Journal of Environmental Impact Assessment (환경영향평가)

Korean Society of Environmental Impact Assessment (한국환경영향평가학회)
권호 표지
DOI QR코드

DOI QR Code

Estimating carbon uptake in forest and agricultural ecosystems of Korea and other countries using eddy covariance flux data

에디 공분산 기반의 플럭스 타워 관측자료를 이용한 국내외 산림과 농업 생태계 탄소 흡수량 분석

  • Received : 2017.01.09
  • Accepted : 2017.03.31
  • Published : 2017.04.30
Download PDF
⟨ Previous Next ⟩

Abstract

Measurements of net ecosystem exchange (NEE) of $CO_2$ based on the eddy covariance technique provide reasonable carbon balance estimates in response to local environmental conditions. In South Korea, the forest ecosystems cover approximately 64% of the total area, thereby strongly affecting regional carbon balances. Cultivated croplands that cover about 17% of the total area should also be considered when calculating the carbon balance of the country. In this study, our objectives were (a) to quantify the range and seasonal variation of NEE at forest ecosystems, including deciduous, coniferous, and mixed forests, and agricultural ecosystems, including rice paddies and a potato field, in South Korea and (b) to compare NEE at ten Fluxnet sites that have the same or similar ecosystems as found in South Korea. The results showed that the forest and agricultural ecosystems were carbon sinks. In Korea, NEE at the forest ecosystems varied between -31 and $-362gC/m^2/yr$, and NEE at the croplands ranged from -210 to $-248gC/m^2/growing$ season. At the deciduous forest, NEE reached low values in late spring, early summer, and early autumn, while at the coniferous forest, it reached low values in spring, early summer, and mid autumn. The young mixed forest was a much stronger carbon sink than the old-growth deciduous and coniferous forests. During each crop growing season, beet had the lowest NEE value within six crops, followed by wither wheat, maize, rice, potato, and soybean. These results will be useful for designing and applying management strategies for the reduction of $CO_2$ emissions.

탄소 흡수원 조성과 같은 토지이용과 관련한 온실가스 감축대안 수립을 위해 다양한 생태계 시스템에서의 온실가스 잠재 흡수량 평가가 요구된다. 이 연구에서는 에디 공분산 기반의 플럭스 타워 관측자료를 활용한 순생태계교환량(Net Ecosystem Exchange: NEE)으로 국내 산림 생태계와 농경지 생태계에서의 탄소 흡수 능력을 추정하였다. 또한 우리나라와 유사한 기후조건의 국외 플럭스 타워 자료를 활용하여 국내 생태계 유형별 탄소 수지 추정결과와 비교분석하였다. 에디 공분산 기법을 이용한 산림과 농경지의 NEE 분석 결과, 우리나라의 산림에서는 연간 $-31gC/m^2/yr$에서 $-362gC/m^2/yr$, 농경지 생태계에서는 작물 재배 기간 동안 $-210gC/m^2/growing$ season에서 $-248gC/m^2/growing$ season의 값을 나타내 산림뿐만 아니라 농경지 생태계도 탄소 흡수 기능이 있음을 확인할 수 있었다. 산림의 경우 임상에 따라 시기별로 서로 다른 탄소 흡수 양상을 보였는데, 활엽수림은 늦봄과 초여름, 초가을에, 침엽수림은 봄과 초여름, 가을중순에 탄소 흡수 능력이 컸다. 또한 숲의 나이가 어리고, 활엽수나 침엽수로만 구성된 단순림보다 혼효림이 더 높은 탄소 흡수 능력을 가지고 있었다. 농작물의 성장기간 동안의 탄소 흡수량은 비트가 가장 컸고, 그 다음은 겨울밀, 옥수수, 벼, 감자, 콩 순으로 나타났다. 이러한 결과들은 향후 산림 및 농경지에서의 탄소저감 정책수립과정에 있어 유용한 기초자료로 활용될 수 있을 것이다.

