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Background: An approach to predict fire growth in an operational setting, with the potential to be used as a decision-support tool for fire management, is described and evaluated. The operational use of fire behaviour models has mostly followed a deterministic approach, however, the uncertainty associated with model predictions needs to be quantified and included in wildfire planning and decision-making process during fire suppression activities. We use FARSITE to simulate the growth of a large wildfire. Probabilistic simulations of fire spread are performed, accounting for the uncertainty of some model inputs and parameters. Deterministic simulations were performed for comparison. We also assess the degree to which fire spread modelling and satellite active fire data can be combined, to forecast fire spread during large wildfires events. Results: Uncertainty was propagated through the FARSITE fire spread modelling system by randomly defining 100 different combinations of the independent input variables and parameters, and running the correspondent fire spread simulations in order to produce fire spread probability maps. Simulations were initialized with the reported ignition location and with satellite active fires. The probabilistic fire spread predictions show great potential to be used as a fire management tool in an operational setting, providing valuable information regarding the spatial-temporal distribution of burn probabilities. The advantage of probabilistic over deterministic simulations is clear when both are compared. Re-initializing simulations with satellite active fires did not improve simulations as expected. Conclusion: This information can be useful to anticipate the growth of wildfires through the landscape with an associated probability of occurrence. The additional information regarding when, where and with what probability the fire might be in the next few hours can ultimately help minimize the negative environmental, social and economic impacts of these fires. [ABSTRACT FROM AUTHOR]

Vegetation burning is a global environmental threat that results in local ecological, economic and social impacts but also has large-scale implications for global change. The burning is usually a result of interacting factors such as climate, land use and vegetation type. Despite its importance as a factor shaping ecological, economic and social processes, countries highly vulnerable to climate change in Central America, such as El Salvador, lack an assessment of this complex relationship. In this study we rely on remotely sensed measures of the Normalized Vegetation Difference Index (NDVI) and thermal anomaly detections by the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor to identify vegetation cover changes and fire occurrences. We also use land use data and rainfall observations derived from the Tropical Rainfall Measuring Mission (TRMM) data to determine the spatial and temporal variability and interactions of these factors. Our results indicate a highly marked seasonality of fire occurrence linked to the climatic variability with a peak of fire occurrences in 2004 and 2013. Low vegetation indices occurred in March-April, around two months after the driest period of the year (December-February), corresponding to months with high detection of fires. Spatially, 65.6% of the fires were recurrent and clustered in agriculture/cropland areas and within 1 km of roads (70%) and only a 4.7% of fires detected were associated with forests. Remaining forests in El Salvador deserve more attention due to underestimated consequences of forest fires. The identification of these clear patterns can be used as a baseline to better shape management of fire regimes and support decision making in this country. Recommendations resulting from this work include focusing on fire risk models and agriculture fires and long-term ecological and economic consequences of those. Furthermore, El Salvador will need to include agricultural fires in the contribution to national accounts emissions. [ABSTRACT FROM AUTHOR]