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6.3.7.4. supy.util.attribute_q2

supy.util.attribute_q2(df_output_A: DataFrame, df_output_B: DataFrame, df_forcing_A: DataFrame, df_forcing_B: DataFrame, min_flux: float = 0.1) → AttributionResult

Decompose q2 (2m specific humidity) differences between two SUEWS scenarios.

The difference in 2m specific humidity is attributed to: - Flux changes (Q_E latent heat flux) - Resistance changes (turbulent exchange efficiency) - Air property changes (rho * L_v)

Parameters:

• df_output_A (pd.DataFrame) – SUEWS output DataFrame for scenario A (reference/baseline)

• df_output_B (pd.DataFrame) – SUEWS output DataFrame for scenario B (modified/test)

• df_forcing_A (pd.DataFrame) – Forcing DataFrame for scenario A. Must contain ‘Tair’, ‘RH’, ‘pres’ columns.

• df_forcing_B (pd.DataFrame) – Forcing DataFrame for scenario B. Must contain ‘Tair’, ‘RH’, ‘pres’ columns.

• min_flux (float, optional) – Minimum flux threshold (W/m2) for resistance calculation. Timesteps with abs(Q_E) < min_flux are flagged. Default 0.1.

Returns:

Container with contributions timeseries, summary statistics, and metadata.

Return type:

AttributionResult

Examples

Compare baseline vs. irrigated scenario:

>>> result = attribute_q2(df_output_baseline, df_output_irrigated,
...                       df_forcing_A=df_forcing, df_forcing_B=df_forcing)
>>> print(result)
q2 Attribution Results
========================================
Mean delta_q2: +0.0012 g/kg

6.3.7.4. Component Breakdown:

flux_total : +0.0008 g/kg (66.7%) resistance : +0.0003 g/kg (25.0%) air_props : +0.0001 g/kg ( 8.3%)

>>> result.plot(kind="bar")  # Visualise contributions