Articles | Volume 23, issue 8
https://doi.org/10.5194/bg-23-2661-2026
https://doi.org/10.5194/bg-23-2661-2026
Research article
 | 
20 Apr 2026
Research article |  | 20 Apr 2026

A machine learning approach to driver attribution of dissolved organic matter dynamics in two contrasting freshwater systems

Daniel Mercado-Bettín, Ricardo Paíz, Valerie McCarthy, Eleanor Jennings, Elvira de Eyto, Angeles M. Gallegos, Mary Dillane, Juan C. Garcia, José J. Rodríguez, and Rafael Marcé

Data sets

Repository: A machine learning approach to driver attribution of dissolved organic matter dynamics in two contrasting freshwater systems Daniel Mercado-Bettín https://doi.org/10.5281/zenodo.19354955

Model code and software

Repository: A machine learning approach to driver attribution of dissolved organic matter dynamics in two contrasting freshwater systems Daniel Mercado-Bettín https://doi.org/10.5281/zenodo.19354955

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Short summary
Understanding what shapes lake water quality is vital in a changing world. We studied dissolved organic matter, a key part of water quality in lakes and the carbon cycle, to analyse its environmental drivers and make predictions, by using machine learning. Tested in lakes in Ireland and Spain, it showed good predictions, even when relying only on climate-soil data, and Julian day. This helps explain how land and climate conditions influence freshwater resources. It can be reproduced worldwide.
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