Journal of Water Resources Planning and Management

Pressure-reducing valves (PRVs) are often used in water distribution networks (WDNs) to regulate pressure for leakage reduction. Optimal management would require the pressure to be constant and as low as possible at the WDN critical node. Such operating conditions can be achieved by means of real-time control (RTC) of the PRVs. Because the pressure RTC of PRVs in WDNs is still an uncommon application, this paper first provides the theoretical framework of control systems and also discusses the benefits of a feedback control over a feedforward scheme. A closed-loop controller was developed, allowing RTC of pressure at the critical node in a WDN by changing the pressure set point in the PRV at the network inlet. To this end, a diaphragm-actuated control valve coupled to an actuator for remote control of the pilot spring was used. Laboratory experiments were carried out to characterize PRV operation and effectiveness of the algorithms. A simplified model of the PRV and numerical simulations to reproduce the pressure transient were also developed. Results show that (1) the algorithm is able to control pressure also in case of abrupt variations of the set point pressure and finite delays; and (2) the numerical model is in good agreement with relevant experiments.

Surrogate-based simulation-optimization techniques are widely applied in developing optimal remediation strategies that increase the efficiency and reduce the cost of surfactant-enhanced aquifer remediation (SEAR) when clearing dense nonaqueous phase liquids (DNAPLs). In such processes, there are many uncertainty factors that may greatly affect the optimization outcome of a SEAR strategy selection. However, previous research on this subject rarely incorporates an uncertainty analysis. This paper presents an uncertainty analysis of both the simulation model and the ensemble surrogate model used to optimize SEAR strategies. Set pair analysis (SPA) and kriging methods were used to build the ensemble surrogate model. The probability distributions of the residuals of the outputs between the ensemble surrogate model and the simulation model, run on 100 testing samples, were analyzed to ascertain the uncertainty of the surrogate model, which was found to be less than 1.5%. The uncertainty of the simulation model was derived by combining a Monte Carlo random simulation with the Sobol’ global sensitivity analysis. Finally, a stochastic nonlinear programming model was established to compute the optimal remediation strategies for the remediation target under different confidence levels. This research will allow decision makers to more confidently select the optimal remediation strategy for a given scenario by balancing the reliability of model prediction with the cost of the remediation strategy according to the demands of the project.

One of the primary concerns in water supply systems is pressure control. High pressure increases both leakage and the risk of pipes bursting, while low pressure can reduce the water supplied to consumers and allow the intrusion of pathogens. Therefore, the operation of the equipment must be optimized to avoid such problems. In high pressure zones, pressure reducing valves (PRVs) are commonly used for pressure control. However, the energy dissipated from head loss could be recovered to produce electrical energy. The use of pumps as turbines (PAT) in this case is an alternative to improve the system efficiency. However, owing to the dynamic operation, PAT performance drops significantly during some periods of the day. The use of variable speeds can solve this problem, but the PAT selection to optimize the operation remains an issue. Therefore, this paper presents a method to simultaneously select PATs and schedule their operation using variable speed to improve energy recovery and reduce leakage. Two networks are studied and the performance in leakage control is compared with the PRV operation.

Streamflow forecasts and common drought indicators were evaluated for suitability to inform drought mitigation actions for the city of Baltimore’s water supply system. Preliminary streamflow forecasts developed by the National Oceanic and Atmospheric Administration’s Mid-Atlantic River Forecast Center (MARFC) were incorporated with other key drought indices, including the standardized precipitation index, days of storage remaining (DSR) index, and Palmer drought severity index in an aggregate drought index to classify drought severity and to trigger drought mitigation actions. Aggregate drought indices were evaluated in the drought action response tool, a simulation model created for this research. Results show that the combined forecast and drought indicators improve water supply operations, allowing the Baltimore system to maintain reliable supply and preserve storage throughout periods of drought. The DSR metric constructed from MARFC forecasts is recommended for incorporation into the city of Baltimore’s drought management plan to facilitate proactive drought response.

Hydromodification is a serious management concern in semiarid regions and is expected to become worse with land use and climate change. Potential stream channel responses range from increased or decreased sediment loads, incision, and dramatic nonequilibrated channel enlargements. The prevalence of hydromodification, particularly in semiarid regions, creates a need for new predictive tools that can support decisions aimed at reducing or mitigating hydromodification effects in a large geographical region. Existing models to screen and predict hydromodification are limited in terms of performance and require time and data. This paper examines three nonlinear learning algorithms—support vector machines, artificial neural networks, and random forests—for predicting changes in stream channel morphology as an indicator of hydromodification. The authors explore the ability of each algorithm to rank the important variables that explain the degree of channel response for streams located in Southern California. Results suggest that variables pertaining to the stream bank morphology, such as bank angle, maximum bank height, and bottom widths, rank high in their ability to predict hydromodification. Among the three algorithms, random forest performance is robust compared to artificial neural networks and support vector machines, given its ability to accommodate small data sample sizes and minimal data preprocessing. The study shows that for complex responses, such as hydromodification of stream channels, preprocessing will continue to be a necessary step for nonlinear algorithms. With proper guidance, there is potential for using nonlinear algorithms to assess stream reaches vulnerable to hydromodification and inform management decisions to manage streamflow and runoff in Southern California.

