Case study of a landslide continuous probability rainfall threshold analysis based on the prediction interval principle

© 2023. The Author(s)..

Bazhong City is located on stratum dominated by red-bed conditions. This type of weak geological condition with sand and mudstone interbedding is very easily affected by climatic conditions to produce rainfall-type landslides. On the basis of landslide data statistics collected in Bazhong City from 2011 to 2019, this paper uses ERA5-Land rainfall data to statistically analyze the correlation between rainfall and landslide events in Bazhong City. The landslide events in Bazhong City are greatly affected by rainfall events lasting for 10 days. Considering the influence of rainfall seepage and other processes, an effective cumulative rainfall-duration threshold curve for Bazhong City is obtained via median nonlinear fitting. Then, on the basis of the prediction interval, the rainfall threshold formula under different landslide occurrence probabilities is obtained and the critical threshold curve with a non-exceeding probability of 1% in Bazhong City is calculated and verified. Subsequently, a continuous probability distribution fitting function of landslide occurrence is established and a continuous probability distribution surface with a good fitting effect in Bazhong City is obtained. This allows a definite probability of whether future rainfall events will induce landslides to be obtained, providing an important basis for engineering disaster prevention and mitigation.

Medienart:

E-Artikel

Erscheinungsjahr:

2023

Erschienen:

2023

Enthalten in:

Zur Gesamtaufnahme - volume:13

Enthalten in:

Scientific reports - 13(2023), 1 vom: 10. Feb., Seite 2434

Sprache:

Englisch

Beteiligte Personen:

Huang, Yu [VerfasserIn]
Zhao, Cuizhu [VerfasserIn]
Jin, Xiaoyan [VerfasserIn]
Zhu, Yan [VerfasserIn]
Peng, Ming [VerfasserIn]
Chen, Zhiyi [VerfasserIn]

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Journal Article

Anmerkungen:

Date Completed 13.02.2023

Date Revised 14.02.2023

published: Electronic

Citation Status PubMed-not-MEDLINE

doi:

10.1038/s41598-023-29625-6

funding:

Förderinstitution / Projekttitel:

PPN (Katalog-ID):

NLM352749547