HR: 0800h
AN: H11C-0308 INVITED     [Abstracts]
TI: Using Diverse Data Types in Ground-Water Models: Issues for Evaluation and Consensus
AU: * Poeter, E P
EM: epoeter@mines.edu
AF: International Ground Wtaer Modeling Center, Colorado School of Mines, Dept of GE 1500 Illinois St, Golden, CO 80401 United States
AB: Investigations have shown that use of diverse data types as observations for evaluation of ground water models is valuable to improving parameter estimates and the conceptual model. Yet use of multiple data types is not common. This practice could be promoted by developing accepted procedures for use of diverse data in model calibration and selection. One issue that arises when using disparate data is how the data types should be weighted. Nearly any choice is arbitrary unless it is related to the measurement error. Use of the inverse measurement variance serves to normalize data of different types by rendering squared residuals dimensionless and of similar magnitude. Use of model error in weighting is challenging because the true model is not known, and identification of the appropriate model is a goal of the process, so as the model is improved the weights would need to be altered, perhaps leading to a circular process. If weights are based on measurement error, then weighted residuals can be used to identify model improvements. Another issue is the use of D-optimality for parameter estimation and model selection. Use of D-optimality strives to minimize the parameter covariance matrix. That is, it balances maximizing sensitivity and minimizing parameter correlation. While this can be useful for designing data collection programs it can lead to bias in calibration and model selection. When designing data collection programs we can vary the observation type and location, thus searching for a data set that maximizes sensitivity and minimizes parameter correlation is reasonable. However, once the data are fixed and we seek the most useful model, we strive for a balance of good fit and parsimony. Insensitivity and parameter correlations may be due to an inadequate data set rather than an inadequate model and it is preferable to seek more meaningful data than to adjust the model. These, and other issues, need to be explored in order for hydrologists to come to agreement on the best approaches. Once there is consensus, illustrative examples and quality technology transfer can expedite implementation of best practices to application.
DE: 1829 Groundwater hydrology
SC: Hydrology [H]
MN: 2004 AGU Fall Meeting