Document Type
Article
Journal/Book Title/Conference
JAWRA Journal of the American Water Resources Association
Volume
61
Issue
2
Publisher
Wiley-Blackwell Publishing, Inc.
Publication Date
3-3-2025
Journal Article Version
Version of Record
Keywords
high-frequency sensor networks, U.S. National Water Model, streamflow forecast, turbidity
First Page
1
Last Page
20
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.
Abstract
As high-frequency sensor networks increasingly enhance data-driven models of water quality, process-based models like the U.S. National Water Model (NWM) are generating accessible forecasts of streamflow at increasingly dense scales. There is now an opportunity to combine these products to construct actionable water quality forecasts. To that end, we couple streamflow forecasts from the NWM to a gradient-boosted decision tree algorithm (LightGBM) trained on 5+ years of high-frequency monitoring data to forecast in-stream turbidity levels in the Catskill Mountains, NY, USA. Results indicate LightGBM models are capable of relatively skillful predictions, which enable robust forecasts for 1–3 days lead times. LightGBM models offer improvements over a simplified linear model across the entire forecast horizon, and more spatially complex models are more resilient to error at shorter lead times (1–3 days). Moreover, interpretation of model features emphasizes high flows as a driver of turbidity in the region. Results suggest that interpretable, flexible, and efficient machine learning algorithms can produce capable water quality forecasts from streamflow forecasts and expand understanding of process dynamics. The use case illustrated here—to our knowledge the first NWM-based water quality forecast—underscores the potential to employ the NWM to expand national water quality forecasting capacity and can overall serve as a guide for similar efforts in basins across the country.
Recommended Citation
Kemper, J.T., Underwood, K.L., Hamshaw, S.D., Davis, D., Siemion, J., Shanley, J.B. and Schroth, A.W. (2025), Leveraging High-Frequency Sensor Data and U.S. National Water Model Output to Forecast Turbidity in a Drinking Water Supply Basin. J Am Water Resour Assoc, 61: e70011. https://doi.org/10.1111/1752-1688.70011