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

The Surface Urban Energy and Water Balance Scheme (SUEWS) has recently been introduced to include a bottom-up approach to modeling carbon dioxide (CO2) emissions and uptake in urban areas. In this study, SUEWS is evaluated against the measured eddy covariance (EC) turbulent fluxes of sensible heat (Q(H)), latent heat (Q(E)), and CO2 (FC) in 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 are extracted by optimization with remotely sensed LAI obtained from a Landsat 7 data product. For the simulation of anthropogenic CO2 components, local traffic and population data are collected. In the model evaluation, the mismatch between the measurement source area and simulation domain is also considered.Using the optimized g(max) and LAI, the modeling of heat fluxes is noticeably improved, showing higher correlation with observations, lower bias, and more realistic seasonal dynamics of Q(E) and Q(H). The effect of the g(max) adjustment is more significant than the LAI adjustment. Compared to heat fluxes, the F-C module shows lower sensitivity to the choices of g(max) and LAI. This can be explained by the low relative contribution of vegetation to the net F-C in the modeled area. SUEWS successfully reproduces the average diurnal cycle of F-C and annual cumulative sums. Depending on the size of the simulation domain, the modeled annual accumulated F-C ranges from 7.4 to 8.7 kgCm(-2)yr(-1), compared to 7.5 kgCm(-2)yr(-1) observed by EC. Traffic is the dominant CO2 source, contributing 59 %-70 % to the annual total CO2 emissions, followed by human metabolism (14 %-18 %), buildings (11 %-14 %), and CO2 release by vegetation and soil respiration (6 %-10 %). Vegetation photosynthesis offsets only 5 %-10 % of the total CO2 emissions. We highlight the importance of choosing the optimal LAI parameters and gmax 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 in mitigating urban CO2 emissions.