An integrated hydrologic Bayesian multimodel combination framework: Confronting input, parameter, and model structural uncertainty in hydrologic prediction

Abstract

The conventional treatment of uncertainty in rainfall-runoff modeling primarily attributes uncertainty in the input-output representation of the model to uncertainty in the model parameters without explicitly addressing the input, output, and model structural uncertainties. This paper presents a new framework, the Integrated Bayesian Uncertainty Estimator (IBUNE), to account for the major uncertainties of hydrologic rainfall-runoff predictions explicitly. IBUNE distinguishes between the various sources of uncertainty including parameter, input, and model structural uncertainty. An input error model in the form of a Gaussian multiplier has been introduced within IBUNE. These multipliers are assumed to be drawn from an identical distribution with an unknown mean and variance which were estimated along with other hydrological model parameters by a Monte Carlo Markov Chain (MCMC) scheme. IBUNE also includes the Bayesian model averaging (BMA) scheme which is employed to further improve the prediction skill and address model structural uncertainty using multiple model outputs. A series of case studies using three rainfall-runoff models to predict the streamflow in the Leaf River basin, Mississippi, are used to examine the necessity and usefulness of this technique. The results suggest that ignoring either input forcings error or model structural uncertainty will lead to unrealistic model simulations and incorrect uncertainty bounds. Copyright 2007 by the American Geophysical Union.