Alpine meadows have become particularly vulnerable to climate change. Variations in precipitation and temperature affect the ecological production of a region. The matrices for ecosystem change include net primary production (NPP), net photosynthesis (PsnNet), and net ecosystem production (NEP), of which NEP is the most important. Using the CENTURY model for alpine meadows, we investigated the response of NEP to climate change from 1969 to 2018 in Gannan, Gansu Province, China. The distribution pattern of NEP in Gannan was simulated with the model. The Mann–Kendall trend test was used to analyze the interannual variation of NEP for the individual counties and the entire study area over a 50-year period. The results showed the annual NEP distribution pattern varied widely in the region, with the highest NEP values in the southeastern part while the northwestern part showed the lowest. The highest NEP was measured in summer with sufficient precipitation and higher temperatures, whereas the lowest values occurred in winter. The total carbon sequestered in the Gannan alpine meadow over the last 50 years was 43580.9 gC, with the average annual NEP of the alpine meadow being approximately 813.62 gC m−2 year−1. Due to the combined effects of precipitation and temperature variations, NEP exhibits significant seasonal and interannual variations. The simulated NEP values of the seven counties fluctuated over the last 50 years, with each county showing an upward trend and the simulated NEP in the entire Gannan area also showing a clear upward trend that mutated around the year 1990. Both precipitation and temperature were found to be significantly and positively correlated with NEP. Precipitation was a significant driving factor, while temperature interacted with precipitation on the carbon budget. The carbon budget in the alpine meadow was stabilized by increasing precipitation and temperature. Estimates from NEP were in satisfactory correlation with measurements (r2=0.74, n=25 at p<0.01). Our findings provide preliminary understanding of the carbon budget and climatic feedback in Gannan alpine meadows which will help to predict carbon fluxes in the terrestrial biosphere and the impacts of climate change.

  • Mapping the distribution pattern of NEP in Gannan from 1969 to 2018.

  • Determining the inter-annual changes in NEP.

  • Evaluating the potential driving mechanisms responsible for the changes in the carbon budget.

Graphical Abstract

Graphical Abstract
Graphical Abstract

Carbon cycle is among the most important scientific concerns in the assessment of universal climate change, both now and in the near future. The relationship between terrestrial ecosystems and climate is carbon cycle in plants, soils, and the atmosphere (Zhao et al. 2012a, 2012b; Stocker 2014). The photosynthetic processes in vegetation (autotrophic) and soil (heterotrophic) respiration influence ecosystem carbon flows. The difference between the amount of carbon received from the atmosphere through photosynthetic processes in the form of CO2 and the amount of carbon released through the soil and vegetation respiration is known as net ecosystem production (NEP) (Ran et al. 2016). The application of NEP measurement can help identify whether a system is a net carbon sink or a net source of carbon. If the NEP is positive, it implies the biosphere is a carbon sink; if the NEP is negative, it implies the biosphere is a source of carbon. Therefore, NEP is critical for assessing carbon storage in ecosystems and underlying carbon cycle regulating activities can be effectively monitored.

Numerous factors such as precipitation, temperature, soil properties, and radiation affect the CO2 flux of ecosystems (Wu et al. 2013; Helbig et al. 2017; Xi et al. 2017). These factors, however, have varying effects in different ecosystems (Lee & Bae 2015; Shah et al. 2015). In arid areas, high temperatures or warming can reduce soil moisture, photosynthesis, and respiration rates, whereas precipitation can increase the soil moisture, promote photosynthesis, and ecosystem respiration (Bragazza et al. 2016; Gustafson et al. 2017). In humid areas, warming increases soil temperature and improves the rate of photosynthesis and respiration, whereas precipitation lowers soil temperature and reduces oxygen availability in the soil, leading to the weakening of photosynthesis and respiration (Barron-Gafford et al. 2012; Schippers et al. 2015). In addition, changes in temperature and precipitation have been demonstrated to affect the soil temperature and moisture, affecting soil vegetation and microbial activities, as well as changing the CO2 flow of the ecosystem (Vargas et al. 2010; Schippers et al. 2015).

Inter-annual fluctuations in NEP have been studied for several decades particularly on temperate ecosystems (Pilegaard et al. 2011; Wilkinson et al. 2012; Froelich et al. 2015), however, synthesis for all ecosystem types was recently proposed by Baldocchi et al. 2018. It has been shown that the causes of interannual NEP variability in ecosystems are manifold and vary from site to site depending on the climate and vegetation type (Keppler et al. 2004). The most commonly identified causes include the following: variability in meteorological conditions (spring temperature, drought intensity or length, radiation), biotic response to environmental forcings (Richardson et al. 2007), long-term trends (Pilegaard et al. 2011), and natural or human-induced disturbances (logging, fires, thinning, insect infestations). However, it is known that, even for a given site, it is generally not possible to explain the inter-annual variability of NEP with a single factor (Pilegaard et al. 2011). In temperate and boreal regions, the availability of water in late summer, together with air temperature in spring, was reported to control the interannual variability of the carbon balance of Canadian Douglas fir stands (Poulter et al. 2013). In subtropical regions, spring temperature and late summer and autumn water content have also been found to be important factors controlling interannual variability in net carbon uptake (Krishnan et al. 2009; Zhang et al. 2011). However, most studies have only considered the immediate effects of environmental factors (Wen et al. 2006; Liu et al. 2014), while the delayed effects that often occur on long time scales have usually been ignored (Dunn et al. 2007).

Grassland occupies over 40% of the world's land area. It is one of the most extensively dispersed types of vegetation (Zhu et al. 2017). Grassland ecosystems store roughly 20% of the vegetation and soil carbon (Nagy et al. 2007), making them major players in the terrestrial carbon cycle (Li et al. 2013). China has a large grassland area and abundant natural resources. China's natural grassland area is currently between 2.80 × 1063.93 × 106 km2, and equivalent to 41.7% of the country's total area (Ministry of Agriculture of China 1996). It plays an important role as a forest carbon sink, and some of the exploitable deposits are substantial (Yang et al. 2015). According to Wang et al. (2017), China's grassland is a huge carbon sink, storing 41.67 PgC of carbon.

Gannan's alpine meadows, located in Gansu Province, China, are extremely vulnerable to climate change and perform a vital role in terrestrial ecosystems. The northwestern highlands and the southeastern area of the region have high NPP values (Zhang et al. 2020). Gannan has considerable rainfall, sufficient sunshine, and excellent soil because of the effect of the southwest monsoon air conditions against the Bay of Bengal.

The CENTURY model was developed by Colorado State University and the United States Department of Agriculture (Parton 1996). It was designed to model grassland carbon dynamics at first, but later parameters were changed and other management strategies were added. It is a popular model for simulating the biogeochemical cycle in terrestrial environments. Several researchers have used the model to estimate the temporal changes of soil carbon. For example, in both Brazil and South Africa, Galdos et al. (2009) employed the model to replicate the temporal dynamics of soil carbon (r2=0.89). In both tropical (De Araújo et al. 2021) and temperate (Bortolon et al. 2011) environments in Brazil, the model has proven great ability to replicate the consequences of varied land-use regimes and management methods. In Sudan, Ardö & Olsson (2003) used the model on soil organic carbon (SOC) estimates and found that the simulated and observed SOC were reasonably consistent with measurements (r2=70, n=13, p<0.01). In China, Tang et al. (2020) estimated the dynamics of SOC using the CENTURY model with a coefficient of determination (r2) of 0.722 and a mean error (ME) of 0.37 at p<0.01 in Shandong province. Furthermore, Zhang et al. (2021) estimated the simulated and observed SOC density (SOCD) and ABVG with correlation coefficients of 0.76 and 0.50, respectively, and a mean absolute percentage error (MAPE) between the observed and simulated SOCD of 0.0824 at p<0.01 using the model for Gansu province, China. This demonstrates that the CENTURY model provides a reliable method for estimating the consequences of climate change on terrestrial ecosystems (Tang et al. 2020; Zhang et al. 2021). Previous studies have primarily focused on the impact of climate change on NPP or NPP on a broad scale (Ollinger et al. 2008; Liu et al. 2015). However, few studies have been undertaken applying the CENTURY model to evaluate the NEP response to climate change, notably in Gannan. Therefore, the goal of this research is to calibrate and verify the CENTURY model towards (1) mapping the distribution pattern of NEP in Gannan from 1969 to 2018, (2) determining seasonal and inter-annual changes in NEP, and (3) evaluating the potential driving mechanisms responsible for the changes in the carbon budget.

