Spatio-temporal snowmelt variability across the headwaters of the Southern Rocky Mountains

doi:10.1007/s11707-017-0641-4

Front. Earth Sci.

2017, Vol. 11

Issue (3) : 505-514 https://doi.org/10.1007/s11707-017-0641-4

RESEARCH ARTICLE

Spatio-temporal snowmelt variability across the headwaters of the Southern Rocky Mountains

S.R. FASSNACHT^1,^2,^3,^4,⁵(

), J.I. LÓPEZ-MORENO⁶, C. MA¹, A.N. WEBER¹, A.K.D. PFOHL⁷, S.K. KAMPF^1,⁴, M. KAPPAS⁵

¹. ESS-Watershed Science, Colorado State University, Fort Collins, CO 80523-1476, USA
². Cooperative Institute for Research in the Atmosphere, Fort Collins, CO 80523-1375, USA
³. Geospatial Centroid at CSU, Fort Collins, CO 80523-1019, USA
⁴. Natural Resources Ecology Laboratory, Fort Collins, CO 80523-1499, USA
⁵. Geographisches Institut, Georg-August-Universität Göttingen, 37077 Göttingen, Germany
⁶. Instituto Pirenaico de Ecología, CSIC, Campus de Aula Dei, 50080 Zaragoza, Spain
⁷. EASC-Watershed Science, Colorado State University, Fort Collins, CO 80523-1482, USA

Download: PDF(1676 KB)
Export: BibTeX | EndNote | Reference Manager | ProCite | RefWorks

Abstract

Understanding the rate of snowmelt helps inform how water stored as snow will transform into streamflow. Data from 87 snow telemetry (SNOTEL) stations across the Southern Rocky Mountains were used to estimate spatio-temporal melt factors. Decreases in snow water equivalent were correlated to temperature at these monitoring stations for eight half-month periods from early March through late June. Time explained 70% of the variance in the computed snow melt factors. A residual linear correlation model was used to explain subsequent spatial variability. Longitude, slope, and land cover type explained further variance. For evergreen trees, canopy density was relevant to find enhanced melt rates; while for all other land cover types, denoted as non-evergreen, lower melt rates were found at high elevation, high latitude and north facing slopes, denoting that in cold environments melting is less effective than in milder sites. A change in the temperature sensor about mid-way through the time series (1990 to 2013) created a discontinuity in the temperature dataset. An adjustment to the time series yield larger computed melt factors.

Keywords melt SWE temperature SNOTEL temperature sensor change

Corresponding Author(s): S.R. FASSNACHT

Just Accepted Date: 28 February 2017 Online First Date: 21 March 2017 Issue Date: 12 July 2017

Cite this article:

S.R. FASSNACHT,J.I. LÓPEZ-MORENO,C. MA, et al. Spatio-temporal snowmelt variability across the headwaters of the Southern Rocky Mountains[J]. Front. Earth Sci., 2017, 11(3): 505-514.

URL:

https://academic.hep.com.cn/fesci/EN/10.1007/s11707-017-0641-4
https://academic.hep.com.cn/fesci/EN/Y2017/V11/I3/505

