Carbon sequestration in vegetation and its change in the Three-North Shelter Forest region of China in 2000-2021

Carbon sequestration in vegetation and its change in the Three-North Shelter Forest region of China in 2000-2021 | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Carbon sequestration in vegetation and its change in the Three-North Shelter Forest region of China in 2000-2021 Li-xia Wang, Ji-xi Gao, Wen-guo Zhang, Wen-ming Shen, Ming-yong Cai, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-1710152/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Carbon sequestration in vegetation can offset a large proportion of carbon emissions, and plays an important role in mitigation climate change. Based on satellite data and ground observation data, we examined carbon sequestration rate and its long-term change in the Three-North Shelter Forest Program (TNSFP) region, where a series of large-scale ecological projects have been implemented. The results indicated that carbon sequestration rate in vegetation was 458.3 ± 45 g C m − 2 yr − 1 in the TNSFP region during the period 2000–2021. Obvious increase trend of carbon sequestration rate was observed covering approximately 90% of the TNSFP region, with an average increase rate of 5.06 ± 4.3 g C m − 2 yr − 1 . There was a larger increase rate of 7.78 ± 2.07 g C m − 2 yr − 1 in forest than that of 3.27 ± 0.55 g C m − 2 yr − 1 in grassland. Total carbon sequestration into vegetation was 26.1054 P g C (1 P g = 10 15 g) in the TNSFP region, with annual carbon sequestration of 1186.6 ± 122.6 T g C (1 T g = 10 12 g) during the period 2000–2021. Our results revealed that both human activities and climate change have positive effect on carbon sequestration rate. Human activities contribute to carbon sequestration increment of 1.7905 Pg C approximately under assumptions that the existing forest transformed from original natural grass land due to afforestation and reforestation. Climate change has promoted carbon sequestration rate due to increase in annual precipitation, but the increase in annual air temperature is harmful in enhancing carbon sequestration rate. Climate change human activities afforestation carbon emissions carbon sequestration. Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Terrestrial vegetation usually acts as a net carbon sink. Carbon sequestration in vegetation offsets a large proportion of carbon emissions, and plays an important role in mitigating world’s climate change. Carbon sequestration in vegetation represents the amount of carbon removed from the atmosphere and stored it in biomass of the terrestrial biosphere through photosynthesis. Ecological restoration program is a useful strategy to enhance carbon capture and storage in vegetation. China has launched six key national ecological restoration projects. It was estimated that the total annual carbon sink in the project region was 132 Tg C per year (1 Tg = 10 12 g), over half of annual carbon sink was attributed to the implementation of these projects (Lu et al., 2018 ). In Northern China, large-scale ecological projects have been implementing, such as Three North Shelter Forest Program (TNSFP, from 1978 to 2050), Natural Forest Conversion Program (NFCP, from 1998 to 2010), Grain for Green Program (GGP, convert farmland to natural grassland or forest from 1998 to 2010), Beijing-Tianjin sands and dust engineering (reforestation/afforestation, prohibition of animal grazing from 2000 to 2012). The TNSFP is the largest afforestation program in the world, with an area of 46.1×10 6 ha. The implementation of the ecological projects has transformed grassland or cropland to forests by afforestation and reforestation. Subsequently, greenness and vegetation cover increased (Duan et al., 2011 ; Zhang et al., 2016 ), biological carbon sequestration was enhanced significantly, and caused a continuous increase in carbon storage (Montagnini & Nair, 2004 ; Xu & Li 2010 ). However, previous reports didn’t provide an adequate quantitative information about biotic carbon sequestration rate and its long-term change rate in different ecosystems across the TNSFP region. It is generally believed that the change of carbon sequestration should attribute to the combined effect of climate change and human activities. It was reported that climatic changes have enhanced plant growth in northern mid-latitudes and high latitudes, and led to increase 6% of net primary production globally during 1982–1999 (Nemani et al., 2003 ). Some reports provided evidence that vegetation carbon stocks increased significantly in northern China in recent years, and ecological projects and climate change had a positive effect on carbon sequestration in vegetation (Sun et al., 2016 ; Ji et al., 2020 ). However, other scientists argued that climate change led to negative impacts on the vegetation in north China and Inner Mongolia (Piao et al., 2015 ). In this study, the TNSFP region was selected as study area. The purposes were (1) to quantify carbon sequestration rate in vegetation, (2) to detect the long-term change of carbon sequestration rate, (3) to identify the effect of climate change and human activities on carbon sequestration rate in the main ecosystems in the TNSFP region. Given that the natural grassland is replaced by planted forest, (4) to quantify carbon sequestration increment due to afforestation and reforestation efforts. Methods And Materials Study area The TNSFP region covers 4,069,000 km 2 , and accounts for 42.4% of China’s land area, ranging between73°26′E and 127°50′E, and between 33°30′N and 50°12′N in Northern China. There is a distance of 4,480 km from east to west, and 560 km ~ 1460 km from south to north. Most of this region belongs to arid and semi-arid climate regions. There is an average annual precipitation of 327.3 mm, and an average annual air temperature of 5.38 ℃ in the TNSFP region in 2000–2021. The spatial pattern of annual precipitation in this region decreases from southeast to northwest. Vegetation types in the TNSFP are mainly composed of desert, grassland, forestland and croplands (Fig. 1 ). One-third (35.52%) of the TNSFP region is covered by the primitive barren desert or sparse grass vegetation. Large-scale ecological projects were implemented to combat desertification and control dust storms since the late 1970s. Vast man-made forests were established in the original natural grass area (Duan et al., 2011 ; Zheng & Zhu 2017 ; Zhu et al., 2019). Data sources Time-series satellite data and ground observation data were used to calculate carbon sequestration rate in vegetation. Land-use in 2010s is provided by Data Center for Resources and Environmental Sciences, Chinese Academy of Sciences (RESDC) ( http://www.resdc.cn ) Normalized difference vegetation index (NDVI) was derived from NASA’s Moderate Resolution Imaging Spectro radiometer sensors. Annual maximal NDVI in each grid cell was extracted from the time-series NDVI using the maximum value composite method to avoid the influence of noise, such as clouds, aerosols, solar elevation angle, and ice-snow cover. Annual precipitation and annual air temperature were acquired from observational data of the 2400 meteorological stations throughout China under the management of the National Meteorological Information Center of the Chinese National Bureau. Elevation with a spatial resolution of 1km × 1km was extracted from a 90 m × 90 m digital elevation model (DEM). To maintain the same spatial resolution, all data were resampled or interpolated into grid cells with a spatial resolution of 1 km ×1 km. Calculation of carbon sequestration Carbon sequestration in vegetation per unit area could be expressed by net primary productivity (NPP), which shows how much carbon is fixed in vegetation per square meter per year, and provides an estimate of annual carbon sequestration rate. A variety of models were developed to simulate carbon sequestration, such as light use efficiency concept (Global Production Efficiency Model; Carnegie-Ames-Stanford Approach, CASA), process-based models (CENTURY, BIOME-BGC), climate-based models (Thornthwaite Memorial model, Miami model and Chikugo model), volume-derived biomass models (Biomass Expansion Factors, BEFs) (Liu et al., 2019 ). Through the comparison of model accuracy and applicability, we selected a statistics-based multiple regression model established by Ji et al. ( 2020 ) ( Eq. 1 ). The model is suitable for Chinese forests, with a fitting coefficient of R 2 = 0.536 ( P < 0.01), which indicated good performance in predicting forest NPP. \(NPP=97.13{NDVI}_{max}+0.022PT+0.128P-9.136T-0.027A +333.67\) ( Eq. 1 ) Where NPP is carbon sequestration rate in forest (g C m − 2 yr − 1 ), NDVI max is annual maximal NDVI (0 < NDVI ≤ 1), P is annual precipitation (mm), T is annual temperature (°C), and A is altitude (m). Carbon sequestration rate in typical grassland was calculated by an exponential model ( Eq. 2 ); Carbon sequestration in semi-arid grassland and desert grassland was calculated by another exponential model ( Eq. 3 ) established by Xu et al. ( 2007 ). The two models output fresh grass yield above ground with an estimated precision of 80%. Fresh grass yield above ground was converted to dry grass yield by applying a dry-wet ratio coefficient of 0.31(Animal Husbandry Vet Bureau of Department of Agriculture P.R.C., 1996), and converted to the total dry grass yield (above and below ground) by ratio coefficient of 5.88 (Wang et al., 2008 ), and then dry grass yield was converted into carbon sequestration rate (g C m − 2 yr − 1 ) using a conversion coefficient of 0.45 (Chai et al., 2014 ). \(Y=385.362\times {\text{e}}^{3.813NDVI}\) ( Eq. 2 ) \(Y=193.585\times {\text{e}}^{4.984NDVI}\) ( Eq. 3 ) Where Y is fresh grass yield (kg ha − 1 yr − 1 ), NDVI is annual maximal NDVI (0 < NDVI ≤ 1). Identify change of carbon sequestration rate and climatic variables To reveal the dynamic processes of vegetation carbon sequestration and climate variables, time-series linear regression analysis was performed to obtain the slope of the regression trend line for each pixel (1km×1km grid cell), where the time series variables (carbon sequestration rate, annual precipitation and air temperature) as dependent variables and time (year) as the independent variable. There was an upward trend or a downward trend for each pixel. If the slope of the regression trend line > 0, the dependent variable exhibits upward trend, whereas if the slope < 0, the dependent variable exhibits downward trend. The absolute value of slope can indicate change rate. The greater the absolute value of slope, the greater the increase rate or decrease rate. Identify effect from climate factors To identify the effect of climate change on carbon sequestration in vegetation, software Arcmap was used to create a random point layer in the TNSFP region, and then the random points were used to extract values corresponding to every random points from the grid layers of carbon sequestration rate, annual precipitation and air temperature (Fig. 2 ). Values corresponding to random points were used to perform correlation analysis, and their correlation coefficient can indicate the effect of climate variables on carbon sequestration rate. To provide a visual description between carbon sequestration rate and climate variables (annual precipitation and air temperature), we drew the scatter diagrams for these variables. Identify effect from human activities There is no natural forest basically in the TNSFP region, and almost all forests are planted in original natural grass land. Under the assumption that the existing forestland came from large-scale afforestation and reforestation in original natural grass land, human-induced carbon sequestration rate could be identified by the difference of carbon sequestration rate after natural grass land is replaced by forest land. Comparing carbon sequestration rates in natural grassland and forest land where grassland has been transformed into forest land, it could provide an estimation on carbon sequestration rate caused by large-scale afforestation and reforestation in the TNSFP region. Results Carbon sequestration rate and its change The average carbon sequestration rate in vegetation (defined by net primary production) was 458.3 ± 45 g C m − 2 yr − 1 in the TNSFP region during the period 2000–2021. There was considerable spatial variability of carbon sequestration rate. Minor carbon sequestration rate (0 ~ 300 g C m − 2 yr − 1 ) occurred in mid-west region, but larger carbon sequestration rate (300 ~ 900 g C m − 2 yr − 1 ) occurred in east of the TNSFP region (Fig. 3 a). Carbon sequestration rate varied in different land use types. There was an average carbon sequestration rate of 613.5 ± 70.2 g C m − 2 yr − 1 in forest land, and 251.4 ± 106.5 g C m − 2 yr − 1 in grass land during 2000–2021. Significant change of carbon sequestration rate was observed during the period 2000–2021. Increase trend of carbon sequestration rate occurred covering approximately 90% of the TNSFP region, and a slight decrease covering 10% area (Fig. 3 b). Carbon sequestration rate had an average increase rate of 5.06 ± 4.3g C m − 2 yr − 1 in the TNSFP region. Higher increase rate (10 ~ 100 g C m − 2 yr − 1 ) occurred in the east and southeast of the TNSFP region, where is the key afforestation area (Fig. 3 b). Significant increase trend of carbon sequestration rate was discovered in the main ecosystems in the TNSFP region in spite of short-term fluctuations. Carbon sequestration rate in grassland had an average increase rate of 3.27 ± 0.55 g C m − 2 yr − 1 , with an increase rate of 3.023 g C m − 2 yr − 1 in high grass, 3.9053 g C m − 2 yr − 1 in middle grass and 2.8852 g C m − 2 yr − 1 in low grass, respectively (Fig. 4 a). Compared with the increase rate in grass land, carbon sequestration rate in forest land had the higher increase rate of 7.78 ± 2.07 g C m − 2 yr − 1 , with an increase rate of 6.14 g C m − 2 yr − 1 in woodland, 7.49 g C m − 2 yr − 1 in shrubwood, 6.74 g C m − 2 yr − 1 in sparse woodland, and 10.78 g C m − 2 yr − 1 in other woodlands, respectively (Fig. 4 b). The amount of carbon sequestration The total amount of carbon sequestration in vegetation was 26.1054 P g C (1 P g C = 10 15 g C) in the TNSFP region during the period 2000–2021, because there is a larger area of 4,069,000 km 2 in the TNSFP region. The average annual carbon sequestration was 1186.6 ± 122.6 T g C (1 T g C = 10 12 g C), which has the capacity to offset 12.8% of annual carbon emission (industrial carbon emissions 9.3 Pg C yr − 1 ) in China (Liu et al., 2021 ). Grass land covers 31.5% of the region, and the amount of carbon sequestration in vegetation was 6.5792 P g C in 2000–2021, with average annual carbon sequestration 299.1 ± 30.5 Tg C. Forest land covers 5.7% of the region, and the amount of carbon sequestration in vegetation was 2.9744 P g C in 2000–2021, with average annual carbon sequestration 135.2 ± 11.8 Tg C (Table 1 ). Table 1 Total amount of annual carbon sequestration in vegetation in the TNSFP region during the period 2000–2021. Year Annual carbon sequestration in all ecosystems (Tg C) Annual carbon sequestration in forest land (Tg C) Annual carbon sequestration in grass land (Tg C) 2000 939.5 ± 77.9 107.6 ± 23.7 253.2 ± 40.1 2001 964.3 ± 80.3 122.1 ± 26.6 242 ± 35.3 2002 1090.7 ± 91.8 126.4 ± 27.3 288.7 ± 41.8 2003 1102.2 ± 93.7 130.6 ± 28.4 288.3 ± 41.8 2004 1082.7 ± 94.3 128.9 ± 27.7 267.4 ± 35.3 2005 1139.3 ± 99.6 134.4 ± 29.1 283.4 ± 40.3 2006 1128.8 ± 98.2 131.8 ± 28.2 285.5 ± 39.3 2007 1093.8 ± 95.8 129.8 ± 27.6 267.4 ± 31.7 2008 1161.6 ± 102.8 133.9 ± 28.9 287.3 ± 39 2009 1087.2 ± 94.6 127.4 ± 27.2 269.7 ± 33.2 2010 1162.7 ± 101.8 133.6 ± 28.5 288.8 ± 35.2 2011 1207.3 ± 107.5 135.5 ± 28.8 300.8 ± 39.1 2012 1280.8 ± 115.6 123.5 ± 26.7 333.1 ± 39 2013 1281.1 ± 114.9 142.4 ± 30.2 323.7 ± 38.6 2014 1197 ± 106.4 135.7 ± 29.1 297 ± 35.6 2015 1224.8 ± 108.9 139.4 ± 30.0 302.1 ± 38.5 2016 1251.9 ± 111.9 142.6 ± 30.4 306.4 ± 32.5 2017 1282.5 ± 113.8 143.1 ± 30.2 320.9 ± 35.8 2018 1355.7 ± 118.9 146.4 ± 30.9 356.4 ± 38.8 2019 1329.1 ± 117.6 145.4 ± 31.1 335.3 ± 38.6 2020 1335.6 ± 118.3 146.1 ± 31.1 338.1 ± 35.8 2021 1406.8 ± 124.7 167.8 ± 36.1 343.9 ± 42 Total 26105.4 2974.4 6579.2 Effect of climate change on carbon sequestration Warm-wet climate trend was observed in the TNSFP region in recent years, because significant increase in annual precipitation occurred covering 83% area of the TNSFP region, with an increase rate of 6 ~ 10 mm per year in forest land, and 4 ~ 7 mm per year in grass land during the period 2000–2021. Significant increase in air temperature occurred covering 90% area of the TNSFP region. There was an average increase rate 0.298 ℃ per decade in forest land, and 0.290 ℃ per decade in grass land, which was higher than the global warming rate (0.2℃ per decade) (IPCC, 2018 ). Warm-wet climate trend has also been confirmed by other studies. It was reported that annual precipitation across China significantly increased at rates of 11.4 mm per decade during the period 1961–2016 (Su et al., 2022 ). It was found that the noticeable increase in carbon sequestration rate was accompanied by the increase in annual precipitation and air temperature during the period 2000–2021 (Fig. 3 b & Fig. 5 ). The co-occurring trends in precipitation, temperature and carbon sequestration rate indicated that carbon sequestration rate in vegetation might suffer a positive effect from the warm-wet climate trend inevitably. Correlation analysis was performed to confirm the relationship between the increasing carbon sequestration rate and the warm-wet climate trend. The results showed a significant positive correlation between annual precipitation and carbon sequestration rate in vegetation, with correlation coefficients of 0.661 in Pearson correlation ( P < 0.01), 0.775 in Spearman Rho correlation ( P < 0.01) and 0.581 in Kendall tau-b correlation ( P < 0.01), respectively (Table 2 ). The trend line of scatter diagram also confirmed a positive correlation between annual precipitation and carbon sequestration rate, with a fitting accuracy R 2 = 0.4371 (Fig. 6 a). The results indicated that increase in annual precipitation is helpful in enhancing carbon sequestration rate. Table 2 Correlation analysis between carbon sequestration rate and climate variables (annual precipitation and annual temperature) Carbon sequestration rate in vegetation Pearson correlation Spearman Rho correlation Kendall tau-b correlation N = 496 N = 496 N = 496 Coefficient Sig. (bilateral) Coefficient Sig. (bilateral) Coefficient Sig. (bilateral) Annual precipitation 0.661 ** < 0.01 0.775 ** < 0.01 0.581 ** < 0.01 Annual air temperature -0.098 * 0.029 -0.322 ** < 0.01 -0.211 ** < 0.01 ** indicates statistically significant correlation at 0.01 confidence level (bilateral) * indicates statistically significant correlation at 0.05 confidence level (bilateral) A slight negative correlation was discovered between annual air temperature and carbon sequestration rate, with correlation coefficients of -0.098 ( P < 0.05) in Pearson correlation, -0.322 ( P < 0.01) in Spearman Rho correlation, and − 0.211 ( P < 0.01) in Kendall tau-b correlation, respectively (Table 2 ). The slight negative correlation was also confirmed by the scatter diagram between annual air temperature and carbon sequestration rate, with a fitting accuracy R 2 = 0.0097 (Fig. 6 b). The slight negative correlation indicated that increase in annual air temperature might be harmful for carbon sequestration rate. It is credible that the favorable effect of the increasing precipitation on carbon sequestration rate is far greater than the adverse effect of the rising temperature, judging by the obvious increase in carbon sequestration rate under the background of the warm-wet climate in recent years in the TNSFP region. Effect of human activities on carbon sequestration Our results showed that large-scale afforestation and reforestation promoted carbon sequestration rate in the TNSFP region. Carbon sequestration rate in forest land was 613.5 ± 70.2 g C m − 2 yr − 1 , larger than 251.4 ± 106.5 g C m − 2 yr − 1 in grass land. Therefore, carbon sequestration rate might increase after transformation from grass land into forest land. It is reasonable assumptions that the existing forest (22478.1×10 3 ha) came from original natural grass land, because there is no natural forest basically, and all the existing forests came from afforestation and reforestation in original natural grass land in the TNSFP region. The results under the assumptions indicated that the additional amount of annual carbon sequestration would be 81.4 ± 10.5 Tg C after grass land into the existing forest land, and the additional carbon sequestration would accumulate to 1790.5 Tg C during the period 2000–2021 (Table 3 ). Thus, approximately 1790.5 Tg C was stored into vegetation in the recent two decades due to afforestation and reforestation in the TNSFP region in the case of favorable climate change. Table 3 Enhanced carbon sequestration assuming that the existing forest was transformed from natural grass land in the TNSFP region Year Forest carbon sequestration rate (g C m − 2 yr − 1 ) Grass carbon sequestration rate(g C m − 2 yr − 1 ) Enhanced amount of carbon sequestration assuming the existing forest from natural grassland(Tg C) 2000 472.4 ± 19.0 215.3 ± 131.8 57.8 2001 537.8 ± 19.3 205.0 ± 117.1 74.8 2002 567.0 ± 29.2 244.3 ± 138.1 72.5 2003 580.3 ± 23.7 243.9 ± 137.7 75.6 2004 586.6 ± 47.7 225.3 ± 117.7 81.2 2005 604.6 ± 36.2 239.6 ± 133.1 82.0 2006 601.1 ± 48.6 240.8 ± 129.8 81.0 2007 592.8 ± 43.0 224.4 ± 107.4 82.8 2008 606.3 ± 44.9 242.0 ± 127.6 81.9 2009 578.5 ± 39.2 226.7 ± 112.4 79.1 2010 606.4 ± 36.1 242.6 ± 118.7 81.8 2011 618.6 ± 46.8 253.2 ± 130.2 82.1 2012 558.6 ± 38.7 278.7 ± 128.9 62.9 2013 656.5 ± 61.5 271.4 ± 129.5 86.6 2014 610.7 ± 33.8 249.2 ± 119.5 81.3 2015 627.9 ± 37.4 254.2 ± 128.9 84.0 2016 656.4 ± 59.7 256.48 ± 113.0 90.0 2017 660.1 ± 56.9 269.0 ± 123.6 87.9 2018 679.5 ± 71.4 297.5 ± 129.7 85.9 2019 662.1 ± 46.5 281.1 ± 130.9 85.6 2020 671.5 ± 62.0 282.0 ± 120.1 87.6 2021 760.4 ± 60.0 288.5 ± 139.8 106.1 Average 613.5 ± 70.2 251.4 ± 106.5 Total 1790.5 Discussions Previous studies discovered that vegetation growth has been improving in the TNSFP region (Duan et al., 2011 ; Zhang et al., 2016 ; Du et al., 2020 ). Our results drew similar conclusion that carbon sequestration rate increase obviously covering approximately 90% of the TNSFP region during 2000–2021. The results indicate carbon sequestration in vegetation has been enhanced, and annual carbon sequestration (1186.6 ± 122.6 T g C) in the TNSFP region has a capacity to offset 12.8% of annual industrial carbon emission (9.3 Pg C yr − 1 ) in China (Liu et al., 2021 ). It was reported that afforestation in China contribute to carbon sequestration of 0.79 Pg C from 1981 to 2008 (He et al., 2015 ). Under the assumptions that the existing forest came from original natural grass land, our results suggested that afforestation and reforestation in the TNSFP region contribute to carbon sequestration of 1.7905 Pg C approximately, which was larger than 0.79 Pg C in China from 1981 to 2008, because the carbon sequestration 1.7905 Pg C included afforestation and reforestation in all periods. It is well known that change of carbon sequestration in vegetation suffer from the complex effect of climate and human activities. Human activities in China have favorable effect on carbon sequestration in vegetation in recent years, because a series of large-scale ecological restoration programs has been implemented (Fang et al., 2018 ; Xu et al., 2021 ). Most of previous studies agreed that climate change has a positive effect on carbon sequestration in vegetation of the TNSFP region (Duan et al., 2011 ; Xu et al., 2017 ). Our results were consisted with previous conclusion that both climate change and human activities have favorable effect on carbon sequestration in vegetation in the TNSFP region. Some studies reported that climate became wetter because annual precipitation increase since the year 2000 (Li et al., 2015 ). Our results confirmed the warm-wet climate trend in the TNSFP region during the period 2000–2021, and the warm-wet climate was helpful for enhancing carbon sequestration rate generally. However, our study provided much more detailed findings that the increase in annual precipitation is helpful, but the