A new daily gridded precipitation dataset based on gauge observations across mainland China

Han, Jingya; Miao, Chiyuan; Gou, Jiaojiao; Zheng, Haiyan; Zhang, Qi; Guo, Xiaoying

doi:https://doi.org/10.5194/essd-2022-373

Preprints

https://doi.org/10.5194/essd-2022-373

Preprints

12 Dec 2022

| 12 Dec 2022

Status: a revised version of this preprint was accepted for the journal ESSD.

A new daily gridded precipitation dataset based on gauge observations across mainland China

Jingya Han, Chiyuan Miao, Jiaojiao Gou, Haiyan Zheng, Qi Zhang, and Xiaoying Guo

Abstract. Freely accessible long-term precipitation estimates with fine spatiotemporal resolution and high-quality play essential roles in hydrologic, climatic, and numerical modeling applications. However, the existing daily gridded precipitation datasets over China either are constructed with insufficient gauge observations or neglect topographic effects and boundary effects on interpolation. Using daily observations from 2,839 gauges across China and nearby regions from 1961 to the present, this study compared eight different interpolation schemes that adjust the climatology based on monthly precipitation constraint and topographic characteristic correction, using an algorithm that combines the daily climatology field with a precipitation ratio field. Results from these eight interpolation schemes are cross validated using 45,992 high-density daily gauge observations from 2015 to 2019 over China. Of these eight schemes, the one with the best performance merges the Parameter-elevation Regression on Independent Slopes Model (PRISM) in the daily climatology field and interpolates station observations into the ratio field using an inverse distance weighting method. This scheme has a correlation coefficient of 0.78, a root-mean-square deviation of 8.8 mm/d, and a Kling-Gupta efficiency of 0.69 for comparisons between the 45,992 high-density gauge observations and the best interpolation scheme for the 0.1° latitude × longitude grid cells from 2015 to 2019. Therefore, this scheme is used to construct a new long-term gauge-based gridded precipitation dataset across mainland China (called CHM_PR, as a member of the China Hydro-Meteorology dataset) with spatial resolutions of 0.5°/0.25°/0.1°. This precipitation dataset is expected to facilitate the advancement of drought monitoring, flood forecasting, and hydrological modeling. Free access to the dataset can be found at https://doi.org/10.6084/m9.figshare.21432123.v2 (Han and Miao, 2022).

Received: 06 Nov 2022 – Discussion started: 12 Dec 2022

Jingya Han et al.

Status: final response (author comments only)

RC1:
'Comment on essd-2022-373', Anonymous Referee #1, 20 Dec 2022

The comment was uploaded in the form of a supplement: https://essd.copernicus.org/preprints/essd-2022-373/essd-2022-373-RC1-supplement.pdf

Citation: https://doi.org/10.5194/essd-2022-373-RC1
- AC1: 'Reply on RC1', Chiyuan Miao, 05 Apr 2023
  
  Dear reviewer,
  We would like to thank you for your thorough review and constructive comments that have helped improve the quality of our manuscript. We have carefully considered the comments and tried our best to address every one of them.
  Our point-by-point responses to your comments are given below.
  Yours sincerely,
  Chiyuan Miao, on behalf of all co-authors
  April 5, 2023
  
  Citation: https://doi.org/10.5194/essd-2022-373-AC1
RC2:
'Comment on essd-2022-373', Anonymous Referee #2, 10 Jan 2023

The objective of the manuscript is to present a new gridded precipitation dataset across mainland China using the best interpolation scheme among the 8 tested. Reliable precipitation data are important to ensure water safety and guarantee water availability and quality. Hence, efforts in creating reliable datasets are quite valuable.

However, the manuscript in its current form lacks a critical discussion on the limitation of the gridded data available and on the selected interpolation scheme. The Authors provide a list of gridded precipitation datasets available (Lines 71-104). However, a critical review of such datasets is missing. They mentioned the sensitivity to interpolation algorithms, but it is too vague. The Authors do not discuss why the scheme considered the optimal (even though it is not an optimal scheme but rather the best, based on some metrics, among the few schemes tested) leads to better goodness of fit metrics. Is such a result expected? Why is such a combination better than the others? It is simply chance? Can this scheme be transferred to other regions?

