Korean Journal of Environmental Biology (환경생물)

Korean Society of Environmental Biology (한국환경생물학회)
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Assessing forest net primary productivity based on a process-based model: Focusing on pine and oak forest stands in South and North Korea

과정기반 모형을 활용한 산림의 순일차생산성 평가: 남북한 소나무 및 참나무 임분을 중심으로

  • Cholho Song (OJEong Resilience Institute, Korea University) ;
  • Hyun-Ah Choi (OJEong Resilience Institute, Korea University) ;
  • Jiwon Son (Department of Environmental Science and Ecological Engineering, Korea University) ;
  • Youngjin Ko (Department of Environmental Science and Ecological Engineering, Korea University) ;
  • Stephan A. Pietsch (International Institute for Applied Systems Analysis) ;
  • Woo-Kyun Lee (OJEong Resilience Institute, Korea University)
  • 송철호 (고려대학교 오정리질리언스연구원) ;
  • 최현아 (고려대학교 오정리질리언스연구원) ;
  • 손지원 (고려대학교 환경생태공학과) ;
  • 고영진 (고려대학교 환경생태공학과) ;
  • ;
  • 이우균 (고려대학교 오정리질리언스연구원)
  • Received : 2023.07.28
  • Accepted : 2023.11.08
  • Published : 2023.12.31
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Abstract

In this study, the biogeochemistry management (BGC-MAN) model was applied to North and South Korea pine and oak forest stands to evaluate the Net Primary Productivity (NPP), an indicator of forest ecosystem productivity. For meteorological information, historical records and East Asian climate scenario data of Shared Socioeconomic Pathways (SSPs) were used. For vegetation information, pine (Pinus densiflora) and oak(Quercus spp.) forest stands were selected at the Gwangneung and Seolmacheon in South Korea and Sariwon, Sohung, Haeju, Jongju, and Wonsan, which are known to have tree nurseries in North Korea. Among the biophysical information, we used the elevation model for topographic data such as longitude, altitude, and slope direction, and the global soil database for soil data. For management factors, we considered the destruction of forests in North and South Korea due to the Korean War in 1950 and the subsequent reforestation process. The overall mean value of simulated NPP from 1991 to 2100 was 5.17 Mg C ha-1, with a range of 3.30-8.19 Mg C ha-1. In addition, increased variability in climate scenarios resulted in variations in forest productivity, with a notable decline in the growth of pine forests. The applicability of the BGC-MAN model to the Korean Peninsula was examined at a time when the ecosystem process-based models were becoming increasingly important due to climate change. In this study, the data on the effects of climate change disturbances on forest ecosystems that was analyzed was limited; therefore, future modeling methods should be improved to simulate more precise ecosystem changes across the Korean Peninsula through process-based models.

본 연구에서는 과정기반 생지화학모형 중 하나인 BGC-MAN (Biogeochemistry Management) 모형을 남북한에 적용하여 산림생태계의 생산성을 나타내는 지표인 순일차생산성(Net Primary Productivity, NPP)을 평가하였다. 기상자료의 경우에는 우리나라 기상청 기후정보포털의 실측 및 동아시아 시나리오 자료를 병행하여 활용하였다. 식생정보로는 소나무(Pinus densiflora) 및 참나무(Quercus spp.) 임분을 대상으로 우리나라의 광릉 및 설마천 유역과 북한 내 양묘장이 있는 것으로 알려져 있는 사리원, 서흥, 해주, 정주, 원산을 대상지로 선정하였다. 생물리적 정보 중경위도, 고도, 사면 방향 등의 지형정보는 SRTM (Shuttle Radar Topography Mission)의 수치표고모델을 활용하였으며, 토양정보 등의 경우에는 HWSD (Harmonized World Soil Database)의 정보를 활용하였다. 관리 요인의 경우에는 1950년의 한국전쟁으로 인한 남북한 산림파괴와 이후 산림의 재조림 과정을 고려하였다. 1991년부터 2100년까지 모의된 NPP의 전체 평균 값은 5.17 Mg C ha-1이었으며, 범위는 3.30~8.19Mg C ha-1로 도출되었다. 또한 기후 시나리오의 변동성이 커짐에 따라서 산림 생산성의 교란이 커졌으며, 소나무 임분의 생장 둔화가 두드러지게 나타났다. 기후변화에 따라 생태계 과정기반 모형의 중요성이 커지는 시점에서 BGC-MAN 모형의 한반도 적용성이 검토되었다. 본 연구의 제한된 자료를 통해서 기후변화에 대한 교란이 산림생태계에 미치는 여러 요인들이 분석된 만큼, 향후 모델링 방법의 보완을 통해 보다 한반도 전역의 정밀한 생태계 변화를 과정기반 모형을 통해 모의할 수 있도록 해야 할 것이다.

