HR: 1340h
AN: H13A-0390    [Abstracts]
TI: Uncertainty in Hydrodynamic Dispersion: Using Data Quality Weights and Statistical Techniques to Develop Parameter Estimates and Distributions.
AU: Aly, A
EM: alaa.aly@nv.doe.gov
AF: Stoller-Navarro Joint Venture, 7710 W. Cheyenne Ave, Bldg 3, Las Vegas, NV 89129 United States
AU: * Denovio, N M
EM: nicole.denovio@nv.doe.gov
AF: Stoller-Navarro Joint Venture, 7710 W. Cheyenne Ave, Bldg 3, Las Vegas, NV 89129 United States
AB: Hydrodynamic dispersion is dependent on the size of a plume and the distance of transport. As a result, it is helpful to know the expected distances of contaminant transport a priori to best assess the range of dispersion values that would be most appropriate for modeling efforts. For large-scale models (scales of several km), with multiple point sources, uncertainty in longitudinal dispersion values is hard to constrain and can be difficult to implement. Data from field tracer tests and contaminated sites was collected, from published literature, to update the data set published by Gelhar et al. (1992). Data quality ranks were assigned based on data collection and interpretation methods. Data were then carefully evaluated to determine if individual data points were providing an undue influence on the distribution of the data. Two approaches were then tested to provide an appropriate model to quantify the uncertainty in longitudinal dispersivity values for transport distances for up to 20 km. The first approach is a probabilistic model where many discrete bins were formed to describe different scales of longitudinal dispersivity data collection, a CDF was fit to each bin. The resulting mixture distribution can then be sampled to obtain the range of values needed for a sensitivity analysis based on scale (travel distance) of the specific simulation. The second approach utilized a linear regression model where a single linear model of longitudinal dispersion was developed as a function of (log-transformed) scale that can be sampled for the observed residuals to complete an uncertainty analysis for transport models. The idea was to fit a linear regression line that can predict longitudinal dispersivity based on scale. Then, for sensitivity analyses, add a quantity based on the empirical distribution of the regression residuals to the linearly predicted value. Both of these approaches were evaluated using not only the typical goodness of fit measures, but also Monte Carlo simulations that were designed to demonstrate the effectiveness of the final models as well how these models reproduce the original data sets.
DE: 1829 Groundwater hydrology
DE: 1832 Groundwater transport
SC: Hydrology [H]
MN: 2004 AGU Fall Meeting