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 ^🇬🇧

Hatari Labs - Watershed and stream network delineation with python and pysheds ^🇬🇧

Gidahatari - Delimitación de límite de cuenca y red hidrica con python y pysheds ^🇪🇸

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

Example usage

See examples/quickstart for more details.

Data available via the USGS HydroSHEDS project.

    # Read elevation and flow direction rasters
    # ----------------------------
    from pysheds.grid import Grid

    grid = Grid.from_raster('n30w100_con', data_name='dem')
    grid.read_raster('n30w100_dir', data_name='dir')
    grid.view('dem')

    # Determine D8 flow directions from DEM
    # ----------------------
    # Fill depressions in DEM
    grid.fill_depressions('dem', out_name='flooded_dem')
    
    # Resolve flats in DEM
    grid.resolve_flats('flooded_dem', out_name='inflated_dem')
    
    # Specify directional mapping
    dirmap = (64, 128, 1, 2, 4, 8, 16, 32)
    
    # Compute flow directions
    # -------------------------------------
    grid.flowdir(data='inflated_dem', out_name='dir', dirmap=dirmap)
    grid.view('dir')

    # Delineate a catchment
    # ---------------------
    # Specify pour point
    x, y = -97.294167, 32.73750

    # Delineate the catchment
    grid.catchment(data='dir', x=x, y=y, dirmap=dirmap, out_name='catch',
               recursionlimit=15000, xytype='label')

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

    # Calculate flow accumulation
    # --------------------------
    grid.accumulation(data='catch', dirmap=dirmap, out_name='acc')
    grid.view('acc')

    # Calculate distance to outlet from each cell
    # -------------------------------------------
    grid.flow_distance(data='catch', x=x, y=y, dirmap=dirmap,
                   out_name='dist', xytype='label')
    grid.view('dist')

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

    # Combine with land cover data
    # ---------------------
    grid.read_raster('nlcd_2011_impervious_2011_edition_2014_10_10.img',
                      data_name='terrain', window=grid.bbox, window_crs=grid.crs)
    grid.view('terrain')

    # Convert catchment raster to vector and combine with soils shapefile
    # ---------------------
    # Read soils shapefile
    import geopandas as gpd
    from shapely import geometry, ops
    soils = gpd.read_file('nrcs-soils-tarrant_439.shp')
    # 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 = soils.intersection(catchment_polygon)

    # Convert soils polygons to raster
    # ---------------------
    soil_polygons = zip(catchment_soils.geometry.values,
                        catchment_soils['soil_type'].values)
    soil_raster = grid.rasterize(soil_polygons, fill=np.nan)

Features

Hydrologic Functions:
- flowdir: DEM to flow direction.
- catchment: Delineate catchment from flow direction.
- accumulation: Flow direction to flow accumulation.
- flow_distance: Compute flow distance to outlet.
- extract_river_network: Extract river network at a given accumulation threshold.
- cell_area: Compute (projected) area of cells.
- cell_distances: Compute (projected) channel length within cells.
- cell_dh: Compute the elevation change between cells.
- cell_slopes: Compute the slopes of cells.
- fill_pits: Fill simple pits in a DEM (single cells lower than their surrounding neighbors).
- fill_depressions: Fill depressions in a DEM (regions of cells lower than their surrounding neighbors).
- resolve_flats: Resolve drainable flats in a DEM using the modified method of Garbrecht and Martz (1997).
- compute_hand : Compute the height above nearest drainage (HAND) as described in Nobre et al. (2011).
Utilities:
- view: Returns a view of a dataset at a given bounding box and resolution.
- clip_to: Clip the current view to the extent of nonzero values in a given dataset.
- set_bbox: Set the current view to a rectangular bounding box.
- snap_to_mask: Snap a set of coordinates to the nearest masked cells (e.g. cells with high accumulation).
- resize: Resize a dataset to a new resolution.
- rasterize: Convert a vector dataset to a raster dataset.
- polygonize: Convert a raster dataset to a vector dataset.
- detect_pits: Return boolean array indicating locations of simple pits in a DEM.
- detect_flats: Return boolean array indicating locations of flats in a DEM.
- detect_depressions: Return boolean array indicating locations of depressions in a DEM.
- check_cycles: Check for cycles in a flow direction grid.
- set_nodata: Set nodata value for a dataset.
I/O:
- read_ascii: Reads ascii gridded data.
- read_raster: Reads raster gridded data.
- 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

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

Performance

Performance benchmarks on a 2015 MacBook Pro:

Flow Direction to Flow Accumulation: 36 million grid cells in 15 seconds.
Flow Direction to Catchment: 9.8 million grid cells in 4.55 seconds.

joycezw/pysheds