the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
29 Mar 2021
29 Mar 2021
Using high-resolution regional climate models to estimate return levels of daily extreme precipitation over Bavaria
- Department of Geography, Ludwig-Maximilians-Universität München, Munich, 80333, Germany
- Department of Geography, Ludwig-Maximilians-Universität München, Munich, 80333, Germany
Abstract. Extreme daily rainfall is an important trigger for floods in Bavaria. The dimensioning of water management structures as well as building codes are based on observational rainfall return levels. In this study, three high-resolution regional climate models (RCMs) are employed to produce 10-year daily rainfall return levels and their performance is evaluated by comparison to observational return levels. The study area is governed by different types of precipitation (stratiform, orographic, convectional) and a complex terrain, with convective precipitation also contributing to daily rainfall levels. The Canadian Regional Climate Model version 5 (CRCM5) at 12 km spatial resolution and the Weather and Forecasting Research model (WRF) at 5 km resolution both driven by ERA-Interim reanalysis data use parametrization schemes to simulate convection. The WRF at 1.5 km resolution driven by ERA5 reanalysis data explicitly resolves convectional processes. Applying the Generalized Extreme Value (GEV) distribution, all three model setups can reproduce the observational return levels with an areal average bias of +6.6 % or less and a spatial Spearman rank correlation of ρ > 0.72. The increase of spatial resolution between the 12 km CRCM5 and the 5 km WRF setup is found to improve the performance in terms of bias (+6.6 % and +3.2 %) and spatial correlation (ρ = 0.72 and ρ = 0.82). However, the finer topographic details of the WRF-ERA5 return levels cannot be evaluated with the observation data because their spatial resolution is too low. Hence, this comparison shows no great further improvement (bias = +1.1 %, ρ = 0.82) of the overall performance compared to the 5 km resolution setup. Uncertainties due to extreme value theory are explored by employing three different approaches for the highest-resolution WRF-ERA5 setup. The GEV distribution with fixed shape parameter (bias = +0.9 %, ρ = 0.79) and the Generalized Pareto (GP: bias = +1.3 %, ρ = 0.81) show almost equivalent results for the 10-year return period, whereas the Metastatistical Extreme Value (MEV) distribution leads to a slight underestimation (bias = -6.2 %, ρ = 0.86). From these results, it follows that high-resolution regional climate models are suitable for generating spatially homogeneous rainfall return level products. In regions with a sparse rain gauge density or low spatial representativeness of the stations due to complex topography, RCMs can support the observational data. Further, RCMs driven by global climate models with emission scenarios can project climate change-induced alterations in rainfall return levels at regional to local scales. This would allow adjustment of structural design and, therefore, adaption to future precipitation conditions.
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Benjamin Poschlod
Status: open (until 11 May 2021)
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RC1: 'Comment on nhess-2021-66', Anonymous Referee #1, 14 Apr 2021
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In the presented study, the author chose two different regional climate models (CNRM and WRF) in three different spatial resolutions (12km, 5km, and 1.5km) driven with two different reanalysis data sets (ERA-Interim and ERA5). The author pointed out the difficulty of a correct and spatial representative estimation of return levels from observational data sets due to their limitations and uncertainties. Using RCM data could fill this gap as return levels of precipitation are of great importance for stakeholders or the insurance industry, also with respect to possible changes in the future regarding climate adaptation. The author used different types of extreme value approaches and validated each by comparing model output with observations and theoretical quantiles which is adequate as each method has specific pros and cons.
Nevertheless, I have some major concerns with the quality of the current version of the manuscript which are listed below followed by minor comments and questions.
Major comments:
1) In the conclusions the author clearly stated the uncertainties arising from different model setups regarding internal climate variability, parametrizations, and further assumptions. Saying so, why did you then choose different RCMs and not only a single one with similar setups, e.g., a COSMO-CLM version in the given (slightly different) resolutions? Furthermore, why did you use ERA-Interim and ERA5 as forcing data and not only the higher resolved and newer ERA5 data for all simulations?
2) The author put lots of effort into the homogenization of pointwise observational data sets. There are several high-res gridded precipitation data sets on the market like REGNIE/HYRAS for Germany (1km, Rauthe et al., 2013), RADOLAN (DWD, 1km), or SPARTACUS (Austria, 1km, Hiebl and Frei, 2017). I agree that even at this high resolution these data sets have limitations when it comes to convection. Nevertheless, DWD and ZAMG put a lot of effort into calibrating these data sets not only with ground measurements but also with radar data and vise versa in the case of RADOLAN. So, I assume these data sets have a higher quality than the homogenized point observations by the author and they have a higher resolution which made the validation of the 1.5km WRF model more robust.
3) When it comes to different extreme value techniques, a proper validation would use every method with every data set and not only a couple of possible combinations like currently presented.
