the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 16 Jan 2023
Hydrological effects of evapotranspiration in the Qilian Mountains forest belt
Abstract. Mountainous areas are the main water-producing and source areas of rivers. Global climate change is transforming the distribution of plants and forms of water use. Therefore, a clear understanding of evapotranspiration in mountainous forest zone is key for understanding the ecohydrological effect of vegetation and its influence on the water cycle of the watershed. We quantified the evapotranspiration processes in the forest belts of the Qilian Mountains as well as their contribution to runoff yield and concentration based on precipitation, soil water, and plant water samples and experimental data. The study showed that transpiration of Qinghai spruce accounted for the highest proportion of evapotranspiration in the entire Qinghai spruce forest ecosystem, with an average of 79 %, which means that transpiration is much greater than evaporation. Soil water content and air humidity were the dominant factors influencing evapotranspiration in Qinghai spruce forest belts. The growing season of Qinghai spruce is characterized by greater evapotranspiration than precipitation in each month. Consequently, the forest zone does not yield flows in the eastern part of the Qilian Mountains. The warming of global temperatures and human activities are likely to trigger shifts in the distribution areas and evapotranspiration regimes of Qinghai spruce, which in turn will lead to a change in water resource patterns in the basin.
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RC1: 'Comment on hess-2022-375', Anonymous Referee #1, 30 Mar 2023
All the comments are in the GeneralComment&CommentedManuscript.pdf file cointaing also the commented manuscript. Most of the highlighted sections will display a comment when hovered.
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AC1: 'Reply on RC1', Guofeng Zhu, 19 Jun 2023
We are deeply grateful for the valuable and encouraging feedback on the manuscript. Your thoughtful revisions and comments are sincerely appreciated.
We have revised the paper greatly, which is mainly reflected in the following aspects:We rearranged the logic of the manuscript and added data, especially for the xylem water of Qinghai spruce. The water vapor recirculation part is clearly expounded, and each parameter is described in detail. We haved carried out uncertainty analysis.We have re-landscaped all the images throughout the article and improved their clarity.We have changed the language to make it easier for native English speakers to make valuable suggestions.We have placed the revised manuscript PDF at the end of the document. The red bolded words, sentences, and subsections in the manuscript represent our editing changes.
We have taken your comments into careful consideration and responded to each one, demonstrating our attention and gratitude towards your input.
ABSTRACT
16 - Maybe need some clarifications.
Response: Based on your comments, we have adjusted this sentence as follows: We collected precipitation, soil water from 0 to 100cm, xylem water from Qinghai Spruce, temperature, relative humidity and rainfall in the eastern Qilian Mountains from 2018 to 2019. We simulated T/ET comprehensively, and quantified the contribution of recirculated water vapor in precipitation. The aim of this study was to clarify the evapotranspiration process and its effect on production and confluence in the forest belt of Qilian Mountain.
INTRODUCTION
The introduction explains quite clearly the general context and gives a reasonable overview of the relative state of the art. The cited literature may be increased in some sections and re-organization of some sentences is advisable (see comments by lines).
The last section of the introduction is a bit too much methods-referred and needs a correction (see
comments by lines). The general objective should be better stated.
Response:These two parts of opinions are very important to the logic of our article, and we have carefully revised them.
34 to 41 - the text is discussing the importance of the spruce forests system referring to the Qinghai local one. Maybe, here a more general discussion about this ecosystem could be more appropriate.
Response:We have adjusted this sentence to a more appropriate content to fit the full text,as follows: As a natural reservoir and purifier, the Qinghai spruce ecosystem has the functions of storing, releasing and purifying water. The Qilian Mountains supply the water resources that human beings depend on for survival in the continental river basin in the arid region, regulate the water cycle in the arid region, and interact with the soil and atmosphere to form a vertical spatial continuum, which not only affects the ecological process of the local plant community, but also changes the regional microclimate by means of latent heat (Ault et al., 2020;Zhang et al.,2021;Eisenhauer et al., 2021).
39 to 41 - This can be better placed in the final part of the introduction. It seems a bit in the middle of the general knowledge section.
Response:Thank you for your suggestion, we have put it in the last paragraph of the introduction.
45 - May be good to add at least a sentence to clarify why ET, which is connected to climate change, is the right parameter to study in this context.
Response:Based on your comments, we have adjusted this sentence as follows: The interaction between soil and vegetation controls rainfall input and water transfer within ecosystem components. It is an important player in climate change mitigation in terms of climate benefits (Rohatyn et al., 2022). Evapotranspiration (ET) is an indispensable part of the terrestrial water and energy cycle. Therefore, exploring the spatio-temporal and component changes of soil evaporation and vegetation transpiration can help us increase the response of vegetation canopy to climate change (Liu et al., 2022). At the ecosystem scale, many studies have classified evapotranspiration (ET) as transpiration (T) and evapotranspiration (E) (Schlesinger et al.,2014).
79 - after "... in central Asia." can reasonable place to insert the previous sentence of lines 39 - 41.
Response: Based on your comments, we have placed it to the previous sentence of lines 39 - 41.
79 to 87 - is too much of a method. Shorter and more on the objectives.
Response: Based on your comments, we have adjusted this sentence as follows: In this study, we observed and analyzed the monthly xylem water, soil water, precipitation stable isotopes and soil water content of Spruce forest in the eastern Qilian Mountains from April to October 2018 and 2019, and used these data to solve the following problems: (1) Quantify the contribution rates of soil evaporation and vegetation transpiration to evapotranspiration of ecosystems; (2) Quantifying the ratio of recirculated water vapor in precipitation; (3) To investigate the evapotranspiration process and its influence on production and confluence in the forest belt of Qilian Mountain. This study provides an effective basis for local water resource use and ecological protection.
STUDY AREA
This section potentially needs quite some updates (see the comments by lines).
Response:We really appreciate your valuable comments. We have revised the description of the study area and created a new overview map of the study area.
Is the vegetation of the monitored basin only composed of the spruce forest? Can mentioning the
vegetation types and distribution be useful (if feasible)?
Response: We think your suggestion is reasonable and have made some adjustments, as follows:
The Qilian Mountains are located in the central part of the Eurasian continent, on the northeastern edge of the Qinghai-Tibet Plateau. The eastern region is dominated by water erosion, with large variations in mountainous terrain and an average elevation of over 4,000 meters. Permafrost is developed at elevations of 3,500 to 3,700 meters, and areas above 4,500 meters are characterized by modern glacier development. The region has a plateau continental climate, with hot summers and cold winters, strong solar radiation, and large temperature differences between day and night. The average annual temperature is below 4℃, with extreme highs of 37.6℃ and extreme lows of -35.8℃. The annual sunshine hours range from 2,500 to 3,300 hours, with a total solar radiation of 5,916 to 15,000 megajoules per square meter. The average annual precipitation is 400 millimeters, and the annual evaporation ranges from 1,137 to 2,581 millimeters. The average wind speed is around 2 meters per second, and the frost-free period lasts from 23.6 to 193 days. The Shiyang River originates from the Daxueshan on the northern side of the Lenglong Ridge in the eastern section of the Qilian Mountains, serving as a major water source for the city of Wuwei. The soil types in the eastern section are diverse, but with low organic matter content. The distribution of vegetation shows distinct zonal characteristics, with mountainous forest-grassland zones (2,600 to 3,400 meters), subalpine shrub-meadow zones (3,200 to 3,500 meters), and high mountain sub-ice-snow sparse vegetation zones (>3,500 meters) at elevations above 2,700 meters. The main types of natural forest vegetation include Qinghai spruce forest, Qilian juniper forest, and Chinese pine forest, with Qinghai spruce being the dominant tree species (Zhu et al., 2022).
89 to 100 - A reference to Fig. 1 is missing. There are many toponyms cited but these are not present in Fig. 1 which makes it quite difficult for readers not familiar with the area to understand the geographical setting of the study area.
Response:Thanks for your suggestion, we have revised Fig.1. We will display the image in the next question.
104 - Fig. 1 Label are too small. A legend of the color code is non-presented. Why are surface water sampling site if surface water is never addressed in the text?
Response:We re-made the map, and reasonably modified the legend and other labels.
Figure 1 Location of the study area and changes in meteorological elements
MATERIALS AND METHODS
The description of the analytical procedures used for isotope determination is completely missing. Please insert it with accurate specifications of the methodology and the associated analytical errors.
Response:In response to your comments, we have inserted the following:
3.2 Experimental Analysis
The isotopic data used in this study mainly include stable isotopes of precipitation, soil water, and xylem water. All isotopic samples were analyzed at the Stable Isotope Laboratory of Northwest Normal University. The precipitation samples were analyzed for hydrogen and oxygen stable isotopes using a liquid water isotope analyzer (DLT-100, Los Gatos Research, USA). After thawing the soil and vegetation samples, they were extracted using a low-temperature vacuum condensation device (LI-2100, LICA United Technology Limited, China), and the extracted water was subjected to isotopic analysis. Each water sample was tested six times to ensure accuracy, with the first two tests considered as interference and only the results of the subsequent four tests were averaged. In order to ensure the accuracy of the measurement results, a parallel sample was collected for each sample, and the average value of the two determination results was taken as the final value(Zhu et al., 2022). The isotopic measurements are represented by δ, which represents the deviation in parts per thousand of the ratio of two stable isotopes in the sample relative to the ratio in a standard sample. The International Atomic Energy Agency (IAEA) defined the Vienna Standard Mean Ocean Water (VSMOW) in 1968 as the standard for isotopic composition, which is derived from distilled seawater and has a similar isotopic composition to Standard Mean Ocean Water (SMOW).
（1）
107 to 110 - It seems that the sampling was done only at one point, but multiple points are reported in Fig. 1. Important, isotopic values are not observed but determined in samples, and samples are collected at certain locations, unless the case of "portable" analyzer (like some CRDLS). The sampling strategies need a clearer explanation.
Response: Based on the reviewer's comments, we rearranged the sampling points needed for the whole paper to avoid the lack of xylem water data. 3.1 Materials Sources has been greatly adjusted to make it more reasonable and clear.
3.1 Materials Sources
3.1.1 Sampling network
Parameter
Station
Qixiang
Hulin
Ninchan
Suidao
Altitude(m)
2543
2721
3068
3448
Local climate
Temperature(℃/a)
3
3.2
3.3
-0.9
Precipitation(mm/a)
510
469.44
394
475
Relative humidity（％/a）
52.9
56.1
66.6
69.2
Samplings number
Precipitation
53
108
91
135
Soil water
220
560
560
560
Xylem water
236
56
56
56
3.1.2 Sample collection
We use an automatic weather station in 2018 and 2019 to record meteorological data, in which a rain gauge is used to collect precipitation and transfer samples to 100ml containers after each rain. Drill holes 0 ~ 5 cm, 5 ~ 10 cm, 10 ~ 20 cm, 20 ~ 30 cm, 30 ~ 40 cm, 30 ~ 50 cm, 50 ~ 60 cm, 60 ~ 70 cm, 70 ~ 80 cm, 80 ~ 90 cm, 90 in the sample plot by using soil drill ~ 100 cm. The soil samples were divided into two parts, one of which was placed in a 50 ml glass bottle. The bottle was sealed with a subfilm and transported to the observation station within 10 hours after the sampling date was marked for cryopreservation to detect stable isotope data. The other part of the sample was placed in a 50ml aluminum box and the soil moisture content was determined by drying method. When collecting plant samples, we used scissors to collect vegetation xylem stems, peeled off the bark, and put them in 50ml glass bottles sealed and frozen for experimental analysis.
117 to 141 - Sections 3.3.1, 3.3.2 and 3.3.3 miss citations. The sources of the reported equations need to be cited.
Response:Based on your comments, we have inserted citations for each formula in the manuscript, and supplement the formula and meaning. as follows:
3.3.1 Isotopic composition of atmospheric water vapour
The stable isotope composition of moisture in ambient air is calculated as follows(Gibson and Reid, 2014;Skrzypek et al., 2015):
（2）
where k=1, or by fitting k to some fraction of 1 as the best fit to the local evaporation line, is the isotopic fractionation factor. Defined by. about 2H and 18O are calculated as follows(Horita and Wesolowski, 1994):
（3）
（4）
3.3.2 Isotopic composition of soil evaporation
The Craig-Gordon model was used to calculate the stable isotopic composition of soil evaporation water vapour, δE, using the following equation(Craig and Gordon, 1965;Yepez et al., 2005).
（5）
where αe(>1) is the equilibrium factor calculated as a function of water surface temperature, δs is the stable isotopic composition of liquid water at the evaporating surface of the soil (0 ~ 10 cm average stable isotopic composition of soil water), δA is the stable isotopic composition of atmospheric water vapour near the surface, εeq represents the equilibrium fractionation corresponding to εeq = (1-1/αe) × 1000, εk is the kinetic fractionation factor of O2 is approximately 18.9‰ and h*is the atmospheric relative humidity(Gibson and Reid, 2010). For δ18O, αe is calculated as follows(Raz-Yaseef et al., 2010):
（6）
Where T is the soil Kelvin temperature (K) at a depth of 5 cm.
3.3.3 Isotopic composition of plant transpiration
When transpiration is strong, leaf water is in "isotopic stable state", that is, the isotopic composition of leaf transpiration water is equivalent to that of water absorbed by the roots of rain plants at noon. Therefore, the stable isotopic composition of water in plant xylem can be used to represent the stable isotopic composition of water vapor in plant transpiration. The expression is as follows(Aron et al., 2020):
（7）
where δx is the isotopic ratio of xylem water and δT is the isotopic ratio of transpiration.
3.3.4 Evapotranspiration isotope assessment
The Keeling Plot model describes the linear relationship between the oxygen isotope composition of atmospheric water vapour and its reciprocal concentration . The intercept of the curve on the Y-axis represents the oxygen isotopic composition of evapotranspiration (δET) and is expressed as(Keeling, 1958;Wang et al., 2015)：
（8）
Where δa and Ca represent the atmospheric water vapour oxygen isotopic composition (‰) and water vapour concentration in the ecosystem boundary layer, δb and Cb represent the background atmospheric water vapour oxygen isotopic composition and background atmospheric water vapour concentration, and δET is the ecosystem evapotranspiration oxygen isotopic composition.
3.3.5 Ecosystem evapotranspiration partitioning
The determination of evapotranspiration by means of biotic and abiotic isotopic water fluxes can be used to improve the understanding of community structure and ecosystem function in Qinghai spruce forests in the Qilian Mountains. Based on the isotope mass balance approach to consider the distribution of major and minor isotopes, the partitioning of evapotranspiration can be achieved using two end-member mixing models (E and T) with the following expression(Kool et al.,2014;Wei et al., 2018):
（9）
where δET, δE and δT are the isotopic compositions of evapotranspiration (ET), soil evapotranspiration (E) and plant evapotranspiration (T), respectively, and the isotopic values of the three can be obtained by both direct observation and model estimation.
