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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

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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.

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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.

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.

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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.

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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

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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.

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Correspondence to Li Li.

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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

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