Glacial Lake Outburst Flood (GLOF) risk mapping in Hunza River Basin (Pakistan) using geospatial

techniques

Arjumand Z. Zaidi Department of Remote Sensing and Geo-information

Sciences, Institute of Space Technology Karachi, Pakistan

Arjumand.zaidi@ist.edu.pk

Zeenat Yasmeen Department of Remote Sensing and Geo-information

Sciences, Institute of Space Technology Karachi, Pakistan

zeenat.raza@yahoo.com

Mohammad Danish Siddiqui Department of Remote Sensing and Geo-information Sciences, Institute of Space Technology

Karachi, Pakistan danish769@yahoo.com

Abstract— In Hindu Kush Himalayas (HKH) region,

observations on glacial fluctuations indicate substantial retreat of glaciers, especially in Pakistan, Nepal, India and China. Recent global temperature rise has allegedly been responsible for the depletion of these glaciers and consequently creation of lakes on their terminus. Several of these lakes have burst and caused flooding or Glacial Lake Outburst Floods (GLOFs) in the recent past. GLOFs have a potential of releasing millions of cubic meters of water in a few hours causing catastrophic flooding downstream and damaging whatever comes into their way. Hazard mapping of elements at risk may enhance the capacity of the vulnerable communities to face these disasters in order to reduce their devastating impacts. Study of GLOFs hazard in Hunza River basin of Pakistan using geospatial techniques consisting of satellite remote sensing, Geographical Information System (GIS), hydraulic and hydrology tools is proposed in this paper. Geospatial techniques have a capacity to acquire information regarding the status of glaciers and glacier lakes at spatial and temporal resolutions beyond the capability of infrequent and point scale in-situ monitoring. Remotely sensed satellite data along with ground based data are used in this study for risk mapping of Passu Lake in Hunza River basin, Pakistan. The outcomes of the proposed study will be helpful for GLOFs risk management and an overall strategy to address possible risks from future GLOFs events in the country.

Keywords—galcial lake outburst flood (GLOF); remote sensing; GIS; hydrologic and hydraulic modeling, risk map

I. INTRODUCTION Glacial Lake Outburst Flood (GLOF) occurs when a dam

containing a glacial lake fails. This is mainly due to the glaciers retreat. As glaciers retreat, glacial lakes are formed behind moraine or ice dams or inside the glaciers. A sudden breach in its walls may lead to a discharge of huge volumes of water and debris. Several of such lakes have been burst in the recent past resulting in a loss of human lives and destruction and damages of infrastructure in the valleys below. Glacier-outburst floods cannot be predicted and therefore, a continuous monitoring and

mapping, both spatial and temporal, as opposed to a limited frequency point measurement can reduce the devastating impact of such hazards. Sometimes it is not easy to avoid natural phenomena causing disasters such as GLOFs, but a prior knowledge about their nature and possible extent can develop a capacity of disaster management authorities to respond and recover from emergency and disaster events. Similarly, hazard maps cannot stop a disastrous event from happening but an effective use of hazard maps can prevent an extreme event from becoming a disaster. This paper proposes a mechanism for reliable and cost effective GLOF hazard mapping and damage assessment using advanced geospatial hydrologic/hydraulic modeling techniques in Hunza River basin.

A. Nature of the Problem Recent global warming has allegedly been responsible for

many alterations in the natural phenomena in many regions of the world. Few of the impacts of the recent climate change in the Hindu Kush Himalayas (HKH) are the creation of glacier lakes on the lower sections of these glaciers. Several of these lakes have burst in the recent past causing Glacial Lake Outburst Floods (GLOFs) that resulted in a loss of human lives and destruction and damages of infrastructure in the valleys below. GLOFs may develop abruptly in a river with no history of outburst flooding and for that reason magnitude and frequency of glacial-outburst floods cannot be predicted using standard statistical methods. Due to remote locations of such lakes, a continuous spatial and temporal monitoring required to reduce the devastating impact of GLOFs is very challenging given the rugged terrains and harsh weather conditions. Remote Sensing (RS) techniques have a capacity to acquire information at spatial and temporal resolutions beyond the capability of infrequent and point scale in-situ monitoring. Utilizing this capability of RS and GIS techniques status of glaciers and glacier lakes can be continuously monitored.

