The die off of large phytoplankton blooms can lead to deoxygenation in the coastal ocean, and have severe impacts on marine life. Ocean colour data can monitor such blooms.
12 September 2022
13 May 2022
By Ben Loveday (Innoflair UG), Hayley Evers-King (EUMETSAT), Marie Smith (CSIR)
Phytoplankton are an essential part of ocean ecosystems — providing the basis of the food web for most of marine life, as well as playing important roles in biogeochemical cycling. However, large phytoplankton blooms can also have negative impacts on marine life, and the human activities that depend on it, such as fisheries and aquaculture.
The southern Benguela is part of one of four upwelling systems in the global ocean. In these systems a combination of wind, coastline, and and the Earth's rotation, lead to movement of surface waters offshore, which are then replaced by cold, nutrient rich waters from the depths. This 'upwelled' water fuels the growth of phytoplankton, leading to productive waters for fisheries and aquaculture. However, some times this growth comes through blooms of toxic phytoplankton species, and/or of blooms of exceptionally high biomass. These are known as harmful algal blooms (HABs), and they can have a variety of negative consequences on both marine and human life.
In the southern Benguela, annual changes in the winds result in an upwelling season that runs from September to April. This is typically when HABs occur.
January to March 2022 saw the growth and persistence of a large dinoflagellate bloom in the St Helena Bay region, focused on the Elands Bay area. High concentrations of chlorophyll-a were observed by the Sentinel-3 satellites during this time. As the biomass levels reach such high concentrations (frequently in excess of 100mg m-3, where typical values for large parts of the ocean range from 0.1-10mg m-3!) customised algorithms are necessary to quantify the biomass. Using the unique spectral bands from the OLCI sensor, an algorithm can be applied to take advantage of the signal created by these blooms in the red part of the visible light spectrum. Figure 1 shows an example of this algorithm applied to an image from Sentinel-3 OLCI during the bloom period. This image can be recreated using a Jupyter Notebook written to accompany this case study — visit the ocean case study repository in the EUMETSAT Gitlab.
Figure 2 (left) shows a time series of data with this algorithm applied, showing how the bloom developed and persisted in the region for several months. As the bloom began to die off, its decomposition resulted in deoxygenation of the coastal waters, which killed marine life, and forced a 'lobster walkout' (Figure 3). This phenomena occurs when the lobsters try to escape the low oxygen conditions and end up on the seashore. Some 700,000 of these lobsters ended up ashore in Elands Bay, following the collapse of the bloom.
Understanding these blooms is vital for managing fisheries and aquaculture activities in the regions, and issuing appropriate human health warnings (if the blooms are caused by toxic species). As such, using satellite data to provide relevant information to marine stakeholders is a key activity undertaken by a consortium of organisations, led by the Council for Scientific and Industrial Research (CSIR) South Africa. The work includes application of regional algorithms for biomass estimation and phytoplankton type (Figure 2, right), as well as distribution of the data through web portals and apps. CSIR are working with a wide range of organisations and marine stakeholders in southern and eastern Africa, as part of the GMES and Africa project of the African Union and European Union. This work is also an important contribution to the UNDP Sustainable Development goals (1, 2, 3, 8 and 14) supporting growth in vital industries, helping to reduce poverty, provide food, and sustainably manage marine life. It also supports several of the UN Ocean Decade challenges — supporting the growth of sustainable aquaculture (challenge 3), developing a sustainable and equitable ocean economy (challenge 4) and increasing resilience to hazards (challenge 6).