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Tree growth–climate relationships of conifer trees and reconstruction of summer season Palmer Drought Severity Index (PDSI) at Pahalgam in Srinagar, India
Quaternary International 254 (2012) 152e158
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Tree growtheclimate relationships of conifer trees and reconstruction of summer season Palmer Drought Severity Index (PDSI) at Pahalgam in Srinagar, India Somaru Ram* Indian Institute of Tropical Meteorology, Dr. Homi Bhabha Road, Pashan, Pune 411008, Maharashtra, India
a r t i c l e i n f o
a b s t r a c t
Article history: Available online 1 October 2011
Tree-ring chronologies of fir (Abies pindrow) and spruce (Picea smithiana) show highly significant positive correlation with Palmer Drought Severity Index (PDSI) as compared to rainfall over the region. This study indicates that soil moisture availability during summer season (AprileSeptember) plays a vital role in developing the annual ring-width pattern. Higher mean, maximum, and minimum temperatures during the summer season are not found to be conducive for the growth of the trees. The summer seasons’ PDSI of the region has been reconstructed from AD 1820e1981. Ó 2011 Elsevier Ltd and INQUA. All rights reserved.
1. Introduction The available climate data from the western Himalaya is limited. Tree-ring records have excellent potential to extend climate analysis from meteorological records over the region. Tree-ring analysis in the Indian subcontinent, especially from the western and central parts of the Himalayan region, has been studied by many scientists (Hughes, 1992; Borgaonkar et al., 1994, 2009; Pant et al., 1995; Bhattacharyya et al., 1997, 2006; Chaudhary and Bhattacharyya, 2000, 2002; Yadav and Singh, 2002; Bhattacharyya and Chaudhary, 2003; Yadav et al., 2006; Singh and Yadav, 2007; Bhattacharyya and Shah, 2009; Yadav, 2009). The millennium-long ring-width chronology of cedar from Garhwal Himalaya has been presented by Singh et al. (2004). However, all these studies are based on the relationship between ring-width index chronologies and climate variables such as rainfall and temperature of the region. Other teak tree-ring width index chronologies from central and peninsular India revealed better response with moisture index and PDSI as compared to rainfall during different seasons (Borgaonkar et al., 2007, 2010; Ram et al., 2008, 2010, 2011a,b; Ram, 2012). Further, they have been used to reconstruct the moisture index back to AD 1866 in central India (Ram et al., 2011b). The aim of this study is to determine a better relationship between ring-width index chronologies and climate variables other than rainfall and temperature. A first attempt has been made to examine the relationship between total ring-width of conifer trees and soil moisture availability, which is indicated by the ring-width
* Fax: þ91 020 25865142. E-mail address: [email protected]. 1040-6182/$ e see front matter Ó 2011 Elsevier Ltd and INQUA. All rights reserved. doi:10.1016/j.quaint.2011.09.026
chronologies and the PDSI respectively, and to examine the wet and dry period over the region based on PDSI reconstruction. 2. Material and methods The ring-width data of fir (Abies pindrow) and spruce (Picea smithiana) have been downloaded from the website (http://www. ncdc.noaa.gov/paleo/treering.html/). Tree core samples have been collected from the Pahalgam area (34 020 N, 75 420 E, 2900 m asl) of Jammu and Kashmir during 1982 (Fig. 1). All core samples are from trees growing at altitude ranging from 2600 m to 2900 m above mean sea level (Borgaonkar et al., 1994, 1999). The sampling site is located in the hills around the Pahalgam where tree-rings were collected. The forest is mainly fir (A. pindrow) and spruce (P. smithiana) (Borgaonkar, 1996). The region is within the summer monsoon regime and has heavy snowfall in winter. Mean correlations of all the radii of A. pindrow and P. smithiana with their respective master series of the site as computed by COFECHA software (Holmes, 1983) were 0.536 and 0.519 respectively. Such high correlations at high altitude show good dating control among the core samples, as well as the presence of a common signal among the samples, climate. Long-term growth trend in the raw ring-width data caused by tree aging were removed by applying an appropriate detrending method. Both ringwidth data were standardized by a two-step procedure. First, both ring-width series were detrended by division through an exponential or linear growth curve (Fritts, 1976). Second, the detrended series were filtered with a spline of 60 and 40 years length respectively with 50% frequency response cut-off. The variances of each detrended series were stabilized to reduce the influence of decreasing sample size in the older parts of the chronologies (Cook,
S. Ram / Quaternary International 254 (2012) 152e158
Ring-width index
1985; Osborn et al., 1997). These indices were then prewhitend using an autoregressive model selected on the basis of the minimum Akaike criterion and combined across all series to discount the influence of outliers. A set of three chronologies: a standard chronology, a residual chronology contain only the high frequency variations, and an arstan chronology reincorporated with the pooled autoregression (Cook, 1985), were developed for both species. The correlation coefficients between the chronologies of two distinct genera are very high (0.41) which is significant at 0.01% during 1781e1982. In addition, to check the coherency between the standard chronologies of two different species, running crosscorrelations have been computed using a 50-year window with 25 years overlap over the entire common period (AD 1781e1982). The strong correlations were observed during the entire chronology (Fig. 2). However, a slightly weaker correlation, but very close to significant during 1856e1905, indicates the influence of site characteristics, especially the nature of soil, slope which reduces the climate signal in tree-ring data. Based on the strong correlation, all core samples of the two distinct genera have been combined to form a single tree-ring
width index chronology, and detrended using the method described above. In total, 21 core samples were included to prepare a single ring-width index chronology. The standard version chronology of the single tree-ring width index chronology along with sample depth and expressed population signal are shown in Fig. 3. The Western Himalaya meteorological network has very few high altitude stations. Most of the meteorological stations are located along the valleys. The high variability of climate within short distances, mainly due to the complex topography, makes it difficult to establish climate/tree-growth relationships at the upper treeline. Therefore, the gridded monthly rainfall, PDSI and temperature data of the same grid square (33.75 N, 73.75 E; 1229 m) nearest to the sampling site were used to determine the tree-growth climate relationship (Fig. 1). The gridded monthly mean, maximum, minimum temperatures and rainfall data were obtained from the Climate Research Unit (CRU, University of East Anglia) (Mitchell and Jones, 2005). The updated global monthly Palmer Drought Severity Index (PDSI) of the same grid was used in the tree growth climate relationship. PDSI (Palmer, 1965) is a meteorological drought index. The PDSI data set developed by Dai et al. (2004) is available on a 2.5 2.5 grid. The data cover the longest period of continuous records of monthly mean, maximum and minimum temperature as well as rainfall, and cover the period 1901e2002. Monthly variations of mean, maximum and minimum temperature and rainfall based on the long-term averages are illustrated (Fig. 4). July has been recorded as the wettest month (199.5 mm). January and February are extremely cold over the region. June (25.8 C) is the hottest month of the year. The region received the highest rainfall (551.1 mm) during monsoon season (JJAS), with less rainfall in winter. The mean temperature ranges from 6.3 C to 25.8 C, maximum temperature varies from 11.9 C to 32.5 C, and minimum temperature ranges from 0.7 C to 20.3 C throughout the year (Fig. 4). The mean, standard deviation and linear trends during different months and seasons for mean temperature, maximum temperature, minimum temperature, rainfall and PDSI were presented in Tables 1 and 2. Correlation between tree-ring chronologies and climate variables such as rainfall, temperature and PDSI were analyzed on monthly (Tables 3 and 4) and seasonal bases (Figs. 5e7) using the DENDROCLIM2002 program (Biondi and Waikul, 2004). The seasons were designated as early winter (prior Octobereprior December),
1.6 1.4 1.2 1.0 0.8 0.6 0.4
Sample depth
Fig. 1. Location map of study site, the values in parentheses are average altitude. Δ, Tree-ring site; C, grid point data considered for rainfall, temperature and PDSI.
