Diurnal Variation Of Suspended Sediment Concentration Coupled GOCI And Numerical Simulation In Coastal Waters Of The East China Sea

Spatial and temporal variation of suspended sediment concentration in coastal waters has not only important scientific research value but also can provide necessary decision aid for economic construction which has great economic value. This paper has made a thorough research on inshore water suspended sediment transport, especially in combination with satellite ocean remote sensing and ocean numerical simulation technology. Relying on the abundant technology and data base in State Key Laboratory of Satellite Ocean Environmental Dynamics in the Second Institute of Oceanography of the State Oceanic Administration, the hourly temporal and spatial variation characteristics of suspended sediment concentration in coastal waters of the East China Sea has been investigated by coupling remote sensing inversion and numerical simulation. We also used numerical simulation to fill in missing data in ocean color remote sensing products and analyzed the quality of restoration with statistical analysis method.Using the high temporal resolution ocean color remote sensing sensor GOCI, the suspended sediment concentration has been derived in coastal waters of the East China Sea, especially in the Hangzhou Bay. The GOCI suspended sediment data are used as the initial field in the sediment transport model to improve the quality of suspended sediment initial field, and are used to verify the numerical simulation results. The hourly variation of suspended sediment in coastal waters of the East China Sea, especially in the Hangzhou Bay are reproduced and analysised by coupling the ocean color remote sensing and suspended sediment numerical model. The results show that the tidal current is the main reason that caused the variation of suspended sediment concentration in the Hangzhou Bay. The research provides a reference for real- time monitoring and forecasting the dynamic variation of the suspended sediment concentration.Because of cloud cover or/and bad weather conditions, there are a variety problem in missing data for ocean color remote sensing, such as the lack of suspended sediment concentration in coastal waters of the East C hina Sea, especially in the Hangzhou Bay. The relationship between areas with and without clouds is built with the suspended sediment transport model, and then the simulation results are extracted and filled in the missing remote sensing inversion data. The recovered quality of GOCI-derived suspended sediment concentrations is good by indirectly compared GOCI data with simulation results which are extracted from cloud-free areas. The method has clear marine dynamic meanings and it is simple, stable and reliable that using the suspended sediment transport numerical model to repair the missing data in remote sensing images. The research provides a reference to improve the utilization rate of ocean color remote sensing data and the long time serial remote sensing inversion data analysis.The main innovations of the thesis are as follows:·Satellite ocean remote sensing technology has some characteristics such as speediness, synchronization, large area, high resolution and so on. GOCI-derived suspended sediment concentrations are used to initialize the COHERENS model, which has improved the quality of the initial field. The applicability of the suspended sediment model is validated by comparing GOCI-derived suspended sediment concentrations with the corresponding numerical simulation results.·The hourly variation characteristics and dynamic mechanism of suspended sediment transport in coastal waters of the East C hina Sea, especially in the Hangzhou Bay has been monitored and analysised. The research provides a reference for real-time monitoring and forecasting the dynamic variation of the suspended sediment concentration.·The missing data in GOCI-derived suspended sediment concentrations are repaired by using suspended sediment transport model. The research provides a reference to improve the utilization rate of ocean color remote sensing data and the long time serial remote sensing inversion data analysis.