Articles | Volume 23, issue 8
https://doi.org/10.5194/bg-23-2661-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/bg-23-2661-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
A machine learning approach to driver attribution of dissolved organic matter dynamics in two contrasting freshwater systems
Daniel Mercado-Bettín
CORRESPONDING AUTHOR
Centre for Advanced Studies of Blanes, Spanish National Research Council, Carrer Accés Cala Sant Francesc, 14, 17300, Blanes, Spain
Ricardo Paíz
School of History and Geography, Dublin City University, D09 YT18, Dublin 9, Ireland
Centre for Freshwater and Environmental Studies, Dundalk Institute of Technology, A91 K584 Dundalk, Co. Louth, Ireland
Valerie McCarthy
School of History and Geography, Dublin City University, D09 YT18, Dublin 9, Ireland
Eleanor Jennings
Centre for Freshwater and Environmental Studies, Dundalk Institute of Technology, A91 K584 Dundalk, Co. Louth, Ireland
Elvira de Eyto
Fisheries & Ecosystem Advisory Services, Marine Institute, F28 PF65 Newport, Co. Mayo, Ireland
Angeles M. Gallegos
Ens d’Abastament d’Aigua Ter-Llobregat, Ctra. Aigües, 6, 08440, Cardedeu, Spain
Mary Dillane
Fisheries & Ecosystem Advisory Services, Marine Institute, F28 PF65 Newport, Co. Mayo, Ireland
Juan C. Garcia
Ens d’Abastament d’Aigua Ter-Llobregat, Ctra. Aigües, 6, 08440, Cardedeu, Spain
José J. Rodríguez
Ens d’Abastament d’Aigua Ter-Llobregat, Ctra. Aigües, 6, 08440, Cardedeu, Spain
Rafael Marcé
Centre for Advanced Studies of Blanes, Spanish National Research Council, Carrer Accés Cala Sant Francesc, 14, 17300, Blanes, Spain
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Short summary
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Lakes and reservoirs are warming across the globe. To better understand how lakes are changing and to project their future behavior amidst various sources of uncertainty, simulations with a range of lake models are required. This in turn requires international coordination across different lake modelling teams worldwide. Here we present a protocol for and results from coordinated simulations of climate change impacts on lakes worldwide.
Leah A. Jackson-Blake, François Clayer, Elvira de Eyto, Andrew S. French, María Dolores Frías, Daniel Mercado-Bettín, Tadhg Moore, Laura Puértolas, Russell Poole, Karsten Rinke, Muhammed Shikhani, Leon van der Linden, and Rafael Marcé
Hydrol. Earth Syst. Sci., 26, 1389–1406, https://doi.org/10.5194/hess-26-1389-2022, https://doi.org/10.5194/hess-26-1389-2022, 2022
Short summary
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We explore, together with stakeholders, whether seasonal forecasting of water quantity, quality, and ecology can help support water management at five case study sites, primarily in Europe. Reliable forecasting, a season in advance, has huge potential to improve decision-making. However, managers were reluctant to use the forecasts operationally. Key barriers were uncertainty and often poor historic performance. The importance of practical hands-on experience was also highlighted.
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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.
Understanding what shapes lake water quality is vital in a changing world. We studied dissolved...
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