153
20 16 12 8 4 0
EPS
1.0 0.8 0.6 0.4 1620 1650 1680 1710 1740 1770 1800 1830 1860 1890 1920 1950 1980
year Fig. 2. Running correlation between standard ring-width index chronologies of fir (Abies pindrow) and spruce (Picea smithiana). The horizontal solid line is significant at 5% level.
Fig. 3. Single tree-ring width index chronology along width sample depth and expressed population signal. Horizontal dashed line represents threshold of 0.85.
154
S. Ram / Quaternary International 254 (2012) 152e158 Table 2 Mean, standard deviation and long-term trends of mean temperature (Tem), maximum temperature (Tmax), minimum temperature (Tmin), rainfall (RF) and Palmer Drought Severity Index (PDSI) during different seasons. 1
Fig. 4. Monthly variation of rainfall, mean, maximum and minimum temperature at grid point (33.75 N,73.75 E) during 1901e2002.
winter (prior Octoberecurrent March), spring (MarcheMay), early summer (MayeJuly), and summer (AprileSeptember). Based on the result of correlation analysis, the single tree-ring width index chronology (t0) and 1-year lagged index, both backward and forward (t 1) and (t þ 1), were tested to examine the relationship with early summer and summer season PDSI. The first principal component (PC1) with eigenvalue >1 explained 76.3% of the common variance was used in the regression analysis (Cook et al., 1999, 2004). The calibration was made on the first half of data set and verification was done on the other half of the data set that were not used in the calibration period (Table 5). To verify the reliability of calibration models, several statistical parameters [T value, reduction of error and coefficient of efficiency (Fritts, 1976; Cook et al., 1999)] were taken into consideration.
2
3
4
5
Tem ( C) 1901e2002
X sd tr
13.1 0.7 0.0
10.9 0.7 0.01b
17.2 1.0 0.01b
24.4 0.7 0.0
22.9 0.6 0.0
Tmax ( C) 1901e2002
X sd tr
19.9 0.7 0.0
17.1 0.7 0.01b
23.2 1.1 0.01a
30.5 0.8 0.0
28.7 0.6 0.0
Tmin ( C) 1901e2002
X sd tr
4.8 0.8 0.01b
11.1 1.0 0.01b
18.4 0.7 0.0
17.1 0.6 0.0
RF (mm) 1901e2002
X sd tr
78.4 52.8 0.28
PDSI 1876e2005
X sd tr
0.7 2.1 0.0
6.4 0.8 0.005a
332.9 97.9 0.44
223.0 76.7 0.36
318.7 147.8 0.77
681.1 212.5 0.45
0.5 1.9 0.0
0.3 2.1 0.0
0.5 2.1 0.0
0.5 2.0 0.0
1, early winter; 2, winter; 3, spring; 4, early summer; 5, summer; X, Mean; Sd, Standard deviation; Tr, Trend in mm/year for rainfall and C/year for temperature. a Significant at 5% level. b Significant at 0.1% level.
a signal to noise ratio of 4.3. The first principal component (PC) over the interval from 1677 to 1980 accounts for 45.3% of the total variance, demonstrating a common pattern among these tree-ring series. Spruce (P. smithiana) chronology shows an expressed population signal of 0.86 and a signal to noise ratio of 6.1. The first principal component accounts for 38.4% of the total variance for the period 1870e1981, clearly indicating its suitability for dendroclimate analysis (Wigley et al., 1984). The statistics of the single ring-width index chronology, such as the signal to noise ratio (4.3) and common variance (35%) indicate the potential of the present chronology in climate studies. The expressed population signal is a measure of correlation between the mean chronology derived from the core samples and the population from which they were drawn (Wigley et al., 1984). The EPS
3. Results 3.1. Chronology statistics The studied chronology of fir (A. pindrow) for the period 1677e1980 exhibits an expressed population signal of 0.81 and
Table 1 Mean, standard deviation and long-term trends of mean temperature (Tem), maximum temperature (Tmax), minimum temperature (Tmin), rainfall (RF) and Palmer Drought Severity Index (PDSI) for different months. 3
4
5
6
9
10
11
Tem ( C) 1901e2002
X Sd Tr
1 6.3 1.5 0.00
2 7.8 1.6 0.01c
12.0 1.4 0.01b
17.3 1.4 0.01c
22.3 1.5 0.00
25.8 0.9 0.00
7 25.2 0.9 0.00
8 24.2 0.7 e0.00
22.3 0.9 0.00
18.2 1.1 0.00
12.8 0.9 0.00
12 8.4 0.9 0.01b
Tmax ( C) 1901e2002
X Sd Tr
11.9 1.5 0.00
13.2 1.8 0.01c
17.6 1.5 0.00
23.4 1.5 0.01
28.8 1.6 0.00
32.5 1.0 0.00
30.2 1.0 0.00
28.9 0.8 0.01c
28.2 1.0 0.00
25.4 1.1 0.00
20.0 0.9 0.00
14.4 1.0 0.01a
Tmin ( C) 1901e2002
X Sd Tr
0.7 1.6 0.00
2.6 1.5 0.01c
6.4 1.4 0.01c
11.3 1.4 0.01c
15.8 1.5 0.00
19.2 0.9 0.00
20.3 0.9 0.00
19.6 0.7 e0.00
16.3 1.0 0.00
11.0 1.1 0.00
5.7 1.1 0.01a
RF (mm) 1901e2002
X Sd Tr
76.0 48.3 0.22
85.0 48.5 0.04
93.4 48.1 0.33a
76.2 41.6 0.14
53.2 31.4 0.11
65.6 42.9 0.25
199.5 139.7 0.29
197.3 121.4 0.20
88.7 74.5 0.29
28.6 29.4 0.02
15.9 20.8 0.17b
33.1 32.7 0.01
PDSI 1876e2005
X Sd Tr
0.6 2.0 0.01
0.3 2.1 0.00
0.3 2.2 0.00
0.3 2.2 0.00
0.5 2.2 0.00
0.4 2.3 0.00
0.5 2.1 0.00
0.6 2.2 0.00
0.7 2.2 0.00
0.7 2.2 0.00
0.7 2.1 0.00
0.6 2.1 0.00
2.4 1.1 0.01b
1, January; 2, February; 3, March; 4, April; 5, May; 6, June; 7, July; 8, August; 9, September; 10, October; 11, November; 12, December; X, Mean; Sd, Standard deviation; Tr, Trend in mm/year for rainfall and C/year for temperature. a Significant at 5% level. b Significant at 1% level. c Significant at 0.1% level.
S. Ram / Quaternary International 254 (2012) 152e158
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Table 3 Bootstrap correlation analysis of fir ring-width index chronology (PHA) with mean temperature (Tem), maximum temperature (Tmax), minimum temperature (Tmin), rainfall (RF) and Palmer Drought Severity Index (PDSI) during 1902e1982. Site PHA
1 Tem Tmax Tmin RF PDSI
þ
2
þ
3
þ
4
þ
5
þ
6
7
þ
8
9
10
þ þ
þ
þ þ
11
12
13
þ þ
þ
exceed 0.85 after AD 1819, a value considered as a reasonable threshold (Fig. 3) (Wigley et al., 1984; Cook and Kairiukstis, 1990). A strong common growth signal was reflected by a large percentage of the total variance being explained by the first principal component (45.5%), and high intercore correlation (0.35). Based on these measures of signal strength, the chronology was found suitable for dendroclimate reconstruction back to AD 1819. Noise associated with decreasing sample depth during the earliest period in the tree-ring record reduces the reliability of the palaeoclimate reconstruction. Therefore, the chronology was truncated at AD 1819 for the purpose of climate reconstruction. The relationship between ring-width index chronologies of different species and monthly climate variables (mean temperature, maximum temperature, minimum temperature, rainfall and PDSI) are shown (Tables 3 and 4). In addition, various seasonal mean of climate variables and their correlations with tree-ring chronologies were presented for the available period to assess the temporal stability in the tree growth climate relationship (Figs. 5e7). As well, 31-year sliding correlation coefficients were computed to examine the stability of the relationship between the single tree-ring width index chronology and summer PDSI and rainfall during1901e1982 (Fig. 8).
