RISICO a system for wide-nation wildfire risk assessment and management Paolo Fiorucci, Francesco Gaetani, Riccardo Minciardi.
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RISICO a system for wide-nation wildfire risk assessment and management Paolo Fiorucci, Francesco Gaetani, Riccardo Minciardi
A general architecture for wildfire risk management
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 RH Slope equivalent wind speed Wind speed Wind direction AspectSlope Fireline Intensity Slope effects on ROS Nowind initial speed onflat terrain InitialRate ofSpread LHV livefuel LHV dead fuel Dead fineFuel moisturemodel Potential fire spreadmodel Equilibrium Moisture Contents Rainfall Snowfall Leaf Growth Model Phenological Model Air Temperature Soil Moisture Conditions Fine live fuel load X Biomass Moisture Model L i v e f i n e f u e l m o i s t u r e a n d l o a d m o d e l s Air temperature Duff Moisture Conditions Wind speed Air temperature Fine dead fuel Load Biomass Load Model Drying/Wetting Rate Fine live moisture conditions Soil Moisture Conditions Dead FineFuel Moisture Conditions
The information 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 k (h) air temperature [ K ] k (h) dew point temperature [ K ] p k (h) cumulate rainfall (t h – t h-1 )[ m ] w k (h) wind speed [ m s -1 ] k (h) wind direction [ rad ] 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.
The land use and vegetation data Source data: CORINE LAND COVER release 2000 (CLC 2000). 16 categories of fuel has been considered and parametrized. average seasonal fuel loads [kg m -2 ]; average seasonal moisture contents [%] only for live fuels; average seasonal Higher Heating Value (HHV) [kJ kg -1 ]. Live fuels Dead fine fuels
SNOW COVER map - MODIS acq. in 2001-01-01 NDVI map – MODIS acquired in 2003-07-18 EO products 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.
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.