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\subsubsection{Data Pre-processing}  Weather station data Pre-processing involved several steps. The first step was 'cleaning' the raw data. The original data presentation was not designed for scientific analysis. Thus,formating the original data was a necessary step for the later analysis.   The computation of wildfire potential usually requires continuous daily data. The required variables are temperature, relative humidity, wind speed and precipitation. Thus, only six stations in the Ecuadroian Andean region were suitable for this study. The selected stations are representative of the ecosystems that are prone to wildfire. The available records in these stations spanned the period 1997-2012. Yet, the period of analysis is short. Reanalysis data was bias-corrected to extend the availability of records.   The bias-correction was necessary for several reasons. First, 1000 hPa reanalysis data does not represent surface level values. There was a 3-hour difference between the time of the two dataset's values. Reanalysis data in the highlands may differ in quality to those at sea-level. Thus, we plotted probability density functions (PDFs) of the reanalysis and weather station datasets. This allowed to determine the differences between their data distributions. Applying linear scaling techniques was a suitable approach to bias-correct the required variables.  
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\subsubsection{Forest Fire Danger Index (FFDI) calculation}  Ecuador currently does not have any scientifically designed wildfire potential index. Therefore, an alternative approach was to use an international index suitable for this region. The McArthur Forest Fire Danger Index (FFDI)  is commonly used in Australia. an Australian wildfire potential index \cite{Noble1980}.  This index uses daily values of maximum temperature, relative humidity and wind speed. The index also incorporates a drought factor. This factor represents moisture in soil. This is indirect measure of the wildfire fuel availability. This empirical index is valid for the Southeastern forest ecosystem of Australia. In this wildfire-prone ecosystem, the eucalyptus is the dominant vegetation. Latin-America experienced a massive introduction of this specie. This occurred for commercial purposes in the early years of the 19th century. Thus, specie \cite{Anchaluisa2013}.Thus,  is reasonable to expect that this index may represent the Ecuadorian wildfire potential. The application of this index with weather station and reanalysis data yielded daily values. With weather station data the calculation comprised the period 1997-2012. The bias-corrected reanalysis data allowed to extend the method for the period 1961-2012. For each year, a sum of the daily FFDI values provided a seasonal metric of wildfire potential. This cumulative FFDI corresponds to the wildfire season July-August-September (JAS). Finally, we calculated the 85th percentile on the seasonal FFDI time series. This determined which were extreme wildfire seasons during the period 1961-2012.  
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