The estimation of mass discharges from contaminated sites is valuable when evaluating the potential risk to down-gradient receptors, when assessing the efficiency of a site remediation, or when determining the degree of natural attenuation. Given the many applications of mass discharge estimation, it is important to quantify the associated uncertainties. Here a rigorous approach for quantifying the uncertainty in the mass discharge across a multilevel control plane is presented. The method accounts for (1) conceptual model uncertainty using multiple conceptual models and Bayesian model averaging (BMA), (2) heterogeneity through Bayesian geostatistics with an uncertain geostatistical model, and (3) measurement uncertainty. Through unconditional and conditional Monte Carlo simulation, ensembles of steady state plume realizations are generated. The conditional ensembles honor all measured data at the control plane for each of the conceptual models considered. The probability distribution of mass discharge is obtained by combining all ensembles via BMA. The method was applied to a trichloroethylene-contaminated site located in northern Copenhagen. Four essentially different conceptual models based on two source zone models and two geological models were set up for this site, each providing substantially different prior mass discharge distributions. After conditioning to data, the predicted mass discharge distributions from each of the four conceptual models all approach each other. This indicates that the data set available at the site is strong and that the estimated mass discharge is robust to the assumed conceptual models and their boundary conditions. On the basis of the results, we discuss which of the conceptual models is most likely to reflect the true site conditions and analyze the relative proportions and importance of uncertainties as well as the impact of different data types on mass discharge uncertainty.
Troldborg, M., Nowak, W., Tuxen, N., Bjerg, P. L., Helmig, R., & Binning, P. J. (2010). Uncertainty evaluation of mass discharge estimates from a contaminated site using a fully Bayesian framework. Water Resources Research, 46, W12552. https://doi.org/10.1029/2010WR009227