Supplementing flash flood reports with impact classifications

In recent years, there has been an increase in flash flood impacts, even as our ability to forecast events and warn areas at risk increases. This increase results from a combination of extreme events and the exposure of vulnerable populations. The issues of exposure and vulnerability to flash floods are not trivial because environmental circumstances in such events are specific and complex enough to challenge the general understanding of natural risks. Therefore, it seems essential to consider physical processes of flash floods concurrently with the impacts they trigger. This paper takes a first step in addressing this need by creating and testing the coherence of an impact-focused database based on two pre-existing public and expert-based survey datasets: the Severe Hazards Analysis and Verification Experiment (SHAVE) and the US National Weather Service (NWS) Storm Data. The SHAVE initiative proposes a new method for collecting near-real-time high-resolution observations on both environmental circumstances and their disastrous consequences (material and human losses) to evaluate radar-based forecasting tools. Forecast verification tools and methods are needed to pursue improving the spatial and temporal accuracy of forecasts. Nevertheless by enhancing SHAVE and NWS datasets with socially and spatially relevant information, we aim at improving future forecast ability to predict the amount and types of impacts.