INVESTIGATING GLACIAL ANOMALIES OF KARAKORUM AS AN EVIDENCE OF CLIMATE CHANGE: A CASE STUDY OF BATURA GLACIER, PAKSITAN.
By:
Tehmina Aziz
Reg. 16F-US-ETH-4650 Session: 2016-18 A Thesis Submitted in Partial Fulfillment of the Requirements for the degree of M.Phil in Geography
Department of Earth Sciences, University of Sargodha, Sargodha, Pakistan
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DECLARATION LETTER
I hereby submit that the research work reported in this thesis titled “Investigating Glacial Anomalies of Karakorum as an evidence of Climate Change: A Case Study of Batura Glacier, Pakistan”. By me and nothing is copied/ stolen/ Plagiarized from any source.
Name: Tehmina Aziz Session: 2016 - 2018 Program: M.Phil. in Geography
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CERTIFICATE OF ORIGINALITY OF RESEARCH WORK
I hereby certify that the research work reported in this thesis titled
“Investigating glacial anomalies of Karakorum as an evidence of
climate change; A case study of Batura Glacier, Pakistan.”
” by Tehmina Aziz Session 2016 - 2018 has been carried under my supervision in partial fulfillment of requirement for the award of degree of M.Phil. in the subject of
Geography and is hereby approved for submission it is further certified that the research work carried out by the scholar is original and nothing is plagiarized in it.
SUPERVISOR
Ms. Asma Shaheen Hashmi Assistant Professor Department of Earth Sciences University of Sargodha.
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ACKNOWLEDGMENT
I am very grateful to Allah Almighty Who is the master of the Day of Judgment and the inventor of all mankind. There are no suitable words for His enormous sanctifications because He bestowed me the courage and boldness to complete my thesis work. I am also appreciative to my affectionate, caring and kind Parents and
Husband to provide me full sustenance during my research work through all possible resources with great persistent. I owe the obligations of appreciation to Department of
The Earth Sciences, which provided me this chance to complete my research in the emerging, evolving and developing field. I am grateful to the research supervisor,
Asma Shaheen Hashmi for her great efforts, direction, appreciated recommendations and kind supervision made this possible. I am thankful to the Chairman of Earth
Sciences Dr. Omer Riaz who provided me the chances to acquire imaginative skills and offering abilities for well preparation for my research.
I would also like to thank the organizations such as PMD, Meteorological
Department of Lahore for providing me the datasets of their meteorological stations. I am highly indebted to a web-source i.e www.meteoblue.com who works in cooperation with the University of Basel, Switzerland. They generate simulation- based data on NOAA/NCEP models. The meteoblue provided me statistical datasets of four meteorological stations free of cost. I highly appreciate their special support for research purpose.
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DEDICATIONS
To
Allah Almighty The Most Gracious and kind, To Prophet Hazrat Muhammad (PBUH)
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Acronyms
Notation Representation
DIP Digital Image processing
DEM Digital Elevation Model
ETM Enhanced Thematic Mapper
GIS Multispectral Scanning Band
MSS Geographical Information system
NASA National Aeronautics and Space
Administration
NDBI Normalized Difference Built -up
NIR Near Infrared Rays
PMD Pakistan Meteorological Department
PCA Principal Component Analysis
RS Remote Sensing
SUPARCO Space and Upper Atmospheric Research Commission
VIS Visible Infrared Band
VPD Vapor pressure deficit
WAPDA Water and Power Development
Authority
HKH Hindukush, Karakorum, Himalaya
IPCC Intergovernmental Panel on Climatic Change
ENSO El Nino or Southern Oscillation
GHG Green House Gas
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CONTENTS
Declaration Latter ii
Certificates of Originality of Research Work iii
Acknowledgement iv
Dedication v
Acronyms vi
Contents vii
List of Tables xii
List of Figures xiii
Abstract xvi
CHAPTER: 1 INTERODUCTION 1
1. Introduction 1 1.1 Rational of the Study 1 1.2 Climate Change 2
1.3 Climate Variability in the World 2
1.4 General Effects of Climate 3 1.5 Effects of Regional Climate Variability 4 1.6 Climate variability in Pakistan 5 1.7 Northern Pakistan in Prospects of Climate Change 6
1.8 Glaciers of Pakistan 7
1.9 Glacial Anomalies in Northern Areas of Pakistan 7
1.10 Factors Affecting to Control Glacial Anomalies 8
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1.11 Climate Variability and Glacial Anomalies 8
1.12 Problem Statement 9 1.13 Study Area 9 1.14 Objectives 10 1.15 Research Questions: 11
1.16 Significance of the Research 11 CHAPTER: 2 LITERATURE REVIEW 12
2.1 Introduction 12
2.2 Monitoring Karakoram Anomaly 12
2.3 Batura Glacier and its anomalies 14
2.4 Factors effecting Batura Glacier 15
2.5 Climate change and glacial anomalies 17
2.6 Climate change and its impact on glacial melting 19
2.7 Glacier assessment by Remote Sensing methods 21
2.8 Glaciers measurement 22
2.9 Glacier retreat and melting measurement 24
2.10 Glacier mass balance 26
CHAPTER: 3 MATERIAL AND METHODS 28
3.1 Methodology 28
3.1.1 Meteorological data 28
3.1.2 Rainfall /Snowfall Data 29
3.1.3 Temperature Data 29
3.2 Satellite Data 30
3.2.1 Satellite Images 30 3.2.2 Digital Elevation model 30
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3.2.3 Land use land cover data 30
3.3 Spatial analysis and methods 31 3.3.1 Rainfall Anomaly Analysis 31 3.3.2 Temperature Anomaly Analysis 31 3.3.3 Normalize Difference Vegetation Index 32
3.3.4 Snow cover index 33
3.3.5 Glacier Index
3.3.6 Slope elevation and aspect analysis of glacier by DEM 34
3.3.7 Glacier anomalies analysis 34 3.4 Precipitation Anomalies Index 34 3.4.1 Procedure of Calculation Anomalies: 35 3.4.2 Correlation Analysis 36 3.5 Regression Analysis 37 3.6 Meteorological Observation Over Batura (Lat,Long) 37 3.6.1 Introduction to the Meteoblue data gathering 37 3.6.2 Introduction to the website 37 3.6.3 Types of Data Sets 38 3.6.4 Formats of data 38 3.6.5 Data Geration 38 3.6.6 Simulation data 38 3.6.7 Limitations of Simulation data 39 3.6.8 Verification 39 3.6.9 Formats of Data 39 3.6.10 Historical Data 39 3.6.11 Meteograph 40 3.6.12 Meteoblue Maps 40
CHAPTER: 4 RESULTS AND DISCUSSION 41
4.1 Vector Data Set Analysis 42
4.4.1 Total Precipitation of Bunji 42
4.1.2 Total precipitation of Astor ix 43
4.1.3 Total precipitation of Gupis 44
4.1.4 Total precipitation in Gilgit 45
4.1.5 Total precipitation of Skardu 45
4.2 Average monthly precipitation in the surrounding of Batura 46
4.2.1 Astor average monthly Precipitation 46
4.2.2 Bunji average monthly precipitation 48
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4.2.3 Gilgit average monthly rainfall 49
4.2.4 Gupis average monthly precipitation 50
4.2.5 Skardu average monthly rainfall 51
4.3.1 Vector data analysis of Temperature Precipitation and Snow of Batura 52
4.3.2 Batura Monthly Temperature in °C since 1985 to 2018 53
4.3.3 Batura total Precipitation 55
4.3.4 Batura Monthly precipitation 52
4.4.5 Batura total Snowfall 56
4.4.6 Batura monthly snowfall in cm during 1985 to 2018 57
4.4.8 The daily mean temperature of the Batura glacier in 1985 to 2018 60
4.4.9 Batura daily total precipitation during 1985 to 2018 60
4.4.10 Batura total daily rainfall since 1985 to 2018 62
4.4.11 Relationship between Temperature and Area under snow 63
4.4.12 Relationship between Area under Snow and amount under snow 64
4.5 Raster Analysis 65 4.5.1Batura Glacier Inventory (True Color Imagery) 65 4.5.2. Batura Glacier 66
4.5.3 Snow cover index 67
4.4 Land cover and land use classification in 1988, 2000,2008, and 2016 68
4.5 Vegetation cover of Batura during 1988 to 2016 69
4.5.1 Batura Land cover 71
4.6 Precipitation Anomalies Index of Batura Glacier 71
4.6.1Annual Precipitation anomalies of Batura Glacier 71
4.6.2 Seasonal Precipitation Anomalies of Batura Glacier 72
4.6.3Monthly Precipitation Anomalies of Batura Glacier 74
4.7 Temperature Anomalies of Batura 76
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4.8 Monthly Anomalies of Temperature in Batura 77
4.9 Seasonal Distribution of Precipitation from 1989 to 2018 78
4.9.1 Batura Precipitation in Monsoon season 78
4.9.2 Batura Precipitation in Western Disturbance season 79
4.9.3 Batura Precipitation in Thunderstorm season 80
4.8 Topographic Examination 81
4.8.1 Contouring 81
4.8.2 Digital Elevation Model (DEM) 82
4.8.3 Watershed 84
4.9 Results 84
CHAPTER: 05 SUMMERY, CONCLUSION AND 88 RECOMMENDATIONS
5.1 Summary 88
5.2 Conclusion 92
5.3 Recommendations 94
6. REFERENCES 97
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List of Tables
Table Title Page
Table 3.3: Satellite Landsat 33
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List of Figures
Figure Title Page
Figure 1.1: Map of study area 10
Figure 4.1: Flow chart of the analysis 41
Figure 4.2 Graphical representation of total received precipitation in
surrounding station of Batura (Bunji) 42
Figure 4.3 Graphical representation of total received precipitation
in surrounding station of Batura (Astor) 43
Figure 4.4: Graphical representation of total received precipitation in
surrounding station of Batura (Gupis) 44
Figure 4.5: Graphical representation of total received precipitation
in surrounding station of Batura (Gilgit) 45
Figure 4.6: Graphical representation of total received precipitation in 46
surrounding station of Batura (Skardu) Data Source:
Pakistan Meteorological Department(PMD) Figure4.7: Graphical representation of monthly precipitation of 47
Astor station
Figure 4.8: Graphical representation of monthly precipitation of 48
Batura station
Figure 4.9: Graphical representation of monthly precipitation of 49
Gilgit station
Figure 4.10: Graphical representation of monthly precipitation of 50 Gupis station
Figure 4.11 Graphical representation of monthly precipitation of 51
Skardu station
Figure 4.12 Graphical representation of monthly Temperature of 52
Batura station
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Figure 4.13 Graphical representation of monthly temperature of 53
Batura station
Figure 4.14: Graphical representation of monthly precipitation of 54
Batura station
Figure 4.15: Graphical representation of monthly precipitation of 55
Batura station
Figure 4.16: Graphical representation of monthly precipitation of 56
Batura station
Figure 4.17: Graphical representation of snowfall of Batura station 57
Figure 4.18: Graphical representation of monthly snowfall of 58
Batura station
Figure 4.19: Graphical representation of daily mean temperature of 59
Batura station
Figure 4.20: Graphical representation of daily maximum temperature 60
of Batura station
Figure 4.21: Graphical representation of daily minimum temperature 60
of Batura station
Figure 4.22: Graphical representation of total precipitation of 61
Batura station
Figure 4.23 Graphical representation of daily amount of snowfall of 62
Batura station
Figure 4.24: Graphical representation of relationship between 63
temperature and area under sow
Figure 4.25: Graphical representation of relationship between 64
area under snow and amount of snow
Figure 4.26: Batura Glacier Inventory (True Color Imagery) 65
Figure 4.27: Satellite image of Batura 66
Figure 4.28: Snow cover of Batura 67
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Figure 4.29: Land cover of Batura 68
Figure 4.30: Graphical representation of snow cover area of Batura 69
Figure 4.31: Graphical representation of barren land of Batura 70
Figure 4.32: Graphical representation of Batura 70
Figure 4.33: Graphical representation of land cover of Batura 71
Figure 4.34: Yearly precipitation anomaly index on 72
Batura station
Figure 4.35: Seasonal Precipitation Anomalies of Batura station 73
Figure 4.36: Monthly anomalies of Precipitation of Batura station 74
Figure 4.37: Temperature yearly anomalies of Batura station 76
Figure 4.38: Monthly temperature anomalies of Batura station 77
Figure 4.39: Distribution Precipitation in Monsoon season 78
Figure 4.40: Distribution of Precipitation in western Disturbance season 79
Figure 4.41: Distribution of precipitation in Thunderstorm season 80
Figure 4.42: Batura Glacier map of Contours 82
Figure 4.43: Digital elevation model of Batura Glacier 83
Figure 4.44: Watershed of Batura Glacier 84
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ABSTRACT
The contrasting climatic findings reported over the Karakorum glaciers are the real impetus behind this study. The anomalies in glacial mass balance over the Karakorum are considered as one of the burning research question among the climate change experts. The present study is afresh case study in order to verify the contrasting evidences of climate change over the Karakorum glacier. Batura glacier inventory is the finest case of testing the recently reported contrary evidences of climate change over the Karakorum region. The study is mainly focused on the spatio-temporal variation in glacial melt rate and snow accumulation in the Batura glacier region since 1988 to 2018. The study employed a combination of research steps which are mainly divided into raster dataset analysis and vector dataset analysis. The raster investigations employed mainly ArcGIS by which land cover classification, Snow cover Index were calculated. The vector dataset processed four meteorological station of Pakistan Meteorological Department (PMD) and one station over Batura by Meteoblue Switzerland. This is the first time in the literature in which the researcher is reporting the point of Batura in this research. The vector dataset generated the precipitation, temperature and snow anomaly index while auto-correlation and regression analysis have also been performed over the climatic parameters. These data were analyzed by spatial regression as a proxy of a glacial anomaly in the Batura region. The study shows in Batura the area under snow was 34 percent of total area in 1988 which reduced at 21 percent of total area in 1998 which again dropped down at 20 percent in 2008 while in 2016 the area under snow slightly increased at 23 percent of the total area. This increase in snow during the last decade is the anomalous evidence which support the hypothesis of the study. The precipitation anomaly index ranges from 1.33487 to 0.00162 which shows decrease in precipitation the negatively deviated years are more than positive abnormality of precipitation while temperature anomalies ranges from -13.8156 to 3.2671 which shows increases in temperature positively deviated years from average temperature are more than negative deviation of temperature from average. These analyses provide the rate of change in ice cover which compared and contrasted with the rate of change in climate parameters. The spatial auto- correlation between glacial retreat and climatic variation has highlighted the fluctuations in both parameters under analysis. The stated hypothesis was tested with findings of correlation which lead us for stating the study as supporting evidence of glacial anomaly under the regional climate change. The analyzed data values and trends were spatiotemporally mapped. The results of the research are additive contribution in the research of climate change impacts on the glaciers. Its recommended that anomalies of other glaciers of Karakorum will further be analyzed and mapped. The reported variations in glacial anomaly over the study area need to be further unlocked by using field data. The future researcher may employ the field expedition for the
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primary data collection. One possible reason may the elevation effect of the reported anomaly in the region.
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CHAPTER: 1
INTRODUCTION The presence of water resources is supposed to great effect by the climate change which is the reality and changing seasonal amount of precipitation and runoff which drives the water resource of Pakistan the greatest mountain ranges of Hindukush, Karakorum and Himalaya meets together in Pakistan and hosting 5000 plus glaciers in the geological limit of Pakistan. This snow and ice melt and monsoon in summer enter to the water of Indus River system. Due to the abrupt increase in temperature frozen water resources declining there reserves at unexpected levels. This not only lessen the ice mass while increasing numbers of glacier lakes also increasing the intensity and rate of destructive hazards in the mountainous areas in recent years.
1.1 Rational of the Study Pakistan faces the high temperature trends in northern areas in recent decades. Due to this the rate of melting glacier ice and snow increases at alarming levels which cause more lake formation in which many lakes are dangerous and have potential for alarming outburst. The rising temperature the shift in snow line also begin upward which is the reason of migrating biodiversity in the region and the glaciers at lower level become melting. The flooding increasing and rock avalanches in Himalayas is reported. And this also affects the water resource in coming years or decades. The ranking of Pakistani Glaciers in the world is in top third largest ice mass on earth after the giant glaciers of the Arctic or Greenland and Antarctic glaciers.
The mountainous region of Himalayas, Karakorum and Hindukush (HKH) mostly receives precipitation in winter season from westerly winds which act as a reservoir. While this snow and rain have more water which release in summer in the Indus River system (IRS) and irrigation system of Pakistan. Almost 15,000 sq.km glaciated area of 5000 glaciers feed the upper Indus basin and the River inflow has 2700 cubic stored volume of ice is equivalent about 14 years of average Indus River System inflows(Ashraf, Naz, & Roohi, 2012). It is realty that the glaciers of whole world are facing reduction in the past century. But in the Hindukush region the rate of glacier melting is highest then all over the world it is reported that Hindukush reducing rapidly than all other parts of the world glaciers. But on the other
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hand the temporal behavior of the Karakorum was not investigated due to the difficult and hard features of terrain and steep slope. (Quincey et al., 2011) the past studies show that some of the Karakoram glaciers are flowing rapidly rather than going back. May be the findings of Hewitt’s not be true for an enormous majority of Hindukush, Karakorum and Himalaya HKH glaciers as, according to PMD (2009), the temperature trends of HKH glaciated region has in increasing generally and the frequency and of events of normal and severe heat wave are increasing in area.
1.2 Climate Change
Climate change is the seasonal cycle in which seasons developed starting from summer to fall to winter and spring and then move back to summer in this time non- tropical areas experience significant temperature fluctuations. The amount of Precipitation can also fluctuates by season almost any climate parameter can vary over the course of the year. Climate change is any structural change in the statistics of temperature pressure and wind which are the climatic parameter and these changes sustain over large time or decades in area.
The difference between climate and weather is that, the day-to-day atmospheric condition of any place is weather and the year-to-year and season-to-season variation in climatic factors is climate (Dines, 2011) for this, the compression of the weather statistics of one period to the other if the statistical values changed then we can say that the climate has changed from one period to another.
This type of changes in climate is famous as global temperature rising and global warming is the major aspect of climate change (Lerner & St. Pierre, 2009). Climate change means the climate change caused by natural or human activities and the change rest for a long period of time (IPCC, 2007). Climate Change dose not only deal with the mean temperatures it is also about such section- relevant states as the frequency of droughts or wet years, changes in daily temperature differences, or the change in the intensity of 24-hour precipitation events (Slater, Singer, & Kirchner, 2015).
1.3 Climate Variability in the World
Climate variability means that variability in the time scale of a couple of years to a period of ten years (shorter than the average period of climate) (Adelman et al., 1990). The conditions of Climatic parameters are inherently fluctuating from one year to another year and then the condition stay whole decades or more than one decade and centuries. During this
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changes goes with and these fluctuations are the part of climate change in average climatic conditions of the year.
The extreme events are always part of the fluctuations of climate change including climate change. The relationships between severe climate events, fluctuation and climate change developed natural hazards in area (Brown, 2014; Leichenko, 2011)When fluctuations and dangerous events discussed together with climate change two important things comes firstly changing weather and the impact of climate change in the variability of area and the change in structure or fluctuation intensity distribution. May the fluctuations increase with climate change but one possibility is that variability increases with climate change but there is little or no consensus on changing variability (Houghton et al., 1996).
Nevertheless, even without any fluctuation in the variability of climate like changes in shape distribution and variance of climate cause a shift in the mean Climate change will compulsory shift in structure and distribution of climate in the area. The intensity of the Phenomenon of dangerous events can be highly affected by small changes in the average (Mearns et al., 1984; Wighley, 1985).
1.4 General Effects of Climate The researchers explored that the average temperature of the world is rising day-by- day. The first evaluation about the temperature rising had been identified in 1896s that this average increase in temperature is due to the industrial emission. The rapid trend of warming was notified in the 1930s. After that, for atmospheric pollutants and for the indication of potential warming of CO2 the first model was developed. “Greenhouse Effect” was known as the idea of global warming in 1962s and the rise in these gases had been proved. Researchers discovered the harmful aerosols during the research of the 1970s to 2000s. These harmful aerosols had chlorofluorocarbons (CFCs). The ozone layer is depleting due to CFCs and issue of global warming and ozone holes were notified (Lerner and St. Pierre, 2009).
It has been notified by the IPCC in 2007 that global temperature increases from 1.8oC to 4oC by the turn of the next century. The impact of climate change observed on agriculture, biodiversity, forest, water, health, and also on the socio-economic sector is highly observed at global level. The developing and less developed world are more affected than the developed world. At the local level, the poor people are a major victim of any climatic anomaly because they have a lack of information and resources. Anthropogenic activities are major responsible for change in the climate trend and due to climate change, the major
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income sources are affected. The increase of Green House Gases (GHGs) emission to the atmosphere alarmingly high from industries at the time of industrial revolution. The rise of temperature of 0.76 C in global temperature was recorded in 20th century and there was 0.6 oC temperature during the first decade of this century (Rasul and Ahmad, 2012).
In the 20th the change in global surface temperature and inter annual climate fluctuations has been reported in many areas of the world (Salinger, 1994; Salinger et al., 1997)The 1982/83 and 1997/98 El Nino and the1991 Mt. Pinatubo volcanic eruption (Salinger et al.,2000 1a) is the reason of fluctuations in the inter annual climate of tropical areas in the late 20th century. Recently IPCC (200 1a) gives the confirmation of temperature increasing trends of Arctic and Antarctic sea ice from day to day break up due to climate change mostly in European Russia, Ukraine and Baltic countries of the World. It is observed and reported that reduction in mountainous glacier in 20thcentury and the temperature increase in permafrost in many glaciated areas were reported.
