Understanding the DayCent model: Calibration, sensitivity, and identifiability through inverse modeling. (April 2015)

• Record Type: Journal Article
• Authors: Necpálová, Magdalena; Anex, Robert P.; Fienen, Michael N.; Del Grosso, Stephen J.; Castellano, Michael J.; Sawyer, John E.; Iqbal, Javed; Pantoja, José L.; Barker, Daniel W.
• Is Part Of: Environmental modelling & software. Volume 66(2015:Apr.)
• Page Start: 110
• Page End: 130
• Publication Date: 2015-04
• Subjects: DayCent model -- Inverse modeling -- PEST -- Sensitivity analysis -- Parameter identifiability -- Parameter correlations; ANPP aboveground net primary productivity -- ARS Agricultural Research Service -- C carbon -- CEC cation-exchange capacity -- CH4 methane -- C/N ratio carbon to nitrogen ratio -- d index of agreement -- DEFAC decomposition factor -- DNDC denitrification decomposition model -- EPA Environmental Protection Agency -- GHG greenhouse gas -- GML Gauss–Marquardt–Levenberg -- NH4+ ammonium cation -- J Jacobian matrix -- N nitrogen -- N2O nitrous oxide -- NPP net primary productivity -- NO3− nitrate anion -- PEST parameter estimation software -- MB mean bias -- RMSE root mean square error -- rRMSE relative root mean square error -- SOC soil organic carbon -- SOM soil organic matter -- SVD singular value decomposition -- SWSR sum of weighted squared residuals -- VSWC volumetric soil water content -- UAN urea ammonium nitrate; Environmental monitoring -- Computer programs -- Periodicals; Ecology -- Computer simulation -- Periodicals; Digital computer simulation -- Periodicals; Computer software -- Periodicals; Environmental Monitoring -- Periodicals; Computer Simulation -- Periodicals; Environnement -- Surveillance -- Logiciels -- Périodiques; Écologie -- Simulation, Méthodes de -- Périodiques; Simulation par ordinateur -- Périodiques; Logiciels -- Périodiques; Computer software; Digital computer simulation; Ecology -- Computer simulation; Environmental monitoring -- Computer programs; Periodicals; Electronic journals; 363.70015118
• DOI: 10.1016/j.envsoft.2014.12.011
• Languages: English
• ISSNs: 1364-8152

Abstract

Abstract: The ability of biogeochemical ecosystem models to represent agro-ecosystems depends on their correct integration with field observations. We report simultaneous calibration of 67 DayCent model parameters using multiple observation types through inverse modeling using the PEST parameter estimation software. Parameter estimation reduced the total sum of weighted squared residuals by 56% and improved model fit to crop productivity, soil carbon, volumetric soil water content, soil temperature, N2 O, and soil NO 3 − compared to the default simulation. Inverse modeling substantially reduced predictive model error relative to the default model for all model predictions, except for soil NO 3 − and NH 4 + . Post-processing analyses provided insights into parameter–observation relationships based on parameter correlations, sensitivity and identifiability. Inverse modeling tools are shown to be a powerful way to systematize and accelerate the process of biogeochemical model interrogation, improving our understanding of model function and the underlying ecosystem biogeochemical processes that they represent. Highlights: Several DayCent submodels were calibrated simultaneously using inverse modeling. Parameter estimation reduced DayCent total sum of weighted squared residuals by 56%. Soil temperature and water content are highly informative in DayCent calibration. Parameter estimation is an efficient way to calibrate soil biogeochemical models. Post-estimation analyses provide unique insights into model structure and function.