On the role of wind and tides in shaping the Gironde River plume (Bay of Biscay)

Toublanc, Florence; Ayoub, Nadia; Marsaleix, Patrick

doi:10.1016/j.csr.2022.104891

Cited by 3 publications

(2 citation statements)

References 49 publications

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“…The Patos Lagoon exhibits a microtidal regime, with a mean range of 0.5 m and diurnal dominance (Moller et al, 2001), having negligible influence on the lagoon's circulation (Moller et al, 2001;Fernandes et al, 2004). As the tidal amplitude is small, the dynamics of the lagoon and turbid plume is controlled by the combined effect Mean, standard deviation and covariance of spectral parameters for each trained class (Lihan et al, 2008), (Thomas and Weatherbee, 2006) Threshold SPM trial-and-error maximum autocorrelation (Constantin et al, 2018), (Zhang et al, 2016), (Petus et al, 2014) percentile 95 th (Longitude, Latitude, time) (Gangloff et al, 2017), (Ody et al, 2022) SPM and Digital Number* trial-and-error and region growing (Teodoro et al, 2008), (Teodoro and Goncalves, 2011) TOA Salinity and R rs (645)* maximum correlation with river discharge (Guo et al, 2017) Salinity** K-means cluster with manual adjustments (when needed) (Korshenko et al, 2023) Stratification salinity index** trial-and-error (Toublanc et al, 2023) Chlorophyll-a trial-and-error of gradient contour (Dzwonkowski and Yan, 2005) PLUMES Turbidity/SPM Similarity of pixels from control points and region growing check this study of wind and river discharge: high river discharge (Q > 2,000 m³s -1 ) overrules the dynamics promoted by winds, whereas, in dry periods, the wind effect becomes the most important forcing mechanism (Fernandes et al, 2002). Marques et al (2010a) verified the importance of the river discharge intensity to its formation and Zavialov et al (2018) identified the importance of the local wind action promoting the plume's stratification (Zavialov et al, 2018).…”

Section: Study Sitementioning

confidence: 99%

“…This automated process is followed by a K-means classification. Threshold techniques use a much simpler approach, by applying a pre-set value commonly determined from trial-and-error (e.g., Petus et al, 2014;Mendes et al, 2017;Toublanc et al, 2023) or statistical assumptions (e.g., Lahet and Stramski, 2010;Saldıás et al, 2012;Gangloff et al, 2017;Maciel et al, 2021). It is, therefore, a challenging task to precisely extract coastal plumes for the same environment/site at different times using a pre-set value or trained class because turbidity is significantly different under different forcing mechanisms (e.g., seasonal variability, or changes due to extreme events).…”

Section: Introductionmentioning

confidence: 99%

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Detecting turbid plumes from satellite remote sensing: State-of-art thresholds and the novel PLUMES algorithm

et al. 2023

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Turbid coastal plumes carry sediments, nutrients, and pollutants. Satellite remote sensing is an effective tool for studying water quality parameters in these turbid plumes while covering a wide range of hydrological and meteorological conditions. However, determining boundaries of turbid coastal plumes poses a challenge. Traditionally, thresholds are the approach of choice for plume detection as they are simple to implement and offer fast processing (especially important for large datasets). However, thresholds are site-specific and need to be re-adjusted for different datasets or when meteorological and hydrodynamical conditions differ. This study compares state-of-the-art threshold approaches with a novel algorithm (PLUMES) for detecting turbid coastal plumes from satellite remote sensing, tested for Patos Lagoon, Brazil. PLUMES is a semi-supervised, and spatially explicit algorithm, and does not assume a unique plume boundary. Results show that the thresholds and PLUMES approach each provide advantages and limitations. Compared with thresholds, the PLUMES algorithm can differentiate both low or high turbidity plumes from the ambient background waters and limits detection of coastal resuspension while automatically retrieving metrics of detected plumes (e.g., area, mean intensity, core location). The study highlights the potential of the PLUMES algorithm for detecting turbid coastal plumes from satellite remote sensing products, which can have significantly positive implications for coastal management. However, PLUMES, despite its demonstrated effectiveness in this study, has not yet been applied to other study sites.

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Section: Study Sitementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Detecting turbid plumes from satellite remote sensing: State-of-art thresholds and the novel PLUMES algorithm

et al. 2023

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How sensitive is a simulated river plume to uncertainties in wind forcing? A case study for the Red River plume (Vietnam)

Nguyen-Duy,

Ayoub,

De-Mey-Frémaux

et al. 2023

Ocean Modelling

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Mixing of the Connecticut River Plume During Ambient Flood Tides: Spatial Heterogeneity and Contributions of Bottom‐Generated and Interfacial Mixing

Whitney,

Spicer,

MacDonald

et al. 2024

JGR Oceans

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The Connecticut River plume is influenced by energetic ambient tides in the Long Island Sound receiving waters. The objectives of this modeling study are (a) characterizing the spatial heterogeneity of turbulent buoyancy fluxes, (b) partitioning turbulent buoyancy fluxes into bottom‐generated and interfacial shear contributions, and (c) quantifying contributions to plume‐integrated mixing within the tidal plume. The plume formed during ambient flood tides under low river discharge, spring tides, and no winds is analyzed. Turbulent buoyancy fluxes (B) and depth‐integrated B through the plume (Bd) are characterized by pronounced spatial heterogeneity. Strong mixing (Bd ∼ 10−5‐10−4 m3/s3) occurs near the mouth, in the nearfield plume turning region, over shoals, and nearshore shallow areas. Low to moderate mixing (Bd ∼ 10−8‐10−6 m3/s3) occupies half the plume. Buoyancy fluxes are first partitioned based on the depth of the shear stress minimum between plume‐generated and bottom‐generated shear maxima. Four other tested partitioning methods are based on open channel flow and stratified shear flow parameterizations. Interfacial and bottom‐generated shear contribute to different areas of intense and moderate mixing. All methods indicate a significant plume mixing role for bottom‐generated mixing, but interfacial mixing is a bigger contributor. Plume‐integrated total and interfacial mixing peak at max ambient flood and the timing of peak bottom‐generated mixing varies among partitioning methods. Two‐thirds of the mixing occurs in concentrated intense mixing areas. A parameter space with the ambient tidal Froude number and plume thickness to depth ratio as axes indicates many tidally modulated plumes are moderately to dominantly influenced by bottom‐generated tidal mixing.

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On the role of wind and tides in shaping the Gironde River plume (Bay of Biscay)

Cited by 3 publications

References 49 publications

Detecting turbid plumes from satellite remote sensing: State-of-art thresholds and the novel PLUMES algorithm

Detecting turbid plumes from satellite remote sensing: State-of-art thresholds and the novel PLUMES algorithm

How sensitive is a simulated river plume to uncertainties in wind forcing? A case study for the Red River plume (Vietnam)

Mixing of the Connecticut River Plume During Ambient Flood Tides: Spatial Heterogeneity and Contributions of Bottom‐Generated and Interfacial Mixing

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