ORIGINAL PAPER

Responses of vegetation distribution to climate changein 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 vegeta-tion distribution and climate change may therefore causeextended changes. A coupled biogeography and biogeochem-istry model called BIOME4 was modified by redefining thebioclimatic limits of key plant function types on the basis ofthe regional vegetation–climate relationships in China. Com-pared to existing natural vegetation distribution, BIOME4 isproven more reliable in simulating the overall vegetationdistribution in China. Possible changes in vegetation distribu-tion were simulated under climate change scenarios by usingthe improved model. Simulation results suggest that regionalclimate change would result in dramatic changes in vegetationdistribution. Climate change may increase the areas coveredby tropical forests, warm-temperate forests, savannahs/drywoodlands and grasslands/dry shrublands, but decrease theareas occupied by temperate forests, boreal forests, deserts,dry tundra and tundra across China. Most vegetation in eastChina, specifically the boreal forests and the tropical forests,may shift their boundaries northwards. The tundra and drytundra on the Tibetan Plateau may be progressively confinedto higher elevation.

1 Introduction

The increase in atmospheric greenhouse gases may result insignificant changes in the climate (IPCC 2007). As a countrylocated in a region dominated by East Asian monsoon, Chinais vulnerable to global climate change. The projections ofnumerous general circulation models (GCMs) indicate that

China may experience an increase in surface air temperature,a rise in the frequency of extreme climate events, an en-hancement of spatial and temporal heterogeneity in precipi-tation and an enlargement of arid regions in the future(TCNARCC 2011). These changes in climate can affectcertain ecological processes, patterns and structures of natu-ral ecosystems, consequently altering the goods and servicesprovided by ecosystems to society. Therefore, an importantaspect of the research on global climate change is to revealthe responses and adaptability of ecosystems to climatechange by investigating the interrelationship between cli-mate 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 vastland area that covers approximately 10 % of the total worldland area. Thus, the climate types in China vary from tropicalin the south to cold temperate in the north and from humid inthe east to dry in the west. These diverse climates togetherwith the complex topography bring about the high biodiver-sity in China. Most of the main vegetation types in the worldcan be found in China. However, the species compositionand the diversity of vegetation in China are sensitive toclimate change, and large-scale vegetation distribution isvulnerable to climate warming (Ni 2011; Zhao et al. 2011).Ni (2011) reported that the climate warming and the generalincrease in precipitation that occurred in China during thepast century lengthened the growing season of vegetationand modified the composition and geographical pattern ofvegetation, particularly in ecotones and tree lines. A greatnumber of paleobotanical studies (Davis and Botkin 1985;Prentice 1986; Huntley 1990; Prentice et al. 1991) haveconfirmed the climate-induced changes in ecosystems. Stud-ies on the development and evolution of vegetation show thatthe vegetation on the Tibetan Plateau is dominated by moun-tain forests during warmer periods and by tundra duringcolder periods (Shi et al. 1998).

D. Zhao : S. Wu (*)Institute of Geographical Sciences and Natural ResourcesResearch, Chinese Academy of Sciences, No. 11 A, Datun Road,Anwai, Beijing 100101, Chinae-mail: wush@igsnrr.ac.cn

Theor Appl ClimatolDOI 10.1007/s00704-013-0971-4

Changes in climate can also alter the distribution patternof vegetation in China. Certain simulations were undertakento assess the potential effect of climate change on vegetation.Zhang and Yang (1993) utilized Holdridge's life zonescheme to analyse the influence of climate change on poten-tial vegetation distribution. By using the BIOME3 model, Niet al. (2001) simulated the potential vegetation distributionand predicted the changes in vegetation distribution underclimate change. Weng and Zhou (2006) developed a poten-tial distribution model to analyse the response of potentialvegetation in China to climate change. However, these stud-ies primarily focused on the applicability of the models inChina. Moreover, their projections for future effects werebased on climate scenarios generated by GCMs at a resolu-tion of 200 to 300 km, which is too coarse for regionalstudies. The initiative to study the potential effects of climatechange is motivated by the increasing scientific and politicalinterests in the interrelationship between climate change andecosystems in regional levels. To date, few studies haveinvestigated the influence of regional climate scenarios onpotential vegetation.

The biogeographical and biogeochemical aspects of eco-logical responses to environmental change are interdependentto each other. BIOME4 (Kaplan et al. 2003), which is aprocess-based terrestrial biosphere model, combines biogeo-graphical and biogeochemical modelling approaches withinsingle framework to simulate the distribution and the structureof global vegetation and its biogeochemical processes. In thisstudy, BIOME4 was adopted to model vegetation dynamics.The model parameters were modified, and the model wascalibrated for the study region on the basis of the distributionand the eco-physiological features of the vegetation types inChina. The modified model was used to project the futureeffects of climate change on vegetation distribution accordingto regional climate scenarios.

