Isotope variations in precipitation and environmental drivers in Anshun during strong El Niño events

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During this period, many parts of the globe experienced frequent extreme weather events. Isotope values in precipitation are important for understanding extreme climates. Therefore, in this work, 140 stable isotope data from precipitation events in the Anshun area from 2015–2016 (including El Niño and La Niña events) were combined with related meteorological data to analyze the characteristics of hydrogen and oxygen isotopes in atmospheric precipitation, as well as their relationships with precipitation, temperature, and atmospheric circulation. The results show that ① the equations of the atmospheric precipitation lines in the study area are δD=8.70δ18O+19.55 (El Niño period) and δD=8.60δ18O+17.23 (La Niña period), which indicate that the atmospheric precipitation in the El Niño period was affected mainly by oceanic water vapor and that there was an imbalance of isotopes in atmospheric precipitation during the La Niña period, with the phenomenon of secondary evaporation. ② isotopes show seasonal variations that are high in the dry seasons and low in the rainy seasons, which are due mainly to the differences in water vapor sources and air mass properties of precipitation in different seasons. ③ The correlation between δ18O and temperature and precipitation in atmospheric precipitation in the study area is not significant, but the correlation can respond sensitively to changes in the ENSO. atmospheric precipitation δ18O δD atmospheric circulation Anshun area Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 1. Introduction Atmospheric precipitation constitutes a fundamental component of the terrestrial water cycle, serving not only to maintain the dynamic equilibrium between surface water and groundwater but also to play a crucial role in regulating the global climate system. Extreme fluctuations in precipitation can lead to severe droughts and floods, posing substantial threats to the sustainable development of human societies (Sun et al., 2021 ). As sensitive indicators of environmental change, the stable isotopes of hydrogen and oxygen in precipitation encapsulate critical physical and chemical information throughout the water cycle. These isotopes have been extensively applied in diverse disciplines, including hydrology, meteorology, ecology, and paleo-climatology (Dublyansky et al., 2018 ). Stable isotopes, although present in low concentrations in precipitation, possess significant resolving power for identifying water vapor sources and atmospheric circulation patterns, while also responding sensitively to environmental changes (Cao et al., 2019 ). Key indicators such as δD, δ¹⁸O, and d-excess reflect variations in temperature, humidity, evaporation, and moisture sources during precipitation processes, offering critical insights into regional water cycle dynamics and climate change (Tian et al., 2021 ; Oza et al., 2022 ). The establishment of the Global Network of Isotopes in Precipitation (GNIP) in 1958 provided a systematic and comprehensive data foundation for studying stable isotopes in precipitation (Froehlich et al., 2004). Craig ( 1961 ) laid the theoretical groundwork for isotope studies by analyzing hydrogen and oxygen isotope ratios in rivers and lakes and formulating the Global Meteoric Water Line (GMWL). In recent years, research has increasingly concentrated on the spatial and temporal distribution patterns of stable isotopes in precipitation and the factors influencing these patterns (Juan et al., 2020 ; Terzer-Wassmuth et al., 2023 ). For instance, in mountainous regions, δ¹⁸O exhibits a strong positive correlation with temperature, whereas in monsoon-affected areas, the precipitation amount effect predominates. In such monsoonal regions, the influence of temperature is often diminished or even inverted, resulting in what is termed an anti-temperature effect (Wen et al., 2016; Xu et al., 2023 ). Additionally, large-scale climatic phenomena such as the El Niño/La Niña and Southern Oscillation (ENSO), the Indian Ocean Dipole (IOD), and the Pacific Intergenerational Oscillation Index (PDO) significantly influence the isotopic composition of precipitation. They do so by regulating factors like moisture sources, temperature, humidity, and atmospheric pressure (Sánchez-Murillo et al., 2023 ). ENSO, which includes El Niño/La Niña events and the Southern Oscillation, occurs on average every 2–7 years, affecting global climate patterns through ocean-atmosphere coupling. During El Niño events, the elevated sea surface temperatures in the central and eastern Pacific strengthen the western Pacific subtropical high, thereby reducing the influx of moist air into southern China. This results in significantly reduced precipitation and higher temperatures, leading to associated climatic anomalies. Conversely, during La Niña events, cooler sea surface temperatures in the central and eastern Pacific weaken the subtropical high, allowing increased southwesterly and southerly moist air flows into southern China, particularly in southwestern regions such as Guizhou. This leads to higher precipitation levels and lower temperatures compared to normal years (Lv et al., 2022 ). These climatic variations not only directly affect fluctuations in precipitation and temperature but also alter the isotopic composition of precipitation. Existing studies indicate that ENSO events cause significant changes in the isotopic composition of precipitation in southeastern China (Qiu et al., 2023 ). However, the underlying mechanisms linking moisture sources to the isotopic characteristics of precipitation in monsoon-dominated regions remain to be further explored. Anshun, Guizhou, situated on the eastern edge of the Yunnan-Guizhou Plateau, lies within the humid subtropical monsoon climate zone, making it an ideal location to investigate the isotopic response of precipitation to specific climatic events. The region receives substantial precipitation, influenced by a combination of oceanic water vapor and monsoon circulation. During the study period, Anshun experienced both a strong El Niño event and a subsequent La Niña event, providing a unique opportunity to examine the effects of extreme climatic events on precipitation isotopes. However, current research on precipitation isotopes in this region remains largely limited to basic characterizations (Mao et al., 2017 ), with a notable lack of systematic studies addressing water vapor transport pathways and regional response mechanisms under the influence of specific climatic phenomena.Using daily precipitation samples collected from 2015 to 2016, this study aims to: 1. Elucidate the temporal variation patterns of δD, δ¹⁸O, and d-excess values; 2. Analyze the coupling relationships between precipitation isotopes and meteorological variables such as temperature and precipitation; 3. Investigate the sources of atmospheric water vapor in the study area through the application of the HYSPLIT trajectory model and water vapor transport mapping. The findings of this research will contribute to a more comprehensive understanding of the regional water cycle, water vapor transport dynamics, and the reconstruction of paleoclimate conditions in the region. 2. Materials and methods 2.1 Study area The city of Anshun (coordinate range: 25°20′N-27°21′N, 105°14′E-107°17′E) is located in the watershed area between the Wujiang River Basin of the Yangtze River system and the Beipanjiang River Basin of the Pearl River system (Fig. 1 ). The topography of the region is characterized by high altitudes in the northwestern part and low altitudes in the southeastern part, and it has a highland subtropical monsoon climate with an average annual temperature of approximately 16°C and annual precipitation in the range of 1205–1656 mm: the region has a large total amount of precipitation but with an uneven seasonal distribution, which is mainly concentrated in June–August, and severe soil erosion during periods of heavy rainfall (Zhang et al., 2023 ). The altitude in the region is approximately 400 m above sea level, and the vertical difference in altitude is approximately 1400 m. Therefore, there are differences in geomorphology and solar radiation, which are jointly influenced by phenomena such as the Indian monsoon and the East Asian monsoon, influencing the atmospheric circulation, resulting in significant climatic variations within the region and obvious microclimatic phenomena. 2.2 Research methods 2.2.1 Sample collection and processing Precipitation monitoring and sample collection were conducted in the study area for two years from 2015–2016. During this period, a total of 140 daily precipitation samples were collected, including 65 in 2015 and 75 in 2016. The sampling site was set up on the top floor of the first teaching building of Anshun College in Guizhou, and the rain sample collector was placed at a distance of approximately 25 m from the ground and approximately 2 m from the floor of the top building to avoid contamination of the samples by the surrounding buildings and ground dust. The precipitation collector used was an inverted triangular plastic funnel, with an upper diameter of 15 cm and a lower diameter of 15 mm. A ping-pong ball was placed inside, and a disposable sealed bag was connected to the bottom of the funnel. This design ensured a sufficient water collection area and effectively prevented isotope fractionation caused by rainwater evaporation. At the end of each precipitation event, the precipitation samples were promptly transferred to 25 ml polyethylene plastic sampling bottles with a syringe, wrapped with a sealing film at the mouth of the bottle, and finally placed in a refrigerator at 4°C for sealing and refrigeration to be ready for subsequent machine measurements. 2.2.2 Sample determination Hydrogen and oxygen stable isotope measurements of all rainwater samples were done at the Isotope Laboratory of Southwest University, using a liquid water isotope analyzer developed by LGR, The working standard and test samples were contained in 2 ml glass test bottles, and the test sample volume was approximately 1.5 ml. All samples were filtered with a 0.45 µm filter membrane and directly loaded into the test bottles without adding other reagents. The duration of a single test for each sample was approximately 2 min, and parallel tests were conducted 6 times. To eliminate the effect of memory, the system automatically removed the first 2 test values and took the average of the last 4 values as the final test value of the sample. After 3 samples were tested, with 9 samples for a group of rounds, each standard curve consisted of the test values for the group of 3 samples and a dynamic standard. The results are expressed in terms of thousandths of a point relative to Vienna standard mean ocean water (R V−SMOW ). The formula is as follows: $$\:{\delta\:}={（\text{R}}_{\text{s}\text{a}\text{m}}-{\text{R}}_{\text{s}\text{t}\text{d}}）∕{\text{R}}_{\text{s}\text{t}\text{d}}\times\:1000（\text{‰}）$$ Here, R sample and R V−SMOW represent the stable isotope proportions of D/H or 18 O/ 16 O within water samples and Vienna standard mean ocean water (V-SMOW), respectively. 2.2.3 Climate data Hourly observational data from the National Aeronautics and Space Administration (NASA) were collected during the study period, covering information on temperature, humidity and rainfall. Commonly used metrics for quantifying ENSO phenomena include the sea surface temperature anomaly (SSTA) in the Niño 3.4 region (5°N-5°S, 170°W-120°W) (Gao et al., 2014 ), as well as the Southern Oscillation Index (SOI) and the Niño 3.4 Index (ONI) provided by the National Oceanic and Atmospheric Administration (NOAA) (Freitas and Mclean, 2013). The information is based on the ENSO measurements provided by the NOAA National Weather Service (NWS) Climate Prediction Centre (CPC) of the United States of America. Atmospheric circulation-related indices, namely, the dipole mode index (DMI), which is used to indicate the intensity of Indian Ocean dipole (IOD) activity, and the Pacific Intergenerational Oscillation Index (PDO), are also included. All the data were preprocessed and statistically analyzed in Excel 2010, analyzed with SPSS 17.0 software, and plotted with Origin 2022 mapping software. 