7th Workshop on Forest Fire Management - EARSeL, European ...

ESTIMATION OF NATIONAL FIRE DANGER RATING SYSTEM 10 HOUR
TIMELAG FUEL MOISTURE CONTENT WITH MSG-SEVIRI DATA
H. Nieto 1 , I. Aguado 1 , E. Chuvieco 1 , I. Sandholt 2
1 Department of Geography, University of Alcalá. Alcalá de Henares, Spain
hector.nieto@uah.es; inmaculada.aguado@uah.es; emilio.chuvieco@uah.es
2 Department of Geography, University of Copenhagen. Copenhagen, Denmark
is@geo.ku.dk
Abstract: The moisture content of fuels is a key factor in both fire ignition
and propagation. Fire meteorological indices are commonly based on temperature,
relative humidity, solar radiation and wind speed, which are only
measured in selected sites that usually are sparsely distributed. In this
study we propose to use remote sensing data to estimate meteorological
data at an adequate spatial and temporal resolution. We will make use of
the Spinning Enhanced Visible and Infrared Imager (SEVIRI) sensor,
onboard the Meteosat Second Generation (MSG) satellites, to estimate air
temperature and relative humidity on an hourly basis. Air temperature and
vapour pressure are combined to calculate Simard’s Equilibrium Moisture
Content and the NFDRS 10 hour timelag fuel moisture content. A meteorological
station located in the Cabañeros National Park (Spain) has been
used to calibrate and validate the results during the year 2005.
1 - Introduction
One of the factors in fire danger management systems is fuel moisture content
(FMC), since it is a critical variable in fire ignition and propagation
(Dimitrakopoulos et al., 2001; Rothermel, 1972). Unlike live vegetation,
which can regulate water losses through stomatal closure, dead fuels tend
to gain or lose moisture until equilibrium with surrounding atmosphere is
achieved. This steady moisture content is called Equilibrium Moisture
Content (EMC), and is primarily affected by temperature and relative humidity
(Viney et al., 1991).
A revision of different dead fuel moisture content models can be found in
Viney (1991). Traditionally, these models have been applied with observed
data from meteorological stations or with forecasted data from Numerical
Weather Prediction (NWP) models. The spatial representation of meteorological
stations is limited, since the network usually is very sparse and stations
are usually located in agricultural or urban areas. On the other hand,
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