Characteristics of Hydrogen–Oxygen Isotopes and Water Vapor Sources of Different Waters in the Ili Kashi River Basin

Abstract

The Ili Kashi River Basin is an area with relatively abundant precipitation within the arid region of Northwest China. Using water samples from atmospheric precipitation, surface water, groundwater, and snow meltwater in the basin from July 2018 to June 2021, the isotope characteristics of the different water bodies in the study area were determined from the perspectives of altitude, season, and interannual changes. Combined with the meteorological data on precipitation and the HYSPLIT model, the water vapor sources of atmospheric precipitation in the Ili Kashi River Basin were tracked and analyzed. Studying the hydrogen and oxygen stable isotopes in the different water bodies in this area can provide substantial scientific support for the generation, development, and change processes of river water resources in Northwest China, and has practical significance for the utilization of water resources. The results derived are as follows. (1) Hydrogen–oxygen isotope changes in the Ili Kashi River Basin were broadly characterized by a continuous enrichment from low-to-high elevations in the summer to a maximum value, followed by gradual depletion, whereas the changes in δ¹⁸O and δD were reversed in autumn. (2) The river water values of δD and δ¹⁸O fluctuated between −107.15‰ and −68.13‰ and between −18.53‰ and −9.66‰, respectively, during the study period. (3) The variation in δ¹⁸O and δD in the precipitation was consistent, showing characteristics of summer enrichment and winter dilution, and the precipitation line equation is δD = 7.30δ¹⁸O + 9.29. (4) In autumn and winter, the groundwater δD and δ¹⁸O values fluctuated between −99.87‰ and −84.95‰ and between −15.50‰ and −10.38‰, respectively; during spring and summer, the δD and δ¹⁸O values varied from −99.27‰ to −87.07‰ and from −15.15‰ to −12.00‰, respectively. The hydrogen–oxygen stable isotope value of the ice–snow meltwater in autumn was higher than that in summer. (5) On the basis of the d-excess variation in each precipitation event over the 3 years and an analysis of the water vapor sources using the HPSPLIT backward trajectory tracking model, the source of water vapor in the study area is primarily the surrounding land water vapor, with the Atlantic Ocean being the main contributor of oceanic water vapor.

1. Introduction

Water is an inorganic substance composed of two main elements—hydrogen and oxygen—which is indispensable to nature and human society [1,2]. Water bodies experience the fractionation of hydrogen–oxygen stable isotopes during the processes of evaporation and condensation [3,4]. Hydrogen and oxygen stable isotopes are the natural tracers of water bodies, sensitive to environmental change, and the essential constituents of water molecules [5]. Atmospheric precipitation is a fundamental source of water resources and a vital link in the water cycle, directly affecting human activities and the ecological environment [6]. A study of the characteristics of hydrogen and oxygen isotopes in different water bodies can reveal the meteorological conditions and water vapor sources that formed the precipitation, which is essential for gaining insight into regional water cycle processes.

The study of isotope hydrology began as early as the 1950s, when Dangsgaard theoretically derived and calculated the effect of condensation temperature on δ¹⁸O values in precipitation and found that the calculated results agreed well with the observed values, thereby pioneering the foundation of stable isotope meteorology [7]. Nakamura et al. [8] concluded that the transformation relationships between water bodies differed between seasons, by studying the stable isotopes of hydrogen and oxygen in the river waters of the Kathmandu Valley during the period of monsoon influence. In China, JianRong Liu et al. [9] analyzed the isotopic characteristics of atmospheric precipitation using CHNIP (China Network of Isotopes in Precipitation) and GNIP (Global Network of Isotopes in Precipitation) data and performed zonal comparisons for the entire country. Li Yaju et al. [10] collected surface snow samples from the Urumqi River source glacier in the Tianshan Mountains and explored the seasonal characteristics of δ¹⁸O values in surface snow, and the effect of water vapor transport on the variation in isotope values in precipitation. Using the hybrid single-particle Lagrangian integrated trajectory (HYSPLIT) model with the hydrogen–oxygen stable isotope tracing method, Li Jiangping et al. [11,12] found that the source of water vapor derived using HYSPLIT tends to be consistent with the distribution of water vapor flux. The HYSPLIT trajectory model is commonly used to track the direction of the movement of particles or gases carried by air currents, allowing for the real-time forecasting of the wind situation, analysis of precipitation, and study of trajectories. The Ili Kashi River is located in northern Xinjiang (China), a semiarid area subject to a complex combination of the monsoon system and atmospheric circulation. The Ili Kashi River, the second-largest tributary of the upper reaches of the Ili River and which flows mainly through Nilka County, is the primary water source for the local population and an essential part of the program for regional ecological protection.

This study takes the Ili Kashi River as the research object and uses data sampling of the surface water, precipitation, groundwater, and surface grain snow meltwater in the basin and analyzes the isotopic characteristics of the different water bodies in the study area to grasp their long-term change patterns, explore the interrelationships between other water bodies, and combines these with the HYSPLIT backward trajectory model to trace the source of water vapor in the study area. The study’s results enrich the study area’s hydrological data, provide crucial scientific support for the generation, development, and change processes of river water resources in northwest China, and are also vital for the economic growth and ecological and environmental protection of the study area.

2. Study Area

The Ili Kashi River (43°25′~44°20′ N, 81°50′~84°45′ E) is located in Ili Prefecture, Northern Xinjiang, China, in the hinterland of the Eurasian continent, which is a region with a continental, northern temperate climate. The river originates at the junction of the Tianshan Mountains between Habirga Mountain and Mount Abujele. It runs through Nilka County in the north part of the Ili Valley and flows westward to Yining County, where it meets the Kunes River. It is one of three tributaries that discharge into the Ili River. There are valley glaciers, ice bucket glaciers, and hanging glaciers that crisscross along the river path, providing a source of ice–snow meltwater for the Ili Kashi River. Glacier precipitation is usually more than 700 mm per annum. The basin (elevation: 800 m–4600 m) is surrounded by mountains, and the river (length: 304 km) presents a feather-like characteristic (Figure 1). Topographically, the basin tilts from the northeast to the southwest, forming a high-elevation terrain in the east and a low-elevation terrain in the west. Climatologically, the bay has decreasing temperature, increasing precipitation, and decreasing evaporation from the west to the east [13,14]. The river’s supply is mainly from ice–snow meltwater, followed by rainfall and groundwater; the annual average runoff is 3.21 billion m³. National observing stations, such as the Nilka weather station and Yining weather station, were established in mountainous areas of the basin to observe standard meteorological elements.

