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GROWTH RESPONSE OF DEODAR (Cedrus deodara), BLUE PINE
(Pinus wallichiana) AND CHIR PINE (Pinus roxburghii) TO CLIMATE
CHANGE IN GALIES FOREST DIVISION – ABBOTTABAD
By
SYED SAID BADSHAH BUKHARI
Department of Environmental Sciences
University of Peshawar
Session: 2009-14
ii
AUTHOR’S DECLARATION
I, Syed Said Badshah Bukhari, hereby state that my PhD thesis titled “GROWTH
RESPONSE OF DEODAR (Cedrus deodara), BLUE PINE (Pinus wallichiana) AND CHIR PINE
(Pinus roxburghii) TO CLIMATE CHANGE IN GALIES FOREST DIVISION - ABBOTTABAD”
is my own work and has not been submitted previously by me for taking any degree
from University of Peshawar or anywhere else in the country/world.
At any time if my statement is found to be incorrect even after my graduation the
university has the right to withdraw my PhD degree.
Syed Said Badshah Bukhari
Dated: 25th September 2018
iii
Anti-Plagiarism Test Certificate (Signed copy attached in the printed copies of the dissertation)
iv
Certificate of Approval (Signed copy attached in the printed copies of the dissertation)
v
DEDICATION
I dedicate this effort, the fruit of my thoughts and study to my parents who always inspired
and supported me to pursue knowledge, truth and virtue and serve the humanity.
vi
ABSTRACT
The present study was conducted to assess climate change and its impacts on growth of
Cedrus deodara, Pinus wallichiana and Pinus roxburghii at Galies Forest Division-
Abbottabad during 1962-2011. The climate parameters of temperature and precipitation
were used to assess climate regimes and climate changes, both on annual and seasonal
basis. Bioclimatic indices regimes and changes therein during 1962-201 were calculated.
The ring-width, early wood and late wood formations, and wood cell diameter and
thickness, were measured for time function analysis and impacts of climate change on
these characteristics.
The findings showed regimes of mean annual maximum temperature of 16.36±0.08 °C,
mean annual minimum temperature 6.08±0.08 °C and mean annual temperature
11.21±0.07 °C, while of precipitation was 889.48±19.43 mm/annum.
Climate Vegetation Productivity Index (CVPI) was ranging from 4,342 to 9,091. The
mean CVPI was calculated at 6,816, which indicated productivity in the range of 163.91-
184.77 cubic feet/acre.
The mean ring-widths of C. deodara, P. wallichiana and P. roxburghii for the time
period of 1962-2011 were 3.08±0.23 mm, 2.54±0.15 mm and 2.62±0.39 mm, with
coefficients of variation of 32.88%, 26.55% and 67.20% respectively. The values of
mean sensitivity of these species for the same period were 0.30±0.11, 0.38±0.11 and
0.29±0.10, with coefficients of variation of 16.56%, 19.50% and 17.53% respectively.
The overall increase in temperature and fluctuations in precipitation affected tree growth,
both in terms of ring-width and intra-ring wood characteristics. The temperature was
found negatively correlated with mean ring-widths and early wood formation in all the
three species, while the correlation with late wood formation and intra-ring wood
characteristics were positive in some cases and negative in others.
Key words: Climate change, Forests, Bioclimatic Indices, Climate Vegetation
Productivity Index, Climate Change Impacts
vii
ACKNOWLEDGEMENT
In the name of Allah, the most merciful and the most benevolent
I bow my head before Allah Almighty Who blessed me with vision, good health,
physical strength and academic vigour to undertake and complete this research work.
The research was conducted under the kind supervision of Professor Dr. S. Shafiqur
Rehman, co-supervision of Professor Dr. Noor Jehan and full assistance of Dr. Ghulam
Ali Bajwa, Coordinator Sericulture, Pakistan Forest Institute.
I am extremely grateful to my supervisor, Professor Dr. S. Shafiqur Rehman and co-
supervisor Professor Dr. Noor Jehan for their motivating spirit, continuous
encouragement and able guidance from conception to completion of this research work.
My special thanks are due for faculty members of Department of Environmental Sciences
and other Departments and Institutes who imparted me basic knowledge, advance
knowledge and scientific training as a PhD scholar, all contributing in undertaking this
research work.
I am highly indebted to Dr. Ghulam Ali Bajwa, Coordinator Sericulture, Pakistan Forest
Institute for his valuable assistance in designing the research study, instrumental
measurement of parameters of interest, data analysis and over all structuring of the thesis.
The technical expertise and assistance provided by Mr. Ghulam Mustafa Nasir, Director,
Forest Products Research Division, and Research Officers and Laboratory staff of
Sericulture Division and Forest Products Research Division are fully acknowledged. I
also wish to express my sincere thanks to Mr. Hakim Shah, Director, Pakistan Forest
Institute, Mr. Muhammad Yousaf Khan, Divisional Forest Officer and colleague
scientists in the Institute who motivated and continuously helped me to fathom the long
academic journey culminating at this thesis.
The research work on the topic involved extensive field visits for collection of cores and
wood discs of sample trees and stumps; I owe special thanks to Mr. Ejaz Qadir, DFO,
Galies Forest Division-Abbottabad and his staff for providing full assistance during the
field visits for collection of the sample wood materials.
Finally, I wish to acknowledge my gratitude to all those individuals and institutions,
researchers and scientists, managers and administrators and my family members,
viii
particularly, my sons, namely, Dr. Syed Kazim Shah Bukhari, Engineer Syed Jafar Shah
Bukhari and Dr. Syed Murtaza Shah Bukhari, and friends who, in several ways,
contributed in undertaking and finishing of this research work.
Syed Said Badshah Bukhari
ix
CONTENTS
AUTHOR’S DECLARATION ii
ANTI-PLAGIARISM TEST CERTIFICATE iii
CERTIFICATE OF APPROVAL iv
DEDICATION v
ABSTRACT vi
ACKNOWLEDGEMENT vii
LIST OF FIGURES xiv
LIST OF TABLES xxii
ABBREVIATIONS xxiv
CHAPTER 1 1
INTRODUCTION 1
1.1 Climate Change 1
1.2 Forest 4
1.3 Climate Change Impacts 5
1.4 Objectives 8
1.5 Scientific Contribution 9
1.6 Limitations of the study 9
CHAPTER 2 11
LITERATURE REVIEW 11
2.1 Climate Change 11
x
2.2 Forest 14
2.2.1 Coniferous Forest 15
2.2.2 Sub-Alpine Forest 15
2.2.3 Dry Temperate Forest 16
2.2.4 Moist Temperate Forest 16
2.2.5 Sub-Tropical Pine Forest 17
2.3 Climate Change and Forest Ecosystems 17
2.4 Climate Change - Growth Response 21
CHAPTER 3 30
MATERIALS AND METHODS 30
3.1 Study Area 30
3.2 Preparation of forest maps 32
3.3 Climate Change Data and Analysis 39
3.4 Bioclimatic Indices 39
3.5 Wood Samples and Measurements 41
3.5.1 Collection and Preparation of Samples 41
3.5.2 Ring-width Measurement 51
3.5.3 Ring-Structure 52
3.6 Time Function and Climate Growth Function 54
3.7 Statistical Design and Analysis 54
CHAPTER 4 56
CLIMATE CHANGE AND BIOCLIMATIC INDICES 56
4.1 Climate 56
4.1.1 Climate Regimes 56
xi
4.1.2 Climate Change Trends 57
4.1.3 Climate Changes 83
4.1.4 Mathematical Expressions of Climate Change
Trends at GFD (1962-2011)
86
4.1.5 Correlation Coefficients Matrix of different Climate
Factors at GFD
87
4.2 Bioclimatic Indices 88
4.2.1 Bioclimatic Indices Regime 88
4.2.2 Changes in Bioclimatic Indices 90
4.2.3 Mathematical Expressions of Changes in
Bioclimatic Indices at GFD (1962-2011)
91
4.3 Climate Vegetation Productivity Index 94
4.4 Discussion 94
CHAPTER 5 100
IMPACTS OF CLIMATE CHANGE ON TREE RINGS AND
RING-WOOD CHARACTERISTICS
100
5.1 Cross-dating of Ring-width data 100
5.2 Standardization of Ring-width data 101
5.3 Mean Sensitivity and Coefficient of Variation 102
5.4 Ring-width and Ring-wood Characteristics of Deodar 103
5.4.1 Time function analysis of Ring-width and Ring-wood
Characteristics of Deodar
103
5.4.2 Mathematical Expressions of Time Function of Ring-
width, Intra- ring wood Formation and Wood Cell Characteristics of
Deodar
113
5.4.3 Decadal changes in Ring-width and Ring-wood
characteristics of Deodar
114
xii
5.4.4 Correlation between Ring-width and Ring-wood
Characteristics of Deodar
117
5.4.5 Impacts of Climate Change on Ring-width of Deodar 119
5.5 Ring-width and Ring-wood Characteristics of Blue pine 140
5.5.1 Time function analysis of Ring-width and Ring-
wood Characteristics of Blue pine
140
5.5.2 Mathematical Expressions of Time Function of
Ring-width, Intra-ring wood Formation and Cell
Characteristics of Blue pine
151
5.5.3 Decadal changes in Ring-width and Ring-wood
Characteristics of Blue pine
152
5.5.4 Correlation between Ring-width and Ring-wood
Characteristics of Blue pine
155
5.5.5 Impacts of Climate Change on Ring-width of Blue
pine
156
5.5.6 Mathematical Expressions of Impacts of
Temperature and Precipitation on Ring-width of Blue pine
175
5.6 Ring-width and Ring-wood Characteristics of Chir pine 177
5.6.1 Time function analysis of Ring-width and Ring-
wood Characteristics of Chir pine
177
5.6.2 Mathematical Expressions of Time Function of
Ring-width, Intra-ring wood Formation and Cell
Characteristics of Chir pine
187
5.6.3 Decadal changes in Ring-width and Ring-wood
Characteristics of Chir pine
188
5.6.4 Correlation between Ring-width and Ring-wood
Characteristics of Chir pine
191
5.6.5 Impacts of Climate Change on Ring-width of Chir
pine
192
xiii
5.6.6 Mathematical Expressions of Impacts of
Temperature and Precipitation on Ring-width of Chir pine
212
5.7 Inter-species Comparison of Correlation Coefficients of
Ring-widths of Cedrus deodara, Pinus wallichiana and
Pinus roxburghii with Climate Parameters
214
5.8 Discussion 217
CHAPTER 6 222
SUMMARY, GENERAL CONCLUSIONS AND
RECOMMENDATIONS 222
6.1 Summary 222
6.2 General Conclusions 238
6.3 Recommendations 241
REFERENCES 242
xiv
LIST OF FIGURES
Figure 2 -1 Moist Temperate forest of Galies Forest Division 15
Figure 3 -1 Land cover map of Galies Forest Division 31
Figure 3 -2 Land cover/Forest map of GFD–Abbottabad (Grid 50x50 Km) 34
Figure 3 -3 Land cover/Forest map of GFD–Abbottabad (Grid 25x25 Km) 35
Figure 3 -4 Land cover/Forest map of GFD–Abbottabad (Grid 15x15 Km) 36
Figure 3 -5 Land cover/Forest map of GFD–Abbottabad (Grid 10x10 Km) 37
Figure 3 -6 Land cover/Forest map of GFD–Abbottabad (Grid 1x1 Km) 38
Figure 3 -7 Core extraction using Presler Borer 43
Figure 3 -8 Samples distribution map of GFD-Abbottabad 48
Figure 3 -9 A magnified section of samples distribution map (1 x 1 km) of
GFD-Abbottabad
49
Figure 3 -10 A magnified section of samples distribution map (0.1 x 0.1
km) of GFD-Abbottabad
50
Figure 3 -11 Preparation of microscopic slides for measuring cell diameter
and cell wall thickness
53
Figure 4 -1 Trend line of Mean Annual Maximum Temp. (°C) vs. Time at
GFD (1962-2011)
59
Figure 4 -2 Trend line of Mean Annual Minimum Temp. (°C) vs. Time at
GFD (1962-2011)
60
Figure 4 -3 Trend line of Mean Annual Temp. (°C) vs. Time at GFD
(1962-2011)
61
Figure 4 - 4 Trend line of Annual Precipitation vs. Time at GFD (1962-
2011)
62
Figure 4 -5 Trend line of Mean Spring Maximum Temp. (°C) vs. Time at
GFD (1962-2011)
63
Figure 4 -6 Trend line of Mean Spring Maximum Temp. (°C) vs. Time at
GFD (1962-2011)
64
xv
Figure 4 -7 Trend line of Mean Spring Temp. (°C) v. Time at GFD (1962-
2011)
65
Figure 4 -8 Trend line of Spring Precipitation vs. Time at GFD (1962-
2011)
66
Figure 4 -9 Trend line of Mean Summer Maximum Temp. (°C) vs. Time at
GFD (1962-2011)
67
Figure 4 -10 Trend line of Mean Summer Minimum Temp. (°C) vs. Time
at GFD (1962-2011)
68
Figure 4 -11 Trend line of Mean Summer Temp. (°C) vs. Time at GFD
(1962-2011)
69
Figure 4 -12 Trend line of Summer Precipitation vs. Time at GFD (1962-
2011)
70
Figure 4 -13 Trend line of Mean Monsoon Maximum Temp. (°C) vs. Time
at GFD (1962-2011)
71
Figure 4 -14 Trend line of Mean Monsoon Minimum Temp. (°C) vs. Time
at GFD (1962-2011)
72
Figure 4 -15 Trend line of Mean Monsoon Temp. (°C) vs. Time at GFD 73
Figure 4 -16 Trend line of Monsoon Precipitation vs. Time at GFD (1962-
2011)
74
Figure 4 -17 Trend line of Mean Autumn Maximum Temp. (°C) vs. Time
at GFD (1962-2011)
75
Figure 4 -18 Trend line of Mean Autumn Minimum Temp. (°C) vs. Time
at GFD (1962-2011)
76
Figure 4 -19 Trend line of Mean Autumn Temp. (°C) vs. Time at GFD
(1962-2011)
77
Figure 4 -20 Trend line of Autumn Precipitation vs. Time at GFD (1962-
2011)
78
Figure 4 -21 Trend line of Mean Winter Maximum Temp. (°C) vs. Time at
GFD (1962-2011)
79
Figure 4 -22 Trend line of Mean Winter Minimum Temp. (°C) vs. Time at
GFD (1962-2011)
80
Figure 4 -23 Trend line of Mean Winter Temp. (°C) vs. Time at GFD
(1962-2011)
81
xvi
Figure 4 -24 Trend line of Winter Precipitation vs. Time at GFD (1962-
2011)
82
Figure 4- 25 Comparison between increases in Maximum Temperature and
Minimum Temperature
85
Figure 4 -26 Trend line of Climate Vegetation Productivity Index at GFD 94
Figure 5 -1 Cross-dating of Ring-width data of Deodar in GFD (1962-
2011)
100
Figure 5 -2 Cross-dating of Ring-width data of Blue pine in GFD (1962-
2011)
101
Figure 5 -3 Cross-dating of Ring-width data of Chir pine in GFD (1962-
2011)
101
Figure 5 -4 Time function of Mean Annual Ring-width of Deodar in GFD
(1962-2011)
105
Figure 5 -5 Time function of Mean Intra-ring Early Wood Formation (%)
of Deodar in GFD (1962-2011)
106
Figure 5 -6 Time function of Mean Intra-ring Late Wood Formation (%) of
Deodar in GFD (1962-2011)
107
Figure 5 -7 Time function of Mean Intra-ring Early Wood Cell Diameter
(µm) of Deodar in GFD (1962-2011)
108
Figure 5 -8 Time function of Mean Intra-ring Early Wood Cell Wall
Thickness (µm) of Deodar in GFD (1962-2011)
109
Figure 5 -9 Intra-ring Early Wood Cell Diameter and Cell Wall Thickness
of Deodar (100x)
110
Figure 5 -10 Time function of Mean Intra-ring Late Wood Cell Diameter
(µm) of Deodar in GFD (1962-2011)
111
Figure 5 -11 Time function of Mean Intra-ring Late Wood Cell Wall
Thickness (µm) of Deodar in GFD (1962-2011)
112
Figure 5 -12 Intra-ring Late Wood Cell Diameter and Cell Wall Thickness
of Deodar (100x)
113
Figure 5 -13 Impact of Mean Annual Maximum Temperature on Ring-
width of Deodar in GFD (1962-2011)
120
Figure 5 -14 Impact of Mean Annual Minimum Temperature on Ring-
width of Deodar in GFD (1962-2011)
121
xvii
Figure 5 -15 Impact of Annual Precipitation on Ring-width of Deodar in
GFD (1962-2011)
122
Figure 5 -16 Impact of Mean Spring Maximum Temperature on Ring-
width of Deodar in GFD (1962-2011)
124
Figure 5 -17 Impact of Mean Spring Minimum Temperature on Ring-width
of Deodar in GFD (1962-2011)
125
Figure 5 -18 Impact of Spring Precipitation on Ring-width of Deodar in
GFD (1962-2011)
126
Figure 5 -19 Impact of Mean Summer Maximum Temperature on Ring-
width of Deodar in GFD (1962-2011)
127
Figure 5 -20 Impact of Mean Summer Minimum Temperature on Ring-
width of Deodar in GFD (1962-2011)
128
Figure 5 -21 Impact of Summer Precipitation on Ring-width of Deodar in
GFD (1962-2011)
129
Figure 5 -22 Impact of Mean Monsoon Maximum Temperature on Ring-
width of Deodar in GFD (1962-2011)
130
Figure 5 -23 Impact of Mean Monsoon Minimum Temperature on Ring-
width of Deodar in GFD (1962-2011)
131
Figure 5 -24 Impact of Monsoon Precipitation on Ring-width of Deodar in
GFD (1962-2011)
132
Figure 5 -25 Impact of Mean Autumn Maximum Temperature on Ring-
width of Deodar in GFD (1962-2011)
133
Figure 5 -26 Impact of Mean Autumn Minimum Temperature on Ring-
width of Deodar in GFD (1962-2011)
134
Figure 5 -27 Impact of Mean Autumn Precipitation on Ring-width of
Deodar in GFD (1962-2011)
135
Figure 5 -28 Impact of Mean Winter Maximum Temperature on Ring-
width of Deodar in GFD (1962-2011)
136
Figure 5 -29 Impact of Mean Winter Minimum Temperature on Ring-
width of Deodar in GFD (1962-2011)
137
Figure 5 -30 Impact of Winter Precipitation on Ring-width of Deodar in
GFD (1962-2011)
138
xviii
Figure 5 -31 Time function of Mean Annual Ring-width of Blue pine in
GFD (1962-2011)
142
Figure 5 -32 Time function of Mean Intra-ring Early Wood Formation (%)
of Blue pine in GFD (1962-2011)
143
Figure 5 -33 Time function of Mean Intra-ring Late Wood Formation (%)
of Blue pine in GFD (1962-2011)
144
Figure 5 -34 Time function of Mean Intra-Ring Early Wood Cell Diameter
(µm) of Blue pine in GFD (1962-2011)
145
Figure 5 -35 Time function of Mean Intra-ring Early Wood Cell Wall
Thickness (µm) of Blue pine
146
Figure 5 -36 Intra-ring Early Wood Cell Diameter and Cell Wall Thickness
of Blue pine (100x)
147
Figure 5 -37 Time function of Mean Intra-ring Late Wood Cell Diameter
(µm) of Blue pine in GFD (1962-2011)
148
Figure 5 -38 Time function of Mean Intra-ring Late Wood Cell Wall
Thickness (µm) of Blue pine in GFD (1962-2011)
149
Figure 5 -39 Intra-ring Late Wood Cell Diameter and Cell Wall Thickness
of Blue pine (100x)
150
Figure 5 -40 Impact of Mean Annual Maximum Temperature on Ring-
width of Blue pine in GFD (1962-2011)
157
Figure 5 -41 Impact of Mean Annual Minimum Temperature on Ring-
width of Blue pine in GFD (1962-2011)
158
Figure 5 -42 Impact of Annual Precipitation on Ring-width of Blue pine in
GFD (1962-2011)
159
Figure 5 -43 Impact of Mean Spring Maximum Temperature on Ring-
width of Blue pine in GFD (1962-2011)
160
Figure 5 -44 Impact of Mean Spring Minimum Temperature on Ring-width
of Blue pine in GFD (1962-2011)
161
Figure 5 -45 Impact of Spring Precipitation on Ring-width of Blue pine in
GFD (1962-2011)
162
Figure 5 -46 Impact of Mean Summer Maximum Temperature on Ring-
width of Blue pine in GFD (1962-2011)
163
xix
Figure 5 -47 Impact of Mean Summer Minimum Temperature on Ring-
width of Blue pine in GFD (1962-2011)
164
Figure 5 -48 Impact of Summer Precipitation on Ring-width of Blue pine
in GFD (1962-2011)
165
Figure 5 -49 Impact of Mean Monsoon Maximum Temperature on Ring-
width of Blue pine in GFD (1962-2011)
166
Figure 5 -50 Impact of Mean Monsoon Minimum Temperature on Ring-
width of Blue pine in GFD (1962-2011)
167
Figure 5 -51 Impact of Monsoon Precipitation on Ring-width of Blue pine
in GFD (1962-2011)
168
Figure 5 -52 Impact of Mean Autumn Maximum Temperature on Ring-
width of Blue pine in GFD (1962-2011)
169
Figure 5 -53 Impact of Mean Autumn Minimum Temperature on Ring-
width of Blue pine in GFD (1962-2011)
170
Figure 5 -54 Impact of Autumn Precipitation on Ring-width of Blue pine in
GFD (1962-2011)
171
Figure 5 -55 Impact of Mean Winter Maximum Temperature on Ring-
width of Blue pine in GFD (1962-2011)
172
Figure 5 -56 Impact of Mean Winter Minimum Temperature on Ring-
width of Blue pine in GFD (1962-2011)
173
Figure 5 -57 Impact of Winter Precipitation on Ring-width of Blue pine in
GFD (1962-2011)
174
Figure 5 -58 Time function of Mean Annual Ring-Width of Chir pine in
GFD (1962-2011)
178
Figure 5 -59 Time function of Mean Intra-ring Early Wood Formation (%)
of Chir pine in GFD (1962-2011)
179
Figure 5 -60 Time function of Mean Intra-ring Late Wood Formation (%)
of Chir pine in GFD (1962-2011)
180
Figure 5 -61 Time function of Mean Intra-ring Early Wood Cell Diameter
(µm) of Chir pine in GFD (1962-2011)
181
Figure 5 -62 Time function of Mean Intra-ring Early Wood Cell Wall
Thickness (µm) of Chir pine in GFD (1962-2011)
182
xx
Figure 5 -63 Intra-ring Early Wood Cell Diameter and Cell Wall Thickness
of Chir pine (100x)
183
Figure 5 -64 Time function of Mean Intra-ring Late Wood Cell Diameter
(µm) of Chir pine in GFD (1962-2011)
184
Figure 5 -65 Time function of Mean Intra-ring Late Wood Cell Wall
Thickness (µm) of Chir pine in GFD (1962-2011)
185
Figure 5 -66 Intra-ring Late Wood Cell Diameter and Cell Wall Thickness
of Chir pine (100x)
186
Figure 5 -67 Impact of Mean Annual Maximum Temperature on Ring-
width of Chir pine in GFD (1962-2011)
193
Figure 5 -68 Impact of Mean Annual Minimum Temperature on Ring-
width of Chir pine in GFD (1962-2011)
194
Figure 5 -69 Impact of Annual Precipitation on Ring-width of Chir pine in
GFD (1962-2011)
195
Figure 5 -70 Impact of Mean Spring Maximum Temperature on Ring-
width of Chir pine in GFD (1962-2011)
197
Figure 5 -71 Impact of Mean Spring Minimum Temperature on Ring-width
of Chir pine in GFD (1962-2011)
198
Figure 5 -72 Impact of Spring Precipitation on Ring-width of Chir pine in
GFD (1962-2011)
199
Figure 5 -73 Impact of Mean Summer Maximum Temperature on Ring-
width of Chir pine in GFD (1962-2011)
200
Figure 5 -74 Impact of Mean Summer Minimum Temperature on Ring-
width of Chir pine in GFD (1962-2011)
201
Figure 5 -75 Impact of Summer Precipitation on Ring-width of Chir pine in
GFD (1962-2011)
202
Figure 5 -76 Impact of Mean Monsoon Maximum Temperature on Ring-
width of Chir pine in GFD (1962-2011)
203
Figure 5 -77 Impact of Mean Monsoon Minimum Temperature on Ring-
width of Chir pine in GFD (1962-2011)
204
Figure 5 -78 Impact of Monsoon Precipitation on Ring-width of Chir pine
in GFD (1962-2011)
205
xxi
Figure 5 -79 Impact of Mean Autumn Maximum Temperature on Ring-
width of Chir pine in GFD (1962-2011)
206
Figure 5 -80 Impact of Mean Autumn Minimum Temperature on Ring-
width of Chir pine in GFD (1962-2011)
207
Figure 5 -81 Impact of Autumn Precipitation on Ring-width of Chir pine in
GFD (1962-2011)
208
Figure 5 -82 Impact of Mean Winter Maximum Temperature on Ring-
width of Chir pine in GFD (1962-2011)
209
Figure 5 -83 Impact of Mean Winter Minimum Temperature on Ring-
width of Chir pine in GFD (1962-2011)
210
Figure 5 -84 Impact of Winter Precipitation on Ring-width of Chir pine in
GFD (1962-2011)
211
xxii
LIST OF TABLES
Table 3 - 1 Site - wise geographical and tree information of Deodar in GFD 43
Table 3 - 2 Site - wise geographical and tree information of Blue pine in
GFD
45
Table 3 - 3 Site - wise geographical and tree information of Chir pine in
GFD
46
Table 4 - 1 Temperature and Precipitation Regimes at GFD (1962-2011) 57
Table 4 - 2 Trend Analysis of Climate Change at GFD (1962-2011) 57
Table 4 - 3 Temperature and precipitation changes at GFD (1962-2011) 84
Table 4 - 4 Mathematical Expressions of Climate Change Trends at GFD
(1962-2011)
86
Table 4 - 5 Correlation Coefficients Matrix among different Climate
Factors at GFD
88
Table 4 - 6 Bioclimatic Indices Regimes at GFD (1962-2011) 88
Table 4 - 7 Changes (%) in Bioclimatic Indices at GFD (1962-2011) 90
Table 4 - 8 Mathematical Expressions of Changes in Bioclimatic Indices at
GFD (1962-2011)
91
Table 5 - 1 (a) Statistics of intra-species variability of annual ring-widths
of Cedrus deodara, Pinus wallichiana and Pinus roxburghii (1962-2011)
102
Table 5 - 1 (b) Statistics of mean sensitivity of mean annual ring-widths of
Cedrus deodara, Pinus wallichiana and Pinus roxburghii for the period
1962-2011
103
Table 5 - 2 Trend Analysis of Ring-width and Ring-wood Characteristics
of Deodar at GFD (1962-2011)
104
Table 5 - 3 Mathematical Expressions of Time Function of Ring-width and
Intra-ring wood Characteristics of Deodar in GFD (1962-2011)
114
Table 5 - 4 Mean Decadal Ring-width and Ring-wood Characteristics of
Deodar in GFD (1962-2011)
116
Table 5 - 5 Correlation Coefficients Matrix between Ring-width and Ring-
wood Characteristics of Deodar in GFD (1962-2011)
119
xxiii
Table 5 - 6 Precipitation and Ring-width of Deodar in GFD (1962-2011) 123
Table 5 - 7 Mathematical Expressions of Impacts of Temperature and
Precipitation on Ring-width of Deodar in GFD (1962-2011)
139
Table 5 - 8 Trend Analysis of Ring-width and Ring-wood Characteristics
of Blue pine at GFD (1962-2011)
141
Table 5 - 9 Mathematical Expressions of Time Function of Ring-width and
Intra-ring Wood Characteristics of Blue pine in GFD (1962-2011)
151
Table 5 - 10 Mean Decadal Ring-width and Ring-Wood Characteristics of
Blue pine in GFD (1962-2011)
153
Table 5 - 11 Correlation Coefficients Matrix between Ring-width and
Ring-wood Characteristics of Blue pine in GFD (1962-2011)
156
Table 5 - 12 Precipitation and Ring-width of Blue pine in GFD (1962-
2011)
160
Table 5 - 13 Mathematical Expressions of Impact of Temperature and
Precipitation on Ring-width of Blue pine in GFD (1962-2011)
176
Table 5 - 14 Trend Analysis of Ring-width and Ring-wood characteristics
of Chir pine at GFD (1962-2011)
177
Table 5 - 15 Mathematical Expressions of Time Function of Ring-width
and Intra-ring Wood Characteristics of Chir pine in GFD (1962-2011)
187
Table 5 - 16 Mean Decadal Ring-width and Ring-Wood Characteristics of
Chir pine in GFD (1962-2011)
189
Table 5 - 17 Correlation Coefficients Matrix between Ring-width and
Ring-wood Characteristics of Chir pine in GFD (1962-2011)
192
Table 5 - 18 Impact of Precipitation on Ring-width of Chir pine in GFD
(1962-2011)
196
Table 5 - 19 Mathematical Expressions of Impact of Temperature and
Precipitation on Ring-width of Chir pine in GFD (1962-2011)
212
Table 5 - 20 Inter-species Comparison of Correlation Coefficients of Ring-
widths of Cedrus deodara, Pinus wallichiana and Pinus roxburghii with
Climate Parameters in GFD (1962-2011)
215
xxiv
ABBREVIATIONS
A Annual
AI Aridity Index
ANOVA Analysis of Variance
AP Alpine Pasture
ARM Annual Ring Measuring
Au Autumn
CACTOS California Conifer Timber Output Simulator
CI Confidence Interval
CRU Climate Research Unit
CV Coefficient of Variation
CV Critical value
CVPI Climate Vegetation Productivity Index
DF Dryness Factor
DFO Divisional Forest Officer
DI Dryness Index
DSTBL Dry Sub-Tropical Broad-Leaved
DT Dry Temperate
DTT Dry Tropical Thorn
EW Early wood
EWCD Early wood cell diameter
EWCWT Early wood cell wall thickness
FAO Food and Agriculture Organization of the United Nations
GFD Galies Forest Division
GPS Global Positioning System
HC Humidity Coefficient
HSD Honest Significance Difference
IP Irrigated Plantation
IPCC Intergovernmental Panel on Climate Change
LW Late wood
LWCD Late wood cell diameter
LWCWT Late wood cell wall thickness
xxv
M Monsoon
Max Maximum
Min Minimum
MF Mangrove Forest
MS Mean Sensitivity
MT Moist Temperate
PEI Precipitation Efficiency Index
PFI Pakistan Forest Institute
PI Prediction Interval
r Coefficient of Correlation
R2 Coefficient of determination
RF Rain Factor
RiF River Forest/Riverine Forest
RW Ring-width
S Spring
SA Sub-Alpine
SE Standard Error
STP Sub-Tropical Pine
Su Summer
T Ton
TEI Temperature Efficiency Index
Temp Temperature
USGCRP United States Global Change Research Program
W Winter
µm Micron
1
CHAPTER 1
INTRODUCTION
1.1 Climate Change
Climate change is the most important contemporary environmental issue with global
dimensions. Climate may be defined as an average weather which may be described in
statistical terms of mean and variability of usually surface variables, such as
temperature, precipitation, wind, etc. In the usage of Intergovernmental Panel on
Climate Change (IPCC), climate change refers to a change in the state of the climate
that can be identified by changes in the mean and/or the variability of its properties and
that persists for an extended period, typically decades or longer. It may be due to
natural processes or anthropogenic changes in the composition of the atmosphere or
land use. The United Nations Framework Convention on Climate Change (UNFCCC)
restricts the climate change to “a change of climate which is attributed directly or
indirectly to human activity that alters the composition of the global atmosphere and
which is in addition to natural climate variability observed over comparable time
periods” (Bukhari and Bajwa, 2012).
As per 4th Assessment Report (IPCC, 2007), climate change is manifested in various
forms. The warming of climate system is unequivocal, as is evident from increases in
global average air and ocean temperatures, widespread melting of snow and ice and
rising global average sea level. The 100-year temperature increasing trend over period
of 1906-2005 is 0.74˚C. The linear warming trend over the last 50 years (0.13˚C per
decade) is nearly twice that for the last 100 years (IPCC, 2007). The rise in temperature
is all over the globe and is higher at higher northern latitudes. The rise in global
average sea level was recorded at an average rate of 1.8 mm/year over the time span of
1961 to 2003 and at an average rate of about 3.1 mm/year from 1993 to 2003. The
extent of snow and ice has reduced, with higher rate during summer. From 1906 to
2005, the precipitation trend exhibited significant increase in some regions of the world
and decrease in others, including parts of southern Asia (IPCC, 2007).
Globally, the extent of area affected by drought has increased since 1970s. A number of
extreme weather events, including hot days and nights, heat spells, heavy precipitation
2
and extreme floods have increased frequency, while cold nights and frost have become
less frequent, at wide range of localities.
The research findings indicate that earth’s climate is changing even faster than
previously estimated. With present anthropogenic activities and physical changes
occurring in nature, the mean global temperature on the earth could rise by seven
degree Celsius as compared to pre-industrial era (1750). This temperature increase
would be faster and higher than the one, the earth experienced at the end of last Ice
Age, about 15,000 years ago; i.e., increase of five degrees Celsius over a period of
5,000 years (Vorholz, 2009).
Climate change is reaching a level to threaten lifestyle and livelihoods in multiple
ways. Rising temperature is causing, inter alia, health problems (Gosling et al., 2009),
increase in intense tropical cyclones and rise in sea levels (IPCC, 2007), changes in
agricultural yields and depletion of ocean oxygen (Shaffer et al., 2009), changes in
forest types and composition (Ravindranath et al., 2006), and extinction of animal and
plant species (Thomas et al., 2004). Due to rising temperature many natural habitats are
shifting towards the poles or into higher latitudes. One of the earliest and most powerful
effects of this warming is the melting of snow packs and mountain glaciers which
precipitate as snow and ice in the winter for release during summer (Svendsen and
Künkel, 2009). For example, the snow-packed water reservoirs in the Himalayas are
melting at a rate of 15.0 m per year, the highest rate in the world, due to rising
temperature (Hasnain, 2009). There are several physical (IPCC, 2007; Grunewald et al.,
2009) and anthropogenic activities (Foley et al., 2005; Falcucci et al., 2007; IPCC,
2007; Vorholz, 2009; Bukhari and Bajwa, 2009) that influence the spatio-temporal
changes in climate processes. Among all these external forcing, anthropogenic
activities are dominant cause of recent global warming (Knutson et al., 2006). Climate
change will affect different regions differently, depending on the extent of increase in
temperature and changes in precipitation in the region. Therefore, proper appreciation
of spatio-temporal variability of temperature and precipitation is of high importance for
many applications, including weather forecasting, climate and environmental research,
determining growth period of plants and estimating evapo-transpiration regimes.
3
Among the anthropogenic activities, emission of greenhouse gases (GHGs) is the
dominant climate changing driver, and these emissions have increased 70% between
1970 and 2004 as compared to pre-industrial era. The major share in increase of GHGs
is of Carbon dioxide (CO2). The annual emissions of Carbon dioxide have grown by
about 80%, from 26 to 38 Giga ton (Gt) between 1970 and 2004. The shares of
different GHGs in total emissions during 2004 were: CO2 (fossil fuel use) 56.6%, CO2
(deforestation and decay of biomass) 17.3%, CO2 (other) 2.8%, methane (CH4) 14.3%,
nitrous oxide (N2O) 7.9% and hydro-chlorofluorocarbons, per fluorocarbons and
sulphur hexafluoride 1.1%. As reported by IPCC, 2007, the emissions of GHGs by
sectors were: energy 25.9%, industry 19.4%, forestry 17.4%, agriculture 13.5%,
transport 13.1%, building 8.0%, and waste materials 2.8%. The global atmospheric
concentrations of GHGs have increased significantly since 1750 and reached to high
levels by 2005: CO2 from 280 ppm to 379 ppm, CH4 from 715 ppb to 1774 ppb and
N2O from 270 ppb to 319 ppb. Aerosols, primarily sulphate, organic carbon, nitrate and
dust, cause a cooling effect both through direct radiative forcing and indirect cloud
albedo forcing. The equilibrium climate sensitivity, measured as the equilibrium global
average surface warming, subsequent to doubling of CO2 concentration, is likely to be
in the range of 2 °C to 4.5 °C, with a best estimate of about 3 °C and is very unlikely to
be less than 1.5 °C. Beside average temperature, anthropogenic forcing has increased
the extreme temperatures and sea level and changed the patterns of winds, storms,
precipitation and drought (IPCC, 2007; Bukhari and Bajwa, 2012).
Climate change is a global phenomenon and Pakistan is no exception to it. Pakistan has
experienced gradual increase in temperature over land and sea, fluctuations and
variations in precipitation, increased frequency of extreme climatic events and changes
in wind and storm patterns. Moreover, the exact magnitude and frequency of these
changes are not uniform in time and space and require empirical data for precise
determination and statistical projections (Sheikh et al., 2012).
The emerging climate change scenarios, particularly down-scaled to local forest types,
will likely have adverse effects on biomass production, biodiversity and forest
ecosystem dynamics. These changes will, in turn, reduce economic, social and
ecological services from these forests with dampening effects on living and livelihood of
forest-dependent communities.
4
1.2 Forest
United Nations Framework Convention on Climate Change (UNFCCC) defines forest
as “a minimum area of land of 0.05-1.0 ha with tree crown cover (or equivalent
stocking level) of more than 10-30% with trees potential to reach of minimum height of
2-5 m at maturity, in situ”. A forest may consist either of closed forest formations
where trees of various layers and undergrowth cover a high proportion of the ground or
open forest with substantial openings in the canopy. Young natural stands and all
plantations which have yet to reach a crown density of 10-30% or tree height of 2-5 m
are categorized as forest, like the areas which are temporarily un-stocked as a result of
human intervention, such as harvesting, or natural causes, but which are expected to
revert to forest.
Pakistan has a lower endowment of forest resources with about 4.6 million ha of forest
land and plantations which sum up to about 5.23% of the total land area of the country.
The per capita forest is 0.03 ha which compares unfavorably with an average world per
capita forest endowment of 0.6 ha. The distribution of forests by forest type is: Conifers
42%, Scrub 34%, Irrigated plantations 6%, River forest 6%, Mangroves 11%, Mazri
0.5% and Linear Plantations 0.5%. These forests are not enough to meet the national
demand of timber, fuel wood and wood based products. The wood production in
Pakistan in 2009 was estimated at 3.983 million m3 and the wood consumption at 4.4 20
million m3, thus indicating a large gap of 0.437 million m3 between production and
consumption (Bukhari, 2011).
Coniferous forests are spread over about 1,946,000 ha in Pakistan, including Azad
State of Jammu and Kashmir (AJK) and Northern Areas (Gilgit-Baltistan) (Wani et al.,
2004), and have multiple uses. The dominant species in coniferous biome in
Pakistan are: Deodar (Cedrus deodara), Blue pine (Pinus wallichiana) and Chir
pine (Pinus roxburghii). The contribution of these species in national coniferous
timber production is about 58.0%, while in Khyber Pakhtunkhwa it is about
88.0%, with species-wise distribution: C. deodara 53.0%, P. wallichiana 20.1%
and P. roxburghii 14.9%. These forests are playing a vital role in national economy
and ecology, especially in the Khyber Pakhtunkhwa province of Pakistan. Khyber
5
Pakhtunkhwa nurtures about 1,073,000 ha of coniferous forests (58.2% of total
coniferous forests of the country) which produce about 30.3% of the national timber
output. Apart from production of timber and non-timber forest produce, these forests
are providing livelihoods to summer grazers and dependent communities and serving
as catchment areas of the Indus River System. The region is also important for eco-
tourism due to its spectacular landforms and greenery. The contribution of forestry
sector and allied services to national economy in current national accounting system is
lower due to lack of marketability of forest ecosystem services, but is much higher in
absolute terms. The functioning and productivity of forest ecosystems are highly
dependent on climatic factors. Therefore, any climate change will likely have
significant effects on their productivity, environmental services and contribution to
local and national economy.
It is, therefore, imperative to assess the impacts of climate change on growth of the
dominant species in coniferous as well as other forest types, which will help devising
a strategy for management of these forests for optimum benefits on sustainable basis.
1.3 Climate Change Impacts
Temperature and precipitation, two of the basic climatic factors bound to change with
increased greenhouse gases concentrations, are primary determinants of global
vegetation patterns with significant impacts on forest ecology (including biodiversity),
plant distribution, productivity and health (Spurr and Barnes, 1980; Smith and Tirpak,
1989; Krischbaum et al., 1996, Bukhari and Bajwa, 2012). Increases in temperature
will not necessarily have simple linear impacts on growth of tree species in different
habitats (Carter, 1996). Also, local, regional and global changes in temperature and
precipitation can influence the occurrence, timing, frequency, duration, extent and
intensity of climatic disturbances (Baker, 1995; Turner et al., 1998). Disturbances, both
human-induced and natural, will configure forest ecosystems by influencing their
composition, structure and functional processes.
A variety of factors affects the tree ring-width and intra-ring wood characteristics.
Some of these factors are specific to location of the tree, its age and close surrounding
conditions, while others are related to wider environmental factors, such as,
6
temperature, rainfall and sunshine (Ahmed 1984; Yeh and Wensel 2000; Suarez et al.,
2009). The tree-ring archive reflects a complete history of climate signals of the
environment, emanating and altered by both biotic and abiotic factors. Species that
show a clear climate signal usually live under limiting conditions. Previously, though
based on limited scale dendrological data, usefulness of tree-ring climate proxy had
been demonstrated by Briffa et al. (1984), Pilcher and Baillie (1980a, b) and Hughes et
al. (1982).
