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Widespread deoxygenation in warming rivers

Nature Climate Change volume 13, pages 1105–1113 (2023)Cite this article

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Abstract

Deoxygenation is commonly observed in oceans and lakes but less expected in shallower, flowing rivers. Here we reconstructed daily water temperature and dissolved oxygen in 580 rivers across the United States and 216 rivers in Central Europe by training a deep learning model using temporal weather and water quality data and static watershed attributes (for example, hydro-climate, topography, land use, soil). Results revealed persistent warming in 87% and deoxygenation in 70% of the rivers. Urban rivers demonstrated the most rapid warming, whereas agricultural rivers experienced the slowest warming but fastest deoxygenation. Mean deoxygenation rates (−0.038 ± 0.026 mg l−1 decade−1) were higher than those in oceans but lower than those in temperate lakes. These rates, however, may be underestimated, as training data are from grab samples collected during the day when photosynthesis peaks. Projected future rates are between 1.6 and 2.5 times higher than historical rates, indicating significant ramifications for water quality and aquatic ecosystems.

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Fig. 1: DO patterns and dynamics.
Fig. 2: Historical trends of WT, DOsat (mg l−1), DO (mg l−1) and DO%sat.
Fig. 3: Deoxygenation rates versus warming rates in US and CE rivers.
Fig. 4: Projected trends and spatial patterns of warming and deoxygenation under SSP2–4.5 and SSP5–8.5 scenarios.
Fig. 5: Projected trends and decadal increases in annual and seasonal days of stress and hypoxia conditions under SSP2–4.5 and SSP5–8.5 scenarios.
Fig. 6: Rates of warming and deoxygenation in different waters.

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Data availability

Discharge and water quality data in the United States were downloaded from the USGS National Water Information System (NWIS) at https://waterdata.usgs.gov/nwis. The historical meteorological datasets in the United States are available from the NLDAS-2 (https://ldas.gsfc.nasa.gov/nldas/v2/forcing) and DAYMET (https://daymet.ornl.gov). Basin characteristics in the United States are from GAGES-II archived at https://water.usgs.gov/GIS/metadata/usgswrd/XML/gagesII_Sept2011.xml. The LamaH-CE paper and dataset including meteorological forcing, discharge and basin attributes is available at https://doi.org/10.5194/essd-13-4529-2021. Due to limits in sharing raw water quality data from providers in the CE region, we recommend accessing data directly from their websites: Water quality data for Austria were obtained from the Federal Ministry of Agriculture, Regions and Tourism at https://wasser.umweltbundesamt.at/h2odb/fivestep/abfrageQdPublic.xhtml. Water quality data in Switzerland were obtained from the Swiss Federal Institute of Aquatic Science and Technology (EAWAG) and Federal Office for the Environment (FOEN) at https://doi.org/10.25678/0004AV. Water quality data for Germany were obtained from the State Agency for the Environment Baden-Württemberg at https://udo.lubw.baden-wuerttemberg.de/public/index.xhtml, and the Bavarian State Office for the Environment at https://www.gkd.bayern.de/en/rivers/chemistry. Supporting data are deposited at https://github.com/LiReactiveWater/WT-DO-US-CE-dataset. The projected downscaled forcing data from the NEX-GDDP-CMIP6 database19 can be found at https://doi.org/10.7917/OFSG3345.

Code availability

The deep learning code and instruction are available on GitHub at https://github.com/LiReactiveWater/WT-DO-US-CE-LSTM. The ‘streamMetabolizer’ R package for calculating DO saturation concentration is available on GitHub at https://github.com/USGS-R/streamMetabolizer. The ‘dataRetrieval’ R package for downloading discharge and water quality data for the United States is available on GitHub https://github.com/USGS-R/dataRetrieval. The ‘bestNormalize’ R package for transforming model inputs is available on GitHub at https://github.com/cran/bestNormalize.

References

  1. Ficklin, D. L. et al. Rethinking river water temperature in a changing, human-dominated world. Nat. Water 1, 125–128 (2023).