3. Data Sources and Research Methods

3.1. Data Sources

The water samples used in this study were collected from July 2018 to June 2021. The samples collected included river water, precipitation, ice–snow meltwater, and groundwater, for a total of 503 water samples. The river, precipitation, and groundwater samples were obtained in the 79th Regiment of Nilka County. Before sampling, 30 mL polyethylene plastic bottles were rinsed thrice with river water. Then, water samples obtained at a depth of 20 cm were collected in the polyethylene plastic bottles with a frequency of once every five days. The Ili Kashi River belongs to a mountainous watershed, and to verify the elevation effect of the stable isotopes of river water in the watershed, the river water sampling procedures along an altitude were repeated at intervals of 300 m above sea level, (m.a.s.l.) and included spring water and ice–snow meltwater. When precipitation occurred, a pre-prepared clean container was placed on the ground in a reasonably open location without interference from trees to collect the precipitation, which was subsequently poured into the polyethylene plastic bottles for storage. For the ice–snow meltwater, the sample was placed in a collection container, where it melt, and was then poured into a polyethylene plastic bottle for storage. The groundwater was sampled with a frequency of once every three days in each season using the same containers and methods. To prevent evaporation, the samples were all sealed with special tape and placed in cold storage.

3.2. Methods

3.2.1. Isotope Measurement

The analyses of the stable isotopes of hydrogen and oxygen in the water samples above were performed at the State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences. The measurement instrument used was a liquid-water stable isotope analyzer (DLT-100, Los Gatos Research Inc., Mountain View, CA, USA) that measured the δD and δ¹⁸O with an accuracy of 0.30‰ and 0.10‰, respectively. Each water sample was measured six times for higher accuracy and then averaged for the study. The results are expressed as thousandths of a percent relative to the Vienna Standard Mean Ocean Water, as follows:

$$\mathsf{\delta }\left(\mathrm{‰}\right)=\left[\frac{{\mathrm{R}}_{\mathrm{s}\mathrm{a}\mathrm{m}\mathrm{p}\mathrm{l}\mathrm{e}}}{{\mathrm{R}}_{\mathrm{V}\mathrm{S}\mathrm{M}\mathrm{O}\mathrm{W}}}-1\right]\times 1000$$

(1)

where R is δD or δ¹⁸O.

3.2.2. Simulation of Atmospheric Precipitation Water Vapor Sources

The HYSPLIT model is suitable for tracing water vapor’s source and transport path [15]. It is a complete system capable of calculating both the simple trajectories of air parcels and complex transport, dispersion, chemical transformation, and deposition simulations. It is one of the atmospheric transport and dispersion models used most widely by the atmospheric science community. This study used a backward trajectory analysis commonly applied to utilize this model to determine the origins of air masses; the trajectory ends at the 79th Regiment of Ili Nilka County in the Ili Kashi River Basin. The longitude and latitude coordinates are 43°80′ N, 82°52′ E; since the study area belongs to a mountainous watershed, where the high altitude reaches more than 3000 m, the operating altitudes were selected as 2000 m, 3000 m, and 4000 m, with a running time of 120 h. Each precipitation event during the three years from July 2018 to June 2021 was analyzed, and the changes in δ¹⁸O, δD, and d-excess were combined with the whole observation period of the study area to investigate the source of precipitation vapor in the Ili. The source of precipitation vapor in the Kashi River basin was also analyzed.

3.2.3. Water Source Composition Analysis

To determine the contributions of river water recharge sources in the Ili Kashi River Basin, the end-members mixing model was applied to derive the specific weights of the water source composition through the isotopic differences present in the different water bodies [16]. The end-members mixture model, proposed by Hooper in 1990, is widely used to analyze potential-contributing water sources [17]. Here, δ¹⁸O is used as an example for the calculation of the isotope mixing ratio as follows [18]:

δ¹⁸O = X₁δ¹⁸O₁ + X₂ δ¹⁸O₂

(2)

X₁ + X₂ = 1

(3)

where the isotope values of the different recharge sources are indicated by δ¹⁸O₁ and δ¹⁸O₂, the isotope values of the recharged water bodies are indicated by δ¹⁸O, and X₁ and X₂ are the recharge ratios of the corresponding recharge sources to the water bodies.

3.2.4. Localized Evaporation of Water Vapor Analysis

Both water vapor formed by local evapotranspiration processes in the region and water vapor transported through atmospheric circulation can cause precipitation in the region. As early as the middle of the 20th century, local evapotranspiration was also shown to be an important source of water vapor [19,20]. Precipitation resulting from local evapotranspiration is called recirculating precipitation, and the contribution of local evaporative water vapor to precipitation is called the precipitation recirculation rate ($\mathsf{\rho }$), which can be calculated using the two-dimensional model of Eltahir [21]:

$$\mathsf{\rho }=\frac{{\mathrm{I}}_{\mathrm{m}}+\mathrm{E}}{{\mathrm{I}}_{\mathrm{m}}+\mathrm{E}+{\mathrm{I}}_{\mathrm{a}}}$$

(4)

where I_m represents the water vapor from the input grid area within the study area (mm/month), Ia represents the water vapor from the input grid area outside the study area (mm/month), and E denotes the evapotranspiration (mm/month), i.e., the mass of water evaporated per unit, and can be expressed as [22]:

E = Q₁·Q₂

(5)

where Q₁ is a function of ambient temperature (T) and raindrop diameter (D) in cm, Q₂ is a function of ambient temperature (T) and relative humidity (RH) in g·cm⁻¹·s⁻¹, and Q₁ and Q₂ can be obtained with bilinear interpolation under different meteorological conditions [23].

3.2.5. Calculation of Water Vapor Transport Contribution

After simulating the backward trajectories using HYSPLIT, clustering the trajectories allows for similar trajectories to be grouped into the same channel, thus defining the contribution of water vapor transport of the different channels [24].

$$\mathrm{Q}=\frac{\sum _{\mathrm{i}=1}^{\mathrm{m}}{\mathrm{q}}_{\mathrm{i}}}{\sum _{\mathrm{i}=1}^{\mathrm{n}}{\mathrm{q}}_{\mathrm{j}}}\times 100\%$$

(6)

where Q is the contribution of the track channel, q_i and q_j are the endpoint specific humidity value or water vapor flux of the track channel, m is the total number of tracks in a particular channel, and n is the total number of all tracks.

4. Results

4.1. Isotopic Characteristics of Different Water Bodies

4.1.1. Elevation Effects on River Water Isotopes

The variation in the stable hydrogen and oxygen isotopes with elevation in summer and autumn is evident in Figure 2. The variation in δD in summer ranges from −96.52‰ to −80.22‰, with the highest value occurring at 1600 m.a.s.l.; the variation in δ¹⁸O ranges from −15.04‰ to −12.28‰, with the highest value also occurring at 1600 m.a.s.l. Although the value of δD initially decreases slightly, it is enriched with elevation, similarly to δ¹⁸O. The depletion of both δD and δ¹⁸O is more significant after the enrichment reaches its maximum value, and continues until the occurrence of the lowest value at 2400 m.a.s.l. A gradual enrichment then occurs from 3000 m.a.s.l., but it remains at a lesser extent than at the lower elevations. Since there are several hydroelectric power stations at different elevations, the hydroelectric power stations open up the river water surface, making the flow velocity slow and evaporation exuberant, which then leads to heavy isotope enrichment, and then makes δD and δ¹⁸O high. In autumn, after the weather turns cold, the δD varies from −91.02‰ to −74.36‰, with the highest value occurring at 1800 m.a.s.l.; from a low elevation to the 1800 m.a.s.l., the variation is almost the same as that in summer, but the enrichment is much higher than in summer, which manifests as a stage of enrichment with rising elevation. The enrichment value is much higher than that in summer, and then depletion continues with rising elevation until the lowest value occurs at 2600 m.a.s.l. The variation in δ¹⁸O ranges from −17.04‰ to −11.88‰, and the highest value occurs at 1400 m.a.s.l., with notable depletion and substantial enrichment throughout.

4.1.2. Seasonal Effects on River Water Isotopes

The daily variations in the hydrogen and oxygen stable isotopes in the river water, precipitation, and groundwater are presented in Figure 3a–c, respectively. As can be seen from

Table 1. δD and δ¹⁸O in Ili Kashi River water.

Years	Samples	δD/‰			δ18O/‰
		Max	Min	Average	Max	Min	Average
2018	Summer	−82.25	−104.86	−90.21	−12.17	−18.53	−14.17
	Autumn	−83.26	−103.67	−91.16	−12.19	−15.27	−13.65
	Winter	−86.24	−107.15	−91.67	−12.24	−15.93	−13.59
2019	Spring	−68.13	−95.45	−86.97	−11.11	−15.87	−13.93
	Summer	−79.62	−95.06	−87.63	−12.38	−16.85	−14.11
	Autumn	−84.59	−88.84	−86.11	−12.67	−14.35	−13.37
	Winter	−84.25	−87.70	−86.15	−11.80	−12.98	−12.25
2020	Spring	−83.91	−104.47	−90.52	−12.58	−15.67	−14.04
	Summer	−77.93	−92.11	−81.99	−12.22	−14.70	−13.43
	Autumn	−75.82	−92.03	−82.14	−9.66	−15.74	−12.86
	Winter	−80.33	−88.26	−83.56	−11.44	−13.97	−12.56
2021	Spring	−81.01	−97.89	−90.29	−11.31	−14.32	−12.73
	Summer	−78.20	−94.97	−83.55	−12.20	−14.03	−12.92

, the variation in δD in the river water of the Ili Kashi River Basin between July 2018 and June 2021 ranges from −107.15‰ to −68.13‰, with a standard deviation of 5.78‰ and a mean value of −85.79‰; the variation in δ¹⁸O ranges from −18.53‰ to −9.66‰, with a standard deviation of 1.16‰ and a mean value of −13.09‰.

The river water data for 2018–2021 encompass four summers, and the range of variation in the δ¹⁸O and δD values in the summer river water during this period are −104.86‰ to −77.93‰ and −18.53‰ to −12.17‰, and the average values are −89.51‰ and −13.66‰, respectively. Autumn is the season with the second-highest enrichment of hydrogen–oxygen stable isotopes. The variation in δ¹⁸O in autumn ranges from −103.67‰ to −75.82‰ and the average value is −86.47‰. The variation in δD ranges from −15.74‰ to −9.66‰ and the average value is −13.29‰. The maximum value according to the mean value also occurred in 2020. The variation in δ¹⁸O values in winter ranges from −107.15‰ to −80.33‰, the average value is −87.13‰, and the total value of the mean value occurred in 2020. The variation in δD values ranges from −13.59‰ to −12.25‰, the average value is −12.80‰, and the total value of the mean value occurred in 2019. Still, the difference from the value in 2020 is negligible. In spring, the value of δ¹⁸O and δD fluctuates between −104.47‰ and −68.13‰ and −15.87‰ and −11.11‰; the average values are −87.01‰ and −13.57‰.

The recharge process of river water is complex, and there are multiple recharge channels. In addition to the direct recharge from precipitation, there are various forms of recharge from precipitation, such as recharge after infiltration into the ground into groundwater and recharge after conversion into soil water and plant water. In addition to precipitation, there is also the recharge of groundwater and the recharge of glacial meltwater. Summer temperatures are high, even if the atmospheric precipitation recharge is more abundant. The high-temperature effect of melting glacial meltwater and snow will dilute the atmospheric precipitation directly recharged to the river, and autumn and winter precipitation is not as abundant as that in summer. These bodies of water regulate each other so that there is little seasonal variation in the river.

