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.
Highlights
In recent decades, climate change has had an increasing impact on water resources, agricultural activities and the environment (Shi and Xu 2008)
Unlike extreme precipitation events whose impact is often readily perceived as severe flooding, the onset of droughts is dependent on many different factors
We demonstrate a study of using the quantile regression (QR) based methods to identify the rainfall trends in two remarkably different climate regions: the Dee river catchment in the UK and the Beijing Metropolitan Area (BMA) in China
Summary
Climate change has had an increasing impact on water resources, agricultural activities and the environment (Shi and Xu 2008). Increased variability of the magnitude and frequency of precipitation and temperature are among the major impacts of climate change (Dinpashoh et al 2014). For water resources planning and management, it is often preferable to use trend analysis of climatic variables such as precipitation, temperature and river flows, which has been widely reported in many studies. Unlike extreme precipitation events whose impact is often readily perceived as severe flooding, the onset of droughts is dependent on many different factors. It takes much longer for the impact of droughts to be fully appreciated than that of heavy precipitation events
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