Assessment of glacial lake prone to glacial lake outburst flood using multi-criteria decision analysis (MCDA) in Changme Khangpu basin, Sikkim Himalaya, India

doi:10.21203/rs.3.rs-2233658/v1

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Research Article

Assessment of glacial lake prone to glacial lake outburst flood using multi-criteria decision analysis (MCDA) in Changme Khangpu basin, Sikkim Himalaya, India

https://doi.org/10.21203/rs.3.rs-2233658/v1

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Changme Khangpu (CK) basin is one of the major glaciated basins in Sikkim Himalaya with a high number of glacial lakes. A glacial lake inventory for the CK basin has been created using a semi-automated method based on Sentinel 2A images from the year 2020 and mapped 134 glacial lakes. Glacial lake outburst floods have emerged as a major concern in the Himalayan region in recent times. 20 glacial lakes were identified as being prone to GLOFs using a set of assessment parameters in the basin. We performed hazard and risk assessments for these glacial lakes using a multi-criteria decision analysis (MCDA) scheme using 13 consistent, exhaustive, and non-redundant criteria, categorized into triggering, mechanism, flood size, and impacts for assessing both the hazard as well as the risk associated with the glacial lakes for providing a comprehensive hazard and risk assessment. Based on this scheme, 14 lakes were categorized into the high-risk class and 6 lakes into the medium-risk class. MCDA scheme allows for assessing the risk scores associated with the glacial lakes by changing the threshold values of assessed criteria. Such sensitivity analysis done for the identified glacial lakes shows that even a slight change in the criteria values can significantly change the risk class of the lake. MCDA scheme can be applied at a regional scale across different lake types for the identification of potential GLOF hotspots and constant monitoring making it suitable for first order assessments.

Sikkim Himalaya

Changme Khangpu basin

glacial lakes

GLOF

hazard and risk assessment

MCDA

Himalayan glaciers have undergone drastic changes in the last two decades, with significant retreat and mass loss (Bolch et al., 2019; Hock et al., 2022). Glacier mass loss rates have doubled between 2000–2016 relative to 1975–2000 across the Himalayas (Maurer et al., 2019), and high retreat rates have also been associated with the presence of proglacial lakes at glacier termini (King et al., 2019; Watson et al., 2020). Glacier thinning and retreat results in the release of a high volume of meltwater, leading to the formation of an increasing number of glacial lakes and supra-glacial ponds and the augmentation of the already existing lakes and ponds. Accelerated growth of pro-glacial lakes and supraglacial lakes has also been documented widely in the Eastern Himalayas (Komori, 2008; Nie et al., 2018; Shukla et al., 2018; Song et al., 2016), with an increase of 8% between 1990 to 2015 (Nie et al., 2016). The rising number of glacial lakes poses concern among local communities about the possibility of an increased risk of glacial lake outburst flood (GLOF) events in the region as a result. Deglaciation and degrading permafrost have also increased the risk of potential mass movement from surrounding areas. However, the link between the increase in the number and volume of lakes and the risk of GLOFs has yet to be understood and is subject to scientific debates. For example, Zhang et al. (2021) argue for an increase in GLOF risk in the coming centuries, while Veh et al. (2019) showed that the average rate of GLOFs in the greater Himalayan region has remained unchanged over the past 3 decades, indicating that GLOF events are not directly linked to the rate of lake increase alone.

A GLOF event is defined as the sudden catastrophic release of a large volume of water from glacial lakes (Clague, 2000; GAPHAZ, 2017), and can be triggered either due to dynamic failure caused by mass movement into the lake, earthquakes, flood waves from a lake situated upstream, blocking of underground outflow channels, extreme climatic events, or self-destructive failure caused by an increase in hydrostatic pressure in the lake or melting of the buried ice core in the moraine dam (Emmer & Cochachin, 2013; Yamada, 1998). The large volume of the water released during such outburst floods incorporates huge amounts of unconsolidated material from the moraine dam as well as the frontal region of the lake often turning into a large accumulated mass flow. GLOFs are characterized by higher peak discharge and greater erosive potential than meteorological floods (Cook et al., 2016; Emmer, 2017). Such events tend to attenuate rapidly but in regions like the Himalayas and Andes where the stream channels are long and steep, these outburst floods have longer flow paths causing widespread destruction (Schwanghart et al., 2016). The physical and socio-economic risks associated with GLOF events have increased manifold over the past decades in the Himalayan regions owing to a rapid increase in the number of settlements, hydropower projects, and infrastructure development downstream (ICIMOD, 2011). For example, in recent years the Himalayas has witnessed the Kedarnath flood, in India (2013), the Gongbatongsha Lake outburst floods (2016) in Tibet and Nepal, Shispher glacial lake outburst, in Pakistan (2022), etc. in the recent past and caused immense destruction downstream. This increased probability of glacier hazards, including GLOF, in the Himalayas, has attracted increased attention from the scientific communities.

