A sequential Bayesian approach for hydrologic model selection and prediction

When a single model is used for hydrologic prediction, it must be capable of estimating system behavior accurately at all times. Multiple-model approaches integrate several model behaviors and, when effective, they can provide better estimates than that of any single model alone. This paper discusses a sequential model fusion strategy that uses the Bayes rule. This approach calculates each model's transient posterior distribution at each time when a new observation is available and merges all model estimates on the basis of each model's posterior probability. This paper demonstrates the feasibility of this approach through case studies that fuse three hydrologic models, auto regressive with exogenous inputs, Sacramento soil moisture accounting, and artificial neural network models, to predict daily watershed streamflow.