Download Responses of vegetation distribution to climate change in China

Theor Appl Climatol
DOI 10.1007/s00704-013-0971-4
ORIGINAL PAPER
Responses of vegetation distribution to climate change
in China
Dongsheng Zhao & Shaohong Wu
Received: 21 September 2012 / Accepted: 8 July 2013
# Springer-Verlag Wien 2013
Abstract Climate plays a crucial role in controlling vegetation distribution and climate change may therefore cause
extended changes. A coupled biogeography and biogeochemistry model called BIOME4 was modified by redefining the
bioclimatic limits of key plant function types on the basis of
the regional vegetation–climate relationships in China. Compared to existing natural vegetation distribution, BIOME4 is
proven more reliable in simulating the overall vegetation
distribution in China. Possible changes in vegetation distribution were simulated under climate change scenarios by using
the improved model. Simulation results suggest that regional
climate change would result in dramatic changes in vegetation
distribution. Climate change may increase the areas covered
by tropical forests, warm-temperate forests, savannahs/dry
woodlands and grasslands/dry shrublands, but decrease the
areas occupied by temperate forests, boreal forests, deserts,
dry tundra and tundra across China. Most vegetation in east
China, specifically the boreal forests and the tropical forests,
may shift their boundaries northwards. The tundra and dry
tundra on the Tibetan Plateau may be progressively confined
to higher elevation.
1 Introduction
The increase in atmospheric greenhouse gases may result in
significant changes in the climate (IPCC 2007). As a country
located in a region dominated by East Asian monsoon, China
is vulnerable to global climate change. The projections of
numerous general circulation models (GCMs) indicate that
D. Zhao : S. Wu (*)
Institute of Geographical Sciences and Natural Resources
Research, Chinese Academy of Sciences, No. 11 A, Datun Road,
Anwai, Beijing 100101, China
e-mail: [email protected]
China may experience an increase in surface air temperature,
a rise in the frequency of extreme climate events, an enhancement of spatial and temporal heterogeneity in precipitation and an enlargement of arid regions in the future
(TCNARCC 2011). These changes in climate can affect
certain ecological processes, patterns and structures of natural ecosystems, consequently altering the goods and services
provided by ecosystems to society. Therefore, an important
aspect of the research on global climate change is to reveal
the responses and adaptability of ecosystems to climate
change by investigating the interrelationship between climate change and ecosystems at different scales (Cox et al.
2004; Hitz and Smith 2004; Wilson et al. 2005; Jiang et al.
2011).
China is dominated by the monsoon climate and has a vast
land area that covers approximately 10 % of the total world
land area. Thus, the climate types in China vary from tropical
in the south to cold temperate in the north and from humid in
the east to dry in the west. These diverse climates together
with the complex topography bring about the high biodiversity in China. Most of the main vegetation types in the world
can be found in China. However, the species composition
and the diversity of vegetation in China are sensitive to
climate change, and large-scale vegetation distribution is
vulnerable to climate warming (Ni 2011; Zhao et al. 2011).
Ni (2011) reported that the climate warming and the general
increase in precipitation that occurred in China during the
past century lengthened the growing season of vegetation
and modified the composition and geographical pattern of
vegetation, particularly in ecotones and tree lines. A great
number of paleobotanical studies (Davis and Botkin 1985;
Prentice 1986; Huntley 1990; Prentice et al. 1991) have
confirmed the climate-induced changes in ecosystems. Studies on the development and evolution of vegetation show that
the vegetation on the Tibetan Plateau is dominated by mountain forests during warmer periods and by tundra during
colder periods (Shi et al. 1998).
D. Zhao, S. Wu
Changes in climate can also alter the distribution pattern
of vegetation in China. Certain simulations were undertaken
to assess the potential effect of climate change on vegetation.
Zhang and Yang (1993) utilized Holdridge's life zone
scheme to analyse the influence of climate change on potential vegetation distribution. By using the BIOME3 model, Ni
et al. (2001) simulated the potential vegetation distribution
and predicted the changes in vegetation distribution under
climate change. Weng and Zhou (2006) developed a potential distribution model to analyse the response of potential
vegetation in China to climate change. However, these studies primarily focused on the applicability of the models in
China. Moreover, their projections for future effects were
based on climate scenarios generated by GCMs at a resolution of 200 to 300 km, which is too coarse for regional
studies. The initiative to study the potential effects of climate
change is motivated by the increasing scientific and political
interests in the interrelationship between climate change and
ecosystems in regional levels. To date, few studies have
investigated the influence of regional climate scenarios on
potential vegetation.
