the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 23 Jul 2024
A New High-Resolution Multi-Drought Indices Dataset for Mainland China
Abstract. Drought indices are crucial for assessing and managing water scarcity and agricultural risks; however, the lack of a unified data foundation in existing datasets leads to inconsistencies that challenge the comparability of drought indices. This study is dedicated to creating CHM_Drought, an innovative and comprehensive long-term meteorological drought dataset with a spatial resolution of 0.1° and data collected from 1961 to 2022 in mainland China. It features six pivotal meteorological drought indices: the standardized precipitation index (SPI), standardized precipitation evapotranspiration index (SPEI), evaporative demand drought index (EDDI), Palmer drought severity index (PDSI), self-calibrating Palmer drought severity index (SC-PDSI), and vapor pressure deficit (VPD), of which SPI, SPEI, and EDDI contain multi-scale features for periods of 2 weeks and 1–12 months. The dataset features a comprehensive application of high-density meteorological station data and a complete framework starting from basic meteorological elements (the China Hydro-Meteorology dataset, CHM). Demonstrating its robustness, the dataset excels in accurately capturing drought events across mainland China, as evidenced by its detailed depiction of the 2022 summer drought in the Yangtze River basin. In addition, to evaluate CHM_Drought, we performed consistency tests with the drought indices calculated based on Climatic Research Unit (CRU) and CN05.1 data and found that all indices had high consistency overall and that the 2-week scale SPI, SPEI, and EDDI had potential early warning roles in drought monitoring. Overall, our dataset bridges the gap in high-precision multi-index drought data in China, and the complete CHM-based framework ensures the consistency and reliability of the dataset, which contributes to enhancing the understanding of drought patterns and trends in China. Free access to the dataset can be found at https://doi.org/10.6084/m9.figshare.25656951.v2 (Zhang and Miao, 2024).
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RC1: 'Comment on essd-2024-270', Anonymous Referee #1, 06 Sep 2024
In this manuscript, multi-drought indices from station data across mainland China has been developed at 0.1 deg resolution for the period of 1961 to 2022. The authors utilized six drought indices such as SPI, SPEI, EDDI, PDSI_China and SC-PDSI through several meteorological parameters. Products derived from the present approach have been compared with other available datasets/products. This long-term high-resolution dataset would be useful for drought management and planning. The manuscript is well-written with reasonable analyses. However, following comments may be addressed to make it more suitable.
Section 2.2: It is suggested to elaborate missing data handling, as it is one of the tricky part of the observational data while considering several meteorological parameters.
Why angular distance-weighted interpolation (ADW) is considered for the higher-resolution gridding? Is it better than optimum interpolation method and state-of-the-art objective analysis techniques? Considering variability of meteorological parameters taken in this study, does ADW reasonable for all parameters?
Why authors have not considered multivariate drought indices including precipitation and soil moisture? An example of such long-term global datasets can be found at https://doi.org/10.1088/1748-9326/7/4/044037
Yangtze River basin may be highlighted in any one figure for the convenience of the global readers.
How empirical constant of expression 8 was determined. It needs to be elaborated.
What is the role of land use/ land cover on drought indices? Is it possible to introduce any new index considering land use/land cover change?
It is suggested to prepare an uncertainty map for each drought index. It would be vital for end users.
Citation: https://doi.org/10.5194/essd-2024-270-RC1 -
AC2: 'Reply on RC1', Chiyuan Miao, 20 Nov 2024
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
November 20, 2024
-
AC2: 'Reply on RC1', Chiyuan Miao, 20 Nov 2024
-
RC2: 'Comment on essd-2024-270', Anonymous Referee #2, 14 Oct 2024
This study produces a long-term and high-resolution meteorological drought dataset with six indices in China. This dataset applied the high-density meteorological station data and incorporates a complete framework. Further, this dataset has been tested and proven capable of capturing the typical drought events across mainland China. This high-precision, multi-index dataset offers valuable potential for further studies on drought patterns and trends, as well as for early warning applications in drought monitoring within China. The manuscript is well-written and logically organized though some details need to be further explained and modified.
