Uncertainty analyses for calibrating a soil carbon balance model to agricultural field trial data in Sweden and Kenya

Abstract

How do additional data of the same and/or different type contribute to reducing model parameter and predictive uncertainties? Most modeling applications of soil organic carbon (SOC) time series in agricultural field trial datasets have been conducted without accounting for model parameter uncertainty. There have been recent advances with Monte Carlo-based uncertainty analyses in the field of hydrological modeling that are applicable, relevant and potentially valuable in modeling the dynamics of SOC. Here we employed a Monte Carlo method with threshold screening known as Generalized Likelihood Uncertainty Estimation (GLUE) to calibrate the Introductory Carbon Balance Model (ICBM) to long-term field trail data from Ultuna, Sweden and Machang'a, Kenya. Calibration results are presented in terms of parameter distributions and credibility bands on time series simulations for a number of case studies. Using these methods, we demonstrate that widely uncertain model parameters, as well as strong covariance between inert pool size and rate constant parameters, exist when root mean square simulation errors were within uncertainties in input estimations and data observations. We show that even rough estimates of the inert pool (perhaps from chemical analysis) can be quite valuable to reduce uncertainties in model parameters. In fact, such estimates were more effective at reducing parameter and predictive uncertainty than an additional 16 years time series data at Ultuna. We also demonstrate an effective method to jointly, simultaneously and in principle more robustly calibrate model parameters to multiple datasets across different climatic regions within an uncertainty framework. These methods and approaches should have benefits for use with other SOC models and datasets as well. (C) 2010 Elsevier B.V. All rights reserved.

Keywords

Soil organic carbon; Soil carbon; Carbon budgets; Model; Modeling; Agriculture; Uncertainty analysis; GLUE; ICBM

Published in

Ecological Modelling
2010, volume: 221, number: 16, pages: 1880-1888
Publisher: ELSEVIER SCIENCE BV

SLU Authors

• Kätterer, Thomas

  • Department of Soil and Environment, Swedish University of Agricultural Sciences
    

UKÄ Subject classification

Environmental Sciences and Nature Conservation
Agricultural Science

Publication identifier

• DOI: https://doi.org/10.1016/j.ecolmodel.2010.04.019

Permanent link to this page (URI)

https://res.slu.se/id/publ/61084