Identification of Drought Events and Correlations with Large-Scale Ocean–Atmospheric Patterns of Variability: A Case Study in Xinjiang, China
Round 1
Reviewer 1 Report
A good job showing the complicated nature of drought. Analysis shows both the spatial and temporal inhomogeneity.
Good use of graphics except figure 2a needs an identification of UF and UB as plotted M_K statistics.
In your conclusion can you speculate on what other drivers may be identified. ENSO is not as convincing as AMO.
Author Response
Response to Reviewer 1 Comments
Point 1: A good job showing the complicated nature of drought. Analysis shows both the spatial and temporal inhomogeneity.
Response 1: We thank the reviewer for recognizing the positive aspects of the work.
Point 2: Good use of graphics except figure 2a needs an identification of UF and UB as plotted M_K statistics.
Response 2: Following the reviewer’s suggestion, we have added at the Page 7 Line 215-220, which provide the identification of UF and UB as plotted M_K statistics. Please see below: In here, UF curve indicate the statistics series of the standard normal distribution, and UB curve indicate the reverse statistics series. As the line UF is over the confidence line (p=0.05, green line), the cross point of UF and UB is the start point of abrupt change in this series.
Figure 2. Temporal variability (a, straight lines denote linear trend) and M-K test (b) of annual SPEI for the period 1961–2015 in Xinjiang. In here, UF curve indicate the statistics series of the standard normal distribution, and UB curve indicate the reverse statistics series. As the line UF is over the confidence line (p=0.05, green line), the cross point of UF and UB is the start point of abrupt change in this series.
Point 3: In your conclusion can you speculate on what other drivers may be identified. ENSO is not as convincing as AMO.
Response 3: Yes. In this paper, we indicated that not all ENSO events are responsible for drought events in this region, which means that these droughts are apparently not explained solely by ENSO events, and other drivers remain to be discovered. Our study suggests the possibility of the ENSO links to the drought in Xinjiang but further analysis is needed for a better understanding of such mechanisms.
Author Response File: 
Author Response.pdf
Reviewer 2 Report
The paper presents an interesting study of spatial and temporal changes in drought occurrence in north-west China, Xinjiang region. The authors apply standardized precipitation-evapotranspiration index SPEI to study drought conditions in the area in the years 1961-2015. The subject of the study is interesting to a wide public. In particular, the influence of teleconnection patterns on drought variability is interesting. However, the authors are asked to put more effort in explaining the results of their research.
Specific comments:
Lines 46-58 in the Introduction require re-writing. The style is confusing and the messages are not clear.
Lines 64-68: style requires correction.
Line 119 12-month SPEI is used in parallel with annual SPEI (e.g. line 158). Please unify the notation. In addition a short definition of 12-month SPEI would be helpful for a reader not familiar with that index.
Lines 138-140 - sentence not clear.
Line 165-166 - not clear - more explanation is required.
Figure 2 - too small, UF and UB not defined.
Figure 3 - a. and b. missing.
Figure 4 - the legend requires better explanation, also in the text the definition of DFD should be given.
Figure 5 - a1, b1 and c1 are missing. There is no Figure 5a3 (line 225).
Lines 205-259 (sections 3.2 and 3.3) require some more work. Those sections are difficult to read and seem to be very chaotic. The figures in the Appendix are referred to without explaining why the Appendix was created. I could not find Fig. A2a3
Lines 252-259. I am not so much convinced about the linearity of Fig. 6 b and c. It is not clear what the authors mean by the “ratio of affected station” .
Lines 323-324: sentence not clear - What do the authors mean?
Figure A2 - a1, b1 and c1 are missing
Author Response
Response to Reviewer 2 Comments
The paper presents an interesting study of spatial and temporal changes in drought occurrence in north-west China, Xinjiang region. The authors apply standardized precipitation-evapotranspiration index SPEI to study drought conditions in the area in the years 1961-2015. The subject of the study is interesting to a wide public. In particular, the influence of teleconnection patterns on drought variability is interesting. However, the authors are asked to put more effort in explaining the results of their research.
We thank the reviewers for their comments to improve the manuscript, as well as the opportunity given by the Editor to submit a revised version. We have addressed all of the points made by each reviewer and we have included a point-by-point response to each reviewer comment below.
Specific comments:
Point 1: Lines 46-58 in the Introduction require re-writing. The style is confusing and the messages are not clear.
Response 1: Thank you for your valuable advice.
Page 2 Line50-62: the statements of “The air temperature is widespread observed to be higher in past decades, especially in arid and semi-arid regions [13]. Central Asia, is one of the largest arid and semi-arid regions in the world, and is likely to be strongly influenced by global warming [14]. Surface air temperature experienced a rapid warming in mid-latitude central Asia, including Xinjiang [15-16]. Xinjiang is located in the arid region of Northwest China. Shi et al. [17] reported a climatic transition from warm-dry to warm-wet in Northwest China based on observed data. Most evidence for hydro-climatic and environmental change in Northwest China have given strong support to this observation [18, 19]. Chen et al [20] found that the average temperature rise in the northwest arid region is up to 0.34 °C/10a (p<0.01), and the precipitation rise showed a dynamic increase of 75% in the late 1980s and 88% in the 1990s for the northwest arid region. Fang et al. [21] also suggested this climatic transition in Xinjiang based on the Coupled Model Inter-comparison Project-Phase 5 (CMIP5) model simulation results. Dramatic changes in climatic conditions can bring about adverse effects, drought is one of them.” was corrected as “Surface air temperature experienced a rapid warming in mid-latitude central Asia, including Xinjiang [13-16]. Xinjiang is located in the arid region of Northwest China. A climatic transition from warm-dry to warm-wet had reported in Xinjiang based on observed data [17]. Most evidence for hydro-climatic and environmental change in Xinjiang have given strong support to this observation [18-21]. Dramatic changes in climatic conditions can bring about adverse effects, drought is one of them.”
Point 2: Lines 64-68: style requires correction.
Response 2: Thank you for your valuable advice.
Page 2 Line68-72: the statements of “Drought affected and damaged area has increased over the past two decades, and the annual damaged area reached 0.36×107 ha in the 1990s [25]. In recent years, different drought indices, study durations, analysis technologies, and regions in Xinjiang have been investigated. SPI and SPEI have been frequently utilized with the analysis of drought [26]” was corrected as “Drought affected and damaged area has increased over the past two decades”, and deleted other sentences.
Point 3: Line 119 12-month SPEI is used in parallel with annual SPEI (e.g. line 158). Please unify the notation. In addition a short definition of 12-month SPEI would be helpful for a reader not familiar with that index.
Response 3: Thank you for your valuable advice. We have added at the Page 5 Line153-156, which provide the identification of the 12-month SPEI and annual SPEI. Please see below:
SPEI were calculated for each month of the year, and time scales of 12 months (referred to as 12-month SPEI) were selected for analysis. Annual SPEI index was calculated at each station for each year of 12-month SPEI index, and the average of all stations were used for the regional analysis.
