Evolution and Mechanism Analysis of Terrestrial Ecosystems in China with Respect to Gross Primary Productivity
Abstract
:1. Introduction
2. Materials and Methods
2.1. Mask Extraction of Grassland, Cropland, and Forest in China
2.2. GPP Data
2.3. Calculation of Peak Photosynthesis Productivity and Carbon Uptake Period
2.4. Partial Correlation Analysis
3. Results
3.1. Spatio-Temporal Variation Characteristics of GPP from Different Data Sources
3.2. Spatial and Temporal Trends in GPPmax from Different Data Sources
3.3. Plant Physiology, Not Phenology, Dominates GPP Variability
4. Discussion
4.1. China’s Terrestrial Ecosystems’ GPP Growth Contributes to Global Warming Mitigation
4.2. Multi-Source Data and Methods Reveal a More Comprehensive Picture in China’s Terrestrial Ecosystems
4.3. The Physiological Role of Plant Should Be Given More Attention to Better Explain Inter-Annual Variability in GPP
4.4. Limitations and Uncertainties
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
- Wang, J.; Xie, Y.; Wang, X.; Dong, J.; Bie, Q. Detecting Patterns of Vegetation Gradual Changes (2001–2017) in Shiyang River Basin, Based on a Novel Framework. Remote Sens. 2019, 11, 2475. [Google Scholar] [CrossRef]
- Chen, C.; Park, T.; Wang, X.; Piao, S.; Xu, B.; Chaturvedi, R.K.; Fuchs, R.; Brovkin, V.; Ciais, P.; Fensholt, R. China and India lead in greening of the world through land-use management. Nat. Sustain. 2019, 2, 122–129. [Google Scholar] [CrossRef] [PubMed]
- Xia, J.; Niu, S.; Ciais, P.; Janssens, I.A.; Chen, J.; Ammann, C.; Arain, A.; Blanken, P.D.; Cescatti, A.; Bonal, D.; et al. Joint control of terrestrial gross primary productivity by plant phenology and physiology. Proc. Natl. Acad. Sci. USA 2015, 112, 2788–2793. [Google Scholar] [CrossRef] [PubMed]
- Zhou, S.; Zhang, Y.; Ciais, P.; Xiao, X.M.; Luo, Y.Q. Dominant role of plant physiology in trend and variability of gross primary productivity in North America. Sci. Rep. UK 2017, 7, 41366. [Google Scholar] [CrossRef] [PubMed]
- Jeong, S.J.; Ho, C.H.; Choi, S.D.; Kim, J.; Lee, E.J.; Gim, H.J. Satellite Data-Based Phenological Evaluation of the Nationwide Reforestation of South Korea. PLoS ONE 2013, 8, e58900. [Google Scholar] [CrossRef] [PubMed]
- Suttie, J.M.; Reynolds, S.G.; Batello, C. Grasslands of the World; Food and Agriculture Organization: Rome, Italy, 2005. [Google Scholar]
- Bulletin on the main data of the Third National Land Survey. People’s Daily, (PRC newspaper 25/8/2021).