Study area

Gannan Prefecture is situated south of Gansu and north of the Qinghai–Tibetan Plateau. As shown in Figure 1, it is located between longitudes 100 °45′E and 104 °45′E, and between latitudes 33 °06′N and 35 °44′N. The region has 27,106 acres of grassland, of which 93.9% is used for animal grazing (ZHANG et al. 2008). Our study region included seven counties of the Gannan region: Zhuoni, Lintan, Luqu, Maqu, Diebu, Xiahe, and Zhouqu. The region is largely a tropical plateau, with an average elevation of over 3,000 m above sea level (Cui et al. 2012). The elevation ranges between 1,172 and 4,777 m above sea level. Temperatures are between −30.6 and 28.9 °C with annual mean values between 1 and 3 °C. The annual precipitation ranges between 400 and 800 mm, with 1,200–1,350 mm of potential evaporation (Liu et al. 2018). The high elevation areas experience cold summers. However, areas of low elevations experience warm summers. There is snow cover on high mountains during the winter seasons.

Figure 1

Location of the study area with the following seven counties: (I) Maqu; (II) Luqu; (III) Xiahe; (IV) Zhuoni; (V) Lintan; (VI) Diebu; and (VII) Zhouqu.

Figure 1

Location of the study area with the following seven counties: (I) Maqu; (II) Luqu; (III) Xiahe; (IV) Zhuoni; (V) Lintan; (VI) Diebu; and (VII) Zhouqu.

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Study sites

Data preparation and sources

The climate data were obtained from www.worldclim.org (accessed on 23 March 2020). The Climatic Research Unit at the University of East Anglia adopted WorldClim 2.1 to downscale these data from de Paula et al. (2020). Average minimum temperature (°C), average maximum temperature (°C), and total precipitation (mm) are the variables provided.

The data for the terrestrial eco-regions are obtained from China's Database for Ecosystems and Ecosystem Services Zoning, which can be found at http://www.ecosystem.csdb.cn/. The spatial distribution patterns of forests, grasslands, shrubs, farmland, towns, deserts, bare land, and other ecosystems are all included.

Soil data were collected from the Harmonized World Soil Database Version 1.2, which was prepared by the Food and Agriculture Organization (FAO)of the United Nations and the International Institute for Applied Systems Analysis (IIASA) (HWSD V1.2). The China Soil Map is based on data from the Second National Soil Survey of China (1995) and is given by the Nanjing Institute of Soil Science (Shi et al. 2004). Bulk density, percentage sand, silt, clay, and the pH of the top and subsoil of each soil type are among the characteristics in the dataset.

To validate our model, the NPP data from which the NEP was derived were obtained from the annual NPP from the sum of all 8-day Net Photosynthesis (PSN) products (MOD17A3H) for the year 2015 from NASA's (LP DAAC) (Team 2020). It provides global exact terrestrial vegetation NPP inter-annual variation data products, with a spatial resolution of 500 m. This data collection has been utilized extensively in studies of regional and worldwide carbon flux and NPP (Zhao et al. 2005; Liu et al. 2017).

Using ArcGIS, a vector file representing the research region was constructed by intersecting the climate, ecological, and soil maps. This resulted in a total of 25 locations spread across Gannan's seven counties (Figure 2). Each of the 25 locations has its soil and climate characteristics. We used this as a boundary map to calculate important statistics unique to each site using the zonal statistics tool in ArcGIS. To get the required input data for the CENTURY model, the mean minimum and maximum temperatures, the mean standard deviation, and skewness of the precipitation of each site were computed with help of the zonal statistics tool in ArcGIS. Figure 3 depicts the procedure.

Figure 2

Spatial distribution of the study sites in the Gannan region, Gansu Province.

Figure 2

Spatial distribution of the study sites in the Gannan region, Gansu Province.

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Figure 3

Flowchart of model simulation.

Figure 3

Flowchart of model simulation.

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The CENTURY model and validation

The CENTURY 4.5 models were used to simulate NEP in the Gannan alpine meadow grassland from 1969 to 2019. Parton et al. (1987) developed this model to predict the dynamic processes of sulphur (S), nitrogen (N), carbon (C), phosphorous (P), and some other elements in grassland ecosystems. It was later applied to forest, crop, and savanna ecosystems following a series of parameter changes. The model's main applications include the following: (i) as an ecosystem analysis tool, (ii) as a test for data consistency, and (iii) as a tool for evaluating the effects of management and climatic changes on ecosystems.

Initialization and parameterization of the CENTURY model

Model simulation involves five main processes: model parameterization, model initialization, model calibration, model simulation, and output of result. The model CENTURY receives input values via 12 ‘100’ files, each of which contains a specific subset of variables. These 12 ‘100’ files include crop.100, cult.100, fert.100, fire.100, fix.100, graz.100, harv.100, irri.100, omad.100, tree.100, trem.100, and .100. According to regional cultivation options, cropping options, fertilizer use options, and fixed parameter files are mainly related to organic matter, grazing options, fire options, irrigation conditions, organic matter addition options, tree species options, tree removal options, and site-specific parameters. Users can change the values in the options to meet parameter settings for different research areas through the FILE100 programme (Tang et al. 2020). For example, the CENTURY model has a set of default parameters. However, each of our study sites has specific vegetation, soil, climate, and management conditions. To account for these site-specific characteristics, several values were modified for all 25 sites. The initialization procedure of the model consists of replicating the state of the soil or vegetation from its initial state to the beginning of the simulation and preparing the simulation for the research period. Thus, we initialized the model by entering the mean monthly precipitation, standard deviation and skewness of precipitation, and mean minimum and maximum temperatures for 50 years, as well as entering the site control parameters such as pH, bulk density, silt, clay, and sand content. The EVENT 100 programme was used to set the event and NPP accumulation was assumed to be influenced only by environmental factors, running the model for thousands of years. In this study, it was set at 2,968 years. Model calibration aims to fine-tune the internal parameters of the model to improve the correlation between the simulated and measured values. Calibration is done by iteratively adjusting the internal parameters until the NPP output is close to the measured NPP, as suggested by Ardö & Olsson (2003). NPP (g/m2) was changed at each iteration until simulated NPP matched measured NPP stocks with considerable accuracy. The new parameters were then used to parameterize the other sites. This was considered the completion of the calibration process of the model CENTURY. The flow chart of the model simulation is shown in Figure 3.

NEP estimates

The difference between plant photosynthesis (net primary production, NPP) and heterotrophic respiration (RH), which releases some of the carbon taken by plants, is the net ecosystem productivity (NEP).

Given NPP and RH, NEP can be determined as indicated in the following equation
formula
(1)
where NPP stands for the net primary productivity (gC m−2) and RH stands for heterotrophic respiration (gC m−2).
The heterotrophic respiratory fraction RH, or soil respiration, is a key contributor to an ecosystem's carbon cycle. The estimated annual heterotrophic respiration (RH) was used according to the empirical relationship derived by a meta-analysis (Bond-Lamberty et al. 2004). The understanding of respiratory processes, especially for vegetation and soil in field situations, is extremely limited (Gao et al. 2013; Xia et al. 2015; Guo et al. 2016). Temperature increases the rate of enzyme reactions during respiration, hence respiration rises as temperature rises (Veroustraete et al. 2002). Respiration could also be measured by direct method and indirect method. In this study, we used the indirect method which calculates the soil respiration value as;
formula
(2)
where T represents temperature and R represents precipitation.