Point 4: Lines 138-140 - sentence not clear.
Response 4: Thank you for your valuable advice.
Page 6 Line183-185: the statements of “The El Niño (La Niña) is such that the 3-month running mean Sea Surface Temperature (SST) deviation for Nino.3.4 (5°S-5°N, 170°W-120°W) continues 0.5°C (−0.5 °C) or higher (lower) for 5 consecutive months or longer” was corrected as “The Nino3.4, as the index describing the ENSO phenomenon was used. The Nino3.4 is the mean Sea Surface Temperature (SST) anomaly in the region bounded by 5°N to 5°S, from 170°W to 120°W”.
Point 5: Line 165-166 - not clear - more explanation is required.
Response 5: Thank you for your valuable advice.
Page 6 Line213: The statement of “This indicates the aggravated drought trend in Xinjiang based on the SPEI” was corrected as “This indicates the increased drought trend in Xinjiang based on the SPEI”.
Point 6: Figure 2 - too small, UF and UB not defined.
Response 6: Following the reviewer’s suggestion, we have added at the Page 7 Line 215-220, which provide the identification of UF and UB as plotted M_K statistics. Please see below:
In here, UF curve indicate the statistics series of the standard normal distribution, and UB curve indicate the reverse statistics series. As the line UF is over the confidence line (p=0.05, green line), the cross point of UF and UB is the start point of abrupt change in this series.
Figure 2. Temporal variability (a, straight lines denote linear trend) and M-K test (b) of annual SPEI for the period 1961–2015 in Xinjiang. In here, UF curve indicate the statistics series of the standard normal distribution, and UB curve indicate the reverse statistics series. As the line UF is over the confidence line (p=0.05, green line), the cross point of UF and UB is the start point of abrupt change in this series.
Point 7: Figure 3 - a. and b. missing.
Response 7: Following the reviewer’s suggestion, we have added the (a) and (b) at the Figure 3. Please see below:
Figure 3. The inter-annual variability of the SPEI values at different timescale in North Xinjiang (a) and South Xinjiang (b) (the vertical axis indicates the timescale from 1 to 24 months, and the horizontal axis indicates the year from 1961 to 2015)
Point 8: Figure 4 - the legend requires better explanation, also in the text the definition of DFD should be given.
Response 8: Following the reviewer’s suggestion, we have added the definition of DFD at the Page 5 Line 167-169 and explained the legend at the Figure 4. Please see below:
Page 5 Line 167-169: Drought frequency difference (DFD) is defined as the average annual number of drought months with different category.
Figure 4. Frequency of drought and wetness variability before and after 1997(a), and spatial distributions of drought frequency difference (DFD) between 1997–2015 and 1961–1996 in Xinjiang (b. mild drought, c. moderate drought, and d. extreme drought), where the DFD is defined as the average annual number of drought months with different category (unit: times per year, 1997–2015 minus 1961–1996)
Point 9: Figure 5 - a1, b1 and c1 are missing. There is no Figure 5a3 (line 225).
Response 9: Following the reviewer’s suggestion, we have added the (a1), (b1) and (c1) at the Figure 5. Please see below:
Figure 5. The first three loading vectors and their corresponding principal components (PC) series for the 1961-2015 (a1–c1: Empirical orthogonal function (EOF) analysis for annual SPEI. a2–c2: Corresponding principal components (PCs) for annual SPEI)
Page 9 Line 282, 285: The statement of “Figure 5a3” was corrected as “Figure 5c3”.
Point 10: Lines 252-259. I am not so much convinced about the linearity of Fig. 6 b and c. It is not clear what the authors mean by the “ratio of affected station” .
Response 10: Following the reviewer’s suggestion, we have modified the Fig. 6, and explained the “ratio of affected station”. Please see below:
Page 11 Line 313-323: The means of magnitude, intensity, and ratio of affected station for different drought durations were used to understand drought severity with duration. Figure 6 plots the relationships of drought magnitude, intensity, and ratio of affected station with duration. Drought magnitude follows an exponential curve with duration, as illustrated by the exponential functions associated with the M-D curves and with a coefficient of determination (R2) of 0.98. This means that drought magnitude increased exponentially with duration. Intensity displayed a linear relationship with duration with a coefficient of determination of 0.58; this implies that drought intensity may increase with duration. A log function was used to determine the A-D relationship, with R2=0.92. This implies that the drought-affected area decreases logarithmically with duration. These relationships would also be useful for drought frequency analysis and drought impact assessment.
Page 5 Line 167-169: Ratio of affected station (A, unit: %) is the ratio of the number of stations where drought occurred to the total number of stations.
Figure 6. Changes of the magnitude (a), intensity (b) and ratio of affected station (c) with duration of the drought events
Point 11: Lines 323-324: sentence not clear - What do the authors mean?
Response 11: Page 15 Line390-391: the statement of “This paper suggests that an obvious aggravating trend of drought in Xinjiang for 1961–2015 is based on SPEI” was corrected as “This paper suggests that an obvious decreasing trend of annual SPEI in Xinjiang for 1961–2015”.
Point 12: Figure A2 - a1, b1 and c1 are missing
Response 12: Following the reviewer’s suggestion, we have added the (a1), (b1) and (c1) at the Figure A2. Please see below:
Figure A2. The first three loading vectors and their corresponding principal components (PCs) series for the 1997-2015 (a1–c1): Empirical orthogonal function (EOF) analysis for annual SPEI. (a2–c2): Corresponding principal components (PCs) for annual SPEI).
Author Response File: Author Response.pdf
Reviewer 3 Report
Summary
This study used the 1961-2015 standardized precipitation evapotranspiration index (SPEI) drought index to assess spatio-temporal drought variability in Northwest China. Moreover, analysis intends to make a link of the drought in NW China to the Atlantic multidecadal variability and El Nino Southern Oscillation. These sound like a valuable piece of literature, but the paper is not in the state in its current form where these merits can be assessed fully. The reason for this is first because the paper not yet fully grammatically sound and also there few fundamental scientific misconceptions which should be fixed (described below in the detailed comments). I also think given the analysis presented in the paper the title of the paper is not appropriate therefore it should either be changed as I suggested below, or the analysis should be substantially changed and redone.
At this stage I would encourage the authors to have the paper go through a set of grammar corrections by a native English speaker. Once this is clear I would be in a better position to evaluate the paper. Therefore, my decision is the manuscript should undertake major revisions before being considered for publication.
I have enclosed comments below (which more significant comments above, and more specific ones below).
