Flyland – A Future Airport in the North Sea? An Integrated Modelling Approach for Marine Ecology

The integration of remote sensing data of suspended particulate matter (SPM) into numerical models is useful to improve the understanding of the temporal and spatial behaviour of SPM in dynamic shelf seas. In this paper a generic method based on the Ensemble Kalman Filtering (EnKF) for assimilating remote sensing SPM data into a transport model is presented. The EnKF technique is used to assimilate SPM data of the North Sea retrieved from the MERIS sensor, into the computational water quality and sediment transport model, Delft3D-WAQ. The satellite data were processed with the HYDROPT algorithm that provides SPM concentrations and error information per pixel, which enables their use in data assimilation. The uncertainty of the transport model, expressed in the system noise covariance matrix, was quantified by means of a Monte Carlo approach. From a case study covering the first half of 2003, it is demonstrated that the MERIS observations and transport model application are sufficiently robust for a successful generic assimilation. The assimilation results provide a consistent description of the spatial-temporal variability of SPM in the southern North Sea and show a clear decrease of the model bias with respect to independent in-situ observations. This study also identifies some shortcomings in the assimilated results, such as over prediction of surface SPM concentrations in regions experiencing periods of rapid stratification/de-stratification. Overall this feasibility study leads to a range of suggestions for improving and enhancing the model, the observations and the assimilation scheme.

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“…Van Gils et al 1993;Los et al 2004;Postma and Hervouet 2008). These models are applied on a domain covering the southern North Sea (Figure 4).…”

Section: Spm Transport Model Descriptionmentioning

confidence: 99%

Improving the description of the suspended particulate matter concentrations in the southern North Sea through assimilating remotely sensed data

et al. 2011

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“…This is a modeling system that, thanks to its several modules, can simulate flows, waves, sediment transports, morphological changes and ecological processes (Roelvink and Van Banning, 1994;Lesser et al, 2004). In this work the DELFT3D FLOW hydrodynamic model (Lesser et al, 2004), the surface wave model SWAN (Booij et al, 1999) and the water quality model DELFT3D-WAQ (Van Gils et al, 1993;Los et al, 2004) were applied.…”

Section: Mathematical Modelsmentioning

confidence: 99%

Modeling the dispersion of viable and total Escherichia coli cells in the artificial semi-enclosed bathing area of Santa Marinella (Latium, Italy)

Bonamano

Madonia

Borsellino

et al. 2015

Marine Pollution Bulletin

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Modeling Our Past, Present, and Future

Glibert

2024

Phytoplankton Whispering: An Introduction to the Physiology and Ecology of Microalgae

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Flyland – A Future Airport in the North Sea? An Integrated Modelling Approach for Marine Ecology

Cited by 3 publications

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Improving the description of the suspended particulate matter concentrations in the southern North Sea through assimilating remotely sensed data

Improving the description of the suspended particulate matter concentrations in the southern North Sea through assimilating remotely sensed data

Modeling the dispersion of viable and total Escherichia coli cells in the artificial semi-enclosed bathing area of Santa Marinella (Latium, Italy)

Modeling Our Past, Present, and Future

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