Developing the Biome-BGC Model to Estimate Net Primary Productivity of Alpine Meadow on the Qinghai-Tibet Plateau

Numerical models are the most convenient instruments to estimate net primary productivity (NPP) of terrestrial vegetation. Process-based Biome-BGC model has been widely used to simulate the storage and flux of water, carbon, and nitrogen within the vegetation, litter, and soil of terrestrial ecosystems. Some researchers used Biome-BGC directly to estimate NPP of the alpine meadow on the Qinghai-Tibet Plateau without assessing its suitability mechanically. However, Biome-BGC has limited applicability to an alpine meadow mainly due to its inability of simulating the regeneration and litterfall processes specific to C3 perennial deciduous grasses, which dominate the alpine meadow on the Qinghai-Tibet Plateau. Our aim was to improve applicability of Biome-BGC to the alpine meadow to accurately estimate its NPP by implementing model development. In this study, C3 perennial deciduous grasses with leaves, non-woody stems, the underground fast-cycling portion, and the underground persistent portion were defined and modelled in the extended Biome-BGC model. Besides, eco-physiological parameters required by the model were also changed to adapt to the adjustment of model structure. After these modifications, the extended Biome-BGC model was validated with the measured NPP from the year of 2012 to 2014 collected at the Zhenqin Station, which is located in the central region of Qinghai-Tibet Plateau. Results showed that, NPP estimates using the extended model are much closer to the measured NPP than those calculated by the original model. On average, model development decreases the relative error of NPP estimation by approximately 30% during the year of 2012-2014.