Regional contributions to interannual variability of net primary production and climatic attributions
Introduction
As the difference between gross primary production (GPP) and autotrophic respiration (Ra), net primary production (NPP) represents the net accumulation of atmospheric carbon dioxide in plants via photosynthesis (Chapin et al. 2006; Morel et al. 2019) and is a key node in the terrestrial carbon cycle (Bloom et al. 2016). The interannual variability (IAV) of NPP is an important metric that reflects the year-to-year variations of plant photosynthesis and production determining land carbon uptake (Le Quere et al. 2009; Musavi et al. 2017; Niu et al. 2017) and the capacity of ecosystem products such as food, fiber, and wood (Imhoff and Bounoua 2006; Imhoff et al. 2004). NPP IAV hence plays an important role in regulating ecosystem services and the global carbon cycle (Fang et al., 2003, Li et al., 2021, Rollinson et al., 2017, Unger et al., 2017, Wang et al., 2020). Quantifying the IAV of annual NPP and identifying regional contributions and dominant drivers are of great significance for regional ecosystem management and climate policy-making (Shiga et al. 2018).
The global terrestrial carbon sink is believed to be mainly dominated by highly productive lands (e.g., tropical forests) (Ahlstrom et al. 2015; Fan et al. 2019), while the IAV of the global plant carbon uptake may be dominated by semi-arid ecosystems (Ahlstrom et al. 2015), especially those in the Southern Hemisphere (Haverd et al. 2016; Poulter et al. 2014). Terrestrial ecosystems in China sequestered about 45% of annual anthropogenic emissions in China per year during 2010 to 2016 (Wang et al. 2020) and are an important component of the global carbon cycle (Li et al. 2019a; Tian et al. 2011; Zhang et al. 2019a). However, the NPP in China showed substantial IAV (Feng et al. 2019) and its regional contributions remain to be explored. The IAV of NPP is largely driven by climate variability and change (He et al. 2019; Jung et al. 2017; Yao et al. 2018; Zhang et al. 2019a). Climate change, especially climate extremes, has exerted great influences on the IAV of plant productivity by changing related physiological and biogeochemical processes (Frank et al. 2015; Piao et al. 2015; Reeves et al. 2014; Wu et al. 2010). The responses of ecosystem productivity to climate change have received growing attention from the research community (Baldocchi et al. 2018; Fang et al. 2018; Liu et al. 2018; Zscheischler et al. 2014). The relationships between the IAV of plant productivity and conventional climatic factors, particularly temperature and precipitation, have been extensively studied (Liang et al. 2015; Zhang et al. 2019a). These two climatic factors can measure climate change to a certain extent; however, these simple indices may not reflect the frequency, intensity, or duration of climate change, which are more profound to plant productivity. Climate extremes such as high temperature days, growing season length, and daily temperature range can more comprehensively reflect the characteristics of climate change (Peterson et al. 2001). Slight changes of these climate regimes may have profound effects on the function of ecosystems (Maestre et al. 2016; Wu et al. 2018). China has experienced dramatic climate changes with substantial spatial variations during the last decades (Niu et al. 2019; Shang et al. 2019) and is one of the regions that are substantially affected by climate extremes (Easterling et al. 2000; Xu et al. 2020b). Hence, the impacts of distinct climatic factors on ecosystem carbon fluxes often exhibit large discrepancies among different regions (Chen et al. 2019; Tian et al. 2011; Zhang et al. 2019a). However, only a few studies assessed the relative importance of multiple climate change indices on plant productivity, and the relative importance of specific climatic drivers to NPP IAV in China over the past decades has barely been addressed. Moreover, dominant climatic drivers to NPP IAV in different regions in China have not been well characterized.
