Fengal slows down on home stretch, economic loss estimated at ₹150 crore
Cyclone Fengal over south-west Bay of Bengal has slowed down in lateral movement to just seven km on Friday afternoon, while on the home stretch towards the Chennai-Mahabalipuram-Puducherry belt.
It featured wind speeds of 70-80 km/hr gusting to 90 km/hr as landfall approached, an India Meteorological Department (IMD) update said in the evening. This was after the national forecaster delayed the landfall from the afternoon as previously announced. Heavy rain and high winds were reported from the coast on both sides of the landfall timeline.
- Also read: TN, Chennai Rains, Cyclone Fengal live updates: Landfall expected along Karaikal-Mahabalipuram stretch tonight
Probable economic loss
Initial estimates suggest cyclone Fengal may cause probable total economic loss of ₹150 crore from wind and rainfall-induced flooding, says Pushpendra Johari, Senior Vice-President, Sustainability at RMSI, a Delhi-based global consultancy.
The cyclone could have potentially impacted at least 15,000 buildings; 1,000 essential facilities; five railway stations; one airport; and approximately 4,000 km of electricity network.
Vishwas Chitale, Senior Programme Lead, Council on Energy, Environment and Water (CEEW), a Delhi-based think tank, said the east coast of India houses hotspots of compounding risks of hazards such as floods, cyclones, and droughts.
Raised cyclone threat
Tamil Nadu is vulnerable to all three extreme events, according to CEEW analysis. It has witnessed a two-fold increase in the number of cyclones in the past decade. At least 11 out of 32 districts in the state are highly exposed to cyclones.
CEEW also finds that the state has a high availability of cyclone multi-hazard early warning systems. These systems play a crucial role in informing the decision-makers and the communities ahead of time to evacuate and reduce the potential losses.
The Ministry of Earth Sciences and the Tamil Nadu state government have also developed CFLOWS-Chennai, a web GIS-based decision-making support system, integrating data and outputs derived from weather forecast models to build India’s first integrated coastal flood warning system.
