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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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[1] 李荣平, 周广胜, 阎巧玲.植物物候模型研究.中国农业气象, 2005, 26(4):210-214. http://www.cnki.com.cn/Article/CJFDTOTAL-ZGNY200504002.htm [2] 裴顺祥, 郭泉水, 辛学兵, 等.国外植物物候对气候变化响应的研究进展.世界林业研究, 2009, 22(6):31-37. http://www.cnki.com.cn/Article/CJFDTOTAL-SJLY200906005.htm [3] Sarvas R.Investigation on the annual cycle of development of forest trees:Autumn dormancy and winter dormancy.Commun Inst For Fenn, 1974, 84:10. [4] Cannell M G R, Smith R I.Thermal time, chill days and prediction of budburst in Picea sitchensis.J Appl Ecol, 1983, 20(1):951-963. [5] Chuine I, Cour P, Rousseau D D.Fitting models predicting dates of flowering of temperate-zone trees using simulated annealing.Plant, Cell & Environment, 1998, 21(5):455-466. https://www.researchgate.net/publication/227650098_ORIGINAL_ARTICLE_OA_220_EN_Fitting_models_predicting_dates_of_flowering_of_temperate-zone_trees_using_simulated_annealing [6] Chuin Cour P, Rousseau D D.Selecting models to predict the timing of flowering of temperature trees implications for tree phenology modellintg.Plant, Cell & Environment, 1999, 22(1):1-13. https://www.researchgate.net/publication/227637078_Selecting_models_to_predict_the_timing_of_flowering_of_temperate_trees_Implications_for_tree_phenology_modelling [7] Chuine I.A unified model for budburst of trees.Journal of Theoretical Biology, 2000, 207(3):337-347. doi: 10.1006/jtbi.2000.2178 [8] Hunter A F, Lechowicz M J.Predicting the timing of budburst in temperate trees.J Appl Ecol, 1992, 29(3):597-604. doi: 10.2307/2404467 [9] Landsberg J J.Apple fruit bud development and growth:Analysis and empirical model.Ann Bot, 1974, 38:1013-1023. doi: 10.1093/oxfordjournals.aob.a084891 [10] Murray M B, Cannell M G R, Smith R I.Date of budburst of fifteen treespecies in Britain following climatic warming.J Appl Ecol, 1989, 26:693-700. doi: 10.2307/2404093 [11] Cannell M G, Smith R I.Thermaltime, chilling days and prediction of budburst in Picea sitchensis.J Appl Ecol, 1983, 20(3):951-963. doi: 10.2307/2403139 [12] Hunter A F, Lechowicz M J.Predicting the timing of budburst in temperate trees.J Appl Ecol, 1992, 2:597-604. [13] 王焕炯, 戴君虎, 葛全胜.1952—2007年中国白蜡树春季物候时空变化分析.中国科学:地球科学, 2012, 42(5):701-710. http://www.cnki.com.cn/Article/CJFDTOTAL-JDXK201205010.htm [14] Chuine I, Yiou P, Viovy N, et al.Historical phenology: Grape ripening as a past climate indicator.Nature, 2004, 432:289. doi: 10.1038/432289a [15] 宋富强, 张一平.