Two-Dimensional Probabilistic Infiltration Analysis in a Hillslope Using First-Order Moment Approach
Affiliation
Univ Arizona, Dept Hydrol & Atmospher SciIssue Date
2019-03-01
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Show full item recordPublisher
WILEYCitation
Tang, R. , Cai, J. and Yeh, T. J. (2019), Two‐Dimensional Probabilistic Infiltration Analysis in a Hillslope Using First‐Order Moment Approach. Groundwater, 57: 226-237. doi:10.1111/gwat.12791Journal
GROUNDWATERRights
© 2018, National Ground Water Association.Collection Information
This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at repository@u.library.arizona.edu.Abstract
A first-order moment analysis method is introduced to evaluate the pore-water pressure variability within a hillslope due to spatial variability in saturated hydraulic conductivity (K-s) during rainfall. The influences of the variance of the natural logarithm of K-s(ln K-s), spatial structure anisotropy of ln K-s, and normalized vertical infiltration flux (q) on the evaluations of the pore-water pressure uncertainty are investigated. Results indicate different responses of pressure head variability in the unsaturated region and the saturated region. In the unsaturated region, a larger variance of ln K-s, a higher spatial structure anisotropy, and a smaller q lead to a larger variability in pressure head, while in the saturated region, the variability in pressure head increases with the increase of variance of ln K-s, the decrease of spatial structure anisotropy, or the increase of q. These variables have great impacts on the range of fluctuation of the phreatic surface within the hillslope. The influences of these three variables on the variance of pressure head within the saturated region are greater than those within the unsaturated region, and the variance of ln K-s has the greatest impact. These results yield useful insight into the effects of heterogeneity on pressure head and uncertainty associated with predicted flow field.Note
12 month embargo; published online: 25 April 2018ISSN
1745-6584PubMed ID
29691842Version
Final accepted manuscriptSponsors
Fundamental Research Funds for the Central Universities, China University of Geosciences (Wuhan) [CUG170686]; China Scholarship Council [201406410032]; National Natural Science Foundation of China [41172282, 41672313]; Strategic Environmental Research and Development Program [ER-1365]; Environmental Security and Technology Certification Program [ER201212]; National Science Foundation-Division of Earth Sciences [1014594]; Outstanding Oversea Professorship award through Jilin University from Department of Education, China; Global Expert award through Tianjin Normal University from the Thousand Talents Plan of Tianjin CityAdditional Links
https://onlinelibrary.wiley.com/doi/full/10.1111/gwat.12791ae974a485f413a2113503eed53cd6c53
10.1111/gwat.12791
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