APPLICATION OF MULTI-PROXY TREE-RING PARAMETERS IN...

APPLICATION OF MULTI-PROXY TREE-RING PARAMETERS IN

THE RECONSTRUCTION OF CLIMATE VIS-À-VIS GLACIAL

FLUCTUATIONS FROM THE EASTERN HIMALAYA

THESIS SUBMITTED TO

LUCKNOW UNIVERSITY

LUCKNOW

UTTAR PRADESH

FOR THE DEGREE OF

DOCTOR OF PHILOSOPHY

IN

BOTANY

BY

MAYANK SHEKHAR, M.Sc. M.Tech.

BIRBAL SAHNI INSTITUTE OF PALAEOBOTANY

LUCKNOW-226007, INDIA

This thesis is dedicated to my beloved parents

i

Acknowledgements

It gives me immense pleasure to express my deep sense of gratitude to

my mentor and Ph.D. supervisor Dr. Amalava Bhattacharyya, Emeritus Scientist, at

Birbal Sahni Institute of Palaeobotany (BSIP), Lucknow, Uttar Pradesh (India) for his

valuable guidance, scientific discussion, encouragements and tremendous moral

support during the entire course of the completion of this dissertation.

I would like to express my gratitude to Prof. Sunil Bajpai, Director, BSIP,

Lucknow (U.P., India) for providing all necessary facilities to me and also for the

encouragements.

I express my sincere gratitude to Dr Santosh Kumar Shah (Santosh Bhiya)

Scientist C (BSIP) for his valuable advices and suggestion on all statistical aspects of

tree-ring analysis and also for moral support.

This work is financially supported by Department of Science and Technology,

New Delhi. DST Project No. ESS/91/38/2005. I extend my sincere thanks to Prof. R.

Ramesh, Director, ISO-IGBB for providing Senior Research Fellowship to me.

I wish to thank the Forest Officials of North Sikkim especially Mr. Pradeep

Kumar (IFS) for giving permission and providing necessary facilities during the

collection of tree-ring samples. I am also grateful to India Meteorological

Department, Pune for providing the climate data.

I am indebted to the entire staff of my laboratory Mrs. Nivedita Mehrotra,

Mrs. Sandhya Misra, Mrs. Archana Singh for their moral support and

encouragements. I would also like to thank to my seniors Dr. Parmindar Singh

Ranhota, Scientist C, Dr. Ruby Ghosh (Didi), Scientist B, Dr. Md. Firoze Quamar,

BSRA, Dr. Gaurav Srivastva, Scientist B, Dr Anumeha Shukla, Scientist B, Birbal

Sahni Institute of Palaeobotany, Lucknow for their moral support and

encouragements.

I would like to thank my friends Er. Devendra Kumar Maurya, Rajesh Kumar

Bhardwaj, Shyum Babu, Entekhab Alam, Muskan Singh and Vivek Singh Chauhan

for helping me in every possible way.

ii

My sincere thanks also goes to my dearest seniors and friends Dr. S. Nawaj

Ali (PRL, Ahmadabad, Gujarat) for the moral support during my dissertation; Mr.

Anshuman Bhardwaj (JRF, DRDO, Chandigrah); Mrs. Pratima Pandey (Research

Associate, Lucknow University) and Ms. Muskan Singh, especially for helping me

while using Remote Sensing and GIS Applications.

I would like to thank Mr. Shaktiman Singh, Research Scholar, Sharda

University (New Delhi) for the critical discussion and suggestion on hydrological

modeling.

I also acknowledge The National Snow and Ice Data Center (NSIDC) with

thanks for providing Mass Balance Data (Mark Dyurgerov, 2005) of Eastern

Himalaya and adjoining countries for analysis.

I extend my thanks to my friends Anish, Vikash, Atual, Preeti, Shikha, Siksha,

who have always supported and encouraged me to pursue this study.

I also acknowledge my sister with thanks who has morally supported me

during the course of the completion of dissertation work.

I would like to express my gratitude to my parents and my brothers Mr.

Shashank Shekhar and Mr. Shardendu Shekhar for all their encouragements and

confidence that they reposed on me.

Finally, I would like to express my deep sense of regard to Almighty for His

divine grace that He bestowed upon me throughout my life.

Mayank Shekhar

Contents Page Acknowledgements i-ii List of Figures iii-vii List of Tables viii Preface ix-x Chapter 1. Introduction

1-16

1.1. Tree-ring as proxy for Paleoenvironment Reconstruction 1

1.2. Theme of the Dissertation 2-4 1.3. Objectives 4 1.4. Overview of forests of Sikkim Himalaya 4-5

1.4.1. Forest covers Sikkim, Eastern Himalaya based on satellite data

5-6

1.4.2. North Sikkim forest cover 6

1.5. Climate of India - An Overview 6-8

1.5.1. Temperature trends 6 1.5.2. Precipitation trends 7-8

1.6. Resume of earlier tree-ring study 9-16 1.6.1. Global context 9

1.6.1.1. Paleoclimatology 9 1.6.1.2. Hydrology 10 1.6.1.3. Glaciology 10 1.6.1.4. The Palmer Drought Severity Index (PDSI) 10

1.6.2. Indian Context 11 1.6.5.1. Western Himalaya 12 1.6.5.2. Eastern Himalaya 12-13 1.6.5.3. Peninsular India 13-14

1.6.5.4. Dendrohydrology 14

1.6.5.5. Dendroglaciology 15 1.6.5.6. Tree Growth and its Relationship with El Nino 15-16 1.6.5.7. The Palmer Drought Severity Index (PDSI) 16

Chapter 2. General Principles & supporting data

17-34

2.1 Selection of tree ring site 17 2.2. Study of tree-rings 17-18 2.3. Acquisition of tree- ring width data 18-20 2.4. Standardization of tree ring data & Chronology preparation 20-22 2.5. Climate and Glacial data 22-25

2.5.1. Regional climate data 22

2.5.1.1. Details of CRU T.S2.1 data and source 24

2.5.1.2. Statistical assessment of climate data 24-25

2.5.1.3. Missing Value estimation in climate data 25

5.1.4. Descriptive statistics of climate data 25 2.5.2. Glacial data 26

2.5.2.1. Glacier Front variation and Mass-balance data 26-27 2.6. Dendroclimatic modeling (Past climate reconstruction) 27-28

2.6.1. Principal component analysis (PCA) 28

2.6.2. Bootstrap Response Function 30

2.6.3 Bootstrap Transfer Function 30-31 2.6.4 Correlation analysis 31 2.6.5. Linear regression method for Climate reconstruction 31

2.7. Dendrohydrological modeling 32-33 2.7.1. Rivers discharge data 32

2.7.2. Climate data for discharge site 32

2.7.3. Correlation analysis for Tree-growth and discharge relationship

33

2.7.4. Discharge Reconstruction method 33 2.8.4.1. Linear regression method for discharge reconstruction

33

2.8. Correlation analysis for tree growth and its relation with PDSI/ El Nino

33-34

2.9. Multiple tree-ring proxies (Earlywood width, Latewood width) 34

Chapter 3. Study area, Site selection and collection of samples and Sample Processing

35-47

3.1. Vegetation Overview of Zemu valley 35 3.2. Tree-ring sampling sites of North Sikkim 35-44

3.2.1 Lachen 38-39 3.2.2. Zema 40 3.2.3. Dozom Khola 40 3.2.4. Talem (TAL) 40 3.2.5. Jakthang (JAK) 40 3.2.6. Yabuk (YAB) 42 3.2.7. Yumthang (YUM) 44

3.3. Zemu Valley (Zemu glacier IN5020105032) 44-45

3.4. Sample Processing 46-47

Chapter 4. Tree ring chronology of Zemu glacier valley 48-55

4.1. Building of Tree-Ring Chronologies 48-53 4.2. Chronology characteristics 53-54

4.2.1. Correlation statistics. 55

Page Chapter 5. Dendroclimatic modeling (Climate calibrations and reconstruction)

56-92

5. Tree Growth/Climate Response Function Analysis 56 5.1. Principal component analysis (PCA) 56

5.1.1. Identification of common patterns of variations in tree growth

56-57

5.2. Significant Climatic variables influencing tree-growth 57

5.2.1. Climatic variables significant in limiting tree-growth at the Zemu Valley.

57-71

5.2.2 Varied Climate–growth responses at altitude gradients 72

5.2.3. Species-specific climate–growth responses. 72-74

5.3. Physiological explanation of tree growth climate relationship 74-76

5.3.1. Positive correlation with March to April temperature 74-75 5.3.2. Negative correlation with June to September 75-76

5.4. Dendroclimatic modeling (Past Climate reconstruction) 77-92 5.4.1. Bootstrap Method for March-April Maximum temperature reconstruction

77-82

5.4.1.1 Salient features of reconstructed March April maximum temperature

83

5.4.2. Linear regression method for Average temperature of (July August) temperature reconstruction

83

5.5. Reliability of the regression model 83-92

5.5.1. Calibration verification of model for temperature

reconstruction.

83-84

5.5.2 Variability in reconstructed climate data. 84-89

5.5.3. Characteristics of reconstructed temperature 90

5.5.6. Cyclic nature of tree based reconstructed climate records

91-92

Chapter 6. Tree growth and glacier relationship 93-98

6.1 Glacier behavior and tree-ring width chronology 93-96 6.2. Reconstructed temperature and glacier fluctuation 97

2.1. Role of Maximum March-April temperature in fluctuation of Zemu glacier.

97

6.2.2. Role of Average July-August temperature in Fluctuation of Zemu glacier.

98

Chapter 7. Dendrohydrological modeling (reconstruction of Discharge) 99-107

7.1. Study area of river discharge 99

7.2 Relationships between Tree growth and Climate 100

7.3. The stream flow reconstruction method 102 7.4. Variability in reconstructed discharge data 103-106 7.5. Cyclic nature in reconstructed January-April discharge.

106-107

Chapter 8. Tree growth and its relation with, El Nino 105-108

8.1. Tree-growth/glacier fluctuation and El Niño relation 105-106

8.2. Tree growth and its relation with PDSI

107-108

Chapter 9. Discussion and conclusions 112-115

9.1 Further work for the improvement of research work 115

Chapter 10. References 116-133

iii

List of Figures Page

Fig.1.1 Forest cover map of Sikkim, Eastern Himalaya (Adopted from Indian State

of Forest Report 2011).

7

Fig. 2.1 Map of India showing position of Zemu glacier and adjoining area in

Sikkim. 2

Fig. 2.2 Monthly variation of total precipitations (bars), mean maximum

temperature (red line), and mean temperature (green line) and mean minimum temperature (pink line) for Gangtok meteorological station.

23

Fig. 2.3 Monthly variation of total precipitations (bars), mean maximum

temperature (red line), and mean temperature (green line) and mean minimum temperature (pink line) for CRU Grid data.

24

Fig. 3.1. Location map of Zemu glacier 36 Fig. 3.2 (a) Satellite map of Zemu glacier and (b) Zemu glacier along with

Vegetation Cover in and around this region based on NDVI (Normalised

Difference Vegetation Index).

37

Fig.3.3 Sampling site Lachen showing forest of Abies densa 39 Fig.3.4. Sampling site Talem showing forest of Abies densa 41 Fig. 3.5. Sampling site Jakthang showing forest of Abies densa and Juniperus

recurva 42

Fig. 3.6: Sampling site Yabuk showing forest of Abies densa and Juniperus recurva 43 Fig. 3.7. (a)Sampling site Yabuk showing zone of Juniperus squmata scrub,(b)

Sampling site Yabuk showing forest of Juniperus recurva 44

Fig.3.8. (a) Satellite map showing location of sample collection site along with

meteorological station and (b) Sketch map route of collection of samples from ZEMA to Zemu glacier site. (the details of the abbreviations of sites and trees are given in Table 2.2)

46

Fig. 3.9. Collection of tree cores from the tree through increment borer. 47 Fig. 3.10. Processing of tree-ring cores. 47 Fig.4.1. Ring-width index chronology of Abies densa from Yabuk 49 Fig.4.2. Ring-width index chronologies of Juniperus squmata from Yabuk 49 Fig. 4.3. Ring-width index chronologies of Juniperus recurva from Yabuk site. 50 Fig. 4.4. Ring-width index chronologies of Abies densa from Zakthang site. 50 Fig. 4.5. Ring-width index chronologies of Abies densa from Talem sites. 50

iv

Fig. 4.6. Ring-width index chronology of Abies densa from Dozamkhola site 50 Fig. 4.7. Ring-width index chronologies of Abies densa Zema site. 51 Fig. 4.8. Ring-width index chronology of Abies densa from Lachen site. 51 Fig. 4.9. Ring-width index chronology of Larix griffithiana from Lachen site. 51 Fig. 4.10. Early wood width index chronology of Larix griffithiana from Lachen site. 52 Fig. 4.11. Late wood width index chronology of Larix griffithiana Lachen site. 52 Fig. 4.12. Ring width index chronology of Abies densa from Yumthang site. 52 Fig.4.13 (a,) and( b). Time-series plots of the two PCs from ring width chronologies

along with altitudinal of gradient of Zemu glacier Sikkim Himalaya. 53

Fig. 5.1. Plot of (a) Response function and (b) Correlation analyses based on

standard chronologies of tree ring indices versus monthly climate data (mean temperature, minimum temperature, maximum temperature and precipitation) for ABDE_YAB. Horizontal pink line indicates significance level (p < 0.05) and red line indicate significance level (p < 0.01) above and below.

58


standard chronologies of tree ring indices versus monthly climate data (mean temperature, minimum temperature, maximum temperature and precipitation) for JUSQ_YAB. Horizontal pink line indicates significance level (p < 0.05) and red line indicate significance level(p < 0.01) above and below

59


standard chronologies of tree ring indices versus monthly climate data (mean temperature, minimum temperature, maximum temperature and precipitation) for JURE_YAB, horizontal pink line indicates significance level (p < 0.05) and red line indicates significance level(p < 0.01)

60

Fig..5.4. Plot of (a) Response function and (b) Correlation analyses based on

standard chronologies of tree ring indices versus monthly climate data (mean temperature, minimum temperature, maximum temperature and precipitation) for ABDE_JAK. Horizontal pink line indicates significance level (p < 0.05) and red line indicate significance level(p < 0.01)

61


standard chronologies of tree ring indices versus monthly climate data (mean temperature, minimum temperature, maximum temperature and precipitation) for ABDE_TAL. Horizontal pink line indicates significance level (p < 0.05) and red line indicate significance level(p < 0.01) above and below

62

Fig. 5.6. Plot of (a) Response function and (b) Correlation analyses based on standard

chronologies of tree ring indices versus monthly climate data (mean temperature, minimum temperature, maximum temperature and precipitation) for ABDE_DOZ. Horizontal pink line indicates significance level (p < 0.05) and red line indicate significance level(p < 0.01) above and below

63

v


standard chronologies of tree ring indices versus monthly climate data (mean temperature, minimum temperature, maximum temperature and precipitation) for ABDE_ZEM. Horizontal pink line indicates significance level (p < 0.05) and red line indicate significance level(p < 0.01) above and below.

64

Fig. 5.8 Plot of (a) Response function and (b) Correlation analyses based on

standard chronologies of tree ring indices versus monthly climate data (mean temperature, minimum temperature, maximum temperature and precipitation) for ABDE_LAC. Horizontal pink line indicates significance level (p < 0.05) and red line indicate significance level(p < 0.01) above and below.

65


standard chronologies of tree ring indices versus monthly climate data (mean temperature, minimum temperature, maximum temperature and precipitation) for LAGR_RW_LAC. Horizontal pink line indicates significance level (p < 0.05) and red line indicate significance level (p < 0.01) above and below.

66


standard chronologies of tree ring indices versus monthly climate data (mean temperature, minimum temperature, maximum temperature and precipitation) for LAGR_EW_LAC. Horizontal pink line indicates significance level (p < 0.05) and red line indicate significance level(p < 0.01) above and below.

67


standard chronologies of tree ring indices versus monthly climate data (mean temperature, minimum temperature, maximum temperature and precipitation) for LAGR_RW_LW_LAC. Horizontal pink line indicates significance level (p < 0.05) and red line indicate significance level(p < 0.01) above and below

68


standard chronologies of tree ring indices versus monthly climate data (mean temperature, minimum temperature, maximum temperature and precipitation) for ABDE_YUM. Horizontal pink line indicates significance level (p < 0.05) and red line indicate significance level(p < 0.01) above and below

69

Fig. 5.13 Principle component plot in rotated space 70 Fig.5.14. Plot of (a) Response function and (b) Correlation analyses based on rotated

principal component (PC) scores [AD 1881–1994 for PC#1, PC#2,] versus monthly climate data (mean temperature, minimum temperature, maximum temperature and precipitation). Horizontal pink line indicates significance level (p < 0.05) and red line indicate significance level (p < 0.01) above and below.

74

Fig.5.15 Tree-ring based reconstructed Maximum March-April temperature in Zemu

Valley (dotted green line is start year of the reliable time span); Actual (green line) and estimated (red line).

79

vi

Fig.5.16 Showing Anomaly in the reconstructed March_April temperature. 80 Fig.5.17 The comparison of actual and reconstructed March April maximum

temperature from AD1966 to 2000. 81

Fig. 5.18 Scatter plot of actual and reconstructed Maximum March-April

temperature with a linear relationship highlighted during the period of 1966–2000.

81

Fig. 5.19 Tree-ring based reconstructed Mean July-August temperatures in Zemu

Valley; (dotted green line is start year of the reliable time span), actual (green line) and estimated (red line).

86

Fig. 5.20 The comparison of actual and reconstructed Maximum temperature of

March-April from 1976 to 1996.

87

Fig. 5.21 Scatter plot of actual and tree-ring reconstructed Late summer mean

temperature (July-August) temperature with a linear relationship highlighted during the period of 1966–2000.

87

Fig. 5.22 Showing Anomaly in the reconstructed July_August temperature 88 Fig. 5.23 (a) Maximum March-April temperature. (b) The wavelet power spectrum.

The power has been scaled by the global wavelet spectrum (at right). The cross-hatched region is the cone of influence, where zero padding has reduced the variance. Black contour is the 95% significance level, using a red-noise (autoregressive lag1) background spectrum. (c) The global wavelet power spectrum (black line).The dashed line is the significance for the global wavelet spectrum, assuming the same significance level and background spectrum as in.

91

Fig. 5.24 (a) July-August mean temperature (b) The wavelet power spectrum. The

power has been scaled by the global wavelet spectrum (at right). The cross-hatched region is the cone of influence, where zero padding has reduced the variance. Black contour is the 95% significance level, using a red-noise (autoregressive lag1) background spectrum. (c) The global wavelet power spectrum (black line). The dashed line is the significance for the global wavelet spectrum, assuming the same significance level and background spectrum as in.

92

Fig. 6.1 Photograph showing the Snout position of Zemu Glacier, North Sikkim

(modified after Luitel et al., 2012) 95

Fig. 6.2 Tree growth and its relation with Zemu glacier 96 Fig. 6.3 Tree growth and its relation with Changmekhangpu glacier

96

Fig. 6.3 Comparison of Fir Chronology of ABDE_YAB with available Mass

balance data of three Chinese glacier (AD 1988-1995) and Nepal glacier (AD 1996-1999).

96

vii

Fig.7.1 Map showing location of tree ring site, meteorological station and discharge gauge station at Lachen, north Sikkim, Eastern Himalaya. For the generation of the map SRTM 30 (digital terrain elevation data set was used)

99

100 Fig.7.2 Photograph showing “Zemu Chuu” at Lachen North Sikkim Fig.7.3 Mean annual variation of “Zemu Chuu” river discharge at Lachen gauging

station (1976-1996) North Sikkim.

101

Fig.7.4 Mean monthly variation of “Zemu Chuu” river discharge at Lachen

gauging station (1976-1996) North Sikkim. 101

Fig.7.5 Correlation plot of standard chronologies of ABDE_ZEM with averaged

monthly Discharge data of Lachen data (1977–1996). Horizontal pink line indicates significance level (p < 0.05) and red line indicate significance level (p < 0.01).

102

Fig.7.6 Reconstruction of January–April discharges of “Zemu Chuu” at Lachen,

North Sikkim since AD 1775. The red line represents reconstructed while green line represents actual data

104

Fig.7.7 (a) The comparison of actual and reconstructed stream flow (January-

April) from 1976 to 1996 (b) Scatter plot of actual and tree-ring reconstructed stream flow (January-April) with a linear relationship highlighted during the period of 1976–1996.

105

Fig.:7.3 (a) January-April _Discharge. (b) The wavelet power spectrum. The power

has been scaled by the global wavelet spectrum (at right). The cross-hatched region is the cone of influence, where zero padding has reduced the variance. Black contour is the 95% significance level, using a red-noise (autoregressive lag1) background spectrum. (c) The global wavelet power spectrum (black line). The dashed line is the significance for the global wavelet spectrum, assuming the same significance level and background spectrum as in

107

Fig. 8.1 Correlation values of mean monthly El Niño 3.4 with standard regional

chronologies PC#1 and PC#2. Monthly variables spanning from January to December. The pink horizontal line indicates 95% confidence limits.

106

Fig. 8.2 Plot of Zemu glacier retreat with mean data of ring-with, and El Nino 3.4 106 Fig. 8.3 Correlation values of mean monthly PDSI of the one grid points with

standard regional chronologies of all site and PC#1 and PC#2. (a) All chronologies. (b) PC#1, PC#2. Monthly variables spanning from November of the previous year to October of the current year. The pink horizontal line indicates 95% and red line indicate 99% confidence limits.

108

viii

List of Tables

Page Table 2.1 Site information and tree-ring chronologies statistics of Zemu Valley. 19

Table 2.2 Site information and tree-ring chronologies statistics Larix griffithiana (EW, LW).

19

Table 2.3 Description of IMD climate data used for temperature records. 22

Table 2.3 Description of IMD climate data used for precipitation records. 23

Table 2.5. Description of CRU T.S 2.1climate data. 23

Table 2.6 General information about the glacier and data. 29

Table.4.1. Correlation matrix for standard tree-ring chronologies. 55

Table.5.1 Summary of the PCA Statistics of tree-ring chronologies. 71

Table.5.2. Summary of rotated principal component retained in PCA. 71

Table.5.3 Monthly climatic models for Zemu Valley based on ABDE_YAB, using bootstrap method.

80

Table 5.4 Statistics of Calibration and Verification for tree-ring reconstruction of maximum March-April temperature.

80

Table. 5.5 Statistics of calibration and verification for tree-ring reconstruction of July-August mean temperature in the common period 1966–2000.

89

Table 7.1 Statistics of calibration and verification for tree-ring reconstruction of January-April Stream flow.

105

Table. 8.1 Correlation value of glacier retreat, mean data of ring-with, and El Nino

3.4.

106

ix

Preface The present dissertation entitled “Application of multi-proxy tree ring

parameters in the reconstruction of climate vis-à-vis glacial fluctuation from the

Eastern Himalaya” is based on multi-proxy tree ring parameters in record of past

climate and glacier fluctuation. Tree-ring samples analyzed were collected mostly in

the form of cores and discs from left over stumps from several conifer taxa growing in

diversified forests ranging from temperate to sub-alpine in and around adjoining area

of Zemu Glacier, Eastern Himalaya.

The research work included in this dissertation is structured into nine chapters.

The beginning with the “Introduction”, where I have introduced briefly significance

of tree-ring as proxy for paleoenvironment reconstruction emphasizing its prospects in

understanding the climatic changes, hydrological changes, glacier fluctuation and its

linkage with Palmer Drought Severity Index (PDSI) and El Nino followed by a brief

account on the overview of forests of Sikkim Himalaya and glimpse of climate of the

India emphasizing Himalayan region have been given. A brief account on resume of

earlier tree-ring study from both global and from India with emphasis to the

Himalayan region and its applications to climate, hydrology, glacier and El Nino have

also been discussed in this chapter.

In the second chapter, “General Principles & supporting data” I have

discussed dendrochronological technique for sample collection and processes

involved in retrieving climate and tree growth relationship to glacial advancement

/retreat etc.

In chapter three i.e. “Study Site selection and collection of samples and

Processing”, a brief account on description of study sites, collection and processing

of tree-ring samples have been discussed.

In chapter four i.e “Tree ring Chronology of Zemu Glacier valley”, I have

discussed chronology development which begins with dating of tree rings exactly to

the calendar years of their formation through cross dating, measurement of ring width

and details of chronology preparation and their quality assessment based on some

selected statistics and regional signal assessment of chronologies through Principal

component.

Chapter five i.e. “Dendroclimatic modeling (Climate reconstruction)”,

contains the details of dendroclimatic modeling from the Eastern Himalayan region,

x

tree growth/climate relationship with the help of Response Function Analysis using

Orthogonal Bootstrap Regression and Correlation analysis, the reconstruction of

climate using tree-ring chronologies using both Bootstrap Transfer Function and

Linear Regression method. At the end of this chapter I have discussed about the

salient features of reconstructed climate and its cyclic behaviour with the help of

wavelet analysis.

In chapter six the “Tree growth and glacier relationship” and data

comparison with adjoining glaciers have been mentioned.

In chapter seven, “Dendrohydrological modeling (Reconstruction of

Discharge)” has been discussed and reconstructed discharge data has been compared

with corresponding other data viz., drought and El Nino Years.

Chapter eight includes “Tree growth and its relation with PDSI, El Nino”

which includes correlation analysis with monthly data set of the same.

Chapter nine, “Discussions and conclusions” is the most important where

results derived from the present study are summarized and salient achievement of

these objectives have been discussed followed by an account on limitations of the

present study along with scope for improvement and further work towards better

understanding of various aspects of climate and climate related phenomena from the

Eastern Himalayan region.

Chapter 1 Introduction

1

1. Introduction In accordance to the global warming, temperature of the Eastern Himalayan

region is also increasing at the rate of 0.01oC/year or more (Shrestha et al., 2010)

when surface temperature of earth as a whole increased by about 0.74° C (+/- 0.18° C)

over the last 100 years (IPCC, 2007). One of the most important indicators of climate

change is the variation of mass of the glaciers. It is general trend that the Himalayan

Glaciers, like other glaciers of the globe, are retreating very fast as indicated by the

movement of snout position towards the higher elevations. The fast retreat of the

Himalayan glaciers is much of societal concern. We are worried about the fate of these

glaciers in respect to the global warming and associated erratic monsoon behavior in

the coming years. For better understanding of glacial movement, it is essential to

explore the variation and changes of climate for longer time span. As widespread,

direct measurements of climate variables are only available for about one to two

centuries back in time and that too are only from limited number of stations which are

inadequate for climate modeling and to established long term link with the retreat and

advancement of glaciers. It is therefore necessary to use indirect indicators or

‘‘proxy’’ to measure climate variability provided by the natural archives of

information present in our environment to reconstruct earlier changes. These natural

archives record by their biological, chemical, or physical nature, climate-related

phenomena. Additionally, information is provided by written archives from historical

documents. Some proxy indicators, including most sediment cores, low accumulation

ice cores, and preserved pollen cannot record climate changes at high temporal

resolution. These indicators generally have poor chronologies because of uncertain

radiometric dating methods or questionable ‘‘age model’’ assumptions (e.g., the

assumption of constant stratigraphic rates between marker or ‘‘dated’’ horizons).

Such proxy indicators are thus only be useful for describing to climate changes in

centennial and often longer timescales. High-resolution, annually and/or seasonally

resolved proxy climate records (historical documents, growth and density

measurements from tree rings, corals, annually resolved ice cores, laminated ocean

and lake sediment cores, and speleothems) can, however, describe year-by-year

patterns of climate in the past centuries (Folland et al., 2001; Jones et al., 2001; Mann,

2001).


2

1.1. Tree-ring as proxy for Paleoenvironment Reconstruction All proxy data are indirect measurements of climate change, and they vary

considerably in their reliability as indicators of long-term climate. Tree ring has

played a pivotal role in high-resolution paleoenvironmental analysis. This is one of

the best proxies for the understanding of climate, because tree ring are environmental

sensitive annual growth rings, which produce precise and reliable chronology of

ecology and climate (Fritts, 1976). For a reliable reconstruction of past changes from

proxy data it is essential that reconstructions based on these indirect climate indicators

be ‘calibrated’’ and independently ‘‘validated’’ against instrumental records during

common intervals of overlap. For the tree, the limiting factor may be internal i.e.

(biogenic) or external (environmental factor). Some of these factors can be a limit in

nutrient availability or growing conditions such as precipitation and temperature.

However, it is important to realize the interconnection of these two factors. Internal

processes cannot operate without supplies delivered by external forces which enhance

the environment signal in tree-ring growth (Fritts, 1976).

1.2. Theme of the Dissertation In this dissertation reconstruction of climate vis-a-vis glacial movements

(advance/retreat) based on multi proxy tree ring parameters i.e., tree ring width data as

a whole or its early wood and latewood separately, have been taken in to

consideration. Although traditional dendroglaciological techniques make it possible to

date the timing of glacier advances and the maximum glacier extent (Luckman, 1988),

but they provide little insight into the attendant climate conditions or duration of

glacier advances (Porter, 1981; Luckman, 2000). By contrast, applied

dendroglaciological research has successfully established linkages between glacier

mass balance and tree-ring-width variability. La Marche and Fritts (1971), Matthews

(1977) and others directly related tree-ring-width variability to climate conditions, and

subsequently to glacier fluctuations. They discerned that high winter snow

accumulation, and short cool and cloudy summers favored positive glacier mass

balance, but were detrimental to tree growth and resulted in narrow annual tree rings.

