Journal of Water Resources Planning and Management

Conservative chemicals (such as sodium chloride) have been utilized to perform tracer studies within drinking water distribution systems. The resulting signals from a tracer study can provide significant quantitative information to assess the ability of a given network model to represent the underlying hydraulic and transport characteristics of the network. Often, however, the resulting observed water quality time-series data are simply visually inspected to assess the ability of the network model to accurately predict water quality transport. The use of standard quantitative metrics, such as arrival times, sum of squared errors (SSE), and correlation analysis at different time lags to assess the differences between the observed and predicted time-series, can provide some useful information but are not sufficient for paired data signals. In this study, the use of dynamic time warping (DTW)—an approach for estimating the similarity between two time series of data—is presented as a method for quantitative analysis of observed and model-predicted conservative chemical time-series data. DTW uses dynamic programming to match the elements of two time series, in a sequential approach, to minimize the SSE of the two signals. Whereas the SSE provides one goodness-of-fit metric, the resulting length of the warping path also provides additional information as to the degree of the alignment between the two data streams.

The impact of implementing an advanced water quality model for simulating the dead-end sections of drinking water distribution networks on the outcomes of booster chlorination optimization is investigated. An advection-dispersion-reaction transport model that accounts for the realistic spatial distribution of water demands along dead-end pipes is linked to a genetic algorithm to find the optimal layout and operation of booster chlorination stations. The objective function is formulated and solved to find the optimal locations and chlorine dosing schedules of the booster stations that minimize the total costs of design and operation of the booster system, while maintaining a sufficient residual throughout the distribution network. The results highlight the importance of considering dispersive solute transport, as well as the excessive residence times encountered in the dead-end branches in the water quality simulations conducted for network optimization problems. While this study addresses the optimization of booster chlorination systems, its implications extend to a wide array of network optimization applications, including pump scheduling for water quality optimization, optimal sensor placement for reactive contaminant detection, and design of real-time boost-response systems.

This paper analyzed the impact of land use and its spatial configuration on streamflow in the Samin catchment ($278{\mathrm{km}}^2$), Indonesia. Historic land use was reconstructed based on satellite images for the years 1982, 1994, 2000, 2006, and 2013. A calibrated and validated Soil and Water Assessment Tool (SWAT) simulated the catchment hydrology of the study area, taking respective land use covers as inputs. A correlation analysis between changes in land use covers and simulated streamflow characteristics was carried out. A land-cover scenario analysis assessed the sensitivity of streamflow characteristics to different future land use covers. The results show that an increase in the settlement connectivity can result in an increase in the ratio of surface runoff to streamflow and a decrease in the ratio of dry-season streamflow to wet-season streamflow, and vice versa. The results suggest that land use pattern management can be an important component in water management.

Supplying high-quality water at competitive cost is a major challenge for water utilities worldwide, especially with ever increasing water quality standards and energy prices. A number of pump scheduling methods for optimizing simultaneously water quality and energy cost have been developed already. However, none of these methods is ideal because of the complexity of water networks and the nonlinear behavior of water flow. In this research, a new optimization method named iterative extended lexicographic goal programming (iELGP) is developed to optimize energy cost and water quality (residual chlorine) in water networks with a mixture of fixed speed pumps (FSPs) and variable speed pumps (VSPs). Two different approaches were used to indirectly improve chlorine. The new method was tested on the C-Town water network and compared with the graph theory method from the literature. The results obtained show the ability of the iELGP method to optimize energy cost and water quality in water networks in a computationally very efficient manner. They also show that the iELGP method can identify lower energy cost pump schedules and do this faster than the comparison method. Using VSPs instead of FSPs improves the water quality and decreases the related energy and maintenance cost in water networks.

Aging infrastructure has caused municipalities to experience an increasing number of water-related challenges, such as noncompliance with water-quality standards or failing infrastructure. Consequentially, with heightened levels of public concern toward drinking water, residents might opt to use bottled or filtered water over tap water. Shrinking cities characteristically have underutilized systems due to chronic urban decline occurring over multiple decades without a corresponding reduction in the infrastructure footprint. Substituting products and using point-of-use filtration systems further exacerbates this problem of decreased demand, and the associated costs may be burdensome to low-income residents, who often comprise upward of 40% of shrinking-city residents. An online survey was deployed in June 2016 to 21 US shrinking cities. A mixed-method approach sought to assess public attitudes toward water received at the tap and to understand the public behavior of using bottled and filtered water in US shrinking cities. Statistical analyses revealed parameters influencing the likelihood of using bottled and filtered water. Qualitative analyses provided insight into how these substitute behaviors are occurring in the household.

