Early Warning of Harmful Algal Bloom Risk Using Satellite Ocean Color and Lagrangian Particle Trajectories

Abstract

Combining Lagrangian trajectories and satellite observations provides a novel basis for monitoring changes in water properties with high temporal and spatial resolution. In this study, a prediction scheme was developed for synthesizing satellite observations and Lagrangian model data for better interpretation of harmful algal bloom (HAB) risk. The algorithm can not only predict variations in chlorophyll-a concentration but also changes in spectral properties of the water, which are important for discrimination of different algal species from satellite ocean color. The prediction scheme was applied to regions along the coast of England to verify its applicability. It was shown that the Lagrangian methodology can significantly improve the coverage of satellite products, and the unique animations are effective for interpretation of the development of HABs. A comparison between chlorophyll-a predictions and satellite observations further demonstrated the effectiveness of this approach: r2 = 0.81 and a low mean absolute percentage error of 36.9%. Although uncertainties from modeling and the methodology affect the accuracy of predictions, this approach offers a powerful tool for monitoring the marine ecosystem and for supporting the aquaculture industry with improved early warning of potential HABs.

New tools and recommendations for a better management of harmful algal blooms under the European Marine Strategy Framework Directive

“…Different EWS for HABs exist in Europe (Ireland, Scotland, England, France, Spain and Portugal) ranging from weekly bulletins based on expert analysis and identification systems. EWS can involve particle tracking models and/or remote sensing data (Lin et al, 2021), statistical (e.g., Generalized Additive Models, GAMs) and machine learning models, or mechanistic full-low trophic ecosystem models (Fernandes-Salvador et al, 2021). One such example is ShellEye that combine remote sensing, modeled hydrographic data, local algae and biotoxin modeled data to forecast water quality for Scottish shellfisheries that can benefit science-based development of harmful algae indicators.…”

Assessing the Performance and Application of Operational Lagrangian Transport HAB Forecasting Systems

“…Here we are concerned with short time scale advection and hence, products composited over a longer period of time will average out this movement. Interpolated products should not have this problem, but must use some extra technique to fill in the gaps, for example using a hydrodynamic model [ (Lin et al 2021), this issue] or machine learning (Vandal and Nemani 2019) to produce intermediate images, which may itself introduce spurious movement. Here we decided to be conservative and only use products which are either daily composites or individual scenes, omitting days with high cloud cover (>30%), to ensure we are comparing to actual bloom development.…”

Bayesian Neural Networks for Estimating Chlorophyll-A Concentration Based on Satellite-Derived Ocean Colour Observations

“…This facilitates detecting spatial and temporal patterns in phytoplankton abundance and productivity, as well as the identification of ecological hotspots [13] and their responses to global environmental changes, such as oceanic warming, ocean acidification, and eutrophication [14]. Furthermore, monitoring oceans via remote sensing can provide early warnings of HABs, thereby enabling effective mitigation of the associated risks [15,16]. Remotely sensed data can also be utilized to calibrate and validate ocean biogeochemical models, which are crucial for predicting the responses of marine ecosystems to global environmental changes [17].…”