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Winter melt trends portend widespread declines in snow water resources

Nature Climate Change volume 11, pages 418–424 (2021)Cite this article

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Abstract

In many mountainous regions, winter precipitation accumulates as snow that melts in the spring and summer, which provides water to one billion people globally. Climate warming and earlier snowmelt compromise this natural water storage. Although snowpack trend analyses commonly focus on the snow water equivalent (SWE), we propose that trends in the accumulation season snowmelt serve as a critical indicator of hydrological change. Here we compare long-term changes in the snowmelt and SWE from snow monitoring stations in western North America and find 34% of stations exhibit increasing winter snowmelt trends (P < 0.05), a factor of three larger than the 11% showing SWE declines (P < 0.05). Snowmelt trends are highly sensitive to temperature and an underlying warming signal, whereas SWE trends are more sensitive to precipitation variability. Thus, continental-scale snow water resources are in steeper decline than inferred from SWE trends alone. More winter snowmelt will complicate future water resource planning and management.

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Fig. 1: On average across western North American stations, snowpack reaches an annual maximum on 1 April when 78% of the annual snow has yet to melt.
Fig. 2: Trends towards more melt before 1 April are three-times more widespread than trends towards a lower 1 April SWE or maximum annual SWE.
Fig. 3: Snowmelt is more sensitive to temperature, whereas SWE is more influenced by precipitation.
Fig. 4: The increasing number of stations with melt is explained by widespread, long-term warming, whereas SWE declines are more sensitive to precipitation variability.

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Data availability

Data used in this paper are publicly available and fully citable at https://doi.org/10.5281/zenodo.4546865.

Code availability

The MATLAB code used to conduct the analysis and create figures is publicly available and fully citable at https://doi.org/10.5281/zenodo.4546596.

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Acknowledgements

K.N.M. and N.P.M. were supported by NASA Applied Sciences Water Resources Program under grant NNX17AF50G. N.A. was supported by the Swiss National Science Foundation (P400P2_180791). We are grateful for the dedicated efforts of the Natural Resources Conservation Service, the California Department of Water Resources, Alberta Environment, the British Columbia Ministry of Environment and the Yukon Government Water Resources Branch to monitor snow water resources.

Author information

Authors and Affiliations

  1. Institute of Arctic and Alpine Research, University of Colorado Boulder, Boulder, CO, USA

    Keith N. Musselman & Noah P. Molotch

  2. Geography, College of Life and Environmental Sciences, University of Exeter, Exeter, UK

    Nans Addor

  3. Aspen Global Change Institute, Basalt, CO, USA

    Julie A. Vano

  4. Department of Geography, University of Colorado Boulder, Boulder, CO, USA

    Noah P. Molotch

  5. NASA Jet Propulsion Laboratory, Pasadena, CA, USA

    Noah P. Molotch

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Contributions

K.N.M. and N.A. designed the study; K.N.M. collected the datasets and conducted the analysis; all the authors contributed to the interpretations of the results and wrote the paper.

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Correspondence to Keith N. Musselman.

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Extended data

Extended Data Fig. 1 Examples of the snowpack and melt metrics used in this study.

Examples of seasonal SWE time series measured at mountain snowpack telemetry stations in (a) continental and (b) maritime climates showing daily SWE (solid black line) and two metrics derived from the daily decrease in SWE: daily melt (blue bars) and cumulative annual melt (red hashed lines) computed for Oct. 1–Aug. 1 of each year. The date of maximum SWE is indicated by the vertical dashed line. Calculations of the fraction of cumulative annual melt that has occurred by the date of maximum SWE (FMmax) are shown. In these examples, FMApr1 is similar to FMmax and is not shown.

Extended Data Fig. 2 On average across western North American stations, snowpack reaches an annual maximum when 88% of annual snow has yet to melt.

As in Fig. 2c,d, but showing the average fraction of cumulative annual melt on the date of maximum SWE.

Extended Data Fig. 3 Trends toward more winter melt are three-times more widespread than trends toward earlier timing of annual maximum SWE.

As in Fig. 2, but showing stations with significant long-term changes in (a) the fraction of cumulative annual melt that has occurred by the date of annual maximum SWE and (b) the date of annual maximum SWE.

Extended Data Fig. 4 Melt is increasing in all snow-dominated months before April 1st with the greatest rate of change in March.

(a) Increases (% per decade) in monthly melt as a fraction of total annual melt (median shown by red circles, lower and upper quartiles indicated by whiskers) from (b) snowpack telemetry stations with data records ≥ 30 years that have statistically significant (p ≤ 0.05) positive trends (in percent of n = 634 stations). Stations with negative trends not shown: <1% in Oct. and Nov., ~2% in Dec., Jan., Feb. and Mar.

Extended Data Fig. 5 Snowmelt increases during winter months are widespread.

Geographic distribution of the monthly melt trends shown in Extended Data Fig. 4 for stations with data records ≥ 30 years (small black markers; n = 634) that have statistically significant (p ≤ 0.05) long-term increases (red markers) and decreases (blue markers) in monthly melt as a fraction of total annual melt for the months of (a) October, (b) November, (c) December, (d) January, (e) February, and (f) March.

Extended Data Fig. 6 Winter melt at lower elevations may be more sensitive to seasonal temperature variations than higher elevations.

As in Fig. 3 but evaluated over six regions with data binned into low, medium and high elevation categories according to the 33rd and 66th percentiles of the regional elevations of stations with long (40 + yr.) records. For visual clarity, shown are linear regressions fit to the centroids of the FMApr1 anomaly (see legend of Fig. 3) for each elevation bin. All regression fits are statistically significant.

Extended Data Fig. 7 Snowmelt trends are highly sensitive to temperature and an underlying warming signal, while SWE trends are more sensitive to precipitation variability.

As in Fig. 2 but showing trends (see legends) in a) temperature, b) precipitation, c) FMApr1 (melt), d) April 1 SWE, and e) date of maximum SWE at the 173 U.S. stations with long records (small black markers) coincident with the PRISM climate data (1979-2019).

Extended Data Fig. 8 Winter melt increases did not predominately occur at lower elevations but were generally more frequent at middle to upper elevations.

The regional distribution (see colors in inset map) of long-term station (black markers in inset map) elevation (see white histogram bars) and the elevation of stations with long-term increases in the fraction of total annual melt before 1 April (see color histogram bars). The vertical lines in the histogram plots indicate the regional median station elevation (dashed black lines) and the median elevation of stations with statistically significant (p < 0.05) melt increases (solid color lines). When the color line is to the right of the dashed line, it indicates that the melt trend is more prevalent among middle to upper elevation sites.

Extended Data Fig. 9 There is limited evidence that April 1st SWE declines have predominately occurred at lower elevations.

As in Extended Data Fig. 8, but for trends in the magnitude of SWE on 1 April.

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Musselman, K.N., Addor, N., Vano, J.A. et al. Winter melt trends portend widespread declines in snow water resources. Nat. Clim. Chang. 11, 418–424 (2021). https://doi.org/10.1038/s41558-021-01014-9

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