HR: 1340h
AN: H13A-1323    [Abstracts]
TI: Optimal Experiment Design for Distributed Parameter Identification in Groundwater Modeling: Case Study ­V Warren Subbasin, California
AU: * Chiu, Y
EM: ycchiu@ucla.edu
AF: Department of Civil and Environmental Engineering, UCLA, 5732 Boelter Hall, UCLA, Los Angeles, CA 90095 United States
AU: Sun, N
EM: nezheng@ucla.edu
AF: Department of Civil and Environmental Engineering, UCLA, 5732 Boelter Hall, UCLA, Los Angeles, CA 90095 United States
AU: Nishikawa, T
EM: tnish@usgs.gov
AF: USGS, 5735 Kearny Villa Rd, San Diego, CA 92123 United States
AU: Yeh, W W
EM: williamy@seas.ucla.edu
AF: Department of Civil and Environmental Engineering, UCLA, 5732 Boelter Hall, UCLA, Los Angeles, CA 90095 United States
AB: This paper develops an optimal experimental design procedure for distributed parameter identification in groundwater modeling using the worst-case parameter (WCP) scenario. The WCP is defined as the parameter that causes the maximum deviation in model application when its structure is simplified. Consequently, the WCP of a structure is a parameter that makes the structure most difficult to homogenize. The criterion adopted for optimal design is either to minimize the total experimental cost subject to information requirement, or to maximize a measure of the information matrix subject to budget constraints. The proposed procedure allows one to find a minimum cost design that will provide sufficient information for identifying the WCP. The concept of WCP and the optimal experimental design procedure are applied to the Warren Subbasin, California. A groundwater flow model and a solute-transport model are developed for the Warren groundwater basin. Historical observations are available for model development and calibration for the period 1956-2001. At the present time, certain parts of the basin are contaminated by nitrate with concentrations exceeding the U.S. Environmental Protection Agency (USEPA) maximum contaminant level (MCL) of 44mg/l. Hence, the objectives of this case study are to: (1) identify the complexity of flow and transport model structures, (2) evaluate the data sufficiency determined by the objectives and accuracy requirement of model application, and (3) propose a conjunctive use program which will decrease the high nitrate concentration while maintaining the water table at the desired level.
DE: 1829 Groundwater hydrology
DE: 1831 Groundwater quality
DE: 1846 Model calibration (3333)
DE: 1880 Water management (6334)
SC: Hydrology [H]
MN: Fall Meeting 2005