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Streamflow seasonality in a snow-dwindling world

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Abstract

Climate warming induces shifts from snow to rain in cold regions1, altering snowpack dynamics with consequent impacts on streamflow that raise challenges to many aspects of ecosystem services2,3,4. A straightforward conceptual model states that as the fraction of precipitation falling as snow (snowfall fraction) declines, less solid water is stored over the winter and both snowmelt and streamflow shift earlier in season. Yet the responses of streamflow patterns to shifts in snowfall fraction remain uncertain5,6,7,8,9. Here we show that as snowfall fraction declines, the timing of the centre of streamflow mass may be advanced or delayed. Our results, based on analysis of 1950–2020 streamflow measurements across 3,049 snow-affected catchments over the Northern Hemisphere, show that mean snowfall fraction modulates the seasonal response to reductions in snowfall fraction. Specifically, temporal changes in streamflow timing with declining snowfall fraction reveal a gradient from earlier streamflow in snow-rich catchments to delayed streamflow in less snowy catchments. Furthermore, interannual variability of streamflow timing and seasonal variation increase as snowfall fraction decreases across both space and time. Our findings revise the ‘less snow equals earlier streamflow’ heuristic and instead point towards a complex evolution of seasonal streamflow regimes in a snow-dwindling world.

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Fig. 1: Spatiotemporal patterns of snowfall fraction and streamflow seasonality across 3,049 catchments over 1950–2020.
Fig. 2: Relationship between mean annual snowfall fraction and streamflow seasonality.
Fig. 3: Impacts of temporal changes in snowfall fraction on streamflow seasonality.
Fig. 4: Impacts of declines in snowfall fraction on the interannual variability of streamflow seasonality.

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Data availability

The hourly ERA5-Land data are available from the Copernicus Climate Change Service (C3S) Climate Date Store at https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-land?tab=overview. Streamflow data are available from four sources: (1) the Global Runoff Data Centre (https://www.bafg.de/GRDC/EN/01_GRDC/grdc_node.html); (2) the USGS National Water Information System (https://waterdata.usgs.gov/nwis/sw); (3) European Water Archive of EURO-FRIEND-Water (https://www.bafg.de/GRDC/EN/04_spcldtbss/42_EWA/ewa_node.html); and (4) the Water Survey of Canada Hydrometric Data (https://wateroffice.ec.gc.ca/). Metadata for the 3,049 catchments used in the analysis are available at https://doi.org/10.5281/zenodo.10692562 (ref. 55). Map figures were created using Natural Earth Data included in the MATLAB software. Source data are provided with this paper.

Code availability

Analyses presented here do not depend on specific code; the approach can be reproduced following the procedures described in the Methods.

References

  1. Bintanja, R. & Andry, O. Towards a rain-dominated Arctic. Nat. Clim. Change 7, 263–267 (2017).

    Article  ADS  Google Scholar 

  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).

    Article  ADS  CAS  PubMed  Google Scholar 

  3. Berghuijs, W. R., Woods, R. A. & Hrachowitz, M. A precipitation shift from snow towards rain leads to a decrease in streamflow. Nat. Clim. Change 4, 583–586 (2014).

    Article  ADS  Google Scholar 

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

    Article  ADS  Google Scholar 

  5. Fritze, H., Stewart, I. T. & Pebesma, E. Shifts in western North American snowmelt runoff regimes for the recent warm decades. J. Hydrometeorol. 12, 989–1006 (2011).

  6. Wasko, C., Nathan, R. & Peel, M. C. Trends in global flood and streamflow timing based on local water year. Water Resour. Res. 56, e2020WR027233 (2020).

    Article  ADS  Google Scholar 

  7. Shi, X., Marsh, P. & Yang, D. Warming spring air temperatures, but delayed spring streamflow in an Arctic headwater basin. Environ. Res. Lett. 10, 064003 (2015).

    Article  ADS  Google Scholar 

  8. Uzun, S. et al. Changes in snowmelt runoff timing in the contiguous United States. Hydrol. Process. 35, e14430 (2021).

    Article  Google Scholar 

  9. Gordon, B. L. et al. Why does snowmelt-driven streamflow response to warming vary? A data-driven review and predictive framework. Environ. Res. Lett. 17, 053004 (2022).

