To quantify the spatial variability of mountain snowpacks, where depths can vary drastically over length scales as small as 1–2 meters in complex terrain, high-resolution remote sensing techniques are often employed. Specifically, repeated airborne LIght Detection And Ranging (Lidar) surveys are capable of recording snow depth distributions at 1–5 meter resolution over very large geographic areas, while additionally providing information about vegetation, slope aspect and terrain roughness. During the second Cold Lands Processes eXperiment (CLPX-II) in the winter of 2006/07, two Lidar surveys were flown nearly three months apart over a vast 750-km2 swath of the Rocky Mountains near Steamboat Springs, Colorado. This work leverages the sheer extent of the CLPX-II Lidar dataset to validate portions of the SNOw Data Assimilation System (SNODAS) operational hydrologic model developed by the National Operational Hydrologic Remote Sensing Center (NOHRSC). Upscaling the high-resolution Lidar-derived snow depths to the much lower spatial resolution of the SNODAS prediction estimates produces a statistically robust dataset of over 900 independent pixel comparisons. Results support the notion that sub pixel-scale processes such as slope, aspect, vegetation density and wind redistribution are important to consider for model predictions of snow water equivalent in mountain regions. To investigate the wind-transport factor, a wind redistribution model based on terrain characteristics is implemented for a specifically wind-scoured study site where snow depths are known at high resolution. The inter-annual consistency of snow depths at the site, observed by multiple Lidar surveys, reveals a close correlation with the terrain parameters produced by the wind model for a known local prevailing wind direction.
Lidar currently remains the most exhaustive method for measuring spatial characteristics of mountain snowpacks, even though it is cost-prohibitive to perform multiple surveys. To monitor temporal variations of snow depth, simple time-lapse photography techniques provide a more efficient way to obtain information about snowpack evolution. A robust and low power method to measure hourly changes in snow depth is presented that involves only three primary components: (1) an off-the-shelf manufactured time-lapse camera, (2) a weatherproof external battery box and (3) an array of secured, brightly painted depth markers. The camera’s physical pixel size as a function of distance is determined prior to installation and a pixel-counting algorithm distinguishes the snow surface at each marker location after the images are captured. The results of the process agree closely with nearby standard ultrasonic sensors and encourage a wider implementation of cameras and depth markers in the near future to aid in various research projects.