pynhd 0.19.4
Access and navigate NHDPlus V2 via various web services, e.g., NLDI and WaterData.
License: MIT
Requires Python: >=3.9

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================ ====================================================================
Package          Description
================ ====================================================================
PyNHD_           Navigate and subset NHDPlus (MR and HR) using web services
Py3DEP_          Access topographic data through National Map's 3DEP web service
PyGeoHydro_      Access NWIS, NID, WQP, eHydro, NLCD, CAMELS, and SSEBop databases
PyDaymet_        Access daily, monthly, and annual climate data via Daymet
PyGridMET_       Access daily climate data via GridMET
PyNLDAS2_        Access hourly NLDAS-2 data via web services
HydroSignatures_ A collection of tools for computing hydrological signatures
AsyncRetriever_  High-level API for asynchronous requests with persistent caching
PyGeoOGC_        Send queries to any ArcGIS RESTful-, WMS-, and WFS-based services
PyGeoUtils_      Utilities for manipulating geospatial, (Geo)JSON, and (Geo)TIFF data
================ ====================================================================

.. _PyGeoHydro: https://github.com/hyriver/pygeohydro
.. _AsyncRetriever: https://github.com/hyriver/async-retriever
.. _PyGeoOGC: https://github.com/hyriver/pygeoogc
.. _PyGeoUtils: https://github.com/hyriver/pygeoutils
.. _PyNHD: https://github.com/hyriver/pynhd
.. _Py3DEP: https://github.com/hyriver/py3dep
.. _PyDaymet: https://github.com/hyriver/pydaymet
.. _PyGridMET: https://github.com/hyriver/pygridmet
.. _PyNLDAS2: https://github.com/hyriver/pynldas2
.. _HydroSignatures: https://github.com/hyriver/hydrosignatures

PyNHD: Navigate and subset NHDPlus database
-------------------------------------------

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Features
--------

PyNHD is a part of `HyRiver <https://github.com/hyriver/HyRiver>`__ software stack that
is designed to aid in hydroclimate analysis through web services.

This package provides access to several hydro-linked datasets:

- `StreamCat <https://www.epa.gov/national-aquatic-resource-surveys/streamcat-dataset>`__
- `WaterData <https://api.water.usgs.gov/>`__
- `NHDPlus MR <https://hydro.nationalmap.gov/arcgis/rest/services/nhd/MapServer>`__
- `NHDPlus HR <https://hydro.nationalmap.gov/arcgis/rest/services/NHDPlus_HR/MapServer>`__
- `NLDI <https://api.water.usgs.gov/>`__
- `PyGeoAPI <https://api.water.usgs.gov/>`__
- `GeoConnex <https://geoconnex.internetofwater.dev/>`__
- `Mainstems <https://www.sciencebase.gov/catalog/item/63cb38b2d34e06fef14f40ad>`__
- `HUC12 Pour Points <https://www.sciencebase.gov/catalog/item/60cb5edfd34e86b938a373f4>`__

These web services can be used to navigate and extract vector data from NHDPlus V2 (both mid-
and high-resolution) database such as catchments, HUC8, HUC12, GagesII, flowlines, and water
bodies.

Moreover, the PyGeoAPI service provides four functionalities:

1. ``flow_trace``: Trace flow from a starting point to up/downstream direction.
2. ``split_catchment``: Split the local catchment of a point of interest at the point's
   location.
3. ``elevation_profile``: Extract elevation profile along a flow path between two points.
4. ``cross_section``: Extract cross-section at a point of interest along a flow line.

PyNHD also provides access to the entire NHDPlus dataset for CONUS (L48) via
``nhdplus_l48`` function. You can get any of the 31 layers that are available in the
NHDPlus dataset. You can also get NHDPlus Value Added Attributes on
`Hydroshare <https://www.hydroshare.org/resource/6092c8a62fac45be97a09bfd0b0bf726/>`__
and `ENHD <https://www.sciencebase.gov/catalog/item/60c92503d34e86b9389df1c9>`__.
These datasets that do not have geometries, include slope and roughness, among other
attributes, for all NHD flowlines. You can use ``nhdplus_vaa`` and ``enhd_attrs``
functions to get these datasets.