    Article  ADS  Google Scholar 

  10. Siirila-Woodburn, E. R. et al. A low-to-no snow future and its impacts on water resources in the western United States. Nat. Rev. Earth .Environ. 2, 800–819 (2021).

    Article  ADS  Google Scholar 

  11. Slatyer, R. A., Umbers, K. D. L. & Arnold, P. A. Ecological responses to variation in seasonal snow cover. Conserv. Biol. 36, e13727 (2022).

    Article  PubMed  Google Scholar 

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

    Article  ADS  Google Scholar 

  13. Ombadi, M., Risser, M. D., Rhoades, A. M. & Varadharajan, C. A warming-induced reduction in snow fraction amplifies rainfall extremes. Nature 619, 305–310 (2023).

    Article  ADS  CAS  PubMed  Google Scholar 

  14. Mu, C. et al. The status and stability of permafrost carbon on the Tibetan Plateau. Earth Sci. Rev. 211, 103433 (2020).

    Article  CAS  Google Scholar 

  15. Knowles, N., Dettinger, M. D. & Cayan, D. R. Trends in snowfall versus rainfall in the western United States. J. Clim. 19, 4545–4559 (2006).

  16. 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).

    Article  ADS  Google Scholar 

  17. Smith, T. & Bookhagen, B. Changes in seasonal snow water equivalent distribution in High Mountain Asia (1987 to 2009). Sci. Adv. 4, e1701550 (2018).

    Article  ADS  PubMed  PubMed Central  Google Scholar 

  18. Barnhart, T. B. et al. Snowmelt rate dictates streamflow. Geophys. Res. Lett. 43, 8006–8016 (2016).

    Article  ADS  Google Scholar 

  19. Musselman, K. N., Addor, N., Vano, J. A. & Molotch, N. P. Winter melt trends portend widespread declines in snow water resources. Nat. Clim. Change 11, 418–424 (2021).

    Article  ADS  Google Scholar 

  20. Milly, P. C. D. & Dunne, K. A. Colorado River flow dwindles as warming-driven loss of reflective snow energizes evaporation. Science 367, 1252–1255 (2020).

    Article  ADS  CAS  PubMed  Google Scholar 

  21. Church, M. Geomorphic thresholds in riverine landscapes. Freshw. Biol. 47, 541–557 (2002).

    Article  Google Scholar 

  22. Brunner, M. I., Slater, L., Tallaksen, L. M. & Clark, M. Challenges in modeling and predicting floods and droughts: a review. WIREs Water 8, e1520 (2021).

    Article  Google Scholar 

  23. Blahušiaková, A. et al. Snow and climate trends and their impact on seasonal runoff and hydrological drought types in selected mountain catchments in Central Europe. Hydrol. Sci. J. 65, 2083–2096 (2020).

    Article  Google Scholar 

  24. Clow, D. W. Changes in the timing of snowmelt and streamflow in Colorado: a response to recent warming. J. Clim. 23, 2293–2306 (2010).

    Article  ADS  Google Scholar 

  25. Cayan, D. R., Kammerdiener, S. A., Dettinger, M. D., Caprio, J. M. & Peterson, D. H. Changes in the onset of spring in the western United States. Bull. Am. Meteorol. Soc. 82, 399–415 (2001).

    Article  ADS  Google Scholar 

  26. McCabe, G. J., Wolock, D. M. & Valentin, M. Warming is driving decreases in snow fractions while runoff efficiency remains mostly unchanged in snow-covered areas of the western United States. J. Hydrometeorol. 19, 803–814 (2018).

  27. Kiewiet, L. et al. Effects of spatial and temporal variability in surface water inputs on streamflow generation and cessation in the rain–snow transition zone. Hydrol. Earth Syst. Sci. 26, 2779–2796 (2022).

    Article  ADS  Google Scholar 

  28. Aguado, E., Cayan, D., Riddle, L. & Roos, M. Climatic fluctuations and the timing of west coast streamflow. J. Clim. 5, 1468–1483 (1992).

    Article  ADS  Google Scholar 

  29. McCabe, G. J. & Clark, M. P. Trends and variability in snowmelt runoff in the western United States. J. Hydrometeorol. 6, 476–482 (2005).