4) The authors conclude that RCMs are better in terms of spatial representativeness of return levels. Saying so I expect cross-validation with existing products like KOSTRA for Germany to clearly point out the benefit of RCMs compared to raw or existing gridded observations.
5) The author concentrated on the return level of 10 years and stated that this is the most important value for the targeted applications. At least for the insurance industry, minimum the 100-year return level better the 200-year values (PML200) are the relevant levels. As all results are specifically related to the 10-year level, I am wondering if the methodology can be adapted/used for higher return levels or if further validation/calibration is necessary in that case. I miss some statements on that in the discussion and conclusions sections.
Additionally to the major comments above, I have some minor comments [page-line/paragraph]:
[Sect. 1] I recommend clearly state the key research questions you are focusing on in this study. For me, it is not clear what the main aims are.
[P3 L81ff] Schröter et al. (2015) analyzed three major flood events in Germany during the past 70 years (1954,2002,2013), which also partly affected your investigation area, concluding that it is not daily/multi-day precipitation amount that triggers major flood events.
[P3 L88ff] “RCM can bridge the gaps” – what about stochastic weather generator or other approaches? Ehmele and Kunz (2019), for example, introduced a semi-physical, 2D, and high-resolved precipitation model mainly based on orographic precipitation which in a statistical sense, gives good results in terms of return levels even for higher return periods.
[Sect. 2] I recommend a reordering of the paragraphs in this section. As your investigation area is restricted to the given data sets, I suggest first describe the data sets and the investigation area afterward.
[Fig.1] Is the study area equal to the model domain? If so, how do you deal with boundary effects?
[P4 L97f] In Fig.2 you give the reference for the data set, I suggest giving it in the text, too.
[Fig.2] Do you have an explanation for the strong “drying” signal in the main Alpine valleys? Please use discrete color separations. See also https://www.nature.com/articles/s41467-020-19160-7
[Sect. 2.2] So I understand that you estimate daily precipitation or at least 24h sums in the moving window by hourly station data, right? If so, please clarify in the text.
[P7 L133] “24h RLs are adjusted to daily values using a reduction”. I do not understand what this reduction is about. Please clarify this in the text.
[Sect. 2.3] Why did you choose exactly these models and not others? There is a huge variety of RCM in 0.11° resolution within the CORDEX project and also high-resolution simulations mainly Germany and Alpine region in the CORDEX FPS convection project. Furthermore, you used WRF v3.6.1 for the 5km and v4.1 for the 1.5km simulations. Are there major differences between the versions? For consistency, the same model version would be better.
[P8 L161ff] For WRF 1.5km, you have 30 simulations with a 1-year length each. Does this have an impact on the comparability with the continuous simulations at coarser resolution?
[Sect.3] I suggest a reordering here, too. Instead of first describing strategies and distributions and then how they are applied in this study, I recommend a structure like 3.1 BM; 3.2 POT, 3.3 MEV each with a short introduction to the method and then directly saying how you will apply it in this study.
[P9 L180ff] It would be helpful for the reader if you can give typical values or magnitude orders of t_wet and t_decluster.
[P9 L192] G is also a CDF, right? Please indicate it.
[P12 L242] Can you explain why the low-res simulations have higher return values than the high-res?
[P13 L277] You mean Fig.5d instead of 5b?
[P15 L289] The 5km WRF seems to have a much stronger orographic signal than the 1.5km, especially the “drying” in the main valleys. Is there any explanation for that?
[P15 293] Fig.5b and later 5e?
[P15 L301] Sure you mean Fig 3d here?
[P15 L305] Fig.5c and 5f, I guess
[P19 L394-398] Maybe I miss something, but I do not get the message from these two paragraphs
[Fig. S5+S6] There is data missing for Switzerland and Austria. Why? I thought you have the data for that regions and time periods.
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RC2: 'Comment on nhess-2021-66', Anonymous Referee #2, 20 Apr 2021
reply
The author estimates 10-years return levels with the Generalized Extreme Value (GEV) distribution from three different Regional Climate Models (RCMs), namely the Canadian Regional Climate Model version 5 (CRMC5) at 12 km resolution, the Weather and Forecasting Research model (WRF) at 5 km resolution and the WRF model at 1.5 km resolution, showing that the finer spatial resolution of the WRF-5km with respect to the 12 km CRCM5 reduces the bias of GEV. Moreover, he investigates uncertainties due to the use of three different extreme value models (GEV with fixed shape parameter, Generalized Pareto (GP) and Metastatistical Extreme Value (MEV) distributions) to estimate the 10-year return period quantiles using the WRF model with the finest (1.5 km) resolution. Through this analysis he concludes that GEV and GP distributions are equivalently biased (~ +1%), while MEV tends to underestimation (~ -6%) and that high-resolution RCMs provide promising results for the estimation of spatially homogeneous rainfall return levels.
