Understanding the Day Cent model: Calibration, sensitivity, and identifiability through inverse modeling
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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 soil3NO− compared to the default simulation. Inverse modeling substantially reduced predictive model error relative to the default model for all model predictions, except for soil 3NO− and 4NH+. 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.
| Publication type | Article |
|---|---|
| Publication Subtype | Journal Article |
| Title | Understanding the Day Cent model: Calibration, sensitivity, and identifiability through inverse modeling |
| Series title | Environmental Modelling and Software |
| DOI | 10.1016/j.envsoft.2014.12.011 |
| Volume | 66 |
| Year Published | 2015 |
| Language | English |
| Publisher | Elsevier |
| Contributing office(s) | Wisconsin Water Science Center |
| Description | 21 p. |
| First page | 110 |
| Last page | 130 |
| Online Only (Y/N) | N |
| Additional Online Files (Y/N) | N |
| Google Analytic Metrics | Metrics page |