3.3.6 Three-component mixing model
Assuming that precipitation vapor is a mixture of advective water vapour and recirculating water vapour, it is understood that the proportion of both precipitation and precipitation water vapour has the same nature. The proportion of precipitation occupied by advective vapour is calculated as follow(Kong et al., 2013; Wang et al., 2022):
（10）
where Ptr, Pev and Padv are precipitation produced by transpiration, surface evaporation and advection, respectively.
This can be calculated using the following formula(Brubaker et al., 1993; Sang et al., 2023):
（11）
（12）
where ftr, fev and fadv are the proportional contributions of transpiration, surface evaporation and advection to precipitation, respectively, and δpv, δtr, δev and δadv values are the stable isotopes in precipitating transpiration, transpiration, surface evaporation and advective vapour, respectively. ftr,fev and fadv are calculated by Isoerror software, based on dual isotopes and three sources(Ver. 1.3.1, https://www.epa.gov/)(Phillips and Gregg, 2001). δpv is calculated using the following formula:
（13）
Using the C-G model to calculate δev, the formula is as follows:
（14）
Including the δs is the isotopic composition of liquid water evaporation front, δadv is advection steam, h is relative humidity, α+ is equilibrium fractionation factor, ℇk is kinetic fractionation factor, ℇ is total fractionation factor.
（15）
（16）
h is the relative humidity, Ck is the kinetic fractionation constant, δ2H is 25.1‰, δ18O is 28.5‰.The weight coefficient θ of small water body is 1, and θ of large water body is 0.5. n ranges from 0.5 (fully turbulent transport, with reduced kinetic fractionation, suitable for lake or saturated soil conditions) to 1 (fully diffused transport, suitable for very dry soil conditions), with a kinetic fractionation coefficient of about 12.2-24.5‰ for ℇk (2H) in a dry atmosphere (h=0). The kinetic separation coefficient of ℇk (18O) is about 13.8-27.7‰.
The advection water vapor isotope δadv in the three-component mixing model needs to be determined by the water vapor isotopic composition at the upwind position. Based on the HYSPLIT model, we found that the eastern Qilian Mountains was controlled by westerly winds, southeast monsoon and plateau monsoon in June, July and August, and by prevailing westerly winds in September and October. The clustering analysis of air masses in different months shows that air masses accumulate at the northern foot of Qilian Mountains and move from low altitude to high altitude along the valley. Xiying, at 2097 m above sea level, is therefore used as a headwind station from April to October. When steam isotopes show a depletion trend along the transport path, isotopic fractionation is assumed to be due to Rayleigh distillation, and the expression is as follow:
（17）
Where δpv-adv is the isotopic composition in the vapor of the winds tation, and F is the ratio between the final vapor and the initial vapor. Since rainfall is positively correlated with the surface vapor pressure of the whole study area (c=1.657e, where c is the water vapor content in mm, e is the surface vapor pressure in hPa,R2=0.94), we used the surface vapor pressure of each site to calculate the value of F. The recirculated water entering the air mass is not considered here, because the contribution of recirculated water to the total air column is very limited, and most of the available precipitation does not result in rainfall but escapes to other areas. If there is no depletion of isotope ratios along the transmission track, the vapor isotope ratio from the upwind station is applied directly, and the Rayleigh distillation equation is not applied.
The δp is corrected by the local evaporation line (LEL), and the LEL slope (SLEL) can be calculated as（Skrzypek et al.，2015):
（18）
Where h is the relative humidity, ℇ is the total fractionation factor, and δPV and δP are the stable isotopic components of water vapor and precipitation.According to our research results, the LEL equation for the study area is δ2H=3.86δ18O-19.88 (R2=0.994, P < 0.0001, n=19).
RESULTS AND ANALYSIS
Comment to Tab. 1. The number of analyzed xylem water samples seems quite low with respect to other water matrices. This can pose a serious problem with the statistical significance of the results. How can the work deal with this?
Response:We recognized this serious problem and expanded the sampling points into four sampling strips at different altitudes(Table 1). Furthermore, we have made revisions to Table 2 in Section 4.1 as follows:
Table 1 Sampling Point Locations and Sample Quantity Information
Parameter
Station
Qixiang
Hulin
Ninchan
Suidao
Altitude(m)
2543
2721
3068
3448
Local climate
Temperature(℃/a)
3
3.2
3.3
-0.9
Precipitation(mm/a)
510
469.44
394
475
Relative humidity（％/a）
52.9
56.1
66.6
69.2
Samplings number
Precipitation
53
108
91
135
Soil water
220
560
560
560
Xylem water
236
56
56
56
Table 2 Stable isotopes of different water bodies during the growing season
Period
Average
δ2H/‰
δ18O/‰
Precipitation
Xylem water
Soil water（0~10cm）
Precipitation
Xylem water
Soil water（0~10cm）
4
-69.15
-39.02
-53.10
-10.25
2.56
-7.10
5
-39.09
-29.78
-45.38
-7.61
4.44
-6.42
6
-31.29
-45.83
-46.08
-5.74
-2.83
-6.12
7
-32.39
-47.63
-47.71
-5.33
-0.97
-7.06
8
-48.88
-44.55
-68.85
-7.79
-2.06
-9.07
9
-29.38
-42.62
-49.20
-6.46
-1.83
-6.79
10
-68.43
-44.57
-54.88
-11.06
-2.25
-7.96
Comment to Figure 4b, is it not clear why δb is represented instead of δET. Does the x-axis represent humidity?
Response:We would be happy to explain the meaning of the x-axis to you. We have provided a clearer explanation of this aspect in our revised section. The fundamental principle of the Keeling plot method is to perform a linear regression of water vapor concentrations (1/[H2O]) at different heights within the ecosystem's boundary layer against stable isotopic compositions (δ18O and δ2H). The resulting Keeling plot is used to estimate δET, with the x-axis representing water vapor concentration. After incorporating additional sampling points and expanding the study timeframe, we have reorganized this section as follows:
The Keeling plot method was used to analyze the stable isotope composition of ecosystem evapotranspiration (Figure 4). Its principle involves linearly fitting the water vapor concentration in the ecosystem boundary layer against the oxygen isotope composition, with the intercept on the y-axis representing the stable isotope value of δET. The results indicate that at different heights within the distribution of deciduous trees, the average δET value is -22.59‰. Throughout the entire growing season, δET does not consistently decrease with increasing elevation. Specifically, near the treeline, there are higher stable isotope values, but in the middle and upper layers of the forest, there is a minimal value, indicating lower and less stable isotopic fractionation in that layer. At an elevation of 3448m, as the number of deciduous trees decreases and shrubs become dominant, the δET value is -21.81‰(Table 3). We found that the stable isotope δE of soil evaporation at depths of 0-10cm is more enriched at lower elevations, particularly in April and May when the isotopic enrichment is more pronounced. From June to August, due to a significant increase in vegetation coverage, soil evaporation intensity decreases. In the early stage of the growing season, when leaves have not fully developed, the stable isotope composition of the xylem exhibits a relatively depleted characteristic. In July and August, when leaves are fully expanded, temperatures rise, and the rainy season in mountainous areas commences, transpiration becomes more intense.
Figure 4 Each sampling point is fitted with a trend line based on the Keeling plot method
Comments to Figure 5a, how can the two data points for which δ18Os is lower than δOE be justified? Which may seem counterintuitive. What is the y-axis representing?
Response:After adding additional sampling points, we have restructured Section 4.3 and incorporated clearer chart types to effectively convey the information we wish to present. As follows:
4.3 T/ET assessment of Qinghai spruce forest ecosystem in different months
We found that the canopy closure of deciduous trees significantly influences the evapotranspiration of the entire ecosystem (Figure 5). In April and May, as temperatures rise, surface vegetation exhibits weaker growth, resulting in a higher proportion of soil evaporation within the ecosystem, while transpiration by vegetation remains relatively low. During the rainy season in June to August, vegetation experiences vigorous growth, and transpiration reaches its peak in July. In September and October, soil evaporation becomes more dominant as temperatures, relative humidity, and rainfall gradually decrease, and deciduous tree leaves become wilted. At lower elevations, the T/ET ratio fluctuates between 0.20 and 0.70 in a distinct pattern, while above the treeline, transpiration ratios fluctuate between 0.20 and 0.80 in a similar pattern. Overall, summer is characterized as the peak season for transpiration, with a minimal contribution from soil evaporation.
Figure 5 The proportion of soil evaporation and vegetation transpiration in evapotranspiration of ecosystem(0 represents missing data)
Comment to Figures 5a and 5b. In both figures, δ18Os is represented. Are these the same data? If yes, why are the values different?
Response: That's a great suggestion. We have removed unnecessary charts and streamlined the content accordingly. What we need to explain here is that through reading literature, we found that δ18O is more reliable than δ2H in calculating soil evaporative isotope and evapotranspiration partitioning. Therefore, only oxygen isotope was calculated in the manuscript. However, in order to make the diagram more clearly reflect the evaluation of T/ET in the spruce forest ecosystem in Qinghai, we remade the them. The modified results have been presented in the previous question for your reference.
Comments to Figure 5c. Some remarkable mistakes are present in this figure. The y-axis is reporting a fraction(0 to 1) value, but the label has the percentage symbol. The y-axis does not have the same interval magnitude. Moreover, see the comment in the pdf on lines 286 to 288
Response: Thank you very much for your feedback. We greatly appreciate your input, and we took particular note of the issue you raised when recreating the charts. We have made the necessary corrections accordingly. Please refer to the revised content in section 4.3 for further details.
252 - Te following is stated "δ18OX>δ18OET>δ18OE" but from the graph in fig. 4a, it seems that except for the first data point δ18OX and δ18OE are more or less equal. A statistical test (like t-test) would probably tell that no significant difference is present between the two sample-populations.
Response: Your point is valid, and we believe that the lack of sufficient sample size has contributed to the observed uncertainty, resulting in the insignificant differences among them. In this latest manuscript, we have presented the isotopic composition of soil evaporation, vegetation transpiration, and ecosystem evapotranspiration in a tabular format (Table 3) for a more straightforward and explicit representation of their interrelationships. As follows:
Table 3 Evaporation and transpiration at different altitudes during the growing season
Site
Type
April
May
June
July
August
September
October
Qixiang
δT
2.22
-5.87
-4.59
-0.72
-1.72
-1.78
-2.26
δE
-30.32
-28.68
-27.33
-29.12
-28.68
-26.32
-27.27
δET
-20.19
-20.05
-11.63
-9.87
-13.56
-15.85
-21.56
Hulin
δT
-5.34
-3.58
-4.13
-0.34
-2.35
-4.25
-1.97
δE
-29.68
-27.28
-25.8
-27.75
-24.56
-25.21
-27.88
δET
-21.59
-22.36
-8.93
-10.17
-11.57
-18.8
/
Ninchan
δT
/
-3.45
-1.98
-1.05
-6.68
/
/
δE
/
-20.57
-26.31
-29.08
-18.22
-18.15
-18.22
δET
/
/
-12.46
-7.57
/
/
/
Suidao
δT
/
-8.45
-6.98
-6.05
-6.68
/
/
δE
-29.79
-27.32
-27.91
-23.83
-28.78
-25.8
-28.06
δET
-24.31
-16.14
-15.19
-10.07
-18.05
-23.02
-18.65
DISCUSSION
Comments to section 5.1.2, figure 7, and lines 190 to 192. The results expressed in the referred section and figure are based on the following parameters reported in lines 190 to 192 “δpv, δtr, δev and δadv values are the stable isotopes in precipitating transpiration, transpiration, surface evaporation, and advective vapour, respectively”. Of these four parameters is never explained how these two δpv and δadv are derived or measured. All this section and elaboration is not understandable.
Response:We fully understand your comments and concerns.We have presented the calculation steps for δpv and δadv in the methodology section, formula (11) to (17) . In order to make Section 5.1.2 clearer and more logical, we have made detailed revisions and explanations in terms of methods, logic, content, and figures. As follows:
5.1.2 Contribution to recirculating water vapour in precipitation
Our previous study in the eastern section of the Qilian Mountains (Zhang et al., 2021) indicated that above an altitude of 2100m, air masses gather from the northern foothills and move along the valley from low to high elevations. From June to August, the atmospheric circulation is influenced by westerlies, southeast monsoons, and plateau monsoons, while from September to October, the westerlies are the dominant factor. Therefore, we selected the Xiying station at an altitude of 2097m as our upwind site. The δpv values showed depletion in April and October, gradually enriching from June to August. The maximum δ2H value was -76.02‰ (Table 5), and the minimum was -184.93‰, while the maximum δ18O value was -11.89‰, and the minimum was -26.38‰. Above 2700 meters, there is a gradual decrease in precipitation vapor with increasing altitude. The δev values exhibited significant fluctuations throughout each month, with different patterns at different locations. At 2721m, the δev range varied between -172.3‰ and 96.39‰. From the forest's lower layer to the upper layer, the isotopic composition of the advected moisture from the valley gradually diminished, resulting in decreasing values of δadv.
