Landslides triggered in the Rasuwa, Nuwakot and Dhading Districts (Nepal) during the M 7.8 Nepal (Gorkha) earthquake of April 25, 2015

Publikation: StudienabschlussarbeitMasterarbeitForschung

Abstract

The Gorkha (Nepal) earthquake of April 25, 2015, with a magnitude of 7.8 Mw, has been the largest earthquake in this area since 1934. Approximately 8800 people were killed during the earthquake, tens of thousands of people were injured and hundreds of thousands of buildings were destroyed. Thousands of landslides were triggered, destroying roads and villages or blocking roads and rivers (Hashash et al., 2015).
Due to the fact that landslides occur where they have previously occurred or in similar conditions, it is important to make an earthquake triggered landslide inventory including all the geomorphometric and spatial landslide specific parameters. To generate this inventory for the three districts Rasuwa, Nuwakot and Dhading, mapping on high resolution Google Earth Pro satellite imagery, combined with QGIS, Excel and R Studio was performed. For each of the detected and by the earthquake activated landslide (in total 8330), the location, surface area, slope angle, aspect ratio, geologic unit, slope aspect, slope relief, elevation, distance to epicenter and ground slip magnitude was determined. Besides, information about affected infrastructure (roads or villages) and whether the landslide was earthquake induced or pre – existing but reactivated was collected. These datasets were statistically analyzed and plotted as diagrams or maps. Further, a map was created including the landslide density and the peak ground acceleration (PGA) (Earthquake Damage Analysis Center, 2015a).
The data evaluation shows that there are four major factors which influenced the susceptibility for landsliding. The first is the ground slip magnitude, which has the highest landslide density between the values of 3.0 m and 5.0 m. Because of the predominantly flat topography in areas of high ground slip magnitudes, the landslide density decreases at the highest magnitudes. The second parameter is the slope angle. The highest landslide susceptibility was located at slope angles of 55° - 60° even though the most present slope angle of the research area was at 25° - 30°. The third factor was the geology. The Augen gneisses and mica granites occurring in the Kuncha Group and Nawakot Group and the mainly shallow marine sediments of the Kuncha Group had by far the highest landslide density. This effect is thought to be due to structural, textural and weathering properties. The forth factor is the slope aspect. A distinct trend of landslides occurring preferably on south and west facing slopes was present. Some authors explain this trend by various factors which can increase the susceptibility of the slope for landsliding already before the earthquake occurs by effecting the vegetation, the weathering, the degree of saturation or the rock mass strength (e.g. Guzzetti et al., 1999; Evans et al., 1999; Nagarajan et al., 2000; Yalcin, 2008). These factors include exposure to sunlight, drying winds, rainfall and discontinuities. Additionally, the ground slip direction can have an influence too. To define the reliability of the data evaluation and to determine the effects of the monsoon on the landslides and the landslide conditions, two week of field reconnaissance were undertaken. This reconnaissance showed a high correlation of the dataset with the actual site conditions. The effect of the monsoon was determined as extreme. It already reshaped the landslides and activated new ones.
Based on this landslide inventory, earthquake triggered landslide hazard and risk assessments can be performed including hazard maps. Implementation of this dataset into a greater inventory can contribute to the development of detailed hazard assessment campaigns on a regional scale.
Originalspracheenglisch
QualifikationMaster of Science
Gradverleihende Hochschule
  • Technische Universität Graz (90000)
Betreuer/-in / Berater/-in
  • Kieffer, Daniel Scott, Betreuer
PublikationsstatusVeröffentlicht - 2018

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landslide
earthquake
slope angle
hazard assessment
road
monsoon
village
weathering
earthquake damage
earthquake epicenter
mica
satellite imagery
marine sediment
discontinuity
risk assessment
relief
surface area
diagram
geology
infrastructure

