watershed_aggregate: aggregate field values over a watershed

watershed_aggregation(grid, field, outlet_id, method, **kwds)[source]

Aggregate a field value over a watershed.

watershed_aggregation calculates aggregate values on the nodes in a watershed that drain to outlet_id. It supports all methods in the numpy namespace that reduce an array to a scalar.

Parameters

  • grid (Landlab model grid) –

  • field (str) – An at-node Landlab grid field that is present on the model grid.

  • outlet_id (int) – Outlet id of the watershed.

  • method (str) – The name of a numpy namespace method.

  • **kwds – Any additional keyword arguments needed by the method.

Returns

out – The aggregate value.

Return type

float

Examples

First an example that only uses the watershed_aggregation function.

>>> from landlab import RasterModelGrid
>>> from landlab.components import FlowAccumulator
>>> from umami.calculations import watershed_aggregation
>>> grid = RasterModelGrid((10, 10))
>>> z = grid.add_zeros("node", "topographic__elevation")
>>> z += grid.x_of_node + grid.y_of_node
>>> fa = FlowAccumulator(grid)
>>> fa.run_one_step()

watershed_aggregation supports all functions in the numpy namespace. Here we show mean and percentile. The latter of which takes an additional argument, q.

>>> watershed_aggregation(grid, "topographic__elevation", 1,  "mean")
5.0
>>> watershed_aggregation(
... grid,
... "topographic__elevation",
... 1,
... "percentile",
... q=10)
1.8

Next, the same calculations are shown as part of an umami Metric.

>>> from io import StringIO
>>> from umami import Metric
>>> file_like=StringIO('''
... oid1_mean:
...     _func: watershed_aggregation
...     outlet_id: 1
...     method: mean
...     field: topographic__elevation
... oid1_10thptile:
...     _func: watershed_aggregation
...     outlet_id: 1
...     method: percentile
...     field: topographic__elevation
...     q: 10
... ''')
>>> metric = Metric(grid)
>>> metric.add_from_file(file_like)
>>> metric.names
['oid1_mean', 'oid1_10thptile']
>>> metric.calculate()
>>> metric.values
[5.0, 1.8]