HR: 1400h
AN: H33B-04    [Abstracts]
TI: Multimodel Prediction of Water Flow in a Field Soil Using Pedotransfer Functions
AU: * Guber, A K
EM: aguber@anri.barc.usda.gov
AF: Department of Environmental Sciences,University of California, A135 Bourns Hall, Riverside, CA 92521, United States
AU: Pachepsky, Y A
AF: Environmental Microbial Safety Laboratory,USDA-ARS-BA-ANRI, Bldg. 173, Rm. 203, BARC-EAST Powder Mill Road, Beltsville, MD 20705, United States
AU: Jacques, D
AF: SCK CEN, Boeretang 200, 2400 Mol, Belgium
AU: van Genuchten, M T
AF: U.S. Salinity Laboratory, USDA-ARS, 450 W. Big Springs Road, Riverside, CA 92507, United States
AU: Nemesh, A
AF: Department of Environmental Sciences,University of California, A135 Bourns Hall, Riverside, CA 92521, United States
AU: Simunek, J
AF: Department of Environmental Sciences,University of California, A135 Bourns Hall, Riverside, CA 92521, United States
AU: Nicholson, T J
AF: U.S. Nuclear Regulatory Commission, Mail Stop T-9C34, Washington, DC 20555, United States
AU: Cady, R E
AF: U.S. Nuclear Regulatory Commission, Mail Stop T-9C34, Washington, DC 20555, United States
AB: Combining predictions using various independent models, often called multimodel prediction, has become a very popular technique in climate prediction and is now increasingly being used also in subsurface hydrology. The objectives of this work were (a) to compare different methods of multimodel prediction of the field soil water regime using pedotransfer functions, and (b) to see whether the calibration of a flow model with field data can be replaced by multimodel predictions. The multimodel prediction in this work consisted of running the Richards model with outputs of individual PTFs and then combining the obtained outputs into a single prediction. We compared weighing predictions from individual models by (1) using only the best model, (2) assigning equal weights, (3) using the unconstrained superensemble (i. e. regressing measured values to outputs of individual models), (4) using singular value decomposition in the regression, (5) using Bayesian model averaging, and (6) applying weights derived from the Kullback-Leibler information for each model. We evaluated the weighing methods in terms of their accuracy (i. e. errors in reproducing the training, or hindcast, datasets), and reliability (i.e., errors in reproducing the test datasets). The two best weighing methods (Bayesian model averaging and regression with singular value decomposition) had average accuracy and reliability RMSE values of about 0.01 cm³cm^-3 at 35 cm depth, and of about 0.005 cm³cm^-3 at larger depths for one month monitoring and 13 months of testing. Calibrating the Richards model resulted in RMSE values of 0.009 cm³cm^-3 at 35 cm depth and from 0.004 to 0.006 cm³cm^-3 at larger depths. This indicates that monitoring of the soil water regime in combination with multimodel prediction instead of calibrating the flow model can be a viable approach to simulating field water flow in the vadose zone.
DE: 1816 Estimation and forecasting
DE: 1847 Modeling
DE: 1865 Soils (0486)
DE: 1866 Soil moisture
DE: 1875 Vadose zone
SC: Hydrology [H]
MN: 2007 Joint Assembly