No Cover Image

Journal article 378 views 32 downloads

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

  • 60092.pdf

    PDF | Version of Record

    Copyright: The Author(s) 2019. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License

    Download (2.83MB)

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...

Full description

Published in: Water Resources Management
ISSN: 0920-4741 1573-1650
Published: Springer Science and Business Media LLC 2019
Online Access: Check full text

URI: https://cronfa.swan.ac.uk/Record/cronfa60092
Tags: Add Tag
No Tags, Be the first to tag this record!
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 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.
Keywords: Climate change; Precipitation; Quantile regression; Trend analysis
College: Faculty of Science and Engineering
Funders: 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.
Issue: 12
Start Page: 4249
End Page: 4264