A systematic review on rainfall thresholds for landslides occurrence

Gonzalez, Fernanda Cristina Gonçalves; Cavacanti, Maria do Carmo Reis; Nahas Ribeiro, Wagner; Mendonça, Marcos Barreto de; Haddad, Assed Naked

doi:10.1016/j.heliyon.2023.e23247

Cited by 2 publications

(1 citation statement)

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“…A challenge persists in accurately discerning whether a landslide is triggered by shortterm or long-term rainfall within the empirical approach [39]. Generally speaking, shallow landslides and slope debris flow are usually triggered by short-term and intense rainfall, and relatively deeper landslides are more likely to occur during prolonged and sustained rainfall events [40,41].…”

Section: Introductionmentioning

confidence: 99%

Derivation of Landslide Rainfall Thresholds by Geostatistical Methods in Southwest China

Xu,

Xiao,

Zhao

et al. 2024

Sustainability

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Deriving rainfall thresholds is one of the most convenient and effective empirical methods for formulating landslide warnings. The previous rainfall threshold models only considered the threshold values for areas with landslide data. This study focuses on obtaining a threshold for each single landslide via the geostatistical interpolation of historical landslide–rainfall data. We collect the occurrence times and locations of landslides, along with the hourly rainfall data, for Dazhou. We integrate the short-term and long-term rainfall data preceding the landslide occurrences, categorizing them into four groups for analysis: 1 h–7 days (H1–7), 12 h–7 days (H12–D7), 24 h–7 days (H24–D7), and 72 h–7 days (H72–D7). Then, we construct a rainfall threshold distribution map based on the 2014–2020 data by means of Kriging interpolation. This process involves applying different splitting coefficients to distinguish the landslides triggered by short-term versus long-term rainfall. Subsequently, we validate these thresholds and splitting coefficients using the dataset for 2021. The results show that the best splitting coefficients for H1–D7, H12–D7, H24–D7, and H72–D7 are around 0.19, 0.52, 0.55, and 0.80, respectively. The accuracy of the predictions increases with the duration of the short-term rainfall, from 48% for H1–D7 to 67% for H72–D7. The performance of these threshold models indicates their potential for practical application in the sustainable development of geo-hazard prevention. Finally, we discuss the reliability and applicability of this method by considering various factors, including the influence of the interpolation techniques, data quality, weather forecast, and human activities.

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Section: Introductionmentioning

confidence: 99%