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UNCERTAINTY IN MODEL PREDICTIONS-PLAUSIBLE OUTCOMES FROM ESTIMATES OF INPUT RANGES
Citation:
Weaver, J W. AND C L. TebesStevens. UNCERTAINTY IN MODEL PREDICTIONS-PLAUSIBLE OUTCOMES FROM ESTIMATES OF INPUT RANGES. Presented at Brownfields 2002, Charlotte, NC, November 13-15, 2002.
Impact/Purpose:
The current focus is to 1.) develop, distribute, and support the FRAMES-3MRA modeling technology, 2) to apply the FRAMES-3MRA modeling technology for the purposes of executing national and site-specific risk assessments, 3) to complete model application case studies to explore model performance issues, such as, model validation, 5) to collaborate with other Federal Agencies in an effort to leverage expertise and resources associated with common modeling interests, and 6) to monitor ongoing developments at the Office of Solid Waste and within the environmental modeling community in an effort to identify new needs for science modules and locate or develop solutions within the FRAMES 3MRA modeling system.
Description:
Models are commonly used to predict the future extent of contamination given estimates of hydraulic conductivity, porosity, hydraulic gradient, biodegradation rate, and other parameters. Often best estimates or averages of these are used as inputs to models, which then transform them into output concentrations. All properties of the subsurface are both uncertain, because of imperfect measurement methods, and subject to point-to-point variability, because of geologic heterogeneity. Where used as purely predictive tools (i.e., in the absence of model calibration to field data), uncertainty and variability lead to the need for assessment of the plausible range of model outcomes. For Brownfields sites, the need to provide rapid assessment of contamination may not allow extensive field and modeling studies, where calibration data sets would be obtained. In those and other cases there is a need for evaluation of model uncertainty given input variation. Our approach is to use various combinations of input parameters to determine the earliest and latest first arrivals, the lowest and highest peak concentration and the shortest and longest duration of contamination. Results of simulations show that even moderate ranges of input variation generate significant differences in model predictions. These differences are greater than obtained from simple one parameter at a time uncertainty analyses, because of combined influences of multiple parameters. For example, hydraulic conductivity, porosity and gradient together determine the seepage velocity, and variation of each of parameter needs to be considered in order to determine the extremes of velocity. The extreme parameter sets were found to be different for each of the three predicted model outputs (first arrival, maximum concentration, duration). This result shows that selection of an average or worst case parameter set depends on the desired output of the model in ways that might not be guessed without performing an uncertainty analysis.