More than one-third of Earth’s landmass is drained by rivers that seasonally freeze over. Ice transforms the hydrologic1,2, ecologic3,4, climatic5 and socio-economic6,7,8 functions of river corridors. Although river ice extent has been shown to be declining in many regions of the world1, the seasonality, historical change and predicted future changes in river ice extent and duration have not yet been quantified globally. Previous studies of river ice, which suggested that declines in extent and duration could be attributed to warming temperatures9,10, were based on data from sparse locations. Furthermore, existing projections of future ice extent are based solely on the location of the 0-°C isotherm11. Here, using satellite observations, we show that the global extent of river ice is declining, and we project a mean decrease in seasonal ice duration of 6.10 ± 0.08 days per 1-°C increase in global mean surface air temperature. We tracked the extent of river ice using over 400,000 clear-sky Landsat images spanning 1984–2018 and observed a mean decline of 2.5 percentage points globally in the past three decades. To project future changes in river ice extent, we developed an observationally calibrated and validated model, based on temperature and season, which reduced the mean bias by 87 per cent compared with the 0-degree-Celsius isotherm approach. We applied this model to future climate projections for 2080–2100: compared with 2009–2029, the average river ice duration declines by 16.7 days under Representative Concentration Pathway (RCP) 8.5, whereas under RCP 4.5 it declines on average by 7.3 days. Our results show that, globally, river ice is measurably declining and will continue to decline linearly with projected increases in surface air temperature towards the end of this century.
This is a preview of subscription content
Subscription info for Chinese customers
We have a dedicated website for our Chinese customers. Please go to naturechina.com to subscribe to this journal.
Get time limited or full article access on ReadCube.
All prices are NET prices.
The code used to acquire, analyse and visualize the dataset can be accessed online at the project’s GitHub page (https://github.com/seanyx/global-river-ice-dataset-from-Landsat). The river ice model and all figures in the paper (including the extended data figures) were made using R statistical software (http://www.R-project.org/).
Beltaos, S. & Prowse, T. River-ice hydrology in a shrinking cryosphere. Hydrol. Process. 23, 122–144 (2009).
Kämäri, M. et al. River ice cover influence on sediment transportation at present and under projected hydroclimatic conditions. Hydrol. Process. 29, 4738–4755 (2015).
Prowse, T. D. River-ice ecology. I: hydrologic, geomorphic, and water-quality aspects. J. Cold Reg. Eng. 15, 1–16 (2001).
Prowse, T. D. River-ice ecology. II: biological aspects. J. Cold Reg. Eng. 15, 17–33 (2001).
Raymond, P. A. et al. Global carbon dioxide emissions from inland waters. Nature 503, 355–359 (2013); erratum 507, 387 (2014).
Stephenson, S. R., Smith, L. C. & Agnew, J. A. Divergent long-term trajectories of human access to the Arctic. Nat. Clim. Change 1, 156–160 (2011).
Rokaya, P., Budhathoki, S. & Lindenschmidt, K.-E. Trends in the timing and magnitude of ice-jam floods in Canada. Sci. Rep. 8, 5834 (2018).
Knoll, L. B. et al. Consequences of lake and river ice loss on cultural ecosystem services. Limnol. Oceanogr. Lett. 4, 119–131 (2019).
Prowse, T. et al. Past and future changes in Arctic lake and river ice. Ambio 40, 53–62 (2011).
Magnuson, J. J. et al. Historical trends in lake and river ice cover in the Northern Hemisphere. Science 289, 1743–1746 (2000).
Prowse, T., Shrestha, R., Bonsal, B. & Dibike, Y. Changing spring air-temperature gradients along large northern rivers: implications for severity of river-ice floods. Geophys. Res. Lett. 37, L19706 (2010).
Bennett, K. E. & Prowse, T. D. Northern Hemisphere geography of ice-covered rivers. Hydrol. Process. 24, 235–240 (2010).
