Journal of Water Resources Planning and Management

Leak detection and localization in water distribution networks (WDNs) is of great significance for water utilities. This paper proposes a leak localization method that requires hydraulic measurements and structural information of the network. It is composed by an image encoding procedure and a recursive clustering/learning approach. Image encoding is carried out using Gramian angular field (GAF) on pressure measurements to obtain images for the learning phase (for all possible leak scenarios). The recursive clustering/learning approach divides the considered region of the network into two sets of nodes using graph agglomerative clustering (GAC) and trains a deep neural network (DNN) to discern the location of each leak between the two possible clusters, using each one of them as inputs to future iterations of the process. The achieved set of DNNs is hierarchically organized to generate a classification tree. Actual measurements from a leak event occurred in a real network are used to assess the approach, comparing its performance with another state-of-the-art technique, and demonstrating the capability of the method to regulate the area of localization depending on the depth of the route through the tree.

Recovery of damaged water supply systems after severe earthquakes is one of the priorities to return to normal conditions. Water supply systems are intrinsically interdependent with other important lifelines such as transportation, energy, health care, and industrial sectors. These interdependencies need to be better understood by means of empirical data and analytical models. This paper is primarily of archival nature and describes empirical impact data of large earthquakes in Chile (Maule 2010), New Zealand (Christchurch 2010–2011), and Japan (Tohoku 2011) on their respective drinking water systems, and summarizes damage observations, emergency actions, and restoration processes within a resilience framework focused on metrics of robustness and rapidity. The archival nature of this article is justified by the paramount importance of systematic data collection to improve network resilience for future analytical models that aim to predict the response and recovery of water networks. Moreover, based on the collected data, the effectiveness of response actions and implemented countermeasures are evaluated relative to the observed earthquake performance of the system components. Important observations are derived to understand the main factors causing water supply system outages, the effectiveness of strategies used, and their capacity to restore the service.

This paper presents a new valve positioning algorithm (VPA) for water network pressure reduction. The proposed algorithm is a graph theory-based algorithm combined with EPANET2.2 to position pressure-reducing valves (PRVs) at the most effective locations, having the most effect on downstream pressure reduction, and then calculates the setpoint for locally controlled valves. Each algorithm solution positions one valve on the edge with the highest-pressure reduction indicator calculated using the depth-first search algorithm. The setpoint is then calculated according to downstream pressure differences to the minimum service pressure to be supplied. The PRV location and setpoint found by the algorithm may be used to install and set locally controlled PRVs or serve as a guide for positioning remote-controlled PRVs. The algorithm is demonstrated on a simple example network using demand-driven analysis (DDA), pressure-driven analysis (PDA), and genetic algorithms (GA) and on a medium and a large example application (DDA). Any number of PRVs may be installed using the algorithm, which returns stable results and very short solution times while maintaining minimum service pressure requirements to the critical consumers.

In this study, three different stormwater control measures (SCMs) pollutant removal models, consisting of the linear regression model, modified Fair and Geyer (MFG) model, and $\mathrm{k}\text{-}{\mathrm{C}}^{*}$ model, were simulated under both steady-state and dynamic hydraulic conditions to evaluate how applying those models to a dynamic modeling framework will affect pollutant removal outputs. Uncertainty of the SCM models was also evaluated using Monte Carlo (MC) and first-order variance estimation (FOVE) approaches. The SCM models were calibrated to data from the International Stormwater Best Management Practices (BMP) database assuming steady-state hydraulic conditions for each event, then applied to the dynamic modeling framework with variable hydraulic conditions dictated by runoff generated from the dynamic watershed model. The linear regression model generated the same pollutant removal results under both steady-state and dynamic conditions. However, both the MFG and $\mathrm{k}\text{-}{\mathrm{C}}^{*}$ models underestimated pollutant removal by 20%–90% under dynamic modeling conditions. In terms of uncertainty, the FOVE method generated prediction intervals that were smaller than the MC method, with 95th percentile outputs generally being 5%–20% lower using FOVE, compared to MC. The smaller prediction intervals generated by the FOVE method partially compensated for the lower pollutant removal generated by the MFG and $\mathrm{k}\text{-}{\mathrm{C}}^{*}$ models under dynamic modeling conditions, such that the 95th percentile outputs generated using MC and steady-state assumptions were very similar to those generated using FOVE and dynamic modeling.