Keywords

References

  1. Ahn SR, Park GA, Jang CH, Kim SJ. 2013. Assessment of climate change impact on evapotranspiration and soil moisture in a mixed forest catchment using spatially calibrated SWAT model. Journal of Korea Water Resources Association. 46(6): 569-583. [Korean Literature] https://doi.org/10.3741/JKWRA.2013.46.6.569
  2. Baldocchi D, Valentini R, Running S, Oechel W, Dahlman R. 1996. Strategies for measuring and modelling carbon dioxide and water vapour fluxes over terrestrial ecosystems. Global Change Biology. 2(3): 159-168. https://doi.org/10.1111/j.1365-2486.1996.tb00069.x
  3. Ceschia E, Beziat P, Dejoux JF, Aubinet M, Bernhofer C, Bodson B, Buchmann N, Carrara A, Cellier P, Di Tommasi P, Elbers J, Eugster W, Grunwald T, Jacobs C, Jans W, Jones M, Kutsch W, Lanigan G, Magliulo E, Marloie O, Moors E, Moureaux C, Olioso A, Osborne B, Sanz M-J, Saunders M. Smith P, Soegaard H, Wattenbach M. 2010. Management effects on net ecosystem carbon and GHG budgets at European crop sites. Agriculture, Ecosystems and Environment. 139(3): 363-383. https://doi.org/10.1016/j.agee.2010.09.020
  4. Ciais P, Wattenbach M, Vuichard, N, Smith P, Piao SL, Don A, Luyssaert S, Janssens IA, Bondeau A, Dechow R, Leip A, Smith PC, Beer C, van der Werf GR, Gervois S, van Oost K, Tomelleri E, Freibauer A, Schulze ED, CarboEurope Synthesis Team. 2009. The European carbon balance: Part 2: croplands. Global Change Biology. 16: 1409-1428.
  5. Ito A, Oikawa T. 2002. A simulation model of the carbon cycle in land ecosystems (Sim-CYCLE): a description based on drymatter production theory and plot-scale validation. Ecological Modelling. 151(2-3):143-176. https://doi.org/10.1016/S0304-3800(01)00473-2
  6. Kirschbaum MUF, Eamus D, Gifford RM, Roxburgh SH, Sands PJ. 2001. Definitions of some ecological terms commonly used in carbon accounting. NEE (Net Ecosystem Exchange) Workshop proceedings. 2001 Apr 18-20. Cooperative research centre for greenhouse accounting. Canberra. Australia. p. 2-5.
  7. Kwon HJ, Lee JH, Lee YK, Lee JW. 2009. Seasonal variations of evapotranspiration observed in a mixed forest in the Seolmacheon catchment. Korean Journal of Agricultural and Forest Meteorology. 11: 39-47. [Korean Literature] https://doi.org/10.5532/KJAFM.2009.11.1.039
  8. Law BE, Turner D, Campbell J, Sun OJ, Van Tuyl S, Ritts WD, Cohen WB. 2004. Disturbance and climate effects on carbon stocks and fluxes across Western Oregon USA. Global Change Biology. 10(9): 1429-1444. https://doi.org/10.1111/j.1365-2486.2004.00822.x
  9. Lee BR. 2015. Remote sensing-based assessment of gross primary production in agricultural ecosystems. Ph.D. Dissertation, University of Bayreuth.
  10. Lee BR, Kang SK, Kim ES, Hwang TH. 2007. Evaluation of a hydro-ecologic model, RHESSys (Regional Hydro-Ecologic Simulation System): parameterization and application at two complex terrain watersheds. Korean Journal of Agricultural and Forest Meteorology. 9: 247-259. [Korean Literature] https://doi.org/10.5532/KJAFM.2007.9.4.247
  11. Lee JT, Lee YS, Kim GY, and Shim KM. 2005. $CO_2$ and water vapor flux measurement by eddy covariance method in a paddy field in Korea. Korea Journal of Agricultural and Forest Meteorology. 7(1):45-50.
  12. Li Y-L, Tenhunen J, Owen K, Schmitt M, Bahn M, Droesler, Otieno D, Schmidt M, Gruenwald Th, Hussain MZ, Mirzae H, Bernhofer C. 2008. Patterns in $CO_2$ gas exchange capacity of grassland ecosystems in the Alps. Agricultural and Forest Meteorology. 148(1): 51-68. https://doi.org/10.1016/j.agrformet.2007.09.002
