Journal of Korea Water Resources Association (한국수자원학회논문집)

Korea Water Resources Association (한국수자원학회)
권호 표지
DOI QR코드

DOI QR Code

Relationship between gross primary production and environmental variables during drought season in South Korea

가뭄 기간 총일차생산량과 환경 변수 간 상관관계 분석

  • Park, Jongmin (School of Civil, Architectural Enginnering and Landscape Architecture, Sungkyunkwan University) ;
  • Lee, Dalgeun (Disaster Information Research Division, National Disaster Management Research Institute) ;
  • Park, Jinyi (Disaster Information Research Division, National Disaster Management Research Institute) ;
  • Choi, Minha (School of Civil, Architectural Engineering and Landscape Architecture, Sungkyunkwan University)
  • 박종민 (성균관대학교 건설환경시스템공학과) ;
  • 이달근 (국립재난안전연구원 재난정보연구실) ;
  • 박진이 (국립재난안전연구원 재난정보연구실) ;
  • 최민하 (성균관대학교 건설환경공학부)
  • Received : 2021.06.11
  • Accepted : 2021.07.30
  • Published : 2021.10.31
Download PDF
⟨ Previous Next ⟩

Abstract

Water stress and environmental drivers are important factors to explain the variance of gross primary production (GPP). Environmental drivers are used to generate GPP in Moderate Resolution Imaging Spectroradiometer (MODIS) algorithm and process-based model. However, MODIS algorithm only consider the vapor pressure deficit (VPD) data while the process-based biogeochemical model also uses limited data to express water stress. We compared the relationship between environmental drivers and GPP from eddy covariance method, MODIS algorithm, and Community Land Model 4 (CLM 4) simulation in normal years and drought years. To consider water stress specifically, we used VPD and evaporative fraction (EF). We evaluated the effects from environmental drivers and EF towards GPP products using the structural equation modeling (SEM) in South Korea. We found that GPP products from MODIS algorithm and model simulation results were not restricted from VPD data if VPD was underestimated. We also found that in the cropland area, irrigation effects can relieve VPD effects to GPP. However, GPP products derived from MODIS and CLM 4 had limitation to explain the irrigation effects to GPP. Overall, these results will enhance the understanding of GPP products derived from MODIS and CLM 4.

총일차생산량은 물 스트레스와 환경 변수에 의해 크게 영향을 받는다. 총일차생산량과 환경변수의 상관관계는 Moderate Resolution Imaging Spectroradiometer (MODIS) 알고리즘과 process-based model에 적용되어 총일차생산량을 계산하는데 활용된다. 그러나 MODIS 알고리즘에서는 물 스트레스를 수증기압차이(vapor pressure deficit)로만 고려하고 있으며, process-based model에서도 제한된 변수만으로 물 스트레스를 표현하여 총일차생산량을 산출하고 있다. 본 연구에서는 에디 공분산 기법, MODIS 알고리즘, 그리고 Community Land Model 4 (CLM 4) 시뮬레이션 결과에서 얻어진 총일차생산량이 환경 변수와 가지는 상관관계를 평년과 가뭄연도를 대상으로 분석하였다. 물 스트레스를 대표하는 지수는 수증기압차이와 evaporative fraction (EF)가 사용되었다. 본 연구에서는 structural equation modeling (SEM)을 활용하여 환경 변수와 EF가 총일차생산량에 끼치는 영향을 수치화하여 평가하였다. SEM을 통해 상관성을 분석한 결과, 수증기압차이가 과소평가될 경우 MODIS 알고리즘과 CLM 4 시뮬레이션에서 생산된 총일차생산량이 수증기압차이로부터 받는 영향이 제한적임을 확인하였다. 에디 공분산 기법으로 산출한 총일차생산량의 상관성 분석 결과, 경작지에서는 관개작업으로 인해 수증기압차이가 총일차생산량에 끼치는 영향이 감소하였으나 MODIS와 CLM 4에서 산출된 총일차생산량 데이터는 이러한 관개작업의 영향을 설명하는데 제한적이었다. 본 연구결과는 MODIS와 CLM 4에서 산출된 총일차생산량의 특성을 이해하고 한계를 분석하는 연구에 도움을 줄 것으로 예상된다.

Keywords

과제정보

This research was supported by a grant (2021-MOIS37-002) of "Intelligent Technology Development Program on Disaster Response and Emergency Management" funded by Ministry of Interior and Safety (MOIS, Korea). This work was supported by BK21 FOUR Project.