4.1.3. Isotopic Characteristics of Precipitation

As shown in Figure 3b, the variation in both δ¹⁸O and δD in the precipitation is broadly similar, although the range of variation in δD and δ¹⁸O in the precipitation is much larger than that in the river water, groundwater, and meltwater. The variation in δD in the precipitation ranges from −266.58‰ to 16.78‰, with a standard deviation of 58.48‰, and the variation in δ¹⁸O ranges from −35.19‰ to 3.60‰, with a standard deviation of 7.72‰. The mean values of δD and δ¹⁸O in the precipitation for the period from July 2018 to June 2019 are −63.62‰ and −9.83‰, respectively. For the 2 years to 2021 and for June 2018, the mean values of δD are −84.87‰ and −77.26‰, respectively, and the mean values of δ¹⁸O are −12.48‰ and −12.10‰, respectively.

4.1.4. Isotopic Characteristics of Groundwater

The hydrogen–oxygen stable isotope variations in the groundwater in the study area are presented in Figure 3c. The groundwater data for the study area were collected from September 2020 onward (every three days) from the wells in the small yards of residents in the middle of the year owing to climatic restrictions; that is, we missed taking water samples from December to February because it was too cold. The data are available until June 2021. As shown in Figure 3c, between September 2020 and November 2020, the δD and δ¹⁸O values in the groundwater fluctuated between −99.87 and −84.95‰ and between −15.50‰ and −10.38‰; during the same period, the river water δ¹⁸O value varied from −15.74‰ to −9.66‰, respectively. Between April 2021 and June 2021, the δD and δ¹⁸O values in the groundwater ranged from −99.27‰ to −87.07‰ and from −15.15‰ to −12.06‰, and the δ¹⁸O variation interval in the river water during the same period was −14.32‰ to −12.01‰, respectively. The hydrogen–oxygen stable isotope values are relatively similar to those of river water during the same period. This indicates a close hydrological relationship between the groundwater and river water in the study area, and shows that the other recharge water mechanisms of these two water bodies might be consistent. The overall small δD values of the groundwater indicate intense evaporation in the middle and downstream reaches of the Ili Kashi River, and this enhanced evaporation might be attributable to the water level [25].

4.1.5. Isotopic Characteristics of Granular Snow Meltwater

Table 2. Stable isotope values of hydrogen and oxygen in surface granular snow meltwater.

Year/Month	Altitude (m)	Sample Type	δD (‰)	δ18O (‰)
2018/07	3377	Snow	−107.14	−15.27
2019/07	3341	Snow	−182.93	−25.31
2020/09	3385	Snow	−56.87	−9.81
2021/07	3375	Snow	−134.85	−18.63

lists the δD and δ¹⁸O values for a single sampling of surface snow in the corresponding time unit. There was no precipitation on the following sampling dates. For the mid-surface powdery snow sampled from July 2018 to July 2021, the highest δD value is −56.87‰ (tested in September 2020), and the lowest value is −182.93‰ (sampled in July 2019). The highest δ¹⁸O value is −9.81‰ (sampled in September 2020), and the lowest is −25.31‰ (sampled in July 2019). All the samples except for those in 2020 (tested in September) were taken in July; the September 2020 value is the most enriched. The hydrogen and oxygen stable isotope data for the river water in the same year also showed the most significant enrichment in autumn. The highest values here probably reflect fresh snowfall as temperatures began to fall.

4.2. Relationship between δD and δ¹⁸O in Different Water Bodies

4.2.1. Precipitation Line Equation

The exposure of surface water to sunlight can lead to evaporation that forms water vapor, which then rises into the air where it cools and condenses into water droplets, and this process is repeated to form precipitation. LMWL is called the local meteoric water line. It is calculated by fitting the δD and δ¹⁸O values in the local hydrogen and oxygen stable isotopes using a least-squares regression. The atmospheric precipitation lines formed by fitting the δD and δ¹⁸O values of the daily precipitation samples from the different years are shown in Figure 4. For the period from July 2018 to June 2019 (Figure 4a), the derived atmospheric precipitation line is δD = 7.68 δ¹⁸O + 11.02 (R² = 0.97). For the period from July 2019 to June 2020 (Figure 4b), the derived atmospheric precipitation line is δD = 6.98 δ¹⁸O + 8.07 (R² = 0.96). For the period from July 2020 to June 2021 (Figure 4c), the derived atmospheric precipitation line is δD = 7.03 δ¹⁸O + 7.00 (R² = 0.96). In comparison to both the global precipitation line (GMWL) equation of δD = 8 δ¹⁸O + 10 first proposed by Craig [26] in 1961, and the Chinese precipitation line equation of δD = 7.90 δ¹⁸O + 8.20 first proposed by Shu Hui Zheng et al. [27] in 1983, the slopes are smaller and the intercepts are smaller, except those in Figure 4a. Smaller slopes and intercepts are usually attributable to secondary evaporation effects on raindrops as they fall, causing the unbalanced fractionation of the hydrogen–oxygen stable isotopes. Moreover, the slope and intercept values that are smaller than those of the GMWL indicate produced under the influence of strong evaporation associated with the regional climate [6].

4.2.2. Analysis of Different Water Bodies

Figure 5 shows the river evaporation line, groundwater line, and local meteoric water line obtained by fitting the data for the 3 years. The calculations were performed in the same way as for the LMWL, with the river evaporation line fitted to the δD and δ¹⁸O values of the river water, and the groundwater line fitted to the δD and δ¹⁸O values of the groundwater, which could be derived by using a least-squares regression. The river water and groundwater samples are mostly distributed near the local meteoric water line in the study area. This distribution suggests that the main recharge source of the groundwater and river water is provided by atmospheric precipitation, and that it undergoes varying degrees of evaporation before recharge [28]. The slope for precipitation is the largest (7.30), while that for river water (2.46) is close to the value for groundwater (2.51). The intercept for precipitation is the largest, and the values for river water and groundwater remain reasonably close to each other. This is because of the combination of the kinetic fractionation effect and the preferential evaporation of light isotopes. When atmospheric water evaporates in an unsaturated condition, it drives the acceleration of the ratio of δD to δ¹⁸O via the fractionation effect in evaporating water vapor; thus, there is an increase (decrease) in d-excess in the water vapor (remaining water body). This reduces the corresponding values for the river water and groundwater, and of the slope relative to the intercept. It shows that atmospheric precipitation in the study area is the main source of recharge for river water and groundwater, and that the recharge is completed after undergoing a mixed exchange with other water bodies and a certain degree of evaporation. The proximity of the slope and intercept values of river water and groundwater proves that there is a certain hydraulic connection between river water and shallow groundwater. In addition to atmospheric precipitation, river water in the Ili Kashi River Basin receives recharging from the groundwater, but the recharge from snow and ice meltwater cannot be ignored.

