Summary The hydrology and water resources of the western U.S. are highly sensitive to climate. As part of the Accelerated Climate Prediction Initiative (ACPI) we coupled the Variable Infiltration Capacity (VIC) macroscale hydrology model to output from the NCAR/DOE Parallel Climate Model (PCM) to generate plausible future streamflows for the next century. These streamflows were then input into reservoir models to assess the implications of climate change on managed water resources. A key step in this process is the removal of bias in climate model predictions using a probability mapping procedure, and downscaling from the spatial scale of GCMs to the hydrology model scale. This step is facilitated by knowledge of the climatology (probability distribution) of the hydrology model forcings from both the climate model and observations. The three focus areas for the ACPI study were the Columbia, the Sacramento-San Joaquin, and the Colorado river basins. The Columbia and Sacramento-San Joaquin results are strongly sensitive to the seasonal shifts in streamflow associated with climate change. These hydrologic shifts are reflected in reductions of “firm” hydropower production, and/or reduction in the system’s ability to meet minimum spring and summer streamflow targets for fisheries protection and enhancement in the case of the Columbia. For the Sacramento-San Joaquin, the fraction of the time that the system would be in “critically dry” status more than doubles by mid-century. In contrast, the Colorado River reservoir system is almost completely insensitive to seasonal runoff shifts, but is highly sensitive to the approximately 10 percent reduction in annual runoff predicted by mid-century under the PCM scenarios. This change would reduce the system’s ability to meet water supply delivery targets, and to meet U.S.-Mexico treaty obligations. Climate Scenarios Global climate simulations, next ~100 yrs Downscaling & Bias Correction Monthly Precip, Temp Hydrologic Model (VIC) Natural Streamflow Reservoir Model DamReleases, Regulated Streamflow Performance Measures Reliability of System Objectives The following Department of Energy/National Center for Atmospheric Research Parallel Climate Model (DOE/NCAR PCM) runs were utilized; Historical; B06.22 (greenhouse CO2+aerosols forcing) Climate Control; B06.45 (CO2+aerosols at 1995 levels) Climate Change; B06.44 (BAU6, future scenario forcing) Climate Change; B06.46 (BAU6, future scenario forcing) Climate Change; B06.47 (BAU6, future scenario forcing) from NCDC observations from PCM historical run raw climate scenario bias-corrected climate scenario month m Note: future scenario temperature trend (relative to control run) removed before, and replaced after, bias-correction step. Hydrologic Model (VIC) The hydrologic model used was the VIC macroscale land surface model (see for model details). The model was run in a 24 hour timestep Water Balance mode at 1/8° resolution. Forcing variables are daily precipitation, maximum and minimum temperatures (from NCDC cooperative observer stations), and wind from NCEP Reanalysis. Soil parameters are taken from the Penn State Soil Geographic Database (STATSGO) and land cover is from the University of Maryland 1-km Global Land Cover product (derived from AVHRR). Water Balance mode assumes that the soil surface temperature is equal to the air temperature for the current time step. The exception to this is that the snow algorithm still solves the surface energy balance at three hour timesteps to determine the fluxes needed to drive accumulation and ablation processes. Inflow Run of River Reservoirs (inflow=outflow + energy) Inflow Storage Reservoirs Releases Depend on: Storage and Inflow Rule Curves (streamflow forecasts) Flood Control Requirements Energy Requirements Minimum Flow Requirements System Flow Requirements System Checkpoint Consumptive use + C ol S im Reservoir Models Separate reservoir models that represents the physical and operational features of each basin were utilized to derive the water resource results. The schematic is for ColSim, a model of the Columbia River reservoir System. Similar models were developed and used for the Colorado and California systems Conclusions / Comparative analysis 1) The mean of the distribution of annual precipitation does not change much over the 21st century, although the variability increases somewhat after the 2030s, with consequent increases in annual runoff variability. 2) The trend in annual temperatures is evident throughout the west, but decadal variations in the rate of increase are also prominent. 3) Columbia River reservoir system primarily provides within- year storage (total storage/mean flow ~ 0.3). California is intermediate (~ 0.3), and Colorado is an over-year system (~4). 4) Climate sensitivities in Columbia basin are dominated by seasonality shifts in streamflow, and may even be beneficial for hydropower. However, fish flow targets would be difficult to meet under an altered climate, and mitigation by altered operation is essentially impossible. 5) The California system operation is dominated by water supply (mostly agricultural), reliability of which would be reduced significantly by a combination of seasonality shifts and reduced (annual) volumes. Partial mitigation by altered operations is possible, but would be complicated by flood related constraints. 