Impacts of Different Socioeconomic Development Levels on Extremely Wet/Dry Events in Mainland China

Round 1

Reviewer 1 Report

The authors use SPEI to define flood and drought events across 552 sites in China and compare this with changes in population and GDP. While this is a large data set that could potentially be used for some interesting research, it is unclear to me what the presented study adds to the literature. The authors fail to describe the relevant literature, what the aim of the study is and what it adds to existing literature. More importantly, the authors do not describe which methods they used and what it is they calculated and why. From what I am able to understand from the methods, I think they are not entirely appropriate and could be refined.

Some more specific comments:

The abstract contains some very detailed statements about the results, this could be a bit more general, explaining the relevance of the study, the methods applied and the overall conclusions of the study.

The first two paragraphs of the introduction provide a lot of general information about flood and drought risk and studies of which I fail to see the relevance for this particular paper. The same for the third paragraph of the introduction, this gives a lot of information about several flood or drought events in China that have been studied, but the relevance of these specific studies and places is not clear. This is then followed by one sentence where it is claimed that there has been little research on the impacts of population and economic and social development without any references to substantiate this claim. In fact, there are quite some studies that investigate the exposure and vulnerability and how they influence flood risk, it may be worth looking into those studies to see what others investigated and what this study adds to the existing literature. The objective of the study, why this is relevant and what it adds to existing literature is not very clear to me. Instead the introduction finishes with a description of indicators that are used, which belongs in the methods section.

From the methods section it is not clear to me how the sites are defined. Is this based on measuring stations? And how is then the socioeconomic level determined? For which area?

The methods provide detailed equations for the Penman-Monteith equation, which in my opinion is not necessary. Instead, it would be good to describe which data and parameters are used to calculate the ET and, more importantly, how are the linear slopes calculated and how are the slopes of the different variables compared? There is a very short section with two sentences on the linear slope estimation, which only describes the names of variables. It is not clear what is being calculated and why.

The authors use the SPEI for a 12 month period to define both drought and flood events. While for drought this is a relevant time period, the 12 month cumulative precipitation is not relevant for flooding. Therefore, I would suggest to the authors to choose a different method for defining flood and drought events (perhaps a threshold method).

The results are difficult to interpret, since it is unclear which methods are being used and what the aim of the study is. In general, the authors seem to draw their conclusions based on estimating relationships between change in GDP/Population and change in SPEI, while they are reporting very low R² values for these fits, which indicates they are not really explaining the variation in the variables.

Author Response

Comments and Suggestions for Authors

The authors use SPEI to define flood and drought events across 552 sites in China and compare this with changes in population and GDP. While this is a large data set that could potentially be used for some interesting research, it is unclear to me what the presented study adds to the literature. The authors fail to describe the relevant literature, what the aim of the study is and what it adds to existing literature. More importantly, the authors do not describe which methods they used and what it is they calculated and why. From what I am able to understand from the methods, I think they are not entirely appropriate and could be refined.

Re: This research investigated the variations of maximum values (SPEI_MAX) and minimum values (SPEI_MIN) of 12-month standardized precipitation evapotranspiration index (SPEI_12-month) for the selected 525 sites at different socioeconomic development levels (SDLs) (classify by population and GDP) in China over 2000-2018. We also analyzed the impacts of increased population/GDP/SDLs on extremely wet/dry events. The linear correlations between SPEI_12-month/SPEI_MAX/SPEI_MIN and population/GDP were conducted for all the sites. The relationship between linear slopes of population (Popu_LS)/GDP(GDP_LS), SPEI_MAX (SPEI_MAX_LS)/SPEI_MIN (SPEI_MIN_LS) were further studied.

We have also updated the methods parts to make clear the methodology. The point-to-point responses are as follows.

Some more specific comments:

• The abstract contains some very detailed statements about the results, this could be a bit more general, explaining the relevance of the study, the methods applied and the overall conclusions of the study.

