Seasonal fire danger forecasts for supporting fire prevention management in an eastern Mediterranean environment: the case of Attica, Greece

Abstract

Abstract. Forest fires constitute a major environmental and socioeconomic hazard in the Mediterranean. Weather and climate are among the main factors influencing forest fire potential. As fire danger is expected to increase under changing climate, seasonal forecasting of meteorological conditions conductive to fires is of paramount importance for implementing effective fire prevention policies. The aim of the current study is to provide high-resolution (∼9 km) probabilistic seasonal fire danger forecasts, utilizing the Canadian Forest Fire Weather Index (FWI) for the Attica region, one of the most fire-prone regions in Greece and the Mediterranean, employing the fifth-generation ECMWF seasonal forecasting system (SEAS5). Results indicate that, depending on the lead time of the forecast, both the FWI and ISI (Initial Spread Index) present statistically significant high discrimination scores and can be considered reliable in predicting above-normal fire danger conditions. When comparing the year-by-year fire danger predictions with the historical fire occurrence recorded by the Hellenic Fire Service database, both seasonal FWI and ISI forecasts are skilful in identifying years with a high number of fire occurrences. Overall, fire danger and its subcomponents can potentially be exploited by regional authorities in fire prevention management regarding preparedness and resources allocation.

Diurnal Temperature Range and Its Response to Heat Waves in 16 European Cities—Current and Future Trends

“…The empirical quantile mapping (EQM) technique was used for the statistical bias correction of daily maximum air temperature, daily minimum air temperature and diurnal temperature range. When using EQM, the observed empirical probability density function (PDF) is used to correct the 1st to 99th percentiles of the predicted empirical probability density function (PDF), while a constant extrapolation is used for lower or higher values falling outside this range [47][48][49][50][51]. This technique is capable of correcting the discrepancies in the distribution of the simulated parameters against the observed ones.…”

“…This technique is capable of correcting the discrepancies in the distribution of the simulated parameters against the observed ones. This is achieved by constructing a transfer function that has been calibrated over the reference period to map quantiles from the empirical cumulative distribution function of the model output onto the corresponding distribution of observations [47][48][49][50][51]. The references indicated above provide further technical details on this technique.…”

Development of a deep learning-based surveillance system for forest fire detection and monitoring using UAV

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Exploring Forest Fire Dynamics: Fire Danger Mapping in Antalya Region, Türkiye

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