1	E A Anderson (2006). Snow Accumulation and Ablation Model – SNOW-17. Update of technical memorandum NWS HYDRO-17 (National weather service river forecast system – snow accumulation and ablation model), Silver Springs, MD, National Oceanographic and Atmospheric Administration, 44pp
2	F Avanzi, C De Michele, A Ghezzi, C Jommi, M Pepe (2014). A processing-modeling routine to use SNOTEL hourly data in snowpack dynamic models. Adv Water Resour, 73: 16–29 https://doi.org/10.1016/j.advwatres.2014.06.011
3	K Brubaker, A Rango, W Kustas (1996). Incorporating radiation inputs into the snowmelt runoff model. Hydrol Processes, 10(10): 1329–1343 https://doi.org/10.1002/(SICI)1099-1085(199610)10:10<1329::AID-HYP464>3.0.CO;2-W
4	S Colbeck (1979). Water-flow through heterogeneous snow. Cold Reg Sci Technol, 1(1): 37–45 https://doi.org/10.1016/0165-232X(79)90017-X
5	K Cooley, P Palmer (1997). Characteristics of snowmelt from NRCS SNOTEL (SNOwTELemetry) sites. Proceedings of the 65th Annual Western Snow Conference, Banff, Alberta, 11pp
6	S F Daly, R E Davis, E Ochs, T Pangburn (2000). An approach to spatially distributed snow modeling of the Sacramento and San Joaquin Basins, California. Hydrol Processes, 14(18): 3257–3271 https://doi.org/10.1002/1099-1085(20001230)14:18<3257::AID-HYP199>3.0.CO;2-Z
7	D DeWalle, Z Henderson, A Rango (2002). Spatial and temporal variations in snowmelt degree-day factors computed from SNOTEL data in the Upper Rio Grande basin. Proceedings of the 70th Western Snow Conference, Granby, CO, 73–81
8	N J Doesken, A Judson (1996). The Snow Booklet. A guide to the science, climatology and measurement of snow in the United States. Department of Atmospheric Sciences, Colorado State University, Fort Collins, Colorado USA, 84 pp
9	K A Dressler, G H Leavesley, R C Bales, S R Fassnacht (2006). Evaluation of gridded snow water equivalent and satellite snow cover products for mountain basins in a hydrologic model. Hydrol Processes, 20(4): 673–688 https://doi.org/10.1002/hyp.6130
10	T Dunne, A Price, S Colbeck (1976). Generation of runoff from subarctic snowpacks. Water Resour Res, 12(4): 677–685 https://doi.org/10.1029/WR012i004p00677
11	S R Fassnacht, D C Deitemeyer, N B H Venable (2014). Capitalizing on the daily time step of snow telemetry data to model the snowmelt components of the hydrograph for small watersheds. Hydrol Processes, 28(16): 4654–4668 https://doi.org/10.1002/hyp.10260
12	S R Fassnacht, K A Dressler, D M Hultstrand, R C Bales, G G Patterson (2012). Temporal inconsistencies in coarse-scale snow water equivalent patterns: Colorado River Basin snow telemetry-topography regressions. Pirineos, 167: 165–186 https://doi.org/10.3989/Pirineos.2012.167008
13	S R Fassnacht, J I López-Moreno, M Toro, D M Hultstrand (2013). Mapping snow cover and snow depth across the lake limnopolar watershed on Byers Peninsula (Livingston Island) in maritime Antarctica. Antarct Sci, 25(2): 157–166 https://doi.org/10.1017/S0954102012001216
14	S R Fassnacht, R M Records (2015). Large snowmelt versus rainfall events in the mountains. J Geophys Res, 120: 2375–2381 https://doi.org/10.1002/2014JD022753
15	S R Fassnacht, G A Sexstone, A H Kashipazha, J I López-Moreno, M F Jasinski, S K Kampf, B C Von Thaden (2016). Deriving snow-cover depletion curves for different spatial scales from remote sensing and snow telemetry data. Hydrol Processes, 30(11): 1708–1717 https://doi.org/10.1002/hyp.10730
