Evaluating underlying causative factors for earthquake-induced landslides and landslide susceptibility mapping in Upper Indrawati Watershed, Nepal

Geoenvironmental Disasters

Tetsuya Kubota¹  Pawan Gautam²  Aril Aditian³ 

[1] Faculty of Agriculture, Kyushu University, 744 Motooka, Nishi-ku, Fukuoka-shi, 819-0395, Fukuoka, Japan;Laboratory of Forest Conservation and Erosion Control, Agro-Environmental Sciences Department, Kyushu University, 744 Motooka, Nishi-ku, Fukuoka-shi, 819-0395, Fukuoka, Japan;Zoological Society of London-Nepal, Kathmandu, Nepal;The AHA Centre, 13120, East Jakarta, Indonesia;

关键词: Earthquake-induced landslide;    Co-seismic landslide;    Landslide distribution;    Landslide susceptibility mapping;    Logistic regression;    Upper-Indrawati Watershed;   

DOI  :  10.1186/s40677-021-00200-3

来源: Springer

【 摘 要 】

The main objective of this study is to understand the overall impact of earthquake in upper Indrawati Watershed, located in the high mountainous region of Nepal. Hence, we have assessed the relationship between the co-seismic landslide and underlying causative factors as well as performed landslide susceptibility mapping (LSM) to identify the landslide susceptible zone in the study area. We assessed the landslides distribution in terms of density, number, and area within 85 classes of 13 causal factors including slope, aspect, elevation, formation, land cover, distance to road and river, soil type, total curvature, seismic intensity, topographic wetness index, distance to fault, and flow accumulation. The earthquake-induced landslide is clustered in Northern region of the study area, which is dominated by steep rocky slope, forested land, and low human density. Among the causal factors, 'slope' showedpositive correlation for landslide occurrence. Increase in slope in the study area also escalates the landslide distribution, with highest density at 43%, landslide number at 4.34/km2, and landslide area abundance at 2.97% in a slope class (> 50°). We used logistic regression (LR) for LSM integrating with geographic information system. LR analysis depicts that land cover is the best predictor followed by slope and distance to fault with higher positive coefficient values. LSM was validated by assessing the correctly classified landslides under susceptibility categories using area under curve (AUC) and seed cell area index (SCAI). The LSM approach showed good accuracy with respective AUC values for success rate and prediction rate of 0.843 and 0.832. Similarly, the decreasing SCAI value from very low to very high susceptibility categories advise satisfactory accuracy of the LSM approach.

【 授权许可】

CC BY