Inventory of Glacial Lakes in the Koshi, Gandaki · some selected basins of HKH region based on satellite images. Analysis of the time series data revealed that the glaciers had lost

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Inventory of Glacial Lakes in the Koshi, Gandaki

and Karnali River basins of Nepal and Tibet,

China Identi f ication of potential ly dangerous glacial lakes and priori t ization for GLOF

risk reduction

Submitted to

UNITED NATIONS DEVELOPMENT PROGRAMME, (UNDP) NEPAL

Submitted by

INTERNATIONAL CENTRE FOR INTEGRATED MOUNTAIN DEVELOPMENT (ICIMOD)

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March 2018

Content 1. Introduction ............................................................................................................................... 1

2. Study area ................................................................................................................................ 3

3. Approach and methodology ....................................................................................................... 5

3. 1 Data sources .......................................................................................... 5

3. 2 Mapping method ...................................................................................... 8

3. 3 Uncertainties and limitations ........................................................................ 9

3. 4 Glacial lake attributes ............................................................................... 11

3. 5 Identification of potentially dangerous glacial lakes .......................................... 13

3. 6 Ranking and prioritization of potentially dangerous glacial lakes ........................ 17

4. Status of glacial lakes in 2015 ................................................................................................ 19

4. 1 Number and area of glacial lakes .............................................................. 20

4. 2 Types of glacial lakes ............................................................................. 21

4. 3 Glacial lake size class ............................................................................. 26

4. 4 Altitudinal distribution of glacial lake ............................................................ 29

4. 5 Distance to the source glacier ................................................................... 31

5. Potentially dangerous glacial lakes and priority lakes for risk reduction ................................. 33

5. 1 Characteristic parameters for lake stability .................................................... 33

5.1.1 Lake characteristics ............................................................................... 33

5.1.2 Dam Characteristics .............................................................................. 35

5.1.3 Mother (Source) glacier characteristics..................................................... 38

5.1.4 Physical condition of surroundings ............................................................ 38

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5. 2 Identification and ranking of potentially dangerous (critical) glacial lakes............ 41

5. 3 Prioritization of PDGL for GLOF risk reduction .............................................. 49

5.3.1 Socioeconomic value ................................................................................ 49

5.3.2 Priority of potentially dangerous glacial lakes for risk reduction ......................... 56

6. Conclusions ............................................................................................................................ 59

7. References ............................................................................................................................. 60

8. Annexes .................................................................................................................................. 66

Table A1: Number and area of glacial lakes by types (2015) in the Koshi, Gandaki and

Karnali basins of the Nepal, TAR, China and India ...................................... 67

Table A2:Identification of potentially dangerous glacial lakes (PDGL) based on the

characteristics of lake, dam and surrounding features including the source glacier.

.......................................................................................................... 68

Table A3: Characteristics of lake, dam, source glacier and surroundings for the identification

of potentially dangerous glacial lakes in Koshi, Gandaki and Karnali basins of Nepal

and TAR, China. (yellow highlighted lake is removed from the list of PDGL) ... 70

Figure A4: Identification of potentially dangerous glacial lakes (PDGL) based on the

characteristics of lake, dam and surrounding features. .................................... 73

Table A5: Information of parameters of all PDGL for dam breach model. ...................... 74

Table A6: Distribution of types of lakes at different elevation zone in pdf. ..................... 74

Table A7: Distribution of glacial lake sizes at different elevation zone in pdf. ................. 74

Table A8: Lake area classes vs type of lakes in pdf. ............................................... 74

Table A9: List of images used in the present study in pdf. ....................................... 74

Soft copy - Glacial Lake Inventory data including shp.file of 2000 .............................. 74

Soft copy - Glacial Lake Inventory data including shp.file of 2015 ............................... 74

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Key findings

The glacial lakes (≥ 0.003 sq km) were mapped for 2015 based on Landsat images using

remote sensing tools and techniques for Koshi, Gandaki and Karnali basins of Nepal and TAR,

China. The study found 3,624 glacial lakes in three basins, of which 2,070 lakes in Nepal,

1,509 lakes in TAR, China and 45 lakes were in India. The lakes larger than 0.02 sq km

were 1,410 in number, which are considered large enough to cause risk in the downstream if

the lake breach. This potential of would be heightened Glacial Lake Outburst Flood (GLOF),

if the lakes are associated with a large retreating glacier and steep sloping landforms at the

surroundings.

A total of the 47 glacial lakes were identified as potentially dangerous (critical) glacial lakes

(PDGL) based on the criteria:1) characteristics of lakes and dam; 2) the activity of the

source glacier and 3) morphology of the surroundings. Other factors such as the extreme

climatic condition, seismic activity and malpractice or human interference on the lake and natural

dam are not considered. Of the 47 lakes identified, 42 lakes are in the Koshi basin, 3 lakes

in the Gandaki basin and 2 lakes are in the Karnali basin. With respect to the political

boundary, 25 PDGL are in the territory of the TAR, China; 21 PDGL in Nepal; and one PDGL

is in the Indian Territory. The number of PDGLs identified in 2011 and present study for Nepal

equal in number same, although on 13 of them are common, while 8 PDGLs are different.

The physical parameters were considered first to categories the PDGLs into three ranks depending

on the hazard level. The socioeconomic parameters were then summated to categorise the

PDGLs into three priorities (priority I, priority II and priority III) for a GLOF risk reduction. Of

the total, 31 lakes are of priority I, 12 lakes are of priority II, and 4 lakes are of priority III.

The priority I lakes are the critical ones, which are at the equilibrium of lake water and dam’s

strength. Slight change in lake water and dam’s strength may breach out, which warrants

immediate action for the potential GLOF mitigation measures. The priority II and III lakes have

a potential of increase in the hazard level and hence need close and regular monitoring. The

water level of four PDGL of priority I had already been lowered in the past to reduce the

GLOF risk: two each from Nepal and the TAR, China. The water level of Tsho Rolpa and

Imja Tsho Lakes of Nepal was lowered by more than 3m and 4m respectively. Similarly, the

water level of GL088066E27933N and GL088075E27946N Lakes in China was also lowered

to reduce the GLOF risk.

Of the 21 PDGLs identified in Nepal, 6 lakes in priority I, 8 lakes in priority II and 9 lakes

are in priority III. Similarly, 14 lakes are in priority I, two lakes are in priority II and 9 lakes

are in priority III in TAR, China and one lake of priority II is in the Kali River of India.

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Acronyms and Abbreviations

ALOS Advance Land Observing Satellite

ASTER Advanced Spaceborne Thermal Emission and Reflection Radiometer

B (c) Cirque Lake

B (o) other bedrock dammed

Co compressed and old dam

DEM Digital Elevation Model

Dl dam length

Dm distance to source glacier

DMS Degree minute second

Ds dam slope

E glacier erosional lake

E (c) Cirque Lake

E (o) other erosional lake

ETM+ Enhanced Thematic Mapper Plus

GCF Green Climate Fund

GIS Geographic Information Systems

GL Glacial lakes

GLIMS Global Land Ice Measurements from Space

GLOBE Global Land One km- Base Elevation Project

GLOF Glacial Lake Outburst Floods

GloVis USGS Global Visualization Viewer

GTOPO Global Topography

HKH Hindu Kush Himalaya

I Ice dammed lake

ICIMOD International Centre for Integrated Mountain Development

IPCC Intergovernmental Panel for Climate Change

I(s) supra-glacial lake

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I(v) lakes dammed by tributary valley glaciers

JAXA Japan Aerospace Exploration Agency

Km kilometer

LIGG Lanzhou Institute of Glaciology and Geocryology

M Moraine dammed lake

M(e) end moraine dammed

M(l) lateral moraine dammed

M(o) other moraine dammed

NASA National Aeronautics and Space Administration

Nc no crest

NDWI Normalized Difference Water Index

NEA Nepal Electricity Authority

NIR Near Infrared

NSIDC National Snow and Ice Data Center

O others glacial lakes

OLI Operational Land Imager

PDGL potentially dangerous glacial lakes

RS Remote sensing

Sm slope of source glacier

Spot Satellite Pour l'Observation de la Terre

Sq. km square kilometer

SRTM Shuttle Radar Topography Mission

TAR Tibet Autonomous Region

TM Thematic Mapper

USGS United States Geological Survey

UNDP United Nations Development Programme

WECS Water and Energy Commission Secretariat

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1. Introduction

The cryosphere (glaciers, snow, river and lake ice and permafrost) is an integral part of the

global climate system with important linkages with ecosystem and socio-economic benefits. The

cryosphere has been changing rapidly in recent decades, and the changes vary depending on

the spatial and temporal scale that they are examined on. Most Himalayan glaciers have been

rapidly melting and shrinking since the 1980s (Bajracharya et al., 2014), concurrent with

climate warming (Bhambri and Bolch, 2009; Bolch et al., 2012; Yao et al., 2012). Glacial

loss and shrinkage not only affects water resources and hydrological processes, but also

influences the formation and expansion of glacial lakes (Yao et al. 2010). The International

Centre for Integrated Mountain Development (ICIMOD) has been involved in glacial lake

inventories and the identification of potentially dangerous glacial lake since 1986 (Ives, 1986).

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 glacial lake outburst

floods (GLOFs) in the Hindu Kush-Himalaya (HKH) region (ICIMOD 2010). A glacial lake

inventory for Nepal and Bhutan was started in 1999 and for selected basins in China, India

and Pakistan were started in 2002 (Mool et al., 2001; ICIMOD 2010).

In 2011, a comprehensive study undertaken by the ICIMOD outlined the status of the glaciers

of the HKH region (Bajracharya et al., 2011). To understand the changes in glacier area and

extent, ICIMOD mapped glaciers from 1980s, 1990, 2000 and 2010 of Nepal, Bhutan, and

some selected basins of HKH region based on satellite images. Analysis of the time series

data revealed that the glaciers had lost almost a quarter of their initial area over the 30-year

period (Bajracharya et al., 2014 b, c, 2016). Moreover, total ice reserves had decreased by

29 percent in Nepal between 1977 and 2010, whilst the number of glacial lakes had increased

by 11 percent (Bajracharya et. al., 2007, 2008). The rapid melting and recession of many

Himalayan glaciers due to of climate change is leading to the formation of new glacial lakes,

whilst the enlargement of existing lakes is increasing the risk that the surrounding moraine dams

will become destabilized(Cruz et al., 2007; IPCC 2007; Rosenzweig et al., 2007). The

moraine dams are mostly composed of loose debris and are susceptible to GLOFs (Randhawa

et al., 2005). Himalayan GLOFs develop at high altitudes and can extend for long distances,

damaging downstream infrastructure (Chen et al., 2007; Osti and Egashira, 2009; Liu et al.,

2014). Examples of previous GLOFs in the Himalayas demonstrates that they constitute a

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serious threat to socio-economic and development endeavors (Rai, 2005; Wang et al., 2014;

Worni et al., 2013). GLOFs from moraine-dammed glacial lakes have been assessed and

modeled in several previous studies (Wang et al., 2012; Osti et al., 2013; Westoby et al.,

2014). Several investigations of glacial lake changes have been conducted in the Himalayas,

and large areal expansions with regional differences have been reported (Mool et al., 2001;

Bolch et al., 2008; Gardelle et al., 2011; Li and Sheng, 2012).

GLOFs are a crucial problem faced by regional countries and people in the Himalayas (Ageta

et al., 2000). Some of them are associated with trans-boundary impacts (Xu Daoming et

al.1989; Yamada and Sharma 1993; Reynolds 1998; Ives et al. 2010). Over 50 GLOF events

that occurred in the region have been reported in the HKH region; however, records are

available only for some areas of China, Nepal, Pakistan and Bhutan (Che et al, 2014;

LIGG/WECS/NEA, 1987). Many more events may remain undocumented or unrecorded (Ives

et al. 2010). An increase of GLOF events in the Himalayas has been reported over the period

of 1940 to 2000, although the trend has been considered statistically insignificant (Richardson

and Reynolds 2000; Bajracharya 2009). Until 2011, Nepal had experienced 24 GLOFs events

(recorded only) where significant damage and loss of life was reported. The Dig Tsho GLOF

of 1985 and the Tampokhari outburst in 1998 both led to considerable loss of life, property

and infrastructure and severely affected the livelihoods of people living in downstream areas

(Dwivedi, Acharya, & Simard, 2000; Vuichard & Zimmermann, 1987).

Although GLOFs are not a recent phenomenon, they have started drawing considerable attention

among scientists after the 1980s as the risk from potential GLOFs has increased (Xu Daoming

1988; Vuichard and Zimmermann 1986, 1987; Chen et al. 2013). About 1.6 million people

living downstream within the territory of Nepal may be at risk from these natural hazards

(Ghimire, 2004). However, GLOF risk can be reduced through the implementation of appropriate

mitigation and adaptation measures. To achieve this, an updated and standardized glacial lake

inventory should be conducted periodically in order to analyze the spatial distribution and temporal

development of glacial lakes, produce a GLOF hazard assessment, and make plans for mitigation

of identified potential GLOF risks. In addition, effective approaches should be available to the

public to help in understanding GLOFs and their risk to the public.

As a part of “Green Climate Fund Readiness Programme for Nepal” led by Ministry of Finance

and to support the Green Climate Fund (GCF) Project proposal formulation by UNDP Nepal,

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a comprehensive mapping and assessment of glacial lakes of Nepal and Tibetan plateau (which

are draining to Nepal) were conducted for 2000 and 2015 using remote sensing (RS) and

a geographic information system (GIS).Different criteria are adapted to identify potentially

dangerous (critical) glacial lakes, and to assess and estimate a qualitative or relative probability

of GLOF and its impacts on downstream communities in Nepal.

2. Study area

The present study area lies within the Koshi, Gandaki and Karnali basins, all of which are

major tributaries of the Ganges River. The catchments of these river basins are transboundary

to the Tibet Autonomous Region (TAR) China (upper section), Nepal (upper and middle

section) and India (mostly lower section). Some of the tributaries are sourced in Tibet and

China before flowing through Nepal to finally merge with the Ganges River in India (Figure

2.1).

Figure 2.1: Study area showing the Koshi, Gandaki and Karnali basins in Nepal and Tibet, China.

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The glaciers and glacial lakes are distributed only in the upper and middle sections of the river

basins. Hence, the study area is confined to Nepal and the TAR, China. The major tributaries

of the Koshi River are the Tamor River, the Arun River (Pumqu River in China), the Dudh

Koshi River, the Tama Koshi (Rongxer River in China), the Likhu River, the Sun Koshi

(Poiqu River in China), and the Indrawati River. The Gandaki River is fed by the Trishuli,

Budhi Gandaki, Seti, Marsyangdi, and Kali Gandaki rivers. Some upper sections of the Budhi

Gandaki and Trishuli rivers lie in the TAR. The Karnali River of western Nepal is also known

as Ghaghara River in India. The main tributaries of the Karnali River are the Mahakali (Kali),

Bheri, Humla Karnali, Mugu Karnali, Kawari, West Seti, and Tila rivers. Some tributaries of the

upper Humla River are sourced in China and the catchment of the upper Kali River spans

Nepal, India and China.

The catchment area of the study area is considered in the TAR, China and Nepal only.

Approximately 57.3%, 88.1% and 95.7% of the total catchment areas of the Koshi, Gandaki

and Karnali basins lay in Nepal respectively. The remainder is located in the TAR, China

(Table 2.1).

Table 2.1: Catchment area of the Karnali, Gandaki and Koshi basins in Nepal and Tibet, China.

Basin China Nepal Total

Area (sq. km)

Area (%) Area (sq.

km) Area (%) Area (sq. km)

Karnali 3016 4.3 67865 95.7 70881 Gandaki 4353 11.9 32116 88.1 36469 Koshi 29450 42.7 39456 57.3 68906

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3. Approach and methodology

The glacial lake inventory consists of water bodies that are situated proximal to present glaciers

as well as those located in lowland areas that were covered by glaciers in the past. The lakes

are generally formed by glacial melt water. Glacial lakes may also exist beneath (subglacial)

or within (englacial) glaciers, but are usually not visible in aerial/optical images, and their

detection is challenging as it requires field-based methods to acquire the necessary information.

Thus, subglacial and englacial lakes are not included in this inventory because they cannot be

mapped from aerial/optical satellite images. To date, the latest glacial lake inventory of Nepal

and adjacent region was conducted between 2003 and 2007 using Landsat ETM+ satellite

images. In the context of climate change and global warming in the region, glaciers are shrinking

and retreating rapidly resulting in large changes in the status of glacial lakes.

Whilst glacial lakes are a source of fresh water, they are also a potential source of disaster if

the dam containing the lake is breached. Knowledge of glacial lakes and related disaster risks

is important to reduce the GLOF risk to the lower riparian community. To understand the latest

status of the glacial lakes, high resolution satellite images are used to produce an inventory of

glacial lakes of 2000 and 2015. A 5 m digital elevation model (DEM) is used to examine

the geometric properties of the lakes.

3. 1 Data sources

Landsat OLI

Landsat satellite images have been widely used to map the extent of glaciers and glacial lakes

globally due to their high spatial resolution and accessibility throughout the region (Bolch et al.