- Wang, Q.; Zhang, Q.; Zhou, W. Grassland Coverage Changes and Analysis of the Driving Forces in Maqu County. Phys. Procedia 2012, 33, 1292–1297. [Google Scholar] [CrossRef]
- He, P.; Ma, X.; Meng, X.; Han, Z.; Liu, H.; Sun, Z. Spatiotemporal evolutionary and mechanism analysis of grassland GPP in China. Ecol. Indic. 2022, 143, 109323. [Google Scholar] [CrossRef]
- Xue, Y.; Liang, H.; Ma, Y.; Xue, G.; He, J. The Impacts of Climate and Human Activities on Grassland Productivity Variation in China. Remote Sens. 2023, 15, 3864. [Google Scholar] [CrossRef]
- He, P.; Ma, X.; Han, Z.; Meng, X.; Sun, Z. Uncertainties of gross primary productivity of Chinese grasslands based on multi-source estimation. Front. Environ. Sci. 2022, 10, 928351. [Google Scholar] [CrossRef]
- Huntzinger, D.N.; Schwalm, C.; Michalak, A.M.; Schaefer, K.; King, A.W.; Wei, Y.; Jacobson, A.; Liu, S.; Cook, R.B.; Post, W.M. The North American Carbon Program Multi-Scale Synthesis and Terrestrial Model Intercomparison Project—Part 1: Overview and experimental design. Geosci. Model Dev. 2013, 6, 2121–2133. [Google Scholar] [CrossRef]
- Wei, L.; Huntzinger, D.N.; Michalak, A.M.; Viovy, P.; Schwalm, W.M. The North American Carbon Program Multi-scale Synthesis and Terrestrial Model Intercomparison Project—Part 2: Environmental driver data. Geosci. Model. Dev. 2014, 7, 2875–2893. [Google Scholar] [CrossRef]
- Zhou, S.; Yu, B.; Schwalm, C.R.; Ciais, P.; Zhang, Y.; Fisher, J.B.; Michalak, A.M.; Wang, W.; Poulter, B.; Huntzinger, D.N.; et al. Response of Water Use Efficiency to Global Environmental Change Based on Output From Terrestrial Biosphere Models. Glob. Biogeochem. Cycle. 2017, 31, 1639–1655. [Google Scholar] [CrossRef]
- Mao, J.; Thornton, P.E.; Shi, X.; Zhao, M.; Post, W.M. Remote Sensing Evaluation of CLM4 GPP for the Period 2000–2009*. J. Clim. 2012, 25, 5327–5342. [Google Scholar] [CrossRef]
- Li, H.; Huang, M.; Wigmosta, M.S.; Ke, Y.; Coleman, A.M.; Leung, L.R.; Wang, A.; Ricciuto, D.M. Evaluating runoff simulations from the Community Land Model 4.0 using observations from flux towers and a mountainous watershed. J. Geophys. Res. Atmos. 2011, 116, D24120. [Google Scholar] [CrossRef]
- Tian, H.; Chen, G.; Zhang, C.; Liu, M.; Sun, G.; Chappelka, A.; Ren, W.; Xu, X.; Lu, C.; Pan, S.; et al. Century-Scale Responses of Ecosystem Carbon Storage and Flux to Multiple Environmental Changes in the Southern United States. Ecosystems 2012, 15, 674–694. [Google Scholar] [CrossRef]
- Jain, A.K.; Meiyappan, P.; Song, Y.; House, J.I. CO2 emissions from land-use change affected more by nitrogen cycle, than by the choice of land-cover data. Glob. Change Biol. 2013, 19, 2893–2906. [Google Scholar] [CrossRef] [PubMed]
- Rosenzweig, C.; Arnell, N.W.; Ebi, K.L.; Lotze-Campen, H.; Raes, F.; Rapley, C.; Smith, M.S.; Cramer, W.; Frieler, K.; Reyer, C.P.O.; et al. Assessing inter-sectoral climate change risks: The role of ISIMIP. Environ. Res. Lett. 2017, 12, 10301. [Google Scholar] [CrossRef]
- Warnant, P.; Francois, L.; Strivay, D.; Gerard, J.C. CARAIB; a global model of terrestrial biological productivity. Glob. Biogeochem. Cycle. 1994, 8, 255–270. [Google Scholar] [CrossRef]
