Journal of
Plant Ecology
Volume 9, Number 1,
Pages 51–60
February 2016
doi:10.1093/jpe/rtv029
Advance Access publication
18 March 2015
available online at
www.jpe.oxfordjournals.org
Growth–climate response and
drought reconstruction from
tree-ring of Mongolian pine in
Hulunbuir, Northeast China
Zhongjie Shi1,†, Lihong Xu2,†, Linshui Dong3,
Jixi Gao4,*, Xiaohui Yang1, Shihai Lü5,
Chaoyang Feng5, Jianxun Shang6, Aiyun Song3,
Hao Guo1 and Xiao Zhang1
1
Institute of Desertification Studies, Chinese Academy of Forestry, 10 Huaishuju Road, Beijing 100091, China
Institute of Forest Ecology, Environment and Protection, Chinese Academy of Forestry, 1 Dongxiaofu, Xiangshan Road,
Beijing 100091, China
3
Key Laboratory of Eco-environmental Science for Yellow River Delta of Shandong Province, Binzhou University, 391
Huanghewu Road, Binzhou 256603, China
4
Nanjing Institute of Environmental Sciences, Ministry of Environmental Protection, 8 Jiangwangmiao Street, Nanjing
2100422, China
5
Chinese Research Academy of Environmental Sciences, 8 Dayangfang, Beiyuan Road, Beijing 100012, China
6
Songliao Water Resources Commission, Ministry of Water Resources, 4188 Jiefang Road, Changchun 130021, China
*Correspondence address. Nanjing Institute of Environmental Sciences, Ministry of Environmental Protection,
No. 8 Jiangwangmiao Street, Nanjing 210042, China. Tel: +86-25-85-28-72-90; Fax: +86-25-85-28-72-78;
E-mail: [email protected]
†
These authors contributed equally to this work.
2
Abstract
Aims
Drought affected by atmosphere–ocean cycle is a dominant factor influencing tree radial growth of sandy Mongolian pine (Pinus
sylvestris var. mongolica) and regional vegetation dynamics in
Hulunbuir, China. However, historical droughts and its correlations
with tree radial growth and atmosphere–ocean cycle in this area
have been little tested.
Methods
We developed tree-ring chronologies of Mongolian pine from
Hulunbuir, Inner Mongolia, China and analyzed the correlations
between tree-ring width index, the normalized difference vegetation
index and Palmer drought severity index (PDSI), then developed a linear model to reconstruct the drought variability from 1829 to 2009.
Long-term trends and its linkages with atmosphere–ocean cycle were
performed by the power spectral, wavelet and teleconnection analysis.
Important Findings
The local moisture variations affected largely the regional vegetation dynamics and tree-ring growth of Mongolia pine in the forest–
grassland transition. Using tree-ring width chronology of Mongolian
pine, the reconstruction explains 49.2% of PDSI variance during
their common data period (1951–2005). The reconstruction gives a
broad-scale regional representation of PDSI in the Hulunbuir area,
with drought occurrences in the 1850s, 1900s, 1920s, mid-1930s
and at the turn of the 21st century. Comparisons with other treering drought reconstructions and historical records reveal some
common drought periods and drying trends in recent decades at
the northern margin zones of the East Asian summer monsoon
(EASM). The drying trends in these zones occurred earlier than
weakening of the EASM. A REDFIT spectral analysis shows significant peaks at 7.2, 3.9, 2.7–2.8, 2.4 and 2.2 years with a 0.05
significance level, and 36.9, 18.1 and 5.0 years with 0.1 significance level. Wavelet analysis also shows similar cycles. Drought
variations in the study area significantly correlated with sea surface
temperatures in the western tropical Pacific Ocean and middle and
northern Indian Ocean, and the Pacific Decadal Oscillation and
North Atlantic Oscillation. This suggests a possible linkage with the
El Niño-Southern Oscillation, the EASM and the Westerlies.
Keywords: tree-rings, dendroclimatology, Mongolian pine,
drought, PDSI, SSTs, East Asian summer monsoon (EASM),
Hulunbuir, China
Received: 22 May 2014, Revised: 9 February 2015, Accepted: 27
February 2015
© The Author 2015. Published by Oxford University Press on behalf of the Institute of Botany, Chinese Academy of Sciences and the Botanical Society of China.
