中国农业气象 ›› 2018, Vol. 39 ›› Issue (03): 177-184.doi: 10.3969/j.issn.1000-6362.2018.03.005

• 论文 • 上一篇    下一篇

基于灰色模型的黑龙江省水稻生育期热量指数分析及预测

王秋京,马国忠,王晾晾,朱海霞,杜春英,姜丽霞   

  1. 1.中国气象局东北地区生态气象创新开放实验室/黑龙江省气象院士工作站/黑龙江省气象科学研究所,哈尔滨 150030; 2.黑龙江省气象台,哈尔滨 150030
  • 收稿日期:2017-06-16 出版日期:2018-03-20 发布日期:2018-03-23
  • 作者简介:王秋京(1979-),女,回族,硕士,高级工程师,主要从事应用气象研究。E-mail:shijianfeila@126.com
  • 基金资助:
    国家自然科学基金项目(31671575)

Prediction on Heat Index of Rice in Heilongjiang Province Based on Grey Model

WANG Qiu-jing, MA Guo-zhong, WANG Liang-liang, ZHU Hai-xia, DU Chun-ying, JIANG Li-xia   

  1. 1. Innovation and Opening Laboratory of Regional Eco-Meteorology in Northeast, China Meteorological Administration/Meteorological Academician Workstation of Heilongjiang Province/Heilongjiang Institute of Meteorological Sciences, Harbin 150030, China; 2. Heilongjiang Meteorological Observatory, Harbin 150030
  • Received:2017-06-16 Online:2018-03-20 Published:2018-03-23

摘要: 选择黑龙江省11个水稻农气观测站点为研究对象,利用1971-2016年逐旬气温资料和水稻发育期资料,将黑龙江省划分为东、西、南三个区域,使用微分方程动态建模方法建立3个区域5、6、7、8月热量指数的灰色预测模型,在此基础上滚动预报水稻生育期的总热量指数,以期开展黑龙江省水稻低温冷害的预测服务。结果表明热量指数能够很好地反映水稻生育期热量条件,且与低温冷害年有很好的对应关系。黑龙江省不同水稻产区热量指数灰色模型模拟结果与原序列关联度均达到0.88以上,通过了关联度检验和残差检验,1971-2010年拟合平均准确率为94.6%~97.6%,且7、8月的预报准确率普遍高于5、6月;2011-2016年的试报准确率均在97%以上,说明各模型的模拟效果很好。利用灰色模型预测黑龙江省水稻生长季热量指数是可行的,可以满足水稻生长发育过程中延迟性冷害的实时评估需求。

关键词: GM(1, 1)模型, 低温冷害, 水稻, 热量指数

Abstract: Based on eleven agro-meteorological observation stations, Heilongjiang province was divided into three regions, namely east region, west region and south region, by using the data of temperature and rice development from 1971 to 2016. The GM (1, 1) forecasting model for the Heat Index was established from May to August for rice in every region with differential equation dynamic modeling. Then the Heat Index was dynamically forecasted, and chilling damage was monitored during growing season of rice. The results showed that the model assessed well the Heat Index during growing season of rice, and the index had corresponded well with chilling damage year of rice. The association degrees were more than 0.88 between simulation results and the original data, and they passed the association degree test and residual tests. The average regression calculating accuracies of these models were 94.6% to 97.6% from 1971 to 2010, and the monthly forecast effects for July and August were generally better than those for May and June in each region. The average forecast accuracy was above 97% from 2011 to 2016. The results indicated that these models had better simulated effect. The GM (1, 1) was feasible to forecast the Heat Index of Rice during growing season, and to achieve dynamic assessment for chilling damage of rice during growing season.

Key words: GM (1, 1) forecasting model, Chilling damage, Rice, The Heat Index