Water and Nitrogen Limitations on Vegetation Dynamics

Abstract

The impact of energy use on climate depends in large part on the response of terrestrial ecosystems that regulate atmospheric CO₂ and climate through exchange and storage of carbon and energy. Water and nitrogen is a dominant regulator of vegetation dynamics and the terrestrial carbon cycle, yet rather simplistic empirical-type models are still used to predict the effect of water and nitrogen limitations on vegetation growth. Therefore, a large amount of uncertainty exists in the current simulation of vegetation dynamics, which substantially affects the reliability of predicted terrestrial carbon fluxes. In this project, we developed next generation water and nitrogen limitation models for vegetation dynamics and plan to integrate these models into DOE-sponsored community land model CLM(ED) in ACME. Our water limitation model is based on mechanistic plant hydraulics with explicit representation of plant water storage and thus enables us to simulate the plant mortality due to hydraulic failure. The nitrogen limitation model is based on the optimization of plant’s nitrogen allocation to maximize the photosynthetic carbon gain under different environmental conditions.  Our analysis showed that drought could reduce the future global vegetation production by ~4%, which is equivalent to 50% of the current fossil fuel carbon release. Our improved hydraulic model simulation at point mode suggests that we can substantially overestimate the vegetation growth if we omit the potential hydraulic damage to plant during droughts. It predicted that there is a high risk of piñon pine mortality in the southwest.  The predicted acclimation of photosynthetic capacity by our nitrogen allocation model could reduce the future global net photosynthesis on the top canopy layer by ~8% under the emission scenario of 8.5, compared to the model that does not change photosynthetic capacity.  Our work of improving the water and nitrogen limitation model in ACME will enable DOE to better predict the vegetation dynamics and its feedback to future climate.