Some detailed suggestions and comments are listed below:
1. Section 2.1 Data: Many datasets are used in this study, while their description in this section is unclear. It would be better to provide a clear classification of these data sources and distinguish between the meteorological data from CMA, CHM, and CN05.1, as all the three datasets seem to originate from gauge observations in China. Further, the CRU dataset is based on global gauge observations with fewer gauges in China. The authors would clarify why these datasets are included in the consistency test.
2. Line 181: FAO-56 Penman-Monteith equation is designed to define the reference crop ET (ET0) using a hypothetical reference crop with an assumed height of 0.12 m. Here, the authors used this equation to calculate PET rather than ET0. I suggest they provide an explanation of PET and ET0 and clarify the calculations used for each.
3. Line 280: why was August 2022 chosen as the node of the 2022 severe drought in the Yangtze River basin? In fact, this drought lasted from summer to autumn. The cumulative water shortage in the months following August may be worse.
4. Line 324: the low consistency between CHM_Drought with CN05.1_Drought is attributed to the poor performance of sparse sites. Does this imply that the data processing method (e.g., interpolation method) affects the accuracy of the production of drought datasets?
5. Section 4.4: While the correlation between VPD and NDVI is discussed, NDVI is influenced by various factors beyond VPD, and NDVI data itself may contain uncertainties. It is unclear why the correlation between VPD and NDVI can be used for the consistency assessment of VPD.
6. Some in-text citations are not listed in the Reference section, the authors should check it out.
Citation: https://doi.org/10.5194/essd-2024-270-RC2 -
AC1: 'Reply on RC2', Chiyuan Miao, 20 Nov 2024
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
November 20, 2024
-
AC1: 'Reply on RC2', Chiyuan Miao, 20 Nov 2024
-
EC1: 'Comment on essd-2024-270', Tobias Gerken, 29 Oct 2024
Based on the reviewers' assessment and my own assessment ,I am now inviting an author response to the reviews.
When preparing the response, the authors should be careful to address the reviewers comments. I recommend to take particular care about the question regarding uncertainty quantification of the drought indicators. This is of particular importance given the fact that there is a low station density in western half of the dataset (especially Qinghai-Tibetan Plateau and Xinjiang).
I also have a question about the comparison to CRU on a 0.5x0.5 degree grid. It seems that the figures displaying this have some interpolation/ smoothing indicating a higher resolution than stated in the manuscript, which should be either removed or clearly explained.
Citation: https://doi.org/10.5194/essd-2024-270-EC1 -
AC3: 'Reply on EC1', Chiyuan Miao, 20 Nov 2024
Dear Dr. Editor,
On behalf of all co-authors, I appreciate you and the reviewers for reviewing our paper (#essd-2024-270 ) 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
November 20 , 2024
-
AC3: 'Reply on EC1', Chiyuan Miao, 20 Nov 2024
Status: closed
-
RC1: 'Comment on essd-2024-270', Anonymous Referee #1, 06 Sep 2024
In this manuscript, multi-drought indices from station data across mainland China has been developed at 0.1 deg resolution for the period of 1961 to 2022. The authors utilized six drought indices such as SPI, SPEI, EDDI, PDSI_China and SC-PDSI through several meteorological parameters. Products derived from the present approach have been compared with other available datasets/products. This long-term high-resolution dataset would be useful for drought management and planning. The manuscript is well-written with reasonable analyses. However, following comments may be addressed to make it more suitable.
Section 2.2: It is suggested to elaborate missing data handling, as it is one of the tricky part of the observational data while considering several meteorological parameters.
Why angular distance-weighted interpolation (ADW) is considered for the higher-resolution gridding? Is it better than optimum interpolation method and state-of-the-art objective analysis techniques? Considering variability of meteorological parameters taken in this study, does ADW reasonable for all parameters?
Why authors have not considered multivariate drought indices including precipitation and soil moisture? An example of such long-term global datasets can be found at https://doi.org/10.1088/1748-9326/7/4/044037
Yangtze River basin may be highlighted in any one figure for the convenience of the global readers.
How empirical constant of expression 8 was determined. It needs to be elaborated.
