Lok Sabha Question No. 645 (UNSTARRED), answered on 2025-12-03
Subject: Heatwaves and floods in cities
Ministry: EARTH SCIENCES
Asked by: Ms. S Jothimani

## GOVERNMENT OF INDIA MINISTRY OF EARTH SCIENCES LOK SABHA

## UNSTARRED QUESTION NO. 645 TO BE ANSWERED ON WEDNESDAY, 3 RD  DECEMBER, 2025

## HEATWAVES AND FLOODS IN CITIES

## 645.    MS. S JOTHIMANI:

Will the Minister of EARTH SCIENCES be pleased to state:

- (a) whether it is true that the ICMR-Global South research has projected urban heatwaves and  erratic  rainfall  events  to  double  in  cities  like  Chennai  by  2030  and  if  so,  the immediate steps taken/being taken by the Government to mitigate these risks;
- (b) the  number  of  extreme  weather  events  including  heatwaves,  flash  floods  and cloudbursts recorded across the Country during the first half of 2025 in comparison with earlier years;
- (c) the status of the Government's efforts to implement "Bharat Forecast System" and urban flood forecasting tools such as C-FLOWS particularly for coastal metros like Chennai; and
- (d) whether enhanced early warning systems and city level decision support platforms are being scaled up in collaboration with IMD and State Governments to improve disaster preparedness and if so, the details thereof?

## ANSWER

## THE MINISTER OF STATE (INDEPENDENT CHARGE) FOR MINISTRY OF SCIENCE AND TECHNOLOGY AND EARTH SCIENCES (DR. JITENDRA SINGH)

- (a) The Government is aware of the earlier reports suggesting that cities like Chennai will face significant rise in heatwave days and extreme rainfall events by 2030. The India Meteorological Department (IMD) continuously monitors extreme weather events such as heatwaves and heavy rainfall across various locations in India, including urban areas, using  station  and  city-based  meteorological  data.  Also,  gridded  rainfall  (25  km resolution)  and  temperature  data  (50  km  resolution)  provided  by  the  IMD  are  also utilised to track these extreme events. Further, IMD consistently issues timely alerts and forecasts at different time scales to the public and concerned stakeholders. Various steps  have  been  taken  to  ensure  effective  dissemination  of  warnings  to  vulnerable populations. IMD's weather information, including alerts  and warnings to the public, is provided through various platforms:
-  Mass Media: Radio/TV, Newspaper network (AM, FM, Community Radio, Private TV), Prasar Bharati, and private broadcasters
-  Weekly & Daily Weather Video
-  Internet  (email), FTP
-  Public Website (mausam.imd.gov.in)
-  IMD Apps:  Mausam/Meghdoot/DAMIN/RAIN ALARM
-  Social Media: Facebook, X, Instagram, BLOG
- i. X: https://twitter.com/Indiametdept
- ii. Facebook:

https://www.facebook.com/India.Meteorological.Department/

- iii. Blog: https://imdweather1875.wordpress.com/
- iv. Instagram: https://www.instagram.com/mausam\_nwfc
- v. YouTube:

https://www.youtube.com/channel/UC\_qxTReoq07UVARm87CuyQw

- (b) IMD  has  data  on  extreme  weather  events  covering  heatwaves  recorded  across  the country. However, since the cloud bursts and associated flash flood events are highly localized events, they need a highly dense network of Automatic Rain-Gauzes (ARGs) and  Automatic  Weather  Stations  (AWSs),  and  flood  Gauzes  for  recording  of  such highly localised rainfall on a real-time basis. Currently, such rainfall data records for longer periods are not available both in India and as well as world-wide. Further these events  mostly  occurr  in  very  remote  areas  over  hilly  areas  of  country  such  as  the Himalayas and Western Ghats, which are still data-sparse regions. So there is no study available  to  find  their  trends  using  longer  period  data.  However,  IMD  has  data  on heatwave  frequencies  in  terms  of  meteorological  sub-division-wise,  across  India. Annexure-1 shows their frequency for the period of 2020-2025 (6 years).
- (c) The Bharat Forecast System (BharatFS) is based on the newly implemented Triangular Cubic Octahedral (TCo) dynamical grid, which enables the model to operate at ~ 6 km horizontal resolution. It has improved representation of orography, better filtering, and better conservation properties. With the increase in horizontal resolution, BharatFS can generate distinct forecasts every 6 km. With the improved skill of BharatFS at such a high  resolution,  the  model  can  provide  better  guidance  for  weather  predictions  and extremes  at  the  district  and  block  levels,  which  are  typically  12  km  or  more.  The BharatFS provides a 10-day forecast of rainfall, temperature, low-pressure genesis, and other  critical  atmospheric  conditions,  enabling  model  guidance  in  issuing  early warnings for multiple sectors.

In 2019, the National Centre for Coastal Research (NCCR) under the Ministry of Earth Sciences (MoES), in collaboration with other MoES institutes and the Tamil Nadu State Government, developed CFLOWS-an urban flood-forecasting system for Chennai. Since then, it is issuing advisory services to the Tamil Nadu State Disaster Department on  a  regular  basis.  This  tool  is  also  integrated  with  TNSMART  for  disaster preparedness. Similar systems are available for Mumbai and Kolkata.