The study of intra-ring wood characteristics also provides a promising tool for
investigation in tree biology and climate change (Fonti et al., 2010), and drawing
climate events within the growing seasons (Campelo et al., 2007; Olivar et al., 2012).
These wood characteristics help to assess impacts of climate variability and change on
tree growth and wood structure (Froux et al., 2002; De Micco et al., 2008; Martinez-
Meier et al., 2008; Hoffmann et al., 2011). Some of the quantifiable intra-ring wood
anatomical characteristics, such as early wood formation and late wood formation, are
highly dependent on climate, and facilitate more conclusive growth-climate relationship
compared to total ring-width only (Wimmer and Grabner, 1997; Rigling et al., 2003;
Cherubini et al., 2003; Campelo et al., 2007; Bogino and Bravo, 2009; Vieira et al.,
2009; Battipaglia et al., 2010; Fonti et al., 2010; Lebourgeois et al., 2010). Novak et al.
(2013) studied the impact of climate parameters on tree-ring widths, early wood and
late wood widths, the transition from early to late wood and the occurrence of intra-
annual rings density fluctuations, as well as the presence of resin canals in early and
late woods, in Aleppo pine (Pinus halepensis) from three sites in Spain and one in
Slovenia. He assessed that wood anatomical features provide complementary
information to that contained in tree-ring widths. Since the samples were obtained from
different sites way apart, it is likely that the results may be generalized over the wide
range of the species distribution pointing a useful proxy for studies on a regional scale.
Coniferous forests are primarily managed on the principle of sustained timber
production for which forest managers/policy makers need growth data to monitor
progress towards forest management plans for achieving targets of timber production
along with sustainable forest resource development. Besides, growth responses of
major tree species could also provide early warning signals to forest managers to put in
place additional management and operational plans, policies or societal actions to
7
achieve sustainable forest resource management for providing desired economic and
socio-ecological services.
There are several methods to evaluate climate change growth response of tree species,
including maintaining growth plots and recording data on visible growth parameters,
such as diameter, plant height, etc., over long periods of time. Maintaining such a large
number of research plots for growth parameters over long periods is a difficult and
costly affair. Apart from long duration, taking exhaustive data is labour intensive. In
contrast, measuring Ring-width and Ring structure, i.e., cell wall, cell radial
dimensions, wood density, etc., are quick and accurate methods to assess climate
growth response of tree species (Ahmed et al., 2010).
Dendrology, the science and study of trees and other wooded plants, and its branches,
including dendrochronology, dendroarchaeology, dendroclimatology, dendroecology,
dendrogeomorphology, dendroglaciology, dendrohydrology, dendropyrochronology
and dendroentomology , has emerged as a distinct applied scientific discipline which
relies primarily on study of tree rings to extract information about parameters of
interest. These parameters include the growth dimension of the tracheid cells and its
anatomical characteristics, which in turn determine the forest productivity and quality
of the timber and other forest produce. The dendrological approach provides reliable
and accurate information on these parameters when applied in a rational manner in
suitable cases. This approach has been adopted for research presented in this
dissertation.
What type of a forest crop can be supported by the specific site or locality is determined
by combinations of climatic factors. Various bioclimatic indices have been developed
to correctly express the interactions between climate and life processes of the plants.
Bioclimatic indices are useful tools to explain the spatial distribution of vegetation units
by the combination of different climatic factors (Gavilán, 2005). They are getting more
importance as they promote the transfer of results from climate modelling to land use
and vegetation science.
A major parameter of interest in forestry is the productivity of a forest which is
dependent on maximum sustainable utilization of the environmental resources. When
8
physiographic and edaphic factors are optimum, the productivity of the site is mainly
determined by suitable combination of climatic factors, for which Climate Vegetation
Productivity Index (CVPI) has been developed by Paterson (1956).
Cedrus deodara, Pinus wallichiana and Pinus roxburghii, along with Picea smithiana
and Abies pindrow, are the dominant species of coniferous biome in mountainous areas
of Pakistan and provide important economic, social and ecological services. Climate
change, with an accelerated trend since the industrial revolution, has the potential to
affect adversely the growth of these species and consequently cause economic, social
and environmental losses. This situation warranted compilation of locality-specific
historic data of climate parameters, its scientific analysis, interpretation and statistical
projections, and impact analysis for the sector of interest. For impact assessment in
forestry sector, tree growth data is the building block which needs to be collected in a
way to cover the entire life span of the tree, through a reliable and easy to handle
method. Ring-width and Ring-structure are dependable bio-tools for estimating growth
and wood quality of forest tree species in response to environmental conditions.
Keeping in view the importance and overall suitability of the tree species: C. deodara,
P. wallichiana and P. roxburghii, representing two coniferous biomes: Moist
Temperate and Sub-Tropical Pine, the present study was conducted in Galies Forest
Division-Abbottabad, to assess the regimes and changes in climate parameters of
temperature and precipitation on annual and seasonal basis, selected bioclimatic indices
and growth response of these species in terms of tree-rings width, early and late wood
formation, and tracheid cells dimensions and cell wall thickness to climate change over
time period of 1962-2011.
1.4 Objectives
This study was primarily aimed to assess the growth response of C. deodara, P.
wallichiana and P. roxburghii to climate change in Galies Forest Division-Abbottabad
over time period of 1962-2011. The specific objectives were set as follows:
1) Analysis of climate change, covering variability of temperature and precipitation
on time scale of annual and five seasons (spring, summer, monsoon, autumn,
winter).
2) Estimation of bioclimatic productivity indices of the study area.
9
3) Assessment of Growth-Response functions of the selected species.
4) Discerning variations and trends in growth of Ring-width and Ring-wood
characteristics of the selected tree species and their association with climate
change.
1.5 Scientific Contribution
The findings of the study were expected to significantly contribute to the scientific
exploration on the subject, particularly understanding the regimes and changes in
climate and bioclimatic indices, and growth response of the selected tree species and
associated anatomical changes in the rings-width, early wood and late wood formation,
ring-cell dimension and cell-wall thickness to climate change in the region focused in
the study. The outcome of the study was further expected to expand the frontiers of
scientific knowledge on the themes covered in this work and enable comparative study
at national, regional and global levels. Besides academic value, the findings would help
and guide forest managers and other stake-holders to design and implement necessary
adaptation strategies to address the negative impacts of climate change on forests.
1.6 Limitations of the study
The methodology adopted for the study had several limitations, enumerated below:
1) There is a lapse rate and local variability of climate with in the study area with a
large altitudinal range, however, the magnitude and impact of the same were
reduced by conducting the study species wise with limited altitudinal ranges and
randomization of the samples across the species altitudinal zones to average out
the variations in data collection and analysis.
2) The climate dataset used for this study was confined to the period 1962-2011 due
to technical and other constraints.
3) Several citations about climate changes were based on the 4th Assessment Report
of IPCC ((IPCC, 2007) and not on the 5th Assessment Report, because at the time
of compilation of this thesis that Report was still under preparation.
10
4) The growth response of tree species is, beside climate change, also affected by
ontogeny and other factors, which creates complication in establishing the causal
relations of the factors of interest. However, ‘standardization’ and appropriate
statistical tools were used to segregate the impacts of climatic variables from
non-climatic factors.
11
CHAPTER 2
LITERATURE REVIEW
2.1 Climate Change
Climate refers to an average of weather or statistical description of mean weather
conditions over a period of several years, typically 2-3 decades, while climate change is
any variation in the climate over a long period of time, but may be restricted only to
those variations which are in excess of natural variability and attributable to human
activities.
McCarthy et al. (2001) reported that the impacts of climate change were noticed both at
global and regional levels. For instance, the rise in mean annual temperature at Western
North America could be 2–5 °C above the range of temperatures that had occurred over
the last 1000 years. The rising temperature would be combined by an increase in winter
precipitation and a decrease in summer precipitation. These changes would
significantly affect human society and ecosystems.
Hulme (2003) reported that the climate of the Earth has never been stable, particularly
during the history and evolution of life on the Earth. The recent glacial periods were 4
°C-5 °C cooler compared to 20th century, while some inter-glacial periods probably
were 1 °C to 2 °C warmer. These climate changes were overwhelmingly natural in
origin and happened on Earth having primitive societies with less population. In fact,
the diurnal and seasonal rhythms of Earth were always regulated by inter-annual, multi-
decadal and millennial variations in climate.
Balling et al. (1998) and Briggs et al. (2005) reported that land cover change and land
degradation either due to anthropogenic activities, deforestation or livestock could
directly increase temperatures. Hulme and Sheard (1999); Johns et al. (2001);
Christensen and Christensen (2003); and Rowell and Jones (2006) reported that the
increase in atmospheric temperature would be compounded with enhanced extreme
weather events (storms, precipitation and droughts), cyclones (hurricanes and
typhoons), etc. The current increasing temperature would affect the global hydrological
12
cycle, and consequently winter temperatures will increase, while frequent and strong
summer droughts will be observed worldwide.
Knutson et al. (2006) reported that among all external forcing, anthropogenic activities
are the dominant cause of recent global warming. Rees and Collins (2006) reported that
the increase in minimum temperature was higher compared to maximum temperature.
Warren et al. (2006) reported that the vulnerable regions and vulnerable communities
would be more prone to drastic impacts of climate change due to its effect as a threat
multiplier combined with inherent vulnerability of insecure communities. The impacts
of climate change would be more severe on poor communities in vulnerable regions.
IPPC (2007) reported that the global surface air temperature had risen by 0.76ºC during
time period of 1850 to 2005. The linear increasing trend of temperature was 0.13 ºC per
decade over the last 50 years and the projected temperature increase is 1.1 ºC to 6.4 ºC
by the end of 21st century. This increase in surface temperature will also result in rise
of sea level. Pyke et al. (2007) reported that land cover and land use were very
important factors which interact with atmospheric conditions to shape the overall
climate. These interactions had great impacts on various ecosystems from regional to
global scales. IPCC (2007) and Grunewald et al. (2009) attributed the climate change,
increasing temperature and precipitation variability, to several physical factors and
Foley et al. (2005); Falcucci et al. (2007); IPCC (2007) and Vorholz (2009) attributed
the same to the anthropogenic activities which affect the spatial and temporal changes
in climate at local and regional levels.
Alavain (2009) reported that water security would be a major conflict issue, both at
inter and intra-regional levels, in water scarcity areas with extreme hydrological
variability and poor endowment of water resources, or inadequate storage capacity,
infrastructure and poor governance. Van de Steeg et al. (2009) and Bukhari and Bajwa
(2009) reported that increased livestock had changed the land cover and land use
pattern. Livestock, besides, were directly responsible for greenhouse gases (18% of all
human-induced greenhouse gases globally) and over-grazing causes deforestation and
deteriorates rangelands. Christy et al. (2001); Houghton et al. (2001); Motha and Baier
(2005); Houghton (2005); Brovkin et al. (2009); Grunewald et al. (2009) and Wu et al.
(2010) reported that out of anthropogenic activities, increasing human population and
13
the activities leading to emission of GHGs were the principal contributors to climate
change. The Economist (2009) reported that besides social problems, changing climate
would cost dearly to global economies. A two-degree increase in global temperature
will devour about one per cent of the world’s total gross domestic product for
undertaking mitigation measures.
Bukhari and Bajwa (2009) reported mean increase in temperature of 0.85 °C (0.77 °C-
0.92 °C) at Peshawar during 1985-2009. The spring season started 15.6 days earlier and
was shortened by 17.8 days. The summer season was extended over seven months
(April-October) with mean maximum temperature >30 °C. There was 30% decrease in
rainfall during recorded time period of 1985-2009. The climate was shifted towards dry
tropical with eight months receiving <25 mm rainfall. The rainfall was reduced
drastically in spring and late summer seasons. Evaporation and wind increased 1.59
times and 1.40 times respectively. The temperature showed negative correlation with
rainfall (r = -0.49) and positive correlation with evaporation (r = 0.78). The range and
coefficient of variation of climate variables indicated higher variability during spring
and autumn seasons. The emerging climate scenario will likely cause multifaceted
effects on vegetative and reproductive growth of plants and habitat characteristics.
Bukhari and Bajwa (2011) studied climate change trends over coniferous forests of
Pakistan for the period 1961-2000 and reported the mean temperature regime between
12.44 °C and 22.54 °C, with the lowest temperature at Alpine Pastures (AP) and the
highest at Sub-Tropical Pine forests (STP). Monsoon was the warmest season followed
by summer. The precipitation regime varied between 266.8 mm and 1071.6 mm. The
highest precipitation was recorded at STP, while the lowest precipitation was at AP.
The highest increase in maximum temperature was 2.03 °C at AP during winter, while
the lowest increase in maximum temperature was 0.08 °C at AP during monsoon. The
highest increase in minimum temperature was 2.61 °C at AP during winter and the
lowest increase in minimum temperature was 0.36 °C at STP monsoon. Temperature
increase was relatively higher at AP compared to other forests types. Temperature
increase during winter was 1-2 °C higher compared to other seasons. Precipitation
decreased by 9.6%, 5.8% and 0.3% at AP, SA and Dry Temperate (DT) respectively,
and increased by 16.7% and 12.3% over Moist Temperate (MT) and STP respectively.
The highest precipitation increase was 71.5% at MT during monsoon, while the highest
14
precipitation decrease was 30% at AP during summer. Precipitation increased at DT
during all seasons, except summer, indicating an elevation-latitudinal movement of
precipitation. The increased temperature and precipitation in MT and DT will likely
enhance plant growth, while the higher temperature increase and precipitation decrease
in AP and SA will have negative effects on plant growth.
Halnaes and Garg (2011) reported that for developing states, the mitigating cost would
be much higher. The unabated increasing temperature would hamper the endeavours to
achieve Millennium Development Goals (MDGs), among which progress on poverty
reduction could slow or even reverse.
Bukhari and Bajwa (2012) reported that mean temperature increase at coniferous
forests was higher compared to other forests types, such as Dry Sub-Tropical Broad-
Leaved (DSTBL), Dry Tropical Thorn (DTT), Irrigated Plantation (IP), Riverine Forest
(RiF) and Mangrove Forest (MF). The results showed increase in mean maximum
temperature between 0.08 °C and 2.03 °C, at coniferous forests during time period of
1961-2000, while the increase in mean minimum temperature was between 0.36 °C and
2.61 °C. Similarly, temperature increase during winter was 1-2 °C higher compared to
other seasons. Precipitation increased by 16.7% and 12.3% at MT and STP
respectively. Contrarily, precipitation decreased by 9.6%, 5.8% and 0.3% at AP, SA
and DT respectively. These climate changes have the potential to affect plant growth as
well as ecological services provided by these forests.
2.2 Forest
Forest may be defined as. –
“A land area of more than 0.5 ha, with a tree canopy cover of more than
10%, which is not primarily under agricultural or other specific non-forest
land use. In case of young forest or where tree growth is climatically
suppressed, the trees should be capable of reaching a height of 5.0 m in situ,
and of meeting the canopy cover requirement (FAO, 2005)”.
15
In Pakistan, forests are divided into nine (09) types depending on vegetation type.
Among these forest types, coniferous forests are the most important, both in the context
of economy and ecology.
2.2.1 Coniferous Forest
In Pakistan, coniferous forests have different biomes: i) Sub-Alpine (SA), ii) Dry
Temperate (DT), iii) Moist Temperate (MT), and iv) Sub-Tropical Pine (STP),
depending upon their geographical locations in terms of altitude and latitude. SA and
DT are located in Greater Himalayan region (winter with snow fall) above 35.25°N,
and MT and STP are present between 33.75°N and 35.25°N in the sub-montane areas
(the monsoon rainfall dominated region) (Bukhari and Bajwa, 2012).
Figure 2-1Moist Temperate forest of Galies Forest Division
2.2.2 Sub-Alpine Forest
Sub-Alpine forests are evergreen formation of coniferous forests located between 3,350
m and 3,800 m elevation in the Himalayas and other mountain ranges, with extensive
covers in Azad Kashmir, Gilgit-Baltistan, Malakand and Hazara Civil Divisions of
Khyber Pakhtunkhwa. Mean annual temperature is about 10ºC and mean monthly
temperature remains below 0ºC for 5-6 months (Champion et al., 1965). The tree
vegetation, such as Himalayan Silver Fir (Abies pindrow) and Blue pine (Pinus
16
wallichiana) are important coniferous species which grow mostly in pure stands with a
lower storey of broad-leaved trees, with Birch (Betula utilis) as the most common
species. Other associates, such as, Prunus spp. and Willow (Salix spp.) and Guch
(Vibernum) bushes complete the vegetation cover. There is a spring flush of herbaceous
flora, including Primula and several composite species. Sub-Alpine forests have
important medicinal plants species, like Aconitum heterophyllum, Chrysanthemum
indicum and Saussurea lappa (Bukhari and Bajwa, 2012).
2.2.3 Dry Temperate Forest
Dry Temperate forests are conspicuous by open evergreen canopies with open scrub
undergrowth, having distribution throughout the dry inner mountain ranges, beyond the
effective reaches of monsoon. DT is primarily located between 1,525 m and 3,350 m
above sea level in Gilgit-Baltistan, Azad Kashmir (Neelum Valley), Khyber
Pakhtunkhwa (Chitral and Kaghan) and Balochistan (Takht-i-Suleman, Shinghar and
Ziarat). The winter season is long and cold with mean temperature between 6ºC and
16ºC. Western disturbances during winter and spring bring considerable snow and
rainfall. The major tree species in DT are: Deodar (Cedrus deodara), Chilgoza (Pinus
gerardiana), Juniper (Juniperus excelsa), Blue pine (Pinus wallichiana), and Spruce
(Picea smithiana). Quercus ilex dominates as pure crop on lower elevations. The
commonly found associates are Fraxinus and Acer spp. DT also contain xerophytic
species, like Daphne, Lonicera, Prunus, Artemisia and Astragalus, and medicinal plant
species, such as, Ephedra nebrodensis, Artemisia maritima, Carum bulbocastanum,
Thymus sp., and Ferula (Bukhari and Bajwa, 2012).
2.2.4 Moist Temperate Forest
Moist Temperate forests extend between 1,375 m and 3,050 m elevation across the
whole stretch of outer ranges of the Himalaya, varying markedly with aspect. MT is
mostly found in Murree, Galies, Kaghan, Dir, Swat and Azad Kashmir. The mean
temperature is about 12.2 °C with mean rainfall of 630-1,500 mm/annum. The major
part of rainfall is received during the monsoon (July to September). The main coniferous
tree species are: Pinus wallichiana, Cedrus deodara, Picea smithiana and Abies
pindrow, while the broad-leaved tree species are: Quercus incana, Q. dilatata and Q.
17
semicarpifolia with Rhododendron arboretum as their commonest associate. The
temperate deciduous tree species, such as Acer, Aesculus, Prunus, Ulmus, Fraxinus,
Corylus and Alnus spp., are found in local consociations. Litsea and Machilus spp. too,
are also found in the moist soil depressions. Shrubs, like Indigofera, Lonicera, Rosa,
Desmodium, Rubus, Viburnum and Strobilanthus spp. and medicinal plant species,
including Punica granatum, Berberis lyceum, Skimmia laureola, Viola serpens,
Dioscorea, Sub-Tropical Pine deltoidea, Valeriana wallichii, Atropa acuminata,
Colchium luteum, Asparagus racemostus, and Mentha piperita are commonly found in
MT (Bukhari and Bajwa, 2012).
2.2.5 Sub-Tropical Pine Forest
Sub-Tropical Pine forests are located between 925 m to 1,675 m elevation and
sometimes ascends up to 2,130 m on ridges with southern exposure. STP is commonly
present in Hazara, Murree and Azad Kashmir. The mean annual temperature range is
between 15ºC and 22ºC, with mean rainfall between 760 mm and 1,270 mm, mainly
falling during monsoon (July and September). Chir pine (Pinus roxburghii) forms
practically whole of the top canopy with Quercus incana as the dominant broad-leaved
associate (Bukhari and Bajwa, 2012).
2.3 Climate Change and Forest Ecosystems
Davis and Botkin (1985) reported that a sustained increase in mean temperature of 1 °C
per annum can introduce considerable changes in the species composition and
distribution. Woodward (1987) reported that growth function and species composition of
forests are highly dependent on surrounding climate. Forest distribution is normally
confined by either limited water availability or extreme temperature. The ratio of actual
evapo-transpiration (the amount allowed by available precipitation) to potential evapo-
transpiration (the amount the atmosphere would take up if soil moisture were not
limiting) determines the maximum leaf area index that can be supported.
Green (1987) reported that the next century will witness the shifting of the potential (or
preferred) geographic ranges of species by approximately 300–500 km, leading to
18
changes and relocation of forest-based industries and substantial socio-economic
impacts.
Pastor and Post (1988) reported that in early Holocene period (about 8,000 years ago)
when the climate became warmer, fire-adapted hardwood species moved northward to
new sites after fire out-breaks, while northern deciduous species, such as sugar maple,
migrated further northward. Forest productivity would increase on soils that retain
adequate moisture. In contrast, productivity would decrease on dry soils where boreal
forest may give way to oak-pine savanna. Rana et al. (1989) estimated carbon
sequestration in central Himalayan temperate forests at 6.3-14 t CO2/ha/yr.
Dobson et al. (1989) reported that reduction in the size of ecosystem has a profound
effect on species abundance, e.g., 10% reduction in ecosystem size will result in loss of
about 50% of species. Based on this projection, it has been estimated that an increase in
temperature of 2 °C would result in 10–50% loss of the animal species currently present
in the Boreal Great Basin mountain ranges.
Brooks et al. (1991) reported that in dry and hot seasons, dry deciduous forests could
burn more frequently and may be perpetually replaced by thorn scrub or savannah
vegetation. Further, changes in forest cover can have profound effects on ground
hydrology, groundwater supplies, surface runoff, sedimentation, and river flows, with
potentially serious socio-economic effects.
Woodward (1987) and Prentice et al. (1992) reported that apart from causing
disturbances in forest ecosystems, climate has a vital role in the survival of many
species, as most species have critical temperature thresholds ranging from +12 °C to –
60 °C. However, many species have narrow ranges of temperature for growth and
reproduction. Groombridge (1992) reported that in general species diversity increases
from temperate to tropical forests.
Melillo et al. (1990) and Dixon et al. (1994) reported that forests are an important part of
the global climatic system, and play a major role in the present and projected future
carbon budget, since they store about 80% of all above ground and 40% of all below
ground terrestrial organic carbon. Thus, forests have a great role in mitigation and
19
adaptation measures against changing climate. Conversely, it is also true that the
changing climate is affecting forests in several ways which are mostly adversely
affecting the forest growth and health. Mitchell et al. (1990) and Greco et al. (1994)
reported that climate-induced desertification problem in semi-arid tropical regions will
become more serious if precipitation declines further.
FAO (1993) reported that the forest ecosystems on the Earth, by and large, have
continued as the least disturbed natural systems from the human influences. They are of
great socio-economic importance, providing timber, pulpwood, fuel wood and many
non-wood products. Globally, forests covered about one-fourth of the Earth’s land
surface in 1990.
Bargali and Singh (1995) reported that Carbon sequestration is directly related with
forest productivity which depends on the age of forest crop. New plantations have more
productivity than natural forests and old plantations and are more efficient in net carbon
sequestration. Mature and old trees have only marginal carbon sequestration due to slow
growth rate and dearth of twigs and leaves Thus, young forest and plantations could help
mitigate greenhouse gas concentration.
Siddiqui et al. (1999) reported that assuming a 0.3 °C rise in temperature and a
precipitation change of 0, +1 and -1% per decade, the three biomes (alpine tundra,
grassland/arid woodlands and deserts) would be reduced in area coverage, and 5 biomes
(cold conifer/mixed woodland, cold conifer/mixed forests, temperate conifer/mixed
forests, warm conifer/ mixed forests, and steppe/arid shrub lands) would increase in area
coverage as a result of climate change.
Spittlehouse and Stewart (2003) reported that the forestry community does need to
evaluate the long-term effects of climate change on forests and determine what the
community should do at present and in future to combat this threat. Forest Managers can
influence the timing and direction of forest adaptation at certain locations, but in most of
the cases the dependent society will have to adjust to the changed forest conditions
arising from natural adaptation. Adapting to climate change with uncertain timing of
impacts requires a set of readily available options. In all such options, a top priority may
20
be given to effective adaptation to emerging forest conditions, along with sustaining the
genetic diversity and resilience of forest ecosystems.
Houghton (2005) and Pachauri and Reisinger (2007) reported that minimizing climate-
based disturbances in forest ecosystems and avoiding deforestation can halt a
considerable emission source of carbon to the atmosphere. Current deforestation and
climate disturbances are releasing about 1,400 to 2,000 Giga ton carbon per year. Millar
et al. (2007) reported that forests which experience frequent disturbances often have
characteristics that increase their resilience against climate disturbances. Fischlin et al.
(2007) reported that globally about 20% to 30% of species (global uncertainty range is
estimated from 10% to 40%, while regional uncertainty from as low as 1% to as high as
80%) will be facing increasingly high risk of extinction by 2100, as global mean
temperature exceed from 2 °C to 3 °C above pre-industrial levels.
USGCRP (2009) reported that climate and climate change affect the composition and
functioning of forest ecosystems and exert great influence in shaping forest health. A
change in climate may aggravate the existing threats to forests, such as pest epidemics,
forest fires, floods, droughts and human interventions. Climate change directly and
indirectly affects the growth and productivity of forests: directly due to changes in
atmospheric Carbon dioxide and climatic factors and indirectly through multiple
complex interactions in forest ecosystems. Climate also affects the frequency and
severity of many forest disturbances.
McKenzie et al. (2009) reported that gradual climate changes, especially atmospheric
changes, result in gradual adaptable changes in ecosystems. However, an acute climate
change, such as high and quick increase in temperature, will cause more frequent and
strong ecological disturbances, which might result in irreversible changes in the
composition and dynamics of forests.
Lasco et al. (2009) reported that climate change and Philippine forests were closely
inter-linked. Climate change was affecting the forests and their capacity to provide
environmental services. Conversely, degradation of the forests caused emission of
Carbon dioxide (CO2) to the atmosphere which contributed to climate change. To
increase the mitigation role of the forests and simultaneously increase their resilience to
21
climate change, suitable policies and programs must be adopted. To be effective, such
policies and programs should be based on scientific research and evidence. Presently, a
number of potential adaptation strategies have been formulated, but are yet to be
adequately tested. The bulk of past researches have focused on the mitigation potential
of terrestrial ecosystems. Huge amount of Carbon is conserved in natural. The stored
Carbon can be emitted to the atmosphere as CO2 gas through deforestation. Sharma et al.
(2010) reported carbon sequestration in coniferous forests of Nepal at 5.12 t
CO2/ha/year.
Ge (2011) studied the effects of climate change and management on the growth of
Norway spruce (Picea abies) in the boreal conditions based on a process-based
ecosystem model simulations, and reported findings on the ways and patterns in which
climate change affected the growth of unmanaged Norway spruce stands in relation to
the water availability, and as how the climate change and varying management regimes
affected the net carbon uptake, total stem wood growth and timber yield of the species at
southern to northern Finland.
Bukhari and Bajwa (2012) reported that the enhanced concentration of CO2 in the
atmosphere seemed to have a profound effect on the biomes area, including shifting of
tree line. The net primary productivity showed an increase in all biomes and scenarios.
However, there is a likelihood of forest dieback and time lag before the prevailing plant
types have enough time to adjust to changed climate and shift to new sites. In the
intervening adjustment period, these species would be vulnerable to ecological and
socio-economic disturbances (e.g., erosion, deforestation, and land-use changes). Thus,
the overall impact of climate change on the forest ecosystems of Pakistan could be
negative.
2.4 Climate Change - Growth Response
The CO2 fertilization effect indicates that the increase of CO2 in the atmosphere
accelerates the rate of photosynthesis in plants and tree growth. However, various
studies have produced divergent evidence, as the effect varies depending on the plant
species, the site, the temperature range, and the availability of water and growth
nutrients.
22
LaMarche et al. (1979) conducted one of the earliest studies which produced evidence
for a possible CO2 fertilization effect in tree rings. The study was based on ring-width
chronologies of high-altitude bristlecone and limber pines in the southwestern United
States, which showed unusually larger growth of ring-widths over the past century. An
explanation was made that the observed enhanced growth was due to CO2 fertilization
as high-altitude plants might be more CO2 limited compared to those at lower altitudes.
However, the study did not present any quantitative modeling to exclude the possible
contribution of favorable climatic change to account for the enhanced growth.
Dieterich and Swetnam (1984) demonstrated that dendrochronological dating was
considerably more reliable and can be used to establish the exact fire-history sequences.
Ahlstrand (1980); Dieterich (1980) and Swetnam and Dieterich (1985) reported the use
of tree-ring data for dating fire scars to reconstruct fire history. Ahlstrand (1980);
Dieterich (1980) and Swetnam and Dieterich (1985) reported that many fire histories
were based upon simple ring counting techniques that could lead to large dating
inaccuracies and uncertain conclusions.
Kienast and Luxmoore (1987) analyzed tree-ring data of naturally grown conifers to
evaluate the possibility of enhanced tree growth due to increased atmospheric CO2.
Samples of tree cores were collected from 34 sites in four different climatic regions in
the northern hemisphere. Growth trends after 1950, when the atmospheric CO2
concentration significantly increased, indicated an increase in ring-widths at eight of
the 34 sites, with moderate temperature or water stress. In four cases the growth
coincided with favorable climatic conditions, while in four cases, the growth increase
exceeded the upper bound expected from CO2 enrichment experiments with seedling
conifer species. Therefore, increased growth in any of the tree-ring chronologies could
not be exclusively attributed to higher atmospheric CO2 concentrations. Jozsa and
Powell (1987) studied representative boreal forest growth and measured ring widths
and density of mature Spruce trees at 11 locations in western Canada. They concluded
that biomass productivity and annual growth layer weights were related to long-term
and yearly climatic variability, but did not present any indication that there was a
systematic growth trend that could be attributed to CO2 fertilization.
23
Schulman (1958; Currey, (1965); Lara and Villalba, (1993) reported that the longevity
of trees (from hundreds to a few thousand years) confounds the detection of
environmental signals. Species with higher longevity possess a greater potential to
record signals over a range of temporal and spatial scales. Suzuki (1990) conducted a
comparative study on the annual ring widths of Abies spectabilis, Pinus wallichiana
and Picea smithiana, from two stands with different crop composition. He reported that
the annual ring widths usually had significant similarities between cores taken from
different trees. These similarities increased with tree size. The climatic change affected
the large trees more strongly than it did the small trees. Annual ring widths were also
correlated with the annual precipitations and its seasonal distribution.
Graumlich (1991) reported that no evidence exist for CO2 fertilization in high-altitude
foxtail pine and other species in the Sierra Nevada. Jacoby et al. (1997) reported that
tree rings provide information about climate change and CO2, but concluded that
overall the present tree-ring evidence for a possible CO2 fertilization effect under
natural environmental conditions seemed very limited.
Bradley and Jones (1992) and Luckman (1996) reported that tree rings act as natural
archives and provide significant proxy data for paleo-environmental studies and
reconstructions. Mann et al. (1998 & 1999) and IPCC (2001) reported that the
information derived from tree-ring series has increasing been used in climate model
validation in the context of global warming assessment. More specifically, the last 1000
years are deemed as a suitable time interval for the assessment of the background
variability in relation to climate change detection and a suitable period in relation to the
life span of several tree species.
Borgaonka et al. (1996) conducted tree‐ring analysis of Cedrus deodara from three
different sites of western Himalaya, including Kufri (Shimla), and reported that
moderately high values of variance exist in the three chronologies which indicate the
high potential of the species for dendroclimatic studies. Response function and
correlation analyses of the tree‐ring‐width data and climate factors showed a significant
negative relationship with summer temperature and positive relationship with summer
precipitation.
24
Yeh and Wensel (2000) observed the variance between actual and predicted growth
rates due to climatic changes for the conifer regions of northern California. They
developed the CACTOS (California conifer timber output simulator) program for
removal of growth variations attributable to biological and cultural factors. The residual
variation was then related with relative precipitation and temperature, while accounting
for effects of elevation, stand density, and species. The results showed that, in addition
to biological and cultural factors, growth variation was related with changes of winter
precipitation and summer temperatures. Winter precipitation and summer temperatures
affected growth in the current and the subsequent years. Further, the relationship
between climate and growth varied with tree densities and species.
Pant et al. (2000) analyzed sample cores of Cedrus deodara collected from two
different sites of western Himalaya by densitometric examination. Data was obtained
for early wood, late wood, minimum, maximum, and mean densities and total ring
width. Most of these variables showed moderately high values of common variance and
signal to noise ratio except latewood and maximum densities. The response function
analyses indicated significant relationships between pre-monsoon summer climate and
early wood density and total ring width.
Dendrochronology normally assumes that once growth trends and disturbance pulses
have been accounted for, climate–growth relationships become age independent.
However, different studies have indicated that tree physiology undergoes changes with
age, which could vary growth‐related climate signals over time. Carrer and Urbinati
(2004) studied the age related consistency of climate-growth responses in tree-ring
series from Larix decidua and Pinus cembra, by comparing their dendrological
statistics. It was found that tree-ring statistics did not change significantly with age in
P. cembra, whereas in L. decidua they appeared to be correlated with age classes. The
response function analysis indicated that climate parameters accounted for a large
fraction of variance in tree ring-widths in both species. Age influence on climate
sensitivity was found uniform.
Goldblum and Rigg (2005) studied the impact of projected future change in monthly
temperature and precipitation compared to General Circulation Model (GCM) monthly
temperature and precipitation conditions for the 2080s on the growth of Sugar maple
25
(Acer saccharum), White spruce (Picea glauca) and Balsam fir (Abies balsamea) at
Ontario, Canada. The sensitivity analysis of the species showed that Sugar maple had
the highest potential for enhanced growth rates under the predicted temperature rise and
altered precipitation pattern. White spruce was assessed likely to show lower increase
in growth, while the balsam fir was likely to confront a decrease in growth potential.
These projected changes would enhance the future status of sugar maple at its northern
limit.
Bouriaud et al. (2005) monitored the intra-annual radial growth variations of two
Norway spruce trees (Picea abies) over a period of four years, at four heights on the
stem, using point dendrometers. The trees were then felled and analyzed for growth and
density parameters. The results indicated that short-term variations in growth rate were
associated with changes in climate parameters and soil water levels. The sensitivity of
radial growth to climate declined with stem height. Wood density responded strongly to
drought events, and was relatively independent of growth rate and climatic conditions
during the early phase of the growing season, but increased with decreasing radial
growth rate later in the growing season.
Khan et al. (2008) conducted dendrochronological study on P. smithiana from District
Dangam of Afghanistan. Twenty eight sample cores were obtained from 15 trees and
cross-dating was done among 24 cores of 12 trees which presented first dated
chronology (1663-2006 AD) from that country. It was indicated that all cores were
highly correlated, indicating similar climatic signals. Ahmed et al. (2009) conducted
dendroclimatic studies on Spruce (Picea smithiana) sample cores obtained from Chera
and Naltar forests, Gilgit Baltistan, Pakistan. He presented six hundred year (1400-2006
AD) chronologies, prepared from highly correlated (r = 0.65 to 0.73) wood samples.
These chronologies were standardized in order to detect long-term climatic trends. The
response function analysis indicated that 37 to 40% variance was due to climatic factors.
Ahmed et al. (2010) studied the core samples of Picea smithiana, Cedrus deodara,
Pinus gerardiana and Juniperus excelsa from seven catchments in the Upper Indus
Basin of Himalayan region of Pakistan, and reported significant inter and intra-species
cross-matching, despite the fact that the samples were obtained from different areas. The
samples of the species showed a high correlation (r = 0.68 to 0.92) with master
chronologies and mean sensitivity in the range of 0.23 to 0.42.
26
Ram et al. (2008) prepared and analyzed tree-ring-width index chronologies of teak
(Tectona grandis) from three sites in Central India. His findings showed existence of
strong correlation among the three site chronologies thus indicated influence of common
climatic signals to the tree growth of the region. Significant positive relationship
between moisture index and tree ring-width variations both during the monsoon months
and on interannual basis indicated the important role of moisture availability at the roots
zone.
Papadopoulos et al. (2009) studied the association between the annual variability of the
Aleppo pine tree ring-widths and the variability of the climate parameters in the Attica
basin for a 45-year period (1959-2003). The results showed that 64% of the total
variance of the tree-rings of the Aleppo pine could be attributed to the common
variability of the climate parameters: precipitation 82.6%, minimum temperature 88.2%
and maximum temperature 88.5% of the total variance of each parameter. Distinct
narrow and wide tree-rings were observed during the years with extreme rainfall or
temperature conditions. The growth of the Aleppo pine showed positive correlations
with the winter and spring precipitations. Conversely, negative correlations were found
with the temperature of the spring months.
Yadav (2009) developed ring-width chronologies of Himalayan pencil juniper
(Juniperus polycarpos), Himalayan pencil cedar (Cedrus deodara) and Chilgoza pine
(Pinus gerardiana) for a millennium and longer from different sites in western
Himalaya. He reported strong precipitation signatures in ring-width measurement series
of these species.
Vaganov et al. (2009) studied correlations between climate and tree ring-width and
anatomy along short to long time scales and examined whether Carbon-13 (13C), a
stable carbon isotope, could be used as an additional parameter to interpret tree-ring
chronologies. The results indicated that climatic variables explained 20% of the
variation in tree ring-width and wood density over consecutive years, while 29-58% of
the variation was explained by autocorrelation between tree rings. The tree ring-width
and 13C values of whole wood were significantly correlated with length of the growing
season, net radiation and vapor pressure deficit. The 13C values were, however, not
27
correlated with precipitation or temperature. A highly significant correlation was also
observed between 13C of the early wood of one year and the late wood of the previous
year, indicating a carry-over effect of the growing conditions of the previous season on
current wood production.
Feliksik and Wilczyński (2009) analyzed the relationships between the diameter
increment of two native species: Norway spruce (Picea abies) and Scots pine (Pinus
sylvestris) and three non-native species: Douglas fir (Pseudotsuga menziesii), Sitka
spruce (P. sitchensis) and Silver fir (Abies alba) in research plot at Poland. The results
indicated that precipitation and temperature of the current growing season and months
preceding that season affected the annual diameter increment of all sampled tree
species. Winter and early spring frosts had a strong negative effect on diameter size.
Norway spruce was found the most resistant to low temperatures but was susceptible to
water deficiency in the soil during spring and summer. The growth of Scots pine was
stimulated by high precipitation in June. The diameter increments of Douglas fir, Sitka
spruce, Scots pine, and Silver fir were more strongly associated with temperature than
precipitation.
Williams et al. (2010) studied the response of trees to inter annual climate variations by
analysis of annual tree ring-width data from 1,097 sites in the continental United States.
A climate-driven statistical growth equation was developed for each site that used
regional climate variables to model ring-width values. These growth models were
applied to predict how tree growth would respond to 21st century climate change, taking
into account four climate projections. The models revealed that productivity of
dominant tree species in the southwestern United States will decrease substantially
during this century, especially in warmer and drier areas. In the northwest, nonlinear
growth relationships with temperature might lead to warming-induced declines in
growth for many trees that historically responded positively to warmer temperatures.
Zafar et al. (2010) investigated the climate sensitivity of tree rings of Picea smithiana
from Haramosh and Bagrot, Gilgit Baltistan, Pakistan. Approximately 550 years
chronology was obtained and quality of cross-dating was found satisfactory by
checking through COFECHA software. ARSTAN program was used to remove non
climatic trends. Mean correlation among samples was high (0.74 to 0.85). Signal
28
strength in Haramosh chronology was found to be higher. The chronology values in
both stands were showing similar trends.
Fernandez et al. (2012) studied the effect of severe drought on growth and anatomy of
pines in stands with different plantation densities, to find the influence of management
practices on the adaptability of the species to climate change. Various growth
parameters were measured in the rings pertaining to 2008-2009, a severe drought year.
It was found that the drought caused decrease in annual growth by 30-38% and 58-65%
with respect to mean growth in previous years and in open versus closed stands
respectively. The higher sensitivity of the latter in this case was opposite to the previous
reports on the same species in similar forests in USA.
(Khan et al. (2013) developed tree-ring-width chronologies covering the past 469 to
595 years of Cedrus deodara from three different sites at Chitral, Hindukush range of
Pakistan, in order to study paleoclimatic records for regions or periods of time for
which no instrumental climatic data was available. Climatic data obtained from the
three weather stations showed strong correlation and was found useful for tree-ring
climate relationships. Correlation function and response function analysis showed that
spring precipitation (March–May) was a critical limiting factor for tree-ring growth,
and temperature prior to November might also play a major role in affecting tree ring-
growth. The results showed that the three sites had continuous relationship which
indicated that only single species from different locations was affected by the same
environmental variables and hence could be used in climate reconstruction in
combination. C. deodara chronologies developed at different locations had several
corresponding narrow and wide marker rings indicating a large macroclimatic response
to regional climatic conditions.
The literature review of dendrological studies on climate change – growth response of
forest tree species reveals distinct pattern of relationships between these variables and
its components, however, the direction and magnitude of the actual and predicted
changes in the morphology, physiology, ring-widths, intra-ring characteristics and
anatomical features of the forest tree species are specific to the species, site and
location, and respond differently to critical climatic factors, its range, intensity and
duration and changes thereof. Therefore, a broad scope is available for conducting
29
research in this field focusing on impact of different climatic factors on growth of
various forest tree species, forest types and ecosystems and changes thereof, at various
locations and geographical regions.