    Article  ADS  Google Scholar 

  30. Stewart, I. T., Cayan, D. R. & Dettinger, M. D. Changes toward earlier streamflow timing across western North America. J. Clim. 18, 1136–1155 (2005).

    Article  ADS  Google Scholar 

  31. Pederson, G. T. et al. Climatic controls on the snowmelt hydrology of the northern Rocky Mountains. J. Clim. 24, 1666–1687 (2011).

    Article  ADS  Google Scholar 

  32. Eisner, S. et al. An ensemble analysis of climate change impacts on streamflow seasonality across 11 large river basins. Clim. Change 141, 401–417 (2017).

    Article  Google Scholar 

  33. Water Year: Glossary of Meteorology (American Meteorological Society, 2012).

  34. Court, A. Measures of streamflow timing. J. Geophys. Res. 67, 4335–4339 (1962).

    Article  ADS  Google Scholar 

  35. Oliver, J. E. Monthly precipitation distribution: a comparative index. Profess. Geogr. 32, 300–309 (1980).

    Article  Google Scholar 

  36. Jefferson, A. J. Seasonal versus transient snow and the elevation dependence of climate sensitivity in maritime mountainous regions. Geophys. Res. Lett. 38, L16402 (2011).

    Article  ADS  Google Scholar 

  37. Nayak, A., Marks, D., Chandler, D. G. & Seyfried, M. Long-term snow, climate and streamflow trends at the Reynolds Creek Experimental Watershed, Owyhee Mountains, Idaho, United States. Water Resour. Res. 46, W06519 (2010).

    Article  ADS  Google Scholar 

  38. Stewart, I. T. Changes in snowpack and snowmelt runoff for key mountain regions. Hydrol. Process. 23, 78–94 (2009).

    Article  ADS  Google Scholar 

  39. Dettinger, M. D. & Cayan, D. R. Large-scale atmospheric forcing of recent trends toward early snowmelt runoff in California. J. Clim. 8, 606–623 (1995).

    Article  ADS  Google Scholar 

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

    Article  ADS  Google Scholar 

  41. Li, D., Wrzesien, M. L., Durand, M., 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).

    Article  ADS  Google Scholar 

  42. Sospedra-Alfonso, R., Melton, J. R. & Merryfield, W. J. Effects of temperature and precipitation on snowpack variability in the Central Rocky Mountains as a function of elevation. Geophys. Res. Lett. 42, 4429–4438 (2015).

    Article  ADS  Google Scholar 

  43. Morán-Tejeda, E., Ignacio López-Moreno, J. & Beniston, M. The changing roles of temperature and precipitation on snowpack variability in Switzerland as a function of altitude. Geophys. Res. Lett. 40, 2131–2136 (2013).

    Article  ADS  Google Scholar 

  44. Scalzitti, J., Strong, C. & Kochanski, A. Climate change impact on the roles of tempearture and precipitation in western U.S. snowpack variability. Geophys. Res. Lett. 43, 5361–5369 (2013).

    Article  ADS  Google Scholar 

  45. Yin, D. & Roderick, M. L. Inter-annual variability of the global terrestrial water cycle. Hydrol. Earth Syst. Sci. 24, 381–396 (2020).

    Article  ADS  Google Scholar 

  46. McMahon, T. A., Peel, M. C., Pegram, G. G. S. & Smith, I. N. A simple methodology for estimating mean and variability of annual runoff and reservoir yield under present and future climates. J. Hydrometeorol. 12, 135–146 (2011).

    Article  ADS  Google Scholar 

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

    Article  ADS  PubMed  PubMed Central  Google Scholar 

  48. Allamano, P., Claps, P. & Laio, F. Global warming increases flood risk in mountainous areas. Geophys. Res. Lett. 36, L24404 (2009).

    Article  ADS  Google Scholar 

  49. Zhang, S. et al. Reconciling disagreement on global river flood changes in a warming climate. Nat. Clim. Change 12, 1160–1167 (2022).

    Article  ADS  MathSciNet  Google Scholar 

  50. Siebert, S. et al. A global data set of the extent of irrigated land from 1900 to 2005. Hydrol. Earth Syst. Sci. 19, 1521–1545 (2015).

    Article  ADS  Google Scholar 

  51. Arino O., Ramos, J., Kalogirou, V., Defourny, P. & Achard, F. GlobCover 2009. In ESA Living Planet Symposium, 27 June https://epic.awi.de/id/eprint/31046/1/Arino_et_al_GlobCover2009-a.pdf (2 July 2010).

