1CLIMATE CHANGE IMPACT ASSESSMENT ON

MELTING GLACIERS USING RS & GIS

GAGANDEEP SINGHROLL NO.- 131861M.TECH- RS & GIS(2013-2015)NIT-Warangal

2CONTENTS

INTRODUCTION

CLIMATE CHANGE - CONCERING ISSUES (GLACIAL MELT)

LITERATURE REVIEW

CASE STUDIES

RESULTS

REFERENCES

3WHAT IS CLIMATE

Climate refers to time and space patterns of precipitation, temperature, and wind.

Climate change is a significant and lasting change in the statistical distribution of weather patterns over periods ranging from decades to millions of years

Glacier is a persistent body of dense ice that moves constantly under its own weight, it forms where accumulation of snow is greater than ablation.

4CONCERNING ISSUE

Rising temperatures due to changing climatic conditions and Global Warming as one of the main reasons has resulted in melting of glaciers all around the world.

Investigations carried out by Intergovernmental Panel on Climate Change (IPCC) have concluded that the earth’s average temperature has increased by 0.6 ± 0.2°C in the 20th century.

This increase in temperature will continue in the 21st century and average surface temperature of the earth can rise by 1.4 to 5.8°C by the end of the century.

This projected rate of warming is much larger than the observed changes during the 20th century.

In Asia the Himalayan region has suffered the wrath of increasing annual temperatures, and from more than a decade now scientists are having a close eye on impact of climate change on Glacial Melt in the Himalayan Region.

5Mapping of glaciers A study - conducted from 1999 to 2004 - generated important

baseline information about glaciers based on the World Glacier Monitoring Service (WGMS) attribute list.

It included the Hindu Kush-Himalayan countries of Bhutan, Nepal, Pakistan, the Ganges Basin in the Tibet Autonomous Region of China, and selected river basins in India (Tista River basin, Himachal Pradesh, and Uttarakhand).

The study identified more than 15,003 glaciers within the surveyed area of 33,344 sq.km.

Majority of glaciers in the region were in a general condition of retreat.

6LITERATURE REVIEW Mapping and monitoring of glaciers has become easier with the advent of

more readily available remote sensing data.

A.V.Kulkarni & J.Srinivasulu (2002) conducted field based spectral reflectance studies - developed NDSI method based on - high reflectance of snow in visible region & low reflectance in SWIR region.

Snow cover monitoring - radiometers developed by SAC-ISRO viz.,

S.No RADIOMETER BANDS

1 Multi-band Ground Truth Radiometer (GTR)

12 spectral bands ranging from visible to near-infrared wavelengths

2 Near-Infrared Ground Truth Radiometer (NIGTR)

SWIR range

3 Ratio-Radiometer (RR) 2 spectral bands, one in the visible range, and another in the SWIR range

This study validates the usefulness of NDSI based method for discriminating snow and clouds on a satellite image.

7LITERATURE REVIEW continued.. B.P.Rathore & Anil Kulkarni (2008) calculated Snow line & future change in areal

extent of glacier was estimated using mass balance, response time and rate of melting at terminus and for permanent snow cover IRS 1D LISS III images were used.

Average areal extent of snow for each month was estimated, using multi-date satellite data of WiFS and Advance Wide Field Sensor (AWiFS) of Indian Remote Sensing Satellite (IRS).

To estimate seasonal snow cover, supervised classification for WiFS and NDSI method for AWiFS was used.

Prajjwal K. Panday & Karen E Frey (2011) studied detection of the timing and duration of snowmelt in the Hindu Kush-Himalaya using QuikSCAT, 2000–2008.

This study provides an overall perspective of regional differences in melt onset, freeze-up, and melt duration that have important implications for glaciological and hydrological processes across the HKH region.

They developed a dynamic threshold based melt detection algorithm calibrated with in situ air temperature records to identify melt and freeze-up events from enhanced resolution data from time series of QuikSCAT radar scatterometer data.

8LITERATURE REVIEW continued.. Prajjwal K. Panday & Henry Bulley (2012) proposed an approach of

glacial lake classification and demonstrated utility of this approach in producing a semi-automated and replicable technique for supraglacial characterization and delineation of glacial lakes.

Supraglacial debris poses a major challenge in automatic delineation of glaciers due to the spectral similarity to the surrounding debris.

Kathryn Semmens & Joan Ramage (2014) analyed brightness temperatures from the passive Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E) used to characterize melt regime patterns over large glacierized areas in Alaska and Patagonia.