3.2. Statistics of climate The mean, standard deviation and linear trends/year of rainfall, temperature and PDSI during the available period for different months and seasons were shown (Tables 1 and 2). Rainfall and temperature are expressed in mm/year and C/year respectively. The mean and minimum temperatures in February, March, April and December showed significant increasing trends. The maximum temperatures in February and December revealed significant increasing trends but August showed a decreasing trend. The minimum temperature in November indicates a significant increasing trend (Table 1). The rainfall in March and November showed significant increasing trends. The mean, maximum and minimum temperatures during winter and spring season showed significant increasing trends (Table 2). Minimum temperature in early winter season indicates
Fig. 5. Correlation between fir ring-width index chronology and seasonal mean, maximum and minimum temperature, rainfall and PDSI. Horizontal solid line is significant at 5% level; 1, early winter; 2, winter; 3, spring; 4, early summer; 5, summer.
a significant increasing trend, representing the climate conditions around the sampling area. The PDSI and rainfall does not show significant increasing/decreasing trends in any seasons (Table 2). 3.3. PDSI reconstruction The predicted PDSI during the different verification period is highly correlated with actual PDSI which is significant at 0.01% level (Table 5). Both the reduction of error (RE) and coefficient of efficiency (CE) values are positive, indicating that regression model had reasonable skill in predicting PDSI (Fritts, 1976; Cook et al., 1994). The calibration model yielded significant statistics in the verification periods, which authenticate the reliability of the reconstruction models (Table 5). However, to capture a higher portion of the low frequency variability in the final reconstruction, PDSI data for the entire period were used to develop a calibration model during the summer season, which captured 13% of the variance in the actual data (1877e1981). Summer season PDSI shows relatively better calibration than early summer (Table 5). The correlation coefficient between reconstructed summer PDSI and actual PDSI showed a much higher value (0.40; significant at 0.01% level), verifying the validity of the present reconstruction of summer PDSI (Fig. 9A). Therefore, summer PDSI was reconstructed back to AD 1820.Both the series showed almost similar patterns after cubic smoothing spline of 30 years (Fig. 9A). 4. Discussion Tables 3 and 4 show the correlation coefficients between ringwidth index chronologies and climate variables (mean temperature, maximum temperature, minimum temperature, rainfall and PDSI,) from last October to October of the current growth year. The correlations were computed over the full period of 1901e1982 for precipitation, 1877e1982 for PDSI and 1901e1982 for mean,
Table 4 Bootstrap correlation analysis of spruce ring-width index chronology (PHP) with mean temperature (Tem), maximum temperature (Tmax), minimum temperature (Tmin), rainfall (RF) and Palmer Drought Severity Index (PDSI) during 1902e1982. Site PHP
1 Tem Tmax Tmin RF PDSI
2 þ þ þ
3
4
5
6
7
8
9
10
11
12
13
þ
þ þ
þ
þ
þ
þ
Negative significant at 5% level; þ positive significant at 5% level 1, prior October; 2, prior November; 3, prior December; 4, January; 5, February; 6, March; 7, April; 8, May; 9, June; 10, July; 11, August; 12, September; 13, October.
Fig. 6. Correlation between spruce ring-width index chronology and seasonal mean, maximum and minimum temperature, rainfall and PDSI. Horizontal solid line is significant at 5% level; 1, early winter; 2, winter; 3, spring; 4, early summer; 5, summer.
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S. Ram / Quaternary International 254 (2012) 152e158
Fig. 7. Correlation between the single tree-ring width index chronology and seasonal mean, maximum and minimum temperature, rainfall and PDSI. Horizontal solid line is significant at 5% level; 1, early winter; 2, winter; 3, spring; 4, early summer; 5, summer.
maximum and minimum temperature. The maximum numbers of significant positive correlations between PDSI and ring-width index chronologies were found as compared to rainfall (Tables 3 and 4). A. pindrow chronology is positively and significantly correlated with current year May, July and September rainfall (Table 3). However, PDSI values of all months except April and August revealed significant positive correlations with tree-ring index (Table 3). Ring-width index chronology of Picea smiathiana revealed significant positive correlations with May and July rainfall (Table 4). Tree-ring index chronology showed significant positive correlations with June, July, August, September and October PDSI (Table 4). For temperature, ring-width index chronologies revealed significant negative correlations with mean, maximum and minimum temperature of May during 1902e1982 (Tables 3 and 4). Ringwidth index chronology is positively correlated with mean, maximum, and minimum temperatures during the prior year November (Table 4). Only significant values between ring-width index chronologies and climate variables have been shown in Tables 3 and 4. Based on the response shown by monthly climate variables, the monthly climate variables have been combined to pool common signal representing the seasonal climate. Seasonally averaged climate may be more representative of moisture conditions during the growing season than a single month. Therefore, the relationship between ring-width index chronologies and climate variables (mean temperature, maximum temperature, minimum temperature, rainfall and PDSI) during different seasons was tested (Figs. 5, 6 and 7). The mean, maximum and minimum temperatures during early winter and winter seasons showed significant positive response with tree growth (Fig. 6). Ring-width index chronology is positively correlated with mean and minimum temperatures of early winter and winter seasons, significant at 5% level (Fig. 7). Higher mean, maximum and minimum temperatures during early winter and winter seasons showed a stronger influence on tree growth. Increased winter temperature at high altitude might increase the ability of roots to absorb water and nutrients from the soil. Moreover, ring-width of high elevation conifers is often reduced by low
Fig. 8. Sliding 31-year correlation coefficient between the single tree-ring width index chronology and PDSI (--) and rainfall (-:-) during summer season. Correlation coefficients are plotted against the central year of the 31-year period. Horizontal solid line reveals significant at 5% level.
winter temperature as a consequence of bud damage, frost desiccation and reduced root activity due to low soil temperature (Korner, 1998). Borgaonkar et al. (1994) also noticed a positive correlation between tree-ring chronology of spruce (P. smithiana) and post-monsoon mean temperature (ON). Generally, persistent low temperatures for long periods during winter at higher elevations are detrimental for both photosynthesis and respiration (Tranquillini, 1964). The low temperature might decrease water movement into trees, resulting in stomata closure and decreased carbon assimilation (Tranquillini, 1964). Ring-width index chronologies of spruce from the upper timberline and other high elevation conifers from west Sichuan and eastern Tibetan Plateau are also sensitive to winter season temperature (Shao and Fan, 1999; Brauning, 2001; Gou et al., 2007; Liang et al., 2008). Tree-ring index chronologies revealed negative correlations with spring mean, maximum and minimum temperature, but not significant. Higher mean, maximum and minimum temperature during spring season is not conducive for tree growth. Higher temperature during spring season might accelerate evaporation and evapotranspiration, resulting in moisture stress. Thus, the reduced soil-moisture availability during the onset of growing season is a deterrent for the growth of the trees. However, tree-ring index chronologies correlated well with spring PDSI, showing a positive correlation which is significant at 5% level (Figs. 5e7). Moisture availability plays a vital role in tree growth process. Ring-width index chronology revealed significant negative correlations with mean, maximum and minimum temperature during early summer and summer (Fig. 6). Growth is negatively correlated with summer maximum temperature and mean, maximum and minimum temperature of the early summer season, significant at 5% level (Fig. 7). However, ring-width index chronology revealed a negative correlation with mean and minimum temperatures of summer season, but not significant (Fig. 7). Higher temperatures during summer might have depleted food reserves
Table 5 Calibration and verification statistics computed for the tree-ring estimation of early summer and summer season PDSI. Season
Early summer Summer
Calibration
Verification
Period
r2
F
Period
r
RE
Pmt
CE
1877e1921 1877e1921 1877e1981
0.12 0.16 0.13
5.6a 8.0b 14.9c
1922e1981 1922e1981
0.40c 0.40c
0.075 0.11
2.371a 2.83b
0.12 0.11
r2 is the square of the correlation coefficient between actual and estimated data; r is the correlation coefficient between actual and estimated data over the verification period; RE is the reduction of error; Pmt is the t value defined using the product mean test; CE is the coefficient of efficiency (Fritts, 1976; Cook et al., 1999). a Significant at 5% level. b Significant at 1% level. c Significant at 0.01% level.
