Journal of Water Resources Planning and Management

State estimation (SE) techniques can be applied to compute the most likely hydraulic state of a water distribution system from the available measurements at a given time. Different approaches exist in the technical literature to undertake such an analysis, but in all of them it is assumed that pump and valve statuses are known beforehand. Such consideration may lead to unrealistic results if real-time unnotified changes in the operation of the network take place, limiting the usefulness of the information provided by telemetry systems. This work eliminates the known-status assumption and presents the concept of topological state estimation (TSE), which not only computes the hydraulic state of the system, but also the current pump and valve status according to the existing measurements. More specifically, a novel methodology for TSE is set out in this paper. The proposed method is derived from the original mixed-integer non-linear programming formulation of the problem, which is transformed in an iterative mixed-integer quadratic programming problem by linearizing some hydraulic constraints. The potential of the methodology is presented by means of an illustrative example and a large case study, in which pumps, gate valves and check valves exist. Results show that TSE would successfully contribute to make the most of available telemetry systems, hence expanding the online monitoring possibilities of water distribution networks.

To enable a water distribution system (WDS) model to yield predictions with a reasonably good match to measurements, pipe resistance coefficients (PRCs) of the model often need to be calibrated. The majority of methods developed recently solve the PRCs’ calibration problem using evolutionary algorithms. The common feature of these methods is to perform the forward computation (i.e., solve the traditional hydraulic equation of networks) repeatedly to search optimal solutions. This paper presents a direct inversion algorithm for PRC calibration of WDSs, which has an identical framework with a global gradient algorithm (GGA) that has been proved to be the most effective method to solve networks and adopted by EPANET. Therefore, all advantages of GGA can be extended in terms of high computational efficiency and convergence, as well as being easy to understand and program. In addition, under the proposed framework, uncertainties propagated from measurement noises, nodal demand uncertainties, and model simplification errors to the estimates can be quantified individually or simultaneously in a flexible manner, enabling the identification of uncertainties source, as well as which kind of effort is preferred to reduce them. Three networks are applied to validate the proposed algorithm. Encouraging results achieved demonstrate that the proposed algorithm is valid for the calibration and uncertainty qualification of PRCs, as well as the uncertainty source identification.

This paper presents a modeling framework for real-time decision support for irrigation scheduling using probabilistic seasonal weather forecasts which are incorporated into a simulation-optimization framework. The simulation of the field processes is performed by the Soil Water Atmosphere Plant (SWAP) model, whereas the optimization is performed by three different stochastic programming methods: implicit approach, explicit single-stage approach and explicit two-stage approach. To evaluate the benefit of the probabilistic forecasts, the irrigation schedules from the different stochastic methods are compared with the best benchmark of perfect forecasts as well as with the real field and the Agriculture Extension Service of Israel schedules. The analysis is performed on a real case study of irrigated chickpeas field in Kibbutz Hazorea, Northern Israel. The results show that incorporating stochastic weather forecasts could lead to substantial improvements compared with current irrigation practices.

This paper presents a methodology for developing efficient and effective operational, short-time response to an unplanned failure event (e.g., a pipe burst or equipment failure) in a water distribution system (WDS). The proposed automated response methodology consists of isolating the failure event followed by the recovery of the affected system part by restoring the flows and pressures to normal conditions. The isolation is typically achieved by manipulating the nearby on/off valves. The recovery, which is the focus of this work, involves selecting an optimal combination of suitable operational interventions from a number of possible choices with the aim to reduce the negative impact of the failure (e.g., volume of water undelivered to consumers) over a prespecified time horizon. The intervention options considered here include valve manipulations, changing the pressure reducing valve’s (PRV) outlet pressure, and installation and use of temporary overland bypasses from nearby hydrants in an adjacent, unaffected part of the network. The optimal mix of interventions is identified by using a multiobjective optimization approach driven by the minimization of the negative impact on the consumers and the minimization of the corresponding number of operational interventions (which acts as a surrogate for operational costs). This methodology has been applied to a real-world water distribution network of C-Town. The results obtained demonstrate the effectiveness of the proposed methodology in identifying the Pareto optimal intervention strategies that could be ultimately presented to the control room operator for making a suitable decision in near real time.

