HR: 11:20h
AN: H32B-04    [Abstracts]
TI: Identification of potential contaminant plume sources under uncertainty
AU: * Vesselinov, V V
EM: vvv@lanl.gov
AF: Hydrology, Geochemistry, and Geology Group Earth and Environmental Sciences Division Los Alamos National Laboratory, EES-6, MS T003, Los Alamos, NM 87501, United States
AU: Birdsell, K
EM: khb@lanl.gov
AF: Hydrology, Geochemistry, and Geology Group Earth and Environmental Sciences Division Los Alamos National Laboratory, EES-6, MS T003, Los Alamos, NM 87501, United States
AU: Broxton, D
EM: broxton@lanl.gov
AF: Hydrology, Geochemistry, and Geology Group Earth and Environmental Sciences Division Los Alamos National Laboratory, EES-6, MS T003, Los Alamos, NM 87501, United States
AU: Longmire, P
AF: Hydrology, Geochemistry, and Geology Group Earth and Environmental Sciences Division Los Alamos National Laboratory, EES-6, MS T003, Los Alamos, NM 87501, United States
AU: Katrzman, D
EM: katzman@lanl.gov
AF: Hydrology, Geochemistry, and Geology Group Earth and Environmental Sciences Division Los Alamos National Laboratory, EES-6, MS T003, Los Alamos, NM 87501, United States
AB: There are various methods that can be applied to identify the potential spatial locations of contaminant sources in regional aquifers. We propose and apply an inverse methodology that takes into account directly the various uncertainties associated with the available hydrogeological information (conceptual uncertainties, observation errors, parameter uncertainties). The technique utilizes a series of forward simulations of probable contaminant transport that encompass predefined uncertainty bounds. The simulations are independent and efficiently performed in parallel. Then the inverse method post processes the results and estimates the spatial distribution of the most probable source locations that are consistent with observations and their uncertainties. In addition, the model predicts the probable contaminant concentrations and their uncertainties. Uncertainties in key model components that impact the results are medium heterogeneity, geochemical processes, and field contaminant concentrations. The analysis will be applied for decision making regarding a next phase of data acquisition. Any new data will be subsequently employed to address the validity of the model predictions and applied methodology.
DE: 1847 Modeling
SC: Hydrology [H]
MN: 2007 Fall Meeting