Quantifying the early snowmelt event of 2015 in the Cascade Mountains, USA by developing and validating MODIS-based snowmelt timing maps

Abstract

Spring snowmelt serves as the major hydrological contribution to many watersheds of the US West. Since the 1970s the conterminous western USA has seen an earlier arrival of spring snowmelt. The extremely low snowpack and early melt of 2015 in the Cascade Mountains may be a harbinger of winters to come, underscoring the interest in advancements in spring snowmelt monitoring. Target-of-opportunity and point measurements of snowmelt using meteorological stations or stream gauges are common sources of these data, however, there have been few attempts to identify snowmelt timing using remote sensing. In this study, we describe the creation of snowmelt timing maps (STMs) which identify the day of year that each pixel of a remotely sensed image transitions from “snow-covered” to “no snow” during the spring melt season, controlling for cloud coverage and ephemeral spring snow storms. Derived from the 500 m MODerate-resolution Imaging Spectroradiometer (MODIS) standard snow map, MOD10A2, this new dataset provides annual maps of snowmelt timing, with corresponding maps of cloud interference and interannual variability in snow coverage from 2001–2015. We first show that the STMs agree strongly with in-situ snow telemetry (SNOTEL) meteorological station measurements in terms of snowmelt timing. We then use the STMs to investigate the early snowmelt event of 2015 in the Cascade Mountains, USA, highlighting the protected areas of Mt. Rainier, Crater Lake, and Lassen Volcanic National Parks. In 2015 the Cascade Mountains experienced snowmelt 41 days earlier than the 2001–2015 average, with 25% of its land area melting>65 days earlier than average. The upper elevations of the Cascade Mountains experienced the greatest snowmelt anomaly. Our results are relevant to land managers and biologists as they plan adaptation strategies for mitigating the effects of climate change throughout temperate mountains.

Keywords

Cascade Mountains / snowmelt / spring / phenology / MODIS / remote sensing

References

[1] Abatzoglou J T, Kolden C A (2013). Relationships between climate and macroscale area burned in the western United States. Int J Wildland Fire, 22(7): 1003–1020

[2] Barrett A (2003). National Operational Hydrologic Remote Sensing Center SNOw Data Assimilation System (SNODAS) Products at National Snow and Ice Data Center. Special Report #11

[3] Billings W D, Bliss L C (1959). An alpine snowbank environment and its effects on vegetation, plant development, and productivity. Ecology, 40(3): 388–397

[4] Brown R D, Brasnett B, Robinson D (2003). Gridded North American monthly snow depth and snow water equivalent for GCM evaluation. Atmos-ocean, 41(1): 1–14

[5] Brubaker K L, Pinker R T, Deviatova E (2005). Evaluation and comparison of MODIS and IMS snow-cover estimates for the continental United States using station data. J Hydrometeorol, 6(6): 1002–1017

[6] Cornelius C, Leingärtner A, Hoiss B, Krauss J, Steffan-Dewenter I, Menzel A (2013). Phenological response of grassland species to manipulative snowmelt and drought along an altitudinal gradient. J Exp Bot, 64(1): 241–251

[7] Crawford C J (2014). MODIS Terra Collection 6 fractional snow cover validation in mountainous terrain during spring snowmelt using Landsat TM and ETM+. Hydrol Processes, 29(1): 128–138

[8] Dickerson-Lange S E, Gersonde R F, Hubbart J A, Link T E, Nolin A W, Perry G H, Roth T R, Wayand N E, Lundquist J D (2017). Snow disappearance timing is dominated by forest effects on snow accumulation in warm winter climates of the Pacific Northwest, United States. Hydrol Processes, 31(10): 1846–1862

[9] Fassnacht S R, Sexstone G A, Kashipazha A H, López-Moreno J I, Jasinski M F, Kampf S K, Von Thaden B C (2016). Deriving snow-cover depletion curves for different spatial scales from remote sensing and snow telemetry data. Hydrol Processes, 30(11): 1708–1717

[10] Ganguly S, Friedl M A, Tan B, Zhang X, Verma M (2010). Land surface phenology from MODIS: characterization of the Collection 5 global land cover dynamics product. Remote Sens Environ, 114(8): 1805–1816

[11] Gleason K E, Nolin A W, Roth T R (2017). Developing a representative snow-monitoring network in a forested mountain watershed. Hydrol Earth Syst Sci, 21(2): 1137–1147

[12] Hall D K, Crawford C J, DiGirolamo N E, Riggs G A, Foster J L (2015). Detection of earlier snowmelt in the Wind River Range, Wyoming, using Landsat imagery, 1972–2013. Remote Sens Environ, 162(1): 45–54

[13] Hall D K, Foster J L, DiGirolamo N E, Riggs G A (2012). Snow cover, snowmelt timing, and stream power in the Wind River Range, Wyoming. Geomorphology, 137(1): 87–93