Hazard and risk assessment studies are the starting point for any comprehensive risk management strategy which provides information on hotspots and helps prioritization of sites where risk management strategies can be implemented. Recently many GLOF hazards and risk assessments follow the integrative concept of climate risk which defines risk as a product of hazard, vulnerability, and exposure (IPCC, 2012). Many such assessment schemes have been developed and applied across the different Himalayan regions in India (A. Aggarwal et al., 2016; S. Aggarwal et al., 2017; Allen et al., 2016; Dubey & Goyal, 2020; Islam & Patel, 2021; Mal et al., 2021; Worni et al., 2013), Nepal (Bolch et al., 2008; Khadka et al., 2021; Rounce et al., 2016), Tibet (Allen, 2019; Duan et al., 2020; Fan et al., 2019; Wang et al., 2011), Pakistan (Ashraf et al., 2012, 2014) and Bhutan (Nagai et al., 2017; Wangchuk et al., 2019). Many of these assessments are specific to regional characteristics, dam types, or assessment parameters which therefore renders many such assessments incomplete in assessing the total risk. Even though many such assessment exists lack of consistency in the assessed parameters and the assessment techniques, makes any comparative studies across region and time extremely difficult. Glacier and Permafrost Hazards in Mountains (GAPHAZ) provides a detailed set of susceptibility and stability assessment parameters for an inclusive risk assessment scheme but no such scheme that encompasses all of the parameters has been developed as of now (GAPHAZ, 2017).

Among the existing scheme, Kougkoulos et al., (2018) provide a multi-criteria decision analysis (MCDA) assessment scheme which can be applied across the region and can take into account all the dam types. This method has been initially applied in the Bolivian Andes (Kougkoulos, et al., 2018) and has not been previously used for any assessment studies in the Eastern Himalayan region. The advantage of this scheme offers a completely desk-based, first-order assessment of lake hazard potential using freely available data inputs and open-access software. In a data-sparse region such as Sikkim, characterized by a large number of lakes of different types, such a scheme is promising. Such a first-order assessment scheme is a useful starting point for evaluating the detailed hazard potential in the region. In this study, we test the application of the MCDA scheme from Kougkoulos et al (2018) and aim to evaluate the first-order GLOF susceptibly of glacial lakes in the Changme Khangpu basin of Sikkim Himalaya using this scheme.

Study area

The study area is located in Sikkim, a small Indian state in the Eastern Himalayan region extending between 27°00’46” and 28°07’48” N and 88°00’58'' and 88°55’25” E. It has a north-south extent of ~ 114 km and an east-west extent of ~ 64 km, covering a total area of about 7096 km² (Census of India, 2011). The state has a large elevation gradient, with elevations ranging from 300 m to 8586 m. This high elevation gradient allows for the state to experience a sub-tropical climate in the south and to tundra climate in the north. Sikkim is drained by the Teesta River and its tributaries, containing four major fifth-order glacierized sub-basins, East Rathong, Talung, Changme Khangpu, and Zemu (Raina & Srivastava, 2008). In this paper, we focus only on the Changme Khangpu (CK) basin (Fig. 1), the easternmost glacierized basin with a total area of around 792 km² out of which ~ 65.77 km² is covered by glaciers (Singh, 2020). The elevation range of the basin extends from 1540 m to 6880 m with about 80% of the basin lying in the range of 3500 to 5500 m. CK basin has around 58 glaciers of which many glaciers are heavily debris-covered such as Changme Khangpu glacier (4.84 km²). Major glaciers in the basin are Khangkyong Khangse (22.54 km²), Tenbawa Khangse (5.7 km²), and Lako Khangse (3.26 km²)

