Journal of Water Resources Planning and Management

The graph model for conflict resolution (GMCR), along with its associated decision support system GMCR II, are employed for systematically studying the strategic aspects of conflict existing among countries in the Aral Sea Basin of Central Asia. Competition for water is increasing in the central Asian countries in the Aral Sea Basin since they became independent in 1991. Disputes have developed between the three downstream countries, Kazakhstan, Turkmenistan, and Uzbekistan, and the two upstream countries, Kyrgyzstan and Tajikistan, in the basin in sharing water in its two main rivers, Syr Darya and Amu Darya. The downstream countries are heavy consumers of water for growing cotton while the upstream countries want to use more water for electricity generation and farming. Accordingly, GMCR II is utilized as a formal mechanism for better understanding the complex conflict existing at present among countries along the Syr Darya and for providing insights as to how these countries may interact with one another in attempting to reach a resolution acceptable to all. In particular, strictly following the present water allocation agreement by all parties is found to be the most preferred resolution for both the upstream and downstream countries.

Developed countries such as the United States have proposed planning frameworks for managing the impacts of drought. This paper describes a process that is a modification to these frameworks for application to developing countries in Africa. A case study for the Niger River in Mali, Africa, is used to illustrate the process. A spreadsheet based multicriterion decision analysis model is presented. The model is used to analyze the sensitivity of management alternative rankings to the type of planning, that is, strategic, tactical, and emergency. It is also used to analyze the sensitivity of the alternative rankings to various stages of a drought and to priorities of different decision makers. The results show the importance of using an approach that integrates the tactical and emergency management with the strategic objectives of drought impacts management.

A new daily time series model for East Doncaster, Melbourne, Australia, is being evaluated. The model depends on the postulate that total water use is made up of base use and seasonal use, where base use is characterized by the water use during winter months and seasonal use on seasonal, climatic, and persistence components. Using the daily data collected by Yarra Valley Water for East Doncaster water supply distribution zone and the corresponding rainfall and temperature data from the Bureau of Meteorology from 1990 to 2000, the base values were calculated based on the lowest months of water usage in a year and were correlated with the day of the week and temperature and rainfall. Results revealed these three factors to be statistically significant and therefore, base use to be climate dependent. The seasonal water use is modeled by a series of three equations. The separation of the random component from the climatic variable resulted to a better $R^2$ of 86%. The model is further validated using different set of data from 2000 to 2001 yielding a $R^2$ of 86%.

Discrete sequential stochastic programming (DSSP) is coupled with the MODSIM basin-wide hydrologic model to estimate the foregone agricultural value associated with water transfers aimed at reducing water shortages for threatened and endangered species habitat. The value of irrigation water in agriculture is estimated for five agriculturally distinct regions of the Platte River Basin. Irrigation water in the upper-most region of the basin, farthest from the critical habitat, was estimated to be of lowest value in agricultural production. Results indicate that even though water transfers originating farther upstream result in less water yielded at the habitat, they appear to be more cost effective.

A methodology is developed based on the solution of optimization models for optimal design of groundwater quality monitoring networks. The optimal monitoring network is time varying as the monitoring wells are installed in stages, considering the transient pollutant transport process. The monitoring locations specified as solution to the optimization model change with time. This ensures additional economy in the installation of the network, compared to a single stage network design. The optimization model incorporates uncertainties in prediction or estimation of some of the aquifer parameters such as hydraulic conductivity and dispersivity. Advective, dispersive, and radioactive decay processes of transport in a two-dimensional groundwater system are considered. Randomly generated aquifer parameter values are used to simulate different realizations of resulting pollutant plumes incorporating uncertainties in predicting the transport process. The simulated pollutant plume realizations are subsequently utilized to obtain cumulative distribution functions (CDFs) of actual concentrations at different spatiotemporal locations. These CDFs are incorporated as an approximated distribution function in the optimization model. These CDFs are used to define chance constraints with associated reliabilities. Both single and multiple objective nonlinear optimization models are developed. Performances of these models are evaluated for different specified values of reliabilities using illustrative problems considering tritium $\left({\mathrm{H}}^3\right)$ as the radioactive pollutant. This monitoring network design model is, in general, applicable to other types of pollutants as well.