    Article  Google Scholar 

  2. Rosamond, M. S., Thuss, S. J. & Schiff, S. L. Dependence of riverine nitrous oxide emissions on dissolved oxygen levels. Nat. Geosci. 5, 715–718 (2012).

    Article  CAS  Google Scholar 

  3. Sundby, B. et al. The effect of oxygen on release and uptake of cobalt, manganese, iron and phosphate at the sediment–water interface. Geochim. Cosmochim. Acta 50, 1281–1288 (1986).

    Article  CAS  Google Scholar 

  4. Jane, S. F. et al. Widespread deoxygenation of temperate lakes. Nature 594, 66–70 (2021).

    Article  CAS  Google Scholar 

  5. Breitburg, D. et al. Declining oxygen in the global ocean and coastal waters. Science 359, eaam7240 (2018).

    Article  Google Scholar 

  6. Blaszczak, J. R. et al. Extent, patterns, and drivers of hypoxia in the world’s streams and rivers. Limnol. Oceanogr. Lett. https://doi.org/10.1002/lol2.10297 (2022).

    Article  Google Scholar 

  7. Bernhardt, E. S. et al. The metabolic regimes of flowing waters. Limnol. Oceanogr. 63, S99–S118 (2018).

    Article  Google Scholar 

  8. Bernhardt, E. S. et al. Light and flow regimes regulate the metabolism of rivers. Proc. Natl Acad. Sci. USA 119, e2121976119 (2022).

    Article  CAS  Google Scholar 

  9. Helton, A. M., Poole, G. C., Payn, R. A., Izurieta, C. & Stanford, J. A. Scaling flow path processes to fluvial landscapes: an integrated field and model assessment of temperature and dissolved oxygen dynamics in a river–floodplain–aquifer system. J. Geophys. Res. Biogeosci. https://doi.org/10.1029/2012JG002025 (2012).

  10. Piatka, D. R. et al. Transfer and transformations of oxygen in rivers as catchment reflectors of continental landscapes: a review. Earth Sci. Rev. 220, 103729 (2021).

    Article  CAS  Google Scholar 

  11. Utz, R. M., Bookout, B. J. & Kaushal, S. S. Influence of temperature, precipitation, and cloud cover on diel dissolved oxygen ranges among headwater streams with variable watershed size and land use attributes. Aquat. Sci. 82, 82 (2020).

    Article  CAS  Google Scholar 

  12. Hancke, K. & Glud, R. N. Temperature effects on respiration and photosynthesis in three diatom-dominated benthic communities. Aquat. Microb. Ecol. 37, 265–281 (2004).

    Article  Google Scholar 

  13. Girard, J. Principles of Environmental Chemistry (Jones & Bartlett Publishers, 2013).

  14. Blaszczak, J. R., Delesantro, J. M., Urban, D. L., Doyle, M. W. & Bernhardt, E. S. Scoured or suffocated: urban stream ecosystems oscillate between hydrologic and dissolved oxygen extremes. Limnol. Oceanogr. 64, 877–894 (2019).

    Article  CAS  Google Scholar 

  15. Carter, A. M., Blaszczak, J. R., Heffernan, J. B. & Bernhardt, E. S. Hypoxia dynamics and spatial distribution in a low gradient river. Limnol. Oceanogr. 66, 2251–2265 (2021).

    Article  Google Scholar 

  16. IPCC Climate Change 2021: The Physical Science Basis (eds Masson-Delmotte, V. et al.) (Cambridge Univ. Press, 2021).

  17. Guo, D. et al. A data-based predictive model for spatiotemporal variability in stream water quality. Hydrol. Earth Syst. Sci. 24, 827–847 (2020).

    Article  Google Scholar 

  18. Zhi, W., Ouyang, W., Shen, C. & Li, L. Temperature outweighs light and flow as the predominant driver of dissolved oxygen in US rivers. Nat. Water 1, 249–260 (2023).