Conversely, warm summer air temperatures and a greater number of sunny days were

shown to result in enhanced glacial ablation, as well as increased radial growth rates


3

and the production of wider annual growth rings Since the growth rings of

climatically sensitive trees respond inversely to the same variables that drive glacier

mass balance, dendroglaciological techniques provide a means for developing

glaciological histories in remote areas without mass balance data. For stream flow reconstructions, tree-ring data are valuable proxy to

reconstruct the long-term discharge data. The records of total discharge from most of

the river gauge stations of India are available for short period which is inadequate for

modeling the long-term properties of river discharge. A short stream flow records is

not statistically reliable for assessing long term trends and shifts in flow volume. In

this context reconstruction of past discharge is very essential and useful for water

resource management.

Palmer Drought Severity Index (PDSI) was a landmark in the development of

drought indices. Tree growth is controlled by soil moisture condition, while soil

moisture balance is a result of the integrative effect of precipitation,

evapotranspiration, physical-chemical properties of soil substrate, etc. In order to

better understanding of drought condition it is necessary to take not only precipitation

but also other hydrological meteorological factors. The PDSI enables measurement of

both wetness (positive value) and dryness (negative values), based on the supply and

demand concept of the water balance equation, and thus incorporates prior

precipitation, moisture supply, runoff and evaporation demand at the surface level.

The Palmer Drought Severity Index (PDSI; Palmer, 1965) is widely used as

meteorological drought index and it was reconstructed long back based on tree-ring

records from diversified geographical region of the globe (Briffa et al., 1994; Cook et

al., 2004, 2010; Dai et al., 2004; D’Arrigo et al., 2006).

El Niño-Southern Oscillation (ENSO) is a coupled Ocean-atmosphere

phenomenon which has impact on global climate and especially on monsoons. The

negative correlation between monsoon rainfall and ENSO are shown by several

workers since long back (Webster and Palmer, 1997 references there in) in which a

weak (strong) monsoon is related to a warm (cold) event through an anomalous

Walker cell driven by tropical East Pacific SST anomalies, has weakened rapidly

since the late 1970s (Chang et al., 2001). Tree ring data has been found useful proxy

to reconstruct ENSO in the background of short instrumental data which could not

prove whether ENSO has changed earlier also or not in long time scale. Recently


4

based on tree ring chronologies from both tropics and mid-latitudes in both

hemispheres a seven century long back history of ENSO was presented (Li et al.,

2013).

In this dissertation I have attempted to reconstruct climate and its linkage with

other climatically induced phenomena like glacier movement, stream flow, PDSI, El

Niño reconstruction based on multi proxy tree ring parameters i.e., tree ring width

data as a whole or early wood and latewood separately. Thus in the course of research

work in this dissertation I tried to achieve the following objectives.

1.3. Objectives

• To prepare tree-ring database based on dating of tree ring from old trees

and shrubs growing on moraine deposits around the Zemu glacier valley

North Sikkim, Eastern Himalaya.

• To establish tree-growth/climatic relationship based on analyses of multi

proxy tree ring data (ring width, early wood width, late wood width,).

• Analysis of climatic changes based on reconstructed long climatic data

derived from tree ring.

• To understand climatic changes and their relationships with glacial

fluctuations

Besides, attempts have also been made to work in the following aspects which are

linked with climate changes.

• Reconstruction of discharge from Zemu Chuu (stream) originated from

Zemu glacier Eastern Himalaya.

• Relationships of tree growth with El Niño and PDSI.

1.4. Overview of forests of Sikkim Himalaya Eastern Himalaya especially its extreme western state, Sikkim, India is well-

known for its biodiversity and is regarded as a botanist's paradise. The steep vertical

climb from the plains of West Bengal to the high altitude areas of Sikkim represents

one of the world's steepest altitude gradients which provides diversified ecological

conditions for the great variety of flora, ranging from the sub tropical to alpine.


5

Fig. 1.1.Forest cover map of Sikkim, Eastern Himalaya (Adopted from Indian State of

Forest Report 2011).

1.4.1. Forest cover of Sikkim, Eastern Himalaya based on satellite data

The forest cover in this area, based on interpretation of satellite data of

December 2008, is 3,369 km2 which is 47.34% of the state's geographical area. In

terms of forest canopy density classes, the state has 500 km2 'area under very dense

forests, 2,161 km2 area under moderately dense forests and 698 km2 area under open

forests. (India State of Forest Report, 2011) (Fig.1.4.) Forest type mapping using

satellite data has been undertaken by Forest Survey of India with reference to ground

truth of forest survey data as per of the Classification of Champion & Seth (1968).

As per this assessment, the state has 10 forest types which belong to six groups, viz.

Tropical Moist Deciduous, Subtropical Broadleaved Hill, Montane Wet Temperate,

Himalayan Moist Temperate, Sub Alpine Forests and Moist Alpine Scrub. (India

State of Forest Report, 2011). Overall assessment of forest types of this region it


6

appears that North Sikkim seems to more potential in terms of tree ring analysis. This

area has a wide geographical and climatic variation which ensue growth of diversified

trees. Several trees and sites in its pristine high mountains close to glaciers provide

ample scope for various aspects of tree ring or dendrochronological studies.

1.4.2. North Sikkim forest cover

North Sikkim geographical area 4,226 (area in Km2) Very dense forest (VDF)

135 Km2, Moderate dense forest (MDF) 890 Km2,Open forest (OF) 292 Km2 total

1,317 Km2 and percentage of GA 31.16 sub 208 Km2. (India State of Forest Report,

2011).

1.5. Climate of India - An Overview

Before taking up the reconstruction of climate of an area it is necessary to

have knowledge of modern climate of the site of investigation and regional climate as

a whole.

1.5.1. Temperature trends

Analysis of temperature data for the period 1901-2009 suggests that annual

mean temperature for the country as a whole has risen by 0.560C over the period. It

has been recorded that annual mean temperature is generally above normal (normal

based on period, 1961-1990 since 1990. This warming is primarily due to rise in

maximum temperature across the country, over larger parts of the data set. However,

since 1990, minimum temperature is steadily rising and rate of its rise is slightly more

than that of maximum temperature (Attri et al., 2010). Warming trend over globe, of

the order of 0.740C has been reported by IPCC (2007). In general, mean maximum

and mean minimum temperature during January are 9.0 to 9.5°C and 3.0 to 3.5°C

respectively and during peak summer it ranges from 19.2 to 19.5 °C and 14.5 to

14.8°C respectively. Relative humidity in the month of July is over 60% and in

October it is over 80% (Rao, 1981). The rainfall along the Tista valley, Sikkim shows

that it is maximum during July -August in most of the sites. There is heavy rainfall at

lower ranges and it decreases sharply at higher elevations where precipitation is


7

mostly in the form of snow (Samui, 1994). The Sikkim experiences a heavy rainfall

due to its proximity with the Bay of Bengal. Pre-monsoon rain occurs in April-May

and Monsoon (South-West) operates normally from the month of May and continues

up to early October Average annual rainfall varies from 1,300 mm at valleys to 4,300

mm at the mountain ridges. The humidity remains very high during the rainy season

(85-97%) (Singh et al., 2009).

1.5.2. Precipitation trends

The all India annual and monsoon rainfall for the period 1901-2009 do not

show any significant trend (Attri et al., 2010). Similarly rainfall for the country as

whole for the same period for individual monsoon months also does not show any

significant trend. The alternating sequence of multi-decadal periods of thirty years

having frequent droughts and flood years are observed in the all India monsoon

rainfall data. The decades 1961-70, 1971-80 and 1981-90 were dry periods. The first

decade (1991-2000) in the next 30 years period already experienced wet period. The

frequency of extreme rainfall (Rainfall ≥ 124.4 mm) shows increasing trend over the

Indian monsoon region during the southwest monsoon season from June to September

(JJAS) and is significant at 98% level. It is also found that the increasing trend of

contribution from extreme rainfall events during JJAS is balanced by a decreasing

trend in category (rainfall ≤ 64.4 mm/day) rainfall events. Similarly on monthly 21

scale, the frequency of extreme rainfall events show significant (95% level) increasing

trend during June and July, whereas during August and September the increasing

trend is not significant statistically. Like the frequency of extreme rainfall events, the

contribution of extreme rainfall to the total rainfall in a season is also showing highly

significant increasing trend during the monsoon season from June to September and

during June and July on monthly scale. It is observed that the mean monthly

contribution of heavy and extreme rainfall events (rainfall > 64.4 mm in a day) during

June-July is 5 to 6% higher than that during August-September and hence contributes

significantly to the total rainfall during the first half of the season (June and July)

(Attri et al., 2010). In the Eastern Himalaya has high variability in summer rainfall is

attributed to orographic influence and variation in location, timing and intensity of

monsoon (Mani, 1981).


8

Monsoon begins earlier than other part of the Himalaya and the area is more

evenly humid than over any other part of the Himalaya because of its proximity to the

Bay of Bengal and direct exposure to the effects of the moisture laden south-west

monsoon. The average rainfall of this region is around 3,000 to 4,000 mm; of which

about more than 75% falls in the monsoon months (June to September) although the

rainy season extends from April to October. Rainfall during winter i.e., from

November to March originates from the north-east monsoon and is negligible,

sometimes nil. January is the coldest month whereas, May or early June before the

burst of monsoon are the hottest time of the year (Mani, 1981).


9

1.6. Resume of earlier tree-ring study

1.6.1. Global context

Some significant progress in the field of tree-ring analysis in global aspect has

been discussed in this section to get the knowledge of the present status of this subject

in different aspects.

1.6.1.1. Paleoclimate

Tree rings as proxy climate indicators have been extensively used for the

reconstruction of the past seasonal temperatures/precipitation/drought, and other

climatic parameters based on measurements of annual ring widths, latewood densities

and other tree parametres (Fritts, 1971, 1976, 1991; Schweingruber, 1987; Jacoby and

D’Arrigo, 1989; Briffa et al., 1992; Cook et al., 1999; Briffa and Osborn, 1999, 2002;

Briffa et al., 2001; Krepkowski et al., 2012). A good amount of tree-ring based

climate reconstruction has been made from different parts of the world. Tree ring

reconstructions offer the advantage of potentially being quite long (e.g., several

millennia) (Cook et al., 2000; Briffa and Osborn, 2002) high resolution climate.

During the most recent decades, there is evidence that the response of tree ring

indicators to climate has changed, particularly at higher latitudes and more so for

density than ring width measurements (Briffa et al., 1998). One suggested source for

this behavior is ‘‘CO2 fertilization’’, the potential enhancement of tree growth at

higher ambient CO2 concentrations. Though it is extremely difficult to establish

existence of this effect (Wigley et al., 1988), however there is evidence that it may

increase annual ring widths in high-elevation drought-stressed trees (Graybill and

Idso, 1993). Recent work making use of climate reconstructions from such trees has

typically sought to remove such influences prior to use in climate reconstruction

(Mann et al., 1998; Mann and Jones, 2003). Other factors have been suggested as

possible explanations for the apparent anomalous tree ring/climate relationships

(Briffa et al., 1998), including the changing seasonality of the climate itself (Vaganov

et al., 1999; Biondi, 2000; Druckenbrod et al., 2003).


10

1.6.1.2. Hydrology

Tree-ring record has now been recognized as a valuable proxy for stream flow

reconstruction to assess the long-term discharge behavior of a river and its

management in various water resources sectors (Meko and Graybill, 1995; Stockton

and Jacoby, 1976; Pederson et al., 2001; Woodhouse, 2001; Meko and Woodhouse,

2005; Woodhouse and Lucas, 2006; Gou et al., 2007; Lara et al., 2008; Axelson et al.,

2009; Liu et al., 2010; D’Arrigo et al., 2011; Margolis et al., 2011; Maxwell et al.,

2011; Wise, 2010; Urrutia et al., 2011). Recently Cook et al. (2013) reconstructed the

Indus River discharge based on a network of tree-ring sites from the Upper Indus

Basin covering the period AD 1452–2008.

1.6.1.3. Glaciology

Tree rings has proven promising proxy to study and date the movement of

glaciers (Luckman, 1988, 1994, 1995, 1996; Schweingruber, 1988). Most early

dendroglaciological investigations focused on determining annually-resolved moraine

ages to date the timing of maximum glacier extent (Tarr and Martin, 1914; Matthes,

1939; Mathews, 1951; Karlén, W. 1984; Braeuning, A. 2006; Gou et al., 2006;

Barclay et al., 2009; Garavaglia et al., 2010), or on documenting glacial recession

rates by dating successional trends on recently deglaciated surfaces (Cooper, 1916;

Lawrence, 1950; Sigafoos and Hendricks, 1961). The age of moraines and other

glacial deposits are commonly determined by counting the tree rings of the oldest tree

found growing on the surface (Smith and Lewis, 2007).

1.6.1.4. The Palmer Drought Severity Index (PDSI) The PDSI is widely used as meteorological drought index and tree ring records

has proven promising proxy to reconstruct PDSI long back from many parts of the

globe (Briffa et al., 1994; Cook et al., 2004; Dai et al., 2004; D’Arrigo et al., 2006; Li

et al., 2010). Positive PDSI values indicate wetter conditions, whereas negative values

indicate drier conditions. Recent development of the self-calibrated PDSI (sc-PDSI)

(Wells et al., 2004), which is spatially comparable and reports extreme wet and dry

events at frequencies expected for rare conditions. Recently, Cook et al. (2010) used

PDSI to demonstrate Asian monsoon failure and mega drought during the Last

Millennium.


11

1.6.2. In Indian Context

The history of tree-ring proxy based studies in Indian subcontinent have been

recorded since long back (Gamble 1902), but their applications are restricted mainly

to forestry aspects. But the beginning of systematic work on tree-ring analysis was by

Bhattacharyya et al. (1992) who explored potential trees for dendrochronological

analysis. Subsequently, a great deal of work; from this part of the globe especially

from the Himalayan region has been carried out by several workers.

1.6.2.1. Western Himalaya

Before 1990’s except climatic reconstruction by Hughes and Davies (1987),

most of the tree ring analyses were on the selection of sampling sites and the

evaluation of tree species suitable for dendroclimatic analysis. Bhattacharyya et al.

(1988) evaluated the potential of six conifers, in the Jammu and Kashmir region. They

demonstrated that two conifers, Cedrus deodara and Pinus gerardiana, exhibited high

age (up to 500 years) and the tree-ring chronologies were suggestive of a drought

response. Climatic reconstructions of spring and summer mean temperature and

precipitation based on well-replicated samples of Abies pindrow and Picea smithiana

were conducted in the Kashmir valley (Hughes and Davies, 1987). Bhattacharyya and

Yadav, (1989b) reported that Cedrus deodara growing in Joshimath, Uttarakhand

attains great age and its growth is inversely related to pre-monsoon temperature and

positively related to precipitation during both summer and winter. Subsequently, there

were a considerable number of studies on the reconstruction of the pre-monsoon

temperature based on tree-ring data of Cedrus deodara either individually

(Borgaonkar et al., 1996; Yadav et al. 1999; Yadav and Singh 2002a) or in

combination with Pinus wallichiana and Picea smithiana (Yadav et al., 1997, 2007).

It is salient that none of these studies produced century-scale negative temperature

anomalies which could be due to a regional impact of the Little Ice Age. Though most

of the climatic reconstructions were restricted to temperature, a few studies examined

the hydrological conditions. Reconstruction of precipitation in the non-monsoon

months (previous October to current May) back to AD 1171 revealed that the wettest

and the driest non-monsoon months occurred in the 14th and the 13th century,

respectively. Both wet and dry spring years were noted during the Little Ice Age


12

(Yadav & Park, 2000; Singh and Yadav, 2005; Singh et al., 2006). Besides ring width,

other tree-ring parameters, e.g., isotopic ratio and wood density have a promising

potential for dendroclimatology in this region. Isotopic analysis of the tree-ring

cellulose extracted from Abies pindrow growing in Gulmarg, Kashmir, revealed that

δ18O was most sensitive to precipitation and mean maximum temperature, whereas

δ13C was sensitive to temperature and δ18O to the amount of clouds and humidity

(Ramesh et al., 1985). In an exploratory analysis of Cedrus deodara at two sites in the

western Himalaya, Pant et al. (2000) suggested that density parameters, viz.,

earlywood, latewood, minimum, maximum, and mean densities, as well as total ring

width may be useful for dendroclimatic studies. Besides temperature or precipitation,

other aspects of environmental issues were also dealt with in some tree-ring studies

The applicability of tree-ring data in palaeo-seismic dating has also been explored.

Tree-ring data of Pinus wallichiana in Agora, Uttarkashi, have been studied to

evaluate the effect of the 1991-earthquake on tree growth (Yadav and Bhattacharyya,

1994).

1.6.2.2. Eastern Himalaya

In comparison to the western Himalaya, tree-ring studies in the eastern part of

the Himalaya are less. Seven conifer species, viz., Abies densa, Juniperus indica,

Larix griffithiana, Pinus roxburghii, P. wallichiana, Taxus baccata and Tsuga

dumosa, growing at diverse ecological sites have been recorded suitable for

dendroclimatic analysis (Chaudhary et al., 1999). Subsequently,a short chronology of

Larix griffithiana, a sub-alpine deciduous conifer growing in Sange, Arunachal

Pradesh, has been recorded suitable for reconstruction of May-temperature

(Chaudhary and Bhattacharyya, 2000). In an another study Pinus kesiya, growing in

and around the Shillong plateau are found not much old and these trees do not show a

common response to climate of Shillong (Chaudhary and Bhattacharyya, 2002).

Maiden report on climate reconstruction from this region is based on tree-ring data of

Abies densa combined from two sites, T-Gompa, Arunachal Pradesh and Yumthang,

Sikkim. This reconstruction (July-September temperature) extended back up to AD

1757. The warmest and coolest 10-year periods are 1978–1987 (+0.25 °C) and 1801–

1810 (-0.31 °C) respectively (Bhattacharyya and Chaudhary, 2003). Buckley et al.

(2005) showed a strong correlation of tree ring data of Pinus merkusii growing at


13

Arunachal pradesh with tropical Indian and Pacific Ocean bands in seasons preceding

the summer monsoon. Tree-ring data at this region have also been recorded suitable in

palaeo-seismic dating. The feasibility of such studies has been analyzed by

Bhattacharyya et al. (2008). In Abies densa, growing at two distantly located sites in

the north-east Himalaya, Yumthang in Sikkim and T-Gompa in Arunachal Pradesh,

the annual growth rings were narrow either in the same year of high intensity

earthquakes or in the subsequent year when an earthquake occurred in this region

during the non-growing season. Recently, a comparative analysis of tree-ring data of

both northeast and northwest Himalayan trees was pursued and evaluated the

suitability taxa and sites for tree-ring studies and climate reconstruction (Shah, 2007).

From the overall review of published data of the Himalayan region I have

recorded that the tree-ring data of the north-east Himalaya, in general were less

sensitive to climate variation in comparison to the western Himalayan sites and trees.

The most salient feature of this analysis was towards understanding climatic

variability during the Little Ice Age and towards linking monsoon with sea-surface

temperature and sea-level pressure in a global perspective. There are several

publications where tree-ring data of the Indian Himalayas have been compared with

those of the Tibetan Himalayas (Guo et al., 2009; Shao et al., 2009; Tian et al. 2009;

Zhang et al., 2009) and report that there was coherence in climate trend in both

regions.

1.6.2.3. Peninsular India

In tropical forest of south and Central India a numbers of groups have been

working to establish good quality tree-ring data network to understand monsoon

variability and related global parameters (e.g. ENSO) in the recent past. In this

context, teak (T. grandis) have been demonstrated as a potential source for high

resolution spatial reconstruction (Pant and Borgaonkar, 1983; Bhattacharyya et al.,

1992, 2007; Borgaonkar et al., 2001, 2010; Shah et al., 2007; Ram, S. 2008, 2010).

These studies indicate a great potential of T. grandis in reconstruction of monsoon

precipitation. It was also observed that the direct influence of temperature and rainfall

was not significant and they investigate the exact role of moisture and rainfall in tree

growth process and their relationship with regional summer month’s moisture index


14

and global parameters such as ENSO using wide network of teak tee-ring data over

the region of Kerala (Ram et al., 2008).

Shah et al. (2007), based on ring width data of teak, reconstructed mean

monsoon precipitation of June-September back to AD 1835 from Hoshangabad,

Central India. The reconstructed climate records show several alternating periods of

high and low monsoon episodes. Many of these low monsoon years have been shown

to coincide with most of the known principal drought years in India. Besides the ring

width, the size of vessels in dated tree-ring sequences of teak has also been found

suitable for climatic analysis. Besides ring width, Bhattacharyya et al. (2007) studied

early wood vessels of teak through image analysis of dated tree rings at

Perambikulam, Kerala, and found that rainfall during October and November

(northeast monsoon) of the previous year and April of the current year is the most

important climatic variable limiting the early wood vessel area. Based on the mean

vessel area of early wood, the northeast monsoon of this region was reconstructed for

the period AD 1743 to 1986 AD.

1.6.2.4. Dendrohydrology

So far only two studies regarding tree-ring based river discharge

reconstruction were available from the western Himalaya (Shah et al., 2013; Singh et

al., 2013). Using tree-ring data of Cedrus deodara (Deodar) growing within the Beas

river basin, Kullu valley, Himachal Pradesh, Stream flow for March-April has been

reconstructed using a simple linear regression transfer function model which goes

back to AD 1834.

In another study (Singh et al., 2013) based on combined tree-ring data of of

Pinus gerardiana (one site) and Cedrus deodara (three sites) from moisture stressed

sites in Kinnaur, the Western Himalaya developed 711 years (AD 1295-2005) long

previous year December to current year July Satluj discharge. The reconstruction

revealed 50-year low and high river discharge happened in the eighteenth and

nineteenth century, respectively. The decreasing tendency in the river discharge

noticed since the 1990s is consistent with the decreasing trend in winter precipitation

in the region.


15

1.6.2.5. Dendroglaciology

Tree-ring data of some conifers growing of the upper tree-line were used in

studying glacial behavior of the Himalayan region. Pinus wallichiana from Kinnaur

(Bhattacharyya and Yadav, 1996) and Abies pindrow growing close to the snout of the

Dokriani Bamak Glacier (Bhattacharyya et al., 2001) exhibited low growth rates

during years with a positive glacial mass balance and with glacial advances in the

Himalayan and Trans-Himalayan region. In another study Bhattacharyya et al. (2006)

reported that the increased tree growth of birch (Betula utilis) growing along moraines

around Bhojbasa, close to the snout of the Gangotri glacier with correspond to the

rapid retreat of the Gangotri glacier. They hypothesized that the fast retreat of this

glacier might be the cumulative effect by several climatic parameters that enhanced

tree growth, i.e., increased precipitation in March, April and June, increased winter

temperature and low snowfall. Borgaonkar et al. (2009) analyzed tree-rings of high-

elevation Cedrus deodara D. Don from Western Himalaya (India) in relation to

climate and glacier fluctuations Singh and Yadav, (2000) studied indications of recent

glacier fluctuations in Gangotri, western Himalaya based on 410-year-old (AD 1590–

1999) ring-width chronology of Pinus wallichiana from Chirbasa. They reported low

growth prior to the 1950s reflecting cooler conditions when glacier should have been

stationary for a long time with some episodic advances. Based on strong correlation

between tree growth and winter temperature they concluded that the winter warmth

was one of the main factors responsible for the twentieth century growth surge (Singh

and Yadav, 2000).

1.6.2.6. Tree growth and its relation with El Nino As mentioned earlier in Section 1.2 in the theme of this dissertation, the

relationship between El Niño events and Indian monsoon has been studied by many

researchers. These studies reveal that the Indian summer monsoon is weaker

(stronger) than normal before (after) the peak of an El Niño in winter, and that the

relationship is opposite for the monsoon and La Niña. However this relationship was

based on short instrumental climate data. Recently attempt has been made to analyze

long-term relationship between E Niño and monsoon based on tree ring data. Ring

widths in both Cedrus deodara and Pinus gerardiana were narrow mostly during

years of deficient rainfall and also in years of an El Niño event, which suggested that


16

these two taxa have excellent potential to reconstruct long records of droughts

(Bhattacharyya and Yadav, 1992). Borgaonkar et al. (2010) studied a 523(AD1481-

2003) Teak tree-ring chronology from Kerala and reported that the frequency of

occurrence of low tree growth in years of deficient Indian monsoon rainfall (droughts)

associated with El Nin˜o since the late 18th century is high. Before that, many low

tree growth years are matched with the known El Nino events. This relationship

indicates strong ENSO-related monsoon signals in the tree-ring records.

1.6.2.7. The Palmer Drought Severity Index (PDSI) Many tree-ring scientists in India developed tree-ring chronologies and

reconstructed PDSI at various locations in India. Teak tree-ring width index

chronologies from central and peninsular India revealed better response with moisture

index and PDSI as compared to rainfall during different seasons (Borgaonkar et al.,

2007, 2010; Ram et al., 2008, 2010, 2011 Ram, 2012). Recently Ram et al. (2012)

from Srinagar at Pahalgam, reconstructed summer seasons’ PDSI of the region that

extended from AD 1820-1981. Yadav 2013, reconstructed PDSI severity index value

in western Himalaya using tree ring data of Pinus gerardiana and Cedrus deodara

which extended back to 1310AD.

Chapter 2 General Principles & Supporting data

17

2.1 Selection of tree ring site For tree ring studies, it is necessary to have knowledge of distribution of trees

growing in the region of investigation. This information could be used in the selection

of suitable trees and site for tree ring analysis. Northern Sikkim seems to be most

promising for tree ring analysis for its variety of trees especially conifers and many

forest sites being close to several glaciers appears to be sensitive for the tree ring/

climate analysis. It is situated the inner dry valleys lying in the rain shadow of the

main Himalayan range. These areas have cold desert-like conditions, although the

aridity is not severe than that of places likes Lahul-Spiti and Ladakh region. The

average elevation is more than 3,000 m with an extremely rugged terrain. There are

two main valleys in the region namely the Lanchung Valley and the Lachen chu

Valley.

Fig.2.1.Map of India showing position of Zemu glacier and adjoining area in Sikkim.

2.2. Study of tree-rings Trees growing mostly in subalpine and temperate forest and a few in

subtropical and tropical forests of India are known to have annual rings

(Bhattacharyya et al., 2009). In the present dissertation I have analyzed trees growing

in sub alpine forest under stressed environmental condition close to Zemu glacier. In

such environment presence of diffuse ring boundaries, wedging of rings, large number


18

of missing and false rings in the ring sequence makes tree ring dating in many sites

complicated or impossible in many cases. To overcome this problem, I have taken

much care to date each ring precisely through the “Cross dating” technique. But

before assigning dates to each rings, I studied all the cores under stereo zoom

microscope to get an idea of early wood and late wood formation and the nature of

ring boundaries. For various analysis different codes for three viz., ABDE, LAGR,

JUIN, JUSQ have been used for Abies densa, Larix griffithiana, Juniperus indica,

Juniperus squamata, respectively (Table.2.1) It has been noted that in ABDE, JUIN,

LAGR, the transition between early wood to late wood cells is gradual but there is

distinct boundary with the early wood cells of subsequent year. The late wood cells

are distinguished from the early wood cells by their dark colour narrow lumen, thick

cell walls and. It is difficult to demarcate a sharp boundary between early wood and

late wood in most species. However, in LAGAR there are distinct colour

demarcations between early wood and late wood cells. Traumatic resin ducts in some

cores of ABDE also mislead as true rings.

2.3. Acquisition of tree- ring width data Tree ring widths of each dated core were measured using increment measuring

stage coupled with a microcomputer which is accurate to a hundredth of a millimetre.

Computer program COFECHA (Holmes, 1983) is used to check dating accuracy. For

each ring width series, COFECHA identifies segments which correlate poorly with

corresponding segments of the master dating series (the mean of all other series) or

which correlate higher at a position other than the position as dated. The cores having

errors were re-examined to evaluate source of errors and corrections were made. For

verification of these corrections, COFECHA was run again on the corrected

measurements to check occurrence of any further errors. Some of the tree ring series

which had problem in dating were deleted and rest were used for building the

chronology. The typical skeleton plot technique of cross dating (Stokes and Smiley,

1968) was used to assign an exact calendar date to each ring. This dating procedure

utilises similarities in ring width or other morphological ring features which vary as a

function of time. The pattern of variation is generally similar among trees growing

throughout the same region for the same time period due to variations in macro

climatic factors (Fritts, 1976).


