Sensitivity analysis and Bayesian calibration for testing robustness of the BASGRA model in different environments

Hjelkrem, Anne-Grete Roer; Höglind, Mats; van Oijen, Marcel; Schellberg, Jürgen; Gaiser, Thomas; Ewert, Frank. 2017 Sensitivity analysis and Bayesian calibration for testing robustness of the BASGRA model in different environments. Ecological Modelling, 359. 80-91. 10.1016/j.ecolmodel.2017.05.015

Before downloading, please read NORA policies.

Preview

Text N517496PP.pdf - Accepted Version Available under License Creative Commons Attribution Non-commercial No Derivatives 4.0. Download (1MB) | Preview

Abstract/Summary

Proper parameterisation and quantification of model uncertainty are two essential tasks in improvement and assessment of model performance. Bayesian calibration is a method that combines both tasks by quantifying probability distributions for model parameters and outputs. However, the method is rarely applied to complex models because of its high computational demand when used with high-dimensional parameter spaces. We therefore combined Bayesian calibration with sensitivity analysis, using the screening method by Morris (1991), in order to reduce model complexity by fixing parameters to which model output was only weakly sensitive to a nominal value. Further, the robustness of the model with respect to reduction in the number of free parameters were examined according to model discrepancy and output uncertainty. The process-based grassland model BASGRA was examined in the present study on two sites in Norway and in Germany, for two grass species (Phleum pratense and Arrhenatherum elatius). According to this study, a reduction of free model parameters from 66 to 45 was possible. The sensitivity analysis showed that the parameters to be fixed were consistent across sites (which differed in climate and soil conditions), while model calibration had to be performed separately for each combination of site and species. The output uncertainty decreased slightly, but still covered the field observations of aboveground biomass. Considering the training data, the mean square error for both the 66 and the 45 parameter model was dominated by errors in timing (phase shift), whereas no general pattern was found in errors when using the validation data. Stronger model reduction should be avoided, as the error term increased and output uncertainty was underestimated.

Item Type:	Publication - Article
Digital Object Identifier (DOI):	10.1016/j.ecolmodel.2017.05.015
UKCEH and CEH Sections/Science Areas:	Dise
ISSN:	0304-3800
Additional Keywords:	Metropolis-Hastings, Morris method, reducing complexity, robustness
NORA Subject Terms:	Agriculture and Soil Science Data and Information
Date made live:	08 Aug 2017 14:39 +0 (UTC)
URI:	https://nora.nerc.ac.uk/id/eprint/517496

Actions (login required)

View Item

Document Downloads

More statistics for this item...