Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

Temperature outweighs light and flow as the predominant driver of dissolved oxygen in US rivers

Nature Water volume 1, pages 249–260 (2023)Cite this article

Subjects

Abstract

The concentration of dissolved oxygen (DO), an important measure of water quality and river metabolism, varies tremendously in time and space. Riverine DO is commonly perceived as regulated by interacting and competing drivers (light, temperature and flow) that define rivers’ climate. Its continental-scale drivers, however, have remained elusive, partly due to the scarcity and spatio-temporal inconsistency of water quality data. Here we show, via a deep learning model (long short-term memory) trained using data from 580 rivers, that temperature predominantly drives daily DO dynamics in the contiguous United States. Light comes a close second, whereas flow imparts minimal influence. This work showcases the promise of using deep learning models for data filling that enables large-scale systematic analysis of patterns and drivers. Results show fairly accurate prediction of DO by temperature alone, and declining DO in warming rivers, which has important implications for water security and ecosystem health in the future climate.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Spatio-temporal DO dynamics.
Fig. 2: Model performance metrics in 580 basins.
Fig. 3: Regional hold-out tests and NSE performance in chemically ungauged basins.
Fig. 4: Model NSE performance under six input scenarios.
Fig. 5: Impact of individual M and BA on model performance.
Fig. 6: Correlations between daily DO and relevant drivers.

Similar content being viewed by others

Data availability

Discharge and biogeochemical data were downloaded from the USGS NWIS77 at the website of https://waterdata.usgs.gov/nwis. The meteorological dataset of DAYMET78 is available from the website of https://daymet.ornl.gov. Basin characteristics of topography, hydro, climate, land cover, soil, and geology from the Geospatial Attributes of Gages for Evaluating Streamflow dataset Version II (GAGES-II)56 are archived at https://water.usgs.gov/GIS/metadata/usgswrd/XML/gagesII_Sept2011.xml.

Code availability

The deep learning LSTM code and instruction are available from the GitHub website at https://github.com/mhpi/hydroDL. The dataRetrieval R package for downloading discharge and biogeochemical data is available at https://github.com/USGS-R/dataRetrieval. Supporting data and script (for example, model inputs, outputs and a downloading sample script) are available at https://github.com/Li-Reactive-Water-Group/NatureWater-US-river-DO-dataset. The scripts for data analysis and plotting were developed in MATLAB (R2018a) and RSTUDIO (v2022.07.1) and are available from the authors upon reasonable request.

References

  1. Tesoriero, A. J., Terziotti, S. & Abrams, D. B. Predicting redox conditions in groundwater at a regional scale. Environ. Sci. Technol. 49, 9657–9664 (2015).

    Article  CAS  PubMed  Google Scholar 

  2. Briggs, M. A. et al. Exploring local riverbank sediment controls on the occurrence of preferential groundwater discharge points. Water 14, 11 (2022).

    Article  Google Scholar 

  3. O’Donnell, B. & Hotchkiss, E. R. Resistance and resilience of stream metabolism to high flow disturbances. Biogeosciences 19, 1111–1134 (2022).

    Article  Google Scholar 

  4. 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 

  5. 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 

  6. Wang, S., Jin, X., Bu, Q., Jiao, L. & Wu, F. Effects of dissolved oxygen supply level on phosphorus release from lake sediments. Colloids Surf. A 316, 245–252 (2008).

    Article  CAS  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  8. Oschlies, A., Brandt, P., Stramma, L. & Schmidtko, S. Drivers and mechanisms of ocean deoxygenation. Nat. Geosci. 11, 467–473 (2018).

    Article  CAS  Google Scholar 

  9. Girard, J. E. Principles of Environmental Chemistry (Jones & Bartlett Learning, 2013).

  10. Tromans, D. Temperature and pressure dependent solubility of oxygen in water: a thermodynamic analysis. Hydrometallurgy 48, 327–342 (1998).

    Article  CAS  Google Scholar 

  11. Ulseth, A. J. et al. Distinct air–water gas exchange regimes in low- and high-energy streams. Nat. Geosci. 12, 259–263 (2019).

