关键词: 青藏高原, 总初级生产力, 统计模型, 光能利用率模型, 过程模型, 精度

Abstract: Gross Primary Productivity (GPP) is a key component of the carbon cycle in terrestrial ecosystems, and its accurate simulation is crucial for understanding the variations of carbon dioxide in the atmosphere. Currently, there are numerous global GPP products developed based on different data sources and models, but systematic assessments of their performance in the Tibetan Plateau are lacking. Methods In this study, we collected GPP data from 17 eddy covariance flux towers on the Tibetan Plateau and comprehensively evaluated the accuracy of 8 global GPP products (GPPGOSIF, GPPNIRv, GPPMOD17, GPPVPM, GPPGLASS, GPPTL-LUE, GPPPML, and GPPBESS) in alpine meadows of the eastern and central Tibetan Plateau using three indicators: coefficient of determination (R2), relative root mean square error (RRMSE), and relative bias (RBIAS).The results showed that GPPVPM (R2=0.60, RRMSE=75.39%, RBIAS=8.73%) and GPPGOSIF (R2=0.58, RRMSE=81.53%, RBIAS=24.94%) generally exhibited relatively high accuracy and were more suitable for the alpine meadows of the Tibetan Plateau. Moreover, the accuracy of GPP products varied significantly in space and time, manifested as follows: seasonally, the accuracy was higher in summer and autumn than in spring; in terms of ecosystem types, typical alpine meadow showed better accuracy than other meadow ecosystems; regarding permafrost types, the accuracy was higher in permafrost zone than in seasonal frost zone; in climate zones, semi-humid region demonstrated higher accuracy than semi-arid region; in terms of drought conditions, the accuracy is greater in non-drought months compared to drought months. This study reveals the uncertainties of global GPP products in the alpine meadows of the Tibetan Plateau and provides important insights for selecting GPP data and improving GPP simulations in this region.

Key words: Tibetan Plateau, Gross Primary Productivity, Statistical Models, Light Use Efficiency Models, Process Models, Accuracy