30
CHAPTER 3
MATERIALS AND METHODS
3.1 Study Area
The study was conducted in Galies Forest Division-Abbottabad (GFD), having an
extensive forest cover, comprising legal categories of reserved forests owned by the
Government with no or few public rights and concessions, Guzara forests and scattered
patches of non-designated forests owned by individuals or communities and burdened
with heavy rights and concessions. The reserved forests were located between 33°55´
and 34°20´ N, and 73°15´ and 73°29´ E, (Khan, 1993), while the Guzara forests were
situated between 33°29´ and 34°21´ N, and 72°55´ and 73°29´ E, in northern Pakistan
(Khan, 1987). The reserved forests were spread almost in a continuous stretch with few
blanks, while Guzara forests were in patches with depleted stock and extensive blanks.
Both categories of the forests were organized on territorial basis into Ranges, Blocks and
Compartments to facilitate its scientific management. The management history and
prescriptions for the reserved forests and Guzara forests in the GFD for a period of 20
years were made in separate Working Plans, while the details of silvicultural and other
operations had been properly recorded in the Compartment History Files, maintained for
each compartment. The land cover map of GFD/District Abbottabad is reproduced
below (Figure 1).
31
Figure 3-1 Land cover map of Galies Forest Division
The forest cover of GFD was assessed at 67,173 ha (37.6% of the total district area),
comprising Moist Temperate 46,899 ha (26.3%), Sub-Tropical Chir pine 16,416 (9.2%)
and Sub-Tropical Broad-leaved 3,858 ha (2.1%) (Bukhari et al., 2012). The blue-pine
was the most abundant species, present frequently in pure stands in the Moist
32
Temperate zone, while deodar stands occupied almost the same altitude, but were
mainly confined to southern and western aspects, rocky sites and steep slopes with
well-drained soil. The Chir-pine was present in pure stands at altitude below Moist
Temperate zone and had been managed under Uniform Shelter-wood Compartment
System for more than a century, resulting into even-aged crops in periodic blocks. As
per 1998 census, Abbottabad district had a human population of 880,666, with many
villages and hamlets in close vicinity of the forest areas. The reserved forests and
Guzara forests are vulnerable to natural factors, like snow-fall, drought, frost, fire,
lightening, land-slides, pest attacks and diseases, while the Guzara forests are in
addition severely exposed to anthropogenic factors, like illicit cutting, lopping,
browsing, grass cutting, deliberate fires and encroachment. These forests have
importance for dendro-climatological studies by virtue of location at the boundary
between tropical and temperate continental climatic interaction (Fowler & Archer,
2006).
3.2 Preparation of forest maps
A set of forest maps was prepared by adopting general methodology comprised of
acquisition and processing of satellite data from accessible sources, digitization of
baseline information from General Topographic (GT) sheets, procured from Survey of
Pakistan and visual interpretation of the satellite images.
The SPOT-5 satellite images (2007-08), having 2.5 m ground resolution, were obtained
from Health Department, Khyber Pakhtunkhwa. These images being in raw format were
processed through geo-rectification, geometric corrections and image enhancement
techniques for efficient use. Geo-rectification was used for positional accuracy, relief
displacement and clearing the images from distortion, geometric correction for removing
geometric distortions due to sensor, earth geometry variations and conversion of data to
coordinates (e.g., latitude and longitude) and geometric correction for making the
images compatible with geometry of large scale maps. Different image enhancement
techniques such as Linear Contrast Stretch and Histogram Equalization were used to
improve the visual quality and interpretability of images.
33
Visual interpretation techniques were applied to delineate forest cover and other land
cover types using Arc GIS software. During this process all pixels in image were
categorized into several land cover classes or themes. All the GIS layers of different
land cover were generated in geo-database for storage and manipulation of the requisite
information. ERDAS Imagine software was used for mosaicking and merging of the
images.
Ground trothing / field verification on sampled sites, selected through stratified random
sampling design, was conducted to test the accuracy of the digital estimates of the forest
cover.
GT sheets in 1:50,000 and 1:15,000 scales were obtained from Survey of Pakistan to
extract baseline information which included district boundaries, settlements, road
infrastructure, etc.
Land cover / forest maps of Abbottabad district were prepared and district baseline
information was over-laid. These maps were properly gridded with coordinates
(Latitude/longitude) at 50x50, 25x25, 15x15, 10x10, and 1x1 Sq. Km., each grid cell
measuring 250,000 ha, 62,500 ha, 22,500 ha, 10,000 ha, and 100 ha respectively
(Figure 3.2, 3.3, 3.4, 3.5 and 3.6). These maps were used for data collection on climate
and sampling of trees.
Statistical software Minitab v. 15.1, XLSTAT and MS Office Excel were used for
graphics and database management.
34
Figure 3-2 Land cover/Forest map of GFD–Abbottabad (Grid 50x50 Km)
35
Figure 3-3 Land cover/Forest map of GFD–Abbottabad (Grid 25x25 Km)
36
Figure 3-4 Land cover/Forest map of GFD–Abbottabad (Grid 15x15 Km)
37
Figure 3-5 Land cover/Forest map of GFD–Abbottabad (Grid 10x10 Km)
38
Figure 3-6 Land cover/Forest map of GFD–Abbottabad (Grid 1x1 Km)
39
3.3 Climate Change Data and Analysis
A gridded map of 0.5 X 0.5 degree (50 km x 50 km) along with climate dataset of
Climate Research Unit (CRU), University of East Anglia, UK, TS v.3.21, reflecting
monthly figures for temperature and precipitation covering the study area, for the
period 1962-2011, was obtained through Global Change Impact Studies Centre
(GCISC), Islamabad. A set of forest maps compatible with the grids pattern of the CRU
map was prepared in the GIS-RS Laboratory of Pakistan Forest Institute, Peshawar for
scaling down the study area, as described in Para 3.2 above. The time period of 1962 -
2011 was focused in the study in view of dataset availability constraint for the study
and time span of prime growth and rotation period of the selected tree species. The
climate parameters of the study area were calculated from the CRU climate dataset by
taking forest area weighted average of the grids covering the study area. The climate
parameters, comprising maximum temperature, minimum temperature, mean
temperature and mean precipitation were used to assess climate regimes along with
standard error (±SE) and climate changes. The climate parameters were calculated both
on vertical (annual) and horizontal (seasonal) basis. Five seasons were marked as spring
(March-April), summer (May-June), monsoon (July-September), autumn (October-
November) and winter (December to February). The monsoon was separated from
summer due to different dynamics in context of temperature, precipitation and plant
growth. Climate data for the study area for a limited period dating back to 1976 were
also available from Kakul Meteorological Observatory, Pakistan Meteorological
Department, located in the study are. The dataset of CRU was compared with the
Observatory data, but no significant variations were observed in the two sets of
corresponding data. Analysis of temperature and precipitation regimes and changing
trends thereof were made by regression analysis and applying Mann Kendall test with
Normal Approximation and Sen’s Slope Estimator method to draw inferences for
meeting the objectives of the study.
3.4 Bioclimatic Indices
What type of a forest crop can be optimally supported by the specific site or locality is
determined not by a single climatic factor, but suitable combinations of climatic factors.
A number of bioclimatic indices have been developed to correctly express the
40
interactions between climate and life processes of the plants. Some of these indices
have since long been in use in forestry in the sub-continent, and were selected for
estimation in this study.
The bioclimatic indices regimes and changes in the indices were calculated. The
indices, namely Temperature Efficiency Index (TEI), Aridity Index (AI), Dryness Index
(DI), Rain Factor (RF), Dryness Factor (DF), Humidity Coefficient (HC) and
Precipitation Efficiency Index (PEI) for the Division were calculated using the
following formulae, as described by Champion et al. (1965), with metric system and
Centigrade scale, except where indicated otherwise:
𝑇𝐸𝐼 = 𝑀𝑎𝑥𝑖𝑚𝑢𝑚 𝑡𝑒𝑚𝑝𝑒𝑟𝑎𝑡𝑢𝑟𝑒 (ᴼ𝐹) − 32
4
𝐷𝐼 =𝑀𝑒𝑎𝑛 𝑎𝑛𝑛𝑢𝑎𝑙 𝑝𝑟𝑒𝑐𝑖𝑝𝑖𝑡𝑎𝑡𝑖𝑜𝑛
2𝑥𝑀𝑒𝑎𝑛 𝑎𝑛𝑛𝑢𝑎𝑙 𝑡𝑒𝑚𝑝𝑒𝑟𝑎𝑡𝑢𝑟𝑒
𝑅𝐹 = 𝑃𝑟𝑒𝑐𝑖𝑝𝑖𝑡𝑎𝑡𝑖𝑜𝑛
𝑇𝑒𝑚𝑝𝑒𝑟𝑎𝑡𝑢𝑟𝑒
DF = 𝑃𝑟𝑒𝑐𝑖𝑝𝑖𝑡𝑎𝑡𝑖𝑜𝑛
𝑇𝑒𝑚𝑝𝑒𝑟𝑎𝑡𝑢𝑟𝑒+7
HC = 𝑀𝑒𝑎𝑛 𝑎𝑛𝑛𝑢𝑎𝑙 𝑝𝑟𝑒𝑐𝑖𝑝𝑖𝑡𝑎𝑡𝑖𝑜𝑛
1.07𝑥𝑀𝑒𝑎𝑛 𝑎𝑛𝑛𝑢𝑎𝑙 𝑡𝑒𝑚𝑝𝑒𝑟𝑎𝑡𝑢𝑟𝑒
PEI = 11.5𝑥𝑃𝑟𝑒𝑐𝑖𝑝𝑖𝑡𝑎𝑡𝑖𝑜𝑛 (𝑖𝑛𝑐ℎ𝑒𝑠)
𝑀𝑎𝑥𝑖𝑚𝑢𝑚 𝑡𝑒𝑚𝑝𝑒𝑟𝑎𝑡𝑢𝑟𝑒 (ᴼ𝐹)−10 𝑥
10
9
41
The potential productivity of the study area was estimated using Climate Vegetation and
Productivity Index (CVPI), following the method as described by Paterson (1956):
10012
Ta
EGPTvCVPI
Where:
Tv = Mean maximum temperature (°C) during the year
Ta = Range between mean maximum temperature (°C) and mean
minimum temperature (°C)
P = Annual precipitation (mm)
G = Growing months, number of months during which the mean
monthly temperature exceeds 3 °C
E = Radiation received at the pole expressed as % age of the
radiation received at the latitude in question
3.5 Wood Samples and Measurements
Sampling of trees and measurement of parameters of interest were made in a planned
manner as described below.
3.5.1 Collection and Preparation of Samples
Based on the information about species and age composition of the forest crop,
obtained from the Working Plans and Compartment History Files, the study area was
divided into two populations comprising Moist Temperate biome and Sub-Tropical
Chir-pine biome. A 2-stage random sampling design was used for samples selection of
twenty trees and twenty stem discs cut at breast height each of C. deodara, P.
wallichiana and P. roxburghii. A forest map of the study area with grid cells measuring
1 x 1 Sq. Km. (100 ha), indicating forest compartment boundaries, was numbered for
each cell and delineated on the basis of information available for the forest
compartments into two populations covering the two biomes: Moist Temperate and
Sub-Tropical Chir pine respectively. The two populations were examined for cells
falling on blanks, or having no tree of over sixty year age, which were excluded from
the sampling frames. The minimum age limit for the sample trees was kept at 60 years
to ensure availability of at least 50 tree-rings in residual chronology after cross-dating.
42
In the first stage sampling twenty cells were selected for P. roxburghii from the
sampling frame of Sub-Tropical Chir pine biome and twenty cells each for C. deodara
and P. wallichiana from the sampling frame of Moist Temperate biome at random
without replacement by draws. The selected cells for C. deodara were compared with
Compartment History File for availability of substantial number of trees of over sixty
year age. In case, this condition was not met, the cell was discarded and substituted by
another cell through a random draw. The process was repeated where needed. The cells
finally selected in the first stage sampling for each of the three species were
superimposed with 0.1 x 0.1 (1.0 ha) grid and each cell was numbered. The second
stage sampling was conducted in a manner analogous to the first stage. The selected 0.1
x 0.1 (1.0 ha) cells were located in the field with the help of compartment boundary
pillars and other conspicuous land feature. Each selected cell area was entered into
from a random point, and the first tree of the relevant species with age of 60 year or
above was selected for analysis. In case, no tree of the requisite species or age was
present in the cell area, that cell was discarded and substituted by another cell through a
random draw. The process was repeated where needed. Circular stem discs of 20 cm
thickness were collected from the stumps of the recently cut trees nearest to the sample
trees for subsequent analysis in the laboratory.
The diameters of the sampled trees along with GPS coordinates and elevation and
general conditions of the sites were recorded on the spot. Increment cores were
extracted using Presler Increment Borer, following the methods as described by Stokes
and Smiley (1968), Ahmed (1984), and Norton and Ogden (1987) (Figure 3.7). The
core samples were preserved in tubes and shifted to the Laboratory. The ring-width was
measured in Annual Ring Measuring Laboratory (ARM Lab.), Pakistan Forest Institute,
Peshawar.
43
Figure 3-7 Core extraction using Presler Borer
Details of sample collection of Deodar, Blue pine and Chir pine are given in Tables 3.1,
3.2 and 3.3 respectively. Samples distribution map of the study area is reproduced in
Figure 3.8 and a magnified version of its section in Figure 3.9. A magnified portion of a
section of the samples distribution map with overlaying grid 0.1 x 0.1 km (1.0 ha) is
reproduced in Figure 3.10.
Table 3-1 Site - wise geographical and tree information of Deodar in GFD
S.
No.
Date GPS
Coordinates
Elevation
(m)
Tree age
(year)
Diameter
(cm)
Forest
Range
Latitude Longitude
1 6-10-12 34° 16'
34.685" N
73° 20'
43.696" E
2299 84 43.4 Thandiani
2 6-10-12 34° 16'
23.173" N
73° 20'
28.269" E
2348 80 41.2 Thandiani
3 6-10-12 34° 16'
23.579" N
73° 20'
45.396" E
2353 83 42.7 Thandiani
4 6-10-12 34° 16'
12.976" N
73° 20'
31.384" E
2333 99 51.1 Thandiani
44
5 6-10-12 34° 16'
9.984" N
73° 20'
37.112" E
2338 80 41.3 Thandiani
6 6-10-12 34° 16'
12.035" N
73° 20'
45.735" E
2319 92 46.0 Thandiani
7 6-10-12 34° 16'
8.906" N
73° 20'
46.479" E
2329 75 44.2 Thandiani
8 6-10-12 34° 15'
57.400" N
73° 20'
45.985" E
2370 70 36.6 Thandiani
9 6-10-12 34° 15'
55.002" N
73° 21'
5.119" E
2367 65 33.0 Thandiani
10 6-10-12 34° 15'
46.295" N
73° 20'
42.392" E
2362 102 58.6 Thandiani
11 6-10-12 34° 15'
42.136" N
73° 21'
15.285" E
2361 116 71.2 Thandiani
12 6-10-12 34° 16'
54.800" N
73° 20'
3.237" E
2355 97 48.4 Thandiani
13 6-10-12 34° 15'
6.253" N
73° 20'
20.807" E
2351 95 52.0 Thandiani
14 6-10-12 34° 15'
13.052" N
73° 21'
2.266" E
2283 72 40.1 Thandiani
15 6-10-12 34° 15'
5.175" N
73° 20'
44.794" E
2294 83 39.4 Thandiani
16 6-10-12 34° 14'
39.161" N
73° 20'
12.999" E
2298 89 39.0 Thandiani
17 6-10-12 34° 14'
51.904" N
73° 20'
5.571" E
2288 68 32.0 Thandiani
18 6-10-12 34° 14'
25.290" N
73° 20'
40.981" E
2281 78 34.8 Thandiani
19 6-10-12 34° 13'
57.591" N
73° 20'
43.867" E
2287 89 44.1 Thandiani
20 6-10-12 34° 13'
40.414" N
73° 20'
38.944" E
2293 61 33.0 Thandiani
45
Table 3-2 Site - wise geographical and tree information of Blue pine in GFD
S.
No
Date GPS
Coordinates
Elevation
(m)
Tree age
(year)
Diameter
(cm)
Forest
Range
Latitude Longitude
1 5-10-12 34° 8'
33.892" N
73° 26'
29.643" E
2095 70 44.6 Bognotar
2 5-10-12 34° 9'
25.114" N
73° 26'
14.026" E
2095 76 58.5 Bognotar
3 5-10-12 34° 7'
54.222" N
73° 23'
2.809" E
2311 106 84.0 Bognotar
4 5-10-12 34° 5'
14.400" N
73° 21'
34.876" E
2329 62 56.2 Bognotar
5 5-10-12 34° 6'
57.411" N
73° 26'
12.620" E
2327 77 48.0 Dongagali
6 6-10-12 34° 6'
18.526" N
73° 25'
31.178" E
2332 80 57.9 Dongagali
7 6-10-12 34° 6'
2.092" N
73° 25'
8.049" E
2332 77 55.5 Dongagali
8 6-10-12 34° 4'
9.976" N
73° 26'
31.767" E
2332 108 59.3 Dongagali
9 6-10-12 34° 2'
42.616" N
73° 24'
6.825" E
2327 68 59.5 Dongagali
10 6-10-12 34° 1'
39.423" N
73° 23'
0.583" E
2335 115 71.0 Dongagali
11 6-10-12 34° 1'
12.553" N
73° 23'
42.695" E
2315 111 56.4 Dongagali
12 6-10-12 34° 0'
4.618" N
73° 23'
26.975" E
2289 80 56.9 Dongagali
13 6-10-12 34° 14'
30.451" N
73° 18'
28.793" E
2289 72 53.6 Thandiani
14 6-10-12 34° 15'
24.700" N
73° 20'
35.825" E
2371 105 58.5 Thandiani
15 6-10-12 33° 59'
53.673" N
73° 22'
49.017" E
2451 76 45.2 Dongagali
46
16 6-10-12 33° 59'
12.313" N
73° 23'
39.204" E
2458 75 62.0 Dongagali
17 6-10-12 34° 2'
53.408" N
73° 25'
41.328" E
2461 77 51.0 Dongagali
18 6-10-12 34° 3'
37.363" N
73° 24'
23.659" E
2325 61 80.0 Dongagali
19 6-10-12 34° 4'
26.106" N
73° 23'
42.609" E
2199 108 55.5 Thandiani
20 6-10-12 34°
4'
6.297" N
73° 25'
12.891" E
2248 71 47.1 Thandiani
Table 3-3 Site - wise geographical and tree information of Chir pine in GFD
S.
No.
Date GPS
Coordinates
Elevation
(m)
Tree age
(year)
Diameter
(cm)
Forest Range
Latitude Longitude
1 5-10-12 34° 5'
35.175" N
73° 21'
11.931" E
2015 87 53.3 Bognotar
2 5-10-12 34° 7'
35.937" N
73° 24'
25.380" E
2015 89 56.2 Bognotar
3 6-10-12 34° 17'
27.910" N
73° 19'
41.993" E
1683 84 52.6 Thandiani
4 6-10-12 34° 17'
57.806" N
73° 20'
24.978" E
1683 85 50.5 Thandiani
5 6-10-12 34° 18'
7.569" N
73° 20'
1.787" E
1683 60 46.0 Thandiani
6 6-10-12 34° 17'
36.793" N
73° 19'
25.872" E
1683 63 46.2 Thandiani
7 6-10-12 34° 18'
7.530" N
73° 20'
15.160" E
1683 71 52.3 Thandiani
8 6-10-12 34° 11'
58.870" N
73° 7'
32.154" E
1510 68 45.1 Abbottabad
47
9 6-10-12 34° 11'
8.366" N
73° 8'
37.620" E
1522 64 51.7 Abbottabad
10 6-10-12 34° 11'
52.302" N
73° 7'
49.016" E
1495 71 56.0 Abbottabad
11 6-10-12 34° 11'
4.944" N
73° 6'
25.821" E
1501 66 61.7 Abbottabad
12 6-10-12 34° 10'
12.968" N
73° 6'
56.169" E
1520 63 47.4 Abbottabad
13 6-10-12 34° 11'
46.491" N
73° 7'
13.027" E
1512 60 48.3 Abbottabad
14 7-10-12 34° 10'
52.288" N
73° 7'
4.863" E
1532 97 49.6 Abbottabad
15 7-10-12 34° 10'
16.465" N
73° 6'
38.620" E
1524 96 51.2 Abbottabad
16 7-10-12 34° 11'
32.764" N
73° 7'
33.039" E
1509 93 60.7 Abbottabad
17 7-10-12 34° 10'
45.670" N
73° 8'
27.464" E
1502 61 51.5 Abbottabad
18 7-10-12 34° 10'
41.087" N
73° 6'
3.702" E
1511 71 51.8 Abbottabad
19 7-10-12 34° 11'
42.579" N
73° 9'
3.077" E
1452 72 51.7 Abbottabad
20 7-10-12 34° 9'
45.939" N
73° 24'
8.687" E
2029 89 57.5 Bognotar
48
Figure 3-8 Samples distribution map of GFD-Abbottabad
49
Figure 3-9 A magnified section of samples distribution map (1 x 1 km) of GFD-Abbottabad
50
Figure 3-10 A magnified section of samples distribution map (0.1 x 0.1 km) of GFD-Abbottabad
51
3.5.2 Ring-width Measurement
The samples collected were air dried to avoid fungal infection. The cores were glued
into grooved iron mounts with transverse surface of the core upwards. The cores were
surfaced using a razor blade and applying successively finer grades sand paper with
orbital sander. The ring-width was measured using Digital Positiometer with
microcomputer-based measuring system. For each tree the most recently formed 50
rings were measured. Besides ring-width, intra ring early wood formation and late
wood formation were measured on absolute scale and as % age of the total ring-width.
The measurements were recorded up to hundredth of a millimeter. The early wood and
late wood junctions in the ring-widths were identified by ocular observations in change
of colour and size of the tracheid cells.
To identify the exact years of formation of the annual rings of the sampled trees, cross-
dating was done in a manner prescribed by Fritts and Swetnam (1989). The procedure
involved both ring counting and ring-width pattern matching, to ensure against
counting error, or errors, caused by missing or false rings. The cross-dating of the three
selected species was separately done for each species by graphical method, plotting the
ring-widths of the sampled trees of each species on a timeline covering the period
1962–2011. In the first step, graphs were constructed by plotting ring-width data of a
batch of five sampled trees at a time, and checked for synchrony by listing and
comparing the narrow rings or other pattern that were present in each sampled tree. If
any ring was found out of sequence, the samples were examined for counting error,
missing or incomplete or false ring and necessary corrections made. In the second step,
species-wise consolidated graphs were developed, using the ring-width data of all 20
samples of each species, duly corrected in the first step, to depict the cross-dated
picture of each species. Both sets of graphs were generated through the use of the
Minitab software.
The growth potential of the seedling and its capacity to respond to climate change
slowly as the seedling grows, matures and attains a dominant position in the canopy of
the forest. These changes produce a downward trend in ring width and variance that are
due to intrinsic factors such as ontogeny or aging and changes in bole geometry. There
are several methods to standardize growth data as reported by Gonzalez and Eckstein
52
(2003), Murtaugh (2003), Biondi and Qeadan (2008), Rozas et al. (2009), Peters et al.
(2015), however, the most commonly used method of ‘standardization’ as described by
Fritts (1976) was used before applying statistical analyses. The procedure involved the
fitting of a curve or straight line to correspond to the average growth potential as it
changed over time. To correct the series for the intrinsically related decline in mean and
variance and remove its effect, the observed data was divided by the value of the curve
to express it as an index or a percent (x 100) of the potential average growth for that
year. Although other site factors and disturbances can affect the observed data, but such
factors were assumed to be uniform on the sampled sites on the basis of field
observations. The standardized data was used for statistical analyses and drawing
inferences.
Mean sensitivity (MS) i.e., response of trees to growth limiting factors, especially
climatic factors, was computed as the relative difference in width from one ring to the
next by using the formula, as described by Fritts (1976) and Rolland (1993):
1n
1t t1t
t1tx
XX
)X2(XMS
1-n
1
Where:
Xt
Xt+1
=
=
Ring-width at year t
Ring-width at year t+1
n = Total number of rings
3.5.3 Ring-Structure
Apart from ring-width, early wood cell diameter, early wood cell wall thickness, late
wood cell diameter and late wood cell wall thickness were measured using Digital
Compound Microscope linked with computer based measuring system, following the
methods described by Hill (1982); Stuiver et al. (1984), and Berish and Ragsdale
(1985). The measurements were recorded up to hundredth of a micrometer. In order to
determine the radial cell diameter and cell wall thickness, stem discs of respective
species from the sampled sites were used. From the cross surface of the discs, wood
samples were cut keeping in view the presence of whole rings from bark to center of
53
the disc. Radial strips of about 2 cm in width were obtained from each wood sample.
The sample blocks were marked and cut from each strip with the help of knife to avoid
the wastage of any portion in growth ring. Size of each block was kept about 2x2x4
cm3, according to the requirement of sledge microtome and getting length-wise full
sections in order to have all the rings present in a block (Figure 3.11). The sample
blocks prepared from each wooden strip were boiled in water to soften them and get
ready for sectioning with a sledge microtome.
Figure 3-11 Preparation of microscopic slides for measuring cell diameter and cell wall thickness
Cross sections of all the sample blocks in each strip were stained in 0.1% aqueous
solution of Safranin, dehydrated in different grades of alcohol, absolute alcohol and
then Xylol and finally mounted in Canada balsam (mounting medium) to make the
slides. The permanent slides of the cross sections were studied under the Nikon
Eclipse 55i microscope and observations were recorded for the measurement of radial
cell diameter and cell wall thickness in early and late wood for 50 growth rings. Five
random observations were recorded per parameter for each ring. The readings were
taken at two diagonals, mid length and width dimensions and one random point in
54
case of rectangular cells and five random points in case of circular or irregular shaped
cells. Thus, at least 1000 observations were recorded for each wood disc.
3.6 Time Function and Climate Growth Function
The time function of ring-width and ring-wood characteristics of C. deodara, P.
wallichiana and P. roxburghii were studied through regression analysis at 95%
Confidence Interval (CI) and Prediction Interval (PI), using standardized data. The time
functions for early wood and late wood formations were plotted from their mean annual
growth as % age of total annual ring-width. The trends in the time series were assessed
by applying Mann Kendall test with Normal Approximation and using Sen’s Slope
Estimator method. Climate growth functions of the three species were assessed using
standardized tree ring-width data series of the three species and grid mean maximum
temperature, minimum temperature and precipitation data by response function analysis
as described by Fritts (1976).
3.7 Statistical Design and Analysis
Parameters of climate, including maximum temperature, minimum temperature, mean
temperature and precipitation and trends thereof were assessed both on annual and
seasonal basis from the monthly climate data for the study area covering period of
1962-2011 using Mann Kendall test with Normal Approximation and Sen’s Slope
Estimator method and regression analysis. Regression curves for the best fit were
drawn and smoothened by Loess method with 0.5 degree of smoothening and at 2 steps,
and mathematical expressions derived to explain the observed trends. The analysis of
variance (ANOVA) table generated by the regression model was used to determine the
level of significance. The correlation between various components of climate was
assessed using Pearson Correlation Coefficients.
Bioclimatic indices, namely, Temperature Efficiency Index (TEI), Aridity Index (AI),
Dryness Index (DI), Rain Factor (RF), Dryness Factor (DF), Humidity Coefficient
(HC) and Precipitation Efficiency Index (PEI) and Climate Vegetation and Productivity
Index (CVPI) were calculated using the climate data and formulae as described in
55
Chapter 3, Para 3.4 and changes therein highlighted. The significance of the changes in
bioclimatic indices was tested using regression analysis.
The results for climate regime, climate change, bioclimatic indices and changes therein
were discussed for significance and impact on trees growth, forest productivity and
forest management.
Trees growth characteristics, including ring-width, early wood formation, late wood
formation, radial cell diameter and cell wall thickness were measured and analyzed
using mean values of samples of 20 trees of each selected species and applying best fit
regression models. There were four replications comprising five trees each. The impact
of climate change on growth parameters of the three tree species was assessed using a
response function, with climate parameters as independent variable. The significance of
the fitted regression models was assessed from the associated ANOVA table generated
by the software. The decadal changes in Ring-width and Ring-wood characteristics of
the three species were tested by grouping the time period into decades and applying 1-
Way analysis of variance (ANOVA). In case of significant ANOVA, the category means
were separated by applying Tukey’s Honest Significance Difference (HSD) test at p =
0.05. In growth response analysis of the three species, mean annual precipitation, taken
as an independent variable, was clustered on decadal basis and the response variables
were tested for significance using 1-Way analysis of variance (ANOVA). In case of
significant ANOVA, the category means were separated by applying Tukey’s Honest
Significance Difference (HSD) test at p = 0.05. The correlations between climate change
and growth characteristics were assessed using Pearson Correlation Coefficients.
Arc GIS software, ERDAS Imagine software, Minitab v. 15.1, XLSTAT and MS Office
Excel were used for data processing, graphics and manuscript formatting.
56
CHAPTER 4
CLIMATE CHANGE AND BIOCLIMATIC INDICES
4.1 Climate
Climate regimes and changes and trends thereof were assessed at Galies Forest Division
(GFD) for the time period 1962–2011.
4.1.1 Climate Regimes
Mean annual maximum temperature, mean annual minimum temperature and mean
annual temperature at GFD, during the time span of 1962-2011, were 16.36±0.08 °C,
6.08±0.08 °C and 11.21±0.07 °C respectively. The highest mean seasonal maximum
temperature was 23.46±0.08 °C during monsoon, which was marginally higher
compared to 23.09±0.15 °C during summer, while the lowest mean seasonal maximum
temperature was 6.78±0.12 °C during winter. The highest mean seasonal minimum
temperature was 13.12±0.07 °C during monsoon, while the lowest mean seasonal
minimum temperature was 2.01±0.14 °C during winter. The mean seasonal minimum
temperature during summer was slightly lower compared to monsoon. The mean
seasonal minimum temperatures of spring and autumn were nearly equal. The mean
seasonal maximum temperature was 18.27±0.07 °C during monsoon and the mean
seasonal minimum temperature was 2.39±0.12 °C during winter (Table 4.1).
Mean annual precipitation at GFD, during 1962-2011, was 889.48±19.43 mm. The
wettest season was monsoon having mean precipitation of 345.06±13.50 mm/season,
while autumn was the driest season with mean precipitation of 46.67±3.01 mm/season.
The spring and winter were moderately wet with mean precipitation of 198.50±9.68
mm/season and 180.53±8.14 mm/season respectively (Table 4.1).
57
Table 4-1 Temperature and Precipitation Regimes at GFD (1962-2011)
Seasons/
Periods
Climate Parameters (mean annual/seasonal)
Max. Temp.
(°C) ±SE
Min. Temp.
(°C) ±SE
Mean Temp.
(°C) ±SE
Precipitation
(mm) ±SE
Spring 13.70±0.17 4.19±0.15 8.93±0.15 198.50±9.68
Summer 23.09±0.15 11.58±0.14 17.32±0.14 116.63±4.59
Monsoon 23.46±0.08 13.12±0.07 18.27±0.07 345.06±13.50
Autumn 16.01±0.11 4.12±0.11 10.05±0.09 46.67±3.01
Winter 6.78±0.12 -2.01±0.14 2.39±0.12 180.53±8.14
Annual 16.36±0.08 6.08±0.08 11.21±0.07 889.48±19.43
4.1.2 Climate Change Trends
Trend analysis of temperature and precipitation data of GFD for the period 1962-2011
showed varying results. The trends were detected by applying Mann Kendall test with
Normal Approximation and Sen’s Slope Estimator method was used to assess the
magnitude of the detected trends. A summary of the resultant statistics and trends is
reproduced in Table 4.2.
Table 4-2 Trend Analysis of Climate Change at GFD (1962-2011)
Climate Parameters Z-Value p-Value Trend Sen’s Slope
Upward Downward
Annual Max. Temp. 4.350 0.000 1.000 0.022
Annual Min. Temp. 4.584 0.000 1.000 0.026
Annual Mean Temp. 4.417 0.000 1.000 0.025
Annual Precipitation 0.34 0.407 0.593 0.391
Spring Max. Temp. 2.376 0.009 0.971 0.032
58
Spring Min. Temp. 3.463 0.000 1.000 0.039
Spring Mean Temp. 3.061 0.001 0.999 0.034
Spring Precipitation - 0.836 0.799 0.201 0.511
Summer Max.
Temp.
2.083 0.019 0.981 0.022
Summer Min. Temp. 2.560 0.005 0.995 0.024
Summer Mean
Temp.
2.459 0.007 0.993 0.025
Summer
Precipitation
- 0.836 0.799 0.201 -0.264
Monsoon Max.
Temp.
2.041 0.021 0.979 0.013
Monsoon Min.
Temp.
1.573 0.058 0.942
0.008
Monsoon Mean
Temp.
2.208 0.014 0.986
0.010
Monsoon
Precipitation
0.586 0.279 0.721
0.500
Autumn Max. Temp. 0.845 0.199 0.801 0.007
Autumn Min. Temp. 2.509 0.006 0.994 0.019
Autumn Mean
Temp.
2.024 0.021 0.979
0.013
Autumn
Precipitation
0.602 0.273 0.727
0.114
Winter Max. Temp. 4.249 0.000 1.000 0.033
Winter Min. Temp. 5.404 0.000 1.000 0.044
Winter Mean Temp. 5.353 0.000 1.000 0.040
Winter Precipitation 0.405 0.344 0.656 0.290
Increasing trend No trend
Scattered plots were constructed and trend lines were drawn for all climate parameters.
Observations were recorded on conspicuous variations/trends in a particular year or
bracket of years. The curves were smoothened through Loess method with 0.5 degree
of smoothening and at 2 steps to have more insight in the pattern of the climate change
in the study area during the focused period. The details of the analysis from visual
interpretation of the data and scattered plots are reproduced in the following paras.
59
The mean annual maximum temperature exhibited an overall increasing trend during
1962-2011. The highest mean annual maximum temperature was 17.40 °C during 1999,
while the lowest mean annual maximum temperature was 15.20 °C during 1965.
Overall, the mean annual maximum temperature remained higher during 1998-2011,
except 2005, compared to 1972-1994. There were 20 years having mean annual
maximum temperature higher than 16.50°C, while there were three years having mean
annual maximum temperature lower than 15.50 °C (Figure 4.1). The highest variability
in mean annual maximum temperature within a year was recorded during 1968,
followed by 1982 and the lowest variability was recorded during 2011.
Figure 4-1Trend line of Mean Annual Maximum Temp. (°C) vs. Time at GFD (1962-2011)
201020001990198019701960
18.0
17.5
17.0
16.5
16.0
15.5
15.0
Year
Ma
xim
um
Te
mp
. (°
C)
Regression
95% CI
95% PI
60
The mean annual minimum temperature exhibited an overall increasing trend during
1962-2011. The highest mean annual minimum temperature observed was 7.61 °C
during 2001, and the lowest mean annual minimum temperature was 4.96 °C during
1975. The mean annual minimum temperature remained above 6.00 °C during 1998 to
2011, with four years, near the turn of the century, having mean annual minimum
temperature higher than 7.00 °C. (Figure 4.2). The highest variability in mean annual
minimum temperature within a year was during 1968, followed by 1975, while the
lowest variability in mean annual minimum temperature within a year was recorded
during 2004, followed by 1989.
201020001990198019701960
8
7
6
5
4
Year
Me
an
min
imu
m T
em
p.
(°C
)
Regression
95% CI
95% PI
Figure 4-2 Trend line of Mean Annual Minimum Temp. (°C) vs. Time at GFD (1962-2011)
61
The mean annual temperature exhibited an overall increasing trend during 1962-2011.
The highest mean annual temperature observed was 12.26 °C during 1999 and 2001.
The lowest mean annual temperature observed was 10.13 °C during 1965, followed by
10.14 °C during 1968. The mean annual temperature increased steadily, except 1986 to
1992, and remained above 11.50 °C during 1995-2011, except 2005 (Figure 4.3). The
increasing trend of mean annual temperature closely followed the trend of annual
maximum temperature. The highest variability in mean annual temperature within a
year was during 1968, while the lowest variability was recorded during 2004.
201020001990198019701960
13.0
12.5
12.0
11.5
11.0
10.5
10.0
Year
Me
an
Te
mp
. (°
C)
Regression
95% CI
95% PI
Figure 4-3 Trend line of Mean Annual Temp. (°C) vs. Time at GFD (1962-2011)
62
The annual precipitation overall exhibited no trend during 1962-2011. The temporal
distribution showed a flattened normal distribution, with peak values around 1986. The
wettest year was 1983, with annual precipitation of 1146.2±25.01 mm/annum, followed
by 1975. The driest year was 1971, with annual precipitation of 561.2±11.60 mm,
followed by 2000 (Figure 4.4). The average annual precipitation remained around 950
mm/annum during late 1970s to mid-1990s, with lower values on both sides. The
highest variability in annual precipitation within a year was during 2006, followed by
1978. In contrast, the lowest variability in annual precipitation within a year was during
2000, followed by 1974.
201020001990198019701960
1300
1200
1100
1000
900
800
700
600
500
Year
Pre
cip
ita
tio
n (
mm
/an
nu
m)
Regression
95% CI
95% PI
Figure 4-4 Trend line of Annual Precipitation vs. Time at GFD (1962- 2011)
63
The mean spring maximum temperature exhibited an overall increasing trend during
1962-2011. The mean spring maximum temperature during 1962-2011 was
13.70±0.17°C. The highest mean spring maximum temperature was 16.80 °C during
2004, while the lowest mean spring maximum temperature was 11.00 °C during 1983.
There was a steady increase in mean spring maximum temperature during 1970s, and
steeper one during 1997 to 2011, except 2006. Conversely, mean spring maximum
temperature decreased slightly during 1986-1994 (Figure 4.5). The mean spring
maximum temperature nearly followed the pattern of mean annual maximum
temperature.
201020001990198019701960
18
17
16
15
14
13
12
11
10
Year
Sp
rin
g M
ax.
Te
mp
. (°
C)
Regression
95% CI
95% PI
Figure 4-5 Trend line of Mean Spring Maximum Temp. (°C) vs. Time at GFD (1962-2011)
64
The mean spring minimum temperature exhibited an overall increasing trend during
1962-2011. The mean spring minimum temperature during 1962-2011 was
4.19±0.15°C. The highest mean spring minimum temperature was 6.25 °C during 2004,
followed by 6.04 °C during 2008. The lowest mean spring minimum temperature was
2.04 °C during 1965. The mean spring minimum temperature increased steadily during
1962-2011, except a slight decline between 1985 and 1995, and increased considerably
during 1998-2011, except 2010 (Figure 4.6). The increase in mean spring minimum
temperature was slightly higher compared to mean spring maximum temperature.
201020001990198019701960
8
7
6
5
4
3
2
1
Year
Sp
rin
g M
in.
Te
mp
. (°
C)
Regression
95% CI
95% PI
Figure 4-6 Trend line of Mean Spring Maximum Temp. (°C) vs. Time at GFD (1962-2011)
65
The mean spring temperature exhibited an overall increasing trend during 1962-2011.
The mean spring temperature during 1962-2011 was 8.93±0.15 °C. The highest mean
spring temperature was 11.58 °C during 2004, followed by 10.79 °C during 2008. The
lowest mean spring temperature was 6.73 °C during 1965. The mean spring
temperature showed a declining trend during 1985-1995, and a rising trend in years
thereafter (Figure 4.7). The increase in mean spring temperature was slightly higher
compared to mean spring maximum temperature, but lower than mean spring minimum
temperature.
201020001990198019701960
13
12
11
10
9
8
7
6
Year
Sp
rin
g m
ea
n T
em
p.
(°C
)
Regression
95% CI
95% PI
Figure 4-7 Trend line of Mean Spring Temp. (°C) v. Time at GFD (1962-2011)
66
The variability in spring precipitation during 1962-2011 exhibited no overall trend. The
mean spring precipitation during 1962-2011 was 198.50±9.68 mm/season. The highest
spring precipitation was 384.10 mm/season during 1983, while the lowest spring
precipitation was 90.50 mm/season during 1999. An increasing trend in mean spring
precipitation was observed during 1981 to 1992, followed by a steady decline (Figure
4.8).
201020001990198019701960
400
300
200
100
0
Year
Sp
rin
g P
pt.
(m
m/m
on
th)
Regression
95% CI
95% PI
Figure 4-8 Trend line of Spring Precipitation vs. Time at GFD (1962- 2011)
67
The mean summer maximum temperature exhibited an overall increasing trend during
1962-2011. The mean summer maximum temperature during 19622011 was
23.09±0.15°C. The highest mean summer maximum temperature was 25.59 °C during
1982, followed by 25.22 °C during 2001. The lowest mean summer maximum
temperature was 20.61 °C during 1987. There was a steady increase in mean summer
maximum temperature, except a small decline during mid-1980s. There were 11 years
having mean summer maximum temperature higher than 24.00 °C. Conversely, there
were eight years having mean summer maximum temperature lower than 22.00 °C
(Figure 4.9).
201020001990198019701960
26
25
24
23
22
21
20
Year
Su
mm
er
Ma
x. T
em
p.
(°C
)
Regression
95% CI
95% PI
Figure 4-9 Trend line of Mean Summer Maximum Temp. (°C) vs. Time at GFD (1962-2011)
68
The mean summer minimum temperature exhibited an overall increasing trend during
1962-2011. The mean summer minimum temperature during the period was
11.58±0.14°C. The highest mean summer minimum temperature was 14.04 °C during
2000, followed by 13.07 °C during 1984. The lowest mean summer minimum
temperature was 9.44 °C during 1987. The mean summer minimum temperature
increased steadily, except a small decline during 19851994. There were ten years
having mean summer minimum temperature higher than 12.50°C, while there were four
years having mean summer minimum temperature lower than 10.50 °C (Figure 4.10).
