1. Optimized Flood Control in the Columbia River Basin for a Global Warming Scenario 1Dept. of Civil and Env. Engineering, UW 2CSES Climate Impacts Group, UW 3U.S. Army Corps of Engineers, Seattle District Se-Yeun Lee 1, Alan F. Hamlet 2,1 Carolyn J. Fitzgerald 3 Stephen J. Burges 1 Dennis P. Lettenmaier 1, 2

2. Motivation

3. Mote P.W.,Hamlet A.F., Clark M.P., Lettenmaier D.P., 2005, Declining mountain snowpack in western North America, BAMS, 86 (1): 39-49 Red - Negative Trend Blue – Positive Trend Trends in April 1 Snowpack from 1950-1997

4. As the West warms, spring flows rise and summer flows drop Stewart IT, Cayan DR, Dettinger MD, 2005: Changes toward earlier streamflow timing across western North America, J. Climate, 18 (8): 1136-1155 Red - Negative Trend Blue – Positive Trend

5.  Increased Winter Flows  Reduced Spring Snowpack  Earlier Melt  Earlier Spring Peak Flow  Decreased Summer Flow Hydrologic Impacts of Global Warming on Snowmelt Dominant Rivers

6. Flood Control vs. Refill Full : Current Climate

7. Flood Control vs. Refill Streamflow timing shifts can reduce the reliability of reservoir refill Full : Current Climate + 2.25 o C : + 2.25 o C No adaption

8. Flood Control vs. Refill Streamflow timing shifts can reduce the reliability of reservoir refill Full : Current Climate : + 2.25 o C plus adaption + 2.25 o C : + 2.25 o C No adaption

9. Objective:  Develop Systems Engineering Procedures for Rebalancing Flood Control and Reservoir Refill Flood Control Refill

10. Test Case: The Columbia River Basin Multi-objective Reservoir System  Flood Control  Hydropower  Instream Flow  Water Supply  Recreation  Navigation

11. Major U.S. Flood Control Checkpoints The Dalles Columbia Falls Bonners Ferry

12. Optimization –Simulation Models  Developing Optimized Flood Control Curves  Testing and refining Proposed Rule Curves Methods

13.  Optimization model developed by the US Army Corps of Engineers  Penalty functions are used to constrain the Columbia River basin system operation Flood control penalties Flood control penalties Storage penalties Storage penalties HEC-PRM (Hydrologic Engineering Center’s Prescriptive Model)

14. ColSim  Flood control  Hydropower  Irrigation  Instream flow  Navigation  Recreation (Columbia Simulation Model)

15. Calibration Strategy Adjust Parameters

16. Calibration Results

17. Calibration Results (Refill) Dam Refill Probability (unit :%) 20 th Century Flow Current FC 20 th Century Flow Optimized FC Arrow8.118.6 Brownlee36.0 Duncan98.8 Dworshak9.314.0 Grand Coulee33.737.2 Hungry Horse29.131.4 Libby54.753.5 Mica66.367.4

18. Calibration Results (Flood Control) Red - Current FC Blue - Optimized FC Bonners Ferry Columbia Falls The Dalles

19. VIC Hydrologic Model  Macroscale Hydrologic Model developed by University of Washington (Variable Infiltration Capacity Hydrologic Model)

20. Climate Change Scenario  Remove historic monthly temperature trends from the daily time step forcing data  Increase the temperatures by 2 o C (annual average) in a seasonal pattern derived from several GCM simulations  Observed precipitation

21. Monthly Simulated Reservoir Inflows : Current Climate : Climate Change Scenario

22. Creating New Flood Control Rule Curves

23. Current Flood Rule Curves  Flood Control Rule Curves are selected by Apr-Jul Flow Volume

24. New Flood Rule Curves  Find Flow Volume Ranges for each category with an equal number of events Number of samples DW Apr-Jul Flow Volume FC 118< 1.8 MAF FC 217< 2.6 MAF FC 317< 2.9 MAF FC 417< 3.2 MAF FC 517>=3.2 MAF

25. New Flood Rule Curves  Find Flow Volume Ranges for each category with an equal number of events Number of samples DW Apr-Jul Flow Volume FC 118< 1.8 MAF FC 217< 2.6 MAF FC 317< 2.9 MAF FC 417< 3.2 MAF FC 517>=3.2 MAF  Use 80th percentile values for each categories as a Flood Rule Curve : 17 ensemble traces : 80th percentile of ensembles FC 4

26. Optimization Model Results Current Climate vs Climate Change Scenario

27. Dworshak storage (Dworshak Apr-Jul flow volume >2.40 MAF) a) 20 th Century Climate b) Climate Change Scenario FebMar : Ensemble traces : 80th percentile of ensembles

28. Libby storage (Libby Apr-Aug flow volume >5.5 MAF) a) 20 th Century Climate b) Climate Change Scenario MarApr : Ensemble traces : 80th percentile of ensembles  The changes in flood control rule curves are different for different projects

29. Simulation Model Results: Reservoir Storage Results

30. Refill Probability of Libby 20 th Century Flow Current FC CC Scenario Flow Current FC CC Scenario Flow HEC-PRM FC June 543971 July 614261 (Libby Apr-Aug flow volume>5.5 MAF) Full

31. Refill Probability of Dworshak 20 th Century Flow Current FC CC Scenario Flow Current FC CC Scenario Flow HEC-PRM FC May0025 June491944 July1400 (Dworshak Apr-Jul flow volume >2.40 MAF)

32. Changes in Reservoir Storage : The Dalles Apr-Aug < 68 MAF : 68 <= The Dalles Apr-Aug <100 MAF : The Dalles Apr-Aug >=100 MAF  Greatest Improvement occurs in Mid flow years ( )

33. Simulation Model Results: Flood-Frequency Results

34. Flood-Frequency Curves for Bonners Ferry

35. Flood-Frequency Curves for Columbia Falls

36. Flood-Frequency Curves for The Dalles

37. Conclusions  Optimization studies provide an objective method for rebalancing flood control and refill objectives in complex reservoir systems in response to hydrologic changes.  The changes in flood control rule curves are different for different projects, corresponding to different changes in flow volume and timing associated with warming in each sub basin.

38. Conclusions  Optimized flood control rule curves show reduced flood evacuation and earlier refill timing; up to one month earlier for a climate change scenario, compared with 20th century climate.  For the climate change scenario, optimized flood rule curves increase reservoir refill as well as the system storage in moderate and high flow years, while providing comparable levels of local and system-wide flood protection in comparison to the current flood control rule curves.

39. Future Work  Extension and refinement of our methods using daily time step optimization and simulation models is needed to evaluate the robustness of these techniques in more detailed planning studies.

40. Questions?