Re: The abstract was revised as follows (page 1 lines 16-38 in the revised manuscript):

“The impacts of human activity (denoted by population), economic and social development (denoted by Gross Domestic Product – GDP) on extremely wet/dry (or drought) events are important for human to tackle extreme hazards. This research aims to investigate the variations of maximum values (SPEI_MAX) and minimum values (SPEI_MIN) of 12-month standardized precipitation evapotranspiration index (SPEI_12-month) for the selected 525 sites at different socioeconomic development levels (SDLs) (classify by population and GDP) in China over 2000-2018, and to analyze the impacts of increased population/GDP/SDLs on extremely wet/dry events. The linear correlations between SPEI_12-month/SPEI_MAX/SPEI_MIN and population/GDP were conducted for all the sites. The relationship between linear slopes of population (Popu_LS)/GDP(GDP_LS), SPEI_MAX (SPEI_MAX_LS)/SPEI_MIN (SPEI_MIN_LS) were further studied. The results showed that, the extremely wet events denoted by SPEI_MAX became worse and the extreme drought events denoted by SPEI_MIN tended to be milder over time. The year 2016 and 2011 were extremely wet and extremely dry in China. There were general increasing trends in SPEI_MAX and decreasing trends in SPEI_MIN as the SDL increased from 1 to 6. This gradual, continuous increase/decrease has been affected potentially for levels 5 and 6. Moreover, extremely wet events were more severe in developed big municipal cities of higher SDLs and extreme drought events were more severe for lower SDLs. This research can supply references for policy makers to prevent extreme disasters.”

• The first two paragraphs of the introduction provide a lot of general information about flood and drought risk and studies of which I fail to see the relevance for this particular paper. The same for the third paragraph of the introduction, this gives a lot of information about several flood or drought events in China that have been studied, but the relevance of these specific studies and places is not clear. This is then followed by one sentence where it is claimed that there has been little research on the impacts of population and economic and social development without any references to substantiate this claim. In fact, there are quite some studies that investigate the exposure and vulnerability and how they influence flood risk, it may be worth looking into those studies to see what others investigated and what this study adds to the existing literature. The objective of the study, why this is relevant and what it adds to existing literature is not very clear to me. Instead the introduction finishes with a description of indicators that are used, which belongs in the methods section.

Re: We shortened the first paragraph and rewritten the following paragraphs in Introduction. The revision is as follows (highlighted parts, pages 1-3 lines 43-145 in the revised manuscript):

1. Introduction

The extreme climate events, including extreme precipitation events [1], extreme temperature events [2], extremely wet/dry events [3], have enormous and potentially severe impacts on human, agriculture, economy and ecology. The extremely wet/dry events have much uncertainty, sensitivity/exposure/vulnerability as well as high risks all over the world historically [4–8]. The extremely wet/dry events were unpredictable severe or unseasonal and led to enormous loss of life and destruction and posed major impediments of human security and sustainable socioeconomic development [9,10].

The evolution and mechanics of mild, severe or extremely wet/dry events [11,12] are complicated, which were caused not only by climate variability, but also human activities. Liu et al. [13] suggested a substantial increase in the Atlantic intertropical convergence zone swing induced severe droughts/floods in the Atlantic-rim countries by using state-of-the-art climate models under a high-emission scenario. Collins [14] tested an increasing trend of flood magnitudes after 1970 in New England watersheds with dominantly natural streamflow, and manifested that this timing was broadly synchronous with a phase change in the low frequency variability of the North Atlantic Oscillation. Daksiya et al. [15] investigated the climate change and urbanization (human activity) effects on flood protection decision-making, they concluded that climate change had a higher impact compared to urbanization on the flood protection decisions.Modarres et al. [7] analyzed the changes of extremely wet/dry events in Iran. They suggested that the increase in flood magnitude and drought severity were attributed partly to annual rainfall negative trend/maximum rainfall increasing trend and land use changes/inappropriate water resources management policies (human activity). Farhidi [16] explored whether environmental policies (of energy consumption, gross domestic product-GDP, population, technology, head of the state’s political affiliation, carbon emission and waste generation) have been impacted by extreme climatic events (like droughts, floods, storms, etc.). They concluded that policymakers make more imminent putting human life is at risk. Brunner et al. [17] stated that the extreme events can be significantly influenced by human flow regulations through hydropower production, water abstraction, or water diversions, however, data on human impacts, such as land use and channel morphology changes, water abstractions, or reservoir regulations, lack sufficient temporal and spatial detail, or are not available at all. Considering data availability, till present the researches of human-activity influences on extreme precipitation events are still limited. The issues about how population and GDP increases affect extreme precipitation events and what were interactions between them are still unclear.

