From January 2010 to December 2022, the collection of river water and precipitation samples was conducted.

River water sampling took place 929 times at the center of Orange Island, with a regular sampling interval of 5 d. Specifically, samples were obtained on the 1st, 6th, 11th, 16th, 21st, and 26th days of each month. The sampling depth of river water was relatively deep to avoid the influences of human activity; moreover, this operation can avoid the evaporation fractionation of surface water during the sampling and ensure adequate mixes of the river water.

At the College of Geographic Science, Hunan Normal University, Changsha (Fig. 1, 112∘56′28′′ E, 28∘11′30′′ N), the sampling of precipitation and the measurements of precipitation amount were conducted at an altitude of 55 m, adjacent to the Yuelu Mountains, throughout the sampling period of this study (2010–2022). For precipitation sampling, a siphon rain gauge was repurposed as a collector, consisting of a 6 cm diameter funnel connected to a glass bottle via a plastic pipe. Both rainfall and snowfall were collected and measured at 08:00 and 20:00 local time on the precipitation day, and the volume-weighted value of the two samplings was used to represent the precipitation isotopic values of the day, while the precipitation amount of the day was calculated by the sum of the two samplings. Snowfall samples were carefully packed in sealed plastic bags and transferred to the glass bottle, which was later melted at room temperature because the glass bottle is sealed and the collection process is completed quickly. Thus the evaporation of precipitation samples is effectively prevented. A total of 1668 precipitation isotopic values was obtained over 1668 precipitation days. Furthermore, considering that the sampling interval for river water was set at 5 d and previous studies have indicated that it may take 3–5 d for precipitation (new water) to significantly contribute to river water (Yao et al., 2016; Xiao et al., 2022b), the precipitation isotopes were volume-weighted in the 5 d interval within this study.

To ensure proper preservation, both the river water and precipitation samples were transferred to clean, sealed, polyethylene bottles (30 mL) and stored in a refrigerator at 0 ∘C. However, few precipitation and river water samples were lost, resulting in some missing data. The isotopic composition of the samples was determined using the off-axis integrated-cavity-output spectroscopy method, specifically conducted with equipment from Los Gatos Research in the USA. The stable isotopic values are represented by the δ (per mil) value of the sample relative to Vienna Standard Mean Ocean Water (V-SMOW) as follows: 1δ2Horδ18O=Rsample/RV-SMOW-1‰, where R is the 2H/1H or 18O/16O ratio.

Comparison of the measured stable isotope values of 160 replicate samples of ultrapure water and its standard composition (known δ2H=-128‰, δ18O=-16.3‰ V-SMOW) showed that the measurement precision was <1 ‰ for δ2H and <0.3 ‰ for δ18O (Lis et al., 2008).

The daily air temperature and evaporation data used in this study were obtained from the National Meteorological Reference Station in Changsha (station code: 57687), specifically utilizing the large evaporator model E-601B. It is worth noting that the evaporation recorded by the E-601B evaporator closely approximates the actual evaporation experienced in small water bodies such as lakes and rivers. As such, it reliably represents the quantity and temporal variations of evaporation within the study area (Hua et al., 2019).

Daily runoff discharge data were obtained from the Xiangtan hydrological station (station code: 61102000). Following the national standard Code for liquid flow measurement in open channels GB 50179-93 (Ministry of Water Resources of the People's Republic of China, 1993), the daily discharge values are calculated by applying the weighted average of intraday instantaneous discharge. This calculation is based on water level observations and a specific stage–discharge curve. As per the guidelines outlined in GB 50179-93, the relative errors in instantaneous discharge measurements range from 2 % for high water levels, 5 % for normal water levels, and 9 % for low water levels. On average, the estimated annual discharge falls within 5 % of the actual value. To facilitate the comparison of runoff discharge with precipitation and evaporation, the daily runoff discharge data (m3d-1) are normalized by dividing them by the basin area (m2) of the measuring cross section. Consequently, the runoff depth (md-1 or mmd-1) and runoff discharge data (m3d-1) are computed and utilized in the subsequent analysis.

Because the samplings of precipitation and river water and the observation of hydrometeorological factors were conducted at the Changsha site, it is necessary to verify the representation of the Changsha site in the Xiangjiang River basin. For the aims of supporting the foundation and reducing the uncertainty of this study, a spatial correlation analysis was conducted between the Changsha site and the surrounding regions based on data from 1979 to 2021, including precipitation isotopes, precipitation amount, evaporation, and air temperature. The analysis employed the simulated precipitation isotope data generated by the iAWBM as detailed by Zhang et al. (2015), which has a spatial resolution of 1.5∘×1.5∘, while the air temperature, evaporation, and precipitation amount data are from the ERA5 reanalysis dataset (https://cds.climate.copernicus.eu, last access: 1 June 2023) published by the European Centre for Medium-Range Weather Forecasts (ECMWF), which has a spatial resolution of 1∘×1∘ (Hersbach et al., 2023). Overall, all the data employed in this spatial correlation analysis were integrated into a 5 d interval.

