TY - JOUR
T1 - Robustness evaluation of the probability-based HTCA model for simulating debris-flow run-out extent
T2 - Case study of the 2010 Hongchun event, China
AU - Ma, Yangfan
AU - Han, Zheng
AU - Li, Yange
AU - Chen, Guangqi
AU - Wang, Weidong
AU - Chen, Ningsheng
AU - Hu, Guisheng
AU - Zhao, Lianheng
AU - Dou, Jie
N1 - Funding Information:
This study was financially supported by the National Key Research and Development Program of China (Grant No. 2018YFD1100401 ); the National Natural Science Foundation of China (Grant No. 52078493 ); the Natural Science Foundation for Excellent Young Scholars of Hunan (Grant No. 2021JJ20057 ); the Natural Science Foundation of Hunan Province (Grant No. 2022JJ30700 ); the Innovation Provincial Program of Hunan Province (Grant No. 2020RC3002 ); the JSPS KAKENHI (Grant No. JP19KK0121 ); and the Science and Technology Plan Project of Changsha (No. kq2106018 ). These financial supports are gratefully acknowledged. The authors also extend gratitude to editor-in-chief and two anonymous reviewers for their insightful comments.
Publisher Copyright:
© 2022 Elsevier B.V.
PY - 2023/1
Y1 - 2023/1
N2 - Probability-based cellular automaton (CA) models are useful alternative tools for simulating the extent of debris flow run-out. Despite that CA models have high computational efficiency, the simulation results are often sensitive to several key parameters, in particular, the steps of Monte-Carlo iterations (MCI), and the spatial resolution of the digital terrain model (DTM) data. This information is fundamental for evaluating the robustness of the CA model in dealing with various cases. This study improves upon the existing hydrodynamic and topography-based cellular automaton (HTCA) model. The basics of the model are briefly reviewed, and the sensitivity of the MCI value and the DTM spatial resolution are analyzed. Various practical scenarios regarding the effects of bed sediment entrainment and check dams are explored, which is beneficial for assessing the capability of the HTCA model in dealing with complex conditions. The August 14, 2010, post-seismic debris-flow event in the Hongchun catchment in the eastern Wenchuan area of China is selected as a case study to demonstrate the influence of key parameters on the simulation. The results illustrate the model's robustness in dealing with specific scenarios. Sensitivity analysis suggests that the MCI value and the DTM spatial resolution have obvious influences on the simulation results. The simulation results of the HTCA model can be improved if the MCI value is chosen based on the resolution of the input DTM data.
AB - Probability-based cellular automaton (CA) models are useful alternative tools for simulating the extent of debris flow run-out. Despite that CA models have high computational efficiency, the simulation results are often sensitive to several key parameters, in particular, the steps of Monte-Carlo iterations (MCI), and the spatial resolution of the digital terrain model (DTM) data. This information is fundamental for evaluating the robustness of the CA model in dealing with various cases. This study improves upon the existing hydrodynamic and topography-based cellular automaton (HTCA) model. The basics of the model are briefly reviewed, and the sensitivity of the MCI value and the DTM spatial resolution are analyzed. Various practical scenarios regarding the effects of bed sediment entrainment and check dams are explored, which is beneficial for assessing the capability of the HTCA model in dealing with complex conditions. The August 14, 2010, post-seismic debris-flow event in the Hongchun catchment in the eastern Wenchuan area of China is selected as a case study to demonstrate the influence of key parameters on the simulation. The results illustrate the model's robustness in dealing with specific scenarios. Sensitivity analysis suggests that the MCI value and the DTM spatial resolution have obvious influences on the simulation results. The simulation results of the HTCA model can be improved if the MCI value is chosen based on the resolution of the input DTM data.
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U2 - 10.1016/j.enggeo.2022.106918
DO - 10.1016/j.enggeo.2022.106918
M3 - Article
AN - SCOPUS:85142157179
SN - 0013-7952
VL - 312
JO - Engineering Geology
JF - Engineering Geology
M1 - 106918
ER -