http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
Generating geographic time series and a combined rainfall-soil moisture model and its statistics
xii, 102 leaves : illustrations ; 28 cm.
A method of generating geographic time series is described when its covariance function is given. It is assumed that the variance-covariance matrix is a positive definite block Toeplitz matrix and then it can be decomposed into a unit lower block triangular matrix, block diagonal matrix, and unit upper block triangular matrix whose elements can be evaluated by using the multivariate Levinson algorithm. Our main objective is to determine a method to speed up this algorithm as a straight- forward implementation requires massive amounts of computer time. The order of an approximating autoregressive process can be found analytically' and numerically if we assume it follows an autoregressive process. We use the Harris County ozone data and Dallas/Fort Worth ozone data provided by the Texas National Resources Conservation Commission (TNRCC) to check the algorithm. The temporal characteristics of the soil moisture field are modeled in this dissertation by using a zero are modeled in this dissertation by using a zero Entekhabi &; Rodriguez-lturbe (1994) and the precipitation forcing by a point process precipitation model (Rodriguez-lturbe, Gupta & Waymire, 1984). The mean, covariance function and the spectral density function of the soil moisture field (both instantaneous and aggregated cases) are analytically derived and verified by simulation experiments under several parameter sets. The probability density function of the soil moisture is also evaluated in this work by simulation methods. These results are then used to evaluate the sampling errors of the soil moisture fields by space-borne sensors and the design of ground truth experiments. The Monsoon '90 data was utilized in this study.
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Includes bibliographical references: pages 97-101.