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Hydrology and Earth System Sciences An interactive open-access journal of the European Geosciences Union
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Volume 16, issue 7
Hydrol. Earth Syst. Sci., 16, 1895–1914, 2012
https://doi.org/10.5194/hess-16-1895-2012
© Author(s) 2012. This work is distributed under
the Creative Commons Attribution 3.0 License.
Hydrol. Earth Syst. Sci., 16, 1895–1914, 2012
https://doi.org/10.5194/hess-16-1895-2012
© Author(s) 2012. This work is distributed under
the Creative Commons Attribution 3.0 License.

Research article 05 Jul 2012

Research article | 05 Jul 2012

Assessing the impact of uncertainty on flood risk estimates with reliability analysis using 1-D and 2-D hydraulic models

L. Altarejos-García1,2, M. L. Martínez-Chenoll1, I. Escuder-Bueno1, and A. Serrano-Lombillo2 L. Altarejos-García et al.
  • 1Instituto de Ingeniería del Agua y Medio Ambiente, UPV, ETSICCP, Edificio 4E – Camino de Vera s/n, 46022 Valencia, Spain
  • 2iPresas Risk Analysis, Centro de Desarrollo Empresarial, UPV, Edificio 9B – Camino de Vera s/n, 46022 Valencia, Spain

Abstract. This paper addresses the use of reliability techniques such as Rosenblueth's Point-Estimate Method (PEM) as a practical alternative to more precise Monte Carlo approaches to get estimates of the mean and variance of uncertain flood parameters water depth and velocity. These parameters define the flood severity, which is a concept used for decision-making in the context of flood risk assessment. The method proposed is particularly useful when the degree of complexity of the hydraulic models makes Monte Carlo inapplicable in terms of computing time, but when a measure of the variability of these parameters is still needed. The capacity of PEM, which is a special case of numerical quadrature based on orthogonal polynomials, to evaluate the first two moments of performance functions such as the water depth and velocity is demonstrated in the case of a single river reach using a 1-D HEC-RAS model. It is shown that in some cases, using a simple variable transformation, statistical distributions of both water depth and velocity approximate the lognormal. As this distribution is fully defined by its mean and variance, PEM can be used to define the full probability distribution function of these flood parameters and so allowing for probability estimations of flood severity. Then, an application of the method to the same river reach using a 2-D Shallow Water Equations (SWE) model is performed. Flood maps of mean and standard deviation of water depth and velocity are obtained, and uncertainty in the extension of flooded areas with different severity levels is assessed. It is recognized, though, that whenever application of Monte Carlo method is practically feasible, it is a preferred approach.

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