2010). Landsat data are used in this study due to their consistent spatial coverage in the

region, high spatial resolution and free accessibility through the GLOVIS web portal

(http://glovis.usgs.gov). We have used the Landsat Operational Land Imager (OLI) to prepare

the current glacial lake inventory of the region. The OLI, built by the Ball Aerospace &

Technologies Corporation, measures the visible, near infrared, and short wave infrared portions

of the spectrum. Its images have 15-meter (49 ft.) panchromatic and 30-meter multi-spectral

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spatial resolutions along a 185 km (115 mile) wide swath. The images cover wide areas of

the Earth whilst retaining sufficient resolution to distinguish features such as glaciers, glacial

lakes, urban centers, farms, forests and other objects. OLI is more reliable and provides

improved performance than ETM+. In addition, the use of satellite images of higher spatial

resolution has been emphasized in this study.

The OLI images acquired covering the region during 2014 to 2016 years were used for mapping

glacial lakes. Images different years and derived from different sensors were also used to verify

the existence of the mapped lakes. The images between September and December were used

primarily because the likelihood of snow or cloud cover is lower during this period than other

months of the year.

Digital elevation model (DEM)

Topographic information of the lakes and adjacent areas was used to identify and categorize

the lakes and establish a ranking of dangerous glacial lakes. Digital elevation models (DEMs)

can be used to extract the topographic parameters of the lakes, associated glaciers, moraines

and the adjacent areas. A DEM can be defined as a regular gridded matrix representation of

the continuous variation of relief over space and is a digital model of land surface form. The

primary requirement of any DEM is that it should have the desired accuracy and resolution and

be devoid of data errors. Their steady and widespread application can be further attributed to

their easy integration within a GIS environment. Before the year 2000, the base elevation

models depicting a global coverage were available in a 1 km resolution, e.g. GTOPO-30

(Global Topography in 30 arc-sec) and GLOBE (The Global Land 1 km- Base Elevation

Project). However, in the last decade, more advanced global DEMs with higher spatial

resolutions, such as the Shuttle Radar Topography Mission (SRTM) (version 4, C-Band DEM

of 3 arc-second, 90 m resolution) and the Advanced Spaceborne Thermal Emission and

Reflection Radiometer (ASTER) (version 2, 30 m resolution), have become available. Apart

from these freely available DEM datasets, stereo images from a number of satellites (e.g.

Cartosat 1, Landsat 7 ETM+, QuickBird, IKONOS, SPOT, ASTER sensors, among others) have

also been used to create DEMs using various software applications for examining landscapes.

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High resolution images are capable of obtaining more accurate surface information. For this

study, we used a 5 m resolution ALOS DEM for Nepal and a 12 m resolution PALSAR DEM

for the TAR, China.

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3. 2 Mapping method

A number of remote sensing methods had been developed for generating glacial lake inventories

(Kääb, 2000; Mool et al. 2001a, b; Huggel et al. 2002, 2006; Ives et al. 2010). We

adopted the method used by Maharjan et al., 2018 for the inventory of glacial lakes. The

method is summarized in Figure 3.1. The ‘Normalized Difference Water Index’ (NDWI; Eq.(1))

method provides an automatic way to detect water bodies, including glacial lakes, on the basis

of Landsat Thematic Mapper (TM) or Enhanced Thematic Mapper Plus (ETM+) images, was

adopted in this study.

Figure 3.1: The remote sensing based glacial lake inventory process.

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NDWI =NIR (or Band 4) − Blue (or Band 1)

NIR (or Band 4) + Blue (or Band 1) …………………………….. (1)

The ratio images of NDWI is created by arithmetic calculation of Band 4 near infrared (NIR)

and Band 1 (Blue) of the Landsat images and the NDWI threshold value is applied to classify

the glacial lakes in the images. A NDWI threshold value of -0.6 to -0.9, as adopted by

Huggel et al. (2002), was used to prepare the inventory of HKH glacial lakes. Although this

automatic classification method can speed up the detection of glacial lakes, this method is not

applicable to a wide region due to some uncertainties created by atmospheric and physical

processes. For example, if lakes are frozen or covered with snow, or cloud cover or shadow

obstructs the image, they cannot be detected using this automatic classification method. In such

cases, manual delineation method was used to map the lakes.

The automated delineation of glacial lakes was validated and modified if necessary by overlaying

the Landsat images over the previous inventory datasets whenever they were available (Mool

et al. 2001a, b, 2003). Thus, any misclassified lakes were corrected, and missing lakes were

added manually. Further, the mapped lakes were overlain with high resolution images if available

in the Google Earth environment for validation.

Generally, pixels in the raster images do not give the homogenous reflectance and represent

only one object unless it is perfectly aligned in single object. Thus, at least four pixels are

required to map the exact boundary of the object (lake) from the images. Therefore, the

smallest glacial lake that can be mapped from the images should be covered by 4 pixels,

which is 0.0036 sq. km area in the case of Landsat images. Hence, the glacial lake area of

0.003 sq. km is the minimum threshold for lake size that has been applied for mapping in

the present glacial lake inventory.

3. 3 Uncertainties and limitations

The uncertainties or accuracy of mapping of glacial lakes or glaciers from the satellite images

used depends, typically, on the spatial resolution, seasonal/temporal snow cover, shadow, and

contrast between the glacial lakes pixels and surroundings pixels (DeBeer and Sharp 2007;

Bajracharya et al. 2014c). Landsat images with the least snow cover and cloud cover were

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selected for mapping to increase the quality of the automatic mapping approach and reduce

manual correction of the boundary. The lake data was overlaid on the high resolution images

in Google Earth and also cross-checked with the previous inventory data wherever it is available

in order to validate and improve the accuracy of mapped lakes from the automatic approach.

Also the glacial lake data were thoroughly checked by overlaying on the same Landsat images

used for automatic mapping along with cross-checking in the high resolution images in Google

Earth and any mismatches of the boundary of the lakes due to the seasonal/temporal snow

cover and shadows were manually corrected using additional Landsat images. Although this

cross-checking improved the quality of the data, the mapped lakes boundary were affected by

various other types of obscurities, which are mostly dependent on image resolution. The

uncertainty of the glacial lake boundary could not be greater than half of the image resolution

(i.e., ±15 m in TM, ETM+ and OLI) (Bajracharya et al. 2014c). Hence the uncertainty of

the glacial lake boundary was estimated by variation of area bounded by the lake polygon,

which is calculated by number of image pixels bounded by each lake polygon and the total

number of image pixels bounded by the 15 m buffer of each lake polygon. The equation used

for calculating total uncertainty is given as:

𝑅𝑀𝑆𝐸 = √∑ (𝑎 − â)2𝑛

𝑖=1

𝑛

Where, ai is the area of glacial lake from the total pixel bounded by glacial lake polygon and

âi is the area of glacial lake from the total pixel bounded by the 15 m buffer of glacial lake

boundary. The total uncertainty of glacial lake area is ±2% and this uncertainty were also

observed in the glacial lake and glacier mapping of the HKH region (Maharjan et al., 2018;

Bajracharya et al., 2014c). Depending on the mapping scale of the glacial lakes, the 30m

resolution of Landsat images satisfies enough to map the lake boundary. The accuracy will be

higher in the high spatial resolution images. Field verification is utmost necessary for the

confirmation of the information before mitigation measures.

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3. 4 Glacial lake attributes

Once the final glacial lake polygons were generated, the attributes of the glacial lake were

generated in ArcGIS. Each lake polygon is given a unique ID containing the longitude and

latitude of the centroid of the l polygon in the same way as the GLIMS ID is developed for

glaciers by National Snow and Ice Data Center (NSIDC), University of Colorado, USA. GLIMS

stands for Global Land Ice Measurement from Space. The GLIMS ID consists of 14 letters,

e.g. GxxxxxxEyyyyyN for glaciers, and 15 letters (GLxxxxxxEyyyyyN) for glacial lakes, where

G stands for Global, N for North, E for East and 5x stands for 3-digit degree decimal latitude

and 6x stands for 3-digit degree decimal longitude. This is the first time that lake IDs are

being used in a lake inventory study (Maharjan et al., 2018. The initial letter “G” in the

GLIMS ID is replaced by “GL” representing ‘Glacial Lake’. Other parameters such as glacial

lake area and elevation etc. were calculated automatically in ArcGIS using the DEM. The lakes

were morphologically classified by manually overlaying high resolution lake images with terrain

data in Google Earth. The lakes were classified as either moraine dammed, ice dammed or

rock dammed (Table 3.1).

Table – 3.1: Classification of Glacial Lakes (modified after ICIMOD 2011) Glacial lake type Code Definition

Moraine dam

med (

M) End-moraine

dammed lake M(e)

Lakes dammed by end (terminal) moraines. Water usually touches the walls of the side moraines. Water is usually held back by the end moraine (dam) but is not necessarily in contact with the glacier. Glacial ice may be present at the bottom of the lake (defined in some other classifications as an advanced form of supraglacial lake).

Lateral moraine dammed lake

M(l)

Lakes dammed by lateral moraine(s) (in the tributary valley, trunk valley, or between the lateral moraine and the valley wall, or at the junction of two moraines). Lake is held back by the outside wall of a lateral moraine, i.e. away from the former glacial path.

Other moraine dammed lake

M(o) Lakes dammed by other moraines (includes kettle lakes and thermo-karst lakes).

Ice dammed Ice-dammed lake I

Lakes dammed by glacier ice, including lakes on the surface of a glacier or lake dammed by glaciers in the tributary/trunk valley, or between the glacier margin and valley wall, or at the junction of two glaciers.

Supra-glacial lake I(s) Bodies of water (pond or lake) on the surface of a glacier. Dammed by tributary valley

glacier I(v)

Lakes dammed by glacier ice with no lateral moraines; can be at the side of a glacier between the glacier margin and valley wall

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Bed rock dam

med

Bedrock dammed lake

B Bodies of water that form as a result of earlier glacial erosion. The lakes accumulate in depressions after the glacier has retreated or melted away.

Cirque lake B(c) A small pond occupying a cirque.

Other glacier erosion lake

B(o) Bodies of water occupying depressions formed by glacial erosion. These are usually located on the mid-slope of hills, but not necessarily in a cirque.

Blocked

Other glacial lakes

O

Lakes formed in a glaciated valley and fed by glacial melt. Damming material is not directly part of the glacial process e.g. debris flow, alluvial, or landslide blocked lakes.

The ‘Albers Equal Area Conic’ projection is used to calculate the area of glacial lake, the unit

of the area adopted were in square kilometer (sq. km). A detailed list of glacial lake attributes

is given in Table 3.2.

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Table 3.2: Fields and formats of glacial lake attributes

S.N. Field Name Type Format Description

1 GLIMS_ID string GLxxxxxxEyyyyyN Combination of longitude (X) and latitude (Y) of the centroid of the lake polygon. GL – glacial lake, E – East, N – North.

2 Basin name string Text Drainage basin name based on maps and literature.

3 Sub-basin name string Text Drainage sub-basin name based on maps and literature.

4 Longitude string DMS Longitude of center of glacial lake. 5 Latitude string DMS Latitude of center of glacial lake.

6 Altitude integer meter above mean sea level (m.a.s.l.)

Water level of glacial lake. Extracted from SRTM.

7 Area float km2 Area of glacial lake. Calculated based on the Albers Equal Area Conic projection.

8 Gl_Type string Text Type of glacial lake.

3. 5 Identification of potentially dangerous glacial lakes

The step-by-step approach to identify critical or potentially dangerous glacial lakes (PDGL)

developed by ICIMOD (Mool et al 2001, ICIMOD, 2011) has been previously applied to the

Koshi basin (Shrestha et al. 2017) and is used in this study with some modifications. The

stability of a lake depends on its characteristics and damming material (Figure 3.2). The dam

should have sufficient strength to hold the lake water if it is to be considered stable, otherwise

the lake may be breached in an outburst flood. Detailed lake and dam features were analyzed

using remote sensing to assess the stability of each lake. However, stable lakes may still

outburst due to the activity of the source glacier and failure of the surroundings, which may

impact the lake and/or dam. Hence, the physical condition of the source glacier and the

surroundings of the lake and dam is also considered in this study. Other triggering agents

including earthquakes, extreme climatic events, unsafe anthropogenic intervention etc. may also

breach the dam, but their potential impact could not be evaluated in this study.

The criteria to identify PDGLs are discussed by Mool et al. (2001), Bajracharya (2007),

and ICIMOD (2011) and are also considered in this study along with some additional criteria

such as catchment of the lake and the elevation difference and length of the dam.

14 | P a g e

Figure 3.2 Identification of potentially dangerous glacial lakes and prioritization for GLOF risk reduction.

The following updated approach has been considered to identify PDGLs using remotely sensed

data:

Criteria

1. Lake characteristics

The current mapping of glacial lakes includes all lakes with areas greater than 0.003

sq. km. If the lakes are larger than 0.02 sq. km, and on a steep slope, an outburst

may occur, causing serious damage, especially in the highly populated areas with

15 | P a g e

extensive infrastructure located downstream. The expansion rates of lakes larger than

0.02 sq. km was analyzed to estimate the possible volumetric increase in water. The

following criteria were used to analyze the stability of the lakes.

I. Lake size and rate of expansion

II. Increase in water level or volume of water

III. Presence of cascading lakes

IV. Intermittent activity of supraglacial lakes

Rapid changes in lake area and the lake’s potential to grow in the near future due to

the size of the catchment, presence of cascading lakes and chances of merging of

supraglacial lakes are assessed. An expansion in lake area indicates an increase in

water volume, which may increase the risk of outburst.

2. Dam characteristics

The condition of the dam is an important parameter to consider in lake stability

assessments. Dams constructed out of thin and loose moraine have a large potential to

rupture. Narrow crested moraines will cause a lake to have a relatively high outburst

potential compared to those with a wide crest. Thus, lakes with thinner moraine crests

may be associated with a cause for GLOFs. Due to erosion and landslides, these

moraine crests are usually angular and narrow at the top. This geometry is vulnerable

to any surge wave generated by ice or snow avalanches, ultimately triggering a GLOF.

The steepness of moraine wall slopes also determines the likelihood of dam outburst.

The main dam characteristics that can be derived from remote sensing are as follows:

I. Type of damming material

II. Crest width

III. Slope of the dam wall

IV. Elevation difference of the moraine (height of the dam)

V. Length of the dam

VI. Erosional activity or presence of landslides on the dam

VII. Presence or absence of drainage outflow

VIII. Breached and closed in the past and the lake refilled again with water

16 | P a g e

IX. Seepage through the damming walls

X. Existence and stability of ice core and/or permafrost within dam

3. Source glacier characteristics

The potential of a lake to burst and cause damage downstream can be heightened if

the lakes are associated with a glacier. If the lake is in contact with a glacier retreating

on a gentle slope, there is possibility for lake expansion. On the other hand, if the

glaciers with crevasses on a steep slope, ice masses may detach from the glacier and

fall into the lake. Falling ice masses can be disturbed the stability of the dam and/or

the lake. Even stable lakes in such an environment can be considered as potentially

dangerous and become a candidate that needs to be monitored. The steepness of the

glacier tongue can also make the associated lake a potentially dangerous. The main

characteristics that can be relevant to the condition of the glacier are as follows:

I. Condition of associated glacier (source glacier)

II. Distance between the glacial lake and the source glacier(s)

III. Steepness of glacier tongue

IV. Debris cover on the lower glacier tongue

V. Presence of crevasses and ponds on the glacier surface

VI. Calving of ice from the glacier front

VII. Icebergs breaking off the glacier terminus and floating into the lake

4. Physical conditions of the surroundings

The physical factors that can destabilize a lake and trigger its outburst can be identified

using remote sensing of the lake surroundings. The factors that are considered in this

study are:

I. Hanging glaciers that are in contact with or very close to the lake

II. Potential rock-fall/slide (mass movements) sites around the lake

17 | P a g e

III. Large snow avalanche sites immediately above the lake

IV. Sudden advance of a glacier towards a lower tributary or the main glacier which

has a well-developed frontal lake.

5. Other factors

Some other unseen factors that can destabilize a lake and/or dam to trigger lake

outbursts are out of the scope of the remote sensing technique used in this study.

Earthquakes and extreme climate events at the vicinity of the lake and dam are other

major triggering factors which may result in a lake outburst. In addition, unscientific and

inappropriate human intervention at the lake and dam may also lead to uncontrolled

breaching. The first two factors are unpredictable. However, there is a need to increase

stakeholder awareness about the danger associated with the lakes and for any further

activity in the lake itself. The following triggering agents are not considered in this study:

I. Earthquake generated waves in the lake, resulting in the deterioration and collapse

of the dam.

II. Extreme climate conditions resulting from excessive and continuous precipitation in

the lake, dam and catchment area.

III. Anthropogenic interference in the lake and dam which may destabilize the

containing walls.

Lake stability depends directly on the physical condition of the lake and the dam. Other factors

which may significantly impact the stability of the lake include the activity of source glacier and

the stability of the surroundings. This study considers data on lake characteristics, dam properties,

source glaciers characteristics and the physical conditions of the area surrounding the lake to

identify potentially dangerous glacial lakes in the study area.

3. 6 Ranking and prioritization of potentially dangerous glacial lakes

Potentially dangerous glacial lakes were identified and ranked based on the physical characteristics

of the lakes and morphology of the dam, source glacier and its surroundings following the

criteria outlined in the section 3.4. Not all of the potentially dangerous glacial lakes do not

constitute an equal risk to the community and infrastructure in the river basins. Based on their

18 | P a g e

socioeconomic importance, the PDGL are prioritized in order to reduce the GLOF risk to the

community. The socioeconomic parameters included in this study were number of household

(Hh), population, road lengths (path, track and trial, motor-able and highway), the number

and type of bridges (wooden, suspension, motor-able, and highway bridges), hydropower

projects (number and capacity of hydropower projects in megawatts), in the path of 500m

buffer zone and the catchment of a potential outburst. The information for preliminary assessment

was derived from Google Earth images and the Nepal census data of 2011.