China has also been suffered from the extremely wet/dry events with large losses. The duration of extremely wet/dry events maybe short, but could cause great damage. As to extremely wet conditions, Wang et al. [18] investigated the extreme stream-flow of the Dongjiang River basin in southern China during 1956-2004. According to the yearbooks, the years 1998 and 2016 experienced extremely wet in China, which have caused agricultural disaster area of 22.29 and 26.1 ×10⁶ ha, the death number of 3656 and 684, the affected population of 2.3 and 1.02 ×10⁹, the direct economic loss of 2484 and 3661 ×10⁹ RMB, and the destroyed houses of 566 and 43 ×10⁴, respectively [19]. In July, 2021, Zhengzhou of Henan Province was hit by a rainstorm of rare high intensity, which caused severe flooding (Zhao et al., 2022; Fan et al. 2022) [20,21]. As to extremely dry conditions, the extreme drought events in 2009-2010 over southwestern China including Yunnan, Sichuan and Guizhou provinces [22] have affected around 21 million people short of drinking water, and economic losses reached nearly US$ 30 billion. The extreme drought between June and Augest in 2022 was one of the worst droughts in around 60 years, which have caused water levels in China’s largest freshwater lake, Poyang Lake, to drop by almost 10m [23]. Although climate variability may have contributed more than huanman activities, due to the complex causes of extreme events, further studies are necessary for reveal the connection between extremely wet/dry events and human activity indicators in China [15].

Previous studies mainly focused on natural characteristics and spatial-temporal variations of extremely wet/dry events. Little works have been carried out on the impacts from human activity (denoted by population), economic and social development (denoted by GDP) on extremely wet/dry events which have complex internal structures. Among the proposed indicators, the standardized precipitation and evapotranspiration index (SPEI) has its multiscalar characteristics and with implications in denoting global warming when compared with SPI [24]. The maximum SPEI (SPEI_MAX) and minimum SPEI (SPEI_MIN) denote extremely wet/dry events well, respectively. Population and GDP could accurately reflect human activity and social development levels of a place because of its stability, which were widely used [25].

The objectives of this study were: (I) to detect the spatial-temporal variations of population, GDP, and the extremely wet/dry indicators, site specifically over 2000-2018 in mainland China. The selected 525 sites are divided into 6 socioeconomic development levels (SDL); (II) to identify the spatial variations of linear slopes (LS) of population (Popu_LS), GDP_LS, and the extreme wet/dry indicators; (III) to analyze the relationship between changes (LS) of extremely wet/dry events and human-activity/SDL in order to further reveal the impacts of population and GDP increase on extreme events; (IV) to investigate the influences of different SDL on extremely wet/dry events.

References:

