Abstract
Snow depth is the easiest snowpack property to measure in the field and is used to estimate the distribution of snow for quantifying snow storage. Often the mean of three snow depth measurements is used to represent snow depth at a location. This location is used as a proxy for an area, typically a digital elevation model (DEM) or remotely sensed pixel. Here, 11, 17, or 21 snow depth measurements were used to represent the mean snow depth of a 30-m DEM pixel. Using the center snow depth measurement for each sampling set was not adequate to represent the pixel mean, and while the use of three snow depth measurements improved the estimate of mean, there is still large error for some pixels. These measurements were then used to determine the variability of snow depth across a pixel. Estimating variability from few points rather than all in a measurement was not sufficient. The sampling size was increased from one to the total per pixel (11, 17, or 21) to determine how many point samples were necessary to approximate the mean snow depth per pixel within five percent. Binary regression trees were constructed to determine which terrain and canopy variables dictated the spatial distribution of the snow depth, the standard deviation of snow depth, and the sample size to within 5% of the mean per pixel. One location was measured in two years just prior to peak accumulation, and it is shown that there was little to no inter-annual consistency in the mean or standard deviation.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Bales R C, Molotch N P, Painter T H, Dettinger M, Rice R, Dozier J (2006). Mountain hydrology of the western United States. Water Resour Res, 42(8): W08432
Balk B, Elder K (2000). Combining binary decision tree and geostatistical methods to estimate snow distribution in a mountain watershed. Water Resour Res, 36(1): 13–26
Blöschl G (1999). Scaling issues in snow hydrology. Hydrol Processes, 13(14–15): 2149–2175
Debarbieux B, Price M F (2012). Mountain regions: a global common good? Mt Res Dev, 32(S1): S7–S11
Deems J S, Fassnacht S R, Elder K J (2006). Fractal distribution of snow depth from LiDAR data. J Hydrometeorol, 7(2): 285–297
Deems J S, Fassnacht S R, Elder K J (2008). Interannual consistency in fractal snow depth patterns at two Colorado mountain sites. J Hydrometeorol, 9(5): 977–988
Dingman S, Seely-Reynolds D, Reynold D (1988). Application of Kriging to estimating mean annual precipitation in a region of orographic influence. J Am Water Resour Assoc, 24(2): 329–339
Dozier J, Frew J (1990). Rapid calculation of terrain parameters for radiation modeling from digital elevation data. IEEE Trans Geosci Remote Sens, 28(5): 963–969
Elder K, Dozier J, Michaelsen J (1991). Snow accumulation and distribution in an alpine watershed. Water Resour Res, 27(7): 1541–1552
Elder K J, Cline D W, Liston G E, Armstrong R (2009). NASA cold land processes experiment (CLPX 2002–2003): field measurements of snowpack properties and soil moisture. J Hydrometeorol, 10(1): 320–329
Erickson T, Williams M, Winstral A (2005). Persistence of topographic controls on the spatial distribution of snow in rugged mountain terrain, Colorado, United States. Water Resour Res, 41(4): W04014
Erxleben J, Elder K, Davis R (2002). Comparison of spatial interpolation methods for estimating snow distribution in the Colorado Rocky Mountains. Hydrol Processes, 16(18): 3627–3649
Fassnacht S R, Deems J S (2006). Measurement sampling and scaling for deep montane snow depth data. Hydrol Processes, 20(4): 829–838
Fassnacht S R, Deitemeyer D C, Venable N B H (2014). Capitalizing on the daily time step of snow telemetry data to model the snowmelt components of the hydrograph for small watersheds. Hydrological Processes, 28(16): 4654–4668
Fassnacht S R, Dressler K A, Bales R C (2003). Snow water equivalent interpolation for the Colorado River Basin from snow telemetry (SNOTEL) data. Water Resour Res, 39(8): 1208
Fassnacht S R, Dressler K A, Hultstrand D M, Bales R C, Patterson G G (2012). Temporal inconsistencies in coarse-scale snow water equivalent patterns: Colorado River Basin snow telemetry-topography regressions. Pirineos, 167(0): 165–185
Fassnacht S R, Heun C, López Moreno J I, Latron J (2010). Variability of snow density measurements in the Esera valley, Pyrenees mountains, Spain. Cuadernos de Investigación Geográfica, 36(1): 59–72
Fassnacht S R, Williams M W, Corrao M V (2009). Changes in the surface roughness of snow from millimetre to metre scales. Ecol Complex, 6(3): 221–229
Fassnacht S R, Wyss D, Heering S M (2017). Räumliches Denken–ein Beispiel im Schnee / A Spatial Thinking Research-Didactic Example in Snow. Geoöko, 37(3-4)
Hultstrand D, Fassnacht S R, Stednick J (2006). Geostatistical methods for estimating snowmelt contribution to the alpine water balance. Proceedings of the Annual Western Snow Conference, (Las Cruces NM), 74: 149–154
Kashipazha A H (2012). Practical snow depth sampling around six snow telemetry (SNOTEL) stations in Colorado and Wyoming, United States. Dissertation for M.S. degree. Watershed Science Program, Colorado State University, Fort Collins, Colorado USA
Logan L (1973). Basin-wide water equivalent estimation from snowpack depth measurements. In: Role of Snow and Ice in Hydrology. IAHS AIHS Publ. 107: 864–884