    Article  Google Scholar 

  19. Thrasher, B. et al. NASA global daily downscaled projections, CMIP6. Sci. Data https://doi.org/10.1038/s41597-022-01393-4 (2022)

  20. Luterbacher, J. et al. European summer temperatures since Roman times. Environ. Res. Lett. 11, 024001 (2016).

    Article  Google Scholar 

  21. Climate at a Glance: National Mapping (NOAA National Centers for Environmental Information, accessed 13 August 2022); https://www.ncei.noaa.gov/cag/

  22. van der Schrier, G., van den Besselaar, E. J. M., Klein Tank, A. M. G. & Verver, G. Monitoring European average temperature based on the E-OBS gridded data set. J. Geophys. Res. Atmos. 118, 5120–5135 (2013).

    Article  Google Scholar 

  23. Thompson, A. M., Kim, K. & Vandermuss, A. J. Thermal characteristics of stormwater runoff from asphalt and sod surfaces 1. J. Am. Water Resour. Assoc. 44, 1325–1336 (2008).

    Article  Google Scholar 

  24. Kinouchi, T., Yagi, H. & Miyamoto, M. Increase in stream temperature related to anthropogenic heat input from urban wastewater. J. Hydrol. 335, 78–88 (2007).

    Article  Google Scholar 

  25. Adeola Fashae, O., Abiola Ayorinde, H., Oludapo Olusola, A. & Oluseyi Obateru, R. Landuse and surface water quality in an emerging urban city. Appl. Water Sci. 9, 25 (2019).

    Article  Google Scholar 

  26. Daniel, M. H. B. et al. Effects of urban sewage on dissolved oxygen, dissolved inorganic and organic carbon, and electrical conductivity of small streams along a gradient of urbanization in the Piracicaba River Basin. Water Air Soil Pollut. 136, 189–206 (2002).

    Article  CAS  Google Scholar 

  27. Welker, T. L., Overturf, K. & Abernathy, J. Effect of aeration and oxygenation on growth and survival of rainbow trout in a commercial serial-pass, flow-through raceway system. Aquac. Rep. 14, 100194 (2019).

    Article  Google Scholar 

  28. Vaquer-Sunyer, R. & Duarte, C. M. Thresholds of hypoxia for marine biodiversity. Proc. Natl Acad. Sci. USA 105, 15452–15457 (2008).

    Article  CAS  Google Scholar 

  29. Ice, G. & Sugden, B. Summer dissolved oxygen concentrations in forested streams of northern Louisiana. South. J. Appl. Forestry 27, 92–99 (2003).

    Article  Google Scholar 

  30. Whitworth, K. L., Baldwin, D. S. & Kerr, J. L. Drought, floods and water quality: drivers of a severe hypoxic blackwater event in a major river system (the southern Murray–Darling Basin, Australia). J. Hydrol. 450-451, 190–198 (2012).

    Article  CAS  Google Scholar 

  31. Calleja, M. L., Al-Otaibi, N. & Morán, X. A. G. Dissolved organic carbon contribution to oxygen respiration in the central Red Sea. Sci. Rep. 9, 4690 (2019).

    Article  Google Scholar 

  32. Zhi, W. et al. From hydrometeorology to river water quality: can a deep learning model predict dissolved oxygen at the continental scale? Environ. Sci. Technol. 55, 2357–2368 (2021).

    Article  CAS  Google Scholar 

  33. Li, J. & Wong, D. W. S. Effects of DEM sources on hydrologic applications. Comput. Environ. Urban Syst. 34, 251–261 (2010).

    Article  Google Scholar 

  34. Preece, R. M. & Jones, H. A. The effect of Keepit Dam on the temperature regime of the Namoi River, Australia. River Res. Appl. 18, 397–414 (2002).

    Article  Google Scholar 

  35. Zaidel, P. A. et al. Impacts of small dams on stream temperature. Ecol. Indic. 120, 106878 (2021).

    Article  Google Scholar 

  36. Zaidel, P. Impacts of Small, Surface-Release Dams on Stream Temperature and Dissolved Oxygen in Massachusetts. MSc thesis, Univ. Massachusetts Amherst (2018).

