Fire burned area from 2001 to present derived from satellite observations Copernicus
A review of existing datasets and methodologies for fire hazard has been previously included in the hazard review document, but needs to be updated.
Wildfire
A wildfire is any uncontrolled burning of biomass and affected man-made assets, which spreads based on environmental conditions. The probability of wildfire occurrence is typically measured by the Fire Weather Index (FWI), possibly in conjunction with a fuel model.
Name
Global Fire Weather Index
Global fire danger re-analysis (1980–2018) for the Canadian Fire Weather Indices
Developer
CSIRO
Vitolo et al.
Hazard process
Wildfire
Wildfire
Resolution
10 km
Analysis type
Probabilistic
Frequency type
Return Period (3 RPs): 2, 5, 10 years
Time reference
Baseline (36 years)
Baseline (1980-2018)
Intensity metric
Fire Weather Index
License
Open data
Notes
The CSIRO datasets (Fig. 8), which drove the wildfire assessment in ThinkHazard and other applications, uses an approach which is entirely based on fire weather index climatology (Fire Weather Index, FWI) to assess both the onset of conditions that will allow fires to spread, as well as the likelihood of fire at any point in the landscape. The method uses statistical modelling (extreme value analysis) of a 36 years fire weather climatology from GFWED to assess the predicted fire weather intensity for specific return period intervals. These intensities are classified based on thresholds using conventions to provide hazard classes that correspond to conditions that can support problematic fire spread in the landscape if an ignition and sufficient fuel were to be present.
Figure 8. CSIRO FWI, RP 30 years.
Despite the fact that CSIRO tried rebalancing the distribution of hazard classes, the resulting FWI is strongly skewed towards extremes, as shown by the number of countries falling in each hazard rank.
FWI
Hazard rank
N. of countries
> 30
High
163
20 – 30
Medium
17
15 – 20
Low
2
<15
Very low
92
This raster shows values up to 300 (ten times the “high” threshold). The raster uses FWI ranks as averaged from various country studies. According to CSIRO report, the FWI method does not account for fuel, but just the meteorological forcing related to wildfire generation, the only masking has been applied for desert areas.
The index is compared to fire frequencies derived from the Global Fire Emissions Database (GFED4, Giglio et al. 2013) for the period 1997-Today, shown as an overlay in Fig. 9. The largest majority of recorded burning happened within the “high” hazard zones (in red), yet we notice some important discrepancies: general hazard overestimation for the Indian sub-continent and Europe; underestimation of fire hazard in some northern regions such as North America and North-East Asia.
Figure 9. FWI from CSIRO and overlay of pixel burnt over the period 1995 to 2016. Light grey values indicate at least 10% of pixel burnt over the period from 1995 to 2016, and black indicates frequent recurrent burning of the entire pixel.
Simply put, meteorological conditions are not sufficient to trigger a wildfire if there is no fire trigger and fuel to burn. A fuel layer should be applied to mask out the areas that cannot produce the hazard (e.g. no vegetation). Values from GlobCover 2009 (resolution 300 m) corresponding to vegetation are used to identify potential fuel and applied as mask for the CSIRO layer. The simple masking of non-vegetated areas produces some improvement, especially for in the Indian sub-continent (Fig. 10).
Figure 10. CSIRO (RP10) masked (white) for non-vegetated areas (300 m).
To test if vegetation aggregation and threshold criteria can further improve the filtering, a mask of vegetated areas is produced from Globcover 2009 on a 10km grid by flagging as “vegetated” only those cells with more than 10% of vegetated area. This is also required to match with the resolution of the FWI layer. The vegetation grid is used as a binary mask, that means that vegetation density is not used as a weight (but that information is stored and available). The effect of this filtering impacts the most in the area of North India/Nepal, with little to no effect in other places (Fig. 11).
Figure 11. Vegetation aggregated on a 0.7 degree cell with criteria vegetation >10% of area.
