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Snow Avalanche Hazard Mapping for Lake Tahoe


According to the National Avalanche Center, every year 25 to 30 people die due to snow avalanches in USA, interestingly termed as "White Death". Record shows that snow avalanches have killed more people in forests than any other natural disaster, while they were participating in recreational activities.

Recently on Jan 17, 2020, a snow avalanche caused several people missing at the Alpine Meadows ski area and near lake Tahoe. Other than human casualty, it also creates blockage on roads and railway tracks and can even destroy power supply lines. Avalanches could be triggered by various reasons such as heavy snowfall, deforestation, vibrations, change in wind direction & speed, etc.

In view of this destructive power of avalanches a study is undertaken near the Lake Tahoe area, California to identify vulnerable locations that could be hit by snow avalanches. Weighted Linear Combination (WLC) method based on combined GIS and Remote Sensing techniques is used in the sample to create a potential hazard map for avalanches. It consists of three steps:

  • Reclassify all the datasets in the same range

  • Assign scores to the categories of each dataset

  • Weight each dataset based on relative importance and add them together

Necessary imports

In [1]:
from arcgis.gis import GIS
import arcgis.raster.functions
from arcgis.raster.functions import aspect, clip, colormap, con, curvature, remap, slope       

Connect to your GIS

In [2]:
gis = GIS(url='', username='arcgis_python', password='amazing_arcgis_123')

Get the data for analysis

Avalanche is a dynamic hazard or in other words has a fast onset and offset period, with favorable conditions impacted by land use-landcover, elevation, slope, curvature, variations in weather. In this study, USA NLCD Land Cover data is used for land cover, and ASTER DEM is used for elevation, slope and curvature to create a potential snow avalanche hazard map for the Lake Tahoe area.

In [3]:
# Aster DEM
dem ="DEM Lake Tahoe", 'imagery layer')[0]
DEM Lake Tahoe
DEM Lake TahoeImagery Layer by api_data_owner
Last Modified: February 28, 2020
0 comments, 1 views
In [4]:
lulc ="LULC Lake Tahoe", 'imagery layer')[0]
Lake Tahoe LULC
Lake Tahoe LULCImagery Layer by api_data_owner
Last Modified: February 28, 2020
0 comments, 1 views
In [5]:
aoi ="study area tahoe", 'feature layer')[0]
Study Area Tahoe
Study Area TahoeFeature Layer Collection by api_data_owner
Last Modified: February 28, 2020
0 comments, 2 views

Once the datasets are ready Weighted Linear Combination (WLC) is carried out. Here all the datasets were reclassified and scores were given to them so that all are in the same number scale. The score ranges between 0 to 5, with high score implying extremely vulnerable area and low score indicating less vulnerable or safe areas.

In [6]:
# Weighted Linear Combination (WLC) 
aoi_layer = aoi.layers[0]
aoi_feature = aoi_layer.query(where='OBJECTID=1')
aoi_feature.features[0].extent = dem.layers[0].extent
aoi_geom = aoi_feature.features[0].geometry
aoi_geom['spatialReference'] = {'wkid':3857}

For further analysis, all the datasets were clipped with the AOI boundary.

Clip and generate rasters from DEM

ASTER DEM imagery layer was used in the study, having a spatial resolution of 30m. The DEM was clipped with the AOI boundary, followed by creating classified rasters of DEM elevation, slope, curvature and aspect rasters from it.

In [7]:
dem_clip = clip(dem.layers[0], aoi_geom)
In [8]:
m ='Lake Tahoe, California')
m.legend = True