increase in annual air temperature is harmful in enhancing carbon sequestration rate. Theoretically rising temperature can prolong the growing season, improve photosynthetic efficiency, and increase plant productivity. Unfortunately, the rising temperature can easily lead to drought, and result in adverse effect on vegetation grown in arid and semi-arid TNSFP region (Xie et al., 2020 ). Conclusions The results provide quantitative information about carbon sequestration rate and its change in the main ecosystems in the TNSFP region. The average annual carbon sequestration rate in vegetation was 458.3 ± 45 g C m − 2 yr − 1 , and annual carbon sequestration rate increased evidently in approximately 90% of the TNSFP region, with an average increase rate 5.06 ± 4.3 g C m − 2 yr − 1 during the period 2000–2021. There was a larger increase rate of 7.78 ± 2.07 g C m − 2 yr − 1 in forest land than that of 3.27 ± 0.55 g C m − 2 yr − 1 in grassland. The amount of annual carbon sequestration into vegetation was 1186.6 ± 122.6 T g C, and accumulated to a total carbon sequestration of 26.1054 P g C in the TNSFP region in 2000–2021. Assuming the existing forest came from original natural grass land, afforestation and reforestation led to carbon sequestration 1.7905 Pg C approximately in the TNSFP region. Our results revealed that both human activities and climate played a positive role in enhancing carbon sequestration rate. More detailed findings indicated that the increase in annual precipitation is helpful, but the increase in annual air temperature is harmful in enhancing carbon sequestration rate. Declarations Acknowledgements We thank the National Meteorological Information Center of the Chinese National Bureau for providing climate data, and thank the Data Center for Resources and Environmental Sciences, Chinese Academy of Sciences for providing land-use data. Author contribution Ji-xi Gao conceived the idea of the article, Li-xia Wang wrote the main manuscript text，Wen-guo Zhang, Wen-ming Shen, Ming-yong Cai, Tong Xiao, Xin-sheng Zhang and Wen-fei Tai performed data analysis. All authors reviewed the manuscript. Data availability Land-use in 2010s is provided by Data Center for Resources and Environmental Sciences, Chinese Academy of Sciences (http://www.resdc.cn). Normalized difference vegetation index (NDVI) was derived from NASA’s Moderate Resolution Imaging Spectro radiometer sensors. Precipitation and air temperature were acquired from the National Meteorological Information Center of the Chinese National Bureau. Funding This study was funded by National Key R & D Program of China (2021YFB3901103). Conflict of interest The authors declare no competing interests. References Animal Husbandry Vet Bureau of Department of Agriculture P.R.C. (1996). Animal Husbandry Vet Central Station of the China. China Grassland Resources. China Science and Technology Press: Beijing, China, pp. 353–358. Chai X., Liang C. Z., Liang M. W., et al. (2014). Seasonal dynamics of belowground biomass and productivity and potential of carbon sequestration in meadow steppe and typical steppe, in Inn Mongolia, China.Acta Ecologica Sinica, 34(19): 5530–5540. Du J. Q., Quan Z. Q., Fang S. F., et al. (2020). Spatiotemporal changes in vegetation coverage and its causes in China since the Chinese economic reform. Environmental Science and Pollution Research, 27: 1144–1159. Duan H. C., Yan C. Z., Tsunekawa A., et al. (2011). Assessing vegetation dynamics in the Three-North Shelter Forest region of China using AVHRR NDVI data. Environ Earth Sci., 64:1011–1020. Fang J. Y., Yu G. R., Liu L.L., et al. (2018). Climate change, human impacts, and carbon sequestration in China. PNAS, 115: 4015–4020. He B., Miao L. J., Cui X. F. (2015). Carbon sequestration from China’s afforestation projects. Environ Earth Sci., 74: 5491–5499. IPCC (2018). Summary for Policymakers. In: Global Warming of 1.5°C. Ji Y. H., Zhou G. S., Wang S. D., Wang L. X. (2020). Prominent vegetation greening and its correlation with climatic variables in northern China. Environmental Monitoring and Assessment, 192(9):636. Ji, Y. H., Zhou G. S., Luo T. X., Dan Y., Zhou L., Lv X. M. (2020). Variation of net primary productivity and its drivers in China’s forests during 2000–2018. Forest Ecosystems, 7, 15. Li Z. C., Guo A. H., Yan H., et al. (2015). Effect of climate change on key ecological protection projects. Advances in Climate Change Research, 11: 179–184. Liu H. J., Shao M. J., Ji Y.M. (2021). The spatial pattern and distribution dynamic evolution of carbon emissions in China: Empirical study based on county carbon emission data. Scientia Geographica Sinica, 41(11):1917–1924. Liu Y. Y., Yang Y., Wang Q., et al. (2019). Assessing the dynamics of grassland net primary productivity in response to climate change at the global scale. Chin. Geogra. Sci., 29, 725–740. Lu F., Hu H. F., Sun W. J., et al. (2018). Effects of national ecological restoration projects on carbon sequestration in China from 2001 to 2010. PNAS, 115: 4039–4044. Montagnini F., Nair P. K. R. (2004). Carbon sequestration: An underexploited environmental benefit of agroforestry systems. Agroforestry Systems, 61: 281–295. Nemani R. R., Keeling C. D., Hashimoto H., et al. (2003). Climate-driven increases in global terrestrial net primary production from 1982 to 1999. Science, 300: 1560–1563. Piao S. L., Yin G. D., Tan J. G., et al. (2015). Detection and attribution of vegetation greening trend in China over the last 30 years. Global Change Biology, 21: 1601–1609. Su, B.; Xiao, C. D.; Zhao, H. Y., et al. (2022). Estimated changes in different forms of precipitation (snow, sleet, and rain) across China: 1961–2016. Atmospheric Research, 270: 106078. Sun Y. R., Zhu J. J., Yan Q. L., et al. (2016). Changes in vegetation carbon stocks between 1978 and 2007 in central Loess Plateau, China. Environ Earth Sci., 75:312. Wang W., Peng S. S., Fang J. Y. (2008). Biomass distribution of natural grasslands and it response to climate change in North China. Arid Zone Research, 25: 90–97. Xie S. D., Mo X. G, Hu S., Chen X. J. (2020). Responses of vegetation greenness to temperature and precipitation in the Three-North Shelter Forest Program. Geographical Research, 39: 152–165. Xu B., Yang X. C., Tao W. G., Tan Z. H., Liu H. Q., Miao J. M. (2007). Remote sensing monitoring upon the grass production in China. Acta Ecologia Sinica, 27(2): 405–413. Xu L., Wen D., Zhu J. X., et al. (2017). Regional variation in carbon sequestration potential of forest ecosystems in China. Chin. Geogra. Sci., 27: 337–350. Xu W. X., Pang Y., Ye M. (2021). Vegetation distribution and regional carbon sequestration in China: based on spatial panel data analysis. Arabian Journal of Geosciences , 14: 1906. Xu X. L., Li K. R. (2010). Biomass carbon sequestration by planted forests in China. Chin. Geogra. Sci., 20: 289–297. Xu X. L., Liu J. Y., Zhang S. W., Li R. D., et al. (2018). China multi period land use and land cover remote sensing monitoring data set (CNLUCC). Data registration and publishing system of resource and environmental science data center of Chinese Academy of Sciences. ( http://www.resdc.cn/DOI : 10.12078/2018070201) . Zhang Y., Peng C. H., Li W. Z., et al. (2016). Multiple afforestation programs accelerate the greenness in the ‘Three North’ region of China from 1982 to 2013. Ecological Indicators, 61: 404–412. Zheng X., Zhu J. J. (2017). A new climatic classification of afforestation in Three-North regions of China with multi-source remote sensing data. Theor. Appl. Climatol., 127: 465–480. Zhu J. J., Zheng X. (2019). The prospects of development of the Three-North Afforestation Program (TNAP): On the basis of the results of the 40-year construction general assessment of the TNAP. Chinese Journal of Ecology, 38: 1600–1610. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-1710152","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":114043680,"identity":"68cc5636-71c2-473c-9837-86f308a996bc","order_by":0,"name":"Li-xia Wang","email":"","orcid":"","institution":"Ministry of Ecology and Environment Center for Satellite Application on Ecology and Environment","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Li-xia","middleName":"","lastName":"Wang","suffix":""},{"id":114043685,"identity":"feb00e98-a520-4a03-82fe-680cb5d8b506","order_by":1,"name":"Ji-xi Gao","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAp0lEQVRIiWNgGAWjYFCCA4wPGAxI1MJsQKoWBjYJ0tTLO54xqy4osLFnYO99/IIoLYYHjqXdnmGQltjAc9zMgjgtDYeP3eYxOJzAIJHGRpyXDBsOthXzGPy3J16LPMPhY8w8BgcYGyTSmB8QpcWA4ViyNI9BcmIbzzE2onQwyM84Y/iZ54+dPT97G/MH4my5cQDCYCM6guT7G+BsIm0ZBaNgFIyCEQcAF0gq1mH5dM8AAAAASUVORK5CYII=","orcid":"","institution":"Ministry of Ecology and Environment Center for Satellite Application on Ecology and Environment","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Ji-xi","middleName":"","lastName":"Gao","suffix":""},{"id":114043686,"identity":"b6a6a263-0599-4422-a4e0-977fa8734f30","order_by":2,"name":"Wen-guo Zhang","email":"","orcid":"","institution":"Ministry of Ecology and Environment Center for Satellite Application on Ecology and Environment","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Wen-guo","middleName":"","lastName":"Zhang","suffix":""},{"id":114043687,"identity":"98f4efe6-f223-4f92-8586-09d6bc79714a","order_by":3,"name":"Wen-ming Shen","email":"","orcid":"","institution":"Ministry of Ecology and Environment Center for Satellite Application on Ecology and Environment","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Wen-ming","middleName":"","lastName":"Shen","suffix":""},{"id":114043688,"identity":"9b396cc0-8536-4c08-9c97-7b42462c8261","order_by":4,"name":"Ming-yong Cai","email":"","orcid":"","institution":"Ministry of Ecology and Environment Center for Satellite Application on Ecology and Environment","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Ming-yong","middleName":"","lastName":"Cai","suffix":""},{"id":114043689,"identity":"1cd42065-9587-467c-9d8a-e75d30b84465","order_by":5,"name":"Tong Xiao","email":"","orcid":"","institution":"Ministry of Ecology and Environment Center for Satellite Application on Ecology and Environment","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Tong","middleName":"","lastName":"Xiao","suffix":""},{"id":114043690,"identity":"6c90ea70-7656-4e00-82db-5d2cadd21a5c","order_by":6,"name":"Xin-sheng Zhang","email":"","orcid":"","institution":"Ministry of Ecology and Environment Center for Satellite Application on Ecology and Environment","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Xin-sheng","middleName":"","lastName":"Zhang","suffix":""},{"id":114043691,"identity":"9741140e-1e9c-4ec3-8559-3882e545244f","order_by":7,"name":"Wen-fei Tai","email":"","orcid":"","institution":"Ministry of Ecology and Environment Center for Satellite Application on Ecology and Environment","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Wen-fei","middleName":"","lastName":"Tai","suffix":""}],"badges":[],"createdAt":"2022-05-31 05:14:12","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-1710152/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-1710152/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":22954661,"identity":"12ad9061-a768-4a95-8981-1924fbb8473f","added_by":"auto","created_at":"2022-06-22 17:40:49","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1806774,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of the main land use types in the TNSFP region in recent years\u003c/p\u003e\u003cp\u003eVegetation types in the TNSFP are mainly composed of desert, grassland, forestland and croplands (\u003cstrong\u003eFig. 1\u003c/strong\u003e). One-third (35.52%) of the TNSFP region is covered by the primitive barren desert or sparse grass vegetation. Large-scale ecological projects were implemented to combat desertification and control dust storms since the late 1970s. Vast man-made forests were established in the original natural grass area (Duan et al., 2011; Zheng \u0026amp; Zhu 2017; Zhu et al., 2019). \u003c/p\u003e","description":"","filename":"Fig.1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-1710152/v1/a81b95656f695d3af4780b34.jpg"},{"id":22954666,"identity":"6fca4937-2128-4fc9-9bb0-6413933b4315","added_by":"auto","created_at":"2022-06-22 17:40:49","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":75882,"visible":true,"origin":"","legend":"\u003cp\u003eThe random point layer created by software Arcmap, and then the random point layer was used to extract carbon sequestration rate, annual precipitation and air temperature corresponding to every random points.