The slope of increase in mean summer minimum temperature was relatively higher
compared to mean summer maximum temperature.
201020001990198019701960
14
13
12
11
10
9
Year
Su
mm
er
Min
. T
em
p.
(°C
)
Regression
95% CI
95% PI
Figure 4-10 Trend line of Mean Summer Minimum Temp. (°C) vs. Time at GFD (1962-2011)
69
The mean summer temperature exhibited an overall increasing trend during 1962-2011.
The mean summer temperature during the period was 17.32±0.14°C. The highest mean
summer temperature was 19.50 °C during 2000, followed by 19.4 °C during 2001. The
lowest mean summer temperature was 15.00 °C during 1987. There was a steady
increase in mean summer temperature over the years, with a higher pace after 1995.
There were 11 years having mean summer temperature higher than 18.00°C, while there
were three years having summer temperature lower than 16.00 °C (Figure 4.11). The
increase in mean summer temperature was relatively more even compared to mean
summer maximum temperature and mean summer minimum temperature.
201020001990198019701960
20
19
18
17
16
15
Year
Su
mm
er
Me
an
Te
mp
. (°
C)
Regression
95% CI
95% PI
Figure 4-11 Trend line of Mean Summer Temp. (°C) vs. Time at GFD (1962-2011)
70
The summer precipitation exhibited no trend during 1962-2011. The mean summer
precipitation during the period was 116.63±4.59 mm/season. The highest summer
precipitation was 217.60 mm/season during 1996, while the lowest summer
precipitation was 61.50 mm/season during 2006. An increasing trend in summer
precipitation was observed during late 1990s and early 2000s, followed by a decline.
There were five years having summer precipitation higher than 150.00 mm.
Conversely, there were 14 years having summer precipitation lower than 100.00 mm
(Figure 4.12).
201020001990198019701960
200
150
100
50
Year
Su
mm
er
Pp
t. (
mm
/mo
nth
)
Regression
95% CI
95% PI
Figure 4-12 Trend line of Summer Precipitation vs. Time at GFD (1962-2011)
71
The mean monsoon maximum temperature exhibited an overall increasing trend during
1962-2011. The mean monsoon maximum temperature during the period was
23.46±0.08°C. The highest mean monsoon maximum temperature was 24.80 °C during
1998, followed by 24.60 °C during 2010. The lowest mean monsoon maximum
temperature was 22.10 °C during 1965. There was a steady increase in mean monsoon
maximum temperature, with a steeper pace after 2002. There were nine years having
mean monsoon maximum temperature higher than 24.00 °C. Conversely, there were
two years having mean monsoon maximum temperature lower than 22.50 °C (Figure
4.13). The ranges of mean monsoon maximum temperature and mean summer
maximum temperature were approximately the same.
201020001990198019701960
25.0
24.5
24.0
23.5
23.0
22.5
22.0
Year
Mo
nso
on
Ma
x. T
em
p.
(°C
)
Regression
95% CI
95% PI
Figure 4-13 Trend line of Mean Monsoon Maximum Temp. (°C) vs. Time at GFD (1962-2011)
72
The mean monsoon minimum temperature exhibited no overall trend during 1962-
2011. The mean monsoon minimum temperature during the period was 13.1±0.07 °C.
The highest mean monsoon minimum temperature was 14.40 °C during 1998, followed
by 14.30 °C during 1997. The lowest mean monsoon minimum temperature was 11.80
°C during 1989. The mean monsoon minimum temperature decreased during 1971-
1985 and increased during 1990s. There were five years having mean monsoon
minimum temperature higher than 13.50 °C, while there were four years having mean
monsoon minimum temperature lower than 12.50 °C (Figure 4.14).
201020001990198019701960
14.5
14.0
13.5
13.0
12.5
12.0
Year
Mo
nso
on
Min
Te
mp
. (°
C)
Regression
95% CI
95% PI
Figure 4-14 Trend line of Mean Monsoon Minimum Temp. (°C) vs. Time at GFD (1962-2011)
73
The mean monsoon temperature exhibited an overall increasing trend during 1962-
2011. The mean monsoon temperature during the period was 18.27±0.07°C. The
highest mean monsoon temperature was 19.60 °C during 1998, followed by 19.20 °C
during 1999. The lowest mean monsoon temperature was 17.2 °C during 1965. There
was a decline in mean monsoon temperature during 1978-86, followed by a steady
increase. There were 13 years having mean monsoon temperature higher than 18.50 °C.
Conversely, there were three years having mean monsoon temperature lower than 17.50
°C (Figure 4.15). The changing trend of mean monsoon temperature closely followed
that of mean monsoon minimum temperature.
201020001990198019701960
20.0
19.5
19.0
18.5
18.0
17.5
17.0
Year
Mo
nso
on
Me
an
Te
mp
. (°
C)
Regression
95% CI
95% PI
Figure 4-15 Trend line of Mean Monsoon Temp. (°C) vs. Time at GFD
74
The monsoon precipitation exhibited no overall trend during 1962-2011. The mean
monsoon precipitation during the period was 345.06±13.50 mm/season. The highest
monsoon precipitation was 573.80 mm/season during 2006, while the lowest monsoon
precipitation was 171.20 mm/season during 1963. There was a steep increase in mean
monsoon precipitation during 1970-1984, followed by a gradual decline. There were
eight years having monsoon precipitation higher than 450.00 mm. Conversely, there
were two years having monsoon precipitation lower than 200.00 mm (Figure 4.16).
201020001990198019701960
600
500
400
300
200
100
0
Year
Mo
nso
on
Pp
t (m
m/a
nn
um
)
Regression
95% CI
95% PI
Figure 4-16 Trend line of Monsoon Precipitation vs. Time at GFD (1962-2011)
75
The mean autumn maximum temperature overall exhibited no trend during 1962-2011.
The mean autumn maximum temperature during the period was 16.01±0.11°C. The
highest mean autumn maximum temperature was 18.10 °C during 1998, followed by
17.50 °C during 1979. The lowest mean autumn maximum temperature was 14.40 °C
during 1967. There was an increase in mean autumn maximum temperature during
1970-1978, followed by a declining trend during 1980-1997. There were six years
having mean autumn maximum temperature higher than 17.00 °C and the same number
of years having mean autumn maximum temperature lower than 15.00 °C years (Figure
4.17).
201020001990198019701960
18
17
16
15
14
Year
Au
tum
n M
ax.
Te
mp
. (°
C)
Regression
95% CI
95% PI
Figure 4-17 Trend line of Mean Autumn Maximum Temp. (°C) vs. Time at GFD (1962-2011)
76
The mean autumn minimum temperature exhibited an overall increasing trend during
1962-2011. The mean autumn minimum temperature during the period was
4.12±0.11°C. The highest mean autumn minimum temperature was 5.70 °C during
2001, followed by 5.60 °C during 1977. The lowest mean autumn minimum
temperature was 2.90 °C during 1968. There were wide fluctuations in mean autumn
minimum temperature over the years. The mean autumn minimum temperature
deceased during 1983-1998, followed by a relatively sharp increase. There were eight
years having mean autumn minimum temperature higher than 5.00 °C, while there were
13 years having mean autumn minimum temperature lower than 3.50 °C (Figure 4.18).
201020001990198019701960
6
5
4
3
2
Year
Au
tum
n M
in.
Te
mp
(°C
)
Regression
95% CI
95% PI
Figure 4-18 Trend line of Mean Autumn Minimum Temp. (°C) vs. Time at GFD (1962-2011)
77
The mean autumn temperature exhibited an overall increasing trend during 1962-2011.
The mean autumn temperature during the period was 10.05±0.09 °C. The highest mean
autumn temperature was 11.70 °C during 1998, followed by 11.30 °C during 2006. The
lowest mean autumn temperature was 8.80 °C during 1968. There was a smooth
increasing trend in mean autumn temperature, except a decline during 1980-1991.
There were 13 years having mean autumn temperature higher than 10.50 °C.
Conversely, there were three years having mean autumn temperature lower than 9.00
°C (Figure 4.19). The increase in mean autumn temperature was relatively more even
compared to mean autumn minimum temperature. The changing trend of mean autumn
temperature followed the pattern of mean autumn maximum temperature.
201020001990198019701960
12
11
10
9
8
Year
Au
tum
n M
ea
n T
em
p.
(°C
)
Regression
95% CI
95% PI
Figure 4-19 Trend line of Mean Autumn Temp. (°C) vs. Time at GFD (1962-2011)
78
The autumn precipitation exhibited no overall trend during 1962-2011. The mean
autumn precipitation during the period was 46.67±3.01 mm/season. The highest autumn
precipitation was 105.70 mm/season during 1996, while the lowest autumn
precipitation was 9.00 mm/season during 1974. An increasing trend in autumn
precipitation was observed during 1981-1997, followed by a decline. There were three
years having autumn precipitation higher than 80.00 mm. Conversely, there were five
years having autumn precipitation lower than 20.00 mm (Figure 4.20).
201020001990198019701960
120
100
80
60
40
20
0
Year
Au
tum
n P
pt
(mm
/an
nu
m)
Regression
95% CI
95% PI
Figure 4-20 Trend line of Autumn Precipitation vs. Time at GFD (1962-2011)
79
The mean winter maximum temperature exhibited an overall increasing trend during
1962-2011. The mean winter maximum temperature during the period was 6.78±0.12
°C. The highest mean winter maximum temperature was 8.50 °C during 2009, followed
by 8.30 °C during 2007. The lowest mean winter maximum temperature was 4.00 °C
during 1968. The mean winter maximum temperature increased steadily over the years,
with a higher pace during 20022011. There were eight years each having mean winter
maximum temperature higher than 7.50 °C and lower than 6.00 °C (Figure 4.21).
201020001990198019701960
10
9
8
7
6
5
4
Year
Win
ter
Ma
x. T
em
p.
(°C
)
Regression
95% CI
95% PI
Figure 4-21 Trend line of Mean Winter Maximum Temp. (°C) vs. Time at GFD (1962-2011)
80
The mean winter minimum temperature exhibited an overall increasing trend during
1962-2011. The mean winter minimum temperature during the period was -
2.0±01.14°C. The highest mean winter minimum temperature was 0.40 °C during 2001,
followed by -0.30 °C during 2003. The lowest mean winter minimum temperature was
-4.90 °C during 1968. There was a steady increase in mean winter minimum
temperature during 1962-2011. There were six years having mean winter minimum
temperature higher than -1.00 °C, while there were eight years having mean winter
minimum temperature lower than -3.00 (Figure 4.22).
201020001990198019701960
1
0
-1
-2
-3
-4
-5
Year
Win
ter
Min
. T
em
p.
(°C
)
Regression
95% CI
95% PI
Figure 4-22 Trend line of Mean Winter Minimum Temp. (°C) vs. Time at GFD (1962-2011)
81
The mean winter temperature exhibited an overall increasing trend during 1962-2011.
The mean winter temperature during the period was 2.39±0.12 °C. The highest mean
winter temperature was 4.00 °C during 2009, followed by 3.80 during 2004. The lowest
mean winter temperature was -0.40 °C during 1968. There were ten years having mean
winter temperature higher than 3.00 °C. Conversely, there were four years having mean
winter temperature lower than 1.00 °C (Figure 4.23). The changing trend of mean
winter temperature closely followed that of the mean winter maximum temperature.
201020001990198019701960
5
4
3
2
1
0
Year
Win
ter
Me
an
Te
mp
. (°
C)
Regression
95% CI
95% PI
Figure 4-23 Trend line of Mean Winter Temp. (°C) vs. Time at GFD (1962-2011)
82
The winter precipitation overall exhibited no trend during 1962-2011. The mean winter
precipitation during the period was 180.53±8.14 mm/season. The highest winter
precipitation was 294.40 mm/season during 2005, while the lowest winter precipitation
was 52.50 mm/season during 1989. There were large fluctuations in winter
precipitation over the period 1962-2011. The mean winter precipitation remained
around 175.00 mm for the first 35 years covered under the study, followed by a gradual
increase to above 200.00 mm during the subsequent fifteen years. There were five years
having winter precipitation higher than 250.00 mm. Conversely, there were nine years
having winter precipitation lower than 125.00 mm (Figure 4.24).
201020001990198019701960
300
250
200
150
100
50
Year
Win
ter
Pp
t (m
m/m
on
th)
Regression
95% CI
95% PI
Figure 4-24 Trend line of Winter Precipitation vs. Time at GFD (1962-2011)
83
4.1.3 Climate Changes
The analysis of climate data showed considerable variations in temperature and
precipitation, both on vertical (across years) and horizontal (across seasons) scales,
during 1962-2011. The mean maximum temperature, mean minimum temperature and
mean annual temperature increased by 1.10°C, 1.32 °C and 1.22 °C, and the mean
annual precipitation by 1.39%, during 1962-2011. The changes in these parameters on
seasonal basis varied from season to season. The increases in temperature parameters
on interannual basis were highly significant (p<0.01) and the increase in precipitation
non-significant (p>0.05). The increase in maximum temperature was highly significant
(p<0.01) during winter, significant (p<0.05) during spring, summer, and autumn and
non-significant (p>0.05) during autumn. The increase in minimum temperature was
highly significant (p<0.01) during spring, summer and winter, significant (p<0.05)
during autumn and non-significant (p>0.05) during monsoon. The increase in mean
temperature was highly significant (p<0.01) during spring and winter and significant
(p<0.05) during summer, monsoon and autumn. On seasonal basis, the changes in
precipitation were: significant (p<0.05) decrease of -14.90% in spring, non-significant
(p>0.05) decrease of -9.95% during summer, and significant (p<0.05) increase of
8.94% during monsoon and non-significant (p>0.05) increase of 11.81% and 12.04%
during autumn and winter respectively (Table 4.3). The significance level (probability
of significance) was based on the ANOVA generated by the regression analysis.
Amongst the seasons, the highest increase in mean maximum temperature of 1.73 °C
was recorded during winter and the lowest increase of 0.50 °C during autumn. The
highest increase in mean minimum temperature of 2.37 °C was recorded during winter
and the lowest increase of 0.35 °C during monsoon. The highest increase in mean
seasonal temperature was recorded during winter, followed by spring and summer. The
lowest increase in mean seasonal temperature was recorded during monsoon, followed
by autumn. The increase in mean maximum temperature and mean minimum
temperature during spring and autumn indicated shortening of winter period and
lengthening of summer period.
84
Table 4-3 Temperature and precipitation changes at GFD (1962-2011)
Seasons/
Periods
Climate parameters and precipitation changes
Max. Temp.
(Δ°C)
Min. Temp.
(Δ°C)
Mean Temp.
(Δ°C)
Precipitation
(Δ %)
Spring 1.47* 1.78** 1.64** - 14.90*
Summer 1.04* 1.16** 1.12* - 9.85ns
Monsoon 0.68* 0.35ns 0.54* 8.94*
Autumn 0.50ns 0.92* 0.73* 11.81ns
Winter 1.73** 2.37** 2.08** 12.04ns
Annual 1.10** 1.32** 1.22** 1.39ns
* Significant (p<0.05); ** highly significant (p<0.01); ns= Non-significant (p>0.05)
The analysis showed an overall increase of 1.39% in mean annual precipitation during
1962-2011. The mean seasonal precipitation increased by 8.94%, 11.81%, and 12.04%
during monsoon, autumn and winter respectively. Conversely, the mean seasonal
precipitation decreased by 14.90% and 9.85% during spring and summer respectively.
85
The analysis of temperature data indicated relatively higher increase in mean minimum
temperature compared to mean maximum temperature (Figure 4.25).
Figure 4-25 Comparison between increases in Maximum Temperature and Minimum Temperature
The slope gradient of mean minimum temperature was higher compared to mean
maximum temperature and mean temperature on annual as well as seasonal basis, thus
indicating warming of night temperature and narrowing down diurnal temperature gap.
The narrowing down of the gap between maximum and minimum temperatures was
more pronounced in monsoon. The results also showed higher variability in mean
minimum temperature compared to mean maximum temperature and mean
temperature. Lower fluctuations were recorded in mean maximum temperature, thus
indicating a uniform increase which was also supported by the linear model fit for the
change in mean maximum temperature.
0.00
0.50
1.00
1.50
2.00
2.50
Spring Summer Monsoon Autumn Winter Annual
Season
Max. Temp. Min. Temp.
86
4.1.4 Mathematical Expressions of Climate Change Trends at GFD (1962-2011)
Mathematical expressions of temperature and precipitation changes at GFD during
1962-2011 showed both linear and quadratic behaviors. Climate parameters of annual
minimum temperature, summer maximum temperature, summer minimum temperature,
summer mean temperature, monsoon maximum temperature, autumn maximum
temperature, autumn mean temperature, winter minimum temperature, winter mean
temperature and winter precipitation exhibited linear functions, while the other 14
parameters followed quadratic pattern of regression response. The R2 for linear response
ranged between 0.01 and 0.50, while the R2 for quadratic function ranged between 0.02
and 0.39, thus indicated good fit of models for some climate parameters and poor fit of
models for others, especially precipitation (Table 4.4).
Table 4-4 Mathematical Expressions of Climate Change Trends at GFD (1962-
2011)
Climate Parameters Mathematical Expressions R2 F(1) 2*, (48) 47*
(p)
Annual Max. Temp. Y = 1306 - 1.321 × X
+ 0.0003 × X2
0.39 15.11 (0.000)
Annual Min. Temp. Y = - 47.58 + 0.027 × X 0.44 37.01 (0.000)
Mean Annual Temp. Y = 1133 - 1.155×X + 0.0003 ×
X2
0.43 17.79 (0.000)
Annual Precipitation Y = - 983447 + 990.8 × X
- 0.249 × X2
0.12 3.12 (0.05)
Spring Max. Temp. Y = 3821 - 3.863 × X + 0.001 ×
X2
0.15 4.09 (0.023)
Spring Min. Temp. Y = 2196 - 2.243×X + 0.0006 ×
X2
0.26 8.43 (0.001)
Spring Mean Temp. Y = 3081 - 3.127 × X + 0.001 ×
X2
0.22 6.55 (0.003)
Spring Precipitation Y = -182887 + 185.0 × X
- 0.047 × X2
0.04 0.87 (0.425)
Summer Max. Temp. Y = - 19.08 + 0.021 × X 0.08 4.17 (0.047)
87
Summer Min. Temp. Y = - 35.43 + 0.0237 × X 0.13 7.11 (0.010)
Summer Mean Temp. Y = - 28.02 + 0.0228 × X 0.11 6.00 (0.018)
Summer Precipitation Y = - 53502 + 54.23 × X
- 0.014 × X2
0.02 0.45 (0.643)
Monsoon Max. Temp. Y = - 3.93 + 0.014 × X 0.12 6.21 (0.016)
Monsoon Min. Temp. Y = 613 - 0.611 × X + 0.0002 ×
X2
0.05 1.34 (0.271)
Monsoon Mean Temp. Y = 1241 - 1.242 × X + 0.0003 ×
X2
0.12 3.31 (0.045)
Monsoon Precipitation Y = - 695351 + 699.9 × X
- 0.176 × X2
0.13 3.47 (0.039)
Autumn Max. Temp. Y = - 4.19 + 0.0102 × X 0.04 1.77 (0.190)
Autumn Min. Temp. Y = 1547 - 1.572 × X + 0.0004 ×
X2
0.13 3.63 (0.034)
Autumn Mean Temp. Y = -19.54 + 0.01489 × X 0.11 6.03 (0.018)
Autumn Precipitation Y = -77864 + 78.34 × X
- 0.0197 × X2
0.04 0.86 (0.428)
Winter Max. Temp. Y = 1935 - 1.976 × X + 0.0005 ×
X2
0.36 13.45 (0.000)
Winter Min. Temp. Y = - 98.24 + 0.0484 × X 0.49 46.78 (0.000)
Winter Mean Temp. Y = - 81.97 + 0.0425 × X 0.50 48.29 (0.000)
Winter Precipitation Y = - 649 + 0.4174 × X 0.01 0.54 (0.465)
Values in parenthesis in the top row are degree of freedom for linear equations and with asterisk
for quadratic equations. The (p) values indicate significance levels.
4.1.5 Correlation Coefficients Matrix of different Climate Factors at GFD
The Pearson Correlation Coefficients matrix showed a highly significant (p<0.01)
positive correlation between maximum temperature and mean temperature (r = 0.94)
and minimum temperature and mean temperature (r = 0.97). The correlation of
precipitation with mean temperature, maximum temperature and minimum temperature
was significant but negative (Table 4.5).
88
Table 4-5 Correlation Coefficients Matrix among different Climate Factors at
GFD
Mean Temp Max. Temp. Min. Temp.
Max. Temp. 0.941**
(0.000)
Min. Temp. 0.966**
(0.000)
0.843**
(0.000)
Precipitation - 0.423**
(0.002)
- 0.458**
(0.001)
- 0.353*
(0.012)
Values in parenthesis are p values; * = Significant (p<0.05); ** = highly significant
(p<0.01)
4.2 Bioclimatic Indices
Bioclimatic indices are tools to explain the spatial distribution of vegetation units by the
combination of different climatic factors.
4.2.1 Bioclimatic Indices Regime
The bioclimatic indices regimes were estimated for GFD for the time period of 1962-
2011, with the results reproduced in Table 4.6.
Table 4-6 Bioclimatic Indices Regimes at GFD (1962-2011)
Indices
Seasons
Annual
(A) Spring
(S)
Summer
(Su)
Monsoon
(M)
Autumn
(Au)
Winter
(W)
TEI
±SE
4.02
±0.07
7.79
±0.06
8.22
±0.03
4.52
±0.03
1.07
±0.06
5.04
±0.04
AI
±SE
20.70
±1.24
6.41
±0.27
17.95
±0.73
4.25
±0.29
61.21
±6.05
73.25
±1.82
89
DI
±SE
11.57
±0.71
3.39
±0.14
9.47
±0.38
2.34
±0.16
38.99
±6.48
39.92
±1.01
RF
±SE
23.14
±0.12
6.78
±0.19
18.94
±0.19
4.68
±0.11
77.97
±0.05
79.84
±0.12
DF
±SE
12.70
±0.12
4.82
±0.20
13.68
±0.20
2.75
±0.11
19.46
±0.05
49.00
±0.12
HC
±SE
21.63
±1.30
6.34
±0.31
17.70
±0.79
4.37
±0.30
72.87
±2.76
74.62
±2.43
PEI
±SE
2.67
±0.14
1.11
±0.05
3.17
±0.13
0.59
±0.04
3.47
±0.16
10.63
±0.25
TEI=Temperature Efficiency Index; AI= Aridity Index; DI= Dryness Index; RF= Rain Factor; DF=
Dryness Factor; HC= Humidity Coefficient; PEI= Precipitation Efficiency Index
Temperature Efficiency Index (TEI) varied considerably among seasons with mean
annual TEI of 5.04±0.04. The highest TEI was estimated during monsoon, while the
lowest TEI was during winter. Similarly, Aridity Index (AI) varied among the seasons
with mean annual AI of 73.25±1.82. The highest AI was in winter and the lowest in
autumn. The mean Dryness Index (DI) and mean Rain Factor (RF) were 39.92±1.01
and 79.84±0.12 respectively. The seasonal regimes of DI and RF followed the pattern
of AI. The highest Dryness Factor and the lowest Dryness Factor (DF) were estimated
in winter and autumn respectively. Conversely, DF was higher in monsoon compared to
spring. Humidity Coefficient (HC) varied among seasons with mean annual HC of
74.62±2.43. The highest HC was 72.87±2.76 in winter and the lowest was 4.37±0.30 in
autumn. The mean annual regime of Precipitation Efficiency Index (PEI) was
10.63±0.25. The highest PEI was estimated for winter and the lowest for autumn.
90
4.2.2 Changes in Bioclimatic Indices
The results showed that mean annual TEI increased by 11.53%. The highest increase
was recorded during winter (55.08%), followed by spring (20.21%) and the lowest
during monsoon (2.99%). Conversely, mean annual AI decreased by 7.92%. The
highest decrease in AI was during winter (59.59%), followed by spring (28.03%).
However, AI increased during monsoon (14.18%) and autumn (7.21%). The changes in
DI and RF were almost similar which followed the pattern of AI. The mean annual DF
decreased by (4.95%), with the highest decrease in spring (23.35%), followed by
summer (13.52%), whereas the highest decrease in HC was during winter (42.82%),
followed by spring (29.32%). Conversely, HC increased during monsoon (5.93%) and
autumn (4.24%). The pattern of changes in PEI were similar to DF (Table 4.7). The
results also indicated that mean annual changes in TEI were positive and highly
significant, while for DI, RF and DF negative and significant and AI, HC and PEI both
negative and non-significant. Similarly, the mean seasonal changes in TEI were
significant and highly significant, AI (Monsoon and winter) and DI (Monsoon) were
significant while in other indices were non-significant.
Table 4-7 Changes (%) in Bioclimatic Indices at GFD (1962-2011)
Indices Spring
(S)
Summer
(Su)
Monsoon
(M)
Autumn
(Au)
Winter
(W)
Annual
(A)
TEI 20.21** 6.67* 2.99* 7.54* 55.08** 11.53**
AI - 28.03ns - 14.67 ns 14.18* 7.21 ns - 59.59* - 7.92 ns
DI - 29.33 ns - 14.94 ns 5.93* 4.23 ns - 42.79 ns - 6.40*
RF - 29.29 ns - 14.92 ns 5.93 ns 4.24 ns - 42.62 ns - 8.72*
DF - 23.35 ns - 13.52 ns 6.76 ns 7.34 ns - 11.83 ns - 4.95*
HC - 29.32 ns - 14.93 ns 5.93 ns 4.24 ns - 42.82 ns - 8.72 ns
PEI - 21.30 ns - 12.92 ns 7.12 ns 8.38 ns - 3.59 ns - 3.57 ns
* Significant (p<0.05); ** highly significant (p<0.01); ns= Non-significant (p>0.05)
91
4.2.3 Mathematical Expressions of Changes in Bioclimatic Indices at GFD (1962-
2011)
Mathematical expressions of changes in bioclimatic indices at GFD during 1962-2011
varied in pattern and exhibited linear, quadratic and polynomial forms (Table 4.8). The
annual and seasonal mathematical expressions for TEI were associated with significant
and highly significant higher values of R2, indicating good fit equations, while the
expressions for other indices were mostly non-significant with smaller values of R2, and
indicating poor fit equations.
Table 4-8 Mathematical Expressions of Changes in Bioclimatic Indices at GFD
(1962-2011)
Bioclimatic
Indices Mathematical Expressions R2
F(1,2,3), (48,47,46)*
(p)
ATEI Y = - 17.23 + 0.01121 x X
0.42 34.88 (0.000)
STEI Y = - 25.93 + 0.01508 × X
0.20 11.94 (0.001)
SuTEI Y = - 12.61 + 0.01027 x X
0.11 6.00 (0.018)
MTEI Y = - 1.60 + 0.004944 x X
0.11 5.85 (0.019)
AuTEI Y = - 8.79 + 0.00670 x X
0.11 6.03 (0.018)
WTEI Y = - 36.89 + 0.01911 x X
0.50 48.29 (0.000)
AAI Y = 318.70 - 0.12350 x X
0.02 0.96 (0.333)
SAI Y = 293.60 - 0.13700 x X
0.05 2.64 (0.111)
SuAI Y = 47.55 - 0.020700 x X
0.08 0.25 (0.271)
MAI Y = - 37769 + 38.02 x X - 0.009565 x X2
0.13 6.61 (0.013)
AuAI Y = - 7526 + 7.577 x X - 0.00190 x X2
0.03 1.52 (0.224)
WAI Y = 137713 - 137.6 x X + 0.03436 x X2 0.15 5.63 (0.022)
ADI Y = - 48011 + 48.45 x X - 0.0122 x X2
0.13 5.63 (0.022)
92
SDI Y = 172.9 - 0.081200 x X
0.06 2.82 (0.099)
SuDI Y = - 1542 + 1.567 x X - 0.00039 × X2 0.03 0.26 (0.610)
MDI Y = - 19967 + 20.10 x X - 0.00505 × X2 0.13 6.62 (0.013)
AuDI
Y = 632028 - 956.6 x X + 0.4826 × X2 -
0.00008 × X3
0.62 1.45 (0.234)
WDI Y = 898.3 - 0.43260 x X
0.19 0.93 (0.341)
ARF Y = - 96022 + 96.91 x X - 0.0244 × X2
0.13 5.63 (0.022)
SRF
Y = 4221064 - 6390 x X + 3.224 × X2-
0.00054 × X3
0.93 0.84 (0.365)
SuRF
Y = 1193311 - 1804 x X + 0.9088 × X2-
0.000153 × X3
0.06 1.58 (0.421)
MRF
Y = - 3725827 + 5607 x X - 2.813 × X2+
0.000470 × X3
0.17 2.36 (0.132)
AuRF Y = 1264056 - 1913 x X + 0.9653 × X2-
0.00016 × X3 0.06 1.45 (0.234)
WRF Y = 1797 - 0.86520 ×X 0.02 0.93 (0.341)
ADF Y = - 57298 + 57.79 X - 0.01456 x X2
0.12 5.91 (0.019)
SDF Y = 2161195 - 3271 x X + 1.650 x X2 -
0.000278 × X3 0.06 0.98 (o.327)
SuDF
Y = 837599 - 1266 x X + 0.6379 × X2 -
0.000107 × X3
0.08 1.61 (0.211)
MDF
Y = - 2673684 + 4024 x X - 2.018 × X2+
0.00033 × X3
0.17 2.38 (0.130)
AuDF
Y = 747935 - 1132 x X + 0.5711 x X2 -
0.000096 × X3
0.06 1.52 (0.224)
WDF Y = - 1103090 + 1671 x X - 0.844 x X2 +
0.000142 × X3 0.02 0.28 (0.599)
AHC Y = - 89740 + 90.57 x X - 0.02283 x X2
0.13 1.14 (0.290)
SHC Y = 3944920 - 5972 x X + 3.013 x X2-
0.000507 × X3 0.06 1.62 (0.267)
93
SuHC Y = 1115244 - 1686 x X + 0.8494 x X2-
0.000143 × X3 0.03 1.28 (0.264)
MHC Y = - 3482082 + 5240 x X - 2.629 x X2+
0.000439 × X3 0.17 0.17 (0.680)
AuHC Y = - 1181361 - 1788 x X + 0.9021 x X2 -
0.000152 ×X3 0.01 0.03 (0.859)
WHC Y = 1679 - 0.80860 x X 0.02 0.07 (0.29)
APEI Y = - 12294 + 12.40 x X - 0.003122 x X2
0.12 0.21 (0.650)
SPEI Y = 441981 - 669.0 x X + 0.3375 x X2 -
0.000057× X3 0.07 1.74 (0.193)
SuPEI Y = 191660 - 289.7 x X + 0.1460 x X2 -
0.000057 × X3 0.06 0.99 (0.325)
MPEI Y = - 617185 + 928.8 x X - 0.4659 x X2 +
0.000078 × X3 0.17 0.25 (0.616)
AuPEI Y = 160315 - 242.6 x X + 0.1224 x X2 -
0.000021 × X3 0.06 0.13 (0.723)
WPEI Y = 1602 - 1.607 x X + 0.000404 x X2 0.01 0.05 (0.816)
* Values in parentheses in the top row are degree of freedom for linear, quadratic and polynomial
equations respectively. The (p) values indicate significance levels.
94
4.3 Climate Vegetation Productivity Index
Climate Vegetation Productivity Index (CVPI) during 1962-2011 ranged between 4,342
and 9,091 with mean of 6,816. The highest CVPI was estimated during 2003, while the
lowest CVPI was estimated during 1971. The pattern of CVPI showed significant
increase (F2, 47= 5.34, p<0.01). The mathematical expression (CVPI = -8059491+ 8104
x X- 2.036 x X2; R2= 0.18) showed a quadratic function of CVPI. The maximum CVPI
calculated was for the time period between 1980 and 2000. A declining trend was
observed in CVPI, after 2000 (Figure 4.26).
Figure 4-26 Trend line of Climate Vegetation Productivity Index at GFD
4.4 Discussion
The present results show an increase of 1.10°C, 1.32 °C and 1.22 °C in mean maximum
temperature, mean minimum temperature and mean annual temperature respectively, at
Galies Forest Division-Abbottabad, during 1962-2011. The temperature changes show
an upward trend both horizontally (across seasons) and vertically (across years). The
analysis of data averaged on interannual basis indicates that the highest increase was
recorded in the minimum temperature (1.32 °C), followed by mean temperature
95
(1.22°C) and maximum temperature (1.10°C), while that averaged on seasonal basis
indicates the highest increase in minimum temperature (2.37°C) during winter and the
lowest increase in minimum temperature (0.35°C) during monsoon. By seasons, the
highest increase in temperature (maximum, minimum and mean) is in winter, followed
by spring, thus indicating extending summer time period. The climate change shows a
feedback mechanism with climate parameters. For instance, maximum temperature and
minimum temperature are positively correlated inter se and with mean temperature.
Conversely, precipitation is negatively correlated with temperature. The range of
variation and coefficients of variation indicate a large seasonal volatility of climate.
The present findings of increasing temperature are higher compared to global average
temperature increase of 0.74 °C during 1906-2005 (IPCC, 2007). However, these
findings are broadly in conformity with Bukhari and Bajwa (2009); where they reported
an increase of 0.92 °C and 0.77 °C (mean 0.85 °C) in maximum and minimum
temperatures respectively in Peshawar, during 1985-2009. Similarly, Bukhari and
Bajwa (2011) reported an increase of 0.56 °C to 0.78 °C in mean temperature over
different forest types of Pakistan. The higher increase in temperature towards the end of
20th century and beginning of 21st century is in corroboration with reports of previous
works (Esper et al., 2002; IPCC, 2007). The increasing temperature trends may be
explained in terms of different degrees of albedo, physical nature of soil surface and
anthropogenic activities. There are several physical (IPCC, 2007; Grunewald et al.,
2009) and anthropogenic activities (Foley et al., 2005; Falcucci et al., 2007; Vorholz,
2009) which influence spatio-temporal changes in climate processes at local and
regional levels. Among all these external forcing, anthropogenic activities have been
considered dominant cause of temperature increase (Knutson et al., 2006).
Furthermore, the higher increase in temperature at local level may be explained in
terms of newly emerging urbanization phenomenon, with associated spree of
construction, infrastructure development and deforestation in the area. The local urban
areas act as heat island. Previously, heat island effects have been reported by Trenberth
et al. (2007); Wu et al. (2010). Higher rates of temperature increase under urban
conditions have also been reported in Karachi-Pakistan during 1976-05 (Sajjad et al.,
2009); where they recorded increase of 2.7°C, 1.2 °C and 1.95 °C in maximum
temperature, minimum temperature and mean annual temperature respectively. The
96
present increase in temperature is lower compared to that reported by Sajjad et al.
(2009), except the minimum temperature. The differences in reported increase in
temperature may be, besides other factors, due to the time period analyzed, as Sajjad et
al. (2009) covered time period between 1976-05, while the present study covers time
period of 1962-2011. Similarly, the increase of 1.10 °C in maximum temperature is,
nevertheless, higher compared to previous reports of 0.94 °C in Lahore-Pakistan during
1975-2007.
The present findings also indicate a higher increase in minimum temperature compared
to maximum temperature, thus indicating that nights are becoming warmer at higher
rates compared to days. The highest increase in temperature is during winter, followed
by spring. Higher increase in minimum temperature during winter is bringing an early
start of spring. These findings are in corroboration with Bukhari and Bajwa (2009, 2011
& 2012).
Many parts of the world have experienced changes in global water cycle, such as the
magnitude and timing of runoff and the frequency and intensity of floods and droughts
and rainfall patterns (Jiang et al., 2007). Temperature is a key parameter of the energy
which affects water cycles of the earth-atmosphere system (Behbahani et al., 2009).
The current findings show significant changes in precipitation. There is an overall
increase of 1.39% precipitation at GFD, however, a significant decrease is observed
during spring and summer. The decrease in precipitation and increase in temperature
during spring and summer, signify an inverse relationship between temperature increase
and magnitude of precipitation during these seasons. The drought periods also increase
with a number of years receiving scant or moderate precipitation. These results are
broadly in line with findings reported previously by Grunewald et al. (2009); Liu et al.
(2010).
The recent climate changes at GFD may further be explained in terms of increased
human population, livestock and urban sprawl in the area, especially during summer
and monsoon, in the recent past. These activities, subsequently, are increasing
greenhouse gases (GHGs) in the area. The combination of increased population and
anthropogenic activities influence the biogeochemical processes which might have
changed climate in the Forest Division, because these factors are dominant reasons of
97
climate changes globally (Brovkin et al., 2004; Motha and Baier, 2005; Grunewald et
al., 2009; Houghton, 2005; Wu, et al., 2010).
Land cover and land use are very important factors which interact with atmospheric
conditions to determine the overall climate. These interactions have great impacts on
various ecosystems from regional to global scales (Pyke et al., 2007). Land cover
change and land degradation either due to anthropogenic activities, deforestation or
livestock can directly increase temperatures (Briggs et al., 2005; Balling et al., 1998).
The increased livestock also change the land cover and land use pattern. Livestock,
besides, directly responsible for GHGs (18% of all human-induced GHGs globally)
cause deforestation as well as deteriorate rangelands (Van de Steeg et al., 2009). In
GFD, grazing and deforestation for timber and fuel wood put pressure on forest
resources. These factors, in addition to urban sprawl and road network, have changed
land cover and land use pattern which subsequently may have resulted in climate
changes.
The observed increase in temperature and precipitation, both horizontal and vertical,
will likely have multiple effects, specifically in terms of (i) altered planting seasons due
to early start of spring as well as extended summer seasons, (ii) poor plant growth, (iii)
low survival of newly planted trees in spring and monsoon seasons, (iv) increased
competition for water among different stakeholders (agricultural, forestry, civic
utilities), (v) change in forest types, species composition, geographical relocation of
plant and animal species, (vi) increased and frequent insect pests and diseases
outbreaks, and (vii) escalated wind damage of forests, as reported by Blennow et al.
(2010); Bukhari and Bajwa (2012). These effects would likely lead to increased cost of
forest management and other economic activities in the area.
Overall, these climate changes present a great threat to the present and, to a much
greater extent, to the coming generations. The mitigation of adverse effects of climate
change on future generations requires advance planning because GHGs, especially
carbon dioxide (CO2) is a long-lived atmospheric gas which makes the climate change a
resilient phenomenon. Moreover, the climate change that we are currently experiencing
is primarily the result of emissions from some time in the past, rather than current
emissions (back loaded effect of climate change) and the full cumulative effects of our
98
current emissions will be realized for some time in the future (delayed/deferred effect
of climate change). The resilient and delayed phenomena of climate change have
serious implications for future generations which call the principle of intergenerational
justice into question.
The present findings show an increase in mean annual and seasonal Temperature
Efficiency Indices and decrease in mean annual Aridity Index (AI). Similarly, changes
have occurred in Dryness Index, Rain Factor, Dryness Factor, Humidity Coefficient
and Precipitation Efficiency Index. The Climate Vegetation Productivity Index (CVPI)
of GFD has an observed range of 4,342 to 9,091, with mean of 6,816. The results
indicate an overall increase in CVPI and a close relationship between climate change
and changing CVPI. The increasing temperature shows a negative impact on CVPI,
especially after 2000. Bioclimatic indices are tools to explain the spatio-temporal
distribution of vegetation by the combination of different climatic factors (Baltas,
2007). These findings are increasingly important for future planning and management
of GFD. Previously, these indices were used to transfer the results from climate
modeling to land use and vegetation science, to predict long-term trends in
desertification (Gavilán, 2005), and in the methodology of pollen forecasting
(Valencia-Barrera, et al., 2002). The mean CVPI of 6,816 puts GFD in ideal site class
with productivity in the range of 163.91-184.77 cubic feet per acre, estimated as per
methodology described by Paterson, 1956, Champion et al., 1965. The increasing trend
of CVPI within certain temperature ranges, however, indicates increasing CVPI. This is
an encouraging finding for management of the GFD.
The changes in bioclimatic indices, both horizontal and vertical, indicate changes in
forest growth and productivity. The climate change coupled with bioclimatic indices
during spring is crucial. The spring is a blossom time and, therefore, reflects biological
responses of vegetation towards temperature. Each plant species requires a specific
amount of heat to break winter dormancy and complete a normal annual cycle of
vegetative and reproductive growth (Bukhari and Bajwa, 2009). The increasing
temperature in winter and spring indicates early onset and completion of spring. Earlier
onset of the spring as well as shifting of seasons is in conformity with Bukhari and
Bajwa (2009) and Liu et al. (2010); who reported an early onset of the spring season.
The early start of spring indicates early sprouting of plants, but shortening of this
99
season reduces the flowering period. Apart from this, day length in March and April is
still short which limits the photosynthetic process and subsequently plants are still in
tender stage when exposed to higher temperatures. This will put plants under further
stress. Further, the poor vegetative growth causes inferior reproductive growth
(flowering, quantity and quality of seed) (Bukhari and Bajwa, 2009 & 2011).
