A method for analyzing atmospheric water line based on precipitation isotopes

Abstract

The application discloses a precipitation isotope-based meteoric water line analysis method, and relates to the technical field of meteoric water line analysis. The method comprises the following steps: arranging a plurality of meteorological sampling sites in a target region, collecting precipitation samples of each precipitation event in a complete hydrological year; determining stable hydrogen isotopes and stable oxygen isotopes based on the precipitation samples; collecting the determination values of stable hydrogen isotopes and stable oxygen isotopes of all samples, and performing linear fitting based on the ordinary least squares method to establish a local meteoric water line equation; comparing the slope and intercept of the local meteoric water line equation with a global meteoric water line to evaluate the secondary evaporation intensity and local climate characteristics of the target region. The application can reflect the LMWL of unique climate and terrain conditions, and deeply explore key meteorological factors and seasonal dynamics affecting isotope composition.

Description

Technical Field

[0001] This invention relates to the field of atmospheric waterline analysis technology, and in particular to an atmospheric waterline analysis method based on precipitation isotopes. Background Technology

[0002] Isotope hydrology is an important method for studying water cycle processes, including the stable generation of hydrogen (δD) and oxygen (δ¹⁸O). 18 O isotopes, as natural tracers, can effectively reveal the sources, transport pathways, and fractionation processes such as evaporation and condensation of precipitation. The Global Meteoric Water Line (GMWL) describes the global precipitation δD and δ¹⁸O. 18 The linear equilibrium relationship between oxygen (O) forms the basic framework for understanding the isotopic composition of atmospheric precipitation. However, under different climatic zones, topographical conditions, and local meteorological conditions, precipitation isotopes are affected by various factors such as temperature, precipitation amount, humidity, altitude, and water vapor source, causing the actual precipitation isotopic relationship to deviate from the GMWL and exhibit significant regional and seasonal differences. Therefore, establishing a Local Meteoric Water Line (LMWL) has irreplaceable scientific value for accurately characterizing regional water cycle features, tracing water movement, and assessing water resource formation mechanisms.

[0003] In China, especially in the arid and semi-arid regions of Northwest China, water scarcity is a prominent issue, making the understanding of the spatiotemporal variations of precipitation isotopes crucial for sustainable water resource management. Existing studies have conducted precipitation isotope observations in typical regions such as the Qinghai-Tibet Plateau and the Loess Plateau, establishing corresponding regional atmospheric waterline equations and revealing the significant impacts of temperature, precipitation, and monsoon activity. However, systematic studies on stable precipitation isotopes are still lacking in Tianshui City, located in the western Qinling Mountains and the middle reaches of the Wei River, a transitional zone between semi-humid and semi-arid conditions. This region has complex topography and is influenced by both the East Asian monsoon and continental climate, resulting in diverse water vapor sources. The temporal variations (e.g., seasonal fluctuations) and spatial differentiations (e.g., altitude and topographic effects) of its precipitation isotope composition have not been fully revealed. Existing regional waterline equations (such as the waterline in the arid Northwest of China) may not accurately represent the local characteristics of transitional zones like Tianshui City, leading to uncertainties in using isotopic methods to analyze key hydrological processes such as local groundwater recharge and surface water-groundwater transformation. Summary of the Invention

[0004] The purpose of this invention is to provide an atmospheric waterline analysis method based on precipitation isotopes, which aims to solve or improve at least one of the above-mentioned technical problems.

[0005] To achieve the above objectives, the present invention provides the following solution: An atmospheric water line analysis method based on precipitation isotopes, comprising: Multiple meteorological sampling stations were set up in the target area to collect precipitation samples from each precipitation event within the complete hydrological year; Stable hydrogen isotopes and stable oxygen isotopes were determined based on the precipitation samples. The measured values ​​of stable hydrogen and stable oxygen isotopes from all samples were compiled, and a local meteorological hydrograph equation was established based on linear fitting using ordinary least squares: δD = a·δ 18 O + b, where δD is a stable hydrogen isotope, δ 18 O is a stable oxygen isotope, a is the slope, and b is the intercept. By comparing the slope and intercept of the local meteorological waterline equation with global meteorological waterlines, the intensity of secondary evaporation and local climate characteristics of the target area are assessed.

[0006] Optionally, the precipitation sample collection process is as follows: For liquid precipitation, clean polyethylene bottles were used for collection, and the bottle openings were sealed with plastic wrap to prevent evaporation; for solid precipitation, it was allowed to thaw naturally in a sealed bag to room temperature before being transferred to the sampling bottle; all samples were frozen immediately after collection until isotope determination was performed.