Data

The glacial lake inventory for the CK basin was created using Sentinel − 2 Multispectral Instrument (MSI) satellite images from the year 2020 (Table.1). In this study, we used the Level-1C orthorectified image providing top-of-atmosphere reflectance obtained from scihub.copernicus.eu. Sentinel 2 MSI imagery has 12 bands in the red, green, blue, near-infrared, and short-wave infrared ranges (10, 20, or 60 m pixel size). We have used the green and near-infrared (NIR) bands in the current study, both of which are available at 10m spatial resolution. Elevation data was based on the Terra Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Global Elevation Model (GDEM) version 3 (NASA/METI/AIST/Japan Spacesystems And U.S./Japan ASTER Science Team, 2019) obtained from earthdata.nasa.gov. For the glacier outlines, we used the Randolph Glacier Inventory 6.0 (RGI v6). The RGI boundaries were updated for the current glacier extent using high spatial resolution imagery from Google Earth.

For the classification schemes, we also used the Global Seismic Hazard Map from Global Seismic Hazard Assessment Program (Giaedini et al., 2003) and bioclimatic variables from WorldClim version 2.1 (Fick & Hijmans, 2017) as auxiliary datasets obtained from http://gmo.gfz-potsdam.de and www.worldclim.orgrespectively.

Table 1

Satellite data used in this study
Sensor	Scene ID	Product	Date	Pixel size (m)	Swath width (km)
Sentinel 2B	S2B_MSIL1C_20201031T043929_N0209_R033_T45RXL_20201031T064857	S2MSI1C	31/10/2020	10	100*100
Sentinel 2B	S2B_MSIL1C_20201031T043929_N0209_R033_T45RXM_20201031T064857	S2MSI1C	31/10/2020	10	100*100

Methodology

Glacial lake mapping

Glacial lake boundaries were delineated using a standard semi-automated, normalized difference water index, (NDWI) (McFeeters, 1996). NDWI takes advantage of the reflectance difference of water in the green and near-infrared (NIR) band to discriminate between water and non-water pixels (NDWI = (\({B}_{green}-{B}_{NIR})/{(B}_{green}+{B}_{NIR})]\) However, while computing NDWI in mountainous terrains, areas under shadow and steep terrain are often misclassified as water pixels (Shukla et al., 2018; Wang et al., 2011). Hence, post-processing shadow masking and visual interpretation have been conducted to remove these misclassification areas. In the final step, manual checking and correction of the lake boundaries were done using high-resolution Google Earth scenes to produce the final glacial lake inventory. We only focused on proglacial lakes in this study; although supraglacial lakes have the potential to produce outburst floods (Racoviteanu et al., 2022) we plan to address them in a separate study as the dynamics associated with such lakes are largely different from the proglacial lakes.

Before the application of the classification scheme, we calculated the area and elevation for all the mapped proglacial and filtered the proglacial lake database based on a set of assessment parameters. We made several assumptions and excluded lakes that met the following criteria: (a) lakes with an area < 0.01 km² were considered to not have significant hazard potential (National Disaster Management Authority, 2020), hence, these glacial lakes were not considered for further assessment (ICIMOD, 2011; Worni et al., 2013); (b) lakes beyond 500 m distance from a glacier were excluded from further analysis based on the assumption that mass movement from surrounding glaciers and steep slopes are one of the most important triggering factors for GLOFs (Wang et al., 2011) ; (c) we excluded slopes of the surrounding area ( within 500 m from the lake boundary) < 25° based on the assumption that this does not pose risk in terms of a mass movement (Wang et al., 2011). After filtering, the remaining lakes were considered for the subsequent analysis.

Multi-criteria decision analysis (MCDA) scheme

In this paper, for the first-order classification, we chose the multi-criteria analysis scheme from Kougkolous et al., 2018. They list 13 criteria across four categories triggering factors, possible mechanisms, flood runout extent, and impact. These criteria were proposed by Kougkolous et al., (2018) based on a compilation from previous studies based on their exhaustiveness, non-redundancy, and consistency. All the criteria values were categorized into three categories based on predefined threshold values. Each of the categories was given a nominal value of 0,2, and 4 for low, medium, and high while assessing risk in MCDA. All the criteria were also given equal weightage for the present study. The thresholds for each criterion and the evaluation method used for computing the criteria are presented in Table 2.