Keywords

Acknowledgement

본 연구는 교육부의 재원으로 한국연구재단의 지원을 받아 수행된 기초연구사업 자율운영형 중점연구소 고려대학교 오정리질리언스연구원(NRF-2021R16A1A10045235)의 세부과제와 산림청(한국임업진흥원)의 전과정 산림관리 모형 개발을 통한 산림 영급구조 개선 시나리오 개발(2022464B10-2224-0201)의 지원에 의하여 수행되었습니다. 또한, 모형의 활용을 허가해 준 국제응용시스템분석연구소와 모델링 시스템을 지원한 고려대학교 오정리질리언스연구원 이지상 연구원에게 감사의 말을 전합니다.

References

  1. Akujarvi A, A Shvidenko and SA Pietsch. 2018. Modelling the impacts of intensifying forest management on carbon budget across a long latitudinal gradient in Europe. Environ. Res. Lett. 14:034012. https://doi.org/10.1088/1748-9326/aaf766
  2. Bae JS, RW Joo and YS Kim. 2012. Forest transition in South Korea: Reality, path and drivers. Land Use Pol. 29:198-207. https://doi.org/10.1016/j.landusepol.2011.06.007
  3. Byun JG, WK Lee, M Kim, DA Kwak, H Kwak, T Park, WH Byun, Y Son, JK Choi, YJ Lee, J Saborowski, DJ Chung and JH Jung. 2013. Radial growth response of Pinus densiflora and Quercus spp. to topographic and climatic factors in South Korea. J. Plant Ecol. 6:380-392. https://doi.org/10.1093/jpe/rtt001
  4. Byun JY, WK Lee, SH Choi, SH Oh, SJ Yoo, TS Kwon, JH Sung and JW Woo. 2012. Vulnerability assessment for forest ecosystem to climate change based on spatio-temporal information. Korean J. Remote Sens. 28:159-171. https://doi.org/10.7780/kjrs.2012.28.1.159
  5. Chae N. 2011. Annual variation of soil respiration and precipitation in a temperate forest(Quercus serrata and Carpinus laxiflora) under East Asian monsoon climate. J. Plant Biol. 54:101-111. https://doi.org/10.1007/s12374-011-9148-9
  6. Cho W, I Kim and D Ko. 2023. Assessment of potential carbon storage in North Korea based on forest restoration strategies. Korea J. Environ. Biol. 41:204-214. https://doi.org/10.11626/kjeb.2023.41.3.204
  7. Choi GM, M Kim, WK Lee, HU Gang, DJ Chung, EJ Ko, BH Yun and CH Kim. 2014a. Estimating radial growth response of major tree species using climatic and topographic condition in South Korea. J. Climate Change Res. 5:127-137. https://doi.org/10.15531/ksccr.2014.5.2.127
  8. Choi HA, WK Lee, SW Jeon, JS Kim, H Kwak, M Kim, J Kim and JT Kim. 2014b. Quantifying climate change regulating service of forest ecosystem - focused on quantifying carbon storage and sequestration. J. Climate Change Res. 5:21-36. https://doi.org/10.15531/ksccr.2014.5.1.21
  9. Choi S, WK Lee, H Kwak, SR Kim, S Yoo, HA Choi, S Park and JH Lim. 2011. Vulnerability assessment of forest ecosystem to climate change in Korea using MC1 model. J. For. Plan. 16:149-161. https://doi.org/10.20659/jfp.16.Special_Issue_149
  10. Choi SE, S Hong, C Song, J Kim, W Kim, R Ha and WK Lee. 2023. Construction of land-use change matrix and estimation of greenhouse gas inventory focusing on settlements in South Korea. Carbon Balanc. Manage. 18:4. https://doi.org/10.1186/s13021-023-00223-3
  11. Cui G, WK Lee, D Kim, EJ Lee, H Kwak, HA Choi, DA Kwak, SW Jeon and W Zhu. 2014. Estimation of forest carbon budget from land cover change in South and North Korea between 1981 and 2010. J. Plant Biol. 57:225-238. https://doi.org/10.1007/s12374-014-0165-3
  12. Duveneck MJ and JR Thompson. 2017. Climate change imposes phenological trade-offs on forest net primary productivity. J. Geophys. Res.-Biogeosci. 122:2298-2313. https://doi.org/10.1002/2017JG004025