19

Table.2.1. Site information and tree-ring chronologies statistics of Zemu Valley

Descriptive statistics of the 12 chronologies Site Location

(Lat. /Lon.) Elev. (m)

Species Species Code

Chronology Time Span (A.D)

No. of Years

Tree/core or Disc samples

Common Period

SD MS AC Rbt EPS PC#1 (%)

SNR

YAB 27°.05'/88°.27 3,810 Juniperus squamata JUSQ 1881-2010 140

28 disc samples 1963-2010 0.189 0.119 0.707 0.077 0.898 14.02 8.781

YAB 27°.52'/88°.41 3,810 Juniperus recurva JURE 1556-2010 456 15/20 1817-2010 0.180 0.145 0.410 0.118 0.409 18.02 2.122

YAB 27°.52'/88°.41 3,810 Abies densa ABDE 1759-2010 252 32/49 1916-2010 0.262 0.153 0.511 0.205 0.860 17.04 6.149

YUM 27°.52'/88°.41 3,596 Abies densa ABDE 1755-1994 240 29 / 50 1892-1984 0.190 0.130 0.887 0.270 0.897 24.16 8.120

ZAK 27°.46'/88°.27 3,503 Abies densa ABDE 1700-2010 311 21/28 1900-2008 0.196 0.107 0.433 0.130 0.765 13.00 3.261

TAL 27°.46'/88°.29 3,157 Abies densa ABDE 1657-2010 354 23/33 1808 2009 0.185 0.103 0.518 0.054 0.628 18.10 1.688

DOZ 27°.46'/88°.30 3,125 Abies densa ABDE 1784-2010 227 2/4 1883 2009 0.274 0.222 0.540 0.079 0.327 30.00 0.487

ZEM 27°.45'/88°.31 2,745 Abies densa ABDE 1628-2007 380 36/56 1875-1996 0.260 0.114 0.520 0.200 0.885 20.09 7.701

LAC 27°.45'/88°.33 2,599 Larix griffithiana LAGR 1733-1994 262 20 / 38 1831-1987 0.220 0.181 0.590 0.389 0.858 35.04 6.046

LAC 27°.45'/88°.33 2,599 Abies densa ABDE 1780-2010 231 11/12 1885 2009 0.162 0.119 0.513 0.107 0.596 13.70 1.473

Table.2.1. Site information and tree-ring chronologies statistics Larix griffithiana (EW, LW). Site Location (Lat. /Lon.) Elev.(m) Species

Code Chronology Time Span (A.D)

No. of Years

Tree/core Common Period

SD MS AC1 Rbt EPS PC#1%

SNR

LAC(EW) 27°45'/88°33 2,599 LAGR 1733-1994 262 20/38 1831-1987 0.236 0.148 0.548 0.377 0.852 35.06 5.773

LAC(LW) 27°45'/88°33 2,599 LAGR 1733-1994 262 20/38 1831-1987 0.238 0.173 0.558 0.339 0.827 35.03 4.775

SD, standard deviation; MS, mean sensitivity; AC1; Autocorrelation ; Rbt, mean inter-series correlation; EPS, expressed population signal; PC#1, percent variance explained by the first principal component (PC#1); NR, signal-to-noise ratio.


20

Cross dating is the process by which the variation in ring width characteristics is

examined to determine whether synchronity exists between series from the same tree

and different trees. If the patterns match then the series are coeval. It is the

fundamental step in any dendrochronological study as it assures that the correct

calendar date is assigned to all rings by identifying and accounting for any missing or

“absent rings”, "false rings" or unusual or indistinct ring boundaries (Stokes and Smi-

ley, 1968). Tree ring are dated through skeleton plot dating. The problem of dating

tree ring sequences in this region due to the presence of double or missing ring is not

serious as these are easily detected. It has been recorded that the presence of missing

ring is more prevalent in trees growing at lower elevations. Thus their occurrence is

more common in Juniperus and Betula .

2.4. Standardization of tree ring data and chronology preparation The computer program (Auto Regressive Standardization) ARSTAN (Cook,

1985) has been used to prepare tree-ring chronologies from the set of well cross dated

tree-ring measurement series. Program ARSTAN produces chronologies from tree-

ring measurement series by detrending and indexing (standardizing) the series, then

applying a biweight robust mean estimation of the mean value function to remove

effects of endogenous stand disturbances. Robust mean has advantage over the

arithmetic mean. The arithmetic mean is no longer minimum variance estimate of the

population mean when outliers are present, and is not guaranteed to be un-biased. In

contrast, robust mean automatically discount the influence of outliers in the

computation of mean, and thus, reduces the variance and bias caused by outliers

(Mosteller and Tukey, 1977). ARSTAN produces three types of chronologies,

"Standard", Residual" and "ARSTAN" (Holmes, 1992). In the "STANDARD"

chronology detrending of measurement series is done first by fitting a curve to model

biological growth trend to each series, and dividing out the growth model. The

chronology is then computed as a robust estimation of the mean value function to

remove effects of endogenous stand disturbances and it enhances the common signal

contained in the data. In the "RESID" chronology autoregressive modelling is

performed on the detrended ring measurement series. Robust estimation of the mean

value function produces a chronology with a strong common signal and without

persistence. The "ARSTAN reincorporates into the residual chronology the


21

persistence structure of a pooled model of autoregression in the entire group of ring

measurement series (Holmes, 1992). The program provides various options of

detrending methods viz., Negative Exponential, Linear Regression, Regional Curve

Standardization, Hugershoff Growth Curve, Cubic Spline smoothing etc. along with

the detailed statistics of each series and chronology.

In the present dissertation, Double-detrending approach used, first a negative

exponential curve, a linear regression or a horizontal line passing through the mean

was used to remove any age-growth trends (Fritts, 1976). Following this, the series

were detrended a second time to reduce the impact of abiotic factors on radial growth

(e.g., competition and defoliation) with a 30 years smoothing spline curve used to

remove biological trend from data because these methods are appropriate for the trees

growing in forest. Both detrending methods are believed to preserve low-frequency

climate variability (Fritts, 1976). Both standard (detrended index) and residual (index

derived from autoregressive modeling) series were included in further analyses. In

chronology development, the standard technique used in dendrochronology has been

used (Fritts, 1976; Hughes et al., 1983; Schweingruber, 1987; Cook and Kairiukstis,

1990). The first step is the removal of overall trends in tree ring sequence to enhance

climate signal. These trends result due to ageing effects of trees and exogenous

disturbances, the exogenous disturbance could be caused by several factors such as

forest fire, insects attack, disease infestation, earthquake shakes, storms frosts and

others. The effect of both, ageing trend and disturbance could be deleted through

Standardization which is accomplished by fitting a growth curve to the ring width

series and then dividing the measured value by the curve value at each year. It

provides a new series of desirable properties which are later averaged together to

transform into tree ring indices series. This new series has a mean of 1.0 and a

relatively constant variance (Fritts, 1976; Cook et al., 1990). The averaging of

standardized ring width values or indices reduces the amount of variability due to non

climatic factors and enhances the ratio of climatic signal to the non climatic signal in

the mean chronology. Various types of curve fitting methods are used in

standardization but the appropriate method to be applied for the chronology building

depends on the type of study and nature of tree growth at a given site. A thorough

review of different standardization methods has been given by Cook et al. (1990,

1997). The negative exponential is very efficient for removing age-size related growth


22

trend in ring width series. Such a growth trend shows an exponential decay as a

function of time after the juvenile period of increasing radial increment has passed.

Other methods include polynomial detrending (Fritts, 1976; Graybill, 1982) and cubic

spline method (Cook and Peters, 1981). The latter one has been found to give better

results than the polynomial, particularly for samples analysed from forest interiors,

where the tree growth is effected due to endogenous disturbances and other causes

(Cook and Peters, 1981). Endogenous disturbances tend to cause by pulses of growth

in single tree resulted due to removal of its neighbouring trees which initiates less

competition among trees especially for micro and macro nutrients, water from soil,

sunlight etc.

2.5. Climate and Glacial data 2.5.1. Regional climate data

Since there are no IMD stations close to study site, Long records of monthly

precipitation and temperature from nearest high elevation sites Gangtok were obtained

from IMD (http://www.imd.gov.in). Monthly mean, maximum, minimum,

temperature and monthly precipitation records from Gangtok (270 20' N, 880 37' E,

1,756 m a.m.s.l, time span: 1966-2000) show that mean annual air temperature during

the period 1966–2000 is 15.170C, with a mean maximum of 22.2370C in June and a

minimum of 4.190C in January. Mean annual total precipitation is 3567.67 mm, with a

maximum monthly sum of 628.65 mm in July (Fig.2.1.).

Table 2.3. Description of IMD climate data used for temperature records. Station Data

Source Location

(Lat. /Lon.)

Elev. (m)

Tmin-Tmax

(Period)

Tmin (MD%)

Tmax (MD%)

Tmean (Period)

Eastern Himalaya

Gangtok IMD 27.20/88.37 1756 1966-2000 1.90 1.67 1966-2000

http://www.imd.gov.in/


23

Table 2.4. Description of IMD climate data used for precipitation records. Station Data

Source Location

(Lat. /Lon.) Elev. (m)

PPT. (Period)

PPT. (MD %)

Eastern Himalaya

Gangtok IMD 27.20/88.37 1756 1966-2000 1.19

Lat., latitude; Lon., longitude; Elev., elevation; PPT., Precipitation; PPT., Precipitation PPT(MD%)., Precipitation Missing data percentage Table 2.5. Description of CRU T.S 2.1climate data

Station Data Source

Location (Lat. /Lon.)

Elev. (m)

(Period)

Eastern Himalaya North Sikkim Grid *CRU T.S 2.1 27.25/ 88.75 1,492 1901-

2000 North Sikkim Grid *(http://www.cgd.ucar.edu/c

as/catalog/climind/pdsi.html)

26.25/ 88.75 1,21 1850-2000

*Indian Meteorological Department (IMD) *The CRU TS 2.1 Climate Dataset has been produced by the Climatic Research Unit (CRU). *(http://www.cgd.ucar.edu/cas/catalog/climind/pdsi.html) for Palmer Drought Severity Index data (PDSI).

0.00

100.00

200.00

300.00

400.00

500.00

600.00

700.00

JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC

Month

Prec

ipita

tion(

mm

)

0.00

5.00

10.00

15.00

20.00

25.00Te

mpe

ratu

re(O

C)

PPT MIN MAX MEAN

Fig.2.2.Monthly variation of total precipitations (bars), mean maximum temperature

(red line), and mean temperature (green line) and mean minimum temperature (pink

line) for Gangtok meteorological station.

http://www.cgd.ucar.edu/cas/catalog/climind/pdsi.html


24

2.5.1.1. Details of Meteorological stations

The Gangtok climate station is located further away from the study site

(approximately 125 km), but provides one of the longest record from Eastern

Himalaya region.

2.5.1.2. Details of CRU T.S2.1 data and source

Temperature and precipitation patterns were also obtained using the most

accurate currently available global database the Climate Research Unit (CRU) dataset.

The CRU TS 2.1 for 1901–2002 (Mitchell and Jones, 2005) dataset for North Sikkim

is used to compare station data of Gangtok. The CRU TS 2.1 is a set of monthly

climate grids. Grid data has been obtained from Water portal met data site. (Source of

CRU T.S2.1 data source) which shows that mean annual air temperature during the

period 1966–2000 is 11.850C, with a mean maximum of 20.870C in June and a

minimum of -1.910C in January. Mean annual total precipitation is 3567.67 mm, with

a maximum monthly sum of 537.925 mm in July (Fig.2.2).

0

100

200

300

400

500

600


Month

Prec

ipita

tion(

mm

)

-5.0000

0.0000

5.0000

10.0000

15.0000

20.0000

25.0000

Tem

pera

ture

(OC)

ppt Mean Temp MIN MAX

Fig.2.3.Monthly variation of total precipitations (bars), mean maximum temperature

(red line), and mean temperature (green line) and mean minimum temperature (pink

line) for CRU Grid data.

2.5.1.3 Statistical assessment of climate data

Analysis of climate data has three main objectives. First, the nature of annual

climatic variability near study site is specified by descriptive statistics of precipitation


25

and temperature time series. Second, to estimate missing value of climate data. Third

homogeneity test of time series data.

2.5.1.4. Missing Value estimation in climate data

Eastern Himalaya climate record is characterized by a common problem

common to meteorological series data for both rainfall and temperature measurements

are missing for a few individual months scattered throughout the series. Therefore, it

is necessary to estimate the missing values both for precipitation and temperature data

set to produce a complete time series suitable for analysis of further tree growth/

climate relationship. This estimation is done by relating climate records from nearby

station that have appropriate month i.e. mean method. Missing temperature data are

usually estimated by a simple linear regression method. The monthly data common to

neighboring stations are used for calibaration, and the appropriate data are submitted

into the equations to obtain a statistical estimate for the missing value. For present

study, the missing values present in the climate data are estimated using the computer

program MET (Holmes, 1996). It approximates missing values by calculating the

mean and standard deviation for the month from all of the previous and following

years of data for that month. It then calculates the departure from the mean for that

month, from data collected at nearby stations. The departure from the mean is

multiplied by the standard deviation of the month, and is added to the monthly

average to arrive at the final estimation. Location of climate stations used for

temperature records are shown in (Fig. 2.1). The details of the available climate

records viz., climate data source, latitude, longitude, elevation, time span and missing

value percentage at different stations of Eastern Himalaya are given in (Table 2.2. to

2.4)

2.5.1.5. Descriptive statistics of climate data

Descriptive univariate statistics specify that to degree of sample variability

indicate the limitations and potential problem in the climate data. The descriptive

statistics also provide a basis for comparing the climate records by suing bivariate and

multivariate statistical analyses. Statistics computed include the median, mean

standard deviation descriptive statistics for both Gangtok (IMD climate data) and as

well for CRU TS. 2.1 (climate data).


26

2.5.2. Glacial data

2.5.2.1. Glacier Front variation and Mass-balance data

This ongoing rapid warming has a profound effect on the Himalayan

environment and may be most visible in the form of rapid retreat of Himalayan

glaciers and diminishing snow fields (Dyurgerov and Meier 2005). From the

Himalayan region, except two extrapolated long dated records on the history of

glacial fluctuations covering time span of last several decades based on data from

several glaciers (Mayewski 1979, 1980), no published records in this aspects are

available. So, I feel necessary to reconstruct Zemu glacier history based on tree ring

proxy. The principal behind glacier reconstruction based on tree ring data is

mentioned in the introduction of chapter 1.1. Simple correlation or matching of tree

growths of fir (Abies densa) growing close to the snout of Zemu glacier, with

available data on glacier advancement and retreat this glaciers. Since the glacier data

of Zemu are of short span. Therefore I have selected data available from five glaciers

other than Zemu which is my site of investigation in this dissertation, to build

relationship variations of tree growth and glacier advancement /retreat data. I have

taken data of glacier area, glacier mass balance (bn) and retreat/ advancement of these

five nearby glaciers located in the Central and Trans Himalayan region from

published record of Dyurgerov M., 2002 and the same data are also available online at

http://nsidc.org/data/g10002.html; Dyurgerov, 2005.

These glaciers are viz., Central Himalaya (Nepal_AX010_NP00005), Eastern

Himalaya (Changmekhangpu_IN02522), and China (E.Tien Shan, S.#1CN0010, Gl.

#1, E.Br.CN0010, Gl. #1, W.Br. CN0010) (Table1, Fig. 6.4) details of these glacier

are shown (Table 2.1).

Zemu glacier the source of River Teesta, is the largest glacier and is located at

North Sikkim and its size is around 90.94 sq kms. This glacier has retreated

approximately 863 m, (Raina, 2009) however, the retreat was punctuated between

1988 and 2000 with an advanced of 92 m. (7.67 m per year). The average rate of

retreat of glaciers in Sikkim has been calculated to be about 13.02 m per year from

1976 to 2005 Cruz, et al. (2007). Observation of Zemu Glacier (1977-1984 ) reveals

that there was retreat of snout 27.7 m a year average 14.10 m per year .with fastest


27

retreat in 1976-78 with advancement in 1988-2000 followed retreat again from 2001-

2005 ( Raina, 2009) .

Glaciers in China have been retreating with an area loss of about 20 per cent

since the Little Ice Age and maximum extent was in the 17th century (Shi and Liu

2000; Su and Shi, 2002;UNEP and WGMS, 2008). Retreat increased during the last

century, especially during the past ten years (Yao et al., 2004; Liu et al., 2006; UNEP

and WGMS, 2008). They reported three pronounced intervals of negative Bn (Mass

balance) during 1988, 1991, 1994 (interval 1988 and 1989 highest),whereas a

positive Bn has been observed in 1989, 1990, 1992, as a result glaciers continue to

maintain terminal positions. The historical behaviour of Nepal glaciers (Lat 27°42'N,

Long. 86°34'E) located within the Eastern Himalayan area suggests that the glacier of

this region has led to mostly negative mass balance conditions over the 1996

,1997,1988 (highest negative bn) 1999 are observed. This rate exceeded over the 21

century (Raina et al., 2009). Over the years, the glacier recession has increased. And

in case of Nepal glacier it is observed that of glacier retreat from India shows similar

pattern i.e. from Changmekhangpu glacier where pronounced intervals of negative Bn

are 1981 to 1986 recorded with respect to highest bn negative bn 1981, 1983, and

1985.Data from the higher elevation Indian glaciers indicate consistently negative

mass balance values, but the extent to which they can be considered regionally

representative is not known. Individual glaciers can respond with great variability to a

changing climate. As discussed earlier, the 2005 IPCC statement about the possible

disappearance of Himalayan glaciers by 2035 is not correct. No evidence was

presented that Himalayan glaciers are receding faster than those in other parts of the

world, as only rates of retreat for the Himalaya were presented.

2.6. Dendroclimatic modeling For dendroclimate modeling (i.e. climate calibration), I have anlaysed tree ring

data of all four species (ABDE, JUIN, LAGR, JUQS) with site specific as well with

regional chronology by using two method, first one Bootstrap Response Function and

second one is Correlation/ Regression method to understand tree growth relationship.

The aim of Tree-ring response function analysis is used to determine which

instrumental climatic variables are best associated with tree-ring width variability.

Ideally such a determination would be accomplished, or verified, through detailed


28

physiological monitoring of trees in their natural environment. A statistical approach

is required because such biological studies on mature trees are currently too time

consuming to perform.

2.6.1. Principal component analysis (PCA)

For the identification of common patterns of variations in tree growth,

Principal Component Analysis assesses the degree of similarity and site-related

differences among varieties. It is a data reduction or structure detection technique, and

is widely used (Briffa, K. R.,1995). The original variables (morphological attributes)

have been transformed into a new set of uncorrelated variables (eigenvectors or

principal components) in such a way that a minimum number of components explain

a maximum percentage of the variance in the dataset. For the present study, PCA was

computed on the correlation matrix with standard tree ring chronologies. The number

of two components retained for further analyses was determined on the basis of the

eigenvalue trace (or scree plot).


29

Table 2.6. General information about the glacier and data.

General information about the glacier and data No Region Country Glacier PSFG Code Lat (N) Long

(E) Elev. Max, (m)

Elev. Med, (m)

Elev. Min, (m)

Length, km

Area, km2

Aspect Mass balance (bn) time period

1 Himalaya Nepal AX010 NP00005 636 27°42' 86°34' 5360 5220 4952 1.7 0.568 E/SE 1996-2000

2 Himalaya India Changmekhan-gpu IN02522 530 27°57' 88°41' 5520 5300 4840 5.6 4.43 S/S 1981-1986

3 E.Tien Shan China Urumqihe S.#1 CN0010 622 43°05' 86°49' 4486 4040 3736 2.2 1.84 NE/NE 1988-1995

4 E.Tien Shan China Gl. #1, E.Br. CN0010 622 43°05' 86°49' 4224 NA 3736 2.2 1.163 NE/NE 1988-1995

5 E.Tien Shan China Gl. #1, W.Br. CN0010 622 43°05' 86°49' 4476 NA 3795 1.95 0.677 NE/NE 1988-1995

Explanation of legends: glacier name, country, region, PSFG number (five digits identifying glacier with minimum elevations (meters), area (the latest information on total area of glacier, in km2), length (the latest information on glacier length, in km), aspect (for some accumulation area-denominator, and for ablation area- numerator) glaciers it is given separately for accumulation area- denominator, and for ablation area- numerator)


30

2.6.2. Bootstrap Response Function

In the present dissertation, “Bootstrap Response Function” and “Correlation

Analysis” have been used to establish tree-growth climate relationship. Bootstrap

response function in the program suite called “3Pbase” (Guiot and Goeury, 1996)

have been used to analyse tree growth climatic relationships. In tree-ring analysis this

method offers the advantage of avoiding errors caused by collinearity among

variables and providing a more realistic estimate of tree response to climate. The

bootstrapping is a very useful technique developed by Efron (1979) to estimate

statistics for unknown population distributions by techniques of Monte Carlo

simulations. The mathematical details are given in Efron (1982), more accessibly in

Efron and Gong (1983), and popularly in Diaconis and Efron (1983). This technique

involves resample of original data matrix to form a new data matrix. Afterward,

another year is randomly drawn and the corresponding data vector is stacked next in

this matrix. In this randomized combination, called ‘‘pseudo-data set’’ matrix, same

year may be drawn several times. A regression is then performed on this pseudo-data

set. This methodology is repeated 200 times, giving 200 regressions and 200

predictions. Summary statistics on the regression coefficients and multiple

correlations were obtained by calculating means and standard deviations for the 200

samples. The bootstrap regression coefficients are judged significant at the 95% level,

if they are twice in absolute value of their standard deviation (Guiot, 1991). The

statistical significance for the correlation between tree-ring and climate are judged at

95% significance level. Equivalent time spans (1966-2000) of mean monthly

temperature, maximum temperature, minimum temperature and monthly precipitation

of Gangtok, (Sikkim) and tree-ring indices and climate variables data were taken.

These climatic variables were taken for the twelve-month period i.e., from November

of the previous year to October of the current year’s.

2.6.3 Bootstrap Transfer Function

Bootstrapped orthogonal regression was also used for transfer function because

Bootstrapped orthogonal regression (Guiot, 1991) was used for estimating tree-

growth/climate relationship seems to work better than cross-validation in many cases

(Efron, 1983). In the simplest form of bootstrapping, instead of repeatedly analyzing

subsets of the data, you repeatedly analyze subsamples of the data. Each subsample is


31

a random sample with replacement from the full sample. Depending on what you

want to do, anywhere from 50 to 2000 subsamples might be used. There are many

more sophisticated bootstrap methods that can be used not only for estimating

generalization error but also for estimating confidence bounds for network outputs

(Efron and Tibshirani 1993).

2.6.4. Correlation Analysis

The Pearson product-moment correlation coefficient is probably the single

most widely used method for summarizing the relationship between two variables.

Under certain assumptions, the statistical significance of a correlation coefficient

depends on just the sample size, defined as the number of independent observations.

If time series are auto correlated, an “effective” sample size, lower than the actual

sample size, should be used when evaluating significance. Finally, it should be

emphasized that the Pearson correlation coefficient measures strength of linear

relationship. Scatterplots are useful for checking whether the relationship is linear.

“Correlation analysis” has also been used for Climate–growth relationships between

tree-ring indices and with climate variables, i.e. maximum temperature (MAX),

minimum temperatures (MIN) and mean or average temperature (MEAN) and

Precipitation (PPT) were taken into account as predictors.

2.6.5. Linear Regression Method for Climate reconstruction Another method, used for climate reconstruction is linear regression method.

First of all, the correlations between the chronology and Gangtok climate data of the

previous November to current year October was calculated. After identifying

significant relationship with tree growth and climate, a regression model has been

developed for reconstruction of climate.


32

2.7. Dendrohydrological modeling Both, Correlation (Tree growth/discharge relation) and Linear Regression

(discharge reconstruction) analyses have been performed for dendrohydrological

modeling. By using correlation method it is determined which months are best

associated with tree-ring width variability and discharge data of these selected months

could be reconstructed through regression analysis. In this dissertation, discharge

reconstruction has been done by using one conifer species viz. Abies densa (ABDE)

(56 cores from 36 trees). These samples were from two localities of this region viz.,

Zema, at the altitudinal ranges from 2,599 m a.s.l to 2,804 m a.s.l (Fig.1). The details

of the samples collected along with site characteristics from three different localities

are given in details in Chapter 3

2.7.1. Rivers discharge data Sikkim is drained by number of Perennial Rivers. However, the two main river

systems are Teesta and Rangit. The Zema Chu (Chu means river) is one of the

tributary of the Teesta river originating from Zemu glaciers (elevation of about 5200

m above m.s.l) North Sikkim. Discharge data for Zemu Chuu recorded in Lachen

gauge stations were obtained from National Hydroelectric power corporation

(NHPC). The available discharge data is extended from 1996 to 1997. The analysis of

annual river discharge of this river at Lachen station shows it has annual water yield

of 637.9 cu.mt/sec, in which the maximum and minimum discharge recorded as

1209.82 cu.mt/sec and 300.59 cu.mt/sec in the year 1984 and 1992 respectively.

Detailed analysis of this data has been discussed in chapter 7.

2.7.2. Climate data for discharge site

There is a lack of spatially disaggregated meteorological records in Sikkim.

Due to the remote location of the study site, no single climate station could be

assumed to be representative of the local climatic conditions. To study the spatial

correlations, maximum temperature (MAX), minimum temperatures (MIN) and mean

or average temperature (MEAN) and Precipitation (PPT) climate data from the

Climate Research Unit (CRU TS 2.1, 0.5°×0.5°, 27°45′N 88°45′E). A PDSI grid

(26°25′N, 88°75′E and 1950–2000) was also chosen for analysis (Fig. 8.3a).


33

2.7.3. Correlation analysis for Tree-growth and discharge relationship

Discharge and climate/ growth relationships were determined by examining

the correlation coefficients between tree-ring indices with climate variables, i.e.

Maximum Temperature (MAX), Minimum Temperatures (MIN), Mean temperature

(MEAN) and monthly Precipitation (PPT) (details in chapter 5) as well as with

discharge were taken into account as predictors. Correlations with Lachen discharge

were positively significant for January-April (r = 0.734., p =0.01). Analysis of the

climate-growth relationship showed a general positive correlation between the radial

growth of ABDE From November to October of the growth year.

2.7.4. Discharge Reconstruction Method

2.7.4.1. Linear regression method for discharge reconstruction Correlations between the chronology and discharge data from the previous

November to current year October has been calculated. Significant correlations were

found only for January to May discharge of the current year (P<0.01). Mean of

January to April discharge were taken into account as predictors, regression model

has been for reconstruction of discharge. No significant correlations between tree-ring

data and precipitation were found. However, the correlations between the PDSI and

chronology were positively associated for most months, but the significant

relationship was recorded only for December and January.

2.8. Correlation analysis for tree growth and its relation with PDSI/

El Nino Correlation analysis between the site specific tree-ring chronologies as well

regional chronologies (developed through PCA) were analysed for relationship with

Palmer Drought Severity Index (PDSI), E Nino 3.4. Palmer Drought Severity Index is

widely used for of meteorological drought over land regions (Dai et al., 2004). The

2.50 X2.50 gridded PDSI values were extracted from the Palmer Drought data set

(http://www.cgd.ucar.edu/cas/catalog/climind/pdsi.html) for one grid points close to

the tree ring sampling locations in the North Sikkim.


34

E Nino 3.4 monthly data was collected from (KNMI Climate Explorer

program (http://climexp.knmi.nl). Details analysis of this data has been discussed in

chapter 8.

2.9. Multiple tree-ring proxies (Earlywood width, Latewood width) Attempt has been made to investigate the potential of using multiple tree ring

parameters in addition to ‘traditional’ ring width data (TRW) for palaeoclimatic

studies, Here, the climatic signals of multiple tree-ring parameters, that are latewood

width (LWW), and earlywood width (EWW) are assessed and correlation of these

parameters with the regional temperature and precipitation data has been done. The

annual Larix increment consists of two, visually well distinguishable parts. Earlywood

(light coloured with large vessels) develops during spring, while latewood (dark

coloured, denser, lacks large vessels) is formed during the latter part of the growing

period. The earlywood width (EWW) and latewood width (LWW) have been

measured and analysed for their response with climate.

Chapter 3 Study area…….…. sample processing

35

3.1. Vegetation Overview of Zemu valley

Zemu glacier is located in the northern part of Sikkim in the Eastern Himalaya

(Fig.3.1). The vegetation cover around this glacier has been analysed using

September month’s Landsat-5 satellite data of 2001 (Fig.3.2) NDVI (Normalised

Difference Vegetation Index) calculated using NIR (Near Infra Red) and red bands of

ETM (Enhanced Thematic Mapper) sensor gave a picture of vegetation cover around

the glacier (Fig. 3.3). The present brief account of the distribution of trees based on

information from Smith and Cave (1913) and my personal observation provide a

general idea of the distribution of trees in the region in terms of their climatic regime.

The Zemu glacier valley area may be divided botanically into three forest types.

These are temperate forest growing from 2,438 to 3,353 m a.s.l, a subalpine shrub

region from 3,353 to 4,267 m a.s.l, and an alpine region from 4,267 to 5,182 m a.s.l.

Trees growing from lower elevation i.e., temperate forest (around 2000 m a.s.l) to

subalpine forest (3,000 m a.s.l ) close to snout of this glacier has been considered for

tree-ring analysis in the present dissertation. Frequent occurrence of rock fall, mass-

movement processes, such as debris flow or snow avalanches in the forest is a

common feature. Fieldwork for collection of samples for the present thesis was made

from seven different sites (Fig.3.8). Two of the study sites are located in close

proximity to Zemu glacier area, namely Yabuk, and Jhakthang. The other five sites

investigated are located in and around Zemu area. Sampling was done starting from

Lachen (2,753 m a.s.l) at lower elevation to Yabuk (3, 953 m a.s.l), higher elevation.

3.2. Tree-ring sampling sites of North Sikkim

One of the objectives of this dissertation is the development of well-replicated

climatically sensitive tree-ring chronologies from the Eastern Himalayan region. For

that I have chosen Zemu glacier valley at altitude gradients from lower elevation up to

the snout of the glacier so that I could extend the existing tree-ring data network of the

Sikkim Himalayan region. For that, a good number of tree-ring samples were

collected during two field trips in the year 2009 and 2010 at this region. Tree-rings

Chapter 3 Study area…………. sample processing

36

cores from the living trees and sections from left over stumps were collected for the

present study. Sampling were made through a transect (Fig.3.9) starting from Lachen

I reached to Zema covering the distance around 3 kilometers by trekking. From there I

proceed to Talem.

