Preprints
https://doi.org/10.5194/nhess-2024-143
https://doi.org/10.5194/nhess-2024-143
22 Aug 2024
 | 22 Aug 2024
Status: this preprint is currently under review for the journal NHESS.

The ability of a stochastic regional weather generator to reproduce heavy precipitation events across scales

Xiaoxiang Guan, Dung Viet Nguyen, Paul Voit, Bruno Merz, Maik Heistermann, and Sergiy Vorogushyn

Abstract. We assess the ability of a regional weather generator to represent the extremity of heavy precipitation events (HPEs) across spatial and temporal scales. To this end, we implement the multi-site non-stationary Regional Weather Generator (nsRWG) for the area of Germany and generate 100 sets of synthetic daily precipitation data spanning 72 years. The weather extremity index (WEI) and its recent cross-scale modification (xWEI) are applied to quantify the cross-scale extremity of synthetic and observed HPEs and to compare their distributions. The results show that the nsRWG excels in replicating the extremity patterns for almost all 7 durations (ranging from 1 to 7 days) considered. The frequency of small-scale 1-day rainfalls is however slightly overestimated. nsRWG aptly reproduces the potential influential areas of HPEs, whether of short or long duration. It is capable of generating precipitation events mirroring the extremity patterns observed during past disaster-causing HPEs in Germany, while simultaneously accommodating their variations. This study demonstrates the potential of the nsRWG for simulating HPE-related hazard and assessing flood risks.

Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this preprint. The responsibility to include appropriate place names lies with the authors.
Xiaoxiang Guan, Dung Viet Nguyen, Paul Voit, Bruno Merz, Maik Heistermann, and Sergiy Vorogushyn

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on nhess-2024-143', Anonymous Referee #1, 25 Sep 2024
    • RC2: 'Reply on RC1', Anonymous Referee #2, 23 Oct 2024
      • AC2: 'Reply on RC2', Xiaoxiang Guan, 05 Dec 2024
    • AC1: 'Reply on RC1', Xiaoxiang Guan, 05 Dec 2024
Xiaoxiang Guan, Dung Viet Nguyen, Paul Voit, Bruno Merz, Maik Heistermann, and Sergiy Vorogushyn
Xiaoxiang Guan, Dung Viet Nguyen, Paul Voit, Bruno Merz, Maik Heistermann, and Sergiy Vorogushyn

Viewed

Total article views: 324 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
180 43 101 324 3 51
  • HTML: 180
  • PDF: 43
  • XML: 101
  • Total: 324
  • BibTeX: 3
  • EndNote: 51
Views and downloads (calculated since 22 Aug 2024)
Cumulative views and downloads (calculated since 22 Aug 2024)

Viewed (geographical distribution)

Total article views: 307 (including HTML, PDF, and XML) Thereof 307 with geography defined and 0 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 06 Dec 2024
Download
Short summary
We evaluated a multi-site stochastic regional weather generator (nsRWG) for its ability to capture the cross-scale extremity of high precipitation events (HPEs) in Germany. We generated 100 realizations of 72 years of daily synthetic precipitation data. The performance was assessed using WEI and xWEI indices, which measure event extremity across spatio-temporal scales. Results show nsRWG simulates well the extremity patterns of HPEs, though it overestimates short-duration, small-extent events.
Altmetrics