Preprints
https://doi.org/10.5194/egusphere-2024-2114
https://doi.org/10.5194/egusphere-2024-2114
05 Aug 2024
 | 05 Aug 2024

An errors-in-variables extreme-value model for estimating interpolated extreme streamflows at ungauged river sections

Duy Anh Alexandre and Jonathan Jalbert

Abstract. Estimating extreme streamflows is critical for delimiting flood zones and designing fluvial infrastructure, but for the vast majority of river sections, no measurements are available. Estimated streamflows at ungauged river sections using spatial interpolation and hydrological modeling are uncertain, and in the context of extreme value analysis, this uncertainty can be crucial when estimating return levels. In the present paper, an errors-in-variables extreme value model is proposed to account for the estimated streamflow uncertainty at ungauged river sections. The true unobserved streamflows correspond to the missing variables in a Bayesian hierarchical model. In this model, the uncertainty of the unobserved streamflows propagates to the uncertainty in the estimated return levels. The model was implemented to estimate the streamflow return levels of 211 ungauged sections of the Chaudière River watershed in Southern Quebec, Canada.

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Duy Anh Alexandre and Jonathan Jalbert

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on egusphere-2024-2114 (An errors-in-variables extreme-value model for estimating interpolated extreme streamflows at ungauged river sections)', Anonymous Referee #1, 10 Sep 2024
  • RC2: 'Comment on EGUSPHERE-2024-2114', Anonymous Referee #2, 19 Sep 2024
Duy Anh Alexandre and Jonathan Jalbert
Duy Anh Alexandre and Jonathan Jalbert

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Short summary
Estimating extreme streamflows is essential for identifying flood-prone areas and designing safe river infrastructure. However, most river sections lack direct measurements of streamflows. Hydrologists use spatial interpolation and hydrological modeling to estimate streamflows in these unmeasured areas, but these estimates come with uncertainties. Our study introduces a new model to better account for these uncertainties, improving the accuracy of predicting extreme streamflows.