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, HCDN 2009, NLCD, CAMELS, and SSEBop databases | |
PyDaymet | Access daily, monthly, and annual climate data via Daymet | |
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 |
PyDaymet is a part of HyRiver software stack that
is designed to aid in hydroclimate analysis through web services. This package provides
access to climate data from
Daymet V4 database using NetCDF
Subset Service (NCSS). Both single pixel (using get_bycoords
function) and gridded data (using
get_bygeom
) are supported which are returned as
pandas.DataFrame
and xarray.Dataset
, respectively. Climate data is available for North
America, Hawaii from 1980, and Puerto Rico from 1950 at three time scales: daily, monthly,
and annual. Additionally, PyDaymet can compute Potential EvapoTranspiration (PET)
using three methods: penman_monteith
, priestley_taylor
, and hargreaves_samani
for
both single pixel and gridded data.
You can find some example notebooks here.
Moreover, under the hood, PyDaymet uses AsyncRetriever for making requests asynchronously with persistent caching. This improves the reliability and speed of data retrieval significantly. AsyncRetriever caches all request/response pairs and upon making an already cached request, it will retrieve the responses from the cache if the server's response is unchanged.
You can control the request/response caching behavior by setting the following environment variables:
HYRIVER_CACHE_NAME
: Path to the caching SQLite database. It defaults to./cache/aiohttp_cache.sqlite
HYRIVER_CACHE_EXPIRE
: Expiration time for cached requests in seconds. It defaults to -1 (never expire).HYRIVER_CACHE_DISABLE
: Disable reading/writing from/to the cache. The default is false.
For example, in your code before making any requests you can do:
import os
os.environ["HYRIVER_CACHE_NAME"] = "path/to/file.sqlite"
os.environ["HYRIVER_CACHE_EXPIRE"] = "3600"
os.environ["HYRIVER_CACHE_DISABLE"] = "true"
You can also try using PyDaymet 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.
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}
}
You can install PyDaymet using pip
after installing libgdal
on your system
(for example, in Ubuntu run sudo apt install libgdal-dev
):
$ pip install pydaymet
Alternatively, PyDaymet can be installed from the conda-forge
repository
using Conda:
$ conda install -c conda-forge pydaymet
You can use PyDaymet using command-line or as a Python library. The commanda-line provides access to two functionality:
- Getting gridded climate data: You must create a
geopandas.GeoDataFrame
that contains the geometries of the target locations. This dataframe must have four columns:id
,start
,end
,geometry
. Theid
column is used as filenames for saving the obtained climate data to a NetCDF (.nc
) file. Thestart
andend
columns are starting and ending dates of the target period. Then, you must save the dataframe as a shapefile (.shp
) or geopackage (.gpkg
) with CRS attribute. - Getting single pixel climate data: You must create a CSV file that
contains coordinates of the target locations. This file must have at four columns:
id
,start
,end
,lon
, andlat
. Theid
column is used as filenames for saving the obtained climate data to a CSV (.csv
) file. Thestart
andend
columns are the same as thegeometry
command. Thelon
andlat
columns are the longitude and latitude coordinates of the target locations.
$ pydaymet -h
Usage: pydaymet [OPTIONS] COMMAND [ARGS]...
Command-line interface for PyDaymet.
Options:
-h, --help Show this message and exit.
Commands:
coords Retrieve climate data for a list of coordinates.
geometry Retrieve climate data for a dataframe of geometries.
The coords
sub-command is as follows:
$ pydaymet coords -h
Usage: pydaymet coords [OPTIONS] FPATH
Retrieve climate data for a list of coordinates.
FPATH: Path to a csv file with four columns:
- ``id``: Feature identifiers that daymet uses as the output netcdf filenames.
- ``start``: Start time.
- ``end``: End time.
- ``lon``: Longitude of the points of interest.
- ``lat``: Latitude of the points of interest.
- ``time_scale``: (optional) Time scale, either ``daily`` (default), ``monthly`` or ``annual``.
- ``pet``: (optional) Method to compute PET. Suppoerted methods are:
``penman_monteith``, ``hargreaves_samani``, ``priestley_taylor``, and ``none`` (default).
- ``snow``: (optional) Separate snowfall from precipitation, default is ``False``.
