Package: pydaymet
PackageDescriptionStatusPyNHDNavigate and subset NHDPlus (MR and HR) using web servicesPy3DEPAccess topographic data through National Map’s 3DEP web servicePyGeoHydroAccess NWIS, NID, WQP, eHydro, NLCD, CAMELS, and SSEBop databasesPyDaymetAccess daily, monthly, and annual climate data via DaymetPyGridMETAccess daily climate data via GridMETPyNLDAS2Access hourly NLDAS-2 data via web servicesHydroSignaturesA collection of tools for computing hydrological signaturesAsyncRetrieverHigh-level API for asynchronous requests with persistent cachingPyGeoOGCSend queries to any ArcGIS RESTful-, WMS-, and WFS-based servicesPyGeoUtilsUtilities for manipulating geospatial, (Geo)JSON, and (Geo)TIFF dataPyDaymet: Daily climate data through DaymetWarningSince the release of Daymet v4 R1 on November 2022, the URL of
Daymet’s server has been changed. Therefore, only PyDaymet v0.13.7+
is going to work, and previous versions will not work anymore.FeaturesPyDaymet is a part ofHyRiversoftware stack that
is designed to aid in hydroclimate analysis through web services. This package provides
access to climate data fromDaymet V4 R1database using NetCDF
Subset Service (NCSS). Both single pixel (usingget_bycoordsfunction) and gridded data (usingget_bygeom) are supported which are returned aspandas.DataFrameandxarray.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, andhargreaves_samanifor
both single pixel and gridded data.For PET computations, PyDaymet accepts four additional user-defined parameters:penman_monteith:soil_heat_flux,albedo,alpha,andarid_correction.priestley_taylor:soil_heat_flux,albedo, andarid_correction.hargreaves_samani: None.Default values for the parameters are:soil_heat_flux= 0,albedo= 0.23,alpha= 1.26, andarid_correction= False.
An important parameter forpriestley_taylorandpenman_monteithmethods
isarid_correctionwhich is used to correct the actual vapor pressure
for arid regions. Since relative humidity is not provided by Daymet, the actual
vapor pressure is computed assuming that the dew point temperature is equal to
the minimum temperature. However, for arid regions, FAO 56 suggests subtracting
minimum temperature by 2-3 °C to account for the fact that in arid regions,
the air might not be saturated when its temperature is at its minimum. For such
areas, you can pass{"arid_correction": True,...}to subtract 2 °C from the
minimum temperature for computing the actual vapor pressure.You can find some example notebookshere.Moreover, under the hood, PyDaymet usesPyGeoOGCandAsyncRetrieverpackages
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.sqliteHYRIVER_CACHE_NAME_HTTP: Path to the caching SQLite database for HTTP requests.
It defaults to./cache/http_cache.sqliteHYRIVER_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:importosos.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 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 viaissue tracker.CitationIf 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}}InstallationYou can install PyDaymet usingpipafter installinglibgdalon your system
(for example, in Ubuntu runsudo apt installlibgdal-dev):$pipinstallpydaymetAlternatively, PyDaymet can be installed from theconda-forgerepository
usingConda:$condainstall-cconda-forgepydaymetQuick startYou 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 ageopandas.GeoDataFramethat contains
the geometries of the target locations. This dataframe must have four columns:id,start,end,geometry. Theidcolumn is used as
filenames for saving the obtained climate data to a NetCDF (.nc) file. Thestartandendcolumns 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. Theidcolumn is used as filenames
for saving the obtained climate data to a CSV (.csv) file. Thestartandendcolumns are the same as thegeometrycommand. Thelonandlatcolumns are
the longitude and latitude coordinates of the target locations.$pydaymet-hUsage: 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.Thecoordssub-command is as follows:$pydaymetcoords-hUsage: 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. Supported methods are:
                ``penman_monteith``, ``hargreaves_samani``, ``priestley_taylor``, and ``none`` (default).
    - ``snow``: (optional) Separate snowfall from precipitation, default is ``False``.

Examples:$catcoords.csvid,lon,lat,start,end,pet
    california,-122.2493328,37.8122894,2012-01-01,2014-12-31,hargreaves_samani$pydaymetcoordscoords.csv-vprcp-vtminOptions:
-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, thegeometrysub-command is as follows:$pydaymetgeometry-hUsage: 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. Supported methods are:
                ``penman_monteith``, ``hargreaves_samani``, ``priestley_taylor``, and ``none`` (default).
    - ``snow``: (optional) Separate snowfall from precipitation, default is ``False``.

Examples:$pydaymetgeometrygeo.gpkg-vprcp-vtminOptions:
-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_bycoordsandget_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 toEPSG:4326). Thedatesargument 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 apetflag for computing PET
and asnowflag for separating snow from precipitation usingMartinez and Gupta (2010)method.
Additionally, we can passtime_scaleto get daily, monthly or annual summaries. This flag
by default is set to daily.frompynhdimportNLDIimportpydaymetasdaymetgeometry=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 thanEPSG:4326, we should pass
it to the functions.coords=(-1431147.7928,318483.4618)crs=3542dates=("2000-01-01","2006-12-31")annual=daymet.get_bycoords(coords,dates,variables=var,loc_crs=crs,time_scale="annual")Additionally, theget_bycoordsfunction accepts a list of coordinates and by setting theto_xarrayflag toTrueit can return the results as axarray.Datasetinstead of
apandas.DataFrame:coords=[(-94.986,29.973),(-95.478,30.134)]idx=["P1","P2"]clm_ds=daymet.get_bycoords(coords,range(2000,2021),coords_id=idx,to_xarray=True)Also, we can use thepotential_etfunction to compute PET by passing the daily climate data.
We can either pass apandas.DataFrameor axarray.Dataset. Note that,penman_monteithandpriestley_taylormethods have parameters that can be passed via theparamsargument,
if any value other than the default values are needed. For example, default value ofalphaforpriestley_taylormethod 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:ContributingContributions are very welcomed. Please readCONTRIBUTING.rstfile for instructions.CreditsCredits toKoen Hufkensfor his implementation of
accessing the Daymet RESTful service,daymetpy.