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Grassland Carbon Budget and Its Driving Factors of the Subtropical and Tropical Monsoon Region in China During 1961 to 2013

Scientific Reportsvolume 7, Article number: 14717 (2017) | Download Citation


The southern grasslands are an integral part of the grassland ecosystems of China and play an essential role in the terrestrial carbon cycle of the country. We reproduced the spatiotemporal dynamics of the carbon budget of southern grasslands from 1961 to 2013 using the Terrestrial Ecosystem Model and our results showed that the annual carbon budget varied from −8.12 to 6.16 Tg C y−1 with an annual average of 0.45 Tg C y−1 during the study period. Overall, southern grasslands acted as a weak carbon sink and sequestrated 23.83 Tg C from 1961 to 2013. At the seasonal scale, southern grasslands acted as a carbon sink in wet seasons but as a carbon source in dry seasons. During the study period, temperature and precipitation were the main factors driving carbon budget dynamics at the seasonal scale, while soil moisture was the main driving factor at the annual scale. Over the entire study region, 71.81% of the area switched to being a carbon sink while only 5.90% remained stable and the strong carbon sinks were mainly found in the southern, northern and western areas of the southern grasslands.


Grasslands are one of the most extensive terrestrial ecosystem on Earth, and they play a crucial role in the circulation of energy and matter, global climate change and the global carbon balance1,2. Based on a national grassland resource survey3, the grasslands of China cover approximately 3.95 × 106 km2, which is approximately 41.1% of the total territory of the country. It has been reported that grasslands account for 20% of the total carbon in both the soil and vegetation on Earth4, and the relationship between the global carbon cycle and the carbon budgets of grasslands has become one of the most important questions in ecology and environmental science5,6,7.

Many previous studies have estimated the productivity and carbon storage of grassland ecosystems8,9, including the annual NPP over different periods10,11,12 and for different types of grasslands13,14,15,16. Most studies on the carbon pools or carbon budgets of grasslands have mainly focused on regions with continuous areas of vegetation cover, such as the grasslands of the Qinghai-Tibetan Plateau17,18,19,20 and Inner Mongolian21,22,23,24. Several studies have investigated the total carbon budget for all the grassland ecosystems in China14,25,26,27,28, and different studies2,13,14,27,29,30,31,32 have found different carbon pool and carbon flux estimates. The vegetation carbon storage and density of the grasslands of China have been found to range from 0.56~4.67 Pg C and 215.80~1148.20 g C m−2, respectively, and the soil carbon storage and density2,14,33 have been found to range from 16.70~41.00 Pg C and 10.00~15.10 kg C m−2, respectively. Consequently, the quantitative assessment of carbon exchange rates and the accurate simulation of the spatiotemporal variation in carbon fluxes are crucial for forecasting trends under future climate change and for understanding the mechanisms that control global change34,35,36.

Southern China is located in the subtropical and tropical monsoon region and experiences distinct seasons, and the grasslands in this zone have a disjunct distribution, are small and minimally affected by human activities. However, although there have been several studies on the carbon budget of southern grasslands37,38,39,40,41,42,43,44,45, there has been relatively little research on the spatiotemporal dynamics of the regional carbon budget of southern China and its control mechanisms since 1961, which limits our understanding of the response of the carbon budget dynamics of southern grassland ecosystems to climate change.

In this work, we calibrated and verified the Terrestrial Ecosystem model using information about nitrogen and carbon fluxes and pools acquired from field observation and the literature. We then simulated and analyzed the spatiotemporal dynamics of the carbon budget and its control mechanisms in southern grasslands from 1961 to 2013.


Model Verification

We verified the TEM results with the observed vegetation and soil organic carbon density data sets from 2011 to 2013 (Fig. 1). First, we removed unreasonable values from the observation data, such as above- or below-ground vegetation densities or soil carbon densities in all soil layers equal to zero, as well as the data for parameter calibration, and we then used the Pauta criterion (3δ method) to exclude the observation data that were more than 3-fold greater than of the standard deviation of the observation data and their average. Finally, we used the remaining observation data to verify the TEM simulation results. The parameterized TEM-simulated vegetation carbon (R2 = 0.50, n = 215) and soil organic carbon (R2 = 0.44, n = 127) data matched the observation data well, exceeding the confidence level of 99%. The average measured vegetation and soil organic carbon density values were 572.70 g C m−2 and 1564.60 g C m−2, respectively, which were close to the corresponding simulated values of 574.94 g C m−2 and 1604.56 g C m−2.

Figure 1
Figure 1

Comparisons of simulated vegetation (a) and soil organic carbon densities (b) with the observation data of 2011~2013.

Temporal Dynamics of Precipitation and Temperature

The average annual temperature in southern grasslands increased remarkably (R2 = 0.44, P < 0.001, N = 53; Fig. 2a) during 1961 to 2013, and the annual precipitation varied greatly from year to year, with an annual average of 1057.35 mm (Fig. 2a). The study region was seriously affected by the Asian Monsoon and experienced considerable seasonal changes in precipitation with obvious wet (April to September) and dry seasons in one year (Fig. 2b). During wet seasons, the total precipitation accounted for 77.16% of the annual precipitation, on average, from 1961 to 2013. The mean temperature was 21.10 °C during wet seasons, which was approximately twice that in dry seasons (10.71 °C). Therefore, the climate was wetter and warmer during wet seasons, especially during the summer season of June to August.

