Synergy of satellite remote sensing and numerical modeling for monitoring of suspended particulate matter

Monitoring and modeling of the distribution of suspended particulate matter (SPM) is an important task, especially in coastal environments. Several SPM models have been developed for the North Sea. However, due to waves in shallow water and strong tidal currents in the southern part of the North Sea, this is still a challenging task. In general there is a lack of measurements to determine initial distributions of SPM in the bottom sediment and essential model parameters, e.g., appropriate exchange coefficients. In many satellite-borne ocean color images of the North Sea a plume is visible, which is caused by the scattering of light at SPM in the upper ocean layer. The intensity and length of the plume depends on the wave and current climate. It is well known that the SPM plume is especially obvious shortly after strong storm events. In this paper a quasi-3-D and a 3-D SPM transport model are presented. Utilizing the synergy of satellite-borne ocean color data with numerical models, the vertical exchange coefficients due to currents and waves are derived. This results in models that for the first time are able to reproduce the temporal and spatial evolution of the plume intensity. The SPM models consist of several modules to compute ocean dynamics, the vertical and horizontal exchange of SPM in the water column, and exchange processes with the seabed such as erosion, sedimentation, and resuspension. In the bottom layer, bioturbation via benthos and diffusion processes is taken into account.

[1]  Roland Doerffer,et al.  Concentrations of chlorophyll, suspended matter, and gelbstoff in case II waters derived from satellite coastal zone color scanner data with inverse modeling methods , 1994 .

[2]  V. Ittekkot,et al.  Facets of Modern Biogeochemistry , 1990 .

[3]  K. Dyer,et al.  Fluxes of suspended matter in the East Anglian plume Southern North Sea , 1998 .

[4]  J. Sündermann Circulation and contaminant fluxes in the North Sea , 1994 .

[5]  Harald Krawczyk,et al.  Capabilities for the retrieval of coastal water constituents (case II) using multispectral satellite data , 1998, Remote Sensing.

[6]  G. Müller,et al.  Lateral Distribution and Sources of Sediment-Associated Heavy Metals in the North Sea , 1990 .

[7]  Wolfgang Rosenthal,et al.  A hybrid parametrical wave prediction model , 1979 .

[8]  T. Pohlmann,et al.  Suspended particulate matter in the Southern North Sea: Application of a numerical model to extend NERC North Sea project data interpretation , 1997 .

[9]  T. Pohlmann,et al.  Currents and Transport in Water , 1994 .

[10]  Jürgen Sündermann,et al.  Numerical simulation and satellite observations of suspended matter in the North Sea , 1994 .

[11]  Jochen Horstmann,et al.  Synergy of remote sensing and numerical modelling for suspended matter transport monitoring , 2002, IEEE International Geoscience and Remote Sensing Symposium.

[12]  V. Casulli,et al.  Stability, accuracy and efficiency of a semi-implicit method for three-dimensional shallow water flow☆ , 1994 .

[13]  T. Aarup,et al.  The North Sea: Satellite colour atlas , 1989 .

[14]  R. Soulsby Dynamics of marine sands : a manual for practical applications , 1997 .

[15]  W. Rosenthal,et al.  Similarity of the wind wave spectrum in finite depth water: 1. Spectral form , 1985 .

[16]  R. Soulsby Dynamics of marine sands , 1997 .