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 were used to perform model inversion by applying Markov chain Monte Carlo simulation. Prior knowledge is mainly based on noninformative 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.
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Water Resources Research, American Geophysical Union, 2018, 54 (7), pp.4376 - 4400. 〈10.1029/2017wr021176〉
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S. Peter, A. Siviglia, J. Nagel, S. Marelli, R. Boes, et al.. Development of Probabilistic Dam Breach Model Using Bayesian Inference. Water Resources Research, American Geophysical Union, 2018, 54 (7), pp.4376 - 4400. 〈10.1029/2017wr021176〉. 〈hal-01893198〉

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