All rights reserved. For permissions, please email: [email protected]
52
INTRODUCTION
The Hulunbuir area of northeast China is a transitional zone
between semiarid and arid conditions, monsoon and non-monsoon climate, forest and steppe, and agricultural land and pastureland. Moisture in the area is affected by the Asian summer
monsoon, and it is extremely sensitive and vulnerable to climatic and environmental changes (Fang et al. 2012). For example, a weakening of the Asian summer monsoon in northern
China since the 1950s corresponds well with a drying tendency
during the period (Jiang and Wang 2005; Xu et al. 2011). Severe
drought events have significantly impacted local populations,
particularly in arid and semiarid regions (Easter et al. 2000). For
instance, one such event in the 1920s and 1930s in northern
China caused widespread famine and tremendous loss of life
(Liang et al. 2007). However, few reliable instrumental records
extend back 100 years in China (Bradley 1999). The situation is even worse in northeast China, where weather records
extend back only 60 years. The limited meteorological records
from this area have hindered our understanding of the processes and mechanisms of past climate variability. Long-term
records from tree-rings have been used to extend these limited
meteorological records and help in detecting the dynamics of
the Asian summer monsoon on longer timescales (Cook et al.
2010; D’Arrigo et al. 2000, 2001; Davi et al. 2009, 2010; Liu et al.
2009a; Mann et al. 2009; Pederson et al. 2001). Here, we use
annual tree-ring width variation as a proxy for drought and for
extending drought records in the Hulunbuir area.
The Palmer drought severity index (PDSI) has been reconstructed for the Ortindag Sand Land (Liang et al. 2007), Guiqing
Mountain (Fang et al. 2010), Kongtong Mountain (Fang et al.
2012), Qilian Mountains (Deng et al. 2013; Lu et al. 2013) and
Guancen Mountain (Sun et al. 2012). The reconstructed drought
variations have been used to explore the connection between
drought and variability of the East Asian summer monsoon
(EASM) on larger spatial and temporal scales (Liu et al. 2010).
Recently, great progress has been made in dendroclimatological
studies in the Inner Mongolia (Liang et al. 2008; Liu et al. 2007,
2010). In addition, annual precipitation and temperature have
been reconstructed for the Hailar region (Bao et al. 2012; Liu
et al. 2009b), Helan Mountain (Liu et al. 2005) and Xinglong
Mountain (Liu et al. 2012). Nevertheless, drought reconstruction has seldom been done for the Hulunbuir area.
The normalized difference vegetation index (NDVI) is an
ideal indicator of vegetation activity such as forest and steppe,
which has been used to study the photosynthesis, land surface
evapotranspiration, exchange of CO2 and energy between the
atmosphere and land surface (Chen et al. 2012). Recently,
many studies had revealed the relationships between the
tree-ring growth and NDVI from forest (Kaufmann et al. 2004;
Leavitt et al. 2008) and grassland (He and Shao 2006; Liang
et al. 2009) at different spatial and temporal scales. However,
no attempt has been made to explore the relationship and
mechanism between NDVI, tree-rings and drought in the
transitional zone of forest and steppe.
Journal of Plant Ecology
The aims of this paper are (i) to test potential relationships
between tree radial growth, NDVI and PDSI change, (ii) to
construct regional drought variability based on the tree-ring
record from Hulunbuir and (iii) to explore links between
reconstructed drought records and the atmosphere–ocean
cycle.
MATERIALS AND METHODS
Study area
The study area is in Hulunbuir of eastern Inner Mongolia,
an ecological transitional zone between steppe and forest,
locating on the present margin of the EASM (Lee and Zhang
2011). The region has a typical temperate continental climate;
it is hot and rainy in summer and cold and dry in winter, with
annual average temperature of −2°C. Temperature in January
averages −25.8°C and can reach a lowest value of −48.5°C,
whereas in July, it averages 19.9°C and can reach a highest
value of 36.7°C. Average annual precipitation is ~350 mm.
This falls mainly from June through August, accounting
for 60–70% of the annual amount. Annual evaporation is
~1210 mm, three times the rainfall. The main regional tree
species is Mongolian pine (Pinus sylvestris var. mongolica).
Tree-ring and NDVI data
The tree-ring samples were collected from living trees in
Xishan Forest Park (49°12ʹN, 119°42ʹE; 515–669 m above sea
level) of Hulunbuir, during October 2009. Cores were taken
from the trees by an increment borer at breast height, with
two or three cores per tree. In total, 44 radii from 26 trees
were used for drought reconstruction.
Tree-ring samples were pre-treated according to the standard protocols of Stokes and Smiley (1968). After the cores
were mounted, dried and sanded, we cross-dated (Fritts 1976)
them using a skeleton diagram (Stokes and Smiley 1968) to
ensure an accurate calendar year for each tree-ring. The width
of each ring was measured with a LINTAB™ 5 ring analyzer
(Frank Rinn, Germany) of accuracy 0.01 mm. Quality control
of cross-dating was done using the COFECHA software program (Holmes 1983), and cores with any ambiguities (eight
total) were eliminated.
