Quantifying surface water evaporation loss rates in the Manas River Basin using stable isotope tracers Open Access

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).

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.

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.

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

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).

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

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).

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.

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.

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.

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.

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 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.