Flood Forecasting Models and Flood Warning System

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Floods have become more frequent in many parts of India. On one hand the Climate Change Scenario in Indian subcontinent with reduced rainy days and also with increased rainfall intensities and cloud burst. Added to these the social  and religious activities like Kumbhmela call for the safety of the human ,ives followed by property loss. It could be seen easily that the forecast models developed by commercial organisations are too rugged with data intensity and there by its operations restricted to highly trained only. Should we not start with simple models with real time data from AWS and ARGs for netter predictions?

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Warnings from the data and ...

Warnings from the data and climate experts should be taken seriously and preparedness to unprecedented rains should be on the state’s priority list alongside with citizen support.

Climate change is real. So, Management is the real KEY! One mistake and there is hardly a comeback.

https://www.jalbharat.com/aftermath-of-kerala-floods-2018/

 

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Thanks Peter Looks ...

Thanks Peter

Looks promising. I would explore and get back

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One could try adapting this ...

One could try adapting this to Indian circumstances:

https://felixschrodinger.wordpress.com/2014/02/16/the-gospel-according-to-noah/

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Automated Early Flood warning ...

Automated Early Flood warning systems are technically possible on any river, based on texts being sent out if the water level crosses a certain determined level, or the rate of rise exceeds a defined rate. however, again, who implements ? 

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automated EFWS are for well instrumented systems. The discussion is regarding select locations as the Kumbh mela events occur on different rivers each year. riverine flood recording may exist at a few locations, while the floods at locations may be flash floods.

Next point is who operates, it is the state or district administration with the help from water resources departments. Thus the operation steps should be simple to the extent for an efficient participation of operator with little trainings.

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