After cross-dating, individual tree-ring width measurements were detrended and standardized to the tree-ring
width index, using the ARSTAN program (available at http://
www.ldeo.columbia.edu/tree-ring-laboratory; Holmes 1983).
Undesirable growth trends related to tree age and stand
dynamics, but unrelated to climatic variations, were removed
from each series during the detrending process. To maximize
the common signal at the lowest frequency possible and to
avoid the impact of different growth rates at different ages,
each tree-ring width sequence was standardized by fitting
a negative exponential or straight line (Gonzalez-Elizondo
et al. 2005; Liu et al. 2009b). All individual index sequences
were combined into a single chronology by calculating the
biweight robust mean, and standard (STD), residual (RES)
Shi et al. | Tree-ring-based growth–climate response and drought reconstruction in Hulunbuir 53
and autoregressive (ARS) chronologies were determined.
The STD chronology contains much lower frequency signals,
and the RES chronology contains only common higher frequency signals. However, the ARS chronology includes both
lower and higher common frequency signals of groups. The
STD version of the chronology was used in further analysis, as it preserves much lower frequency signals (Cook and
Kairiukstis 1990). The average percentage of absent rings in
the samples was 0.02%.
Subsample signal strength (SSS) was used to assess adequacy of replication in early years of the chronology (Wigley
et al. 1984). To obtain the longest chronology and ensure reliability of reconstruction, the SSS of the first year of the chronology was restricted to at least 0.85. This threshold corresponds
to a minimum sample depth of six trees beginning in 1829.
NDVI, a normalized ratio of the near-infrared and red spectral
reflections, was employed in this study as a proxy for vegetation growth and has been widely used in studies on vegetation
growth at regional and global scales (Huber et al. 2011; Pettorelli
et al. 2005; Piao et al. 2011). The data were derived from the
NOAA/AVHRR Land data set (available at http://daac.gsfc.
nasa.gov/) processed by the Global Inventory Monitoring and
Modeling Studies group, with a spatial resolution of 8 × 8 km
and had been collected at 15-day intervals (Tucker et al. 2005)
from 1982 to 2006. These data were processed to account for
orbital drift, to minimize cloud cover, to compensate for sensor
degradation and sensor intercalibration differences and to consider the effects of stratospheric volcanic aerosols (Tucker et al.
2005). Using the 15-day NDVI data of each month, the monthly
NDVI data were generated by the maximum value composite
method and the annual and season NDVIs were generated separately by computing averages of the respective months. In this
study, NDVI in the area (48–50°N, 118–120°E) were utilized.
et al. 2007), we also investigated correlation between tree-ring
width indices and regional PDSI data. PDSI is a meteorological drought metric based on a water balance model, in which
temperature, precipitation and soil characteristics are considered (Dai et al. 2004; Palmer 1965). PDSI has been widely
used in global. Positive and negative PDSI values correspond
to wet and dry conditions, respectively. The PDSI herein was
developed by Dai et al. (2004), and features a 2.5° × 2.5° gridding system. A monthly PDSI grid (48.75°N, 118.75°E) nearest our sites for 1951–2005 was used for the analysis.
Statistical analysis
Correlation analysis between the tree-ring width index and
instrumental meteorological records was performed for the
reliable time period of 1951–2005. And the correlations
between NDVI and tree-ring width index/climatic variables
were analyzed during 1982–2006. The PDSI reconstruction
was conducted based on a split calibration-verification procedure designed to test model reliability (Fang et al. 2010).
Statistics were used to evaluate model capability, including
simple correlation coefficient (R), R-squared (R2), sign test (S),
t-statistics, reduction of error (RE) and coefficient of efficiency
(CE). Values of RE and CE > 0 indicate rigorous model skill
(Cook et al. 1999). Teleconnections between regional droughts
and remote oceans were analyzed by correlations between
regional droughts and sea surface temperature (SST; Buckley
et al. 2007; Li et al. 2008). We used the HadISST1 SST data
set (Rayner et al. 2003), and spatial correlation was analyzed
using the KNMI Climate Explorer (available at www.knmi.
nl). Power spectral analysis was performed by PAleontological
STatistics software (Hammer et al. 2001). Wavelet analysis
was done using software available online (available at www.
ion.researchsystems.com/IONScript/wavelet/; Torrence and
Compo 1998).
Climate data
Precipitation and temperature data from nearby Hailar meteorological station (49°13ʹN, 119°45ʹE) were compared with
the tree-ring width data for testing the climate–growth relationship. As some other studies in the moisture-sensitive area
showed tree-ring width to be significantly correlated with
PDSI in Northern China (Fang et al. 2009; Li et al. 2007; Liang
RESULTS
Chronology statistics
Figure 1 shows the STD chronology and sample sizes during 1805–2009. Statistical characteristics of STD chronology
are listed in Table 1. Of the 44 radii, 6 were >180 years old,
Figure 1: STD ring-width index chronology and the number of samples for each year from Hailar. Thick line depicts 11-year moving average.