Table 5 Isotopic Composition of Precipitation Vapor, Surface Evaporation Vapor, and Vegetation Transpiration Vapor at Different Months and Altitudes
Month
Type
isotope
April
May
June
July
August
September
October
Qixiang
δpv
δ2H/‰
-141.95
-123.83
-99.87
-115.99
-128.34
-120.9
-152.43
δ18O/‰
-19.27
-16.58
-14.04
-15.91
-18.6
-17.22
-22.34
δev
δ2H/‰
-
-125.69
-123.69
-117.98
-134.57
-
-
δ18O/‰
-
-30.21
-29.56
-28.62
-31.19
-
-
δtr
δ2H/‰
-39.9
-29.32
-46.19
-49.58
-45.15
-42.66
-44.64
δ18O/‰
2.22
-5.87
-4.59
-0.72
-1.72
-1.78
-2.26
δadv
δ2H/‰
-145.57
-83.25
-81.12
-92
-109.62
-100.53
-122.62
δ18O/‰
-20.24
-11.93
-10.73
-12.06
-15.31
-13.46
-18.16
Hulin
δpv
δ2H/‰
-129.93
-123.29
-98.68
-113.98
-124.16
-118.52
-164.82
δ18O/‰
-17.54
-16.62
-13.18
-15.48
-17.33
-17.88
-22.46
δev
δ2H/‰
-114.24
-117.01
-107.75
-123.44
-106.92
-96.39
-172.3
δ18O/‰
-14.77
-16.35
-15.03
-16.7
-14.53
-12.78
-24.82
δtr
δ2H/‰
-24.12
-39.62
-35.97
-26.44
-35.85
-38.39
-40.53
δ18O/‰
-5.34
-3.58
-4.13
-0.34
-2.35
-4.25
-1.97
δadv
δ2H/‰
-112.79
-115.67
-106.61
-122.19
-106.49
-95.7
-170.54
δ18O/‰
-14.59
-16.15
-14.88
-16.54
-14.46
-12.69
-24.57
Ninchan
δpv
δ2H/‰
-
-76.02
-139.21
-135.74
-129.96
-113.71
-184.93
δ18O/‰
-
-11.89
-19.87
-18.7
-17.91
-16.77
-26.38
δev
δ2H/‰
-
-83.76
-81.42
-92.42
-110.24
-101.11
-123.42
δ18O/‰
-
-12.01
-10.76
-12.09
-15.37
-13.49
-18.27
δtr
δ2H/‰
-
-25.58
-46.77
-37.77
-43.66
-
-
δ18O/‰
-
-3.45
-1.98
-1.05
-6.68
-
-
δadv
δ2H/‰
-162.36
-113.49
-111.45
-106.16
-122.12
-114.67
-141.33
δ18O/‰
-22.73
-15.94
-15.28
-14.46
-16.96
-16.58
-20.42
Suidao
δpv
δ2H/‰
-167.86
-128.08
-124.95
-117.32
-137.73
-130.44
-155.52
δ18O/‰
-22.9
-18.02
-17.28
-16.04
-19.09
-18.64
-21.59
δev
δ2H/‰
-164.16
-114.52
-112.37
-106.9
-122.96
-115.47
-142.85
δ18O/‰
-22.98
-16.07
-15.41
-14.56
-17.08
-16.69
-20.64
δtr
δ2H/‰
-
-25.58
-46.77
-37.77
-43.66
-
-
δ18O/‰
-
-8.45
-6.98
-6.05
-6.68
-
-
δadv
δ2H/‰
-162.38
-113.51
-111.47
-106.18
-122.14
-114.69
-141.36
δ18O/‰
-22.73
-15.94
-15.28
-14.47
-16.97
-16.58
-20.42
In July, the ratio of vegetation transpiration to precipitation vapor is significantly higher compared to other months. The temperatures in the lower layers of the forest are relatively high, and the middle to upper layers are densely populated with spruce, resulting in a higher ftr (transpiration ratio) throughout the entire growing season. Both the early and late stages of the growing season exhibit noticeably higher fev (evaporation ratio) compared to other months, with the middle and upper parts of the forest having a higher proportion of evaporated vapor. The average fadv (advected vapor ratio) is 72%, with contributions exceeding 70% for all months except June and July (Figure 7).
Figure 7 Comparison of fadv (advective water vapour contribution), fev (surface evaporation water vapour contribution) and ftr (plant transpiration water vapour contribution) for each of period
-
AC1: 'Reply on RC1', Guofeng Zhu, 19 Jun 2023
-
RC2: 'Comment on hess-2022-375', Anonymous Referee #2, 14 May 2023
General comment
The authors of this manuscript aimed to assess the role of transpiration, compared to other water fluxes, in a spruce forest in the Qilian Mountains, China. In their analyses, the authors exploited the usefulness of stable isotopes of hydrogen and oxygen to investigate water fluxes in the soil-plant-atmosphere continuum.
Although this manuscript may be interesting for the readers of Hydrology and Earth System Sciences, the current version presents a poor description of the sampling approaches and laboratory methodologies, and the dataset does not seem sufficient to support the main findings (only a growing season was considered, and the authors collected only 7 xylem samples). Furthermore, the authors should have determined the uncertainty in their estimates, in order to assess the impacts on the main results.
In terms of presentation quality, the manuscript would benefit from a thorough revision of the English by a native speaker, and the authors should upload high-resolution figures with labels that are readable.
Specific comments
- Section 1: I think the introduction lacks some paragraphs describing the usefulness of stable isotopes of hydrogen and oxygen to investigate water fluxes in the soil-plant-atmosphere continuum. Furthermore, the novelty of this work is not clear (it seems a case study, but there are many like this one), and the specific objectives and/or the research questions should be clearly addressed.
- Section 2: The authors should add the area of the catchment and of the study site (it is unclear whether it is a subcatchment, a hillslope or a plot), and more specific topographic characteristics (e.g., elevation range, slope, aspect etc.). Lithology, soil type and texture should be described as well.
- Section 3.1: In this section, the authors should provide clear and detailed information about meteorological data and water sample collection. About meteorological data, the authors should clarify whether the station is located in the study site (is it representative of the local meteorological conditions?) and the temporal resolution of the measurements. About water samples, details should include temporal (monthly, weekly, daily or event timescale?) and spatial resolution (how many locations for each water source? At which elevation?), as well as depth for soil water samples (and how many locations?) and xylem samples (how many trees and locations were considered?). Furthermore, in this section the authors should describe the methodological approaches used for precipitation collection (what kind of collector was used?) and extraction of water samples from soil and plants (what kind of plant tissue was collected?).
- Section 3: This section also lacks a paragraph reporting how water samples were transported and stored before isotopic analyses, and details about laboratory analyses. These details should include the methodology used for isotopic analyses of the different water samples, the uncertainty in the isotopic measurements, any information about protocols used to ensure the quality of the analyses.
- In Section 3.3.5 and 3.3.6, the authors should clarify if they have assessed the uncertainty in their estimations, for instance, by error propagation and based on the uncertainty in the isotopic analyses and isotopic variability among various samples.
- Table 1: The number of xylem water samples (only 7) used for this study is too low. I think these few samples alone cannot support findings and the conclusions.
- Figure 3a: The authors report soil depths here, but they should have described those sampling depths in a previous section. Furthermore, the authors should clearly explain why soil depth varies for various sampling times.
- Figure 7: The authors should indicate the uncertainty in their estimates, clearly in the text and by error bars in this figure.
- I do not understand the purpose of Section 5.2 and the title does not reflect the content of the section.Technical corrections
- Figure 1: Labels are difficult to read. I suggest increasing their size and resolution.
- Line 107: “Water isotopes…were observed” is an unusual phrasing; please change the verb.
- Figure 2: Again, labels are difficult to read. I suggest increasing their size and resolution.
- Figure 3, 4, 5 and 7: Please increase the size of the labels.
- Figure 8a: The legend is unreadable; please increase the size and the resolution of the labels.Citation: https://doi.org/10.5194/hess-2022-375-RC2 -
AC2: 'Reply on RC2', Guofeng Zhu, 19 Jun 2023
RESOPNE TO REVIEWER2
Thank you very much for taking the time to provide valuable feedback on our manuscript. Your input is highly significant in improving the quality of our manuscript.
To demonstrate our appreciation for your feedback, we have diligently addressed each of your comments and made significant revisions, particularly in the following areas. We rearranged the logic of the manuscript and added data, especially for the xylem water of Qinghai spruce. The water vapor recirculation part is clearly expounded, and each parameter is described in detail. We haved carried out uncertainty analysis.We have re-landscaped all the images throughout the article and improved their clarity.We have changed the language to make it easier for native English speakers to make valuable suggestions.We have placed the revised manuscript PDF at the end of the document. The red bolded words, sentences, and subsections in the manuscript represent our editing changes.
We sincerely hope that you will recognize the genuine effort we have put into this manuscript and give us another opportunity to review and incorporate your suggested modifications.
General comment
The authors of this manuscript aimed to assess the role of transpiration, compared to other water fluxes, in a spruce forest in the Qilian Mountains, China. In their analyses, the authors exploited the usefulness of stable isotopes of hydrogen and oxygen to investigate water fluxes in the soil-plant-atmosphere continuum.
Respone:Thank you for your affirmation of our work. It serves as an ongoing motivation for us to continue our research in the current direction. We greatly appreciate your support and encouragement.
Although this manuscript may be interesting for the readers of Hydrology and Earth System Sciences, the current version presents a poor description of the sampling approaches and laboratory methodologies, and the dataset does not seem sufficient to support the main findings (only a growing season was considered, and the authors collected only 7 xylem samples). Furthermore, the authors should have determined the uncertainty in their estimates, in order to assess the impacts on the main results.
Respone:This issue indeed serves as a critical weakness in our manuscript. Therefore, in this revised version, we have made several adjustments. Firstly, we have adjusted the research timeframe to cover two growing seasons from 2018 to 2019. Additionally, we have increased the number of sampling points to four at different vertical heights and expanded the number of wood water samples to 404. Furthermore, we have conducted an uncertainty analysis (Section 5.2) to assess the uncertainties associated with our methodology.
In terms of presentation quality, the manuscript would benefit from a thorough revision of the English by a native speaker, and the authors should upload high-resolution figures with labels that are readable.
Respone:To underscore our determination to enhance the quality of the manuscript, we have undertaken a complete revamp of all the visuals in the article. Furthermore, we have made language revisions throughout the manuscript to ensure it is better suited for native English speakers to read and comprehend.
Specific comments
Section 1: I think the introduction lacks some paragraphs describing the usefulness of stable isotopes of hydrogen and oxygen to investigate water fluxes in the soil-plant-atmosphere continuum. Furthermore, the novelty of this work is not clear (it seems a case study, but there are many like this one), and the specific objectives and/or the research questions should be clearly addressed.
Respone:Thank you for your feedback. We have inserted the following sentence into the introduction:
The interaction between soil and vegetation controls rainfall input and water transfer within ecosystem components. It is an important player in climate change mitigation in terms of climate benefits (Rohatyn et al., 2022). In the face of complex climate changes, unpredictable weather variations, and continual alterations in surface coverage, the subsystems comprising the atmospheric, soil, and vegetation components in ecohydrological systems undergo corresponding changes in resilience, fragility, and sensitivity. Water moves from the soil through plant roots and stems, eventually reaching the leaves. During photosynthesis, water vapor is released into the atmosphere through open stomata. Stable isotopes of hydrogen and oxygen serve as natural tracers, enabling monitoring of the vertical migration and transformation of water in the ecosystem(Goodwell et al., 2018).
The semi-arid natural environment influences the hydrological processes in the soil-plant-atmosphere continuum. However, the contribution of vegetation transpiration (T) to water fluxes in these mountainous regions, which rely on rainfall and snowmelt as water sources, remains unclear. Furthermore, most studies have focused solely on partitioning evapotranspiration within the ecosystem, which obscures the fate of water vapor. In our research, we analyze the water vapor fluxes from soil evaporation and vegetation transpiration into the atmosphere, which is crucial for understanding water loss dynamics and ensuring appropriate allocation of water resources in the upstream and downstream regions of mountainous areas.
Section 2: The authors should add the area of the catchment and of the study site
(it is unclear whether it is a subcatchment, a hillslope or a plot), and more specific topographic characteristics (e.g., elevation range, slope, aspect etc.). Lithology, soil type and texture should be described as well.
Respone:We have made revisions to Section 2 based on your feedback, as follows:
2. Study area
The Qilian Mountains are located in the central part of the Eurasian continent, on the northeastern edge of the Qinghai-Tibet Plateau. The eastern region is dominated by water erosion, with large variations in mountainous terrain and an average elevation of over 4,000 meters. Permafrost is developed at elevations of 3,500 to 3,700 meters, and areas above 4,500 meters are characterized by modern glacier development. The region has a plateau continental climate, with hot summers and cold winters, strong solar radiation, and large temperature differences between day and night. The average annual temperature is below 4℃, with extreme highs of 37.6℃ and extreme lows of -35.8℃. The annual sunshine hours range from 2,500 to 3,300 hours, with a total solar radiation of 5,916 to 15,000 megajoules per square meter. The average annual precipitation is 400 millimeters, and the annual evaporation ranges from 1,137 to 2,581 millimeters. The average wind speed is around 2 meters per second, and the frost-free period lasts from 23.6 to 193 days. The Shiyang River originates from the Daxueshan on the northern side of the Lenglong Ridge in the eastern section of the Qilian Mountains, serving as a major water source for the city of Wuwei. The soil types in the eastern section are diverse, but with low organic matter content. The distribution of vegetation shows distinct zonal characteristics, with mountainous forest-grassland zones (2,600 to 3,400 meters), subalpine shrub-meadow zones (3,200 to 3,500 meters), and high mountain sub-ice-snow sparse vegetation zones (>3,500 meters) at elevations above 2,700 meters. The main types of natural forest vegetation include Qinghai spruce forest, Qilian juniper forest, and Chinese pine forest, with Qinghai spruce being the dominant tree species (Zhu et al., 2022).
Figure 1 Location of the study area and changes in meteorological conditions
Section 3.1: In this section, the authors should provide clear and detailed information about meteorological data and water sample collection. About meteorological data, the authors should clarify whether the station is located in the study site (is it representative of the local meteorological conditions?) and the temporal resolution of the measurements. About water samples, details should include temporal (monthly, weekly, daily or event timescale?) and spatial resolution (how many locations for each water source? At which elevation?), as well as depth for soil water samples (and how many locations?) and xylem samples (how many trees and locations were considered?). Furthermore, in this section the authors should describe the methodological approaches used for precipitation collection (what kind of collector was used?) and extraction of water samples from soil and plants (what kind of plant tissue was collected?)
Respone:Thank you very much for your meticulous feedback on this section. We have taken your comments seriously and made the following revisions:
1.1 Materials Sources
- Sampling network
Table 1 Sampling Point Locations and Sample Quantity Information
Parameter
Station
Qixiang
Hulin
Ninchan
Suidao
Altitude(m)
2543
2721
3068
3448
Local climate
Temperature(℃/a)
3
3.2
3.3
-0.9
Precipitation(mm/a)
510
469.44
394
475
Relative humidity（％/a）
52.9
56.1
66.6
69.2
Samplings number
Precipitation
53
108
91
135
Soil water
220
560
560
560
Xylem water
236
56
56
56
3.1.2 Sample collection
We use an automatic weather station in 2018 and 2019 to record meteorological data, in which a rain gauge is used to collect precipitation and transfer samples to 100ml containers after each rain. Drill holes 0 ~ 5 cm, 5 ~ 10 cm, 10 ~ 20 cm, 20 ~ 30 cm, 30 ~ 40 cm, 30 ~ 50 cm, 50 ~ 60 cm, 60 ~ 70 cm, 70 ~ 80 cm, 80 ~ 90 cm, 90 in the sample plot by using soil drill ~ 100 cm. The soil samples were divided into two parts, one of which was placed in a 50 ml glass bottle. The bottle was sealed with a subfilm and transported to the observation station within 10 hours after the sampling date was marked for cryopreservation to detect stable isotope data. The other part of the sample was placed in a 50ml aluminum box and the soil moisture content was determined by drying method. When collecting plant samples, we used scissors to collect vegetation xylem stems, peeled off the bark, and put them in 50ml glass bottles sealed and frozen for experimental analysis.