Major Comments:
I struggled a bit with this paper - principally because the writing is sometimes not grammatically correct, and statements are sometimes really long and hard to follow i.e lines 30-31:
“Drought is one of the most serious, widespread, and costliest natural disaster, caused impacts to 30 agriculture, ecosystems, hydrology, economic, and social activities “ which should be “Drought is one of the most serious, widespread and costliest natural disasters causing yearly… ‘ or ‘with impacts on’
Sometimes statements are too long – please sorthen statements longer than 2 lines line in lines 132-134 (the AMO definition).
You should try avoiding if possible using formulations like 'clearly different' (line 173) , 'obviously similar' or ‘the trend became even worse’ (line 175). I suggest to leave the science with its methods and numbers show results instead of using words as ‘obvious’, ‘even worse’ etc. Scientific writing is different to scientific oral communications and ‘clearly different’ or ‘obviously similar’ sounds like a non-scientific way of describing things. I suggest consulting a science writing book for eliminating such mistakes – they are really easy to fix once you read a bit on them ( like “ The Craft of Scientific Writing” https://www.amazon.co.uk/Craft-Scientific-Writing-Michael-Alley/dp/1441982876). I also found a bit non-scientific the fact that you write things as ‘the expected standards’ (line 98) or ‘extreme inspection’ (line 99). Again, such adjectives are not useful when it comes to science – please describe with methods and numbers what ‘expected standards’ means as well as what ‘extreme inspection’ means.
The paper needs more clarity and to be rewritten more as 1 whole than the present version which seems few different piece of work glued together with places where the reader is expected to have went through an entire body of literature to be familiar with what you are doing. For example, given that the SPEI index is at the core of this study writing the formula or the index along with a paragraph on it, rather than only citing other studies ( lines 113 to 115) would be useful.
Science wise, one of the main conclusions of the paper is that (line 21-22) “Both AMO and ENSO 21 events have a strong influence on drought in Xinjiang”. I m not sure I agree with that. I think the current analysis can’t draw such a conclusion based on a correlation of indices but no further causality or mechanistic analysis. I would formulate that coclusion in a milder way ‘Our study suggests the posibiloity of the AMO and ENSO links to the drought in Xianjiang but further analysis is needed for a better understanding of such mechanisms’. I would not list this as a major conclusion of the paper but rather as a possible direction of future research.
In Figure 2 a SPEI index for Central Asia is illustrated however this index is not defined spatially. I could not find where you described the latitude-longitude box used for this index. Please define it somewhere in the text. There is in line 30 mentioning of the index in Xinjiang but is not clear what coordinates you use.
Lines 30-35 introduce AMO and ENSO as ‘circulation indices’. Calling AMO and ENSO circulation indices is not fully correct. AMO is an index built based on SST anomalies variability while ENSO ( not the SO – southern Oscillation) although coupled ocean-atmosphere variability is not an exclusively ‘circulation’ related index. It is not clear therefore what you wanted to refer to as ‘circulation indexes. What kind of circulation did you have in mind? Was it oceanic or atmospheric circulation? As it comes across from the paper I think you tried to link the SPEI index over Nothern China to atmospheric circulation patterns globally. If this was the case the AMO and ENSO are probably not the best choice given the title of your paper and I’d suggest using indices like NAO, AO, some Jet Stream related indices or simply geopotential heights patterns at various altitudes. If however what you want is to link AMO and ENOS to your SPEI index you should change the title of the paper to ‘Identification of drought events and correlations with 2 large-scale modes of variability: A case study in the 3 Xinjiang, China” or use other indices than AMO and ENSO.
For plot 3, I didn’t understand where you chosen a box over which you averaged/a latitude line. Once you’ll have a clear definition of which boxes you chosen can you please justify your choice ? My main concern is your trends might change significantly if your box would be different ? Your results are highly dependent on such a box. Could you therefore justify your choice of the domain over which you plotted the Howmoller ?
Could you do a significance test of the trends you found and plotted in your figures ? Are these trends significant ? ( in Figure 3 for example )
Also for Plot 3 how do you know this trend is this trend part of a natural variability cycle and can be an oscillation rather than a trend ? It seems to have attentive positive and negative phases therefore is hard to asses if is indeed a trend.
In Figures 7 and 8, could you please explain why did you defined your AMO index as a 5 years running mean ? (see previous literature Clement 2016, Shea , Zhang ) the index is defined as > 10 years running mean. Is there any particular justification for you defining the AMO as a 5 years mean? I suggest, in case you do need to define it as a 5 years mean not to use the usual index acronym AMO 'Atlantic Multidecadal Variability' but rather call it an Atlantic SST index - it will bring less confusion to the reviewers and readers. My main concern is, if so your paper title as well as the whole analysis will have to be changed because you are not dealing with circulation patterns at decadal to multidecadal scales but much shorter ones therefore your analysis will change accordingly (along with the results).
A last concern – is not really clear how you link the AMO and ENSO variability to the SPEI index using a correlation methodology. I don’t think you should assume causality based on that therefore maybe some more analysis should be done on this – I would reformulate Conclusion 3 and specify is a rather open direction of further research than a conclusion per se.
Hope the comments will be useful to improve this manuscript.
Author Response
Response to Reviewer 3 Comments
This study used the 1961-2015 standardized precipitation evapotranspiration index (SPEI) drought index to assess spatio-temporal drought variability in Northwest China. Moreover, analysis intends to make a link of the drought in NW China to the Atlantic multidecadal variability and El Nino Southern Oscillation. These sound like a valuable piece of literature, but the paper is not in the state in its current form where these merits can be assessed fully. The reason for this is first because the paper not yet fully grammatically sound and also there few fundamental scientific misconceptions which should be fixed (described below in the detailed comments). I also think given the analysis presented in the paper the title of the paper is not appropriate therefore it should either be changed as I suggested below, or the analysis should be substantially changed and redone.
At this stage I would encourage the authors to have the paper go through a set of grammar corrections by a native English speaker. Once this is clear I would be in a better position to evaluate the paper. Therefore, my decision is the manuscript should undertake major revisions before being considered for publication.
We thank the reviewers for their comments to improve the manuscript, as well as the opportunity given by the Editor to submit a revised version. We have addressed all of the points made by each reviewer and we have included a point-by-point response to each reviewer comment below.
We have used the review period to revise some sentences, figures, and some minor mistakes and repetitions of text through the manuscript. We also invited the International Science Editing (http://www.internationalscienceediting. com) to check the writing and grammatical errors in this manuscript. These changes will not influence the content and framework of the paper. And here we did not list the changes but marked in revised paper.
Point 1: I struggled a bit with this paper - principally because the writing is sometimes not grammatically correct, and statements are sometimes really long and hard to follow i.e lines 30-31: “Drought is one of the most serious, widespread, and costliest natural disaster, caused impacts to 30 agriculture, ecosystems, hydrology, economic, and social activities “ which should be “Drought is one of the most serious, widespread and costliest natural disasters causing yearly… ‘ or ‘with impacts on’
Response 1: Thank you for your valuable advice. We have invited the International Science Editing (http://www.internationalscienceediting. com) to check the writing and grammatical errors in this manuscript.