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B. Background of Previous Work According to World Meteorological Organization (2011),

the first decade of the 21th century (2001-2010) was the warmest decade recorded over the globe. Due to rise in global temperature, most of the world glaciers are subject to depletion with a few exceptions and are posing serious challenges to water security [1]. The heavily glacierized region of Pakistan has also experienced a similar trend of rising annual mean temperatures along with significantly increased heat waves over the southern slopes of HKH [2]. Rapid retreat of glaciers increases run-off and contributes to the growth of glacial lakes [3] that may burst and destroy downstream river valley. These GLOFs have adverse socioeconomic impacts and are massive threats for downstream located elements at risk including, human lives, infrastructure, natural forests, agriculture lands, settlements, and other assets.

International Centre for Integrated Mountain Development (ICIMOD) has developed an inventory of glaciers, glacier lakes, and potential GLOFs affected by global warming in mountains of Himalayan regions. Around 2,420 glacial lakes are identified in the HKH region of Pakistan of which 52 lakes are declared as potentially dangerous lakes [4]. This inventory is very useful for study of GLOFs in the HKH region of Pakistan. The proposed study is based on one of these dangerous glacier lakes at the terminus of Passu Glacier in Hunza Watershed. Passu Lake had two (02) outbursts in last two (02) decades blocking Karakoram Highway (KKH) and destroying settlements located along Hunza River [5]. Since glacier lakes may burst abruptly without any prior history of flooding and for that reason a continuous monitoring and mapping, both spatial and temporal, is required to reduce the devastating impact of such hazards. Remote Sensing and GIS techniques along with hydraulic and hydrologic simulations have a capacity to acquire information regarding status of glaciers and glacier lakes at spatial and temporal resolutions and to map flood extent. Several studies, around the world, have already used satellite images for monitoring glaciers and glacier lakes and hydraulic and hydrologic models (HEC-RAS, HEC-GeoRAS, HEC-HMS, HEC-GeoHMS, Arc Hydro and others) for GLOFs mapping [6], [7], [8], [9], [10]. Study of GLOFs hazard in Hunza watershed of Pakistan using satellite remote sensing, GIS, hydraulic and hydrologic tools is proposed here.

C. Purpose and Significance A broader area can be monitored or mapped utilizing satellites data and extent of glaciers and presence of glacier lakes can be detected at selected sites. The main purpose of this research was monitoring, and risk and hazard mapping of dangerous glacial lake in Hunza valley utilizing RS, GIS and hydraulic/hydrology tools and techniques. Passu Lake has natural drainage and apparently the probability of its outburst is low [11] but the expected losses in case of GLOF are huge. The outcomes of the proposed study are helpful to address possible risks from future GLOFs events.

D. Addressing the Problem The study presented in this paper includes temporal

mapping of glacial lake extent at the study area using RS and GIS tools based on satellite images and available maps and

reports. Passu Lake at Hunza river basin has a past history of flooding at a random interval of 2-5 years. The severity of the potential flooding of Passu Lake may be assessed for several criteria like prior history of flooding, area, lake expansion rate, surrounding environment, socio-economic parameters such as economic activities in downstream river valleys, and downstream settlements and infrastructure. Hydrologic tools and hydraulic simulation models, integrated with GIS, were used to perform watershed based analyses to delineate catchment area and runoff flow pattern and flood mapping. State-of-the-art software packages include, but not limited to, ERDAS Imagine, ArcGIS 10, ArcHydro, HEC-GeoRAS and HEC-RAS. Satellite data used in this study were derived from various satellite systems (Landsat TM, and Advanced Spaceborne Thermal Emission and Reflection Radiometer- ASTER Global Digital Elevation Model). Salient features of the proposed project are:

