naa a22 4500 510749399 CHVBK 20180411083059.0 cr unu---uuuuu 180411e20130901xx s 000 0 eng 10.1007/s11270-013-1677-z doi (NATIONALLICENCE)springer-10.1007/s11270-013-1677-z Nitrous Oxide Emissions from Cropland: a Procedure for Calibrating the DayCent Biogeochemical Model Using Inverse Modelling [Elektronische Daten] [Rashad Rafique, Michael Fienen, Timothy Parkin, Robert Anex] 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 2years 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. Springer Science+Business Media Dordrecht, 2013 DayCent model nationallicence Inverse modelling nationallicence Parameter Estimation (PEST) nationallicence Nitrous oxide nationallicence Sensitivity analysis nationallicence Automatic calibration nationallicence Validation nationallicence Rafique Rashad Department of Biological System Engineering, University of Wisconsin—Madison, 53706, Madison, WI, USA aut Fienen Michael United States Geological Survey, Wisconsin Water Science Center, 53562, Middleton, WI, USA aut Parkin Timothy USDA-ARS, National Laboratory for Agriculture and the Environment, 50011, Ames, IA, USA aut Anex Robert Department of Biological System Engineering, University of Wisconsin—Madison, 53706, Madison, WI, USA aut Water, Air, & Soil Pollution Springer Netherlands 224/9(2013-09-01), 1-15 0049-6979 224:9<1 2013 224 11270 https://doi.org/10.1007/s11270-013-1677-z text/html Onlinezugriff via DOI 1 research-article jats NATIONALLICENCE 856 40 https://doi.org/10.1007/s11270-013-1677-z text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Rafique Rashad Department of Biological System Engineering, University of Wisconsin—Madison, 53706, Madison, WI, USA aut NATIONALLICENCE 700 1- Fienen Michael United States Geological Survey, Wisconsin Water Science Center, 53562, Middleton, WI, USA aut NATIONALLICENCE 700 1- Parkin Timothy USDA-ARS, National Laboratory for Agriculture and the Environment, 50011, Ames, IA, USA aut NATIONALLICENCE 700 1- Anex Robert Department of Biological System Engineering, University of Wisconsin—Madison, 53706, Madison, WI, USA aut NATIONALLICENCE 773 0- Water, Air, & Soil Pollution Springer Netherlands 224/9(2013-09-01), 1-15 0049-6979 224:9<1 2013 224 11270 Metadata rights reserved Springer special CC-BY-NC licence nationallicence BK010053 XK010053 XK010000 NATIONALLICENCE NATIONALLICENCE NL-springer