Apart from forest growth and productivity, the present findings of changing climate and
bioclimatic indices at GFD indicate changes in vegetation composition. It has been
reported that a change in the mean annual temperature, as small as 1 °C over a
sustained period is sufficient to bring about changes in species composition and
distribution of many tree species (IPCC, 1996). A number of climate-vegetation models
have also shown that certain climatic regimes are associated with particular plant
communities or groups (Holdrige, 1947; Thornthwaite, 1948; Walter, 1985; Whittaker,
1975), and change in the climatic regimes may induce changes in vegetation
composition. The long summer combined with long monsoon may also change the
basic composition of seasonal rhythms and subsequently flora and fauna of GFD. These
seasonal variations might cause extinction of some floral and faunal species by facing
climatic conditions beyond their critical survival ranges. Besides disturbance of
biological processes and biodiversity, the climate change and seasonal variations also
affect a number of physical processes and livelihood activities in the area, particularly
in agriculture, livestock, water supply, housing, construction and tourism sectors.
100
CHAPTER 5
IMPACTS OF CLIMATE CHANGE ON TREE RINGS AND RING-
WOOD CHARACTERISTICS
5.1 Cross-dating of Ring-width data
To establish precise chronology of the sampled trees of the three selected species, cross-
dating was done for the period 1962-2011 as per procedure described by Fritts and
Swetnam (1989). The details of the procedure are given in Chapter 3, Para 3.5.2. The
cross-dating output of Deodar, Blue pine, and Chir pine are presented in Figures 5.1, 5.2
and 5.3 respectively. Each colour-shaded line in the Figures represents ring-width size of
one of the 20 sampled trees of the particular species. The thickness of the lines is
proportional to the annual ring-width size of the sampled trees and the vertical variations
across the lines indicate variability in the year-wise growth of the tree-rings across the
20 sampled trees.
Figure 5-1 Cross-dating of Ring-width data of Deodar in GFD (1962-2011)
101
Figure 5-2 Cross-dating of Ring-width data of Blue pine in GFD (1962-2011)
Figure 5-3 Cross-dating of Ring-width data of Chir pine in GFD (1962- 2011)
5.2 Standardization of Ring-width data
The biological growth trend of ontogeny - decrease in ring-widths with increasing tree
age – and effects of other non-climatic site factors were removed by applying
'standardization' procedure as described by Fritts (1976). The details of the procedure
are given in Chapter 3, Para 3.5.2.
102
5.3 Mean Sensitivity and Coefficient of Variation
A sample of twenty trees each of C. deodara, P. wallichiana and P. roxburghii was
selected in random, and variability of their intra-species annual ring-widths were
assessed, as reflected in Table 5.1(a). The mean annual ring-widths of C. deodara, P.
wallichiana and P. roxburghii for the period 1962-2011 were 3.08±0.23 mm, 2.54±0.15
mm and 2.62±0.39 mm and the variances were 1.03 mm, 0.46 mm and 3.10 mm
respectively. The variability of intra-species annual ring-widths was the highest in P.
roxburghii, followed by C. deodara and P. wallichiana. The variability in samples for
the same species was caused by both climatic and non-climatic factors, like ontogeny,
site disturbances and changes in forest crop conditions. The sensitivity analysis was
done to describe the impacts of climate factors on the growth parameters.
Table 5-1 (a) Statistics of intra-species variability of annual ring-widths of Cedrus
deodara, Pinus wallichiana and Pinus roxburghii (1962-2011)
Statistics
Tree species
C. deodara P. wallichiana P. roxburghii
Mean ring-width (mm) 3.08 2.54 2.62
Standard Error (mm) 0.23 0.15 0.39
Variance (σ2) (mm) 1.03 0.46 3.10
Coefficient of Variation (CV)
(%)
32.88 26.55 67.20
Two important tree-ring measures: mean sensitivity (MS) and coefficient of variation
(CV) were calculated for the means of 20 samples each of the three selected species.
The mean sensitivity was calculated to describe variability of high frequency
component of the ring-width due to climatic fluctuations, while the coefficient of
variation was calculated for low frequency component variability induced either by
climate or by other long term influences. The statistics calculated for Cedrus deodara,
Pinus wallichiana and Pinus roxburghii for the period 1962-2011 are presented in
Table 5.1(b).
103
Table 5-1 (b) Statistics of mean sensitivity of mean annual ring-widths of Cedrus
deodara, Pinus wallichiana and Pinus roxburghii for the period 1962-2011
Statistics
Tree species
C. deodara P. wallichiana P. roxburghii
Mean ring-width (mm) 3.08 2.54 2.62
Mean Sensitivity (MS) 0.30 0.38 0.29
Standard Error (mm) 0.11 0.11 0.10
Variance (σ2) (mm) 0.26 0.25 0.21
Coefficient of Variation (CV)
(%)
16.56 19.50 17.53
The mean sensitivity of mean annual ring-width of C. deodara, P. wallichiana and P.
roxburghii for the period 1961-2011were estimated at 0.30±0.0.11, 0.38±0.0.11 and
0.29±0.0.10 respectively. The highest mean sensitivity of 0.38 was estimated for P.
wallichiana and the lowest of 0.29 for P. roxburghii. The mean annual ring-width for
the period 1962-2011 was relatively larger in C. deodara (3.08 mm) compared to P.
wallichiana (2.54 mm) and P. roxburghii (2.62 mm). The variance of mean annual ring-
widths of C. deodara and P. wallichiana were nearly of equal magnitude, while that of
P. roxburghii was slightly lower compared to the other two species. The highest
coefficient of variation of 19.50% was observed in P. wallichiana and the lowest
coefficient of variation of 17.53% in P. roxburghii. The results of mean sensitivity and
coefficient of variation calculated for the three species indicated enough variability in
growth statistics to enable analysis of its time function, correlation and regression with
climate parameters and changes thereof.
5.4 Ring-width and Ring-wood Characteristics of Deodar
5.4.1 Time function analysis of Ring-width and Ring-wood Characteristics of
Deodar
The time function responses of ring-width and ring-wood characteristics of Deodar
were studied through regression analysis at 95% Confidence Interval (CI) and
Prediction Interval (PI), using the standardized data. The trend in the time series was
104
assessed by applying Mann Kendall test with Normal Approximation and using Sen’s
Slope Estimator method. A summary of the trend analysis is reproduced in Table 5.2.
Table 5-2 Trend Analysis of Ring-width and Ring-wood Characteristics of Deodar
at GFD (1962-2011)
Species /Tree Growth
Characteristics
Z-Value p-Value Trend Sen’s
Slope Upward Downward
Ring-width -6.115 1.000 0.000 0.026
Early wood formation -7.077 1.000 0.000 0.155
Late wood formation 6.608 0.000 1.000 0.153
Early wood cell diameter -1.205 0.886 0.114 0.140
Early wood cell wall
thickness
1.372 0.085 0.915 0.001
Late wood cell diameter 2.627 0.004 0.996 0.012
Late wood cell wall
thickness
4.851 0.000 1.000 0.005
Increasing trend No trend Decreasing trend
The time function analysis of ring-width and ring-wood characteristics of Deodar for
the period 1962-2011 showed highly significant (p<0.01) downward trend in ring-width
and early wood formation, highly significant (p<0.01) upward trend in late wood
formation, late wood cell diameter and late wood cell wall thickness, and no trend in
early wood cell diameter and early wood cell wall thickness.
105
The analysis of time function response of mean annual ring-widths of Deodar for the
period 1962-2011 indicated a wide range of variation in annual ring-widths across the
cores in a quadratic pattern, with an overall decreasing trend. The mean annual ring-
width ranged from 2.24±0.25 mm to 4.37±0.26 mm, with a mean value of 3.08±0.23
mm. The largest mean annual ring-width was recorded during 1962, while the smallest
mean annual ring-width was recorded during 2011. The mean annual ring-width
declined incessantly from 1962 to 1990, remained stable during 1991-2000 and
increased slightly between 2000 and 2011. There were four years having mean annual
ring-widths larger than 4.0 mm, while there were 24 years having mean annual ring-
widths smaller than 3.0 mm (Figure 5.4). The highest variability in mean annual ring-
widths across the cores was recorded during 2000.
Figure 5-4 Time function of Mean Annual Ring-width of Deodar in GFD (1962-2011)
106
The analysis of time function response of mean intra-ring early wood formation of
Deodar for the period 1962-2011 showed a large variation in a linear pattern, with an
overall declining trend. The mean intra-ring early wood formation was 75.64±0.36% of
the mean annual wood formation. The largest mean intra-ring early wood formation
was 80.72±0.88% during 1962, while the smallest mean intra-ring early wood
formation was 71.34±1.51% during 2005. The time function response of mean intra-
ring early wood formation did not follow the pattern of mean annual ring-width. There
were ten years having mean intra-ring early wood formation higher than 78.0%, while
there were eight years having mean intra-ring early wood formation lower than 73.0%
(Figure 5.5). The highest variability in mean intra-ring early wood formation across the
cores was during 2002.
Figure 5-5 Time function of Mean Intra-ring Early Wood Formation (%) of Deodar in GFD (1962-2011)
107
The analysis of time function response of mean intra-ring late wood formation of
Deodar for the period 1962-2011 showed a large variation in a quadratic pattern, with
an overall significant increasing trend. The mean intra-ring late wood formation was
24.53±0.37% of the mean annual wood formation. The largest mean intra-ring late
wood formation was 28.66±2.01% during 2006, while the smallest mean intra-ring late
wood formation was 19.11±0.92% during 1962. The mean intra-ring late wood
formation showed an opposite trend to that of mean intra-ring early wood formation
and mean annual ring-width. The slope gradient of time function of mean intra-ring late
wood formation was relatively smaller compared to time function of mean intra-ring
early wood formation. The mean intra-ring late wood formation increased steadily over
1962-2011, except a slight decline after 2000. There were 25 years having mean intra-
ring late wood formation higher than 25.0%, while there were five years having mean
intra-ring late wood formation lower than 21.0% (Figure 5.6). The highest variability in
mean intra-ring late wood formation across the cores was recorded in 1980.
Figure 5-6 Time function of Mean Intra-ring Late Wood Formation (%) of Deodar in GFD (1962-2011)
108
The mean intra-ring early wood cell diameter of Deodar during 1962-2011 ranged from
33.38±1.10 µm to 37.59±3.15 µm, with a mean of 35.85±0.14 µm. The time function
response of mean intra-ring early wood cell diameter showed a quadratic behavior, with
no significant overall trend. The largest mean intra-ring early wood cell diameter was
recorded during 1963, while the smallest mean intra-ring early wood cell diameter was
recorded during 2002. The mean intra-ring early wood cell diameter decreased
gradually between 1965 and 1990, but increased gradually between 1995 and 2011.
There were eight years each having mean intra-ring early wood cell diameter larger
than 37.0 µm and smaller than 35.0 µm (Figure 5.7). The highest variability in mean
intra-ring early wood cell diameter across the cores was recorded in 1963.
Figure 5-7 Time function of Mean Intra-ring Early Wood Cell Diameter (µm) of Deodar in GFD (1962-2011)
109
The mean intra-ring early wood cell wall thickness of Deodar during 1962-2011 ranged
between 1.93±0.06 µm and 2.33±0.32 µm, with a mean of 2.08±0.01 µm. The time
function response of mean intra-ring early wood cell wall thickness showed a
polynomial pattern, with no significant overall trend. The largest mean intra-ring early
wood cell wall thickness was recorded during 1977, while the smallest mean intra-ring
early wood cell wall thickness was recorded during 1989. The mean intra-ring early
wood cell wall thickness showed an increasing trend during 1962-70 and 2005-2010,
but a decreasing trend during 1980-2004. There were four years having mean intra-ring
early wood cell wall thickness larger than 2.20 µm, while there were five years having
mean intra-ring early wood cell wall thickness smaller than 2.00 µm (Figure 5.8). The
highest variability in mean intra-ring early wood cell wall thickness was recorded in
1977.
Figure 5-8 Time function of Mean Intra-ring Early Wood Cell Wall Thickness (µm) of Deodar in GFD (1962-2011)
110
A microscopic picture (100×) of intra-ring early wood cell diameter and cell wall
thickness of Deodar is depicted below (Figure 5.9).
Figure 5-9 Intra-ring Early Wood Cell Diameter and Cell Wall Thickness of Deodar (100x)
111
The mean intra-ring late wood cell diameter of Deodar during 1962-2011 ranged
between 14.60±0.59 µm and 16.86±0.69 µm, with a mean of 15.56±0.07 µm. The time
function response of mean intra-ring late wood cell diameter showed a quadratic
response, with an overall increasing trend. The largest mean intra-ring late wood cell
diameter was recorded during 2011, while the smallest mean intra-ring late wood cell
diameter was recorded during 1964. The mean intra-ring late wood cell diameter
decreased slightly during 1970-85, but increased steadily after 1990. There were nine
years having mean intra-ring late wood cell diameter larger than 16.0 µm, while there
were five years having mean intra-ring late wood cell wall diameter smaller than 15.0
µm (Figure 5.10). The highest variability in mean intra-ring late wood cell diameter
across the cores was recorded in 1979. The mean intra-ring late wood cell diameter was
significantly smaller compared to mean intra-ring early wood cell diameter.
Figure 5-10 Time function of Mean Intra-ring Late Wood Cell Diameter (µm) of Deodar in GFD (1962-2011)
112
The mean intra-ring late wood cell wall thickness of Deodar during 1962-2011 ranged
between 3.52±0.12 µm and 4.00±0.13 µm, with a mean of 3.76±0.02 µm. The time
function response of mean intra-ring late wood cell wall thickness showed a small
variation in a quadratic pattern, with an overall increasing trend. The largest mean intra-
ring late wood cell wall thickness was recorded during 2003, while the smallest mean
intra-ring late wood cell wall thickness was recorded during 1964. The mean intra-ring
late wood cell wall thickness increased gradually during 1962-2003, but decreased
afterwards. There were 18 years having mean intra-ring late wood cell wall thickness
larger than 3.80 µm, while there were four years having mean intra-ring late wood cell
wall thickness smaller than 3.60 µm (Figure 5.11). The highest variability in mean
intra-ring late wood cell wall thickness across the cores was recorded in 1996. The
mean intra-ring late wood cell wall thickness showed a changing trend which was
opposite to that of mean intra-ring late wood cell diameter. Similarly, the changing
trend of mean intra-ring early cell wall thickness and mean late wood cell wall
thickness followed different patterns. The mean intra-ring late wood cell wall thickness
was significantly (p<0.05) larger compared to mean intra-ring early wood cell wall
thickness. The slope gradient of mean late wood cell wall thickness was considerably
higher compared to that of mean early wood cell wall thickness.
Figure 5-11 Time function of Mean Intra-ring Late Wood Cell Wall Thickness (µm) of Deodar in GFD (1962-2011)
113
A microscopic picture (100×) of intra-ring late wood cell diameter and cell wall
thickness of Deodar is depicted in Figure 5.12.
Figure 5-12 Intra-ring Late Wood Cell Diameter and Cell Wall Thickness of Deodar (100x)
5.4.2 Mathematical Expressions of Time Function of Ring-width, Intra- ring wood
Formation and Wood Cell Characteristics of Deodar
Mathematical expressions of time functions of mean annual ring-width, mean intra-ring
wood formation and wood cell characteristics of Deodar showed a mix of linear,
quadratic and polynomial behaviors. The mean annual ring-width, mean intra-ring early
wood formation, mean intra-ring late wood formation, mean intra-ring late wood cell
diameter and mean intra-ring late wood cell wall thickness showed highly significant
(p<0.01) changes with time. Conversely, temporal changes in mean intra-ring early
wood cell diameter and mean intra-ring early wood cell wall thickness were non-
significant (p>0.05). The R2 ranged between 0.07 and 0.77. The highest R2 value was
estimated for mean intra-ring early wood formation, followed by mean intra-ring late
wood formation. The lowest R2 value was calculated for mean intra-ring early wood
cell wall thickness, followed by mean intra-ring early wood cell diameter. The analysis
indicated that linear model had good fit for time function of mean intra-ring early
wood formation, quadratic model had good fit for mean annual ring-width, mean intra-
ring late wood formation and mean intra-ring late wood cell wall thickness, but poor fit
114
for mean intra-ring early wood cell wall diameter and mean intra-ring early wood cell
thickness. The polynomial model had good fit for time function of early wood cell wall
thickness (Table 5.3).
Table 5-3 Mathematical Expressions of Time Function of Ring-width and Intra-
ring wood Characteristics of Deodar in GFD (1962-2011)
Tree Growth
Characteristics
Mathematical Expressions R2 F(1,2,3), (48,47,46)*
(p)
Ring-width Y = 3357-3.350×X + 0.00084
× X2
0.69 52.08 (0.00)
Early wood formation Y = 380.3 - 0.1534 × X 0.77 162.17(0.00)
Late wood formation Y = -10277 + 10.22 × X
- 0.0025 × X2
0.74 67.55 (0.00)
Early wood cell
diameter
Y = 6343 - 6.343 × X +
0.0016 × X2
0.10 2.73 (0.075)
Early wood cell wall
thickness
Y = -55641+ 83.95 × X -
0.0422 × X2 + 0.00001 × X3
0.07 1.15 (0.340)
Late wood cell
diameter
Y = 2567 - 2.580 × X +
0.0007 × X2
0.20 5.74 (0.006)
Late wood cell wall
thickness
Y = -813.2 + 0.8178 × X -
0.0002 × X2
0.56 29.51 (0.000)
* Values in parentheses in the top row are degree of freedom for linear, quadratic and
polynomial equations respectively. The (p) values indicate significance levels.
5.4.3 Decadal changes in Ring-width and Ring-wood characteristics of Deodar
A highly significant (F4, 15= 400.56; p<0.01) difference was recorded in mean decadal
ring-widths of Deodar, with a decreasing trend, during 1962-2011. The overall
difference in mean decadal ring-widths among the decades was significant (Tukey’s
HSD, CV 0.11; p=0.05). The largest mean decadal ring-width was 3.90±0.01 mm
during 1962-71, which was significantly different from mean decadal ring-width during
1972-81. The difference in mean decadal ring-widths of 1982-91 and 1992-01 was non-
115
significant (p>0.05). The smallest mean decadal ring-width was 2.61±0.01 mm during
2002-11 (Table 5.3).
A highly significant (F4, 15= 51.66; p<0.01) difference was recorded in mean decadal
intra-ring early wood formation of Deodar, with a decreasing trend, during 1962-2011.
The overall difference in mean decadal intra-ring early wood formation among the
decades was significant (Tukey’s HSD, CV 1.49; p=0.05). The largest mean decadal
intra-ring early wood formation was 79.19±0.41% during 1962-71, which was
significantly higher compared to mean decadal intra-ring early wood formation during
1972-81. The smallest mean decadal intra-ring early wood formation was 73.42±0.44%
during 2002-11. The difference in mean decadal intra-ring early wood formation among
decades, 1992-01 and 2001-11, was non-significant. The mean decadal intra-ring early
wood formation followed the pattern of mean decadal ring-widths (Table 5.4).
A highly significant (F4, 15= 87.26; p<0.01) difference was recorded in mean decadal
intra-ring late wood formation of Deodar, with an increasing trend, during 1962-2011.
The overall difference in mean decadal intra-ring late wood formation among the
decades was significant (Tukey’s HSD, CV 1.15; p=0.05). The trend of mean decadal
intra-ring late wood formation followed a pattern opposite to mean decadal intra-ring
ring-width and mean decadal intra-ring early wood formation. The largest mean
decadal intra-ring late wood formation was 27.10±0.32% during 2002-11, which was
not significantly different from 1992-2001. The smallest mean decadal intra-ring late
wood formation was 21.02±0.33% which was significantly lower compared to 1972-81
(Table 5.4).
116
Table 5-4 Mean Decadal Ring-width and Ring-wood Characteristics of Deodar
in GFD (1962-2011)
Decades
/CV
Tree ring Characteristics
RW±SE
(mm)
EW±SE
(%)
LW±SE
(%)
EWCD
±SE
(µm)
EWCWT
±SE
(µm)
LWCD
±SE
(µm)
LWCWT
±SE
(µm)
1962-71 3.90±
0.01 a
79.19±
0.41 a
21.02±
0.33 d
37.16±
0.14 a
1.98±
0.01c
15.54±
0.11b
3.57±
0.01d
1972-81 3.10±
0.01 b
77.03±
0.27 b
23.24±
0.27 c
35.08±
0.23 b
2.05±
0.02 bc
15.31±
0.09 bc
3.73±
0.01c
1982-91 2.92±
0.01 c
75.33±
0.21 c
25.08±
0.09 b
35.08±
0.19 b
2.09±
0.01ab
15.63±
0.13 b
3.78±
0.01bc
1992-01 2.87±
0.05 c
73.50±
0.31 d
26.38±
0.25 a
35.37±
0.33 b
2.10±
0.00 ab
14.83±
0.02 c
3.84±
0.01b
2002-11 2.61±
0.01 d
73.42±
0.44 d
27.10±
0.32 a
35.13±
0.31b
2.15±
0.02 a
16.83±
0.23 a
3.98±
0.03 a
CV 0.11 1.49 1.15 1.09 0.06 0.59 0.07
Mean values within a column sharing same alphabets are not significantly different (Tukey’s
HSD, p=0.05); RW= Ring-width; EW= Early wood; LW= Late wood; EWCD= Early wood cell
diameter; EWCW= Early wood cell wall thickness; LWCD= Late wood cell diameter; LWCW=
Late wood cell wall thickness S
A highly significant (F4, 15= 17.11; p<0.01) difference was recorded in mean decadal
intra-ring early wood cell diameter of Deodar, with an overall decreasing trend, during
1962-2011. The difference in mean decadal intra-ring early wood cell diameter among
the decades was significant (Tukey’s HSD, CV 1.09; p=0.05). The largest mean decadal
intra-ring early wood cell diameter was 37.16±0.14 µm during 1962-71, which was
significantly larger compared to 1972-81. The smallest mean decadal intra-ring early
wood cell diameter was 35.08±0.19 µm during 1982-91. The mean decadal intra-ring
early wood cell diameter did not vary significantly among the decades, 1972-81, 1982-
91, 1992-01 and 2002-11 (Table 5.4).
117
A highly significant (F4, 15= 18.73; p<0.01) difference was recorded in mean decadal
intra-ring early wood cell wall thickness of Deodar, with an increasing trend, during
1962-2011. The difference in mean decadal intra-ring early wood cell wall thickness
among the decades was significant (Tukey’s HSD, CV 0.06; p= 0.05). The largest mean
decadal intra-ring early wood cell wall thickness was 2.15± 0.02 µm during 2002-11,
which was not significantly higher compared to 1982-91 and 1992-01. The smallest
mean decadal intra-ring early wood cell wall thickness was 1.98±0.01 µm during 1962-
71, which was not significantly different from the corresponding values during 1972-81
(Table 5.4).
A highly significant (F4, 15= 29.79; p<0.01) difference was recorded in mean decadal
intra-ring late wood cell diameter of Deodar, with an overall increasing trend, during
1962-2011. The difference in mean decadal intra-ring late wood cell diameter among
the decades was significant (Tukey’s HSD, CV 0.59; p= 0.05). The largest mean
decadal intra-ring late wood cell diameter was 16.83±0.23 µm during 2002-11, followed
by 15.63±0.13 µm during 1982-91. The smallest mean decadal intra-ring late wood cell
diameter was 14.83±0.02 µm during 1992-01. The mean decadal intra-ring late wood
cell diameter did not vary significantly between 1962-71 and 1982-91(Table 5.4).
A highly significant (F4, 15= 78.93; p<0.01) difference was recorded in mean decadal
intra-ring late wood cell wall thickness of Deodar, with an increasing trend, during
1962-2011. The difference in mean decadal intra-ring late wood cell wall thickness
among the decades was significant (Tukey’s HSD, CV 0.07; p= 0.05). The trend of
mean decadal intra-ring late wood cell wall thickness was similar to mean decadal intra-
ring late wood formation. The largest mean decadal intra-ring late wood cell wall
thickness was 3.98±0.03 µm during 2001-11, which was significantly larger compared
to 1992-2001. The smallest mean decadal intra-ring late wood cell wall thickness was
3.57±0.01 µm during 1962-71 (Table 5.4).
5.4.4 Correlation between Ring-width and Ring-wood Characteristics of Deodar
The analysis of Pearson Correlation Coefficients matrix of Deodar growth data for
1962-2011 revealed a highly significant (p<0.01) and positive correlation of mean
annual ring-width with mean intra-ring early wood formation (r = 0.90) and mean intra-
118
ring early wood cell diameter (r = 0.78), but highly significant (p<0.01) and negative
with mean intra-ring early wood cell wall thickness (r = -0.87), mean intra-ring late
wood formation (r = -0.93) and mean intra-ring late wood cell wall thickness (r = -0.93).
The correlation was, however, non-significant (p>0.05) and negative with mean intra-
ring late wood cell diameter (r = -0.31). The correlation of mean intra-ring early wood
formation was highly significant (p<0.01) and positive with mean intra-ring early wood
cell diameter (r = 0.62), but highly significant (p<0.01) and negative with mean intra-
ring early wood cell wall thickness (r = -0.82), mean intra-ring late wood formation (r =
-0.95) and mean intra-ring late wood cell wall thickness (r = -0.86). The correlation of
mean intra-ring early wood formation was non-significant (p>0.05) and negative with
mean intra-ring late wood cell diameter (r = -0.19). The correlation of mean intra-ring
early wood cell diameter was highly significant (p<0.01) and negative with mean intra-
ring early wood cell wall thickness (r = -0.57), mean intra-ring late wood formation (r =
-0.63) and mean intra-ring late wood cell wall thickness (r = 0.61), but non-significant
(p>0.05) and negative with mean intra-ring late wood cell diameter (r = -0.04). The
correlation of mean intra-ring early cell wall thickness was highly significant (p<0.01)
and positive with mean intra-ring late wood formation (r = 0.88) and mean intra-ring
late wood cell wall thickness (r = 0.88), but non-significant (p>0.05) and positive with
mean intra-ring late wood cell diameter (r = 0.40). The correlation of mean intra-ring
late wood formation was highly significant (p<0.01) and positive with mean intra-ring
late wood cell wall thickness (r = 0.91), but non-significant (p>0.05) and positive with
mean intra-ring late wood cell wall diameter (r = 0.29). The correlation of mean intra-
ring late wood cell diameter was significant (p<0.05) and positive with mean intra-ring
late wood cell wall thickness (r = 0.44) (Table 5.5).
119
Table 5-5 Correlation Coefficients Matrix between Ring-width and Ring-wood
Characteristics of Deodar in GFD (1962-2011)
Ring-wood
Characteristics
RW EW EWCD EWCWT LW LWCD
EW 0.90**
(0.000)
EWCD 0.78**
(0.000)
0.62**
(0.003)
EWCWT - 0.87**
(0.000)
- 0.82**
(0.000)
- 0.57**
(0.008)
LW - 0.93**
(0.000)
- 0.95**
(0.000)
- 0.63**
(0.003)
0.88**
(0.000)
LWCD - 0.31ns
(0.191)
- 0.19 ns
(0.419)
- 0.04 ns
(0.870)
0.40 ns
(0.079)
0.29 ns
(0.217)
LWCWT - 0.93**
(0.000)
- 0.86**
(0.000)
- 0.61**
(0.004)
0.88**
(0.000)
0.91**
(0.000)
0.44*
(0.05)
Values in ( ) are p-values; * Significant at 95%; ** Significant at 99%; ns= Non-significant; RW=
Ring-width; EW= Early wood; EWCD= Early wood cell diameter; EWCW= Early wood cell wall
thickness; LW= Late wood; LWCD= Late wood cell diameter; LWCW= Late wood cell wall
thickness
5.4.5 Impacts of Climate Change on Ring-width of Deodar
The impacts of climate change on mean annual ring-widths of Deodar during 1962-2011
were assessed using response functions of ring-widths with temperature (maximum and
minimum) and precipitation, both on annual and seasonal basis.
120
The mean annual maximum temperature showed a significant (F2, 47= 4.78; p<0.05)
impact on mean annual ring-widths of Deodar, exhibiting a quadratic pattern, with an
overall declining trend. Most of the mean annual ring-width responses were observed
between 16.0˚C and 17.0˚C. The mean annual maximum temperature below 15.5˚C
showed positive impact on mean annual ring-width. Similarly, the mean annual
maximum temperature above 17.0˚C also showed slightly positive impact on mean
annual ring-width (Figure 5.13).
Figure 5-13 Impact of Mean Annual Maximum Temperature on Ring-width of Deodar in GFD (1962-2011)
121
The mean annual minimum temperature showed a highly significant (F2, 47= 6.97;
p<0.05) impact on mean annual ring-widths of Deodar, exhibiting a quadratic pattern.
Relatively better growth response (mean annual ring-width >3.50 mm) was observed
between 5.0˚C and 5.5˚C. A positive impact of minimum temperature was also observed
above 7.0˚C (Figure 5.14).
Figure 5-14 Impact of Mean Annual Minimum Temperature on Ring-width of Deodar in GFD (1962-2011)
122
The annual precipitation showed a significant (F1, 48 = 5.60; p<0.05) impact on mean
annual ring-widths of Deodar. The mean annual ring-width decreased with increasing
annual precipitation. A large variation in mean annual ring-width response was noted
across the observed range of precipitation, with growth response more clustered around
annual precipitation range of 800-1100 mm (Figure 5.15).
Figure 5-15 Impact of Annual Precipitation on Ring-width of Deodar in GFD (1962-2011)
123
The 1-way analysis of variance of mean annual ring-width of deodar with mean decadal
precipitation showed that the largest mean annual ring-width was 3.40±0.10 mm when
annual precipitation ranged between 600 mm and 700 mm. The smallest mean annual
ring-width was 2.78±0.09 mm when annual precipitation was higher than 1000 mm
(Table 5.6).
Table 5-6 Precipitation and Ring-width of Deodar in GFD (1962-2011)
Precipitation range
(mm/annum)
Mean Annual Ring-width
(mm)
Standard Error (SE)
501-600 3.46* 0.00
601-700 3.40 0.10
701-800 3.28 0.24
801-900 3.10 0.15
901-1000 3.13 0.12
>1001 2.78 0.09
* Single value
124
The mean spring maximum temperature showed a significant (F1, 48= 3.35; p<0.05)
impact on mean annual ring-widths of Deodar. The mean annual ring-width declined
with increasing mean annual maximum temperature (Figure 5.16).
Figure 5-16 Impact of Mean Spring Maximum Temperature on Ring-width of Deodar in GFD (1962-2011)
125
The mean spring minimum temperature showed a non-significant (F1, 48 = 3.13; p>0.05)
negative impact on mean annual ring-widths of Deodar. The mean annual ring-width
decreased with increasing mean spring minimum temperature. Relatively better growth
response was observed between 2.0˚C and 4.0˚C with mean annual ring-width larger
than 3.0 mm (Figure 5.17).
Figure 5-17 Impact of Mean Spring Minimum Temperature on Ring-width of Deodar in GFD (1962-2011)
126
The spring precipitation showed a non-significant (F2, 47=0.49; p>0.05) positive impact
on mean annual ring-widths of Deodar. There was an overall increase in mean annual
ring-width with increasing spring precipitation (Figure 5.18).
Figure 5-18 Impact of Spring Precipitation on Ring-width of Deodar in GFD (1962-2011)
127
The mean summer maximum temperature showed a non-significant (F3, 46= 0.31;
p>0.05) impact on mean annual ring-widths of Deodar. The response exhibited a
polynomial pattern (Figure 5.19).
Figure 5-19 Impact of Mean Summer Maximum Temperature on Ring-width of Deodar in GFD (1962-2011)
128
The mean summer minimum temperature showed a non-significant (F3, 46= 0.73;
p>0.05) impact on mean annual ring-widths of Deodar. The overall mean annual ring-
width response to summer minimum temperature was negative and exhibited a
quadratic function. Relatively better growth response (mean annual ring-width >3.25
mm) was observed between 9.0˚C and 12.0˚C (Figure 5.20).
Figure 5-20 Impact of Mean Summer Minimum Temperature on Ring-width of Deodar in GFD (1962-2011)
129
The summer precipitation showed a non-significant (F2, 47=1.39; p>0.05) impact on
mean annual ring-widths of Deodar. The mean annual ring-width increased with
increasing precipitation up to 150 mm, followed by dampening and declining pattern,
with a quadratic function (Figure 5.21). The impact of summer precipitation showed a
contrasting effect on mean annual ring-width compared to spring precipitation.
Figure 5-21 Impact of Summer Precipitation on Ring-width of Deodar in GFD (1962-2011)
130
The mean monsoon maximum temperature showed a non-significant (F2, 47=1.04;
p>0.05) impact on mean annual ring-widths of Deodar. The mean annual ring-width
decreased with increasing mean monsoon maximum temperature, except a small
increase during 2000-11. Relatively better growth response (mean annual ring-width
>3.00 mm) was measured at mean monsoon maximum temperature between 22.0˚C and
24˚C (Figure 5.22).
Figure 5-22 Impact of Mean Monsoon Maximum Temperature on Ring-width of Deodar in GFD (1962-2011)
131
The mean monsoon minimum temperature showed a non-significant (F2, 47= 0.76;
p>0.05) impact on mean annual ring-widths of Deodar. The overall impact of
increasing mean monsoon minimum temperature was positive, with a trend contrasting
to that of mean monsoon maximum temperature. Relatively better growth response
(ring-width >3.50 mm) was observed at 13.5˚C (Figure 5.23).
Figure 5-23 Impact of Mean Monsoon Minimum Temperature on Ring-width of Deodar in GFD (1962-2011)
132
The monsoon precipitation showed a non-significant (F1, 48=1.39; p>0.05) impact on
mean annual ring-widths of Deodar. The mean annual ring-width decreased with
increasing monsoon precipitation. Relatively better growth response (mean annual ring-
width >3.5 mm) was observed when monsoon precipitation was ranged between 170
mm/season and 350 mm/season (Figure 5.24). The impact of monsoon precipitation
followed closely the pattern of impact of mean monsoon maximum temperature,
however, the slope gradient of monsoon precipitation was relatively higher compared to
that of mean monsoon maximum temperature.
Figure 5-24 Impact of Monsoon Precipitation on Ring-width of Deodar in GFD (1962-2011)
133
The mean autumn maximum temperature showed a non-significant (F2, 47= 0.82;
p>0.05) impact on mean annual ring-widths of Deodar. The mean annual ring-width
responded positively to increasing autumn maximum temperature. The mean annual
ring-widths were found more clustered around 16.0˚C. Relatively better growth
response (mean annual ring-width >3.25 mm) was observed at 16.0˚C to 17˚C (Figure
5.25).
Figure 5-25 Impact of Mean Autumn Maximum Temperature on Ring-width of Deodar in GFD (1962-2011)
134
The mean autumn minimum temperature showed a highly significant (F2, 47 =5.11;
p<0.01) impact on mean annual ring-widths of Deodar. The mean annual ring-width
decreased slightly with increasing mean autumn minimum temperature. Relatively
better growth response (mean annual ring-width >3.25 mm) was observed at minimum
temperature ranged from 2.0˚C to 3.0˚C (Figure 5.26).
Figure 5-26 Impact of Mean Autumn Minimum Temperature on Ring-width of Deodar in GFD (1962-2011)
135
The autumn precipitation showed a non-significant (F1, 48= 0.76; p>0.05) impact on
mean annual ring-widths of Deodar. The mean annual ring-width decreased marginally
with increasing autumn precipitation. Relatively better growth response (mean annual
ring-width >3.50 mm) was observed when autumn precipitation ranged from 25.0
mm/season to 75.0 mm/season (Figure 5.27).
Figure 5-27 Impact of Mean Autumn Precipitation on Ring-width of Deodar in GFD (1962-2011)
136
The mean winter maximum temperature showed a highly significant (F1, 48= 10.33;
p<0.01) impact on mean annual ring-widths of Deodar. The mean annual ring-width
decreased with increasing mean winter maximum temperature. Relatively better growth
response (mean annual ring-width >3.50 mm) was observed at 4.0˚C to 7.0˚C (Figure
5.28).
Figure 5-28 Impact of Mean Winter Maximum Temperature on Ring-width of Deodar in GFD (1962-2011)
137
The mean winter minimum temperature showed a highly significant (F1, 48= 18.02;
p<0.01) impact on mean annual ring-widths of Deodar. The mean annual ring-width
decreased with increasing mean winter minimum temperature. Relatively better growth
response (mean annual ring-width >3.50 mm) was observed at - 4.0˚C and - 2.0˚C
(Figure 5.29).
Figure 5-29 Impact of Mean Winter Minimum Temperature on Ring-width of Deodar in GFD (1962-2011)
138
The winter precipitation showed a non-significant (F3, 46=1.32; p>0.05) impact on mean
annual ring-widths of Deodar. The mean annual ring-width showed a polynomial
response. Relatively better growth was found (mean annual ring-width >3.0 mm) when
mean winter precipitation ranged from 50.0 mm/season to 250.0 mm/season (Figure
5.30).
Figure 5-30 Impact of Winter Precipitation on Ring-width of Deodar in GFD (1962-2011)
139
5.4.6 Mathematical Expressions of Impacts of Temperature and Precipitation on
Ring-width of Deodar
Mathematical expressions of impacts of climate change on mean annual ring-widths of
deodar showed linear to polynomial patterns (Table 5.7). The mean annual minimum
temperature showed higher impact on mean annual ring-width (R2 = 0.23) compared to
mean annual maximum temperature (R2 = 0.17). Among the seasons, mean winter
minimum temperature showed the highest impact (R2 = 0.27), followed by mean winter
maximum temperature (R2 = 0.18) and mean autumn minimum temperature (R2 = 0.18).
The impacts of temperature, both maximum and minimum during other seasons, were
marginal. The impacts of mean annual precipitation and mean monsoon precipitation
were significant. The impact of mean monsoon precipitation was higher (R2 = 0.14)
compared to mean annual precipitation (R2 = 0.10). There was no significant difference
in the impacts of other seasonal precipitation.
Table 5-7 Mathematical Expressions of Impacts of Temperature and Precipitation
on Ring-width of Deodar in GFD (1962-2011)
Climate Parameters Mathematical Expressions R2 F(1,2,3), (48,47,46)*
(p)
Mean Max. Temp. Y = 122.3 - 4.31 × X + 0.4292 ×
X2
0.17 4.78 (0.013)
Mean Min. Temp. Y = 19.92 - 5.216 × X + 0.3985
× X2
0.23 6.97 (0.002)
Mean Precipitation Y = 4.150 - 0.0012 × X 0.10 5.60 (0.022)
Spring Max. Temp. Y = 3.951 - 0.06334 × X 0.02 1.16 (0.287)
Spring Min. Temp. Y = 3.586 - 0.1200 × X 0.06 3.13 (0.083)
Spring Precipitation Y = 3.616 - 0.0053 × X +
0.00001 × X2
0.02 0.49 (0.614)
Summer Max. Temp. Y = -324.9 + 42.97 × X -1.871 ×
X2 + 0.0271×X3
0.02 0.31 (0.815)
Summer Min. Temp. Y = 11.83 - 1.432 × X + 0.0581
× X2
0.03 0.80 (0.457)
140
Summer Precipitation Y = 1.831 + 0.019 × X - 0.0001
× X2
0.06 1.39 (0.258)
Monsoon Max. Temp. Y = 78.55 - 6.277 × X + 0.1304
× X2
0.04 1.04 (0.360)
Monsoon Min. Temp. Y = 42.98 - 6.082 × X + 0.232 ×
X2
0.03 0.76 (0.475)
Monsoon
Precipitation
Y = 4.498 - 0.007 × X + 0.0001
× X2
0.14 3.80 (0.030)
Autumn Max. Temp. Y = 30.03 - 3.377 × X + 0.1056
× X2
0.03 0.82 (0.448)
Autumn Min. Temp. Y = 9.774 - 3.186 × X + 0.3662
× X2
0.18 5.11 (0.010)
Autumn Precipitation Y = 3.223 - 0.003 × X 0.02 0.76 (0.388)
Winter Max. Temp. Y = 4.757 - 0.2470 × X 0.18 10.33 (0.002)
Winter Min. Temp. Y = 2.551 - 0.2648 × X 0.27 18.02 (0.000)
Winter Precipitation Y = 3.983 - 0.02 × X + 0.0001 ×
X 2- 0.00001 × X3
0.08 1.32 (0.281)
* Values in parentheses in the top row are degree of freedom for linear, quadratic and
polynomial equations respectively. The (p) values indicate significance levels.
5.5 Ring-width and Ring-wood Characteristics of Blue pine
5.5.1 Time function analysis of Ring-width and Ring-wood Characteristics of Blue
pine
The time function responses of ring-width and ring-wood characteristics of Blue pine
were studied through regression analysis at 95% Confidence Interval (CI) and
Prediction Interval (PI), using the standardized data. The trend in the time series was
assessed by applying Mann Kendall test with Normal Approximation and using Sen’s
Slope Estimator method. A summary of the trend analysis is reproduced in Table 5.8.
141
Table 5-8 Trend Analysis of Ring-width and Ring-wood Characteristics of Blue
pine at GFD (1962-2011)
Increasing trend No trend Decreasing trend
The time function analysis of ring-width and ring-wood characteristics of Blue pine for
the period 1962-2011 showed highly significant (p<0.01) downward trend in ring-width
and early wood formation, highly significant (p<0.01) upward trend in late wood
formation, significant (p<0.05) downward trend in early wood cell wall thickness and
no trend in early wood cell diameter, late wood cell diameter and late wood cell wall
thickness.
