Journal of Water Resources Planning and Management

Water quality management policies, which are proposed to prevent, control, or treat environmental problems related to quality of water, are broad and complex issues. We have various types of water resources, different water uses, and a lot of decision parameters with several levels of decision makers involved. Moreover, there are a lot of strategies and technologies available to be applied for water quality management and so environmental decision makers are required to evaluate and prioritize them in order to choose the best possible plan for each particular problem. To provide a comprehensive but easy to use tool in the assessment and evaluation of water quality policies, the concept of water quality index (WQI) has been developed. Due to the abovementioned complexities, to get this index, there is a need for a methodology to not only structure and identify information relevant to the problem but also to help users reach a decision. Designing a multiple-attribute decision support expert system, which makes expert knowledge available to nonexpert users, can do this. In doing so, we may encounter qualitative or linguistic assessments in the index making process. Thus, fuzzy set theory can be applied to recognize this inherent fuzziness of such a process. Briefly, in this study we propose a fuzzy multiple-attribute decision support expert system to compute the water quality index and to provide an outline for the prioritization of alternative plans based on the amount of improvements in WQI. At the end, applicability and usefulness of the proposed methodology is revealed by a case study.

This paper investigates the influence of different hydrological input data on the management simulation of a water resources system (WRS). Three complete simulations were carried out using synthetic inflow series generated with several stochastic models: an autoregressive moving average (ARMA) model, the Lane condensed temporal disaggregation model, and a nonlinear model based on a multilayer perceptron artificial neural network (MLP-ANN) with a random component embedded. The validation of the stochastic models was performed using comparisons of relevant drought statistics from synthetic series with those from the historical records. Since the analyzed WRS includes five inflow sites, multivariate models were applied. The MLP-ANN model showed the best performance. The management simulations of the WRS were executed with the decision support system AQUATOOL under a probabilistic approach. This approach gives probabilities of demand failures of the WRS, which were used to evaluate the influence of the three applied stochastic models on the simulation results. Significant differences were found.

The regional simulation model (RSM) is a conjunctive groundwater surface-water hydrological model under development at the South Florida Water Management District. The model is designed to allow a flexible, extensible expression of a wide variety of natural hydrologic processes, as well as anthropogenic water resource control schemes in order to facilitate alternative management scenario evaluations. The management module of the RSM is the management simulation engine (MSE). The MSE is based on a multilayer hierarchical control architecture, which naturally encompasses the local control of hydraulic structures, as well as the coordinated subregional and regional control of multiple structures. MSE emphasizes independent abstraction of hydrological state information and managerial decision algorithms, facilitating the interoperation and compatibility of diverse management algorithms. The MSE architecture is described in this paper.

In this study, the QUAL2E model is linked with a genetic algorithm (GA) in order to conduct the calibration and verification of the model. The efficacy of the optimization model was tested for different observation data quality represented by the perfect and noisy data assumptions. Four cases were studied. In the first (base) case, calibration was conducted for the reach-variable reaeration coefficient $\left(K_2\right)$ and the sediment oxygen demand rate $\left(K_4\right)$ . In the second case, the number of sampling points was increased. The third case investigated the impact of objective function formulation itself on the optimization model performance. Finally, a high number of parameters were calibrated using the weighted and unweighted objective functions. In general, it was not possible to reach the exact values of the parameters at all reaches. However, the resulting water quality profiles were satisfactory and close to the goal. The performance of the optimization model improved for better observation data quality and increased number of sampling points. Furthermore, it was observed that the objective function formulation impacted the performance of optimization, depending on the observation data quality. Results of the fourth case suggested that weighted objective function minimized the domination of water quality constituents with higher orders of magnitude in concentration values in search for the best solution.