4.3. D-Excess Analysis

4.3.1. Analysis of D-Excess in River Water, Surface Granular Snow Meltwater, and Groundwater

(1) River water d-excess analysis: The values of d-excess variation in the river water within the period from July 2018 to June 2021 range from −12.61‰ to 45.07‰ and the average value is 19.65‰. The variation range is smaller than the precipitation deuterium surplus variation range (−34.45‰~3.60‰), because the atmospheric precipitation is more easily affected by weather changes, making the surface water deuterium surplus variation factor more singular. However, comparing mean values, the mean deuterium surplus of surface water is larger than the mean deuterium surplus of precipitation (17.58‰) and, combined with the geographical environment of the upper reaches of the Ili Kashi River Basin, it can be found that the effect of glacial meltwater distributed around the upper reaches contributes to the overall enrichment of the deuterium surplus of surface water.

(2) Groundwater d-excess analysis: The values of d-excess variation in the groundwater in the study area from September to December 2020 range from −1.88‰ to 27.30‰, with an average value of 11.35‰. The range of variation from April to July 2021 is between 3.83‰ and 26.39‰, with an average value of 15.16‰. The d-excess values of the groundwater in the study area are larger in the spring and summer than in the autumn and winter, similar to those of river water, with the maximum value occurring in July. Because of the high precipitation in summer, the temperature does not rise immediately after the rain stops and, during this short process, there is less evaporation, which leads to more and more infiltration, so that the precipitation during the rainy season recharges the groundwater more strongly, and the maximum value occurs.

(3) Surface granular snow meltwater d-excess analysis: The surface granular snow meltwater samples are all from approximately 3300 m.a.s.l. (

Table 3. Different water bodies d-excess (‰).

Year/Month	River Water			Year/Month	Ground Water			Year/Month	Snow
	Max	Min	Avg		Max	Min	Avg
2018/07–2018/12	43.49	10.24	19.07	2020/09–2020/12	27.30	−1.88	11.35	2018/07	15.02
2019/01–2020/01	40.70	7.39	21.55					2019/07	19.55
2020/01–2021/01	45.07	−12.61	21.88	2021/04–2021/06	26.39	3.65	15.16	2020/09	21.61
2021/01–2021/06	25.95	3.83	14.24					2021/07	14.19

). The samples were obtained in July of 2018, 2019, and 2021, and in September 2020. The highest value of 21.61‰ is from September 2020, followed by 19.55‰ in July 2019, 15.02‰ in July 2018, and 14.19‰ in July 2021.

4.3.2. Analysis of D-Excess in Precipitation

The range of the interannual fluctuation of δ¹⁸O in the precipitation in the Ili Kashi River Basin is from −34.45‰ to 3.60‰, and the pattern of interannual variation broadly shows enrichment in summer and depletion in winter (Figure 6). The variation in d-excess is in the range of −32.75‰ to 77.37‰, and the variation in d-excess is less consistent than that of δ¹⁸O, with higher values occurring in the autumn and winter. The d-excess values of atmospheric precipitation in the Ili Kashi River Basin are high, and only 40 precipitation samples in the 3-year dataset have a d-excess value lower than the global average (10‰). The existence of certain differences in the d-excess values of precipitation from different sources of water vapor was previously verified by Aizen et al. [29] (2 citations). If the source of the water vapor that forms precipitation comes from the Arctic Ocean, the d-excess value is <7.8‰; if it comes from the Atlantic Ocean, the d-excess is in the range of 7.00‰~12.00‰, and the d-excess of locally recirculated water vapor precipitation is >12.0‰. The Ili Kashi River is located in the western hinterland of the Tianshan Mountains, which is an area usually influenced by westerly winds, where water vapor can be transported over long distances. The rich vegetation within the watershed can transpire a substantial volume of water vapor such that the water vapor can be recirculated locally, which leads to an overall high value of d-excess in the study area [30]. The seasonal mean d-excess values of the Ili Kashi River are high, that is, >10‰ in each season, and indicate substantial local water vapor recirculation in each season. However, most of the local water vapor recirculation is also formed by the secondary transformation of ocean-derived water vapor. Broadly speaking, the d-excess values are higher in autumn and winter than in summer. Summer precipitation is mostly from Atlantic water vapor and local water vapor circulation transported by the westerly wind belt, while autumn and winter precipitation are influenced by Arctic air masses, increased water vapor from the Arctic Ocean. The temperature in autumn is not as high as that in summer, and the precipitation in summer is greater than that in autumn, when the annual precipitation has almost reached its terminal amount; thus, making the summer d-excess value smaller than that of the fall [31,32,33].

4.4. Links between Different Water Bodies

The upper reaches of the Ili Kashi River basin can rise above 3000 m in elevation and are rich in precipitation, while the temperature is low. Large glaciers are distributed around the upstream section, and there are also seasonal glaciers, which become an important recharge source for the river water when the temperature warms up. Thus, based on the very strong similarity of the mean δ¹⁸O values of the surface water and groundwater, and the overlapping distribution of surface water and groundwater around the LMWL, it is shown that there is a frequent mutual exchange of water between the mountain rivers and groundwater, reflecting the importance of the mountain water cycle for inland rivers. In this paper, the river water in the Ili Kashi River Basin is viewed as a mixed water body jointly recharged by precipitation and shallow groundwater, and the groundwater is viewed as a mixed water body comprising snow and ice meltwater and original groundwater [34,35]. On the basis of the available shallow groundwater data, the composition of the water sources of the water bodies was analyzed using an end-members mixing model and the combining of river water data, precipitation data, and snow and ice meltwater data for the basin for the same period. The δ¹⁸O values were used as tracers, and the average δ¹⁸O values for each water sample (

Table 4. Different water body parameter values.