19 | P a g e

4. Status of glacial lakes in 2015

The glacial lakes equal to and larger than 0.003 sq. km were mapped from Landsat satellite

images between 2014 and 2016 for the Koshi, Gandaki and Karnali basins of Nepal and TAR

(Figure 4.1). A unique identity (GLIMS ID) is assigned to each individual glacial lake present

in the study area. The lake area, elevation and topographic features of individual glacial lakes

were calculated from the 5 m ALOS DEM for Nepal and 12 m PALSAR DEM for the TAR

using ArcGIS. Lake size was divided into 7 different classes according to their size. The type

of lake was identified and classified into bedrock dammed, moraine dammed, ice dammed and

other lake classes. The altitudinal distribution of lakes was analyzed using DEMs and lake

boundaries in ArcGIS. The distance to the source glacier was measured manually only for

moraine dammed lakes equal to or larger than 0.02 sq. km.

Figure 4.1: Distribution of glacial lakes in the Koshi, Gandaki and Karnali basins of Nepal and the TAR, China.

20 | P a g e

4. 1 Number and area of glacial lakes

A total of 3,624 glacial lakes were mapped in the Koshi, Gandaki and Karnali basins of Nepal

and the TAR. The Koshi basin had the largest number (2064) of glacial lakes, followed by

the Karnali basin (1128) and the Gandaki basin (432). The distribution and area of glacial

lakes in the Koshi, Gandaki and Karnali basins is shown in Figure 4.1 and given in Table 4.1.

Table 4.1: Number and area of glacial lakes in the sub-basins of the three different basins (2015).

Basin Sub-basin Number Area

Count % sq. km % Avg. (sq. km)

Koshi

Tamor 283 7.81 9.12 4.68 0.03 Arun 909 25.08 68.58 35.15 0.08 Dudh Koshi 355 9.80 16.92 8.67 0.05 Likhu 17 0.47 0.41 0.21 0.02 Tama Koshi 307 8.47 14.68 7.52 0.05 Sun Koshi 181 4.99 22.13 11.34 0.12 Indrawati 12 0.33 0.16 0.08 0.01 Sub-total 2064 56.95 132.01 67.65 0.06

Gandaki

Trishuli 242 6.68 8.19 4.20 0.03 Budhi Gandaki 49 1.35 1.58 0.81 0.03 Marsyangdi 59 1.63 6.22 3.19 0.11 Seti 4 0.11 0.15 0.08 0.04 Kali Gandaki 78 2.15 3.05 1.56 0.04 Sub-total 432 11.92 19.19 9.83 0.04

Karnali

Bheri 164 4.53 9.20 4.72 0.06 Tila 82 2.26 4.11 2.11 0.05 Mugu 239 6.59 6.22 3.19 0.03 Kawari 28 0.77 1.04 0.53 0.04 West Seti 51 1.41 1.43 0.73 0.03 Humla 498 13.74 20.21 10.36 0.04 Kali 63 1.74 1.53 0.78 0.02 Karnali 3 0.08 0.19 0.10 0.06 Sub-total 1128 31.13 43.93 22.51 0.04

Total 3624 100.00 195.12 100.00 0.05 The Arun sub-basin of the Koshi basin had the highest number and total area of glacial lakes

among the sub-basins and also a higher sum than the Gandaki and Karnali basins. The Arun

sub basin contributes more than a quarter of the total number of lakes in the inventory and

over a third of lakes by total lake area. The Humla Karnali sub-basin of the Karnali basin

contributes the second highest number of glacial lakes (13.7%) and is the third highest in

total lake area (10.36%). The Sun Koshi sub-basin of the Koshi basin consists of only 181

21 | P a g e

lakes but is the second highest by total glacial lake area (11.34%) and is the highest in

terms of average lake area. The Sun Koshi sub-basin therefore has a higher proportion of

large lakes than other basins in the study area. The Tamor, Dudh Koshi and Tama Koshi

sub-basins each contribute to approximately 7 %to 10 % of the total number of glacial lakes,

and 4 % to 8% of the total lake area. Other sub-basins in the study area account for less

than 5 % of the total number of lakes and total lake area in the study region. Very few

glacial lakes exist in the Likhu, Indrawati, Seti and Kawari sub-basins.

Glacial lakes occupy a combined area of 195.12 sq. km in the study area (Table 4.1). Of

this, ~132.02 sq. km (67.65%) is in the Koshi basin, 19.18 sq. km (9.83%) is in the

Gandaki basin, and 43.94 sq. km (22.51%) is sourced from the Karnali basin (Figure 4.2).

The average mean area of the glacial lakes within each sub basin ranges from 0.01 in the

Indrawati sub-basin to 0.12 sq. km in the Sun Koshi sub-basin. Both of these are sub-basins

of the Koshi basin. The average mean area of glacial lakes is 0.06 sq. km in the Koshi basin

and 0.04 sq. km in the Karnali and Gandaki basins. The overall average mean area of the

glacial lakes in the study area is 0.05 sq. km per lake.

Figure 4.2: Number and total area of glacial lakes in three different basins.

4. 2 Types of glacial lakes

Glacial lake are often formed as the result of glacier retreat, a process which may leave behind

large debris deposits. Lakes may be formed either within part of the eroded landform (e.g.

bed rock dammed) or build behind a dam formed by moraines, ice and/or landslide debris.

20

64

43

2

11

28

13

2.0

2

19

.18

43

.94

1

10

100

1000

10000

Koshi Gandaki Karnali

Number Area (km2)

22 | P a g e

The lakes in this inventory were classified based on the damming material such as bedrock

dammed (B), moraine dammed (M), ice dammed (I), and others (O) including a landslide

dam, moraine or ice dam adjoining the glacier. Moraine dammed lakes are split into more

detailed classes including end moraine (e), lateral moraine (l) and other moraine (o).

Similarly, ice dammed lakes are classified into supraglacial (s) and valley (v) types. Bedrock

dams are split into cirque (c) and other erosional landforms (o) (Table 3.1). The majority

of the glacial lakes in all basins of the study area are moraine dammed (Table 4.2). Moraine

dammed lakes comprise about 55% of the total lakes (2002) followed by bedrock dammed

lakes (35%) (1256) and ice dammed lakes including others (10%) (339). Among the

moraine dammed lakes, the end-moraine dammed lakes comprise about 15.8% (573) and

nearly 2.3% (82) were lateral moraine dammed type. The other moraine dammed lakes

contribute to more than 37.2 % (1347) of the total. Among the ice-dammed lakes, supra-

glacial lakes comprise 9.3 % (337) and other ice-dammed lakes contribute to only 0.1% (2)

of the total. Cirque glacial lakes constitute about 7.8% (281) of the total, whereas other

erosional glacial lakes comprise ~26.9% (975). In general, moraine-dammed lakes consist of

loose, coarse material with little cementing content. This composition is easy to erode and thus

lake moraines comprised of this material are vulnerable to GLOFs. The distribution of lake types

within each basins is shown in Figure 4.3.

Table 4.2: Number, and area of glacial lakes in three basins by lake type (2015). Major Basin Koshi Gandaki Karnali Total

Type No. Area (sq. km)

No. Area (sq. km)

No. Area (sq. km)

No. Area (sq. km)

Moraine-dammed lake (M)

M(e)

359 74.98 75 9.93 139 11.72 573 96.63

M(l) 37 7.48 20 1.44 25 1.92 82 10.84 M(o)

668 12.61 143 3.47 536 12.54 1347 28.62

Sub-total 1064 95.07 238 14.84 700 26.18 2002 136.09 Ice-dammed lake

(I) I(s) 234 3.36 46 0.36 57 0.46 337 4.18 I(v) 0 0 0 0 2 0.04 2 0.04

Sub-total 234 3.36 46 0.36 59 0.5 339 4.22

Bedrock dammed /erosion lake (B)

B(c)

132 7.27 31 1.33 118 5.42 281 14.02

B(o)

625 24.06 116 2.55 234 5.7 975 32.31

23 | P a g e

Sub-total 757 31.33 147 3.88 352 11.12 1256 46.33 Others O 9 2.26 1 0.1 17 6.14 27 8.5

Total 2064 132.02 432 19.18 1128 43.94 3624 195.14

The distribution of different glacial lake types for each sub-basin is displayed in Table 4.3.

The total number and area of moraine dammed lakes are comparatively higher than bedrock

dammed, ice-dammed and other types of lakes across all of the basins. The Arun sub-basin

has the highest number of moraine dammed lakes, followed by the Humla Karnali sub-basin of

the Karnali basin. The Dudh Koshi and Tama Koshi sub-basins of the Koshi basin have more

than 200 moraine dammed lakes and more than 100 moraine dammed lakes are located in

the Tamor, Sun Koshi, Trishuli, and Mugu Karnali sub-basins. The sub-basins of the Koshi,

Gandaki and Karnali basins had fewer moraine dammed lakes. The moraine dammed lakes in

the Koshi basin are comparatively more vulnerable to GLOFs than those in the other basins.

The ice dammed lakes present in this inventory are mostly supraglacial lakes, which are dynamic

in nature, appearing and disappearing periodically depending on glacier melt rates. The growth

and merging of supraglacial lakes are characteristic of this class of lakes, which ultimately

convert into moraine dammed lakes. Imja Tsho and Tsho Rolpa are typical examples of moraine

dammed lakes that have developed from supraglacial lakes (Bajracharya et al., 2007). This

type of lake are more vulnerable to GLOFs. If the supraglacial lakes do not merge, there is

high possibility of lake disappearance over time. The Dudh Koshi sub-basin contains the highest

number of ice dammed (supraglacial) lakes.

Figure 4.3: Number and percentage of the different types of glacial lakes in the three basins.

0

10

20

30

40


Nu

mb

er (

%)

Moraine-dammed lake (M) Bedrock-dammed lake (E) Ice-dammed lake (I) Others (O)

24 | P a g e

Bedrock dammed lakes are the most stable type of lakes. These lakes have a lower probability

of GLOF occurrence; however, flash floods may be generated by falling ice, snow or debris

within the lake, causing the water to overflow the bedrock sill. There is little possibility of

breaching of the bedrock dam by foreign masses landing on the dam or in the lake.

25 | P a g e

Table 4.3: Number and area of glacial lakes by types (2015) in the sub-basins and basins of the Koshi, Gandaki and Karnali River of Nepal and the TAR B

asin

Sub Basin

Number Area (km2)


Ice-dammed lake (I)

Bedrock-dammed lake (E)

Other Total


Ice-dammed lake (I)


Other Total

M(e) M(l) M(o) I(s) I(v) B(c) B(o) O M(e) M(l) M(o) I(s) I(v) B(c) B(o) O

Ko

shi

Tamor 37 9 78 36 0 34 87 2 283 3.00 0.17 1.50 0.38 0.00 2.03 1.60 0.46 9.12

Arun 167 4 218 39 0 64 411 6 909 36.80 0.23 4.64 0.98 0.00 3.78 20.36 1.79 68.58

Dudh Koshi 64 17 134 103 0 4 32 1 355 9.77 3.04 2.22 1.40 0.00 0.17 0.31 0.01 16.92

Likhu 2 0 8 0 0 3 4 0 17 0.15 0.00 0.11 0.00 0.00 0.10 0.05 0.00 0.41

Tama Koshi 55 2 161 38 0 15 36 0 307 9.31 1.19 2.56 0.30 0.00 0.77 0.56 0.00 14.68

Sun Koshi 34 5 64 18 0 11 49 0 181 15.96 2.85 1.50 0.30 0.00 0.41 1.12 0.00 22.13

Indrawati 0 0 5 0 0 1 6 0 12 0.00 0.00 0.08 0.00 0.00 0.02 0.06 0.00 0.16

Sub-Total 359 37 668 234 0 132 625 9 2064 74.98 7.48 12.61 3.36 0.00 7.27 24.06 2.26 132.01

Gan

dak

i

Trishuli 34 13 62 35 0 18 80 0 242 3.13 0.49 1.80 0.29 0.00 0.97 1.52 0.00 8.19

Budhi Gandaki 11 2 18 8 0 6 4 0 49 0.76 0.12 0.28 0.06 0.00 0.10 0.27 0.00 1.58

Marsyangdi 14 3 23 3 0 4 12 0 59 4.89 0.37 0.61 0.02 0.00 0.15 0.19 0.00 6.22

Seti 0 0 0 0 0 0 3 1 4 0.00 0.00 0.00 0.00 0.00 0.00 0.05 0.10 0.15

Kali Gandaki 16 2 40 0 0 3 17 0 78 1.15 0.47 0.77 0.00 0.00 0.13 0.53 0.00 3.05

Sub-Total 75 20 143 46 0 31 116 1 432 9.93 1.44 3.47 0.36 0.00 1.33 2.55 0.10 19.19

Kar

nal

i

Bheri 21 1 56 3 0 16 65 2 164 1.44 0.01 1.07 0.02 0.00 0.73 0.98 4.96 9.20

Tila 2 0 20 2 0 24 32 2 82 0.52 0.00 1.32 0.01 0.00 1.50 0.54 0.23 4.11

Mugu 29 0 127 7 0 31 40 5 239 1.91 0.00 2.38 0.10 0.00 1.03 0.65 0.16 6.22

Kawari 5 0 10 0 0 6 7 0 28 0.50 0.00 0.21 0.00 0.00 0.27 0.06 0.00 1.04

West Seti 9 3 22 9 0 4 3 1 51 0.84 0.14 0.24 0.08 0.00 0.10 0.01 0.00 1.43

Humla 61 16 285 22 2 31 74 7 498 6.10 1.63 6.77 0.16 0.04 1.53 3.18 0.79 20.21

Kali 12 5 16 14 0 3 13 0 63 0.42 0.14 0.55 0.08 0.00 0.07 0.27 0.00 1.53

Karnali 0 0 0 0 0 3 0 0 3 0.00 0.00 0.00 0.00 0.00 0.19 0.00 0.00 0.19

Sub-Total 139 25 536 57 2 118 234 17 1128 11.72 1.92 12.54 0.46 0.04 5.42 5.70 6.14 43.93

Total 573 82 1347 337 2 281 975 27 3624 96.63 10.84 28.61 4.18 0.04 14.02 32.31 8.50 195.12

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4. 3 Glacial lake size class

The glacial lakes were classified into seven different size classes: Class 1 (<= 0.02 sq. km),

Class 2 (>0.02 – 0.05 sq. km), Class 3 (>0.05 – 0.1 sq. km), Class 4 (>0.1 – 0.5

sq. km), Class 5 (>0.5 – 1 sq. km), Class 6 (>1 – 5 sq. km) and Class 7 (> 5 sq.

km).

Class 1 lakes are most frequent (2214) contribute 61 % of the total inventory (Table 4.4).

However, the total area of these lakes is only 20.25 sq km and their contribution to the total

lake area is only 10.38%. The average area of Class 1 glacial lakes is 0.01 sq km per lake.

The Class 1 glacial lakes are not analyzed further for the identification of potentially dangerous

glacial lakes. Class 2 lakes are the second highest in terms of total lake numbers and contribute

about 21 % to the total lake area. The number of glacial lakes decrease with respect to the

increasing class number. However, although class 4 lakes are ranked 4th in terms of their

absolute number, they are associated with the greatest total area of glacial lakes. Only one

class 7 lake is present in the inventory (0.03%) with an area of 5.41 sq km. Although this

is the largest glacial lake, it contributes to only 2.77% of the total lake area.

Table 4.4 shows the distribution of glacial lake numbers and areas in different size classes

within the basins. The Koshi basin exhibits the highest number of lakes in all classes. The

largest glacial lake is also present in the Koshi basin. There is an inverse relationship between

the number and area of lakes according to the size classes. Similar observations were made

in the Pumqu (Arun) River basin (Che et al. 2014) and the Poiqu (Bhote Koshi) basin

(Wang et al. 2014) with large numbers of smaller lakes of the same size as the class 1

and 2 lakes here.

Table 4.4: Number and area of glacial lakes by size class (2015) in the Koshi, Gandaki and Karnali basins of Nepal and TAR, China.

Class

Koshi Gandaki Karnali Total

No

Area (sq. km) No

Area (sq. km) No

Area (sq. km) No Area (sq. km)

Total Avg Total Avg Total Avg Total Avg

1 1239 11.04 0.01 261 2.46 0.01 714 6.75 0.01 2214 20.25 0.01

2 433 13.35 0.03 95 2.94 0.03 231 7.19 0.03 759 23.48 0.03

27 | P a g e

3 181 12.54 0.07 38 2.65 0.07 88 6.40 0.07 307 21.59 0.07

4 163 35.24 0.22 36 6.84 0.19 90 16.27 0.18 289 58.34 0.20

5 29 21.24 0.73 1 0.92 0.92 4 2.40 0.60 34 24.56 0.72

6 18 33.19 1.84 1 3.38 3.38 1 4.92 4.92 20 41.50 2.08

7 1 5.41 5.41 0 0.00 0 0 0.00 0 1 5.41 5.41

Total 2064 132.01 0.06 432 19.19 0.04 1128 43.93 0.04 3624 195.13 0.05

Table 4.5 displays the proportional distribution of lake size verses types of glacial lake. A

considerable number of lakes consist of other moraine and end moraine dammed lake types.