참고문헌

  1. Ambika, A.K., and Mishra, V. (2020). "Substantial decline in atmospheric aridity due to irrigation in India." Environmental Research Letters, Vol. 15, No. 12, 124060. https://doi.org/10.1088/1748-9326/abc8bc
  2. Aubinet, M., Vesala, T., and Papale, D. (2012). Eddy covariance: A practical guide to measurement and data analysis. Springer Science & Business Media, Berlin, Heidelberg, Germany.
  3. Borges, C.V., Minatel, I.O., Gomez-Gomez, H.A., and Lima, G.P.P. (2017). Medicinal plants: Influence of environmental factors on the content of secondary metabolites. Medicinal Plants and Environmental Challenges, Springer, Cham, pp. 259-277.
  4. Dong, J., Xiao, X., Wagle, P., Zhang, G., Zhou, Y., Jin, C., Torn, M., Meyers, T., Suyker, A., Wang, J., Yan, H., Biradar, C., and Moore III, B. (2015). "Comparison of four EVI-based models for estimating gross primary production of maize and soybean croplands and tallgrass prairie under severe drought." Remote Sensing of Environment, Vol. 162, pp. 154-168. https://doi.org/10.1016/j.rse.2015.02.022
  5. Falge, E., Baldocchi, D., Olson, R., Anthoni, P., Aubinet, M., Bernhofer, C., Burba, G., Ceulemans, R., Clement, R., Dolman, H., Granier, A., Gross, P., Grunwald, T., Hollinger, D., Jensen, N.O., Katul, G., Keronen, P., Kowalski, A., Lai, C.T., Law, B.E., Meyers, T., Moncrieff, H., Moors, E., Munger, JW., Pilegaard, K., Rannik, U., Rebmann, C., Suyker, A., Tenhunen, J., Tu, K., Verma, S., Vesala, T., Wilson, K., and Wofsy, S. (2001). "Gap filling strategies for defensible annual sums of net ecosystem exchange." Agricultural and Forest Meteorology, Vol. 107, No. 1, pp. 43-69. https://doi.org/10.1016/S0168-1923(00)00225-2
  6. Fan, Y., Chen, J., Shirkey, G., John, R., Wu, S. R., Park, H., and Shao, C. (2016). "Applications of structural equation modeling (SEM) in ecological studies: an updated review." Ecological Processes, Vol. 5, No. 1, pp. 1-12. https://doi.org/10.1186/s13717-016-0044-6
  7. Flanagan, L.B., Wever, L. A., and Carlson, P.J. (2002). "Seasonal nd interannual variation in carbon dioxide exchange and carbon balance in a northern temperate grassland." Global Change Biology, Vol. 8, No. 7, pp. 599-615. https://doi.org/10.1046/j.1365-2486.2002.00491.x
  8. Fu, Z., Ciais, P., Bastos, A., Stoy, P.C., Yang, H., Green, J.K., Wang, B., Yu, W., Huang, Y., Knohi, A., Sigut, L., Gharun, M., Cuntz, M., Arriga, N., Roland, M., Peichl, M., Migliavacca, M., Cremonese, E., Varlagin, A., Brummer, C., Gourlez de la Motte, L., Fares, S., Buchmann, N., El-Madany, T., S., Pitacco, A., Vendrame, N., Li, Z., Vinc, C., and Koebsch, F. (2020). "Sensitivity of gross primary productivity to climatic drivers during the summer drought of 2018 in Europe." Philosophical Transactions of the Royal Society B, Vol. 375, No. 1810, 20190747. https://doi.org/10.1098/rstb.2019.0747
  9. Goodrich, J.P., Campbell, D.I., Clearwater, M.J., Rutledge, S., and Schipper, L.A. (2015). "High vapor pressure deficit constrains GPP and the light response of NEE at a Southern Hemisphere bog." Agricultural and Forest Meteorology, Vol. 203, pp. 54-63. https://doi.org/10.1016/j.agrformet.2015.01.001
  10. Grace, J. (2004). "Understanding and managing the global carbon cycle." Journal of Ecology, Vol. 92, No. 2, pp. 189-202. https://doi.org/10.1111/j.0022-0477.2004.00874.x
  11. Grace, J.B. (2006). Structural equation modeling and natural systems. Cambridge University Press, UK.
  12. Hong, I., Lee, J.H., and Cho, H.S. (2016). "National drought management framework for drought preparedness in Korea (lessons from the 2014-2015 drought)." Water Policy, Vol. 18, No. S2, pp. 89-106. https://doi.org/10.2166/wp.2016.015