Figure 2
Figure 2

Inter-annual (a) and monthly (b) variations of temperature and precipitation during 1961 to 2013.

Temporal Dynamics of the Carbon Budget

In the TEM, net ecosystem production (NEP) was defined as the difference between net primary production (NPP) and heterotrophic respiration (RH). The annual NEP in southern grasslands ranged from −8.12 to 6.16 Tg C y−1 from 1961 to 2013, with an annual average of 0.45 Tg C y−1 (Fig. 3a). Thus, the entire study region (0.29 × 106 km2) acted as a weak carbon sink and fixed 23.83 Tg C during the study period with large inter-annual variations. Compared with NEP, the annual NPP and RH showed different temporal dynamics (Fig. 3b); both showed significantly increasing trends from 1961 to 2013 (R2 = 0.35, P < 0.001, N = 53 and R2 = 0.68, P < 0.001, N = 53, respectively). The rate of increase of NPP (0.18 Tg C y−1) was greater than that of RH (0.17 Tg C y−1), and the difference between these rates was the basis for the annual fluctuations in NEP in southern grasslands from 1961 to 2013. The average annual NPP was 93.63 Tg C y−1 during 1961 to 2013, and the annual RH ranged from 89.9 to 97.1 Tg C with an annual average of 93.2 Tg C y−1.

Figure 3
Figure 3

Inter-annual and monthly variations of carbon budget during 1961 to 2013.

The seasonal variations in monthly NPP and RH exhibited the same pattern, decreasing from July to December and increasing from January to July (Fig. 3d), and the differences in the monthly NPP and RH led to the seasonal variation in NEP. The seasonal variation in monthly NEP showed a distinct bimodal pattern with the maximum appearing in May and October (Fig. 3c). The NEP in summer was close to zero because almost all the carbon fixed by grasslands from the atmosphere was released by the soil under high temperature and high moisture conditions. Therefore, the southern grasslands acted as a carbon sink in wet seasons and as a carbon source in dry seasons.

Response of NEP to Climate Variables

Climatic variables, such as precipitation, air temperature, soil temperature and so on, could affect the NEP of grasslands through their influence on NPP and RH. At the seasonal scale, southern grasslands were strongly affected by the monsoon climate and characterized by high temperature and precipitation in wet seasons (Fig. 3b), but the NEP was much lower in dry seasons (Fig. 3b). The linear regression results showed that soil and air temperature accounted for most of the seasonal variation in NEP followed by precipitation and soil moisture (Table 1). We used stepwise regression to evaluate the influences of selected climatic variables on NEP and 71.4% (P < 0.001) of the seasonal variation in southern grasslands could be explained by precipitation and air temperature.

Table 1 Linear regressions between NEP (NEP(y), g C m−2 month−1) and climatic variables on monthly scale, covering radiation (NIRR, W m−2), soil temperature (Ts,  °C), mean air temperature (TAIR,  °C), mean soil moisture (SM, %) and precipitation (PRECIP, mm month-1) in southern China during 1961 to 2013.

At the annual scale, there was a strong positive correlation between annual NEP and annual soil moisture, which meant that an increase in annual soil moisture would lead to an increase in the annual NEP of southern grasslands. However, statistical analysis indicated there was an insignificant correlation between annual NEP and annual mean air temperature or annual precipitation.

Spatial Dynamics of Grassland NEP in Southern China

From 1961 to 2013, TEM simulations indicated that southern grasslands acted as a carbon sink with a high standard deviation (3.38 Tg C y−1), which was the difference between the average annual NPP and RH of the total vegetated area of 0.29 × 106 km2. Therefore, the annual NEP of southern grasslands in China was 1.53 g C m−2 y−1 with significant spatial variation; large carbon sinks were in the southern, western and northern portions of the southern grasslands (e.g., Guangxi, Hubei, and Sichuan Provinces), which experienced high precipitation and high temperatures. Central Yunnan Province, which has low temperatures and low precipitation mainly acted as a carbon source (Fig. 4a).

Figure 4
Figure 4

Spatial distribution patterns of average annual NEP (a) and annual NEP’s increasing rate (b) from 1961 to 2013 in southern grasslands (This picture was made by ArcGIS 10.2,

The spatial distribution of the areas with increasing NEP indicated that 71.81% of the study area tended to be a carbon sink from 1961 to 2013 (Fig. 4b). These areas are mainly in most areas in southern grasslands. Less than 22.26% of the total grasslands area acted as carbon sources. These areas were distributed mainly in the central of Yunnan province. Only approximately 5.93% of the study area was in a steady condition (0.00~0.01 g C m−2 y−1).


Only a few studies have been conducted on the carbon budget of southern grasslands (Table 2). For instance, Sun et al.44 estimated that southern grasslands sequestered carbon at a rate of 3.25 to 81.98 g C m−2 y−1 during 2001 to 2010 using an improved BIOME-BGC model. In contrast, we estimated a much lower carbon sink of 1.53 g C m−2 y−1 from 1961 to 2013, which was much lower than that of other types of grasslands, e.g., 158.00 g C m−2 y−1 for the temperate grasslands of northern China during the growing season46, 11.25 g C m−2 y−1 for the temperate grasslands of China24 by TEM from 1961 to 2007, 88.21 g C m−2 y−1 for the grasslands of Inner Mongolia47 and 4.00~192.50 g C m−2 y−1 for the grasslands of the Tibetan Plateau20,48,49,50,51,52. The estimates of Sun et al.44 were quite different from those of this study due to the large difference in the scopes of the two studies. The research by Sun et al.44 was based on samples from three typical grasslands (mountain steppe meadow, typical grassland mountain and slope and typical mountain meadow) in southern China, while this study covered all grasslands in southern China.

