Jean-François Soussana

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Documents écrit par Jean-François Soussana (188) | parle de Jean-François Soussana (20)

Jean-François Soussana (16 avr. 2023)
Renáta Sándor et al. (13 janv. 2023)
Dominique Arrouays et al. (2023)
Marta Dondini et al. (2023)
There is a growing realization that the complexity of model ensemble studies depends not only on the models used but also on the experience and approach used by modelers to calibrate and validate results, which remain a source of uncertainty. Here, we applied a multi-criteria decision-making method to investigate the rationale applied by modelers in a model ensemble study where 12 process-based different biogeochemical model types were compared across five successive calibration stages. The modelers shared a common level of agreement about the importance of the variables used to initialize their models for calibration. However, we found inconsistency among modelers when judging the importance of input variables across different calibration stages. The level of subjective weighting attributed by modelers to calibration data decreased sequentially as the extent and number of variables provided increased. In this context, the perceived importance attributed to variables such as the fertilization rate, irrigation regime, soil texture, pH, and initial levels of soil organic carbon and nitrogen stocks was statistically different when classified according to model types. The importance attributed to input variables such as experimental duration, gross primary production, and netecosystem exchange varied significantly according to the length of the modeler’s experience. We argue that the gradual access to input data across the five calibration stages negatively influenced the consistency of the interpretations made by the modelers, with cognitive bias in “trial-and-error” calibration routines. Our study highlights that overlooking human and social attributes is critical in the outcomes of modeling and model intercomparison studies. While complexity of the processes captured in the model algorithms and parameterization is important, we contend that (1) the modeler’s assumptions on the extent to which parameters should be altered and (2) modeler perceptions of the importance of model parameters are just as critical in obtaining a quality model calibration as numerical or analytical details.
Fabrizio Albanito et al. (20 sept. 2022)
D. Arrouays et al. (2022)
Marisa Peyre et al.
Bertrand Guenet et al. (2021)
Bertrand Guenet et al. (2021)
Pamela Mcelwee et al. (11 août 2020)

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Manuel Martin et al. (17 févr. 2021)
Antoine Tardif (10 déc. 2013)
Antoine Tardif (10 déc. 2013)
Antoine Tardif (10 déc. 2013)
Danièle Revel (25 sept. 2013)
D Richard et al. (2013)
François Bertrand et al. (2013)
François Bertrand et al. (2013)
François Bertrand et al. (2013)
Gianni Bellocchi et al. (2013)
Amélie Cantarel (25 mars 2011)
Amélie Cantarel (25 mars 2011)

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