Calculating evaporation loss rates of surface water poses a significant challenge in remote regions, where meteorological data are scarce. To address this challenge, our study has adopted stable isotope tracers to determine the loss rates within the Manas River Basin. Utilizing the stable isotope composition in surface water and precipitation from April to October 2023 in the basin, this study aimed to estimate loss rates using the Craig–Gordon model and the Rayleigh fractionation model across mountainous and plain regions. The local meteoric water line has a smaller slope and intercept than the global meteoric water line, indicating that the precipitation is subjected to strong sub-cloud evaporation. The surface water evaporation line revealed further evaporation after runoff formation, with a decreasing slope from upstream to downstream, indicating increased evaporation loss. We compared the performance of three isotope tracer methods (δ18O, δD, and deuterium excess (d-excess)) for calculating evaporation. In the mountainous region of the watershed, the average evaporation rate per kilometer was 0.023% (δ18O), 0.028% (δD), and 0.030% (d-excess). In contrast, these values were notably elevated in the plain region, reaching 0.106% (δ18O), 0.141% (δD), and 0.117% (d-excess). The Rayleigh fractionation model using d-excess is the most effective among the three evaporation tracing methods.

  • Determining surface water evaporation rates is difficult in remote regions due to limited meteorological data.

  • We compared the performance of three isotope tracer methods (δ18O, δD, and d-excess) for calculating evaporation.

  • The Rayleigh fractionation model utilizing d-excess outperforms the other two methods for tracking evaporation.

The arid region of Northwest China, which encompasses most of Xinjiang, western Inner Mongolia, and northwestern Gansu, covers approximately 24.5% of China's land area. However, the arid region's groundwater and surface water constitute only 5.5 and 3.5% of China's total water resources, respectively, making water resources a significant challenge in the arid region (Deng 2018). In the context of global climate change, the arid region of Northwest China has experienced significant warming, with the average annual temperature rising by 0.32 °C/decade. This increase in temperature led to an increase in evaporation losses in the arid region of Northwest China (Chen et al. 2023). Therefore, quantitatively assessing the regional evaporation rate is essential for comprehending the stability of water resources under climate warming conditions and for supporting sustainable water resource management in the arid regions of Northwest China (Li et al. 2013).

Various methods exist for estimating water evaporation, including direct observation, evaporation modeling, and indirect inverse algorithms (Coertjens et al. 2015). Among these methods, the direct observation method has the highest accuracy, yet it is accompanied by the high costs associated with equipment installation and ongoing maintenance. Evaporation models offer predictions closely aligned with actual observations, yet they necessitate difficult-to-obtain data for their precision. The indirect inverse algorithm calculates the evaporation of a water body using hydrological data, yet it is characterized by comparatively lower accuracy. In recent years, isotope tracer technology has rapidly advanced, offering a promising new approach for quantifying water evaporation. This technology can effectively address the challenges associated with conducting surface water evaporation research in remote regions with limited meteorological data.

Numerous researchers have conducted water evaporation research using isotope technology, providing crucial data for local ecological protection and sustainable water resource management. Among these studies, the Craig–Gordon (C–G) model serves as a key theoretical basis for understanding changes in the stable isotope compositions of liquid and gaseous water during evaporation (Craig & Gordon 1965). Since then, the C–G model has been further used to study the evaporation process of lakes in order to reveal their isotopic dynamics during the evaporation process (Gonfiantini 1986). In recent years, the C–G model has also been applied to river evaporation studies, successfully estimating surface water evaporation losses in the South-to-North Water Diversion Project (Chen & Tian 2021) and the Shiyang River Basin (Sun et al. 2021). British scientist Lord Rayleigh introduced the concept of Rayleigh fractionation, which was initially formulated within the context of the evaporation process in liquid mixtures (Rayleigh 1902). Rayleigh fractionation is an equilibrium process in an open system, where vapor is immediately separated from the liquid phase upon evaporation, maintaining equilibrium at the water–vapor interface (Hu et al. 2007). The Rayleigh fractionation model, enhanced by incorporating deuterium excess (d-excess) measurements in conjunction with the abundance of hydrogen and oxygen stable isotopes (δD and δ18O), allows for the determination of the evaporative loss rate in a water body (Huang & Pang 2012; Hu et al. 2018).

The Manas River Basin (MRB), located in the arid and semi-arid region of Northwest China, is a typical inland river basin that serves as the largest oasis farming region in Xinjiang and the economic core region in the northern foothills of the Tianshan Mountains (Fan et al. 2012). In recent years, the volatility and uncertainty of water resources have been increasing, which is attributed to human activities such as reservoir construction and arable land expansion in the basin, as well as the effects of global warming. These limited water resources have become a major bottleneck restricting economic and social development, and there is an urgent need to further improve the efficiency of water resource utilization in the basin (Pan et al. 2023).