Point-by-point comments:

Abstract: I suggest the Authors revise the abstract. The primary objective of the paper (the new gridded data) and the temporal coverage (from when to when) should be better highlighted. Moreover, it should be clearer why the interpolation method selected is the best among the ones tested and how it addresses the limitations of currently available products. RMSE and other metrics as presented are not enough to judge the goodness of the method. How does this perform compared to the others? Why does it perform better?

Lines 80-81: what does the following sentence mean? “Through a fusion of remote sensing products and reanalysis datasets into in situ station data”. Remote sensing products and reanalysis data generated gauged precipitation dataset? Or gauged data were combined with remote sensing products and reanalysis data?

Section 2.3 and 2.4: Which method did the Authors use to re-grid the data? Where the raw data can be found?

Line 173: is it possible to eliminate interpolation errors?

Line 200: in the 30-year mean daily precipitation, was there any trend in the data or inhomogeneity?

Lines 245 - 325: Four interpolation methods to construct the field of ratio. Still missing how they differ, why those have been chosen, and why they provide different results.

Section 4.1 (starting line 369). My suggestion is to revise the term “optimal” for a scheme since there is no optimal scheme but simply the scheme having better metrics compared to the other schemes tested. The question of why such a combination of methods leads to better goodness of fit metrics is not answered. Why is such a combination better compared to the others? It is simply chance? Can this combination be transferred to other regions? Since the schemes perform differently depending on the topography (369-372), how do these differences affect the overall performance of the scheme? Are the metrics’ values listed (lines 375-380) average over the 45k stations used for verification?

Lines 479: Optimal interpolation scheme. Again, there is no optimal but the best among the ones tested

Citation: https://doi.org/10.5194/essd-2022-373-RC2
- AC2: 'Reply on RC2', Chiyuan Miao, 05 Apr 2023
  
  Dear reviewer,
  We would like to thank you for your thorough review and constructive comments that have helped improve the quality of our manuscript. We have carefully considered the comments and tried our best to address every one of them.
  Our point-by-point responses to your comments are given below.
  Yours sincerely,
  Chiyuan Miao, on behalf of all co-authors
  April 5, 2023
  
  Citation: https://doi.org/10.5194/essd-2022-373-AC2
CC1:
'Comment on essd-2022-373', Hanqing Chen, 29 Jan 2023

The authors have produced a new ground-based daily gridded precipitation dataset for mainland China, which is very meaningful work. Nevertheless, I noted that the new precipitation product only provides the precipitation variable, and information about the number of rain gauges used in each grid is missing, which is not good for data users to use this dataset. Consequently, could the authors provide the number of stations in each grid box?

Citation: https://doi.org/10.5194/essd-2022-373-CC1
- AC3: 'Reply on CC1', Chiyuan Miao, 05 Apr 2023
  
  Dear Dr. Editor,
  On behalf of all co-authors, I appreciate you and the reviewers for reviewing our paper (#essd-2022-373 ) and providing valuable comments, which are valuable in improving the quality of our manuscript. We have carefully considered the comments and tried our best to address every one of them, and the manuscript has been revised accordingly. An item-by-item reply to the Reviewers is shown as follows.
  We hope that the revision is acceptable, and I look forward to hearing from you soon.
  Sincerely yours,
  Dr. Chiyuan Miao
  April 5 , 2023
  
  Citation: https://doi.org/10.5194/essd-2022-373-AC3

Jingya Han et al.

Data sets

A new daily gridded precipitation dataset based on gauge observations across mainland China Jingya Han; Chiyuan Miao https://doi.org/10.6084/m9.figshare.21432123.v2

Jingya Han et al.

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Short summary

Constructing a high-quality, long-term daily precipitation dataset is essential to current hydrometeorology research. This study aims to construct a long-term daily precipitation dataset with different spatial resolutions based on 2,839 gauge observations. The constructed precipitation dataset shows reliable quality compared with the other available precipitation products, and is expected to facilitate the advancement of drought monitoring, flood forecasting, and hydrological modeling.


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