Natural Hazards

, Volume 62, Issue 3, pp 887–899

Glacial lakes and glacial lake outburst flood in a Himalayan basin using remote sensing and GIS


    • National Institute of Hydrology
  • Anil K. Lohani
    • National Institute of Hydrology
  • R. D. Singh
    • National Institute of Hydrology
  • Anju Chaudhary
    • National Institute of Hydrology
  • L. N. Thakural
    • National Institute of Hydrology
Original Paper

DOI: 10.1007/s11069-012-0120-x

Cite this article as:
Jain, S.K., Lohani, A.K., Singh, R.D. et al. Nat Hazards (2012) 62: 887. doi:10.1007/s11069-012-0120-x


Glacial hazards relate to hazards associated with glaciers and glacial lakes in high mountain areas and their impacts downstream. The climatic change/variability in recent decades has made considerable impacts on the glacier life cycle in the Himalayan region. As a result, many big glaciers melted, forming a large number of glacial lakes. Due to an increase in the rate at which ice and snow melted, the accumulation of water in these lakes started increasing. Sudden discharge of large volumes of water with debris from these lakes potentially causes glacial lake outburst floods (GLOFs) in valleys downstream. Outbursts from glacier lakes have repeatedly caused the loss of human lives as well as severe damage to local infrastructure. Monitoring of the glacial lakes and extent of GLOF impact along the downstream can be made quickly and precisely using remote sensing technique. A number of hydroelectric projects in India are being planned in the Himalayan regions. It has become necessary for the project planners and designers to account for the GLOF also along with the design flood for deciding the spillway capacity of projects. The present study deals with the estimation of GLOF for a river basin located in the Garwhal Himalaya, India. IRS LISSIII data of the years 2004, 2006 and 2008 have been used for glacial lake mapping, and a total of 91 lakes have been found in the year 2008, and out of these, 45 lakes are having area more than 0.01 km2. All the lakes have been investigated for vulnerability for potential bursting, and it was found that no lake is vulnerable from GLOF point of view. The area of biggest lake is 0.193, 0.199 and 0.203 km2 in the years 2004, 2006 and 2008, respectively. Although no lake is potentially hazardous, GLOF study has been carried out for the biggest lake using MIKE 11 software. A flood of 100-year return period has been considered in addition to GLOF. The flood peak at catchment outlet comes out to be 993.74, 1,184.0 and 1,295.58 cumec due to GLOF; 3,274.74, 3,465.0 and 3,576.58 cumec due to GLOF; and 100-year return flood together considering breach width of 40, 60 and 80 m, respectively.


GLOFGlacierNDSIMike 11

1 Introduction

The Himalayan region has permanent snow fields, and in winter, most of the high-altitude regions experience snowfall. Himalayan glaciers are an important source of fresh water for northern Indian rivers and water reservoirs (Kumar et al. 2005). According to a report by the International Centre for Integrated Mountain Development (ICIMOD), there are 15,000 glaciers and 9,000 glacial lakes in the Himalaya (Mool 2005). The isolated lakes found in the mountains and valleys far from the glaciers may not have a glacial origin. The isolated lakes above 3,500 msl are considered to be the remnants of the glacial lakes left due to the retreat of the glaciers (Campbell 2005). In the recent past, some of the glaciers have retreated due to climate warming, not only affecting water resources and hydrological processes, but also causing the expansion of glacial lakes (Yao et al. 2010).

The lakes located at the snout of the glacier are mainly dammed by the lateral or end moraine, where there is high tendency of breaching. Such lakes could be dangerous as they may hold a large quantity of water. Breaching and the instantaneous discharge of water from such lakes can cause flash floods enough to create enormous damage in the downstream areas. In order to assess the possible hazards from such lakes, it is therefore essential to have a systematic inventory of all such lakes formed at the high altitudes. This is feasible by identifying them initially through satellite images (and aerial photographs, if available) and to assess their field setting subsequently. Besides making a temporal inventory, a regular monitoring of these lakes is also required to assess the change in their nature and aerial extent.

