Development of the SSiB4/Triffid/DayCent-SOM Model to study the impact of Nitrogen availability to carbon cycle

The nitrogen availability affects the carbon and water cycles and climate through feedback processes; and are highly related to anthropogenic activities. Nitrogen availability regulates the capture, allocation, and turnover of carbon, and, therefore, regulates photosynthesis capacity in many terrestrial ecosystems. In this study, we couple a biophysical and dynamic model (SSiB4/ Triffid) with the soil nitrogen dynamics model, DayCent-SOM, to simulate the impact of soil nitrogen limitation on the plant growth and carbon and water cycles. The DayCent-SOM includes the parameterizations of direct root and mycorrhizal-associated nitrogen uptake, symbiotic biological nitrogen fixation, nitrogen mineralization, nitrification, immobilization, volatilization, atmospheric deposition, and leaching.

In recent years, nitrogen limitation has been included in some land surface carbon models, which were developed particularly for environmental, climate, and agricultural applications. However, these developments are still at preliminary stage. Studies have shown that N-limitation is related to the maximum rate of carboxylation (VCmax), which depends on the leaf nitrogen. To incorporate the limitation of nitrogen in the coupled system, we first modify the Carbon-Nitrogen ratio of leaf, wood, and root of each plant functional type based on the observation and some simulation results from DayCent-SOM. Then, based on Plant N uptake and fixation, when the potential uptake nitrogen is less than the N demand, nitrogen will limit the plant growth, which causes the Net Primary Products (NPP) and leaf area INDEX (LAI) lower than expected. We have tested several strategies to present this N-limitation under the constrain of the satellite products.

The 2-D coupled SSiB4/Triffid/DayCent-SOM model is tested over the globe from 1948 to 2007. The satellite derived global LAI, and ground measurement-based global GPP products were applied for evaluation. Preliminary results show that the addition of nitrogen dynamics improves the model simulation in these variable, which leads to better carbon cycle.