Combining satellite and geospatial technologies for rainstorms hazard soft mapping.

Multiple Damaging Hydrological Events are rapidly developing into worldwide disasters with effects to the vi- able habitat for humankind and ecosystems. This research describes how data assimilation friendly models combining re- motely sensed and ground hydrological data could be used for developing a soft geovisual communication in order to re- duce the uncertainty in rainstorm hazard mapping. For this, a set of sequential GIScience rules was utilized for converting coding data of a Rainstorm Hazard Index (RHI) from point record to spatial information using TRMM-NASA satellite rain data as covariate. Examples of probability estimation for different precipitation durations, ranging from 3 to 48 hours and the quantification of hydrological hazard fields were used with probability maps of damaging rainstorms prone-areas for the test-region of Southern Italy. Results show that sub-regional rainstorm hazard modelling can provide probability maps for damaging events in Italy with a spatial variability resolution of around 20 km. Spatially finer estimates (e.g., at local-scale: < 10 km) can be ensured only with the availability of more accurate and detailed remote sensing rain data.