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

evaluate the effects of various combinations of precipitation, climate, and land use on basin response. Response to normal and extreme rainfall and snowmelt can be simulated to evaluate changes in water-balance relations, streamflow regimes, soil-water relations, and groundwater recharge. Each hydrologic component used for generation of streamflow is represented within PRMS by a process algorithm that is based on a physical law or an empirical relation with measured or calculated characteristics

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David M. Bjerklie, Thomas J. Trombley, and Roland J. Viger

groundwater surface water flow model was used. In this model, most of the algorithms used were the same as used in this study; however, this model was operated on an hourly time step and required additional routing procedures and included a transfer function between HRUs and a more complex shallow subsurface flow zone that accounts for a two-layer unsaturated zone above the saturated groundwater zone ( Goode et al. 2010 ). In the study presented here, we are not as interested in subdaily routing of flows

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William Battaglin, Lauren Hay, and Markstrom Steve

, stepwise approach that applied a shuffled complex evolution global search algorithm ( Hay et al. 2006a ). This approach ensured that the models produced not only accurate runoff simulation but also realistic estimates of other hydrologic variables (e.g., snow-covered area or snowpack water equivalent). Given the uncertainty in climate modeling, it is desirable to use more than one GCM to obtain a range of potential future climatic conditions. An analysis of available output from the World Climate

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

. Structural error is related to how well the watershed model represents the physical, water, and energy processes in the basin. Hay et al. ( Hay et al. 2011 ) provide an extensive discussion on uncertainty associated with the PRMS method of simulating potential evapotranspiration. PRMS uses an empirically based algorithm to simulate PET with air temperature and precipitation as input. Another source of model uncertainty is the lack of feedback mechanisms between PRMS and climate change. PRMS was

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

precipitation station data to HRUs ( Hay and Clark 2000 ; Hay and Clark 2003 ). For further information, see Viger et al. ( Viger et al. 2010 ). 4.3. Model calibration PRMS was calibrated by using Luca ( Hay and Umemoto 2006 ; Hay et al. 2006 ), a multiple-objective, stepwise, automated procedure for hydrologic model calibration and the associated graphical user interface (GUI). The calibration procedure uses the Shuffled Complex Evolution global search algorithm ( Duan et al. 1993 ; Duan et al. 1992

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