Skillful mid-term temperature predictions (up to five years out) offer a potential opportunity for water managers, especially in the Colorado River Basin (CRB), where streamflows are sensitive to temperature. The purpose of this paper is to develop and demonstrate a framework for how mid-term temperature predictions can be incorporated into streamflow forecasting and operational projections. The framework consists of three steps. First, 5-year average temperature predictions are obtained from two large ensemble climate model datasets. Second, hindcasts from the Ensemble Streamflow Predictions (ESP), an operationally used forecast method in the CRB, are post-processed using the 5-year average temperature predictions; specifically, a tercile-based block bootstrap resampling approach generates weighted streamflow ensembles called WeighESP. Third, ESP and WeighESP are run through an operational model, the Colorado River Mid-term Modeling System (CRMMS). Compared to ESP, WeighESP marginally improves streamflow forecast accuracy in the multi-year hindcasts up to five years out (i.e., years 1-5, 2-5, 2-4, and 2-3). The multi-year hindcasts show median annual root mean square error (RMSE) improvements between 437,000 and $\mathrm{771,000}{\mathrm{m}}^3$ (354 and 625 thousand acre-feet). Improvements in streamflow accuracy are more pronounced for the most recent hindcast run dates through 2016, partially due to ESP being run with climate time series data from 1981 to 2010. Next, CRMMS translates the streamflow forecasts into operational projections of end of calendar year (EOCY) pool elevations. WeighESP improves the accuracy of EOCY predictions, but mainly for longer leads of 3- and 4-years. For the 4-year lead, the median RMSE improves by 1.1 and 0.7 m (3.5 and 2.3 ft) for Lakes Powell and Mead, respectively. Although marginal improvements in pool elevation could be beneficial, not being realized until longer leads is a limitation. This study describes the need for better predictive tools at the mid-term timescale and underscores the importance of evaluating improvements in streamflow forecasts in decision-relevant terms.

This study presents an approximated model for pressure transient simulations as well as a local wall shear stress analysis to control and dislodge biofilm growth in water distribution systems. It demonstrates the potential of taming hydraulic transients to manage and disrupt the growth of biofilm colonies attached to the inner walls of pipeline systems. The systems are subjected to consecutive controlled transient pressure waves by manipulating valves positioned strategically along the water distribution system. Because the controlled transient waves are generated at different locations in the system, the interference properties of the waves can be capitalized on at points where different pressure waves can come together and merge in the system, thus creating high pressures and powerful shear stresses. Nevertheless, it is vital to keep the head pressure confined within the allowed pressure range to avert extreme devastating pressures and ensure the integrity of the system. Three case study applications of increasing complexity are presented to demonstrate the potential of this approach. These transient wave simulations implemented the Lagrangian-based wave characteristic transient model, and the optimization of valve operation was governed by an evolutionary genetic algorithm. The results indicate that it is possible to trigger transient events for biofilm management purposes without exceeding the allowed critical pressure in various network layouts.

Flow–ecology relationships are critical for developing and adaptively managing environmental flows. However, uncertainty often arises from data limitations and an incomplete understanding of the spatial and temporal attributes inherent to each relationship. Accounting for sources of uncertainty is critical given the mounting interest in implementing environmental flows at large scales, often with limited information. We used the South Fork Eel River watershed in northern California as a case study to demonstrate how data gaps and uncertainty in flow–ecology relationships may be better quantified. A rigorous literature review revealed that few flow–ecology relationships related directly to the flow regime, and none spanned the full range of hydrologic or geomorphic variability exhibited across the watershed. Identified data gaps informed several sensitivity analyses within a Bayesian network model which showed that the modeled ecological outcome differed by as much as 50% depending on the type and magnitude of uncertainty. This study presents a general regional framework for quantifying spatial and temporal data gaps that can be applied to other watersheds and information types to improve representation of uncertainty in flow–ecology relationships and to inform environmental flow design.