Journal of Water Resources Planning and Management

Implementation of monitoring strategy for increasing the efficiency of groundwater pollutant source characterization is often necessary, especially when inadequate and arbitrary concentration measurement data are initially available. The research reported in this paper focuses on estimating three main parameters that are essential for efficient and accurate characterization of groundwater pollution sources, as follows: (1) location of source, (2) its starting time of release, and (3) duration of its activity. Most of the methodologies developed so far for unknown pollutant source identification have not adequately addressed the complexities involved with estimation of starting time of release and the duration of activity. Estimation of the time gap between the first observation of contamination in the aquifer at a location and the starting time of release is important for source identification. The main complexity arises due to the fact that the spatial location and the duration of activity of a pollutant source are interrelated. Therefore, explicitly specifying one and solving for the other simplifies the source characterization problem. In the research reported in this paper, both the source location and starting time of release are treated as explicit unknowns. The developed methodology uses dynamic time warping (DTW) distance as a cost function in the linked simulation–optimization model to design a monitoring network to efficiently estimate source characteristics including the starting time of release of unknown groundwater pollutant source. Performance of the developed methodology is evaluated with data obtained from a real aquifer. The evaluation results demonstrate that pollutant source characterisation based on pollutnat concentration measurements obtained from a designed monitoring network consisting of a fraction of total observation wells available compares very well with that based on all concentration information recorded at all the 74 monitoring wells over a period of 4 years. These evaluation results demonstrate the potential use of the developed methodology for efficient identification of unknown contaminant source in an aquifer.

This paper presents a decision-scaling based framework to determine whether one or more preselected planning alternatives for a multiobjective water-resources system are robust to a variety of nonstationary hydroclimatic conditions and modeling uncertainties. The decision-scaling methodology is advanced beyond previous applications with an efficient procedure to select realizations of climate variability and Bayesian methods to assess the effects of hydrologic uncertainty. Monte Carlo simulations are used to identify long-term planning alternatives that are robust despite the hydroclimatic uncertainties. A new metric is proposed to define robustness in this context. The framework is coupled with a host of long-term projections to understand the likelihood of potential future changes and provide useful guidance for planning. The effects of climate model downscaling and credibility on the decision process are discussed. The approach is demonstrated in a case study for a dual-purpose surface water reservoir in Texas. The results suggest that both internal climate variability and hydrologic uncertainty can substantially alter the assessment of system robust for long-term planning purposes.

New water resources management methodologies are needed to address increasing demands and future uncertainty for urban water resources. Adaptive water demand management strategies provide an approach to improve the efficiency of water system operations and meet water demands by adapting flexibility to increasing stresses, such as droughts. This study simulates adaptive water demand management through the development of a complex adaptive system modeling framework, which couples cellular automata modeling, agent-based modeling, and hydrologic modeling to simulate land-use change, consumer behaviors, management decisions, the rainfall-runoff process, and reservoir storage. The model is applied to simulate the effect of demand management strategies on reductions in municipal water demands and on the sustained storage in a surface water supply reservoir. Historic and projected climate change hydroclimatic time series are used to assess the effectiveness of domestic water restrictions, including outdoor watering restrictions, a rainwater harvesting rebate program, and a high-density land-use change policy. Strategies are adaptively implemented based on the amount of water storage available. The framework is applied to evaluate strategies for the Arlington, Texas, metropolitan area that historically suffers from severe droughts. The framework provides an approach to evaluate a combination of multiple strategies for effectively managing the increasing stresses caused by urbanization, population growth, and climate change. Results demonstrate that adaptive demand management strategies that respond to water shortages result in long-term per capita demand reductions. For climate projections that forecast severe water shortages, development density strategies are more effective than rainwater harvesting strategies, and a combination of strategies can reduce the need for interbasin transfers and maintain reservoir volumes.

Agent-based systems have been developed for many scientific applications and simulation studies to model a group of actors and their interactions based on behavioral rules. Agent-based models and multiagent systems simulate the emergence of system-level properties based on the actions of adaptive agents that interact with other agents, react to environmental signals, and optimize decisions to achieve individual goals. In water resources planning and management, agent-based modeling has been applied to explore, simulate, and predict the performance of infrastructure design and policy decisions as they are influenced by human decision making, behaviors, and adaptations. The goal of this paper is to provide a comprehensive introduction to agent-based modeling for water resources researchers, students, and practitioners, and to explore water resources systems as complex adaptive systems that can be studied using agent-based modeling. Agent-based modeling is defined, and the characteristics of complex adaptive systems that necessitate its use are described. A literature review is presented to demonstrate research in the field that uses agent-based modeling to gain insight for water resources management. Two illustrative case studies of agent-based water resources systems models are developed and described. The case studies demonstrate the use of reactive and active (e.g., optimizing) agents for simulating water resources planning problems. The limitations in applying agent-based modeling and the insights that are expected from further investigations are summarized.

Water distribution networks are not inert transport systems. The high-quality water produced at water treatment works is subject to a variety of complex and interacting physical, chemical, and biological interactions within these highly variable, high-surface reactors. In particular, the aging and deteriorating asset condition in water distribution systems can result in a degradation of water quality delivered to the customer, often experienced as discoloration caused by increasing amounts of fine particulate matter. Here, it is proposed that by assessing measured turbidity over time, in particular its correlation with local hydraulics, an assessment of change in risk of fouling can be obtained and asset deterioration inferred. This paper presents a methodology for pairwise monitoring of a hydraulic parameter (flow or pressure) and turbidity using wavelet-based semblance analysis—a novel methodology from another domain, which is applied for the first time to water quality data in distribution systems. It is suggested and subsequently explored through case studies that an increasing (anti) correlation of the turbidity with the (pressure) flow diurnal cycle will be indicative of increasing fouling risk. This can be further supported through evaluation of the rate and magnitude of drift and through assessment of the change in magnitude of the daily turbidity profile. The composite of these approaches is applied to an extensive data set from a United Kingdom distribution system revealing the effectiveness of the analysis preflushing and postflushing (reducing discoloration events by 64–89%). With increasing proliferation of monitoring devices and real-time data acquisition the potential for online systems and well-informed proactive management is apparent.