Species /Tree Growth
Characteristics
Z-Value p-Value Trend Sen’s Slope
Upward Downward
Ring-width - 4.718 1.000 0.000 - 0.023
Early wood formation - 4.366 1.000 0.000 - 0.092
Late wood formation 4.316 0.000 1.000 0.090
Early wood cell diameter 0.686 0.246 0.754 0.007
Early wood cell wall
thickness
- 2.141 0.984 0.016
- 0.002
Late wood cell diameter 0.234 0.407 0.593 0.001
Late wood cell wall
thickness
- 1.539 0.938 0.061
- 0.002
142
The analysis of time function response of mean annual ring-widths of Blue pine for the
period 1962-2011 indicated a large variation in mean annual ring-widths across the
cores in a quadratic pattern, with an overall decreasing trend. The mean annual ring-
width ranged from 1.85±0.27 to 3.33±0.31 mm, with a mean value of 2.54±0.15. The
largest mean annual ring-width was recorded during 1962, while the smallest mean
annual ring-width was recorded during 2002. The mean annual ring-width was steadily
higher during 1960s, but decreased gradually during 1978-86, and increased again
during 1990s followed by a considerable decrease. There were 13 years having mean
annual ring-width larger than 3.0 mm, while there were nine years having mean annual
ring-width smaller than 2.0 mm (Figure 5.31). The highest variability in mean annual
ring-width across the cores was recorded during 1971.
Figure 5-31 Time function of Mean Annual Ring-width of Blue pine in GFD (1962-2011)
143
The analysis of time function response of mean intra-ring early wood formation of Blue
pine for the period 1962-2011 showed a large variation in a quadratic pattern, with a
declining trend. The mean intra-ring early wood formation was 76.67±0.21% of the
mean annual wood formation. The largest mean intra-ring early wood formation was
78.87±1.51% during 1973, while the smallest early wood formation was 72.12±1.80%
during 2009. The time function response of mean intra-ring early wood formation was
almost similar to that of mean annual ring-width, but with a lower slope gradient. There
were nine years having mean intra-ring early wood formation higher than 78.0%, while
there were ten years having mean intra-ring early wood formation lower than 73.0%
(Figure 5.32). The highest variability in mean intra-ring early wood formation across
the cores was recorded in 1971.
Figure 5-32 Time function of Mean Intra-ring Early Wood Formation (%) of Blue pine in GFD (1962-2011)
144
The analysis of time function response of mean intra-ring late wood formation of Blue
pine for the period 1962-2011 showed a large variation in a quadratic pattern, with an
overall increasing trend. The mean intra-ring late wood formation was 23.37±0.20% of
the mean annual wood formation. The largest mean intra-ring late wood formation was
28.97±1.95% during 2002, while the smallest mean intra-ring late wood formation was
20.53±1.42% during 1976. The time function response of mean intra-ring late wood
formation exhibited opposite trend to that of mean intra-ring early wood formation. The
mean intra-ring late wood formation increased steadily from 1962 onwards, except a
slight decline during 1990s and 2005-08. There were 20 years having mean intra-ring
late wood formation higher than 25.0%, while there were two years having mean intra-
ring late wood formation lower than 21.0% (Figure 5.33). The highest variability in
intra-ring late wood formation across the cores was recorded in 1998.
Figure 5-33 Time function of Mean Intra-ring Late Wood Formation (%) of Blue pine in GFD (1962-2011)
145
The mean intra-ring early wood cell diameter of Blue pine during 1962-2011 ranged
from 40.05±1.48 µm to 45.07±1.08 µm, with a mean of 42.57±0.16 µm. The time
function response of mean intra-ring early wood cell diameter exhibited a polynomial
behavior, with no overall significant trend. The largest mean intra-ring early wood cell
diameter was recorded during 1975, while the smallest mean intra-ring early wood cell
diameter was recorded during 1964. The mean intra-ring early wood cell diameter
increased during 1970-80 and after 2003, but decreased during 1990-2000. There were
46 years having mean intra-ring early wood cell diameter larger than 41.0 µm, while
there were four years having mean intra-ring early wood cell diameter between 40.0
and 41.0 µm (Figure 5.34). The highest variability in mean intra-ring early wood cell
diameter across the cores was recorded in 2009.
Figure 5-34 Time function of Mean Intra-Ring Early Wood Cell Diameter (µm) of Blue pine in GFD (1962-2011)
146
The mean intra-ring early wood cell thickness of Blue pine during 1962-2011 ranged
from 2.25±0.06 µm to 2.57±0.19 µm, with a mean of 2.38±0.01 µm. The time function
response of mean intra-ring early wood cell wall thickness showed a quadratic
behavior, with an overall decreasing trend. The largest mean intra-ring early wood cell
wall was recorded during 1979, while the smallest mean intra-ring early wood cell wall
was recorded during 1964. The mean intra-ring early wood cell wall thickness
decreased slightly during 1985-88. There were five years having mean intra-ring early
wood cell wall thickness larger than 2.5 µm, while there were 11 years having mean
intra-ring early wood cell wall thickness smaller than 2.30 µm (Figure 5.35). The
highest variability in mean intra-ring early wood cell wall thickness was recorded in
1982.
Figure 5-35 Time function of Mean Intra-ring Early Wood Cell Wall Thickness (µm) of Blue pine
147
A microscopic picture (100×) of intra-ring early wood cell diameter and cell wall
thickness of Blue pine is depicted in Figure 5.36.
Figure 5-36 Intra-ring Early Wood Cell Diameter and Cell Wall Thickness of Blue pine (100x)
148
The mean intra-ring late wood cell diameter of Blue pine during 1962-2011 ranged
from 17.42±0.61 µm to 19.55±0.50 µm, with a mean of 18.28±0.07 µm. The time
function response of mean intra-ring late wood cell diameter showed a quadratic
behavior, with no overall significant trend. The largest mean intra-ring late wood cell
diameter was recorded during 1966, while the smallest mean intra-ring late wood cell
diameter was recorded during 1990. The mean intra-ring late wood cell diameter
decreased during 1975-95, but increased gradually from 1995 onwards. There were
eight years having mean intra-ring late wood cell diameter larger than 19.0 µm, while
there were 13 years having mean intra-ring late wood cell wall diameter smaller than
18.0 µm (Figure 5.37). The highest variability in mean intra-ring late wood cell
diameter across the cores was recorded in 1991. The mean intra-ring late wood cell
diameter was significantly (p<0.05) smaller compared to mean intra-ring early wood
cell diameter.
Figure 5-37 Time function of Mean Intra-ring Late Wood Cell Diameter (µm) of Blue pine in GFD (1962-2011)
149
The mean intra-ring late wood cell wall thickness of Blue pine during 1962-2011
ranged from 3.90±0.13 µm to 4.30±0.13 µm, with a mean of 4.07±0.01 µm. The time
function response of mean intra-ring late wood cell thickness showed a quadratic
behavior, with no overall significant trend. The largest mean intra-ring late wood cell
wall thickness was recorded during 1976, while the smallest mean intra-ring late wood
cell wall thickness was recorded during 1999. The highest variability in mean intra-ring
late wood cell wall thickness across the cores was recorded in 1987. The mean intra-
ring late wood cell wall thickness showed a slightly declining trend during 1970-2000.
There were seven years having mean intra-ring late wood cell wall thickness larger than
4.3 µm, while there were 12 years having mean intra-ring late wood cell wall thickness
smaller than 4.0 µm (Figure 5.38). The highest variability in mean intra-ring late wood
cell wall thickness across the cores was recorded in 1987. The mean intra-ring late
wood cell wall thickness was significantly (p<0.05) higher compared to mean intra-ring
early wood cell wall thickness.
Figure 5-38 Time function of Mean Intra-ring Late Wood Cell Wall Thickness (µm) of Blue pine in GFD (1962-
2011)
150
A microscopic picture (100×) of intra-ring late wood cell diameter and cell wall
thickness of Blue pine is depicted in Figure 5.39
Figure 5-39Intra-ring Late Wood Cell Diameter and Cell Wall Thickness of Blue pine (100x)
151
5.5.2 Mathematical Expressions of Time Function of Ring-width, Intra-ring wood
Formation and Cell Characteristics of Blue pine
Mathematical expressions of mean annual ring-width, mean intra-ring wood formation
and cell characteristics of Blue pine showed a mix of quadratic and polynomial
behaviors of time function. The mean annual ring-width, mean intra-ring early wood
formation and mean intra-ring late wood formation showed highly significant (p<0.01)
changes with time. The mean intra-ring early wood cell diameter, mean intra-ring early
wood cell wall thickness and mean intra-ring late wood cell diameter showed
significant (p<0.05) temporal response. Conversely, temporal change in mean intra-ring
late wood cell wall thickness was non-significant (p>0.05). The R2 ranged between 0.06
and 0.51. The highest R2 value was calculated for mean intra-ring annual ring-width,
while the lowest R2 value was calculated for mean intra-ring late wood intra-ring cell
wall thickness. The models used for time function response indicated good fit of the
models for mean annual ring-width, mean intra-ring wood formation and mean wood
cell characteristics except mean intra-ring late wood cell wall thickness (Table 5.9).
Table 5-9 Mathematical Expressions of Time Function of Ring-width and Intra-
ring Wood Characteristics of Blue pine in GFD (1962-2011)
Tree Growth
Characteristics
Mathematical Expressions R2 F (2,3) (47,46) * (p)
Ring-width Y = 2421 - 2.412 × X +
0.0006 × X2
0.51 24.73 (0.000)
Early wood formation Y = 9697 - 9.591 × X +
0.0024 × X2
0.41 16.48 (0.000)
Late wood formation Y = -12831 + 12.85 × X-
0.003 × X2
0.42 16.89 (0.000)
Early wood cell diameter Y = -1375115 + 2075 × X -
1.044 × X2 + 0.0002 × X3
0.17 3.09 (0.036)
Early wood cell wall
thickness
Y = 172.9 - 0.1698 × X +
0.00004 × X2
0.12 3.26 (0.047)
Late wood cell
diameter
Y = 4400 - 4.413 × X +
0.0011 × X2
0.17 4.84 (0.012)
Late wood cell wall
thickness
Y = 269.6 -0.2660 × X +
0.0001 × X2
0.06 1.51 (0.230)
* Values in parentheses in the top row are degree of freedom for quadratic and polynomial
equations respectively. The (p) values indicate significance levels.
152
5.5.3 Decadal changes in Ring-width and Ring-wood Characteristics of Blue pine
A highly significant (F4, 15= 272.25; p<0.01) difference was recorded in mean decadal
ring-widths of Blue pine, with a decreasing trend, during 1962-2011. The overall
difference in mean ring-widths among the decades was significant (Tukey’s HSD, CV
0.14; p=0.05). The largest mean decadal ring-width was 3.40±0.04 mm during 1962-71,
which was significantly different from 1972-81. The difference in mean decadal ring-
widths of 1972-81 and 1982-91 was non-significant (p>0.05). The smallest mean
decadal ring-width was 2.06±0.02 mm during 2002-1 (Table 5.10).
A highly significant (F4, 15= 16.9; p<0.01) difference was recorded in mean decadal
intra-ring early wood formation of Blue pine, with a decreasing trend, during 1962-
2011. The overall difference in mean decadal intra-ring early wood formation among
the decades was significant (Tukey’s HSD, CV 0.14; p=0.05). The largest mean
decadal intra-ring early wood formation was 77.37±0.32% during 1962-71, which was
significantly higher compared to mean decadal intra-ring early wood formation during
1972-81. The smallest mean decadal intra-ring early wood formation was 73.63±0.53%
during 2002-11. The difference in mean decadal intra-ring early wood formation among
decades, 1982-91, 1992-2001, 2001-2011 was non-significant (p>0.05) (Table 5.10).
153
Table 5-10 Mean Decadal Ring-width and Ring-Wood Characteristics of Blue pine
in GFD (1962-2011)
Mean values within a column sharing same alphabets are not significantly different (Tukey’s HSD,
p=0.05); RW= Ring-width; EW= Early wood; LW= Late wood; EWCD= Early wood cell diameter;
EWCW= Early wood cell wall thickness; LWCD= Late wood cell diameter; LWCW= Late wood cell wall
thickness
A highly significant (F4, 15= 57.15; p<0.01) difference was recorded in mean decadal
intra-ring late wood formation of Blue pine, with an increasing trend, during 1962-
2011. The overall difference in mean decadal intra-ring late wood formation among the
decades was significant (Tukey’s HSD, CV 1.02; p=0.05). The trend of mean decadal
intra-ring late wood formation followed a pattern opposite to mean decadal ring-width
and mean decadal intra-ring early wood formation. The largest mean decadal intra-ring
late wood formation was 26.09±0.36 mm during 2002-2011, which was not
significantly different from 1992-2001. The smallest mean decadal intra-ring late wood
formation was 21.92±0.04 mm. The mean decadal intra-ring late wood formation
during 1962-71 was significantly (p<0.05) lower compared to 1972-81 (Table 5.10).
A highly significant (F4, 15= 26.65; p<0.01) difference was recorded in mean decadal
intra-ring early wood cell diameter of Blue pine, with an overall increasing trend,
Decades
/CV
Tree-ring Characteristics
RW±SE
(mm)
EW±SE
(%)
LW±SE
(%)
EWCD
±SE
(µm)
EWC
WT±SE
(µm)
LWCD
±SE
(µm)
LWCWT
±SE
(µm)
1962-71 3.40±
0.04 a
77.37±
0.32 a
21.92±
0.04 d
41.56±
0.08 c
2.51±
0.01 a
17.88±
0.01 c
4.33±
0.04 a
1972-81 2.58±
0.03 b
77.04±
0.36 ab
22.92±
0.16 c
42.10±
0.19b c
2.43±
0.00 b
17.82±
0.02 c
4.10±
0.03 b
1982-91 2.57±
0.04 b
75.32±
0.45 bc
24.31±
0.28 b
42.44±
0.18 b
2.38±
0.01 c
18.07±
0.16 c
4.05±
0.02 bc
1992-01 2.21±
0.03 c
73.65±
0.48 c
25.65±
0.20 a
42.62±
0.10 b
2.35±
0.01 d
18.52±
0.09 b
4.02±
0.01 bc
2002-11 2.06±
0.02 d
73.63±
0.53 c
26.09±
0.36 a
44.02±
0.26 a
2.33±
0.00 d
19.39±
0.02 a
3.92±
0.05 c
CV 0.14 4.37 1.02 0.77 0.02 0.36 0.15
154
during 1962-2011. The difference in mean decadal intra-ring early wood cell diameter
among the decades was significant (Tukey’s HSD, CV 0.77; p=0.05). The largest mean
decadal intra-ring early wood cell diameter was 44.02±0.26 µm during 2002-11, which
was significantly higher compared to 19912001. The smallest mean decadal intra-ring
early wood cell diameter was 41.56±0.08 µm during 1962-71. The mean decadal intra-
ring early wood cell diameter did not vary significantly between decades: 1962-71 and
1972-81, and 1982-91 and 1992-01(Table 5.10).
A highly significant (F4, 15= 179.39; p<0.01) difference was recorded in mean decadal
intra-ring early wood cell wall thickness of Blue pine, with a decreasing trend, during
1962-2011. The difference in mean decadal intra-ring late wood cell wall thickness
among the decades was significant (Tukey’s HSD, CV 0.02; p= 0.05). In contrast to
mean decadal intra-ring early wood cell diameter, mean decadal intra-ring early wood
cell wall thickness decreased over time. The largest mean decadal intra-ring early wood
cell wall thickness was 2.51± 0.01 µm, which was significantly higher compared to
1982-91. The smallest mean decadal intra-ring early wood cell wall thickness was
2.33± 0.00 µm during 2002-11. The difference in mean decadal intra-ring early wood
cell wall thickness did not vary significantly between decades 1992-01 and 2002-
11(Table 5.10).
A highly significant (F4, 15= 62.30; p<0.01) difference was recorded in mean decadal
intra-ring late wood cell diameter of Blue pine, with a decreasing trend, during 1962-
2011. The difference in mean decadal intra-ring late wood cell diameter among the
decades was significant (Tukey’s HSD, CV 0.36; p= 0.05). The largest mean decadal
intra-ring mean decadal intra-ring late wood cell diameter was 19.39± 0.02 µm during
2002-11, which was significantly higher compared to 1992-2001. The smallest mean
decadal intra-ring late wood cell diameter was 17.88± 0.01 µm during 1962-71. The
mean decadal intra-ring late wood cell diameter did not vary significantly among the
decades of 1962-71, 1972-81 and 1982-91(Table 5.10).
A highly significant (F4, 15= 19.47; p<0.01) difference was recorded in mean decadal
intra-ring late wood cell wall thickness of Blue pine, with a decreasing trend, during
1962-2011. The difference in mean decadal intra-ring late wood cell wall thickness
among the decades was significant (Tukey’s HSD, CV 0.15; p= 0.05). The pattern of
155
change in the mean decadal intra-ring late wood cell wall thickness was similar to the
mean decadal intra-ring early wood cell wall thickness. The largest mean decadal intra-
ring late wood cell wall thickness was 4.33± 0.04 µm during 1962-71, which was
significantly higher compared to 197281. The smallest mean decadal intra-ring late
wood cell wall thickness was 3.92±0.05 µm during 2002-2011. The mean decadal intra-
ring late wood cell wall thickness did not vary among 1982-91, 1992-01 and 2002-2013
(Table 5.10).
5.5.4 Correlation between Ring-width and Ring-wood Characteristics of Blue pine
The analysis of Pearson Correlation Coefficients matrix of Blue pine growth data for
1962-2011 revealed a highly significant (p<0.01) and positive correlation of mean
annual ring-width with mean intra-ring early wood formation (r = 0.80), mean intra-ring
early wood cell wall thickness (r = 0.96) and mean intra-ring late wood cell wall
thickness (r = 0.89). The correlation of mean annual ring-width was highly significant
(p<0.01) and negative with mean intra-ring early wood cell wall diameter (r = -0.80),
mean intra-ring late wood formation (r = -0.89) and mean intra-ring late wood cell
diameter (r = -0.70). The correlation of mean intra-ring early wood formation was
highly significant (p<0.01) and positive with mean intra-ring early wood cell wall
thickness (r = 0.84) and mean intra-ring late wood cell wall thickness (r = 0.69), but
highly significant (p<0.01) and negative with mean intra-ring early wood cell diameter
(r = -0.74), mean intra-ring late wood cell formation (r = -0.91) and mean intra-ring
late wood cell diameter (r = -0.75). The correlation of mean intra-ring early wood cell
diameter was highly significant (p<0.01) and positive with mean intra-ring late wood
formation (r = 0.78) and mean intra-ring late wood cell diameter (r = 0.86), but highly
significant (p<0.01) and negative with mean intra-ring early wood cell wall thickness (r
= -0.81) and mean intra-ring late wood cell wall thickness (r = -0.71). The correlation of
mean intra-ring early wood cell wall thickness was highly significant (p<0.01) and
positive with mean intra-ring late wood cell wall thickness (r = 0.90), but highly
significant (p<0.01) and negative with mean intra-ring late wood formation (r = -0.94)
and mean intra-ring late wood cell diameter (r = -0.73). The correlation of mean intra-
ring late wood formation was highly significant (p<0.01) and positive with mean intra-
ring late wood cell diameter (r = 0.80), but highly significant (p<0.01) and negative
with mean intra-ring late wood cell wall thickness (r = -0.86). The correlation of mean
156
intra-ring late wood cell diameter was highly significant (p<0.01) and negative with
mean intra-ring late wood cell wall thickness (r = -0.68) (Table 5.11).
Table 5-11 Correlation Coefficients Matrix between Ring-width and Ring-wood
Characteristics of Blue pine in GFD (1962-2011)
Tree Growth
Characteristics
RW EW EWCD EWCWT LW LWCD
EW 0.80**
(0.000)
EWCD - 0.80**
(0.000)
- 0.74**
(0.000)
EWCWT 0.96**
(0.000)
0.84**
(0.000)
- 0.81**
(0.000)
LW - 0.89**
(0.000)
- 0.91**
(0.000)
0.78**
(0.000)
- 0.94**
(0.000)
LWCD - 0.70**
(0.000)
- 0.75**
(0.000)
0.86**
(0.000)
- 0.73**
(0.000)
0.80**
(0.000)
LWCWT 0.89**
(0.000)
0.69**
(0.000)
- 0.71**
(0.000)
0.91**
(0.000)
- 0.86**
(0.000)
- 0.68**
(0.001)
Values in ( ) are p-values; ** Significant at 99%; RW= Ring-width; EW= Early wood;
EWCD= Early wood cell diameter; EWCW= Early wood cell wall thickness; LW= Late
wood; LWCD= Late wood cell diameter; LWCW= Late wood cell wall thickness
5.5.5 Impacts of Climate Change on Ring-width of Blue pine
The impacts of climate change on mean annual ring-widths of Blue pine during 1962-
2011 were assessed using response functions of ring-widths with temperature
(maximum and minimum) and precipitation, both on annual and seasonal basis.
157
The mean annual maximum temperature showed a highly significant (F1, 48= 25.10;
p<0.01) impact on mean annual ring-widths of Blue pine, exhibiting a linear pattern
with a declining trend. Most of the ring-width responses were observed between 16.0˚C
and 17.0˚C. The mean annual maximum temperature below 15.5˚C showed positive
impact on mean annual ring-width, while the mean annual maximum temperature
above 17.0˚C showed negative impact on mean annual ring-width (Figure 5.40).
Figure 5-40 Impact of Mean Annual Maximum Temperature on Ring-width of Blue pine in GFD (1962-2011)
158
The mean annual minimum temperature showed a highly significant (F1, 48=18.77;
p<0.01) impact on mean annual ring-widths of Blue pine, exhibiting a linear pattern.
The mean annual ring-width decreased with increasing mean annual minimum
temperature. Relatively better growth response (mean annual ring-width >3.25 mm)
was observed between 5.5˚C and 6.5˚C (Figure 5.41).
Figure 5-41 Impact of Mean Annual Minimum Temperature on Ring-width of Blue pine in GFD (1962-2011)
159
The annual precipitation showed a non-significant (F1, 48= 0.65; p>0.05) impact on
mean annual ring-widths of Blue pine. The mean annual ring-width decreased with
increasing annual precipitation. A large variation in mean annual ring-width response
was noticed across the observed range of precipitation, with relatively better growth
(mean annual ring-width >3.00 mm) when annual precipitation was in the range of 800-
1000 mm (Figure 5.42).
Figure 5-42 Impact of Annual Precipitation on Ring-width of Blue pine in GFD (1962-2011)
160
The 1-way analysis of variance of mean annual ring-width of Blue pine with mean
decadal precipitation showed that the largest mean annual ring-width was 2.64±0.33
mm when annual precipitation was ranged between 600 mm and 700 mm. The smallest
mean annual ring-width was 2.50±0.22 mm when annual precipitation ranged from 701
mm to 800 mm (Table 5.12).
Table 5-12 Precipitation and Ring-width of Blue pine in GFD (1962- 2011)
Precipitation Range
(mm/annum)
Mean Annual Ring-width
(mm)
Standard Error (SE)
501-600 3.25* 0.00
601-700 2.64 0.33
701-800 2.50 0.22
801-900 2.58 0.14
901-1000 2.60 0.12
>1001 2.41 0.13
*Single value
The mean spring maximum temperature showed a highly significant (F1, 48=7.21;
p<0.01) negative impact on mean annual ring-widths of Blue pine. The mean annual
ring-width declined with increasing maximum temperature (Figure 5.43).
Figure 5-43 Impact of Mean Spring Maximum Temperature on Ring-width of Blue pine in GFD (1962-2011)
161
The mean spring minimum temperature showed a highly significant (F1, 48 = 10.04;
p<0.01) negative impact on mean annual ring-widths of Blue pine. The mean annual
ring-width decreased with increasing mean spring minimum temperature. Relatively
better growth response (ring-width >3.0 mm) was observed between 3.0˚C and 4.0˚C
(Figure 5.44).
Figure 5-44 Impact of Mean Spring Minimum Temperature on Ring-width of Blue pine in GFD (1962-2011)
162
The spring precipitation showed a non-significant (F1, 48=0.15; p>0.05) positive
impact on mean annual ring-widths of Blue pine. The mean annual ring-width
increased with increasing spring precipitation. (Figure 5.45).
Figure 5-45 Impact of Spring Precipitation on Ring-width of Blue pine in GFD (1962-2011)
163
The mean summer maximum temperature showed a highly significant (F1, 48=7.84;
p<0.01) negative impact on mean annual ring-widths of Blue pine, in a linear pattern
with declining trend (Figure 5.46).
Figure 5-46 Impact of Mean Summer Maximum Temperature on Ring-width of Blue pine in GFD (1962-2011)
164
The mean summer minimum temperature showed a significant (F1, 48= 6.86; p<0.05)
negative impact on mean annual ring-widths of Blue pine. The mean annual ring-width
decreased with increasing summer minimum temperature. Relatively better growth
response (mean annual ring-width >3.00 mm) was observed between 10.5.˚C and
11.5˚C (Figure 5.47).
Figure 5-47 Impact of Mean Summer Minimum Temperature on Ring-width of Blue pine in GFD (1962-2011)
165
The summer precipitation showed a non-significant (F1, 48=0.60; p>0.05) impact on
mean annual ring-widths of Blue pine. The mean annual ring-width increased with
increasing summer precipitation. Relatively better growth (mean annual ring-width
>3.25 mm) was observed when summer precipitation was in the range of 100-150
mm/season (Figure 5.48).
Figure 5-48 Impact of Summer Precipitation on Ring-width of Blue pine in GFD (1962-2011)
166
The mean monsoon maximum temperature showed a non-significant (F1, 48=2.43;
p>0.05) impact on mean annual ring-widths of Blue pine. The mean annual ring-width
decreased with increasing mean maximum monsoon temperature. Relatively better
growth response was observed (mean annual ring-width >3.00 mm) at mean monsoon
maximum temperature of 23.0˚C-24.0˚C, with some fluctuations (Figure 5.49).
Figure 5-49 Impact of Mean Monsoon Maximum Temperature on Ring-width of Blue pine in GFD (1962-2011)
167
The mean monsoon minimum temperature showed a non-significant (F1, 48=0.39;
p>0.05) negative impact on mean annual ring-widths of Blue pine. The mean annual
ring-width decreased with increasing mean annual monsoon minimum temperature.
Relatively better growth response (mean annual ring-width >3.25 mm) was observed
between 13.0˚C and 13.5˚C (Figure 5.50).
Figure 5-50 Impact of Mean Monsoon Minimum Temperature on Ring-width of Blue pine in GFD (1962-2011)
168
The monsoon precipitation showed a non-significant (F1, 48=1.39; p>0.05) impact on
mean annual ring-widths of Blue pine. The mean annual ring-width decreased slightly
with increasing monsoon precipitation. Relatively better growth response (mean annual
ring-width >3.00 mm) was observed when the monsoon precipitation was in the range
of 250-350 mm/season, with some fluctuations (Figure 5.51).
Figure 5-51 Impact of Monsoon Precipitation on Ring-width of Blue pine in GFD (1962-2011)
169
The mean autumn maximum temperature showed a non-significant (F1, 48=0.55;
p>0.05) impact on mean annual ring-widths of Blue pine. The mean annual ring-width
decreased with increasing mean autumn maximum temperature. The mean annual ring-
width were clustered around 16.0˚C (Figure 5.52).
Figure 5-52 Impact of Mean Autumn Maximum Temperature on Ring-width of Blue pine in GFD (1962-2011)
170
The mean autumn minimum temperature showed a highly significant (F1, 48=10.2;
p<0.01) negative impact on mean annual ring-widths of Blue pine. The mean annual
ring-width decreased with increasing mean autumn minimum temperature. Relatively
better growth response (mean annual ring-width >3.25 mm) was observed between
3.0˚C and 3.5˚C. The mean annual ring-width decreased steadily with mean autumn
minimum temperature above 4.0˚C (Figure 5.53).
Figure 5-53 Impact of Mean Autumn Minimum Temperature on Ring-width of Blue pine in GFD (1962-2011)
171
The autumn precipitation showed a non-significant (F1, 48=0.37; p>0.05) impact on
mean annual ring-widths of Blue pine. The mean annual ring-width decreased
marginally with increasing autumn precipitation. Relatively better growth (mean annual
ring-width >3.00 mm), with few fluctuations, was observed when autumn precipitation
was in the range of 30-60 mm/season (Figure 5.54).
Figure 5-54 Impact of Autumn Precipitation on Ring-width of Blue pine in GFD (1962-2011)
172
The mean winter maximum temperature showed a highly significant (F1, 48=13.03;
p<>0.01) negative impact on mean annual ring-widths of Blue pine. The mean annual
ring-width decreased with increasing mean winter maximum temperature. The mean
annual ring-width was relatively higher (>2.50 mm) at 6.0˚C-7.0˚C, however, it
declined steadily with mean winter maximum temperature higher than 7.5˚C (Figure
5.55).
Figure 5-55 Impact of Mean Winter Maximum Temperature on Ring-width of Blue pine in GFD (1962-2011)
173
The mean winter minimum temperature showed a highly significant (F1, 48=34.28;
p<0.01) negative impact on mean annual ring-widths of Blue pine. The mean annual
ring-width decreased with increasing mean winter minimum temperature. Relatively
better growth response (mean annual ring-width >3.00 mm) was observed between -
3.0˚C and -2.0˚C. The mean annual ring-width decreased steadily with mean winter
minimum temperature above -1.0˚C (Figure 5.56).
Figure 5-56 Impact of Mean Winter Minimum Temperature on Ring-width of Blue pine in GFD (1962-2011)
174
The winter precipitation showed a non-significant (F1, 48=0.18; p>0.05) impact on mean
annual ring-widths of Blue pine. The mean annual ring-width decreased marginally
with increasing winter precipitation. Relatively better growth (mean annual ring-width
>3.00 mm) was observed when winter precipitation was in the range of 150-250
mm/season (Figure 5.57). The impact of winter precipitation on ring-width was almost
similar to that of autumn precipitation.
Figure 5-57 Impact of Winter Precipitation on Ring-width of Blue pine in GFD (1962-2011)
175
5.5.6 Mathematical Expressions of Impacts of Temperature and Precipitation on
Ring-width of Blue pine
Mathematical expressions of impacts of climate change on mean annual ring-width of
Blue pine showed a linear pattern (Table 5.13). The mean annual maximum
temperature (R2 = 0.34) showed a higher negative impact on mean annual ring-width
compared to mean annual minimum temperature (R2 = 0.28). Among the seasons, mean
winter minimum temperature showed the highest (R2= 0.41) impact followed by mean
winter maximum temperature (R2 =0.21), mean autumn minimum temperature (R2 =
0.18), mean spring minimum temperature (R2 = 0.17) and mean summer maximum
temperature (R2 = 0.14). The impacts of mean spring maximum temperature and mean
summer minimum temperature were the same. The impacts of mean monsoon
maximum temperature and mean monsoon minimum temperature and mean autumn
maximum temperature were non-significant. The mean autumn maximum temperature
showed the least impact on mean annual ring-width among all seasons. The impacts of
mean annual precipitation and mean seasonal precipitations were non-significant.
Among the mean seasonal precipitations, the mean autumn precipitation showed the
highest impact on mean annual ring-width.
176
Table 5-13 Mathematical Expressions of Impact of Temperature and
Precipitation on Ring-width of Blue pine in GFD (1962-2011)
Climate Parameters Mathematical
Expressions
R2 F(1), (48)* (p)
Mean Max. Temp. Y = 5.500 - 0.486 × X 0.34 25.10 (0.000)
Mean Min. Temp. Y = 10.57 - 0.4900 × X 0.28 18.77 (0.000)
Mean Precipitation Y = 2.913 - 0.0004 × X 0.01 0.65 (0.425)
Spring Max. Temp. Y = 4.527 - 0.145 × X 0.13 7.21 (0.010)
Spring Min. Temp. Y = 3.365 - 0.196 × X 0.17 10.04 (0.003)
Spring Precipitation Y = 2.464 + 0.0004 × X 0.03 0.15 (0.705)
Summer Max. Temp. Y = 6.456 -0.1695 × X 0.14 7.84 (0.007)
Summer Min. Temp. Y = 4.653 - 0.1823 × X 0.13 6.86 (0.012)
Summer Precipitation Y = 2.346 + 0.0017 × X 0.01 0.60 (0.444)
Monsoon Max. Temp. Y = 6.838 - 0.183 × X 0.05 2.43 (0.126)
Monsoon Min. Temp. Y = 3.791 - 0.095 × X 0.08 0.39 (0.533)
Monsoon Precipitation Y = 2.844 - 0.0009 × X 0.03 1.39 (0.244)
Autumn Max. Temp. Y = 3.621 - 0.0673 × X 0.02 0.55 (0.460)
Autumn Min. Temp. Y = 3.641 - 0.2667 × X 0.18 10.20 (0.002)
Autumn Precipitation Y = 2.63 - 0.002 × X 0.08 0.37 (0.547)
Winter Max. Temp. Y = 4.329 - 0.264 × X 0.21 13.03 (0.001)
Winter Min. Temp. Y = 3.641 - 0.267 × X 0.41 34.28 (0.000)
Winter Precipitation Y = 2.637 - 0.0015 × X 0.04 0.18 (0.675)
* Values in parentheses in the top row are degree of freedom for linear equation. The (p)
values indicate significance levels.
177
5.6 Ring-width and Ring-wood Characteristics of Chir pine
5.6.1 Time function analysis of Ring-width and Ring-wood Characteristics of Chir
pine
The time function responses of ring-width and ring-wood characteristics of Chir pine
were studied through regression analysis at 95% Confidence Interval (CI) and Prediction
Interval (PI), using the standardized data. The trend in the time series was assessed by
applying Mann Kendall test with Normal Approximation and using Sen’s Slope
Estimator method. A summary of the trend analysis is reproduced in Table 5.14.
Table 5-14 Trend Analysis of Ring-width and Ring-wood characteristics of Chir
pine at GFD (1962-2011)
Species /Tree Growth
Characteristics
Z-Value p-Value Trend Sen’s
Slope Upward Downward
Ring-width -0.753- 0.774 0.226 -0.013
Early wood formation -0.335 0.631 0.369 -0.004
Late wood formation -0.753 0.774 0.226 -0.013
Early wood cell
diameter
-2.275 0.989 0.011
-0.029
Early wood cell wall
thickness
-0.134 0.553 0.447
0.000
Late wood cell
diameter
-0.452 0.674 0.326
-0.003
Late wood cell wall
thickness
-3.714 1.000 0.000
-0.006
Increasing trend No trend decreasing trend
The time function analysis of ring-width and ring-wood characteristics of Chir pine
for the period 1962-2011 showed highly significant (p<0.01) downward trend in late
wood cell wall thickness and significant (p<0.05) downward trend in early wood cell
diameter and no trend in ring-width, early wood formation, late wood formation,
early wood cell wall thickness and late wood cell diameter.
178
The analysis of time function response of ring-widths of Chir pine for the period 1962-
2011 indicated a wide range of variation in mean annual ring-widths across the cores in
a quadratic pattern, with no overall significant trend. The mean annual ring-width
ranged between 2.00±0.39 mm and 3.66±0.55 mm, with a mean value of 262±0.39 mm.
The largest mean annual ring-width was recorded during 1988, while the smallest mean
annual ring-width was recorded during 2011. The ring-width increased during 1970s
and late 1980s, but declined incessantly from 1995 to 2011. There were ten years
having mean annual ring-width larger than 3.0 mm, while there were 24 years having
mean annual ring-width lower than 2.50 mm (Figure 5.58). The highest variability in
mean annual ring-width across the cores was recorded during 1983.
Figure 5-58 Time function of Mean Annual Ring-Width of Chir pine in GFD (1962-2011)
179
The analysis of time function response of mean intra-ring early wood formation of Chir
pine for the period 1962-2011 showed a highly significant (F2, 47= 10.50; p<0.01)
change in a quadratic pattern, with no overall significant trend. There was an increasing
growth in early wood formation up to 1990, followed by a decline. The mean intra-ring
early wood formation was 66.67±0.21% of mean annual wood formation. The largest
mean early wood formation was 69.95±1.94% during 1988, while the smallest mean
intra-ring early wood formation was 64.22±1.31% during 2002. The time function
response of mean intra-ring early wood formation showed an opposite trend to that of
mean annual ring-width. There were 11 years having mean intra-ring early wood
formation higher than 68.0%, while there were eight years having mean intra-ring early
wood formation lower than 65.0% (Figure 5.59). The highest variability in mean intra-
ring early wood formation across the cores was during 1983.
Figure 5-59 Time function of Mean Intra-ring Early Wood Formation (%) of Chir pine in GFD (1962-2011)
180
The analysis of time function response of mean intra-ring late wood formation of Chir
pine showed a highly significant (F2, 47= 6.79; p<0.01) change in a quadratic pattern,
with no overall significant trend. There was a declining growth in late wood formation
up to 1993, followed by an increasing tendency. The mean intra-ring late wood
formation was 32.97±0.20% of mean annual wood formation. The largest mean intra-
ring late wood formation was 35.58±2.17% during 1983, while the smallest mean intra-
ring late wood formation was 29.92±1.90% during 1988. The mean intra-ring late wood
formation showed a changing trend opposite to that of mean intra-ring early wood
formation. The slope gradient of time function of mean intra-ring late wood formation
was relatively smaller compared to mean intra-ring early wood formation. There were
five years each having mean intra-ring late wood formation higher than 35.0% and
lower than 31.0% (Figure 5.60). The highest variability in mean intra-ring late wood
formation across the cores was recorded in 1983.
Figure 5-60 Time function of Mean Intra-ring Late Wood Formation (%) of Chir pine in GFD (1962-2011)
181
The mean intra-ring early wood cell diameter of Chir pine during 1962-2011 ranged
from 48.46±1.51 µm to 54.53±1.25 µm, with a mean of 51.50±0.19 µm. The time
function response of mean intra-ring early wood cell diameter showed a highly
significant (F2, 47= 5.27; p<0.05) change, with a linear pattern and declining trend. The
largest mean intra-ring early wood cell diameter was recorded during 1974, while the
smallest mean intra-ring early wood cell diameter was recorded during 2001. There
were five years having mean intra-ring early wood cell diameter larger than 53.0 µm,
while there were eight years having mean intra-ring early wood cell diameter smaller
than 50.0 µm (Figure 5.61). The largest variability in mean intra-ring early wood cell
diameter across the cores was recorded in 1982.
Figure 5-61 Time function of Mean Intra-ring Early Wood Cell Diameter (µm) of Chir pine in GFD (1962-2011)
182
The mean intra-ring early wood cell thickness of Chir pine during 1962-2011 ranged
from 2.70±0.08 µm to 2.38±0.08 µm, with a mean of 2.55±0.01 µm. The time function
response of mean intra-ring early wood cell wall thickness showed a non-significant
(F2, 47= 1.60; p>0.05) change, with a quadratic pattern and no overall significant trend.
The largest mean intra-ring early wood cell wall thickness was recorded during 1966,
while smallest mean intra-ring early wood cell wall thickness was recorded during
1975. The mean intra-ring early wood cell wall thickness decreased during 1962-90,
but increased after 2000. There was only one year having mean intra-ring early wood
cell wall thickness larger than 2.7 µm and two years having mean intra-ring early wood
cell wall thickness smaller than 2.40 µm (Figure 5.62). The highest variability in mean
intra-ring early wood cell wall thickness was recorded in 1981.
Figure 5-62 Time function of Mean Intra-ring Early Wood Cell Wall Thickness (µm) of Chir pine in GFD (1962-
2011)
183
A microscopic picture (100×) of intra-ring early wood cell diameter and cell wall
thickness of Chir pine is depicted in Figure 5.63.
Figure 5-63 Intra-ring Early Wood Cell Diameter and Cell Wall Thickness of Chir pine (100x)
184
The mean intra-ring late wood cell diameter of Chir pine during 1962-2011 ranged
from 19.39±0.76 µm to 22.12±1.57 µm, with a mean of 20.72±0.10 µm. The time
function response of mean intra-ring late wood cell diameter showed a non-significant
(F2, 47= 0.76; p>0.05) change, with a quadratic response and no overall significant trend.
The largest mean intra-ring late wood cell dia7meter was recorded during 1996, while
the smallest mean intra-ring late wood cell diameter was recorded during 1980. The
mean intra-ring late wood cell diameter decreased gradually during 1962-95, but
increased gradually thereafter. There were 14 years having mean intra-ring late wood
cell diameter larger than 21.0 µm and eight years having mean intra-ring late wood cell
diameter smaller than 21.0 µm (Figure 5.64). The highest variability in mean intra-ring
late wood cell diameter across the cores was recorded in 1984. The mean intra-ring late
wood cell diameter was significantly (p<0.05) smaller compared to mean intra-ring
early wood cell diameter.
Figure 5-64 Time function of Mean Intra-ring Late Wood Cell Diameter (µm) of Chir pine in GFD (1962-2011)
185
The mean intra-ring late wood cell wall thickness of Chir pine during 1962-2011
ranged from 4.56±0.19 µm to 5.24±0.18 µm, with a mean of 4.86±0.02 µm. The time
function response of mean intra-ring late wood cell wall thickness showed a highly
significant (F2, 47= 7.79; p<0.01) change, with a linear function and declining trend. The
largest mean intra-ring late wood cell wall thickness was recorded during 1970, while
smallest mean intra-ring late wood cell wall thickness was recorded during 1984. There
were ten years having mean intra-ring late wood cell wall thickness larger than 5.00 µm
and 12 years having mean intra-ring late wood cell wall thickness smaller than 4.75 µm
(Figure 5.65). The highest variability in mean intra-ring late wood cell wall thickness
across the cores was recorded in 1982. The mean intra-ring late wood cell wall
thickness was significantly (p<0.05) higher compared to mean intra-ring early wood
cell wall thickness. The time function responses of mean intra-ring early and mean
intra-ring late wood cell wall thickness were also different. The slope gradient of mean
intra-ring late wood cell wall thickness was considerably higher compared to mean
intra-ring early wood cell wall thickness.
