Design Flood Estimation: Exploring the Potentials and Limitations of Two Alternative Approaches

Abstract

The design of flood defence structures requires the estimation of flood water levels corresponding to a given probability of exceedance, or return period. In river flood management, this estimation is often done by statistically analysing the frequency of flood discharge peaks. This typically requires three main steps. First, direct measurements of annual maximum water levels at a river cross-section are converted into annual maximum flows by using a rating curve. Second, a probability distribution function is fitted to these annual maximum flows to derive the design peak flow corresponding to a given return period. Third, the design peak flow is used as input to a hydraulic model to derive the corresponding design flood level. Each of these three steps is associated with significant uncertainty that affects the accuracy of estimated design flood levels. Here, we propose a simulation framework to compare this common approach (based on the frequency analysis of annual maximum flows) with an alternative approach based on the frequency analysis of annual maximum water levels. The rationale behind this study is that high water levels are directly measured, and they often come along with less uncertainty than river flows. While this alternative approach is common for storm surge and coastal flooding, the potential of this approach in the context of river flooding has not been sufficiently explored. Our framework is based on the generation of synthetic data to perform a numerical experiment and compare the accuracy and precision of estimated design flood levels based on either annual maximum river flows (common approach) or annual maximum water levels (alternative approach).

A Design Water Discharge Maxima Forecasting Method Based on Observation Data Using Plotting Position Formulas

“…Therefore, the probabilistic methods based on hydrological observation data statistic analysis have been more popular in studying and predicting diverse design hydrological characteristics. In particular, it is in the case of forecasting maxima water discharges relating to riverine floods [1][2][3].…”

“…Usually, the time series of observed maxima water discharges are considered and analyzed in the frame of the stationary hypothesis. To forecast design discharges of low annual probability of exceedance, parametrical probability distributions are used as predictive models [2]. Returning to the problem of flood risk management, it should be reminded that Directive 2007/60/EC (the EU Flood Risk Directive) [5] defines flood risk quantitatively as "the combination of the probability of a flood event and the potential adverse consequences for human health, the environment, cultural heritage and economic activity associated with a flood event".…”

Spring flood frequency analysis in the Southern Buh River Basin, Ukraine

“…Since the natural disaster as extreme floods is the basis for planning and design of various hydraulic structures, hydrological forecasting, flood risk reflection characteristics such as trends of extreme floods and its changes, and its formation conditions, the probable maximal flood and its characteristics have a great practical importance. The determining of the probable maximal flood is the practical importance, especially for the planning, design, and operation of hydrotechnical structures (Apel et al, 2004;Blöschl et al, 2013;Okoli et al, 2019).…”

“…Hence, the important task is obtaining reliable flood estimates. This can be achieved by using appropriate methodological approaches (McKerchar and Macky, 2001;Kjeldsen, 2015;Okoli et al, 2019).…”

Caracterização de eventos extremos de precipitação e suas consequências em um contexto de mudanças climáticas

“…Geralmente, a caracterização de eventos extremos é baseada na análise estatística de frequência (NGUYEN;NGUYEN, 2019). Este processo envolve o ajuste de uma distribuição de probabilidade que seja adequada para descrever as variáveis de interesse observadas (BARATTI et al, 2012;OKOLI et al, 2019a). O modelo ajustado é, então, extrapolado para associar eventos de magnitude específica a um período de retorno, que expressa a raridade de um evento, de acordo com o seu tempo médio de recorrência .…”