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Climate change drives low dissolved oxygen and increased hypoxia rates in rivers worldwide

Nature Climate Change volume 15, pages 1348–1354 (2025)Cite this article

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Abstract

Increased water temperatures under climate change will probably cause decreases in dissolved oxygen and an associated increase in the number of days with hypoxia. This could have major consequences for freshwater ecosystems, but the extent of this threat remains unclear. Here we analyse trends in dissolved oxygen concentrations and days with stress and hypoxia in rivers worldwide between the periods 1980–2019 and 2020–2100 under global change. To achieve this, we train a hybrid process-based and machine learning model on approximately 2.6 million observations of dissolved oxygen, and we apply this model under both past and future conditions globally. The model projects significant decreasing trends in dissolved oxygen in most of the world’s rivers, resulting in on average 8.8 ± 2.3 more hypoxia days per decade globally between the years 2020 and 2100, and indicating a potentially major threat to freshwater ecosystems worldwide.

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Fig. 1: Spatial patterns and trends in DO, stress and hypoxia days over the historic period (1980–2019).
Fig. 2: Average trends in DO concentration per decade over the period 1980–2019.
Fig. 3: Time-series analyses of global average DO, total number of stress days and total number of hypoxia days (1980–2019).
Fig. 4: Future projections (2020–2100) of DO concentrations, stress days and hypoxia days (SSP 3-RCP 7.0).
Fig. 5: Future projections (2020–2100) of DO concentrations, stress days and hypoxia days (SSP 3-RCP 7.0).
Fig. 6: Time series of the total number of hypoxia days with the ensemble mean (thick line) and uncertainty bounds (± s.d.) of the five GCMs for the hybrid DynQual_Random Forest model.

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Data availability

Daily DO output from DynQual_Random Forest over the periods 1980–2019 and 2020–2100 is available via Yoda at https://doi.org/10.24416/UU01-0QHX53 (ref. 63). DO monitoring data are available via Zenodo at https://doi.org/10.5281/zenodo.15308434 (ref. 64).

Code availability

Python scripts associated with the analysis of this study and development of the hybrid DynQual_Random Forest model are available via Zenodo at https://doi.org/10.5281/zenodo.13329996 (ref. 65) and GitHub at https://github.com/SustainableWaterSystems/DYNQUAL/tree/feature/dissolved_oxygen.

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Acknowledgements

We thank J. Wang (Utrecht University) for their inputs related to biogeochemistry. We would like to thank A. Schipper (Radboud University and PBL Netherlands Environmental Assessment Agency) for providing valuable feedback on this study. The calculations of the study were computed on the Dutch national supercomputer Snellius with the support of SURFsara. D.J.G. and M.T.H.v.V. were financially supported by the Netherlands Scientific Organisation VIDI grant no. VI.Vidi.193.019. In addition, M.T.H.v.V. acknowledges funding from the European Research Council B-WEX grant no. 101039426.

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Authors and Affiliations

  1. Department of Physical Geography, Utrecht University, Utrecht, the Netherlands

    Duncan J. Graham, Marc F. P. Bierkens, Edward R. Jones, Edwin H. Sutanudjaja & Michelle T. H. van Vliet

  2. Unit Subsurface and Groundwater Systems, Deltares, Utrecht, the Netherlands

    Marc F. P. Bierkens

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Contributions

D.J.G. performed the model simulations and data analyses and wrote the original drafts of the paper and methods. D.J.G., M.T.H.v.V. and M.F.P.B. conceptualized the study. M.T.H.v.V. and M.F.P.B. supervised the project and contributed to the writing. E.H.S. and E.R.J. assisted in the project. All authors contributed to editing.

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Correspondence to Duncan J. Graham.

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Nature Climate Change thanks Li Li, Robert Schwefel and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Process-based model evaluation (DynQual).