A number of glacial lakes have been identified and found expanding as a consequence of climate change and glacier thinning in previous studies (Reynolds 2000; Ageta et al. 2000; Benn et al. 2000). ICIMOD has been involved in glacier and glacial lake inventory and the identification of potentially dangerous glacial lake since 1986. Between 1999 and 2005, ICIMOD in collaboration with partners in different countries embarked on the preparation of an inventory of glaciers and glacial lakes and identification of potential sites for GLOF (ICIMOD 2010). Watanabe et al. (1994) assessed the potentials of glacial lake outburst in Khumbu Himalaya based upon comparisons with the change in volume of lake water. Sakai et al. (2000) evaluated the rate of expansion of a glacial lake in Rolwaling Himalaya, Nepal Himalaya by applying heat balance method. An automatic detection method of the lake surface using a normalized difference water index (NDWI) was recently attempted on Imja Glacial Lake by Bolch et al. (2008). Although some studies have asserted the expansion rate as posing a risk of a glacial lake outburst flood (GLOF) (Quincey et al.2007), determining the trigger and proof strength of the damming moraine is more important. Lichtenegger et al. (2008) utilized microwave data to monitor the variations of Imja Lake in Khumbu—Everest Region of Nepal. Molnia (2009) employed TERRA-ASTER images to identify and monitor formation of glacier related lakes of Afghanistan glaciers. Thompson et al. (2010) employed differential GPS survey to map the perimeter of major lakes in the glacier terminal region. The accuracy of the previous methods is the ability to analyze the difference between the features such as cast shadow and glacial lake. Detecting the lakes in cast shadow is the most critical element in automated detection approach (Frey et al. 2010). Raj (2010) has carried out a study for Zanskar basin, J&K, India, and peak discharge from the lake was estimated using empirical formula. Ukita et al. 2011 made a glacial lake inventory of Bhutan, using Advanced Land Observing Satellite (ALOS) data.

2 The study area

The Alaknanda valley in Uttaranchal, India, has a vast potential for water resources development, substantial of which is yet to be harnessed. The valley is rich in forest wealth, herbal plants, magnesite, dolomite, talc etc. The river Alaknanda, a major tributary of Ganga, originates from KAMET Glacier above Badrinath at an elevation of about 7,800 m. It generally flows in the North to South direction and is met by a number of tributaries, all from the Indian side. These tributaries are Saraswati, Dhauli Ganga, Nandakini, Pindar and Mandakini River. At Deoprayag, it joins with river Bhagirathi and moves downstream by the name ‘Ganga’. Alaknanda valley in the Himalayas has steep slopes which are quite suitable for harnessing the hydropower potential by way of constructing runoff-the-river or storage schemes depending upon the geographical conditions. Accordingly, a number of hydropower schemes have been envisaged on river Alaknanda and its tributaries, many of which are in different stages of construction/investigations. The average bed slope of Alaknanda River is 29 m/km, and the actual slope in many of the smaller stretches is much more. The Dhauliganga River which joins the river Alaknanda near Joshimath is the major tributary of Alaknanda River. The total catchment area of Alaknanda at its confluence with Dhauliganga River near Joshimath is 4,508 km2 out of which 2,700 km2 is snow bound. In the present study, catchment of river Alaknanda extends from latitude 30° 15′ 00″N to 31° 07′ 00″N and longitude 79° 15′ 00″E to 80° 15′ 00″E. It is completely mountainous, significant part of which is covered by snow.

3 Data used

The basic materials used for the compilation of inventory of glacial lakes are different type of satellite images, topographic maps and published maps, field report and available literatures. Medium- to high-resolution satellite images of different dates are more useful in the inventory of glacial lakes. The combination of satellite remote sensing data and the digital elevation model (DEM) was also used for better interpretations and more accurate results for the inventory of glacial lakes in the three dimensions. For glacial lake identification from satellite data, the images should be with least snow cover and cloud free. In this study, IRS 1 D LISS III data of September 7, 2004, November 15, 2006 and IRS P6 LISSIII data of September 15, 2008 were used for the identification of glacial lakes.

Since no cross-section details were available for the study area, DEM generated using Advanced Spaceborne Thermal Emission and Reflection Radiometer sensor (ASTER) has been used for this purpose. ASTER is an imaging instrument that is flying on the TERRA satellite launched in December 1999 as part of NASA’s Earth Observing System (EOS). ASTER represents a revolution in the remote sensing community because of the availability of its imagery and its superior resolution. ASTER resolution ranges from 15 to 90 m, depending on the wavelength. The instrument records in three bands as follows: the Visible and Near Infrared (VNIR), the Shortwave Infrared (SWIR), and the Thermal Infrared (TIR), oriented on the nadir and backward looking. There are 14 spectral bands all together spanning the visible and infrared spectra, so the sensor is susceptible to cloud cover and cannot record images at night. Because of its off-nadir sensor pointing capability, ASTER can collect the stereo pairs necessary to generate high-resolution DEMs (using bands 3N and 3B).The ASTER onboard the Terra satellite has produced 30-m resolution elevation data. There is a fairly complete coverage of world at this high resolution, and data are free. Japan’s Ministry of Economy, Trade and industry (METI) and NASA announced the release of the ASTER Global Digital Elevation Model (GDEM) on June 29, 2009. The GDEM was created by stereo-correlating the 1.3 million scene ASTER VNIR archive, covering the Earth’s land surface between 83N and 83S latitudes. The GDEM is produced with 30-m postings and is formatted in 1 × 1 degree tiles as GeoTIFF files. The GDEM is referenced to the WGS84/EGM96 geoid. The GDEM’s pre-production accuracy estimates were 20 m at 95% confidence for vertical data and 30 meters at 95% confidence for horizontal data.

