pydaymet.pet

Core class for the Daymet functions.

Module Contents

pydaymet.pet.potential_et(clm: pandas.DataFrame, coords: tuple[float, float], crs: CRSTYPE, method: Literal[penman_monteith, priestley_taylor, hargreaves_samani] = ..., params: dict[str, float] | None = ...) → pandas.DataFrame

pydaymet.pet.potential_et(clm: xarray.Dataset, coords: None = None, crs: None = None, method: Literal[penman_monteith, priestley_taylor, hargreaves_samani] = ..., params: dict[str, float] | None = ...) → xarray.Dataset

Compute Potential EvapoTranspiration for both gridded and a single location.

Parameters:

• clm (pandas.DataFrame or xarray.Dataset) – The dataset must include at least the following variables:

  • Minimum temperature in degree celsius

  • Maximum temperature in degree celsius

  • Solar radiation in in W/m2

  • Daylight duration in seconds

  Optionally, for penman_monteith, wind speed at 2-m level will be used if available, otherwise, default value of 2 m/s will be assumed. Table below shows the variable names that the function looks for in the input data.

  pandas.DataFrame	xarray.Dataset
  tmin (degrees C)	tmin
  tmax (degrees C)	tmax
  srad (W/m2)	srad
  dayl (s)	dayl
  u2m (m/s)	u2m

• coords (tuple of floats, optional) – Coordinates of the daymet data location as a tuple, (x, y). This is required when clm is a DataFrame.

• crs (str, int, or pyproj.CRS, optional) – The spatial reference of the input coordinate, defaults to EPSG:4326. This is only used when clm is a DataFrame.

• method (str, optional) – Method for computing PET. Supported methods are penman_monteith, priestley_taylor, and hargreaves_samani. The penman_monteith method is based on Allen et al.[1] assuming that soil heat flux density is zero. The priestley_taylor method is based on Priestley and TAYLOR[2] assuming that soil heat flux density is zero. The hargreaves_samani method is based on Hargreaves and Samani[3]. Defaults to hargreaves_samani.

• params (dict, optional) – Model-specific parameters as a dictionary, defaults to None. Valid parameters are:

  • penman_monteith: soil_heat_flux, albedo, alpha, and arid_correction.

  • priestley_taylor: soil_heat_flux, albedo, and arid_correction.

  • hargreaves_samani: None.

  Default values for the parameters are: soil_heat_flux = 0, albedo = 0.23, alpha = 1.26, and arid_correction = False. An important parameter for priestley_taylor and penman_monteith methods is arid_correction which 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 dewpoint 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.

Returns:

pandas.DataFrame or xarray.Dataset – The input DataFrame/Dataset with an additional variable named pet (mm/day) for pandas.DataFrame and pet for xarray.Dataset.

References

[1]

Richard G Allen, Luis S Pereira, Dirk Raes, Martin Smith, and others. Crop evapotranspiration-guidelines for computing crop water requirements-fao irrigation and drainage paper 56. Fao, Rome, 300(9):D05109, 1998.

[2]

Charles Henry Brian Priestley and Robert Joseph TAYLOR. On the assessment of surface heat flux and evaporation using large-scale parameters. Monthly weather review, 100(2):81–92, 1972.

[3]

George H. Hargreaves and Zohrab A. Samani. Estimating potential evapotranspiration. Journal of the Irrigation and Drainage Division, 108(3):225–230, sep 1982. URL: https://doi.org/10.1061%2Fjrcea4.0001390, doi:10.1061/jrcea4.0001390.