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

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

doi:https://doi.org/10.5194/nhess-25-3075-2025

Articles | Volume 25, issue 9

https://doi.org/10.5194/nhess-25-3075-2025

© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/nhess-25-3075-2025

© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 25, issue 9

Research article

|

05 Sep 2025

Research article |

| 05 Sep 2025

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

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

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Final revised paper (published on 05 Sep 2025)
Preprint (discussion started on 22 Aug 2024)

Interactive discussion

Status: closed

RC1:
'Comment on nhess-2024-143', Anonymous Referee #1, 25 Sep 2024

The comment was uploaded in the form of a supplement: https://nhess.copernicus.org/preprints/nhess-2024-143/nhess-2024-143-RC1-supplement.pdf

Citation: https://doi.org/10.5194/nhess-2024-143-RC1
- RC2:
  'Reply on RC1', Anonymous Referee #2, 23 Oct 2024
  
  This study presents a novel evaluation approach for WGs to examine their ability to capture cross-scale HPEs. The core method encompasses the WEI and xWEI, where the former focuses on the extremity of events at a single spatiotemporal scale, while the latter emphasizes the overall extreme performance of events across multiple scales. Their results indicate that nsRWG is capable of reproducing most HPEs in terms of duration (1 to 7 days), especially those that historically triggered disasters. However, nsRWG slightly overestimates the frequency of short-term (1-2 days) HPEs with smaller WEI areas. The manuscript was good in general, but I have some questions and comments to further improve the quality.
  1. The introduction only briefly mentions Stochastic weather generators (WGs), and then directly jumps to Section 3.1, which discusses the version of the a stochastic multi-site non-stationary regional weather generator (nsRWG) used in this study. It would be better to explain the similarities and differences between the two systems for the general readers.
  2. In Section 3.2, the explanation regarding how the return periods (P_(t,i)) used in the calculation process are obtained is somewhat unclear. Please provide further clarification.
  3. This study only uses the cross-scale weather extremity index (xWEI) as an assessment standard to compare and discuss the outputs of E-OBS (observational data) and nsRWG (simulated data). Both xWEI and the nsRWG methods were referred to other studies (this study only combined two systems). Given the amount of content for publication, it might be not enough. Please find a way to extend the current analysis or results based on the research objectives. For example: (1) Incorporate other models as a control group to evaluate the advantages of nsRWG compared to other technologies; (2) Use traditional validation scores or provide additional reference to illustrate the differences in Figure 7 for various cases; (3) Further explain the relevant statistics constructed in this paper and their practical applications in the prevention and control of Heavy Precipitation Events (HPEs).
  
  Citation: https://doi.org/10.5194/nhess-2024-143-RC2
  - AC2: 'Reply on RC2', Xiaoxiang Guan, 05 Dec 2024
    
    We appreciate the thoughtful feedback from the reviewer on our manuscript. The reviewer’s comments have helped us clarify and improve key aspects of our study.
    In this revised version, we have addressed each of the reviewer’s suggestions as follows:
    Expanded the introduction to include a detailed explanation of stochastic weather generators (WGs), their characteristics, and applications. This provides additional context for readers unfamiliar with WGs and highlights the importance of the nsRWG model in addressing HPEs.
    
    Clarified the return period calculation process in Section 3.2, specifying how the duration-dependent GEV distribution is used to derive intensity-duration-frequency (IDF) relationships and estimate grid-cell return periods.
    
    Emphasized the rationale for focusing on WEI and xWEI metrics and provided further elaboration on their relevance for understanding and managing heavy precipitation events (HPEs).
    
    Discussed practical applications of our findings in greater detail, including how the metrics can inform flood risk management and support analyses using spatial counterfactuals for disaster prevention.
    
    A detailed point-by-point response is provided in the attached document. Additionally, the revised manuscript reflects these updates in the introduction, methodology, and discussion sections.
    We believe the revisions enhance the clarity, depth, and applicability of our study. We look forward to your feedback on this revised manuscript and are happy to provide further clarifications if needed.
    
    Citation: https://doi.org/10.5194/nhess-2024-143-AC2
- AC1:
  'Reply on RC1', Xiaoxiang Guan, 05 Dec 2024
  We sincerely thank the reviewer for the feedback on our manuscript. The comments provided have been invaluable in refining and clarifying our work.
  In this revision, we have addressed all the reviewer’s comments as follows:
  Expanded the explanation of WEI and xWEI in the Methods and Results sections, particularly clarifying their link to event extension and duration (see Figure 2 and Section 4.2).
  
  Improved the coherence, clarity, and precision of key descriptions throughout the manuscript, including the methodology (e.g., SANDRA, EtA, and HPE analysis) and result interpretation.
  
  Made specific revisions to streamline discussions, address technical comments, and include requested clarifications, such as elaborating on spatial counterfactuals and refining Figures 4 and 5 explanations.
  
  A detailed point-by-point response to each comment is provided in the attached response document. We have also uploaded supplementary material to support the updates made to the manuscript, including additional references and data clarifications.
  We believe the revisions significantly improve the clarity and robustness of the manuscript. We hope the updated version meets the standards of NHESS and look forward to your feedback.
  
  Citation: https://doi.org/10.5194/nhess-2024-143-AC1

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision | EF: Editorial file upload

ED: Reconsider after major revisions (further review by editor and referees) (20 Feb 2025) by Lindsay Beevers

AR by Xiaoxiang Guan on behalf of the Authors (10 Mar 2025) Author's response Author's tracked changes Manuscript

ED: Referee Nomination & Report Request started (04 Apr 2025) by Lindsay Beevers

RR by Anonymous Referee #2 (22 Jun 2025)

ED: Publish as is (26 Jun 2025) by Lindsay Beevers

AR by Xiaoxiang Guan on behalf of the Authors (27 Jun 2025) Manuscript

Short summary

We evaluated a multi-site stochastic regional weather generator (nsRWG) for its ability to capture the cross-scale extremity of heavy-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 spatiotemporal scales. The results show that nsRWG simulates the extremity patterns of HPEs well, although it overestimates short-duration small-extent events.

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

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Peer review completion

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