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.

This paper addresses the doubts regarding the spatial characteristics of the commonly used rules for parallel reservoir system operation. The rules based on aggregation-decomposition determine the system total release first and then assign this release to individual reservoirs, without considering the water demand distribution in the river network. In this paper, a conceptual model for parallel reservoir systems with distributed water demands is proposed. Three specific optimality conditions are derived for determining the optimal analytical solution. A rigorous proof shows that the aggregation-decomposition-based rules are a special case of the derived rules. An efficient algorithm is then developed based on the optimality conditions and shortage allocation index (SAI), in which a larger SAI indicates taking a higher percentage of the system water shortage, as release or storage. Unlike traditional algorithms that modify the violated variables empirically, we propose a criterion in terms of relative deviation indicators to determine the crucial priority of variable modification. This criterion can effectively address constraint violations. The optimal rules along with the solution algorithm are then demonstrated by the operation of a parallel reservoir system in the Shiyang River Basin, China. The results show that the proposed rules and algorithm are more efficient and effective than traditional algorithms and aggregation-decomposition-based rules, especially in dry seasons with more binding constraints.

Hydraulic accidents or abnormal situations, also known as leakages, cause not only water losses but also service interruptions and other negative effects. To facilitate the rapid response of water utilities and reduce water losses caused by undiscovered leakages, a timely, efficient, and accurate detection method is required. This paper describes a novel framework for multiple overlapping leak detection and isolation in the water distribution system. The proposed method has three stages: an estimation stage to extract leak-induced data from the huge monitoring data, an identification stage to detect the presence of overlapping leaks from multiscale difference series, and a localization stage to pinpoint the leak hotspots. The proposed method detects seven leakages in real-life networks and localizes 16 overlapping leakages with an economic score of €167,981 in L-town 2019. Comparisons show that the framework can accurately detect it in a fast response time and effectively localize the leaking pipeline with good applicability.

Accurate interpretation of longitudinal dispersion of solutes within distribution pipes increases the reliability of the predictions of water distribution systems (WDS) water quality analysis models. However, the estimation of longitudinal dispersion from fundamental principles is complicated. Thus, a longitudinal diffusion coefficient, a nonphysical constant depending on the flow and pipe properties, is generally applied to characterize the dispersion mechanism during the advective-dispersive-reactive (ADR) modeling in WDS. Many empirical/semiempirical formulas exist for calculating the coefficient values. Several of them have been applied in WDS water quality modeling research also. However, these formulas have shortcomings concerning overestimating and/or underestimating the longitudinal dispersion coefficient value under transitional/turbulent flow regimes and depicting the transient nature of longitudinal dispersion under laminar flow settings. As yet, no effort has been made to comparatively analyze the implications of the performance of these formulas in ADR modeling in WDS. This paper attempted to critically examine the competence of the prevailing state of the art to accurately represent longitudinal dispersion in pipes from a WDS water quality modeling perspective. The results established that the conceptual dissimilarities in incorporating dispersion memory and transient characteristics between the formulas for laminar regimes have significant impacts on regulating the scale of longitudinal dispersion in the ADR model predictions. The relative variances between the formulas for transitional/turbulent flow settings were found comparatively less significant concerning the ADR model outputs. This study’s findings can advance the state of the art to minimize the failure risks involved in simulating the concentration profiles when dispersive transport is dominant over advection in WDS.

Landscape water budgets provide a benchmark of efficiency in residential outdoor water consumption, as well as a means of assessing overirrigation and possible water savings through conservation. This study investigates outdoor water conservation potential by analyzing trends, patterns, and drivers of overirrigation across 14,300 single-family residential customers in a central Texas city over a 5-year period (2015–2019). Customers exceeding their budgets were categorized using a bivariate framework based on temporal and quantitative measures of overirrigation. Between one-third and two-thirds of the customers exceeded their budgets during at least 1 month of the growing season, wasting an average of $\mathrm{423,000}{\mathrm{m}}^3$ of water annually. Approximately 10% of customers contributed 56% of the total overirrigation during the study period. Overirrigation occurred throughout the growing season, but the number of customers and total volume wasted were highest in the fall months. Both overirrigation frequency and the amount wasted per unit of area tended to increase with property market value and decrease with landscape size. The methods and findings presented in this study can help water utilities evaluate potential outdoor water savings in their service areas and identify candidate customers for conservation interventions, such as landscape irrigation audits and educational workshops on efficient landscape watering.

