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Climate change decisive for Asia’s snow meltwater supply

Nature Climate Change volume 11, pages 591–597 (2021)Cite this article

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Abstract

Streamflow in high-mountain Asia is influenced by meltwater from snow and glaciers, and determining impacts of climate change on the region’s cryosphere is essential to understand future water supply. Past and future changes in seasonal snow are of particular interest, as specifics at the scale of the full region are largely unknown. Here we combine models with observations to show that regional snowmelt is a more important contributor to streamflow than glacier melt, that snowmelt magnitude and timing changed considerably during 1979–2019 and that future snow meltwater supply may decrease drastically. The expected changes are strongly dependent on the degree of climate change, however, and large variations exist among river basins. The projected response of snowmelt to climate change indicates that to sustain the important seasonal buffering role of the snowpacks in high-mountain Asia, it is imperative to limit future climate change.

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Fig. 1: SWE and historical absolute and relative trends.
Fig. 2: SWE climatology for the period 1979–2019.
Fig. 3: Snowmelt hydrographs for four melt regimes.
Fig. 4: Relative contributions of rainfall, snowmelt and glacier melt.
Fig. 5: Response of snowmelt to changes in precipitation and temperature.

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Data availability

Data generated by this study are available online for download at https://doi.org/10.5281/zenodo.4715786. This includes daily 0.05° grids for 1979–2019, EOC projections and the bottom-up elasticity output for both SWE and snowmelt. Additional model outputs and derivatives are available from the authors upon request. Pre-processed input data to run the snow model are available at https://doi.org/10.5281/zenodo.4715955. Precipitation and temperature fields from ERA5 reanalysis data29 used in this study are available from the Copernicus Climate Data Store at https://cds.climate.copernicus.eu/. CMIP6 data44 used in this study are available at https://pcmdi.llnl.gov/CMIP6/. MODIS snow cover data77 are available at https://nsidc.org/data/MOD10A1/versions/6, land surface temperature data83 at https://doi.org/10.5067/MODIS/MOD11A2.006 and water mask80 at https://doi.org/10.5067/MODIS/MOD44W.006. SRTM elevation data81 are available at https://srtm.csi.cgiar.org/. HydroSHEDS basin outlines24 are available from https://www.hydrosheds.org/. Glacier outlines from the Randolph Glacier Invertory101 are available at https://www.glims.org/RGI/.

Code availability

Code for the snow model presented in this study is available at https://doi.org/10.5281/zenodo.4715953. Code for the glacier model9 is available at https://doi.org/10.5281/zenodo.2548689. Other code is available from the authors upon request.

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Acknowledgements

This study was financially supported by the Strategic Priority Research Program of the Chinese Academy of Sciences within the Pan-Third Pole Environment framework (grant agreement no. XDA20100300), by the European Research Council under the European Union’s Horizon 2020 research and innovation programme (grant agreement no. 676819) and by the Netherlands Organization for Scientific Research under the Innovational Research Incentives Scheme VIDI (grant agreement 016.181.308). We thank H. Lievens for providing the snowdepth data prior to publication and J. Norris for providing the High Asia high-resolution WRF downscaling that was developed by the Climate Variations and Change research group at the University of California Santa Barbara.

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Authors and Affiliations

  1. Department of Physical Geography, Faculty of Geosciences, Utrecht University, Utrecht, the Netherlands

    Philip D. A. Kraaijenbrink, Emmy E. Stigter & Walter W. Immerzeel

  2. Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing, China

    Tandong Yao

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  1. Philip D. A. Kraaijenbrink
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  2. Emmy E. Stigter
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  4. Walter W. Immerzeel
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Contributions

P.D.A.K., W.W.I. and T.Y. designed the study; P.D.A.K. performed all analyses and wrote the manuscript with contributions and suggestions from E.E.S., T.Y. and W.W.I.; and P.D.A.K., W.W.I. and E.E.S. developed the snow model concept.

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Correspondence to Philip D. A. Kraaijenbrink.

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The authors declare no competing interests.

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Peer review information Nature Climate Change thanks Bodo Bookhagen, Yukiko Hirabayashi 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 ERA5 temperature and precipitation.

2-m temperature climatology (a), trends in annual mean 2-metre temperature (a), and trends in basin-averaged annual 2-m temperature (c). Annual cumulative precipitation (d), trends in annual precipitation (e), and trends in basin-averaged annual precipitation (f). The dot overlay in the trend maps (b, e) indicates areas where trends are significant (p ≤ 0.05). The colours in the bar plots (c, f) indicate basin-average temperature and precipitation climatology, and correspond to the colour scales of panel a and d, respectively. The climatologies and trends of all panels are determined for the period 1979–2019 from the ERA5 gridded reanalysis dataset29.

Extended Data Fig. 2 Snowmelt hydrographs for all river basins.

Snowmelt hydrographs for the historical (1979–1999) and present day (1999–2019) periods for individual river basins (a-l). Shading indicates the 95% confidence interval for the present-day hydrograph. The colour of the shading indicates one of four identified melt season types (Fig. 3). The dashed lines are hydrographs associated with model runs forced with ensemble mean climate projections for the SSP-RCP experiments within CMIP644 for the end of century (2071–2100). All hydrographs are based on average five-day-sum climatologies.

Extended Data Fig. 3 Sensitivity of snow water equivalent and snowmelt to temperature change.

Relative change in the basin-wide mean annual snowmelt, mean annual SWE and peak SWE under changing temperatures with respect to the reference period (2000–2019) for all basins (a-l). The dashed vertical lines indicate the relative position of 1.5 °C and 2.0 °C temperature rise scenarios1 with respect to pre-industrial climate (1851–1880), determined per basin from entire CMIP6 ensemble (Supplementary Table 1).

Extended Data Fig. 4 Projected losses in snow and glacier meltwater by the EOC.

Simulated loss of annual snow (left column) and glacier (right column) meltwater by the end of century (2071–2100) for the SSP-RCP ensembles (Supplementary Table 1) with respect to present day (2000–2019) for all basins and the entire HMA (rows). Annual meltwater volume (km3) in the reference period is annotated in black left of the vertical bars. The errors bars indicate one standard deviation.

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Supplementary Methods, Figures 1–7 and Tables 1–3.

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Kraaijenbrink, P.D.A., Stigter, E.E., Yao, T. et al. Climate change decisive for Asia’s snow meltwater supply. Nat. Clim. Chang. 11, 591–597 (2021). https://doi.org/10.1038/s41558-021-01074-x

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