Hydrology and Earth System Sciences
Hydrology and Earth System Sciences
HESS
Articles | Volume 30, issue 20
Articles | Volume 30, issue 20
https://doi.org/10.5194/hess-30-6579-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-30-6579-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 30, issue 20
Technical note
 | Highlight paper
 | 
27 Oct 2026
Technical note | Highlight paper |  | 27 Oct 2026

Technical note: Machine learning metamodelling for global sensitivity analysis

Patricio Yeste, Lieke A. Melsen, João Paulo L. F. Brêda, Nicolás Tacoronte, Andrea Saltelli, Giulia Vannucci, Roberta Siciliano, and Axel Bronstert

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Editorial statement
This timely, meticulous and accessible technical note brings important insights to the hydrological community on the new opportunities for sensitivity analysis (SA) offered by explainable AI. It also addresses the issue of computational efficiency, which remains an important barrier to application of SA to complex models. By reconciling machine leaning techniques with classical SA, the paper is relevant to the broader environmental modelling, sensitivity analysis, and explainable AI communities.
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Short summary
Understanding which factors most influence streamflow is key for accurate hydrologic modelling. This study shows that machine learning methods, like random forests and neural networks, can efficiently identify the most important model inputs, producing results similar to traditional sensitivity analysis but with far less computation. This approach helps scientists explore complex models faster and more reliably, improving insights into how catchments respond to changing conditions.
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