In this study, we examined the spatiotemporal dynamics of the stable isotope composition in surface water and precipitation within the basin. The local meteoric water line (LMWL) and the local evaporation line (LEL) served as indicators for assessing evaporation intensity in the basin. We utilized the C–G model and the Rayleigh fractionation model, utilizing stable isotope tracers (δ18O, δD, and d-excess), to accurately estimate the evaporation loss rate of surface water in both mountainous and plain regions of the MRB. The utilization of stable isotope tracers serves as a tool for the precise quantification of evaporation patterns across the basin scale, which is crucial for enhancing water resource management and ensuring the sustainable utilization of water within the basin (Jin et al. 2022; Xie et al. 2022).

Study area

The MRB is located at the southern edge of the Junggar Basin, with its geographical coordinates ranging from 84°44′E to 86°50′E and 43°4′N to 46°0′N (Figure 1). The basin is delimited by the Tashi River in the east, the Kuitun River in the west, the Tianshan Mountains in the south, and the Gurbantunggut Desert in the north. The general topography of the MRB is characterized by higher elevations in the southern part (mountainous region) and lower elevations in the northern part (plain region). The MRB is influenced by the prevailing westerly winds and polar cold airflow, leading to a typical temperate continental climate. The region is characterized by scant rainfall, with an average annual rainfall of approximately 100–200 mm, contrasted by substantial evaporation rates, with an average annual evaporation of approximately 800–1,600 mm. The winters are cold, with record lows plummeting to −42.8 °C, while the summers are hot, with record highs soaring to 43.1 °C. The average annual temperature fluctuates within a narrow range, from 4.7 to 5.7 °C. The region experiences significant annual temperature differences and large diurnal temperature variations (Kang et al. 2023).
Figure 1

Overview of the study region and distribution of sampling sites in the MRB.

Figure 1

Overview of the study region and distribution of sampling sites in the MRB.

Close modal

Sample collection

In this study, sampling sites were set up in the mountainous and plain regions of the MRB to examine the spatial variations in stable hydrogen and oxygen isotopes in surface water and precipitation (Figure 1). A total of 34 sampling sites were established, comprising 28 surface water sampling sites and 6 precipitation sampling sites. Rain barrels were used to collect precipitation, and ping-pong balls were placed above the funnel to prevent water loss through evaporation. Surface water samples were systematically collected from a variety of sources, including rivers, canals, and reservoirs (lakes). Both surface water and precipitation were collected from April to September 2023, with a consistent monthly collection protocol for better comparison. Water samples were filtered using 0.22-μm membrane filters and subsequently transferred to rinsed polyethylene bottles, which were then sealed with Parafilm. All the collected water samples were refrigerated at 0–4 °C and transported to the laboratory for measurement.

Lab measurements

Surface water and precipitation samples were stored in dry, clean glass bottles and subsequently analyzed using a liquid water isotope analyzer (model DLT-100, Los Gatos Research, San Jose, CA, USA) at Shihezi University. The stable isotope compositions of hydrogen and oxygen (δD and δ18O) were expressed in per mil (‰) relative to the standard Vienna standard mean ocean water (V-SMOW):
(1)
where is the ratio of 18O/16O or D/H in the sample, and is the ratio of 18O/16O or D/H in V-SMOW, and the measurement accuracies of δ18O and δD were ±0.15 and ±0.4‰, respectively.
The excess, a second-order parameter that combines both oxygen and hydrogen isotopic species, is defined by the following expression (Dansgaard 1964):
(2)

Isotope tracer technology

In this study, we used the C–G model and the Rayleigh fractionation model to determine the evaporation rate of surface water from the MRB, specifically in both mountainous and plain regions throughout the sampling period.

The C–G model divides the surface of a body of water into three layers: (1) the water–vapor interface, which is characterized by evaporation and condensation that achieve equilibrium within its boundary layer; (2) the diffusion layer, which is mainly controlled by molecular diffusion mechanisms; and (3) the turbulence layer, which is predominantly influenced by turbulent transport and eddy diffusion. The C–G model calculates the distribution of the water vapor exchange fluxes and isotopic compositions for each of the three layers by integrating the physical processes that occur within them. This provides a comprehensive understanding of the distribution of water vapor exchange fluxes and isotopic compositions for each layer.

Based on the principle of the model, the calculated water body evaporation to inflow ratio can be expressed as follows:
(3)
where δP is the stable isotope value at the inlet, δQ is the stable isotope value at the outlet, m is the enrichment slope, and δ* is the isotope enrichment limit. can be expressed as follows:
(4)
where h is the relative humidity (RH), is the free atmosphere stable isotope content, and is the total enrichment factor.
The total enrichment factor represents the sum of the kinetic fractionation enrichment factor and the equilibrium fractionation enrichment factor, which can be expressed as follows:
(5)
where is the equilibrium fractionation enrichment factor, and is the kinetic fractionation enrichment factor.