López-Moreno J I, Fassnacht S R, Begueria S, Latron J (2011). Variability of snow depth at the plot scale: implications for mean depth estimation and sampling strategies. Cryosphere, 5(3): 617–629
López-Moreno J I, Fassnacht S R, Heath J T, Musselman K, Revuelto J, Latron J, Morá n-Tejeda E, Jonas T (2013). Small scale spatial variability of snow density and depth over complex alpine terrain: implications for estimating snow water equivalent. Adv Water Resour, 55: 40–52
López-Moreno J I, Nogués Bravo D (2006). Interpolating local snow depth data: an evaluation of methods. Hydrol Processes, 20(10): 2217–2232
López-Moreno J I, Revuelto J, Fassnacht S R, Azorín-Molina C, Vicente-Serrano S M, Morá n-Tejeda E, Sexstone G A (2015). Snowpack variability across various spatio-temporal resolutions. Hydrol Processes, 29(6): 1213–1224
Meiman J R (1968). Snow accumulation related to elevation, aspect, and forest canopy. In Proceedings of Snow Hydrology Workshop Seminar, Canadian National Committee of the International Hydrological Decade, Fredericton NB, pp. 35–47
Meromy L, Molotch N, Link T, Fassnacht S R, Rice R (2013). Subgrid variability of snow water equivalent at operational snow stations in the western USA.Hydrol Processes, 27(17): 2383–2400
Molotch N, Brooks P, Burns S, Litvak M, Monson R, McConnell J, Musselman K (2009). Ecohydrological controls on snowmelt partitioning in mixed conifer sub alpine forests. Ecohydrology, 2(2): 129–142
Molotch N P, Bales R C (2005). Scaling snow observations from the point to the grid-element: implications for observation network design. Water Resour Res, 41: W11421
Moriasi D N, Arnold J G, Van Liew M W, Bingner R L, Harmel R D, Veith T L (2007). Model evaluation guidelines for systematic quantification of accuracy in watershed simulations. Trans ASABE, 50(3): 885–900
Mould B (1985). New Day Rising. Chapter 1 in New Day Rising, Solid State Tuners, Taylor, Texas
Nash J E, Sutcliffe J V (1970). River flow forecasting through conceptual models part I–A discussion of principles. J Hydrol (Amst), 10(3): 282–290
Neumann N, Derksen C, Smith C, Goodison B (2006). Characterizing local scale snow cover using point measurements during the winter season. Atmos-ocean, 44(3): 257–269
Rice R, Bales R (2010). Embedded sensor network design for snow cover measurements around snow pillow and snow course sites in the Sierra Nevada of California. Water Resour Res, 46(3): W03537
Sexstone G A, Fassnacht S R (2014). What drives basin scale spatial variability of snowpack properties in the Front Range of northern Colorado? Cryosphere, 8(2): 329–344
Sturm M, Wagner A (2010). Using repeated patterns in snow distribution modeling: an Arctic example. Water Resour Res, 46(12): W12549
Trujillo E, Ramírez J A, Elder K J (2007). Topographic, meteorologic, and canopy controls on the scaling characteristics of the spatial distribution of snow depth fields. Water Resour Res, 43(7): W07409
U.S. Department of Agriculture (2011). Snow Survey and Water Supply Forecasting. National Engineering Handbook Part 622, Water and Climate Center, Natural Resources Conservation Service
U.S. Department of Agriculture (2016). Snow Survey and Water Supply Forecasting Program. National Water and Climate Center, Natural Resources Conservation Service
U.S. Geological Survey (2018). The National Map. U.S. Department of Interior
Veatch W, Brooks P, Gustafson J, Molotch N (2009). Quantifying the effects of forest canopy cover on net snow accumulation at a continental, mid latitude site. Ecohydrology, 2(2): 115–128
Wallace C, Gass L (2008). Elevation derivatives for Mojave Desert tortoise habitat models. US Geological Survey Open File Report 2008-1283, 7 pp
Webb R (2017). Using ground penetrating radar to assess the variability of snow water equivalent and melt in a mixed canopy forest, Northern Colorado. Front Earth Sci, 11(3): 482–495
Winstral A, Elder K, Davis R (2002). Spatial snow modeling of wind redistributed snow using terrain based parameters. J Hydrometeorol, 3(5): 524–538
Winstral A, Marks D (2002). Simulating wind fields and snow redistribution using terrain based parameters to model snow accumulation and melt over a semi arid mountain catchment. Hydrol Processes, 16(18): 3585–3603
Acknowledgements
Funding was provided by a grant from the Colorado Water Conservancy Board award POGG1 PDAA 201800000044 (Automated Non-Telemetered Snow Depth Monitoring for Water Supply Forecasting by Improved Basin-wide Snowpack Water Storage Estimation; PI Fassnacht), NOAA award NA07NWS4620016 (PI N.P. Molotch), and NASATerrestrial Hydrology Program award NNX11AQ66G (PI M.F. Jasinski). The field surveys were undertaken by Colorado State University student volunteers, including those enrolled in the WR575 (Snow Hydrology Field Methods) class in March 2009. Thanks are due to all those who helped in the field and those who digitized the data. One reviewer made useful comments that helped restructure this paper.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Fassnacht, S.R., Brown, K.S.J., Blumberg, E.J. et al. Distribution of snow depth variability. Front. Earth Sci. 12, 683–692 (2018). https://doi.org/10.1007/s11707-018-0714-z
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11707-018-0714-z