Evaluation of the BMA probabilistic inflow forecasts using TIGGE numeric precipitation predictions based on artificial neural network

Reservoir inflow forecasting is a crucial task for reservoir management. Without considering precipitation predictions, the lead time for inflow is subject to the concentration time of precipitation in the basin. With the development of numeric weather prediction (NWP) techniques, it is possible to forecast inflows with long lead times. Since larger uncertainty usually occurs during the forecasting process, much attention has been paid to probabilistic forecasts, which uses a probabilistic distribution function instead of a deterministic value to predict the future status. In this study, we aim at establishing a probabilistic inflow forecasting scheme in the Danjiangkou reservoir basin based on NWP data retrieved from the Interactive Grand Global Ensemble (TIGGE) database by using the Bayesian model averaging (BMA) method, and evaluating the skills of the probabilistic inflow forecasts. An artificial neural network (ANN) is used to implement hydrologic modelling. Results show that the corrected TIGGE NWP data can be applied sufficiently to inflow forecasting at 1-3 d lead times. Despite the fact that the raw ensemble inflow forecasts are unreliable, the BMA probabilistic inflow forecasts perform much better than the raw ensemble forecasts in terms of probabilistic style and deterministic style, indicating the established scheme can offer a useful approach to probabilistic inflow forecasting.