The end moraine dammed (M(e)) lakes are higher in all size classes of the lakes (Figure

4.4). Fifty-five moraine dammed lakes are larger than 0.5 sq km and out of this, 17 lakes

are larger than 1 sq km. Most of the larger lakes are moraine dammed lakes, although some

are blocked lakes (Table 4.6). The danger level of larger glacial lakes dammed by ice or

moraine are high.

Table 4.5: Number of glacial lakes by size class and lake type (2015).

Class Size Types of lake

Total M(e) M(l) M(o) I(s) I(v) B(c) B(o) O

Class 1 <0.02 138 34 936 301 1 108 688 8 2214 Class 2 0.02 - 0.05 142 14 305 28 1 75 187 7 759 Class 3 0.05 - 0.1 106 11 68 4 0 62 54 2 307 Class 4 0.1 - 0.5 149 17 37 4 0 36 38 8 289 Class 5 0.5 - 1.0 23 5 0 0 0 0 6 0 34 Class 6 1.0 - 5.0 14 1 1 0 0 0 2 2 20 Class 7 >5.0 1 0 0 0 0 0 0 0 1

Total 573 82 1347 337 2 281 975 27 3624

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Figure 4.4: Number of glacial lakes by size class and lake type (2015) of Koshi, Gandaki and Karnali basins of Nepal and TAR, China.

29 | P a g e

4. 4 Altitudinal distribution of glacial lake

The glacial lakes are found between 2400 and 6100 m.a.s.l. (Table 4.6). Around 6000

m.a.s.l. is the highest elevation of debris covered glaciers in the Nepal Himalaya (Bajracharya

et al. 2014). Above this elevation, the landforms are steeper and mostly exist in a frozen

state meaning that no

(permanent/perennial) lakes can

exist above this altitude. Figure 4.5

displays the distribution of glacial

lakes in 100 m elevation zones.

Only one glacial lake is located at

2400 m.a.s.l. in the Seti River sub-

basin of the Gandaki basin - the

rest are above 3300 m.a.s.l. The

majority of glacial lakes (58.3%)

are located at elevations between

5000 and 6000 m.a.s.l. Similarly,

40.3% of the total lakes are located

between 4000 and 5000 m.a.s.l.

About 99% of the glacial lakes are

present between 4000 and 6000

m.a.s.l. Similar altitudinal

differences in glacial lake distribution

have been reported in the Himalaya

in other studies (Nie et al. 2013;

Wang et al. 2014). For example,

in Poiqu basin, Wang et al. (2014)

showed that glacial lakes are distributed within the altitudinal range of 4420 to 5860, with

majority of the lakes (76%) situated at elevations>5000 m.a.s.l. The altitudinal distribution

of lakes shown in Figure 4.5 clearly demonstrates that a larger proportion of moraine dammed

Figure 4.5: Altitudinal distribution of number and various types of lakes in the study area.

0 400

2000

2200

2400

2600

2800

3000

3200

3400

3600

3800

4000

4200

4400

4600

4800

5000

5200

5400

5600

5800

6000

6200

Number

Ele

vati

on

(m

asl)

M(e) M(l) M(o) I(s) I(v) B(c) B(o) O

30 | P a g e

lakes occurs at higher elevations whilst erosional and others types of glacial lake exist at lower

elevations.

Figure 4.6 indicates that the percentage of small glacial lakes (<0.02 sq. km) is

higher in all the altitudinal zones except in below 2500 m.a.s.l. However, the percentage

of large glacial lakes (>0.1 sq. km) is comparatively higher in altitudinal zone between

3500 and 5500 m.a.s.l.

Table 4.6: Distribution of different types of glacial lakes in 1000 m elevation zones Elevation Zone 3000 - 4000 4000 - 5000 5000 - 6000 6000 - 7000 Total

Type No % No % No % No % No %

M

M(e) 7 15.56 180 12.33 386 18.26 0 0 573 15.81

M(l) 0 0 43 2.95 37 1.75 2 50 82 2.26

M(o) 6 13.33 387 26.51 953 45.08 1 25 1347 37.17

I

I(s) 7 15.56 178 12.19 152 7.19 0 0 337 9.3

I(v) 0 0 0 0 2 0.09 0 0 2 0.06

I(o) 0 0 0 0 0 0 0 0 0 0

B B(c) 7 15.56 202 13.84 72 3.41 0 0 281 7.75

B(o) 12 26.67 452 30.96 510 24.12 1 25 975 26.9

Others O 6 13.33 18 1.23 2 0.09 0 0 27 0.75

Total Number 45 1460 2114 4 3624

% 1.24 40.29 58.33 0.11 100

Note: Only one glacial lake (0.03%) is below 3000masl

Figure 4.6: Number of glacial lakes in percent by size class in different elevation bands.

0

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Class 1 (<0.02 km2) Class 2 (0.02 - <0.05 km2) Class 3 (0.05 - <0.1 km2)Class 4 (0.1 - <0.5 km2) Class 5 (0.5 - <1 km2) Class 6 (1 - <5 km2)Class 7(≥5 km2)

31 | P a g e

4. 5 Distance to the source glacier

The distance of glacial lakes to the source glaciers is another important factor in the stability

of lakes. The distance from the lake to the source glacier is generated based on the 2005

lake data (Maharjan et al., 2018) for glacial lakes equal to and larger than 0.02 sq. km.

Lakes that are more than 10 km away from the source glacier are not considered as glacial

lakes as these are defined by their contact or close proximity to their source glacier. Changing

glacier melt and retreat are closely associated with changes in the glacial lake environment.

Lakes that are located within the glaciers are typically ice dammed lake and are about 13% in

Koshi, 17% in Gandaki and 8% in Karnali basin (Table 4.7). The lakes in contact with the

glacier are mostly moraine dammed lake and about 2% each of the lakes are in the Koshi

and Karnali basins and 5 % in the Gandaki basin. The lakes at a distance less than 500m

are 38%, 62% and 33% in Koshi, Gandaki and Karnali basins respectively. Rest the majority

of lakes are more than 500m away from the source glaciers, which are not so much concerned

with the stability of lakes and dam.

Figure 4.7 show the distribution of number and area of lakes and distance to glaciers. The

number and area of lakes had decreases with the increase in distance with the glaciers. Lakes

closer to the glacier are higher in number and larger in lake area. Mostly the lakes far away

from the glaciers are erosional or other types of lakes. Most of the moraine dammed lakes are

within the 5 km distance from the glaciers. Highest number of moraine dam lakes are within

2 km distance from the glaciers.

Table 4.7: Distribution of number and area of glacial lakes at different elevation zones

Distance to source glaciers (m)


No % Area (sq km)

% No % Area (sq km)

% No % Area (sq km)

%

Within 271 13 3.17 3 68 17 0.72 4 96 8 0.91 2 Contact with 50 2 11.31 9 20 5 1.80 10 26 2 2.05 5 >0 - <100 59 3 15.73 13 38 9 1.74 10 50 4 1.79 4 100 - <200 76 4 9.24 8 26 6 1.28 7 45 4 1.79 4 200 - <500 343 16 26.95 22 100 25 3.10 17 184 15 7.09 16 500 - <1,000 387 19 13.25 11 66 16 5.32 30 240 20 9.06 20

32 | P a g e

1,000 - <2,000 259 12 13.81 11 31 8 1.78 10 192 16 5.89 13 2,000 - <5,000 357 17 13.93 11 24 6 0.86 5 239 20 12.20 27 5,000 - <10,000

225 11 7.23 6 31 8 1.13 6 82 7 3.25 7

≥ 10,000 60 3 8.49 7 1 0 0.02 0 50 4 1.58 3 Total 2087 100 123.10 100 405 100 17.75 100 1204 100 45.60 100

a- Koshi

b- Gandaki

0

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100

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300

-0.5 1.5 3.5 5.5 7.5 9.5 11.5 13.5 15.5 17.5 19.5 21.5 23.5 25.5 27.5 29.5

Are

a (k

m2)

Nu

mb

er

Distance from Glaciers (km)

Nunber Area

0

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-0.5 1.5 3.5 5.5 7.5 9.5 11.5 13.5 15.5 17.5 19.5 21.5 23.5 25.5 27.5 29.5

Are

a (k

m2 )

Nu

mb

er


Number Area

33 | P a g e

c- Karnali

Figure 4.7: Glacial lake number and area distribution to the distance from glacier (blue line: snout of glacier) in 200m zone in the study area: a- Koshi basin, b- Gandaki basin, c- Karnali basin.

5. Potentially dangerous glacial lakes and priority lakes for risk

reduction

5. 1 Characteristic parameters for lake stability

5.1.1 Lake characteristics Changes in the glacial lakes mirror changes in their source glaciers. In the 33 year period

from 1977 to 2010, the glaciers of Nepal have decreased by almost a quarter of their initial

area (Bajracharya et al. 2014). Accordingly, the number of glacial lakes in the Koshi basin

has increased from 1,160 in 1977 to 2,168 in 2010 and the total area of lakes has increased

from 94.4 sq. km to 127.6 sq. km in 2010 (Shrestha et al., 2017). The number of lakes

has increased by 86.9% and the total lake area has increased by 35.1%. We had mapped

the glacial lakes for 2000 and 2015 and compared this inventory against existing data

gathered in 2005 to produce a change assessment (Table 5.1). The number of glacial lakes

in Koshi basin decreased from 2119 in 2000 to 2087 in 2005 and to 2064 in 2015. In

contrast, the number of glacial lakes has increased in the Gandaki basin from 377 in 2000

to 405 in 2005 and 432 in 2015. Similarly, the glacial lakes in the Karnali basin have

0

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140

-0.5 1.5 3.5 5.5 7.5 9.5 11.5 13.5 15.5 17.5 19.5 21.5 23.5 25.5 27.5 29.5

Are

a (k

m2 )

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Number Area

34 | P a g e

increased from 1105 in 2000 to 1204 in 2005 but decreased to 1128 in 2015. This decrease

in number of glacial lakes is not an indication of the loss of glacial lakes as this pattern is

produced by the merging of lakes with neighboring glacial lakes. As a result, the total area

of the glacial lakes has increased from 179.56 sq km in 2000 to 186.44 sq km in 2005

and 195.39 sq km in 2015. The increase in number of glacial lakes is indicative of the

rapid melting of glaciers and formation of new lakes particularly dammed by glacial ice and

moraines. Table 5.1 shows the number and area of glacial lakes in 2000, 2005 and 2015.

The threshold value of distance to the glacier is slightly different in 2005 than in the present

study. Hence the number of glacial lakes in 2005 is slightly higher than 2015 in the Koshi

and Karnali basins. However, the lake area had increased by 12% in the Koshi basin, 8%

in the Gandaki basin and 1.27% in the Karnali basin between 2000 and 2015. The number

of supraglacial lakes in the Koshi basin is higher and merging of supraglacial lakes reduces

the number of lakes but overall lake area had increased.

Table 5.1: Number and total area of glacial lakes in 2000, 2005 and 2015

Basin

2000 2005 2015±1year Difference (2000 to 2015)

No Area

No Area

No Area

No % Area (sq km)

% (sq km)

(sq km)

(sq km)

Koshi 2119 118.42 2087 123.10 2064 132.27 - 55

- 2.60

13.9 11.70

Gandaki 377 17.76 405 17.75 432 19.19 55 14.59 1.4 8.07 Karnali 1105 43.38 1204 45.60 1128 43.93 23 2.08 0.6 1.27 Total 3601 179.56 3696 186.44 3624 195.39 23 0.64 15.8 8.82

This study has identified about 1410 glacial lakes that have an area equal to and larger than

0.02 sq. km. In comparison to the glacial lake inventory of 2000 and 2015, the number of

lakes with areas equal to or larger than 0.02 sq km has increased in all basins (Table

5.2). Sixty-nine lakes in the Koshi basin, 31 lakes in the Gandaki basin and 38 lakes in

the Karnali basin have increased since t the inventory of 2015. This increase in lake area

is associated with a corresponding increase in GLOF risk. All lakes with an area equal to or

larger than 0.02 sq. km are analyzed further to understand the stability of the lake and dam.

35 | P a g e

Lakes upstream and downstream of these lakes are also examined as these may impact lake

stability.

Table 5.2: Number and area of glacial lakes equal to and larger than 0.02 sq km in 2000 and 2015.

Basin

2000 2015 Difference

No Area

(sq km) No

Area (sq km)

No Area

(sq km) Koshi 771 106.64 840 121.49 69 14.85 Gandaki 140 13.97 171 16.73 31 2.76 Karnali 381 32.67 419 37.47 38 4.8

An enlargement in lake area increases the potential energy of the reservoir, whilst a decrease

in the dam area reduces the dam’s strength leading to the breaching of the dam. Types and

the sizes of the glacial lakes are given in the Table 4.5. About 2214 lakes smaller than

0.02 sq. km are excluded from the analysis of potentially dangerous glacial lakes. In addition,

a further 480 lakes including valley lakes (1), blocked lakes (bedrock dammed lake) (281-

108=73) (Table 4.5), and other blocked lakes (19) are also excluded from the potentially

dangerous glacial lake analysis. A total of 2694 lakes are excluded and 896 lakes remain

to identify the potentially dangerous glacial lakes for level 2.

Analysis for PDGL (Level 1) = Total lakes – Class 1 – I(s+v) – B(c+o) - O = 896 (1)

The number of glacial lakes derived from the Level 1 (result of equation 1) is further

analyzed incorporating the dam characteristics of respective glacial lakes.

5.1.2 Dam Characteristics The flow of glacier meltwater can be obstructed by rocky terrain, glacier ice, loose moraine

and landslides to form a lake. The obstructing feature functions like a natural dam. Rapid

glacial melt results in either an increase in surface runoff or lake expansion. The expanded

lake will either increase the potential energy to weaken the dam or flow over the dam. The

condition of the dam is important in determining the stability of the lake. Most of the lakes

close to the glacier snout are dammed by loose moraine. When lake waters flow over thin

and loose moraine dams, these structures may be easily eroded resulting in a GLOF. Lakes

36 | P a g e

dammed by narrow crest moraines have a relatively high potential for outburst compared to

lakes with wide crest dams. Thus, dams with thinner crests are associated with a higher

GLOF risk. Due to erosion and mass movements, dam crests are usually angular and narrow

at the top. Any surge wave generated by ice or snow avalanches may ultimately trigger a

GLOF. The steepness of the slope of the moraine wall also determines the likelihood of the

outburst from a lake. Ice dammed and moraine dammed lakes are particularly susceptible to

instability. Bedrock dammed lakes are more stable than lakes with other types of natural dams

as the stability of other dammed lakes depends on the characteristics of dammed features.

The main characteristics of the lake dams were analyzed for glacial lakes equal to and larger

than 0.02 sq. km. The characteristics of these dams includes: 1) no dam crest (nc) –

inflow and outflow of lake is equivalent; 2) compressed and old dam material (co) – more

stable than loose debris; 3) dam length greater than 200m (dl) – reduces the erosional

capacity of overflow; 4) the outer slope of dam is less than 20 degrees (ds) – a lower

gradient will have less erosional capacity. These features define the main characteristics of a

dam stability. Based on these parameters, eight lakes identified as PDGL in 2011 (listed in

Table 5.3) were removed from the PDGL list in this study. Processing through four levels

of analysis, the remaining 47 lakes are identified as potentially dangerous glacial lakes (Table

5.4 and Figure 5.1).

37 | P a g e

Table 5.3: Glacial lakes identified as potentially dangerous (PDGL) in 2011 that have been removed from the PDGL list in this study.

S.N. Lake ID/Name valley Description

1. kotam_gl_0135/ Nagma Pokhari

Tamor Outburst in 1980. Confined lake water outflow through the moraine and debris almost for 1700 m then only the drop at steeper slope. No further GLOF is expected.

2. kodud_gl_0193/ Tam Pokhari

Dudh Koshi

Outburst in 1998, presence of confined channel wider than 45 m and flow through the moraine and debris for almost 1450 m then only flow on the river valley. No further GLOF is expected.

3. gakal_gl_0004 Kaligandaki Blocked by possibly old landslide debris, existing wide and confined lake out let at the side of the dam, no further GLOF is expected.

4. koaru_gl_0012/ Barun Pokhari

Arun

GLOF evidence in the past and the river valley is full of alluvial fan with gentle slope for about 300m,, no damming, compact debris at downstream less chances of huge ice avalanches to create GLOF.

5. gabud_gl_0009/ Birendratal

Budhigandaki

No damming, erosional land feature, compact debris at downstream, in contact with retreating glacier, in case of glacier topple only the possibility of overflow of splash water. No GLOF is expected.

6. koaru_gl_0016 Arun

No damming, erosional land feature, compact debris at downstream, fed by lake at the glacier snout, dam length of about 1500m then landslide and steep slope. No GLOF is expected.

7. gakal_gl_0008 Kaligandaki

On medial moraine, old and compact moraine. confined surface flow, lake to glacier distance is about 1km, no chances of ice fall, landslide or by any means to break the moraine.

8. gakal_gl_0022 Kaligandaki Valley and blocked lake with outlet, no chances of ice fall, landslide or by any means to GLOF.

The lakes with the dam characteristics of no crest (nc), compressed and old dam material

(co), dam length greater than 200m (dl) and outer slope of dam less than 20 degrees

(ds) is assumed to be a stable lake and dam and hence will be subtracted from the level1

for the further analysis in the identification of potentially dangerous glacial lakes. A total of

295 lakes remained for the analysis of level 3.