- Smith, B.; Wårlind, D.; Arneth, A.; Hickler, T.; Leadley, P.; Siltberg, J.; Zaehle, S. Implications of incorporating N cycling and N limitations on primary production in an individual-based dynamic vegetation model. Biogeosciences 2014, 11, 2027–2054. [Google Scholar] [CrossRef]
- Bondeau, A.; Smith, P.C.; Zaehle, S.; Schaphoff, S.; Lucht, W.; Cramer, W.; Gerten, D.; Lotze Campen, H.; Müller, C.; Reichstein, M.; et al. Modelling the role of agriculture for the 20th century global terrestrial carbon balance. Glob. Change Biol. 2007, 13, 679–706. [Google Scholar] [CrossRef]
- Guimberteau, M.; Zhu, D.; Maignan, F.; Huang, Y.; Yue, C.; Dantec-Nédélec, S.; Ottlé, C.; Jornet-Puig, A.; Bastos, A.; Laurent, P.; et al. ORCHIDEE-MICT (v8.4.1), a land surface model for the high latitudes: Model description and validation. Geosci. Model. Dev. 2018, 11, 121–163. [Google Scholar] [CrossRef]
- Zheng, Y.; Shen, R.; Wang, Y.; Li, X.; Liu, S.; Liang, S.; Chen, J.M.; Ju, W.; Zhang, L.; Yuan, W. Improved estimate of global gross primary production for reproducing its long-term variation, 1982–2017. Earth Syst. Sci. Data. 2020, 12, 2725–2746. [Google Scholar] [CrossRef]
- Yuan, W.; Liu, S.; Zhou, G.; Zhou, G.; Tieszen, L.L.; Baldocchi, D.; Bernhofer, C.; Gholz, H.; Goldstein, A.H.; Goulden, M.L.; et al. Deriving a light use efficiency model from eddy covariance flux data for predicting daily gross primary production across biomes. Agric. For. Meteorol. 2007, 143, 189–207. [Google Scholar] [CrossRef]
- Yuan, W.; Cai, W.; Xia, J.; Chen, J.; Liu, S.; Dong, W.; Merbold, L.; Law, B.; Arain, A.; Beringer, J.; et al. Global comparison of light use efficiency models for simulating terrestrial vegetation gross primary production based on the LaThuile database. Agric. For. Meteorol. 2014, 192, 108–120. [Google Scholar] [CrossRef]
- Wang, M.; Sun, R.; Zhu, A.; Xiao, Z. Evaluation and Comparison of Light Use Efficiency and Gross Primary Productivity Using Three Different Approaches. Remote Sens. 2020, 12, 1003. [Google Scholar] [CrossRef]
- Wang, J.; Sun, R.; Zhang, H.; Xiao, Z.; Zhu, A.; Wang, M.; Yu, T.; Xiang, K. New Global MuSyQ GPP/NPP Remote Sensing Products From 1981 to 2018. IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens. 2021, 14, 5596–5612. [Google Scholar] [CrossRef]
- Piao, S.; Fang, J.; Zhou, L.; Ciais, P.; Zhu, B. Variations in satellite-derived phenology in China’s temperate vegetation. Glob. Change Biol. 2006, 12, 672–685. [Google Scholar] [CrossRef]
- Cong, N.; Piao, S.; Chen, A.; Wang, X.; Lin, X.; Chen, S.; Han, S.; Zhou, G.; Zhang, X. Spring vegetation green-up date in China inferred from SPOT NDVI data: A multiple model analysis. Agric. For. Meteorol. 2012, 165, 104–113. [Google Scholar] [CrossRef]
- Yin, Y.; Zhao, R.; Yang, Y.; Meng, Q.; Ying, H.; Cassman, K.G.; Cong, W.; Tian, X.; He, K.; Wang, Y.; et al. A steady-state N balance approach for sustainable smallholder farming. Proc. Natl. Acad. Sci. USA 2021, 118, 1. [Google Scholar] [CrossRef]
- Bao, S.; Wutzler, T.; Koirala, S.; Cuntz, M.; Ibrom, A.; Besnard, S.; Walther, S.; Igut, L.; Moreno, A.; Weber, U. Environment-sensitivity functions for gross primary productivity in light use efficiency models. Agric. For. Meteorol. 2022, 312, 108708. [Google Scholar] [CrossRef]