  52. Lehner, B. et al. High-resolution map** of the world’s reservoirs and dams for sustainable river-flow management. Front. Ecol. Environ. 9, 494–502 (2011).

    Article  Google Scholar 

  53. Muñoz-Sabater, J. et al. ERA5-Land: a state-of-the-art global reanalysis dataset for land applications. Earth Syst. Sci. Data 13, 4349–4383 (2021).

    Article  ADS  Google Scholar 

  54. Sloat, L. L. et al. Increasing importance of precipitation variability on global livestock grazing lands. Nat. Clim. Change 8, 214–218 (2018).

    Article  ADS  Google Scholar 

  55. Han, J. et al. Metadata for the 3049 catchments. Zenodo https://zenodo.org/records/10692562 (2024).

  56. Lettenmaier, D. P. & Gan, T. Y. Hydrologic sensitivities of the Sacramento-San Joaquin River Basin, California, to global warming. Water Resour. Res. 26, 69–86 (1990).

    Article  ADS  CAS  Google Scholar 

  57. Nash, L. L. & Gleick, P. H. Sensitivity of streamflow in the Colorado Basin to climatic changes. J. Hydrol. 125, 221–241 (1991).

    Article  ADS  Google Scholar 

  58. Jeton, A. E., Dettinger, M. D. & Smith, J. L. Potential Effects of Climate Change on Streamflow, Eastern and Western Slopes of the Sierra Nevada, California and Nevada Water-Resources Investigations Report 95-4260 (USGS, 1996).

  59. Yang, D. et al. Siberian Lena River hydrologic regime and recent change. J. Geophys. Res. Atmos. 107, ACL14-1–ACL14-10 (2002).

    Article  Google Scholar 

  60. Dettinger, M. D., Cayan, D. R., Meyer, M. K. & Jeton, A. E. Simulated hydrologic responses to climate variations and change in the Merced, Carson and American River Basins, Sierra Nevada, California, 1900–2099. Clim. Change 62, 283–317 (2004).

    Article  Google Scholar 

  61. Jasper, K., Calanca, P., Gyalistras, D. & Fuhrer, J. Differential impacts of climate change on the hydrology of two alpine river basins. Clim. Res. 26, 113–129 (2004).

    Article  Google Scholar 

  62. Regonda, S. K., Rajagopalan, B., Clark, M. & Pitlick, J. Seasonal cycle shifts in hydroclimatology over the western United States. J. Clim. 18, 372–384 (2005).

    Article  ADS  Google Scholar 

  63. Zierl, B. & Bugmann, H. Global change impacts on hydrological processes in Alpine catchments. Water Resour. Res. 41, W02028 (2005).

    Article  ADS  Google Scholar 

  64. Hodgkins, G. A. & Dudley, R. W. Changes in the timing of winter–spring streamflows in eastern North America, 1913–2002. Geophys. Res. Lett. 33, L06402 (2006).

    Article  ADS  Google Scholar 

  65. Hamlet, A. F., Mote, P. W., Clark, M. P. & Lettenmaier, D. P. Twentieth-century trends in runoff, evapotranspiration and soil moisture in the western United States. J. Clim. 20, 1468–1486 (2007).

    Article  ADS  Google Scholar 

  66. Moore, J. N., Harper, J. T. & Greenwood, M. C. Significance of trends toward earlier snowmelt runoff, Columbia and Missouri Basin headwaters, western United States. Geophys. Res. Lett. 34, L16402 (2007).

    Article  ADS  Google Scholar 

  67. Yang, D., Zhao, Y., Armstrong, R., Robinson, D. & Brodzik, M.-J. Streamflow response to seasonal snow cover mass changes over large Siberian watersheds. J. Geophys. Res. Earth Surf. 112, F02S22 (2007).

    Article  ADS  Google Scholar 

  68. Burn, D. H. Climatic influences on streamflow timing in the headwaters of the Mackenzie River Basin. J. Hydrol. 352, 225–238 (2008).

    Article  ADS  Google Scholar 

  69. Hidalgo, H. G. et al. Detection and attribution of streamflow timing changes to climate change in the western United States. J. Clim. 22, 3838–3855 (2009).

