Development of high spatial resolution Forest Fire Index for boreal conditions Applications to Helsinki Testbed area - Preliminary results- Andrea Vajda, FMI Mesoscale Atmospheric Network Course, February 13, 2007

Ferbuary 13, 2007Mesoscale Atmospheric Network Course, HY Outline  The Finnish Forest Fire Index calculation  Objectives  Helsinki Testbed applications - Downscaling soil moisture data - Spatial variation of temperature and precipitation - Adjustment of the rainfall gauges and weather radar precipitation data  Next steps

Ferbuary 13, 2007Mesoscale Atmospheric Network Course, HY Soil surface moisture as the indicator of the fire risk Potential evaporation on a 10 km * 10 km grid Penman-Monteith equation The forest fire index calculation in Finland

Ferbuary 13, 2007Mesoscale Atmospheric Network Course, HY Soil surface moisture as the indicator of the fire risk Surface layer Precipitation Evaporation Infiltration and run-off The drying/wetting of soil surface due to evaporation/precipitation is based on results obtained from field measurements Estimates of soil moisture content Forest fire index FFI ≥4 → Forest fire warning The forest fire index calculation in Finland (cont’d)

Ferbuary 13, 2007Mesoscale Atmospheric Network Course, HY Spatial analysis of fire index at 9.00 a.m. Provincial (weighted) averages at 9.00 a.m. warning no warning Reference: [-24h] [-12h] [0h] [+12h] [+24h] Click to get past or forecast situation! Forest fire risk assessment for Finland

Ferbuary 13, 2007Mesoscale Atmospheric Network Course, HY Objectives  To develop high spatial resolution (1*1 km 2 ) forest fire index for boreal condition (e.g. Finland)  To apply the weather radar precipitation observation to the forest fire index calculation  To apply and test the high resolution FFI on Helsinki Testbed area  To use a SVAT model (COUP Model) in boreal forest vegetation fire risk assessment to be tested in the Helsinki Testbed area (Nurmijärvi observatory)

Ferbuary 13, 2007Mesoscale Atmospheric Network Course, HY High resolution FFI development- Helsinki Testbed Application Spatial variation of soil moisture on August 4, *10 km 2 1*1 km km 250 km 10 km*10 km1 km*1 km

Ferbuary 13, 2007Mesoscale Atmospheric Network Course, HY High resolution FFI development- Helsinki Testbed Application Kriging spatial interpolation method with 1*1 km 2 spatial resolution (Vajda and Venäläinen, 2003) Z (X) = M(X) + E(X) M(x, y, h, l, s) = a 0 + a 1 x + a 2 y + a 3 x 2 + a 4 y 2 + a 5 xy +a 6 h + a 7 s + a 8 l  Surface classification and land-cover data: from Global Land Cover 2000 database (EC, Joint Research Centre, 2003)  Elevation data: from the Global Land One-km Base Elevation (GLOBE) project database (NOAA National Data Centres, 1998)  Lake and sea coverage data: calculated using the previous two dataset

Ferbuary 13, 2007Mesoscale Atmospheric Network Course, HY Spatial variation of air temperature measured at 12 h on 3-4 August, 2006 Input data: from 106 stations Spatial resolution: 1*1 km km 250 km

Ferbuary 13, 2007Mesoscale Atmospheric Network Course, HY Spatial variation of precipitation based on gauge measurements on August 3, *1 km 2 Input data : daily rainfall sum from 99 gauges

Ferbuary 13, 2007Mesoscale Atmospheric Network Course, HY Application of weather radar precipitation data to fire risk calculation Rain measurements - Circles: Radar 20-60km - Dots: Manual observations - Big diamonds: FD12P - Small diamonds: Potential FD12P - Triangles: Automatic snow depth - Squares: Weighing gauge

Ferbuary 13, 2007Mesoscale Atmospheric Network Course, HY Spatial variation of precipitation based on radar observation on August 3, *1 km 2 r= 0.38

Ferbuary 13, 2007Mesoscale Atmospheric Network Course, HY The trend and the fluctuation of the spatial variation of precipitation obtained by the combination of gauge and radar data M (X)Z(X)

Ferbuary 13, 2007Mesoscale Atmospheric Network Course, HY Rainfall data obtained from gauge- radar adjustment

Ferbuary 13, 2007Mesoscale Atmospheric Network Course, HY Work to be done  To test/verify the adjustment method and analyze the results using a longer time period  To demonstrate the influence of high spatial resolution precipitation information on the variation of fire risk indices  To apply the high resolution FFI to Helsinki Testbed area Finland  Explore SVAT methods to estimate vegetation fire risk