GIS-WRAP
Assessment and forecast of weather extreme events
GIS-WRAP (Geographical Information System and Weather Risk Awareness Platform) is a toolbox for the mapping and assessment of multi-risk scenarios (forest fires, floods, extreme weather). It combines weather observation and forecast, and thematic and remote sensing data in a time-dependent geographical platform to feed models in an interoperable fashion. Outcomes in the form of risk indices, warnings, and vulnerability reports, are communicated through an advanced visualization interface.
Technology demonstrated in relevant environment.
Representative model or prototype system, which is well beyond that of TRL 5, is tested in a relevant environment. Represents a major step up in a technology’s demonstrated readiness. Examples include testing a prototype in a high-fidelity laboratory environment or in a simulated operational environment.
Representative model or prototype system, which is well beyond that of TRL 5, is tested in a relevant environment. Represents a major step up in a technology’s demonstrated readiness. Examples include testing a prototype in a high-fidelity laboratory environment or in a simulated operational environment.
Most of GIS-WRAP components, and a preliminary pilot version of the integrated system, have been tested in operational use for risk assessment in real forest fires, along the last four fire campaigns in Madrid Region (Spain), while some other services -yet experimental-, such as the integration of satellite-based forest fuel moisture monitoring, have been tested separately in other areas, such as Basque Country Region (Spain). Feedback from end-users at the operational bodies has been duly collected and used for further improvement and adaptation of the system. Currently, testing of components for the integration of vulnerability in the risk assessment process is taking place.
How does it work?
GIS-WRAP seeks and collects weather observation and forecast data from external datasets according to quality and resolution criteria. Meteorological data are processed, if required, using spatial interpolation and numerical downscaling techniques, and are 2D/3D shown through visualization consoles. This data is then combined with spatial thematic layers to retrieve additional risk variables and indexes, and warnings are generated after a threshold-based approach is set up. Modelling techniques are finally used to simulate the evolution of hazard and its induced effects (spread of forest fires or floods), its intensity, and the area affected. Vulnerability maps and reports are also generated for supporting operational decisions.
By logging in you can access to more detailed information on the innovation