hydrotools.nwm-client 9.2.1

Retrieve National Water Model data from various sources.

OWPHydroTools :: NWM Client

This subpackage implements various interfaces to retrieve National Water Model (NWM) data from various sources including Google Cloud Platform, NOMADS, local directories, or a generic web server directory listing. The primary use for this tool is to populate pandas.Dataframe objects with NWM streamflow data. See the NWM Client Documentation for a complete list and description of the currently available methods. To report bugs or request new features, submit an issue through the OWPHydroTools Issue Tracker on GitHub.

Installation

In accordance with the python community, we support and advise the usage of virtual environments in any workflow using python. In the following installation guide, we use python's built-in venv module to create a virtual environment in which the tool will be installed. Note this is just personal preference, any python virtual environment manager should work just fine (conda, pipenv, etc. ).

# Create and activate python environment, requires python >= 3.8
$ python3 -m venv env
$ source env/bin/activate
$ python3 -m pip install --upgrade pip wheel

# Install nwm_client
$ python3 -m pip install hydrotools.nwm_client

Usage

The following example demonstrates how one might use hydrotools.nwm_client to retrieve NWM streamflow forecasts.

Code

View compatible configurations

# Import the NWM Client
from hydrotools.nwm_client.NWMFileClient import NWMFileClient

# Instantiate model data client
model_data_client = NWMFileClient()

# Print compatible model configurations
#  Note that not all data sources contain the full range of available 
#  National Water Model data. This client defaults to Google Cloud Platform
#  Which has the largest amount of *operational* forecast data.
#  Also note that not all configurations are available for the entire
#  archive of NWM operational forecast data. For example, the configurations 
#  for Alaska only became available after August 2023.
print(model_data_client.catalog.configurations)

Example output

['analysis_assim', 'analysis_assim_alaska', 'analysis_assim_alaska_no_da', 'analysis_assim_extend', 'analysis_assim_extend_no_da', 'analysis_assim_extend_alaska', 'analysis_assim_extend_alaska_no_da', 'analysis_assim_hawaii', 'analysis_assim_hawaii_no_da', 'analysis_assim_no_da', 'analysis_assim_puertorico', 'analysis_assim_puertorico_no_da', 'analysis_assim_long', 'analysis_assim_long_no_da', 'long_range_mem1', 'long_range_mem2', 'long_range_mem3', 'long_range_mem4', 'medium_range_alaska_mem1', 'medium_range_alaska_mem2', 'medium_range_alaska_mem3', 'medium_range_alaska_mem4', 'medium_range_alaska_mem5', 'medium_range_alaska_mem6', 'medium_range_alaska_no_da', 'medium_range_mem1', 'medium_range_mem2', 'medium_range_mem3', 'medium_range_mem4', 'medium_range_mem5', 'medium_range_mem6', 'medium_range_mem7', 'medium_range_no_da', 'short_range', 'short_range_alaska', 'short_range_hawaii', 'short_range_hawaii_no_da', 'short_range_puertorico', 'short_range_puertorico_no_da']

Retrieving data from google cloud

# Import the NWM Client
from hydrotools.nwm_client.NWMFileClient import NWMFileClient

# Instantiate model data client
#  By default, NWM values are in SI units
#  If you prefer US standard units, nwm_client can return
#  values in US standard units by setting the unit_system parameter 
#  to MeasurementUnitSystem.US
# 
# from hydrotools.nwm_client.NWMClientDefaults import MeasurementUnitSystem
# model_data_client = NWMFileClient(unit_system=MeasurementUnitSystem.US)
model_data_client = NWMFileClient()

# Retrieve forecast data
forecast_data = model_data_client.get(
    configurations = ["short_range"],
    reference_times = ["20210101T01Z"],
    nwm_feature_ids = [724696]
    )

# Look at the data
print(forecast_data.head())

Example output

       reference_time  nwm_feature_id          value_time      value measurement_unit variable_name configuration usgs_site_code
0 2021-01-01 01:00:00          724696 2021-01-01 02:00:00  56.340000           m3 s-1    streamflow   short_range       01013500
1 2021-01-01 01:00:00          724696 2021-01-01 17:00:00  56.090000           m3 s-1    streamflow   short_range       01013500
2 2021-01-01 01:00:00          724696 2021-01-01 16:00:00  56.119999           m3 s-1    streamflow   short_range       01013500
3 2021-01-01 01:00:00          724696 2021-01-01 15:00:00  56.149998           m3 s-1    streamflow   short_range       01013500
4 2021-01-01 01:00:00          724696 2021-01-01 14:00:00  56.180000           m3 s-1    streamflow   short_range       01013500

Retrieving data from Azure Blob Storage

# Import the NWM Client
from hydrotools.nwm_client.NWMFileClient import NWMFileClient
from hydrotools.nwm_client.AzureFileCatalog import AzureFileCatalog
import pandas as pd

# Instantiate model data client
catalog = AzureFileCatalog()
model_data_client = NWMFileClient(catalog=catalog)

# Set reference time
yesterday = pd.Timestamp.utcnow() - pd.Timedelta("1D")

# Retrieve forecast data
forecast_data = model_data_client.get(
    configurations = ["short_range"],
    reference_times = [yesterday],
    nwm_feature_ids = [724696]
    )

