Articles | Volume 18, issue 5
https://doi.org/10.5194/os-18-1451-2022
https://doi.org/10.5194/os-18-1451-2022
Research article
 | 
13 Oct 2022
Research article |  | 13 Oct 2022

A clustering approach to determine biophysical provinces and physical drivers of productivity dynamics in a complex coastal sea

Tereza Jarníková, Elise M. Olson, Susan E. Allen, Debby Ianson, and Karyn D. Suchy

Data sets

SalishSeaCast ERDDAP The SalishSeaCast Model Team https://salishsea.eos.ubc.ca/erddap/index.html

Model code and software

NEMO-3.6-CLUSTER Salish Sea MEOPAR project contributors https://github.com/SalishSeaCast/NEMO-3.6-CLUSTER

tjarnikova/CLUSTER_OS: Supplementary Code for Jarnikova et al. 2022 (v1.0.0) Tereza Jarnikova https://doi.org/10.5281/zenodo.7144696

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Short summary
Understanding drivers of phytoplankton biomass in dynamic coastal regions is key to predicting present and future ecosystem functioning. Using a clustering-based method, we objectively determined biophysical provinces in a complex estuarine sea. The Salish Sea contains three major distinct provinces where phytoplankton dynamics are controlled by diverse stratification regimes. Our method is simple to implement and broadly applicable for identifying structure in large model-derived datasets.