Toward reduction of model uncertainty: Integration of Bayesian model averaging and data assimilation

Multimodeling in hydrologic forecasting has proved to improve upon the systematic bias and general limitations of a single model. This is typically done by establishing a new model as a linear combination or a weighted average of several models with weights on the basis of individual model performance in previous time steps. The most commonly used multimodeling method, Bayesian model averaging (BMA), assumes a fixed probability distribution around individual models' forecast in establishing the prior and uses a calibration period to determine static weights for each individual model. More recent work has focused on a sequential Bayesian model selection technique with weights that are adjusted at each time step in an attempt to accentuate the dynamics of an individual model's performance with respect to the system's response. However, these approaches still assume a fixed distribution around the individual models' forecast. A new sequential Bayesian model‐averaging technique is developed incorporating a sliding window of individual model performance around the forecast. Additionally, this new technique relaxes the fixed distribution assumption in establishing the prior utilizing a particle filter data assimilation method that reflects both the performance dynamics of the models' forecasts along with their uncertainty. A comparative analysis of the different BMA strategies is performed across different rates of change in the hydrograph. Results show that methods employing the particle filter show higher probabilistic skill in high ranges of volatility but are overconfident in medium and low ranges of volatility. Key Points Model averaging with dynamic weights significantly outperforms the static BMA Integration of BMA and DA is shown to significantly reduce forecast uncertainty Dynamically changing the number of models avg'd increase the performance