Journal of Water Resources Planning and Management

The localization of sources of pressure transients is essential for proactively managing and reducing the adverse effects of these transients in water supply networks (WSNs). This paper addresses the issue of localizing transient sources in a WSN where there is uncertainty about the network connectivity. If closed valves or blockages in the pipes are not accounted for, it can result in inaccurate knowledge of the network connectivity, leading to incorrect estimations about the sources of pressure transients. The problem is challenging due to the added uncertainties in estimating the velocity of pressure waves and determining their arrival times at pressure monitoring sites. In order to systematically investigate this problem, the paper presents a novel theoretical framework for localizing the source of pressure transients in WSNs with uncertain connectivity. The problem is formulated as a mixed-integer quadratic program, which consists of minimizing the difference between analytical and measured pressure wave arrival times for multiple pairs of time-synchronized sensors. Unlike previous approaches, the k-shortest path (with respect to time) routing problem is incorporated into the problem formulation to account for multiple potential wave propagation paths. The optimization problem is then solved using an off-the-shelf solver, and a methodology is developed to ensure the reliability of the optimization approach. We apply the proposed methodology to numerically simulated pressure transient data for a benchmark WSN and compare it against a previously published method. The results show a notable improvement in accurately localizing the source of a transient in the presence of unknown closed valves or pipe blockages. Provided pressure wave speeds are predetermined, the proposed methodology is able to simultaneously localize the source of a pressure transient and validate the assumed hydraulic connectivity of a WSN. In this way, any irregularities or uncertainties in network connectivity can also be periodically detected and validated.

Water supply systems (WSS) transport water from the source to the population, requiring a significant amount of energy, particularly for hydraulic pumps. To improve the efficiency of WSS, the use of hydraulic models and optimization algorithms can provide valuable insights for their operation and management. However, the efficient combination of these tools requires an effective and reliable sensitivity analysis. This work presents a novel and precise calculation of the sensitivities for the pump scheduling problem using a real continuous formulation and the extended period simulation (EPS) hydraulic analysis, as used in the generality of hydraulic simulators, such as EPANET. The work was validated through comparison with the finite-difference method (FDM) in two numerical case studies: a simple pump-tank network and the AnyTown Modified benchmark. For the last case study, an optimization process was carried out comparing the results obtained by the FDM and the analytical approach previously validated. The analytical sensitivities were found to be more accurate and require lower computational resources than the alternative approaches.

Dynamic planning of water infrastructure requires identifying signals for adaptation, including measures of system performance linked to vulnerabilities. However, it remains a challenge to detect projected changes in performance outside the envelope of natural variability, and to identify whether such detections can be attributed to one or more uncertain drivers. This study investigated these questions using a combination of ensemble simulation, nonparametric tests, and variance decomposition, which were demonstrated for a case study of the Sacramento–San Joaquin River Basin, California. We trained a logistic regression classifier to predict future detections given observed trends in performance over time. The scenario ensemble includes coupled climate and land-use change through the end of the century, evaluated using a multireservoir simulation model to determine changes in water supply reliability and flooding metrics relative to the historical period (1951–2000). The results show that the reliability metric is far more likely to exhibit a significant change within the century, with the most severe scenarios tending to be detected earlier, reflecting long-term trends. Changes in flooding often are not detected due to natural variability despite severe events in some scenarios. We found that the variance in detection times is attributable largely to the choice of climate model, and also to the emissions scenario and its interaction with the choice of climate model. Finally, in the prediction model for both cases, reliability and flooding, the model learns to associate more-recent observations of system performance with nonstationarity detection. These findings underscore the importance of differentiating between long-term change and natural variability in identifying signals for adaptation.

