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.

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.

Concerns over the impacts of urban growth have prompted the development and adoption of water-demand management strategies. Water and energy savings from increasingly efficient technologies, diversified water sources, and water savings policies are typically quantified from an individual demand-side basis, but network-wide potential is not well studied. This paper studies the effects of residential demand profiles on the performance of urban water networks, in response to emerging demand management strategies. Hydraulic simulations were conducted to assess the performance of base and conservation demand scenarios. Five metrics of network performance are suggested to evaluate responses to scenarios: water loss, water age, peak flow, energy input, and loss. The results revealed that network performance for energy and flow metrics improved under the conservation scenario; however, the conservation scenario had a negative impact on water age and losses throughout the network. The results indicate that the potential benefits from these demand profiles cannot be fully realized without adjustments in network operation and may come at a cost in terms of water quality. This work suggests an initial tool for evaluating network-wide effects of demand management strategies.

Mechanical reliability analysis of water distribution networks (WDNs) refers to the assessment to provide correct service to customers under abnormal functioning conditions due to system component failures. Mechanical reliability depends on the isolation valve system (IVS) effectiveness in isolating a network segment and on the failure probability. The main aim of this work was the formal development of reliability indicators considering pressure-driven analysis (PDA) for customer demand and topological changes. Three factors influencing reliability were identified: (1) the hydraulic behavior of the still-connected network; (2) the topological performance of the IVS; and (3) the topological performance of the IVS with respect to unintended isolation. The reliability performance assessment of the network over time depends on the hydraulic capacity of the WDN under abnormal functioning conditions and on two IVS performance topological indicators of the risk of disconnection and unintended isolations. The hydraulic capacity was computed using PDA, whereas the topological risks were computed considering the unsupplied demand in the isolated segment and the normalized failure probability, i.e., the probability of a segment disconnection for an assumed failure occurrence in the network. The development of a modularity index for an IVS was achieved in a consistent framework and the integrated design of an IVS and district metering areas was introduced. The framework allows the analysis of the isolation valve failure. A real Apulian WDN was used to discuss and demonstrate the theoretical findings.

This study details a novel procedure for analyzing water demands in the nonresidential sector (i.e., commercial, industrial, and institutional users). Nonresidential customers are classified into “subsectors” based on economic, land-use, and property appraisal data sets and analyzed using a linear mixed-effects regression modeling framework, which controls for random fluctuations around mean monthly parcel-level water demand, for a 4-year study period in Austin, Texas. Classification of nonresidential customers can improve the explanatory power of statistical models over models without any classification ($R^2=0.635$ and 0.431, respectively). Additional improvement is seen by explicitly using the economic, land-use, and property data on which subsectors are based ($R^2=0.773$), at the cost of computational expense and added model complexity. Results indicate that the subsector classification provides the best explanation of variation in monthly water usage at the parcel level, followed by conditioned floor area and number of employees. These results can improve traditional water demand forecasting techniques for the nonresidential sector and reveal subsector-specific trends that might otherwise be obscured without classification of customers.

Water distribution networks have to face the challenge of providing for a growing population with efficient management of water and energy. Pressure control in water networks is an efficient strategy to reduce leakage, which is usually carried out by installing pressure-reducing valves. Because the operation of these elements involves energy dissipation, the replacement of pressure-reducing valves by hydropower turbines has been widely analyzed. Thus, in this work a methodology focused on the detection of the optimal location and number of pumps as turbines (PATs) to maximize hydropower generation in gravity water distribution networks is proposed. The methodology considers a mathematical optimization and evaluates the real PAT performance at each node. Installation costs and payback period have been considered to determine the viability of installations. The application of this methodology to six water distribution networks showed potential hydropower locations in three, with values of daily recovered energy from 43 to 231 kWh. Although the optimization methodology developed here is not suitable for complex looped networks due to the computational time required, for branched networks it is a powerful tool offering decision makers information about hourly power, total installation costs, and payback period at each potential node in a network.

