Development of Probabilistic Dam Breach Model Using Bayesian Inference

Peter, Samuel; Siviglia, Annunziato; Nagel, Joseph B.; Marelli, Stefano; Boes, Robert M.; Vetsch, David; Sudret, Bruno

doi:10.1029/2017wr021176

Cited by 19 publications

(16 citation statements)

References 69 publications

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“…Results are given in Figure 9 and Table S2 for different experimental designs. The PCE degree shown in the appendix is the PCE degree with the lowest LOO in the array of the given degrees (i.e., [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15] [54]. The results showed that with the increasing ED the value of LOO decreases, and the degree of the calculated PCE changes as well.…”

Section: Step C Uncertainty Propagationmentioning

confidence: 99%

Metamodeling for Uncertainty Quantification of a Flood Wave Model for Concrete Dam Breaks

et al. 2020

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Uncertainties in instantaneous dam-break floods are difficult to assess with standard methods (e.g., Monte Carlo simulation) because of the lack of historical observations and high computational costs of the numerical models. In this study, polynomial chaos expansion (PCE) was applied to a dam-break flood model reflecting the population of large concrete dams in Switzerland. The flood model was approximated with a metamodel and uncertainty in the inputs was propagated to the flow quantities downstream of the dam. The study demonstrates that the application of metamodeling for uncertainty quantification in dam-break studies allows for reduced computational costs compared to standard methods. Finally, Sobol’ sensitivity indices indicate that reservoir volume, length of the valley, and surface roughness contributed most to the variability of the outputs. The proposed methodology, when applied to similar studies in flood risk assessment, allows for more generalized risk quantification than conventional approaches.

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Section: Step C Uncertainty Propagationmentioning

confidence: 99%

Metamodeling for Uncertainty Quantification of a Flood Wave Model for Concrete Dam Breaks

et al. 2020

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“…For embankment and moraine-dammed lakes, there are two main approaches to computing dam failure: (i) numerical models based on mass and momentum balance equations supplemented by erosion laws (Begam et al, 2018;Castro-Orgaz & Hager, 2013;Faeh, 2007;Larocque et al, 2013), and (ii) conceptual models based on a series of differential equations accounting for the various processes at play (Capart, 2013;Peter et al, 2018;Walder & O'Connor, 1997). For ice-dammed lakes, the water's change phase should also be considered.…”

Section: Drainage and Dam Breakmentioning

confidence: 99%

Hydraulic Reconstruction of the 1818 Giétro Glacial Lake Outburst Flood

Ancey

Bardou

Funk

et al. 2019

Water Resources Research

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In the spring of 1818, ice avalanches from the Giétro Glacier created an ice dam, which in turn formed a glacial lake in the Drance Valley (Canton of Valais, Switzerland). Today, its maximum volume is estimated to have been 25×106 m3. Cantonal authorities commissioned an engineer named Ignaz Venetz to mitigate the risk of the ice dam's failure. He supervised the construction of a tunnel through which a large volume of water was drained as the lake rose (9×106 m3 according to his estimates and 11×106 m3 according to our model). After 2.5 days of slow drainage, the ice dam failed on 16 June 1818 and caused major flooding in the Drance Valley up to 40 km downstream, resulting in about 40 deaths. Venetz's lake monitoring notes, numerous testimonies gathered in the disaster's aftermath, and our field survey have made it possible to collect a wealth of information on this event, which is one of the world's major documented glacial lake outburst floods. Reconstructing major outburst floods remains challenging because not only do they involve enormous volumes of water spreading over long distances but they are also associated with additional physical processes such as massive erosion; intense transport of ice, sediment, and debris; and damage to vegetation and buildings. This paper attempts to reconstruct the 1818 Giétro flood by focusing on its water component. We develop a simple model to estimate the initial hydrograph during the slow drainage and failure phases. The flood's features are deduced by solving the shallow‐water equations numerically. The computational framework involves six free parameters, of which five are constrained by physical considerations. Using iterative manual parameter adjustments, we matched the numerical simulations to the historical data. We found that the peak discharge was close to 14,500 m3/s, the flood's front velocity was about 6 m/s, and flow depth varied considerably along the River Drance's bed (from 30 m just downstream of the ice dam to 2 m on the alluvial fan, 24 km west of the dam). To achieve a good agreement between computations and historical data, we had to select a high value for the Manning friction coefficient n (with n as large as 0.08 s/m1/3). As the Drance Valley is narrow, high flow resistance caused the flood's leading edge to behave like a plug, moving at a fairly constant velocity, with little dependence on what happened behind it. This result may explain why a simple flood routing model is able to reproduce the flood's features, because in an Alpine valley, a lateral spreading of the water volume is limited.

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“…Focusing the attention in dam engineering field, the most interesting examples aim to determine the model parameters for the risk assessment of earth fill dams [31,32,33,34].…”

Section: Interpretation Of the Dam Structural Behaviour From Monitorimentioning

confidence: 99%

Concrete gravity dams model parameters updating using static measurements

Sevieri

Andreini

Falco

et al. 2019

Engineering Structures

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The structural control of concrete gravity dams is of primary importance. In this context, numerical models play a fundamental role both to assess the vulnerability of gravity dams and to control their behaviour during normal operativity and after extreme events. In this regard, data monitoring represents an important source of information for numerical model calibrations. This study proposes a novel probabilistic procedure, defined in the Bayesian framework, to calibrate the parameters of finite elements models of dams. To this aim, monitoring data and the results of material tests are used as reference information. The computational burden is reduced by using a new hybridpredictive model of the dam displacements. An application on an Italian dam shows the feasibility of the proposed procedure.

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Development of Probabilistic Dam Breach Model Using Bayesian Inference

Cited by 19 publications

References 69 publications

Metamodeling for Uncertainty Quantification of a Flood Wave Model for Concrete Dam Breaks

Metamodeling for Uncertainty Quantification of a Flood Wave Model for Concrete Dam Breaks

Hydraulic Reconstruction of the 1818 Giétro Glacial Lake Outburst Flood

Concrete gravity dams model parameters updating using static measurements

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