54
Journal of Plant Ecology
Table 1: statistical characteristics of STD chronology
Basic statistics
STD
First-order autocorrelation
0.41
Mean sensitivity
0.26
SD
Signal-to-noise ratio
EPS
0.28
35.81
0.97
Variance in first eigenvector
54.44%
First year where SSS > 0.85 (number of trees)
1829 (6)
while 21 were >150 years old. Mean tree-ring width was
0.99 ± 0.28 mm (mean ± standard deviation [SD]). Narrow
rings mainly occurred in 1836, 1853, 1856, 1907, 1926, 1986,
1987, 2003, 2004, 2006, 2007 and 2008 (Fig. 1), which displayed an obvious growth decrease in the end of 20th and
beginning of the 21st century. During the past 204 years, the
occurrences of narrow rings were most frequent in the recent
30 years. More serious, the locally missing rings occurred
in 1987.
Mean sensitivity, a measure of relative difference in widths
between adjacent rings, was 0.26. This indicates that tree
growth was sensitive to changes of local environment. The
first-order autocorrelation coefficient was 0.41, indicating
that tree growth in a given year had an impact on growth the
following year (Fritts 1976). The expressed population signal (EPS; Wigley et al. 1984), used to represent an acceptable
level of chronology confidence, was 0.97, much higher than
the EPS threshold 0.85. High variance in the first eigenvector
(54.44%) and signal-to-noise ratio (35.81) indicates that the
chronology contains considerable common signals and is thus
suitable for climate change study.
Tree-ring and NDVI series coherence
Figure 2 showed the tree-ring width and NDVI series and
their correlations during 1982–2006. The tree-ring width
series corresponds well with the regional annual NDVI
series (Fig. 2a). The tree-ring width series display high correlations with the regional NDVI series (Fig. 2b). The correlation coefficient reached up to highest value (r = 0.71,
P < 0.01) in the monthly scale. The annual mean NDVI
significantly correlated with tree-ring width (r = 0.43,
P < 0.05) during 1982–2006. NDVI has significant correlations with the tree-ring widths during the growing season
(May–September), especially during the period of May–July
(r = 0.74, P < 0.01). The rapid growth of Mongolia pine
occurred mainly in the period of May to September, especially during May to July (Zhao 1963). However, the accelerated growth for the steppe happened on the period of May
to July. The vegetation on April was still in the dormant
phase. A significant correlation was found between the
tree-ring width and NDVI range of April to July (r = 0.58,
P < 0.01). In addition, the spatial correlation between mean
May–July NDVI and tree-ring widths also showed a higher
correlation within the wide regions (see Supplementary
Figure S1). The high correlations may be caused mainly by
the similar phenological processes for the forest and steppe.
These results also showed the coherence of regional vegetation dynamic from the satellite and tree-ring width from
our observation.
Tree-ring and NDVI response to climatic variables
Correlation for the STD chronology with climatic variables
from Hailar meteorological station (49°13ʹN, 119°45ʹE) and
monthly PDSI (48.75°N, 118.75°E) is shown in Fig. 3. Treerings correlate negatively with mean temperature, and significant correlations at the 0.05 level were found in April
and June–September of the current year (Fig. 3a). Tree-rings
were significantly positively correlated with precipitation
during May–August in the current year (P < 0.05), and the
highest correlation coefficient was observed in July (Fig. 3a).
Correlation with PDSI, a direct metric of moisture conditions (Dai et al. 2004), showed that tree radial growth was
positively correlated with PDSI for all months between the
previous October and the current September (Fig. 3b). This
indicates that moisture availability during these seasons is
the primary limiting factor to radial growth of Mongolia pine
in the study area. The highest correlation of 0.70 is found in
current May–July PDSI data. The study area is in a semiarid
region, and annual precipitation cannot meet demands of
tree evapotranspiration. Tree growth in this area is largely
moisture-stressed. As a metric of soil moisture considering
both precipitation and temperature, PDSI is a better indicator of moisture than simply precipitation. In addition, spatial
correlation between the tree-ring index and the entire grid
of the PDSI from Dai et al.(2004) showed that the tree-ring
record reflects the average May–July PDSI over a sizable area
around the sampling site (see Supplementary Figure S2).
