Abstract: The rapid expansion of tea plantations in the hilly regions of southeastern China significantly impacts regional carbon cycle. The Biome-BGC model, commonly used to quantify carbon fluxes, lacks sufficient representation of artificial management processes. We integrated the measured and remote-sensed leaf area index (LAI) to improve the Biome-BGC model, enhancing its simulation capabilities for the artificial management processes in tea plantations. The results showed that LAI was a crucial intermediate variable in the Biome-BGC model. Accurate simulation of LAI was the key to improve the model’s precision in simulating carbon fluxes in tea plantations. The improved model significantly enhanced the simulation accuracy of gross primary productivity (GPP) and ecosystem respiration (RE), with 5-year average GPP and RE values of 1.26 and 1.19 kg C·m^-2, respectively. The daily-scale R² values reached 0.55 and 0.80, representing an increase of 44.5% for GPP and a decrease of 0.9% for RE compared to the original model. The root mean square error (RMSE) values were 0.887 and 1.030 g C·m^-2·d^-1, representing reductions of 50.3% for GPP and 68.4% for RE compared to the original model, respectively. At the month scale, the improved model significantly reduced the overestimation of original model resulted from insufficient representation of artificial pruning for tea plantations. The improved model could dynamically depict the impact of LAI fluctuations caused by pruning on the carbon cycle and its applicability across different time scales had been verified, which would provide technical support for quantitative research on carbon cycling in tea plantations with high-intensity anthropogenic management.

Key words: Biome-BGC model, tea plantation, carbon flux simulation, leaf area index