Analysis for PDGL (Level 2) = Level 1 – nc- co – dl - ds = 295 (2)

38 | P a g e

5.1.3 Mother (Source) glacier characteristics Whilst lake and dam characteristics determine whether a lake is considered as stable, lake

stability may be disturbed by its source glacier. This may occur either by rapid melting of

glacier ice, resulting rapid growth of glacial lake, or by the toppling/collapse of a large mass

of glacier ice to disturb the lake flow or damage the dam, causing it to breach. For lakes

in direct contact with their source glacier, steep slopes and large lake-glacier elevation

differences may allow large ice masses to fall into the lake, creating a large wave which

may result in the rupture of the dam. This process is considered potentially dangerous, and

glacial lakes that exhibit these properties become candidates for potentially dangerous lakes

that need to be monitored. The information on the condition of hanging glaciers, distance to

lakes, steepness of glacier tongues, debris cover on lower glacier tongues, presence of

crevasses and ponds on glacier surfaces, and the possibility of toppling/collapsing ice from

glacier fronts or icebergs breaking off the glacier terminus and floating into the lake are also

examined in this study to identify potentially dangerous glacial lakes.

Lakes situated further than 200m from the glacier terminus (dm) and those with source

glaciers with surface slopes less than 60 degrees (sm) are assumed to be stable and are

removed from further detailed analysis.

5.1.4 Physical condition of surroundings The stability of the surroundings of the glacial lake and dam are additional important factors

which may destabilize the lake and dam. Much of the study area is located at high altitude

where snow/ice avalanches and landslides commonly occur. These phenomena can disturb

the dam and the lake to trigger a GLOF. A total of 243 lakes were safe both from the

source glacier and surroundings, the remaining 52 lakes were analyzed for level 4.

Analysis for PDGL (Level 3) = Level 2 - dm - sm - (S) = 52 (4)

39 | P a g e

The remaining 52 glacial lakes were thoroughly checked in the high resolution satellite images

available in Google Earth and identified 47 glacial lakes were potentially dangerous and

selected for the potential GLOF risk reduction. Number and total area of the lakes at different

level are given in the Annex (Table A).

Table 5.4: Identification of potentially dangerous glacial lakes (PDGL) based on the characteristics of lake, dam and surrounding features including the source glacier.

Country Basin

Lake inventory

Lake size and type

Dam characteristics

Surrounding features

PDGL

Zero level First level Second level Third level Fourth level Nr Nr Nr Nr Nr

Nepal

Koshi 834 199 91 19 18 Gandaki 255 65 18 2 2 Karnali 981 241 39 1 1 Sub-total 2070 505 148 22 21

China


India


Total 3624 896 295 52 47

40 | P a g e

Figure 5.1: Identification of potentially dangerous glacial lakes (PDGL) based on the characteristics of lake, dam and surrounding features including the source glacier.

20

70

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Lake Inventory Lake size andtype

Damcharacteristics

Surroundingfeatures

PDGL

Nu

mb

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Nepal China India

41 | P a g e

5. 2 Identification and ranking of potentially dangerous

(critical) glacial lakes

In this current study, a remote sensing approach combined with a geographic information

system was used to identify the potentially dangerous glacial lakes within the large population

of lakes in the study area. First, the small lakes (≤0.02 sq km) were excluded. The

remaining lakes were then evaluated using a range of geomorphological and physical criteria

which assessed factors related to the lake, dam, source (mother) glacier and surrounding

area to identify the potentially dangerous glacial lakes.

Out of 3624 mapped lakes, 1410 lakes are equal to and larger than 0.02 sq.km. This

area of water is considered large enough to cause damage downstream if the lake was to

rupture. This potential is heightened if the lakes are associated with a large and retreating

glacier. Out of 1410 lakes, 1230 lakes were removed based on the damming condition, the

activity of the source glaciers and their surroundings. The remaining 180 lakes were analyzed

further to identify potentially dangerous glacial lakes. Of these, 47 glacial lakes were identified

as potentially dangerous including 42 lakes in the Koshi, 3 in the Gandaki and 2 in the

Karnali basins (Figure 5.2). Out of these, 25 PDGL lakes are in the TAR with transboundary

to Nepal, 21 PDGLs are situated in Nepal and one is located in India. Table 5.5 show the

PDGLs by river basin and country, and Table 5.6 displays the characteristics of the lakes,

dams, source glaciers and the surroundings of the potentially dangerous glacial lakes.

Table 5.5: Summary of potentially dangerous glacial lakes in the Koshi, Gandaki and Karnali basins of Nepal and the TAR, China.

Basin Sub-basin Nepal China India Total

Koshi

Tamor 4 x x

42 Arun 4 13 x Dudh Koshi 9 x x Tama Koshi 1 7 x Sun Koshi x 4 x

Gandaki Trishuli 1 1 x

3 Marsyangdi 1 x x

Karnali Kali x x 1 2

Humla 1 x x

42 | P a g e

Total 21 25 1 47

The PDGLs were ranked to determine the priority of lakes for potential GLOF risk reduction.

Precise ranking was not possible. However, outbursts can occur that have no historic

precedence, especially in view of the current atmospheric warming. It is important to remember

this limitation. Also, many of the GLOFs that have occurred have effectively disrupted the

retaining end moraine dams to the extent where the likelihood of subsequent outbursts in

the same locality is minimal. Another consideration is that where potential outbursts are

tentatively identified in areas remote from human activity, they should not be prioritized.

Reliable determination of the degree of glacial lake instability, at least in most cases, will

require detailed glaciological and geotechnical field investigation (Ives et al. 2010).

S.N. Lake ID S.N. Lake ID S.N. Lake ID S.N. Lake ID S.N. Lake ID

1 GL087945E27781N 11 GL087591E28229N 21 GL087092E27798N 31 GL086476E27861N 41 GL085870E28360N







43 | P a g e

8 GL087636E28093N 18 GL086304E28374N 28 GL086858E27687N 38 GL086314E28194N



Figure 5.2 Location of potentially dangerous glacial lakes in the Koshi, Gandaki and Karnali basins of Nepal and TAR, China. The key physical parameters applied in the ranking were the distance between the lake outlet

and dam crest and lakes enlargement over time, changes in the boundary conditions of the

associated glaciers (frontal retreat), and the distance between the lake and the glacier:

whether the two were in contact, close, or less than 500m apart. Lakes farther than 500m

from their associated glaciers were not considered as potentially dangerous. The rating of

the moraine dams included height, width, and steepness. Surroundings of the lake area

included factors such as possible rock or debris slides, hanging glaciers, and potential

avalanche paths. The danger level of the PDGL are categorized in to 3 levels:

Rank I – Large lake and possibility of expansion due to calving of glaciers. Lake close

to loose moraine end, no overflow through the moraine dam, steep outlet

slope, hanging source glacier, chances of snow and/or ice avalanches and

landslide at the surroundings may impact to the lake and dam.

Rank II – Confined lake-outlet, lake-outlet close to compact and old end moraine,

hanging lake, distinct seepage at the bottom of end moraine dam, gentle out

moraine slope.

Rank III – Confined lake-outlet, gentle outward dam slope, large lake but shallow depth,

moraine dam more than 200m, old and compact moraine.

Of the 47 lakes reviewed, 31 lakes were classed as Rank I, 12 lakes as Rank II, and 4

lakes as Rank III (Table 5.6). The water level was already lowered from four of the lakes

under Rank I, by more than 3m in Tsho Rolpa and 4m in Imja Tsho of Nepal and two

lakes GL088066E27933N and GL088075E27946N in China.

44 | P a g e

Table 5.6: List of potentially dangerous glacial lakes in Koshi, Gandaki and Karnali basins of Nepal and TAR, China.

S.N. Lake ID/Name Rank Description River basin Country

1 GL087945E27781N I Less dam length, steep side slope and landslide, source glacier hanging but far from the lake.

Tamor Nepal

2 GL087934E27790N III Lake close to dam end but confined outlet, hanging glacier, steep side wall slope.

Tamor Nepal

3 GL087893E27694N III Shallow lake at steep slope and short dam length, hanging mother glacier

Tamor Nepal

4 GL087749E27816N I Glacier in contact, less dam length, possibilities of avalanches, landslide on outer slope of dam.

Tamor Nepal

5 GL087596E27705N I

Lake expanding, cascading lake overflow may trigger the outburst, less dam width, erosion at end moraine.

Arun Nepal

6 GL087632E27729N III Lake outlet close to moraine dam end (2m), high gradient dam,

Arun Nepal

7 GL087771E27926N I Possibilities of lake expansion, calving source glacier, lake outlet near to dam end, steep side wall.

Arun China

8 GL087636E28093N I

Possibilities of lake expansion, lake outlet near to dam end, marking of outlet drainage, seepage at the bottom of the dam, steep outward dam slope and steep side wall.

Arun China

9 GL087626E28052N I

Possibilities of lake expansion, lake outlet near to dam end, no clear outlet drainage, seepage at the bottom of the dam, steep and eroded side wall with landslide and rock blocks, hanging source glacier.

Arun China

10 GL087563E28178N III

Large lake and expanding, no distinct lake outlet and surface runoff is appeared after 500m down the slope, one side steep wall,

Arun China

11 GL087591E28229N II

Large lake at the extreme end of moraine, dry outlet channel for almost 370m and then appeared the river flow, old and compact moraine, hanging source glacier.

Arun China

12 GL087930E27949N I Possibilities of merging of small lake, less dam width, calving

Arun China

45 | P a g e

S.N. Lake ID/Name Rank Description River basin Country source glacier, steep side slope with possibilities of snow avalanches and landslide,

13 GL088002E27928N I

Large lake and fed by many tributary glaciers, old and dry lake outlet channel of about 40m and then major seepage appeared, calving source glacier, PDGL GL088019E27928N is at the tributary valley.

Arun China

14 GL088019E27928N I

Possibilities of lake expansion, lake outlet at the dam end, couple of overflow channels, moraine dam seems old and compact, calving source glacier,

Arun China

15 GL088066E27933N I

Lake water lowered and done compaction at end moraine dam, calving glacier, possibilities of expansion, short dam length.

Arun China

16 GL088075E27946N I

Lake water lowered, lake expanding on both ends, dead ice on the end moraine, large source glaciers, large PDGL GL088066E27933N at left valley may damage the lake

Arun China

17 GL088288E28017N II

Lake at close to dam end but confined wide outlet channel, compact and old moraine, calving and steep slope source glacier, high gradient of moraine dam.

Arun China

18 GL086304E28374N II Ice fall from source glacier, large lake, old moraine

Arun China

19 GL087134E28069N I

Possibilities of expansion of lake, Lake outlet almost at the dam end, minor overflow but many seepage, possibilities of landslide on the side wall.

Arun China

20 GL087095E27829N II Hanging lake connected with the retreating glacier, landslide at the side wall.

Arun Nepal

21 GL087092E27798N

Lower Barun I

Possibilities of expansion of lake, calving source glacier, chances of landslide and ice avalanches at the right side wall of the lake, one lake and couple of small lakes at the at the upper catchment.

Arun Nepal

46 | P a g e


22 GL086977E27711N I

Lake expanding towards glacier, short dam length and steep, calving source glacier, high chances of ice toppling and avalanches

Dudh Nepal

23 GL086958E27755N

Chamlang II

Hanging glaciers, high chances of ice avalanches, lake formation near the end moraine, Ice underneath the dam but the dam length is more than 500m

Dudh Nepal

24 GL086957E27783N

Hongu 2 I

Hanging glacier, chances of avalanches, short dam length and steep slope with many erosional features

Dudh Nepal

25 GL086935E27838N

Hongu1 I

Lake expanding towards the retreating glacier snout, hanging lakes on both side of the valley, lake outlet is close to dam end. Many cascading lakes in the lower old moraine., PDGL GL086928E27850N is at the hanging valley

Dudh Nepal

26 GL086928E27850N I

Lake out let near to the dam end, dam outer slope is steep, cascading lake in upstream, chances of landslide and ice avalanches at upstream, may also trigger to the lake GL086935E27838N

Dudh Nepal

27 GL086917E27832N I

Close to source glacier, short dam length and steep side slope with erosional features, Ice underneath the dam,

Dudh Nepal

28 GL086858E27687N I

Few meters of freeboard, outer dam slope steeper, hanging source glacier, chances of landslide, ice avalanches from the head water and from right valley.

Dudh Nepal

29 GL086925E27898N

Imja Tsho I

Lake water lowered by 4m in 2016, lake expanding level reduced, ice underneath end moraine, merging of supraglacial pond

Dudh Nepal

30 GL086612E27779N

Lumding I

Lake expanding rapidly, in contact with calving glacier, 3 hanging lakes at the side valley, continues dam slope

Dudh Nepal

47 | P a g e


31 GL086476E27861N

Tsho Rolpa I

Lake water lowered in 2000, expanding rapidly with the retreat of calving source glacier, steep moraine dam and side moraine is thin and danger, hanging lake in tributary glacier.

Tama Nepal

32 GL086447E27946N I

Lake expanding and possibility of merging with supraglacial ponds, evidences of small supra lake outburst at end moraine, dead ice at the end moraine, fed by three glaciers and calving at source glacier.

Tama China

33 GL086500E28033N II

Lake expanding, calving source glacier, additional small lake at end moraine, steep slope end moraine, one large hanging glacial lake in the side valley,

Tama China

34 GL086520E28073N I lake extension is close to end moraine, hanging source glacier, erosional features at left valley wall

Tama China

35 GL086530E28135N II

Pond and ice in end moraine, confined outlet with gentle slope, hanging lake in the tributary valley, steep and calving source glacier,

Tama China

36 GL086532E28185N I

Lake snout at end of dam, possibilities of expansion, calving source glacier and crevasses near the lake

Tama China

37 GL086371E28238N I

Lake expanding in contact with long cascading glacier, no free board, short inward dam length, one supraglacial lake at the side valley

Tama China

38 GL086314E28194N I

Lake extension is close to end moraine, hanging source glacier with steep slope, hanging lake at the side valley,

Tama China

39 GL086157E28303N I

Lake extension up to end moraine, crevasse on the hanging source glacier, possibilities of ice avalanches

Sun China

40 GL086225E28346N II Lake expanding on calving source glacier, short dam length but clear outlet in the lake

Sun China

41 GL085870E28360N

Ganxico II

Larger expanding lake, small outlet, gentle dam, possibility of

Sun China

48 | P a g e

S.N. Lake ID/Name Rank Description River basin Country ice in end moraine dam, possibilities of landslide and ice avalanches from side slope.

42 GL085838E28322N

Lumichimi II

Fast growing lake, steep source glacier, short end moraine dam, narrow lake outlet

Sun China

43 GL085630E28162N I

Lake at extreme dam end, chances of landslide from the right side wall and ice avalanches from the hanging source glacier.

Trishuli Nepal

44 GL085494E28508N II

Lake is expanding and at debris covered glacier and hanging moraine, steep outer dam slope, chances of snow and ice avalanches, lake end and end moraine is about 100m.

Trishuli China

45 GL084485E28488N

Thulagi I

Large lake and expanding on the debris covered source glacier, possibility of landslide and snow avalanches from the side walls, evidences of subsidence of old and compact end moraine.

Marsyangdi Nepal

46 GL082673E29802N II

Shallow lake but close to crest, overhanging boulder protecting the erosion of the dam, hanging source glacier with many crevasses.

Mugu Nepal

47 GL080387E30445N I

Lake close to top dam end, chances of expansion of lake due to debris cover source glacier, steep outer dam slope, chances of landslide at upstream,

Kali India

49 | P a g e

5. 3 Prioritization of PDGL for GLOF risk reduction

5.3.1 Socioeconomic value

The socioeconomic assessment of the regions downstream of the identified potentially dangerous

glacial lakes was carried out only for Nepal. The socioeconomic parameters included the

number of household, population, the extent of motor-able road and highway sections, the

number and type of bridges (wooden, suspension, motor-able, and highway bridges), and

hydropower projects (number and capacity of hydropower projects in megawatts) in the

500m buffer zone in the path of a potential outburst and in the catchment.

Population and households

GLOF events have downstream impacts at different levels: individual household, ward/VDC,

district, and national. At a household level, impacts are either direct from inundation or

indirect by secondary erosion or landslides. At the ward/VDC level, people are affected by

a loss of natural resources and service infrastructure. At the district level, damage to physical

infrastructure disrupts the flow of goods and services, and at national level power supplies

are disrupted because of damage to hydroelectricity projects, affecting populations living far

beyond the GLOF area. The downstream population and infrastructure were estimated for the

basins Tama Koshi, Dudh Koshi, Arun and Tamor down to the confluence with the Sun

Koshi River. Similarly the information for Marsyangdi River and Trishuli River were calculated

down to the confluence with the major Narayani River (Figure 5.3).

The downstream population along the river valley of PDGLs were estimated based on the

population census data of 2011 and the maximum number of house hold and population who

could be directly affected along the river valley of about 500m buffer zone are counted on

the high resolution satellite images available on Google Earth. Of these, at the household

level, the number of people likely to be affected by a potential GLOF varied from 10,000

in Sun Koshi and 19,000 number in Dudh Koshi (Table 5.7). The remaining population

50 | P a g e

could suffer a loss of environmental resources and service infrastructure. The maximum

number who could be indirectly affected through infrastructural damage and loss of goods

and services ranges from 51,000 for Tama Koshi to 62,500 for in Sun Koshi (Table 5.8).

