Articles | Volume 16, issue 14
https://doi.org/10.5194/gmd-16-4213-2023
https://doi.org/10.5194/gmd-16-4213-2023
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

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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.