Data space inversion for efficient uncertainty quantification using an integrated surface and sub-surface hydrologic model

Delottier, Hugo; Doherty, John; Brunner, Philip

doi:https://doi.org/10.5194/gmd-16-4213-2023

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.

https://doi.org/10.5194/gmd-16-4213-2023

© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 16, issue 14

Model description paper

|

26 Jul 2023

Model description paper |

| 26 Jul 2023

Data space inversion for efficient uncertainty quantification using an integrated surface and sub-surface hydrologic model

Hugo Delottier, John Doherty, and Philip Brunner

Data sets

Dataset used for: Data space inversion for efficient uncertainty quantification using an integrated surface and subsurface hydrologic model Hugo Delottier, John Doherty, and Philip Brunner https://doi.org/10.5281/zenodo.7707975

Model code and software

Code developed for: Data space inversion for efficient uncertainty quantification using an integrated surface and subsurface hydrologic model John Doherty https://doi.org/10.5281/zenodo.7913402

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.