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.
This is a preview of subscription content, access via your institution
Open Access articles citing this article.
npj Climate and Atmospheric Science Open Access 17 November 2023
Nature Communications Open Access 02 October 2023
Anthropogenic forcing and Pacific internal variability-determined decadal increase in summer precipitation over the Asian water tower
npj Climate and Atmospheric Science Open Access 19 May 2023
Access Nature and 54 other Nature Portfolio journals
Get Nature+, our best-value online-access subscription
$29.99 / 30 days
cancel any time
Subscribe to this journal
Receive 12 print issues and online access
$209.00 per year
only $17.42 per issue
Rent or buy this article
Prices vary by article type
Prices may be subject to local taxes which are calculated during checkout
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/.
IPCC Special Report on the Ocean and Cryosphere in a Changing Climate (WMO, 2019).
Viviroli, D., Dürr, H. H., Messerli, B., Meybeck, M. & Weingartner, R. Mountains of the world, water towers for humanity: typology, mapping, and global significance. Water Resour. Res. 43, W07447 (2007).
Immerzeel, W. W., van Beek, L. P. H. & Bierkens, M. F. P. Climate change will affect the Asian water towers. Science 328, 1382–1385 (2010).
Immerzeel, W. W. et al. Importance and vulnerability of the world’s water towers. Nature 577, 364–369 (2019).
Biemans, H. et al. Importance of snow and glacier meltwater for agriculture on the Indo-Gangetic Plain. Nat. Sustain. 2, 594–601 (2019).
Brun, F., Berthier, E., Wagnon, P., Kääb, A. & Treichler, D. A spatially resolved estimate of high mountain Asia glacier mass balances, 2000–2016. Nat. Geosci. 10, 668–673 (2017).
Shean, D. E. et al. A systematic, regional assessment of high mountain Asia glacier mass balance. Front. Earth Sci. 7, 1–19 (2020).
Nie, Y. et al. Glacial change and hydrological implications in the Himalaya and Karakoram. Nat. Rev. Earth Environ. 2, 91–106 (2021).
Kraaijenbrink, P. D. A., Bierkens, M. F. P., Lutz, A. F. & Immerzeel, W. W. Impact of a global temperature rise of 1.5 degrees Celsius on Asia’s glaciers. Nature 549, 257–260 (2017).
Huss, M. & Hock, R. Global-scale hydrological response to future glacier mass loss. Nat. Clim. Change 8, 135–140 (2018).
Rounce, D. R., Hock, R. & Shean, D. E. Glacier mass change in high mountain Asia through 2100 using the open-source Python glacier evolution model (PyGEM). Front. Earth Sci. 7, 1–20 (2020).
Kapnick, S. B., Delworth, T. L., Ashfaq, M., Malyshev, S. & Milly, P. C. D. Snowfall less sensitive to warming in Karakoram than in Himalayas due to a unique seasonal cycle. Nat. Geosci. 7, 834–840 (2014).
Armstrong, R. L. et al. Runoff from glacier ice and seasonal snow in High Asia: separating melt water sources in river flow. Reg. Environ. Change 19, 1249–1261 (2019).
Hammond, J. C., Saavedra, F. A. & Kampf, S. K. Global snow zone maps and trends in snow persistence 2001–2016. Int. J. Climatol. 38, 4369–4383 (2018).
Lievens, H. et al. Snow depth variability in the Northern Hemisphere mountains observed from space. Nat. Commun. 10, 4629 (2019).
Dozier, J., Bair, E. H. & Davis, R. E. Estimating the spatial distribution of snow water equivalent in the world’s mountains. Wiley Interdiscip. Rev. Water 3, 461–474 (2016).
Bormann, K. J., Brown, R. D., Derksen, C. & Painter, T. H. Estimating snow-cover trends from space. Nat. Clim. Change 8, 924–928 (2018).
Smith, T. & Bookhagen, B. Changes in seasonal snow water equivalent distribution in high mountain Asia (1987 to 2009). Sci. Adv. 4, 1–8 (2018).
Smith, T. & Bookhagen, B. Assessing multi-temporal snow-volume trends in high mountain Asia from 1987 to 2016 using high-resolution passive microwave data. Front. Earth Sci. 8, 1–13 (2020).