[0007] Optionally, the measurement accuracy of the stable hydrogen isotope and the stable oxygen isotope is not less than ±0.2‰ and ±0.6‰, respectively, and is expressed as a percentage deviation relative to the Vienna standard mean seawater.

[0008] Optionally, the formulas for calculating the slope and intercept of the ordinary least squares method are as follows: Where n represents the number of samples, and x represents δ 18 O value, y represents δ 2 H value, a The slope of the atmospheric water level line. b This is the intercept of the atmospheric water level line.

[0009] Optionally, the assessment of the secondary evaporation intensity and local climate characteristics of the target area by comparing the slope and intercept of the local meteorological waterline equation with global meteorological waterlines specifically includes: Seasonal divisions were performed based on the set collection time, and local meteorological water lines were constructed according to the division results. The differences in slope and intercept were compared to analyze the impact of seasonal changes on isotope fractionation. Based on the basic location information of the sampling sites, local meteorological water lines were constructed and spatial comparative analysis was performed; By correlating precipitation isotope values ​​with contemporaneous meteorological data, the intensity of secondary evaporation and local climate characteristics of the target area were assessed, and δ¹² values ​​were plotted. 18 O and temperature, δ 18 The graph shows the relationship between O and precipitation, and the regression coefficient is calculated.

[0010] Optionally, the seasonal division based on the set collection time specifically includes: setting April to October of each year as the monsoon season and November of each year to March of the following year as the non-monsoon season.

[0011] Optionally, the basic location information includes differences in altitude, topography, and water vapor source for each sampling station.

[0012] Optionally, the correlation analysis process includes statistical analysis of temperature and precipitation effects.

[0013] According to specific embodiments provided by the present invention, the present invention discloses the following technical effects: This invention discloses an atmospheric waterline analysis method based on precipitation isotopes. The method includes deploying multiple meteorological sampling stations within a target area to collect precipitation samples from each precipitation event throughout a complete hydrological year; determining stable hydrogen and oxygen isotopes based on the precipitation samples; summarizing the measured values ​​of stable hydrogen and oxygen isotopes from all samples and performing linear fitting using ordinary least squares to establish a local meteorological waterline equation; and evaluating the secondary evaporation intensity and local climate characteristics of the target area by comparing the slope and intercept of the local meteorological waterline equation with global meteorological waterlines. This invention can reflect the unique climatic and topographical conditions of the LMWL and deeply explore the key meteorological factors and seasonal dynamics affecting isotopic composition. Attached Figure Description

[0014] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0015] Figure 1 This is a flowchart illustrating the atmospheric water line analysis method based on precipitation isotopes of the present invention. Figure 2 This is a schematic diagram of the spatial variation of precipitation isotopes in this embodiment; Figure 3 This is a schematic diagram of the atmospheric water line in this embodiment; Figure 4 This is a schematic diagram illustrating the relationship between isotopes and temperature in this embodiment; Figure 5 This is a schematic diagram illustrating the relationship between isotopes and precipitation in this embodiment. Detailed Implementation

[0016] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0017] The purpose of this invention is to provide an atmospheric waterline analysis method based on precipitation isotopes, which aims to solve or improve at least one of the above-mentioned technical problems.

[0018] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0019] like Figure 1 As shown, this invention provides an atmospheric water line analysis method based on precipitation isotopes, comprising: Multiple meteorological sampling stations are deployed within the target area to collect precipitation samples from each precipitation event throughout the complete hydrological year. As a specific implementation method, the precipitation sample collection process is as follows: For liquid precipitation, clean polyethylene bottles were used for collection, and the bottle openings were sealed with plastic wrap to prevent evaporation. For solid precipitation, it was allowed to thaw naturally in a sealed bag to room temperature before being transferred to the sampling bottle. All samples were immediately frozen after collection and stored until isotope determination was performed. The measurement accuracy of the stable hydrogen isotopes and stable oxygen isotopes was not less than ±0.2‰ and ±0.6‰, respectively, and expressed as a percentage deviation relative to the Vienna standard mean seawater.

[0020] Stable hydrogen isotopes and stable oxygen isotopes were determined based on the precipitation samples.

[0021] The measured values ​​of stable hydrogen and stable oxygen isotopes from all samples were compiled, and a local meteorological hydrograph equation was established based on linear fitting using ordinary least squares: δD = a·δ 18 O + b, where δD is a stable hydrogen isotope, δ 18 O represents the stable oxygen isotope, a is the slope, and b is the intercept.