- Ma, J.; Xiao, X.; Miao, R.; Li, Y.; Chen, B.; Zhang, Y.; Zhao, B. Trends and controls of terrestrial gross primary productivity of China during 2000–2016. Environ. Res. Lett. 2019, 14, 84032. [Google Scholar] [CrossRef]
- Weiwei, Q.I.; Haishan, N.; Shiping, W.; Yanjie, L.; Lirong, Z. Simulation of effects of warming on carbon budget in alpine meadow ecosystem on the Tibetan. Acta Ecol. Sin. 2012, 32, 1713–1722. [Google Scholar] [CrossRef]
- Friedlingstein, P.O.M.J. Global Carbon Budget. Sci. Data Earth Syst. 2023, 15, 5301–5369. [Google Scholar] [CrossRef]
- Han, J.; Guo, C.; Ye, S.; Zhang, L.; Li, S.; Wang, H.; Yu, G. Effects of diffuse photosynthetically active radiation on gross primary productivity in a subtropical coniferous plantation vary in different timescales. Ecol. Indic. 2020, 115, 106403. [Google Scholar] [CrossRef]
- Liu, Y.; Zhou, R.; Ren, H.; Zhang, W.; Zhang, Z.; Zhang, Z.; Wen, Z. Evaluating the dynamics of grassland net primary productivity in response to climate change in China. Glob. Ecol. Conserv. 2021, 28, e01574. [Google Scholar] [CrossRef]
- Gampe, D.; Zscheischler, J.; Reichstein, M.; O Sullivan, M.; Smith, W.K.; Sitch, S.; Buermann, W. Increasing impact of warm droughts on northern ecosystem productivity over recent decades. Nat. Clim. Change 2021, 11, 772–779. [Google Scholar] [CrossRef]
- Fang, J.; Guo, Z.; Piao, S.; Chen, A. Terrestrial vegetation carbon sinks in China, 1981–2000. Sci. China Ser. D Earth Sci. 2007, 50, 1341–1350. [Google Scholar] [CrossRef]
- Guirui, Y.; Xuanran, L.; Oiufeng, W.; Shenggong, L. Carbon Storage and Its Spatial Pattern of Terrestrial Ecosystem in China. J. Resour. Ecol. 2010, 2, 97–109. [Google Scholar]
- Frankenberg, C.; Fisher, J.B.; Worden, J.; Badgley, G.; Saatchi, S.S.; Lee, J.; Toon, G.C.; Butz, A.; Jung, M.; Kuze, A.; et al. New global observations of the terrestrial carbon cycle from GOSAT: Patterns of plant fluorescence with gross primary productivity. Geophys. Res. Lett. 2011, 38, L17706. [Google Scholar] [CrossRef]
- Piao, S.; He, Y.; Wang, X.; Chen, F. Estimation of China’s terrestrial ecosystem carbon sink: Methods, progress and prospects. Sci. China Earth Sci. 2022, 65, 641–651. [Google Scholar] [CrossRef]
- Zaehle, S.; Sitch, S.; Smith, B.; Hatterman, F. Effects of parameter uncertainties on the modeling of terrestrial biosphere dynamics. Glob. Biogeochem. Cycle. 2005, 19, GB3020. [Google Scholar] [CrossRef]
- Huete, A.; Didan, K.; Miura, T.; Rodriguez, E.P.; Gao, X.; Ferreira, L.G. Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sens. Environ. 2002, 83, 195–213. [Google Scholar] [CrossRef]
- Piao, S.P.P.N. Growing season extension and its impact on terrestrial carbon cycle in the Northern Hemisphere over the past 2 decades. Glob. Biogeochem. Cycle. 2007, 21, GB3018. [Google Scholar] [CrossRef]
- Starr, G.R.E.; Oberbauer, S.F.; Pop, E.R.I.C. Effects of lengthened growing season and soil warming on the phenology and physiology of Polygonum bistorta. Glob. Change Biol. 2010, 6, 357–369. [Google Scholar] [CrossRef]
