Spatial Modeling of Extreme Temperature in Northeast Thailand
The objective of the present study was to examine and predict the annual maximum temperature in the northeast of Thailand by using data from 25 stations and employing spatial extreme modeling which is based on max-stable process (MSP) using schlatter’s method. We analyzed extreme temperature data us...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-04-01
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| Series: | Atmosphere |
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| Online Access: | https://www.mdpi.com/2073-4433/13/4/589 |
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| author | Prapawan Senapeng Thanawan Prahadchai Pannarat Guayjarernpanishk Jeong-Soo Park Piyapatr Busababodhin |
| author_facet | Prapawan Senapeng Thanawan Prahadchai Pannarat Guayjarernpanishk Jeong-Soo Park Piyapatr Busababodhin |
| author_sort | Prapawan Senapeng |
| collection | DOAJ |
| description | The objective of the present study was to examine and predict the annual maximum temperature in the northeast of Thailand by using data from 25 stations and employing spatial extreme modeling which is based on max-stable process (MSP) using schlatter’s method. We analyzed extreme temperature data using the MSP using latitude, longitude, and altitude variables. Our result showed that the maximum temperature has an increasing trend. The return level estimates of the study areas from both the local generalized extreme value (GEV) model and MSP models show that the Nong Khai, Maha Sarakham, and Khon Kaen stations had higher return levels than the other stations for every return period, whereas Pak Chong Agromet had the lowest return levels. Furthermore, the results showed that MSP modeling is more suitable than point-wise GEV distribution. We realize that the spatial extreme modeling based on MSP provides more precise and robust return levels as well as some indices of the maximum temperatures for both the observation stations and the locations with no observed data. The results of this study are consistent with those of some previous studies. The increasing trend in return levels could affect agriculture and the surrounding environment in northeast Thailand. Spatial extreme modeling can be beneficial in the impact management and vulnerability assessment under extreme event scenarios caused by climate change. |
| first_indexed | 2024-03-09T11:09:40Z |
| format | Article |
| id | doaj.art-0175c25745ec475ab1583ca8435062b0 |
| institution | Directory Open Access Journal |
| issn | 2073-4433 |
| language | English |
| last_indexed | 2024-03-09T11:09:40Z |
| publishDate | 2022-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Atmosphere |
| spelling | doaj.art-0175c25745ec475ab1583ca8435062b02023-12-01T00:47:06ZengMDPI AGAtmosphere2073-44332022-04-0113458910.3390/atmos13040589Spatial Modeling of Extreme Temperature in Northeast ThailandPrapawan Senapeng0Thanawan Prahadchai1Pannarat Guayjarernpanishk2Jeong-Soo Park3Piyapatr Busababodhin4Digital Innovation Research Cluster for Integrated Disaster Management in the Watershed, Mahasarakham University, Maha Sarakham 44150, ThailandDepartment of Mathematics and Statistics, Chonnam National University, Gwangju 61186, KoreaFaculty of Interdisciplinary Studies, Nong Khai Campus, Khon Kaen University, Nong Khai 43000, ThailandDepartment of Mathematics and Statistics, Chonnam National University, Gwangju 61186, KoreaDigital Innovation Research Cluster for Integrated Disaster Management in the Watershed, Mahasarakham University, Maha Sarakham 44150, ThailandThe objective of the present study was to examine and predict the annual maximum temperature in the northeast of Thailand by using data from 25 stations and employing spatial extreme modeling which is based on max-stable process (MSP) using schlatter’s method. We analyzed extreme temperature data using the MSP using latitude, longitude, and altitude variables. Our result showed that the maximum temperature has an increasing trend. The return level estimates of the study areas from both the local generalized extreme value (GEV) model and MSP models show that the Nong Khai, Maha Sarakham, and Khon Kaen stations had higher return levels than the other stations for every return period, whereas Pak Chong Agromet had the lowest return levels. Furthermore, the results showed that MSP modeling is more suitable than point-wise GEV distribution. We realize that the spatial extreme modeling based on MSP provides more precise and robust return levels as well as some indices of the maximum temperatures for both the observation stations and the locations with no observed data. The results of this study are consistent with those of some previous studies. The increasing trend in return levels could affect agriculture and the surrounding environment in northeast Thailand. Spatial extreme modeling can be beneficial in the impact management and vulnerability assessment under extreme event scenarios caused by climate change.https://www.mdpi.com/2073-4433/13/4/589climate changegeneralized extreme value distributionspatial analysisreturn levelmax-stable process |
| spellingShingle | Prapawan Senapeng Thanawan Prahadchai Pannarat Guayjarernpanishk Jeong-Soo Park Piyapatr Busababodhin Spatial Modeling of Extreme Temperature in Northeast Thailand Atmosphere climate change generalized extreme value distribution spatial analysis return level max-stable process |
| title | Spatial Modeling of Extreme Temperature in Northeast Thailand |
| title_full | Spatial Modeling of Extreme Temperature in Northeast Thailand |
| title_fullStr | Spatial Modeling of Extreme Temperature in Northeast Thailand |
| title_full_unstemmed | Spatial Modeling of Extreme Temperature in Northeast Thailand |
| title_short | Spatial Modeling of Extreme Temperature in Northeast Thailand |
| title_sort | spatial modeling of extreme temperature in northeast thailand |
| topic | climate change generalized extreme value distribution spatial analysis return level max-stable process |
| url | https://www.mdpi.com/2073-4433/13/4/589 |
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