[0022] By comparing the slope and intercept of the local meteorological waterline equation with global meteorological waterlines, the intensity of secondary evaporation and local climate characteristics of the target region are assessed. This step specifically includes: Seasonal divisions were made based on the set collection time, and local meteorological water lines were constructed according to the division results. The differences in slope and intercept were compared to analyze the impact of seasonal changes on isotope fractionation. Specifically, April to October of each year was set as the monsoon season, and November to March of the following year was set as the non-monsoon season.

[0023] Local meteorological waterlines are constructed based on the basic location information of the sampling stations, and spatial comparative analysis is performed; wherein, the basic location information includes the differences in altitude, topography and water vapor source of each sampling station.

[0024] By correlating precipitation isotope values ​​with contemporaneous meteorological data, the intensity of secondary evaporation and local climate characteristics of the target area were assessed, and δ¹² values ​​were plotted. 18 O and temperature, δ 18 A graph showing the relationship between temperature (O) and precipitation was used to calculate the regression coefficients. The correlation analysis process included statistical analysis of the temperature and precipitation effects.

[0025] Based on the above technical solution, the following embodiments are provided.

[0026] Taking Tianshui City as an example, Tianshui City is located in southeastern Gansu Province, situated between the western section of the Qinling Mountains and the middle reaches of the Wei River. It administers 2 districts and 5 counties, covering a total area of ​​14,300 square kilometers. Its geographical coordinates lie between 104°56′ and 106°73′ east longitude and 34°07′ and 35°17′ north latitude. It borders Baoji City of Shaanxi Province to the east, Dingxi City to the west, Longnan City to the south, and Pingliang City to the north. The northwest is dominated by loess ridges and gullies, while the southeast is surrounded by the Qinling and Guanshan Mountains, forming a vast forest zone.

[0027] This embodiment selected six research sites (Wushan, Gangu, Qin'an, Qinzhou, Zhangjiachuan, and Qingshui) in Tianshui City, Gansu Province. Sample collection was conducted by professionals from the local meteorological bureau, covering two complete hydrological years from January 2021 to December 2022. During the sampling process, all precipitation between 08:00 on a given day and 08:00 the following day was defined as a precipitation event. After each precipitation event, samples were immediately collected into clean 50 ml polyethylene sampling bottles. For liquid precipitation, the sampling bottles were sealed with plastic wrap to reduce data errors caused by evaporation; for solid precipitation, it was allowed to thaw naturally to room temperature in a sealed plastic bag before being transferred to the sampling bottles. All samples were labeled with the precipitation date and immediately frozen for storage until laboratory analysis was completed.

[0028] Precipitation samples were analyzed in the Stable Isotope Laboratory of the College of Geography and Environmental Sciences, Northwest Normal University, using a DLT-100 liquid water isotope analyzer developed by Los Gatos Research, Inc. To avoid data errors caused by the injection needle memory effect, each sample was measured six times, and the average of the last four measurements was taken as the final result. δ 18 The measurement accuracies for O and δD are ±0.2‰ and ±0.6‰, respectively.

[0029] δ obtained through experimental analysis and calculation 18 Both O and δD values ​​are expressed as a percentage deviation relative to the Vienna Standard Mean Seawater (VSMOW): In this equation, in the precipitated sample 18 O / 16 The ratio of O is used R s This indicates that, in SMOW 18 O / 16 The ratio of O is used R SMOW express.

[0030] Hourly weather data: To ensure spatial correspondence between the collected precipitation samples and the meteorological dataset, hourly meteorological data were obtained from seven key selected local meteorological stations. This data covers various meteorological parameters relevant to a complete precipitation event, including air pressure, temperature, relative humidity, precipitation, and water vapor pressure. The analysis used Beijing time (China National Standard Time), which is consistent with UTC (Coordinated Universal Time) +8 time zone.

[0031] General least squares method: This embodiment uses the ordinary least squares (OLS) method to simulate and analyze the atmospheric water level (AWL) equation for the study area and each sampling station. The slope (a) and intercept (b) of the AWL are calculated as follows: Where n represents the number of samples, and x represents δ 18 O value, y represents δ 2 H value.

[0032] Experimental results: I. Spatiotemporal Variation of Precipitation Isotopes 1. Changes over time: Due to differences in topography, meteorological conditions, and regional variations in the influence of monsoons, stable isotopes in precipitation exhibit monthly variations. Current research has found that stable isotope values ​​in precipitation in the arid northwest of China continuously increase from January to July, then gradually decrease from August to December. In the Loess Plateau region located in the eastern part of the study area, observed precipitation isotope values ​​gradually enrich from May to July, while showing a decreasing enrichment trend from August to September.

