Geoscientific Model Development
Geoscientific Model Development
GMD
Articles | Volume 16, issue 14
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

Viewed

Total article views: 2,863 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
1,862 901 100 2,863 93 147
  • HTML: 1,862
  • PDF: 901
  • XML: 100
  • Total: 2,863
  • BibTeX: 93
  • EndNote: 147
Views and downloads (calculated since 15 Mar 2023)
Cumulative views and downloads (calculated since 15 Mar 2023)

Viewed (geographical distribution)

Total article views: 2,863 (including HTML, PDF, and XML) Thereof 2,839 with geography defined and 24 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 06 Dec 2025
Download
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.
Read more
Share