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Applicability Analysis of Phenological Models in the Flowering Time Prediction of Ornamental Plants in Beijing Area
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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。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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Key words:
- phenological models;
- flowers blooming;
- prediction;
- applicability
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图 2 SW模型、UniChill模型和统计模型模拟的杭州早樱始花期和盛花期
Fig. 2 Effectiveness of SW Model, UniChill Model and Statistical Model for simulating the flowering date and the full flowering date of Prunus discoidea
图 3 SW模型、UniChill模型和统计模型模拟的白玉兰始花期和盛花期
Fig. 3 Effectiveness of SW Model, UniChill Model and Statistical Model for simulating the flowering date and the full flowering date of Magnolia denudata
图 4 SW模型、UniChill模型和统计模型模拟的山桃始花期和盛花期
Fig. 4 Effectiveness of SW Model, UniChill Model and Statistical Model for simulating the first flowering date and the full flowering date of Amygdalus davidiana
图 5 1982—2012年观测与模拟的山桃始花期和盛花期序列
Fig. 5 The observed and simulated the first flowering date and the full flowering date of Amygdalus davidiana from 1982 to 2012
表 1 本研究所采用观赏植物及物候资料一览表
Table 1 Phenological data used in this study
物种 观测地点 资料时段 资料来源 杭州早樱 玉渊潭公园 1998—2012年 自主观测 白玉兰 密云农业
气象试验站1996—2012年 农业气象
试验站观测山桃 颐和园公园 1981—2012年 中国物候观测网 
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表 2 SW模型模拟3种植物始花期和盛花期的模型参数及其检验结果
Table 2 Model parameters and test results of SW Model for simulating the first flowering date and the full flowering date of three plants
模拟项目 模型参数 内部检验 交叉检验 t0 Tb G/(℃·d) ERMS/d ES ERMS/d ES 杭州早樱始花期 1 3.0 131.7 1.59 0.94* 2.35 0.88* 杭州早樱盛花期 1 3.0 149.5 1.88 0.91* 1.93 0.91* 白玉兰始花期 1 3.0 124.5 2.91 0.84* 3.58 0.74* 白玉兰盛花期 1 3.0 146.5 2.30 0.87* 2.35 0.85* 山桃始花期 28 0.0 152.3 2.45 0.90* 2.75 0.88* 山桃盛花期 28 0.0 180.1 3.05 0.85* 3.16 0.84* 注：*表示达到0.001显著性水平。
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表 3 UniChill模型模拟3种植物始花期和盛花期的模型参数及其检验结果
Table 3 Model parameters and test results of UniChill Model for simulating the first flowering date and the full flowering date of three plants
模拟项目 模型参数 内部检验 交叉检验 a d e k l C G/(℃·d) ERMS/d ES ERMS/d ES 杭州早樱始花期 0.09 3.94 16.66 -0.29 15.59 72.12 3.59 1.69 0.93* 3.26 0.75* 杭州早樱盛花期 0.09 3.94 16.66 -0.28 15.59 72.12 4.06 2.27 0.87* 2.82 0.81* 白玉兰始花期 0.07 4.00 12.39 -0.23 15.94 31.18 5.27 2.74 0.84* 3.79 0.68* 白玉兰盛花期 0.07 4.00 12.39 -0.23 15.94 31.18 6.00 2.22 0.86* 2.49 0.82* 山桃始花期 0.07 3.52 18.16 -0.21 15.78 87.14 4.55 2.33 0.91* 2.73 0.88* 山桃盛花期 0.07 3.52 18.16 -0.21 15.78 87.15 4.87 2.51 0.90* 2.68 0.88* 注：*表示达到0.001显著性水平。
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表 4 统计模型模拟3种植物始花期和盛花期的模型参数及其检验结果
Table 4 Model parameters and test results of Statistic Model for simulating the first flowering date and the full flowering date of three plants
模拟项目 模型参数 内部检验 交叉检验 h z 月份 ERMS/d ES ERMS/d ES 杭州早樱始花期 -3.76 113.15 3 2.20 0.85* 2.67 0.78* 杭州早樱盛花期 -3.53 113.95 3 2.15 0.86* 2.37 0.83* 白玉兰始花期 -3.97 114.98 3 3.62 0.70* 4.24 0.59* 白玉兰盛花期 -3.38 114.65 3 3.31 0.67* 3.84 0.56* 山桃始花期 -3.67 104.30 3 3.57 0.77* 3.82 0.74* 山桃盛花期 -3.89 108.88 3 3.47 0.80* 3.75 0.77* 注：*表示达到0.001显著性水平。
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表 5 SW模型、UniChill模型和统计模型的交叉检验结果对比
Table 5 Cross-validation results of SW Model, UniChill Model and Statistic Model
模型 ES ERMS/d SW模型 0.74~0.91 1.93~3.58 UniChill模型 0.68~0.88 2.49~3.79 统计模型 0.56~0.83 2.37~4.24
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表 6 SW模型、UniChill模型和统计模型预测观赏植物开花期准确率 (单位：%)
Table 6 Predicting accuracy of SW Model, UniChill Model and Statistical Model for simulating the flowering date of three plants (unit:%)
观赏植物 SW模型 UniChill模型 统计模型 始花期 盛花期 始花期 盛花期 始花期 盛花期 杭州早樱 93.75 93.75 81.25 75.00 87.50 87.50 白玉兰 66.67 88.89 70.59 88.89 41.18 55.56 山桃 92.86 85.71 85.71 82.14 64.29 67.86
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