- Moors, E.J. Influence of spring and autumn phenological transitions on forest ecosystem productivity. Philos. Trans. R. Soc. B Biol. Sci. 2010, 365, 3227–3246. [Google Scholar]
- Keenan, T.F.; Gray, J.; Friedl, M.A.; Toomey, M.; Bohrer, G.; Hollinger, D.Y.; Munger, J.W.; O Keefe, J.; Schmid, H.P.; Wing, I.S. Net carbon uptake has increased through warming-induced changes in temperate forest phenology. Nat. Clim. Change 2014, 4, 598–604. [Google Scholar] [CrossRef]
- Zscheischler, J.; Reichstein, M.; Harmeling, S.; Rammig, A.; Tomelleri, E.; Mahecha, M.D. Extreme events in gross primary production: A characterization across continents. Biogeosciences 2014, 11, 2909–2924. [Google Scholar] [CrossRef]
- Piao, S.; Wang, X.; Park, T. Characteristics, drivers and feedbacks of global greening. Nat. Rev. Earth Environ. 2020, 1, 14–27. [Google Scholar] [CrossRef]
- Zhao, Y.; Peng, J.; Ding, Z.; Qiu, S.; Liu, X.; Wu, J.; Meersmans, J. Divergent dynamics between grassland greenness and gross primary productivity across China. Ecol. Indic. 2022, 142, 109100. [Google Scholar] [CrossRef]
- Zhao, Y.; Wang, L.; Jiang, Q.; Wang, Z. Sensitivity of gross primary production to precipitation and the driving factors in China’s agricultural ecosystems. Sci. Total Environ. 2024, 948, 174938. [Google Scholar] [CrossRef]
- Li, Y.; Zhang, Y.; Lv, J. Interannual variations in GPP in forest ecosystems in Southwest China and regional differences in the climatic contributions. Ecol. Inform. 2022, 69, 101591. [Google Scholar] [CrossRef]
- Liu, H.; Wang, Z.; Wang, Z.; Zeng, Y.; Xue, P.; Zhang, M. Stability of the ecosystem gross primary productivity increasing in Chinese forestry ecological engineering area. Agric. Ecosyst. Environ. 2023, 356, 108636. [Google Scholar] [CrossRef]
- Shao, J.; Zhou, X.; Luo, Y.; Zhang, G.; Yan, W.; Li, J.; Li, B.; Dan, L.; Fisher, J.B.; Gao, Z. Uncertainty analysis of terrestrial net primary productivity and net biome productivity in China during 1901–2005. J. Geophys. Res. Biogeosci. 2016, 121, 1372–1393. [Google Scholar] [CrossRef]
- Cheng, S.; Huang, J.; Ji, F.; Lin, L. Uncertainties of soil moisture in historical simulations and future projections. J. Geophys. Res.-At. 2017, 122, 2239–2253. [Google Scholar] [CrossRef]
- Exbrayat, J.; Bloom, A.A.; Falloon, P.; Ito, A.; Smallman, T.L.; Williams, M. Reliability ensemble averaging of 21st century projections of terrestrial net primary productivity reduces global and regional uncertainties. Earth Syst. Dyn. 2018, 9, 153–165. [Google Scholar] [CrossRef]
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Sun, H.; Cheng, Y.; An, Q.; Liu, L. Evolution and Mechanism Analysis of Terrestrial Ecosystems in China with Respect to Gross Primary Productivity. Land 2024, 13, 1346. https://doi.org/10.3390/land13091346
Sun H, Cheng Y, An Q, Liu L. Evolution and Mechanism Analysis of Terrestrial Ecosystems in China with Respect to Gross Primary Productivity. Land. 2024; 13(9):1346. https://doi.org/10.3390/land13091346
Chicago/Turabian StyleSun, Hanshi, Yongming Cheng, Qiang An, and Liu Liu. 2024. "Evolution and Mechanism Analysis of Terrestrial Ecosystems in China with Respect to Gross Primary Productivity" Land 13, no. 9: 1346. https://doi.org/10.3390/land13091346