Spatial and Temporal Variability in Precipitation Concentration over Mainland China, 1961–2017

Round 1

Reviewer 1 Report

The current study by Lu eta l examines the spatial and temporal precipitation variability in mainland China from 1961 to 2017. While the overall quality of the methods and discussion are good, it is not clear about the overall novelty and actual contributions of this study, There are multitude of studies that have been published on the spatiao-temporal precipitation variability over China, and thus it is not clear what are the new contributions of this study.Some of the studies on China I have listed below:

Li, Zhi, Fen-li Zheng, Wen-zhao Liu, and Dennis C. Flanagan. "Spatial distribution and temporal trends of extreme temperature and precipitation events on the Loess Plateau of China during 1961–2007." Quaternary International 226, no. 1-2 (2010): 92-100.

Zhang, Huan, and Panmao Zhai. "Temporal and spatial characteristics of extreme hourly precipitation over eastern China in the warm season." Advances in atmospheric sciences 28, no. 5 (2011): 1177.

Miao, Chiyuan, Hamed Ashouri, Kuo-Lin Hsu, Soroosh Sorooshian, and Qingyun Duan. "Evaluation of the PERSIANN-CDR daily rainfall estimates in capturing the behavior of extreme precipitation events over China." Journal of Hydrometeorology 16, no. 3 (2015): 1387-1396.

None of the above studies have been cited in the paper. There are also several papers dealing precipitation variability also:

Li, Xuemei, Fengqing Jiang, Lanhai Li, and Guigang Wang. "Spatial and temporal variability of precipitation concentration index, concentration degree and concentration period in Xinjiang, China." International Journal of Climatology 31, no. 11 (2011): 1679-1693.

Liang, Liqiao, Lijuan Li, and Qiang Liu. "Precipitation variability in Northeast China from 1961 to 2008." Journal of Hydrology404, no. 1-2 (2011): 67-76.

In addition, I have several minor comments that can help improve the paper:

-- It would strengthen the manuscript if some of the following relevant references are also included in the study:

Seneviratne et al., 2014. No pause in the increase of hot temperature extremes Nat. Clim. Change, 4 (2014), pp. 161-163

Sen Roy, S. and R.C. Balling Jr., 2009: Evaluation of extreme precipitation indices using daily records (1910-2000) from India. Weather. 64, 149-152.

Easterling DR, Evans JL, Groisman PY, Karl TR, Kunkel KE, Ambenje P. 2000. Observed variability and trends in extreme climate events: a brief review. Bulletin of the American Meteorological Society 81: 417–425.

Song, X., J. Zhang, A. AghaKouchak, S. Sen Roy, Y. Xuan, G. Wang, R. He, X. Wang, and C. Liu. 2014. Rapid urbanization and changes in trends and spatio-temporal characteristics of precipitation in the Beijing metropolitan area. Journal of Geophysical Research – Atmospheres. 119, 11,250–11,271, DOI: 10.1002/2014JD022084.

-- Line 92: Please clarify what you mean by "integrity rate of higher than 95%"?

-- Line 95: Please clarify what you mean by "estimated by averaging the precipitation data for the same dates from neighboring stations. " More than one station was used to interpolate the missing values? Or only one station? How were these "neighboring" stations selected?

-- Line 100: Please explain more about how the eight regions boundary was demarcated. Why not use existing boundaries from one of the already published regional studies.

-- Line 115: Figure 1: What is the spatial distribution of the station network? It seems like they are more clustered in the eastern half of the country. In Region VIII, Tibetan Plateau, there is a very sparse network of stations particularity in the western section.

-- Line 151: How many total classes? What were the class intervals? How were they determined?

-- Figure 3: Does it show  the Results of MK test?

Again I am not convinced about the original contributions of the .study. having said that, the paper is overall well written with rigorous methods.

Author Response

Response to review on manuscript: water-483493

Title: Spatial and Temporal Variability in Precipitation Concentration over mainland China, 1961-2017

Journal: water

Corresponding Author:  Shanhu Jiang

Authors:  Yujie Lu, Shanhu Jiang, Liliang Ren, Linqi Zhang, Menghao Wang, Ruolan Liu, and Linyong Wei

Email:  [email protected]

Dear Editors:

Thank you for your letter and for the reviewers’ comments concerning our manuscript (water-483493). Those comments are all valuable and very helpful for revising and improving our paper, as well as the important guiding significance to our researches. We have studied comments carefully and have made correction which we hope meet with approval. Revised portion are marked in red and highlight in the paper. Below is the item-by-item response to you and the two reviewers’ comments. Our responses appear in blue font.

