Effect of temperature change on CO2 Flux in temperate mixed forest ecosystem

Yuan Gong, Yinlong Zhang

Article ID: 1596
Vol 3, Issue 2, 2020

VIEWS - 380 (Abstract) 230 (PDF)

Abstract


Forest is the main carbon sink of terrestrial ecosystem. Due to the unique growth characteristics of plants, the response of their growth status and physiological activities to climate change will affect the carbon cycle process of forest ecosystem. Based on the local scale CO2 flux and temperature observation data recorded by the FLUXNET registration site and Harvard Forest FLUX observation tower from 2000 to 2012, combined with the phenological model, this paper analyzes the impact of temperature changes on CO2 flux in temperate forest ecosystems. The results show that: (1) the maximum NEE in 2000–2012 was 298.13 g·m-2·a-1, which occurred in 2010. Except in the 2010 and 2011, the annual NEE in other years was negative. (2) NEE, GPP, temperature and phenology models have good fitting effects (R2 > 0.8), which shows that the stable period of photosynthesis in temperate mixed forest ecosystem is mainly concentrated in summer, and vegetation growth is the dominant factor of carbon cycle in temperate mixed forest ecosystem. (3) The linear fitting results of the change time points of air temperature (maximum point, minimum point and 0 point date) and the change time points of NEE and GPP (maximum point, minimum point and 0 point date) show that there is a significant positive correlation between air temperature and CO2 flux (P < 0.01), and the change of air temperature affects the carbon cycle process of temperate mixed forest ecosystem.


Keywords


Mixed Forest; Ecosystem; Eddy Correlation System; CO2 Flux; Phenological Model

Full Text:

PDF


References


1. Wiesner S, Staudhammer CL, Loescher HW, et al. Interactions among abiotic drivers, disturbance and gross ecosystem carbon exchange on soil respiration from subtropical pine savannas. Ecosystems 2018; 21: 1639–1658.

2. Starr G, Staudhammer CL, Wiesner S, et al. Carbon dynamics of Pinus palustris ecosystems following drought. Forests 2016; 7(5). doi: 10.3390/f7050098.

3. Starr G, Staudhammer CL, Loescher HW, et al. Time series analysis of forest carbon dynamics: recovery of Pinus palustris physiology following a prescribed fire. New Forests 2015; 46: 63–90.

4. Gong Y, Guo Z, Zhang K, et al. Impact of vegetation on CO2 flux of a subtropical urban ecosystem. Acta Ecologica Sinica 2019; 39(2): 530–541.

5. Wright JK, Williams M, Starr G, et al. Measured and modelled leaf and stand-scale productivity across a soil moisture gradient and a severe drought. Plant, Cell & Environment 2013; 36(2): 467–483.

6. Pan Y, Blrdsey RA, Fang J, et al. A large and persistent carbon sink in the world’s forests. Science 2011; 333: 988–993.

7. Ji X, Lu J, Yang J, et al. Variation characteristics and influencing factors of carbon flux in coniferous and broad-leaved mixed forest in Fengyang Mountain. Journal of Northeast Forestry University 2019; 47(3): 49–55.

8. Niu X, Jiang H, Zhang J, et al. Characteristics of CO2 flux in an old growth mixed forest in Tianmu Mountain, Zhejiang, China. Chinese Journal of Applied Ecology 2016; 27(1): l–8.

9. Wang C, Yu G, Zhou G, et al. Dinghuashan changlv zhenkuoye hunjiaolin CO2 tongliang gusuan (Chinese) [Estimation of CO2 flux in Dinghushan evergreen coniferous and broad-leaved mixed forest]. Science in China (Series D: Earth Sciences) 2006; 36(S1): 119–129.

10. Xu L, Zhang X, Shi P, et al. Net ecosystem carbon dioxide exchange of alpine meadow in the Tibetan Plateau from August to October. Acta Ecologica Sinica 2005; 25(8): 1948–1952.

11. Barford CC, Wofsy SC, Goulden ML, et al. Factors controlling long- and short-term sequestration of atmospheric CO2 in a mid-latitude forest. Science 2001; 294: 1688–1691.

12. Bassow SL, Bazzaz FA. How environmental conditions affect canopy leaf-level photosynthesis in four deciduous tree species. Ecology 1998; 79(8): 2660–2675.

13. Richardson AD, Andy Black T, Ciais P, et al. Influence of spring and autumn phenological transitions on forest ecosystem productivity. Philosophical Transactions of the Royal Society B: Biological Sciences 2010; 365: 3227–3246.