Fig.3.1.Map of India showing position of Zemu glacier and adjoining area in Sikkim


37

Fig. 3.2. (a) Satellite map of Zemu glacier and (b) Zemu glacier along with

Vegetation Cover in and around this region based on NDVI (Normalised Difference

Vegetation Index)

Zemu glacier is about 11 kilometer from Talem. Besides, samples were

collected from two other places i.e. from Zakthang and Yabuk in between Talem and

snout of the glacier. Zakthang is 2-3 Km from Talem and from Yabuk to Zemu glacier

is about 3-4 kilometer (Fig. 3.4). Tree-line is located at Yabuk which is close to the

Snout of the Zemu glacier. Juniper Squmata scrubs are flourishing close to glacier

snout. It had been quite a hard task to find sufficient aged trees as most forest are

disturbed due to human activities, and natural disasters. Some undisturbed forest sites

are in much remote areas, but those were beyond my reach with existing facilities.


38

Even that, after careful selection of trees a large number of tree cores were collected

from trees growing in a wide variety of ecological settings. These tree cores or

samples were collected through Increment Borers. Generally two cores in opposite

direction at the breast height of the trees were collected. In many cases it was not

possible to collect more than one core, as the other side was not approachable due to

steep slope. In some cases, to get longer samples from the active portion of the trunk,

more than one core were collected where tree had heart rot inside. Depending upon

the availability of suitable trees in the area, generally 10-20 trees were sampled. In

some cases only few trees were sampled with a view to understand the

dendrochronological potentiality of the taxa and site. In this dissertation, for the

convenient of analysis each of these tree-ring sites are abbreviated by its first three

letters. Similarly, name of taxa is abbreviated by four letters in which first two letters

are of generic and later two are from specific name. A List of the tree-ring samples

collected from different sites along with the site and species code and number of cores

collected are shown in (Table 3.1). Total 445cores from 236 trees were collected

from two conifer trees species viz. Abies densa, Juniperus recurva, and one broad

leaved taxa Betula utilis Beside 36 wood stump and 2 wood stump of Juniperus

squamata, Betula utilis were also collected (Fig.3.7). These samples were from

several localities of this region viz., Lachen, Dozom Khola, Talem, Zakthang and

Yabuk, at altitudinal ranges from 2599 to 3953 m.a.s.l. (Fig.3.1).The details of the

samples collected along with site characteristics from different localities are given

below.

3.2.1 Lachen (means ‘big pass’) is a town in North Sikkim and located at an elevation

of 2,753 m a.s.l.. This town is located about 129 km from Gangtok. This site is amidst

mixed conifers and Rhododendron forests. Abies densa growing along with Lachen

river, a tributary of the Teesta River (Fig.3.3). It is also known as Lachen-chu. Tree-

ring samples are collected from the Abies-Rhododendron forest above Lachen

Township. From this forest site, 57 tree cores from 29 trees of Abies densa and 29

trees and 50 core of Larix griffithiana were collected. It was found that most of the


39

trees in this locality are rotten from inside. (Alt of 2,735 m a.s.l). The town forms the

base to the Chopta Valley and Gurudongmar Lake.

Fig. 3.3. Sampling site Lachen showing forest of Abies densa


40

3.2.2. Zema

This place is by the side of the Zema river (Zemu chu). Crossing the Zemu

Chu to westward up the Zemu valley through the forest, the route is quite steep and

one has to walk on path which is broken here and there due to landslides and those

have to be crossed by temporarily made log bridges. Here forest is of mixed conifer

type, especially Abies densa with Rhododendron. From this forest site, 78 tree cores

from 38 trees of Abies densa are collected.

3.2.3. Dozom Khola

This site is on the way to Talem at 3125 m.a.s.l. Here trees appear to be not

much old. From the young patch of Abies densa, Only 4 cores from 3 trees were

collected.

3.2.4. Talem (TAL)

After crossing Zema II trekking through steep slope by the side of the zema

chu I reached Talem. This site is on the left bank of the Zemu chu at an altitude of

3157 m a.s.l. The forest is mostly covered by Abies and Betula with thick

undergrowth (Fig.3.4). Tree cores were collected from 47 trees, in which 56 cores are

from 33 trees of Abies densa and 28 cores are from 14 trees of Betula utilis. Most of

the huge girth trees of Abies densa in this site are also found rotten from inside.

3.2.5. Jakthang (JAK)

This site is on the left bank of the Zemu chu at an altitude of 3503 m a.s.l. The

dominant conifer taxa of this forest is Abies densa associated with few Juniperus

recurva and few Betula utlis. From this forest 66 cores from 34 trees of Abies densa

and 26 cores from 13 trees of Juniperus recurva were collected (Fig.3.5). Some of the

trees are found damaged by boulders and covered by thick moss cushion and rotten

due to fungal activities.


41


42

3.2.6. Yabuk (YAB)

This site (3, 953 m a.s.l) lies in the northern part of North Sikkim District at an

altitude of 3953 m a.s.l. It is about 18 km from Green Lake, on the Lachen- Gangtok

trek route. From Jakthang to Yabuk route there are several ascents and descends on

the route. This path is often wiped out of floods, covered by boulders and pebbles by

landsides. Being close to glacier this site experience very low temperature and high

speed wind. Tree-ring sampling: Abies- Betula –Juniperus site (Fig.3.6). A total

number of 208 tree-ring cores from 110 trees (100 cores from 52 trees of Abies densa,

25 cores from 13 trees of Betula utilis and 83 cores from 45 trees of Juniperus recurva

were collected from this site. In this site except Betula utilis and Juniperus recurva

other one taxa Abies densa, collected for tree-ring analysis, was found mostly rotten


43

inside. Most of the trees with huge girth were rotten from inside. New seedling of

Abies densa growing at upper line of tree, which suggest the migration of trees to

higher altitude in response to global warming.

Fig.3.6.Sampling site Yabuk showing forest of Juniperus recurva and Abies densa


44

3.2.7 Yumthang (YUM)

This site is 24 km. north of Lachung at an altitude of 3,880 m a.s.l. The area is

characterized by sub-alpine forest in which taxa are Abies densa and Rhododendron

conspicuous. These trees are growing on moderately steep slopes forming high stand

density. Samples were collected from ABDE growing 50 m. up slope from Yumthang

Forest Guest House, and attain 10 to 15 m. height and 1-2.5 m. in girth. 46 cores from

27 trees were collected.

3.3. Zemu Valley (Zemu glacier IN5020105032)

Zemu glacier is the largest glacier in the eastern Himalayas with a total surface

area of about 42 km2 and a liner length of about 20 km (Fig. 3.8). It is situated

between the north latitude 270 40' N and 270 41' N and 880 10' E and 880 23' E. The

glacier originates from the eastern slopes of the mount Kangchenjunga (8,536 m a.s.l),

world third highest peak. The glacier can be approached from Lachen in north

Sikkim, which in turn is connected to Gangtok, (capital of Sikkim) by an all weather

road. This glacier initially, flows towards north east changing to easterly course for

Fig.3.7(a)Sampling site Yabuk showing zone of Juniperus squmata scrub

(b) Collection of disc sample of Juniperus squmata (disc) from left over stumps at Yabuk.


45

the greater part of its flow downstream with an average gradient of 1 in 10 m. It is fed

by as 12 tributary glaciers from the southern side. Since last several decades the

glacier was under continuous observation of the GSI and is reported to have vacated

an area of snout 52,443 km2 along the snout front, from 1965 onwards with in

interruption of periods 1979-80 and 1984-1985 when the glacier snout has been

reported to have shown slight advancement.

a.


46

Fig.3.8. (a) Satellite map showing location of sample collection site along with

meteorological station and (b) Sketch map route of collection of samples from ZEMA

to Zemu glacier site. (The details of the abbreviations of sites and trees are given in

Table 2.2).

3.4. Sample Processing The cores were mounted in grooved wooden stick through glue in such a way

that the marking line (sheen caused by the shearing action of the spoon) on the sides

of core and edges of grooves remain parallel vertically (Fig 3.10). The specimen

number and such notes as name of tree and site and date of collection were written on

the mount. The surface of the cores were cut by sharp edged blade and polished using

several grades of sand papers that to make surface smooth for the rings become

visible to study under stereo zoom binocular microscope.

b.


47

Fig.3.9 Collection of tree cores from the tree through increment borer

Fig. 3.10. Processing of tree-ring cores.

Chapter 4 Building of Tree-Ring Chronologies

48

4.1 Building of Tree-Ring Chronologies In sum total 445 cores from 236 trees were collected from three different

conifer trees species viz. Abies densa, Juniperus recurva, and one broad leaved taxa

Betula utilis. Besides 36 and 2 wood stump of Juniperus squamata and Betula utilis

respectively were also collected from Zemu valley. These samples were from several

localities of this region viz., Lachen, Dozom Khola, Talem, Zakthang and Yabuk, at

the altitudinal ranges from 2599 to 3953 m a.s.l. (Fig. 3.8.). Due to dating problem

some samples tree ring data from all trees and sites could not be used. Thus in this

dissertation, the tree-ring data of ABDE from all sites and JURE data of one site

considered, other site of JURE from JAK were not studied. The same problem with

Betula utilis has been recorded although samples were collected from several sites

like, YAB, JAK, and TAL. These remaining works will be taken up later.

In the present dissertation total 12 tree-ring chronologies comprising ten of

ring-width and one each of late wood and early wood tree ring chronologies were

prepared. In the preparation of these chronologies, the computer programme

“ARSTAN” has been used which produces three versions of chronologies

"STANDARD", “RESIDUAL" and "ARSTAN", intend to contain a maximum

common signal and minimum amount of noise in the tree ring series. In the

"STANDARD" chronology detrending of measurement series is done first by fitting a

curve to model biological growth trend to each series, and dividing out the growth

model. The chronology is then computed as a robust estimation of the mean value

function to remove effects of endogenous stand disturbances and it enhances the

common signal contained in the data. In the "RESIDUAL" chronology autoregressive

modelling is performed on the detrended ring measurement series. Robust estimation

of the mean value function produces a chronology with a strong common signal and

without persistence. The "ARSTAN reincorporates into the residual chronology, the

persistence structure of a pooled model of autoregression in the entire group of ring

measurement series. These chronologies prepared are mostly from Abies densa, and

one each from Juniperus squmata, Larix griffithiana and Juniperus indica. These

chronologies along with samples size are shown in (Fig. 4.1). These are from sites

viz., YAB, JAK, TAL, DOZ, ZEMA, LACH, and YUM (Fig. 3.8.). Six chronologies

of Abies densa each from sites YAB, JAK, TAL, DOZ, ZEMA, LACH. One

chronology of Abies densa from YUM. One chronology of Juniperus squmata is from


49

YAB. Beside, one chronology of LAGR from Lachen, two chronologies of late wood

and early wood of LAGR. Time span of chronology prepared from Abies densa of

Zema is extends from AD 1628 to 2007(380 years), From Jakthang, extending from

AD 1700 to 2010 (311 year), Talem AD 1678-2010 (333 years) and Yabuk extends

from AD 1759 to 2010 (252 years). Besides, tree rings the shortest chronology

prepared is one from stumps of JUSQ, AD 1881-2010, (140 years). The longest

chronologies in these lists are JURE which extend from AD 1556 to 2010 (456 year).

Samples of LAGR collected from LAC were less in number (only 9 cores) but these

samples exhibit good cross dating with samples of same species collected from

Lachung (approx. 40 kms apart). These two chronologies were merged together to

form one chronology (LAC) representing LAGR of northern Sikkim region. These

chronologies along with samples size are shown in (Figs 4.1).

Fig.4.1.Ring-width index chronology of Abies densa from Yabuk

Fig.4.2.Ring-width index chronology of Juniperus squmata from Yabuk

0.00

0.20

0.40

0.60

0.80

1.00

1.20

1.40

1.60

1870 1880 1890 1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000

Year

Ring

wid

th In

dex

0

20

40

60

80

100

120

Num

be o

f rad

ii

JUSQ_YAB


50

0.0

0.5

1.0

1.5

2.0

1700

1710

1720

1730

1740

1750

1760

1770

1780

1790

1800

1810

1820

1830

1840

1850

1860

1870

1880

1890

1900

1910

1920

1930

1940

1950

1960

1970

1980

1990

2000

2010

Year

Ring

-wid

ht In

ices

0

5

10

15

20

25

30

35

Num

ber o

f rad

ii

ABDE JAK

Fig. 4.3 Ring-width index chronology of Juniperus recurva from Yabuk site.

Fig. 4.4. Ring-width index chronology of Abies densa from Zakthang site.

Fig. 4.5. Ring-width index chronology of Abies densa from Talem sites.

Fig. 4.6. Ring-width index chronology of Abies densa from Dozamkhola site

0

5

10

15

20

25

0.0

0.5

1.0

1.5

2.0

1550

1570

1590

1610

1630

1650

1670

1690

1710

1730

1750

1770

1790

1810

1830

1850

1870

1890

1910

1930

1950

1970

1990

2010

Num

ber

of r

adii

Rin

g-w

idht

Inic

es

Year

JURE YAB

051015202530

0.0

0.5

1.0

1.5

2.0

2.5

1650

1670

1690

1710

1730

1750

1770

1790

1810

1830

1850

1870

1890

1910

1930

1950

1970

1990

2010

Num

ber

of ra

dii

Rin

g-w

idth

Indi

ces

Year

ABDE_TAL

0.00

0.50

1.00

1.50

2.00

1780

1790

1801

1812

1823

1834

1845

1856

1867

1878

1889

1900

1911

1922

1933

1944

1955

1966

1977

1988

1999

2010

Year

Ring

-wid

ht In

ices

0

1

2

3

4

5

Num

ber o

f rad

ii

ABDE DOZ


51

0.0

0.5

1.0

1.5

2.0

1620

1630

1640

1650

1660

1670

1680

1690

1700

1710

1720

1730

1740

1750

1760

1770

1780

1790

1800

1810

1820

1830

1840

1850

1860

1870

1880

1890

1900

1910

1920

1930

1940

1950

1960

1970

1980

1990

2000

2010

Year

Ring

-wid

ht In

ices

0

10

20

30

40

50

60

Num

ber o

f rad

ii

ABDE_ZEMA

0.0

0.5

1.0

1.5

2.0

1780

1790

1800

1810

1820

1830

1840

1850

1860

1870

1880

1890

1900

1910

1920

1930

1940

1950

1960

1970

1980

1990

2000

2010

Year

Ring

-wid

ht In

ices

0246810121416

Num

ber o

f rad

ii

ABDE_LAC

0.00

0.50

1.00

1.50

2.00

1700

1741

1752

1763

1774

1785

1796

1807

1818

1829

1840

1851

1862

1873

1884

1895

1906

1917

1928

1939

1950

1961

1972

1983

1994

Year

Ring

-wid

th In

dice

s

0

5

10

15

20

25

30

Num

ber o

f rad

ii

LAGR_EW_LAC

Fig. 4.7. Ring-width index chronology of Abies densa Zema site.

Fig.4.8.Ring-width index chronology of Abies densa from Lachen site.

Fig. 4.9. Ring-width index chronology of Larix griffithiana from Lachen site.

0

5

10

15

20

25

30

0.0

0.5

1.0

1.5

2.0

2.5

1700

1741

1752

1763

1774

1785

1796

1807

1818

1829

1840

1851

1862

1873

1884

1895

1906

1917

1928

1939

1950

1961

1972

1983

1994

Num

ber

of ra

dii

Rin

g-w

idth

Ind

ices

Year

LAGR_RW_LAC


52

0.0

0.5

1.0

1.5

2.0

1700

1741

1752

1763

1774

1785

1796

1807

1818

1829

1840

1851

1862

1873

1884

1895

1906

1917

1928

1939

1950

1961

1972

1983

1994

Year

Ring

-wid

th In

dice

s

0

5

10

15

20

25

30

Num

ber o

f rad

ii

LAGR_LW_LAC

0.00

0.50

1.00

1.50

2.00

2.50

1750

1760

1770

1780

1790

1800

1810

1820

1830

1840

1850

1860

1870

1880

1890

1900

1910

1920

1930

1940

1950

1960

1970

1980

1990

2000

Year

Ring

-wid

th In

dice

s

05101520253035404550

Num

ber o

f rad

ii

ABDE_YUM

Fig. 4.10.Early wood width index chronology of Larix griffithiana from Lachen site.

Fig. 4.11. Late wood width index chronology of Larix griffithiana from Lachen site.

Fig. 4.12. Ring width index chronology of Abies densa from Yumthang site.

-3-2-10123

1880

1886

1892

1898

1904

1910

1916

1922

1928

1934

1940

1946

1952

1958

1964

1970

1976

1982

1988

1994

PC#1a.


53

Fig.4.13. a, b. Time-series plots of the two PCs from ring width chronologies along

with altitudinal of gradient of Zemu glacier Sikkim Himalaya..

4.2. Chronology characteristics To assess the quality of the chronology, a number of standard statistical

parameters used in dendrochronology were calculated for the standardized

chronologies. The standard deviation (SD) estimates the variability of measurements

for the whole series; the mean sensitivity (MS) measures year-to-year variation in

tree-ring width and is thus considered as an estimate of the extent to which the

chronology reflects local climate variation (Cook and Kairiukstis, 1990). That is also

an indicator of the relative changes in ring-width variance between consecutive years;

Common signal strength was evaluated by mean interseries correlation (Rbt) and by

the percent variance explained by the first principal component (PC#1). The high

first-order autocorrelations (AC1) reflect a high persistence of the ring-width

chronologies, indicating a significant impact of previous year’s climate on current

year’s ring width, probably caused by carry-over effects of carbohydrates used for

early wood formation (Fritts, 1976). The expressed population signal (EPS) quantifies

the degree to which the constructed chronology portrays the hypothetically perfect

one (Wigley et al. 1984). EPS value of 0.85 as a threshold for the reliability of

chronologies (Wigley et al., 1984) has been used. The expressed population signal

(EPS) and signal-to-noise ratio (SNR) are functions of Rbt and sample size, and

evaluate the signal strength of the site chronologies.

Descriptive statistics, of the twelve ring-width chronologies are shown in

(Tables. 1). In the present study mean sensitivity ranging from 0.103 to 0.222 for ring

width and 0.148 to 0.173 for late wood and early wood respectively and standard

-3-2-10123

1880

1886

1892

1898

1904

1910

1916

1922

1928

1934

1940

1946

1952

1958

1964

1970

1976

1982

1988

1994

PC#2b.


54

deviation ranging from 0.180 to 0.274 for ring width and 0.236 to 0.238 for late wood

and early wood, have been found low in all chronologies (Table 1). The chronologies

generally display a low year-to-year variability (mean sensitivity, MS), which is

typical for conifers growing in humid environments. It ranges from 0.42 to 0.63, in

which values are lowest for LAGR_LAC and highest for ABDE_YAB. The first order

autocorrelation coefficient measures the strength of dependence of a given year’s

chronology value on the value immediately preceding it. This value has been found

high in all the conifers studied 0.410 to 0.590. Percentage of variance and signal/noise

ratio account for the first principal component of tree ring indices are measures of

strength of signal common to trees at a site. Here, the percentage of variance ranges

from 35.04 to 13.24. But In LAGR_LAC variance is comparatively higher i.e. 35.06,

as compared to ABDE chronologies (Table 2.2). In ABDE chronologies, SNR is

comparatively better for ABDE_YUM (8.21) and ABDE_ZEM (7.701) sites, although

it is very low at ABDE_DOZ (0.487) because there are only four cores. In other trees

viz., JUIN and JUSQ, SNR ratio is low (0.122). Correlation among and within trees at

different sites is low to moderate (Table 3.1). However, chronologies from lower

elevation sites (e.g. ABDE_LAC, LAGR_LAC ABDE_ZEMA, ABDE_DOZ and

ABDE_TAL) display a higher standard deviation (SD) and MS (Table 2). Mean inter-

series correlations (Rbt) range from 0.118 to 0.399, and expressed population signals

(EPS) vary between 0.409 and 0.897. The first principal component (PC#1) explains

more than 13 % of the total variance in all individual series except ABDE_JAK and

the signal-to-noise ratio (SNR) ranges from 2.7 (ABD_LAC) to 35.1 (LAGR_LAC)

(Table 2.1). The amount of variance explained by PC#1 and the high SNR values

indicate that the chronologies contain strong common signals. The combination of

relatively high values of Rbt and EPS confirms that our chronologies are suitable for

growth–climate relationship studies (Wigley et al., 1984).


55

4.2.1. Correlation statistics Inter correlation of both standard chronologies for 12 sites for the common

period from 1891-1994 were analysed and results are listed in (Table 1). In general,

agreement among these chronologies is poor. However, comparatively better

correlation (r = 0.421 for the RES and r = 0.385 for the STD chronologies), between

YAB, JAK, TAL, DOZ, ZEM, LAC and YUM chronologies have been found. This

indicates a common variability in regional climate which might have a significant role

in controlling the growth of ABDE. But both the chronologies (JUSQ_YAB and

JUIN_YUB) exhibit quite low or negative correlation with other species. The

correlation results shows that significant correlation exists among all the seven sites

with different species, except ABDE_LACH (Lowest correlation) with the highest

values between ABDE_YUM and ABDE_YAB (r=0.653, p<0.01); ABDE_YUM and

ABDE_JAK (r=0.507, p< 0.01) and the lowest correlation between (ABDE_LAC and

ABDE_YUM; (0.221)) and (JUSQ_YAB_ABDE_YUM); Except for JUSQ_YAB

and ABDE_LACH, in general had a good correlation with other sites (Table 4.1).

Pearson Correlation among the seven sites of ABDE showed a good correlation

among each site with highest correlation (r =0.653; p < 0.01) between YUB and YUM

sites. Thus, three sites of ABDE in Zemu transect, YUM, YAB and ZEMA have a

good correlation with each other (p< 0.01), with highest being among between

ABDE_YUM ABDE_YAB(r=0.653, p<0.01). But their correlations with JUSQ_YAB

and ABDE_LAC are low (Table 4.1). It is highest for TAL and LAGR _RW,

LAGR_EW, LAGR_LW (r= 0.7092, 0.6617, 0.6243, 0.6612, 0.5441, p < 0.05) and

lowest for ABDE of LAC at LAGR_RW of LAC (0.08, p < 0.05) (Table 4.1). The

PCA of the tree-ring chronologies contributes four principal components, identified

with an Eigen value >1.0 which together explains 21.25% of the variance in the

original data set (Table 5.1).

Table.4.1. correlation matrix for standard tree-ring chronologies

Chapter 5 Dendroclimatic modeling

5. Tree Growth/Climate Response Function Analysis Tree-growth/climate relationships were analyzed from trees growing along the

transect of Zemu glacier valley i.e., at lower elevation (2,753 m .a.s.l) to higher

elevation (3,953 m a.s.l) using both Correlation Analysis (CA) and Response function

Analysis (RFA). The use of CA is an initial interpretive guide prior to RFA (Blasing

et al., 1984), later is a multiple regression technique using the monthly climatic data

and ring-width data to determine which climatic variable are best associated with the

tree growth (Fritts, 1976). Here, I have used Bootstrap Response Function Analysis

(Guiot, 1991) using software 3PBase (Guiot and Goeury, 1996). In tree-ring analysis

this method offers the advantage of avoiding errors caused by collinearity among

variables and providing a more realistic estimate of tree response to climate.

5.1. Principal component analysis (PCA)

Basic principal of PCA was discussed earlier in (Chapter 2, section 2.4 .1). In

this section I have given its applications for establishing tree growth climate

relationship of my study sites.

5.1.1. Identification of common patterns of variations in tree growth

After careful examination of descriptive statistics of tree-ring chronologies, I

have performed PCA to find out optimized combination of variables. Then using

standard criteria, I picked the principle components which show the most variation in

the data. PCA rotates the coordinate space of our original variables in such a way that

the longest axis (PC#1) projects the most data. The length of the axis is referred to as

its eigenvalue and is a measure of the variance in the data. Subsequent axes are made

perpendicular to PC1 and explain progressively less and less of the variation in the

data. The relationship between the new variables, or principle components, and their

original variables is determined by their loadings. A higher loading means that the

variable is more closely related to the principle component (Table 5.1). The PCA

scores are the original data rotated into their new coordinate space. The components

with eigen values < 1 were discarded. The outputs from the PCA analysis are

illustrated in (Figs. 5.13). The eigenvalue >1 suggests that should first two principle

components should be retained. Because trees rarely respond to a single climate


57

variable, it is desirable to use a combination of variables to derive a tree response

function. An optimum combination of variables would be one in which the first

derived variable described the largest proportion of variance in the data. Subsequent

variables would be orthogonal and uncorrelated and would describe increasingly less

variation in the data. The process of arranging the original variables in this fashion is

called Principle Component Analysis. After deciding on a number of components to

retained for analysis, one can then model the climate growth relationship using

regression analysis with an optimum combination of variables (Cook and Kairiukstis,

1989).

The tree-ring width data of ABDE LAGR, JUSQ, JUIN (7 individual

chronologies) (Table.4.1) have considered in this study. All these chronologies have

been included as potential predictors of climate to capture the regional scale climate

variability. To assess the agreement among the ten individual standard chronologies,

correlation and principal component analyses (PCA) were applied over the common

period (1881–1994 AD) i.e., for time span of higher replication of site chronologies.

This assures that the signal in each record is representative of the stand growth,

reducing the noise associated with low series replication in early periods of the

chronologies. After identifying common patterns of variations in the records from

each species, the ring width series from individual chronologies, contributing to a PC

with a factor loading> 0.60, were combined to develop regional composite

chronologies (Fig.4.13).

5.2. Significant Climatic variables influencing tree-growth

In the present dissertation, RFA and CA have been performed using standard

tree-ring chronologies of all sites as well with principal components scores of PC#1,

and PC#2, derived from PCA (discussed earlier in Section 5.2.1) with the climatic

data of Gangtok. Climatic variables used for both correlation analysis and Response

Function are (mean temperature, minimum temperature, maximum temperature and

precipitation). These climatic variables were taken for the twelve-month period i.e.,

from November of the previous year to October of the current year’s. Tree-growth

climate relationships of these four conifer species have been illustrated in (Fig.5.14).


58

In RFA of ABDE_YAB (Fig. 5.1a) there was a positive significant

relationship in both maximum and mean temperature of March-April and with

precipitation of May where as it was negative with minimum and mean temperature

of July-August, and precipitation of March.

-5.000-4.000-3.000-2.000-1.0000.0001.0002.0003.0004.0005.000

pNOV pDEC JAN FEB MAR APR MAY JUN JUL AUG SEP OCT

Regr

essi

on c

oeffi

cent

/SD

YAB_MEAN YAB_MAX YAB_MIN YAB_PPT 95CL 95CL

-0.6000

-0.4000

-0.2000

0.0000

0.2000

0.4000

0.6000


Corr

elat

ion

coef

ficie

nts

YAB _MEAN YAB_MAX YAB_MIN YAB_PPT 95%CL 95%CL 99%CL 99%CL

Fig. 5.1.Plot of (a) Response function and (b) Correlation analyses based on standard

chronologies of tree ring indices versus monthly climate data (mean temperature,

minimum temperature, maximum temperature and precipitation) for ABDE_YAB.

Horizontal pink line indicates significance level (p < 0.05) and red line indicate

significance level (p < 0.01) above and below.

However, in the Correlation analysis of ABDE_YAB (Fig. 5.1b) a positive

correlation was recorded with maximum and mean temperature of March-April

(p<0.01) where as negative correlation was found with minimum and mean

a.

b.


59

temperature of May–September (p< 0.05) but it is highly significant only for mean

temperature of July-August (p<0.01). In case of precipitation, March was found

negative while May is positive. Thus, Correlation Analysis exhibits almost similar

results that of Response Function Analysis. Moreover, in both analyses, a positive

relationship with maximum temperature of March-April, and negative relationship

with mean temperature of July-Aug (p < 0.01) were noted.

In case of JUSQ_YAB, in RFA, a positive relationship was noted with

precipitation of May–June, where as it was negatively associated with maximum and

mean temperature of January (Fig.5.2a).

But in Correlation Analysis, only positive significant relationship was recorded with

precipitation of May but it was negative with maximum temperature of January (Fig.

5.2b).

a.

-0.400

-0.300

-0.200

-0.100

0.000

0.100

0.200

0.300

0.400


Corre

latio

n co

effic

ient

s

JUSQ_MEAN JUSQ_MIN JUSQ_MAX JUSQ_PPT 95%CL 95%CL

-3.000

-2.000

-1.000

0.000

1.000

2.000

3.000


Regr

essi

on c

oeffi

cent

/SD

JUSQ_MEAN JUSQ_MAX JUSQ_MIN JUSQ_PPT 95CL 95CLb.

a.

Fig.5.2. Plot of (a) Response function and (b) Correlation analyses based on standard chronologies of tree ring indices versus monthly climate data (mean temperature, minimum temperature, maximum temperature and precipitation) for JUSQ_YAB. Horizontal pink line indicates significance level (p < 0.05) above and below.


60

In case JURE_YAB; RFA revealed a negative relationship between tree

growths and mean temperature, minimum temperature of January but no significant

relationship existed with precipitation (Fig.5.3a). In case of Correlation Analysis, a

negative relationship was recorded with minimum and mean temperature of January

whereas it was negative with mean and maximum temperature of August-September

(Fig 5.3b).

