In order to reveal temporal variations of autumn phenology of herbaceous plants and their climatic attributions, we analyzed changing trend of the common brown-down date of Taraxacum mongolicum and simulated interannual variation of the common brown-down date using plant phenological and meteorological data at 47 phenological stations in eastern China’s temperate zone from 1992 to 2012 and statistical methods. Results show that 1) the common brown-down date of Taraxacum mongolicum was delayed at 34 stations from 1992 to 2012, of which significant delaying trends were observed at 22 stations. By contrast, the common brown-down date of Taraxacum mongolicum advanced at 13 stations, of which significant advancing trends were detected at five stations. 2) The common brown-down date of Taraxacum mongolicum at individual stations correlates mainly negative with average temperature during the growing season (the period from leaf unfolding to common browndown) but positive with relative soil moisture and daily minimum temperature in autumn. 3) Among the 30 effective optimal models at individual stations (p<0.05), the common brown-down date of Taraxacum mongolicum was influenced by relative soil moisture at 22 stations, by daily minimum temperature at 19 stations and by average temperature during the growing season at 21 stations. Moreover, simulation accuracy of the model was significantly affected by interannual variation of the common brown-down date of Taraxacum mongolicum, namely, the smaller the interannual variation of the common brown-down date at a station, the higher the simulation accuracy of the model.

为了揭示草本植物秋季物候的时间变化及其气候归因, 利用中国东部温带47个站点1992—2012年的植物物候与气象数据和统计方法, 分析蒲公英黄枯普期的变化趋势, 并模拟黄枯普期的年际变化。结果表明: 1) 在1992—2012年期间, 34个站点的蒲公英黄枯普期呈推迟的趋势, 其中22个站点呈显著推迟的趋势, 相比之下, 13个站点的蒲公英黄枯普期呈提前的趋势, 其中5个站点呈显著提前的趋势; 2) 各站蒲公英黄枯普期与生长季(展叶始期到黄枯普期的期间)的平均温度主要呈负相关关系, 但与秋季土壤相对湿度和秋季日最低温度主要呈正相关关系; 3) 在 30 个单站有效最优模型(p<0.05)中, 蒲公英黄枯普期受秋季土壤相对湿度影响的站点有22个, 受秋季日最低温度影响的站点有19个, 受生长季平均温度影响的站点有21个。此外, 模型模拟的精度受到蒲公英黄枯普期年际波动的显著影响, 即在一个站点处的黄枯普期年际波动越小, 模型的模拟精度越高。