Standart input data

Standart input data #

Each of these fields plays an important role in calculating fire probabilities and simulating wildfire behavior. Below is a step-by-step guide for configuring the FLAM tab:

  • Click on the Open button next to the Population field and select the appropriate population data file, which is typically in raster format (e.g., BaseY2000.tif). This dataset is necessary to calculate the Human Ignition Probability (P_h), as it captures the population density in the modeled area.
  • In the Fuel field, click Open and select the biomass or fuel availability data (e.g., Fuel_gC_sqm.tif). This dataset contains information about the quantity of fuel available in the area, which will influence the Ignition Probability by Fuel Availability (P_b). The fuel data should match the region and period of your study.
  • Lightning Data is optional. If available, click the Open button next to the Lightning field and provide a lightning dataset. This dataset will be used to calculate the Probability of Natural Ignition (P_I) caused by lightning strikes. If lightning data is not available, you can leave this field blank.
  • For the Fire Shape field, click Open and select the shapefile that contains information about the delineated burned areas (e.g., biome.shp) or the area coverage you want to process. This data will be used to map the historical fire locations and inform the model about the spatial distribution of fire occurrences.
  • Finally, click Open next to the Results field and select the folder where the model output will be saved. This is the location where all the processed data and results from the FLAM simulations will be stored (e.g., AFEIM_Johanna). Once everything is set, you can Save your configuration by clicking the Save button. This ensures that all your selected input data is stored for future use or edits. You can also click Preprocess to initiate the preprocessing step, which prepares the data and configures the model for simulations. The preprocessing step checks the input data for consistency and ensures that everything is ready for running simulations. After preprocessing, you can proceed with running the simulations to calculate fire probabilities and analyze wildfire risk. You can go on configuring the other sub-models like G4M and BeWhere, where you load further data specific to these models.