Comparing Design Ground Snow Load Prediction in Utah and Idaho

Abstract

Snow loads in the western United States are largely undefined due to complex geography and climates, leaving the individual states to publish detailed studies for their region, usually through the local Structural Engineers Association (SEAs). These associations are typically made up of engineers not formally trained to develop or evaluate spatial statistical methods for their regions and there is little guidance from ASCE 7. Furthermore, little has been written to compare the independently developed design ground snow load prediction methods used by various western states. This paper addresses this topic by comparing the accuracy of a variety of spatial methods for predicting 50 year (i.e. design) ground snow loads in Utah and Idaho. These methods include, among others, the current Utah snow load equations, Idaho's normalized ground snow loads based on inverse distance weighting, two forms of Kriging, and the authors' adaptation of PRISM. The accuracy of each method is evaluated by measuring the mean absolute error using ten fold cross validation on datasets obtained from Idaho's 2015 snow load report, Utah's 1992 snow load report, and a new Utah ground snow load dataset. These results show that regression-based Kriging and PRISM methods have the lowest cross validated errors across all three datasets. These results also show that normalized ground snow loads, which are a common way of accounting for elevation in traditional interpolation methods, do not fully account for the effect of elevation on ground snow

Interval-Valued Kriging Models for Geostatistical Mapping with Imprecise Inputs

“…The Utah report follows this precedent (Bean et al, 2018), although other distributions have been used in other western states (Sack, 2015). Bean et al (2019) discusses the shortcomings of the distribution fitting approach to estimating design snow loads. Nevertheless, for the purpose of comparison, we likewise assume that any set of annual maximums resulting from Step 1 can be appropriately modeled by a log-normal distribution.…”

Extreme Value Snow Water Equivalent and Snowmelt for Infrastructure Design Over the Contiguous United States

“…Extreme snow can damage infrastructure and buildings (American National Standards Institute, 1972; American Society of Civil Engineers, 2017; Sack, 2015). Heavy snow loads on roofs can cause structural failures (Bean et al, 2019; Geis et al, 2012). From 1989 to 2009, 1,029 snow‐induced building collapse incidents in the U.S. caused 19 fatalities and 146 injuries with each incident costing up to $200 million (Geis et al, 2012).…”