Spatiotemporal index for analyzing controls on snow climatology: application in the Colorado Front Range

Abstract

Mountain snowpacks are important water supplies that are susceptible to climate change, yet snow measurements are sparse relative to snowpack heterogeneity. We used remote sensing to derive a spatiotemporal index of snow climatology that reveals patterns in snow accumulation, persistence, and ablation. Then we examined how this index relates to climate, terrain, and vegetation. Analyses were based on Moderate Resolution Imaging Spectroradiometer eight-day snow cover from 2000 to 2010 for a mountain watershed in the Colorado Front Range, USA. The Snow Cover Index (SCI) was calculated as the fraction of years that were snow covered for each pixel. The proportion of SCI variability explained by independent variables was evaluated using regression analysis. Independent variables included elevation, northing, easting, slope, aspect, northness, solar radiation, precipitation, temperature, and vegetation cover. Elevation was the dominant control on SCI patterns, due to its influence on both temperature and precipitation. Grouping SCI values by elevation, we identified three distinct snow zones in the basin: persistent, transitional, and intermittent. The transitional snow zone represents an area that is sensitive to losing winter snowpack. The SCI can be applied to other basins or regions to identify dominant controls on snow cover patterns and areas sensitive to snow loss.

What drives basin scale spatial variability of snowpack properties in northern Colorado?

“…Terrain variables derived within GIS at each of the snowpack measurement locations have similar averages when compared to the 50 % Snow Cover Index (SCI) (Richer et al, 2013) (Fig. 1), for both 2011 and 2012.…”

“…We focus on the portion of this basin that shows persistent snow cover near peak snow accumulation; this region is responsible for the majority of hydrologic input to the river system. To define this area, we use the Snow Cover Index (SCI) at 50 % (Richer et al, 2013), which represents the area that was snow-covered at least 50 % of the time from 2000 to 2010 during early April. The SCI is calculated based on the Moderate Resolution Imaging Spectroradiometer (MODIS)/Terra 8-day snow cover products.…”

Hydrologic flow path development varies by aspect during spring snowmelt in complex subalpine terrain

“…From a basin-scale perspective, elevation has been shown to influence the depth and persistence of a snowpack (Richer et al, 2013;Molotch and Meromy, 2014;Sexstone and Fassnacht, 2014), while at finer resolutions the spatial variability of both accumulation and melt may be controlled by aspect (C. J. López-Moreno et al, 2013;Hinckley et al, 2014), and snow in forested areas is affected by interception during accumulation, shortwave radiation shading, and longwave radiation influences prior to and during melt (Storck et al, 2002;Musselman et al, 2008;Molotch et al, 2009;Adams et al, 2011;Webb, 2017).…”

The Contribution of Physical Geographers to Sustainability Research

“…The impacts of climate change on snowpack distribution as a water resource in the Colorado Front Range has also been investigated [21]. Interestingly, a study of the relationship between weather and urban water consumption in Seoul, South Korea, showed an expected relationship between maximum daily temperature and water consumption, but also produced a surprising conclusion that wind speed is also an important control because of its impact on evaporative cooling [71].…”

“…A GIS-based model, WetSpass, was used to estimate the water balance of a watershed in Ethiopia in order to determine sustainable levels of groundwater abstraction [20]. By using multiyear snowpack data from the Colarado Front Range and relating it to elevation, location, slope aspect, solar radiation, temperature, precipitation, and vegetation cover, it has also been possible to identify areas that are most vulnerable to climate change [21].…”