Model validation test

It is challenging to get observed NPP data for vast fields to assist in model verification (Gao et al. 2013). To verify our calibrated CENTURY model, we authenticated the NEP simulated by the model using NEP values for Gannan (25 data points in Figure 2) from June 2015. Observed NPP data of Gannan were downloaded from Running & Zhao (2019). NEP was then derived by using observed NPP (Equation (1)). We then validated our model by comparing the outputs of the model which is the simulated NEP with a set of independently observed data from Running & Zhao (2019). We then calculated the coefficient of determination, r2 between each pair of variables as follows:
formula
(3)

The coefficient of determinant is a statistical measure that determines the proportion of variance in a regression model. In other words, it demonstrates how well a set of data fits into a regression model (the goodness of fit). The closer r2 is to 1, the better the fitting effect, and the more accurate the fitting function.

We also calculated the Margin of error, ME as follows:
formula
(4)
where Si is the simulated NEP value at the ith site validation point, is the observed NEP value at the ith site validation point, is the average at the observed NEP values of Gannan validation point from the period 1969 to 2018, and S is the average value of the simulated NEP value of the site validation point from 1969 to 2018. ME is a statistical measure of the difference between actual and expected results and the closer it is to 1, the more efficient the simulation (Foereid et al. 2007).

Trend and significance test of simulated NEP and the climatic variables

The trend of the simulated NEP for the past 50 years was calculated using Sen's slope (Sen 1968). Sen's slope prevents the loss of time series data and the impact of data distribution on analysis findings, as well as outlier interference on time series data (Yin et al. 2011). It outperforms the competition when it comes to time series trend analysis. Sen's slope can be calculated using the following formula:
formula
(5)
where and are the sequence values at periods j and i, respectively, and S is Sen's slope. If the time series data are trending upwards; otherwise, they are trending downwards. Sen's slope is calculated by taking the sequence's median value. It can effectively decrease noise interference, but it cannot perform a significance evaluation of the sequence trend on its own.

The Mann–Kendall (MK) trend test method is a non-parametric test method for detecting the significance of the trends of climate variables; this has been widely used since it was suggested by the World Meteorological Organization (WMO) (Wu et al. 2005; Daneshvar Vousoughi et al. 2013; Gocic & Trajkovic 2013). The MK test has the advantage of not requiring the sample to follow a specific distribution and is unaffected by missing values or outliers; as a result, the MK method was developed to assess the importance of long-term NPP and NEP data trends (Wu et al. 2005; Wang et al. 2013). The following is the statistical test method.

For the time series define the standardized test statistic Z:
formula
(6)
formula
(7)
formula
(8)
where the sequence values at time j and i are and , respectively. The number of data is denoted by the letter n. When n>8, the test statistic S is roughly normally distributed, with the following mean and variance:
formula
(9)
formula
(10)

If at a significance level , the hypothesis that the current time series data has no trend is rejected, and there is an obvious trend shift; is the value corresponding to the standard normal function distribution table at significance level. For the significance test, the significance levels and were used. The change trend of the data series passes the significance test with a confidence of 90 or 95% when is greater than 1.65 or 1.96, respectively. In addition, the significance test findings were classified into two categories based on the positive Sen trend: significant decrease, relatively significant increase, and significant increase (Table 2).

Table 1

The summary statistics of the output variables for the various counties

Net ecosystem production (NEP)
gC/m2
CountySitesMinMaxAvg.Std.
Zhuoni 68.86 1,342.31 705.59 900.47 
65.04 1,336.12 703.52 902.95 
63.86 1,340.61 702.24 902.8 
Lintan 62.47 1,262.34 662.41 848.44 
63.72 1,294.12 678.92 870.02 
Luqu 65.12 1,276.29 670.71 856.43 
64.35 1,259.31 661.83 844.96 
65.71 1,281.06 673.39 859.38 
63.93 1,279.65 671.79 859.64 
Maqu 58.99 1,252.31 655.65 843.8 
56.47 1,248.52 652.5 842.91 
55.06 1,273.49 664.28 861.56 
Diebu 71.64 1,378.25 724.95 923.91 
72.12 1,391.08 731.6 932.65 
72.05 1,385.32 728.69 928.62 
71.36 1,373.03 722.2 920.42 
Xiahe 68.31 1,329.01 698.66 891.45 
68.07 1,318.34 693.21 884.07 
65.46 1,309.75 687.61 879.85 
66.09 1,312.26 689.18 881.18 
Zhouqu 82.81 1,491.11 813.62 1,033.04 
83.45 1,478.31 780.88 986.32 
82.16 1,484.76 783.46 991.79 
82.37 1,469.34 775.86 980.74 
82.74 1,478.56 780.65 986.99 
Net ecosystem production (NEP)
gC/m2
CountySitesMinMaxAvg.Std.
Zhuoni 68.86 1,342.31 705.59 900.47 
65.04 1,336.12 703.52 902.95 
63.86 1,340.61 702.24 902.8 
Lintan 62.47 1,262.34 662.41 848.44 
63.72 1,294.12 678.92 870.02 
Luqu 65.12 1,276.29 670.71 856.43 
64.35 1,259.31 661.83 844.96 
65.71 1,281.06 673.39 859.38 
63.93 1,279.65 671.79 859.64 
Maqu 58.99 1,252.31 655.65 843.8 
56.47 1,248.52 652.5 842.91 
55.06 1,273.49 664.28 861.56 
Diebu 71.64 1,378.25 724.95 923.91 
72.12 1,391.08 731.6 932.65 
72.05 1,385.32 728.69 928.62 
71.36 1,373.03 722.2 920.42 
Xiahe 68.31 1,329.01 698.66 891.45 
68.07 1,318.34 693.21 884.07 
65.46 1,309.75 687.61 879.85 
66.09 1,312.26 689.18 881.18 
Zhouqu 82.81 1,491.11 813.62 1,033.04 
83.45 1,478.31 780.88 986.32 
82.16 1,484.76 783.46 991.79 
82.37 1,469.34 775.86 980.74 
82.74 1,478.56 780.65 986.99 
Table 2

Statistical table for the significant test of the climatic variables trend

No.CountyNo. of sitesArea (km2)Area ratio (%)Significance types
Zhuoni 1,782 2.24 Relative significant increase 
II Lintan 1,026 1.29 Highly significant increase 
III Luqu 7,069 9.22 Significant increase 
IV Maqu 4,118 5.37 Significant increase 
Diebu 5,335 6.96 Highly significant increase 
VI Xiahe 1,329 1.73 Relative significant increase 
VII Zhouqu 54,459 71.06 Highly significant increase 
No.CountyNo. of sitesArea (km2)Area ratio (%)Significance types
Zhuoni 1,782 2.24 Relative significant increase 
II Lintan 1,026 1.29 Highly significant increase 
III Luqu 7,069 9.22 Significant increase 
IV Maqu 4,118 5.37 Significant increase 
Diebu 5,335 6.96 Highly significant increase 
VI Xiahe 1,329 1.73 Relative significant increase 
VII Zhouqu 54,459 71.06 Highly significant increase 

Validation of the model

Figure 4 shows the relationship between the simulated NEP and observed NEP.

Figure 4

Correlation analysis between observed and simulated NEP for 25 sites in the Gannan region for July 2015.

Figure 4

Correlation analysis between observed and simulated NEP for 25 sites in the Gannan region for July 2015.

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The linear model between the observed NEP and simulated NEP is as shown below:
formula
(11)
where YNEP(sim) and XNEP(obs) are the simulated NEP and observed NEP, respectively. The intercept and coefficient are highly significant (p=<0.01). The coefficient of determination (r2) was also calculated to show the strength of the relations. The relation was significant (r2=0.74, p<0.01, ME=0.38, Figure 4), indicating that the model's estimation accuracy was satisfactory, hence the onsite calibrated CENTURY model was used to support the research on changes in NEP in the study area.

