Global Change Biology Global Change Biology (2015), doi: 10.1111/gcb.12846

Carbon stock and its responses to climate change in Central Asia CHAOFAN LI1,2,3, CHI ZHANG1,3, GEPING LUO1,3, XI CHEN1,3, BAGILA MAISUPOVA4, A B D U L L O A . M A D A M I N O V 5 , Q I F E I H A N 6 and B E K M A M A T M . D J E N B A E V 7 1 State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi, Xinjiang 830011, China, 2University of Chinese Academy of Sciences, Beijing 100049, China, 3CAS Research Center for Ecology and Environment of Central Asia, Urumqi, Xinjiang 830011, China, 4Almaty Branch of Kazakh Scientific Research Institute of Forestry, Ministries of Agriculture, Almaty 480050, Republic of Kazakhstan, 5Department for Ecology & Plants Resources, Institute of Botany, Plant Physiology and Genetic, Academy of Science Republic of Tajikistan, Dushanbe 734017, Republic of Tajikistan, 6School of Remote Sensing, Nanjing University of Information Science & Technology, Nanjing 210044, China, 7Biology and Soil Institute of National Academy of Sciences of the Kyrgyz Republic, Bishkek 720071, Kyrgyz Republic

Abstract Central Asia has a land area of 5.6 9 106 km2 and contains 80–90% of the world’s temperate deserts. Yet it is one of the least characterized areas in the estimation of the global carbon (C) stock/balance. This study assessed the sizes and spatiotemporal patterns of C pools in Central Asia using both inventory (based on 353 biomass and 284 soil samples) and process-based modeling approaches. The results showed that the C stock in Central Asia was 31.34– 34.16 Pg in the top 1-m soil with another 10.42–11.43 Pg stored in deep soil (1–3 m) of the temperate deserts. They amounted to 18–24% of the global C stock in deserts and dry shrublands. The C stock was comparable to that of the neighboring regions in Eurasia or major drylands around the world (e.g. Australia). However, 90% of Central Asia C pool was stored in soil, and the fraction was much higher than in other regions. Compared to hot deserts of the world, the temperate deserts in Central Asia had relatively high soil organic carbon density. The C stock in Central Asia is under threat from dramatic climate change. During a decadal drought between 1998 and 2008, which was possibly related to protracted La Ni~ na episodes, the dryland lost approximately 0.46 Pg C from 1979 to 2011. The largest C losses were found in northern Kazakhstan, where annual precipitation declined at a rate of 90 mm decade 1. The regional C dynamics were mainly determined by changes in the vegetation C pool, and the SOC pool was stable due to the balance between reduced plant-derived C influx and inhibited respiration. Keywords: arid ecosystem model, carbon stock, Central Asia, climate change, dryland, temperate desert Received 24 September 2014 and accepted 13 November 2014

Introduction Dryland, including arid and semiarid regions in over 40% of the terrestrial area (Lal, 2001), plays an indispensable role in the global carbon (C) cycle (Reynolds et al., 2007). Dryland ecosystems account for approximately 1/3 of the vegetation C (VEGC) (Allen-Diaz et al., 1996) and 27% of the soil organic C (SOC) (Zafar et al., 2005) in the world and are sensitive to climate change (Smith et al., 2000). Recent studies also suggested that dryland ecosystems have strong C sequestration potential (Rotenberg & Yakir, 2010; Evans et al., 2014). In particular, cold/temperate deserts can store large amounts of SOC (9.7–9.9 kg C m 2), comparable to temperate moist forests (9.3–12 kg C m 2) (Zinke, 1984). Despite its importance and sensitivity to climate change, large uncertainties exist regarding dryland C Correspondence: Chi Zhang, tel. +86 991 7823127, fax +86 991 7823127, e-mail: [email protected] CL and XC contributed equally to this paper.

© 2015 John Wiley & Sons Ltd

storage and its responses to climate change (Trumper et al., 2008). Reports about the C stock in cold/temperate deserts were particularly rare because most studies have focused on hot deserts located in tropical/subtropical regions (Wu, 2001). Using the selection criteria ‘desert/dryland’ and ‘carbon/biomass’ for the title, abstract, and keywords, we searched scientific publications during 1993–2013 in the scholarly databases Web of Science (http://apps.webofknowledge.com) and Google Scholar (http://scholar.google.com) and found that less than 16% of the studies focused on cold/temperate deserts/drylands. Unlike in tropical/subtropical drylands, ecosystems in temperate drylands are constrained by temperature as well as precipitation (Nemani et al., 2003). Considering the relatively high warming rate in global cool temperate regions in recent decades (Trenberth et al., 2007), it is important to assess the spatiotemporal pattern of the temperate dryland C pools in response to climate change (Wu & Overton, 2002). 1

2 C . L I et al. The majority (80–90%) of the world’s cold/temperate deserts locate in Central Asia (Encyclopedia Britannica, 1997; see Fig. S1). Lying in the middle of the Eurasian continent, Central Asia includes Kazakhstan, Kyrgyzstan, Tajikistan, Turkmenistan, Uzbekistan, and Xinjiang Province of China (Cowan, 2007; Hu et al., 2014) (Fig. 1). It is a unique region characterized by strong geographical heterogeneity, complex meteorological systems, dynamic land-use history, and vast waterstressed plant communities that are vulnerable to climate change (Pauw, 2007; Lioubimtseva & Henebry, 2009; Xi et al., 2012). Strong gradients in temperature and precipitation (from < 50 mm a 1 in the deserts to > 900 mm a 1 in the windward slopes of the Tianshan Mountains and Fergana Valley) arise because of the orography that has elevation difference up to 7000 m (Bohner, 2006). In recent decades, Central Asia has experienced a complex set of dramatic climate and atmospheric changes, such as rising temperature, increasing precipitation fluctuations, and elevating CO2 concentration in the atmosphere (Lioubimtseva et al., 2005; Cruz et al., 2007). Since 1979, the averaged air temperature in the region has increased at a mean decadal rate of 0.39 °C, about twice the warming rate in Europe and larger than the average rate (0.29 °C) for global land areas (Hu et al., 2014). The overall regional precipitation has shown a slight decrease in the past 50–60 years (Lioubimtseva & Henebry, 2009). The change was highly spatially variable, with significant increase (by 7–16%) in XJ from 1981 to 2007 (Sorg et al., 2012; Li et al., 2013). These spatiotemporal variations in temperature and precipitation should have strong

impacts on the dryland ecosystems of Central Asia (Wu & Overton, 2002). Despite its large area (5.6 9 106 km2) and potential importance to the global C cycle, the IPCC (2007) found Central Asia to be one of the most uncertain areas in the estimation of the global C stock/balance. The lack of observational data (partially due to insufficient translation of Russian literatures) made it difficult to gain a comprehensive view of the C balance in Central Asia (Yohe et al., 2006; Lioubimtseva & Henebry, 2009). Except for a few studies that focused on certain species, for example, Thevs et al. (2013), regional assessments on the VEGC stock were still unavailable in Central Asia. Sommer & De Pauw (2011) assessed the SOC in the top 30 cm of soil in the five Central Asia states. They ignored the deep-soil organic C storage which could be remarkably high in drylands (Rumpel & K€ ogel-Knabner, 2011; Gong et al., 2012). Furthermore, because information regarding SOC was unavailable for many soil groups (e.g. Eutric Histosol) in Central Asia, Sommer & De Pauw (2011) had to rely on data from other regions (many of which were not temperate dryland) in their estimation. Similarly, because of a lack of in situ observations, Lal (2004) had to use data from the hot desert ecosystems in Morocco and Syria in his estimate of the C sequestration potential in Central Asia. Large uncertainties exist in the results of these studies. This study estimated the VEGC and SOC pools of Central Asia’s drylands using both data inventory and process-based modeling approaches. Furthermore, driven by daily climate datasets from 1979 to 2011, the ecosystem C dynamics was simulated and their