16	M He, T S Hogue, K J Franz, S A Margulis, J A Vrugt (2011). Characterizing parameter sensitivity and uncertainty for a snow model across hydroclimatic regimes. Adv Water Resour, 34(1): 114–127 https://doi.org/10.1016/j.advwatres.2010.10.002
17	R Hock (2003). Temperature index melt modelling in mountain areas. J Hydrol (Amst), 282(1–4): 104–115 https://doi.org/10.1016/S0022-1694(03)00257-9
18	R E Horton (1915). The melting of snow. Mon Weather Rev, 43(12): 599–605 https://doi.org/10.1175/1520-0493(1915)43<599:TMOS>2.0.CO;2
19	J Johnson, D Marks (2004). The detection and correction of snow water equivalent pressure sensor errors. Hydrol Processes, 18(18): 3513–3525 https://doi.org/10.1002/hyp.5795
20	J B Johnson, G L Schaefer (2002). The influence of thermal, hydrologic, and snow deformation mechanisms on snow water equivalent pressure sensor accuracy. Hydrol Processes, 16(18): 3529–3542 https://doi.org/10.1002/hyp.1236
21	R P Julander, J Curtis, A Beard (2007). The SNOTEL Temperature Dataset. Mountain Views Newsletter, 1(2): 4–7 https://doi.org/10.1.1.226.2144
22	S K Kampf, E E Richer (2014). Estimating source regions for snowmelt runoff in a Rocky Mountain basin: tests of a data-based conceptual modeling approach. Hydrol Processes, 28(4): 2237–2250 https://doi.org/10.1002/hyp.9751
23	M Kumar, D Marks, J Dozier, M Reba, A Winstral (2013). Evaluation of distributed hydrologic impacts of temperature-index and energy-based snow models. Adv Water Resour, 56: 77–89 https://doi.org/10.1016/j.advwatres.2013.03.006
24	G H Leavesley (1989). Problems of snowmelt runoff modelling for a variety of physiographic and climatic conditions. Hydrological Sciences, 34(6): 617–634 https://doi.org/10.1080/02626668909491371
25	D R Legates, G J McCabe Jr (1999). Evaluating the use of “goodness-of-fit” measures in hydrologic and hydroclimatic model validation. Water Resour Res, 35(1): 233–241 https://doi.org/10.1029/1998WR900018
26	R K Linsley Jr (1943). A simple procedure for the day-to-day forecasting of runoff from snow-melt. Eos (Wash DC), 24(3): 62–67
27	J I López-Moreno, B Alvera, J Latron, S R Fassnacht (2010). Instalación y Uso de un Colchón de Nieve para la Monitorización del Manto de Nieve, Cuenca Experimental de Izas (Pirineo Central). Cuadernos de Investigación Geográfica, 36(1): 73–85 https://doi.org/10.18172/cig.1228
28	J Lydon, M Schulz (1986). Round. Track 4 on Album, Elektra, New York, NY
29	C Ma, S R Fassnacht, S K Kampf (2016). Homogenization of High Elevation Temperature Data Across Colorado. Oral presentation at the 36th Annual AGU Hydrology Days Conference, Fort Collins, Colorado, March 21–23
30	J Martinec, A Rango, R Roberts (2008). Snowmelt Runoff Model User’s Manual, Updated Edition 2008, WinSRM Version 1.11. New Mexico State University Agricultural Experiment Station, Special Report 100
31	M J Menne, C N Williams Jr, R S Vose (2015). United States Historical Climatology Network (USHCN) Daily Dataset. National Climatic Data Center, National Oceanic and Atmospheric Administration, available at:<>(last access: 13 November 2015)
32	L Meromy, N P Molotch, T E Link, S R Fassnacht, R Rice (2013). Subgrid variability of snow water equivalent at operational snow stations in the western USA. Hydrol Processes, 27(17): 2383–2400 https://doi.org/10.1002/hyp.9355
33	P Morrisey (2015). Personal Communication. Idaho Snow Survey Office, Natural Resources Conservation Service, U.S. Department of Agriculture