  37. Hartmann, J., Lauerwald, R. & Moosdorf, N. GLORICH-Global river chemistry database. PANGAEA https://doi.org/10.1594/PANGAEA.902360 (2019).

  38. Diamond, J. S. et al. Hypoxia is common in temperate headwaters and driven by hydrological extremes. Ecol. Indic. 147, 109987 (2023).

    Article  CAS  Google Scholar 

  39. Kaushal, S. S. et al. Rising stream and river temperatures in the United States. Front. Ecol. Environ. 8, 461–466 (2010).

    Article  Google Scholar 

  40. Jastram, J. D. & Rice, K. C. Air- and Stream-Water-Temperature Trends in the Chesapeake Bay Region, 1960–2014 (US Department of the Interior, US Geological Survey, 2015).

  41. Michel, A., Brauchli, T., Lehning, M., Schaefli, B. & Huwald, H. Stream temperature and discharge evolution in Switzerland over the last 50 years: annual and seasonal behaviour. Hydrol. Earth Syst. Sci. 24, 115–142 (2020).

    Article  Google Scholar 

  42. IPCC Climate Change 2014: Impacts, Adaptation, and Vulnerability (eds Field, C. B. et al.) (Cambridge Univ. Press, 2014).

  43. Bulgin, C. E., Merchant, C. J. & Ferreira, D. Tendencies, variability and persistence of sea surface temperature anomalies. Sci. Rep. 10, 7986 (2020).

    Article  CAS  Google Scholar 

  44. O’Reilly, C. M. et al. Rapid and highly variable warming of lake surface waters around the globe. Geophys. Res. Lett. 42, 10,773–10,781 (2015).

    Google Scholar 

  45. Dokulil, M. T. et al. Increasing maximum lake surface temperature under climate change. Clim. Change https://doi.org/10.1007/s10584-021-03085-1 (2021).

  46. Xie, C., Zhang, X., Zhuang, L., Zhu, R. & Guo, J. Analysis of surface temperature variation of lakes in China using MODIS land surface temperature data. Sci. Rep. 12, 2415 (2022).

    Article  CAS  Google Scholar 

  47. Schmidtko, S., Stramma, L. & Visbeck, M. Decline in global oceanic oxygen content during the past five decades. Nature 542, 335–339 (2017).

    Article  CAS  Google Scholar 

  48. Bograd, S. J. et al. Oxygen declines and the shoaling of the hypoxic boundary in the California Current. Geophys. Res. Lett. 35, L12607 (2008).

    Article  Google Scholar 

  49. Pierce, S. D., Barth, J. A., Shearman, R. K. & Erofeev, A. Y. Declining oxygen in the Northeast Pacific. J. Phys. Oceanogr. 42, 495–501 (2012).

    Article  Google Scholar 

  50. Li, L. et al. Climate controls on river chemistry. Earths Future 10, e2021EF002603 (2022).

    Article  CAS  Google Scholar 

  51. Hochreiter, S. & Schmidhuber, J. Long short-term memory. Neural Comput. 9, 1735–1780 (1997).

    Article  CAS  Google Scholar 

  52. Klingler, C., Schulz, K. & Herrnegger, M. LamaH-CE: LArge-SaMple DAta for hydrology and environmental sciences for Central Europe. Earth Syst. Sci. Data 13, 4529–4565 (2021).

    Article  Google Scholar 

  53. Falcone, J. A. GAGES-II: Geospatial Attributes of Gages for Evaluating Streamflow (US Geological Survey, 2011).

  54. Fang, K., Kifer, D., Lawson, K., Feng, D. & Shen, C. The data synergy effects of time‐series deep learning models in hydrology. Water Resour. Res. https://doi.org/10.1029/2021WR029583 (2022).

    Article  Google Scholar 

  55. Moore, R. B. et al. User’s Guide for the National Hydrography Dataset plus (NHDPlus) High Resolution Open-File Report (US Geological Survey, 2019).