Table 2

Evaluation criteria and risk thresholds
Criteria		Data source	Risk threshold values
Criteria		Data source	Low risk	Medium risk	High risk
TRIGGERS	Regional seismic activity	PGA values were obtained from Global Seismic Hazard Map	< 0.5 ms^− 1	0.5–3.9 ms^− 1	> 3.9 ms^− 1
	Intense precipitation events	BIOCLIM indicators BIO 4 and BIO 15 were obtained from the WorldClim database	< 50	50–100	> 100
	High-temperature events		< 50	50–100	> 100
MECHANISMS	Dam freeboard	Google Earth Pro using the elevation profile feature.	> 15m	5-15m	< 5m
	Dam type	Visual interpretation in Google Earth Pro.	Bedrock	Moraine	Ice
	Slope steepness surrounding the lake	Slope maps were prepared using ASTER GDEM in QGIS.	< 30°	30–45°	> 45°
	Distance between a lake and the steepest slope	Google Earth Pro using the measure distance feature.	250 -500m	10-250m	0-10m
	Distance between lake and parent glacier	Google Earth Pro using the measure distance feature.	250 -500m	10-250m	0-10m
	Parent glacier snout steepness	Slope maps prepared using ASTER GDEM in QGIS	< 15°	15–25°	> 25°
FLOOD EXTENT	Travel distance of GLOF	Google Earth Pro using the measure distance feature and elevation difference.	3–7°	7–11°	> 11°
FLOOD EXTENT	Lake volume	Empirical area-volume equation (Cook & Quincey, 2015a).	< 10⁶ m³	10⁶-10⁷ m³	> 10⁷ m³
IMPACT	Potential loss of human life	Sourced from the Census of India dataset (Census of India, 2011).	< 10	10-1000	> 1000
IMPACT	Potential loss of infrastructure		Agricultural fields	Houses, bridges, etc	Hydropower, mining

In terms of triggering factors, intense precipitation and high-temperature events have been known to drastically increase the probability of lake outbursts (Mir et al., 2018; Prakash & Nagarajan, 2017; Wang et al., 2011). Intense temperature events are associated with degrading permafrost and destabilization of the surrounding area. We used the BIOCLIM² indicators BIO 4 and BIO 15 which represent the temperature and precipitation seasonality respectively as surrogates for intense precipitation events and high-temperature events. Seismic activity has been known to generate mass movement and in many cases even trigger a dam failure (Jha & Khare, 2017; Mir et al., 2018; Prakash & Nagarajan, 2017; Rounce et al., 2016). Maximum peak ground acceleration (PGA_max) is a comprehensive proxy for measuring regional seismic activity.

In terms of conditioning factors or mechanisms, we based our choices of criteria on existing studies. Dam freeboard is used for assessing if any mass movement will create wave displacement high enough for dam overtopping, which may lead to eventual partial or complete erosion of the dam (McKillop & Clague, 2007; Worni et al., 2013; Xin et al., 2012). Dam type is among the most important conditioning parameters for GLOFs, ice and moraine-dammed lakes have a larger predisposition towards producing outbursts than bedrock-dammed lakes which are considered more stable (Huggel et al., 2004; Worni et al., 2013). Both of these were assessed visually in Google Earth pro. Previous studies showed that rockfall, avalanche, and icefall are the most important triggers of GLOFs, and the probability of any such mass movement is increased by the presence of steep slopes around the glacial lakes (Wang et al., 2011). We calculated these based on slope maps derived from ASTER GDEM using QGIS. Furthermore, lakes located in the vicinity of steeper slopes are considered to have a higher possibility of having any mass movements produced impact the lake. A threshold of 500 m for the distance between the lake and the steep slope was considered in the present study (Wang et al., 2011); this was calculated using buffer functionality in QGIS.

Calving from the parent glacier often adds a large volume into the lake causing overtopping and in some cases complete failure of the lake (Allen, 2019; Khadka et al., 2021). Lakes that are connected to their parent glaciers are considered to be the most hazardous; they also allow for the future growth of the lake due to the retreating glacier. Therefore lakes within 500 m of the glacier have been considered to have a more significant GLOF trigger possibility (Cook et al., 2016; Wang et al., 2011). Snout steepness directly controls the possibility of any calving or avalanche into the lake (Che et al., 2014; Duan et al., 2020; Fan et al., 2019; Jha & Khare, 2017; Wang et al., 2011)

In terms of flood extent, we assessed the GLOF travel distance and lake volume. Flood wave extends are determined by the type of flow and the slope through which the flow travel, generally stopping at average downstream slopes of 3–11°. The average slope between a potential GLOF and infrastructure at risk was used to ascertain the travel distance of a GLOF. Lake volume determines the outburst volume and the peak discharge associated with any potential outburst. Lake volume was calculated using an empirical equation V = 0.1217 A^1.4129 (Cook & Quincey, 2015b).

