Comparison of Trend Preserving Statistical Downscaling Algorithms Toward an Improved Precipitation Extremes Projection i
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Comparison of Trend Preserving Statistical Downscaling Algorithms Toward an Improved Precipitation Extremes Projection in the Headwaters of Blue Nile River in Ethiopia Getachew Tegegne 1
& Assefa M. Melesse
1
Received: 13 April 2020 / Accepted: 12 October 2020/ # Springer Nature Switzerland AG 2020
Abstract
Projected changes in precipitation extremes can greatly impact the natural environment. Hence, the precipitation extremes must be precisely estimated with an appropriate bias correction algorithm to provide reliable information for the formulation of climate change impact adaptation and mitigation strategies. However, there is a lack of studies that discuss the effect of bias correction algorithms on the reproduction of precipitation extremes in the Blue Nile River Basin. This study compared three commonly used bias correction algorithms: the quantile mapping (QM), detrended QM (DQM), and quantile delta mapping (QDM). The QDM and DQM algorithms outperformed the standard QM bias correction algorithm in preserving the raw climate models projected relative changes of precipitation extremes. The performance differences between the standard QM and other bias correction algorithms (DQM and QDM) were more pronounced in the projection of extreme daily precipitation. Conversely, the projection of dry and wet spells was less sensitive for the choice of the bias correction algorithm. In general, the climate change impact analysis with the QDM algorithm revealed the increase in the frequency and severity of precipitation extremes. Moreover, the results showed the increase (decrease) in the maximum length of dry (wet) spells; indicating the increase in the severity of the meteorological droughts in the future that could potentially reduce the rainfed agricultural productivity of the region. Keywords Precipitation extremes . Bias correction algorithms . Blue Nile River basin
* Getachew Tegegne [email protected]
1
Department of Earth and Environment, Florida International University, Miami, FL 33199, USA
Tegegne G., Melesse A.M.
1 Introduction The intensity and frequency of climatic extremes will change in the future owing to global warming (Dai 2011; Tegegne et al. 2020a; Van Loon et al. 2016). Several studies in different regions worldwide have reported a significant impact of climate change on the natural environment (Alexander et al. 2006; Anandhi et al. 2016; Bai et al. 2007; Bengtsson and Rana 2014; Dessu and Melesse 2013; Dosio et al. 2019; Gao et al. 2020; Gebrechorkos et al. 2019; Harpa et al. 2019; Klein Tank and Können 2003; Mariotti et al. 2014; Tegegne et al. 2019, 2020a, c; Tegegne and Melesse 2020; Worqlul et al. 2018). Several studies have also assessed the impact of climate change on water resources variability by using the hydrological models forced with the downscaled climate data (Abtew and Melesse 2014; Abtew et al. 2009a, b; Dessu and Melesse 2013; Fiseha et al. 2014; Mango et al. 2011; Melesse et al. 2009, 2011a, b, 2014; Setegn et al. 2011). Most of the previous studies reported that climate ch
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