2.2.4 HYSPLIT model We utilized the Lagrangian Integrated Trajectory (HYSPLIT) Model, version 4.2, developed by the National Oceanic and Atmospheric Administration (NOAA), alongside meteorological data from the Global Data Assimilation System (GDAS) ( https://ftp.arl.noaa.gov/pub/archives/ ), with a spatial resolution of 2.5° × 2.5°. The model was employed to simulate atmospheric air mass transport pathways and precipitation processes, which were subsequently clustered to identify the moisture sources influencing the study area during different time periods. Since the majority of atmospheric water vapor is concentrated within 2 km above sea level and has a typical retention time of several days to one week (Bedaso et al., 2020), simulations were conducted at an altitude corresponding to 850 hPa with a backward trajectory duration of 168 hours (7 days). To enhance temporal resolution, four trajectories were generated daily at 00:00, 06:00, 12:00, and 18:00 UTC. 2.2.5 Water vapor flux To further determine the water vapor source and transformation process, water vapor transport maps were developed via European Centre for Medium-Range Weather Forecasts (ECMWF) monthly scale reanalysis data, in which latitudinal and longitudinal winds and specific humidity data were obtained from the Fifth Generation of Reanalysis Data (Fifth Generation of Reanalysis Data) published by the ECMWF. The data were obtained from the Fifth Generation of ECMWF Atmospheric Reanalysis (ERA5) issued by the ECMWF ( https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-pressure-levels-monthly-means?tab=form ). 3. Results and analyses 3.1 Time-varying characteristics of isotopes in atmospheric precipitation During the monitoring period, Fig. 2 illustrates the trends of stable isotope contents (δD, δ 18 O, and D-excess), temperature (T), and precipitation (P) in the precipitation samples from the Anshun atmospheric field over time. The shaded areas in the figure highlight the strong El Niño period (January 2015–April 2016) and the weak La Niña period (August–December 2016). A linear relationship between δD and δ 18 O was observed due to isotopic fractionation, resulting in similar trends for both isotopes, as shown in Fig. 2 . The precipitation and temperature patterns showed higher values in summer and lower values in winter, with rainfall concentrated from May to September. This pattern aligns with the typical rain-heat system characteristics of Southwest China, and it is worth noting that rainfall in 2015 was significantly greater than in 2016.During the El Niño period, the maximum and minimum values of δD and δ 18 O occurred in May and October, respectively. The δD values ranged from − 147.44‰ to 16.37‰, while δ 18 O values ranged from − 19.80‰ to 0.18‰. The maximum deuterium excess occurred in December (29.83‰), and the minimum value was recorded in September (3.60‰).During the La Niña period, the maximum and minimum values of δD and δ 18 O occurred in May and August, respectively. The δD values ranged from − 131.32‰ to 19.21‰, while δ 18 O values ranged from − 17.70‰ to 14.98‰. The maximum deuterium excess was observed in December (23.83‰), and the minimum value occurred in July (2.14‰). 3.2 Atmospheric precipitation lines in Anshun The relationship between hydrogen and oxygen stable isotopes in water during rainfall is important for studying changes in water isotopes during the atmospheric cycle. In this study, two atmospheric precipitation lines for Anshun city during the El Niño and La Niña periods were established on the basis of 140 daily rainfall δD and δ 18 O measured data collected from 2015–2016, respectively, as follows: δD = 8.70δ 18 O + 19.55 (El Niño period; n = 65, p < 0.05) δD = 8.60δ 18 O + 17.23 (La Niña period; n = 75, p < 0.05) The results reveal larger slope and intercept values compared to the Global Meteoric Water Line (GMWL) equation, δD = 8δ¹⁸O + 10, proposed by Craig ( 1961 ), as well as the Local Meteoric Water Line (LMWL) equation for the region, δD = 7.8δ¹⁸O + 8.2, calculated by Shuhui Zheng (1983). Notably, the slope during the La Niña period is smaller than that observed during the El Niño period. The meteoric water line equation reflects the type and degree of isotopic fractionation occurring during precipitation. A slope deviating from 8 indicates varying degrees of non-equilibrium isotopic fractionation during the precipitation process. Meanwhile, the intercept represents the global mean atmospheric precipitation value, and an intercept greater than 10 suggests a significant imbalance in isotopic fractionation between the vapor and liquid phases during the formation of precipitation clouds. These findings align with observations from neighboring regions of the study area (Zhang et al., 2023 ). 3.3 Characteristics of deuterium excess values To quantify and compare the degree of non-equilibrium of atmospheric precipitation during evaporation and condensation in different regions, Dansgaard ( 1964 ) first introduced the concept of D-excess, which is calculated as D-excess = δD-8δ 18 O. Its global average value is approximately 10‰, which is equivalent to the intercept of the precipitation line in the region when the slope is 8. Under relatively humid climatic conditions, the kinetic fractionation is weak and the formation of deuterium surplus in precipitation is low, while under relatively dry climatic conditions, the air humidity is relatively small, and the kinetic fractionation of water evaporation is also strong, and the formation of deuterium surplus in precipitation will be higher. As can be seen from Fig. 4 , the range of deuterium surplus values in 2015 was 3.61‰-29.83‰, with a mean value of 13.44‰, and the range of deuterium surplus values in 2016 was 2.14‰-26.52‰, with a mean value of about 12.76‰.The D-excess values in 2016 were more negative than those in 2015. During the year the deuterium surplus values were below the global average in July-September and above the global average in all other months. In 2015, higher precipitation months (e.g., May and June) were accompanied by higher D-excess values, whereas in lower precipitation months (e.g., January and December), D-excess values were relatively low. A similar trend was observed in 2016, where despite an overall decrease in precipitation in 2016 compared to 2015, months with higher precipitation (e.g., May and August) were still associated with higher D-excess values. 4. Discussion 4.1 Relationships between isotopes in rainfall and the temperature and rainfall amount Dansgaard ( 1964 ) proposed the effects of temperature, precipitation, elevation, etc., on the basis of the Rayleigh model, in which temperature is considered one of the most important controlling factors (Guo et al., 2021 ). This is because the condensation of water vapor inside clouds is key to the formation of atmospheric precipitation, and the temperature directly affects the condensation temperature of water vapor inside clouds, which in turn affects the δ values of hydrogen and oxygen isotopes in atmospheric precipitation. Specifically, the lower the temperature is, the more pronounced the hydrogen and oxygen isotope fractionation in atmospheric precipitation, and the larger the stable isotope fractionation coefficients are, resulting in lower isotope values of precipitation, which is a phenomenon that is particularly common in mid- and high-latitude regions (Ma et al., 2023). However, at low latitudes and in coastal areas, the effect of volume is more significant and often masks the effect of temperature, leading to a weakening of the effect of temperature of δ 18 O in precipitation or even an inverse effect of temperature (Guo et al., 2017 ). In the study of atmospheric precipitation in Anshun city, as shown in Table 1, δD, δ 18 O and temperature did not significantly affect the temperature during the occurrences of El Niño and La Niña, which was accompanied by a weak negative correlation, i.e., an inverse effect of temperature. In contrast, a significant effect of temperature was found at the transition point between El Niño events and La Niña events (summer 2016). The inverse effect of temperature was also observed in a study by Zhang Lin et al. (2008) at low latitudes in China, but the effect of temperature was not significant or even absent at the seasonal scale. In another study, Wang Tao's team (2013) reported that in the Nanjing region, although no significant effect of temperature was detected on a year-round scale, such an effect occurred in winter. This finding is similar to that found for Anshun, probably because, relative to high-latitude inland areas, mid- and low-latitude areas are affected by many factors, such as air humidity, condensation temperature, and monsoon winds, which mask the effect of temperature (Li et al., 2016 ). In addition, Anshun is in a middle- to low-latitude region and is affected by the oceanic monsoon, which increases the number of disturbance factors, and the El Niño phenomenon brought a large amount of precipitation to southern China during the study period, which masked the effect of temperature to a certain extent. Therefore, the precipitation in this region is controlled by many factors, and considering only a single temperature factor cannot fully explain its pattern of change. Table 1 Correlation coefficients of precipitation δD and δ 18 O with precipitation and temperature in Anshun, Guizhou, China isotope factor El Niño La Niña vintage SP SU FA WI annual δD P -0.20 -0.40* 2015 0.21 0.28 -0.28 -0.26 -0.17 2016 -0.52* -0.31 -0.21 0.72 -0.31** T -0.21 -0.32 2015 0.34 -0.13 -0.37* -0.09 -0.26 2016 -0.15 0.35* 0.19 0.21 -0.33** δ 18 O P -0.16 -0.40* 2015 0.26 0.26 -0.25 -0.25 -0.07 2016 -0.47* -0.34 -0.20 0.69 -0.30** T -0.13 -0.23 2015 0.43 -0.19 -0.34 -0.08 -0.06 2016 -0.06 0.37* 0.31 0.46 -0.32** Note: P: precipitation; T: temperature ** Represent correlations significant at p values of <0.01 * Represent correlations significant at p values of <0.05 According to previous studies (Yamanaka et al., 2007 ), in the midlatitude oceanic monsoon climate zone, where rain and heat coincide, there is an inverse relationship between the stable isotope δ 18 O ratio in precipitation and precipitation, i.e., the rainfall effect. However, in this study area, no significant correlation was observed between δ 18 O and precipitation at the seasonal scale, but the effect of rainfall was significant during the La Niña period. This implies that the amount of precipitation is not the overall determinant of δ 18 O variations. In the Anshun area, for example, the δ 18 O values show seasonal variations, which are closely related to the atmospheric circulation and the sources of water vapor in different seasons (Wang et al., 2020 ). Fractionation of water vapor occurs during transport and condensation, leading to the fact that even if precipitation decreases, the δ 18 O values may still become more negative. Therefore, it can be concluded that seasonal water vapor sources and the motions of atmospheric circulation play crucial roles in the variations in δD and δ 18 O during atmospheric precipitation in the Anshun region. In addition, by analyzing two atmospheric precipitation lines during El Niño and La Niña (Fig. 3 ), it was found that there are significant differences in the compositions of hydrogen and oxygen isotopes in precipitation during these two periods. These differences may be closely related to changes in meteorological conditions and water vapour sources during precipitation formation. During El Niño, it is possible that more oceanic water vapour is transported to the study area, resulting in the slope and intercept of the precipitation line being closer to the characteristics of the global atmospheric precipitation line. In contrast, during La Niña, secondary evaporation effects may occur, leading to lower slopes and reduced D-excess values of the precipitation lines (Fig. 4 ). These variations further demonstrate the different effects of different atmospheric phenomena on the isotopic composition of precipitation in the Anshun region, highlighting the key role of atmospheric circulation and water vapour sources in precipitation isotope variations. 