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  Simulation of CO2 Effects on Plant’s Stomatal Conductance by Machine Learning for Land Surface Models

Chitsaz, N., Guan, H., Shanafield, M., Batelaan, O. (2023): Simulation of CO2 Effects on Plant’s Stomatal Conductance by Machine Learning for Land Surface Models, XXVIII General Assembly of the International Union of Geodesy and Geophysics (IUGG) (Berlin 2023).
https://doi.org/10.57757/IUGG23-1759

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Datensatz-Permalink: https://gfzpublic.gfz-potsdam.de/pubman/item/item_5017832 Versions-Permalink: https://gfzpublic.gfz-potsdam.de/pubman/item/item_5017832_1
Genre: Konferenzbeitrag

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Chitsaz, Nastaran1, Autor
Guan, Huade1, Autor
Shanafield, Margaret1, Autor
Batelaan, Okke1, Autor
Affiliations:
1IUGG 2023, General Assemblies, 1 General, International Union of Geodesy and Geophysics (IUGG), External Organizations, ou_5011304              

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 Zusammenfassung: The estimation of plant transpiration is an important factor to comprehend the effects of environmental variables on global water balance. The land surface models (LSMs) reflect plant response to the environmental variables changes by simulation of stomatal conductance (gs). However, the plant responses are not well understood and vary by the climate and vegetation types. In this study, we reviewed the simulation of gs within different LSMs. These gs simulation models were calibrated and tested by Bayesian Markov chain Monte Carlo and 10th fold cross-validation. The uncertainty of each gs simulation model to input variables was determined by global sensitivity analysis. The results show the challenging issues in generalization in these models due to high sensitivity and dependency on parameterization in semi-empirical models and complex environmental stress functions in empirical models. The machine learning (ML) model was used to optimally discover the functional relationship between key environmental variables in the gs simulation by following the physical constraints. The high accuracy and low uncertainty to input variables in g­s simulation highlighted the ML model's importance for future studies. The new approach could be used to improve the integrity of the fundamental scientific processes in LSMs for transpiration simulation and water balance prediction.

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Sprache(n): eng - Englisch
 Datum: 2023
 Publikationsstatus: Final veröffentlicht
 Seiten: -
 Ort, Verlag, Ausgabe: -
 Inhaltsverzeichnis: -
 Art der Begutachtung: -
 Identifikatoren: DOI: 10.57757/IUGG23-1759
 Art des Abschluß: -

Veranstaltung

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Titel: XXVIII General Assembly of the International Union of Geodesy and Geophysics (IUGG)
Veranstaltungsort: Berlin
Start-/Enddatum: 2023-07-11 - 2023-07-20

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Titel: XXVIII General Assembly of the International Union of Geodesy and Geophysics (IUGG)
Genre der Quelle: Konferenzband
 Urheber:
Affiliations:
Ort, Verlag, Ausgabe: Potsdam : GFZ German Research Centre for Geosciences
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