Examples:
$ cat coords.csv
id,lon,lat,start,end,pet
california,-122.2493328,37.8122894,2012-01-01,2014-12-31,hargreaves_samani
$ pydaymet coords coords.csv -v prcp -v tmin
Options:
-v, --variables TEXT Target variables. You can pass this flag multiple
times for multiple variables.
-s, --save_dir PATH Path to a directory to save the requested files.
Extension for the outputs is .nc for geometry and .csv
for coords.
--disable_ssl Pass to disable SSL certification verification.
-h, --help Show this message and exit.
And, the geometry
sub-command is as follows:
$ pydaymet geometry -h
Usage: pydaymet geometry [OPTIONS] FPATH
Retrieve climate data for a dataframe of geometries.
FPATH: Path to a shapefile (.shp) or geopackage (.gpkg) file.
This file must have four columns and contain a ``crs`` attribute:
- ``id``: Feature identifiers that daymet uses as the output netcdf filenames.
- ``start``: Start time.
- ``end``: End time.
- ``geometry``: Target geometries.
- ``time_scale``: (optional) Time scale, either ``daily`` (default), ``monthly`` or ``annual``.
- ``pet``: (optional) Method to compute PET. Suppoerted methods are:
``penman_monteith``, ``hargreaves_samani``, ``priestley_taylor``, and ``none`` (default).
- ``snow``: (optional) Separate snowfall from precipitation, default is ``False``.
Examples:
$ pydaymet geometry geo.gpkg -v prcp -v tmin
Options:
-v, --variables TEXT Target variables. You can pass this flag multiple
times for multiple variables.
-s, --save_dir PATH Path to a directory to save the requested files.
Extension for the outputs is .nc for geometry and .csv
for coords.
--disable_ssl Pass to disable SSL certification verification.
-h, --help Show this message and exit.
Now, let's see how we can use PyDaymet as a library.
PyDaymet offers two functions for getting climate data; get_bycoords
and get_bygeom
.
The arguments of these functions are identical except the first argument where the latter
should be polygon and the former should be a coordinate (a tuple of length two as in (x, y)).
The input geometry or coordinate can be in any valid CRS (defaults to EPSG:4326
). The
dates
argument can be either a tuple of length two like (start_str, end_str)
or a list of
years like [2000, 2005]
. It is noted that both functions have a pet
flag for computing PET
and a snow
flag for separating snow from precipitation using
Martinez and Gupta (2010) method.
Additionally, we can pass time_scale
to get daily, monthly or annual summaries. This flag
by default is set to daily.
from pynhd import NLDI
import pydaymet as daymet
geometry = NLDI().get_basins("01031500").geometry[0]
var = ["prcp", "tmin"]
dates = ("2000-01-01", "2000-06-30")
daily = daymet.get_bygeom(geometry, dates, variables=var, pet="priestley_taylor", snow=True)
monthly = daymet.get_bygeom(geometry, dates, variables=var, time_scale="monthly")
If the input geometry (or coordinate) is in a CRS other than EPSG:4326
, we should pass
it to the functions.
coords = (-1431147.7928, 318483.4618)
crs = "epsg:3542"
dates = ("2000-01-01", "2006-12-31")
annual = daymet.get_bycoords(coords, dates, variables=var, loc_crs=crs, time_scale="annual")
Also, we can use the potential_et
function to compute PET by passing the daily climate data.
We can either pass a pandas.DataFrame
or a xarray.Dataset
. Note that, penman_monteith
and priestley_taylor
methods have parameters that can be passed via the params
argument,
if any value other than the default values are needed. For example, default value of alpha
for priestley_taylor
method is 1.26 (humid regions), we can set it to 1.74 (arid regions)
as follows:
pet_hs = daymet.potential_et(daily, methods="priestley_taylor", params={"alpha": 1.74})
Next, let's get annual total precipitation for Hawaii and Puerto Rico for 2010.
hi_ext = (-160.3055, 17.9539, -154.7715, 23.5186)
pr_ext = (-67.9927, 16.8443, -64.1195, 19.9381)
hi = daymet.get_bygeom(hi_ext, 2010, variables="prcp", region="hi", time_scale="annual")
pr = daymet.get_bygeom(pr_ext, 2010, variables="prcp", region="pr", time_scale="annual")
Some example plots are shown below:
Contributions are very welcomed. Please read CONTRIBUTING.rst file for instructions.
Credits to Koen Hufkens for his implementation of accessing the Daymet RESTful service, daymetpy.