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Advisor(s)
Abstract(s)
Dead fine fuel moisture content (FM) is one of the most important determinants of fire behavior. Fire scientists
have attempted to effectively estimate FM for nearly a century, but we are still lacking broad scale evaluations of
the different approaches for prediction. Here we tackle this problem by taking advantage or a recently compiled
global fire behavior database (BONFIRE) gathering 1603 records of 1h (i.e., <6 mm diameter or thickness) dead
fuel moisture content from measurements before experimental fires. We compared the results of models routinely
used by different agencies worldwide, empirical models, semi-mechanistic models and also non-linear and
machine learning approaches based on either temperature and relative humidity or vapor pressure deficit (VPD).
A semi-mechanistic model based on VPD showed the best performance across all FM ranges and a historical
model developed in Australia (MK5) was additionally recommended for low fuel moisture estimations. We also
observed significant differences in FM dynamics between vegetation types with FM in grasslands more responsive
to changes in atmospheric dryness than woody ecosystems. The addition of computational complexity through
machine learning is not recommended since the gain in model fit is small relative to the increase in complexity.
Future research efforts should concentrate on predictions at low FM (<10 %) as this is the range most significant
for fire behavior and where the poorest model performance was observed. Model predictions are available from
https://hcfm.shinyapps.io/shinyfmd/.
Description
Keywords
Dead fine fuel moisture content Models Vapor pressure deficit Vegetation type
Citation
Rodrigues, Marcos; Dios, Víctor Resco de; Sil, Ângelo Filipe; Cunill Camprubí, Àngel; Fernandes, Paulo M. (2024). VPD-based models of dead fine fuel moisture provide best estimates in a global dataset. Agricultural and Forest Meteorology. ISSN 0168-1923. 346, p. 1-10
Publisher
Elsevier