1.5 Effects of Regional Climate Variability The development of a joint consensuses of the world that the climate change is the biggest threat in the modern world for humankind and is likely to have big consequences for the economic and the social activities like human health affect by this as well as management of food security and natural resource. The global warming creates harmful impacts are in the whole world like severe weather conditions for example storms floods tornado and droughts are increasing in intensity and rate of events are abrupt and large scale. Due to this 400-500 natural disasters in year at average effects the world increased from 125 in 1980s (Disaster Risk Reduction: Global Review 2007).
Earlier to the 20th century air surface temperature of Northern Hemisphere was 0.5 C average and changes from the order back to AD1000. Various climate structures reconstructed describe that in the start of 20th century slow cooling held. While the temperature of globe increased at 0.6 C and 1990s was reported the warmest decade on record. In the latter part of the century the greatest warming patterns on mid-latitude. Simultaneously the frequency of ice particles of air has decreased in may land areas and due to this tropics and subtropics has been drying. Because of the global warming decadal scale fluctuation induced the pacific Oscillation this cause the climate change which causes decadal changes in climate averages the fluctuations in tropical and subtropical areas due to the El Nino or Southern Oscillation and small portion of mid latitude areas. Europe and Africa faces
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disturbance in climate from the North Atlantic Oscillation. The temperature rise and variations in climate increased the temperature of the globe and cause the unprecedented in the history of human settlements in the world (Salinger, 2005).
The effects of ENSO and La-Nina are climatic variability. The Worldwide droughts, hurricanes and flood are the effects of these variables. From more than 400 years, the droughts conditions in the Philippine, South Africa, Australia and India are highly linked to ENSO. In Pacific, Tahiti and Polynesia, the strong hurricanes occurred. The season of Atlantic hurricane become weak in the El-Nino while strengthen in La-Nina. Due to these conditions, flood accord in Bolivia, Ecuador, Peru, Cuba, Southwestern United State and Mountain states. During the season of El-Nino, the precipitation remains high in southwestern united and pacific remained wet than the La-Nina. These conditions originate in one place and affect the whole world (Christopherson, 2012).
The precipitation variability and changes in oceanic ecosystem is the accelerating recession of most glaciers on the earth according to the 4th report of IPCC. The fresh water availability is also another serious threat due to climate change which is decreasing especially in large river basins and badly affects more than a billion people by the 2050s. (Lead Pakistan web site)
1.6 Climate variability in Pakistan The climate change structure around the world created a disturbing threat to dwellers of the earth. The small and developing countries like Pakistan are highly affected by the weather related diseases like flash floods, hurricanes etc. Pakistan is the seventh highly effected country by climate change. The industrialized nations are responsible for the emission GHGs. The key contributors of the global warming are US, China, Russia, UK, Germany, Australia, Canada, Japan and Korea (Ashraf, 2018). Pakistan is highly affected by the climate change because there is warm climate. It has been expected that the temperature will increase from the global average temperature with the time in Pakistan due to its warm climate and geographical region. It mostly lies in arid and semi-arid conditions. The 60% land area of Pakistan receives less than 250mm rainfall and 24% between 250-500mm. the glaciers of Hindu Kush, Karakoram and Himalayan mountains are fed the rivers of Pakistan. The economy of this country is highly sensitive to climate change. It has also faces high variability in monsoon season which causes large floods and droughts. In Pakistan, the increase of 4 C temperatures is expected till 2100 and spatio -temporal variability in rainfall
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is also high. The deltaic region and Southern sea is affected by both the local weather conditions and climate change (Rasul and Ahmad, 2012).
The increasingly volatile was reported in the past different decades in Pakistan, and the pattern presumes to continue. Most importantly rise in the power and occurrence of severe weather conditions, integrate with unpredictable frequent and intense floods and droughts due to monsoon rains. The Hindu Kush –Karakoram-Himalayan (HKH) glaciers are reducing towards down because of the global temperature raise and carbon deposits from trans boundary pollution source and water inflow threatening into the Indus River(IRS).(Javed, 2016; Kafle)
1.7 Northern Pakistan in Prospects of Climate Change
Climate change creating impacts on mountains is vital because mountains covers the 20% of surface of the Earth provide 10 percent residence to human and 50 percent fresh water. They are storehouses of genetic diversity that help feed the world. Mountainous areas are under severe conditions due to climate change and many other factors (e.g., deforestation, overexploitation of natural resources and unsustainable agricultural practices). When the forests cut down and prepare the land for forming, mining than the flow of water becomes rapid which creates floods, land slide, avalanches etc. due to the loss of forests and fertile soil, the water runoff increase, overall ecosystem degrade and loss of overall biodiversity (including extinction of species of plants and animals, genetic biodiversity and habitats). Lives in the mountainous areas become harder due to the climate change and variability. It is also expected for further disruption in the environment of these areas. The sensitivity of mountain ecosystems to climate change is particularly high because of their breakable nature, topography, steep gradients, and diversity of ecosystems (Hussain,, et al., 2005).
Pakistan contributes very little to the overall less than 1% of Greenhouse Gas (GHG) emissions (among the lowest in the world) but remains extremely impacted by the negative effects of climate change and Pakistan has also few technical and financial capacities to adapt to its adverse impact. Himalayas Glaciers are melting which is the reason of increase in flooding and in the coming decades flooding will affect the water resource of the country. This means River flow reduced due to the glacier melting and reduction. The reduction in water will also create negative impacts on biodiversity loss and also reduce availability of fresh water for the people.
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1.8 Glaciers of Pakistan
The mountainous north of Pakistan has the longest and largest mid-latitude glaciers, approximately, having 15,000 km2 areas. A number of glaciers are in the Hindu Kush region and Swat, Kohistan areas, the Nanga Parbat Himalaya, and the Karakoram Himalaya. Some important glaciers are Balt Bare, Batura, Biafo, Gorshai, Raikot, Siachen, Tirich Mir, and Yangtze. Glaciers have different types of flow and different standards. In these glaciers medial and lateral moraines had strongly convoluted in five glaciers, while there is 13 glaciers convoluted at medial and lateral moraines whereas 7 glaciers had prominent tributary ice overriding or changing locations from main glacier ice While 14 glaciers had marked depression below the lateral moraines surface. Moreover 76 glaciers had lateral and medial moraines that were slightly sinuous locate or offset by crevasses. 9 glaciers had early melting that is called light-color DE glaciation region behind 106 glaciers had extensive areas of debris-covered down wasting or stagnant ice. The had 47 glaciers of linear moraines recent major melting or declining of ice front that left light-colored deglacierized terrain behind and accordant tributary glaciers (Jilani, Haq and Naseer, 2007)
1.9 Glacial Anomalies in Northern Areas of Pakistan
Glacial anomaly is glacier swings between developments and decreasing snow falls on the peaks which slowly compress and turns to ice while melting and evaporation loss of ice started in lower down the glaciers. The HKH region is among the most highly glaciered mountain area in the world and glaciers this area is an important water resource for Pakistan. The area needs attention because its glaciers retreat observed worldwide on average. While many glaciers are withdrawn as a result of global warming, the glaciers of the Karakoram region of the Indus River Basin are stable for past four decades in South Asia. This condition has been labeled the “Karakoram Anomaly”.
In the late 1990’s wide spread proof of glacier expansion was commence in the central Karakoram in comparatively to a worldwide decrease of a mountain glaciers. The growth of glacier purely in center basin of the glacier in the highest areas of the glacier and grow rapidly after decades of withdrawal. The distinguishing characteristics of environmental of the region necessitate interactions among air masses and its seasonal system and there top climatic or verticality effects glaciers on extreme altitudinal range, the complex nature and distribution of ice masses and temperature of the climatic sensitivities of heavy
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versus thin supra glacial debris falls in allover the complex and critical elevation of the region.
1.10 Factors Affecting to Control Glacial Anomalies
The anomaly in Karakorum was firstly reported in 2005 and since then, the causes of expansion determines by scientists in glaciers region the region includes the world’s second largest mountain K2(Fowler H., 2017). There may be many factors of these anomalies. Normally slope and morphological factor controlling the structure of features and reactions of the glaciers to climate change. The important factors are following firstly reduction in glacier elevation. Secondly the growth of supraglacial ponds is a further controlling factor of glacier elevation change that is, where supraglacier ponds develop, the glacier register further surface lowering. Thirdly Debris coverage was not found to be significantly responsible for the development of supraglacier ponds, changes in elevation or shifts in snowline altitude. Fourthly increase in precipitation and decrease in temperature from past 50 years in Valley climate stations and small reduction in summer which highlighted positive trends in glacier mass balance.
Still the unexpected growth is of the glacier is difficult as is their imprisonment to glaciers from the highest watersheds while the other parts are continuously reducing. Temperature shifts in ice masses with high altitude ranges supposed to face more hard conditions and leads rapid relocation of glacier ice mass by the elevation (Hewitt, 2009).
1.11Climate Variability and Glacial Anomalies
Pakistan’s 70 percent fresh water resource depends on glaciers in the mountainous north of Himalayas and Hindu Kush. The rivers are also depending on glaciers in western and eastern parts of the country. Climate change is the one cause of temperature increase in glaciated region of Pakistan. (Singh, Bassignana-Khadka, Singh Karky, & Sharma, 2011). The Himalayan’s glaciers are receding faster than the other world and if remain continuous, it will be very high by the year of 2035 (Bolch et al., 2012). Overall, the increase in temperature in the northern region of Pakistan has also increased the snow and glacier melting in these areas. The year-to-year average temperature rise in winter season have negative effect on the accumulation of snow and summer maximum temperature has severe impact on the depletion of snow and glacier of the mountains of northern areas. The increasing trend in precipitation both in the high and sub- mountain areas may because good effects in the mountain area. But the area is extremely
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complex and sensitive due to human activities which cause degradation in region increased precipitation could improve the land degradation process in these areas but surface run off, soil erosion, and landslides increases in the region. Due to these processes the sedimentation shifts towards downstream, reduction in the storage capacity of our reservoirs and decline in irrigation water in the country (Hussain, et al., 2005). 1.12 Problem Statement Pakistan is an agrarian country with flourishing industrial sector and its population is increasing at fast rate. Proper use of water in each sector is very important and highly demanding. But water resources are also depleting at a higher rate. In Pakistan there are not so much opportunities for researchers to seek knowledge at higher level in the field of Geo- informatics and earth observations. It is reported by international center for integrated mountain development (ICMOD) survey in 2005 that Pakistan has 5,218 on the bases of remotely sensed data of the region the research are very few of the Karakorum glaciers characteristics. It is believed that the total Karakorum glaciers are stable while the Himalayan glaciers were going back towards reduction due to the increasing temperature of glaciated region. So there is need to model the climate and glaciers on Geo-Spatial parameters.
1.13 Study Area Batura Glacier 57 kilometers (35 mi) in length, after the polar region the glacier is longest and largest on in the world. It is located in Gilgit Baltistan region of Pakistan below the Gojal region. The flow direction of glacier is west to east. The lower part is called grey sea of rocks and gravelly moraine, while the area is bordered by some summer villages found and pastures in which herbs of sheep, goats, cows and yaks and the area where roses and juniper trees are commonly grow. After the polar region Batura glacier is the largest one. The height above sea level is 7,500 meter in the north of Passu. It is located at the longitudes of 36o 30`N to 36o 40`N and 74o 22` 33``E to 74o 52` 30``E. It feeds River Hunza in northern Pakistan which flows west to east. River Hunza meets by the Gilgit and the Naltar Rivers before it flows into the Indus River. The previous study shows that the area ender ice and ice free areas in the year 1992 was 98 km2 and 25 km2 respectively, whereas in the year 2000 the area under ice decreased to 81km2 and consequently increasing of the area free of ice free are to 42km2 (Jilani and Haq, 2007).
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Figure 1.1 Map of Study Area (Batura)
1.14 Objectives The present study is aimed at investigating reported glacial anomalies over the Karakorum under the recent changing climatic regime. The study is focused on then monitoring of Batura glacier during the last three decades with the following specific objectives:
1. To investigate the Karakorum anomaly by monitoring the melt and accumulation rate of Batura glacier as a case study. 2. To measure the impact of climate change on the size and mass balance of Batura glacier with the help of satellite imagery and field data. 3. To analyze the change in climatic variables i.e. temperature and precipitation in the study area by using field and remotely sensed data on temporal basis.
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1.15 Research Questions:
This study will deal following research questions: 1. How to investigate the Karakorum anomaly by monitoring the melt and accumulation rate of Batura glacier as a case study. 2. How to measure the impact of climate change on the size and mass balance of Batura glacier with the help of satellite imagery and field data. 3. How to analyze the change in climatic variables i.e. temperature and precipitation in the study area by using field and remotely sensed data on temporal basis.
1.16 Significance of the Research
Glaciers have been well established as valuable indicators of the severity of regional climate change and constitute an important part of the local and global hydrological cycle (Molina, 2006). Figure 2 The health of the glaciers and ice sheets has a significant influence on global sea level rise. A significant contribution to sea level rise comes from alpine glaciers most of which have been identified to be declining in overall volume at an accelerating rate since 1990 (Larsen et al. 2006; Peduzzi et al. 2010). It is necessary to monitor changes in glaciers parameters such as thickness and area extent of the glaciers, the future researcher will be able to learn these new techniques and their implementation to solve the spatial and water problems in Pakistan, as it would be great prospect for me and my country. This research would be beneficial for the planning and management projects in the field of climate and water resources for future trends in Pakistan.
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CHAPTER: 2 LITERATURE REVIEW 2.1 Introduction
The first chapter elaborated the grounds and research questions for the study, which compose the basis for the assortment of the pre easy to get to literature for making it understandable data and information to be collected and the plan of the research.
Consequently, this chapter begins by emphasizing the idea and processes of technically monitoring glacial anomalies and climate. Although climate and glaciers have been widespread analyzed in recent decades, in spite of it stays the subject of discussion among environmental scientists. Here along these first ideas and processes of observing glacial anomaly and climate are inspected, the part moreover highlights relationship between temperature, rainfall and glacial dynamics. It also discovers the progress and role of satellite technology and keeps an eye on glaciers assessment as well as human activities aggravation the process in Batura glacier.
2.2 Monitoring Karakoram Anomaly
Senese (2018) this research analyzed Landsat thematic Mapper images of 2009, 2010, 2011 and 2013 presenting main particulars at the central Karakoram National Park (CKNP the biggest fortified region of the highest parks of Pakistan and world), all the data and particulars is offered in view of the CKNP as a whole and by distributing it into five major basins (Upper Indus, Shigar, Gilgit, Shyok and Hunza). The inventory of glacier in detail reports 608 ice bodies covering 3680 km² with an overall glacier volume of ca.532 km³. The key aim of this exploration modeled the melt water from ice extirpation over the phase in 2011 from 23 July to 9 August. The overall melt quantity was ca.1.5 km³. Lastly, this research considered about lakes of glacier (4 km² area covered by 202 water bodies) who are potentially in dangerous situations and two lakes were discovered having such conditions.
Shafique (2018) on the earth surface ice sheets are the biggest store of fresh water. Northern Pakistan is secured with probably the biggest mid latitude ice sheets on the planet. The greater part of the ongoing researches on the locale lead to conflicting outcomes that glaciers in the Karakoram Mountains have indicated either progression, withdraw or stable practices and in this way coin the expression “Karakoram anomaly.” In this investigation, temporal images of Landsat satellite, obtained in the long periods of 1977, 1999, 2001, 2007
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and 2014, were applied to assess the temporal elements of the preferred glaciers in the Hunza valley, Northern Pakistan, including the Ghulkin, Batura, Gulmit and Passu. The development of diagrams was utilizing slope gradient and normalized different snow index tracked by editing. The Landsat resultant glacier inventories were demonstrated through the better resolution WorldView-2 satellite imageries and field perceptions. Assessing the temporal glaciers results shows that the chosen glaciers are withdrawing; on the other hand, the melting rates are varying relying upon the supra glacier debris cover. The progression or melting of glaciers is additionally affected by the precipitation and mean annual temperature records.
Forsythe (2017) identifying the mechanics driving spatially diverse glaciers mass balance systems in the Himalaya and Karakoram anomaly, is pivotal for understanding regional water asset directions. According to methodology stream flows reliant on glacier melt water are positively and strongly correlated with summer air temperature in Karakorum, which demonstrate current anomalous cooling. Study explain these stream flows and temperature inconsistencies through a system of circulation- the Karakoram vortex- recognized utilizing a areal circulation metric that measures the power and relative position of the westerly jet, responses of the winter temperature to this metric are uniform across south Asia, although the summer reaction of the Karakoram differs from whatever remains of the Himalaya. This is because of seasonal shrinkage of the Karakoram vortex through its connection with monsoon of the south Asia. Results shows that inter annual fluctuation in the Karakoram vortex, quantified by circulation metric, clarifies the variations in energy constrained removal showed in river flows over the Himalaya and Karakoram.
Hewitt (2005) analyzes conceivable clarification for this apparently abnormal behavior, utilizing proof from momentary observing projects, low-elevation weather stations, and the idiosyncratic ecological qualities of the region. The last mentioned include cooperation among regional air mass climatology, its verticality, and seasonality or topoclimate impacts on glaciers with tremendous altitudinal range, climatically sensitivities of overwhelming versus thin supraglacial debris, and complicated temperature divisions in ice masses with ice fall all through basic elevations. Valley stations of climate demonstrate increments in precipitation in the course of the last 50 years and minute mollification in summer temperatures, which may point out positive patterns in glacier mass balance. On the other hand, the unexpectedness of the expansions is challenging, just like their internment to glaciers from the peaked watersheds while others keep on withdrawing. Thermal shifts in ice
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masses with extraordinary height ranges might be much more critical, prompting to an accelerated redistribution of ice mass by elevation.
2.3 Batura Glacier and its anomalies
Baig (2018) this investigation calculates the temporal decadal changes in the ice mass region of Hunza river basin selected four glaciers (Batura, Gulkhin, Hussaini and Passu). An endeavor has been made to explore the connection among determined estimation of precipitation and glacier ice area changes, run off and temperature. An arrangement of field and satellite based approach is connected. Outcome incorporates maps of glacier ice hypsometers of eight frigid ice sub regions of Hunza river basin for 3 years for example 1989, 2002 and 2010. The outcomes demonstrate a diminishing pattern in the area of glacier under ice region implying a decrease of 20.47 percent with the biggest decrease being in the lower elevation bands, results shows there is not any final answer with respect to why glacier ice in the region of Karakoram is acting uniquely in contrast to the near global sign of glacial ice changes. Climatic information and data from high altitudes are expected to discover answer for this anomalous conduct.
Bolch (2017) past geodetic appraisals of mass fluctuation and changes in the glaciated region of Karakoram especially Batura uncovered balanced budgets or a conceivable minor mass gain since 2000. Signs of longer term steadiness exist however just not very many mass budget examinations are accessible before 2000. Here, in light of 1973 Hexagon KH-9 ∼ 2009 SRTM DTM and the ASTER, Hunza river basin was by and large in balance or indicated slight inconsequential mass loss inside the period 1973 – 2009. Heterogeneous behavior and regular surge actions were also characteristic of the period before 2000. Surge and non-surge G2 type glaciers appeared by and large no significantly dissimilar values. On the other hand, some individual glacial mass change rates varied altogether for the period before and after ∼ 2000.
Zhou & Li (2017) an anomalously minor glacier mass expand amid 2000 to 2010 has currently been accounted in the Karakoram Batura region, elevation and mass change quantified utilizing DEM created from KH-9 imageries procured amid 1973 – 1980 and the 1 arc-second SRTM DEM. Analyst found a minor mass loss −0.09 ± 0.03 m w.e. a-1 (12366 square kilometer) for 1973 – 2000, which is a smaller amount negative than the world average rate for 1971 – 2009 (−0.31 ± 0.19 m w.e. a−1) inside the Karakoram region, the
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trends of glacier change was temporally and spatially heterogeneous. Specifically, an almost steady state in the central Karakoram (−0.04 ± 0.05 m w.e. a−1 amid the period 1974–2000) infers that anomaly of Karakorum dates back to the 1970s. Joined with the past examinations, research finalized that the glaciers of the Karakoram range overall were in an almost balanced state amid the 1970s to the 2010s,
Bishop (1998) directed an examination of the expansive Batura glacier in Pakistan to decide whether spectral fluctuations can be measured and utilized to describe glacier surface. In particular, SPOT Panchromatic satellite information and data were assessed for distinguishing features of ice mass structure coming about because of ice movement, supraglacial fluvial action, and ablation. Imageries semi variorum investigation was led. For reviewing spectral variation trends and fractal analysis was utilized to explore scale dependent variability in the data. Outcomes show that spectral variation from fields of ice sera’s can reveal fractal distinctiveness, though most surface features on the ice sheet show a change in the fractal aspect over various ranges in scale. The fractal dimension was observed to be helpful for separating among glacier surfaces, for example, debris covered ice and white ice. Attributes of the scale dependent nature of quantifying the geometric characteristics and debris load eventually decided the capability of class distinctness.
2.4 Factors effecting Batura Glacier
Eberhardt and Miehe (2007) this research work clarifies the appended medium scale vegetation map (1:60 000) of the Batura valley, the furthest limit of the event of vascular plant was 5,200 m. as indicated by the arid climate and subtropical latitude , the overall vegetation characteristics of the Batura valley is steppe or desert like. Species wise vegetation is low (around 380 kinds of vascular plant) and covers just about 10% of the aggregate region by the side of Batura Glacier. Twenty six vegetation developments were mapped. Open dwarf shrub and herbaceous vegetation prevails, while woodland sections, closed grass and thicker shrubs networks possess just little zones. The anthropogenic effect on the vegetation cover, for the most part through grazing, is commonly viewed as high. Present status, though, the nature, degree and explicit impacts of anthropic-zoogenic impact are exceptionally separated locally and because of the present socio-economic conversion in the area, are clearly subject to current changes. Parts of woods and forest, dwarf and thicker shrub are restricted to small zones. In spite of its common depauperate, patchy and fragmented trait, the vegetation of the Batura valley shows noteworthy local diversity and reveals an enormous altitudinal gradient.