The kinetic fractionation enrichment factor is controlled by the RH h.

For , can be expressed as follows:
(6)
For , can be expressed as follows:
(7)
The equilibrium fractionation factor is temperature-dependent. For , is determined by the following empirical formula:
(8)
For , is determined by the following empirical formula:
(9)
where T is the temperature of the water surface, which can also be calculated using atmospheric temperature.
The equilibrium fractionation enrichment factor is expressed as follows:
(10)
The enrichment slope m in Equation (3) can be expressed as follows:
(11)
The free atmosphere stable isotope content in Equation (4) can be expressed as follows:
(12)
where is the measured stable isotope value of precipitation.

In our study, we employed the Hydrocalculator software (Skrzypek et al. 2015), which is accessible online at http://hydrocalculator.gskrzypek.com, to calculate the evaporation rate using the C–G model. By inputting the relevant isotope and meteorological data (such as temperature and RH), the software accurately computes the evaporation loss rate. In our research, we estimated the stable isotope composition of atmospheric water vapor from the stable isotope composition of precipitation. Considering the varying states of water bodies, the software enables a choice between steady-state and unsteady-state models, with our analysis favoring the steady-state approach for its applicability.

The Rayleigh process is a phase equilibrium process that occurs in an open system, where it is assumed that water vapor immediately separates from the system after evaporation from the liquid phase, and that equilibrium is constantly maintained between the liquid and vapor phases at the surface water–water vapor interface (Rayleigh 1902; Hu et al. 2007).

Rayleigh fractionation can be expressed as follows:
(13)
where f is the ratio of the remaining part of the liquid phase, is the ratio of heavy and light isotopes in the liquid phase at the initial moment, is the ratio of heavy and light isotopes in the liquid phase at the final moment, and is the fractionation factor between vapor and liquid water, which can be expressed as follows:
(14)
where is the kinetic fractionation factor between liquid water and vapor, is the equilibrium fractionation factor between liquid water and vapor.
Combining Equation (13) with Equation (1), we obtain the following equation:
(15)
Combining Equation (15) with Equation (2), we obtain another formula for calculating the evaporation loss from water bodies:
(16)
where and are the initial isotope abundances in the water body.

Thus, the proportion of evaporation loss from water bodies (E/I = 1 − f) within the watershed can be determined using the above equations.

Stable isotope characterization of precipitation

As depicted in Figure 2, the obtained stable isotope data in precipitation from the basin exhibited varying δD values, ranging from −65.10 to −13.28‰, with a mean value of −33.80‰, alongside δ18O values that range from −10.30 to 0.63‰, with a mean value of −4.98‰. In the mountainous region, the δD values of precipitation vary from −65.10 to −19.92‰, averaging at −36.58‰, while δ18O values range from −10.30 to −3.22‰, averaging at −5.33‰. In the plain region, the δD values range from −55.59 to −13.28‰, averaging at −26.53‰, and the δ18O values fluctuate between −6.74 and 0.63‰, averaging at −3.32‰. The stable isotope data from precipitation in the mountainous region consistently showed lower values when compared with the plain region.
Figure 2

Characteristics of distributions for precipitation δD (a) and δ18O (b) in the MRB.

Figure 2

Characteristics of distributions for precipitation δD (a) and δ18O (b) in the MRB.

Close modal

The Global Meteoric Water Line (GMWL), with the formula: δD = 8δ18O + 10, was derived from a global analysis of surface freshwater and precipitation samples (Craig 1961). The LMWL was obtained through linear regression of the stable isotope data of precipitation in the region. The slope and intercept of the LMWL are key indicators for evaluating the influence of evaporation on precipitation during descent (Li et al. 2017; Tian et al. 2021a).

As depicted in Figure 3, the LMWL in the MRB is represented by the equation δD = 5.5δ18O − 6.41 (R2 = 0.89, N = 29); the LMWL in the mountainous region is as follows: δD = 6.12δ18O − 1.49 (R2 = 0.96, N = 15); and the LMWL in the plain region is as follows: δD = 5.29δ18O − 8.39 (R2 = 0.72, N = 14).
Figure 3

The local meteoric water lines for the overall basin, the mountainous region, and the plain region.

Figure 3

The local meteoric water lines for the overall basin, the mountainous region, and the plain region.

Close modal

Our research reveals that the intercept and slope of the LMWL in the mountainous and plain regions of the MRB are smaller than the GMWL, indicating that precipitation in the MRB is strongly affected by evaporation during its descent, and non-equilibrium fractionation leads to the enrichment of heavy isotopes in the precipitation. Precipitation in the mountainous region of the basin tends to be more distributed on the left side of the GMWL, whereas precipitation in the plain region is more prevalent on the right side of the GMWL, which indicates that precipitation in the plain region undergoes stronger sub-cloud evaporation than mountain precipitation during the formation and descent processes.

