Calibration of the Fire Weather Index over Mediterranean Europe based on fire activity retrieved from MSG satellite imagery

Abstract

Here we present a procedure that allows the operational generation of daily maps of fire danger over Mediterranean Europe. These are based on integrated use of vegetation cover maps, weather data and fire activity as detected by remote sensing from space. The study covers the period of July–August 2007 to 2009. It is demonstrated that statistical models based on two-parameter generalised Pareto (GP) distributions adequately fit the observed samples of fire duration and that these models are significantly improved when the Fire Weather Index (FWI), which rates fire danger, is integrated as a covariate of scale parameters of GP distributions. Probabilities of fire duration exceeding specified thresholds are then used to calibrate FWI leading to the definition of five classes of fire danger. Fire duration is estimated on the basis of 15-min data provided by Meteosat Second Generation (MSG) satellites and corresponds to the total number of hours in which fire activity is detected in a single MSG pixel during one day. Considering all observed fire events with duration above 1h, the relative number of events steeply increases with classes of increasing fire danger and no fire activity was recorded in the class of low danger. Defined classes of fire danger provide useful information for wildfire management and are based on the Fire Risk Mapping product that is being disseminated on a daily basis by the EUMETSAT Satellite Application Facility on Land Surface Analysis.

How a new fire‐suppression policy can abruptly reshape the fire‐weather relationship

“…Fire duration is very short in the mediterranean eurozone; over the period 2007-2009, more than 80% of fires lasted less than 3 hours and 100% of forest fires did not last more than a day (DaCamara et al 2014). As in the other southern European countries (see Moreira et al 2011), humans are responsible for most of fire ignitions (97% during the 1973-2006 period; www.…”

Fire danger rating over Mediterranean Europe based on fire radiative power derived from Meteosat

“…Several approaches have been proposed involving different techniques to rate indices of fire danger against fire history over a given period and study area. Examples of such techniques include logistic regression and percentile analysis (Andrews et al, 2003), cluster analysis (Dymond et al, 2005) and threshold setting based on a geometric progression (Van Wagner, 1987) or on values of probability (DaCamara et al, 2014). Fire history traditionally consists of ground observations of fire occurrence (Anderson and Englefield, 2001), fire load (Merrill and Alexander, 1987), suppression difficulty (Kiil et al, 1977) and area burned (San-Miguel-Ayanz et al, 2012).…”

“…The EFFIS product relies on a traditional calibration approach where the lower threshold of the class of highest fire danger is estimated from FWI values associated with burned areas of more than 500 ha, and the subsequent thresholds are defined by a geometric progression (San-Miguel-Ayanz et al, 2012). In the case of the LSA SAF FRM product, calibration is performed by fitting a generalized Pareto (GP) model to the duration of fire episodes derived from hot spot observations from space (DaCamara et al, 2014). When calibrating indices of fire danger over large areas such as the Mediterranean basin, the spatial and temporal consistency of historical records of fire activity derived from remotely sensed information provided by the same sensors present an important advantage over ground-based data, where the time and location of the fire event and the associated burned area are usually obtained by visual inspection and the information recorded depends on policies that vary from country to country as well as on criteria that may change over time (Pereira et al, 2011).…”

Regional variation in fire weather controls the reported occurrence of Scottish wildfires

“…For example, in New Zealand, fire behaviour and fuel moisture in similar shrub-dominated fuel types and temperate climates have proven to be challenging to model (Alexander, 2008). Globally a number of countries have investigated or adopted the Canadian Fire Weather Index System (FWI System; Table 1) to provide forecasts of wildfire danger (e.g., DaCamara et al, 2014; Simpson et al, 2014). A limited system currently exists in the form of the Met Office Fire Severity Index (MOFSI; Kitchen et al, 2006) developed for England and Wales in response to the CROW Act.…”