Abstract
Suspended Particulate Matter(SPM) is at the core of estuarine functioning in light limited ecosystems. By determining light availability, it exerts fundamental control on primary production which is the base of the estuarine foodweb. Therefore, understanding SPM dynamics is crucial for evaluating estuarine health and predicting its response to environmental change. Its accurate representation in ecosystem models is critical for effective evaluation of anthropogenic impacts on estuarine ecosystems. Despite decades of research into the fundamental study of SPM dynamics, such accurate representation remains a critical research gap. Data-based approaches suffer from inherent limitations of sensor data (no spatial accuracy) and satellite data (no temporal accuracy), while mechanistic models of sediment are computationally demanding and often not reliable. In this project I will develop a major advancement in coupled sediment and ecosystem modelling by using an innovative data assimilation approach involving the combination of sensor, in-situ and satellite data to capture SPM dynamics across estuarine systems.
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