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Scalable, adaptive and risk-informed design of hydrological sensor networks

Nature Water volume 3, pages 1144–1154 (2025)Cite this article

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Abstract

Stream monitoring networks are essential for understanding and managing Earth’s water resources, yet their deployment is rarely coordinated at the system scale to meet these objectives. We present a data-driven framework for the design of streamflow monitoring networks that improves hydrological predictions while also accommodating socio-environmental constraints. This approach uses a rank-revealing QR decomposition to isolate monitoring sites that best capture the spatiotemporal structure of hydrological time series obtained from retrospective simulations. Evaluated using 44 years of reanalysis data, we find that our sensor placement approach enables better reconstructions of streamflow at ungauged locations compared with existing methods. Our approach accommodates incremental expansion of existing gauge networks and integrates operational priorities, such as flood risk, without compromising the accuracy of hydrological predictions. Demonstrated across diverse hydrological regimes, this framework provides a scalable and robust method for gauge network design that will empower water managers to make more informed decisions.

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Fig. 1: Comparison of the streamflow reconstruction performance of our framework and various benchmarks.
Fig. 2: Analysis of sensor rankings and their relationships with hydrological and physical attributes.
Fig. 3: Network expansion analysis for sensor placement.
Fig. 4: Integration of flood risk into sensor placement framework and its impact on distribution and accuracy.
Fig. 5: Model-agnostic sensor network planning demonstrated across diverse hydrological settings.
Fig. 6: Overview of sensor placement approach.

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Data availability

The data sources accessed for this study include the National Water Model Retrospective Dataset (https://registry.opendata.aws/nwm-archive/), GloFAS (https://doi.org/10.24381/cds.a4fdd6b9), the USGS gauge inventory (https://doi.org/10.5066/P9J5CK2Y), CAMELS-BR (https://doi.org/10.5281/zenodo.3964745) and Bangladesh Water Development Board (http://www.hydrology.bwdb.gov.bd/index.php?pagetitle=discharge). Accessed geographic datasets include USGS National Hydrography Dataset Plus v2.1 (https://www.usgs.gov/national-hydrography/national-hydrography-dataset), FEMA National Risk Index (https://hazards.fema.gov/nri/data-resources), PRISM climate data (https://prism.oregonstate.edu/) and SEDAC population density data (https://data.ghg.center/browseui/index.html#sedac-popdensity-yeargrid5yr-v4.11/).

Code availability

The code is available at https://github.com/future-water/hydrological-sensor-network-design.

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Acknowledgements

J.O. was supported by a University Graduate Continuing Fellowship from the University of Texas at Austin.

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  1. Fariborz Maseeh Department of Civil, Architectural and Environmental Engineering, The University of Texas at Austin, Austin, TX, USA

    Jeil Oh & Matt Bartos

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  1. Jeil Oh
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J.O. designed the study with input from M.B. J.O. performed the research and prepared the figures. J.O. and M.B. analysed the results and wrote the paper.

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Correspondence to Jeil Oh.

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Oh, J., Bartos, M. Scalable, adaptive and risk-informed design of hydrological sensor networks. Nat Water 3, 1144–1154 (2025). https://doi.org/10.1038/s44221-025-00496-7

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