HR: 0800h
AN: H21C-0697    [Abstracts]
TI: Using the Chi-Squared Curve to Assimilate Large Data Sets from Different Sources
AU: * Mead, J
EM: jmead@boisestate.edu
AF: Department of Mathematics Boise State University, 1910 University Dr., Boise, ID 83725, United States
AU: Treneva, R
EM: raynatreneva@mail.boisestate.edu
AF: Department of Mathematics Boise State University, 1910 University Dr., Boise, ID 83725, United States
AU: Gribb, M
EM: mgribb@boisestate.edu
AF: Department of Civil Engineering Boise State University, 1910 University Dr., Boise, ID 83725, United States
AU: McNamara, J
EM: jmcnamara@boisestate.edu
AF: Department of Geosciences Boise State University, 1910 University Dr., Boise, ID 83725, United States
AB: We will describe the chi-squared curve method for parameter estimation recently developed by Mead (2007) and Mead and Renaut (submitted). The chi-squared curve method is considerably more efficient, and as accurate as traditional L-curve and cross-correlation methods for parameter estimation. This method involves forming a maximum likelihood estimation problem, and here we will include soil moisture and pressure head data from both in-situ, and laboratory core measurements. We assume all data contain errors, thus this method does not calibrate a model with data, rather parameter estimates are found within a priori data uncertainty ranges. A priori estimates of laboratory errors are given by repeated measurements on cores, while those for in-situ measurements are given by the chi-squared curve method.
DE: 1846 Model calibration (3333)
DE: 1866 Soil moisture
DE: 1873 Uncertainty assessment (3275)
DE: 3260 Inverse theory
SC: Hydrology [H]
MN: 2007 Fall Meeting