), which are not detailed, concrete, and controllable enough for systematic water management into the future. Some recent strategies adding additional factors could be further improved. This study focuses on decomposing the factor of production scale into more detailed drivers (population, consumption pattern, and per capita consumption volume). This was achieved by extending the IO-SDA model with an impact = population × affluence × technology (IPAT) analysis. The resultant IO-IPAT-SDA approach developed a compartmentalized decomposition for water use drivers and evaluated their impacts on water use changes by different final users. This model can be used in scenario forecasting to evaluate drivers and potential effects into the future. As a test case, the study assessed the driving factors of water use in Beijing from 1987 to 2020 and found that Beijing’s production-related water use per year decreased from 3.92 to $2.68\times {10}^9{\mathrm{m}}^3$ during 1987–2010, since water use efficiency gains offset potential water use increases by other drivers. If Beijing maintains the current development patterns, production-related water use would increase another 51.1% by 2020. Forecasting scenarios show that relying on improvements in water use efficiency alone will not stabilize Beijing’s future water use, but a comprehensive control of various drivers could maintain sustainable levels of water usage.

A gradient-based network assessment is proposed as a way to understand and improve the cost and hydraulic performance of distribution networks. It seeks to reduce the computation time of optimizing water distribution networks with varying demands by using a sequence of shorter time cycles to approximate a fuller range of costs. To allow system evolution, diameters are adjusted after each cycle according to anticipated capital, energy, and damage costs. Accounting for risks of low- and high-pressure hydraulic failure not only adds needed redundancy to the network, but also helps guide the gradient search. The method was applied to the well-known network of “Anytown.” Results indicate that the technique can be effectively applied to different scenarios and generate robust solutions. Furthermore, its lower computational intensity should facilitate its application as part of a broader optimization process and thus better enable the assessment of more storage, pumping, and control alternatives.

This study describes a binary integer programming model for mutually-operating fixed and mobile sensors in water distribution systems. The proposed method applies a deterministic optimization scheme for maximizing the monitored volume within network clusters. For a given budget, the model determines the ratio of mobile sensors to fixed sensors along with their placement and release strategies. Through assessing the benefit of placing each fixed sensor and the time and location of mobile sensors release, the combination of fixed and mobile sensors is determined. Utilizing mobile sensors for water quality monitoring is still in its infancy. Such sensors are equipped with self-powered sensing, sampling, data acquisition, and wireless transmission units. The model initiates with the combined operation of mobile and fixed sensors. It then explores the benefits of mobile sensors compared to fixed. The two battle of the water sensor networks (BWSN) are utilized for demonstrating the model’s capabilities. Mobile sensors are found to be beneficial to water distribution system monitoring when operated in conjunction with static sensors.

The planning, design, and construction of hydropower dams are occurring at a rapid pace in the Mekong/Lancang River Basin in Southeast Asia. For years the river and its tributaries transported an average of 160 million metric tons of sediment per year to the South China Sea. Reservoirs are expected to trap significant fractions of this sediment, rendering much of it unavailable to transport nutrients and maintain the river’s geomorphic structure and ecological habitats. This paper introduces and applies a method for identifying and evaluating alternative dam siting, design, and operating (SDO) policy options that could help maintain more natural sediment regimes and for evaluating the effect of these sediment-management strategies on hydropower production. This paper focuses on the planned Lower Sesan 2 (LSS2) Dam in Cambodia. This dam would prevent a significant source of sediment from reaching critical Mekong ecosystems, such as Cambodia’s Tonle Sap Lake and the Mekong Delta in Vietnam. The concept of replacing LSS2 as planned with several smaller alternative dams with SDO modifications to enable reservoir sediment flushing is evaluated. These modifications could increase sediment discharge from this site by as much as three to four times compared to current plans but reduce short-term annual energy production by a third or more. Results demonstrate that most of the loss in energy production associated with SDO modifications (at least 94% in the cases considered) results from reduced reservoir size as opposed to modified reservoir operations. It is also found that, from an operational standpoint, sediment flushing is likely best conducted during the transition period between the dry and wet seasons.

This article investigates the improvement of the operation of a four-reservoir system in the Seine River basin, France, by use of deterministic and ensemble weather forecasts and real-time control. In the current management, each reservoir is operated independently from the others and following prescribed rule-curves, designed to reduce floods and sustain low flows under the historical hydrological conditions. However, this management system is inefficient when inflows are significantly different from their seasonal average and may become even more inadequate to cope with the predicted increase in extreme events induced by climate change. In this work, a centralized real-time control system is developed to improve reservoirs operation by exploiting numerical weather forecasts that are becoming increasingly available. The proposed management system implements a well-established optimization technique, model predictive control (MPC), and its recently modified version that can incorporate uncertainties, tree-based model predictive control (TB-MPC), to account for deterministic and ensemble forecasts respectively. The management system is assessed by simulation over historical events and compared to the no-forecasts strategy based on rule-curves. Simulation results show that the proposed real-time control system largely outperforms the no-forecasts management strategy, and that explicitly considering forecast uncertainty through ensembles can compensate for the loss in performance due to forecast inaccuracy.

Many global municipal water distribution systems (WDS) are in need of repair. To save energy and greenhouse gas emissions due to pumping, pipes that need to be replaced can be enhanced by being enlarged and made of a smoother material. To choose which pipes to thus enhance first given a limited budget, an algorithm is introduced based on enhancing the pipe that requires the most effective power, which is defined as the product of discharge, specific weight, and headloss. The algorithm was verified with complete enumeration, life-cycle, and environmental-impact modeling on seven realistic WDSs. It was found that the additional cost and greenhouse gasses (GHG) emitted from purchasing and manufacturing a larger pipe are quickly recovered by the reduced cost and GHG emissions due to less pumping being required. The proposed pipe enhancement should be verified with modeling, however, since increased payback periods for both costs and GHG emissions can result if an increase in pipe size causes water to travel a longer path.