In terms of impacts, the risk possessed by any GLOF event can be expressed as the impact on the downstream pollution and infrastructure that is exposed to the potential GLOF. The multi-criteria decision analysis of the selected lakes was performed using the SMAA-TRI (Stochastic Multi-criteria Acceptability Analysis), an open-source software package based on the ELECTRE (ELimination Et Choice Translating REality) TRI method (http://smaa.fi/) which allows for alternatives to be assigned predefined categories in a sorting problem (Roy, 1996). Finally, the selected glacial lakes were assigned to three risk classes: low, medium, and high-risk probability.

Sensitivity analysis

The MCDA scheme allows for checking the robustness of the model by alternating the λ-cutting level, which represents the minimum proportion of criteria that must be met before any lake is assigned to a given risk category. The λ cutting value is generally always set between 0.5 and 1.0. The λ cutting the value of 0.5, 0.6, 0.7, 0.8, and 0.9, refers to a minimum of 50%, 40%, 30%, 20%, and 10% respectively of the criteria need to be evaluated as an anyone risk category for the lake to be assigned that category (Damart et al., 2007). For the present study, the risk scores were evaluated at a λ cutting value of 0.65 (denoted here as λ_0.65). We consider this to be a conservative value to assess the associated risk of glacial lakes (Kougkoulos, Cook, Edwards, et al., 2018).

Glacial lake database

We identified and mapped a total of 134 glacial lakes in the CK basin for the year 2020 covering a total area of 4.75 km². Our results are in good agreement with the previous inventories for the region prepared using higher resolution data for the years, 2014,2017and 2018 (Abdul Hakeem et al., 2018; Shukla et al., 2018; Islam & Patel, 2021). The lake area in the basin ranges from 0.001 km² to 0.458 km² with a mean lake area of 0.04 ± 0.06 km². Small lakes (< 0.01 km²) are the most frequent (n = 61 or 46% of the total lakes) however, they only cover 5.34% of the total lake area in the basin (Fig. 2). In contrast, 12 large lakes (9% of the total lakes) with an area between 0.1–0.5 km² cover almost 50% (2.4 km²) of the total lake area in the basin.

All the mapped glacial lakes in the basin lie within the elevation range of ~ 4200 to 5550 m with a mean elevation of 5015 m. The highest number of glacial lakes (around 57%) lies in the altitude range of 5000–5500 m followed by 4500–5000 m, 4000–4500 m, and 5500–6000 m respectively (Fig. 3).

GLOF probability assessment

The area threshold selection of 0.01 km² resulted in 72 out of the 134 glacial lakes being selected for further assessment. Subsequently, 38 lakes were eliminated as they were more than 500 m away from any glacier boundary. Of the remaining 34 glacial lakes, 14 lakes were excluded as the slope of the surrounding area within 500 m from the lake boundary was less than 25 °. After applying this assessment, 20 lakes were selected as glacial lakes prone to GLOF and were considered for the ranking scheme. The MCDA scheme was applied to these lakes to calculate the hazard and risk scores associated with each lake. The scores against each risk category which represents the level of confidence with which the lake is placed in that group were computed (Table 3). Based on these risk scores vulnerable glacial lake was assigned a risk class. Of the 20 glacial lakes prone to GLOF 14 glacial lakes were categorized in the high-risk class, 6 in the medium-risk class, and no lake in the low-risk class. Their spatial distribution has been shown in Fig. 4. The risk scores under the high-risk class for all glacial lakes except GL_CK_088 and GL_CK_091 are more than 0.34 suggesting the high susceptibility of the basin to producing GLOF events.