Thanks and appreciate your time.

Shanhu Jiang

State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Hohai University, Nanjing 210098, China

Response for editor and reviewers:

Thanks for your careful reading and positive evaluations. We have revised our manuscript according to the reviewers’ suggestions. We hope the revisions can make our paper more acceptable. To better demonstrate the core content and objectives of the article, we have changed the title to “Spatial and Temporal Variability in Precipitation Concentration over mainland China, 1961-2017” in the revised paper.

In the revised manuscript, for response for the suggestions of reviewers, we have added eleven new references:

Shouraseni, S. R.; Balling, R. C. Evaluation of extreme precipitation indices using daily records (1910–2000) from India. Weather 2010, 64, 149-152, doi: 10.1002/wea.385.

Easterling, D. R.; Evans, J. L.; Groisman, P. Y.; Karl, T. R.; Ambenje, P. Observed variability and trends in extreme climate events: a brief review. B. Am. Meteorol. Soc. 2000, 81, 417-426, doi:10.1175/1520-0477(2000)0812.3.CO;2.

Donat, M. G.; L. V. Alexander; H. Yang; I. Durre; R. Vose; R. J. H. Dunn; K. M. Willett et al. "Updated analyses of temperature and precipitation extreme indices since the beginning of the twentieth century: The HadEX2 dataset." J. Geophys. Res. Atmos. 2013, 118, 2098-2118. doi:10.1002/jgrd.50150.

Song, X.; Zhang, J.; Aghakouchak, A.; Roy, S. S.; Xuan, Y.; Wang, G.; Liu, C. Rapid urbanization and changes in trends and spatio-temporal characteristics of precipitation in the Beijing metropolitan area. J. Geophys. Res. Atmos. 2014, 119, 11,250–11,271, doi:10.1002/2014JD022084.

Li, X.M.; Jiang, F.Q.; Li, L.H.; Wang, G.Q. Spatial and temporal variability of precipitation concentration index, concentration degree and concentration period in Xinjiang, China. Int. J. Climatol. 2011, 31, 1679-1693, doi:10.1002/joc.2181.

Miao, C.Y.; Hamed, A.; Kuo-Lin, H.; Soroosh, S.; Duan, Q.Y. Evaluation of the PERSIANN-CDR daily rainfall estimates in capturing the behavior of extreme precipitation events over China. J. Hydrol. 2015, 16, 1387-1396, doi:10.1175/JHM-D-14-0174.1.

Li, Z.; Zheng, F.; Liu, W.; Flanagan, D. C. Spatial distribution and temporal trends of extreme temperature and precipitation events on the loess plateau of china during 1961–2007. Quaten. Int. 2010, 226, 92-100, doi:10.1016/j.quaint.2010.03.003.

Zhang, H.; Zhai, P. Temporal and spatial characteristics of extreme hourly precipitation over eastern china in the warm season. Adv. Atmos. Sci. 2011, 28, 1177, doi:10.1007/s00376-011-0020-0．

Chen, S.; Hong, Y.; Cao, Q.; Gourley, J. J.; Kirstetter, P. E.; Yong, B. Similarity and difference of the two successive v6 and v7 trmm multisatellite precipitation analysis performance over china. J. Geophys. Res. Atmos. 2013, 118, 13,060-13,074, doi:10.1002/2013JD019964.

Qian, W.; Lin, X. Regional trends in recent precipitation indices in China. Meteorol. Atmos. Phys. 2005, 90, 193, doi:10.1007/s00703-004-0101-z.

Tang, Z.; Wang, Z.; Zheng, C.; Fang, J. Biodiversity in China’s mountains. Front. Ecol. Environ. 2006, 4, 347–352, doi:10.1890/1540-9295(2006)004[0347:BICM]2.0.CO;2.

