Seasonal streamflow prediction by a combined climate-hydrologic system for river basins of Taiwan

Abstract

A combined, climate-hydrologic system with three components to predict the streamflow of two river basins of Taiwan at one season (3-month) lead time for the NDJ and JFM seasons was developed. The first component consists of the wavelet-based, ANN-GA model (Artificial Neural Network calibrated by Genetic Algorithm) which predicts the seasonal rainfall by using selected sea surface temperature (SST) as predictors, given that SST are generally predictable by climate models up to 6-month lead time. For the second component, three disaggregation models, Valencia and Schaake (VS), Lane, and Canonical Random Cascade Model (CRCM), were tested to compare the accuracy of seasonal rainfall disaggregated by these three models to 3-day time scale rainfall data. The third component consists of the continuous rainfall-runoff model modified from HBV (called the MHBV) and calibrated by a global optimization algorithm against the observed rainfall and streamflow data of the Shihmen and Tsengwen river basins of Taiwan. The proposed system was tested, first by disaggregating the predicted seasonal rainfall of ANN-GA to rainfall of 3-day time step using the Lane model; then the disaggregated rainfall data was used to drive the calibrated MHBV to predict the streamflow for both river basins at 3-day time step up to a season's lead time. Overall, the streamflow predicted by this combined system for the NDJ season, which is better than that of the JFM season, will be useful for the seasonal planning and management of water resources of these two river basins of Taiwan.