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Balaji Rajagopalan, Kenneth Nowak University of Colorado, Boulder, CO James Prairie USBR

A Streamflow Generation Technique Under Climate Change Using Paleo and Observational Data for Colorado River. Balaji Rajagopalan, Kenneth Nowak University of Colorado, Boulder, CO James Prairie USBR Ben Harding Amec, Boulder Marty Hoerling ESRL/NOAA Hydrology Days, 2008.

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Balaji Rajagopalan, Kenneth Nowak University of Colorado, Boulder, CO James Prairie USBR

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  1. A Streamflow Generation Technique Under Climate Change Using Paleo and Observational Data for Colorado River Balaji Rajagopalan, Kenneth Nowak University of Colorado, Boulder, CO James Prairie USBR Ben Harding Amec, Boulder Marty Hoerling ESRL/NOAA Hydrology Days, 2008

  2. UC CRSS stream gauges LC CRSS stream gauges Lees Ferry

  3. Below normal flows into Lake Powell 2000-2004 62%, 59%, 25%, 51%, 51%, respectively 2002 at 25% lowest inflow recorded since completion of Glen Canyon Dam Some relief in 2005 105% of normal inflows Not in 2006 ! 73% of normal inflows Current 2007 forecast 130% of normal inflows Recent conditions in the Colorado River Basin Colorado River at Lees Ferry, AZ 5-year running average

  4. Paleo Reconstructions observed record Woodhouse et al. 2006 Stockton and Jacoby, 1976 Hirschboeck and Meko, 2005 Hildalgo et al. 2002

  5. Past Flow Summary • Paleo reconstructions indicate • 20th century one of the most wettest • Long dry spells are not uncommon • 20-25% changes in the mean flow • Significant interannual/interdecadal variability • Rich variety of wet/dry spell sequences • All the reconstructions agree greatly on the ‘state’ (wet or dry) information • How will the future differ?

  6. IPCC 2007 AR4 Projections Wet get wetter and dry get drier… Southwest Likely to get drier

  7. IPCC 2007 Southwest North America Regional Findings Annual mean warming likely to exceed global mean Western NA warming between 2C and 7C at 2100 In Southwest greatest warming in summer Precipitation likely to decrease in southwest Snow season length and depth very likely to decrease Less agreement on the upper basin climate – important for water generation in the basin Stuff and m

  8. National Geographic, Feb 2008 Science, February 1, 2008

  9. Colorado River Climate Change Studies over the Years Early Studies – Scenarios, About 1980 Stockton and Boggess, 1979 Revelle and Waggoner, 1983* Mid Studies, First Global Climate Model Use, 1990s Nash and Gleick, 1991, 1993 McCabe and Wolock, 1999 (NAST) IPCC, 2001 More Recent Studies, Since 2004 Milly et al.,2005, “Global Patterns of trends in runoff” Christensen and Lettenmaier, 2004, 2006 Hoerling and Eischeid, 2006, “Past Peak Water?” Seager et al, 2007, “Imminent Transition to more arid climate state..” IPCC, 2007 (Regional Assessments) Barnett and Pierce, 2008, “When will Lake Mead Go Dry?” National Research Council Colorado River Report, 2007 Stuff and m

  10. A PDF of model outputs is not a PDF of the future.

  11. Models Precip and Temp Biases Models show consistent errors (biases) Western North America is too cold and too wet Weather models show biases, too Can be corrected

  12. Christensen & Lettenmaier, 2006Colorado River Projections - Mean Results 11AR 4 Models, 2 Scenarios B1(Low) & A2 (High) Very different results from C&L, 2004 Increased Winter Precipitation important Caveats: Does hydrology model understate summer drying? Means can be deceptive

  13. 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

  14. Regression CRSSCRMM Hydrology Models:NWSRFSVICPRMS Hypothetical Scenarios Progression of Data and Models in studies about the influence of climate change on streamflows in the Colorado River Basin 3.Water Supply Operations Model 1.Climate Change Data Source 2.Flow Generation Technique Wet / Dry Spell Sequence General CirculationModel Temperature Precipitation Streamflow OR Reservoir storage Hydroelectric powerUB Releases Stuff and m

  15. Recent Dry Spell not unusual, based on Paleo reconstructions • Colorado River System has enormous storage of approx 60MAF ~ 4 times the average annual flow - consequently, • wet and dry sequences are crucial for system risk/reliability assessment • Current climate change flow ensembles are poor at generating the wet/dry sequences, summer inflows, and temperature sensitive - using these flows as is leads to • over estimation of ET • over estimation of demands • over estimation of system risk • Streamflow generation tool that can generate flow scenarios in the basin that are realistic in • wet and dry spell sequences • Magnitude • Need for combining all the available information Motivation

  16. Need for Combination(Paleo, Observational and Climate Change projection) • Paleo reconstructions are • Good at providing ‘state’ (wet or dry) information • Poor with the magnitude information • Observations are reliable with the state and magnitude • Climate change projections have • Uncertain sequence and magnitude information • Reasonable projections of the mean flow • Observed Annual average flow (15MAF) is used to define wet/dry state.

