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John Risley, Hamid Moradkhani, Lauren Hay, and Steve Markstrom

runoff under various emission scenarios. Stewart et al. ( Stewart et al. 2004 ) created regression relations between streamflow timing and precipitation and temperature indices. In their analysis, they used National Center for Atmospheric Research climate model output under a “business as usual” greenhouse-gas emissions scenario ( Washington et al. 2000 ) and projected springtime snowmelt occurring earlier than the historic average across much of western North America for the 1995–2099 period. To

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William Battaglin, Lauren Hay, and Markstrom Steve

for the first 20–30 years of the simulation period ( Figures 3 , 4 ). After ~2040, the central tendencies for the three scenarios diverge with the smallest projected changes projected for the B1 scenario and the largest projected changes and largest range of projections for the A2 scenario. In both basins, baseline conditions are less than the lowest simulated temperature estimates from all three scenarios after 2040. A regression analysis is used to identify projected changes in the central

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Kathryn M. Koczot, Steven L. Markstrom, and Lauren E. Hay

the end of the twenty-first century as compared with PRMS simulations from the other baselines (warm PDO). The combined range of uncertainty simulated from all baseline conditions represents a more realistic range of uncertainty for PRMS simulations of the GCM projections. Figure 13. Simulated range in 11-yr moving averages of streamflow for emission scenarios (a) B1, (b) A1B, and (c) A2 using the 1994, 1989, and 1970 baseline conditions. 4.5. Regression analysis Table 3 shows the results of a

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Roland J. Viger, Lauren E. Hay, Steven L. Markstrom, John W. Jones, and Gary R. Buell

server provides access to daily streamflow data values from the USGS National Water Information System (available online at ) and daily minimum and maximum temperature and precipitation data values from the National Weather Service (NWS) Cooperative Observer Program (COOP; available online at ). A three-dimensional multiple linear regression, based on longitude x , latitude y , and elevation z , is used to distribute temperature and

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Lauren E. Hay, Steven L. Markstrom, and Christian Ward-Garrison

central tendency was calculated as a mean annual value using the last 11 years of each 12-yr window (the first year was reserved for PRMS model initialization). The results of a regression analysis ( Table 4 ) show the projected change (slope) in the central tendency and associated adjusted R 2 (adjR2) based on the central tendencies of the five GCMs for each of the three emission scenarios by basin. The slope indicates the change in the central tendency for the selected variable by year (center of

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