Applicability Analysis of Phenological Models in the Flowering Time Prediction of Ornamental Plants in Beijing Area

Zhang Aiying; Wang Huanjiong; Dai Junhu; Ding Deping

Vol.25, NO.4, 2014

Article Contents

Article Navigation > Journal of Applied Meteorological Science > 2014 > 25(4): 483-492

Zhang Aiying, Wang Huanjiong, Dai Junhu, et al. Applicability analysis of phenological models in the flowering time prediction of ornamental plants in Beijing area. J Appl Meteor Sci, 2014, 25(4): 483-492.

Citation:

Zhang Aiying, Wang Huanjiong, Dai Junhu, et al. Applicability analysis of phenological models in the flowering time prediction of ornamental plants in Beijing area. J Appl Meteor Sci, 2014, 25(4): 483-492.

Citation:

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Applicability Analysis of Phenological Models in the Flowering Time Prediction of Ornamental Plants in Beijing Area

1.
Beijing Meteorological Service Center, Beijing 100089
2.
Institute of Geographic Science and Natural Resources, CAS, Beijing 100101
3.
University of Chinese Academy of Sciences, CAS, Beijing 100049

Received Date: 2014-01-11
Rev Recd Date: 2014-05-05
Publish Date: 2014-07-31

Abstract

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.
- phenological models;
- flowers blooming;
- prediction;
- applicability

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References(38)

Figures and Tables

Fig. 1 Location of phenological and meteorological stations

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Fig. 2 Effectiveness of SW Model, UniChill Model and Statistical Model for simulating the flowering date and the full flowering date of Prunus discoidea

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Fig. 3 Effectiveness of SW Model, UniChill Model and Statistical Model for simulating the flowering date and the full flowering date of Magnolia denudata

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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

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Fig. 5 The observed and simulated the first flowering date and the full flowering date of Amygdalus davidiana from 1982 to 2012

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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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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
模拟项目	t₀	T_b	G/(℃·d)	E_RMS/d	E_S	E_RMS/d	E_S
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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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
模拟项目	a	d	e	k	l	C	G/(℃·d)	E_RMS/d	E_S	E_RMS/d	E_S
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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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
模拟项目	h	z	月份	E_RMS/d	E_S	E_RMS/d	E_S
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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Table 5 Cross-validation results of SW Model, UniChill Model and Statistic Model

模型	E_S	E_RMS/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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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
观赏植物	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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