Thapa, A. & Muhammad, S. Contemporary snow changes in the Karakoram region attributed to improved MODIS data between 2003 and 2018. Water 12, 2681 (2020).
Huss, M. et al. Toward mountains without permanent snow and ice. Earths Future 5, 418–435 (2017).
Livneh, B. & Badger, A. M. Drought less predictable under declining future snowpack. Nat. Clim. Change 10, 452–458 (2020).
Qin, Y. et al. Agricultural risks from changing snowmelt. Nat. Clim. Change 10, 459–465 (2020).
Lehner, B. & Grill, G. Global river hydrography and network routing: baseline data and new approaches to study the world’s large river systems. Hydrol. Process. 27, 2171–2186 (2013).
Hock, R. Temperature index melt modelling in mountain areas. J. Hydrol. 282, 104–115 (2003).
Stigter, E. E. et al. The importance of snow sublimation on a Himalayan glacier. Front. Earth Sci. 6, 108 (2018).
Sarangi, C. et al. Dust dominates high-altitude snow darkening and melt over high-mountain Asia. Nat. Clim. Change 10, 1045–1051 (2020).
Brock, B. W., Willis, I. C. & Sharp, M. J. Measurement and parameterization of albedo variations at Haut Glacier d’Arolla, Switzerland. J. Glaciol. 46, 675–688 (2000).
ERA5 Reanalysis (ECMWF, 2017).
Hall, D. K., Riggs, G. A., Foster, J. L. & Kumar, S. V. Development and evaluation of a cloud-gap-filled MODIS daily snow-cover product. Remote Sens. Environ. 114, 496–503 (2010).
Putkonen, J. K. Continuous snow and rain data at 500 to 4400 m altitude near Annapurna, Nepal, 1999–2001. Arct. Antarct. Alp. Res. 36, 244–248 (2004).
Kirkham, J. D. et al. Near real-time measurement of snow water equivalent in the Nepal Himalayas. Front. Earth Sci. 7, 1–18 (2019).
Grünewald, T. & Lehning, M. Are flat-field snow depth measurements representative? A comparison of selected index sites with areal snow depth measurements at the small catchment scale. Hydrol. Process. 29, 1717–1728 (2015).
Ceglar, A., Toreti, A., Balsamo, G. & Kobayashi, S. Precipitation over monsoon Asia: a comparison of reanalyses and observations. J. Clim. 30, 465–476 (2017).
Cannon, F., Carvalho, L. M. V., Jones, C. & Norris, J. Winter westerly disturbance dynamics and precipitation in the western Himalaya and Karakoram: a wave-tracking approach. Theor. Appl. Climatol. 125, 27–44 (2016).
Thapa, K., Endreny, T. A. & Ferguson, C. R. Atmospheric rivers carry non-monsoon extreme precipitation into Nepal. J. Geophys. Res. Atmospheres 123, 5901–5912 (2018).
Ménégoz, M., Gallée, H. & Jacobi, H. W. Precipitation and snow cover in the Himalaya: from reanalysis to regional climate simulations. Hydrol. Earth Syst. Sci. 17, 3921–3936 (2013).
Bookhagen, B. & Burbank, D. W. Toward a complete Himalayan hydrological budget: spatiotemporal distribution of snowmelt and rainfall and their impact on river discharge. J. Geophys. Res. Earth Surf. 115, 1–25 (2010).
Lutz, A. F., Immerzeel, W. W., Shrestha, A. B. & Bierkens, M. F. P. Consistent increase in High Asia’s runoff due to increasing glacier melt and precipitation. Nat. Clim. Change 4, 587–592 (2014).
Wulf, H., Bookhagen, B. & Scherler, D. Differentiating between rain, snow, and glacier contributions to river discharge in the western Himalaya using remote-sensing data and distributed hydrological modeling. Adv. Water Resour. 88, 152–169 (2016).
Pritchard, H. D. Asia’s shrinking glaciers protect large populations from drought stress. Nature 569, 649–654 (2019).
Pepin, N. et al. Elevation-dependent warming in mountain regions of the world. Nat. Clim. Change 5, 424–430 (2015).
