Journal of Water Resources Planning and Management

Forum papers are thought-provoking opinion pieces or essays founded in fact, sometimes containing speculation, on a civil engineering topic of general interest and relevance to the readership of the journal. The views expressed in this Forum article do not necessarily reflect the views of ASCE or the Editorial Board of the journal.

Coastal ecosystems are at great risk from climate change and sea level rise, potentially resulting in loss of habitat and economic costs to society. This study aimed to investigate whether users’ attitudes toward climate risks have any bearing on how they value ecosystem services and express the desire to mitigate risks. The study used psychometric measures to characterize users’ attitudes toward climate risk. The findings were integrated into an economic discrete choice model to elicit public willingness to pay (WTP) for improved fishery and overall ecosystem health resulting from increased freshwater inflows in the Florida Everglades. The respondents’ WTP was strongly dependent on their perceptions of the risk posed by sea level rise. Adjusting WTP for risk perception resulted in a 40.03% increase in annual estimated recreational benefits from unadjusted WTP. Water management agencies in Florida may wish to consider people’s attitudes toward risk when designing their public education and engagement programs in support of ecosystem restoration.

Consumer demand estimation is a key step in real-time drinking water system (DWS) modeling used for demand forecasting, optimal operations, and water quality management. Consumer nodes in a DWS are generally clustered to reduce the number of unknown demands to be estimated from a limited number of measurement locations. A clustering methodology using the self-organizing map (SOM) is presented, which groups consumer nodes based on sensitivity of measurements to perturbations in the consumer demands and through the use of exogenous consumer information representative of, for example, socioeconomic information. The SOM algorithm not only developed demand clusters, but also provided intuitive visualization of the high-dimensional sensitivity space, which can provide important visual clues about the clustering problem such as the maximum number of clusters that can reasonably be formed and sharpness of the clusters. When applied to an example network, the sensitivity-based SOM clusters improved the performance in representing the observed measurements and demand estimate uncertainty, but reduced the performance in representing the overall network hydraulics relative to the actual clusters. Incorporating exogenous information about the actual clusters demonstrated the potential for providing trade-offs between representing the limited observed hydraulic information and the overall network hydraulics. The results from the SOM algorithm clearly demonstrate a need for clustering approaches that incorporate network-specific information (e.g., measurement locations, sensitivity information, and exogenous data) to develop demand estimates that are capable of representing observed information while adequately capturing overall system dynamics.

Long-term snowpack decline is among the best-understood impacts of climate change on water resources systems. This trend has been observed for decades and is projected to continue even in climate projections in which total runoff volumes do not change significantly. For basins in which snowpack has historically provided intra-annual water storage, snowpack decline creates several issues that may require adaptation to infrastructure, operations, or both. This study develops an approach to analyze vulnerabilities and adaptations specifically focused on the challenge of snowpack decline, using the northern California reservoir system as a case study. We first introduce an open-source daily time-step simulation model of this system, which is validated against historical observations of operations. Multiobjective vulnerabilities to snowpack decline are then examined using a set of downscaled climate scenarios to capture the physically based effects of rising temperatures. A statistical analysis shows that the primary impacts include water supply shortage and lower reservoir storage resulting from the seasonal shift in runoff timing. These challenges identified from the vulnerability assessment inform proposed adaptations to operations to maintain multiobjective performance across the ensemble of plausible future scenarios, which include other uncertain hydrologic changes. To adapt seasonal reservoir management without the cost of additional infrastructure, we specifically propose and test adaptations that parameterize the structure of existing operating policies: a dynamic flood control rule curve and revised snowpack-to-streamflow forecasting methods to improve seasonal runoff predictability given declining snowpack. These adaptations are shown to mitigate the majority of vulnerabilities caused by snowpack decline across the scenario ensemble, with remaining opportunities for improvement using formal policy search and dynamic adaptation techniques. The coupled approach to vulnerability assessment and adaptation is generalizable to other snowmelt-dominated water resources systems facing the loss of seasonal storage due to rising temperatures.

Due to the high costs associated with maintaining and upgrading water infrastructure systems, selecting an appropriate water rate to charge for water service is an important step for water utilities. Understanding the consumption patterns under different rate conditions is important because revenues generated from consumers form the backbone of water utilities’ financial model. This paper describes the results of a statistical analysis based on a survey of household attitudes toward water service reliability and quality in a Midwestern US city to identify influential factors that are likely to be associated with supporting or opposing water rate increases, as well as influencing water consumption patterns after such rate increases. Using a multivariate binary probit approach, the statistical estimations cover a range of socioeconomic factors affecting individuals’ likelihood of supporting increases in water rates. The results provide insights as to how individuals are likely to respond to water rate increases based on the reliability of current water services and the quality of the supplied water.

