Spatiotemporal Expansion of Algal Blooms in Coastal China Seas.

The coastal seas of China are increasingly threatened by algal blooms, yet their comprehensive spatiotemporal mapping and understanding of underlying drivers remain challenging due to high turbidity and heterogeneous water conditions. We developed a singular value decomposition-based algorithm to map these blooms using two decades of MODIS-Aqua satellite data, spanning from 2003 to 2022. Our findings indicate significant algal activity along the Chinese coastline, impacting an average annual area of approximately 1.8 × 105 km2. The blooms exhibit peak intensity in August, while the maximum affected area occurs in September, featuring multifrequency outbreaks in spring, and pronounced large-scale events in summer and autumn. Notably, our analysis demonstrates a robust 67% increase in bloom occurrences over the study period. This expansion is primarily attributed to increased nutrient inflow from terrestrial sources linked to human activity and precipitation, compounded by rising global sea surface temperatures. These spatiotemporal insights are critical for devising effective management strategies and policies to mitigate the impacts of algal blooms.

Remotely sensing harmful algal blooms in the Red Sea.

Harmful Algal Blooms (HABs) are of global concern, as their presence is often associated with socio-economic and environmental issues including impacts on public health, aquaculture and fisheries. Therefore, monitoring the occurrence and succession of HABs is fundamental for managing coastal regions around the world. Yet, due to the lack of adequate in situ measurements, the detection of HABs in coastal marine ecosystems remains challenging. Sensors on-board satellite platforms have sampled the Earth synoptically for decades, offering an alternative, cost-effective approach to routinely detect and monitor phytoplankton. The Red Sea, a large marine ecosystem characterised by extensive coral reefs, high levels of biodiversity and endemism, and a growing aquaculture industry, is one such region where knowledge of HABs is limited. Here, using high-resolution satellite remote sensing observations (1km, MODIS-Aqua) and a second-order derivative approach, in conjunction with available in situ datasets, we investigate for the first time the capability of a remote sensing model to detect and monitor HABs in the Red Sea. The model is able to successfully detect and generate maps of HABs associated with different phytoplankton functional types, matching concurrent in situ data remarkably well. We also acknowledge the limitations of using a remote-sensing based approach and show that regardless of a HAB's spatial coverage, the model is only capable of detecting the presence of a HAB when the Chl-a concentrations exceed a minimum value of ~ 1 mg m-3. Despite the difficulties in detecting HABs at lower concentrations, and identifying species toxicity levels (only possible through in situ measurements), the proposed method has the potential to map the reported spatial distribution of several HAB species over the last two decades. Such information is essential for the regional economy (i.e., aquaculture, fisheries & tourism), and will support the management and sustainability of the Red Sea's coastal economic zone.