2 Methods and data

2.1 The BIOME4 model

BIOME4 (Kaplan et al. 2003) is a coupled biogeography andbiogeochemistry model that simulates the distribution andstructure of global vegetation. The model functions on thebasis of 12 plant functional types (PFTs), which represent allmajor vegetation types on earth from the arctic tundra to thetropical rainforest. The computational core of BIOME4 is acoupled carbon and water flux scheme. This scheme deter-mines the leaf area index (LAI) that maximizes the netprimary productivity (NPP) for any given PFT on the basisof the daily time step simulation of soil water balance,canopy conductance, photosynthesis and respiration. BI-OME4 implicitly simulates competition between PFTs as a

function of the relative value of NPP. On the basis of theidentity of the most successful and second most successfulPFT and their sustainable LAI, each model grid cell isassigned a biome according to a set of semi-empiricalrules. The model is sensitive to changes in climate be-cause of the NPP responses and the stomatal conductanceto water and heat (Kaplan 2001). BIOME4 has a horizon-tal grid resolution of 50×50 km. The model inputs includeCO2, soil texture, monthly surface temperature, precipitationand cloudiness. We provide only a short overview on BI-OME4 because Kaplan et al. (2003) have already describedBIOME4 in detail.

BIOME4 can predict the distributions of 26 biomesaltogether. For simplicity of analysis and facilitative com-parisons, the biome classification used in BIOME4 wasreconstructed following the assignment scheme used byHarrison and Prentice (2003) to generate nine mega-biomes(tropical forest, warm-temperate forest, temperate forest,boreal forest, savanna and dry woodland, grassland and dryshrubland, desert, dry tundra, tundra) that would representthe vegetation types in China (Table 1). A set of widelyaccepted bioclimatic and eco-physiological parameters wasused 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 inChina. Thus, certain the bioclimate limiting factors wereupdated for key PFTs on the basis of the relationship betweenclimate and vegetation distribution in China and publishedliterature to capture the boundary of vegetation types inChina more accurately (CVEC, CAS 1980; Ni et al. 2001;CVAEC, CAS 2001; Zhang et al. 2007; Weng and Zhou2006). For example, the model tends to overestimate theelevation of alpine tree lines at lower latitudes. Thus, a min-imum heat requirement was set for boreal forest at 500 grow-ing degree days (5 °C base). Given that tropical forest in Chinamainly consist of tropical monsoon forests, distinguishingevergreen forest from deciduous forest in the actual vegetationmap is difficult. Therefore, tropical evergreen forest and trop-ical 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 ofa given biome in the study area. The biome mean centre isusually used to track changes in the distribution or to com-pare the distributions of different types of biomes. The biomemean centre is given as follows (Hart 1954; Yue et al. 2011.)

X tð Þ ¼

Xi¼1

n tð Þxi tð Þ

n tð Þ ; Y tð Þ ¼

Xi¼1

n tð Þyi tð Þ

n tð Þ ð1Þ

D. Zhao, S. Wu

where t is the period of climate change; xi and yi are thelongitude and latitude coordinates for the ith grid cell of agiven biome type, respectively; and n is equal to the totalnumber of a given biome type. ( X tð Þ; Y tð Þ� �

) is the meancentre of a given biome type.

The shift distance and direction of the biome type fromt to t+1 are respectively calculated in ArcGIS 10.0 soft-ware (ESRI, Redlands, California, USA).

2.3 Kappa statistic

Kappa statistic is widely used to assess the agreement be-tween two maps with categorical data. The kappa statistic κis formulated as follows.

κ ¼ po−peð Þ.

1−peð Þ: ð2Þ

κ is approximately zero when agreement is no better thanrandom and reaches unity when agreement is perfect. Byletting pij be the proportion of the total number of grid cellsthat belongs to category i in one map and belongs to the

category j in another, the sum of these proportions is theoverall proportion of observed agreement po, as follows.

po ¼Xi¼1

m

pii ð3Þ

where m is the number of vegetation types and pii is theproportions of grid cells on which both maps agree. Theoverall expected value of agreement pe due to chance aloneis calculated as.

pe ¼Xi¼1

m

pi:p:i ð4Þ

An individual kappa statistic can also be calculated foreach category i, as follows.

κi ¼ pii−pi:p:ið Þ.

pi:þ p:ið Þ.2−pi:p:i

h i: ð5Þ

As suggested by Monserud and Leemans (1992), a kappavalue <0.4 is considered poor or very poor. A kappa valueranging from 0.4 to 0.55 is considered fair, from 0.55 to 0.7good, 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 theclimate change research group of the Institute of Environ-ment and Sustainable Development in Agriculture of theChinese Academy of Agricultural Sciences. The researchgroup used the Providing Regional Climate for ImpactsStudies (PRECIS) system (Jones et al. 2004) to create high-resolution (50×50 km) climate data scenarios in China forthe late twenty-first century on the basis of greenhouse gasemission scenarios of the IPCC Special Report on EmissionScenarios (SRES) (Xu 2004). The general circulation modelUKMO_HadCM3 was used to obtain the boundary condi-tions for PRECIS simulations. UKMO_HadCM3 is consid-ered more effective in simulating the spatial and temporalcharacteristics of climate change in China compared with theother global circulation models that contributed to the fourthassessment report of the IPCC (Miao et al. 2012). Theeffectiveness of PRECIS to simulate terrestrial climatechange in China was validated by applying the reanalysisdata derived from the European Centre for Medium-RangeWeather Forecasts as lateral boundary conditions. The re-gional climate scenarios simulated by PRECES have beenwidely utilized to assess the effect of climate change on theecosystems in China (Xiong et al. 2009,Wu et al. 2010, Zhaoet al. 2013a). The projected climatic data that include the A2,B2 and A1B emission scenarios from 1961 to 2080 wereused in this study. Among all the IPCC proposed scenarios,A1B is considered a high-emission scenario that is charac-terized by rapid economic growth, global population that