This study explored the influence of various environmental factors, including temperature, RH, altitude, and seasonality, on the isotopic signatures of precipitation. We conducted a linear regression analysis between the δ18O values of precipitation and these selected factors, generating corresponding linear equations (Figure 4). The linear relationship between precipitation δ18O and temperature (T) is δ18O = 0.26T − 10.57, indicating a moderate positive correlation; the linear relationship between precipitation δ18O and RH is δ18O = −0.15RH − 1.13, indicating a moderate negative correlation; the linear relationship between precipitation δ18O and latitude (LAT) is δ18O = 2.18LAT − 102, indicating a high positive correlation; and the linear relationship between precipitation δ18O and altitude is δ18O = −0.0014ALT − 4.19, indicating a moderate negative correlation. Furthermore, these isotopic signatures of precipitation are preserved to some extent in river water, while the isotopic composition of river water is subsequently influenced and modified by additional factors such as evaporation.
Figure 4

The effects of temperature (a), RH (b), LAT (c), and altitude (d) on the oxygen isotopic values of precipitation.

Figure 4

The effects of temperature (a), RH (b), LAT (c), and altitude (d) on the oxygen isotopic values of precipitation.

Close modal

Stable isotope characterization of surface water

As depicted in Figure 5, the stable isotopic composition of the surface water in the MRB was characterized by a variation in δ18O ranging from −12.76 to −1.30‰, with a mean value of −9.70‰, and a variation in δD ranging from −81.34 to −21.99‰, with a mean value of −65.58‰. The isotopic composition of surface water in the mountainous region exhibited a range of −12.76 to −9.27‰ for δ18O, with a mean value of −10.72‰, while the variation range for δD was −81.34 to −58.70‰, with a mean value of −68.48‰. The isotopic composition of surface water in the plain region was characterized by a variation in δ18O ranging from −12.50 to −1.30‰, with a mean value of −9.23‰, and a variation in δD ranging from −80.66 to −21.99‰, with a mean value of −64.24‰. We found that the stable isotope fluctuations of surface water in the mountainous region were smaller compared to the plain regions.
Figure 5

Characteristics of distributions for surface water δD (a) and δ18O (b) in the MRB

Figure 5

Characteristics of distributions for surface water δD (a) and δ18O (b) in the MRB

Close modal
As depicted in Figure 6, the slope and intercept of the LEL served as indicators for assessing the evaporation intensity of surface water in the basin (Tian et al. 2021b). The LEL equation for the mountainous region was obtained as δD = 5.53δ18O − 9.21 (R2 = 0.65, N = 42); and the LEL equation for the plain region was obtained as δD = 4.66δ18O − 21.23 (R2 = 0.96, N = 91). The notably lower slopes of the LEL compared to the LMWL suggest that evaporation significantly alters the stable isotope composition of precipitation, leading to a greater enrichment of stable isotopes in surface water. Furthermore, the slope and intercept of the LEL in these regions are decreasing from the mountainous region to the plain region, indicating that the degree of evaporation in the basin is gradually increasing from the upper reaches to the middle and lower reaches of the Manas River.
Figure 6

LELs of surface water in the overall basin, the mountainous region, and the plain region.

Figure 6

LELs of surface water in the overall basin, the mountainous region, and the plain region.

Close modal

As shown in Figure 6, the surface water isotope data points in the mountainous region of the MRB are found on both sides of the LMWL, indicating that precipitation is not the only source of surface water recharge in the mountainous regions. In contrast, the surface water isotope data points in the plain region of the basin are mainly located on the right side of the LMWL, demonstrating that precipitation is the primary source of surface water recharge in this plain region, particularly in the middle and lower reaches of the Manas River. It is also worth noting that other potential sources of surface water recharge, such as groundwater, glaciers, and snowmelt, have a negligible influence on the isotopic composition of surface water in the region.

Calculation of evaporation rates

In this study, the route ‘Kenswat Reservoir (E1)-Shihuyao Village (E2)-Zuandongziqu Village (E3)-Entrance to the West Bank Drainage (E4)-Middle Reaches to the West Bank Drainage (E5)-Lower Reaches to the West Bank Drainage (E6)’ was selected, with a total of six sampling sites (Figure 1). The total length of the route was approximately 120 km. The water flowed from E1 to E6, and all the water in the river flowed into E4 at the river confluence during the sampling period. For the mountainous region of the MRB, the ‘E1-E3’ was employed to determine the surface water evaporation rate, while the ‘E4-E6’ was used to evaluate the surface water evaporation rate in the plain region of the basin. To calculate the evaporation rate of surface water, local meteorological data (such as air temperature RH) were considered, and calculations were performed using the C–G model and the Rayleigh fractionation model to determine the cumulative evaporation rate along the route. This allowed for the calculation of the average evaporation rate per kilometer of surface water in both the mountainous and plain regions of the MRB.