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The environmental and phytogeography indication is being checked on by the Batura valley. The human impact on the cover of vegetation, for the most part through grazing of residential stock, is normally high. Though, the precise nature and explicit impacts of anthropic- zoogenic intervention differ and are in this way subject to ongoing environmental and socio- economic changes in the area.
Prestrud (1997) demonstrate that water draining area engaged by dynamic alpine glaciers caption uncovering rates more prominent than the worldwide mean rate however don’t surpass rate in non-glacial catchments with comparable water discharge. Electrical conductivity was utilized to quantify of solute concentration. Preeminent recognized chemical denudation of glaciers rates were obtained from discharge measurements over minimum one year and reiterate water sampling. The finest documented chemical denudation of glaciers rates are belonged from at least one year discharge measurement and water sampling, in this group are Batura, Haut and Gornergletscher. Silica denudation rates are noticeably lower in ice covered catchments than in their no glacial corresponding parts. For the reason that sediment yield was lofty from glaciers, this proposes that water flux, instead of physical erosion, applies the essential control on chemical erosion by glaciers. Calcium and potassium applications were high in respect to different captions in glacial water, most likely because of dissolution of solvent trace stages, such as caption leaching from biotitic and carbonates, uncovered by commination. Particular weathering of biotitic may outcome in higher 87Sr/86Sr in glaciers runoff than anticipated from entire rock structures.
Maohuan (1982) ice temperature information and data gathered utilizing a copper resistance thermometer from the region of continental category glaciers in china and Batura since 1959 was examined. Formulae for the temperature graded of the dynamic layer and for yearly mean temperature profiles are proposed. Continental type glaciers temperature in china was quite low, rising quickly with depth. A noteworthy piece of the base of most ice sheets comes to the pressure – melting point, with basal sliding. The extraordinary tremendous valley glacier will transform from a cold glaciers addicted to a temperate one when it go downs to the locale where the atmosphere was mild, the temperature of ice to the lower bound of the dynamic layer at the height of balance line was 1.8 – 3.7 degree higher than the yearly mean air temp at a similar dimension. The western region of ch’i-lien shan might be the position where the alpine glaciers temperature was the lowest of the low and middle latitude. The infiltration zone was warmed considerably by the recon gelation and infiltration
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process. A plan representing how the lower bound temperature of the active layer transforms with glacial sectors is drawn.
2.5 Climate change and glacial anomalies
Marzeion and Champollion (2018) utilized precipitation and temperature fields from the inter comparison Coupled Model phase 5 project productivity to compel a glacier progression model, measuring mass reactions in future climate changes. Research uncover that existing glacier mass is in alteration with the recent climate, with 36 ± 8% mass loss is as of now dedicated in light of past emission of greenhouse gases. Subsequently, predicting future releases will have just exceptionally constrained effect on glacier mass modification in the 21st century. No noteworthy contrasts somewhere in the range of 1.5 and 2 K warming situations are noticeable in the ocean level involvement of glaciers accrued with in the 21st century. In the long standing, though, mitigation will apply strong control, proposing that driven measures are essential for the long term protection of glaciers.
Doughty (2017) topical model exploration recommends that inter annual climate variation could be perplexing the elucidation of glacier variances as climatic signals. Paleo climatic analysis of moraine positions and connected cosmogenic presentation ages may have huge vulnerabilities if the glacier being referred to was delicate to inter annual variation. In this examination the impending for inter annual precipitation and temperature fluctuation was source of great shifts in glacier length at some stage in the Holocene. Utilizing a coupled mass balance and ice flow model, reproduce the reaction of Cameron glacier, a minor mountain glacier in south Alps New Zealand, to two sorts of climate constraining: inconsistent climate and equilibrium climate. Research equilibrium outcomes imply a net warming pattern commencing the early Holocene (10.69±0.41 ka; 2.7 °C cooler than current) to the belatedly Holocene (CE 1864; 1.3 °C cooler than current). Inter annual climate variation can’t represent the Holocene glacial variances in this valley.
Gonzalez (2017) this exploration was summarizes Southern Central Pyrenees accessible paleo environmental lacustrine information and data for the previous 20,000 years and demonstrates a new order from mid elevation (Lake Estanya Holocene record). Multi proxy examinations of lake inventories have recognized large hydrological and vegetation variations amid last glacial period, Holocene and deglaciation time frame at decadal, centennial and even millennial scales and achieved their diverse nature, timing and intensity.
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The assessment shows that landscape elements in the Pyrenees have been significantly organized by long term and unexpected climatic modifications, since the middle Holocene, and principally since medieval occasions, by anthropogenic actions as new changing specialist. Even though high inward unpredictability portrayed each site, normal temporal patterns are confirmed, and also a indicative western-eastern incline superimposed to the projected altitudinal one. Thus, the history of Pyrenees environment exhibits a generally high level of inward rationality across space.
Roe (2017) the close worldwide of glaciers in the course of the only remaining century gives probably the most iconic imagery for imparting the truth of human environmental amend to people in general. Shockingly, be that as it may, there has not been a quantitative groundwork for ascribing with draw to climatic modification, with the exception of in the worldwide overall. This breach, between scientific basis and people observation, is because of vulnerabilities in numerical demonstrating and the minor length of glacier mass balance evidences. Research demonstrate a technique for singular glacial change dependent on the signal to noise proportion, a strong metric that is insensible to vulnerabilities in glacier elements. Utilizing only glacial and meteorological monitoring, and the traits decadal reaction time of glaciers, exhibit that monitored retreats of individual glaciers correspond to some of the most astounding signal to noise rations of environmental and climatic change yet verified. In this manner, in numerous spots, the centennial scale retreat of the nearby icy masses does for use establish absolute proof of climate change.
Zhang and Barker (2017) Glacial environment is set apart by sudden, millennial scale climate modifications recognized as Dansgaard Oeschger Cycles. The majority articulated stadial coolings, Heinrich occasions, are connected with gigantic chunk of ice releases to the North of Atlantic. These dealings have been associated to variability in the potency of the Atlantic meridional overturn circulation. On the other hand, those components that direct to unexpected changes amid weak and burly circulation course routines stay vague. Research present entirely coupled atmospheric ocean model, slow changes in barometrical CO2 meditation can trigger unexpected atmosphere changes, linked with a routine of bi-stability circulation of Atlantic meridional overturning .beneath transitional glacial circumstances. Investigation discover that modification in atmospheric CO2 focuses modify the transport of environmental moisture crosswise over central parts of America, which tweaks the freshwater budget of the north side of Atlantic and consequently deep water development. According to results, an alter in atmospheric CO2 dimensions of about 15 ppmv- correspondent to
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variability amid Dansgaard Oeschger cycles restraining Heinrich occasions – is adequate reason to transition between well-built interstadial and a weak stadial circulation approach. Exploration direct that atmospheric CO2 may provide as a negative response to conversion among sturdy and weak circulation sorts.
2.6 Climate change and its impact on glacial melting
Sorg and Beniston (2012) environment obsessed changes in glacier sustained stream flow routines have coordinate ramifications on freshwater supply, hydropower and irrigation prospective. Consistent record about future and current glaciations and spillover is critical for water distribution, a complicated task in central region of Asia, research shows that glacier reduction is mainly marked in fringe, bring down elevation extends close to the thickly populace forelands, where summers are without water and dry and where ice and glacial melt water is fundamental for water accessibility. Seasonal runoff maxima reallocates have previously been monitored in a few waterways, and it is recommended that summer runoff will additionally diminish in these streams if precipitation and release from defrosting permafrost bodies don’t remunerate adequately for water shortages.
Immerzeel (2010) above than 1.4 billion inhabitants rely on water on and after the Yangtze, Indus, Yellow, Brahmaputra and Ganges rivers. In this examination Normalized melt index was utilized over the time frame 2001 to 2007 to measure the significance of ice assets and upstream snow of these basins, imperative in supporting seasonal water accessibility, was probably going to be influenced considerably by environmental change, yet to what degree is nevertheless uncertain. At this point, outcome shows that melt water is enormously essential in the Brahmaputra basin and vital for the Indus basin yet assumes just a mild job for the Yellow River, Ganges and Yangtze. An immense contrast additionally survives between food security and basins in the degree to which climatic change was anticipated to influence water accessibility. The Indus and Brahmaputra are mainly vulnerable to decreases of stream flow, compromising the Nourishment security of an expected 60 million people.
Xu (2009) the mighty Himalayas clasp the largest mass of glacier outward polar boundaries and are the origin of the top 10 mightiest streams in Asia. Quick decline in the size of Himalayan ice sheets because of environmental change was happening. The falling impacts of ice and snow loss and increasing temperature in the area are affecting, for example biodiversity, water availability worldwide feedback and biological territory shifts.
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Environmental change will likewise have social, political and ecological effects that will. Asia possibly increased the vulnerability of agricultural production water provisions for humans crosswise. It is required to comprehension of global and local environmental change with the goal that alleviation and adjustment methodologies can be distinguished and actualized.
Akhtar and Booij (2008) this examine present evaluation of water assets varies in three stream basins in the Karakorum, Hindukush and Himalaya territory connected with environmental change. The future climate and current climate SRES A2 situation 2017 – 2100 are imitation by PRECIS regional climatic model with 25 × 25 km spatial resolution. Two models of HBV are intended to measure the future release. Temperature and precipitation series related to future are built through the approach of delta change HBV Met, results presents overall increase in precipitation and temperature on the way to end of twenty first century. In a changes climate 0 % glacial scenario, HBV Met indicate severe decline in water reserves and as a whole enhancement in both models for 50% and 100% glacial scenarios. One of the main reasons for bad outcomes of the approach of delta change is that in this methodology the recurrence of daily precipitation was not changed and everyday variation in temperature was not accurately measured.
Haeberli (1998) change in climate in the Alps of Europe amid the twenty century has been exemplify by augments in least temperature of round about 2ºC, extra mild enhance in maximum temperature, minor pattern in data of precipitation, and as a whole diminish of solar radiation length through to the middle of 1980s. 30% to 40% in surface area in Alps of Europe has lost and nearly half its inventive volume. The assessed aggregate ice mass volume in the European Alps was a quantity of $130\ {\r m km}^{3}$ for the mid-1970s, however firmly negative balances of mass had reason an extra thrashing of around 10 to 20 % of this stay behind glacier volume since 1980. Imitation of high resolution climatic for $\text {double-} {\r m CO}{2}$ circumstances utilizing regional climatic model by the way of 20 – km horizontal grid offer commonly winter higher temperature, a progressively marked increment in temperatures of summer, signs that temperature builds supplementary at higher heights than at lower elevations, as well as higher / additional exceptional precipitation in the season of winter, although much dryer situations in summer season. Under this pressure, the Alps of Europe would lose real parts of their ice mass cover within decades.
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2.7 Glacier assessment by Remote Sensing methods
Paul and Molg (2013) this exploration utilized glacial outline as a support for change evaluation. By matching outlines for debris and clean secured glaciers, as got from numerous and single digitizing by similar or the unusual forecasters on medium resolution 30 m and very high 1m remote sensing information and data, in opposition to one another and to glacier outlines got from mechanized classification of Landsat data, outcomes illustrate a high variation in the analysis of glacier parts covered by debris, generally autonomous of the spatial resolution (regional diversity were up to 30%) and a general good agreement for clean ice with adequate complexity to the encompassing landscape (difference ~ 5%). The dissimilarity of the involuntarily was belonged from a reference value as minor as the standard deviation of the manual digitization from a few examiners. In view of these outcomes, we abstracted that computerized mapping of clean ice is favorable to manual digitization and propose utilizing the latter technique just for needed corrections of inaccurately glacier mapped parts.
Smith and Booth (2006) this exploration demonstrate the outcomes of an experimentation to contrast glacier geomorphology remotely sensed mapping with 1: 10,000 scale field diagram. The field mapping is authenticated in opposition to LiDAR high resolution images of a region ice covered amid the Younger Dyras and originate to offer a effectively trustworthy, if not comprehensive, illustration of the glacier geomorphology. The trial encompasses of contrasting the digital elevation model with field mapping and Landsat satellite image of 100 km² area of Glasgow north and central Scotland, that was previous glaciated amid the glacial maximum and amid the Younger Dryas, in that order c. 14.5 and 11.5 cal. Ka BP. For this intention behind this activity, research focused on glacier lineaments (tail, flutes, crag and drumlins) however deliberation was additionally given to eskers, moraine and ridges. Quantitative versus qualitative correlation was applied and the outcomes demonstrate that of the remotely sensed information and data sets, only NEXT Map Great Britain™ offered domino effect that explained any calculation to the field mapping. OS Profile® and OS Panorama® offered very poor approximations, and alternate techniques not succeed to give data and information to significant value.
Kaab (2005) utilizing space borne and air remote sensing practices appropriate for permafrost and glacier hazard evaluation. Various strategies of change detection and image classification support studies of high mountain hazard. Optical stereo data linked to Digital
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terrain models, laser scanning or synthetic aperture radar shows one of the mainly significant information sets for examining high mountain procedures. Fusion of DTM from shuttle radar topography mission with satellite stereo obtained DTMs is a good method to join the benefits of the two innovations. Huge modifications in terrain volume, for example, from deposits of avalanche can in reality be estimated even by reiterate satellite DTMs. Multi temporal information and data can be utilized to infer surface dislocation on landslides, permafrost and glaciers. Merging DTMS, outcomes from multi temporal data from change detection and spectral imageries classification and displacement measurements considerably enhances the detection of hazard and risk magnitude.
Paul (2002) another Swiss glacier record is to be gathered from the data of satellite for the year of 2000. Research explained here and defines two key goals: Digital elevation model and accuracy assessment of diverse techniques for glacier grouping with Landsat thematic mapper; the GIS supported strategies for programmed extraction of individual ice masses from ordered satellite information and data and calculation of three dimensional glacier parameters (for example orientation, lowest, slope, highest and median) by fusion with digital elevation models. Results of TM4 and TM5 uncover the most appropriate glacier map.
2.8 Glaciers measurement
Phan and Lefevre (2017) this research analyzed Argentiere glacier positioned in the Alps of France and discussed glacier flow approximation utilizing Synthetic Aperture Radar imagery information. The main aim is to develop a weighted graph model created from characteristic points to quantify the dislocation vectors situated at their locations. Actually, characteristics points are proficient of catching the images contextual and radiometric data and information. At that point, by encoding their association and inter link, a graph representation was capable to differentiate both geometry and intensity material from the imageries content, which was more helpful for texture tracking mission. In this exploration, applied a graph based similitude measure to follow the local texture data in the region of every keypoint in series to figure out its association from the other imageries and measure the connected displacement. This approach was checked by utilizing high resolution TerraSAR- X scenes. Investigation fundamental exploratory outcomes demonstrate the algorithms ability to give a quick and trustworthy approximation of glacial flows, particularly over vastly structured and textured areas.
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Carturan (2016) explorer analyzed the La Mare valley glacier located in the central part of the Ortles Cevedale eastern part of the Italy, ice mass balance is a main variable for the observing techniques of the world climatic system, actually the persistence of long haul perceptions is presently imperiled by the approaching eradication of a few observed glaciers. According to the methodology snowline data were mapped every 2 and three weeks amid the season of ablation, manual snow covered maps were collected and digitized, second degree polynomial function were utilized for winter mass balance, in geodetic way two LiDAR surveys was conducted, results of La Mare glacier shows mass balance was negative from 2003 to 2012, significantly positive in 2014 and close to zero in 2013, outcomes indicate gathered information are valuable for testing and optimizing two process projected in the literature for extrapolating quantifications to inaccessible regions.
Blake (1994) the main aim of this exploration was to quantify downhill at the base of Trapridge Glacier, Canada Yukon region by utilizing a “Drag spool”. Exploration depicts this straightforward and reasonable method and tool and its installation and management. From 1990 – 1992 seven areas were tooled with drag spool. At six of the destination basal sliding, amid the time of monitor, represented for 50 – 70 % of the overall flow monitored at the glacial surface. The commitment from ice creep was recognized to be minor, so majority of the staying surface movement should be ascribed to sub glacial sediment deformation. For the 7th site the monitored downhill rate was ~90% of the aggregate stream, a sign that the sliding involvement differs spatially over the bed. Diurnal variability in the reaction of one of our instruments come into view to be correlated to sub glacier water stress vacillations and are inferred in patterns of alteration in sliding velocity instead of the closing and opening of basal cavities.
Scambos (1992) west Antarctica was selected as test area, main objective was image to image cross correlation programming connected to sets of computerized satellite imageries to map the delineate speed areas of moving ice. This strategy utilizes minute scale glacier surface characteristics, for example snow dunes and crevasse scars as indicators on the face of the moving ice. Disarticulations of the surface features was mapped by choosing the minor imagery zones focused on particular features, or by separating a huge area of thickly featured glacier surface into areas of grid and exploring the successive image for coordinating regions utilizing a cross correlation algorithm. Interpolation of the peak correlation rate enables the relocations to be estimated to sub pixel accuracy, bringing about exceptionally velocity quantifications. Cross correlation was likewise connected to offer image co registration in
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zones without bed rock revelation. In this type of zones, inconspicuous extensive scale topographic undulations in the glacier surface, linked to hidden bedrock configurations, might be connected by utilizing big image regions and low pass filtered scenes. The two sorts of utilization are illustrated, utilizing ice stream D and E in west Antarctica. A map of high resolution of the velocity field of the middle part of ice stream E, produced by the displacement estimating practice, is introduced. The utilization of cross correlation programming is beneficent enhancement over past physically based photogrammetric techniques for velocity estimation.
2.9 Glacier retreat and melting measurement
Wood (2018) in late decades, tidewater ice sheets Greenland northwest participated fundamentally to rise ocean level. The key aim was to reveal a complicated spatial trend of retreat. At that point explorer utilized novel monitoring of water temperature and bathymetry from Greenland Ocean melting NASA’s mission to calculate the track of warm, Atlantic salty water holding the evolution of thirty seven glaciers. Modeled ocean persuaded undercutting of calving edges contrasted with ice front retreat and ice advection monitored by system of satellite from 1985 – 2015 demonstrate that 35 glaciers retreated when cumulative variability in ocean induced undercutting ascended over the scope of seasonal fluctuations of calving front locations, whereas two glaciers remaining on colder water and shallow sills did not move back. Variations in the monitored timing of retreat are clarified by residual vulnerabilities in bathymetry, the occurrence of minor floating segments, and non-efficient blending of water in shallow fjords. Generally speaking, rising sea temperature pushed the retreat; however 71% calving procedures overwhelm ablation.
Aubry and Mark (2018) this work done analysis on Cuchillacocha glacier in Peru. Tropical ice masses comprise an imperative wellspring of water for population. Be that as it may, researcher comprehension of glacier melt forms is as yet constrained. One monitored method that has not been evaluated for tropical glaciers is the melting sourced by the long wave emission transfer. The main aim was to utilize high resolution temperature of surface acquired from the image of thermal infrared Cordillera Blanca, Peru Cuchillacocha Glacier in June 2014 to measure a edge long wave flux. That long wave flux, getting the glacier margin from the side of neighboring uncovered rock, fluctuate among81 and 120 Wm-2 day by day. That flux is fused into a melt model on physical basis to review the net solar radiation at the modeled glacier margin. The imitation outcomes represent an increment in the energy
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accessible for melt by an aggregate of 106 W m – 2 amid the day when contrasted with the simulation where the long wave margin flux is not represented. This esteem corresponds to an expansion in ablation of ~ 1.7 m at the ice sheet margin for the span of the dried season.
Gabbud and Lane (2015) research presented how another age of earthly laser scanner can be utilized to examine glacial ablation and additional components of glacier hydrodynamics at unusually high temporal and spatial resolution. The study area under analysis was Haut glacier d’Arolla alpine valley glacier, Switzerland. Ultra-long range scanner RIEGL VZ – 6000 was utilized for examining the aim. Having a laser explicitly planned for estimation of ice cover and snow surfaces. Investigation center around two timescales day by day and seasonal. Results explain that a system of near infrared scanning can offer high exactness elevation change and ablation information from long ranges, and over moderately extensive segments of the glacial surface. Researcher utilize it to calculate spatial variability in the trend of surface melt at the seasonal scale, as managed by both perspective and disparity debris cover. On daily base scale is calculated the impacts of give- associated dissimilarities in ice surface content of debris on spatial trends of ablation. Every day scale estimations point to conceive hydraulic jacking of the glacier coupled with short term water pressure ascends.
Pratap (2015) the majority of the focal Himalayan ice sheets have debris layer of inconsistent thickness, which to a great extent influence the rate of ablation. The main aim was to relate glacial surface melting to debris cover thickness. In methodology thirty were utilized to quantify clean ice of Dokriani Glacier and ablation for debris covered (7 km²) from 2009/10 to 2012/2013. Exploration discovered noteworthy altitude wise distinction in the rate of debris covered ice melting and clean. Outcome initiates a high correlation (R2 = 0.92) among altitude and yearly clean ice ablation and a very minor correlation (R2 = 0.14) among altitude ice melting with debris covered. Ablation was most extreme under thicknesses of debris 0f 1 – 6 cm and least under 40 cm. even a thickness of minor debris of 1-2 cm decreases ice melting as judge against to that of clean ice on yearly basis. As a whole, ice ablation ice covered amid the research time was monitored to be 37% not exactly clean ice ablation. Significant down wasting was additionally monitored in the study area with aggregate yearly ablation of 1.82 m w.e.a-1 in a related timeframe.
