Uncertainty of mass discharge estimates from contaminated sites using a fully bayesian framework
Mass discharge estimates are increasingly being used in the management of contaminated sites and uncertainties related to such estimates are therefore of great practical importance. We present here a rigorous approach for quantifying the uncertainty in the mass discharge across a multilevel control plane given sparse field measurements. The method accounts for i) conceptual model uncertainty through Bayesian model averaging, ii) heterogeneity through Bayesian geostatistics with an uncertain geostatistical model, iii) uncertainty in the source characterization and the solute transport parameters and iv) measurement uncertainty. Through Monte Carlo simulation, an ensemble of unconditional steady-state plume realizations is generated numerically. By use of the Kalman Ensemble Generator (similar to the Ensemble Kalman Filter), the parameter field realizations are conditioned on site-specific data. In this way a posterior ensemble of realizations, all honouring the measured data at the control plane, are generated for each of the conceptual models considered. The ensembles from different conceptual models are combined via Bayesian model averaging, yielding the overall probability distribution of mass discharge. Elements of the method are illustrated with results from a synthetic test case.
M.Troldborg W.Nowak P.J.Binning P.L.Bjerg R.Helmig
Department of Environmental Engineering, Technical University of Denmark, MiljevejB113, 2800 Kgs.Lyn Institute of Hydraulic Engineering, Universitat Stuttgart, Pfaffenwaldring 61,70569 Stuttgart,German Department of Environmental Engineering, Technical University of Denmark, Miljevej B113,2800 Kgs.Lyn Department of Environmental Engineering, Technical University of Denmark, Miljevej B113, 2800 Kgs.Ly Institute of Hydraulic Engineering, Universitat Stuttgart, Pfaffenwaldring 61, 70569 Stuttgart, Germ
国际会议
武汉
英文
71-74
2009-09-20（万方平台首次上网日期，不代表论文的发表时间）