Protecting vulnerable communities: a case study of index-based flood insurance in India, powered by flood modeling and remotely sensed rainfall

The poor across the world is vulnerable to floods and drought disasters, which have a detrimental effect on the lives and livelihoods of the poor. Weather-based index insurance is one of the ways of dealing with these disasters. Protecting against floods and providing risk cover against losses due to floods has been a major area of concern for any government. Risk transfer through insurance is an important component in managing agricultural risks from extreme flood events. The study developed the first of its kind of design and implementation of an index-based flood insurance (IBFI) product with the advanced use of satellite data and flood models to estimate crop losses due to floods. IBFI insurance product uses two different data elements, and the first one is based on the flood model using HEC-HMS and HEC-RAS that uses inputs from NASA GPM bias-corrected satellite rainfall estimates, observed water level and discharge data, river characteristics, and digital elevation model to generate flood depth and flood duration to develop predetermined thresholds based on the historical flood events between 1991 and 2015 and the second IBFI product uses only satellite data from NASA MODIS Terra and Aqua satellite data and the Copernicus Sentinel-1 SAR data to generate flood depth and flood duration to develop predetermined thresholds based on the historical flood events and economic losses. More than 7000 farming households in Bihar (India) and northern Bangladesh have signed up for a pilot IBFI scheme, which went live in 2017. The participating farmers have received insurance compensation for crop losses of over $US160,000. In addition to the insurance product implementation, the research evaluated the farming willingness to pay, developing business models for scaling, social equity, and economic benefits of derisk disasters. IBFI initiative promotes a closer linkage between risk transfer and risk reduction that could make this a more sustainable and robust financial instrument for flood-affected communities and reduce the burden of postdisaster relief funds for the government. In summary, index insurance using open-access satellite imagery is a win-win opportunity as it brings down the data development cost, lower insurance premiums, quick settlement, and greater transparency among various users.