Abstract
The spatial distribution of snow plays a vital role in Arctic climate, hydrology, and ecology due to its fundamental influence on the water balance, thermal regimes, vegetation, and carbon flux. However, for earth system modelling, the spatial distribution of snow is not well understood, and therefore, it is not well modeled, which can lead to substantial uncertainties in snow cover representations. To capture key hydro-ecological controls on snow spatial distribution, we carried out intensive field studies over multiple years for two small (2017–2019, ~2.5 km2) sub-Arctic study sites located on the Seward Peninsula of Alaska. Using an intensive suite of field observations (> 22,000 data points), we developed simple models of spatial distribution of snow water equivalent (SWE) using factors such as topographic characteristics, vegetation characteristics based on greenness (normalized different vegetation index, NDVI), and a simple metric for approximating winds. The most successful model was the random forest using both study sites and all years, which was able to accurately capture the complexity and variability of snow characteristics across the sites. Approximately 86 % of the SWE distribution could be accounted for, on average, by the random forest model at the study sites. Factors that impacted year-to-year snow distribution included NDVI, elevation, and a metric to represent coarse microtopography (topographic position index, or TPI), while slope, wind, and fine microtopography factors were less important. The models were used to predict SWE at the locations through the study area and for all years. The characterization of the SWE spatial distribution patterns and the statistical relationships developed between SWE and its impacting factors will be used for the improvement of snow distribution modelling in the Department of Energy’s earth system model, and to improve understanding of hydrology, topography, and vegetation dynamics in the Arctic and sub-Arctic regions of the globe.
Highlights
Covering the land for more than six months each year, snow plays a vital role in the climate, hydrology, and ecosystems of the Arctic and sub-arctic
The characterization of the snow water equivalent (SWE) spatial distribution patterns and the statistical relationships developed between SWE and its impacting factors will be used for the improvement of snow distribution modelling in the Department of Energy’s earth system 30 model, and to improve understanding of hydrology, topography, and vegetation dynamics in the Arctic and sub-Arctic regions of the globe
The extensive snow depth and density dataset from this study is of high value for calibrating and validating 595 physically-based models of snow distribution, which is being undertaken in current work by the authors
Summary
Covering the land for more than six months each year, snow plays a vital role in the climate, hydrology, and ecosystems of the Arctic and sub-arctic. Our goal is to build a statistical model to 1) characterize the spatial pattern of the end-of-winter snow distribution, 2) identify the key factors controlling the spatial distribution, and 3) predict the snow distribution for the local study sites. The statistical snow distribution model will be used to validate and improved snow redistribution in Department of Energy (DOE)’s Energy Exascale Earth System model (E3SM) land surface model (ELM) and to improve understanding of hydrology, topography, and vegetation 140 dynamics in the Arctic and sub-Arctic regions of the globe.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
