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.
Recently there has been a lot of effort to model extremes of spatially dependent data. These efforts seem to be divided into two distinct groups: the study of max-stable processes, together with the development of statistical models within this framework; the use of more pragmatic, flexible models using Bayesian hierarchical models (BHM) and simulation based inference techniques. Each modeling strategy has its strong and weak points. While max-stable models capture the local behavior of spatial extremes correctly, hierarchical models based on the conditional independence assumption, lack the asymptotic arguments the max-stable models enjoy. On the other hand, they are very flexible in allowing the introduction of physical plausibility into the model. When the objective of the data analysis is to estimate return levels or kriging of extreme values in space, capturing the correct dependence structure between the extremes is crucial and max-stable processes are better suited for these purposes. However when the primary interest is to explain the sources of variation in extreme events Bayesian hierarchical modeling is a very flexible tool due to the ease with which random effects are incorporated in the model. In this paper we model a data set on Portuguese wildfires to show the flexibility of BHM in incorporating spatial dependencies acting at different resolutions.
Abstract. We present a procedure that allows the operational generation of daily
forecasts of fire danger over Mediterranean Europe. The procedure combines
historical information about radiative energy released by fire events with
daily meteorological forecasts, as provided by the Satellite Application
Facility for Land Surface Analysis (LSA SAF) and the European Centre for
Medium-Range Weather Forecasts (ECMWF). Fire danger is estimated based on
daily probabilities of exceedance of daily energy released by fires occurring
at the pixel level. Daily probability considers meteorological factors by
means of the Canadian Fire Weather Index (FWI) and is estimated using a daily
model based on a generalized Pareto distribution. Five classes of fire danger
are then associated with daily probability estimated by the daily model. The
model is calibrated using 13 years of data (2004–2016) and validated
against the period of January–September 2017. Results obtained show that
about 72 % of events releasing daily energy above 10 000 GJ belong to the
“extreme” class of fire danger, a considerably high fraction that is more
than 1.5 times the values obtained when using the currently
operational Fire Danger Forecast module of the European Forest Fire
Information System (EFFIS) or the Fire Risk Map (FRM) product disseminated by
the LSA SAF. Besides assisting in wildfire management, the procedure is
expected to help in decision making on prescribed burning within the
framework of agricultural and forest management practices.
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