Table 2 Carbon budget of different study regions from different studies.

The TEM-simulated NPP of southern grasslands was 318.15 g C m−2 y−1, which was within the range of previous estimates40,41,44,45 of 191.96~389.14 g C m−2 y−1. In comparison, our estimated average regional NPP was lower than that simulation by Sun et al.42 (1081.78 g C m−2 y−1) using the Global Production Efficiency Model, by Sun et al.43 (656.30 g C m−2 y−1) using the improved comprehensive and sequential classification system (CSCS) and by Sun et al.39 (471.62 g C m−2 y−1) using a new climate productivity model. Our estimated annual NPP increased (0.88 g C m−2 y−1) from 1981 to 2000 and decreased (1.01 g C m−2 y−1) from 2000 to 2011.and Sun et al.40 similarly found an increasing trend from 1981 to 2000 with the GLO-PEM model and a decreasing trend from 2000 to 2011 by the improved CASA model. Earlier studies reported the NPP of different grasslands types. For instance, the simulated NPP ranged from 259.90 to 290.23 g C m−2 y−1 in Inner Mongolian grasslands47,53,54,55,56 and from 92.50 to 334.45 g C m−2 y−1 in Tibetan Plateau grasslands19,20,48,51,52,57,58,59. In general, the NPP of southern grasslands was similar to that of Inner Mongolian grasslands and slightly higher than the NPP of Qinghai-Tibetan Plateau grasslands per unit area.

Our simulated RH ranged from 300.90~339.00 g C m−2 y−1 from 1961 to 2013 with an annual average of 317.16 g C m−2 y−1, which was similar to that estimated by Liu45 with a range from 79.61 to 719.14 g C m−2 y−1 of the grasslands of the Qinghai-Tibetan Plateau18,48,52 and 132.00~866.00 g C m−2 y−1 for temperate grasslands60,61 but lower than 470.00~900.00 g C m−2 y−1 for tropical grasslands60.

The differences of in the grassland carbon budgets of these studies37,38,39,40,41,42,43,44,45 probably resulted for the following reasons. (1) The study regions and areas were different. The studies by Sun et al.41 and Liu45 were administered in south China and eastern Tibet, respectively, and the study by Sun et al.38 was administered in southern China, which had a grassland area of 0.6 × 106 km2 of grasslands area. (2) The data sources in previous studies were also quite different. For example, Sun et al.38 and Liu45 used data from the moderate-resolution imaging spectroradiometer (MODIS) to estimate grassland NPP, and Sun et al.41,44 used observation data from 2009 to 2010 and data from the 1980 Chinese grassland resource inventory, respectively. (3) Different estimation methods were used. For example, Sun et al.38 estimated the NPP of grasslands based on the relationship between NPP and the normalized difference vegetation index (NDVI); Liu45 estimated NPP and carbon storage with the improved Comprehensive Sequence Classification System (CASA) model; Sun et al.44 estimated NPP and NEP based on the BIOME-BGC model and Sun et al.42 used the Global Production Efficiency Model to simulate NPP. (4) Different study periods were used. Liu45, Sun et al.41,44 performed their studies from 2001 to 2010; Sun et al.42 analyzed the temporal and spatial dynamics from 1981 to 2000; Sun et al.38 worked in 2011; and Sun et al.39 performed research from 2000 to 2011.

In this study, the area of the southern grasslands was approximately 0.29 × 106 km2, with an annual mean NEP of 0.45 Tg C y−1, an annual mean NPP of 93.63 Tg C y−1 and an annual mean RH of 93.20 Tg C y−1. The annual mean NEP, NPP and RH of the grasslands of China were 8.44~54.40 Tg C30,69, 490.40~1392.00 Tg C62,63,64,65,66,67,68,69,70 and 320.00~3068.40 Tg C71,72,73, respectively, based on the results of previous studies (Table 3). Therefore, our results showed that southern grasslands accounted for 0.83~5.33% of the NEP, 6.73~19.09% of the NPP and 3.04~29.13% of the RH of the grasslands of China.

Table 3 Southern grasslands contribute to carbon budget in China’s grasslands.

The seasonal variation in NEP indicated that the dry season was a key period for annual net carbon emissions. In the wet season (June to August), southern grasslands acted as a weak carbon sink and were close to carbon neutral because the carbon fixed by the vegetation was equal to the amount released from the soil under warm and moist conditions. In contrast, the maximum monthly NEP was observed in May and October. Our study showed that the seasonal and annual variations in NEP were mainly driven by NPP.

Our analyses suggested that temperature (air temperature and soil temperature) was a key factor influencing the NEP of southern grasslands at the seasonal scale, accounting for 45% of the total variance in NEP. Future warming will tend to obviously positively correlate with the NEP of southern grasslands. Meanwhile, the results from our study suggested that water conditions (precipitation and soil moisture) also had strongly positive effects on the NEP of southern grasslands at the seasonal scale. Overall, all selected climatic variables could explain approximately 94.7% (P < 0.001) of the seasonal variation in NEP.

