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Statistical flood frequency analysis coupled with hydrograph scaling iscommonly used to generate design floods to assess dam safety assessment. Thesafety assessments can be highly sensitive to the choice of the statisticalflood frequency model. Standard dam safety assessments are typically based on asingle distribution model of flood frequency, often the Log Pearson Type III orGeneralized Extreme Value distributions. Floods, however, may result frommultiple physical processes such as rain on snow, snowmelt or rainstorms. Thiscan result in a mixed distribution of annual peak flows, according to the causeof each flood. Engineering design choices based on a single distributionstatistical model are vulnerable to the effects of this potential structuralmodel error. To explore the practicality and potential value of implementingmixed distribution statistical models in engineering design, we compare thegoodness of fit of several single- and mixed-distribution peak flow models, aswell as the contingent dam safety assessment at Pueblo, Colorado as a didacticexample. Summer snowmelt and intense summer rainstorms are both key drivers ofannual peak flow at Pueblo. We analyze the potential implications for theannual probability of overtopping-induced failure of the Pueblo Dam as adidactic example. We address the temporal and physical cause separationproblems by building on previous work with mixed distributions. We find a MixedGeneralized Extreme Value distribution model best fits peak flows observed inthe gaged record, historical floods, and paleo floods at Pueblo. Finally, weshow that accounting for mixed distributions in the safety assessment at PuebloDam increases the assessed risk of overtopping.

Effects of Mixed Distribution Statistical Flood Frequency Models on Dam Safety Assessments: A Case Study of the Pueblo Dam, USA