    Article  ADS  Google Scholar 

  70. Godsey, S. E., Kirchner, J. W. & Tague, C. L. Effects of changes in winter snowpacks on summer low flows: case studies in the Sierra Nevada, California, USA. Hydrol. Process. 28, 5048–5064 (2014).

    Article  ADS  Google Scholar 

  71. Morán-Tejeda, E., Lorenzo-Lacruz, J., López-Moreno, J. I., Rahman, K. & Beniston, M. Streamflow timing of mountain rivers in Spain: recent changes and future projections. J. Hydrol. 517, 1114–1127 (2014).

    Article  ADS  Google Scholar 

  72. Burn, D. H. & Whitfield, P. H. Changes in floods and flood regimes in Canada. Can. Water Res. J. 41, 139–150 (2016).

    Article  Google Scholar 

  73. Dudley, R. W., Hodgkins, G. A., McHale, M. R., Kolian, M. J. & Renard, B. Trends in snowmelt-related streamflow timing in the conterminous United States. J. Hydrol. 547, 208–221 (2017).

    Article  ADS  Google Scholar 

  74. Burn, D. H. & Whitfield, P. H. Changes in flood events inferred from centennial length streamflow data records. Adv. Water Res. 121, 333–349 (2018).

    Article  Google Scholar 

  75. Kam, J., Knutson, T. R. & Milly, P. C. D. Climate model assessment of changes in winter–spring streamflow timing over North America. J. Clim. 31, 5581–5593 (2018).

    Article  ADS  Google Scholar 

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Acknowledgements

This study is financially supported by the National Natural Science Foundation of China (nos. 42041004 and 42071029), the Ministry of Science and Technology of China (grant no. 2023YFC3206603) and the Department of Science and Technology of Yunnan Province (grant no. 202203AA080010). T.R.M. acknowledges support from CSIRO Environment.

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  1. These authors contributed equally: Juntai Han, Ziwei Liu

Authors and Affiliations

  1. State Key Laboratory of Hydroscience and Engineering, Department of Hydraulic Engineering, Tsinghua University, Bei**g, China

    Juntai Han, Ziwei Liu, Dawen Yang, Taihua Wang, Ying Hou, Yuhan Guo, Changming Li & Yuting Yang

  2. Key Laboratory of Hydrosphere Sciences of the Ministry of Water Resources, Bei**g, China

    Juntai Han, Ziwei Liu, Dawen Yang, Taihua Wang, Ying Hou, Yuhan Guo, Changming Li & Yuting Yang

  3. School of Civil, Aerospace and Design Engineering, University of Bristol, Bristol, UK

    Ross Woods

  4. CSIRO Environment, Canberra, New South Wales, Australia

    Tim R. McVicar

  5. Australian Research Council Centre of Excellence for Climate Extremes, Canberra, New South Wales, Australia

    Tim R. McVicar

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Contributions

Y.Y., J.H. and Z.L. initiated the idea and designed the study with suggestions from R.W., T.R.M. and D.Y. J.H. and Z.L. processed the data and performed the analysis with help from Y.H., Y.G. and C.L. Y.Y. drafted the manuscript. All authors contributed to results discussion and the review and editing of the manuscript. Y.Y. acquired funding for this research.

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Correspondence to Yuting Yang.

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Extended data figures and tables

Extended Data Fig. 1 Spatial and temporal coverages of the streamflow records.

a | Spatial coverage of the 3049 catchments used herein. b | Spatial distribution of areas where the 1950–2020 mean annual snowfall fraction exceeded 0.1 over the Northern Hemisphere. c | Histogram of data length for the 3049 catchments. Numbers in c above each column indicate the count of catchments falling into each category.

Extended Data Fig. 2 Distribution of mean annual snowfall fraction in the elevation—latitude domain for the 3049 catchments

.

Extended Data Fig. 3 Spatiotemporal patterns of precipitation seasonality across 3049 catchments over 1950–2020.

a | Spatial distribution of mean annual precipitation timing (represented by the centre of mass of precipitation or CTP). b | Spatial distribution of mean annual seasonal precipitation variation (represented by PCI). c | Spatial distribution of trends in annual CTP. d | Spatial distribution of trends in annual PCI. In c and d, grey dots indicate that the trends are not statistically significant (p > 0.1, t-test).

