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Author |
Makowski, D. |
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Title  |
A simple Bayesian method for adjusting ensemble of crop model outputs to yield observations |
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Journal Article |
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Year |
2017 |
Publication |
European Journal of Agronomy |
Abbreviated Journal |
Europ. J. Agron. |
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Volume |
88 |
Issue |
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Pages |
76-83 |
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Keywords |
Bayesian method; Climate change; Ensemble modelling; Uncertainty; Yield; Linear-Approach; Climate-Change; CO2 |
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Abstract |
Multi-model forecasting has drawn some attention in crop science for evaluating effect of climate change on crop yields. The principle is to run several individual process-based crop models under several climate scenarios in order to generate ensembles of output values. This paper describes a simple Bayesian method – called Bayes linear method- for updating ensemble of crop model outputs using yield observations. The principle is to summarize the ensemble of crop model outputs by its mean and variance, and then to adjust these two quantities to yield observations in order to reduce uncertainty. The adjusted mean and variance combine two sources of information, i.e., the ensemble of crop model outputs and the observations. Interestingly, with this method, observations collected under a given climate scenario can be used to adjust mean and variance of the model ensemble under a different scenario. Another advantage of the proposed method is that it does not rely on a separate calibration of each individual crop model. The uncertainty reduction resulting from the adjustment of an ensemble of crop models to observations was assessed in a numerical application. The implementation of the Bayes linear method systematically reduced uncertainty, but the results showed the effectiveness of this method varied in function of several factors, especially the accuracy of the yield observation, and the covariance between the crop model output and the observation. (C) 2015 Elsevier B.V. All rights reserved. |
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2017-08-07 |
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English |
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ISSN |
1161-0301 |
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Article |
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Notes |
CropM, ft_macsur |
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no |
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Call Number |
MA @ admin @ |
Serial |
5171 |
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Author |
Refsgaard, J.C.; Madsen, H.; Andréassian, V.; Arnbjerg-Nielsen, K.; Davidson, T.A.; Drews, M.; Hamilton, D.P.; Jeppesen, E.; Kjellström, E.; Olesen, J.E.; Sonnenborg, T.O.; Trolle, D.; Willems, P.; Christensen, J.H. |
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Title |
A framework for testing the ability of models to project climate change and its impacts |
Type |
Journal Article |
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Year |
2014 |
Publication |
Climatic Change |
Abbreviated Journal |
Clim. Change |
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Volume |
122 |
Issue |
1-2 |
Pages |
271-282 |
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Keywords |
simulation-models; shallow lakes; predictions; calibration; ensembles; terminology; uncertainty; temperature; adaptation; validation |
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Abstract |
Models used for climate change impact projections are typically not tested for simulation beyond current climate conditions. Since we have no data truly reflecting future conditions, a key challenge in this respect is to rigorously test models using proxies of future conditions. This paper presents a validation framework and guiding principles applicable across earth science disciplines for testing the capability of models to project future climate change and its impacts. Model test schemes comprising split-sample tests, differential split-sample tests and proxy site tests are discussed in relation to their application for projections by use of single models, ensemble modelling and space-time-substitution and in relation to use of different data from historical time series, paleo data and controlled experiments. We recommend that differential-split sample tests should be performed with best available proxy data in order to build further confidence in model projections. |
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English |
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0165-0009 1573-1480 |
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Notes |
CropM |
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no |
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Call Number |
MA @ admin @ |
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4688 |
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