Understanding the trade-offs associated with the two operation stages of hydropower generation is critical for guiding efficient reservoir operations. Previous studies showed that the marginal return in Stage 2 is always higher than the marginal cost in Stage 1 if variations in tailwater levels are ignored. However, the tailwater level often raises faster than the reservoir water level, and thus reduces the net hydraulic head of the reservoirs with reaction turbines, especially in large-scale and low-head reservoirs. Therefore, this study considers the variations in tailwater levels and theoretically evaluates the marginal utility of the total power generation (TPG) in the two stages. It is found that the variation of TPG presents diminishing marginal utility and is closely related to the topography characteristics of the reservoir and downstream channels, as well as the relationship between releases in the two stages. The marginal return in Stage 2 might be lower than the marginal cost in Stage 1 when the release in Stage 2 exceeds that in Stage 1 for the reservoirs in which the tailwater level is sensitive to discharge and hydraulic head. This suggests that the carryover storage equalizing the marginal utility in the two stages is optimal, i.e., satisfying the marginal utility principle. Otherwise, the marginal return is always higher than the marginal cost, which suggests that as much carryover storage as possible should be saved. Following these findings, the reservoir states, represented by the storage difference and the total inflow in the two stages, that satisfy the marginal utility principle are identified. Further, the optimal carryover storage under different levels of inflow and storage difference is derived. The theoretical findings are verified with four hydropower plants in China. Results confirm the theoretical findings and show that two-stage hydropower generation coupled with operating rules can greatly improve the performance of hydropower generation.

Leakages in water distribution networks (WDNs) are estimated to globally cost $39\mathrm{billion}\mathrm{USD}/\mathrm{year}$ and cause water and revenue losses, infrastructure degradation, and other cascading effects. Their impacts can be prevented and mitigated with prompt identification and accurate leak localization. In this work, we propose the leakage identification and localization algorithm (LILA), a pressure-based algorithm for data-driven leakage identification and model-based localization in WDNs. First, LILA identifies potential leakages via semisupervised linear regression of pairwise sensor pressure data and provides the location of their nearest sensors. Second, LILA locates leaky pipes relying on an initial set of candidate pipes and a simulation-based optimization framework with iterative linear and mixed-integer linear programming. LILA is tested on data from the L-Town network devised for the Battle of Leakage Detection and Isolation Methods. Results show that LILA can identify all leakages included in the data set and locate them within a maximum distance of 374 m from their real location. Abrupt leakages are identified immediately or within 2 h, while more time is required to raise alarms on incipient leakages.

Flow regulation through dams increases water and energy security for society but also threatens the natural equilibrium of river basins, leaving ecosystems and communities more vulnerable. While recovering flow regime dynamics to mitigate environmental impacts is a necessary goal, its effective implementation depends on the capacity to predict the expected outcomes to multiple competing users. Although such impacts can be measured between tangible economic uses, identifying the ecological trade-offs remains a challenge. To guide the design of environmental flows and support improved ecosystem restoration, we propose a methodology framework that builds an ensemble of flow regime options based on the naturalized flow regime range variability and quantifies the ecosystem response of each option in terms of migratory fish abundance with an artificial neural network model. The flow regime options with significant responses were called functional flow regimes because they provide conditions for the recruitment success of migratory fish species, which are vulnerable to flow dynamic synchronization. Our findings indicate that functional flow regimes may still produce relevant ecological responses even without fully recovering the natural flow regime. Specific levels of magnitude, frequency, duration, and timing of a flow regime can be combined to achieve a desired level of ecological response, while there is a clear threshold above which performance gains are smaller, indicating the presence of diminishing marginal performance gains when designing environmental flows. Knowing the trade-offs of different levels of flow regime recovery gives flexibility to the negotiation process between users and managers, leading to improved reservoir operation to meet multiple competing water needs.

The COVID-19 pandemic affected the operation of water utilities across the world. In the context of utilities, new protocols were needed to ensure that employees can work safely, and that water service is not interrupted. This study reports on how the operations of 27 water utilities worldwide were affected by the COVID-19 pandemic. Interviews were conducted between June and October 2020; respondents represent utilities that varied in population size, location, and customer composition (e.g., residential, industrial, commercial, institutional, and university customers). Survey questions focused on the effects of the pandemic on water system operation, demand, revenues, system vulnerabilities, and the use and development of emergency response plans (ERPs). Responses indicate that significant changes in water system operations were implemented to ensure that water utility employees could continue working while maintaining safe social distancing or alternatively working from home. A total of 23 of 27 utilities reported small changes in demand volumes and patterns, which can lead to some changes in water infrastructure operations and water quality. Utilities experienced a range of impacts on finances, where most utilities discussed small decreases in revenues, with a few reporting more drastic impacts. The pandemic revealed new system vulnerabilities, including supply chain management, capacity of staff to perform certain functions remotely, and finances. Some utilities applied existing guidance developed through ERPs with slight modifications, other utilities developed new ERPs to specifically address unique conditions induced by the pandemic, and a few utilities did not use or reference their existing ERPs to change operations. Many utilities suggested that lessons learned would be used in future ERPs, such as personnel training on pandemic risk management or annual mock exercises for preparing employees to better respond to emergencies.

