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A BENCHMARKING ANALYSIS FOR FIVE RADIONUCLIDE VADOSE ZONE MODELS (CHAIN, MULTIMED_DP, FECTUZ, HYDRUS, AND CHAIN 2D) IN SOIL SCREENING LEVEL CALCULATIONS
Citation:
Chen, J., R. Drake, Z. Lin, AND D G. Jewett*. A BENCHMARKING ANALYSIS FOR FIVE RADIONUCLIDE VADOSE ZONE MODELS (CHAIN, MULTIMED_DP, FECTUZ, HYDRUS, AND CHAIN 2D) IN SOIL SCREENING LEVEL CALCULATIONS. The Waste Management 2002 Symposium, Tucson, AZ, February 24-28, 2002.
Description:
Five radionuclide vadose zone models with different degrees of complexity (CHAIN, MULTIMED_DP, FECTUZ, HYDRUS, and CHAIN 2D) were selected for use in soil screening level (SSL) calculations. A benchmarking analysis between the models was conducted for a radionuclide (99Tc) release scenario at the Las Cruces Trench Site in New Mexico. Sensitivity of three model outputs to the input parameters were evaluated and compared among the models. The three outputs were peak contaminant concentrations, time to peak concentrations at the water table, as well as those of time to exceed the contaminant=s maximum critical level at a representative receptor well. Model parameters investigated include soil properties such as bulk density, water content, soil water retention parameters and hydraulic conductivity. Chemical properties examined include distribution coefficient, radionuclide half-life, dispersion coefficient, and molecular diffusion. Other soil characteristics such as recharge rate, was also examined. Model sensitivity was quantified in the form of sensitivity and relative sensitivity coefficients. Relative sensitivities were used to compare the sensitivities of different parameters. The analysis indicates that soil water content, recharge rate, saturated soil water content, and soil retention parameter, $, have a great influence on model outputs. In general, the results of sensitivities and relative sensitivities using five models are similar for a specific scenario. Slight differences were observed in predicted peak contaminant concentrations due to different mathematical treatment among models. The results of benchmarking and sensitivity analysis would facilitate the model selection and application of the model in SSL calculations.