  13. Luyssaert S, Schulze ED, Borner A, Knohl A, Hessenmoller D, Law BE, Ciais P, Grace J. 2008. Old-growth forests as global carbon sinks. Nature. 455(7210): 213-215. https://doi.org/10.1038/nature07276
  14. Moon BK, Hong JK, Lee BR, Yun JI, Park EW, and Kim J. 2003. $CO_2$ and energe exchange in a rice paddy for the growing season of 2002 in Hari, Korea. Korea Journal of Agricultural and Forest Meteology. 5(2): 51-60.
  15. Ogutu BO, Dash J, Dawson TP. 2013. Developing a diagnostic model for estimating terrestrial vegetation gross primary productivity using the photosynthetic quantum yield and earth observation data. Global Change Biology. 19(9): 2878-2892. https://doi.org/10.1111/gcb.12261
  16. Ottander C, Oquist G. 1991. Recovery of photosynthesis in winter-stressed Scots pine. Plant, Cell and Environment. 14:345-349. https://doi.org/10.1111/j.1365-3040.1991.tb01511.x
  17. Owen KE, Tenhunen J, Reichstein M, Wang Q, Falge E, Geyer R, Xiao X, Stoy P, Ammann C, Arain A, Aubinet, M, Aurela, M, Bernhofer C, Chojnicki BH, Granier A, Gruenwald T, Hadley J, Heinesch B, Hollinger D, Knohl A, Kutsch W, Lohila A, Meyers T, Moors E, Moureaux C, Pilegaard K Saigusa N, Verma S, Vesala T, Vogel C. 2007. Linking flux network measurements to continental scale simulations: ecosystem carbon dioxide exchange capacity under non-waterstressed conditions. Global Change Biology. 13(4): 734-760. https://doi.org/10.1111/j.1365-2486.2007.01326.x
  18. Papale D, Reichstein M, Aubinet M. 2006. Towards a standardized processing of net ecosystem exchange measured with eddy covariance technique: algorithms and uncertainty estimation. Biogeosciences. 3:571-583. https://doi.org/10.5194/bg-3-571-2006
  19. Pontailler JY, Faille A, Lemee G. 1997. Storms drive successional dynamics in natural forests: a case study in Fontainebleau forest (France). Forest Ecology and Management. 98(1): 1-15. https://doi.org/10.1016/S0378-1127(97)00073-X
  20. Reichstein M, Falge E, Baldocchi D, Papale D, Aubinet M, Berbigier P, Bernhofer C, Buchmann N, Gilmanov T, Granier A, Grunwald T, Havrankova K, Ilvesniemi H, Janous D, Knohl A, Laurila T, Lohila A, Loustau D, Matteucci G, Meyers T, Miglietta F, Ourcival J-M, Pumpanen J, Rambal S, Rotenberg E, Sanz M, Tenhunen J, Seufert G, Vaccari F, Vesala T, Yakir D, Valentini R. 2005. On the separation of net ecosystem exchange into assimilation and ecosystem respiration: review and improved algorithm. Global Change Biology. 11(9): 1424-1439. https://doi.org/10.1111/j.1365-2486.2005.001002.x
  21. Reichstein M, Moffat AM. 2014. REddyProc: data processing and plotting utilities of (half-) hourly eddy-covariance measurements. R package version 0.8-2.
  22. Richardson AD, Black TA, Ciais P, Delbart N, Friedl MA, Gobron N, Hollinger DY, Kutsch WL, Longdoz B, Luyssaert S, Migliavacca M, Montagnani L, Munger JW, Moors E, Piao S, Rebmann C, Reichstein M, Saigusa N, Tomelleri E, Vargas R, Varlagin A. 2010. Influence of spring and autumn phenological transitions on forest ecosystem productivity. Philosophical Transactions of the Royal Society of London, Series B: Biological Sciences. 365(1555): 3227-3246. https://doi.org/10.1098/rstb.2010.0102
  23. Running SW, Nemani RR, Heinsch FA, Zhao M, Reeves M, Hashimoto H. 2004. A continuous satellite-derived measure of global terrestrial primary production. Bioscience. 54(6): 547-560. https://doi.org/10.1641/0006-3568(2004)054[0547:ACSMOG]2.0.CO;2
