Journal of Water Resources Planning and Management

The California Department of Water Resources has developed a water resources planning model (DWRSIM) to simulate the combined operation of the California State Water Project (SWP) and the Federal Central Valley Project (CVP) as discussed by Barnes and Chung (1986). A least‐cost network flow programming procedure, the Out‐of‐Kilter Algorithm (OKA), has been incorporated into DWRSIM to perform the operation of the CVP‐SWP facilities south of the Sacramento‐San Joaquin Delta. The OKA is an improvement over the traditional mass balance accounting approach of reservoir and streamflow routing in that it increases the flexibility of the model. This flexibility is essential in the management and planning of complex water systems such as the CVP‐SWP system. An added benefit of the OKA over the mass balance approach is that the OKA is a computationally more efficient algorithm, resulting in a substantial savings in computer execution time. Since a typical operation study using DWRSIM involves a study period of approximately 60 years, and since a typical study may involve numerous model runs, the resulting time savings can be significant.

A methodology to optimize the operation of water supply pumps is developed for use in minimizing pump operation costs. The developed methodology is directly applicable to complete water distribution systems or isolated pressure zones with a single dominant storage facility and multiple pump stations. The optimal pump operation methodology involves two basic phases: the development of an optimal tank trajectory, and the development of an optimal pump operating policy to achieve the optimal trajectory. The optimal trajectory is determined using dynamic programming while the associated pump policy is determined using an explicit enumeration scheme. In order to test the applicability of the developed methodology it was applied to the third high pressure zone of the Washington D.C. water system. To examine the impact of a variable electric rate schedule and a variable system demand schedule on the resulting optimal pump policy, the methodology was applied to four different representative days. Annual energy usage cost savings of approximately 6.7% were projected.

This paper describes a computer model that can be used to analyze long‐term capacity expansion strategies for power generation systems consisting of hydrothermal plants and transmission networks. The objective is to determine the optimum capacity expansion schedule that minimizes the present worth of capacity cost and operating cost, subject to meeting peak power demand and energy demand. The problem is formulated as a large scale mixed‐integer program and solved by Lagrangean relaxation decomposing into two‐level hierarchy. At the top level is the capacity problem, where the capacity expansion decisions are made. At the second level is the operating problem, where the operational decisions on power generation and transmission are made. The feedback information is conveyed by the Lagrange multiplier.

This paper describes the assessment of subsurface water in the river bed of Bharathapuzha at a location near Thrithala in Palghat District of Kerala State, India for a rural water supply scheme which is to service a rural population of about 0.5 million. Field data obtained through hydrologic and hydrogeologic data monitoring, geophysical surveys, and aquifer performance tests are analyzed to obtain estimates of the critical period of water demand when the river can be expected to be dry, saturated thickness as well as permeabilities of the aquifers, and gradients of subsurface flow. These results are in turn used to quantify the groundwater contribution from both banks into the river course, subsurface flow along the river course, and subsurface storage within the river bed at the proposed location of the scheme. The feasible subsurface water withdrawal through an infiltration gallery‐based water supply scheme is finally estimated as $50\times {10}^{6}L/\mathrm{day}$ during periods other than the critical period and $25\times {10}^{6}L/\mathrm{day}$ during the critical period.

An overall study has been made to develop an optimum water resources utilization plan in the Cho Shui River Basin in Taiwan, the Republic of China (ROC). This paper represents a part of this overall study. It focuses on the optimum diversion plan and preliminary design of diversion amounts, tunnel sizes and capacities of power plants. Two diversion schemes, scheme I and scheme II, are identified and evaluated based on economic criteria. The optimization procedure of the present study consists of two phases: the first phase deals with the separate optimization of each subsystem consisting of diversions and power plants; the second phase deals with the economic evaluation of the combined subsystems. The optimum capacities of diversions and power plants in each scheme are determined successfully by using Rosenbrock's technique of nonlinear programming.

In the monitoring of water distribution systems, the inaccuracy of input data contributes greatly to the inaccuracy of system state estimates calculated from them. It is important, therefore, that the system operators are given not only the values of flows and pressures in the network at any instant of time but also that they have some indication of how reliable these values are. The quantification of the inaccuracy of calculated flows and pressures caused by the input data uncertainty is called here confidence limit analysis. Several confidence limit analysis algorithms are presented. These include a Monte Carlo simulation method, an optimization method, and a sensitivity matrix technique. The performance of these algorithms is assessed in terms of their suitability for real‐time control or design stage applications. Results are presented for a realistic test network.

The Washington D.C. Metropolitan Area (WMA) has experienced rapid population growth in the 1980s. Water supply yield analysis techniques are developed in this paper to assess adequacy of the current WMA water supply system to meet escalating future demands for water. In the statistical model developed for analyzing water supply yield, “annual yield” is a random variable representing the maximum yield the water supply system can provide in a given year. Randomness in annual yield may be attributed solely to randomness in supply or to randomness in both supply and demand. Annual yield random variables are dependent on the operating rules used for the two upstream reservoirs that serve the WMA. The dependence is simple because the operating rules are completely specified by past streamflow and two “operating parameters.” The operating parameters are chosen to maximize specified attributes of the annual yield distribution, represented by the “weighted yield.” The sum of the historic yield values of the individual components of the WMA water supply system is 482 mgd, a value dangerously close to current mean water use. Historic yield values for the joint system yield models exceed 700 mgd, indicating that the current water supply system is quite reliable.

An implicit mathematical model is developed for use in calibrating hydraulic network models. The calibration model uses a nonlinear optimization algorithm along with a general network solver to adjust selected model parameters for either steady state or dynamic operating conditions. Adjustable model parameters include: pipe roughness, source grades, and nodal demands. Both a steady state and an extended period calibration example are presented.