Improved Flood Forecasting Systems Launched Nationwide

Improved Flood Forecasting Systems Launched Nationwide.webp

New Delhi, March 11 District-level early warning systems for floods and cyclones are operational across vulnerable districts of India, enabling timely alerts and improved disaster preparedness, Union Minister Jitendra Singh said on Wednesday.

In a written reply to the Lok Sabha, Singh stated that the India Meteorological Department (IMD) prepares and updates district-wise early warnings four times a day, with forecasts valid for up to seven days, particularly for heavy rainfall and cyclone-related events.

"These warnings are disseminated through multiple communication channels, including the Common Alerting Protocol (CAP), mobile applications, official websites, social media platforms, and messaging services, to ensure timely access for authorities and the public," Singh said.

The minister also said that currently, flood forecasts are issued at 350 forecasting stations, including 150 inflow forecast stations on major dams and barrages, and 200 level forecast stations on major rivers, established in consultation with states and Union Territories.

The Central Water Commission (CWC) — the nodal organisation for flood warnings and forecasting — has developed basin-wise prediction models based on rainfall-runoff mathematical modelling, enabling seven-day advance advisory forecasts, in addition to short-range flood forecasts with response times of up to 24 hours, according to Singh.

The minister highlighted the web-based "C-Flood" platform, which provides two-day advance inundation forecasts up to the village level through detailed inundation maps and water-level predictions.
 
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c-flood platform central water commission common alerting protocol cyclone warnings disaster preparedness district-level alerts early warning systems flood forecasting flood forecasting stations india meteorological department inflow forecasting inundation forecasts rainfall-runoff modelling river level forecasting water level prediction
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