Fig. 1 The study area and the distribution of major plant functional types (PFTs) in Central Asia. © 2015 John Wiley & Sons Ltd, Global Change Biology, doi: 10.1111/gcb.12846

CENTRAL ASIA’S C STOCK UNDER CHANGING CLIMATE 3 spatiotemporal patterns were analyzed. To assess the C stock, the inventory approach linked the C densities of different vegetation types to the vegetation map of Central Asia. VEGC densities were estimated from 353 field observations based on literature review and field surveys (Table 1). SOC densities were estimated based on measurements of 284 soil profiles. It was reported that the desert shrubs in Central Asia had a mean root depth of 3 m (Xu, 2008), and a large amount of its SOC was located in deep soil (Rumpel & K€ ogel-Knabner, 2011; Gong et al., 2012). Therefore, we estimated the SOC stocks of temperate deserts in both the 0–1 m and 0–3 m depths. Estimations based on the inventory method were compared to the model simulation results. The arid ecosystem model (AEM) used in this study is a processed-based ecosystem model which has been developed for and validated against the dryland ecosystems in Central Asia (Zhang et al., 2013). The AEM can model the deep root distribution of desert plants and the related C and water processes in deep soil. The inventory approach can make full use of the observational data but cannot reflect the spatiotemporal patterns of C pools in response to environmental controls; the modeling approach can predict environmental effects but has lower thematic resolution due to the limitation in computational efficiency and difficulties in model parameterization (Wu & Li, 2006). Using the two complementary approaches, this study aimed to assess the size and distribution of the ecosystem C stock in Central Asia’s drylands and characterize the spatiotemporal responses of the C pools to changing climate at the regional scale.

Materials and methods

Study region Our study area (34.3°–55.4°N, 46.5°–96.4°E) consists of Xinjiang Province (XJ), China, and five Central Asian states (hereafter, CAS): Kazakhstan, Kyrgyzstan, Tajikistan, Turkmenistan, and Uzbekistan (Fig. 1). We will refer this region as ‘Central Asia’. There have been discrepancies and discussions about the usage of the term ‘Central Asia’. In some publications, ‘Central Asia’ refers to western China and Mongolia (Le Houerou, 2005); in others, it refers to the five CAS in the former Soviet Union [e.g. Mayhew et al. (2004)]. Cowan (2007) reviewed the usages and suggested that in the English language publications, ‘Central Asia’ includes the five CAS as well as areas in northwestern China and Mongolia. Following Goudie (2002) and Hu et al. (2014), we only included the five CAS and Xinjiang Province (XJ) of China in this study because the two contiguous regions share the same climate and hydrologic and ecological systems (Chen & Luo, 2013).

Because this study area is located deep inside the Eurasian continent and because the basins in the area are in the rain shadows of high mountain ranges, its climate is arid and semiarid (Lioubimtseva & Cole, 2006). Taking up about a quarter of the total study area, the low-lying desert and oasis areas are typically at elevations from 200 to 400 m above sea level. Grasslands are at elevations from 300 to 500 m, and the mountains in the eastern part of the study area are higher than 1000 m. Above the foothills, the mountains are covered by forests, alpine meadows, and shrubs before rising into glaciers at higher elevations. Excluding the mobile sand desert and glacier-covered areas, Central Asia’s drylands cover a land area of 4.7 9 106 km2.

The inventory approach Following Lioubimtseva et al. (1998), the C stock of Central Asia’s drylands was estimated based on the area and mean C density of the main ecosystem types. According to Zhang et al. (2007) and Rachkovskaya (1995), the dryland ecosystems in Central Asia were classified into 5 major types, that is, temperate desert, grassland, evergreen needleleaf forest and deciduous broadleaf forest, cropland, and built-up lands (Table 1). Considering the wide distribution and high diversity of the temperate deserts in this region, the desert type was further divided into five subclasses, that is, temperate shrubby desert, temperate semishrubby and dwarf semishrubby desert, temperate succulent holophytic dwarf semishrubby desert, alpine cushion dwarf semishrubby desert, and temperate dwarf semi-arborous desert (Zhang et al., 2007). In Central Asia, the temperate dwarf semi-arborous desert communities are dominated by Haloxylon, including Haloxylon ammadendron communities, Haloxylon persicum communities, and mixed Haloxylon communities (Table 1). Similarly, the grassland was divided into three subclasses, including desert steppe, typical steppe, and mountain steppe, according to Zhang (2004) and Zhang et al. (2007). In total, we have identified 13 vegetation types, including both major vegetation types and their subclasses. The mean VEGC density and SOC density of each vegetation type were estimated from 637 observations (353 VEGC samples and 284 SOC samples) (Table 1). These observational data were either derived from a compilation of 22 literatures or collected in field surveys (see Tables S1 and S2 for a list of the data and the data sources). This study also considered the built-up land (Table 1), where VEGC and SOC were calculated by assuming the built-up areas to be covered by 25% grass, 25% forest, and 50% impervious surface (Zhang, 2004; Sun, 2007). The impervious surface was assumed to have zero C stock (Bell et al., 2011). Finally, the VEGC and SOC of each vegetation type were multiplied by its area to estimate the total C stocks. The areas of cropland and built-up lands were estimated based on the European Space Agency’s GlobCover 2009 land cover map (due.esrin.esa.int/globcover/; accessed 6 February 2013). Except croplands and built-up lands, the areas of the other 12 vegetation types were derived from the vegetation map of China (Zhang et al., 2007) and the vegetation map of the five CAS (Rachkovskaya, 1995).

