Bitte beachten Sie: Im Zeitraum vom 21.12.2024 bis zum 07.01.2025 werden auf hohPublica keine Anfragen oder Publikationen durch das KIM bearbeitet. Please note: KIM will not process any requests or publications on hohPublica between December 21, 2024 and January 7, 2025.
 

Diagnosing similarities in probabilistic multi-model ensembles: An application to soil–plant-growth-modeling

dc.contributor.authorSchäfer Rodrigues Silva, Aline
dc.contributor.authorWeber, Tobias K. D.
dc.contributor.authorGayler, Sebastian
dc.contributor.authorGuthke, Anneli
dc.contributor.authorHöge, Marvin
dc.contributor.authorNowak, Wolfgang
dc.contributor.authorStreck, Thilo
dc.contributor.corporateSchäfer 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
dc.contributor.corporateWeber, Tobias K. D.; Department of Biogeophysics, Institute of Soil Science and Land Evaluation, University of Hohenheim, Stuttgart, Germany
dc.contributor.corporateGayler, Sebastian; Department of Biogeophysics, Institute of Soil Science and Land Evaluation, University of Hohenheim, Stuttgart, Germany
dc.contributor.corporateGuthke, Anneli; Junior Research Group for Statistical Model-Data Integration, Cluster of Excellence “Data-Integrated Simulation Science”, University of Stuttgart, Stuttgart, Germany
dc.contributor.corporateHöge, Marvin; Department of Systems Analysis, Integrated Assessment and Modelling, Eawag-Swiss Federal Institute of Aquatic Science and Technology, Dübendorf, Switzerland
dc.contributor.corporateNowak, 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
dc.contributor.corporateStreck, Thilo; Department of Biogeophysics, Institute of Soil Science and Land Evaluation, University of Hohenheim, Stuttgart, Germany
dc.date.accessioned2024-12-20T08:03:22Z
dc.date.available2024-12-20T08:03:22Z
dc.date.issued2022
dc.date.updated2024-12-02T06:44:28Z
dc.description.abstractThere 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.en
dc.description.sponsorshipOpen Access funding enabled and organized by Projekt DEAL.
dc.description.sponsorshipDeutsche Forschungsgemeinschaft http://dx.doi.org/10.13039/501100001659
dc.description.sponsorshipDeutsche Forschungsgemeinschaft http://dx.doi.org/10.13039/501100001659
dc.description.sponsorshipUniversität Stuttgart (1023)
dc.identifier.urihttps://doi.org/10.1007/s40808-022-01427-1
dc.identifier.urihttps://hohpublica.uni-hohenheim.de/handle/123456789/17057
dc.language.isoeng
dc.rights.licensecc_by
dc.subjectMulti-model ensembles
dc.subjectEnergy statistics
dc.subjectModel set visualization
dc.subjectCrop modeling
dc.subject.ddc630
dc.titleDiagnosing similarities in probabilistic multi-model ensembles: An application to soil–plant-growth-modelingen
dc.type.diniArticle
dcterms.bibliographicCitationModeling Earth Systems and Environment, 8 (2022), 4, 5143-5175. https://doi.org/10.1007/s40808-022-01427-1. ISSN: 2363-6211
dcterms.bibliographicCitation.issn2363-6211
dcterms.bibliographicCitation.issue4
dcterms.bibliographicCitation.journaltitleModeling earth systems and environment
dcterms.bibliographicCitation.originalpublishernameSpringer
dcterms.bibliographicCitation.originalpublisherplaceHeidelberg
dcterms.bibliographicCitation.pageend5175
dcterms.bibliographicCitation.pagestart5143
dcterms.bibliographicCitation.volume8
local.export.bibtex@article{Schäfer Rodrigues Silva2022-06-19, doi = {10.1007/s40808-022-01427-1}, author = {Schäfer Rodrigues Silva, Aline and Weber, Tobias K. D. and Gayler, Sebastian et al.}, title = {Diagnosing similarities in probabilistic multi-model ensembles: an application to soil–plant-growth-modeling}, journal = {Modeling Earth Systems and Environment}, year = {2022-06-19}, volume = {8}, number = {4}, pages = {5143--5175}, }
local.export.bibtexAuthorSchäfer Rodrigues Silva, Aline and Weber, Tobias K. D. and Gayler, Sebastian et al.
local.export.bibtexKeySchäfer Rodrigues Silva2022-06-19
local.export.bibtexPages5143--5175
local.export.bibtexType@article

Files

Original bundle

Now showing 1 - 1 of 1
Loading...
Thumbnail Image
Name:
s40808-022-01427-1.pdf
Size:
7.02 MB
Format:
Adobe Portable Document Format

License bundle

Now showing 1 - 1 of 1
No Thumbnail Available
Name:
license.txt
Size:
7.85 KB
Format:
Item-specific license agreed to upon submission
Description: