For wildfire predictions, the lag between the time when wildfire precursors prevail and a wildfire event is important information because appropriate wildfire-favoring environments often need time to develop. Here, the mean time lags weighted by partial correlations between wildfires and precursors are ...
Predictions for other days are presented in Fig. 9. Although we did not set a goal to train model to predict the velocity of wild fire spreading in each direction, we computed it as a part of the post-processing. The achieved results are presented in Table 6. For the first day the ...
As the final step, we conducted a sensitivity analysis to understand how model predictions would be affected by simple changes in climate and vegetation variables. The goal was not, for example, to create projections of wildfire probability in response to a real climate scenario, but instead to ...
In a Wednesday press conference on the subject of the 2024 wildfire season forecast, Canadian Emergency Preparedness Minister Harjit Sajjan saidongoing drought, low snowpack levels, and a forecast for warmer-than-average spring weather are combining t...
We integrated the widely used SPread and InTensity of FIRE (SPITFIRE) fire module into the spatially explicit individual-based dynamic global vegetation model (SEIB-DGVM) to improve the accuracy of fire predictions and then simulated future fire regimes to better understand their imp...
‘CFD’)50and integrated remote sensing, the origins and development of which are well encapsulated elsewhere51,52. These often rely on the integration of data such as fuel type, topography, fuel density, obstacles, and measurements of radiant heat flux53. Predictions may then be made as to ...
Modeling techniques have suggested that if individuals in a community take steps to decrease their exposure to wildfire smoke, based on predictions of an increase in PM2.5in their area of 20 µg/m3or more, asthma-related ED visits can be expected to decrease [144]. However, in order to...
Uncertainty associated with model predictions of surface and crown fire rates of spread Environ Model Software, 47 (2013), pp. 16-28, 10.1016/j.envsoft.2013.04.004 View PDFView articleView in ScopusGoogle Scholar Csardi and Nepusz, 2006 G. Csardi, T. Nepusz The igraph software package fo...
. The same predictors also well predict the corresponding fire carbon emissions. Independent predictions for spring burned area in 2019 and 2020 are very close to observations, with a mean absolute percentage error of only 3.0%. The findings of this study provide a possibility for guarding humans...
The impact of US wildland fires on ozone and particulate matter: a comparison of measurements and CMAQ model predictions from 2008 to 2012. Int J Wildland Fire. 2018;27:684–98. CAS Google Scholar Wegesser TC, Franzi LM, Mitloehner FM, Eiguren-Fernandez A, Last JA. Lung antioxidant ...