Landslide susceptibility in Rasuwa District of central Nepal after the 2015 Gorkha Earthquake

Abstract

The 2015 Gorkha Earthquake (7.8 Mw) triggered several landslides in central Nepal with major damages in 14 districts. Among them, the Rasuwa district at the north of Kathmandu Valley faced severe landslides due to rugged topography, complex geology and improper land use development. The landslides had blocked the Pasang Lhamu Highway and dammed the Trishuli River at many places. A total of 1416 landslide locations were detected in the district from high resolution satellite images in Google Earth. In this study, landslide susceptibility was modeled in the Rasuwa District by considering slope, aspect, elevation, geology, peak ground acceleration (PGA), land use, drainage proximity and thrust proximity as the predictive factors for landslide occurrences. The landslide inventory was split into 70% and 30% portions as the training dataset and testing dataset respectively. The results from modified frequency ratio (FR) suggest that effect of geology with prediction rate 2.52 is the highest among all factors and is followed by elevation (2.38) and drainage proximity (2.12). The results were verified using area under curve (AUC) and the prediction rate was found to be 79.14%. The computed landslide susceptibility map is helpful for land use planning and landslide risk reduction measure in the Rasuwa District.

Assessing Soil Erosion Susceptibility Using Revised Morgan-Morgan-Finney Model: A Case Study from Kulekhani Watershed, Makawanpur, Nepal

“…The Kulekhani Reservoir boasts an overall storage capacity of 85.3 million m 3 (Schreier et al, 1995). Kulekhani Watershed comprising steep hills and narrow valleys has uneven terrain (Pokharel and Thapa, 2018). The slope of the watershed ranges from 0° to 68.95° and elevation ranges from 1362 m to 2595 m at the dam site and the peak of Simbhanjyang of the Mahabharat range respectively (Fig.…”

Assessment of earthquake-induced landslide inventories and susceptibility maps using slope unit-based logistic regression and geospatial statistics

“…We investigated five existing inventories over the impacted region produced by Zhang et al 34 , Gnyawali et al 32 , Roback et al 33 , Kargel et al 31 , Pokharel and Thapa 35 at different times after the earthquake event. These five inventories are referred to as Inventories A, B, C, D and E, respectively.…”

“…Pokharel and Thapa 35 used 1.5 m pan-sharpened SPOT-5 satellite images acquired before Aril 2015 and freely available satellite images available after April 2015 to prepare a polygon-based landslide inventory in Rasuwa district (1544 km 2 ), including 1416 polygons. Most of the landslides were delineated using the images acquired in May 2015, before the monsoon.…”

Remote Sensing Approaches and Related Techniques to Map and Study Landslides

“…Some researchers used slightly different susceptible classes to develop landslide susceptibility maps such as unstable, quasi-stable, moderately stable, and stable (e.g., [12]). Some studies also used susceptibility indices: very high, high, moderate, low, and very low (e.g., [59][60][61]79]).…”