This paper develops a new domain knowledge–based initial design method for the optimization of water distribution network design. The new initial water distribution network design method, termed as head loss-based design preconditioner (HDP), is based on head loss analysis in the supplying path from source to user. The new HDP-preconditioned search is compared with two algorithms: one preconditioned on a velocity-based initial design method and a simple genetic algorithm without preconditioning. Results show the HDP head loss–based method outperforms the prescreened heuristic sampling method (PHSM) in terms of the quality of the initial solutions and computational efficiency on all three cases. HDP also outperforms stochastic initialization on two of the three cases. The results obtained imply that the proposed domain knowledge–based design method HDP also would be able to provide effective starting conditions for other optimization algorithms, besides genetic algorithm for large water distribution systems, since most optimization methods are greatly assisted by a good starting condition.

The lead contamination of drinking water in homes and buildings remains an important public health concern. In order to assess strategies to measure and reduce exposure to lead from drinking water, models are needed that incorporate the multiple factors affecting lead concentrations in premise plumbing systems (PPS). In this study, the use of EPANET, a commonly used hydraulic and water quality model for water distribution systems, was assessed for its ability to predict lead concentrations in PPS. The model was calibrated and validated against data collected from multiple experiments in the EPA’s Home Plumbing Simulator that contained a lead service line and other lead sources. The EPANET’s first-order saturation kinetics model was used to simulate the dissolution of lead in the lead service line. A version of EPANET was developed to include one-dimensional mass dispersion. Modeling results were compared to experimental data, and recommendations were made to improve the EPANET-based modeling framework for predicting lead concentrations in PPS.

As is much of the world, California increasingly is challenged by water scarcity. A recent multiyear drought depleted surface reservoir and groundwater storage in many places of the state. The Sustainable Groundwater Management Act (SGMA), passed in 2014, promises sustainable groundwater management in California and suggests managed aquifer recharge (MAR) as one of the key practices to eliminate groundwater overdraft by groundwater sustainability agencies. Questions remain, however, about the amount of water available for MAR. Conjunctive management provides the opportunity to modify reservoir operations and enhance recharge long before any drought occurs. However, the amount by which reoperation of surface reservoirs can increase the available water for MAR has not been thoroughly investigated. Folsom reservoir is operated to meet a variety of objectives, including flood control, water supply, hydropower, and environmental flow. The inclusion of water discharge for groundwater recharge adds another objective for the operation of the reservoir and complicates the decision-making. Various management strategies were developed and applied to evaluate performance of the system during a historical period, and a new objective was added to maximize the available water for recharge from Folsom reservoir. Although the reoperation strategy offers additional storage in the system and increases the expected value of recharge from 280 to 430 million cubic meter (mcm) per year, trade-offs between different objectives showed that new operating rules perform quite satisfactorily, with nonsignificant deficits and violations of old objectives.

A multiobjective water resource allocation model (GWAS) was constructed to incorporate the objectives of socioeconomic water use, power generation, and river ecological flow. The new model can simulate both rule-based operating schemes and optimized operating schemes for reservoirs with two kinds of solving algorithms and was applied to the Fuhe River basin. Fifteen socioeconomic water use units, 23 ecological flow control sections in the river channel, and 22 reservoirs were considered in the case study. The advantages and disadvantages of each rule-based operating scheme and the global optimization were compared under four computational schemes with the GWAS model. The competitive relationship among socioeconomic water use, power generation, and ecological water use under empirical rules, and the global optimal operation, were revealed. The GWAS model is an improvement of the traditional water resource allocation model and enables managers to compare empirical rule-based schemes and form a better, globally-optimized scheme.

This study presents a performance assessment of small water systems (SWSs) through the lens of drinking water quality management. The performance assessment is based on five criteria: treatment and disinfection; water quality issues; operators’ capabilities; infrastructure and funding; and operational characteristics. Each criterion is composed of six performance indicators. Each indicator is rated using one of the three qualitative classes, namely, good, average, and poor. The qualitative classes are later transformed into numerical scores, which are then aggregated using a weighted sum method. The aggregated scores divided by a maximum possible score in the respective performance criteria give the overall performance level for a particular water system. The proposed performance assessment framework has been demonstrated using data collected from 66 SWSs representing three types of local bodies (regional districts, municipalities, and improvement districts) in British Columbia, Canada. The respondents included operators, engineers, managers, and technicians. The results showed the overall performance level of water systems of regional districts was comparatively better, followed by municipalities, and then improvement districts.

Water distribution systems (WDSs) are critical infrastructures that need to be resilient to cope with and quickly recover from exceptional conditions in an uncertain and challenging future. To build resilience in the design of WDSs, it is essential to explore indicators that can effectively quantify the level of system resilience. On the basis of the optimization of rehabilitation designs of three benchmark WDSs, four resilience related indicators are investigated, i.e., Todini’s index, which is a surrogate and indirect performance indicator, and three direct performance indicators—failure duration, failure magnitude, and a severity-based resilience index. These indicators are widely used in the literature yet have not been comprehensively examined and compared. Results show that strong correlations exist between the four resilience-related indicators, indicating that optimization using any one indicator is likely to improve system resilience measured by other indicators. Nevertheless, they have distinctive advantages and disadvantages. In particular, the severity-based resilience index is effective in identifying nodes susceptible to the occurrence of failures and slow in recovery. Todini’s index can be assessed without the need to set up failure scenarios, which is an advantage compared to the other three resilience indicators; however, its correlations with direct resilience indicators are weaker in WDSs with tanks.