The Once and Future Pulse of Colorado River Flow Mitigating Water Supply Risk Under Changing Climate Balaji Rajagopalan Department of Civil, Environmental and Architectural Engineering And Cooperative Institute for Research in Environmental Sciences (CIRES) University of Colorado Boulder, CO 23 February, 2010 Presentation to Michael Kinter-Meyer Energy and Environment Directorate Pacific Northwest National Laboratory

Key Questions What is the Colorado River System-wide Water supply risk profile under climate change? Need to consider the entire syste (~60AF Storage) Need to generate streamflow scenarios consistent with climate projections and combining (Paleo?) Is there flexibility within the existing management framework? Can Management Mitigate the future risk? Rajagopalan et al. (2009, WRR)

Colorado River Basin Overview 7 States, 2 Nations Upper Basin: CO, UT, WY, NM Lower Basin: AZ, CA, NV Fastest Growing Part of the U.S. Over 1,450 miles in length Basin makes up about 8% of total U.S. lands Highly variable Natural Flow which averages 15 MAF 60 MAF of total storage 4x Annual Flow 50 MAF in Powell + Mead Irrigates 3.5 million acres Serves 30 million people Very Complicated Legal Environment Denver, Albuquerque, Phoenix, Tucson, Las Vegas, Los Angeles, San Diego all use CRB water DOI Reclamation Operates Mead/Powell Source:Reclamation 1 acre-foot = 325,000 gals, 1 maf = 325 * 10 9 gals 1 maf = 1.23 km 3 = 1.23*10 9 m 3

Colorado River Demand - Supply UC CRSS stream gauges LC CRSS stream gauges

Colorado River at Lees Ferry, AZ Recent conditions in the Colorado River Basin Paleo Context Below normal flows into Lake Powell %, 59%, 25%, 51%, 51%, respectively 2002 at 25% lowest inflow recorded since completion of Glen Canyon Dam Some relief in % of normal inflows Not in 2006 ! 73% of normal inflows 2007 at 68% of Normal inflows 2008 at 111% of Normal inflows 5 year running average

Winter and Summer Precipitation Changes at 2100 – High Emissions Summer Hatching Indicates Areas of Strong Model Agreement

StudyClimate Change Technique (Scenario/GC M) Flow Generation Technique (Regression equation/Hydrologic model) Runoff ResultsOperations Model Used [results?] Notes Stockton and Boggess, 1979 ScenarioRegression: Langbein's 1949 US Historical Runoff- Temperature- Precipitation Relationships +2C and -10% Precip = ~ -33% reduction in Lees Ferry Flow Results are for the warmer/drier and warmer/wetter scenarios. Revelle and Waggoner, 1983 ScenarioRegression on Upper Basin Historical Temperature and Precipitation +2C and -10% Precip= -40% reduction in Lee Ferry Flow +2C only = -29% runoff, -10% Precip only = -11% runoff. Nash and Gleick, 1991 and 1993 Scenario and GCM NWSRFS Hydrology model runoff derived from 5 temperature & precipitation Scenarios and 3 GCMs using doubled CO2 equilibrium runs. +2C and -10% Precip = ~ -20% reduction in Lee Ferry Flow Used USBR CRSS Model for operations impacts. Many runoff results from different scenarios and sub- basins ranging from decreases of 33% to increases of 19%. Christensen et al., 2004 GCMUW VIC Hydrology model runoff derived from temperature & precipitation from NCAR GCM using Business as Usual Emissions. +2C and -3% Precip at 2100 = -17% reduction in total basin runoff Created and used operations model, CRMM. Used single GCM known not to be very temperature sensitive to CO2 increases. Hoerling and Eischeid, 2006 GCMRegression on PDSI developed from 18 AR4 GCMs and 42 runs using Business as Usual Emissions. +2.8C and ~0% Precip at = -45% reduction in Lee Fee Flow Christensen and Lettenmaier, 2006 GCMUW VIC Hydrology Model runoff using temperature & precipitation from 11 AR4 GCMs with 2 emissions scenarios. +4.4C and -2% Precip at = -11% reduction in total basin runoff Also used CRMM operations model. Other results available, increased winter precipitation buffers reduction in runoff.