The climatic variables had an impact on the regional
vegetation dynamics. NDVI in May was significantly and
positively correlated with the precipitation and temperature. However, NDVI on June, July and August was found
a significantly positive correlation with the precipitation on
the previous month, respectively. In particular, the correlation analysis showed May–September NDVI was mainly
influenced by the May–July precipitation. Compared with
the above analysis, the higher correlations were displayed
between NDVI and PDSI. NDVI in May, June and July was
significantly correlated with their previous monthly PDSI,
respectively (see Supplementary Table S1). The May–July
NDVI was found a highest correlation with the concurrent
PDSI (r = 0.74, P < 0.01). Above results displayed a similar
climate response pattern for the regional NDVI and tree-ring
growth of Mongolia pine, showing some common limiting
factors for their growth.
PDSI reconstruction and validation
A simple linear regression model was used to reconstruct the
PDSI from May to July of the current year: PDSI57 = 4.98*STD
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Shi et al. | Tree-ring-based growth–climate response and drought reconstruction in Hulunbuir 55
Figure 2: tree-ring width and NDVI series (a), their correlations (b) from 1982 through 2006.
Figure 3: correlation analyses of STD chronologies with monthly
mean temperature and total monthly precipitation (a) and monthly
PDSI (b). Dashed lines indicate 0.05 significance level.
– 5.17, where PDSI57 is average May–July PDSI and STD represents the tree-ring STD chronology. The reconstructed PDSI
agrees well with instrumental records (Fig. 4), explaining
49.2% of total variance during the calibration period 1951–
2005 (Table 2). To further evaluate statistical reliability of the
reconstruction model, a split-period, calibration-verification
procedure was used. As shown in Table 2, the calibration and
verification results for the split sub-periods 1951–1978 and
1981–2005 generally show a good model fit. This is for the
two most rigorous tests of model validation, RE and CE. These
tests demonstrate the validity of the regression model.
Drought variability
The May–July PDSI, reconstructed from 1829 to 2009 for
the Hulunbuir area, has mean value −0.25 and SD = ±1.42.
This accords with the normal moisture level (PDSI = 0.0 ± 0.5)
in the central and western Great Plains of the USA (Palmer
1965). In this study, the value of −0.25 was considered as the
normal moisture condition, and years with values greater
than twice the SD were defined as extreme dry or wet years.
During the 181 years of reconstructed PDSI (1829–2009),
extremely dry years were 1853, 1856, 1987 and 2003, and
extremely wet years were 1934 and 1984. There is greater
frequency of severe dry years during recent decades, especially after ~1986. We defined years with PDSI >1 SD above
the mean as wet years, and those with PDSI <1 SD below
the mean as dry years. The full PDSI reconstruction displays
strong interannual variation across the entire period (Fig. 5).
There were 28 dry years and 32 wet years in the reconstructed series, accounting for 15.47% and 17.68% of the
total, respectively. Dry periods of 2 years or more were found
during 1905–09, 1926–28, 1950–51, 1986–87, 2001–03 and
2006–08. Wet periods exceeding 2 years were during 1846–
48, 1868–69, 1881–83, 1932–34, 1939–41, 1953–56, 1983–84
and 1989–91. The PDSI reconstruction from the Monsoon
Asian Drought Atlas (Cook et al. 2010) shows trends similar to
our reconstruction (see Supplementary Figure S4). Droughts
are shown in the 1850s, 1900s, 1920s, mid-1930s and turn of
the 21st century.
On the decadal timescale, the reconstructed PDSI series
also reveals drought variability. An 11-year moving average
of reconstructed PDSI (Fig. 5) shows distinct alternations of
dry and wet intervals. There were six dry intervals with PDSI
below the mean: 1837–39, 1851–62, 1889–1912, 1920–32,
1963–74 and 1997–2009. There were six wet intervals with
PDSI above the mean: 1829–36, 1840–50, 1863–88, 1913–
19, 1933–62 and 1975–96. The longest wet period lasted
for 30 years, from 1933 to 1962, and the longest dry period
was 24 years, from 1889 to 1912. Figure 5 also demonstrates
that after the 1940s, the reconstructed PDSI shows a longterm descending trend. This trend is more significant after
the 1980s.
Significant (95% confidence level) spectral peaks were
revealed at 7.2, 3.9, 2.7–2.8, 2.4 and 2.2 years (Fig. 6) by the
REDFIT spectral analysis (Hammer et al. 2001). We also found
periods of 36.9, 18.1 and 5.0 years at a 90% confidence level.
56
Journal of Plant Ecology
Wavelet analysis showed similar cycles to those found in our
reconstruction by power spectral analysis (Fig. 7). To some
extent, the power resided within the typical El Niño-Southern
Oscillation (ENSO) variability bands during the entire reconstruction. The greatest power at decadal timescale, expressed
in the range from 30 to 40 years, was from the 1950s to the
turn of the 21st century. Power in the 10–20 year range was
found from the 1840s–60s and 1890s–1910s. Power within
the 8–12 and 4–8 year ranges was in the 1920s–40s and
1980s–90s (Fig. 7).
