nwm

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

Please note that nwm package is deprecated. This package now only serves as an example to demonstrate how to implement Basic Model Interface (BMI) for research datasets as the CSDMS Data Component.

Get Started

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
dataset.attrs

# plot data
plt.figure(figsize=(9,5))
dataset.plot()
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()
data_comp.initialize('config_file.yaml')

# 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()
    data_comp.update()

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

# plot data
plt.figure(figsize=(9,5))
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')