Optimizing seasonally variable photosynthetic parameters based on joint carbon and water flux constraints

Terrestrial biosphere models (TBMs) often adopt the Farquhar biochemical model coupled with the Ball-Berry stomatal conductance (gs) model to simulate ecosystem carbon and water fluxes. The parameters m, representing the sensitivity of gs to the photosynthetic rate, and Vcmax25, representing the leaf photosynthetic capacity, are two pivotal parameters but the two main sources of uncertainties in TBM simulations. The temporal variations of m in TBMs are still elusive, due to the lack of direct observations. It also remains unclear how accurate estimates of m and Vcmax25 can improve the simulations of carbon and water fluxes. In this study, we used a Bayesian parameter optimization approach to estimate seasonally varying m and Vcmax25 from eddy covariance observations in a mixed forest stand at the Borden Forest Research Station located in southern Ontario, Canada and used in-situ observations of m and Vcmax25 for validation. Three strategies were tested for optimizing m and Vcmax25, including the carbon, water, and carbon-water coupling scenarios. m and Vcmax25 optimized from carbon-water coupling constraints shows best correlations with the measured m (R2 = 0.70) and Vcmax25 (R2 = 0.70). By incorporating optimized m and Vcmax25with seasonal variations, we found considerable improvements in the estimated gross primary productivity (GPP) and evapotranspiration (ET) compared with constant m and Vcmax25, with R2 increasing from 0.78 to 0.85 for GPP, from 0.65 to 0.71 for ET and RMSE reducing from 2.579 g C m−2 d−1 to 2.038 g C m−2 d−1 for GPP, from 1.151 mm d−1 to 0.137 mm d−1 for ET. This study proposes an effective approach to retrieve m and Vcmax25 for TBMs and demonstrates the efficacy of incorporating seasonally variable m and Vcmax25 for reducing the uncertainties in GPP and ET simulations, which supports accurate quantifications of land-atmosphere exchanges.