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Nepal Hazard Risk Assessment
1 ANNEXURE 1
GIS METHODOLOGY FOR ASSESSMENT OF LANDSLIDE HAZARD
1.1 INTRODUCTION
The major triggering factors for landslides are extreme precipitation events, strong earthquakes, and
human activity. The strong correlation between landslides and other natural hazard triggers like
hurricanes or earthquakes has resulted in an underestimation of their socio-economic impact. This
underestimation contributes to reduced awareness and concern of both authorities and general public
about landslide risk.
1.2 MODEL FOR LANDSLIDE HAZARD EVALUATION
The term "landslide" in this study refers to events involving gravity-driven rapid mass movement downslope,
like rockslides, debris flows, and rainfall- and earthquake-induced slides; which pose a threat to
human life. Slow moving slides have significant economic consequences for constructions and
infrastructure, but rarely cause any fatalities.
to the information available in landslide inventories and physical processes. The general approach used
in the present study is a modified and improved version of the approach used by Nadim et al. (2006).
One of the key improvements in the present model is the increased resolution on the DEM and
consequently the slope data. In previous studies a 30 arc second resolution was used, whereas the present
study uses the 3 arc seconds SRTM dataset.
The hazard maps are divided in precipitation induced landslide hazard and earthquake induced landslide
hazard. The landslide hazard indices were estimated using the following equations:
H r = (S r x Six S h x S v )xT p (1)
He = (Sr X Si X Sh X Sv) x T s (2)
where Hr and He are landslide hazard indices for rainfall and earthquake induced landslides respectively,
Sr is the slope factor within a selected grid, Sl is lithological (or geological) conditions factor, S h
describes the soil moisture condition, T p is the precipitation factor and T s describes the seismic
conditions. The index Sv described the vegetation cover.
1.2.1 SLOPE FACTOR SR
The slope factor represents the natural landscape ruggedness within a grid unit. In February 2000, NASA
collected elevation data for much of the world using a radar instrument aboard the Space Shuttle. The
raw data collected on the mission were processed over three years. NASA has now released a global
elevation dataset called SRTM3, referring to the name of the mission and the resolution of the data,
which is 3 arc-seconds, or approximately 90 by 90 m per data sample near the equator. The SRTM3 data
set covers the globe from 60 degrees south latitude to 60 degrees north latitude. The vertical accuracy is
estimated such that 90% of posts are within 16m tolerance of the actual position.
Table 1.1. Slope angle classification.
Figure 1.1. Schematic approach for landslide hazard and risk evaluation.
As part of the "Natural Disaster Hotspots" project of The World Bank (Dilley et al., 2005), (Nadim et
al., 2006) adopted a simplified first-pass analysis method for identifying the global landslide hazard and
risk "hotspots",. The scale of their analysis was a grid of roughly 1km x 1km pixels where landslide
hazard, defined as the annual probability of occurrence of a potentially destructive landslide event, was
estimated by an appropriate combination of the triggering factors (mainly extreme precipitation and
seismicity) and susceptibility factors (slope, lithology, and soil moisture). The principles of the method
are depicted in Figure 1.1. The weights of different triggering and susceptibility factors were calibrated
Range of slopes angle (unit 1/100 degrees) Classification Sr
0000 - 0100 Very low 0
0101 - 0600 Low 1
0601 - 1200 Moderate 2
1201 - 1800 Medium 3
1801 - 2400 High 4
2401 - 4000 Very high 5
4001 - 4500 Medium 3
4501 - 9000 Medium 3
In the analyses, the slope data were reclassified on a geographical grid (WGS84). Cells were distributed
in the 6 different categories (0 - 5) presented in the category table above.
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