Responds to the reviewer’s comments:

Reviewer #1:

The current study by Lu eta l examines the spatial and temporal precipitation variability in mainland China from 1961 to 2017. While the overall quality of the methods and discussion are good, it is not clear about the overall novelty and actual contributions of this study. There are multitude of studies that have been published on the spatiao-temporal precipitation variability over China, and thus it is not clear what are the new contributions of this study. In addition, I have several minor comments that can help improve the paper. Again I am not convinced about the original contributions of the study. having said that, the paper is overall well written with rigorous methods.

Response:

Thanks for your careful reading and pertinent suggestions. Based on your suggestions, we have revised our manuscript thoroughly again. Also, your good suggestions are precious experiences for my future research.

And now section by section:

1.      Response to comment: (There are multitude of studies that have been published on the spatio-temporal precipitation variability over China, and thus it is not clear what are the new contributions of this study.)

Response: Thanks again to the reviewer on suggesting to properly address the significance of the work. I am sorry for not being able to highlight the background and the contribution of this manuscript in the early version. In the revised version, I have added your suggested references and more explanations to the impact of this work as follows:

“The trend of significant increases or decreases in daily or monthly precipitation in most countries is directly related to changes in the same signs of the amount of precipitation during heavy and extreme precipitation events [5 ,6].” (Line 40)

“Although the low frequency of observed extreme events, heavy rainfall occurring in a few day accounts for a large proportion of annual rainfall and may lead to flooding. A better technique to evaluate rainfall distribution and variability is to consider the contribution of several rainy days with largest amount of rainfall to total rainfall. The timing and rate of annual precipitation concentration are also the characteristics of heavy rainfall.” (Line 51)

“Such as an increase in recent natural disasters, including a spring drought in Southwestern China and flood disasters that cause considerable social losses almost every year.” (Line 72)

“However, most studies have focused on analyzing patterns of extreme precipitation events using indices of extreme climate events [19,20]. Compared to the low frequency of observed extreme events, the exploration of precipitation concentration can improve understanding of major disasters and provide warnings. Li et al. [15] calculated precipitation CIs in Xinjiang and concluded that the Kaidu River basin and Southern Xinjiang have higher precipitation CIs, with most parts of Xinjiang are characterized by no significant trends in precipitation CIs. Huang et al. [22] studied the two precipitation concentration indices and found that the precipitation concentration in Qinghai showed a significant irregularity of the monthly rainfall distribution and highly homogeneous distribution of the daily rainfall. These PCI and CI studies mainly focused on some major river basins or regions in China, only a few studies have focused on mainland China.” (Line 76)

“Analyzing and understanding the spatial-temporal characteristics and impacts of precipitation concentration index in China is the key to understanding and solving water problems in China, especially in the context of climate change and human activities. And the combination of the PCI index with the CI index could be a more efficient methodology because of their different mechanisms and evaluation criteria.” (Line 90)

“Thus, a detailed analysis of precipitation concentration in China could enhance the understanding of the statistical characteristics of precipitation extremes, and provide support for making macro decisions about river affairs and the prevention of natural disasters.” (Line 96)

2.      Response to comment: (Line 92: Please clarify what you mean by "integrity rate of higher than 95%"?)

Response: We are very sorry for our negligence of not clearly describing the filtering process for meteorological stations. The "integrity rate of higher than 95%" means that if the missing daily precipitation data of the station account for more than 5% of 57 years, the station will be excluded. Otherwise, the missing data of selected station will be filled by using the interpolation method of the adjacent station or this station, and 774 available precipitation stations will be obtained.

“If the missing daily precipitation data of the station account for more than 5% of 57 years, the station will be excluded.” (Line 138)

3.      Response to comment: (Line 95: Please clarify what you mean by "estimated by averaging the precipitation data for the same dates from neighboring stations. " More than one station was used to interpolate the missing values? Or only one station? How were these "neighboring" stations selected?)

Response: Thank you for pointing this out. It meant that if consecutive days were missing, values were estimated by simple linear correlation based on data from neighboring stations (R²>0.95). More than one station was used to interpolate the missing values. Besides these missing data, we calculated the correlation coefficient of rainfall data among stations and chose the stations which R² are greater than 0.95 as the neighboring station to interpolate the missing values.

