ANSWER
MINISTER FOR MINISTRY OF SCIENCE AND TECHNOLOGY AND
MINISTRY OF EARTH SCIENCES
(DR. HARSH VARDHAN)
(a) Forecasting and prevention of floods are the responsibilities of the Central Water Commission (CWC), Ministry of Water Resources. However, India Meteorological Department (IMD) supports flood warning services of Central Water Commission (CWC) by providing observed and predicted rainfall. In order to meet specific requirements of flood warnings by CWC, India Meteorological Department (IMD) operates Flood Meteorological Offices (FMOs) at 13 locations viz., Agra, Ahmedabad, Asansol, Bhubaneshwar, Guwahati, Hyderabad, Jalpaiguri, Lucknow, New Delhi, Patna, Srinagar, Bengaluru and Chennai. Apart from this, IMD also supports Damodar Valley Corporation (DVC) by providing Quantitative Precipitation Forecast (QPF) for Damodar river basin areas for their flood forecasting activities. Flood Meteorological Offices (FMO) provide meteorological support to the CWC for issuing flood warnings well in advance in respect of 153 river basins.
CWC issues flood forecasts as a non-structural measure of flood management, to concerned State Governments depending on the requisition from them at identified locations. CWC also issues inflow forecasts to identified reservoirs for proper reservoir regulation. Flood forecast formulation methodology used by CWC includes:
Conventional statistical correlation methodology: It includes gauge to gauge correlation between base station (upstream of forecasting station) and forecasting station. This method provides advance warning time from 6 to 24 hrs depending upon the terrain. IMD provides 3 day Quantitative Precipitation Forecast (QPF) in ranges of 0, 0.1-10mm, 11-25 mm, 26-37mm, 38-50 mm, 51-75 mm, 76-100 mm and >100 mm for various river sub-basins to the concerned Divisional Flood Control Room of CWC through their Flood Meteorological Offices spread all over the country.
Rainfall-Runoff mathematical modeling technology: It includes mathematical modeling of river basin based on rainfall runoff methodology. Input taken is rainfall provided by IMD through its Automatic Weather Station (AWS) & Automatic Rain Gauge (ARG) stations and CWC telemetry stations. The three days advance forecast is generated using various available rainfall data products as a major input into the system like IMD Gridded Rainfall product and other global rainfall products such as GSMaP (Global Satellite Mapping of Precipitation), GPM (Global Precipitation Measurement) and the IMD forecasted rainfall data (WRF- Weather Research and Forecast, GFS- Global Forecast System). Numerical Weather Prediction (NWP) model products viz. Weather Research and Forecast (WRF) model which is given for 3 days and Global Forecasting System (GFS) model product which is given for 10 days are seamlessly shared by IMD for use in mathematical models by CWC. Mathematical model has been used for formulation of advisories and these advisories are shared with stake holders using dedicated website.
Besides this, CWC is also providing inundation forecast in the same platform using 2-dimensional models for main Brahmaputra and is also shared with beneficiaries through the same website.
Modernization of dissemination of flood forecasts: Dissemination of flood forecasts have been modernised by having a dedicated website. The flood information is also shared via various social media platforms of CWC Flood Forecast dissemination system. Daily Flood Situation Report cum Advisories are also shared with all stake holders as well as general public. CWC has signed an MOU with M/s Google Inc for using their vast repository of high resolution Digital Elevation Models and the power of dissemination to send alerts regarding inundation through Google Platform using the flood forecasts issued by CWC.
(b) Flood Forecasting by CWC uses all the latest technology including remote-sensing, Geographical Information System (GIS), Internet, Artificial Intelligence and Machine Learning in development/ running/ formulation and calibration of Mathematical models and for providing Inundation Alerts which are closely at par with international standards.
(c)-(d) Yes Sir. At the end of flood season CWC prepares an Appraisal of flood forecasting activity in which the accuracy/performance of the forecasts are compiled. In conventional methodology of forecast, a level forecast is deemed to be accurate if the forecasted level is within +/- 0.15 m from the actual river water level attained at the forecasted time. Similarly, for inflow forecast, if the forecasted inflow is within +/- 20% from the actual inflow into the reservoir. Statement showing the accuracy of the system for the period 2000-2020 is given in Annexure-I.
(e) Ministry of Earth Sciences has not received any proposals in this regard.
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Annexure I
FLOOD FORECASTING PERFORMANCE FROM 2000 TO 2020
(as provided by Central Water Commission)
Year No.of Level Forecasts issued No.of Inflow Forecasts issued Total No.of Forecasts issued
Total Within +/-15 cm of deviation from actual Accuracy (%) Total Within +/-20% cumec of deviation from actual Accuracy (%) Total Within +/-15 cm or +/-20% cumec of deviation from actual Accuracy (%)
2000 5622 5504 97.90 821 747 90.99 6443 6251 97.02
2001 4606 4533 98.42 857 809 94.40 5463 5342 97.79
2002 3618 3549 98.09 623 602 96.63 4241 4151 97.88
2003 5989 5789 96.66 611 586 95.91 6600 6375 96.59
2004 4184 4042 96.61 705 654 92.77 4889 4696 96.05
2005 4323 4162 96.28 1295 1261 97.37 5618 5423 96.53
2006 5070 4827 95.21 1593 1550 97.30 6663 6377 95.71
2007 6516 6339 97.28 1707 1651 96.72 8223 7990 97.17
2008 5670 5551 97.90 1021 1003 98.24 6691 6554 97.95
2009 3343 3298 98.65 667 629 94.30 4010 3927 97.93
2010 6491 6390 98.44 1028 988 96.11 7519 7378 98.12
2011 4848 4795 98.91 1143 1109 97.03 5991 5904 98.55
2012 4200 4136 98.47 831 803 96.63 5031 4939 98.17
2013 5741 5471 95.30 1319 1289 97.73 7060 6760 95.75
2014 3884 3804 97.94 888 863 97.18 4772 4667 97.80
2015 3500 3429 97.97 572 562 98.25 4072 3991 98.01
2016 4969 4891 98.43 1270 1057 83.23 6239 5948 95.34
2017 5085 4975 97.84 1212 926 76.40 6297 5901 93.71
2018 4969 4871 98.03 1882 1624 86.29 6851 6495 94.80
2019 6004 5773 96.15 3750 2678 71.41 9754 8451 86.64
2020 8243 8133 98.67 3478 3065 88.13 11721 11198 95.54
Average 5089 4965 97.56 1299 1165 89.68 6388 6129 95.95
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