Journal of Water Resources Planning and Management

Assessing the long-term reliability and vulnerability of municipal water supply systems often employs system modeling to analyze performance. Generally, decision-makers have long been in search of a metric that presents a comprehensive assessment of water-supply systems. The system’s condition is often evaluated by a univariate measure of reliability, resiliency, or vulnerability, which individually cannot provide a comprehensive understanding of the system’s performance. In this study, instead of an individual measure, the joint probability distribution of reliability and vulnerability is used to assess the performance of water systems. To quantify the joint probability distribution between reliability and vulnerability, different copula functions are tested, and the most appropriate one is selected. The copula function couples one-dimensional marginal distributions of reliability and vulnerability to form the cumulative distribution function (CDF) of the joint probability. Then, a novel index, the Water System Performance Index (WSPI), is derived from the copula CDF based on exceedance and nonexceedance probability of these two metrics. WSPI presents simultaneous information about the frequency and magnitude of failures in water systems. The proposed WSPI increases with an increase in reliability and a decrease in vulnerability of the system and vice versa. The WSPI is demonstrated and tested using two reservoirs of the Salt Lake City Department of Public Utilities (SLCDPU) water system. WSPI, first, is employed to present the performance of the system during the historical period of 1981–2010. Then, WSPI values are estimated for the system under future climate projections. Results suggest the WSPI provides a useful tool for managers and stakeholders to represent simultaneous information about frequency, magnitude, and recovery period of a system under different failure conditions.

Water users in California’s hybrid water rights system have different legal priorities to available surface water in times of water scarcity. A set of two linear programming models was developed to determine curtailments of water use under drought conditions according to riparian and appropriative water right doctrines with spatially varying water availability and water rights within a basin. The models were implemented in spreadsheets and extended to estimate water right reliability and factors of safety in water rights administration. Alternate methods for calculating water use curtailments are discussed. Curtailments from the models are compared with actual water shortage notices issued by the state for the Eel River, California for June 30, 2014. Analyzing water use curtailments with an algorithm in spreadsheet software offers a mechanistic, transparent, accessible, and precise approach derived from legal doctrines to support water rights administration during drought.

This paper presents a novel meta-heuristic approach for real-time flood control. The approach combines model predictive control (MPC) with a reduced genetic algorithm (RGA) to quickly find near-optimal solutions. The main control objective is to reduce the flood damage cost in an entire river basin. A fast conceptual model for the rivers and floodplains is used to compute the inundation levels and damages. The hydraulic component of the model is identified and calibrated to a full hydrodynamic model. The Demer basin, a flood-prone area in Belgium, is considered as a case study. Results show that the RGA converges faster to a near-optimal solution than a standard genetic algorithm. Furthermore, MPC-RGA outperforms the current programmable logic controller (PLC) based regulation by anticipating to rainfall forecasts.

Many burst detection methods, including a prediction stage, have been developed in order to identify bursts in a timely manner. These methods require vast historical data to produce accurate predictions. The clustering-based method proposed in this paper only requires one day of time series data. In clustering analysis, cosine distance is used to evaluate dissimilarity between flow data. Incorporating cosine distance enables this method to fully use the temporal varying correlation between the data from multiple flow sensors. By doing this, data variations caused by sudden weather changes, festivals, and periodic changes in water demand are correctly classified as normal conditions in pipe networks. This method was applied in a real multi-inlet and multioutlet district metering area (DMA). The results show that it can achieve a low false positive rate and few false alarms and be sensitive to relatively large bursts. This method has the potential to be used in different types of DMA.

Flood assessment in urban catchments is usually addressed through the combination of geographic information systems (GISs) and stormwater models. However, the coupled use of these tools involves a level of detail in terms of hydrological modeling that can be beyond the scope of overall flood management planning strategies. This research consists of the development of a methodology based on multiple regression analysis (MRA) to assess flood risk in urban catchments according to their morphologic characteristics and the geometrical and topological arrangement of the drainage networks into which they flow. Stormwater models were replaced by a combination of multiple linear regression (MLR), multiple nonlinear regression (MNLR), and multiple binary logistic regression (MBLR), which enabled identifying influential parameters in the maximum runoff rates generated in urban catchments, modeling the magnitude of peak flows across them, and estimating flood risk in the nodes of sewer networks, respectively. The results obtained through a real urban catchment located in Espoo, Finland, demonstrated the usefulness of the proposed methodology to provide an accurate replication of flood occurrence in urban catchments due to intense storm events favored by climate change, information that can be used to plan and design preventative drainage strategies.

