Journal of Water Resources Planning and Management

Recent large-scale flooding in northern California and the scale of the 1993 flood of the Mississippi and Missouri Rivers have demonstrated that floodplain management should be conducted for regions, rather than only for individual floodplains. Economic activities connect each floodplain to other floodplains and to areas not affected by flooding. A method is presented to estimate the economic effects of flooding over a region of interacting floodplains and other lands by incorporating a Leontief economic input-output model with a probabilistic description of the potential overtopping in a system of levees. Expected economic damages may be greater if flooding reduces the availability of floodplain products that are used as inputs to other sectors of the economy. The model is used to evaluate the flood risk for an example of a distributed system of levees.

This article reports results from a two-year research project to evaluate the effectiveness of drought contingency plans. The research developed a metalevel framework for evaluating plans before a drought. In addition, it applied this framework to an in-depth case study of a major metropolitan region, Atlanta, Georgia. This research discovered that drought plans have limited effectiveness when disconnected from larger scale and longer term planning. The requirement to prepare a plan does not necessarily invoke a substantive drought planning process. Disjointed local level plans, instead of coordinated planning effects, have limited ability to mitigate impacts in widespread drought. Because the faith placed in plans may be misplaced, metropolitan regions may be unwittingly poised for a disaster. Recommendations include stronger links between drought plans and drought planning, inclusion of stakeholders and agency expertise, mechanisms for interagency coordination, and improved integration between drought plans and strategic water resources plans.

A simulation-optimization model has been developed for the optimal management of the city of Santa Barbara's water resources during a drought. The model, which links ground-water simulation with linear programming, has a planning horizon of 5 years. The objective is to minimize the cost of water supply subject to: water demand constraints, hydraulic head constraints to control seawater intrusion, and water capacity constraints. The decision variables are monthly water deliveries from surface water and ground water. The state variables are hydraulic heads. The drought of 1947–51 is the city's worst drought on record, and simulated surface-water supplies for this period were used as a basis for testing optimal management of current water resources under drought conditions. The simulation-optimization model was applied using three reservoir operation rules. In addition, the model's sensitivity to demand, carryover [the storage of water in one year for use in a later year(s)], head constraints, and capacity constraints was tested.

An optimal operation model development method for a typical south Indian irrigation system is developed. This method envisions three phases of modeling. First is a simulation model to simulate the command area of the reservoir, consisting of paddy fields both drawing directly from the canals from the reservoir and also drawing water through tanks in the command area. This simulation model is used to determine the expected demand sequences by simulating the command area with historical data. Second is a Stochastic Dynamic Programming (SDP) model to obtain an optimal release policy. This SDP model considers both demand and inflow as stochastic, and both are assumed to follow first-order Markov chain model. Third is simulation using the optimal release policy from the SDP model. This simulation model is used to study the degree of failure associated with adoption of the optimal operating policy for different reservoir storages at the start of the crop seasons.

An integrated optimization model is developed for planning irrigation systems. In this work, surface reservoir capacity, ground-water and spring withdrawal, delivery system capacities (including canals, pumping stations, and tunnels), hectares of land to be developed for irrigation, and cropping pattern are considered as interacting parts of the system. Moreover, the effect of the costs due to drainage, land leveling, and irrigation network construction are also considered. The model is capable of integrating other decision variables. The system is optimized by means of a chance-constraint optimization model. The model uses mixed integer linear programming to maximize the net benefit associated with the development. To linearize a collection of interrelated nonlinear cost functions, a new generalized technique is developed. Regarding the linearization of nonlinear cost functions, the technique reduces the size of the models and excludes any possible infeasibility that may occur in the models. Results generated by the application of the model, along with the sensitivity analysis, provide a tool to select the optimum design considering the varieties of criteria involved.

This study presents a surface-to-subsurface two-dimensional (2D) model for the design of infiltration trench basins. It assists engineers in estimating the size of an infiltration basin and in determining the subsurface geometry required for having an adequate hydraulic conductivity. The Federal Aviation Administration (FAA) method is revised to predict the detention storage volume by maximizing the volume difference between the design storm runoff and the basin infiltration rate. Diffusion of the wetting front beneath the basin is depicted by the streamline-equipotential flow-net model derived in this study with consideration of nonisotropic permeability. The required subsurface geometry is found to be related to the width of the basin, and the length of the saturation zone is proportional to the ratio of infiltration rate-to-coefficient of permeability.

Ground-water remediation optimization models were formulated using a statistical optimization methodology, chance-constrained programming (CCP), to account for uncertainty in the coefficients of the models. Several models were formulated that depended on which set of coefficients were considered uncertain. Such models were either mixed-integer linear programming models or mixed-integer nonlinear programming models. The CCP method transformed the probabilistic models to deterministic models. The deterministic models are easier to solve and use less computer memory and less storage space than probabilistic models. Results are presented that demonstrate the models formulated. The results showed that incorporating uncertainty into a ground-water optimization model using CCP could be a practical method for making decisions on well locations and pumping rates in ground-water remediation.

In some states the design of branched pipes in rural water systems must be carried out based on pressure drops calculated using expected instantaneous peak flows. These instantaneous peak flows are based on the number of users served by the pipe link, and field measurements have led to various tables of recommended values. Hydraulic calculations using instantaneous peak flows require a special approach. This approach, which can be incorporated into a pipe network hydraulic model, is described. An example illustrates the significant differences possible in a hydraulic analysis using a conventional approach based on a peak per-capita demand and a modified one based on satisfying instantaneous peak flow requirements.