Running RAVEN on a multi-basin NetCDF file in parallel

Here we use birdy’s WPS client to launch the GR4JCN hydrological model on the server for 2 basins in parallel. The NetCDF is thus a 2d file. We then analyze the outputs.

#Import necessary packages and start the service
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 for gr4jcn for both catchments. Can either be a string of comma separated values, a list, an array or a named tuple.
gr4jcn1 ='0.529, -3.396, 407.29, 1.072, 16.9, 0.947'
gr4jcn2 ='0.28, -3.6, 380.9, 1.092, 14.6, 0.831'

params=[gr4jcn1,gr4jcn2]

# Forcing files. Raven uses the same forcing files for all and extracts the information it requires for each model.
ts=TESTDATA['input2d']

# Model configuration parameters.
config = dict(
    start_date=[dt.datetime(2000, 1, 1),dt.datetime(2000, 1, 1)],
    end_date=[dt.datetime(2002, 1, 1),dt.datetime(2002, 1, 1)],
    area=[4250.6,5000],
    elevation=[843.0,780],
    latitude=[54.4848,48.0],
    longitude=[-123.3659,-122.99],
    nc_index=[0,0],
    )

# Launch the WPS to get the multi-model results.  Note the "gr4jcn" and "hmets" keys.
resp=wps.raven_gr4j_cemaneige(ts=str(ts),params=params, **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)

# Print the diagnostics for both catchments
print(diagnostics)

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.

from pandas.plotting import register_matplotlib_converters
register_matplotlib_converters()

# Plot the hydrographs for both basins
hydrograph.q_sim.isel(nbasins=0).plot.line(hue='realization')
hydrograph.q_sim.isel(nbasins=1).plot.line(hue='realization')

The individual configuration files for each model run can be accessed as a Zip archive.

[hydrograph, storage, solution, diagnostics, rv] = resp.get(asobj=False)
print ("RV configuration", rv)