Table 1 Allocation of original biome used in BIOME4 to mega-biomeclassification used in this study

Original biome classification Mega-biome classificationused in this study

Tropical evergreen rainforest Tropical forestTropical semi-deciduous forest

Tropical deciduous forest

Warm mixed forest Warm-temperate forest

Temperate deciduous forest Temperate forestTemperate conifer forest

Cool mixed forest

Cool conifer forest

Cold mixed forest

Evergreen taiga/montane forestDeciduous taiga/montane forest

Boreal forest

Tropical savanna Savanna and dry woodlandTemperate sclerophyll woodland

Temperate broadleaved savanna

Open conifer woodland

Tropical xerophytic shrubland Grassland and dry shrublandTemperate xerophytic shrubland

Tropical grassland

Temperate grassland

Desert DesertBarren

Graminoid–forb tundra Dry tundra

Shrub tundra TundraDwarf shrub tundra

Prostrate shrub tundra

Cushion forbs–lichen–moss tundra

Responses of vegetation in China

peaks in the mid-century and declines thereafter, and rapidintroduction of new and more efficient technologies. A2 isthe scenario with high emissions of greenhouse gases. In A2,self-reliance and local identities are emphasized, populationincreases continuously, and economic development is re-gionally oriented. B2 represents moderate emissions of thegreenhouse gases. B2 is characterized by a continuous butmoderate increase in population and a moderate economicgrowth that focuses on local solutions to economic, socialand 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 termwas evaluatedaccording to the average simulation of 30 years.

The projection from PRECIS indicates that the annualaverage temperature in China during the long-term willincrease from 3.80 °C (A1B) to 2.63 °C (B2) comparedwith the baseline term (Table 3), in which the highesttemperature increase of approximately 5 °C (A1B) isdetermined in northwest China. The lowest temperatureincrease may occur in southwest and southeast China,with an average increment of 2 °C (B2) (Fig. 1). Thetotal annual precipitation in the long-term scenario isprojected to increase from 17.63 % (A1B) to 9.3 % (B2)(Table 3). The highest temperature increase is projected tooccur in northwest China and is 60% greater than the baseline

term. A slight decrease of approximately 5 % may exist innortheast China (Fig. 2).

2.5 Vegetation data

The simulated output by BIOME4 was compared with thepotential distribution of natural vegetation derived from theVegetation 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 mapinto a raster dataset in the ArcInfo Grid format with spatialresolution of 50×50 km. A total of 573 actual vegetationtypes were reclassified into 26 potential biome types in theBIOME4 according to the scheme formulated by Ni et al.

Table 3 Average changes in temperature and precipitation under SRESA1B, A2 and B2 scenarios in China from PRECIS simulation relative tobaseline (1961–1990)

Periods Temperature (°C) Precipitation (%)

A1B A2 B2 A1B A2 B2

Near-term (1991–2020) 0.81 0.44 0.73 5.88 4.02 5.61

Mid-term (2021–2050) 2.28 1.63 1.66 12.36 8.61 6.01

Long-term (2051–2080) 3.80 3.15 2.63 17.65 16.13 9.30

Table 2 Bioclimate limiting factors for each plant functional type

PFTs Tc min (°C) Tc max(°C)

Tmin min(°C)

Tmin max(°C)

GDD5 min GDD0

minTwm min(°C)

Twm max(°C)

Tropical monsoon forest [Tropicalevergreen forest; Tropicalraingreen forest]

−3 [0] 12 [10]

Temperate broadleaved forest −8 5 1,500 [1,200] 12 [10]

Temperate deciduous forest −2 [−15] −8 1,200

Temperature evergreen coniferforest

−2 10 900

Boreal evergreen forest −25 [−32.5] −2 500 [no limit] 21

Boreal deciduous forest 5 −10 500 [no limit] 21

Temperature grass 0 550

Tropical grass −3 10

Desert woody plant −45 500 10

Tundra shrub 50 15

Cold herbaceous 50 12 [15]

Lichen/forb 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 bracketsgiven by Kaplan (2001) are replaced by the italics value in this study

Tc mean temperature of the coldest month, Tmin absolute minimal temperature, GDD5 the growing degree days of over 5 °C, GDD0 the growingdegree days of over 0 °C, Twm mean temperature of the warmest month

D. Zhao, S. Wu

(2001, 2011), who produced a potential vegetation for Chinaby using 1:4 million actual vegetation map on the basis of thefloristic and bioclimate criteria. The 26 biomes were furtherreconstructed to the same nine major biomes in the same wayas for the BIOME4 PFTs.

2.6 Soil data

In this study, the soil texture data from the map of soil texturetypes (1:14,000,000) (Zhang et al. 2004) were adopted. Thedata contain the geographical distribution of different soiltexture types and the proportions of mineral grains in the topsoil. The soil textures were reclassified as clay, silt, sand,silty sand, sandy clay, silty clay and clay with silt and sandaccording to the FAO classification standard for soil texture(Ni et al. 2001) to meet the requirements of BIOME4. The

soil data were then transformed into grid format andresampled to the spatial resolution of 50×50 km.

