Databases of the glaciers and glacial lakes of Himalayan-China Region, based on medium- to large-scale topographic maps, have not been developed prior to the present study. For the glacier inventory the study used the methodology developed by the Temporary Technical Secretary for the World Glacier Inventory (Muller et al. 1977), and for the glacial lake inventory, the methodology developed by the Lanzhou Institute of Glaciology and Geocryology (LIGG) (LIGG/Water and Energy Commission Secretariat (WECS)/Nepal Electricity Authority [NEA] 1988) was used with modification. The present methodology for the compilation of inventories of glaciers and glacial lakes of Himalayan-China Region is applied using medium-scale maps.

The topographic maps based on aerial photographs and field verification, in the 1960s–1980s on a scale of 1:50,000, are the only map series that cover the whole of Himalayan-China Region on a medium scale. Based on this map series, spatial and attribute databases of glaciers and glacial lakes were developed.

Creating inventories of and monitoring glaciers and glacial lakes can be done quickly and correctly using a combination of satellite images simultaneously with topographic maps. The multi-stage approach of using remotely-sensed data and field data increases the ability and accuracy of the work. The integration of visual and digital image analysis with a geographic information system (GIS) can provide very useful tools for the study of glaciers, glacial lakes, and Glacial Lake Outburst Floods (GLOFs).

Analysts’ experiences and adequate field knowledge of the physical characteristics of the glacier and lake and their associated features are always necessary for the interpretation of the topographic maps, and satellite images. Evaluation of spectral responses by different surface cover types in different bands of satellite images is necessary. Different techniques of digital image enhancement and spectral classification of ground features are useful for the study of glaciers and lakes. Different spectral band combinations in False Colour Composite (FCC) and individual spectral bands were used to study glaciers and glacial lakes using knowledge of image interpretation keys.

The Digital Elevation Model (DEM) is useful to decide the rules for discrimination of features and land-cover types in GIS techniques and for better perspective viewing and presentations. A DEM suitable for the present study of the whole country has been available. In this study, the DEM has played a key role not only to inventory the glaciers and glacial lakes, but to identify the potentially dangerous lakes. We propose that the DEM should been used in the further inventory of glaciers and glacial lakes.

The inventory of glaciers and glacial lakes of Himalayan-China Region as a whole is divided into eight basins, namely, the Jiazhagangge, Daoliqu, Majiacangbu, Jilongcangbu, Poiqu, Pumqu, Rongxer, and Zangbuqin basin. Altogether 77 potentially danger lakes were identified in this study.

The characteristic features of the identified potentially dangerous lakes in general are:

  • moraine-dammed glacial lakes in contact or very near to large glaciers,
  • merging of supraglacial lakes at the glacier tongue,
  • some new lakes of considerable size formed at glacier tongues,
  • lakes rapidly growing in size
  • rejuvenation of lakes after a past glacial lake outburst event.

There are several possible methods for mitigating the impact of GLOF surge, for monitoring, and for early warning systems. Careful evaluation by detailed studies of lakes, mother glaciers, damming materials, and the surrounding conditions are essential in choosing the appropriate method and in starting mitigation measures.

Summary of Glaciers and Glacial Lakes of Himalaya - China region

S.N.

Sub basins Name

Glaciers

Glacial Lakes

Number

Area
(km2)

Number

Area
(km2)

1

Jiazhagangge

96

143.30

14

0.52

2

Daoliqu

43

60.60

7

0.38

3

Majiacangbu

147

216.16

69

4.73

4

Jilongcangbu

180

418.61

72

3.32

5

Poiqu

127

230.52

91

15.66

6

Pumqu

716

1408.15

383

52.01

7

Rongxer

205

301.22

183

8.40

8

Zangbuqin

64

85.75

5

0.18

Total

1578

2864.33

824

85.19