HR: 1340h
AN: H23B-1316    [Abstracts]
TI: Uncertainty Assessment of Soil Hydraulic Parameter Estimated From Cokriging and Artificial Neural Network
AU: * Deng, H
EM: hldeng@magnet.fsu.edu
AF: School of Computational Science and Department of Geological Sciences, Florida State University, Tallahassee, FL 32306, United States
AU: Ye, M
EM: mingye@scs.fsu.edu
AF: School of Computational Science and Department of Geological Sciences, Florida State University, Tallahassee, FL 32306, United States
AU: Schaap, M G
EM: mschaap@cals.arizona.edu
AF: Department of Soil Water and Environmental Sciences, University of Arizona, Tucson, AZ 85721, United States
AU: Khaleel, R
EM: Raziuddin_Khaleel@rl.gov
AF: Fluor Government Group, P. O. Box 1050, Richland, WA 99352, United States
AB: To characterize heterogeneity of soil hydraulic parameters, we developed a method to incorporate measurements of soil texture, bulk density, soil hydraulic parameter, and moisture content. The method first uses cokriging to estimate heterogeneous soil texture and bulk density (pedotransfer variables) using the moisture content as secondary variables that are abundant at the site. The heterogeneous pedotransfer variables are then used as input to an artificial neural network (ANN) developed to estimate heterogeneous soil hydraulic parameters, which are then used to simulate a field injection experiment at the U.S. Department of Energy's Hanford Site, WA. Although a large amount of uncertainty exists in the cokriging and ANN estimates, only the mean estimates are used for the numerical simulation. It is unknown to what extent the estimation uncertainty of cokriging and ANN affects the simulated injection experiment, and hence the motivation for this study. The uncertainty of parameter estimates is investigated in two steps. First, uncertainty of the ANN estimates is quantified using mean and variance of the estimates obtained from a bootstrap method. A total of 10,000 bootstraps are generated to obtain a statistically meaningful variance. The bootstrap estimates are Gaussian. A Latin Hypercube Sampling (LHS) method is used to generate 10 realizations from the entire distribution. A Monte Carlo simulation is conducted to evaluate propagation of uncertainty of the ANN estimates via numerical simulation of the field injection experiment. In the second step, uncertainty of cokriging estimates is assessed in a similar manner. Based on cokriging mean and variance and the assumed Gaussian distribution of cokriging estimates, 10 realizations of the pedotransfer variables are generated using the LHS method. For each of the realization, 10 realizations of soil hydraulic parameters are generated by the ANN. As a result, a total 100 realizations of the soil hydraulic parameters are generated for a Monte Carlo simulation. Uncertainty of the simulated injection experiment is evaluated next. Results indicate that the uncertainty of the cokriging estimates affects the injection experiment simulations considerably more than uncertainty of the ANN estimates.
DE: 1829 Groundwater hydrology
DE: 1847 Modeling
DE: 1866 Soil moisture
DE: 1873 Uncertainty assessment (3275)
DE: 1875 Vadose zone
SC: Hydrology [H]
MN: 2007 Fall Meeting