the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Deep-learning statistical downscaling of precipitation in the middle reaches of the Yellow River: A Residual in Residual Dense Block based network
He Fu
Jianing Guo
Chenguang Deng
Jie Wu
Zhengguo Shi
Cailing Wang
Abstract. The middle reaches of the Yellow River (MRYR), located in northern China, are the transition zone between semi-arid and semi-humid climates. As one of the climate-sensitive regions in China, MRYR has fragile ecological environment and serious soil loss, which leads to geological disasters such as landslides, collapses and mudslides caused by extreme precipitation to occur. However, scarceness of the high-resolution precipitation data over MRYR limits the assessment of the environmental impacts caused by climate change, especially for extreme precipitation. In this paper, we design a Residual in Residual Dense Block based network (RRDBNet) model for the statistical downscaling of precipitation in MRYR, and compare the proposed RRDBNet with the generalized linear regression model and two popular deep learning-based models. The results show that the proposed RRDBNet model has a good performance on precipitation simulations, which can well reproduce the spatial-temporal characteristics of high-resolution precipitation. Especially, RRDBNet has substantial improvements in extreme precipitation compared with other models. On the probability density function of daily precipitation, it is further demonstrated that RRDBNet performs better on extreme precipitation frequency. Our results suggest that the statistical downscaling based on RRDBNet may be an effective tool for historical and future climate simulations from global climate models.
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He Fu et al.
Status: open (until 27 Oct 2023)
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RC1: 'Comment on gmd-2023-158', Anonymous Referee #1, 24 Aug 2023
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Review for “Deep-learning statistical downscaling of precipitation in the middle reaches of the Yellow River: A Residual in Residual Dense Block based network” By Fu et al.
In this study, Fu et al. proposed a new deep learning approach, namely, Residual in Residual Dense Block based network (RRDBNet), to downscale ERA5 data at the middle reaches of the Yellow River (MRYR) in China. The proposed method was compared with three other methods (GLM, CNN and RDBNet). The authors concluded that RRDBNet performed better than other methods in terms of difference, RMSE and correlartion coefficient (CC). However, the study lacks novelty and improvements are very trivial. Thus, I suggest rejecting for publishing it at GMD. My major concerns are as follows:
- The models were trained with daily data, but all the evaluations were performed at aggregated time scale (annual, monthly and seasons). How does the model perform in daily time scale (both daily statistics and extremes)? The aggregated time scales may not be critical and may hide important information on model evaluation.
- The rationale of the proposed method is not clear. Compared to other deep learning methods, what are its advantages and why did the author propose this method? Without deep understanding the model itself, the manuscript gives readers limited insights.
- The authors claimed the proposed method is much better than the other three methods, which may not be true. Given the stochastic nature of deep learning models and the slightly better statistics, the superiority may be purely due to stochasticity itself. Training the model multiple times may help discriminate the superiority and stochasticity. Furthermore, it is not fair to compare different deep learning models without giving model complexity (e.g., the number of trainable parameters).
Minor comments
- In the introduction section, the authors described many GCM downscaling. However, this study is not about GCM downscaling but reanalysis data, which is very different story and may mislead readers. Thus, the introduction needs to be rewritten.
- Line 69: the reanalysis data ERA5 has spatial resolution of 0.25x0.25 degree not 2x2 degree. Furthermore, the authors selected 5 predictors without giving any reasons.
- Line 89: the three parameters came out first time without any explanations. Y ∈ Rt°x100°x159 came out without further information.
- Lines 114 to 119: Does the statement about batch normalization come from model testing? If that is true, this statement needs to be included in the results section. If it is not true, where this come from?
- Line 121: how did you get B=0.2?
- Line 139: The statement “The final precipitation is obtained by multiplying p with the random values of the distribution with shape α scale β.” Why?
- Line 142: “In the training phase” was repeated.
Citation: https://doi.org/10.5194/gmd-2023-158-RC1 -
CEC1: 'Comment on gmd-2023-158', Juan Antonio Añel, 05 Sep 2023
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Dear authors,
After checking your manuscript, a couple of issues regarding the code availability need to be fixed. They are related to the GitHub repositories that you mention in your statement. GitHub is not a suitable repository for scientific publication archival. Fortunately, in the case of your manuscript, you can solve this quickly. First, one of the GitHub repositories you mentioned already has a copy in Zenodo, which is linked in GitHub; it is https://zenodo.org/record/5007540. The other one corresponds to downscaleR.keras, and this code is under the GPLv3 license, so you can take it and create a new Zenodo repository containing it. This is what you should do.
Therefore, please modify the Code availability statement in your manuscript in any future reviewed version, changing the GitHub links for the Zenodo repositories and their DOIs. Also, please reply to this comment with the details for the Zenodo repository for downscaleR.keras, as this should have been addressed already at the submission of your manuscript.Juan A. Añel
Geosci. Model Dev. Executive Editor
Citation: https://doi.org/10.5194/gmd-2023-158-CEC1 -
RC2: 'Comment on gmd-2023-158', Anonymous Referee #2, 26 Sep 2023
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The authors have used a residual-based approach to downscale precipitation over the reaches of China. The residual-based approach significantly improves on traditional machine learning approaches when compared to observational data.
While many aspects of the research are useful in this manuscript, there is little novelty in this approach, and many aspects of the research have been widely investigated elsewhere. The authors have many redundant figures, and the manuscript could be condensed significantly. Additionally, you only use 4 years of data to evaluate extremes. I suggest doing another experiment with a larger test set.
He Fu et al.
He Fu et al.
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