“if consecutive days were missing, values were estimated by simple linear correlation based on data from neighboring stations (R²>0.95).” (Line 142)

4.      Response to comment: (Line 100: Please explain more about how the eight regions boundary was demarcated. Why not use existing boundaries from one of the already published regional studies.)

Response: As requested by the reviewer we have added the details about the demarcation of eight regions boundary. There are many ways of dividing China's regional boundaries. We chose the most suitable and universal demarcation method according to the references.

“Figure 1(a) shows the topographic variability of the digital elevation model (DEM). Considering the altitude, China can be roughly divided into three regions :(1)>3000 m, (2) 1000-3000 m, and (3) <1000 m.” (Line 111)

“To analyze the spatial and temporal variations of precipitation concentration indices at different scales more clearly, as well as their mechanisms, we divided the mainland of China into eight regions according to Chen et al. [24] depending on the annual mean precipitation distribution [25], mountain ranges [26], and elevations.” (Line 113)

5.      Response to comment: (Line 115: Figure 1: What is the spatial distribution of the station network? It seems like they are more clustered in the eastern half of the country. In Region VIII, Tibetan Plateau, there is a very sparse network of stations particularity in the western section.)

Response: It is true that the meteorological stations in Northwest China and Qinghai-Tibet Plateau are rare and unevenly distributed. Considering the Reviewer’s questions, we have added the following sentences on this issue in the end of our revised manuscript.

“It should be pointed out the meteorological stations in Northwest China and Qinghai-Tibet Plateau are rare and unevenly distributed, which may lead to some uncertainties in the analysis results.” (Line 434)

6.      Response to comment: (Line 151: How many total classes? What were the class intervals? How were they determined?)

Response: Thank you very much for pointing out ambiguities and we have polished this portion in our revised manuscript. In terms of the statistical structure of daily precipitation, the distribution of its amount frequencies is adjustable by negative exponential distributions. To classify and tabulate the daily precipitation amounts by length (the tablet was too long, we didn’t put it in our manuscript), their absolute frequencies decrease exponentially, starting with the lowest class. Then we used a limit of 0.1 mm/day to separate wet and dry days and 1mm as the precipitation interval to classify the precipitation limits in ascending order. Thus, each station has a different number of classes. The station with the largest daily rainfall record has the largest total classes (640).

“In this study, a rainy day is defined as a day with least 0.1 mm rainfall and 1 mm precipitation is used as the precipitation interval to classify the precipitation limits in ascending order. Irregularity of rainfall distribution was measured by determining percentage of rain contributed by days falling in each class.” (Line 178)

7.      Response to comment: (Figure 3: Does it show the Results of MK test?)

Response: Based on the referee’s comment, we have re-write the figure captions of Figure 3, Figure 5 and Figure 8.

“Figure 3. Spatial distribution of rainfall stations displaying negative or positive rainfall trends across China. (a) Overall station trends (Blue downward triangles denote decreasing trends; red upward triangles denote increasing trends) and (b) stations exhibiting statistically significant trends (Blue circles denote decreasing trends; red circles denote increasing trends; bigger circles show the trend exhibits a 95% significance level detected by MK test; smaller circles show the trend exhibits a 90% significance level detected by MK test).” (Line 282)

“Figure 5. Spatial distribution of rainfall stations displaying negative or positive PCI trends across China. (a) Overall station trends (Blue downward triangles denote decreasing trends; red upward triangles denote increasing trends) and (b) stations exhibiting statistically significant trends (Blue circles denote decreasing trends; red circles denote increasing trends; bigger circles show the trend exhibits a 95% significance level detected by MK test; smaller circles show the trend exhibits a 90% significance level detected by MK test).” (Line 324)

“Figure 8. Spatial distribution of the stations displaying negative or positive CI trends across China. (a) Overall station trends (Blue downward triangles denote decreasing trends; red upward triangles denote increasing trends) and (b) stations exhibiting statistically significant trends (Blue circles denote decreasing trends; red circles denote increasing trends; bigger circles show the trend exhibits a 95% significance level detected by MK test; smaller circles show the trend exhibits a 90% significance level detected by MK test).” (Line 402)

We tried our best to improve the manuscript and made some changes in the manuscript. These changes will not influence the content and framework of the paper. And here we did not list the changes but marked in red in revised paper. We appreciate for Editors/Reviewers’ warm work earnestly, and hope that the correction will meet with approval.

