Decreasing uncertainty in flood frequency analyses by including historic flood events in an efficient bootstrap approach

Anouk Bomers,Suzanne J M H Hulscher,Ralph M J Schielen

doi:10.5194/nhess-19-1895-2019

Abstract

Abstract. Flood frequency curves are usually highly uncertain since they are based on short data sets of measured discharges or weather conditions. To decrease the confidence intervals, an efficient bootstrap method is developed in this study. The Rhine river delta is considered as a case study. We use a hydraulic model to normalize historic flood events for anthropogenic and natural changes in the river system. As a result, the data set of measured discharges could be extended by approximately 600 years. The study shows that historic flood events decrease the confidence interval of the flood frequency curve significantly, specifically in the range of large floods. This even applies if the maximum discharges of these historic flood events are highly uncertain themselves.

Highlights

Floods are one of the main natural hazards to cause large economic damage and human casualties worldwide as a result of serious inundations with disastrous effects
We find that using only the 1901 data set results in larger design discharges compared to the two extended data sets
An advantage of the proposed method is that any kind of historical information can be used to extend the data set of annual maximum discharges as long as the information can be translated into discharges

Summary

Introduction

Floods are one of the main natural hazards to cause large economic damage and human casualties worldwide as a result of serious inundations with disastrous effects. Design discharges associated with a specific return period are used to construct flood defences to protect the hinterland from severe floods. These design discharges are commonly determined with the use of a flood frequency analysis (FFA). The basic principle of an FFA starts with selecting the annual maximum discharges of the measured data set, or peak values that exceed a certain threshold (Schendel and Thongwichian, 2017). These maximum or peak values are used to identify the parameters of a probability distribution. Discharges corresponding to any return period can be derived

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