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2.10 Glacier mass balance
Shepherd (2017) this work examine the mass balance change of west Antarctica, Antarctic Peninsula and east Antarctica and key goal of this research from 1992 to 2017. In the sense of methodology collective satellite monitoring of its gravitation attraction, changing volume and flow with the help of modeling and satellite were applied, on the whole 24, 24 and 23 individual approximates of mass change were calculated within the identified geographical boundary. Monitoring results demonstrate that mean sea level increase 7.6 ± 3.9 in millimeters because it lost 2720 ± 1390 billion tones ice during that period. Ocean determined melting has reasoned rates of ice thrashing from western Antarctica to augment from 53 ± 29 billion to 159 ± 26 billion tones every year. Ice shelf fall down has enhanced the velocity of ice loss in peninsula Antarctica from 7 ± 13 billion to 33 ± 16 billion tons each year. Researcher discovers maximum variability in and among model calculates of balance of surface mass as well as glacier isostatic modification for Eastern Antarctica, with its aggregate rate of mass expand amid the period 1992 to 2017 (5 ± 46 billion tones every year) being the slightest certain.
Berthier (2007) remote sensing information and data to observe glacial altitudinal changes and mass balances in the Himachal Pradesh, Spiti, India was analyzed and major aim of this examination. According to the methodology Calculations are acquired by contrasting 2000 Shuttle Radar Topographic Mission to the 2004 digital elevation model. On mainly ice sheets, an apparent thinning was estimated to low altitude, even on tongues covered by debris. Somewhere in the range of 1999 and 2004, we acquire a general explicit mass balance of – 0.7 to -0.85 m/a contingent upon the density utilize for the vanished (or achieved) material in the gathering zone. The tempo of ice vanish id double higher than the long haul (1977 – 1999) mass balance evidence for Himalaya representing an expansion in the velocity of glacial wastage. To review whether these ice thrashing are size reliant, all glaciers were categorized into three sections according to their areal degree. Outcomes indicate each of the three sections illustrate ice loss the Yantis being higher for ice sheets greater than 30 km². In the matter of the standard of glacier Chhota Shigri, a fine conformity was originated between the satellite explanations and the mass balance quantified on the field amid hydrological years 2002 – 2003 and 2003 – 2004.
Rabtel (2006) Alpine glaciers in French Alps are exceptional delicate to climatic variances, and their balance of mass can be utilized as an marker of local scale environmental
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change. The major objective of this study was to quantify mass balance by utilizing remote sensing information and data. Snowline calculations from the side of remotely sensed imageries noted at the end of the hydrological year offer a useful proxy of the equilibrium line. Mass balance can be extracted from the equilibrium line altitude deviations. Three sound recorded glaciers mass balance was quantified at ground level with a stake system were preferred to appraise the accuracy of the technique over the 1994 – 2002 timeframe. Outcomes attained by ground calculations and remote sensing are evaluated and demonstrated significant correlation (r2 > 0.89), mutually for the ELA as well as balance of the mass, signifying that the remote sensing strategies can be pertained to glaciers where no ground information and data available.
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CHAPTER: 3
MATERIAL AND METHODS
This research work was conducted to the glacial anomalies of Karakorum as an evidence of climate change; in the research the availability of data was in different formats and types. To build interoperability among all datasets to perform GIS the data was prepared in software affable format by using several metrological data, satellite images and other remotely sensed parameters of temporal datasets. Most of the Meteorological data satellite data was in manual traditional file system except the data of few places was in electrical file system. So, all data was organized accordingly. The database for meteorological data (rainfall, temperature (min, max) was generated in MS Excel. The GIS analysis was performed to achieve the required results. The detail is given in this chapter.
Software used:
1. ArcGIS 10.1 2. Erdas 2010 3. MS Excel 2010 3.1 Methodology
To get the required objectives, following methodology was used for data processing and analysis. There was different method used to investigate the anomaly of Batura Glacier. The physical methods applied related parameters DEM (Digital Elevation Model) and Interpolation made a use of Landsat images.
3.1.1 Meteorological data
The HKH region stretches for more than 2000 Kilometers in length from east to West. Along this mountain range there is a considerable variability in climate condition including varying source regions and type of precipitation (e.g. Bocchiola and Diolaiuti, 2013), influencing the behavior and evolution of cryosphere. The HKH region nests about 60,000 km2 of ice bodies, glaciers, glacierets and perennial surface ice in climatic regimes (Kaab et al., 2012) and it is considered the third pole of our planet (Winigar et al., 2005; Smiraglia et al.,2007; Kehrwald et al.,2008). This large mountain system delivers water for agriculture, human consumption and power production and more than 50% of water in the Indus River originating from the Karakorum comes from snow and glacier melt (Immerzeel et al., 2010).
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The most recent observations indicate that glaciers are retreating globally with the increase in the global temperature. However, some of the glaciers in the eastern and central HKH glaciers are subject to general retreat and have lost significant amount of mass and area (Salerno et al., 2008; Bolch et al., 2011). Karakoram mountain ranges are reported as surging with positive mass balances and advancing, especially since the 1990 to 2016. Various efforts have been made to explain the state and fate of the HKH glaciers in the recent past. To identify the reasons for the establishment of Batura glacier anomaly, monthly mean climatic variables for last three decades, reported from meteorological observatories at the valley floors in HKH region are analyzed. The climatic variables of temperature and precipitation, monthly mean data from 2007 to 2015 is taken from Regional Meteorology Centre of Hunza station are used. The role of different near surface and upper atmosphere metrological variables in maintaining positive mass balance of the glaciers and development of the Karakoram Anomaly can be explained. Batura glacier receive accumulation from precipitation during the Indian monsoon in summer snowfall occurs in winter through westerly atmospheric circulations (Bookhagen and Burbank, 2010; Kaab et al., 2012; Fowler and Archer, 2006).
3.1.2 Rainfall /Snowfall Data
The rainfall and snowfall data is important data to know the climatic variables in Hunza district that directly affect the glacial accumulation or volume. Hunza meteorological station is situated 2156.0 meter above sea level and its Latitudinal 3619 longitudinal 74 39 in extent. Monthly rainfall data is taken from 2007 to 2015 from PMD (Pakistan Meteorology Department). The data was also obtained from other surrounding meteorological stations (Gilgit, Gupis,Astore and Skardu). The Meteoblue, Switzerland also provided the data of three very important stations including Batura glacier. Sakar (Pakistan Afghanistan border) and Diyar are the two more stations which are located in the 50 km radius of my study area. This study is of its first kind which contains the data of these three meteorological stations.
3.1.3 Temperature Data
In this research the temperature data is important to correlate the snow melting and glacier anomalies. For this purpose minimum and maximum temperature for 2007 to 2015 was obtained. Overall warming is observed in the region. Summer temperature is decreasing a decade ago now found increasing in updated time series. The overall gradient is still negative.
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The winter mean and maximum temperature are increasing with accelerated trends. Both maximum and minimum temperatures in summer are not diverging anymore and diurnal temperature range is decreasing in most recent decades (Bashir, Furrukh 2016).
The use of meteorological data of different months and years of significant variations of temperature change was also acquired.
3.2 Satellite Data
The two types of data metrological data and satellite data was obtained to find the objectives of this research. Satellite remote sensing technique is practical approach that is used for assessment of changing in thickness, surface area and glacier mass balance. Data obtained for this research confined with interval of ten years.
3.2.1 Satellite Images
Image processing was necessary to analyze the glacier. The long term changes are observed by analyzing remotely sensed imagery. In remote sensing there are variety of image analysis and techniques which are used to conclude better results of the observed climate change in Batura glacier. The land sat imagery that is used in this raster analysis was captured during 1988 to 2016.
3.2.2 Digital Elevation model
Digital elevation model is the cell based digital data of the earth’s surface. The data is used to calculate elevation above sea level and other characteristics of the earth surface. The Digital elevation model is Raster representation of a continuous surface, usually referencing the surface of the earth. The accuracy of the digital elevation model is based on primarily by the resolution location and distance of data sampling points. Error in DEM is usually classified as either sinks or peaks. The USGS DEM is arrays of regularly spaced elevation data of the world. The projection which is used for generation of data is Mercator projection. It is a three dimensional elevation data use for surface analysis of the earth.
3.2.3 Land use land cover data
Land use and land cover classification of data sets of the Batura glacier were calculated. The purpose of the land use and land cover classification was to know the land use pattern of the area under study. There are multiple classifications in the world. The land
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use classification is held according to the need of user. Remote sensing provides multiple techniques which satisfy the user. For development of this classification remote sensing offers us different software here we are using one of them which is Eradas Imagine 2013. For the classification of Batura glacier three classes were developed which are snow, barren land and vegetation cover on the data sets of four different years starting from 1988, 2000, 2008 and 2016. The calculated values are discussed in further chapter of this thesis. For image classification of land use land cover of the area different classes or signature were developed in Eradas Imagine 2013 of the data sets of four years. The more the signature the more the results are correct. Than we reclassify the images in ArcGIS. The results and analysis of this classification will discuss in further chapter of the document.
3.3 Spatial analysis and methods
Temperature and rainfall data was obtained from PMD (Pakistan Meteorological Department). This data was obtained in raw form. After the tabulation of the raw form data interpolation method was applied to construct maps. These maps expressed trend change on the basis of temporal and spatial trend
3.3.1 Rainfall Anomaly Analysis
Anomaly analysis is technique of data mining in which rare events are observed from the majority of data. Abnormal behavior of continuous data was detected in this type of data analysis. In this study rainfall anomaly analysis is performed to see amorous behavior of rainfall of Batura glacier by using rainfall data during 1985 to 2018 from different stations installed by PMD (Pakistan Meteorological Department). The data were taken from different altitudes and locations near and within Batura glacier. During analysis data were tabulated, interpolated, statistically and geographically analyzed to find out anomalies and changes in data during last thirty years.
3.3.2 Temperature Anomaly Analysis
To find out the abnormalities of the temperature distribution of the Batura glacier during last thirty years temperature anomaly analysis were required. This is one of the most imported analyses of the study because glacier melting is very much depend on the temperature distribution of area under study. For this analysis data were collected from different climatic stations of the Batura and nearby the area. The data of temperature were
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also statistically and geographically analyzed to observe changes in temperature of the glacier during last three decade.
3.3.3 Normalize Difference Vegetation Index
The index, Normalize Difference Vegetation Index was used to know the deference Near Infrared Rays (NIR) and R values of visible reflectance over total of two values (Iqbal et al, 1997).
The Normalize Difference Vegetation Index is a specific measure of Chlorophyll abundance and energy absorption. It was used to calculate the vegetation condition and phonology in a range of ecosystem.
The ranges of these NDVI values are between +1 to −1. Those values close to
+1 denotes good vegetation condition. The NDVI calculated to each image to analysis the vegetation condition on temporal basis as given in (Eq. 3.1) (Tuker et. al., 1989).
NDVI= (IR−R) / (IR−R)
IR= Near Infrared band reflectance (Band 4)
R= Red band in reflectance (Band 3)
This reflectance on the images showed different classes of vegetation. Due to this index, it was known that the values which were near to +1 have dense vegetation and the values that are close to −1 values showed scattered and thin vegetation (Abbas, 1999).
Normalize difference vegetation Index calculates vegetation cover by using near-infrared radiations of electromagnetic spectrum the visible portion of the spectrum is 0.4 to 0.7 neno meter while the infrared range is ˗1 to +1 neno meter. When values are near -1 it may be water body or water content is high while when values are near +1 it means that the area has dense green leaves but when NDVI is zero then there is not green leaves it may be the urban area.
Formula:
(푵푰푹−푹푬푫) NDVI= (푵푰푹+푹푬푫)
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3.3.4 Snow cover index (NDSI)
The normalized difference snow index is used to measure the presence of snow in the raster data. This is the more authentic way to detect snow in imagery. Snow have high reflection of visible long wave length and low short wave infrared reflectance of infrared waves (Riggs et all,. 2016)
Snow cover is normally measured by visible long wave and short wave infrared reflectance. Formula is given below:
NDSI= ((band 4 –band 6)/ (band 4 +band 6))
If the results are grater then 0.0 means have some snow present while if NDSI is less than or equals to 0.0 than it is a snow free land surface.
The Normalized Difference Snow Index (NDSI) snow cover is an index that is related to the presence of snow in a pixel and is a more accurate description of snow detection as compared to Fractional Snow Cover (FSC). Snow typically has very high visible (VIS) reflectance and very low reflectance in the shortwave infrared (SWIR), a characteristic used to detect snow by distinguishing between snow and most cloud types. Snow cover is detected using the NDSI ratio of the difference in VIS and SWIR reflectance; NDSI = ((band 4-band 6) / (band 4 + band 6)). A pixel with NDSI > 0.0 is considered to have some snow present. A pixel with NDSI <= 0.0 is a snow free land surface
Table 3.3 Satellite Landsat
Satellite Path Row Resolution Date
Landsat 5 150 34,35 30m July 1988 Landsat 5 150 34,35 30m July 1998 Landsat 5 150 34,35 30m July 2008 Landsat 8 150 34,35 30m July 2016 3.3.5 Glacier index
The glacier index is calculated to know the information about glaciers. The remotely sensed aerial photograph and data sets are used for calculation glaciers volume and other characteristics.
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3.3.5 Slope elevation and aspect analysis of glacier by DEM
Digital elevation model is used to measuring glacier slop and aspect and elevation of area (Klein et all., 1998). Digital elevation model is used to generate maps of elevation, slop and aspect of the area. Geographical information system analysis DEM performed to calculate terrain and topography of the area under study.
3.3.6 Glacier anomalies analysis
In present due to high temperature sea ice decline the frozen features of the earth are changing. Most of the world facing decline in glaciers but some stubborn are not like Karakorum of the Himalayas. The climate of the HinduKush, Karakorum and Himalaya ranges stretching over 2000 kilometers (Chistoph Mayyaer). To detect anomaly or abnormal behaviors of climate in Batura glacier analysis were performed on raster and vector data of the Batura glacier.
3.4 Precipitation Anomalies Index
Rainfall Anomaly Index (RAI)
From the precipitation data the Annual/ Monthly Rainfall Anomaly index can be analyze frequency and intensity of the dry and rainy years. RAI developed and firstly used by Rooy (1965) and adopted by Freitas (2005), constitute the Following
Equation:
N-N RAI= 3 For Positive anomalies M-N
N-N RAI= - 3 For Negative anomalies X-N
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Where N- current monthly/ yearly rainfall in order words, of the monthly /year when RAI will be generated (mm); N- monthly /yearly average rainfall of the historical series (mm);
M- average of the ten highest monthly/ yearly precipitations of the historical series (mm); X- average of the ten lowest monthly /yearly precipitation of the historical series (mm); and
positive anomalies have their values above average and negative anomalies have their values below average.
3.4.1 Procedure of Calculation Anomalies:
The following steps were followed while anomaly calculation,
1. Add historical rainfall values 2. Again copy these values two more times in sheet 3. Sort first copy of historical rainfall values smallest to largest 4. Sort second copy largest to smallest 5. Select first 10 values of these sorts we only need this from sorted values 6. Delete other sorted values that we don’t need these sorted values 7. Calculate the averages of top ten smallest and largest values 8. Also calculate the total yearly historical rainfall averages Now we have three averages a. Average of top ten smallest rainfall values b. Average of top ten largest Rainfall Values c. Average of Total historical rainfall Now apply the formula
In which we need this
Xa= Smallest 10 values average
Ma= Largest 10 values
Na= yearly Averages
Now calculate anomalies of Negative and positive rainfall anomalies:
1. Put equal sign in excel cell 2. Select first rainfall year value and minus it from total annual average “Na”
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3. Drag this for all years values 4. Now sort this calculation to smallest to largest For Negative Anomalies:
1. Put equal sign 2. Minus three times * select first negative value in excel cell and divided it with Smallest 10 average vale which we have “Xa” 3. Now we just need to sort years column from smallest to largest because years are random 4. Now copy anomalies and past only values 5. Now delete other columns 6. Now you have one year column and other rainfall anomalies value. 3.4.2 Correlation Analysis
Correlation studies may be designed either to determine whether and how asset of variables are related or test hypotheses regarding expected relations. Variables to be correlated should be selected on the basis of some rational. The relation should be a logical one which needs to be investigating. A correlation coefficient is a decimal number between -1.00 and +1.00. it describes both the size and direction of the relation between two variables. If the correlation coefficient is near 0.00 the variable are not related. If the correlation coefficient near +1.00 indicates that the variables are strongly related. Correlation coefficient of +1.00 to -1.00 represent the same strength but different directions of relation between two variables(Gay, Mills, & Airasian, 2012).
Formula:
∑(x−mx)(y−my)
R =−−−−−−−−−−−−−−−−−−−−−
∑(x−mx)(y−my)∑(x−mx)2
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3.5 Regression Analysis
Regression analysis is a statistical measure that use for estimating the relationship among two variables dependent and independent variables. Regression analysis helps to understand how the typical value of the dependent variable changes by independent variable. Regression analysis is also use to understand dependent and independent variables (“Regression analysis,” 2018) for the presentation of regression analysis different techniques. There are different models of regression analysis like linear regression, scattered plot, simple regression equation (Gay et al., 2012).
Formula:
(∑y) (∑x²)- (∑x) (∑x y)
a =−−−−−−−−−−−−−−−−−−−−−
n ((∑x²) – ((∑x)²
3.6 Meteorological Obervation Over Batura (Lat, Long)
The data of Batura Glacier points have been taken on a special request sent by the researcher. I sent request to Meteorological observatory for temperature, precipitation and snowfall data requirement gor my study on Batura Glacier. The process of data observation by obsevatry given below.
3.6.1 Introduction to the Meteoblue data gathering :
University of the Basel, Switzerland, and U.S National Oceanic and atmospheric administration and the national centers for environmental prediction start working together in metrological field in 2006. After Sandoz chemical disaster a local university made this company. The site have archived continuous data of past 30 years without any gap. The sites offers all weather variables from throughout the earth. Meteoblue uses NOAA Satellites GOES-18.
3.6.2 Introduction to the website:
The website is providing data related to the weather for casts like shower, thunderstorms, temperature, precipitation, cloud cover and wind on hourly bases. This website is the first in the world that offers weather prediction into graphs. This university and local researcher groups are famous for their
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weather Predictions Company and website from where data purchase and download into different formats. The sites provides variety of graphs maps and simulation models.
3.6.3 Types of Data Sets:
The website meteoblue is providing hourly data of weather since 1985. The major types of data enlisted below:
1. Temperature (2m above ground) 2. Ralitive humidity (2m above ground) 3. Pressure at sea level 4. Precipitation amount mm/m² 5. Snowfall cm/m² 6. Total cloud cover in percent 7. Low mid and high cloud cover ( in percent) 8. Solar radiation w/m² 9. Wind speed and direction (10m) km/h, m/s, bft, kn, mph 10. Wind speed and direction ( 900hpa) km/h, m/s, bft, kn, mph
3.6.4 Formats of data
1. Tables 2. Graphs 3. Models 4. Meteoblue Maps 5. Meteogram 3.6.5 Data Genration:
The sites offer weather data of 30 years from 1985 to present. The data is continuous and combine. The meteoblue provided all parameters of weather from all over the world and the calculation of lacal and global weather data models.
3.6.6 Simulation data:
The company provide worldwide historical weather simulation data along with the spatial resolution of 4 to 30km. this data collection started in 1985 and the high resolution data collection was started in 2008. This data is complete and have no gaps. The company have data verification system of their weather that’s why top quality data is available. The data is suitable for areal and regional weather
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assessment for longer time span but not good for the extreme local conditions for this measurement data is use.
3.6.7 Limitations of Simulation data:
Resolution of data fluctuate at different locations and times so the micro climates of narrow valleys or mountain tops not sensed. But still analysis is possible of these area but little errors in results of these narrow areas and mountain may have.
3.6.8 Verification:
The data authenticity is highly secure because of the high quality standards and methods of data verification and validation. This improves the weather models quality.
3.6.9 Formats of Data:
The data is available in multiple formats . it can be download as CSV files, common evaluation comparison format, GDD, and frequency analysis.
Nature of data:
1. Historical Data 2. Interactive interface to analyse data 3. Compare multiple years 4. Frequency analysis histogram and wind rose
3.6.10 Historical Data:
30 years of historical data can be downloaded with high resolution of hourly bases. Historical data includes the following parameters like temperature, precipitation, clouds and winds.
Interactive interface to analyse data:
The pakage of data have large amount of weather data interactive graphs and comparisons are possible of 30 years historical data or you can compare it with other places.
Compare multiple years:
With help of data weather conditions for seasons were calculated. As well as annual comparison is also possible.
Frequency analysis histograms:
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Meteoblue also offers frequency analysis and histogram and wind rose distribution of wind data.
3.6.12 Meteograph:
5- day meteograph is a temperature chart with weather pictograms with sun timing. Also shows cloud covers of area. Forecasts for wind direction and precipitation.
3.6.13 Meteoblue Maps:
Meteoblue maps provides wind streamlines, wind speed, temperature and precipitation maps with animation. Different altitude layers can be used in these maps.
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CHAPTER: 4
RESULTS AND DISCUSSION
This chapter consists of in depth analysis the study. The analysis are primarily divided into two major categories; First Statistical Analysis of point data of different meteorological parameters especially precipitation, temperature and snow amount since 1985. The second part comprises the rater dataset analysis of satellite images classifications since 1988. The first portion covers the climate change aspects by discussing the statistical trends of meteorological parameter over the region under study. The vector data sets have been processed through different statistical operations. The statistical summaries give the best picture of the changing climate regime over the study area. The analysis shows that the recent studies results are in line with most of the studies conducted over the glaciated region.