[14] Hall D K, Riggs G A (2007). Accuracy assessment of the MODIS snow products. Hydrol Processes, 21(12): 1534–1547

[15] Hall D K, Riggs G A (2016). MODIS/Terra Snow Cover Daily L3 Global 500 m Grid, Version 6. NASA National Snow and Ice Data Center Distributed Active Archive Cente https://doi.org/10.5067/MODIS/MOD10A1.006

[16] Hall D K, Salomonson V V, Riggs G A (2006). MODIS/Terra Snow Cover 8-Day L3 Global 500m Grid. Version 5. National Snow and Ice Data Center

[17] Homan J W, Luce C H, McNamara J P, Glenn N F (2011). Improvement of distributed snowmelt energy balance modeling with MODIS-based NDSI-derived fractional snow-covered area data. Hydrol Processes, 25(4): 650–660

[18] Inouye D W (2008). Effects of climate change on phenology, frost damage, and floral abundance of Montane Wildflowers. Ecology, 89(2): 353–362

[19] Inouye D W, Morales M A, Dodge G J (2002). Variation in timing and abundance of flowering by Delphinium barbeyi Huth (Ranunculaceae): the roles of snowpack, frost, and La Niña, in the context of climate change. Oecologia, 130(4): 543–550

[20] Iorio J P, Duffy P B, Govindasamy B, Thompson S L, Khairoutdinov M, Randall D (2004). Effects of model resolution and subgrid-scale physics on the simulation of precipitation in the continental United States. Clim Dyn, 23(3–4): 243–258

[21] Jönsson P, Eklundh L (2004). TIMESAT‒ a program for analysing time-series of satellite sensor data. Comput Geosci, 30(8): 833–845

[22] Klein A G, Barnett A C (2003). Validation of daily MODIS snow cover maps of the Upper Rio Grande River Basin for the 2000–2001 snow year. Remote Sens Environ, 86(2): 162–176

[23] Littell J S, McKenzie D, Peterson D L, Westerling A L (2009). Climate and wildfire area burned in western U.S. ecoprovinces, 1916—2003. Ecol Appl, 19(4): 1003–1021

[24] Lundquist J D, Dickerson-Lange S E, Lutz J A, Cristea N C (2013). Lower forest density enhances snow retention in regions with warmer winters: a global framework developed from plot-scale observations and modeling. Water Resour Res, 49(10): 6356–6370

[25] Margulis S A, Cortés G, Girotto M, Durand M (2016b). A Landsat-Era Sierra Nevada snow reanalysis (1985–2015). J Hydrometeorol, 17(4): 1203–1221

[26] Margulis SA, Cortés G, Girotto M, Huning LS, Li D, Durand M (2016a). Characterizing the extreme 2015 snowpack deficit in the Sierra Nevada (USA) and the implications for drought recovery. Geophysical Research Letters, 43(12): 2016GL068520. DOI:10.1002/2016GL068520

[27] McCabe G J, Clark M P, Hay L E (2007). Rain-on-snow events in the western United States. Bull Am Meteorol Soc, 88(3): 319–328

[28] Moore C, Kampf S, Stone B, Richer E (2015). A GIS-based method for defining snow zones: application to the western United States. Geocarto Int, 30(1): 62–81

[29] Mote P W, Hamlet A F, Clark M P, Lettenmaier D P (2005). Declining mountain snowpack in western North America. Bull Am Meteorol Soc, 86(1): 39–49

[30] Musselman K N, Molotch N P, Margulis S A (2017). Snowmelt response to simulated warming across a large elevation gradient, southern Sierra Nevada, California. Cryosphere, 11(6): 2847–2866

[31] Narasimhan R, Stow D (2010). Daily MODIS products for analyzing early season vegetation dynamics across the North Slope of Alaska. Remote Sens Environ, 114(6): 1251–1262

[32] Nolin A W, Daly C (2006). Mapping “At Risk” Snow in the Pacific Northwest. J Hydrometeorol, 7(5): 1164–1171

[33] Nolin A W, Sproles E A, Crumley R L, Wilson A, Mar E, van de Kerk M, Prugh L (2017). Cloud-based Computing and Applications of New Snow Metrics for Societal Benefit. AGU Fall Meeting Abstracts, 22

[34] NPS (2016a). Science and Learning Center.

[35] NPS (2016b). Ongoing and Past Research.

[36] NPS (2016c). Klamath Network Inventory and Monitoring.

[37] NRCS (2016). NRCS National Water and Climate Center | SNOTEL Data & Products.

[38] O’Leary III D S, Bloom T D, Smith J C, Zempf C R, Medler M J (2016). A new method comparing snowmelt timing with annual area burned. Fire Ecol, 12(1): 41–51

[39] O’Leary III D S, Hall D K, Medler M J, Matthews R A, Flower A (2017). Snowmelt Timing Maps Derived from MODIS for North America, 2001‒2015. Oak Ridge National Laboratory.