Once again, thank you very much for your comments and suggestions.

Author Response File: Author Response.doc

Reviewer 2 Report

The manuscript presents an interesting results mainly for readers and practitioners from China but research approach can be interesting also for other readers of Water journal.

My comments are as follows.

I recommend to change the sections in chapter 2 – to move the second paragraph as the first and to move the first paragraph as the second.

Please response: Have you tested the data for homogeneity? What are the results of this test?

At page8, line 255 you mention using 724 stations but before 774 stationed is mentioned – what is the correct value?

At page 13, line 356 – formal mistake – instead of “come” should be “some”.

I recommend use for Figs 6 and 8 the same scales for precipitation (right axis) to be more comparable.

Author Response

Response to review on manuscript: water-483493

Title: Spatial and Temporal Variability in Precipitation Concentration over mainland China, 1961-2017

Journal: water

Corresponding Author: Shanhu Jiang

Authors: Yujie Lu, Shanhu Jiang, Liliang Ren, Linqi Zhang, Menghao Wang, Ruolan Liu, and Linyong Wei

Email: [email protected]

Dear Editors:

Thank you for your letter and for the reviewers’ comments concerning our manuscript (water-483493). Those comments are all valuable and very helpful for revising and improving our paper, as well as the important guiding significance to our researches. We have studied comments carefully and have made correction which we hope meet with approval. Revised portion are marked in red and highlight in the paper. Below is the item-by-item response to you and the two reviewers’ comments. Our responses appear in blue font.

Thanks and appreciate your time.

Shanhu Jiang

State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Hohai University, Nanjing 210098, China

Response for editor and reviewers:

Thanks for your careful reading and positive evaluations. We have revised our manuscript according to the reviewers’ suggestions. We hope the revisions can make our paper more acceptable. To better demonstrate the core content and objectives of the article, we have changed the title to “Spatial and Temporal Variability in Precipitation Concentration over mainland China, 1961-2017” in the revised paper.

In the revised manuscript, for response for the suggestions of reviewers, we have added eleven new references:

Shouraseni, S. R.; Balling, R. C. Evaluation of extreme precipitation indices using daily records (1910–2000) from India. Weather 2010, 64, 149-152, doi: 10.1002/wea.385.

Easterling, D. R.; Evans, J. L.; Groisman, P. Y.; Karl, T. R.; Ambenje, P. Observed variability and trends in extreme climate events: a brief review. B. Am. Meteorol. Soc. 2000, 81, 417-426, doi:10.1175/1520-0477(2000)0812.3.CO;2.

Donat, M. G.; L. V. Alexander; H. Yang; I. Durre; R. Vose; R. J. H. Dunn; K. M. Willett et al. "Updated analyses of temperature and precipitation extreme indices since the beginning of the twentieth century: The HadEX2 dataset." J. Geophys. Res. Atmos. 2013, 118, 2098-2118. doi:10.1002/jgrd.50150.

Song, X.; Zhang, J.; Aghakouchak, A.; Roy, S. S.; Xuan, Y.; Wang, G.; Liu, C. Rapid urbanization and changes in trends and spatio-temporal characteristics of precipitation in the Beijing metropolitan area. J. Geophys. Res. Atmos. 2014, 119, 11,250–11,271, doi:10.1002/2014JD022084.

Li, X.M.; Jiang, F.Q.; Li, L.H.; Wang, G.Q. Spatial and temporal variability of precipitation concentration index, concentration degree and concentration period in Xinjiang, China. Int. J. Climatol. 2011, 31, 1679-1693, doi:10.1002/joc.2181.

Miao, C.Y.; Hamed, A.; Kuo-Lin, H.; Soroosh, S.; Duan, Q.Y. Evaluation of the PERSIANN-CDR daily rainfall estimates in capturing the behavior of extreme precipitation events over China. J. Hydrol. 2015, 16, 1387-1396, doi:10.1175/JHM-D-14-0174.1.

Li, Z.; Zheng, F.; Liu, W.; Flanagan, D. C. Spatial distribution and temporal trends of extreme temperature and precipitation events on the loess plateau of china during 1961–2007. Quaten. Int. 2010, 226, 92-100, doi:10.1016/j.quaint.2010.03.003.

