Journal of Water Resources Planning and Management

Societies are facing major challenges in allocating water resources to growing water demands due to population growth and industrial and agricultural developments. With increasing water scarcity, the need to increase agricultural water productivity is receiving significant attention in developing countries. Among alternative options for meeting increasing water demand, improving productivity has received considerable attention. Therefore, planning of water systems to face future development conditions needs further studies on land, water use, and resources as well as consideration of objectives to maximize crop production to achieve the maximum net return. Because climate change is likely to have impact on the hydrological cycle and consequently on the available water resources and agricultural water demand, there are concerns about the effects of climate change on agricultural productivity. Considering climate change impacts, in order to optimize agricultural productivity, practical frameworks and models based on comprehensive researches and studies need to be developed. The goal of this study is to develop an agricultural planning model in order to optimize the cultivated area, crop pattern, and irrigation efficiency based on the estimations of crop water requirements, water availability, and water allocation to irrigation. For this purpose, the Aharchay watershed located in the northwestern part of Iran is selected as the case study. For crop water requirement estimation, the output of an atmosphere-ocean general circulation model statistically downscaled and used as inputs to a computer model. Then water supply resources are assessed using a water allocation planning tool. The results of this study show the significance of integration and using different tools and methods in assessing and allocating water resource in regions with limited or scarce water resources.

In this paper, a recently developed stochastic programming technique that includes reliability constraints is used to solve the operations optimization problem of the Parambikulam-Aliyar project (PAP), a multireservoir system in India. The use of reliability constraints as chance constraints in reservoir operations optimization have been around for some time, but is still a challenging problem because either the results are not good or they cannot be applied in problems with more than one or two reservoirs when such techniques depend on discretization. The new implementation of chance constraints based on a previous model extended to multireservoir systems provides better results than so far known. This work is easy to apply because it requires only a standard nonlinear programming solver.

Communities depend on increasingly complex critical infrastructures to meet residents’ daily needs and to facilitate emergency response. The potential for cascading failures could intensify the consequences of disasters and complicate the process of planning for effective emergency response. This article presents a methodology for conducting a vulnerability analysis based on simulation of water distribution’s role in emergency response under various complex-disaster scenarios in which incapacity of the water system is coupled with an urban fire. The analysis reported here was run on a virtual (but detailed and realistic) small-town water distribution system to permit candid discussion of the insights derived from the vulnerability analysis and to avoid disclosing the vulnerabilities of a real system. The analysis methodology presented may be adapted to fit the needs of real communities. The results of these simulations are used to demonstrate a procedure for designing and testing mitigation methods to improve the community’s robustness and resiliency. Most of the mitigation techniques involve infrastructure upgrades, and a cost-benefit analysis of these approaches is included as an aid to decision making. Discussion of how utility managers and emergency planners may adapt the analysis technique to their own systems is included throughout.

This work presents a postoptimization methodology for refining the solutions found by adaptive search algorithms used in the design of large water distribution networks. The approach uses two heuristics to search for an optimal combination of pipes that, after a reduction of their diameters, will maximize cost savings while continuing to meet design constraints. Adaptive search methods are often used to design urban water distribution networks when the number of pipes in the network is insignificant. For complex, real-world networks, however, such methods are computationally demanding, and they have difficulty finding near-global optima. To identify a solution as close to the global optimum (and in which no pipe can be reduced without violating pressure constraint), requires a high-speed computer potentially running for a long time and also probably some good fortune. The postoptimization approach presented in this paper is shown to be an efficient complement to heuristic search algorithms used in the design of real-world networks. In a network created with the aid of a genetic algorithm, the proposed heuristics found that 4.37% of the pipes with a diameter greater than the minimum could be further reduced without causing hydraulic failure.