3 Results

3.1 Comparison of simulated result

The simulated vegetation distribution (Fig. 3a) was com-pared 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 distribu-tion and boundaries accurately. All kappa values for theindividual vegetation types are listed in Table 4. Thekappa values indicate good simulation results for warm-temperate forest, temperate forest and desert; fair results

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

Responses of vegetation in China

for tropical forest, boreal forest, grassland and dry shrubland;but poor results for savanna and dry woodland, dry tundraand tundra.

In the simulated vegetation distribution, dry woodlandwas mixed with other types instead of occupying clearlydefined areas. Dry woodland and temperate forest sharedthe same PFT during simulation. However, they are differ-entiated spatially in biome assignment according to whethertheir PFT is a primary PFT or not. Sharing key PFT amongbiomes is a key reason for mismatches because the modelcannot differentiate the biomes under similar bioclimaticconditions. Figure 1 shows that tundra and dry tundra typesare generally not simulated accurately and the modifiedBIOME4 cannot predict their boundaries accurately. Thisproblem may be a matter of PFT definition and biomeassignment. Certain issues on vegetation classification madethe assignment of alpine vegetation to tundra biomes diffi-cult or incorrect in certain cases, consequently affecting the

comparisons between the simulated result and the actualvegetation distribution.

3.2 Effect of climate change on simulated vegetationdistribution

The overall vegetation distribution was not changed signif-icantly by climate change in the near-term compared with thebaseline term. However, the grassland and dry shrublandbiome and the savanna and dry woodland biome are sensitiveto warming climate. These types of areas are expected toincrease significantly from the baseline term (Table 5).Grassland and dry shrubland will begin to invade the Greaterand the Lesser Higgnan Mountains (Figs. 4 and 5), replacingthe temperate forest and the boreal forest. Meanwhile, grass-land and dry shrubland will spread slightly to the interior ofthe Tarim Basin, particularly under the A1B scenario, inwhich the mean centre moved towards the west (Fig. 6). In

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

D. Zhao, S. Wu

north China, savanna and dry woodland will expand totemperate forests in relatively arid areas and in certain cal-careous 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 theTibetan Plateau, the dry tundra under A1B, A2 and B2scenarios will slightly decline compared with that in thebaseline term. No obvious changes were observed in thedistribution of forest vegetation.

Fig. 3 Vegetation distributionsimulated by the modifiedBIOME4 model (a) and derivedfrom the Vegetation Atlas ofChina (b)

Responses of vegetation in China

For the mid-term, the boreal forest in northeast Chinawill experience a substantial northward shift in its distri-bution and will be partly replaced by expanding grass-lands (Fig. 5). Thus, the mean centre of the boreal forestwill experience maximum shift distance longer than470 km towards the southwest under the A1B scenario(Table 7). The savanna and dry woodland biome and thegrassland and dry shrubland biome will follow the trendof that in the near-term. In north China, the savanna anddry woodland biome is expected to expand, changingfrom the existing mixed distribution with temperate for-est to a more continues coverage, particularly in A1Band A2 scenarios. The temperate forest will move itsmean centre towards northeast (Table 7), and the distri-bution area of the temperate forest will continue todecrease. The mean centre of the warm-temperate forestis 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) willalso be replaced by warm-temperate forests composed ofcontinues coverage together with the eastern warm-temperate forest. The tropical forest will spread to coveran area that is twice as large as than that in the baseline(Table 5). However, this area will occupy approximatelyless than 3 % of the total land area of China. On theTibetan Plateau, the temperate forest and the boreal for-est will begin to invade the interior of the plateau andwill mostly replace the original tundra and dry tundra,resulting in a decline in the area of these two vegetationtypes.

For the long-term under the warmest climate, the borealforest in the northern part of China is projected to shrink to ascattering of small fragments in the Great Higgnan Moun-tains. On the Tibetan Plateau, the distribution of the borealforest will continue to expand into the interior of the plateau(Fig. 5). The mean centre of the boreal forest will experiencea prominent 400 km shift towards the southwest (Table 8).The area of grassland and dry shrubland is projected toincrease by 60 % relative to the baseline term under theA1B scenario, which is greater than those in the A2 and B2scenarios. The mean centre of the grassland and dry shrub-land will move southwest, occupying most of north China.The area of savanna and dry woodland will be approximatelysix times greater under A1B and A2 scenarios during thebaseline period, replacing more temperate forests in northChina. The warm-temperate forest biome will continue toshift northeast and will be the largest biome in China occu-pying >20 % of the area. The tropical forest is projected toincrease in area by three times from the baseline term, mainlyfocusing on south China. The mean centre of the tropicalforest will shift northeast. From the baseline term, the meancentre of the tropical forest will move over 200 km for A1B,A2 and B2 scenarios.

