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

Author

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  • Salam A. Abbas

    (Swansea University Bay Campus)

  • Yunqing Xuan

    (Swansea University Bay Campus)

  • Xiaomeng Song

    (China University of Mining and Technology)

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.

Suggested Citation

  • Salam A. Abbas & Yunqing Xuan & Xiaomeng Song, 2019. "Quantile Regression Based Methods for Investigating Rainfall Trends Associated with Flooding and Drought Conditions," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(12), pages 4249-4264, September.
    • Handle: RePEc:spr:waterr:v:33:y:2019:i:12:d:10.1007_s11269-019-02362-0
    DOI: 10.1007/s11269-019-02362-0
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    References listed on IDEAS

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    1. Salam A. Abbas & Yunqing Xuan, 2019. "Development of a New Quantile-Based Method for the Assessment of Regional Water Resources in a Highly-Regulated River Basin," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(9), pages 3187-3210, July.
    2. Koenker, Roger W & Bassett, Gilbert, Jr, 1978. "Regression Quantiles," Econometrica, Econometric Society, vol. 46(1), pages 33-50, January.
    3. G. Tsakiris & D. Pangalou & H. Vangelis, 2007. "Regional Drought Assessment Based on the Reconnaissance Drought Index (RDI)," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 21(5), pages 821-833, May.
    4. I. Nalbantis & G. Tsakiris, 2009. "Assessment of Hydrological Drought Revisited," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 23(5), pages 881-897, March.
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    1. Ufuk Beyaztas & Han Lin Shang & Aylin Alin, 2022. "Function-on-Function Partial Quantile Regression," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 27(1), pages 149-174, March.
    2. Wilson Kalisa & Tertsea Igbawua & Fanan Ujoh & Igbalumun S. Aondoakaa & Jean Nepomuscene Namugize & Jiahua Zhang, 2021. "Spatio-temporal variability of dry and wet conditions over East Africa from 1982 to 2015 using quantile regression model," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 106(3), pages 2047-2076, April.
    3. Ioannis M. Kourtis & Harris Vangelis & Dimitris Tigkas & Anna Mamara & Ioannis Nalbantis & George Tsakiris & Vassilios A. Tsihrintzis, 2023. "Drought Assessment in Greece Using SPI and ERA5 Climate Reanalysis Data," Sustainability, MDPI, vol. 15(22), pages 1-19, November.

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