Study On The Medium And Long-term Forecasting Models Of BPH’s Immigration In Jiangsu Province

In recent30years, because of the global climate change, the immigration of BPH tended to be complicated and it brought serious threats to the rice production in China. The complicated weather and climate conditions not only had direct impacts on the evolving of rice and brown planthoppers (BPH), Nilapavata lugens (Stal), but also had the indirect influences on the life processes by altering BPH’s survival, breeding, immigration, feeding and infesting environment. In recent10years, the occurrence of BPH in Jiangsu provice became obvious heavier. Therefore it had significant theoretical values on improving the research levels of agricultural meteorological disasters in China by contructing the medium and long-term forecasting models of BPH’s immigration in Jiangsu Provience and it also had profound practical significance on improving our ability of agricultural meteorological services, forecasting the occurrence of disease and pest, preventing and reducing disasters, guaranteeing food security and the development of economy.Based on the data of BPH, the SST data on the Pacific Ocean, the temperature data covered the Indochina Peninsula, the conventional surface and upper-air meteorological data, in this paper, some correlation analysis and regression analysis were used to analyze the relationship between BPH’s immigration and SST field the Pacific Ocean, the temperature field on the Indochina Peninsula, the characteristic factors of atmospheric circulation, the climate variables in Jiangsu Province and the impact of global climate anomalies on the catastrophic immigrations of BPH in Jiangsu Province was discussed. Main impact factors corresponding to different periods which effect on the immigration of BPH were selected, and medium and long-term forecast models for the BPH’s immigration were established. The main research conclusions were as follows:(1) The occurrence degrees of BPH were inclined to Grade4or Grade5in the same year or the next year of El Nino event occured and the dates of BPH’s first immigration peaks lagged behind1to14months from the beginning dates of El Nino events.(2) The influence of rainfalling on the immigrations of BPH in Jiangsu Province was remarkable and confined. There were the more immigrations and the heavier occurrence degrees of BPH in the years of greater precipitation and more rainfall days. The precipitation, rainfall days and rainfall intensity of June to October in a year were used as the factors to establish the forecasting equations for the BPH’s immigration occurrence grades by a step-wise multiple linear regression analysis and the fitting effect of the equations was good. The extraordinary air temperature during Semtemper to October in Jiangsu Province had an obvious impact on the heavy occurrences (Grade4or Grade5) of BPH’s immigrations. In the years of high autumn temperature, the occurrence degrees of BPH were Grade4or Grade5.(3) The case analysis indicated that there existed a certain and consistent relative region between BPH’s immigration, SSTA of Pacific Ocean, the temperature field on Indo-China Peninsula, the circulation factors and climate factors in Gaoyou Station, Tongzhou Station and Yixing Station in Jiangsu Province. It had significant correlations between BPH’s immigrations of different periods and SSTA of Pacific Ocean, the temperature field on Indo-China Peninsula, the circulation factors and the climate factors.(4) The above significant SSTA, the temperature field of Indo-China Peninsula and the circulation factors were used as the key factors to establish forecasting models for the immigration of BHP in the three stations. In Gaoyou Station, a model using SSTA as a predicting factor and two models using the atmospheric circulation characteristic variables as predicting factors were established. In Tongzhou Station, three models using SSTA as predictors and one model using the temperature of Indo-China Peninsula as the predictors and five models using circulation variables as the predictors were established. In Yixing Station, a model using SSTA as the predictors and two models using the temperature of Indo-China Peninsula as the predictors and three models using circulation variables as the predictors were established. The historical fitting accordance were mostly above70%and the predictive precision were all above66.7%. These illustrated the the effect of the models were good and these models were feasible in the predicting practice of BPH’s immigration.