At the annual scale, the results indicated a positive relationship between the annual NEP and the annual soil moisture, but we found no remarkable relationship between annual NEP and any of other climatic variables. In contrast, Sun et al.44 found a negative correlation between annual NEP and annual mean temperature, but correlation analysis detected no significant relationship between precipitation and NEP in southern grasslands.


Study Region

The study region, which accounts for 24.60% of the total terrestrial area of China, covers approximately 2.36 × 106 km2 and is located in southern China. Administratively, southern China includes 17 provinces and municipalities, e.g., Yunnan (excluding the Qinghai-Tibet Plateau), Sichuan (excluding the western portion of the Qinghai-Tibet Plateau), Guangxi, Hunan, and Hubei (Fig. 5). The southern grasslands cover an area of approximately 0.29 × 106 km2, which accounts for 12.50% of southern China and 7.50% of the grasslands of China. The southern grasslands are mainly characterized by tropical tussock and tropical shrub-tussock vegetation based on the second grassland investigation in China, and the altitude varies by two or three staircases over complicated terrain. The eastern region is mainly composed of plains and hills, while the western region is mainly composed of basins and plateaus. Furthermore, the study area is mainly characterized by temperate monsoon, subtropical monsoon, and tropical monsoon climates. The summer is hot and humid and the winter is warm in the tropical monsoon climate region and cold in the subtropical monsoon climate region, although the air is relatively warm and moist45. The frost-free period is more than 300 d with abundant precipitation; the precipitation ranges from 800 mm to 1600 mm in most areas that are suitable for growing forage74.

Figure 5
Figure 5

Distribution of southern grasslands (This picture was made by ArcGIS 10.2,

Data Source

The Terrestrial Ecosystem Model (TEM 5.0) requires input of monthly climate data sets (precipitation, temperature, cloud cover, and solar radiation) and longitude and latitude location information as well as soil texture, altitude, grassland distribution, and atmospheric CO2 data (Table 4). Daily climate data sets were obtained from basic and reference meteorological stations from National Meteorological Information Center, China Meteorological Administration (CMA) from 1961 to 2013. In this study, the TEM was applied to grid-cell for regional-scale simulation at a 1-month time scale, and each grid was 10 km × 10 km. First, we interpolated temperature and precipitation in southern China to a resolution of 10 km × 10 km using ANUSPLIN4.375 and then calculated the solar radiation based on grid temperature and precipitation data sets using the method described by Thornton and Running76. Finally, we aggregated the daily climate data into monthly time steps to drive the TEM. The elevation data sets were taken from the National Fundamental Geographic Information System of China, and the soil texture data sets were collected from Nanjing Institute of Soil, the Chinese Academy of Sciences (CAS).The vegetation data sets were provided by the national grassland resource survey (Fig. 5). All the above spatial data sets were rearranged at a resolution of 10 km × 10 km. The atmospheric CO2 data from 1961 to 2013 were gathered from National Oceanic and Atmospheric Administration (NOAA), United States Department of Commerce (

Table 4 Environmental Variables for the TEM.

The vegetation and soil carbon density observation data from 2011 to 2013 grasslands investigation were employed for TEM calibration and verification. As the southern grasslands are scattered, we randomly selected ten uniformly distributed meteorological stations in southern China to achieve the appropriate parameterization (Fig. 6a). Then, we chose survey samples within 50 km of each meteorological station using ArcGIS software as the calibration dataset (19 samples in total), and finally, the remaining survey samples were used to verify the TEM simulation results (Fig. 6b).

Figure 6
Figure 6

Distributions for 10 select meteorological stations in southern grasslands (a) and investigation stations to verified the model (b) (This picture was made by ArcGIS 10.2,


The TEM is a regional- and global-scale, process-based biogeochemical ecosystem model that uses spatial climate, soil, vegetation and elevation data to simulate the sizes of carbon and nitrogen pools and the fluxes of terrestrial ecosystems at the month time scale77,78. The model framework and details have been described in previous studies64,77,78,79,80. This work used TEM version 5.0, which coupled TEM version 4.2 with a soil thermal model and is used widely for high altitudes and latitudes78,81,82,83,84,85.

At the monthly scale, the TEM defined the difference between net primary production (NPP) and soil heterotrophic respiration (RH) as net ecosystem production (NEP), and the difference between gross primary production (GPP) and plant autotrophic respiration (RA) was defined as NPP. These algorithms have been described in many previous studies48,77,79,80.

Model Parameterization, Verification and Simulation

TEM parameterization requires monthly climate data sets and vegetation- and soil-specific parameters related to the nitrogen and carbon processes in vegetation and soil. For TEM calibration, the required parameters and their sources are shown in Table 5. The parameterization must follow three criteria within a certain tolerance (1%): (1) the simulated annual NPP and GPP are close to the measured values; (2) the simulated annual nitrogen uptake matches the measured value; and (3) the simulated annual NEP is close to zero24,78.

Table 5 Southern grassland ecosystem parameterization method.

After TEM calibration, we used the soil and vegetation carbon density field observation data from the 2011 to 2013 grassland survey to verify the simulated results by comparing the average simulated results for 2011~2013 with the observations. When multiple sample sites located in one grid cell, we calculated the mean value of these sample sites.

