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Quantile Regression Based Methods for Investigating Rainfall Trends Associated with Flooding and Drought Conditions

Salam A. Abbas, Yunqing Xuan Orcid Logo, Xiaomeng Song

Water Resources Management, Volume: 33, Issue: 12, Pages: 4249 - 4264

Swansea University Author: Yunqing Xuan Orcid Logo

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DOI (Published version): 10.1007/s11269-019-02362-0

Abstract

Conducting trend analysis of climatic variables is one of the key steps in many climate change impact studies inwhich the trend is often checked against aggregated variables. In addition, there is a strong need to explore the trendof data in different regimes. The quantile regression (QR) based meth...

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Published in: Water Resources Management
ISSN: 0920-4741 1573-1650
Published: Vienna Springer Science and Business Media LLC 2019
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URI: https://cronfa.swan.ac.uk/Record/cronfa38948
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spelling 2018-04-04T23:28:54.4160270 v2 38948 2018-03-05 Quantile Regression Based Methods for Investigating Rainfall Trends Associated with Flooding and Drought Conditions 3ece84458da360ff84fa95aa1c0c912b 0000-0003-2736-8625 Yunqing Xuan Yunqing Xuan true false 2018-03-05 CIVL Conducting trend analysis of climatic variables is one of the key steps in many climate change impact studies inwhich the trend is often checked against aggregated variables. In addition, there is a strong need to explore the trendof data in different regimes. The quantile regression (QR) based method fits this need very well as it can revealtemporal dependencies of the variable in question, not only for the mean value, but also for its quantiles. As such,tendencies revealed by the QR will be immensely helpful in practice where different mitigation methods need to beconsidered for various level of severities. In this study, the linear quantile regression method is employed to analysethe long-term trend of rainfall records in two climatically different regions: The Dee river catchment in the UK withdaily rainfall data over 1970-2004 and the Beijing metropolitan area in China with monthly rainfall data from 1950 to2012. Two quantiles are used to represent extreme wet condition (98% quantile) and severe dry condition (2%quantile). The results show that the quantile regression is able to reveal the patterns of both extremely wet and dryconditions of the areas. The clear difference between the trends at chosen quantiles manifests the utility of using QRin this context. Journal Article Water Resources Management 33 12 4249 4264 Springer Science and Business Media LLC Vienna 0920-4741 1573-1650 climate change, trend analysis, rainfall, quantile regression 1 9 2019 2019-09-01 10.1007/s11269-019-02362-0 http://dx.doi.org/10.1007/s11269-019-02362-0 COLLEGE NANME Civil Engineering COLLEGE CODE CIVL Swansea University 2018-04-04T23:28:54.4160270 2018-03-05T15:48:37.9149063 Salam A. Abbas 1 Yunqing Xuan 0000-0003-2736-8625 2 Xiaomeng Song 3 38948__16708__c417684b1b714263bb501005a67b3306.pdf abbas2020.pdf 2020-02-27T09:35:09.9811768 Output 2964984 application/pdf Version of Record true 2020-02-27T00:00:00.0000000 false Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
title Quantile Regression Based Methods for Investigating Rainfall Trends Associated with Flooding and Drought Conditions
spellingShingle Quantile Regression Based Methods for Investigating Rainfall Trends Associated with Flooding and Drought Conditions
Yunqing Xuan
title_short Quantile Regression Based Methods for Investigating Rainfall Trends Associated with Flooding and Drought Conditions
title_full Quantile Regression Based Methods for Investigating Rainfall Trends Associated with Flooding and Drought Conditions
title_fullStr Quantile Regression Based Methods for Investigating Rainfall Trends Associated with Flooding and Drought Conditions
title_full_unstemmed Quantile Regression Based Methods for Investigating Rainfall Trends Associated with Flooding and Drought Conditions
title_sort Quantile Regression Based Methods for Investigating Rainfall Trends Associated with Flooding and Drought Conditions
author_id_str_mv 3ece84458da360ff84fa95aa1c0c912b
author_id_fullname_str_mv 3ece84458da360ff84fa95aa1c0c912b_***_Yunqing Xuan
author Yunqing Xuan
author2 Salam A. Abbas
Yunqing Xuan
Xiaomeng Song
format Journal article
container_title Water Resources Management
container_volume 33
container_issue 12
container_start_page 4249
publishDate 2019
institution Swansea University
issn 0920-4741
1573-1650
doi_str_mv 10.1007/s11269-019-02362-0
publisher Springer Science and Business Media LLC
url http://dx.doi.org/10.1007/s11269-019-02362-0
document_store_str 1
active_str 0
description Conducting trend analysis of climatic variables is one of the key steps in many climate change impact studies inwhich the trend is often checked against aggregated variables. In addition, there is a strong need to explore the trendof data in different regimes. The quantile regression (QR) based method fits this need very well as it can revealtemporal dependencies of the variable in question, not only for the mean value, but also for its quantiles. As such,tendencies revealed by the QR will be immensely helpful in practice where different mitigation methods need to beconsidered for various level of severities. In this study, the linear quantile regression method is employed to analysethe long-term trend of rainfall records in two climatically different regions: The Dee river catchment in the UK withdaily rainfall data over 1970-2004 and the Beijing metropolitan area in China with monthly rainfall data from 1950 to2012. Two quantiles are used to represent extreme wet condition (98% quantile) and severe dry condition (2%quantile). The results show that the quantile regression is able to reveal the patterns of both extremely wet and dryconditions of the areas. The clear difference between the trends at chosen quantiles manifests the utility of using QRin this context.
published_date 2019-09-01T03:49:26Z
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score 11.013731

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