4.2 Effects of changes in atmospheric circulation on isotopes in precipitation 4.2.1 Water vapor sources To investigate the effect of different water vapor sources on stable isotopes in precipitation in the Anshun area, six simulations were conducted using the HYSPLIT model for the rainy season (May–October) and dry season (November–March) during 2015–2016. The results indicate a significant difference in water vapor sources between the dry and rainy seasons. As shown in Figures a, d, and f, the dry season is characterized by water vapor mainly originating from the northwest and southwest directions, predominantly from continental sources, with some contributions from oceanic vapor. The long transport distances and significant evaporation effects during the dry season lead to enrichment of heavy isotopes (δ 18 O and δD) in the water vapor, resulting in higher isotope values.Previous studies indicate that water vapor in dry season precipitation over Southwest China primarily originates from westerly winds and re-evaporation of inland water, resulting in lower air humidity and higher stable isotope ratios, along with elevated deuterium excess (d-excess) in precipitation (Wang et al., 2023 ). During the rainy season (Figures b, e), water vapor mainly originates from southern humid regions, such as the Bay of Bengal and the South China Sea. As these air masses move inland, heavy isotopes in precipitation are progressively depleted due to intense fractionation. Consequently, precipitation during the rainy season exhibits significantly lower isotope values compared to the dry season. Moreover, simulations of water vapor sources during the 2016 rainy season revealed that approximately 5% of the water vapor originated from the western Pacific Ocean—an outcome that differs from prior studies on the sources of water vapor in southern China (Li et al., 2024 ; Xu et al., 2022 ). This anomaly may be related to the La Niña event that followed the strong El Niño of 2016. Under La Niña conditions, the subtropical high-pressure ridge over the western Pacific shifted westward, channeling Pacific water vapor into mainland China. The contribution of water vapor from the western Pacific suggests that the western Pacific subtropical high plays an important regulatory role in the distribution of water vapor in the East Asian monsoon region (Ke et al., 2023 ). This observation aligns with the trend shown in Fig. 4 , indicating that during La Niña years, water vapor is more likely to originate from wetter oceanic sources. In summary, the isotopic differences in water vapor sources between the dry and rainy seasons are primarily influenced by transport pathways, fractionation effects, and the characteristics of source regions. These factors reflect the seasonal variability of the regional monsoon climate and the diverse spatial distribution of water vapor sources. 4.2.2 Characteristics of δ 18 O in precipitation during ENSO periods ENSO is a large-scale ocean‒atmosphere event that occurs in the equatorial eastern Pacific Ocean, and it has a period of 2–7 year. La Niña is referred to as an ENSO cold event, and El Niño is referred to as an ENSO warm event. The SOI is generally negative during El Niño, whereas the SOI is positive when La Niña occurs; in general, the SOI is significantly negatively correlated with the SST. According to the National Oceanic and Atmospheric Administration (NOAA), a La Niña (or El Niño) event is recognized when the 3-month sliding average of sea surface temperature anomalies (SSTA) in the Niño 3.4 area is ≤ -0.5°C (≥ 0.5°C) for 5 consecutive months (Ren et al., 2014 ). Counting from April 2015, the mean sea surface temperature (SST) in the Niño 3.4 sea area was greater than 0.5°C for 13 consecutive months (from April 2015 to April 2016), and this phenomenon was recognized as a strong period of El Niño events. In fact, signs of El Niño events have been visible since September 2014, when sea surface temperature anomalies in the Niño 3.4 region started to show positive values and gradually increased, heralding the beginning of the El Niño phenomenon. After August 2015, the sea surface temperature anomalies in the central and eastern Pacific Ocean increased to more than 2°C, which indicated the onset of an extremely intense ENSO event, which then reached its peak in November and December 2015 and continued until April 2016, when it ended. At that time, the SSTA values decreased by approximately 1°C and continued to decrease, resulting in a 22-month streak of positive values for the El Niño phenomenon. The sea surface temperatures returned to normal between May and July 2016, and then a weak La Niña event occurred between August and December 2016. The SSTA values also decreased by approximately 1°C and continued to decrease, ending a 22-month streak of positive values for the El Niño phenomenon. The SSTA values also decreased by approximately 1°C and continued to decrease. According to modern meteorological records, 2015 was considered one of the years with the highest average global temperatures on record. In the same year, southern China experienced more precipitation. However, it is not clear whether these anomalous increases in precipitation were directly related to strong El Niño events (Zhai et al., 2016 ). Most studies indicate that the effect of the El Niño phenomenon on precipitation in China may be related to changes in the intensity of summer winds from the Indian and Western Pacific Oceans (Gao et al., 2014 ; Zhang, 2015 ). Different ENSO events cause changes in monsoon activities, subtropical high pressure, and the Intertropical Convergence Zone (ITCZ), which in turn affect the distribution, sources, and isotopes of precipitation (Santos et al., 2019 ; Zhou et al., 2019 ). In fact, monsoon activity is often considered a manifestation of intraseasonal migration in the ITCZ (Geen et al., 2020 ). As shown in Figure. 2, there is a difference in the variation in isotopes in rainfall between 2015 and 2016, which suggests that ENSO events influenced the location and intensity of the ITCZ throughout the year. On this basis, in this study, the impact of changes in the ITCZ during ENSO on rainfall in the study area is analyzed by exploring water vapor transport under the influence of summer winds during the summer and half of 2015 to 2016 (e.g., Figure. 6). During April, Southwest China has not yet entered the rainy season, and westerly wind-dominated water vapor accounts for a large portion of the water vapor, with local internal circulation. Owing to the close proximity of water vapor transport, the isotopes in rainfall were less fractionated during the water vapor transport process, resulting in higher isotope values of precipitation in April. In May, the monsoon activities that affect precipitation began, and with the shift in convective activities in the ITCZ to the low-latitude Arabian Sea–Bay of Bengal region, the water vapor flux gradually increased compared with that in April, and heavy isotopes were preferentially deposited by oceanic water vapor in the process of transport, resulting in the depletion of heavy isotopes contained in the remaining water vapor mass. The magnitude of rainfall in the study area reached a small peak in the year's rainfall, and the δ 18 O values also rapidly decreased, indicating that the rainy season was influenced by summer winds. Some researchers (Huang et al., 2016 ) noted that the strongest phase of large-scale monsoon circulation occurs from June–August each year, and this phase is also the most isotopically depleted period for atmospheric precipitation. In this study, isotopes in rainfall generally showed a gradual depletion from June–August, and in 2015, during the El Niño period, lower values were recorded until the end of July, but the lowest value of the year occurred in mid-October, which may be due to the El Niño anomaly that led to an increase in rainfall in southern China. The water vapor transport map (Figure. 6) also shows that the water vapor sources were similar in June 2015 and 2016, both with the migration of the ITCZ to the north, with strong convection occurring in the Arabian Sea‒Bay of Bengal continuum, as well as on the west coast of the Pacific Ocean. The sustained increase in the oceanic source of water vapor in the study area resulted in a subsequent decrease in the isotope δ 18 O in rainfall. In July, the common feature in both years was that the oceanic source of water vapor at this time was almost exclusively from the Arabian Sea–Bay of Bengal region, and the previous convective activity in the western Pacific Ocean had almost disappeared; the difference was that the convective activity in the Arabian Sea–Bay of Bengal continuum was enhanced in July 2015 compared with June 2015, whereas the convective activity in this region weakened in July 2016 compared with June 2016, which was supported by the fact that measurements of isotopes in rainfall in July 2015 started to increase in July 2016. The continuous decrease in the δ 18 O values in July 2015 was due to the fractionation of heavy isotopes under the prolonged influence of the same air mass, which caused the depletion of δ 18 O in the remaining water vapor one additional time, which reflected the effects of rainfall. In August 2015, the convective activities in the low-latitude western Pacific and South China Sea ITCZ almost disappeared, the convective activities in the Bay of Bengal were significantly weakened, the source of water vapor from the distant oceans was reduced, and the locally internally circulating water vapor accounted for a relatively high percentage of water vapor, with high temperatures and evaporation, which led to the elevation of the δ 18 O values in precipitation. The convective activities of the ITCZ in August 2016 were very different from the convective phenomena in 2015 and were much greater than those in August 2015 and July 2016 in the South China Sea, the west coast of the Pacific Ocean, the Bay of Bengal and the Arabian Sea, in which convective activities were significantly strengthened. The center of strong convection extended northward, shown on the water vapor transport map (Figure. 6) and water vapor source map (Figure. 4 and Figure. 2); the rainfall in the study area was high during this period, and the high precipitation led to the rainfall δ 18 O values in August 2016 reaching the lowest value in the year. In September, which is the last month of the summer period, the rainfall decreased, most of the rainfall originated from water vapor circulating within the interior, and all the rainfall δ 18 O values appeared to be elevated. 4.3 Key factors affecting isotope changes in Anshun The aim of this study is to investigate the key factors influencing the variation of precipitation hydrogen and oxygen isotopes (δD, δ¹⁸O) in the Anshun region (Figure. 7). Although the analysis revealed no significant correlation between the isotopes and meteorological factors (e.g., temperature, precipitation, humidity, barometric pressure) or major climatic indices (e.g., Nino 3.4 sea surface temperature (SST), the Southern Oscillation Index (SOI), the Oceanic Niño Index (ONI), the Indian Ocean Dipole (IOD), and the Pacific Intergenerational Oscillation Index(PDO), this suggests that the isotopic characteristics of precipitation in the study area result from a complex and dynamic combination of multiple factors, making it difficult for a single factor to fully explain the observed variability. However, the results of this study also highlight the significant influence of the El Niño/La Niña and Southern Oscillation (ENSO) on precipitation isotopes (Figure. 8). Since September 2014, the development of the El Niño phenomenon has led to an increase in oceanic water vapor from the nearby Pacific Ocean, which has progressively penetrated deeper into the Anshun region. During this period, the SOI remained negative, primarily driven by El Niño, resulting in higher δ¹⁸O values between June and September 2015 due to the influence of summer winds, while lower δ¹⁸O values were observed during La Niña. According to the "circulation effect" proposed by Tan Ming (2014) and the analysis of atmospheric circulation in the study area, during El Niño, the proportion of water vapor from the distant Indian Ocean decreases, while the contribution of water vapor from the nearer Pacific Ocean increases significantly, leading to higher δ¹⁸O values in atmospheric precipitation compared to those observed during La Niña. The inter annual variation in δ¹⁸O values in the Pearl River Delta region is significantly positively correlated with ENSO events and negatively correlated with the SOI (Friedman, 1953 ), reflecting the response of precipitation isotopes to the aforementioned changes in water vapor. However, it is important to note that the study was limited to a relatively short monitoring period of two years (2015–2016). Large-scale atmospheric circulation phenomena, such as ENSO, typically vary on time scales ranging from inter annual to decadal, and short-term data may not adequately reflect their full variability and long-term impacts. Future research should include longer time-series of isotope data to better understand the relationship between atmospheric circulation patterns and precipitation isotopes, and to assess the generalization of these findings across multiple ENSO events. 