The evaluation of water systems based on historical statistics is problematic when shifts in the hydrologic system occur due to a changing climate. An explicit link to thermodynamic and dynamic pathways in the climate system that may control water system performance is missing from current operational policies prescribed by regulation manuals within the US. In response, this study contributes an extended version of an existing weather regime (WR)–based stochastic weather generator (SWG) that allows (1) hourly simulation, (2) over the entire year, and (3) with a corrected representation of extremes. A range of climate scenarios is developed to demonstrate the insights that can be gained from linking the impacts of climate change to their thermodynamic and dynamic causal mechanisms, in this case for inflows to the Don Pedro Reservoir within the Tuolumne River Watershed of California. Application of the WR-SWG and water system modeling chain shows that the magnitude of flood events can be heavily influenced by antecedent hydrologic factors such as snow water equivalent (SWE) and soil moisture. Our results suggest that, under all climate change scenarios, SWE decreases as temperature increases and contributes more (sometimes up to 2.5 times more than the baseline) inflow as part of rain-on-snow events. The monthly reservoir inflows show the potential to cause extreme floods as the average rate of inflow increases by up to 80% with temperature increases, whereas SWE tends to increase by 50%, adding water to the stream during the high flow season. In addition to the temperature increase, if the water-holding capacity of the atmosphere increases with Clausius-Clapeyron scaling, reservoir inflows are projected to increase. This provides insight for risk-hedging policies: winter storm and spring snowmelt release and storage decisions that drive flood and drought risk, respectively.

Rainfall is a critical input variable of statistical streamflow forecasting models at subseasonal to seasonal time scales. This study presents a framework for evaluating the utility of a high-resolution experimental winter seasonal climate reforecasts for Florida (CLIFF) in improving streamflow forecasts. The CLIFF forecasts were coproduced through a scientist–stakeholder group of the Florida Water and Climate Alliance. The framework consists of a statistical streamflow generation model, four different sets of rainfall inputs, and distinct metrics for evaluating the resulting streamflow forecasts. The four sets of rainfall inputs include rainfall climatology, observed rainfall, NOAA-based seasonal rainfall forecasts, and CLIFF-based rainfall forecasts. Because NOAA ensemble precipitation forecasts were not available in this study, NOAA-based categorical precipitation outlooks were postprocessed via a hidden Markov chain model to obtain the corresponding NOAA-based seasonal rainfall forecasts. Streamflow forecasts based on rainfall climatology served as a reference. Different evaluation metrics, including Spearman correlation, mean absolute percent error (MAPE), and rank probability skill score (RPSS), were employed to evaluate model performance. The framework was demonstrated for streamflow forecasts for two rivers in the southwest of Florida, serving as a major source of a regional water supply agency. A retrospective streamflow forecasting model was designed for the dry season [November, December, January, and February (NDJF) months] for each of the 20 years from 2000 to 2019. Results revealed that CLIFF-based streamflow forecasts are a promising alternative to NOAA-based forecasts. Deterministic streamflow forecasts based on CLIFF rainfall have a smaller mean absolute percent error (MAPE) compared with the NOAA-based streamflow forecasts. Although NOAA-based probabilistic streamflow forecasts outperformed CLIFF-based probabilistic streamflow forecasts for the winter forecasting periods of November, December, and January, the latter forecasts performed better for the forecasting period of February. Thus, the two probabilistic forecasts are complementary. Although the results are limited to the study area, it has general application for evaluating the utility of different rainfall forecasts in providing deterministic/probabilistic streamflow forecasts.

