xrspatial.flood.flood_depth_vegetation#

xrspatial.flood.flood_depth_vegetation(hand_agg: DataArray, slope_agg: DataArray, mannings_n: float | DataArray, unit_discharge: float, name: str = 'flood_depth_vegetation') → DataArray[source]#

Compute vegetation-adjusted flood depth via Manning’s normal depth.

For each cell, the equilibrium (normal) depth for steady uniform flow is:

h = (q * n / sqrt(tan(slope)))^(3/5)

where q is unit discharge (m^2/s), n is Manning’s roughness, and slope is terrain slope in degrees. The flood depth is h - HAND where h > HAND, and NaN elsewhere.

Higher roughness (denser vegetation) produces deeper, slower water.

Parameters:

  • hand_agg (xarray.DataArray) – 2D HAND raster (output of hand()).

  • slope_agg (xarray.DataArray) – 2D slope raster in degrees.

  • mannings_n (float or xarray.DataArray) – Manning’s roughness coefficient. A scalar applies uniformly; a DataArray (e.g. output of vegetation_roughness()) allows spatially varying roughness.

  • unit_discharge (float) – Unit discharge in m^2/s (discharge per unit width, > 0).

  • name (str, default 'flood_depth_vegetation') – Name of the output DataArray.

Returns:

2D float64 flood depth grid. NaN where not inundated or where inputs are NaN.

Return type:

xarray.DataArray

Notes

Supports NumPy, CuPy, Dask with NumPy, and Dask with CuPy backed xarray DataArrays.

Examples

>>> import numpy as np
>>> import xarray as xr
>>> from xrspatial import flood_depth_vegetation
>>> hand = xr.DataArray(
...     np.array([[0.0, 1.0]]), dims=['y', 'x'])
>>> slope = xr.DataArray(
...     np.array([[10.0, 10.0]]), dims=['y', 'x'])
>>> depth = flood_depth_vegetation(
...     hand, slope, mannings_n=0.10, unit_discharge=0.5)