    Article  CAS  Google Scholar 

  12. 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).

  13. 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 

  14. Arroita, M., Elosegi, A. & Hall, R. O. Jr Twenty years of daily metabolism show riverine recovery following sewage abatement. Limnol. Oceanogr. 64, S77–S92 (2019).

    Article  CAS  Google Scholar 

  15. Diamond, J. S. et al. Light and hydrologic connectivity drive dissolved oxygen synchrony in stream networks. Limnol. Oceanogr. https://aslopubs.onlinelibrary.wiley.com/doi/abs/10.1002/lno.12271 (2022).

  16. Amon, R. M. & Benner, R. Photochemical and microbial consumption of dissolved organic carbon and dissolved oxygen in the Amazon River system. Geochim. Cosmochim. Acta 60, 1783–1792 (1996).

    Article  CAS  Google Scholar 

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

    Article  Google Scholar 

  18. 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 

  19. Caraco, N. F. et al. Dissolved oxygen declines in the hudson river associated with the invasion of the zebra mussel (Dreissena polymorpha). Environ. Sci. Technol. 34, 1204–1210 (2020).

    Article  Google Scholar 

  20. Palmer, M. J., Chételat, J., Jamieson, H. E., Richardson, M. & Amyot, M. Hydrologic control on winter dissolved oxygen mediates arsenic cycling in a small subarctic lake. Limnol. Oceanogr. https://doi.org/10.1002/lno.11556 (2021).

  21. Canadell, M. B., Gómez‐Gener, L., Clémençon, M., Lane, S. N. & Battin, T. J. Daily entropy of dissolved oxygen reveals different energetic regimes and drivers among high-mountain stream types. Limnol. Oceanogr. 66, 1594–1610 (2021).

    Article  CAS  Google Scholar 

  22. Guo, D. et al. Key factors affecting temporal variability in stream water quality. Water Resour. Res. 55, 112–129 (2019).

    Article  CAS  Google Scholar 

  23. Burns, D. A. et al. Monitoring the riverine pulse: applying high-frequency nitrate data to advance integrative understanding of biogeochemical and hydrological processes. WIREs Water 6, e1348 (2019).

    Article  Google Scholar 

  24. 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  PubMed  Google Scholar 

  25. Shen, C. A transdisciplinary review of deep learning research and its relevance for water resources scientists. Water Resour. Res. 54, 8558–8593 (2018).

    Article  Google Scholar 

  26. Moatar, F., Abbott, B. W., Minaudo, C., Curie, F. & Pinay, G. Elemental properties, hydrology, and biology interact to shape concentration-discharge curves for carbon, nutrients, sediment, and major ions. Water Resour. Res. 53, 1270–1287 (2017).

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

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

    Article  Google Scholar 

  29. 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).

  30. Kratzert, F. et al. Towards learning universal, regional, and local hydrological behaviors via machine learning applied to large-sample datasets. Hydrol. Earth Syst. Sci. 23, 5089–5110 (2019).

    Article  Google Scholar 

  31. 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. 58, e2021WR029583 (2022).

    Article  Google Scholar 

  32. Doran, P. M. in Bioprocess Engineering Principles 2nd edition (ed. Doran, P. M.) 379–444 (Academic Press, 2013).

  33. Townsend, S. A., Webster, I. T. & Schult, J. H. Metabolism in a groundwater-fed river system in the Australian wet/dry tropics: tight coupling of photosynthesis and respiration. J. North Am. Benthol. Soc. 30, 603–620 (2011).

    Article  Google Scholar 

  34. Davison, I. R. Environmental effects on algal photosynthesis: temperature. J. Phycol. 27, 2–8 (1991).

    Article  Google Scholar 

  35. 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 

  36. Zhi, W. & Li, L. The shallow and deep hypothesis: subsurface vertical chemical contrasts shape nitrate export patterns from different land uses. Environ. Sci. Technol. 54, 11915–11928 (2020).