2C to 6 C -40% to +30% Runoff changes in ~115% ~80% CRB Runoff From C&L Precipitation, Temperatures and Runoff in Triangle size proportional to runoff changes: Up = Increase Down = Decrease Green = Blue = Red =

Scale Matters Runoff Efficiency (How much Precip actually runs off) Varies Greatly from ~5% (Dirty Devil) to > 40% (Upper Mainstem) You can’t model the basin at large scales and expect accurate results GCMs (e.g. Milly, Seager) and H&E 2006 may get the right answer, but miss important topographical effects 14.4% 16.1% 24.9% 14.1% 6.3% 9.9% 11.8% 2.4% % of Total Runoff

Most runoff comes from small part of the basin > 9000 feet Very Little of the Runoff Comes from Below 9000’ (16% Runoff, 87% of Area) 84% of Total Runoff Comes from 13% of the Basin Area – all above 9000’ % Total Runoff Basin Area Runoff

Future Flow Summary Future projections of Climate/Hydrology in the basin based on current knowledge suggest Increase in temperature with less uncertainty Decrease in streamflow with large uncertainty Uncertain about the summer rainfall (which forms a reasonable amount of flow) Unreliable on the sequence of wet/dry (which is key for system risk/reliability) The best information that can be used is the projected mean flow Clearly, need to combine paleo + observed + projection to generate plausible flow scenarios

System Risk Streamflow Simulation Prairie et al. (2008) WRR System Water Balance Model Management Alternatives (Reservoir Operation + Demand Growth) Rajagopalan et al. (2009), WRR

Lees Ferry Natural Flow (15.0) + Intervening flows (0.8) - Upper Basin Consumptive Use (4.5+) Evaporation (varies with stage; 1.4 avg declining to 1.1) “Bank Storage is near long-term equilibrium’ LB Consumptive Use + MX Delivery + losses (9.6) Climate Change -20% LF flows over 50 years Initial Net Inflow = +0.4 Water Balance Model: Our version

Water Balance Model Storage in any year is computed as: Storage = Previous Storage + Inflow - ET- Demand Upper and Lower Colorado Basin demand = 13.5 MAF/yr Total Active Storage in the system 60 MAF reservoir Initial storage of 30 MAF (i.e., current reservoir content) Inflow values are natural flows at Lee’s Ferry, AZ + Intervening flows between Powell and Mead and below Mead ET computed using Lake Area – Lake volume relationship and an average ET coefficient of Transmission Losses ~6% of Releases

Flow and Demand Trends applied to the simulations Red – demand trend 13.5MAF – 14.1MAF by 2030 Blue – mean flow trend 15MAF – 12MAF By MAF/year Under 20% - reduction

AlternativeDemandShortage Policy Initial Storage A 7.5 MaF to LB, 1.5 MaF to MX and UB deliveries per EIS depletion schedule 333 KaF DS when S < 36%, 417 KaF DS when S < 30% and 500 KaF DS when S <23% 30 MAF B 7.5 MaF to LB, 1.5 MaF to MX and UB deliveries per EIS depletion schedule 5% DS when S < 36%, 6% DS when S < 30% and 7% DS when S < 23% 30 MAF C 7.5 MaF to LB, 1.5 MaF to MX and UB deliveries at a 50% rate of increase as compared to the EIS depletion schedule 5% DS when S < 36%, 6% DS when S < 30% and 7% DS when S < 23% 30 MAF D 7.5 MaF to LB, 1.5 MaF to MX and UB deliveries at a 50% rate of increase as compared to the EIS depletion schedule 5% DS when S < 36%, 6% DS when S < 30% and 7% d DS when S < 23% 60 MAF* E 7.5 MaF to LB, 1.5 MaF to MX and UB deliveries at a 50% rate of increase as compared to the EIS depletion schedule 5% DS when S < 50%, 6% DS when S < 40%, 7% d DS when S < 30% and 8 % DS when S < 20% 30 MAF Management and Demand Growth Combinations Table 1 Descriptions of alternatives considered in this study. (LB = Lower Basin, MX = Mexico, UB = Upper Basin, DS = Delivery Shortage and S = Storage). Per EIS depletion schedule the total deliveries are projected to be 13.9 MaF by 2026 and 14.4 MaF by * One alternative with full initial storage (E) illustrates the effects of a full system.

Natural Climate Variability

Climate Change – 20% reduction Climate Change – 10% reduction

20% Reduction 10% Reduction Shortage Volume Under Climate Change

Initial Demand – 12.7MaF Actual Average Consumption In the recent decade Sensitivity to Initial Demand - 20% reduction Initial Demand – 13.5MaF

Summary Water supply risk (i.e., risk of drying) is small (< 5%) in the near term ~2026, for any climate variability (good news) Risk increases dramatically by about 7 times in the three decades thereafter (bad news) Risk increase is highly nonlinear There is flexibility in the system that can be exploited to mitigate risk. Considered alternatives provide ideas Smart operating policies and demand growth strategies need to be instilled Demand profiles are not rigid Delayed action can be too little too late Water supply risk occurs well before any ‘abrupt’ climate change – even under modest changes Nonlinear response

What do we do?