[0033] Based on the precipitation isotope statistics described above, a time series analysis was conducted on the monthly average precipitation isotopes from six sampling stations in Tianshui City for the two complete hydrological years of 2021 and 2022. (δ¹² values ​​for each station are missing from the original text.) 18 The high values ​​of O are mainly concentrated between May and June. This is because Tianshui is located in the transition zone from semi-humid to semi-arid, during which time the water vapor content is abundant. Light isotopes in the water vapor undergo preferential fractionation during evaporation, leading to the enrichment of heavy isotopes in the precipitation.

[0034] Furthermore, two distinct periods of low values ​​were observed throughout the hydrological year: one in mid-September and the other in late November. The mid-September low was primarily attributed to convective activity in the Bay of Bengal and the Intertropical Convergence Zone (ITCZ) of the Northwest Pacific during the summer monsoon season. Additionally, long-distance transport of oceanic water vapor also led to the depletion of heavy isotopes (continental effect). The late November low was mainly associated with solid precipitation (snowfall). During solid precipitation, isotopic equilibrium fractionation occurs; as snow falls from the cloud base to the ground, the isotopic ratio remains stable due to minimal evaporation, resulting in relative isotopic depletion.

[0035] Specifically, from January to March, the stable isotope values ​​in precipitation steadily increased, peaking around June at all monitoring stations. This is because the water vapor content is relatively high in June, causing lighter isotopes in the precipitation to preferentially fractionate during evaporation, resulting in the remaining water vapor becoming enriched in heavier isotopes, thus increasing the isotope values. After June, the stable isotope values ​​in precipitation gradually decreased because precipitation at this time mainly occurs in solid form (snow). Studies have shown that when snow falls from the cloud base to the ground, isotope equilibrium fractionation dominates, with almost no evaporation; therefore, the heavy isotope content in precipitation is relatively low compared to summer.

[0036] By comparing isotope variation data from 2021 and 2022, the monthly isotope variation trends at the six monitoring stations were largely consistent. The lowest values ​​occurred in September at Gangu, Wushan, and Qinzhou, while Zhangzichuan experienced a significant low value period in November for both years. This indicates that the isotope variations at Gangu, Wushan, and Qinzhou were influenced by the Intertropical Convergence Zone (ITCZ) in the Bay of Bengal and convective activity in the Northwest Pacific. Furthermore, the long-distance transport of oceanic water vapor also led to a continuous decrease in heavy isotope content at Gangu, Wushan, and Qinzhou during this transport process.

[0037] The lowest values ​​occurred around November 2021, while in January 2022 they occurred in January, similar to the pattern observed at the Qin'an station. This difference may be due to the relatively high snowfall at both the Qin'an and Qingshui stations in January 2021. When snow falls from the cloud base to the ground, evaporation is minimal, leading to relative depletion of isotopes.

[0038] 2. Spatial changes: Studies have shown that the spatial distribution of stable isotopes in precipitation in arid regions of Northwest China is influenced by both latitude and altitude. This distribution is also related to factors such as topography, water sources, and meteorological conditions, exhibiting a certain degree of spatial variability. To clarify the spatial distribution characteristics of stable isotopes in precipitation in Tianshui City, this embodiment uses precipitation isotope values ​​from various sampling stations to draw spatial distribution maps of isotopes during the monsoon and non-monsoon periods (e.g., ...). Figure 2 (As shown). In this embodiment, April to October is defined as the monsoon season, and November to March of the following year is defined as the non-monsoon season.

[0039] During the monsoon season, precipitation isotope values ​​show a gradual decreasing trend from south to north. The sampling station with the highest precipitation isotope value is located in Wushan, while the lowest value is found in Qin'an. Precipitation during the monsoon season mainly originates from water vapor brought by the southern monsoon. In the early stages of precipitation, the heavy isotope content in the water vapor is relatively high, but because heavy isotopes are more likely to condense into precipitation due to their larger mass, the heavy isotope values ​​at the southern sampling stations are higher. As precipitation continues, the heavy isotope content gradually decreases, leading to a corresponding decrease in the isotope values ​​of subsequent precipitation.

[0040] During the non-monsoon season, isotope values ​​gradually enrich from south to north or from west to east, with the highest values ​​appearing in Zhangjiachuan and the lowest values ​​appearing in Wushan, indicating high values ​​in mountainous areas and low values ​​in the Weihe River Valley.

[0041] Furthermore, the spatial distribution of stable isotopes in precipitation in Tianshui City is significantly influenced by topography, especially during relatively dry non-monsoon periods. In the western part of the West Qinling Mountains and the eastern region dominated by the Xiaochangshan and Liupanshan Mountains, δ¹² isotopes... 18 The O values ​​are significantly lower, while those in the central part of the Weihe River Valley are relatively higher. This distribution pattern arises because as moist air masses rise along the mountain slopes, the temperature decreases with increasing altitude, thus promoting water vapor condensation and increasing isotope fractionation efficiency. Therefore, stable isotopes in precipitation are negatively correlated with altitude. However, this correlation is not significant during the monsoon season.