Year/Month	Water Type	Number of Samples	δ18O		Rate\%
			AVG	Standard Deviation
2020/09 - 2020/11	Ground water	21	−13.24	1.18	77%
	Precipitation	8	−10.10	9.57	23%
	River	19	−12.53	1.93	-
2021/04 - 2021/06	Ground water	30	−13.74	0.81	48%
	Precipitation	16	−6.39	3.84	52%
	River	30	−12.89	0.49	-

) were selected separately and substituted into the model to calculate the relative contribution of both precipitation and groundwater in the study area to river water in the basin. In summer, under the effect of high temperatures and snow and ice melt, water is able to recharge directly in the upper reaches of the river. In autumn, precipitation gradually changes from rainfall to snowfall, and 23% of the river water in the Ili Kashi River Basin is recharged by precipitation and 77% by groundwater, of which 5% of the groundwater is recharged by snow and ice meltwater and 95% is original groundwater. During the spring and summer, precipitation increases, temperatures increase, and the groundwater recharge resulting from snow and ice meltwater gradually increases. During this period, 52% of the recharge of surface water in the Ili Kashi River basin came from precipitation and 48% from groundwater, of which 19% of the groundwater consisted of snow and ice meltwater and 81% was original groundwater. If the δD or d-excess surplus calculations are used, there may be an error of up to 5%.

4.5. HYSPLIT-Based Water Vapor Source Analysis

The stable isotopic variation in hydrogen and oxygen in precipitation is influenced by local meteorological factors and inseparably related to the source and transport of water vapor from atmospheric precipitation. The δ¹⁸O-weighted average values for the summer months (June, July, and August) in the Ili Kashi River basin are −6.10‰, −5.61‰, and −6.38‰, which are higher than those of other seasons; during this period, the water vapor in the study area mostly comes from the Atlantic Ocean and is transported to the interior of Asia and Europe via the westerly wind belt, and is joined by near-source oceanic water vapor and local evaporative water vapor on the way. The δ¹⁸O-weighted average values from September to November are −12.15‰, −19.10‰, and −20‰, respectively. From September to November, the weighted-average values of δ¹⁸O are −12.15‰, −19.10‰ and −20.82‰, which is a gradual increase compared to the summer, when the Atlantic water vapor starts to decrease slowly, the temperature decreases, the precipitation decreases, the relative humidity of air also decreases, and the local evaporative water vapor joining along the way also decreases; thus, the δ¹⁸O gradually decreases. From December to February, the weighted-average values of δ¹⁸O are −22.83‰, −26.27‰, and −21.80‰, respectively, when, in addition to the transport of water vapor by the westerly wind belt, there is the joint action of the Arctic air mass and weak local evaporation along the way. The δ¹⁸O values thus drop to their lowest values. From March to May, the temperature warms up, precipitation gradually increases, the relative humidity in the air increases, and the δ¹⁸O values gradually increase; during this period, the study area’s δ¹⁸O weighted-average values are −15.42‰, −13.21‰, −7.22‰.

Atmospheric water vapor is derived mainly from surface water bodies such as rivers, lakes, and oceans, but also from moist soil, animals, and vegetation, and is evaporated into the air continuously [36]. The snow and ice in cold regions are also slowly rising. As verified by Aizen, and mentioned above [29] (2 citations), this water vapor enters the atmosphere and becomes clouds that cause rain. In addition to the influence of δ¹⁸O and d-excess in the precipitation of local meteorological elements, this is inseparably related to the source and transport of water vapor from atmospheric precipitation. Figure 7 shows the backward trajectory of water vapor sources that was obtained using the HYSPLIT model for 2018–2021, and according to the seasonal division of each water body isotope analyzed. March, April, and May of each year were classified as spring; June, July, and August were classified as summer; September, October, and November were classified as autumn; and December, January, and February were classified as winter. Figure 7 shows the plots for the water-vapor-source backward trajectories for each precipitation or snowfall from July 2018 to June 2021 for the same month. The purple line in the first six charts represents January–June 2021, the purple line in the final six charts represent July–December 2018, the red line represents January–December 2019, and the orange line represents January–December 2020. The maximum and minimum elevations set for the trajectories were 4000 and 2000 m, respectively. The three years of backward trajectory data show that the water vapor recirculation of precipitation in the study area was weak, accounting for only 8% of all the precipitation during this period; land-based water vapor accounted for more than half of the total precipitation, at 52% of the total precipitation; and the ocean sources of water vapor that could reach the study area were the Atlantic Ocean, Arctic Ocean, Mediterranean Sea, Black Sea, Caspian Sea, and the Arabian Sea. Among all the water vapor marine sources, the Atlantic Ocean had a relatively large proportion of 18% of the total water vapor sources and 38% of the marine water vapor; the Arctic Ocean had a lower rate of water vapor recirculation in the study area, with 5% of the total and 10% of the marine water vapor; and the Mediterranean Sea, the Black Sea, the Caspian Sea, and the Baltic Sea also belong in the same the direction as the Atlantic Ocean, and have a proportion of 8%, 3%, 3%, and 2%, respectively, of the total water vapor sources. The Arabian Sea had a tiny and negligible proportion. As shown in

Table 5. Precipitation formed from different sources of water vapor and its percentage of total precipitation.