We then extrapolated the parameterized and verified TEM to southern China for 1961 to 2013. For zone modeling, we first ran the TEM at each grid cell to equilibrium using the long-term average monthly meteorological data sets of 1961~2013. Then, to explain the impact of inter-annual climate variability, we used the 1961~1975 climate data to spin up 45 years of the model of the undisturbed ecosystem. Finally, we used atmospheric CO2 concentrations and monthly climate data for 1961~2013 to run the model.

Lastly, after model simulation, we aggregated the simulated results from each grid to the whole study region by multiplying the grasslands area data and the simulated results grid by grid.

Data availability statement

The datasets used during the current study such as vegetation and soil carbon density are available from grasslands investigation during 2011 to 2013 (this project was funded by the Strategic Priority Research Program—Climate Change: Carbon Budget and Related Issues of the Chinese Academy of Sciences [grant number XDA05050408]), climate data sets are available from National Meteorological Information Center, China Meteorological Administration (CMA), the elevation data sets are available from the National Fundamental Geographic Information System of China, the soil texture data sets are available from Nanjing Institute of Soil, the Chinese Academy of Sciences (CAS), the vegetation data sets are available from the national grassland resource survey, and the atmospheric CO2 data is availabe from National Oceanic and Atmospheric Administration (NOAA), United States Department of Commerce (

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  1. 1.

    Scurlock, J. M. O., Johnson, K. & Olson, R. J. Estimating net primary productivity from grassland biomass dynamics measurements. Glob. Change Biol. 8, 736–753 (2002).

  2. 2.

    Li, K. R., Wang, S. & Cao, M. Vegetation and soil carbon storage in China. Sci. China Ser. D 47, 49–57 (2004).

  3. 3.

    Department of Animal Husbandry and Veterinary, Institute of Grassland, Chinese Academy of Agricultural Sciences (DAHV), and General Station of Animal Husbandry and Veteriary, China Ministry of Agriculture (GSAHV). Rangeland resources of China. Beijing: China Agricultural Science and Technology Press (1996).

  4. 4.

    Adams, J. M., Faure, H., Faure-Denard, L., Mcglade, J. M. & Woodward, F. I. Increases in terrestrial carbon storage from the Last Glacial Maximum to the present. Nature 348, 711–714 (1990).

  5. 5.

    Chapin, F. S. III et al. Reconciling carbon-cycle concepts, terminology, and methods. Ecosystems 9, 1041–1050 (2006).

  6. 6.

    Chapin, F. S. III et al. The changing global carbon cycle: linking plant-soil carbon dynamics to global consequences. J. Ecol. 97, 840–850 (2009).

  7. 7.

    Yu, G. R., Fang, H. J., Fu, Y. L. & Wang, Q. F. Research on carbon budget and carbon cycle of terrestrial ecosystems in regional scale: a review. Acta Ecol. Sin. 31, 5449–5459 (2011).

  8. 8.

    Zhou, G. S. & Zhang, X. S. Study on NPP of natural vegetation in China under global climate change. Acta Phytoecol. Sin. 20, 11–19 (1996).

  9. 9.

    Fang, J. Y. et al. Interannual variability in net primary production and precipitation. Science 293, 1723 (2001).

  10. 10.

    Xiao, X. et al. Net primary production of terrestrial ecosystems in China and its equilibrium response to changes in climate and atmospheric CO2 concentration. Acta Phytoecol. Sin. 22, 97–118 (1998).

  11. 11.

    Sun, R. & Zhu, Q. J. Effect of climate change on terrestrial net primary productivity in China. J. Remote Sens. 5, 61–62 (2001).

  12. 12.

    Piao, S. L. et al. Changes in vegetation net primary productivity from 1982 to 1999 in China. Global Biogeochem. Cy. 19, 1605–1622 (2005).

  13. 13.

    Ni, J. Carbon storage in terrestrial ecosystems of China: estimates at different spatial resolutions and their responses to climate change. Climatic Change 49, 339–358 (2001).

  14. 14.

    Ni, J. Carbon storage in grasslands of China. J. Arid Environ. 50, 205–218 (2002).

  15. 15.

    Ni, J. Forage yield-based carbon storage in grasslands of China. Climatic Change 67, 237–246 (2004).

  16. 16.

    Ni, J. Estimating net primary productivity of grasslands from field biomass measurements in temperate northern China. Plant Ecol. 174, 217–234 (2004).

  17. 17.

    Piao, S. L. & Fang, J. Y. Terrestrial net primary production and its spatio-temporal patterns in Qinghai-Xizang Plateau, China during 1982–1999. J. Natural Res. 17, 373–380 (2002).

  18. 18.

    Wang, G. X., Qian, J., Cheng, G. D. & Lai, Y. M. Soil organic carbon pool of grassland soils on the Qinghai–Tibetan plateau and its global implication. Sci. Total Environ. 291, 207–217 (2002).

  19. 19.

    Zhang, Y. L. et al. Spatial and temporal variability in the net primary production of alpine grassland on the Tibetan Plateau since 1982. J. Geogr. Sci. 24, 269–287 (2014).

  20. 20.

    Yan, L., Zhou, G. S., Wang, Y. H., Hu, T. Y. & Sui, X. H. The spatial and temporal dynamics of carbon budget in the alpine grasslands on the Qinghai-Tibetan Plateau using the Terrestrial Ecosystem Model. J. Clean Prod. 108, 189–196 (2015).

