the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
A D-vine copula-based quantile regression towards merging satellite precipitation products over a rugged topography at the upper Tekeze Atbara Basin of the Nile Basin
Mohammed Abdallah
Lijun Chao
Abubaker Omer
Khalid Hassaballah
Kidane Welde Reda
Linxin Liu
Tolossa Lemma Tola
Omar M. Nour
Abstract. Precipitation is a vital key element in various studies of hydrology, flood prediction, drought monitoring, and water resources management. The main challenge in conducting studies over remote regions with rugged topography is that weather stations are usually scarce and unevenly distributed. However, open-sourced satellite-based precipitation products (SPPs) with the suitable resolution provide alternative options in these data-scarce regions, typically associated with high uncertainty. To reduce the uncertainty of individual satellite products, we have proposed a D-vine Copula-based Quantile Regression (DVQR) model to merge multiple SPPs with rain gauges (RGs). DVQR model was employed during the 2001–2017 summer monsoon seasons and compared with two other quantile regression methods based on the Multivariate Linear (MLQR) and the Bayesian Model Averaging (BMAQ), respectively, and two traditional merging methods: the simple modeling average (SMA) and the one-outlier-removed average (OORA) using the descriptive and categorical statistics. The rugged topography region of the Upper Tekeze-Atbara Basin in Ethiopia was selected as the study region. The Results indicated that the precipitation data estimates with DVQR, MLQR, and BMAQ models and traditional merging methods outperformed the downscaled SPPs. Monthly evaluations reveal that all products perform better in July and September than in June and August due to precipitation variability. DVQR, MLQR, and BMAQ models exhibit higher accuracy than the traditional merging methods over UTAB. The DVQR model substantially improved all the statistical metrics considered over BMAQ and MLQR models. However, DVQR model does not outperform BMAQ and MLQR models in the probability of detection (POD) and false alarm ratio (FAR), although it has the best frequency bias index (FBI) and critical success index (CSI) among all the employed models. Overall, the newly proposed merging approach improves the quality of SPPs and demonstrates the value of the proposed DVQR model in merging multiple SPPs over rugged topography regions such as UTAB.
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Mohammed Abdallah et al.
Status: open (until 05 Oct 2023)
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RC1: 'Comment on hess-2023-179', Anonymous Referee #1, 29 Aug 2023
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This study presents a new study on how to apply a D-vine copulas-based quantile regression method to merge different satellite precipitaiton products. A comparision of this proposed new method with other existing methods was then conducted. Overall, this new method apparently shows a better performance than the other methods. To further improve the quality of this manuscript, I think that there are several issues that the authors should consider to address.
- In the abstract, more quantitative results are needed.
- Lines 107-115: This paragraph is important to summarize why you conduct this study and what objectives your study have. This paragraph need be rephrased. The data input information need be moved to the Methodology section.
- Per the requirement of HESS, all figures need be inserted into the text. I suggest re-organizing this manuscript by making this methodology as a generic method. For example, a better tile should be “A D-vine copula-based quantile regression for merging satellite precipitation products over rugged topography: A case study at the upper Tekeze Atbara Basin of the Nile Basin”. In addition, you need describe the methodology as a generic method first and then describe the case study area.
- Discussion is kind of missing. The authors need provide some descriptions on the advantages, potential limitations, further studies on this topic in this discussion.
- Discussion can be combined with the conclusion section. The conclusions need be more concised and just describe the major findings.
Citation: https://doi.org/10.5194/hess-2023-179-RC1 -
RC2: 'Comment on hess-2023-179', Anonymous Referee #2, 20 Sep 2023
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The authors presented a data merging method to combine multiple remote-sensing rainfall products and rain gauge observation. The method was applied in a basin located in Ethiopia and its performance was compared against other data merging methods. This manuscript is generally well-designed with clear results. However, there are several major concerns that should be addressed.
1. the impacts of different scales. The rain gauge observes rainfall at a local or point scale, while the satellite products are in km or tens of km scale. When the authors try to compare the merged products against rain gauge observation, the mismatch of scale should be discussed in more detail and carefully.
2. The validation. The authors take a ten-fold way to train and validate their results. However, only ten stations are available. it is suggested the authors use the other 65 stations for validation.
3. The reliability of downscaled soil moisture data. the authors downscale soil moisture to 0.01 degree, but did not show the validation results. Soil moisture is a highly spatial heterogeneous variable. The authors should check and validate the downscaled soil moisture data.
Some minor issues, like
4. L34, what are the downscaled SPPs, from where? The abstract should be self-explained.
5. L36, what is UTAB?
6. In the abstract part, it is better to show the results in a more quantitative way, like adding some values of the metrics used in this study.
7. L109, "Here in this present study", please be concise.
8. L110, SPP has been defined before, do not need to do it again.
9. L148, what is the meaning of "another NOAA-CPC"?
10.L151, what is the "PM"?
Citation: https://doi.org/10.5194/hess-2023-179-RC2
Mohammed Abdallah et al.
Mohammed Abdallah et al.
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