Historical Statistics of Percipitation Data from Batura and srounding Climatic Satations
Graphical Ternds of pericipitation records of Batura and Surronding Areas
long term Trend Analysis
Statistical Summary of Vector Data
Raster Data Analysis
Image Clasification
Ice cover and amount calculations
Corelation Analysis
Figure 4.1 Flow chart of the analysis
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4.1 Vector Data Set Analysis
For investigation of glacial anomalies of Karakorum as an evidence of climate change, the in- depth study of Batura glacier was conducted. Firstly, for investigating the historical background of climatic trends, the data of five climatic stations installed in surrounding of glacier Batura were analyzed. Especially the historical trends of rainfall were investigated during 1978 to 2015. The above figure have five graphical trends of rainfall in the surrounding of Batura glacier the names of stations are following, Bunji, Astor, Gilgit, Gupis and Skardu.
3.4.1 Total Precipitation of Bunji
The Bunji is the nearby station to Batura and temporal precipitation trend was analyzed from 1978 to 2015, which has great variability in these years.
Bunji (Total Precipitation in mm since 1978 to 2015) 400 350 300 250 200 150 100 50
0
1990 1996 2015 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1991 1992 1993 1994 1995 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014
Figure 4.2 Graphical representation of total received precipitation in surrounding station of Batura (Bunji) Data Source: Pakistan Meteorological Department
The above graph is showing total rainfall received in Bunji which is at the near Batura glacier. The graph is showing maximum rainfall 339.4mm in 2003 while the minimum precipitation was received in 2001 which is 74.3mm. The average rainfall during these 38 years was 171.59mm at this station. The grand total during 38 years was 6520.4mm in recent years the amount of precipitation is declining.
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4.1.2 Total precipitation of Astor
The temporal trend of precipitation was analyzed for Astor and graphical trend was analyzed. Almost data of 37 years were used in analysis. Precipitation data from 1978 to 2015 was used for analyzing the temporal trend of precipitation in Astor. The detail of trend in different years are following.
Astor (Total precipitation in mm since 1978 to 2015) 1000 900 800 700 600 500 400 300 200 100 0 1978198019821984198619881990199219941996199820002002200420062008201020122014
Figure 4.3 Graphical representation of total received precipitation in surrounding station of Batura (Astor) Data Source: Pakistan Meteorological Department
The figure 4.3 is showing graphical trend of Astor total rainfall in mm. The highest amount of rainfall is 857.7mm in 1996 while the amount of minimum precipitation received was 2627.7mm in 2007 and the average range of received rainfall in Astor was 484.86mm which is higher than Bunji station. During the years of 1978 to 1982 Astor receives the average maximum rainfall was 86.42 mm while the minimum precipitation was 15.32mm. if we move towards 1983 to 1987 the average precipitation ranges from 9.98 mm to 132.84 mm. in 1988 to 1992 maximum average was 106.64 while minimum average was 5.86 mm. The years 1993 to 1997 the average minimum precipitation is 12.66 mm while the maximum average was 107.1 mm. in 1998 to 2002 the maximum average was 117.4 mm while minimum average was 14.08 mm while in 2003 to 2007 maximum and minimum averages ranges from 11.52 mm to 84.78 mm in 2008 to 2012 this average was 0.68 mm to 76.26 mm and in 2013 to 2015 the maximum average was 74.06667 mm to 12.3 mm.
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4.1.3 Total precipitation of Gupis
The next graph 4.4 is showing precipitation trend of Gupis historical rainfall trend in Karakorum the highest received rainfall in Gupis was 675.6 mm in 1999 while the minimum value was 5.3 in 1982.
(d) Gupis (Total precipitation in mm since 1978 to 2015)
800
700 600
500 400 300 200 100 0
Figure 4.4 Graphical representation of total received precipitation in surrounding station of Batura (Gupis) Data Source: Pakistan Meteorological Department
Here the mean value is 203.69 and the grand sum was 7740.4 mm during last 38 years. Precipitation fluctuate during 1978 to 2015 in 1978 to 1982 the maximum averaged received precipitation was 36.06 mm while the minimum value was 0 mm the amount more dropped down in 1983 to 1987 the maximum average was 26.68 mm while minimum average was 0.94 mm which is very low amount. In 1988 to 1992 the minimum average precipitation more dropped down while the maximum value was 39.94 mm in the years of 1993 to 1979 precipitation average s ranges from 3.51 mm to 54.82 mm which is slightly increased from previous years the amount of precipitation increased in 1998 to 2002 the maximum amount was 107.76 mm in 2003 to 2007 maximum average was 119.76 mm and minimum average was 1.82 mm in 2013 to 2015 the precipitation again dropped down and the maximum average was 0 mm and lowest average amount was 0 mm.
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4.1.4 Total precipitation in Gilgit
The graph 4.5 is showing Gilgit here the highest value is 285.95 mm in 2010 while the minimum was 64.4mm in 1983 and the average was 145.98 during 1978 to 2015 and the overall received rainfall during 38 years was 14598mm.
(c) Gilgit (Total precipitation in mm since1978 to 2015) 350
300
250
200
150
100
50
0 1978198019821984198619881990199219941996199820002002200420062008201020122014
Figure 4.5 Graphical representation of total received precipitation in surrounding station of Batura (Gilgit) Data Source: Pakistan Meteorological Department
In 1978 to 2015 the total averages of rainfall were fluctuated. In 1978 to 1982 the maximum average was 50.8 mm while minimum precipitation value was 1.68 at average in 1983 to 1987 maximum amount was 25.68 mm and minimum average was 0.46 mm in 1988 to 1992 maximum average was 29.87 while minimum average was 2.84 mm in 1993 to 1997 the average ranges from 3.5 to 28.4 while in 1998 to 2002 the average total precipitation was 39.86 mm and minimum 1.32 mm 2003 to 2007 the values ranged from 2.09 to 33.22 mm 2013 to 2015 the precipitation ranged from 0.95 mm to 26.9667 mm.
4.1.5 Total precipitation of Skardu
The below figure 4.6 is showing total rainfall at fifth station near Batura glacier of Karakorum which is Skardu. In this area the peak value was received in 2010 it was 495.4 and the lowest value was received in 2007 which was 109mm and the average rainfall during 1978 to 2015 was 235mm while the total received rainfall during 38 years was 8951.15 mm in Skardu.
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(e) Sakerdu (Total Precipitation in mm since 1978 to 2015) 600
500
400
300
200
100
0 1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012 2014
Figure 4.6 Graphical representation of total received precipitation in surrounding station of Batura (Skardu) Data Source: Pakistan Meteorological Department
These graphs are representing the areal climatic conditions in the surrounding areas of Batura glacier in Karakorum Pakistan. To find out the anomalies in climatic history rainfall related data is one of the most important climatic parameter. The yearly trend shows the average of precipitation during 1978 to 1982 ranges from 6.26 mm to 23.84 mm in 1983 to 1987 the maximum range is 43.58 mm to 5.84 minimum precipitation in 1988 to 1992 the highest average value was 54.625 mm which is highest in all years and the minimum value was 2.78 mm in 1993 to 1997maximum average was 45.7 mm to the 4.01 mm minimum average while in 1998 to 2002 the precipitation maximum average was 53.38 while minimum average was 1.74in 2003 to 2007 the maximum amount was 4.59 mm during 2008 to 2012 maximum range was 64.7 mm and minimum range was 4.59 mm in 2013 to 2015 the maximum average was 64.7 mm and minimum average was 3.8667 mm. The trend show slightly increasing precipitation in Skardu but is still less amount of precipitation.
4.2. Average monthly precipitation in the surrounding of Batura
The average monthly precipitation is subject to ice mass accumulation in the glacier and it depicts the seasonal variability in the region.
4.2.1 Astor average monthly Precipitation
The Figure 4.7 shows the monthly trend of rainfall during 1978 to 2015 in Astor station of Karakorum Pakistan January receives the highest rainfall during 2003 to 2007 while lowest in
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1978 to 1982. February receives highest rainfall in 2008 to 2012 while the lowest amount in 1983 1987. March have maximum amount in 1988 to 1992.
Astor Average Monthly Precipitation in mm 140 Jan 120 Feb Mar 100 Apr 80 May 60 June Jul 40 Aug 20 Sep 0 Oct 1978 to 1983 to 1988 to 1993 to 1998 to 2003 to 2008 to 2013 to Nov 1982 1987 1992 1997 2002 2007 2012 2015 Dec
Figure 4.7 Graphical representation of monthly precipitation of Astor station
The peak of April was in 1998 to 2002. The rainfall in May, June July august have similar trends of rainfall while the end of the year also have similar values of monthly rainfall. The above monthly trends of rainfall during 38 years for these eight groups of data were generated. During the period of each group the average received rainfall in months were shown. In first group the maximum value is 86.42mm in March of 1978 to 1982 while during this time span the minimum value was 15.32mm received in August. If we move further during 1983 to 1987 the maximum value of monthly rainfall was 132.84mm in January while the minimum value was 9.98 mm only in September. During 1988 to 1982 the highest amount of rainfall received in March which was 106.64 and the lowest amount received in November which was only 5.66mm. in the further next five years the month March received maximum rainfall about 107.1mm and the lowest amount was 12.66 mm in September. While if we see 1998 to 2002 the maximum rainfall received in April which is about 117.1mm and minimum trend was in October almost 14.08 mm rainfall received in Astor. While in 2003 to 2007 April with 84.78 with the decreasing trends of average rainfall in the month, While October received lowest rainfall only 11.52mm. The amount of rainfall was decreasing in 2003 to 2012 as well as in 2013 to 2015. The highest amount of rainfall received in 1983 to 1987 while the lowest total amount received in 1998 to 2002
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4.2.2 Bunji average monthly precipitation
The figure 4.8 shows the graphical representation of monthly rainfall in Bunji station of Karakorum Pakistan. There are monthly fluctuations of average monthly rainfall in the area. Here April, May have greater rainfall while January , February have very insufficient amount of rainfall while October November and December also had very less rainfall in Bunji The above monthly rainfall received in Bunji near Batura glacier.
Bunji Average Monthly Rainfall in mm 50 1978 to 1982 45 1983 to 1987 40 35 1988 to 1992 30 1993 to 1997 25 1998 to 2002 20 15 2003 to 2007 10 2008 to 2012 5 2013 to 2015 0 Jan Feb Mar Apr May June Jul Aug Sep Oct Nov Dec
Figure4.8 Graphical representation of monthly precipitation of Batura station
During the years of 1978 to 1982 the maximum rainfall received in April with the value of 42.96mm and lowest amount received in November with only 2.1mm. If move further from 1983 to 1987 the maximum amount was 37.98 in May while the lowest average was only 3.56 in February. In 1988 to 1992 the heavy rainfall received in July which is very low in history only 19.78mm and in these are lowest amount were 0 in November. The next group shows again low rainfall in the area with highest amount of 32.96 in May and lowest in January only 4.42. In 1998 to 2002 the peak month was April and the lowest value received in October with on 1.04mm. During 2003 to 2007 38.68mm is the highest value in May while 1.08 is the lowest value in November. In the years of 2008 to 2012 the heavy rainfall received in August which was 43.58mm and the lowest value is 0.74 in November. In 2013 to 2015 31.96 was the highest monthly value in September while the 3.55mm is lowest value in November. In Bunji total highest amount received during2008 to 2012 monthly total was 207.23 mm and the lowest total amount received in 1988 to 1992 which was 120.96mm.
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4.2.3 Gilgit average monthly rainfall
The figure 4.9 shows the monthly trend of annually received rainfall in Gilgit in the graphical trend April, May, and August receives higher rainfall while the other months of the year have very less rainfall and the trend was also similar with some fluctuations.
Gilgit Average Monthly Precipitation in mm 60 1978 to 1982 50 1983 to 1987 1988 to 1992 40 1993 to 1997 30 1998 to 2002 20 2003 to 2007 2008 to 2012 10 2013 to 2015 0 Jan Feb Mar Apr May June Jul Aug Sep Oct Nov Dec
Figure4.9Graphical representation of monthly precipitation of Gilgit station
Source of Data: www.meteoblue.com
The above monthly average values of received rainfall in Gilgit during 1978 to 2015. During the years of 1978 to 1982 the maximum values were 50.8mm in April while the lowest values are 1.68mm in January. While in 1983 to 1987 the amount of monthly rainfall in Gilgit were decreasing in April 25.68mm rainfall was received and the lowest value received in January which was only 0.46. The above table shows that in 1993 to 1997 the monthly amount of rainfall again decreasing in Gilgit the maximum amount was 29.87 while the minimum amount was 0.46 in the months of May and January respectively. During the years of 1998 to 2002 the monthly highest amount of rainfall received in April with the amount of 39.86mm and the lowest amount was received
In December with only 1.32mm during 2003 to 2007 the highest amount were 33.22 which is decreasing from previous highest amount in Gilgit and the lowest amount in these years were 2.09mm only in November. While in the years of 2008 to 2015 the monthly amount of rainfall again decreasing in the region with the maximum value of 34.92 mm in May while the lowest amount received in November with only 0.88mm. the monthly total highest value was received in 2008 to 2012 with the total value of 180.15mm and the lowest total was received in 1983 to 1987 it was 131.38. The average the monthly rainfall was decreasing in Gilgit.
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4.2.4 Gupis average monthly precipitation
The figure 4.10 shows the linear tends of monthly rainfall in Gupis Karakaram Pakistan. The monthly rainfall data retrieved from Gupis Pakistan meterological department. The figure illustrate that Gupis receives highest rainfall in March, April and May during 2003 to 2012 while in 1998 to 2002 this month also have high rainfall.
Gupis Average Monthly Precipitation in mm 1978 to 1982 140 1983 to 1987 120 1988 to 1992 100 1993 to 1997 80 1998 to 2002 60 2003 to 2007 40 2008 to 2012 20 2013 to 2015 0 Jan Feb Mar Apr May June Jul Aug Sep Oct Nov Dec
Figure 4.10 Graphical representation of monthly precipitation of Gupis station
February and March comparative less rainfall While the other months of the years also have less rainfall with similar trend. The high fluctuations in the monthly values of received rainfall in this region. The annually analysis in table shows the maximum value about 36.06 in the month of April and the minimum valve of only zero in December during the years of 1978 to 1982. While the next five years shows variations in months with 26.68 in August while maximum value is less than previous five years but lowest value is again in December with only 0.94mm rainfall in Gupis. The further five years again shows maximum value 39.94 in May while the lowest value showed in November which is only0.36mm. During the years 1993 to 1997 the heavy rainfall was received in June which is highest from all previous years in the region While the minimum amount was 3.51 mm in January. The maximum amount again increases in next five years which is 107.28mm again highest from all previous years in April while the lowest amount was 0 in again in January. The table shows that January and November received lowest rainfall in the Gupis while April and May received the highest amount of rainfall. During 2003 to 2007 the highest amount of rainfall is 119.76mm in April while the lowest amount was 1.82mm in November. The years 2008 to
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2012 the peak month was Feb with 23.2mm rainfall while the lowest value received in November which is only 1.14 at average. The total highest monthly amount received in 2003 to 2007 which was 361.82mm and the lowest total received in 1978 to 1982 which was only 65.8 mm in the region.
4.2.5 Sakerdu average monthly rainfall
The figure 4.1 monthly fluctuations of rainfall in Sakerdu. The line graph shows that in 2008 to 2012 February receives maximum rainfall while the other month of these years dose not heavy sufficient rainfall. In 2013 to 2015 August and September receives maximum rainfall the start of the year the rainfall was at average while in October, November, and December area receives very less rainfall the reason probably that was the season of snowfall.
Skardu Average Montthly Precipitation in mm 70 1978 to 1982 60 1983 to 1987 50 1988 to 1992 1993 to 1997 40 1998 to 2002 30 2003 to 2007 20 2008 to 2012 10 2013 to 2015 0 Jan Feb Mar Apr May June Jul Aug Sep Oct Nov Dec
Figure 4.11 Graphical representation of monthly precipitation of Skardu station
The figure 4.11 illustrates the monthly trend of average rainfall received in different years in Skardu. The table shows the data of thirty eight years. The first five years shows the highest amount of received rainfall in March with only 23.84 mm in average while the minimum amount was 6.23 in September. Furthermore in 1983 to 1987 the month receiving highest rainfall was March with 43.58mm rainfall while the lowest amount was received in September which is 5.84mm. While during 1988 to 1992 the monthly trend has great fluctuation with the highest amount of 54.625mm in the month of March while the lowest amount received in the month of November which is only 2.78mm. the years 1993 to 1997 the highest rainfall received again in March with average amount of 48.16mm and the
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minimum amount was 4.01 mm in October. The next five years again have heavy rainfall in Skardu with the average of 53.38mm in April which is highest of all previous April in Skardu. While during these years the lowest value was 2.78 in November. During the next years the highest amount was in February with the amount of 64.3mm while the lowest amount was in again November with only 4.14 rainfalls in Skardu. The last years or this data have again 64.7 in September while the lowest values were in October with only 3.8667mm. the overall highest total was received in the years of 2008 to 2012 which was 314.4mm while the lowest monthly total was received in 1978 to 1982 which was 170.26mm.
4.3.1 Vector data analysis of Temperature Precipitation and Snow of Batura:
The Climatic vector Data of Batura glacier was analyzed for investigation of the glacier anomalies of Karakorum as an evidence of climate change. The three major climatic parameters were consider in this study were Snowfall, Temperature and Rainfall.
Batura (Average annual Temperature in Degree Culsius) 2.5 2 1.5 1 0.5 0
-0.5
1985 1992 1999 1987 1988 1989 1990 1991 1993 1994 1995 1996 1997 1998 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 -1 1986 -1.5 -2 Figure: 4.12 Graphical representation of monthly Temperature of Batura station
Source of Data: www.meteoblue.com
The above figure 4.12 graphical trend is showing the average annual temperature of Batura Glacier during1985 to 2018 December 20th of 2018. The diagram showed that the area received highest temperature in 1990 which was 2.29 °C while the lowest was -1.4 °C in 1996. And the average was 0.58 °C in Batura. The graph shows that the temperature fluctuation is between 2.5 °C to -1.5 °C. The Batura glacier has high fluctuations between this temperatures ranged during these 33 years. The previous studies by (Hewitt, 2005) temperature influence glacier energy to bring cold and ice melting point. The present study highlighted the fluctuation in temperature of Batura.
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4.3.2 Batura Monthly Temperature in °C since 1985 to 2018
The figure 4.13 shows the monthly variation in temperature during 1985 to 2018. The maximum temperature was less than 15 °C in July; August while the most of the month in years received -1°C at average but there is high variation in this range of temperature
Batura (Average Montly Temperature in °C since 1985 to 2018)
20 15 10 5 0
-5
1989 2001 2013 1986 1987 1988 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2014 2015 2016 2017 2018 -10 1985 -15 -20 Jun Feb Mar apr May Jun Jul Aug Setp Oct Nov Dec
Figure: 4.13 Graphical representation of monthly temperature of Batura station
Data source: www.meteoblue.com
The above figure 4.13showing the monthly temperature received during 1985 to 2018 in Batura Glacier. The table shows the average range of the temperature received in all months during 33 years. The data were grouped into seven classes and the average temperature of each class showed in the above table. The studies shows that the precipitation are decisive for glacier nourishment (Hewitt, 2005) the present study shows decrease in precipitation amount.
4.3.3 Batura Total Precipitation
The figure 4.14 showed the great fluctuation in total annual precipitation during 1985 to 2018. The total annual precipitation Ranges from 305.9mm to 836.5mm in Batura Glacier.
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Batura (Total Pericipitation in mm since 1985 to 2018) 900 800 700 600 500 400 300 200 100
0
1993 2018 1985 1986 1987 1988 1989 1990 1991 1992 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017
Figure 4.14 Graphical representation of monthly precipitation of Batura station
Data source: www.meteoblue.com
The highest amount of rainfall received in 2004 while the lowest value received in 1997 which was 305.9mm and the average total amount of rainfall is 530mm in Batura. The grand total of the rainfall received in all years is 18022mm which Batura received during 1985 to 2018. The trend shows that in 1985 the total precipitation was about 490mm in Batura while in next three years the amount increases upt0 787mm than the amount of precipitation slightly decreases in 1988 and remain similar till 1992 with little variations. While in 1995 the precipitation amount again dropped down to 388.4 mm in Batura with gradual ups and downs this decline continues till 2001 the total received amount was 355.8mm. when we further see the graph after 2001 from 2002 to 2003 the abrupt increase comes in the total amount of precipitation of the area and the amount reached up to 836.5mm in 2004. The line graph shows that after the year 2005 the drop down again started and the amount decline start Freon 510mm in 2005 than 330mm in 2007. After 2007 the trend of precipitation again started to rise from 423mm in 2009 to 741.5mm in 2010. But the raise is not greater than 2004 but it is the higher amount of received rainfall in the Batura during last 5 years. After that precipitation decreases in the area with only one peak in 603.7mm in 2015 overall the amount of precipitation were decreasing in last 10 years. The previous studies by (Hewitt, 2005) indicates seasonal migration of precipitation.
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4.3.4Batura Monthly precipitation
The figure 4.15 shows the fluctuation in monthly rainfall during the years of 1985 to 2018. How the different months receives the rainfall in January February March, April and august and September receives high rainfall in Batura while October November Decembmer have lesser amount of rainfall in Batura.
