Assessment of seismically-induced landslide susceptibility after the 2015 Gorkha earthquake, Nepal

2017 ◽  
Vol 78 (3) ◽  
pp. 1829-1842 ◽  
Author(s):  
Suchita Shrestha ◽  
Tae-Seob Kang
Keyword(s):  
Landslide Susceptibility ◽  
Gorkha Earthquake ◽  
2015 Gorkha Earthquake

Landslide susceptibility assessment of the region affected by the 25 April 2015 Gorkha earthquake of Nepal

2016 ◽  
Vol 13 (11) ◽  
pp. 1941-1957 ◽  
Author(s):  
Amar Deep Regmi ◽  
Megh Raj Dhital ◽  
Jian-qiang Zhang ◽  
Li-jun Su ◽  
Xiao-qing Chen
Keyword(s):  
Landslide Susceptibility ◽  
Susceptibility Assessment ◽  
Gorkha Earthquake ◽  
Landslide Susceptibility Assessment ◽  
2015 Gorkha Earthquake

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

2019 ◽  
Vol 59 ◽  
pp. 79-88 ◽  
Author(s):  
Badal Pokharel ◽  
Prem Bahadur Thapa
Keyword(s):  
Land Use ◽  
Landslide Susceptibility ◽  
Google Earth ◽  
Training Dataset ◽  
Slope Aspect ◽  
Gorkha Earthquake ◽  
Central Nepal ◽  
Prediction Rate ◽  
2015 Gorkha Earthquake ◽  
High Resolution Satellite Images

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.

scholarly journals Rapid Terrain Assessment for Earthquake-Triggered Landslide Susceptibility With High-Resolution DEM and Critical Acceleration

2021 ◽  
Vol 9 ◽  
Author(s):  
Season Maharjan ◽  
Kaushal Raj Gnyawali ◽  
Dwayne D. Tannant ◽  
Chong Xu ◽  
Pascal Lacroix
Keyword(s):  
High Resolution ◽  
Landslide Susceptibility ◽  
Earthquake Ground Motion ◽  
Depth Information ◽  
Gorkha Earthquake ◽  
Ground Acceleration ◽  
Critical Acceleration ◽  
Field Reconnaissance ◽  
2015 Gorkha Earthquake ◽  
Glaciated Valley

Earthquake ground motion often triggers landslides in mountainous areas. A simple, robust method to quickly evaluate the terrain’s susceptibility of specific locations to earthquake-triggered landslides is important for planning field reconnaissance and rescues after earthquakes. Different approaches have been used to estimate coseismic landslide susceptibility using Newmark’s sliding block model. This model requires an estimate of the landslide depth or thickness, which is a difficult parameter to estimate. We illustrate the use of Newmark sliding block’s critical acceleration for a glaciated valley affected by the 2015 Gorkha earthquake in Nepal. The landslide data came from comparing high-resolution pre- and post-earthquake digital elevation models (DEMs) derived from Spot 6/7 images. The areas where changes were detected provided an inventory of all the landslides triggered by the earthquake. The landslide susceptibility was modeled in a GIS environment using as inputs the pre-earthquake terrain and slope angles, the peak ground acceleration from the 2015 Gorkha earthquake, and a geological map. We exploit the depth information for the landslides (obtained by DEM difference) to apply the critical acceleration model. The spatial distribution of the predicted earthquake-triggered landslides matched the actual landslides when the assumed landslide thickness in the model is close to the median value of the actual landslide thickness (2.6 m in this case). The landslide predictions generated a map of landslide locations close to those observed and demonstrated the applicability of critical acceleration for rapidly creating a map of earthquake-triggered landslides.

scholarly journals The Influence of Surface Topography on the Weak Ground Shaking in Kathmandu Valley during the 2015 Gorkha Earthquake, Nepal

Sensors ◽  
2020 ◽  
Vol 20 (3) ◽  
pp. 678
Author(s):  
Mark van der Meijde ◽  
Md Ashrafuzzaman ◽  
Norman Kerle ◽  
Saad Khan ◽  
Harald van der Werff
Keyword(s):  
Numerical Simulations ◽  
Surface Topography ◽  
Seismic Energy ◽  
Spectral Element ◽  
Kathmandu Valley ◽  
Ground Displacement ◽  
Gorkha Earthquake ◽  
Ground Shaking ◽  
Earthquake Scenarios ◽  
2015 Gorkha Earthquake

It remains elusive why there was only weak and limited ground shaking in Kathmandu valley during the 25 April 2015 Mw 7.8 Gorkha, Nepal, earthquake. Our spectral element numerical simulations show that, during this earthquake, surface topography restricted the propagation of seismic energy into the valley. The mountains diverted the incoming seismic wave mostly to the eastern and western margins of the valley. As a result, we find de-amplification of peak ground displacement in most of the valley interior. Modeling of alternative earthquake scenarios of the same magnitude occurring at different locations shows that these will affect the Kathmandu valley much more strongly, up to 2–3 times more, than the 2015 Gorkha earthquake did. This indicates that surface topography contributed to the reduced seismic shaking for this specific earthquake and lessened the earthquake impact within the valley.

Characterizing the Kathmandu Valley sediment response through strong motion recordings of the 2015 Gorkha earthquake sequence

2017 ◽  
Vol 714-715 ◽  
pp. 146-157 ◽  
Author(s):  
S. Rajaure ◽  
D. Asimaki ◽  
E.M. Thompson ◽  
S. Hough ◽  
S. Martin ◽  
...  
Keyword(s):  
Strong Motion ◽  
Earthquake Sequence ◽  
Kathmandu Valley ◽  
Gorkha Earthquake ◽  
2015 Gorkha Earthquake

scholarly journals Comparison of Earthquake-Triggered Landslide Inventories: A Case Study of the 2015 Gorkha Earthquake, Nepal

Geosciences ◽  
2019 ◽  
Vol 9 (10) ◽  
pp. 437 ◽  
Author(s):  
Meena ◽  
Tavakkoli Piralilou
Keyword(s):  
Risk Mitigation ◽  
Landslide Susceptibility Mapping ◽  
Gorkha Earthquake ◽  
Area Distribution ◽  
2015 Gorkha Earthquake ◽  
The World ◽  
Mapping Techniques ◽  
Event Based ◽  
The Impact

Despite landslide inventories being compiled throughout the world every year at different scales, limited efforts have been made to critically compare them using various techniques or by different investigators. Event-based landslide inventories indicate the location, distribution, and detected boundaries of landslides caused by a single event, such as an earthquake or a rainstorm. Event-based landslide inventories are essential for landslide susceptibility mapping, hazard modeling, and further management of risk mitigation. In Nepal, there were several attempts to map landslides in detail after the Gorkha earthquake. Particularly after the main event on 25 April 2015, researchers around the world mapped the landslides induced by this earthquake. In this research, we compared four of these published inventories qualitatively and quantitatively using different techniques. Two principal methodologies, namely the cartographical degree of matching and frequency area distribution (FAD), were optimized and applied to evaluate inventory maps. We also showed the impact of using satellite imagery with different spatial resolutions on the landslide inventory generation by analyzing matches and mismatches between the inventories. The results of our work give an overview of the impact of methodology selection and outline the limitations and advantages of different remote sensing and mapping techniques for landslide inventorying.

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