Red Cross Develops Innovative Mechanism To Predict & Prepare For Flood Risks

The Red Cross Red Crescent Climate Centre has revealed a new and innovative mechanism which will help at-risk communities predict and prepare for flood risks, and help mobilize resources to support those communities.

The new “forecast-based financing” will be unveiled at a special session at the upcoming 2017 World Hydropower Congress, and according to hosts, the International Hydropower Association (IHA), the hydropower sector “can be an important partner for testing and developing this mechanism.”

The difference with this idea of forecast-based financing is that, currently, most flood relief funding is only available once disaster has struck a region or community — after people have already died and suffered tremendous loss. This new method opens up the idea of funding in advance of a potential disaster, using scientific forecasting and flood risk management.

“Hydropower dams are natural partners for this initiative, as it is possible to predict when flooding is likely to occur, but often there is a lack of communication between operators and affected communities,” said Pablo Suarez, associate director for research and innovation at the Red Cross Red Crescent Climate Centre, who is one of the architects behind the scheme. “This is where the Red Cross, together with governments and other partners, can help mobilise money when disaster is likely to strike. That money can save lives and protect valuable assets.”

Pablo Suarez explains that the concept isn’t entirely new, but could nevertheless make a significant impact for at-risk communities in developing countries:

“This approach is already in use in countries like Switzerland, where plenty of preparedness measures exist, including finances to take action. But in developing countries we often notice two things are missing. Firstly, information isn’t reaching the communities that need it. Dam operators may know a flood is likely, but even if they are informing an authority, the information isn’t always trickling down. Secondly, even where early warning systems do exist, there is often no funding to deliver the necessary preventive measures in a timely manner.”

Forecast-based financing is currently being implemented at the Nangbéto Dam in Togo, Africa, and is using machine learning systems to help hydropower operators predict flood risks as well as enable them to communicate these risks to at-risk communities in a clear and timely manner. Currently, conventional forecasts using meteorology and hydrology aren’t yet reliable enough to mobilize resources for humanitarian action. An example of just how disastrous things can get, in 2010 a major flood took place on the Mono River in Togo, and it took 34 days for disaster relief funding from international sources to reach the Red Cross in Togo.

The Red Cross team are using what little available data there is combined with machine learning to create a model called FUNES which is based on a self-learning algorithm that aims to predict likely overspill of the dam. The new system has already been put to the test, when in September of last year, unusual rainfall patterns triggered an early warning of likely flooding, which released funding and preventative measures for the region to protect downstream communities.