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Winter melt trends portend widespread declines in snow water resources

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.

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.

References

  1. 1.

    Li, D., Wrzesien, M. L., Durand, Adam, J. & Lettenmaier, D. P. How much runoff originates as snow in the western United States, and how will that change in the future? Geophys. Res. Lett. 44, 6163–6172 (2017).

    Google Scholar 

  2. 2.

    Barnett, T. P., Adam, J. C. & Lettenmaier, D. P. Potential impacts of a warming climate on water availability in snow-dominated regions. Nature 438, 303–309 (2005).

    CAS  Google Scholar 

  3. 3.

    Church, J. E. Snow surveying: its principles and possibilities. Geogr. Rev. 23, 529–563 (1933).

    Google Scholar 

  4. 4.

    Garen, D. C. Improved techniques in regression-based streamflow volume forecasting. J. Water Resour. Plan. Manage. 118, 654–670 (1992).

    Google Scholar 

  5. 5.

    Pagano, T. Soils, snow and streamflow. Nat. Geosci. 3, 591–592 (2010).

    CAS  Google Scholar 

  6. 6.

    Anghileri, D. et al. Value of long‐term streamflow forecasts to reservoir operations for water supply in snow‐dominated river catchments. Water Resour. Res. 52, 4209–4225 (2016).

    Google Scholar 

  7. 7.

    Palmer, P. L. The SCS snow survey water supply forecasting program: current operations and future directions. in Proc. 56th Annual Western Snow Conference 43–51 (Western Snow Conference, 1988).

  8. 8.

    Abramovich, R. Uses of natural resources conservation service snow survey data and products. in Proc. 75th Western Snow Conference 103–113 (Western Snow Conference, 2007).

  9. 9.

    Nijssen, B., O’Donnell, G. M., Hamlet, A. F. & Lettenmaier, D. P. Hydrologic sensitivity of global rivers to climate change. Climatic Change 50, 143–175 (2001).

    CAS  Google Scholar 

  10. 10.

    Boisvenue, C. & Running, S. W. Impacts of climate change on natural forest productivity—evidence since the middle of the 20th century. Global Change Biol. 12, 862–882 (2006).

    Google Scholar 

  11. 11.

    Musselman, K. N. et al. Projected increases and shifts in rain-on-snow flood risk over western North America. Nat. Clim. Change 8, 808–812 (2018).

    Google Scholar 

  12. 12.

    Ford, C. M., Kendall, A. D. & Hyndman, D. W. Effects of shifting snowmelt regimes on the hydrology of non-Alpine temperate landscapes. J. Hydrol. 590, 125517 (2020).

    Google Scholar 

  13. 13.

    Immerzeel, W. W., Van Beek, L. P. & Bierkens, M. F. Climate change will affect the Asian water towers. Science 328, 1382–1385 (2010).

    CAS  Google Scholar 

  14. 14.

    Qin, Y. et al. Agricultural risks from changing snowmelt. Nat. Clim. Change 10, 459–465 (2020).

    Google Scholar 

  15. 15.

    Shindell, D. et al. Simultaneously mitigating near-term climate change and improving human health and food security. Science 335, 183–189 (2012).

    CAS  Google Scholar 

  16. 16.

    Westerling, A. L. Increasing western US forest wildfire activity: sensitivity to changes in the timing of spring. Phil. Trans. R. Soc. B 371, 20150178 (2016).

    Google Scholar 

  17. 17.

    Livneh, B. & Badger, A. M. Drought less predictable under declining future snowpack. Nat. Clim. Change 10, 452–458 (2020).

    Google Scholar 

  18. 18.

    Musselman, K. N., Clark, M. P., Liu, C., Ikeda, K. & Rasmussen, R. Slower snowmelt in a warmer world. Nat. Clim. Change 7, 214–219 (2017).

    Google Scholar 

  19. 19.

    Vano, J. A. et al. Climate Change impacts on water management in the Puget Sound region, Washington State, USA. Climatic Change 102, 261–286 (2010).

    Google Scholar 

  20. 20.

    Thackeray, C. W., Derksen, C., Fletcher, C. G. & Hall, A. Snow and climate: feedbacks, drivers, and indices of change. Curr. Clim. Change Rep. 5, 322–333 (2019).

    Google Scholar 

  21. 21.

    Addor, N. et al. Robust changes and sources of uncertainty in the projected hydrological regimes of Swiss catchments. Water Resour. Res. 50, 7541–7562 (2014).