The aim of water distribution design is to size and configure a system so that it meets existing and future demands while providing pressures above a minimum level for service and fire protection. Extended period simulation (EPS) is used in design to determine network pressures under varied diurnal demand patterns. In EPS, diurnal demands are almost invariably assumed to change in unison, or in statistical terms, to be strongly correlated in space. This paper first tests this common assumption by investigating the extent to which cross correlation in demand affects the mean and standard deviation of pressure heads in water networks, and then investigates how cross correlated demands can influence capital costs in network design. Preliminary findings from two examples indicate that the standard deviation of pressure head and capital costs can be sensitive to the level of cross correlation between nodal demands. Thus a realistic assessment of cross correlation in demand can lead to a more economical design.

Subsystem identification in water distribution systems is important for repairing pipes and containing damages. In this paper, a method is proposed to identify the smallest subsections of a network called segments that are enclosed between valves suited to isolation. A dual network representation in terms of segments as nodes and valves as links permit easy visualization of segment expansion resulting from progressive valve failures. Segment deletions often lead to the disconnection of other parts of a network from supply. A technique is presented to track these unintended isolations. The algorithms are applied to a sample network illustrating the various aspects.

Contemporary heuristic search procedures [e.g., evolutionary algorithms (EAs)] continue to offer increased capabilities for systematic search for a range of water resources and environmental management problems. These problems are often riddled, however, with numerous unquantifiable issues that are important when making decisions, but escape being incorporated in the system model. The mathematically optimal solution to such an incompletely defined model may be found unrealistic or altogether incorrect for the real problem. Optimization procedures could still be made useful if they can be utilized effectively to generate, in addition to the optimal solution, a small number of different alternatives that are near optimal. Alternatives with maximal differences in the decision variable values are expected to perform differently with respect to the unmodeled issues, providing valuable choices when making decisions. Although successful alternative generation procedures have been reported for mathematical programming-based search procedures, they are yet to be explored fully for EAs. This paper describes an extensive investigation of a new EA-based alternatives generation procedure, the evolutionary algorithm to generate alternatives (EAGA). A previously published regional wastewater treatment optimization study is used as a basis for establishing and demonstrating the capabilities of EAGA, and the set of results from the previous study is used as a benchmark for comparing the performance of EAGA. Comparisons of results indicate that EAGA is effective in generating good alternative solutions that perform differently with respect to several unmodeled issues. EAGA is sufficiently flexible to be applied to a wide range of water resources and environmental management problems. Further, EAGA can be applied to any problem that is set up to be solved using an evolutionary algorithm.

Water-rights driven surface-water allocations for irrigated agriculture can be simulated using the farm process for MODFLOW-2000. This paper describes and develops a model, which simulates routed surface-water deliveries to farms limited by streamflow, equal-appropriation allotments, or a ranked prior-appropriation system. Simulated diversions account for deliveries to all farms along a canal according to their water-rights ranking and for conveyance losses and gains. Simulated minimum streamflow requirements on diversions help guarantee supplies to senior farms located on downstream diverting canals. Prior appropriation can be applied to individual farms or to groups of farms modeled as “virtual farms” representing irrigation districts, irrigated regions in transboundary settings, or natural vegetation habitats. The integrated approach of jointly simulating canal diversions, surface-water deliveries subject to water-rights constraints, and groundwater allocations is verified on numerical experiments based on a realistic, but hypothetical, system of ranked virtual farms. Results are discussed in light of transboundary water appropriation and demonstrate the approach’s suitability for simulating effects of water-rights hierarchies represented by international treaties, interstate stream compacts, intrastate water rights, or ecological requirements.

A risk-based evaluation is performed in the meeting of future water demands in the Ganges Delta of Bangladesh (GDB). This evaluation is based on reliability, resiliency, and vulnerability performance indicators, which have been newly defined to capture the time varying characteristics of the Ganges River system. The analysis includes the impacts of climate change on both demands and resources, and the generation of synthetic flows of the Ganges River. The values of the indicators reveal that the expected demand of the GDB up to the year 2050 can be supplied with the proposed Ganges Barrage under the “no change” and “most likely” climatic scenarios, provided that the groundwater remains usable. However, if an additional upstream diversion from the transboundary rivers takes place in India and/or a “possible adverse” change in climate occurs, the consequences may be devastating.