For reservoirs with multiple water demands, losses in water use utility because of shortages in the water supply from historical operation decisions can be considered information to improve the current decisions. This study proposes an improved hedging model that uses the parameter optimization method to incorporate the effects of historical decisions. A new cumulative water shortage impact index is proposed to evaluate the severity of historical water shortages and their impact on release decisions, and the Palmer drought severity index (PDSI) is incorporated to improve the hedging model. Based on the new index, a weighting factor of previously derived hedging rules is optimized, which represents the relative importance of water release and water storage, and more realistic hedging rules and operation decisions for reservoir managers are obtained. The new model is employed at the Danjiangkou Reservoir, which serves as one of the major storage points on the middle route of the south-to-north water transfer project (SNWTP). The results show the following: (1) when the reservoir inflow is insufficient, the initial weighting factor (initial $w$) and its corresponding scaling factor can be obtained to guide decisions by considering the forecasted inflow, starting regulation level (SRL), and expected water level at the end of year (WLE); (2) by following the optimal solution, the water delivery process becomes more uniform, and a more reasonable water supply order can be determined according to the unit water supply benefit for each water user; and (3) in an average dry year and in the driest year in the historical record, with initial $w$ values of 0.7 and 0.15, the SRL is greater than 158 m, and the delivery rates of the SNWTP water in the initial stages should be controlled at approximately 0.85 and 0.4, respectively.

The apportionment of waters of the Indus River System between the provinces of Pakistan is widely hailed as a historic agreement. This agreement (herein referred to as the Accord) was signed into effect in 1991, just over 25 years ago. The Accord lacks a clearly stated objective and hence it is difficult to review the Accord against its objective. This paper presents a detailed thematic review of the Accord and interprets the literature and data sets that have become available over the last 25 years. Although the Accord leaves room for interpretation, which is often biased to a particular perspective, an obvious starting point that has been highlighted in the literature is to improve water accounting in the Indus basin and to clarify and document the Operating Rules. Over the next 25 years, demographic change, socioeconomic change, and climate change in the Indus Basin will place this Accord under increased scrutiny.

Drought indices assimilate meteorological and/or hydrological information to come up with a comprehensible index. Over the last few decades, hundreds of drought indices have been developed in order to improve monitoring and impact assessment. For a particular drought event, these multiple indices sometimes indicate different levels of drought severity, creating confusion among stakeholders and posing challenges for decision making. To overcome the problem, this study suggests a novel methodology using a Bayesian network. There are several advantages of this proposed method: (1) it pools information from multiple drought indices and comes up with a better estimate for drought severity; (2) instead of a deterministic drought-severity outcome from the individual indices, it offers probabilistic estimates for drought severity; and (3) it reduces the uncertainty of the individual drought indices. The robustness of the methodology is further checked with a case study of an actual drought event in South Korea.

Dual water systems are becoming an important urban water management strategy as more utilities adopt increasingly integrated approaches that enable matching source water quality to the intended use, more efficient use of resources, use of nontraditional local water sources, and more resilient systems. Four alternative strategies for the dual supply of raw water for nonpotable municipal uses (e.g., landscape irrigation and fire supply) and treated water for potable uses were evaluated in this study. The alternative strategies included centralized and decentralized water treatment approaches, varying distribution system scales, and integration of existing irrigation ditches with raw water landscape irrigation systems. Multiple criteria decision analysis (MCDA) was used to conduct a triple bottom line (TBL) evaluation of the alternative strategies versus maintaining a conventional water supply system. This methodology allowed for the inclusion of incommensurable performance indicators and stakeholder preferences in the decision process, which was instrumental in garnering stakeholder support. The study found that the alternative strategies provide many social and environmental benefits versus a conventional system that may justify initial capital costs; a predominately gravity-fed distribution system favored centralized water treatment alternatives to decentralized options, existing irrigation canals for municipal use provide unique social and environmental benefits important to the community, and the optimum strategies are dependent on local conditions and community priorities.

State estimation (SE) involves estimating state variables of interest that cannot be directly measured by using measurable variables. In water distribution system (WDS) SE, nodes are often aggregated to reduce the number of unknowns. To achieve high SE accuracy, the optimal observation locations in the WDS should be determined. This paper proposes an optimal meter placement and node grouping (OMPNG) model for WDS demand estimation (DE). The nonlinear Kalman filter (NKF) method is used to estimate the nodal group demand (NGD) from pipe flow measurements at meter locations. A k-means clustering method is introduced to generate the initial node grouping for the proposed OMPNG model. An elitism-based genetic algorithm is employed to minimize the sum of the NGD root-mean-square errors (RMSEs). The proposed OMPNG model was applied to the modified Austin network DE problem, and the results were compared with those obtained by optimizing node grouping with fixed meter locations based only on engineering sense. The results showed that the proposed OMPNG model significantly improves the DE accuracy and reliability.