# Look at the data
print(forecast_data.head())

Example output

       reference_time  nwm_feature_id          value_time      value measurement_unit variable_name configuration usgs_site_code
0 2022-08-07 18:00:00          724696 2022-08-07 19:00:00  20.369999           m3 s-1    streamflow   short_range       01013500
1 2022-08-07 18:00:00          724696 2022-08-08 10:00:00  24.439999           m3 s-1    streamflow   short_range       01013500
2 2022-08-07 18:00:00          724696 2022-08-08 09:00:00  24.469999           m3 s-1    streamflow   short_range       01013500
3 2022-08-07 18:00:00          724696 2022-08-08 08:00:00  24.490000           m3 s-1    streamflow   short_range       01013500
4 2022-08-07 18:00:00          724696 2022-08-08 07:00:00  24.510000           m3 s-1    streamflow   short_range       01013500

Retrieving data from Nomads

# Import the NWM Client
from hydrotools.nwm_client.NWMFileClient import NWMFileClient
from hydrotools.nwm_client.HTTPFileCatalog import HTTPFileCatalog
import pandas as pd

# Instantiate model data client
catalog = HTTPFileCatalog("https://nomads.ncep.noaa.gov/pub/data/nccf/com/nwm/prod/")
model_data_client = NWMFileClient(catalog=catalog)

# Set reference time
yesterday = pd.Timestamp.utcnow() - pd.Timedelta("1D")

# Retrieve forecast data
forecast_data = model_data_client.get(
    configurations = ["short_range"],
    reference_times = [yesterday],
    nwm_feature_ids = [724696]
    )

# Look at the data
print(forecast_data.head())

Example output

       reference_time  nwm_feature_id          value_time      value measurement_unit variable_name configuration usgs_site_code
0 2022-08-07 18:00:00          724696 2022-08-07 19:00:00  20.369999           m3 s-1    streamflow   short_range       01013500
1 2022-08-07 18:00:00          724696 2022-08-08 10:00:00  24.439999           m3 s-1    streamflow   short_range       01013500
2 2022-08-07 18:00:00          724696 2022-08-08 09:00:00  24.469999           m3 s-1    streamflow   short_range       01013500
3 2022-08-07 18:00:00          724696 2022-08-08 08:00:00  24.490000           m3 s-1    streamflow   short_range       01013500
4 2022-08-07 18:00:00          724696 2022-08-08 07:00:00  24.510000           m3 s-1    streamflow   short_range       01013500

Retrieving data from a private file server

# Import the NWM Client
from hydrotools.nwm_client.NWMFileClient import NWMFileClient
from hydrotools.nwm_client.HTTPFileCatalog import HTTPFileCatalog
from hydrotools.nwm_client.NWMClientDefaults import MeasurementUnitSystem
import ssl

# Create ssl context
context = ssl.create_default_context(cafile="/path/to/my/ca-bundle.crt")

# Instantiate model data client
catalog = HTTPFileCatalog(
    "https://path-to-my-private-server.com/nwm/2.2/", 
    ssl_context=context
    )
model_data_client = NWMFileClient(
    catalog=catalog,
    unit_system=MeasurementUnitSystem.US,
    ssl_context=context
)

# Retrieve forecast data
forecast_data = model_data_client.get(
    configurations = ["short_range"],
    reference_times = ["2022-06-01T13"],
    nwm_feature_ids = [724696]
    )

# Look at the data
print(forecast_data.head())

Example output

       reference_time  nwm_feature_id          value_time        value measurement_unit variable_name configuration usgs_site_code
0 2022-06-01 13:00:00          724696 2022-06-01 14:00:00  3586.910645           ft^3/s    streamflow   short_range       01013500
1 2022-06-01 13:00:00          724696 2022-06-02 05:00:00  2167.260986           ft^3/s    streamflow   short_range       01013500
2 2022-06-01 13:00:00          724696 2022-06-02 04:00:00  2168.673584           ft^3/s    streamflow   short_range       01013500
3 2022-06-01 13:00:00          724696 2022-06-02 03:00:00  2172.558350           ft^3/s    streamflow   short_range       01013500
4 2022-06-01 13:00:00          724696 2022-06-02 02:00:00  2177.855469           ft^3/s    streamflow   short_range       01013500

System Requirements

We employ several methods to make sure the resulting pandas.DataFrame produced by nwm_client are as efficient and manageable as possible. Nonetheless, this package can potentially use a large amount of memory.

The National Water Model generates multiple forecasts per day at over 3.7 million locations across the United States. A single forecast could be spread across hundreds of files and require repeated calls to the data source. The intermediate steps of retrieving and processing these files into leaner DataFrame may use several GB of memory. As such, recommended minimum requirements to use this package are a 4-core consumer processor and 8 GB of RAM.

Development

This package uses a setup configuration file (setup.cfg) and assumes use of the setuptools backend to build the package. To install the package for development use:

$ python3 -m venv env
$ source env/bin/activate
$ python3 -m pip install -U pip
$ python3 -m pip install -U setuptools
$ python3 -m pip install -e .[develop]

To generate a source distribution:

$ python3 -m pip install -U wheel build
$ python3 -m build

The packages generated in dist/ can be installed directly with pip or uploaded to PyPI using twine.