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supy.util.calib_g¶
-
supy.util.
calib_g
(df_fc_suews, g_max=33.1, lai_max=5.9, s1=5.56, method='cobyla', prms_init=None, debug=False)[source]¶ Calibrate parameters for modelling surface conductance over vegetated surfaces using
LMFIT
.Parameters: - df_fc_suews (pandas.DataFrame) – DataFrame in SuPy forcing format
- g_max (numeric, optional) – Maximum surface conductance [mm s-1], by default 30
- lai_max (numeric, optional) – Maximum LAI [m2 m-2], by default 6
- s1 (numeric, optional) – Wilting point (WP=s1/g6, indicated as deficit [mm]) related parameter, by default 5.56
- method (str, optional) – Method used in minimisation by
lmfit.minimize
: details refer to itsmethod
. - prms_init (lmfit.Parameters) – Initial parameters for calibration
- debug (bool, optional) – Option to output final calibrated
ModelResult
, by default False
Returns: dict, or `ModelResult <lmfit –
- dict: {parameter_name -> best_fit_value}
ModelResult
- Note:
Parameters for surface conductance: g1 (LAI related), g2 (solar radiation related), g3 (humidity related), g4 (humidity related), g5 (air temperature related), g6 (soil moisture related)
Return type: ModelResult>` if
debug==True
Note
For calibration validity, turbulent fluxes, QH and QE, in
df_fc_suews
should ONLY be observations, i.e., interpolated values should be avoided. To do so, please placenp.nan
as missing values for QH and QE.