the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
The ability of a stochastic regional weather generator to reproduce heavy precipitation events across scales
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.
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Status: final response (author comments only)
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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
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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.Â
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.Â
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
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RC2: 'Reply on RC1', Anonymous Referee #2, 23 Oct 2024
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