Analysis of maximum precipitation in Thailand using non‐stationary extreme value models
Abstract Non‐stationarity in heavy rainfall time series is often apparent in the form of trends because of long‐term climate changes. We have built non‐stationary (NS) models for annual maximum daily (AMP1) and 2‐day precipitation (AMP2) data observed between 1984 and 2020 years by 71 stations and b...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2023-04-01
|
| Series: | Atmospheric Science Letters |
| Subjects: | |
| Online Access: | https://doi.org/10.1002/asl.1145 |
| _version_ | 1797854104448401408 |
|---|---|
| author | Thanawan Prahadchai Yonggwan Shin Piyapatr Busababodhin Jeong‐Soo Park |
| author_facet | Thanawan Prahadchai Yonggwan Shin Piyapatr Busababodhin Jeong‐Soo Park |
| author_sort | Thanawan Prahadchai |
| collection | DOAJ |
| description | Abstract Non‐stationarity in heavy rainfall time series is often apparent in the form of trends because of long‐term climate changes. We have built non‐stationary (NS) models for annual maximum daily (AMP1) and 2‐day precipitation (AMP2) data observed between 1984 and 2020 years by 71 stations and between 1960 and 2020 by eight stations over Thailand. The generalized extreme value (GEV) models are used. Totally, 16 time‐dependent functions of the location and scale parameters of the GEV model are considered. On each station, a model is selected by using Bayesian and Akaike information criteria among these candidates. The return levels corresponding to some years are calculated and predicted for the future. The stations with the highest return levels are Trad, Samui, and Narathiwat, for both AMP1 and AMP2 data. We found some evidence of increasing (decreasing) trends in maximum precipitation for 22 (10) stations in Thailand, based on NS GEV models. |
| first_indexed | 2024-04-09T20:01:37Z |
| format | Article |
| id | doaj.art-1a6941c14cb641419b7e5ea9093b657b |
| institution | Directory Open Access Journal |
| issn | 1530-261X |
| language | English |
| last_indexed | 2024-04-09T20:01:37Z |
| publishDate | 2023-04-01 |
| publisher | Wiley |
| record_format | Article |
| series | Atmospheric Science Letters |
| spelling | doaj.art-1a6941c14cb641419b7e5ea9093b657b2023-04-03T02:06:07ZengWileyAtmospheric Science Letters1530-261X2023-04-01244n/an/a10.1002/asl.1145Analysis of maximum precipitation in Thailand using non‐stationary extreme value modelsThanawan Prahadchai0Yonggwan Shin1Piyapatr Busababodhin2Jeong‐Soo Park3Department of Mathematics and Statistics Chonnam National University Gwangju South KoreaDepartment of Mathematics and Statistics Chonnam National University Gwangju South KoreaDepartment of Mathematics Mahasarakham University Mahasarakham ThailandDepartment of Mathematics and Statistics Chonnam National University Gwangju South KoreaAbstract Non‐stationarity in heavy rainfall time series is often apparent in the form of trends because of long‐term climate changes. We have built non‐stationary (NS) models for annual maximum daily (AMP1) and 2‐day precipitation (AMP2) data observed between 1984 and 2020 years by 71 stations and between 1960 and 2020 by eight stations over Thailand. The generalized extreme value (GEV) models are used. Totally, 16 time‐dependent functions of the location and scale parameters of the GEV model are considered. On each station, a model is selected by using Bayesian and Akaike information criteria among these candidates. The return levels corresponding to some years are calculated and predicted for the future. The stations with the highest return levels are Trad, Samui, and Narathiwat, for both AMP1 and AMP2 data. We found some evidence of increasing (decreasing) trends in maximum precipitation for 22 (10) stations in Thailand, based on NS GEV models.https://doi.org/10.1002/asl.1145heavy rainfallMann–Kendall testmaximum likelihood estimationmodel diagnosticsparametric bootstraptropical cyclone |
| spellingShingle | Thanawan Prahadchai Yonggwan Shin Piyapatr Busababodhin Jeong‐Soo Park Analysis of maximum precipitation in Thailand using non‐stationary extreme value models Atmospheric Science Letters heavy rainfall Mann–Kendall test maximum likelihood estimation model diagnostics parametric bootstrap tropical cyclone |
| title | Analysis of maximum precipitation in Thailand using non‐stationary extreme value models |
| title_full | Analysis of maximum precipitation in Thailand using non‐stationary extreme value models |
| title_fullStr | Analysis of maximum precipitation in Thailand using non‐stationary extreme value models |
| title_full_unstemmed | Analysis of maximum precipitation in Thailand using non‐stationary extreme value models |
| title_short | Analysis of maximum precipitation in Thailand using non‐stationary extreme value models |
| title_sort | analysis of maximum precipitation in thailand using non stationary extreme value models |
| topic | heavy rainfall Mann–Kendall test maximum likelihood estimation model diagnostics parametric bootstrap tropical cyclone |
| url | https://doi.org/10.1002/asl.1145 |
| work_keys_str_mv | AT thanawanprahadchai analysisofmaximumprecipitationinthailandusingnonstationaryextremevaluemodels AT yonggwanshin analysisofmaximumprecipitationinthailandusingnonstationaryextremevaluemodels AT piyapatrbusababodhin analysisofmaximumprecipitationinthailandusingnonstationaryextremevaluemodels AT jeongsoopark analysisofmaximumprecipitationinthailandusingnonstationaryextremevaluemodels |