Zhang, H.; Zhai, P. Temporal and spatial characteristics of extreme hourly precipitation over eastern china in the warm season. Adv. Atmos. Sci. 2011, 28, 1177, doi:10.1007/s00376-011-0020-0．

Chen, S.; Hong, Y.; Cao, Q.; Gourley, J. J.; Kirstetter, P. E.; Yong, B. Similarity and difference of the two successive v6 and v7 trmm multisatellite precipitation analysis performance over china. J. Geophys. Res. Atmos. 2013, 118, 13,060-13,074, doi:10.1002/2013JD019964.

Qian, W.; Lin, X. Regional trends in recent precipitation indices in China. Meteorol. Atmos. Phys. 2005, 90, 193, doi:10.1007/s00703-004-0101-z.

Tang, Z.; Wang, Z.; Zheng, C.; Fang, J. Biodiversity in China’s mountains. Front. Ecol. Environ. 2006, 4, 347–352, doi:10.1890/1540-9295(2006)004[0347:BICM]2.0.CO;2.

Responds to the reviewer’s comments:

Reviewer #2:

The manuscript presents an interesting result mainly for readers and practitioners from China but research approach can be interesting also for other readers of Water journal.

Response:

Thanks for your careful reading and pertinent suggestions. Based on your suggestions, we have revised our manuscript thoroughly again. Also, your good suggestions are precious experiences for my future research.

And now section by section:

1.      Response to comment: (I recommend to change the sections in chapter 2 to move the second paragraph as the first and to move the first paragraph as the second.)

Response: Thank you for pointing this out. The paragraphs order in chapter 2 have been changed.

2.      Response to comment: (Have you tested the data for homogeneity? What are the results of this test?)

Response: As for the reviewer’s concern, the results of data homogeneity test have been supplemented in the revised manuscript. Quality and homogeneity control were applied to the datasets using RClimDex software. The software can pick out the outliers and then we dealt with these outliers using our judgment. The Illogical data, such as negative values, were treated as missing value. 226 stations of 824 stations was found to have missing value. And about 6% of 824 stations didn’t pass the test. After rejecting 50 stations, 774 stations were finally selected for this study.

“Furthermore, homogeneity tests were also performed using RclimDex software package (http://etccdi.pacificclimate.org/software.shtml). The software can pick out the illogical data, such as precipitation values below 0 mm, were treated as missing value.” (Line 133)

3.      Response to comment: (At page8, line 255 you mention using 724 stations but before 774 stationed is mentioned - what is the correct value?)

Response: We are very sorry for our mistake and incorrect writing, the number 724 has been corrected to 774.

“The spatial distribution of multi-year average PCI values is shown in Figure 4, which is derived from the monthly precipitation data sets of each year from 1961 to 2017 using 774 national meteorological stations in China.”（Line 292）

4.      Response to comment: (At page 13, line 356 - formal mistake - instead of “come” should be “some”.)

Response: Once again, thank you very much for pointing out our incorrect writing. The “come” has been corrected into “some”.

“Comparing the results of Table 2, some regions such as NW, SW, and XJ show remarkable increasing trends of up to 25.5%, 18.9%, and 41.8% respectively.” (Line 397)

5.      Response to comment: (I recommend use for Figs 6 and 9 the same scales for precipitation (right axis) to be more comparable.)

Response: Thank you for your careful work. We have made adjustment according to the Reviewer's comments.

Figure 6. Time series of annual precipitation (blue solid line) and PCI (orange solid line) for the eight subregions in China. Dashed lines show the corresponding linear trend.

Figure 9. Time series of annual precipitation (blue solid line) and CI (orange solid line) for eight subregions in China. Dashed lines are the corresponding linear trend.

We tried our best to improve the manuscript and made some changes in the manuscript. These changes will not influence the content and framework of the paper. And here we did not list the changes but marked in red in revised paper. We appreciate for Editors/Reviewers’ warm work earnestly, and hope that the correction will meet with approval.

Once again, thank you very much for your comments and suggestions.

Author Response File: Author Response.docx

Round 2

Reviewer 1 Report

The authors have adequately responded to all my comments except my major comment about the original contributions of this paper. It is upto the editor to decide based on the journal impact whether this isimpactful enough or not.