NEP distribution pattern in Gannan

All the 25 study sites were grouped into the seven counties of Gannan (Table 1). Each of the counties had a specific number of study sites. For each of the counties, the average and standard deviation of NEP was calculated. Among the seven counties, Diebu and Zhouqu which are located in the southeast part of the region, had the most accumulated NEP, with Maqu in the southwestern region and Lintan in the northeastern regions having the least accumulation of NEP. The computed average NEP at intervals of at least 5 years for each site was then interpolated to obtain a spatial distribution map of the study area (Gannan). Figure 5 illustrates the spatial distribution of NEP in Gannan from 1969 to 2018 for the past 50 years.

Figure 5

The distribution pattern of the annual NEP of 5-year intervals from 1969 to 1973, 1974 to 1978, 1979 to 1983, 1984 to 1988, and 1989 to 1993, 1994 to 1998, 1999 to 2003, 2004 to 2008, 2009 to 2013, and 2014 to 2018 in the Gannan region.

Figure 5

The distribution pattern of the annual NEP of 5-year intervals from 1969 to 1973, 1974 to 1978, 1979 to 1983, 1984 to 1988, and 1989 to 1993, 1994 to 1998, 1999 to 2003, 2004 to 2008, 2009 to 2013, and 2014 to 2018 in the Gannan region.

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Higher values of NEP were found in the southeast throughout the simulation period. This was attributed to an increase in average precipitation and temperature coupled with the dominant vegetation cover in the southeast area (alpine meadow). The soil type of the area was a contributing factor as well. The alpine meadow grasses grow well in the Mollic, Gelic, and haptic soil which take dominance in the area. However, lower values of NEP were recorded in the northwestern part of the region, where the average precipitation and temperature were relatively low. Comparatively the alpine meadow in this area was on the low side with Eutric and Luvic soils being the major soil types that support grass growth in the area. Generally, it was observed that the NEP trend showed a reduction of carbon budget in the alpine meadow grassland from the southeastern part to the central and northwestern part of Gannan (Figure 6). Higher NEP values occurred between 1994 and 1998 in the year 1995 and then between 1999 and 2003 in the year 2000. The annual NEP between 1989 and 1993 recorded the lowest NEP, specifically in 1989 than that of the previous years, and increased afterward until 2018. Between 1969 and 2018, the annual NEP significantly (p<0.05) increased by 35% in parts of the southeast, whereas the increase in most parts of the northeast was approximately 7%. This could be a result of intensive land management to fight desertification. NEP has always been on an increasing trend throughout the study period (Figure 6). The grassland was not disturbed during the period of study so there was a gradual accumulation of NEP. The mean annual NEP of the Gannan alpine meadow from 1969 to 2018 is equal to 813.62 gC m−2 year−1.

Figure 6

The MK test of the trend of annual average NEP changes in the the following seven counties of Gannan from 1969 to 2018: (a) Zhuoni; (b) Lintan; (c) Luqu; (d) Maqu; (e) Diebu; (f) Xiahe; (g) Zhouqu; and (h) the entire Gannan region. U(t) is the forward series and U′(t) is the backward series.

Figure 6

The MK test of the trend of annual average NEP changes in the the following seven counties of Gannan from 1969 to 2018: (a) Zhuoni; (b) Lintan; (c) Luqu; (d) Maqu; (e) Diebu; (f) Xiahe; (g) Zhouqu; and (h) the entire Gannan region. U(t) is the forward series and U′(t) is the backward series.

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Figure 7

The monthly and seasonal variation of NEP in Gannan; (a) monthly variations of NEP and (b) seasonal variations of NEP in Gannan.

Figure 7

The monthly and seasonal variation of NEP in Gannan; (a) monthly variations of NEP and (b) seasonal variations of NEP in Gannan.

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Interannual variation of annual average NEP

We further demonstrated the interannual variation of NEP in Gannan, both in the seven counties studied and in the entire study region. Figure 6 shows the interannual variation of NEP in Gannan and the individual counties between 1969 and 2018.

In each of the seven counties there was a significant sudden NEP fluctuation indicating an upward trend. This upward trend of NEP was evident in six counties, namely Lintan, Luqu, Maqu, Diebu, Xiahe, and Zhouqu. In Lintan, a significant increasing trend started with U(t) values from 2003 to 2018 (Figure 6(b)). In Luqu, the trend can be seen in 1988 and was significant with U(t) values between 1995 and 2018 (Figure 6(c)). In Maqu, a significant trend started with U(t) values from 1993 to 2018 (Figure 6(d)). In Diebu, there was also a significant NEP upward trend with U(t) values from 1990 to 2018 (Figure 6(e)), whereas in the Xiahe county, a significant trend began with U(t) values from 1992 to 2018 (Figure 6(f)). Figure 6(g) shows that in Zhouqu, a significant NEP upward trend began with U(t) values greater than 2 from 1991 to 2018. The overall increasing trend of NEP in all counties was the result of a combined climate variability and increasing atmospheric CO2. An upward trend was generally observed in Zhuoni from NEP and was significant at U(t) between 2000 and 2014. However, a decreasing trend of NEP was observed after 2014, which could be attributed to inadequate rainfall hindering grass growth.

We also analysed the inter-annual variations in the average annual NEP characteristics for the entire Gannan from 1969 to 2018 (Figure 6(h)). From our findings, the annual average NEP value was lowest during the study period in the late 1970s and mid-1980s, fluctuating between −3.5 and 2 (Figure 6(h)). The low NEP value recorded during this period was the result of a decline in average rainfall during these years. After 1989, it entered a relatively stable growth trend. The forward series U′(t) and backward series UI(t) curves intersect at a point in 1990; the intersection lies between the critical line of the 5% significance level, and the forward series U(t) curve crosses the critical line of the 0.05 significance level after 1998, indicating that the annual average of NEP in Gannan transitioned from lower significant mutations to high mutations and mutations occurred in 1990. After 1991, the NEP began to increase and remained at a level that is a positive anomaly for many years. The increases in these years are the result of sufficient rainfall, increased temperatures and maximum light levels, which promote grass production in the summer season.

Monthly and seasonal variation of NEP in Gannan, from 1968 to 2018

Again we analyzed the monthly and seasonal variation of NEP for the study period. The NEP values varied greatly from month to month with seasonal differences in grassland NEP as a result of the combined effect of both temperature and precipitation. Figure 7 shows both the monthly and seasonal variation of NEP in Gannan between 1969 and 2018.

The least values of NEP were recorded in December through to January and February the following year which were below 0 gC/m2/month, accounting for a total grassland NEP of 32.54 gC/m2 (representing about 4% of the total NEP of the study region Figure 7(a)). This basically indicates a carbon source since a negative NEP depicts a carbon source. This could be attributed to low temperatures and poor hydrothermal conditions, which are not favorable for grass growth during this period, indicating that low temperature inhibits carbon sink. The NEP, on the other hand, rose in March, April, through to October before falling in November, also accounting for a total grassland NEP of 781.08 gC/m2 (representing 96% of the total NEP of the entire study region; Figure 7(a)). The appreciation was above 0 gC/m2/month, which indicates a carbon sink. This is because a positive NEP shows a period or area is a carbon sink. The increase in NEP is due to a rise in temperature and adequate precipitation, which allow photosynthetic tissue to remain in grass and produce carbohydrates to meet the grass's growth and respiration needs. From Figure 7(a), it could be observed that the maximum monthly average NEP was recorded in August with a value of 40.46 gC/m2/month, while the minimum value was seen in the month of January with a value of −2.23 gC/m2/month.

The clear seasonal differences in the NEP in the study area over the research period are depicted in Figure 7(b). Among the four seasons, summer had the highest NEP with 606.71 gC/m2 representing 74.57% of the total. This is due to the fact that there is enough light, nutrients, water, air, and a good temperature during this season, all of which are essential for photosynthesis, which is required for grass growth, resulting in a higher NEP record in the summer. The winter season, however, had the lowest NEP, at 10.25 gC/m2 accounting for only 1.26% of the total annual NEP in Gannan.