8. Jayadas, A.; Ambujam, N.K. A Quantitative Assessment of Vulnerability of Farming Communities to Extreme Precipitation Events in Lower Vellar River Sub-Basin, India. Environ Dev Sustain 2022, doi:10.1007/s10668-022-02645-4.
9. Liu, Y.; Cai, W.; Lin, X.; Li, Z. Increased Extreme Swings of Atlantic Intertropical Convergence Zone in a Warming Climate. Nat. Clim. Chang. 2022, 12, 828–833, doi:10.1038/s41558-022-01445-y.
10. Daksiya, V.; Mandapaka, P.V.; Lo, E.Y.M. Effect of Climate Change and Urbanisation on Flood Protection Decision-Making. Journal of Flood Risk Management 2021, 14, e12681, doi:10.1111/jfr3.12681.
11. Farhidi, F.; Madani, K.; Crichton, R. Have Extreme Events Awakened Us? Sustainability 2022, 14, 7417, doi:10.3390/su14127417.
12. Brunner, M.I.; Slater, L.; Tallaksen, L.M.; Clark, M. Challenges in Modeling and Predicting Floods and Droughts: A Review. WIREs Water 2021, 8, e1520, doi:10.1002/wat2.1520.
13. Zhao, X.; Li, H.; Qi, Y. Are Chinese Cities Prepared to Manage the Risks of Extreme Weather Events? Evidence from the 2021.07.20 Zhengzhou Flood in Henan Province 2022.
14. Fan, J.; Liu, B.; Ming, X.; Sun, Y.; Qin, L. The Amplification Effect of Unreasonable Human Behaviours on Natural Disasters. Humanit Soc Sci Commun 2022, 9, 1–10, doi:10.1057/s41599-022-01351-w.
15. Mallapaty, S. China’s Extreme Weather Challenges Scientists Trying to Study It. Nature 2022, 609, 888–888, doi:10.1038/d41586-022-02954-8.

 

• From the methods section it is not clear to me how the sites are defined. Is this based on measuring stations? And how is then the socioeconomic level determined? For which area?

Re：Yes, the data are based on measuring stations.

The socioeconomic level was determined by classifying GDP, Popu and GDP&Popu. Please refereed to the detail description of the section 2.2 as follows (page 4, lines 162-173):

“A total of 525 sites were selected for the study. The observed weather data were collected from the Chinese Meteorological Data Sharing Service Network (http://data.cma.cn/) with strict quality control. The missing data were interpolated by linear regression equation with the arithmetic average of adjacent days or months. 

The population data and GDP data at the selected 525 sites were collected from the National of Bureau Statistics, the Statistical yearbook of China (http://www.stats.gov.cn/) and the Winds Database (https://www.wind.com.cn/). The selected 525 sites were divided into 6 SDLs first by population in 2018, then by GDP in 2018, finally by both population and GDP in 2018 (Table 1).

Table 1. SDLs of the selected sites classified by population, GDP and both. RMB-Chinese currency.

SDL	Classified by population (×104)	Classified by GDP (×108 RMB)	Classified by both population (×104) and GDP (×108 RMB)
1	<50	<100	Population<50&GDP<100
2	50-100	100-400	50≤Population<100&100≤GDP<400
3	100-300	400-1000	100≤Population<300&400≤GDP<1000
4	300-500	1000-2000	300≤Population<500&1000≤GDP<2000
5	500-1000	2000-10000	500≤Population<1000&2000≤GDP<10000
6	≥1000	≥10000	Population≥1000& GDP≥10000

• The methods provide detailed equations for the Penman-Monteith equation, which in my opinion is not necessary. Instead, it would be good to describe which data and parameters are used to calculate the ET and, more importantly, how are the linear slopes calculated and how are the slopes of the different variables compared? There is a very short section with two sentences on the linear slope estimation, which only describes the names of variables. It is not clear what is being calculated and why.

Re：We removed the detailed descriptions of the Penman-Monteith equation (Equations 2 to 5) and shortened the descriptions, the revisions are shown as follows (pages 5-6, lines 203-211):

“The albedo (=0.23), the actual and maximum possible sunshine durations, the Stefan-Boltzmann constant (4.903×10^-9 MJ K^-4 m^-2 d^-1), the maximum and minimum absolute temperatures, and the coefficients a_s and b_s in the Angstrom equation used for calculating ET₀. In order to get better accuracy, the calibrated values of a_s and b_s based on the observed daily R_s at 139 stations of China were used [30], which were also used for the nearby stations using the Thiessen polygon method in ArcGIS 10.2 software. Detail computation procedures of the other variables in Equation 1 is referred to Allen et al. [29].”