Since the filtering through the vegetation mask does not look sufficient to fix the apparent overestimation of fire hazard provided by CSIRO layers, new global datasets were explored and compared. Updated fire indices from Vitolo et al (2019) are aggregated from 38 years of global reanalysis of wildfire danger (Fig. 12). The dataset used to produce the analysis and the final products are available for download.
Figure 12. FWI 100-year mean (1980-2018) from Vitolo et al. masked for vegetation <10%.
The whole dataset consists of seven indices, each of which describes a different aspect of the effect that fuel moisture and wind have on fire ignition probability and its behavior, if started. Three indices measure the soil moisture: Fine Fuel Moisture Code (FFMC), Duff Moisture Code (DMC), Drought Code (DC). From these, the FWI model generates two fire behaviour indeces: Initial Spread Index (ISI), Build Up Index (BUI). Then the model generates the Fire Weather Index (FWI) and Daily Severity Rating (DSR). For convenience, each index is archived separately. All datasets are calculated using a daily time step by interpolating the atmospheric fields at local noon when fire conditions are considered to be at their worst. Fig. 13 shows how much larger the CSIRO value is compared to Vitolo. Even in area that both data rank as high class, the difference in value is enormous.
Figure 13. Difference in the FWI index is calculated as CSIRO(value) – Vitolo(value).
To better understand how hazard ranking matches with observations, the FWI is compared with a fire density map (fig. 14) produced from the NASA MODIS fire archive M6 (2000 to present) and distributed by FIRMS. Points representing fire events are counted on the same grid as FWI: only “vegetation fire” type is considered (Type = 0). Confidence value (0-100) can be also used to filter out uncertain events. Threshold of 30% confidence is applied. This reduces the sample from 42 to 38 thousand records. FRP (Fire Radiative Power, expressed in MW) depicts the pixel-integrated fire radiative power and can be potentially used as weight for event severity.
Figure 14. Point density map of vegetation fire events (confidence >30%) from MODIS remote sensing using Fire Radiative Power as unit of intensity.
The MODIS map appears consistent (at least in relative terms) to the one from GFED4 (fig. 15).
Figure 15. Burned ground from GFED4.
In both cases, we can notice some important differences when comparing empirical fire maps against the FWI rankings. See central Africa in fig. 16 as example.
Figure 16. Comparing MODIS event grid and Vitolo FWI index.
One partial explanation is provided by the fact that MODIS fire archive considers agricultural fires as vegetation fires, as found when comparing vegetation mask and MODIS events. The vegetation map masks the MODIS grid almost perfectly; one notable exception, the Punjab region, which is in fact excluded as it is identified as post-flooding agricultural land. The high number of events here are waste fires from agricultural activities, as confirmed by NASA focus also in central Africa. These are fires that do not require any FWI severity or natural fuel to happen, posing an issue on using these observed fires as validation for FWI. Further details about wildfire data and comparison are found in a dedicated doc.
Wildfire hazars is currently not included in the screening.
There are some recent options in terms of third-party global datasets to evaluate:
A review of existing datasets and methodologies for fire hazard has been previously included in the hazard review document, but needs to be updated.
Wildfire
A wildfire is any uncontrolled burning of biomass and affected man-made assets, which spreads based on environmental conditions. The probability of wildfire occurrence is typically measured by the Fire Weather Index (FWI), possibly in conjunction with a fuel model.
The CSIRO datasets (Fig. 8), which drove the wildfire assessment in ThinkHazard and other applications, uses an approach which is entirely based on fire weather index climatology (Fire Weather Index, FWI) to assess both the onset of conditions that will allow fires to spread, as well as the likelihood of fire at any point in the landscape. The method uses statistical modelling (extreme value analysis) of a 36 years fire weather climatology from GFWED to assess the predicted fire weather intensity for specific return period intervals. These intensities are classified based on thresholds using conventions to provide hazard classes that correspond to conditions that can support problematic fire spread in the landscape if an ignition and sufficient fuel were to be present.
Despite the fact that CSIRO tried rebalancing the distribution of hazard classes, the resulting FWI is strongly skewed towards extremes, as shown by the number of countries falling in each hazard rank.