  56. Spahr, N. E., Dubrovsky, N. M., Gronberg, J. M., Franke, O. & Wolock, D. M. Nitrate Loads and Concentrations in Surface-Water Base Flow and Shallow Groundwater for Selected Basins in the United States, Water Years 1990–2006 (US Geological Survey, 2010).

  57. Mueller, D. K. & Spahr, N. E. Nutrients in Streams and Rivers Across the Nation—1992–2001 Report No. 2006-5107 (US Geological Survey, 2006).

  58. Moriasi, D. N., Gitau, M. W., Pai, N. & Daggupati, P. Hydrologic and water quality models: performance measures and evaluation criteria. T. ASABE 58, 1763–1785 (2015).

    Article  Google Scholar 

  59. Wei, Z. DeepWater: deep learning for water quality. Zenodo https://doi.org/10.5281/zenodo.8199995 (2023)

  60. Feng, D., Fang, K. & Shen, C. Enhancing streamflow forecast and extracting insights using long-short term memory networks with data integration at continental scales. Water Resour. Res. https://doi.org/10.1029/2019WR026793 (2020).

    Article  Google Scholar 

  61. Kratzert, F., Klotz, D., Brenner, C., Schulz, K. & Herrnegger, M. Rainfall–runoff modelling using Long Short-Term Memory (LSTM) networks. Hydrol. Earth Syst. Sci. 22, 6005–6022 (2018).

    Article  Google Scholar 

  62. Fang, K., Shen, C., Kifer, D. & Yang, X. Prolongation of SMAP to spatiotemporally seamless coverage of continental U.S. using a deep learning neural network. Geophys. Res. Lett. 44, 11,030–11,039 (2017).

    Article  Google Scholar 

  63. Wang, Y.-H., Gupta, H. V., Zeng, X. & Niu, G.-Y. Exploring the potential of long short-term memory networks for improving understanding of continental- and regional-scale snowpack dynamics. Water Resour. Res. https://doi.org/10.1029/2021WR031033 (2022).

    Article  Google Scholar 

  64. Graf, R., Zhu, S. & Sivakumar, B. Forecasting river water temperature time series using a wavelet–neural network hybrid modelling approach. J. Hydrol. 578, 124115 (2019).

    Article  Google Scholar 

  65. Gallice, A., Schaefli, B., Lehning, M., Parlange, M. B. & Huwald, H. Stream temperature prediction in ungauged basins: review of recent approaches and description of a new physics-derived statistical model. Hydrol. Earth Syst. Sci. 19, 3727–3753 (2015).

    Article  Google Scholar 

  66. Jackson, F. L., Fryer, R. J., Hannah, D. M., Millar, C. P. & Malcolm, I. A. A spatio-temporal statistical model of maximum daily river temperatures to inform the management of Scotland’s Atlantic salmon rivers under climate change. Sci. Total Environ. 612, 1543–1558 (2018).

    Article  CAS  Google Scholar 

  67. Zhu, S., Nyarko, E. K. & Hadzima-Nyarko, M. Modelling daily water temperature from air temperature for the Missouri River. PeerJ 6, e4894 (2018).

    Article  Google Scholar 

  68. Zhu, S. & Heddam, S. Prediction of dissolved oxygen in urban rivers at the Three Gorges Reservoir, China: extreme learning machines (ELM) versus artificial neural network (ANN). Water Qual. Res. J. 55, 106–118 (2020).

    Article  CAS  Google Scholar 

  69. Yu, X., Shen, J. & Du, J. A machine-learning-based model for water quality in coastal waters, taking dissolved oxygen and hypoxia in Chesapeake Bay as an example. Water Resour. Res. https://doi.org/10.1029/2020wr027227 (2020)

  70. Liu, X. et al. Estimation of the key water quality parameters in the surface water, middle of northeast China, based on Gaussian process regression. Remote Sens. 14, 6323 (2022).