Figure 7 illustrates the variations in stable isotopes at the sampling sites. With the progression of water flow, evaporation led to an enrichment of heavier stable isotopes in surface water. The δ18O and δD values exhibited an increasing trend along the course, whereas the d-excess showed a decreasing trend. Specifically, the δ18O values ranged from −11.61‰ to −9.09‰, δD values ranged from −76.06‰ to −63.93‰, and the d-excess values ranged from 8.82‰ to 16.80‰.
Figure 7

Isotopic signatures along the E1–E6 route in the MRB.

Figure 7

Isotopic signatures along the E1–E6 route in the MRB.

Close modal
By combining the stable isotope data with the corresponding meteorological data from the above sampling sites, this study calculates the cumulative evaporation rate change along the course using the C–G model (using δ18O and δD tracers) and the Rayleigh fractionation model (using d-excess tracer). As illustrated in Figure 8, the calculated cumulative evaporation ratio estimated based on δ18O was 9.74%; the cumulative evaporation ratio based on δD was 12.78%; and the cumulative evaporation ratio based on d-excess was 10.92%.
Figure 8

Changes in cumulative evaporation rates along the E1–E6 route in the MRB.

Figure 8

Changes in cumulative evaporation rates along the E1–E6 route in the MRB.

Close modal
Figure 9 presents the average evaporation rates per kilometer in both mountainous and plain regions of the MRB, deduced from cumulative evaporation rates and water flow distances. In the mountainous region, the rates were calculated at 0.023% per kilometer for δ18O, 0.027% for δD, and 0.030% for d-excess. In contrast, the plain region exhibited higher rates: 0.106% for δ18O, 0.141% for δD, and 0.117% for d-excess. The findings from both the C–G and Rayleigh fractionation models confirm a significantly higher evaporation rate for surface water in the plain region compared to the mountainous areas of the MRB.
Figure 9

Average evaporation rates per kilometer in mountainous and plain regions of the MRB.

Figure 9

Average evaporation rates per kilometer in mountainous and plain regions of the MRB.

Close modal

Past research has predominantly utilized oxygen isotopes rather than hydrogen isotopes for tracing the hydrological cycle and evaporation processes since the measurement techniques for oxygen isotopes have historically been more precise and reliable (Gat 1970; Gibson & Edwards 2002; Coenders-Gerrits et al. 2014). The fractionation mechanisms of oxygen isotopes (both equilibrium and kinetic fractionation) have been extensively studied and well understood, while the fractionation mechanisms for hydrogen isotopes are more complex, especially regarding kinetic fractionation (Craig & Gordon 1965; Zuber 1983). As depicted in Figure 9, the calculated values of δ18O and d-excess are fairly consistent with each other, regardless of whether the calculations are conducted in the mountainous or plain regions. The value of δD only correlates well with those of δ18O and d-excess in the mountainous area. In the plain area, however, the calculated value of δD is notably higher. This further underscores the greater reliability of the calculated results for δ18O and d-excess compared to δD.

When comparing the two calculation methods, δ18O (C–G model) and d-excess (Rayleigh fractionation model), it is evident that the δ18O method involves only a single stable isotope. In contrast, the Rayleigh fractionation model (d-excess) incorporates both hydrogen and oxygen stable isotopes through the d-excess formula (d-excess = δD − 8δ18O), thereby offering a more stable and consistent input signal. In arid regions, surface water is often replenished by groundwater from various sources and ages. The d-excess method is more stable and less affected by groundwater mixing than the δ18O method (Hu et al. 2018). In conclusion, the Rayleigh fractionation model utilizing d-excess outperforms the other two isotopic tracers in terms of tracking evaporation, indicating that it is more reliable than the C–G model.

Factors affecting evaporation of surface water

During the regulation and storage of runoff, reservoirs alter the velocity and discharge of downstream rivers, deviating from their natural state. These changes can impact the isotopic characteristics of surface water and the estimation of evaporation losses. To mitigate these effects, we communicate with local water resource management authorities before sampling to avoid periods of reservoir capacity adjustment.

During river flow, groundwater, and surface water continuously exchange and replenish each other. This interaction alters the stable isotope values of surface water, complicating the estimation of evaporation losses. Current technology cannot yet precisely quantify this process. Additionally, agricultural irrigation in the watershed leads to return flows, which further change the stable isotope composition of surface water. The d-excess method utilized in our study is more stable and less affected by mixing from groundwater and other sources.

