Recent amendments to design ground snow load requirements in ASCE 7-22 have reduced the size of case study regions by 91% from what they were in ASCE 7-16, primarily in western states. This reduction is made possible through the development of highly accurate regional generalized additive regression models (RGAMs), stitched together with a novel smoothing scheme implemented in the R software package remap, to produce the continental- scale maps of reliability-targeted design ground snow loads available in ASCE 7-22. This approach allows for better characterizations of the changing relationship between temperature, elevation, and ground snow loads across the Conterminous United States. RGAMs are shown to have 10% or better improvement in mean absolute mapping error in two independently created datasets when compared to traditional mapping techniques. Potential implications and limitations of incorporating mapping accuracy into the reliability-targeted load calculation are demonstrated and discussed.

Document Type




File Format

.Rdata, .gz, .csv, .R

Viewing Instructions

To run the example, download all files into the same folder and set your working directory in R to be the same as the location of these files. Note that you may need to update other R packages in order to install the tar.gz packages described previously.

Publication Date



American Society of Civil Engineers, Structural Engineering Institute


Utah State University

Award Number

American Society of Civil Engineers, Structural Engineering Institute 202827


This folder contains a dataset, a shapefile, two R packages, and script that reproduces estimates of cross-validated accuracy in the companion manuscript. The associated data represents a consolidation of information from the following sources.

- The National Oceanic and Atmospheric Administration (NOAA) Global Historical Climatological Network - Daily (GHCND) ( Version: August 25th, 2020.

- Gridded climate normals from the PRISM Climate Group (

- US EPA's Environmental Ecoregions (

Data processing and models were created using R Statistical Software with the help of the tidyverse, sf, mgcv, automap, gstat, snowload, and remap packages.

Referenced by

Wagstaff, J., Bean, B., Wheeler, J., Maguire, M., & Sun, Y. (in press). Regionalized Mapping of Reliability-Targeted Design Snow Loads in the United States.

Bean, B., Maguire, M., Sun, Y., Wagstaff, J., Al-Rubaye, S. A., Wheeler, J., Jarman, S., & Rogers, M. (2021). The 2020 National Snow Load Study. Mathematics and Statistics Faculty Publications. Paper 276.



Code Lists



Mathematics | Statistics and Probability


Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.




Additional Files

bean_2023_readme.txt (5 kB)
MD5: 528610921fad22fbf40fbdf09cb604cf (2414 kB)
MD5: b43797b5111d136c743d82c94f52a5e1

eco3_simp.Rdata (225 kB)
MD5: 5bb41fcbd4714c06eded18c73f7d1baf

remap_0.3.0.tar.gz (583 kB)
MD5: 705d4261a005d8155bacad09aeeba708

rtsl.csv (670 kB)
MD5: e7cb546a391afd87452f16fd87702247

rtsl_cross_validation.R (9 kB)
MD5: fcb8b69f8df5aa4780135c1d97375768

snowload_1.0.0.901.tar.gz (1285 kB)
MD5: 06a1f8be086c3864647737cbdb041585