Figure 5-65Time function of Mean Intra-ring Late Wood Cell Wall Thickness (µm) of Chir pine in GFD (1962-2011)
186
A microscopic picture (100×) of intra-ring late wood cell diameter and cell wall
thickness of Chir pine is depicted in Figure 5.66.
Figure 5-66 Intra-ring Late Wood Cell Diameter and Cell Wall Thickness of Chir pine (100x)
187
5.6.2 Mathematical Expressions of Time Function of Ring-width, Intra-ring wood
Formation and Cell Characteristics of Chir pine
Mathematical expressions of time functions of ring-width, intra-ring wood formation
and cell characteristics of Chir pine showed a mix of linear and quadratic behaviors.
The mean annual ring-width, mean intra-ring early wood formation, mean intra-ring
late wood formation, mean intra-ring early wood cell wall thickness, mean intra-ring
late wood cell diameter and mean intra-ring late wood cell wall thickness followed a
quadratic function, while mean intra-ring early wood cell diameter followed a linear
function. The R2 ranged between 0.03 and 0.31. The highest R2 value was estimated for
mean intra-ring early wood formation followed by mean intra-ring late wood cell wall
thickness. The lowest R2 value was calculated for mean intra-ring late wood cell
diameter followed by mean intra-ring early wood cell wall thickness. The results of
time function response indicated good fit of the models for mean annual ring-width,
mean intra-ring early, mean intra-ring late wood formation and mean intra-ring late
wood cell wall thickness. The models were poorly fit for mean intra-ring early wood
cell diameter, mean intra-ring early wood cell wall thickness and mean intra-ring late
wood cell diameter (Table 5.15).
Table 5-15 Mathematical Expressions of Time Function of Ring-width and Intra-
ring Wood Characteristics of Chir pine in GFD (1962-2011)
Tree Growth
Characteristics
Mathematical Expressions R2 F (1) 2, (48) 47 * (p)
Ring-width Y = - 4901 + 4.94 × X +
0.0012 × X2
0.30 9.88 (0.000)
Early wood formation Y = - 17081 + 17.27 × X -
0.0044 × X2
0.31 10.50 (0.000)
Late wood formation Y = 13649 - 13.70 × X +
0.0034 × X2
0.22 6.79 (0.003)
Early wood cell diameter Y = 109.3 - 0.0291 × X 0.10 5.27 (0.026)
Early wood cell wall
thickness
Y = 457.4 - 0.4580 × X +
0.0001 × X2
0.06 1.60 (0.213)
Late wood cell diameter Y = 2425 - 2.418 × X +
0.0006 × X2
0.03 0.76 (0.473)
Late wood cell wall
thickness
Y = 470.5 - 0.4631 × X +
0.0001 × X2
0.25 7.79 (0.001)
* Values in parentheses ( ) in the top row are degree of freedom for linear functions and
outside parenthesis for quadratic functions. The (p) values indicate significance levels.
188
5.6.3 Decadal changes in Ring-width and Ring-wood Characteristics of Chir pine
A highly significant (F4, 15= 8889.78; p<0.01) difference was recorded in mean decadal
ring-widths of Chir pine, with an overall irregular declining trend, during 1962-2011.
The difference in mean decadal ring-widths among the decades was significant
(Tukey’s HSD, CV 0.03; p=0.05). The largest mean decadal ring-width was 3.77± 0.01
mm during 1972-81, which was significantly different from mean decadal ring-widths
during 1962-71 and 1982-91. The difference in mean decadal ring-widths of 1992-01
and 2002-11 was significant (p<0.05). The smallest mean decadal ring-width was 2.22±
0.01 mm during 2002-11 (Table 5.16).
An overall highly significant (F4, 15= 29.81; p<0.01) difference was recorded in mean
decadal intra-ring early wood formation of Chir pine, with a decreasing trend, during
1962-2011. The mean decadal intra-ring early wood formation varied significantly on
decadal basis (Tukey’s HSD, CV 1.13; p=0.05). The largest mean decadal intra-ring
early wood formation was 68.23±0.25% during 1962-71, which was significantly
higher compared to mean decadal intra-ring early wood formation measured during
1972-81. The smallest mean decadal intra-ring early wood formation was 64.50±0.04%
during 2002-11. The mean decadal intra-ring early wood formation during 1992-01 was
not significantly different (p>0.05) from mean decadal intra-ring early wood formation
during 2001-11. The mean decadal intra-ring early wood formation did not follow the
pattern of change in mean decadal ring-width (Table 5.16).
A highly significant (F4, 15= 601.90; p<0.01) difference was recorded in mean decadal
intra-ring late wood formation of Chir pine, with a decreasing trend, during 1962-2011.
The overall difference in mean decadal intra-ring late wood formation among the
decades was significant (Tukey’s HSD, CV: 0.39; p=0.05). The changes in mean
decadal intra-ring late wood formation followed the pattern of changes in mean decadal
intra-ring early wood formation. The largest mean decadal late wood formation was
36.91±0.06% during 1962-71, which was significantly different from mean decadal late
wood formation during 1972-81. The smallest mean decadal late wood formation was
31.73±0.14% during 2002-11 which was not significantly different from 1992-01
(Table 5.16).
189
Table 5-16 Mean Decadal Ring-width and Ring-Wood Characteristics of Chir pine
in GFD (1962-2011)
Decades
/CV
Tree-ring Characteristics
RW±SE
(mm)
EW±SE
(%)
LW±SE
(%)
EWCD
±SE
(µm)
EWCWT
±SE
(µm)
LWCD
±SE
(µm)
LWCWT
±SE
(µm)
1962-71 2.85±
0.01 b
68.23±
0.25 a
36.91±
0.06 a
53.44±
0.25 a
2.46±
0.01 c
21.94±
0.16 b
5.26±
0.01*a
1972-81 3.77±
0.01 a
66.46±
0.36 b
33.03±
0.07 b
52.26±
0.10 b
2.56±
0.02 b
21.02±
0.18bc
4.89±
0.01 b
1982-91 2.64±
0.00 c
66.46±
0.07 b
32.16±
0.02 c
50.59±
0.15 c
2.48±
0.01 c
21.26±
0.11 b
4.82±
0.03bc
1992-01 2.33±
0.02 d
65.27±
0.37 c
31.83±
0.11 cd
51.11±
0.23 c
2.54±
0.00 b
20.58±
0.19 c
4.79±
0.02 c
2002-11 2.22±
0.01 e
64.50±
0.04 c
31.73±
0.14 d
51.34±
0.17 c
2.76±
0.02 a
19.29±
0.11 d
4.57±
0.02 d
CV 0.03 1.13 0.39 0.80 0.02 0.68 0.09
Mean values within a column sharing same alphabets are not significantly different (Tukey’s
HSD, p=0.05); RW= Ring-width; EW= Early wood; LW= Late wood; EWCD= Early wood cell
diameter; EWCW= Early wood cell wall thickness; LWCD= Late wood cell diameter; LWCW=
Late wood cell wall thickness
A highly significant (F4, 15= 35.92; p<0.01) difference was recorded in mean decadal
intra-ring early wood cell diameter of Chir pine, with an overall decreasing trend, during
1962-2011. The difference in mean decadal intra-ring early wood cell diameter among
the decades was significant (Tukey’s HSD, CV: 0.80; p=0.05). The largest mean decadal
intra-ring early wood cell diameter was 53.44±0.25 µm during 1962-71, which was
significantly higher compared to 197281. The smallest mean decadal intra-ring early
wood cell diameter was 51.34±0.17 µm during 2002-11. The mean decadal intra-ring
early wood cell diameter did not vary significantly among 1982-91, 1992-01 and 2002-
11 (Table 5.16).
A highly significant (F4, 15= 475.93; p<0.01) difference was recorded in mean decadal
intra-ring early wood cell wall thickness of Chir pine, with an overall increasing trend,
190
during 1962-2011. The difference in mean decadal intra-ring early wood cell wall
thickness among the decades was significant (Tukey’s HSD, CV: 0.02; p= 0.05). The
largest mean decadal intra-ring early wood cell wall thickness was 2.76±0.02 µm
during 2002-11, which was significantly higher compared to 1992-01. The smallest
mean decadal intra-ring early wood cell wall thickness was 2.46±0.01 µm during 1962-
71 which was significantly different from 1972-81 (Table 5.16).
A highly significant (F4, 15= 40.69; p<0.01) difference was found in mean decadal intra-
ring late wood cell diameter of Chir pine, with an overall decreasing trend, during
1962-2011. The difference in mean decadal intra-ring late wood cell diameter was
significant among decades (Tukey’s HSD, CV 0.68; p= 0.05). The largest mean decadal
intra-ring late wood cell diameter was 21.94±0.16 µm during 1962-71, followed by
21.26±0.11 µm during 1982-91. The smallest mean decadal intra-ring late wood cell
diameter was 19.29±0.11 µm during 2001-11. The mean decadal intra-ring late wood
cell diameter during 2001-11 was significantly different from 1992-01 (Table 5.16).
A highly significant (F4, 15= 151.54; p<0.01) difference was recorded in mean decadal
intra-ring late wood cell wall thickness of Chir pine, with a decreasing trend, during
1962-2011. The difference in mean decadal intra-ring late wood cell wall thickness
among the decades was significant (Tukey’s HSD, CV: 0.09; p= 0.05). The largest
mean decadal intra-ring late wood cell wall thickness was 5.26± 0.01 µm during 1962-
71 during 2001-11, which was significantly higher compared to 1972-81. The smallest
mean decadal intra-ring late wood cell wall thickness was 4.57± 0.02 µm during 2002-
11. The mean decadal intra-ring late wood cell wall thickness did not vary between
1972-81 and 1982-91 (Table 5.16). The pattern of change in mean decadal intra-ring
late wood cell wall thickness was similar to mean decadal intra-ring late wood
formation.
191
5.6.4 Correlation between Ring-width and Ring-wood Characteristics of Chir pine
The analysis of Pearson Correlation Coefficients matrix of Chir pine growth data for
1962-2011 revealed a significant (p<0.05) and positive correlation of mean annual ring-
width with mean intra-ring early wood formation (r = 0.47), mean intra-ring early wood
cell diameter (r = 0.45) and mean intra-ring late wood cell diameter (r = 0.46). The
correlation of mean annual ring-width was non-significant (p>0.05) and negative with
mean intra-ring early wood cell wall thickness (r = 0.34), but non-significant (p>0.05)
and positive with mean intra-ring late wood formation (r = 0.31) and mean intra-ring
late wood cell wall thickness (r = 0.42). The correlation of mean intra-ring early wood
formation was highly significant (p<0.01) and positive with mean intra-ring early wood
cell diameter (r = 0.64), mean intra-ring late wood formation (r = 0.83), mean intra-ring
late wood cell diameter (r = 0.82) and mean intra-ring late wood cell wall thickness (r =
0.92), but highly significant (p<0.01) and negative with mean intra-ring early wood cell
wall thickness (r = -0.75). The correlation of mean intra-ring early wood cell diameter
was highly significant (p<0.01) and positive with mean intra-ring late wood formation
(r = 0.86) and mean intra-ring late wood cell wall thickness (r = 0.75), significant
(p<0.05) and positive with mean intra-ring late wood cell diameter (r = 0.46), but non-
significant (p>0.05) and negative with mean intra-ring early wood cell wall thickness (r
= -0.25). The correlation of mean intra-ring early wood cell wall thickness was highly
significant (p<0.01) and negative with mean intra-ring late wood cell wall diameter (r =
-0.90) and mean intra-ring late wood cell wall thickness (r = -0.75), but significant
(p<0.05) and negative with mean intra-ring late wood formation (r = -0.52). The
correlation of mean intra-ring late wood formation was highly significant (p<0.01) and
positive with mean intra-ring late wood cell wall diameter (r = 0.70) and mean intra-
ring late wood cell wall thickness (r = 0.93). The correlation of mean intra-ring late
wood cell diameter was highly significant (p<0.01) and positive with mean intra-ring
late wood cell wall thickness (r = 0.84) (Table 5.17)
192
Table 5-17 Correlation Coefficients Matrix between Ring-width and Ring-wood
Characteristics of Chir pine in GFD (1962-2011)
Values in ( ) are p-values; * significant (p<0.05); ** highly significant (p<0.01); RW= Ring-
width; EW= Early wood; EWCD= Early wood cell diameter; EWCW= Early wood cell wall
thickness; LW= Late wood; LWCD= Late wood cell diameter; LWCW= Late wood cell wall
thickness
5.6.5 Impacts of Climate Change on Ring-width of Chir pine
The impacts of climate change on ring-widths of Chir pine during 1962-2011 were
assessed using response functions of ring-widths with temperature (maximum and
minimum) and precipitation, both on annual and seasonal basis.
Tree
Growth
Parameters
RW EW EWCD EWCWT LW LWCD
EW 0.47*
(0.037)
EWCD 0.45*
(0.049)
0.64**
(0.003)
EWCWT -0.34
(0.146)
-0.75**
(0.000)
-0.25
(0.297)
LW 0.31
(0.179)
0.83**
(0.000)
0.86**
(0.000)
-0.52*
(0.018)
LWCD 0.46*
(0.041)
0.82**
(0.000)
0.46*
(0.039)
-0.90**
(0.000)
0.70**
(0.000)
LWCWT 0.42
(0.069)
0.92**
(0.000)
0.75**
(0.000)
-0.75**
(0.000)
0.93**
(0.000)
0.84**
(0.000)
193
The mean annual maximum temperature showed a highly significant (F2, 47= 3.48;
p<0.05) impact on mean annual ring-widths of Chir pine, exhibiting a quadratic pattern
with a slightly declining trend. Most of the ring-width responses were observed around
16.0˚C. The mean annual maximum temperature of 15.5˚C showed positive impact on
ring-width. (Figure 5.67).
Figure 5-67 Impact of Mean Annual Maximum Temperature on Ring-width of Chir pine in GFD (1962-2011)
194
The mean annual minimum temperature showed a significant (F2, 47= 3.04; p<0.05)
impact on mean annual ring-widths of Chir pine, exhibiting a quadratic pattern.
Overall, the mean annual ring-width decreased with increasing mean annual minimum
temperature. Relatively better growth response (mean annual ring-width >3.0 mm) was
observed between 5.0˚C and 5.5˚C. A positive impact of mean annul minimum
temperature was observed above 5.5˚C and 7.0˚C (Figure 5.68). The mean annual ring-
width of 2.56±0.31 mm at 6.0˚C was significantly (p<0.05) smaller compared to mean
annual ring-width at 5.0˚C. Conversely, mean annual ring-width of 2.57±0.66 mm at
7.0˚C did not differ significantly (p>0.05) from mean annual ring-width at 5.0˚C.
Figure 5-68 Impact of Mean Annual Minimum Temperature on Ring-width of Chir pine in GFD (1962-2011)
195
The annual precipitation showed a non-significant (F3, 46= 2.11; p>0.05) impact on
mean annual ring-widths of Chir pine, in a polynomial pattern. The mean annual ring-
width, as a whole, slightly decreased with increasing annual precipitation. Most of the
mean annual ring-width responses were observed in annual precipitation range of 800-
1000 mm (Figure 5.69).
Figure 5-69 Impact of Annual Precipitation on Ring-width of Chir pine in GFD (1962-2011)
196
The 1-way analysis of variance of mean annual ring-width of deodar with mean decadal
precipitation showed that the largest mean annual ring-width was 3.46±0.40 mm when
annual precipitation was between 501 and 600 mm, followed by mean annual ring-
width 3.28±0.42 mm at annual precipitation of 801-900 mm. The smallest mean annual
ring-width was 2.76±0.40 mm when annual precipitation was between >1000
mm/annum, followed by mean annual ring-width of 2.89±0.39 mm at annual
precipitation range of 901-1000 mm (Table 5.18).
Table 5-18 Impact of Precipitation on Ring-width of Chir pine in GFD (1962-
2011)
Precipitation range
(mm/annum)
Mean Annual Ring-width
(mm)
Standard Error
(SE)
501-600 3.64* 0.00
601-700 3.02 0.77
701-800 3.04 0.49
801-900 3.28 0.42
901-1000 2.89 0.39
>1001 2.76 0.40
*Single value
197
The mean spring maximum temperature showed a significant (F3, 46= 3.45; p<0.05)
impact on mean annual ring-widths of Chir pine. As a whole, mean annual ring-width
slightly increased with increasing mean spring maximum temperature, with a
polynomial function (Figure 5.70).
Figure 5-70 Impact of Mean Spring Maximum Temperature on Ring-width of Chir pine in GFD (1962-2011)
198
The mean spring minimum temperature showed a non-significant (F3, 46 = 1.46; p>0.05)
impact on mean annual ring-widths of Chir pine, in a polynomial pattern and slightly
declining trend. Relatively better growth response was observed around 3.0˚C and
5.0˚C, with mean annual ring-width larger than 3.0 mm (Figure 5.71).
Figure 5-71 Impact of Mean Spring Minimum Temperature on Ring-width of Chir pine in GFD (1962-2011)
199
The spring precipitation showed a non-significant (F3, 46=1.76; p>0.05) impact on mean
annual ring-widths of Chir pine. There was an overall slight increase in mean annual
ring-width with increasing spring precipitation (Figure 5.72).
Figure 5-72 Impact of Spring Precipitation on Ring-width of Chir pine in GFD (1962-2011)
200
The mean summer maximum temperature showed a non-significant (F3, 46= 1.08;
p>0.05) impact on mean annual ring-widths of Chir pine, in polynomial pattern and an
overall slightly declining trend (Figure 5.73).
Figure 5-73 Impact of Mean Summer Maximum Temperature on Ring-width of Chir pine in GFD (1962-2011)
201
The mean summer minimum temperature showed a non-significant (F3, 46= 1.52;
p>0.05) impact on mean annual ring-widths of Chir pine. The mean annual ring-width
response was of polynomial pattern, with an overall declining trend. Relatively better
growth response in terms of mean annual ring-width (mean annual ring-width >2.80
mm) was observed around 11.0˚C (Figure 5.74).
Figure 5-74 Impact of Mean Summer Minimum Temperature on Ring-width of Chir pine in GFD (1962-2011)
202
The summer precipitation showed a non-significant (F3, 46=1.51; p>0.05) impact on
mean annual ring-widths of Chir pine, in a quadratic pattern and an overall decreasing
trend (Figure 5.75). The overall impact of summer precipitation on mean annual ring-
width was similar to that of spring precipitation.
Figure 5-75 Impact of Summer Precipitation on Ring-width of Chir pine in GFD (1962-2011)
203
The mean monsoon maximum temperature showed a non-significant (F2, 47= 0.42;
p>0.05) impact on mean annual ring-widths of Chir pine, in a polynomial pattern, with
an increasing trend when mean monsoon maximum temperature ranged between 23.0˚C
and 24˚C. A relatively better growth response (mean annual ring-width >2.80 mm) was
measured between mean monsoon maximum temperature of 23.0˚C and 24˚C (Figure
5.76).
Figure 5-76 Impact of Mean Monsoon Maximum Temperature on Ring-width of Chir pine in GFD (1962-2011)
204
The mean monsoon minimum temperature showed a non-significant (F2, 47= 0.52;
p>0.05) impact on mean annual ring-widths of Chir pine, in a linear pattern and
decreasing trend. Relatively better growth response (mean annual ring-width >3.0 mm)
was observed at 13.0˚C to 14.0˚C (Figure 5.77). The trend of impact of mean monsoon
minimum temperature was contrasting with that of mean monsoon maximum
temperature.
Figure 5-77 Impact of Mean Monsoon Minimum Temperature on Ring-width of Chir pine in GFD (1962-2011)
205
The monsoon precipitation showed a non-significant (F2, 47=2.26; p>0.05) impact on
mean annual ring-widths of Chir pine, exhibiting a quadratic pattern. As whole there
was an increasing trend in mean annual ring-width with increasing monsoon
precipitation, which followed the impact trend of spring precipitation with lower slope
gradient. Relatively better growth response (mean annual ring-width >3.0 mm) was
observed when monsoon precipitation was ranged between 200 mm/season and 350
mm/season and larger than 500 mm/season (Figure 5.78).
Figure 5-78 Impact of Monsoon Precipitation on Ring-width of Chir pine in GFD (1962-2011)
206
The mean autumn maximum temperature showed a non-significant (F3, 46= 1.41;
p>0.05) impact on mean annual ring-widths of Chir pine, in a quadratic pattern.
Overall, mean annual ring-width slightly declined with increasing mean autumn
maximum temperature. The mean annual ring-widths were found clustered at 16.0˚C.
Relatively better growth response (mean annual ring-width >3.0 mm) was observed at
14.0˚C, 16.0˚C and 17˚C (Figure 5.79).
Figure 5-79 Impact of Mean Autumn Maximum Temperature on Ring-width of Chir pine in GFD (1962-2011)
207
The mean autumn minimum temperature showed a highly significant (F3, 46= 5.28;
p<0.01) impact on mean annual ring-widths of Chir pine, in a weak polynomial pattern.
The mean annual ring-width decreased slightly with increasing mean autumn minimum
temperature. Relatively better growth response (mean annual ring-width >3.0 mm) was
observed between 3.0˚C and 4.0˚C (Figure 5.80). The impact trend of mean autumn
minimum temperature on mean annual ring-width was similar to that of mean autumn
maximum temperature, however, the slope gradient of mean autumn temperature was
smaller.
Figure 5-80 Impact of Mean Autumn Minimum Temperature on Ring-width of Chir pine in GFD (1962-2011)
208
The autumn precipitation showed a non-significant (F3, 46= 2.02; p>0.05) impact on
mean annual ring-widths of Chir pine. The mean annual ring-width decreased marginally
with increasing autumn precipitation, in a polynomial pattern. Relatively better growth
response (mean annual ring-width >3.0 mm) was observed when autumn precipitation
was around 20.0 mm/season, and between 60.0 to 80.0 mm/season (Figure 5.81). In
general, the impact of autumn precipitation on mean annual ring-width followed the
pattern of monsoon precipitation.
Figure 5-81 Impact of Autumn Precipitation on Ring-width of Chir pine in GFD (1962-2011)
209
The mean winter maximum temperature showed a non-significant significant (F3, 46=
1.27; p<0.01) impact on mean annual ring-widths of Chir pine. The overall mean annual
ring-width increased marginally with increasing mean winter maximum temperature, in
a polynomial pattern. The growth was better (mean annual ring-width >3.00 mm)
between 6.0˚C and 7.5˚C (Figure 5.82).
Figure 5-82 Impact of Mean Winter Maximum Temperature on Ring-width of Chir pine in GFD (1962-2011)
210
The mean winter minimum temperature showed a significant (F2, 47= 2.74; p<0.05)
impact on mean annual ring-widths of Chir pine, in a quadratic pattern and overall
slightly declining trend. The mean annual ring-width decreased gradually with mean
winter minimum temperature lower than -3.5˚C and higher than -1.0˚C. Relatively
better growth response (mean annual ring-width >3.0 mm) was observed from -3.0˚C to
-2.0˚C (Figure 5.83). The impact of increasing mean winter minimum temperature was
more profound compared to mean winter maximum temperature.
Figure 5-83 Impact of Mean Winter Minimum Temperature on Ring-width of Chir pine in GFD (1962-2011)
211
The winter precipitation showed a non-significant (F3, 46= 0.31; p>0.05) impact on
mean annual ring-widths of Chir pine. The mean annual ring-width showed a
polynomial response, with a slightly declining trend. Relatively better growth response
(mean annual ring-width >3.0 mm) was found when winter precipitation ranged from
175.0 mm/season to 250.0 mm/season (Figure 5.84).
Figure 5-84 Impact of Winter Precipitation on Ring-width of Chir pine in GFD (1962-2011)
212
5.6.6 Mathematical Expressions of Impacts of Temperature and Precipitation on
Ring-width of Chir pine
Mathematical expressions of impacts of climate change on mean annual ring-widths of
Chir pine showed a mix of quadratic and polynomial functions. The mean annual
maximum temperature (R2 = 0.13) showed a higher impact on mean annual ring-width
compared to mean annual maximum temperature (R2 = 0.12) and mean annual
precipitation (R2 = 0.12). Among the seasons, mean autumn minimum temperature
showed the highest (R2 = 0.26) impact, followed by mean spring maximum (R2 = 0.18)
and mean winter minimum temperature (R2 = 0.11). The impacts of mean temperatures,
both maximum and minimum during other seasons, were marginal. The impact of mean
monsoon precipitation (R2 = 0.09) was relatively higher compared to impacts of other
mean seasonal precipitations. The results indicated poor to good fit of the models for
the impacts of maximum temperature, minimum temperature and precipitation on the
ring-width of Chir pine (Table 5.19).
Table 5-19 Mathematical Expressions of Impact of Temperature and Precipitation
on Ring-width of Chir pine in GFD (1962-2011)
Climate Parameters Mathematical Expressions R2 F(2,3), (47,46)* (p)
Mean Max. Temp. Y = - 68.66 + 8.81 × X +
0.272 × X2
0.13 3.48 (0.039)
Mean Min. Temp. Y = 69.40 - 11.40 × X +
0.8812 × X2
0.12 3.04 (0.05)
Mean Precipitation Y = - 37.85 + 0.2680 × X -
0.0003 × X2 + 0.0000 × X3
0.12 2.11 (0.113)
Spring Max. Temp. Y = 417.1 - 82.28 ×X +
5.851 × X2 - 0.1382 × X3
0.18 3.45 (0.024)
Spring Min. Temp. Y = 47.15 - 9.287 × X +
1.947 × X2 - 0.1326 × X3
0.09 1.46 (0.237)
Spring Precipitation Y = 34.42 - 0.01858 × X +
0.0001×X2
0.07 1.76 (0.184)
213
Summer Max. Temp. Y = - 740 + 98.8 × X - 4.193
× X2 + 0.05907 × X3
0.04 1.08 (0.624)
Summer Min. Temp. Y = - 94.5 + 33.41 × X -
2.881 ×X2 + 0.0818 × X3
0.03 1.52 (0.672)
Summer Precipitation Y = 2.458 + 0.005 × X +
0.0031 × X2
0.06 1.51 (0.402)
Monsoon Max. Temp. Y = 6754 - 854.2 × X +
36.18 × X2 - 0.5107 × X3
0.03 0.42 (0.666)
Monsoon Min. Temp. Y = - 0.28 + 0.496 × X -
0.024 × X2
0.02 0.52 (0.855)
Monsoon
Precipitation
Y = 37.90 - 0.0283 × X +
0.0001 × X2
0.09 2.26 (0.116)
Autumn Max. Temp. Y = 1922 - 351.6 × X +
21.76 × X2 - 0.4477 × X3
0.08 1.41 (0.253)
Autumn Min. Temp. Y = 114.4 - 54.70 × X +
11.92 × X2 - 0.8444 × X3
0.26 5.28 (0.003)
Autumn Precipitation Y = 32.56 + 0.0455 × X -
0.0011 × X2 + 0.00001 × X3
0.01 2.02 (0.124)
Winter Max. Temp. Y = 21.22 + 8.33 × X - 1.653
× X2 + 0.0995 × X3
0.08 1.27 (0.297)
Winter Min. Temp. Y = 32.49 + 0.0164 × X2 +
0.1009 × X2
0.11 2.74 (0.075)
Winter Precipitation Y = 3.31 + 0.014 × X -
0.0001 × X2 - 0.000001 × X3
0.09 0.3 (0.736)
* Values in parentheses in the top row are degree of freedom for quadratic and polynomial
equations respectively. The (p) values indicate significance levels.
214
5.7 Inter-species Comparison of Correlation Coefficients of Ring-widths of Cedrus
deodara, Pinus wallichiana and Pinus roxburghii with Climate Parameters
The mathematical expressions of correlation coefficients of ring-widths of Cedrus
deodara, Pinus wallichiana and Pinus roxburghii with climate parameters at GFD for
the period 1962-2011 (Tables 5.4.6, 5.5.6 and 5.6.6) indicated linear patterns in some
cases and non-linear patterns in others. A matrix of Pearson Correlation Coefficients
drawn for ring-widths of Cedrus deodara, Pinus wallichiana and Pinus roxburghii with
climate parameters at GFD for the period 1962-2011 (Table 5.20) indicated the
comparative direction and strength of the relationships. As the Pearson Correlation
Coefficient predicts the correct direction and strength of only linear relationship of
associated variables, therefore, the interpretation of the matrix were confined to linear
pattern only. The correlation between mean annual ring-width of Deodar with mean
annual precipitation was negative and significant, with winter maximum temperature
and winter minimum temperature negative and highly significant and with summer
maximum temperature and summer minimum temperature negative and non-
significant. The correlation between mean annual ring-width of Blue pine with summer
minimum temperature was negative and significant, with annual maximum
temperature, annual minimum temperature, spring maximum temperature, spring
minimum temperature, summer maximum temperature, winter maximum temperature
and winter precipitation was negative and highly significant, with spring precipitation
and summer precipitation positive and non-significant and with the rest of the climatic
variables negative and non-significant. The correlation between mean annual ring-
width of Chir pine being for non-linear relationships could lead to false results and have
not been interpreted.
215
Table 5-20 Inter-species Comparison of Correlation Coefficients of Ring-widths of
Cedrus deodara, Pinus wallichiana and Pinus roxburghii with Climate Parameters
in GFD (1962-2011)
Climate
parameters
Deodar Blue pine Chir pine
Mean Max. Temp. - 0.302
(0.033)
- 0.530*
(0.000)
- 0.199
(0.166)
Mean Min. Temp. - 0.339
(0.016)
- 0.586*
(0.000)
- 0.211
(0.141)
Mean
Precipitation
- 0.323 *
(0.022)
- 0.115*
(0.425)
0.004
(0.980)
Spring Max.
Temp.
- 0.154 *
(0.283)
- 0.361*
(0.010)
- 0.236
(0.098)
Spring Min.
Temp.
- 0.247 *
(0.083)
- 0.416*
(0.003)
- 0.173
(0.230)
Spring
Precipitation
- 0.034
(0.812)
0.055*
(0.703)
0.153
(0.288)
Summer Max.
Temp.
- 0.077
(0.596)
- 0.375*
(0.007)
- 0.143
(0.323)
Summer Min.
Temp.
- 0.122
(0.398)
- 0.354*
(0.012)
- 0.152
(0.291)
Summer
Precipitation
0.119
(0.412)
0.111*
(0.444)
0.108
(0.455)
Monsoon Max.
Temp.
- 0.172
(0.232)
- 0.219*
(0.126)
- 0.116
(0.421)
216
Monsoon Min.
Temp.
- 0.023
(0.871)
- 0.090*
(0.533)
0.021
(0.887)
Monsoon
Precipitation
- 0.347
(0.014)
- 0.168*
(0.244)
- 0.064
(0.661)
Autumn Max.
Temp.
0.022
(0.881)
- 0.107*
(0.460)
0.039
(0.789)
Autumn Min.
Temp.
- 0.069
(0.632)
- 0.419*
(0.002)
- 0.046
(0.749)
Autumn
Precipitation
- 0.125*
(0.388)
- 0.087*
(0.547)
- 0.074
(0.607)
Winter Max.
Temp.
- 0.421*
(0.002)
- 0.462*
(0.001)
- 0.143
(0.322)
Winter Min.
Temp.
- 0.522*
(0.000)
- 0.645*
(0.000)
- 0.303
(0.032)
Winter
Precipitation
- 0.168
(0.243)
- 0.061*
(0.675)
- 0.075
(0.606)
* Linear relationship. Values in ( ) are p-values; significant (p<0.05); highly significant (p<0.01); non-
significant (p˃0.05)
217
5.8 Discussion
The present study assesses climate change and its impacts on growth, in terms of tree
ring-width and intra-ring wood characteristics, of C. deodara P. wallichiana and P.
roxburghii in Galies Forest Division-Abbottabad. The mean annual ring-widths of C.
deodara P. wallichiana and P. roxburghii, over time period of 1962-2011 were
3.08±0.23 mm, 2.54±0.15 mm and 2.62±0.39 mm respectively. There are several
corresponding narrow and wide marker rings in the species which indicate a growth
response to climatic conditions. Previously, such corresponding narrow and wide marker
rings in C. deodara have been reported from Chitral-Hindukush Range of Pakistan
(Khan et al., 2013) and Aleppo pine in Attica basin (Papadopoulos et al., 2009). The
mean sensitivity (MS) ranged from 0.29 to 0.38, while coefficient of variation (CV)
ranged from 17.53% to19.50%. The present mean sensitivity range is broadly in
agreement with mean sensitivity values of 0.23 to 0.42 reported earlier by Ahmed et al.
(2010) for Picea smithiana, C. deodara, Pinus gerardiana and Juniperus excelsa in the
Upper Indus Basin of Himalayan region of Pakistan. However, differences in present
MS values and those reported by Ahmed and colleagues may be due to site variation.
Besides tree species, MS depends on site (Bogino and Brawo, 2009). The present MS
values show considerable variability of high frequency component of the ring-widths
due to climatic fluctuations, while CV shows low frequency variability caused either by
climate or other long term influences. These tree-ring statistics provide adequate
variation to estimate impacts of climate change through correlation function as
suggested by Rolland (1993) and Speer (2010).
A large variation is observed in intra-ring wood characteristics across the tree species.
In Deodar, mean intra-ring early wood formation is 66.67±0.21%, while mean late
intra-ring wood formation is 32.97±0.20%. The mean intra-ring early wood cell
diameter is 42.57±0.16 µm, with a cell wall thickness of 2.38±0.01 µm. The mean
intra-ring late wood cell diameter is 18.28±0.07 µm, with a cell wall thickness of
4.07±0.01 µm. In Blue pine, mean intra-ring early wood formation is 75.64±0.36%,
while mean intra-ring late wood formation is 24.53±0.37%. The mean intra-ring early
wood cell diameter is 35.85±0.14 µm, with a cell wall thickness of 2.08±0.01 µm. The
mean intra-ring late wood cell diameter is 15.56±0.07 µm, with a cell wall thickness of
3.76±0.02 µm. In Chir pine, mean intra-ring early wood formation is 66.67±0.21%,
218
while mean intra-ring late wood formation is 32.97±0.20%. The mean intra-ring early
wood cell diameter is 51.50±0.19 µm, with a cell wall thickness of 2.55±0.01 µm. The
mean intra-ring late wood cell diameter is 20.72±0.10 µm, with a cell wall thickness of
4.86±0.02 µm. These results indicate a two way change in the cell anatomy. The cell
diameter decreases along the ring from early wood to late wood, and, conversely, the
cell wall thickness increases along the ring from early wood to late wood. These
findings are in conformity with earlier findings of Olano (2012)
The present findings reveal a decrease in ring-width with time. A similar change pattern
is followed by early wood formation, early wood cell wall thickness and late wood cell
wall thickness, however, early wood cell diameter, late wood formation and late wood
cell diameter proceed in opposite direction. The early wood formation closely follows
ring-width pattern. This relationship of early wood formation and ring-width is the
likely consequence of that early wood constitutes a major part of ring-width, i.e.,
75.64±0.36%, 66.67±0.21%, 66.67±0.21% of ring-width of P. wallichiana, C. deodara
and P. roxburghii respectively. In corroboration with this trend, the present results
indicate positive correlation between ring-width and early wood formation. Similar
correlation pattern between ring-width and early wood formation and changes in cell
diameter and cell wall thickness of early wood and late wood have been reported in
Juniperus thurifera, by Olano (2012). Bouriaud et al. (2005) has also reported the
increase in cell wall thickness (wood density) with decreasing radial growth rate in
Scots pine.
The observed change in ratio between early wood formation and late wood formation
may have significant effects on tree growth and wood quality. In case of larger early
wood formation, it is more likely that the resin canals will occur in early wood and that
the transition from early wood to late wood will be gradual. Such phenomenon was
observed in Scots pine (Novak et al., 2013). Conversely, larger late wood formation
enhances likelihood of L-ring formation. L-ring formation is related to summer stop
and later restart with another growing cycle in autumn, if the conditions are favorable
(Luis et al., 2011). Such favorable conditions provide longer growing season and
consequently increase in late wood formation. The observed increase in temperature
during autumn and late wood formation indicates longer growth period and L-ring
formation. The complex relationship between ring-width and intra-ring wood features
219
suggests that use of combination of ring-width and intra-ring wood characteristics can
help better interpretation of tree-growth related physiological processes than the use of
tree ring-width alone. Moreover, producing different types and forms of cells by tree
species in different time periods on annual basis may also be interpreted as an
important adaptation of trees which helps in maintaining the balance among the
capacity to conduct water, resistance to cavitation and mechanical stability (Novak et
al., 2013). All this could play an important role in acclimatization or/and adaptation to
new changed climatic conditions. The combination of ring-width and intra-ring wood
characteristics can also be used as a climate multi-proxy and to improve the predictions
of the potential impact of climate change on tree growth and survival. This use of
combination of ring-width and intra-ring wood characteristics for better prediction of
impact of climate change on tree growth and survival has formerly been reported by
Wimmer and Grabner (1997); Wimmer et al. (2000); McCarroll et al. (2003);
Rathgeber et al. (2005); Battipaglia et al., (2010); Campelo et al. (2007); Luis et al.
(2011).
The present study indicates that the information archived in tree-ring statistics and
intra-ring wood characteristics are highly sensitive to year-to-year variation in climatic
conditions. However, the tree species respond differently to these conditions. Some
species are sensitive to temperature while others are sensitive to precipitation. The
pattern and extent of such variability and inferences therefrom for Deodar, Blue pine
and Chir pine have been described in paras 5.4.6, 5.5.6 and 5.6.6, with related
mathematical expressions in Tables 5.6, 5.11 and 5.16 respectively. The critical
limiting factor function of seasonal temperatures and precipitations for ring-width (tree
ring-growth) of C. deodara in Chitral-Hindukush Range has also earlier been reported
by Khan et al. (2013). Similarly, Bouriaud et al. (2005) has reported the strong
influence of precipitation on ring-widths of Beech, Oak and Ash, and of maximum
temperature on Scots pine. A positive influence of January and June-August
precipitation was found on radial growth of Quercus ilex subsp. Ballota (Corcuera et
al., 2004). The positive correlation of spring precipitation and negative correlation of
spring temperature with radial growth of Blue pine and Chir pine are supported by
Bogino et al. (2009) and Papadopoulos et al. (2009). The negative correlation between
ring-width and winter precipitation and positive correlation with summer precipitation
are in agreement with Novak et al. (2013).
220
The impact of climate change on radial growth is in array of Blue pine> Deodar> Chir
pine as indicated by their respective mean sensitivity values of 0.30±0.11, 0.38±0.11 and
0.29±0.10. The different levels of association between growth variation in conifers and
climate changes have been substantiated by Yeh and Wensel (2000) where they found
influences of winter precipitation and summer temperature on coniferous species in
northern California. Nonetheless, the influence of different seasonal climate elements is
integrated into tree ring-width, but since such seasonal climate elements are correlated
among themselves differently, the isolation of the individual climate relationship is
difficult to determine.
The present findings indicate that global warming or any other modification in seasonal
climatic regimes, apart from modifying the growth rates, may also induce changes in
wood structure. For instance, the changes in the early wood and/or late wood widths
may induce modifications in the hydraulic and mechanical properties of wood which
consequently may affect water transport and plant survival (Froux et al., 2002; De
Micco et al., 2008).
The present findings of Climate Vegetation Productivity Index combined with ring-
width data can provide reasonably accurate estimates of yield and biomass production of
C. deodara, P. wallichiana, and P. roxburghii in GFD. The dendrochronological
analysis provides accurate basis to predict growth and yield of trees on large scales,
covering several stands or soil conditions, and over long time series. The variations in
ring-width at breast height have been intensively used for assessing stand yield for
research and practical purposes (Telewski et al., 1999; Bouriaud et al., 2005). Tree ring
analysis can also support identification of various climatic factors that have played a
major role in forest growth and biomass production. Any long term cooling, even of
0.5°C, could adversely affect biomass productivity in boreal forests (Parker and Jozsa,
1973). The foremost important assumption in such type of studies is that ring increment
at breast height is an unbiased predictor of tree volume or biomass increment in conifer
species. The negatively influenced ring-width by increasing temperature and great
variation in precipitation may reduce biomass production of three under study conifer
species. Previously, fluctuations had been found in annual growth at an inter-annual time
step in Fagus sylvatica. These fluctuations were influenced by climate during the
221
growing season, particularly drought events. Ring-area increments were more strongly
reduced at breast height compared to upper parts of the tree during dry years (Bouriaud
et al., 2005).
222
CHAPTER 6
SUMMARY, GENERAL CONCLUSIONS AND
RECOMMENDATIONS
6.1 Summary
Climate change is the most serious global issue having multifaceted impacts on
environment, socio-economic and economic parameters. The changing climate is also
affecting forest productivity, health and biodiversity. The present study was conducted
during 2009-14 to assess climate change, bioclimatic indices and their impacts on the
growth of three major coniferous species, i.e., C. deodara, P. Wallachia, P. roxburghii
in Galies Forest Division-Abbottabad (Pakistan) during 1962-2011.
Literature review was done to extract the updated research knowledge on the subject,
and identify gaps for undertaking further research.
The materials and methods used were secondary data on climate parameters obtained
from web-based archives of Climate Research Unit, University of East Anglia, UK, and
local Observatory, satellite imageries, General Topographic (GT) sheets procured from
Survey of Pakistan and primary data collected from sampled trees increment cores and
stem discs samples from the study area. The maps were prepared in GIS-RS
Laboratory, Pakistan Forest Institute, Peshawar and the samples collected were studied
and analyzed in Annual Ring Measuring Laboratory (ARM Lab.), Pakistan Forest
Institute, Peshawar, using instruments, including orbital sander, sledge microtome,
Canada balsam, Digital Positiometer with Microcomputer-based measuring system and
digital compound microscope linked with computer based measuring system.