  21. 21.

    Ma, W. H. et al. The carbon storage levels of temperate grassland vegetation in Inner Mongolia. J. Arid Land Res. Environ. 20, 192–195 (2006).

  22. 22.

    Yang, Y. H. et al. Soil carbon stock and its changes in northern China’s grasslands from 1980s to 2000s. Global Change Biol. 16, 3036–3047 (2010).

  23. 23.

    Wang, G. C., Zhang, W. & Huang, Y. Temporal and spatial variation characteristics of NPP in Inner Mongolian grasslands from 1981 to 2001. Pratacult. Sci. 28, 2016–2025 (2011).

  24. 24.

    Sui, X. H. & Zhou, G. S. Carbon dynamics of temperate grassland ecosystems in China from 1951 to 2007: an analysis with a process-based biogeochemistry model. Environ. Earth Sci. 68, 521–533 (2013).

  25. 25.

    Wang, J., Hu, D. & Sun, Z. Simulation of terrestrial C cycle balance model in Tibet. J. Geogr. Sci. 13, 316–322 (2003).

  26. 26.

    Tian, H. Q. et al. Patterns of soil nitrogen storage in China. Global Biogeochem. Cy. 20, GB1001(1-9) (2006).

  27. 27.

    Piao, S., Fang, J., Zhou, L., Tan, K. & Tao, S. Changes in biomass carbon stocks in China’s grasslands between 1982 and 1999. Global Biogeochem. Cy. 21, GB2002(1-10) (2007).

  28. 28.

    Fang, J. Y., Yang, Y. H., Ma, W. H. & Shen, H. H. Ecosystem carbon stocks and their changes in China’s grasslands. Sci. China Life Sci. 53, 757–765 (2010).

  29. 29.

    Fang, J. Y., Liu, G. H. & Xu, S. L. Carbon pools in terrestrial ecosystems in China. Hot Spots in Modern Ecology, China Science and Technology Press, Beijing 251–277 (1996).

  30. 30.

    Fang, J. Y., Guo, Z. D., Piao, S. L. & Chen, A. P. Terrestrial vegetation carbon sinks in China, 1981–2000. Sci. China Earth Sci. 50, 1341–1350 (2007).

  31. 31.

    Piao, S. L., Fang, J. Y., He, J. S. & Xiao, Y. Spatial distribution of grassland biomass in China. Acta Phytoecologica Sinica 28, 491–498 (2004).

  32. 32.

    Fan, J. W. et al. Carbon storage in the grasslands of China based on field measurements of above-and below-ground biomass. Climatic Change 86, 375–396 (2008).

  33. 33.

    Xie, Z. B. et al. Soil organic carbon stocks in China and changes from 1980s to 2000s. Global Change Biol. 13, 1989–2007 (2007).

  34. 34.

    Falkowski, P. et al. The global carbon cycle: a test of our knowledge of earth as a system. Science 290, 291–296 (2000).

  35. 35.

    Houghton, R. A. Balancing the global carbon budget. Annu. Rev. Earth Planet Sci. 35, 313–347 (2007).

  36. 36.

    Yu, G. R., Wang, Q. F. & Zhu, X. J. Methods and uncertainties in evaluating the carbon budgets of regional terrestrial ecosystems. Prog. Geogr. 30, 103–313 (2011).

  37. 37.

    Sun, C. M. et al. Estimation model and the application of grassland NPP based on precipitation and temperature in south China. Ecol. Environ. Sci. 22, 1458–1461 (2013).

  38. 38.

    Sun, C. M. et al. Remote sensing estimation and application of grassland NPP based on MODIS data in southern China. Acta Pratacult. Sin. 22, 11–17 (2013).

  39. 39.

    Sun, C. M., Zhong, X. C., Chen, C., Gu, T. & Chen, W. Evaluating the grassland net primary productivity of southern China from 2000 to 2011 using a new climate productivity model. J. Integr. Agr. 15, 1638–1644 (2016).

  40. 40.

    Sun, Z. G. The study of productivity and carbon storage estimation in the southern of China’s grasslands. Nanjing: Nanjing University (2012).

  41. 41.

    Sun, Z. G., Sun, C. M., Zhou, W., Ju, W. M. & Li, J. L. Evaluating the net primary productivity in the grasslands of southern China from 2001 to 2010 using a new land portfolio assessment model. Plant Ecol. 214, 1223–1232 (2013).

  42. 42.

    Sun, Z. G. et al. Evaluation of net primary productivity and its spatial and temporal patterns in southern China’s grasslands. Rangeland J. 35, 331–318 (2013).

  43. 43.

    Sun, Z. G., Sun, C. M., Zhou, W., Ju, W. M. & Li, J. L. Classification and Net Primary Productivity of the Southern China’s Grasslands Ecosystem Based on Improved Comprehensive and Sequential Classiifcation System (CSCS) Approach. J. Integr. Agr. 13, 893–903 (2014).

  44. 44.

    Sun, Z. G., Chen, Y. Z., Ju, W. M., Zhou, W. & Li, J. L. Productivity of different types of grassland plots and their responses to climate change in the southern China. Resources and Environment in the Yangtze Basin 24, 609–616 (2015).

  45. 45.

