Assessment of Extreme Precipitation in Future through Time-Invariant and Time-Varying Downscaling Approaches

Abstract

Skill of a time-varying downscaling approach, namely Time-Varying Downscaling Model (TVDM), against time-invariant Statistical Downscaling Model (SDSM) approach for the assessment of precipitation extremes in the future is explored. The downscaled precipitation is also compared with a Regional Climate Model (RCM) product obtained from Coordinated Regional Climate Downscaling Experiment (CORDEX). The potential of downscaling the extreme events is assessed considering Bhadra basin in India as the study area through different models (SDSM, TVDM and RCM) during historical period (calibration: 1951–2005, testing: 2006–2012). Next, the changes in precipitation extremes during future period (2006–2035) have been assessed with respect to the observed baseline period (1971–2000), for different Representative Concentration Pathway (RCP) scenarios. All the models indicate an increasing trend in the precipitation, for the monsoon months and maximum increase is noticed using RCP8.5. The annual precipitation during the future period (RCP8.5) is likely to increase by 7.6% (TVDM) and 4.2% (SDSM) in the study basin. An increase in magnitude and number of extreme events during the future period is also noticed. Such events are expected to be doubled in number in the first quarter of the year (January–March). Moreover, the time-invariant relationship (in SDSM) between causal-target variables is needed to be switched with time-varying (TVDM). This study proves that the time-varying property in TVDM is more beneficial since its performance is better than SDSM and RCM outputs in identifying the extreme events during model calibration and testing periods. Thus, the TVDM is a better tool for assessing the extreme events.