Articles | Volume 21, issue 8
https://doi.org/10.5194/hess-21-4021-2017
https://doi.org/10.5194/hess-21-4021-2017
Research article
 | 
10 Aug 2017
Research article |  | 10 Aug 2017

Residual uncertainty estimation using instance-based learning with applications to hydrologic forecasting

Omar Wani, Joost V. L. Beckers, Albrecht H. Weerts, and Dimitri P. Solomatine

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Peer-review completion

AR: Author's response | RR: Referee report | ED: Editor decision
ED: Publish subject to minor revisions (further review by Editor) (27 Jun 2017) by Bettina Schaefli
AR by Omar Wani on behalf of the Authors (27 Jun 2017)  Author's response    Manuscript
ED: Publish subject to minor revisions (further review by Editor) (28 Jun 2017) by Bettina Schaefli
AR by Omar Wani on behalf of the Authors (28 Jun 2017)  Author's response    Manuscript
ED: Publish as is (05 Jul 2017) by Bettina Schaefli
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Short summary
We generate uncertainty intervals for hydrologic model predictions using a simple instance-based learning scheme. Errors made by the model in some specific hydrometeorological conditions in the past are used to predict the probability distribution of its errors during forecasting. We test it for two different case studies in England. We find that this technique, even though conceptually simple and easy to implement, performs as well as some other sophisticated uncertainty estimation methods.