Reservoir Operation and Flood Management
System Framework
Guna Paudyal, M.Eng. Ph.D.
Senior Water Resources Management Exp...
Drought
Irrigation
Hydropower
Domestic
water
Water
quality
Challenges & Technology Requirements
Trans-boundary
Flood
Flood...
Modern IT Based solutions help us…
© DHI
…manage, organise and
analyse large amounts
of data
…make wise and robust
water m...
46 major and medium reservoirs
Operated with rigid operational
rule curves: keep the reservoirs
full towards the end of ra...
Sutlej & Beas
Catchments (in India) Decision supports required:
• To attain as high a level as possible
in Bhakra and Pong...
Reservoir Operation & Flood Management System Framework
To save lives
To minimize damage
To reduce risk
Data
Collection
Transmission
& Reception
Emergency
Response
Forecasts
Diss...
Time Delay
Time Delay
Time Delay
Time Delay
NOW!
Future!
Hydrological modelling technology helps to
get additional forecas...
ProcessInputs Outputs
Precipitation,
Evaporation, Flows
Real Time data from
RTDAS, met forecasts
Reservoir Details, water
...
6 December, 2012© DHI #11
Hydro-met Network (300 telemetry stations)
A Knowledgebase system containing
• Historical hydro-...
Interactive Reservoir Operation System
Dissemination system
Flood Bulletin
SMS & E-mail alerts
15
Trial Operation 2013 Monsoon
16
STATISTICAL ANALYSIS OF RESERVOIR WATER LEVEL
6 December, 2012© DHI #17
1-day forecast
comparison
2-day forecast
comparison
Optimization of Reservoir
Operational short term during flood
emergencies
Results at Koyna
Reservoir Operational Guidance System (ROS)
Results at Arjunwad (Koyna Complex)
Optimum operation during flood season (Khadakwasala example)
Example of
Khadakwasala complex
(average year)
Long term operation for optimum water resources management
Optimization of Reservoir Operation
(long term operation – planning)
Optimization to satisfy irrigation and water demands
580
585
590
595
600
605
610
615
10/06/01
10/08/01
10/10/01
10/12/01
1...
The BBMB RTDSS Process
Data Acquisition
System
Telemetry Data
IMD Data
RIMES Forecast
Modis Snow
Imageries
NASA Satellite
...
Over view of the MIKE Customized RTDSS
Flood Forecasting including inundation d/s of Nangal
Thank you
Specific presentation on the BBMB RTDSS
Details of Krishna - Bhima RTDSS
System Demos: C.S. Modak, Dr. Pandit, A...
Real time decision support system in reserrvoir and flood management system framework
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Real time decision support system in reserrvoir and flood management system framework

  1. 1. Reservoir Operation and Flood Management System Framework Guna Paudyal, M.Eng. Ph.D. Senior Water Resources Management Expert Team Leader RTDSS Projects (HP-2)
  2. 2. Drought Irrigation Hydropower Domestic water Water quality Challenges & Technology Requirements Trans-boundary Flood FloodsDroughtsOperational Seasonal Strategic Multiple objectives, stakeholders Inflow forecast Reservoir operation Optimization Food forecasting Warning dissemination Benefits
  3. 3. Modern IT Based solutions help us… © DHI …manage, organise and analyse large amounts of data …make wise and robust water management decisions …get the full benefit of real-time monitoring and early warning systems …optimise operations and planning
  4. 4. 46 major and medium reservoirs Operated with rigid operational rule curves: keep the reservoirs full towards the end of rainy season. But when heavy rain occurs in catchments, then the reservoirs are operated releasing sudden floods downstream causing damaging floods. High Level Government commission: Floods of 2005 and 2006 were devastating, strong needs of Integrated operation of reservoirs were felt. Reservoir operations should consider downstream flooding more explicitly, in addition to other water uses. Krishna-Bhima basins, 70,000 sq.km) Ujjani = 3,350 MCM Khadakwasala = 800 MCM Koyna= 3,000 MCM