Table 3

The risk score and risk class for the selected glacial lakes prone to GLOF
Lake Id	Risk scores			Risk class
Lake Id	Low	Medium	High	Risk class
GL_CK_031	0.08	0.14	0.78	High Risk
GL_CK_034	0.08	0.14	0.78	High Risk
GL_CK_038	0.00	0.21	0.79	High Risk
GL_CK_045	0.02	0.07	0.91	High Risk
GL_CK_050	0.00	0.21	0.79	High Risk
GL_CK_076	0.11	0.30	0.58	High Risk
GL_CK_077	0.00	0.41	0.58	High Risk
GL_CK_081	0.04	0.62	0.34	Medium Risk
GL_CK_086	0.29	0.36	0.35	Medium Risk
GL_CK_088	0.33	0.52	0.15	Medium Risk
GL_CK_091	0.34	0.52	0.15	Medium Risk
GL_CK_094	0.12	0.31	0.57	High Risk
GL_CK_096	0.11	0.31	0.58	High Risk
GL_CK_101	0.03	0.19	0.78	High Risk
GL_CK_105	0.11	0.30	0.58	High Risk
GL_CK_109	0.29	0.36	0.34	Medium Risk
GL_CK_118	0.34	0.08	0.58	High Risk
GL_CK_123	0.14	0.52	0.34	Medium Risk
GL_CK_125	0.23	0.19	0.58	High Risk
GL_CK_134	0.11	0.31	0.58	High Risk
GL_CK_109	0.29	0.36	0.34	Medium Risk
GL_CK_118	0.34	0.08	0.58	High Risk
GL_CK_123	0.14	0.52	0.34	Medium Risk
GL_CK_125	0.23	0.19	0.58	High Risk
GL_CK_134	0.11	0.31	0.58	High Risk

GLOF assessment in Sikkim Himalaya

Sikkim Himalaya is known to be seismically active and lies in seismic zone IV (Pal et al., 2008; State Disaster Management Authority, 2015). Peak ground acceleration which is used for regional seismic activity values consistently lies in the medium-risk category. Hydrometeorological parameters viz. intense precipitation events and high-temperature events values also lie either in the high or medium risk category throughout the basin respectively. These values suggest that the triggering parameters are efficient in initiating GLOFs in the region.

Dam freeboard and the steepest slope surrounding the lake, also have values in the high-risk category for all the lakes. The value for dam freeboard is essentially zero meters for all the lakes, as they have outlet channels. CK basin is characterized by steep slopes with more than 50% of the basin area with a slope of more than 45°. The other mechanism parameters viz. distance between the lake and steepest slope, the distance between the lake and parent glacier, and parent glacier snout steepness largely have a mix of medium to high values except for the dam type. All the glacial lakes are either moraine or bedrock dammed representing medium and low risk in the CK basin.

The travel distance of GLOF which is determined by the slope of the flow path of any potential GLOF to the nearest habitat has a mix of low and medium risk values, except for glacial lake GL_CK_045 while all the glacial lakes prone to GLOF have lake volume that lies in the medium-risk category.

The potential loss of human life and potential loss of infrastructure in the downstream region increase the risk associated with any GLOF. Lachung, Yumthang, and Dombang are the major habitation centers located in the path of potential GLOF events. The growing population combined with the high tourist influx makes this region very risk-prone in case of any GLOF event. Any GLOF event in the CK basin will ultimately drain into river Lachung Chu and could have a detrimental effect on the 1200 MWt Teesta III Hydro Power Project, a major hydel power project is also situated at Chungthang. In the context of the chosen criteria, the categorization of selected glacial lakes into high and medium-risk categories presents an alarming situation for the future in terms of potential GLOF events.

Sensitivity analysis

The risk class of the glacial lakes has been further assessed at different sensitivity levels taking the advantage of the robustness of the ranking scheme. Risk scores for the vulnerable glacial lakes at λ_0.70 (λ cutting the value of 0.70) and λ_0.75 (λ cutting value of 0.75) were evaluated and presented in Table 4. All lakes that were categorized into high-risk class at λ0.65 continue to remain in the same class however the absolute risk score increases with the increasing λ cutting value. Risk scores of the remaining lakes have also changed, however, only four lakes viz. GL_CK_086, GL_CK_088, GL_CK_109, and GL_CK_123 jump from medium to high-risk class with increasing λ cutting value i.e., λ_0.70 and λ_0.75.

