Services for the European Open Science Cloud

CyberHAB

Using data cloud services to manage harmful algae blooms

Harmful Algal Blooms (HABs) happen when toxic microalgae proliferate beyond control and take over rivers, lakes or ponds with costly environmental and socioeconomic impacts, for example: on fisheries, or on the availability of drinking water. At sea, this phenomenon causes red tides. Blooms and red tides are caused by a combination of meteorological, hydrodynamic and biogeochemical factors that are difficult to pin down with certainty.

For these reasons, managing algal blooms is a challenge for local governments, environmental agencies and the people that depend on healthy water bodies for their livelihood. Despite the investment in waste management and monitoring systems, current methods and processes are still far from ideal.

Ecohydros believes that deploying new technologies and big data analytics can pave the way for better and more efficient ways to manage harmful algal blooms.

Challenge

Extracting meaningful information from monitoring data is a computational challenge. The data covers hundreds of variables and parameters that need to undergo treatment, processing and analysis before they can be used in visualization tools. The predictive models also require calibration in the short and medium term in an at least semi-automatic way, performing sweeps of numerous parameters in multiple combinations, forcing the system to use high demand iterations. All this leads to an increase in the demand for computing beyond what a standard company or a standard computer center can provide.

Work plan

The main goal is to demonstrate the technical and economic advantages of applying e-infrastructures to the early warning and integral management of HABs in different pilot cases, exploiting Data Cloud Services (DCS) to support the key processes required (data processing, modelling, integration of images).

For this purpose, the following partial objectives will be achieved:

  • Integration of all data processes into Data Cloud Services, including satellite images from MERIS and the forthcoming Sentinel-3 Ocean and Land Colour Instrument (OLCI), as soon as available.
  • Integration of iterative optimization tools for 4D modelling of HABs.
  • Demonstration of a HAB early detection and prediction event using the improved CIS with DCS in a cyanobacterial freshwater bloom (Water Agency as customer).

Partners