The biogeographical and biogeochemical aspects of ecological responses to environmental change are interdependent
to each other. BIOME4 (Kaplan et al. 2003), which is a
process-based terrestrial biosphere model, combines biogeographical and biogeochemical modelling approaches within
single framework to simulate the distribution and the structure
of global vegetation and its biogeochemical processes. In this
study, BIOME4 was adopted to model vegetation dynamics.
The model parameters were modified, and the model was
calibrated for the study region on the basis of the distribution
and the eco-physiological features of the vegetation types in
China. The modified model was used to project the future
effects of climate change on vegetation distribution according
to regional climate scenarios.
2 Methods and data
2.1 The BIOME4 model
BIOME4 (Kaplan et al. 2003) is a coupled biogeography and
biogeochemistry model that simulates the distribution and
structure of global vegetation. The model functions on the
basis of 12 plant functional types (PFTs), which represent all
major vegetation types on earth from the arctic tundra to the
tropical rainforest. The computational core of BIOME4 is a
coupled carbon and water flux scheme. This scheme determines the leaf area index (LAI) that maximizes the net
primary productivity (NPP) for any given PFT on the basis
of the daily time step simulation of soil water balance,
canopy conductance, photosynthesis and respiration. BIOME4 implicitly simulates competition between PFTs as a
function of the relative value of NPP. On the basis of the
identity of the most successful and second most successful
PFT and their sustainable LAI, each model grid cell is
assigned a biome according to a set of semi-empirical
rules. The model is sensitive to changes in climate because of the NPP responses and the stomatal conductance
to water and heat (Kaplan 2001). BIOME4 has a horizontal grid resolution of 50×50 km. The model inputs include
CO2, soil texture, monthly surface temperature, precipitation
and cloudiness. We provide only a short overview on BIOME4 because Kaplan et al. (2003) have already described
BIOME4 in detail.
BIOME4 can predict the distributions of 26 biomes
altogether. For simplicity of analysis and facilitative comparisons, the biome classification used in BIOME4 was
reconstructed following the assignment scheme used by
Harrison and Prentice (2003) to generate nine mega-biomes
(tropical forest, warm-temperate forest, temperate forest,
boreal forest, savanna and dry woodland, grassland and dry
shrubland, desert, dry tundra, tundra) that would represent
the vegetation types in China (Table 1). A set of widely
accepted bioclimatic and eco-physiological parameters was
used to define PFTs and to assign biomes (Prentice et al.
1992; Haxeltine and Prentice 1996; Kaplan et al. 2003).
However, some of parameters were not suitable for PFTs in
China. Thus, certain the bioclimate limiting factors were
updated for key PFTs on the basis of the relationship between
climate and vegetation distribution in China and published
literature to capture the boundary of vegetation types in
China more accurately (CVEC, CAS 1980; Ni et al. 2001;
CVAEC, CAS 2001; Zhang et al. 2007; Weng and Zhou
2006). For example, the model tends to overestimate the
elevation of alpine tree lines at lower latitudes. Thus, a minimum heat requirement was set for boreal forest at 500 growing degree days (5 °C base). Given that tropical forest in China
mainly consist of tropical monsoon forests, distinguishing
evergreen forest from deciduous forest in the actual vegetation
map is difficult. Therefore, tropical evergreen forest and tropical raingreen forest were unified into tropical monsoon forest
(Table 2).
2.2 Model of biome mean centre
The biome mean centre is the average x and y coordinates of
a given biome in the study area. The biome mean centre is
usually used to track changes in the distribution or to compare the distributions of different types of biomes. The biome
mean centre is given as follows (Hart 1954; Yue et al. 2011.)
nðt Þ
X
X ðt Þ ¼
nð t Þ
X
y i ðt Þ
xi ðt Þ
i¼1
nð t Þ
; Y ðt Þ ¼
i¼1
nð t Þ
ð1Þ
Responses of vegetation in China
Table 1 Allocation of original biome used in BIOME4 to mega-biome
classification used in this study
Original biome classification
Tropical evergreen rainforest
Tropical semi-deciduous forest
Tropical deciduous forest
Warm mixed forest
Temperate deciduous forest
Temperate conifer forest
Cool mixed forest
Cool conifer forest
Cold mixed forest
Evergreen taiga/montane forest
Deciduous taiga/montane forest
Tropical savanna
Temperate sclerophyll woodland
Temperate broadleaved savanna
Open conifer woodland
Tropical xerophytic shrubland
Temperate xerophytic shrubland
Tropical grassland
Temperate grassland
Desert
Barren
Graminoid–forb tundra
Shrub tundra
Dwarf shrub tundra
Prostrate shrub tundra
Cushion forbs–lichen–moss tundra
Mega-biome classification
used in this study
category j in another, the sum of these proportions is the
overall proportion of observed agreement p o, as follows.
po ¼
pii
ð3Þ
i¼1
Tropical forest
Warm-temperate forest
Temperate forest
m
X
where m is the number of vegetation types and p ii is the
proportions of grid cells on which both maps agree. The
overall expected value of agreement p e due to chance alone
is calculated as.
pe ¼
m
X
pi :p:i
ð4Þ
i¼1
Boreal forest
Savanna and dry woodland
Grassland and dry shrubland
Desert
Dry tundra
Tundra
where t is the period of climate change; x i and y i are the
longitude and latitude coordinates for the ith grid cell of a
given biome type, respectively; and
n is equal
to the total
number of a given biome type. ( X ðt Þ; Y ðt Þ ) is the mean
centre of a given biome type.
