Widespread deoxygenation in warming rivers

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

Article 

Google Scholar 

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 

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 

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

Article 
CAS 

Google Scholar 

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

Article 

Google Scholar 

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 

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

Article 

Google Scholar 

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 

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).

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 

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 

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 

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

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 

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 

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

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 

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 

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

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

Article 

Google Scholar 

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

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 

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 

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 

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 

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 

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 

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 

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 

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 

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 

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 

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

Article 

Google Scholar 

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 

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

Article 

Google Scholar 

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

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

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 

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

Article 

Google Scholar 

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).

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 

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

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 

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 

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).

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 

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 

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 

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 

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

Article 
CAS 

Google Scholar 

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

Article 
CAS 

Google Scholar 

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 

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

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 

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

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).

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

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 

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

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 

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 

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 

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 

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 

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 

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 

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 

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 

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)

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 

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