Observed vs. predicted variability in non-algal suspended particulate matter concentration in the English Channel in relation to tides and waves

Abstract

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][2004][2005][2006][2007][2008][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.

Seasonal and interannual variability of coccolithophore blooms in the North East-Atlantic Ocean from a 18-year time-series of satellite water-leaving radiance

“…One possibility could be to exploit further the concept advanced by Shutler et al (2010) that the dynamics of coccolithophores and mineral SPM are different. To this purpose it is possible to consider that mineral SPM responds quickly to waves and tides (Rivier et al, 2012;20 Gohin et al, 2015) while coccolithophore blooms respond to more complex environmental factors. The seasonal and interannual variability obtained in this work could be used in future studies to analyse the impact of the climatic and environmental changes in the North East Atlantic which might considerably affect coccolithophore blooms.…”

Construction of Multi-Year Time-Series Profiles of Suspended Particulate Inorganic Matter Concentrations Using Machine Learning Approach

“…In this experiment, almost 11 years of time series data of the four variables (Hs, Hs50, Ubar and Vbar) were used as observables, and 15 months of SPIM profiles from the ROMS hydro-sedimentary model were considered as hidden data [34] in order to derive ≈11 years of SPIM profiles.…”

“…The period of 50 days is reported by Rivier et al [34] so as to give the best coefficient of determination in a linear regression between satellite data and the modeled output for 10 locations in the English Channel.…”

“…This station is subject to tides and swells. The last station, M4, is located in the eastern part of the English Channel, where the tidal action is reduced in comparison to the other stations and the waves are weak to moderate [26,33,34]. …”

“…We have selected the hourly time series of significant wave height (Hs) from the WW3 model output from the English Channel. The time series of Hs50 has been extracted from Hs, which is as the running average of Hs from the preceding 50 days and is arbitrarily defined by Rivier et al in 2012 [34]:…”