There is an increasing need for skillful runoff season (i.e., spring) streamflow forecasts that extend beyond a 12-month lead time for water resources management, especially under multiyear droughts and particularly in basins with highly variable streamflow, large storage capacity, proclivity to droughts, and many competing water users such as in the Colorado River Basin (CRB). Ensemble streamflow prediction (ESP) is a probabilistic prediction method widely used in hydrology, including at the National Oceanic and Atmospheric Administration (NOAA) Colorado Basin River Forecasting Center (CBRFC) to forecast flows that the Bureau of Reclamation uses in their water resources operational decision models. However, it tends toward climatology at 5-month and longer lead times, causing decreased skill, particularly in forecasts critical for management decisions. We developed a modeling approach for seasonal streamflow forecasts using a machine learning technique, random forest (RF), for runoff season flows (April 1–July 31 total) at the important gauge of Lees Ferry, Arizona, on the CRB. The model predictors include antecedent basin conditions, large-scale climate teleconnections, climate model projections of temperature and precipitation, and the mean ESP forecast from CBRFC. The RF model is fitted and validated separately for lead times spanning 0 to 18 months over the period 1983–2017. The performance of the RF model forecasts and CBRFC ESP forecasts are separately assessed against observed streamflows in a cross validation mode. Forecast performance was evaluated using metrics including relative bias, root mean square error, ranked probability skill score, and reliability. Measured by ranked probability skill score, RF outperforms a climatological benchmark at all lead times and outperforms CBRFC’s ESP hindcasts for lead times spanning 6 to 18 months. For the 6- to 18-month lead times, the RF ensemble median had a root mean square error that was between $\sim 410$- and $\sim 620\text{-}\text{thousand}$ acre-feet lower than that of the ESP ensemble median (i.e., RF reduced ensemble median RMSE by $-9\%$ to $-12\%$ relative to ESP). Reliability was comparable between RF and ESP. More skillful long-lead cross-validated forecasts using machine learning methods show promise for their use in real time forecasts and better informed and efficient water resources management; however, further testing in various decision models is needed to examine RF forecasts’ downstream impacts on key water resources metrics like robustness, reliability, and vulnerability.

Water distribution networks (WDNs) are essential infrastructure systems, providing vital drinking and process water for urban health and economic vitality. The unfolding choices of how and when to maintain these aging networks reflect the values (priorities) invoked by decision makers, and, in turn, these choices determine the value of the service delivered, ranging from adequate water delivery to its alignment with broader environmental, social, and economic objectives. However, the extent to which asset management strategies acknowledge and incorporate diverse values through their priorities, assumptions, and objectives, remains limited both in practice and in modeling research. This paper proposes a computational model to evaluate the performance of long-term pipe maintenance strategies through Monte Carlo simulations of sequential pipe maintenance activities, capturing the probabilistic nature of pipe failure and its impacts across multidimensional performance metrics. Specifically, the model explores how operator priorities affect the choice of different repair and replacement strategies, and the effects of these value profiles across multiple dimensions including both system-level performance and the perception of different stakeholders such as users and service regulators. An illustrative example of a theoretical distribution network shows how valuing pipe replacement over short-term repair can both reduce pipe failure risk and lead to notable improvements in service, environmental, and monetary outcomes. Through an exploration of stakeholder value perception, this study shows that there is potential for alignment between societal objectives such as water losses and energy use, and between global service outcomes and direct maintenance costs for network operators. This study provides a novel exploration of the relationship between value (i.e., preference, priorities, and objectives) across stakeholders, and proposes methodological improvements in pipe maintenance modeling to better reflect the uncertain nature of WDN operation and a generalized and granular approach to pipe maintenance modeling.

Pressure pipe failure is a common problem for water utilities worldwide and can result in high costs and disruption of customers. This study analyzed the failure rates of iron, polyvinyl chloride (PVC), asbestos cement (AC), and polyethylene (PE) mains in the water network of the Auckland, New Zealand, over a six-year period. Correlations between failure rates and a range of contributing factors were studied, and multilinear regression and machine learning (random forest, gradient boosted decision tree) models were then used to predict pipe failure rates and prioritize pipes for replacement. The study found the most important factors influencing failure rates to be diameter, age, and modeled pressure for all materials. The failure rates of all materials increased within a relatively narrow band up to a 30 year age. For pressure, failure rates were observed to increase linearly with pressure for iron and AC, while plastics (PVC and PE) displayed nonlinear trends with pressure having a greater relative impact at higher values. The pressure trends were observed for each material when considering all pipes, but also when grouping pipes by diameter or age. A focused investigation using scatterer interferometry data captured by the SENTINEL-1 satellite did not find any correlation between pipe failures and any identified ground movements from adjacent reflective surfaces. The gradient boosted decision tree model was able to include a high fraction of failing pipes in a prioritized pipe replacement list limited to 1% of the system length.