Page 1 Line33-34: the statement of “Drought is one of the most serious, widespread, and costliest natural disaster, caused impacts to……” was corrected as “Drought is one of the most serious, widespread and costliest natural disaster with impacts on the ……”.
Point 2: Sometimes statements are too long – please sorthen statements longer than 2 lines line in lines 132-134 (the AMO definition).
Response 2: Thank you for your valuable advice.
Page 6 Line 176-180: the statement of “The AMO is the multi-decadal fluctuation pattern of North Atlantic sea surface temperature (SST) variations, with an index defined as the area average over the entire North Atlantic of lowpass filtered (LF) annual mean SST anomalies, after removing any linear trend [33-34]” was corrected as “The AMO is a coherent mode of natural variability occurring in the North Atlantic Ocean with an estimated period of 60-80 years. It defined as detrended 10-year low-pass filtered annual mean SST anomalies over the North Atlantic (0N-65N, 80W-0E) [33-34]”.
Point 3: You should try avoiding if possible using formulations like 'clearly different' (line 173) , 'obviously similar' or ‘the trend became even worse’ (line 175). I suggest to leave the science with its methods and numbers show results instead of using words as ‘obvious’, ‘even worse’ etc. Scientific writing is different to scientific oral communications and ‘clearly different’ or ‘obviously similar’ sounds like a non-scientific way of describing things. I suggest consulting a science writing book for eliminating such mistakes – they are really easy to fix once you read a bit on them ( like “ The Craft of Scientific Writing” https://www.amazon.co.uk/Craft-Scientific-Writing-Michael-Alley/dp/1441982876). I also found a bit non-scientific the fact that you write things as ‘the expected standards’ (line 98) or ‘extreme inspection’ (line 99). Again, such adjectives are not useful when it comes to science – please describe with methods and numbers what ‘expected standards’ means as well as what ‘extreme inspection’ means.
Response 3: Thank you for your valuable advice. We have used the review period to revise some sentences, figures, and some minor mistakes and repetitions of text through the manuscript. We also invited the International Science Editing (http://www.internationalscienceediting. com) to check the writing and grammatical errors in this manuscript. These changes will not influence the content and framework of the paper. And here we did not list the changes but marked in revised paper.
Point 4: The paper needs more clarity and to be rewritten more as 1 whole than the present version which seems few different piece of work glued together with places where the reader is expected to have went through an entire body of literature to be familiar with what you are doing. For example, given that the SPEI index is at the core of this study writing the formula or the index along with a paragraph on it, rather than only citing other studies ( lines 113 to 115) would be useful.
Response 4: Thank you for your valuable advice.
We have added the calculation of the SPEI index at the Page 4-5 Line 125-152. Please see below:
SPEI combines the precipitation and the PET data; we followed Thornthwaite model approach to calculate the PET. Following this approach, the monthly PET (mm) is calculated using
(1)
where N is maximum number of sun hours, NDM is number of days in the month, T is average air temperature (℃) and I is heat index, which is calculated as the sum of 12 monthly index values:
; (2)
and m is a coefficcient that depends on I:
m=6.75×10-7I3-7.71×10-5I2+1.79×10-2I+0.492. (3)
The deficit or surplus accumulation of a climate water balance at different time scales is calculated by the difference between the precipitations (P) and PET for the month i:
Di=Pi－PETi (4)
The calculated Di values are aggregated at different time series, following the same procedure as that for the SPI. The difference in a given time n depends on the chosen time scale k (months):
(5)
Next, normalize the water balance into a log-logistic probability distribution to obtain the SPEI index series. The probability density function of a three-parameter log-logistic distributed variable is expressed as:
(6)
where , , and are the scale, shape and origin parameters, respectively, for D values in the range ( > D <∞). Thus, the probability distribution function of the D series is given by:
(7)
The F(x) value is then transformed to a normal variable by means of the following approximation:
(8)
Where W is a probability-weighted moment, for P≤0.5 is the probability of exceeding a determined D value, P=1−F(x). If P＞0.5, then P is replaced by 1−P and the sign of the resultant SPEI is reversed. The constants are C0=2.515517, C1=2.515517, C2=2.515517, d1=2.515517, d2=2.515517 and d3=2.515517.
Point 5: Science wise, one of the main conclusions of the paper is that (line 21-22) “Both AMO and ENSO 21 events have a strong influence on drought in Xinjiang”. I m not sure I agree with that. I think the current analysis can’t draw such a conclusion based on a correlation of indices but no further causality or mechanistic analysis. I would formulate that coclusion in a milder way ‘Our study suggests the posibiloity of the AMO and ENSO links to the drought in Xianjiang but further analysis is needed for a better understanding of such mechanisms’. I would not list this as a major conclusion of the paper but rather as a possible direction of future research.
Response 5: Thank you for your valuable advice.
In Abstract, we have deleted the statement of “Both AMO and ENSO events have a strong influence on drought in Xinjiang”, and added the statement of “Our study suggests the posibiloity of the AMO and ENSO links to the drought in Xinjiang but further analysis is needed for a better understanding of such mechanisms”.
Point 6: In Figure 2 a SPEI index for Central Asia is illustrated however this index is not defined spatially. I could not find where you described the latitude-longitude box used for this index. Please define it somewhere in the text. There is in line 30 mentioning of the index in Xinjiang but is not clear what coordinates you use.
Response 6: Thank you for your valuable advice.
In Table 1: We have added the latitude-longitude and elevation information of the 55 selected meteorological stations in Xinjiang.
In Table 3: We have deleted the Figure 2b, and added the spatial trend analysis for the annual SPEI at 55 meteorological stations.
Table 1. Basic information about the 55 selected meteorological stations in Xinjiang.