We added the detailed descriptions of the analysis for linear slope, the revisions are shown as follows (page 7, lines 229-239):

 “2.4. Linear slope estimation

In order to compare how the changes of population and GDP impact the extremely wet/dry events, the linear slope (LS) values of annual population, GDP and SPEI, SPEI_MAX_LS, SPEI_MIN over 2000 - 2018 versus time (year) are obtained (simplified as Popu_LS, GDP_LS, SPEI_LS, SPEI_MAX_LS, SPEI_MIN_LS below, respectively). A negative LS indicates that the time series has a downward trend, while a positive LS indicates an upward trend. The Pearson correlations between pairs of Popu_LS/GDP_LS and SPEI_LS/SPEI_MAX_LS/SPEI_MIN_LS are conducted to reveal the responses of extreme wet/dry events to human activity-induced population and GDP changes. The larger the coefficient of determination (R²), the closer the relationship between two variables. The calculation is implemented through R 3.4.3.”

• The authors use the SPEI for a 12 month period to define both drought and flood events. While for drought this is a relevant time period, the 12 month cumulative precipitation is not relevant for flooding. Therefore, I would suggest to the authors to choose a different method for defining flood and drought events (perhaps a threshold method).

Re：First, we found that the term “flooding” is not suitable for describing the extremely wet events because the formation of flooding is a complex process and not only related to high water deficit, while for dry events, both term “drought” and “dry” is suitable. Therefore we changed the term flooding to “wet” and drought to “dry” (we also used drought under some conditions) in the revised paper.

Second, a 12-month SPEI could be used to explore the impact of precipitation change on hydrological systems (Ayugi et al., 2020; Polong et al., 2019). Ojara et al. (2022) concluded that the temporal development of drought and wet events using 3-month SPEI had higher temporal frequencies, which stabilized over the 12-month SPEI timescale. Therefore, the 12-month SPEI has higher stability than smaller timescales and it can still keep the details of temporal variations when compared to timescales longer than 12-month.

References:

29. Ayugi, B.; Ngoma, H.; Babaousmail, H.; Karim, R.; Iyakaremye, V.; Lim Kam Sian, K.T.C.; Ongoma, V. Evaluation and Projection of Mean Surface Temperature Using CMIP6 Models over East Africa. Journal of African Earth Sciences 2021, 181, 104226, doi:10.1016/j.jafrearsci.2021.104226.
30. Polong, F.; Chen, H.; Sun, S.; Ongoma, V. Temporal and Spatial Evolution of the Standard Precipitation Evapotranspiration Index (SPEI) in the Tana River Basin, Kenya. Theor Appl Climatol 2019, 138, 777–792, doi:10.1007/s00704-019-02858-0.
31. Ojara, M.A.; Yunsheng, L.; Babaousmail, H.; Sempa, A.K.; Ayugi, B.; Ogwang, B.A. Evaluation of Drought, Wet Events, and Climate Variability Impacts on Maize Crop Yields in East Africa During 1981–2017. Int. J. Plant Prod. 2022, 16, 41–62, doi:10.1007/s42106-021-00178-w.

• The results are difficult to interpret, since it is unclear which methods are being used and what the aim of the study is. In general, the authors seem to draw their conclusions based on estimating relationships between change in GDP/Population and change in SPEI, while they are reporting very low R²values for these fits, which indicates they are not really explaining the variation in the variables.

Re：SPEI (as well as the maximal, minimal values) and the socioeconomic indices (popu, GDP) are used for investigate the influences of human-activities on extremely wet/dry events. The linear slopes of these variables and the mutual correlations are also studied to find the change extent and the changes between pairs of variables. Our aim is to investigate how human-activity affect the extremely wet/dry events in China.

The low R² values (0.00002-0.12) were observed in Figures 4 and 6, but we can find that there were connections between change in GDP/Population and change in SPEI. As GDP_LS /Popu_LS increased, the response of extremely wet/dry events changed at the six SDLs, namely from decrease response to increase response when SDL increased from 1 to 6, which implied increasing population&GDP intensified extreme events in China.