The study is interesting and shows potential for evaluating extremes in a changing climate, the manuscript is well written and easy to follow. I have some major comments, and minor comments follow.
Major comments:
1) The use of the high-resolution products (REGNIE, RADOLAN, SPARTACUS) would avoid to homogenize the gauge precipitation values and would make possible a more accurate validation of the RCMs with the finest resolution. Why not considering them?
2) Why only return level of 10 years? I understand the concern of the author that 30 years of data are few for estimating higher quantiles, but return periods higher than 10 (e.g., 100) years are more relevant for engineering applications/(re)insurance purposes and the challenge is indeed to estimate them with the availability of short time series. How would the estimation of higher return levels compare e.g. with the official ones from KOSTRA? As the manuscript is presented now, the conclusion stated in the abstract “it follows that high-resolution regional climate models are suitable for generating spatially homogenous rainfall return level products” is not fully supported by the analysis, since only the 10-years return levels have been evaluated.
3) The study area is characterized by some high-elevated regions affected by orographic precipitation. I'm wondering if using all the values as “ordinary events” in the MEV might not respect the independence hypothesis required by the MEV framework. See for example Marra et al. (2018) and Miniussi et al. (2020) for some discussion on temporal correlation.
4) Why using a GEV distribution with a constant shape parameter and not, for example, a Gumbel? Previous studies (e.g., Grieser et al. (2007)) have shown that the Gumbel distribution is a good model for precipitation in the Bavarian area, and its location parameter has a strong correlation with altitude, while its scale parameter has a noisy pattern (except for the Bavarian Alps). Moreover, you say that the shape parameter based on all the three RCM setups is centered around a value close to 0.114, in line with the one recommended by Papalexiou and Koutsoyiannis (2013): is this really a fair comparison, as these shape parameter values are already affected by estimation uncertainty?
Minor comments.
Section 3.
L225: Another title for section 3.3 would be more appropriate
L226-227: please add a couple of words about the adjustment, so that the reader understands it directly from here without the need to go looking at the reference.
L239: you state that “the location and scale parameter are governed by the topography”. From Figure 3 one can notice that the spatial pattern of the location parameter is somehow coherent with topography, but the noise for the scale parameter does not make its pattern straightforward to understand. Maybe also the colors scale is not helping.
L240: why a chaotic pattern for the shape parameter? Is it related to the uncertainty that one can get due to the limited series available to estimate it?
L259: you mention you made a “goodness of fit” (despite its limitation in prediction) for the GEV and the GP distributions. Have you made a similar analysis also for the Weibull distribution?
L264: in L253-255 you mention that for sample sizes > 50 estimation via ML is recommended. Why then using PWM for the Weibull distribution in the MEV framework?
Section 4.
L287 and 310 (captions of Figures 4 and 6): “difference calculated as climate model return level minus observational return level” -> difference between the return level from the climate model and the observational one. Why using of the absolute error instead of the relative error?
L454-457: in Zorzetto et al. (2016) the analysis has been made by means of a cross-validation approach, so that the sample used for parameter calibration is independent from the one used for testing the performance of GEV and MEV distributions. When GEV is fitted and tested on the same sample (unless the sample is shorter, i.e. 10-20 years, when issues in the parameter estimation –especially for the shape parameter- might arise), it usually outperforms MEV, but it is not flexible in prediction.
A curiosity: are you considering or discarding snow events?
Supplementary material.
FigS2: how are the 95% confidence intervals computed?
You have the example for the Munich grid cell, and only for GEV-LMOM and GP models, why not for GEV-ML and MEV? Moreover, a comprehensive validation of all the extreme value models for the whole area would add value to the analysis.
FigS5-S6: now the REGNIE product is shown; why not showing the observation-based product used in the analysis? It would be also useful to evaluate differences among the products (even if for some events only).
References
Grieser, J., Staeger, T., & Schonwiese, C. D. (2007). Estimates and uncertainties of return periods of extreme daily precipitation in Germany. Meteorologische Zeitschrift, 16(5), 553–564. https://doi.org/10.1127/0941-2948/2007/0235
Marra, F., Nikolopoulos, E. I., Anagnostou, E. N., & Morin, E. (2018). Metastatistical Extreme Value analysis of hourly rainfall from short records: Estimation of high quantiles and impact of measurement errors. Adv. Wat. Res., 117, 27–39. https://doi.org/10.1016/j.advwatres.2018.05.001
Miniussi, A., Villarini, G., & Marani, M. (2020). Analyses Through the Metastatistical Extreme Value Distribution Identify Contributions of Tropical Cyclones to Rainfall Extremes in the Eastern United States. Geophysical Research Letters, 47(7). https://doi.org/10.1029/2020GL087238
Benjamin Poschlod
Benjamin Poschlod
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