    Article  CAS  PubMed  Google Scholar 

  37. Stewart, B. et al. Streams as mirrors: reading subsurface water chemistry from stream chemistry. Water Resour. Res. 58, e2021WR029931 (2022).

    Article  CAS  Google Scholar 

  38. Hirsch, R. M., Moyer, D. L. & Archfield, S. A. Weighted regressions on time, discharge, and season (wrtds), with an application to chesapeake bay river inputs. J. Am. Water Resour. Assoc. 46, 857–880 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  39. Rodell, M. et al. The global land data assimilation system. Bull. Am. Meteorol. Soc. 85, 381–394 (2004).

    Article  Google Scholar 

  40. Berg, P., Almén, F. & Bozhinova, D. HydroGFD3.0 (Hydrological Global Forcing Data): a 25 km global precipitation and temperature data set updated in near-real time. Earth Syst. Sci. Data 13, 1531–1545 (2021).

    Article  Google Scholar 

  41. Hersbach, H. et al. The ERA5 global reanalysis. Q. J. R. Meteorolog. Soc. 146, 1999–2049 (2020).

    Article  Google Scholar 

  42. Do, H. X., Gudmundsson, L., Leonard, M. & Westra, S. The global streamflow indices and metadata archive (GSIM)—part 1: the production of a daily streamflow archive and metadata. Earth Syst. Sci. Data 10, 765–785 (2018).

    Article  Google Scholar 

  43. Virro, H., Amatulli, G., Kmoch, A., Shen, L. & Uuemaa, E. GRQA: global river water quality archive. Earth Syst. Sci. Data https://doi.org/10.5194/essd-2021-51 (2021).

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Troch, P. A., Carrillo, G., Sivapalan, M., Wagener, T. & Sawicz, K. Climate–vegetation–soil interactions and long-term hydrologic partitioning: signatures of catchment co-evolution. Hydrol. Earth Syst. Sci. 17, 2209–2217 (2013).

    Article  Google Scholar 

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

    Article  PubMed  Google Scholar 

  47. Ni, W., Li, M., Ross, A. C. & Najjar, R. G. Large projected decline in dissolved oxygen in a eutrophic estuary due to climate change. J. Geophys. Res. Oceans 124, 8271–8289 (2019).

    Article  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

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

  50. Danladi Bello, A.-A., Hashim, N. & Mohd Haniffah, M. Predicting impact of climate change on water temperature and dissolved oxygen in tropical rivers. Climate 5, 58 (2017).

    Article  Google Scholar 

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

    Article  Google Scholar 

  52. Perkins-Kirkpatrick, S. E. & Lewis, S. C. Increasing trends in regional heatwaves. Nat. Commun. 11, 3357 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. 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).

  54. 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 

  55. Gorelick, N. et al. Google Earth Engine: planetary-scale geospatial analysis for everyone. Remote Sens. Environ. 202, 18–27 (2017).

    Article  Google Scholar 

  56. Falcone, J. A. GAGES-II: Geospatial Attributes of GAGES for Evaluating Streamflow (US Geological Survey, 2011); https://water.usgs.gov/GIS/metadata/usgswrd/XML/gagesII_Sept2011.xml

  57. Hirsch, R. M. & De Cicco, L. A. User Guide to Exploration and Graphics for RivEr Trends (EGRET) and dataRetrieval: R Packages for Hydrologic Data Report No. 2328-7055 (US Geological Survey, 2015).

  58. Sterle, G. et al. CAMELS-Chem: augmenting CAMELS (Catchment Attributes and Meteorology for Large-sample Studies) with atmospheric and stream water chemistry data. Hydrol. Earth Syst. Sci. Discuss. 2022, 1–23 (2022).

    Google Scholar 

  59. 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).

  60. Greff, K., Srivastava, R. K., Koutník, J., Steunebrink, B. R. & Schmidhuber, J. LSTM: a search space odyssey. IEEE Trans Neural Netw. Learn. Syst. 28, 2222–2232 (2016).