34	J E Nash, J V Sutcliffe (1970). River flow forecasting through conceptual models part I — A discussion of principles. J Hydrol (Amst), 10(3): 282–290 https://doi.org/10.1016/0022-1694(70)90255-6
35	Natural Resources Conservation Service (2016a). Snow Telemetry (SNOTEL) Data Collection Network Brochure. National Water and Climate Center, U.S. Department of Agriculture, available at:
36	Natural Resources Conservation Service (2016b). NRCS: National Water and Climate Center SNOTEL data network. U.S. Department of Agriculture, available at:<>, (last accessed: 16 December 2016)
37	A Ohmura (2001). Physical basis for the temperature-based melt-index method. J Appl Meteorol, 40(4): 753–761 https://doi.org/10.1175/1520-0450(2001)040<0753:PBFTTB>2.0.CO;2
38	N Omani, R Srinivasan, P K Smith, R Karthikeyan (2016). Glacier mass balance simulation using SWAT distributed snow algorithm. Hydrol Sci J, https://doi.org/10.1080/02626667.2016.1162907
39	J Oyler, S Z Dobrowski, A P Ballantyne, A E Klene, S W Running (2015). Artificial amplification of warming trends across the mountains of the Western United States. Geophys Res Lett, 42(1): 153–161 https://doi.org/10.1002/2014GL062803
40	G G Patterson (2016). Seasonal Snowpack Trends about Rocky Mountain National Park. Unpublished Ph.D. dissertation, Watershed Science, Colorado State University, Fort Collins, Colorado USA, 105pp+ 1 appendix
41	R A Pielke Sr, T Stohlgren, L Schell, W Parton, N Doesken, K Redmond, J Moeny, T McKee, T G F Kittel (2002). Problems in evaluating regional and local trends in temperature: an example from eastern Colorado, USA. Int J Climatol, 22(4): 421–434 https://doi.org/10.1002/joc.706
42	A Rango, J Martinec (1995). Revisiting the degree-day method for snowmelt computations. J Am Water Resour Assoc, 31(4): 657–669 https://doi.org/10.1111/j.1752-1688.1995.tb03392.x
43	E E Richer, S K Kampf, S R Fassnacht, C Moore (2013). Spatiotemporal index for analyzing controls on snow climatology: application in the Colorado Front Range. Phys Geogr, 34: 85–107 https://doi.org/10.1080/02723646.2013.787578
44	G A Sexstone, S R Fassnacht (2014). What drives basin scale spatial variability of snowpack properties in Northern Colorado? Cryosphere, 8(2): 329–344 https://doi.org/10.5194/tc-8-329-2014
45	C Tobin, B Schaefli, L Nicotina, S Simoni, G Barrenetxea, R Smith, M Parlange, A Rinaldo (2013). Improving the degree-day method for sub-daily melt simulations with physically-based diurnal variations. Adv Water Resour, 55: 149–164 https://doi.org/10.1016/j.advwatres.2012.08.008
46	U.S. Army Corps of Engineers (1956). Snow Hydrology. Summary report of the snow investigation, North Pacific Division, Portland, OR, 437pp
47	U.S. Geological Survey (2016). The USGS Land Cover Institute (LCI). U.S. Department of the Interior, available at:<>
48	A Valéry, V Andréassian, C Perrin (2014). As simple as possible but not simpler: what is useful in a temperature-based snow-accounting routine? Part 2- Sensitivity analysis of the Cemaneige snow accounting routine on 380 catchments. Journal of Hydrology, 517: 1176–1187
49	R W Webb, S R Fassnacht, M N Gooseff (in review). Defining the Diurnal Pattern of Snowmelt using a Gamma Distribution Function. Journal of the American Water Resources Association, (JAWRA-16-0153-P) (submitted July 2016)
50	H Zeinivand, F De Smedt (2009). Hydrological modeling of snow accumulation and melting on river basin scale. Water Resources Management, 23(11): 2271–2287 https://doi.org/10.1007/s11269-008-9381-2