[0042] II. Variations in Precipitation Isotopes during Monsoon and Non-Monsoon Seasons Comprehensive analysis shows that the δ of precipitation 18O and δD show a significant correlation, and stable isotopes at different sites differ markedly between the monsoon season (April to October) and the non-monsoon season (November to March of the following year). Temporally, isotope values ​​are higher during the monsoon season than during the non-monsoon season, a trend consistent with observations in the arid northwest of China and the upper reaches of the Yellow River. Similarly, isotope studies of precipitation in the Loess Plateau show that hydrogen and oxygen isotope peaks occur during the monsoon summer (May to October), while the lowest values ​​occur during the non-monsoon winter (November to April of the following year). Overall, δD... 18 O and δ 2 The H values ​​show a consistent trend, exhibiting clear seasonal fluctuations: higher values ​​during the monsoon season (April to October) and lower values ​​during the non-monsoon season (November to March of the following year). The stable trends at the six sampling stations suggest that these stations may be influenced by similar moisture sources.

[0043] Further analysis of seasonal isotope variations in the study area in 2021 revealed more significant changes at Wushan, Gangu, Zhangjiachuan, and Qingshui stations, with higher values ​​during the monsoon season and lower values ​​during the non-monsoon season. In contrast, Qinzhou station did not show obvious seasonal variations because most of its precipitation events are concentrated in the monsoon season from June to August. Hydrogen and oxygen isotope values ​​at Qin'an and Qinzhou stations gradually increased from January to March and gradually decreased from April to December, showing less pronounced seasonal variations. This pattern is closely related to the valley topography of these two stations. During the relatively dry non-monsoon season, the central Weihe River Valley exhibits relatively higher isotope values. This is because as moist air masses rise along the mountain slopes, the temperature decreases with increasing altitude, which enhances the water vapor condensation process and increases the efficiency of isotope fractionation. Therefore, stable isotopes in precipitation are negatively correlated with altitude. However, this correlation largely disappears during the monsoon season.

[0044] The precipitation variation at Wushan and Gangu stations in 2022 was smaller than that in 2021. This may be related to the limited number of precipitation samples collected during non-monsoon months, leading to a discrepancy between the isotopic variation analysis results for the winter half-year and those of 2021. In contrast, Zhangjiachuan and Qingshui stations still showed significant seasonal variations during the monsoon season of 2022. This may be related to the fact that both stations are located within the Guanshan natural zone—the seasonal differences in this region are more pronounced than in the Weihe River Valley and the northern Weihe River Basin. Meanwhile, the precipitation variation at Qin'an and Qinzhou stations, which belong to the Weihe River Valley natural zone, did not show significant differences compared to 2021.

[0045] III. Atmospheric Water Line Equation By analyzing more than 400 precipitation samples collected from GNIP stations worldwide, it was found that δ 18 O and δ 2 There is a significant linear relationship between H and H, expressed as δ 2 H = 8δ 18O +10. This widely used and promoted theory is known as the Global Meteorological Waterline (GMWL). This atmospheric waterline equation effectively reflects the degree of isotopic fractionation: a slope of 8 indicates the existence of isotopic equilibrium fractionation during precipitation, while the intercept reflects the degree to which deuterium deviates from equilibrium. This equation is of great significance for understanding the evaporation intensity and water cycle processes in the study area.

[0046] Local meteorological waterline (LMWL) reflects precipitation δ in a specific region. 2 H and δ 18 The linear relationship between O and LMWL. As a fundamental tool for tracking hydrological processes using stable hydrogen and oxygen isotopes, it reflects the region's natural geographical features and meteorological conditions. If the slope of LMWL is lower than the global average, it indicates the presence of non-equilibrium isotope fractionation during precipitation, mainly influenced by secondary evaporation under clouds.

[0047] Based on precipitation samples from six sampling stations in Tianshui City for two complete hydrological years, local meteorological waterlines for 2021 and 2022 were established. The equation for the 2021 atmospheric waterline is δ. 2 H = 7.093 × δ 18 O+4.551, δ in 2022 2 H = 7.728 × δ 18 O+5.786 (e.g.) Figure 3 (As shown). The slopes and intercepts of these two equations are both lower than the global meteorological waterline and the Chinese meteorological waterline.