The mixing of rainfall and snowmelt with river water results in a modification of the stable isotope compositions in river water, thereby creating discrepancies between samples taken from upstream and downstream locations. Nevertheless, snowmelt was absent from the study area and confined to higher elevations and southern regions of the watershed during this study. Additionally, sampling was conducted during a period of minimal precipitation to reduce rainfall impacts. Consequently, extreme weather events and snowmelt did not influence the study's outcomes.

In this study, we utilized the Hydrocalculator software to perform a sensitivity analysis on two key meteorological parameters: temperature and humidity. As shown in Figure 10, when other variables are held constant, the ratio of evaporation loss to inflow (E/I) consistently increases with rising temperature and decreases with increasing RH. Notably, changes in temperature or humidity have a more pronounced impact on evaporation rates calculated using δD compared to those using δ18O, highlighting the greater sensitivity of the δD method. However, the d-excess method exhibits greater stability, with its model outputs being less affected by variations in temperature and humidity. This stability is likely because the d-excess method focuses on the initial and final isotopic compositions while being insensitive to the changes in temperature and humidity during the evaporation process.
Figure 10

The impact of temperature and humidity on evaporation rates.

Figure 10

The impact of temperature and humidity on evaporation rates.

Close modal

Implications for water resource management

In arid and semi-arid regions, global warming and climate change have led to increased evaporation of surface water, exacerbating water scarcity and its impact on societal development. To address this issue, reservoir surfaces can be covered with materials such as black polyethylene balls (Han et al. 2017). This method effectively blocks ultraviolet radiation, reduces water loss, and is both cost-efficient and easily scalable.

In our study area, agricultural production requires a substantial amount of irrigation water. When irrigation exceeds crop requirements, it leads to excessive and unnecessary evaporation, wasting water resources. Moreover, excess irrigation water can seep into rivers, potentially affecting water quality and ecological balance. To address these issues, promoting high-efficiency water-saving irrigation techniques, such as drip irrigation, within the watershed can effectively conserve water and reduce the negative impacts of irrigation return flow.

This study explores the characteristics of stable isotope changes in water bodies throughout the mountainous and plain regions of the MRB and estimates the evaporation rates of surface water in the MRB using isotope tracers. The main conclusions are as follows:

  • (1) Precipitation in the MRB displays distinct spatial patterns in hydrogen and oxygen stable isotopes, with enrichment observed in the plain region and depletion in the mountainous region. The LMWL in the basin, characterized by a slope lower than the typical 8 and an intercept below 10, suggests that precipitation undergoes significant sub-cloud evaporation. The isotopic values of atmospheric precipitation exhibit positive correlations with temperature and latitude, while displaying negative correlations with RH and altitude.

  • (2) Stable isotopes of surface waters in the MRB show a pattern of depletion in the mountainous region and enrichment in the plain region. The lower intercept and slope of the LEL compared to the LMWL and GMWL signify additional evaporation after runoff formation. The decrease in LEL slope from mountainous upstream to plain downstream regions mirrors the progressive enrichment of these isotopes and escalating evaporative loss.

  • (3) In the MRB, the average evaporation rates per kilometer for the mountainous region were 0.023% (δ18O), 0.027% (δD), and 0.030% (d-excess), while in the plain region, they were notably higher at 0.106% (δ18O), 0.141% (δD), and 0.117% (d-excess). Analysis from both the C–G model and the Rayleigh fractionation model indicates that surface water evaporation in the plain region substantially exceeds that in the mountainous region.

  • (4) The evaporation rate calculated using the Rayleigh fractionation model (using a d-excess tracer) is more reliable than that calculated using the C–G model (using δ18O and δD tracers). Evaporation loss is positively correlated with temperature and negatively correlated with RH.

Our research is subject to certain limitations. The sampling period was relatively short, and surface water sampling was conducted only during dry periods. Our results are theoretical values derived from model calculations and lack comparison with field measurements. Future studies should extend the sampling period, increase sampling frequency, and validate results against empirical data to refine the findings and improve accuracy.

This research was supported by the National Natural Science Foundation of China (52269006 and 42106228); Projects of Xinjiang Production and Construction Corps (2022BC001, 2023TSYCCX0114, 2022DB023, and 2023AB059); Project of Shihezi (2023NY01); and The Third Xinjiang Scientific Expedition Program (2021xjkk0804). The research was also supported by the Key Laboratory of Cold and Arid Regions Eco-Hydraulic Engineering of Xinjiang Production & Construction Corps.

Data cannot be made publicly available; readers should contact the corresponding author for details.

The authors declare there is no conflict.