DISCUSSION
Drought–growth relationships
Previous studies in arid and semiarid areas of North China
have shown that tree growth is primarily determined by
effective moisture (Fang et al. 2009, 2010; Li et al. 2007; Liang
et al. 2007). The moisture-sensitive growth pattern is apparent from negative correlations with temperature and positive correlations with precipitation. Temperature affects tree
growth via soil moisture amount, and this is supported by the
negative correlations. The reconstruction agrees with this pattern. But compared to precipitation, temperature is more significant in causing drought stress as demonstrated by higher
coefficients, which is also evident at Guiqing Mountain (Fang
et al. 2010), Kongtong Mountain (Fang et al. 2012), Guancen
Mountain (Sun et al. 2012), and Ortindag Sand Land (Liang
et al. 2007).
During the growing season, higher temperature can accelerate tree transpiration and respiration, simultaneously decrease
carbohydrate accumulation in stems. Annual effective evapotranspiration is about five to seven times greater than total precipitation (Bao et al. 2012), which means that high temperature
would exacerbate the large soil moisture deficits. Temperatureinduced drought may be common in extremely dry regions
or locations with sandy soil, where potential evapotranspiration is considerably higher than actual evapotranspiration.
For such areas, drought-growth correlations are expected to
be high, which may account for the high PDSI–growth correlations in the Hulunbuir area. These findings are similar to
results of studies of the Qinghai-Tibet Plateau (Gou et al. 2008;
Qin et al. 2008; Shi et al. 2010), north-central Shaanxi Province
(Cai et al. 2008), Lüliang Mountains (Cai et al. 2010), and Funiu
Mountains (Tian et al. 2009).
The occurrence of missing or narrow rings indicates that
drought stress sometimes controls its survival limit (Liang
et al. 2014). The dry and warm year appear to cause the lower
tree-ring growth, even missing rings. In the past 30 years,
7 years with the narrower rings were consistent with the
droughts in the study area. In particular, the drought in 1987
was the most serious in the past 181 years, the locally missing
rings occurred. Similar to the relationship between tree-ring
width and drought, the lower NDVI values also coincide with
the drought events. The high correlations were observed
among the ring width, NDVI and PDSI, particularly in the
growing season, which appears to be a common feature of
the response of the tree growth, regional vegetation on the
climate for the grassland and forest in the semiarid and arid
regions of northern China. The significant linkage between
NDVI and ring growth is a result of their common phenological processes. It is more important that there is a common
and similar influence of seasonal moisture on the growth of
different vegetation types in the study area.
The PDSI data here faithfully represent local drought conditions, and strongly correlated with tree growth and regional
vegetation dynamics. The most significant moisture limitation
is during May and June, which have relatively high temperature and light precipitation (Fig. 3b) and indicate a greater soil
moisture limitation.
Drought variability
Figure 4: comparison of observed and reconstructed May–July PDSI
from 1951 to 2005.
The 1920s–30s drought was reported in other studies of arid
and semiarid regions in North China (Fang et al. 2009; Liang
Table 2: statistics of calibration and verification tests of the PDSI57 reconstruction
Calibration
Period
Verification
r
r2
S
T
Period
r
RE
CE
S
T
1981–2005
0.73
0.54
19/6*
4.25
1951–1980
0.68
0.65
0.40
26/4**
4.61
1951–1978
0.75
0.56
26/2**
4.38
1979–2005
0.69
0.29
0.25
20/7*
4.27
1951–2005
0.70
0.49
45/10**
6.09
—
—
—
—
—
—
*P < 0.05, **P < 0.01.
Shi et al. | Tree-ring-based growth–climate response and drought reconstruction in Hulunbuir 57
Figure 5: reconstructed PDSI and 11-year smoothed average reconstruction.
Figure 6: spectral density of reconstructed PDSI during 1829–2009
in Hulunbuir area. Spectral analysis is based on REDFIT method.
et al. 2006; Liu et al. 2005, 2009b, 2010; Yang et al. 2011;
Zhang et al. 2011). This drought was widespread and varied in
intensity and length across this region, leading to tremendous
loss of life and severe damage to the economy.
The dry epoch after 1986, especially after 1995, was the
most severe over the past 181 years in the Hulunbuir drought
reconstruction. This is consistent with an observed drying
trend since the 1980s (Deng et al. 2013; Fang et al. 2009, 2010,
2012; Li et al. 2007; Shi et al. 2007), which was also recorded
at Xinglong Mountain (Fang et al. 2009; Liu et al. 2013),
Guiqing Mountain (Fang et al. 2010), Kongtong Mountain
(Fang et al. 2012) and Qilian Mountain (Deng et al. 2013;
Yang et al. 2011). This drought has increased the risk of water
resource and ecosystem vulnerability, especially in our study
area. The droughts occurred in 1987 and 2003 were the most
severe events during 1829–2009, and were centered on an
area similar to that for which our tree-rings are representative (see Supplementary Figure S3), strongly corresponding
to warm phases of ENSO. In addition, the wet intervals during 1863–88 and 1933–62 also correspond to the longest wet
intervals from a tree-ring-based PDSI reconstruction in the
Ortindag Sand Land (Liang et al. 2007).
