Articles | Volume 16, issue 14
https://doi.org/10.5194/gmd-16-4213-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-16-4213-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model description paper
| 
26 Jul 2023
Model description paper |
| 26 Jul 2023
| 26 Jul 2023
Data space inversion for efficient uncertainty quantification using an integrated surface and sub-surface hydrologic model
Hugo Delottier
CORRESPONDING AUTHOR
Centre for Hydrogeology and Geothermics (CHYN), University of Neuchâtel, Neuchâtel, Switzerland
John Doherty
Watermark Numerical Computing, Corinda, QLD, Australia
Philip Brunner
Centre for Hydrogeology and Geothermics (CHYN), University of Neuchâtel, Neuchâtel, Switzerland
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Short summary
Long run times are usually a barrier to the quantification and reduction of predictive uncertainty with complex hydrological models. Data space inversion (DSI) provides an alternative and highly model-run-efficient method for uncertainty quantification. This paper demonstrates DSI's ability to robustly quantify predictive uncertainty and extend the methodology to provide practical metrics that can guide data acquisition and analysis to achieve goals of decision-support modelling.
Long run times are usually a barrier to the quantification and reduction of predictive...
