The COFLUX module:
Adrian Rocha (PI) and Michael Lemmon, University of Notre Dame
Remote sensing and eddy covariance datasets provide valuable information on ecosystem functioning in the remote but globally important arctic region. The size of such datasets have increased exponentially over the past two decades, and the state of the art statistical techniques are urgently needed in order to make inferences on how these systems are responding to climate change. Unfortunately, many of these statistical techniques require steep learning curves that can hinder or limit their use. Here we propose to develop an easy to use remote sensing and eddy covariance data assimilation application as a MATLAB GUID to expand the use of ecosystem modelling and data assimilation techniques to a broader audience. Remote sensing and eddy covariance data will be assimilated into a canopy-atmosphere resistance gas exchange and energy balance model (ALEX) with an ensemble Kalman filter (EnKF). The ALEX model is ideal for our application
because it uses a light use efficiency model to capture plant photosynthesis. This allows the use of remote sensing datasets to use water and carbon fluxes data to characterize plant stomata via the Ball-Berry equation. Both eddy covariance and remote sensing data will be assimilated into the model via the EnKF, where coupling between carbon and water fluxes will be determiend as the slope and intercept of the Ball-Berry equation.