With the aims of building the empirical relationships between the river water isotopes and the hydrometeorological factors and identifying the controlling factor that influences the river water isotopes, multiple linear regressions (MLRs) were used to build the prediction model of the river water isotopes. The variables include the precipitation isotope and the hydrometeorological factors such as precipitation amount, air temperature, evaporation, and runoff. Linear regression and a stepwise regression method were applied in MLRs using SPSS for Windows version 22.0 (SPSS Inc., SPSS Statistics 22.0). All the variables were taken as input variables, and one or more independent variables were retained in the prediction models.

Table 1 presents the monthly average values of air temperature (∘C), precipitation (mm), evaporation (mm), and runoff discharge (108 m3), and the results illustrate the uneven distribution of these factors throughout the year. Based on the monthly patterns of these hydrometeorological factors (Table 1) and the previous findings (Qin et al., 2006; Yao et al., 2016), four distinct runoff periods have been identified: a rainless period, a spring flood period, a major flood period, and a summer drought period. The rainless period spans from October to the following February, characterized by low air temperature, minimal precipitation, evaporation, and runoff discharge. In this period, the runoff discharge exhibits slight fluctuations except for isolated peaks resulting from major rainfall events. The spring flood period, occurring in March and April, marks an increase in runoff discharge, and this can be attributed to relatively higher precipitation amounts as well as moderate air temperature and evaporation in spring. The major flood period, extending from May to mid-July, exhibits a rapid surge in runoff discharge, often reaching peak values due to intensive precipitation events. The summer drought period spans from mid-July to September, while a significant decrease in runoff discharge is always observed. This decline can be attributed to high air temperature, leading to increased evaporation in this period. Additionally, the scarcity of precipitation events and relatively low precipitation amounts contribute to the reduced runoff discharge in this period. Overall, the analysis highlights the seasonal variations in air temperature, precipitation, evaporation, and runoff discharge, leading to distinct runoff periods throughout the year.

From 2010 to 2022, the 5 d volume-weighted precipitation δ2H (δ2HP) exhibited large seasonality throughout the year, as depicted in Figs. 2 and 3. Notably, the 5 d volume-weighted δ2HP ranged from -133.0 ‰ to 39.1 ‰, with a standard deviation of 27.6 ‰. The 5 d volume-weighted δ2HP showed maximum values in March, while the minimum values occurred in September (Fig. 2c). Furthermore, the 5 d volume-weighted δ2HP had the following order: spring flood period > rainless period > major flood period > summer drought period (Fig. 3b). The seasonal variations in the precipitation isotopes primarily resulted from different vapor sources, upstream effects, circulation patterns, and local meteorological factors in different seasons (H. Zhou et al., 2019; Xiao et al., 2023). The δ2H values of river water (δ2HR) ranged from -63.7 ‰ to -21.7 ‰, with a standard deviation of 6.1 ‰ (Figs. 2 and 3).

The magnitude of δ2HR was ranked as follows: spring flood period, major flood period, rainless period, and summer drought period (Fig. 3b). The most depleted and enriched isotopic values in river water in the sampling period occurred on 1 September 2013 and 21 May 2014, respectively (Fig. 2). The seasonal variations in river water isotopes generally aligned with those observed in precipitation, indicating that river water directly derives from the precipitation input and is influenced by its isotopic composition. Moreover, the local meteoric water line (LMWL) and the river water line (RWL) have relatively close slopes of 8.3 and 8.05, respectively (Fig. 3a). However, the δ2HR exhibited a relatively smaller range of variation and a more attenuated temporal pattern compared to the δ2HP. This is likely due to the processes that precipitation undergoes before recharging river water, such as evaporation and mixing with older waters in the subsurface, which significantly reduce the variability in δ2HR (Xiao et al., 2022b, 2023).

