Preprints
https://doi.org/10.5194/hess-2021-546
https://doi.org/10.5194/hess-2021-546

  30 Nov 2021

30 Nov 2021

Review status: this preprint is currently under review for the journal HESS.

Performance-based comparison of regionalization methods to improve the at-site estimates of daily precipitation

Abubakar Haruna1, Juliette Blanchet2, and Anne-Catherine Favre1 Abubakar Haruna et al.
  • 1Univ. Grenoble Alpes, Grenoble INP, CNRS, IRD, IGE, 38000 Grenoble, France
  • 2Univ. Grenoble Alpes, CNRS, IRD, Grenoble INP, IGE, 38000 Grenoble, France

Abstract. In this article, we compare the performances of three regionalization approaches in improving the at-site estimates of daily precipitation. The first method is built on the idea of conventional RFA (Regional Frequency Analysis) but is based on a fast algorithm that defines distinct homogeneous regions relying on their upper tail similarity. It uses only the precipitation data at hand without the need for any additional covariate. The second is based on the region-of-influence (ROI) approach in which neighborhoods, containing similar sites, are defined for each station. The third is a spatial method that adopts Generalized Additive Model (GAM) forms for the model parameters. In line with our goal of modeling the whole range of positive precipitation, the chosen marginal distribution model is the Extended Generalized Pareto Distribution (EGPD) on which we apply the three methods. We consider a dense network composed of 1176 daily stations located within Switzerland and in neighboring countries. We compute different criteria to assess the models' performances both in the bulk of the distribution as well as in the upper tail. The results show that all the regional methods offered improved robustness over the local EGPD model. While the GAM method is more robust and reliable in the upper tail, the ROI method is better in the bulk of the distribution.

Abubakar Haruna et al.

Status: open (until 24 Feb 2022)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on hess-2021-546', Anonymous Referee #1, 05 Jan 2022 reply
    • AC1: 'Reply on RC1', Abubakar Haruna, 17 Jan 2022 reply

Abubakar Haruna et al.

Abubakar Haruna et al.

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Short summary
Reliable prediction of floods depends on the quality of the input data such as precipitation. However, estimation of precipitation from the local measurements is known to be difficult, especially for extremes. Regionalization improves the estimates by increasing the quantity of data available for estimation. Here, we compare three regionalization methods based on their robustness and reliability. We apply the comparison to a dense network of daily stations within and outside Switzerland.