HR: 1340h
AN: H23B-1314    [Abstracts]
TI: Accounting for Transport Parameter Uncertainty in Geostatistical Groundwater Contaminant Release History Estimation
AU: * Ostrowski, J
EM: ojill@umich.edu
AF: The University of Michigan Department of Civil and Environmental Engineering, 183 EWRE Bldg. 1351 Beal Ave., Ann Arbor, MI 48109, United States
AU: Shlomi, S
EM: shaharsh@umich.edu
AF: The University of Michigan Department of Civil and Environmental Engineering, 183 EWRE Bldg. 1351 Beal Ave., Ann Arbor, MI 48109, United States
AU: Michalak, A
EM: amichala@umich.edu
AF: The University of Michigan Department of Civil and Environmental Engineering, 183 EWRE Bldg. 1351 Beal Ave., Ann Arbor, MI 48109, United States
AB: The process of estimating the release history of a contaminant in groundwater relies on coupling a limited number of concentration measurements with a groundwater flow and transport model in an inverse modeling framework. The information provided by available measurements is generally not sufficient to fully characterize the unknown release history; therefore, an accurate assessment of the estimation uncertainty is required. The modeler's level of confidence in the transport parameters, expressed as pdfs, can be incorporated into the inverse model to improve the accuracy of the release estimates. In this work, geostatistical inverse modeling is used in conjunction with Monte Carlo sampling of transport parameters to estimate groundwater contaminant release histories. Concentration non-negativity is enforced using a Gibbs sampling algorithm based on a truncated normal distribution. The method is applied to two one-dimensional test cases: a hypothetical dataset commonly used in validating contaminant source identification methods, and data collected from a tetrachloroethylene and trichloroethylene plume at the Dover Air Force Base in Delaware. The estimated release histories and associated uncertainties are compared to results from a geostatistical inverse model where uncertainty in transport parameters is ignored. Results show that the a posteriori uncertainty associated with the model that accounts for parameter uncertainty is higher, but that this model provides a more realistic representation of the release history based on available data. This modified inverse modeling technique has many applications, including assignment of liability in groundwater contamination cases, characterization of groundwater contamination, and model calibration.
DE: 1829 Groundwater hydrology
DE: 1832 Groundwater transport
DE: 1847 Modeling
DE: 1869 Stochastic hydrology
DE: 1873 Uncertainty assessment (3275)
SC: Hydrology [H]
MN: 2007 Fall Meeting