Drivers of Global Wildfires — Statistical analyses Master Thesis Seminar, 2010 Hongxiao Jin Supervisor: Dr. Veiko Lehsten Division of Physical Geography.

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Drivers of Global Wildfires — Statistical analyses Master Thesis Seminar, 2010 Hongxiao Jin Supervisor: Dr. Veiko Lehsten Division of Physical Geography and Ecosystems Analysis Department of Earth and Ecosystem Sciences Lund University Photo:

Presentation Outline Introduction Data Methods Results Conclusions Master Thesis Seminar, 2010

Introduction Master Thesis Seminar, 2010 Impacts of wildfires in earth system affect ecosystem accelerate carbon cycle influence climate Wildfire drivers scale-dependent local scale landscape scale regional and global scale Fire representations DGVMs DGVMs should include fire models based on statistical relations burned area

Master Thesis Seminar, 2010 Data Fire data Collection 5 MODIS Level 3 Monthly Tiled 500m Burned Area Product April 2000 to March 2009 lon -180º to +180º, lat 53.22ºS to 75.55ºN 500 m resolution sinusoidal projection 266 tiles nominal size 10º  10º 2400 rows  2400 columns tiles are overlapped hierarchical data format (*.hdf) 24,408 files Regridded to 0.25º  0.25º to get burned area ratio (BAR) and burn date

Master Thesis Seminar, 2010 Data Driver data Precipitation TRMM+NCEP Surface air temperature NCEP Forest cover IIASA Grass cover IIASA Cultivation IIASA Urban IIASA Soil nutrient availability IIASA Population density CIESIN Topographical roughness USGS Wind speed NCEP Air relative humidity NCEP Soil moisture NOAA

Master Thesis Seminar, 2010 Methods Correlation analyses Response variable Nine years’ mean annual BAR Individual annual case (2004) Explanatory variable 13 variables for nine years’ mean annual BAR 17 variables for individual annual case (2004) Linear correlation Pearson correlation Generalized linear correlation

Master Thesis Seminar, 2010 Methods Modelling Generalized linear model 14 regions and global binomial distribution logit link function linear combination of explanatory variables 1 st order, 2 nd order and 2-times interaction stepAIC of R 50% of samples as training (except for TENA &CEAM), world data 5% of samples as training Random forest regression global 500 trees 5 out of 13 variables 5% of samples as training

Master Thesis Seminar, 2010 Results and discussions Burned area

Master Thesis Seminar, 2010 Fire seasons Results and discussions

Master Thesis Seminar, 2010 Results and discussions Fire seasons and peak fire month of 14 regions. Red numbers indicate the peak fire month of each region. Winter-spring fires happened between 23.5ºN and 23.5ºS and summer-autumn fires happened outside this zone. Four red rectangles had summer-autumn fire seasons, different from their main regions of the regional division scheme.

Master Thesis Seminar, 2010 Drivers Pearson correlation coefficient Generalized liner correlation From the most to the least important: MeanT, IntraR, Grass, RainNoFire, InterR, RainFireSeason, Nutrient, MeanR, Topography, Forest, Population, Urban, Cultivation Results and discussions

Master Thesis Seminar, 2010 Variable importance estimated by random forest regression. The variable importance is give by the measure of the mean squared error increasing percent (% Increase of Mean Squared Error) when that variable is permuted. Variable importance given by randomForest Population ? Urban ? Cultivation? Results and discussions

Master Thesis Seminar, 2010 Models Observed vs. Modelled Results and discussions

Master Thesis Seminar, 2010 Observed Global RF Global GLM Modelled vs. Observed Regional GLM Results and discussions

Master Thesis Seminar, 2010 Conclusions Burned area 3.85% of the global land area burned each year (335.74± km 2 ). Savanna fires account for 83.1% and Africa contributes 72% of the world total. Fire seasons Globally 4 latitudinal zones, 2 fire seasons (August and December) Drivers Mean annual temperature is the most important driver of global wildfire. The next most important driver is grass cover. Each region has slightly different sequences of wildfire drivers. Models The regional GLMs have better prediction performance than global GLM and random forest. The global random forest regression is superior to the global GLM.

Master Thesis Seminar, 2010 Thanks!