4 Methodology

A digital database of glaciers and glacial lakes is necessary to identify the potentially dangerous glacial lakes. To identify the individual glaciers and glacial lakes, different image enhancement techniques are useful. However, complemented by the visual interpretation method (visual pattern recognition), with the knowledge and experience of the terrain conditions, glacier and glacial lake inventories and monitoring can be made. With different spectral band combinations in false color composite (FCC) and in individual spectral bands, glaciers and glacial lakes can be identified and studied using the knowledge of image interpretation keys: color, tone, texture, pattern, association, shape, shadow, etc. Different color composite images highlight different land-cover features. The lake water in color composite images ranges in appearance from light blue to blue to black. In the case of frozen lakes, it appears white. Sizes are generally small, having circular, semicircular or elongated shapes with very fine texture and are generally associated with glaciers in the case of high lying areas or rivers in the case of low lying areas. The ERDAS imagine 9.3 and Arc GIS 9.3 have been used for the processing of satellite data and GIS analysis. In the present study, map of Alaknanada basin has been prepared using ASTER DEM and drainage map is shown in Fig. 1. The total area is 4,782.9 km2, and the elevation values range from a minimum of 1,238 m to maximum of 7,785 m with in the study area.
Fig. 1

Drainage map of the study area

The present study has utilized mainly digital remote sensing data from the IRS-1D/P6 LISS-III sensor. When applying basic techniques of multispectral classification similar to those used for the normalized difference vegetation index, NDVI (Hardy and Burgan 1999), a normalized difference water index (NDWI) for lake detection was used (Huggel 1998).
$$ {\text{NDWI}} = \frac{{B_{\text{NIR}} - B_{\text{blue}} }}{{B_{\text{NIR}} + B_{\text{blue}} }} $$

As a result of spectral reflection, some self-shadowed areas are misclassified as lakes. These areas have been found with the help of DEM, the DEM was over-laid on NDWI image. It could thus be assured that only lake appeared as black spots. After that, manual delineation of lakes has been carried out. Lake boundaries were digitized using ERDAS Imagine vector module tools. The digitized polygons have been assigned polygon IDs. The area of the lake was calculated using the digital techniques by counting the number of pixels falling inside the water body polygon. The geographic latitude and longitude of the center of the lake has been computed using attributes information of the polygon later. There may be a possibility of some lakes which are snow covered and cannot be fully distinguishable in the satellite data. The lakes or water bodies which are partly snow covered or fully snow covered and could not be distinguishable are not reported.

5 Glacial lake outburst flood (GLOF) simulation

The criteria for identifying potentially dangerous glacial lakes are based on field observations, processes and records of past events, geomorphological and geotechnical characteristics of the lake and surroundings, and other physical conditions. Criteria such as area/volume of lake, breaching evidences, condition of lake and its surrounding have been investigated. For this purpose, remote sensing data in conjunction with Google Earth have been used. It was observed that no lake is located in ablation zone of glacier and not connected with mother glacier. There were no breaching evidences, and area of lake was not expanded much in the past. Therefore, in the present study, breaching of the biggest lake has been considered for simulation.

The MIKE 11 model has been used to model GLOF simulation in India (Sharma et al. 2009). MIKE11 is a professional engineering software package for the simulation of one-dimensional flow in estuaries, rivers, irrigation systems, channels and other water bodies. It is a dynamic, user-friendly one-dimensional modeling tool for the detailed design, management and operation of both simple and complex river and channel systems. The hydrodynamic module (HD) contains an implicit, finite difference computation of unsteady flows in river and estuaries. The formulation can be applied to branched and looped networks and flood plains.

The dam break model setup consists of a single or several channels, reservoirs, dam break structures and other auxiliary dam structures such as spillways, sluices. The river is represented in a model by cross-sections at regular intervals. However, due to highly unsteady nature of dam break flood propagation, it is advisable that the river course is described as accurately as possible through the use of a dense grid of cross-sections, particularly where the cross-section is changing rapidly. Further, the cross-sections shall extend as far as the highest modeled water level, which normally will be in excess of highest recorded flood level. In the present study, the lake has been represented as dam break structures having certain crest level and crest length. The dam breach parameters have been specified as a time series and assigned to corresponding lake. The glacial lake has been represented as reservoir in the model by its elevation–surface area relation, at chainage “0” km of the reservoir branch.

