real time decision support system in reserrvoir and flood management system framework
TRANSCRIPT
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Reservoir Operation and Flood Management System Framework
Guna Paudyal, M.Eng. Ph.D. Senior Water Resources Management ExpertTeam Leader RTDSS Projects (HP-2)
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Drought
Irrigation
Hydropower
Domesticwater
Water quality
Challenges & Technology Requirements
Trans-boundary
Flood
Floods DroughtsOperational Seasonal Strategic
Multiple objectives, stakeholders
Inflow forecastReservoir operation OptimizationFood forecastingWarning disseminationBenefits
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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
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46 major and medium reservoirsOperated 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 MCMKhadakwasala = 800 MCM
Koyna= 3,000 MCM
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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
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Reservoir Operation & Flood Management System Framework
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To save livesTo 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
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Time Delay
Time Delay
Time Delay
Time Delay
NOW!
Future!
Hydrological modelling technology helps to get additional forecast lead time
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ProcessInputs Outputs
Precipitation, Evaporation, FlowsReal Time data from RTDAS, met forecasts
Reservoir Details, water demands
Predicted Runoff
Hydrographsfrom all
sub-catchments
Catchment Rainfall-runoff
model
Overview of the Modelling Process
Hydrodynamic River routingFlood Forecast Models
Data AssimilationInundation mapping tools
Data from RTDAS/ Web sites, River & flood Plain topography
Flood Forecast, Early warningFlood maps
Basin Simulation model
Optimal Water Allocation
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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
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Interactive Reservoir Operation System
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Dissemination system
Flood Bulletin
SMS & E-mail alerts
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15
Trial Operation 2013 Monsoon
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16
STATISTICAL ANALYSIS OF RESERVOIR WATER LEVEL
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6 December, 2012© DHI #17
1-day forecast comparison
2-day forecast comparison
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Optimization of Reservoir Operational short term during flood emergencies
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Results at Koyna
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Reservoir Operational Guidance System (ROS)
Results at Arjunwad (Koyna Complex)
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Optimum operation during flood season (Khadakwasala example)
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Example of Khadakwasala complex (average year)
Long term operation for optimum water resources management
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Optimization of Reservoir Operation(long term operation – planning)
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Optimization to satisfy irrigation and water demands
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Optimized WL observed WL
Wat
er L
evel
(m
)
Depleted during dry & average years, filled up in flood years (Pawana)
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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
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Over view of the MIKE Customized RTDSS
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Flood Forecasting including inundation d/s of Nangal
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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