HR: 16:15h
AN: H14A-02 INVITED     [Abstracts]
TI: Parameter Uncertainty and Model Reliability in Groundwater modeling
AU: * Yeh, W
EM: williamy@seas.ucla.edu
AF: UCLA, 5732B, Boelter Hall, UCLA, Los Angeles, CA 90095 United States
AB: Due to data limitation in both quantity and quality, a distributed parameter such as hydraulic conductivity must be approximated by a finite dimensional form when calibrating a conceptual groundwater model. This reduction in parameter dimension is known as parameterization and is necessary in order to obtain a stable and unique solution of the inverse problem. It has been pointed out by a number of researchers that in model calibration, one must consider simultaneously the parameter dimension, parameter pattern and parameter values. For a given set of observations, the least-squares error used for model calibration decreases when the parameter dimension increases. However, the parameter uncertainty error increases when over-parameterization occurs. The higher the parameter dimension, the more the required data. Over-parameterization occurs when information provided by the data is insufficient. Parameter uncertainty is usually represented by a norm of the covariance matrix of the estimated parameters. However, parameter uncertainty alone does not address the requirements of model application. To evaluate the model reliability in prediction, an additional criterion based on the error in model application must be considered. It is well understood that an over-simplified model structure may not be able to both fit the observed data and produce reliable predictions. On the other hand, a complex model structure may cause over-parameterization when data are limited. If a model is over-parameterized, the reliability of model prediction will decrease rather than increase. This paper reviews methods that have been developed to estimate the parameter uncertainty error as well as the model structure error when a simplified model structure is used to replace a more complex model structure.
DE: 3260 Inverse theory
DE: 1829 Groundwater hydrology
DE: 1869 Stochastic processes
SC: Hydrology [H]
MN: 2004 AGU Fall Meeting