We utilized the monthly potential evapotranspiration dataset of China, with a spatial resolution of 0.0083333° (approximately 1 km), covering the period from January 1990 to December 2021. The data is measured in units of 0.1 mm. This dataset is derived from the China 1 km monthly mean temperature, minimum temperature, and maximum temperature dataset(Peng et al.,2022;Dinget al.,2020;Ding et al.,2021), using the Hargreaves equation for estimating potential evapotranspiration (Peng et al., 2017). The formula is as follows: PET = 0.0023 × S0 × (MaxT − MinT)0.5 × (MeanT + 17.8), where PET represents potential evapotranspiration in mm/month, MaxT, MinT, and MeanT are the monthly maximum temperature, minimum temperature, and mean temperature, respectively. S0 represents the theoretical solar radiation reaching the top of the Earth's atmosphere, calculated based on solar constant, distance between Earth and the sun, Julian day, and latitude. For data storage convenience, the values are stored as int16 type in NETCDF (nc) files. The surface evapotranspiration data were obtained from the MODIS-based daily surface evapotranspiration data of the Qilian Mountains (2019), with a spatial resolution of 0.01°（Yao et al., 2017;Yao et al., 2020）.
Section 3: This section also lacks a paragraph reporting how water samples were transported and stored before isotopic analyses, and details about laboratory analyses. These details should include the methodology used for isotopic analyses of the different water samples, the uncertainty in the isotopic measurements, any information about protocols used to ensure the quality of the analyses.
Respone: Thank you for your suggestions. We have rewritten this section based on your feedback. Here is the revised version:
3.2 Experimental Analysis
The isotopic data used in this study mainly include stable isotopes of precipitation, soil water, and xylem water. All isotopic samples were analyzed at the Stable Isotope Laboratory of Northwest Normal University. The precipitation samples were analyzed for hydrogen and oxygen stable isotopes using a liquid water isotope analyzer (DLT-100, Los Gatos Research, USA). After thawing the soil and vegetation samples, they were extracted using a low-temperature vacuum condensation device (LI-2100, LICA United Technology Limited, China), and the extracted water was subjected to isotopic analysis. Each water sample was tested six times to ensure accuracy, with the first two tests considered as interference and only the results of the subsequent four tests were averaged (Zhu et al., 2022). The isotopic measurements are represented by δ, which represents the deviation in parts per thousand of the ratio of two stable isotopes in the sample relative to the ratio in a standard sample. The International Atomic Energy Agency (IAEA) defined the Vienna Standard Mean Ocean Water (VSMOW) in 1968 as the standard for isotopic composition, which is derived from distilled seawater and has a similar isotopic composition to Standard Mean Ocean Water (SMOW).
（1）
In Section 3.3.5 and 3.3.6, the authors should clarify if they have assessed the uncertainty in their estimations, for instance, by error propagation and based on the uncertainty in the isotopic analyses and isotopic variability among various samples.
Respone: This is a great question, and we have made modifications to the content based on your feedback. We have addressed the uncertainties related to sections 3.3.5 and 3.3.6 separately in section 5.2 Uncertainty Analysis. Additionally, we have presented how we avoided isotopic variations among samples in section 3.2 Experimental Analysis.
Table 1: The number of xylem water samples (only 7) used for this study is too low. I think these few samples alone cannot support findings and the conclusions
Respone:Your concern is entirely justified, and in the revised manuscript, we have addressed it by adding more sampling points and extending the sampling duration, thus avoiding any potential unreliability in the analysis results. The additional information on the sampling points is provided below:
Table 1 Sampling Point Locations and Sample Quantity Information
Parameter
Station
Qixiang
Hulin
Ninchan
Suidao
Altitude(m)
2543
2721
3068
3448
Local climate
Temperature(℃/a)
3
3.2
3.3
-0.9
Precipitation(mm/a)
510
469.44
394
475
Relative humidity（％/a）
52.9
56.1
66.6
69.2
Samplings number
Precipitation
53
108
91
135
Soil water
220
560
560
560
Xylem water
236
56
56
56
We have revised the original Table 1 in Section 4.1 and renamed it as Table 2. Furthermore, we have made adjustments to the content of the table.As follows:
During the growth season of Qinghai spruce, the stable isotopes of precipitation exhibit specific patterns of fluctuation (Table 2). In the early stages of growth, the hydrogen and oxygen isotope values are generally low. As the temperature gradually increases, the extent of water evaporation and loss intensifies, leading to an enrichment of stable isotopes. The average δ2H value of precipitation throughout the growth season is -45.52‰, fluctuating roughly between -238.62‰ and 63.43‰. The average δ18O value is -7.75‰, fluctuating roughly between -31.49‰ and 14.79‰. There is not a significant depletion or enrichment of stable isotopes in the wood tissues, with a fluctuation range of -76.95‰ to 23.87‰ for δ2H and -11.92‰ to 24.77‰ for δ18O. Shallow soil water shows a less pronounced enrichment of heavy isotopes compared to precipitation and wood tissues, with a lower degree of fluctuation observed during late spring and the beginning of summer.
Table 2 Stable isotopes of different water bodies during the growing season
Period
Average
δ2H/‰
δ18O/‰
Precipitation
Xylem water
Soil water（0~10cm）
Precipitation
Xylem water
Soil water（0~10cm）
4
-69.15
-39.02
-53.10
-10.25
2.56
-7.10
5
-39.09
-29.78
-45.38
-7.61
4.44
-6.42
6
-31.29
-45.83
-46.08
-5.74
-2.83
-6.12
7
-32.39
-47.63
-47.71
-5.33
-0.97
-7.06
8
-48.88
-44.55
-68.85
-7.79
-2.06
-9.07
9
-29.38
-42.62
-49.20
-6.46
-1.83
-6.79
10
-68.43
-44.57
-54.88
-11.06
-2.25
-7.96
Figure 3a: The authors report soil depths here, but they should have described those sampling depths in a previous section. Furthermore, the authors should clearly explain why soil depth varies for various sampling times.
Respone:Thank you for your feedback. What I intended to convey here is to utilize soil moisture content at different depths to reflect the information of unbalanced fractionation in soil evaporation. However, it seems that our original figures and sampling points deviated from this intention. Therefore, we have reorganized this section accordingly, as follows:
Unsaturated water vapor leads to non-equilibrium fractionation during the process of precipitation, with an average d-excess value of 16.58‰ throughout the growing season (Figure 3, a). In May and September, due to higher relative humidity compared to other periods, the evaporation rate of water vapor is faster. The deuterium values show slow fluctuations from June to August, with significant fluctuations starting from mid-August, indicating that local evaporation is gradually enhanced over time due to the influence of temperature and relative humidity, leading to increased non-equilibrium evaporation. The average lc-excess value of precipitation in the lower layer of forest distribution is -8.18‰, while the average lc-excess value of precipitation in the middle, upper-middle, and canopy layers is close to 0. This is because the fractionation effect of evaporation is more pronounced at lower elevations. At higher elevations, influenced by rainfall and snowmelt, the soil moisture content in all soil layers is above 30% (Figure 3, b). Towards the end of the growing season, as temperatures decrease, tree leaves fall to the forest floor, forming a litter layer that retains moisture in the soil.
Figure 3 (a) Variation in soil water content, (b) Comparison between atmospheric water vapour oxygen isotopes and d-excess
Figure 7: The authors should indicate the uncertainty in their estimates, clearly in the text and by error bars in this figure.
Respone:Thank you for your feedback. We have restructured section 5.1.2 "Contribution to recirculating water vapor in precipitation" and conducted an analysis of the associated uncertainties. We have also redesigned the tables and figures accordingly. In Figure 7, we have compared the contribution rates of each component only for the growing season. As there are only values available for 7 months for each component, I have not included error bars for visual clarity. I hope you understand this approach. As follows:
5.1.2 Contribution to recirculating water vapour in precipitation
Our previous study in the eastern section of the Qilian Mountains (Zhang et al., 2021) indicated that above an altitude of 2100m, air masses gather from the northern foothills and move along the valley from low to high elevations. From June to August, the atmospheric circulation is influenced by westerlies, southeast monsoons, and plateau monsoons, while from September to October, the westerlies are the dominant factor. Therefore, we selected the Xiying station at an altitude of 2097m as our upwind site. The δpv values showed depletion in April and October, gradually enriching from June to August. The maximum δ2H value was -76.02‰ (Table 5), and the minimum was -184.93‰, while the maximum δ18O value was -11.89‰, and the minimum was -26.38‰. Above 2700 meters, there is a gradual decrease in precipitation vapor with increasing altitude. The δev values exhibited significant fluctuations throughout each month, with different patterns at different locations. At 2721m, the δev range varied between -172.3‰ and 96.39‰. From the forest's lower layer to the upper layer, the isotopic composition of the advected moisture from the valley gradually diminished, resulting in decreasing values of δadv.
Table 5 Isotopic Composition of Precipitation Vapor, Surface Evaporation Vapor, and Vegetation Transpiration Vapor at Different Months and Altitudes
Month
Type
isotope
April
May
June
July
August
September
October
Qixiang
δpv
δ2H/‰
-141.95
-123.83
-99.87
-115.99
-128.34
-120.9
-152.43
δ18O/‰
-19.27
-16.58
-14.04
-15.91
-18.6
-17.22
-22.34
δev
δ2H/‰
-
-125.69
-123.69
-117.98
-134.57
-
-
δ18O/‰
-
-30.21
-29.56
-28.62
-31.19
-
-
δtr
δ2H/‰
-39.9
-29.32
-46.19
-49.58
-45.15
-42.66
-44.64
δ18O/‰
2.22
-5.87
-4.59
-0.72
-1.72
-1.78
-2.26
δadv
δ2H/‰
-145.57
-83.25
-81.12
-92
-109.62
-100.53
-122.62
δ18O/‰
-20.24
-11.93
-10.73
-12.06
-15.31
-13.46
-18.16
Hulin
δpv
δ2H/‰
-129.93
-123.29
-98.68
-113.98
-124.16
-118.52
-164.82
δ18O/‰
-17.54
-16.62
-13.18
-15.48
-17.33
-17.88
-22.46
δev
δ2H/‰
-114.24
-117.01
-107.75
-123.44
-106.92
-96.39
-172.3
δ18O/‰
-14.77
-16.35
-15.03
-16.7
-14.53
-12.78
-24.82
δtr
δ2H/‰
-24.12
-39.62
-35.97
-26.44
-35.85
-38.39
-40.53
δ18O/‰
-5.34
-3.58
-4.13
-0.34
-2.35
-4.25
-1.97
δadv
δ2H/‰
-112.79
-115.67
-106.61
-122.19
-106.49
-95.7
-170.54
δ18O/‰
-14.59
-16.15
-14.88
-16.54
-14.46
-12.69
-24.57
Ninchan
δpv
δ2H/‰
-
-76.02
-139.21
-135.74
-129.96
-113.71
-184.93
δ18O/‰
-
-11.89
-19.87
-18.7
-17.91
-16.77
-26.38
δev
δ2H/‰
-
-83.76
-81.42
-92.42
-110.24
-101.11
-123.42
δ18O/‰
-
-12.01
-10.76
-12.09
-15.37
-13.49
-18.27
δtr
δ2H/‰
-
-25.58
-46.77
-37.77
-43.66
-
-
δ18O/‰
-
-3.45
-1.98
-1.05
-6.68
-
-
δadv
δ2H/‰
-162.36
-113.49
-111.45
-106.16
-122.12
-114.67
-141.33
δ18O/‰
-22.73
-15.94
-15.28
-14.46
-16.96
-16.58
-20.42
Suidao
δpv
δ2H/‰
-167.86
-128.08
-124.95
-117.32
-137.73
-130.44
-155.52
δ18O/‰
-22.9
-18.02
-17.28
-16.04
-19.09
-18.64
-21.59
δev
δ2H/‰
-164.16
-114.52
-112.37
-106.9
-122.96
-115.47
-142.85
δ18O/‰
-22.98
-16.07
-15.41
-14.56
-17.08
-16.69
-20.64
δtr
δ2H/‰
-
-25.58
-46.77
-37.77
-43.66
-
-
δ18O/‰
-
-8.45
-6.98
-6.05
-6.68
-
-
δadv
δ2H/‰
-162.38
-113.51
-111.47
-106.18
-122.14
-114.69
-141.36
δ18O/‰
-22.73
-15.94
-15.28
-14.47
-16.97
-16.58
-20.42
In July, the ratio of vegetation transpiration to precipitation vapor is significantly higher compared to other months. The temperatures in the lower layers of the forest are relatively high, and the middle to upper layers are densely populated with spruce, resulting in a higher ftr (transpiration ratio) throughout the entire growing season. Both the early and late stages of the growing season exhibit noticeably higher fev (evaporation ratio) compared to other months, with the middle and upper parts of the forest having a higher proportion of evaporated vapor. The average fadv (advected vapor ratio) is 72%, with contributions exceeding 70% for all months except June and July (Figure 7).
Figure 7 Comparison of fadv (advective water vapour contribution), fev (surface evaporation water vapour contribution) and ftr (plant transpiration water vapour contribution) for each of period
I do not understand the purpose of Section 5.2 and the title does not reflect the content of the section.
Respone:I completely agree with your viewpoint, and therefore, I have removed that section from the manuscript. Instead, I have revised section 5.2 to focus on uncertainty analysis. As follows:
5.2 Uncertainty analysis
A higher sample size can reduce the margin of error. Therefore, we utilized isotopic data from four sites over a two-year period to evaluate the model. We used 404 xylem samples to calculate the contribution ratio of transpiration to ecosystem evapotranspiration. We examined the uncertainty of the model evaluation. When analyzing the evaporation characteristics in a semi-arid natural environment using the Craig-Gordon isotopic model, we first eliminated the influence of solar radiation and other meteorological variables on the calculation results. We focused on temperature, relative humidity, water vapor, and the initial isotopic values of water bodies. Particularly in semi-arid environments, the variations in temperature and relative humidity are crucial (Hernández-Pérez et al., 2020). To verify the calculation results, we found a strong correlation between the isotopes of soil evaporation and relative humidity, as demonstrated by the fitting of δE against relative humidity and temperature. This also indicates the reliability of the results obtained through the Craig-Gordon isotopic model. We employed the Keeling plot method to calculate δET, which is based on isotopic mass balance and a two-endmember mixing model. This method assumes that the isotopic composition of the background atmosphere and source remains constant, with a very low probability of isotopic spatial variation (Good et al., 2012; Kool et al., 2014). Due to the higher reliability of oxygen isotopes compared to hydrogen isotopes (Han et al., 2022; Kale et al., 2022), we solely used oxygen isotopes to calculate the T/ET values. The results indicate that transpiration significantly outweighs evaporation during July and August, which aligns with previous research findings (Zhu et al., 2022). The correlation between T/ET and soil moisture content suggests that soil moisture is a crucial factor driving the variations in transpiration and evaporation ratios. Additionally, the estimation of isotopic composition of advected water vapor from the upwind sites contributes to increased uncertainty. In our study area, the sites are predominantly influenced by valley winds, with water vapor moving from the valley bottom to higher altitudes. Therefore, we selected lower elevation areas in the valley bottom as the source region for advected water vapor (Zhang et al., 2021).
Figure 8 Correlation analysis of factors affecting uncertainty in impact assessment
Technical corrections
- Figure 1: Labels are difficult to read. I suggest increasing their size and resolution.
- Line 107: “Water isotopes…were observed” is an unusual phrasing; please change the verb.
- Figure 2: Again, labels are difficult to read. I suggest increasing their size and resolution.
- Figure 3, 4, 5 and 7: Please increase the size of the labels.
- Figure 8a: The legend is unreadable; please increase the size and the resolution
of the labels.
Respone: We have carefully addressed your feedback regarding the figures. In order to enhance the overall quality of the paper, we have recreated all the figures in our manuscript, removing any inappropriate sentences.
We would like to invite you to review the significant changes we have made in the revised manuscript. Attached below is the PDF of the manuscript:
-
AC2: 'Reply on RC2', Guofeng Zhu, 19 Jun 2023
Status: closed
-
RC1: 'Comment on hess-2022-375', Anonymous Referee #1, 30 Mar 2023
All the comments are in the GeneralComment&CommentedManuscript.pdf file cointaing also the commented manuscript. Most of the highlighted sections will display a comment when hovered.
-
AC1: 'Reply on RC1', Guofeng Zhu, 19 Jun 2023
We are deeply grateful for the valuable and encouraging feedback on the manuscript. Your thoughtful revisions and comments are sincerely appreciated.
We have revised the paper greatly, which is mainly reflected in the following aspects:We rearranged the logic of the manuscript and added data, especially for the xylem water of Qinghai spruce. The water vapor recirculation part is clearly expounded, and each parameter is described in detail. We haved carried out uncertainty analysis.We have re-landscaped all the images throughout the article and improved their clarity.We have changed the language to make it easier for native English speakers to make valuable suggestions.We have placed the revised manuscript PDF at the end of the document. The red bolded words, sentences, and subsections in the manuscript represent our editing changes.
We have taken your comments into careful consideration and responded to each one, demonstrating our attention and gratitude towards your input.
ABSTRACT
16 - Maybe need some clarifications.
Response: Based on your comments, we have adjusted this sentence as follows: We collected precipitation, soil water from 0 to 100cm, xylem water from Qinghai Spruce, temperature, relative humidity and rainfall in the eastern Qilian Mountains from 2018 to 2019. We simulated T/ET comprehensively, and quantified the contribution of recirculated water vapor in precipitation. The aim of this study was to clarify the evapotranspiration process and its effect on production and confluence in the forest belt of Qilian Mountain.
INTRODUCTION
The introduction explains quite clearly the general context and gives a reasonable overview of the relative state of the art. The cited literature may be increased in some sections and re-organization of some sentences is advisable (see comments by lines).
The last section of the introduction is a bit too much methods-referred and needs a correction (see
comments by lines). The general objective should be better stated.
Response:These two parts of opinions are very important to the logic of our article, and we have carefully revised them.
34 to 41 - the text is discussing the importance of the spruce forests system referring to the Qinghai local one. Maybe, here a more general discussion about this ecosystem could be more appropriate.
Response:We have adjusted this sentence to a more appropriate content to fit the full text,as follows: As a natural reservoir and purifier, the Qinghai spruce ecosystem has the functions of storing, releasing and purifying water. The Qilian Mountains supply the water resources that human beings depend on for survival in the continental river basin in the arid region, regulate the water cycle in the arid region, and interact with the soil and atmosphere to form a vertical spatial continuum, which not only affects the ecological process of the local plant community, but also changes the regional microclimate by means of latent heat (Ault et al., 2020;Zhang et al.,2021;Eisenhauer et al., 2021).
39 to 41 - This can be better placed in the final part of the introduction. It seems a bit in the middle of the general knowledge section.
Response:Thank you for your suggestion, we have put it in the last paragraph of the introduction.
45 - May be good to add at least a sentence to clarify why ET, which is connected to climate change, is the right parameter to study in this context.
Response:Based on your comments, we have adjusted this sentence as follows: The interaction between soil and vegetation controls rainfall input and water transfer within ecosystem components. It is an important player in climate change mitigation in terms of climate benefits (Rohatyn et al., 2022). Evapotranspiration (ET) is an indispensable part of the terrestrial water and energy cycle. Therefore, exploring the spatio-temporal and component changes of soil evaporation and vegetation transpiration can help us increase the response of vegetation canopy to climate change (Liu et al., 2022). At the ecosystem scale, many studies have classified evapotranspiration (ET) as transpiration (T) and evapotranspiration (E) (Schlesinger et al.,2014).
79 - after "... in central Asia." can reasonable place to insert the previous sentence of lines 39 - 41.
Response: Based on your comments, we have placed it to the previous sentence of lines 39 - 41.
79 to 87 - is too much of a method. Shorter and more on the objectives.
Response: Based on your comments, we have adjusted this sentence as follows: In this study, we observed and analyzed the monthly xylem water, soil water, precipitation stable isotopes and soil water content of Spruce forest in the eastern Qilian Mountains from April to October 2018 and 2019, and used these data to solve the following problems: (1) Quantify the contribution rates of soil evaporation and vegetation transpiration to evapotranspiration of ecosystems; (2) Quantifying the ratio of recirculated water vapor in precipitation; (3) To investigate the evapotranspiration process and its influence on production and confluence in the forest belt of Qilian Mountain. This study provides an effective basis for local water resource use and ecological protection.
STUDY AREA
This section potentially needs quite some updates (see the comments by lines).
Response:We really appreciate your valuable comments. We have revised the description of the study area and created a new overview map of the study area.
Is the vegetation of the monitored basin only composed of the spruce forest? Can mentioning the
vegetation types and distribution be useful (if feasible)?
Response: We think your suggestion is reasonable and have made some adjustments, as follows:
The Qilian Mountains are located in the central part of the Eurasian continent, on the northeastern edge of the Qinghai-Tibet Plateau. The eastern region is dominated by water erosion, with large variations in mountainous terrain and an average elevation of over 4,000 meters. Permafrost is developed at elevations of 3,500 to 3,700 meters, and areas above 4,500 meters are characterized by modern glacier development. The region has a plateau continental climate, with hot summers and cold winters, strong solar radiation, and large temperature differences between day and night. The average annual temperature is below 4℃, with extreme highs of 37.6℃ and extreme lows of -35.8℃. The annual sunshine hours range from 2,500 to 3,300 hours, with a total solar radiation of 5,916 to 15,000 megajoules per square meter. The average annual precipitation is 400 millimeters, and the annual evaporation ranges from 1,137 to 2,581 millimeters. The average wind speed is around 2 meters per second, and the frost-free period lasts from 23.6 to 193 days. The Shiyang River originates from the Daxueshan on the northern side of the Lenglong Ridge in the eastern section of the Qilian Mountains, serving as a major water source for the city of Wuwei. The soil types in the eastern section are diverse, but with low organic matter content. The distribution of vegetation shows distinct zonal characteristics, with mountainous forest-grassland zones (2,600 to 3,400 meters), subalpine shrub-meadow zones (3,200 to 3,500 meters), and high mountain sub-ice-snow sparse vegetation zones (>3,500 meters) at elevations above 2,700 meters. The main types of natural forest vegetation include Qinghai spruce forest, Qilian juniper forest, and Chinese pine forest, with Qinghai spruce being the dominant tree species (Zhu et al., 2022).
89 to 100 - A reference to Fig. 1 is missing. There are many toponyms cited but these are not present in Fig. 1 which makes it quite difficult for readers not familiar with the area to understand the geographical setting of the study area.
Response:Thanks for your suggestion, we have revised Fig.1. We will display the image in the next question.
104 - Fig. 1 Label are too small. A legend of the color code is non-presented. Why are surface water sampling site if surface water is never addressed in the text?
Response:We re-made the map, and reasonably modified the legend and other labels.
Figure 1 Location of the study area and changes in meteorological elements
MATERIALS AND METHODS
The description of the analytical procedures used for isotope determination is completely missing. Please insert it with accurate specifications of the methodology and the associated analytical errors.
Response:In response to your comments, we have inserted the following:
3.2 Experimental Analysis
The isotopic data used in this study mainly include stable isotopes of precipitation, soil water, and xylem water. All isotopic samples were analyzed at the Stable Isotope Laboratory of Northwest Normal University. The precipitation samples were analyzed for hydrogen and oxygen stable isotopes using a liquid water isotope analyzer (DLT-100, Los Gatos Research, USA). After thawing the soil and vegetation samples, they were extracted using a low-temperature vacuum condensation device (LI-2100, LICA United Technology Limited, China), and the extracted water was subjected to isotopic analysis. Each water sample was tested six times to ensure accuracy, with the first two tests considered as interference and only the results of the subsequent four tests were averaged. In order to ensure the accuracy of the measurement results, a parallel sample was collected for each sample, and the average value of the two determination results was taken as the final value(Zhu et al., 2022). The isotopic measurements are represented by δ, which represents the deviation in parts per thousand of the ratio of two stable isotopes in the sample relative to the ratio in a standard sample. The International Atomic Energy Agency (IAEA) defined the Vienna Standard Mean Ocean Water (VSMOW) in 1968 as the standard for isotopic composition, which is derived from distilled seawater and has a similar isotopic composition to Standard Mean Ocean Water (SMOW).
（1）
107 to 110 - It seems that the sampling was done only at one point, but multiple points are reported in Fig. 1. Important, isotopic values are not observed but determined in samples, and samples are collected at certain locations, unless the case of "portable" analyzer (like some CRDLS). The sampling strategies need a clearer explanation.
Response: Based on the reviewer's comments, we rearranged the sampling points needed for the whole paper to avoid the lack of xylem water data. 3.1 Materials Sources has been greatly adjusted to make it more reasonable and clear.
3.1 Materials Sources
3.1.1 Sampling network
Parameter
Station
Qixiang
Hulin
Ninchan
Suidao
Altitude(m)
2543
2721
3068
3448
Local climate
Temperature(℃/a)
3
3.2
3.3
-0.9
Precipitation(mm/a)
510
469.44
394
475
Relative humidity（％/a）
52.9
56.1
66.6
69.2
Samplings number
Precipitation
53
108
91
135
Soil water
220
560
560
560
Xylem water
236
56
56
56
3.1.2 Sample collection
We use an automatic weather station in 2018 and 2019 to record meteorological data, in which a rain gauge is used to collect precipitation and transfer samples to 100ml containers after each rain. Drill holes 0 ~ 5 cm, 5 ~ 10 cm, 10 ~ 20 cm, 20 ~ 30 cm, 30 ~ 40 cm, 30 ~ 50 cm, 50 ~ 60 cm, 60 ~ 70 cm, 70 ~ 80 cm, 80 ~ 90 cm, 90 in the sample plot by using soil drill ~ 100 cm. The soil samples were divided into two parts, one of which was placed in a 50 ml glass bottle. The bottle was sealed with a subfilm and transported to the observation station within 10 hours after the sampling date was marked for cryopreservation to detect stable isotope data. The other part of the sample was placed in a 50ml aluminum box and the soil moisture content was determined by drying method. When collecting plant samples, we used scissors to collect vegetation xylem stems, peeled off the bark, and put them in 50ml glass bottles sealed and frozen for experimental analysis.
117 to 141 - Sections 3.3.1, 3.3.2 and 3.3.3 miss citations. The sources of the reported equations need to be cited.
Response:Based on your comments, we have inserted citations for each formula in the manuscript, and supplement the formula and meaning. as follows:
3.3.1 Isotopic composition of atmospheric water vapour
The stable isotope composition of moisture in ambient air is calculated as follows(Gibson and Reid, 2014;Skrzypek et al., 2015):
（2）
where k=1, or by fitting k to some fraction of 1 as the best fit to the local evaporation line, is the isotopic fractionation factor. Defined by. about 2H and 18O are calculated as follows(Horita and Wesolowski, 1994):
（3）
（4）
3.3.2 Isotopic composition of soil evaporation
The Craig-Gordon model was used to calculate the stable isotopic composition of soil evaporation water vapour, δE, using the following equation(Craig and Gordon, 1965;Yepez et al., 2005).
（5）
where αe(>1) is the equilibrium factor calculated as a function of water surface temperature, δs is the stable isotopic composition of liquid water at the evaporating surface of the soil (0 ~ 10 cm average stable isotopic composition of soil water), δA is the stable isotopic composition of atmospheric water vapour near the surface, εeq represents the equilibrium fractionation corresponding to εeq = (1-1/αe) × 1000, εk is the kinetic fractionation factor of O2 is approximately 18.9‰ and h*is the atmospheric relative humidity(Gibson and Reid, 2010). For δ18O, αe is calculated as follows(Raz-Yaseef et al., 2010):
（6）
Where T is the soil Kelvin temperature (K) at a depth of 5 cm.
3.3.3 Isotopic composition of plant transpiration
When transpiration is strong, leaf water is in "isotopic stable state", that is, the isotopic composition of leaf transpiration water is equivalent to that of water absorbed by the roots of rain plants at noon. Therefore, the stable isotopic composition of water in plant xylem can be used to represent the stable isotopic composition of water vapor in plant transpiration. The expression is as follows(Aron et al., 2020):
（7）
where δx is the isotopic ratio of xylem water and δT is the isotopic ratio of transpiration.
3.3.4 Evapotranspiration isotope assessment
The Keeling Plot model describes the linear relationship between the oxygen isotope composition of atmospheric water vapour and its reciprocal concentration . The intercept of the curve on the Y-axis represents the oxygen isotopic composition of evapotranspiration (δET) and is expressed as(Keeling, 1958;Wang et al., 2015)：
（8）
Where δa and Ca represent the atmospheric water vapour oxygen isotopic composition (‰) and water vapour concentration in the ecosystem boundary layer, δb and Cb represent the background atmospheric water vapour oxygen isotopic composition and background atmospheric water vapour concentration, and δET is the ecosystem evapotranspiration oxygen isotopic composition.
3.3.5 Ecosystem evapotranspiration partitioning
The determination of evapotranspiration by means of biotic and abiotic isotopic water fluxes can be used to improve the understanding of community structure and ecosystem function in Qinghai spruce forests in the Qilian Mountains. Based on the isotope mass balance approach to consider the distribution of major and minor isotopes, the partitioning of evapotranspiration can be achieved using two end-member mixing models (E and T) with the following expression(Kool et al.,2014;Wei et al., 2018):
（9）
where δET, δE and δT are the isotopic compositions of evapotranspiration (ET), soil evapotranspiration (E) and plant evapotranspiration (T), respectively, and the isotopic values of the three can be obtained by both direct observation and model estimation.
3.3.6 Three-component mixing model
Assuming that precipitation vapor is a mixture of advective water vapour and recirculating water vapour, it is understood that the proportion of both precipitation and precipitation water vapour has the same nature. The proportion of precipitation occupied by advective vapour is calculated as follow(Kong et al., 2013; Wang et al., 2022):
（10）
where Ptr, Pev and Padv are precipitation produced by transpiration, surface evaporation and advection, respectively.
