Multi-model integrated error correction for streamflow simulation based on Bayesian model averaging and dynamic system response curve

Error correction methods play an important role in improving the reliability and accuracy of hydrological modeling. The Dynamic system response curve (DSRC) is a novel and effective error correction method, but it may have the problem of over-correction. Therefore, two multi-model integrated error correction models based on DSRC and Bayesian model averaging (BMA) were proposed in this paper, namely DSRC-BMA and BMA-DSRC. The Sunshui River catchment is selected for a case study. First, three hydrological models including Xinanjiang model (XAJ), Hydrologiska Fyrans Vattenbalans modell (HBV) and vertically hybrid yield model (VHY) were employed. Then, a standard BMA model and three DSRC-based models were constructed separately. Finally, two multi-model integrated error correction models (DSRC-BMA and BMA-DSRC) were applied. The performance of these nine models was compared by Nash-Sutcliffe Efficiency coefficient (NSE), root mean squared error (RMSE) and percent bias (PBIAS). Results showed that the DSRC-based models presented better results than the standard BMA method and most DSRC-based models. Moreover, the uncertainty in BMA, DSRC-BMA and BMA-DSRC models were assessed. The 90% confidence interval of the BMA-DSRC model had high containing ratio values and low average relative bandwidth. Overall, the proposed multi-model integrated error correction methods are effective and can be applicable in improving streamflow modeling.