Advanced Modeling Using Land-use History and Remote Sensing to Improve Projections of Terrestrial Carbon Dynamics

Quantifying, attributing, and projecting terrestrial carbon dynamics can provide valuable information in support of climate mitigation policy to limit global warming to 1.5 °C. Current modeling efforts still involve considerable uncertainties, due in part to knowledge gaps regarding efficient and accurate scaling of individual-scale ecological processes to large-scale dynamics and contemporary ecosystem conditions (e.g., successional states and carbon storage), which present strong spatial heterogeneity. To address these gaps, this research aims to leverage decadal advances in land-use modeling, remote sensing, and ecosystem modeling to improve the projection of terrestrial carbon dynamics at various temporal and spatial scales. Specifically, this research examines the role of land-use modeling and lidar observations in determining contemporary ecosystem conditions, especially in forest, using the latest land-use change dataset, developed as the standard forcing for CMIP6, and observations from both airborne lidar and two state-of-the-art NASA spaceborne lidarmissions, GEDI and ICESat-2. Both land-use change dataset and lidar observations are used to initialize a newly developed global version of the ecosystem demography (ED) model, an individual-based forest model with unique capabilities to characterize fine-scale processes and efficiently scale them to larger dynamics. Evaluations against multiple benchmarking datasets suggest that the incorporation of land-use modeling into the ED model can reproduce the observed spatial pattern of vegetation distribution, carbon dynamics, and forest structure as well as the temporal dynamics in carbon fluxes in response to climate change, increased CO2, and land-use change. Further, the incorporation of lidar observations into ED, largely enhances the model’s ability to characterize carbon dynamics at fine spatial resolutions (e.g., 90 m and 1 km). Combining global ED model, land-use modeling and lidar observation together can has great potential to improve projections of future terrestrial carbon dynamics in response to climate change and land-use change