Utilities are increasingly considering the use of temporary water transfers to augment their supplies during periods of drought, an alternative that is often less expensive than expanding safe yields through new infrastructure. Understanding the volume and timing of transfers is important for developing contracts between buyer and seller and can be challenging due to the transient nature of drought, a situation complicated by the uncertainties associated with climate change. While transfer arrangements have received some attention in the literature, the effects of climate change on such agreements remain unexplored. This paper investigates these impacts using an improved method for developing new hydro-climate scenarios. A technique for producing stochastic time series of inflows is described, one which effectively replicates the autocorrelation present in the historic record. Unlike autoregressive (and similar) models that assume complete stationarity, the modified fractional Gaussian noise (mFGN) method preserves the seasonal patterns in the correlation structure, thereby providing some advantages when modifying historical streamflow records to reflect alternative hydro-climate scenarios. Alternative scenarios are developed for the Research Triangle region of North Carolina, an area with several utilities currently seeking to use a system of risk-based transfer agreements as a means to meet demand during droughts. This study simulates hydrologic conditions and transfer activity from 2010 to 2025 under a variety of climate scenarios. Results indicate that increased variability in inflows, with no change in the mean, corresponds to slight increases in transfer activity. However, when increased variability is paired with modest decreases in expected inflows (7%), transfer activity is doubled.

Hydropower plays an important role in electric power systems. It not only interacts with many non-hydropower generation activities but also competes with other water users (industrial, commercial, residential, and agricultural) for limited water resources. Therefore, the objective of this study is to investigate an optimized water allocation scheme within a water resource and electric power management system through developing an inexact water resource and electric power systems planning model (WPEM). WPEM is based on the interval-parameter programming and mixed-integer programming techniques; thus, it can deal with dynamics of capacity expansion and uncertainties associated with system management. The developed method is then applied to a power system with water shortage issues in the future. The results of scenario analysis indicate that WPEM could help get insights into the tradeoff between system benefit and water utilization as well as that between system benefit and environmental concern. Thus, the modeling solutions could be used by water managers for supporting the effective allocation of water among power production, industrial process, agricultural irrigation, commercial activity, and residential use in case of water shortage.

This work addresses the issue of recovering the release history of a pollutant injection in water distribution systems through the application of geostatistical tools. Geostatistical tools are applied to the inverse problem of recovering the release history of a pollutant injected into a water distribution pipeline network in this paper. This application assesses the uncertainty in the concentration measurements and the release function by considering them as random variables characterized by their statistical moments. Performance of the geostatistical application to steady and unsteady flow conditions hinges on numerical modelling of the hydraulic behavior of the water distribution system and evaluation of a transfer function that characterizes the probability of contaminant migration at control points in the network. A sensitivity analysis is carried out and it proves that the methodology is stable with respect to measurement errors and uncertainty in the network description (roughness coefficients and water demands). The analysis of error terms, the relative error of the released contaminant mass and the root-mean square error of the recovered release function, demonstrates that the geostatistical methodology is able to characterize the release history of a contaminant in a water distribution pipeline system.

Bayesian belief networks are graphical probabilistic analysis tools for representing and analyzing problems involving uncertainty. The problem of monitoring the propagation of a contaminant in a water distribution system can be represented by using Bayesian networks (BNs). The presented methodology proposes the use of BN statistics to estimate the likelihood of the injection location of a contaminant and its propagation in the system. A clustering method, previously developed by the authors, is first applied to formulate a simplified representation of the distribution system based on nodal connectivity properties. Given evidence from clusters, information is combined through probabilistic inference using BNs to find the most likely source of contamination and its propagation in the network. The conditional independence assumptions with the BNs allow efficient calculation of the joint probabilities and diagnostic and predictive queries (e.g., the most likely event given evidence or the probability of an outcome given starting conditions). In addition, a theoretic information measure is suggested to evaluate the significance of the clusters relying on the BN model of the system and possible optimal sensor locations. The proposed methodology is developed and tested on two water supply systems.

