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Sensitivity analysis of a sensitivity analysis: We are likely overlooking the impact of distributional assumptions

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  • Paleari, Livia
  • Confalonieri, Roberto

Abstract

Although uncertainty in input factor distributions is known to affect sensitivity analysis (SA) results, a standard procedure to quantify its impact is not available. We addressed this problem by performing a SA (generating sample of parameter distributions) of a SA (generating samples of parameter values for each generated distribution) of the WARM rice model using the Sobol’ method. The sample of distributions was generated using distributions of jackknife statistics calculated on literature values. This allowed mimicking the differences in distributions that could derive from different selection of literature sources. Despite the very low plasticity of WARM, the ranks of the two most relevant parameters was overturned in 22% of the cases and, in general, differed from what achieved in earlier SAs performed on the same model under similar conditions. SA results were mainly affected by uncertainty in distribution of parameters involved in non-linear effects or interacting with others. The procedure identified parameters whose uncertainty in distribution can alter SA results, i.e., parameters whose distributions could need to be refined.

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  • Paleari, Livia & Confalonieri, Roberto, 2016. "Sensitivity analysis of a sensitivity analysis: We are likely overlooking the impact of distributional assumptions," Ecological Modelling, Elsevier, vol. 340(C), pages 57-63.
    • Handle: RePEc:eee:ecomod:v:340:y:2016:i:c:p:57-63
    DOI: 10.1016/j.ecolmodel.2016.09.008
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    References listed on IDEAS

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    1. Confalonieri, R. & Bellocchi, G. & Bregaglio, S. & Donatelli, M. & Acutis, M., 2010. "Comparison of sensitivity analysis techniques: A case study with the rice model WARM," Ecological Modelling, Elsevier, vol. 221(16), pages 1897-1906.
    2. Confalonieri, Roberto & Acutis, Marco & Bellocchi, Gianni & Donatelli, Marcello, 2009. "Multi-metric evaluation of the models WARM, CropSyst, and WOFOST for rice," Ecological Modelling, Elsevier, vol. 220(11), pages 1395-1410.
    3. Stockle, Claudio O. & Martin, Steve A. & Campbell, Gaylon S., 1994. "CropSyst, a cropping systems simulation model: Water/nitrogen budgets and crop yield," Agricultural Systems, Elsevier, vol. 46(3), pages 335-359.
    4. Confalonieri, R. & Bregaglio, S. & Acutis, M., 2012. "Quantifying plasticity in simulation models," Ecological Modelling, Elsevier, vol. 225(C), pages 159-166.
    5. Toshichika Iizumi & Masayuki Yokozawa & Motoki Nishimori, 2011. "Probabilistic evaluation of climate change impacts on paddy rice productivity in Japan," Climatic Change, Springer, vol. 107(3), pages 391-415, August.
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    1. Paleari, Livia & Movedi, Ermes & Zoli, Michele & Burato, Andrea & Cecconi, Irene & Errahouly, Jabir & Pecollo, Eleonora & Sorvillo, Carla & Confalonieri, Roberto, 2021. "Sensitivity analysis using Morris: Just screening or an effective ranking method?," Ecological Modelling, Elsevier, vol. 455(C).
    2. Wagener, Thorsten & Pianosi, Francesca, 2019. "What has Global Sensitivity Analysis ever done for us? A systematic review to support scientific advancement and to inform policy-making in earth system modelling," Earth Arxiv g9ma5, Center for Open Science.
    3. Bregaglio, Simone & Ginaldi, Fabrizio & Raparelli, Elisabetta & Fila, Gianni & Bajocco, Sofia, 2023. "Improving crop yield prediction accuracy by embedding phenological heterogeneity into model parameter sets," Agricultural Systems, Elsevier, vol. 209(C).
    4. Mirko Ginocchi & Ferdinanda Ponci & Antonello Monti, 2021. "Sensitivity Analysis and Power Systems: Can We Bridge the Gap? A Review and a Guide to Getting Started," Energies, MDPI, vol. 14(24), pages 1-59, December.
    5. Moulin, Thibault & Perasso, Antoine & Gillet, François, 2018. "Modelling vegetation dynamics in managed grasslands: Responses to drivers depend on species richness," Ecological Modelling, Elsevier, vol. 374(C), pages 22-36.

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