RISICO a system for widenation wildfire risk assessment
- Slides: 11
RISICO a system for wide-nation wildfire risk assessment and management Paolo Fiorucci, Francesco Gaetani, Riccardo Minciardi
A general architecture for wildfire risk management Real-time weather conditions Topographic and territorial data Meteo forecast Historical forest fires database Climate data Earth Obs. data Static hazard assessment procedure Other sensors data Fire detection and fire tracking procedures In field measurement Direct observation delimitation and measurement of the burned areas Dynamic hazard assessment procedure Urban and forest planning Preoperational resources management procedures Exposed elements data Active fire risk management Real-time resource allocation procedure Definition of administrative measures
Dynamic hazard assessment The main drivers of wildfire process are: ➲ ➲ ➲ Vegetation; Meteorological conditions; Topography; Defining suitable semi-empirical models it is possible to define an estimation of the potential behaviour of a fire eventually ignited in a certain space time window.
Dynamic hazard assessment Discretizing the considered area it is possible to define for each grid cell: Vegetation type (dead and live fuel load, live fuel moisture, Higher Heating Value); ➲ Meteorological condition and/or forecast (Air temperature, relative humidity, rainfall, wind speed and direction); ➲ Elevation ➲ Slope ➲ Aspect ➲
RISICO: a system for dynamic fire risk assessment Rainfall Snowfall RH Air temperature No wind initial speed on flat terrain Drying/Wetting Rate Slope effects on ROS Dead Fine Fuel Moisture Conditions Air temperature X Initial Rate of Spread Dead fine Fuel moisture model LHV dead fuel Slope Wind speed Duff Moisture Conditions Soil Moisture Conditions Air Temperature Fireline Intensity Potential fire spread model Wind direction Slope equivalent wind speed Aspect Live fine fuel moisture and load models Equilibrium Moisture Contents Wind speed Soil Moisture Conditions Phenological Model Leaf Growth Model Biomass Load Model Biomass Moisture Model Slope Fine dead fuel Load Fine live LHV live fuel load Fine live moisture conditions
The meteorological information METEO FORECASTED DATA The system receives daily the outputs of a meteorological Limited Area Model (LAM), namely COSMO LAMI consisting of a set of data discretized in time steps of three hours, over a time horizon of 72 hours, defined over a regular grid composed by 57200 cells having a side corresponding to 0. 05 degrees tk (h) air temperature rk (h) dew point temperature pk (h) cumulate rainfall (th – th-1 ) wk (h) wind speed hk (h) wind direction METEO OBSERVED DATA Each run of the system is fed by new fresh data obtained from the extended national Meteorological Observation Network. Information relevant to 24 h cumulate precipitation and temperature observed by almost 3000 meteo stations is interpolated to obtain the fields defining the zero state of the daily run. [K] [m] [m s-1] [rad]
The land use and vegetation data Source data: CORINE LAND COVER release 2000 (CLC 2000). 16 categories of fuel has been considered and parametrized. Live fuels Dead fine fuels • average seasonal fuel loads [kg m-2]; • average seasonal moisture contents [%] only for live fuels; • average seasonal Higher Heating Value (HHV) [k. J kg-1].
EO products NDVI map – MODIS acquired in 2003 -07 -18 Remote Sensing data provide valuable information for the characterization of the state of vegetation, mapping of fuel types and vegetation properties at different temporal and spatial scales including the global, regional and landscape levels. SNOW COVER map - MODIS acq. in 2001 -01 -01
RISICO graphical user interface
RISICO graphical user interface
Towards GRID architecture ➲ ➲ ➲ Only a high resolution grid allows to represent the vegetation and topographical heterogeneity; When several fires are active simultaneously civil protection managers need to know which fire determine the highest risk for the people; GRID architecture allows to introduce new perspective in potential risk assessment by means of propagation models able to give in output the potential burnt area in a given time interval and the potential damage considering the exposed elements.