Brooks, R. N., Prowse, T. D. & O’Connell, I. J. Quantifying Northern Hemisphere freshwater ice. Geophys. Res. Lett. 40, 1128–1131 (2013).
Brown, D. R. N. et al. Changing river ice seasonality and impacts on interior Alaskan communities. Weather Clim. Soc. 10, 625–640 (2018).
Park, H. et al. Quantification of warming climate-induced changes in terrestrial Arctic river ice thickness and phenology. J. Clim. 29, 1733–1754 (2016).
Ionita, M., Badaluta, C.-A., Scholz, P. & Chelcea, S. Vanishing river ice cover in the lower part of the Danube basin—signs of a changing climate. Sci. Rep. 8, 7948 (2018).
Smith, L. C. Trends in Russian Arctic river-ice formation and breakup, 1917 to 1994. Phys. Geogr. 21, 46–56 (2000).
Cooley, S. W. & Pavelsky, T. M. Spatial and temporal patterns in Arctic river ice breakup revealed by automated ice detection from MODIS imagery. Remote Sens. Environ. 175, 310–322 (2016).
Pavelsky, T. M. & Smith, L. C. Spatial and temporal patterns in Arctic river ice breakup observed with MODIS and AVHRR time series. Remote Sens. Environ. 93, 328–338 (2004).
Prowse, T., Bonsal, B. R., Duguay, C. R. & Lacroix, M. P. River-ice break-up/freeze-up: a review of climatic drivers, historical trends and future predictions. Ann. Glaciol. 46, 443–451 (2007).
Collins, M. et al. in Climate Change 2013: The Physical Science Basis (eds Stocker, T. F. et al.) 1029–1136 (Cambridge Univ. Press, 2013).
Allen, G. H. & Pavelsky, T. M. Global extent of rivers and streams. Science 361, 585–588 (2018).
Pekel, J.-F., Cottam, A., Gorelick, N. & Belward, A. S. High-resolution mapping of global surface water and its long-term changes. Nature 540, 418–422 (2016).
Yamazaki, D. et al. MERIT Hydro: a high-resolution global hydrography map based on latest topography datasets. Wat. Resour. Res. 55, 5053–5073 (2019).
Gorelick, N. et al. Google Earth Engine: planetary-scale geospatial analysis for everyone. Remote Sens. Environ. 202, 18–27 (2017).
Zhu, Z. & Woodcock, C. E. Object-based cloud and cloud shadow detection in Landsat imagery. Remote Sens. Environ. 118, 83–94 (2012).
Hall, D. K., Riggs, G. A. & Barton, J. S. Algorithm Theoretical Basis Document (ATBD) for the MODIS Snow and Sea Ice-Mapping Algorithms (NASA, 2001); https://eospso.gsfc.nasa.gov/sites/default/files/atbd/atbd_mod10.pdf
Copernicus Climate Change Service (C3S) ERA5: Fifth Generation of ECMWF Atmospheric Reanalyses of the Global Climate (C3S Climate Data Store, 2017); https://cds.climate.copernicus.eu/cdsapp#!/home
Lacroix, M. P., Prowse, T. D., Bonsal, B. R., Duguay, C. R. & Menard, P. River ice trends in Canada. In 13th Workshop on Ice Covered Rivers 41–55 (Committee on River Ice Processes and the Environment, 2005).
Thrasher, B., Maurer, E. P., McKellar, C. & Duffy, P. B. Technical note: bias correcting climate model simulated daily temperature extremes with quantile mapping. Hydrol. Earth Syst. Sci. 16, 3309–3314 (2012).
Foga, S. et al. Cloud detection algorithm comparison and validation for operational Landsat data products. Remote Sens. Environ. 194, 379–390 (2017).
Beaton, A., Whaley, R., Corston, K. & Kenny, F. Identifying historic river ice breakup timing using MODIS and Google Earth Engine in support of operational flood monitoring in Northern Ontario. Remote Sens. Environ. 224, 352–364 (2019).
