In this article we investigate the use of statistical methods for wildfire risk assessment in the Mediterranean Basin using three meteorological covariates, the 2 m temperature anomaly, the 10 m wind speed and the January–June rainfall occurrence anomaly. We focus on two remotely sensed characteristic fire variables, the burnt area (BA) and the fire radiative power (FRP), which are good proxies for fire size and intensity respectively. Using the fire data we determine an adequate parametric distribution function which fits best the logarithm of BA and FRP. We reconstruct the conditional density function of both variables with respect to the chosen meteorological covariates. These conditional density functions for the size and intensity of a single event give information on fire risk and can be used for the estimation of conditional probabilities of exceeding certain thresholds. By analysing these probabilities we find two fire risk regimes different from each other at the 90 % confidence level: a “background” summer fire risk regime and an “extreme” additional fire risk regime, which corresponds to higher probability of occurrence of larger fire size or intensity associated with specific weather conditions. Such a statistical approach may be the ground for a future fire risk alert system.

In order to better manage fire risk, several methods have been investigated.
Among the first are the fire risk indices, such as the Canadian Fire Weather
Index

The detection of fires is performed using the fire products from MODIS (Moderate
Resolution Imaging Spectroradiometer), an instrument carried on
board of the Aqua and Terra polar heliosynchronous orbiting satellites. The
recorded fire variables are the burnt area (BA) and the fire radiative power (FRP)
which can be seen as a proxy of the fire intensity. We focus on the
Mediterranean Basin. We therefore select the fires occurring within the box
[35, 50

Our weather database was built upon the ERA-Interim reanalysis of the
European Center for Medium-range Weather Forecast (ECMWF)

WS

Evolution of 5th (blue), 25th (green), 50th (red),
75th (cyan) and 95th (purple) quantiles of BA (data set BA

Our first attempt at linking fire and weather data used regression techniques
to forecast the conditional mean. This approach failed, with maximum

AD2R values for all different distributions and for all data sets. The AD2R values for the chosen distributions are in bold.

Figure

As BA and FRP have very skewed distributions it becomes easier to study
their logarithm. We therefore from this point onward only discuss the
modelling of log

The parametric forms that are tested for the distributions of the transformed fire variables are the following:

the Exponential distribution,

the Normal distribution,

the Cauchy distribution,

the Gamma distribution,

the Logistic distribution,

the Log-Normal distribution,

the Generalized extreme value (GEV) distribution,

Here

If

We fitted all these distributions for each data set (BA

Normalized histograms, modelled densities

Figure

The general framework of our methodology is the parametric estimation of the
conditional probability density function of BA or FRP with respect to

Estimated probabilities of fire size (BA) exceeding the 2000 ha threshold (BA

Estimated probabilities of fire size (BA) exceeding the 2000 ha threshold
(BA

Estimated probabilities of fire intensity (FRP) exceeding the 200 ha threshold.
The

Mean values of the standard deviations calculated from the nearest neighbours search (

Figures

Probabilities of observing a

Normalized histograms of the estimated probabilities (black), PDFs of
the mixture model (red) and normalized histograms of the 90 %-level confidence
intervals lengths (blue).

Let us illustrate the information provided by our method by focusing on the
2003 megafire event in Portugal. We take the largest wildfire event of the
BA

Regarding the uncertainties of the method the mean standard deviation of the
meteorological covariates have been calculated (Table

For BA

Statistical modelling of burnt area (BA) and fire radiative power (FRP)
was investigated in this article. Using maximum goodness-of-fit techniques
the density functions of log

Our model allows to discriminate accurately jumps between “background” summer fire risk regime and an “extreme” additional fire risk regime, corresponding to higher probability of occurrence of larger fire size or intensity associated with specific weather conditions;

our model provides information for both the fire size and the fire intensity;

our model provides an estimation of the probability of risk to exceed given values of fire size and fire intensity each time meteorological forcing data are available, that is typically on an hourly to 6-hourly basis;

our model includes enhanced fire risk preconditioning by precipitation occurrence anomaly during the preceding months.

This work contributes to the HyMeX program (HYdrological cycle in The
Mediterranean EXperiment –