ORIGINAL RESEARCH article
Front. Mar. Sci.
Sec. Ocean Observation
Volume 12 - 2025 | doi: 10.3389/fmars.2025.1558747
NUTRIENT ESTIMATION IN THE PERUVIAN UPWELLING SYSTEM BASED ON A NEURAL NETWORK APPROACH
Provisionally accepted
Cristhian Asto1,2*
Anthony Bosse3
Alice Pietri1,4
Raphaëlle Sauzède5
Michelle Graco2
Dimitri Gutiérrez2,6
François Colas4,7- 1UMR7159 Laboratoire d'Océanographie et du Climat Expérimentations et Approches Numériques (LOCEAN), Paris, Île-de-France, France
- 2Dirección General de Investigaciones Oceanográficas y Cambio Climático (DGIOCC), Instituto del Mar del Perú (IMARPE), Callao, Peru
- 3Aix Marseille Univ, Université de Toulon, CNRS, IRD, MIO, Marseille, France
- 4Institut de Recherche Pour le Développement (IRD), Marseille, Provence-Alpes-Côte d'Azur, France
- 5UMR7093 Laboratoire d'océanographie de Villefranche (LOV), Villefranche-sur-Mer, Provence-Alpes-Côte d'Azur, France
- 6Laboratorio de Ciencias del Mar, Facultad de Ciencias y Filosofía, Centro de Investigación para el Desarrollo Integral y Sostenible (CIDIS), Universidad Peruana Cayetano Heredia, Lima, Peru
- 7UMR6523 Laboratoire d'Oceanographie Physique et Spatiale (LOPS), Plouzane, Brittany, France
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This study presents a regionally trained version of the "CArbonate system and Nutrients concentration from hYdrological properties and Oxygen using a Neural network" (CANYON) method, named CANYON-PU, for estimating primary macronutrients (phosphates, silicates, and nitrates) in the Peruvian Upwelling System (PUS). Using a neural network approach, the model was trained using extensive biogeochemical data spanning between 2003 and 2021, collected by the Peruvian Institute of Marine Research (IMARPE). Variables representing the low-frequency variability related to ENSO were introduced in the training and significantly improved the performance of the algorithm. The performance of CANYON-PU was validated against independent datasets and demonstrated an improvement in accuracy over the global CANYON model that struggled to represent the nutrient distribution in the PUS mainly due to the lack of samples in its training. Therefore,CANYON-PU successfully captured nutrient variability across different spatial and temporal scales, showcasing its applicability to diverse datasets, including high-frequency data such as profiling floats or gliders. This work highlights the effectiveness of neural networks for representing the nutrient distribution within highly variable ecosystems like the PUS.
Keywords: Peruvian upwelling system, Nutrients, Neural Network, El Niño, Gliders, Profiling float
Received: 10 Jan 2025; Accepted: 26 May 2025.
Copyright: © 2025 Asto, Bosse, Pietri, Sauzède, Graco, Gutiérrez and Colas. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Cristhian Asto, UMR7159 Laboratoire d'Océanographie et du Climat Expérimentations et Approches Numériques (LOCEAN), Paris, 75252, Île-de-France, France
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