Lok Sabha Question No. 2524 (UNSTARRED), answered on 2022-03-16
Subject: Hydro-Meteorological Calamities
Ministry: EARTH SCIENCES
Asked by: Shashi Tharoor

GOVERNMENT OF INDIA MINISTRY OF EARTH SCIENCES LOKSABHA UNSTARRED QUESTION NO. 2524 I TO BE ANSWERED ON WEDNESDAY, 16TH MARCH, 2022

## HYDRO-METEOROLOGICAL CALAMITIES

## 2524. DR. SHASHI THAROOR:

Will the Minister of EARTH SCIENCES be pleased to state:

- (a) whether the Minister concurs with the statement made by the Union Minister of Agriculture and Farmers Welfare on India losing 5.04 million hec ares i! 2021 and 36 million hectares of crop area since 2016 due to hydro-meteorological ca amities and if so, the details thereof; /
- whether the Ministry has identified districts in Kerala particularly prone t soil loss due to soil erosion caused by heavy rains and if so, the details thereof ahd if not, the reasons therefor; / (b)
- whether the Ministry has implemented measures to improve soil 9uality for improving durability to heavy rains if so, the details thereof and if not, reasons Itheref<!>r; (c)
- the details of the increasing number of instances of hydro-mete rolog cal calamities predicted for this decade, due to climate change; (d)
- whether the Ministry can ascertain accurate prediction of similar future events by the weather forecasting systems despite increasing uncertainty; and (e)
- if so, the details thereof and if not, the reasons therefor? (t)

## ANSWER

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

- (a) This is the information provided by the Hon'ble Union Ministe of griculture and Farmers Welfare during a reply to the Lok Sabha question throukh the/ data available with the respective ministry. Ministry of Earth Sciences (MoES) lis mafdated to issue forecasts and warnings related to day to day weather e!ents hrough India Meteorological Department (IMD). Operational Agromet Adviso~ Servi~es (AAS), i.e. Gramin Krishi Mausam Sewa (GKMS) scheme, rendered by IMD jOinty with Indian Council of Agricultural Research (ICAR) and State Agricultural yniver~ities, is a step towards weather-based crop and livestock management strategies and operations for the benefit of farming community in the country. Under the scheme, rrlediuni range weather forecast at district and block level is generated and based on t~e forfcast, Agromet Advisories are prepared by the 130 Agromet Field Units (AMIfUs) ltcated in State Agricultural Universities, ICAR Institutes, IITs etc. and 199 Dr'strict rgromet Units (DAMUs)at KVKs under ICAR network and communicated to he fa ers on every Tuesday and Friday.
- Indian Institute of Soil and Water Conservation (IISWC) a reserrch i~stitution under ICAR has identified few districts of Kerala prone to erosion using Iprocess based estimation of soil erosion. Based on the comparison between prevriling fnd permissible rate of soil erosion, the districts namely Ernakulum, Idukki1 Kollam, Kottayam, Kozikode, Mallapuram, Palakad, Thriruvananthapuram, Thrissur an Waynad are identified as vulnerable to soil erosion. (b)
- Ministry of Earth Sciences (MoES) has not implemented meas res to improve soil quality for improving durability to heavy rains However, GoveIent f Kerala had published various information on Kerala soils and is available at (c)

http://www.keralasoils.gov.inlindex.php/2016-04-27-09-26-39/S0ilsrf-kerla#red-soiIS

- As per the latest IPCC reports, global warming is likely to conti~e and there is high probability for these extreme events to increase. The trends of vari us e~treme weather events over Indian region are computed and are given in figures (1 &2) ,n Annexure-I. It contains the ratio of severe cyclonic stormto the total cyclonic s~ormsover the North Indian Ocean for the period 1891 to 2020 (Fig.1) and the trend of ~eavy rainfall events occurred during monsoon season (JJAS) for the period 1989 to 2018 (Fig. ). (d)
- (e)-(f) Yes Sir. During the past few years, IMD has been continuouslJ impr ving weather and warnings are issued by IMD at the national, State and district lerels. I has a network of State Meteorological Centres for better coordination with St~te an district level agencies. With the upgradation of observations and prediction syst~m, noticeable improvements have been made in the recent past in the skill of predict on, especially with respect to heavy-rainfall, heat-wave, thunderstorm and cyclones.

The weather forecast accuracy is verified by IMD and errors Cd s~ill scores are calculated seasonwise and annually. Details of improvement t Weather Forecast Accuracy are follows: ~