Table 4 Discrimination accuracy of simulated vegetation types inChina by the modified BIOME4

Vegetation types Accuracy (%) Kappa value

Tropical forest 42.2 0.41

Warm-temperate forest 76.4 0.76

Temperate forest 60.4 0.60

Boreal forest 62.8 0.49

Savanna/dry woodland 16.7 0.15

Grassland/dry shrubland 48.3 0.47

Desert 83.0 0.82

Dry tundra 28.8 0.24

Tundra 37.9 0.33

Overall 61.8 0.55

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 0.88 1.33 1.23 1.55 2.13 1.88 1.78 4.68 3.38 2.15

Warm-temperate forest 19.33 20.75 20.35 20.23 23.63 21.90 22.15 22.93 22.98 23.48

Temperate forest 24.23 23.30 20.70 22.23 18.45 19.68 20.73 18.55 17.80 19.18

Boreal forest 6.05 5.38 5.23 5.05 5.70 5.45 5.23 5.33 5.95 5.65

Savanna/dry woodland 0.55 1.63 2.70 1.53 3.35 2.78 1.48 3.83 3.63 2.23

Grassland/dry shrubland 12.50 14.80 14.50 14.70 16.50 15.53 16.28 19.98 19.20 18.70

Desert 15.73 13.33 15.40 15.40 13.40 14.83 14.33 10.60 12.45 12.93

Dry tundra 8.33 7.25 7.53 7.00 6.45 6.55 6.48 5.40 5.45 5.38

Tundra 12.43 12.25 12.38 12.33 10.40 11.43 11.58 8.73 9.18 10.33

D. Zhao, S. Wu

4 Discussions

The modified BIOME4 was used to simulate the vegetationdistribution in China under climate change characterized byrising temperature and increasing precipitation. The resultsindicate that climate change can result in shifts in vegetationdistribution. However, the effects of such changes varied indifferent vegetation types. The results are consistent withprevious studies except for certain discrepancies.

The simulation suggests that forest vegetation is likely toshift northwards because of climate change, which is consis-tent with previous studies (Ni et al. 2001; Zhao et al. 2002;Weng and Zhou 2006). Forest biomes show considerableresilience to climate change. The areas occupied by all forestvegetation types except tropical forest varied by less than30 % among all scenarios. Given the small percentage(0.88 %) of area occupied by tropical forests, the tropicalforest biome may have a large proportional increase. Thisfinding also explains why the warm-temperate forest doesnot shift its western boundary far, but mainly moves itsboundary northwards in all climate scenarios. This phenom-enon can be fundamentally associated with the steep climategradient in the western boundary of warm-temperate forestsbecause the mountains in this area increase in elevationabruptly. Topographical factors play an important role in theresponse of forest shift to climate change because the vegeta-tion's moving velocity caused by temperature change is sev-eral orders of magnitude lower in mountainous regions com-pared with flat regions (Loarie et al. 2009). The temperate

forest in the simulation decreased by 20 to 25 % in thelong-term from the baseline, which is consistent with theresults obtained by Wang et al. (2011), who suggested thattemperate forests may decrease with a significant north-ward shift because of warming. A projection from Wu(2003) also showed that the temperate forest in northeastChina will significantly shift its boundary northwards,with an areal coverage decrease of 20 to 35 %, which issimilar to the projections in this study. The boreal forest,which is the most sensitive to climate warming because of lowtemperature environment and low productivity, may shrink toa scattering of small fragments on the Greater HiggnanMoun-tains, but may expand into the interior of the Tibetan Plateau.Previous studies (Ni et al. 2001, Wang et al. 2011) haveobtained similar results on the boreal forest. Although tem-perature affects the distribution of the boreal vegetation,moisture availability is likely the more important factor. Inthe climate change scenarios, much of boreal forest region innortheast China is projected slight increase in precipitation,but increase in precipitation will likely be offset with increasesin evapotranspiration. In addition, warming on permafrostcondition can modify soil water availability and can furtherresult in increase in drought stress during summer (Wilmkinget al. 2004). These changes in climate can induce boreal forestnorthward shift beyond China.

The Tibetan Plateau is regarded by many researchers as aregion vulnerable to climate change because of its uniquelocation, special climate and high altitude. In this study, asimilar observation was suggested by the results. As a result

Fig. 4 Elevation map of Chinawith major geographical regionmentioned in this text

Responses of vegetation in China

of warming, tundra and dry tundra may be converted intoforest that invades into the interior of the plateau. On thebasis of a simulation by BIOME3, Ni et al. (2001) also foundthat tundra would be largely reduced on the plateau andwould be replaced by forests. In addition, the desert inQaidam Basin may be replaced gradually by grassland ordry shrubland, which require increased precipitation. Theconversion risk of vegetation types in China may furtherincrease in the future because climate warming is faster onthe 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 northChina may be largely reduced, whereas the woodland mayexpand significantly. The temperate forest only remained asa narrow belt from the northeast to the southwest of northChina. Zhao et al. (2002) indicated that climate changes inthe future would transform the vegetation in north China into

dry woodland and grassland, which is consistent with theobservation in this study. This transformation can beassociated with the rising temperature. An increase intemperature may reduce water effectiveness through en-hanced evapotranspiration, which limits temperate forestsurvival. By contrast, dry woodland and grassland canadapt to such changes. Piao et al. (2009) found that anincrease in temperature alone does not benefit vegetationgrowth in temperate ecosystems because of water control.Studies on global terrestrial NPP suggest that water avail-ability is the strongest limiting factor for vegetationgrowth in the mid-latitude Eurasian vegetation (Nemaniet al. 2003).

Three climate scenarios (A2, B2 and A1B) were adoptedto predict the vegetation distribution in China. The simula-tion results from BIOME4 under possible future conditions,which are represented by three scenarios, exhibit general

Fig. 5 Simulated biomes in near-term (1991–2020) (top row), mid-term (2021–2050) (middle row) in long-term (2051–2080) (bottom row) andunder A1B (left column), A2 (middle column) and B2 (right column) scenarios

D. Zhao, S. Wu

consistency. If the ecosystemmodel can be considered realisticto a certain extent and assuming that the three scenarios canrepresent a proportion of the uncertainty, then the overallpattern of change in the vegetation in China in the comingcentury may quantitatively resemble the simulated results inthis study. In addition, the vegetation distribution in threewarming periods, namely near-term, mid-term and long-term,

was examined to improve our understanding of the effects ofthe different warming levels.