In any dam break study, prediction of the dam breach parameters and timing of the breach are very important factors. But prediction of these parameters is extremely difficult. The important aspects to deal when considering the failure of dam are, time of failure, extent of overtopping before failure, size, shape and time of the breach formation. Estimation of the dam break flood depends on these parameters. Important breach characteristics that are needed as input to the existing dam break models are (1) initial and final breach width and level; (2) shape of the breach; (3) time duration of breach development; and (4) reservoir level at time of start of breach.

Arc-GIS and ERDAS Imagine software were used to delineate cross-sections of the stream. For this purpose the vector layer of the stream and the buffer lines along the stream on the both side of stream at the distance of 1 km were created. The stream was divided at the distance of 5 km from lake side, and the cross-section layer was created. ERDAS Imagine Software was used to overlay DEM of basin and vector layer of cross-section. The Spatial Profile Viewer in ERDAS allows to visualize the reflectance spectrum of a polyline of data file values in a single band of data (one-dimensional mode) or in many bands (perspective three-dimensional mode). This is being used to create a height cross-section profile along a route. This helps in interpreting changes in elevation along a planned route and in identifying the sections of the route which are particularly steep or flat. Inquire cursor of ERDAS Imagine was used to extract the elevation values at each pixel.

There is no estimate available for volume of glacial lakes in Gharwal Himalaya from their water spread areas. However, some estimates are available for glacial lakes in Swiss Alps, as given by Huggel et al. (2002). In the absence of information on the volume of glacial lakes, it is considered appropriate to use the same relationships developed for the lakes in Swiss Alps for estimating the water volume for the lakes in this area. The empirical relations as available in the study by Huggel et al. (2002) are:
$$ {\text{The}}\,{\text{lake}}\,{\text{volume}}\,V = 0. 10 4\,{\text{A}}^{ 1. 4 2} $$
where V is the lake volume in m3 and A is the lake area in m2.

6 Results

In this study, glacial lakes have been delineated using remote sensing technique and manual delineation approach. The area of this lake varies from 0.192 to 0.203 km2 for the years 2004–2008. The lake area in the years 2004, 2006 and 2008 is given in the Table 1. Most of the lakes are situated above 3,000 masl. In the year 2004, there were total 99 lakes, out of which 28 lakes were above 4,000 masl while 61 lakes were above 5,000 m. In the year 2004, there were 58 lakes below 0.01 km2, 39 lakes below 0.10 km2 and 2 lakes were above 0.1 km2. Therefore, there are total 41 lakes above 0.01 km2.
Table 1

Lakes of different area and above an altitude in the study area


Area <0.01 km2

Area <0.1 km2

Area >0.1 km2

Elevation >4,000 m

Elevation >5,000











































The area of the biggest lake is 0.233 km2 in 2008, and it is located in Dhauliganga River. The GLOF study has been carried out for this lake. The resulting dam breach flood; that is, GLOF has been routed through Dhauliganga River along with 100-year return period flood in the valley. The Dhauliganga River from glacial lake location down to the catchment outlet (total length 92 km) has been represented in the model by a number of cross-sections at an interval of 5 kms, developed from DEM. However, at some critical locations where the topography is varying within 5 kms, cross-sections at closer interval i.e. at an interval of 1 km have been given as input in the model. The total reach from lake to the outlet is shown in Fig. 2. All the cross-sections obtained at different interval are shown in Fig. 3.
Fig. 2

Locations of cross-sections at 5-km interval downstream of lake
Fig. 3

Cross-sections at 5-km interval downstream of lake

The altitude of the biggest lake is 4,663 m. The surface area of the lake and altitude are given in Table 2. The volume of the lake is calculated using Eq. 2, and it comes out to be 4.35 Mm3. The breach width has been taken as 40, 60 and 80 m, and breach depth is taken as 10 m. The side slope has been taken as 0.75H: 1V. The breach development time has been taken as 1 h. The Manning’s roughness coefficient has been taken as 0.040 considering the bouldery beds and hilly terrain of Himalayan Rivers and large debris flow associated with GLOF.
Table 2

Elevation–area relationship of glacial lakes

Glacial lake (volume 3.578 Mcum)

Elevation (m)

Surface area (m2)







The 100-year return period flood value available at the catchment outlet is distributed along the study reach on the basis of the catchment areas of the contributing tributaries. There are mainly three streams which are meeting with the stream coming from lake up to catchment outlet. The distributed flood values have been taken as lateral inflows of magnitude corresponding to 100-year flood with their impingement locations in the Dhuliganga River, as given in Table 3.
Table 3

Lateral inflow considered corresponding to 100-year return period flood

Sl. no.