Batura (Average Monthly Precipitation in mm since 1985 to 2018) Jun 180 Feb 160 Mar 140 apr 120 May 100 Jun 80 Jul 60 Aug 40 Setp 20 0 Oct
Nov
1995 1998 2015 1986 1987 1988 1989 1990 1991 1992 1993 1994 1996 1997 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2016 2017 2018 1985 Dec
Figure 4.15 Graphical representation of monthly precipitation of Batura station
Data source: www.meteoblue.com
The above line graph shows the annual monthly representation of rainfall amount. The highest amount of rainfall in January was 115.3 mm in 2006 while the lowest amount of January was 10mm in 1978. While if we discuss February the highest amount received in 2011 which was 132mm and the lowest was in 2016 with only 18mm. march received highest rainfall in 1999 which was 155mm and the lowest in 2002 with only 37.8mm. the month April also have variations in the graph the highest value of April was in 2017 which was 115.5mm while the lowest value was showed in 1997 which is only 14.1mm. The next month May also have great variation during all these years the highest amount of may receive in 1993 with total 136mm while the lowest value was in 0.7 in 2006. June received highest rainfall in 1987 which was 105.9mm while the lowest amount of June was in 0.1 in 2018. The month July receives the highest in 2011 which was 109.2mm while received in 2018 which was only 0.3mm. August receives highest rainfall in 2004 the amount was151.3mm while the lowest amount receives in also 2018 the amount was 0.8mm. The month of September received the highest rainfall in 1992 which is 66mm while the lowest amount received in 1988 the amount was 1.2mm. October received respectively low rainfall in the month here the highest amount was 151.1mm in 1987 while the lowest amount of October was in 2013
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which was only 4.4mm. During these years the month of November have highest amount of rainfall in 151.1 mm in 1987 while the lowest amount received in 1997 the amount was 4.4mm. The second last month of the year have highest rainfall in 1993 and the total received amount was 95.4mm while the smallest amount was in 2007 and the amount was zero. The last month December also showed variation in above graph the highest value of rainfall in December was 94.4mm in 2008 while the lowest amount was in 2010 which was 4.3mm. the previous studies shows that the annual loss of the precipitation are not healthy for glacier health (Hewitt, 2005). The present study highlighted the loss in precipitation monthly during temporal analysis. The above monthly values of the rainfall received in Batura Glacier Region of Karakorum. The data were grouped into seven classes and the average of each month calculated. In the following tabulation all months have great variation in received rainfall amount of rainfall during 1985 to 2018. According to the figure 4.15 the months of January, February and March received heavy rainfall in the region and April May and June have average amount while July to December area receives less amount of rainfall.
4.4.5 Batura total Snowfall
The figure 4.16 is showing the total annual rainfall received in Batura Glaciated region the maximum snowfall recorded in the year 1987 and the recorded amount was 41.994 cm while the lowest amount recorded in 2001 and the amount was 13.831cm. And the average recorded average snowfall in the Batura glacier is 26.38cm per year.
Batura (Total Annual Snowfall in cm)
600
500
400
300
200
100
0
2001 2013 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2014 2015 2016 2017 2018
Figure 4.16 Graphical representation of annual snowfall of Batura station
Source of Data: www.meteoblue.com
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In all these years Batura receives 896.93 cm Snowfall which was the total of all years during 1985 to 2018. The above trend line shows that the total annual amount of snowfall in Batura decreasing slowly with some up and downs but during the last 10 years the trend decreases. In 1970s, Chinese glaciologists first showed that, to sustain Batura Glacier, upper basin snowfall heavy (Hewitt, 2011).but present study shows it is declining in recent years.
4.4.6 Batura monthly snowfall in cm during 1985 to 2018
The figure 4.17 line graph is showing monthly trends of received snowfall in Batura glacier since 1985 to 2018. In these 34 years what are the monthly fluctuation in snowfall of the area. It started from January the highest snowfall received in 2006 and the amount was 80.71cm while the lowest amount received in 1989 which was only 7cm and the average of January snowfall was 30.977cm in Batura.
Batura ( Monthly Snowfall in cm) Jun Feb 140 Mar 120 apr 100 80 May 60 Jun 40 Jul 20 Aug 0 Setp
Oct
1994 2004 2014 1986 1987 1988 1989 1990 1991 1992 1993 1995 1996 1997 1998 1999 2000 2001 2002 2003 2005 2006 2007 2008 2009 2010 2011 2012 2013 2015 2016 2017 2018 1985 Nov
Figure 4.17 Graphical representation of snowfall of Batura station
Data source: www.meteoblue.com
The next month February received highest snowfall in 2011 and the amount was 97.3cm and the lowest amount was in 2000 which was only 11.27cm. The next month March is the heavy snowfall receiving month March received highest snowfall of about 117.39cm in 1996 while the lowest amount was only 12cm in 2008and the average was 61.934cm. April receives highest snowfall in 1994 the amount was 116.27cm in Batura while the lowest snowfall was amount in 1997 was only 9.03cm and the average amount was 51.281cm. The month of May received highest amount of snowfall in 1993 and that amount was 91.56cm while the lowest amount was received 0.49cm in 2017 and the average was snowfall 34.341cm in the same year. In mid of the year the month of June received highest snowfall in
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1987 the amount was 52.22cm while the lowest amount receives in 2003 and 2008 and the amount were zero cm only and the average amount of snowfall in June was 9.3059cm in Batura. The July received highest snowfall in 2006 the mount was 18.41 only and the lowest amount of snowfall was received during the month of July in years 1985,1986,1998, 2000, 2003, 2004, 2012, 2015 2017 and 2018 as well receives zero snowfall in Batura it is the effect of climate change and temperature increase while the average amount of July was only 2.9812cm this month receives very less snowfall in the year of1985,1986,1994,1998, 2003, 2004, 2007,2008, 2012, 2015, 2017, 2018. August also received very less snowfall the highest in 1997 the amount was 15.19cm while the lowest amount was 0cm was received in 1987, 1990,1996, 2001,2002 and 2003 as well as 2011, 2015 2016 and 2018 also receives 0cm snowfall while the average was 1.6985cm in Batura. The month of September also received less snowfall the highest amount was 43.68 in 1992 while the lowest amount was 0 in 1988, 1990,1993 And 1996 while the average precipitation was 7.0206cm in Batura. The month of October received heavy snowfall in Batura the highest amount was received in 1987 the amount was 105.77cm and the lowest amount was 3.01 cm in 2016 while the average was 15.904cm. November also received sufficient amount of snowfall in Batura with the highest amount of 66.78 in 1993 while the lowest amount was in 2007 which was 0cm and the average amount was 22.47cm. The last month December have highest in 1990 with the amount of 58.52cm and lowest amount was 3.01cm in in 2010 the average was 33.145cm in December. The previous studies showed that snowfall rapidly concentrated down slope or below snowlines that accelerating transformation of glacier to ice (Hewitt, 2011). This study showed decline in snowfall this is the one of the reason of glacial transformation into ice.
Batura (Average Annual Snowfall in cm ) 45 40 35 30 25 20 15 10 5
0
1996 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018
Figure 4.18 Graphical representation of monthly snowfall of Batura station
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Data source: www.meteoblue.com
The figure 4.18 showed the average annual snowfall receives in Batura glacier since 1985 to 2018. The line graph shows that the amount of average snowfall was slowly declining in the Batura glaciated region of Karakorum. In 1985 the total amount was 289.3cm which increases in 1987 at 503cm. but after that the amount remains stable tile 1994 but then it dropped down in 1995 with the mount of 247.94cm than it also dropped down in 2001in 2003 to 2004 slightly raised with amount of 385.49cm than again the amount of snowfall is decreasing till 2018 with little variations. The study analyzed the change and during 1989 to 2001 compared with 2002 to 2010 there was 1.16% decline in ice that is due to decreasing snow fall in the area (Baig, Khan, & Din, 2018).
The average snowfall received in Batura during 1985 to 2018 and the average monthly trend is showing in the figure 4.18. The monthly fluctuations showed in the all months in Batura glaciated region of Karakorum. The data of all years were grouped in 7 classes and the average amount of each month showed in the figure 4.18. The tabulation was required to investigate the average amount of snowfall in each month of the year. Previous study by (Hewitt, 2005) indicated negative impact of temperature on monthly amount of precipitation. This study also highlighted increase in temperature that might be the reason of decrease in precipitation.
4.4.8 The Daily mean temperature of the Batura glacier from 1985 to 2018
The daily trend of temperature was also analyzed of Batura Glacier from 1985 to 2018. The recorded received temperature in Batura region was graphically analyzed. The graphical representation shows the daily maximum mean temperature of Batura glacier.
Batura Daily Maximum Temperature in Degree Culsuis 30 25 20 15 10 5 0
-5 1
328 655 982
3598 8503 1309 1636 1963 2290 2617 2944 3271 3925 4252 4579 4906 5233 5560 5887 6214 6541 6868 7195 7522 7849 8176 8830 9157 9484 9811
10465 10792 11119 11446 11773 12100 -10 10138 -15 -20
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Figure 4.19 Graphical representation of daily mean temperature of Batura station
Data source: www.meteoblue.com
The figure 4.19 showed the daily received Mean temperature in Batura Glaciated region the temperature was the daily mean of the hourly received temperature in Batura. Maximum temperature was 18.56 degree Celsius -23.21 above2m from ground. Average 0.644.
Batura Daily Mean Temperature in degree Culsius 30
20
10
0
Year
2005 1986 1987 1988 1989 1990 1991 1991 1992 1993 1994 1995 1996 1997 1998 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2012 2013 2014 2015 2016 2017 2018 -10 1985
-20
-30
Figure 4.20 Graphical representation of daily maximum temperature of Batura station
Data source: www.meteoblue.com
The above graph showed daily received Maximum temperature in Batura glaciated region. Maximum received temperature was 25.2 degree Celsius. While the lowest amount was - 14.35 degree Celsius while the average was -4.66764 degree Celsius.
Batura Daily Minimum Temperature in Degree Culsius 20
10
0
Year
1995 1996 1997 1986 1987 1988 1989 1990 1991 1992 1993 1993 1994 1998 1999 2000 2001 2002 2002 2003 2004 2005 2006 2007 2008 2009 2010 2010 2011 2012 2013 2014 2015 2016 2017 2018 -10 1985
-20
-30
-40
60
Figure 4.21 Graphical representation of daily minimum temperature of Batura station
Data source: www.meteoblue.com
The figure 4.21 shows the daily minimum temperature of Batura Glaciated region above 2 m height above the ground. In this data minimum the highest value was 15.34 degree Celsius while the minimum value was -32.36 degree Celsius while the average of minimum temperature was -4.47184 degree Celsius. The previous study by (Hewitt, 2005) highlighted influence of temperature on glacier. The present study highlighted fluctuations in temperature of Batura.
4.4.9 Batura Daily total precipitation during 1985 to 2018
Batura Total Precipitation Daily Sum in mm 60 50 40 30 20 10
0
Year
1998 2013 1985 1986 1987 1988 1989 1990 1991 1992 1993 1993 1994 1995 1996 1997 1999 2000 2001 2002 2002 2003 2004 2005 2006 2007 2008 2009 2010 2010 2011 2012 2014 2015 2016 2017 2018
Figure 4.22 Graphical representation of total precipitation of Batura station
Data source: www.meteoblue.com
The data of precipitation was firstly analyzed on daily trend for this data from 1985 to 2018 were collected. The daily total precipitation data were statistically and graphically analyzed. The daily sum was used for precipitation analysis.
The figure4.22 graph shows the daily sum of precipitation in Batura glaciated region of Karakorum Pakistan. The daily maximum received rainfall is 48mm in 2016 the values of rainfall ranges from 0mm to 48mm daily rainfall. The daily precipitation in Batura glacier has great fluctuation. The daily amount of precipitation was highest during 1987, 1993, 2006 and 2016.
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4.4.10 Batura total daily rainfall since 1985 to 2018
The figure 4.23 shows the daily amount of snowfall in Batura glacier since 1985 to 2018. The values range from 0cm to 34.02cm in a day. The maximum value was 34.02cm in 2016 while other years also have snowfall with the amount of 10m to 20cm.
Batura Daily Amount of Snowfall sum in cm 40 35 30 25 20 15 10 5
0
Year
1987 2002 2017 1985 1986 1988 1989 1990 1991 1991 1992 1993 1994 1995 1996 1997 1998 1998 1999 2000 2001 2003 2004 2005 2005 2006 2007 2008 2009 2010 2011 2012 2012 2013 2014 2015 2016 2018 Figure 4.23 Graphical representation of daily amount of snowfall of Batura station
Data source: www.meteoblue.com
The daily trend shows minimum snowfall 0cm and the average 0.86751cm in a day. (Hewitt, 2005) said that the ice lower down due to the increase in temperature. This might be the reason of present decline in daily snow amount in Batura Glacier. (Hewitt, 2005) the amount of ice mass in the glacier influenced by increasing trend of temperature while the present study highlighted the fluctuation of temperature in the increasing trends
4.4.11 Relationship between Temperature and Area under snow
The regression analysis used to find the relation between different variables the linear trend of data showed by regression line. The analysis shows the relationship between area under snow and temperature.
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Temprature VS Area Under Snow
3500 y = -34.133x + 70468 R² = 0.5179 3000 tempratur 2500
2000 Snow ( Area in sq km )
1500 Linear (Snow ( Area in sq km 1000 ))
500
0 1980 1990 2000 2010 2020
Figure 4.24 Graphical representation of relationship between temperature and area under sow
The above scatter plot shows the relationship between temperature in degree Celsius of Batura glacier and area under snow in sq. km. There is negative relation between temperature and area under snow. With the increases of temperature the area under snow decreases. The amount of area under snow declined since 1980 to 2018. The value of y is -34.133x +70468 while the R²=0.5179
4.4.12 Relationship between Area under Snow and amount under snow
The relationship between area under snow and amount of snow find out by regression analysis. The linear representation of data show the amount of snow during 1980, 1990, 2000, 2010,and 2015 with area under snow.
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4.5 Raster Analysis The raster analysis performed on remotely sensed data sets and satellite imagery. Raster data from USGS and other observatories used for raster analysis. The condition of Batura region from 1988 to
Area Under Snow VS amount of Snow 3500
3000 y = -34.133x + 70468 2500 R² = 0.5179
2000
1500 Snow ( Area in sq km ) 1000 Amount of snow 500
0 Linear (Snow ( Area in sq 1980 1990 2000 2010 2020 km )) -500 Figure 4.25 Graphical representation of relationship between area under snow and amount of snow
The figure 4.25 showed the relationship between areas under snow verses amount of snow in Batura glacier. That is negative relationship between area under snow and the amount of snow during 1980 to 2020. The amount of snow is decreasing due to the decline in the area under snow. The value of y is -34.133=70468 while the value of R² is 0.5179
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2018 in different seasons were analyzed. Moreover the vegetation cover, land use, and barren land also Winter (December 1988 to 2018) Summer (July 1988 to 2018)
(a) December 1988 (b) July1988
(c) December 1998 (d) July1998
(e) December 2008 (f) July2008
(g) December 2018 (h) July2018
Figure 4.26 Glacier Inventory (True Color Imagery)
Source: USGS Retrieved on December 2018
calculated. The above figures are showing the True color images of the Batura glacier in winter and summer since 1988 to 2018 the December and July of 1988, 1998, 2008 and 2018.
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4.5.2. Batura Glacier
The figure 4.27map shows glaciated area of the Batura glaciated region of Karakorum. In 1988, 200, 2008, 2016 the percentage of glaciated area was 34 percent in 1988 previous studies shows in 2011 it was 32 percent(Hewitt, 2011)while it dropped down in 1998 at the percentage of 21. Further in 2008 the percentage again decreases at the 20% while the area slightly increases in 2016 at the 23%. The study by (Hewitt, 2011) showed Karakorum glaciers have decline since 1920s to 1960s. Comparison of both studies showed that decline is still continuing in the Karakorum glacier.
Figure 4. 27Satellite image of Batura
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4.5.3 Snow cover index
The figure 4.28 map shows the snow cover index of the Batura glaciated region of the Karakoram Pakistan during 1988 to 2016. The percentage of snow cover in Batura was 34 percent in 1988 while in 1998 it was 21 percent which is less then the pervious.
F Figure 4.28 Snow cover of Batura using snow cover index
If we move further the snow cover agin dropped down in 2008 with on percent decrease and the percentage was 20. And the last year 2016 in snow index have slight increses in the snow cover from pervious two year and here the percentage was 23. (Immerzeel, van Beek, & Bierkens, 2010) said there is increase in the high Karakaram snow cover. This shows that the fluctuation in snow cover still facing increase whiles in some years it was decreasing as well.
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4.4 Land cover and land use classification in 1988, 2000, 2008, and 2016
The map shows the land use and land cover classification of Batura Glaciated region of Karakorum during 1988 to 2016. The figure shows three major classes of the area the classes are Snow cover, Barren land and the vegetation cover.
Figure 4.29 Land cover of Batura
The barren land was 54 percent in 1988 while decreases 62 percent in 1998 then again increases in 2008 at the percentage of 74 and in 2016 the amount was 67 percent. The vegetation cover is very less in the area only 12 percent in 1988 while increase at 17 percent in 1998 then dropped down in 20098 at 6 percent while 10 percent in 2016 . The snow area showed 34 percent, 21, 20, and 23 percent in the analysis. Previous studies by (Hewitt, 2011) showed glacier areas in Karakorum exhibited down ward shift as compared to snow fall. The present study also showed the decline in snow cover area.
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4.5 Vegetation covers of Batura during 1988 to 2016
The figure 29 shows vegetation cover and barren land area of Batura glacier in square kilometers and percentage. In 1988 the total snow cover area was 29661.1818 km2 which is the 34% of the total area highest in all these years. While in 1998 it was 1837.0494 km2 which was 21%of total area the snow cover decrease in this year. In 2008 the snow cover was 1682.5383 km2 and 20%of total area the snow cover again decreased in this year. While in 2016 area was 1987.3449 km2 with almost 23%. While the barren land in 1988 was 4648.4226 km2 in 54%. In 1998 it increased at 5301.5589 km2 almost 62%. In 2008 the barren land area was 6417.0657 km2 which was almost 74% while in 2016 the area was 5784.2073 km2 which is almost 67%. (Hewitt, 2005) temperature and changing pattern of precipitation impacted the vegetation cover.
The graph showed the vegitation cover of the Batura glacier in karakarm Pakistan. In 1988 the vegetation cover was 1013.2182 km2 which was 12%of the total area. The vegetation inreased in 1998 the area was 1484.2143 km2 which was 17 %. In next year 2008 the area of vegetation was 851.2704 km2 almost 10%of the total area of Batura.
Batura Snow cover ( Area in sq km ) 3500
3000
2500
2000
1500
1000
500
0 1988 1998 2008 2016
Figure 30 Graphical representation of snow covers area of Batura
The bar graph shows the snow cover trend in Batura Glacier in square kilometer. The snow cover area is decreasing from 1988 to 2016.
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Batura Area of Barren Land in Percentage
1988, 54% 2016, 67%
1998, 62% 2008, 74%
Figure 4.31 Graphical representation of barren land of Batura
The pie chart shows the barren land in percentage of the Batura glacier. The area was highest in 2008 the area was 74 percent of the total area While lowest in 1998 almost 62 percent of the total area.
Batura Vegetation Cover ( Area in sq km ) 1600 1484.2143 1400 1200 1000 1013.2182 800 851.2704 600 523.2186 400 200 0 1988 1998 2008 2016
Figure 4.2 Graphical representation of Batura
The figure 4.32 shows the vegetation cover of the Batura glacier the line shows the clear decrease in vegetation of Batura.
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4.5.1Batura Land cover
The figure shows the area of snow cover, Barren land and vegetation of the Batura glacier. The graphical representation shows the clear decrease in the snow cover area of Batura while the barren land is increasing as well as the vegetation cover is very small and also decreasing.
Batura Land Cover Area 7000 6000 5000 4000 3000 2000 1000 0 1988 1998 2008 2016 Snow ( Area in sq km ) Barren land ( Area in sq km ) Vegetation ( Area in sq km )
Figure 4.33 Graphical representation of land cover of Batura
4.6 Precipitation Anomalies Index of Batura Glacier
The precipitation anomaly is the quantitative measure to analyze the shift and change in the pattern, intensity and amount if precipitation in the temporal scale.
4.6.1 Annual Precipitation Anomalies of Batura Glacier
The purpose of precipitation anomalies is to find out the results of received rainfall in an area that does not fit the pattern of precipitation in the area under study. In the scientific form anomalies called deviations. This part of the chapter deals with yearly, seasonal and monthly precipitation anomalies of Batura glacier.
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Yearly Anomalies of Batura Precipitation 1.6 1.4 1.2 1 0.8 0.6 0.4 0.2
0
1991 1992 1986 1987 1988 1989 1990 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 1985
Figure 4.34 yearly precipitation anomaly index on Batura station
The above figure 4.34 shows the annual precipitation anomalies index of Batura Glacier Karakorum Pakistan. There are great negative fluctuations in the total amount of precipitation in Batura glacier. The values of anomalies in above graph ranged from 0.00162 to 1.33487 during 1985 to 2018. There are 23 negative anomalies while three positive anomalies. The years of negative anomalies are 2005,1985, 2017,1998,2016, 2014, 2008, 2013, 2002, 1995, 2000, 2001, 1997, 1990, 2012, 2006, 2011, 1994, 1996, 1992, and 2003 which showed decreasing trend of precipitation. While the years of positive anomalies are 2010,1987, 2004 with more precipitation.
4.6.2 Seasonal Precipitation Anomalies of Batura Glacier
According to the classification of seasons in Pakistan developed by Fazal Karim Khan, in Pakistan precipitation does not occur throughout the year. The precipitation in Pakistan held during two seasons summer, monsoons and the winter rainfall called western Disturbance and the thunderstorms receives very small rainfall (Khan, 2006).
