this is for holding javascript data
Daniel Pazmino edited untitled.tex
over 8 years ago
Commit id: 71b745c3bd4aa2d121528af1c6f6321d286ca53d
deletions | additions
diff --git a/untitled.tex b/untitled.tex
index d85e094..b132755 100644
--- a/untitled.tex
+++ b/untitled.tex
...
\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 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 changes in
soil. soil moisture. This is
an 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.
On the other side of the world, Latin-America
—including Ecuador— experienced a massive introduction of this 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. 1963-2012. 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. 1963-2012.
\subsubsection{Investigation of ENSO influence over wildfire seasons}
Two WE used two approaches
drove the investigation of to investigate ENSO's link over wildfire seasons. Firstly, it was important to know the influence of ENSO in the months leading the wildfire season JAS. Thus, we
calculated applied linear correlations of
ENSO indexes seasonal averages
of ENSO indices with FFDI values.
This These averages comprised three-months seasons starting from October-November-December (OND) of the previous year. The correlation
also included the SOI and
Nino Niño 3.4 values for the concurrent JAS season. A second approach compared the extreme wildfire seasons with ENSO's categories of intensity. We used the categories proposed by the National Oceanic and Atmospheric Administration (NOAA).
\section{Results}
...