Running a multi-member hydrological ensemble on the Raven Server

Here we use birdy’s WPS client to launch the GR4JCN hydrological model using two sets of parameters.

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 and hmets.
# It's not possible at the moment to pass multiple parameters as a nested list due to the WPSClient limitations.
# Use a list of strings instead.
params = ['0.529, -3.396, 407.29, 1.072, 16.9, 0.947', '0.4, -3.96, 307.29, 1.072, 16.9, 0.947']

# Forcing files. Raven uses the same forcing files for all and extracts the information it requires for each model.
ts=TESTDATA['raven-gr4j-cemaneige-nc-ts']

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

# 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)

The hydrograph and storage output are netCDF files where the simulations are concatenated along the params dimension. The solution output is a list of rvc files, and the diagnostics a list of performance metrics comparing simulations with observations.

[hydrograph, storage, solution, diagnostics, rv] = resp.get(asobj=True)
hydrograph

xarray.Dataset

• Dimensions:
  • nbasins: 1
  • params: 2
  • time: 732
• Coordinates: (2)
  • basin_name
    (nbasins)
    object
    ...

    long_name :

    Name/ID of sub-basins with simulated outflows

    cf_role :

    timeseries_id

    units :

    1

    array(['Salmon'], dtype=object)

  • time
    (time)
    datetime64[ns]
    2000-01-01 ... 2002-01-01

    standard_name :

    time

    array(['2000-01-01T00:00:00.000000000', '2000-01-02T00:00:00.000000000',
           '2000-01-03T00:00:00.000000000', ..., '2001-12-30T00:00:00.000000000',
           '2001-12-31T00:00:00.000000000', '2002-01-01T00:00:00.000000000'],
          dtype='datetime64[ns]')

• Data variables: (4)
  • q_obs
    (time, nbasins)
    float64
    ...

    units :

    m**3 s**-1

    long_name :

    Observed outflows

    array([[ nan],
           [11.1],
           [10.9],
           ...,
           [11.7],
           [12.1],
           [12.3]])

  • q_in
    (time, nbasins)
    float64
    ...

    units :

    m**3 s**-1

    long_name :

    Observed inflows

    array([[nan],
           [nan],
           [nan],
           ...,
           [nan],
           [nan],
           [nan]])

  • precip
    (time)
    float64
    ...

    units :

    mm d**-1

    long_name :

    Precipitation

    array([     nan, 2.478706, 0.628235, ..., 0.      , 0.003882, 0.      ])

  • q_sim
    (params, time, nbasins)
    float64
    ...

    units :

    m**3 s**-1

    long_name :

    Simulated outflows

    array([[[ 0.      ],
            [ 0.165788],
            ...,
            [13.330653],
            [13.25446 ]],
    
           [[ 0.      ],
            [ 0.123831],
            ...,
            [11.191918],
            [11.114937]]])

• Attributes: (11)

  Conventions :

  CF-1.6

  featureType :

  timeSeries

  history :

  Created on 2020-05-01 17:35:52 by Raven

  description :

  Standard Output

  title :

  Simulated river discharge

  references :

  Craig J.R. and the Raven Development Team Raven user's and developer's manual (Version 2.8) URL: http://raven.uwaterloo.ca/ (2018).

  comment :

  Raven Hydrological Framework version 2.9 rev#254

  model_id :

  gr4jcn

  time_frequency :

  day

  time_coverage_start :

  2000-01-01 00:00:00

  time_coverage_end :

  2002-01-01 00:00:00

from pandas.plotting import register_matplotlib_converters
register_matplotlib_converters()


hydrograph.q_sim.isel(nbasins=0).plot.line(hue='params')

[<matplotlib.lines.Line2D at 0x7f1c8632c2b0>,
 <matplotlib.lines.Line2D at 0x7f1c84e988d0>]

print(diagnostics)

['observed data series,filename,DIAG_NASH_SUTCLIFFE,DIAG_RMSE,\nHYDROGRAPH,/tmp/pywps_process_mv5m3m6u/Salmon-River-Near-Prince-George_meteo_daily.nc,-0.117301,37.9493,\n', 'observed data series,filename,DIAG_NASH_SUTCLIFFE,DIAG_RMSE,\nHYDROGRAPH,/tmp/pywps_process_mv5m3m6u/Salmon-River-Near-Prince-George_meteo_daily.nc,-0.0559845,36.8933,\n']