Probabilistic collocation method for uncertainty analysis of soil infiltration in flood modelling

Huang, Y. and Qin, X. S. (2014). Probabilistic collocation method for uncertainty analysis of soil infiltration in flood modelling. In: Hubert Chanson and Luke Toombes, Hydraulic structures and society - Engineering challenges and extremes. 5th IAHR International Symposium on Hydraulic Structures, Brisbane, Australia, (1-8). 25-27 June 2014. doi:10.14264/uql.2014.40

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Author Huang, Y.
Qin, X. S.
Title of paper Probabilistic collocation method for uncertainty analysis of soil infiltration in flood modelling
Conference name 5th IAHR International Symposium on Hydraulic Structures
Conference location Brisbane, Australia
Conference dates 25-27 June 2014
Proceedings title Hydraulic structures and society - Engineering challenges and extremes
Journal name ISHS 2014 - Hydraulic Structures and Society - Engineering Challenges and Extremes: Proceedings of the 5th IAHR International Symposium on Hydraulic Structures
Place of Publication Brisbane, Australia
Publisher The University of Queensland
Publication Year 2014
Year available 2014
Sub-type Fully published paper
DOI 10.14264/uql.2014.40
Open Access Status DOI
ISBN 9781742721156
Editor Hubert Chanson
Luke Toombes
Start page 1
End page 8
Total pages 8
Language eng
Abstract/Summary The probabilistic collocation method (PCM) based on the Karhunen-Loevè expansion (KLE) and Polynomial chaos expansion (PCE) is applied for uncertainty analysis of flood inundation modelling. The floodplain hydraulic conductivity (KS) is considered as one of the important parameters in a 2-dimensional (2D) physical model FLO-2D (with Green-Ampt infiltration method) and has a nonlinear relationship with the flood simulation results, such as maximum flow depths (hmax). In this study, due to the spatial heterogeneity of soil, log-transformed Ks was assumed a random field in spatiality with normal distribution and decomposed with KLE in pairs of corresponding eigenvalues and eigenfuctions. The hmax random field is expanded by a second-order PCE approximation and the deterministic coefficients in PCE are solved by FLO-2D. To demonstrate this method, a simplified flood inundation case was used, where the mean and variance of the simulation outputs were compared with those from direct Monte Carlo Simulation. The comparison indicates that PCM could efficiently capture the statistics of flow depth in flood modelling with much less computational requirements.
Keyword Karhunen-Loevè expansion
PCM
PCE
Flood inundation modelling
Monte Carlo Simulation
Q-Index Code E1
Q-Index Status Provisional Code
Institutional Status Non-UQ

 
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Created: Tue, 13 May 2014, 14:25:34 EST by Anthony Yeates on behalf of Scholarly Communication and Digitisation Service