-2.500-2.000-1.500-1.000-0.5000.0000.5001.0001.5002.0002.500


Regr

essi

on c

oeffi

cent

/SD

JURE_MEAN JURE_MAX JURE_MIN JURE_PPT 95CL 95CL

In the next site towards lower elevation, ABDE_JAK, in its RFA, a positive

and significant relationship was noted with mean and minimum temperature of March

while a negative relation was with minimum temperature of January (Fig.5.4a). On

a.

b.

Fig. 5.3. Plot of (a) Response function and (b) Correlation analyses based on standard chronologies of tree ring indices versus monthly climate data (mean temperature, minimum temperature, maximum temperature and precipitation) for JURE_YAB, horizontal pink line indicates significance level (p < 0.05) and red line indicates significance level(p < 0.01) above and below

-0.400

-0.300

-0.200

-0.100

0.000

0.100

0.200

0.300

0.400


Corr

elat

ion

coef

ficie

nts

JURE_MEAN JURE_MIN JURE_MAX JURE_PPT 95%CL 95%CL


61

the other hand in Correlation Analysis, a positive relationship with only mean and

minimum temperature of March (Fig 5.4b) was recorded.

-3.000

-2.000

-1.000

0.000

1.000

2.000

3.000

4.000


Regr

essi

on c

oeffi

cent

/SD

JAK_MEAN JAK_MAX JAK_MIN JAK_PPT 95CL 95CL

-0.400

-0.300

-0.200

-0.100

0.000

0.100

0.200

0.300

0.400


Corr

elat

ion

coef

ficie

nts

JAK_MEAN JAK_MIN JAK_MAX JAK_PPT 95%CL 95%CL

Fig.5.4. Plot of (a) Response function and (b) Correlation analyses based on standard


minimum temperature, maximum temperature and precipitation) for ABDE_JAK.

Horizontal pink line indicates significance level (p < 0.05) above and below

In ABDE_TAL, RFA; a negative relationship was noted with maximum

temperature of previous year December and with precipitation, whereas a negative

and significant relationship with maximum temperature of May and September and

with minimum temperature of July-August were recorded. In case of precipitation

only January exhibits negative relationship (Fig. 5.5a). However, in case of

Correlation Analysis, a negative relationship with minimum temperature of previous

a.

b.


62

November to current year February and mean temperature with previous November to

January were recorded. A negative relationship was with mean, maximum and

minimum temperature of May to September, except maximum temperature of January

though it was related but not significant (Fig.5.5b.) In case of precipitation a negative

relationship was with January recorded.

-0.800

-0.600

-0.400

-0.200

0.000

0.200

0.400


Corr

elat

ion

coef

ficie

nts

TAL_MEAN TAL_MIN TAL_MAX TAL_PPT 95%CL 99%CL 95%CL


chronologies of tree ring indices versus monthly climate data (mean temperature, minimum

temperature, maximum temperature and precipitation) for ABDE_TAL. Horizontal pink line

indicates significance level (p < 0.05) and red line indicate significance level(p < 0.01) above

and below

In case of ABDE_DOZ_ RFA, a negative relationship was with precipitation

of April and June while mean and maximum temperature of March was positive.

-4.000

-3.000

-2.000

-1.000

0.000

1.000

2.000

3.000


Regr

essi

on c

oeffi

cent

/SD

TAL_MEAN TAL_MAX TAL_MIN TAL_PPT 95CL 95CL

a.

b.


63

Whereas in CA, a negatively significant relationship was with mean and

minimum temperature of January and precipitation of April and June (p<0.05). Thus,

a common relationship between tree growth and precipitation was recorded in both

RFA and Correlation analysis. A negative significant relationship for the precipitation

in April and June recorded in both analysis (Fig. 5.6a and 5.6b). It is generally typical

for lower elevation site were growth is mostly limited by precipitation since this site is

located at the lower elevation and also responds similar way.

-3.000

-2.000

-1.000

0.000

1.000

2.000

3.000


Regr

essi

on c

oeffi

cent

/SD

DOZAM_MEAN DOZAM_MAX DOZAM_MIN DOZAM_PPT 95CL 95CL

-0.500

-0.400

-0.300

-0.200

-0.100

0.000

0.100

0.200

0.300

0.400


Corr

elat

ion

coef

ficie

nts

DOZAM_MEAN DOZAM_MIN DOZAM_MAX DOZAM_PPT 95%CL 95%CL



minimum temperature, maximum temperature and precipitation) for ABDE_DOZ.

Horizontal pink line indicates significance level (p < 0.05) above and below

a.

b.


64

In case of ABDE_ZEMA; RFA, a negative relationship with precipitation of

January recorded at significant level, and a positive and negative relationship with

mean temperature of previous year November and current year of September

recorded (Fig.5.7a). While in Correlation Analysis, a negative relationship with

precipitation of January recorded and also a negative relationship with mean and

maximum temperature of September (p<0.05) at significant level (Fig.5.7b) recorded.

In both analyses the common result observed in case of precipitation.

In case of ABDE_LACH; a negative and significant relationship with May

precipitation and mean and minimum temperature also positively associated with

-2.500-2.000-1.500-1.000-0.5000.0000.5001.0001.5002.0002.500


Reg

ress

ion

co

effi

cen

t/S

D

ZEMA_MEAN ZEMA_MAX ZEMA_MIN ZEMA_PPT 95CL 95CL

a.

-0.500

-0.400

-0.300

-0.200

-0.100

0.000

0.100

0.200

0.300

0.400


Cor

rela

tion

coef

ficie

nts

ZEMA_MEAN ZEMA_MIN ZEMA_MAX ZEMA_PPT 95%CL 95%CL

b.

Fig. 5.7. Plot of (a) Response function and (b) Correlation analyses based on standard chronologies of tree ring indices versus monthly climate data (mean temperature, minimum temperature, maximum temperature and precipitation) for ABDE_ZEM. Horizontal pink line indicates significance level (p < 0.05) above and below


65

July-September temperature recorded (Fig.5.8a) While in Correlation Analysis, a

Negative Significant relationship for May on the other hand positive relationship with

mean temperature August at (p<0.05) level (Fig.5.8b) recorded.

-2.500-2.000-1.500-1.000-0.5000.0000.5001.0001.5002.0002.500


Regr

essi

on c

oeffi

cent

/SD

LACH_MEAN LACH_MAX LACH_MIN LACH_PPT 95CL 95CL

-0.500

-0.400

-0.300

-0.200

-0.100

0.000

0.100

0.200

0.300

0.400

0.500


Corr

elat

ion

coef

ficie

nts

LACH_MEAN LACH_MIN LACH_MAX LACH_PPT 95%CL 99%CL 95%CL 99%CL



minimum temperature, maximum temperature and precipitation) for ABDE_LAC.

Horizontal pink line indicates significance level (p < 0.05) and red line indicate

significance level(p < 0.01) above and below

In case of LAGR_RW_LAC_Climate response to Latewood and Early wood:

the aim of this study was to analyze the effects of climatic factors (i.e. monthly mean

temperature and total precipitation) on radial growth (early wood width, latewood

width, and total ring width). In RFA for LAGR_RW; a negative relationship with

mean and minimum temperature for January only (Fig.5.9a), noted while in

a.

b.


66

Correlation Analysis a negative relation with minimum temperature of the whole

year at (p<0.05) level. But the highest significant negative relationship with minimum

and mean temperature of May to September at (p<0.01) level. There was no signal of

precipitation observed (Fig.5.9b).

-4.000

-3.000

-2.000

-1.000

0.000

1.000

2.000

3.000


Regr

essi

on c

oeffi

cent

/SD

LAGR_RW_MEAN LAGR_RW_MAX LAGR_RW_MIN LAGR_RW_PPT 95CL 95CL



minimum temperature, maximum temperature and precipitation) for

LAGR_RW_LAC. Horizontal pink line indicates significance level (p < 0.05) and red

line indicate significance level (p < 0.01) above and below

LAGR_LW_LAC; in RFA, a negative relationship was noted with mean,

maximum and minimum temperature for January and Maximum temperature for May

-0.800

-0.600

-0.400

-0.200

0.000

0.200

0.400


Co

rrela

tio

n c

oeff

icie

nts

LAR_RW_MEAN LAR_RW_MIN LAR_RW_MAX LAR_RW_PPT 95%CL 99%CL

a.

b.


67

(Fig.5.10a). Where as in Correlation Analysis a negative relationship with mean and

minimum temperature for previous year November to February recorded. There was a

negative relationship with minimum and mean temperature for May to September also

(Fig.5.10b).


standard chronologies of tree ring indices versus monthly climate data (mean

temperature, minimum temperature, maximum temperature and precipitation) for

LAGR_RW_LW_LAC. Horizontal pink line indicates significance level (p < 0.05)

and red line indicate significance level (p < 0.01) above and below

LAGR_EW_LAC; RFA revealed a negative relationship with mean and

minimum temperature for January only (Fig.5.11a). While in Correlation Analysis, a

negative relationship was noted with minimum temperature for January to September

-0.700-0.600-0.500-0.400-0.300-0.200-0.1000.0000.1000.2000.300


Corr

elat

ion

coef

ficie

nts

LAR_LW_MEAN LAR_LW_MIN LAR_LW_MAX LAR_LW_PPT 95%CL 99%CL

-4.000

-3.000

-2.000

-1.000

0.000

1.000

2.000

3.000


Reg

ress

ion

coef

ficen

t/SD

LAGR_LW_MEAN LAGR_LW_MAX LAGR_LW_MIN LAGR_LW_PPT 95CL 95CL

a.

b.


68

(at significant level p<0.01). Mean temperature also displayed a negative relationship

for previous year November to March and May to September at significant level.

Thus, the major difference between RFA and CA in this case, where the later shared

the strongest climatic response with minimum temperature (Fig5.11b).

Fig. 5.11 Plot of (a) Response function and (b) Correlation analyses based on standard


minimum temperature, maximum temperature and precipitation) for

LAGR_EW_LAC. Horizontal pink line indicates significance level (p < 0.05) and red

line indicate significance level (p < 0.01) above and below

-0.800

-0.600

-0.400

-0.200

0.000

0.200

0.400


Corr

elat

ion

coef

ficie

nts

LAR_EW_MEAN LAR_EW_MIN LAR_EW_MAX LAR_EW_PPT 95%CL 99%CL

-4.000

-3.000

-2.000

-1.000

0.000

1.000

2.000

3.000


Regr

essi

on c

oeffi

cent

/SD

LAGR_EW_MEAN LAGR_EW_MAX LAGR_EW_MINLAGR_EW_PPT 95CL 95CL

a.

b.


69

ABDE_YUM; in RFA, (Fig. 5.12a) there was a positive significant

relationship with maximum temperature of March-April and where as it was negative

with minimum and mean temperature of July-August (but at significant level), and

precipitation of May. However, in the Correlation analysis of ABDE_YUM; (Fig.

5.12b) a negative correlation was found with maximum and mean temperature of

May–September (p< 0.05) but it is highly significant only for mean temperature of

July-August (p<0.01). In case of precipitation, March is found negative.

Fig. 5.12.Plot of (a) Response function and (b) Correlation analyses based on standard

chronologies of tree ring indices versus monthly climate data (mean temperature, minimum

temperature, maximum temperature and precipitation) for ABDE_YUM. Horizontal pink line

indicates significance level (p < 0.05) and red line indicate significance level(p < 0.01) above

and below

-3

-2

-1

0

1

2

3

PNOV PDEC JAN FEB MAR APR MAY JUN JUL AUG SEP OCT

Regr

essi

on c

oeffi

cent

/SD

YUM_MAX YUM_MIN YUM_MEAN YUM_PPT 95CL 95CL

a.

b.

-0.600

-0.500

-0.400

-0.300

-0.200

-0.100

0.000

0.100

0.200

0.300

0.400


Corr

elat

ion

coef

ficie

nts

YUM_MEAN YUM_PPT YUM_MIN YUM_MAX 95%CL 99%CL 95%CL

b.


70

5.2.1. Climatic variables significant in limiting tree-growth at the Zemu Valley

Tree-growth variability was also analyzed using principal component analysis

(PCA) for the common period 1881-1994 of standard chronologies of all the trees

viz., ABDE, JUSQ, JURE, and LAGR. The plot of the PCA loading coefficients

displayed groups of chronologies with similar growth patterns (Table.).

Fig. 5.13.Principle component plot in rotated space

JUSQ_YAB and JURE_YAB and ABDE_LAC chronologies were scattered,

covering nearly all the range of the first axis values, they were in an intermediate

position i.e. between YAB, JAK, TAL, DOZ, ZEM, LAC and YUM chronologies In

this analysis it was found. Since JUSQ_YAB chronologies negatively correlated with

other chronologies so it appeared in separate axis. So for the final PC score two

species JUSQ_YAB and JURE_YAB were discarded from all and final three PCs

developed. Despite the diversity of species, habitats and climatic regimes, a common

macroclimatic signal expressed by the first principal component (PC#1) was found.


71

Moreover, considering the PC#1 scores as a regional chronology, significant relations

were established with Gangtok data set. The PC#1 and the second PC (PC#2) of the

chronology PCA were significant, representing 30.37% and 25.03% of the total

variance, respectively.

Table.5.1.Summary of the PCA Statistics of tree-ring chronologies.

Total Variance Explained

Component Initial Eigenvalues

Extraction Sums of Squared Loadings

Rotation Sums of Squared Loadings

Total % of

Variance Cumulative

% Total % of

Variance Cumulative

% Total % of

Variance Cumulative

%

n0 1 2.605 37.216 37.216 2.605 37.216 37.216 2.126 30.375 30.375

2 1.273 18.189 55.405 1.273 18.189 55.405 1.752 25.030 55.405

Extraction Method: Principal Component Analysis.

Table.5.2. Summary of rotated principal component retained in PCA

Component Matrixa

Component

1 2

ABDE_YUM .790 -.346

ABDE_YAB .735 -.412

ABDE_JAK .664 -.375

ABDE_TAL .581 .259

ABDE_DOZ .469 .237

LAGR_RW_LAC .489 .614

ABDE_ZEM .449 .586


72

5.2.2 Varied Climate–growth responses at altitude gradients

The Response Function Analysis emphasizes that there was a markedly

diverse tree-growth responses to climatic variations at these sites located at altitude

gradients from lower to higher elevations. Conifers growing mostly at high and

middle elevations were recorded highly sensitive to temperature variations except

ABDE_JAK at middle-elevation site where growth was less sensitive to climate

variability (Fig. 5.2.4).This might be tress growing at this site was highly disturbed as

landslide are common as evident by the presence of loose boulders in forest floor of

this site. In contrary lower elevation sites like ABDE_TAL, ABDE_DOZ,

ABDE_LAC, were clearly limited by precipitation. All these high elevation sites

recorded significant relationship to Feb-March and July-October temperature,

However among all these high elevation sites ABDE (ABDE_YAB) is highly

sensitive because at this site abundant rainfall is generally combined with enhanced

cloudiness and reduced radiation input which lower the temperatures at the highest

elevation of study site.

5.2.3. Species-specific climate–growth responses

It has been noted that trees ABDE, JUSQ and JURE showed a different

climate-response behavior, (Fig.5.5) in respect to each other. A comparison between

JUSQ, ABDE both respond to pre monsoon temperature and late summer variations

in different way, In the pre monsoon maximum temperature shows positive relation

with ABDE_YAB but JUSQ doesn’t show the same while in late summer season

(June–August), minimum and average temperature limit radial growth of fir but not

for JUSQ, JURE (compare Figs. 5 and 6). Hence, tree-ring chronologies of ABDE

from high-elevation conifer sites might be used to reconstruct pre monsoon maximum

temperature as well late summer temperature. Warm conditions in the growing season

have a positive effect on radial growth, but for other ABDE site the correlations were

not found at high significant level. (Table 4.1).


73

-3.000

-2.000

-1.000

0.000

1.000

2.000

3.000


Regr

essi

on c

oeffi

cent

/SD

PC#1_MAX PC#1 MEAN PC#1_MIN PC#1PPT 95CL 95CL

-2.50

-2.00

-1.50

-1.00

-0.50

0.00

0.50

1.00

1.50

2.00

2.50


Regr

essi

on c

oeffi

cent

/SD

PC#2 MIN PC#2_MEAN PC#2_MAX PC#2PPT 95CL 95CL

b.

a.

-0.600

-0.400

-0.200

0.000

0.200

0.400

0.600


Corr

elat

ion

coef

ficie

nts

PC#1_MEAN PC#1_MIN PC#1_MAX PC#1_PPT 95CL 99 CL 95CL

c.


74

-0.600

-0.400

-0.200

0.000

0.200

0.400

0.600


Corr

elat

ion

coef

ficie

nts

PC#2_MIN PC#2_MAX PC#2_PPT PC#2_MEAN 95CL 99 CL 95CL

Fig.5.14. Plot of (a, b) Response function and (c, d) Correlation analyses based on

rotated principal component (PC) scores [AD 1881–1994 for PC#1, PC#2,] versus

monthly climate data (mean temperature, minimum temperature, maximum

temperature and precipitation). Horizontal pink line indicates significance level (p <

0.05) and red line indicate significance level (p < 0.01) above and below.

5.3. Physiological explanation of tree growth climate relationship

The correlation analyses suggest that in this region the influence of rainfall on

tree growth is weaker than that of temperature. Precipitation at higher elevation sites

does not reach thresh hold limits to reduce tree growth as no significant correlations

were recorded between monthly precipitation and the tree-ring chronologies,

suggesting that moisture is not limiting factors for radial growth at tree line. Here

attempts have been made to find out logical explanation for tree growth climate

relationship in terms of physiolological aspects of trees.

5.3.1. Positive correlation with March to April temperature

At tree-line locations, the growth period is relatively short and most active

tracheid formation occurs at the beginning of the summer (Rossi et al., 2006).

Therefore, for production of wide tree rings is the combination of initiation of early

cambial activity with enough available resources, i.e., stored carbohydrates and other

substances produced in the previous growth period and favorable climate conditions

during the late spring and early summer months of the growth year is needed. The

d.


75

effect of previous year climate is expressed by strong first-order autocorrelation often

found in tree-line trees (Fritts, 1976). A positive response to spring and summer

temperatures has also been found in a number of studies of P. cembra in the European

Alps (Carrer and Urbinati, 2004) and the Carpathian Mountains (Kern and Popa

2007).

5.3.2. Negative correlation with June to September:

Low temperatures in June and July could slow down the initiation of cambial

activity and first stages of cell division and development. In addition to this, unusually

low temperatures during this period can also slow down the formation of secondary

cell walls, and deposition of lignin, and can therefore be responsible for production of

light rings or narrow tree rings in extreme years (Gindl, 1999; Rossi et al., 2006). The

growth of conifer in the Eastern Himalaya is often controlled by late summer

temperature (Bhattacharyya et al., 2003).

Dendroclimatological studies of high-elevation conifer sites on eastern Tibetan

plateau and in Eastern Himalaya suggested that tree-ring chronologies were an

indicator of late summer temperature (Bräuning and Mantwill, 2004; Bräuning, 2006;

Bhattacharyya and Chaudhary, 2003). Similar results were reported by various studies

in nearby areas (Fan et al., 2009; Liang et al., 2008; Bräuning, 2006). Bhattacharyya

et al. (2003) Suggested that late summer temperature in Eastern Himalaya is vital for

growth of Abies densa. In the study area, sampled trees are assumed to be growth

limited by a single climate variable (usually temperature). Because of Forest

desiccation and reduced root activity due to low soil temperature that restricts tree

growth. Liang et al. (2008) suggested that mean summer minimum temperature was

the major limiting factor to tree-ring growth of P. likiangensis var. balfouriana in the

source region of the Yangtze River on the Tibetan Plateau. The growth of conifer in

the Eastern Himalaya is often controlled by late summer temperature (Bhattacharyya

et al., 2003). Abies densa sensitive to summer drought stress, as reflected by the

negative correlations with June and September temperatures. This suggests that tree

growth is limited in years with dry and hot summers. Such findings are remarkable for

tree-line sites, where tree growth mostly reacts positively to high summer


76

temperatures (Korner, 1998; Buntgen et al., 2006; Carrer et al., 2004) leaves are not

frozen (Brauning, 2001; Chabot and Hicks, 1982; Havranek and Tranquillini, 1995;

Pederson et al., 2004). Highest temperature in the study area occurred from June to

August, which is earlier than the highest precipitation timing (from July to

September). That is to say, the higher the temperature is in the growth season, the

more intensive the soil evaporations and plant transpirations are, and this leads to

worse conditions for tree growth. The negative correlation between tree-ring width

and the mean temperature from June to September suggests temperature can be the

most limiting climatic factor of trees in Zemu area, by accelerating soil water

evaporation and tree’s transpiration Higher winter temperature might advance the

beginning of the growing season.


77

5.4. Dendroclimatic modeling (Past Climate reconstruction) In general, in stressed environments sites, inter annual tree growth variations

according to changes in growth limiting climate variables are conspicuous. The

process of deriving a climate from tree growth and extending it beyond the

instrumentally measured climate or meteorological records is referred to as a

calibration. The outcome is a chronology of climate as old as the oldest preserved tree

in the tree ring series (Fritts, 1976). Details methodologies involved in the climate

reconstruction were discussed in the chapter 2.

To reconstruct the past climate variations, the instrumental records

(temperature and precipitation) were regressed against the regional chronology. Out

of 12 tree ring chronology made from four conifers, only two sites chronologies of

ABDE i.e. ABDE_YAB and ABDE_ZEM were recorded statistically reliable for

climate and others were statistically weak for reconstruction. In case of ABDE_YAB,

A clear and high significant positive relationship to maximum March-April and a

negative to mean temperature of July- August recorded. With precipitation, there were

no persistent patterns of correlation with the tree ring data among the all site

chronologies. For maximum March April temperature reconstruction, I applied only

Bootstrap method was applied because it qualifies the statistical significance level

successfully in both RFA and CA. For reconstruction of Mean July-Augst

temperature, I have used Linear regression method, because in this process the July-

August negative relationship are highly significant at 99% confidence level, even

though signals were also seen also in RFA but it just touched 95% level.

5.4.1. Bootstrap Method for March_April Maximum temperature reconstruction

Average March-April Maximum temperature has been observed significant

controlling growth of fir at tree line of Zemu glacier area in the both methods, i.e.

Bootstrap and Correlation. Since response function analysis using Bootstrap shows

better possibility for reconstruction (Table 5.3). Significant climatic variables limiting

tree growth and these climatic parameter have been selected for the reconstruction

through Bootstrapped transfer function (Guiot, 1991). Reliability of the reconstruction


78

models is tested following the usual procedure adopted for calibration and

verification. In the present study total climatic data has been divided in to two sub-

periods, AD 1966 to 1983 and 1984 to 2000. Initially, the calibration was made on the

first half of the dataset and the independent verification was done on the other half

and vice-versa. Finally, for the final reconstruction of Maximum March_April

temperature, regression coefficients were calculated for the whole period of 1966–

2000 and various calibration and verification statistics were calculated (Table 2). The

advantage of Bootstrapping seems to perform better than cross-validation in many

cases (Efron, 1983). In the simplest form of bootstrapping, instead of repeatedly

analyzing subsets of the data, subsamples of the data repeatedly analyze. Each

subsample is a random sample with replacement from the full sample. The reliability

of the Bootstrap orthogonal regression model was evaluated by statistics on

calibration and verification periods. Evaluative statistics provided for the calibration

period are the Pearson correlation coefficient (r), the coefficient of determination (R2),

R2 adjusted where as for the verification period, it is t-test, reduction of error (RE),

and sign test (ST). Values of RE was positive, indicating a significant skill present in

the estimates of late summer temperature. All calculations were made by using DPL

programme.

The reconstructed data of the Maximum temperature for March -April extends

back to 1759 to 2000 but the reliable reconstruction is only from 1821 to 2000 (Fig.

5.9.1). Because before A.D. 1821 the EPS value of the tree-ring chronology of YAB

site was below 0.85, so the statistically reliable reconstruction may be considered for

1821 to 2000 AD. Descriptive statistics reveals that in this series values are for mean

(19.822oC); standard deviation (0.412) maximum (20.972oC) and minimum

(18.821oC). The reconstructed MA temperature series indicated annual to decadal

scale variations (Fig. 5.3). A 5-year of moving average was calculated to identify

variations in pre- summer temperature. The value greater than that of maximum and

minimum represents hot and cold months respectively observed in the reconstruction.

The reconstructed temperature series for the last 180 years showed annual to

multiyear fluctuations punctuated with cool and warm periods.


17.000

18.000

19.000

20.000

21.000

22.000

23.000

1750

1760

1770

1780

1790

1800

1810

1820

1830

1840

1850

1860

1870

1880

1890

1900

1910

1920

1930

1940

1950

1960

1970

1980

1990

2000

Tem

pera

ture

o C

18.000

18.500

19.000

19.500

20.000

20.500

21.000

21.500

1820

1824

1828

1832

1836

1840

1844

1848

1852

1856

1860

1864

1868

1872

1876

1880

1884

1888

1892

1896

1900

1904

1908

1912

1916

1920

1924

1928

1932

1936

1940

1944

1948

1952

1956

1960

1964

1968

1972

1976

1980

1984

1988

1992

1996

2000

Year

Max

imum

Tem

pera

ture

o C

(Rec

onst

ruct

ed)

12

14

16

18

20

22

24

Max

imum

Tem

pera

ture

o C

(Act

ual)

Fig.5.15 Tree-ring based

reconstructed Maximum

March-April temperature in Zemu Valley (dotted green

line is start year of the reliable

time span); Actual (green line)

and estimated (red line).


80

Site _Name Model Name Calibration Verification YABUK ABDE_YAB/ Max temperature_(March-April) 0.273± 0.1267 a 0.204± 0.105 b

Calibration Verification Calibration Verification

(1966-1983) (1984-2000) (1984-2000) (1966-1983)

r 0.723* 0.477* 0.477* 0.7233* R2 0.523* 0.231 0.231 0.5232* RE

0.4453*

0.7119*

t-value

4.0409*

5.0492* Sign test

16/1

15/3

-3

-2

-1

0

1

2

3

4

1820

1830

1840

1850

1860

1870

1880

1890

1900

1910

1920

1930

1940

1950

1960

1970

1980

1990

2000

Year

Max

imum

Tem

pera

ture

o C

( Mar

ch-A

pril)

R2 is the square of the correlation coefficient calculated between actual and estimated data. t-value is derived using the product mean test. The departures from the actual and reconstructed series are multiplied in each year; the means of negative and positive products are calculated, and the difference between the means is tested with student’s t-test. Sign test is number of agreement/total values with the correct deviation (first number) from the mean. Reduction of error statistics in any positive value demonstrates skill in reconstruction (varies between -1.0 and +1.0).

a The value is averaged multiple correlation coefficients from 200 replications and their standard deviations

b Indicates the independent verification are significant at 95% level

Table.5.3.Monthly climatic models for Zemu Valley based on ABDE_YAB using bootstrap method.

Table 5.4 Statistics of calibration and verification for tree-ring reconstruction of maximum March-April temperature

Fig.5.16. Showing Anomaly in the reconstructed March_April temperature.


81

.

Fig5.18. Scatter plot of actual and tree-ring reconstructed Maximum (March-April) temperature with a linear relationship highlighted during the period of 1966–2000.

Fig.5.17 The comparison of actual and reconstructed Maximum March-April temperature from 1966 to 2000

16.00

18.00

20.00

22.00

24.00

1966

1968

1970

1972

1974

1976

1978

1980

1982

1984

1986

1988

1990

1992

1994

1996

1998

2000

Year

Tem

pera

ture

(0 C)

ReconstuctionActual

R2 = 0.3757

19.000

19.500

20.000

20.500

21.000

17.00 18.00 19.00 20.00 21.00 22.00 23.00

Actual temperature (0C)

Reco

nstu

ctue

d te

mpe

ratu

re (

0 C)


5.4.1.1 Salient features of reconstructed March April maximum temperature

The reconstruction indicates that the observed value is higher than mean value i.e.

warmer years includes:

low temperatures than mean value i.e. represents cold years are:

1822 1823 1824 1825 1826 1826 1827 1828 1836 1837 1841 1845

1848, 1857 1858 1865 1875 1876 1877 1878 1879 1880 1881 1883

1884 1885 1886 1888 1889 1891 1893 1894 1895 1896 1897 1898

1899 1900 1901 1902 1903 1904 1911 1912 1913 1914 1917 1918

1919 1923 1924 1925 1926 1927 1939 1940 1941 1942 1948 1949

1950 1952 1953 1954, 1955, 1956 1957 1961 1963 1966 1967 1968

1969 1970 1971 1972 1973 1974 1975 1979 1985 1986.

1821 1829 1830 1831 1832 1833 1834 1835 1838 1839 1840 ,1842

1843 1844 1846 1847 1849 1850 1851 1852 1853 1854 1855 1856

1859 1860 1861 1862 1863 1864 1866 1867 1868 1869 1870 1871

1872 1873 1874 1882 1887 1890 1892 1905 1906 1907 1908 1909

1910 1915 1916 1920 1921 1922 1928 1937 1938 1943 1944 1946

1947 1951 1959 1960 1962 1964 1965 1976 1977 1978 1980 1981

1982 1983 1984 1987 1988 1989 1990 1991 1992 1993 1994 1995

1996 ,1997 ,1998 ,1999 2000


83

5.4.2. Linear regression method for Average temperature of (July August)

temperature reconstruction

Correlation analysis of the climate-growth relationship showed high

significant negative correlation between the radial growth of ABDE from YABUK

site and mean temperature of July-August (Fig. 5.10) and positive correlation with

average (March-April) temperature. The highest correlation between tree rings and

July to August) temperature (r = - 0.693; p < 0.01). Therefore, average July-August

temperature used as the climate variable for the reconstruction. A simple linear

regression model was obtained to reconstruct (July-August) temperature history of the

study area (Cook and Kairiukstis, 1990). Linear regression model used between tree-

ring indices and temperature for the 1966–2000 calibration periods.