Comparison with other studies
A severe drought in North China from 1904 to 1909 was captured by the Hulunbuir PDSI reconstruction. According to
historical records (Ding 2008), the drought began in western
Heilongjiang Province (the Hulunbuir area of today) in 1904.
Figure 7: wavelet plots for reconstructed May–July PDSI. Areas
surrounded by thick black contours (5% significant level, using rednoise [ARS lag 1] background spectrum) are regions of significant
power at corresponding timescales.
The drought was continuous thereafter, and there was no
rainfall from spring to autumn of 1909. Only 30–40% of crops
compared with a normal harvest year were harvested during
this period because of severe summer drought. This illustrates
the reliability of the sequence reconstructed here.
In the mid-late 1920s, an extreme drought disaster caused
serious social and economic problems in northern China. In
1926, April–July precipitation was 50% lower than the average in Hailar. The drought recurred in summer 1927, and its
continuation prevented crop harvesting in 1928 (Ding 2008;
Shen 2008). Moreover, in 1935, most areas around Hulunbuir
were subjected to continuous drought in spring and summer;
this drought was especially severe in Hailar (Shen 2008). Such
continuous drought agrees with results of tree-ring temperature reconstruction in many regions of northern China (Cai
et al. 2008; Liang et al. 2007).
According to historical records, the continuous severe
drought between 1876 and 1878 represents one of the
greatest natural hazards during the Qing Dynasty in middle
and northern China. In the latter region, this event affected
1.6–2 billion people (~50% of the total Chinese population) and led to 10 million fatalities (Li et al. 1994; Shen
2008). However, this extreme drought was not evident in
the Hulunbuir reconstruction, which shows a normal state
during this period.
58
We also compared the reconstructed drought variation
with seven other tree-ring-based PDSI reconstructions near
our sampling sites (see Supplementary Figure S4; Cook et al.
2010; Deng et al. 2013; Fang et al. 2010, 2012; Liang et al.
2007). Our reconstruction shows drought intervals similar to
the other seven drought reconstructions (see Supplementary
Figure S4). Over the past 181 years, there were common climate trends among the various series, including the wet period of the 1860s–70s (see Supplementary
Figure S4, period I), drought periods of the 1920s–30s (see
Supplementary Figure S4, period II) and the turn of the 20th
century (except at Guiqing Mountain; see Supplementary
Figure S4, period III).
These reconstructions also show a slow drying trend in
recent decades (see Supplementary Figure S4 a~h). By comparing these trends at different sites, we found that the drying trend started at the end of the 1930s at Hulunbuir (Cook
et al., 2010; this study) and Helan Mountain (Fang et al.
2010), and at Ortindag Sand Land (Liang et al. 2007) and
the eastern Qilian Mountains (Deng et al. 2013) since the
1950s. This trend began in the 1970s at Kongtong Mountain
(Fang et al. 2012) and Xinglong Mountain (Fang et al. 2010),
and in the 1980s at Guiqing Mountain (Fang et al. 2010;
see Supplementary Figure S4). The eight studies (including
this study) were done in the transitional zones of arid and
semiarid climate, and at the margin of the EASM. Despite
some differences, this comparison demonstrates a spatial
and temporal link to PDSI in these environmentally sensitive EASM margin zones. In addition, the common trends
within the eight PDSI time series reveal a drying trend during the second half of the 20th century in these regions, and
this trend is consistent with weakening of the EASM (Jiang
and Wang 2005; Liu et al. 2003). Another study has shown
that EASM has been weakening since the 1950s (Jiang
and Wang 2005; Xu et al. 2011), and sites within marginal
monsoon zones experienced this weakening earlier than in
monsoon zones. In addition, the weakening in the northern
marginal monsoon zones occurred earlier than the south
marginal zones.
Linkages with remote oceans
The 2–4-year peak cycle found in some moisture-related
reconstructions by tree-ring data in North China (Deng et al.
2013; Fang et al. 2009, 2010, 2012; Liu et al. 2009b) falls
within the range of ENSO variability (Allan and Parker 1996),
suggesting a link between droughts and ENSO. And the link
was supported by the negative correlation between the PDSI
and western tropical Pacific SSTs (see Supplementary Figure
S5), i.e. the higher SSTs induced the occurrence of drought
in Hulunbuir area. Spatial correlation between PDSI and SST
within the instrumental period (1951–2005) also displayed
patterns similar to those of Guiqing Mountain (Fang et al.
