An algorithmic calibration approach to identify globally optimal parameters for constraining the DayCent model

Rashad Rafique, Sandeep Kumar, Yiqi Luo, Gerard Kiely, Ghassem Asrar

Research output: Contribution to journalArticlepeer-review

17 Scopus citations

Abstract

The accurate calibration of complex biogeochemical models is essential for the robust estimation of soil greenhouse gases (GHG) as well as other environmental conditions and parameters that are used in research and policy decisions. DayCent is a popular biogeochemical model used both nationally and internationally for this purpose. Despite DayCent's popularity, its complex parameter estimation is often based on experts' knowledge which is somewhat subjective. In this study we used the inverse modelling parameter estimation software (PEST), to calibrate the DayCent model based on sensitivity and identifiability analysis. Using previously published N2O and crop yield data as a basis of our calibration approach, we found that half of the 140 parameters used in this study were the primary drivers of calibration differences (i.e. the most sensitive) and the remaining parameters could not be identified given the data set and parameter ranges we used in this study. The post calibration results showed improvement over the pre-calibration parameter set based on, a decrease in residual differences 79% for N2O fluxes and 84% for crop yield, and an increase in coefficient of determination 63% for N2O fluxes and 72% for corn yield. The results of our study suggest that future studies need to better characterize germination temperature, number of degree-days and temperature dependency of plant growth; these processes were highly sensitive and could not be adequately constrained by the data used in our study. Furthermore, the sensitivity and identifiability analysis was helpful in providing deeper insight for important processes and associated parameters that can lead to further improvement in calibration of DayCent model.

Original languageEnglish (US)
Pages (from-to)196-200
Number of pages5
JournalEcological Modelling
Volume297
DOIs
StatePublished - Feb 1 2015
Externally publishedYes

Keywords

  • Corn yield
  • DayCent model
  • Identifiability
  • NO
  • ParameterESTimation (PEST)
  • Sensitivity analysis

ASJC Scopus subject areas

  • Ecological Modeling

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