The R² values in Figure 5 were 0.28, 0.40, 0.26 and 0.47, which was not low. It showed increasing human activity may have led to more extremely dry events than extremely wet events (higher slopes). This result is reasonable since greater population consume more water for living and agricultural production.

The R² values in Figure 7 are 0.65, 0.12 and 0.83. It showed less connections between SPEI_MAX (extremely wet events) vs. SDL than SPEI_MIN (extremely dry events) vs. SDL. This results confirmed results from Figure 4, also showed closer connections between human-activity on extremely dry events.

The R² values in Figure 10 are 0.30, 0.57 and 0.50. It showed as SDL increased, changes in precipitation and D increased while change in ET_o decreased. This result partially explained the connections between extremely wet/dry events and human activities, as shown in Figure 7.

It is reasonable that the correlations between some variables are low because there was great climate variability with complex circulation process.

Thank you very much for addressing so much helpful comments which led us to think deeper about our research results.

Author Response File: Author Response.pdf

Reviewer 2 Report

Article:

Impacts of different socioeconomical development levels on extreme flooding/drought events in mainland China

Authors: Qingfeng Zhang, Yi Li, Qiaoyu Hu, Ning Yao, Xiaoyan Song, Fenggui Liu, Bakhtiyor Pulatov, Qingtao Meng and Puyu Feng

Journal: Water

Manuscript No.: 1918528

 

This research investigated the variations of maximum values of 12-month standardized precipitation evapotranspiration index (SPEI_12-month) and minimum values of SPEI_12-month for different socioeconomical levels in China over 2000-2018. The manuscript addressed an important issue in extreme event researches. Some quantitative results were shown. There are some interesting findings that worth attention. Before accepted, I suggest a minor revision for the manuscript. Please make some changes if needed and reply the below comments point to point: (1) The authors use “socioeconomical development level” to show the scales of the studied sites or region. But in many cases they also use “socioeconomical level”. I suggest to consistently use “socioeconomical development level” or simplified to “SDL” from the beginning to the end, including in tables and Figures. (2) In Eq. (9), you use P(D), while in Eq. (10), you use P, is P and P(D) same? If same, I suggest you use P(D) consistently. In addition, in lines 158 and 164 you describe D= (=P-ET0 in mm), here P is precipitation. Please differ these terms accurately. (3) When you describe the computation processes of SPEI, Please reference to Spatiotemporal Comparison of Drought in Shaanxi–Gansu–Ningxia from 2003 to 2020 Using Various Drought Indices in Google Earth Engine. you use both x and D for Independent Variable. I suggest you only use D and keep consistently in the entire manuscript. (4) Line 168, remove “(similar with SPI)” because you didn’t study SPI in this paper. (5) Line 190, remove events from “extreme flooding/drought events conditions”. (6) Too long titles for sections 3.4.1, 3.5 and 3.6.2.

 

(7) Shorten the conclusion and show the highlighted conclusions.

Author Response

This research investigated the variations of maximum values of 12-month standardized precipitation evapotranspiration index (SPEI_12-month) and minimum values of SPEI_12-month for different socioeconomical levels in China over 2000-2018. The manuscript addressed an important issue in extreme event researches. Some quantitative results were shown. There are some interesting findings that worth attention. Before accepted, I suggest a minor revision for the manuscript.

Re: We thank the reviewer’s positive comments very much.

Please make some changes if needed and reply the below comments point to point:

• The authors use “socioeconomical development level” to show the scales of the studied sites or region. But in many cases they also use “socioeconomical level”. I suggest to consistently use “socioeconomical development level” or simplified to “SDL” from the beginning to the end, including in tables and Figures. 

Re: We have simplified “socioeconomical development level” to “SDL” in the revised manuscript. Therefore it was clear in the revised manuscript for smooth reading.

• In Eq. (9), you use P(D), while in Eq. (10), you use P, is P and P(D) same? If same, I suggest you use P(D) consistently. In addition, in lines 158 and 164 you describe D= (=P-ET0 in mm), here P is precipitation. Please differ these terms accurately.

