|Rafique, Rashid -|
|Fienen, Michael -|
|Anex, Robert -|
Submitted to: Journal of Environmental Modeling and Software
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: August 19, 2013
Publication Date: N/A
Interpretive Summary: Nitrous oxide is a potent greenhouse gas produced in agricultural soils. There are many agricultural management and environmental factors that influence the amount of nitrous oxide produced, including: i) fertilizer, ii) tillage, iii) crop rotation, iv) temperature, and v) precipitation. In addition soil type can have a strong influence on emissions. Since it is very costly to measure total nitrous oxide emissions on a given field, and since nitrous oxide emissions vary from year to year as a function of the weather, mathematical modeling of nitrous oxide emissions is a strategy to investigate and estimate the interactive effects of agriculture, climate, and soil type on annual nitrous oxide emissions. However, since many of the mathematical models used for nitrous oxide prediction integrate the complex interactions of soil, management, and climate, the accuracy of the predictions are dependant upon how well the models are calibrated. This paper describes a procedure for calibration of the mathematical model, DAYCENT. The model calibration procedure was tested using nitrous oxide data collected from a two-year study conducted on corn/soybean fields in Ames, Iowa. It was found that model calibration improved estimation of emissions by up to 67%. This model calibration procedure will be useful to scientists in their efforts to predict the influence of agricultural management on soil nitrous oxide emissions.
Technical Abstract: DayCent is a biogeochemical model of intermediate complexity widely used to simulate greenhouse gases (GHG), soil organic carbon (SOC) and nutrients in crop, grassland, forest and savannah ecosystems. Although this model has been applied to a wide range of ecosystems, it is still typically parameterized through a traditional "trial and error" approach and has not been calibrated using statistical inverse modelling (i.e., algorithmic parameter estimation). The aim of this study is to establish and demonstrate a procedure for calibration of DayCent to improve estimation of GHG emissions. We coupled DayCent with the PEST parameter estimation software for universal inverse modelling. The PEST software can be used for calibration through regularized inversion as well as model sensitivity and uncertainty analysis. The DayCent model was analysed and calibrated using N2O flux data collected over two years at the Iowa State University Agronomy and Agricultural Engineering Research Farms, Boone, IA. Crop year 2003 data were used for model calibration and 2004 data were used for validation. The optimization of DayCent model parameters using PEST significantly reduced model residuals relative to the default DayCent parameter values. Parameter estimation improved the model performance by reducing the sum of weighted squared residual difference between measured and modelled outputs by up to 67%. For the calibration period, simulation with the default model parameter values underestimated mean daily N2O flux by 98%. After parameter estimation, the model underestimated the mean daily fluxes by 35%. During the validation period the calibrated model reduced sum of weighted squared residuals by 20% relative to the default simulation. Sensitivity analysis performed provides important insights into the model structure providing guidance for model improvement.