Improved modeling of canopy transpiration for temperate forests by incorporating a LAI-based dynamic parametrization scheme of stomatal slope

Abstract

The ecosystem-level conductance-photosynthesis models, which represent a linearly coupled relationship between canopy stomatal conductance (Gs) and CO2 assimilation, have been increasingly used for modeling transpiration (Tc). As a key parameter in these models, the slope parameter (G1) has been observed to vary considerably over the seasons in the field, but is often parametrized with a biome-specific temporally constant G1, resulting in large potential uncertainty. Here we hypothesized that G1 varies with leaf area index (LAI) phenology and soil water content (SWC) seasonality, and accurate characterization of G1 seasonality offers an avenue to improve Tc modelling. To test these hypotheses, we first investigated the seasonality of Eddy flux-derived G1 and then explored its relationship with satellite-derived LAI and field-observed SWC seasonality at 12 temperate forest FLUXNET sites across the Northern Hemisphere. Last, we cross-compared the two schemes of model parameterization of G1 for modeling Tc: (1) a constant G1 (FIX) and (2) a dynamic G1 parameterized using the selected variables (DYN). Our results show G1 displays considerable seasonal variations across all sites, with a minimum value in mid-summer. Further variance partitioning analysis demonstrates that the seasonal variations in G1 show direct linkages with LAI phenology rather than SWC seasonality likely associated with leaf aging and ontogeny development. Last, we found relative to the FIX model, the DYN model (using LAI for G1 parameterization) significantly reduced the model uncertainty in terms of RMSE by 24.6 ± 11.8% and 32.0 ± 8.7%, respectively for Gs and Tc at a daily scale. These results collectively improve our understanding of the dynamic pattern and proximate controls of G1 seasonality, and highlight the effectiveness of using remote sensing-derived LAI phenology for improved characterization of G1 seasonality that ultimately contributes to the improved process model simulations of the seasonal dynamics of Gs and Tc across temperate forest landscapes.