In this study, an inexact multistage fuzzy-stochastic programming (IMFSP) model was developed for water resources management under uncertainty. In the IMFSP model, the uncertainties existing in the objective functions, and the constraints’ left side and right side are expressed as fuzzy sets so that the uncertainties can be directly communicated into the optimization process and various water allocation schemes can be obtained under different $\alpha$-cut levels at a plurality of periods regardless of the shapes of their membership functions. The IMFSP model can not only generate the most possible, lower and upper bound values for all possible decision schemes but it can also provide information about the degrees of membership of the decision variables between the lower and upper bound values. Meanwhile, the scenario-based multistage stochastic programming was incorporated in the IMFSP model to adequately reflect the dynamic variations and uncertain information of the water resources system. Therefore, the IMFSP model offers significant improvements over existing multistage stochastic programming models for water resources management with respect to the capacity of tackling various uncertainties and their interrelationships. Additionally, a simple hypothetical case for water resources management was provided for demonstrating applicability of the IMFSP model to support decision making within multiple scenarios during many time periods under uncertainty. In general, the proposed IMFSP has only been demonstrated and tested on a hypothetical example. Future research can be directed toward expanding/testing it on more complex real-world problems.

The study aims to propose a simulation-optimization model for deciding the optimal combination of detention ponds, which comprises a two-dimensional physiographic drainage-inundation model and a nondominated sorting genetic algorithm II (NSGA-II). Dian-Bao River Basin in southern Taiwan is chosen as the study area. Five detention-pond candidates with different sizes and locations are adopted for optimizing their combination. One-day design rainfalls for different return periods (i.e., 2, 5, 10, and 25 years) are used as the model input. During the optimization process, two conflicting objectives (i.e., the investment cost and the inundation-damage cost) are minimized to obtain the Pareto-optimal solutions by using the NSGA-II. Based on the posterior approach, the compromise solutions for different return periods are obtained. A cost-benefit analysis is further used to evaluate the compromise solutions for different return periods. The optimal combination of detention ponds for the return period (e.g., 25 years in the study case) with the highest value of direct benefit-cost ratio can be suggested for decision making.

A novel tool is proposed that couples the nondominated sorting genetic algorithm (NSGAII) with support vector regression (SVR) and nonlinear programming (NLP) to optimize monthly operation rules for hydropower generation. The SVR-NSGAII is applied to calculate the optimized release for hydropower generation by minimizing (1) the error committed by the SVR in extracting the optimized operation rule, and (2) the number of input variables used as predictors (the parsimony feature) in a regression model. The SVR calculates the optimized reservoir release for hydropower generation based on input variables and parameters values that are found by the NSGAII. An evaluation of results obtained for the Karoon-4 reservoir of Iran indicates that the SVR-NSGAII is well suited to calculate the optimal hydropower reservoir operation rule in real time with approximately 90% accuracy.

Multiobjective genetic programming is used to calculate optimal reservoir-operating rules under baseline and climatic-change conditions. The rules are calculated based on river inflows to the Aidoghmoush Reservoir (located in East Azerbaijan, Iran), storage volume, and downstream irrigation demands. The objective functions are the maximization of the reliability of meeting irrigation demand and the minimization of the vulnerability to irrigation deficits in a baseline period (1987–2000) and a future period (2026–2039), the latter influenced by climatic change. The optimization results show that reservoir-operating rules that take into account changing climate would lead to improvements in reservoir performance on the order of 29–32% relative to operating rules based on baseline climatic conditions.

The aim of this paper is to analyze the effects of considering the mutual dependence of the pulse duration and intensity inside water demand generation models that operate with a high temporal resolution, i.e., 1 s time step, and at the scale of the individual user. To this end, a Poisson model was developed and applied to a literature case study. The Poisson model was able to represent the two variables or their respective logarithms as dependent variables following a bivariate normal distribution. The results of the new model were analyzed in comparison with the results of a model, in which the pulse intensity and the logarithm of the duration were represented as independent random variables. The analysis showed that taking into account the mutual dependence of the variables leads to improvements, and thus it is recommended. In fact, when it is taken into account, more consistent synthetic water demand pulses can be obtained, which are in better agreement with those measured in terms of overall daily demand volume.

Descriptive energy metrics, calculated for each component, represent how the hydraulic state of a network evolves and how energy flows vary temporally and spatially. More specifically, these metrics describe how the energy supplied is partitioned between the energy that is dissipated, lost, and delivered throughout the system. The metrics are meant to support planning, from local (e.g., pump or pipe renewal) to system-wide (e.g., leakage or pressure management) decisions. Whereas aggregate results are indicators of system capacity, efficiency, greenhouse gas emissions, and costs, the comparison of component metrics allows for the identification of specific pipes, tanks, or pumps for which changes would be most beneficial. Furthermore, analysis of the temporal variation of energy flows facilitates the assessment of operation under multiple scenarios. The metrics are applied to a case study of the Toronto water distribution system and show, based on two scenarios provided by Toronto Water, that on average, less than 27% of the energy supplied is actually delivered to users. This system inefficiency has important economic and environmental repercussions. Nevertheless, changes to operations, such as improved pump maintenance or scheduling, have significant potential to lower costs and exploit lower greenhouse gas emission factors.