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United States Department of Agriculture

Agricultural Research Service

Research Project: QUANTIFYING AND MONITORING NUTRIENT CYCLING, CARBON DYNAMICS AND SOIL PRODUCTIVITY AT FIELD, WATERSHED AND REGIONAL SCALES

Location: Hydrology and Remote Sensing Laboratory

Title: The general ensemble biogeochemical modeling system (GEMS) and its applications to agriculture systems in the United States

Authors
item Liu, Shuguang
item McCarty, Gregory
item Mirsky, Steven
item Ochsner, Tyson
item Baumgart-Getz, A
item Prokopy, L
item Shao, G
item Chan, S
item Njoku, E
item Kerr, Y
item Allen, R
item Morse, A
item Shi, J

Submitted to: Book Chapter
Publication Type: Book / Chapter
Publication Acceptance Date: October 1, 2011
Publication Date: June 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.

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.

Last Modified: 12/20/2014
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