The Probabilistic Flood Prediction Based on Implementation of the Schaake Shuffle Method over the Huaihe Basin

Daily precipitation records of 19 rain gauges over the Huaihe Wangjiaba-Dapoling catchment and single-value forecasts of 24-hour cumulative precipitation of the Global Forecast System (GFS) with lead time up to 14 days from 1 January 1981 to 31 December 2003 are employed to construct a probability forecast model which can generate ensemble forecast based on conditional meta-Gaussian distribution. Several single-value forecasts could be computed by this model using forecasts of the GFS for daily mean areal precipitation (MAP) and cumulative MAP for each lead time (1-14 days) over 3 sub-catchments in the Huaihe Basin. Then a method is implemented to reorder the ensemble output to recover the space-time variability in precipitation, namely Schaake shuffle method. Ensembles are then reordered 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 MAP 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 recovered, 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 Hydrologic Station is carried out. It shows that MAP ensemble forecasts can provide the maximum estimation of possibility of the future hydrologic 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 hydrologic uncertainty is also given.