Palazzi, E., Filippi, L. & von Hardenberg, J. Insights into elevation-dependent warming in the Tibetan Plateau–Himalayas from CMIP5 model simulations. Clim. Dyn. 48, 3991–4008 (2017).
Eyring, V. et al. Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization. Geosci. Model Dev. 9, 1937–1958 (2016).
Jiang, J., Zhou, T., Chen, X. & Zhang, L. Future changes in precipitation over Central Asia based on CMIP6 projections. Environ. Res. Lett. 15, 054009 (2020).
Ridley, J., Wiltshire, A. & Mathison, C. More frequent occurrence of westerly disturbances in Karakoram up to 2100. Sci. Total Environ. 468–469, S31–S35 (2013).
Hasson, S. U., Pascale, S., Lucarini, V. & Böhner, J. Seasonal cycle of precipitation over major river basins in South and Southeast Asia: a review of the CMIP5 climate models data for present climate and future climate projections. Atmos. Res. 180, 42–63 (2016).
Rogelj, J. et al. Scenarios towards limiting global mean temperature increase below 1.5 °C. Nat. Clim. Change 8, 325–332 (2018).
IPCC Special Report on Global Warming of 1.5 °C (eds Masson-Delmotte, V. et al.) (WMO, 2018).
Immerzeel, W. W., Pellicciotti, F. & Bierkens, M. F. P. Rising river flows throughout the twenty-first century in two Himalayan glacierized watersheds. Nat. Geosci. 6, 742–745 (2013).
Huss, M. & Hock, R. A new model for global glacier change and sea-level rise. Front. Earth Sci. https://doi.org/10.3389/feart.2015.00054 (2015).
Comola, F. et al. Scale-dependent effects of solar radiation patterns on the snow-dominated hydrologic response. Geophys. Res. Lett. 42, 3895–3902 (2015).
Sicart, J. E., Hock, R. & Six, D. Glacier melt, air temperature, and energy balance in different climates: the Bolivian tropics, the French Alps, and northern Sweden. J. Geophys. Res. Atmos. 113, D24113 (2008).
Essery, R., Morin, S., Lejeune, Y. & B Ménard, C. A comparison of 1701 snow models using observations from an alpine site. Adv. Water Resour. 55, 131–148 (2013).
Magnusson, J. et al. Evaluating snow models with varying process representations for hydrological applications. Water Resour. Res. 51, 2707–2723 (2015).
Avanzi, F. et al. Model complexity and data requirements in snow hydrology: seeking a balance in practical applications. Hydrol. Process. 30, 2106–2118 (2016).
Pellicciotti, F. et al. An enhanced temperature-index glacier melt model including the shortwave radiation balance: development and testing for Haut Glacier d’Arolla, Switzerland. J. Glaciol. 51, 573–587 (2005).
Heynen, M., Pellicciotti, F. & Carenzo, M. Parameter sensitivity of a distributed enhanced temperature-index melt model. Ann. Glaciol. 54, 311–321 (2013).
Franz, K. J., Hogue, T. S. & Sorooshian, S. Operational snow modeling: addressing the challenges of an energy balance model for National Weather Service forecasts. J. Hydrol. 360, 48–66 (2008).
Slater, A. G., Barrett, A. P., Clark, M. P., Lundquist, J. D. & Raleigh, M. S. Uncertainty in seasonal snow reconstruction: relative impacts of model forcing and image availability. Adv. Water Resour. 55, 165–177 (2013).
Lapo, K. E., Hinkelman, L. M., Raleigh, M. S. & Lundquist, J. D. Impact of errors in the downwelling irradiances on simulations of snow water equivalent, snow surface temperature, and the snow energy balance. Water Resour. Res. 51, 1649–1670 (2015).
Maussion, F. et al. The open global glacier model (OGGM) v1.1. Geosci. Model Dev. 12, 909–931 (2019).
Cohen, J., Ye, H. & Jones, J. Trends and variability in rain-on-snow events. Geophys. Res. Lett. 42, 7115–7122 (2015).