Biofuel development to comply with the Renewable Fuel Standard (RFS) would alter conventional crop patterns in agricultural watersheds. As a result, the hydrologic response of the watersheds will exhibit different and often opposing effects on agrohydrological system variables such as riverine nitrate-N load and streamflow. Conventional modeling approaches treat those externalities as regulatory constraints, often fail to consider the hierarchical nature of the decision-making process, and end with unrealistic solutions. This study therefore proposes an alternative decision-modeling framework for biofuel development to optimize a water-quality objective under different levels of streamflow requirement in the watershed. A bilevel programming model is established to mimic the hierarchical decision-making process in environmental regulation. The model is applied to the Sangamon River basin, a typical agricultural watershed in central Illinois, to determine the optimal locations and type of ethanol biorefineries as policy instruments. The results show that the proposed instruments can effectively guide the decisions in biofuel development to meet the environmental objectives in the watershed, although adopting the proposed framework yields a lower profit than the conventional models, which is the price of a more realistic solution to the hierarchical decision problem. The results also highlight the importance of spatial heterogeneity and identifying an appropriate spatial scale to design effective environmental policies in biofuel development.

A multiobjective optimal meter placement (MOMP) model is proposed for water distribution system (WDS) pipe burst detection to (1) minimize the normalized total meter cost, (2) maximize the detection probability, (3) minimize the rate of false alarms, and (4) maximize the meter network’s mechanical reliability. The mechanical reliability is a meter network’s ability to continuously provide informative data/measurement even under measurement failures originating from either missing measurements for time steps or meter malfunction. A novel method for quantifying mechanical reliability is developed based on the simulation of a single-meter failure. In addition to meter locations, the optimal ratio between the pressure and pipe flow meters was identified for a predefined number of meters. The proposed MOMP model was demonstrated on two networks from the literature with different configurations, characteristics, and numbers of components. The results suggest that the proposed MOMP model should be used to determine the optimal meter location and best set of meters for high detectability and mechanical reliability of a specific network of interest.

In many agricultural watersheds, channel erosion is a major source of sediment. In these watersheds, a comprehensive approach to reducing channel erosion can aid in the feasibility of meeting water quality goals. In the Minnesota River Basin (MRB), a large agricultural watershed, increased peak flows have contributed to greater sediment loading from within the channel. This study focuses on three components of channel restoration that are important in developing a strategic framework: sediment source reduction, working with private landowners, and the economics of restoration. A synthesis of sediment research was done to assess the sediment reduction benefit of restoration projects. While field erosion is the largest source of gross erosion, most of the sediment from fields is not delivered to the river. In the MRB, sediment from stream banks and bluffs is the dominant source, thus requiring water storage and/or stream management in order to reduce sediment load. Landowners favored edge-of-field practices that minimize the conversion of farmland and have minimal government intervention. Economic analysis of restoration showed that the most cost-effective restoration projects reduced large amounts of sediment on short stretches of lower-order streams. However ecological benefits, project sustainability, and local cultural issues suggest a role for smaller stream restoration projects. Sediment reduction estimates and assessment of ecosystem service benefits from stream restoration projects need to be further developed.