Station name | WMO number | Latitude (°N) | Longitude (°E) | Elevation (m) | Station name | WMO number | Latitude (°N) | Longitude (°E) | Elevation (m) |
Habahe | 51053 | 86.40 | 48.05 | 534 | Hejing | 51559 | 86.40 | 42.32 | 1102 |
Jimunai | 51059 | 85.87 | 47.43 | 984 | Yanqi | 51567 | 86.57 | 42.08 | 1057 |
burqin | 51060 | 86.87 | 47.70 | 476 | Heshuo | 51568 | 86.80 | 42.25 | 1087 |
Altay | 51076 | 88.08 | 47.73 | 737 | Turpan | 51573 | 89.20 | 42.93 | 37 |
Tacheng | 51133 | 83.00 | 46.73 | 537 | Shansan | 51581 | 90.23 | 42.85 | 399 |
Hoboksar | 51156 | 85.72 | 46.78 | 1294 | Baicheng | 51633 | 81.90 | 41.78 | 1230 |
Qinghe | 51186 | 90.38 | 46.67 | 1220 | Luntai | 51642 | 84.25 | 41.78 | 978 |
Alataw | 51232 | 82.58 | 45.18 | 286 | Korla | 51656 | 86.13 | 41.75 | 933 |
Bole | 51238 | 82.07 | 44.90 | 533 | Torugart | 51701 | 75.40 | 40.52 | 3507 |
Tuoli | 51241 | 83.60 | 45.93 | 1078 | Atux | 51704 | 76.17 | 39.72 | 1299 |
Karamay | 51243 | 84.85 | 46.28 | 446 | Wuqia | 51705 | 75.25 | 39.72 | 2178 |
Baitash | 51288 | 90.53 | 45.37 | 1655 | Aketao | 51708 | 75.95 | 39.15 | 1325 |
Wenquan | 51330 | 81.02 | 44.97 | 1354 | Akqi | 51711 | 78.45 | 40.93 | 1986 |
Mosuowan | 51353 | 86.10 | 45.02 | 347 | Tikanlik | 51765 | 87.70 | 40.63 | 847 |
Shihezi | 51356 | 86.05 | 44.32 | 444 | Ruoqiang | 51777 | 88.17 | 39.03 | 889 |
Caijiahu | 51365 | 87.53 | 44.20 | 441 | Tashkurgan | 51804 | 75.23 | 37.78 | 3094 |
Qitai | 51379 | 89.57 | 44.02 | 794 | Shache | 51811 | 77.27 | 38.43 | 1232 |
Yining | 51431 | 81.33 | 43.95 | 664 | Zepu | 51815 | 77.27 | 38.18 | 1275 |
Gongliu | 51435 | 82.23 | 43.47 | 776 | Pishan | 51818 | 78.28 | 37.62 | 1376 |
Xinyuan | 51436 | 83.30 | 43.45 | 929 | Cele | 51826 | 80.80 | 37.02 | 1337 |
Zhaosu | 51437 | 81.13 | 43.15 | 1855 | Hetian | 51828 | 79.93 | 37.13 | 1375 |
Urumqi | 51463 | 87.62 | 43.78 | 919 | Minfeng | 51839 | 82.72 | 37.07 | 1410 |
Bluntai | 51467 | 86.30 | 42.73 | 1738 | Qiemo | 51855 | 85.55 | 38.15 | 1248 |
Daxigou | 51468 | 86.83 | 43.10 | 3544 | Yutian | 51931 | 81.65 | 36.85 | 1423 |
Daban | 51477 | 88.32 | 43.35 | 1104 | Barko | 52101 | 93.00 | 43.60 | 1651 |
Qijiaojin | 51495 | 91.63 | 43.48 | 874 | Yiwu | 52118 | 94.70 | 43.27 | 1730 |
Kumux | 51526 | 88.22 | 42.23 | 924 | Hami | 52203 | 93.52 | 42.82 | 738 |
Bayanbulak | 51542 | 84.15 | 43.03 | 2459 |
Table 3. Spatial trend analysis for the annual SPEI at 55 meteorological stations.
Station name | Trend (decade) | Station name | Trend (decade) | Station name | Trend (decade) |
Habahe | 0.03 | Xinyuan | 0.00 | Wuqia | -0.01 |
Jimunai | -0.08 | Zhaosu | -0.14 | Aketao | 0.37 |
burqin | 0.04 | Urumqi | 0.31 | Akqi | 0.15 |
Altay | 0.15 | Bluntai | -0.02 | Tikanlik | -0.52 |
Tacheng | -0.16 | Daxigou | 0.02 | Ruoqiang | -0.20 |
Hoboksar | -0.19 | Daban | -0.13 | Tashkurgan | -0.14 |
Qinghe | -0.11 | Qijiaojin | -0.51 | Shache | -0.18 |
Alataw | -0.03 | Kumux | -0.23 | Zepu | -0.12 |
Bole | -0.12 | Bayanbulak | -0.08 | Pishan | -0.25 |
Tuoli | -0.15 | Hejing | -0.14 | Cele | -0.37 |
Karamay | 0.06 | Yanqi | -0.30 | Hetian | -0.37 |
Baitash | -0.02 | Heshuo | 0.19 | Minfeng | -0.41 |
Wenquan | 0.26 | Turpan | -0.41 | Qiemo | -0.56 |
Mosuowan | -0.13 | Shansan | -0.47 | Yutian | -0.11 |
Shihezi | -0.11 | Baicheng | 0.07 | Barko | -0.25 |
Caijiahu | -0.08 | Luntai | -0.40 | Yiwu | -0.14 |
Qitai | 0.14 | Korla | -0.36 | Hami | -0.12 |
Yining | 0.00 | Torugart | -0.02 | ||
Gongliu | -0.15 | Atux | 0.06 |
Bold values indicate the significant at the 0.05 confidence level.
Point 7: Lines 30-35 introduce AMO and ENSO as ‘circulation indices’. Calling AMO and ENSO circulation indices is not fully correct. AMO is an index built based on SST anomalies variability while ENSO ( not the SO – southern Oscillation) although coupled ocean-atmosphere variability is not an exclusively ‘circulation’ related index. It is not clear therefore what you wanted to refer to as ‘circulation indexes. What kind of circulation did you have in mind? Was it oceanic or atmospheric circulation? As it comes across from the paper I think you tried to link the SPEI index over Nothern China to atmospheric circulation patterns globally. If this was the case the AMO and ENSO are probably not the best choice given the title of your paper and I’d suggest using indices like NAO, AO, some Jet Stream related indices or simply geopotential heights patterns at various altitudes. If however what you want is to link AMO and ENOS to your SPEI index you should change the title of the paper to ‘Identification of drought events and correlations with 2 large-scale modes of variability: A case study in the 3 Xinjiang, China” or use other indices than AMO and ENSO.
Response 7: Thank you for your valuable advice.
Yes, you are right. The AMO and ENSO should be large-scale ocean- atmospheric circulation patterns. The statement of “circulation indices” was corrected as “large-scale patterns”.
We have changed the title of the paper to ‘Identification of drought events and correlations with large-scale ocean- atmospheric patterns of variability: A case study in the Xinjiang, China”
Point 8: For plot 3, I didn’t understand where you chosen a box over which you averaged/a latitude line. Once you’ll have a clear definition of which boxes you chosen can you please justify your choice ? My main concern is your trends might change significantly if your box would be different ? Your results are highly dependent on such a box. Could you therefore justify your choice of the domain over which you plotted the Howmoller ?
Response 8: Figure 3 indicated the inter-annual variability of the SPEI values at different timescale in North Xinjiang (a) and South Xinjiang (b), and the vertical axis indicates the timescale from 1 to 24 months, and the horizontal axis indicates the year from 1961 to 2015.
