Articles | Volume 21, issue 5
https://doi.org/10.5194/hess-21-2497-2017
https://doi.org/10.5194/hess-21-2497-2017
Technical note
 | 
12 May 2017
Technical note |  | 12 May 2017

Dealing with uncertainty in the probability of overtopping of a flood mitigation dam

Eleni Maria Michailidi and Baldassare Bacchi

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Subject: Engineering Hydrology | Techniques and Approaches: Stochastic approaches
Uncertainty estimation of regionalised depth–duration–frequency curves in Germany
Bora Shehu and Uwe Haberlandt
Hydrol. Earth Syst. Sci., 27, 2075–2097, https://doi.org/10.5194/hess-27-2075-2023,https://doi.org/10.5194/hess-27-2075-2023, 2023
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FarmCan: a physical, statistical, and machine learning model to forecast crop water deficit for farms
Sara Sadri, James S. Famiglietti, Ming Pan, Hylke E. Beck, Aaron Berg, and Eric F. Wood
Hydrol. Earth Syst. Sci., 26, 5373–5390, https://doi.org/10.5194/hess-26-5373-2022,https://doi.org/10.5194/hess-26-5373-2022, 2022
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Identifying sensitivities in flood frequency analyses using a stochastic hydrologic modeling system
Andrew J. Newman, Amanda G. Stone, Manabendra Saharia, Kathleen D. Holman, Nans Addor, and Martyn P. Clark
Hydrol. Earth Syst. Sci., 25, 5603–5621, https://doi.org/10.5194/hess-25-5603-2021,https://doi.org/10.5194/hess-25-5603-2021, 2021
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Characteristics and process controls of statistical flood moments in Europe – a data-based analysis
David Lun, Alberto Viglione, Miriam Bertola, Jürgen Komma, Juraj Parajka, Peter Valent, and Günter Blöschl
Hydrol. Earth Syst. Sci., 25, 5535–5560, https://doi.org/10.5194/hess-25-5535-2021,https://doi.org/10.5194/hess-25-5535-2021, 2021
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Objective functions for information-theoretical monitoring network design: what is “optimal”?
Hossein Foroozand and Steven V. Weijs
Hydrol. Earth Syst. Sci., 25, 831–850, https://doi.org/10.5194/hess-25-831-2021,https://doi.org/10.5194/hess-25-831-2021, 2021
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Cited articles

Ariff, N. M., Jemain, A. A., Ibrahim, K., and Wan Zin, W. Z.: IDF relationships using bivariate copula for storm events in Peninsular Malaysia, J. Hydrol., 470–471, 158–171, https://doi.org/10.1016/j.jhydrol.2012.08.045, 2012.
Aronica, G. T., Candela, A., Fabio, P., and Santoro, M.: Estimation of flood inundation probabilities using global hazard indexes based on hydrodynamic variables, Phys. Chem. Earth A/B/C, 42–44, 119–129, https://doi.org/10.1016/j.pce.2011.04.001, 2012.
Asquith, W.: lmomco – L-moments, censored L-moments, trimmed L-moments, L-comoments, and many distributions, r package version 2.1.4, http://www.cran.r-project.org/package=lmomco (last access: 10 May 2017), 2015.
Autorità di bacino del fiume Po: Caratteristiche del bacino del fiume Po e primo esame dell'impatto ambientale delle attività umane sulle risorse idriche, Report, Parma, Italy, 2006.
Balistrocchi, M. and Bacchi, B.: Modelling the statistical dependence of rainfall event variables through copula functions, Hydrol. Earth Syst. Sci., 15, 1959–1977, https://doi.org/10.5194/hess-15-1959-2011, 2011.
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Short summary
In this research, we explored how the sampling uncertainty of flood variables (flood peak, volume, etc.) can reflect on a structural variable, which in our case was the maximum water level (MWL) of a reservoir controlled by a dam. Next, we incorporated additional information from different sources for a better estimation of the uncertainty in the probability of exceedance of the MWL. Results showed the importance of providing confidence intervals in the risk assessment of a structure.