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

Salam Abbas, Yunqing Xuan Orcid Logo, Xiaomeng Song

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

Swansea University Authors: Salam Abbas, 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 where trend is often checked against aggregated variables. However, there is also a strong need to investigate the trend of the data in different regimes – examples include high flow versus...

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Published in: Water Resources Management
ISSN: 0920-4741 1573-1650
Published: Springer Science and Business Media LLC 2019
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URI: https://cronfa.swan.ac.uk/Record/cronfa60092
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spelling 2022-06-14T12:32:23.8621586 v2 60092 2022-05-27 Quantile Regression Based Methods for Investigating Rainfall Trends Associated with Flooding and Drought Conditions 8ab3f5b149c6b7e8a25210dab3cc2dce Salam Abbas Salam Abbas true false 3ece84458da360ff84fa95aa1c0c912b 0000-0003-2736-8625 Yunqing Xuan Yunqing Xuan true false 2022-05-27 Conducting trend analysis of climatic variables is one of the key steps in many climate change impact studies where trend is often checked against aggregated variables. However, there is also a strong need to investigate the trend of the data in different regimes – examples include high flow versus low flow, and heavy precipitation versus prolonged dry period. For this matter, quantile regression (QR) based methods are preferred as they can reveal the temporal dependencies of the variable in question for not only the mean value, but also its quantiles. As such, the tendencies revealed by the QR methods are more informative and helpful in studies where different mitigation methods need to be considered at different severity levels.In this paper, we demonstrate the use of several quantile regressions methods to analyse the long-term trend of rainfall records in two climatically different regions: The Dee River catchment in the United Kingdom, for which daily rainfall data of 1970–2004 are available; and the Beijing Metropolitan Area in China for which monthly rainfall data from 1950 to 2012 are available. Two quantiles are used to represent heavy rainfall condition (0.98 quantile) and severe dry condition (0.02 quantile). The trends of these two quantiles are then estimated using linear quantile regression before being spatially interpolated to demonstrate their spatial distribution (for Dee river only). The method is also compared with traditional indices such as SPI. The results show that the quantile regression method can reveal patterns for both extremely wet and dry conditions of the areas. The clear difference between trends at the chosen quantiles manifests the utility of QR in this context. Journal Article Water Resources Management 33 12 4249 4264 Springer Science and Business Media LLC 0920-4741 1573-1650 Climate change; Precipitation; Quantile regression; Trend analysis 1 9 2019 2019-09-01 10.1007/s11269-019-02362-0 COLLEGE NANME COLLEGE CODE Swansea University Salam A. Abbas has been supported by the scholarship provided by the Higher Committee for Education Development in Iraq; Yunqing Xuan has been partly supported by the Royal Academy of Engineering’s UK-China Urban Flooding Research Programme (Grant: UUFRIP\10021), which are both gratefully acknowledged. 2022-06-14T12:32:23.8621586 2022-05-27T11:09:42.7511376 Faculty of Science and Engineering School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Civil Engineering Salam Abbas 1 Yunqing Xuan 0000-0003-2736-8625 2 Xiaomeng Song 3 60092__24308__7ffad3eb9afa4268bfa82b29ca2b3f4f.pdf 60092.pdf 2022-06-14T12:28:15.3105685 Output 2964984 application/pdf Version of Record true Copyright: The Author(s) 2019. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License true eng http://creativecommons.org/licenses/by/4.0/
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
Salam Abbas
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 8ab3f5b149c6b7e8a25210dab3cc2dce
3ece84458da360ff84fa95aa1c0c912b
author_id_fullname_str_mv 8ab3f5b149c6b7e8a25210dab3cc2dce_***_Salam Abbas
3ece84458da360ff84fa95aa1c0c912b_***_Yunqing Xuan
author Salam Abbas
Yunqing Xuan
author2 Salam 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
college_str Faculty of Science and Engineering
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hierarchy_top_id facultyofscienceandengineering
hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Civil Engineering{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Civil Engineering
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description Conducting trend analysis of climatic variables is one of the key steps in many climate change impact studies where trend is often checked against aggregated variables. However, there is also a strong need to investigate the trend of the data in different regimes – examples include high flow versus low flow, and heavy precipitation versus prolonged dry period. For this matter, quantile regression (QR) based methods are preferred as they can reveal the temporal dependencies of the variable in question for not only the mean value, but also its quantiles. As such, the tendencies revealed by the QR methods are more informative and helpful in studies where different mitigation methods need to be considered at different severity levels.In this paper, we demonstrate the use of several quantile regressions methods to analyse the long-term trend of rainfall records in two climatically different regions: The Dee River catchment in the United Kingdom, for which daily rainfall data of 1970–2004 are available; and the Beijing Metropolitan Area in China for which monthly rainfall data from 1950 to 2012 are available. Two quantiles are used to represent heavy rainfall condition (0.98 quantile) and severe dry condition (0.02 quantile). The trends of these two quantiles are then estimated using linear quantile regression before being spatially interpolated to demonstrate their spatial distribution (for Dee river only). The method is also compared with traditional indices such as SPI. The results show that the quantile regression method can reveal patterns for both extremely wet and dry conditions of the areas. The clear difference between trends at the chosen quantiles manifests the utility of QR in this context.
published_date 2019-09-01T04:17:54Z
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