Investigating statistical bias correction with temporal subsample of the upper Ping River Basin, Thailand

This study aims to investigate different statistical bias correction techniques to improve the output of a regional climate model (RCM) of daily rainfall for the upper Ping River Basin in Northern Thailand. Three subsamples are used for each bias correction method, which are (1) using full calibrate...

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Autor principal:	Srisunee Wuthiwongtyohtin
Formato:	article
Lenguaje:	EN
Publicado:	IWA Publishing 2021
Materias:	daily rainfall regional climate model statistical bias correction subsample upper ping river basin Environmental technology. Sanitary engineering TD1-1066 Environmental sciences GE1-350
Acceso en línea:	https://doaj.org/article/de993e80e1094277a8c6afd400c19ac2
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spelling	oai:doaj.org-article:de993e80e1094277a8c6afd400c19ac22021-11-05T19:01:42ZInvestigating statistical bias correction with temporal subsample of the upper Ping River Basin, Thailand2040-22442408-935410.2166/wcc.2020.021https://doaj.org/article/de993e80e1094277a8c6afd400c19ac22021-08-01T00:00:00Zhttp://jwcc.iwaponline.com/content/12/5/1631https://doaj.org/toc/2040-2244https://doaj.org/toc/2408-9354This study aims to investigate different statistical bias correction techniques to improve the output of a regional climate model (RCM) of daily rainfall for the upper Ping River Basin in Northern Thailand. Three subsamples are used for each bias correction method, which are (1) using full calibrated 30-year-period data, (2) seasonal subsampling, and (3) monthly subsampling. The bias correction techniques are classified into three groups, which are (1) distribution-derived transformation, (2) parametric transformation, and (3) nonparametric transformation. Eleven bias correction techniques with three different subsamples are used to derive transfer function parameters to adjust model bias error. Generally, appropriate bias correction methods with optimal subsampling are locally dependent and need to be defined specifically for a study area. The study results show that monthly subsampling would be well established by capturing the monthly mean variation after correcting the model's daily rainfall. The results also give the best-fitted parameter set of the different subsamples. However, applying the full calibrated data and the seasonal subsamples cannot substantially improve internal variability. Thus, the effect of internal climate variability of the study region is greater than the choice of bias correction methods. Of the bias correction approaches, nonparametric transformation performed best in correcting daily rainfall bias error in this study area as evaluated by statistics and frequency distributions. Therefore, using a combination of methods between the nonparametric transformation and monthly subsampling offered the best accuracy and robustness. However, the nonparametric transformation was quite sensitive to the calibration time period. HIGHLIGHTS Coupling the statistical bias correction and optimal subsample is a new attempt to experiment for this tropical climate study area.; Appropriate bias correction methods are locally dependent.; Comparing climate models' results before and after bias correction.; Effect of subsamples (time window) in statistical bias correction daily rainfall.;Srisunee WuthiwongtyohtinIWA Publishingarticledaily rainfallregional climate modelstatistical bias correctionsubsampleupper ping river basinEnvironmental technology. Sanitary engineeringTD1-1066Environmental sciencesGE1-350ENJournal of Water and Climate Change, Vol 12, Iss 5, Pp 1631-1653 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	daily rainfall regional climate model statistical bias correction subsample upper ping river basin Environmental technology. Sanitary engineering TD1-1066 Environmental sciences GE1-350
spellingShingle	daily rainfall regional climate model statistical bias correction subsample upper ping river basin Environmental technology. Sanitary engineering TD1-1066 Environmental sciences GE1-350 Srisunee Wuthiwongtyohtin Investigating statistical bias correction with temporal subsample of the upper Ping River Basin, Thailand
description	This study aims to investigate different statistical bias correction techniques to improve the output of a regional climate model (RCM) of daily rainfall for the upper Ping River Basin in Northern Thailand. Three subsamples are used for each bias correction method, which are (1) using full calibrated 30-year-period data, (2) seasonal subsampling, and (3) monthly subsampling. The bias correction techniques are classified into three groups, which are (1) distribution-derived transformation, (2) parametric transformation, and (3) nonparametric transformation. Eleven bias correction techniques with three different subsamples are used to derive transfer function parameters to adjust model bias error. Generally, appropriate bias correction methods with optimal subsampling are locally dependent and need to be defined specifically for a study area. The study results show that monthly subsampling would be well established by capturing the monthly mean variation after correcting the model's daily rainfall. The results also give the best-fitted parameter set of the different subsamples. However, applying the full calibrated data and the seasonal subsamples cannot substantially improve internal variability. Thus, the effect of internal climate variability of the study region is greater than the choice of bias correction methods. Of the bias correction approaches, nonparametric transformation performed best in correcting daily rainfall bias error in this study area as evaluated by statistics and frequency distributions. Therefore, using a combination of methods between the nonparametric transformation and monthly subsampling offered the best accuracy and robustness. However, the nonparametric transformation was quite sensitive to the calibration time period. HIGHLIGHTS Coupling the statistical bias correction and optimal subsample is a new attempt to experiment for this tropical climate study area.; Appropriate bias correction methods are locally dependent.; Comparing climate models' results before and after bias correction.; Effect of subsamples (time window) in statistical bias correction daily rainfall.;
format	article
author	Srisunee Wuthiwongtyohtin
author_facet	Srisunee Wuthiwongtyohtin
author_sort	Srisunee Wuthiwongtyohtin
title	Investigating statistical bias correction with temporal subsample of the upper Ping River Basin, Thailand
title_short	Investigating statistical bias correction with temporal subsample of the upper Ping River Basin, Thailand
title_full	Investigating statistical bias correction with temporal subsample of the upper Ping River Basin, Thailand
title_fullStr	Investigating statistical bias correction with temporal subsample of the upper Ping River Basin, Thailand
title_full_unstemmed	Investigating statistical bias correction with temporal subsample of the upper Ping River Basin, Thailand
title_sort	investigating statistical bias correction with temporal subsample of the upper ping river basin, thailand
publisher	IWA Publishing
publishDate	2021
url	https://doaj.org/article/de993e80e1094277a8c6afd400c19ac2
work_keys_str_mv	AT srisuneewuthiwongtyohtin investigatingstatisticalbiascorrectionwithtemporalsubsampleoftheupperpingriverbasinthailand
_version_	1718444072875589632

Investigating statistical bias correction with temporal subsample of the upper Ping River Basin, Thailand

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