Figure 5.3: Household and population estimated from the catchment area for sub-basins of Koshi and Gandaki River.

Table 5.7 Household distribution and estimation of population in 500m buffer zone along the river based on the Google images.

Basin Sub-basin Household (HH) Population estimated

Nepal China Nepal China

Koshi

Tamor 2,966 0 13,894 0

Arun 3,008 5,414 13,156 na

Dudh Koshi 4,175 0 18,553 0

Tama Koshi 2,689 275 10,801 na

Sun Koshi 2,332 1,377 9,821 na

Gandaki Trishuli 3,066 154 12,057 na

Marsyangdi 4,435 0 17,433 0

51 | P a g e

The household and population calculated down to the confluence with the Sun Koshi River.

The Tama Koshi and Dudh Koshi river confluences with the almost west-east flowing Sun

Koshi River, whereas for the Arun River and Tamor River are considered up to the confluence

of Tamor and Sun Koshi.

Table 5.8: Household and population downstream within the Koshi River valley that could be affected (based on Census, 2011).

S.N. Lake ID / Name Basin HH POP Male Female Basin

HH POP Male Female

1 GL087945E27781N

Tamor

12466 58462 27370 31092

12572 58883 27591 31292 2 GL087934E27790N 12466 58462 27370 31092

3 GL087893E27694N 12396 58261 27286 30975

4 GL087749E27816N 12413 58320 27308 31012

5 GL087596E27705N

Arun

12343 56108 26571 29537

12961 59275 28150 31125

6 GL087632E27729N 12343 56108 26571 29537

7 GL087771E27926N 12479 56741 26884 29857

8 GL087636E28093N 12479 56741 26884 29857

9 GL087626E28052N 12479 56741 26884 29857

10 GL087563E28178N 12519 56925 26978 29947

11 GL087591E28229N 12519 56925 26978 29947

12 GL087930E27949N 12519 56925 26978 29947

13 GL088002E27928N 12519 56925 26978 29947

14 GL088019E27928N 12519 56925 26978 29947

15 GL088066E27933N 12519 56925 26978 29947

16 GL088075E27946N 12519 56925 26978 29947

17 GL088288E28017N 12519 56925 26978 29947

18 GL086304E28374N 12519 56925 26978 29947

19 GL087134E28069N 12519 56925 26978 29947

20 GL087095E27829N 11782 53859 25551 28308

21 GL087092E27798N / Lower Barun

11782 53859 25551 28308

22 GL086977E27711N

Dudh Koshi

6549 31481 15076 16405

11205 51993 25221 26772

23 GL086958E27755N / Chamlang

6549 31481 15076 16405

24 GL086957E27783N / Hongu 2

6549 31481 15076 16405

25 GL086935E27838N / Hongu 1

6549 31481 15076 16405

26 GL086928E27850N 6549 31481 15076 16405

27 GL086917E27832N 6549 31481 15076 16405

28 GL086858E27687N 5948 28692 13769 14923

29 GL086925E27898N / Imja Tsho

7241 33127 15873 17254

30 GL086612E27779N / Lumding

6096 29154 13873 15281

52 | P a g e

S.N. Lake ID / Name Basin HH POP Male Female Basin

HH POP Male Female

31 GL086476E27861N / Tsho Rolpa

Tama Koshi

12010 50643 23867 26776

12104 50972 24036 26936

32 GL086447E27946N 12071 50898 24010 26888

33 GL086500E28033N 12071 50898 24010 26888

34 GL086520E28073N 12071 50898 24010 26888

35 GL086530E28135N 12071 50898 24010 26888

36 GL086532E28185N 12071 50898 24010 26888

37 GL086371E28238N 12071 50898 24010 26888

38 GL086314E28194N 12071 50898 24010 26888

39 GL086157E28303N

Sun Koshi

14744 62493 29969 32524

14744 62493 29969 32524

40 GL086225E28346N 14744 62493 29969 32524

41 GL085870E28360N / Ganxico

14744 62493 29969 32524

42 GL085838E28322N / Lumichimi 14744 62493 29969 32524

Note: HH – House hold; Pop – Population

Exposure of infrastructure

Several existing and proposed hydropower projects are commissioned along or near the river

valley that they are threatened from the GLOF risk (Table 5.9 and Figure 5.4). The roads,

tracks & trails, and bridges built along the river valley are at higher risk (Table 5.10).

The hydro-powers are already on operational and some are under construction and plan in

the river basins like Tamor, Tama Koshi, Sun Koshi and Trishuli (Table 5.9). The level

of exposure to a potential GLOF in basins such as the Bhotekoshi/Sunkoshi and Marsyangdi

is comparatively higher because of big hydropower projects - Marsyangdi and Middle

Marsyangdi at Marsyangdi and Bhotekoshi, Sanima and Sunkoshi hydropower at

Bhotekoshi/Sunkoshi. The total capacity of hydropower in Marsyandi basin is 922 MW but

only two plants are under operation generating 74 MW. Similarly, a capacity of 208 MW is

planned from seven hydropowers in Bhotekoshi/Sunkoshi while only 61 MW is operational

from four plant. The highway and main trails in these basins are well developed and are

likely to be affected to varying extent depending on a GLOF event. For example, the

Lumuchimi Lake (GL085838E28322N) and Ganxi Co Lake (GL085870E28360N) in the

Poiqu basin in China can damage a huge loss in the downstream settlements such as

Liping, Tatopani, Larcha, Barhabise, Lamosangu and Khandichaur along the Kodari Highway

53 | P a g e

in the Bhotekoshi/Sunkoshi basin in Nepal. It can destruct the settlements, markets, bridge,

trials, roads, highway and other infrastructure on its path. Other lakes like Imja Tsho

(GL086925E27898N) at Dudh Koshi, Thulagi (GL084485E28488N) at Marsyangdi and

Tsho Rolpa (GL086476E27861N) at Tama Koshi also have high risk in the downstream

area as the flow of people, goods, settlements, markets and infrastructure have increased

along these valleys due to the growth of tourism and trade. At Tama Koshi basin, the seven

plants (implemented/proposed) can produce 1,053 MW of energy and the lake Tsho Rolpa

(GL086476E27861N) can affect two hydropower producing 70 MW and 160 km of main

trail in a GLOF event (Table 5.9 and 5.10). A GLOF from Imja Tsho (GL086925E27898N)

and Lumding (GL086612E27779N) Lakes at Dudh Koshi can damage the main trail, 39

bridges trials and tracts, settlements and markets but not a hydropower plant (683 MW)

as three plants are under implementation phase. Several hydropower are in implementation

phase in Tamor (five plants producing 811 MW) and Trishuli (eleven plants producing 633

MW) basins so the damages can occur only in the trails, settlements and other goods that

exists along the river valley. Two hydropower are in operation generating 38 MW of energy

in the downstream of Trishuli. In Arun basin, two hydropower are in implanting phase that

can produce 1000 MW of energy. A GLOF from Lower Barun (GL087092E27798N) in

Arun basin can pose risk to 1 bridge road, 30 bridge trails and tracks, 260 main trails and

37 other roads (Table 5.10). However, this risk can be reduced if proper attention is given

to GLOF risks and implement a mitigation and adaptation measures in the areas directly

susceptible to the GLOF risk.

Table 5.9: Location of hydropower projects in the river valley susceptible to a potential GLOF risk. SN River Hydro Power Project Latitude Longitude MW Status

1 Tamor Upper Tamor 27.4531 87.7083 415 GLA

2 Tamor Middle Tamor 27.3992 87.6717 57 GLA

3 Tamor Tamor Storage 27.0344 87.5242 200 SLI

4 Tamor Tamor Mewa 27.3333 87.5833 101 SLI

5 Kabeli Kabeli-A 27.2281 87.6819 38 GLI

6 Arun Arun 3 27.5000 87.2000 900 SLI

7 Barun Khola Lower Barun Khola 27.6833 87.3444 10 SLI

8 Dudh Koshi Dudhkoshi-6 27.6667 86.6917 93 SLA

9 Dudh Koshi Dudhkoshi-2 27.3603 86.6667 240 SLI

10 Dudh Koshi Dudhkoshi-4 27.5583 86.6667 350 SLI

54 | P a g e

SN River Hydro Power Project Latitude Longitude MW Status

11 Dudh koshi Dudhkoshi Storage 27.1139 86.5833 300 SLI

12 TamaKoshi Tamakoshi-3 TA-3 27.4928 86.0761 650 GLA

13 Tamakoshi Upper Tamakoshi-A 27.9167 86.1967 85 SLA

14 TamaKoshi Tamakoshi-V 27.7583 86.1833 87 SLI

15 Sipring Sipring Khola 27.8106 86.2297 10 OP

16 Jum Jum Khola 27.9378 86.2131 62 SLA

17 Khimti Khimti -I 27.4744 86.1011 60 OP

18 Lapche Upper Lapche Khola 28.0500 86.1456 99 SLI

19 Bhote Koshi Middle Bhotekoshi -1 27.8833 85.9103 40 GLA

20 Bhote koshi Madhya Bhotekoshi 27.8189 85.8636 102 GLI

21 Bhote Koshi Upper Bhotekoshi 27.9111 85.9203 45 OP

22 Sun Koshi Sun Koshi 27.7500 85.8361 10 OP

23 Sun Koshi Sunkoshi Small 27.7667 85.8833 3 OP

24 Sun Koshi Lower Balephi-3 27.7194 85.7625 5 SLI

25 BhairabKund Bhairab Kund Khola 27.9311 85.9314 3 OP

26 Bhote koshi Rasuwagadhi 28.2347 85.3561 111 GLI

27 Bhote koshi Rasuwa Bhotekoshi 28.1750 85.3333 120 SLI

28 Bhote koshi Chilime Bhotekosi 28.1556 85.3222 53 SLA

29 Trishuli Devighat Cascade 27.8522 85.0967 10 GLA

30 Trishuli Trishuli Galchhi 27.7978 84.9722 75 GLA

31 Trishuli Upper Trishuli 3A 28.0225 85.1878 60 GLI

32 Trishuli Third Trishuli Nadi 27.8019 84.7867 20 GLI

33 Trishuli Upper Trishuli 3B 27.9867 85.1697 37 GLI

34 Trishuli Devighat 27.8853 85.1319 14 OP

35 Trishuli Trishuli 27.9192 85.1458 24 OP

36 Trishuli Middle Trishuli 27.9633 85.1750 65 SLI

37 Trishuli Upper Trishuli-1 28.0742 85.2111 216 SLI

38 Trishuli Upper Trishui-2 28.1286 85.3011 102 SLI

39 Trishuli Langtang Khola 28.1514 85.3428 10 GLA

40 Marsyangdi Upper Marsyangdi -2 28.3678 84.3583 600 GLA

41 Marsyangdi Upper Marsyangdi 1 28.3139 84.3917 138 GLA

42 Marsyangdi Marsyangdi Besi 28.2000 84.3542 50 GLA

43 Marsyangdi Upper Marsyangdi A 28.2853 84.3653 50 GLI

44 Marsyangdi Madhya Marsyangdi 28.1389 84.4050 70 OP

45 Nyadi Lower Nyadi Hp 28.3083 84.4014 10 SLA

46 Radhi Radhi Small 28.3967 84.4094 4 OP

55 | P a g e

Note: MW – Mega Watt; GLI - Generation license issued; GLA - Generation license applied; SLA – Survey license applied; OP – Operational project

Figure 5.4: Location of hydropower projects in the river valley to a potential GLOF risk.

Table 5.10: Road, bridge, track and trails within 500m zone along the river valley to a potential GLOF risk.

Sub-Basin Bridge Road

Bridge Trails & tracks

District Road Highway Main Trail Other Road

No Length (km)

No Length (km)

No Length (km)

No Length (km)

No Length (km)

No Length (km)

Tamor 2 0.26 29 3.01 6 8.18 300 253.93 20 24.1 Arun 1 0.14 30 3.10 260 256.40 37 40.4 Dudh Koshi 39 4.23 159 135.70 165 237.2 Tama Koshi 5 0.36 34 3.06 10 8.30 195 159.72 98 136.9 Sun Koshi 13 1.43 19 2.41 77 60.02 177 102.97 109 156.1 Trishuli 41 3.84 32 3.49 31 19.21 170 95.89 341 218.16 53 62.9 Marshyangdi 33 3.61 21 12.21 44 34.86 199 147.55 46 67.5 Mugu Karnali 1 0.07 37 4.35 1 0.00 392 382.59 43 49.4 Mahakali 17 1.46 168 135.63 259 336.0 Grand Total 62 5.91 166 17.73 31 19.21 257 164.21 1468 1133.97 830 1110.4

56 | P a g e

5.3.2 Priority of potentially dangerous glacial lakes for risk reduction

The PDGL is ranked and prioritized considering both of the socioeconomic and physical

parameters. The physical parameters include the boundary conditions of the associated glaciers

and lake enlargement over time; the distance between lake and glacier; the height, width

and steepness of the moraine dams; physical condition of the surroundings (potential for

rock and debris slides and avalanches); and the presence of hanging glaciers. The socio-

economic parameters include the size of downstream settlements, the number and type of

bridges, hydropower projects, the agricultural land, and any other important infrastructure or

activities of economic value. Based on these parameters, the PDGL were categorized into:

I) high priority lakes – requires extensive field investigation and GLOF risk reduction activities;

II) medium priority lakes –requires close monitoring and reconnaissance field surveys; and

III) low priority lakes –warrants periodic observation.

The key physical parameters were considered first for the selection of PDGL and later

categorized into three ranks depending on the level of hazard. The socioeconomic parameters

were then added to categories the priorities for a GLOF risk reduction. Eighteen lakes falls

under the Priority 1, 11 lakes in Priority II and again 18 lakes in priority III to reduce the

potential GLOF risk (Table 5.11). Among them 6 lakes of Priority I, 8 lakes of Priority II

and 7 lakes of Priority III are in Nepal. Similarly, 12 lakes of Priority I, 2 lakes of Priority

II and 11 lakes of Priority III are in TAR, China and one lake of priority II is in India.

Depending upon the Rank level and priority level, PDGLs were further classified into four

level of risks: very high, high, medium or moderate and low. Of the total, 17 PDGLs are

in very high (11 in TAR, China and 6 in Nepal (Figure 5.5 and Table 5.11). Similarly,

57 | P a g e

11 PDGLs are in high risk (3 in TAR, China and 7 in Nepal). These high and very high

risk lakes requires extensive field investigation and mitigation measures to reduce GLOF risks.

Figure 5.5: Priority level of potentially dangerous glacial lakes in TAR, China and Nepal.

58 | P a g e

Table 5.11: Priority of PDGL for risk reduction S.N. Lake ID Name River basin Country Rank Priority Remark 1 GL087945E27781N Tamor Nepal I II

2 GL087934E27790N Tamor Nepal III II

3 GL087893E27694N Tamor Nepal III III

4 GL087749E27816N Tamor Nepal I II

5 GL087596E27705N Arun Nepal I I

6 GL087632E27729N Arun Nepal III III

7 GL087771E27926N Arun China I I


9 GL087626E28052N Arun China I II

10 GL087563E28178N Arun China III III

11 GL087591E28229N Arun China II III


13 GL088002E27928N Arun China I III


15 GL088066E27933N Arun China I I Lowered 16 GL088075E27946N Arun China I I Lowered 17 GL088288E28017N Arun China II III

18 GL086304E28374N Arun China II III


20 GL087095E27829N Arun Nepal II III

21 GL087092E27798N Lower Barun Arun Nepal I II

22 GL086977E27711N Dudh Nepal I II

23 GL086958E27755N Chamlang Dudh Nepal II III

24 GL086957E27783N Hongu 2 Dudh Nepal I I

25 GL086935E27838N Hongu 1 Dudh Nepal I II

26 GL086928E27850N Dudh Nepal I III

27 GL086917E27832N Dudh Nepal I III

28 GL086858E27687N Dudh Nepal I I

29 GL086925E27898N Imja Tsho Dudh Nepal I I Lowered 30 GL086612E27779N Lumding Dudh Nepal I II

31 GL086476E27861N Tsho Rolpa Tama Nepal I I Lowered 32 GL086447E27946N Tama China I I

33 GL086500E28033N Tama China II III

34 GL086520E28073N Tama China I III

35 GL086530E28135N Tama China II III

36 GL086532E28185N Tama China I I

37 GL086371E28238N Tama China I II

38 GL086314E28194N Tama China I I

39 GL086157E28303N Sun China I I

40 GL086225E28346N Sun China II I

41 GL085870E28360N Ganxico Sun China II III

42 GL085838E28322N Lumichimi Sun China II III

43 GL085630E28162N Trishuli Nepal I I

44 GL085494E28508N Trishuli China II III

45 GL084485E28488N Thulagi Marsyangdi Nepal I II

59 | P a g e

S.N. Lake ID Name River basin Country Rank Priority Remark 46 GL082673E29802N Mugu Nepal II III

47 GL080387E30445N Kali India I II

6. Conclusions

Altogether, 3624 lakes larger than 0.003 sq. km were mapped from the Koshi, Gandaki

and Karnali basins of Nepal and the TAR, China, based on Landsat OLI images of 2015

years using remote sensing and geographic information systems. Validation of remote sensing

data with the field observation is necessary to implement any kind of formal decision. Out

of these, 1410 lakes are larger than 0.02 sq km, which are considered large enough to

cause damage downstream if they rupture; this potential would be heightened if they are

associated with a large and retreating source glacier. A total of 844 lakes that are either

erosional or valley type without crest on their damming material, or flow directly through their

moraine with a low gradient were removed from the analysis. A longitudinal profile from

lake-outlet to dam end of 52 lakes were plotted to understand the dam length, slope of

the damming material and dam crest. Based on above criteria a total of 47 lakes were

identified as potentially dangerous in the study area. Altogether 21 PDGLs were identified in

2011 and from this study as well in Nepal but in the present PDGL list 13 lakes are from

old list and 8 lakes were newly identified.

