Influence of below-threshold rainfall on landslide occurrence based on Japanese cases

Soichi Kaihara (), Noriko Tadakuma, Hitoshi Saito and Hiroaki Nakaya
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Soichi Kaihara: Eight-Japan Engineering Consultants Inc
Noriko Tadakuma: Eight-Japan Engineering Consultants Inc
Hitoshi Saito: Nagoya University
Hiroaki Nakaya: Ministry of Land Infrastructure Transport and Tourism

Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, 2023, vol. 115, issue 3, No 21, 2307-2332

Abstract: Abstract Critical rainfall events are used in landslide early warning systems to predict the occurrence and severity of landslides. In the present study, historical critical rainfall events triggering landslides in Japan were analyzed. We mainly considered the amount of exceeding/nonexceeding rainfall (referred to as critical rainfall here) within an existing 1-km grid covering Japan where landslides occurred. Furthermore, this study could inform the LEWS operational performance. For this purpose, we used historical landslide records retrieved from a Japanese inventory, radar-based rainfall data (1-km grid resolution), and critical rainfall data collected over the past 17 years. Nearly equal numbers of rainfall events were identified with rainfall below and exceeding the critical rainfall level. The probability that a series of rainfall events could cause a landslide was approximately 1.15% when the critical rainfall level was exceeded and 0.09% when the critical rainfall level was not exceeded, with a difference of approximately 10 times. It was also found that even if critical rainfall was not exceeded, in the case of debris flows and slope failures, rainfall exceeding the critical rainfall level occurred one or two days before. In the case of landslides, there occurred rainfall exceeding the critical rainfall level one or two weeks before, and if critical rainfall was exceeded during a subsequent rainfall event, a landslide could occur. In addition, operational evaluation of the Japanese LEWS revealed a recall value of 0.486 in regard to the occurrence prediction accuracy, which was related to the fact that almost half of the rainfall events exhibited rainfall not exceeding the reference rainfall level. The nonoccurrence prediction accuracy reached 0.935, which was greatly influenced by true negative data of nonexceeding rainfall events, accounting for most of the data.

Keywords: Critical rainfall; Landslide; Antecedent precipitation index; Geological classification; Early warning systems (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/s11069-022-05639-7

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