Snow water equivalent interpolation for the Colorado River Basin from snow telemetry (SNOTEL) data

Abstract

[1] Inverse weighted distance and regression nonexact techniques were evaluated for interpolating methods snow water equivalent (SWE) across the entire Colorado River Basin of the western United States. A 1-km spacing was used for the gridding of snow telemetry (SNOTEL) measurements for the years 1993, 1998, and 1999, which on average, represented higher than average, average, and lower than average snow years. Because of the terrain effects, the regression techniques (hypsometric elevation and multivariate physiographic parameter) were found to be superior to the weighted distance approaches (inverse distance weighting squared, and optimal power inverse distance weighting). A regression detrended inverse weighted distance method was developed for the hypsometric and multivariate techniques in order to preserve the point SNOTEL data. On the basis of root mean square error analysis and estimates of SWE volumes in different elevation zones for the entire basin and for subbasins the elevation detrended method with a point by point regression was found to be the most appropriate technique. Various search radii and anisotropies of the search ellipse were tested with the hypsometric method, producing only small difference in the root mean square error and SWE volumes.

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

“…Bivariate relations showed that SWE increased with increasing elevation, with the steepness of this trend being greater in WY 2011 than 2012. The strength of the correlation between SWE and elevation for WY 2011 (r = 0.75) and WY 2012 (r = 0.68) suggests that elevation is the most important physiographic variable for driving the distribution of SWE across the study domain, which is consistent with previous findings from studies evaluating SWE at the basin scale (e.g., Fassnacht et al, 2003;Jost et al, 2007;Harshburger et al, 2010). As UTM Northing increases, SWE decreases in WY 2011, suggesting northern regions of the study area receive less snow than southern regions (as suggested by J. Meiman, personal communication, 2010), yet this trend was not apparent in the low snow year of 2012.…”

“…Eighty percent of all residual values (n = 2613) fell within ±50 mm, and the variance of the model residuals was on average within 12.8 % of the observed values for the calibration data set. Within site variability of SWE has been conservatively estimated to be 15 to 25 % (Jonas et al, 2009), which suggests that the error observed from the model is within the natural range of SWE variability at a site (Fassnacht et al, 2008). The small range of error suggests that estimating SWE from snow depth measurements through a snow density model works due to the conservative nature of snow density; 52 % of snow density data values ranged from 250 to 350 kg m −3 .…”

“…These studies have used SNOTEL data to interpolate SWE over large basins (e.g., Fassnacht et al, 2003), as well as manual field snowpack measurements over small catchments (e.g., Elder et al, 1991). However, few studies have analyzed snow's spatial variability at the basin scale using both operational and field-based measurements.…”

“…Spatially continuous coordinates of UTM Easting and Northing can be correlated with the distribution of snow in various ways that depend on site location and scale. Previous studies have used distance to a mountain barrier and distance to ocean or source of moisture (e.g., Fassnacht et al, 2003;López-Moreno and Nogués-Bravo, 2006), which can also be represented by UTM Easting for the study site due to its geographic orientation. Furthermore, given the scale of the study, UTM Easting and Northing represent different regions within the study area that are thought to display different patterns of snow accumulation and ablation due to the variability of meteorology and storm tracks.…”

“…However, few studies have analyzed snow's spatial variability at the basin scale using both operational and field-based measurements. Operational measurements can provide regional knowledge on the spatial distribution of snow (e.g., Fassnacht et al, 2003), yet cannot accurately characterize the basin scale variability that vitally impacts snowmelt and runoff . It has been recommended that future research should focus on more accurate estimation of SWE at the basin (100s to 1000s km 2 ) and regional (10 000s to 100 000s km 2 ) scale to effectively assess and manage mountain water resources (Viviroli et al, 2011).…”