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Plotting an hydrograph

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Flow duration curves

A flow duration curve represents how often any given flow discharge is likely to be equaled or exceeded. The x axis corresponds to probabilities of exceedence, while the y axis corresponds to stream flow discharges.

To construct a flow duration curve (FDC), stream flow discharges are ranked in decreasing order and associated with a given plotting position: if n is the number of flow data, the highest flow is associated with 1/(n+1) and the lowest flow with n/(n+1).

The function plot_fdc provides a convenient way to plot FDCs.

This function requires a time series as input, either as a 1D or a 2D series. In that second case, each row should correspond to a given period of flow records (usually a year).

By default, if the series is 1D, only one FDC will be plotted. If the series is 2D, one FDC will be plotted for each row of the series.

A 1D series can also be converted to an annual frequency using the starting_month parameter. This parameter precises the month at which the year should start. For example, water years are usually defined as starting in April and lasting to the following march. To plot FDCs for each water year, use starting_month=4.

>>> import numpy as np
>>> import scikits.hydroclimpy as hydro
>>> import scikits.hydroclimpy.enso as enso
>>> import as usgs
>>> import scikits.hydroclimpy.plotlib as cpl

Let’s import the streamflow data for the North Oconee River in Athens, GA. The corresponding USGS code of the gage is ‘02217770’.

>>> series = usgs.load_usgs_flows('02217770')

Let’s create a figure and add a subplot.

>>> fig = cpl.figure()
>>> fsp = fig.add_subplot(111)

Let’s call the plot_fdc function to plot the corresponding flow duration curve. By default, the curve is plotted on the current subplot. We can also select a particular subplot with the subplot parameter. We will use the additional parameters lw to specify the linewidth of the curve, ls for the linestyle, c for the color, and zorder for the priority order.

>>> cpl.plot_fdc(series, subplot=fsp, lw=2, ls='-', c='k', zorder=10)

Let’s add the FDCs for each water year. By default, the multiple curves are drawn using a linewidth of 0.8 and a grey color.

>>> cpl.plot_fdc(series, subplot=fsp, starting_month=4)

We can now finalize the plot by adding some labels and legends.

>>> fsp.set_ylabel("Flows (cfs)", fontweight='bold')
>>> fig.suptitle("Flow duration curve for the North Oconee River at Athens, GA",
                 fontweight="bold", fontsize=12)
>>> fig.savefig('athens_fdc.png')

[source code, hires.png, pdf]


Adding ENSO information

The plot_fdc function accepts also ClimateSeries objects to add ENSO related information to the plot. This option can be activated by setting the plot_enso optional boolean flag to True. Naturally, an ENSOIndicator must have been associated with the input series.

When the input series is 1D, individual points will plotted with the color associated with their ENSO condition, provided a marker is explicitly given as an input parameter.

When the input series is 2D, each individual year will be associated with an ENSO condition, depending on the lag optional parameter. By default, the lag parameter is calculated as the time difference between the first month of the ENSO indicator reference (or October, if the ENSO indicator does not have any reference season) and the starting_month parameter. For example, if starting_month is 4 (April) and the reference season runs from October to December included, the lag will be 6 months, and each water year will be associated with the ENSO conditions prevailing the previous October.

In both cases, the colors corresponding to each ENSO conditions are available through the ENSOcolors dictionary.

Let us replot the previous FDCs with ENSO information. First, we need to associate an ENSO indicator to the series.

>>> ONI = enso.load_oni()
>>> series = enso.set_ensoindicator(series, ONI)

We can now create a new figure and a new plot.

>>> fig = cpl.figure()
>>> cpl.plot_fdc(series, enso=True, marker='o')
>>> cpl.plot_fdc(series, enso=True, starting_month=4)
>>> cpl.gca().legend()
>>> fig.savefig('athens_fdc_enso.png')