  13. Huang, Y., Ryu, Y., Jiang, C., Kimm, H., Kim, S., Kang, M., and Shim, K. (2018). "BESS-Rice: A remote sensing derived and biophysical process-based rice productivity simulation model." Agricultural and Forest Meteorology, Vol. 256, pp. 253-269. https://doi.org/10.1016/j.agrformet.2018.03.014
  14. Hudiburg, T.W., Law, B.E., and Thornton, P.E. (2013). "Evaluation and improvement of the Community Land Model (CLM4) in Oregon forests." Biogeosciences, Vol. 10, No. 1, pp. 453-470. https://doi.org/10.5194/bg-10-453-2013
  15. Im, E.S., Marcella, M.P., and Eltahir, E.A. (2014). "Impact of potential large-scale irrigation on the West African monsoon and its dependence on location of irrigated area." Journal of Climate, Vol. 27, No. 3, pp. 994-1009. https://doi.org/10.1175/JCLI-D-13-00290.1
  16. Jung, C.G., Lee, Y.G., Kim, S.J., and Jang, C.H. (2015). "Quantitative Study of CO2 based on Satellite Image for Carbon Budget on Flux Tower Watersheds." Journal of The Korean Society of Agricultural Engineers, Vol. 57, No. 3, pp. 109-120. https://doi.org/10.5389/KSAE.2015.57.3.109
  17. Lasslop, G., Reichstein, M., Papale, D., Richardson, A.D., Arneth, A., Barr, A., Stoy P., and Wohlfahrt, G. (2010). "Separation of net ecosystem exchange into assimilation and respiration using a light response curve approach: Critical issues and global evaluation." Global Change Biology, Vol. 16, No. 1, pp. 187-208. https://doi.org/10.1111/j.1365-2486.2009.02041.x
  18. Lawrence, D.M., Oleson, K.W., Flanner, M.G., Thornton, P.E., Swenson, S.C., Lawrence, P.J., Zeng, X., Yang, Z-L., Levis, S., Sakaguchi, K., Bonan, G.B., and Slater, A.G. (2011). "Parameterization improvements and functional and structural advances in version 4 of the community land model." Journal of Advances in Modeling Earth Systems, Vol. 3, No. 1. https://doi.org/10.1029/2011MS00045
  19. Lloyd, J., and Taylor, J.A. (1994). "On the temperature dependence of soil respiration." Functional Ecology, Vol. 8, No. 3, pp. 315-323. https://doi.org/10.2307/2389824
  20. Malek-Madani, G., Walter-Shea, E.A., Nguy-Robertson, A.L., Suyker, A., and Arkebauer, T.J. (2020). "Modeling gross primary production of Midwestern US maize and soybean croplands with satellite and gridded weather data." Remote Sensing, Vol. 12, No. 23, 3956. https://doi.org/10.3390/rs12233956
  21. Mao, J., Thornton, P.E., Shi, X., Zhao, M., and Post, W.M. (2012). "Remote sensing evaluation of CLM4 GPP for the period 2000-09." Journal of Climate, Vol. 25, No. 15, pp. 5327-5342. https://doi.org/10.1175/JCLI-D-11-00401.1
  22. Mu, Q., Zhao, M., Heinsch, F.A., Liu, M., Tian, H., and Running, S.W. (2007). "Evaluating water stress controls on primary production in biogeochemical and remote sensing based models." Journal of Geophysical Research: Biogeosciences, Vol. 112, No, G1.
  23. Oleson, K.W., Lawrence, D.M., Gordon, B., Flanner, M.G., Kluzek, E., Lawrence, P.J., Levis, S., Swenson, S.C., Thornton, P.E., Dai, A., Decker, M., Dickinson, R., Feddema, J., Heald, C.L., Hoffman, F., Lamarque, J.-F., Mahowald, N., Niu, G.-Y., Qian, T., Randerson, J., Running, S., Sakaguchi, K., Slater, A., Stockli, R., Wang, A., Yang, Z.-L., Zeng, Xi., and Zeng, Xu. (2010). Technical description of version 4.0 of the Community Land Model (CLM). NCAR Technical Note NCAR/TN-461+STR, National Center for Atmospheric Research, Boulder, CO, U.S., p. 174.
  24. Pau, S., Detto, M., Kim, Y., and Still, C.J. (2018). "Tropical forest temperature thresholds for gross primary productivity." Ecosphere, Vol. 9, No. 7, e02311. https://doi.org/10.1002/ecs2.2311
  25. Sheffield, J., Goteti, G., and Wood, E.F. (2006). "Development of a 50-year high-resolution global dataset of meteorological forcings for land surface modeling." Journal of Climate, Vol. 19, No. 13, pp. 3088-3111. https://doi.org/10.1175/JCLI3790.1
