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Fetch and process data from the National Water Model

Project description


Documentation Status MIT license Binder

Python library to fetch and process the National Water Model (NWM) NetCDF datasets.

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Install package

$ pip install nwm

Download NWM Data

You can launch binder to test and run the code below.

Example 1: use NwmHs class to download data (Recommended method)
import matplotlib.pyplot as plt
from nwm import NwmHs

# get data from National water model HydroShare App
nwm_data = NwmHs()
dataset = nwm_data.get_data(archive='harvey', config='short_range', geom='channel_rt', variable='streamflow',
                           comid=[5781915], init_time=0, start_date='2017-08-23')

# show metadata

# plot data
plt.xlabel('Year 2017')
plt.ylabel('{} ({})'.format(dataset.variable_name,dataset.variable_unit))
plt.title('Short range streamflow forecast for Channel 5781915 during Harvey Hurricane Event')


Example 2: use BmiNwmHs class to download data (Demonstration of how to use BMI)
import matplotlib.pyplot as plt
import numpy as np
import cftime

from nwm import BmiNwmHs

# initiate a data component
data_comp = BmiNwmHs()

# get variable info
var_name = data_comp.get_output_var_names()[0]
var_unit = data_comp.get_var_units(var_name)
print(' variable_name: {}\n var_unit: {}\n'.format(var_name, var_unit))

# get time info
start_time = data_comp.get_start_time()
end_time = data_comp.get_end_time()
time_step = data_comp.get_time_step()
time_unit = data_comp.get_time_units()
time_steps = int((end_time - start_time)/time_step) + 1
print(' start_time:{}\n end_time:{}\n time_step:{}\n time_unit:{}\n time_steps:{}\n'.format(start_time, end_time, time_step, time_unit, time_steps))

# initiate numpy arrays to store data
stream_value = np.empty(1)
stream_array = np.empty(time_steps)
cftime_array = np.empty(time_steps)

for i in range(0, time_steps):
    data_comp.get_value(var_name, stream_value)
    stream_array[i] = stream_value
    cftime_array[i] = data_comp.get_current_time()

time_array = cftime.num2date(cftime_array, time_unit, only_use_cftime_datetimes=False, only_use_python_datetimes=True)

# plot data
plt.plot(time_array, stream_array)
plt.xlabel('Year 2017')
plt.ylabel('{} ({})'.format(var_name, var_unit))
plt.title('Short range streamflow forecast for Channel 5781915 during Harvey Hurricane Event')

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