Figure 3. The inter-annual variability of the SPEI values at different timescale in North Xinjiang (a) and South Xinjiang (b) (the vertical axis indicates the timescale from 1 to 24 months, and the horizontal axis indicates the year from 1961 to 2015)
We have added the Table 3 of the spatial trend for the annual SPEI at 55 meteorological station, the bold values indicate the significant at the 0.05 confidence level.
Table 3. Spatial trend analysis for the annual SPEI at 55 meteorological stations.
Station name | Trend (decade) | Station name | Trend (decade) | Station name | Trend (decade) |
Habahe | 0.03 | Xinyuan | 0.00 | Wuqia | -0.01 |
Jimunai | -0.08 | Zhaosu | -0.14 | Aketao | 0.37 |
burqin | 0.04 | Urumqi | 0.31 | Akqi | 0.15 |
Altay | 0.15 | Bluntai | -0.02 | Tikanlik | -0.52 |
Tacheng | -0.16 | Daxigou | 0.02 | Ruoqiang | -0.20 |
Hoboksar | -0.19 | Daban | -0.13 | Tashkurgan | -0.14 |
Qinghe | -0.11 | Qijiaojin | -0.51 | Shache | -0.18 |
Alataw | -0.03 | Kumux | -0.23 | Zepu | -0.12 |
Bole | -0.12 | Bayanbulak | -0.08 | Pishan | -0.25 |
Tuoli | -0.15 | Hejing | -0.14 | Cele | -0.37 |
Karamay | 0.06 | Yanqi | -0.30 | Hetian | -0.37 |
Baitash | -0.02 | Heshuo | 0.19 | Minfeng | -0.41 |
Wenquan | 0.26 | Turpan | -0.41 | Qiemo | -0.56 |
Mosuowan | -0.13 | Shansan | -0.47 | Yutian | -0.11 |
Shihezi | -0.11 | Baicheng | 0.07 | Barko | -0.25 |
Caijiahu | -0.08 | Luntai | -0.40 | Yiwu | -0.14 |
Qitai | 0.14 | Korla | -0.36 | Hami | -0.12 |
Yining | 0.00 | Torugart | -0.02 | ||
Gongliu | -0.15 | Atux | 0.06 |
Bold values indicate the significant at the 0.05 confidence level.
Point 9: Could you do a significance test of the trends you found and plotted in your figures ? Are these trends significant ? ( in Figure 3 for example )
Response 9: Table 4 showed the significance test of the SPEI trends at 1, 3, 6, 12 and 24-months time scales, and the bold values indicated the significant at the 0.05 confidence level.
Table 4. Trend analysis for the annual SPEI in Xinjiang and different regions
1-month/decade | 3-month/decade | 6-month/decade | 12-month/decade | 24-month/decade | |
Xinjiang | -0.03 | -0.06 | -0.09 | -0.12 | -0.15 |
North Xinjiang | -0.006 | -0.02 | -0.06 | -0.08 | -0.11 |
South Xinjiang | -0.06 | -0.10 | -0.14 | -0.16 | -0.20 |
Bold values indicate the significant at the 0.05 confidence level.
Point 10: In Figures 7 and 8, could you please explain why did you defined your AMO index as a 5 years running mean ? (see previous literature Clement 2016, Shea , Zhang ) the index is defined as > 10 years running mean. Is there any particular justification for you defining the AMO as a 5 years mean? I suggest, in case you do need to define it as a 5 years mean not to use the usual index acronym AMO 'Atlantic Multidecadal Variability' but rather call it an Atlantic SST index - it will bring less confusion to the reviewers and readers. My main concern is, if so your paper title as well as the whole analysis will have to be changed because you are not dealing with circulation patterns at decadal to multidecadal scales but much shorter ones therefore your analysis will change accordingly (along with the results).
Response 10: Thank you for your valuable advice.
We have defined the AMO index as detrended 10-year low-pass filtered annual mean SST anomalies over the North Atlantic (0N-65N, 80W-0E). Table 7b showed the AMO index, and black line indicates the monthly SSTA over the North Atlantic, and blue line indicates the 121 month smoothed AMO index.
Figure 7. The 1962–2015 monthly SPEI at 12-time scales (Red line indicates the 121 month smoothed SPEI index) and the AMO index (blue line indicates the 121 month smoothed AMO index).
Point 11: A last concern – is not really clear how you link the AMO and ENSO variability to the SPEI index using a correlation methodology. I don’t think you should assume causality based on that therefore maybe some more analysis should be done on this – I would reformulate Conclusion 3 and specify is a rather open direction of further research than a conclusion per se.
Response 11: Thank you for your valuable advice.
You are right. In this paper, we only analyzed the possibility correlation between AMO /ENSO variability and SPEI index using a correlation methodology. Our study suggests the possibility of the AMO and ENSO links to the drought in Xinjiang but further analysis is needed for a better understanding of such mechanisms.
Author Response File: Author Response.pdf
Round 2
Reviewer 3 Report
Second Review
The authors significantly improved the manuscript. I was very impressed by the speed of the response of the authors however not all the comments I made were addressed and especially the science related questions answered. Whilst the manuscript is a clear improvement in comparison to the earlier submission, there are few minor points the authors should address.
My first comment and (restated) questions are below.
Can you also please check the spelling of the second author’s name -
(i.e Dilinuer Tuoliewubiek ) – when I do a google Scholar search of this name I get no findings as if the author doesn’t exist ??
The authors didn’t yet address few points like:
Can you have the paper read by a native English speaker – just improve a bit the way of writing in English.
Sometimes statements are too long – please make shorter statements.
Science wise, one of the main conclusions of the paper is “Both AMO and ENSO 21 events have a strong influence on drought in Xinjiang”. I m not sure I agree with that. I think the current analysis can’t draw such a conclusion based on a correlation of indices but no further causality or mechanistic analysis. I would formulate that conclusion in a milder way ‘Our study suggests the possibility of the AMO and ENSO links to the drought in Xianjiang but further analysis is needed for a better understanding of such mechanisms’. I would not list this as a major conclusion of the paper but rather as a possible direction of future research.
In Figure 2 a SPEI index for Central Asia is illustrated however this index is not defined spatially. I could not find where you described the latitude-longitude box used for this index. Please define it somewhere in the text. There is in line 30 mentioning of the index in Xinjiang but is not clear what coordinates you use.
For plot 3, I didn’t understand where you chosen a box over which you averaged/a latitude line. Once you’ll have a clear definition of which boxes you chosen can you please justify your choice ? My main concern is your trends might change significantly if your box would be different ? Your results are highly dependent on such a box. Could you therefore justify your choice of the domain over which you plotted the Howmoller ?
Could you do a significance test of the trends you found and plotted in your figures ? Are these trends significant ? ( in Figure 3 for example )
Also for Plot 3 how do you know this trend is this trend part of a natural variability cycle and can be an oscillation rather than a trend ? It seems to have attentive positive and negative phases therefore is hard to asses if is indeed a trend.
