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Drought less predictable under declining future snowpack

Nature Climate Change volume 10, pages 452–458 (2020)Cite this article

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Abstract

Mountain snowpack serves as an immense natural water reservoir, and knowledge of snow conditions helps predict seasonal water availability and offers critical early warning of hydrologic drought. This paradigm faces an impending challenge given consensus that a smaller fraction of future precipitation will fall as snow. Here, we apply downscaled hydrologic simulations from 28 climate model projections to show that by mid-century (2036–2065), 69% of historically snowmelt-dominated areas of the western United States see a decline in the ability of snow to predict seasonal drought, increasing to 83% by late century (2070–2099). Reduced predictability arises when peak snowpack approaches zero or because of decreased warm-season runoff efficiency. Changes in drought prediction skill show significant (P < 0.01) elevation dependence, with lower-elevation coastal areas most impacted by warming. Ancillary predictive information can only partially mitigate snow-based predictability losses to 65% of areas, underscoring the importance of declining future snowpack.

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Fig. 1: Conceptual hypothesis showing how declines in future snow can affect seasonal water supply prediction.
Fig. 2: Correspondence between simulated and observed 1 April SWE over the historical period and projected future SWE changes demonstrate a dependence on elevation.
Fig. 3: Projected reductions in drought prediction skill for the majority of the study area via the ETS.
Fig. 4: Mechanisms responsible for late-century reductions in drought prediction skill relative to historic.
Fig. 5: Projected improvements in drought prediction skill when considering ancillary predictors relative to the snow-only case, via the ETS.

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Data availability

All data used in this analysis are publicly available. Snowpack observations were obtained from the NRCS Snow Telemetry (SNOTEL) (http://www.wcc.nrcs.usda.gov/snotel/) and the NRCS Snow Course (https://www.wcc.nrcs.usda.gov/snowcourse/snow-course-sites.html) networks in the United States and the British Columbia Automated Snow Station Network (https://catalogue.data.gov.bc.ca/dataset/archive-automated-snow-weather-station-data) for Canadian stations. A total of 992 stations across these networks met the criteria of greater than 20 years of data and a climatological average 1 April SWE of at least 100 mm and at least 25% of water-year-to-date precipitation to ensure that basins are truly snowmelt dominated. Observations of seasonal streamflow volumes were obtained from US Geological Survey gauges (http://waterdata.usgs.gov/nwis/). We excluded any basins with known regulation, where streamflow is altered by human activity upstream such as irrigation diversions and reservoir releases, on the basis of information provided by both GAGES-II54 and HCDN55 networks. The hydrologically downscaled CMIP5 data used in this study are publicly available: https://gdo-dcp.ucllnl.org/downscaled_cmip_projections/dcpInterface.html.

Code availability

Model codes used to implement the statistical model and to calculate and evaluate changes in ETS are provided at https://doi.org/10.5281/zenodo.3630827.

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Acknowledgements

We acknowledge funding support, NOAA Grant # NA16OAR4310132 Advancing the Use of Drought Early Warning Systems in the Upper Colorado River Basin, NOAA Grant # NA15OAR4310144 The Western Water Assessment: Building Climate Resilience by Design, as well as graphical support from the Cooperative Institute for Research in Environmental Sciences (CIRES) at the University of Colorado Boulder.

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Authors and Affiliations

  1. Department of Civil, Environmental and Architectural Engineering, University of Colorado Boulder, Boulder, CO, USA

    Ben Livneh

  2. Cooperative Institute for Research in Environmental Sciences, Boulder, CO, USA

    Ben Livneh

  3. Universities Space Research Association, Columbia, MD, USA

    Andrew M. Badger

  4. NASA Goddard Space Flight Center, Greenbelt, MD, USA

    Andrew M. Badger

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Contributions

B.L. proposed the overall concept for the study and wrote the paper. B.L. and A.M.B. designed components of the analysis. A.M.B. ran nearly all statistical analyses and generated nearly all graphics. B.L. and A.M.B. contributed to interpretations of the results.

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Correspondence to Ben Livneh.

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The authors declare no competing interests.

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Peer review information Nature Climate Change thanks Dongyue Li and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Predictive framework example.

Example (top) of time-varying April 1 SWE ‘predictor’ information (blue line) and target flow (black line) together with the statistical predictive model (red line) during a 30-year calibration. The subsequent 5-year application period shows the model prediction (purple line) from which predictive skill is calculated. The procedure is repeated every 5 years through the end of the 21st century (bottom); historical data are used from the Yampa R.at Maybell, CO.

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Supplementary Information

Description of the validation basins, models and data sources, Supplementary Figs. 1–11 and Supplementary Tables 1–3.

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Livneh, B., Badger, A.M. Drought less predictable under declining future snowpack. Nat. Clim. Chang. 10, 452–458 (2020). https://doi.org/10.1038/s41558-020-0754-8

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