© 2015 John Wiley & Sons Ltd, Global Change Biology, doi: 10.1111/gcb.12846

27

231

0.02

1.48

Cropland

Forest

31

0.09

0.72

17

9

0.02

0.27 0.45

22

0.07

74

6

0.23

2.36

56

0.05

38 19 17

75

0.05

1.25 0.96 0.15

9

0.43

Grassland

8 50

0.14 0.57

Shrubby desert Semishrubby and dwarf semishrubby desert Succulent holophytic dwarf semishrubby desert Alpine cushion dwarf semishrubby desert Temperate dwarf semiHaloxylon arborous desert ammadendron Haloxylon persicum Mixed haloxylon Summary Mean Total area and C stocks Desert steppe Typical steppe Mountain steppe Summary Mean Total area and C stocks Evergreen needleleaf forest Deciduous broadleaf forest Summary Mean Total area and C stocks Rain-fed cropland Irrigated cropland Summary Mean

Temperate desert

77

38

5

33

128

39 45 44

41

34.82  12.17 3.04  1.06

11.03  9.70 0.96  0.85

8.85  2.87 6.37  2.06

11.33  3.63

2.92  2.08

0.64  0.21 0.46  0.15

42.99  15.14

2.94 6.77 8.21 4.84 11.44

13.85  12.35

4.08 8.84 17.04 6.84 16.18

     0.13 0.12 0.27 0.13 0.32

    

0.30 0.49 0.57 0.40 0.94

3.35  2.66 4.96  3.93

0.40  0.34 0.60  0.50

0.27  0.29

0.09  0.05

0.37  0.34

0.18  0.17

0.96  0.85

0.17  0.12 0.19  0.13

VEGC

0–1 m

SOC

VEGC

Ecosystem type

SOC

Area (106 km2)

No. of observations

Inventory study

10.39  1.34 15.38  1.99

0–3 m*

   

0.12 0.74 1.29 0.28

28.03  17.94 2.17 5.91  4.33 9.41  5.55 7.71  5.54 5.55

0.27  0.19 0.59  0.32 0.48  0.32 0.34

14.87  20.94

32.60  18.38

2.26  1.20 8.67  9.49 12.48  23.86 5.52  3.59 13.06

4.16  3.27 6.15

0–1 m

15.76  13.11 1.22

8.95  11.17

18.13  13.51

0.24 0.81 0.60 0.50 1.18

0.87  1.30 1.28

VEGC

SOC

Modeling study

11.89  9.35 17.58

0–3 m*

Table 1 Calculating the vegetation and soil C stocks (Pg; 1 P = 1015) of the dryland ecosystems in Central Asia from the vegetation carbon (VEGC, kg C m 2) and soil organic carbon (SOC, kg C m 2) densities of different ecosystem types based on the inventory and modeling studies

4 C . L I et al.

© 2015 John Wiley & Sons Ltd, Global Change Biology, doi: 10.1111/gcb.12846

27

231

0.02

1.48

Cropland

Forest

31

0.09

0.72

17

9

0.02

0.27 0.45

22

0.07

74

6

0.23

2.36

56

0.05

38 19 17

75

0.05

1.25 0.96 0.15

9

0.43

Grassland

8 50

0.14 0.57

Shrubby desert Semishrubby and dwarf semishrubby desert Succulent holophytic dwarf semishrubby desert Alpine cushion dwarf semishrubby desert Temperate dwarf semiHaloxylon arborous desert ammadendron Haloxylon persicum Mixed haloxylon Summary Mean Total area and C stocks Desert steppe Typical steppe Mountain steppe Summary Mean Total area and C stocks Evergreen needleleaf forest Deciduous broadleaf forest Summary Mean Total area and C stocks Rain-fed cropland Irrigated cropland Summary Mean

Temperate desert

77

38

5

33

128

39 45 44

41

34.82  12.17 3.04  1.06

11.03  9.70 0.96  0.85

8.85  2.87 6.37  2.06

11.33  3.63

2.92  2.08

0.64  0.21 0.46  0.15

42.99  15.14

2.94 6.77 8.21 4.84 11.44

13.85  12.35

4.08 8.84 17.04 6.84 16.18

     0.13 0.12 0.27 0.13 0.32

    

0.30 0.49 0.57 0.40 0.94

3.35  2.66 4.96  3.93

0.40  0.34 0.60  0.50

0.27  0.29

0.09  0.05

0.37  0.34

0.18  0.17

0.96  0.85

0.17  0.12 0.19  0.13

VEGC

0–1 m

SOC

VEGC

Ecosystem type

SOC

Area (106 km2)

No. of observations

Inventory study

10.39  1.34 15.38  1.99

0–3 m*

   

0.12 0.74 1.29 0.28

28.03  17.94 2.17 5.91  4.33 9.41  5.55 7.71  5.54 5.55

0.27  0.19 0.59  0.32 0.48  0.32 0.34

14.87  20.94

32.60  18.38

2.26  1.20 8.67  9.49 12.48  23.86 5.52  3.59 13.06

4.16  3.27 6.15

0–1 m

15.76  13.11 1.22

8.95  11.17

18.13  13.51

0.24 0.81 0.60 0.50 1.18

0.87  1.30 1.28

VEGC

SOC

Modeling study

11.89  9.35 17.58

0–3 m*

Table 1 Calculating the vegetation and soil C stocks (Pg; 1 P = 1015) of the dryland ecosystems in Central Asia from the vegetation carbon (VEGC, kg C m 2) and soil organic carbon (SOC, kg C m 2) densities of different ecosystem types based on the inventory and modeling studies

4 C . L I et al.

© 2015 John Wiley & Sons Ltd, Global Change Biology, doi: 10.1111/gcb.12846

6 C . L I et al. (b) 10

Observed SOC density (kg C m–2)

Observed VEGC density (kg C m–2)

(a) GRS DS DBF 1

ENF (×10)

0.1

y = 0.68 x R² = 0.55 0.01 0.01

0.1

n = 154; RMSE = 2.44 1

10

Simulated VEGC density (kg C m–2)

100

GRS DS CRP

10

DBF ENF

1

y = 0.97 x R² = 0.88 0.1 0.1

1

n = 103; RMSE = 4.10 10

100

Simulated SOC density (kg C m–2)

Fig. 2 Comparing the simulated vegetation carbon (VEGC) (a) and soil organic carbon (SOC) (b) against field observations. Plant functional types: grassland (GRS), desert shrubland (DS), evergreen needleleaf forest (ENF), deciduous broadleaf forest (DBF), and cropland (CRP).

dryland detritus pools (Foereid et al., 2011). Like most ecosystem models, the AEM adopted the concept of plant functional types (PFTs) to describe vegetation distribution. The model has been parameterized for the six major PFTs in Central Asia: irrigated cropland (ICRP), grassland (GRS), temperate deciduous broadleaf forest (DBF), temperate evergreen needleleaf forest (ENF), phreatophytic shrub (PS, represented by Tamarix), and nonphreatophytic shrub (NPS, represented by Haloxylon). The model’s performance has been evaluated by sensitivity analyses and model validations against field observations, including daily evapotranspiration, annual productivities, and C storage at several sites in XJ. A detailed description of the model, its parameters, and evaluation results can be found in Zhang et al. (2013).

