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 08.05.2006 at 9.00 a.m.

Provincial (weighted) averages08.05.2006 at 9.00 a.m.

warningno warning

Reference: http://virpo.fmi.fi/metsapalo/

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Forest fire risk assessment for Finland

Ferbuary 13, 2007Mesoscale Atmospheric Network Course, HY

Objectives

To develop high spatial resolution (1*1 km2) 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, 2006

200 km

250 km

10 km*10 km 1 km*1 km

Ferbuary 13, 2007Mesoscale Atmospheric Network Course, HY

High resolution FFI development- Helsinki Testbed Application

Kriging spatial interpolation method with 1*1 km2 spatial resolution (Vajda and Venäläinen, 2003)

Z (X) = M(X) + E(X)

M(x, y, h, l, s) = a0 + a1x + a2y + a3x2 + a4y2 + a5xy +a6h + a7s + a8l

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 km2

200 km

250 km

Ferbuary 13, 2007Mesoscale Atmospheric Network Course, HY

Spatial variation of precipitation based on gauge measurements on August 3, 2006

1*1 km2

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, 2006

1*1 km2

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