Changes in climate conditions

We also presented the spatial distribution of changes in climate conditions and the significance tests of their trends. Figure 8 shows the spatial distribution of average annual precipitation (P) and temperature (T) and their trends and significance tests in Gannan.

Figure 8

The distribution pattern of mean annual (a) temperature (T) and (b) precipitation (P); annual trends of (c) temperature (T) and (d) precipitation (P); and the significance test of (e) temperature (T) and (f) precipitation (P) from 1969 to 2018 (I: Maqu, II: Luqu, III: Xiahe, IV: Zhuoni, V: Lintan, VI: Diebu, and VII: Zhouqu).

Figure 8

The distribution pattern of mean annual (a) temperature (T) and (b) precipitation (P); annual trends of (c) temperature (T) and (d) precipitation (P); and the significance test of (e) temperature (T) and (f) precipitation (P) from 1969 to 2018 (I: Maqu, II: Luqu, III: Xiahe, IV: Zhuoni, V: Lintan, VI: Diebu, and VII: Zhouqu).

Close modal

The research region's mean annual precipitation ranged between 400 and 800 mm, while the annual mean temperature was between 1.0 and 3.0 °C. Across the whole study region, the climatic trends differed. With the exception of a few areas in the southern part of the study region, the northern, central, and southeastern areas of the research regions were characterized by increasing precipitation (Figure 8(d)). The average annual temperature increased across the research area, with the rate of increase exceeding 0.011 °C/year as it travelled north, as well as in some portions of the southeastern region (Figure 8(c)).

The MK method was again used to test the significance of the trend of climate variables in the study region from 1969 to 2018 (Figure 8(e) and 8(f)). The spatial distribution pattern of significance was relatively consistent with the spatial distribution of change trends. (1) The areas with significant increase were Luqu and Maqu, covering 7,069 and 4,118 km2, respectively (Table 2). (2) The areas with relatively significant increase were Zhuoni and Xiahe, covering 1,782 and 1,329 km2, respectively. (3) The areas with highly significant increases were Lintan with 1,026 km2 and Zhouqu with 54,459 km2.

Inter-annual NEP response to climatic factors

For each of the 25 study sites, we used R Studio software to calculate the correlation coefficient r of the average annual NEP and the two climatic factors between 1969 and 2018 across the study region. We then interpolated the values to obtain a map of the spatial distribution in the region. Figure 9 shows the correlation coefficient r of NEP between temperature and precipitation.

Figure 9

The distribution pattern of the partial correlation coefficient (r) between the two climatic variables and the simulated NEP in Gannan Alpine meadow from 1969 to 2018: (a) temperature against NEP and (b) precipitation against NEP.

Figure 9

The distribution pattern of the partial correlation coefficient (r) between the two climatic variables and the simulated NEP in Gannan Alpine meadow from 1969 to 2018: (a) temperature against NEP and (b) precipitation against NEP.

Close modal

As illustrated in Figure 9(a), the NEP showed a significant positive correlation with temperature (r: 0.68–0.98). The positive responses of the NEP to annual average temperature were stronger in Luqu (sites G, H, I, and F), Zhuoni (sites A–C), and Zhouqu (sites U–Y) with r2 value of 0.98 (p<0.05, Figure 9(a)), with the least recorded in Maqu with r2 value of 0.68 (p<0.05, Figure 9(a)). The northeastern and southwestern parts of Gannan alpine meadow showed considerable positive responses of simulated NEP to temperature change (r>0.50, p<0.05, Figure 9(a)). Generally, the NEP in our research region showed a clear positive response to rising temperatures, which might be due to the fact that rainfall in those places met the rising water demand as temperatures rose.

The NEP exhibited a positive correlation with precipitation in all of Gannan's study sites, with correlation r values ranging from 0.75 to 0.97 (Figure 9(b)). With rising temperatures, the NEP's positive response to annual average precipitation variance got stronger. The positive responses of the NEP to annual average precipitation were stronger in Diebu (sites M–P), Lintan (sites D and E), and Luqu (sites G, H, I, and F) with r2 values of 0.97 (p<0.05, Figure 9(b)). Maqu (sites J–L) had the least correlations between NEP and precipitation, with r-value of 0.75 (p<0.05, Figure 9(b)). In the northeastern and southern areas of the Gannan alpine meadow, the NEP revealed a substantial positive connection with precipitation (r>0.65, p<0.05, Figure 9(b)). Increased precipitation in the southeast could result in more cloud cover, which is unfavorable to photosynthesis.

Dynamics in the carbon budget (NEP) in response to climate change

We also used R Studio software to show a summary of the response of the simulated dynamics of NEP to climate change. Figure 10 shows the summary of the correlations between NEP and the climatic factors.

Figure 10

A summary of the correlations (r) between the NEP and climate conditions from 1969 to 2018. NEP is the net ecosystem production; TEM is the temperature; and PRE is the precipitation.

Figure 10

A summary of the correlations (r) between the NEP and climate conditions from 1969 to 2018. NEP is the net ecosystem production; TEM is the temperature; and PRE is the precipitation.

Close modal

A compilation of the long-term association among the simulated NEP and meteorological parameters was carried out to summarize the correlations in Figure 9 and clarify the mechanisms responsible for changes in the carbon budget of the Gannan Alpine meadow (Figure 10). In general, the NEP value of the Alpine meadows had comparable responses to climatic factors: precipitation was significantly positively associated with the NEP, as was temperature. This means that the two climate conditions have an impact on the productivity of the Gannan alpine meadow grassland.

The annual average NEP of alpine grassland in Gannan, Gansu Province, China, was replicated using the CENTURY model from 1969 to 2018, and the probable causes of variations in the carbon budget were investigated. Using the MK trend test, the temporal pattern in Gannan NEP grassland fluctuated from −3.5 to 2 and mutated about 1990 and indicated a definite increasing trend, which was consistent across all seven counties in Gannan. The changed trend of annual average NEP in the alpine meadow of Gannnan in the last 50 years was generally due to climate change, soil properties and length of historical data which led to the variation in the MK test among counties despite the upward trend (Ma et al. 2021). Our findings are consistent with previous studies where the trends of GPP and NPP in various grassland types increased (Li et al. 2013; Chen et al. 2016). In recent years, studies in some parts of Africa have found that the annual pattern of NPP calculated by the model showed a significant increase in NPP from 9.97 PgC/year in the 1980s to 10.21 PgC/year in the 1990s and 10.49 PgC/year in the 2000s (Pan et al. 2015).

In this current study, the alpine meadow grassland NEP values to climate change was investigated by integrating simulated values with real meteorological variables. The spatial distribution of the annual average NEP was generally consistent across all 10 divisions with respect to the last 50 years in Gannan. The NEP decreased from the southeast to the northwest in all cases. Zhouqu, Diebu, and Lintan counties recorded high values for grassland NEP in Gannan while Maqu and Zhuoni had the lowest values for grassland NEP. Most soils in the southeast include mollic and haptic, which are relatively fertile and support grass development (Catling 1992), resulting in a large number of NEP in the southeast while the dormant soils in the northwest of Gannan are eutric and luvic soils. These soils are not rich in nutrients and therefore do not support grass development, resulting in a decline of NEP. Measures such as erosion prevention, leaching and application of calcium content mannure can help to revive the soil fertility of the northwestern part of Gannan to facilitate the growth of alpine meadow grasses to increase the accumulation of NEP.