  5. 5. Sutlej & Beas Catchments (in India) Decision supports required: • To attain as high a level as possible in Bhakra and Pong Reservoirs at the end of the monsoon filling period, depending on the acceptable risk of spilling. • In the event the Reservoir levels exceeds the FRL, to manage spills to minimise downstream flooding. • to the cushion to leave at the end of the depletion period to meet minimum demands. • to schedule the flow diverted through the Beas Satluj Link for optimal irrigation and hydropower
  6. 6. Reservoir Operation & Flood Management System Framework
  7. 7. To save lives To minimize damage To reduce risk Data Collection Transmission & Reception Emergency Response Forecasts Dissemination Flood Forecasting & Early Warning System As quickly as possible Making information travel faster than flood water As much time as possible before flood start
  8. 8. Time Delay Time Delay Time Delay Time Delay NOW! Future! Hydrological modelling technology helps to get additional forecast lead time
  9. 9. ProcessInputs Outputs Precipitation, Evaporation, Flows Real Time data from RTDAS, met forecasts Reservoir Details, water demands Predicted Runoff Hydrographs from all sub-catchments Catchment Rainfall-runoff model Overview of the Modelling Process Hydrodynamic River routing Flood Forecast Models Data Assimilation Inundation mapping tools Data from RTDAS/ Web sites, River & flood Plain topography Flood Forecast, Early warning Flood maps Basin Simulation model Optimal Water Allocation
  10. 10. 6 December, 2012© DHI #11 Hydro-met Network (300 telemetry stations) A Knowledgebase system containing • Historical hydro-met data • Links to RTDAS and Web based data • GIS and other data • Data analysis tools A suite of models • Catchment hydrology (rainfall-runoff) • Hydro-dynamics • Reservoir operation • Forecasting • Optimization
  11. 11. Interactive Reservoir Operation System
  12. 12. Dissemination system Flood Bulletin SMS & E-mail alerts
  13. 13. 15 Trial Operation 2013 Monsoon
  14. 14. 16 STATISTICAL ANALYSIS OF RESERVOIR WATER LEVEL
  15. 15. 6 December, 2012© DHI #17 1-day forecast comparison 2-day forecast comparison
  16. 16. Optimization of Reservoir Operational short term during flood emergencies
  17. 17. Results at Koyna
  18. 18. Reservoir Operational Guidance System (ROS) Results at Arjunwad (Koyna Complex)
  19. 19. Optimum operation during flood season (Khadakwasala example)
  20. 20. Example of Khadakwasala complex (average year) Long term operation for optimum water resources management
  21. 21. Optimization of Reservoir Operation (long term operation – planning)
  22. 22. Optimization to satisfy irrigation and water demands 580 585 590 595 600 605 610 615 10/06/01 10/08/01 10/10/01 10/12/01 10/02/02 10/04/02 10/06/02 10/08/02 10/10/02 10/12/02 10/02/03 10/04/03 10/06/03 10/08/03 10/10/03 10/12/03 10/02/04 10/04/04 10/06/04 10/08/04 10/10/04 10/12/04 10/02/05 10/04/05 10/06/05 10/08/05 10/10/05 10/12/05 10/02/06 10/04/06 10/06/06 10/08/06 10/10/06 10/12/06 10/02/07 10/04/07 WaterLevel(m) Optimized WL observed WL Depleted during dry & average years, filled up in flood years (Pawana)
  23. 23. The BBMB RTDSS Process Data Acquisition System Telemetry Data IMD Data RIMES Forecast Modis Snow Imageries NASA Satellite Precipitation Manual Observation Data Data Storage and Management System Architecture Data Flow Backup and Security Modeling Tools Weighted Rainfall Rainfall Runoff Snow Melt Hydrodynamic Allocation Model Flood Models Results Visualization and Dissemination Realtime DSS Interface Workstations Remote Locations Website – Dashboard Daily Reports Email and SMS Alerts
  24. 24. Over view of the MIKE Customized RTDSS
  25. 25. Flood Forecasting including inundation d/s of Nangal
  26. 26. Thank you Specific presentation on the BBMB RTDSS Details of Krishna - Bhima RTDSS System Demos: C.S. Modak, Dr. Pandit, Amit Garg, Sagarika Discussion on Technology: Claus Skotner, DHI Denmark

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