caa a22 4500 528784005 20200905030840.0 cr unu---uuuuu 180924e201807 xx s 000 0 eng 10.3929/ethz-b-000284227 doi 10.1029/2017wr021176 doi (ETHRESEARCH)oai:www.research-collection.ethz.ch:20.500.11850/284227 Development of Probabilistic Dam Breach Model Using Bayesian Inference [Elektronische Daten] [Samuel Peter, Annunziato Siviglia, Joseph B. Nagel, Stefano; id_orcid 0000-0002-9268-9014 Marelli, Robert M. Boes, David F. Vetsch, Bruno; id_orcid 0000-0002-9501-7395 Sudret] Water Resour. Res. Open access ethresearch 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. In Copyright - Non-Commercial Use Permitted http://rightsstatements.org/page/InC-NC/1.0 ethresearch Dam break modeling ethresearch Bayesian inversion ethresearch hierarchical models ethresearch Peter Samuel joint author Siviglia Annunziato joint author Nagel Joseph B. joint author Marelli Stefano; id_orcid 0000-0002-9268-9014 joint author Boes Robert M. joint author Vetsch David F. joint author Sudret Bruno; id_orcid 0000-0002-9501-7395 joint author Water Resources Research s.l. : American Geophysical Union (AGU) 54 (7), pp. 4376-4400 0043-1397 http://hdl.handle.net/20.500.11850/284227 text/html WWW-Backlink auf das Repository (Open access) BK010053 XK010053 XK010000 1 Journal Article ethresearch ETHRESEARCH ETHRESEARCH ETHRESEARCH Journal Article Open access ETHRESEARCH 856 40 http://hdl.handle.net/20.500.11850/284227 text/html WWW-Backlink auf das Repository (Open access) ETHRESEARCH 700 1- Peter Samuel joint author ETHRESEARCH 700 1- Siviglia Annunziato joint author ETHRESEARCH 700 1- Nagel Joseph B. joint author ETHRESEARCH 700 1- Marelli Stefano; id_orcid 0000-0002-9268-9014 joint author ETHRESEARCH 700 1- Boes Robert M. joint author ETHRESEARCH 700 1- Vetsch David F. joint author ETHRESEARCH 700 1- Sudret Bruno; id_orcid 0000-0002-9501-7395 joint author ETHRESEARCH 773 0- Water Resources Research s.l. : American Geophysical Union (AGU) 54 (7), pp. 4376-4400 0043-1397