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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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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 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.
Keywords: climate change, trend analysis, rainfall, quantile regression
Issue: 12
Start Page: 4249
End Page: 4264

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