Approximate Bayesian Computation for Diagnostic Model Calibration and Evaluation

In this talk I will discuss theory, concepts and applications of Approximate Bayesian Computation (ABC) for diagnostic model calibration and evaluation. This statistical methodology relaxes the need for an explicit likelihood function in favor of one or multiple different summary statistics rooted in hydrologic theory that together have a more clear and compelling diagnostic power than some average measure of the size of the error residuals. A few illustrative case studies are used to demonstrate that ABC is relatively easy to implement, and readily employs signature based indices to analyze and pinpoint which part of the model is malfunctioning and in need of further improvement.