14. Curtis PS, Hanson PJ, Bolstad P, et al. Biometric and eddy-covariance based estimates of annual carbon storage in five eastern North American deciduous forests. Agricultural and Forest Meteorology 2002; 113(1/2/3/4): 3–19.

15. Niu S, Fu Y, Gu L, et al. Temperature sensitivity of canopy photosynthesis phenology in northern ecosystems. In: Schwartz M. Phenology: An integrative environmental science. Dordrecht: Springer; 2013; 503–519.

16. Yi C, Rustic G, Xu X, et al. Climate extremes and grassland potential productivity. Environmental Research Letters 2012; 7(3): 035703. doi: 10.1088/1748-9326/7/3/035703.

17. Gu L, Post WM, Baldocchi DD, et al. Characterizing the seasonal dynamics of plant community photosynthesis across a range of vegetation types. In: Asko N (editor). Phenology of ecosystem processes: Applications in global change research. New York: Springer; 2009. p. 35–58.

18. Massman WJ, Lee X. Eddy covariance flux corrections and uncertainties in long-term studies of carbon and energy exchanges. Agricultural and Forest Meteorology 2002; 113(1/2/3/4): 121–144.

19. Hollinger DY, Richardson AD. Uncertainty in eddy covariance measurements and its application to physiological models. Tree Physiology 2005; 25(7): 873–885.

20. Baldocchi DD, Meyers TP, Wilson KB. Correction of eddy-covariance measurements incorporating both advective effects and density fluxes. Boundary-Layer Meteorology 2000; 97(3): 487–511.

21. Kim JH, Hwang T, Schaaf CL, et al. Seasonal variation of source contributions to eddy-covariance CO2 measurements in a mixed hardwood-conifer forest. Agricultural and Forest Meteorology 2018; 253/254: 71–83.

22. Barraza V, Restrepo-Coupe N, Huete A, et al. Passive microwave and optical index approaches for estimating surface conductance and evapotranspiration in forest ecosystems. Agricultural and Forest Meteorology 2015; 213: 126–137.

23. Wilson KB, Hanson PJ, Mulholland PJ, et al. A comparison of methods for determining forest evapotranspiration and its components: Sap-flow, soil water budget, eddy covariance and catchment water balance. Agricultural and Forest Meteorology 2001; 106(2): 153–168.

24. Baldocchi D, Falge E, Gu L, et al. FLUXNET: A new tool to study the temporal and spatial variability of ecosystemscale carbon dioxide, water vapor, and energy flux densities. Bulletin of the American Meteorological Society 2001; 82(11): 2415–2434.

25. Falge E, Baldocchi D, Tenhunen J, et al. Seasonality of ecosystem respiration and gross primary production as derived from FLUXNET measurements. Agricultural and Forest Meteorology 2002; 113(1/2/3/4): 53–74.

26. Williams M, Richardson AD, Reichstein M, et al. Improving land surface models with FLUXNET data. Biogeosciences 2009; 6(7): 1341–1359.

27. Fisher JB, Tu KP, Baldocchi DD. Global estimates of the land-atmosphere water flux based on monthly AVHRR and ISLSCP-II data, validated at 16 FLUXNET sites. Remote Sensing of Environment 2008; 112(3): 901–919.

28. Lipovetsky S. Double logistic curve in regression modeling. Journal of Applied Statistics 2010; 37(11): 1785–1793.

29. Liu L, Liang L, Schwartz MD, et al. Evaluating the potential of MODIS satellite data to track temporal dynamics of autumn phenology in a temperate mixed forest. Remote Sensing of Environment 2015; 160: 156–165.

30. Acharya D, Rani A, Agarwal S, et al. Application of adaptive Savitzky-Golay filter for EEG signal processing. Perspectives in Science 2016; 8: 677–679.

31. Shao Y, Lunetta RS, Wheeler B, et al. An evaluation of time-series smoothing algorithms for land-cover classifications using MODIS-NDVI multi-temporal data. Remote Sensing of Environment 2016; 174: 258–265.

32. Fontana F, Rixen C, Jonas T, et al. Alpine grassland phenology as seen in AVHRR, VEGETATION, and MODIS NDVI time series—A comparison with in situ measurements. Sensors (Basel) 2008; 8(4): 2833–2853.

33. Bracho R, Starr G, Gholz HL, et al. Controls on carbon dynamics by ecosystem structure and climate for southeastern US slash pine plantations. Ecological Monographs 2012; 82(1): 101–128.




DOI: https://doi.org/10.24294/sf.v3i2.1596

Refbacks

  • There are currently no refbacks.


Copyright (c) 2020 Yuan Gong, Yinlong Zhang

Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

This site is licensed under a Creative Commons Attribution 4.0 International License.