\u0026nbsp;\u003c/p\u003e","description":"","filename":"Fig.2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-1710152/v1/fe98f7f5a20e8c370407d77b.jpg"},{"id":22954662,"identity":"a1a5e7fa-5145-4709-aa56-87249b476ba1","added_by":"auto","created_at":"2022-06-22 17:40:49","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":213238,"visible":true,"origin":"","legend":"\u003cp\u003eCarbon sequestration rate in vegetation with spatial resolution of 1 km × 1 km (a) and the statistically significant (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05) change of carbon sequestration rate in vegetation (b) at each grid cell in the TNSFP region during 2000–2021. (Positive values indicate an increasing trend; negative values indicate a decreasing trend).\u0026nbsp;\u003c/p\u003e","description":"","filename":"Fig.3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-1710152/v1/bfcccb897ede06d83ad706cf.jpg"},{"id":22955573,"identity":"337d4471-b485-4c67-b507-ab28c261efe2","added_by":"auto","created_at":"2022-06-22 17:45:49","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":234019,"visible":true,"origin":"","legend":"\u003cp\u003eChange trend of annual carbon sequestration rate of (high, middle and low) grass lands (a) and (woodland, shrub wood, sparse woodland and other woodland) forest lands (b) in the TNSFP region during 2000-2021\u0026nbsp;\u003c/p\u003e","description":"","filename":"Fig.4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-1710152/v1/6995afe1e5b3f2d9e532c630.jpg"},{"id":22954665,"identity":"deeb1096-9e31-483a-b39d-f4d94af43b67","added_by":"auto","created_at":"2022-06-22 17:40:49","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":203656,"visible":true,"origin":"","legend":"\u003cp\u003eWarm-wet climate trend occurred because annual precipitation increase covering 83% of the TNSFP region, annual air temperature increase covering 90% area during the period 2000-2021.\u003c/p\u003e","description":"","filename":"Fig.5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-1710152/v1/1d588dbe6f9f5b1b4c653214.jpg"},{"id":22955574,"identity":"34dff50b-8e12-4efe-a2e6-06108a24bd49","added_by":"auto","created_at":"2022-06-22 17:45:49","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":125843,"visible":true,"origin":"","legend":"\u003cp\u003eScatter diagrams between carbon sequestration rate and annual precipitation (a), between carbon sequestration rate and annual air temperature (b).\u003c/p\u003e","description":"","filename":"Fig.6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-1710152/v1/0edf7bc872cddd319422ed7c.jpg"},{"id":22955605,"identity":"25b35df3-25d7-4293-832e-5bc72ef5fc6f","added_by":"auto","created_at":"2022-06-22 17:45:57","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":943085,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-1710152/v1/5534c251-271f-47ca-a4be-7ad2185f62c3.pdf"},{"id":22955577,"identity":"1a2fc342-be9b-45be-b446-675c7f1ea8a2","added_by":"auto","created_at":"2022-06-22 17:45:50","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":773104,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-1710152/v1/a9d80973-3844-4de2-9c86-5b32c02bce94.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Carbon sequestration in vegetation and its change in the Three-North Shelter Forest region of China in 2000-2021","fulltext":[{"header":"Introduction","content":"\u003cp\u003eTerrestrial vegetation usually acts as a net carbon sink. Carbon sequestration in vegetation offsets a large proportion of carbon emissions, and plays an important role in mitigating world\u0026rsquo;s climate change. Carbon sequestration in vegetation represents the amount of carbon removed from the atmosphere and stored it in biomass of the terrestrial biosphere through photosynthesis. Ecological restoration program is a useful strategy to enhance carbon capture and storage in vegetation. China has launched six key national ecological restoration projects. It was estimated that the total annual carbon sink in the project region was 132 Tg C per year (1 Tg\u0026thinsp;=\u0026thinsp;10\u003csup\u003e12\u003c/sup\u003e g), over half of annual carbon sink was attributed to the implementation of these projects (Lu et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn Northern China, large-scale ecological projects have been implementing, such as Three North Shelter Forest Program (TNSFP, from 1978 to 2050), Natural Forest Conversion Program (NFCP, from 1998 to 2010), Grain for Green Program (GGP, convert farmland to natural grassland or forest from 1998 to 2010), Beijing-Tianjin sands and dust engineering (reforestation/afforestation, prohibition of animal grazing from 2000 to 2012). The TNSFP is the largest afforestation program in the world, with an area of 46.1\u0026times;10\u003csup\u003e6\u003c/sup\u003e ha. The implementation of the ecological projects has transformed grassland or cropland to forests by afforestation and reforestation. Subsequently, greenness and vegetation cover increased (Duan et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Zhang et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), biological carbon sequestration was enhanced significantly, and caused a continuous increase in carbon storage (Montagnini \u0026amp; Nair, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Xu \u0026amp; Li \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). However, previous reports didn\u0026rsquo;t provide an adequate quantitative information about biotic carbon sequestration rate and its long-term change rate in different ecosystems across the TNSFP region.\u003c/p\u003e \u003cp\u003eIt is generally believed that the change of carbon sequestration should attribute to the combined effect of climate change and human activities. It was reported that climatic changes have enhanced plant growth in northern mid-latitudes and high latitudes, and led to increase 6% of net primary production globally during 1982\u0026ndash;1999 (Nemani et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). Some reports provided evidence that vegetation carbon stocks increased significantly in northern China in recent years, and ecological projects and climate change had a positive effect on carbon sequestration in vegetation (Sun et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Ji et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). However, other scientists argued that climate change led to negative impacts on the vegetation in north China and Inner Mongolia (Piao et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn this study, the TNSFP region was selected as study area. The purposes were (1) to quantify carbon sequestration rate in vegetation, (2) to detect the long-term change of carbon sequestration rate, (3) to identify the effect of climate change and human activities on carbon sequestration rate in the main ecosystems in the TNSFP region. Given that the natural grassland is replaced by planted forest, (4) to quantify carbon sequestration increment due to afforestation and reforestation efforts.\u003c/p\u003e"},{"header":"Methods And Materials","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy area\u003c/h2\u003e \u003cp\u003eThe TNSFP region covers 4,069,000 km\u003csup\u003e2\u003c/sup\u003e, and accounts for 42.4% of China\u0026rsquo;s land area, ranging between73\u0026deg;26\u0026prime;E and 127\u0026deg;50\u0026prime;E, and between 33\u0026deg;30\u0026prime;N and 50\u0026deg;12\u0026prime;N in Northern China. There is a distance of 4,480 km from east to west, and 560 km\u0026thinsp;~\u0026thinsp;1460 km from south to north. Most of this region belongs to arid and semi-arid climate regions. There is an average annual precipitation of 327.3 mm, and an average annual air temperature of 5.38 ℃ in the TNSFP region in 2000\u0026ndash;2021. The spatial pattern of annual precipitation in this region decreases from southeast to northwest.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eVegetation types in the TNSFP are mainly composed of desert, grassland, forestland and croplands (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). One-third (35.52%) of the TNSFP region is covered by the primitive barren desert or sparse grass vegetation. Large-scale ecological projects were implemented to combat desertification and control dust storms since the late 1970s. Vast man-made forests were established in the original natural grass area (Duan et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Zheng \u0026amp; Zhu \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Zhu et al., 2019).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eData sources\u003c/h2\u003e \u003cp\u003eTime-series satellite data and ground observation data were used to calculate carbon sequestration rate in vegetation. Land-use in 2010s is provided by Data Center for Resources and Environmental Sciences, Chinese Academy of Sciences (RESDC) (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.resdc.cn\u003c/span\u003e\u003cspan address=\"http://www.resdc.cn\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) Normalized difference vegetation index (NDVI) was derived from NASA\u0026rsquo;s Moderate Resolution Imaging Spectro radiometer sensors. Annual maximal NDVI in each grid cell was extracted from the time-series NDVI using the maximum value composite method to avoid the influence of noise, such as clouds, aerosols, solar elevation angle, and ice-snow cover. Annual precipitation and annual air temperature were acquired from observational data of the 2400 meteorological stations throughout China under the management of the National Meteorological Information Center of the Chinese National Bureau. Elevation with a spatial resolution of 1km \u0026times; 1km was extracted from a 90 m \u0026times; 90 m digital elevation model (DEM). To maintain the same spatial resolution, all data were resampled or interpolated into grid cells with a spatial resolution of 1 km \u0026times;1 km.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eCalculation of carbon sequestration\u003c/h2\u003e \u003cp\u003eCarbon sequestration in vegetation per unit area could be expressed by net primary productivity (NPP), which shows how much carbon is fixed in vegetation per square meter per year, and provides an estimate of annual carbon sequestration rate. A variety of models were developed to simulate carbon sequestration, such as light use efficiency concept (Global Production Efficiency Model; Carnegie-Ames-Stanford Approach, CASA), process-based models (CENTURY, BIOME-BGC), climate-based models (Thornthwaite Memorial model, Miami model and Chikugo model), volume-derived biomass models (Biomass Expansion Factors, BEFs) (Liu et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Through the comparison of model accuracy and applicability, we selected a statistics-based multiple regression model established by Ji et al. (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) (\u003cb\u003eEq.\u0026nbsp;1\u003c/b\u003e). The model is suitable for Chinese forests, with a fitting coefficient of \u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.536 (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01), which indicated good performance in predicting forest NPP.\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(NPP=97.13{NDVI}_{max}+0.022PT+0.128P-9.136T-0.027A +333.67\\)\u003c/span\u003e \u003c/span\u003e (\u003cb\u003eEq.\u0026nbsp;1\u003c/b\u003e)\u003c/p\u003e \u003cp\u003eWhere \u003cem\u003eNPP\u003c/em\u003e is carbon sequestration rate in forest (g C m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e yr\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e), \u003cem\u003eNDVI\u003c/em\u003e\u003csub\u003emax\u003c/sub\u003e is annual maximal \u003cem\u003eNDVI\u003c/em\u003e (0\u0026thinsp;\u0026lt;\u0026thinsp;\u003cem\u003eNDVI\u003c/em\u003e\u0026thinsp;\u0026le;\u0026thinsp;1), \u003cem\u003eP\u003c/em\u003e is annual precipitation (mm), \u003cem\u003eT\u003c/em\u003e is annual temperature (\u0026deg;C), and \u003cem\u003eA\u003c/em\u003e is altitude (m).