Bayesian selection of hydro-morphodynamic models under computational time constraints

A variety of empirical formulas to predict river bed evolution with hydro-morphodynamic river models exists. Modelers lack objective guidance of how to select the most appropriate one for a specific application. Such guidance can be provided by Bayesian model selection (BMS). Its applicability is however limited by high computational costs. To transfer it to computationally expensive river modeling tasks, we propose to combine BMS with model reduction based on arbitrary Polynomial Chaos Expansion. To account for approximation errors in the reduced models, we introduce a novel correction factor that yields a reliable model ranking even under strong computational time constraints. We demonstrate our proposed approach on a case study for a 10-km stretch of the lower Rhine river. The correction factor may shield us from misleading model ranking results. In our case, the correction factor was shown to increase the confidence in model selection.