Development of statistical models for maximum daily rainfall in upper northern region of Thailand

© 2015, Chiang Mai University. All rights reserved. The upper northern region of Thailand is the originated place of all the major tributaries of the Chao Phraya River in central Thailand. Hence, the extreme precipitation event occurred on many days continuously in upper northern part was directly f...

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Main Authors: Manad Khamkong, Putipong Bookkamana
Format: Journal
Published: 2018
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http://cmuir.cmu.ac.th/jspui/handle/6653943832/54168
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spelling th-cmuir.6653943832-541682018-09-04T10:26:14Z Development of statistical models for maximum daily rainfall in upper northern region of Thailand Manad Khamkong Putipong Bookkamana Biochemistry, Genetics and Molecular Biology Chemistry Materials Science Mathematics Physics and Astronomy © 2015, Chiang Mai University. All rights reserved. The upper northern region of Thailand is the originated place of all the major tributaries of the Chao Phraya River in central Thailand. Hence, the extreme precipitation event occurred on many days continuously in upper northern part was directly found to be the cause of widespread flooding in several areas especially in the central parts. This paper focuses on the statistical modeling development of the annual maxima of daily (AMR1) and 2-day (AMR2) rainfall data in upper north Thailand based on a generalized extreme value (GEV) distribution. AMR1 and AMR2 for the years from 1957 to 2012 in the rainy season were modelled for sixteen locations in the upper northern region. The study found that only the Pua Nan location was being a GEV model in which the location parameter changes depending on quadratic trend while the others were being stationary GEV models from AMR1. For AMR2 only the Ngao Lampang location was being a GEV model in which the location parameter changes depending on linear trend while the others were being stationary GEV. The 95% confidence interval estimations of daily rainfalls return level for various return periods of the Nan River and Yom River were higher than that of other locations. Information obtained from our study is very useful for agencies involved in water management in terms of strategic planning and also scarce rainfalls prevention related to public safety, among others. 2018-09-04T10:08:53Z 2018-09-04T10:08:53Z 2015-01-01 Journal 01252526 2-s2.0-84946569202 https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84946569202&origin=inward http://cmuir.cmu.ac.th/jspui/handle/6653943832/54168
institution Chiang Mai University
building Chiang Mai University Library
country Thailand
collection CMU Intellectual Repository
topic Biochemistry, Genetics and Molecular Biology
Chemistry
Materials Science
Mathematics
Physics and Astronomy
spellingShingle Biochemistry, Genetics and Molecular Biology
Chemistry
Materials Science
Mathematics
Physics and Astronomy
Manad Khamkong
Putipong Bookkamana
Development of statistical models for maximum daily rainfall in upper northern region of Thailand
description © 2015, Chiang Mai University. All rights reserved. The upper northern region of Thailand is the originated place of all the major tributaries of the Chao Phraya River in central Thailand. Hence, the extreme precipitation event occurred on many days continuously in upper northern part was directly found to be the cause of widespread flooding in several areas especially in the central parts. This paper focuses on the statistical modeling development of the annual maxima of daily (AMR1) and 2-day (AMR2) rainfall data in upper north Thailand based on a generalized extreme value (GEV) distribution. AMR1 and AMR2 for the years from 1957 to 2012 in the rainy season were modelled for sixteen locations in the upper northern region. The study found that only the Pua Nan location was being a GEV model in which the location parameter changes depending on quadratic trend while the others were being stationary GEV models from AMR1. For AMR2 only the Ngao Lampang location was being a GEV model in which the location parameter changes depending on linear trend while the others were being stationary GEV. The 95% confidence interval estimations of daily rainfalls return level for various return periods of the Nan River and Yom River were higher than that of other locations. Information obtained from our study is very useful for agencies involved in water management in terms of strategic planning and also scarce rainfalls prevention related to public safety, among others.
format Journal
author Manad Khamkong
Putipong Bookkamana
author_facet Manad Khamkong
Putipong Bookkamana
author_sort Manad Khamkong
title Development of statistical models for maximum daily rainfall in upper northern region of Thailand
title_short Development of statistical models for maximum daily rainfall in upper northern region of Thailand
title_full Development of statistical models for maximum daily rainfall in upper northern region of Thailand
title_fullStr Development of statistical models for maximum daily rainfall in upper northern region of Thailand
title_full_unstemmed Development of statistical models for maximum daily rainfall in upper northern region of Thailand
title_sort development of statistical models for maximum daily rainfall in upper northern region of thailand
publishDate 2018
url https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84946569202&origin=inward
http://cmuir.cmu.ac.th/jspui/handle/6653943832/54168
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