中国农业气象 ›› 2020, Vol. 41 ›› Issue (11): 695-706.doi: 10.3969/j.issn.1000-6362.2020.11.002

• 农业生物气象栏目 • 上一篇    下一篇

基于线性生长假设的作物积温模型稳定性比较

栾青,郭建平,马雅丽,张丽敏,王婧瑄,李伟伟   

  1. 1.山西省气候中心,太原 030006;2.中国气象科学研究院,北京 100081;3.南京信息工程大学气象灾害预警预报与评估协同创新中心,南京 210044;4.辽宁省葫芦岛市气象局,葫芦岛 125000;5.内蒙古自治区气象服务中心,呼和浩特 010051;6.山西省侯马市气象局,侯马 043000
  • 收稿日期:2020-07-06 出版日期:2020-11-20 发布日期:2020-11-12
  • 通讯作者: 郭建平,E-mail:gjp@cma.gov.cn E-mail:gjp@cma.gov.cn
  • 作者简介:栾青,E-mail:luanqing2003@163.com
  • 基金资助:
    国家自然科学基金(31571559);中国气象科学研究院科技发展基金(2019KJ006);公益性行业(气象)科研专项(GYHY201306038)

Comparison of Model’s Stability about Integrated Temperature Based on Linear Hypotheses

LUAN Qing, GUO Jian-ping, MA Ya-li, ZHANG Li-min, WANG Jing-xuan, LI Wei-wei   

  1. 1. Shanxi Climate Center, Taiyuan 030006, China; 2.Chinese Academy of Meteorological Sciences, Beijing 100081; 3.Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science and Technology, Nanjing 210044; 4.Huludao Meteorological Bureau, Huludao 125000; 5.Inner Mongolia Meteorological Service Center, Hohhot 01005l; 6. Houma Meteorological Bureau of Shanxi Province, Houma 043000
  • Received:2020-07-06 Online:2020-11-20 Published:2020-11-12

摘要: 利用山西省2个冬小麦观测站、3个春玉米观测站和3个夏玉米观测站长时间序列的作物生育期观测资料和地面气象观测资料,基于4种作物生长发育速率线性假设,建立了作物不同生育阶段的活动积温(Aa)和4种有效积温模型,并对各积温模型的稳定性进行统计分析与检验。结果表明:以变异系数为指标检验各模型稳定性时,活动积温模型最稳定,考虑作物三基点温度的有效积温模型(Ae4)次之,仅考虑作物下限温度的有效积温模型(Ae1)及考虑作物上、下限温度的有效积温模型(Ae2和Ae3)最不稳定。以生育期模拟偏差和生育期模拟准确率为指标检验各模型稳定性时,Aa模型对作物生育期的模拟效果最好,稳定性最高;4种有效积温模型中,Ae1、Ae2和Ae3模型模拟效果无显著差异,准确率和稳定性高于Ae4模型。各积温模型在春玉米和夏玉米出苗−抽雄期和抽雄−成熟期的稳定性表现一致,出苗−抽雄期各积温模型的稳定性高于抽雄−成熟期;冬小麦在出苗−抽穗期和抽穗−成熟期各积温模型的稳定性表现因地区不同而有所差异。因此,在实际应用中,还需根据作物种植区域、品种类型以及生育期选取合适的基点温度,综合分析多种积温模型稳定性,选取稳定性更高的积温模型。

关键词: 积温, 线性假设, 稳定性, 变异系数, 模拟准确率

Abstract: Integrated temperature, as a measure of heat, has been widely used in the prediction of crop development period, yield, diseases and insect pests. However, more and more studies showed that the integrated temperature is unstable, and the stability of different integrated temperature models is different. Therefore, it is great significant to analyze and understand the stability of different integrated temperature models for the application of integrated temperature in agricultural meteorological work. In this paper, four linear hypotheses about response of growth and development rate to temperature and five integrated temperature models were made. The first linear hypothesis is that the growth and development rate of crops increases linearly with the increase of temperature when the average daily temperature (T) is higher than the lower limit temperature (Tb). It is the hypothesis of the active integrated temperature model (Aa) and the first effective integrated temperature model (Ae1). The second hypothesis is that when the T is between the lower limit temperature (Tb) and the upper limit temperature (Tu) of crops, the growth and development rate of crops increases linearly with the increase of temperature and reaches the maximum (1.0); when the T exceeds Tu, the growth and development rate of crops remains constant with the increase of temperature. It is the hypothesis of the second effective integrated temperature model (Ae2). The third hypothesis is that when the T is between Tb and Tu, the growth and development rate of crops increases linearly with the increase of temperature and reaches the maximum (1.0); when the T exceeds Tu, the growth and development of crops stagnate. It is the hypothesis of the third effective integrated temperature model (Ae3). The fourth hypothesis is that when the T is between Tb and the optimum temperature (T0), the growth and development rate of crops increases linearly with the increase of temperature and reaches the maximum (1.0); when the T is between T0 and Tu, the growth and development rate of crops decreases linearly with the increase of temperature and decreases to 0.0; when the T exceeds Tu, the growth and development of crops stagnate. It is the hypothesis of the fourth effective integrated temperature model (Ae4). Based on these hypotheses, long time series of crop development period observation data and surface meteorological observation data of two winter wheat stations, three spring maize stations and three summer maize stations in Shanxi Province were selected to calculate the active integrated temperature and four effective integrated temperature. Using the coefficient of variation, the average simulation deviation of the crop growth period and the simulation accuracy of the crop growth period as indicators, the stability of the five integrated temperature models were evaluated. The result showed that the coefficient of variation (CV) of Aa model during different growth stages for three representative crops in each station were between 0.062 and 0.143; the CV of Ae1, Ae2 and Ae3 models were between 0.073 and 0.201; the CV of Ae4 model were between 0.072 and 0.179. That is, when using the CV as an indicator to test the stability of each model, the stability of Aa model was highest, that of the Ae4 model was the second and that of the Ae1, Ae2 and Ae3 models were the weakest. The average simulation deviation (SD) of Aa model for different growth periods of three representative crops in each station were between 1.3 and 5.8 days; the SD of Ae1, Ae2 and Ae3 models were between 1.5 and 6.6 days; the SD of Ae4 model were between 2.2 and 6.7 days. The simulation accuracy (SA) of Aa model for different growth periods of three representative crops in each station were between 39.5% and 92.3%; the SA of Ae1, Ae2 and Ae3 models were between 28.6% and 87.2%; the SA of Ae4 model were between 26.5% and 84.6%. That is, when using the SD and SA as the indicators, the Aa model had the best simulation effect for different growth periods of the crops and had the highest stability. The simulation accuracy and stability of Ae1, Ae2 and Ae3 models had no significant difference and were higher than those of Ae4 model. For spring maize and summer maize, the stability of each integrated temperature model was consistent from emergence to tasseling and from tasseling to maturity, and the stability of each integrated temperature model from emergence to tasseling was higher than that from tasseling to maturity. While the stability of each integrated temperature model for winter wheat from jointing to heading and from heading to maturity varied from region to region. Therefore, in practical applications, it is also necessary to select the appropriate base point temperature according to the crop planting region, variety relationship and growth period, and to select a more stable integrated temperature model based on the comprehensive analysis of the stability of multiple integrated temperature models.

Key words: Integrated temperature, Linear hypotheses, Stability, Coefficient of variation, Simulation accuracy of growth period