Month	Year	Precipitation/mm	Recirculation Rate	Land Vapor	Atlantic Ocean	Arctic Ocean	Mediterranean Sea	Black Sea	Caspian Sea	Baltic Sea	Arabian Sea
1	2019	6.8	28%	33%	6%	-	11%	6%	11%	-	5%
	2020	7.4	7%	13%	40%	7%	13%	-	7%	6%	7%
	2021	18.7	-	50%	11%	5%	17%	6%	-	6%	5%
2	2019	30.4	10%	43%	10%	7%	20%	3%	3%	4%	-
	2020	22.3	-	50%	21%	8%	17%	-	4%	-	-
	2021	39.4	4%	58%	13%	-	12%	13%	-	-	-
3	2019	20.7	23%	44%	11%	-	-	-	-	-	22%
	2020	27.8	10%	38%	33%	5%	14%	-	-	-	-
	2021	26.4	-	41%	30%	7%	4%	-	11%	7%	-
4	2019	90.9	11%	52%	6%	-	25%	6%	-	-	-
	2020	17.4	27%	46%	20%	-	-	-	-	-	7%
	2021	17.9	6%	58%	7%	6%	11%	6%	6%	-	-
5	2019	35.3	11%	38%	17%	17%	-	11%	6%	-	-
	2020	29.4	13%	60%	13%	-	7%	-	7%	-	-
	2021	48.9	4%	54%	38%	-	-	4%	-	-	-
6	2019	35.1	8%	63%	17%	6%	3%	-	3%	-	-
	2020	36	8%	66%	13%	13%	-	-	-	-	-
	2021	72	5%	46%	25%	13%	8%	-	-	3%	-
7	2018	76.7	11%	67%	12%	-	4%	-	4%	2%	-
	2019	24.4	8%	42%	29%	4%	5%	4%	4%	4%	-
	2020	57.6	10%	60%	18%	4%	-	-	7%	1%	-
8	2018	37.8	-	38%	19%	9%	14%	10%	5%	5%	-
	2019	55.6	5%	55%	28%	-	6%	6%	-	-	-
	2020	50.5	6%	57%	17%	-	3%	7%	10%	-	-
9	2018	29.4	17%	67%	8%	-	-	-	8%	-	-
	2019	39.1	6%	66%	6%	11%	-	-	11%	-	-
	2020	16.9	-	73%	27%	-	-	-	-	-	-
10	2018	26.2	-	33%	50%	-	17%	-	-	-	-
	2019	27.7	15%	48%	10%	-	5%	19%	-	3%	-
	2020	20.7	17%	58%	25%	-	-	-	-	-	-
11	2018	16.4	-	33%	17%	42%	-	8%	-	-	-
	2019	33.5	8%	50%	13%	13%	13%	-	-	3%	-
	2020	13.6	8%	58%	13%	13%	-	-	8%	-	-
12	2018	25.9	-	53%	13%	14%	13%	-	7%	-	-
	2019	19.8	19%	42%	19%	-	10%	10%	-	-	-
	2020	11.6	-	58%	25%	-	-	-	17%	-	-
amount to		1166.2	8%	52%	18%	5%	8%	3%	3%	2%	1%

, for every month, both Atlantic Ocean and land-based water vapor were delivered to the study area, with the land-based water vapor also mainly produced from the same direction as the Atlantic Ocean.

5. Discussion

5.1. Isotope Change Analysis

Atmospheric precipitation is the main contributing factor to the water cycle in Xinjiang, which is a semiarid region. The analysis of the stable isotopes of hydrogen and oxygen in the atmospheric precipitation and in other water bodies can further characterize the water cycle. Further exploration reveals that the variation in the stable isotopes of hydrogen and oxygen in precipitation has a wider range in comparison to that of river water, groundwater, and snow meltwater. Consistent with the results of a study by Feng Xiancheng et al. [37] (2 citations), who investigated the spatial and temporal characteristics of runoff water chemistry and hydrogen–oxygen stable isotope changes in the Ili Kashi River from December 2017 to November 2018 and their environmental importance, the equation line for the Ili Kashi River Basin has slope and intercept values lower than those of the local atmospheric precipitation line because of strong evaporative fractionation. However, there is some interannual variation in the local meteoric water line for the study area. The local meteoric water line is larger than the GMWL during the study of Feng Xiancheng et al. [37] (2 citations); however, this study found that the slope and intercept of the local meteoric water line for the latter 2 years are smaller than the GMWL, except for the intercept of the local meteoric water line from July 2018 to June 2019, which skews larger than the GMWL. The local meteoric water line of the Ili Kashi River Basin is highly variable; the isotopic variation in the river water in the basin is not very significant either within or between years, but the variation is still large with respect to elevation. The overall trend of an increase and decrease in the hydrogen–oxygen isotopes in the river water runoff with elevation in summer is consistent with the results of Feng Xiancheng et al. Moreover, we found that δD and δ¹⁸O show opposite changes with elevation in autumn; that is, the δD values decrease with elevation, while the δ¹⁸O values show an overall trend of increase and decrease in summer. However, the enrichment of hydrogen–oxygen stable isotopes in the runoff occurs at some elevations both in summer and autumn, which might reflect the influence of tributaries. The seasonality of hydrogen and oxygen stable isotopes in precipitation is more significant, and the results obtained from the 3 years of data are consistent with the isotopic characteristics of precipitation in the Ili Kashi River derived by Zeng Kangkang et al. [38] (2 citations), using isotopic data from July 2017 to June 2018. The hydrogen–oxygen isotopes of precipitation in the study area are highest in July every year and lowest in January, showing an overall characteristic of low values in winter and high values in summer. This is related to the source of the water vapor in the study area and the influencing winds. Xinjiang lies in the region of the mid-latitude westerly wind belt. However, Xinjiang is far from the ocean; the westerly winds are wind bands generated by dynamical factors and have little to do with the thermal differences between land and sea. And the terrain west of Xinjiang to the Atlantic Ocean is reasonably flat and open. Therefore, in summer, the westerly winds can provide the long-distance transport of a bit of precipitation moisture. In winter, most of the regional water vapor in the Ili Kashi River Basin is produced by the westerly wind belt interacting with the winter monsoon.

The hydrogen and oxygen stable isotope values of the groundwater are lower than those of the river water in the basin because of evaporation from the river water’s surface; therefore, the hydrogen and oxygen isotope values of the surface water are higher than those of the local atmospheric precipitation and groundwater, which is helpful for determining the hydraulic connections between these water bodies. When precipitation is converted to river water and then evaporates, the slope of the precipitation line changes accordingly. The slope and intercept of precipitation in the study area are large in comparison to those of river water and groundwater, indicating that atmospheric precipitation in the study area recharges the river water and groundwater after being mixed and exchanged with other water bodies and a experiencing a certain degree of evaporation.