Additional validation. Because the consistency between model results and field measurements is essential to establish the credibility of simulated C stocks, the model validation was updated with new VEGC and SOC observations. In total, there were 353 VEGC and 284 SOC sampling plots, among which 166 VEGC plots and 87 SOC plots were recently obtained in our field surveys (Tables S1 and S2). Because our model simulation was conducted under a spatial resolution of 38 9 38 km (mainly limited by the climate inputs; see section Model inputs), several samples may share one simulation grid. As a result, there were 154 pairs of observed VEGC vs. simulated VEGC and 103 pairs of observed SOC vs. simulated SOC for the model validation. Although the model tended to overestimate the VEGC of desert shrubs (Fig. 2a), its predictions overall matched well with the field observations for both VEGC (Fig. 2a; Adj.-R2 = 0.55, P < 0.05) and SOC (Fig. 2b; Adj.-R2 = 0.88, P < 0.05). Model inputs. The climate dataset (including daily precipitation, maximum, minimum, and average temperature, solar radiation, and relative humidity) for 1979–2011 was developed by integrating the 6-hourly Climate Forecast System Reanalysis (CFSR) with a spatial resolution of 38 9 38 km provided by The U.S. National Centers for Environmental Prediction

(NCEP) (http://rda.ucar.edu/pub/cfsr.html; accessed 23 January 2013). CFSR is one of the latest global reanalysis climate datasets, which has been widely applied to climate change studies (Saha et al., 2010; Ebisuzaki & Zhang, 2011; Wang et al., 2011) and matches up well with in situ climate observations in Central Asia (Hu et al., 2014). The PFT map was developed from multiple data sources (Fig. 1). The potential vegetation distribution map was developed by combining the 10-km-resolution vegetation map of China (Zhang et al., 2007) and the 25-km-resolution vegetation map of the five CAS (Rachkovskaya, 1995). We used the GlobCover 2009 land cover map (due.esrin.esa.int/globcover/; accessed 6 February 2013) developed by the European Space Agency to determine the distribution of irrigated cropland, rain-fed cropland, and built-up areas. According to the studies of Sun (2007) and Zhang (2004), we assumed the built-up areas in Central Asia to be covered by 50% impervious area, 25% grass, and 25% forest. Because of their extremely low biological activities, the sand dunes in the Taklimakan Desert were excluded from this simulation (Fig. 1). Other input datasets include (1) the topographic maps (elevation, slope, and aspects) derived from the 30-m-resolution ASTER (the Advanced Spaceborne Thermal Emission and Reflection Radiometer) Global Digital Elevation Model Version 2 dataset (ASTER GDEM, v2) (http://gdem.ers dac.jspacesystems.or.jp/; accessed 23 February 2013), (2) the soil maps (bulk density, volumetric content of sand and clay, and pH) based on the HWSD (Harmonized World Soil Database) version 1.2 global soil dataset (webarchive.iiasa.ac.at/Research/LUC/External-World-soil-database/HTML/HWSD_Data.html?sb=4; accessed 25 February 2013), (3) the atmospheric CO2 concentrations from 1979 to 2011 according to the Mauna Loa observations (http://cdiac.esd.ornl.gov/ftp/trends/co2/maunaloa.co2; accessed 23 February 2013), and (4) the groundwater table of XJ derived from the 1:4 million groundwater table depth map of Xinjiang (Dong & Deng, 2005). All the input maps were gridded into 38 km resolution to match the resolution of the CFSR climate data.

© 2015 John Wiley & Sons Ltd, Global Change Biology, doi: 10.1111/gcb.12846

CENTRAL ASIA’S C STOCK UNDER CHANGING CLIMATE 7 C stock (Pg)

30 20

UZB

19.17 17.15 22.96

0

KGZ

KAZ

10 5

21.31

2.40 2.24

3.18 3.09

1m

3m

2.14 1.81

1m

3m

1m

2.19 1.88

3m

VEGC_INV KAZ

VEGC_MOD SOC_INV

XJ

UZB SOC_MOD

KGZ

TKM

C stock (Pg)

TJK

30 20

6.72

10 5 0

XJ

TJK

TKM

2.31

3.94 2.38

1m

4.17

3m

1.13 0.96

1.19 1.04

1m

3m

10.82 11.22 6.73

1m

3m

Fig. 3 Vegetation carbon (VEGC) and soil organic carbon (SOC) stocks in the six subregions of Central Asia. VEGC_INV and SOC_INV are the inventoried (INV) VEGC and SOC stocks, and VEGC_MOD and SOC_MOD are the modeling (MOD) carbon stocks. KAZ, Kazakhstan; KGZ, Kyrgyzstan; TJK, Tajikistan; TKM, Turkmenistan; UZB, Uzbekistan; XJ, Xinjiang in China. 1 m and 3 m indicate the soil depths of the temperate deserts in the SOC stock calculations. Error bars indicate the standard deviation for the inventory approach and the temporal standard deviation of the mean value during 1979–2011 for the modeling approach, respectively.

Model simulation. To establish a baseline for the C and water pools, the model was run to an equilibrium state with initial climate datasets and CO2 concentrations of 1979. Because daily climate maps before 1979 are not available for the study region, the climate mean during the first decade of the study period (i.e. 1979–1989) was used. Then, a spin-up run of 1500 (150 spins 9 10 yr per spin) was set up to prevent any abnormal fluctuations due to the sudden switch from the equilibration state to the transient state. To match the initial climate conditions for the equilibrium run, each spin was driven by a 10-years detrended climate dataset based on the climate data from 1979 to 1989. After initialization, we applied the time series data for climate and CO2 to simulate the C and nitrogen dynamics from 1979 to 2011.

Results

The carbon stock in Central Asia The VEGC stock in Central Asia was 3.04  1.88 Pg and 4.12 Pg (1 P = 1015) from the inventory and

modeling methods, respectively (Table 1). The SOC stock to 1 m depth (SOC1m) was 27.15 Pg and 30.82  18.61 Pg from the modeling and inventory methods, respectively (Table 1). Another 10.42– 11.43 Pg of SOC was stored in deep soil (1–3 m) in the temperate deserts of Central Asia. Our model simulation estimated the litter C pool (LTRC) to be 0.3 Pg in Central Asia. Thus, the total ecosystem C (TOTC) stock in Central Asia was estimated to be 31.34–34.16 Pg for 1 m soil depth or 43.00–44.58 Pg if the deep-soil (1–3 m) C stock in the temperate deserts was included. There were strong spatial heterogeneities in the C stock distribution. Northern Kazakhstan, the Altai Mountains, and the mid-altitude areas of the Tianshan Mountains had relatively high C density compared to that of the temperate deserts in Turkmenistan, Uzbekistan, and the Gurbantunggut Desert in northern XJ (Fig. 3). In this study, we ignored the organic C storage of the mobile sand dunes in the Taklimakan Desert. Among the subregions, Kazakhstan and XJ together

© 2015 John Wiley & Sons Ltd, Global Change Biology, doi: 10.1111/gcb.12846

6 C . L I et al. (b) 10

Observed SOC density (kg C m–2)

