Balbi 2022

Workflow information

  • Documentation page:

  • Version: 1.0

  • Date of record creation: 2024-11-24

  • Date of upload to firebench:

  • Version/tag/commit firebench: 0.3.0a1

Configuration

  • Rate of spread model: Balbi 2022 using firebench.ros_models.Balbi_2022_fixed_SFIRE implementation.

  • Input dataset: firebench/data/ros_model_validation/Anderson_2015, Table A1

  • Complementary fuel data from Scott and Burgan 40.

  • If fuel data is missing, use firebench.tools.find_closest_fuel_class_by_properties to retrieve the closest fuel category using total fuel load and fuel height with default weights.

  • Use Baughman_generalized_wind_reduction_factor_unsheltered to compute wind reduction factor considering that the input wind height is above vegetation.

Specific inputs

Some fuel properties are considered as constant:

  • fuel_density: 32 lb ft-3

  • fuel_chaparral_flag: 0 [-]

  • fuel_mineral_content_total: 0.0555 [-]

  • fuel_mineral_content_effective: 0.01 [-]

Results

Fig.1 shows that Balbi 2022 is over predicting the rate fo spread in most cases.

blockdiagram

Fig. 1 : Comparison of expected and computed rate of spread for Anderson 2015 dataset.

Data

  • 01_generate_data.py: 844b5c6e877925377dea85b1b6a517f3c2aa085954e5ff6dfc67a0bbfc328ae6

  • 02_plot_data.py: 2b613aec56ed5afb697262856e19a0b99203eb924ed54ed70487cc47a3e205bd

  • 03_create_record.py: 7f4090fba976d4e2c9688fc697c7329724807108dad92362003fd9435b0c192d

  • firebench.log: c5711d26513540d9bdcefa00862a293ead9ac18a594769388bef47d336066fba

  • output_data.h5: ce9d76689d3318b6f18c9591d60f6408e8b53bf41486a3882545376d27776cd6

  • anderson_2015_validation.png: 3e7512f10b617f195d02c54e68e65e8753f108531978a51dddc404bd3f811efb