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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.

  • 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 its method.
  • prms_init (lmfit.Parameters) – Initial parameters for calibration
  • debug (bool, optional) – Option to output final calibrated ModelResult, by default False

dict, or `ModelResult <lmfit

  1. dict: {parameter_name -> best_fit_value}
  2. ModelResult

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


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 place np.nan as missing values for QH and QE.