Researchers develop statistical model to predict floods

Researchers have developed a novel statistical model for prediction of floods and its duration. It also accurately determines the timescale of flooding.

"It is possible to predict the duration of floods by coupling atmospheric dynamics and land surface conditions in the watershed," said study researcher Nasser Najibi from the City College of New York.

For the study published in the journal Nature Research, researchers analysed data from the Missouri river basin from the last 50 years to develop the Bayesian network statistical model.

The research team found that long duration floods first require high flow conditions in rivers created by recurrent high-intensity rainfall events, which is then followed by a large stable long-lived low-pressure system -- a storm cell. These conditions may then result in large-scale devastating floods.

In shorter duration floods, however, this land-atmospheric coupling is negligible thus explaining why not all storms result in widespread flooding.

The researchers said that with the help of this statistical model, potential risk imposed by longer duration floods on critical infrastructure systems such as flood control dams, bridges and power plants can be mitigated.

It is also possible to predict how long the duration of flooding and inundation will be, they said.

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