This can be calculated using the following formula(Brubaker et al., 1993; Sang et al., 2023):
（11）
（12）
where ftr, fev and fadv are the proportional contributions of transpiration, surface evaporation and advection to precipitation, respectively, and δpv, δtr, δev and δadv values are the stable isotopes in precipitating transpiration, transpiration, surface evaporation and advective vapour, respectively. ftr,fev and fadv are calculated by Isoerror software, based on dual isotopes and three sources(Ver. 1.3.1, https://www.epa.gov/)(Phillips and Gregg, 2001). δpv is calculated using the following formula:
（13）
Using the C-G model to calculate δev, the formula is as follows:
（14）
Including the δs is the isotopic composition of liquid water evaporation front, δadv is advection steam, h is relative humidity, α+ is equilibrium fractionation factor, ℇk is kinetic fractionation factor, ℇ is total fractionation factor.
（15）
（16）
h is the relative humidity, Ck is the kinetic fractionation constant, δ2H is 25.1‰, δ18O is 28.5‰.The weight coefficient θ of small water body is 1, and θ of large water body is 0.5. n ranges from 0.5 (fully turbulent transport, with reduced kinetic fractionation, suitable for lake or saturated soil conditions) to 1 (fully diffused transport, suitable for very dry soil conditions), with a kinetic fractionation coefficient of about 12.2-24.5‰ for ℇk (2H) in a dry atmosphere (h=0). The kinetic separation coefficient of ℇk (18O) is about 13.8-27.7‰.
The advection water vapor isotope δadv in the three-component mixing model needs to be determined by the water vapor isotopic composition at the upwind position. Based on the HYSPLIT model, we found that the eastern Qilian Mountains was controlled by westerly winds, southeast monsoon and plateau monsoon in June, July and August, and by prevailing westerly winds in September and October. The clustering analysis of air masses in different months shows that air masses accumulate at the northern foot of Qilian Mountains and move from low altitude to high altitude along the valley. Xiying, at 2097 m above sea level, is therefore used as a headwind station from April to October. When steam isotopes show a depletion trend along the transport path, isotopic fractionation is assumed to be due to Rayleigh distillation, and the expression is as follow:
（17）
Where δpv-adv is the isotopic composition in the vapor of the winds tation, and F is the ratio between the final vapor and the initial vapor. Since rainfall is positively correlated with the surface vapor pressure of the whole study area (c=1.657e, where c is the water vapor content in mm, e is the surface vapor pressure in hPa,R2=0.94), we used the surface vapor pressure of each site to calculate the value of F. The recirculated water entering the air mass is not considered here, because the contribution of recirculated water to the total air column is very limited, and most of the available precipitation does not result in rainfall but escapes to other areas. If there is no depletion of isotope ratios along the transmission track, the vapor isotope ratio from the upwind station is applied directly, and the Rayleigh distillation equation is not applied.
The δp is corrected by the local evaporation line (LEL), and the LEL slope (SLEL) can be calculated as（Skrzypek et al.，2015):
（18）
Where h is the relative humidity, ℇ is the total fractionation factor, and δPV and δP are the stable isotopic components of water vapor and precipitation.According to our research results, the LEL equation for the study area is δ2H=3.86δ18O-19.88 (R2=0.994, P < 0.0001, n=19).
RESULTS AND ANALYSIS
Comment to Tab. 1. The number of analyzed xylem water samples seems quite low with respect to other water matrices. This can pose a serious problem with the statistical significance of the results. How can the work deal with this?
Response:We recognized this serious problem and expanded the sampling points into four sampling strips at different altitudes(Table 1). Furthermore, we have made revisions to Table 2 in Section 4.1 as follows:
Table 1 Sampling Point Locations and Sample Quantity Information
Parameter
Station
Qixiang
Hulin
Ninchan
Suidao
Altitude(m)
2543
2721
3068
3448
Local climate
Temperature(℃/a)
3
3.2
3.3
-0.9
Precipitation(mm/a)
510
469.44
394
475
Relative humidity（％/a）
52.9
56.1
66.6
69.2
Samplings number
Precipitation
53
108
91
135
Soil water
220
560
560
560
Xylem water
236
56
56
56
Table 2 Stable isotopes of different water bodies during the growing season
Period
Average
δ2H/‰
δ18O/‰
Precipitation
Xylem water
Soil water（0~10cm）
Precipitation
Xylem water
Soil water（0~10cm）
4
-69.15
-39.02
-53.10
-10.25
2.56
-7.10
5
-39.09
-29.78
-45.38
-7.61
4.44
-6.42
6
-31.29
-45.83
-46.08
-5.74
-2.83
-6.12
7
-32.39
-47.63
-47.71
-5.33
-0.97
-7.06
8
-48.88
-44.55
-68.85
-7.79
-2.06
-9.07
9
-29.38
-42.62
-49.20
-6.46
-1.83
-6.79
10
-68.43
-44.57
-54.88
-11.06
-2.25
-7.96
Comment to Figure 4b, is it not clear why δb is represented instead of δET. Does the x-axis represent humidity?
Response:We would be happy to explain the meaning of the x-axis to you. We have provided a clearer explanation of this aspect in our revised section. The fundamental principle of the Keeling plot method is to perform a linear regression of water vapor concentrations (1/[H2O]) at different heights within the ecosystem's boundary layer against stable isotopic compositions (δ18O and δ2H). The resulting Keeling plot is used to estimate δET, with the x-axis representing water vapor concentration. After incorporating additional sampling points and expanding the study timeframe, we have reorganized this section as follows:
The Keeling plot method was used to analyze the stable isotope composition of ecosystem evapotranspiration (Figure 4). Its principle involves linearly fitting the water vapor concentration in the ecosystem boundary layer against the oxygen isotope composition, with the intercept on the y-axis representing the stable isotope value of δET. The results indicate that at different heights within the distribution of deciduous trees, the average δET value is -22.59‰. Throughout the entire growing season, δET does not consistently decrease with increasing elevation. Specifically, near the treeline, there are higher stable isotope values, but in the middle and upper layers of the forest, there is a minimal value, indicating lower and less stable isotopic fractionation in that layer. At an elevation of 3448m, as the number of deciduous trees decreases and shrubs become dominant, the δET value is -21.81‰(Table 3). We found that the stable isotope δE of soil evaporation at depths of 0-10cm is more enriched at lower elevations, particularly in April and May when the isotopic enrichment is more pronounced. From June to August, due to a significant increase in vegetation coverage, soil evaporation intensity decreases. In the early stage of the growing season, when leaves have not fully developed, the stable isotope composition of the xylem exhibits a relatively depleted characteristic. In July and August, when leaves are fully expanded, temperatures rise, and the rainy season in mountainous areas commences, transpiration becomes more intense.
Figure 4 Each sampling point is fitted with a trend line based on the Keeling plot method
Comments to Figure 5a, how can the two data points for which δ18Os is lower than δOE be justified? Which may seem counterintuitive. What is the y-axis representing?
Response:After adding additional sampling points, we have restructured Section 4.3 and incorporated clearer chart types to effectively convey the information we wish to present. As follows:
4.3 T/ET assessment of Qinghai spruce forest ecosystem in different months
We found that the canopy closure of deciduous trees significantly influences the evapotranspiration of the entire ecosystem (Figure 5). In April and May, as temperatures rise, surface vegetation exhibits weaker growth, resulting in a higher proportion of soil evaporation within the ecosystem, while transpiration by vegetation remains relatively low. During the rainy season in June to August, vegetation experiences vigorous growth, and transpiration reaches its peak in July. In September and October, soil evaporation becomes more dominant as temperatures, relative humidity, and rainfall gradually decrease, and deciduous tree leaves become wilted. At lower elevations, the T/ET ratio fluctuates between 0.20 and 0.70 in a distinct pattern, while above the treeline, transpiration ratios fluctuate between 0.20 and 0.80 in a similar pattern. Overall, summer is characterized as the peak season for transpiration, with a minimal contribution from soil evaporation.
Figure 5 The proportion of soil evaporation and vegetation transpiration in evapotranspiration of ecosystem(0 represents missing data)
Comment to Figures 5a and 5b. In both figures, δ18Os is represented. Are these the same data? If yes, why are the values different?
Response: That's a great suggestion. We have removed unnecessary charts and streamlined the content accordingly. What we need to explain here is that through reading literature, we found that δ18O is more reliable than δ2H in calculating soil evaporative isotope and evapotranspiration partitioning. Therefore, only oxygen isotope was calculated in the manuscript. However, in order to make the diagram more clearly reflect the evaluation of T/ET in the spruce forest ecosystem in Qinghai, we remade the them. The modified results have been presented in the previous question for your reference.
Comments to Figure 5c. Some remarkable mistakes are present in this figure. The y-axis is reporting a fraction(0 to 1) value, but the label has the percentage symbol. The y-axis does not have the same interval magnitude. Moreover, see the comment in the pdf on lines 286 to 288
Response: Thank you very much for your feedback. We greatly appreciate your input, and we took particular note of the issue you raised when recreating the charts. We have made the necessary corrections accordingly. Please refer to the revised content in section 4.3 for further details.
252 - Te following is stated "δ18OX>δ18OET>δ18OE" but from the graph in fig. 4a, it seems that except for the first data point δ18OX and δ18OE are more or less equal. A statistical test (like t-test) would probably tell that no significant difference is present between the two sample-populations.
Response: Your point is valid, and we believe that the lack of sufficient sample size has contributed to the observed uncertainty, resulting in the insignificant differences among them. In this latest manuscript, we have presented the isotopic composition of soil evaporation, vegetation transpiration, and ecosystem evapotranspiration in a tabular format (Table 3) for a more straightforward and explicit representation of their interrelationships. As follows:
Table 3 Evaporation and transpiration at different altitudes during the growing season
Site
Type
April
May
June
July
August
September
October
Qixiang
δT
2.22
-5.87
-4.59
-0.72
-1.72
-1.78
-2.26
δE
-30.32
-28.68
-27.33
-29.12
-28.68
-26.32
-27.27
δET
-20.19
-20.05
-11.63
-9.87
-13.56
-15.85
-21.56
Hulin
δT
-5.34
-3.58
-4.13
-0.34
-2.35
-4.25
-1.97
δE
-29.68
-27.28
-25.8
-27.75
-24.56
-25.21
-27.88
δET
-21.59
-22.36
-8.93
-10.17
-11.57
-18.8
/
Ninchan
δT
/
-3.45
-1.98
-1.05
-6.68
/
/
δE
/
-20.57
-26.31
-29.08
-18.22
-18.15
-18.22
δET
/
/
-12.46
-7.57
/
/
/
Suidao
δT
/
-8.45
-6.98
-6.05
-6.68
/
/
δE
-29.79
-27.32
-27.91
-23.83
-28.78
-25.8
-28.06
δET
-24.31
-16.14
-15.19
-10.07
-18.05
-23.02
-18.65
DISCUSSION
Comments to section 5.1.2, figure 7, and lines 190 to 192. The results expressed in the referred section and figure are based on the following parameters reported in lines 190 to 192 “δpv, δtr, δev and δadv values are the stable isotopes in precipitating transpiration, transpiration, surface evaporation, and advective vapour, respectively”. Of these four parameters is never explained how these two δpv and δadv are derived or measured. All this section and elaboration is not understandable.
Response:We fully understand your comments and concerns.We have presented the calculation steps for δpv and δadv in the methodology section, formula (11) to (17) . In order to make Section 5.1.2 clearer and more logical, we have made detailed revisions and explanations in terms of methods, logic, content, and figures. As follows:
5.1.2 Contribution to recirculating water vapour in precipitation
Our previous study in the eastern section of the Qilian Mountains (Zhang et al., 2021) indicated that above an altitude of 2100m, air masses gather from the northern foothills and move along the valley from low to high elevations. From June to August, the atmospheric circulation is influenced by westerlies, southeast monsoons, and plateau monsoons, while from September to October, the westerlies are the dominant factor. Therefore, we selected the Xiying station at an altitude of 2097m as our upwind site. The δpv values showed depletion in April and October, gradually enriching from June to August. The maximum δ2H value was -76.02‰ (Table 5), and the minimum was -184.93‰, while the maximum δ18O value was -11.89‰, and the minimum was -26.38‰. Above 2700 meters, there is a gradual decrease in precipitation vapor with increasing altitude. The δev values exhibited significant fluctuations throughout each month, with different patterns at different locations. At 2721m, the δev range varied between -172.3‰ and 96.39‰. From the forest's lower layer to the upper layer, the isotopic composition of the advected moisture from the valley gradually diminished, resulting in decreasing values of δadv.
Table 5 Isotopic Composition of Precipitation Vapor, Surface Evaporation Vapor, and Vegetation Transpiration Vapor at Different Months and Altitudes
Month
Type
isotope
April
May
June
July
August
September
October
Qixiang
δpv
δ2H/‰
-141.95
-123.83
-99.87
-115.99
-128.34
-120.9
-152.43
δ18O/‰
-19.27
-16.58
-14.04
-15.91
-18.6
-17.22
-22.34
δev
δ2H/‰
-
-125.69
-123.69
-117.98
-134.57
-
-
δ18O/‰
-
-30.21
-29.56
-28.62
-31.19
-
-
δtr
δ2H/‰
-39.9
-29.32
-46.19
-49.58
-45.15
-42.66
-44.64
δ18O/‰
2.22
-5.87
-4.59
-0.72
-1.72
-1.78
-2.26
δadv
δ2H/‰
-145.57
-83.25
-81.12
-92
-109.62
-100.53
-122.62
δ18O/‰
-20.24
-11.93
-10.73
-12.06
-15.31
-13.46
-18.16
Hulin
δpv
δ2H/‰
-129.93
-123.29
-98.68
-113.98
-124.16
-118.52
-164.82
δ18O/‰
-17.54
-16.62
-13.18
-15.48
-17.33
-17.88
-22.46
δev
δ2H/‰
-114.24
-117.01
-107.75
-123.44
-106.92
-96.39
-172.3
δ18O/‰
-14.77
-16.35
-15.03
-16.7
-14.53
-12.78
-24.82
δtr
δ2H/‰
-24.12
-39.62
-35.97
-26.44
-35.85
-38.39
-40.53
δ18O/‰
-5.34
-3.58
-4.13
-0.34
-2.35
-4.25
-1.97
δadv
δ2H/‰
-112.79
-115.67
-106.61
-122.19
-106.49
-95.7
-170.54
δ18O/‰
-14.59
-16.15
-14.88
-16.54
-14.46
-12.69
-24.57
Ninchan
δpv
δ2H/‰
-
-76.02
-139.21
-135.74
-129.96
-113.71
-184.93
δ18O/‰
-
-11.89
-19.87
-18.7
-17.91
-16.77
-26.38
δev
δ2H/‰
-
-83.76
-81.42
-92.42
-110.24
-101.11
-123.42
δ18O/‰
-
-12.01
-10.76
-12.09
-15.37
-13.49
-18.27
δtr
δ2H/‰
-
-25.58
-46.77
-37.77
-43.66
-
-
δ18O/‰
-
-3.45
-1.98
-1.05
-6.68
-
-
δadv
δ2H/‰
-162.36
-113.49
-111.45
-106.16
-122.12
-114.67
-141.33
δ18O/‰
-22.73
-15.94
-15.28
-14.46
-16.96
-16.58
-20.42
Suidao
δpv
δ2H/‰
-167.86
-128.08
-124.95
-117.32
-137.73
-130.44
-155.52
δ18O/‰
-22.9
-18.02
-17.28
-16.04
-19.09
-18.64
-21.59
δev
δ2H/‰
-164.16
-114.52
-112.37
-106.9
-122.96
-115.47
-142.85
δ18O/‰
-22.98
-16.07
-15.41
-14.56
-17.08
-16.69
-20.64
δtr
δ2H/‰
-
-25.58
-46.77
-37.77
-43.66
-
-
δ18O/‰
-
-8.45
-6.98
-6.05
-6.68
-
-
δadv
δ2H/‰
-162.38
-113.51
-111.47
-106.18
-122.14
-114.69
-141.36
δ18O/‰
-22.73
-15.94
-15.28
-14.47
-16.97
-16.58
-20.42
In July, the ratio of vegetation transpiration to precipitation vapor is significantly higher compared to other months. The temperatures in the lower layers of the forest are relatively high, and the middle to upper layers are densely populated with spruce, resulting in a higher ftr (transpiration ratio) throughout the entire growing season. Both the early and late stages of the growing season exhibit noticeably higher fev (evaporation ratio) compared to other months, with the middle and upper parts of the forest having a higher proportion of evaporated vapor. The average fadv (advected vapor ratio) is 72%, with contributions exceeding 70% for all months except June and July (Figure 7).
