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- W2109658532 abstract "This study tested a first-order perturbation method based on Karhunen-Loevè expansion (FP-KLE), to analyze flood inundation modeling under uncertainty. The floodplain roughness over a 2-dimensional domain was assumed to be a statistically heterogeneous field with log-normal distributions. Firstly, we attempted to use KLE to decompose the random field of log-transferred floodplain roughness N(x), which was based on the eigenvalues and eigenfunctions of the covariance function of N(x), and a set of orthogonal normal random variables. Secondly, the maximum flow depths were expanded by the first-order perturbation method by using the same set of random variables as used in the KLE decomposition. Then, a flood inundation model, named FLO-2D, was adopted to numerically solve the corresponding perturbation expansions. To illustrate the methodology, a one-in-five-years flood event was chosen as the study case. The results indicated that the mean of the maximum flow-depth field obtained from the proposed method was fairly close to that from Monte Carlo Simulation (MCS), but the standard deviation was somewhat higher. However, the FP-KLE method was computationally more efficient than MCS. The study verified the applicability of FP-KLE in handling uncertainties of flood modeling in a more efficient manner; further test with multiple inputs of random fields is desired." @default.
- W2109658532 created "2016-06-24" @default.
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- W2109658532 date "2014-01-01" @default.
- W2109658532 modified "2023-10-14" @default.
- W2109658532 title "Uncertainty analysis for flood inundation modelling with a random floodplain roughness field" @default.
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- W2109658532 doi "https://doi.org/10.1186/2193-2697-3-9" @default.
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