    Google Scholar 

  22. 22.

    Lievens, H. et al. Snow depth variability in the Northern Hemisphere mountains observed from space. Nat. Commun. 10, 4629 (2019).

    Google Scholar 

  23. 23.

    Girotto, M., Musselman, K. N. & Essery, R. L. H. Data assimilation improves estimates of climate-sensitive seasonal snow. Curr. Clim. Change Rep. 6, 81–94 (2020).

    Google Scholar 

  24. 24.

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

    Google Scholar 

  25. 25.

    Mote, P. W., Li, S., Lettenmaier, D. P., Xiao, M. & Engel, R. Dramatic declines in snowpack in the western US. npj Clim. Atmos. Sci. 1, 2 (2018).

    Google Scholar 

  26. 26.

    Siler, N., Proistosescu, C. & Po‐Chedley, S. Natural variability has slowed the decline in western US snowpack since the 1980s. Geophys. Res. Lett. 46, 346–355 (2019).

    Google Scholar 

  27. 27.

    Pierce, D. W. et al. Attribution of declining western U.S. snowpack to human effects. J. Clim. 21, 6425–6444 (2008).

    Google Scholar 

  28. 28.

    Pendergrass, A. G., Knutti, R., Lehner, F., Deser, C. & Sanderson, B. M. Precipitation variability increases in a warmer climate. Sci. Rep. 7, 17966 (2017).

    Google Scholar 

  29. 29.

    Oki, T. & Kanae, S. Global hydrological cycles and world water resources. Science 313, 1068–1072 (2006).

    CAS  Google Scholar 

  30. 30.

    Pagano, T., Garen, D. & Sorooshian, S. Evaluation of official western US seasonal water supply outlooks, 1922–2002. J. Hydrometeorol. 5, 896–909 (2004).

    Google Scholar 

  31. 31.

    Daly, C., Neilson, R. P. & Phillips, D. L. A statistical–topographic model for mapping climatological precipitation over mountainous terrain. J. Appl. Meteorol. 33, 140–158 (1994).

    Google Scholar 

  32. 32.

    Luce, C. H., Lopez‐Burgos, V. & Holden, Z. Sensitivity of snowpack storage to precipitation and temperature using spatial and temporal analog models. Water Resour. Res. 50, 9447–9462 (2014).

    Google Scholar 

  33. 33.

    Lehner, F., Wahl, E. R., Wood, A. W., Blatchford, D. B. & Llewellyn, D. Assessing recent declines in Upper Rio Grande runoff efficiency from a paleoclimate perspective. Geophys. Res. Lett. 44, 4124–4133 (2017).

    Google Scholar 

  34. 34.

    Cline, D. W., Bales, R. C. & Dozier, J. Estimating the spatial distribution of snow in mountain basins using remote sensing and energy balance modeling. Water Resour. Res. 34, 1275–1285 (1998).

    CAS  Google Scholar 

  35. 35.

    Dressler, K., Fassnacht, S. & Bales, R. A comparison of snow telemetry and snow course measurements in the Colorado River basin. J. Hydrometeorol. 7, 705–712 (2006).

    Google Scholar 

  36. 36.

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

    Google Scholar 

  37. 37.

    Sun, F., Hall, A., Schwartz, M. B., Walton, D. & Berg, N. Twenty-first-century snowfall and snowpack changes over the Southern California mountains. J. Clim. 29, 91–110 (2016).

    Google Scholar 

  38. 38.

    Howat, I. M. & Tulaczyk, S. Climate sensitivity of spring snowpack in the Sierra Nevada. J. Geophys. Res. Earth Surf. 110, F04021 (2005).

    Google Scholar 

  39. 39.

    Mote, P. W. Climate-driven variability and trends in mountain snowpack in western North America. J. Clim. 19, 6209–6220 (2006).

    Google Scholar 

  40. 40.

    Harpold, A. A. et al. Soil moisture response to snowmelt timing in mixed‐conifer subalpine forests. Hydrol. Process. 29, 2782–2798 (2015).

    Google Scholar 

  41. 41.

    Henn, B., Musselman, K. N., Lestak, L., Ralph, F. M. & Molotch, N. P. Extreme runoff generation from atmospheric river driven snowmelt during the 2017 Oroville Dam spillways incident. Geophys. Res. Lett. 47, e2020GL088189 (2020).