The shift distance and direction of the biome type from
t to t +1 are respectively calculated in ArcGIS 10.0 software (ESRI, Redlands, California, USA).
2.3 Kappa statistic
Kappa statistic is widely used to assess the agreement between two maps with categorical data. The kappa statistic κ
is formulated as follows.
.
κ ¼ ðpo −pe Þ ð1−pe Þ:
ð2Þ
κ is approximately zero when agreement is no better than
random and reaches unity when agreement is perfect. By
letting p ij be the proportion of the total number of grid cells
that belongs to category i in one map and belongs to the
An individual kappa statistic can also be calculated for
each category i, as follows.
.h
.
i
κi ¼ ðpii −pi :p:i Þ ðpi : þ p:i Þ 2−pi :p:i :
ð5Þ
As suggested by Monserud and Leemans (1992), a kappa
value <0.4 is considered poor or very poor. A kappa value
ranging from 0.4 to 0.55 is considered fair, from 0.55 to 0.7
good, from 0.7 to 0.85 very good, and >0.85 excellent.
2.4 Climate data
The climate data used in this study were provided by the
climate change research group of the Institute of Environment and Sustainable Development in Agriculture of the
Chinese Academy of Agricultural Sciences. The research
group used the Providing Regional Climate for Impacts
Studies (PRECIS) system (Jones et al. 2004) to create highresolution (50×50 km) climate data scenarios in China for
the late twenty-first century on the basis of greenhouse gas
emission scenarios of the IPCC Special Report on Emission
Scenarios (SRES) (Xu 2004). The general circulation model
UKMO_HadCM3 was used to obtain the boundary conditions for PRECIS simulations. UKMO_HadCM3 is considered more effective in simulating the spatial and temporal
characteristics of climate change in China compared with the
other global circulation models that contributed to the fourth
assessment report of the IPCC (Miao et al. 2012). The
effectiveness of PRECIS to simulate terrestrial climate
change in China was validated by applying the reanalysis
data derived from the European Centre for Medium-Range
Weather Forecasts as lateral boundary conditions. The regional climate scenarios simulated by PRECES have been
widely utilized to assess the effect of climate change on the
ecosystems in China (Xiong et al. 2009, Wu et al. 2010, Zhao
et al. 2013a). The projected climatic data that include the A2,
B2 and A1B emission scenarios from 1961 to 2080 were
used in this study. Among all the IPCC proposed scenarios,
A1B is considered a high-emission scenario that is characterized by rapid economic growth, global population that
D. Zhao, S. Wu
Table 2 Bioclimate limiting factors for each plant functional type
PFTs
Tc min (°C)
Tc max
(°C)
T min max
(°C)
GDD5 min
GDD0
min
−3 [0]
Tropical monsoon forest [Tropical
evergreen forest; Tropical
raingreen forest]
Temperate broadleaved forest
Temperate deciduous forest
Temperature evergreen conifer
forest
Boreal evergreen forest
Boreal deciduous forest
Temperature grass
Tropical grass
Desert woody plant
Tundra shrub
Cold herbaceous
Lichen/forb
T min min
(°C)
−8
−2 [−15]
−2
−25 [−32.5]
−2
5
Twm min
(°C)
Twm max
(°C)
12 [10]
5
1,500 [1,200]
−8
10
1,200
900
−10
0
500 [no limit]
500 [no limit]
550
−3
−45
12 [10]
21
21
10
10
500
50
50
15
12 [15]
10 [15]
The original PFTs in square brackets in the first column were unified into a new PFT in this study. The original parameter values in square brackets
given by Kaplan (2001) are replaced by the italics value in this study
Tc mean temperature of the coldest month, T min absolute minimal temperature, GDD 5 the growing degree days of over 5 °C, GDD 0 the growing
degree days of over 0 °C, Twm mean temperature of the warmest month
peaks in the mid-century and declines thereafter, and rapid
introduction of new and more efficient technologies. A2 is
the scenario with high emissions of greenhouse gases. In A2,
self-reliance and local identities are emphasized, population
increases continuously, and economic development is regionally oriented. B2 represents moderate emissions of the
greenhouse gases. B2 is characterized by a continuous but
moderate increase in population and a moderate economic
growth that focuses on local solutions to economic, social
and environmental stability.