S. Ram / Quaternary International 254 (2012) 152e158 4
A
4 2
PDSI
2
PDSI
0
157
A
0 -2
-2 -4 4
-4 4
B PDSI
2
PDSI
B
2
0
0 -2 -4 1820
-2
1840
1860
-4
1880
1900 year
1920
1940
1960
1980
1820 1830 1840 1850 1860 1870 1880 1890 1900 1910 1920 1930 1940 1950 1960 1970 1980
year Fig. 9. A: Comparison of actual (dashed line) and reconstructed PDSI (solid line) during summer season. Values are smoothed with a 30-year cubic spline to indicate low frequency variations. B: The smoothed line superimposed on the reconstruction is a 30-year cubic spline fit.
for the following year tree growth by enhancing potential evapotranspiration demands and reducing soil moisture. In addition, during summer, higher temperature may have a direct effect on loss of evaporation and the depletion of soil moisture for tree growth. However, these results are consistent with the findings of Borgaonkar et al. (1994). Tree-ring index chronologies are significantly correlated with all seasons of PDSI (Figs. 5e7), showing better correlation with PDSI during different seasons than does rainfall (Li et al., 2007; Fang et al., 2009; Cook et al., 2010). The moisture availability during the growing season is found to be conducive for tree growth as compared to rainfall over the region. Both temperature and precipitation play an important role in modulating tree growth in the study area, as both affect soil moisture availability (LeBlanc and Terrell, 2001). This results show that trees growing in the same region but at different locations might have been controlled by the moisture availability of the region. Several other studies from central and peninsular India also revealed significant positive relationships between teak tree-ring chronologies and PDSI (Borgaonkar et al., 2007, 2010; Ram et al., 2011a,b; Ram, 2012). The highest correlations have been found between tree-ring chronologies and PDSI over central and peninsular India as compared to rainfall. Correlation analysis with climate data indicated that the tree growth rates are mainly influenced by soil moisture availability during the summer season. To examine the stability of the relationship, 31-year sliding correlation coefficients have been computed between the single tree-ring chronology and summer season PDSI and rainfall for the common period 1901e1982 (Fig. 8). Fig. 8 reveals that tree-ring chronology is more compatible with PDSI than rainfall from 1920 to 1962, showing the variation in the relationship over time. The mean value of summer PDSI reconstruction is 0.40, which is within the scale of the defined normal moisture status (Palmer, 1965; Alley, 1984; PDSI ¼ 0.0 0.5). However the reconstructed PDSI values fall mainly within 2.3. PDSI values of 0.50 to 0.99 represent incipient drought, values of 0.50e0.99 represent incipient wet spell, values of 1.00 to 1.99 represent mild drought conditions, values of 1.00e1.99 represent slightly wet, values of 2.00 to 2.99 represent moderate drought, and values of 2.00e2.99 represent moderate wet conditions (Fig. 9B). Further, to verify the validity of the present reconstruction, it has been compared with the reconstruction of regional PDSI (JuneeJulyeAugust) of Western Himalaya after merging four grid
Fig. 10. Comparison between two series, A: Reconstruction of summer PDSI (JuneeJulyeAugust; Cook et al., 2010) and B: The reconstructed summer PDSI (AprileSeptember) in western Himalaya. The smoothed line on the reconstruction series indicates 30 years cubic spline fit.
points (Fig. 10; Cook et al., 2010; 31.25 N, 73.75 E; 33.75 N, 73.75 E; 31.25 N, 76.25 E; 33.75 N, 76.25 E) near sampling sites. Low soil moisture occurred during 1830e1834, 1848e1849, 1859e1876, 1887e1901, 1933e1953, 1964e1973, and high soil moisture during 1836e1839, 1857,1927e1930, 1979e1981. These records of low and high soil moisture periods are associated with the findings of Cook et al. (2010). The longest periods of the low soil moisture in the present reconstruction occurred during 1859e1876. Some differences exist between the present and earlier reconstruction (Cook et al., 2010) because of differences in seasonality as well as geographical setting of the western Himalaya. However, both the reconstructed series revealed somewhat similar patterns in the Western Himalaya (Fig. 10). The calibration and verification tests revealed the reasonable stability of the treegrowth climate relationship. 5. Conclusions The results of the present study show that tree growth is very sensitive to moisture availability of the region. The tree-ring index correlated well with the PDSI as compared to rainfall and temperature, although this study is based on only a single site. This effort will contribute to understanding drought trends and their characteristics in the western Himalaya. For better understanding of tree growth climate relationship, more tree-ring data from different geographical regions might be useful to a develop large-scale and long-term drought network in the western Himalaya region. Acknowledgements The author is thankful to Prof. B.N. Goswami, Director, IITM, Pune, and Dr. N. Singh, Head, C & H division, IITM, Pune for encouragement while carrying out this work. The author is also thankful to Mr. S.S. Mulye, C & H division, IITM, Pune for his help in preparation of map of study site. The author gratefully acknowledges the NOAA website for providing tree-ring data. The author thanks reviewers for useful comments to improve the manuscript. References Alley, W.M., 1984. The Palmer drought severity index: limitations and assumptions. Journal of Climate and Applied Meteorology 23, 1100e1109. Bhattacharyya, A., Yadav, R.R., Chaudhary, V., 1997. The Himalayan conifers and their perspectives in dendroclimate studies. Himalayan Geology 18, 169e176.
