Understanding the DayCent model: Calibration, sensitivity, and identifiability through inverse modeling

Date
2015-04-01
Authors
Castellano, Michael
Sawyer, John
Necpálová, Magdalena
Anex, Robert
Fienen, Michael
Del Grosso, Stephen
Barker, Daniel
Castellano, Michael
Sawyer, John
Iqbal, Javed
Pantoja, Jose
Barker, Daniel
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Volume Title
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Altmetrics
Research Projects
Organizational Units
Agronomy
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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, N2O, and soilNO3 compared to the default simulation. Inverse modeling substantially reduced predictive model error relative to the default model for all model predictions, except for soil NO3 and NH4+. 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.

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Keywords
DayCent model, Inverse modeling, PEST, Sensitivity analysis, Parameter identifiability, Parameter correlations
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