- Probability of Detection (POD) for heavy rainfall warning with 24 lead period is 74% in 2021, which has improved by 51% in year 2021 as cdmpare to their skill between 2002-20. False Alarm Rate (FAR) and Missing Rate (1'1 ) are 26% in 2021, which has improved by 21% & 53% respectively in year 2021 as co+pared to their skill between 2002-20. (Fig.-3 in Annexure-I) ill
- Probability of Detection (POD) for heat wave warning with 24 ~ leaiferiod is 97% in 2021, which has improved by 15% as compared to their skill be een 2014-20. False Alarm Rate (FAR) and Missing Rate (MR) are 2% & 3%lrespe tively in 2021, which has improved by 63% & 82% respectively as compared t10 their skill between 2014-20. (Fig.-4 in Annexure-I) I
- The annual average Tropical Cyclone (TC) landfall point foreca~t err0I[' in 2021 have been 16.4 km, 10.6 km and 19.8 km respectively for 24, 36 and 18 hrs rgainst the past five year (2016-2020) average error of31.9 km, 43.7 km and 61.$ km ~ed on data of 2016-2020. Considering the diameter of the central region (eye) a~ lO-ll km, there was almost zero error in landfall point forecasts of cyclone in 2021 up 0 48 hours in advance. $
- The annual average TC track forecast errors in 2021 have been 3 km, 91 km and 164 km respectively for 24,48 and 72hrs lead period against the past rve y~ar (2016-2020) average error of 77, 117 and 159 km based on data of 2fI6-20ro, (Fig.-5 in Annexure-I)
- The TC track forecast skills compared to climatology and per istenc forecast have been 75%, 82% and 68% respectively for the 24, 48 and 72 hrs lead afainst the long period average (2016-2020) skill of 64%, 76% & 78% respectiv ·ly. (Flg.-5).
- The annual average absolute error(AE) in the TC intensity (wind) forecast has been 6.2 knots, 9.5 knots and 10.8 knots (108 nautical miles per hour)lrespe~tivelY for 24, 48 and 72 hrs lead period of forecast against the past five year(12016020) average error of 7.9, 11.4 and 14.1 knots. The skill in intensity forecast as compared to persistence forecast was 63.2%, 78.4% and 85.6% against the long period average (2016-20) skill of52.2, 72.1 and 75.1 for 24,48 and 72 hours lead perio '. (Fig.-5)
- Probability of Detection (POD) for thunderstorm warning with ~4 hr ead period is 86% in 2021 against 31% in 2016. (Fig.-6 in Annexure-I) I
- Probability of Detection (POD) for thunderstorm warning with 3 h urly nowcast during March to June 2021 has been 79%.

Moreover, various new initiatives, as mentioned below, have been undertaken by IMD, MoES for betterment of prediction and dissemination of warningslOf ext I erne weather events that may cause natural disasters.

1. The observational network of the department has been enhance with installation of more number of Automatic Weather Stations (AWSs) and A1tomatfc Raingauges (ARGs) across the country. I
2. Thirty three Doppler Weather Radars (DWRs) are operation I across the country with 4 DWRs being commissioned in January 2022 at New Delhi, [Leh, Mumbai and Chennai. Doppler Weather Radars provide adequate warning if the event of approach of Cyclonic Storms, Monsoon Depressions, Thunderstorms etc. DWR network also provides vital information for nowcasting pUlloses Ion mesoscale convective weather developments anywhere in the country.
3. Multi-Mission Meteorological Data Receiving & Processi g Sys em has been established and dedicated to the nation for augmentation of sa ellite derived products. ,
4. 203 new raingauge stations have been added in the D,istrict1wise Rainfall Monitoring Scheme taking the total number of stations to 4940.
5. Location specific forecast for 7 days and nowcast for nex~ 3 hours have been extended to 1164 and 1089 stations respectively covering 739listricts in the country.
6. NWP Model based gridded rainfall data are provided to Central Wat r Commission for their flood forecasting model for all 153 river catchments land Extended Range model products for 10 river basins alongwith quantitative pretiPitatitn forecast for all river catchments valid upto next five days. I
7. With operationalization of Flash Flood Guidance system, generatia and issue of Flash Flood Guidance has commenced for all watersheds ofthf counE'
8. Impact based forecast is already in practice for cyclone. The,.same is extended to heavy rainfall and heatwaves.
9. ofWMO. 10. The multi-model ensemble (MME) based Extended range ~redicti n system and long range forecasting system have been developed and impl~f1ente in IMD.
9. Common Alert Protocol (CAP) has been implemented as per WMO standard for severe weather warning. It is being utilized for Global MUltiJ11Hazartl Alert System t
11. IMD has launched seven of its services (Current Weather, Nowcast! City Forecast, Rainfall Information, Tourism Forecast, Warnings and Cycl?ne) with 'UMANG' mobile App for use by public. Moreover, !MD had derelopetl mobile App 'MAUSAM' for weather forecasting, 'Megbdoot' fo~ Agrl met advisory dissemination and 'Damini' for lightning alert. The commonAlert rotocol (CAP) developed by NDMA is also being implemented for dissemination of warning by IMD.

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Fi~~~~~e t;:e seri~~of rati~of severe Cyclonic storms to tota~YclOniC stbrm over North Indian Ocean for the period (1891 to 2020).

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Fig. 2: Trend in Frequency of Heavy Rainfall Days during monsoon (HAS seas n for the period (1989-2018).

Fig. 3: Heavy rainfall warning skill during 2021(FAR (False Alarm ate), (Missing Rate), PoD (Probability of Detection) and CSI (Critical Success Index).IMD has I igh skill for heavy rainfall warning upto Day 5 as POD for Day 5 is more than 50%.

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Fig.4: All India Summer months (April to June) 2021vs 2014-20 heat wav skill cores

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Fig.S: Annual average (a) track forecast errors (km) and (b) during 2021 compared to long period average errors during 2016-20. skill (%)

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Fig.6: Skill Scores for 24 hr Thunderstorm forecast by IMD during past 6 I 16to 2021)

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