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The use of ocean colour classification algorithms, linked to water quality gradients, can be a useful tool for mapping river plumes in both tropical and temperate systems. This approach has been applied in operational water quality programs in the Great Barrier Reef to map river plumes and assess trends in marine water composition and ecosystem health during flood periods. In this study, we used the Forel–Ule colour classification algorithm for Sentinel-3 OLCI imagery in an automated process to map monthly, annual and long-term plume movement in the temperate coastal system of Liverpool Bay (UK). We compared monthly river plume extent to the river flow and in situ water quality data between 2017–2020. The results showed a strong positive correlation (Spearman's rho = 0.68) between the river plume extent and the river flow and a strong link between the FUI defined waterbodies and nutrients, SPM, turbidity and salinity, hence the potential of the Forel–Ule index to act as a proxy for water quality in the temperate Liverpool Bay water. The paper discusses how the Forel–Ule index could be used in operational water quality programs to better understand river plumes and the land-based inputs to the coastal zones in UK waters, drawing parallels with methods that have been developed in the GBR and Citclops project. Overall, this paper provides the first insight into the systematic long-term river plume mapping in UK coastal waters using a fast, cost-effective, and reproducible workflow. The study created a novel water assessment typology based on the common physical, chemical and biological ocean colour properties captured in the Forel–Ule index, which could replace the more traditional eutrophication assessment regions centred around strict geographic and political boundaries. Additionally, the Forel–Ule assessment typology is particularly important since it identifies areas of the greatest impact from the land-based loads into the marine environment, and thus potential risks to vulnerable ecosystems. [ABSTRACT FROM AUTHOR]

Suspended sediment is a significant threat to the Great Barrier Reef (GBR) ecosystem. This catchment pollutant stems primarily from terrestrial soil erosion. Bulk masses of sediments have potential to propagate from river plumes into the mid-shelf and outer-shelf regions. Existing sediment forecasting methods suffer from the problem of low-resolution predictions, making them unsuitable for wide area coverage. In this paper, a novel sediment distribution prediction model is proposed to augment existing water quality management programs for the GBR. This model is based on the state-of-the-art Transformer network in conjunction with the well-known finite element analysis. For model training, the emerging physics-informed neural network is employed to incorporate both simulated and measured sediment data. Our proposed Finite Element Transformer (FE-Transformer) model offers accurate predictions of sediment across the entire GBR. It provides unblurred outputs, which cannot be achieved with previous next-frame prediction models. This paves a way for accurate forecasting of sediment, which in turn may lead to improved water quality management for the GBR. • Vision Transformer is proposed for next-frame prediction. • Finite element analysis is integrated with the Vision Transformer. • Sediment distribution in the GBR is forecasted using the proposed FE-Transformer. • PINN is employed to merge sediment PDE solutions with in-situ measured data. • The proposed model produces highly accurate unblurred output frames. [ABSTRACT FROM AUTHOR]

• River plumes are a source of pollutants in the Great Barrier Reef, Australia. • Plume dispersion is simulated using a coupled Lagrangian-Eulerian-ocean model. • Lagrangian particle density is computed from application of Voronoi diagrams. • Lagrangian particle densities and Eulerian tracer concentrations are comparable. • Lagrangian model is validated using satellite imagery and satellite Chlorophyll-a. In this paper, we describe methods to verify the adequacy and accuracy of Lagrangian particles from a Lagrangian model to reproduce the concentrations of a passive tracer from an Eulerian-model in river plumes. The modelling simulates plumes from two major rivers discharging in the Great Barrier Reef (GBR), Australia, under real-world scenarios. The study has been a part of a major project to aid in the protection of the GBR system from the impacts of extreme events and climate change. We employ the Regional Ocean Modelling System (ROMS) activated with its built-in Lagrangian model, and forced with wind fields from global models and recorded river volume discharges. The ROMS-Lagrangian model tracks the Lagrangian particles using the spatially interpolated velocities computed on the Eulerian ROMS three-dimensional (3D) grid. The Lagrangian particles are released in the river in proportion to the measured river volume flux. We apply a novel technique that exploits Voronoi polygon areas to convert Lagrangian particle separation into a concentration field. This facilitates comparison with the passive tracer concentrations driven by the Eulerian velocities computed on the ROMS 3D grid. We evaluate the fate of Lagrangian particles activated on a 4-km grid resolution with those of equivalent Eulerian tracer concentrations. For validation, we compare the Lagrangian particles with the Eulerian passive tracer simulated using a higher model grid resolution of 500-m, and found that the computed Lagrangian particle concentrations showed similar overall patterns compared to the passive tracer concentrations from both the coarse 4-km, and high-resolution 500-m Eulerian models. The spatial extent of the particles was in better agreement with the coarse 4-km model than with the higher resolution 500-m model. The clustering of particles resulted in structures at finer scales than the Eulerian model, over the high particle density areas that compared well against observations. The Lagrangian particles are able to capture the general river plume patterns seen in the satellite MODIS images and the satellite derived Chlorophyll-a. We further compare the online ROMS model with an offline particle tracker, OceanPARCELS and determined the similarity of results from both online and offline methods. Overall, our study demonstrates the viability to use Lagrangian particles to reproduce the Eulerian tracer properties, which can be combined with particle properties include tracer age providing to enhance the guidance on the distribution and concentration of effluent from localised flooding river events. [ABSTRACT FROM AUTHOR]