Acknowledgements I would like to thank my advisors for their guidance in my master's degree research. Thank you to Larry Weber for your guidance in my research process and for providing the opportunity to participate in the IWA project. Thank you to Antonio Arenas Amado for your expertise and guidance with technical and research questions. I am grateful for your patience and advice in learning about hydrologic modeling and research. Thank you also to my committee member Witold Krajewski for

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... ing on my committee and recommending the use of RainyDay in my research. I am thankful for the support of my fellow students, especially Maral Razmand for her help with meteorology inputs and her patience in troubleshooting research problems. Thank you to the other master's students: Michael Krasowski, Anthony Emigh, and Matthew Meulemens. It has been good to have your support and friendship while I pursued this degree. Finally, thank you to my friends and family who have given me their love and support. I am very lucky to have you all in my life. iii Abstract On June 13, 2008, after many days of rain, the Cedar River flooded the city of Cedar Rapids. With a peak discharge of 139,987 cfs and at 19.12 feet above flood stage, the 2008 flood of Cedar Rapids was the largest flood in the city's historic record. As rivers rose, the city had received forecasts of an incoming flood as early as June 8. Then, on June 12, it began to rain in Cedar Rapids. Finally, on June 13, 2008, the Middle Cedar crested at 31.12 feet. This thesis project modeled a variety of rainfall patterns on June 12, 2008, to determine the effect of varying rainfall intensity and location on the magnitude of the 2008 flood of Cedar Rapids. Using a method known as Stochastic Storm Transposition (SST), I overwrote precipitation data in a hydrologic model of the Middle Cedar Watershed with rainfall data extracted from specific storm events that occurred in the Upper Midwest. We used a physicallybased, semi-distributed hydrologic model known as GHOST (Generic Hydrologic Overland-Subsurface Toolkit) developed by Marcela Politano at the University of Iowa. Traditionally, hydrologic modeling for watersheds has used design storms to create rainfall inputs in flood modeling. These design storms have uniform rainfall timing and accumulation patterns across a watershed and are determined by designated equations for a geographic region. In large watersheds such as the Middle Cedar (2,400 square miles), design storms are not physically realistic because of their uniformity. Additionally, design storms fail to capture unique storm patterns such as high intensity periods or the movement of a storm across a watershed. By implementing SST into GHOST, we used physically realistic storm events that have unique rainfall patterns and intensities within a designated return period. SST extracts rainfall data from real storm events and transposes the storm patterns onto watersheds to provide physically realistic rainfall data for hydrologic modeling. A tool called RainyDay, developed by Professor Daniel Wright at the University of Wisconsin, provided the storm transpositions used in this research. We assigned the storm transpositions return periods created by RainyDay, corresponding to their average transposed rainfall across the Middle Cedar Watershed. Replacing the June 12 rainfall with RainyDay's two-year transposed storm events (average rain accumulation 1.8 inches) resulted in modeled flood peaks larger than the unaltered June 12 flood peak. Storm transpositions of 5-, 10-, and 2,000-year return periods showed even larger peaks, illustrating the potential for floods much larger than the 2008 flood. iv In addition to the analysis of flood magnitude in 2008, we modeled a set of storm transposition scenarios for a variety of soil-moisture conditions. The increased discharge levels in scenarios with high soil moisture emphasize the importance of initial conditions in flooding scenarios. Finally, we modeled the effect that two-year RainyDay storms would have had on the 2016 flood of Cedar Rapids had they occurred on the day before the peak. The two-year transpositions showed that with an impending flood crest smaller than the 2008 crest, several two-year RainyDay scenarios would have resulted in floods nearly equal in magnitude to the 2008 flood event. Our manipulation of the rainfall in the Middle Cedar Watershed on June 12, 2008, using the GHOST model provided the opportunity to re-examine the influence that a specific day of rainfall had on the 2008 flood of Cedar Rapids. The potential for higher flooding under conditions of repeated rainfall and high soil moisture illustrates the susceptibility of the Middle Cedar Watershed to future flood events under similar conditions. Applying SST in hydrologic modeling also provided an opportunity to model a variety of rainfall scenarios and to better understand watershed responses to nuanced and physically realistic rainfall patterns. v Public Abstract On June 13, 2008, after many days of rain, the Cedar River flooded the city of Cedar Rapids at 19.12 feet above flood stage, making it the largest flood in the historic record and exceeding the 500-year flood level. To study the effect of varying rainfall patterns and intensities on June 12, the day before the peak, we transposed real storms that occurred elsewhere in the Midwest into a model of the watershed. Our model is a physically-based, semi-distributed hydrologic model; by overwriting meteorological radar data in this model for June 12, 2008, we were able to simulate the effects of a variety of storm events. We established that a rainfall event with 1.8 inches of accumulation on average across the watershed would have created a significantly larger flood. Traditionally, storm scenarios for flood modeling consist of engineered design storms, which are uniform across time and space, with patterns determined by a series of equations. By modeling the effects of physically complex storm events that actually happened elsewhere in the region, we can study details that more closely reflect natural phenomena. This study demonstrates the potential for the application of storm transposition in hydrologic modeling and flood impact studies.