Predicting residential water demand is challenging because of two technical questions: (1) which data and variables should be used and (2) which modeling technique is most appropriate for high prediction accuracy. To address these issues, this article investigates 12 statistical techniques, including parametric models and machine learning (ML) models, to predict daily household water use. In addition, two data scenarios are adopted, one with only 6 variables, generally available to cities and water utilities (general scenario), and one with all 19 variables available from the Residential End-Use 2016 database (REU 2016 scenario). The results for the REU 2016 scenario indicate that ML models outperform linear models. In particular, gradient boosting regression (GBR) performs best with an $R_{adj}^2$ of 0.69 compared to 0.54 for linear regression. The performance gap between ML and linear models becomes even wider for the general scenario with an $R_{adj}^2$ of 0.60 for GBR compared to 0.33 for linear regression. The finding in this article can be useful to researchers, municipalities, and utilities seeking novel modeling techniques that can provide consistent modeling performance—i.e., high prediction accuracy—depending on data availability. Future work could include the development of new measures to increase the interpretability of ML models to better understand causal relationships between independent variables and daily household water use.

The paper proposes a burst detection method that relies on shape similarity analysis of time series subsequences (i.e., slices of time series). Subsequence libraries are constructed using flow (or water demand) data. Increase-rate distance is used to evaluate the shape similarity between subsequences, and abnormal subsequences are those that have low shape similarity with others. An abnormal subsequence searching algorithm first is used to remove abnormal subsequences, and the remaining subsequences are used to form reference libraries. Then the shape similarity between newly collected subsequences and reference libraries is evaluated to detect bursts. In the detection, a modified version of the abnormal subsequence searching algorithm can reduce the number of false alarms by finding the don’t-care segment in subsequences and improve the method’s detection ability by crossover between night subsequences. The method was applied to a network’s hydraulic model and three real-life district metering areas. Results show that the method’s detection performance is only slightly affected by seasonal changes of data and is insensitive to data sets from different networks.

A water distribution system (WDS) is designed and managed to provide a reliable water supply, that is, to properly respond to water user demand, particularly in critical operating conditions such as in times of peak demand. Therefore, the assessment of the influence of water demand characteristics is an essential requirement in the context of WDS reliability. In this paper the impact of the pattern of hourly demand on WDS performance is analyzed for a system subject to aging processes and temporary pipe unavailability and affected by water losses with different leakage levels. The hydraulic deficit that can occur when the pressure falls below the minimum service value is used as a performance index, and its relevance is analyzed without and with preventive maintenance. The case of the synthetic Anytown network is analyzed, but the procedure has general validity and can be applied to any real WDS. Defined in a prescribed temporal horizon the pipe replacement prioritization without preventive maintenance, the effects of pipe substitutions are analysed as a function of different scheduling times to quantify the reduction of the hydraulic deficit. The results show the capability of the proposed approach to define a pipe replacement prioritization and the related scheduling time, in view of the relevance that these aspects could have in any economic analysis developed to define a proper maintenance strategy.

The need to improve the accuracy of mathematical models describing hydraulic networks (e.g., water distribution systems) poses numerous challenges regarding the problems of parameter calibration and optimal sampling. In this paper, a comparison is made between different sensor placing strategies, including a novel, direct (iteration-free) algorithm. The approach used is based on maximizing both the sensitivity and the so-called hydraulic distance of the sampling points without the need for optimization. Based on these sampled measurements, the pipe roughness coefficient of each pipe is calibrated individually (without grouping) by solving the resulting underdetermined linear system with singular value decomposition. The efficiency of the approach is demonstrated on case studies, including artificial (e.g., Anytown and C-town) and real-life water distribution systems through detailed statistical comparison.

Most agent-based model (ABM) applications in water resources management and planning relied on hypothetical systems for method testing and policy analysis. Although these ABM studies can help provide guidance for evaluating or designing water management policies in an exploratory way, there still exists a gap in translating the numerical results from hypothetical studies into the implementation of water policies in the real world. This study aimed to fill this gap. In this paper, we developed an ABM for the water depot–based water allocation system that has emerged to distribute a large quantity of freshwater for shale oil development activities at the Bakken (North Dakota). The ABM was then calibrated against recorded annual water uses from 2007 to 2014 before it was used to evaluate water policies and to devise effective water management strategies in the Bakken region. Our analysis shows that the authorization of the Western Area Water Supply Project, implementation of the In Lieu of Irrigation program, and accelerated issuance of temporary surface water permits were the most effective water policies adopted during the recent oil boom in terms of mitigating water shortage for hydraulic fracturing and limiting permit violations. The ideal range of the proposed permit-to-use ratio in the Bakken region should be 3.0–7.0. The use of agent-based modeling for water management in the Bakken region can help other policymakers and managers develop water policies to address increased industrial water demands associated with the unconventional oil and gas development in their regions.

