Estimating Future Burned Areas under Changing Climate in the EU-Mediterranean Countries. Amatulli, G., Camia, A., & San-Miguel-Ayanz, J. 450-451:209–222.
Estimating Future Burned Areas under Changing Climate in the EU-Mediterranean Countries [link]Paper  doi  abstract   bibtex   
The impacts of climate change on forest fires have received increased attention in recent years at both continental and local scales. It is widely recognized that weather plays a key role in extreme fire situations. It is therefore of great interest to analyze projected changes in fire danger under climate change scenarios and to assess the consequent impacts of forest fires. In this study we estimated burned areas in the European Mediterranean (EU-Med) countries under past and future climate conditions. Historical (1985-2004) monthly burned areas in EU-Med countries were modeled by using the Canadian Fire Weather Index (CFWI). [] Monthly averages of the CFWI sub-indices were used as explanatory variables to estimate the monthly burned areas in each of the five most affected countries in Europe using three different modeling approaches (Multiple Linear Regression – MLR, Random Forest – RF, Multivariate Adaptive Regression Splines – MARS). MARS outperformed the other methods. Regression equations and significant coefficients of determination were obtained, although there were noticeable differences from country to country. [] Climatic conditions at the end of the 21st Century were simulated using results from the runs of the regional climate model HIRHAM in the European project PRUDENCE, considering two IPCC SRES scenarios (A2-B2). The MARS models were applied to both scenarios resulting in projected burned areas in each country and in the EU-Med region. Results showed that significant increases, 66\,% and 140\,% of the total burned area, can be expected in the EU-Med region under the A2 and B2 scenarios, respectively.
@article{amatulliEstimatingFutureBurned2013,
  title = {Estimating Future Burned Areas under Changing Climate in the {{EU}}-{{Mediterranean}} Countries},
  author = {Amatulli, Giuseppe and Camia, Andrea and San-Miguel-Ayanz, Jesús},
  date = {2013-04},
  journaltitle = {Science of The Total Environment},
  volume = {450-451},
  pages = {209--222},
  issn = {0048-9697},
  doi = {10.1016/j.scitotenv.2013.02.014},
  url = {https://doi.org/10.1016/j.scitotenv.2013.02.014},
  abstract = {The impacts of climate change on forest fires have received increased attention in recent years at both continental and local scales. It is widely recognized that weather plays a key role in extreme fire situations. It is therefore of great interest to analyze projected changes in fire danger under climate change scenarios and to assess the consequent impacts of forest fires. In this study we estimated burned areas in the European Mediterranean (EU-Med) countries under past and future climate conditions. Historical (1985-2004) monthly burned areas in EU-Med countries were modeled by using the Canadian Fire Weather Index (CFWI).

[] Monthly averages of the CFWI sub-indices were used as explanatory variables to estimate the monthly burned areas in each of the five most affected countries in Europe using three different modeling approaches (Multiple Linear Regression -- MLR, Random Forest -- RF, Multivariate Adaptive Regression Splines -- MARS). MARS outperformed the other methods. Regression equations and significant coefficients of determination were obtained, although there were noticeable differences from country to country.

[] Climatic conditions at the end of the 21st Century were simulated using results from the runs of the regional climate model HIRHAM in the European project PRUDENCE, considering two IPCC SRES scenarios (A2-B2). The MARS models were applied to both scenarios resulting in projected burned areas in each country and in the EU-Med region. Results showed that significant increases, 66\,\% and 140\,\% of the total burned area, can be expected in the EU-Med region under the A2 and B2 scenarios, respectively.},
  keywords = {*imported-from-citeulike-INRMM,~INRMM-MiD:c-13096535,~to-add-doi-URL,climate-change,fire-danger-rating,fire-weather-index,machine-learning,mediterranean-region,regression,wildfires}
}

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