Observed VEGC density (kg C m–2)

(a) GRS DS DBF 1

ENF (×10)

0.1

y = 0.68 x R² = 0.55 0.01 0.01

0.1

n = 154; RMSE = 2.44 1

10

Simulated VEGC density (kg C m–2)

100

GRS DS CRP

10

DBF ENF

1

y = 0.97 x R² = 0.88 0.1 0.1

1

n = 103; RMSE = 4.10 10

100

Simulated SOC density (kg C m–2)

Fig. 2 Comparing the simulated vegetation carbon (VEGC) (a) and soil organic carbon (SOC) (b) against field observations. Plant functional types: grassland (GRS), desert shrubland (DS), evergreen needleleaf forest (ENF), deciduous broadleaf forest (DBF), and cropland (CRP).

dryland detritus pools (Foereid et al., 2011). Like most ecosystem models, the AEM adopted the concept of plant functional types (PFTs) to describe vegetation distribution. The model has been parameterized for the six major PFTs in Central Asia: irrigated cropland (ICRP), grassland (GRS), temperate deciduous broadleaf forest (DBF), temperate evergreen needleleaf forest (ENF), phreatophytic shrub (PS, represented by Tamarix), and nonphreatophytic shrub (NPS, represented by Haloxylon). The model’s performance has been evaluated by sensitivity analyses and model validations against field observations, including daily evapotranspiration, annual productivities, and C storage at several sites in XJ. A detailed description of the model, its parameters, and evaluation results can be found in Zhang et al. (2013).

Additional validation. Because the consistency between model results and field measurements is essential to establish the credibility of simulated C stocks, the model validation was updated with new VEGC and SOC observations. In total, there were 353 VEGC and 284 SOC sampling plots, among which 166 VEGC plots and 87 SOC plots were recently obtained in our field surveys (Tables S1 and S2). Because our model simulation was conducted under a spatial resolution of 38 9 38 km (mainly limited by the climate inputs; see section Model inputs), several samples may share one simulation grid. As a result, there were 154 pairs of observed VEGC vs. simulated VEGC and 103 pairs of observed SOC vs. simulated SOC for the model validation. Although the model tended to overestimate the VEGC of desert shrubs (Fig. 2a), its predictions overall matched well with the field observations for both VEGC (Fig. 2a; Adj.-R2 = 0.55, P < 0.05) and SOC (Fig. 2b; Adj.-R2 = 0.88, P < 0.05). Model inputs. The climate dataset (including daily precipitation, maximum, minimum, and average temperature, solar radiation, and relative humidity) for 1979–2011 was developed by integrating the 6-hourly Climate Forecast System Reanalysis (CFSR) with a spatial resolution of 38 9 38 km provided by The U.S. National Centers for Environmental Prediction

(NCEP) (http://rda.ucar.edu/pub/cfsr.html; accessed 23 January 2013). CFSR is one of the latest global reanalysis climate datasets, which has been widely applied to climate change studies (Saha et al., 2010; Ebisuzaki & Zhang, 2011; Wang et al., 2011) and matches up well with in situ climate observations in Central Asia (Hu et al., 2014). The PFT map was developed from multiple data sources (Fig. 1). The potential vegetation distribution map was developed by combining the 10-km-resolution vegetation map of China (Zhang et al., 2007) and the 25-km-resolution vegetation map of the five CAS (Rachkovskaya, 1995). We used the GlobCover 2009 land cover map (due.esrin.esa.int/globcover/; accessed 6 February 2013) developed by the European Space Agency to determine the distribution of irrigated cropland, rain-fed cropland, and built-up areas. According to the studies of Sun (2007) and Zhang (2004), we assumed the built-up areas in Central Asia to be covered by 50% impervious area, 25% grass, and 25% forest. Because of their extremely low biological activities, the sand dunes in the Taklimakan Desert were excluded from this simulation (Fig. 1). Other input datasets include (1) the topographic maps (elevation, slope, and aspects) derived from the 30-m-resolution ASTER (the Advanced Spaceborne Thermal Emission and Reflection Radiometer) Global Digital Elevation Model Version 2 dataset (ASTER GDEM, v2) (http://gdem.ers dac.jspacesystems.or.jp/; accessed 23 February 2013), (2) the soil maps (bulk density, volumetric content of sand and clay, and pH) based on the HWSD (Harmonized World Soil Database) version 1.2 global soil dataset (webarchive.iiasa.ac.at/Research/LUC/External-World-soil-database/HTML/HWSD_Data.html?sb=4; accessed 25 February 2013), (3) the atmospheric CO2 concentrations from 1979 to 2011 according to the Mauna Loa observations (http://cdiac.esd.ornl.gov/ftp/trends/co2/maunaloa.co2; accessed 23 February 2013), and (4) the groundwater table of XJ derived from the 1:4 million groundwater table depth map of Xinjiang (Dong & Deng, 2005). All the input maps were gridded into 38 km resolution to match the resolution of the CFSR climate data.

© 2015 John Wiley & Sons Ltd, Global Change Biology, doi: 10.1111/gcb.12846

CENTRAL ASIA’S C STOCK UNDER CHANGING CLIMATE 9 (a)

y = 0.04 x + 7.13 R² = 0.26

7

600

5 3 1 1979

Annual mean temperature

800

400

y = –5.84 x + 518.78 R² = 0.48 1983

1987

1991

Precipitation (mm)

Temperature (oC)

9

Annual total precipitation

La Niña events 1995

1999

2003

2007

200 2011

(c)

(b)

Fig. 5 Spatial and temporal changes in temperature and precipitation in Central Asia from 1979 to 2011. (a) Temporal changes in annual temperature and precipitation. The La Ni~ na records were obtained from the Golden Gate Weather Services (http://ggweath er.com/enso/oni.htm); (b) spatial pattern of the temperature trend in 1979–2011; (c) spatial pattern of the precipitation trend in 1979–2011.

Cumulative C change (Pg C)

1979 0.2

1983

1987

1991

1995

1999

2003

2007

2011

0 –0.2 –0.4

LTRC

–0.6

SOC

–0.8

VEGC Protracted La Niña

TOTC –1

Protracted La Niña

Fig. 6 Carbon storage changes of different carbon pools relative to 1979 in Central Asia during 1979–2011. VEGC denotes vegetation carbon storage; SOC, soil organic carbon storage; LTRC, litter carbon storage; and TOTC, total carbon storage. The two protracted La Ni~ na records between 1998 and 2009 were highlighted based on the reports from the Golden Gate Weather Services (http://ggweath er.com/enso/oni.htm).

when the precipitation was only 80% of the long-term (1979–2011) mean and the mean annual air temperature (5.62 °C) was higher than the long-term mean (5.54 °C). The drier and warmer climate exacerbated the xeric conditions in Central Asia drylands and took a toll on

the regional C stock. Our model simulation showed that the dryland ecosystems in Central Asia lost approximately 0.46 Pg C (~0.02 Pg C yr 1) from 1979 to 2011, mainly from the VEGC pool (0.57 Pg C loss) (Fig. 6). It is noteworthy that Central Asia drylands were nearly C