[0048] 1. Atmospheric water lines under the activity scale of each station: Differences in geographical location, climate conditions, and environmental factors experienced during water vapor transport lead to differences in isotope fractionation, resulting in variations in the slope and intercept of local meteorological water lines in different regions. Therefore, studying atmospheric water lines at different sampling points is of great significance for understanding the stable isotope characteristics of precipitation in Tianshui City.

[0049] Analysis of atmospheric water vapor line distribution at six meteorological stations in Tianshui City in 2021 shows that the maximum slope occurred at Wushan Station (7.781), while the minimum slope occurred at Zhangjiachuan Station (6.543). The slopes, from highest to lowest, are: Wushan Station (7.781), Qinzhou Station (7.393), Gangu Station (7.281), Qin'an Station (6.823), Qingshui Station (6.574), and Zhangjiachuan Station (6.543). Although the slopes at the other six stations, except for Wushan Station, are all lower than the slope of the Global Meteorological Water Vapor Line (GMWL), the slopes at these stations are also lower than the slope of the atmospheric water vapor line equation (7.41) derived by Liu et al. for Northwest China.

[0050] Observational data show that the maximum evaporation intercept occurred at Wushan Station (8.868), while the minimum intercept was located at Zhangjiajie Station (1.384). The intercepts, sorted from highest to lowest value, are: Wushan Station (8.868), Qinzhou Station (6.012), Gangu Station (4.783), Qingshui Station (3.141), Qin'an Station (3.064), and Zhangjiajie Station (1.384). Analysis of the slope and intercept data clearly shows that the evaporation effect was most significant at Zhangjiajie Station in 2021, while the evaporation effect was weakest at Wushan Station among these six monitoring stations.

[0051] Analysis of atmospheric water vapor line distribution at six stations in Tianshui City in 2022 shows that the steepest slope was observed at Qin'an Station (8.294), while the shortest slope was at Gangu Station (7.443). The slopes, from smallest to largest, are: Gangu (7.443), Qingshui (7.654), Wushan (7.683), Qinzhou (7.802), Zhangjiachuan (7.910), and Qin'an (8.294). It is noteworthy that, except for Qin'an Station, the slopes at all other stations are lower than the slopes of the global meteorological water vapor line (GMWL).

[0052] Observational data show that Jinzhou Station had the highest evaporation intercept value (10.401), while Qin'an Station had the lowest (2.618). The intercept values, sorted from smallest to largest, are: Qin'an Station (2.618), Gangu Station (3.274), Wushan Station (6.389), Qingshui Station (7.785), Zhangjiachuan Station (7.865), and Jinzhou Station (10.401). Analysis of the slope and intercept data reveals that Gangu Station experienced the most significant evaporation effect in 2022, while Qin'an Station had the weakest evaporation effect among the six monitoring stations.

[0053] Comparative analysis of atmospheric water vapor lines at various meteorological stations in Tianshui City from 2021 to 2022 shows that, except for Wushan Station, the slopes of the atmospheric water vapor line equations at the other five stations all exhibit an upward trend. Increased precipitation or prolonged rainfall duration can weaken the isotope fractionation effect to some extent, thus leading to an increase in slope. This trend may be related to the increased frequency of precipitation events at each station. Notably, the slope of the atmospheric water vapor line equation at Wushan Station decreased in 2022 compared to 2021, which is likely related to the enhanced evaporation effect at that station in 2022.

[0054] 2. Atmospheric water system scale during monsoon and non-monsoon seasons: Based on this study, the atmospheric waterline equations for the monsoon and non-monsoon periods were fitted at various sampling points within the study area in 2021. The deviation between the slope of the atmospheric waterline equation and the slope of the Global Meteorological Waterline (GMWL) mainly stems from non-equilibrium isotope fractionation. The study indicates that in Northwest China, raindrops undergo a certain degree of secondary evaporation beneath clouds during their descent. Furthermore, it is believed that isotope fractionation occurs less strongly in solid snowfall during the non-monsoon period, leading to higher hydrogen and oxygen isotope values ​​during the monsoon period and lower values ​​during the non-monsoon period. However, the differences in the slopes of the atmospheric waterline equations between the monsoon and non-monsoon periods at the six sampling stations within the study area over the two years were relatively small, with the monsoon slope slightly higher than the non-monsoon slope. Most precipitation events in this region occur during the monsoon period, and increased precipitation or prolonged rainfall duration can, to some extent, weaken the isotope fractionation effect. Therefore, the slightly higher slope of the atmospheric waterline equation during the monsoon period compared to the non-monsoon period may be related to the strong precipitation characteristics of the monsoon season.