The periods were divided into the baseline term (1961–
1990), the near-term (1991–2020), the mid-term (2021–
2050) and the long-term (2051–2080). Each term was evaluated
according to the average simulation of 30 years.
The projection from PRECIS indicates that the annual
average temperature in China during the long-term will
increase from 3.80 °C (A1B) to 2.63 °C (B2) compared
with the baseline term (Table 3), in which the highest
temperature increase of approximately 5 °C (A1B) is
determined in northwest China. The lowest temperature
increase may occur in southwest and southeast China,
with an average increment of 2 °C (B2) (Fig. 1). The
total annual precipitation in the long-term scenario is
projected to increase from 17.63 % (A1B) to 9.3 % (B2)
(Table 3). The highest temperature increase is projected to
occur in northwest China and is 60 % greater than the baseline
term. A slight decrease of approximately 5 % may exist in
northeast China (Fig. 2).
2.5 Vegetation data
The simulated output by BIOME4 was compared with the
potential distribution of natural vegetation derived from the
Vegetation Atlas of China at a scale of 1:1 million (CVAEC,
CAS 2001). ArcGIS 10.0 (ESRI, Redlands, California,
USA) was used to transform the digitalized vegetation map
into a raster dataset in the ArcInfo Grid format with spatial
resolution of 50×50 km. A total of 573 actual vegetation
types were reclassified into 26 potential biome types in the
BIOME4 according to the scheme formulated by Ni et al.
Table 3 Average changes in temperature and precipitation under SRES
A1B, A2 and B2 scenarios in China from PRECIS simulation relative to
baseline (1961–1990)
Periods
Near-term (1991–2020)
Mid-term (2021–2050)
Long-term (2051–2080)
Temperature (°C)
Precipitation (%)
A1B
A2
B2
A1B
A2
B2
0.81
2.28
3.80
0.44
1.63
3.15
0.73
1.66
2.63
5.88
12.36
17.65
4.02
8.61
16.13
5.61
6.01
9.30
Responses of vegetation in China
Fig. 1 Change of annual mean temperature in near-term (1991–2020) (top), mid-term (2021–2050) (middle) and long-term (2051–2080) (bottom)
relative to the baseline term (1961–1990) projected by the PRECIS under SRES A1B (left), A2 (middle) and B2 (right) scenarios
(2001, 2011), who produced a potential vegetation for China
by using 1:4 million actual vegetation map on the basis of the
floristic and bioclimate criteria. The 26 biomes were further
reconstructed to the same nine major biomes in the same way
as for the BIOME4 PFTs.
soil data were then transformed into grid format and
resampled to the spatial resolution of 50×50 km.
2.6 Soil data
3.1 Comparison of simulated result
In this study, the soil texture data from the map of soil texture
types (1:14,000,000) (Zhang et al. 2004) were adopted. The
data contain the geographical distribution of different soil
texture types and the proportions of mineral grains in the top
soil. The soil textures were reclassified as clay, silt, sand,
silty sand, sandy clay, silty clay and clay with silt and sand
according to the FAO classification standard for soil texture
(Ni et al. 2001) to meet the requirements of BIOME4. The
The simulated vegetation distribution (Fig. 3a) was compared with the natural vegetation map of China (Fig. 3b).
The result agrees well with an overall kappa value of 0.56.
The modified BIOME4 captured most vegetation distribution and boundaries accurately. All kappa values for the
individual vegetation types are listed in Table 4. The
kappa values indicate good simulation results for warmtemperate forest, temperate forest and desert; fair results
3 Results
D. Zhao, S. Wu
Fig. 2 Change of annual mean precipitation in near-term (1991–2020) (top), mid-term (2021–2050) (middle) and long-term (2051–2080) (bottom)
relative to the baseline term (1961–1990) projected by the PRECIS under SRES A1B (left), A2 (middle) and B2 (right) scenarios
for tropical forest, boreal forest, grassland and dry shrubland;
but poor results for savanna and dry woodland, dry tundra
and tundra.
In the simulated vegetation distribution, dry woodland
was mixed with other types instead of occupying clearly
defined areas. Dry woodland and temperate forest shared
the same PFT during simulation. However, they are differentiated spatially in biome assignment according to whether
their PFT is a primary PFT or not. Sharing key PFT among
biomes is a key reason for mismatches because the model
cannot differentiate the biomes under similar bioclimatic
conditions. Figure 1 shows that tundra and dry tundra types
are generally not simulated accurately and the modified
BIOME4 cannot predict their boundaries accurately. This
problem may be a matter of PFT definition and biome
assignment. Certain issues on vegetation classification made
the assignment of alpine vegetation to tundra biomes difficult or incorrect in certain cases, consequently affecting the
comparisons between the simulated result and the actual
vegetation distribution.