Modeling the pipe failure phenomenon is essential for any water utility. The linear extension of the Yule process (LEYP), proposed in 2009, was intended to be a synthesis of the pipe failure models available at the time. Since then, two major improvements have been proposed. First, in 2012 and 2014, several papers introduced time-dependent factors into the LEYP model. Second, in 2016, the LEYP with Selective Survival (LEYP2s) was proposed to deal with the selective survival bias. The objectives of this paper were (1) from a theoretical point of view, to show how temporal covariates also can be introduced into the LEYP2s model; and (2) from a practical point of view, to prove the feasibility and interest of a LEYP2s model with time-dependent variables, using a case study. The mathematical consequences of taking into account temporal factors within the LEYP2s model are presented, together with the solutions found that allow the calculation of the model likelihood and thus the estimation of the model parameters. These solutions were applied to the black polyethylene service line network of the city of Bordeaux, France. Three time-dependent covariates were considered: air temperature, pressure regime, and type of disinfectant. This case study proved the feasibility of a LEYP2s model with temporal variables. The results showed that the time-dependent factors considered did not improve the ranking of the service lines, but did improve the annual prediction of the network failure rate.

In many rural communities across the world, a systemic lack of environmental monitoring and technical resources can pose significant barriers in the understanding and management of the local food–energy–water (FEW) systems. Further, nexus features and solutions in rural communities can be extremely place based and require a robust engagement with community stakeholders whose local knowledge is critical to understanding and framing of appropriate decision problems. In this case study, a participatory framing approach was used to coproduce a conceptual model to adaptively manage water systems in a hyperlocalized FEW nexus in the rural village E. Palaguttapalli Gram Panchayat in Andhra Pradesh in India. We collected qualitative data regarding the perceptions and experiences of farming in the village to identify key stressors of interest, socioeconomic conditions, and natural drivers of the FEW system and individual and collective actions to manage these stressors. Results indicate that key stressors of both groundwater depletion and dwindling labor supply were felt throughout the village. However, sociocultural differences between the hamlets influenced the preferred individual and collective FEW management actions to manage these stressors, particularly for scarce water resources. The codeveloped conceptual model of the FEW system reflects the unique natural and socioeconomic conditions, as well as preferred actions to manage key stressors of interest in this village. The proposed approach to frame FEW nexus management problems serves as a useful guide for engaging communities in decision-making involving similar data-sparse and hyperlocalized FEW nexus ecosystems.

Abstract

The focus of this study is the 1938 Rio Grande Compact water supply. The compact is an interstate agreement signed in 1938 between the states of Colorado, New Mexico, and Texas to evenly apportion the waters of the Rio Grande Basin. A shortage in deliveries is at the center of a lawsuit that began in 2011, went to the Supreme Court in 2013, and has not been resolved. This research statistically characterized the differences between water available and water delivered by Colorado and New Mexico, signatories of the Rio Grande Compact with Texas, to understand changes over time and uncover potential autocorrelations. Unexpected changes in water deliveries can have serious financial impacts on communities, thus the importance of shedding light into the behavior of the differences between water available and delivered. One of the findings of the research was that the annual credit/debit time series for Colorado was significantly correlated with the current and immediately previous scheduled delivery as well as with itself in the two previous years, and the behavior is explained by a transfer function model (TFM). For New Mexico, the annual credit/debit series did not display a clear pattern; therefore, it was not possible to characterize its relationship with scheduled delivery with the method examined. This technical note contributes to the extensive body of literature of the Rio Grande Compact in a way no other study has done as well as contributing to the wealth of literature related to the use of the transfer function model in hydrology. The main contribution of the study is the methodology developed and used, which could be useful to other river systems and transboundary water sharing agreements.

Practical Applications

This research statistically characterized the differences between water available and water delivered, also known as credits and debits, by Colorado and New Mexico, signatories of the Rio Grande Compact with Texas, to understand changes over time and uncover potential autocorrelations. A legal conflict between the signatories of the compact has lasted over a decade. Texas alleges that New Mexico is extracting groundwater, shortening their share of the water. Unexpected changes in water deliveries can have serious financial impacts on communities so shedding light on the differences between water available and water delivered can contribute to the debate. One of the research findings was that the annual credit/debit time series for Colorado was significantly correlated with the current and immediately previous scheduled delivery as well as with itself in the two previous years, and the behavior is explained by a TFM. For New Mexico, the annual credit/debit series did not display a clear pattern; therefore, it was not possible to characterize its relationship with scheduled delivery with the method examined. The main contribution of the study is the methodology developed and used, specifically the TFM, which could be tweaked to fit other river systems and transboundary water sharing agreements.