- (d) Yes. MoES has developed advanced early warning systems for severe weather events such  as  cyclones,  heavy  rainfall,  and  other  extreme  conditions.  These  systems  are supported by a state-of-the-art observation network, including surface and upper-air instruments, Doppler Weather Radars, remote-sensing platforms, and high-resolution dynamical models. The India Meteorological Department, in coordination with other MoES  centres  and  State  Governments,  has  developed  an  end-to-end  GIS-based Decision Support System. This system serves as the front end of the national earlywarning framework, enabling timely detection and monitoring of weather hazards and strengthening disaster preparedness across the country, including in urban areas.

IMD has adopted new techniques and technology from time to time to detect, monitor, and provide timely early warnings for the entire country, including urban areas, and cities regularly affected by all types of extreme weather events like cyclones, heavy rainfall,  etc.,  which  have  devastating  impacts  on  human  lives,  livelihoods,  and infrastructure. IMD has released a "Climate Hazard & Vulnerability Atlas of India", helping  States  and  disaster-management  agencies  identify  hotspots  and  prepare  for extreme weather  events,  which  cause extensive  damage  and economic, human,  and animal losses. The same can be accessed at https://imdpune.gov.in/hazardatlas/abouthazard.html.

IMD has launched seven of its services (Current  Weather,  Nowcast,  City Forecast, Rainfall Information, Tourism Forecast, Warnings, and Cyclone) with the 'UMANG' Mobile  App  for  use  by  the  public.  Moreover,  IMD  developed  a  mobile  App, 'MAUSAM' for weather forecasting, 'Meghdoot' for Agromet advisory dissemination, and 'Damini' for lightning alerts. The Common Alert Protocol (CAP), developed by the National  Disaster  Management  Authority  (NDMA),  is  also  being  implemented  to disseminate warnings and alerts by the IMD. Further, a new Central Sector Scheme, Mission Mausam, has been launched by MoES to make the country 'weather-ready and climate-smart'.

Annexure-1

|                         |   2020 |   2021 |   2022 |   2023 |   2024 |   2025 |
|-------------------------|--------|--------|--------|--------|--------|--------|
| Assam &Meghalaya        |      0 |      0 |      0 |      0 |      1 |      0 |
| Nmmt                    |      0 |      0 |      0 |      0 |      0 |      0 |
| Shwb &Sikkim            |      0 |      0 |      1 |     15 |     11 |      1 |
| Gangetic West Bengal    |      0 |      3 |      8 |     27 |     31 |      4 |
| Odisha                  |      0 |      3 |     11 |     24 |     37 |      7 |
| Jharkhand               |      0 |      0 |     27 |     16 |     23 |      4 |
| Bihar                   |      0 |      0 |     13 |     29 |     30 |      4 |
| East U.P.               |      2 |      0 |     33 |     11 |     33 |      8 |
| West U.P.               |      2 |      2 |     28 |      5 |     32 |      5 |
| Uttarakhand             |      0 |      0 |      5 |      0 |     10 |      0 |
| Har. Chd& Delhi         |      0 |      2 |     37 |      5 |     30 |     11 |
| Punjab                  |      0 |      0 |     22 |      3 |     27 |      7 |
| Himachal Pradesh        |      0 |      2 |     38 |      0 |     18 |     10 |
| Jammu& kashmir& ladakh  |      0 |      0 |     19 |      0 |     11 |     13 |
| West Rajasthan          |      5 |      6 |     58 |      3 |     29 |     33 |
| East Rajasthan          |      0 |      4 |     28 |      0 |     23 |     21 |
| West Madhya Pradesh     |      4 |      2 |     42 |      4 |     24 |      7 |
| East Madhya Pradesh     |      0 |      0 |     34 |     13 |     26 |     10 |
| Gujarat region          |      0 |      0 |     13 |      1 |     14 |      7 |
| Saurashtra &Kutch       |      6 |     12 |     25 |      4 |     16 |     15 |
| Konkan &Goa             |      0 |      4 |      2 |      6 |      4 |      1 |
| Madhya Maharashtra      |      2 |      0 |      2 |      1 |      8 |      1 |
| Marathwada              |      3 |      0 |      0 |      0 |      3 |      3 |
| Vidarbha                |      0 |      2 |     18 |     11 |     11 |      8 |
| Chhattisgarh            |      2 |      0 |      3 |     12 |     13 |      1 |
| Coastal A.P. &Yanam     |      0 |      0 |      0 |     22 |     11 |      0 |
| Telangana               |      2 |      0 |      0 |     14 |     12 |      1 |
| Rayalaseema             |      0 |      0 |      0 |      1 |     16 |      0 |
| Tamil., Pudu. &Karaikal |      0 |      2 |      0 |      1 |     13 |      0 |
| Coastal Karnataka       |      0 |      0 |      0 |      2 |      3 |      0 |
| N. I. Karnataka         |      0 |      0 |      0 |      0 |     18 |      0 |
| S. I. Karnataka         |      0 |      0 |      0 |      0 |     10 |      0 |
| Kerala &Mahe            |      0 |      0 |      0 |      0 |      6 |      0 |

*****