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Tim Bardsley, Andrew Wood, Mike Hobbins, Tracie Kirkham, Laura Briefer, Jeff Niermeyer, and Steven Burian

the NOAA Colorado Basin River Forecast Center's operational hydrologic modeling framework and to simplify communication with water managers in the United States, results are reported in imperial units. The primary objective of this study is to inform water management and long-range planning decisions through a partial bottom-up assessment ( Brown and Wilby 2012 ) of SLC system sensitivities to potential vulnerabilities in water supplies given climate operational adaptation options and measures

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M. Sekhar, M. Shindekar, Sat K. Tomer, and P. Goswami

(RMSE), mean absolute error (MAE), and correlation were computed. Figure 10a shows the RMSE, MAE, and correlation for the all the GCMs. Based on the figure, the GCMs can be classified into two categories: showing relatively better and poor behavior with the measured rainfall. Worldwide, the skill of GCMs at forecasting time- and space-averaged rainfall, like all-India seasonal precipitation, is quite poor and the skill decreases as the spatial scale is reduced to single station. Thus, GCM

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Mohammad Karamouz, Erfan Goharian, and Sara Nazif

(GCMs), which are the most advanced tools currently available in this field. GCMs are widely applied for understanding the climate, weather forecasting, and projecting climate change. In the past few years, various studies have investigated the hydrological impact of climate change (e.g., Boorman and Sefton 1997 ; Bergström et al. 2001 ; Gao et al. 2002 ; Christensen et al. 2004 ; Chen et al. 2007 ). Charlton et al. ( Charlton et al. 2006 ) examined the impact of climate change on flood hazard

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