Water managers in the western United States and throughout the world are facing the increasing challenge of supplying a variety of water demands under the growing stresses of climate variability and of population and economic growth. Accurate streamflow forecasts are key to the water resources planning and decision-making process. There is growing evidence that variability in western streamflow is modulated by large-scale ocean-atmospheric features. Traditional forecasting techniques, however, do not systematically utilize large-scale climate information. In this study we present a framework for incorporating climate information into water resources decision-making and demonstrate the method on the semiarid Truckee-Carson Basin in Nevada where environmental, agricultural, and municipal demands compete for a limited supply of water. In this basin, water managers must plan carefully to meet the demands of timing and required flowrates. The framework presented in this paper consists of a streamflow forecast system that is based on large-scale climate information. The forecasts are then incorporated in a decision-support tool to aid in water resources planning and decision making. Previous work on the Truckee-Carson Basin has demonstrated that incorporating climate information into the forecast can increase the accuracy (or skill) and lead time of forecasts. This paper focuses on the decision-support system that is used to evaluate decision strategies and demonstrates possible water management improvements. The performance of the framework and the improved forecasts are evaluated on the Truckee-Carson system by using a suite of years from the historical record.

Traditional drought management planning has focused on curtailing demand through various conservation measures when regional hydroclimatic indicators reach specific trigger levels. This study suggests a methodology that can augment traditional drought management methods in two ways. First, regional hydroclimatic indicators, which are not necessarily indicative of the impacts of droughts on individual water storage systems, are replaced with system specific indicators in the context of supply reliability, or likelihood of system failure. Second, the study illustrates how economically optimal programs for conjunctive supply and demand management can be developed once the susceptibility of a specific water supply system to drought conditions is quantitatively understood. The method links a stochastic simulation program with a mixed-integer linear program aimed at achieving target levels of supply reliability at minimum economic cost. Results from a case study in Western Massachusetts were used to guide capital planning by indicating a clear need for emergency water sources. Results were also used during a recent drought to justify the avoidance of premature tapping of emergency supplies which could have cost up to $750,000.

Methodology and algorithm to calibrate pipe wall demand coefficients for chlorine decay using an input-output model framework are developed and applied. An input-output model of water quality in water distribution systems developed previously provides information that is not available using traditional simulation approaches: the various flow paths between particular input sources and output nodes, and their associated time delays and impacts on output node water quality. Such information constitutes a complete description of the input-output behavior under typical assumptions of first-order chemical decay or production reactions. With the input-output model, the chlorine concentration at output locations can be expressed explicitly as a function of the concentrations at upstream input locations, and pipe wall demand coefficients. Thus the sensitivity of water quality at the outputs to wall demand coefficients can be derived analytically and calculated efficiently. The sensitivity information is also used to study uncertainty in wall demand coefficient estimates that are caused by chlorine measurement errors.

Booster disinfectant is introduced within a distribution system to maintain disinfectant residuals and avoid high dosages near water sources. Assuming that first order reaction kinetics apply to chlorine decay, an integer linear programming optimization problem is posed to determine booster locations and their injection rates. The formulation avoids long water quality simulations by adding constraints requiring the concentrations at the beginning and end of the design period to be the same. The optimization problem is divided into two levels. The upper level selects the booster locations using a genetic algorithm, if more than a few boosters are included, or enumeration, if the number of boosters and/or potential locations is relatively small. Given a set of boosters from the upper level, the lower level minimizes the chlorine mass to be injected to maintain required residuals. The approach is applied to the Brushy Plains system for alternative numbers of allowable boosters.