Probabilistic streamflow forecast based on spatial post-processing of TIGGE precipitation forecasts

Ensemble precipitation forecast is effective in reducing the uncertainty and providing reliable probabilistic streamflow forecast. However, for operational applications, precipitation forecasts must go through bias correction in mean and spread. Although post-processing methods, such as BMA, have demonstrated good performance in ensemble-based calibration, the spatial correlation between stations may be altered after post-processing. In this research, ensemble precipitation forecasts of four NWP models, including ECMWF, UKMO, NCEP, and CMA within the TIGGE database, was bias-corrected and post-processed using quantile mapping and BMA for a case study basin in Iran. The ECC method was then used to recover the spatial correlation of ensemble forecasts. Subsequently, probabilistic streamflow forecast was conducted using post-processed precipitation forecasts. The results showed that the errors in the mean and spread of ensemble precipitation forecasts were corrected for each of the four NWP models while the ECC method was effective in maintaining spatial correlation. Furthermore, the results of probabilistic streamflow forecast showed that the performance of the forecast models improved after post processing, with the ECMWF model providing the best forecasts. More work is recommended to improve the impact of the ECC method on NWP models’ performance.