5. Conclusions 1 The isotope values of atmospheric precipitation in Anshun are low in summer and high in winter, which is due mainly to differences in seasonal water vapor sources. In the summer half-year, precipitation mainly originates from oceanic water vapor carried by summer winds, with high humidity and heavy isotope depletion, and the δ 18 O (δD) values are low; in the winter half-year, under the influence of continental air masses, precipitation mainly originates from westerly circulation and inland water vapor re-evaporation, the air humidity is low, and the δ 18 O (δD) values are high. 2 The equation of the atmospheric precipitation line in the Anshun area was established on the basis of the δ 18 O and δD values of atmospheric precipitation collected from 2015–2016, and the slope of precipitation decreased during the La Niña phenomenon, which implies that there is a secondary evaporation effect between hydrogen and oxygen isotopes in precipitation. However, during the El Niño phenomenon, the slope and intercept of the precipitation line were more similar to those of the global atmospheric precipitation line, which indicates that the precipitation during this period was mainly controlled by water vapor of marine origin. 3 There are no key factors driving the isotope changes in rainfall in the study area during the monitoring period. However, the characteristic changes in δ 18 O are sensitive to movements in atmospheric circulation and changes in ENSO. Declarations Competing Interests The authors declare no competing interests Author Contributions All authors contributed to the study conception and design. Conceptualization, Dayun Zhu and Jialu Wang; Data curation, Ju Ni; Funding acquisition, Dayun Zhu and Jialu Wang; Methodology, Ronghan Li; Resources, Jialu Wang; Software, Yurong Han; Supervision, Dayun Zhu; Writing-original draft, Ju Ni; Writing-review & editing, Dayun Zhu. Data Availability Statement The data presented in this study are available on request from the corresponding author. Acknowledgments This work was supported by National Natural Science Foundation of China (No. 42361010); The Guiding Fund Project of Government’s Science and Technology (No. Qian Ke He Zhong Yin Di[2023]005) and the Academic Talent Plan of Guizhou Normal University (No. Qian Shi Xin Miao[2022]B31). References Bedaso Z, Wu S Y. (2020). Daily precipitation isotope variation in Midwestern United States: Implication for hydroclimate and moisture source[J]. Science of the Total Environment. 713: 136631. Cao, F., Gao, T., Dan, L., Ma, Z., Chen, X., Zou, L., et al., (2019). Synoptic-scale atmospheric circulation anomalies associated with summertime daily precipitation extremes in the middle–lower reaches of the Yangtze River Basin. Climate Dynamics. 53, 3109-3129. Craig, H. (1961). Isotopic variations in meteoric waters. Science. 133(3465), 1702-1703. 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Changes in East Asian summer monsoon and summer rainfall over eastern China during recent decades. Science Bulletin. 60(13), 1222-1224. Zheng, S., Hou, F., Ni B. (1983). Stable isotope study of hydrogen and oxygen in atmospheric precipitation in China. Science Bulletin. (13):801-806. Zhou, H., Zhang, X., Yao, T., Hua, M., Wang, X., Rao, Z., et al., (2019). Variation of δ 18 O in precipitation and its response to upstream atmospheric convection and rainout: A case study of Changsha station, south-central China. Science of the Total Environment. 659, 1199-1208. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 15 Feb, 2025 Read the published version in Stochastic Environmental Research and Risk Assessment → Version 1 posted Editorial decision: Accepted 25 Jan, 2025 Reviews received at journal 23 Jan, 2025 Reviewers agreed at journal 21 Jan, 2025 Reviewers invited by journal 29 Nov, 2024 Submission checks completed at journal 29 Nov, 2024 First submitted to journal 28 Nov, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5177698","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":384897360,"identity":"61ffafaf-c029-478e-9a6a-78fa8e45b913","order_by":0,"name":"Ju Ni","email":"","orcid":"","institution":"School of Karst Science, Guizhou Normal University","correspondingAuthor":false,"prefix":"","firstName":"Ju","middleName":"","lastName":"Ni","suffix":""},{"id":384897361,"identity":"2c77ddc5-543c-4046-b392-cf005aaa2beb","order_by":1,"name":"Dayun Zhu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA20lEQVRIiWNgGAWjYDACCTBiYGBjbz/4IKGihgQtfDxnkg0enDlGghY5CQczyYctzIR1yM/uMbzxs+1wHpsEQ1pFYgMbA397dwJeLYxzzhhb9rYdLmaTbjx2I3GHDIPEmbMb8Gphlsgxk+BtO5zYJnMg7UbiGTYGA4lc/FrYgFok/4K0SCSYFSS2MRPWwgPUIs0L1cJAlBYJibRia5lz6YltwECWSDhzjIegX+RnJG+8+abMOnF+e/vBjz8qauT423vxawEDRjYklxJWDgZ/iFQ3CkbBKBgFIxMAAM3+R/YqIPb1AAAAAElFTkSuQmCC","orcid":"","institution":"School of Karst Science, Guizhou Normal University","correspondingAuthor":true,"prefix":"","firstName":"Dayun","middleName":"","lastName":"Zhu","suffix":""},{"id":384897362,"identity":"4851b063-e624-4f08-ae01-1154f293c856","order_by":2,"name":"Jialu Wang","email":"","orcid":"","institution":"School of Resources and Environmental Engineering, Anshun University","correspondingAuthor":false,"prefix":"","firstName":"Jialu","middleName":"","lastName":"Wang","suffix":""},{"id":384897363,"identity":"4ba39a18-106c-4ea1-a4af-4516b556c309","order_by":3,"name":"Ronghan Li","email":"","orcid":"","institution":"School of Karst Science, Guizhou Normal University","correspondingAuthor":false,"prefix":"","firstName":"Ronghan","middleName":"","lastName":"Li","suffix":""},{"id":384897364,"identity":"a3c9e8a0-9855-4370-b8e5-3a775e482f46","order_by":4,"name":"Yurong Han","email":"","orcid":"","institution":"School of Karst Science, Guizhou Normal University","correspondingAuthor":false,"prefix":"","firstName":"Yurong","middleName":"","lastName":"Han","suffix":""}],"badges":[],"createdAt":"2024-09-30 04:23:08","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5177698/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5177698/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s00477-025-02925-1","type":"published","date":"2025-02-15T15:58:04+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":70661503,"identity":"a4c5eebd-4183-428a-b02e-1bb764a86ca0","added_by":"auto","created_at":"2024-12-05 11:01:09","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":19843600,"visible":true,"origin":"","legend":"\u003cp\u003eGeographic location of the sampling sites\u003c/p\u003e","description":"","filename":"Fig1.png","url":"https://assets-eu.researchsquare.com/files/rs-5177698/v1/eb00d4649ea60a76d8807066.png"},{"id":70661502,"identity":"fef6984b-5804-43cd-8d09-15c67cadb962","added_by":"auto","created_at":"2024-12-05 11:01:09","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":3639473,"visible":true,"origin":"","legend":"\u003cp\u003eTemporal and spatial variations in isotopes (δD and δ\u003csup\u003e18\u003c/sup\u003eO) in rainfall, rainfall (P), temperature (T), and deuterium excess (D-excess) during the 2015–2016 period. The pink shaded area represents the El Niño period, and the blue shaded area represents the La Niña period.\u003c/p\u003e","description":"","filename":"Fig2.png","url":"https://assets-eu.researchsquare.com/files/rs-5177698/v1/ea25af5bcdf92ee053ea427d.png"},{"id":70661501,"identity":"e2a39674-c6e9-4cb1-8fb9-27fb96e86d4a","added_by":"auto","created_at":"2024-12-05 11:01:09","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1129585,"visible":true,"origin":"","legend":"\u003cp\u003eThe local meteoric water line (LMWL) during the El Niño and La Niña periods; GMWL: global meteoric water line (Craig, 1961).\u003c/p\u003e","description":"","filename":"Fig3.png","url":"https://assets-eu.researchsquare.com/files/rs-5177698/v1/efa82de932e16ad001f6830f.png"},{"id":70662927,"identity":"e9e31c2b-975d-4bfc-8cfc-aa42d18b2a1a","added_by":"auto","created_at":"2024-12-05 11:17:10","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":3904466,"visible":true,"origin":"","legend":"\u003cp\u003eCharacteristics of the temporal changes in deuterium excess over the study period\u003c/p\u003e","description":"","filename":"Fig4.png","url":"https://assets-eu.researchsquare.com/files/rs-5177698/v1/7cae3bf2b5d294a9a24da929.png"},{"id":70661515,"identity":"1067ec73-1194-487c-88da-89f418bffd78","added_by":"auto","created_at":"2024-12-05 11:01:10","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":47094857,"visible":true,"origin":"","legend":"\u003cp\u003eSources and clustering of water vapor in the rainy and dry seasons, 2015-2016\u003c/p\u003e","description":"","filename":"Fig5.png","url":"https://assets-eu.researchsquare.com/files/rs-5177698/v1/a86dcc8340b3347126066997.png"},{"id":70662629,"identity":"f6889c6d-4fc5-4aab-9f00-b75b951178b7","added_by":"auto","created_at":"2024-12-05 11:09:10","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":10849046,"visible":true,"origin":"","legend":"\u003cp\u003eMean water vapor flux in the whole atmosphere and the dispersion of the water vapor flux in the summers of 2015–2016;red triangles represent the Anshun site\u003c/p\u003e","description":"","filename":"Fig6.png","url":"https://assets-eu.researchsquare.com/files/rs-5177698/v1/0df067144a7e147afe4d592f.png"},{"id":70661508,"identity":"f7603f11-c2f1-4c36-9030-108b67fc28d6","added_by":"auto","created_at":"2024-12-05 11:01:10","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":723175,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelations of multiple environmental factors with isotopes in rainfall\u003c/p\u003e\n\u003cp\u003e** Represent correlations significant at \u003cem\u003ep\u003c/em\u003e values of \u0026lt;0.01\u003c/p\u003e\n\u003cp\u003e* Represent correlations significant at \u003cem\u003ep\u003c/em\u003e values of \u0026lt;0.05\u003c/p\u003e","description":"","filename":"Fig7.png","url":"https://assets-eu.researchsquare.com/files/rs-5177698/v1/80a0711e93287b2a4565a2a9.png"},{"id":70662926,"identity":"4d4792ec-6367-4592-a4c2-788bab220f57","added_by":"auto","created_at":"2024-12-05 11:17:09","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":1755383,"visible":true,"origin":"","legend":"\u003cp\u003eTemporal variations in δ\u003csup\u003e18\u003c/sup\u003eO, SST, and SOI\u003c/p\u003e","description":"","filename":"Fig8.png","url":"https://assets-eu.researchsquare.com/files/rs-5177698/v1/db1290fd52738d22ebc8598c.png"},{"id":76487681,"identity":"18ace653-e752-43ef-ad98-5687cea4a8c1","added_by":"auto","created_at":"2025-02-17 16:11:10","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":78810067,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5177698/v1/e39459b5-0770-4a8f-a6c0-555543159fe2.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Isotope variations in precipitation and environmental drivers in Anshun during strong El Niño events","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eAtmospheric precipitation constitutes a fundamental component of the terrestrial water cycle, serving not only to maintain the dynamic equilibrium between surface water and groundwater but also to play a crucial role in regulating the global climate system. Extreme fluctuations in precipitation can lead to severe droughts and floods, posing substantial threats to the sustainable development of human societies (Sun et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). As sensitive indicators of environmental change, the stable isotopes of hydrogen and oxygen in precipitation encapsulate critical physical and chemical information throughout the water cycle. These isotopes have been extensively applied in diverse disciplines, including hydrology, meteorology, ecology, and paleo-climatology (Dublyansky et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eStable isotopes, although present in low concentrations in precipitation, possess significant resolving power for identifying water vapor sources and atmospheric circulation patterns, while also responding sensitively to environmental changes (Cao et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Key indicators such as δD, δ\u0026sup1;⁸O, and d-excess reflect variations in temperature, humidity, evaporation, and moisture sources during precipitation processes, offering critical insights into regional water cycle dynamics and climate change (Tian et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Oza et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The establishment of the Global Network of Isotopes in Precipitation (GNIP) in 1958 provided a systematic and comprehensive data foundation for studying stable isotopes in precipitation (Froehlich et al., 2004). Craig (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e1961\u003c/span\u003e) laid the theoretical groundwork for isotope studies by analyzing hydrogen and oxygen isotope ratios in rivers and lakes and