the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Probabilistic downscaling of EURO-CORDEX precipitation data for the assessment of future areal precipitation extremes of different durations
Abstract. This work presents a methodology to inspect the changing statistical properties of precipitation extremes with climate change. Data from regional climate models for the European continent (EURO-CORDEX 11) were used. The use of climate model data requires first an inspection of the data and a correction of the biases of the meteorological model. Both the correction of biases of the point precipitation and those of the spatial structure were performed. For this purpose, a quantile-quantile transformation of the point precipitation and a spatial recorrelation method were used. Once bias-corrected, the data from the regional climate model were downscaled to a finer spatial scale using a stochastic method with equally probable outcomes. This enables the assessment of the corresponding uncertainties. The downscaled fields were used to derive area-depth-duration-frequency (ADDF) curves, and area-reduction-factors (ARF) for selected regions in Germany. The estimated curves were compared to those derived from a reference weather radar data set. While the corrected and downscaled data show good agreement with the observed reference data over all temporal and spatial scales, the future climate simulations indicate an increase in the estimated areal rainfall depth for future periods. Moreover, the future ARFs for short durations and large spatial scales increase compared to the reference value, while for longer durations the difference is smaller.
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RC1: 'Comment on hess-2023-288', Anonymous Referee #1, 24 Jun 2024
Dear authors,
Some suggestions for improvement of the manuscript.
Improved Bias Correction Techniques: Implement more sophisticated bias correction methods, potentially incorporating machine learning algorithms to handle non-linear associations and outliers effectively.Implement higher resolution models, such as convection-permitting models (CPMs), whenever possible to enhance the precision of localized extreme precipitation projections. Engaging in partnerships with computational resources can assist in reducing the substantial expenses associated with computation.
Extend the analysis by incorporating longer historical data periods to encompass a broader range of climate variability and trends. This would establish a more extensive foundation for comparing with future forecasts.
Wider Geographical Analysis: Extend the geographical range to encompass multiple regions with diverse climatic conditions. This would facilitate comprehension of regional disparities and enhance the applicability of the results.
Enhanced Uncertainty Quantification: Incorporate a more rigorous framework for quantifying uncertainties, such as Monte Carlo simulations or ensemble approaches, to offer a more comprehensive evaluation of the uncertainties in the projections.
Integrate the findings with practical impact studies, such as flood risk assessments and water resource management, to establish the real-world applications and advantages of the enhanced downscaling methodology.
Citation: https://doi.org/10.5194/hess-2023-288-RC1 -
AC1: 'Reply on RC1', Abbas El Hachem, 15 Jul 2024
We thank the reviewer for his suggestions regarding our manuscript. In our opinion there are a lot of different possible methods which one could use for this research. The problem requires however a chain of methods including bias correction, spatial structure correction etc. In the paper we selected a set of methodologically compatible techniques. Unfortunately, if one would follow all ideas suggested by the reviewer then it would increase the volume of the paper to the size of 2 to three research papers.
Citation: https://doi.org/10.5194/hess-2023-288-AC1
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AC1: 'Reply on RC1', Abbas El Hachem, 15 Jul 2024
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RC2: 'Comment on hess-2023-288', Anonymous Referee #2, 25 Jun 2024
General comments to the authors:
Probabilistic downscaling of EURO-CORDEX precipitation data for the assessment of future areal precipitation extremes of different durations
Abbas El Hachem, Jochen Seidel, and András Bárdossy.
The paper describes a methodology to derive area-depth-duration-frequency and area-reduction-factors in a robust way with consideration of changing climate. The experiment is well-designed and explained, and the paper is well-structured to address the relevant scientific questions.
Specific comments:
I’d like to see the criteria used by the authors to choose MPI-M-MPI-LR-GERICS-REMO2015-v1 as a single model to assess the climate change impact instead of using an ensemble from EURO-CORDEX. Also, I would like to see 1-2 sentences regarding your experience with extrapolation issue in Line 265-270 in case the modelled precipitation is extremely larger than the observed extreme.
Technical corrections:
Title: it would be better to make it clear the temporal resolution to be discussed in the paper, e.g.,” hourly to daily”.
Line 36:” Euro-CORDEX” - >” Euro-CORDEX CMIP5” .
Line 41:” RCM data” - > ”RCM”.
Line 116:”(Bardossy and Hörning, 2016b, a)” , what does “a” refer to?
Line 147:” the first reference …” rephrase the sentence, perhaps?
Line 157:” In order that…” please rephrase the sentence.
Line 161:” any …” please rephrase the sentence.
Line 172:” In (Bardosy ..)” please remove the parenthesis.
Line 212:”ρi”, what does i stand for? Please make it clear.
Line 475:” Panel (b) of Figure 13”, Isn’t it Figure 12?
Comments on references: please check the order of the references between Line 605 – 620; also, it would be better to list other relevant references rather than abstracts from conferences.
Citation: https://doi.org/10.5194/hess-2023-288-RC2 - AC2: 'Reply on RC2', Abbas El Hachem, 15 Jul 2024
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