A Sensitivity Analysis of Simulated Infiltration Rates to Uncertain Discretization in the Moisture Content Domain
KAUST DepartmentDivision of Physical Science and Engineering, King Abdullah University of Science and Technology, Thuwal 23955-6900, Saudi Arabia
Permanent link to this recordhttp://hdl.handle.net/10754/656344
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AbstractAn unconditionally mass conservative hydrologic model proposed by Talbot and Ogden provides an effective and fast technique for estimating region-scale water infiltration. It discretizes soil moisture content into a proper but uncertain number of hydraulically interacting bins such that each bin represents a collection of pore sizes. To simulate rainfall-infiltration, a two-step alternating process runs until completion; and these two steps are surface water infiltration into bins and redistribution of inter-bin flow. Therefore, a nonlinear dynamical system in time is generated based on different bin front depths. In this study, using rigorous mathematical analysis first reveals that more bins can produce larger infiltration fluxes, and the overall flux variation is nonlinear with respect to the number of bins. It significantly implies that a greater variety of pore sizes produces a larger infiltration rate. An asymptotic analysis shows a finite change in infiltration rates for an infinite number of bins, which maximizes the heterogeneity of pore sizes. A corollary proves that the difference in the predicted infiltration rates using this model can be quantitatively bounded under a specific depth ratio of the deepest to the shallowest bin fronts. The theoretical results are demonstrated using numerical experiments in coarse and fine textured soils. Further studies will extend the analysis to the general selection of a suitable number of bins.
CitationLiu, L., & Yu, H. (2019). A Sensitivity Analysis of Simulated Infiltration Rates to Uncertain Discretization in the Moisture Content Domain. Water, 11(6), 1192. doi:10.3390/w11061192
SponsorsFunding: This research was sponsored by the Natural Science Foundation for Young Scientists of Jiangsu Province (grant no. BK20180450), the Natural Science Foundation of Nanjing University of Posts and Telecommunications NUPTSF (grant no. NY219080) and the China Postdoctoral Science Foundation (grant no. 2018T110531).
Acknowledgments: The authors gratefully acknowledge the assistance of Talbot, Ogden and Craig Douglas for their extraordinary insights and comments in the development of this paper. The authors also thank King Abdullah University of Science and Technology for the support on computational resources.
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