Data-constrained modeling of terrestrial gross primary production over the Tibetan Plateau for 2003–2019

Abstract

The gross primary productivity (GPP) of terrestrial ecosystem is the largest carbon flux between atmosphere and land surface. However, accurately simulating ecosystem GPP remains a great challenge for most land surface models (LSMs) due to the biased leaf area index (LAI) simulated by the models. In this study, we use remotely sensed LAI dataset for the period 2003–2019 to drive the Farquhar model and simulate the spatial-temporal changes of GPP for the alpine ecosystem over Tibetan Plateau. The annual GPP over the Tibetan Plateau is estimated to be 540.8 ± 27.3 Tg C yr−1, which is consistent with the benchmark datasets derived from flux tower measurements and remote sensing estimations. The GPP for the past two decades has been increasing at a rate of 2.6 Tg C yr−2. The canopy greening featured by increasing LAI contributed to 27.2 % of the increasing trend in GPP, while the direct impacts from warming and wetting climate contributed to 65.2 % of GPP changes. The contribution from the direct impact of elevated atmosphere CO2 concentration is only 6.7 %. In this study we show that constraining Farquhar model with remotely sensed LAI datasets could provide reliable simulation of GPP. Given the great challenge in modeling LAI and the considerable overestimation of LAI in current LSMs, our results highlight the importance of improving LAI modeling in LSMs and the necessity of constraining LAI when tuning the parameters for photosynthesis module.