(a-e) process-based simulations of dissolved oxygen (DO) from DynQual v1.1 (blue) compared with monitoring data (black) for five different stations across North America, Europe, New Zealand, Asia and South America. (f-g) Model validation of DynQual for different classes of water temperature (Tw) and river discharge (Q). Grey denotes observation data. Boxplots: median, first quartile (Q1), third quartile (Q3), Q1 − 1.5IQR, Q3 + 1.5IQR where IQR is Interquartile range.

Extended Data Fig. 2 Basin-scale validation of DynQual_Random Forest and DynQual.

(a) Water quality monitoring stations with dissolved oxygen concentration measurements available based on 7 water quality datasets53,54,55,56,57,58,59. (b) normalised Root-Mean-Squared-Error (nRMSE) of DynQual_Random Forest using all available dissolved oxygen concentration data. Note: median nRMSEs are estimated per river basin based on HydroSHEDs level 4 (c) nRMSE histograms: DynQual_Random Forest (red) and DynQual (blue). (d) nRMSEs of DynQual: all data. (e) nRMSEs of DynQual_Random Forest using dissolved oxygen concentration data below the 10th percentiles. (f) nRMSE histograms: data below 10th percentiles. (g) nRMSEs of DynQual: dissolved oxygen data below the 10th percentiles (h) Average nRMSE of DynQual_Random Forest for each year between 1980–2019. (i) Percentage biases (PBIAS) of DynQual_Random Forest (red) and DynQual (blue): all data. (j) Average nRMSE of DynQual for each year between 1980–2019. River basin shapefiles in b, d, e and g adapted with permission from ref. 60, Wiley.

Extended Data Fig. 3 Station level validation of DynQual_Random Forest and DynQual.

(a) Spatial patterns with Means Errors (ME) of DynQual_Random Forest (b) Root-Mean-Squared-Errors (RMSE) of DynQual_Random Forest. (c) ME of DynQual (d) RMSE of DynQual for dissolved oxygen monitoring stations globally. (e-h) Boxplots with dissolved oxygen (DO) concentrations for stress days (< 5 mg/l) and hypoxia days (DO data < 3 mg/l) for DynQual_Random Forest and DynQual. Boxplots: median, first quartile (Q1), third quartile (Q3), Q1 − 1.5IQR, Q3 + 1.5IQR where IQR is Interquartile range.

Extended Data Fig. 4 Regional and local validation of DynQual_Random Forest and DynQual.

(a) Median value of the Root Mean Squared Error, normalised by the mean (nRMSE), for each region and three month period (Jan-March, April-June, July-Sept, Oct-Dec) for the process-based model DynQual (b) median nRMSE value for each region and three-month period for the hybrid DynQual_Random Forest model (c) boxplots comparing the observed dissolved oxygen concentrations (grey) during periods of hypoxia (DO < 3 mg/l) with the simulations from DynQual_Random Forest (red) and DynQual (blue). Boxplots: median, first quartile (Q1), third quartile (Q3), Q1 − 1.5IQR, Q3 + 1.5IQR where IQR is Interquartile range. (d-g) Time-series of daily DO concentrations simulated by the hybrid DynQual_Random Forest model for four stations across Asia, North America, Australia/New Zealand and South America compared to monitoring data (black).

Extended Data Fig. 5 Further validation metrics of DynQual_Random Forest and DynQual.

(a) Median in Root-Mean-Squared-Errors (RMSEs) per river basin for DynQual_Random Forest (b) RMSEs for DynQual (blue) and DynQual_Random Forest (red) (c) median RMSEs per river basin for DynQual (d) lower 10th percentiles of dissolved oxygen (DO) data: median RMSEs per basin for DynQual_Random Forest (e) RMSEs for DynQual and DynQual_Random Forest (DO data < 10th percentiles) (f) median RMSEs for DynQual (DO data < 10th percentiles) (g) mean annual RMSE values (DynQual_Random Forest) (h) Mean absolute errors (MAE) DynQual and DynQual_Random Forest (i) Mean annual RMSE values for DynQual (j) Frequency of stress days (DO < 5 mg/l) simulated and observed (k) Frequency of hypoxia days (DO < 3 mg/l) simulated and observed. River basin shapefiles in a, c, d and f adapted with permission from ref. 60, Wiley.