2010), the Qilian Mountains (Deng et al. 2013) and northeastern Tibetan Plateau (Li et al. 2008). Some studies in northeast
China have shown a climate response of EASM to moisture
advection from the western tropical Pacific (Huang et al. 2007;
Journal of Plant Ecology
Sun et al. 2007). This suggests a teleconnection between local
droughts and the EASM.
Drought variability was greatly affected by the Pacific
Decadal Oscillation (PDO). On an annual scale, our reconstructed PDSI correlates with May–July PDO for 1900–2009
(r = 0.122, P < 0.2). On a decadal scale, this PDSI also significantly correlated with May–July PDO (r = 0.257, P < 0.01).
Prevailing cool PDO regimes from 1920 to 1932 and 1963 to
1974, and warm PDO regimes from 1933 to 1962 and 1975
to the mid-1990s (Mantua and Hare 2002), correspond with
most of our dry and wet periods.
Another study showed that the Hulunbuir area is within
the zone of the Westerlies (Lee and Zhang 2011). There is significant correlation (r = 0.189, P < 0.05) between our reconstructed PDSI and the North Atlantic Oscillation (NAO), which
is an indicator of the strength of these Westerlies. This indicates
their possible influence, simultaneous with the period of EASM
decline. This influence was also found in the Qilian Mountains
within the EASM margin zone (Deng et al. 2013; Zhang et al.
2009) and Northwest China (Lee and Zhang 2011).
CONCLUSIONS
A tree-ring chronology from Mongolia pine spanning 1829–
2009 was developed for the Hulunbuir area of Northeast
China. The PDSI was reconstructed using a model that
explained 49.2% of variance. Spatial correlation analyses
highlighted the broad-scale regional representativeness of
our reconstruction. The moisture-sensitive growth pattern
showed negative correlations with temperature and positive
correlations with precipitation in the arid or semiarid region.
The local moisture variations affected largely the regional
vegetation dynamics and tree-ring growth of Mongolia pine
in the forest–grassland transition. The strong PDSI–growth
relationship demonstrates a capability for modeling local
drought conditions. Drought reconstruction showed strong
interannual and decadal variability, and drought events were
revealed in the 1850s, 1900s, 1920s, mid-1930s and at the
turn of the 21st century. Comparisons with other tree-ring
drought reconstructions and certain historical records, they
disclose jointly some common drought periods and drying
trends in northern margin zones of the EASM. The latter drying trends preceded EASM weakening. The REDFIT spectral
analysis showed significant peaks at 7.2, 3.9, 2.7–2.8, 2.4
and 2.2 years with 95% confidence level, and 36.9, 18.1 and
5.0 years with 90% confidence level. The wavelet analysis
showed cycles similar to those in the reconstruction by power
spectral analysis. Further, drought variations in our reconstruction significantly correlated with SSTs in the western
tropical Pacific, mid and northern Indian Ocean, the PDO and
NAO. These suggest links to ENSO, EASM and the Westerlies.
SUPPLEMENTARY DATA
Supplementary material is available at Journal of Plant
Ecology online.
Shi et al. | Tree-ring-based growth–climate response and drought reconstruction in Hulunbuir 59
FUNDING
‘948’ Project of State Forestry Administration China (20154-27), International S&T Cooperation Program of China
(2015DFR31130), National Natural Science Foundation of
China (41271033, 41471029 and 41371500) and The Lecture
and Study Program for Outstanding Scholars from Home and
Abroad (CAFYBB2011007).
Davi N, Jacoby G, Fang K, et al. (2010) Reconstructing drought variability for Mongolia based on a large‐scale tree ring network:
1520–1993. J Geophys Res 115:D22103.
Davi NK, Jacoby GC, D’Arrigo RD, et al. (2009) A tree ring-based
drought index reconstruction for far-western Mongolia 1565–
2004. Int J Climatol 29:1508–14.
Deng Y, Gou XH, Gao LL, et al. (2013) Aridity changes in the eastern
Qilian Mountains since AD 1856 reconstructed from tree-rings.
Quatern Int 283:78–84.
ACKNOWLEDGEMENTS
Ding YH (2008) Meteorological Disasters Dictionary of China: Vol.
Comprehensive. Beijing, China: Meteorological Press.
We thank Prof. Xuemei Shao and Dr Keyan Fang for their great
help and meticulous guidance. We are also thankful to anonymous
reviewers for their thoughtful suggestions to improve this manuscript
significantly.
Conflict of interest statement. None declared.
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