3.2 Effect of climate change on simulated vegetation
distribution
The overall vegetation distribution was not changed significantly by climate change in the near-term compared with the
baseline term. However, the grassland and dry shrubland
biome and the savanna and dry woodland biome are sensitive
to warming climate. These types of areas are expected to
increase significantly from the baseline term (Table 5).
Grassland and dry shrubland will begin to invade the Greater
and the Lesser Higgnan Mountains (Figs. 4 and 5), replacing
the temperate forest and the boreal forest. Meanwhile, grassland and dry shrubland will spread slightly to the interior of
the Tarim Basin, particularly under the A1B scenario, in
which the mean centre moved towards the west (Fig. 6). In
Responses of vegetation in China
Fig. 3 Vegetation distribution
simulated by the modified
BIOME4 model (a) and derived
from the Vegetation Atlas of
China (b)
north China, savanna and dry woodland will expand to
temperate forests in relatively arid areas and in certain calcareous or salinized soils, especially under the B2 scenario,
in which the mean centre will move 191 km southwestwards
(Table 6). In addition, most forest biomes in China will begin
to shift their northern boundary forward (Fig. 6). On
Tibetan Plateau, the dry tundra under A1B, A2 and
scenarios will slightly decline compared with that in
baseline term. No obvious changes were observed in
distribution of forest vegetation.
the
B2
the
the
D. Zhao, S. Wu
Table 4 Discrimination accuracy of simulated vegetation types in
China by the modified BIOME4
Vegetation types
Accuracy (%)
Kappa value
Tropical forest
Warm-temperate forest
Temperate forest
Boreal forest
42.2
76.4
60.4
62.8
0.41
0.76
0.60
0.49
Savanna/dry woodland
Grassland/dry shrubland
Desert
Dry tundra
Tundra
Overall
16.7
48.3
83.0
28.8
37.9
61.8
0.15
0.47
0.82
0.24
0.33
0.55
For the mid-term, the boreal forest in northeast China
will experience a substantial northward shift in its distribution and will be partly replaced by expanding grasslands (Fig. 5). Thus, the mean centre of the boreal forest
will experience maximum shift distance longer than
470 km towards the southwest under the A1B scenario
(Table 7). The savanna and dry woodland biome and the
grassland and dry shrubland biome will follow the trend
of that in the near-term. In north China, the savanna and
dry woodland biome is expected to expand, changing
from the existing mixed distribution with temperate forest to a more continues coverage, particularly in A1B
and A2 scenarios. The temperate forest will move its
mean centre towards northeast (Table 7), and the distribution area of the temperate forest will continue to
decrease. The mean centre of the warm-temperate forest
is expected to experience a northeastward shift (Fig. 6),
in which a significant area of the biome expands. The
temperate forests around the Sichuan Basin (Fig. 4) will
also be replaced by warm-temperate forests composed of
continues coverage together with the eastern warmtemperate forest. The tropical forest will spread to cover
an area that is twice as large as than that in the baseline
(Table 5). However, this area will occupy approximately
less than 3 % of the total land area of China. On the
Tibetan Plateau, the temperate forest and the boreal forest will begin to invade the interior of the plateau and
will mostly replace the original tundra and dry tundra,
resulting in a decline in the area of these two vegetation
types.
For the long-term under the warmest climate, the boreal
forest in the northern part of China is projected to shrink to a
scattering of small fragments in the Great Higgnan Mountains. On the Tibetan Plateau, the distribution of the boreal
forest will continue to expand into the interior of the plateau
(Fig. 5). The mean centre of the boreal forest will experience
a prominent 400 km shift towards the southwest (Table 8).
The area of grassland and dry shrubland is projected to
increase by 60 % relative to the baseline term under the
A1B scenario, which is greater than those in the A2 and B2
scenarios. The mean centre of the grassland and dry shrubland will move southwest, occupying most of north China.
The area of savanna and dry woodland will be approximately
six times greater under A1B and A2 scenarios during the
baseline period, replacing more temperate forests in north
China. The warm-temperate forest biome will continue to
shift northeast and will be the largest biome in China occupying >20 % of the area. The tropical forest is projected to
increase in area by three times from the baseline term, mainly
focusing on south China. The mean centre of the tropical
forest will shift northeast. From the baseline term, the mean
centre of the tropical forest will move over 200 km for A1B,
A2 and B2 scenarios.