  17. Generate system state Generate flow conditionally (K-NN resampling) Proposed ApproachModification to Prairie et al. (2008, accepted, WRR) Nonhomogeneous Markov Chain Model on the observed & Paleo data f (x_t | S_t) Threshold for Re-sampling based On future mean flow Projection

  18. window = 2h +1 Discrete kernal function h Source: Rajagopalan et al., 1996

  19. Nonhomogenous Markov model with Kernel smoothing (Rajagopalan et al., 1996) • TP for each year are obtained using the Kernel Estimator • h determined with LSCV • 2 state, lag 1 model was chosen • ‘wet (1)’ if flow above annual median of observed record; ‘dry (0)’ otherwise. • AIC used for order selection (order 1 chosen)

  20. Transition Probabilities

  21. Flow Generation Steps • Nonhomogenous Markov Chain • Lag-1, 2-state, moving window Markov chain on the Paleo recons • Transition probability for each year • Select a mean flow from the climate change projections • Generate 100-year state sequences from the NHMM transition probabilities (S_t) • Divide the observed streamflows into ‘wet’ and ‘dry’ states using this mean flow threshold • Conditionally re-sample streamflows from the observations based on the state – i.e., re-sample from the conditional PDF f(x_t | S_t) • Repeat steps 3-5, 1000 times to generate multi-century ensembles • Compute suite of statistics • Drought Length • Surplus Length • PDF of simulated streamflow • Flow statistics (mean, standard deviation, skew, etc.) • Using Water Balance model compute the statistics of system risk/reliability

  22. Storage in any year is computed as: • Storage = Previous Storage + Inflow - ET- Demand • Upper and Lower Colorado Basin demand = 13.5 MAF/yr • Lakes Powell and Mead are modeled as one 50 MAF reservoir • Initial storage of 30 MAF (i.e., current reservoir content) • Inflow values are natural flows at Lee’s Ferry, AZ • ET computed using Lake Area – Lake volume relationship and an average ET coefficient of 0.436 • Shortage EIS Criteria: • When storage reaches less than 36% capacity, demand (release) is reduced by 5% Water Balance Model

  23. Combined Area-volume RelationshipET Calculation ET coefficients/month (Max and Min) 0.5 and 0.16 at Powell 0.85 and 0.33 at Mead Average ET coefficient : 0.436 ET = Area * Average coefficient * 12

  24. PDF of generated streamflows 8MAF 10MAF 12MAF

  25. Drought and Surplus Statistics Surplus Length Surplus volume flow Drought Length Threshold (e.g., mean 15MAF) time Drought Deficit

  26. Drought Length Distribution (15MAF threshold) 8MAF 10MAF 12MAF

  27. Surplus Length Distribution (15MAF threshold) 8MAF Paleo 12MAF Observed 8MAF 10MAF 12MAF

  28. Deficit/Shortage Over 100-year traces Without Shortage Criteria With Shortage Criteria

  29. Probability of Reservoir Drying Up (based on 10000 years of simulation) Without Shortage Criteria With Shortage Criteria

  30. Probability of Reservoir Drying in any year over a 100-yr period (including intervening flows) Flow between Powell and Mead & Flow below mead (1.07 MAF) With 12MAF mean Are included in the water balance

  31. Years to first drying of the reservoir over a 100-yr period Without Shortage Criteria With Shortage Criteria

  32. Summary Statistics Without Shortage Criteria With Shortage Criteria

  33. Lake Level Risks based on Scenarios from Prairie et al. (2008) With CRSS Lake mead Lake Powell

  34. Summary • A flexible, simple and robust framework to combine paleo, observed and climate projection information • Streamflow scenarios generated have realistic wet/dry sequences – important for system risk/reliability estimation • Coupling these with simple water balance model indicate • Increased system risk (reservoir drying, unable to meet demands etc.) during the next century • Consistent with estimates from Paleo and Observed data (Prairie et al., 2006) • Shortage criteria reduces the system risk for 20-30% reduction in mean flow • The scenarios need to be driven through full CRSS model for system wide risk/reliability estimation

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