formulating the Global Meteoric Water Line (GMWL). In recent years, research has increasingly concentrated on the spatial and temporal distribution patterns of stable isotopes in precipitation and the factors influencing these patterns (Juan et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Terzer-Wassmuth et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). For instance, in mountainous regions, δ\u0026sup1;⁸O exhibits a strong positive correlation with temperature, whereas in monsoon-affected areas, the precipitation amount effect predominates. In such monsoonal regions, the influence of temperature is often diminished or even inverted, resulting in what is termed an anti-temperature effect (Wen et al., 2016; Xu et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAdditionally, large-scale climatic phenomena such as the El Ni\u0026ntilde;o/La Ni\u0026ntilde;a and Southern Oscillation (ENSO), the Indian Ocean Dipole (IOD), and the Pacific Intergenerational Oscillation Index (PDO) significantly influence the isotopic composition of precipitation. They do so by regulating factors like moisture sources, temperature, humidity, and atmospheric pressure (S\u0026aacute;nchez-Murillo et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). ENSO, which includes El Ni\u0026ntilde;o/La Ni\u0026ntilde;a events and the Southern Oscillation, occurs on average every 2\u0026ndash;7 years, affecting global climate patterns through ocean-atmosphere coupling. During El Ni\u0026ntilde;o events, the elevated sea surface temperatures in the central and eastern Pacific strengthen the western Pacific subtropical high, thereby reducing the influx of moist air into southern China. This results in significantly reduced precipitation and higher temperatures, leading to associated climatic anomalies. Conversely, during La Ni\u0026ntilde;a events, cooler sea surface temperatures in the central and eastern Pacific weaken the subtropical high, allowing increased southwesterly and southerly moist air flows into southern China, particularly in southwestern regions such as Guizhou. This leads to higher precipitation levels and lower temperatures compared to normal years (Lv et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). These climatic variations not only directly affect fluctuations in precipitation and temperature but also alter the isotopic composition of precipitation. Existing studies indicate that ENSO events cause significant changes in the isotopic composition of precipitation in southeastern China (Qiu et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). However, the underlying mechanisms linking moisture sources to the isotopic characteristics of precipitation in monsoon-dominated regions remain to be further explored.\u003c/p\u003e \u003cp\u003eAnshun, Guizhou, situated on the eastern edge of the Yunnan-Guizhou Plateau, lies within the humid subtropical monsoon climate zone, making it an ideal location to investigate the isotopic response of precipitation to specific climatic events. The region receives substantial precipitation, influenced by a combination of oceanic water vapor and monsoon circulation. During the study period, Anshun experienced both a strong El Ni\u0026ntilde;o event and a subsequent La Ni\u0026ntilde;a event, providing a unique opportunity to examine the effects of extreme climatic events on precipitation isotopes. However, current research on precipitation isotopes in this region remains largely limited to basic characterizations (Mao et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), with a notable lack of systematic studies addressing water vapor transport pathways and regional response mechanisms under the influence of specific climatic phenomena.Using daily precipitation samples collected from 2015 to 2016, this study aims to: 1. Elucidate the temporal variation patterns of δD, δ\u0026sup1;⁸O, and d-excess values; 2. Analyze the coupling relationships between precipitation isotopes and meteorological variables such as temperature and precipitation; 3. Investigate the sources of atmospheric water vapor in the study area through the application of the HYSPLIT trajectory model and water vapor transport mapping. The findings of this research will contribute to a more comprehensive understanding of the regional water cycle, water vapor transport dynamics, and the reconstruction of paleoclimate conditions in the region.\u003c/p\u003e"},{"header":"2. Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Study area\u003c/h2\u003e \u003cp\u003eThe city of Anshun (coordinate range: 25\u0026deg;20\u0026prime;N-27\u0026deg;21\u0026prime;N, 105\u0026deg;14\u0026prime;E-107\u0026deg;17\u0026prime;E) is located in the watershed area between the Wujiang River Basin of the Yangtze River system and the Beipanjiang River Basin of the Pearl River system (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The topography of the region is characterized by high altitudes in the northwestern part and low altitudes in the southeastern part, and it has a highland subtropical monsoon climate with an average annual temperature of approximately 16\u0026deg;C and annual precipitation in the range of 1205\u0026ndash;1656 mm: the region has a large total amount of precipitation but with an uneven seasonal distribution, which is mainly concentrated in June\u0026ndash;August, and severe soil erosion during periods of heavy rainfall (Zhang et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The altitude in the region is approximately 400 m above sea level, and the vertical difference in altitude is approximately 1400 m. Therefore, there are differences in geomorphology and solar radiation, which are jointly influenced by phenomena such as the Indian monsoon and the East Asian monsoon, influencing the atmospheric circulation, resulting in significant climatic variations within the region and obvious microclimatic phenomena.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Research methods\u003c/h2\u003e \u003cdiv id=\"Sec5\" class=\"Section3\"\u003e \u003ch2\u003e2.2.1 Sample collection and processing\u003c/h2\u003e \u003cp\u003ePrecipitation monitoring and sample collection were conducted in the study area for two years from 2015\u0026ndash;2016. During this period, a total of 140 daily precipitation samples were collected, including 65 in 2015 and 75 in 2016. The sampling site was set up on the top floor of the first teaching building of Anshun College in Guizhou, and the rain sample collector was placed at a distance of approximately 25 m from the ground and approximately 2 m from the floor of the top building to avoid contamination of the samples by the surrounding buildings and ground dust. The precipitation collector used was an inverted triangular plastic funnel, with an upper diameter of 15 cm and a lower diameter of 15 mm. A ping-pong ball was placed inside, and a disposable sealed bag was connected to the bottom of the funnel. This design ensured a sufficient water collection area and effectively prevented isotope fractionation caused by rainwater evaporation. At the end of each precipitation event, the precipitation samples were promptly transferred to 25 ml polyethylene plastic sampling bottles with a syringe, wrapped with a sealing film at the mouth of the bottle, and finally placed in a refrigerator at 4\u0026deg;C for sealing and refrigeration to be ready for subsequent machine measurements.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003e2.2.2 Sample determination\u003c/h2\u003e \u003cp\u003eHydrogen and oxygen stable isotope measurements of all rainwater samples were done at the Isotope Laboratory of Southwest University, using a liquid water isotope analyzer developed by LGR, The working standard and test samples were contained in 2 ml glass test bottles, and the test sample volume was approximately 1.5 ml. All samples were filtered with a 0.45 \u0026micro;m filter membrane and directly loaded into the test bottles without adding other reagents. The duration of a single test for each sample was approximately 2 min, and parallel tests were conducted 6 times. To eliminate the effect of memory, the system automatically removed the first 2 test values and took the average of the last 4 values as the final test value of the sample. After 3 samples were tested, with 9 samples for a group of rounds, each standard curve consisted of the test values for the group of 3 samples and a dynamic standard. The results are expressed in terms of thousandths of a point relative to Vienna standard mean ocean water (R\u003csub\u003eV\u0026minus;SMOW\u003c/sub\u003e). The formula is as follows:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:{\\delta\\:}={（\\text{R}}_{\\text{s}\\text{a}\\text{m}}-{\\text{R}}_{\\text{s}\\text{t}\\text{d}}）∕{\\text{R}}_{\\text{s}\\text{t}\\text{d}}\\times\\:1000（\\text{\u0026permil;}）$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eHere, R\u003csub\u003esample\u003c/sub\u003e and R\u003csub\u003eV\u0026minus;SMOW\u003c/sub\u003e represent the stable isotope proportions of D/H or \u003csup\u003e18\u003c/sup\u003eO/\u003csup\u003e16\u003c/sup\u003eO within water samples and Vienna standard mean ocean water (V-SMOW), respectively.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003e2.2.3 Climate data\u003c/h2\u003e \u003cp\u003eHourly observational data from the National Aeronautics and Space Administration (NASA) were collected during the study period, covering information on temperature, humidity and rainfall. Commonly used metrics for quantifying ENSO phenomena include the sea surface temperature anomaly (SSTA) in the Ni\u0026ntilde;o 3.4 region (5\u0026deg;N-5\u0026deg;S, 170\u0026deg;W-120\u0026deg;W) (Gao et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), as well as the Southern Oscillation Index (SOI) and the Ni\u0026ntilde;o 3.4 Index (ONI) provided by the National Oceanic and Atmospheric Administration (NOAA) (Freitas and Mclean, 2013). The information is based on the ENSO measurements provided by the NOAA National Weather Service (NWS) Climate Prediction Centre (CPC) of the United States of America. Atmospheric circulation-related indices, namely, the dipole mode index (DMI), which is used to indicate the intensity of Indian Ocean dipole (IOD) activity, and the Pacific Intergenerational Oscillation Index (PDO), are also included. All the data were preprocessed and statistically analyzed in Excel 2010, analyzed with SPSS 17.0 software, and plotted with Origin 2022 mapping software.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e \u003ch2\u003e2.2.4 HYSPLIT model\u003c/h2\u003e \u003cp\u003eWe utilized the Lagrangian Integrated Trajectory (HYSPLIT) Model, version 4.2, developed by the National Oceanic and Atmospheric Administration (NOAA), alongside meteorological data from the Global Data Assimilation System (GDAS) (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://ftp.arl.noaa.gov/pub/archives/\u003c/span\u003e\u003cspan address=\"https://ftp.arl.noaa.gov/pub/archives/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), with a spatial resolution of 2.5\u0026deg; \u0026times; 2.5\u0026deg;. The model was employed to simulate atmospheric air mass transport pathways and precipitation processes, which were subsequently clustered to identify the moisture sources influencing the study area during different time periods. Since the majority of atmospheric water vapor is concentrated within 2 km above sea level and has a typical retention time of several days to one week (Bedaso et al., 2020), simulations were conducted at an altitude corresponding to 850 hPa with a backward trajectory duration of 168 hours (7 days). To enhance temporal resolution, four trajectories were generated daily at 00:00, 06:00, 12:00, and 18:00 UTC.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003e2.2.5 Water vapor flux\u003c/h2\u003e \u003cp\u003eTo further determine the water vapor source and transformation process, water vapor transport maps were developed via European Centre for Medium-Range Weather Forecasts (ECMWF) monthly scale reanalysis data, in which latitudinal and longitudinal winds and specific humidity data were obtained from the Fifth Generation of Reanalysis Data (Fifth Generation of Reanalysis Data) published by the ECMWF. The data were obtained from the Fifth Generation of ECMWF Atmospheric Reanalysis (ERA5) issued by the ECMWF (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-pressure-levels-monthly-means?tab=form\u003c/span\u003e\u003cspan address=\"https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-pressure-levels-monthly-means?tab=form\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"3. Results and analyses","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Time-varying characteristics of isotopes in atmospheric precipitation\u003c/h2\u003e \u003cp\u003eDuring the monitoring period, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e illustrates the trends of stable isotope contents (δD, δ\u003csup\u003e18\u003c/sup\u003eO, and D-excess), temperature (T), and precipitation (P) in the precipitation samples from the Anshun atmospheric field over time. The shaded areas in the figure highlight the strong El Ni\u0026ntilde;o period (January 2015\u0026ndash;April 2016) and the weak La Ni\u0026ntilde;a period (August\u0026ndash;December 2016).