High pressure in water distribution networks may lead to more leakages, bursts, and excessive consumption. Therefore, an effective strategy to improve the reliability of the network is pressure management. Many indices have been proposed to quantify the hydraulic performance and reliability of water networks. The fuzzy reliability index is one of the indices proposed to focus on nodal pressure and pipe head losses. Fixed outlet pressure reducing valves (PRVs) are low cost and efficient tools for pressure management programs. In this study, a simulation-optimization model is developed to find optimum locations and set pressures for PRVs in the existing networks to maximize the fuzzy reliability index and minimize the costs of performing pressure management program. Applying the model in a high-pressure case study water distribution network shows that using some PRVs can balance and limit the pressure in a specified range. Moreover, applying the Young conflict resolution theory on solutions of multiobjective optimization allows decision makers to select appropriate option for pressure management considering their available budget and constraints.

This study performs an extensive investigation to explore critical factors that affect the accuracy of the first-order second-moment (FOSM) approximation when it is used as a nodal pressure head uncertainty estimation method for a water distribution system (WDS). The applicability of FOSM for WDS calibration, abnormality detection, and network design is examined. Uncertainties are considered in nodal demands, peak demand factors, and pipe roughness coefficients. To quantify the accuracy of FOSM, results are compared with those from Monte Carlo simulation (MCS). The accuracy of FOSM is tested based on WDS peak demand conditions, topology, and pipe diameter by applying it to 21 real and hypothetical WDSs. Results reveal that FOSM provides accurate variances estimation of nodal pressure heads and the extreme values of 1st and 99th percentile nodal pressure heads compared to MCS. In addition, accurate uncertainty estimations are obtained from FOSM even under the peak demand conditions. FOSM accuracy is lower for a branched system relative to looped and gridded systems and independent of the pipe size.

Optimization of short-term reservoir operation normally involves ramping constraints of outflows and water elevations at short time steps (e.g., hourly). Random search algorithms, such as genetic algorithms, have been widely used in optimization of reservoir operation. When applying random search algorithms to hourly reservoir operation, two important issues arise. The first one is the frequent violation of ramping constraints on the hourly reservoir outflows because of the random nature of the optimization algorithm. In other words, the optimization struggles to meet the ramping constraints when finding feasible solutions. The second issue is the zigzag fluctuation of the hourly decision variables as a result of the random search, which is unrealistic to implement in practice. In this study, the Savitzky-Golay smoothing filter (also known as least-squares filter) is incorporated periodically within the routine of the Nondominated Sorting Genetic Algorithm (NSGA-II). The goal of this study is to smooth out the decision variable functions without deteriorating the performance of the optimization algorithm. The performance of the proposed approach is quantified through three indexes using a multireservoir system with 3,360 decision variables as the test case. The results show that use of the Savitzky-Golay filter not only provides a solution to the two aforementioned issues, but also significantly improves the performance of the NSGA-II for hourly reservoir operation. The optimal decisions obtained using the proposed approach display similar hourly variability to decisions of actual reservoir operation.

The traditional approach to the design of water distribution systems (WDS) is based on the concept of topological and energy redundancy, resulting in systems with many loops and with nodal pressure higher than design requirements. In the early days of water supply networks, this oversizing was not seen as a problem. Currently, however, with aging water networks and pressure on utility finances, the cost of infrastructure maintenance and renewal constitutes a significant proportion of utility budgets. This paper proposes a novel approach that enables a water utility to better invest limited budgets by dividing the water system into a primary and a secondary group of pipes, with the former being the focus of the field investigation, maintenance, and renewal activities. The primary network is identified such that it guarantees the minimum hydraulic performance while achieving maximum topological redundancy in the overall system. The methodology is based on the use of local and global topological and energy metrics in conjunction with a heuristic optimization technique. A case study confirms that minimum nodal pressure can be substantially preserved by focusing activities on less than 70% of the pipes in the network, which means a reduction of over 30% of the total length of infrastructure and, consequently, of related field investigation and maintenance costs. The analysis of the local and global performance indexes also provides various insights and useful information for utility management.

