The impact of tides and waves on near-surface suspended sediment concentrations in the English Channel

Guillou, Nicolas; Rivier, Aurélie; Chapalain, Georges; Gohin, Francis

doi:10.1016/j.oceano.2016.06.002

Cited by 16 publications

(8 citation statements)

References 29 publications

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“…Such extremely high concentrations are, however, only found in e.g., estuaries with very high river discharge or during storms [2,4]. Typical coastal suspended sediment concentrations are 5 to 1000 times lower in the range of 1–200 mg·L −1 e.g., [2,28]. As far as we know, only a few studies have investigated the effects of sediments on aquatic viruses in a more ecological setting, and with inconclusive results [23,29,30].…”

Section: Introductionmentioning

confidence: 99%

Sediments from Arctic Tide-Water Glaciers Remove Coastal Marine Viruses and Delay Host Infection

Maat

Prins

Brussaard

2019

Viruses

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Over the past few decades, the Arctic region has been strongly affected by global warming, leading to increased sea surface temperatures and melting of land and sea ice. Marine terminating (tide-water) glaciers are expected to show higher melting and calving rates, with an increase in the input of fine sediment particles in the coastal marine environment. We experimentally investigated whether marine viruses, which drive microbial interactions and biogeochemical cycling are removed from the water column through adsorption to glacier-delivered fine sediments. Ecologically relevant concentrations of 30, 100 and 200 mg·L−1 sediments were added to filtered lysates of 3 cultured algal viruses and to a natural marine bacterial virus community. Total virus removal increased with sediment concentration whereby the removal rate depended on the virus used (up to 88% for an Arctic algal virus), suggesting a different interaction strength with the sediment. Moreover, we observed that the adsorption of viruses to sediment is a reversible process, and that desorbed viruses are still able to infect their respective hosts. Nonetheless, the addition of sediment to infection experiments with the Arctic prasinovirus MpoV-45T substantially delayed host lysis and the production of progeny viruses. We demonstrate that glacier-derived fine sediments have the potency to alter virus availability and consequently, host population dynamics.

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Section: Introductionmentioning

confidence: 99%

Sediments from Arctic Tide-Water Glaciers Remove Coastal Marine Viruses and Delay Host Infection

Maat

Prins

Brussaard

2019

Viruses

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“…• The satellite algorithm overestimates the SPIM concentration when finer particles dominate the suspension during storms [33].…”

Section: Time Series Of Surface Spimmentioning

confidence: 99%

“…This continental shelf sea also experiences large temporal variability in particle concentrations and particle-size class distributions [30,31]. In the English Channel, tidal currents and coastal waves primarily control particle resuspension and turbidity [30,32,33]. The vertical distributions of SPM in water columns of the English Channel largely depend upon two physical processes: re-suspension of bottom particles and advection of these particles by currents and waves.…”

Section: Study Areamentioning

confidence: 99%

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

et al. 2017

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Hydro-sedimentary numerical models have been widely employed to derive suspended particulate matter (SPM) concentrations in coastal and estuarine waters. These hydro-sedimentary models are computationally and technically expensive in nature. Here we have used a computationally less-expensive, well-established methodology of self-organizing maps (SOMs) along with a hidden Markov model (HMM) to derive profiles of suspended particulate inorganic matter (SPIM). The concept of the proposed work is to benefit from all available data sets through the use of fusion methods and machine learning approaches that are able to process a growing amount of available data. This approach is applied to two different data sets entitled "Hidden" and "Observable". The hidden data are composed of 15 months (27 September 2007 to 30 December 2008) of hourly SPIM profiles extracted from the Regional Ocean Modeling System (ROMS). The observable data include forcing parameter variables such as significant wave heights (Hs and Hs50 (50 days)) from the Wavewatch 3-HOMERE database and barotropic currents (Ubar and Vbar) from the Iberian-Biscay-Irish (IBI) reanalysis data. These observable data integrate hourly surface samples from 1 February 2002 to 31 December 2012. The time-series profiles of the SPIM have been derived from four different stations in the English Channel by considering 15 months of output hidden data from the ROMS as a statistical representation of the ocean for ≈11 years. The derived SPIM profiles clearly show seasonal and tidal fluctuations in accordance with the parent numerical model output. The surface SPIM concentrations of the derived model have been validated with satellite remote sensing data. The time series of the modeled SPIM and satellite-derived SPIM show similar seasonal fluctuations. The ranges of concentrations for the four stations are also in good agreement with the corresponding satellite data. The high accuracy of the estimated 25 h average surface SPIM concentrations (normalized root-mean-square error-NRMSE of less than 16%) is the first step in demonstrating the robustness of the method.

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“…In contrast, weaker winds and lower wave heights in the dry season leaded to low turbidity in the study domain. The wave effect on turbidity has also been found in coastal regions around the world, including the Yalu estuary [4], the estuary of the Yangtze River [39], and the English Channel [40]. Through the analysis of variation (ANOVA) [41] for the five years data of the wet and dry seasons, p-value < 0.01 at Station B and =0.04 at IMOS Station, respectively, indicating the turbidity seasonal variation in the inner harbour is more significant, compared with the deep waters in the outer harbour.…”

Section: Factors Controlling the Seasonal Variability Of Turbiditymentioning

confidence: 99%

Using 250-M Surface Reflectance MODIS Aqua/Terra Product to Estimate Turbidity in a Macro-Tidal Harbour: Darwin Harbour, Australia

et al. 2018

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Turbidity is an indicator of the quality of water and usually exhibits variability associated with changing hydrodynamic conditions, which can be reflected in the sediment dynamics in coastal regions. Darwin Harbour is a typical macro-tidal, well mixed, and complex environment influenced by industries, human activities, and natural factors-including winds, currents, river discharges, waves, and tides. As a case study, hydrodynamics and sediment dynamics in Darwin Harbour are investigated using moderate resolution imaging spectroradiometer (MODIS) measurements. This study focuses on understanding the variability of turbidity, mechanisms that control the variations of turbidity and analyzing field data to determine the main factors that influence the sediment dynamics in Darwin Harbour. The results of this study illustrate the seasonal turbidity variation is mainly influenced by the wind waves. The dredging campaigns in 2013 and 2014 wet seasons contributed to the rise of turbidity in Darwin Harbour. The action of tidal currents appears to be the dominant factor controlling the turbidity pattern in a spring-neap cycle and the turbidity intra-tidal variation. In addition, the turbidity maximum zone (TMZ) near Charles Point is formed by the tidal current convergence based on the results of current modelling.

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The impact of tides and waves on near-surface suspended sediment concentrations in the English Channel

Cited by 16 publications

References 29 publications

Sediments from Arctic Tide-Water Glaciers Remove Coastal Marine Viruses and Delay Host Infection

Sediments from Arctic Tide-Water Glaciers Remove Coastal Marine Viruses and Delay Host Infection

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

Using 250-M Surface Reflectance MODIS Aqua/Terra Product to Estimate Turbidity in a Macro-Tidal Harbour: Darwin Harbour, Australia

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