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Hjelkrem, A.-G.R.; Höglind, M.; van Oijen, M.; Schellberg, J.; Gaiser, T.; Ewert, F. |
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Title |
Sensitivity analysis and Bayesian calibration for testing robustness of the BASGRA model in different environments |
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Journal Article |
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Year |
2017 |
Publication |
Ecological Modelling |
Abbreviated Journal |
Ecol. Model. |
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359 |
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80-91 |
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Keywords |
Metropolis-hasting; Morris method; Reducing complexity; Robustness |
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Abstract |
Highlights • The parameters to be fixed were consistent across sites. • Model calibration must be performed separately for each specific case. • Possible to reduce model parameters from 66 to 45. • Strong model reductions must be avoided. • The error term for the training data were characterised by timing (phase shift). Abstract 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. |
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0304-3800 |
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CropM, LiveM, ft_macsur |
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MA @ admin @ |
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5010 |
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Author |
Höglind, M. |
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Europeisk landbruk i et klima i endring (MACSUR) |
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2014 |
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CropM |
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Bioforsk-konferansen 2014, Hamar Norway, 2014-02-05 to 2014-02-06 |
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MA @ admin @ |
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2485 |
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Höglind, M., Persson T. |
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Title |
Yields and harvest security in Nordic forage production in the future – examples from simulation studies using the LINGRA model |
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2013 |
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CropM |
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NJF Seminar 455 Nordic Forage Model Applications- predicting forage yield and quality in a variable and changing climate, Forssa, Finland, NJF Report Vol. 9 No. 1 2013, p 4-5., 2013-01-30 to 2013-01-31 |
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MA @ admin @ |
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2484 |
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Höglind, M.; Persson, T.; van Oijen, M. |
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Title |
Breeding forage grasses: simulation modelling as a tool to identify important cultivar characteristics for winter survival and yield under future climate conditions in Norway |
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2014 |
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CropM |
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MACSUR CropM International Symposium and Workshop: Modelling climate change impacts on crop production for food security, Oslo, Norway, 2014-02-10 to 2014-02-12 |
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MA @ admin @ |
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2487 |
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Höglind, M.; Persson, T.; van Oijen, M. |
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Breeding forage grasses: simulation modelling as a tool to identify important cultivar characteristics for winter survival and yield under future climate conditions in Norway |
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2014 |
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CropM |
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Conference on Genetic Resources for Food and Agriculture in a Changing Climate, Lillehammer, Norway., 2014-01-27 to 2014-01-29 |
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MA @ admin @ |
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2488 |
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