Adapting rainfall bias-corrections to improve hydrological simulations generated from climate model forcings

Abstract

Global circulation models (GCMs) provide important insights into future climate change. Bias-correction of downscaled GCM output is integral to any hydrological investigations of climate change due to discrepancies between the statistics of downscaled GCM simulations and observations. Many bias-correction techniques have been developed to support hydrological applications. However, there have been few comparisons of the sensitivity of hydrological simulations to different bias-correction assumptions. This paper investigates the importance of two common assumptions: (i) simultaneously correcting bias at multiple time scales, and (ii) explicitly handling rainfall autocorrelation; in quantile mapping of downscaled GCM rainfall data for hydrological simulation. Four quantile mapping methods are applied to correct bias in dynamically downscaled reanalysis and historical GCM simulations of rainfall for 11 catchments in south-eastern Australia and the performance of bias-corrected rainfall and streamflow simulations is evaluated.All quantile mapping methods investigated can effectively eliminate bias in monthly and annual rainfall totals. Quantile mapping methods that consider differences in the temporal dependence (autocorrelation) structure of downscaled GCM and observed rainfall are most effective in reducing bias in rainfall sequencing statistics, such as probability of consecutive wet or dry days. Streamflow simulations of mean and high streamflow percentiles are underestimated when generated using rainfall that is corrected using quantile mapping methods that do not consider differences in the temporal dependence structure of downscaled GCM and observed rainfall. Future hydrological investigations of climate change should therefore adopt methods that explicitly consider the temporal dependence structures of downscaled GCM and observed rainfall.