Title
Session 4 - Using uncertainty to guide characterization, closure, and long-term management of an underground nuclear test site
Location
University of Nevada Las Vegas, Stan Fulton Building
Start Date
1-6-2007 2:02 PM
End Date
1-6-2007 2:09 PM
Description
Protecting the public from radionuclide contaminated groundwater is the focus of investigations of the Shoal underground nuclear test site in rural Nevada. A preliminary stochastic groundwater flow and transport model was developed using historic site data and information from four characterization wells. The resulting uncertainty in predictions was unacceptable to the U.S. DOE, prompting additional data collection. A Data Decision Analysis used Monte Carlo methods with the model to identify optimum data collection activities for uncertainty reduction. The model was revised with data from new wells and tests, and used to determine confidence levels for a site contaminant boundary. An optimum monitoring system was designed, the installation of which provided another opportunity to reduce uncertainty as data were collected for model validation. These data can feed back into the stochastic flow and transport model to cull poorly performing model realizations and reduce uncertainty in the model predictions.
Keywords
Groundwater contamination; Groundwater -- Pollution; Monitoring wells; Nevada; Nuclear testing; Nuclear weapons -- Testing; Shoal underground nuclear test site; Underground nuclear explosions; Well monitoring
Disciplines
Environmental Monitoring | Nuclear
Language
English
Permissions
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COinS
Session 4 - Using uncertainty to guide characterization, closure, and long-term management of an underground nuclear test site
University of Nevada Las Vegas, Stan Fulton Building
Protecting the public from radionuclide contaminated groundwater is the focus of investigations of the Shoal underground nuclear test site in rural Nevada. A preliminary stochastic groundwater flow and transport model was developed using historic site data and information from four characterization wells. The resulting uncertainty in predictions was unacceptable to the U.S. DOE, prompting additional data collection. A Data Decision Analysis used Monte Carlo methods with the model to identify optimum data collection activities for uncertainty reduction. The model was revised with data from new wells and tests, and used to determine confidence levels for a site contaminant boundary. An optimum monitoring system was designed, the installation of which provided another opportunity to reduce uncertainty as data were collected for model validation. These data can feed back into the stochastic flow and transport model to cull poorly performing model realizations and reduce uncertainty in the model predictions.