The model was designed as:

Average temperature for (July August) temperature = 21.086-1.8424*YAB CRN

In this equation, average temperature for (July August) (in oC) represents the

reconstructed average temperature and YAB_CRN represents the tree-ring width

index of ABDE_YAB. The ability of this equation has been tested to build climate

reconstruction by applying it to the validation dataset. The correlation coefficient is

−0.694 (n=35, P<0.01), and variance explanation (R2) is 23.6% (R2 adj=19.0%) in the

calibration period. The function F test value is 8.439. Split calibration verification

results (Table 1) show that it has a high correlation coefficient both in the calibration

and verification periods. It also passed the Sign test (P<0.05) and Reduction of error

(RE), statistical tests for paleoclimate reconstruction. The model was applied to the

pre-instrument ring widths to reconstruct past climate.

5.5. Reliability of the regression model

5.5.1. Calibration verification of model for temperature reconstruction

Past climate was reconstructed from tree rings by a linear regression model. The

statistics from splitting samples into calibration-verification intervals showed a


84

significant correlation between the reconstructed and the actual temperatures during

the calibration and verification periods (Table 1).The reliability of the regression

model was evaluated by statistics on calibration and verification periods. Evaluative

statistics provided for the calibration period are the Pearson correlation coefficient

(R), the coefficient of determination (R2), and F test (F) and for the verification period

are reduction of error (RE), Values of RE was positive, indicating a significant skill in

the estimates of late summer temperature, sign test (ST), and Durbin-Watson test

(DW) (Fritts 1976; Cook et al. 1994), DW value was about 1.5, indicating

insignificant autocorrelation in the model residuals and target climate data. These

results demonstrated the reliability of our regression model. R2, RE, are all measures

of shared variance between climate and tree rings, and a positive RE is evidence for a

valid regression model. The results of the sign test and product mean test also

exceeded the 95% confidence level

5.5.2 Variability in reconstructed climate data.

The reconstructed data of the Late summer temperature (July-August) history

since extends back to 1759 to 2000, but the reliable reconstruction is only from 1821

to 2000 (Fig. 5.19). Because before A.D. 1821 the EPS value of the tree-ring

chronology of YAB site was below 0.85, so the statistically reliable reconstruction

may be considered for AD 1821 to 2000. Descriptive statistics showed that, Mean

(19.299 OC); standard deviation (0.305) maximum (20.07 OC) and minimum (18.327

OC). The reconstructed summer temperature series indicated annual to decadal scale

variations (Fig. 5.15). A 5-year of moving average was calculated to identify

variations in late summer temperature. The Value greater than that of Maximum and

minimum represents Hot and cold month in the reconstruction. The reconstructed

temperature series for the last 180 years showed annual to multiyear fluctuations

punctuated with cool and warm periods.

The reconstructed temperature showed that warm episodes occurred in the

intervals A.D. 1821–2000, with regular interval of cooler summer (Fig. 5.b).

Although continues low temperature then mean value i.e. a cold interval in 1822 to


85

1828, 1836 to 1841, 1857 to 1860, 1875 to 1877, 1879 to 1881, 1883 to 1886, 1888 to

1891, 1895 to 1904, 1910 to 1914, 1916 to 1920 1939 to 1942, 1944 1945, 1947 to

1958 1960 to 1967, 1970, 1973 to 1979, 1984, 1985, 1987 1988, 1993, 1994, 2000.

Notable warm anomaly of the reconstruction are from 1831 to 1835, 1842 to 1847,

1850 to 1856, 1861 to 1874, 1905 to 1909 etc. In the last warm period from A.D.

1991 to present, the temperatures of all the years were above the long-term mean.


17.00

17.50

18.00

18.50

19.00

19.50

20.00

20.50

21.00

1820

1830

1840

1850

1860

1870

1880

1890

1900

1910

1920

1930

1940

1950

1960

1970

1980

1990

2000

Year

Tem

pera

ture

o C

Reconstuctucted(Jul-Aug) Actual temperature

17.0

17.5

18.0

18.5

19.0

19.5

20.0

20.5

21.0

1750

1760

1770

1780

1790

1800

1810

1820

1830

1840

1850

1860

1870

1880

1890

1900

1910

1920

1930

1940

1950

1960

1970

1980

1990

2000

Year

Tem

pera

ture

oC

Fig. 5.19. Tree-ring based reconstructed Mean July-August temperatures in Zemu Valley; (dotted green line is start year of the reliable time span), actual (green line) and estimated (red line).


87

Fig. 5.20. The comparison of actual and reconstructed Mean temperature of July-August from 1976 to 1996.

Fig. 5.21. Scatter plot of actual and tree-ring reconstructed Mean July-August

temperature with a linear relationship highlighted during the period of 1966–2000.

17.00

17.50

18.00

18.50

19.00

19.50

20.00

20.50

21.00

21.50

22.0019

66

1967

1968

1969

1970

1971

1972

1973

1974

1975

1976

1977

1978

1979

1980

1981

1982

1983

1984

1985

1986

1987

1988

1989

1990

1991

1992

1993

1994

1995

1996

1997

1998

1999

2000

Year

Tem

pera

ture

o C

Actual temperatureReconstructed temperature

r= 0.464, p<0.05

R2 = 0.2362

18.40

18.60

18.80

19.00

19.20

19.40

19.60

19.80

20.00

17.00 17.50 18.00 18.50 19.00 19.50 20.00 20.50 21.00

Actual temperature(0C)

Reco

nstu

cted

tem

pera

ture

(0 C)

a.

b.


88

-4

-3

-2

-1

0

1

2

318

20

1830

1840

1850

1860

1870

1880

1890

1900

1910

1920

1930

1940

1950

1960

1970

1980

1990

2000

Mea

n Te

mpe

ratu

re

0 C(J

uly-

Augu

st)

Fig.5.22. Showing Anomaly in the reconstructed July-August temperature


Table5.5.Statistics of calibration and verification for tree-ring reconstruction of July-August

mean temperature in the common period 1966–2000.

Split-sample calibration-verification Period Calibration R R² Adjusted

R² F Verificatio

n Sign-test

RE RMSE DW

1966-1983 0.470 0.281 0.229 5.464*

1984-2000 11/2- 0.169* 0.540 0.928

1984-2000 0.718 0.527 0.494 15.617*

1966-1983 8/1- 0.441* 0.697 0.723

Full calibration

1966-2000 0.464 0.236 0.190 8.439*

R correlation coefficient, R2 explained variance, F F-test, Sign-test sign of paired observed

and estimated departures from their mean on the basis of the number of

agreements/disagreements, Pmt product mean test, RE reduction of error, RMSE is a

frequently used measure of the differences between values predicted by a model or an

estimator and the values actually observed, DW Durbin–Watson test, * p < 0.05.

Higher temperature than mean values:

1821 1829 1830 1831 1832 1833 1834

1835 1842 1843

1844

1846 1847 1850 1851 1852 1853 1854 1855 1856

1861 1862

1863

1864 1865

1866 1867

1868 1869 1870

1871 1872

1873 1874 1878 1882 1887 1892 1894 1905

1906 1907 1908 1909 1915 1921 1922 1924 1927 1928

1929

1930 1938 1943

1946 1959 1968 1969 1971

1972

1980 1981 1982 1983 1986 1989, 1990

1991

1992 1995

1996 1997 1998 1999

Lower temperature than mean value:

1822 1823 1824 1825 1826 1827

1828 1836 1837, 1838,

1839, 1840 1841 1845 1848, 1849 1857 1858 1859 1860 1875 1876 1877 1879 1880 1881 1883 1884 1885 1886 1888 1889, 1890 1891 1893 1895, 1896, 1897 1898, 1899, 1900, 1901, 1902 1903 1904, 1910 1911 1912 , 1913, 1914, 1916, 1917, 1918, 1919, 1920 1923 1925, 1926, 1931, 1932, 1933 1934, 1935, 1936, 1937, 1939,

1940, 1941,

1942 1944,

1945, 1947, 1948, 1949, 1950, 1951, 1952, 1953, 1954 1955,


90

1956, 1957 1960 1961 1962 1963, 1964 1965, 1966, 1967, 1970 1973 1974,

1975, 1976, 1977, 1978, 1979,

1984,

1985,

1987 1988, 1993, 1994, 2000.

5.5.3. Characteristics of reconstructed temperature

In order to identify whether this climatic reconstruction (July- August)

represents features that are coherent over a large spatial scale, I compared the present

reconstruction data with other tree-ring based temperature reconstructions. The

present reconstruction shows a good coherence with the climatic reconstruction made

from the nearby other site of the Eastern Himalaya. In that region cool late-summer

temperature during 1782-1786, 1830-1831,1899,1993 and 1975 and warmer summers

during 1777-1779, 1817, 1843, 1904-1906, 1926-1927, and 1980-1982 reported by

Bhattachryya et al. (2003) have also matched with the present reconstruction. This

similarity of data also supports the authenticity of the present reconstruction. In order

to identify whether our reconstruction represents features that are coherent over a

large spatial scale, I also compared present data with Tibet and other higher elevation

sites of China. Fan et al. (2009) reconstructed summer temperatures in the central

Hengduan Mountains China, and reported warm episodes during A.D. 1780s, 1810s–

1820s, 1840s–1850s, 1920s–1940s,1990s-present, were are in agreement with warm

periods occurred at 1780s, 1820s–1850s, 1930s and 1990s in the Zemu region. The

most salient finding in the reconstruction of temperature from Zemu glacier valley is

the cooling during the major part of the nineteenth century in comparison to warming

in the 20th century, particularly 1830 to 1870 and 1900 to 2000. These finding are also

well corroborative with study of China. Based on instrumental data and other proxies

(ice cores, tree rings), Wang et al. (2004) showed that temperature anomalies during

the period 1920–1950 are noticeable positive over China throughout the last century,

especially in southwestern China and on the Tibetan Plateau. Warm conditions around

1950, and the cool period around 1970 were also reported in West Sichuan (Shao and

Fan, 1999) and Tibet (Briffa et al., 2001; Brauning and Mantwill, 2004). The

pronounced negative summer temperature trends from 1970 to 1990 on the Tibetan


91

Plateau are probably the consequence of enhanced cloudiness and rainfall at the upper

treeline and thus of increasing monsoon intensity (Brauning and Mantwill, 2004).

5.5.6. Cyclic nature of tree based reconstructed climate records

Wavelet analyses provide insights into recurrent variability within a time

series that has changed in strength and frequency (Gedalof and Smith, 2001b; Rigozo

et al., 2001). These analyses has been undertaken at the interactive website

(www.atoc.colorado.edu/research/wavelets/) developed by Torrence and Compo (1998).A

visual examination of reconstructed series of Maximum March-April temperature and

mean July-August temperature suggests that it has cyclic trends (Fig. 5.23) and Fig

5.24) respectively. For wavelet analysis has confirmed a dominant mode of variability

of less than 16 years and 8 years (Fig. 5.23 and 5.24).


92

Fig. 5.23 (a) Maximum March-April temperature. (b) The wavelet power spectrum.

The power has been scaled by the global wavelet spectrum (at right). The cross-

hatched region is the cone of influence, where zero padding has reduced the variance.

Black contour is the 95% significance level, using a red-noise (autoregressive lag1)

background spectrum. (c) The global wavelet power spectrum (black line).The dashed

line is the significance for the global wavelet spectrum, assuming the same

significance level and background spectrum as in.

Fig. 5.24 (a) July-August mean temperature (b) The wavelet power spectrum. The

power has been scaled by the global wavelet spectrum (at right). The cross-hatched

region is the cone of influence, where zero padding has reduced the variance. Black

contour is the 95% significance level, using a red-noise (autoregressive lag1)

background spectrum. (c) The global wavelet power spectrum (black line). The

dashed line is the significance for the global wavelet spectrum, assuming the same

significance level and background spectrum as in.

Chapter 6 Tree growth and glacier relationship

93

Among several factors controlling glacier dynamics and tree growth, climate

have pivotal role in both cases. Thus there might be relationship between

advancement/retreat with variation of tree growth low/high at least in trees growing

close to the glacier. Based on this assumption in this chapter of dissertation, an

attempt has been made to analyze tree growth/ glacier fluctuations relationship, with

special reference to the Zemu Glacier area of the Eastern Himalaya. Glaciers develop

where mass gain (by snowfall and avalanches) exceeds mass loss (by melting and

calving). Generally Lower temperatures and greater snowfall favor mass gain

(accumulation) and conversely, higher temperatures favor mass loss (ablation). The

sum of accumulation and ablation over any time period is the mass budget. If ablation

dominates over several years, the mass flux is reduced and the glacier starts to retreat

this event could be reflected as increased radial growth rates and the production of

wider annual growth rings. Conversely, if net annual accumulation (positive balance)

dominates for a long time, the glacier flow speed increases and eventually advances.

As mentioned earlier in chapter 1 positive glacier mass balance are detrimental to tree

growth and resulted in narrow annual tree rings.

6.1. Glacier behavior and tree-ring width chronology

The correlation analysis or simple matching of the data was made between the

ring-width chronology of fir (Abies densa ) from Yabuk site close to snout of the

Zemu glacier (Fig. 6.1) and available glacier fluctuation data (Fig.6.2). A reverse

Relationship between Zemu glacier fluctuation and tree-ring width of fir is observed.

In initial phase of correlation diagram (Fig.6.2) reveals average tree-growth is high

during 1976-78(fastest retreat) in second phase when glacier shows advancement in

1988-2000 tree-growth get suppressed and in last phase (2001-2005) again tree

growth increased when again retreat starts. As mentioned earlier in the chapter 2 that

during the last 100 years, the Zemu glacier advanced 1988- 2000 is in bracket years of

the longest period of low tree-growth as indicated in our data also during this period.

The other prominent high growth periods recorded in tree ring data were around

1776-1804 and 2007-2010. The high growth indices during this time span possibly

reflect the retreat of glacier. Since glacier behaviour data are very limited in Zemu

area (Eastern Himalaya), and mass balance records from Himalayan glaciers are


94

extremely rare and of short duration (Zemp et al. 2009) thus to understand that any

relationship existed in longer time scale and considering synchrony in regional glacier

behavior, I simply correlated tree-ring data with regional glacier fluctuation history of

Eastern Himalaya, Central Himalaya (Nepal) and China, which are taken form

published record (Dyurgerov, 2005). It shows that during the twentieth century, most

of the glaciers in the China E.Tien Shan CN0010 (Fig.6.4) have undergone period of

general retreat. Although three pronounced intervals of negative Mass balance (bn)

1988, 1991, and 1994 observed, whereas a positive mass balance has been observed in

1989, 1990, 1992, as a result, glaciers continue to maintain terminal positions. In

matching with the ring-width of fir for these periods shows a reverse relationship, i.e.

higher tree growth observed in 1988, 1991 but after 1992 a continuous increasing

trend of tree growth observed till 1994 and then decreases when glacier exhibit

increasing trend in negative mass balance. In respect to Central Himalayan (Nepal)

glacier, (Lat. 27°42’N, Long. 86°34'E) where data is available for four years from

1996 to 1999 showing continuous negative mass balance (Dyurgerov ,2002), tree-

growth shows increase trend till 1998 and since then a sharp downfall have been

observed both in bn as well tree growth. Changmekhangpu glacier India (Fig.6.3).

having little older records of glacier retreat to that of Tibetan glaciers also shows

similar pattern, where pronounced intervals of negative mass balance are recorded

from 1981 to 1986. Tree-growth shows increasing trend with respect to highest

negative mass balance 1981, 1983, and 1985. These preliminary results show

potential to established relationship between climate, glacier and tree-growth. This

ongoing rapid global warming has a much effect on the Himalayan environment and

this is clearly visible in the rapid retreat of Himalayan glaciers (Dyurgerov and Meier

2005). Data from the higher elevation Himalayan glaciers indicate consistently

negative mass balance values, but the extent to which they can be considered

regionally as representative is not known (Armstrong, R. L. 2010). The 2005 IPCC

statement about the possible disappearance of Himalayan glaciers by 2035. No

evidence was presented that Himalayan glaciers are receding faster than those in other

parts of the world, as in this model only rates of retreat for the Himalayan glacier

were presented. Although simple correlation analysis revels negative relationship with


95

retreat of Zemu glacier and mass balance of chinese and Nepal glaciers i.e.

Significant value were bn1, -0.285, bn2, -0.258, bn3, -0.384 but not at high significant

level, because of short time span of glacier record data. But definitely it gives a clear

indication of trends of negative relationship with the glacier mass balance data that

may further be explained if long term data for common time period may be available.

Fig. 6.1. Photograph showing the Snout position of Zemu Glacier, North Sikkim

(modified after Luitel et al., 2012).


96

-50-40-30-20-10

01020

1976-1978 1988- 2000 2001-2005

Year

Ret

reat

rat

e(m

)

0.850

0.900

0.950

1.000

1.050

Ave

rage

Rin

g-w

idth

inde

x

Average Retreat rate Average Ring-width

Fig.6.2.Tree growth and its relation with Zemu glacier

-450-400-350-300-250-200-150-100-50

0

1981 1982 1983 1984 1985 1986

Net

mas

s B

lanc

e

0.00

0.20

0.40

0.60

0.80

1.00

1.20

1.40

Tree

-rin

g w

idth

Indi

ces

Net mass Blance ABDE_YAB_

Fig.6.3. Tree growth and its relation with Changmekhangpu glacier

Fig.6.4. Comparison of Fir Chronology of ABDE_YAB with available Mass balance data of three Chinese glacier (AD 1988-1995) and Nepal glacier (AD 1996-1999).

-1400.0

-1200.0

-1000.0

-800.0

-600.0

-400.0

-200.0

0.0

200.0

400.0

1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999

Gla

cier

mas

s ba

lanc

e

0.000

0.200

0.400

0.600

0.800

1.000

1.200

Tree

-rin

g in

dex

Himalaya Nepal AX010 NP00005 E.Tien Shan China Gl. #1, E.Br. CN0010 E.Tien Shan China Gl. #1, W.Br. CN0010

E.Tien Shan China Urumqihe S.#1 CN0010 Tree-ring Chronology


97

6.2. Reconstructed temperature and glacier fluctuation

Warm and cold periods in our reconstruction were also generally in phase with

the periods of retreat and advance of the glaciers of this region. The notable

characteristic of the reconstruction was the increase temperature in two time span ie.

from 1830 to 1870 and 1940 to present. It coincided with a growing mean annual

temperature of the Eastern Himalaya region (Shrestha et al., 2010). The higher retreat

of glacier observed during 1976-1978, (-41.25 m), and in recent years 2001-2005, (-

3.17 m) (Raina et al., 2009) are under the bracket year of increased temperature in our

study. In accordance with the decreasing trend of summer temperature, an

advancement of Zemu glacier has been reported in 1988-2000 (7.67m). The cold

period around 1870 to 1900 correspond to Zemu glacier advancement. Hence, tree

rings seem to be a good indicator of past climate and glacier fluctuations in the Zemu

area.

6.2.1. Role of Maximum March-April temperature in fluctuation of Zemu glacier

Geomorphological records reveal that the retreat was very fast for the period

of 1976-1978 at the Zemu glacier (Raina et. al., 2009) which also coincide with the

period of increased maximum March–April temperature, both in reconstructed as well

as in the observed data. During this time span temperatures were higher than the

average value. In contrast, for the period of advancement of glacier i.e., 1988 to 2000;

both the reconstructed as well observed Maximum temperature of March- April was

below to mean i.e. (19.5130 C).

6.2.2. Role of Average July-August temperature in Fluctuation of Zemu glacier

It is of general observation that the increased temperature enhances the rate of

retreat of a glacier and it is opposite for the low temperature. But here, I have

recorded contradictory response among July-August temperature/ tree-growth/glacier

behaviour. As per published record (Raina et. al., 2009) and was mentioned earlier

also in section 2.5.2. that the retreat of Zemu glacier was very fast during 1976-1978

when temperature of July-August at Zemu Valley are below the mean value for the

years 1976, 1977, 1978 (18.7370 C, 18.5730 C, and 19.2970 C respectively). In


98

contrast, for the period of 1988 to 2000, the recorded period of Zemu glacier

advancement (Raina et. al., 2009) temperature of July-August are found high in most

of these years. This relationship i.e., increased temperature here related to glacier

advancement may be due to combined effect of temperature and precipitation during

these months. As per yearly meteorological record of this region, both temperature

and precipitation remain high during July–August. Thus precipitation in the higher

reaches during these months in the form of snow enhances accumulation and increase

snow depth. Moreover increased temperature during these months causes higher

evaporation which also makes the surface temperature low thus weakening the effect

of temperature. As mentioned earlier in the Introduction of this dissertation that the

rapid retreat of this glacier is linked with the global warming but this inverse

relationship recorded with temperature suggest that the July-August temperature does

not play much role in glacier retreat. Thus it appears that the temperature of March-

April, other significant climatic variables limiting the tree growth of this region may

have significant role in glacier advancement/ retreat in comparison to the variation of

summer temperature of this region. In support of this view it has been observed that

increased July-August temperature of prior years are found correlated with the low

March April temperature of following year i.e., current year.

Chapter 7 Dendrohydrological modeling

99

7. 1. Study area of river discharge The river Teesta is one of the main Himalayan Rivers, which originates from the

glaciers of Sikkim at an elevation of over 8,500 m a.s.l. It is being snow fed by the glaciers

Zemu, Changame Khanpu, Talung etc. flows through the states of Sikkim and West Bengal

in Indian Territory and then to Bangladesh (NHPC, 2009). It is a major source of irrigation

and hydropower generation (NHPC, 2009). But an obvious question comes here, is about the

need of enough discharge to support both irrigation and hydropower generation? this is

important because little is known about the long-term properties of the Zemu river at Lachen

hydrologic regime and its principal contributors, snow and glacier melt water. Zemu chu a

first stage of the river originating from Zemu glaciers and generally flows in north-south

direction, at a steeper gradient.

Fig.7.1. Map showing location of tree ring site, meteorological station and discharge gauge station at Lachen, north Sikkim, Eastern Himalaya. For the generation of the map SRTM 30 (digital terrain elevation data set was used)


100

Fig.7.2. Photograph showing “Zemu Chuu” at Lachen North Sikkim.

7.2 Relationships between Tree growth and Climate The correlations between the chronology and climate data have calculated from the

previous November to current year October. Significant correlations were found only of

corresponding period when tree is predictable in the correlation analysis previous year

November to current year October year (P<0.01; Figure 7.5) details has been discussed in

chapter 5. Meanwhile, the correlations between the PDSI and chronology were significant

(P<0.05) only for the month of January, reaching the 95% confidence level (details in chapter


101

8). High elevations snow and ice would not increase soil moisture until the next warm period

arrives.

12.00

17.00

22.00

27.00

32.00

37.00

42.00

1976

1977

1978

1979

1980

1981

1982

1983

1984

1985

1986

1987

1988

1989

1990

1991

1992

1993

1994

1995

1996

Year

Stre

amflo

w(m

3/s)

Fig.7.3. Mean annual variation of “Zemu Chuu” river discharge at Lachen gauging station

(1976-1996) North Sikkim.

0.00

10.00

20.00

30.00

40.00

50.00

60.00

70.00


Stre

amflo

w(m

3 /s)

Fig.7.4. Mean monthly variation of “Zemu Chuu” river discharge at Lachen gauging station

(1976-1996) North Sikkim.


102

0.000

0.100

0.200

0.300

0.400

0.500

0.600

0.700

0.800


Corr

elat

ion

coef

ficie

nts

Discharge 95%CL 99%CL

Fig.7.5. Correlation plot of standard chronologies of ABDE_ZEM with averaged monthly

Discharge data of Lachen data (1977–1996). Horizontal pink line indicates significance level

(p < 0.05) and red line indicate significance level (p < 0.01).

7. 3. The stream flow reconstruction method

Correlation analysis of the discharge-growth relationship showed high significant

positive correlation between the radial growth of ABDE from ZEM site and mean discharge

of January May (Fig.7.5). The highest correlation is between tree rings and mean January-

April disharge (r = 0.708; p < 0.01). Therefore, average January –April discharge is used as

the variable for reconstruction. A simple linear regression model was obtained to reconstruct

Jaunary-April discharge history of the study area (Cook and Kairiukstis, 1990). For

reconstruction of discharge at Lachen, a Linerar Regression Model approach is used. A

transform function model between the tree-ring width index and the January- April discharge

for the calibration (1976-1996) periods.

January-April (mean discharge) = -5.4118605608796+15.6225756538604* TRW_ CRN

Here, mean discharge of March-May represents the reconstructed discharge from

November of the former year to October of the current year; TRW_CRN represents the tree-

ring width index. The correlation coefficient is 0. 708 (N=22, P<0.01), and variance


103

explanation (R 2) is 50.2% The function F test value is 5.521; Split calibration method used

segmented verification results (Table 1) show that it has a high correlation coefficient both in

the calibration and verification periods. It also passed the sign test (P<0.05). Reduction of

error (RE) and PMT are two important statistical tests for paleoclimate reconstruction. The

reliability of the regression model was evaluated by statistics on calibration and verification

periods by using Split-sample Calibration-Verification method. Evaluative statistics provided

for the calibration period are the Pearson correlation coefficient (R), the coefficient of

determination (R2), and F test (F) and for the verification period are Reduction of error (RE),

Sign test (ST), (RMSE) is a frequently used measure of the differences between values

predicted by a model or an estimator and the values actually observed, Product Mean Test

(Pmt) and Durbin-Watson test (DW) (Fritts, 1976; Cook et al., 1994). R2, RE, are all

measures of shared variance between climate and tree rings, and a positive RE is evidence for

a valid regression model (Table 7.1). The sign test counts the number of agreements and

disagreements between the reconstructed and the instrumental climate data, while Pmt

measures the level of agreement between the actual and estimated values and takes into

account the sign and magnitude of departures from the calibration average. The DW statistic

tests for the autocorrelation in the residuals between model and target climate data.

7.4. Variability in reconstructed discharge data

Descriptive statistics of reconnected January-April Discharge history since 1775

(Figure 7.a) data shows, mean discharge was 9.996 standard deviation was 2.602 maximum

discharge 18.366 and minimum discharge 3.446. The reconstructed river discharge series

indicated annual to decadal scale variations (Fig.7.1). The value greater than that of

Maximum discharge (18.366) and lower than that of minimum discharge (3.446) represent

Highest and lowest discharge year (dry years) respectively.

Extremely lowest discharge years (1782, 1799, 1814, 1837, 1838, 1839, 1840, 1848

and 1932, 1933, 1967 1978 and highest discharge years (1776, 1777, 1778 1786, 1787, 1788)

1791, 1792, 1802, 1810, 1819, 1821, 1823, 1824, 1829, 1830, 1833, 1834, 1835, 1842, 1843

1850,1851,1852,1853,1854,1855,1856,1857,1863,1869,1870,1871,1872,1873,1891,1900,190

1,1912,1914,1915,1916,1928,1929,1930,1931,1944,1945,1946,1950, 1958 1975, 1990 were

observed in the reconstruction.


104

2.0

4.0

6.0

8.0

10.0

12.0

14.0

16.0

18.0

20.017

70

1780

1790

1800

1810

1820

1830

1840

1850

1860

1870

1880

1890

1900

1910

1920

1930

1940

1950

1960

1970

1980

1990

2000

Year

Reco

nstru

cted

Stre

amflo

w (m

3 /s) Reconstructed Actual

Fig7.6. Reconstruction of January–April discharges of “Zemu Chuu” at Lachen, North Sikkim since AD 1775. The red line represents

reconstructed while green line represents actual data.


105

R2 = 0.5017

7

8

9

10

11

12

13

14

3 4 5 6 7 8 9 10 11 12 13 14

Actual Streamflow (m3/s)

Reco

nstru

cted

Stre

amflo

w (m

3 /s)

Fig.7.7. (a) The comparison of actual and reconstructed stream flow (January-April) from 1976 to 1996. (b) Scatter plot of actual and tree-ring reconstructed stream flow (January-April) with a linear relationship highlighted during the period of 1976–1996.

Split-sample calibration-verification Calibration

Period R R² F verification

Period Sign-test Pmt RMSE RE DW

1976-1986 0.806 0.649 14.817* 1987-1996 6+/2- 0.350 6.093 0.441 2.496

1987-1996 0.580 0.336 3.518 1976-1986 3+/1- 0.211 2.338 0.125 0.761

1976-1996

0.708 0.502 5.521

R correlation coefficient, R2 explained variance, F F-test, Sign-test sign of paired observed and estimated departures from their mean on the basis of the number of agreements/disagreements, Pmt product mean test, RE reduction of error, RMSE is a frequently used measure of the differences between values predicted by a model or an estimator and the values actually observed, DW Durbin–Watson test * p < 0.05.

