Journal of Korean Society of Forest Science (한국산림과학회지)

Korean Society of Forest Science (한국산림과학회)
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Recent Changes in Bloom Dates of Robinia pseudoacacia and Bloom Date Predictions Using a Process-Based Model in South Korea

최근 12년간 아까시나무 만개일의 변화와 과정기반모형을 활용한 지역별 만개일 예측

  • Kim, Sukyung (Department of Agriculture, Forestry and Bioresources, Seoul National University) ;
  • Kim, Tae Kyung (Department of Agriculture, Forestry and Bioresources, Seoul National University) ;
  • Yoon, Sukhee (Korea Association of Soil and Water Conservation) ;
  • Jang, Keunchang (Forest ICT Research Center, National Institute of Forest Science) ;
  • Lim, Hyemin (Forest Tree Improvement and Biotechnology Division, National Institute of Forest Science) ;
  • Lee, Wi Young (Forest Tree Improvement and Biotechnology Division, National Institute of Forest Science) ;
  • Won, Myoungsoo (Forest ICT Research Center, National Institute of Forest Science) ;
  • Lim, Jong-Hwan (Forest Ecology Division, National Institute of Forest Science) ;
  • Kim, Hyun Seok (Department of Agriculture, Forestry and Bioresources, Seoul National University)
  • 김수경 (서울대학교 농림생물자원학부) ;
  • 김태경 (서울대학교 농림생물자원학부) ;
  • 윤석희 (사방협회) ;
  • 장근창 (국립산림과학원 산림ICT연구센터) ;
  • 임혜민 (국림산림과학원 임목자원연구과) ;
  • 이위영 (국림산림과학원 임목자원연구과) ;
  • 원명수 (국립산림과학원 산림ICT연구센터) ;
  • 임종환 (국립산림과학원 산림생태연구과) ;
  • 김현석 (서울대학교 농림생물자원학부)
  • Received : 2021.05.13
  • Accepted : 2021.06.29
  • Published : 2021.09.30
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Abstract

Due to climate change and its consequential spring temperature rise, flowering time of Robinia pseudoacacia has advanced and a simultaneous blooming phenomenon occurred in different regions in South Korea. These changes in flowering time became a major crisis in the domestic beekeeping industry and the demand for accurate prediction of flowering time for R. pseudoacacia is increasing. In this study, we developed and compared performance of four different models predicting flowering time of R. pseudoacacia for the entire country: a Single Model for the country (SM), Modified Single Model (MSM) using correction factors derived from SM, Group Model (GM) estimating parameters for each region, and Local Model (LM) estimating parameters for each site. To achieve this goal, the bloom date data observed at 26 points across the country for the past 12 years (2006-2017) and daily temperature data were used. As a result, bloom dates for the north central region, where spring temperature increase was more than two-fold higher than southern regions, have advanced and the differences compared with the southwest region decreased by 0.7098 days per year (p-value=0.0417). Model comparisons showed MSM and LM performed better than the other models, as shown by 24% and 15% lower RMSE than SM, respectively. Furthermore, validation with 16 additional sites for 4 years revealed co-krigging of LM showed better performance than expansion of MSM for the entire nation (RMSE: p-value=0.0118, Bias: p-value=0.0471). This study improved predictions of bloom dates for R. pseudoacacia and proposed methods for reliable expansion to the entire nation.

최근 급격한 봄철 기온 상승과 기후변화의 영향으로 한반도에 분포하고 있는 아까시나무의 개화 시기가 변화하면서 지역간에 동시 개화 현상(simultaneous blooming)이 관측되고 있다. 이러한 변화는 국내 양봉 산업에 큰 변화를 초래하였고, 이로 인해 정확도 높은 아까시나무 개화시기 정보에 대한 수요가 증가하고 있다. 따라서, 본 연구를 통해 아까시나무의 지역별 개화 시기 변화를 잘 설명할 수 있는 신뢰도 높은 개화 시기 예측 모형을 개발하고자 하였다. 이를 위해 지난 12년(2006~2017년)간 전국 26개 지점에서 관측된 아까시나무 만개일 자료와 과거 일기온 복원 자료를 활용하여 봄철 기온 및 아까시나무 만개일 변화의 경향성을 권역별로 파악하고, 과정기반모형을 활용하여 지역 통합 모형(SM)과 함께 지역적 특성을 반영하는 세 모형-SM에 지점별 보정계수를 도입한 수정 통합 모형(MSM), 권역별로 모수를 추정하는 권역별 통합 모형(GM), 관측 지점별로 모수를 추정하는 지역 모형(LM)-을 도출, 성능을 비교하였다. 기온 및 만개일의 경향 분석 결과, 남부 지역에 비해 봄철 기온 상승률이 2배 이상 높았던 중북부 내륙 지역의 경우 만개일이 빠른 속도로 앞당겨져, 결과적으로 남서부 해안 지역과의 만개일 차이는 1년에 0.7098일씩 감소하였다(p-value=0.0417). 전체 지역에 대한 모형의 성능 비교 결과, 지역 특이성이 반영되지 않은 SM에 비해서 MSM은 24% 이상, LM은 15% 이상 감소한 RMSE 값을 나타냈다. 또한 LM과 MSM의 예측 알고리즘을 전국 범위로 확대하여 4년 간(2014~2017년) 16개의 추가 관측 지점을 대상으로 검증한 결과, LM에 코크리깅(Co-kriging)기법을 적용한 방법이 보정계수 전국 분포도를 추정하여 SM을 보정하는 방법보다 예측력이 더 뛰어났으며, 오차의 분포는 두 모형 간에 통계적으로 유의한 차이를 보였다(RMSE: p-value=0.0118, Bias: p-value=0.0471). 본 연구는 아까시나무의 개화 시기 예측에 있어 지역 단위 예측의 신뢰도를 향상시키고 모형을 넓은 지역 범위로 확대, 적용하기 위한 방안을 제시하였다.

Keywords

Acknowledgement

본 연구는 산림청(국립산림과학원) '산악지역 영향예보 기반 구축 및 맞춤형 산악기상·기후 서비스 체계 개발'(과제번호: FE0500-2018-02)과 산림청(한국임업진흥원) 산림과학기술 연구개발사업'(FTIS 2020185D10-2122-AA02)'의 지원에 의하여 이루어진 것입니다.

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