Table 4

Risk scores at different λ cutting values
Lake Id	Risk Score at λ_0.65			Risk Score at λ_0.70			Risk Score at λ_0.75
Lake Id	Low	Medium	High	Low	Medium	High	Low	Medium	High
GL_CK_031	0.08	0.13	0.79	0.06	0.05	0.88	0.04	0.02	0.94
GL_CK_034	0.08	0.13	0.79	0.06	0.05	0.88	0.04	0.02	0.94
GL_CK_038	0.00	0.21	0.79	0.01	0.11	0.88	0.01	0.05	0.95
GL_CK_045	0.02	0.07	0.92	0.01	0.03	0.96	0.01	0.01	0.98
GL_CK_050	0.00	0.21	0.79	0.01	0.11	0.88	0.01	0.05	0.95
GL_CK_076	0.12	0.31	0.58	0.12	0.15	0.72	0.09	0.06	0.85
GL_CK_077	0.00	0.41	0.58	0.01	0.26	0.73	0.01	0.14	0.85
GL_CK_081	0.04	0.62	0.34	0.06	0.45	0.49	0.09	0.26	0.65
GL_CK_086	0.29	0.36	0.35	0.32	0.19	0.49	0.28	0.07	0.65
GL_CK_088	0.33	0.52	0.15	0.42	0.33	0.25	0.45	0.16	0.39
GL_CK_091	0.34	0.52	0.15	0.42	0.33	0.25	0.44	0.17	0.39
GL_CK_094	0.12	0.30	0.59	0.12	0.16	0.72	0.10	0.06	0.84
GL_CK_096	0.11	0.30	0.58	0.12	0.15	0.73	0.09	0.06	0.85
GL_CK_101	0.03	0.18	0.79	0.03	0.09	0.88	0.02	0.03	0.95
GL_CK_105	0.12	0.31	0.58	0.12	0.15	0.72	0.09	0.06	0.85
GL_CK_109	0.29	0.36	0.34	0.32	0.19	0.49	0.28	0.07	0.65
GL_CK_118	0.34	0.08	0.58	0.25	0.03	0.72	0.15	0.01	0.85
GL_CK_123	0.14	0.52	0.34	0.18	0.33	0.49	0.19	0.16	0.65
GL_CK_125	0.23	0.19	0.58	0.20	0.07	0.72	0.13	0.02	0.85
GL_CK_134	0.11	0.30	0.58	0.12	0.15	0.73	0.09	0.06	0.85

Comparison with previous studies

Multiple glacial lakes in the CK basin have been identified as vulnerable glacial lakes in previous studies and summarised in Table 5. It is important to note that these studies used different assessment parameters and schemes for the identification of glacial lakes prone to GLOF. The current scheme uses consistent, exhaustive, and non-redundant criteria for assessing both the hazard as well as the risk associated with the glacial lakes providing a comprehensive assessment. The present scheme focuses on large-scale assessment and identification of GLOF hotspots and in such cases, it is better to err on the side of caution.

Table 5

Risk class identified for vulnerable glacial lakes in this study and its comparison with previous studies.
Sl. No.	Lake Id	Location	Present Study	Previous Studies
1.	GL_CK_031	27.87N 88.75E	High Risk
2.	GL_CK_034	27.91N 88.79E	High Risk
3.	GL_CK_038	27.97N 88.80E	High Risk
4.	GL_CK_045	27.82N 88.66E	High Risk	Mal et al. 2021, Islam and Patel. 2021
5.	GL_CK_050	27.96N 88.80E	High Risk	Worni et al. 2013
6.	GL_CK_076	27.86N 88.87E	High Risk
7.	GL_CK_077	27.86N 88.86E	High Risk	Dubey and Goyal. 2020, Mal et al. 2021
8.	GL_CK_081	27.81N 88.80E	Medium Risk
9.	GL_CK_086	27.83N 88.76E	Medium Risk
10.	GL_CK_088	27.85N 88.81E	Medium Risk	Mal et al. 2021
11.	GL_CK_091	27.87N 88.80E	Medium Risk	Worni et al. 2013
12.	GL_CK_094	27.86N 88.75E	High Risk	Mal et al. 2021, Islam and Patel. 2021, Aggarawal et al. 2017
13.	GL_CK_096	27.88N 88.74E	High Risk
14.	GL_CK_101	27.89N 88.76E	High Risk	Mal et al. 2021, Islam and Patel. 2021
15.	GL_CK_105	27.94N 88.78E	High Risk
16.	GL_CK_109	27.92N 88.67E	Medium Risk	Mal et al. 2021, Worni et al. 2013
17.	GL_CK_118	27.95N 88.70E	High Risk	Worni et al. 2013
18.	GL_CK_123	27.96N 88.71E	Medium Risk
19.	GL_CK_125	27.97N 88.73E	High Risk
20.	GL_CK_134	27.87N 88.86E	High Risk