Additionally, you can get many more derived attributes at NHD catchment-level
through two sources:

- Select Attributes for NHDPlus Version 2.1 Reach Catchments from an item on
  `ScienceBase <https://sciencebase.usgs.gov>`__
- EPA's `StreamCat <https://www.epa.gov/national-aquatic-resource-surveys/streamcat-dataset>`__
  dataset.

They both include hundreds of attributes such as hydroclimate properties, water quality,
urbanization, and population. In addition to NHD catchment summaries, they also have
their network-accumulated values (both upstream and divergence-routed). You can use
``nhdplus_attrs``, ``epa_nhd_catchments``, ``streamcat`` functions to get these datasets.

Additionally, PyNHD offers some extra utilities for processing the NHD flowlines:

- ``flowline_xsection`` and ``network_xsection``: Get cross-section lines along a flowline
  at a given spacing or a network of flowlines at a given spacing.
- ``flowline_resample`` and ``network_resample``:
  Resample a flowline or network of flowlines based on a given spacing. This is
  useful for smoothing jagged flowlines similar to those in the NHDPlus database.
- ``prepare_nhdplus``: For cleaning up the data frame by, for example, removing tiny networks,
  adding a ``to_comid`` column, and finding terminal flowlines if it doesn't exist.
- ``topoogical_sort``: For sorting the river network topologically which is useful for routing
  and flow accumulation.
- ``vector_accumulation``: For computing flow accumulation in a river network. This function
  is generic, and any routing method can be plugged in.

These utilities are developed based on an R package called
`nhdplusTools <https://github.com/USGS-R/nhdplusTools>`__ and a Python package
called `nldi-xstool <https://code.usgs.gov/wma/nhgf/toolsteam/nldi-xstool>`__.

All functions and classes that request data from web services use ``async-retriever``
that offers response caching. By default, the expiration time is set to never expire.
All these functions and classes have two optional parameters for controlling the cache:
``expire_after`` and ``disable_caching``. You can use ``expire_after`` to set the expiration
time in seconds. If ``expire_after`` is set to ``-1``, the cache will never expire (default).
You can use ``disable_caching`` if you don't want to use the cached responses. The cached
responses are stored in the ``./cache/aiohttp_cache.sqlite`` file.

You can find some example notebooks `here <https://github.com/hyriver/HyRiver-examples>`__.

Moreover, under the hood, PyNHD uses
`PyGeoOGC <https://github.com/hyriver/pygeoogc>`__ and
`AsyncRetriever <https://github.com/hyriver/async-retriever>`__ packages
for making requests in parallel and storing responses in chunks. This improves the
reliability and speed of data retrieval significantly.

You can control the request/response caching behavior and verbosity of the package
by setting the following environment variables:

* ``HYRIVER_CACHE_NAME``: Path to the caching SQLite database for asynchronous HTTP
  requests. It defaults to ``./cache/aiohttp_cache.sqlite``
* ``HYRIVER_CACHE_NAME_HTTP``: Path to the caching SQLite database for HTTP requests.
  It defaults to ``./cache/http_cache.sqlite``
* ``HYRIVER_CACHE_EXPIRE``: Expiration time for cached requests in seconds. It defaults to
  one week.
* ``HYRIVER_CACHE_DISABLE``: Disable reading/writing from/to the cache. The default is false.
* ``HYRIVER_SSL_CERT``: Path to a SSL certificate file.

For example, in your code before making any requests you can do:

.. code-block:: python

    import os

    os.environ["HYRIVER_CACHE_NAME"] = "path/to/aiohttp_cache.sqlite"
    os.environ["HYRIVER_CACHE_NAME_HTTP"] = "path/to/http_cache.sqlite"
    os.environ["HYRIVER_CACHE_EXPIRE"] = "3600"
    os.environ["HYRIVER_CACHE_DISABLE"] = "true"
    os.environ["HYRIVER_SSL_CERT"] = "path/to/cert.pem"

You can also try using PyNHD without installing
it on your system by clicking on the binder badge. A Jupyter Lab
instance with the HyRiver stack pre-installed will be launched in your web browser, and you
can start coding!

Moreover, requests for additional functionalities can be submitted via
`issue tracker <https://github.com/hyriver/pynhd/issues>`__.

