Improved assessment of maximum streamflow for risk management of hydraulic infrastructures. A case study

ABSTRACT

Understanding the risks associated with the likelihood of extreme events and their respective consequences for the stability of hydraulic infrastructures is essential for flood forecasting and engineering design purposes. Accordingly, a hydrological methodology for providing reliable estimates of extreme discharge flows approaching hydraulic infrastructures was developed. It is composed of a preliminary assessment of missing data, quality and reliability for statistically assessing the frequency of flood flows, allied to parametric and non-parametric methods. Model and parameter uncertainties are accounted for by the introduced and proposed modified model averaging (modified MM) approach in the extreme hydrological event's prediction. An assessment of the parametric methods accuracy was performed by using the non-parametric Kernel Density Estimate (KDE) as a benchmark model. For demonstration and validity purposes, this methodology was applied to estimate the design floods approaching the case study ‘new Hintze Ribeiro bridge’, located in the Douro river, one of the three main rivers in Portugal, and having one of Europe's largest river flood flows. Given the obtained results, the modified MM is considered a better estimation method.

KEYWORDS:

• Flood events
• hydraulic infrastructures
• KDE
• modified MM

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

The data that support the findings of this study are available from the corresponding author, Bento, A.M., upon reasonable request.

Additional information

Funding

The first author thanks the Fundação para a Ciência e a Tecnologia (FCT) for the financial support through the PhD scholarship PD/BD/127798/2016. This manuscript is an outcome of the Doctoral Program INFRARISK – ‘Analysis and Mitigation of Risks in Infrastructures’. This research was co-supported by FCT's strategic funding UIDB/04423/2020 and UIDP/04423/2020. Streamflow data was partly made accessible thanks to 'Energias de Portugal (EDP).