    Article  Google Scholar 

  71. Appling, A. P., Hall, R. O., Yackulic, C. B. & Arroita, M. Overcoming equifinality: leveraging long time series for stream metabolism estimation. J. Geophys. Res. Biogeosci. 123, 624–645 (2018).

    Article  CAS  Google Scholar 

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Acknowledgements

This study was supported by the Barry and Shirley Isett professorship endowment to L.L. and a seed grant from the Institute of Computation and Data Science at Penn State University to L.L.

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Authors and Affiliations

  1. Department of Civil and Environmental Engineering, The Pennsylvania State University, University Park, PA, USA

    Wei Zhi, Jiangtao Liu & Li Li

  2. The National Key Laboratory of Water Disaster Prevention, Yangtze Institute for Conservation and Development, Key Laboratory of Hydrologic-Cycle and Hydrodynamic-System of Ministry of Water Resources, Hohai University, Nanjing, China

    Wei Zhi

  3. Institute for Hydrology and Water Management, University of Natural Resources and Life Sciences, Vienna, Austria

    Christoph Klingler

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Contributions

W.Z. conceived the idea and compiled data for 580 US rivers. C.K. acquired and prepared data from Central Europe. J.L. downloaded and processed the daily downscaled forcing data from the NEX-GDDP-CMIP6. W.Z. trained the deep learning model. W.Z. wrote the first draft of the paper together with L.L. W.Z. and L.L. iterated and edited multiple versions to shape the ideas, figures and main message of the paper. C.K. edited the paper. L.L. finalized the paper.

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Correspondence to Li Li.

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Extended data

Extended Data Fig. 1 Monthly dynamics and variations of WT and DO in US and CE rivers.

Lines and dots are the model and measured data of WT (a) and DO (b), respectively, for the means of the temporally (that is, 1981–2019) and spatially averaged monthly values from all US and CE rivers. Shade areas and error bars are mean ± standard deviation to indicate monthly variability across US (n = 580) and CE (n = 216) rivers.

Extended Data Fig. 2 Model performance of DO and WT in the testing period.

Lower magnitude values closer to 0 in the percent bias (Pbias, a) and root mean square error (RMSE, b) indicate more accurate model results. Values closer to 1 in the Pearson’s correlation coefficient (Pcorr, c) indicates positive correlations and better capture of seasonality. Boxes show the median values (middle line) and the interquartile range (IQR), which is the range between the first quartile (Q1) and third quartile (Q3). The lower and upper whiskers extend to Q1 − 1.5 × IQR and Q3 + 1.5 × IQR, respectively.

Extended Data Fig. 3 Model NSE performance comparison against Gaussian Process Regression (GPR) model.

Three GPR model scenarios (that is, GPR-r200, GPR-r100, GPR-ind) were compared to the long-short term memory (LSTM) for DO (a, c) and WT (b, d) performance. In the top panel of Empirical Cumulative Distribution Function (ECDF), the lower LSTM curve tending toward the right side (NSE = 1.0) indicates better model performance for both DO and WT. The bottom panel similarly shows higher LSTM performances. The boxes show the median values (middle line) and the interquartile range (IQR), which is the range between the first quartile (Q1) and third quartile (Q3). The lower and upper whiskers extend to Q1 − 1.5 × IQR and Q3 + 1.5 × IQR, respectively. The three GRP scenarios refer to two regional models trained with 200 and 100 basins (that is, GPR-r200 and GPR-r100) and one individual model (that is, GPR-ind) trained for each individual basin (Methods).

Extended Data Fig. 4 Historical warming trends in air temperature over 1981 to 2019.

The top, middle, and bottom panels are daily average temperature (Tavg), daily maximum temperature (Tmax), and daily minimum temperature (Tmin), respectively. The spatial patterns of WT warming rates (Fig. 2a) are similar to Tmin warming rates. The side boxes show the median values (middle line) and the interquartile range (IQR), which is the range between the first quartile (Q1) and third quartile (Q3). The lower and upper whiskers extend to Q1 − 1.5 × IQR and Q3 + 1.5 × IQR, respectively. A previous study showed that water temperature no longer increases linearly with the increase in air temperature when it rises above 25 °C as heat is increasingly lost as evaporative cooling increase71. It is therefore possible that water temperature has a closer relationship with Tmin as it tends not to increase above 25 °C as Tmax and Tavg.

