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

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I - PRE-FIRE PLANNING AND MANAGEMENT
November - 140 observations) and Winter-Spring (December to April - 100
observations) burnt area trends.
To correct for the bias in the climate change model, the projected future
fire danger conditions were estimated as the difference between the scenario
and the control period of the RCM-HIRHAM experiment, and than
added to the control period conditions assessed with the ERA-40 dataset.
The multiple regression models were than applied to the A2/B2 scenarios
results to predict the potential burnt areas in each country.
4 - Results
The stepwise multiple linear regression analysis, performed for all the countries
during the Summer-Autumn period, produces an adjusted r 2 over 0.70.
On the contrary for Winter-Spring period r 2 was ranging between 0.45-0.69.
The stepwise selection points out common sub-indexes (DC, ISI) for the
Summer-Autumn models, on the contrary remarkable differences were presented
for the selection of the sub-indexes (FFMC, DC, ISI) concerning the
Winter-Spring models (Table 1). Monthly average prediction and observation
is depicted in Figure 1. A well noted model fitting is visible for the
Summer-Autumn period. On the contrary the Winter-Spring period shows a
not linearity trend. The burnt prediction for the two climate change scenarios
are extremely evident in August (Figure 1). In Figure 2 is reported
predicted and observed area trend for the different time periods considering
the months variability of the ISI and DC indexes.
5 - Conclusion
Sub-indexes future projection point out a clear increase of their average
values al EU-Med level and also at country level. Fire weather and burnt
area are strongly linked by a robust correlation. Nonetheless, alternative
no-standard regression models (MARS, GAM) should be tested to smooth
the highest values of the identified exponential function. More climate
change simulation models should be tested to come out with an ensemble
model able to drive internal variability parameters. The understanding of
future fire activity and consequent burnt area can help the European member
states to adapt and develop mitigation plans to prevent dramatic fire
events.
References
Christensen, J.H., Christensen, O.B., Lopez, P., Van Meijgaard, E., Botzet,
M., 1996. The HIRHAM4 Regional Atmospheric Climate Model. DMI
Scientific Report 96-4, Danish Meteorological Institute, Copenhagen,