Leveraging Trillions of Pixels for Flood Mitigation Decisions Support in the Rio Salado Basin, Argentina

The Rio Salado River Basin in Argentina is an economically important region that generates 25-30 percent of Argentina's grain and meat production. Between 2000-2011, floods in the basin caused nearly US$4.5 billion in losses and affected 5.5 million people. With the goal of developing cost-efficient flood monitoring and prediction capabilities in the Rio Salado Basin to support decision making, Cloud to Street is developing satellite based analytics to cover information gaps and improve monitoring capacity. This talk will showcase the Flood Risk Dashboard developed by Cloud to Street to support monitoring and decision-making at the level of provincial and national water management agencies in the Rio Salado Watershed. The Dashboard is based on analyzing thousands of MODIS, Landsat, and Sentinel scenes in Google Earth Engine to reconstruct the spatial history of flooding in the basin. The tool, iteratively designed with the end-user, shows a history of floodable areas with specific return times, exposed land uses and population, precipitation hyetographs, and spatial and temporal flood trends in the basin. These trends are used to understand both the impact of past flood mitigation investments (i.e. wetland reconstruction) and identify shifting flood risks. Based on this experience, we will also describe best practices on making remote sensing "flood dashboards" for water agencies.