1. Hydrology and hydraulic modeling for delineation of drainage basin and assessing impact of GLOF downstream

2. Identification of potential risk areas and element at risks downstream due to flooding.

II. MATERIALS AND METHODS

A. Study Area The northern Pakistan is a mountainous region and

numerous glaciers and glacial lakes are found in high mountain range of Hindu Kush Himalayas (HKH) region. Glacier and glacial lakes have been a hazard to people and property located downstream [12]. According to ICIMOD’s inventory published in 2005, a total of 2,420 glacial lakes have been identified in ten (10) river basins of HKH region of Pakistan. The maximum glacial lakes are identified in Gilgit River basin (614) followed by Indus (574), Swat (255) and Shingo (238) River basins. In Gilgit River basin out of 614 glacial lakes, 380 lakes have been characterized as major lakes which are about 62 % of the total lakes. These major lakes contribute about 93 % of the lake area of the basin. Glacier thinning and retreat in HKH region has resulted in the formation of new glacial lakes, expansion of existing lakes and glacier lakes outburst floods (GLOFs). There were 52 potentially dangerous lakes identified in Pakistani HKH mountain ranges. The site selected for this study is Passu lake in Hunza River watershed. Passu Lake is located at the terminus of 38 Km long East-West oriented Passu glacier in HKH region. Passu subwatershed within which Passu Lake is situated was selected for floodplain mapping (Figure 1).

B. Methodology 1. Hydrologic and Hydraulic Modeling

Drainage basin, subbasins and stream network of Hunza River watershed were delineated using 30 meter resolution ASTER Digital Elevation Model (DEM) and Arc Hydro software. Arc Hydro is a geospatial and temporal data model for water resources that operates within Environmental Systems Research Institute’s (ESRI) GIS mapping software ArcGIS [13]. The DEM processing is necessary to remove misleading elevation values and was done using ‘Fill Sink’

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function of Arc Hydro. Watershed and stream network delineation were performed with Arc Hydro after DEM processing.

Hydraulic modeling of Passu Lake and Hunza River was done for flood mapping and to evaluate the downstream impact

of GLOF. Hydraulic simulation models HEC-RAS and HEC-GeoRAS were used for this purpose. HEC-RAS, developed by the U.S. Army Corps of Engineers Hydrologic Engineering Center (HEC), is designed to perform one-dimensional hydraulic modeling [13]. Hunza River reach flowing through Passu subwatershed was used for creating HEC-RAS geometry

Figure 2: HEC-GeoRAS Geometry Layers

Figure 1: Study Area

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layers in HEC-GeoRAS that has GIS interfacing capabilities (Figure 2). HEC-GeoRAS processes geospatial data in ArcGIS for preparing geometric layers to be imported into HEC-RAS. The layers created for this study were stream centerline, channel banks, flow paths, and river cross sections.

The geometric layers created for this study were imported in HEC-RAS for further analysis. HEC-RAS is capable of analyzing both steady flow and unsteady flow data. For this study steady flow data analysis was performed for calculating flood inundation area and water depths at various predefined cross sections. Manning roughness coefficient was arbitrary selected as 0.03 for main channel and 0.04 for floodplain. A higher roughness coefficient for floodplain was selected to account for higher resistance to flow in vegetated areas. Although a multiple number of flow profiles can be added in the model but a single flow profile was used here based on probable maximum discharge. For calculating flow or discharge an empirical relationship developed by Huggel et al. [14] was used where volume, V (in m3), of a glacial lake can be expressed as a function of lake area A (in m2):

V = 0.104A1.42 (1)

The above relationship is based on glacial lakes data in North America, South America, the Himalayas, Iceland, and the European Alps [10]. Area of Passu Lake was determined by digitizing the lake extent in April 2005 Google Earth image. Comparing other images of different time periods (both Google Earth and Landsat TM), the April 2005 image showed the maximum lake area. More recent images were analyzed but either the area was smaller or the images had cloud covers. The lake area determined from 2005 Google Earth image was 125,300 m2. Using equation (1), the volume of Passu Lake was estimated to be 1.8 x 106 m3. Equations (2) and (3) were used to calculate maximum discharge assuming the worst case scenario when a GLOF event will cause bursting out of all water in the lake [7].

PE = 9800 x h x V (2)

Qmax = 0.00013 x PE0.6 (3)

Where:

h = Height of the moraine dam (approximately 46 m from the top to the bottom of the dam, measured from Google Earth image)

PE = Potential energy of the lake water (calculated to be 8.1 x 10 11 J)

Qmax = Maximum probable flow (calculated to be 1,817.6 m3/s)

After entering flow data, reach boundary conditions were defined. Downstream slope of 0.004 m/m for normal depth computation of reach was selected and model run command was executed. The water surface profile data was exported from HEC-RAS model and converted into GIS layers (flood extent and inundation) by HEC-GeoRAS. Finally floodplain delineation was done by subtracting the terrain grid (DEM) from water surface grid for the given flow profile.