Extended Data Fig. 5 Basin attributes of US and CE rivers.

The attributes include mean elevation (top row), relative humidity (2nd row), stream order (3rd row), drainage area (4th row), and dominant land use (last row). Stream order is the modified Strahler order that accounts for flow splits55. The side boxes show the median values (middle line) and the interquartile range (IQR), which is the range between the first quartile (Q1) and third quartile (Q3). The lower and upper whiskers extend to Q1 − 1.5 × IQR and Q3 + 1.5 × IQR, respectively.

Extended Data Fig. 6 Correlations between changing rates and basin attributes.

The correlations include warming (a, b) and deoxygenation (c, d) rates with basin area (left column) and basin slope (right column).

Extended Data Fig. 7 Projected temperature and precipitation variables under SSP2-4.5 and SSP5-8.5 scenarios.

Variables include daily average (a), maximum (b), and minimum (c) air temperature and precipitation (d). The historical period covers 1981 to 2019. The solid line as the average of all 796 basins. The projection period spans from 2020 to 2100 with color lines from 10 CMIP6 models. The solid line and black shading represent the mean and two standard deviations of the 10 CMIP6 models, respectively. Temperature (a-c) shows consistent warming trends while the projected precipitation (d) generally exhibits a stable but decreased trends compared to historical period.

Extended Data Fig. 8 Model inputs for historical prediction and future projection.

Two types of inputs are required to run the model for DO and WT, that is, time-series of daily hydro-meteorological forcing and constant basin attributes. In the historical prediction, meteorological forcing data are from the NLDAS-2, DAYMET, and LamaH-CE (see Methods). Basin attributes are from the GAGES-II and LamaH-CE. In the future projection, historical variables of temperature (Tavg, Tmax, Tmin) and precipitation were replaced (red box) with these projected variables from the NEX-GDDP-CMIP6 dataset while others that are not available remained the same as in the historical periods (blue box). With these new projected temperature and precipitation variables, their long-term means were updated as new basin attributes (red box) in the future scenarios.

Extended Data Fig. 9 Modeled seasonal trends of warming and deoxygenation under SSP2-4.5 and SSP5-8.5 scenarios.

The top and bottom panels show warming (a, b) and deoxygenation (c, d), respectively. The historical period covers 1981 to 2019 with the solid line as the average of all 796 basins. The projection period spans from 2020 to 2100 with thin color lines from the 10 CMIP6 models and bold black lines as the model mean ± 2 std. In each panel, the long-dash, dotted, dashed, and dot-dash lines represent the seasons of summer, autumn, spring, and winter, respectively.

Extended Data Fig. 10 Projected changing rates of stress and hypoxia days under SSP2-4.5 and SSP5-8.5 scenarios.

Stress (red) and hypoxia (blue) days were counted with daily DO concentration less than 5.0 and 3.0 mg/L, respectively. In the top panel (a), the long-dash, dotted, dashed, and dot-dash lines represent the seasons of summer, autumn, spring, and winter, respectively. In the maps, annual day rate (b) and seasonal day rates (c–f) are the average of 10 CMIP6 models. The side boxes show the median values (middle line) and the interquartile range (IQR), which is the range between the first quartile (Q1) and third quartile (Q3). The lower and upper whiskers extend to Q1 − 1.5 × IQR and Q3 + 1.5 × IQR, respectively. Note a river can exhibit both stress and hypoxia conditions. CE is not included because all rivers have zero stress and hypoxia days in both scenarios.

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Zhi, W., Klingler, C., Liu, J. et al. Widespread deoxygenation in warming rivers. Nat. Clim. Chang. 13, 1105–1113 (2023). https://doi.org/10.1038/s41558-023-01793-3

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