Calling MOHYSE on the Raven server

Here we use birdy’s WPS client to launch the MOHYSE hydrological model on the server and analyze the output.

from birdy import WPSClient

from example_data import TESTDATA
import datetime as dt
from urllib.request import urlretrieve
import xarray as xr
import numpy as np
from matplotlib import pyplot as plt
import os

# Set environment variable WPS_URL to "http://localhost:9099" to run on the default local server
url = os.environ.get("WPS_URL", "https://pavics.ouranos.ca/twitcher/ows/proxy/raven/wps")
wps = WPSClient(url)

# The model parameters. Can either be a string of comma separated values, a list, an array or a named tuple.
# Mohyse also requires HRU parameters
params = '1.00, 0.0468, 4.2952, 2.6580, 0.4038, 0.0621, 0.0273, 0.0453'
hrus = '0.9039, 5.6179775'

# Forcing files
ts=TESTDATA['raven-mohyse-nc-ts']

# Model configuration parameters
config = dict(
    start_date=dt.datetime(2000, 1, 1),
    end_date=dt.datetime(2002, 1, 1),
    area=4250.6,
    elevation=843.0,
    latitude=54.4848,
    longitude=-123.3659,
    )

# Let's call the model
resp = wps.raven_mohyse(ts=str(ts), params = params, hrus=hrus, **config)

# And get the response
# With `asobj` set to False, only the reference to the output is returned in the response.
# Setting `asobj` to True will retrieve the actual files and copy the locally.
[hydrograph, storage, solution, diagnostics, rv] = resp.get(asobj=True)

Since we requested output objects, we can simply access the output objects. The dianostics is just a CSV file:

print(diagnostics)

observed data series,filename,DIAG_NASH_SUTCLIFFE,DIAG_RMSE,
HYDROGRAPH,/tmp/pywps_process_6uslcp5c/input.nc,0.194612,32.2196,

The hydrograph and storage outputs are netCDF files storing the time series. These files are opened by default using xarray, which provides convenient and powerful time series analysis and plotting tools.

hydrograph.q_sim

<xarray.DataArray 'q_sim' (time: 732, nbasins: 1)>
array([[ 0.      ],
       [ 0.      ],
       [ 0.      ],
       ...,
       [15.83282 ],
       [15.143114],
       [14.480141]])
Coordinates:
  * time        (time) datetime64[ns] 2000-01-01 2000-01-02 ... 2002-01-01
    basin_name  (nbasins) object ...
Dimensions without coordinates: nbasins
Attributes:
    units:      m**3 s**-1
    long_name:  Simulated outflows

from pandas.plotting import register_matplotlib_converters
register_matplotlib_converters()

hydrograph.q_sim.plot()

[<matplotlib.lines.Line2D at 0x7f9868a9cf98>]

print("Max: ", hydrograph.q_sim.max())
print("Mean: ", hydrograph.q_sim.mean())
print("Monthly means: ", hydrograph.q_sim.groupby(hydrograph.time.dt.month).mean(dim='time'))

Max:  <xarray.DataArray 'q_sim' ()>
array(130.83915822)
Mean:  <xarray.DataArray 'q_sim' ()>
array(48.38849311)
Monthly means:  <xarray.DataArray 'q_sim' (month: 12, nbasins: 1)>
array([[ 6.24447576],
       [ 2.55609967],
       [ 9.11604814],
       [62.74908332],
       [92.79608734],
       [60.44819211],
       [59.04811531],
       [57.81612411],
       [63.59451548],
       [53.42205803],
       [80.20394387],
       [32.01984468]])
Coordinates:
    basin_name  (nbasins) object ...
  * month       (month) int64 1 2 3 4 5 6 7 8 9 10 11 12
Dimensions without coordinates: nbasins