Observed vs. predicted variability in non-algal suspended particulate matter concentration in the English Channel in relation to tides and waves
The study of water clarity is essential to understand variability in biological production, particularly in coastal seas. The spatial and temporal variability of non-algal suspended particulate matter (SPM) in surface waters of the English Channel was investigated and related to local forcing by means of a large satellite dataset covering the study area with a spatial resolution of 1.2 km and a daily temporal resolution. This analysed dataset is a time series of non-algal SPM images derived from MODIS and MERIS remote-sensing reflectance by application of an IFREMER semi-analytical algorithm over the period 2003–2009. In a first step, the variability of time series of MODIS images was analysed through temporal autocorrelation functions. Then, non-algal SPM concentrations were assessed in terms of site-specific explanatory variables such as tides, wind-generated surface-gravity wave amplitudes and chlorophyll-a (Chl-a), based on three statistical models with fitting parameters calibrated on a dataset of merged MERIS/MODIS images gathered from 2007 to 2009 over the whole English Channel. Correlogram analysis and the first model highlight the local patterns of the influence of the tide, especially the neap–spring cycle, on non-algal surface SPM. Its effect is particularly strong in the central and eastern English Channel and in the western coastal areas. The second model shows that waves prevail as driver at the entrance of the English Channel. The most sophisticated of the three statistical models, although involving only three explanatory variables—the tide, waves and Chl-a—is able to estimate non-algal surface SPM with a coefficient of determination reaching 70% at many locations.
KeywordsSuspended Particulate Matter Suspended Particulate Matter Concentration Water Column Stratification Spring Tidal Cycle Surface Suspended Particulate Matter
The authors are grateful to the MyOcean (European Commission) project and the Space Agencies for providing ocean colour data from MODIS (NASA) and MERIS (ESA), and to the IOWAGA (European Commission) and PREVIMER (French National Pilot Project of Coastal Oceanography) projects for providing wave data. The raw MODIS data were provided by the MarCoast2 project (ESA). This paper is also a contribution to the CHannel integrated Approach for marine Resource Management (CHARM) Phase 3 project (INTERREG IV A France (Channel) – England cross-border European cooperation programme, co-financed by the European Regional Development Fund). David Bowers (Bangor University, UK) is warmly thanked for his help in reading and improving the final manuscript. Quinten Vanhellemont (Management Unit of the North Sea Mathematical Models, Belgium) is acknowledged for providing satellite products generated by the algorithm of B. Nechad and colleagues. The authors acknowledge constructive assessments by two anonymous reviewers.
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