The year is divided into following seasons:
1. Monsoon (July, August, Sep) 2. Western Disturbance (Dec, Jan, Feb, March 3. Thunderstorm (Oct, November, April, May, January (Khan, 2006)
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Seasonal Anomalies 10 9 8 7 6 5 4 3 2 1
0
1986 1999 2014 1985 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2015 2016 2017 2018 Monsoon Anomilies Western Disturbance Anomalies Thunderstrom Anomalies
Figure 4.35 Seasonal Precipitation Anomalies of Batura Station
The above figure 4.35 shows the seasonal precipitation anomalies index of Batura glacier Karakaram Pakistan. The graph shows positive and negative anomalies of three seasons in Batura glacier. The brown line showed the anomalies of monsoon season, the green lines showed the values of anomalies due to western disturbance while the purple lines showed the anomalies of the thunderstorm. In Monsoon season anomalies ranges from 0.1081 to 0.1081 during 1985 to 2018. Monsoon showed positive anomalies for the years 1985, 1988, 1991,1994, 1997, 2002, 2005, 2010, and 1998 while the years of negative anomalies years are 1988, 1989, 1989, 1992, 1999, 2003, 2013, 2014, 2015, and 2016. The values of western disturbance anomalies ranged from 0.0024 to 2.1229 during the years 1985 to 2018. The years of positive anomalies are 1987, 1988, 1990, 1991, 1992, 1995, 1997, 2000, 2001, 2002, 2003, 2013, 2014, 2017, 2018 while negative anomalies years 1985, 1986, 1989, 1993, 1994, 1996, 1998, 1999, 2004, 2006, 2017, 2008,2010, 2015, 2016. The thunderstorm anomalies ranged from 0.0024 to 3.6582 during 1985 to 2018 while the positive anomalies years are 1998, 2014, 1990, 2001, 2006, 2012 and 1987 While the negative anomalies are 1998, 2018, 2016, 2015, 1994, 2010, 2009, and 1988.
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4.6.3 Monthly Precipitation Anomalies of Batura Glacier
The figure 4.36 shows the monthly precipitation anomalies since year 1985 to 2018. The fluctuations for all months are discussed below. The January has nine negative anomalies while twenty six positive anomalies.
Monthly Anomiliesof Batura Glacier Prescipiitation Index 25 January Feb 20 Maarch 15 April 10 May 5 June
0 July
1997 2008 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018
Figure 4.36 Monthly anomalies of precipitation of Batura station
The years of negative anomalies are 2004, 1994, 1989, 2005, 1999, 1996, 1998, and 2009. While positive anomalies are 2017, 1991, 1988, 2002, 2008, 1992, 1990, 2016, 2013, 1997, 2000, 2010, 2006, 2003, 2015, 2018, 1993, 2014, 2001, 2011, 2012, 1986, 1995, 2007, and 1987. The previous study showed that in the recent decades most of the glaciers have undergo negative mass balance. The decline was reported in the glaciers comparatively to Batura also negative trends with higher positive anomalies (Hewitt, 2005).
The anomalies of February 17 negative anomalies and 17 positive anomalies out of 34 years since 1985 to 2018. The February anomalies ranged from 0.04267 since 1985 to 2018. The years of negative anomalies are 1996, 2015, 2012, 2004, 1999, 1993, 1989, 1991, 1990, 1987, 2014, 1998, 2013, 1994, 2009, 2005. While the Positive anomalies are found in the years 2006,2003, 1992, 2010, 2017, 2008, 2001, 1995, 2018, 1997, 2002, 1985, and 2000.
March anomalies since 1985 to 2018 ranged from 0.00951 to 4.98885. The years of negative anomalies are 1985, 2004, 2014, 2010, 2016, 1987, 2007, 1994, 2003, 2015, 1998, 1995, 1991, 1993, 1992, 1990, 1986. While years of positive anomalies are 1988, 2012,1997, 2018, 1999, 1996, 2017, 2009, 2017, 2009, 2011, 2000, 2013, 2002, 2006, 2001, 2008.
April anomalies since 1985 to 2019 ranged from 0.20446 to 6.31472. April have negative anomalies during 1990, 2009, 1989, 1986, 2018, 2003, 2016, 2002, 2005, 2010, 2017,, while
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positive anomalies during 2004, 2006, 1999, 1987, 1988, 2014,1994, 2013, 1991, 2008, 1985,1992, 1995, 2011, 2007,2001, 1997 and 2000.
During the month of May, precipitation anomalies ranged from 0.05231 to 10.5258 during 1985 to 2018. The negative anomalies during years of 2008, 1995, 2007, 2018,1986, 2009, 2012, 2002, 2015, while the positive anomalies during 1990, 1988, 1999, 1998, 2006, 1997, 2011, 2017, 2014, 2016, 2003, 1993, 1989, 1985, 2000, 2010, 2013, 1991, 2005, 1992, 1987, 1994.
June anomalies raged from 0.02365 to 11.4909. the years of negative anomalies are 2013, 1992, 1989, 1993, 1985, 1991,2002, 1986, 1996, 1988, 1990, while positive anomalies in 2017, 2003, 1994, 2012, 1998, 2015,2011, 1998, 2015, 2011, 2014, 1997, 2016, 2008, 2009, 2007, 2006, 2005, 2018.
July anomalies during 1985 to 2018 ranged from 0.06796 to 16.0877. The negative anomalies during the years of 2015, 1999, 2001, 2006, 1996, 1993, 1989, 1978 were found while positive anomalies during 2012, 2004, 1010, 2013, 1986, 2011, 1988, 1991, 2008,2014, 2000, 2002, 1985, 2017, 1994, 2009, 2003, 1998,2018 were exhibited.
August precipitation anomalies ranged from 0.14439 to 15.0255 during 1985 to 2018. The negative precipitation anomalies during years of 1997, 1999, 1988, 2003, 2007, 2014 were analyzed, while the negative anomalies are during the years of 2012, 2015, 1986, 2013, 2006, 2005, 2010, 2016, 1989, 1992, 2009, 2004, 1991, 1985, 1993, 1995, 2000, 1994, 2002, 1987, 1998, 1990, 2017, 2001, 1996, 2018 were found.
September anomalies of precipitation ranged from 0.0403 to 22.546 during 1985 to 2018. The negative anomalies years are 2015, 2008, 2006, 2004, 2000, 2009, 2013, 1989, 2007 while positive anomalies during the years of 2010, 2011, 2012, 2003, 1991, 1992, 2014, 1995, 1999, 2001, 1994, 1998, 2005, 2016, 1997, 1985, 1987, 2018, 1993, 1996, 1988, 1990, 2017.
October precipitation anomalies ranged from 0.3592 to 8.7989 during 1985 to 2018. The years of negative anomalies were 2005, 2000, 2004, 2015, 2014, 1985. While the positive anomalies were during the years 2011, 1993, 2012, 2018, 1989, 1991, 1995, 1988, 1992, 1994, 2008, 2009, 2017, 1990, 1998, 2001, 2001, 2007, 2006, 2003, 1986, 1987, 2010, 2002, 2016, 1997, 2013, 1999.
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The November anomalies of precipitation index showed that a value of anomalies ranged from 0.0216 to 17.8866 during 1985 to 2018. The negative anomalies were 2003 2014, 2006, 2009, 2000, 2010, 2018, 2016, 2011, 1989, while positive anomalies were in 2004, 1999, 2015, 1993, 2012, 2006, 1987, 1996, 2008, 1994, 1990, 1992, 2002, 1998, 2013, 1995, 2010, 1988, 2017, 2007.
The December precipitation ranged from 0.1035 to 10.906 during 1985 to 2018. December showed negative anomalies during years of 2001, 1989, 1987, 2003, 2007, 1988, 1993, 2012, 1992, 1985 and 2006. While the positive anomalies were analyzed during 2004, 1986, 2002, 1990, 1994, 2017, 2008, 2000, 1991, 2015, 1997, 2011, 1998, 2014, 2016, 2013, 2005, 1996, 2018, 2010, 1999.
This is the monthly precipitation anomaly index of Batura glacier since 1985 to 2018. (Hewitt, 2005) the study indicates seasonal migration. The present study indicates negative anomalies in rainfall that might be the reason of seasonal migration in Batura glacier.
4.7 Temperature Anomalies of Batura
The figure 4.37 is showing temperature anomaly index during 1985 to 2018 of Batura Glacier. the positive and negative anomalies shows the fluctuation of average temperature on two different ends the values not fit in the average of minimum temperature shows decrease in temperature while the value that do not fit in the maximum temperature are showing increase in temperature.
Temperatur Anomalies 6 4 2 0
-2
1992 2012 1985 1986 1987 1988 1989 1990 1991 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2013 2014 2015 2016 2017 2018 -4 -6 -8 -10 -12 -14 -16
Figure 4.37 Temperature yearly anomalies of Batura station
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The temperature anomalies showed decreased from average temperature are 1996, 2018, 1997, 1995, 2015, 2009, 1989, 2006, 2007, 2014, 1998, 1993, 2005, 201, 1994, 2012 while the anomalies which shows the increase in temperature was higher for the years 2003, 1992, 2000, 1999, 2016, 2011, 2010, 2002, 2001, 2008, 1991,1987, 2013, 1986, 2013, 1986, 1988, 1985, 2004, 1990.
4.8 Monthly Anomalies of Temperature in Batura
The below figure show the monthly increasing anomalies and decreasing anomalies of temperature of Batura Glacier Karakoram Pakistan. The graph shows clear fluctuations in increasing temperature months then months with below temperature.
Batura Monthly Temperature Anomalies Jan 30 Feb 25 Marc 20 h 15 April 10 May
5 June 0 July -5 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009 2011 2013 2015 2017 -10 Aug -15 Sep
Figure 4.38 Monthly temperature anomalies of Batura station
The anomalies showed the increase in temperature are those values that does not fit in the average higher temperature of Batura Glacier while the anomalies which showed decreasing temperatures are those values that does not fit in the average of lowest temperature in Batura glacier. The years with the high monthly temperature anomalies are more than the years of lowest temperature anomalies during 1985 to 2018 in Batura glacier.
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4.9 Seasonal Description of Precipitation from 1989 to 2018
The seasonal description of precipitation data shows the variation in amount of precipitation received in different seasons of Batura glacier from 1989 to 2018. The data is divided into three decades 1989, to 1998, 1999 to 2008, and 2009 to 2018 while three major seasons were discussed here namely monsoon, western disturbance, and thunderstorm.
4.9.1 Batura precipitation in Monsoon season
The trends of Batura precipitation in monsoon season were fluctuate in all decades. The amount of precipitation in monsoon season in first decade was less than other two decades. During 1999 to 2008 the amount of precipitation increased while again slightly decreases in 2009 to 2018.
Figure 4.39 Distribution Precipitation in Monsoon season
Data source: www.meteoblue.com
(Khan, 2006) Monsoon season of Pakistan was the tail end of the monsoon winds which enters in Pakistan from India, the division of season by F. K Khan shows start of monsoon in July and the season ends in early September. The season is the major precipitation in South Asia and contributor of the total precipitation in the region and very important for agriculture the abnormality in monsoon creates adverse effect on crops (Imran et al., 2014).
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The above seasonal map of the Aliabad (Batura regions) shows amount of precipitation receiving three decades. The variation in amount of monsoon is clearly observed while the detail is discussed below. In the first decade during 1989 to 1998 ranges from 49mm to 60 mm while average amount is 57.78375 mm. The area near the point of Batura glacier receives maximum rainfall which is 96.75mm during this decade. While in the second decade during 1999 to 2008 monsoon precipitation ranges from 33.545 mm to 202.53 mm while the average value is 79.83438 mm. while according to this map precipitation increases in this decade near the Batura point. In first decade Batura received less precipitation almost 55 mm while in second the amount goes up to 79.83438. While the precipitation in third decade ranges from 254.11 mm to 61.215 mm while the average amount is 106.585.still the highest amount received near Batura glacier.
4.9.2 Batura Precipitation in Western Disturbance season
The trends of precipitation in western disturbance were almost smellier with lesser fluctuations. While the direction of precipitation varies in different decades the amount of precipitation received in western disturbance season during 1989 to 2018 shows in the following figure.
Figure 4.40 Distribution of Precipitation in western Disturbance season
Data source: www.meteoblue.com
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The season western disturbance in Pakistan during December to March the winds enter in Pakistan from Iran and Afghanistan. Pakistan receives small amount of precipitation from this wind (Khan, 2006).
The above figure shows the amount of received precipitation Aliabad (Batura Region) in three decades during 1989 to 2018. The first decade shows the precipitation ranges from 115 mm to 191 mm. Batura point shows maximum precipitation the amount is 184 mm. while the second decade shows Aliabad (Batura Region) in 1999 to 2008 the amount ranges from 116.346 mm to 178.441 mm. and the average value is 59.48 mm in Batura. The highest amount of precipitation receives in Batura region and the value is 174 mm. while the third decade 2009 to 2018 the precipitation ranges from 130.571 mm to 196.388 mm. and the average is 65.51 mm. in this decade overall region receives heavy precipitation.
4.9.3 Batura Precipitation in Thunderstorm season
The trend of precipitation in thunderstorm season in three decades shows little fluctuation in amount of received precipitation in Batura. during 1989 to 1998 the amount of precipitation in this season is less than the other two decades.
Figure 4.41 Distribution of precipitation in Thunderstorm season
Data source: www.meteoblue.com
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In Pakistan thunderstorms season is divided into two different periods first on is April to June in this season northern areas receives precipitation and the amount is 125 mm and in the rest of Pakistan 50 mm precipitation. While the second season October and November northern mountains receives precipitation and the amount is 50 mm (Khan, 2006).
The figure shows the Thunderstorm season of Aliabad (Batura Region) in three decades from 1989 to 2018. The first decade 1989 to 1998 shows precipitation ranges from 145.063 mm to 192.272 mm while the average 44.814 mm. the season shows heavy precipitation while the maximum amount receives near Batura Region. While the next decade 1999 to 2008 shows precipitation ranges from 139.246 mm to 190.895 mm while the average was monthly 36.768 mm. the maximum amount shows above Batura Region and the amount is 188 mm. The third decade shows the amount of precipitation ranges from 129.609 mm to 187.812 mm. and monthly average is 36.494 mm. the maximum amount in Batura region is 186 above Batura region.
4.8 Topographic Examination
The under studied region (Batura Glacier Inventory) has extreme array of geological and physiological characteristics. Its topographical arrangement is considered as wonder in the geography. The topographic examination was necessary to understand the glacio- meteorological behavior of the Batura inventory. In this regard, the Digital Elevation Model (DEM), Contouring map and Watershed have been calculated by processing raster data sets of the area under study. The image has been taken from Google Earth Pro as km file and further processed in ArcGIS.
4.8.1 Contouring
Contours are imaginary lines which connects point of equal height above sea level. If contours are closely spaced it means that land is very steep, if contours are widely spaced it means the land is more flat. Imaginary line joining points of equal elevation above or below a datum Contour lines have a specific contour interval is the vertical distance between contours CI is a function of scale and units Relief is the highest elevation shown minus the lowest. Slopes are represents by contours. By linking up all the contour patterns on map the whole area can understood easily (Singh,, 2011.).
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Figure 4.42 Batura Glacier map of Contours
The given map of contours has been drawn around Batura Glacier inventory, Bat. The lines are of high contrast due to its abrupt changes in rise and depression of the Karakorum. Batura Sar is at 7500m and lowest value is 2000m. The scale of contours is 500m that’s why values are approximate because on the scale of 100m there is very abrupt change in the area. Contours are very abrupt near Batura Sar and closely placed but as we go to the lower elevation areas and Batura glacier and Passu region contours are wide. Map is showing that high contours area near Batura Sar in the middle of study area but as we go southward and western side of the map the contours are showing the less height.
The map showed elevation of area ranged from highest 7770.9 km to lowest 1787.6 km above sea level. The map represents the elevation and the slop of the area. Spacing between contours indicates slope of the area closely spaced contour shows steepness of slop and the widely spaced contours shows lesser slope (Singh, 2011).
4.8.2 Digital Elevation Model (DEM)
Digital elevation model is the 3d CG elevation model of terrain surface. A Digital Elevation Model (DEM) is a specialized database that represents the relief of a surface between points of known elevation. By interpolating known elevation data from sources such as ground surveys and photogrammetric data capture, a rectangular digital elevation model grid can be created. Present DEM image is the map of Batura it is showing that a depression is on the
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southeast side. Dem is showing the height at Batura Sar in the center and a few points toward the north side(Abd Aziz, 2008).
The development of a DEM involves spatial interpolation techniques to estimate values at un sampled locations using the elevation measurements taken at surveyed locations. A key feature of topographical data is each observation relates to a particular location in space (Abd Aziz, 2008). However in this research ASTER DEM was used to analyzed the feature and topographical analysis of the area.
Figure 4.43 Digital elevation model of Batura Glacier
The figure 4.43 map shows the stream channels of Batura Glacier passing in Batura Glacier, as well as Batura sar is also highlighted in the map were the elevations is high. The other side is Batura Pasu at lower elevation. The shape of Batura basin is also highlighted with green color lines in the map. The map showed streams of Batura with blue color lines. While elevation ranges from 1787.6 km to 7770.9 km. the variation of three different points are highlighted in map. The first point is Batura glacier at highest elevation while the Batura Glacier below the Batura Sar while Batura Pasu is more below than Batura Sar. The stream slopes are also shown on map.
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4.8.3 Watershed
Watershed is an area of land that feeds all water the water running under it and draining of it into the water body. Watershed map shows the streams and the basins of area. Present map of Batura Glacier is showing that Batura Sar, Passu and Batura are feeding the same area .Major streams are also in this area .this is the huge area of watershed in this region and its direction is in southeast side.
Figure 4.44 Watershed of Batura Glacier
Watershed map is showing the major water sources of the region Batura and Pasu streams and basin of the area. It is concluded that the highest basin was Batura Glacier and the second highest basin was Batura Sar and respectively Batura at lesser slope than Batura glacier and Batura Ssar.
4.9 Results
Historically climatic trends showed the following results. Precipitation trends of five station showed in 2003 maximum rainfall is 339.4 mm while minimum value was in 2001 74.3 mm. The total received precipitation is 6520.4 mm for this year. The next station Astor have lowest value in 2627.7 mm in 2007 while maximum value was 857.7 mm in 1996 and the average precipitation in 38 years was 484.86 mm. Gilgit precipitation ranged from 285.95
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mm in 2010 which was the highest precipitation value during 38 years while the minimum value was 64.4 mm in 1993 and the average of 38 years was 145.98 mm. Gupis maximum was in 1999 with 675.6 mm and minimum was 5.3mm in 1982 and the average value of Gupis was 7740.4 during 1978 to 2015. Results of Skardu precipitation ranged from 4.94.4 mm in 2010 to lowest 109 mm in 2007 while the average value is 235 mm.
Monthly results showed Astor in January receives highest precipitation during years of 2003 to 2007 and the received precipitation was 53.66 mm in average while the lowest during 1978 to 1982 and the value was 29.76 at average. February received highest precipitation during 2003 to 2007 with 56.36 mm, and lowest in 1983 to 1987 with the amount of 27.74 mm. March received highest precipitation in 1978 to 1982 the value is 86.42 mm in average April also received highest precipitation in 1978 to 1982 with the amount of 85.22 at average. May received highest rainfall during 2013 to 2015 while lowest in 1998 to 2002 value was 37.08 mm. while June July August and September not have rainfall in Astor the amounts in these months ranges from 32.1 mm to 10 mm. Bunji receives highest in January 2008 to 2012 with very low precipitation of 16.1 mm while lowest value is 2.1 during 1978 to 1982. In Bunji May received highest rainfall with 38.68 mm average to 10.94 mm during 1978 to 2015. The monthly rainfall in Gilgit ranged from 0.88 to 50.8 in Aril. Monthly precipitation of Gupis ranged highest in the month of April with the values of 107.28 mm to 119.76 during years of 1993 to 2007 while minimum values in January, October and December with the value of 0 mm. The next station is Skardu here maximum rainfall received in February 1993 to 1997 average amount was 33.9 mm while the lowest amount was 4.01 mm in October 1993 to 2002.
Graphical representations showed fluctuations throughout the year in Astor during 1978 to 1992 the values of total rain fall were high while in recent years the amount of precipitation decreased. Bunji Graph shows highest rainfall in March, April, May and August. While other months showed average precipitation with some fluctuations. Gilgit showed highest rainfall during March, April while other months not have heavy precipitation. Gupis also showed maximum precipitation in March April during years of 2003 to 2007 and 1998 to 2002. Skardu showed fluctuation in precipitation throughout the years while the highest precipitation showed in 2013 to 2015 September.
Batura climatic data results are summarized following. The monthly Temperature ranges from lowest -1.4 Degree Celsius and the highest 2.29 degree Celsius. And the average
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temperature is 0.58 degree Celsius. Monthly temperature of Batura station ranges from - 1degree Celsius to 15 degree Celsius. And the average monthly value is -1 degree Celsius.
Batura monthly precipitation trends shows the precipitation of Batura is declining in recent years while the maximum precipitation receives in 2004 and minimum in 2018. There is great fluctuation in monthly average amount of precipitation in Batura During all months of the years since 1985 to 2018.
The snowfall results shows that the amount of snow fall in centimeter is also declining slowly in the recent years while the highest snowfall receives in 1989 and lowest in 2018. The monthly snowfall fluctuates in all month of the years since 1985 to 2018 while the amount is decreasing in the area in recent years.
Daily results of Batura station daily mean ranges from -23.21 degree Celsius to 18.56 degree Celsius while the average daily mean value is 0.644 degree Celsius. The minimum temperature ranges from minimum -32.36 degree Celsius to maximum 15.34 degree Celsius and the average daily temperature is 4.47184 of minimum temperature. Maximum temperature ranges from -4.66764 degree Celsius to 25.2 degree Celsius and the average is - 4.66764 degree Celsius.
Batura total daily precipitation results ranges from 0 mm to 48mm in a day while the average is 20 mm. Batura snowfall ranges from 0 cm to 34.02 in a day.