© 2015 John Wiley & Sons Ltd, Global Change Biology, doi: 10.1111/gcb.12846

10 C . L I et al. (a)

(b)

(c)

(d)

Fig. 7 Changes in total ecosystem carbon density (a), vegetation carbon density (b), soil organic carbon density (c), and litter carbon density (d) from 1979 to 2011. The dashed and solid circles outline the strong carbon source in northern Kazakhstan and the strong carbon sink in northern Xinjiang, respectively.

neutral before the drought period (Fig. 6). In the first 5 years (1998–2002) of the drought period, the ecosystem lost large amounts (0.32 Pg) of VEGC. The C stock started to recover from 2009 to 2010, when the precipitation rebounded. The spatial pattern of carbon dynamics in response to climate change from 1979 to 2011. The changes in temperature and precipitation from 1979 to 2011 were not uniform across the study region. To illustrate the spatial pattern of climate change, the linear trends of temperature and precipitation from 1979 to 2011 were computed for each simulation pixel (Fig. 5b,c). The results showed strong warming in southeastern XJ and the Gurbantunggut Desert in northern XJ (≥ 0.06 °C yr 1) (Fig. 5a). Precipitation decreased significantly in northern Kazakhstan, Kyrgyzstan, and southwestern XJ, while it increased in northern and southeastern XJ (Fig. 5b). As the climate became drier, the ecosystem C stock decreased by approximately 0.3 kg C m 2 in northern Kazakhstan. In contrast, the ecosystem C stock increased by approxi-

mately 0.1 kg C m 2 in northern XJ in response to a warmer and wetter climate (Fig. 5 and Fig. 7a). Our results showed that the spatial patterns of the VEGC density changes and TOTC density changes were highly correlated with an Adj.-R2 to be 0.98 (Fig. 7a, b). This may indicate that the VEGC pool was the major component that controlled the regional C dynamics in response to climate change.

Discussion

Assessment of the carbon stock in Central Asia – comparison with previous studies Assessments of C balance have been comprehensively conducted in many regions in the world. Yet, Central Asia remains poorly known and uncertain, because of very limited climatic, ecological, and soil data. In the latest IPCC report (Fifth Assessment Report; ipcc.ch), the C budgets in Central Asia are unavailable. We found that no published literature reported the contemporary VEGC storage in Central Asia. Thevs et al.

© 2015 John Wiley & Sons Ltd, Global Change Biology, doi: 10.1111/gcb.12846

C E N T R A L A S I A ’ S C S T O C K U N D E R C H A N G I N G C L I M A T E 11 (2013) assessed the C stock of the Haloxylon ecosystems in Central Asia. According to their study, the mean VEGC density of the temperate dwarf semi-arborous desert ecosystem was approximately 0.15–0.30 kg C m 2, agreeing with our inventory data (0.09–0.37 kg C m 2 in Table 1). There were a few regional studies that have analyzed the status of the SOC stock in Central Asia (Lal, 2007a). Based on the SOC information extracted from the ISRIC-WISE v3.1 global soil database (Batjes, 2009) and from a few other reports, Sommer & De Pauw (2011) estimated the potential SOC of the native soils in the CAS region to be approximately 20 Pg in the topsoil (30 cm). Their study also indicated approximately 1 Pg SOC lost during 1982–2000 due to agricultural conversion and rangeland degradation. Therefore, according to Sommer & De Pauw (2011), the contemporary topsoil SOC stock in the CAS region was approximately 19 Pg C (or 4.8 kg C m 2 30 cm 1). It should be noted that the ISRIC-WISE soil database that was largely based on soil profiles from the mountain steppe or meadow ecosystems in Central Asia might overestimate the SOC of the dryland (Lioubimtseva & Adams, 2002). In comparison, our results indicated the SOC storage in the topsoil (30 cm) of the CAS region to be approximately 12–14 Pg. According to Xie et al. (2007), the top 1-m depth SOC stock of XJ was 6.9 Pg, excluding the mobile

sand desert and glacier-covered areas. This estimation was close to our results of 5.7–6.0 Pg.

Carbon storage comparison with other drylands and regions Our estimated VEGC density in Central Asia drylands, 0.36–0.58 kg C m 2, was comparable to the global mean VEGC in desert and semidesert, 0.35– 0.40 kg C m 2 (Gibbs, 2006; Houghton et al., 2009) (Table 2). The aboveground VEGC of the temperate deserts in Central Asia was approximately 0.11– 0.13 kg C m 2, close to the reported values in the Mojave Desert (0.09–0.12 kg C m 2) (Rundel & Gibson, 2005) and the Chihuahuan Desert (0.08–0.16 kg C m 2) (Huenneke & Schlesinger, 2006). The total VEGC of the temperate deserts was 0.40–0.87 kg C m 2, higher than the VEGC in the Sahel hot desert (0.26 kg C m 2) (Woomer et al., 2004) but lower than that in the Sonoran hot desert (1.6 kg C m 2) (B urquez et al., 2010). A comparison between the SOC of the temperate deserts in Central Asia and the SOC of other desert types or regions is shown in Table 3. In top 1 m depth, the SOC of the temperate deserts in Central Asia was approximately 3.35–4.16 kg C m 2 (Table 1), slightly lower than the global mean SOC in deserts/semideserts (4.37 kg C m 2) estimated by Watson et al. (2000). We

Table 2 Global comparison of vegetation carbon (VEGC) in deserts Studies

Desert type or region

C pool

VEGC (kg C m 2)

Rundel & Gibson (2005) Huenneke & Schlesinger (2006) This study Woomer et al. (2004) Houghton et al. (2009) Gibbs (2006)

Mojave Desert Chihuahuan Desert Temperate desert and desert steppe Sahel Transition Zone Global desert Global nonpolar desert and semidesert, and sparse vegetation Sonoran Desert Temperate desert and desert steppe

Aboveground VEGC

0.09–0.12 0.08–0.16 0.11–0.13 0.26 0.35 0.4

B urquez et al. (2010) This study

Total VEGC

1.6 0.36–0.58

Table 3 Global comparison of soil organic carbon (SOC) in deserts Studies

Desert type or region

Soil depth

SOC (kg C m 2)

Hoffmann et al. (2012)* Amundson (2001)† Feng et al. (2002) Henry et al. (2009) Watson et al. (2000) This study (1-m soil depth) Jobbagy & Jackson (2000) This study (3-m soil depth)

Hot rocky desert in Negev, Middle East Global hot desert Desert in China Desert in Africa Global desert/semidesert Central Asia temperate desert Global desert Central Asia temperate desert

0–1 m

0.31 1.4 2.32 2.5 4.37 3.35–4.16 11.5  8.2 10.39–11.89

*Soil depth was variable. †Soil depth was not specified. © 2015 John Wiley & Sons Ltd, Global Change Biology, doi: 10.1111/gcb.12846

