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논문 기본 정보

자료유형
학술저널
저자정보
정재은 (경희대학교 생태시스템공학과/생명자원과학연구원) 권은영 (경희대학교 생태시스템공학과/생명자원과학연구원) 정유란 (경희대학교 생태시스템공학과/생명자원과학연구원) 윤진일 (경희대학교 생태시스템공학과/생명자원과학연구원)
저널정보
한국농림기상학회 한국농림기상학회지 한국농림기상학회지 제7권 제2호
발행연도
2005.1
수록면
148 - 155 (8page)

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An accurate prediction of blooming date is crucial for many authorities to schedule and organize successful spring flower festivals in Korea. The Korea Meteorological Administration (KMA) has been using regression models combined with a subjective correction by forecasters to issue blooming date forecasts for major cities. Using mean monthly temperature data for February (observed) and March (predicted), they issue blooming date forecasts in late February to early March each year. The method has been proved accurate enough for the purpose of scheduling spring festivals in the relevant cities, but cannot be used in areas where no official climate and phenology data are available. We suggest a thermal time-based two-step phenological model for predicting the blooming dates of spring flowers, which can be applied to any geographic location regardless of data availability. The model consists of two sequential periods: the rest period described by chilling requirement and the forcing period described by heating requirement. It requires daily maximum and minimum temperature as an input and calculates daily chill units until a pre-determined chilling requirement for rest release. After the projected rest release date, it accumulates daily heat units (growing degree days) until a pre- determined heating requirement for flowering. Model parameters were derived from the observed bud-burst and flowering dates of cherry tree (Prunus serrulata var. spontanea) at KMA Seoul station along with daily temperature data for 1923-1950. The model was applied to the 1955-2004 daily temperature data to estimate the cherry blooming dates and the deviations from the observed dates were compared with those predicted by the KMA method. Our model performed better than the KMA method in predicting the cherry blooming dates during the last 50 years (MAE = 2.31 vs. 1.58, RMSE = 2.96 vs. 2.09), showing a strong feasibility of operational application.

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