    Liu, S. Y. Carbon storage changes in Grassland of southern China between 1982 to 2012 by remote sensing process model. Chengdu: Chengdu University of Technology (2014).

  46. 46.

    Zhang, L., Guo, H. D., Jia, G. S. & Wylie, B. Net ecosystem productivity of temperate grasslands in northern China: An upscaling study. Agr. Forest Meteorol. 184, 71–81 (2014).

  47. 47.

    Dai, E. F., Huang, Y., Wu, Z. & Zhao, D. S. Spatial-temporal features of carbon source-sink and its relationship with climate factors in Inner Mongolia grassland ecosystem. Acta Geogr. Sin. 71, 21–34 (2016).

  48. 48.

    Zhuang, Q. L., He, J., Lu, Y. Y., Ji, L., Xiao, J. F. & Luo, T. X. Carbon dynamics of terrestrial ecosystems on the Tibetan Plateau during the 20th century: an analysis with a process-based biogeochemical model. Global Ecol. Biogeogr. 19, 649–662 (2010).

  49. 49.

    Kato, T. et al. Temperature and biomass influences on interannual changes in CO2, exchange in an alpine meadow on the Qinghai-Tibetan Plateau. Global Change Biol. 12, 1285–1298 (2006).

  50. 50.

    Pei, Z. Y., Zhou, C. P., OuYang, H. & Yang, W. B. A carbon budget of alpine steppe area in the Tibetan Plateau. Geogr. res. 29, 102–110 (2010).

  51. 51.

    Yi, S. H., Wang, X. Y., Qin, Y., Xiang, B. & Ding, Y. J. Responses of alpine grassland on Qinghai–Tibetan plateau to climate warming and permafrost degradation: a modeling perspective. Environ. Res. Lett. 9, 1195–1206 (2014).

  52. 52.

    Pei, Z. Y., OuYang, H., Zhou, C. P. & Xu, X. L. Carbon balance in an alpine steppe in the Qinghai—Tibet plateau. J. Integr. Plant Biol. 51, 521–526 (2009).

  53. 53.

    Mu, S. J. et al. Spatio-temporal variation analysis of grassland net primary productivity and its relationship with climate over the past 10 years in Inner Mongolia. Acta Pratacult. Sin. 22, 6–15 (2013).

  54. 54.

    Zhang, F., Zhou, G. S. & Wang, Y. H. Dynamics simulation of nep primary productivity by a satellite data driven CASA model in Inner Mongolian typical steppe. China. J. Plant Ecol. 32, 786–797 (2008).

  55. 55.

    Zhu, W. Q. et al. Estimating Net Primary Productivity of Terrestrial Vegetation Based on GIS and RS:A Case Study in Inner Mongolia, China. J. Remote Sens. 9, 300–307 (2004).

  56. 56.

    Bao, G., Bao, Y. H., ALateng, T. Y., Wu, Z. & Zhao, D. S. Estimation of vegetation net primary productivity using MODIS data and CASA model in Inner Mongolia. The National Agricultural Remote Sensing Technology Conference 247–256 (2009).

  57. 57.

    Zhou, C. P., Hua, O. Y., Wang, Q. X., Watanabe, M. & Sun, Q. Q. Estimation of Net Primary Productivity in Tibetan Plateau. Acta Geogr. Sin. 59, 74–79 (2004).

  58. 58.

    Piao, S., Fang, J. & He, J. Variations in Vegetation Net Primary Production in the Qinghai-Xizang Plateau, China, from 1982 to 1999. Climatic Change 74, 253–267 (2006).

  59. 59.

    Tan, K. et al. Application of the ORCHIDEE global vegetation model to evaluate biomass and soil carbon stocks of Qinghai-Tibetan grasslands. Global Biogeochem Cy. 24, 425–427 (2010).

  60. 60.

    Wang, G. C., Du, R., Kong, Q. Z. & Lv, D. R. Experimental study on soil respiration characteristics of typical temperate steppe in China. Chinese Sci. Bull. 49, 692–696 (2004).

  61. 61.

    Liu, L. X., Dong, Y. S. & Qi, Y. C. The Development on the Soil Respiration of Grassland Ecosystem. Prog. Geogr. 23, 35–42 (2004).

  62. 62.

    Jiang, L. P. et al. A research of net primary productivity model of grassland based on MODIS data. Chinese J. grassland 28, 72–76 (2006).

  63. 63.

    Liu, M. L. Land-use/land-cover change and terrestirial ecosystem phytomass carbon pool and production in China. Beijing: Chinese Academy of Sciences (2001).

  64. 64.

    Wang, Y. Y. et al. Valuation of the organic matter produced by terrestrial vegetation ecosystems in China in 1995 based on remote sensing. Ecol. Environ. 14, 455–459 (2005).

  65. 65.

    Tao, B., Li, K. R., Shao, X. M. & Cao, M. K. Temporal and spatial pattern of net primary production of terrestrial ecosystems in China. Acta Geogr. Sin. 58, 372–380 (2003).

  66. 66.

    Zhang, H.L. The feature analysis on Chin’s terrestrial NPP spatial-temporal change in recent five years. Nanjing: Nanjing Normal University (2006).

  67. 67.

    He, Y., Dong, W. J., Ji, J. J. & Dan, L. The net primary production simulation of terrestrial ecosystems in China by AVIM. Adv. Earth Sci. 20, 345–349 (2005).