It is also noteworthy that NPP estimates from different studies usually present remarkable differences (Feng et al. 2019; Liang et al. 2015; Piao et al. 2005), and the large discrepancies in magnitude and dynamics among different studies are caused by different model inputs, structures, parameters, and time spans (Chen et al. 2011; Feng et al. 2019; Shao et al. 2016). Until now, most studies based on models driven by remote sensing observations mainly focused on the NPP before 2015 due to the data availability of the Global Inventory Modeling and Mapping Studies (GIMMS3g) Normalized Difference Vegetation Index (NDVI) (Tucker et al. 2005). Consequently, our knowledge about NPP dynamics after 2015 is quite limited. The continuously updated NDVI of the Satellite Pour l'Observation de la Terre (SPOT) VEGETATION Collection 3 (SPOT/VGT-C3 NDVI) with a high spatial resolution (1 km × 1 km) offered us an opportunity to investigate the recent NPP dynamics at large scales (Baret et al. 2013; Maisongrande et al. 2004; Verger et al. 2014). The Carnegie-Ames-Stanford (CASA) model (Potter et al. 1993) based on remote sensing observations (NDVI), climatic factors, and environmental features has been demonstrated as a simpler and more efficient tool for investigating the dynamics of NPP compared with other process-oriented ecosystem models (Liang et al. 2015; Sun et al. 2019).
Here we examined the IAV of annual NPP and its regional contributions and climatic drivers for China's terrestrial ecosystems over the period 1982-2018 using the CASA model and long-term NDVI datasets. The specific objectives of this study are to: (1) quantify the contributions of different regions to the interannual variability of model-based NPP estimates; (2) identify the dominant climatic drivers to the interannual variability of model-based NPP estimates at different scales (i.e. grid cell scale, climatic regions, and different ecosystem types).
Section snippets
The CASA model
We used the Carnegie-Ames-Stanford (CASA) biosphere model (Potter et al. 1993) to produce the monthly gridded NPP data with a spatial resolution of 1 km × 1 km over China from 1982 to 2018. The model forcing data mainly include monthly climatic and NDVI data. The NPP in place x and at time t is a function of plant absorbed photosynthetically active radiation (APAR) and light utilization efficiency (ε) (Potter et al. 1993):where APAR is determined by the incoming
Evaluation of simulated NPP
We evaluated the simulated annual NPP driven by SPOT/VGT-C3 NDVI and GIMMS3g NDVI with the MODIS NPP product (2000-2015), respectively (Fig. 2a, b). Both annual NPP estimates derived from the two NDVI datasets were very consistent with the MODIS NPP product. The estimates forced by the two NDVI datasets in Central China and Southeastern China were slightly overestimated, while those in southwestern China, northeastern China, and eastern China were slightly underestimated. Overall, the simulated
Discussion
The comprehensive comparisons between our estimates and multiple NPP datasets suggested that the CASA model in this study well captured the magnitude and dynamics of NPP in China (Fig. 2, Fig. S4). Our estimated NPP illustrated a decreasing trend in southwestern China (Fig. 3b), which was also revealed by a prvious study (Feng et al. 2019). Simultaneously, we found that NPP in most of the semi-humid region increased during the past 37 years, and the enhanced vegetation greenness observed by the
Conclusions
In this study, we investigated the regional contributions to the national NPP IAV and identified the dominant climatic drivers in different regions based on a more comprehensive NPP estimation and climate change indices over China during the past 37 years (1982-2018). We found that the country's total NPP increased with an average rate of 15.2 Tg C yr−1 accompanied by large interannual variation ranging from -0.29 to 0.22 Pg C. The humid region dominated this large national NPP IAV, where NPP
Declaration of Competing Interest
We declare that we have no financial and personal relationships with other people or organizations that can inappropriately influence our work, there is no professional or other personal interest of any nature or kind in any product, service and/or company that could be construed as influencing the position presented in, or the review of, the manuscript entitled, “Regional contributions to interannual variability of net primary production and climatic attributions”.
Acknowledgements
This study was funded by the Strategic Priority Research Program of Chinese Academy of Sciences (XDB40020205), the National Science Foundation of China (31961143011), the Fundamental Research Funds for the Central Universities (xzy022020008), the National Key Research and Development Program of China (2019YFC0507403), the Shaanxi Major Theoretical and Practical Program (20ST-106), the National Thousand Youth Talent Program of China, Shaanxi Hundred Talent Program, and the Young Talent Support
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