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Hughes, M.K., 1992. Dendroclimate evidence from the western Himalaya. In: Bradley, R.S., Jones, P.D. (Eds.), Climate since AD1500. Routledge, London. Korner, C., 1998. A re-assessment of high elevation tree line positions and their explanation. Oecologia 115, 445e459. LeBlanc, D., Terrell, M., 2001. Dendroclimate analysis using Thornthwaite-Mathertype evapotranspiration models: a bridge between dendroecology and forest simulation models. Tree-ring Research 57, 55e56. Li, J., Chen, F., Cook, E.R., Gou, X., Zhang, Y., 2007. Drought reconstruction for north central China from tree-rings: the value of the Palmer drought severity index. International Journal of Climatology 27, 903e909. Liang, E.Y., Shao, X.M., Qin, N.S., 2008. Tree-ring based summer temperature reconstruction for the source region of the Yangtze River on Tibetan Plateau. Global and Planetary Change 61, 313e320. Mitchell, T.D., Jones, P.D., 2005. An improved method of constructing a database of monthly climate observations and associated high resolution grids. International Journal of Climatology 25, 693e712. Osborn, T.J., Briffa, K.R., Jones, P.D., 1997. Adjusting variance for sample size in treering chronologies and other regional mean time series. Dendrochronologia 15, 89e99. Palmer, W.C., 1965. Meteorological Drought. In: Weather Bureau Research Paper 45. United States Department of Commerce, Washington DC. Pant, G.B., Borgaonkar, H.P., Rupa Kumar, K., 1995. Climate during the past 250 years over the western Himalaya: a dendroclimate reconstruction. In: Quaternary Environments and Geoarchaeology of India. Geological Society of India, Bangalore, Memoir, vol. 32, pp. 78e97. Ram, S., Borgaonkar, H.P., Sikder, A.B., 2008. Tree-ring analysis of teak (Tectona grandis L.F.) in central India and its relationship with rainfall and moisture index. Journal of Earth System Science 117 (5), 637e645. Ram, S., Borgaonkar, H.P., Sikder, A.B., 2010. Varying strength of the relationship between tree-rings and summer month moisture index (AprileSeptember) over central India: a case study. Quaternary International 212, 70e75. Ram, S., Borgaonkar, H.P., Sikder, A.B., 2011a. Growth and climate relationship in teak trees from Conolly’s plot, South India. Current Science 100 (5), 630e633. Ram, S., Borgaonkar, H.P., Munot, A.A., Sikder, A.B., 2011b. Tree-ring variation in teak (Tectona grandis L.) from Allapalli, Maharashtra in relation to moisture and Palmer drought severity index, India. Journal of Earth System Science 120 (4), 713e721. Ram, S., 2012. On the recent strengthening of the relationship between Palmer drought severity index and teak (Tectona grandis L.F.) tree-ring width chronology from Maharashtra, India: a case study. Quaternary International 248, 92e97. Shao, X.M., Fan, J.M., 1999. Past climate of west Sichuan Plateau as reconstructed from ring-widths of dragon spruce. Quaternary Science 1, 81e89. Singh, J., Yadav, R.R., 2007. Dendroclimate potential of millennium-long ring-width chronology of Pinus gerardiana from Himachal Pradesh, India. Current Science 93 (6), 833e836. Singh, J., Yadav, R.R., Dubey, B., Chaturvedi, R., 2004. Millennium-long ring width chronology of Himalayan cedar from Garhwal Himalaya and its potential in climate change studies. Current Science 86 (4), 590e593. Tranquillini, W., 1964. The physiology of plants at high altitudes. Annual Review of Plant Physiology 15, 345e362. Wigley, T.M.L., Briffa, K.R., Jones, P.D., 1984. On the average value of correlated time series, with applications in dendroclimatology and hydrometeorology. Journal of Climate and Applied Meteorology 23, 201e213. Yadav, R.R., Singh, J., 2002. Tree-ring based spring temperature patterns over the past four centuries in western Himalaya. Quaternary Research 57, 299e305. Yadav, R.R., Singh, J., Dubey, B., Misra, K.G., 2006. A 1584-year ring width chronology of juniper from Lahul, Himachal Pradesh: prospect of developing millennialong records. Current Science 90 (8), 1122e1126. Yadav, R.R., 2009. Tree-ring imprints of long-term changes in climate in western Himalaya. Journal of Bioscience 34 (5), 699e707.
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Tree growtheclimate relationships of conifer trees and reconstruction of summer season Palmer Drought Severity Index (PDSI) at Pahalgam in Srinagar, India Somaru Ram* Indian Institute of Tropical Meteorology, Dr. Homi Bhabha Road, Pashan, Pune 411008, Maharashtra, India
a r t i c l e i n f o
a b s t r a c t
Article history: Available online 1 October 2011
Tree-ring chronologies of fir (Abies pindrow) and spruce (Picea smithiana) show highly significant positive correlation with Palmer Drought Severity Index (PDSI) as compared to rainfall over the region. This study indicates that soil moisture availability during summer season (AprileSeptember) plays a vital role in developing the annual ring-width pattern. Higher mean, maximum, and minimum temperatures during the summer season are not found to be conducive for the growth of the trees. The summer seasons’ PDSI of the region has been reconstructed from AD 1820e1981. Ó 2011 Elsevier Ltd and INQUA. All rights reserved.
1. Introduction The available climate data from the western Himalaya is limited. Tree-ring records have excellent potential to extend climate analysis from meteorological records over the region. Tree-ring analysis in the Indian subcontinent, especially from the western and central parts of the Himalayan region, has been studied by many scientists (Hughes, 1992; Borgaonkar et al., 1994, 2009; Pant et al., 1995; Bhattacharyya et al., 1997, 2006; Chaudhary and Bhattacharyya, 2000, 2002; Yadav and Singh, 2002; Bhattacharyya and Chaudhary, 2003; Yadav et al., 2006; Singh and Yadav, 2007; Bhattacharyya and Shah, 2009; Yadav, 2009). The millennium-long ring-width chronology of cedar from Garhwal Himalaya has been presented by Singh et al. (2004). However, all these studies are based on the relationship between ring-width index chronologies and climate variables such as rainfall and temperature of the region. Other teak tree-ring width index chronologies from central and peninsular India revealed better response with moisture index and PDSI as compared to rainfall during different seasons (Borgaonkar et al., 2007, 2010; Ram et al., 2008, 2010, 2011a,b; Ram, 2012). Further, they have been used to reconstruct the moisture index back to AD 1866 in central India (Ram et al., 2011b). The aim of this study is to determine a better relationship between ring-width index chronologies and climate variables other than rainfall and temperature. A first attempt has been made to examine the relationship between total ring-width of conifer trees and soil moisture availability, which is indicated by the ring-width
* Fax: þ91 020 25865142. E-mail address: [email protected]. 1040-6182/$ e see front matter Ó 2011 Elsevier Ltd and INQUA. All rights reserved. doi:10.1016/j.quaint.2011.09.026
chronologies and the PDSI respectively, and to examine the wet and dry period over the region based on PDSI reconstruction. 2. Material and methods The ring-width data of fir (Abies pindrow) and spruce (Picea smithiana) have been downloaded from the website (http://www. ncdc.noaa.gov/paleo/treering.html/). Tree core samples have been collected from the Pahalgam area (34 020 N, 75 420 E, 2900 m asl) of Jammu and Kashmir during 1982 (Fig. 1). All core samples are from trees growing at altitude ranging from 2600 m to 2900 m above mean sea level (Borgaonkar et al., 1994, 1999). The sampling site is located in the hills around the Pahalgam where tree-rings were collected. The forest is mainly fir (A. pindrow) and spruce (P. smithiana) (Borgaonkar, 1996). The region is within the summer monsoon regime and has heavy snowfall in winter. Mean correlations of all the radii of A. pindrow and P. smithiana with their respective master series of the site as computed by COFECHA software (Holmes, 1983) were 0.536 and 0.519 respectively. Such high correlations at high altitude show good dating control among the core samples, as well as the presence of a common signal among the samples, climate. Long-term growth trend in the raw ring-width data caused by tree aging were removed by applying an appropriate detrending method. Both ringwidth data were standardized by a two-step procedure. First, both ring-width series were detrended by division through an exponential or linear growth curve (Fritts, 1976). Second, the detrended series were filtered with a spline of 60 and 40 years length respectively with 50% frequency response cut-off. The variances of each detrended series were stabilized to reduce the influence of decreasing sample size in the older parts of the chronologies (Cook,
S. Ram / Quaternary International 254 (2012) 152e158
Ring-width index
1985; Osborn et al., 1997). These indices were then prewhitend using an autoregressive model selected on the basis of the minimum Akaike criterion and combined across all series to discount the influence of outliers. A set of three chronologies: a standard chronology, a residual chronology contain only the high frequency variations, and an arstan chronology reincorporated with the pooled autoregression (Cook, 1985), were developed for both species. The correlation coefficients between the chronologies of two distinct genera are very high (0.41) which is significant at 0.01% during 1781e1982. In addition, to check the coherency between the standard chronologies of two different species, running crosscorrelations have been computed using a 50-year window with 25 years overlap over the entire common period (AD 1781e1982). The strong correlations were observed during the entire chronology (Fig. 2). However, a slightly weaker correlation, but very close to significant during 1856e1905, indicates the influence of site characteristics, especially the nature of soil, slope which reduces the climate signal in tree-ring data. Based on the strong correlation, all core samples of the two distinct genera have been combined to form a single tree-ring
width index chronology, and detrended using the method described above. In total, 21 core samples were included to prepare a single ring-width index chronology. The standard version chronology of the single tree-ring width index chronology along with sample depth and expressed population signal are shown in Fig. 3. The Western Himalaya meteorological network has very few high altitude stations. Most of the meteorological stations are located along the valleys. The high variability of climate within short distances, mainly due to the complex topography, makes it difficult to establish climate/tree-growth relationships at the upper treeline. Therefore, the gridded monthly rainfall, PDSI and temperature data of the same grid square (33.75 N, 73.75 E; 1229 m) nearest to the sampling site were used to determine the tree-growth climate relationship (Fig. 1). The gridded monthly mean, maximum, minimum temperatures and rainfall data were obtained from the Climate Research Unit (CRU, University of East Anglia) (Mitchell and Jones, 2005). The updated global monthly Palmer Drought Severity Index (PDSI) of the same grid was used in the tree growth climate relationship. PDSI (Palmer, 1965) is a meteorological drought index. The PDSI data set developed by Dai et al. (2004) is available on a 2.5 2.5 grid. The data cover the longest period of continuous records of monthly mean, maximum and minimum temperature as well as rainfall, and cover the period 1901e2002. Monthly variations of mean, maximum and minimum temperature and rainfall based on the long-term averages are illustrated (Fig. 4). July has been recorded as the wettest month (199.5 mm). January and February are extremely cold over the region. June (25.8 C) is the hottest month of the year. The region received the highest rainfall (551.1 mm) during monsoon season (JJAS), with less rainfall in winter. The mean temperature ranges from 6.3 C to 25.8 C, maximum temperature varies from 11.9 C to 32.5 C, and minimum temperature ranges from 0.7 C to 20.3 C throughout the year (Fig. 4). The mean, standard deviation and linear trends during different months and seasons for mean temperature, maximum temperature, minimum temperature, rainfall and PDSI were presented in Tables 1 and 2. Correlation between tree-ring chronologies and climate variables such as rainfall, temperature and PDSI were analyzed on monthly (Tables 3 and 4) and seasonal bases (Figs. 5e7) using the DENDROCLIM2002 program (Biondi and Waikul, 2004). The seasons were designated as early winter (prior Octobereprior December),
1.6 1.4 1.2 1.0 0.8 0.6 0.4
Sample depth
Fig. 1. Location map of study site, the values in parentheses are average altitude. Δ, Tree-ring site; C, grid point data considered for rainfall, temperature and PDSI.