\u003c/p\u003e \u003cp\u003eCarbon sequestration rate in typical grassland was calculated by an exponential model (\u003cb\u003eEq.\u0026nbsp;2\u003c/b\u003e); Carbon sequestration in semi-arid grassland and desert grassland was calculated by another exponential model (\u003cb\u003eEq.\u0026nbsp;3\u003c/b\u003e) established by Xu et al. (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). The two models output fresh grass yield above ground with an estimated precision of 80%. Fresh grass yield above ground was converted to dry grass yield by applying a dry-wet ratio coefficient of 0.31(Animal Husbandry Vet Bureau of Department of Agriculture P.R.C., 1996), and converted to the total dry grass yield (above and below ground) by ratio coefficient of 5.88 (Wang et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2008\u003c/span\u003e), and then dry grass yield was converted into carbon sequestration rate (g C m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003eyr\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) using a conversion coefficient of 0.45 (Chai et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(Y=385.362\\times {\\text{e}}^{3.813NDVI}\\)\u003c/span\u003e \u003c/span\u003e (\u003cb\u003eEq.\u0026nbsp;2\u003c/b\u003e)\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(Y=193.585\\times {\\text{e}}^{4.984NDVI}\\)\u003c/span\u003e \u003c/span\u003e (\u003cb\u003eEq.\u0026nbsp;3\u003c/b\u003e)\u003c/p\u003e \u003cp\u003eWhere \u003cem\u003eY\u003c/em\u003e is fresh grass yield (kg ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003eyr\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e), \u003cem\u003eNDVI\u003c/em\u003e is annual maximal \u003cem\u003eNDVI\u003c/em\u003e (0\u0026thinsp;\u0026lt;\u0026thinsp;\u003cem\u003eNDVI\u003c/em\u003e\u0026thinsp;\u0026le;\u0026thinsp;1).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eIdentify change of carbon sequestration rate and climatic variables\u003c/h2\u003e \u003cp\u003eTo reveal the dynamic processes of vegetation carbon sequestration and climate variables, time-series linear regression analysis was performed to obtain the slope of the regression trend line for each pixel (1km\u0026times;1km grid cell), where the time series variables (carbon sequestration rate, annual precipitation and air temperature) as dependent variables and time (year) as the independent variable. There was an upward trend or a downward trend for each pixel. If the slope of the regression trend line\u0026thinsp;\u0026gt;\u0026thinsp;0, the dependent variable exhibits upward trend, whereas if the slope\u0026thinsp;\u0026lt;\u0026thinsp;0, the dependent variable exhibits downward trend. The absolute value of slope can indicate change rate. The greater the absolute value of slope, the greater the increase rate or decrease rate.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eIdentify effect from climate factors\u003c/h2\u003e \u003cp\u003eTo identify the effect of climate change on carbon sequestration in vegetation, software Arcmap was used to create a random point layer in the TNSFP region, and then the random points were used to extract values corresponding to every random points from the grid layers of carbon sequestration rate, annual precipitation and air temperature (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Values corresponding to random points were used to perform correlation analysis, and their correlation coefficient can indicate the effect of climate variables on carbon sequestration rate. To provide a visual description between carbon sequestration rate and climate variables (annual precipitation and air temperature), we drew the scatter diagrams for these variables.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eIdentify effect from human activities\u003c/h2\u003e \u003cp\u003eThere is no natural forest basically in the TNSFP region, and almost all forests are planted in original natural grass land. Under the assumption that the existing forestland came from large-scale afforestation and reforestation in original natural grass land, human-induced carbon sequestration rate could be identified by the difference of carbon sequestration rate after natural grass land is replaced by forest land. Comparing carbon sequestration rates in natural grassland and forest land where grassland has been transformed into forest land, it could provide an estimation on carbon sequestration rate caused by large-scale afforestation and reforestation in the TNSFP region.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eCarbon sequestration rate and its change\u003c/h2\u003e \u003cp\u003eThe average carbon sequestration rate in vegetation (defined by net primary production) was 458.3\u0026thinsp;\u0026plusmn;\u0026thinsp;45 g C m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003eyr\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e in the TNSFP region during the period 2000\u0026ndash;2021. There was considerable spatial variability of carbon sequestration rate. Minor carbon sequestration rate (0\u0026thinsp;~\u0026thinsp;300 g C m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003eyr\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) occurred in mid-west region, but larger carbon sequestration rate (300\u0026thinsp;~\u0026thinsp;900 g C m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003eyr\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) occurred in east of the TNSFP region (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea). Carbon sequestration rate varied in different land use types. There was an average carbon sequestration rate of 613.5\u0026thinsp;\u0026plusmn;\u0026thinsp;70.2 g C m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003eyr\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e in forest land, and 251.4\u0026thinsp;\u0026plusmn;\u0026thinsp;106.5 g C m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003eyr\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e in grass land during 2000\u0026ndash;2021.\u003c/p\u003e \u003cp\u003eSignificant change of carbon sequestration rate was observed during the period 2000\u0026ndash;2021. Increase trend of carbon sequestration rate occurred covering approximately 90% of the TNSFP region, and a slight decrease covering 10% area (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb). Carbon sequestration rate had an average increase rate of 5.06\u0026thinsp;\u0026plusmn;\u0026thinsp;4.3g C m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003eyr\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e in the TNSFP region. Higher increase rate (10\u0026thinsp;~\u0026thinsp;100 g C m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003eyr\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) occurred in the east and southeast of the TNSFP region, where is the key afforestation area (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eSignificant increase trend of carbon sequestration rate was discovered in the main ecosystems in the TNSFP region in spite of short-term fluctuations. Carbon sequestration rate in grassland had an average increase rate of 3.27\u0026thinsp;\u0026plusmn;\u0026thinsp;0.55 g C m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003eyr\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, with an increase rate of 3.023 g C m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003eyr\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e in high grass, 3.9053 g C m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003eyr\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e in middle grass and 2.8852 g C m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003eyr\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e in low grass, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea). Compared with the increase rate in grass land, carbon sequestration rate in forest land had the higher increase rate of 7.78\u0026thinsp;\u0026plusmn;\u0026thinsp;2.07 g C m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003eyr\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, with an increase rate of 6.14 g C m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003eyr\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e in woodland, 7.49 g C m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003eyr\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e in shrubwood, 6.74 g C m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003eyr\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e in sparse woodland, and 10.78 g C m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003eyr\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e in other woodlands, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eThe amount of carbon sequestration\u003c/h2\u003e \u003cp\u003eThe total amount of carbon sequestration in vegetation was 26.1054 P g C (1 P g C\u0026thinsp;=\u0026thinsp;10\u003csup\u003e15\u003c/sup\u003e g C) in the TNSFP region during the period 2000\u0026ndash;2021, because there is a larger area of 4,069,000 km\u003csup\u003e2\u003c/sup\u003e in the TNSFP region. The average annual carbon sequestration was 1186.6\u0026thinsp;\u0026plusmn;\u0026thinsp;122.6 T g C (1 T g C\u0026thinsp;=\u0026thinsp;10\u003csup\u003e12\u003c/sup\u003e g C), which has the capacity to offset 12.8% of annual carbon emission (industrial carbon emissions 9.3 Pg C yr\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) in China (Liu et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Grass land covers 31.5% of the region, and the amount of carbon sequestration in vegetation was 6.5792 P g C in 2000\u0026ndash;2021, with average annual carbon sequestration 299.1\u0026thinsp;\u0026plusmn;\u0026thinsp;30.5 Tg C. Forest land covers 5.7% of the region, and the amount of carbon sequestration in vegetation was 2.9744 P g C in 2000\u0026ndash;2021, with average annual carbon sequestration 135.2\u0026thinsp;\u0026plusmn;\u0026thinsp;11.8 Tg C (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eTotal amount of annual carbon sequestration in vegetation in the TNSFP region during the period 2000\u0026ndash;2021.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYear\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAnnual carbon sequestration in all ecosystems (Tg C)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAnnual carbon sequestration in forest land (Tg C)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAnnual carbon sequestration in grass land (Tg C)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e939.5\u0026thinsp;\u0026plusmn;\u0026thinsp;77.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e107.6\u0026thinsp;\u0026plusmn;\u0026thinsp;23.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e253.2\u0026thinsp;\u0026plusmn;\u0026thinsp;40.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e964.3\u0026thinsp;\u0026plusmn;\u0026thinsp;80.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e122.1\u0026thinsp;\u0026plusmn;\u0026thinsp;26.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e242\u0026thinsp;\u0026plusmn;\u0026thinsp;35.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1090.7\u0026thinsp;\u0026plusmn;\u0026thinsp;91.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e126.4\u0026thinsp;\u0026plusmn;\u0026thinsp;27.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e288.7\u0026thinsp;\u0026plusmn;\u0026thinsp;41.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1102.2\u0026thinsp;\u0026plusmn;\u0026thinsp;93.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e130.6\u0026thinsp;\u0026plusmn;\u0026thinsp;28.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e288.3\u0026thinsp;\u0026plusmn;\u0026thinsp;41.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1082.7\u0026thinsp;\u0026plusmn;\u0026thinsp;94.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e128.9\u0026thinsp;\u0026plusmn;\u0026thinsp;27.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e267.4\u0026thinsp;\u0026plusmn;\u0026thinsp;35.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1139.3\u0026thinsp;\u0026plusmn;\u0026thinsp;99.