Re: We use Pre for precipitation in the text and Figures so the it can be distinguished with probability (P).

• When you describe the computation processes of SPEI, Please reference to Spatiotemporal Comparison of Drought in Shaanxi–Gansu–Ningxia from 2003 to 2020 Using Various Drought Indices in Google Earth Engine. you use both x and D for Independent Variable. I suggest you only use D and keep consistently in the entire manuscript.

Re: We have cited the recommended reference in the revised manuscript.

We use only D in the revised manuscript to avoid confusion.

References:

36. Zhao, X.; Xia, H.; Liu, B.; Jiao, W. Spatiotemporal Comparison of Drought in Shaanxi–Gansu–Ningxia from 2003 to 2020 Using Various Drought Indices in Google Earth Engine. Remote Sensing 2022, 14, 1570, doi:10.3390/rs14071570.

 

• Line 168, remove “(similar with SPI)” because you didn’t study SPI in this paper.

Re: “(similar with SPI)” was removed from the manuscript.

• Line 190, remove events from “extreme flooding/drought events conditions”.

Re: “events” has been removed from “extreme flooding/drought events conditions”, and “floodin/drought” was changed to “wet/dry”.

• Too long titles for sections 3.4.1, 3.5 and 3.6.2.

Re：Title of 3.4.1 was changed to “LSs of socioeconomic - dry/wet indices in different SDLs”, title of 3.5 was changed to “The variations of LSs for dry events at different SDLs”，and title of 3.6.2 was changed to “Occurrence of extremely wet/dry events at different SDLs”

• Shorten the conclusion and show the highlighted conclusions.

Re：The conclusions were shortened and the highlighted points were indicated (pages 16-17, lines 509-535):

“The selected total 525 sites were divided into 6 SDLs by population and GDP in 2018. The population and GDP of each level tended to increase, especially in big cities such as Beijng, Shanghai, Guangzhou, Shenzhen which belonged to SDL 6 and located mostly in developed eastern China. Extremely wet events denoted by SPEI_MAX have become worse and extremely dry events denoted by SPEI_MIN turned to be milder over time. The years 2016 with averaged SPEI_MAX of SPEI_12-month of 1.83 and 2011 with averaged SPEI_MIN of SPEI_12-month of -1.58 were found to be extremely wet/dry years in China, respectively. This gradual, continuous increase of SPEI_MAX or decrease of SPEI_MIN has been affected potentially by the fast extension of population and rapid growing of GDP and urbanization, especially for SDLs of 5 and 6. The extremely wet events were more severe in developed cities at high levels and the extremely dry events were more severe in less developed sites at low levels.

The main reason may be urban rain island effect, heat island effect and the underlying surface condition changes. This research investigated the variations of extremely wet/dry events in different SDLs in China from social and economic respects. It could be supplied as references for policy makers to prevent extremely wet/dry events for prefecture-level or county-level cities in different levels. Further studies need to be carried out on the contribution of human activities and climate change to extremely wet/dry events.”

Thank you very much for addressing so much helpful comments which led us to think deeper about our research results.

Author Response File: Author Response.pdf

Reviewer 3 Report

1.     This paper reveals the statistical correction of urbanization with extreme floods and drought. As in common sense, extreme floods should occur in the low-lying and downstream areas of the catchment. And extreme flooding always transforms into by flow path derived from the terrain. How do the authors deal with this phenomenon?

2.     What is the standard for describing extreme flooding events? The Flooding may be different in a different areas.

3.     What is the inner reason that the socio-economical levels affected extreme flooding and extreme drought?

 

4.     As in Line 304, The highest 303 occurrence times of extreme flooding events were 22 times at Yumen in Gansu province. What is the reason the extreme flooding events occur here? 

 

 

5.     Please pay attention to adding a legend, scale, and compass for all subfigures in the graphic. And the legend should contain all the elements drawn on the map.