Figure 7 Comparison of fadv (advective water vapour contribution), fev (surface evaporation water vapour contribution) and ftr (plant transpiration water vapour contribution) for each of period
-
AC1: 'Reply on RC1', Guofeng Zhu, 19 Jun 2023
-
RC2: 'Comment on hess-2022-375', Anonymous Referee #2, 14 May 2023
General comment
The authors of this manuscript aimed to assess the role of transpiration, compared to other water fluxes, in a spruce forest in the Qilian Mountains, China. In their analyses, the authors exploited the usefulness of stable isotopes of hydrogen and oxygen to investigate water fluxes in the soil-plant-atmosphere continuum.
Although this manuscript may be interesting for the readers of Hydrology and Earth System Sciences, the current version presents a poor description of the sampling approaches and laboratory methodologies, and the dataset does not seem sufficient to support the main findings (only a growing season was considered, and the authors collected only 7 xylem samples). Furthermore, the authors should have determined the uncertainty in their estimates, in order to assess the impacts on the main results.
In terms of presentation quality, the manuscript would benefit from a thorough revision of the English by a native speaker, and the authors should upload high-resolution figures with labels that are readable.
Specific comments
- Section 1: I think the introduction lacks some paragraphs describing the usefulness of stable isotopes of hydrogen and oxygen to investigate water fluxes in the soil-plant-atmosphere continuum. Furthermore, the novelty of this work is not clear (it seems a case study, but there are many like this one), and the specific objectives and/or the research questions should be clearly addressed.
- Section 2: The authors should add the area of the catchment and of the study site (it is unclear whether it is a subcatchment, a hillslope or a plot), and more specific topographic characteristics (e.g., elevation range, slope, aspect etc.). Lithology, soil type and texture should be described as well.
- Section 3.1: In this section, the authors should provide clear and detailed information about meteorological data and water sample collection. About meteorological data, the authors should clarify whether the station is located in the study site (is it representative of the local meteorological conditions?) and the temporal resolution of the measurements. About water samples, details should include temporal (monthly, weekly, daily or event timescale?) and spatial resolution (how many locations for each water source? At which elevation?), as well as depth for soil water samples (and how many locations?) and xylem samples (how many trees and locations were considered?). Furthermore, in this section the authors should describe the methodological approaches used for precipitation collection (what kind of collector was used?) and extraction of water samples from soil and plants (what kind of plant tissue was collected?).
- Section 3: This section also lacks a paragraph reporting how water samples were transported and stored before isotopic analyses, and details about laboratory analyses. These details should include the methodology used for isotopic analyses of the different water samples, the uncertainty in the isotopic measurements, any information about protocols used to ensure the quality of the analyses.
- In Section 3.3.5 and 3.3.6, the authors should clarify if they have assessed the uncertainty in their estimations, for instance, by error propagation and based on the uncertainty in the isotopic analyses and isotopic variability among various samples.
- Table 1: The number of xylem water samples (only 7) used for this study is too low. I think these few samples alone cannot support findings and the conclusions.
- Figure 3a: The authors report soil depths here, but they should have described those sampling depths in a previous section. Furthermore, the authors should clearly explain why soil depth varies for various sampling times.
- Figure 7: The authors should indicate the uncertainty in their estimates, clearly in the text and by error bars in this figure.
- I do not understand the purpose of Section 5.2 and the title does not reflect the content of the section.Technical corrections
- Figure 1: Labels are difficult to read. I suggest increasing their size and resolution.
- Line 107: “Water isotopes…were observed” is an unusual phrasing; please change the verb.
- Figure 2: Again, labels are difficult to read. I suggest increasing their size and resolution.
- Figure 3, 4, 5 and 7: Please increase the size of the labels.
- Figure 8a: The legend is unreadable; please increase the size and the resolution of the labels.Citation: https://doi.org/10.5194/hess-2022-375-RC2 -
AC2: 'Reply on RC2', Guofeng Zhu, 19 Jun 2023
RESOPNE TO REVIEWER2
Thank you very much for taking the time to provide valuable feedback on our manuscript. Your input is highly significant in improving the quality of our manuscript.
To demonstrate our appreciation for your feedback, we have diligently addressed each of your comments and made significant revisions, particularly in the following areas. We rearranged the logic of the manuscript and added data, especially for the xylem water of Qinghai spruce. The water vapor recirculation part is clearly expounded, and each parameter is described in detail. We haved carried out uncertainty analysis.We have re-landscaped all the images throughout the article and improved their clarity.We have changed the language to make it easier for native English speakers to make valuable suggestions.We have placed the revised manuscript PDF at the end of the document. The red bolded words, sentences, and subsections in the manuscript represent our editing changes.
We sincerely hope that you will recognize the genuine effort we have put into this manuscript and give us another opportunity to review and incorporate your suggested modifications.
General comment
The authors of this manuscript aimed to assess the role of transpiration, compared to other water fluxes, in a spruce forest in the Qilian Mountains, China. In their analyses, the authors exploited the usefulness of stable isotopes of hydrogen and oxygen to investigate water fluxes in the soil-plant-atmosphere continuum.
Respone:Thank you for your affirmation of our work. It serves as an ongoing motivation for us to continue our research in the current direction. We greatly appreciate your support and encouragement.
Although this manuscript may be interesting for the readers of Hydrology and Earth System Sciences, the current version presents a poor description of the sampling approaches and laboratory methodologies, and the dataset does not seem sufficient to support the main findings (only a growing season was considered, and the authors collected only 7 xylem samples). Furthermore, the authors should have determined the uncertainty in their estimates, in order to assess the impacts on the main results.
Respone:This issue indeed serves as a critical weakness in our manuscript. Therefore, in this revised version, we have made several adjustments. Firstly, we have adjusted the research timeframe to cover two growing seasons from 2018 to 2019. Additionally, we have increased the number of sampling points to four at different vertical heights and expanded the number of wood water samples to 404. Furthermore, we have conducted an uncertainty analysis (Section 5.2) to assess the uncertainties associated with our methodology.
In terms of presentation quality, the manuscript would benefit from a thorough revision of the English by a native speaker, and the authors should upload high-resolution figures with labels that are readable.
Respone:To underscore our determination to enhance the quality of the manuscript, we have undertaken a complete revamp of all the visuals in the article. Furthermore, we have made language revisions throughout the manuscript to ensure it is better suited for native English speakers to read and comprehend.
Specific comments
Section 1: I think the introduction lacks some paragraphs describing the usefulness of stable isotopes of hydrogen and oxygen to investigate water fluxes in the soil-plant-atmosphere continuum. Furthermore, the novelty of this work is not clear (it seems a case study, but there are many like this one), and the specific objectives and/or the research questions should be clearly addressed.
Respone:Thank you for your feedback. We have inserted the following sentence into the introduction:
The interaction between soil and vegetation controls rainfall input and water transfer within ecosystem components. It is an important player in climate change mitigation in terms of climate benefits (Rohatyn et al., 2022). In the face of complex climate changes, unpredictable weather variations, and continual alterations in surface coverage, the subsystems comprising the atmospheric, soil, and vegetation components in ecohydrological systems undergo corresponding changes in resilience, fragility, and sensitivity. Water moves from the soil through plant roots and stems, eventually reaching the leaves. During photosynthesis, water vapor is released into the atmosphere through open stomata. Stable isotopes of hydrogen and oxygen serve as natural tracers, enabling monitoring of the vertical migration and transformation of water in the ecosystem(Goodwell et al., 2018).
The semi-arid natural environment influences the hydrological processes in the soil-plant-atmosphere continuum. However, the contribution of vegetation transpiration (T) to water fluxes in these mountainous regions, which rely on rainfall and snowmelt as water sources, remains unclear. Furthermore, most studies have focused solely on partitioning evapotranspiration within the ecosystem, which obscures the fate of water vapor. In our research, we analyze the water vapor fluxes from soil evaporation and vegetation transpiration into the atmosphere, which is crucial for understanding water loss dynamics and ensuring appropriate allocation of water resources in the upstream and downstream regions of mountainous areas.
Section 2: The authors should add the area of the catchment and of the study site
(it is unclear whether it is a subcatchment, a hillslope or a plot), and more specific topographic characteristics (e.g., elevation range, slope, aspect etc.). Lithology, soil type and texture should be described as well.
Respone:We have made revisions to Section 2 based on your feedback, as follows:
2. Study area
The Qilian Mountains are located in the central part of the Eurasian continent, on the northeastern edge of the Qinghai-Tibet Plateau. The eastern region is dominated by water erosion, with large variations in mountainous terrain and an average elevation of over 4,000 meters. Permafrost is developed at elevations of 3,500 to 3,700 meters, and areas above 4,500 meters are characterized by modern glacier development. The region has a plateau continental climate, with hot summers and cold winters, strong solar radiation, and large temperature differences between day and night. The average annual temperature is below 4℃, with extreme highs of 37.6℃ and extreme lows of -35.8℃. The annual sunshine hours range from 2,500 to 3,300 hours, with a total solar radiation of 5,916 to 15,000 megajoules per square meter. The average annual precipitation is 400 millimeters, and the annual evaporation ranges from 1,137 to 2,581 millimeters. The average wind speed is around 2 meters per second, and the frost-free period lasts from 23.6 to 193 days. The Shiyang River originates from the Daxueshan on the northern side of the Lenglong Ridge in the eastern section of the Qilian Mountains, serving as a major water source for the city of Wuwei. The soil types in the eastern section are diverse, but with low organic matter content. The distribution of vegetation shows distinct zonal characteristics, with mountainous forest-grassland zones (2,600 to 3,400 meters), subalpine shrub-meadow zones (3,200 to 3,500 meters), and high mountain sub-ice-snow sparse vegetation zones (>3,500 meters) at elevations above 2,700 meters. The main types of natural forest vegetation include Qinghai spruce forest, Qilian juniper forest, and Chinese pine forest, with Qinghai spruce being the dominant tree species (Zhu et al., 2022).
Figure 1 Location of the study area and changes in meteorological conditions
Section 3.1: In this section, the authors should provide clear and detailed information about meteorological data and water sample collection. About meteorological data, the authors should clarify whether the station is located in the study site (is it representative of the local meteorological conditions?) and the temporal resolution of the measurements. About water samples, details should include temporal (monthly, weekly, daily or event timescale?) and spatial resolution (how many locations for each water source? At which elevation?), as well as depth for soil water samples (and how many locations?) and xylem samples (how many trees and locations were considered?). Furthermore, in this section the authors should describe the methodological approaches used for precipitation collection (what kind of collector was used?) and extraction of water samples from soil and plants (what kind of plant tissue was collected?)
Respone:Thank you very much for your meticulous feedback on this section. We have taken your comments seriously and made the following revisions:
1.1 Materials Sources
- Sampling network
Table 1 Sampling Point Locations and Sample Quantity Information
Parameter
Station
Qixiang
Hulin
Ninchan
Suidao
Altitude(m)
2543
2721
3068
3448
Local climate
Temperature(℃/a)
3
3.2
3.3
-0.9
Precipitation(mm/a)
510
469.44
394
475
Relative humidity（％/a）
52.9
56.1
66.6
69.2
Samplings number
Precipitation
53
108
91
135
Soil water
220
560
560
560
Xylem water
236
56
56
56
3.1.2 Sample collection
We use an automatic weather station in 2018 and 2019 to record meteorological data, in which a rain gauge is used to collect precipitation and transfer samples to 100ml containers after each rain. Drill holes 0 ~ 5 cm, 5 ~ 10 cm, 10 ~ 20 cm, 20 ~ 30 cm, 30 ~ 40 cm, 30 ~ 50 cm, 50 ~ 60 cm, 60 ~ 70 cm, 70 ~ 80 cm, 80 ~ 90 cm, 90 in the sample plot by using soil drill ~ 100 cm. The soil samples were divided into two parts, one of which was placed in a 50 ml glass bottle. The bottle was sealed with a subfilm and transported to the observation station within 10 hours after the sampling date was marked for cryopreservation to detect stable isotope data. The other part of the sample was placed in a 50ml aluminum box and the soil moisture content was determined by drying method. When collecting plant samples, we used scissors to collect vegetation xylem stems, peeled off the bark, and put them in 50ml glass bottles sealed and frozen for experimental analysis.
We utilized the monthly potential evapotranspiration dataset of China, with a spatial resolution of 0.0083333° (approximately 1 km), covering the period from January 1990 to December 2021. The data is measured in units of 0.1 mm. This dataset is derived from the China 1 km monthly mean temperature, minimum temperature, and maximum temperature dataset(Peng et al.,2022;Dinget al.,2020;Ding et al.,2021), using the Hargreaves equation for estimating potential evapotranspiration (Peng et al., 2017). The formula is as follows: PET = 0.0023 × S0 × (MaxT − MinT)0.5 × (MeanT + 17.8), where PET represents potential evapotranspiration in mm/month, MaxT, MinT, and MeanT are the monthly maximum temperature, minimum temperature, and mean temperature, respectively. S0 represents the theoretical solar radiation reaching the top of the Earth's atmosphere, calculated based on solar constant, distance between Earth and the sun, Julian day, and latitude. For data storage convenience, the values are stored as int16 type in NETCDF (nc) files. The surface evapotranspiration data were obtained from the MODIS-based daily surface evapotranspiration data of the Qilian Mountains (2019), with a spatial resolution of 0.01°（Yao et al., 2017;Yao et al., 2020）.
