Improving Sediment Transport Prediction by Assimilating Satellite Images in a Tidal Bay Model of Hong Kong

Abstract

Numerical models being one of the major tools for sediment dynamic studies in complex coastal waters are now benefitting from remote sensing images that are easily available for model inputs. The present study explored various methods of integrating remote sensing ocean color data into a numerical model to improve sediment transport prediction in a tide-dominated bay in Hong Kong, Deep Bay. Two sea surface sediment datasets delineated from satellite images from the Moderate Resolution Imaging Spectra-radiometer (MODIS) were assimilated into a coastal ocean model of the bay for one tidal cycle. It was found that remote sensing sediment information enhanced the sediment transport model ability by validating the model results with in situ measurements. Model results showed that root mean square errors of forecast sediment both at the surface layer and the vertical layers from the model with satellite sediment assimilation are reduced by at least 36% over the model without assimilation.

Assessment of Total Suspended Sediment Distribution under Varying Tidal Conditions in Deep Bay: Initial Results from HJ-1A/1B Satellite CCD Images

“…The water depth near the coastline in the inner bay is lower than in other water bodies, and high turbidity can therefore be found in this area due to re-suspension caused by the greatest velocity in the process of ebb tide. In the central deeper water area near the mouth of Deep Bay, the suspended sediments are therefore higher because of the lower water depth near the coastline [62]; further investigation would be needed to discuss the cause of this formation in the future. We observed a non-turbid water plume at the central mouth of the bay.…”

Remote Sensing Big Data for Water Environment Monitoring: Current Status, Challenges, and Future Prospects

“…(2010) coupled MERIS images and ECOMSED (a hydrodynamic, wave, and sediment transport model) to simulate the transport process of SPM in the Bohai Sea. Likewise, Delft3D‐FLOW is also integrated with various remote sensing images, such as MODIS, and has been widely applied in various waters, including oceans, reservoirs, and lakes (Zhang et al., 2014, 2015). Solanki et al.…”

“…For example, Chen et al (2010) coupled MERIS images and ECOMSED (a hydrodynamic, wave, and sediment transport model) to simulate the transport process of SPM in the Bohai Sea. Likewise, Delft3D-FLOW is also integrated with various remote sensing images, such as MODIS, and has been widely applied in various waters, including oceans, reservoirs, and lakes (Zhang et al, 2014(Zhang et al, , 2015. Solanki et al (2015) developed a coupling method for data analysis and bio-physical processes, which effectively mapped the surface profiles to understand the linkage of the sea surface temperature, Chla concentrations, and surface height anomalies with marine fishery resources.…”

“…Remote sensing separates the atmospheric influence from the radiation received by the satellites/sensors and extracts the composition and characteristic information of water based on the remote sensing images. In recent years, the vigorous development of digital earth (Gore, 1998), big data, and cloud computing has promoted the extensive applications of remote sensing big data for water environment monitoring, such as water mapping (Alevizos, 2020; Chen et al., 2017; Ji et al., 2018; Murphy et al., 2018; Sagawa et al., 2019; Traganos et al., 2018; Wang et al., 2020; Zhou et al., 2019), quantitative estimation of key water quality parameters (Arabi et al., 2020; Cao et al., 2019; Chen et al., 2021; El Hourany et al., 2019; Eugenio et al., 2020; Griffin et al., 2018; Kõuts et al., 2007; Lin et al., 2018), flood and drought monitoring (Cretaux et al., 2011; Jiao et al., 2021), marine surveying and mapping (Chen et al., 2004; Choi et al., 2020; Feng et al., 2020; Ling et al., 2020; Prakash et al., 2021; Solanki et al., 2015; Wang et al., 2017; Xu et al., 2019; Zhang et al., 2014), and marine climate (Medina‐Lopez & Urena‐Fuentes, 2019).…”