Stochastic dynamic programming is one of the most widely used optimization techniques for water system optimization. In this study, four methods for estimating transition probabilities have been evaluated to determine how they influence water system performance for short-term operating policies. The methods are counting, ordinary least-squares regression, robust linear model regression and multivariate conditional distribution. Two discretization schemes—equal-width interval and equal-frequency and data transformation—have also been included in the study as sources of uncertainty. The study was carried out for three water systems: the Outardes River, Manicouagan River, and Lac Saint-Jean, located in Quebec, Canada. The results show that the water system configuration played a significant role in the performance of the transition probabilities. The discretization scheme and data transformation had a considerable influence on the counting and regression methods, whereas they had less of an impact on the multivariate conditional distribution. The robust linear models with equal-frequency discretization without data transformation gave satisfactory results for all the water systems.

Operational strategies to mitigate combined sewer overflows (CSOs) in older urban areas may be enhanced through real-time decision support provided to sewer operators. During severe rainfall events, real-time hydraulic simulations, coupled with control algorithms, can explore a large number of potential changes to control procedures at short time intervals to provide dynamic feedback and optimization. A model predictive control (MPC) genetic algorithm was developed in previous work and tested offline to explore the efficiency and effectiveness of alternative MPC approaches. This paper extends the MPC methodology to evaluate potential impacts of long-term capital investments on CSO frequency. An alternative strategy to mitigating CSOs in real time with sluice gates may involve replacing small-diameter pipes that cause high hydraulic grade lines throughout the system. CSO reductions may also be significantly enhanced through consideration of larger spatial scales. Replacing conduits is effective but expensive, and optimization over a larger spatial extent (without conduit replacement) has been shown to reduce CSOs by 14%. Optimization over the entire large-scale system is recommended for future work.

An increasing shortage of drinking water resources around the world has been observed, especially for people living in absolute poverty. Hence, meeting demand for drinking water with required quality needs advanced control technology to operate the drinking water distribution systems (DWDSs). This paper aims to implement robust parameter estimation and an advanced output prediction algorithm on a nonlinear water quality model for the purpose of monitoring water quality in DWDSs. The advanced algorithm was tested for robustly predicting the outputs of the water quality model in DWDSs incorporating disinfectant by-products (DBPs). The application of the point-parametric model (PPM) for robustly estimating parameters of the nonlinear water quality model considering DBPs by utilizing a multi-input multioutput (MIMO) model structure is stretched. The piecewise constant parameters within the time-varying parameter model structure explaining the dynamics of water quality considering DBPs in DWDSs under input disturbance are acquired by applying modified piecewise continuity algorithms. In addition, this paper for the first time describes the robustness of the PPM framework on a MIMO nonlinear water quality model under multihydraulic operation status with advanced operating procedures. Simulation results suggest that the PPM technology and its piecewise continuity algorithm are appropriate for robustly feasible model predictive control (RFMPC) on water quality in DWDSs with DBPs involved.

In this paper, a new mathematical framework is proposed for maximizing the self-cleaning capacity (SCC) of drinking water distribution systems by controlling the diurnal peak flow velocities in the pipes under normal operation. This is achieved through an optimal change of the network connectivity (topology). This paper proposes an efficient algorithm for the network analysis of valve closures, which allows enforcing favorable changes in the flow velocities for maximizing the SCC by determining an optimal set of links to isolate in the forming of a more branched network, while concurrently satisfying the hydraulic and regulatory pressure constraints at the demand nodes. Multiple stochastic demands from an end-use demand model are generated to test the robustness in the improved SCC for the modified network connectivity under changing demand. An operational network model is used to demonstrate the efficacy of the proposed approach.

Managing aging infrastructure has become one of the greatest challenges for water utilities, particularly when faced with selecting the most critical pipes for rehabilitation from among the thousands of candidates. This paper presents a set of novel yet practical energy metrics that quantify energy interactions at the spatial resolution of individual water mains to help utilities identify pipes for rehabilitation. The metrics are demonstrated using a benchmark system and two large, complex systems. The results show that the majority of pipes have good energy performance but that an important minority of outlier pipes have low energy efficiency and high energy losses due to friction and leakage. Pumping and tank operations tend to drive energy efficiency and energy losses in pipes close to water sources, whereas diurnal variation in demand drives energy performance of mains located far away from water sources. The new metrics of energy lost to friction and energy lost to leakage can provide information on energy performance in a pipe that is complementary to the traditional measures of unit head loss and leakage flow.

