Assessing Ecosystem Carbon Dynamics over North America by Integrating Eddy Covariance, MODIS, and New Ecological Data through Upscaling and Model-data Synthesis
National Science Foundation (NSF)
Jingfeng Xiao (PI), Scott Ollinger (Co-PI)
Improved understanding of ecological processes that drive terrestrial carbon cycling is essential for predicting future changes in the Earth’s carbon-climate system. Although there has been considerable progress, remaining uncertainties are substantial and models that produce continental-scale carbon budgets still exhibit enormous variability over space and time. In recent years, the accumulation of site-level carbon flux data, development of sophisticated new modeling techniques and improvements in satellite information retrieval have opened new opportunities that have yet to be fully exploited. We propose to capitalize on these advances in several important ways. We plan to explicitly integrate continuous, high-frequency eddy covariance measurements with satellite and aircraft remote sensing data and new spatial data on key ecosystem properties (disturbance/stand age, aboveground biomass, leaf nitrogen). These data sources will be brought together using rigorous methods of upscaling and model-data synthesis to create gridded carbon flux estimates and associated estimates of uncertainty, and to examine sources of variability in carbon fluxes over North America.
The principal innovations in the work we propose are the explicit integration of continuous eddy flux measurements with a variety of remote sensing data streams and new spatial data on key ecological properties (disturbance/stand age, aboveground biomass, and leaf nitrogen) through upscaling and model-data fusion. Our approaches will account for the effects of disturbance, stand age and nitrogen availability by using new sources of ecological data. We will use two upscaling methods to link eddy flux data with plot, regional and continental scale observations. Data-assimilation techniques will be used to constrain model parameters and improve model performance for simulating carbon dynamics. We will also conduct a state-of-the-art uncertainty analysis to examine sources of uncertainty in model parameters and algorithms, eddy flux measurements, remotely-sensed data, and other driving variable and to quantify uncertainties in the resulting flux estimates and carbon budgets. Our flux estimates will be used to examine terrestrial carbon sinks and sources, and identify the main sources of the interannual variability of carbon fluxes, particularly the impacts of extreme climate events and disturbances. This work will be the first time such an approach will be used in a continental analysis, and will provide a new benchmark for North American ecosystem carbon dynamics.
Our results will be of clear value for a number of future global change assessments (e.g., IPCC, USGCRP, and NACP) and will provide methods that can be employed using data from new ecological observation networks and ecological forecasting activities. Specifically, the development of new approaches for continental-scale ecological analysis and forecasting across towers, plots, aircraft observation platforms and global satellite sensors is a high priority of the forthcoming National Ecological Observatory Network (NEON) and will be essential to its success. Beyond our own immediate results, we see potential for numerous NEON-related activities that compare variability in carbon metabolism with ecological variables such as species diversity, prevalence of invasive species, and susceptibility to changes in future climate.