\u003c/p\u003e \u003cp\u003eA linear relationship between δD and δ\u003csup\u003e18\u003c/sup\u003eO was observed due to isotopic fractionation, resulting in similar trends for both isotopes, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. The precipitation and temperature patterns showed higher values in summer and lower values in winter, with rainfall concentrated from May to September. This pattern aligns with the typical rain-heat system characteristics of Southwest China, and it is worth noting that rainfall in 2015 was significantly greater than in 2016.During the El Ni\u0026ntilde;o period, the maximum and minimum values of δD and δ\u003csup\u003e18\u003c/sup\u003eO occurred in May and October, respectively. The δD values ranged from \u0026minus;\u0026thinsp;147.44\u0026permil; to 16.37\u0026permil;, while δ\u003csup\u003e18\u003c/sup\u003eO values ranged from \u0026minus;\u0026thinsp;19.80\u0026permil; to 0.18\u0026permil;. The maximum deuterium excess occurred in December (29.83\u0026permil;), and the minimum value was recorded in September (3.60\u0026permil;).During the La Ni\u0026ntilde;a period, the maximum and minimum values of δD and δ\u003csup\u003e18\u003c/sup\u003eO occurred in May and August, respectively. The δD values ranged from \u0026minus;\u0026thinsp;131.32\u0026permil; to 19.21\u0026permil;, while δ\u003csup\u003e18\u003c/sup\u003eO values ranged from \u0026minus;\u0026thinsp;17.70\u0026permil; to 14.98\u0026permil;. The maximum deuterium excess was observed in December (23.83\u0026permil;), and the minimum value occurred in July (2.14\u0026permil;).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Atmospheric precipitation lines in Anshun\u003c/h2\u003e \u003cp\u003eThe relationship between hydrogen and oxygen stable isotopes in water during rainfall is important for studying changes in water isotopes during the atmospheric cycle. In this study, two atmospheric precipitation lines for Anshun city during the El Ni\u0026ntilde;o and La Ni\u0026ntilde;a periods were established on the basis of 140 daily rainfall δD and δ\u003csup\u003e18\u003c/sup\u003eO measured data collected from 2015\u0026ndash;2016, respectively, as follows:\u003c/p\u003e \u003cp\u003eδD\u0026thinsp;=\u0026thinsp;8.70δ\u003csup\u003e18\u003c/sup\u003eO + 19.55 (El Ni\u0026ntilde;o period; n\u0026thinsp;=\u0026thinsp;65, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05)\u003c/p\u003e \u003cp\u003eδD\u0026thinsp;=\u0026thinsp;8.60δ\u003csup\u003e18\u003c/sup\u003eO + 17.23 (La Ni\u0026ntilde;a period; n\u0026thinsp;=\u0026thinsp;75, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05)\u003c/p\u003e \u003cp\u003eThe results reveal larger slope and intercept values compared to the Global Meteoric Water Line (GMWL) equation, δD\u0026thinsp;=\u0026thinsp;8δ\u0026sup1;⁸O\u0026thinsp;+\u0026thinsp;10, proposed by Craig (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e1961\u003c/span\u003e), as well as the Local Meteoric Water Line (LMWL) equation for the region, δD\u0026thinsp;=\u0026thinsp;7.8δ\u0026sup1;⁸O\u0026thinsp;+\u0026thinsp;8.2, calculated by Shuhui Zheng (1983). Notably, the slope during the La Ni\u0026ntilde;a period is smaller than that observed during the El Ni\u0026ntilde;o period. The meteoric water line equation reflects the type and degree of isotopic fractionation occurring during precipitation. A slope deviating from 8 indicates varying degrees of non-equilibrium isotopic fractionation during the precipitation process. Meanwhile, the intercept represents the global mean atmospheric precipitation value, and an intercept greater than 10 suggests a significant imbalance in isotopic fractionation between the vapor and liquid phases during the formation of precipitation clouds. These findings align with observations from neighboring regions of the study area (Zhang et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Characteristics of deuterium excess values\u003c/h2\u003e \u003cp\u003eTo quantify and compare the degree of non-equilibrium of atmospheric precipitation during evaporation and condensation in different regions, Dansgaard (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e1964\u003c/span\u003e) first introduced the concept of D-excess, which is calculated as D-excess\u0026thinsp;=\u0026thinsp;δD-8δ\u003csup\u003e18\u003c/sup\u003eO. Its global average value is approximately 10\u0026permil;, which is equivalent to the intercept of the precipitation line in the region when the slope is 8. Under relatively humid climatic conditions, the kinetic fractionation is weak and the formation of deuterium surplus in precipitation is low, while under relatively dry climatic conditions, the air humidity is relatively small, and the kinetic fractionation of water evaporation is also strong, and the formation of deuterium surplus in precipitation will be higher. As can be seen from Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, the range of deuterium surplus values in 2015 was 3.61\u0026permil;-29.83\u0026permil;, with a mean value of 13.44\u0026permil;, and the range of deuterium surplus values in 2016 was 2.14\u0026permil;-26.52\u0026permil;, with a mean value of about 12.76\u0026permil;.The D-excess values in 2016 were more negative than those in 2015. During the year the deuterium surplus values were below the global average in July-September and above the global average in all other months. In 2015, higher precipitation months (e.g., May and June) were accompanied by higher D-excess values, whereas in lower precipitation months (e.g., January and December), D-excess values were relatively low. A similar trend was observed in 2016, where despite an overall decrease in precipitation in 2016 compared to 2015, months with higher precipitation (e.g., May and August) were still associated with higher D-excess values.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cdiv id=\"Sec15\"\u003e\n \u003ch2\u003e4.1 Relationships between isotopes in rainfall and the temperature and rainfall amount\u003c/h2\u003e\n \u003cp\u003eDansgaard (\u003cspan\u003e1964\u003c/span\u003e) proposed the effects of temperature, precipitation, elevation, etc., on the basis of the Rayleigh model, in which temperature is considered one of the most important controlling factors (Guo et al., \u003cspan\u003e2021\u003c/span\u003e). This is because the condensation of water vapor inside clouds is key to the formation of atmospheric precipitation, and the temperature directly affects the condensation temperature of water vapor inside clouds, which in turn affects the \u0026delta; values of hydrogen and oxygen isotopes in atmospheric precipitation. Specifically, the lower the temperature is, the more pronounced the hydrogen and oxygen isotope fractionation in atmospheric precipitation, and the larger the stable isotope fractionation coefficients are, resulting in lower isotope values of precipitation, which is a phenomenon that is particularly common in mid- and high-latitude regions (Ma et al., 2023). However, at low latitudes and in coastal areas, the effect of volume is more significant and often masks the effect of temperature, leading to a weakening of the effect of temperature of \u0026delta;\u003csup\u003e18\u003c/sup\u003eO in precipitation or even an inverse effect of temperature (Guo et al., \u003cspan\u003e2017\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eIn the study of atmospheric precipitation in Anshun city, as shown in Table 1, \u0026delta;D, \u0026delta;\u003csup\u003e18\u003c/sup\u003eO and temperature did not significantly affect the temperature during the occurrences of El Ni\u0026ntilde;o and La Ni\u0026ntilde;a, which was accompanied by a weak negative correlation, i.e., an inverse effect of temperature. In contrast, a significant effect of temperature was found at the transition point between El Ni\u0026ntilde;o events and La Ni\u0026ntilde;a events (summer 2016). The inverse effect of temperature was also observed in a study by Zhang Lin et al. (2008) at low latitudes in China, but the effect of temperature was not significant or even absent at the seasonal scale. In another study, Wang Tao\u0026apos;s team (2013) reported that in the Nanjing region, although no significant effect of temperature was detected on a year-round scale, such an effect occurred in winter. This finding is similar to that found for Anshun, probably because, relative to high-latitude inland areas, mid- and low-latitude areas are affected by many factors, such as air humidity, condensation temperature, and monsoon winds, which mask the effect of temperature (Li et al., \u003cspan\u003e2016\u003c/span\u003e). In addition, Anshun is in a middle- to low-latitude region and is affected by the oceanic monsoon, which increases the number of disturbance factors, and the El Ni\u0026ntilde;o phenomenon brought a large amount of precipitation to southern China during the study period, which masked the effect of temperature to a certain extent. Therefore, the precipitation in this region is controlled by many factors, and considering only a single temperature factor cannot fully explain its pattern of change.\u003c/p\u003e\n \u003cdiv\u003e\u0026nbsp;\u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 1\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003e\u003cstrong\u003eCorrelation coefficients of precipitation \u0026delta;D and \u0026delta;\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e18\u003c/strong\u003e\u003c/sup\u003e\u003cstrong\u003eO with precipitation and temperature in Anshun, Guizhou, China\u003c/strong\u003e\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eisotope\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003efactor\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eEl Ni\u0026ntilde;o\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLa Ni\u0026ntilde;a\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003evintage\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSP\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSU\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eFA\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eWI\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eannual\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"4\"\u003e\n \u003cp\u003e\u0026delta;D\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"2\"\u003e\n \u003cp\u003e-0.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"2\"\u003e\n \u003cp\u003e-0.40*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.52*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.31**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"2\"\u003e\n \u003cp\u003e-0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"2\"\u003e\n \u003cp\u003e-0.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.37*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.26\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.35*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.33**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"4\"\u003e\n \u003cp\u003e\u0026delta;\u003csup\u003e18\u003c/sup\u003eO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"2\"\u003e\n \u003cp\u003e-0.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"2\"\u003e\n \u003cp\u003e-0.40*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.47*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.30**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"2\"\u003e\n \u003cp\u003e-0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"2\"\u003e\n \u003cp\u003e-0.