Raster analysis Results shows the total area of glacier is declining from 1988 to 2016 from 34 percent to 23 percent. The snow cover area is decreasing from 1988 to 2016 from 34 percent to 23 percent. Land use and land cover classification shows the barren land was increasing from 54 to 67 percent while vegetation cover is very low and also decreasing in recent years. The total sow cover area was 2961.1818 sq. km in 1988 decreases in 1998 at 1837.0494 sq. km the decrease is 13 percent in snow cover area one percent increases comes in 2008 the area is 1682.5383 which is 20 percent of total area. While in 2016 the area was 1987.3449 sq. km slightly increases from previous. The barren land area in 1988 was 4648.4226 almost 54 percent of the total while in 1998 it was 62 percent of total area the area is 5301.5589 sq. km while in 2008 the area was 6417.0657 sq. km almost 74 percent of the total area while in 2016 the barren land was 5784.2073 sq. km almost 67 percent of the total Batura Glacier. If we discuss the vegetation cover results of Batura it is also decreasing from 1988 to 2016 the vegetation was in 1988 almost 1013.2182 which is almost 12 percent of the total Batura
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glacier while in 1998 it increases at 1484.2143 sq. km almost 17 percent of the total in 2008 it again dropped down at 6 percent which was almost 523.2186 sq. km. while in 2016 851.2704 sq. km almost 10 percent of the total Batura glacier. The vegetation cover is very small in the area but faces great fluctuation.
The results of precipitation anomaly index of Batura glacier shows the flowing trends in yearly, monthly, and seasonal anomalies. Firstly the yearly precipitation anomaly index ranges from 000162 to 1.33487 during 1985 to 2018. There are 23 negative anomalies and only 3 positive anomalies. Secondly seasonal precipitation anomaly index given in discussion which shows the fluctuation in three seasons monsoon, western disturbance and thunderstorm. In the monsoon season anomalies ranges from 0.1081 to 0.1081 during 1985 to 2018. The major positive anomalies are 8 and negative 10 during 1985 to 2018. While during the season of western disturbance the precipitation anomalies ranged from 0.0024 to 2.1229 during 1985 to 2018 the numbers of positive anomalies are 15 while the numbers of negative anomalies are 15. The thunderstorm anomalies ranges from0.0024 to 3.6582 includes seven positive anomalies while 8 negative anomalies. The third part of the anomaly index shows the monthly anomalies of the precipitation of Batura glacier. The monthly results are given below. January have 26 positive anomalies and 8 negative anomalies. February have 17 positive and 17 negative anomalies. March have 17 negative and 17 positive anomalies. April have 11 negative and 18 positive anomalies. While May have 18 negative and 11 positive anomalies. June have 10 negative and 19 positive anomalies. July have 8 negative and 19 positive anomalies. August have 6 positive and 27 Negative anomalies. September faces 9 negative anomalies while 23 positive anomalies. November have 10 negative anomalies while 21 positive anomalies. The month December has 11 negative anomalies while 20 positive anomalies.
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CHAPTER: 05 SUMMERY, CONCLUSION AND RECOMMENDATIONS
5.1 SUMMERY:
Climate change is realty of the present century that modifying the conditions of water resource in Pakistan due to the seasonal change of the precipitation patterns of Pakistan. The availability of water resources is necessary for human survival. The accelerating climate change stressed water resources in the world. There are evidences of climate changes effect on glacier of Karakorum region of Pakistan. In recent years, glacial lake outburst are the reasons of increasing frequency of mountain hazards during recent years and the alarming temperature increases in northern region of Pakistan. The present case study based on investigating glacial anomalies of climate change study of Batura glacier as an evidence of climate change in Pakistan. In this study thirty-two years climatic data of precipitation, snowfall a temperature of Batura glacier were analyzed while thirty-eight years data of climatic data were analyzed from other points of the glaciated region. The climatic station including Astor, Bunji,Gupis, Gilgit, Skardu and the major one id the Batura glacier. There are three major objective is to investigate the Karakorum anomaly by monitoring the melt and accumulation rate of Batura glacier as a case study. The second objective based on to measure the impact of climate change on the size and mass balance of Batura glacier with the help of satellite imagery and field data. The last but not the least objective was to analyze the change in climatic variable i.e temperature and precipitation in the study area by using field and remotely sensed data on temporal basis. The significance of study highlighted by (Molina, 2006) said that glaciers indicated severity of regional climate change. (Larsenet et al, 2006; Peduzzi et al, 2010) said health of glacier and ice sheets create impact on sea level raising sea level is due to the decline in alpine glaciers since 1990. The investigation of glacier thickness and extent is important for future research of water problems of Pakistan. The research would be fruitful for climate and water resource planning and management project in Pakistan. In this glacier anomaly analysis of Karakorum to major types of data was used number one is vector data including climatic parameter such as temperature precipitation and snowfall. Second type of data is raster data in which satellite images, satellite data sets and other remotely sensed data were used. The sampling and data collection were based on 32 to 38 years data varying the numbers of years at different stations. There are total six stations from where secondary data were collected. Five station from the
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different points of Karakorum region near Batura glacier while the sixth one is Batura glacier itself. The names of climatic station Astor, Bunji, Gilgit, Gupis, Sekerdu from these points thirty-eight years data were collected of the climatic parameter is precipitation while the sixth station which is major station is Batura from this station three climatic parameters are collected and the parameters are temperature, precipitation and snowfall. This data were almost thirty- two years date back to present. Moreover the raster data were downloaded from USGS. Three types of data were downloaded true color imagery from 1988 to 2018. The vector data were analyzed, mapped, tabulated and statistical analysis were also performed in excel. The raster analyses were held in two software named ArcGIS 10.4 and Eradas Imagine 2016. Three major analysis of remotely sensed data were performed NDVI which is normalize difference vegetation index. The second analysis was land use land cover analysis of the area under study was performed, while the presentation of result was majorly divided into five steps. Number one is the historical presentation of climatic data near Batura stations. Secondly monthly averages were tabulated and graphically presented of the surrounding stations in third step graphical representation of monthly data of surrounding stations. At fourth step results of Batura station were presented in detailed in which graphical trends, tables of annually, monthly and daily trends were presented here. While in the fifth part of the analysis raster data were presented in which image classification of Batura glacier were explained three major classes were developed, land use and land cover, barren land, snow cover and vegetation cover were classified and calculated, at the end of this chapter graphs of raster data were presented. The precipitation of Bunji was ranged from 0mm to 339.4mm. Astor average precipitation was 484.8mm. Gilgit average value is 145.98 mm. Gupis Average was 203.69 mm. while Skardu average is 235mm. the results of Batura vector data results are following Batura annual temperature during 1985 to 2018 ranges from -1.4 degree Celsius to 2.29 and the average is 0.58 mm. Monthly average is ranges 15 degree Celsius to -1 degree Celsius. While the annually precipitation average is 530mm. monthly precipitation ranges from 0mm to 145mm. snowfall monthly amount is ranges from 0.49 cm to 117.39 cm mean daily temperature ranges from -23.21 to 18.5 degree Celsius average maximum temperature from -4.66764 to 25.2 degree Celsius. Daily minimum temperature ranges from -32.36 to 15.34 degree Celsius. Daily precipitation ranges from 0mm to 48 mm. snowfall rages from 0cm to 34.02cm in a day. In raster analysis percentage of glacier was 34 percent in 1988 and 21 percent in 1998 while in 2008 it was 20 percent in 2016 it is 23 percent. This analysis shows the variation of glacier during 1988 to 2016. Land cover classify into three. Classes were snow area, barren land and vegetation. Barren land ranges from 54 percent to 67. While
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vegetation ranged from 12 to 10 percentages. While in the fifth chapter summery, results and recommendation were given.
Chapter four deals with results and discussion in this chapter, the results were presented into 5 different parts for a clearer understanding of the results of performed analysis. Firstly the vector data analysis was presented in which total precipitation of each station graphically presented names of stations are Bunji, Batura, Astor, Sakerdu, Gilgit and. Data from each station were grouped into three parts yearly, monthly and daily. The total annual precipitation of Bunji from 1978 to 2015 was ranges from 339.4mm to 74.3mm and the total annual sum of precipitation was 6520.4mm. The total annual precipitation of Astor ranges from 857.7mmto 15.32mm from 1978 to 2015. There are great variations in annual precipitation of Astor. The values of Gupis ranged from 36.06mm to 0mm and the grand total was 7740.7mm. Gilgit values range from 50.8mm to 1.68mm and the average value is 2.84mm. Sakerdu annually precipitation ranges from 495.4 to 109mm and the grand total was 895.4mm. After annual representation of vector data monthly trends were shown in which the data were grouped into 8 groups or classes. Here the monthly fluctuations of precipitation were showed. The first group of Astor ranges from the maximum 86.42mm in March to 15.32mm. the 2nd group ranges from 132.84mm to 9.98mm January the maximum while the minimum value was in September. The 3rd group ranges from maximum in March with the value of 106.64mm to lowest 5.66mm in September. The 4th group ranges from maximum in 107.1mm to 12.66mm in September. The 5th group ranges from maximum value in April 117.1mm to 14.08mm in October. In 6th group, values ranges from 84.78mm to 11.52mm maximum in April while minimum in October. While in 7th and 8th groups the monthly amount of rainfall decreasing. The next station is Bunji here April and may receive greater precipitation while in October November and December have a lesser amount of precipitation. The monthly trends of groups are the following: during 1978 to 1982 amount of precipitation ranges from 42.96mm to 2.1mm. In 2nd group, values ranges from 37.98mm to 3.56mm in the 3rd group the range is 19.28mm to 0mm. In the 4th group the values are between 32.96mm to 4.42mm. In the 5th group the values range from highest in April and Lowest in October 1.04mm. In 6th group, values are 38.68mm to 1.08mm the 7th group ranges from 43.58mm to 0.74mm and the last group range from 31.96 to 3.55mm. The next station is Gilgit the higher precipitation receives in April, May, and August. In 1st group, values ranges from 50.8mm to 1.68mm maximum in April while minimum in January. The 2nd group ranges from 25.68mm to 6.46mm the 3rd group range from 29.82mm to 6.46mm 5th group
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ranges from 33.22mm to 2.09mm in the 7th group values range from 34.92mm to 0.88mm while the last group range from 180.15mm to 131.38mm. the next station is Gupis 1st group ranges from 36.06mm to 0mm in the 2nd group values range from 26.68mm to 0.94mm the 3rd group ranges from 39.94mm to 0.36mm the 4th group ranges from 107.28mm to 3.51mm 5th group ranges from 119.76mm to 0mm. 6th 23.2mm to 1.82mm the 7th group ranges from 23.2mm to 1.14mm. The last group ranges from 361.82mm to 65.8mm. The next station is Sakerdu February August, and September receives heavy rainfall while October, November and December receives very less rainfall. The 1st group ranges from 23.84mm to 6.23mm in 2nd group values ranges between 43.58mm to 5.84mm 3rd group range from 54.625mm to 2.78mm the 4th group ranges from 48.16mm to 4.01mm. 5th group range from 53.38mm to 2.78mm in 6th group the 7th group ranges from 64.3mm to 4.14mm and the 8th group ranges from 64.7 to 3.8667mm. The next variable of vector data is temperature of Batura station the annual representation ranges from highest 2.29°C to minimum -1.4°C in 1996. While the monthly results shows maximum less than 15°C and minimum -1°C. The next variable is Batura station precipitation the annual values range from 305.9mm to 800mm while the average value is 530mm. The monthly values ranges from 0m m to 160mm in different months. The annual snow fall during 1985 to 2018 ranges from 41.994 cm to 13.831 cm while the average value is 26.38 cm. The monthly snowfall ranges from 80.71 cm to 7 cm while the average value is 30.977 cm in Batura. The annual average snowfall ranges between 58.52 cm to 3.01 cm. the daily mean temperature of the Batura glacier in 1985 to 2018 ranges from 18.56 °C to -23.21°C while the maximum temperature ranges from 25.2 °C to 14.35 °C while the average value is -4.66764 °C while the daily minimum temperature ranges from 15.34 °C to -32.36 . Daily amount of snowfall shows range from 0cm to 34.02cm. The last section of the vector data presentation detailed variables of Batura station was discussed. The statistical analysis was performed in which the relationship between temperature and area under snow in sq.km and there is negative relation between temperature and area under snow was find. Another relationship was found between area under snow and amount of snow that is also negative relationship between them. Raster analysis was performed on remotely sensed satellite imagery of winter and summer during 1988 to 2018. The area under snow is decreasing from 1988 to 2016. The snow cover index shows the decrease in percentage of snow in different years. The land cover and land use pattern shows three major classes of land use in Batura which is vegetation, snow, and barren land. Barren land increases in past years while vegetation cover increases and snow cover decreases. The next part of the thesis deals with the anomalies index of the Batura Glacier firstly the precipitation anomalies were calculated. There are more negative anomalies of precipitation than positive. There are 23 negative and only three positive anomalies in yearly precipitation anomalies index. While in seasonal anomalies index shows the anomalies of three seasons of the region
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Monsoon, western disturbance, and thunderstorm anomalies. After that monthly anomalies of batura were calculated. In the next part of analysis temperature anomalies were calculated in which negative anomalies are more than positive anomalies which means that temperature of Bature was increasing in past years. Monthly anomalies of temperature were also calculated which also shows the increasing temperature trends. The thesis also presents the seasonal distribution of precipitation in different groups. The great fluctuation in monsoon precipitation during 1989 to 2018 was noticed. Western disturbance of precipitation also fluctuates simultaneously thunderstorm season also face fluctuation in Batura. the last part of the analysis deals with the Topographic examination of Batura Glacier in which contouring highest elevation around 7770.9 km and lowest value 1787.6 km. DEM also shows the shape of the Batura basin and highlighted Batura Sar and Batura Pasu. Watershed shows the stream flow in Batura glacier.
5.2 Conclusion
The present study gives the following conclusions regarding Batura glacier anomalies. Rainfall is an important indicator of glacier health the historical trend showed that Bunji, Astor, Gilgit Gupis and Sakerdu facing decline in precipitation during the years of 1978 to 2015. The monthly trends of Astor showed very low amount of precipitation while within is March April may have sufficient amount of rainfall, January and February receives average rainfall. Other months receives very low amount of rainfall. Bunji also receives very less amount of rainfall on monthly bases, same with the Gilgit, Gupis and Skardu rainfall monthly pattern. The rainfall showed high fluctuations during the years 1978 to 2015. The average temperature of Batura showed increase in recent years. The monthly precipitation also has high fluctuation during all months of the year while the daily temperature is also increasing in recent years. The total precipitation of Batura decreased in the recent years. The fluctuation of monthly precipitation showed Batura received highest rainfall in February, March, April and May. Other month receives average rainfall while the monthly rainfall declining during 1978 to 2915. Total annual snowfall is decreasing in slowly during 1985 to 2018 n Batura station but the decrease is become fast in recent years. Batura monthly snowfall amount is also decreasing trend in the recent year’s average annual snowfall facing decline in the recent years. Batura mean daily temperature facing increasing trend of temperature. While the maximum temperature also increasing Batura daily minimum temperature is also increasing. Batura daily precipitation facing fluctuation throughout the years but in recent years it was at average. The daily amount snowfall is at average in recent years with some declining trends. The raster analysis shows the total area of glacier reducing during 1988 to 2016 while the barren land is increasing. On the other hand vegetation cover of Batura glacier is also
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decreasing. The area under snow also face decline. The results of remote sensing analysis were compared with the vector data analysis. The yearly, seasonal and monthly precipitation meteorological anomaly index showed more negative anomalies than positive anomalies. During 1985 to 2018 the numbers of negative anomalies were 21 while only three positive analysis. The next part of the analysis deals with the temperature and precipitation anomalies of Batura Glacier. The index of anomalies shows negative anomalies of precipitation during 1985 to 2018. While the seasonal anomalies shows the fluctuation in seasonal precipitation were shown in three groups named as monsoon (July, August, Sep) western Disturbance (Dec, Jan, Feb, March) and thunderstorm (Oct, November, April, May, January). Monsoon anomalies have positive trends from 1985 to 1998 while in 1988 to 2018 faces negative anomalies. western disturbance face negative and thunderstorm face positive anomalies. monthly anomalies shows the fluctuation of anomalies in different of months of different years. The precipitation anomalies are high in negative anomalies while lesser positive anomalies which indicates that decline in precipitation of Batura region. The temperature anomalies show that negative anomalies are higher than positive anomalies which indicate that the temperature of Batura is increasing day by daily. The monthly anomalies show the trends of negative and positive in different month of the year. During 1985 to 2017 the next part of analysis deals with the seasonal distribution of precipitation during three decades. Firstly monsoon season were discussed in which clear variation in precipitation were observed increase in precipitation in Batura. The distribution of precipitation in western Disturbance shows clear decline in precipitation. The third part of this section fluctuation in this season. The last part of the thesis were developed topographical examination of Batura glacier. Firstly contouring held on data which shows elevation of the slop. DEM show the shape of the Batura glaciers and main Sars in it. Watershed shows the direction and flow of the streams and its shape. The analysis shows the detailed investigation of the climatic variables of Batura glacier in which the trends of precipitation, temperature and snowfall were examine with different statistical, geographical and special analysis. In which annually monthly and daily fluctuations change, and anomalies were calculated. Moreover the statistical analysis was also performed. It is concluded that it is clear decline in precipitation shows while temperature increase is also show the high temperature in Batura glacier. Snowfall is also declining in the region. The results show that the climatic trends of Batura have clear impacts of climate change. The present climate change is damaging the glaciers of the region which is important water resource for Pakistan. The temperature anomalies of Batura show negative trends which means the temperature of region is raising due to climate
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changes. Annual and monthly anomalies show that temperature of the glacier is raising in past decades. While the precipitation anomalies also have negative anomalies seasonal anomalies show more positive trends in monsoon season while thunderstorm and western disturbance have negative anomalies are than positive anomalies. The third part of anomalies is about snowfall anomalies. The monthly anomalies show 26 positive anomalies while 8 negative anomalies but the negative anomalies are recorded in recent past years. The fifth chapter deals with the statistical summary results and discussion of the thesis. The research explores the climatic variables. The detailed statistical summary was given of temperature, Precipitation, and snowfall. Annual, monthly and daily trends of variables were graphically presented. Vector and raster analysis were performed on data. Raster analysis were performed on land set and satellite imagery on the region. The years of the imagery were 1988, 200, 2008, and 2016. The true color images of same years were also given in the analysis section. The raster analysis shows that area under snow was decreasing from previous years the barren land is increasing while vegetation cover is also decreasing in study area.
5.3 RECOMMENDATIONS
• The recent study has seek to explore the abnormalities of Batura glaciated region as an evidence of climate change in Pakistan and its impacts on the water resources of Pakistan. • The future researchers should use the anomaly analysis to find out the impacts and abnormalities of the climate change in the glaciated regions. And calculate the amount of glacier in the area of study. The same results are used for comparing the historical trends of areal and temporal variations in climatic data. • The future researchers should compare the amount of precipitation, snowfall, temperature and its impact on land use land cover of the area while it is recommended to save records of land use land cover parameter such as glaciated area, snow cover, barren land and vegetation. • Remote sensing techniques should use to explore anomalies in the area. While ArcGIS use to find out areal and temporal maps of the area. • The glacier anomaly analysis is necessary to find out the present condition of the glacier because it great impact in outburst glaciated lake and water resource of the area.
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• It is recommended to use image and data set classification with more classes by this more accurate results comes to great snap short of the glacier. • It is recommended that to create more opportunities or this kind of research in the study area. • It is recommended that more climatic stations should install in the study area. • It is recommended that government should developed climate research institution in Pakistan or researcher groups that focused on this burning global issues of climate change have creating impact on Pakistan water resource and other climate change related problems will reported because Pakistan is one of the top countries that will effected by climate change. • The study has both academic and public sector benefits this research is used for future researches, government, glacier study, mountainous hazards study, climate change related problems and water management and problem reporting project managers should use this study for future analysis. • It is recommended that government should invest in these types of studies or create more scholarships in this field of research. • The Government should encourage researchers to come in this field of study that is useful to Indus water management problems and projects. • The study is useful for climate change protections policies in Pakistan. • Future researchers use remotely sensed data and analyzing software for classifications and calculations. By creating models and mapped results by using geographical and statistical software, make it a clearer picture of the glacier for good researchers and planning. • The study observes that recent excessive temperature change create declining impacts on glaciers future research should focus on this issue in the area. • On the bases of my research study, it is clearly said that the Batura glacier had clear evidence of anomalies and abnormalities during 1978 to 2018 the glacier facing clear decline it’s slow but happing which accelerates the climate change effects of the water resources and availability of water in Pakistan. • The study recommends that climate change protection policies should improve and it should globally highlight at international climate protection forums. • It is highly recommended that other glaciated regions of Pakistan Karakorum should also study in the field of research.
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• Batura is a very difficult terrain to visit due to that there are very fewer numbers of observation points in this area. • It is recommended that government should install more metrological and observational points so that further observation possible. • Due to the lack of observation points and highly difficult terrain visitor and researcher very rarely visit the glacier government should make visitor friendly policy so that observers and researchers visit this place and investigate. • Station installation also encourages visitors to go and collect the evidence of climate change in this region. • Field visits should plan by the government or policymakers. Field visits important for the development of research. • There is a lack of interest among people to go there government and climate change researchers should develop policies to increase their interests. • Nationwide climate change policies should be developed in Pakistan to minimize the impact of climate change. • The scientific observation of climate change monitoring and assessment should develop by environmental and climate change research groups worldwide. • More reservoirs should construct in the region to reduce the loss of water. • Pakistan Met department should install more climate observation station in this region to save the climatic records of climate.
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