Temporary flood defenses constitute a supplementary approach to permanent engineering solutions for flood management. However, their deployment strategy is a challenge because it depends on uncertain short-term weather conditions. The strategies that later proved to be insufficient or underused can have high social and environmental costs. Real options analysis (ROA) provides a mechanism to include flexibility in decision making to adapt the deployment strategies to future conditions and handle the aforementioned challenge in temporary flood defenses planning. To apply ROA principles, we combined multistage stochastic programming and a scenario tree. The methodology provided adaptive and flexible deployment decisions as uncertain future weather conditions were resolved. The proposed formulation was applied to nine flood-affected locations in Carlisle, northwest England, and the implications of the results were investigated. The results showed that there is a value achieved by building ROA in design of temporary defense deployment planning under uncertainty.

The world faces mounting challenges related to food, energy, and water security. Modeling approaches have emerged in the last decade to address this problem with mixed outcomes across a range of boundaries, including local, regional, national, and by research agendas. This paper delves into a comprehensive meta-analysis of the literature to identify the prevalence and strengths of these emergent approaches on the agendas they were applied to, the boundary levels, nexus dimensions, and the perspectives of the social and political dynamics. The research highlights the critical gaps that remain in the intersection of the different nexus agendas. A crucial observation was the scarcity of food, energy, and water models that incorporate technology adoption and economic implementation of nexus projects. On the core dimensions of the nexus, there is an important opportunity to include ecosystems, soil health, human health, and waste as key nexus dimensions. Although it is difficult to include social and political dynamics in nexus studies, this research identified proxies including (1) stakeholder interactions; (2) the intersection of access, security, and education; and (3) trade patterns and measures of prosperity.

There are often planned (for example, regular maintenance) and unplanned (for example, pipe bursts) interruptions in a water distribution network (WDN). Therefore, part of the isolation valves must be closed to isolate the part (segment) of the network that contains one or more pipes. Isolation of the target pipe segment with minimum possible disruption has been a problem to be solved. A large number of studies have been conducted to optimize the design of isolation valve placement in new WDNs, but less attention has been given to reducing the isolation zone and improving the reliability of old WDNs by adding optimally placed isolation valves. Therefore, this paper proposes a multiobjective optimization model for adding optimally located isolation valves to old WDNs, which considers the dual objectives of economy and reliability. The installation or removal of isolation valves can cause the original segments to split or merge, so this paper proposes the use of the segment-valve (SV) graph local update (SVLU) algorithm instead of the seed-filling algorithm to construct the SV graph. The optimization model was applied to the valve layout modification of part of the WDN in Changshu, Jiangsu Province, China, and the results showed that the model can solve the optimization solution quickly (15.358 s). Moreover, the use of the SVLU algorithm improved the efficiency of the solved model by 26.71%.

Hydraulic models can provide efficient and cost-effective ways for water utilities to evaluate changes in operating conditions (e.g., population dynamics, disasters), thereby increasing system resiliency during crises. Unfortunately, model development remains out of reach for many utilities because of high software costs, data needs, or personnel requirements. This study seeks to classify hydraulic modeling data needs, identify success factors and challenges associated with model development, and determine whether modeling a subzone of a larger water distribution network can provide useful insights during a crisis, specifically the COVID-19 pandemic. At the pandemic onset, we began developing a hydraulic model of the water distribution system of the University of Texas at Austin campus—a subsystem of the water distribution network of Austin, Texas—to understand how spatiotemporal changes in water demands impacted system performance. We found that the completed model can offer useful insight into the impacts of demand changes within the modeled subsystem (e.g., potential locations of water stagnation). However, the data collection and processing challenges encountered (e.g., siloed collection efforts, lack of standardization, lengthy processing) reflect barriers to model development and use. The amount of time required to gather and process the necessary data shows that model development cannot occur during a time-sensitive crisis, likely rendering any insight too late for use. Here, we make recommendations to address data-related challenges and support utilities in incorporating hydraulic modeling into emergency planning.

This paper presents the results of a pilot study funded by Metropolitana Milanese to investigate the use of transient tests for the diagnosis of the water distribution network of Milan (Italy). The peculiar aspects of the application of transient-based techniques to a complex system in an urban environment are discussed. Synchronous pressure signals acquired at 2,048 and 1,000 Hz at different measurement sections are shown and analyzed by auto- and cross-correlation, and wavelet transform. The effects of the shape of the pressure wave introduced in the system by the manoeuvre of a valve and the effects of repetition of the same manoeuvre on the reliability of the results are discussed. The main practical lessons learned about the use of hydrants, the topological and effective distances of measurement sections, the need for the introduction of pressure waves of given shapes, the information carried by the pressure signals for the diagnosis, and the use of simplified wavelet analysis are also presented and discussed.