Researchers predict floods with a new, more effective method

In a recent study, researchers have proposed an alternative method for predicting floods. This method is more appropriate for analysing flood frequency in a changing environment.

It is crucial to accurately predict flood frequency and severity to reduce physical and economic losses, as more of us are vulnerable to its effects than ever before. This has been caused by changes in climate, land use, infrastructure, and population growth in recent decades.

Conventional analysis of flood frequency assumes that flooding follows historical patterns and the methods used often do not consider changing conditions, such as climate change, river regulation, and land cover variation. This creates a higher risk of underestimating the frequency and severity of floods and designing less resilient infrastructure.

The new and resilient method is published in the Journal of Hydrology.

Using nonstationary models to make more reliable estimations

The team of researchers proposed a model that is a type of nonstationary flood frequency analysis. Nonstationary models provide more reliable estimations for water-related structures and flood prevention measures, as they consider variations of factors influencing flood frequency.

Despite nonstationary frequency analysis now being a widely researched topic for measuring floods, there is a lack of consensus on the most appropriate methods. The existing models are either too complicated or too expensive for engineers or hydrologists to implement in practice.

In 2021, Mengzhu Chen, the first author of the paper, published a study that used a different model of nonstationary flood frequency across the UK. From this, she found there were limitations in applying this approach to practices like engineering design and hydraulic structure design.

“We were unable to express the model as a simple mathematical formula, which made it difficult to interpret and calculate. Therefore, we wanted to find a more suitable model,” Chen explained.

Bridging the gap in hydraulic research

To assess and compare different modelling techniques in the current study, the researchers analysed historical data of floods from 161 catchments across the UK.

These areas, also known as watersheds or drainage basins, have natural boundaries such as ridges, hills, or mountains, and all surface water drains to a common channel to form rivers or creeks.

The team found that the ‘fractional polynomial-based regression’ model is the most flexible, effective, and user-friendly among all the models. This method is an emerging tool in certain applied research areas like medical statistics and clinical research but is currently used very little in the hydrology field.

Chen said: “Currently, there is a gap between hydraulic research and practice, as most practitioners are not familiar with nonstationary models, even though they have gained popularity in academia.

“The findings of our recent study provide recommendations to hydrologists and engineers to help them choose from the available analysis models.

“For practitioners, the fractional polynomial model we propose in our paper can be an additional valuable tool for application. It can be expressed as a mathematical formula and is more user-friendly.”

Can this method effectively predict floods in the future?

The primary purpose of nonstationary flood frequency analysis is to provide estimations for the design, construction, and management of water-related infrastructure.

“Nevertheless, there’s a long way to go before nonstationary methods can be widely used in practice. A more user-friendly, straightforward, and generally agreed-upon approach for nonstationary flood frequency analysis is still worth exploring in the future,” Chen said.

“We also need further investigation into the complex underlying factors influencing flood frequency to help prepare for future extreme weather events,” she concluded.

The research team consists of Mengzhu Chen and Professor Konstantinos Papadikis from XJTLU, Dr Changhyun Jun from Chung-Ang University, and Professor Neil Macdonald from the University of Liverpool.

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