Downscaling precipitation or bias-correcting streamflow? Some implications for coupled general circulation model (CGCM)-based ensemble seasonal hydrologic forecast

摘要

[1] The progress in forecasting seasonal climate by using coupled atmosphere-ocean-land general circulation models (CGCMs) has increased the use of CGCM-based hydrologic forecasting in recent years. A common procedure is to downscale the meteorological forcings and use them as inputs to hydrologic models to provide ensemble forecasts. Less attention has been paid to bias correcting the hydrologic forecasts directly generated by CGCM. In this study, we show that either downscaling precipitation for hydrologic model or directly bias-correcting CGCM streamflow increases the efficiency skill score greatly as compared to the original CGCM streamflow forecast, and bias correcting the streamflow from hydrologic model with downscaled precipitation leads to a further skill increase. Bias-correcting CGCM streamflow is more skillful and reliable than downscaling precipitation for hydrologic modeling in terms of ensemble forecasts, as verified by the ranked probability skill score and the rank histogram. While bias-correcting streamflow from CGCM can provide useful forecasts, combining the downscaled CGCM forcings and bias-corrected hydrologic output through the CGCM-hydrology forecasting approach does gain additional skill of accuracy and discrimination.