Hydrology and Earth System Sciences
Hydrology and Earth System Sciences
HESS
Articles | Volume 29, issue 18
Articles | Volume 29, issue 18
https://doi.org/10.5194/hess-29-4457-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-29-4457-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 29, issue 18
Research article
 | 
17 Sep 2025
Research article |  | 17 Sep 2025

Interrogating process deficiencies in large-scale hydrologic models with interpretable machine learning

Admin Husic, John Hammond, Adam N. Price, and Joshua K. Roundy

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Short summary
We used interpretable machine learning to evaluate the accuracy of two continental-scale hydrologic models. We analyzed a suite of catchment attributes and found that soil water content had the biggest impact on model performance, especially in dry areas. Key thresholds for variables like precipitation and road density were identified, which could guide future improvements in these models. Our findings highlight the potential of data-driven methods to inform process-based models.
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