deltametrics.mobility.calculate_channel_abandonment

deltametrics.mobility.calculate_channel_abandonment(chmap, basevalues=None, basevalues_idx=None, window=None, window_idx=None)

Calculate channel abandonment.

Measure the number of channelized pixels that are no longer channelized as a signature of channel mobility based on method in Liang et al 2016 [1]. This requires providing an input channel map, and setting parameters for the min/max values to compare to, and the time window over which the evaluation can be done.

This calculation is performed by first identifying the number of channel pixels at some base time. Then, for each time step after the base time, we measure the number of pixels that are no longer channelized. This measurement is used to calculate the fraction of channel pixels that are abandoned relative to the base channel map for each time step. Multiple base times can be provided. The results are returned as a 2-D array in which each row represents the abandonment values associated with a given base value and the columns are associated with each time lag.

[1]

Liang, Man, Wonsuck Kim, and Paola Passalacqua. “How much subsidence is enough to change the morphology of river deltas?.” Geophysical Research Letters 43, no. 19 (2016): 10-266.

Parameters:

• chmap (list, xarray.DataArray, numpy.ndarray) – Either a list of 2-D deltametrics.mask, xarray.DataArray, or numpy.ndarray objects, or a t-x-y 3-D xarray.DataArray or numpy.ndarray with channel mask values.

• basevalues (list, int, float, optional) – List of time values to use as the base channel map. (or single value)

• basevalues_idx (list, optional) – List of time indices to use as the base channel map. (or single value)

• window (int, float, optional) – Duration of time to use as the time lag (aka how far from the basemap will be analyzed).

• window_idx (int, float, optional) – Duration of time in terms of indices (# of save states) to use as the time lag.

Returns:

PwetA – A 2-D array of the abandoned fraction of the channel over the window of time. It is of shape len(basevalues) x time_window so each row represents the fraction of the channel that has been abandonded, and the columns are associated with each time lag.

Return type:

ndarray