The distribution of hydrogen–oxygen stable isotopes in ice and snow meltwater has been shown to be influenced by two main processes: sublimation and the exchange of water vapor within the snowpack, and the interpenetrating exchange of meltwater at the boundary between where the snow has finished melting and the surface snow has not yet melted. Kinetic isotope fractionation is the main manifestation of the isotope effect during snow and ice melting, which is related to ambient temperature. The hydrogen–oxygen stable isotope values of the snow meltwater in September 2020 in the sampled data are significantly larger than the values in July of other years because of the higher temperature in summer and the onset of relatively low temperatures in autumn.

5.2. Water Vapor Source Analysis

On the basis of the backward trajectory model of water vapor sources in the study area, and considering the wind and rain temperature map used to investigate oceanic water vapor in the study area, oceanic water vapor has been found to be transported by the westerly winds to the Ili Kashi River Basin and then forms precipitation under normal circumstances. Kangkang Zeng et al. [38] (2 citations) explored the source of the water vapor in the Ili Kashi River Basin for a single, selected monthly maximum precipitation event from July 2017 to June 2018, and reported that Atlantic Ocean water vapor accounts for 68.6% of the total water vapor in the study area. In this study, a clustering analysis of the HYSPLIT backward trajectory model was performed for each precipitation event during the period from July 2018 to June 2021. The oceanic water vapor that reached the study area was sourced from the Atlantic Ocean, Arctic Ocean, Mediterranean Sea, Black Sea, Caspian Sea, and Arabian Sea. Of these, the Atlantic and Arctic oceans were the dominant sources, followed by the Mediterranean Sea and, to a lesser extent, the Arabian Sea. The source of water vapor in the northern Xinjiang region has been considered to be indirectly transported from the Atlantic and Arctic Oceans. Still, based on the three years of backward trajectories of water vapor, it was found that indirect transport was not the case in all instances. There were instances of direct long-distance transport from the Atlantic and Arctic Oceans. Still, indirect forms of transport account for more than half of the total, with the vast majority of this terrestrial water vapor coming from the direction of the Atlantic Ocean, while the Mediterranean Sea, the Caspian Sea, the Black Sea, etc., also come from the direction of the Atlantic Ocean, and are all transported via the westerly wind belt into the interior of the sub-European continent, until they are transported to the study area. In contrast, water vapor from the Arctic Ocean is influenced by a combination of the westerly wind belt and polar air masses. Because of the short transport distance, the vapor travels northward to the study area. Although the study area belongs to the northwestern arid and semiarid region, it is located in the northern part of Xinjiang. It belongs to the northern frontier region, with rich glacial resources along the river source, and is one of the more humid regions in Xinjiang. Under continental climate conditions, the temperature difference between day and night is significant; the ground heats up rapidly after enhancement with solar radiation, the temperature rises sharply, clouds increase due to the enhancement of a convective upward movement, and cumulonimbus clouds often form after the secondary transformation of the water vapor transported from the ocean. This leads to local water vapor recirculation in the study area, which mainly occurs in the summer season; however, in low-temperature, low-precipitation conditions, the local water vapor recirculation phenomenon is almost non-existent.

6. Conclusions

The analysis of the hydrogen–oxygen stable isotopes of the different water bodies in the Ili Kashi River Basin and the information obtained using the HYSPLIT model allowed the following conclusions.

(1) The variation with elevation in the hydrogen–oxygen stable isotopes in the runoff from the Ili Kashi River is significant. The δ¹⁸O value rises to a maximum in summer and autumn at an elevation of 1600 m, and then falls and rises again, whereas the δD value varies as above with elevation in summer, but the opposite is true in autumn. The intra-annual variation shows that the stable isotopes of hydrogen and oxygen in the river waters of the basin are characterized by seasonal variation. The interannual variation is not regular, and the comparison of the hydrogen and oxygen stable isotopes in the river water of the basin over the 3-year study period reveals the greatest enrichment and largest variation in 2020.

(2) The variation in the hydrogen–oxygen stable isotopes in the precipitation is much greater than that in the other water bodies, and the day-to-day variations in the δD and δ¹⁸O values in the precipitation converge. According to the intra-annual variation, the hydrogen–oxygen stable isotopes in the precipitation are most enriched in summer and most depleted in winter for all 3 years.

(3) The values of the hydrogen–oxygen stable isotopes in the groundwater fluctuate very little, with δD in the range of −99.87‰ to −84.95‰ and δ¹⁸O in the range of −15.15‰ to −12.06‰, that is, similar to the values of the hydrogen–oxygen stable isotopes in river water during the same period. The highest values in snow meltwater occurred in September 2020, namely autumn, but all the other snow meltwater sampling was performed in the summer, which accounts for the higher values of hydrogen–oxygen stable isotopes in snow meltwater in autumn in comparison with summer.

(4) The derived local meteoric water line, δD = 7.30 δ¹⁸O + 9.29, is smaller than the GMWL. In the relationship between δD and δ¹⁸O, the slope and intercept of the river water evaporation line and groundwater line are smaller than the atmospheric precipitation line for the local area of the Ili Kashi River Basin. This result indicates that atmospheric precipitation in the study area undergoes a mixed exchange with other water bodies and a certain degree of evaporation before completing the recharge of river water and groundwater, and there are frequent hydraulic connections between the surface water and groundwater.

(5) The study area is influenced by the westerly circulation, the Arctic air masses, etc., which complete the oceanic water vapor transport. By analyzing the HYSPLIT tracking model for every precipitation event in the study area during the three years, it was found that oceanic water vapor can be driven to the study area from the Atlantic Ocean, the Arctic Ocean, the Mediterranean Sea, the Black Sea, and the Caspian Sea, etc., of which the Atlantic Ocean is the most important, and accounts for 18% of the total source of water vapor, and 38% of the total source of oceanic water vapor.