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e134.4\u0026thinsp;\u0026plusmn;\u0026thinsp;29.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e283.4\u0026thinsp;\u0026plusmn;\u0026thinsp;40.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1128.8\u0026thinsp;\u0026plusmn;\u0026thinsp;98.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e131.8\u0026thinsp;\u0026plusmn;\u0026thinsp;28.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e285.5\u0026thinsp;\u0026plusmn;\u0026thinsp;39.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1093.8\u0026thinsp;\u0026plusmn;\u0026thinsp;95.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e129.8\u0026thinsp;\u0026plusmn;\u0026thinsp;27.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e267.4\u0026thinsp;\u0026plusmn;\u0026thinsp;31.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1161.6\u0026thinsp;\u0026plusmn;\u0026thinsp;102.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e133.9\u0026thinsp;\u0026plusmn;\u0026thinsp;28.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e287.3\u0026thinsp;\u0026plusmn;\u0026thinsp;39\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1087.2\u0026thinsp;\u0026plusmn;\u0026thinsp;94.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e127.4\u0026thinsp;\u0026plusmn;\u0026thinsp;27.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e269.7\u0026thinsp;\u0026plusmn;\u0026thinsp;33.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1162.7\u0026thinsp;\u0026plusmn;\u0026thinsp;101.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e133.6\u0026thinsp;\u0026plusmn;\u0026thinsp;28.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e288.8\u0026thinsp;\u0026plusmn;\u0026thinsp;35.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1207.3\u0026thinsp;\u0026plusmn;\u0026thinsp;107.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e135.5\u0026thinsp;\u0026plusmn;\u0026thinsp;28.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e300.8\u0026thinsp;\u0026plusmn;\u0026thinsp;39.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1280.8\u0026thinsp;\u0026plusmn;\u0026thinsp;115.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e123.5\u0026thinsp;\u0026plusmn;\u0026thinsp;26.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e333.1\u0026thinsp;\u0026plusmn;\u0026thinsp;39\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1281.1\u0026thinsp;\u0026plusmn;\u0026thinsp;114.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e142.4\u0026thinsp;\u0026plusmn;\u0026thinsp;30.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e323.7\u0026thinsp;\u0026plusmn;\u0026thinsp;38.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1197\u0026thinsp;\u0026plusmn;\u0026thinsp;106.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e135.7\u0026thinsp;\u0026plusmn;\u0026thinsp;29.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e297\u0026thinsp;\u0026plusmn;\u0026thinsp;35.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1224.8\u0026thinsp;\u0026plusmn;\u0026thinsp;108.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e139.4\u0026thinsp;\u0026plusmn;\u0026thinsp;30.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e302.1\u0026thinsp;\u0026plusmn;\u0026thinsp;38.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1251.9\u0026thinsp;\u0026plusmn;\u0026thinsp;111.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e142.6\u0026thinsp;\u0026plusmn;\u0026thinsp;30.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e306.4\u0026thinsp;\u0026plusmn;\u0026thinsp;32.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1282.5\u0026thinsp;\u0026plusmn;\u0026thinsp;113.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e143.1\u0026thinsp;\u0026plusmn;\u0026thinsp;30.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e320.9\u0026thinsp;\u0026plusmn;\u0026thinsp;35.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1355.7\u0026thinsp;\u0026plusmn;\u0026thinsp;118.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e146.4\u0026thinsp;\u0026plusmn;\u0026thinsp;30.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e356.4\u0026thinsp;\u0026plusmn;\u0026thinsp;38.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1329.1\u0026thinsp;\u0026plusmn;\u0026thinsp;117.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e145.4\u0026thinsp;\u0026plusmn;\u0026thinsp;31.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e335.3\u0026thinsp;\u0026plusmn;\u0026thinsp;38.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1335.6\u0026thinsp;\u0026plusmn;\u0026thinsp;118.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e146.1\u0026thinsp;\u0026plusmn;\u0026thinsp;31.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e338.1\u0026thinsp;\u0026plusmn;\u0026thinsp;35.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1406.8\u0026thinsp;\u0026plusmn;\u0026thinsp;124.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e167.8\u0026thinsp;\u0026plusmn;\u0026thinsp;36.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e343.9\u0026thinsp;\u0026plusmn;\u0026thinsp;42\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTotal\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26105.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2974.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6579.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e \u003ch2\u003eEffect of climate change on carbon sequestration\u003c/h2\u003e \u003cp\u003eWarm-wet climate trend was observed in the TNSFP region in recent years, because significant increase in annual precipitation occurred covering 83% area of the TNSFP region, with an increase rate of 6\u0026thinsp;~\u0026thinsp;10 mm per year in forest land, and 4\u0026thinsp;~\u0026thinsp;7 mm per year in grass land during the period 2000\u0026ndash;2021. Significant increase in air temperature occurred covering 90% area of the TNSFP region. There was an average increase rate 0.298 ℃ per decade in forest land, and 0.290 ℃ per decade in grass land, which was higher than the global warming rate (0.2℃ per decade) (IPCC, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Warm-wet climate trend has also been confirmed by other studies. It was reported that annual precipitation across China significantly increased at rates of 11.4 mm per decade during the period 1961\u0026ndash;2016 (Su et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIt was found that the noticeable increase in carbon sequestration rate was accompanied by the increase in annual precipitation and air temperature during the period 2000\u0026ndash;2021 (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb \u0026amp; Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). The co-occurring trends in precipitation, temperature and carbon sequestration rate indicated that carbon sequestration rate in vegetation might suffer a positive effect from the warm-wet climate trend inevitably.\u003c/p\u003e \u003cp\u003eCorrelation analysis was performed to confirm the relationship between the increasing carbon sequestration rate and the warm-wet climate trend. The results showed a significant positive correlation between annual precipitation and carbon sequestration rate in vegetation, with correlation coefficients of 0.661 in Pearson correlation (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01), 0.775 in Spearman Rho correlation (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01) and 0.581 in Kendall tau-b correlation (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01), respectively (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The trend line of scatter diagram also confirmed a positive correlation between annual precipitation and carbon sequestration rate, with a fitting accuracy \u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.4371 (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ea). The results indicated that increase in annual precipitation is helpful in enhancing carbon sequestration rate.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCorrelation analysis between carbon sequestration rate and climate variables (annual precipitation and annual temperature)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"6\" nameend=\"c7\" namest=\"c2\"\u003e \u003cp\u003eCarbon sequestration rate in vegetation\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003ePearson correlation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eSpearman Rho correlation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eKendall tau-b correlation\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;496\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;496\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;496\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCoefficient\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSig. (bilateral)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCoefficient\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSig. (bilateral)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCoefficient\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSig. (bilateral)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnnual precipitation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.661\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.775\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.581\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnnual air temperature\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.098\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.029\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.322\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.211\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e\u003csup\u003e**\u003c/sup\u003eindicates statistically significant correlation at 0.01 confidence level (bilateral)\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e\u003csup\u003e*\u003c/sup\u003e indicates statistically significant correlation at 0.05 confidence level (bilateral)\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eA slight negative correlation was discovered between annual air temperature and carbon sequestration rate, with correlation coefficients of -0.098 (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) in Pearson correlation, -0.322 (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01) in Spearman Rho correlation, and \u0026minus;\u0026thinsp;0.211 (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01) in Kendall tau-b correlation, respectively (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The slight negative correlation was also confirmed by the scatter diagram between annual air temperature and carbon sequestration rate, with a fitting accuracy \u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.0097 (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eb). The slight negative correlation indicated that increase in annual air temperature might be harmful for carbon sequestration rate.\u003c/p\u003e \u003cp\u003eIt is credible that the favorable effect of the increasing precipitation on carbon sequestration rate is far greater than the adverse effect of the rising temperature, judging by the obvious increase in carbon sequestration rate under the background of the warm-wet climate in recent years in the TNSFP region.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section3\"\u003e \u003ch2\u003eEffect of human activities on carbon sequestration\u003c/h2\u003e \u003cp\u003eOur results showed that large-scale afforestation and reforestation promoted carbon sequestration rate in the TNSFP region. Carbon sequestration rate in forest land was 613.5\u0026thinsp;\u0026plusmn;\u0026thinsp;70.2 g C m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003eyr\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, larger than 251.4\u0026thinsp;\u0026plusmn;\u0026thinsp;106.5 g C m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003eyr\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e in grass land. Therefore, carbon sequestration rate might increase after transformation from grass land into forest land.