Predicting Utah Ground Snow Loads with PRISM

Abstract

Despite their importance in building design, ground snow load requirements remain largely undefined for the western United States. This paper proposes an adaptation of the Parameter-Elevation Relationships on Independent Slopes Model (PRISM) for predicting ground snow loads, using Utah as the area of interest to test the efficacy of the algorithm. It can be concluded that data filtering is exceptionally important to effective extreme-value estimations. In addition, using the log transform of the resulting 50-year ground snow load estimates as the response variable satisfies the basic linearity assumptions required for any linear regression–based estimator. A comparison of PRISM predictions with the current ground snow load requirements defined for Utah shows that PRISM yields lower absolute errors and less bias than the current methods used in the state. These predictions also eliminate existing discrepancies in ground snow load requirements along county boundaries within Utah and provide the framework for the elimination of ground snow load requirement discrepancies along state boundaries throughout the western United States.