This paper presents a short-term hydropower scheduling model to determine the number of operating units and the power in each quarter hour and for each hydroplant. A reservoir operation and hydropower unit commitment problem are formulated and solved using linear programming (LP) and dynamic programming (DP), respectively. Two temporal scales are coupled with each other in formulating the reservoir operation problem, with the quarter-hourly and hourly scales used for power and water balances, respectively. The model has its advantages in adapting to preferences of shift engineers by prioritizing the objective and constraints, as well as in taking into account the roles of reservoirs in balancing power demands. The model and procedure are applied to deal with the Yunnan provincial hydropower system that consists of 37 reservoirs, believed to be one of the largest-scale problems ever reported. The experience from this work suggests that the gap between academy and industry is mainly attributable to modeling, not the methods or algorithms used to solve the models.

This work presents the technical characteristics and the regulation system of a complex water supply system (WSS) in an Italian Alpine valley. The WSS faced multiple challenges: water supply over a large area, hydropower generation, and coordination among multiple local sources and networks. The development of an optimal feedback-control algorithm for the supervisory control system was key to guaranteeing the operation of this modern WSS. This regulation scheme and the rationale for its development are described in this paper. A customized numerical model of the WSS was developed in order to test the operating rules through suitable numerical simulations. Results show that the proposed algorithm satisfies the objectives of the WSS and respects its tight constraints. The analysis of the case study evidences the advantages of the coordination among the municipal water networks, quantifies the hydropower generation potential in the WSS, and highlights the key roles of automation and remote control in modern water systems. Finally, the case study presented here provides an efficient technical solution for the hydraulic regulation of a high-pressure water main connecting a cascade of small tanks in mountainous regions.

This paper presents a new multiobjective approach to solve sedimentation problems behind weirs and low-head dams. The multicriteria decision analysis (MCDA) framework is used to improve reservoir operation rules for Sangju Weir in South Korea. A series of stage and discharge constraints can be developed to include consideration for reservoir sedimentation, hydropower generation, flood control, water supply, irrigation and drainage, and environment. Seasonally changing operation rules can help mitigate reservoir sedimentation while improving hydropower production, water supply, water quality, and environmental issues. Based on a 22-year daily reservoir operation simulation, improved operation rules to mitigate reservoir sedimentation include: (1) a nonflood season stage kept high (EL 47.0 m); (2) a flood season stage between EL 47.0 m and EL 44.5 m depending on the magnitude of the upstream flow discharge; and (3) gates should be opened during floods ($Q>600{\mathrm{m}}^3/\mathrm{s}$).

The transboundary Apalachicola-Chattahoochee-Flint (ACF) Basin in the southeastern United States has a long history of competition for water resources and litigation surrounding these conflicts. This case study applies the decision-scaling approach to explore the spatiotemporal impacts to water supply deficits in the ACF Basin from natural climate variability, from change in mean precipitation and temperature, and from shifts in municipal and industrial (M&I) water demand. System performance is characterized by reliability, vulnerability, and effective life (i.e., years until crossing an unacceptable performance threshold). The results indicate that long-term water supply reliability and vulnerability are sensitive to, in decreasing order of importance, changes in mean precipitation, mean M&I demand, and mean temperature. In the short term, natural climate variability causes the most uncertainty in vulnerability. Reliability (and the corresponding effective life) is uniform across the basin because of a shared water supply curtailments management system, whereas vulnerability (and the corresponding effective life) varies greatly. In particular, metropolitan Atlanta exhibits high sensitivity and vulnerability to stressors because of its location in the headwaters and high demand levels.

This paper presents the implementation of stochastic dual dynamic programming (SDDP) to generate water-value functions for operations planning of the British Columbia (BC) Hydro system. An inflow model is developed to generate stochastic seasonal volumes forecasts and monthly inflows for the Peace River and upper Columbia River in Canada. A model is developed to model storage spaces for flood control and storage account operations to comply with the Columbia River Treaty (CRT) and subsequent agreements between Canada and the United States. Two novel algorithms are developed. The first uses SDDP and an updating process to generate the end of planning horizon water-value function, and the second uses SDDP to generate monthly water-value functions with a new stopping criterion for SDDP. Analysis results of the water-value functions illustrate the importance of globally optimizing reservoirs and storage accounts and modeling stochastic inflows. Results of simulation studies show significant benefits of using water-value functions over currently used methods and the need for modeling inflow uncertainties and storage account operations.