Takács, K., Kern, Z. & Nagy, B. Impacts of anthropogenic effects on river ice regime: examples from Eastern Central Europe. Quat. Int. 293, 275–282 (2013).
Gossart, A. et al. An evaluation of surface climatology in state-of-the-art reanalyses over the Antarctic Ice Sheet. J. Clim. 32, 6899–6915 (2019).
Funding was provided to T.M.P. by a subcontract from the SWOT Project Office at the NASA/Caltech Jet Propulsion Laboratory. We thank S. Lindsey at the Alaska-Pacific River Forecast Center for providing us with the NWS Alaska river break-up and freeze-up records, and W. Dolan for help with geolocating Alaskan river ice records.
The authors declare no competing interests.
Peer review information Nature thanks John Kimball, Gerhard Krinner and Homa Kheyrollah Pour 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.
Extended data figures and tables
a, Data availability map for the decades 1984–1994 and 2008–2018 based on the river ice extent dataset. Black indicates no data or no studied river. b, Percentage of successful river ice observations for each month of each decade. The percentage was calculated by taking the ratio between the number of observations used in the historical analysis and the total number of Landsat river observations when no filters (cloud, topographic shadow and river length) were applied.
Extended Data Fig. 2 Monthly maps of the changes in river ice extent between 1984–1994 and 2008–2018.
Black indicates no data or no studied river.
Extended Data Fig. 3 Modelled average monthly river ice difference between 2009–2029 and 2080–2100 using CESM SAT output (RCP 4.5).
The percentage point change over the Northern Hemisphere is listed under the month, with the percentage change in parentheses.
Extended Data Fig. 4 Modelled average monthly river ice difference between 2009–2029 and 2080–2100 using CESM SAT.
a, Model output under RCP 8.5. b, Model output under RCP 4.5. Only the months that showed obvious changes in percentage points (June, July and and August) were mapped. The percentage point changes and percentage changes over the Southern Hemisphere are listed in the tables on the right.
Extended Data Fig. 5 Modelled ice duration zones between 2009–2029 and 2080–2100 using CESM modelled SAT.
a, The Northern Hemisphere under RCP 4.5. b, c, The Southern Hemisphere under RCP 8.5 (b) and RCP 4.5 (c). Areas showing obvious changes are marked by red rectangles.
Extended Data Fig. 6 Sensitivity of the changes in annual maximum river ice extent to the changes in global mean SAT.
The sensitivity was assessed for three models (CESM1-BGC, GFDL-ESM2M and MIROC-ESM).
Extended Data Fig. 7 Landsat sampling difference between the historical period 1984–1994 and 2008–2018.
a, Distribution of the temporal sampling difference within each month. b, Temporal sampling difference and its relationship with the difference in the ice extent. c, Distribution of the spatial sampling difference within the 5° × 5° tiles. d, Spatial sampling difference and its relationship with the difference in the ice extent.
Extended Data Fig. 8 Summary of river ice duration decline based on temperature outputs from three CMIP5 models.
a, Difference in the global mean SAT across 21 CMIP5 models between 2006–2036 and 2069–2099. The three models used to assess future river ice change are marked with red rectangles. b, Decline in global mean river ice duration between 2009–2029 and 2080–2100 for the three selected models.
Extended Data Fig. 9 Evaluating Landsat-derived river ice conditions against in situ river ice records.
a, The accuracy of Landsat-derived river ice extents when evaluated against in situ reports of river ice condition. b, Monthly evaluation of Landsat-derived river ice estimates. c, Examples of differences in definition between remotely sensed and ground-based ice conditions. The GRWL centrelines are shown in images c2 and c4 to indicate the river. Satellite images courtesy of the US Geological Survey.
About this article
Cite this article
Yang, X., Pavelsky, T.M. & Allen, G.H. The past and future of global river ice. Nature 577, 69–73 (2020). https://doi.org/10.1038/s41586-019-1848-1