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. 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
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. Time Delay
Time Delay
Time Delay
Time Delay
NOW!
Future!
Hydrological modelling technology helps to
get additional forecast lead time
9.
10. 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
23. 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)
24. 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
27. 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
