17 January 2024 | Joe R. McNorton and Francesca Di Giuseppe
The paper presents a novel mid-complexity model for predicting fuel characteristics, which combines data-driven and analytical methods to provide real-time forecasts and climate records. The model integrates meteorological variables, land surface modeling, and satellite observations to estimate fuel load and moisture content, including both live and dead fuels. The output is partitioned into live and dead fuels, with wood and foliage components. The model was validated using independent datasets and extreme wildfire case studies, showing reasonable agreement with observed data. The resulting 10-year dataset, with a spatial resolution of approximately 9 km and daily updates, is the first of its kind and will be regularly updated. The model's ability to predict fuel characteristics is crucial for effective wildfire management and prevention strategies, as it can help assess landscape flammability and fire risk. The validation results suggest that the model can accurately estimate fuel load and moisture content, providing valuable insights into fire dynamics and contributing to the development of global fire rate-of-spread models.The paper presents a novel mid-complexity model for predicting fuel characteristics, which combines data-driven and analytical methods to provide real-time forecasts and climate records. The model integrates meteorological variables, land surface modeling, and satellite observations to estimate fuel load and moisture content, including both live and dead fuels. The output is partitioned into live and dead fuels, with wood and foliage components. The model was validated using independent datasets and extreme wildfire case studies, showing reasonable agreement with observed data. The resulting 10-year dataset, with a spatial resolution of approximately 9 km and daily updates, is the first of its kind and will be regularly updated. The model's ability to predict fuel characteristics is crucial for effective wildfire management and prevention strategies, as it can help assess landscape flammability and fire risk. The validation results suggest that the model can accurately estimate fuel load and moisture content, providing valuable insights into fire dynamics and contributing to the development of global fire rate-of-spread models.