Colorado River Water Availability Assessment Under Climate Variability Annie Yarberry 1, Balaji Rajagopalan 2,3 and James Prairie 4 1. Humboldt State University, Arcata, CA 2. University of Colorado, Boulder, CO 3. CIRES, University of Colorado, Boulder, CO 4. USBR, University of Colorado, Boulder, CO AGU Fall 2010

Background 60 MAF reservoir storage 4 times annual flow 50 MAF in Lake Powell and Mead Increasing Demand Decreasing Streamflows Compacts/agreements made in the wettest part of early 20 th century Under stress in recent decades Water supply risk and sustainability

Background… 2000 ~ 2008 Declining lakes Mead and Powell 5 years of 10 maf/yr 66% of average flows Worst drought in historic record How bad can it go?? Climate change portends 0 ~ 25% reduction in the coming 4-5 decades Lake Mead Volume in Millions of Acre-Feet

Climate Change – Back to the Future? Long dry epochs are very common 20 th century unusually wet Climate change studies indicate a consensus of 0 ~ 30% decline in mean flows in 4-5 decades

Climate Change – Water supply Risk What is the risk to water supply under climate change – can management mitigate? (Rajagopalan et al., 2009) Basin-wide simple water balance model Entire storage as a ‘bath tub’ Stochastic streamflow ensembles from observed+paleo+climate change projections (Prairie et al., 2008) 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 approximately 7 times in the three decades thereafter (bad news) Smart operating policies and demand growth strategies need to be instilled

This Study – Research Question What is the probability distribution of optimal “yield” from the given storage capacity in the basin and ensemble of streamflow sequences? Can be a complementary tool for stakeholders to make risk-based planning and development decisions.

Methodology – Constrained Optimization (Linear) Y=Yield (MAF) Spill t =Overflow (MAF) Q t =Paleo-reconstructed inflow (MAF/yr) K=Reservoir capacity (MAF) S t-1 =Previous year storage (MAF) S t =Current storage (MAF) Minimum Storage is specified System storage = 60MAF Average storage is computed for the optimal yield Y opt, as the average of:

Methodology… 10, year ensembles of streamflow sequences each generated using observed and paleo flows (Prairie et al., 2008) – Natural Variability 10 and 20 percent linear flow reductions applied to incorporate climate change projections (Rajagopalan et al., 2009) – Climate Change Projections For each ensemble: optimal yield average storage standard deviation of storage For the three scenarios - natural variability, 10, and 20% flow reductions – we explored five reservoir conditions Initially full Minimum storage at: Zero MAF; and 15%, 30%, and 40% of capacity

Results Natural Variability – Minimum Storage of Zero Optimal Yield (MAF/yr) Average Storage (MAF) Storage Standard Deviation (MAF) 20% Streamflow Reduction – Minimum Storage of Zero

Results All Scenarios – Minimum Storage at Zero Optimal Yield (MAF/yr) Average Storage (MAF) Storage Standard Deviation (MAF) Natural Variability 10% Flow Reduction 20% Flow Reduction

Results.. Natural Variability - Minimum Storage at 40% Capacity Optimal Yield (MAF/yr) Average Storage (MAF) Storage Standard Deviation (MAF) 20% Streamflow Reduction - Minimum Storage at 40% Capacity

Results.. All Scenarios - Minimum Storage at 40% Capacity Optimal Yield (MAF/yr) Average Storage (MAF) Storage Standard Deviation (MAF) Natural Variability 10% Flow Reduction 20% Flow Reduction 16 35

Results.. Condition Yield (MAF/yr) Reliability (%) Minimum storage of zero Natural Variability % flow reduction Minimum storage at 40% capacity Natural Variability % flow reduction

Results..

Summary and Conclusion A simple system-wide water balance model to assess ‘Optimal Yield’ for a given storage and streamflow sequence was developed for the Colorado River Basin Natural variability, 10% and 20% reduction in mean flows due to climate change were considered Reliability of current consumption ~ 12.7MaF ~99% when the system is let to go dry for any flow scenario Drops to ~82% when minimum storage is set to 24MaF and for 20% reduction due to climate change scenario

Summary and Conclusion Reliability of planned demand of ~ 13.5MaF Drops to ~57% when minimum storage is set to 24MaF and for 20% reduction due to climate change scenario Higher demands have progressively less reliability The PDFs of ‘optimal yields’ provide stakeholders with estimates of risks for various scenarios If specific sub-system ‘yields’ and their risks are desired – full system model (e.g., CRSS) needs to be run

Acknowledgements NSF-REU Program at University of Colorado at Boulder, Summer, 2010

Thank You! Questions?

References Rajagopalan, B., K. Nowak, J. Prairie, M. Hoerling, B. Harding, J. Barsugli, A. Ray and B. Udall; Water supply risk on the Colorado River: Can management mitigate?, Water Resources Research, 45, W08201, Christensen, N., A. Wood, N. Voisin, D. Lettenmaier, and R. Palmer; The effects of climate change on the hydrology and water resources of the colorado river basin, Climatic Change, 62, (1-3), U.S. Department of Interior, U.S. Bureau of Reclamation; Colorado River interim guidelines for lower basin shortages and the coordinated operations for lake powell and lake mead, Final EIS, 2007.

Climate Change Studies 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