pygeohydro 0.17.1

Description:

pygeohydro 0.17.1

Package
Description
Status



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: Retrieve Geospatial Hydrology Data



































Features
PyGeoHydro (formerly named hydrodata) is a part of
HyRiver software stack that
is designed to aid in hydroclimate analysis through web services. This package provides
access to some public web services that offer geospatial hydrology data. It has three
main modules: pygeohydro, plot, and helpers.
PyGeoHydro supports the following datasets:

gNATSGO for
US soil properties.
SoilGrids
for seamless global soil properties.
Derived Soil Properties
for soil porosity, available water capacity, and field capacity across the US.
NWIS for daily mean streamflow observations
(returned as a pandas.DataFrame or xarray.Dataset with station attributes),
SensorThings API
for accessing real-time data of USGS sensors.
CAMELS for accessing streamflow
observations (1980-2014) and basin-level attributes of 671 stations within CONUS.
Water Quality Portal for accessing current and
historical water quality data from more than 1.5 million sites across the US,
NID for accessing the National Inventory of Dams
web service,
HCDN 2009 for identifying sites
where human activity affects the natural flow of the watercourse,
NLCD 2021 for land cover/land use, imperviousness
descriptor, and canopy data. You can get data using both geometries and coordinates.
WBD for accessing
Hydrologic Unit (HU) polygon boundaries within the US (all HUC levels).
SSEBop for daily actual
evapotranspiration, for both single pixel and gridded data.
Irrigation Withdrawals for estimated
monthly water use for irrigation by 12-digit hydrologic unit in the CONUS for 2015
STN for access USGS Short-Term Network (STN)
eHydro for accessing USACE
Hydrographic Surveys that includes topobathymetry data
NFHL for accessing
FEMA’s National Flood Hazard Layer (NFHL) data.

Also, it includes several other functions:

interactive_map: Interactive map for exploring NWIS stations within a bounding box.
cover_statistics: Categorical statistics of land use/land cover data.
overland_roughness: Estimate overland roughness from land use/land cover data.
streamflow_fillna: Fill missing daily streamflow values with day-of-year averages.
Streamflow observations must be at least for 10-year long.

The plot module includes two main functions:

signatures: Hydrologic signature graphs.
cover_legends: Official NLCD land cover legends for plotting a land cover dataset.
descriptor_legends: Color map and legends for plotting an imperviousness descriptor dataset.

The helpers module includes:

nlcd_helper: A roughness coefficients lookup table for each land cover and imperviousness
descriptor type which is useful for overland flow routing among other applications.
nwis_error: A dataframe for finding information about NWIS requests’ errors.

You can find some example notebooks here.
Moreover, under the hood, PyGeoHydro uses
PyGeoOGC and
AsyncRetriever 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:
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 PyGeoHydro 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.


Citation
If you use any of HyRiver packages in your research, we appreciate citations:
@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 PyGeoHydro using pip after installing libgdal on your system
(for example, in Ubuntu run sudo apt install libgdal-dev). Moreover, PyGeoHydro has an optional
dependency for using persistent caching, requests-cache. We highly recommend installing
this package as it can significantly speed up send/receive queries. You don’t have to change
anything in your code, since PyGeoHydro under-the-hood looks for requests-cache and
if available, it will automatically use persistent caching:
$ pip install pygeohydro
Alternatively, PyGeoHydro can be installed from the conda-forge repository
using Conda:
$ conda install -c conda-forge pygeohydro


Quick start
We can obtain river topobathymetry data using the EHydro class. We can subset
the dataset either using a geometry or a bounding box, based on their ID, or SQL query:
from pygeohydro import EHydro

ehydro = EHydro("points")
topobathy = ehydro.bygeom((-122.53, 45.57, -122.52, 45.59))
We can explore the available NWIS stations within a bounding box using interactive_map
function. It returns an interactive map and by clicking on a station some of the most
important properties of stations are shown.
import pygeohydro as gh

bbox = (-69.5, 45, -69, 45.5)
gh.interactive_map(bbox)



We can select all the stations within this boundary box that have daily mean streamflow data from
2000-01-01 to 2010-12-31:
from pygeohydro import NWIS

nwis = NWIS()
query = {
"bBox": ",".join(f"{b:.06f}" for b in bbox),
"hasDataTypeCd": "dv",
"outputDataTypeCd": "dv",
}
info_box = nwis.get_info(query)
dates = ("2000-01-01", "2010-12-31")
stations = info_box[
(info_box.begin_date <= dates[0]) & (info_box.end_date >= dates[1])
].site_no.tolist()
Then, we can get the daily streamflow data in mm/day (by default the values are in cms)
and plot them:
from pygeohydro import plot

