Snowmelt-driven floods result in large societal and economic impacts on local communities including infrastructure failures in the U.S. However, the current U.S. government standard design precipitation maps are based on liquid precipitation data (e.g. National Oceanic and Atmospheric Administration’s Precipitation-Frequency Atlas 14; NOAA Atlas 14) with very limited guidance on snowmelt-driven floods. In this study, we developed 25- and 100-year return level design maps of snow water equivalent (SWE) and 1-day and 7-day snowmelt including precipitation events (e.g. rain-on-snow) using long-term observation-based gridded SWE developed by University of Arizona (UA) incorporating the national snow model product (SNOw Data Assimilation System; SNODAS) over the contiguous U.S. (CONUS). For the 44 U.S. states where the NOAA Atlas 14 maps are available, the design snowmelt values from this study exceed the standard design values in 23% of the total extent. The snowmelt values exceed the NOAA Atlas 14 design precipitation by up to 171 and 254 mm in the northeastern U.S.; 127 and 225 mm in the north-central U.S.; and 191 and 425 mm the western mountain U.S. for the 25- and 100-year return levels, respectively. A comparison of 7-day design snowmelt between with and without precipitation shows that including precipitation results in an average increase of 42 mm and 68 mm for 25- and 100-year return levels, respectively, over snowmelt that do not include precipitation. The design snowmelt maps from this study complement the NOAA Atlas 14 design precipitation and provide additional guidance on infrastructure design for snowmelt-driven floods in the CONUS.

Snow distribution is a function of interactions among static variables, such as terrain, vegetation, and soil properties, and dynamic meteorological variables, such as wind speed and direction, solar radiation, and soil moisture that occur over a range of spatial scales. However, identifying the dominant physical drivers responsible for spatial patterns of the snowpack, particularly for ephemeral, shallow snowpacks, has been challenged due to the lack of the high-resolution snowpack and physical variables with high vertical accuracy as well as inherent limitations in traditional approaches. This study uses an Unpiloted Aerial System (UAS) lidar-based snow depth and static variables (1-m spatial resolution) to analyze field-scale spatial structures of snow depth and apply the Maximum Entropy (MaxEnt) framework to identify primary controls over open terrain and forests at the Thompson Farm Research Observatory, New Hampshire, United States. We found that, among nine topographic and soil variables, plant functional type and terrain roughness contribute up to 80% and 76% of relative importance in MaxEnt to predicting locations of deeper or shallower snowpacks, respectively, across the landscape. Soil variables, such as organic matter and saturated hydraulic conductivity, were also important controls (up to 70% and 81%) on snow depth spatial variations for both open and forested landscapes suggesting spatial variations in soil variables under snow can control thermal transfer among soil, snowpack, and surface-atmosphere. This work contributes to improving land surface and snow models by informing parameterization of the sub-grid scale snow depths, downscaling remotely sensed snow products, and understanding field scale snow states.