  26. Shin, H.J., Park, M.J., and Kim, S.J. (2012). "Evaluation of forest watershed hydro-ecology using measured data and RHESSys model-For the Seolmacheon Catchment." Journal of Korea Water Resources Association, Vol. 45, No. 12, pp. 1293-1307. https://doi.org/10.3741/JKWRA.2012.45.12.1293
  27. Spielmann, F.M., Wohlfahrt, G., Hammerle, A., Kitz, F., Migliavacca, M., Alberti, G., Ibrom, A., El-Madany, T.S, Gerdel, K., Moreno, G., Kolle, O., Karl, T., Peressotti, A., and Delle Vedove, G. (2019). "Gross primary productivity of four European ecosystems constrained by joint CO2 and COS flux measurements." Geophysical Research Letters, Vol. 46, No. 10, pp. 5284-5293. https://doi.org/10.1029/2019GL082006
  28. Stocker, B.D., Zscheischler, J., Keenan, T.F., Prentice, I.C., Seneviratne, S.I., and Penuelas, J. (2019). "Drought impacts on terrestrial primary production underestimated by satellite monitoring." Nature Geoscience, Vol. 12, No. 4, pp. 264-270. https://doi.org/10.1038/s41561-019-0318-6
  29. Tabari, H., and Aghajanloo, M.B. (2013). "Temporal pattern of aridity index in Iran with considering precipitation and evapotranspiration trends." International Journal of Climatology, Vol. 33, No. 2, pp. 396-409. https://doi.org/10.1002/joc.3432
  30. Taylor, S.E., Klein, L.C., Lewis, B.P., Gruenewald, T.L., Gurung, R.A., and Updegraff, J.A. (2000). "Biobehavioral responses to stress in females: Tend-and-befriend, not fight-or-flight." Psychological Review, Vol. 107, No. 3, pp. 411-429. https://doi.org/10.1037/0033-295X.107.3.411
  31. Turner, D.P., Ritts, W.D., Cohen, W.B., Gower, S.T., Running, S.W., Zhao, M., Costa, M.H., Kirschbaum, A.A., Ham, J.M., Saleska, S.R., and Ahl, D.E. (2006). "Evaluation of MODIS NPP and GPP products across multiple biomes." Remote Sensing of Environment, Vol. 102, No. 3-4, pp. 282-292. https://doi.org/10.1016/j.rse.2006.02.017
  32. Wang, Y., Zhou, L., Jia, Q., and Ping, X. (2019). "Direct and indirect effects of environmental factors on daily CO2 exchange in a rainfed maize cropland - A SEM analysis with 10 year observations." Field Crops Research, Vol. 242, 107591. https://doi.org/10.1016/j.fcr.2019.107591
  33. Williams, I.N., Torn, M.S., Riley, W.J., and Wehner, M.F. (2014). "Impacts of climate extremes on gross primary production under global warming." Environmental Research Letters, Vol. 9, No. 9, 094011. https://doi.org/10.1088/1748-9326/9/9/094011
  34. Wutzler, T., Lucas-Moffat, A., Migliavacca, M., Knauer, J., Sickel, K., Sigut, L., Menzer, O., and Reichstein, M. (2018). "Basic and extensible post-processing of eddy covariance flux data with REddyProc." Biogeosciences, Vol. 15, No. 16, pp. 5015-5030. https://doi.org/10.5194/bg-15-5015-2018
  35. Yu, Z., Wang, J., Liu, S., Rentch, J. S., Sun, P., and Lu, C. (2017). "Global gross primary productivity and water use efficiency changes under drought stress." Environmental Research Letters, Vol. 12, No. 1, 014016. https://doi.org/10.1088/1748-9326/aa5258
  36. Yu, T., Sun, R., Xiao, Z., Zhang, Q., Liu, G., Cui, T., and Wang, J. (2018). "Estimation of global vegetation productivity from global land surface satellite data." Remote Sensing, Vol. 10, No. 2, 327. https://doi.org/10.3390/rs10020327
  37. Zhang, L., Mao, J., Shi, X., Ricciuto, D., He, H., Thornton, P., Yu, G., Li, P., Liu, M., Ren, X., Han, S., Li, Y., Yan, J., Hao, Y., and Wang, H. (2016). "Evaluation of the community land model simulated carbon and water fluxes against observations over ChinaFLUX sites." Agricultural and Forest Meteorology, Vol. 226, pp. 174-185. https://doi.org/10.1016/j.agrformet.2016.05.018