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