著者

水 珠子 川谷 真由美 石田(坂根) 千津恵 甲斐 敬子 鬼束 千里 棚町 祥子 小瀬 千晶 山崎 あかね 辻 雅子 鈴木 太朗 久野 一恵 酒元 誠治 Tamako MIZU Mayumi KAWATANI Chizue ISHIDA(SAKANE) Keiko KAI Chisato ONITUKA Shouko TANAMACHI Chiaki KOSE Akane YAMASAKI Masako TSUji Tarou SUZUKI Kazue KUNO Seiji SAKEMOTO

雑誌

島根県立大学短期大学部松江キャンパス研究紀要 (ISSN:18826768)

巻号頁・発行日

vol.53, pp.77-84, 2015-03-31

著者

Keiko Kai Mikiko Kainuma Naomi Murakoshi Kenji Omasa

出版者

日本農業気象学会

雑誌

農業気象 (ISSN:00218588)

巻号頁・発行日

vol.48, no.5, pp.771-774, 1993 (Released:2010-03-16)

参考文献数

11

被引用文献数

4 8

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Strong correlations were found between blooming dates and meteorological factors. Based on these correlations, predictive maps of blooming dates in the Japanese Islands were proposed for each case of 1, 2 and 3°C of warming. The correlation was tested for the blooming dates of Prunus yedoensis, Prunus mume, Camellia japonica, Taraxacum, Rhododendron kaempferi, Wistaria floribunda, Lespedeza bicolor, Hydrangea macrophylla, Lagerstroemia indica, Miscanthus sinensis, etc., using the data of monthly mean temperatures, warming indices and cold indices from 102 meteorological stations in Japan between 1953-1990. Simple and multiple regression analyses were used for the correlation.Among meteorological factors, the strongest correlation was shown for monthly mean temperatures. Notably, the strongest was obtained for the case of Prunus yedoensis. The cold index and mean temperature of the previous December also showed the best correlation for species such as Prunus mume and Camellia japonica. Strong correlations between the leaf color-changing dates of Ginkgo biloba and Acer palmatum and the monthly mean temperature were found in one month of autumn. In these species, there was a delay of 2-7 days with a 1 degree increase in mean temperature.The 30-year 1km2 temperature-climate mesh-file developed by the Japan Meteorological Agency was used for the phenological estimation and predictive maps of blooming dates. Each observatory station was classified according to its annual mean temperature. Blooming for each mesh was estimated through monthly mean temperatures and regression equations of corresponding stations. Then, distribution maps of predictive blooming dates distinguished by 5-day divisions were made.