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ARS Home » Northeast Area » Beltsville, Maryland (BARC) » Beltsville Agricultural Research Center » Hydrology and Remote Sensing Laboratory » Research » Publications at this Location » Publication #274729
Title: The general ensemble biogeochemical modeling system (GEMS) and its applications to agriculture systems in the United States

Author
item LIU, SHUGUANG - Collaborator
item McCarty, Gregory
item Mirsky, Steven
item OCHSNER, TYSON - Oklahoma State University
item BAUMGART-GETZ, A - Oklahoma State University
item PROKOPY, L - Purdue University
item SHAO, G - Purdue University
item CHAN, S - Jet Propulsion Laboratory
item NJOKU, E - Jet Propulsion Laboratory
item KERR, Y - Collaborator
item ALLEN, R - University Of Idaho
item MORSE, A - Collaborator
item SHI, J - University Of California

Submitted to: Book Chapter
Publication Type: Book / Chapter
Publication Acceptance Date: 10/1/2011
Publication Date: 6/1/2012
Citation: Liu, S., Mccarty, G.W., Mirsky, S.B., Ochsner, T., Baumgart-Getz, A., Prokopy, L.S., Shao, G., Chan, S., Njoku, E., Kerr, Y., Allen, R.G., Morse, A., Shi, J. 2012. The general ensemble biogeochemical modeling system (GEMS) and its applications to agriculture systems in the United States. Managing Agricultural Greenhouse Gases. Amsterdam:Elsevier p. 309-323.

Interpretive Summary:

Technical Abstract: The General Ensemble Biogeochemical Modeling System (GEMS) was developed for a proper integration of well-established ecosystem biogeochemical models with various spatial databases to simulate biogeochemical cycles over large areas. Major driving variables include land cover and land use, climate, soils, disturbances, and various management activities. GEMS uses two approaches to quantify the uncertainty of model outputs. First, to reduce biases in individual models, it uses multiple site-scale biogeochemical models to perform model simulations. Second, it adopts Monte Carlo ensemble simulations of each simulation unit (one site/pixel or group of sites/pixels with similar biophysical conditions) to incorporate uncertainties and variability (as measured by variances and covariance) of input variables into model simulations. In this paper, we illustrated the applications of GEMS at the site and regional scales with an emphasis on incorporating agricultural practices. Challenges in modeling soil carbon dynamics and greenhouse emissions are also discussed.