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Fire date model

Fire date model. Problematic. The date of burn has a significant on fire severity in boreal forest. This effect is taken into account in TEM, rather using a lookup table (Yi et al. 2010) or a continuous model (Genet et al. 2013). ALFRESCO does simulate fire occurrence but not fire date.

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Fire date model

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  1. Fire date model

  2. Problematic • The date of burn has a significant on fire severity in boreal forest. • This effect is taken into account in TEM, rather using a lookup table (Yi et al. 2010) or a continuous model (Genet et al. 2013). • ALFRESCO does simulate fire occurrence but not fire date. • The TEM group usually assess the date of burn (DOB) for periods outside the historical records, in order to reflect the current population of DOB. • The problem with this approach is that is doesn’t take into account the potential effect of climate change on DOB.

  3. Presentation of the database

  4. Correlation with climate • Pearson correlation coefficient between the julian date of burn (DOB) and monthly precipitation and temperature of the year of burn (YOB) Monthly air temperature Monthly Precipitation

  5. Correlation with climate • Pearson correlation coefficient between the julian date of burn (DOB) and monthly precipitation and temperature of the year of burn (YOB) Monthly air temperature Monthly Precipitation Synthetic variables: Tsp = mean March through June Psp = mean January through May Tsm = mean July and August Psm = mean June through August

  6. Correlation with topographic descriptors • The effect of topography varies with the spatial scales at which it is considered

  7. Correlation with topographic descriptors • The effect of topography varies with the spatial scales at which it is considered

  8. Correlation with the size of the fire scar • The size of the fire scar has only a significant effect in the tundra ecoregion.

  9. Predictive model of date of burn per ecoregion • The explanatory variables are inter-correlated and the year the fires occurs should also be taken into account since several scars can occur the same year. • The colinearity among explanatory variable has been taken into account using a PCA among these variables (centered scaled) and using the orthogonized components as explanatory variables • The effect or repeated observation (fire scar) per year has been taken into account using a mixed model with the principal components of the above PCA.

  10. Response function per ecoregion • This graph showed the regression coefficient of the centered scaled explanatory variables for each of the three ecoregions.

  11. Explanatory power and verification Although the explanatory power of this model is quite poor, it reproduces some of the inter-annual variability of DOB at the regional scale. The model also allows to reproduce the potential effect of climate change on DOB. The climate of the year prior to fire hasn’t been considered but might have a significant effect of DOB. Given the high variability of the data, the use of smaller ecoregions might also allow to increase the model’s power.

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