Out of 47 PDGL, 42 lakes are in the Koshi basin, 3 are in the Gandaki basin and 2 are

in the Karnali basin. Twenty-five PDGL are in the territory of the TAR, China and 21 lakes

in Nepal; and one PDGL is in Indian Territory. Of the total, 31 lakes are in Rank I, 12

lakes in Rank II, and 4 lakes in Rank III. The lake water level already had lowered in four

lakes to reduce the GLOF risk: two each in Nepal and the TAR, China. The lake water

level of Tsho Rolpa and Imja Tsho of Nepal were lowered by more than 3m and 4m

respectively. The lake water level of GL088066E27933N and GL088075E27946N lakes in

China were also lowered to reduce the GLOF risk.

Based on the ranking and socioeconomic value 18 lakes each were categorized in priority I

and priority III and 11 lakes in priority II to reduce the potential GLOF risk. Among them 6

lakes of priority I, 8 lakes of priority II and 7 lakes of priority III are in Nepal, whilst 12

lakes of Priority I, 2 lakes of priority II and 11 likes of priority III are in TAR, China. One

lake of priority II is in the Kali River of India.

60 | P a g e

The findings of the number of glacial lakes, identification of potentially dangerous glacial

lakes, ranking of the PDGL and the prioritization of lakes for GLOF risk reduction will be

useful in designing GLOF risk management and reduction strategies in Nepal. It is hoped

that the findings of this study will serve as a resource guide and provide materials for

assessing GLOF hazards, socioeconomic vulnerability, and GLOF impacts downstream in

Nepal.

7. References

Ageta, Y., et al., (2000) Expansion of glacier lakes in recent decades in the Bhutan Himalayas, Debris-covered glaciers. IAHS publ, Wallingford, pp. 165–176.

Bajracharya, SR; Maharjan, SB; Shrestha, F; Guo, W; Liu, S; Immerzeel, W.W; Shrestha, B; (2015) The glaciers of the Hindu Kush Himalaya: current status and observed changes from 1980s to 2010. IJWRD, Routledge Taylor & Francis Group. Vol. 31, No.2, 161-173

Bajracharya, S. R., Maharjan, S. B., and Shrestha, F. (2014a) Understanding dynamics of Himalayan glaciers: scope and challenges of remote sensing. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XL-8, ISPRS Technical Commission VIII Symposium, 09 – 12 December 2014, Hyderabad, India.

Bajracharya, S. R., Maharjan, S. B., Shrestha, F., Bajracharya, O. R., and Baidya, S. (2014b) Glacier status in Nepal and decadal change from 1980 to 2010 based on Landsat data. Kathmandu: ICIMOD.

Bajracharya, SR; Maharjan, SB; Shrestha, F; (2014c) The status and decadal change of glaciers in Bhutan from 1980's to 2010 based on the satellite data. Annals of Glaciology. 55(66): 159-166. doi: 10.3189/2014AoG66A125.

Bajracharya, S. R. and Shrestha, B. (eds.) (2011) The status of glaciers in the Hindu Kush Himalayan region. Kathmandu: ICIMOD.

Bajracharya, S. R., (2011) Change in glacial environment of Everest region, Nepal. Proceedings of the 10th International symposium on High Mountain Remote Sensing Cartography. Grazer Schriften der Geographie und Raumforschung. Band45. 83-90.

Bajracharya, S. R. and Mool, P. K., (2009) Glaciers, glacial lakes and glacial lake outburst floods in the Everest region. Nepal. Annals of Glaciology, 50 (53) London, UK. 81 – 86pp.

61 | P a g e

Bajracharya, S R., (2009) Glacial lake outburst flood disaster risk reduction in Nepal. Asia-Pacific Symposium on new technologies for prediction and mitigation of sediment disasters. 18-19 Nov 2009, Tokyo, Japan. JSECE publication No. 55. 34-35.

Bajracharya, S. R., Mool, P. K., Shrestha, B. R., (2008) Mapping of glacial lakes and GLOF in the Himalaya in Policy priorities for sustainable mountain development. Proceedings and selected papers from the ICIMOD regional policy workshop. ICIMOD. 245s-252s pp.

Bajracharya, S. R., Mool, P. K., & Shrestha, B. R. (2007) Impact of Climate Change on Himalayan Glaciers and Glacial Lakes. Kathmandu.

Bajracharya, S. R., and Mool, P K., (2005) ‘Growth of hazardous glacial lakes in Nepal.’ In Yoshida, M; Upreti, BN; Bhattarai, TN; Dhakal, S (eds) Proceedings of the JICA Regional Seminar on Natural Disaster Mitigation and Issues on Technology Transfer in South and Southeast Asia, 30 September – 13 October 2004, pp131-148. Kathmandu: Tribhuvan University, Tri-Chandra Campus, Department of Geology with Japan International Cooperation Agency.

Bhambri, R., Bolch, T., (2009) Glacier mapping: a review with special reference to the Indian Himalayas. Progress in Physical Geography 33 (5), 672-704.

Bolch, T., Kulkarni, A., Kääb, A., Huggel, C., Paul, F., Cogley, J. G., Frey, H., Kargel, J. S., Fujita, K., Scheel, M., Bajracharya, S., and Stoffel, M. (2012) The State and Fate of Himalayan Glaciers, Science, vol. 336, pp. 310-314.

Bolch, T., Pieczonka, T., and Benn, D. I. (2011) Multi-decadal mass loss of glaciers in the Everest area (Nepal Himalaya) derived from stereo imagery, The Cryosphere, 5, 349-358, doi:10.5194/tc-5-349-2011.

Bolch, T., Buchroithner, M., Peters, J., Baessler, M., Bajracharya, S., (2008) Identification of glacier motion and potentially dangerous glacial lakes in the Mt. Everest region/ Nepal using spaceborne imagery. Nat. Hazards Earth Syst. Sci. 8 (6), 1329–1340

Carey, M., Huggel, C., Bury, J., Portocarrero, C., Haeberli, W. (2012) An integrated socio-environmental framework for glacier hazard management and climate change adaptation: Lessons from Lake 513, Cordillera Blanca, Peru. Climate Change 112:733–767.

Che, T., Xiao, L., and Liou, Y.-A. (2014) Changes in Glaciers and Glacial Lakes and the Identification of Dangerous Glacial Lakes in the Pumqu River Basin, Xizang (Tibet). Advances in Meteorology, 2014, 1–8. doi:10.1155/2014/903709.

Chen, N. S., Hu, G. S., Deng, W., Khanal, N., Zhu, Y. H., Han, D. (2013) On the water hazards in the trans-boundary Kosi River basin. Natural Hazards and Earth System Sciences 13:795–808

Chen, X.Q., Cui, P., Li, Y., Yang, Z., Qi, Y.Q., 2007 Changes in glacial lakes and glaciers of post-1986 in the Poiqu River basin, Nyalam, Xizang (Tibet). Geomorphology 88 (3-4), 298–311.

62 | P a g e

Clague, J. J. and Evans S. G. (2000) A review of catastrophic drainage of moraine-dammed lakes in British Columbia. Quaternary Science Reviews (19): 17-18.

Cruz, R. V., Harawawa, H., Lal, M., Wu, S., Anokhin, Y., Punsalmaa, B., Honda, Y., Jafari, M., Li, C., Ninh, N. H. (2007): Asia. In M. L. Parry, O. F. Canziani, J. P. Palutikof, P. J. van der Linden, & C. E. Hanson (Eds.), Climate change (2007) Impacts, Adaptation and Vulnerability, Contribution of Working Group II to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change (pp. 469–506). Cambridge, UK: Cambridge University Press.

Dwivedi, S. K., Acharya, M. D., & Simard, R. (2000) The Tamp Pokhari glacier lake outburst flood of 3 September (1988) Journal of Nepal Geological Society, 22, 539–546.

Gardelle, J., Arnaud, Y., and Berthier, E. (2011) Contrasted evolution of glacial lakes along the Hindu Kush Himalaya mountain range between 1990 and 2009. Global and Planetary Change, 75(1-2), 47–55. doi:10.1016/j.gloplacha.2010.10.003.

Ghimire, M. (2004) “Review of Studies on Glacier Lake Outburst Floods and Associated Vulnerability in the Himalayas.” Himalayan Review 35 (2004): 49-64

Huggel, C., Kääb, A., Haeberli, W., Teysseire, P., and Paul, F. (2002) Remote sensing based assessment of hazards from glacier lake outbursts: a case study in the Swiss Alps, Can. Geotech. J. 39: 316-330.

Huggel, C., Kääb, A., and Salzmann, N. (2006) Evaluation of QuickBird and Ikonos Imagery for Assessment of High-Mountain Hazards. EARSeL eProceedings 5, 1/2006: 51-62.

ICIMOD (2011) Glacial Lakes and Glacial Lakes Outburst Floods in Nepal. Kathmandu: ICIMOD.

IPCC. (2007) In P. J. van der Linden, C. E. H. M. L. Parry, O. F. Canziani, & J. P. Palutikof (Eds.), Climate change 2007: Impacts, adaptation and vulnerability. Contribution of working group II to the fourth assessment report of the intergovernmental pannel on climate change (p. 976). Cambridge, UK: Cambridge University Press.

IPCC, 2014 Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Field, C.B., V.R. Barros, D.J. Dokken, K.J. Mach, M.D. Mastrandrea, T.E. Bilir, M. Chatterjee, K.L. Ebi, Y.O. Estrada, R.C. Genova, B. Girma, E.S. Kissel, A. N. Levy, S. MacCracken, P.R. Mastrandrea, and L.L. White (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 1132pp.

Ives, J. D., Shrestha, R. B., and Mool, P. K., (2010) Formation of Glacial Lakes in the Hindu-Kush Himalayas and GLOF Risk Assessment. Kathmandu: ICIMOD.

63 | P a g e

Ives, JD (1986) Glacial lake outburst floods and risk engineering in the Himalaya: A review of the Langmoche disaster, Khumbu Himal, 4 August1985, Occasional paper No 5. Kathmandu: ICIMOD

Kääb, A. (2000) Photogrammetry for early recognition of high mountain hazards: new techniques and applications. Physics and Chemistry of the Earth 25: 765–770.

Khanal, N.R., Mool, P.K.; Shrestha, A.B.; Rasul, G.; Ghimire, P.K.; Shrestha, R.B.; and Joshi, S.P. (2015) A comprehensive approach and methods for glacial lake outburst flood risk assessment, with examples from Nepal and the transboundary area, IJWRD, 31:2, 219-237, DOI: 10.1080/07900627.2014.994116.

Li, J., Sheng, Y., (2012) An automated scheme for glacial lake dynamics mapping using Landsat imagery and digital elevation models: a case study in the Himalayas. Int.J. Remote Sens. 33 (16), 5194–5213.

LIGG/WECS/NEA (1988) Report on first expedition to glaciers and glaciers lakes in the Pumqu (Arun) and Poiqu (Bhote-Sun Kosi) river basin, Xizang (Tibet), China, Sino-Nepalese investigation of glacial lake outburst floods in the Himalaya, Science Press, Beijing, China, 192pp. plus maps.

Liu, C. and Sharma, C. K. (eds.) (1988) Report on the first expedition to glaciers and glacier- lake in the Pumqu (Arun) and Poiqu (Bhote-Sun Kosi) River Basins, Xizang (Tibet). Beijing, Science Press.

Liu, J.J., Cheng, Z.L., Li, Y., (2014) The 1988 glacial lake outburst flood in Guangxieco Lake, Tibet, China. Nat. Hazards Earth Syst. Sci. 14 (11), 3065–3075.

Maharjan, SB., Lizong, W., Xiao, G., Shrestha, F., Mool, PK., Bajracharya, SR., Shrestha, RB., Khanal, NR., Shahi, S., Baral, P. The status of glacial lakes in the Hindu Kush Himalayan region. (in press).

McKillop RJ, Clague JJ. (2006) A procedure for making objective preliminary assessments of outburst flood hazard from moraine-dammed lakes in southwestern British Columbia. Nat. Hazards 41:131–157.

Mool, P. K., Bajracharya, S. R., and Joshi, S. P. (2001a) Inventory of Glaciers, Glacial Lakes, and Glacial Lake Outburst Floods: Monitoring and early warning systems in the Hindu Kush-Himalayan Regions – Nepal, ICIMOD, Kathmandu, ISBN 92 9115 331 1, 363pp.

Mool, P. K., Wangda, D., Bajracharya, S. R., Kunzang, K., Gurung, D. R., and Joshi, S. P. (2001b) Inventory of Glaciers, Glacial Lakes, and Glacial Lake Outburst Floods: Monitoring and early warning systems in the Hindu Kush-Himalayan Region – Bhutan, ICIMOD, Kathmandu, ISBN 92 9115 345 1, 227pp.

Mool, P. K., and Bajracharya, S. R. (2003) Inventory of Glaciers, Glacial Lakes and the Identification of Potential Glacial Lake Outburst Floods (GLOFs) Affected by Global Warming in the Mountains of Himalayan Region: Tista Basin, Sikkim Himalaya, India, ICIMOD, Kathmandu, 134pp.

64 | P a g e

Osti, R., Egashira, S., (2009) Hydrodynamic characteristics of the Tam Pokhari Glacial Lake outburst flood in the Mt. Everest region, Nepal. Hydrol. Process. 23 (20), 2943–2955.

Rai, S.C., (2005) An overview of glaciers, glacier retreat, and subsequent impacts in Nepal, India and China. WWF Nepal Program (79 pp.).

Randhawa, S.S., Sood, R.K., Rathore, B.P., Kulkarni, A.V., (2005) Moraine-Dammed Lakes study in the chenab and The Satluj River Basins using IRS data. J. Indian Soc. Remote Sens. 33 (2), 285–290.

Reynolds, J. M. (1998) High-altitude glacial lake hazard assessment and mitigation: a Himalayan perspective. Geological Society, London, Engineering Geology Special Publications, 15: 25-34.

Richardson, S. D. and Reynolds, J. M. (2000) An overview of glacial hazards in the Himalayas, Quaternary International 65/66, 31-47.

Rosenzweig, C., Casassa, G., Karoly, J., Imeson, A., Liu, C., Menzel, A., Rawlins, S., Root, T. L., Seguin, B., Tryjanowski, P. (2007) Assessment of observed changes and responses in natural and managed systems. In: P. J. van der L. and C. E. H. M. L. Parry, O. F. Canziani, J. P. Palukof (Ed.), Climate Change 2007: Impacts, Adaptation and Vulnerability. Contribution of Working Group II to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change (pp. 79–131). Cambridge, UK: Cambridge University Press.

Seneviratne, S.I., N. Nicholls, D. Easterling, C.M. Goodess, S. Kanae, J. Kossin, Y. Luo, J. Marengo, K. McInnes, M. Rahimi, M. Reichstein, A. Sorteberg, C. Vera, and X. Zhang, (2012) Changes in climate extremes and their impacts on the natural physical environment. In: Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation [Field, C.B., V. Barros, T.F. Stocker, D. Qin, D.J. Dokken, K.L. Ebi, M.D. Mastrandrea, K.J. Mach, G.-K. Plattner, S.K. Allen, M. Tignor, and P.M. Midgley (eds.)]. A Special Report of Working Groups I and II of the Intergovernmental Panel on Climate Change (IPCC). Cambridge University Press, Cambridge, UK, and New York, NY, USA, pp. 109-230.

Shrestha, F., Xiao, G., Khanal, N.R., Maharjan, S.B., Shrestha, R.B., Lizong, W., Mool, P., Bajracharya, S. (2017) Decadal glacial lake changes in the Koshi basin, central Himalaya, from 1977 to 2010, derived from Landsat satellite images. Journal of Mountain Science 14(10).

Vuichard, D. and Zimmermann M. (1987) The 1985 catastrophic drainage of a moraine-dammed lake, Khumbu Himal, Nepal: Causes and consequences. Mt. Res. Dev. 7:91–110.

Vuichard, D. and Zimmermann, M. (1986) The Langmoche Flash-Flood, Khumbu Himal, Nepal. Mountain Research and Development, 6(1), 90–94.

65 | P a g e

Wang, W., Xiang, Y., Gao, Y., Lu, A., Yao, T., (2014) Rapid expansion of glacial lakes caused by climate and glacier retreat in the Central Himalayas. Hydrol. Process. 29 (6), 859–874.

Worni, R., Huggel, C., Stoffel, M., (2013) Glacial lakes in the Indian Himalayas — From an area-wide glacial lake inventory to on-site and modeling based risk assessment of critical glacial lakes. Sci. Total Environ. 468–469, S71–S84.

Wang, X., et al., (2012) An approach for estimating the breach probabilities of moraine dammed lakes in the Chinese Himalayas using remote-sensing data. Nat. Hazards Earth Syst. Sci. 12 (10), 3109–3122.

