Modeling Forest Wildfires at Regional Scales

This paper sets the following objectives: (i) presenting, (ii) testing, and (iii) evaluating a set of mathematical techniques to forecast the number of forest wildfires (No), the burned area (A), and the mean burned area (MA), on annual basis at regional scales. A comprehensive wildfire data set for coniferous forests of the State of Durango, Mexico was used to fit (1970–2011) and to validate (2012–2016) some modeling techniques. Most tested probabilistic and stochastic models hardly explain 70% of the wildfire variance. However, the teleconnection approach using a combination of large scale and local hydroclimate anomalies better predicted both data sets; explaining nearly 80% of the wildfire variance for fitting and for validating models. Results stress the complexity of interactive factors including the stochastic and underlying physical process that makes the prediction of wildfires losing precision and they should be further considered in future conceptual models. Therefore, proposing a more physical-based and conceptual models including Montecarlo models is an integral component of this paper; with the goal of increasing prediction capabilities and assisting decision-makers on the prevention activities inherent to better control wildfires. This proposed conceptual model stresses the need for using the probabilistic, stochastic and physical techniques to improve sub-model parameterization. Furthermore, the use of Monte Carlo simulation techniques would extract the most likely future scenarios for predicting the risk of high-severity wildfire regimes in temperate forests elsewhere.