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.37*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.32**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eNote: P: precipitation; T: temperature\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e** Represent correlations significant at \u003cem\u003ep\u003c/em\u003e values of \u0026lt;0.01\u003c/p\u003e\n \u003cp\u003e* Represent correlations significant at \u003cem\u003ep\u003c/em\u003e values of \u0026lt;0.05\u003c/p\u003e\n \u003cp\u003eAccording to previous studies (Yamanaka et al., \u003cspan\u003e2007\u003c/span\u003e), in the midlatitude oceanic monsoon climate zone, where rain and heat coincide, there is an inverse relationship between the stable isotope \u0026delta;\u003csup\u003e18\u003c/sup\u003eO ratio in precipitation and precipitation, i.e., the rainfall effect. However, in this study area, no significant correlation was observed between \u0026delta;\u003csup\u003e18\u003c/sup\u003eO and precipitation at the seasonal scale, but the effect of rainfall was significant during the La Ni\u0026ntilde;a period. This implies that the amount of precipitation is not the overall determinant of \u0026delta;\u003csup\u003e18\u003c/sup\u003eO variations. In the Anshun area, for example, the \u0026delta;\u003csup\u003e18\u003c/sup\u003eO values show seasonal variations, which are closely related to the atmospheric circulation and the sources of water vapor in different seasons (Wang et al., \u003cspan\u003e2020\u003c/span\u003e). Fractionation of water vapor occurs during transport and condensation, leading to the fact that even if precipitation decreases, the \u0026delta;\u003csup\u003e18\u003c/sup\u003eO values may still become more negative. Therefore, it can be concluded that seasonal water vapor sources and the motions of atmospheric circulation play crucial roles in the variations in \u0026delta;D and \u0026delta;\u003csup\u003e18\u003c/sup\u003eO during atmospheric precipitation in the Anshun region.\u003c/p\u003e\n \u003cp\u003eIn addition, by analyzing two atmospheric precipitation lines during El Ni\u0026ntilde;o and La Ni\u0026ntilde;a (Fig. \u003cspan\u003e3\u003c/span\u003e), it was found that there are significant differences in the compositions of hydrogen and oxygen isotopes in precipitation during these two periods. These differences may be closely related to changes in meteorological conditions and water vapour sources during precipitation formation. During El Ni\u0026ntilde;o, it is possible that more oceanic water vapour is transported to the study area, resulting in the slope and intercept of the precipitation line being closer to the characteristics of the global atmospheric precipitation line. In contrast, during La Ni\u0026ntilde;a, secondary evaporation effects may occur, leading to lower slopes and reduced D-excess values of the precipitation lines (Fig. \u003cspan\u003e4\u003c/span\u003e). These variations further demonstrate the different effects of different atmospheric phenomena on the isotopic composition of precipitation in the Anshun region, highlighting the key role of atmospheric circulation and water vapour sources in precipitation isotope variations.\u003c/p\u003e\n \u003cdiv\u003e\u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec16\"\u003e\n \u003ch2\u003e4.2 Effects of changes in atmospheric circulation on isotopes in precipitation\u003c/h2\u003e\n \u003cdiv id=\"Sec17\"\u003e\n \u003ch2\u003e4.2.1 Water vapor sources\u003c/h2\u003e\n \u003cp\u003eTo investigate the effect of different water vapor sources on stable isotopes in precipitation in the Anshun area, six simulations were conducted using the HYSPLIT model for the rainy season (May\u0026ndash;October) and dry season (November\u0026ndash;March) during 2015\u0026ndash;2016. The results indicate a significant difference in water vapor sources between the dry and rainy seasons. As shown in Figures a, d, and f, the dry season is characterized by water vapor mainly originating from the northwest and southwest directions, predominantly from continental sources, with some contributions from oceanic vapor. The long transport distances and significant evaporation effects during the dry season lead to enrichment of heavy isotopes (\u0026delta;\u003csup\u003e18\u003c/sup\u003eO and \u0026delta;D) in the water vapor, resulting in higher isotope values.Previous studies indicate that water vapor in dry season precipitation over Southwest China primarily originates from westerly winds and re-evaporation of inland water, resulting in lower air humidity and higher stable isotope ratios, along with elevated deuterium excess (d-excess) in precipitation (Wang et al., \u003cspan\u003e2023\u003c/span\u003e). During the rainy season (Figures b, e), water vapor mainly originates from southern humid regions, such as the Bay of Bengal and the South China Sea. As these air masses move inland, heavy isotopes in precipitation are progressively depleted due to intense fractionation. Consequently, precipitation during the rainy season exhibits significantly lower isotope values compared to the dry season.\u003c/p\u003e\n \u003cp\u003eMoreover, simulations of water vapor sources during the 2016 rainy season revealed that approximately 5% of the water vapor originated from the western Pacific Ocean\u0026mdash;an outcome that differs from prior studies on the sources of water vapor in southern China (Li et al., \u003cspan\u003e2024\u003c/span\u003e; Xu et al., \u003cspan\u003e2022\u003c/span\u003e). This anomaly may be related to the La Ni\u0026ntilde;a event that followed the strong El Ni\u0026ntilde;o of 2016. Under La Ni\u0026ntilde;a conditions, the subtropical high-pressure ridge over the western Pacific shifted westward, channeling Pacific water vapor into mainland China. The contribution of water vapor from the western Pacific suggests that the western Pacific subtropical high plays an important regulatory role in the distribution of water vapor in the East Asian monsoon region (Ke et al., \u003cspan\u003e2023\u003c/span\u003e). This observation aligns with the trend shown in Fig. \u003cspan\u003e4\u003c/span\u003e, indicating that during La Ni\u0026ntilde;a years, water vapor is more likely to originate from wetter oceanic sources. In summary, the isotopic differences in water vapor sources between the dry and rainy seasons are primarily influenced by transport pathways, fractionation effects, and the characteristics of source regions. These factors reflect the seasonal variability of the regional monsoon climate and the diverse spatial distribution of water vapor sources.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec18\"\u003e\n \u003ch2\u003e4.2.2 Characteristics of \u0026delta;\u003csup\u003e18\u003c/sup\u003eO in precipitation during ENSO periods\u003c/h2\u003e\n \u003cp\u003eENSO is a large-scale ocean‒atmosphere event that occurs in the equatorial eastern Pacific Ocean, and it has a period of 2\u0026ndash;7 year. La Ni\u0026ntilde;a is referred to as an ENSO cold event, and El Ni\u0026ntilde;o is referred to as an ENSO warm event. The SOI is generally negative during El Ni\u0026ntilde;o, whereas the SOI is positive when La Ni\u0026ntilde;a occurs; in general, the SOI is significantly negatively correlated with the SST. According to the National Oceanic and Atmospheric Administration (NOAA), a La Ni\u0026ntilde;a (or El Ni\u0026ntilde;o) event is recognized when the 3-month sliding average of sea surface temperature anomalies (SSTA) in the Ni\u0026ntilde;o 3.4 area is \u0026le; -0.5\u0026deg;C (\u0026ge;\u0026thinsp;0.5\u0026deg;C) for 5 consecutive months (Ren et al., \u003cspan\u003e2014\u003c/span\u003e). Counting from April 2015, the mean sea surface temperature (SST) in the Ni\u0026ntilde;o 3.4 sea area was greater than 0.5\u0026deg;C for 13 consecutive months (from April 2015 to April 2016), and this phenomenon was recognized as a strong period of El Ni\u0026ntilde;o events. In fact, signs of El Ni\u0026ntilde;o events have been visible since September 2014, when sea surface temperature anomalies in the Ni\u0026ntilde;o 3.4 region started to show positive values and gradually increased, heralding the beginning of the El Ni\u0026ntilde;o phenomenon. After August 2015, the sea surface temperature anomalies in the central and eastern Pacific Ocean increased to more than 2\u0026deg;C, which indicated the onset of an extremely intense ENSO event, which then reached its peak in November and December 2015 and continued until April 2016, when it ended. At that time, the SSTA values decreased by approximately 1\u0026deg;C and continued to decrease, resulting in a 22-month streak of positive values for the El Ni\u0026ntilde;o phenomenon. The sea surface temperatures returned to normal between May and July 2016, and then a weak La Ni\u0026ntilde;a event occurred between August and December 2016. The SSTA values also decreased by approximately 1\u0026deg;C and continued to decrease, ending a 22-month streak of positive values for the El Ni\u0026ntilde;o phenomenon. The SSTA values also decreased by approximately 1\u0026deg;C and continued to decrease.\u003c/p\u003e\n \u003cp\u003eAccording to modern meteorological records, 2015 was considered one of the years with the highest average global temperatures on record. In the same year, southern China experienced more precipitation. However, it is not clear whether these anomalous increases in precipitation were directly related to strong El Ni\u0026ntilde;o events (Zhai et al., \u003cspan\u003e2016\u003c/span\u003e). Most studies indicate that the effect of the El Ni\u0026ntilde;o phenomenon on precipitation in China may be related to changes in the intensity of summer winds from the Indian and Western Pacific Oceans (Gao et al., \u003cspan\u003e2014\u003c/span\u003e; Zhang, \u003cspan\u003e2015\u003c/span\u003e). Different ENSO events cause changes in monsoon activities, subtropical high pressure, and the Intertropical Convergence Zone (ITCZ), which in turn affect the distribution, sources, and isotopes of precipitation (Santos et al., \u003cspan\u003e2019\u003c/span\u003e; Zhou et al., \u003cspan\u003e2019\u003c/span\u003e). In fact, monsoon activity is often considered a manifestation of intraseasonal migration in the ITCZ (Geen et al., \u003cspan\u003e2020\u003c/span\u003e). As shown in Figure. 2, there is a difference in the variation in isotopes in rainfall between 2015 and 2016, which suggests that ENSO events influenced the location and intensity of the ITCZ throughout the year. On this basis, in this study, the impact of changes in the ITCZ during ENSO on rainfall in the study area is analyzed by exploring water vapor transport under the influence of summer winds during the summer and half of 2015 to 2016 (e.g., Figure. 6).\u003c/p\u003e\n \u003cp\u003eDuring April, Southwest China has not yet entered the rainy season, and westerly wind-dominated water vapor accounts for a large portion of the water vapor, with local internal circulation. Owing to the close proximity of water vapor transport, the isotopes in rainfall were less fractionated during the water vapor transport process, resulting in higher isotope values of precipitation in April. In May, the monsoon activities that affect precipitation began, and with the shift in convective activities in the ITCZ to the low-latitude Arabian Sea\u0026ndash;Bay of Bengal region, the water vapor flux gradually increased compared with that in April, and heavy isotopes were preferentially deposited by oceanic water vapor in the process of transport, resulting in the depletion of heavy isotopes contained in the remaining water vapor mass. The magnitude of rainfall in the study area reached a small peak in the year\u0026apos;s rainfall, and the \u0026delta;\u003csup\u003e18\u003c/sup\u003eO values also rapidly decreased, indicating that the rainy season was influenced by summer winds. Some researchers (Huang et al., \u003cspan\u003e2016\u003c/span\u003e) noted that the strongest phase of large-scale monsoon circulation occurs from June\u0026ndash;August each year, and this phase is also the most isotopically depleted period for atmospheric precipitation. In this study, isotopes in rainfall generally showed a gradual depletion from June\u0026ndash;August, and in 2015, during the El Ni\u0026ntilde;o period, lower values were recorded until the end of July, but the lowest value of the year occurred in mid-October, which may be due to the El Ni\u0026ntilde;o anomaly that led to an increase in rainfall in southern China. The water vapor transport map (Figure. 