Citation
--------
If you use any of HyRiver packages in your research, we appreciate citations:

.. code-block:: bibtex

    @article{Chegini_2021,
        author = {Chegini, Taher and Li, Hong-Yi and Leung, L. Ruby},
        doi = {10.21105/joss.03175},
        journal = {Journal of Open Source Software},
        month = {10},
        number = {66},
        pages = {1--3},
        title = {{HyRiver: Hydroclimate Data Retriever}},
        volume = {6},
        year = {2021}
    }

Installation
------------

You can install PyNHD using ``pip`` after installing ``libgdal`` on your system
(for example, in Ubuntu run ``sudo apt install libgdal-dev``):

.. code-block:: console

    $ pip install pynhd

Alternatively, PyNHD can be installed from the ``conda-forge`` repository
using `Conda <https://docs.conda.io/en/latest/>`__
or `Mamba <https://github.com/conda-forge/miniforge>`__:

.. code-block:: console

    $ conda install -c conda-forge pynhd

Quick start
-----------

Let's explore the capabilities of ``NLDI``. We need to instantiate the class first:

.. code-block:: python

    from pynhd import NLDI, WaterData, NHDPlusHR
    import pynhd as nhd

First, let's get the watershed geometry of the contributing basin of a
USGS station using ``NLDI``:

.. code-block:: python

    nldi = NLDI()
    station_id = "01031500"

    basin = nldi.get_basins(station_id)

The ``navigate_byid`` class method can be used to navigate NHDPlus in
both upstream and downstream of any point in the database. Let's get the ComIDs and flowlines
of the tributaries and the main river channel upstream of the station.

.. code-block:: python

    flw_main = nldi.navigate_byid(
        fsource="nwissite",
        fid=f"USGS-{station_id}",
        navigation="upstreamMain",
        source="flowlines",
        distance=1000,
    )

    flw_trib = nldi.navigate_byid(
        fsource="nwissite",
        fid=f"USGS-{station_id}",
        navigation="upstreamTributaries",
        source="flowlines",
        distance=1000,
    )

We can get other USGS stations upstream (or downstream) of the station
and even set a distance limit (in km):

.. code-block:: python

    st_all = nldi.navigate_byid(
        fsource="nwissite",
        fid=f"USGS-{station_id}",
        navigation="upstreamTributaries",
        source="nwissite",
        distance=1000,
    )

    st_d20 = nldi.navigate_byid(
        fsource="nwissite",
        fid=f"USGS-{station_id}",
        navigation="upstreamTributaries",
        source="nwissite",
        distance=20,
    )

We can get more information about these stations using GeoConnex:

.. code-block:: python

    gcx = GeoConnex("gauges")
    stations = st_all.identifier.str.split("-").str[1].unique()
    gauges = gpd.GeoDataFrame(
        pd.concat(gcx.query({"provider_id": sid}) for sid in stations),
        crs=4326,
    )

Instead, we can carry out a spatial query within the basin of interest:

.. code-block:: python

    gauges = pynhd.geoconnex(
        item="gauges",
        query={"geometry": basin.geometry.iloc[0]},
    )

Now, let's get the
`HUC12 pour points <https://www.sciencebase.gov/catalog/item/5762b664e4b07657d19a71ea>`__:

.. code-block:: python

    pp = nldi.navigate_byid(
        fsource="nwissite",
        fid=f"USGS-{station_id}",
        navigation="upstreamTributaries",
        source="huc12pp",
        distance=1000,
    )

.. image:: https://raw.githubusercontent.com/hyriver/HyRiver-examples/main/notebooks/_static/nhdplus_navigation.png
    :target: https://github.com/hyriver/HyRiver-examples/blob/main/notebooks/nhdplus.ipynb
    :align: center

Also, we can get the slope data for each river segment from the NHDPlus VAA database:

.. code-block:: python

    vaa = nhd.nhdplus_vaa("input_data/nhdplus_vaa.parquet")

    flw_trib["comid"] = pd.to_numeric(flw_trib.nhdplus_comid)
    slope = gpd.GeoDataFrame(
        pd.merge(flw_trib, vaa[["comid", "slope"]], left_on="comid", right_on="comid"),
        crs=flw_trib.crs,
    )
    slope[slope.slope < 0] = np.nan

