Simulating heat and CO₂ fluxes in Beijing using SUEWS V2020b: Sensitivity to vegetation phenology and maximum conductance

Abstract. The Surface Urban Energy and Water Balance Scheme (SUEWS) has recently been introduced to include a bottomup approach to modelling carbon dioxide (CO₂) emissions and sink in urban areas. In this study, SUEWS is evaluated against radiation flux observations and eddy covariance (EC) measured turbulent fluxes of sensible heat (Q_H), latent heat (Q_E), and CO₂ (F_C) at a densely built neighborhood in Beijing. The model sensitivity to maximum conductance (g_max) and leaf area index (LAI) is examined. Site-specific g_max is obtained from observations over local vegetation species, and LAI parameters by optimization with remotely sensed LAI obtained from a MODIS/Terra data product. For simulation of anthropogenic CO₂ components, local traffic and population data are collected. In model evaluation, the mismatch between the measurement source area and simulation domain is also considered.

Using the optimized g_max and LAI, the modelling of heat fluxes is noticeably improved, showing higher correlation with observations, lower bias, and more realistic seasonal dynamics of Q_E and Q_H. In comparison to heat fluxes, the F_C module shows lower sensitivity to the choice of g_max and LAI. This can be explained by the low relative contribution of vegetation to net F_C in the modelled area. SUEWS successfully reproduces the average diurnal cycle of F_C and annual cumulative sums. Depending on the size of the simulation domain, the modelled annual accumulated F_C ranges from 7.2 to 8.5 kg C m⁻² yr⁻¹, when compared to 7.5 kg C m⁻² yr⁻¹ observed by EC. Traffic is the dominant CO₂ source, contributing 63–73 % to the annual total CO₂ emissions, followed by human metabolism (14–18 %), respiration released by vegetation and soil (6–11 %) and building heating (6–9 %). Vegetation photosynthesis offsets only 4–8 % of the total CO₂ emissions. We highlight the importance of choosing optimal LAI parameters and g_max when SUEWS is used to model surface fluxes. The F_C module of SUEWS is a promising tool in quantifying urban CO₂ emissions at the local scale, and therefore assisting to mitigate urban CO₂ emissions.