pyet.temperature.blaney_criddle#
- pyet.temperature.blaney_criddle(tmean, lat, a=-1.55, b=0.96, k=0.65, wind=None, rhmin=None, n=None, nn=None, py=None, method=0, clip_zero=True)[source]#
Potential evapotranspiration calculated according to Blaney and others (1952).
- Parameters:
tmean (pandas.Series or xarray.DataArray) – average day temperature [°C].
lat (float or xarray.DataArray, optional) – the site latitude [rad].
a (float, optional) – calibration coefficient for method 0 [-].
b (float, optional) – calibration coefficient for method 0 [-].
k (float, optional) – calibration coefficient for method 1 [-].
wind (float or pandas.Series or xarray.DataArray, optional) – mean day wind speed [m/s].
rhmin (float or pandas.Series or xarray.DataArray, optional) – mainimum daily relative humidity [%].
n (float or pandas.Series or xarray.DataArray, optional) – actual duration of sunshine [hour].
nn (float or pandas.Series or xarray.DataArray, optional) – maximum possible duration of sunshine or daylight hours [hour].
py (float or pandas.Series or xarray.DataArray, optional) – percentage of actual day-light hours for the day compared to the number of day-light hour during the entire year [-].
method (float, optional) – 0 => Blaney Criddle after Schrödter (1985) 1 => Blaney Criddle after Xu and Singh (2001) 2 => FAO-24 Blaney Criddle after McMahon et al. (2013).
clip_zero (bool, optional) – if True, replace all negative values with 0.
- Returns:
Potential evapotranspiration [mm d-1].
- Return type:
float or pandas.Series or xarray.DataArray containing the calculated
Examples
>>> pet_blaney_criddle = blaney_criddle(tmean, lat)
Notes
Method = 0; Based on (Schrödter, 1985).
\[PET=a+b(py(0.46 * T_{mean} + 8.13))\]Method = 1; Based on (Xu and Singh, 2001).
\[PET=kpy(0.46 * T_{mean} + 8.13)\]Method = 2; Based on (McMahon et al., 2013).
\[PET=k_1+b_{var}(py(0.46*T_{mean} + 8.13))\], where:
\[k1 = (0.0043RH_{min}-\frac{n}{N}-1.41)\]\[bvar =e_0+e1 RH_{min}+e_2 \frac{n}{N} + e_3 u_2 + e_4 RH_{min} \frac{n}{N} + e_5 * RH_{min} * u_2\]\[e_0=0.81917, e_1 = -0.0040922, e_2 = 1.0705, e_3 = 0.065649, e_4 = -0.0059684, e_5 = -0.0005967.\]