Integrating earth observation data in hydrological runoff models

The remote sensing and GIS communities are still separate worlds with their own tools and data formats. It is extremely difficult to easily share data among scientists representing these communities without performing some cumbersome conversions. This paper shows in a case study how these two worlds can benefit from each other by implementing online satellite derived soil moisture in a GIS based operational flood early warning system. We obtained near real time satellite data from the currently active satellite microwave sensor AQUA AMSR-E from the National Snow and Ice Data Center data pool and converted the data to soil moisture maps with the Land Parameter Retrieval Model. The soil moisture maps, with a spatial resolution of 0.1 degree and temporal resolution of approximately 1 day, were converted in a gridded format and directly added to an operational Flood Early Warning System. The developed opportunity to directly visualize soil moisture in such a system appears to be a powerful tool, because it creates the ability to study both the spatial and temporal evolution of soil moisture within the river basin. Furthermore, near real time qualitative information on soil moisture conditions prior to rainfall events, such as generated by our system, can even lead to more accurate estimations for flood hazard conditions. Finally, the current and future role and value of remote sensing products in flood forecasting systems are discussed.