[0055] Furthermore, based on the atmospheric water vapor line equations for each sampling point within the study area during the monsoon and non-monsoon periods in 2021, the slopes of the atmospheric water vapor line equations at Wushan, Zhangjiachuan, and Qingshui stations were all lower during the monsoon period than during the non-monsoon period. The monsoon period slopes for these three stations were 7.930, 7.634, and 8.065, respectively, while the non-monsoon slopes were 8.547, 7.813, and 8.197, respectively. For the seasonally distinct Wushan, Zhangjiachuan, and Qingshui stations, precipitation is mainly concentrated during the monsoon period, when the relatively higher temperatures cause significant secondary evaporation of raindrops below the clouds during their descent. In contrast, the lower temperatures during the non-monsoon period make it easier for water vapor to reach equilibrium, resulting in more pronounced isotope fractionation.

[0056] At Qin'an and Qinzhou stations, the slopes of the atmospheric water vapor line equations during the monsoon and non-monsoon periods showed little difference, with the slope being higher during the monsoon period. This indicates that the intensity of secondary evaporation under clouds is higher at these two stations during the non-monsoon period than during the monsoon period, possibly related to the concentration of precipitation events during the monsoon season. However, the slopes at Gangu station during the monsoon and non-monsoon periods differed significantly from those at other stations, possibly due to the larger temperature difference between the monsoon and non-monsoon periods in the Gangu area.

[0057] Furthermore, by comparing and analyzing the atmospheric water vapor lines at various stations during the monsoon and non-monsoon periods from 2021 to 2022, it was found that the water vapor line trends at Wushan and Zhangjiachuan stations in 2022 were basically consistent with those in 2021, with the slope during the non-monsoon period being higher than that during the monsoon period. However, at Qingshui station, the slope during the monsoon period in 2022 was actually higher than that during the non-monsoon period, which may be related to the shorter duration of monsoon precipitation compared to 2021. The water vapor line slope changes at Gangu and Qin'an stations also showed differences, which is likely related to fluctuations in the number of precipitation events. As for Qinzhou station, due to the limited number of precipitation samples during the non-monsoon period in 2022, it was difficult to accurately analyze the slope relationship between the monsoon and non-monsoon periods.

[0058] 3. Relationship between stable isotopes of precipitation and meteorological factors: Meteorological factors have long been considered key determinants of the stable isotopic composition of precipitation. The temperature effect refers to the phenomenon that isotopic values ​​increase with rising ambient temperature and decrease with falling temperature. In atmospheric precipitation processes, changes in stable isotopes are closely related to meteorological conditions, with temperature being a crucial factor influencing evaporation and condensation, significantly impacting stable isotope fractionation. Recent studies on temperature and precipitation effects have provided new insights into the fluctuations of hydrogen and oxygen isotope values ​​in different regions. [The text then abruptly shifts to a different topic:] δ¹⁴ ... 18 A study on the relationship between O value and temperature found that in areas north of the Tropic of Cancer, δ 18 O values ​​are positively correlated with temperature, but negatively correlated in low-latitude regions. On shorter timescales, the temperature effect is usually negligible. Furthermore, researchers have found that the precipitation effect is more significant in low-latitude regions, indicating a negative correlation between precipitation isotope values ​​and precipitation amount.

[0059] This embodiment uses precipitation isotope data from the study area from 2021 to 2022 to plot the relationship between precipitation isotopes and temperature at each sampling station. Figure 4 Among the sampling stations, Wushan, Gangu, and Qingshui stations showed significant temperature effects, with the regression coefficient at Qingshui station being 0.486‰ / °C, close to the δ value of the Northwest region. 18 The regression coefficient for the relationship between oxygen and temperature was 0.58‰ / °C. In contrast, the temperature effects at Qin'an and Zhangjiachuan stations were weaker, with regression coefficients of 0.103‰ / °C and 0.089‰ / °C, respectively. No temperature effect was observed at Qinzhou station. This indicates that the seasonal variation of isotopes at Qinzhou station was not significant during either the monsoon or non-monsoon seasons, explaining the absence of a temperature effect. Since isotopic fractionation is influenced by multiple factors, and temperature is a major factor affecting water vapor condensation and evaporation, high temperatures during precipitation enhance evaporation, leading to an increase in isotopic values. Although Qin'an and Zhangjiachuan stations showed almost no temperature effect, their δ¹⁸O values ​​were significantly higher than those at temperatures below 0°C. 18 The O value increases with rising temperature, indicating a certain degree of temperature effect. However, since precipitation is mainly concentrated in summer, the influence of temperature on isotope fractionation is partially offset by local moisture circulation and secondary evaporation under clouds, making temperature a secondary factor in isotope fractionation.

