HR: 0800h
AN: H21C-0699    [Abstracts]
TI: Distributed Parameter Estimation Using a Regularization Approach
AU: * Pokhrel, P
EM: pokhrel@hwr.arizona.edu
AF: University of Arizona, Hydrology and Water Resources Harshbarger bld 1133 E North Campus Drive, Tucson, AZ 85719, United States
AU: Gupta, H V
EM: hoshin.gupta@hwr.arizona.edu
AF: University of Arizona, Hydrology and Water Resources Harshbarger bld 1133 E North Campus Drive, Tucson, AZ 85719, United States
AU: Wagener, T
EM: thorsten@engr.psu.edu
AF: Pennsylvania State University, Civil and Environmental Engineering 226B Sackett Bldg. Pennsylvania State University, University Park, PA 16802, United States
AB: The high dimensionality of the parameter search space can be solved by the introduction of additional information about the parameters. In this research the information contained in the apriori parameter estimates, derived using the soil data and the method developed by the National Weather Service, was used to identify regularization equations. These regularization equations were then used to constrain the parameter variability during the calibration process and reduce the dimension of the calibration problem. The study of spatial variability of apriori parameters with respect to the NRCS based curve numbers and the depth of soil showed some recognizable trends that could be exploited in the form of some simple regression equations. These equations, along with some inter parameter relations, were used as regularization equations. Calibration of the coefficients of the regularization equations instead of the Sacramento Soil Moisture Accounting Model parameters reduced the dimension of the problem from 858 to 33 unknowns and resulted in significant reduction in the objective function values.
DE: 1805 Computational hydrology
DE: 1816 Estimation and forecasting
DE: 1839 Hydrologic scaling
DE: 1846 Model calibration (3333)
DE: 1848 Monitoring networks
SC: Hydrology [H]
MN: 2007 Fall Meeting