The deaths of army personnel who were buried under a snow avalanche in Siachen Glacier on February 3, 2016, shocked the nation. Soldiers, trekkers and researchers working on the Himalaya are at constant danger from snow, ice and rock avalanches. Often, these avalanches are triggered by tectonic events like earthquakes (Huggel et al., 2007).
The Himalaya is tectonically very active and any tremors or earthquakes can result in a hazardous avalanche by breaking off large masses of snow, ice and/or rock. To mitigate the risks of avalanche, one needs hazards zonation, mapping, and an early warning system to be in place (Salzmann et al., 2004). In India, the Snow Avalanche Study Establishment (SASE) issues warning for snow avalanches; this is mostly done for the movement of the Indian Army in glaciated regions. In 2009, the National Disaster Management Authority (NDMA) issued guidelines to minimise the impact of landslides and snow avalanches on life, property and economic activity. However, due to remote location, complicated terrain, a harsh environment and political restrictions, it is not possible to monitor mountain hazards through field observations and issue warnings well ahead of time.
GIS tools and remote sensing data
Advancing geospatial science—remote sensing technology and geographic information systems (GIS) have efficient and promising capabilities in providing robust and fast mapping of potential avalanche zones, modelling the route of the avalanche and predicting such occurrences (Kääb et al., 2005). GIS combined with remote sensing data can be the most useful tool for hazard mapping of debris flow and snow/ice/rock avalanches.
The launching of high-resolution satellites in recent decades, emergence of sensor technologies and development of sophisticated tools have posed remote sensing as effective and efficient alternatives to monitor, assess and manage glacier related hazards. The optical spectral region of remote sensing is most suitable for glacier hazards assessment. For this, the nature, characteristics, size and growth of hazards decide the selection of remote sensing data.
Referencing, analysis, modelling and sharing of observed data can be made possible in a GIS environment. Data can be obtained from various sources such as the Indian Remote Sensing (IRS) satellite through the National Remote Sensing Centre (NRSC), the National Snow and Ice Data Centre and United States Geological Survey (USGS). Medium-resolution data from Landsat TM/ETM+/OLI/TIR, ASTER and IRS LISS III can cover regional to global scale hazard assessment, whereas high-resolution data from Resources at (LISS IV), Quickbird and IKONOS can provide detailed information. Some selected data can be freely downloaded from Bhuvan (IRS datasets) and USGS (data acquired by NASA).
For avalanche mapping, the most commonly required base datasets are digital elevation model (DEM) and multispectral image. The topographical details of the surface terrain such as elevation, slope, steepness, orientation and hill-shade are derived from DEM using GIS software. Availability of a topographical map can sometimes aid in identifying old avalanche sites and correlate with current avalanche sites. The usage of multispectral data provides additional details of the area such as snow coverage and distribution, ice and rock dissemination and pro-glacial environment, etc.Visual interpretation and analysis of these data sets are easily possible. Continuous monitoring of avalanche sites can be done by utilising multitemporal images.
The monitoring of occurrence of ice avalanches and the settings of early warning systems for mitigation require high-quality data and tools for systematic regional coverage. The combination of GIS tools with remote sensing data has been found to be useful for hazard mapping in particular with respect to debris flow and snow/ice/rock avalanches. Fusion of multispectral data with the DEMs is the most promising method for the monitoring and assessment of glacier hazards.
Digital elevation model
A DEM is used to define terrain parameters such as elevation, slope, aspect and curvature of the avalanche and give a firsthand estimate of runout distances and possible risks to people and structures. Computer based models in the GIS environment are capable of creating avalanche representations and hence predict events. These models need accurate and high resolution remote sensing data sets.
DEM used for avalanche study can be of high to medium resolution, ranging between grid cells of size of 5m to 90m. The Cartosat-1 is an Indian satellite that provides high resolution (2.5m) stereo data for good quality DEM, which can be used for accurate mapping of potential avalanching zones and hence prediction. Global DEMs like SRTM (30m and 90m resolution) and ASTER GDEM (30m) are freely available and can be utilised extensively in avalanche study.
The precision of the DEM used in identification of avalanche prone areas, mapping of these zones and in modeling the path of the avalanche is very crucial. The higher the resolution of the DEM, the more accurate will be the avalanche study. A DEM is used in classifying and dividing the mountains/glaciated terrain into elevation classes. The higher elevation areas can be assigned to have maximum potential for avalanche.
The most crucial parameter responsible for avalanches is the steepness of the mountain and glacier peaks. The steepness is measured by the slope of the region. A slope map can be derived from a DEM in GIS. Studies have shown that slopes ranging between 25° and 45° are most dangerous and prone to avalanches (Snehmani et al.,2013). The other parameter in avalanche mapping is derivation of aspect/orientation which is again derived from DEM in GIS. The aspect is the direction of the maximum slope of the terrain surface, which has an indirect influence on snow pack instability. It also influences on the amount of radiation received by the terrain and hence melting. The aspect of a surface can be classified into nine classes. These classes are N, NE, E, SE, S, SW, W, NW and Flat. Due to differential radiation, different aspects have different potentials to cause avalanches.
Curvature is another essential factor in determination of avalanche events and derived by GIS from DEMs. The plan and profile curvatures decide the route and flow of the avalanching material along the avalanche path. The land cover type such as ice, snow, rock, vegetation, barren surface are also crucial factors influencing the intensity and impact of an avalanche.
Figures 1 a, b and c show the avalanche snow/ice avalanche zones of Hamtah glacier, Lahaul-Spiti, Himachal Pradesh. The elevation and slope maps show the vastly elevated and steep areas of the glacier which are highly prone for avalanche.
Recent advancements in remote sensing and GIS capabilities have greatly benefited the study of snow pack properties and snow cover distribution. Simulation and prediction of avalanches can be aided significantly by advanced snowpack characteristics models. Remote sensing data can efficiently provide snow cover area, grain size, albedo and snow water equivalent. The meteorological data along with snow pack data can provide better understanding of avalanche activities. The combination of geospatial science, improved data resolution, knowledge of snow pack characteristics and advanced GIS technology can thus facilitate efficient avalanche research and prediction.
Huggel, C., Caplan-Auerbach, J., Waythomas, C.F., & Wessels, R.L. (2007). Monitoring and modeling ice-rock avalanches from ice-capped volcanoes: A case study of frequent large avalanches on Iliamna Volcano, Alaska. Journal of Volcanology and Geothermal Research, 168, 114–136.
Kääb, A., Huggel, C., Guex, S., Paul, F., Salzmann, N., Schmutz, K., Schneider, D., & Weidmann, Yvo. (2005). Glacier hazard assessment in mountains using satellite optical data. EARSeL eProceedings, 4(1), 79-93.
National Disaster Management Authority, Government of India. (2009). National Disaster Management Guidelines—Management of Landslides and Snow Avalanches. Retrieved from http://nidm.gov.in/pdf/guidelines/new/landslidessnowavalanches.pdf
Salzmann, N, Kääb, A., Huggel, C., Allgower, B., & Haeberli, W. (2004). Assessment of the hazard potential of ice avalanches using remote sensing and GIS-modelling. Norsk Geografisk Tidsskrift–Norwegian Journal of Geography, 58, 74–84.
Snehmani, Bhardwaj, A., Pandit, A., & Ganju, A. (2014). Demarcation of potential avalanche sites using remote sensing and ground observations: a case study of Gangotri glacier. Geocarto International, 29(5), 520-535. Retrieved from DOI: 10.1080/ 10106049.2013.807304.