2. Risk Mapping of Potential Risk Areas

Identification of potential risk areas downstream due to flooding was done by overlapping the GIS layers of elements at

risk with flood inundation map. The elements at risk used for this study were settlements and roads/highways. For future studies, agricultural areas, trees and utilities will also be added for risk evaluation.

III. RESULTS AND DISCUSSIONS

Risk map was developed by overlay of element at risk layers with flood map. Potential risk areas are shown in Figure 3. Segments of Karakoram highway are within risk zones and few settlements along the river have potential to get effected by future GLOF events. Hunza River blockage due to landslide in 2010 had also resulted flooding in the area downstream of Passu Lake. The flood water is still not completely drained out and the situation may get worse if flood water after GLOF also reaches there.

The flood inundation was based on moraine dam bursting and releasing all water in the lake. Though this may be considered as the worst case scenario but precipitation data from extreme rain events has not been considered here and therefore its influence is yet to be analyzed. Also high resolution data should be used to refine this analysis that may enhance the estimation of GIS data like lake area, dam height, locations of elements at risk, and other related geometric parameter.

IV. CONCLUSIONS The proposed study will develop a mechanism for reliable and cost effective monitoring of glacier lakes and GLOFs in near real time, hazard mapping and risk and damage assessment using advance geospatial hydrologic/hydraulic modeling techniques. Knowledge of risk is essential for all disaster risk reduction activities either these are through policies, development planning, or other structural or non structural control measures. Hazard and risk maps are used in all phases of disaster management (DM); Mitigation, Preparedness, Response and Recovery. The hazard and risk maps cannot stop a disastrous phenomenon from happening but an effective use of hazard maps can prevent an extreme event from becoming a disaster. Sometimes it is not easy to avoid natural phenomena causing disasters such as GLOFs, but a prior knowledge about their nature and possible extent can develop a capacity of DM authorities to respond and recover from emergency and disaster events. Greater capacity to face these disasters also reduces their impacts. The vulnerable areas include portions of Karakoram Highway and some villages downstream to Passu Lake along Hunza River. Any future GLOF event due to Passu Lake outburst may cause damages to these elements. The outcomes of this study will be helpful in reducing the adverse impacts of future GLOFs events in Passu subwatershed and will be useful for developing early warning systems in the vulnerable areas.

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[3] S. R. Bajracharya, and P. Mool, “Glaciers, glacial lakes and glacial lake outburst floods in the Mount Everest region- Nepal.” Annals of Glaciology 50(53) PP. 81-86, 2009.

[4] ICIMOD, “Inventory of the Glaciers and Glacial Lakes of HKH Region,” 2005.

[5] R. Roohi, A. Ashraf, N. Mustafa and T. Mustafa, “Preparatory assessment Report on Community Based survey for Assesment Glacial lake Outburst Flood hazards in Hunza River Basin.” PP. 44, 2008.

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[7] Weicai Wang, Xiaoxin Yang and Tandong Yao, “Evaluation of ASTER GDEM and SRTM and their suitability in hydraulic modelling of a glacial lake outburst flood in southeast Tibet.” Hydrol. Process. 26, 213–225, 2012.

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[10] Christian Huggel, Wilfried Haeberli, Andreas Kääb, Daniel Bieri, and Shaun Richardson, “An assessment procedure for glacial hazards in the Swiss Alps.” Can. Geotech. J. 41: 1068–1083, 2004.

[11] G. Rasul, Q. Z. Chaudhry and A. Mahmood, “Glaciers and Glacial Lakes under Changing Climate in Pakistan.” Pak. J. Met. Vol.8, Issue 15. PP. 01-07, 2011.

[12] ICIMOD, “Glaciers in the Hindu Kush Himalayas new inventory and data released,” 2011.

[13] David R. Maidment, Archydro: Gis for Water Resources, ESRI Press, 2002.

[14] Christian Huggel, Andreas Kääb, Wilfried Haeberli, P. Teysseire, and F. Paul, “Remote sensing based assessment of hazards from glacier lake outbursts: a case study in the Swiss Alps.” Canadian Geotechnical Journal, 39: 316–330, 2002.

Figure 3: Risk Mapping of Passu subwatershed

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