0–3 m

12 C . L I et al. 180

C stock (Pg)

150 120

VEGC SOC (1 m) SOC (3 m) TOTEC

90 60 30 0

Central Middle Asia East

South Asia

Australia

Europe

China

Global desert & dry shrubland

Fig. 8 Comparing the vegetation carbon (VEGC), soil organic carbon (SOC), and total ecosystem carbon (TOTC) stock in Central Asia against reports from other regions of the world. The VEGC and SOC stock of Central Asia are the averages calculated from both the inventory and modeling approaches. One and 3 m in the brackets indicate the soil depths of the temperate deserts. Reports include the following: Trumper et al. (2008) on the Middle East and South Asia; Spain et al. (1983), Berry & Roderick (2006), Grace et al. (2006b), and Cosier et al. (2009) on Australia; Smith et al. (1997), Powlson et al. (1998), and Kaplan et al. (2012) on Europe; Fang et al. (1996), Wang et al. (2003), Li et al. (2004), Xie et al. (2007), Yang et al. (2007), and Ni (2013) on China; and Trumper et al. (2009) on global deserts and shrublands. Error bar shows one standard deviation.

found that the SOC of the temperate deserts could be 1–2 times higher than the global mean SOC of hot deserts [1.4 kg C m 2, Amundson (2001)] and approximately 10 times the SOC of a hot rocky desert in the Middle East. While most studies were limited to the top 1 m (or shallower) soil depth, an empirical model prediction (Jobb agy & Jackson, 2000) showed that the global desert SOC in the top 3 m depth was 11.5  8.2 kg C m 2. This estimation was consistent to our findings (10.39–11.89 kg C m 2 SOC to 3 m depth) in the Central Asia deserts (Table 1). For all of Central Asia drylands, the mean TOTC density in the top 1-m soil was approximately 6.6–7.3 kg C m 2, comparable to that in Australia, ~7.1  1.4 kg C m 2 (Berry & Roderick, 2006; Grace et al., 2006a; Cosier et al., 2009). With a large land area of 5.6 9 106 km2, the C stock of Central Asia is an important component of the Eurasia C budget (Fig. 8). The total C stock of Central Asia (43–44.58 Pg) was comparable to the C stocks of the Middle East (44 Pg) and South Asia (54 Pg) (Trumper et al., 2008). It is approximately 38% of the C stock of Europe [based on reports from Kaplan et al. (2012), Powlson et al. (1998), and Smith et al. (1997)], and 36% of China’s C stock [based on reports from Fang et al. (1996), Wang et al. (2003), Li et al. (2004), Xie et al. (2007), Yang et al. (2007), and Ni (2013)]. The C stock in Central Asia amounted to 18–24% of the global C storage in deserts and dry shrublands (Trumper et al., 2009) and was comparable to the C stock of major drylands of the world, such as Australia [54 Pg; based on reports from Berry & Roderick (2006), Grace et al. (2006b), and Cosier et al. (2009)]. It should be noted that the C stock of Central Asia is predominantly (~90%) belowground. In

comparison, VEGC accounted for approximately 45% of the C stock in Australia (Fig. 8). Despite its importance and uniqueness, Central Asia dryland ecosystems have been largely overlooked in global C cycle research, compared to the interest in other regions of the world (Lioubimtseva & Adams, 2002; Lal, 2004, 2007b). Our study further revealed that 65–68% of the SOC in temperate deserts was stored in deep soil (under the top 1 m). Plant roots in desert ecosystems can grow deep into soil in search of water (Goedhart & Pataki, 2011), and SOC in deep soil layers accounted for a remarkable fraction of the full soil profile in deserts (Jobbagy & Jackson, 2000; Wang et al., 2010; Rumpel & K€ ogel-Knabner, 2011). However, previous field soil surveys in Central Asia mainly focused on upper layers within the top 1 m and could have underestimated the C stock in Central Asia (Lioubimtseva & Adams, 2002; Sommer & De Pauw, 2011). Current C modeling studies had similar problems in drylands. Although deep roots and related deep-soil water and C processes were important to the structure and functions of dryland ecosystems (Goedhart & Pataki, 2011), deep-soil mechanisms were rarely considered in ecosystem models that have been widely used in regional or global C cycle research (Schimel et al., 1994; Tian et al., 2011). In comparison, the AEM used in this study can address the root distribution in deep soil and the groundwater uptake process by phreatophytes (Zhang et al., 2013). Therefore, our AEM simulation could show the relatively high ecosystem C stock supported by the shallow groundwater around the Taklimakan Desert in southern XJ’s arid lands (Fig. 3), where other models would fail. (A map of the groundwater table is provided in the Fig. S2).

© 2015 John Wiley & Sons Ltd, Global Change Biology, doi: 10.1111/gcb.12846

C E N T R A L A S I A ’ S C S T O C K U N D E R C H A N G I N G C L I M A T E 13

The carbon dynamics in response to climate change During the past decades, the Eurasian continent, especially its northern forest, has been suggested to be a huge C sink (Myneni et al., 2001; Schimel et al., 2001; Piao et al., 2011). Little attention has been paid to its dryland ecosystems, however. The dryland ecosystems in Central Asia faced strong climate change from 1979 to 2011 (Hu et al., 2014). The warming rate of 0.4 °C decade 1 was larger than the average rate for the Northern Hemisphere (0.3 °C decade 1) (Trenberth et al., 2007) and was about twice as large as the warming rate in Europe (Simmons et al., 2004). The drylands also endured a decadal drought from 1998 to 2008. In some regions of northern Kazakhstan, annual precipitation declined as much as 90 mm decade 1 (Chen & Luo, 2013). As a result, we found significant C losses (about 0.3 kg C m 2) in northern Kazakhstan from 1979 to 2011 (outlined by dashed circles in Fig. 7a, b). This finding was supported by multiple remote sensing studies, which showed severe degradation in the Normalized Difference Vegetation Index (NDVI) during the last 2 or 3 decades in northern Kazakhstan, that mark the region as one of the most conspicuous vegetation browning hot spots in the world (Beurs et al., 2009; Piao et al., 2011; De Jong et al., 2012; Gessner et al., 2013). After the 1980s, the NDVI and ecosystem productivity have increased in northern XJ, a region that shares similar dryland biomes with the latitudinally neighboring Kazakhstan, according to remote sensing studies (Fang et al., 2003; Chen et al., 2005; Piao et al., 2005). Similarly, we found that the TOTC and VEGC of the northern XJ drylands have increased by approximately 0.1 kg C m 2 since 1979 (outlined by solid circles in Fig. 7a,b). This change was due to the increased regional precipitation, which reduced water stresses and promoted vegetation growth in northern XJ from 1979 to 2011 (Shi et al., 2007; Li et al., 2013). The similarity in vegetation types and the difference in climate change patterns between the two neighboring subregions of Central Asia provide an ideal comparative case study for exploring the mechanisms of climate change and dryland ecosystem interactions at regional scale. Previous studies indicated that the severe drought in Central Asia (Fig. 5c) may have resulted from oceanic sources related to the cold phases of the El Ni~ no-Southern Oscillation (ENSO) (Barlow et al., 2002; Syed et al., 2006). Our simulation showed that the two major C loss events, 1998–2002 (0.33 Pg C lost) and 2005–2009 (0.13 Pg C lost), were coincident with the protracted La Ni~ na episodes in the same periods (http://ggweath-

