The Probabilistic Flood Prediction Based on Reconstructing Space–Time Variability in Ensemble over the Huaihe Basin

As we know that ensemble prediction from various weather centers implemented on catchment hydrology can improve early flood warning significantly. This research constructed ensemble forecast based on the single-value forecasts by the conditional meta-Gaussian distribution over three sub-catchments in the Huaihe Basin, China. Then a method is implemented to recorder the ensemble output to recover the space-time variability in precipitation, namely Schaake shuffle method. Ensemble are then recordered to match the original order of the selection of historical data. Using this approach, the observed inter sub-catchments correlations, intervariable correlations, and the observed temporal persistence are almost entirely recovered. This reordering methodology is applied in recovering the space-time variability in modeled streamflow for twelve flood processes over the Huaihe Basin. Results demonstrate that the observation of discharge is included in the interval between the 5th percentage and the 95th percentage forecasts of discharge that is generated by mean areal precipitation ensemble forecasts which is calculated from the conditional meta-Gaussian distribution model and Schaake shuffle. Several members can capture the flood peak flow and the corresponding peak time. Using approach of Schaake Shuffle, sub-catchment correlations of each ensemble member forecasting could be re-covered, which are closer to the observation.

A test of flood forecasting result from precipitation probability forecasts of conditional meta-Gaussian distribution model and Schaake shuffle for the stream between Dapoling to Wangjiaba Hydrological Station is carried out. It shows that MAP ensemble forecasts can provide the maximum estimation of possibility of the future hydrological events for flood forecasting comparing to the single-value MAP forecast of GFS model. And a comprehensive interval which includes the factor that can lead to hydrological uncertainty is also given.