Science Helps to Reduce Flood Risk Around the World

July 24, 2015 6:05 pm Published by Leave a comment

New computer models that estimate the social and economic impacts of flooding are helping to protect people from devastating floods, and to reduce the economic losses associated with them. In a new study published in Nature Climate Change, international researchers led by Dr. Philip Ward of the Institute for Environmental Studies at the VU University Amsterdam, review how global flood risk models are being used in practice to reduce flood impacts around the world.

Dr. Philip Ward: “It’s great to see that the science we’ve been developing for the last few years is now really making a difference on the ground. We are working together closely with many international organisations, like the Red Cross, World Bank, and reinsurance companies, to contribute to reducing flood risk. Together with Deltares, World Resources Institute, PBL Netherlands Environmental Assessment Agency, and Utrecht University, we are now also developing free and easy to use web-tools, like the Aqueduct Global Flood Analyzer, to make our science easily accessible to all.”

The World Bank Group (WBG) and the Global Facility for Disaster Reduction and Recovery (GFDRR) are using these models to improve national-level decisions on disaster risk management. Dr. Alanna Simpson, Senior DRM Specialist, and co-author of the study states: “We often need information on flood hazard and risk rapidly to respond to Government requests for information – sometimes within days or weeks. This information may be used to prioritise investment in risk reduction or to understand which areas may have received the greatest damage from a recent flood event. Traditional flood modelling requires a significant investment in resources and time to produce results, and these new models allow us to answer some questions rapidly while we still have the attention of the decision-maker. Clearly there are limitations associated with global models, especially with their use at local levels, but certainly this approach represents a massive step forward in understanding and managing disaster risks.”

The Red Cross Red Crescent Climate Centre is using forecasts from global flood risk models to prepare for floods before they occur. In this way, hopefully a large part of the damages and suffering caused by floods can actually be prevented. In north-eastern Uganda, they are working together with the Uganda Red Cross and German Red Cross to test such a system. A forecast-based financing system is being established to trigger preventative actions before a flood occurs, based on forecasts from the GloFAS global flood forecasting model. These are large-scale actions like distributing water purification tablets in entire villages. Such “no regret” measures are appropriate for the high false alarm rates inherent in global flood models, since they are beneficial to the local communities even if the flood does not actually happen. Erin Coughlan de Perez, Senior Climate Specialist for the Red Cross Red Crescent Climate Centre, and co-author of the study, states that “Global flood models can provide risk information across timescales in places where this information might not otherwise be available. This includes long-term flood risks as well as a real-time indication of when flood risk is heightened. Our work centres on selecting the correct actions that can be triggered by this risk information, linking it with local systems”.

As the world’s population continues to grow, and the climate changes, the impacts of floods are expected to worsen. However, that trend can be broken if effective flood risk management strategies are adopted. Global flood risk models can help in developing those strategies, especially when scientists and users work together towards a common goal of disaster risk reduction.

Full citation: Ward, P.J., Jongman, B., Salamon, P., Simpson, A., Bates, P., De Groeve, T., Muis, S., Coughlan de Perez, E., Rudari, R., Trigg, M.A., Winsemius, H.C., 2015. Usefulness and limitations of global flood risk models. Nature Climate Change, 5, 712-715, doi: 10.1038/nclimate2742