Browsing by Subject "Model set visualization"

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Diagnosing similarities in probabilistic multi-model ensembles: An application to soil–plant-growth-modeling
(2022) Schäfer Rodrigues Silva, Aline; Weber, Tobias K. D.; Gayler, Sebastian; Guthke, Anneli; Höge, Marvin; Nowak, Wolfgang; Streck, Thilo; Schäfer Rodrigues Silva, Aline; Department of Stochastic Simulation and Safety Research for Hydrosystems, Institute for Modelling Hydraulic and Environmental Systems/Cluster of Excellence “Data-Integrated Simulation Science”, University of Stuttgart, Stuttgart, Germany; Weber, Tobias K. D.; Department of Biogeophysics, Institute of Soil Science and Land Evaluation, University of Hohenheim, Stuttgart, Germany; Gayler, Sebastian; Department of Biogeophysics, Institute of Soil Science and Land Evaluation, University of Hohenheim, Stuttgart, Germany; Guthke, Anneli; Junior Research Group for Statistical Model-Data Integration, Cluster of Excellence “Data-Integrated Simulation Science”, University of Stuttgart, Stuttgart, Germany; Höge, Marvin; Department of Systems Analysis, Integrated Assessment and Modelling, Eawag-Swiss Federal Institute of Aquatic Science and Technology, Dübendorf, Switzerland; Nowak, Wolfgang; Department of Stochastic Simulation and Safety Research for Hydrosystems, Institute for Modelling Hydraulic and Environmental Systems/Cluster of Excellence “Data-Integrated Simulation Science”, University of Stuttgart, Stuttgart, Germany; Streck, Thilo; Department of Biogeophysics, Institute of Soil Science and Land Evaluation, University of Hohenheim, Stuttgart, Germany
There has been an increasing interest in using multi-model ensembles over the past decade. While it has been shown that ensembles often outperform individual models, there is still a lack of methods that guide the choice of the ensemble members. Previous studies found that model similarity is crucial for this choice. Therefore, we introduce a method that quantifies similarities between models based on so-called energy statistics. This method can also be used to assess the goodness-of-fit to noisy or deterministic measurements. To guide the interpretation of the results, we combine different visualization techniques, which reveal different insights and thereby support the model development. We demonstrate the proposed workflow on a case study of soil–plant-growth modeling, comparing three models from the Expert-N library. Results show that model similarity and goodness-of-fit vary depending on the quantity of interest. This confirms previous studies that found that “there is no single best model” and hence, combining several models into an ensemble can yield more robust results.