IASbhai Daily Editorial Hunt | 8th Oct 2020

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Dear Aspirants
IASbhai Editorial Hunt is an initiative to dilute major Editorials of leading Newspapers in India which are most relevant to UPSC preparation –‘THE HINDU, LIVEMINT , INDIAN EXPRESS’ and help millions of readers who find difficulty in answer writing and making notes everyday. Here we choose two editorials on daily basis and analyse them with respect to UPSC MAINS 2020.

EDITORIAL HUNT #177 :“New Flood Forecasting Method | UPSC

New Flood Forecasting Method | UPSC

J. Harsha
New Flood Forecasting Method | UPSC

J. Harsha is Director, Central Water Commission, Government of India.


Playing catch up in flood forecasting technology


India needs a technically capable workforce that can master ensemble weather and flood forecast models



Outdated technologies and a lack of technological parity between multiple agencies and their poor water governance decrease crucial lead time during floods . Elucidate -(GS 3)


  • Acting quickly
  • Calculation of floods
  • What is Ensemble technology


  • TAKING ACTIONS : There are many times during flood events, when the end users (district administration, municipalities and disaster management authorities) receive such forecasts and have to act quickly.
  • COMPELLING SCENARIOS : These compelling scenarios are often experienced across most flood forecast river points

In Assam, Bihar, Karnataka, Kerala or Tamil Nadu.

Compare this with another form of flood forecast (known as the “Ensemble forecast”) that provides a lead time of 7-10 days ahead.

  • FORECASTING FEATURES : Ensemble forecast has probabilities assigned to different scenarios of water levels and regions of inundation.
  • PROBABILITIES AHEAD : For instance probable chances of the water level exceeding the danger level is 80%, with likely inundation of a village nearby at 20% can be notified.
  • BETTER RESILIENCE : The “Ensemble flood forecast” certainly helps local administrations with better decision-making and in being better prepared than in a deterministic flood forecast.
  • GRADUAL SHIFT : The United States, the European Union and Japan have already shifted towards “Ensemble flood forecasting” alongwith “Inundation modelling”.
  • INDIAN FORECASTING : India has only recently shifted towards “Deterministic forecast” (i.e. “Rising” or “Falling” type forecast per model run).



  • ALARM CENTRES : The India Meteorological Department (IMD) issues meteorological or weather forecasts while the Central Water Commission (CWC) issues flood forecasts at various river points.
  • END USER AGENCIES : The end-user agencies are disaster management authorities and local administrations.
  • COMMAND CENTRES : The advancement of flood forecasting depends on how quickly rainfall is estimated and forecast by the IMD and how quickly the CWC integrates the rainfall forecast (also known as Quantitative Precipitation Forecast or QPF) with flood forecast.
  • DATA DISSEMINATION : It also is linked to how fast the CWC disseminates this data to end user agencies.
  • LEAD TIME : The length of time from issuance of the forecast and occurrence of a flood event termed as “lead time” is the most crucial aspect of any flood forecast.

Technology plays a part in increasing lead time.

  • CALCULATION OF RAINFALL : IMD has about 35 advanced Doppler weather radars to help it with weather forecasting.
  • TECHNOLOGICAL INTERVENTION : Compared to point scale rainfall data from rain gauges,Doppler weather radars can measure the likely rainfall directly from the cloud reflectivity over a large area.

Thus the lead time can be extended by up to three days.

  • OUTDATED METHODS : The advantage of advanced technology becomes infructuous because most flood forecasts at several river points across India are based on outdated statistical methods that enable a lead time of less than 24 hours.
  • DATA DRIVEN BY : This is contrary to the perception that India’s flood forecast is driven by Google’s most advanced Artificial Intelligence (AI) techniques!
  • HYDROLOGICAL DATA : These statistical methods fail to capture the hydrological response of riverbasins between a base station and a forecast station.They cannot be coupled with QPF too.

This bypasses the data deficiencies and shortcomings of forecasts based on statistical methods.

  • GOOGLE AI : IT has adopted the hydrological data and forecast models derived for diverse river basins across the world for training AI to issue flood alerts in India.


  • RAINFALL-RUNOFF MODELS : Telangana shows that it is only recently that India has moved to using hydrological (or simply rainfall-runoff models) capable of being coupled with QPF.

The United States which is estimated to have a land area thrice that of India, has about 160 next generation S-band Doppler weather radars (NEXRAD) with a range of 250-300 km.

  • INDIA’s ESTIMATES : India will need at least an 80-100 S-band dense radar network to cover its entire territory for accurate QPF.
  • LIMITATIONS : Else, the limitations of altitude, range, band, density of radars and its extensive maintenance enlarge the forecast error in QPF which would ultimately reflect in the CWC’s flood forecast.
  • ERROR MARGINS : Conspicuously, the error margin is always away from the public gaze.
  • FORECASTING ERRORS  : A single error increases and the burden of interpretation shifts to hapless end user agencies.The outcome is an increase in flood risk and disaster.

      IASbhai Windup: 


  • MINIMUM INTERVAL : Beyond a lead time of three days, a deterministic forecast becomes less accurate.
  • DEVELOPED WORLD : The developed world has shifted from deterministic forecasting to ensemble weather models that measure uncertainty by causing perturbations in initial conditions.
  • LEAD TIME WITH ENSEMBLE : Probabilities are then computed for different flood events, with a lead time beyond 10 days.
  • LONG WAY AHEAD : India has a long way to go before mastering ensemble model-based flood forecasting.

The IMD has begun testing and using ensemble models for weather forecast through its 6.8 peta flops supercomputers (“Pratyush” and “Mihir”)

  • TECHNOLOGICAL PARITY : The forecasting agency has still to catch up with advanced technology , in order to couple ensemble forecasts to its hydrological models.
  • COMPATIBILITY : IMD has to modernise not only the telemetry infrastructure but also raise technological compatibility with river basin-specific hydrological, hydrodynamic and inundation modelling.
  • SKILLED MANPOWER : To meet that objective, it needs a technically capable workforce that is well versed with ensemble models and capable of coupling the same with flood forecast models.
  • PROBABILISTIC FORECAST : With integration between multiple flood forecasting agencies, end user agencies can receive probabilistic forecasts.

This will give them ample time to decide, react, prepare and undertake risk-based analysis and cost-effective rescue missions, reducing flood hazard across the length and breadth of India.

       SOURCES:   THE HINDU EDITORIAL HUNT | New Flood Forecasting Method | UPSC


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