Chen
Y.
&
Tian
L.
(
2021
)
Canal surface evaporation along the China's South-to-North Water Diversion quantified by water isotopes
,
Science of The Total Environment
,
779
,
146388
.
doi:10.1016/j.scitotenv.2021.146388
.
Chen
Y.
,
Li
Z.
,
Xu
J.
,
Shen
Y.
,
Xing
X.
,
Xie
T.
,
Li
Z.
,
Yang
L.
,
Xi
H.
,
Zhu
C.
,
Fang
G.
,
Si
J.
&
Zhang
Y.
(
2023
)
Changes and protection suggestions in water resources and ecological environment in arid region of Northwest China
,
Bulletin of Chinese Academy of Sciences
,
38
(
3
),
385
393
.
doi:10.16418/j.issn.1000-3045.20230120005
.
Coenders-Gerrits
A. M. J.
,
Van der Ent
R. J.
,
Bogaard
T. A.
,
Wang-Erlandsson
L.
,
Hrachowitz
M.
&
Savenije
H. H. G.
(
2014
)
Uncertainties in transpiration estimates
,
Nature
,
506
(
7487
),
E1
E2
.
doi:10.1038/nature12925
.
Coertjens
L.
,
Donche
V.
,
Maeyer
S. D.
,
Vanthournout
G.
&
Petegem
P. V.
(
2015
)
Estimating evaporation in semi-arid regions facing data scarcity: example of the El Haouareb dam (merguellil catchment, central Tunisia)
,
Journal of Hydrology Regional Studies
,
3
(
9
),
265
284
.
doi:10.1016/j.ejrh.2014.11.007
.
Craig
H.
(
1961
)
Isotopic variations in meteoric waters
,
Science
,
133
(
3465
),
1702
1703
.
doi:10.1126/science.133.3465.1702
.
Craig
H.
&
Gordon
L. I.
, (
1965
)
Deuterium and oxygen-18 variation in the ocean and the marine atmosphere
. In:
Tongiorgi
E.
(ed.)
Stable Isotope in Oceanographic Studies and Paleotemperatures
,
National Research Council Nuclear Geology Laboratory,
pp.
9
122
.
Dansgaard
W.
(
1964
)
Stable isotopes in precipitation
,
Tellus
,
16
(
4
),
436
468
.
doi: 10.3402/tellusa.v16i4.8993
.
Fan
W.
,
Wu
P.
,
Han
Z.
&
Yao
B.
(
2012
)
Influencing factors analysis of reference crop evapotranspiration and modification of Hargreaves method in Manas River Basin
,
Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE)
,
28
(
8
),
19
24
.
doi:CNKI:SUN:NYGU.0.2012-08-004
.
Gat
J. R.
(
1970
)
Environmental isotope balance of Lake Tiberias
, In:
Proceedings of IAEA Symposium on Isotope Hydrology, Vienna, Austria, 9-13 March 1970. Vienna: IAEA
,
151
162
.
Gibson
J. J.
&
Edwards
T. W. D.
(
2002
)
Regional water balance trends and evaporation-transpiration partitioning from a stable isotope survey of lakes in northern Canada
,
Global Biogeochemical Cycles
,
16
(
2
),
1026
.
doi:10.1029/2001GB001839
.
Gonfiantini
R.
, (
1986
)
Environmental isotopes in lake studies
. In:
Fritz
P.
&
Fontes
J. C.
(eds.)
Handbook of Environmental Isotope Geochemistry
, Amsterdam: Elsevier, pp.
113
168
.
Han
K.
,
Shi
K.
,
Yan
X.
,
Lyu
J.
&
Yang
Y.
(
2017
)
Study on the inhibition rate of the still water evaporation under the PE floating ball coverage in arid zone plain reservoir
,
Journal of Water Resources and Water Engineering
,
04
,
235
239
.
doi:10.11705/j.issn.1672-643X.2017.04.40
.
Hu
H.
,
Bao
W.
,
Wang
T.
&
Qu
S.
(
2007
)
Derivation of Rayleigh fractionation formula and its experiment study in water evaporation
,
Journal of Hydraulic Engineering
,
(S1)
,
314
317
.
doi:10.13243/j.cnki.slxb.2007.s1.044
.
Hu
Y.
,
Liu
Z.
,
Zhao
M.
,
Zeng
Q.
,
Zeng
C.
,
Chen
B.
,
Chen
C.
,
He
H.
,
Cai
X.
,
Ou
Y.
&
Chen
J.
(
2018
)
Using deuterium excess, precipitation and runoff data to determine evaporation and transpiration: a case study from the Shawan Test Site, Puding, Guizhou, China
,
Geochimica et Cosmochimica Acta
,
242
,
21
33
.
doi:10.1016/j.gca.2018.08.049
.
Huang
T.
&
Pang
Z.
(