Another factor that promotes grass growth in the southeast is climate variables (Xu et al. 2018) such as precipitation and temperature which positively correlate with SOC and vegetation biomass. The southeastern part of Gannan has relatively high temperature and sufficient precipitation, which affects the growth of alpine meadows and leads to an increase in NEP. However, the northwestern part of Gannan is known to be freezing cold in most years, which inhibits the growth of grass and leads to low coverage of NEP. A similar result was reported by Liu et al. (2015), where net primary productivity of vegetation and its response to climate change during 2001–2008, the NPP values in the Tibetan Plateau were found to be high in the southeastern part and low in the northwestern part. According to WANG et al. (2011), the average annual NPP in Gannan grassland between 2001 and 2008 was 483.41 gC m−2 year−1, which is consistent with our result. The results of our study show that the average annual NEP between 1969 and 2018 is 813.62 gC m−2 year−1.

The carbon budget of Gannan alpine meadows was found to be in a carbon sink condition during the study period. Our results indicate that monthly and seasonal variations of NEP in Gannan from 1969 to 2018 show that values of NEP vary widely from month to month, with differences in NEP. Alpine meadow NEP peaks in Gannan occur between April and October each year, mostly during the summer season. Due to favorable meteorological conditions, NEP also began to rise, reaching its peak in July or August. This agrees with the findings of Hatfield & Prueger (2015), who stated that the rate of growth and development of grasses depends on the surrounding temperature and that each grassland type has a specific temperature range represented by a minimum, maximum, and an optimum. The other favorable conditions that contributed to the increase in NEP levels in summer, making the carbon budget in Gannan a carbon sink, include adequate light, availability of water, moisture, and nutrient (Velásquez et al. 2018). On the contrary, as temperatures dropped and foliage began to wilt, the NEP gradually began to decline. This occurs between November through to March of the following year (winter). This result is also consistent with the findings of Zhang et al. (2020) who stated that the maximum monthly average NPP values occur in July and August each year, while the minimum values occur in the winter months (November, December, January, February, and March).

In Gannan study locations, the correlation (r) between NEP and average annual precipitation (Figure 9(b)) was higher in the alpine meadow. Seasonal precipitation variations have a greater influence on ecosystem C-cycles than annual precipitation changes (Liu et al. 2016; Van den Brink et al. 2018). This result suggests that the carbon budget in the Gannan alpine meadow is primarily controlled by increases in precipitation. The importance of precipitation for carbon uptake in the Gannan grassland is becoming increasingly important. In their studies on the Tibetan Plateau, Shen et al. (2014) and Shen et al. (2015) reported that precipitation can influence plant phenology and soil respiration in the context of climate change. At all study sites in Gannan, the correlation (r) between the NEP and annual mean temperature (Figure 9(b)) was also high in the alpine meadow. This finding suggests that the carbon budget in the Gannan alpine meadow is controlled by increasing temperatures. In a temperate ecosystem, warming has a beneficial effect on carbon storage (Wolf et al. 2016). However, seasonal changes in CO2 flux from alpine grasslands amid warming were likely influenced by water availability, as shown in a study by Zhu et al. (2017). Our results are consistent with those of Lenihan et al. (2008), who found that in California, USA, grass growth will increase dramatically in a future drier and high temperature with adequate rainfalls. Both average annual precipitation and temperature were positively correlated with annual NEP. This implies that precipitation and temperature variations in the Gannan region had a greater effect on the carbon budget of Alpine Meadow grassland. In the Gannan region, an increase in temperature will have a positive effect on NEP in future years. High temperatures lead to a longer growing season and higher productivity, while precipitation enhances the positive effects of warming on photosynthesis by mitigating water shortages. Steadily increasing precipitation also improves vegetation photosynthesis and ecosystem carbon uptake, especially in the arid and semiarid zones.

Moreover, the response of grasslands NEP to climate change and anthropogenic activities is a complicated process that experts are concerned about (Zhao et al. 2017). However, due to the variety of different activities, it is difficult to measure the impact of some specific human activities on grassland NEP (Miao et al. 2015). At the same time, the interdependence of climate variables and anthropogenic activities makes it particularly challenging to determine which areas are controlled by climate variables and which by human activities. In further studies, grassland NEP will be modelled with climatic conditions in mind and compared to actual grassland NEP to determine whether climatic variables or human activities are the driving forces behind the variation in grassland NEP. The discrepancy between grass planting and ecological recovery and improvement in grassland productivity needs further investigation in conjunction with longer time series analyses.

In this study, the CENTURY model was used to analyse the carbon budget response to climate change in Gannan alpine grasslands. The alpine meadow grasslands in the Gannan region of China have been a carbon sink for the past 50 years. However, due to anthropogenic activities and intensive livestock grazing, some areas in the region seem to be a carbon source. The region became a carbon sink due to governmental interventions carried out by policy makers. From our findings, there were obvious seasonal changes and interannual variations of NEP, as might be expected from the variations in temperature and precipitation, with the highest NEP values in summer. The annual average NEP over the past 50 years was estimated at 813.62 gC m−2 year−1. The southeastern part of the study site had higher NEP values than the northwestern part. From the trend analysis of the MK test on the periodic changes in the time series of the annual average of NEP in Gannan from 1969 to 2018, the annual average of NEP fluctuated but showed a clear upward trend in all counties of the region. Climate variables, i.e., both precipitation and temperature, were statistically significant and positively correlated with NEP. Increased precipitation and temperature stabilized the carbon budget on the pasture. Future research combining NEP estimates such as the CASA model with data from NOAA or MODIS to determine the grassland NEP on the Gannan alpine grassland is needed in the study region.

This work was supported by the Key Research and Development Program of Gansu Province, China (21YF5WA096), the Young Tutor Support Fund Project of the Gansu Agriculture University (GAU-QDFC-2019-03), and the Natural Science Foundation of Gansu Province, China (1606RJZA077 and 1308RJZA262) Funding.

The authors declare that there is no conflict of interest.

Data cannot be made publicly available; readers should contact the corresponding author for details.

