Nonlinear Processes in Geophysics
Nonlinear Processes in Geophysics
NPG
Articles | Volume 33, issue 3
Articles | Volume 33, issue 3
https://doi.org/10.5194/npg-33-425-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/npg-33-425-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 33, issue 3
Research article
 | 
25 Aug 2026
Research article |  | 25 Aug 2026

Bayesian data selection to quantify the value of data for landslide runout calibration

V. Mithlesh Kumar, Anil Yildiz, and Julia Kowalski

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Short summary
The reliability of Bayesian calibration depends on the quality and availability of observational data. But are we choosing the right data? We address this question by measuring the information gained during calibration to quantify how data selection influences the Bayesian calibration of physics-based landslide runout models. We find that more data does not always yield better results – observations that capture the dynamics governed by a parameter are more effective for its calibration.
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