The response of ecosystems in China to climate change ischaracterized by substantial regional differences. The north-ern part of the north China Plain is a sensitive area. The warmforest in this area is projected to degrade into savannaand dry woodland because of warming and drying. The

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)

Responses of vegetation in China

biodiversity in this area would face higher risks of damagebecause of the high-level floods and increased human activ-ities. The tundra vegetation may shrink into the interior of theTibetan Plateau and may be partly replaced by forests be-cause of the increase in temperature and precipitation. Thisphenomenon may significantly affect the livelihood of localherdsmen. Given that the tundra on the Tibetan Plateau ismainly dominated by alpine meadow (Ni and Herzschun2011), the plateau is not only an ecosystem but also a keyresource for the local people. According to the future climatetrend from TCNARCC (2011), warming may be strongest innorthwest China and precipitation will likely increase signif-icantly. Consequently, the desert may retreat and may bereplaced gradually by other vegetation types that requireincreased precipitation, resulting in the transformation ofmost areas in northwest China into oases.

Carbon sequestration in terrestrial ecosystems is closelyassociated with vegetation distribution (Piao et al. 2009).Climate change influences carbon budget not only directlyby affecting the flux but also indirectly by affecting vegeta-tion distribution. Table 4 shows that climate change mayincrease the tropical forest distribution, which is beneficialto the carbon storage in terrestrial ecosystems because

tropical forests are considered the greatest carbon sinks fortheir strong carbon sequestration capacity (Cox et al. 2004).The boreal forest is projected to decrease in the future andwill be replaced by grassland and dry shrubland. This changecan result in an adverse effect to carbon sequestration be-cause boreal forests can sequestrate more carbon than grass-lands and dry shrublands could (Lindroth et al. 1998). Thisvegetation change is beneficial to the carbon storage in theTibetan Plateau because of the increase in forest distributionand the decrease in desert distribution. However, climatewarming can accelerate permafrost thawing and can enhancesoil microbial activities, thus inducing more carbon emissionfrom the soil. This temperature change can partly offset thepositive effects of vegetation change to the carbon seques-tration in the Tibetan Plateau (Zhao et al. 2013b).

In this study, three scenarios from PRECIS were used toinvestigate the potential response of Chinese vegetation toclimate 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 indescribing the ecological mechanisms, and time lags associ-ated with succession and migration are not modelled explic-itly, which bias the result. Such reasons can result in certain

Table 6 Shift distance (inkilometre) and direction of meancentre for each vegetation typefrom baseline (1961–1990) tonear-term (1991–2020) underthree climate scenarios

Vegetation type A1B A2 B2

Distance Direction Distance Direction Distance Direction

Tropical forest 63 Northwest 133 Northeast 148 Northeast

Warm-temperate forest 42 Northeast 35 Northeast 48 Northeast

Temperate forest 62 Northwest 83 Northwest 64 Northwest

Boreal forest 365 Southwest 102 Southwest 395 Southwest

Savanna/dry woodland 92 Northeast 81 Southwest 191 Southwest

Grassland/dry shrubland 358 West 60 Northwest 66 West

Desert 37 Northeast 11 Northeast 8 Northwest

Dry tundra 43 Northeast 29 West 55 West

Tundra 48 Northwest 28 Northwest 14 East

Table 7 Shift distance (inkilometre) and direction of meancentre for each vegetation typefrom near-term (1991–2020) tomid-term (2021–2050) underthree climate scenarios

Vegetation type A1B A2 B2

Distance Direction Distance Direction Distance Direction

Tropical forest 174 Northeast 37 Northeast 30 Northwest

Warm-temperate forest 102 Northeast 53 Northeast 40 Northeast

Temperate forest 83 Northwest 55 Northwest 46 Northwest

Boreal forest 386 Southwest 471 Southwest 255 Southwest

Savanna/dry woodland 121 Southwest 31 Southwest 39 Southeast

Grassland/dry shrubland 134 Southwest 113 Northwest 134 West

Desert 42 Northeast 8 Northeast 31 West

Dry tundra 64 East 21 West 33 West

Tundra 48 Northeast 35 Northeast 38 West

D. Zhao, S. Wu

uncertainties in the simulation results. The results simulatedby BIOME4 are also free from the disturbance of humanactivities. The great expansion in human activity can havemajor influences on vegetation dynamics. Therefore, thisstudy could be further improved by integrating human dis-turbances in the simulation.

5 Conclusions

In this study, a coupled biogeography and biogeochemistrymodel (BIOME4) was modified by redefining the bioclimat-ic limits of key PFTs to simulate the responses of biomedistribution to future climate change in China. A comparisonbetween the simulated biome and the vegetation map showsthat the modified model can produce realistic predictions ofbiome distribution patterns in China. Three climate scenarios(A2, B2 and A1B) were used to drive the modified BIOME4model to show the uncertainties in the simulations. Threeperiods, 1991–2020 (near-term), 2021–2050 (mid-term) and2051–2080 (long-term), were used for comparison with thebaseline term 1961–1990 to quantify the difference of thebiome responses to climate change under different scenarios.