153
20 16 12 8 4 0
EPS
1.0 0.8 0.6 0.4 1620 1650 1680 1710 1740 1770 1800 1830 1860 1890 1920 1950 1980
year Fig. 2. Running correlation between standard ring-width index chronologies of fir (Abies pindrow) and spruce (Picea smithiana). The horizontal solid line is significant at 5% level.
Fig. 3. Single tree-ring width index chronology along width sample depth and expressed population signal. Horizontal dashed line represents threshold of 0.85.
154
S. Ram / Quaternary International 254 (2012) 152e158 Table 2 Mean, standard deviation and long-term trends of mean temperature (Tem), maximum temperature (Tmax), minimum temperature (Tmin), rainfall (RF) and Palmer Drought Severity Index (PDSI) during different seasons. 1
Fig. 4. Monthly variation of rainfall, mean, maximum and minimum temperature at grid point (33.75 N,73.75 E) during 1901e2002.
winter (prior Octoberecurrent March), spring (MarcheMay), early summer (MayeJuly), and summer (AprileSeptember). Based on the result of correlation analysis, the single tree-ring width index chronology (t0) and 1-year lagged index, both backward and forward (t 1) and (t þ 1), were tested to examine the relationship with early summer and summer season PDSI. The first principal component (PC1) with eigenvalue >1 explained 76.3% of the common variance was used in the regression analysis (Cook et al., 1999, 2004). The calibration was made on the first half of data set and verification was done on the other half of the data set that were not used in the calibration period (Table 5). To verify the reliability of calibration models, several statistical parameters [T value, reduction of error and coefficient of efficiency (Fritts, 1976; Cook et al., 1999)] were taken into consideration.
2
3
4
5
Tem ( C) 1901e2002
X sd tr
13.1 0.7 0.0
10.9 0.7 0.01b
17.2 1.0 0.01b
24.4 0.7 0.0
22.9 0.6 0.0
Tmax ( C) 1901e2002
X sd tr
19.9 0.7 0.0
17.1 0.7 0.01b
23.2 1.1 0.01a
30.5 0.8 0.0
28.7 0.6 0.0
Tmin ( C) 1901e2002
X sd tr
4.8 0.8 0.01b
11.1 1.0 0.01b
18.4 0.7 0.0
17.1 0.6 0.0
RF (mm) 1901e2002
X sd tr
78.4 52.8 0.28
PDSI 1876e2005
X sd tr
0.7 2.1 0.0
6.4 0.8 0.005a
332.9 97.9 0.44
223.0 76.7 0.36
318.7 147.8 0.77
681.1 212.5 0.45
0.5 1.9 0.0
0.3 2.1 0.0
0.5 2.1 0.0
0.5 2.0 0.0
1, early winter; 2, winter; 3, spring; 4, early summer; 5, summer; X, Mean; Sd, Standard deviation; Tr, Trend in mm/year for rainfall and C/year for temperature. a Significant at 5% level. b Significant at 0.1% level.
a signal to noise ratio of 4.3. The first principal component (PC) over the interval from 1677 to 1980 accounts for 45.3% of the total variance, demonstrating a common pattern among these tree-ring series. Spruce (P. smithiana) chronology shows an expressed population signal of 0.86 and a signal to noise ratio of 6.1. The first principal component accounts for 38.4% of the total variance for the period 1870e1981, clearly indicating its suitability for dendroclimate analysis (Wigley et al., 1984). The statistics of the single ring-width index chronology, such as the signal to noise ratio (4.3) and common variance (35%) indicate the potential of the present chronology in climate studies. The expressed population signal is a measure of correlation between the mean chronology derived from the core samples and the population from which they were drawn (Wigley et al., 1984). The EPS
3. Results 3.1. Chronology statistics The studied chronology of fir (A. pindrow) for the period 1677e1980 exhibits an expressed population signal of 0.81 and
Table 1 Mean, standard deviation and long-term trends of mean temperature (Tem), maximum temperature (Tmax), minimum temperature (Tmin), rainfall (RF) and Palmer Drought Severity Index (PDSI) for different months. 3
4
5
6
9
10
11
Tem ( C) 1901e2002
X Sd Tr
1 6.3 1.5 0.00
2 7.8 1.6 0.01c
12.0 1.4 0.01b
17.3 1.4 0.01c
22.3 1.5 0.00
25.8 0.9 0.00
7 25.2 0.9 0.00
8 24.2 0.7 e0.00
22.3 0.9 0.00
18.2 1.1 0.00
12.8 0.9 0.00
12 8.4 0.9 0.01b
Tmax ( C) 1901e2002
X Sd Tr
11.9 1.5 0.00
13.2 1.8 0.01c
17.6 1.5 0.00
23.4 1.5 0.01
28.8 1.6 0.00
32.5 1.0 0.00
30.2 1.0 0.00
28.9 0.8 0.01c
28.2 1.0 0.00
25.4 1.1 0.00
20.0 0.9 0.00
14.4 1.0 0.01a
Tmin ( C) 1901e2002
X Sd Tr
0.7 1.6 0.00
2.6 1.5 0.01c
6.4 1.4 0.01c
11.3 1.4 0.01c
15.8 1.5 0.00
19.2 0.9 0.00
20.3 0.9 0.00
19.6 0.7 e0.00
16.3 1.0 0.00
11.0 1.1 0.00
5.7 1.1 0.01a
RF (mm) 1901e2002
X Sd Tr
76.0 48.3 0.22
85.0 48.5 0.04
93.4 48.1 0.33a
76.2 41.6 0.14
53.2 31.4 0.11
65.6 42.9 0.25
199.5 139.7 0.29
197.3 121.4 0.20
88.7 74.5 0.29
28.6 29.4 0.02
15.9 20.8 0.17b
33.1 32.7 0.01
PDSI 1876e2005
X Sd Tr
0.6 2.0 0.01
0.3 2.1 0.00
0.3 2.2 0.00
0.3 2.2 0.00
0.5 2.2 0.00
0.4 2.3 0.00
0.5 2.1 0.00
0.6 2.2 0.00
0.7 2.2 0.00
0.7 2.2 0.00
0.7 2.1 0.00
0.6 2.1 0.00
2.4 1.1 0.01b
1, January; 2, February; 3, March; 4, April; 5, May; 6, June; 7, July; 8, August; 9, September; 10, October; 11, November; 12, December; X, Mean; Sd, Standard deviation; Tr, Trend in mm/year for rainfall and C/year for temperature. a Significant at 5% level. b Significant at 1% level. c Significant at 0.1% level.
