Le système d’observation et de recherche en environnement côtier de Thau – RECThau

International audienceCe Système d’Observation (SO) a été créé en 2009 au démarrage de l’OSUOREME à Montpellier. Il avait le double objectif de 1.structurer et pérenniserdes observations dans la lagune et sur la côte jusqu’alors ponctuelles voiresporadiques et 2. initier une coordination des différentes observations sur lelittoral languedocien dans l’espace géographique de la lagune de Thau afind’engendrer de nouvelles collaborations aussi bien en termes méthodologiquesque de nouveaux projets de recherche.Depuis 2015, le SO affiche une nouvelle appellation et une nouvellestructuration en trois Tâches d’Observation (TO) :- la TO du Bassin-Versant de la lagune de Thau (TO BV-Thau) : regroupe lesobservations météo-hydrologiques sur le bassin versant de la lagune de Thau.Ces observations sont mises en place depuis plus de 15 ans sur la Vène,principal contributeur du bassin.- la TO de la lagune de Thau (TO Suivi-Thau) : regroupe les observations deparamètres météorologiques et de paramètres physico-chimiques etbiologiques effectuées dans la colonne d’eau de la lagune de Thau. Cette TOest en partie en relation avec les expérimentations effectuées dans lesmésocosmes in situ de MEDIMEER installés sur la lagune de Thau,- la TO côtière au large de la lagune de Thau et sur le plateau continental enface de la ville de Sète (SOMLIT-Sète) : regroupe les observations desvariables physico-chimiques et biologiques sous 30 m de fond au large deSète, jusqu’alors dénommée Suivi-Côte. Elle a intégré le réseau SOMLIT(Service national d’Observation du Milieu LITtoral) en 2015. Les mesures sefont également à Haute Fréquence sur la station BESSète et elle est ainsimembre fondateur du réseau COAST-H

Data quality control considerations in multivariate environmental monitoring: experience of the French coastal network SOMLIT

International audienceIntroduction While crucial to ensuring the production of accurate and high-quality data—and to avoid erroneous conclusions—data quality control (QC) in environmental monitoring datasets is still poorly documented. Methods With a focus on annual inter-laboratory comparison (ILC) exercises performed in the context of the French coastal monitoring SOMLIT network, we share here a pragmatic approach to QC, which allows the calculation of systematic and random errors, measurement uncertainty, and individual performance. After an overview of the different QC actions applied to fulfill requirements for quality and competence, we report equipment, accommodation, design of the ILC exercises, and statistical methodology specially adapted to small environmental networks (<20 laboratories) and multivariate datasets. Finally, the expanded uncertainty of measurement for 20 environmental variables routinely measured by SOMLIT from discrete sampling—including Essential Ocean Variables—is provided. Results, Discussion, Conclusion The examination of the temporal variations (2001–2021) in the repeatability, reproducibility, and trueness of the SOMLIT network over time confirms the essential role of ILC exercises as a tool for the continuous improvement of data quality in environmental monitoring datasets

Table_1_Data quality control considerations in multivariate environmental monitoring: experience of the French coastal network SOMLIT.xls

IntroductionWhile crucial to ensuring the production of accurate and high-quality data—and to avoid erroneous conclusions—data quality control (QC) in environmental monitoring datasets is still poorly documented.MethodsWith a focus on annual inter-laboratory comparison (ILC) exercises performed in the context of the French coastal monitoring SOMLIT network, we share here a pragmatic approach to QC, which allows the calculation of systematic and random errors, measurement uncertainty, and individual performance. After an overview of the different QC actions applied to fulfill requirements for quality and competence, we report equipment, accommodation, design of the ILC exercises, and statistical methodology specially adapted to small environmental networks (Results, Discussion, ConclusionThe examination of the temporal variations (2001–2021) in the repeatability, reproducibility, and trueness of the SOMLIT network over time confirms the essential role of ILC exercises as a tool for the continuous improvement of data quality in environmental monitoring datasets.</p

DataSheet_1_Data quality control considerations in multivariate environmental monitoring: experience of the French coastal network SOMLIT.docx

IntroductionWhile crucial to ensuring the production of accurate and high-quality data—and to avoid erroneous conclusions—data quality control (QC) in environmental monitoring datasets is still poorly documented.MethodsWith a focus on annual inter-laboratory comparison (ILC) exercises performed in the context of the French coastal monitoring SOMLIT network, we share here a pragmatic approach to QC, which allows the calculation of systematic and random errors, measurement uncertainty, and individual performance. After an overview of the different QC actions applied to fulfill requirements for quality and competence, we report equipment, accommodation, design of the ILC exercises, and statistical methodology specially adapted to small environmental networks (Results, Discussion, ConclusionThe examination of the temporal variations (2001–2021) in the repeatability, reproducibility, and trueness of the SOMLIT network over time confirms the essential role of ILC exercises as a tool for the continuous improvement of data quality in environmental monitoring datasets.</p