Climate parameters, including maximum temperature, minimum temperature, mean
temperature and precipitation, and trends thereof were assessed, both on annual and
seasonal basis, from the monthly climate data for the study area covering a period of
1962-2011. Bioclimatic indices, namely, TEI, AI, DI, RF, DF, HC and PEI and CVPI
were calculated and changes therein were assessed. Trees growth characteristics,
including ring-width, early wood formation, late wood formation, radial cell diameter
223
and cell wall thickness were measured and analyzed using mean values of samples of 20
trees, with four replications comprising five trees of each selected species. The data were
organized on annual, seasonal and decadal basis and analyzed by applying Mann
Kendall test with Normal Approximation and Sen’s Slope Estimator method, regression
analysis, 1-Way analysis of variance (ANOVA, Tukey’s Honest Significance Difference
(HSD) test and Pearson Correlation formula.
Arc GIS software, ERDAS Imagine software Statistical software, Minitab v. 15.1,
XLSTAT and MS Office Excel were used for data processing, graphics and database
management.
The results indicated that mean annual maximum temperature, mean annual minimum
temperature and mean annual temperature at GFD, during the time period of 1962-
2011, were 16.36±0.08 °C, 6.08±0.08 °C and 11.21±0.07 °C respectively. The highest
mean seasonal maximum temperature was 23.46±0.08 °C during monsoon, which was
marginally higher compared to 23.09±0.15 °C during summer, while the lowest mean
seasonal maximum temperature was 6.78±0.12 °C during winter. The highest mean
seasonal minimum temperature was 13.12±0.07 °C during monsoon, while the lowest
mean seasonal minimum temperature was 2.01±0.14 °C during winter. The mean
seasonal minimum temperature during summer was slightly lower compared to
monsoon. The mean seasonal minimum temperatures of spring and autumn were nearly
equal. The mean seasonal maximum temperature was 18.27±0.07 °C during monsoon
and the mean seasonal minimum temperature was 2.39±0.12 °C during winter. Mean
annual precipitation at GFD, during 1962-2011, was 889.48±19.43 mm. The wettest
season was monsoon having mean precipitation of 345.06±13.50 mm/season, while
autumn was the driest season with mean precipitation of 46.67±3.01 mm/season. The
spring and winter were moderately wet with mean precipitation of 198.50±9.68
mm/season and 180.53±8.14 mm/season respectively.
The results of trend analysis of climate change at GFD during 1962– 2011 indicated an
upward trend in mean maximum and mean minimum temperatures on annual scale as
well as on seasonal scales, except monsoon minimum temperature and autumn
maximum temperature which did not exhibit any trend. The mean precipitation on
annual scale and seasonal scales also did not exhibit any trend. The regression analysis
224
and associated ANOVA table also produced similar results, except for mean spring
precipitation and mean autumn precipitation where the changes were indicated as
negative and significant and positive and significant respectively.
The results indicated changes, mostly upward, in maximum, minimum and mean
temperature, both on annual and seasonal basis at GFD during 1962-2011. The mean
maximum temperature, mean minimum temperature and mean annual temperature
increased by 1.10 °C, 1.32 °C and 1.22 °C, and the mean annual precipitation by
1.39%, during 1962-2011. The changes in these parameters on seasonal basis varied
from season to season. The increases in temperature parameters on interannual basis
were highly significant (p<0.01) and the increase in precipitation non-significant
(p>0.05). The increase in maximum temperature was highly significant (p<0.01) during
winter, significant (p<0.05) during spring, summer, and autumn and non-significant
(p>0.05) during autumn. The increase in minimum temperature was highly significant
(p<0.01) during spring, summer and winter, significant (p<0.05) during autumn and
non-significant (p>0.05) during monsoon. The increase in mean temperature was
highly significant (p<0.01) during spring and winter, significant (p<0.05) during
summer, monsoon and autumn. On seasonal basis, the changes in precipitation were:
significant (p<0.05) decrease of -14.90% in spring, non-significant (p>0.05) decrease of
-9.95% during summer, and significant (p<0.05) increase of 8.94% during monsoon
and non-significant (p>0.05) increase of 11.81% and 12.04% during autumn and winter
respectively. Among the seasons, the highest increase was 2.37 °C in mean minimum
temperature during winter. The lowest increase was 0.35 °C in mean minimum
temperature during monsoon. The increase in mean minimum temperature was
relatively higher than mean maximum temperature. The increase in mean maximum
temperature and mean minimum temperature during spring and autumn indicated
shortening of winter period and extending summer period. The analysis showed an
overall increase of 1.39% in mean annual precipitation during 1962-2011. The mean
seasonal precipitation increased by 8.94%, 11.81%, and 12.04% during monsoon,
autumn and winter respectively. Conversely, the mean seasonal precipitation decreased
by 14.90% and 9.85% during spring and summer respectively.
Mathematical expressions of temperature and precipitation changes during 1962-2011
showed both linear and quadratic behaviors. The R2 for linear functions ranged between
225
0.01 and 0.50, while the R2 for quadratic functions ranged between 0.02 and 0.39, thus
indicating good fit of models for some climate parameters and poor fit of models for
others, especially precipitation.
The Pearson Correlation Coefficients matrix showed a highly significant (p<0.01)
positive correlation between maximum temperature and mean temperature (r =
0.94) and minimum temperature and mean temperature (r = 0.97). The correlation
of precipitation with mean temperature, maximum temperature and minimum
temperature was significant but negative.
The bioclimatic indices regimes varied along years and among seasons. During 1962-
2011, mean annual regime of TEI was 5.04±0.04, while monsoon and winter had the
highest and the lowest regimes of 4.52±0.03 and 1.07±0.06 respectively. The mean
annual regime of AI was 73.25±1.82, while the highest AI regime of 61.21±6.05 was
during winter and the lowest 4.25±0.29 in autumn. The mean annual regime of DI and
mean annual regime of RF were 39.92±1.01 and 79.84±0.12 respectively. The highest
and the lowest regimes of DI and RF followed the pattern of AI. The mean annual
regime of DF was 49.00±0.12, while winter and autumn had the highest and the lowest
DF regimes of 19.46±0.05 and 2.75±0.11 respectively. The DF was higher in monsoon
compared to spring. The HC varied among seasons with mean annual HC of
74.62±2.43. The highest HC was 72.87±2.76 in winter and the lowest was 4.37±0.30 in
autumn. The mean annual regime of PEI was 10.63±0.25. The highest PEI was
estimated for winter and the lowest for autumn.
During 1962-2011, increase in mean annual TEI was 11.53%, with the highest increase
of 55.08% during winter. Conversely, the mean annual AI decreased by 7.92%, with the
highest decrease of -59.59% during winter. The mean annual DI, mean annual RF,
mean annual DF, mean annual HC and mean annual PEI decreased by -6.40%, -8.72%,
-4.95%, -8.72% and -3.57% respectively. The highest decrease in mean annual DI,
mean annual RF and mean annual HC of -42.79%, -42.62% and -42.82% was during
winter and mean annual DF and mean annual PEI of -23.35% and -21.30% was during
spring respectively. The results also indicated that mean annual changes in TEI were
positive and highly significant, while in DI, RF and DF were negative and significant.
The changes in AI, HC and PEI were negative and non-significant. The mean seasonal
226
changes in TEI were positive and significant in summer and autumn and highly
significant in spring and winter. The mean seasonal changes in all other indices were
negative in spring, summer and winter and positive in monsoon and autumn. The mean
seasonal changes in AI during monsoon and winter and in DI during monsoon were
significant, while in all other indices were non-significant.
Mathematical expressions of changes in bioclimatic indices at GFD during 1962-2011
varied in pattern and exhibited linear, quadratic and polynomial forms. The annual and
seasonal mathematical expressions for TEI were associated with significant and highly
significant higher values of R2, indicating good fit of equations, while the expressions
for other indices were mostly non-significant with smaller values of R2, and indicating
poor fit of equations.
The Climate Vegetation Productivity Index (CVPI) ranged between 4,342 and 9,091,
with a mean of 6,816. The highest CVPI was estimated during 2003 and the lowest
during 1971, with an overall increasing trend. The mean CVPI of 6,816 puts GFD in
ideal site class with productivity in the range of 163.91-184.77 cubic feet/acre.
Cross-dating of the samples of the selected trees was done to establish precise
chronology of the annual growth rings for the period 1962-2011. The results were
graphically reproduced, where the thickness of the lines was kept proportional to the
annual ring-width size of the sampled trees and the vertical variations across the lines
indicated variability in the year-wise growth of the tree-rings across the 20 sampled
trees.
The biological growth trend of ontogeny - decrease in ring-widths with increasing tree
age – and effects of other non-climatic site factors were removed by applying
'standardization' procedure, and the standardized data was used for analysis of the
results.
The results of sensitivity analysis revealed that the mean annual ring-widths of C.
deodara, P. wallichiana and P. roxburghii for the period 1962-2011 were 3.08 mm,
2.54 mm and 2.62 mm, the standard error of the mean annual ring-widths were 0.23
mm, 0.15 mm and 0.39 mm and the variances were 1.0 mm, 0.46 mm and 3.10 mm
227
respectively. The variability of intra-species annual ring-widths was the highest in P.
roxburghii, followed by C. deodara and P. wallichiana. The variability in samples for
the same species was caused by both climatic and non-climatic factors, like ontogeny,
site disturbances and changes in forest crop conditions. The sensitivity analysis was
done to describe the impacts of climate factors on the growth parameters. The mean
sensitivity was calculated to describe variability of high frequency component of the
ring-width due to climatic fluctuations, while the coefficient of variation was calculated
for low frequency component variability induced either by climate or by other long
term influences. The values of mean sensitivity of mean annual ring-width of C.
deodara, P. wallichiana and P. roxburghii for the period 1961-2011were estimated at
0.30±0.11, 0.38±0.11 and 0.29±0.10 respectively. The highest mean sensitivity of 0.38
was estimated for P. wallichiana and the lowest of 0.29 for P. roxburghii. The mean
annual ring-width for the period 1962-2011 was relatively larger in C. deodara (3.08
mm) compared to P. wallichiana (2.54 mm) and P. roxburghii (2.62 mm). The variance
of mean annual ring-widths of C. deodara and P. wallichiana were nearly of equal
magnitude, while that of P. roxburghii was slightly lower compared to the other two
species. The highest coefficient of variation of 19.50% was observed in P. wallichiana
and the lowest coefficient of variation of 17.53% in P. roxburghii. The results of mean
sensitivity and coefficient of variation calculated for the three species indicated enough
variability in growth statistics to enable analysis of its time function, correlation and
regression with climate parameters and changes thereof.
The time function analysis of ring-width and ring-wood characteristics of Deodar for
the period 1962-2011 showed highly significant (p<0.01) downward trend in ring-width
and early wood formation, highly significant (p<0.01) upward trend in late wood
formation, late wood cell diameter and late wood cell wall thickness, and no trend in
early wood cell diameter and early wood cell wall thickness. The mean annual ring-
width showed a quadratic time function, and ranged from 2.24±0.25 mm to 4.37±0.26
mm, with a mean value of 3.08±0.23. The mean intra-ring early wood formation
showed a linear time function, and ranged from 71.34±1.51% to 80.72±0.88%, with a
mean value of 75.64±0.36% of the mean annual wood formation. The mean intra-ring
late wood formation showed a quadratic time function, and ranged from 19.11±0.92%
to 28.66±2.01%, with a mean value of 24.53±0.37% of the mean annual wood
formation. The mean intra-ring early wood cell diameter showed a quadratic time
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function, and ranged from 33.38±1.10 µm to 37.59±3.15 µm, with a mean value of
35.85±0.14 µm. The mean intra-ring early wood cell wall thickness showed a
polynomial time function, and ranged from 1.93±0.06 µm to 2.33±0.32 µm, with a
mean value of 2.08±0.01 µm. The mean intra-ring late wood cell diameter showed a
quadratic time function, and ranged between 14.60±0.59 µm and 16.86±0.69 µm, with
a mean of 15.56±0.07 µm. The mean intra-ring late wood cell wall thickness showed a
quadratic time function, and ranged from 3.52±0.12 µm to 4.00±0.13 µm, with a mean
of 3.76±0.02 µm.
Mathematical expressions of time functions of mean annual ring-width, mean intra-ring
wood formation and wood cell characteristics of Deodar showed a mix of linear,
quadratic and polynomial behaviors. The mean annual ring-width, mean intra-ring early
wood formation, mean intra-ring late wood formation, mean intra-ring late wood cell
diameter and mean intra-ring late wood cell wall thickness showed highly significant
(p<0.01) changes with time. Conversely, temporal changes in mean intra-ring early
wood cell diameter and mean intra-ring early wood cell wall thickness were non-
significant (p>0.05). The R2 ranged between 0.07 and 0.77. The analysis indicated that
linear model had good fit for time function of mean intra-ring early wood formation,
quadratic model had good fit for mean annual ring-width, mean intra-ring late wood
formation and mean intra-ring late wood cell wall thickness, but poor fit for mean intra-
ring early wood cell wall diameter and mean intra-ring early wood cell thickness. The
polynomial model had good fit for time function of early wood cell wall thickness.
The decadal analysis of ring-width and ring-wood characteristics of Deodar for the
period 1962-2011 showed a highly significant (F4, 15= 400.56; p<0.01) difference in
mean decadal ring-widths, with a decreasing trend. The overall difference in mean
decadal ring-widths among the decades was significant (Tukey’s HSD, CV 0.11;
p=0.05). A highly significant (F4, 15= 51.66; p<0.01) difference was recorded in mean
decadal intra-ring early wood formation, with a decreasing trend. The overall difference
in mean decadal intra-ring early wood formation among the decades was significant
(Tukey’s HSD, CV 1.49; p=0.05). A highly significant (F4, 15= 87.26; p<0.01)
difference was recorded in mean decadal intra-ring late wood formation, with an
increasing trend. The overall difference in mean decadal intra-ring late wood formation
among the decades was significant (Tukey’s HSD, CV 1.15; p=0.05). A highly
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significant (F4, 15= 17.11; p<0.01) difference was recorded in mean decadal intra-ring
early wood cell diameter, with an overall decreasing trend. The difference in mean
decadal intra-ring early wood cell diameter among the decades was significant (Tukey’s
HSD, CV 1.09; p=0.05). A highly significant (F4, 15= 17.11; p<0.01) difference was
recorded in mean decadal intra-ring early wood cell thickness, with an overall
decreasing trend. The difference in mean decadal intra-ring early wood cell diameter
among the decades was significant (Tukey’s HSD, CV 1.09; p=0.05). A highly
significant (F4, 15= 29.79; p<0.01) difference was recorded in mean decadal intra-ring
late wood cell diameter, with an overall increasing trend. The difference in mean
decadal intra-ring late wood cell diameter among the decades was significant (Tukey’s
HSD, CV 0.59; p= 0.05). A highly significant (F4, 15= 78.93; p<0.01) difference was
recorded in mean decadal intra-ring late wood cell wall thickness, with an increasing
trend. The difference in mean decadal intra-ring late wood cell wall thickness among
the decades was significant (Tukey’s HSD, CV 0.07; p= 0.05).
The mean annual ring-width of Deodar exhibited a highly significant and positive
correlation with mean intra-ring early wood formation (r = 0.90) and mean intra-ring
early wood cell diameter (r = 0.78), but highly significant and negative with mean intra-
ring early wood cell wall thickness (r = -0.87), mean intra-ring late wood formation (r =
-0.93) and mean intra-ring late wood cell wall thickness (r = -0.93). The correlation
was, however, non-significant and negative with mean intra-ring late wood cell
diameter (r = -0.31). The correlation of mean intra-ring early wood formation was
highly significant and positive with mean intra-ring early wood cell diameter (r = 0.62),
but highly significant and negative with mean intra-ring early wood cell wall thickness
(r = -0.82), mean intra-ring late wood formation (r = -0.95), and mean intra-ring late
wood cell wall thickness (r = -0.86). The correlation of mean intra-ring early wood
formation was non-significant and negative with mean intra-ring late wood cell
diameter (r = -0.19). The correlation of mean intra-ring early wood cell diameter was
highly significant and negative with mean intra-ring early wood cell wall thickness (r =
-0.57), mean intra-ring late wood formation (r = -0.63) and mean intra-ring late wood
cell wall thickness (r = -0.61), but non-significant and negative with mean intra-ring late
wood cell diameter (r = -0.04). The correlation of mean intra-ring early cell wall
thickness was highly significant and positive with mean intra-ring late wood formation
(r = 0.88) and mean intra-ring late wood cell wall thickness (r = 0.88), but non-
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significant and positive with mean intra-ring late wood cell diameter (r = 0.40). The
correlation of mean intra-ring late wood formation was highly significant and positive
with mean intra-ring late wood cell wall thickness (r = 0.91), but non-significant and
positive with mean intra-ring late wood cell wall diameter (r = 0.29). The correlation of
mean intra-ring late wood cell diameter was significant and positive with mean intra-
ring late wood cell wall thickness (r = 0.44).
The mean annual maximum temperature, mean annual minimum temperature and mean
annual precipitation had significant negative impacts on mean annual ring-width of
Deodar. Similarly, the mean maximum temperature during winter, the mean minimum
temperature during autumn and winter had significant negative impacts on mean annual
ring-width. A large variation in mean annual ring-width response was noted across the
observed range of precipitation, with growth response more clustered around annual
precipitation range of 800-1100 mm. The mean annual precipitation between 600
mm/annum and 700 mm/annum produced the largest mean annual ring-width of
3.40±0.10 mm. The mean precipitation during monsoon (July-September) had a
positive impact on mean annual ring-width. The largest ring-width was 3.47±0.19 mm
when monsoon precipitation ranged from 250 mm/season to 350 mm/season.
Mathematical expressions of impacts of climate change on mean annual ring-widths of
deodar showed linear to polynomial patterns. The mean annual minimum temperature
showed higher impact on mean annual ring-width (R2 = 0.23) compared to mean annual
maximum temperature (R2 = 0.17). Among the seasons, mean winter minimum
temperature showed the highest impact (R2 = 0.27), followed by mean winter maximum
temperature (R2 = 0.18) and mean autumn minimum temperature (R2 = 0.18). The
impacts of temperature, both maximum and minimum during other seasons, were
marginal. The impacts of mean annual precipitation and mean monsoon precipitation
were significant. The impact of mean monsoon precipitation was higher (R2 = 0.14)
compared to mean annual precipitation (R2 = 0.10). There was non-significant
difference in the impacts of other seasonal precipitation.
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The time function analysis of ring-width and ring-wood characteristics of Blue pine for
the period 1962-2011 showed highly significant (p<0.01) downward trend in ring-width
and early wood formation, highly significant (p<0.01) upward trend in late wood
formation, significant (p<0.05) downward trend in early wood cell wall thickness and
no trend in early wood cell diameter, late wood cell diameter and late wood cell wall
thickness. The mean annual ring-width showed a quadratic time function, and ranged
from 1.85±0.27 to 3.33±0.31 mm, with a mean value of 2.54±0.15. The mean intra-ring
early wood formation showed a quadratic time function, and ranged from 72.12±1.80%
to 78.87±1.51%, with a mean value of 76.67±0.21% of the mean annual wood
formation. The mean intra-ring late wood formation showed a quadratic time function,
and ranged from 20.53±1.42% to 28.97±1.95%, with a mean value of 23.37±0.20 of the
mean annual wood formation. The mean intra-ring early wood cell diameter showed a
polynomial time function, and ranged from 40.05±1.48 µm to 45.07±1.08 µm, with a
mean of 42.57±0.16 µm. The mean intra-ring early wood cell wall thickness showed a
quadratic time function, and ranged from 2.25±0.06 µm to 2.57±0.19 µm, with a mean
of 2.38±0.01 µm. The mean intra-ring late wood cell diameter showed a quadratic time
function, and ranged from 17.42±0.61 µm to 19.55±0.50 µm, with a mean of
18.28±0.07 µm. The mean intra-ring late wood cell wall thickness showed a quadratic
time function, and ranged from 3.90±0.13 µm to 4.30±0.13 µm, with a mean of
4.07±0.01 µm.
Mathematical expressions of mean annual ring-width, mean intra-ring wood formation
and cell characteristics of Blue pine showed a mix of quadratic and polynomial
behaviors of time function. The mean annual ring-width, mean intra-ring early wood
formation and mean intra-ring late wood formation showed highly significant (p<0.01)
changes with time. The mean intra-ring early wood cell diameter, mean intra-ring early
wood cell wall thickness and mean intra-ring late wood cell diameter showed
significant (p<0.05) temporal response. Conversely, temporal change in mean intra-ring
late wood cell wall thickness was not significant (p>0.05). The R2 ranged between 0.06
and 0.51. The highest R2 value was calculated for mean intra-ring annual ring-width,
while the lowest R2 value was calculated for mean intra-ring late wood intra-ring cell
wall thickness. The models used for time function response indicated good fit of the
models for mean annual ring-width, mean intra-ring wood formation and mean wood
cell characteristics except mean intra-ring late wood cell wall thickness.
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The decadal analysis of ring-width and ring-wood characteristics of Blue pine for the
period 1962-2011 showed a highly significant (F4, 15= 272.25; p<0.01) difference in
mean decadal ring-widths, with a decreasing trend. The overall difference in mean
decadal ring-widths among the decades was significant (Tukey’s HSD, CV 0.14;
p=0.05). A highly significant (F4, 15= 16.9; p<0.01) difference was recorded in mean
decadal intra-ring early wood formation, with a decreasing trend. The overall difference
in mean decadal intra-ring early wood formation among the decades was significant
(Tukey’s HSD, CV 0.14; p=0.05). A highly significant (F4, 15= 57.15; p<0.01)
difference was recorded in mean decadal intra-ring late wood formation, with an
increasing trend. The overall difference in mean decadal intra-ring late wood formation
among the decades was significant (Tukey’s HSD, CV 1.02; p=0.05). A highly
significant (F4, 15= 26.65; p<0.01) difference was recorded in mean decadal intra-ring
early wood cell diameter, with an overall increasing trend. The difference in mean
decadal intra-ring early wood cell diameter among the decades was significant (Tukey’s
HSD, CV 0.77; p=0.05). A highly significant (F4, 15= 179.39; p<0.01) difference was
recorded in mean decadal intra-ring early wood cell diameter, with an overall
decreasing trend. The difference in mean decadal intra-ring early wood cell diameter
among the decades was significant (Tukey’s HSD, CV 0.02; p= 0.05). A highly
significant (F4, 15= 62.30; p<0.01) difference was recorded in mean decadal intra-ring
late wood cell diameter, with an overall decreasing trend. The difference in mean
decadal intra-ring late wood cell diameter among the decades was significant (Tukey’s
HSD, CV 0.36; p= 0.05). A highly significant (F4, 15= 19.47; p<0.01) difference was
recorded in mean decadal intra-ring late wood cell wall thickness, with a decreasing
trend. The difference in mean decadal intra-ring late wood cell wall thickness among
the decades was significant (Tukey’s HSD, CV 0.15; p= 0.05).
The mean annual ring-width of Blue pine exhibited a highly significant and positive
correlation with mean intra-ring early wood formation (r = 0.80), mean intra-ring early
wood cell wall thickness (r = 0.96) and mean intra-ring late wood cell wall thickness (r
= 0.89). The correlation was, however, highly significant and negative with mean intra-
ring early wood cell wall diameter (r = -0.80), mean intra-ring late wood formation (r =
-0.89) and mean intra-ring late wood cell diameter (r = -0.70). The correlation of mean
intra-ring early wood formation was highly significant and positive with mean intra-
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ring early wood cell wall thickness (r = 0.84) and mean intra-ring late wood cell wall
thickness (r = 0.69), but highly significant and negative with mean intra-ring early wood
cell diameter (r = -0.74), mean intra-ring late wood cell formation (r = -0.91) and mean
intra-ring late wood cell diameter (r = -0.75). The correlation of mean intra-ring early
wood cell diameter was highly significant and positive with mean intra-ring late wood
formation (r = 0.78) and mean intra-ring late wood cell diameter (r = 0.86), but highly
significant and negative with mean intra-ring early wood cell wall thickness (r = -0.81)
and mean intra-ring late wood cell wall thickness (r = -0.71). The correlation of mean
intra-ring early wood cell wall thickness was highly significant and positive with mean
intra-ring late wood cell wall thickness (r = 0.90), but highly significant and negative
with mean intra-ring late wood formation (r = -0.94) and mean intra-ring late wood cell
diameter (r = -0.73). The correlation of mean intra-ring late wood formation was highly
significant and positive with mean intra-ring late wood cell diameter (r = 0.80), but
highly significant and negative with mean intra-ring late wood cell wall thickness (r = -
0.86). The correlation of mean intra-ring late wood cell diameter was highly significant
and negative with mean intra-ring late wood cell wall thickness (r = -0.68).
The mean annual maximum temperature and mean annual minimum temperature had
significant negative impacts on mean annual ring-width of Blue pine. By seasons,
maximum temperature and minimum temperature during spring, summer and winter
and minimum temperature during autumn had negative impacts on mean annual ring-
width. The annual precipitation showed a non-significant impact on mean annual ring-
widths of Blue pine. The mean annual ring-width decreased with increasing annual
precipitation. A large variation in mean annual ring-width response was noticed across
the observed range of precipitation, with relatively better growth (mean annual ring-
width >3.00 mm) when annual precipitation was in the range of 800-1000 mm. The
precipitation between 600 mm/annum and 700 mm/annum had the largest mean annual
ring-width of 2.64±0.33 mm.
Mathematical expressions of impacts of climate change on mean annual ring-width of
Blue pine showed a linear pattern. The mean annual maximum temperature (R2 = 0.34)
showed a higher negative impact on mean annual ring-width compared to mean annual
minimum temperature (R2 = 0.28). Among the seasons, mean winter minimum
temperature showed the highest (R2= 0.41) impact followed by mean winter maximum
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temperature (R2 =0.21), mean autumn minimum temperature (R2 = 0.18), mean spring
minimum temperature (R2 = 0.17) and mean summer maximum temperature (R2 =
0.14). The impacts of mean spring maximum temperature and mean summer minimum
temperature were the same. The impacts of mean monsoon maximum temperature and
mean monsoon minimum temperature and mean autumn maximum temperature were
non-significant. The mean autumn maximum temperature showed the least impact on
mean annual ring-width among all seasons. The impacts of mean annual precipitation
and mean seasonal precipitations were non-significant. Among the mean seasonal
precipitations, the mean autumn precipitation showed the highest impact on mean
annual ring-width.
The time function analysis of ring-width and ring-wood characteristics of Chir pine for
the period 1962-2011 showed highly significant (p<0.01) downward trend in late wood
cell wall thickness and significant (p<0.05) downward trend in early wood cell diameter
and no trend in ring-width, early wood formation, late wood formation, early wood cell
wall thickness and late wood cell diameter. The mean annual ring-width showed a
quadratic time function, and ranged from 2.00±0.39 mm to 3.66±0.55 mm, with a mean
value of 2.62±0.39 mm. The mean intra-ring early wood formation showed a quadratic
time function, and ranged from 64.22±1.31% to 69.95±1.94%, with a mean value of
66.67±0.21%, of mean annual wood formation. The mean intra-ring late wood
formation showed a quadratic time function, and ranged from 29.92±1.90% to
35.58±2.17%, with a mean value of 32.97±0.20% of the mean annual wood formation.
The mean intra-ring early wood cell diameter showed a linear time function, and ranged
from 48.46±1.51 µm to 54.53±1.25 µm, with a mean of 51.50±0.19 µm. The mean
intra-ring early wood cell wall thickness showed a quadratic time function, and ranged
from 2.70±0.08 µm to 2.38±0.08 µm, with a mean of 2.55±0.01 µm. The mean intra-
ring late wood cell diameter showed a quadratic time function, and ranged from
19.39±0.76 µm to 22.12±1.57 µm, with a mean of 20.72±0.10 µm. The mean intra-ring
late wood cell wall thickness showed a linear time function, and ranged from 4.56±0.19
µm to 5.24±0.18 µm, with a mean of 4.86±0.02 µm.
Mathematical expressions of time functions of ring-width, intra-ring wood formation
and cell characteristics of Chir pine showed a mix of linear and quadratic behaviors.
The mean annual ring-width, mean intra-ring early wood formation, mean intra-ring
235
late wood formation, mean intra-ring early wood cell wall thickness, mean intra-ring
late wood cell diameter and mean intra-ring late wood cell wall thickness followed a
quadratic function, while mean intra-ring early wood cell diameter followed a linear
function. The R2 ranged between 0.03 and 0.31. The highest R2 value was estimated for
mean intra-ring early wood formation, followed by mean intra-ring late wood cell wall
thickness. The lowest R2 value was calculated for mean intra-ring late wood cell
diameter followed by mean intra-ring early wood cell wall thickness. The results of
time function response indicated good fit of the models for mean annual ring-width,
mean intra-ring early, mean intra-ring late wood formation and mean intra-ring late
wood cell wall thickness. The models were poorly fit for mean intra-ring early wood
cell diameter, mean intra-ring early wood cell wall thickness and mean intra-ring late
wood cell diameter.
The decadal analysis of ring-width and ring-wood characteristics of Chir pine for the
period 1962-2011 showed a highly significant (F4, 15= 8889.78; p<0.01) difference in
mean decadal ring-widths, with an irregular declining trend. The overall difference in
mean decadal ring-widths among the decades was significant (Tukey’s HSD, CV 0.03;
p=0.05). A highly significant (F4, 15= 29.81; p<0.01) difference was recorded in mean
decadal intra-ring early wood formation, with a decreasing trend. The overall difference
in mean decadal intra-ring early wood formation among the decades was significant
(Tukey’s HSD, CV 1.13; p=0.05). A highly significant (F4, 15= 601.90; p<0.01)
difference was recorded in mean decadal intra-ring late wood formation, with an
increasing trend. The overall difference in mean decadal intra-ring late wood formation
among the decades was significant (Tukey’s HSD, CV: 0.39; p=0.05). A highly
significant (F4, 15= 35.92; p<0.01) difference was recorded in mean decadal intra-ring
early wood cell diameter, with an overall increasing trend. The difference in mean
decadal intra-ring early wood cell diameter among the decades was significant (Tukey’s
HSD, CV: 0.80; p=0.05). A highly significant (F4, 15= 475.93; p<0.01) difference was
recorded in mean decadal intra-ring early wood cell diameter, with an overall
decreasing trend. The difference in mean decadal intra-ring early wood cell diameter
among the decades was (Tukey’s HSD, CV: 0.02; p= 0.05). A highly significant (F4,
15= 40.69; p<0.01) difference was recorded in mean decadal intra-ring late wood cell
diameter, with an overall decreasing trend. The difference in mean decadal intra-ring
late wood cell diameter among the decades was significant (Tukey’s HSD, CV 0.68; p=
236
0.05). A highly significant (F4, 15= 151.54; p<0.01) difference was recorded in mean
decadal intra-ring late wood cell wall thickness, with a decreasing trend. The difference
in mean decadal intra-ring late wood cell wall thickness among the decades was
significant (Tukey’s HSD, CV: 0.09; p= 0.05).
The mean annual ring-width of Chir pine exhibited a significant and positive correlation
with mean intra-ring early wood formation (r = 0.47), mean intra-ring early wood cell
diameter (r = 0.45) and mean intra-ring late wood cell diameter (r = 0.46). The
correlation was, however, non-significant and negative with mean intra-ring early wood
cell wall thickness (r = - 0.34), but non-significant and positive with mean intra-ring
late wood formation (r = 0.31) and mean intra-ring late wood cell wall thickness (r =
0.42). The correlation of mean intra-ring early wood formation was highly significant
and positive with mean intra-ring early wood cell diameter (r = 0.64), mean intra-ring
late wood formation (r = 0.83), mean intra-ring late wood cell diameter (r = 0.82) and
mean intra-ring late wood cell wall thickness (r = 0.92), but highly significant and
negative with mean intra-ring early wood cell wall thickness (r = - 0.75). The
correlation of mean intra-ring early wood cell diameter was highly significant and
positive with mean intra-ring late wood formation (r = 0.86) and mean intra-ring late
wood cell wall thickness (r = 0.75), significant and positive with mean intra-ring late
wood cell diameter (r = 0.46), but non-significant and negative with mean intra-ring
early wood cell wall thickness (r = -0.25). The correlation of mean intra-ring early
wood cell wall thickness was highly significant and negative with mean intra-ring late
wood cell wall diameter (r = -0.90) and mean intra-ring late wood cell wall thickness (r
= -0.75), but significant and negative with mean intra-ring late wood formation (r = -
0.52). The correlation of mean intra-ring late wood formation was highly significant
and positive with mean intra-ring late wood cell wall diameter (r = 0.70) and mean
intra-ring late wood cell wall thickness (r = 0.93). The correlation of mean intra-ring
late wood cell diameter was highly significant and positive with mean intra-ring late
wood cell wall thickness (r = 0.84).
The mean annual maximum temperature, mean annual minimum temperature and mean
autumn minimum temperature had significant negative impacts on mean annual ring-
width of Chir pine, while annual precipitation showed a non-significant impact on mean
annual ring-widths in a polynomial pattern. The largest mean annual ring-width was
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3.35±0.40 mm when annual precipitation was >1,001 mm/annum. Relatively better
growth response of Chir pine (mean annual ring-width >3.0 mm) was observed at
certain points and ranges of annual and seasonal temperatures and precipitation.
Mathematical expressions of impacts of climate change on mean annual ring-widths of
Chir pine showed a mix of quadratic and polynomial functions. The mean annual
maximum temperature (R2 = 0.13) showed a higher impact on mean annual ring-width
compared to mean annual maximum temperature (R2 = 0.12) and mean annual
precipitation (R2 = 0.12). Among the seasons, mean autumn minimum temperature
showed the highest (R2 = 0.26) impact, followed by mean spring maximum (R2 = 0.18)
and mean winter minimum temperature (R2 = 0.11). The impacts of mean temperatures,
both maximum and minimum during other seasons, were marginal. The impact of mean
monsoon precipitation (R2 = 0.09) was relatively higher compared to impacts of other
mean seasonal precipitations.
The mathematical expressions of correlation coefficients of ring-widths of Cedrus
deodara, Pinus wallichiana and Pinus roxburghii with climate parameters indicated
linear patterns in some cases and non-linear patterns in others. A matrix of Pearson
Correlation Coefficients drawn for ring-widths of Cedrus deodara, Pinus wallichiana
and Pinus roxburghii with climate parameters indicated the comparative direction and
strength of the relationships. As the Pearson Correlation Coefficient predicts the
direction and strength of only linear relationship of associated variables, therefore, the
interpretation of the matrix were confined to linear pattern only. The correlation
between mean annual ring-width of Deodar with mean annual precipitation was
negative and significant, with winter maximum temperature and winter minimum
temperature negative and highly significant and with summer maximum temperature
and summer minimum temperature negative and non-significant. The correlation
between mean annual ring-width of Blue pine with summer minimum temperature was
negative and significant, with annual maximum temperature, annual minimum
temperature, spring maximum temperature, spring minimum temperature, summer
maximum temperature, winter maximum temperature and winter precipitation was
negative and highly significant, with spring precipitation and summer precipitation
positive and non-significant and with the rest of the climatic variables negative and
238
non-significant. The correlation between mean annual ring-width of Chir pine being for
non-linear relationships could lead to false results and have not been interpreted.
6.2 General Conclusions
Based on the results of this study, the following general conclusions are drawn for
GFD during 1962-2011:
The mean annual maximum temperature, mean annual minimum temperature,
mean annual temperature and mean annual precipitation were 16.36±0.08 °C,
6.08±0.08 °C, 11.21±0.07 °C and 889.48±19.43 mm/annum respectively.
The monsoon was the warmest season, followed by summer, while winter was
the coldest season, followed by autumn.
The mean maximum and mean minimum temperatures, on annual as well as
seasonal scales, exhibited an upward trend, except monsoon minimum
temperature and autumn maximum temperature, which along with mean
precipitation on annual and seasonal scales, did not exhibit any trend.
The regression analysis produced similar results, except for mean spring
precipitation and mean autumn precipitation where the changes were significant
and negative and significant and positive respectively.
The mean maximum temperature, mean minimum temperature and mean
annual temperature increased by 1.10 °C, 1.32 °C and 1.22 °C, and the mean
annual precipitation by 1.39%.
The observed temporal increases in annual temperature parameters were highly
significant and in annual precipitation non-significant.
The highest increase was in mean minimum temperature during winter, and the
lowest in mean minimum temperature during monsoon.
The increase in mean minimum temperature was relatively higher than mean
maximum temperature.
The increase in mean maximum temperature and mean minimum temperature
during spring and autumn indicated shortening of winter period and extending
summer period.
Among the seasons, the highest increase occurred in minimum temperature.
Winter was becoming relatively warmer followed by autumn compared to other
seasons.
239
The mean seasonal precipitation increased during monsoon, autumn and winter,
and decreased during spring and summer.
An overall increase in precipitation was recorded, with the highest increase in
winter followed by autumn and the highest decrease in spring followed by
summer.
The mathematical expressions of temperature and precipitation changes showed
a mix of linear and quadratic behaviors. The values of R2 indicated good fit of
models for some climate parameters and poor fit of models for others, especially
precipitation.
The correlations between maximum temperature and mean temperature and
minimum temperature and mean temperature were highly significant and
positive, and of precipitation with mean temperature, maximum temperature and
minimum temperature were significant and negative.
The bioclimatic indices varied considerably with climate change.
The TEI increased both vertically and horizontally, but the other six indices
decreased vertically and on seasonal basis during spring, summer and winter.
The decrease of the indices in some seasons, particularly spring and summer,
had strong limiting impacts on forest growth, productivity and composition.
The Climate Vegetation Productivity Index (CVPI) ranged between 4,342 and
9,091, with a mean value of 6,816, and exhibited an increasing trend in a
quadratic pattern.
The mean annual ring-widths of C. deodara, P. wallichiana and P. roxburghii
were 3.08±0.23 mm, 2.54±0.15 mm and 2.62±0.39 mm and the coefficients of
variation were 32.88%, 26.55% and 67.20% respectively.
The variability of intra-species annual ring-widths was the highest in P.
roxburghii, followed by C. deodara and P. wallichiana.
The mean sensitivity of mean annual ring-width of C. deodara, P. wallichiana
and P. roxburghii were estimated at 0.30±0.11, 0.38±0.11 and 0.29±0.10
respectively.
The time function analysis of ring-widths and early wood formation of C.
deodara and P. wallichiana and P. roxburghi indicated an overall downward
trends for the first two and no trends for the third one, while late wood
formation showed downward trend for the first two and no trend for the third
240
one. The cell early and late wood diameters and cell wall thickness showed
varying trends: upward, downward and no-trend.
The analysis of time functions of ring-widths and ring-widths characteristics of
the three species showed a mix of linear, quadratic and polynomial patterns,
with a wide range of R2 values, indicating good fit of the models for some cases
and poor fit of the models for others.
The analysis of ring-widths and ring-wood characteristics on inter-decadal basis
indicated highly significant to significant differences amongst the decades in
some cases and non-significant differences in others.
The results indicated correlations between ring-widths and ring-widths
characteristics of the three species, varying in direction, magnitude and
significance levels.
The analysis of impacts of various climate parameters on ring-widths of the
three species indicated a mix of linear, quadratic and polynomial patterns, with
a wide range of R2 values, indicating good fit of the models for some cases and
poor fit of the models for others.
The impact of increasing mean annual precipitation on the ring-widths of the
three species was negative.
The overall increase in temperature and precipitation affected the growth of
ring-width, early wood formation, late wood formation and intra-ring wood
characteristics in different directions and varying magnitudes.
The impact of climate change on radial growth was the highest in Blue pine,
followed by Deodar and Chir pine.
The reduced early wood formation and increased late wood formation had
changed anatomical properties of the wood produced.
The combination of study of ring-width and wood anatomical features can help
better understanding of climate-growth relationship than the use of tree ring-
width alone. Therefore, this combination can be used as a climate multi-proxy
to enhance prediction of potential impacts of climate change on tree growth and
its adaptability and survival.
This study provides a basis for using short-term climate-growth data to make
long-term growth projections with growth adjusted to long-term climate
conditions.
241
6.3 Recommendations
Based on the findings of the present study, the following recommendations are made:
1. The present study has shown considerable changes in climate at GFD and their
impacts on the tree growth and biomass production. Therefore, climate change
estimates and scenarios may be made an integral part of Forest Management
Plans, and future wood volume and yield estimates should be assessed in light of
climate change scenarios.
2. The tree ring and intra-ring wood characteristics are good indicators of yield and
biomass productivity; hence, these parameters should be used in addition to
inventory data for scientific management of forests.
3. The climate change trends in terms of temperature and precipitation regimes
across five seasons may be used as guiding principles in adaption strategies for
scientific forest management and other economic and socio-economic activities in
the area.
4. The observed climate changes and the trends thereof may be factored in planning
and designing of forest fire control systems and integrated pests management
plans.
5. Research studies may be conducted to assess the impacts of climate changes on
silvicultural characteristics of various forest tree species, associate broad-leaved
species, shrubs and ground flora, medicinal plants, and forage grasses and forbs,
shift in tree lines and trans- migration of species in the region.
6. Growth research trials and genetic engineering studies of forest species may be
undertaken to promote new species and varieties with best adaption to emerging
climatic conditions in the area.
7. Research studies may be conducted on the impacts of observed climate changes
on local flora, fauna and biodiversity in the region.
8. The results of this study indicated that Deodar, Blue pine and Chir pine have
high dendro-climatic value. Further research may be conducted on the same
species in other areas.
242
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