Supplementary files for "Creating a Universal Depth-To-Load Conversion Technique for the Conterminous United States using Random Forests"

As part of an ongoing effort to update the ground snow load maps in the
United States, this paper presents an investigation into snow densities
for the purpose of predicting ground snow loads for structural engineering
design with ASCE 7. Despite their importance, direct measurements of snow
load are sparse when compared to measurements of snow depth. As a result,
it is often necessary to estimate snow load using snow depth and other
readily accessible climate variables. Existing depth-to-load conversion
methods, each of varying complexity, are well suited for snow load
estimation for a particular region or station network, but none are
consistently effective across regions and station networks. This paper
proposes a random forests regression model for estimating annual maximum
snow loads in the conterminous United States that makes use of climate
reanalysis data and overcomes the limitations of existing methods.
The effectiveness of the random forest model is demonstrated through
accuracy comparisons of existing depth-to-load conversion techniques
using a compilation of national and state-level data sources. The accuracy
comparisons show that the random forest model is competitive for all regions
and station networks, while other methods are competitive for only certain
regions or station networks. These results highlight the feasibility of
developing a single depth-to-load conversion method that appropriately
characterizes region and climate specific differences in the snow
depth/load relationship across the conterminous United States.
Such universal models are an essential component for creating a unified set
of national snow load requirements that eliminate the case study regions
currently defined in ASCE 7.