Journal of Water Resources Planning and Management

As society looks to provide the most effective identification of possible intrusion events, issues of sensor placement in water distribution systems are drawing increased attention. A multiple objective optimization problem with two competitive objectives is formulated herein: (1) minimize time delay, and (2) maximize sensor detection redundancy. The two objectives are evaluated, based on a prebuilt database containing the array of potential intrusion events and detection information. Pareto fronts are developed to assess impacts of increasing numbers of sensors by nondominated genetic algorithm-II (NSGA-II). Further, Pareto front performance improvement of increasing numbers of sensors is quantified by average normalized Euclidean distance to identify the point of diminishing marginal return aiming to provide rationale for estimating the number of sensors needed for a water distribution system. A case study is conducted for the City of Guelph water distribution system. It is observed that increasing the numbers of sensors results in better performance on the Pareto front. The Pareto front improvement rate indicates five sensors as the point of diminishing marginal return, which provides a basis for determining the number of sensors needed for the Guelph water distribution system.

Urban regions are complex and dynamic with many external and internal factors driving growth, movement, and composition. Forecasting regional characteristics important for long-term planning is inherently difficult for such settings, but quantitative estimates of the changes in urban environments are necessary for engineers and planners. This study examines how population growth, land use, pricing policy, and climate change affect residential water demands in the Puget Sound region. A spatially disaggregate water demand model is coupled with an advanced urban simulation model (UrbanSim) to generate demands at a detailed spatial resolution over a 30 year planning horizon. A baseline scenario is compared with output from UrbanSim for three different planning scenarios.

Stochastic dual dynamic programming (SDDP) is one of the few methods available to solve multipurpose-multireservoir operation problems in a stochastic environment. This algorithm requires that the one-stage optimization problem be a convex program so that the efficient Benders decomposition scheme can be implemented to handle the large state-space that characterizes multireservoir operation problems. When working with hydropower systems, one usually assumes that the production of hydroelectricity is dominated by the release term and not by the head (storage) term to circumvent the nonlinearity of the hydropower production function. Although this approximation is satisfactory for high head power stations for which the difference between the maximum and the minimum head is small compared to the maximum head, it may no longer be acceptable when a significant portion of the energy originates from low and/or medium head power plants. Recent developments improve the representation of the nonlinear hydropower function through a convex hull approximation of the true hydropower function. A network of hydropower plants and irrigated areas in the Nile Basin is used to illustrate the difference between the two SDDP formulations on the energy generation and the allocation decisions.

This paper discusses the development of a software-aided approach to best management practice (BMP) selection. Supported with input from a geographic information system (GIS), this software provides an algorithmic, mathematically based alternative to the inherently subjective approaches currently used to choose BMPs for management of storm-water runoff. The BMP selection software was applied to a study site located in the town of Blacksburg, Virginia. The software was utilized to identify BMP alternatives for a distributed storm-water management approach on the site. When compared with a base model with no runoff management, the traditional, detention-based management approach yielded 13.0% greater runoff rate reduction than did the distributed model. By contrast, the distributed storm-water management approach greatly outperformed the detention-based approach in removing pollutants from surface runoff. Although both runoff management strategies removed a significant portion of suspended sediment from the demonstration site’s runoff, the distributed model achieved a nearly 40% greater removal efficiency. Similarly, although both runoff management approaches remove a significant portion of total phosphorus from the demonstration site’s runoff, the distributed model achieved a 38% greater removal efficiency.

A total maximum daily load (TMDL) under Section 303(d) of the U.S. Clean Water Act is the maximum amount of pollutant that a water body can assimilate without violating applicable water quality standards (WQSs). The TMDL regulatory process includes development of limitations for estimated pollutant sources and an allowance for a margin of safety (MOS). This paper describes a methodology for determining the MOS on the basis of the classical engineering concept of a design safety margin, the difference between the expected capacity and expected load. In risk-based design, the design safety margin is selected to produce an acceptable failure risk over a design period. This principle can be readily applied to MOS determination, as illustrated here by application to phosphorus TMDLs in Massachusetts. The application includes evaluation of adopted MOS values and development of risk-based MOS values at specified levels of exceedance risk. The risk-based MOS values varied widely from the established MOS values, illustrating a type of inconsistency produced by current approaches to selecting the MOS.

Computational cost is a critical issue for large-scale water-resource optimization under uncertainty, since time-intensive Monte Carlo simulations are often required to evaluate over multiple parameter realizations. This paper presents an efficient approach for replacing most Monte Carlo simulations with surrogate models within a noisy genetic algorithm (GA). The surrogates are trained to predict the posterior expectations online on the basis of stochastic decision theory, using Monte Carlo simulation results created during the GA run. The surrogates, which in this application are neural networks, are adaptively updated to improve their prediction performance as the search progresses. A Latin hypercube sampling method is used to efficiently sample parameters for the Monte Carlo simulation, and the sampling results are archived so that the estimate of posterior expectation can be iteratively improved in an efficient manner. In addition, the GA is modified to incorporate hypothesis tests in its selection operator to account for sampling noise. The method is applied to a field-scale groundwater remediation design case study, whereas the primary source of uncertainty stems from hydraulic conductivity values in the aquifers. The results show that the method identified more reliable and cost-effective solutions with 86–90% less computational effort than the purely physically based noisy GA approach.

This paper introduces a method that assists in the evaluation of project and policy alternatives by identifying stakeholder reactions not only to the predicted economic, environmental, and social impacts but also to the distribution and balance of those impacts among all stakeholders. Gross impact (GI) and degree of impact imbalance (IB) evaluation criteria are adopted for multiobjective analyses in two water resources planning studies. In both studies, stakeholders converted various quantitative and qualitative indicators of economic, environmental, social, financial, institutional, and technical project objectives into GI and IB measures for the evaluation and ranking of alternative projects or policies. In these two studies, having both the GI and IB measures helped the stakeholders during the project formulation and screening, ranking, and consensus-building phases of the studies. Although applied to water resources planning projects, this approach to multiobjective stakeholder-driven planning or policy analyses could be applied in various other fields where stakeholder participation in decision making is involved.