[40] O’Leary III D S, Kellermann J L, Wayne C (2018). Snowmelt timing, phenology, and growing season length in conifer forests of Crater Lake National Park, USA. Int J Biometeorol, 62(2): 273–285

[41] Ramsay B H (1998). The interactive multisensor snow and ice mapping system. Hydrol Processes, 12(10–11): 1537–1546

[42] Rangwala I, Miller J R (2012). Climate change in mountains: a review of elevation-dependent warming and its possible causes. Clim Change, 114(3–4): 527–547

[43] Riggs G A, Hall D K, Román M O (2017). Overview of NASA’s MODIS and visible infrared imaging radiometer suite (VIIRS) snow-cover earth system data records. Earth Syst Sci Data, 9(2): 765–777

[44] Roth T R, Nolin A W (2017). Forest impacts on snow accumulation and ablation across an elevation gradient in a temperate montane environment. Hydrol Earth Syst Sci, 21(11): 5427–5442

[45] Semmens K A, Ramage J (2012). Investigating correlations between snowmelt and forest fires in a high latitude snowmelt dominated drainage basin. Hydrol Processes, 26(17): 2608–2617

[46] Sherwood J A, Debinski D M, Caragea P C, Germino M J (2017). Effects of experimentally reduced snowpack and passive warming on montane meadow plant phenology and floral resources. Ecosphere, 8(3): e01745

[47] Simic A, Fernandes R, Brown R, Romanov P, Park W (2004). Validation of VEGETATION, MODIS, and GOES+ SSM/I snow-cover products over Canada based on surface snow depth observations. Hydrol Processes, 18(6): 1089–1104

[48] Sproles E A, Nolin A W, Rittger K, Painter T H (2013). Climate change impacts on maritime mountain snowpack in the Oregon Cascades. Hydrol Earth Syst Sci, 17(7): 2581–2597

[49] Sproles E A, Roth T R, Nolin A W (2017). A spatial-probabilistic assessment of the extraordinarily low snowpacks of 2014 and 2015 in the Oregon Cascades. Cryosphere, 11(1): 331–341

[50] Steltzer H, Landry C, Painter T H, Anderson J, Ayres E (2009). Biological consequences of earlier snowmelt from desert dust deposition in alpine landscapes. Proc Natl Acad Sci USA, 106(28): 11629–11634

[51] Stewart I T (2009). Changes in snowpack and snowmelt runoff for key mountain regions. Hydrol Processes, 23(1): 78–94

[52] Swenson S C, Lawrence D M (2012). A new fractional snow-covered area parameterization for the Community Land Model and its effect on the surface energy balance. J Geophys Res D Atmospheres, 117(D21): D21107

[53] Tahir A A, Chevallier P, Arnaud Y, Ahmad B (2011). Snow cover dynamics and hydrological regime of the Hunza River basin, Karakoram Range, Northern Pakistan. Hydrol Earth Syst Sci, 15(7): 2275–2290

[54] Totland Ø, Alatalo J M (2002). Effects of temperature and date of snowmelt on growth, reproduction, and flowering phenology in the arctic/alpine herb, Ranunculus glacialis. Oecologia, 133(2): 168–175

[55] University of Wyoming (2017). Grand Teton National Park Service Research Needs 2017.

[56] USGS (2014). Geologic Provinces of the United States: Pacific.

[57] USGS (2017). National Elevation Dataset (NED) The Long Term Archive.

[58] USGS (2018). United States Geological Survey Current Water Data for the Nation.

[59] USGSA (2017). Data.gov US General Services Administration. In: Data.gov.

[60] Westerling A L, Hidalgo H G, Cayan D R, Swetnam T W (2006). Warming and earlier spring increase western U.S. forest wildfire activity. Science, 313(5789): 940–943

[61] Westerling AL (2016). Increasing western US forest wildfire activity: sensitivity to changes in the timing of spring. Philosophical Transactions of the Royal Society B: Biological Sciences, 371(1696): 1–10. DOI:10.1098/rstb.2015.0178

[62] Wolf A, Zimmerman N B, Anderegg W R L, Busby P E, Christensen J (2016). Altitudinal shifts of the native and introduced flora of California in the context of 20th-century warming. Glob Ecol Biogeogr, 25(4): 418–429

[63] Yu S, Bhave P V, Dennis R L, Mathur R (2007). Seasonal and regional variations of primary and secondary organic aerosols over the continental United States: semi-empirical estimates and model evaluation. Environ Sci Technol, 41(13): 4690–4697

[64] Zhang X, Friedl M A, Schaaf C B (2006). Global vegetation phenology from moderate resolution imaging spectroradiometer (MODIS): evaluation of global patterns and comparison with in situ measurements. J Geophys Res Biogeosci, 111(G4): G04017 doi:10.1029/2006JG000217

RIGHTS & PERMISSIONS

Higher Education Press and Springer-Verlag GmbH Germany, part of Springer Nature