S. Ram / Quaternary International 254 (2012) 152e158
155
Table 3 Bootstrap correlation analysis of fir ring-width index chronology (PHA) with mean temperature (Tem), maximum temperature (Tmax), minimum temperature (Tmin), rainfall (RF) and Palmer Drought Severity Index (PDSI) during 1902e1982. Site PHA
1 Tem Tmax Tmin RF PDSI
þ
2
þ
3
þ
4
þ
5
þ
6
7
þ
8
9
10
þ þ
þ
þ þ
11
12
13
þ þ
þ
exceed 0.85 after AD 1819, a value considered as a reasonable threshold (Fig. 3) (Wigley et al., 1984; Cook and Kairiukstis, 1990). A strong common growth signal was reflected by a large percentage of the total variance being explained by the first principal component (45.5%), and high intercore correlation (0.35). Based on these measures of signal strength, the chronology was found suitable for dendroclimate reconstruction back to AD 1819. Noise associated with decreasing sample depth during the earliest period in the tree-ring record reduces the reliability of the palaeoclimate reconstruction. Therefore, the chronology was truncated at AD 1819 for the purpose of climate reconstruction. The relationship between ring-width index chronologies of different species and monthly climate variables (mean temperature, maximum temperature, minimum temperature, rainfall and PDSI) are shown (Tables 3 and 4). In addition, various seasonal mean of climate variables and their correlations with tree-ring chronologies were presented for the available period to assess the temporal stability in the tree growth climate relationship (Figs. 5e7). As well, 31-year sliding correlation coefficients were computed to examine the stability of the relationship between the single tree-ring width index chronology and summer PDSI and rainfall during1901e1982 (Fig. 8).
3.2. Statistics of climate The mean, standard deviation and linear trends/year of rainfall, temperature and PDSI during the available period for different months and seasons were shown (Tables 1 and 2). Rainfall and temperature are expressed in mm/year and C/year respectively. The mean and minimum temperatures in February, March, April and December showed significant increasing trends. The maximum temperatures in February and December revealed significant increasing trends but August showed a decreasing trend. The minimum temperature in November indicates a significant increasing trend (Table 1). The rainfall in March and November showed significant increasing trends. The mean, maximum and minimum temperatures during winter and spring season showed significant increasing trends (Table 2). Minimum temperature in early winter season indicates
Fig. 5. Correlation between fir ring-width index chronology and seasonal mean, maximum and minimum temperature, rainfall and PDSI. Horizontal solid line is significant at 5% level; 1, early winter; 2, winter; 3, spring; 4, early summer; 5, summer.
a significant increasing trend, representing the climate conditions around the sampling area. The PDSI and rainfall does not show significant increasing/decreasing trends in any seasons (Table 2). 3.3. PDSI reconstruction The predicted PDSI during the different verification period is highly correlated with actual PDSI which is significant at 0.01% level (Table 5). Both the reduction of error (RE) and coefficient of efficiency (CE) values are positive, indicating that regression model had reasonable skill in predicting PDSI (Fritts, 1976; Cook et al., 1994). The calibration model yielded significant statistics in the verification periods, which authenticate the reliability of the reconstruction models (Table 5). However, to capture a higher portion of the low frequency variability in the final reconstruction, PDSI data for the entire period were used to develop a calibration model during the summer season, which captured 13% of the variance in the actual data (1877e1981). Summer season PDSI shows relatively better calibration than early summer (Table 5). The correlation coefficient between reconstructed summer PDSI and actual PDSI showed a much higher value (0.40; significant at 0.01% level), verifying the validity of the present reconstruction of summer PDSI (Fig. 9A). Therefore, summer PDSI was reconstructed back to AD 1820.Both the series showed almost similar patterns after cubic smoothing spline of 30 years (Fig. 9A). 4. Discussion Tables 3 and 4 show the correlation coefficients between ringwidth index chronologies and climate variables (mean temperature, maximum temperature, minimum temperature, rainfall and PDSI,) from last October to October of the current growth year. The correlations were computed over the full period of 1901e1982 for precipitation, 1877e1982 for PDSI and 1901e1982 for mean,
Table 4 Bootstrap correlation analysis of spruce ring-width index chronology (PHP) with mean temperature (Tem), maximum temperature (Tmax), minimum temperature (Tmin), rainfall (RF) and Palmer Drought Severity Index (PDSI) during 1902e1982. Site PHP
1 Tem Tmax Tmin RF PDSI
2 þ þ þ
3
4
5
6
7
8
9
10
11
12
13
þ
þ þ
þ
þ
þ
þ
Negative significant at 5% level; þ positive significant at 5% level 1, prior October; 2, prior November; 3, prior December; 4, January; 5, February; 6, March; 7, April; 8, May; 9, June; 10, July; 11, August; 12, September; 13, October.
Fig. 6. Correlation between spruce ring-width index chronology and seasonal mean, maximum and minimum temperature, rainfall and PDSI. Horizontal solid line is significant at 5% level; 1, early winter; 2, winter; 3, spring; 4, early summer; 5, summer.
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S. Ram / Quaternary International 254 (2012) 152e158
Fig. 7. Correlation between the single tree-ring width index chronology and seasonal mean, maximum and minimum temperature, rainfall and PDSI. Horizontal solid line is significant at 5% level; 1, early winter; 2, winter; 3, spring; 4, early summer; 5, summer.
maximum and minimum temperature. The maximum numbers of significant positive correlations between PDSI and ring-width index chronologies were found as compared to rainfall (Tables 3 and 4). A. pindrow chronology is positively and significantly correlated with current year May, July and September rainfall (Table 3). However, PDSI values of all months except April and August revealed significant positive correlations with tree-ring index (Table 3). Ring-width index chronology of Picea smiathiana revealed significant positive correlations with May and July rainfall (Table 4). Tree-ring index chronology showed significant positive correlations with June, July, August, September and October PDSI (Table 4). For temperature, ring-width index chronologies revealed significant negative correlations with mean, maximum and minimum temperature of May during 1902e1982 (Tables 3 and 4). Ringwidth index chronology is positively correlated with mean, maximum, and minimum temperatures during the prior year November (Table 4). Only significant values between ring-width index chronologies and climate variables have been shown in Tables 3 and 4. Based on the response shown by monthly climate variables, the monthly climate variables have been combined to pool common signal representing the seasonal climate. Seasonally averaged climate may be more representative of moisture conditions during the growing season than a single month. Therefore, the relationship between ring-width index chronologies and climate variables (mean temperature, maximum temperature, minimum temperature, rainfall and PDSI) during different seasons was tested (Figs. 5, 6 and 7). The mean, maximum and minimum temperatures during early winter and winter seasons showed significant positive response with tree growth (Fig. 6). Ring-width index chronology is positively correlated with mean and minimum temperatures of early winter and winter seasons, significant at 5% level (Fig. 7). Higher mean, maximum and minimum temperatures during early winter and winter seasons showed a stronger influence on tree growth. Increased winter temperature at high altitude might increase the ability of roots to absorb water and nutrients from the soil. Moreover, ring-width of high elevation conifers is often reduced by low
Fig. 8. Sliding 31-year correlation coefficient between the single tree-ring width index chronology and PDSI (--) and rainfall (-:-) during summer season. Correlation coefficients are plotted against the central year of the 31-year period. Horizontal solid line reveals significant at 5% level.