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@phdthesis{a5b3f72591b44805b655a138c0d63ef8,
title = "Landslides triggered in the Rasuwa, Nuwakot and Dhading Districts (Nepal) during the M 7.8 Nepal (Gorkha) earthquake of April 25, 2015",
abstract = "The Gorkha (Nepal) earthquake of April 25, 2015, with a magnitude of 7.8 Mw, has been the largest earthquake in this area since 1934. Approximately 8800 people were killed during the earthquake, tens of thousands of people were injured and hundreds of thousands of buildings were destroyed. Thousands of landslides were triggered, destroying roads and villages or blocking roads and rivers (Hashash et al., 2015).Due to the fact that landslides occur where they have previously occurred or in similar conditions, it is important to make an earthquake triggered landslide inventory including all the geomorphometric and spatial landslide specific parameters. To generate this inventory for the three districts Rasuwa, Nuwakot and Dhading, mapping on high resolution Google Earth Pro satellite imagery, combined with QGIS, Excel and R Studio was performed. For each of the detected and by the earthquake activated landslide (in total 8330), the location, surface area, slope angle, aspect ratio, geologic unit, slope aspect, slope relief, elevation, distance to epicenter and ground slip magnitude was determined. Besides, information about affected infrastructure (roads or villages) and whether the landslide was earthquake induced or pre – existing but reactivated was collected. These datasets were statistically analyzed and plotted as diagrams or maps. Further, a map was created including the landslide density and the peak ground acceleration (PGA) (Earthquake Damage Analysis Center, 2015a). The data evaluation shows that there are four major factors which influenced the susceptibility for landsliding. The first is the ground slip magnitude, which has the highest landslide density between the values of 3.0 m and 5.0 m. Because of the predominantly flat topography in areas of high ground slip magnitudes, the landslide density decreases at the highest magnitudes. The second parameter is the slope angle. The highest landslide susceptibility was located at slope angles of 55° - 60° even though the most present slope angle of the research area was at 25° - 30°. The third factor was the geology. The Augen gneisses and mica granites occurring in the Kuncha Group and Nawakot Group and the mainly shallow marine sediments of the Kuncha Group had by far the highest landslide density. This effect is thought to be due to structural, textural and weathering properties. The forth factor is the slope aspect. A distinct trend of landslides occurring preferably on south and west facing slopes was present. Some authors explain this trend by various factors which can increase the susceptibility of the slope for landsliding already before the earthquake occurs by effecting the vegetation, the weathering, the degree of saturation or the rock mass strength (e.g. Guzzetti et al., 1999; Evans et al., 1999; Nagarajan et al., 2000; Yalcin, 2008). These factors include exposure to sunlight, drying winds, rainfall and discontinuities. Additionally, the ground slip direction can have an influence too. To define the reliability of the data evaluation and to determine the effects of the monsoon on the landslides and the landslide conditions, two week of field reconnaissance were undertaken. This reconnaissance showed a high correlation of the dataset with the actual site conditions. The effect of the monsoon was determined as extreme. It already reshaped the landslides and activated new ones. Based on this landslide inventory, earthquake triggered landslide hazard and risk assessments can be performed including hazard maps. Implementation of this dataset into a greater inventory can contribute to the development of detailed hazard assessment campaigns on a regional scale.",
author = "Christoph Zambanini",
year = "2018",
language = "English",
school = "Graz University of Technology (90000)",

}

TY - THES

T1 - Landslides triggered in the Rasuwa, Nuwakot and Dhading Districts (Nepal) during the M 7.8 Nepal (Gorkha) earthquake of April 25, 2015

AU - Zambanini, Christoph

PY - 2018

Y1 - 2018

N2 - The Gorkha (Nepal) earthquake of April 25, 2015, with a magnitude of 7.8 Mw, has been the largest earthquake in this area since 1934. Approximately 8800 people were killed during the earthquake, tens of thousands of people were injured and hundreds of thousands of buildings were destroyed. Thousands of landslides were triggered, destroying roads and villages or blocking roads and rivers (Hashash et al., 2015).Due to the fact that landslides occur where they have previously occurred or in similar conditions, it is important to make an earthquake triggered landslide inventory including all the geomorphometric and spatial landslide specific parameters. To generate this inventory for the three districts Rasuwa, Nuwakot and Dhading, mapping on high resolution Google Earth Pro satellite imagery, combined with QGIS, Excel and R Studio was performed. For each of the detected and by the earthquake activated landslide (in total 8330), the location, surface area, slope angle, aspect ratio, geologic unit, slope aspect, slope relief, elevation, distance to epicenter and ground slip magnitude was determined. Besides, information about affected infrastructure (roads or villages) and whether the landslide was earthquake induced or pre – existing but reactivated was collected. These datasets were statistically analyzed and plotted as diagrams or maps. Further, a map was created including the landslide density and the peak ground acceleration (PGA) (Earthquake Damage Analysis Center, 2015a). The data evaluation shows that there are four major factors which influenced the susceptibility for landsliding. The first is the ground slip magnitude, which has the highest landslide density between the values of 3.0 m and 5.0 m. Because of the predominantly flat topography in areas of high ground slip magnitudes, the landslide density decreases at the highest magnitudes. The second parameter is the slope angle. The highest landslide susceptibility was located at slope angles of 55° - 60° even though the most present slope angle of the research area was at 25° - 30°. The third factor was the geology. The Augen gneisses and mica granites occurring in the Kuncha Group and Nawakot Group and the mainly shallow marine sediments of the Kuncha Group had by far the highest landslide density. This effect is thought to be due to structural, textural and weathering properties. The forth factor is the slope aspect. A distinct trend of landslides occurring preferably on south and west facing slopes was present. Some authors explain this trend by various factors which can increase the susceptibility of the slope for landsliding already before the earthquake occurs by effecting the vegetation, the weathering, the degree of saturation or the rock mass strength (e.g. Guzzetti et al., 1999; Evans et al., 1999; Nagarajan et al., 2000; Yalcin, 2008). These factors include exposure to sunlight, drying winds, rainfall and discontinuities. Additionally, the ground slip direction can have an influence too. To define the reliability of the data evaluation and to determine the effects of the monsoon on the landslides and the landslide conditions, two week of field reconnaissance were undertaken. This reconnaissance showed a high correlation of the dataset with the actual site conditions. The effect of the monsoon was determined as extreme. It already reshaped the landslides and activated new ones. Based on this landslide inventory, earthquake triggered landslide hazard and risk assessments can be performed including hazard maps. Implementation of this dataset into a greater inventory can contribute to the development of detailed hazard assessment campaigns on a regional scale.

