Forecasting Models Of WBPH’s Immigration Based On The Backgrounds Of Atmospheric Circulation

In recent30years, white back planthopper (WBPH), Sogatella furcifera (Horvath) has been one of great pests endangering rice growth in China, along with the increase in its occurrence areas and immigration frequencies. The rice-growing region of the middle and lower reaches of the Yangtze River is one of the serious regions where WBPH endangered. The main factors affecting WBPH’s migration and occurrence grades are biological factors and physics factors, including the rice growth conditions, cropping systems, distribution of insect resources, meteorological conditions geographical environment and so on. Among them, the atmospheric circulation backgrounds and the atmospheric dynamic fields influence WBPH’s taking-off, flight in the air and landfalling directly.In order to reveal the influence of the atmospheric circulation on the immigration of WBPH, in this paper, the WBPH’s lighting catches of125plant protection stations in China during1979to2010was collected to investigate the temporal and spatial pattern of WBPH’s migrations, a typical case of migration northward and a typical migration southward case were selected respectively and their landfalling characteristics were discussed. The reanalyzed meteorological grid data from NCEP (National Center for Environment Predicting in USA) during1979to2011and the pentad lighting catches of WBPH in the rice-growing regions of the middle and lower reaches of the Yangtze River in the same period were collected to discuss the influence of the atmospheric circulation characteristic variables on the immigrations of WBPH. After the analysis of the correlations between the immigration amount of WBPH and the main atmospheric circulation characteristic variables, two kinds of methods, the Back Propagation Neural Network and the Support Vector Machines, were used to establish the short-term forecast models of WBPH’s immigration occurrence grades during the beginning periods of immigration, the peak periods of migration northward, the peak periods of migration southward and the ending periods of immigration for13plant protection stations in the middle and lower reaches of the Yangtze River. The results showed as follows:(1) In China, the immigrations of WBPH’s populations occur in the beginning of March to the end of November of a year. The migration processes of WBPH in March and November are the rarest and the great migration events in July is the most. The immigration peaks and landfalling heads of WBPH’s migration northward were more than of the migration southward. In the rice-growing region of the middle and lower reaches of the Yangtze River, the immigration peaks of WBPH’s migration northward were more than the migration southward. WBPH’s catastrophic immigrations happened in the periods of its migration northward more frequently than the migration southward. (2) The atmospheric general circulation on850hPa isobaric surface was one of important factors controlled and influenced the immigrations of WBPH’s populations. On this level, there were coincident air streams carrying the populations with a northern exposure during the migrations northward of WBPH and there were concurrent airflows carrying them with a southern exposure during the migration southward. The regions with cyclonic shears of wind directions were advantageous to the taking-off and emigrating of the pest and the regions with anticyclonic shears of wind directions were favorable to the immigrating and landfalling on850hPa. The strong subsiding airflow on the vertical velocity field could compel WBPH’s populations to landfall cosmically and the strong ascending air could promote their taking-off.(3) There were most significant correlations between the immigration amount of WBPH and the area index of the western Pacific Subtropical High (IA), the western ridge point index of the western Pacific Subtropical High (Iw), the westerly strength index (IEARW), the East Asia major trough index (H500) on the isobaric surface of500hPa in the preceding pentad respectively. Among these correlations, the correlation between the immigration amount of WBPH and IEARWw was negative and the correlations between the immigration amount of WBPH and IA, IW and H50were positive. The correlation coefficient between the immigration amount of WBPH and Iw was0.397as the least value in all of the correlation coefficients but the correlation coefficients between it and IA, IEARW and H500were larger than0.78. There were significant positive correlations between the immigration amount of WBPH and the geopotential height (hgt), vertical velocity (omega), zonal wind speed (uwnd) and meridional wind speed (vwnd) on the isobaric surface of850hPa in the preceding pentad respectively. Among these correlations, the correlation coefficient between WBPH’s immigration amount and hgt was0.354as the least value but the correlation coefficients between it and omega, uwnd and vwnd were larger than0.8.(4) The atmospheric circulation characteristic variables of significant correlation between them and the immigration amount of WBPH were selected as the predictors. After the WBPH’s immigration amounts were divided into5occurrence grades,4forecasting models of WBPH’s occurrence grades during the beginning periods of immigration, the peak periods of migration northward, the peak periods of migration southward and the ending periods of immigration based on the method of Back Propagation Neural Network were established and the extension examination accuracy rates of these models stabled above70%. Therefore, these models were proved to be available for the short-term forecast of WBPH pendant occurrence grades in the middle and lower reaches of the Yangtze River.(5)With the same predictors as the above method of Back Propagation Neural Network,4forecasting models of WBPH’s occurrence grades during the beginning periods of immigration, the peak periods of migration northward, the peak periods of migration southward and the ending periods of immigration based on the method of the Support Vector Machines were established and the extension examination accuracy rates of these models stabled above80%. This method was more accurate and stable than the method of Back Propagation Neural Network.All of the above results were significant to reveal the impact of meteorological factors on WBPH’s immigrations.early warn and predict accurately its immigrations, and to prevent and control effectively its endangering in the practice.