Objective-oriented groundwater modeling for conjunctive-use planning of surface water and groundwater

In this dissertation, an objective-oriented model is constructed for conjunctive use planning of surface water and groundwater for the Warren groundwater basin in Southern California. The town of Yucca Valley located in the southwest part of the Mojave Desert in southern California relies on ground-water pumping from the Warren ground-water basin as its sole source of water supply. This significant dependency has resulted in a large imbalance between ground-water pumpage and natural recharge, causing groundwater levels in the basin to decline more than 90 meters from the late 1940s to 1994. Consequently, an artificial recharge program proposed by the Hi-Desert Water District (HDWD), which provides water service to the town of Yucca Valley, was implemented for the purpose of recovering the ground-water levels; however, the rise in ground-water levels has caused nitrate (NO3) concentration to increase simultaneously. The goal of conjunctive use planning is to decrease high nitrate concentration while maintaining groundwater levels at desired elevations and meeting water demand. A management problem is formulated that minimizes the total cost over the proper choices of the time-varying pumping and recharge rates at pre-specified wells and surface ponds.;To make the solution of the management problem reliable, an accurate simulation model to predict groundwater head and nitrate concentration distributions under different management alternatives is desired. A model constructed by the traditional process may not be acceptable because of its uncertainty in model prediction. The objective-oriented model construction approach seeks a representative model that has the simplest structure, requires the minimum data for identification, but can produce reliable results for a given model application. With the data from the Warren groundwater basin, we show how to incorporate management objectives into the construction of an objective-oriented model, identify the parameter structure and its corresponding parameter values, solve the generalized inverse problem (GIP) effectively by finding the worst-case parameter (WCP), evaluate the sufficiency of existing data and, finally, find a robust experiment design when the existing data are insufficient. Results of this case study show that the presented methodology is useful in practice because (1) data sufficiency can be judged before conducting actual field experiments, and (2) the identified WCP drastically reduces the computation time for constructing an objective-oriented model.;In the management problem, an optimization/management model is formulated with a linear objective function and nonlinear constraints. The response matrix approach is used to link the optimization model with the simulation model. Because of nonlinearity, the response matrix is updated and iteration is required for convergence. A systematic scheme is also developed for finding a feasible initial policy. Three different scenarios are considered in the management model. The results obtained from each scenario are analyzed and discussed.