\u003c/p\u003e \u003cp\u003eIt is reasonable assumptions that the existing forest (22478.1\u0026times;10\u003csup\u003e3\u003c/sup\u003e ha) came from original natural grass land, because there is no natural forest basically, and all the existing forests came from afforestation and reforestation in original natural grass land in the TNSFP region. The results under the assumptions indicated that the additional amount of annual carbon sequestration would be 81.4\u0026thinsp;\u0026plusmn;\u0026thinsp;10.5 Tg C after grass land into the existing forest land, and the additional carbon sequestration would accumulate to 1790.5 Tg C during the period 2000\u0026ndash;2021 (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Thus, approximately 1790.5 Tg C was stored into vegetation in the recent two decades due to afforestation and reforestation in the TNSFP region in the case of favorable climate change.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eEnhanced carbon sequestration assuming that the existing forest was transformed from natural grass land in the TNSFP region\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYear\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eForest carbon sequestration rate\u003c/p\u003e \u003cp\u003e(g C m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003eyr\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGrass carbon sequestration rate(g C m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003eyr\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEnhanced amount of carbon sequestration assuming the existing forest from natural grassland(Tg C)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e472.4\u0026thinsp;\u0026plusmn;\u0026thinsp;19.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e215.3\u0026thinsp;\u0026plusmn;\u0026thinsp;131.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e57.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e537.8\u0026thinsp;\u0026plusmn;\u0026thinsp;19.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e205.0\u0026thinsp;\u0026plusmn;\u0026thinsp;117.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e74.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e567.0\u0026thinsp;\u0026plusmn;\u0026thinsp;29.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e244.3\u0026thinsp;\u0026plusmn;\u0026thinsp;138.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e72.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e580.3\u0026thinsp;\u0026plusmn;\u0026thinsp;23.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e243.9\u0026thinsp;\u0026plusmn;\u0026thinsp;137.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e75.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e586.6\u0026thinsp;\u0026plusmn;\u0026thinsp;47.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e225.3\u0026thinsp;\u0026plusmn;\u0026thinsp;117.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e81.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e604.6\u0026thinsp;\u0026plusmn;\u0026thinsp;36.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e239.6\u0026thinsp;\u0026plusmn;\u0026thinsp;133.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e82.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e601.1\u0026thinsp;\u0026plusmn;\u0026thinsp;48.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e240.8\u0026thinsp;\u0026plusmn;\u0026thinsp;129.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e81.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e592.8\u0026thinsp;\u0026plusmn;\u0026thinsp;43.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e224.4\u0026thinsp;\u0026plusmn;\u0026thinsp;107.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e82.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e606.3\u0026thinsp;\u0026plusmn;\u0026thinsp;44.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e242.0\u0026thinsp;\u0026plusmn;\u0026thinsp;127.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e81.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e578.5\u0026thinsp;\u0026plusmn;\u0026thinsp;39.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e226.7\u0026thinsp;\u0026plusmn;\u0026thinsp;112.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e79.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e606.4\u0026thinsp;\u0026plusmn;\u0026thinsp;36.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e242.6\u0026thinsp;\u0026plusmn;\u0026thinsp;118.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e81.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e618.6\u0026thinsp;\u0026plusmn;\u0026thinsp;46.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e253.2\u0026thinsp;\u0026plusmn;\u0026thinsp;130.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e82.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e558.6\u0026thinsp;\u0026plusmn;\u0026thinsp;38.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e278.7\u0026thinsp;\u0026plusmn;\u0026thinsp;128.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e62.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e656.5\u0026thinsp;\u0026plusmn;\u0026thinsp;61.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e271.4\u0026thinsp;\u0026plusmn;\u0026thinsp;129.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e86.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e610.7\u0026thinsp;\u0026plusmn;\u0026thinsp;33.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e249.2\u0026thinsp;\u0026plusmn;\u0026thinsp;119.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e81.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e627.9\u0026thinsp;\u0026plusmn;\u0026thinsp;37.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e254.2\u0026thinsp;\u0026plusmn;\u0026thinsp;128.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e84.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e656.4\u0026thinsp;\u0026plusmn;\u0026thinsp;59.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e256.48\u0026thinsp;\u0026plusmn;\u0026thinsp;113.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e90.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e660.1\u0026thinsp;\u0026plusmn;\u0026thinsp;56.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e269.0\u0026thinsp;\u0026plusmn;\u0026thinsp;123.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e87.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e679.5\u0026thinsp;\u0026plusmn;\u0026thinsp;71.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e297.5\u0026thinsp;\u0026plusmn;\u0026thinsp;129.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e85.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e662.1\u0026thinsp;\u0026plusmn;\u0026thinsp;46.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e281.1\u0026thinsp;\u0026plusmn;\u0026thinsp;130.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e85.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e671.5\u0026thinsp;\u0026plusmn;\u0026thinsp;62.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e282.0\u0026thinsp;\u0026plusmn;\u0026thinsp;120.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e87.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e760.4\u0026thinsp;\u0026plusmn;\u0026thinsp;60.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e288.5\u0026thinsp;\u0026plusmn;\u0026thinsp;139.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e106.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAverage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e613.5\u0026thinsp;\u0026plusmn;\u0026thinsp;70.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e251.4\u0026thinsp;\u0026plusmn;\u0026thinsp;106.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTotal 1790.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Discussions","content":"\u003cp\u003ePrevious studies discovered that vegetation growth has been improving in the TNSFP region (Duan et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Zhang et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Du et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Our results drew similar conclusion that carbon sequestration rate increase obviously covering approximately 90% of the TNSFP region during 2000\u0026ndash;2021. The results indicate carbon sequestration in vegetation has been enhanced, and annual carbon sequestration (1186.6\u0026thinsp;\u0026plusmn;\u0026thinsp;122.6 T g C) in the TNSFP region has a capacity to offset 12.8% of annual industrial carbon emission (9.3 Pg C yr\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) in China (Liu et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIt was reported that afforestation in China contribute to carbon sequestration of 0.79 Pg C from 1981 to 2008 (He et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Under the assumptions that the existing forest came from original natural grass land, our results suggested that afforestation and reforestation in the TNSFP region contribute to carbon sequestration of 1.7905 Pg C approximately, which was larger than 0.79 Pg C in China from 1981 to 2008, because the carbon sequestration 1.7905 Pg C included afforestation and reforestation in all periods.\u003c/p\u003e \u003cp\u003eIt is well known that change of carbon sequestration in vegetation suffer from the complex effect of climate and human activities. Human activities in China have favorable effect on carbon sequestration in vegetation in recent years, because a series of large-scale ecological restoration programs has been implemented (Fang et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Xu et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Most of previous studies agreed that climate change has a positive effect on carbon sequestration in vegetation of the TNSFP region (Duan et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Xu et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Our results were consisted with previous conclusion that both climate change and human activities have favorable effect on carbon sequestration in vegetation in the TNSFP region.\u003c/p\u003e \u003cp\u003eSome studies reported that climate became wetter because annual precipitation increase since the year 2000 (Li et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Our results confirmed the warm-wet climate trend in the TNSFP region during the period 2000\u0026ndash;2021, and the warm-wet climate was helpful for enhancing carbon sequestration rate generally. However, our study provided much more detailed findings that the increase in annual precipitation is helpful, but the increase in annual air temperature is harmful in enhancing carbon sequestration rate. Theoretically rising temperature can prolong the growing season, improve photosynthetic efficiency, and increase plant productivity. Unfortunately, the rising temperature can easily lead to drought, and result in adverse effect on vegetation grown in arid and semi-arid TNSFP region (Xie et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThe results provide quantitative information about carbon sequestration rate and its change in the main ecosystems in the TNSFP region. The average annual carbon sequestration rate in vegetation was 458.3\u0026thinsp;\u0026plusmn;\u0026thinsp;45 g C m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003eyr\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, and annual carbon sequestration rate increased evidently in approximately 90% of the TNSFP region, with an average increase rate 5.06\u0026thinsp;\u0026plusmn;\u0026thinsp;4.3 g C m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003eyr\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e during the period 2000\u0026ndash;2021. There was a larger increase rate of 7.78\u0026thinsp;\u0026plusmn;\u0026thinsp;2.07 g C m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003eyr\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e in forest land than that of 3.27\u0026thinsp;\u0026plusmn;\u0026thinsp;0.55 g C m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003eyr\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003ein grassland. The amount of annual carbon sequestration into vegetation was 1186.6\u0026thinsp;\u0026plusmn;\u0026thinsp;122.6 T g C, and accumulated to a total carbon sequestration of 26.1054 P g C in the TNSFP region in 2000\u0026ndash;2021. Assuming the existing forest came from original natural grass land, afforestation and reforestation led to carbon sequestration 1.7905 Pg C approximately in the TNSFP region. Our results revealed that both human activities and climate played a positive role in enhancing carbon sequestration rate. More detailed findings indicated that the increase in annual precipitation is helpful, but the increase in annual air temperature is harmful in enhancing carbon sequestration rate.