Baldocchi
D.
,
Chu
H.
&
Reichstein
M.
2018
Inter-annual variability of net and gross ecosystem carbon fluxes: A review
.
Agricultural and Forest Meteorology
249
,
520
533
.
Barron-Gafford
G. A.
,
Scott
R. L.
,
Jenerette
G. D.
,
Hamerlynck
E. P.
&
Huxman
T. E.
2012
Temperature and precipitation controls over leaf-and ecosystem-level CO2 flux along a woody plant encroachment gradient
.
Global Change Biology
18
,
1389
1400
.
Bond-Lamberty
B.
,
Wang
C.
&
Gower
S. T.
2004
A global relationship between the heterotrophic and autotrophic components of soil respiration?
Global Change Biology
10
,
1756
1766
.
Bortolon
E. S. O.
,
Mielniczuk
J.
,
Tornquist
C. G.
,
Lopes
F.
&
Bergamaschi
H.
2011
Validation of the Century model to estimate the impact of agriculture on soil organic carbon in Southern Brazil
.
Geoderma
167
,
156
166
.
Bragazza
L.
,
Buttler
A.
,
Robroek
B. J.
,
Albrecht
R.
,
Zaccone
C.
,
Jassey
V. E.
&
Signarbieux
C.
2016
Persistent high temperature and low precipitation reduce peat carbon accumulation
.
Global Change Biology
22
,
4114
4123
.
Catling
D.
1992
History of Research
. In:
Rice in Deep Water
.
(P. MacMillan, ed.)
Springer
,
London, UK
.
Cui
X.
,
Guo
Z. G.
,
Liang
T. G.
,
Shen
Y. Y.
,
Liu
X. Y.
&
Liu
Y.
2012
Classification management for grassland using MODIS data: a case study in the gannan region, China
.
International Journal of Remote Sensing
33
,
3156
3175
.
Daneshvar Vousoughi
F.
,
Dinpashoh
Y.
,
Aalami
M. T.
&
Jhajharia
D.
2013
Trend analysis of groundwater using non-parametric methods (case study: ardabil plain)
.
Stochastic Environmental Research Risk Assessment
27
,
547
559
.
De Araújo Neto
R. A.
,
Maia
S. M. F.
,
Althoff
T. D.
,
Cerri
C. E. P.
,
de Carvalho
A. L.
&
Menezes
R. S. C.
2021
Simulation of soil carbon changes due to conventional systems in the semi-arid region of Brazil: adaptation and validation of the century model
.
Carbon Management
12
(
4
),
399
410
.
de Paula
G.
,
Maia
V.
,
Aguiar-Campos
N.
,
Souza
C.
,
Farrapo
C.
,
Araujo
F.
,
Fagundes
N.
,
Coelho
P.
,
Morel
J.
&
Santos
A.
2020
.
Climate change and forest dynamics: three decades of monitoring. Authorea Preprints.
Dunn
A. L.
,
Barford
C. C.
,
Wofsy
S. C.
,
Goulden
M. L.
&
Daube
B. C.
2007
A long-term record of carbon exchange in a boreal black spruce forest: means, responses to interannual variability, and decadal trends
.
Global Change Biology
13
,
577
590
.
Foereid
B.
,
Barthram
G.
&
Marriott
C. A.
2007
The CENTURY model failed to simulate soil organic matter development in an acidic grassland
.
Nutrient Cycling in Agroecosystems
78
,
143
153
.
Froelich
N.
,
Croft
H.
,
Chen
J. M.
,
Gonsamo
A.
&
Staebler
R. M.
2015
Trends of carbon fluxes and climate over a mixed temperate–boreal transition forest in southern ontario, Canada
.
Agricultural Forest Meteorology
211
,
72
84
.
Galdos
M. V.
,
Cerri
C. C.
,
Cerri
C. E. P.
,
Paustian
K.
&
Van Antwerpen
R.
2009
Simulation of soil carbon dynamics under sugarcane with the CENTURY model
.
Soil Science Society of America Journal
73
(
3
),
802
811
.
Gao
Y.
,
Zhou
X.
,
Wang
Q.
,
Wang
C.
,
Zhan
Z.
,
Chen
L.
,
Yan
J.
&
Qu
R.
2013
Vegetation net primary productivity and its response to climate change during 2001–2008 in the Tibetan plateau
.
Science of the Total Environment
444
,
356
362
.
Guo
L.
,
Hao
C.
,
Wu
S.
,
Zhao
D.
&
Gao
J.
2016
Analysis of changes in net primary productivity and its susceptibility to climate change of inner Mongolian grasslands using the CENTURY model
.
Geographical Research
35
,
271
284
.
Hatfield
J. L.
&
Prueger
J. H.
2015
Temperature extremes: effect on plant growth and development
.
Weather and Climate Extremes
10
,
4
10
.
Helbig
M.
,
Chasmer
L. E.
,
Desai
A. R.
,
Kljun
N.
,
Quinton
W. L.
&
Sonnentag
O.
2017
Direct and indirect climate change effects on carbon dioxide fluxes in a thawing boreal forest–wetland landscape
.
Global Change Biology
23
,
3231
3248
.
Keppler
F.
,
Kalin
R. M.
,
Harper
D. B.
,
McRoberts
W. C.
&
Hamilton
J. T.
2004
Carbon isotope anomaly in the major plant C 1 pool and its global biogeochemical implications
.
Biogeosciences
1
(
2
),
123
131
.
Krishnan
P.
,
Black
T. A.
,
Jassal
R. S.
,
Chen
B.
&
Nesic
Z.
2009
Interannual variability of the carbon balance of three different-aged douglas-fir stands in the pacific northwest
.
Journal of Geophysical Research: Biogeosciences
114
(
G4
),
1
18
.
Li
X. L.
,
Gao
J.
,
Brierley
G.
,
Qiao
Y. M.
,
Zhang
J.
&
Yang
Y. W.
2013
Rangeland degradation on the qinghai-Tibet plateau: implications for rehabilitation
.
Land Degradation Development
24
,
72
80
.
Liu
Y.
,
Zhou
Y.
,
Ju
W.
,
Wang
S.
,
Wu
X.
,
He
M.
&
Zhu
G.
2014
Impacts of droughts on carbon sequestration by China's terrestrial ecosystems from 2000 to 2011
.
Biogeosciences
11
,
2583
2599
.
Liu
S.
,
Yang
Y.
,
Shen
H.
,
Hu
H.
,
Zhao
X.
,
Li
H.
,
Liu
T.
&
Fang
J.
2018
No significant changes in topsoil carbon in the grasslands of northern China between the 1980 and 2000 s
.
Science of the Total Environment
624
,
1478
1487
.
Miao
L.
,
Jiang
C.
,
Xue
B.
,
Liu
Q.
,
He
B.
,
Nath
R.
&
Cui
X.
2015
Vegetation dynamics and factor analysis in arid and semi-arid inner Mongolia
.
Environmental Earth Sciences
73
,
2343
2352
.
Ministry of Agriculture of China, D. O. A. H. A. V.
1996
General Station of Animal Husbandry and Veterinary of Ministry of Agriculture of China
.
China Science and Technology Press
,
Beijing
,
Rangeland and Resource of China
.
Nagy
Z.
,
Pintér
K.
,
Czóbel
S.
,
Balogh
J.
,
Horváth
L.
,
Fóti
S.
,
Barcza
Z.
,
Weidinger
T.
,
Csintalan
Z.
&
Dinh
N.
2007
The carbon budget of semi-arid grassland in a wet and a dry year in Hungary
.
Agriculture, Ecosystems Environment
121
,
21
29
.
Ollinger
S. V.
,
Richardson
A. D.
,
Martin
M. E.
,
Hollinger
D. Y.
,
Frolking
S. E.
,
Reich
P. B.
,
Plourde
L. C.
,
Katul
G. G.
,
Munger
J. W.
,
Oren
R.
&
Smith
M. L.
2008
Canopy nitrogen, carbon assimilation, and albedo in temperate and boreal forests: Functional relations and potential climate feedbacks
.
Proceedings of the National Academy of Sciences
105
(
49
),
19336
-
19341
.
Pan
S.
,
Dangal
S. R.
,
Tao
B.
,
Yang
J.
&
Tian
H.
2015
Recent patterns of terrestrial net primary production in Africa influenced by multiple environmental changes
.
Ecosystem Health Sustainability
1
,
1
15
.
Parton
W.
1996
The CENTURY model
. In:
Evaluation of Soil Organic Matter Models
.
(D. S. Powlson, P. Smith & J. U. Smith, eds.)
Springer
,
Berlin, Heidelberg
, pp.
283
291
.
Parton
W. J.
,
Schimel
D. S.
,
Cole
C. V.
&
Ojima
D.
1987
Analysis of factors controlling soil organic matter levels in great plains grasslands
.
Soil Science Society of America Journal
51
,
1173
1179
.
Pilegaard
K.
,
Ibrom
A.
,
Courtney
M. S.
,
Hummelshøj
P.
&
Jensen
