Nitrous Oxide Emissions from Cropland: a Procedure for Calibrating the DayCent Biogeochemical Model Using Inverse Modelling

被引:25
|
作者
Rafique, Rashad [1 ,4 ]
Fienen, Michael N. [2 ]
Parkin, Timothy B. [3 ]
Anex, Robert P. [1 ]
机构
[1] Univ Wisconsin, Dept Biol Syst Engn, Madison, WI 53706 USA
[2] US Geol Survey, Wisconsin Water Sci Ctr, Middleton, WI 53562 USA
[3] ARS, USDA, Natl Lab Agr & Environm, Ames, IA 50011 USA
[4] Univ Oklahoma, Dept Microbiol & Plant Biol, Norman, OK 73019 USA
来源
WATER AIR AND SOIL POLLUTION | 2013年 / 224卷 / 09期
关键词
DayCent model; Inverse modelling; Parameter Estimation (PEST); Nitrous oxide; Sensitivity analysis; Automatic calibration; Validation; CARBON-DIOXIDE; SOIL; N2O; DENITRIFICATION; SIMULATIONS; SYSTEMS;
D O I
10.1007/s11270-013-1677-z
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
DayCent is a biogeochemical model of intermediate complexity widely used to simulate greenhouse gases (GHG), soil organic carbon 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 parameter estimation (PEST) software for 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 2 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.
引用
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页数:15
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