import earthkit.hydro as ekh
import numpy as np

network = ekh.river_network.load("efas", "5", use_cache=False)

Cache disabled.

Catchment statistics

Instead of computing upstream statistics over an entire field, it is possible to compute statistics just at specific locations of interest. All metrics are available.

field = np.ones(network.shape) # or load array/xarray from file

ekh.catchments.sum(network, field, locations = {
    "gauge_1": (70.475, 28.32),
    "gauge_2": (42.225, 50.24)
})

<xarray.DataArray 'out' (node_index: 2)> Size: 16B
array([ 13372., 545096.])
Coordinates:
  * node_index  (node_index) int64 16B 26066 4153831
    lat         (node_index) float64 16B 28.32 50.24
    lon         (node_index) float64 16B 70.47 42.23
    name        (node_index) <U7 56B 'gauge_1' 'gauge_2'

xarray.DataArray

'out'

• node_index: 2

• 1.337e+04 5.451e+05

  array([ 13372., 545096.])

• Coordinates: (4)
  • node_index
    (node_index)
    int64
    26066 4153831

    array([  26066, 4153831])

  • lat
    (node_index)
    float64
    28.32 50.24

    array([28.325     , 50.24166667])

  • lon
    (node_index)
    float64
    70.47 42.23

    array([70.475, 42.225])

  • name
    (node_index)
    <U7
    'gauge_1' 'gauge_2'

    array(['gauge_1', 'gauge_2'], dtype='<U7')

• Indexes: (1)
  • node_index
    PandasIndex

    PandasIndex(Index([26066, 4153831], dtype='int64', name='node_index'))

• Attributes: (0)