qobs = nwis.get_streamflow(stations, dates, mmd=True)
plot.signatures(qobs)
By default, get_streamflow returns a pandas.DataFrame that has a attrs method
containing metadata for all the stations. You can access it like so qobs.attrs.
Moreover, we can get the same data as xarray.Dataset as follows:
qobs_ds = nwis.get_streamflow(stations, dates, to_xarray=True)
This xarray.Dataset has two dimensions: time and station_id. It has
10 variables including discharge with two dimensions while other variables
that are station attitudes are one dimensional.
We can also get instantaneous streamflow data using get_streamflow. This method assumes
that the input dates are in UTC time zone and returns the data in UTC time zone as well.
date = ("2005-01-01 12:00", "2005-01-12 15:00")
qobs = nwis.get_streamflow("01646500", date, freq="iv")
We can query USGS stations of type “stream” in Arizona using SensorThings API
as follows:
odata = {
"filter": "properties/monitoringLocationType eq 'Stream' and properties/stateFIPS eq 'US:04'",
}
df = sensor.query_byodata(odata)
Irrigation withdrawals data can be obtained as follows:
irr = gh.irrigation_withdrawals()
We can get the CAMELS dataset as a geopandas.GeoDataFrame that includes geometry and
basin-level attributes of 671 natural watersheds within CONUS and their streamflow
observations between 1980-2014 as a xarray.Dataset, like so:
attrs, qobs = gh.get_camels()
The WaterQuality has a number of convenience methods to retrieve data from the
web service. Since there are many parameter combinations that can be
used to retrieve data, a general method is also provided to retrieve data from
any of the valid endpoints. You can use get_json to retrieve stations info
as a geopandas.GeoDataFrame or get_csv to retrieve stations data as a
pandas.DataFrame. You can construct a dictionary of the parameters and pass
it to one of these functions. For more information on the parameters, please
consult the Water Quality Data documentation.
For example, let’s find all the stations within a bounding box that have Caffeine data:
from pynhd import WaterQuality

bbox = (-92.8, 44.2, -88.9, 46.0)
kwds = {"characteristicName": "Caffeine"}
wq = WaterQuality()
stations = wq.station_bybbox(bbox, kwds)
Or the same criterion but within a 30-mile radius of a point:
stations = wq.station_bydistance(-92.8, 44.2, 30, kwds)
Then we can get the data for all these stations the data like this:
sids = stations.MonitoringLocationIdentifier.tolist()
caff = wq.data_bystation(sids, kwds)



Moreover, we can get land use/land cove data using nlcd_bygeom or nlcd_bycoods functions,
percentages of land cover types using cover_statistics, and overland roughness using
overland_roughness. The nlcd_bycoords function returns a geopandas.GeoDataFrame
with the NLCD layers as columns and input coordinates as the geometry column. Moreover,
the nlcd_bygeom function accepts both a single geometry or a geopandas.GeoDataFrame
as the input.
from pynhd import NLDI

basins = NLDI().get_basins(["01031450", "01318500", "01031510"])
lulc = gh.nlcd_bygeom(basins, 100, years={"cover": [2016, 2019]})
stats = gh.cover_statistics(lulc["01318500"].cover_2016)
roughness = gh.overland_roughness(lulc["01318500"].cover_2019)



Next, let’s use ssebopeta_bygeom to get actual ET data for a basin. Note that there’s a
ssebopeta_bycoords function that returns an ETA time series for a single coordinate.
geometry = NLDI().get_basins("01315500").geometry[0]
eta = gh.ssebopeta_bygeom(geometry, dates=("2005-10-01", "2005-10-05"))



Additionally, we can pull all the US dams data using NID. Let’s get dams that are within this
bounding box and have a maximum storage larger than 200 acre-feet.
nid = NID()
dams = nid.get_bygeom((-65.77, 43.07, -69.31, 45.45), 4326)
dams = nid.inventory_byid(dams.id.to_list())
dams = dams[dams.maxStorage > 200]
We can get also all dams within CONUS with maximum storage larger than 2500 acre-feet:
conus_geom = gh.get_us_states("contiguous")

dam_list = nid.get_byfilter([{"maxStorage": ["[2500 +inf]"]}])
dams = nid.inventory_byid(dam_list[0].id.to_list(), stage_nid=True)

conus_dams = dams[dams.stateKey.isin(conus_geom.STUSPS)].reset_index(drop=True)



The WBD class allows us to get Hydrologic Unit (HU) polygon boundaries. Let’s
get the two Hudson HUC4s:
from pygeohydro import WBD

wbd = WBD("huc4")
hudson = wbd.byids("huc4", ["0202", "0203"])
The NFHL class allows us to retrieve FEMA’s National Flood Hazard Layer (NFHL) data.
Let’s get the cross-section data for a small region in Vermont:
from pygeohydro import NFHL

nfhl = NFHL("NFHL", "cross-sections")
gdf_xs = nfhl.bygeom((-73.42, 43.28, -72.9, 43.52), geo_crs=4269)


Contributing
Contributions are very welcomed. Please read
CONTRIBUTING.rst
file for instructions.


Credits
This package was created based on the audreyr/cookiecutter-pypackage project template.