6) also shows that the water vapor sources were similar in June 2015 and 2016, both with the migration of the ITCZ to the north, with strong convection occurring in the Arabian Sea‒Bay of Bengal continuum, as well as on the west coast of the Pacific Ocean. The sustained increase in the oceanic source of water vapor in the study area resulted in a subsequent decrease in the isotope \u0026delta;\u003csup\u003e18\u003c/sup\u003eO in rainfall. In July, the common feature in both years was that the oceanic source of water vapor at this time was almost exclusively from the Arabian Sea\u0026ndash;Bay of Bengal region, and the previous convective activity in the western Pacific Ocean had almost disappeared; the difference was that the convective activity in the Arabian Sea\u0026ndash;Bay of Bengal continuum was enhanced in July 2015 compared with June 2015, whereas the convective activity in this region weakened in July 2016 compared with June 2016, which was supported by the fact that measurements of isotopes in rainfall in July 2015 started to increase in July 2016. The continuous decrease in the \u0026delta;\u003csup\u003e18\u003c/sup\u003eO values in July 2015 was due to the fractionation of heavy isotopes under the prolonged influence of the same air mass, which caused the depletion of \u0026delta;\u003csup\u003e18\u003c/sup\u003eO in the remaining water vapor one additional time, which reflected the effects of rainfall.\u003c/p\u003e\n \u003cp\u003eIn August 2015, the convective activities in the low-latitude western Pacific and South China Sea ITCZ almost disappeared, the convective activities in the Bay of Bengal were significantly weakened, the source of water vapor from the distant oceans was reduced, and the locally internally circulating water vapor accounted for a relatively high percentage of water vapor, with high temperatures and evaporation, which led to the elevation of the \u0026delta;\u003csup\u003e18\u003c/sup\u003eO values in precipitation. The convective activities of the ITCZ in August 2016 were very different from the convective phenomena in 2015 and were much greater than those in August 2015 and July 2016 in the South China Sea, the west coast of the Pacific Ocean, the Bay of Bengal and the Arabian Sea, in which convective activities were significantly strengthened. The center of strong convection extended northward, shown on the water vapor transport map (Figure. 6) and water vapor source map (Figure. 4 and Figure. 2); the rainfall in the study area was high during this period, and the high precipitation led to the rainfall \u0026delta;\u003csup\u003e18\u003c/sup\u003eO values in August 2016 reaching the lowest value in the year. In September, which is the last month of the summer period, the rainfall decreased, most of the rainfall originated from water vapor circulating within the interior, and all the rainfall \u0026delta;\u003csup\u003e18\u003c/sup\u003eO values appeared to be elevated.\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec19\"\u003e\n \u003ch2\u003e4.3 Key factors affecting isotope changes in Anshun\u003c/h2\u003e\n \u003cp\u003eThe aim of this study is to investigate the key factors influencing the variation of precipitation hydrogen and oxygen isotopes (\u0026delta;D, \u0026delta;\u0026sup1;⁸O) in the Anshun region (Figure. 7). Although the analysis revealed no significant correlation between the isotopes and meteorological factors (e.g., temperature, precipitation, humidity, barometric pressure) or major climatic indices (e.g., Nino 3.4 sea surface temperature (SST), the Southern Oscillation Index (SOI), the Oceanic Ni\u0026ntilde;o Index (ONI), the Indian Ocean Dipole (IOD), and the Pacific Intergenerational Oscillation Index(PDO), this suggests that the isotopic characteristics of precipitation in the study area result from a complex and dynamic combination of multiple factors, making it difficult for a single factor to fully explain the observed variability. However, the results of this study also highlight the significant influence of the El Ni\u0026ntilde;o/La Ni\u0026ntilde;a and Southern Oscillation (ENSO) on precipitation isotopes (Figure. 8). Since September 2014, the development of the El Ni\u0026ntilde;o phenomenon has led to an increase in oceanic water vapor from the nearby Pacific Ocean, which has progressively penetrated deeper into the Anshun region. During this period, the SOI remained negative, primarily driven by El Ni\u0026ntilde;o, resulting in higher \u0026delta;\u0026sup1;⁸O values between June and September 2015 due to the influence of summer winds, while lower \u0026delta;\u0026sup1;⁸O values were observed during La Ni\u0026ntilde;a. According to the \u0026quot;circulation effect\u0026quot; proposed by Tan Ming (2014) and the analysis of atmospheric circulation in the study area, during El Ni\u0026ntilde;o, the proportion of water vapor from the distant Indian Ocean decreases, while the contribution of water vapor from the nearer Pacific Ocean increases significantly, leading to higher \u0026delta;\u0026sup1;⁸O values in atmospheric precipitation compared to those observed during La Ni\u0026ntilde;a. The inter annual variation in \u0026delta;\u0026sup1;⁸O values in the Pearl River Delta region is significantly positively correlated with ENSO events and negatively correlated with the SOI (Friedman, \u003cspan\u003e1953\u003c/span\u003e), reflecting the response of precipitation isotopes to the aforementioned changes in water vapor.\u003c/p\u003e\n \u003cp\u003eHowever, it is important to note that the study was limited to a relatively short monitoring period of two years (2015\u0026ndash;2016). Large-scale atmospheric circulation phenomena, such as ENSO, typically vary on time scales ranging from inter annual to decadal, and short-term data may not adequately reflect their full variability and long-term impacts. Future research should include longer time-series of isotope data to better understand the relationship between atmospheric circulation patterns and precipitation isotopes, and to assess the generalization of these findings across multiple ENSO events.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"5. Conclusions","content":"\u003cp\u003e1 The isotope values of atmospheric precipitation in Anshun are low in summer and high in winter, which is due mainly to differences in seasonal water vapor sources. In the summer half-year, precipitation mainly originates from oceanic water vapor carried by summer winds, with high humidity and heavy isotope depletion, and the δ\u003csup\u003e18\u003c/sup\u003eO (δD) values are low; in the winter half-year, under the influence of continental air masses, precipitation mainly originates from westerly circulation and inland water vapor re-evaporation, the air humidity is low, and the δ\u003csup\u003e18\u003c/sup\u003eO (δD) values are high.\u003c/p\u003e \u003cp\u003e2 The equation of the atmospheric precipitation line in the Anshun area was established on the basis of the δ\u003csup\u003e18\u003c/sup\u003eO and δD values of atmospheric precipitation collected from 2015\u0026ndash;2016, and the slope of precipitation decreased during the La Ni\u0026ntilde;a phenomenon, which implies that there is a secondary evaporation effect between hydrogen and oxygen isotopes in precipitation. However, during the El Ni\u0026ntilde;o phenomenon, the slope and intercept of the precipitation line were more similar to those of the global atmospheric precipitation line, which indicates that the precipitation during this period was mainly controlled by water vapor of marine origin.\u003c/p\u003e \u003cp\u003e3 There are no key factors driving the isotope changes in rainfall in the study area during the monitoring period. However, the characteristic changes in δ\u003csup\u003e18\u003c/sup\u003eO are sensitive to movements in atmospheric circulation and changes in ENSO.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors contributed to the study conception and design. Conceptualization, Dayun Zhu and Jialu Wang; Data curation, Ju Ni; Funding acquisition, Dayun Zhu and Jialu Wang; Methodology, Ronghan Li; Resources, Jialu Wang; Software, Yurong Han; Supervision, Dayun Zhu; Writing-original draft, Ju Ni; Writing-review \u0026amp; editing, Dayun Zhu.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data presented in this study are available on request from the corresponding author.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by National Natural Science Foundation of China (No. 42361010); The Guiding Fund Project of Government\u0026rsquo;s Science and Technology (No. Qian Ke He Zhong Yin Di[2023]005) and the Academic Talent Plan of Guizhou Normal University (No. Qian Shi Xin Miao[2022]B31).\u0026nbsp;\u003c/p\u003e\n"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBedaso Z, Wu S Y. 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Science of the Total Environment. 659, 1199-1208.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"stochastic-environmental-research-and-risk-assessment","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"serr","sideBox":"Learn more about [Stochastic Environmental Research and Risk Assessment](https://www.springer.com/journal/477)","snPcode":"477","submissionUrl":"https://submission.nature.com/new-submission/477/3","title":"Stochastic Environmental Research and Risk Assessment","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"atmospheric precipitation, δ18O, δD, atmospheric circulation, Anshun area","lastPublishedDoi":"10.21203/rs.3.rs-5177698/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5177698/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":" Extreme weather triggered by El Niño events poses a serious threat to the economy and society. 2015 was one of the years with the highest average global temperatures since the El Niño event was recorded. During this period, many parts of the globe experienced frequent extreme weather events. Isotope values in precipitation are important for understanding extreme climates. Therefore, in this work, 140 stable isotope data from precipitation events in the Anshun area from 2015–2016 (including El Niño and La Niña events) were combined with related meteorological data to analyze the characteristics of hydrogen and oxygen isotopes in atmospheric precipitation, as well as their relationships with precipitation, temperature, and atmospheric circulation. The results show that ① the equations of the atmospheric precipitation lines in the study area are δD=8.70δ18O+19.55 (El Niño period) and δD=8.60δ18O+17.23 (La Niña period), which indicate that the atmospheric precipitation in the El Niño period was affected mainly by oceanic water vapor and that there was an imbalance of isotopes in atmospheric precipitation during the La Niña period, with the phenomenon of secondary evaporation. ② isotopes show seasonal variations that are high in the dry seasons and low in the rainy seasons, which are due mainly to the differences in water vapor sources and air mass properties of precipitation in different seasons. ③ The correlation between δ18O and temperature and precipitation in atmospheric precipitation in the study area is not significant, but the correlation can respond sensitively to changes in the ENSO.","manuscriptTitle":"Isotope variations in precipitation and environmental drivers in Anshun during strong El Niño events","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-12-05 11:01:04","doi":"10.21203/rs.3.rs-5177698/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Accepted","date":"2025-01-25T14:47:36+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-01-23T10:23:44+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"159795418507573365672967456690243332936","date":"2025-01-21T08:28:55+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-11-29T11:40:07+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-11-29T11:32:33+00:00","index":"","fulltext":""},{"type":"submitted","content":"Stochastic Environmental Research and Risk Assessment","date":"2024-11-28T14:26:30+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"stochastic-environmental-research-and-risk-assessment","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"serr","sideBox":"Learn more about [Stochastic Environmental Research and Risk Assessment](https://www.springer.com/journal/477)","snPcode":"477","submissionUrl":"https://submission.nature.com/new-submission/477/3","title":"Stochastic Environmental Research and Risk Assessment","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"477dcc26-ae00-440c-89c4-5dc1b99b9616","owner":[],"postedDate":"December 5th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-02-17T16:04:34+00:00","versionOfRecord":{"articleIdentity":"rs-5177698","link":"https://doi.org/10.1007/s00477-025-02925-1","journal":{"identity":"stochastic-environmental-research-and-risk-assessment","isVorOnly":false,"title":"Stochastic Environmental Research and Risk Assessment"},"publishedOn":"2025-02-15 15:58:04","publishedOnDateReadable":"February 15th, 2025"},"versionCreatedAt":"2024-12-05 11:01:04","video":"","vorDoi":"10.1007/s00477-025-02925-1","vorDoiUrl":"https://doi.org/10.1007/s00477-025-02925-1","workflowStages":[]},"version":"v1","identity":"rs-5177698","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5177698","identity":"rs-5177698","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}