Section 3: This section also lacks a paragraph reporting how water samples were transported and stored before isotopic analyses, and details about laboratory analyses. These details should include the methodology used for isotopic analyses of the different water samples, the uncertainty in the isotopic measurements, any information about protocols used to ensure the quality of the analyses.
Respone: Thank you for your suggestions. We have rewritten this section based on your feedback. Here is the revised version:
3.2 Experimental Analysis
The isotopic data used in this study mainly include stable isotopes of precipitation, soil water, and xylem water. All isotopic samples were analyzed at the Stable Isotope Laboratory of Northwest Normal University. The precipitation samples were analyzed for hydrogen and oxygen stable isotopes using a liquid water isotope analyzer (DLT-100, Los Gatos Research, USA). After thawing the soil and vegetation samples, they were extracted using a low-temperature vacuum condensation device (LI-2100, LICA United Technology Limited, China), and the extracted water was subjected to isotopic analysis. Each water sample was tested six times to ensure accuracy, with the first two tests considered as interference and only the results of the subsequent four tests were averaged (Zhu et al., 2022). The isotopic measurements are represented by δ, which represents the deviation in parts per thousand of the ratio of two stable isotopes in the sample relative to the ratio in a standard sample. The International Atomic Energy Agency (IAEA) defined the Vienna Standard Mean Ocean Water (VSMOW) in 1968 as the standard for isotopic composition, which is derived from distilled seawater and has a similar isotopic composition to Standard Mean Ocean Water (SMOW).
（1）
In Section 3.3.5 and 3.3.6, the authors should clarify if they have assessed the uncertainty in their estimations, for instance, by error propagation and based on the uncertainty in the isotopic analyses and isotopic variability among various samples.
Respone: This is a great question, and we have made modifications to the content based on your feedback. We have addressed the uncertainties related to sections 3.3.5 and 3.3.6 separately in section 5.2 Uncertainty Analysis. Additionally, we have presented how we avoided isotopic variations among samples in section 3.2 Experimental Analysis.
Table 1: The number of xylem water samples (only 7) used for this study is too low. I think these few samples alone cannot support findings and the conclusions
Respone:Your concern is entirely justified, and in the revised manuscript, we have addressed it by adding more sampling points and extending the sampling duration, thus avoiding any potential unreliability in the analysis results. The additional information on the sampling points is provided below:
Table 1 Sampling Point Locations and Sample Quantity Information
Parameter
Station
Qixiang
Hulin
Ninchan
Suidao
Altitude(m)
2543
2721
3068
3448
Local climate
Temperature(℃/a)
3
3.2
3.3
-0.9
Precipitation(mm/a)
510
469.44
394
475
Relative humidity（％/a）
52.9
56.1
66.6
69.2
Samplings number
Precipitation
53
108
91
135
Soil water
220
560
560
560
Xylem water
236
56
56
56
We have revised the original Table 1 in Section 4.1 and renamed it as Table 2. Furthermore, we have made adjustments to the content of the table.As follows:
During the growth season of Qinghai spruce, the stable isotopes of precipitation exhibit specific patterns of fluctuation (Table 2). In the early stages of growth, the hydrogen and oxygen isotope values are generally low. As the temperature gradually increases, the extent of water evaporation and loss intensifies, leading to an enrichment of stable isotopes. The average δ2H value of precipitation throughout the growth season is -45.52‰, fluctuating roughly between -238.62‰ and 63.43‰. The average δ18O value is -7.75‰, fluctuating roughly between -31.49‰ and 14.79‰. There is not a significant depletion or enrichment of stable isotopes in the wood tissues, with a fluctuation range of -76.95‰ to 23.87‰ for δ2H and -11.92‰ to 24.77‰ for δ18O. Shallow soil water shows a less pronounced enrichment of heavy isotopes compared to precipitation and wood tissues, with a lower degree of fluctuation observed during late spring and the beginning of summer.
Table 2 Stable isotopes of different water bodies during the growing season
Period
Average
δ2H/‰
δ18O/‰
Precipitation
Xylem water
Soil water（0~10cm）
Precipitation
Xylem water
Soil water（0~10cm）
4
-69.15
-39.02
-53.10
-10.25
2.56
-7.10
5
-39.09
-29.78
-45.38
-7.61
4.44
-6.42
6
-31.29
-45.83
-46.08
-5.74
-2.83
-6.12
7
-32.39
-47.63
-47.71
-5.33
-0.97
-7.06
8
-48.88
-44.55
-68.85
-7.79
-2.06
-9.07
9
-29.38
-42.62
-49.20
-6.46
-1.83
-6.79
10
-68.43
-44.57
-54.88
-11.06
-2.25
-7.96
Figure 3a: The authors report soil depths here, but they should have described those sampling depths in a previous section. Furthermore, the authors should clearly explain why soil depth varies for various sampling times.
Respone:Thank you for your feedback. What I intended to convey here is to utilize soil moisture content at different depths to reflect the information of unbalanced fractionation in soil evaporation. However, it seems that our original figures and sampling points deviated from this intention. Therefore, we have reorganized this section accordingly, as follows:
Unsaturated water vapor leads to non-equilibrium fractionation during the process of precipitation, with an average d-excess value of 16.58‰ throughout the growing season (Figure 3, a). In May and September, due to higher relative humidity compared to other periods, the evaporation rate of water vapor is faster. The deuterium values show slow fluctuations from June to August, with significant fluctuations starting from mid-August, indicating that local evaporation is gradually enhanced over time due to the influence of temperature and relative humidity, leading to increased non-equilibrium evaporation. The average lc-excess value of precipitation in the lower layer of forest distribution is -8.18‰, while the average lc-excess value of precipitation in the middle, upper-middle, and canopy layers is close to 0. This is because the fractionation effect of evaporation is more pronounced at lower elevations. At higher elevations, influenced by rainfall and snowmelt, the soil moisture content in all soil layers is above 30% (Figure 3, b). Towards the end of the growing season, as temperatures decrease, tree leaves fall to the forest floor, forming a litter layer that retains moisture in the soil.
Figure 3 (a) Variation in soil water content, (b) Comparison between atmospheric water vapour oxygen isotopes and d-excess
Figure 7: The authors should indicate the uncertainty in their estimates, clearly in the text and by error bars in this figure.
Respone:Thank you for your feedback. We have restructured section 5.1.2 "Contribution to recirculating water vapor in precipitation" and conducted an analysis of the associated uncertainties. We have also redesigned the tables and figures accordingly. In Figure 7, we have compared the contribution rates of each component only for the growing season. As there are only values available for 7 months for each component, I have not included error bars for visual clarity. I hope you understand this approach. As follows:
5.1.2 Contribution to recirculating water vapour in precipitation
Our previous study in the eastern section of the Qilian Mountains (Zhang et al., 2021) indicated that above an altitude of 2100m, air masses gather from the northern foothills and move along the valley from low to high elevations. From June to August, the atmospheric circulation is influenced by westerlies, southeast monsoons, and plateau monsoons, while from September to October, the westerlies are the dominant factor. Therefore, we selected the Xiying station at an altitude of 2097m as our upwind site. The δpv values showed depletion in April and October, gradually enriching from June to August. The maximum δ2H value was -76.02‰ (Table 5), and the minimum was -184.93‰, while the maximum δ18O value was -11.89‰, and the minimum was -26.38‰. Above 2700 meters, there is a gradual decrease in precipitation vapor with increasing altitude. The δev values exhibited significant fluctuations throughout each month, with different patterns at different locations. At 2721m, the δev range varied between -172.3‰ and 96.39‰. From the forest's lower layer to the upper layer, the isotopic composition of the advected moisture from the valley gradually diminished, resulting in decreasing values of δadv.
Table 5 Isotopic Composition of Precipitation Vapor, Surface Evaporation Vapor, and Vegetation Transpiration Vapor at Different Months and Altitudes
Month
Type
isotope
April
May
June
July
August
September
October
Qixiang
δpv
δ2H/‰
-141.95
-123.83
-99.87
-115.99
-128.34
-120.9
-152.43
δ18O/‰
-19.27
-16.58
-14.04
-15.91
-18.6
-17.22
-22.34
δev
δ2H/‰
-
-125.69
-123.69
-117.98
-134.57
-
-
δ18O/‰
-
-30.21
-29.56
-28.62
-31.19
-
-
δtr
δ2H/‰
-39.9
-29.32
-46.19
-49.58
-45.15
-42.66
-44.64
δ18O/‰
2.22
-5.87
-4.59
-0.72
-1.72
-1.78
-2.26
δadv
δ2H/‰
-145.57
-83.25
-81.12
-92
-109.62
-100.53
-122.62
δ18O/‰
-20.24
-11.93
-10.73
-12.06
-15.31
-13.46
-18.16
Hulin
δpv
δ2H/‰
-129.93
-123.29
-98.68
-113.98
-124.16
-118.52
-164.82
δ18O/‰
-17.54
-16.62
-13.18
-15.48
-17.33
-17.88
-22.46
δev
δ2H/‰
-114.24
-117.01
-107.75
-123.44
-106.92
-96.39
-172.3
δ18O/‰
-14.77
-16.35
-15.03
-16.7
-14.53
-12.78
-24.82
δtr
δ2H/‰
-24.12
-39.62
-35.97
-26.44
-35.85
-38.39
-40.53
δ18O/‰
-5.34
-3.58
-4.13
-0.34
-2.35
-4.25
-1.97
δadv
δ2H/‰
-112.79
-115.67
-106.61
-122.19
-106.49
-95.7
-170.54
δ18O/‰
-14.59
-16.15
-14.88
-16.54
-14.46
-12.69
-24.57
Ninchan
δpv
δ2H/‰
-
-76.02
-139.21
-135.74
-129.96
-113.71
-184.93
δ18O/‰
-
-11.89
-19.87
-18.7
-17.91
-16.77
-26.38
δev
δ2H/‰
-
-83.76
-81.42
-92.42
-110.24
-101.11
-123.42
δ18O/‰
-
-12.01
-10.76
-12.09
-15.37
-13.49
-18.27
δtr
δ2H/‰
-
-25.58
-46.77
-37.77
-43.66
-
-
δ18O/‰
-
-3.45
-1.98
-1.05
-6.68
-
-
δadv
δ2H/‰
-162.36
-113.49
-111.45
-106.16
-122.12
-114.67
-141.33
δ18O/‰
-22.73
-15.94
-15.28
-14.46
-16.96
-16.58
-20.42
Suidao
δpv
δ2H/‰
-167.86
-128.08
-124.95
-117.32
-137.73
-130.44
-155.52
δ18O/‰
-22.9
-18.02
-17.28
-16.04
-19.09
-18.64
-21.59
δev
δ2H/‰
-164.16
-114.52
-112.37
-106.9
-122.96
-115.47
-142.85
δ18O/‰
-22.98
-16.07
-15.41
-14.56
-17.08
-16.69
-20.64
δtr
δ2H/‰
-
-25.58
-46.77
-37.77
-43.66
-
-
δ18O/‰
-
-8.45
-6.98
-6.05
-6.68
-
-
δadv
δ2H/‰
-162.38
-113.51
-111.47
-106.18
-122.14
-114.69
-141.36
δ18O/‰
-22.73
-15.94
-15.28
-14.47
-16.97
-16.58
-20.42
In July, the ratio of vegetation transpiration to precipitation vapor is significantly higher compared to other months. The temperatures in the lower layers of the forest are relatively high, and the middle to upper layers are densely populated with spruce, resulting in a higher ftr (transpiration ratio) throughout the entire growing season. Both the early and late stages of the growing season exhibit noticeably higher fev (evaporation ratio) compared to other months, with the middle and upper parts of the forest having a higher proportion of evaporated vapor. The average fadv (advected vapor ratio) is 72%, with contributions exceeding 70% for all months except June and July (Figure 7).
Figure 7 Comparison of fadv (advective water vapour contribution), fev (surface evaporation water vapour contribution) and ftr (plant transpiration water vapour contribution) for each of period
I do not understand the purpose of Section 5.2 and the title does not reflect the content of the section.
Respone:I completely agree with your viewpoint, and therefore, I have removed that section from the manuscript. Instead, I have revised section 5.2 to focus on uncertainty analysis. As follows:
5.2 Uncertainty analysis
A higher sample size can reduce the margin of error. Therefore, we utilized isotopic data from four sites over a two-year period to evaluate the model. We used 404 xylem samples to calculate the contribution ratio of transpiration to ecosystem evapotranspiration. We examined the uncertainty of the model evaluation. When analyzing the evaporation characteristics in a semi-arid natural environment using the Craig-Gordon isotopic model, we first eliminated the influence of solar radiation and other meteorological variables on the calculation results. We focused on temperature, relative humidity, water vapor, and the initial isotopic values of water bodies. Particularly in semi-arid environments, the variations in temperature and relative humidity are crucial (Hernández-Pérez et al., 2020). To verify the calculation results, we found a strong correlation between the isotopes of soil evaporation and relative humidity, as demonstrated by the fitting of δE against relative humidity and temperature. This also indicates the reliability of the results obtained through the Craig-Gordon isotopic model. We employed the Keeling plot method to calculate δET, which is based on isotopic mass balance and a two-endmember mixing model. This method assumes that the isotopic composition of the background atmosphere and source remains constant, with a very low probability of isotopic spatial variation (Good et al., 2012; Kool et al., 2014). Due to the higher reliability of oxygen isotopes compared to hydrogen isotopes (Han et al., 2022; Kale et al., 2022), we solely used oxygen isotopes to calculate the T/ET values. The results indicate that transpiration significantly outweighs evaporation during July and August, which aligns with previous research findings (Zhu et al., 2022). The correlation between T/ET and soil moisture content suggests that soil moisture is a crucial factor driving the variations in transpiration and evaporation ratios. Additionally, the estimation of isotopic composition of advected water vapor from the upwind sites contributes to increased uncertainty. In our study area, the sites are predominantly influenced by valley winds, with water vapor moving from the valley bottom to higher altitudes. Therefore, we selected lower elevation areas in the valley bottom as the source region for advected water vapor (Zhang et al., 2021).
Figure 8 Correlation analysis of factors affecting uncertainty in impact assessment
Technical corrections
- Figure 1: Labels are difficult to read. I suggest increasing their size and resolution.
- Line 107: “Water isotopes…were observed” is an unusual phrasing; please change the verb.
- Figure 2: Again, labels are difficult to read. I suggest increasing their size and resolution.
- Figure 3, 4, 5 and 7: Please increase the size of the labels.
- Figure 8a: The legend is unreadable; please increase the size and the resolution
of the labels.
Respone: We have carefully addressed your feedback regarding the figures. In order to enhance the overall quality of the paper, we have recreated all the figures in our manuscript, removing any inappropriate sentences.
We would like to invite you to review the significant changes we have made in the revised manuscript. Attached below is the PDF of the manuscript:
-
AC2: 'Reply on RC2', Guofeng Zhu, 19 Jun 2023
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