In this study, the simulation suggests that regional climatechange would result in dramatic changes in vegetation dis-tribution in China. The areas with increased vegetation dis-tribution under the A2, B2 and A1B scenarios include thetropical forest biome, warm-temperate forest biome, savan-nah and dry woodland biome, and grassland and dry shrub-land biome. The areas with decreased vegetation includetemperate forest, boreal forest, desert, dry tundra and tundra.In east China, a prominent movement northwards was ob-served for each vegetation distribution in response to futureclimate change. Under the three scenarios, boreal forestshifted the greatest distance northwards, followed by tropicalforest and temperature forest. Savanna and dry woodlandbiome, and grassland and dry shrubland biome expanded thedistribution. On the Tibetan Plateau, the area of forests

increased by spreading into the interior of the plateau, where-as tundra and dry tundra retreated towards the northwest. Thedesert in northwest China may be reduced and replaced bygrassland and dry shrubland because of increased precipita-tion. The responses of vegetation distribution to climatechange at a regional scale will contribute to our understand-ing of the vulnerability of regional ecosystems to climatechange.

Acknowledgments This study was supported by the StrategicPriority 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 Environmentand Sustainable Development in Agriculture, Chinese Academy ofAgriculture Sciences, for providing climate scenario data. We appreciatetwo anonymous reviewers for their insightful comments on an earlierversion of this manuscript.

References

Cox PM, Betts RA, Collis M, Harris PP, Huntingford C, Jones CD(2004) Amazonian forest dieback under climate-carbon cycle pro-jections for the 21st century. Theor Appl Climatol 78:137–156

CVEC, CAS (The China Vegetation Editorial Committee, ChineseAcademy of Sciences) (1980) Vegetation of China. Science,Beijing

CVAEC, CAS (The China Vegetation Atlas Editorial Committee, Chi-nese Academy of Sciences) (2001) Atlas of China vegetation(1:1,000,000). Science, Beijing

Davis MB, Botkin DB (1985) Sensitivity of cool-temperate forests andtheir fossil pollen record to rapid temperate change. J Quat Sci23:327–340

Harrison SP, Prentice IC (2003) Climate and CO2 controls on globalvegetation distribution at the last maximum: analysis based onpalaeovegetation data, biome modelling and palaeoclimate simu-lation. Glob Change Biol 9:983–1004

Hart JF (1954) Central tendency in areal distribution. Econ Geogr30:48–59

Haxeltine A, Prentice IC (1996) BIOME3: an equilibrium biospheremodel based on ecophysiological constraints, resource availability

Table 8 Shift distance (inkilometre) and direction of meancentre for each vegetation typefrom mid-term (2021–2050) tolong-term (2051–2080) underthree climate scenarios

Vegetation type A1B A2 B2

Distance Direction Distance Direction Distance Direction

Tropical forest 84 Northwest 44 Northeast 44 Northeast

Warm-temperate forest 89 Northeast 85 Northeast 42 Northeast

Temperate forest 79 Northeast 64 Northeast 43 Northwest

Boreal forest 621 Southwest 444 Southwest 475 Southwest

Savanna/dry woodland 46 Southwest 54 Northeast 44 Northeast

Grassland/dry shrubland 371 Southwest 403 Southwest 213 West

Desert 109 East 48 East 20 Northeast

Dry tundra 26 West 31 Northwest 15 Northeast

Tundra 50 East 30 Northwest 37 Northwest

Responses of vegetation in China

and competition among plant functional types. Global BiogeochemCycles 10:693–709

Hitz S, Smith J (2004) Estimating global impacts from climate change.Global Environ Change 14:201–218

Huntley B (1990) European vegetation history: palaeovegetation mapsfrom pollen data—13000 yr BP to present. J Quat Sci 5:103–122

IPCC (2007) Climate change 2007: the physical science basis. In:Solomon S, Qin D, Manning M, Chen Z, Marquis M, AverytKB, Tignor M, Miller HL (eds) Contribution of Working Group Ito the Fourth Assessment Report of the Intergovernmental Panelon Climate Change. Cambridge University Press, Cambridge

Jiang DB, Zhang Y, Lang XM (2011) Vegetation feedback under futureglobal warming. Theor Appl Climatol 106:211–227

Jones R, Noguer M, Hassell D, Hudson D, Wilson S, Jenkins G,Mitchell J (2004) Generating high resolution climate change sce-narios using PRECIS. Met Office Hadley Centre, Exeter

Kaplan JO (2001) Geophysical applications of vegetation modeling.Ph.D. dissertation, Lund University, Lund, Sweden

Kaplan JO, Bigelow NH, Prentice IC, Harrison SP, Bartlein PJ,Christensen TR, Cramer W, Matveyeva NV, McGuire AD, MurrayDF, Razzhivin VY, Smith B, Walker DA, Anderson PM, AndreevAA, Brubaker LB, Edwards ME, Lozhkin AV (2003) Climatechange and arctic ecosystems II: modeling, paleodata-model com-parisons, and future projections. J Geophys Res 108:1–12

Lindroth A, Grelle A, Morén AS (1998) Long-term measurements ofboreal forest carbon balance reveal large temperature sensitivity.Global Change Biol 4:443–450

