Multiple Damaging Hydrological Events are rapidly developing into worldwide disasters with effects to the viable habitat for humankind and ecosystems. This research describes how data assimilation friendly models combining remotely sensed and ground hydrological data could be used for developing a soft geovisual communication in order to reduce 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.
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