[1]	Ci SONG, Qiu YIN. Research on vertical spatial characteristic of satellite infrared hyperspectral atmospheric sounding data[J]. Front. Earth Sci., 2022, 16(2): 265-276.
[2]	Munkhdulam OTGONBAYAR, Clement ATZBERGER, Erdenesukh SUMIYA, Sainbayar DALANTAI, Jonathan CHAMBERS. Estimation of bioclimatic variables of Mongolia derived from remote sensing data[J]. Front. Earth Sci., 2022, 16(2): 323-339.
[3]	Qiang XU, Hangbing LIN, Yue ZHAO, Bo WANG, Bin MA, Rong DING, Jianxin WANG, Tao HOU. Evolution of pore structure in organic shale with type III kerogen and high kaolinite content in Ningwu Basin[J]. Front. Earth Sci., 2021, 15(4): 831-848.
[4]	Xin NIE, Yu WAN, Da GAO, Chaomo ZHANG, Zhansong ZHANG. Evaluation of the in-place adsorbed gas content of organic-rich shales using wireline logging data: a new method and its application[J]. Front. Earth Sci., 2021, 15(2): 301-309.
[5]	Jiancheng QIN, Buda SU, Hui TAO, Yanjun WANG, Jinlong HUANG, Tong JIANG. Projection of temperature and precipitation under SSPs-RCPs Scenarios over northwest China[J]. Front. Earth Sci., 2021, 15(1): 23-37.
[6]	Emre ÇOLAK, Filiz SUNAR. Spatial pattern analysis of post-fire damages in the Menderes District of Turkey[J]. Front. Earth Sci., 2020, 14(2): 446-461.
[7]	Linan YUAN, Jingjuan LIAO. Exploring the influence of various factors on microwave radiation image simulation for Moon-based Earth observation[J]. Front. Earth Sci., 2020, 14(2): 430-445.
[8]	Sukh TUMENJARGAL, Steven R. FASSNACHT, Niah B.H. VENABLE, Alison P. KINGSTON, Maria E. FERNÁNDEZ-GIMÉNEZ, Batjav BATBUYAN, Melinda J. LAITURI, Martin KAPPAS, G. ADYABADAM. Variability and change of climate extremes from indigenous herder knowledge and at meteorological stations across central Mongolia[J]. Front. Earth Sci., 2020, 14(2): 286-297.
[9]	S.R. FASSNACHT, J.I. LÓPEZ-MORENO. Patterns of trends in niveograph characteristics across the western United States from snow telemetry data[J]. Front. Earth Sci., 2020, 14(2): 315-325.
[10]	Shiyong YAN, Ke SHI, Yi LI, Jinglong LIU, Hongfeng ZHAO. Integration of satellite remote sensing data in underground coal fire detection: A case study of the Fukang region, Xinjiang, China[J]. Front. Earth Sci., 2020, 14(1): 1-12.
[11]	Yiannis KAMARIANAKIS, Xiaoxiao LI, B. L. TURNER II, Anthony J. BRAZEL. On the effects of landscape configuration on summer diurnal temperatures in urban residential areas: application in Phoenix, AZ[J]. Front. Earth Sci., 2019, 13(3): 445-463.
[12]	Yaoping CUI, Xiangming XIAO, Russell B DOUGHTY, Yaochen QIN, Sujie LIU, Nan LI, Guosong ZHAO, Jinwei DONG. The relationships between urban-rural temperature difference and vegetation in eight cities of the Great Plains[J]. Front. Earth Sci., 2019, 13(2): 290-302.
[13]	Qianfeng WANG, Jingyu ZENG, Song LENG, Bingxiong FAN, Jia TANG, Cong JIANG, Yi HUANG, Qing ZHANG, Yanping QU, Wulin WANG, Wei SHUI. The effects of air temperature and precipitation on the net primary productivity in China during the early 21st century[J]. Front. Earth Sci., 2018, 12(4): 818-833.
[14]	Donal O’Leary III, Dorothy Hall, Michael Medler, Aquila Flower. Quantifying the early snowmelt event of 2015 in the Cascade Mountains, USA by developing and validating MODIS-based snowmelt timing maps[J]. Front. Earth Sci., 2018, 12(4): 693-710.
[15]	Kazuyoshi SUZUKI, Milija ZUPANSKI. Uncertainty in solid precipitation and snow depth prediction for Siberia using the Noah and Noah-MP land surface models[J]. Front. Earth Sci., 2018, 12(4): 672-682.

Viewed

Full text

Abstract

Cited

Shared

Discussed