pysheds

🌎 Simple and fast watershed delineation in python.

Documentation

Read the docs here 📖.

Media

Hatari Labs - Elevation model conditioning and stream network delineation with python and pysheds ^:uk:

Hatari Labs - Watershed and stream network delineation with python and pysheds ^:uk:

Gidahatari - Delimitación de límite de cuenca y red hidrica con python y pysheds ^:es:

Earth Science Information Partners - Pysheds: a fast, open-source digital elevation model processing library ^:uk:

Example usage

Example data used in this tutorial are linked below:

• Elevation: elevation.tiff
• Terrain: impervious_area.zip
• Soil Polygons: soils.zip

Additional DEM datasets are available via the USGS HydroSHEDS project.

Read DEM data

# Read elevation raster
# ----------------------------
from pysheds.grid import Grid

grid = Grid.from_raster('elevation.tiff')
dem = grid.read_raster('elevation.tiff')

Condition the elevation data

# Condition DEM
# ----------------------
# Fill pits in DEM
pit_filled_dem = grid.fill_pits(dem)

# Fill depressions in DEM
flooded_dem = grid.fill_depressions(pit_filled_dem)

# Resolve flats in DEM
inflated_dem = grid.resolve_flats(flooded_dem)

Elevation to flow direction

# Determine D8 flow directions from DEM
# ----------------------
# Specify directional mapping
dirmap = (64, 128, 1, 2, 4, 8, 16, 32)

# Compute flow directions
# -------------------------------------
fdir = grid.flowdir(inflated_dem, dirmap=dirmap)

Compute accumulation from flow direction

# Calculate flow accumulation
# --------------------------
acc = grid.accumulation(fdir, dirmap=dirmap)

Delineate catchment from flow direction

# Delineate a catchment
# ---------------------
# Specify pour point
x, y = -97.294, 32.737

# Snap pour point to high accumulation cell
x_snap, y_snap = grid.snap_to_mask(acc > 1000, (x, y))

# Delineate the catchment
catch = grid.catchment(x=x_snap, y=y_snap, fdir=fdir, dirmap=dirmap, 
                       xytype='coordinate')

# Crop and plot the catchment
# ---------------------------
# Clip the bounding box to the catchment
grid.clip_to(catch)
clipped_catch = grid.view(catch)

Extract the river network

# Extract river network
# ---------------------
branches = grid.extract_river_network(fdir, acc > 50, dirmap=dirmap)

Compute flow distance from flow direction

# Calculate distance to outlet from each cell
# -------------------------------------------
dist = grid.distance_to_outlet(x=x_snap, y=y_snap, fdir=fdir, dirmap=dirmap,
                               xytype='coordinate')

Add land cover data

# Combine with land cover data
# ---------------------
terrain = grid.read_raster('impervious_area.tiff', window=grid.bbox,
                           window_crs=grid.crs, nodata=0)
# Reproject data to grid's coordinate reference system
projected_terrain = terrain.to_crs(grid.crs)
# View data in catchment's spatial extent
catchment_terrain = grid.view(projected_terrain, nodata=np.nan)

Add vector data

# Convert catchment raster to vector and combine with soils shapefile
# ---------------------
# Read soils shapefile
import pandas as pd
import geopandas as gpd
from shapely import geometry, ops
soils = gpd.read_file('soils.shp')
soil_id = 'MUKEY'
# Convert catchment raster to vector geometry and find intersection
shapes = grid.polygonize()
catchment_polygon = ops.unary_union([geometry.shape(shape)
                                     for shape, value in shapes])
soils = soils[soils.intersects(catchment_polygon)]
catchment_soils = gpd.GeoDataFrame(soils[soil_id], 
                                   geometry=soils.intersection(catchment_polygon))
# Convert soil types to simple integer values
soil_types = np.unique(catchment_soils[soil_id])
soil_types = pd.Series(np.arange(soil_types.size), index=soil_types)
catchment_soils[soil_id] = catchment_soils[soil_id].map(soil_types)

Convert from vector to raster

soil_polygons = zip(catchment_soils.geometry.values, catchment_soils[soil_id].values)
soil_raster = grid.rasterize(soil_polygons, fill=np.nan)

Features

• Hydrologic Functions:
  • flowdir : Generate a flow direction grid from a given digital elevation dataset.
  • catchment : Delineate the watershed for a given pour point (x, y).
  • accumulation : Compute the number of cells upstream of each cell; if weights are given, compute the sum of weighted cells upstream of each cell.
  • distance_to_outlet : Compute the (weighted) distance from each cell to a given pour point, moving downstream.
  • distance_to_ridge : Compute the (weighted) distance from each cell to its originating drainage divide, moving upstream.
  • compute_hand : Compute the height above nearest drainage (HAND).
  • stream_order : Compute the (strahler) stream order.
  • extract_river_network : Extract river segments from a catchment and return a geojson object.
  • extract_profiles : Extract river segments and return a list of channel indices along with a dictionary describing connectivity.
  • cell_dh : Compute the drop in elevation from each cell to its downstream neighbor.
  • cell_distances : Compute the distance from each cell to its downstream neighbor.
  • cell_slopes : Compute the slope between each cell and its downstream neighbor.
  • fill_pits : Fill single-celled pits in a digital elevation dataset.
  • fill_depressions : Fill multi-celled depressions in a digital elevation dataset.
  • resolve_flats : Remove flats from a digital elevation dataset.
  • detect_pits : Detect single-celled pits in a digital elevation dataset.
  • detect_depressions : Detect multi-celled depressions in a digital elevation dataset.
  • detect_flats : Detect flats in a digital elevation dataset.
• Viewing Functions:
  • view : Returns a "view" of a dataset defined by the grid's viewfinder.
  • clip_to : Clip the viewfinder to the smallest area containing all non- null gridcells for a provided dataset.
  • nearest_cell : Returns the index (column, row) of the cell closest to a given geographical coordinate (x, y).
  • snap_to_mask : Snaps a set of points to the nearest nonzero cell in a boolean mask; useful for finding pour points from an accumulation raster.
• I/O Functions:
  • read_ascii: Reads ascii gridded data.
  • read_raster: Reads raster gridded data.
  • from_ascii : Instantiates a grid from an ascii file.
  • from_raster : Instantiates a grid from a raster file or Raster object.
  • to_ascii: Write grids to delimited ascii files.
  • to_raster: Write grids to raster files (e.g. geotiff).

pysheds supports both D8 and D-infinity routing schemes.

Installation

pysheds currently only supports Python 3.

Using pip

You can install pysheds using pip:

$ pip install pysheds

Using anaconda

First, add conda forge to your channels, if you have not already done so:

$ conda config --add channels conda-forge

Then, install pysheds:

$ conda install pysheds

Installing from source

For the bleeding-edge version, you can install pysheds from this github repository.

$ git clone https://github.com/mdbartos/pysheds.git
$ cd pysheds
$ python setup.py install

or

$ git clone https://github.com/mdbartos/pysheds.git
$ cd pysheds
$ pip install .

Performance

Performance benchmarks on a 2015 MacBook Pro (M: million, K: thousand):

Speed tests were run on a conditioned DEM from the HYDROSHEDS DEM repository (linked above as elevation.tiff).

Citing

If you have used this codebase in a publication and wish to cite it, consider citing the zenodo repository:

@misc{bartos_2020,
    title  = {pysheds: simple and fast watershed delineation in python},
    author = {Bartos, Matt},
    url    = {https://github.com/mdbartos/pysheds},
    year   = {2020},
    doi    = {10.5281/zenodo.3822494}
}