Additionally, we can obtain cross-section lines along the main river channel with 4 km spacing
and width of 2 km using ``network_xsection`` as follows:

.. code-block:: python

    from pynhd import NHD

    distance = 4000  # in meters
    width = 2000  # in meters
    nhd = NHD("flowline_mr")
    main_nhd = nhd.byids("COMID", flw_main.index)
    main_nhd = pynhd.prepare_nhdplus(main_nhd, 0, 0, 0, purge_non_dendritic=True)
    main_nhd = main_nhd.to_crs("ESRI:102003")
    cs = pynhd.network_xsection(main_nhd, distance, width)

Then, we can use `Py3DEP <https://github.com/hyriver/py3dep>`__
to obtain the elevation profile along the cross-section lines.

Now, let's explore the PyGeoAPI capabilities. There are two ways that you can access
PyGeoAPI: ``PyGeoAPI`` class and ``pygeoapi`` function. The ``PyGeoAPI`` class
is for querying the database for a single location using tuples and list while the
``pygeoapi`` function is for querying the database for multiple locations at once
and accepts a ``geopandas.GeoDataFrame`` as input. The ``pygeoapi`` function
is more efficient than the ``PyGeoAPI`` class and has a simpler interface. In future
versions, the ``PyGeoAPI`` class will be deprecated and the ``pygeoapi`` function
will be the only way to access the database. Let's compare the two, starting by
``PyGeoAPI``:

.. code-block:: python

    pygeoapi = PyGeoAPI()

    trace = pygeoapi.flow_trace((1774209.63, 856381.68), crs="ESRI:102003", direction="none")

    split = pygeoapi.split_catchment((-73.82705, 43.29139), crs=4326, upstream=False)

    profile = pygeoapi.elevation_profile(
        [(-103.801086, 40.26772), (-103.80097, 40.270568)],
        numpts=101,
        dem_res=1,
        crs=4326,
    )

    section = pygeoapi.cross_section((-103.80119, 40.2684), width=1000.0, numpts=101, crs=4326)

Now, let's do the same operations using ``pygeoapi``:

.. code-block:: python

    import geopandas as gpd
    import shapely.geometry as sgeom
    import pynhd as nhd

    coords = gpd.GeoDataFrame(
        {
            "direction": ["up", "down"],
            "upstream": [True, False],
            "width": [1000.0, 500.0],
            "numpts": [101, 55],
        },
        geometry=[
            sgeom.Point(-73.82705, 43.29139),
            sgeom.Point(-103.801086, 40.26772),
        ],
        crs=4326,
    )
    trace = nhd.pygeoapi(coords, "flow_trace")
    split = nhd.pygeoapi(coords, "split_catchment")
    section = nhd.pygeoapi(coords, "cross_section")

    coords = gpd.GeoDataFrame(
        {
            "direction": ["up", "down"],
            "upstream": [True, False],
            "width": [1000.0, 500.0],
            "numpts": [101, 55],
            "dem_res": [1, 10],
        },
        geometry=[
            sgeom.MultiPoint([(-103.801086, 40.26772), (-103.80097, 40.270568)]),
            sgeom.MultiPoint([(-102.801086, 39.26772), (-102.80097, 39.270568)]),
        ],
        crs=4326,
    )
    profile = nhd.pygeoapi(coords, "elevation_profile")

.. image:: https://raw.githubusercontent.com/hyriver/HyRiver-examples/main/notebooks/_static/split_catchment.png
    :target: https://github.com/hyriver/HyRiver-examples/blob/main/notebooks/pygeoapi.ipynb
    :align: center

Next, we retrieve mid- and high-resolution flowlines within the bounding box of our
watershed and compare them using ``WaterData`` for mid-resolution, ``NHDPlusHR`` for
high-resolution.