N. O.
2011
Increasing net CO2 uptake by a danish beech forest during the period from 1996 to 2009
.
Agricultural Forest Meteorology
151
,
934
946
.
Poulter
B.
,
Pederson
N.
,
Liu
H.
,
Zhu
Z.
,
D'arrigo
R.
,
Ciais
P.
,
Davi
N.
,
Frank
D.
,
Leland
C.
&
Myneni
R.
2013
Recent trends in inner Asian forest dynamics to temperature and precipitation indicate high sensitivity to climate change
.
Agricultural Forest Meteorology
178
,
31
45
.
Ran
Y.
,
X
L. I.
,
Sun
R.
,
Kljun
N.
,
Zhang
L.
,
Wang
X.
&
Zhu
G.
2016
Spatial representativeness and uncertainty of eddy covariance carbon flux measurements for upscaling net ecosystem productivity to the grid scale
.
Agricultural Forest Meteorology
230
,
114
127
.
Richardson
A. D.
,
Hollinger
D. Y.
,
Aber
J. D.
,
Ollinger
S. V.
&
Braswell
B. H.
2007
Environmental variation is directly responsible for short-but not long-term variation in forest-atmosphere carbon exchange
.
Global Change Biology
13
,
788
803
.
Running
S.
&
Zhao
M.
2019
Daily GPP and Annual NPP (MOD17A2H/A3H) and Year-end Gap-Filled (MOD17A2HGF/A3HGF) Products NASA Earth Observing System MODIS Land Algorithm
.
NASA LP DAAC
. .
Schippers
P.
,
Sterck
F.
,
Vlam
M.
&
Zuidema
P. A.
2015
Tree growth variation in the tropical forest: understanding effects of temperature, rainfall and CO 2
.
Global Change Biology
21
,
2749
2761
.
Sen
P. K.
1968
Estimates of the regression coefficient based on Kendall's tau
.
Journal of the American Statistical Association
63
,
1379
1389
.
Shen
M.
,
Zhang
G.
,
Cong
N.
,
Wang
S.
,
Kong
W.
&
Piao
S.
2014
Increasing altitudinal gradient of spring vegetation phenology during the last decade on the qinghai–Tibetan plateau
.
Agricultural Forest Meteorology
189
,
71
80
.
Shi
X.
,
Yu
D.
,
Warner
E.
,
Pan
X.
,
Petersen
G.
,
Gong
Z.
&
Weindorf
D.
2004
Soil database of 1: 1,000,000 digital soil survey and reference system of the Chinese genetic soil classification system
.
Soil Survey Horizons
45
,
129
136
.
Stocker
T.
2014
Climate Change 2013: the Physical Science Basis: Working Group I Contribution to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change
.
Cambridge University Press
,
Cambridge, UK
.
Team, A.
2020
Application for Extracting and Exploring Analysis Ready Samples (AppEEARS). Ver. 2.49. NASA EOSDIS Land Processes Distributed Active Archive Center (LP DAAC), USGS/Earth Resources Observation and Science (EROS) Center, Sioux Falls, South Dakota, USA. USGS/Earth Resourc. Observ. Sci.
.
Van Den Brink
P. J.
,
Boxall
A. B.
,
Maltby
L.
,
Brooks
B. W.
,
Rudd
M. A.
,
Backhaus
T.
,
Spurgeon
D.
,
Verougstraete
V.
,
Ajao
C.
&
Ankley
G. T.
2018
Toward sustainable environmental quality: priority research questions for Europe
.
Environmental Toxicology Chemistry
37
,
2281
2295
.
Vargas
R.
,
Baldocchi
D. D.
,
Querejeta
J. I.
,
Curtis
P. S.
,
Hasselquist
N. J.
,
Janssens
I. A.
,
Allen
M. F.
&
Montagnani
L.
2010
Ecosystem CO2 fluxes of arbuscular and ectomycorrhizal dominated vegetation types are differentially influenced by precipitation and temperature
.
New Phytologist
185
,
226
236
.
Velásquez
A. C.
,
Castroverde
C. D. M.
&
He
S. Y.
2018
Plant-Pathogen warfare under changing climate conditions
.
Current Biology : CB
28
,
R619
R634
.
Veroustraete
F.
,
Sabbe
H.
&
Eerens
H.
2002
Estimation of carbon mass fluxes over Europe using the C-Fix model and euroflux data
.
Remote Sensing of Environment
83
,
376
399
.
Wang
Y.
,
Xia
W.-T.
&
Liang
T.-G.
2011
Spatial-temporal dynamics simulation of grassland net primary productivity using a satellite data-driven CASA model in gannan prefecture
.
Acta Prataculturae Sinica
20
,
316
.
Wang
D.
,
Liu
W.
&
Huang
X.
2013
Trend analysis in vegetation cover in Beijing based on Sen+ mann-Kendall method
.
Jisuanji Gongcheng yu Yingyong
49
,
13
17
.
Wang
Z.
,
Yang
Y.
,
J
L. I.
,
Zhang
C.
,
Chen
Y.
,
Wang
K.
,
Odeh
I.
&
Qi
J.
2017
Simulation of terrestrial carbon equilibrium state by using a detachable carbon cycle scheme
.
Ecological Indicators
75
,
82
94
.
Wen
X.-F.
,
Yu
G.-R.
,
Sun
X.-M.
,
Li
Q.-K.
,
Liu
Y.-F.
,
Zhang
L.-M.
,
Ren
C.-Y.
,
Fu
Y.-L.
&
Li
Z.-Q.
2006
Soil moisture effect on the temperature dependence of ecosystem respiration in a subtropical pinus plantation of southeastern China
.
Agricultural Forest Meteorology
137
,
166
175
.
Wilkinson
M.
,
Eaton
E.
,
Broadmeadow
M.
&
Morison
J.
2012
Inter-annual variation of carbon uptake by a plantation oak woodland in south-eastern England
.
Biogeosciences
9
,
5373
5389
.
Wolf
S.
,
Keenan
T. F.
,
Fisher
J. B.
,
Baldocchi
D. D.
,
Desai
A. R.
,
Richardson
A. D.
,
Scott
R. L.
,
Law
B. E.
,
Litvak
M. E.
&
Brunsell
N. A.
2016
Warm spring reduced carbon cycle impact of the 2012 US summer drought
.
Proceedings of the National Academy of Sciences
113
,
5880
5885
.
Wu
S.
,
Yin
Y.
,
Zheng
D.
&
Yang
Q.
2005
Climate changes in the Tibetan plateau during the last three decades
.
Acta Geographica Sinica
60
,
3
11
.
Wu
C.
,
Chen
J. M.
,
Black
T. A.
,
Price
D. T.
,
Kurz
W. A.
,
Desai
A. R.
,
Gonsamo
A.
,
Jassal
R. S.
,
Gough
C. M.
&
Bohrer
G.
2013
Interannual variability of net ecosystem productivity in forests is explained by carbon flux phenology in autumn
.
Global Ecology Biogeography
22
,
994
1006
.
Xi
C.
,
Peili
S.
,
Ning
Z.
,
Ben
N.
,
Yongtao
H.
&
Xianzhou
Z.
2017
Biophysical regulation of carbon flux in different rainfall regime in a northern Tibetan alpine meadow
.
Journal of Resources Ecology
8
,
30
41
.
Xia
L.
,
Wang
F.
,
Mu
X.
,
Jin
K.
,
Sun
W.
,
Gao
P.
&
Zhao
G.
2015
Water use efficiency of net primary production in global terrestrial ecosystems
.
Journal of Earth System Science
124
,
921
931
.
Xu
L.
,
Yu
G.
,
He
N.
,
Wang
Q.
,
Gao
Y.
,
Wen
D.
,
S
L. I.
,
Niu
S.
&
Ge
J.
2018
Carbon storage in China's terrestrial ecosystems: a synthesis
.
Scientific Reports
8
,
2806
.
Yin
H.
,
Li
Z.
,
Wang
Y.
&
Cai
F.
2011
Assessment of desertification using time series analysis of hyper-temporal vegetation indicator in inner Mongolia
.
Acta Geographica Sinica
66
,
653
661
.
Zhang
R.
,
Zhao
X.-Y.
&
Zhao
H.-L.
2008
Researching on the synthesis competitiveness in the high cold pasturing area——a case of gannan autonomy state
.
Journal of Northwest Normal University
01
.
Zhang
W.-J.
,
Wang
H.-M.
,
Yang
F.-T.
,
Yi
Y.-H.
,
Wen
X.-F.
,
Sun
X.-M.
,
Yu
G.-R.
,
Wang
Y.-D.
&
Ning
J.-C.
2011
Underestimated effects of low temperature during early growing season on carbon sequestration of a subtropical coniferous plantation
.
Biogeosciences
8
,
1667
1678
.
Zhao
M.
,
Heinsch
F. A.
,
Nemani
R. R.
&
Running
S. W.
2005
Improvements of the MODIS terrestrial gross and net primary production global data set
.
Remote Sensing of Environment
95
,
164
176
.
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