DSC Publications

Core Information
Title: Effect of Water Retention Parameters on Predictive Uncertainty of Unsaturated Flow and Contaminant Transport
Abstract: Uncertainty assessment of flow and contaminant transport in the vadose zone entails probability density functions (PDFs) of soil hydraulic parameters. A non-conventional maximum likelihood (ML) approach is used in this study to estimate the PDFs of water retention parameters (e.g., van Genuchten α and m) for a situation common in field-scale modeling where core samples are sparse and prior PDFs of the parameters are unknown. In this situation, the non-conventional ML approach approximates the PDFs as multivariate Gaussian. The approach is non-conventional, since it gives the PDFs; conventional ML approaches yield ML parameter estimates and parameter uncertainty bounds, but not the PDFs. This study develops a method of estimating the mean and covariance of the multivariate Gaussian PDF based on results of least square methods that can be easily obtained in practice. The developed method is applied to and evaluated through numerical simulation of unsaturated flow and tracer transport at the proposed Yucca Mountain (YM) geological repository. Another focus of this study is to investigate effect of the uncertainty in the water retention parameters on the predictive uncertainty. By comparing the predictive uncertainty before and after incorporating the random water retention parameters, it is found that the random water retention parameters have limited effect on the mean predictions of the state variables including percolation flux, normalized cumulative mass arrival, and tracer travel time from the potential repository to the water table. However, incorporating the uncertainty in the water retention parameters significantly increases the magnitude and spatial extent of predictive uncertainty of the state variables. In particular, incorporating the random water retention parameters significantly changes the 5th and 95th percentiles of the tracer travel time by tens of thousands of years.
Keywords: Not Provided
Author Information
1. Pan, Feng[ Desert Research Institute ]Graduate
2. Ye, Ming[ FSU/DSC ]Neither
3. Zhu, Jianting[ Desert Research Institute ]Neither
Detailed Scientific Article Information
Journal Name: Vadose Zone Journal
Volume: 8
Page Range: 158-166
Article Number: Not Provided
Number of Pages: Not Provided
Year of Publication: 2009
Refereed: Yes
Digital Object Identifier (DOI), if available: doi:10.2136/vzj2008.0092
Official Url: Not Provided
ISSN: Not Provided
Subjects Information
1. Geology
2. Statistics

Contact: pubs@sc.fsu.edu