INVITED Statistical Post-Processing To Improve Hydrometeorological Forecasts

Hydrologic forecasts based on direct outputs from a hydrologic model contain significant uncertainty from various sources, including model inputs, initial/boundary conditions and model structure/model parameters. The uncertainty leads to various biases in the hydrologic forecasts. Before issuing final hydrologic forecasts to the forecast users, it is necessary to remove these biases. A statistical post-processor is an effective tool than can be used to remove various biases from the hydrometeorological forecasts. In this paper, we demonstrated the effectiveness of two post-processing methods in China's Huai River basin and the French Broad river basin in the U.S. Results clearly show that post-processing can significantly improve the raw hydrometeorological forecasts. An interesting observation is that post-processing can achieve the same degree of improvement in streamflow simulation as model calibration. This suggests that, for basins where calibration cannot be done properly due to data issues (i.e., streamflow regulations), we can use post-processing to compensate for the lack of model calibration.