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Quantile Regression Based Methods for Investigating Rainfall Trends Associated with Flooding and Drought Conditions
Water Resources Management, Volume: 33, Issue: 12, Pages: 4249 - 4264
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Conducting trend analysis of climatic variables is one of the key steps in many climate changeimpact studies where trend is often checked against aggregated variables. However, there isalso a strong need to investigate the trend of the data in different regimes – examples includehigh flow versus low...
|Published in:||Water Resources Management|
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Conducting trend analysis of climatic variables is one of the key steps in many climate changeimpact studies where trend is often checked against aggregated variables. However, there isalso a strong need to investigate the trend of the data in different regimes – examples includehigh flow versus low flow, and heavy precipitation versus prolonged dry period. For thismatter, quantile regression (QR) based methods are preferred as they can reveal the temporaldependencies of the variable in question for not only the mean value, but also its quantiles. Assuch, the tendencies revealed by the QR methods are more informative and helpful in studieswhere different mitigation methods need to be considered at different severity levels.In thispaper, we demonstrate the use of several quantile regressions methods to analyse the long-termtrend of rainfall records in two climatically different regions: The Dee River catchment in theUnited Kingdom, for which daily rainfall data of 1970–2004 are available; and the BeijingMetropolitan 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 drycondition (0.02 quantile). The trends of these two quantiles are then estimated using linearquantile 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. Theresults show that the quantile regression method can reveal patterns for both extremely wet anddry conditions of the areas. The clear difference between trends at the chosen quantilesmanifests the utility of QR in this context.
Climate change; Precipitation; Quantile regression; Trend analysis