    Google Scholar 

  42. 42.

    Berghuijs, W. R., Woods, R. A., Hutton, C. J. & Sivapalan, M. Dominant flood generating mechanisms across the United States. Geophys. Res. Lett. 43, 4382–4390 (2016).

    Google Scholar 

  43. 43.

    Brooks, P. D., Williams, M. W. & Schmidt, S. K. Microbial activity under alpine snowpacks, Niwot Ridge, Colorado. Biogeochemistry 32, 93–113 (1996).

    Google Scholar 

  44. 44.

    Edwards, A. C., Scalenghe, R. & Freppaz, M. Changes in the seasonal snow cover of alpine regions and its effect on soil processes: a review. Quat. Int. 162, 172–181 (2007).

    Google Scholar 

  45. 45.

    Shukla, S. & Lettenmaier, D. Seasonal hydrologic prediction in the United States: understanding the role of initial hydrologic conditions and seasonal climate forecast skill. Hydrol. Earth Syst. Sci. 15, 3529–3538 (2011).

    Google Scholar 

  46. 46.

    Harpold, A. A., Sutcliffe, K., Clayton, J., Goodbody, A. & Vazquez, S. Does including soil moisture observations improve operational streamflow forecasts in snow‐dominated watersheds? J. Am. Water Resourc. Assoc. 53, 179–196 (2017).

    Google Scholar 

  47. 47.

    Koch, J. et al. Inter-comparison of three distributed hydrological models with respect to seasonal variability of soil moisture patterns at a small forested catchment. J. Hydrol. 533, 234–249 (2016).

    Google Scholar 

  48. 48.

    Günther, D., Marke, T., Essery, R. & Strasser, U. Uncertainties in snowpack simulations—assessing the impact of model structure, parameter choice, and forcing data error on point‐scale energy balance snow model performance. Water Resour. Res. 55, 2779–2800 (2019).

    Google Scholar 

  49. 49.

    Pflug, J., Liston, G., Nijssen, B. & Lundquist, J. Testing model representations of snowpack liquid water percolation across multiple climates. Water Resour. Res. 55, 4820–4838 (2019).

    Google Scholar 

  50. 50.

    Immerzeel, W. W. et al. Importance and vulnerability of the world’s water towers. Nature 577, 364–369 (2020).

    CAS  Google Scholar 

  51. 51.

    Serreze, M. C., Clark, M. P., Armstrong, R. L., McGinnis, D. A. & Pulwarty, R. S. Characteristics of the western United States snowpack from snowpack telemetry (SNOTEL) data. Water Resour. Res. 35, 2145–2160 (1999).

    Google Scholar 

  52. 52.

    Trujillo, E. & Molotch, N. P. Snowpack regimes of the Western United States. Water Resour. Res. 50, 5611–5623 (2014).

    Google Scholar 

  53. 53.

    Gilbert, R. O. Statistical Methods for Environmental Pollution Monitoring (John Wiley & Sons, 1987).

  54. 54.

    Mann, H. B. Nonparametric tests against trend. Econometrica 13, 245–259 (1945).

    Google Scholar 

  55. 55.

    Kendall, M. G. Rank Correlation Methods (Charles Griffin & Co. Ltd, 1948).

    Google Scholar 

  56. 56.

    Yue, S. & Pilon, P. A comparison of the power of the t test, Mann–Kendall and bootstrap tests for trend detection. Hydrol. Sci. J. 49, 21–37 (2004).

    Google Scholar 

  57. 57.

    Sen, P. K. Estimates of the regression coefficient based on Kendall’s tau. J. Am. Stat. Assoc. 63, 1379–1389 (1968).

    Google Scholar 

  58. 58.

    Lettenmaier, D. P., Wood, E. F. & Wallis, J. R. Hydro-climatological trends in the continental United States, 1948–88. J. Clim. 7, 586–607 (1994).

    Google Scholar 

  59. 59.

    Martel, J.-L., Mailhot, A., Brissette, F. & Caya, D. Role of natural climate variability in the detection of anthropogenic climate change signal for mean and extreme precipitation at local and regional scales. J. Clim. 31, 4241–4263 (2018).

    Google Scholar 

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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.

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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.

Corresponding author

Correspondence to Keith N. Musselman.

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Peer review information Nature Climate Change thanks the anonymous reviewers for their contribution to the peer review of this work.

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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. (2021). https://doi.org/10.1038/s41558-021-01014-9

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