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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]ΒΆ

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_dq_base (humidity related), g_dq_shape (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.