9CASE STUDY 1

TITILE- Monitoring of Gangotri glacier using remote sensing & ground observations

AUTHORS- H.S. Negi, N.K. Thakur, A. Ganju, Snehmani

JOURNAL- Journal of Earth System Science(2012)

In this study, Gangotri glacier was monitored using Indian Remote Sensing (IRS) LISS-III sensor data in combination with field collected snow-meteorological data for a period of seven years (2001–2008).

10STUDY AREA & DATA USED Gangotri glacier - longitude 78°59'30" and 79º17'45"E,

latitude 30º43'00" and 30º57'15"N

The total study area - approx. 417 km² (54% glaciated).

Length – apprx. 30 km.

LISS-III sensor data - IRS-1C/1D satellites - monitor snow cover

33 multitemporal cloud-free satellite imageries - Oct 2001 to May 2008

PAN imagery of IRS satellite (for the mapping of latest glacier extent)

False colour composite (FCC) of visible and near-infrared images used to map glacier boundary, due to significant difference in reflectance from glacial and non-glacial features.

• Green (0.52–0.59 μm), • Red (0.62–0.68 μm), • NIR (0.77–0.86 μm)• SWIR (1.55–1.70 μm) wavelength regions. • 23.5 m spatial resolution in first three bands (VNIR)• 70.5 m spatial resolution in SWIR band• swath of 141 km.

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Study area shown by IRS-1C, LISS-III image of 9 August 2005; Pink polygon represents Gangotri sub-basin; Green polygon represents glacier boundary based on SoI mapsheet of 1962 and yellow points are settlements.

12METHODOLOGY

The generation of digital database and preprocessing of LISS-III satellite data were carried out in ERDAS imagine.

The normalized difference snow index (NDSI) - high and low reflectance of snow in visible(green) & shortwave infrared (SWIR) regions - snow cover area

NDSI - discriminates between snow and cloud - cloud reflectance is high in SWIR region.

The snow versus non-snow area was classified using the NDSI threshold value of 0.4, which is the value generally reported for multispectral data

13METHODOLOGY cont…

The snow cover analysis - satellite & ground data on seasonal basis. (Daily snow-melt data such as temperatures (maximum and minimum), fresh snowfall and rainfall recorded from field observatory were analyzed for comparison/ validation of snow cover area and snow characteristics).

The topographic analysis - A DEM of the study area was generated at 6 m pixel resolution using contours and spot heights of 1:50,000 scale map sheets.

The images were classified - unsupervised classification

14RESULTS The digital database of Gangotri glacier consisting different GIS layers

such as glacier boundary, sub-basin boundary, and settlements generated using mapsheets and satellite imageries

The seasonal snow cover area analysis shows an overall decreasing trend for winter.

An upward shifting trend of wet snow line was observed in the beginning of melt period, i.e., in May and dominant wet snow conditions were observed between May and October.

The change in glacier health is effectively confined to the abrupt slope changes, such as the walls of open crevasses and other holes on the glacier surface.

This study has shown that the changes on glacier surface are due to climatic and topographic (local geomorphology) factors, A deglaciation of 6% in overall area of Gangotri glacier was observed between the years 1962 and 2006.

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Estimated snow cover area using satellite data of(a) winter season (November–April) and (b) summer season(May–October).

Comparison of estimated dry and wet snow coverarea using satellite data of (a) winter season (November–April) and (b) summer season (May–October).

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Changes in glacio-morphology constrained by topography. (a) LISS-III data of 10 October 2001; (b) LISS-III data of 9 October 2006; (c) change detection image with red colour indicating snow class present in earlier image changedinto another class/decreased reflectance in later image; (d) Cartosat-1 image of 28 September 2006.

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18CASE STUDY 2

TITILE- Glacier Change in Mount Suphan Using Remote Sensing and Meteorological Data

AUTHORS- Dogukan Dogu Yavalı, M. Kirami Olgen

PUBLISHER- BALWOIS 2008, Conference on Water Observation and Information System for Decision Support

Mount Suphan was studied using Landsat images. MSS false colour composite images and TM4/TM5 band combination used for delineating glaciers, and applied the change detection techniques to determine the change on glacial area.

Also, the results were compared with meteorological data & it was concluded that there was a recession in Mount Suphan glaciers due to global climate change.

