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supy.util.calib_g(df_fc_suews, ser_ra, g_max, lai_max, wp_smd, 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
  • ser_ra (pandas.Series) – Series with RA, aerodynamic resistance, [s m-1]
  • g_max (numeric) – Maximum surface conductance [mm s-1]
  • lai_max (numeric) – Maximum LAI [m2 m-2]
  • wp_smd (numeric) – Wilting point indicated as soil moisture deficit [mm]
  • method (str, optional) – Method used in minimisation by lmfit.minimize: details refer to its method.
  • prms_init (lmfit.Parameters, optional) – 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: g_lai (LAI related), g2 (solar radiation related), g_q1 (humidity related), g_q2 (humidity related), g_ta (air temperature related), g_smd (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.