A new method is presented for the steady-state analysis of water supply networks. This method is based on the same fundamental concepts as the gradient method, with the difference that for each element in the network, two approximations are made for the energy equation, one disregarding the head variation in the upstream node and the other doing the same for the downstream node. These equations, together with node continuity equations, provide an explicit method for determining the desired heads and flows without the need for the solution of a system of equations, as is commonly used for this type of analysis. Because the elements (e.g., pipes, valves, and pumps) present the same type of treatment, the new method facilitates parallel processing. An illustrative example is presented for comparing the results obtained using the gradient method (implemented in EPANET) and the proposed new method.

Streams and their associated biological communities are among our most valuable natural resources. Humans rely on the environmental services provided by streams in a myriad of ways. However, in some areas, excessive groundwater pumping exacerbates the already critical pressure on streamflow and must be managed through effective planning. Based on economic and hydrogeological concepts, this study estimates the quantity of streamflow depletion that is attributable to groundwater pumping and the negative impact on the socioeconomic system if groundwater pumping must be constrained to restore streamflow. The primary objective of this paper is to develop a multiobjective nonlinear optimization model to simulate the tradeoffs between streamflow restoration and economic welfare loss in a Chicago suburban county, McHenry County. The multiobjective optimization was conducted at both county and municipality levels. An evolutionary algorithm, the nondominated sorting genetic algorithm, was used to solve the optimization model and to identify the tradeoff curve (Pareto frontier). Comparing municipal Pareto frontiers shows spatially heterogeneous costs of preserving streamflow through various shadow prices and also the different capacities of restoring streamflow. The results include discussion of the shapes of the Pareto frontier, the sensitivity of the pumping boundary constraints, and return flow coefficients. It is concluded that the multiobjective optimization model provides a useful framework to consider conflicting objectives in a typical environmental management and planning process, and that the findings can help decision-makers and planners to formulate effective groundwater pumping strategies.

The ability to monitor the flow and how water transforms throughout water networks would greatly improve the management of those distribution systems. The sensor placement problem attempts to find the locations of monitoring devices that would optimally observe water quality and protect consumers from accidents and intrusions of contaminants. In some related critical scenarios, the absence of information about possible contamination events, including knowledge of the injection sources, contaminant types or mass, time of pollution, and the variability of water network input parameters, such as the nodal demands and the pipe roughness coefficients, raises several challenges that must be addressed by the sensor network design process. In this paper, the authors describe a stochastic parameterization and analysis of uncertainty for the design of sensor networks aimed at maximizing the probability of detection of accidents and intrusions in water distribution networks. A challenge with such an approach, in particular, when applied to large urban water networks is the size of the ensuing computational model and the associated numerical optimization problem. This problem is compounded in the presence of uncertainty, where the need to deal with a large number of statistically plausible scenarios underscores the need for efficient yet credible algorithms for addressing the sensor placement issue. In addition to modeling aspects, the authors also address a challenge associated with the computational feasibility and performance of relevant numerical algorithms. Specifically, and through the use of submodular cost functions, the authors are able to solve the optimization problem with a greedy algorithm, yielding sufficient computational performance that allows for the description of stochastic water demands in the context of Monte Carlo simulations. Imperfect sensors are further accounted for while maintaining submodularity. The results show that the optimal sensor network layout is highly dependent on whether uncertainty in the stochastic parameters has been introduced and whether individual sensors can fail or not.

One attractive possibility for saving energy in water distribution systems is the introduction of variable speed pumps (VSPs). However, to assess the cost effectiveness of using VSPs, a correct estimate of VSP energy consumption, and therefore efficiency, is essential. This task involves estimating the efficiency of various components: pump, motor, and variable speed drive. Hydraulic solvers, which are used to check the hydraulics of the system, usually use the affinity laws to describe the pump behavior in a VSP pumping system. This paper demonstrates the inaccuracy of the popular hydraulic solver EPANET 2, which does not properly take into account the affinity laws in the efficiency computation when the speed changes for VSP operations. The correction of the code is presented and an additional improvement of the toolkit to directly retrieve the efficiency is introduced.