|Del Grosso, Stephen - Steve|
|OGLE, S - Colorado State University|
|PARTON, W - Colorado State University|
|BREIDT, F - Colorado State University|
Submitted to: Global Biogeochemical Cycles
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 9/30/2009
Publication Date: 3/8/2010
Citation: Del Grosso, S.J., Ogle, S.M., Parton, W.J., Breidt, F.J. 2010. Estimating Uncertainty in N2O Emissions from US Cropland Soils. Global Biogeochemical Cycles Vol. 24, GB1009, 12 pp, doi:10.1029/2009GB003544.
Interpretive Summary: Nitrous oxide (N2O) is an important greenhouse gas and agricultural soils are the primary human related source. To estimate agricultural N2O emissions at national scales, it is necessary to use models. It is also desirable to quantify uncertainty in these model generated estimates. We present a rigorous method to quantify the uncertainties by combining a Monte Carlo analysis with an empirically-based approach. This method accounts for uncertainties in key model input data and structural errors associated with model predictions due to imperfections in model algorithms and parameterization. One simulation representing the dominant weather, soil type, and N inputs was performed for each major crop in each of the ~3,000 agricultural counties in the US. Then, 300 counties were randomly selected for the Monte Carlo simulations, and 100 simulations were performed for each county by randomly drawing model inputs for weather, soils, and N inputs from probability distribution functions. Next, a structural uncertainty estimator was developed to account for model prediction error. Using this approach, we estimated soil N2O emission of 201 Gg N for major US crops in 2007 with a 95% confidence interval of 133-304 Gg N.
Technical Abstract: A Monte Carlo analysis was combined with an empirically-based approach to quantify uncertainties in soil N2O emissions from US croplands estimated with the DAYCENT simulation model. Only a subset of croplands was simulated in the Monte Carlo analysis which was used to infer uncertainties across the larger spatio-temporal domain. Specifically, one simulation representing dominant weather, soil type, and N inputs was performed for each major commodity crop in the 3000 counties occurring within the conterminous US. We randomly selected 300 counties for the Monte Carlo analysis and randomly drew model inputs from probability distribution functions (100 iterations). A structural uncertainty estimator was developed by deriving a statistical equation from a comparison of DAYCENT simulated N2O emissions with measured emissions from experiments in North America. We estimated soil N2O emission of 201 Gg N from major commodity crops in 2007 with a 95% confidence interval of 133-304 Gg N.