Extended Data Fig. 6 Hydro-climatic conditions during training, testing and simulating at the global level.

(a-d) Statistical distributions of water temperature (Tw) and river discharge (Q) during 1980-2019 and 2020-2100. CDF: cumulative distribution function. (e-f) Model validation of DynQual_Random Forest and DynQual during periods of high water temperatures (Tw > 30 °C) and low discharge (Q < 30m3/s). Boxplots: median, first quartile (Q1), third quartile (Q3), Q1 − 1.5IQR, Q3 + 1.5IQR where IQR is Interquartile range.

Extended Data Fig. 7 Global trends in hydro-climatic variables, stress and hypoxia 1980-2019.

(a) Average trend in water temperature over the period 1980-2019. (b) Average trend in river discharge over the period 1980-2019. (c) Average trend in biochemical oxygen demand (BOD) concentration over the period 1980-2019. (d) Average absolute trend in the number of stress days (DO < 5 mg/l) over the period 1980-2019 using DynQual_Random Forest (e) Average absolute trend in the number of hypoxia days (DO < 3 mg/l) over the period 1980-2019 using DynQual_Random Forest. Grey denotes mean discharge < 10m3/s. Basemaps adapted from ref. 19 under a Creative Commons license CC BY 4.0.

Extended Data Fig. 8 Future trends in hydro-climatic variables 2020-2100.

(a) Average trend in water temperature over the period 2020-2100 based on five Global Climate Models (GCMs) for SSP3-RCP7.0 scenario. (b) Average trend in river discharge over the period 2020-2100 based on five GCMs. (c) Average trend in biochemical oxygen demand (BOD) concentration over 2020-2100 based on five GCMs. Grey denotes mean discharge < 10m3/s. Basemaps adapted from ref. 19 under a Creative Commons license CC BY 4.0.

Extended Data Fig. 9 Future trends in stress and hypoxia 2020-2100.

(a) Average absolute trend in the number of stress days (DO < 5 mg/l) over 2020-2100 based on five Global Climate Models (GCMs) for SSP3-RCP7.0 scenario using DynQual_Random Forest. (b) Average absolute trend in the number of hypoxia days (DO < 3 mg/l) over 2020-2100 based on five GCMs using DynQual_Random Forest. Grey denotes mean discharge < 10m3/s. Basemaps adapted from ref. 19 under a Creative Commons license CC BY 4.0.

Extended Data Fig. 10 Exposure of fish species to hypoxia.

(a) Freshwater fish species richness (SR) distribution (data from ref. 18). (b) Average trends in the number of freshwater fish species exposed to hypoxia per decade from 2020 to 2100 based on five Global Climate Models (GCMs) for SSP3-RCP7.0 scenario and using DynQual_Random Forest (percentage change) (c) Time-series of the total number of fish species exposed to hypoxia (DO < 2 mg/l) during 2020-2100 with the ensemble mean (thick line) and uncertainty bounds (±− standard deviation) of the 5 GCMs (DynQual_Random Forest) (d) Time-series of the total number of fish species exposed to hypoxia (DO < 3 mg/l) during 2020-2100 with the ensemble mean (thick line) and uncertainty bounds of the 5 GCMs (±− standard deviation) (DynQual_Random Forest). Panel a adapted with permission from ref. 18 under a Creative Commons license CC-BY 4.0. Basemap in b adapted from ref. 19 under a Creative Commons license CC BY 4.0.

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Graham, D.J., Bierkens, M.F.P., Jones, E.R. et al. Climate change drives low dissolved oxygen and increased hypoxia rates in rivers worldwide. Nat. Clim. Chang. 15, 1348–1354 (2025). https://doi.org/10.1038/s41558-025-02483-y

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