Table 5 Simulated area of each vegetation type in the baseline, A1B, A2 and B2 scenarios (in percent)
Vegetation type
Baseline (1961–1990)
Near-term (1991–2020)
Mid-term (2021–2050)
Long-term (2051–2080)
A1B
A2
B2
A1B
A2
B2
A1B
A2
B2
Tropical forest
Warm-temperate forest
0.88
19.33
1.33
20.75
1.23
20.35
1.55
20.23
2.13
23.63
1.88
21.90
1.78
22.15
4.68
22.93
3.38
22.98
2.15
23.48
Temperate forest
Boreal forest
Savanna/dry woodland
Grassland/dry shrubland
Desert
Dry tundra
Tundra
24.23
6.05
0.55
12.50
15.73
8.33
12.43
23.30
5.38
1.63
14.80
13.33
7.25
12.25
20.70
5.23
2.70
14.50
15.40
7.53
12.38
22.23
5.05
1.53
14.70
15.40
7.00
12.33
18.45
5.70
3.35
16.50
13.40
6.45
10.40
19.68
5.45
2.78
15.53
14.83
6.55
11.43
20.73
5.23
1.48
16.28
14.33
6.48
11.58
18.55
5.33
3.83
19.98
10.60
5.40
8.73
17.80
5.95
3.63
19.20
12.45
5.45
9.18
19.18
5.65
2.23
18.70
12.93
5.38
10.33
Responses of vegetation in China
Fig. 4 Elevation map of China
with major geographical region
mentioned in this text
4 Discussions
The modified BIOME4 was used to simulate the vegetation
distribution in China under climate change characterized by
rising temperature and increasing precipitation. The results
indicate that climate change can result in shifts in vegetation
distribution. However, the effects of such changes varied in
different vegetation types. The results are consistent with
previous studies except for certain discrepancies.
The simulation suggests that forest vegetation is likely to
shift northwards because of climate change, which is consistent with previous studies (Ni et al. 2001; Zhao et al. 2002;
Weng and Zhou 2006). Forest biomes show considerable
resilience to climate change. The areas occupied by all forest
vegetation types except tropical forest varied by less than
30 % among all scenarios. Given the small percentage
(0.88 %) of area occupied by tropical forests, the tropical
forest biome may have a large proportional increase. This
finding also explains why the warm-temperate forest does
not shift its western boundary far, but mainly moves its
boundary northwards in all climate scenarios. This phenomenon can be fundamentally associated with the steep climate
gradient in the western boundary of warm-temperate forests
because the mountains in this area increase in elevation
abruptly. Topographical factors play an important role in the
response of forest shift to climate change because the vegetation's moving velocity caused by temperature change is several orders of magnitude lower in mountainous regions compared with flat regions (Loarie et al. 2009). The temperate
forest in the simulation decreased by 20 to 25 % in the
long-term from the baseline, which is consistent with the
results obtained by Wang et al. (2011), who suggested that
temperate forests may decrease with a significant northward shift because of warming. A projection from Wu
(2003) also showed that the temperate forest in northeast
China will significantly shift its boundary northwards,
with an areal coverage decrease of 20 to 35 %, which is
similar to the projections in this study. The boreal forest,
which is the most sensitive to climate warming because of low
temperature environment and low productivity, may shrink to
a scattering of small fragments on the Greater Higgnan Mountains, but may expand into the interior of the Tibetan Plateau.
Previous studies (Ni et al. 2001, Wang et al. 2011) have
obtained similar results on the boreal forest. Although temperature affects the distribution of the boreal vegetation,
moisture availability is likely the more important factor. In
the climate change scenarios, much of boreal forest region in
northeast China is projected slight increase in precipitation,
but increase in precipitation will likely be offset with increases
in evapotranspiration. In addition, warming on permafrost
condition can modify soil water availability and can further
result in increase in drought stress during summer (Wilmking
et al. 2004). These changes in climate can induce boreal forest
northward shift beyond China.
The Tibetan Plateau is regarded by many researchers as a
region vulnerable to climate change because of its unique
location, special climate and high altitude. In this study, a
similar observation was suggested by the results. As a result
D. Zhao, S. Wu
Fig. 5 Simulated biomes in near-term (1991–2020) (top row), mid-term (2021–2050) (middle row) in long-term (2051–2080) (bottom row) and
under A1B (left column), A2 (middle column) and B2 (right column) scenarios
of warming, tundra and dry tundra may be converted into
forest that invades into the interior of the plateau. On the
basis of a simulation by BIOME3, Ni et al. (2001) also found
that tundra would be largely reduced on the plateau and
would be replaced by forests. In addition, the desert in
Qaidam Basin may be replaced gradually by grassland or
dry shrubland, which require increased precipitation. The
conversion risk of vegetation types in China may further
increase in the future because climate warming is faster on
the plateau than on the rest of the country (Zhao et al. 2011).
