xrspatial.fire.rate_of_spread#

xrspatial.fire.rate_of_spread(slope_agg: DataArray, wind_speed_agg: DataArray, fuel_moisture_agg: DataArray, fuel_model: int = 1, name: str = 'rate_of_spread') → DataArray[source]#

Rate of spread using a simplified Rothermel model.

Uses the Anderson 13 fuel model lookup table and computes per-pixel spread rate from slope, wind speed, and fuel moisture.

Supports NumPy, CuPy, Dask with NumPy, and Dask with CuPy backed xarray DataArrays; the output backend matches the input.

Parameters:

  • slope_agg (xr.DataArray) – Terrain slope in degrees.

  • wind_speed_agg (xr.DataArray) – Mid-flame wind speed in km/h.

  • fuel_moisture_agg (xr.DataArray) – Dead fuel moisture content as a fraction (0-1).

  • fuel_model (int, default=1) – Anderson 13 fuel model number (1-13).

  • name (str, default='rate_of_spread') – Name of output DataArray.

Returns:

Rate of spread in m/min (float32). This is the default input unit of fireline_intensity(), so the result can be passed there directly.

Return type:

xr.DataArray

Examples

>>> import numpy as np, xarray as xr
>>> from xrspatial import rate_of_spread
>>> slope = xr.DataArray(np.full((2, 2), 10.0, dtype='f4'))
>>> wind = xr.DataArray(np.full((2, 2), 10.0, dtype='f4'))
>>> moisture = xr.DataArray(np.full((2, 2), 0.06, dtype='f4'))
>>> rate_of_spread(slope, wind, moisture, fuel_model=1).values
array([[106.6344, 106.6344],
       [106.6344, 106.6344]], dtype=float32)