100-year flood m3/s

Adopted lateral inflow m3/s


10 km down stream from lake in the river Dhauliganga




34 km down stream from lake in the river Dhauliganga




50 km down stream from lake in the river Dhauliganga




67 km down stream from lake in the river Dhauliganga




84 km down stream from lake in the river Dhauliganga




90 km down stream from lake in the river Dhauliganga



MIKE 11 software was applied for generation of flood hydrograph for three cases (breach width, 40, 60 and 80 m). These flood hydrographs (including 100-year return flood ordinate) at just downstream of the lake and at catchment outlet are shown in Fig. 4, and flood hydrographs excluding 100-year return flood are shown in Fig. 5 for breach width of 80 m. The flood hydrograph at outlet shows the flood ordinates as a sum of GLOF ordinates and 100-year flood ordinates. The total flood peak, the flood peak due to 100-year flood, the flood peak due to GLOF and its travel time from GLOF site, is given in Table 4.
Fig. 4

GLOF hydrograph (including 100-year flood ordinates) at catchment outlet considering 80 m breach width
Fig. 5

GLOF hydrograph (excluding 100-year flood ordinates) at catchment outlet considering 80 m breach width

Table 4

Flood peak due to glacial lake outburst considering 80 m breach width


Distance (km) from glacial lake

Total flood peak (m3/s)

100-year flood peak (m3/s)

GLOF peak (m3/s)

Travel time (h-min)

40 m

60 m

80 m

40 m

60 m

80 m

Just d/s of lake










Catchment outlet










It can be seen from the Table 4 that the GLOF peak for the above breach parameters is 1,315.94, 1,640.5 and 1,861.51 cumec for breach width 40, 60 and 80 m, respectively. The same get mitigated to 993.74, 1,184.0 and 1,295.80 cumec for breach width 40, 60 and 80 m, respectively, at the outlet. The time of travel of flood peak from the lake site to outlet is about 1 h and 10 min. It is inferred from the study that the outburst of glacial lake with peak flood of 1,295.80 cumec at the outlet gives the worst case scenario of GLOF.

7 Limitations

Glacial lake outburst flow modeling process is nothing but approximation of a physical phenomenon through which the physical phenomenon, and its effects can be studied for water resources structure design and flood management. In GLOF modeling, assumptions are mainly associated with the breach parameters, especially, breach width and breach depth, which has impact on flood peak and arrival times. In general, glacial lake bursting mechanism and formation of breach in glacial lakes are not fully understood. Furthermore, the high velocity associated with GLOF can cause significant scour of channels associated with bed as well as bank erosion. Change in the channel cross-section due to GLOF is neglected due to limitations in modeling such a complicated physical process. Generally, GLOF creates a large amount of transported debris, and this may be accumulate at constricted cross-sections, where it acts as a temporary dam and partially or completely restricts the flow, resulting variation in flood peak arrival time. This aspect has also been neglected due to limitations in modeling of such a complicated physical process. These limitations have an effect mainly on the conservative side. Even with the assumptions and limitations outlined above, hydrodynamic modeling serves very useful purpose, as it provides reasonable estimate of glacial lake outburst flood, thus enabling the appropriate estimation of design flood.

8 Conclusions

The integration of visual and digital image analysis with a Geographic Information System (GIS) can provide very useful tools for the study of glacial lakes and glacial lake outburst floods (GLOFs). The Alakanada basin up to catchment outlet covers an area of 4,782 km2. The elevation values ranges from a minimum of 1,238 m to maximum of 7,785 m with in the study area.

In the study basin, total lakes found are 91 and the lakes with area greater than 0.01 km2 are 41, and area covered under these lakes is 1.522 km2 in the year 2008. Almost all the lakes are above altitude of 4,000 m and below 6,000 m. The biggest lake has been identified, and the area of this lake is 0.233 km2 in the year 2008. For GLOF study, MIKE 11 software has been used. In this study, 100-year return flood has also been considered in addition to GLOF peak at the catchment outlet. The flood peak at the lake site is 1,315.94, 1,640.5 and 1,861.51 cumec for breach width of 40, 60 and 80 m, respectively. Total flood peak at outlet is 3,274.74, 3,465.0 and 3,576.58 cumec. The flood peak due to GLOF only is computed as 993.74, 1,184.0 and 1,295.58 cumec.

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© Springer Science+Business Media B.V. 2012