Ecosystem services (ESs) including provisioning, regulating, and cultural services for sustaining and fulfilling human well-being are formed based on various conditions, processes, and components of natural and artificial ecosystems. Spatial conservation planning for sustainable ecosystem development requires precise information on the local potential for providing hydrological ecosystem services in a watershed. The principal challenges in spatially conserving multiple ESs are that they are not independent of each other, and that the interactive relationships between them are in the forms of trade-offs and synergies. The rationality of spatial priority conservation on reconciling multiple ESs will be enhanced if the interactive relationships are recognized at the planning stage. Such a framework is developed by integrating a hydrology model into a systematic conservation model, and it is illustrated with data about multiple hydrological ESs in the Teshio watershed, with the aim of developing a priority conservation ranking map that reconciles competing and compromising interactive relationships among multiple services. The water yield, sediment, and organic nutrient retentions concentrated in eastern and some northern areas with higher precipitation, more forest lands, and steeper slope, but the inorganic nutrient retention concentrated in riverine and southwestern places with agricultural lands. The spatial priority conservation ranking map of an individual ES is closely related to its spatially distributed pattern. The spatial priority conservation areas for sediment and organic nutrient retentions are traded off against those for inorganic nutrient retention. There are obvious differences between the spatial priority conservation ranking maps of individual ESs and those of multiple services together. The spatial priority conservation areas for multiple ESs together simultaneously include southwestern, riverine, eastern, and some northern places of the study watershed, which can balance the conflicts existing between sediment and organic nutrient retentions and inorganic nutrient retention. The systematic priority conservation areas of hydrological ESs provide an effective tool for identifying trade-offs between environmental protection and agricultural economic development. The proposed framework in this study can be applicable to similarly structural prioritization problems of other watersheds, which can sustain ecosystem conservation and economic development.

This paper aims to illustrate the hidden bargain decision-making process undertaken by a reservoir operator and basin farmers along with water allocation of the Al-Tharthar reservoir located on the Tigris river. The amount of water discharged from the reservoir is unilaterally determined according to traditional rule curve–based policy. The water discharged from agricultural zones to the stream was observed to be significantly less than the amount estimated based on the reservoir operational policy, particularly in the dry season. The reservoir operator announces the operational policy that will be applied for the coming year to the basin farmers. Both stakeholders are assumed to have incentives to cheat each other. Given their past experience, the farmers may not adhere to their announced allocation, and for the same reason, the reservoir operator may provide false information about water availability in anticipation of the farmers’ response. Noncooperative game theory was applied in an extensive form to model the behind-the-scenes bargaining process between the reservoir operator and basin farmers. The result provided a perspective that can be used to examine the validity of traditional reservoir operational policy on basin management as well as stakeholders’ payoffs under the interactive decision-making situation. The conclusion is that the traditional operational policy implemented for basin management at the Al-Tharthar reservoir was well designed, although the situation of mutual trust between the reservoir operator and farmers emerges in only one-sixth of the cases.

Extreme climate events are expected to become more severe or frequent under climate change projections, and these future changes continue to be an urgent concern for the aging water infrastructure in East Asia and Pacific countries. In this study, the authors suggest an approach to dam rehabilitation assessment to prepare for the complex dam safety and environmental issues caused by climate change and aging dam facilities in South Korea. The authors review the validity of each evaluation component and the appropriateness of all criteria through research into both domestic and foreign cases, literature review, and consultation with dam rehabilitation assessment experts. In addition, the authors propose quantitative evaluation indices and a comprehensive importance evaluation of dam rehabilitation using a survey-based Delphi–analytic hierarchy process (AHP) method, in accordance with the professional experience of panel participants.

In this work, a linear system theory approach produces an analytical solution of contaminant mass transport equations for its subsequent use in multiobjective optimization for optimal waste allocation for a hypothetical multireach, multiwaste source river. The solution scheme used provided contaminant concentrations at the chosen checkpoint at any time, thus allowing for a solution to the optimization problem using a conventional mathematical iterative search method (sequential quadratic programming). The study conceptually investigates the effects of different input waste loads on the optimization with cost and equity objectives. The results suggest that in some cases, equity measure does not protect dischargers downstream, but rather balances all the dischargers with the most restricted dischargers. Moreover, 25–43% fuller utilization of the river’s assimilative capacity can be achieved by rearranging input waste loads between the discharges even in comparison with eliminating the most restricted waste sources.