CK basin has a high density of glacial lakes, as well as a high number of glacial lakes vulnerable to GLOF. These lakes are mostly moraine and bedrock dammed located at an above altitude of 4000 m. Understanding the hazard and risk posed by these lakes is important in the current scenario of climate extremes, high climatic variability, and the resulting glacial and geomorphological dynamics. 134 glacial lakes covering a total area of 4.75 sq. have been mapped in the CK basin using Sentinel 2A MSI imagery. A first-order hazard and risk assessment were carried out for the identification of glacial lakes vulnerable to GLOF based on multicriteria decision analysis (MCDA). Based on the assessment of 13 parameters 20 lakes were identified as glacial lakes with high vulnerability toward producing a GLOF. 14 of the 20 vulnerable glacial lakes in the basin were categorized into high-risk class and the other six lakes as medium-risk class. The risk posed by the vulnerable glacial lakes is accentuated by the presence of steep slopes, high hydro-meteorological variability, high seismicity, and the presence of a significant downstream population in the basin. Sensitivity analysis shows that even a slight lowering of the λ cutting values increases the number of lakes in the high-risk class to 18. This strongly demonstrates that even small changes in the criteria values of glacial lakes can significantly increase the risk associated with the lakes. The presence of a high number of vulnerable glacial lakes in the CK basin renders it a potential hotspot for future GLOF events. Regular monitoring of all the glacial lakes irrespective of their risk class thus becomes very important. This entails careful attention in terms of regular monitoring of all glacial lakes in the region through first-order assessments. These regular first-order assessments also need to be combined with detailed assessments of the vulnerable glacial lakes. The present MCDA scheme uses freely available data and open-source software and can be applied across regions with different lake types making it one of the most useful methods for regional first-order assessment and constant monitoring, especially in regions such as Sikkim Himalayas.

Authors’ Contributions:

Rajeev Rajak: Conceptualization, Methodology, Computation and modelling Writing-original draft, review, and editing; Rakesh K Ranjan: Supervision, Writing- review and editing; Adina E. Racoviteanu: Supervision, Writing- review and editing; Sargam Lohar: Computation; Krity Rai- Computation, Writing- review and editing; Bidyutjyoti Baruah: Preparation of maps, Review and editing; Thupstan Angchuck: Review and editing. Anirudra Rai-Computation; Khushboo Sharma: Review and editing; Aparna Gupta: Review and editing

Data availability

Most of the datasets used in the study are freely available. The multi criteria decision analysis criteria dataset for the glacial lakes prone to GLOF are available from the corresponding authors upon request.

Competing interests.

The authors declare no competing interests.

Acknowledgments

Authors acknowledge the Department of Science and Technology, Government of India funded Centre of Excellence on Water Resources, Cryosphere and Climate Change at the Department of Geology, Sikkim University for supporting this work (DST/CCP/CoE/186/2019 (G)). Rajeev Rajak acknowledges the Council of Scientific & Industrial Research (CSIR) for a Junior Research Fellowship for his Ph.D. work. We are also thankful to Copernicus Open Access Hub for providing the Sentinel 2A imagery, to worldclim.org for the climatic variables dataset and to U.S. Geological survey and NASA Land Processes Distributed Active Archive Centre (LP DAAC) for the ASTER GDEM data.

Funding

Rajeev Rajak is funded through the Council of Scientific & Industrial Research Junior Research Fellowship (CSIR-JRF) award 09/1114(0003)/2018-EMR-I). for his Ph.D. Centre of Excellence on Water Resources, Cryosphere and Climate Change at the Department of Geology, Sikkim University is funded by the Department of Science and Technology, Government of India (DST/CCP/CoE/186/2019 (G)).

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No competing interests reported.

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Assessment of glacial lake prone to glacial lake outburst flood using multi-criteria decision analysis (MCDA) in Changme Khangpu basin, Sikkim Himalaya, India

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Abstract

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1. Introduction

2. Materials And Methods

Study area

Data

Methodology

Glacial lake mapping

Multi-criteria decision analysis (MCDA) scheme

Sensitivity analysis

3. Results And Discussion

Glacial lake database

GLOF probability assessment

GLOF assessment in Sikkim Himalaya

Sensitivity analysis

Comparison with previous studies

4. Conclusion

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References

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