    Article  PubMed  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  62. Rahmani, F. et al. Exploring the exceptional performance of a deep learning stream temperature model and the value of streamflow data. Environ. Res. Lett. https://doi.org/10.1088/1748-9326/abd501 (2020).

  63. Ma, K. et al. Transferring hydrologic data across continents-leveraging data-rich regions to improve hydrologic prediction in data-sparse regions. Water Resour. Res. https://doi.org/10.1029/2020wr028600 (2021).

  64. Feng, D., Lawson, K. & Shen, C. Mitigating prediction error of deep learning streamflow models in large data‐sparse regions with ensemble modeling and soft data. Geophys. Res. Lett. https://doi.org/10.1029/2021gl092999 (2021).

  65. Fang, K., Pan, M. & Shen, C. P. The value of SMAP for long-term soil moisture estimation with the help of deep learning. IEEE Trans. Geosci. Remote 57, 2221–2233 (2019).

    Article  Google Scholar 

  66. Peterson, R. A. Finding optimal normalizing transformations via bestNormalize. R J. 13, 294–313 (2021).

    Article  Google Scholar 

  67. 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 

  68. Gal, Y. & Ghahramani, Z. A theoretically grounded application of dropout in recurrent neural networks. Adv. Neural Inf. Process. Syst. 29, 1019–1027 (2016).

    Google Scholar 

  69. 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, 030–011,039 (2017).

    Article  Google Scholar 

  70. Zhao, N., Fan, Z. & Zhao, M. A new approach for estimating dissolved oxygen based on a high-accuracy surface modeling method. Sensors 21, 3954 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  71. Stajkowski, S., Zeynoddin, M., Farghaly, H., Gharabaghi, B. & Bonakdari, H. A methodology for forecasting dissolved oxygen in urban streams. Water 12, 2568 (2020).

    Article  Google Scholar 

  72. Stefan, H. G. & Fang, X. Dissolved oxygen model for regional lake analysis. Ecol. Modell. 71, 37–68 (1994).

    Article  CAS  Google Scholar 

  73. 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 

  74. 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).

  75. 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).

  76. Sivapalan, M. Prediction in ungauged basins: a grand challenge for theoretical hydrology. Hydrol. Processes 17, 3163–3170 (2003).

    Article  Google Scholar 

  77. National Water Information System data available on the World Wide Web (USGS, 2016); http://waterdata.usgs.gov/nwis/

  78. Thornton, M. M. et al. Daymet: daily surface weather data on a 1-km grid for North America, version 4. ORNL DAAC https://doi.org/10.3334/ORNLDAAC/1840 (2020).

Download references

Acknowledgements

The project was supported by the Barry and Shirley Isett professorship to W.Z. and L.L. from the Department of Civil and Environmental Engineering at Penn State University. W.O. and C.S. were supported by the Office of Biological and Environmental Research of the United States Department of Energy under contract DE-SC0016605.

Author information

Authors and Affiliations

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

    Wei Zhi, Chaopeng Shen & Li Li

  2. School of Hydraulic Engineering, Dalian University of Technology, Dalian, China

    Wenyu Ouyang

Authors
  1. Wei Zhi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Wenyu Ouyang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Chaopeng Shen
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Li Li
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

W.Z. conceived the idea and carried out the data retrieval and model development. W.O. and C.S. helped with the technical issues related to model simulations. W.Z. developed the first draft, upon which W.Z. and L.L. iterated multiple versions for figure design, content structure and key message development. L.L. finalized the paper.

Corresponding author

Correspondence to Li Li.

Ethics declarations

Competing interests

The authors declare no competing interests.

Peer review

Peer review information

Nature Water thanks Alice Carter, Ryan Utz, Danlu Guo and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Supplementary Information

Supplementary Figs. 1–8 and Table 1.

Reporting Summary

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhi, W., Ouyang, W., Shen, C. et al. Temperature outweighs light and flow as the predominant driver of dissolved oxygen in US rivers. Nat Water 1, 249–260 (2023). https://doi.org/10.1038/s44221-023-00038-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s44221-023-00038-z

This article is cited by

Search

Advanced search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing