  Harmful Algal and Cyanobacterial Blooms (HABs), occurring in inland and
maritime waters, pose threats to natural environments by producing toxins that
affect human and animal health. In the past, HABs have been assessed mainly by
the manual collection and subsequent analysis of water samples and occasionally
by automatic instruments that acquire information from fixed locations. These
procedures do not provide data with the desirable spatial and temporal
resolution to anticipate the formation of HABs. Hence, new tools and
technologies are needed to efficiently detect, characterize and respond to HABs
that threaten water quality. It is essential nowadays when the world's water
supply is under tremendous pressure because of climate change,
overexploitation, and pollution. This paper introduces DEVS-BLOOM, a novel
framework for real-time monitoring and management of HABs. Its purpose is to
support high-performance hazard detection with Model Based Systems Engineering
(MBSE) and Cyber-Physical Systems (CPS) infrastructure for dynamic
environments.