3.000

5.000

7.000

9.000

11.000

13.000

15.000

1976

1977

1978

1979

1980

1981

1982

1983

1984

1985

1986

1987

1988

1989

1990

1991

1992

1993

1994

1995

1996

Year

Stre

amflo

w (m

3 /s)

ReconstructedActual

Table 7.1.Statistics of calibration and verification for tree-ring reconstruction of January-April Stream flow


106

High discharge years:

1775, 1776, 1777, 1778, 1779, 1784, 1786, 1787, 1788, 1789, 1790,1791,1792,1793,

1794, 1795, 1796,1797,1798,1801,1802,1803,1805,1806,1808,1809,1810,1812,1818,1819,

1820, 1821, 1822,1823,1824,1827,1828,1829,1830,1832,1833,1834,1835,1842.1843,1850,

1851, 1852, 1853,1854,1855,1856,1857,1863,1869,1870,1871,1872,1873,1878,1879,1882,

1883, 1886, 1888,1889,1890,1891,1892,1897,1898,1900,1901,1902,1907,1908,1911,1912,

1913, 1914, 1915,1916,1925,1927,1928,1929,1930,1931,1944,1945,1946,1947,1948,1949,

1950, 1956, 1957,1958,1959,1960,1961,1970,1971,1972,1974,1975,1976,1979,1981,1986,

1988, 1989, 1990, 1991, 1994, 1995.

Low discharge years:

1780, 1781, 1782,1783,1785,1799,1800,1804,1807,1811,1813,1814,1815,1816,1817,1825,

1826, 1831, 1836,1837,1838,1839,1840,1841,1844,1845,1846,1847,1848,1849,1858,1859,

1860, 1861, 1862,1864,1865,1866,1867,1868,1874,1875,1876,1877,1880,1881,1884,1885,

1887, 1893, 1894,1895,1896,1899,1903,1904,1905,1906,1909,1910,1917,1918,1919,1920,

1921, 1922, 1923,1924,1926,1932,1933,1934,1935,1936,1937,1938,1939,1940,1941,1942,

1943, 1951, 1952,1953,1954,1955,1962,1963,1964,1965,1966,1967,1968,1969,1973,1977,

1978, 1980, 1982, 1983,1984,1985,1987,1992,1993,1996.

7.5. Cyclic nature in reconstructed January-April discharge.

Wavelet analyses provide insights into recurrent variability within a time series that

has changed in strength and frequency (Gedalof and Smith, 2001b; Rigozo et al., 2001). ).A

visual examination of reconstructed series of mean January-April discharge suggests that it

has cyclic trends (Fig. 7.8). Wavelet analysis has confirmed a dominant mode of variability

of less than 16 years and 8 years (Fig. 7.8).


107

Fig.7.8. (a) January-April _Discharge. (b) The wavelet power spectrum. The power has been

scaled by the global wavelet spectrum (at right). The cross-hatched region is the cone of

influence, where zero padding has reduced the variance. Black contour is the 95%

significance level, using a red-noise (autoregressive lag1) background spectrum. (c) The

global wavelet power spectrum (black line). The dashed line is the significance for the global

wavelet spectrum, assuming the same significance level and background spectrum as in.

Chapter 8 Tree growth and its relation with PDSI and El Nino

105

8.1. Tree-growth/glacier fluctuation and El Niño relation To precisely attribute glacier retreat to a particular climate forcing requires

detailed knowledge and understanding of the climatic changes that have taken place in

the 20th century; unfortunately the details of Zemu glacier fluctuation history is

available only for limited time scale. As far as El Niño 3.4 concern with in general,

negative mass balances are correlated with El Niño conditions, while positive mass

balances predominate during La Niña conditions (Vincent et al., 2009). Earlier studies

also supported this, that during negative Southern Oscillation Index values (El Niño,

warm tropical Pacific SSTA in the eastern equatorial Pacific) accumulation is reduced

and ablation is enhanced at a monthly timescale and over longer periods (Francou et

al., 1995a, b, 2000; Ribstein et al., 1995; Wagnon et al., 2001; Francou et al., 2003,

2004; Favier et al., 2004a, b). In this dissertation, the relationship of retreat (retreat is

result of prolonged negative mass balance) and El Niño for Zemu glacier has been

analysed. In this Chapter correlation analysis has been performed between averaged

tree ring data, and average data of El Niño 3.4, and all India rainfall for the period of

Zemu glacier history. Correlation analysis between ABDE_YAB chronology, glacier

fluctuation data and average El Niño 3.4 data shows a negative correlation between

retreat and El Niño (r = -0.942, 90% significance level) and between tree ring width

and El Niño 3.4 also negative correlation has been observed. Relation with average

data of respective period of Zemu glacier history and all India rainfall is positive i.e.

(r = 0.953, 95% Significance level). The more frequent El Niño years between AD

1909-1965, 1965-1975 and 1975-1986 and between AD 1986-2005 may be

responsible for Zemu glacial fluctuation (Fig.8.1, and Table.8.1). However, this

conclusion is not supported by solid evidence because the data set is very limited.

Although correlation between regional chronology of principle component scores

PC#1, PC#2, were negatively correlated but not at significance level. For the use of

glacier fluctuation as a climate proxy it essential to identify the significant climatic

factor controlling their behaviour, i.e. establishing a transfer function between current

climate and glacier variations. However, the accelerated glacier retreat at the end of

the 19th century is in agreement with the results of Torrence and Webster (1999), who


106

used SST in the ELNiño-3 zone (5°S–5°N, 90°–150°W) and monthly precipitation

measurements in India (both available since 1871), and concluded that the period

from 1875 to 1920 was characterized by a high frequency of El Niño events.

-0.400

-0.350

-0.300

-0.250

-0.200

-0.150

-0.100

-0.050

0.000


corr

elat

ion

coef

ficen

ts

PC#1 PC#2 95CL

Fig. 8.1 Correlation values of mean monthly El Niño 3.4 with standard regional

chronologies PC#1 and PC#2. Monthly variables spanning from January to

December. The pink horizontal line indicates 95% confidence limits.

0

5

10

15

20

25

30

35

1909-1965 1965-1975 1975-1986 1986-2005

Gla

cier

retre

at (m

)

-0.20

0.00

0.20

0.40

0.60

0.80

1.00

1.20

1.40

Mea

n da

ta

annual retreat(m) YAB_CRN EL Nino 3.4

Fig. 8.2. Plot of Zemu glacier retreat with mean data of ring-with, and El Nino 3.4 Table 8.1 Correlation value of glacier retreat, mean data of ring-with, and El Nino 3.4

Retreat(m) ABDE_YAB ABDE_YAB 0.402 1.000

El Niño 3.4 -0.942* -0.684*

All India rainfall 0.530 0.953*

* Indicate 95% confidence limit,


107

8.2. Tree growth and its relation with PDSI

Tree growth is also controlled by soil moisture condition, while soil moisture

balance is a result of the integrative effect of precipitation, evapotranspiration,

physical-chemical properties of soil substrate, etc. In order to better understanding of

drought condition it is necessary to take not only precipitation but also other

hydrological meteorological factors. Based on water balance model, Palmer Drought

severity index was developed, considering monthly air temperature, precipitation, and

local available soil water content. In this context to find out the relationship of tree

growth with the Palmer Drought Severity Index (PDSI) of the site (88.75'E 26.25'N).

For that, average tree-ring chronologies of all sites have also been compared with 2.50

x 2.50 gridded Palmer Drought Severity Index (PDSI) datasets (Dai et al., 2004). For

this analysis monthly dataset from AD 1967 to 2000 has been used. The analysed,

relationship of regional chronology i.e PC#1, PC#2 with PDSI has been analysed. The

monthly variables were taken for the twelve-month period i.e., from November of the

previous year to October of the current year’s. Correlation analysis of all ABDE

,JURE, JUSQ chronologies with PDSI, a positive correlation were recorded for almost

all site except ABDE_DOZ and JUSQ_YAB sites where negative correlation was

found, only month of April of JUSQ_YAB (p<0.05). In case of ABDE_ZEM,

previous year December and January were found Positive and significant (p<0.05). In

case of regional chronologies i.e PC#1, PC#2, with the monthly PDSI values of grid

points, showed a strong positive relationship with the respective site chronologies and

PC#1 PC#2. A positive relation correlation was recorded for all months with

significance of January month ((p<0.05) where as with PC#2 a negative correlation

recorded with February –April (Fig.8.3).


108

(a)

-0.300

-0.200

-0.100

0.000

0.100

0.200

0.300


Corr

elat

ion

coef

ficie

nts

PC#1 PC#2 95CL 95CL 99 CL 99 CL

(b) Fig.8.3. Correlation values of mean monthly PDSI of the one grid points with standard regional chronologies of all site and PC#1 and PC#2. (a) All chronologies. (b) PC#1, PC#2. Monthly variables spanning from November of the previous year to October of the current year. The pink horizontal line indicates 95% and red line indicate 99% confidence limits.

-0.300

-0.200

-0.100

0.000

0.100

0.200

0.300


Corr

elat

ion

coef

ficie

nts

ABDE_YUM ABDE_JAK JUIN_JAK JUSQ_YAB ABDE_LACABDE_TAL ABDE_YAB ABDE_ZEM LAGR_RW_LAC ABDE_DOZ95CL 95CL 99 CL

Chapter 9 Discussion & conclusion

112

The research work included this dissertation was carried out using multi-proxy

tree ring parameters i.e., width of tree ring as a whole, early wood and latewood

separately, the data has provided high resolution temporal climate reconstruction for

longer time span (250 year) 1758 to 2010 AD. This reconstructed climate data was

analyzed to seek a long term linkage with El Niño and advancement and retreat of

Zemu glacier. This work is also supplemented with discharge reconstruction of Zemu

Chu (river) originated from Zemu glacier. In this pursuit, a good amount of tree ring

data has been generated from Zemu glacier valley, especially from seven sites of it

through a transect from lower elevation site, Lachen (2,753 m a.s.l) to high elevation

site, Yabuk (3,953 m), however, the latter site is close to the snout of the Zemu

glacier. The present study portrayed that the tree ring width data of several conifers

(Abies densa, Larix grifithiana, Juniperus recurva, and Juniperus squmata) and also

early wood and late wood data of Larix grifithiana were potential for tree ring

analysis. These trees are suitable for the tree ring analysis for their clear, datable tree

ring sequences. In this dissertation twelve tree ring chronology were developed. The

longest chronology from Zemu glacier valley was made from Juniperus recurva

growing at tree line, close to snout of glacier. It covers time span of 1556 to 2010 AD

(456 year). The other chronologies have variable lengths viz., Zema (380 years

extending 1628 AD to 2007AD), Jakthang (311 year extending from 1700 to 2010

AD, Talem from 1678-2010 AD., 333 years and the Juniperus squmata from 1881-

2010 AD 140 years. Thus, except a few, most of these trees were found less than 350

years old. Some trees of Juniper spp are found very old and could be used for

preparing long tree ring chronologies of above 450 years. In addition, Lachen

chronology of LAGR_RW extends form 1733-1994 AD, 262 years along with its

other two chronologies of late wood 17733-1994 AD 262 years and early wood 1733-

1994 AD 262 years were also developed.

Tree-ring chronology statistics i.e. descriptive statistics, of the twelve ring-

width chronologies exhibit low to moderate relationship among all sites. They have

mean sensitivity ranging from 0.103 to 0.222 for ring width and 0.148 to 0.173 for

late wood and early wood respectively. Standard deviation has been found low in all

chronologies ranging from 0.180 to 0.274 for ring width and 0.236 to 0.238 for late

wood and early wood respectively. These chronologies in general display a low year-


113

to-year variability (mean sensitivity, MS), which is typical in agreement with conifers

growing in humid environments.

Percentage of variance and signal/noise ratio which measure strength of signal

common to trees at a site account for the first principal component of tree ring indices

ranging from13.24 to 35.04 and 0.122 to 8.21 respectively. The combination of

statistics discussed above confirms that most of these chronologies are suitable for

growth–climate relationship studies. An overall result of tree growth climate

relationships of high-elevation conifers along an elevation gradient in the Zemu

glacier area North Sikkim, Eastern Himalaya, shows positive relationship to March-

April (i.e. for maximum and Mean temperature) and significant negative reationship

to July-September late summer temperatures. These months are found to be the most

consistent climatic factor limiting radial growth of fir at high to middle elevations.

However, the magnitude of growth responses to climate is species and habitat

specific.

Though several climate variables are found as limiting factors for the tree

growths at the Zemu glacier valley but reconstruction is only possible for March-April

maximum temperature (1821 to 2000 AD) and July to August mean temperature

(1821 to 2000 AD) since others including precipitation were found not much

significant statistically.

Reconstructed July to August mean temperature indicates that years 1907

(20.07°C) and 1837 (18.32°C) were recorded warmest and coolest years respectively.

In longer time span warmer and cool periods were reported during 1830 to 1870 and

1870 to 1900 respectively. In order to identify whether this reconstruction represents

features that are coherent over a large spatial scale. A comparison between tree-ring

based temperature reconstructions of this region with nearby regions. The warm

episodes during A.D. 1780s, 1810s–1820s, 1840s–1850s, 1920s, 1940s,1990s,

reported from Hengduan Mountains China (Fan et al., 2009) were in agreement with

the warm periods occurred at 1780s, 1820s–1850s, 1930s and 1990s in my present

study. The most salient finding in the reconstruction is the cooling during the major

part of the nineteenth century in comparison to warming in the 20th century.

The reconstructed data of the Maximum temperature for March _April extends

back to since 1821. The reconstructed temperature series for the last 180 years

showed annual to multiyear fluctuations punctuated with cool and warm periods 1822


114

to 1828 1836 to1841, 1857 to 1865 1875 to1889 1891to1904 1911 to 1986 warmer

years and cool years are during 1832 to 1835, 1840 to 1874 1905 to1910 1915 to

1922, 1959 to 1962, 1966 to 1874, 1905 to 1910, 1964 to 1878, 1982 to 2000.

Tree-ring record of 250 yrs from this site provides a new insight towards

understanding the dynamic behavior of the monsoonal glacier in the Eastern

Himalaya in relation to climate change. Tree-ring width index are low during AD

1988-2000 which is synchronous to a phase of glacier advancements. Similarly, higher

during 1976-1978, 2001-2005, that correspond to the periods of rapid retreat of

glaciers.

River discharge reconstruction of Zemu River at Lachen which originating

from the Zemu glacier is the pioneer attempt from the Eastern Himalaya. The

reconstruction of discharge is vital for the understanding of long term river discharge

fluctuation from Eastern Himalaya and significant for water resource management

Based on tree ring width chronology of Fir as proxy, January–April discharge was

reconstructed which extended back to AD 1775. In present study extremely lowest

discharge years (1782, 1799, 1814, 1837,1838,1839,1840, 1848 and 1932, 1933, 1967

1978 and highest discharge years (1776, 1777, 1778 1786, 1787, 1788) 1791, 1792,

1802, 1810, 1819, 1821,1823, 1824, 1829, 1830, 1833, 1834, 1835, 1842, 1843

1850,1851,1852,1853,1854,1855,1856,1857,1863,1869,1870,1871,1872,1873,1891,

1900, 1901, 1912, 1914, 1915, 1916, 1928, 1929, 1930, 1931, 1944, 1945, 1946,

1958, 1975, 1990 were observed based on the reconstruction. The relation with tree-

ring index and the monthly PDSI data were developed for this region the tree-ring

chronologies of fir and monthly or seasonal PDSI were positively correlated for all

month starting from previous November to current year October, but was statistically

significant for only the month of January.

Correlation analysis between ABDE_YAB chronology and with the glacier

fluctuation (advance), positive mass balance and average El Niño 3.4 data show a

negative correlation. Correlation between retreat and El Niño is -0.942 (90%

significance level) where as between tree ring width and El Niño 3.4 also negative

correlation. It has been observed that period of Zemu glacier history (advance) and all

India rainfall is positive i.e. r = 0.953 (95% Significance level). The more frequent El

Niño years between AD 1909-1965, 1965-1975 and 1975-1986 and between AD 1986


115

to 2005 may be responsible for Zemu glacial fluctuation (retreat). However, this

conclusion is based on limited data.

9.1 Further work for the improvement of research work A comparison of the tree-ring and glacier fluctuations (advancement /retreat or

mass balance positive/ negative) records indicates that the trees in the area are much

sensitive and respond to glacier fluctuations. In the absence of long term instrumental

records, they permit exploration of the relative contribution of changes in temperature

to net mass balance. An overall assessment of analyses of climate, stream flow and El

Niño signatures in Zemu glacier area presented in the present research work appears

to have great promise and should look forward to a more comprehensive analysis

based on development of tree-ring data in both spatial and temporal coverage close to

the snout of the glaciers of the Eastern Himalaya. Because of the complex landscape

in this mountainous region, more tree-ring data are required for better understanding

of the regional climate variability. Though careful interpretation and selection is

required, tree-ring based proxy records of climate would allow the reconstruction of

continuous records of glacier. The substantial glacier recession of this century has

continued to expand the opportunities for advancing dendroglaciological studies by

exposing the remains of tree/shrubs buried by glacier advances at various times

during Little Ice Age or other period of advances during the Holocene. These findings

would help for the development of millennia-long tree-ring chronologies in glaciated

sites. The development of long tree-ring chronologies also allows for comparison of

accurately dated deposits over large areas, essential for assessing the synchroneity of

global glacier activity to climate forcing mechanisms at different timescales. Due to

increased human pressure in the Himalayas it is hard to find wood pieces in the

exposed sediments as are these used as fuel or for construction purposes. Though it is

difficult but with the extensive field survey there may be chances to get buried wood

or old trees. A detailed analysis through multidisciplinary approach would bring out

valuable information regarding glacier advancement and retreat in greater details from

the Himalyan region.

Chapter 10 Bibliography

116

Attri, S. D., Tyagi, A., 2010. Climate Profile of India. Contribution to the Indian Network of Climate Change Assessment (National Communication-II), Ministry of Environment and Forests 1501, 1-129.

Barclay, D.J., Wiles, G.C., Calkin, P.E., 2009. Tree-ring cross dates for a First Millennium AD advance of Tebenkof Glacier, southern Alaska. Quaternary Research, 71, 22–26.

Bhattacharyya, A., LaMarche Jr, V. C., Telewski, F. W., 1988. Dendrochronological reconnaissance of the conifers of northwest India. Tree-Ring Bulletin 48, 21-30.

Bhattacharyya, A., Yadav, R. R., Borgaonkar, H. P., Pant, G. B., 1992.Growth-ring analysis of Indian tropical trees; dendroclimatic potential. Current Science 62, 736–741.

Bhattacharyya, A., Yadav, R.R., 1996. Dendrochronological reconnaissance of Pinus wallichiana to study glacial behaviour in the western Himalaya. Current Science70, 739–744.

Bhattacharyya, A., Yadav, R. R., 1999. Climatic reconstructions using tree-ring data from tropical and temperate regions of India—a review. IAWA Journal 20, 311-316.

Bhattacharyya A., Chaudhary., V., 2003. Late-summer temperature reconstruction of the Eastern Himalayan region based on tree-ring data of Abies densa Arctic. Antarctic Alpine research 35, 196 – 202.

Bhattacharyya, A., Shah, S. K., Chaudhary, V., 2006. Would tree-ring data of Betula utilis have potential for the analysis of Himalayan glacial fluctuations? Current Science 91, 754–761.

Bhattacharyya, A., Eckstein, D., Shah, S.K., Chaudhary, V., 2007.Analyses of climatic changes around Parambikulam, south India, based on earlywood mean vessel area of teak. Current Science 93, 1159–1164.

Biondi, F., Lange, C. B., Hughes, M. K., Berger, W. H., 1997. Inter-decadal signals during the last millennium (AD 1117–1992) in the Varve record of Santa Barbara Basin, California. Geophysical Research Letters, 24, 193-196. Biondi, F., 2000. Are climate-tree growth relationships changing in north-central idaho, USA?. Arctic, Antarctic, and Alpine Research, 111-116. Borgaonkar, H.P., Pant, G.B, Rupa Kumar. K., 1994. Dendroclimatic reconstruction of summer precipitation at Srinagar Kashmir India since the late 18th century. The Holocene 4, 299–306.


117

Borgaonkar, H. P., Pant, G. B., Rupa Kumar, K., 1996. Ring‐width variations in Cedrus deodara and its climatic response over the Western Himalaya. International Journal of climatology 16, 1409-1422.

Borgaonkar, H. P., Pant, G. B., Kumar, K. R., 1999. Tree-ring chronologies from Western Himalaya and their dendroclimatic potential IAWA J 20, 295–309.

Borgaonkar, H. P., Ram, S., Sikder, A. B., 2009. Tree-ring analysis of high elevation Cedrus deodara D. Don from Western Himalaya in relation to climate and glacier fluctuations. Dendrochronologia 27, 59-69.

Borgaonkar, H. P., Sikder, A. B., Ram, S., Pant, G. B., 2010. El Niño and related monsoon drought signals in 523-year-long ring width records of teak (Tectona grandis L.F.) trees from south India. Palaeogeography, Palaeoclimatology, Palaeoecology 285, 74-84.

Borgaonkar, H. P., 2011. Dendroclimatology and climate change: Indian perspective. Journal of the Indian Academy of Wood Science 8, 52-61.

Brauning, A., 2001. Combined view of various tree ring parameters from different forest habitats in Tibet for the reconstruction of seasonal aspects of Asian Monsoon variability, Palaeobotanist 50, 1–12.

Bräuning, A., Mantwill, B., 2004. Summer temperature and summer monsoon history on the Tibetan plateau during the last 400 years recorded by tree rings. Geophysical Research Letters 31, 24.

Braeuning, A., 2006. Tree-ring evidence of "Little Ice Age" glacier advances in southern Tibet. The Holocene 16, 369–380.

Briffa, K. R., Jones, P. D., Schweingruber, F. H., 1992. Tree-ring density reconstructions of summer temperature patterns across western North America since 1600. Journal of Climate 5, 735-754. Briffa, K. R., Jones, P. D., 1993. Global surface air temperature variations during the twentieth century: Part 2, implications for large-scale high-frequency palaeoclimatic studies. The Holocene 3, 77-88. Briffa, K. R., Jones, P. D., Hulme, M., 1994. Summer moisture variability across Europe, 1892–1991: an analysis based on the Palmer drought severity index. International Journal of Climatology 14, 475-506.


118

Briffa, K. R., 1995, Interpreting high-resolution proxy climate data the example of dendrochronology, in Analysis of Climate Variability: Applications of Statistical Techniques, edited by H. von Storch and A. Navarra, 77– 94, Springer-Verlag, New York.

Briffa, K. R., Osborn, T. J., 1999, Seeing the wood from the trees, Science 284, 926–927.

Briffa, K. R., Osborn, T. J., Schweingruber, F. H., Harris, I. C., Jones, P. D., Shiyatov, S. G., Vaganov, E. A., 2001. Low-frequency temperature variations from a northern tree ring density network. Journal of Geophysical Research, 106, 2929–2941.

Briffa, K. R., Osborn, T. J., 2002, Blowing hot and cold, Science 295, 2227–2228.

Briffa, K. R., Schweingruber, F. H., Jones, P. D., Osborn, T. J., Shiyatov, S. G., Vaganov, E. A., 1998. Reduced sensitivity of recent tree-growth to temperature at high northern latitudes. Nature 391, 678–682. Buckley, B. M., Palakit, K., Duangsathaporn, K., Sanguantham, P., Prasomsin, P., 2007. Decadal scale droughts over northwestern Thailand over the past 448 years: links to the tropical Pacific and Indian Ocean sectors. Climate Dynamics 29, 63–71.

Büntgen, U., Frank, D. C., Schmidhalter, M., Neuwirth, B., Seifert, M., Esper, J. 2006. Growth/climate response shift in a long subalpine spruce chronology. Trees, 20, 99–110.

Carrer, M., Urbinati, C., 2004. Age-dependent tree-ring growth responses to climate in Larix decidua and Pinus cembra. Ecology 85, 730–740.

Chabot, B. F., Hicks, D. J., 1982. The ecology of leaf life spans. Annual Review of Ecology and Systematics, 13, 229–259.

Chalise, S. R., Kansakar, S. R., Rees, G., Croker, K., Zaidman, M. (2003). Management of water resources and low flow estimation for the Himalayan basins of Nepal. Journal of Hydrology 282, 25-35. Chang, C. P., Harr, P., Ju, J., 2001. Possible roles of Atlantic circulations on the weakening Indian monsoon rainfall-ENSO relationship. Journal of Climate 14, 2376-2380. Chaudhary, V., Bhattacharyya, A., Yadav, R.R., 1999. Tree-ring studies in the Eastern Himalaya. IAWA J 20, 317–324.


119

Cook, E.R, Kairiukstis, L.A., 1990. Methods of dendrochronology: applications in the environmental sciences. Kluwer Academic Publishers, Dordrecht 394 pp.

Cook, E.R., Peters, K., 1981. The smoothing spline: a new approach to standardizing forest interior tree-ring series for dendroclimatic studies. Tree-ring Bulletin 41, 45-53 Cook, E.R., 1985. A Time Series Approach to Tree-ring Standardisation. Ph.D. thesis. University of Arizona, Tucson, Arizona USA. Cook, E. R., Meko, D. M., Stahle, D. W., Cleaveland, M. K., 1999. Drought reconstructions for the continental United States. Journal of Climate, 12, 1145-1162.

Cook, E. R., Buckley, B. M., D'Arrigo, R. D., Peterson, M. J., 2000. Warm-season temperatures since 1600 BC reconstructed from Tasmanian tree rings and their relationship to large-scale sea surface temperature anomalies. Climate Dynamics, 16, 79-91.

Cook, E. R., Krusic, P. J., Jones, P. D., 2003. Dendroclimatic signals in long tree-ring chronologies from the Himalayas of Nepal. International Journal of Climatology, 23, 707-732. Cook, E. R., Woodhouse, C. A., Eakin, C. M., Meko, D. M., Stahle, D. W., 2004. Long-term aridity changes in the western United States. Science 306, 1015-1018. Cook, E. R., Anchukaitis, K. J., Buckley, B. M., D’Arrigo, R. D., Jacoby, G. C., Wright, W. E., 2010. Asian monsoon failure and megadrought during the last millennium. Science, 328, 486-489. . Cooper, W. S., 1916. Plant successions in the Mount Robson Region, British Columbia. The Plant World 19, 211–238.

Cropper, J.P., 1985. Tree-Ring Response Functions: An Evaluation by Means of Simulations. Ph.D dissertation, The University of Arizona, Tucson. University Microfilms International, Ann Arbor. Cruz, R., 2007. Asia. Pages 469–506 in M. Parry, et al. editors. Climate change 2007: impacts, adaptation and vulnerability. Contribution of Working Group II to the Fourth assessment report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge, United Kingdom.


120

Dai, A., Trenberth, K. E., Qian, T., 2004. A global dataset of Palmer Drought Severity Index for 1870-2002: Relationship with soil moisture and effects of surface warming. Journal of Hydrometeorology 5, 1117-1130. Dai, A., 2011. Characteristics and trends in various forms of the Palmer Drought Severity Index during 1900–2008. Journal of Geophysical Research: Atmospheres (1984–2012), 116, D12115, doi:10.1029/2010JD015541. D'Arrigo, R. D., Jacoby, G. C., Krusic, P. J., 1994. Progress in dendroclimatic studies in Indonesia. Terrestrial, Atmospheric and Oceanic Sciences, 5, 349-363. D'Arrigo, R., Wilson, R., Jacoby, G., 2006. On the long-term context for late twentieth century warming. Journal of Geophysical Research, 111, D03103. Druckenbrod, D. L., Mann, M. E., Stahle, D. W., Cleaveland, M. K., Therrell, M. D., Shugart, H. H., 2003. Late-eighteenth-century precipitation reconstructions from James Madison's Montpelier plantation. Bulletin of the American Meteorological Society 84, 57-71. Dyurgerov, Mark., 2002. "Glacier Mass Balance and Regime: Data of Measurements and Analysis." INSTAAR Occasional Paper No. 55, ed. M. Meier and R. Armstrong. Boulder, CO: Institute of Arctic and Alpine Research, University of Colorado. Distributed by National Snow and Ice Data Center, Boulder, CO. Efron, B and Gong, G., 1983. A leisurely look at the bootstrap, the jackknife, and cross-validation. The American Statistician 37, 36 – 48. Efron, B., 1979. Bootstrap methods: another look at jackknife. The Annals of Statistics 7, 1 – 26. Efron, B., 1982. The Jackknief, the bootstrap and other resampling plans. Society for Industrial and Applied Mathematics, Providence, RI, pp. 92. Efron, B., 1983, Estimating the Error Rate of a Prediction Rule: Improvement of Cross-Valuation. Journal of the American Statistical Association 78, 316–331. Efron, B., & Tibshirani, R. J. (1993). Confidence intervals based on bootstrap “tables,”. An Introduction to the Bootstrap. London, UK, Chapman and Hall/CRC, 160-162.


121

Efron, B. and Tibshirani, R.J.1997 "Improvements on cross-validation: The .632+ bootstrap method," Journal of the American Statistical Association, 92, 548-560. Esper J, Cook, E.R., Schweingruber. F.H., 2002. Low-frequency signals in long tree-ring chronologies for reconstructing past temperature variability. Science 295, 2250–2253 Fan, Z.X, Bräuning, A, Bao Y, Cao K.F., 2009. Tree ring density-based summer temperature reconstruction for the central Hengduan Mountains in southern China. Glob Planet Change 65, 1–11 Favier, V., Wagnon, P., Chazarin, J-P., Maisincho, L., Coudrain, A., 2004. One-year measurements of surface heat budget on the ablation zone of Antizana Glacier 15, Ecuadorian Andes. Journal of Geophysical Research 109, D18105. doi:10.1029/ 2003JD004359.