This raster shows values up to 300 (ten times the “high” threshold). The raster uses FWI ranks as averaged from various country studies. According to CSIRO report, the FWI method does not account for fuel, but just the meteorological forcing related to wildfire generation, the only masking has been applied for desert areas.
The index is compared to fire frequencies derived from the Global Fire Emissions Database (GFED4, Giglio et al. 2013) for the period 1997-Today, shown as an overlay in Fig. 9. The largest majority of recorded burning happened within the “high” hazard zones (in red), yet we notice some important discrepancies: general hazard overestimation for the Indian sub-continent and Europe; underestimation of fire hazard in some northern regions such as North America and North-East Asia.
Simply put, meteorological conditions are not sufficient to trigger a wildfire if there is no fire trigger and fuel to burn. A fuel layer should be applied to mask out the areas that cannot produce the hazard (e.g. no vegetation). Values from GlobCover 2009 (resolution 300 m) corresponding to vegetation are used to identify potential fuel and applied as mask for the CSIRO layer. The simple masking of non-vegetated areas produces some improvement, especially for in the Indian sub-continent (Fig. 10).
To test if vegetation aggregation and threshold criteria can further improve the filtering, a mask of vegetated areas is produced from Globcover 2009 on a 10km grid by flagging as “vegetated” only those cells with more than 10% of vegetated area. This is also required to match with the resolution of the FWI layer. The vegetation grid is used as a binary mask, that means that vegetation density is not used as a weight (but that information is stored and available). The effect of this filtering impacts the most in the area of North India/Nepal, with little to no effect in other places (Fig. 11).
Since the filtering through the vegetation mask does not look sufficient to fix the apparent overestimation of fire hazard provided by CSIRO layers, new global datasets were explored and compared. Updated fire indices from Vitolo et al (2019) are aggregated from 38 years of global reanalysis of wildfire danger (Fig. 12). The dataset used to produce the analysis and the final products are available for download.
The whole dataset consists of seven indices, each of which describes a different aspect of the effect that fuel moisture and wind have on fire ignition probability and its behavior, if started. Three indices measure the soil moisture: Fine Fuel Moisture Code (FFMC), Duff Moisture Code (DMC), Drought Code (DC). From these, the FWI model generates two fire behaviour indeces: Initial Spread Index (ISI), Build Up Index (BUI). Then the model generates the Fire Weather Index (FWI) and Daily Severity Rating (DSR). For convenience, each index is archived separately. All datasets are calculated using a daily time step by interpolating the atmospheric fields at local noon when fire conditions are considered to be at their worst. Fig. 13 shows how much larger the CSIRO value is compared to Vitolo. Even in area that both data rank as high class, the difference in value is enormous.
To better understand how hazard ranking matches with observations, the FWI is compared with a fire density map (fig. 14) produced from the NASA MODIS fire archive M6 (2000 to present) and distributed by FIRMS. Points representing fire events are counted on the same grid as FWI: only “vegetation fire” type is considered (Type = 0). Confidence value (0-100) can be also used to filter out uncertain events. Threshold of 30% confidence is applied. This reduces the sample from 42 to 38 thousand records. FRP (Fire Radiative Power, expressed in MW) depicts the pixel-integrated fire radiative power and can be potentially used as weight for event severity.
The MODIS map appears consistent (at least in relative terms) to the one from GFED4 (fig. 15).
Figure 15. Burned ground from GFED4.
In both cases, we can notice some important differences when comparing empirical fire maps against the FWI rankings. See central Africa in fig. 16 as example.
One partial explanation is provided by the fact that MODIS fire archive considers agricultural fires as vegetation fires, as found when comparing vegetation mask and MODIS events. The vegetation map masks the MODIS grid almost perfectly; one notable exception, the Punjab region, which is in fact excluded as it is identified as post-flooding agricultural land. The high number of events here are waste fires from agricultural activities, as confirmed by NASA focus also in central Africa. These are fires that do not require any FWI severity or natural fuel to happen, posing an issue on using these observed fires as validation for FWI. Further details about wildfire data and comparison are found in a dedicated doc.