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

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Code and data availability

Code in Python and R for the snowmelt timing development and quantitative analysis are available from the corresponding author. Snowmelt Timing Maps including cloud interference, count, and mean calculations for 2001‒2015 are available from the Oak Ridge National Laboratory, USA. https://doi.org/10.3334/ORNLDAAC/1504

Acknowledgements

This material is based upon work supported by the National Science Foundation Graduate Research Fellowship Program under Grant No. DGE1322106. Co-author Hall was funded through NASA grant 80NSSC17K0172. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation. We thank Dr. Jherime Kellermann, Chris Wayne, Crater Lake National Park, the University of Washington, and the George Melendez Wright Foundation for supporting the Young Leaders in Climate Change program, during which D. O’Leary III developed earlier iterations of the STMs. We also thank Dr. Christopher Crawford, Dr. Andrew Bunn, and Dr. David Wallin for their suggestions in refining the STMs.

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