AB - The Gorkha (Nepal) earthquake of April 25, 2015, with a magnitude of 7.8 Mw, has been the largest earthquake in this area since 1934. Approximately 8800 people were killed during the earthquake, tens of thousands of people were injured and hundreds of thousands of buildings were destroyed. Thousands of landslides were triggered, destroying roads and villages or blocking roads and rivers (Hashash et al., 2015).Due to the fact that landslides occur where they have previously occurred or in similar conditions, it is important to make an earthquake triggered landslide inventory including all the geomorphometric and spatial landslide specific parameters. To generate this inventory for the three districts Rasuwa, Nuwakot and Dhading, mapping on high resolution Google Earth Pro satellite imagery, combined with QGIS, Excel and R Studio was performed. For each of the detected and by the earthquake activated landslide (in total 8330), the location, surface area, slope angle, aspect ratio, geologic unit, slope aspect, slope relief, elevation, distance to epicenter and ground slip magnitude was determined. Besides, information about affected infrastructure (roads or villages) and whether the landslide was earthquake induced or pre – existing but reactivated was collected. These datasets were statistically analyzed and plotted as diagrams or maps. Further, a map was created including the landslide density and the peak ground acceleration (PGA) (Earthquake Damage Analysis Center, 2015a). The data evaluation shows that there are four major factors which influenced the susceptibility for landsliding. The first is the ground slip magnitude, which has the highest landslide density between the values of 3.0 m and 5.0 m. Because of the predominantly flat topography in areas of high ground slip magnitudes, the landslide density decreases at the highest magnitudes. The second parameter is the slope angle. The highest landslide susceptibility was located at slope angles of 55° - 60° even though the most present slope angle of the research area was at 25° - 30°. The third factor was the geology. The Augen gneisses and mica granites occurring in the Kuncha Group and Nawakot Group and the mainly shallow marine sediments of the Kuncha Group had by far the highest landslide density. This effect is thought to be due to structural, textural and weathering properties. The forth factor is the slope aspect. A distinct trend of landslides occurring preferably on south and west facing slopes was present. Some authors explain this trend by various factors which can increase the susceptibility of the slope for landsliding already before the earthquake occurs by effecting the vegetation, the weathering, the degree of saturation or the rock mass strength (e.g. Guzzetti et al., 1999; Evans et al., 1999; Nagarajan et al., 2000; Yalcin, 2008). These factors include exposure to sunlight, drying winds, rainfall and discontinuities. Additionally, the ground slip direction can have an influence too. To define the reliability of the data evaluation and to determine the effects of the monsoon on the landslides and the landslide conditions, two week of field reconnaissance were undertaken. This reconnaissance showed a high correlation of the dataset with the actual site conditions. The effect of the monsoon was determined as extreme. It already reshaped the landslides and activated new ones. Based on this landslide inventory, earthquake triggered landslide hazard and risk assessments can be performed including hazard maps. Implementation of this dataset into a greater inventory can contribute to the development of detailed hazard assessment campaigns on a regional scale.

M3 - Master's Thesis

ER -