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IIT Madras researchers collect real-time data during Cyclone Nivar

Data collected will help mitigate the impact of future cyclones

Researchers at the Indian Institute of Technology (IIT), Madras, found one little silver lining during Nivar, the devastating cyclone. A team comprising both faculty and students from IIT, in collaboration with the Tamil Nadu State Disaster Management Authority (TNSDMA), collected critical real-time data that will help mitigate the impact of future cyclones.

IIT Madras, along with Anna University, IIT Bombay and the National Centre for Coastal Research (NCCR), developed a pilot flood forecasting system with the funding support from the Principal Scientific Adviser, Government of India, after the megafloods that hit Chennai in December 2015.

The initiative funded 15 automatic weather stations, rain gauges and six water level recorders in 2017. However, Cyclone Nivar was the first time the project was able to collect sufficient real-time ground truth data in terms of river discharge rate, as the monsoon over the last three years have been below normal.

The team, led by Balaji Narasimhan, professor in the civil engineering department, measured river discharges at several critical places across the Adyar river, aiming to collect real-time data during the cyclone. Equipped with the acoustic current profiler, a sublime device, the team measured river currents and flow depths across the width of the river to get the integrated flow rate of the river.

Phanindra Reddy, a government official for disaster management and mitigation, said that the data collected during this field project by the students and faculty of IIT Madras will play an important role to operationalise the Real Time Flood Forecasting (RTFF) and Spatial Decision Support System (SDSS).

Narasimhan emphasised the need to develop rating curves at critical sections of the rivers to set up a robust flood management system. Rating curves help understand the volumetric flow rate (in m3/s or ft3/s) for different flow depths (in m or ft). An efficiently developed rating curve will allow an integrated network of water level sensors to monitor the river discharges remotely at critical river stretches.