Modeling Forest Wildfires at Regional Scales
Geofisica Internacional, ISSN: 2954-436X, Vol: 62, Issue: 3, Page: 563-579
2023
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
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Metrics Details
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Article Description
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.
Bibliographic Details
http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85198374873&origin=inward; http://dx.doi.org/10.22201/igeof.2954436xe.2023.62.3.1713; http://revistagi.geofisica.unam.mx/index.php/RGI/article/view/1713; http://www.scielo.org.mx/scielo.php?script=sci_arttext&pid=S0016-71692023000300563&lng=en&tlng=en; http://www.scielo.org.mx/scielo.php?script=sci_abstract&pid=S0016-71692023000300563&lng=en&tlng=en; http://www.scielo.org.mx/scielo.php?script=sci_arttext&pid=S0016-71692023000300563; http://www.scielo.org.mx/scielo.php?script=sci_abstract&pid=S0016-71692023000300563; https://dx.doi.org/10.22201/igeof.2954436xe.2023.62.3.1713
Universidad Nacional Autonoma de Mexico
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