Hope my comments were useful and good luck with the review and corrections.
Author Response
Response to Reviewer 3 Comments
The authors significantly improved the manuscript. I was very impressed by the speed of the response of the authors however not all the comments I made were addressed and especially the science related questions answered. Whilst the manuscript is a clear improvement in comparison to the earlier submission, there are few minor points the authors should address.
We thank the reviewers for their comments to improve the manuscript, as well as the opportunity given by the Editor to submit a revised version. We have addressed all of the points made by each reviewer and we have included a point-by-point response to each reviewer comment below.
We have used the review period to revise some sentences, figures, and some minor mistakes and repetitions of text through 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 revised paper.
Point 1: Can you also please check the spelling of the second author’s name -
(i.e Dilinuer Tuoliewubieke)– when I do a google Scholar search of this name I get no findings as if the author doesn’t exist ??
Response 1: We have checked the author’s name, and the Dilinuer Tuoliewubieke is a Ph.D. student in the Institute of Desert Meteorology, China Meteorological Administration.
Point 2: Can you have the paper read by a native English speaker – just improve a bit the way of writing in English. Sometimes statements are too long – please make shorter statements.
Response 2: Thank you for your valuable advice. We have invited a native English speaker to check the writing and grammatical errors in this manuscript. These changes will not influence the content and framework of the paper. And here we did not list the changes but marked in revised paper.
Point 3: Science wise, one of the main conclusions of the paper is that (line 21-22) “Both AMO and ENSO events have a strong influence on drought in Xinjiang”. I m not sure I agree with that. I think the current analysis can’t draw such a conclusion based on a correlation of indices but no further causality or mechanistic analysis. I would formulate that coclusion in a milder way ‘Our study suggests the posibiloity of the AMO and ENSO links to the drought in Xianjiang but further analysis is needed for a better understanding of such mechanisms’. I would not list this as a major conclusion of the paper but rather as a possible direction of future research.
Response 3: I’d like to endorse your opinion. In this paper, we only suggests the possible links between SPEI index and large-scale ocean-atmospheric circulation patterns (ie., AMO and ENSO) based on a correlation analysis, and the mechanistic analysis are scarce. Thus, the further analysis is needed for a better understanding of such mechanisms. Thank you for your valuable advice.
In Abstract, we have deleted the statement of “Both AMO and ENSO events have a strong influence on drought in Xinjiang”, and added the statement of “This study suggests the possibility of AMO and ENSO links to drought in Xinjiang but further analysis is needed for a better understanding of such mechanisms”.
Point 4: In Figure 2 a SPEI index for Central Asia is illustrated however this index is not defined spatially. I could not find where you described the latitude-longitude box used for this index. Please define it somewhere in the text. There is in line 30 mentioning of the index in Xinjiang but is not clear what coordinates you use.
Response 4: Thank you for your valuable advice. We have added the defined spatially, latitude-longitude box and coordinates information at the Page 4 Line 143-146 and Table 1.
Page 4 Line 143-146: Spatially, the regional SPEI index was calculated at the average of all stations in Xinjiang. The latitude, longitude, and elevation information at 55 selected meteorological stations are listed in Table 1. The World Geodetic System 1984 (WGS84) coordinates was used in this paper.
Table 1. Basic information about the 55 selected meteorological stations in Xinjiang.
Station name | WMO number | Latitude (°N) | Longitude (°E) | Elevation (m) | Station name | WMO number | Latitude (°N) | Longitude (°E) | Elevation (m) |
Habahe | 51053 | 86.40 | 48.05 | 534 | Hejing | 51559 | 86.40 | 42.32 | 1102 |
Jimunai | 51059 | 85.87 | 47.43 | 984 | Yanqi | 51567 | 86.57 | 42.08 | 1057 |
burqin | 51060 | 86.87 | 47.70 | 476 | Heshuo | 51568 | 86.80 | 42.25 | 1087 |
Altay | 51076 | 88.08 | 47.73 | 737 | Turpan | 51573 | 89.20 | 42.93 | 37 |
Tacheng | 51133 | 83.00 | 46.73 | 537 | Shansan | 51581 | 90.23 | 42.85 | 399 |
Hoboksar | 51156 | 85.72 | 46.78 | 1294 | Baicheng | 51633 | 81.90 | 41.78 | 1230 |
Qinghe | 51186 | 90.38 | 46.67 | 1220 | Luntai | 51642 | 84.25 | 41.78 | 978 |
Alataw | 51232 | 82.58 | 45.18 | 286 | Korla | 51656 | 86.13 | 41.75 | 933 |
Bole | 51238 | 82.07 | 44.90 | 533 | Torugart | 51701 | 75.40 | 40.52 | 3507 |
Tuoli | 51241 | 83.60 | 45.93 | 1078 | Atux | 51704 | 76.17 | 39.72 | 1299 |
Karamay | 51243 | 84.85 | 46.28 | 446 | Wuqia | 51705 | 75.25 | 39.72 | 2178 |
Baitash | 51288 | 90.53 | 45.37 | 1655 | Aketao | 51708 | 75.95 | 39.15 | 1325 |
Wenquan | 51330 | 81.02 | 44.97 | 1354 | Akqi | 51711 | 78.45 | 40.93 | 1986 |
Mosuowan | 51353 | 86.10 | 45.02 | 347 | Tikanlik | 51765 | 87.70 | 40.63 | 847 |
Shihezi | 51356 | 86.05 | 44.32 | 444 | Ruoqiang | 51777 | 88.17 | 39.03 | 889 |
Caijiahu | 51365 | 87.53 | 44.20 | 441 | Tashkurgan | 51804 | 75.23 | 37.78 | 3094 |
Qitai | 51379 | 89.57 | 44.02 | 794 | Shache | 51811 | 77.27 | 38.43 | 1232 |
Yining | 51431 | 81.33 | 43.95 | 664 | Zepu | 51815 | 77.27 | 38.18 | 1275 |
Gongliu | 51435 | 82.23 | 43.47 | 776 | Pishan | 51818 | 78.28 | 37.62 | 1376 |
Xinyuan | 51436 | 83.30 | 43.45 | 929 | Cele | 51826 | 80.80 | 37.02 | 1337 |
Zhaosu | 51437 | 81.13 | 43.15 | 1855 | Hetian | 51828 | 79.93 | 37.13 | 1375 |
Urumqi | 51463 | 87.62 | 43.78 | 919 | Minfeng | 51839 | 82.72 | 37.07 | 1410 |
Bluntai | 51467 | 86.30 | 42.73 | 1738 | Qiemo | 51855 | 85.55 | 38.15 | 1248 |
Daxigou | 51468 | 86.83 | 43.10 | 3544 | Yutian | 51931 | 81.65 | 36.85 | 1423 |
Daban | 51477 | 88.32 | 43.35 | 1104 | Barko | 52101 | 93.00 | 43.60 | 1651 |
Qijiaojin | 51495 | 91.63 | 43.48 | 874 | Yiwu | 52118 | 94.70 | 43.27 | 1730 |
Kumux | 51526 | 88.22 | 42.23 | 924 | Hami | 52203 | 93.52 | 42.82 | 738 |
Bayanbulak | 51542 | 84.15 | 43.03 | 2459 |
Point 5: For plot 3, I didn’t understand where you chosen a box over which you averaged/a latitude line. Once you’ll have a clear definition of which boxes you chosen can you please justify your choice ? My main concern is your trends might change significantly if your box would be different ? Your results are highly dependent on such a box. Could you therefore justify your choice of the domain over which you plotted the Howmoller ?
