Journal article 96 views 10 downloads
Quantile Regression Based Methods for Investigating Rainfall Trends Associated with Flooding and Drought Conditions
Water Resources Management, Volume: 33, Issue: 12, Pages: 4249 - 4264
PDF | Version of Record
Copyright: The Author(s) 2019. This article is distributed under the terms of the Creative Commons Attribution 4.0 International LicenseDownload (2.83MB)
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...
|Published in:||Water Resources Management|
Springer Science and Business Media LLC
Check full text
No Tags, Be the first to tag this record!
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.
Climate change; Precipitation; Quantile regression; Trend analysis
Faculty of Science and Engineering
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.