Campus Units
Agronomy
Document Type
Article
Publication Version
Published Version
Publication Date
4-2015
Journal or Book Title
Environmental Modeling & Software
Volume
66
First Page
110
Last Page
130
DOI
10.1016/j.envsoft.2014.12.011
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.
Rights
© 2014 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Copyright Owner
The Authors
Copyright Date
2014
Language
en
File Format
application/pdf
Recommended Citation
Necpálová, Magdalena; Anex, Robert P.; Fienen, Michael N.; Del Grosso, Stephen J.; Castellano, Michael J.; Sawyer, John E.; Iqbal, Javed; Pantoja, Jose L.; and Barker, Daniel W., "Understanding the DayCent model: Calibration, sensitivity, and identifiability through inverse modeling" (2015). Agronomy Publications. 99.
https://lib.dr.iastate.edu/agron_pubs/99
