the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Statistical and Machine Learning Downscaling Methods to Assess Changes to Rainfall Amounts and Frequency in Climate Change Context- CMIP 6
David Antonio Jimenez Osorio
Andrea Menapace
Ariele Zanfei
Eber José de Andrade Pinto
Bruno Brentan
Abstract. General Circulation Models (GCMs) simulations result on grids ranging from 50 km to 600 km, and, therefore, this coarse spatial resolution requires data processing, whereby the application of downscaling techniques has become a standard procedure. The main approaches employed are Statistical DownScaling (SDS) and Dynamic DownScaling (DDS). The former SDS consists of Linear Methods (LM), Stochastic Weather Generators, and Artificial Intelligence DownScaling techniques (IADS). Being computationally less demanding and highly portable, most studies apply LM, and IADS approaches to develop the downscaling. However, it is needed to evaluate whether these approaches allow obtaining representative, in the development of rainfall frequency analysis (RFA), in the estimative of the total precipitation (TP) and the number of rainy days (RD) both water year and multiannual level, as well as identify whether any of these approaches provide better results for the last generation of GCM’s made available for CMIP 6. On this basis and considering only the models with a horizontal resolution of 100 km that participated in the SSP1-2.6 and/or SSP5-8.5 scenarios of CMIP6, the present study aim to evaluate the performance of Delta Method (DM), Quantile Mapping (QM) and Regression Trees (RT) to develop RFA, estimate the TP and RD, based on rainfall series obtained by DownScaling, respect to estimative developed with historical records. The results show that the application of DM, RT and QM does not guarantee a temporal correlation between the TP and RD estimated with DownScaling and historical series, likewise, it is observed that in the estimation of RFA, the application of RT generates better results than QM. Finally, it is evident that not applying any DownScaling technique and applying QM generates similar results.
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David Antonio Jimenez Osorio et al.
Status: final response (author comments only)
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RC1: 'Comment on hess-2023-55', Kyunghun Kim, 07 Aug 2023
This study applied three statistical downscaling methods (Delta method, Quantile mapping, and regression trees) on CMIP6, the newest climate change scenario. It estimated total precipitation and the number of rainy days, and conducted frequency analysis for testing results of the downscaling methods. However, this study has lacks some fundamental aspects. Firstly, it exhibits numerous issues related to paper formatting and writing errors. Futhermore, it fails to differentiate itself from related researches, and omits any discussions about the results. Since there are too many things to be fixed, only the most cirtical problems about the paper are presented as follows:
1) The terminology lacks consistency. When employing abbreviations, they should be introduced initially, and these same abbreviations should be consistently used for referring to the corresponding terms.
2) When listing references in the sentence, they should be arranged chronologically, from the most recent to the earliest. However, this paper’s reference are currently presented in a disorganized manner.
3) The rationale for conducting this study in the introduction is not clearly established, and the presentation of past related researches are overly succinct.
4) Every choice made in the study must be supported by evidence. However, there is no clear rationale for the selection of the three downscaling methods or choosing RMBH as the study area, etc.
5) There is a lack of understanding of the concepts and methodologies used in this study. Firstly the commonly used Markov chain methods, nonlinear methods for introducing statistical downscaling methods are not presented. Futhermore, a minimum of 30 years is necessary for the frequency analysis, but this study set as 20 years. The important aspect in applying machine learning method is to appropriately define parameters. However this study just used default values provided by the program.
6) When presenting the results as figure, the range of axes on the figures varies from one figure to other. This inconsistency could lead the reader to misinterpret the results. The results were presented; however there was no discussion regarding the underlying reasons for these outcomes.
7) The major problem lies in the lack of novelty compared from related papers. This type of paper has been previously explored and merely applying CMIP6 does not constitute novelty.
Citation: https://doi.org/10.5194/hess-2023-55-RC1 -
RC2: 'Comment on hess-2023-55', Anonymous Referee #2, 24 Aug 2023
The manuscript compared three downscaling techniques, i.e., the Delta Method (DM), Quantile Mapping (QM), and Regression Trees (RT), in downscaling CMIP6 climate model simulations in a region of Brazil. Downscaled precipitation was used to estimate total precipitation, the number of precipitation days, and precipitation quantiles, which were compared against observed data. The results suggest using Regression Trees for precipitation frequency analysis, while using Quantile Mapping for estimating multi-year total precipitation and precipitation days.
The major problem of the manuscript is lacking innovative points, since it only compared three well-developed techniques and didn’t propose any new modifications. Downscaling climate models based on gauge data is also an old problem that has been investigated by many studies. The conclusion does not seem to provide new insights into the current research field. Also, the manuscript has lots of writing and formatting issues that need to be revised. Here are several comments:
- The title included “assess changes to rainfall amounts and frequency in climate change text,” but I didn’t find content related to evaluating changes in precipitation due to climate change. The downscaling methods didn’t incorporate nonstationarity as well. This manuscript focused on evaluating the performance of downscaling techniques in downscaling climate models. Therefore, the title needs to be adjusted to match the main content.
- In line 205, “the multiannual level” needs to be clarified. Please clearly state that the precipitation metrics were computed over multiple years. In line 235, you seemed to mention that the number of years is the same as the period of station record. Please move this description to the methodology section.
- In Figures 2 and 3, the plot axis has a different range for each downscaling method, which makes it very difficult to compare. Including results from all the stations makes it more complicated to read. I suggest computing the average metrics from all the stations and comparing the three methods’ results in the same plot.
- The manuscript should add a discussion section to interpret and discuss the results. One potential topic is to discuss the reason why a certain downscaling technique performs better. For example, it is reasonable that regression trees and quantile mapping performed better in correcting precipitation quantiles because it is designed to correct the precipitation distribution. In contrast, the delta method only applied a multiplication factor to the precipitation amount and led to greater bias in precipitation quantiles.
- Lots of sentences need to be rephrased to improve clarity. Here are some places:
Line 51: “However, in practice, the main approaches employed are Statistical DownScaling techniques (SDS) and Dynamic DownScaling, however, into the SDS we found:…”
Line 177: “It was decided to train and validate the model based on the observed and simulated precipitation quantiles since it was not evident temporal correlation between the magnitudes of rainfall events;”
Line 214: “For total precipitation and the number of rainy days per hydrologic year, the high RMSE, low NSE and KGE, and R less than 0.6 and greater than -0.6 show that there is no temporal correlation between total precipitation and the number of rainy days per hydrologic year…”
There are many more places to revise. Please reduce the length of many long sentences and correct the grammar.
- In Line 60, “Neural Networks (ANNs)” should be “Artificial Neural Networks (ANNs)”. “Vector Support Machine” should be “Support Vector Machine.”
Citation: https://doi.org/10.5194/hess-2023-55-RC2
David Antonio Jimenez Osorio et al.
David Antonio Jimenez Osorio et al.
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