The area of grassland may enlarge in all three scenarios,
particularly in northeast China. The temperate forest in north
China may be largely reduced, whereas the woodland may
expand significantly. The temperate forest only remained as
a narrow belt from the northeast to the southwest of north
China. Zhao et al. (2002) indicated that climate changes in
the future would transform the vegetation in north China into
dry woodland and grassland, which is consistent with the
observation in this study. This transformation can be
associated with the rising temperature. An increase in
temperature may reduce water effectiveness through enhanced evapotranspiration, which limits temperate forest
survival. By contrast, dry woodland and grassland can
adapt to such changes. Piao et al. (2009) found that an
increase in temperature alone does not benefit vegetation
growth in temperate ecosystems because of water control.
Studies on global terrestrial NPP suggest that water availability is the strongest limiting factor for vegetation
growth in the mid-latitude Eurasian vegetation (Nemani
et al. 2003).
Three climate scenarios (A2, B2 and A1B) were adopted
to predict the vegetation distribution in China. The simulation results from BIOME4 under possible future conditions,
which are represented by three scenarios, exhibit general
Responses of vegetation in China
Fig. 6 Mean centre for biome types with longer shift distances under A1B (a), A2 (b) and B2 (c) scenarios. In the figures, B refers to baseline term
(1961–1990), N refers to near-term (1991–2020), M refers to mid-term (2021–2050) and L refers to long-term (2051–2080)
consistency. If the ecosystem model can be considered realistic
to a certain extent and assuming that the three scenarios can
represent a proportion of the uncertainty, then the overall
pattern of change in the vegetation in China in the coming
century may quantitatively resemble the simulated results in
this study. In addition, the vegetation distribution in three
warming periods, namely near-term, mid-term and long-term,
was examined to improve our understanding of the effects of
the different warming levels.
The response of ecosystems in China to climate change is
characterized by substantial regional differences. The northern part of the north China Plain is a sensitive area. The warm
forest in this area is projected to degrade into savanna
and dry woodland because of warming and drying. The
D. Zhao, S. Wu
Table 6 Shift distance (in
kilometre) and direction of mean
centre for each vegetation type
from baseline (1961–1990) to
near-term (1991–2020) under
three climate scenarios
Vegetation type
A1B
Distance
Direction
Distance
B2
Direction
Distance
Direction
Tropical forest
63
Northwest
133
Northeast
148
Northeast
Warm-temperate forest
Temperate forest
Boreal forest
Savanna/dry woodland
Grassland/dry shrubland
Desert
Dry tundra
Tundra
42
62
365
92
358
37
43
48
Northeast
Northwest
Southwest
Northeast
West
Northeast
Northeast
Northwest
35
83
102
81
60
11
29
28
Northeast
Northwest
Southwest
Southwest
Northwest
Northeast
West
Northwest
48
64
395
191
66
8
55
14
Northeast
Northwest
Southwest
Southwest
West
Northwest
West
East
biodiversity in this area would face higher risks of damage
because of the high-level floods and increased human activities. The tundra vegetation may shrink into the interior of the
Tibetan Plateau and may be partly replaced by forests because of the increase in temperature and precipitation. This
phenomenon may significantly affect the livelihood of local
herdsmen. Given that the tundra on the Tibetan Plateau is
mainly dominated by alpine meadow (Ni and Herzschun
2011), the plateau is not only an ecosystem but also a key
resource for the local people. According to the future climate
trend from TCNARCC (2011), warming may be strongest in
northwest China and precipitation will likely increase significantly. Consequently, the desert may retreat and may be
replaced gradually by other vegetation types that require
increased precipitation, resulting in the transformation of
most areas in northwest China into oases.
Carbon sequestration in terrestrial ecosystems is closely
associated with vegetation distribution (Piao et al. 2009).
Climate change influences carbon budget not only directly
by affecting the flux but also indirectly by affecting vegetation distribution. Table 4 shows that climate change may
increase the tropical forest distribution, which is beneficial
to the carbon storage in terrestrial ecosystems because
Table 7 Shift distance (in
kilometre) and direction of mean
centre for each vegetation type
from near-term (1991–2020) to
mid-term (2021–2050) under
three climate scenarios
A2
Vegetation type
Tropical forest
Warm-temperate forest
Temperate forest
Boreal forest
Savanna/dry woodland
Grassland/dry shrubland
Desert
Dry tundra
Tundra
tropical forests are considered the greatest carbon sinks for
their strong carbon sequestration capacity (Cox et al. 2004).
