US-China Carbon Consortium (USCCC)



The USCCC's mission is to facilitate a better understanding of the environmental factors that influence the rate and magnitude of both carbon sequestration and water cycling across a range of ecosystems and climates while using mutually agreed upon measurement protocols and equipment and through a collaborated network of data sharing and analysis.

Goal

To develop a network of study sites sponsored by individual institutions to share data and results with the purpose of assessing the impacts of climate change and humans on the carbon and hydrologic cycles at broad spatial scales. USCCC research is designed to test following scientific hypotheses:

  • Human disturbances increase variability and uncertainty of carbon sequestration and water cycle of a landscape in time and space primarily via influencing landscape structure (i.e., composition).
  • Human disturbance regimes in US and China are significantly different and models describing the CO2 and H2O cycles should be different.

Approach

To explore the underline mechanisms controlling the fluxes of dominant ecosystems in both North America and Eastern Asian continents using an ecosystem approach. A central piece of the research is the flux towers using eddy-covariance method to measure directly and continuously the net ecosystem exchange of CO2, H2O, energy, and other greenhouse species. Integrated modeling tools are used to scaling up site level data to the continental scales. USCCC shares data with the FLUXNET committee and contributes to synthesis research.

Achievement

Eight annual workshops have been held in China; Trained over 30 scientists and graduate students in ecosystem sciences through scientific exchange program; More than 40 peer-reviewed journal papers and one special issue in Agricultural and Forest Meteorology (2009); participated in international synthesis activities (FLUXNET). These sites are providing unique understanding about functions of managed ecosystems. Outputs from this work are being used to develop new and improved models to predict future impacts of climate change.