19STUDY AREA & DATA USED Mt. Suphan is a stratovolcano located in eastern Turkey

It is the second highest volcano in Turkey

with an elevation of 4,058 m

The largest glacier on the Mount Suphan is

Hızır Glacier located on the northern slope

of the crater

Glacier is 1.5 km long, 3400 meters high

Length of the glacier is 1.2 kilometers.

20

The LANDSAT data available on NASA’s GLCF for Mount Suphan

Colour composite of Landsat images: a) false-colour (321=RGB) MSS image, b) TM image (543=RGB), c) ETM image (543=RGB). Except for the image in 1989, the images are acquired during a time of minimum snow cover. Cyan colour represents snow in the image of 1989.

21METHODOLOGY

Different kinds of methods for mapping or monitoring the glaciers with remote sensing

(A) Manual delineation of the glacier outline from different colour combinations,

(B) Segmentation of ratio images

(C) Various supervised and unsupervised classification techniques.

MSS image - manual delineation - FCC

TM image (snow covered) - the glaciers have to be distinguished from snow. Glaciers are the results of metamorphosis of snow, (spectral properties very similar).

22METHODOLOGY cont… Dividing near infrared (0.76-0.90 μm) part of spectrum by middle

infrared (1.55-1.75 μm) part - band ratio techniques.

Result- high values over ice and snow

low values over other terrain,

Formula for TM is TM4/TM5.

Then, snow and glacier was separated from other terrain with a

threshold.

After defining the glaciers, the outline of it was manually digitized and the areas are calculated in ArcGIS 9.2.

Meteorological data obtained from Turkish State Meteorological Service was used to determine the change in climatic conditions.

Trend analysis

23RESULTS

Areal change of glaciers on Mount Suphan. The colored lines show the extent of glacier forthe dates indicated in the legend. The background image is ETM (543=RGB) image of the year 2000.

Image AnalysisThe total glacier area in Mount Suphan ,1977 with 1.20 km² was retreated to 1.09 km² in 1989, and 0.33 km² in 2000

24Climatic Factors

Trends of two variables, temperature and precipitation, are responsible for glacier regime and mass balance.

The assessment of annual maximum temperatures show little decrease trend in 61 years

However, annual minimum temperatures are clearly in an increasing trend .

trend in annual maximum temperature series

trend in annual minimum temperature series

25 To sum up, the qualitative analysis of satellite imagery of Mount

Suphan shows clear recession of glaciers. 3/4 of total glacier area lost in 23 years.

The analysis of climatic factors shows this recession is coherent with the warming trend.

26SUMMARY Presently, the most successful strategy is based on the

combination of remote sensing, modelling with Geographical Information Systems (GIS) or numerical models and other local field surveys to study the glaciers and the impact of climate change on their melting.

The use of remote sensing data and techniques and geographic information system (GIS) data, complemented by field verification, is an effective method for the mapping and inventorying of glaciers.

27REFERENCES Field based spectral reflectance studies to develop NDSI Method for snow

cover monitoring- A.V. Kulkarni, J. Srinivasulu , S.S. Manjul , P. Mathur - JOURNAL OF INDIAN SOCIETY OF REMOTE SENSING, 2002

Effect of global warming on snow ablation pattern in the Himalaya- D. T. Mourya et al. Current Science, Vol. 83, No. 2. 2002.

Understanding future changes in snow and glacier melt runoff due to globalwarming in Wangar Gad basin, India - B.P.Rathore & Anil Kulkarni – CURRENT SCIENCE JOURNAL OF RESEARCH, 2008

Glacier Change in Mount Suphan Using Remote Sensing and Meteorological Data- D. D. Yavalı & M. Kirami Olgen , BALWOIS 2008, Conference on Water Observation and Information System for Decision Support, 2008

Detection of the timing and duration of snowmelt in the Hindu Kush-Himalaya using QuikSCAT - Prajjwal K. Panday & Karen E Frey – IOP SCIENCE, ENVIRONMENTAL RESEARCH LETTERS, 2011

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Supraglacial lake classification in the everest region of Nepal- Prajjwal K. Panday & Henry Bulley - SPRINGER LINK, 2012

Monitoring of Gangotri glacier using remote sensing & ground observations- H.S. Negi, N.K. Thakur, A. Ganju, Snehmani Journal of Earth System Science, 2012

Melt Patterns and Dynamics in Alaska and Patagonia Derived from Passive Microwave Brightness Temperatures - Kathryn Semmens & Joan Ramage - MDPI, REMOTE SENSING JOURNAL, 2014