Across many river basins in the arid Western United States, upstream surface water reservoirs store snowmelt runoff to meet downstream water demand. A collaborative modeling research program in the Truckee River Basin iteratively convenes researchers and local water managers to (1) assess water management challenges under climate change, (2) identify strategies to adapt water management, (3) prioritize research and modeling activities, and (4) collaboratively review findings. This paper presents selected research program results that identify fixed date–based reservoir operations based on stationary climate as a barrier to adapt to warmer temperatures, earlier Sierra Nevada snowmelt runoff, and shifts in streamflow timing. Using an integrated hydrologic and operations model tailored to the river basin, researchers demonstrate that under a warmer climate, earlier peak streamflow compromises reservoir storage. Simulations that allow for earlier storage absorb streamflow timing shifts, providing measurable benefits upstream in the reservoir and downstream for diverse water-use communities. Researchers review simulation results with managers to assess the on-the-ground potential and identify additional research opportunities that meet local information needs. This paper illustrates the utility of integrating local knowledge with applied climate science research to support adaptive water management in snow-fed river basins.

Model predictive control (MPC) has shown to be an efficient technique for real-time flood control. The evaluation of the control performance is, however, typically restricted to a limited set of flood events. In this paper, the control performance is evaluated for a long-term time series of 116 years of meteorological data as well as after climate scenarios. Such an evaluation became feasible thanks to the use of a computationally efficient MPC approach based on a fast conceptual river model and an adapted genetic algorithm. The uncertainties related to the river model and the rainfall forecasts were accounted for. The influence of these uncertainties on the MPC control performance was, however, found to be limited after applying data assimilation. Comparing the proposed MPC approach to a standard programmable logic control (PLC)-based regulation shows that – despite the presence of uncertainties – MPC outperforms the PLC-based approach because it strongly reduces the incurred damage cost, the flood risk, and the frequency of flooding. This is still the case after considering the climate scenarios.

Population growth, socioeonomic development, and climate change will add pressures on limited water resources, especially in the arid Arabian Peninsula countries, where overutilization of renewable and nonrenewable aquifers takes place. Meeting future challenges requires institutions to be proactive and adapt to regional environmental transformations, political arrangements, and international norms and legal practices. Currently, in the Arabian Peninsula, there are no established groundwater-sharing agreements groundwater-sharing agreements for the transboundary aquifers. This could potentially spur tensions among competing water users. The existing unsustainable practices are caused by lack of coordination among the sharing states, as well as by lack of national regulation. The present work analyzes the regional transboundary aquifer development status and the probable consequences of the present trajectory. It reviews the structure of existing institutional arrangements for transboundary aquifers and proposes a regional cooperation mechanism that takes into account region’s environmental, political, social, and economic dimensions. A road map toward comanagement of the region’s shared groundwater is described that would promote equitable and responsible groundwater sharing. The suggested cooperation framework emphasizes hydrodiplomacy, transparency, joint development and management, sharing of information, exchange of experience, funded projects, and training programs.

This paper presents a novel methodology for the generation of demand time series at water distribution network (WDN) users. After subdividing the day into an integer number of time steps with order of magnitude of 1 h, the methodology is based on two phases. First, it generates, for each user and for each time step of the day, demand time series of the first attempt, which are consistent with the measured time series in terms of mean, standard deviation, and skewness. This is done with a beta probability distribution with tunable bounds or with a gamma distribution with shift parameter. In the refinement phase, rank cross-correlations between users and at all temporal lags are imposed on the generated demand time series through a single Copula-based re-sort. The effectiveness of the methodology is proven in two real case studies with different numbers of users—namely, the literature case study of Milford, Ohio, and a novel Italian site. The demand time series obtained from the spatial aggregation of the generated user demand time series preserves very well mean and standard deviation of the measured aggregated demand time series. The preservation of skewness and temporal cross-correlations at all lags is very satisfactory. A procedure is also presented to reconcile the generated demand time series with demand pulses generated at fine time step, thus enabling reconstruction of demand at any time step.