The boreal forest is projected to decrease in the future and
will be replaced by grassland and dry shrubland. This change
can result in an adverse effect to carbon sequestration because boreal forests can sequestrate more carbon than grasslands and dry shrublands could (Lindroth et al. 1998). This
vegetation change is beneficial to the carbon storage in the
Tibetan Plateau because of the increase in forest distribution
and the decrease in desert distribution. However, climate
warming can accelerate permafrost thawing and can enhance
soil microbial activities, thus inducing more carbon emission
from the soil. This temperature change can partly offset the
positive effects of vegetation change to the carbon sequestration in the Tibetan Plateau (Zhao et al. 2013b).
In this study, three scenarios from PRECIS were used to
investigate the potential response of Chinese vegetation to
climate change. However, numerous scenarios are available,
which would likely yield varied results. No model is perfect,
and BIOME4 is no exception, it uses simplified ways in
describing the ecological mechanisms, and time lags associated with succession and migration are not modelled explicitly, which bias the result. Such reasons can result in certain
A1B
A2
B2
Distance
Direction
Distance
Direction
Distance
Direction
174
102
83
386
121
134
42
64
48
Northeast
Northeast
Northwest
Southwest
Southwest
Southwest
Northeast
East
Northeast
37
53
55
471
31
113
8
21
35
Northeast
Northeast
Northwest
Southwest
Southwest
Northwest
Northeast
West
Northeast
30
40
46
255
39
134
31
33
38
Northwest
Northeast
Northwest
Southwest
Southeast
West
West
West
West
Responses of vegetation in China
Table 8 Shift distance (in
kilometre) and direction of mean
centre for each vegetation type
from mid-term (2021–2050) to
long-term (2051–2080) under
three climate scenarios
Vegetation type
A1B
Distance
A2
Direction
Distance
B2
Direction
Distance
Direction
Tropical forest
84
Northwest
44
Northeast
44
Northeast
Warm-temperate forest
Temperate forest
Boreal forest
Savanna/dry woodland
Grassland/dry shrubland
Desert
Dry tundra
Tundra
89
79
621
46
371
109
26
50
Northeast
Northeast
Southwest
Southwest
Southwest
East
West
East
85
64
444
54
403
48
31
30
Northeast
Northeast
Southwest
Northeast
Southwest
East
Northwest
Northwest
42
43
475
44
213
20
15
37
Northeast
Northwest
Southwest
Northeast
West
Northeast
Northeast
Northwest
uncertainties in the simulation results. The results simulated
by BIOME4 are also free from the disturbance of human
activities. The great expansion in human activity can have
major influences on vegetation dynamics. Therefore, this
study could be further improved by integrating human disturbances in the simulation.
increased by spreading into the interior of the plateau, whereas tundra and dry tundra retreated towards the northwest. The
desert in northwest China may be reduced and replaced by
grassland and dry shrubland because of increased precipitation. The responses of vegetation distribution to climate
change at a regional scale will contribute to our understanding of the vulnerability of regional ecosystems to climate
change.
5 Conclusions
In this study, a coupled biogeography and biogeochemistry
model (BIOME4) was modified by redefining the bioclimatic limits of key PFTs to simulate the responses of biome
distribution to future climate change in China. A comparison
between the simulated biome and the vegetation map shows
that the modified model can produce realistic predictions of
biome distribution patterns in China. Three climate scenarios
(A2, B2 and A1B) were used to drive the modified BIOME4
model to show the uncertainties in the simulations. Three
periods, 1991–2020 (near-term), 2021–2050 (mid-term) and
2051–2080 (long-term), were used for comparison with the
baseline term 1961–1990 to quantify the difference of the
biome responses to climate change under different scenarios.
In this study, the simulation suggests that regional climate
change would result in dramatic changes in vegetation distribution in China. The areas with increased vegetation distribution under the A2, B2 and A1B scenarios include the
tropical forest biome, warm-temperate forest biome, savannah and dry woodland biome, and grassland and dry shrubland biome. The areas with decreased vegetation include
temperate forest, boreal forest, desert, dry tundra and tundra.
In east China, a prominent movement northwards was observed for each vegetation distribution in response to future
climate change. Under the three scenarios, boreal forest
shifted the greatest distance northwards, followed by tropical
forest and temperature forest. Savanna and dry woodland
biome, and grassland and dry shrubland biome expanded the
distribution. On the Tibetan Plateau, the area of forests
Acknowledgments This study was supported by the Strategic
Priority Research Program of the Chinese Academy of Sciences
(XDA05090308), National Basic Research Program of China
(2011CB403206), and National Natural Science Foundation of China
(40901058). We thank Prof. Yinlong Xu from Institute of Environment
and Sustainable Development in Agriculture, Chinese Academy of
Agriculture Sciences, for providing climate scenario data. We appreciate
two anonymous reviewers for their insightful comments on an earlier
version of this manuscript.
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