Identifying spatial patterns of water distribution pipe breaks can improve the understanding of the significant factors that promote the deterioration of water infrastructure systems. Such understanding can be translated into better failure prediction models or leveraged by water municipalities to improve their maintenance operations and asset management. Herein, spatial autocorrelation analysis, based on global and local Moran’s I, is proposed for the identification of failure clusters in water distribution networks. The proposed approach extends traditional spatial autocorrelation analysis by accounting for network structure in the formulation of the spatial weights and is adaptable to different levels of spatial resolution. For the studied system, the results revealed that pipe failures exhibit a significant degree of spatial clustering. The locations of statistically significant hot- and coldspots of pipe failures were identified. Characteristics of the underlying pipe network, including pipe density, material, and age, were found to be the main drivers for spatial clustering in the failure data. Beyond water distribution networks, the proposed computational approach can be applied to detect and locate patterns of spatial events in other networked infrastructure systems and to identify local network characteristics that give rise to such patterns.

This paper presents a novel wastewater pipe condition rating model that builds on published literature and expands on industry-accepted and used guidelines and practices that currently consider mostly structural and operational defects in estimating pipe condition. A series of pipe internal and external, as well as hydraulic factors and parameters are identified and included in the proposed methodology to obtain a comprehensive rating of the pipe’s current condition. By using the analytic hierarchy process (AHP) multicriteria decision analysis, subject matter expert knowledge and input is used to determine relative importance weights of carefully selected and defined criteria and factors that affect wastewater pipe condition. The level of agreement between subject matter expert opinions is tested by employing statistical analysis. An in-depth sensitivity analysis using clustering is presented to determine the main criteria’s sensitivity to changing relative importance weights. For validation purposes, the proposed model is applied to a small portion of a US wastewater collection system. The results show satisfactory average validity percentage.

Real-time water distribution system (WDS) hydraulic models are used in water utilities to facilitate the planning and operation of the water distribution system. As a critical model input, spatiotemporally varying nodal water demands significantly affect the performance and applicability of such WDS models. Thus, real-time nodal demands must be calibrated for reliability before their use. The main difficulty for real-time calibration is the lack of observed data sufficient to determine thousands of nodal demands accurately in a network. To address the difficulty, this study proposes a formal Bayesian approach to determine nodal demands in WDS hydraulic modeling by explicitly taking prior water demand information into account and coupling more information to constrain the nodal water demand modeling. Application of the approach on a simple hypothetical network and a field network in a city of eastern Zhejiang Province, China demonstrates that by adding prior information, the nodal demand can be uniquely determined in real time. The approach limits uncertainty propagation and improves the robustness of the real-time model calibration and analysis.

Several countries promote a regionalization of the drinking water sector; however, few decision support tools are adapted to the intermunicipal level to aid in regional decisions. The aim of this paper is to describe and demonstrate a probabilistic cost-benefit analysis approach to assess the societal effects of regional water supply interventions to constitute support for decision makers. A special focus is given to the quantification of effects on consumers’ health, water supply reliability, and operation and maintenance costs. The uncertainties of the quantified values are represented by probability distribution functions and analyzed by means of Monte Carlo simulations. The proposed approach was demonstrated in the Göteborg region in Sweden, for which five alternative interventions were evaluated. In conclusion, the proposed approach facilitates the identification and prioritization of societal effects so that costs and benefits normally overlooked in evaluation processes can be explicitly considered and addressed. The paper provides a transparent handling of uncertainties and enables a structured approach to improve decision makers’ ability in making informed choices on regional water supply alternatives.