Westoby, M.J., et al., (2014) Modelling outburst floods from moraine-dammed glacial lakes. Earth Sci. Rev. 134, 137–159.

WECS (1987) Study of Glacier Lake Outburst Floods in the Nepal Himalaya: Phase 1, Interim Report, May 1987, WECS Report No. 4/1/200587/1/1, Seq. No. 251, Kathmandu, Nepal: WECS.

Xu D. (1988) Characteristics of Debris Flow Caused by Outburst of Glacial Lake in Boqu River, Xizang, China, 1981. Geo Journal 17:569–580.

Xu Daoming; Liu Chaohai; and Feng Qinghua (1989) ‘Dangerous glacial lake and outburst features in Xizang Himalayas’. Acta Geographica Sinica, 44(3): 343-253.

Yamada, T. and Sharma, C. K. (1993) Glacier lakes and outburst floods in the Nepal Himalaya. In: Snow and Glacier Hydrology (ed. by G. J. Young) (Proc. Kathmandu Symp., November 1992), 319-330. IAHS Publ. no. 218.

Yao, T.D., Thompson, L., Yang, W., Yu, W.S., Gao, Y., Guo, X.J., Yang, X.X., Duan, K.Q., Zhao, H.B., Xu, B.Q., Pu, J.C., Lu, A.X., Xiang, Y., Kattel, D.B., Joswiak, D., (2012) Different glacier status with atmospheric circulations in Tibetan Plateau and surroundings. Nature Climate Change 2 (9), 663e667.

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8. Annexes

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Table A1: Number and area of glacial lakes by types (2015) in the Koshi, Gandaki and Karnali basins of the Nepal, TAR, China and India

Country Basin

Number Area (km2)


Ice-dammed lake (I)

Bedrock-dammed lake

(E) Others

Total


Ice-dammed lake (I)


Others Total

M(e) M(l) M(o) I(s) I(v) B(c) B(o) O M(e) M(l) M(o) I(s) I(v) B(c) B(o) O

Nepal

Koshi 142 29 275 142 0 69 174 3 834 18.215 3.255 4.972 1.804 0 3.566 2.647 0.462 34.921 Gandaki 45 8 88 38 0 16 59 1 255 7.188 1.164 1.782 0.266 0 0.721 1.467 0.102 12.69 Karnali 116 23 494 33 2 111 186 16 981 9.903 1.901 11.018 0.308 0.04 5.149 2.95 6.116 37.385 Sub-total 303 60 857 213 2 196 419 20 2070 35.306 6.32 17.772 2.378 0.04 9.436 7.064 6.68 84.996

China

Koshi 217 8 393 92 0 63 451 6 1230 56.765 4.227 7.637 1.552 0 3.701 21.41 1.794 97.086 Gandaki 30 12 55 8 0 15 57 0 177 2.746 0.275 1.685 0.094 0 0.611 1.088 0 6.499 Karnali 13 2 30 16 0 4 36 1 102 1.429 0.019 1.011 0.114 0 0.206 2.507 0.022 5.308 Sub-total 260 22 478 116 0 82 544 7 1509 60.94 4.521 10.333 1.76 0 4.518 25.005 1.816 108.893

India

Koshi 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Gandaki 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Karnali 10 0 12 8 0 3 12 0 45 0.383 0 0.507 0.04 0 0.067 0.239 0 1.236 Sub-total 10 0 12 8 0 3 12 0 45 0.383 0 0.507 0.04 0 0.067 0.239 0 1.236

Total 573 82 1347 337 2 281 975 27 3624 96.629 10.841 28.612 4.178 0.04 14.021 32.308 8.496 195.125

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Table A2:Identification of potentially dangerous glacial lakes (PDGL) based on the characteristics of lake, dam and surrounding features including the source glacier.

Country Basin

Lake Inventory Lake size and type Dam characteristics Surrounding features PDGL Zero Level First level Second Level Third Level Fourth Level

Number Area (sq.km)

Number Area (km2)

Number Area (km2)

Number Area (km2)

Number Area (km2)

Nepal

Koshi 834 34.921 199 24.23 91 16.5 19 9.88 18 9.80 Gandaki 255 12.69 65 9.35 18 5.6 2 1.07 2 1.07 Karnali 981 37.385 241 18.81 39 3.4 1 0.03 1 0.03 Sub-total 2070 84.996 505 52.40 148 25.4 22 10.97 21 10.90

China

Koshi 1230 97.086 309 66.28 123 39.8 28 30.25 24 29.56 Gandaki 177 6.499 52 4.25 17 1.1 1 0.28 1 0.28 Karnali 102 5.308 22 2.25 3 0.1 0 0.00 0 0.00 Sub-total 1509 108.893 383 72.78 143 41.0 29 30.53 25 29.84

India

Koshi 0 0 0 0.00 0 0.0 0 0.00 0 0.00 Gandaki 0 0 0 0.00 0 0.0 0 0.00 0 0.00 Karnali 45 1.236 8 0.78 4 0.3 1 0.12 1 0.12 Sub-total 45 1.236 8 0.78 4 0.3 1 0.12 1 0.12

Total 3624 195.13 896 125.96 295 66.8 52 41.62 47 40.85

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Table A3: Characteristics of lake, dam, source glacier and surroundings for the identification of potentially dangerous glacial lakes in Koshi, Gandaki and Karnali basins of Nepal and TAR, China

Char

acte

ristic

s

Parameters

Lake ID

GL086476E27861N

GL087092E27798N

GL086925E27898N

GL086612E27779N

GL086958E27755N

GL086957E27783N

GL086935E27838N

GL087095E27829N

GL085870E28360N

GL088066E27933N

GL086304E28374N

GL086225E28346N

GL085838E28322N

GL086447E27946N

GL087930E27949N

GL086532E28185N

GL088002E27928N

GL086500E28033N

Lake

Lake size (sq. km) 1.60 1.91 1.37 1.22 0.86 0.87 0.30 0.12 4.82 0.96 4.02 0.58 5.41 1.77 0.84 0.68 1.11 0.60 Expansion rate (sq.km/yr) 0 0.06 0.03 0.02 0 -0 -0 0 0.08 0.01 0 0.01 0.14 0.05 0.01 0.01 0 0.02 Presence of cascading lakes 0 1 0 1 0 0 1 1 0 0 0 0 0 0 1 0 1 1 Activity of supraglacial lakes 0 1 1 0 0 0 1 0 0 0 0 0 1 1 0 0 0 1

Dam

damming material m m m m m m m m m m m m m m m m m m Length of the dam (m) 398 930 751 560 1273 1107 330 361 390 300 701 110 540 1290 520 290 510 671 Crest width (m) 15 15 50 15 20 30 5 20 10 10 0 0 10 0 0 0 0 30 Dam height (m) 159 128 55 62 212 382 43 61 60 77 56 19 94 205 105 92 66 75 Inner slope of the dam (deg) 3 6 8 2 4 10 3 5 1 1 0 0 0 0 0 0 0 1 Outer slope of the dam (deg) 23 8 10 3 9 19 5 9 7 19 9 7 10 11 12 19 8 14 Landslides on the dam 0 1 0 0 1 1 0 1 0 0 0 0 0 0 0 1 0 0 Lake outlet drainage 1 1 1 0 1 0 1 1 1 1 1 1 0 1 1 0 1 0 Breached in past 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Seepage through the dam 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Existence of ice core 0 1 1 1 1 1 1 0 1 1 1 0 1 1 0 0 1 1

Mot

her

Gla

cier

Distance to source glacier (m) 0 0 0 0 180 0 0 371 0 0 0 0 292 0 0 0 0 0 Debris cover on the glacier tongue

1 1 1 1 0 0 1 0 1 1 0 1 1 1 1 0 0 1

Presence of crevasses and ponds 1 1 1 1 1 1 0 1 1 1 1 1 1 1 0 1 1 1 Toppling/collapsing of glacier ice 1 1 1 1 1 0 0 0 1 1 1 1 1 1 1 1 1 1

Surroun dings

Hanging glacier close to lake 1 1 1 1 1 1 0 0 1 1 0 0 1 0 0 1 1 1 Potential rock-fall/slide 0 0 0 1 1 1 0 0 1 0 0 1 1 0 1 1 0 0

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Potential snow/ice avalanche 1 1 0 1 1 1 0 0 1 0 0 0 0 0 0 1 0 0 Sudden advance of a glacier 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

Table A3: Characteristics of lake, dam, source glacier and surroundings for the identification of potentially dangerous glacial lakes in Koshi, Gandaki and Karnali basins of Nepal and TAR, China. (yellow highlighted lake is removed from the list of PDGL)

Char

acte

ristic

s

Parameters

Lake ID

GL088075E27946N

GL085819E28297N

GL086530E28135N

GL087632E27729N

GL087596E27705N

GL087619E27725N

GL086858E27687N

GL086928E27850N

GL087893E27694N

GL086917E27832N

GL086977E27711N

GL087749E27816N

GL086157E28303N

GL085782E28271N

GL087945E27781N

GL087934E27790N

GL086314E28194N

GL086371E28238N

Lake

Lake size (sq. km) 1.48 0.32 1.00 0.03 0.03 0.07 0.31 0.45 0.02 0.37 0.05 0.18 0.62 0.08 0.05 0.15 0.28 0.29 Expansion rate (sq.km/yr) 0.04 0 0.01 0 0 0.01 0 -0 0 -0 0 0 0 0 0 0 0 -0 Presence of cascading lakes 1 0 0 0 1 0 0 1 0 0 0 0 0 1 0 0 0 1 Activity of supraglacial lakes 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

Dam

damming material m m m m m m m m m m m m m m m m m m Length of the dam (m) 750 620 802 220 380 340 440 296 170 590 430 560 520 530 400 360 255 234 Crest width (m) 30 20 0 0 0 0 0 30 0 0 60 40 0 40 20 0 0 20 Dam height (m) 74 97 106 63 158 58 172 45 51 128 129 221 102 145 124 128 101 99 Inner slope of the dam (deg) 2 8 0 0 0 0 0 3 0 4 12 4 0 5 3 0 0 6 Outer slope of the dam (deg) 8 7 15 15 23 13 19 13 18 14 20 26 9 25 18 23 19 24 Landslides on the dam 0 0 1 0 1 0 1 1 1 0 1 1 0 1 1 1 1 1 Lake outlet drainage 1 0 1 1 1 1 1 0 0 1 0 1 1 1 1 0 0 0 Breached in past 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Seepage through the dam 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 Existence of ice core 1 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0

Mot

her

Gla

cier

Distance to source glacier (m) 0 630 0 309 1095 0 135 367 1018 0 0 0 306 1950 626 504 690 0 Debris cover on the glacier tongue 1 0 1 0 1 1 1 0 0 1 1 0 1 1 0 0 0 0

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Presence of crevasses and ponds 1 0 0 0 1 1 0 0 0 0 1 1 1 1 1 1 1 1 Toppling/collapsing of glacier ice 1 0 1 0 1 1 0 0 0 0 0 1 1 0 0 0 0 0

Surroun dings

Hanging glacier close to lake 1 1 1 0 0 0 1 1 0 0 0 0 1 0 0 1 0 1 Potential rock-fall/slide 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 Potential snow/ice avalanche 0 1 0 0 0 0 1 0 0 0 0 1 1 0 0 1 1 0 Sudden advance of a glacier 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

Table A3: Characteristics of lake, dam, source glacier and surroundings for the identification of potentially dangerous glacial lakes in Koshi, Gandaki and Karnali basins of Nepal and TAR, China. (yellow highlighted lake is removed from the list of PDGL)

Char

acte

ristic

s

Parameters

Lake ID

GL086520E28073N

GL087771E27926N

GL087626E28052N

GL087636E28093N

GL087076E28100N

GL087134E28069N

GL088288E28017N

GL088019E27928N

GL087053E28226N

GL087591E28229N

GL087563E28178N

GL084485E28488N

GL085494E28508N

GL085630E28162N

GL082673E29802N

GL080387E30445N

Lake

Lake size (sq. km) 0.24 1.01 0.19 0.80 0.12 0.28 0.51 0.15 0.17 0.87 1.12 0.93 0.28 0.14 0.03 0.12 Expansion rate (sq.km/yr) 0 0.01 0 0.01 0 0 0 0.01 0 0 0.02 0 0.01 0 0 0 Presence of cascading lakes 0 1 1 0 0 0 0 0 0 1 0 0 0 0 1 0 Activity of supraglacial lakes 0 1 0 1 0 1 0 0 0 0 0 0 0 0 0 0

Dam

damming material m m m m m m m m m m m m m m m m Length of the dam (m) 350 190 270 0 450 260 340 469 390 411 800 1310 830 900 210 270 Crest width (m) 30 0 0 50 0 0 20 0 30 0 10 20 20 50 0 30 Dam height (m) 149 74 111 109 95 82 97 98 89 59 141 192 339 223 99 127 Inner slope of the dam (deg) 12 0 0 0 0 0 4 0 1 0 2 2 0 7 0 12 Outer slope of the dam (deg) 23 22 29 18 12 16 17 11 13 5 8 10 28 15 26 29 Landslides on the dam 1 1 1 1 0 1 0 0 0 1 0 0 1 1 1 1 Lake outlet drainage 0 0 0 0 0 1 1 1 1 0 0 1 0 0 0 0 Breached in past 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Seepage through the dam 0 0 1 1 1 0 0 0 0 1 1 0 0 0 0 0 Existence of ice core 0 0 0 0 0 0 0 1 1 0 0 1 0 0 0 0

Mot

he r G

laci

er Distance to source glacier (m) 563 0 605 0 806 113 108 0 268 34 1320 0 0 497 623 283

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Debris cover on the glacier tongue 0 1 0 1 0 1 0 0 0 0 1 1 1 0 0 1 Presence of crevasses and ponds 1 1 1 0 0 1 1 1 1 1 1 1 1 1 1 0 Toppling/collapsing of glacier ice 0 1 1 1 0 0 0 1 0 1 1 1 1 0 0 0

Surroun- dings

Hanging glacier close to lake 1 0 1 0 0 0 1 0 0 1 1 0 1 1 0 0 Potential rock-fall/slide 1 0 1 1 0 0 0 0 1 0 1 1 0 0 0 1 Potential snow/ice avalanche 0 0 1 1 0 0 0 0 1 1 1 0 1 0 0 0 Sudden advance of a glacier 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

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Figure A4: Identification of potentially dangerous glacial lakes (PDGL) based on the characteristics of lake, dam and surrounding features.

Basin Sub Basin

Lake Inventory Lake size and type Dam characteristics Surrounding features PDGL

Zero Level First level Second Level Third Level Fourth Level

Number Area (km2)

Number Area (km2)

Number Area (km2)

Number Area (km2) Number Area (km2)

Koshi

Tamor 283 9.12 52 4.01 21 2.49 4 0.40 4 0.40

Arun 909 68.58 202 40.01 70 21.18 20 15.77 17 15.41

Dudh Koshi 355 16.92 96 13.95 47 9.32 9 5.73 9 5.73

Likhu 17 0.41 4 0.22 3 0.19 0 0.00 0 0.00

Tama Koshi 307 14.68 95 12.42 50 9.37 8 6.38 8 6.38

Sun Koshi 181 22.13 58 19.88 22 13.72 6 11.83 4 11.43

Indrawati 12 0.16 1 0.03 1 0.03 0 0.00 0 0.00

Sub-Total 2064 132.01 508 90.51 214 56.31 47 40.12 42 39.36

Gandaki

Trishuli 242 8.19 56 4.94 22 1.69 2 0.42 2 0.42

Budhi Gandaki 49 1.58 12 0.97 4 0.27 0 0.00 0 0.00

Marsyangdi 59 6.22 22 5.68 8 4.71 1 0.93 1 0.93

Seti 4 0.15 0 0.00 0 0.00 0 0.00 0 0.00

Kali Gandaki 78 3.05 27 2.02 1 0.03 0 0.00 0 0.00

Sub-Total 432 19.19 117 13.61 35 6.70 3 1.34 3 1.34

Karnali

Bheri 164 9.20 28 1.99 5 0.44 0 0.00 0 0.00

Tila 82 4.11 14 1.73 5 0.85 0 0.00 0 0.00

Mugu 239 6.22 55 3.31 10 0.40 1 0.03 1 0.03

Kawari 28 1.04 6 0.58 2 0.08 0 0.00 0 0.00

West Seti 51 1.43 7 0.94 0 0.00 0 0.00 0 0.00

Humla 498 20.21 149 12.35 18 1.66 0 0.00 0 0.00

Kali 63 1.53 12 0.93 6 0.37 1 0.12 1 0.12

Karnali 3 0.19 0 0.00 0 0.00 0 0.00 0 0.00

Sub-Total 1128 43.93 271 21.84 46 3.79 2 0.15 2 0.15

Total 3624 195.12 896 125.96 295 66.80 52 41.62 47 40.85

74 | P a g e

Separate file

Table A5: Information of parameters of all PDGL for dam breach model. Table A6: Distribution of types of lakes at different elevation zone in pdf. Table A7: Distribution of glacial lake sizes at different elevation zone in pdf. Table A8: Lake area classes vs type of lakes in pdf. Table A9: List of images used in the present study in pdf.

Soft copy

Soft copy - Glacial Lake Inventory data including shp.file of 2000 Soft copy - Glacial Lake Inventory data including shp.file of 2015

Documents

Inventory of Glacial Lakes in the Koshi, Gandaki · some selected basins of HKH region based on satellite images. Analysis of the time series data revealed that the glaciers had lost