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Insight into the Characteristics and Triggers of Loess Landslides during the 2013 Heavy Rainfall Event in the Tianshui Area, China
- Source :
- Remote Sensing, Vol 15, Iss 17, p 4304 (2023)
- Publication Year :
- 2023
- Publisher :
- MDPI AG, 2023.
-
Abstract
- The 2013 heavy rainfall event (from June to July) in the Tianshui area triggered the most serious rainfall-induced group-occurring landslides since 1984, causing extensive casualties and economic losses. To better understand the characteristics and triggers of these loess landslides, we conducted a detailed analysis of the landslides and relevant influencing factors. Based on the detailed rainfall-induced landslide database obtained using visual interpretation of remote sensing images before and after rainfall, the correlation between the landslide occurrence and different influencing factors such as terrain, geomorphology, geology, and rainfall condition was analyzed. This rainfall event triggered approximately 54,000 landslides with a total area of 67.9 km2, mainly consisting of shallow loess landslides with elongated type, shallow rockslides, collapses, and mudflows. The landslides exhibited a clustered distribution, with the majority concentrated in two specific areas (i.e., Niangniangba and Shetang). The abundance index of landslides was closely associated with the hillslope gradient, total rainfall, and drainage (river) density. The landslide area density (LAD) was positively correlated with these influential factors, characterized by either an exponential or a linear relationship. The Middle Devonian Shujiaba formation (D2S) was identified to be highly susceptible to landslides, and the landslide events therein accounted for 35% of the total landslide occurrences within 22% of the study area. In addition, the E-SE aspect was more prone to landslides, while the W-NW aspect exhibited a low abundance of landslides.
Details
- Language :
- English
- ISSN :
- 20724292
- Volume :
- 15
- Issue :
- 17
- Database :
- Directory of Open Access Journals
- Journal :
- Remote Sensing
- Publication Type :
- Academic Journal
- Accession number :
- edsdoj.6da1165ec6b44e9fb5f83273983874b2
- Document Type :
- article
- Full Text :
- https://doi.org/10.3390/rs15174304