.. code-block:: python

    mr = WaterData("nhdflowline_network")
    nhdp_mr = mr.bybox(basin.geometry[0].bounds)

    hr = NHDPlusHR("flowline")
    nhdp_hr = hr.bygeom(basin.geometry[0].bounds)

.. image:: https://raw.githubusercontent.com/hyriver/HyRiver-examples/main/notebooks/_static/hr_mr.png
    :target: https://github.com/hyriver/HyRiver-examples/blob/main/notebooks/nhdplus.ipynb
    :align: center

An alternative to ``WaterData`` and ``NHDPlusHR`` is the ``NHD`` class that
supports both the mid- and high-resolution NHDPlus V2 data:

.. code-block:: python

    mr = NHD("flowline_mr")
    nhdp_mr = mr.bygeom(basin.geometry[0].bounds)

    hr = NHD("flowline_hr")
    nhdp_hr = hr.bygeom(basin.geometry[0].bounds)

Moreover, ``WaterData`` can find features within a given radius (in meters) of a point:

.. code-block:: python

    eck4 = "+proj=eck4 +lon_0=0 +x_0=0 +y_0=0 +datum=WGS84 +units=m +no_defs"
    coords = (-5727797.427596455, 5584066.49330473)
    rad = 5e3
    flw_rad = mr.bydistance(coords, rad, loc_crs=eck4)
    flw_rad = flw_rad.to_crs(eck4)

Instead of getting all features within a radius of the coordinate, we can snap to the closest
feature ID using NLDI:

.. code-block:: python

    comid_closest = nldi.comid_byloc((x, y), eck4)
    flw_closest = nhdp_mr.byid("comid", comid_closest.comid.values[0])

.. image:: https://raw.githubusercontent.com/hyriver/HyRiver-examples/main/notebooks/_static/nhdplus_radius.png
    :target: https://github.com/hyriver/HyRiver-examples/blob/main/notebooks/nhdplus.ipynb
    :align: center

Since NHDPlus HR is still at the pre-release stage let's use the MR flowlines to
demonstrate the vector-based accumulation. Based on a topological sorted river network
``pynhd.vector_accumulation`` computes flow accumulation in the network.
It returns a data frame that is sorted from upstream to downstream that
shows the accumulated flow in each node.

PyNHD has a utility called ``prepare_nhdplus`` that identifies such
relationships among other things such as fixing some common issues with
NHDPlus flowlines. But first, we need to get all the NHDPlus attributes
for each ComID since ``NLDI`` only provides the flowlines' geometries
and ComIDs which is useful for navigating the vector river network data.
For getting the NHDPlus database we use ``WaterData``. Let's use the
``nhdflowline_network`` layer to get required info.

.. code-block:: python

    wd = WaterData("nhdflowline_network")

    comids = flw_trib.nhdplus_comid.to_list()
    nhdp_trib = wd.byid("comid", comids)
    flw = nhd.prepare_nhdplus(nhdp_trib, 0, 0, purge_non_dendritic=False)

To demonstrate the use of routing, let's use ``nhdplus_attrs`` function to get a list of available
NHDPlus attributes

.. code-block:: python

    char = "CAT_RECHG"
    area = "areasqkm"

    local = nldi.getcharacteristic_byid(comids, "local", char_ids=char)
    flw = flw.merge(local[char], left_on="comid", right_index=True)


    def runoff_acc(qin, q, a):
        return qin + q * a


    flw_r = flw[["comid", "tocomid", char, area]]
    runoff = nhd.vector_accumulation(flw_r, runoff_acc, char, [char, area])


    def area_acc(ain, a):
        return ain + a


    flw_a = flw[["comid", "tocomid", area]]
    areasqkm = nhd.vector_accumulation(flw_a, area_acc, area, [area])

    runoff /= areasqkm

Since these are catchment-scale characteristics, let's get the catchments
then add the accumulated characteristic as a new column and plot the
results.

.. code-block:: python

    wd = WaterData("catchmentsp")
    catchments = wd.byid("featureid", comids)

    c_local = catchments.merge(local, left_on="featureid", right_index=True)
    c_acc = catchments.merge(runoff, left_on="featureid", right_index=True)

.. image:: https://raw.githubusercontent.com/hyriver/HyRiver-examples/main/notebooks/_static/flow_accumulation.png
    :target: https://github.com/hyriver/HyRiver-examples/blob/main/notebooks/nhdplus.ipynb
    :align: center

More examples can be found `here <https://pygeohydro.readthedocs.io/en/latest/examples.html>`__.

Contributing
------------

Contributions are very welcomed. Please read
`CONTRIBUTING.rst <https://github.com/hyriver/pynhd/blob/main/CONTRIBUTING.rst>`__
file for instructions.