Folland, C. K., Karl,T. R., Christy, J. R., Clarke, R. A., Gruza, G. V., Jouzel, J., Mann, M. E., Oerlemans, J., Salinger, M. J., Wang, S. W., 2001, Observed climate variability and change, in Climate Change 2001: The Scientific Basis, edited by J. T. Houghton et al., pp. 99–181, Cambridge Univ. Press, New York.

Francou, B., Ramírez, E., Cáceres, B., Mendoza, J., 2000. Glacier evolution in the Tropical Andes during the last decades of the 20th century: Chacaltaya, Bolivia, and Antizana, Ecuador. Ambio 29, 416–422.

Francou, B., Vuille, M., Wagnon, P., Mendoza, J., Sicart, J. E., 2003. Tropical climate change recorded by a glacier in the central Andes during the last decades of the twentieth century: Chacaltaya, Bolivia, 16 S. Journal of Geophysical Research 108, 4059 DOI:10.129/2002JD002473.

Fritts HC (1976) Tree-rings and climate. Academic Press, London pp 567

Fritts, H.C, Guiot, J., Gordon, G.A., 1990.Verification. In: Cook ER, Kairiukstis LA (eds) Methods of dendrochronology: applications in the environmental sciences. Kluwer, Dordrecht, pp 178–185.

Fritts, H. C., 1991, Reconstructing Large-scale Climatic Patterns From Tree-Ring Data, 286 pp., Univ. of Ariz. Press, Tucson. Fritts, H. C. 1976 Tree Rings and Climate. Academic Press, London.


122

Fritts, H. C., Blasing, T. J., Hayden, B. P., Kutzbach, J. E., 1971. Multivariate techniques for specifying tree-growth and climate relationships and for reconstructing anomalies in paleoclimate. Journal of applied meteorology, 10, 845-864. Garavaglia, V., Pelfini, M., Bollati, I., 2010. The influence of climate change on glacier geomorphosites: the case of two Italian glaciers (Miage Glacier, Forni Glacier) investigated through dendrochronology. Geomorphologie- Relief Processus Environnement 153–164.

Gedalof, Z. E., Smith, D. J., 2001. Interdecadal climate variability and regime-scale shifts in Pacific North America. Geophysical Research Letters 28, 1515-1518. Gindl, W., 1999. Climatic significance of light rings in timberline spruce, Picea abies, Austrian Alps. Arctic, Antarctic, and Alpine Research 242-246. Gou X., Chen F., Yang M., Jacoby, G., Peng J., Zhang Y., 2006. A comparison of tree-ring records and glacier variations over the past 700 years, northeastern Tibetan Plateau. Annals of Glaciology 43, 86-90. Gou, X., Chen, F., Cook, E., Jacoby, G., Yang, M., Li, J., 2007. Streamflow variations of the Yellow River over the past 593 years in western China reconstructed from tree rings. Water Resources Research 43, W06434. Graybill, D. A., Idso, S. B., 1993. Detecting the aerial fertilization effect of atmospheric CO2 enrichment in tree-ring chronologies. Global Biogeochemical Cycles, 7(1), 81–95. Guiot, J., 1985. The extrapolation of recent climatological series with spectral canonical regression. Journal of Climatology 5,325–335.

Guiot, J., 1990. Methods of calibration. In: Methods of Dendrochronology: Application to Environmental Sciences, Cook, E., Kairiukstis, L., (Eds.) pp. 165–178. Kluwer Academic Press and IlASA, Dordrecht.

Guiot, J., 1991. The bootstrapped response function. Tree-ring bulletin, 51, 39–41. Guiot, J., Goeury, C., 1996. PPPBASE, a software for statistical analysis of paleoecological and paleoclimatological data. Dendrochronologia, 14, 295–300.


123

Hardman, G., Reil, O.E., 1936. The relationship between tree-growth and stream runoff in the Truckee River basin, California-Nevada. The University of Nevada Agricultural Research Station, Bulletin No.141, pp. 1–38. Havranek, M., Tranquillini, W., 1995. Physiological processes during their winter dormancy and their ecological significance, in: Ecophysiology of coniferous forest, edited by: Smith, W. K. and Hinkley, T. M., Academic Press, New York, USA, 95–124

Hawley, F.M., 1937. Relationship of southern cedar growth to precipitation and runoff. Ecology 8,398-405.

Holmes, R.L., 1983.Computer-assisted quality control in tree-ring dating and measurement. Tree-Ring Bull 43, 69–78.

Hughes, M. K., Graumlich, L. J., 1996. Multimillennial dendroclimatic studies from the western United States. Nato Asi Series I, Global Environmental Change 41, 109-124.

Hughes, M.K., Kelly, P.M., Pilcher, J.R., LaMarche, V.C. Jr. (Eds.) 1982. Climate from Tree Rings. Cambridge University Press, Cambridge. 233 pp.

India State of Forest Report, 2011. Forest Survey of India, (Ministry of Environment and Forests) Government of India, Dehradun. 214-218 pp.

Metz, B., Davidson, O., De Coninck, H. C., Loos, M., Meyer, L. A. 2005. IPCC, 2005. IPCC special report on carbon dioxide capture and storage. Prepared by Working Group III of the Intergovernmental Panel on Climate Change. Cambridge, United Kingdom and New York, NY, USA, 442 pp.

IPCC, 2007: Climate Change 2007: The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change [Solomon, S.D., Qin, M., Manning, Z., Chen, M., Marquis, K.B., Averyt, M., Tignor, H.L., Miller (Eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 996 pp.

Isdale, P. J., Stewart, B. J., Tickle, K. S., Lough J. M., 1998. Paleohydrological variation in a tropical river catchment: A reconstruction using flourescent bands in corals of the Great Barrier Reef, Australia, Holocene 8, 1–8.


124

Jacoby Jr, G. C., D'Arrigo, R., 1989. Reconstructed Northern Hemisphere annual temperature since 1671 based on high-latitude tree-ring data from North America. Climatic Change, 14, 39-59. Jacoby, G. C., D’Arrigo. R. D., 1990. Teak (Tectona grandis L. F.), a tropical species of large scale dendroclimatic potential, Dendrocronologia 8, 83–98. Jones, P. D., Mann, M. E., 2004. Climate over past millennia. Reviews of Geophysics 42, RG2002.

Karl, T. R., Knight, R. W., 1998. Secular trends of precipitation amount, frequency, and intensity in the United States. Bulletin of the American Meteorological society, 79, 231-241. Kaspari S, Mayewski P, Kang S, Sneed S, Hou S, Hooke R, Kreutz K, Introne D, Handley M, Maasch K, Qin D, Ren J., 2007. Reduction in northward incursions of the South Asian monsoon since 1400 A.D. inferred from a Mt. Everest ice core. Geophys Res Lett 34:L16701. doi:10.1029/2007GL030440

Kern, Z., Popa, I., 2007. Climate–growth relationship of tree species from a mixed stand of Apuseni Mts., Romania. Dendrochronologia, 24,109-115.

Körner, C., 1998. A re-assessment of high elevation treeline positions and their explanation. Oecologia 115, 445–459.

LaMarche V.C., Mooney, H.A., 1967. Altithermal timberline advance in western United States. Nature 213, 980–982

LaMarche, V. C., Fritts, H. C., 1971. Tree rings, glacial advance, and climate in the Alps. Zeitschrift fur Gletscherkunde und Glazialgeologie 7, 125–131.

LaMarche, V. C., 1973. Holocene climatic variations inferred from treeline fluctuations in the White Mountains, California. Quaternary Research, 3, 632–660

LaMarche, V. C., 1974. Paleoclimatic Inferences from Long Tree-Ring Records Intersite comparison shows climatic anomalies that may be linked to features of the general circulation. Science 183, 1043–1048.

Li, J., Xie, S. P., Cook, E. R., Huang, G., D'Arrigo, R., Liu, F., Zheng, X. T., 2011. Interdecadal modulation of El Niño amplitude during the past millennium. Nature Climate Change, 1, 114–118.

http://dx.doi.org/10.1029/2007GL030440


125

Li, J., Xie, S. P., Cook, E. R., Morales, M. S., Christie, D. A., Johnson, N. C., Chen, F., D’Arrigo, R., Fowler, A.M., Gou, X ., Fang, K., 2013. El Nino modulations over the past seven centuries. Nature Climate Change 2, 1–5

Liang, E., Shao, X., Eckstein, D., Huang, L., Liu, X. 2006. Topography- and species-dependent growth responses of Sabina przewalskii and Picea crassifolia to climate on the northeast Tibetan Plateau. Forest Ecology and Management, 236, 268–277.

Liang, E., Shao, X., Qin, N., 2008. Tree-ring based summer temperature reconstruction for the source region of the Yangtze River on the Tibetan Plateau. Global and Planetary Change 61, 313–320.

Luckman, B. H., 1988. Dating the moraines and recession of Athabasca and Dome Glaciers, Alberta, Canada. Arctic and Alpine Research 20, 40–54

Luckman, B.H., 1994. Glacier fluctuation and tree-ring records for the last millennium in the Canadian Rockies. Quaternary Science Reviews 12, 441-450.

Luckman, B.H., 1995. Calendar-dated, early 'Little Ice Age' glacier advance at Robson Glacier, British Columbia, Canada. The Holocene, 5,149–159.

Luckman, B.H., 1996. Dendroglaciology at Peyto Glacier, Alberta, Canada. In: J.S. Dean, D.M. Meko, and T.W. Swetnam, eds., Tree Rings, Environment, and Humanity. Radiocarbon 1996, 679–688.

Luckman, B. H., Briffa, K. R., Jones, P. D., Schweingruber, F. H., 1997. Tree-ring based reconstruction of summer temperatures at the Columbia Icefield, Alberta, Canada, AD 1073-1983. The Holocene, 7, 375-389.

Luckman, B. H., 2000. The Little Ice Age in the Canadian Rockies. Geomorphology 32, 357–384.

Luitel, K. K., Shrestha, D. G., Sharma, N. P., Sharma, R. K., 2012. Impact of Climate Change on East-Rathong Glacier In Rangit Basin, West Sikkim. Climate Change in Sikkim Patterns, Impacts and Initiatives. Information and Public Relations Department, Government of Sikkim, Gangtok.

Managave, S. R., Sheshshayee, M. S., Borgaonkar, H. P., Ramesh, R., 2010. Past break‐monsoon conditions detectable by high resolution intra‐annual δ18O analysis of teak rings. Geophysical Research Letters 37, 5.


126

Managave, S. R., Sheshshayee, M. S., Borgaonkar, H. P., Ramesh, R., 2010. Intra-annual oxygen isotope variations in central Indian teak cellulose: possibility of improved resolution for past monsoon reconstruction. Current Science, 98, 930-937.

Mani, A., 1981., The climate of the Himalayas. In: Lall, J.S., Moddie, A.D. (Eds). The Himalayas–Aspects of Change, Oxford University Press. 3-15 pp.

Mann, M. E., 2001. Climate during the past millennium. Weather, 56, 91-102.

Mann, M. E., Park, J., 1994. Global‐scale modes of surface temperature variability on interannual to century timescales. Journal of Geophysical Research: Atmospheres (1984–2012), 99(D12), 25819-25833.

Mann, M. E., Bradley, R. S., Hughes, M. K., 1998. Global-scale temperature patterns and climate forcing over the past six centuries. Nature, 392, 779-787.

Masters, T., 1995. Advanced Algorithms for Neural Networks: A C++ Sourcebook, NY: John Wiley and Sons, ISBN 0-471-10588-0

Matthews, F., 1939. Report of Committee on Glaciers, April 1939, Eos Trans. AGU, 20, 518–523. Matthews, J. A., 1977. Glacier and climatic fluctuations inferred from tree-growth variations over the last 250 years, central southern Norway. Boreas 6, 1–24.

Mathews, W. H., 1951. Historic and prehistoric fluctuations of alpine glaciers in the Mount Garibaldi map-area, southwestern British Columbia. Journal of Geology 59, 357–380.

Mayewski, P.A., Jeschke, P.A., 1979. Himalayan and Trans- Himalayan glacier fluctuations since A.D. 1812. Arctic and Alpine Research 11, 267 – 287.

Mayewski, P. A., Pregent, G. P., Jeschke, P. A., Ahmad, N., 1980. Himalayan and Trans-Himalayan glacier fluctuations and the south Asian monsoon record. Arctic and Alpine Research, 171-182.

Meko, D. M., 1981. Applications of Box-Jenkins methods of time series analysis to the reconstruction of drought from tree rings, Ph.D. thesis, 149 pp., Univ. of Ariz., Tucson.


127

Meko, D., Cook, E. R., Stahle, D. W., Stockton, C. W., Hughes, M. K., 1993. Spatial patterns of tree-growth anomalies in the United States and southeastern Canada. Journal of Climate, 6, 1773-1786. Meko, D.M., Graybill, D.A., 1995. Tree-ring reconstruction of upper Gila River discharge. Water Resour. Bull. 314, 605–616. Meko, D., Graybill, D. A., 1995. Tree‐ring reconstruction of upper gila rwer discharge. JAWRA Journal of the American Water Resources Association 31, 605-616. Meko, D. M., Therrell, M. D., Baisan, C. H., Hughes, M. K., 2001. Sacramento River flow reconstructed to AD 869 From Tree Rings. JAWRA Journal of the American Water Resources Association 37, 1029-1039. Pant, G. B., Kumar, K. R., Borgaonkar, H. P., Okada, N., Fujiwara, T., & Yamashita, K., 2000. Climatic response of Cedrus deodara tree-ring parameters from two sites in the western Himalaya. Canadian Journal of Forest Research 30, 1127-1135. Payette, S., Fillion, L., Delwaide, A., Begin, C., 1989. Reconstruction of tree-line vegetation response to long-term climate change. Nature 341, 429–432

Pederson, N., Jacoby, G., D’Arrigo, R., Cook, E., Buckley, B., Dugarjav, C., Mijiddorj, R., 2001. Hydrometeorological reconstructions for northeastern Mongolia derived from tree rings: 1651-1995. Journal of Climate 14, 872-881. Porter, S. C., 1981. Glaciological evidence of Holocene climatic change. In Climate and history–studies in past climates and their impact on man (T. M. L. Wigley, M. J. Ingram and G. Farmer, Eds.), 82–110 pp. Cambridge University Press,Cambridge. Pumijumnong, N., Eckstein, D., Sass, U., 1995. Tree-ring research on Tectona grandis in Northern Thailand. IAWA J 16, 385–392 Raina, V. K., 2009. Himalayan glaciers: a state-of-art review of glacial studies, glacial retreat and climate change. Himalayan glaciers: a state-of-art review of glacial studies, glacial retreat and climate change. Somaru, R., Borgaonkar, H. P., Sikder, A. B., 2011. Growth and climate relationship in teak trees from Conolly’s plot, South India. Current Science 100, 630-633.


128

Ram, S., Borgaonkar, H.P., Sikder, A.B., 2008.Tree-ring analysis of teak (Tectona grandis LF) in Central India and its probable linkage with moisture fluctuation: a case study. Journal of Earth System Science 117, 637–645

Ram, S., Borgaonkar, H. P., Sikder, A. B., 2010. Varying strength of the relationship between tree-rings and summer month moisture index (April–September) over central India: a case study. Quaternary International 212, 70-75.

Ram, S., 2012. Tree growth–climate relationships of conifer trees and reconstruction of summer season Palmer Drought Severity Index (PDSI) at Pahalgam in Srinagar, India. Quaternary International 254, 152-158.

Ram, S., 2012. On the recent strengthening of the relationship between Palmer Drought Severity Index and teak Tectona grandis tree-ring width chronology from Maharashtra, India: A case study. Quaternary International 248, 92-97.

Ramesh, R., Bhattacharyya, S.K., Pant, G.B., 1989. Climatic significance of dD variation in a tropical tree species from India. Nature 337,149–150

Rao, R.K, Juneja, K.B.S., 1971. A handbook for field identification of fifty important timbers of India. Publication of Forest Research Institute and colleges, Dehra Dun .

Ribstein, P., Tiriau, E., Francou, B., Saravia, R.,1995. Tropical climate and glacier hydrology: acase study in Bolivia. Journal of Hydrology 165, 221–234.

Rossi, S., Deslauriers, A., Anfodillo, T., Morin, H., Saracino, A., Motta, R., Borghetti, M., 2006. Conifers in cold environments synchronize maximum growth rate of tree-ring formation with day length. New Phytologist 170, 301-310.

Sano, M., Buckley, B. M., & Sweda, T., 2009. Tree-ring based hydroclimate reconstruction over northern Vietnam from Fokienia hodginsii: eighteenth century mega-drought and tropical Pacific influence. Climate dynamics 33, 331-340. Schulman, E., 1945. Runoff histories in tree rings of the Pacific slope. The Geographical Review 5, 59-73. Schulman, E., 1951. Tree-ring indices of rainfall,temperature, and river flow. In T.F. Malone, ed.,Compendium of Meteorology,.American Meteorological Society, 1024-1029 pp. Schulman, E., 1945. Runoff histories in tree rings of the Pacific slope. Geographical Review 35, 59-73.


129

Schweingruber, F.H., 1987. Tree Rings: basics and applications of Dendrochronology.” Kluwer Academic Publishers, Dordrecht, Netherlands. 276 pp.

Shah, S.K, Bhattacharyya, A., Chaudhry, V., 2007. Reconstruction of June–September precipitation based on tree-ring data of teak (Tectona grandis L.) from Hoshangabad, Madhya Pradesh, India. Dendrochronologia 25, 57–64.

Shao, J., 1993, "Linear model selection by cross-validation," J. of the American Statistical Association 88, 486-494.

Shrestha, A. B., Devkota, L. P., 2010. Climate change in the Eastern Himalayas: observed trends and model projections. International Centre for Integrated Mountain Development (ICIMOD).pp 13.

Sigafoos, R.S., Hendricks, E.L., 1961. Botanical evidence of the modern history of Nisqually Glacier, Washington. U.S. Geological Survey Professional Paper 387-A: 1–20.

Singh, J., Yadav, R. R., 2000. Tree-ring indications of recent glacier fluctuations in Gangotri, Western Himalaya, India. Current Science 79, 1598-1601.

Singh, J., Yadav, R.R., Dubey, B., Chaturvedi, R., 2004. Millennium-long ring-width chronology of Himalayan cedar from Garhwal Himalaya and its potential in climate change studies. Current Science 86, 590-593. Singh, J., Yadav, R.R., 2005. Spring precipitation variations over the western Himalaya, India since AD 1731 as deduced from tree rings. Journal of Geophysical Research 110, D01110. http://dx.doi.org/10.1029/2004JD004855. Singh, J., Park, W.-K., Yadav, R.R., 2006. Tree-ring-based hydrological records for western Himalaya, India, since AD 1560. Climate Dynamics 26, 295-303. Singh, J., Yadav, R.R., 2007. Dendroclimatic potential of millennium-long ring-width chronology of Pinus gerardiana from Himachal Pradesh, India. Current Science 93, 833-836. Singh, J., Yadav, R.R., Wilmking, M., 2009. A 694-year tree-ring based rainfall reconstruction from Himachal Pradesh, India. Climate Dynamics 33, 1149-1158. Singh, J., Yadav, R.R., 2012. Application of tree-ring data in development of long term discharge of River Satluj. Current Science 103, 1452-1454.


130

Singh, K. K., Gaira, K. S., Rai, L. K., 2011. Agricultural scenario vis-a-vis the pollinator elements of the Sikkim Himalayan region. Biodiversity of Sikkim-Exploring and Conserving a Global Hotspot. Information and Public Relations Department, Government of Sikkim, 427-441.

Smith, D., Lewis, D., 2007.Glacial landforms, tree-rins/Dendrochronlogiology, pp 988-199.

Smith, W.W., Cave, G.H., 1913.The vegetation of Zemu and Llonakh valley of Sikkim. Records of the Botanical Survey of India 4 (5).

Somaru, R., Borgaonkar, H. P., Sikder, A. B., 2008. Tree-ring analysis of teak (Tectona grandis LF) in Central India and its probable linkage with moisture fluctuation: a case study. Journal of Earth System Science117, 637-645.

Stockton, C.W., Jacoby, G.C., 1976. Long-term surface water supply and streamflow trends in the upper Colorado River basin based on tree-ring analyses. Lake Powell Res. Proj. Bull. 18, 1–70. Stokes, M.A., Smiley, T.L., 1968. An Introduction to Tree-ring Dating. The University of Chicago Press, Chicago. Su, Z., Y. Shi., 2002. Response of monsoonal temperate glaciers to global warming since the Little Ice Age. Quaternary International 97-98, 123-131. Sumi., R.P., 1994.Rainfall variation along testa valley in Mountainous slope of Sikkik Mausam 45, 165-170.

Tarr, R. S., Martin, L., 1914. Alaskan glacier studies of the National Geographic Society in the Yakutat Bay, Prince William Sound and lower Copper River regions. National Geographic Society. Tibshirani, R., 1996. "A comparison of some error estimates for neural network models," Neural Computation, 8, 152-163. Torrence, C., Compo, G. P., 1998. A practical guide to wavelet analysis. Bulletin of the American Meteorological society, 79(1), 61-78.


131

UNEP and WGMS. 2008. Global glacier changes: facts and fi gures. United Nations Environment Programme and World Glacier Monitoring Service. 88 p. http://www.grid.unep.ch/glaciers/.

UNEP. 2008. Global Glacier Changes: facts and figures. WGMS.

Urban, F., Cole, J., Overpeck, J., 2000. Influence of mean climate change on climate variability from a 155-year tropical Pacific coral record. Nature 407,989-993. Urrutia, R.B., Lara, A., Villalba, R., Christie, D.A., Le Quesne, C., Cuq, A., 2011. Multicentury tree ring reconstruction of annual streamflow for the Maule River watershed in south central Chile. Water Resources Research 47,W06527. http://dx.doi.org/10.1029/2010WR009562. Vaganov, E. A., Hughes, M. K., Kirdyanov, A. V., Schweingruber, F. H., Silkin, P. P. 1999. Influence of snowfall and melt timing on tree growth in subarctic Eurasia. Nature 400(6740), 149-151.

Webster, P. J., Palmer, T. N., 1997. The past and the future of El Niño. Nature, 390, 562-564.

Wagnon, P., Ribtein, P., Francou, B., Sicart, J.E., 2001. Anomalous heat and mass budget of glacier Zongo, Bolivia, during the 1997/1998 El Niño year. Journal of Glaciology 47, 21–28.

Wigley, T. M. L., Jones, P. D., Briffa., K. R., 1988. Detecting the effects of acidic deposition and CO2-fertilization on tree growth, in Methods of Dendrochronology Applications in the Environmental Science, Kairiukstis, L., et al. (Eds.),. 239–253 pp, Polish Acad. of Sci., Warsaw.

Woodhouse, C.A., 2000. Extending hydrologic records with tree rings. Water Resour: Impact 2, 25–27. Woodhouse, C.A., Meko, D.M., 2002. Introduction to tree-ring based streamflow reconstructions. Southwest Hydro 1, 14–15. Wu, X, Shao, X., 1995. Status and prospects of dendrochronological study in Tibetan Plateau. Dendrochronologia 13, 89–98.


132

Yadav, R.R., Park, W.K., Bhattacharyya, A., 1997. Dendroclimatic reconstruction of April-May temperature fluctuations in the western Himalaya of India since A.D. 1698. Quaternary Research 48, 187-191.

Yadav R.R., Park W.K., Bhattacharya, A., 1999. Spring temperature fluctuations in the western Himalayan region as reconstructed from tree-rings; AD 1390–1987. The Holocene 9, 85–90

Yadav, R.R., Park, W.K., 2000. Precipitation reconstruction using ring-width chronology of Himalayan cedar from western Himalaya: preliminary results. Proceedings, Indian Academy of Sciences (Earth andPlanetary Science) 109, 339-345. Yadav, R.R., Singh, J., 2002. Tree-ring-based spring temperature patterns over the past four centuries in western Himalaya. Quaternary Research 57, 299-305. Yadav, R. R., Park, W. K., Singh, J., Dubey, B., 2004. Do the western Himalayas defy global warming? Geophysical Research Letters 31, L17201. Yadav, R.R., 2011a. Tree-ring evidence of 20th century precipitation surge in monsoon shadow zone of western Himalaya, India. Journal of Geophysical Research 116. http://dx.doi.org/10.1029/2010JD014647. Yadav, R.R., 2011b. Long-term hydroclimatic variability in monsoon shadow zone of western Himalaya, India. Climate Dynamics. http://dx.doi.org/10.1007/s00382-010-0800-8. Yadav, R.R., Braeuning, A., Singh, J., 2011. Tree ring inferred summer temperaturevariations over the last millennium in western Himalaya, India. Climate Dynamics 36, 1545-1554. Yadav, R.R., 2013. Tree ring-based seven-century drought records for the Western Himalaya, India. Journal of Geophysical Research 118. http://dx.doi.org/10.1029/ 2012JD018661. Yao, T., Wang, Y., Liu, S., Pu, J., Shen, Y., Lu, A., 2004. Recent glacial retreat in High Asia in China and its impact on water resource in Northwest China. Science in China Series D: Earth Sciences,47, 1065-1075.

http://dx.doi.org/10.1029/


133

Zemp, M., Hoelzle, M., Haeberli, W., 2009. ‘Six decades of glacier mass balance observations: a review of the worldwide monitoring network.’ Annals of Glaciology 50,101-111

Summary

1

Title of this dissertation “Application of multi-proxy tree- ring parameters in

the reconstruction of past climate vis-à-vis glacier fluctuations from the Eastern

Himalaya”. The research work included this dissertation was carried out using multi-

proxy tree ring parameters i.e. width of tree ring as a whole, early wood and latewood

separately provided high resolution temporal climate reconstruction for longer time-

span (1759 to 2010 AD). This reconstructed climate data was analyzed to seek a long-

term linkage with El Niño and advancement and retreat of Zemu glacier. This work is

also supplemented with reconstruction discharge of Zemu Chu originated from Zemu

glacier. In this pursuit, a good amount of tree ring data has been generated from Zemu

glacier. These data are from several conifer trees viz., Abies densa, Juniperus recurva,

Larix griffithiana (Ring width, late wood, and early wood) growing sub alpine forest

and Juniperus squamata scrub growing at the tree-line zone close to snout of the

Zemu glacier. I reconstructed summer temperature in Zemu area and also explored

glacier and tree growth relationship of North Sikkim Eastern Himalaya. In this

context, I developed 12 chronologies from a transect of lower (m) to higher elevation

close to snout of the Zemu glacier and established site-specific climate growth

response. After that I made regional chronology using PCA to find out common

variance to enhance tree growth relation at regional level i.e. First PC#1 and the

second PC (PC#2) of the chronology PCA were significant, representing 37.21% and

18.18% of the total variance, respectively. Regional Response Function Analysis

suggested that average February-March temperature and July-September temperature

were limiting factors for tree-growth. I reconstructed maximum temperature of

March-April using Bootstrap Regression Method, maximum March-April temperature

was successfully reconstructed for the Zemu glacier valley regions. Secondly, I

reconstructed mean temperature of July-August using Linear Regression Method, viz.,

Correlated tree-ring chronologies (TRCs) were used as predictor variables in linear

regression (LR) to determine the optimal regression model with the highest skill.

Average July-August temperature was successfully reconstructed for the various

regions with R2 values ranging from 23.60%. And thirdly, I reconstructed January-

April discharge of Zemu Chu (1775 to 1996 AD) a tributary of Tista river using linear

regression (LR) and R2values ranging from 50.2%. Established relationships with

tree growth and glacier fluctuation history with available glacier fluctuation data.

Beside this, analysis of the relation of tree growth/ glacier retreat and El Nino 3.4 has

also been made. The major contributions of this dissertation are first, maximum

Summary

2

March-April temperature reconstruction. Secondly, mean July-August temperature

reconstruction in Zemu glacier valley North Sikkim (Eastern Himalaya) India, and

thirdly the first successful hydrological reconstruction, and conclusive evidence of

climate change signals and 3rd is glacier history with relation to tree growth and El

Nino 3.4. Generated data will allow for a better understanding of climate,

hydrological variability in the region and glacier history of this region. Based on tree-

ring, the data generated can be used to enhance the understanding of

paleoenvironments. A comparison of the tree-ring and glacier fluctuations

(advancement/retreat or mass balance positive/negative) records indicate that the trees

in the area are much sensitive and respond to glacier fluctuations. An overall

assessment of analyses of climate, stream flow and El Niño signatures in Zemu

glacier area presented in the present research work appears to have great promise and

should look forward to a more comprehensive analysis based on development of tree-

ring data in both spatial and temporal coverage close to snout of the glaciers of the

Eastern Himalaya. Because of the complex landscape in this mountainous region,

more tree-ring data are required for better understanding of the regional climate

variability. In the absence of long-term instrumental records, they permit exploration

of the relative contribution of changes in temperature to net mass balance. A detailed

analysis through multidisciplinary approach would bring out valuable information

regarding glacier advancement and retreat in greater details from the Himalyan

region.

APPLICATION OF MULTI-PROXY TREE-RING PARAMETERS IN...

Documents

Transcript of APPLICATION OF MULTI-PROXY TREE-RING PARAMETERS IN...