6) The Colorado system is sensitive primarily to changes in annual streamflow volumes. A low runoff ratio makes the system highly sensitive to modest changes in precipitation (in winter, especially in the headwater sub-basins). Sensitivity to alternative operations is modest, and mitigation possibilities by increased storage would be ineffective (even if otherwise feasible). Columbia River BasinCaliforniaColorado River Basin References Christensen, N.S., Wood, A.W., Voisin, N., Lettenmaier, D.P. and R.N. Palmer, 2004, Effects of Climate Change on the Hydrology and Water Resources of the Colorado River Basin, Climatic Change Vol. 62, Issue 1-3, , January. Liang, X, D.P. Lettenmaier, E. F. Wood, and S. J. Burges, 1994: “A simple hydrologically based model of land surface water and energy fluxes for general circulation models.” J. Geophys. Res.(D7), 14,415-14, 428 Payne, J.T., A.W. Wood, A.F. Hamlet, R.N. Palmer and D.P. Lettenmaier, 2004, Mitigating the effects of climate change on the water resources of the Columbia River basin, Climatic Change Vol. 62, Issue 1-3, , January. Van Rheenen,N.T., A.W. Wood, R.N. Palmer and D.P. Lettenmaier, 2004, Potential Implications of PCM Climate Change Scenarios for Sacramento - San Joaquin River Basin Hydrology and Water Resources, Climatic Change Vol. 62, Issue 1-3, , January Wood, A.W., L.R. Leung, V. Sridhar and D.P. Lettenmaier, 2004, Hydrologic implications of dynamical and statistical approaches to downscaling climate model outputs, Climatic Change Vol. 62, Issue 1-3, , January Zhu,C., D.W. Pierce, T.P. Barnett, A.W. Wood, and D.P. Lettenmaier, 2004, Evaluation of Hydrologically Relevant PCM Climate Variables and Large-scale Variability over the Continental U.S., Climatic Change Vol. 62, Issue 1-3, 45-74, January 1. Bias Correction: On a monthly and PCM grid cell specific basis, a percentile mapping approach was used to correct for bias in GCM output biases. Bias Correction & DownscalingOverall Approach Climate Model Scenarios Selected Results VIC-scale monthly simulation Observed mean fields (1/8 –1/4 degree) Monthly PCM anomaly (T42) Interpolated to VIC scale The spatial distribution of predicted changes in mean annual runoff for the control and BAU periods 1-3 (averaged over the 3 ensembles) relative to simulated historic conditions. Downscaled Colorado River basin average annual temperature for PCM ensemble climate simulations. Blue line shown is simulated historic average while red is static 1995 control climate. A C B Figure A; Simulated total January 1 storage. Figure B; Simulated average annual release from Imperial Dam to Mexico and probability that annual release targets are met. Figure C; Simulated total energy production. 2. Spatial Disaggregation: PCM scale monthly anomalies were interpolated to the 1/8 degree hydrology model grid and applied the 1/8 degree observed climatological mean fields. 3. Temporal Disaggregation: 1/8 degree monthly fields were disaggregated to a daily time step by resampling observed daily patterns by month and rescaling/shifting them to reproduce the bias-corrected and spatially disaggregated monthly mean fields. Downscaled Columbia River basin average annual temperature, precipitation and derived runoff. Blue line shown is simulated historic average; dotted is static 1995 control climate; gray lines are the three future climate ensembles and the red thick line is their average. Downscaled California average annual temperature, precipitation and derived runoff. Blue line shown is simulated historic average; dotted is static 1995 control climate; gray lines are the three future climate ensembles and the red thick line is their average. Percent change of future ensemble mean snow water equivalent (SWE) for two decades (2050s and 2090s) relative to averages. Selected Water Resources Results: (right) Fish flow target performance under different operating strategies for future Periods 1-3 ( ; ; ); (below) Trade-offs between (left) flood protection benefits and hydropower revenues, and between (right) fish flows and hydropower, in Periods 1 and 2 (Period 3 not shown). Percent change of future ensemble mean snow water equivalent (SWE) for the first five decades of the 21 st century relative to control run (1990s climate) averages. Fractions of water year type determination that governs water allocations in the Central Valley for an historical period and three future periods. Monthly depiction of reservoir storage in the San Joaquin R. system for the control climate and five future periods. Reservoir system results derived from routing the simulated streamflows from VIC through the Colorado River Reservoir Model (CRRM). CRRM represent 9 of the rivers reservoirs (85% of total Colorado River storage lies in Lake Powell and Lake Mead) and operates on a monthly timescale. Acknowledgements The US Department of Energy’s Accelerated Climate Prediction Initiative provided funding for this research. Publications were also supported by the Joint Institute for the Study of the Atmosphere and Ocean (JISAO) under NOAA Cooperative Agreement #NA17RJ1232. Assessing the Implications of Climate Change for Western Water Resources Andrew W. Wood 1, Dennis P. Lettenmaier 1, and Tim P. Barnett 2 1. Department of Civil and Environmental Engineering, Box , University of Washington, Seattle, WA Climate Research Division, Scripps Institution of Oceanography, La Jolla, CA CCSP Workshop Arlington, Virginia November 14 – 16, % 86% 82% 83%