  24. Saigusa N, Yamamoto S, Murayama S, Kondo H. 2005. Inter-annual variability of carbon budget components in an AsiaFlux forest site estimated by long-term flux measurements. Agricultural and Forest Meteorology. 134(1-4): 4-16. https://doi.org/10.1016/j.agrformet.2005.08.016
  25. Shin HJ, Park MJ, Kim SJ. 2012. Evaluation of forest watershed hydro-ecology using measured data and RHESSys model -For the Seolmacheon Catchment-. Journal of Korea Water Resources Association. 45(12): 1293-1307. [Korean Literature] https://doi.org/10.3741/JKWRA.2012.45.12.1293
  26. Son Y, Park IH, Yi MJ, Jin HO, Kim DY, Kim RH, Hwang JO. 2004. Biomass production and nutrient distribution of a natural oak forest in central Korea. Ecological Research. 19(1): 21-28. https://doi.org/10.1111/j.1440-1703.2003.00617.x
  27. Thomas C, Martin JG, Goeckede M, Siqueira MB, Foken T, Law BE, Loescher HW, Katul G. 2008. Estimating daytime subcanopy respiration from conditional sampling methods applied to multi-scalar high frequency turbulence time series. Agricultural and Forest Meteorology. 148(8-9): 1210-1229. https://doi.org/10.1016/j.agrformet.2008.03.002
  28. Wang YP, Polglase PJ. 1995. Carbon balance in the tundra, boreal forest and humid tropical forest duringClimate change: scaling up from leaf physiology and soil carbon dynamics. Plant, Cell and Environment. 18(10): 1-20. https://doi.org/10.1111/j.1365-3040.1995.tb00538.x
  29. Wattenbach M, Sus O, Vuichard N, Lehuger S, Gottschalk P, Li L, Leip A, Williams M, Tomelleri E, Kutsch WL, Buchmann N, Eugster W, Dietiker D, Aubinet M, Ceschia E, Beziat P, Guenwald T, Hastings A, Osborne B, Ciais P, Cellier P, Smith P. 2010. The carbon balance of European croplands: a cross-site comparison of simulation models. Agriculture, Ecosystems and Environment. 139(3): 419-453. https://doi.org/10.1016/j.agee.2010.08.004
  30. White MA, Thornton PE, Running SW, Nemani RR. 2000. Parameterization and sensitivity analysis of the BIOME-BGC terrestrial ecosystem model: net primary production controls. Earth Interactions. 4(3): 1-85. https://doi.org/10.1175/1087-3562(2000)004<0001:ITEASI>2.3.CO;2
  31. Wu C, Niu Z, Tang Q, Huang W, Rivard B, Feng J. 2009. Remote estimation of gross primary production in wheat using chlorophyll-related vegetation indices. Agricultural and Forest Meteorology. 149(6-7): 1015-1021. https://doi.org/10.1016/j.agrformet.2008.12.007
  32. You S, Lee W-K, Son Y, Ito A. 2012. Estimation of vegetation carbon budget in South Korea using ecosystem model and spatiotemporal environmental information. Korean Journal of Remote Sensing. 28(1): 145-157. [Korean Literature] https://doi.org/10.7780/kjrs.2012.28.1.145
  33. Zhao P, Luers J. 2012. Improved determination of daytime net ecosystem exchange of carbon dioxide at croplands. Biogeosciences Discussions. 9(3): 2883-2919. https://doi.org/10.5194/bgd-9-2883-2012

Cited by

  1. 산림의 CO2 흡수량 평가를 위한 통계 및 공간자료의 활용성 검토 - 안산시를 대상으로 - vol.27, pp.2, 2017, https://doi.org/10.14249/eia.2018.27.2.124
  2. 머신러닝 기법의 산림 총일차생산성 예측 모델 비교 vol.21, pp.1, 2017, https://doi.org/10.5532/kjafm.2019.21.1.29
  3. Post-2020에 연계한 온실가스 항목의 환경영향평가 개선 방안 vol.28, pp.5, 2019, https://doi.org/10.14249/eia.2019.28.5.483
  4. 에디 공분산 플럭스 자료를 이용한 논, 밭, 과수원의 연간 탄소 흡수량 추정 및 비교 vol.39, pp.4, 2017, https://doi.org/10.5338/kjea.2020.39.4.40