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Digital Library

of the European Council for Modelling and Simulation

 

Title:

A Model for Predicting the Amount of Photosynthetically Available Radiation from BGC-ARGO Float Observations in the Water Column

Authors:

Frederic Stahl, Lars Nolle, Oliver Zielinski, Ahlem Jemai

Published in:

 

 

(2022). ECMS 2022, 36th Proceedings
Edited by: Ibrahim A. Hameed, Agus Hasan, Saleh Abdel-Afou Alaliyat, European Council for Modelling and Simulation.

 

DOI: http://doi.org/10.7148/2022

ISSN: 2522-2422 (ONLINE)

ISSN: 2522-2414 (PRINT)

ISSN: 2522-2430 (CD-ROM)

 

ISBN: 978-3-937436-77-7
ISBN: 978-3-937436-76-0(CD)

 

Communications of the ECMS , Volume 36, Issue 1, June 2022,

Ålesund, Norway May 30th - June 3rd, 2022

 

Citation format:

Frederic Stahl, Lars Nolle, Oliver Zielinski, Ahlem Jemai (2022). A Model for Predicting the Amount of Photosynthetically Available Radiation from BGC-ARGO Float Observations in the Water Column, ECMS 2022 Proceedings Edited By: Ibrahim A. Hameed, Agus Hasan, Saleh Abdel-Afou Alaliyat, European Council for Modeling and Simulation.

doi:10.7148/2022-0174

DOI:

https://doi.org/10.7148/2022-0174

Abstract:

Modern oceanography uses, amongst other platforms, automated diving devices, which are drifting with the ocean current whilst continuously collecting vertical profiles of environmental parameters. One of the important parameters is photosynthetically available radiation (PAR). It was studied in this work whether the PAR values can be reconstructed by combinations of measurements from the remaining onboard sensors with specific wavelength. If a reconstruction of PAR is possible, this would allow allocating the sensor with a further specific wavelength instead of PAR. Having available more spectral information could for example enable natural science researchers to better distinguish phytoplankton or UV radiation. Therefore, data from three different expeditions from different regions of the world were used to model PAR using multiple linear regression and regression trees (RT). multiple linear regression achieved an R2 value of 0.970 for the combined dataset and RT achieved an R2 value of 0.960. Hence, the models are accurate enough to predict the PAR parameter without the need for a dedicated PAR sensor. Thus the PAR sensor could be allocated instead with a further specific wavelength.

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