Identifying And Predicting Impacts Of Climate Conditions Of The Indochina Peninsula On Catastrophic Immigration Of Nilaparvata Lugens (St(?)l) In South China

Brown plant hopper(BPH),Nilaparvata lugens(St(?)l),is a kind of long-distance migratory rice pest,its natal origin distributes in the Indochina Peninsula.The main rice producing areas in China are located in the south of Qinling Mountains and Huaihe River Valley.In order to make the accurate early warning for the BPH's catastrophic immigrations come from the Indochina Peninsula and prevent from their damage effectively,it is necessary to a deep and systematic research on the dynamics of BPH's population and the climatic conditions in the overseas natal origin areas.In this thesis,the catch data on the BPH's lighting traps at plant protection stations in China from 1980 to 2016,to identify correlations between the occurrence grades of BPH and the meteorological factors affecting the immigration heads of BPH in the south of China and the key predicting factors were screened.With the same time,CMIP5 models combined with the Taylor diagrams were used to test the performance of their simulation on the meteorological factors influenced on BPH's immigration such as temperature,relative humidity,precipitation and wind speed in the Indochina Peninsula.The optimal models which simulate the above meteorological factors and the output data from these optimal models under the two emission scenarios(RCP4.5,RCP8.5)of greenhouse gas were used to analyze the correlation between these meteorological factors.Consequently,regression analysis,BP neural network,and SVM model were used to forecast the total annual immigration of the BPH in the representative stations and their advantages and disadvantages were compared.The results were as follows:(1)Most of the abnormal climate occurrence areas in the Indochina Peninsula were distributed in the north part.The occurrence frequency of abnormal climate in the north part was higher than the frequency of the south part,with the characteristics of the frequencies progressively descending from north to south in an annular pattern.(2)If the ground temperature of the Indochina Peninsula was higher than the average temperature and the relative humidity was greater than the average relative humidity,it brought about partially heavy or heavy occurrences of BPH immigration in south China.However,if the ground temperature of the Indochina Peninsula was lower than the average temperature and the relative humidity was less than the average relative humidity,it brought about partially light or light occurrences of BPH immigration in south China.(3)By comparing the correct rates of back substitution and prediction accuracy of all three models,we found that all three models had certain capabilities of predicting the occurrence grades of BPH in south China.The predicting capability of the SVM model was the best,the BP neural network was the second best,and the multiple linear regression model was the worst,indicating that the SVM model was more suitable for predicting the occurrence of BPH in rice production.(4)The 8 CMIP5 models in this study have a certain ability to simulate the historical climate characteristics of Southeast Asia.The temperature simulation results of BNU-CSM1-1 model developed by China was the best,the humidity effect simulation of CESM1-CAM5 model was the best by United States;the rainfall effect simulation of HadGEM2-AO model was the best by Korea;the zonal wind simulation effect of BCC-CSM1-1 model was the best by China;the meridional wind spring simulation effect of the HadGEM2-AO model was the best by South Korea,and the winter simulation effect of BCC-CSM1-1 model was the best by China.Moreover,the simulation effect of the southeast coast of Indochina Peninsula was better than that of northern area,then the simulation ability of wind speed and temperature was better than precipitation and relative humidity,and the winter simulation was better than spring.(5)By comparing the correct rates of back substitution,prediction accuracy and stability of all three models under RCP climate scenarios,we found that all three models had certain capabilities of predicting the occurrence grades of BPH in south China.The predicting capability of the SVM model was the best,the BP neural network was the second best,and the multiple linear regression model was the worst,and the prediction effect under RCP4.5 was better than RCP8.5.This indicated that the SVM model was most suitable for predicting the occurrence of BPH in rice production under RCP4.5 climate scenarios.(6)Through the comparative analysis of southern China from 2017 to 2027 of BPH forecast occurrence grades results and risk zoning prediction map,it was found that the occurrence degrees of BPH in Guangxi,Jiangxi and other regions should be serious in the future period of 11 years and some corresponding defense measures should be made according to its planting systems and geographical environments.