Technical Papers
Aug 12, 2022

Simple Method for Shallow Landslide Prediction Based on Wide-Area Terrain Analysis Incorporated with Surface and Subsurface Flows

Publication: Natural Hazards Review
Volume 23, Issue 4

Abstract

During intense rainfall, shallow landslides often occur on natural slopes due to the rising groundwater level. This study proposed a simple model for rainfall-induced shallow landslide prediction in broad areas based on an infinite homogeneous slope assumption and a simplified hydrological process. For this purpose, a numerical model, TAG_FLOW, was developed in Fortran 90. Its structure was divided into four modules: (1) a surface infiltration module based on the Green-Ampt model, (2) a groundwater-level simulation based on a previously developed simple one-dimensional vertical prediction method combined with horizontal groundwater distribution, (3) a surface water control module based on two-dimensional shallow water equations and a depression removal process, and (4) slope stability estimated using the assumption of an infinite homogeneous slope. The simplified hydrological process for the surface and subsurface flows was validated using a tilted V-catchment model and tested with the Abdul and Gillham system. For the performance evaluation of TAG_FLOW, we applied the model to analyze rainfall-induced landslides for an area in North Kyushu during the historically large torrential rainfall event in July 2017. The landslide prediction capacity of the TAG_FLOW model was evaluated by the TRIGRS model and the actual landslide map. The results confirmed that both groundwater and surface water contributed to the occurrence of landslides in the study area. The prediction performance of the TAG_FLOW model was slightly better than that of the TRIGRS model, but both models overpredicted landslides in the study area.

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Data Availability Statement

All data, models, and code generated or used during the study appear in the published article.

Acknowledgments

This research was supported by a Ministry of Education, Culture, Sports, Science and Technology scholarship and a Japan Science and Technology Agency Strategic International Collaborative Research Program grant (e-ASIA Joint Research Program), titled “Establishment of a Landslide Monitoring and Prediction System” (Grant JPMJSC18E3). The authors are grateful to the Kyushu Regional Development Bureau, Ministry of Land, Infrastructure, Transport and Tourism of Japan for providing the DEM data, the Ministry of Land, Infrastructure, Transport and Tourism of Japan for the XRAIN data, the Geospatial Information Authority of Japan for the landslide map, and the Geological Survey of Japan for the geological map data. The authors also appreciate the support of Dr. Takatsugu Ozaki, Ms. Akino Watanate, and other members of the e-ASIA Joint Research Program.

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Natural Hazards Review
Volume 23Issue 4November 2022

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Received: Dec 31, 2021
Accepted: May 19, 2022
Published online: Aug 12, 2022
Published in print: Nov 1, 2022
Discussion open until: Jan 12, 2023

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Nguyen Van Thang [email protected]
Ph.D. Student, Graduate School of Science and Technology, Gunma Univ., 1-5-1, Tenjin-cho, Kiryu, Gunma 376-8515, Japan (corresponding author). Email: [email protected]
Akihiko Wakai
Professor, Graduate School of Science and Technology, Gunma Univ., 1-5-1, Tenjin-cho, Kiryu, Gunma 376-8515, Japan.
Go Sato
Professor, Graduate School of Environmental Informations, Teikyo Heisei Univ., 4-21-2 Nakano, Nakano-ku, Tokyo 164-8530, Japan.
Doctor, Dept. of Civil Engineering, Thuyloi Univ., 175 Tay Son, Dong Da, Hanoi 100000, Vietnam. ORCID: https://orcid.org/0000-0002-8034-8650
Nanaha Kitamura
Master’s Student, Graduate School of Science and Technology, Gunma Univ., 1-5-1, Tenjin-cho, Kiryu, Gunma 376-8515, Japan.

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