Extended Data Fig. 4 Relationship between trends in snowfall fraction and streamflow seasonality across the 3049 catchments over 1950–2020.

a | Relationship between trends in snowfall fraction (fs) and CTQ. b | Relationship between trends in fs and QCI. The red solid lines show the best linear fits.

Extended Data Fig. 5 Changes in streamflow timing between the two 10-year periods with the highest and lowest snowfall fraction.

a | Changes in streamflow timing (ΔCTQ) as a function of mean annual snowfall fraction. b | ΔCTQ as a function of elevation. For each box, whiskers represent the 1st and 99th percentile, the top and bottom of the box are the 25th and 75th percentile and the median is represented by the horizontal line internal to the coloured box. The red solid line shows the best linear fit. The slopes of the linear regression models are quantified and the level of statistical significance is calculated using a t-test.

Extended Data Fig. 6 Streamflow timing in response to changes in precipitation timing and snowmelt onset data.

a | Sensitivities of CTQ to precipitation timing (εCTQ_CTP) and melt onset date (εCTQ_MOD) as a function of \(\bar{{f}_{s}}\) for the 3049 catchments. b | Histogram of sensitivities of precipitation timing and melt onset date to declines in snowfall fraction (εCTP_fs and εMOD_fs) for the 3049 catchments. The snowmelt onset date (MOD) is determined following ref. 24, where MOD is calculated as the beginning of the first 5-day period during which snow water equivalent declined by more than 2.5 cm.

Extended Data Fig. 7 Changes in seasonal streamflow and precipitation between low and high snowfall fraction periods.

a-c | Number of catchments showing changes in (a) warm season streamflow, (b) cold season streamflow and (c) cold season precipitation. d-f | Relationship of changes in snowfall fraction (Δfs) and changes in (d) warm season streamflow (ΔQ), (e) cold season streamflow (ΔQ) and (f) cold season precipitation (ΔP) between low- and high-fs periods. In d, e and f, whiskers represent the 1st and 99th percentile, the top and bottom of the box are the 25th and 75th percentile and the median is represented by the horizontal line internal to the coloured box. The red solid lines show the best linear fits.

Extended Data Fig. 8 Impact of changes in snowfall fraction on interannual variability of streamflow seasonality.

a | Relationship of relative change in interannual variability of streamflow timing (ΔCVCTQ/CVCTQ) and that of precipitation timing (ΔCVCTP/CVCTP) between the two 20-year periods with the highest and lowest snowfall fraction (fs). b | Relationship of relative change in interannual variability of streamflow seasonal variation (ΔCVQCI/CVQCI) and that of precipitation seasonal variation (ΔCVPCI/CVPCI) between two 20-year periods with the highest and lowest fs. In both plots, catchments are divided into 5 groups based on their relative changes in fs between the two 20-year periods and lines are the best linear fit across catchments in each group. The slopes of the linear regression models are presented in the inset, denoted by asterisks “*” and “**” to signify statistical significance at the 95% and 99% confidence levels (t-test), respectively.

Extended Data Fig. 9 Influence of temporal variations in snowfall fraction on streamflow timing along an elevation gradient for 3049 catchments.

a | Elevation-dependent slopes in the linear regression between precipitation timing and snowfall fraction (εCTP_fs) for the 3049 catchments. b | Elevation-dependent slopes in the linear regression between snowmelt timing and snowfall fraction (εMOD_fs) for the 3049 catchments. c | Elevation-dependent changes in streamflow timing (ΔCTQ) resulting from alterations in precipitation timing (ΔCTP) between high- and low-fs periods for the 3049 catchments. d | Elevation-dependent ΔCTQ induced by changes in snowmelt timing (ΔMOD) between high- and low-fs periods for the 3049 catchments. For each box, whiskers represent the 1st and 99th percentile, the top and bottom of the box are the 25th and 75th percentile and the median is represented by the horizontal line internal to the coloured box. The red solid line shows the best linear fit.

Extended Data Table 1 Overview of existing studies assessing changes in streamflow timing in snow-affected regions
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Han, J., Liu, Z., Woods, R. et al. Streamflow seasonality in a snow-dwindling world. Nature 629, 1075–1081 (2024). https://doi.org/10.1038/s41586-024-07299-y

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