Livneh, B., Xia, Y., Mitchell, K. E., Ek, M. B. & Lettenmaier, D. P. Noah LSM Snow model diagnostics and enhancements. J. Hydrometeorol. 11, 721–738 (2010).
Dozier, J. & Painter, T. H. Multispectral and hyperspectral remote sensing of alpine snow properties. Annu. Rev. Earth Planet. Sci. 32, 465–494 (2004).
Gautam, R., Hsu, N. C., Lau, W. K.-M. & Yasunari, T. J. Satellite observations of desert dust-induced Himalayan snow darkening. Geophys. Res. Lett. 40, 988–993 (2013).
Painter, T. H., Skiles, S. M., Deems, J. S., Brandt, W. T. & Dozier, J. Variation in rising limb of Colorado River snowmelt runoff hydrograph controlled by dust radiative forcing in snow. Geophys. Res. Lett. 45, 797–808 (2018).
Singh, P., Kumar, N. & Arora, M. Degree-day factors for snow and ice for Dokriani Glacier, Garhwal Himalayas. J. Hydrol. 235, 1–11 (2000).
Braithwaite, R. J. Temperature and precipitation climate at the equilibrium-line altitude of glaciers expressed by the degree-day factor for melting snow. J. Glaciol. 54, 437–444 (2008).
Pfeffer, W. T. & Humphrey, N. F. Formation of ice layers by infiltration and refreezing of meltwater. Ann. Glaciol. 26, 83–91 (1998).
Saloranta, T. et al. A model setup for mapping snow conditions in high-mountain Himalaya. Front. Earth Sci. 7, 1–18 (2019).
Stigter, E. E. et al. Energy and mass balance dynamics of the seasonal snowpack at two high-altitude sites in the Himalaya. Cold Reg. Sci. Technol. 183, 103233 (2021).
Samimi, S. & Marshall, S. J. Diurnal cycles of meltwater percolation, refreezing, and drainage in the supraglacial snowpack of Haig Glacier, Canadian Rocky Mountains. Front. Earth Sci. 5, 1–15 (2017).
Heilig, A. et al. Seasonal and diurnal cycles of liquid water in snow—measurements and modeling. J. Geophys. Res. Earth Surf. 120, 2139–2154 (2015).
Wever, N. et al. Verification of the multi-layer SNOWPACK model with different water transport schemes. Cryosphere 9, 2271–2293 (2015).
Stigter, E. E., Wanders, N., Saloranta, T. M., Shea, J. M. & Bierkens, M. F. P. Assimilation of snow cover and snow depth into a snow model to estimate snow water equivalent and snowmelt runoff in a Himalayan catchment. Cryosphere 11, 1647–1664 (2017).
Hall, D. K. & Riggs, G. A. MODIS/Terra Snow Cover Daily L3 Global 500m SIN Grid, Version 6 (NASA National Snow and Ice Data Center Distributed Active Archive Center, accessed 6 December 2019); https://doi.org/10.5067/MODIS/MOD10A1.006
Gorelick, N. et al. Google Earth Engine: planetary-scale geospatial analysis for everyone. Remote Sens. Environ. 202, 18–27 (2017).
Zhang, H. et al. Ground-based evaluation of MODIS snow cover product V6 across China: implications for the selection of NDSI threshold. Sci. Total Environ. 651, 2712–2726 (2019).
Carroll, M. L. et al. MOD44W MODIS/Terra Land Water Mask Derived from MODIS and SRTM L3 Global 250m SIN Grid V006 (NASA EOSDIS Land Processes DAAC, accessed 5 November 2019); https://doi.org/10.5067/MODIS/MOD44W.006
Farr, T. et al. The shuttle radar topography mission. Rev. Geophys. 45, RG2004 (2007).
Mukul, M., Srivastava, V., Jade, S. & Mukul, M. Uncertainties in the Shuttle Radar Topography Mission (SRTM) heights: insights from the Indian Himalaya and Peninsula. Sci. Rep. 7, 41672 (2017).