Loarie SR, Duffy PB, Hamilton H, Asner GP, Field CB, Ackerly DD(2009) The velocity of climate change. Nature 462:1052–1055

Miao C, Duan Q, Yang L, Borthwick AGL (2012) On the applicabilityof temperature and precipitation data from CMIP3 for China.PLoS One 7(9):e44659. doi:10.1371/journal.pone.0044659

Monserud RA, Leemans R (1992) Comparing global vegetation mapswith the Kappa statistic. Ecol Model 62:275–293

Nemani RR, Keeling CD, Hashimoto H, Jolly WM, Piper SC, TuckerCJ, Myneni RB, Running SW (2003) Climate-driven increase inglobal terrestrial net primary production from 1982 to 1999. Sci-ence 300:1560–1563

Ni J (2011) Impacts of climate change on Chinese ecosystems: keyvulnerable regions and potential thresholds. Reg Environ Change11(Suppl 1):S49–S64

Ni J, Herzschun U (2011) Simulating biome distribution on the TibetanPlateau using a modified global vegetation model. Arct AntarctAlp Res 43(3):429–441

Ni J, Sykes MT, Prentice IC, Cramer W (2001) Modeling the vegetationof China using the process-based equilibrium terrestrial biospheremodel BIOME3. Global Ecol Biogeogr 9:463–479

Prentice IC (1986) Vegetation response to past climatic variation.Vegetatio 67:131–141

Piao SL, Ciais P, Friedlingstein P, de Noblet-Duconudre N, Cadule P,Viovy N, Wang T (2009) Spatiotemporal patterns of terrestrialcarbon cycle during the 20th century. Global Biogeochem Cy 23,GB4026. doi:10.1029/2008GB003339

Prentice IC, Bartlein PJ, Webb T (1991) Vegetation and climate changein eastern North America since the last glacial maximum. Ecology72:2038–2056

Prentice IC, Cramer W, Harrison SP, Leemans R, Monserud RA, Solo-mon AM (1992) A global biome model based on plant physiologyand dominance, soil properties and climate. J Biogeogr 19:117–134

Shi YF, Li JJ, Li BY (1998) Uplift and environmental changes of theTibetan Plateau in the late Cenozoic. Guangdong Science andTechnology Press, Guangzhou (In Chinese)

TCNARCC (Taskforce on China's National Assessment Report onClimate Change) (2011) China's national assessment report onclimate change. Science, Beijing

Wang H, Ni J, Prentice IC (2011) Sensitivity of potential naturalvegetation in China to projected changes in temperature, pre-cipitation and atmospheric CO2. Reg Environ Change 11:715–727

Weng ES, Zhou GS (2006) Modeling distribution changes of vegetationin China under future climate change. Environ Model and Assess11:45–48

Wilmking M, Juday GP, Barber VA, Zald HSJ (2004) Recent climatewarming forces contrasting growth responses of white spruce attreeline in Alaska through temperature thresholds. Global ChangeBiol 10:1724–1736

Wilson K, Newton A, Echeverr C, Weston C, Burgman M (2005) Avulnerability analysis of the temperate forests of south centralChile. Biol Conserv 122:9–21

Wu SH, Yin YH, Zhao DS, Huang M, Shao XM, Dai EF (2010) Impactof future climate change on terrestrial ecosystems in China. Int JClimatol 30:866–873

Wu ZF (2003) Assessment of eco-climatic suitability and climatechange impacts of broad-leaved Korean pine forest in northeastChina. Chinese J Appl Ecol 14:771–775 (in Chinese)

Xiong W, Conway D, Lin ED, Holman I (2009) Potential impacts ofclimate change and climate variability on China's rice yield andproduction. Clim Res 40:23–35

Xu YL (2004) High resolution climatic scenarios in China based onRCM from the Hadley Center. Clim Change Newslett 3:6–7 (inChinese)

Yue TX, Fan ZM, Chen CF, Sun XF, Li BL (2011) Surface modeling ofglobal terrestrial ecosystems under three climate change scenario.Ecol Model 222:2342–2361

Zhang SH, Peng GB, Huang M (2004) The feature extraction and datafusion of regional soil textures based on GIS techniques. ClimEnviron Res 6:65–79 (in Chinese)

Zhang XS, Yang DA (1993) A study on climate–vegetation interactionin China: the ecological model for global change. Coenoses8:105–119

Zhang XS, Vegetation Map of China Editorial Committee, ChineseAcademy of Science (2007) Vegetation map of China and itsgeographic pattern: illustration of the vegetation map of the Peo-ple's Republic China (1:10,000,000). Geographical PublishingHouse, Beijing (in Chinese)

Zhao DS, Wu SH, Yin YH, Yin Z-Y (2011) Vegetation distribution onTibetan Plateau under climate change scenario. Reg EnvironChange 11:905–915

Zhao DS, Wu SH, Yin YH (2013a) Responses of terrestrial ecosystems'net primary productivity to future regional climate change inChina. PLoS One 8(4):e60849. doi:10.1371/journal.pone.0060849

Zhao DS, Wu SH, Yin YH (2013b) Dynamic responses of soil organiccarbon to climate change in the Three-River Headwater region ofthe Tibetan Plateau. Clim Res 56:21–23

Zhao MS, Neilson PR, Yan XD, Dong WJ (2002) Modeling thevegetation of China under changing climate. J Geogr Sci 57:28–38(in Chinese)

D. Zhao, S. Wu