  13. Eum S, S Kang and D Lee. 2005. A simulation study to investigate climatic controls on net primary production (NPP) of a rugged forested landscape in The Mid-western Korean Peninsula. Korean J. Agric. For. Meteorol. 7:67-78.
  14. Heo M, C Song, J Kim and WK Lee. 2022. Applicability of biogeochemical models to forest ecosystems in South Korea. J. Climate Change Res. 13:409-428. https://doi.org/10.15531/ksccr.2022.13.4.409
  15. Hong M, C Song, M Kim, J Kim, SG Lee, CH Lim, K Cho, Y Son and WK Lee. 2022. Application of integrated Korean forest growth dynamics model to meet NDC target by considering forest management scenarios and budget. Carbon Balanc. Manag. 17:5. https://doi.org/10.1186/s13021-022-00208-8
  16. Kang S, D Lee, J Lee and SW Running. 2006. Topographic and climatic controls on soil environments and net primary production in a rugged temperate hardwood forest in Korea. Ecol. Res. 21:64-74. https://doi.org/10.1007/s11284-005-0095-0
  17. Kang S, JH Lim, ES Kim and N Cho. 2016. Modelling analysis of climate and soil depth effects on pine tree dieback in Korea using BIOME-BGC. Korean J. Agric. For. Meteorol. 18:242-252. https://doi.org/10.5532/KJAFM.2016.18.4.242
  18. Kim D, CH Lim, C Song, WK Lee, D Piao, S Heo and SW Jeon. 2016a. Estimation of future carbon budget with climate change and reforestation scenario in North Korea. Adv. Space Res. 58:1002-1016. https://doi.org/10.1016/j.asr.2016.05.049
  19. Kim HS, Y Luo, F Noulekoun, NJ Noh, J Lee and Y Son. 2021b. Carbon and nitrogen turnover times of South Korean forests estimated via data-model fusion. J. Geophys. Res.-Biogeosci. 126:e2021JG006368. https://doi.org/10.1029/2021JG006368
  20. Kim M, F Kraxner, N Forsell, C Song and WK Lee. 2021a. Enhancing the provisioning of ecosystem services in South Korea under climate change: The benefits and pitfalls of current forest management strategies. Reg. Envir. Chang. 21:6. https://doi.org/10.1007/s10113-020-01728-0
  21. Kim M, WK Lee, GM Choi, C Song, CH Lim, J Moon, D Piao, F Kraxner, A Shividenko and N Forsell. 2017a. Modeling stand-level mortality based on maximum stem number and seasonal temperature. For. Ecol. Manag. 386:37-50. https://doi.org/10.1016/j.foreco.2016.12.001
  22. Kim M, WK Lee, WA Kurz, DA Kwak, S Morken, CE Smyth and D Ryu. 2016b. Estimating carbon dynamics in forest carbon pools under IPCC standards in South Korea using CBM-CFS3. iForest 10:83-92. https://doi.org/10.3832/ifor2040-009
  23. Kim M, WK Lee, Y Son, S Yoo, GM Choi and DJ Chung. 2017b. Assessing the impacts of topographic and climatic factors on radial growth of major forest forming tree species of South Korea. For. Ecol. Manage. 404:269-279. https://doi.org/10.1016/j.foreco.2017.08.048
  24. Kim SN, WK Lee, Y Son, Y Cho and MS Lee. 2009. Applicability of climate change impact assessment models to Korean forest. J. Korean Soc. For. Sci. 98:33-48.
  25. Kwon H, J Lee, Y Lee, JW Lee, SW Jung and J Kim. 2009. Seasonal variations of evapotranspiration observed in a mixed forest in the Seolmacheon catchment. Korean J. Agric. For. Meteorol. 11:39-47. https://doi.org/10.5532/kjafm.2009.11.1.039
  26. Kwon SS, SH Choi and SD Lee. 2012. Estimation of spatial-temporal net primary productivity and soil carbon storage change in the capital area of South Korea under climate change. J. Environ. Impact Assess. 21:757-765. https://doi.org/10.14249/eia.2012.21.5.757