Wan, Z., Hook, S. & Hulley, G. MOD11A2 MODIS/Terra Land Surface Temperature/Emissivity 8-Day L3 Global 1km SIN Grid V006 (NASA EOSDIS Land Processes DAAC, accessed 5 November 2019); https://doi.org/10.5067/MODIS/MOD11A2.006
Wagnon, P. et al. Seasonal and annual mass balances of Mera and Pokalde glaciers (Nepal Himalaya) since 2007. Cryosphere 7, 1769–1786 (2013).
Litt, M. et al. Glacier ablation and temperature indexed melt models in the Nepalese Himalaya. Sci. Rep. 9, 5264 (2019).
Dee, D. P. et al. Toward a consistent reanalysis of the climate system. Bull. Am. Meteorol. Soc. 95, 1235–1248 (2014).
Palazzi, E., Von Hardenberg, J. & Provenzale, A. Precipitation in the Hindu-Kush Karakoram Himalaya: observations and future scenarios. J. Geophys. Res. Atmos. 118, 85–100 (2013).
Immerzeel, W. W., Wanders, N., Lutz, A. F., Shea, J. M. & Bierkens, M. F. P. Reconciling high-altitude precipitation in the upper Indus basin with glacier mass balances and runoff. Hydrol. Earth Syst. Sci. 19, 4673–4687 (2015).
Tarek, M., Brissette, F. P. & Arsenault, R. Evaluation of the ERA5 reanalysis as a potential reference dataset for hydrological modelling over North America. Hydrol. Earth Syst. Sci. 24, 2527–2544 (2020).
Albergel, C. et al. ERA-5 and ERA-Interim driven ISBA land surface model simulations: which one performs better? Hydrol. Earth Syst. Sci. 22, 3515–3532 (2018).
Huang, J., Rikus, L. J., Qin, Y. & Katzfey, J. Assessing model performance of daily solar irradiance forecasts over Australia. Sol. Energy 176, 615–626 (2018).
Urraca, R. et al. Evaluation of global horizontal irradiance estimates from ERA5 and COSMO-REA6 reanalyses using ground and satellite-based data. Sol. Energy 164, 339–354 (2018).
Graham, R. M., Hudson, S. R. & Maturilli, M. Improved performance of ERA5 in Arctic gateway relative to four global atmospheric reanalyses. Geophys. Res. Lett. 46, 6138–6147 (2019).
Beck, H. E. et al. Daily evaluation of 26 precipitation datasets using Stage-IV gauge-radar data for the CONUS. Hydrol. Earth Syst. Sci. 23, 207–224 (2019).
Orsolini, Y. et al. Evaluation of snow depth and snow cover over the Tibetan Plateau in global reanalyses using in situ and satellite remote sensing observations. Cryosphere 13, 2221–2239 (2019).
Jiang, Q. et al. Evaluation of the ERA5 reanalysis precipitation dataset over Chinese Mainland. J. Hydrol. 595, 125660 (2020).
Chen, Y. et al. Spatial performance of multiple reanalysis precipitation datasets on the southern slope of central Himalaya. Atmos. Res. 250, 105365 (2021).
Betts, A. K., Chan, D. Z. & Desjardins, R. L. Near-surface biases in ERA5 over the Canadian Prairies. Front. Environ. Sci. 7, 207–224 (2019).
Wang, C., Graham, R. M., Wang, K., Gerland, S. & Granskog, M. A. Comparison of ERA5 and ERA-Interim near-surface air temperature, snowfall and precipitation over Arctic sea ice: effects on sea ice thermodynamics and evolution. Cryosphere 13, 1661–1679 (2019).
Taylor, K. E., Stouffer, R. J. & Meehl, G. A. An overview of CMIP5 and the experiment design. Bull. Am. Meteorol. Soc. 93, 485–498 (2012).
Pfeffer, W. T. et al. The Randolph Glacier Inventory: a globally complete inventory of glaciers. J. Glaciol. 60, 537–552 (2014).
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.
The authors declare no competing interests.
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.
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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.
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.
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).
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.
About this article
Cite this article
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
This article is cited by
Anthropogenic forcing and Pacific internal variability-determined decadal increase in summer precipitation over the Asian water tower
npj Climate and Atmospheric Science (2023)
Nature Geoscience (2023)
Nature Water (2023)
Nature Communications (2023)
npj Climate and Atmospheric Science (2023)