HR: 1340h
AN: H13A-0399    [Abstracts]
TI: Parameter Uncertainty Analysis of a Regional Subsurface Water Flow Model Using Single and Multi-Objective Calibration
AU: * Schoups, G
EM: gerrit@stanford.edu
AF: Hydrologic Sciences, Department of Land Air and Water Resources, University of California, Davis, One Shields Avenue, Davis, CA 95616 United States
AU: Hopmans, J W
EM: jwhopmans@ucdavis.edu
AF: Hydrologic Sciences, Department of Land Air and Water Resources, University of California, Davis, One Shields Avenue, Davis, CA 95616 United States
AU: Young, C A
EM: cayoung@ucdavis.edu
AF: Hydrologic Sciences, Department of Land Air and Water Resources, University of California, Davis, One Shields Avenue, Davis, CA 95616 United States
AU: Vrugt, J A
EM: jvrugt@science.uva.nl
AF: Institute for Biodiversity and Ecosystem Dynamics, University of Amsterdam, Nieuwe Achtergracht 166, Amsterdam, 1018 WV Netherlands
AU: Wallender, W W
EM: wwwallender@ucdavis.edu
AF: Hydrologic Sciences, Department of Land Air and Water Resources, University of California, Davis, One Shields Avenue, Davis, CA 95616 United States
AB: Regional-scale modeling of variably-saturated subsurface water flow is affected by uncertainties associated with the model parameters and the appropriate model structure. These uncertainties may be reduced by comparing the model predictions to measurements using inverse modeling, resulting in posterior parameter distributions that are conditioned on the data used in the calibration. We present the calibration of a regional distributed subsurface water flow model for a 1,400 km2 irrigated agricultural area in the western San Joaquin Valley of California. Two global optimization algorithms were used to identify optimal parameter values and their uncertainties using data on spatially distributed local water table depth measurements, district-average groundwater pumping and district-average subsurface drainage data. Using the single objective function approach, the three measurement types were weighted into a single objective function for global optimization purposes. Additionally, a three-objective multi-criteria optimization problem was formulated in which no prior weighting of the individual objectives was specified. The single-objective optimization approach resulted in identifiable parameters with relatively small uncertainties, however, most likely values for various optimized parameter approached the outer bounds of their physical-realistic ranges. In the multi-objective approach, the objective function of each measurement type was treated independently, so that no subjective preferences were assigned a priori. The estimated Pareto set exhibited large parameter uncertainty, indicating possible model structural inadequacies.
DE: 1829 Groundwater hydrology
DE: 1836 Hydrologic budget (1655)
DE: 1842 Irrigation
SC: Hydrology [H]
MN: 2004 AGU Fall Meeting