Modeling Near-Surface Suspended Sediment Concentration in the English Channel

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

doi:10.3390/jmse3020193

Cited by 13 publications

(11 citation statements)

References 43 publications

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“…These four points correspond to the locations retained by Guillou et al [26] for the local evaluation of numerical model predictions. These stations are, in particular, affected by different combinations of storms and tides [26].…”

Section: Study Areamentioning

confidence: 93%

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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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Section: Study Areamentioning

confidence: 93%

“…These stations are, in particular, affected by different combinations of storms and tides [26]. Station M1, located near the Isle of Bréhat, Brittany coast, is strongly influenced by the spring/neap tidal cycle and is also exposed to swells coming from the west.…”

Section: Study Areamentioning

confidence: 99%

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

et al. 2017

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“…Pendekatan pemodelan merupakan metode yang sangat baik menginterpretasikan proses-proses yang terjadi di perairan pesisir. Permodelan transpor sedimen telah dilakukan di teluk (Sugianto, 2009), di ambang/selat (Guillou et al, 2015), dan di perairan pesisir (Susiati et al, 2010;Ummami et al, 2014). Tujuan penelitian ini untuk menunjukkan pola sebaran sedimen kohesif dan menghubungkannya dengan sedimentasi di TAD.…”

Section: Pendahuluanunclassified

Cohesive Sediment Transport Modeling on Inner Ambon Bay

Noya

Purba²,

Koropitan³

et al. 2017

J. Ilmu dan Teknologi Kelautan Tropis

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The presence of cohesive sediment in the water column can reduce light penetration and affect photosynthesis process, and it can be disrupted the primary productivity of aquatic, and sedimentation of coastal waters. The objective of this research was to determine the cohesive sediment distribution pattern and the relationship with sedimentation. MIKE 3 FM modeling was used to understand the process of sediment transport and sedimentation on Inner Ambon Bay. Sediment transport modeling method was divided into two stages: the hydrodynamic modeling (baroclinic) and sediment transport (mud transport) modeling. The model results indicate current patterns in the Inner Ambon Bay is influenced by the tidal factor. Suspended sediment dispersed vertically from the surface to a depth of 30 m with concentration of about 3.5-15 Kg/m3. The maximum consentration of the suspended sediment occurs at head of the bay (around Waiheru, Passo, and Lateri). Model simulations for 30 days showed the rate of erosion is about 1.04-6.15 Kg/m2/s, while in Inner Ambon Bay the erosion about 9.07x10-8Kg/m2/s only occurred in T1 station. Sedimentation associated with the cohesive sediment accumulation and it was shown by bed level. In addition, the simulation showed bed level in sill ranged at 0.01-0.19 cm and 0.47 mm/day on average, while in the Inner Ambon Bay it ranged from 1.75-10.01 cm, and the sedimentation rate was approximately 39.9 mm/day.

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“…Secara teori gesekan dasar akan maksimum pada area yang dangkal dimana dinamika aliran arus lebih dinamis jika dibandingkan dengan area perairan yang lebih dalam. Hal ini berpengaruh pada tingkat ketebalan sedimen dasar (Carniello et al, 2012;Safak et al, 2013;Guillou et al, 2015). Dapat dikatakan bahwa akumulasi sedimen kohesif atau sedimentasi yang berhubungan dengan ketebalan lapisan hasil simulasi selama 30 hari pada area ambang berkisar antara 0,01 -0,19 cm atau rata-rata 0,47 mm/hari.…”

Section: Sedimentasi (Deposit Sedimen Kohesif)unclassified

Cohesive Sediment Transport Modeling on Inner Ambon Bay

Noya¹,

Purba²,

Koropitan³

et al. 2017

J. Ilmu dan Teknologi Kelautan Tropis

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ABSTRAKAdanya sedimen kohesif di perairan dapat mengurangi penetrasi cahaya ke dalam air dan fotosintesis sehingga produktifitas primer perairan dapat terganggu. Pengendapan sedimen kohesif di perairan pesisir dapat mengakibatkan sedimentasi. Tujuan peneltian ini menunjukkan pola sebaran sedimen kohesif dan hubungannya dengan sedimentasi yang terjadi di Teluk Ambon Dalam (TAD). Pemodelan menggunakan MIKE 3 FM untuk menginterpretasikan proses sedimentasi pada TAD. Pengerjaan pemodelan dibagi menjadi 2 tahap yaitu pemodelan hidrodinamika (baroclinic) dan transport sedimen (sedimen transport). Hasil model menunjukkan bahwa pola pergerakan arus yang terjadi pada perairan TAD dipengaruhi oleh faktor pasang surut. Pola penyebaran sedimen kohesif secara vertikal mulai dari permukaan sampai dengan kedalaman 30m sekitar 3,5-15 Kg/m 3 . Konsentrasi sedimen kohesif tertinggi terjadi pada pangkal teluk (di sekitar Waiheru, Passo, dan Lateri). Hasil simulasi model selama 30 hari menunjukan bahwa tingkat erosi pada ambang (sill) berkisar antara 1,04-6,15 Kg/m 2 /s, sedangkan pada TAD hanya terjadi pada stasiun T1 yaitu 9,07x10 -8 Kg/m 2 /s. Sedimentasi yang berhubungan akumulasi sedimen kohesif ditunjukan oleh ketebalan lapisan (bed level). Ketebalan lapisan sedimen pada area ambang yaitu 0,01-0,19 cm atau rata-rata 0,47 mm/hari, sedangkan pada TAD berkisar antara 1,75-10,01 cm, atau dengan nilai rata-rata sekitar 39,9 mm/hari.

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Modeling Near-Surface Suspended Sediment Concentration in the English Channel

Cited by 13 publications

References 43 publications

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

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

Cohesive Sediment Transport Modeling on Inner Ambon Bay

Cohesive Sediment Transport Modeling on Inner Ambon Bay

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