[0060] Total precipitation is another key factor influencing stable isotope values ​​in precipitation. The precipitation effect refers to the negative correlation between stable isotope values ​​in precipitation and total precipitation. When precipitation is abundant, the relative humidity of the surrounding environment increases, which weakens the enrichment of heavy isotopes caused by the temperature effect. Furthermore, higher relative humidity reduces the local water cycle, which typically involves isotope-enriched water bodies. This reduction in the water cycle also leads to the depletion of stable isotopes in precipitation. The precipitation effect is commonly observed in low-latitude coastal areas or islands, where the climate is dominated by maritime moisture and influenced by moist sea air masses throughout the year. Regions with a significant precipitation effect typically exhibit significant interannual variability in precipitation and distinct wet and dry seasons.

[0061] Correlation analysis of stable isotopes of precipitation and precipitation at six sampling stations in the study area (e.g.) Figure 5 As shown in the figure, the overall impact of total precipitation is not significant. Only Qinzhou and Zhangjiajie stations showed a weak effect of total precipitation, with regression coefficients of -0.029 and -0.068, respectively. This further explains why Qinzhou station did not show a temperature effect.

[0062] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. The same or similar parts between the various embodiments can be referred to each other.

[0063] This document uses specific examples to illustrate the principles and implementation methods of the present invention. The descriptions of the above embodiments are only for the purpose of helping to understand the core ideas of the present invention. Furthermore, those skilled in the art will recognize that, based on the ideas of the present invention, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of the present invention.

Claims

1. A method for analyzing atmospheric water lines based on precipitation isotopes, characterized in that, include: Multiple meteorological sampling stations were set up in the target area to collect precipitation samples from each precipitation event within the complete hydrological year; Stable hydrogen isotopes and stable oxygen isotopes were determined based on the precipitation samples. The measured values ​​of stable hydrogen and stable oxygen isotopes from all samples were compiled, and a local meteorological hydrograph equation was established based on linear fitting using ordinary least squares: δD = a·δ 18 O + b, where δD is a stable hydrogen isotope, δ 18 O is a stable oxygen isotope, a is the slope, and b is the intercept. By comparing the slope and intercept of the local meteorological waterline equation with global meteorological waterlines, the intensity of secondary evaporation and local climate characteristics of the target area are assessed.

2. The atmospheric water line analysis method based on precipitation isotopes according to claim 1, characterized in that, The process of collecting the precipitation samples is as follows: For liquid precipitation, clean polyethylene bottles were used for collection, and the bottle openings were sealed with plastic wrap to prevent evaporation; for solid precipitation, it was allowed to thaw naturally in a sealed bag to room temperature before being transferred to the sampling bottle; all samples were frozen immediately after collection until isotope determination was performed.

3. The atmospheric water line analysis method based on precipitation isotopes according to claim 1, characterized in that, The measurement accuracy of the stable hydrogen isotope and the stable oxygen isotope shall be no less than ±0.2‰ and ±0.6‰, respectively, and expressed as a percentage deviation relative to the Vienna standard mean seawater.

4. The atmospheric water line analysis method based on precipitation isotopes according to claim 1, characterized in that, The formulas for calculating the slope and intercept of the ordinary least squares method are as follows: Where n represents the number of samples, and x represents δ 18 O value, y represents δ 2 H value, a The slope of the atmospheric water level line. b This is the intercept of the atmospheric water level line.

5. The atmospheric water line analysis method based on precipitation isotopes according to claim 1, characterized in that, The assessment of the secondary evaporation intensity and local climate characteristics of the target region by comparing the slope and intercept of the local meteorological waterline equation with global meteorological waterlines specifically includes: Seasonal divisions were performed based on the set collection time, and local meteorological water lines were constructed according to the division results. The differences in slope and intercept were compared to analyze the impact of seasonal changes on isotope fractionation. Based on the basic location information of the sampling sites, local meteorological water lines were constructed and spatial comparative analysis was performed; By correlating precipitation isotope values ​​with contemporaneous meteorological data, the intensity of secondary evaporation and local climate characteristics of the target area were assessed, and δ¹² values ​​were plotted. 18 O and temperature, δ 18 The graph shows the relationship between O and precipitation, and the regression coefficient is calculated.

6. The atmospheric water line analysis method based on precipitation isotopes according to claim 5, characterized in that, The seasonal division based on the set collection time specifically includes: setting April to October of each year as the monsoon season and November of each year to March of the following year as the non-monsoon season.

7. The atmospheric water line analysis method based on precipitation isotopes according to claim 5, characterized in that, The basic location information includes differences in altitude, topography, and water vapor source for each sampling station.

8. The atmospheric water line analysis method based on precipitation isotopes according to claim 5, characterized in that, The correlation analysis process includes statistical analysis of temperature and precipitation effects.

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