Author Response

• This paper reveals the statistical correction of urbanization with extreme floods and drought. As in common sense, extreme floods should occur in the low-lying and downstream areas of the catchment. And extreme flooding always transforms into by flow path derived from the terrain. How do the authors deal with this phenomenon?

Re: Through thinking, we found that the term “flooding” is not suitable for describing the extremely wet events because the formation of flooding is a complex process and not only related to high water deficit, while for dry events, both term “drought” and “dry” is suitable. Therefore we changed the term flooding to “wet” and drought to “dry” (we also used drought under some conditions for “dry”) in the revised paper. Thus in this research the extremely wet events only referred to high D (precipitation –ET₀) conditions.

• What is the standard for describing extreme flooding events? The Flooding may be different in a different area.

Re：The standard/classification of extremely wet events are when SPEI≥2. Yes, we agree well with the reviewer that the extremely wet events are different in a different area.

Table 2. Wet/dry severity level classified by SPEI value.

SPEI range	Severity level
SPEI≥2	Extremely wet
1.5≤SPEI<2	Severely wet
1.0≤SPEI<1.5	Moderately wet
0.5≤SPEI<1.0	Mildly wet
-0.5<SPEI<0.5	Normal

• What is the inner reason that the socio-economical levels affected extreme flooding and extreme drought?

Re: We thank the reviewer addressed a very good question which let us to think deeper about the issue. For formally respond to the comment, we added following paragraph in Discussion of the revised manuscript (page 7, lines 488-507):

“The inner reason that the SDLs affected extreme wet/dry events are complex [16,17] [16,17]. The extremely wet/dry events in China are both affected by climate variability and human-activities, of which, climate change played more roles [46,47]. From one hand, climate has great variability and its changes are affected by diverse factors. For China, the uneven precipitation distribution with synchronically rainfall-heat as well as summer monsoon cause generally wet/hot events in summer and cold/dry events in winter [18] [18,48]. In addition, the atmospheric circulations such as ENSO, west Pacific subtropical ridge, tropical cyclone or typhoon also contributed to the extremely wet/dry events [19, 22, 23] [19,22,23]. From another hand, various human-activities intensified the severity of the wet/dry events [49]. These human-activities include the industrial activities which increased the greenhouse  gas concentrations [50], agricultural management activities which consumed more water in plantings [51], the water allocation/diversion/adjustment  activities which changed the natural water distribution and evapotranspiration patterns of waterbodies and watersheds [52], the over-exploration of water resources, and the urbanization (denoted by increasing population and GDPs) which have generated more urban inland inundation events. These human activities have disturbed the natural processes, induced a more vulnerable and sensitivity world, and intensified the occurrence of the extremely wet/dry events. In this paper we only studied the SDL effects on extremely wet/dry events, ef-fects of more types of human-activities on extremely wet/dry events are necessary in future study.”

• As in Line 304, The highest 303 occurrence times of extreme flooding events were 22 times at Yumen in Gansu province. What is the reason the extreme flooding events occur here?

Re：For discuss this comments, we added the following paragraph in the revised manuscript as follows (page 14, lines 406-415):

“In fact, Yumen in Gansu is a site with low precipitation. The extreme wet events occurred here 22 times were shown by SPEI_MAX. Except Yumen, the results also showed high occurrence times of extreme events in several sites of northwest China (arid and semi-arid zone). Since SPEI is a standardized index, it showed variations of D around average levels. Such extreme wet events in some low precipitation areas were observed because at the occurred times the D values were much higher than the average D. This was a shortcoming of standardized indices (not only SPEI) which denoted the high/low variations of D compared to the referenced standard (average value) but had nothing to do with the actual value of average D. Thus the high/low values maybe exaggerated if D values is relatively low.”  

• Please pay attention to adding a legend, scale, and compass for all subfigures in the graphic. And the legend should contain all the elements drawn on the map.

Re：We re-drew all the figures as you required.

Thank you very much for addressing so much helpful comments which led us to think deeper about our research results.

Author Response File: Author Response.pdf

Round 2

Reviewer 3 Report

This manuscript can be accepted.