Tides, influenced by astronomical and meteorological factors, have a direct impact on coastal areas.
Astronomical tides are caused by the gravitational forces between the Moon, the Sun, and the Earth, following a predictable cycle. During full and new moons, when these celestial bodies are aligned, the well-known spring tides occur, significantly raising water levels and increasing the risk of flooding. Conversely, during the first and third quarters of the Moon, when the Sun, Earth, and Moon form a right angle, neap tides take place, characterized by smaller variations in sea level.
Meteorological tides, on the other hand, are variable and depend on factors such as atmospheric pressure, strong winds, or heavy rainfall, which can rapidly raise sea levels during storms or low-pressure systems.
Both types of tides affect sea levels but in different ways. Understanding both is crucial for disaster management and prevention.
For coastal communities and areas near estuaries, predicting these fluctuations is essential to prevent damage. Thanks to advanced data integration on a single platform, FAST provides these municipalities with a key tool, powered by Artificial Intelligence, enabling them to anticipate flooding caused by the influence of these tides on rivers, while effectively managing and activating response protocols.
How does it work? By combining data from various predictive models—both astronomical and meteorological tides—specifically tailored to estuaries and coastal regions. This way, local authorities can obtain precise real-time estimates of sea behavior, especially in areas vulnerable to flooding.
Municipalities can thus activate emergency protocols before damage occurs, protect critical infrastructure, and make informed decisions to ensure public safety. Moreover, using FAST not only helps mitigate immediate risks but also fosters more sustainable coastal management, anticipating extreme events and improving preparedness for adverse weather conditions.
Request a demo and discover how FAST can help your municipality predict floods and enhance protection.
