Quantile Regression Based Methods for Investigating Rainfall Trends Associated with Flooding and Drought Conditions

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 regimesexamples 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.

Drought Assessment in Greece Using SPI and ERA5 Climate Reanalysis Data

“…Several researchers have assessed the impacts of climate change on different regions around the globe [2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17]. In addition, various studies have assessed rainfall trends for different regions around the world (e.g., [18][19][20][21][22][23][24][25][26][27][28][29][30][31]).…”

Long‐Term Trends in the Distribution of Ocean Chlorophyll

“…To quantify changes in CHL distribution, we estimate trends in different distribution quantiles via QR (Koenker & Bassett, 1978). While assessing change in the mean of climate variables using ordinary least squares (OLS) provides extremely valuable information, it does not provide insight into changing extremes and how overall variability is related to time‐varying events (Abbas et al., 2019). The main difference with OLS is that QR substitutes the conditional mean function in OLS for a conditional quantile function (Koenker & Bassett, 1978; Koenker & D’Orey, 1987).…”

حول المنهجية شبه المعلمية لتقدير الانموذج التجزيئي الضبابي: مراجعة

“…In same year Arefi (2020) suggested quantiles of fuzzy data that can present a loss function between fuzzy numbers, the fuzzy response variable, and the fuzzy parameters through the utilization of empirical data. Risk management must vary on the region's climatic conditions Abbas et al (2019). And as a result, it offers a more reliable inference in the presence of outliers than mean regression In contrast to mean regression.…”