Moment-based metrics for global sensitivity analysis of hydrological systems

Dell'Oca, Aronne; Riva, Monica; Guadagnini, Alberto

doi:https://doi.org/10.5194/hess-21-6219-2017

Articles | Volume 21, issue 12

https://doi.org/10.5194/hess-21-6219-2017

© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.

https://doi.org/10.5194/hess-21-6219-2017

© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.

Articles | Volume 21, issue 12

Research article

|

08 Dec 2017

Research article |

| 08 Dec 2017

Moment-based metrics for global sensitivity analysis of hydrological systems

Aronne Dell'Oca, Monica Riva, and Alberto Guadagnini

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Final revised paper (published on 08 Dec 2017)
Preprint (discussion started on 06 Mar 2017)

Interactive discussion

Status: closed

AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment

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- Supplement

SC1: 'Referee comment', Holger Class, 30 May 2017
- RC1: 'Referee comment', Holger Class, 31 May 2017
  - AC1: 'Response to Prof. H. Class', Aronne Dell'Oca, 06 Jul 2017
RC2: 'Review of Dell'Oca et al.', Jacob Bensabat, 22 Jun 2017
- AC2: 'Response to Dr. J. Bensabat', Aronne Dell'Oca, 06 Jul 2017

Peer-review completion

AR: Author's response | RR: Referee report | ED: Editor decision

ED: Reconsider after major revisions (further review by Editor and Referees) (07 Aug 2017) by Bill X. Hu

AR by Aronne Dell Oca on behalf of the Authors (23 Aug 2017) Author's response Manuscript

ED: Referee Nomination & Report Request started (05 Sep 2017) by Bill X. Hu

RR by Holger Class (11 Sep 2017)

RR by Jacob Bensabat (08 Oct 2017)

ED: Publish subject to technical corrections (10 Oct 2017) by Bill X. Hu

AR by Aronne Dell Oca on behalf of the Authors (17 Oct 2017) Manuscript

Short summary

We propose new metrics to assist global sensitivity analysis of Earth systems. Our approach allows assessing the impact of model parameters on the first four statistical moments of a target model output, allowing us to ascertain which parameters can affect some moments of the model output pdf while being uninfluential to others. Our approach is fully compatible with analysis in the context of model complexity reduction, design of experiment, uncertainty quantification and risk assessment.