Analysis of maximum precipitation in Thailand using non-stationary extreme value models

被引：7

作者：

Prahadchai, Thanawan ^{[1
]}

Shin, Yonggwan ^{[1
,3
]}

Busababodhin, Piyapatr ^{[2
]}

Park, Jeong-Soo ^{[1
]}

机构：

[1] Chonnam Natl Univ, Dept Math & Stat, Gwangju, South Korea

[2] Mahasarakham Univ, Dept Math, Maha Sarakham, Thailand

[3] Chonnam Natl Univ, Dept Math & Stat, Gwangju 61186, South Korea

来源：

ATMOSPHERIC SCIENCE LETTERS | 2023年 / 24卷 / 04期

基金：

新加坡国家研究基金会;

关键词：

heavy rainfall; Mann-Kendall test; maximum likelihood estimation; model diagnostics; parametric bootstrap; tropical cyclone; TEMPERATURE; RAINFALL;

D O I：

10.1002/asl.1145

中图分类号：

P3 [地球物理学]; P59 [地球化学];

学科分类号：

0708 ; 070902 ;

摘要：

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.

引用

页数：11

共 27 条

[11]

IPCC, 2022, Compassionate Reasoning: Changing the Mind to Change the World

[12] Robust estimation for longitudinal data based upon minimum Hellinger distance [J].