| 物候模型在北京观赏植物开花期预测中的适用性 |
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| 引用本文： | 张爱英,王焕炯,戴君虎,丁德平.物候模型在北京观赏植物开花期预测中的适用性[J].应用气象学报,2014,25(4):483-492. |
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| 作者姓名： | 张爱英 王焕炯 戴君虎 丁德平 |
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| 作者单位： | 1.北京市气象服务中心，北京 100089 |
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| 基金项目： | 北京市自然科学基金项目（8112028），国家自然科学基金项目(41030101) |
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| 摘 要： | 以北京地区玉渊潭公园杭州早樱 (1998—2012年)、密云农业气象试验站白玉兰 (1996—2012年) 及颐和园公园山桃 (1981—2012年) 物候观测资料和海淀、密云气象站的1981—2012年逐日平均气温观测资料为基础，分别应用国际通用的3种物候模型 (SW模型、UniChill模型和统计模型) 对以上植物的始花期和盛花期建模，并评估模型适用性。结果表明:SW模型在北京地区3种观赏植物开花期预测中适用性最高，其交叉检验的均方根误差仅为1.93~3.58 d, 其次为UniChill模型 (均方根误差为2.49~3.79 d)，统计模型效果最差 (均方根误差为2.36~4.24 d)。因此，推荐在观赏植物开花期预测业务中采用SW模型。
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| 关 键 词： | 物候模型 开花期 预测 适用性 |
| 收稿时间： | 2014-01-11 |
| 修稿时间： | 5/5/2014 12:00:00 AM |
Applicability Analysis of Phenological Models in the Flowering Time Prediction of Ornamental Plants in Beijing Area |
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| Zhang Aiying,Wang Huanjiong,Dai Junhu and Ding Deping.Applicability Analysis of Phenological Models in the Flowering Time Prediction of Ornamental Plants in Beijing Area[J].Quarterly Journal of Applied Meteorology,2014,25(4):483-492. |
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| Authors: | Zhang Aiying Wang Huanjiong Dai Junhu and Ding Deping |
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| Affiliation: | 1.Beijing Meteorological Service Center, Beijing 1000892.Institute of Geographic Science and Natural Resources, CAS, Beijing 1001013.University of Chinese Academy of Sciences, CAS, Beijing 100049 |
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| Abstract: | In recent years, with the tourism booming and the increasing demands for flower-appreciation, the prediction of flowering date of ornamentals plants becomes more and more important. For a long time, phenological models are widely used in agriculture field, but rarely applied in predicting flowering time of ornamental plants.Based on phenological data of three ornamentals plants (Prunus discoidea, Magnolia denudata and Amygdalus davidiana) in Beijing Area, corresponding meteorological data during the period of 1981-2012 at Haidian and Miyun meteorological stations, three phenological models (SW Model, UniChill Model and Statistical Model) for simulating the first flowering date and the full flowering date of the above three plants are developed. In the experimental process, the least square fitting is introduced in computing parameters, including linear least square fitting in Statistical Model and nonlinear least square fitting in SW Model and UniChill Model. Moreover, the simulating annealing approach is used to obtain the analytic solutions for SW Model and UniChill Model. Results show that SW Model performs well in simulating the first flowering date and the full flowering date of Prunus discoidea, the full flowering date of Magnolia denudata, and the first flowering date and the full flowering date of Amygdalus davidiana. Besides, SW Model is the most applicable model with the root mean square error (RMSE) of external verification between 1.93-3.58 days. UniChill Model ranks the second with the RMSE of 2.49-3.89 days, and Statistical Model has the largest uncertainty with the RMSE of 2.37-4.24 days. As far as prediction accuracy is concerned, SW Model also ranks the first, and for more than 85% of years, the prediction error is within 3 days.Above all, SW Model is recommended for predicting the flowering dates of the ornamental plants in Beijing Area. But Statistical Model based on daily average temperature, considering the comprehensive effect of light and moisture and plant physiological processes, may perform better. With the increasing urban heat island effect in Beijing Area, the deviation caused by urban heat island effect should be removed during the application of SW Model. |
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| Keywords: | phenological models flowers blooming prediction applicability |
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