What is the role of land use/ land cover on drought indices? Is it possible to introduce any new index considering land use/land cover change?
It is suggested to prepare an uncertainty map for each drought index. It would be vital for end users.
Citation: https://doi.org/10.5194/essd-2024-270-RC1 -
AC2: 'Reply on RC1', Chiyuan Miao, 20 Nov 2024
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
November 20, 2024
-
AC2: 'Reply on RC1', Chiyuan Miao, 20 Nov 2024
-
RC2: 'Comment on essd-2024-270', Anonymous Referee #2, 14 Oct 2024
This study produces a long-term and high-resolution meteorological drought dataset with six indices in China. This dataset applied the high-density meteorological station data and incorporates a complete framework. Further, this dataset has been tested and proven capable of capturing the typical drought events across mainland China. This high-precision, multi-index dataset offers valuable potential for further studies on drought patterns and trends, as well as for early warning applications in drought monitoring within China. The manuscript is well-written and logically organized though some details need to be further explained and modified.
Some detailed suggestions and comments are listed below:
1. Section 2.1 Data: Many datasets are used in this study, while their description in this section is unclear. It would be better to provide a clear classification of these data sources and distinguish between the meteorological data from CMA, CHM, and CN05.1, as all the three datasets seem to originate from gauge observations in China. Further, the CRU dataset is based on global gauge observations with fewer gauges in China. The authors would clarify why these datasets are included in the consistency test.
2. Line 181: FAO-56 Penman-Monteith equation is designed to define the reference crop ET (ET0) using a hypothetical reference crop with an assumed height of 0.12 m. Here, the authors used this equation to calculate PET rather than ET0. I suggest they provide an explanation of PET and ET0 and clarify the calculations used for each.
3. Line 280: why was August 2022 chosen as the node of the 2022 severe drought in the Yangtze River basin? In fact, this drought lasted from summer to autumn. The cumulative water shortage in the months following August may be worse.
4. Line 324: the low consistency between CHM_Drought with CN05.1_Drought is attributed to the poor performance of sparse sites. Does this imply that the data processing method (e.g., interpolation method) affects the accuracy of the production of drought datasets?
5. Section 4.4: While the correlation between VPD and NDVI is discussed, NDVI is influenced by various factors beyond VPD, and NDVI data itself may contain uncertainties. It is unclear why the correlation between VPD and NDVI can be used for the consistency assessment of VPD.
6. Some in-text citations are not listed in the Reference section, the authors should check it out.
Citation: https://doi.org/10.5194/essd-2024-270-RC2 -
AC1: 'Reply on RC2', Chiyuan Miao, 20 Nov 2024
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
November 20, 2024
-
AC1: 'Reply on RC2', Chiyuan Miao, 20 Nov 2024
-
EC1: 'Comment on essd-2024-270', Tobias Gerken, 29 Oct 2024
Based on the reviewers' assessment and my own assessment ,I am now inviting an author response to the reviews.
When preparing the response, the authors should be careful to address the reviewers comments. I recommend to take particular care about the question regarding uncertainty quantification of the drought indicators. This is of particular importance given the fact that there is a low station density in western half of the dataset (especially Qinghai-Tibetan Plateau and Xinjiang).
I also have a question about the comparison to CRU on a 0.5x0.5 degree grid. It seems that the figures displaying this have some interpolation/ smoothing indicating a higher resolution than stated in the manuscript, which should be either removed or clearly explained.
Citation: https://doi.org/10.5194/essd-2024-270-EC1 -
AC3: 'Reply on EC1', Chiyuan Miao, 20 Nov 2024
Dear Dr. Editor,
On behalf of all co-authors, I appreciate you and the reviewers for reviewing our paper (#essd-2024-270 ) 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
November 20 , 2024
-
AC3: 'Reply on EC1', Chiyuan Miao, 20 Nov 2024
Data sets
A New High-Resolution Multi-Drought Indices Dataset for Mainland China: CHM_Drought Qi Zhang and Chiyuan Miao https://doi.org/10.6084/m9.figshare.25656951.v2
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