  27. Lee J, CH Lim, GS Kim, A Markandya, S Chowdhury, SJ Kim, WK Lee and Y Son. 2018. Economic viability of the national-scale forestation program: The case of success in the Republic of Korea. Ecosyst. Serv. 29:40-46. https://doi.org/10.1016/j.ecoser.2017.11.001
  28. Lim HJ, YH Lee and HJ Kwon. 2010. Evaluation of community land model version 3.5-dynamic global vegetation model over deciduous forest in Gwangneung, Korea. Korean J. Agric. For. Meteorol. 12:95-106. https://doi.org/10.5532/kjafm.2010.12.2.095
  29. Lim J, JH Shin, GZ Jin, JH Chun and JS Oh. 2003. Forest stand structure, site characteristics and carbon budget of the Kwangneung Natural Forest in Korea. Korean J. Agric. For. Meteorol. 5:101-109.
  30. Lindsay K, GB Bonan, SC Doney, FM Hoffman, DM Lawrence, MC Long, NM Mahowald, JK Moore, JT Randerson and PE Thornton. 2014. Preindustrial-control and twentieth-century carbon cycle experiments with the Earth System Model CESM1(BGC). J. Clim. 27:8981-9005. https://doi.org/10.1175/JCLI-D-12-00565.1
  31. Noh NJ. C Kim, SW Bae, WK Lee, TK Yoon, H Muraoka and Y Son. 2013. Carbon and nitrogen dynamics in a Pinus densiflora forest with low and high stand densities. J. Plant Ecol. 6:368-379. https://doi.org/10.1093/jpe/rtt007
  32. Noh NJ, Y Son, SK Lee, TK Yoon, KW Seo, C Kim, WK Lee, SW Bae and J Hwang. 2010. Influence of stand density on soil CO2 efflux for a Pinus densiflora forest in Korea. J. Plant Res. 123:411-419. https://doi.org/10.1007/s10265-010-0331-8
  33. Piao D, M Kim, GM Choi, J Moon, H Yu, WK Lee, SW Wang, SW Jeon, Y Son, YM Son and G Choi. 2018. Development of an integrated DBH estimation model based on stand and climatic conditions. Forests 9:155. https://doi.org/10.3390/f9030155
  34. Piao D, WK Lee, Y Zhu, M Kim, C Song. 2016. Assessment of Forest Degradation and Carbon Storage for REDD+ Project in North Korea. Korean J. Envion. Biol. 34:1-7. https://doi.org/10.11626/kjeb.2016.34.1.001
  35. Pietsch SA, H Hasenauer and PE Thornton. 2005. BGC-model parameters for tree species growing in central European forests. For. Ecol. Manag. 211:264-295. https://doi.org/10.1016/j.foreco.2005.02.046
  36. Pietsch SA. 2014. Modelling Ecosystem Pools and Fluxes. Implementation and Application of Biogeochemical Ecosystem Models. Institute of Silviculture Department of Forest and Soil Sciences, University of Natural Resources and Life Sciences (BOKU). Vienna, Austria.
  37. Shin HJ, JJ Park and SJ Kim. 2012. Evaluation of forest watershed hydro-ecology using measured data and RHESSys Model-For the Seolmacheon catchment-. J. Korea Water Resour. Assoc. 45:1293-1307 https://doi.org/10.3741/jkwra.2012.45.12.1293
  38. Song C, SA Pietsch, M Kim, S Cha, E Park, A Shvidenko, D Schepaschenko and WK Lee. 2019. Assessing forest ecosystems across the vertical edge of the mid-latitude ecotone using the biogeochemistry management model (BGC-MAN). Forests 10:523. https://doi.org/10.3390/f10060523
  39. White MA, PE Thornton, SW Running and RR Nemani. 2000. Parameterization and sensitivity analysis of the BIOME-BGC Terrestrial Ecosystem Model: Net primary production controls. Earth Interact. 4:1-85. https://doi.org/10.1175/1087-3562(2000)004<0003:PASAOT>2.0.CO;2
  40. Yoo S, WK Lee, Y Son and A Ito. 2012. Estimation of vegetation carbon budget in South Korea using ecosystem model and spatio-temporal environmental information. Korean J. Remote Sens. 28:145-157. https://doi.org/10.7780/kjrs.2012.28.1.145