winter temperature as a consequence of bud damage, frost desiccation and reduced root activity due to low soil temperature (Korner, 1998). Borgaonkar et al. (1994) also noticed a positive correlation between tree-ring chronology of spruce (P. smithiana) and post-monsoon mean temperature (ON). Generally, persistent low temperatures for long periods during winter at higher elevations are detrimental for both photosynthesis and respiration (Tranquillini, 1964). The low temperature might decrease water movement into trees, resulting in stomata closure and decreased carbon assimilation (Tranquillini, 1964). Ring-width index chronologies of spruce from the upper timberline and other high elevation conifers from west Sichuan and eastern Tibetan Plateau are also sensitive to winter season temperature (Shao and Fan, 1999; Brauning, 2001; Gou et al., 2007; Liang et al., 2008). Tree-ring index chronologies revealed negative correlations with spring mean, maximum and minimum temperature, but not significant. Higher mean, maximum and minimum temperature during spring season is not conducive for tree growth. Higher temperature during spring season might accelerate evaporation and evapotranspiration, resulting in moisture stress. Thus, the reduced soil-moisture availability during the onset of growing season is a deterrent for the growth of the trees. However, tree-ring index chronologies correlated well with spring PDSI, showing a positive correlation which is significant at 5% level (Figs. 5e7). Moisture availability plays a vital role in tree growth process. Ring-width index chronology revealed significant negative correlations with mean, maximum and minimum temperature during early summer and summer (Fig. 6). Growth is negatively correlated with summer maximum temperature and mean, maximum and minimum temperature of the early summer season, significant at 5% level (Fig. 7). However, ring-width index chronology revealed a negative correlation with mean and minimum temperatures of summer season, but not significant (Fig. 7). Higher temperatures during summer might have depleted food reserves
Table 5 Calibration and verification statistics computed for the tree-ring estimation of early summer and summer season PDSI. Season
Early summer Summer
Calibration
Verification
Period
r2
F
Period
r
RE
Pmt
CE
1877e1921 1877e1921 1877e1981
0.12 0.16 0.13
5.6a 8.0b 14.9c
1922e1981 1922e1981
0.40c 0.40c
0.075 0.11
2.371a 2.83b
0.12 0.11
r2 is the square of the correlation coefficient between actual and estimated data; r is the correlation coefficient between actual and estimated data over the verification period; RE is the reduction of error; Pmt is the t value defined using the product mean test; CE is the coefficient of efficiency (Fritts, 1976; Cook et al., 1999). a Significant at 5% level. b Significant at 1% level. c Significant at 0.01% level.
S. Ram / Quaternary International 254 (2012) 152e158 4
A
4 2
PDSI
2
PDSI
0
157
A
0 -2
-2 -4 4
-4 4
B PDSI
2
PDSI
B
2
0
0 -2 -4 1820
-2
1840
1860
-4
1880
1900 year
1920
1940
1960
1980
1820 1830 1840 1850 1860 1870 1880 1890 1900 1910 1920 1930 1940 1950 1960 1970 1980
year Fig. 9. A: Comparison of actual (dashed line) and reconstructed PDSI (solid line) during summer season. Values are smoothed with a 30-year cubic spline to indicate low frequency variations. B: The smoothed line superimposed on the reconstruction is a 30-year cubic spline fit.
for the following year tree growth by enhancing potential evapotranspiration demands and reducing soil moisture. In addition, during summer, higher temperature may have a direct effect on loss of evaporation and the depletion of soil moisture for tree growth. However, these results are consistent with the findings of Borgaonkar et al. (1994). Tree-ring index chronologies are significantly correlated with all seasons of PDSI (Figs. 5e7), showing better correlation with PDSI during different seasons than does rainfall (Li et al., 2007; Fang et al., 2009; Cook et al., 2010). The moisture availability during the growing season is found to be conducive for tree growth as compared to rainfall over the region. Both temperature and precipitation play an important role in modulating tree growth in the study area, as both affect soil moisture availability (LeBlanc and Terrell, 2001). This results show that trees growing in the same region but at different locations might have been controlled by the moisture availability of the region. Several other studies from central and peninsular India also revealed significant positive relationships between teak tree-ring chronologies and PDSI (Borgaonkar et al., 2007, 2010; Ram et al., 2011a,b; Ram, 2012). The highest correlations have been found between tree-ring chronologies and PDSI over central and peninsular India as compared to rainfall. Correlation analysis with climate data indicated that the tree growth rates are mainly influenced by soil moisture availability during the summer season. To examine the stability of the relationship, 31-year sliding correlation coefficients have been computed between the single tree-ring chronology and summer season PDSI and rainfall for the common period 1901e1982 (Fig. 8). Fig. 8 reveals that tree-ring chronology is more compatible with PDSI than rainfall from 1920 to 1962, showing the variation in the relationship over time. The mean value of summer PDSI reconstruction is 0.40, which is within the scale of the defined normal moisture status (Palmer, 1965; Alley, 1984; PDSI ¼ 0.0 0.5). However the reconstructed PDSI values fall mainly within 2.3. PDSI values of 0.50 to 0.99 represent incipient drought, values of 0.50e0.99 represent incipient wet spell, values of 1.00 to 1.99 represent mild drought conditions, values of 1.00e1.99 represent slightly wet, values of 2.00 to 2.99 represent moderate drought, and values of 2.00e2.99 represent moderate wet conditions (Fig. 9B). Further, to verify the validity of the present reconstruction, it has been compared with the reconstruction of regional PDSI (JuneeJulyeAugust) of Western Himalaya after merging four grid
Fig. 10. Comparison between two series, A: Reconstruction of summer PDSI (JuneeJulyeAugust; Cook et al., 2010) and B: The reconstructed summer PDSI (AprileSeptember) in western Himalaya. The smoothed line on the reconstruction series indicates 30 years cubic spline fit.
points (Fig. 10; Cook et al., 2010; 31.25 N, 73.75 E; 33.75 N, 73.75 E; 31.25 N, 76.25 E; 33.75 N, 76.25 E) near sampling sites. Low soil moisture occurred during 1830e1834, 1848e1849, 1859e1876, 1887e1901, 1933e1953, 1964e1973, and high soil moisture during 1836e1839, 1857,1927e1930, 1979e1981. These records of low and high soil moisture periods are associated with the findings of Cook et al. (2010). The longest periods of the low soil moisture in the present reconstruction occurred during 1859e1876. Some differences exist between the present and earlier reconstruction (Cook et al., 2010) because of differences in seasonality as well as geographical setting of the western Himalaya. However, both the reconstructed series revealed somewhat similar patterns in the Western Himalaya (Fig. 10). The calibration and verification tests revealed the reasonable stability of the treegrowth climate relationship. 5. Conclusions The results of the present study show that tree growth is very sensitive to moisture availability of the region. The tree-ring index correlated well with the PDSI as compared to rainfall and temperature, although this study is based on only a single site. This effort will contribute to understanding drought trends and their characteristics in the western Himalaya. For better understanding of tree growth climate relationship, more tree-ring data from different geographical regions might be useful to a develop large-scale and long-term drought network in the western Himalaya region. Acknowledgements The author is thankful to Prof. B.N. Goswami, Director, IITM, Pune, and Dr. N. Singh, Head, C & H division, IITM, Pune for encouragement while carrying out this work. The author is also thankful to Mr. S.S. Mulye, C & H division, IITM, Pune for his help in preparation of map of study site. The author gratefully acknowledges the NOAA website for providing tree-ring data. The author thanks reviewers for useful comments to improve the manuscript. References Alley, W.M., 1984. The Palmer drought severity index: limitations and assumptions. Journal of Climate and Applied Meteorology 23, 1100e1109. Bhattacharyya, A., Yadav, R.R., Chaudhary, V., 1997. The Himalayan conifers and their perspectives in dendroclimate studies. Himalayan Geology 18, 169e176.
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