1. Energy and Water
Climate change impact assessment on hydro-electric project using multi-model climate ensemble
Vinod Chilkoti, PhD Candidate
Tirupati Bolisetti, Associate Professor
Ram Balachandar, Professor

2. Contents Introduction Objective Methodology Case study Results
Hydrological modeling
Power generation
Climate change projections
Case study
Results
Conclusions
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3. Introduction Electricity generation –
non-renewable ~ 78%
renewable sources ~ 22% (source:
~ 280%
(source: KeyWorld_Statistics_2015,
Burning more fossil fuels to generate the electricity
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4. Introduction Climate change
~ 200%
40% of energy-related CO2 emissions is from electricity generation
Focusing on renewable sector should help in reducing the emission
Hydropower contributes 85% in renewable sector
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5. Objective
If Hydropower can deliver clean and sustainable energy, does climate change has effect on it?
Climate change impacts assessment on Hydropower generation
Broadly quantify the changes in energy generation
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6. Methodology Climate change impact assessment (CCIA) process Input data
Climate modeling
Role of climate scientist
Climate model simulation
Model output
Reading model output
Downscaling and Bias correction
Climate change impact assessment (CCIA) study
Engineers’ Interest area
Hydrological model simulation
Impact Assessment

7. Methodology CCIA for hydropower project
Power (P) a Rate of water flow (Q)
Important to quantify water
Hydrological modeling
Power generation
Climate change projections
Climate change impact assessment (CCIA)
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8. Case Study C.H. Corn Hydroelectric Plant, 12MW
Located on Ochlockonee River
20 miles southwest of Tallahassee
One of only two hydroelectric plants in state of Florida
~3% of Tallahassee’s typical power need of 425 MW
C.H. Corn Hydroelectric Power Plant
FLORIDA
Source:
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9. Hydrological Modeling
Modeling the hydrological system to generate flow series at a given project location
Climate Inputs: Precipitation & Temperature
MOPEX dataset : 438 US catchments
Conceptual model HYMOD has been used
Model is calibrated and validated based observed flow data on
Calibration period : (15 years)
Validation period : (5 years)
Flow data available at USGS Gage on Ochlockonee River
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10. Hydrological Modeling
Flow series of the calibrated hydrological model
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11. Power Generation Annual hydropower generation
Monthly hydropower generation
Source for actual hydropower energy generation data:
Source for actual hydropower energy generation data:
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12. Climate change projections
Multi-model climate ensemble
Regional climate models (RCM); resolution ~ 50km
Climate projection for future period : years 2091 – 2100
Coordinated Regional Downscaling Experiment (CORDEX)
Source F. Giorgi, 2008
CORDEX (NAM)
Source:
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13. Climate change projections
Multi-model climate ensemble
Model No
Regional Climate Model (RCM)
Driving Global Climate Model (GCM)
RCM
Modeling Agency*
GCM 1
CanRCM4
CCCma
CanESM2 2
RCA4
SMHI 3
EC-EARTH
ICHEC 4
HIRHAM5
DMI 5
CRCM5
UQAM 6
MPI-ESM-LR
MPI-M
* CCCma- Canadian Center for Climate Modeling and Analysis
SMHI – Swedish Meteorological and Hydrological Institute
DMI – Danish Meteorological Institute
ICHEC – Irish Center for High End Computing
UQAM-Université du Québec à Montréal 
MPI –Max Planck Institute of Meteorology
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14. Results Projection (2091-2100) for Precipitation
Annual change in precipitation depth ~ +14%
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15. Results Projection (2091-2100) for Precipitation
Monthly precipitation ensemble
Seasonal precipitation ensemble
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16. Results Projection (2091-2100) for Temperature
Max. Temperature ensemble
Min. Temperature ensemble
Annual change in min. temperature ~ C
Annual change in max. temperature ~ +2.00C
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17. Results Projection (2091-2100) for Streamflow
Obtained by forcing the projected climate data into hydrological model
Annual change in streamflow ~ +21%
Change in winter ~ +72%; summer ~ -24%
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18. Results Projection (2091-2100) for streamflow
Monthly streamflow ensemble
Seasonal streamflow ensemble
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19. Results Projection (2091-2100) for hydropower generation
Estimated streamflow is used for energy calculations
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20. Results Projection (2091-2100) for hydropower generation
Monthly energy generation ensemble
Seasonal energy generation ensemble
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21. Results Projection (2091-2100) for hydropower generation
Change w.r.t present ~ +56% % % %
~ % (annual average)
With more electricity demand in summer-peaking states, this trend may worry the decision-makers
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22. Results
Results from climate Model-1 & Model-2 were mostly extreme among all models
Their results were influencing the annual average change statistics
These models were governed by similar boundary conditions
Imperative to carry out CCIA study using climate model ensemble
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23. Conclusions
Climate change is bound to have a significant effect on the precipitation and temperature patterns
Increased precipitation and temperature
Cumulative effect on the hydrological regime
Seasonal effects are more worrying
Important to study projections through multi-model climate ensembles
Climate model uncertainty is a major source of uncertainty
Hydropower is projected to increase in winter/autumn and decrease in summer
Hydropower can deliver clean and sustainable energy
Infrastructure need to be adapted to this varying pattern of future electricity supply
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24. Acknowledgements
Partial funding support by the following is gratefully acknowledged:
Natural Sciences and Engineering Research Council (NSERC) of Canada
University of Windsor
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25. THANK YOU
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