Response 5: In Figure 3, the vertical axis represents the timescale from 1 to 24 months of the SPEI index, the horizontal axis represents the month from 1961 to 2015, and the legend represents the monthly SPEI value. Thus, the Figure 3 represents the monthly variability of the SPEI values at different timescale in North Xinjiang (a) and South Xinjiang (b) from 1961 to 2015.
Figure 3 shows the variability of monthly SPEI at different timescales (1 to 24 months) from 1961 to 2015 in Xinjiang. The variability of SPEI at the 24 time scales all exhibited a drying trend in South Xinjiang (Table 4). The SPEI values were different before and after 1997, especially in 2005. Normal and wet conditions were observed before 1997, whereas droughts occurred frequently after 1997.
The box is not a Howmoller diagrams. In this box, the value represents the monthly SPEI value at the 1 to 24 months from 1961 to 2015, and is not significantly test. We have added the Table 3 of the spatial trend for the annual SPEI at 55 meteorological station, the bold values indicate the significant at the 0.05 confidence level.
Figure 3. The inter-annual variability of the SPEI values at different timescale in North Xinjiang (a) and South Xinjiang (b) (the vertical axis indicates the timescale from 1 to 24 months, and the horizontal axis indicates the year from 1961 to 2015)
Table 3. Spatial trend analysis for the annual SPEI at 55 meteorological stations.
Station name | Trend (decade) | Station name | Trend (decade) | Station name | Trend (decade) |
Habahe | 0.03 | Xinyuan | 0.00 | Wuqia | -0.01 |
Jimunai | -0.08 | Zhaosu | -0.14 | Aketao | 0.37 |
burqin | 0.04 | Urumqi | 0.31 | Akqi | 0.15 |
Altay | 0.15 | Bluntai | -0.02 | Tikanlik | -0.52 |
Tacheng | -0.16 | Daxigou | 0.02 | Ruoqiang | -0.20 |
Hoboksar | -0.19 | Daban | -0.13 | Tashkurgan | -0.14 |
Qinghe | -0.11 | Qijiaojin | -0.51 | Shache | -0.18 |
Alataw | -0.03 | Kumux | -0.23 | Zepu | -0.12 |
Bole | -0.12 | Bayanbulak | -0.08 | Pishan | -0.25 |
Tuoli | -0.15 | Hejing | -0.14 | Cele | -0.37 |
Karamay | 0.06 | Yanqi | -0.30 | Hetian | -0.37 |
Baitash | -0.02 | Heshuo | 0.19 | Minfeng | -0.41 |
Wenquan | 0.26 | Turpan | -0.41 | Qiemo | -0.56 |
Mosuowan | -0.13 | Shansan | -0.47 | Yutian | -0.11 |
Shihezi | -0.11 | Baicheng | 0.07 | Barko | -0.25 |
Caijiahu | -0.08 | Luntai | -0.40 | Yiwu | -0.14 |
Qitai | 0.14 | Korla | -0.36 | Hami | -0.12 |
Yining | 0.00 | Torugart | -0.02 | ||
Gongliu | -0.15 | Atux | 0.06 |
Bold values indicate the significant at the 0.05 confidence level.
Point 6: Could you do a significance test of the trends you found and plotted in your figures ? Are these trends significant ? ( in Figure 3 for example )
Response 6: The Figure 3 represents the monthly SPEI value at the 1 to 24 months from 1961 to 2015, and is not significantly test. We have added the Table 3 of the spatial trend for the annual SPEI at 55 meteorological station, the bold values indicate the significant at the 0.05 confidence level. Table 4 also showed the significance test of the SPEI trends at 1, 3, 6, 12 and 24-months time scales, and the bold values indicated the significant at the 0.05 confidence level.
Table 4. Trend analysis for the annual SPEI in Xinjiang and different regions
1-month/decade | 3-month/decade | 6-month/decade | 12-month/decade | 24-month/decade | |
Xinjiang | -0.03 | -0.06 | -0.09 | -0.12 | -0.15 |
North Xinjiang | -0.006 | -0.02 | -0.06 | -0.08 | -0.11 |
South Xinjiang | -0.06 | -0.10 | -0.14 | -0.16 | -0.20 |
Bold values indicate the significant at the 0.05 confidence level.
Point 7: Also for Plot 3 how do you know this trend is this trend part of a natural variability cycle and can be an oscillation rather than a trend ? It seems to have attentive positive and negative phases therefore is hard to asses if is indeed a trend.
Response 7: In this paper, the trend changes and inflection point of the SPEI value were identified using the nonparametric Mann–Kendall test method. Annual SPEI clearly exhibits a decreasing trend and greater decadal fluctuations, with a trend magnitude of -0.12/decade (p<0.05) during 1961–2015 (Figure 2a). A significant change point occurred in 1997 (Figure 2b), consistent with inflection points found for temperature in Central Asia (Li et al. 2015).
SPEI estimation is based on the difference between monthly mean precipitation and PET, the variability of P and PET has a significant trend change in Xinjiang. Thornthwaite (1948) correlated monthly mean temperature with PET, as determined from the water balance. From 1961 to 2015, annual mean temperature in Xinjiang, China, experienced a significant increasing trend, with a rate of increase of 0.31◦C/10a. The warming trend started to intensify during the late 1980s, then increased sharply in 1997. Annual precipitation remained relatively stable from the 1960s to the mid-1980s, then began to increase sharply in 1987 (Yao et al., 2018, PeerJ).
Figure 2. Temporal variability (a, straight lines denote linear trend) and M-K test (b) of annual SPEI for 1961–2015 in Xinjiang. The UF curve indicates the statistical series of the standard normal distribution, and the UB curve indicates the reverse statistical series. Because the line UF is above the confidence line (p=0.05, green line), the crossing point of UF and UB is the start of abrupt change in this series.
Temporal changes of (A) annual mean temperature (B) and annual precipitation in Xinjiang
during 1961–2015.
Author Response File: Author Response.pdf