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u0026nbsp;\u003c/strong\u003eWe thank\u0026nbsp;the National Meteorological Information Center of the Chinese National Bureau\u0026nbsp;for providing climate data, and thank the Data Center for Resources and Environmental Sciences, Chinese Academy of Sciences for providing land-use data.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contribution\u003c/strong\u003e Ji-xi Gao conceived the idea of the article,\u0026nbsp;Li-xia Wang\u0026nbsp;wrote the main manuscript text，Wen-guo Zhang,\u0026nbsp;Wen-ming Shen, Ming-yong Cai, Tong Xiao, Xin-sheng Zhang and Wen-fei Tai\u0026nbsp;performed data analysis. All authors reviewed the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e Land-use in 2010s is provided by Data Center for Resources and Environmental Sciences, Chinese Academy of Sciences (http://www.resdc.cn). Normalized difference vegetation index\u0026nbsp;(NDVI) was derived from NASA\u0026rsquo;s Moderate Resolution Imaging Spectro radiometer sensors. Precipitation and air temperature were acquired from the National Meteorological Information Center of the Chinese National Bureau.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e This study was funded by National Key R \u0026amp; D Program of China (2021YFB3901103).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest\u003c/strong\u003e The authors declare no competing interests.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAnimal Husbandry Vet Bureau of Department of Agriculture P.R.C. (1996). Animal Husbandry Vet Central Station of the China. China Grassland Resources. China Science and Technology Press: Beijing, China, pp.\u0026nbsp;353\u0026ndash;358.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChai X., Liang C. Z., Liang M. W., et al. (2014). Seasonal dynamics of belowground biomass and productivity and potential of carbon sequestration in meadow steppe and typical steppe, in Inn Mongolia, China.Acta Ecologica Sinica, 34(19): 5530\u0026ndash;5540.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDu J. Q., Quan Z. Q., Fang S. F., et al. (2020). Spatiotemporal changes in vegetation coverage and its causes in China since the Chinese economic reform. Environmental Science and Pollution Research, 27: 1144\u0026ndash;1159.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDuan H. C., Yan C. Z., Tsunekawa A., et al. (2011). Assessing vegetation dynamics in the Three-North Shelter Forest region of China using AVHRR NDVI data. Environ Earth Sci., 64:1011\u0026ndash;1020.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFang J. Y., Yu G. R., Liu L.L., et al. (2018). Climate change, human impacts, and carbon sequestration in China. PNAS, 115: 4015\u0026ndash;4020.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHe B., Miao L. J., Cui X. F. (2015). Carbon sequestration from China\u0026rsquo;s afforestation projects. Environ Earth Sci., 74: 5491\u0026ndash;5499.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIPCC (2018). Summary for Policymakers. In: Global Warming of 1.5\u0026deg;C.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJi Y. H., Zhou G. S., Wang S. D., Wang L. X. (2020). Prominent vegetation greening and its correlation with climatic variables in northern China. Environmental Monitoring and Assessment, 192(9):636.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJi, Y. H., Zhou G. S., Luo T. X., Dan Y., Zhou L., Lv X. M. (2020). Variation of net primary productivity and its drivers in China\u0026rsquo;s forests during 2000\u0026ndash;2018. Forest Ecosystems, 7, 15.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi Z. C., Guo A. H., Yan H., et al. (2015). Effect of climate change on key ecological protection projects. Advances in Climate Change Research, 11: 179\u0026ndash;184.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu H. J., Shao M. J., Ji Y.M. (2021). The spatial pattern and distribution dynamic evolution of carbon emissions in China: Empirical study based on county carbon emission data. Scientia Geographica Sinica, 41(11):1917\u0026ndash;1924.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu Y. Y., Yang Y., Wang Q., et al. (2019). Assessing the dynamics of grassland net primary productivity in response to climate change at the global scale. Chin. Geogra. Sci., 29, 725\u0026ndash;740.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLu F., Hu H. F., Sun W. J., et al. (2018). Effects of national ecological restoration projects on carbon sequestration in China from 2001 to 2010. PNAS, 115: 4039\u0026ndash;4044.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMontagnini F., Nair P. K. R. (2004). Carbon sequestration: An underexploited environmental benefit of agroforestry systems. Agroforestry Systems, 61: 281\u0026ndash;295.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNemani R. R., Keeling C. D., Hashimoto H., et al. (2003). Climate-driven increases in global terrestrial net primary production from 1982 to 1999. Science, 300: 1560\u0026ndash;1563.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePiao S. L., Yin G. D., Tan J. G., et al. (2015). Detection and attribution of vegetation greening trend in China over the last 30 years. Global Change Biology, 21: 1601\u0026ndash;1609.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSu, B.; Xiao, C. D.; Zhao, H. Y., et al. (2022). Estimated changes in different forms of precipitation (snow, sleet, and rain) across China: 1961\u0026ndash;2016. Atmospheric Research, 270: 106078.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSun Y. R., Zhu J. J., Yan Q. L., et al. (2016). Changes in vegetation carbon stocks between 1978 and 2007 in central Loess Plateau, China. Environ Earth Sci., 75:312.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang W., Peng S. S., Fang J. Y. (2008). Biomass distribution of natural grasslands and it response to climate change in North China. Arid Zone Research, 25: 90\u0026ndash;97.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXie S. D., Mo X. G, Hu S., Chen X. J. (2020). Responses of vegetation greenness to temperature and precipitation in the Three-North Shelter Forest Program. Geographical Research, 39: 152\u0026ndash;165.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXu B., Yang X. C., Tao W. G., Tan Z. H., Liu H. Q., Miao J. M. (2007). Remote sensing monitoring upon the grass production in China. Acta Ecologia Sinica, 27(2): 405\u0026ndash;413.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXu L., Wen D., Zhu J. X., et al. (2017). Regional variation in carbon sequestration potential of forest ecosystems in China. Chin. Geogra. Sci., 27: 337\u0026ndash;350.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXu W. X., Pang Y., Ye M. (2021). Vegetation distribution and regional carbon sequestration in China: based on spatial panel data analysis. \u003cem\u003eArabian Journal of Geosciences\u003c/em\u003e, 14: 1906.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXu X. L., Li K. R. (2010). Biomass carbon sequestration by planted forests in China. Chin. Geogra. Sci., 20: 289\u0026ndash;297.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXu X. L., Liu J. Y., Zhang S. W., Li R. D., et al. (2018). China multi period land use and land cover remote sensing monitoring data set (CNLUCC). Data registration and publishing system of resource and environmental science data center of Chinese Academy of Sciences. (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.resdc.cn/DOI\u003c/span\u003e\u003cspan address=\"http://www.resdc.cn/DOI\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.12078/2018070201)\u003c/span\u003e\u003cspan address=\"10.12078/2018070201)\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang Y., Peng C. H., Li W. Z., et al. (2016). Multiple afforestation programs accelerate the greenness in the \u0026lsquo;Three North\u0026rsquo; region of China from 1982 to 2013. Ecological Indicators, 61: 404\u0026ndash;412.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZheng X., Zhu J. J. (2017). A new climatic classification of afforestation in Three-North regions of China with multi-source remote sensing data. Theor. Appl. Climatol., 127: 465\u0026ndash;480.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhu J. J., Zheng X. (2019). The prospects of development of the Three-North Afforestation Program (TNAP): On the basis of the results of the 40-year construction general assessment of the TNAP. Chinese Journal of Ecology, 38: 1600\u0026ndash;1610.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Climate change, human activities, afforestation, carbon emissions, carbon sequestration. ","lastPublishedDoi":"10.21203/rs.3.rs-1710152/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-1710152/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eCarbon sequestration in vegetation can offset a large proportion of carbon emissions, and plays an important role in mitigation climate change. Based on satellite data and ground observation data, we examined carbon sequestration rate and its long-term change in the Three-North Shelter Forest Program (TNSFP) region, where a series of large-scale ecological projects have been implemented. The results indicated that carbon sequestration rate in vegetation was 458.3\u0026thinsp;\u0026plusmn;\u0026thinsp;45 g C m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003eyr\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e in the TNSFP region during the period 2000\u0026ndash;2021. Obvious increase trend of carbon sequestration rate was observed covering approximately 90% of the TNSFP region, with an average increase rate of 5.06\u0026thinsp;\u0026plusmn;\u0026thinsp;4.3 g C m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003eyr\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e. There was a larger increase rate of 7.78\u0026thinsp;\u0026plusmn;\u0026thinsp;2.07 g C m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003eyr\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e in forest than that of 3.27\u0026thinsp;\u0026plusmn;\u0026thinsp;0.55 g C m\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003eyr\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003ein grassland. Total carbon sequestration into vegetation was 26.1054 P g C (1 P g\u0026thinsp;=\u0026thinsp;10\u003csup\u003e15\u003c/sup\u003e g) in the TNSFP region, with annual carbon sequestration of 1186.6\u0026thinsp;\u0026plusmn;\u0026thinsp;122.6 T g C (1 T g\u0026thinsp;=\u0026thinsp;10\u003csup\u003e12\u003c/sup\u003e g) during the period 2000\u0026ndash;2021. Our results revealed that both human activities and climate change have positive effect on carbon sequestration rate. Human activities contribute to carbon sequestration increment of 1.7905 Pg C approximately under assumptions that the existing forest transformed from original natural grass land due to afforestation and reforestation. Climate change has promoted carbon sequestration rate due to increase in annual precipitation, but the increase in annual air temperature is harmful in enhancing carbon sequestration rate.\u003c/p\u003e","manuscriptTitle":"Carbon sequestration in vegetation and its change in the Three-North Shelter Forest region of China in 2000-2021","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2022-06-22 17:40:46","doi":"10.21203/rs.3.rs-1710152/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"6756da7a-51bf-4e4d-be07-837ce00bd6ba","owner":[],"postedDate":"June 22nd, 2022","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2022-06-22T17:40:47+00:00","versionOfRecord":[],"versionCreatedAt":"2022-06-22 17:40:46","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-1710152","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-1710152","identity":"rs-1710152","version":["v1"]},"buildId":"ehx78VzkSd0WSzXnipQa-","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}