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.

The use of drinking water distribution system models has been around for decades and requires good demand estimates to ensure adequate hydraulic and water quality representation. Traditional demand estimation processes are capable of estimating demands, often for highly skeletonized systems, with approximations to represent uncertainties in demand estimates and hydraulic states. This study implemented a Markov chain Monte Carlo (MCMC) algorithm to estimate hourly demand multipliers and uncertainties for a synthetic network using a previously developed clustering algorithm to reduce the number of unknowns. The MCMC approach also provided the flexibility to accommodate potential spatial correlation in demand multipliers through, for example, the use of a Markov Random Field (MRF) prior. The MCMC algorithm produced adequate representation of demand multipliers, similar to weighted least squares (WLS), and improved representation of the uncertainties relative to the approximations based on WLS results. The incorporation of the MRF prior resulted in more spatially correlated demand multipliers but did not provide any significant benefits for representing the network being studied. Increasing the number of clusters, reducing measurement uncertainty, and including additional flow measurements (rather than pressure) improved the ability to represent system-wide flows. However, increasing the number of clusters also resulted in larger uncertainties in the demand multiplier estimates as the estimation problem became more ill-conditioned. A generalized discussion associated with clustering approaches and measurement locations is included to provide a broader perspective on demand estimation.

This research article presents a model-based framework for detecting and localizing leaks in water distribution networks (WDNs). The framework uses optimization and systematic search space reduction. The method employs two stages: (1) the search space reduction (SSR) stage and (2) the leakage detection and localization stage (LDL). During SSR, the number of decision variables is reduced along with the range of possible values, while trying to preserve the optimum solution. Then, at the LDL stage, the size and area of a leak are found. The leak localization method is formulated as an optimization problem, which identifies leakage node locations and their associated emitter coefficients. This is achieved such that the differences between the simulated and field-observed values for pressure head and flow are minimized. The optimization problem is solved by using a genetic algorithm. A model that has already been calibrated at least according to threshold standards is necessary for this methodology. Two case studies are discussed in this paper including a real WDN example with artificially generated data, which investigated the limits of this method. The second case study is a real water system in the United Kingdom, where the method was implemented to detect a leak event that actually happened. The results suggest that leaks that cause a hydraulic impact larger than the sensor data error can be detected and localized with this method. The real case outcome shows that the presented method can reduce the search area for finding the leak to within 10% of the WDN (by length). The method can also contribute to more timely detection and localization of leakage hotspots, thus reducing economic and environmental impacts. The optimization model for predicting leakage hotspots can be effective despite the recognized challenges of model calibration and physical measurement limitations from the pressure and flow field tests.

Short-term hydrogeneration scheduling aims at minimizing water consumption for the next 7–15 days on an hourly basis, while satisfying the electrical load as well as many operational, regulatory, and safety requirements. In an ever-changing environment, planners need to make decisions quickly and often adapt their schedules to new conditions. They need a tool that is fast, reactive, and flexible. This paper presents a new solution approach that provides, within a few minutes of computation, near-optimal solutions to hard problems, in which one seeks to determine the number of committed generating units and turbined and spilled flows on an hourly basis for a planning horizon of 10 days. Our solution approach is based on tabu search with new neighborhoods, allowing simultaneous modifications for several plants and time periods. It can handle multiobjective problems, as well as nonlinear and nonconvex constraints. A decomposition mechanism allows parallelism and search acceleration for long horizons and large production systems. Computational experiments on real instances from Hydro-Québec show that even with the most naïve initial solution, our approach finds solutions almost as good as mixed integer linear programming solutions up to 235 times faster than conventional solvers and with less modeling limitations. Starting from a previous planning solution yields even better results.

In the face of a highly uncertain future, there is a need for water utilities to develop structured approaches for the long-term strategic design of water distribution networks (WDNs). A new conceptual framework for developing an integrative approach based on a multicriteria decision analysis (MCDA), embracing an optimization model to size flexible alternatives, is proposed. The flexible solutions are evaluated through MCDA for all the criteria (investment costs, carbon emissions, resilience, and reliability of WDNs) across all the scenarios generated for the sake of robustness and will help to adapt WDNs to changing conditions over a long planning horizon, divided into phases. The alternatives are ranked through two different MCDA methods, Preference Ranking Organization METHod for Enrichment of Evaluations (PROMETHEE) and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), so that decision makers will have more comprehensive information for analyzing highly ranked design solutions and after the first phase, solutions for the other phases can be reassessed by the same dynamic adaptive framework.

A traditional single-scenario design approach considers the most probable future scenario, which is very risky and may result in high supplementary cost or overpayment (i.e., regret cost). An alternative involves simultaneously considering multiple scenarios. This study proposes a novel two-phase multiscenario-based design approach to optimize the layout and hydraulic design (determining pipe sizes and manhole depths) of an urban sewer system. In the first phase, multiple individual scenarios are adopted to independently identify the optimal layout for each scenario and the requisite hydraulic design. The aim of the second phase is to determine robust solutions for the sewer layout and hydraulic design to minimize construction cost with a constraint on acceptable regret costs over multiple scenarios. The proposed two-phase optimization method was demonstrated with a hypothetical example of an urban drainage system. The results indicate that the proposed multiscenario optimization approach produces a robust sewer network solution that performs well and is cost-effective for different scenarios.

Cascade pumping stations (CPSs) in open-channel water transfer systems usually consume vast amounts of energy and are expensive to operate. For these CPS systems, scheduling optimization is a critical approach to save energy. In this paper, an optimized scheduling method based on station skipping is proposed, in which the use of one or more pumping stations can be reduced and the allocation of the gross head to other stations is optimized. In this method, a one-dimensional hydrodynamic model and a complex optimization model were established. The Miyun Reservoir Regulation and Storage Project in Beijing was selected for a case study. The results indicate that (1) some stations with low heads could be skipped at low or medium discharges compared with the design discharge; and (2) the optimization scheme with skipping pumping stations has a more substantial energy-saving effect than that without skipping any stations under certain conditions. This proposed method makes it possible to save energy and reduce cost at low or medium required discharges in such CPS systems.

Diarrheal diseases, notably cholera, have been shown to be related to episodic seasonal variability in river discharge, predominantly low flows, in regions where water and sanitation infrastructure are inadequate. Forecasting river discharge in transboundary international basins a few months in advance remains elusive because the necessary geophysical data are unavailable or are not shared with stakeholders. We hypothesized that river discharge in large river basins is directly related to upstream water conditions that lead to generation of high and low flows. Using the Ganges-Brahmaputra-Meghna Rivers as an example and Bayesian regressive models, we showed that terrestrial water storage (TWS) anomalies from the Gravity Recovery and Climate Experiment (GRACE) can provide reliable estimates of flows, which are essential hydroclimatic variables for predicting endemic cholera, with an overall accuracy of 70% and up to 60 days in advance, without ancillary ground-based data.