Predictive Modeling and Simulation System for the Management of Harmful Cyanobacteria Blooms

Water scarcity is increasing due to climate change, overexploitation and pollution. In addition, water bodies contain Harmful Cyanobacteria Blooms (HCBs) that produce toxins that are harmful to health, economy and environment. So far, these blooms have been assessed mainly by manual collection and analysis, or with the help of automatic instruments that acquire information from fixed locations. However, although having Early-Warning Systems (EWSs) to detect HCBs would be ideal, the procedures used do not usually provide data with sufficient resolution to anticipate their formation. Therefore, it is necessary to develop techniques and tools that combine data collection procedures with numerical simulations to detect, characterize, predict and respond to these outcrops. For this, it is proposed to implement a system for prediction and analysis of HCBs as part of an integral solution for its monitoring and management in real time, supported by a Model Based Systems Engineering (MBSE) infrastructure.