er.com/enso/oni.htm; accessed 20 June 2014) (Fig. 5 and Fig. 6). According to climate modeling studies, the persistent La Ni~ na episodes in the recent decades resulted from a remarkable warming of the Indian and west Pacific oceans that enhanced the zonal contrast in sea surface temperature between the east and west tropical Pacific (Hoerling & Kumar, 2003). This abnormal warming was partly attributed to the ocean’s responses to increased greenhouse gases in the atmosphere (Knutson & Manabe, 1998; Yeh et al., 2009). If the future climate is warmer (IPCC Fifth Assessment Report; ipcc.ch), and the frequency and intensity of the ENSO are growing (Bastos et al., 2013), the sustainability of the dryland ecosystems in Central Asia would be threatened by more droughts in the 21st century. It is noteworthy that the majority of the C losses in response to a warmer and drier climate were from the VEGC pool (Fig. 6), although the SOC pool was far larger than the VEGC pool in Central Asia (Fig. 3). This was probably because the SOC stock was determined by the balance between plant-derived C influx (litterfall) and heterotrophic respiration (Rh), both of which had similar responses to climate change. A meta-analysis on warming effects suggested that while biomass was sensitive to warming, the C pools in litter and mineral soil were generally not significantly altered because the warming-induced changes in plant-derived C influx were roughly offset by the warming-stimulated litter decomposition and soil respiration (Lu et al., 2012). In another review, Wu et al. (2011) concluded that decreased precipitation could reduce Rh as well as biomass and productivity. According to their analysis, the dryland Rh was especially sensitive to precipitation change. For the SOC pool of the drought-stressed drylands in Central Asia, the reduced plant-derived C influx was balanced by the suppressed Rh, which according to our study decreased by 14.5% in response to the 1998–2008 drought. However, this balance may be disrupted by human activities in Central Asia (Lioubimtseva, 2014). The rapid expansion of irrigated land may reduce soil water deficits and stimulate Rh (Pielke et al., 2007). The large-scale ecological restoration project in the Tarim River Basin in XJ may also unintentionally trigger strong C losses from the dryland soil due to periodical artificial flooding (Zhang et al., 2010).

Uncertainties Like most traditional plots studies, our study design did not allow a quantitative assessment of the sampling quality. To make full use of available data, our inventory study combined a field survey and multiple inventory datasets from previous studies without a

© 2015 John Wiley & Sons Ltd, Global Change Biology, doi: 10.1111/gcb.12846

14 C . L I et al. consistent sampling design [e.g. Lioubimtseva (2007), Takata et al. (2007), Ren et al. (2009), and Yang et al. (2009)]. Moreover, the previous studies disproportionally focused on Kazakhstan and Xinjiang, China, while some other regions were not sampled because of the inaccessibility due to either their remote locations (e.g. the Kyzyl Kum Desert in Uzbekistan) or political restrictions (e.g. field sampling in Turkmenistan). As a result, the distribution of observational data was spatially biased to the northern and eastern part of the study region. Unfortunately, we were unable to evaluate the uncertainties from this spatial bias, which limited the subsequent usefulness of the data (Yang et al., 2010). Another source of uncertainty is related to the spatial representativeness of the samples (Stein & Ettema, 2003). In particular, our assessment on the forest VEGC stock in Central Asia was mainly based on Luo (1996) who focused on Xinjiang, China. Our simulation showed that the forest VEGC of Xinjiang (10.00 kg C m 2) could be much lower than the regional mean (15.76 kg C m 2), a result indicating the inventory study (that indicated the forest VEGC=11.03 kg C m 2) might have underestimated the forest VEGC stock in Central Asia. Overlooking important processes such as soil erosion and land-use/land cover change (LULCC) also caused uncertainties in our modeling results. Soil erosions, particularly wind erosion that could remove 5–15 g SOC m 2 yr 1 from the desert ecosystems (Yan et al., 2005), have important impacts on SOC in the dryland of Central Asia (Lal, 2007b). The AEM model, however, did not consider the erosion effect, which may partially explain why the modeled SOC was 0.80 kg m 2 higher than the inventory SOC in the deserts (Table 1). The dramatic LULCC change (esp. after 1991) in Central Asia also profoundly impacted the regional C stock (Beurs & Henebry, 2004; Lioubimtseva & Henebry, 2012). The cropland in the CAS region declined 22.03% from 1991 to 2009 (Han et al., 2012). The large-scale cropland abandonment and reduced grazing intensity in the northern grassland of Kazakhstan were probably responsible for a 0.006–0.052 increase of NDVI in the region (Beurs & Henebry, 2004; Wright et al., 2012). While the cropland abandonment in the CAS region might have enhanced ecosystem productivity and SOC sequestration (Beurs et al., 2009; Sommer & De Pauw, 2011), it could reduce the VEGC ( 0.026 Tg yr 1) and SOC ( 0.004 Tg yr 1) pools in Xinjiang, China (Wang et al., 2014). The disparities were possibly caused by the different cropland management and climate regimes between the two areas. Overlooking the LULCC process and its complex interactions with climate change in Central Asia may lead to big uncertainties in C cycle simulation.

Acknowledgements This study was supported by the National Basic Research Programs of China (Grant No. 2014CB954204), the National Natural Science Foundation of China (Project 41401118), and the ‘Hundred Talents Program’ of the Chinese Academy of Sciences (granted to Chi Zhang). We are grateful to Prof. Yuanrun Zheng of IB-CAS, Prof. Yuanming Zhang, Prof. Weikang Yang, Dr. Ye Tao, Dr. Wenqiang Xu, Dr. Gang Yin, Ms. Yan Yan of XIEGCAS, and Prof. Qi Hu of University of Nebraska–Lincoln for their generous help. We also want to thank the editor and reviewers for their valuable advices and suggestions that helped us improve this manuscript.

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Supporting Information Additional Supporting Information may be found in the online version of this article: Figure S1. Global temperate desert located in Central Asia (Smith, 1997) (available at: http://www.britannica.com/EBchecked/ topic/158992/desert). Figure S2. Groundwater table of Central Asia. Table S1. Vegetation carbon (VEGC) density (kg C m 2) of typical vegetation types in Central Asia. Table S2. Soil organic carbon (SOC) density (kg C m 2) of major vegetation types in depth of 1 m in Central Asia. Table S3. Allometric models for above- (Ba) and below-ground biomass (Bb) of desert shrubs.

© 2015 John Wiley & Sons Ltd, Global Change Biology, doi: 10.1111/gcb.12846