Spatial Modeling of Extreme Temperature in Northeast Thailand

The objective of the present study was to examine and predict annual maximum temperature in northeast Thailand by using data from 25 stations employing spatial extreme modeling which is based on max-stable process (MSP) schlatter’s method. We analyzed MSP latitude, longitude, altitude variables. Our result showed that has an increasing trend. return level estimates areas both local generalized value (GEV) model models show Nong Khai, Maha Sarakham, Khon Kaen had higher levels than other for every period, whereas Pak Chong Agromet lowest levels. Furthermore, results more suitable point-wise GEV distribution. realize provides precise robust as well some indices temperatures observation locations with no observed data. this are consistent those previous studies. trend could affect agriculture surrounding environment Thailand. Spatial can be beneficial impact management vulnerability assessment under event scenarios caused climate change.