Title: Development of probabilistic dam breach model using Bayesian inference,
Abstract: Dam breach models are commonly used to predict outflow hydrographs of
potentially failing dams and are key ingredients for evaluating flood risk. In
this paper a new dam breach modeling framework is introduced that shall improve
the reliability of hydrograph predictions of homogeneous earthen embankment
dams. Striving for a small number of parameters, the simplified physics-based
model describes the processes of failing embankment dams by breach enlargement,
driven by progressive surface erosion. Therein the erosion rate of dam material
is modeled by empirical sediment transport formulations. Embedding the model
into a Bayesian multilevel framework allows for quantitative analysis of
different categories of uncertainties. To this end, data available in
literature of observed peak discharge and final breach width of historical dam
failures was used to perform model inversion by applying Markov Chain Monte
Carlo simulation. Prior knowledge is mainly based on non-informative
distribution functions. The resulting posterior distribution shows that the
main source of uncertainty is a correlated subset of parameters, consisting of
the residual error term and the epistemic term quantifying the breach erosion
rate. The prediction intervals of peak discharge and final breach width are
congruent with values known from literature. To finally predict the outflow
hydrograph for real case applications, an alternative residual model was
formulated that assumes perfect data and a perfect model. The fully
probabilistic fashion of hydrograph prediction has the potential to improve the
adequate risk management of downstream flooding.