  68. 68.

    Zhu, W. Q., Pan, Y. Z. & Zhang, J. S. Estimation of net primary productivity of Chinese terrestrial vegetation based on remote sensing. J. Plant Ecol. 31, 413–424 (2007).

  69. 69.

    Zhou, W. Temporal and spatial dynamics of grassland ecosystem productivity and its affecting factors in China. Nanjing: Nanjing University (2014).

  70. 70.

    Feng, X. et al. Net primary productivity of China’s terrestrial ecosystems from a process model driven by remote sensing. J. Environ. Manage 85, 563–573 (2007).

  71. 71.

    Yi, L. M. Soil respiration of different land-use types and its climatic factors in China. Heilongjiang Sci. Technol. Inf. (32), 23 (2010).

  72. 72.

    Zhan, X. Y., Yu, G. R., Zheng, Z. M. & Wang, Q. F. Carbon emission and spatial pattern of soil respiration of terrestrial ecosystems in China: based on geostatistic estimation of flux measurement. Prog. Geogr. 31, 97–108 (2012).

  73. 73.

    Dong, Y. S. & Qi, Y. C. Research progress of carbon cycle process in grassland ecosystem. Geogr. Res. 25, 183 (2006).

  74. 74.

    Xu, L. L., Bao, S. M. & Li, R. T. Analysis on the social and economical benefit of nourishing pasturage by grass planting in the South of China. Journal of East China Institute of Technology (Social Science Edition) 23, 93–96 (2004).

  75. 75.

    Hutchinson, M. F. ANUSPLIN Version4. 3 User Guide. Canberra: The Australia National University. Center for Resource and Environment Studies (2004).

  76. 76.

    Thornton, P. E. & Running, S. W. An improved algorithm for estimating incident daily solar radiation from measurements of temperature, humidity, and precipitation. Agr. Forest Meteorol. 93, 211–228 (1999).

  77. 77.

    Raich, J. W. et al. Potential net primary productivity in South America: application of a global model. Ecol. Appl. 1, 399–429 (1991).

  78. 78.

    Zhuang, Q. L. et al. Carbon cycling in extratropical terrestrial ecosystems of the Northern Hemisphere during the 20th century: a modeling analysis of the influences of soil thermal dynamics. Tellus B 55, 751–776 (2003).

  79. 79.

    Mcguire, A. D. et al. Interactions between carbon and nitrogen dynamics in estimating net primary productivity for potential vegetation in North America. Global Biogeochem. Cy. 6, 101–124 (1992).

  80. 80.

    Mcguire, A. D. et al. Equilibrium responses of global net primary production and carbon storage to doubled atmospheric carbon dioxide: sensitivity to changes in vegetation nitrogen concentration. Global Biogeochem. Cy. 11, 173–189 (1997).

  81. 81.

    Zhuang, Q. L., Romanovsky, V. & Mcguire, A. D. Incorporation of a permafrost model into a large—scale ecosystem model: Evaluation of temporal and spatial scaling issues in simulating soil thermal dynamics. J. Geophys. Res. Atmos. (1984–2012) 106, 33649–33670 (2001).

  82. 82.

    Zhuang, Q. L. et al. Modeling soil thermal and carbon dynamics of a fire chronosequence in Interior Alaska. J. Geophys. Res. Atmos. (1984–2012) 107, FFR 3-1–FFR 3-26 (2002).

  83. 83.

    Zhuang, Q. L. et al. CO2 and CH4 exchanges between land ecosystems and the atmosphere in northern high latitudes over the 21st century. Geophys. Res. Lett. 33, 130–142 (2006).

  84. 84.

    Euskirchen, E. S. et al. Importance of recent shifts in soil thermal dynamics on growing season length, productivity, and carbon sequestration in terrestrial high-latitude ecosystem. Global Change Biol. 12, 731–750 (2006).

  85. 85.

    Balshi, M. S. et al. The role of historical fire disturbance in the carbon dynamics of the pan-boreal region: a processbased analysis. J. Geophys. Res. 112, 339–343 (2007).

  86. 86.

    Melillo, J. M. et al. Global climate change and terrestrial net primary production. Nature 363, 234–240 (1993).

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This work was supported by China Special Fund for Meteorological Research in the Public Interest (Major projects)[grant number GYHY201506001-3] and the Strategic Priority Research Program—Climate Change: Carbon Budget and Related Issues of the Chinese Academy of Sciences [grant number XDA05050408]. We thank the special carbon funding for supporting our grassland field investigation that provided data for this work. We also want to acknowledge the help and support from Jun Tang, Qiuling Wang and Jian Song.

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  1. State Key Laboratory of Vegetation and Environmental Change, Institute of Botany, Chinese Academy of Sciences, Beijing, 100093, China

    • Li Zhang
    • , GuangSheng Zhou
    •  & YongFei Bai
  2. University of Chinese Academy of Sciences, Beijing, 100049, China

    • Li Zhang
  3. Chinese Academy of Meteorological Sciences, Beijing, 100081, China

    • GuangSheng Zhou
    •  & YuHe Ji


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Zhang L. and Zhou G.S. wrote the main manuscript text, Ji Y.H. and Bai Y.F. prepared data and drew figures. All authors reviewed the manuscript.

Competing Interests

The authors declare that they have no competing interests.

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Correspondence to GuangSheng Zhou.

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