scholarly journals 2015 Nepal Earthquake: A Mass Wasting Balance

2020 ◽  
Author(s):  
Paolo Frattini ◽  
Andrea Valagussa ◽  
Elena Valbuzzi ◽  
Giovanni B. Crosta
Keyword(s):  
High Mobility ◽  
Google Earth ◽  
Constant Thickness ◽  
Mass Wasting ◽  
Landslide Area ◽  
Nepal Earthquake ◽  
Scar Area ◽  
Representative Subset ◽  
Entire Dataset ◽  
2015 Nepal Earthquake

<p>Following the 7.8 Mw earthquake that struck Nepal on April 25th, 2015, a high-resolution earthquake-induced landslide inventory was prepared. 21,151 landslides have been mapped using Google Earth’s pre- and post-earthquake images, helicopter footage and Google Crisis data. For a representative subset of landslides (~7%), the main scar area was manually distinguished from the landslide transport and deposition areas. Starting from this subset of scar areas, six different relationships between scar area and total landslide area were attained for six different intervals of the landslide aspect ratio (AR, i.e. ratio between landslide length and width) which is used as a proxy of landslide mobility. These relationships were used to estimate the scar area for the entire dataset. For landslides with AR lower than 3 (i.e. low-mobility landslides) the total volume was calculated with the equations proposed by Larsen et al. (2010) by using the total landslide area values. For landslides with an AR larger than 3 (i.e. high-mobility landslides) the volume was computed by applying the equation by Larsen et al. (2010) to landslide scar area only, and considering a constant thickness for the runout area (1m based on field activities). By comparing the landslide denudation and mass wasting to uplift and subsidence measured by InSAR (ALOS-2 satellite data) following the Nepal earthquake, the net volume change in the earthquake-affected area was calculated.</p>

scholarly journals Role of landslides on the volume balance of the Nepal 2015 earthquake sequence

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
A. Valagussa ◽  
P. Frattini ◽  
E. Valbuzzi ◽  
G. B. Crosta
Keyword(s):  
High Mobility ◽  
Vertical Displacement ◽  
Landslide Inventory ◽  
Nepal Earthquake ◽  
Volume Balance ◽  
Landslide Volume ◽  
Order Of Magnitude ◽  
2015 Nepal Earthquake

AbstractThe 7.8 Mw earthquake that struck Nepal on April 25th, 2015 triggered over 21,000 landslides over an area of more than 25,000 km2. These landslides contributed to mass wasting, partially compensating the tectonic uplift by the earthquake. In this paper we quantify the volume balance resulting from the 2015 earthquake uplift (or subsidence) and landslide erosion. Starting from a new complete earthquake-induced landslide inventory, we calculated landslide volume by adopting different strategies for low-mobility and high-mobility landslides, considering also the potential supply of sediments to the drainage network. The results show that the contribution of earthquake-induced landslides to erosion is about one order of magnitude smaller than the vertical coseismic displacement. We found landslide volume values, due to the 2015 Nepal earthquake, ranging between 251 (− 15/ + 16) Mm3 up to 1503 (− 183/ + 210) Mm3 based on the adopted method, and a volume due to coseismic vertical displacement of 2134 (± 1269) Mm3 for the whole area. The volume balance of the 2015 Nepal earthquake is strongly dominated by tectonic displacement. We show that these estimates depend on several uncertainties. We identified and quantified uncertainties related to: (1) the choice of empirical volume-area scaling relationships and their parameters; (2) the completeness and quality of landslide inventory through comparison with available inventories; (3) the approach adopted for the assessment of elongated landslide volume; (4) the InSAR displacement data.

Co-seismic and Post-seismic Changes in ZTD and TEC of the 2015 Nepal Earthquake

2021 ◽  
Author(s):  
A. Akilan ◽  
S. Padhy ◽  
V. P. Dimri ◽  
H. Schuh ◽  
K. K. Abdul Azeez
Keyword(s):  
Nepal Earthquake ◽  
2015 Nepal Earthquake

scholarly journals Post-Earthquake Night-Time Light Piecewise (PNLP) Pattern Based on NPP/VIIRS Night-Time Light Data: A Case Study of the 2015 Nepal Earthquake

2020 ◽  
Vol 12 (12) ◽  
pp. 2009 ◽  
Author(s):  
Shengjun Gao ◽  
Yunhao Chen ◽  
Long Liang ◽  
Adu Gong
Keyword(s):  
Infrared Imaging ◽  
Random Noise ◽  
Development Rate ◽  
Night Time ◽  
Nepal Earthquake ◽  
Decomposition Procedure ◽  
Spatiotemporal Changes ◽  
Reconstruction Rate ◽  
2015 Nepal Earthquake

Earthquakes are unpredictable and potentially destructive natural disasters that take a long time to recover from. Monitoring post-earthquake human activity (HA) is of great significance to recovery and reconstruction work. There is a strong correlation between night-time light (NTL) and HA, which aid in the study of spatiotemporal changes in post-earthquake human activities. However, seasonal and noise impact from National Polar-Orbiting Partnership Satellite Visible Infrared Imaging Radiometer Suite (NPP/VIIRS) data greatly limits their application. To tackle these issues, random noise and seasonal fluctuation of NPP/VIIRS from January 2014 to December 2018 is removed by adopting the seasonal-trend decomposition procedure based on loess (STL). Based on the theory of post-earthquake recovery model, a post-earthquake night-time light piecewise (PNLP) pattern is explored by employing the National Polar-Orbiting Partnership Satellite Visible Infrared Imaging Radiometer Suite (NPP/VIIRS) monthly data. PNLP indicators, including pre-earthquake development rate (kp), recovery rate (kr1), reconstruction rate (kr2), development rate (kd), relative reconstruction rate (krp) and loss (S), are defined to describe the PNLP pattern. Furthermore, the 2015 Nepal earthquake is chosen as a case study and the spatiotemporal changes in different areas are analyzed. The results reveal that: (1) STL is an effective algorithm for obtaining HA trend from the time series of denoising NTL; (2) the PNLP pattern, divided into four phases, namely the emergency phase (EP), recovery phase (RP-1), reconstruction phase (RP-2), and development phase (DP), aptly describes the variation in post-earthquake HA; (3) PNLP indicators are capable of evaluating the recovery differences across regions. The main socio-economic factors affecting the PNLP pattern and PNLP indicators are energy source for lighting, type of building, agricultural economy, and human poverty index. Based on the NPP/VIIRS data, the PNLP pattern can reflect the periodical changes of HA after earthquakes and provide an effective means for the analysis and evaluation of post-earthquake recovery and reconstruction.

Post-disaster Waste Management: Lessons Learnt from 2015 Nepal Earthquake

2019 ◽  
pp. 465-483 ◽  
Author(s):  
Nivesh Dugar ◽  
Sailesh Karanjit ◽  
Nawa Raj Khatiwada ◽  
Surya Man Shakya ◽  
Anish Ghimire
Keyword(s):  
Waste Management ◽  
Nepal Earthquake ◽  
Lessons Learnt ◽  
Post Disaster ◽  
2015 Nepal Earthquake
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