2012
)
The role of deuterium excess in determining the water salinisation mechanism: a case study of the arid Tarim River Basin, NW China
,
Applied Geochemistry
,
27
(
12
),
2382
2388
.
doi:10.1016/j.apgeochem.2012.08.015
.
Jin
K.
,
Zhang
Q.
,
Lu
Y.
,
Hu
Y.
&
Rao
W.
(
2022
)
Research on stable isotopes and hydrochemical features of lakes water in Badain Jaran Desert
,
Yangtze River
,
53
(
04
),
65
72
.
doi:10.16232/j.cnki.1001-4179.2022.04.011
.
Kang
W.
,
Zhou
Y.
,
Sun
Y.
,
Zhou
J.
,
Cao
Y.
,
Lu
H.
&
Tu
Z.
(
2023
)
Distribution and coenrichment of arsenic and fluorine in the groundwater of the Manas River Basin in Xinjiang
,
Arid Zone Research
,
40
(
09
),
1425
1437
.
doi:10.13866/j.azr.2023.09.06
.
Li
Z.
,
Wang
N. A.
,
Li
Y.
,
Zhang
Z.
,
Li
M.
,
Dong
C.
&
Huang
R.
(
2013
)
Runoff simulations using water and energy balance equations in the lower reaches of the Heihe River, Northwest China
,
Environmental Earth Sciences
,
70
,
1
12
.
doi:10.1007/s12665-012-2099-8
.
Li
W.
,
Li
C.
,
Jia
D.
,
Hao
S.
,
Liu
Z.
&
Li
R.
(
2017
)
Change of stable isotopes in summer precipitation in central Inner Mongolia
,
Arid Zone Research
,
34
(
06
),
1214
1221
.
doi:10.13866/j.azr.2017.06.02
.
Pan
Y.
,
Yang
G.
,
Xue
L.
,
Xu
X.
,
Gulishengmu
A.
,
Huang
Z.
,
Wang
W.
&
Ran
M.
(
2023
)
Application of DSF-GWO algorithmization of planting structure in Manas River irrigation region
,
Journal of Drainage and Irrigation Machinery Engineering(JDIME)
,
41
(
9
),
943
951
.
doi:10.3969/j.issn.1674-8530.22.0164
.
Rayleigh
L.
(
1902
)
LIX. On the distillation of binary mixtures
,
The London, Edinburgh, and Dublin Philosophical Magazine and Journal of Science
,
4
(
23
),
521
537
.
doi:10.1080/14786440209462876
.
Skrzypek
G.
,
Mydłowski
A.
,
Dogramaci
S.
,
Hedley
P.
,
Gibson
J. J.
&
Grierson
P. F.
(
2015
)
Estimation of evaporative loss based on the stable isotope composition of water using Hydrocalculator
,
Journal of Hydrology
,
523
,
781
789
.
doi:10.1016/j.jhydrol.2015.02.010
.
Sun
Z.
,
Zhu
G.
,
Zhang
Z.
,
Xu
Y.
,
Yong
L.
,
Wan
Q.
,
Ma
H.
,
Sang
L.
&
Liu
Y.
(
2021
)
Identifying surface water evaporation loss of inland river basin based on evaporation enrichment model
,
Hydrological Processes
,
35
(
3
),
e14093
.
doi:10.1002/hyp.14093
.
Tian
L.
,
Gao
Y.
,
Yang
G.
,
Schwartz
B.
,
Cai
B.
,
Ray
C.
,
Li
Y.
&
Wu
H.
(
2021a
)
Isotopic tracers of sources of water for springs from the Edwards Aquifer, Central Texas, USA
,
Hydrology Research
,
52
,
787
803
.
doi:10.2166/nh.2021.011
.
Tian
L.
,
Gao
Y.
,
Yang
G.
,
Schwartz
B.
,
Cai
B.
,
Lei
G.
,
Shi
G.
,
Ray
C.
,
Sok
S.
,
Martinez
E.
,
Li
Y.
&
Wu
H.
(
2021b
)
The evolution of hydrochemical and isotopic signatures from precipitation, surface water to groundwater in a typical karst watershed, Central Texas, USA
,
Isotopes in Environmental and Health Studies
,
57
,
492
515
.
doi:10.1080/10256016.2021.1948410
.
Xie
S.
,
Meng
Y.
,
Liu
G.
&
Li
L.
(
2022
)
Variations of hydrogen and oxygen isotopes in waterbody of Dadu River during flood period and their influencing factors
,
Yangtze River
,
53
(
03
),
55
60
.
doi:10.16232/j.cnki.1001-4179.2022.03.008
.
Zuber
A.
(
1983
)
On the environmental isotope method for determining the water balance components of some lakes
,
Journal of Hydrology
,
61
,
409
427
.
doi:10.1016/0022-1694(83)90004-5
.

Author notes

These authors contributed equally to the article.

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