动态物候模型发展及其在全球变化研究中的应用, 生态学杂志, 2007, 26(1):115-120. http://www.cnki.com.cn/Article/CJFDTOTAL-STXZ200701023.htm [16] Morin X, Viner D, Chuine I.Tree species range shifts at a continental scale:New predictive insights from a process-based model.J Ecol, 2008, 96:784-794. doi: 10.1111/jec.2008.96.issue-4 [17] Spieksma F T M, Emberlin J C, Hjelmroos M, et al.Atmospheric birch (Betula) pollen in Europe:Trends and fluctuations in annual quantities and the starting dates of the seasons.Grana, 1995, 34(1):51-57. doi: 10.1080/00173139509429033 [18] 戴君虎, 王焕炯, 葛全胜.近50年中国温带季风区植物花期霜冻风险变化.地理学报, 2013, 68(5):593-601. doi: 10.7605/gdlxb.2013.05.047 [19] 李荣平, 周广胜, 王笑影, 等.不同物候模型对东北地区作物发育期模拟对比分析.气象与环境学报, 2012, 28(3):25-30. http://www.cnki.com.cn/Article/CJFDTOTAL-LNQX201203005.htm [20] 张谷丰, 孙雪梅, 张志春, 等.物候模型预测稻纵卷叶螟发生期的应用研究.福建农业学报, 2013, 28(2):148-153. http://www.cnki.com.cn/Article/CJFDTOTAL-FJNX201302013.htm [21] 张福春.北京春季的树木物候与气象因子的统计学分析.地理研究, 1983, 2(2):55-64. http://www.cnki.com.cn/Article/CJFDTOTAL-DLYJ198302005.htm [22] 郝日明, 张璐, 张明娟, 等.影响南京桂花秋季开花期变化的关键气候因子研究.植物资源与环境学报, 2006, 15(3):31-34. http://www.cnki.com.cn/Article/CJFDTOTAL-ZWZY200603006.htm [23] 刘流, 甘一忠.桃花迟早年型的冬季气候特点及花期预测.气象, 2006, 32(1):113-116. doi: 10.11898/1001-7313.20060119 [24] 韩亚东, 于长文, 刘雪峰.京桃春季物候期与气温之间的关系.安徽农业科学, 2007, 35(15):4517-4518. doi: 10.3969/j.issn.0517-6611.2007.15.057 [25] 陈正洪, 肖玫, 陈璇.樱花花期变化特征及其与冬季气温变化的关系.生态学报, 2008, 28(11):5209-5217. doi: 10.3321/j.issn:1000-0933.2008.11.002 [26] 张菲, 邢小霞, 李仁杰.利用地温构建菏泽牡丹花期预测模型.中国农业气象, 2008, 29(1):87-89. http://www.cnki.com.cn/Article/CJFDTOTAL-ZGNY200801020.htm [27] 祝廷成, 钟章成, 李建东.植物生态学.北京:高等教育出版社, 1988. http://www.cnki.com.cn/Article/CJFDTOTAL-SYQY201603027.htm [28] 徐琳, 陈效逑, 杜星.中国东部暖温带刺槐花期空间格局的模拟与预测.生态学报, 2013, 33(12):3584-3593. http://www.cnki.com.cn/Article/CJFDTOTAL-STXB201312004.htm [29] 韩小梅, 申双和.物候模型研究进展.生态学杂志, 2008, 27(1):89-95. http://www.cnki.com.cn/Article/CJFDTOTAL-STXZ200801015.htm [30] 谭美, 王四清.观赏植物生长模拟模型研究进展.园艺学报, 2010, 37(9):1523-1530. http://www.cnki.com.cn/Article/CJFDTOTAL-YYXB201009027.htm [31] 赵平, 南素兰.气候和气候变化领域的研究进展.应用气象学报, 2006, 17(6):725-735. doi: 10.11898/1001-7313.20060610 [32] 任玉玉, 任国玉, 周江兴.我国大陆大尺度气候观测网的理想密度和分布.应用气象学报, 2012, 23(2):205-213. doi: 10.11898/1001-7313.20120209 [33] 曲绍厚, 宋锡铭, 李玉英.北京城区的气象效应.地球物理学报, 1981, 24(2):229-237. http://www.cnki.com.cn/Article/CJFDTOTAL-DQWX198102011.htm [34] 郑祚芳, 刘伟东, 王迎春.北京地区城市热岛时空分布特征.南京气象学院学报, 2006, 29(5):694-699. http://cpfd.cnki.com.cn/Article/CPFDTOTAL-ZGQX200510001629.htm [35] 张光智, 徐祥德, 王继志, 等.北京及周边地区城市尺度热岛特征及其演变.应用气象学报, 2002, 13(特刊):43-50. http://www.cnki.com.cn/Article/CJFDTOTAL-YYQX2002S1004.htm [36] 杨玉华, 徐祥德, 翁永辉.北京城市边界层热岛的日变化周期模拟.应用气象学报, 2003, 14(1):61-68. http://qikan.camscma.cn/jams/ch/reader/view_abstract.aspx?file_no=20030107&flag=1 [37] 郑祚芳, 范水勇, 王迎春.城市热岛效应对北京夏季高温的影响.应用气象学报, 2006, 17(增刊Ⅰ):48-53. http://www.cnki.com.cn/Article/CJFDTOTAL-YYQX2006S1006.htm [38] 石英, 高学杰, 吴佳.华北地区未来气候变化的高分辨率数值模拟.应用气象学报, 2010, 21(5):580-589. doi: 10.11898/1001-7313.20100507 -
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