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

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

Neistoty v povodňovom modelovaní

“…Uncertainties in flood modeling can be also found in other input data, which is also being given a considerable attention in scientific literature. These uncertainties are represented by the roughness coefficients (Dimitriadis et al, 2016), cross-section spacing (Ali, Di Baldassarre, Solomatine, 2015), data on the modeled stream centreline, river banks or hydraulic structure along the reach such as bridges (Altarejos-García et al, 2012), etc.…”

“…Nezanedbateľné neistoty v povodňovom modelovaní možno hľadať aj v ďalších vstupných dátach, ktorým sa tiež v literatúre venuje značná pozornosť. Ide o koeficienty drsnosti povrchu (Dimitriadis et al, 2016), vzdialenosť medzi priečnymi profilmi koryta (Ali, Di Baldassarre, Solomatine, 2015), údaje o samotnom modelovanom toku, brehových čiarach, či prekážkach na toku ako sú mosty alebo hrádze (Altarejos-García et al, 2012), atď.…”

Integración de la incertidumbre natural temporal y espacial asociada al coeficiente de rugosidad Manning en modelos bidimensionales de inundaciones rápidas

“…El índice de intensidad de flujo (I DF ) no es una variable del modelo numérico de aguas someras, sin embargo, su cálculo depende de la profundidad y la velocidad producto de las simulaciones hidrodinámicas. El I DF propuesto por Jakob et al (2012) relaciona la profundidad [16] y la magnitud de la velocidad al cuadrado (ecuación ( 9)); los valores para I DF se toman como estimaciones de la severidad del flujo (véase Altarejos-García et al, 2012).…”