Chronic population decline in cities nationwide has resulted in increased per capita costs for continued service of water and wastewater infrastructure. Due to the high fixed costs associated with water and wastewater infrastructures and this chronic population decline, the financial burden to maintain and operate the fixed-grid footprint has become a challenge for those who reside within these shrinking cities. With this in mind, the understanding of public opinion toward the provision of service provided by critical infrastructure sectors is crucial for informed decision making by both utilities and state agencies. Hence, this study seeks to present a new methodological approach to assess the willingness to pay for the provision of water and wastewater services, and to evaluate the drivers of the willingness to pay in shrinking cities. A latent class Tobit regression modeling approach is proposed to identify and understand the influential variables associated with a stated preference survey on the willingness to pay for increased rates in shrinking cities. In addition, the modeling approach can more accurately capture the unobserved heterogeneity across the nationwide shrinking cities. The results illustrated that the latent class Tobit outperformed the more basic Tobit model and accounted for unobserved heterogeneity. Moreover, characteristics determining an increased willingness to pay for improved reliability of water and wastewater service included the responsibility of paying the water bill, homeownership, and income level. Characteristics determining a decreased willingness to pay for improved reliability of water and wastewater service included gender, awareness of chronic population decline, having no income, marital status, and employment status.

Aiming at the minimum power consumption for water pumping in a cascade pumping station system, we established a complex nonlinear mathematical model by taking optimized lift head and blade angle (or speed) of pump units as decision variables. The on-site optimal operation mode conforming to single-station water restriction was obtained by water decomposition–dynamic programming aggregation solution of the optimal operation model of pump stations under different discrete heads. After that, the constraint of total system lift head was considered. By taking lift heads of pump stations as decision variables, the original model was transformed into a one-dimensional dynamic programming aggregation model, which was solved by dynamic programming method. Through numerical simulation of unsteady flow in an interstage water channel, the optimal head distribution was modified to obtain optimal head distribution and unit optimization operation modes of pumping stations.

Leakage or pipe burst detection, often carried out by using the pressure sensor systems (PSSs) within a water distribution network (WDN), is critical to enable such networks to operate in a safe manner. The majority of previous studies have focused on either the advancement of detection equipment or the development of detection algorithms (leakage localization or sensor deployment). In contrast, this paper proposes a methodology to investigate the underlying capacity of existing PSSs for pipe burst detection using a set of quantitative metrics. These metrics focus on the identification of (1) nodes where bursts cannot be detected by the PSS (undetectable nodes); (2) undetectable demands, derived from the demands of undetectable nodes; (3) detection dimension, representing the number of sensors that can simultaneously detect a burst at a node; (4) the spatial region where bursts within each subregion can be detected by a particular sensor with a minimum outflow; and (5) the detectable threshold, which indicates the minimum burst outflow of each node triggering its associated pressure sensor. The proposed methodology has been applied to two real-world WDNs with varying sizes and configurations. Results show that the former two metrics can indicate the spatial distribution of the nodes at which bursts can be detected, and the latter three metrics can successfully reveal the PSS’s detection dimension and spatial regions, as well as the detectable threshold of each node. Such improved understanding offers guidance to develop effective burst-management strategies and to facilitate decision-making processes regarding sensor placement and burst localization.

Ongoing changes in global climate are expected to alter the hydrologic regime of many river basins worldwide, expanding historically observed variability as well as increasing the frequency and intensity of extreme events. Understanding the vulnerabilities of water systems under such uncertain and variable hydrologic conditions is key to supporting strategic planning and design adaptation options. In this paper, we contribute a multiobjective assessment of the impacts of hydrologic uncertainty on the operations of multipurpose water reservoirs systems in arid climates. We focus our analysis on the Dez and Karoun river system in Iran, which is responsible for the production of more than 20% of the total hydropower generation of the country. A system of dams controls most of the water flowing to the lower part of the basin, where irrigation and domestic supply are strategic objectives, along with flood protection. We first design the optimal operations of the system using observed inflows and evaporation rates. Then, we simulate the resulting solutions over different ensembles of stochastic hydrology to partition the impacts of streamflow and evaporation uncertainty. Numerical results show that system operations are extremely sensitive to alterations of both uncertainty sources. In particular, we show that in this arid river basin, long-term objectives are mainly vulnerable to inflow uncertainty, whereas evaporation rate uncertainty mostly affects short-term objectives. Our results suggest that local water authorities should properly characterize hydrologic uncertainty in the design of future operations of the expanded network of reservoirs, possibly also investing in the improvement of the existing monitoring network to obtain more reliable data for modeling streamflow and evaporation processes.