Derivation of Landslide Rainfall Thresholds by Geostatistical Methods in Southwest China

Zhongyuan Xu, Zhilin Xiao (), Xiaoyan Zhao, Zhigang Ma, Qun Zhang, Pu Zeng and Xiaoqiong Zhang
Additional contact information
Zhongyuan Xu: Sichuan Institute of Land and Space Ecological Restoration and Geological Hazard Prevention, Chengdu 610036, China
Zhilin Xiao: Sichuan Institute of Land and Space Ecological Restoration and Geological Hazard Prevention, Chengdu 610036, China
Xiaoyan Zhao: Faculty of Geosciences and Engineering, Southwest Jiaotong University, Chengdu 611756, China
Zhigang Ma: Sichuan Institute of Land and Space Ecological Restoration and Geological Hazard Prevention, Chengdu 610036, China
Qun Zhang: Sichuan Institute of Land and Space Ecological Restoration and Geological Hazard Prevention, Chengdu 610036, China
Pu Zeng: Natural Resources and Planning Bureau of Ya’an City, Ya’an 625099, China
Xiaoqiong Zhang: Natural Resources and Planning Bureau of Ya’an City, Ya’an 625099, China

Sustainability, 2024, vol. 16, issue 10, 1-15

Abstract: 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.

Keywords: rainfall threshold; landslide warning; geostatistical interpolation (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2071-1050/16/10/4044/pdf (application/pdf)
https://www.mdpi.com/2071-1050/16/10/4044/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:16:y:2024:i:10:p:4044-:d:1393001

Access Statistics for this article

Sustainability is currently edited by Ms. Alexandra Wu

More articles in Sustainability from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().