Preprints
https://doi.org/10.5194/hess-2024-64
https://doi.org/10.5194/hess-2024-64
14 Mar 2024
 | 14 Mar 2024
Status: this preprint is currently under review for the journal HESS.

A data-centric perspective on the information needed for hydrological uncertainty predictions

Andreas Auer, Martin Gauch, Frederik Kratzert, Grey Nearing, Sepp Hochreiter, and Daniel Klotz

Abstract. Uncertainty estimates are fundamental to assess the reliability of predictive models in hydrology. We use the framework of Conformal Prediction to investigate the impact of temporal and spatial information on uncertainty estimates within hydrological predictions. Integrating recent information significantly enhances overall uncertainty predictions, even with substantial gaps between updates. While local information yields good results on average, it proves insufficient for peak flow predictions. Incorporating global information improves the accuracy of peak flow bounds, corroborating findings from related studies. Overall, the study underscores the importance of continuous data updates and the integration of global information for robust and efficient uncertainty estimation.

Andreas Auer, Martin Gauch, Frederik Kratzert, Grey Nearing, Sepp Hochreiter, and Daniel Klotz

Status: open (until 09 May 2024)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on hess-2024-64', Carlo Albert, 11 Apr 2024 reply
Andreas Auer, Martin Gauch, Frederik Kratzert, Grey Nearing, Sepp Hochreiter, and Daniel Klotz
Andreas Auer, Martin Gauch, Frederik Kratzert, Grey Nearing, Sepp Hochreiter, and Daniel Klotz

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Short summary
This work examines the impact of temporal and spatial information on the uncertainty estimation of streamflow forecasts. The study emphasizes the importance of data updates and global information for precise uncertainty estimates. We use Conformal Prediction to show that recent data enhances the estimates, even if only available infrequently. Local data yields reasonable average estimations but falls short for peak flow events. The use of global data significantly improves these predictions.