Water distribution networks (WDNs) need to guarantee that water is delivered with adequate quality. This paper compares the performance of 12 multiobjective procedures to limit water quality deterioration in a WDN through the optimal operation of valves. The first objective (ObF1) is to minimize the water age, chosen as a surrogate parameter of quality deterioration, and the second objective (ObF2) is to minimize the number of valve closures. The 12 procedures are derived from the combination of 4 different optimization algorithms and 3 formulations of ObF1, namely, to minimize the maximum, the arithmetic mean, and the demand-weighted mean water age. The optimization algorithms considered are random search (RS), Loop for Optimal Valve Status Configuration (LOC), and a combination of each of these two with the Archive-based Micro Genetic Algorithm. The procedures are tested on two networks of different complexity. Results show how LOC is able to find near-optimal solutions using a fraction of the computational time required by a brute force search. Furthermore, among the ObF1 formulations, the use of the averages (either arithmetic or demand-weighted) gives better results in terms of impact on the population served by a WDN.

This paper shows how heuristic techniques can be used to account for engineering aspects in the application of a water distribution network (WDN) partitioning algorithm. In fact, being based on graph-theory concepts, most WDN partitioning algorithms fail to consider explicitly such aspects as the number of boundary pipes and the similarity of district metered areas (DMAs) in terms of number of nodes, total demand, and total pipe length, which are often considered by water utility managers to make their decisions. The algorithm considered is the fast-greedy partitioning algorithm (FGPA), based on the original formulation of modularity as an indicator of the strength of WDN partitioning. This algorithm operates by merging the elementary parts of the WDN in sequential steps until the desired number of district metered areas is reached. Two heuristic optimization techniques were combined with FGPA to propose different merging combinations: the former reproduces some specific features of the simulated annealing algorithm while the latter is based on the multiobjective genetic algorithm. Applications were carried out on a real WDN considering the actual system of isolation valves. The partitioning solutions obtained by the traditional FGPA without heuristics and by a literature algorithm based on spectral clustering were taken as benchmark. The results proved that the former heuristic can help in obtaining numerous WDN partitioning solutions with high modularity. The performance of these solutions can be evaluated in terms of practical engineering aspects to help WDN managers make an informed choice about the ultimate solution. If the trade-off between engineering criteria needs to be thoroughly analyzed in the context of WDN partitioning, the latter heuristic, in which FGPA creates DMAs through information encoded in proper weights, can be effectively used. Compared to the benchmark solutions, the FGPA with the latter heuristic can yield solutions with fewer boundary pipes and better demand uniformity over the DMAs.

The uncertainty of water resources availability is a growing problem in California as agricultural industrialization, population growth, and climate change affect water resources. The intense manipulation of the hydrological regime has led to the depletion of the water resources in the state and the subsequent use of various adaptive management strategies to cope with environmental conditions and social concerns. The historical imbalance between water pumping and replenishment in Pajaro Valley has led to overdrafted aquifers, seawater intrusion, and salinization. The objective of this study is to estimate the sustainable carrying capacity of agricultural land in Pajaro Valley while preventing groundwater overdraft. A groundwater box model was built and calibrated using historical data to represent current and future hydrology and water management strategies. An optimization model maximized the economic profit using the agricultural acreage as the decision variable with a set of constraints aimed at determining the sustainable carrying capacity of the groundwater basin. Model constraints include total land and water availability, crop acreage, agricultural water use, and historical demand. In the Pajaro Valley, agricultural operations must use less water more efficiently, which means changes in crop types, size of activities, and fallowing land in parts of the basin. Results of the optimal scenario over 25 years show a 15% reduction of total agricultural acreage, 8.5% reduction in food production, average profit loss of 4%, and a 79% reduction in aquifer depletion. This study provides an overall vision of what can be accomplished with coordinated land use planning using strategies that harmonize individual decisions and shared natural resources.

The terminal effect of urbanization on streams is their burial in culverts, with their disappearance from the visible landscape eventually resulting in their disappearance from the local perspective. This case study describes the historical research, logistical planning, and execution of a mapping and hydraulic monitoring program to characterize a stream with intermittent flooding issues that was buried under the Roanoke Virginia, central business district at the beginning of the twentieth century. This includes (1) a review and synthesis of the historical literature pertaining to the stream and springs; (2) a description of the methodology used to map the stream tunnels in a confined space under high vehicular traffic; and (3) the deployment and preliminary results of a distributed flow-depth monitoring program. The methodology presented can be used by other localities addressing flooding issues caused by buried streams, and the results of the monitoring program demonstrate the value of this effort in directing infrastructure improvement spending and identifying water quality issues. A complete characterization of buried streams is critical to provide a benchmark as Roanoke and other localities invest in stream and watershed capital-improvement projects with the intent of improving hydrologic function.