The relationships between the δ2HR and the corresponding 5 d accumulated precipitation and evaporation and average runoff discharge and air temperature in different periods are illustrated in Fig. 4. Based on the relationship between the 5 d volume-weighted δ2HP and the corresponding accumulated precipitation in different runoff periods (Fig. S1 in the Supplement; left panel), it is evident that an “amount effect” is observed in the precipitation isotopes across various runoff periods. Specifically, the increased precipitation amounts consistently result in more isotope-depleted precipitation. Furthermore, the δ2HR exhibits a positive correlation versus the corresponding 5 d volume-weighted δ2HP, with a correlation coefficient of 0.55 and p<0.001 (Fig. S2 in the Supplement). This suggests that precipitation input is the primary factor influencing δ2HR. However, as shown in Fig. 4, the δ2HR exhibited relatively weak correlations with the corresponding 5 d accumulated precipitations in the rainless period, major flood period, and summer drought period, as indicated by low correlation coefficient values and p>0.1 (Fig. 4a, i, and m). Although a positive correlation existed between δ2HR and the corresponding 5 d accumulated precipitations in the spring flood period, their correlation coefficients were not exceptionally high (Fig. 4e). The weak correlation between the δ2HR and the precipitation amount or runoff discharge can be attributed to the expansive area and water reserves in the Xiangjiang River basin – that is, after precipitation falls in the basin and the new input precipitation within 5 d may influence the δ2HR to some extent. However, it tends to mix with old waters, such as groundwater, soil water, and river water consisting of a high proportion of older water components in the subsurface flow paths (Xiao et al., 2023), thereby attenuating the impact of precipitation input and predominantly shaping the river water isotopes.

The δ2HR exhibits a consistent relationship with the corresponding 5 d average air temperature and accumulated evaporation within each respective runoff period. However, positive or negative correlations may be observed in these relationships across the four runoff periods. Specifically, in the spring flood period and summer drought period, the δ2HR demonstrates either a highly significant (p<0.001) or non-significant (0.1>p>0.05 or p>0.1) positive correlation with the corresponding 5 d average air temperature and accumulated evaporation (Fig. 4g, h and o, p). Conversely, in the rainless period and major flood period, a highly significant (p<0.001 or p<0.01) negative correlation is observed (Fig. 4c, d and k, i). The negative relationship between the δ2HR and the corresponding 5 d average air temperature or accumulated evaporation may seem counter-intuitive, as river water isotopes typically become enriched with increasing air temperature and progressing evaporation (Gibson et al., 2016; Jiang et al., 2021). This discrepancy can be explained by considering the seasonality of the relationship between precipitation isotopes and air temperature in different runoff periods (Fig. S1; right panel). For instance, the 5 d volume-weighted δ2HP gradually decreases as the corresponding average air temperature increases in the major flood period, but it increases as the corresponding average air temperature decreases in the rainless period (Figs. 2 and 3). This would lead to the negative relationship between the 5 d volume-weighted δ2HP and the corresponding average air temperature in the rainless period and major flood period (Fig. S1b and f), subsequently resulting in the negative relationship between the δ2HR and the corresponding 5 d average air temperature in these two periods (Fig. 4c, d and k, i). Moreover, as there is strong consistency between evaporation and air temperature (Allen et al., 2005), this alignment causes evaporation to also exhibit a negative correlation with δ2HR in these two periods (Fig. 4). Therefore, in the major flood period and rainless period, the influences of air temperature and evaporation on river water isotopes are somewhat masked by the seasonality of precipitation isotopes.

In the spring flood period and summer drought period, the river water isotopes exhibited an enrichment trend with the increasing corresponding average air temperature and accumulated evaporation (Fig. 4g, h and o, p). However, these relationships may be somewhat misleading due to the positive correlation observed between the 5 d volume-weighted δ2HP and the corresponding average air temperature in the spring flood period (p<0.05) and summer drought period (p>0.1) (Fig. S1d and h). This suggests that the positive correlation between river water isotopes and air temperature or evaporation may also be influenced by the seasonality of precipitation isotopes. Moreover, in the spring flood period, the increases in precipitation amount and runoff discharge lead to more isotope-enriched river water, as indicated in Fig. 4e and f. This phenomenon can be attributed to the most enriched precipitation isotopes occurring in the spring flood period (Figs. 2c and 3b), as the precipitation amount and runoff discharge gradually increase in this period (Fig. 2a). This contributes to the positive relationship observed between the δ2HR and the corresponding accumulated precipitation or average runoff discharge. Consequently, based on the findings of this section, when interpreting the relationships between the river water isotopes and hydrometeorological factors, it is important to consider the uncertainty arising from the seasonality of precipitation isotopes.

As shown in Fig. 4, the relationship between the δ2HR and various factors may not be significant or may contradict common sense – that is, precipitation input and evaporation are likely the major driving factors that change the isotopic compositions of river water. For instance, the river water isotopes tend to be more depleted, and the slope of RWL tends to be high in heavy-precipitation events, while the river water generally exhibits gradually enriched isotopes and a lower evaporation line slope on rainless days with high air temperature and evaporation (Gibson et al., 2016; Yang et al., 2020; Jiang et al., 2021). Therefore, under the river water sample collection at a 5 d interval, it is essential to analyze the influences of the individual hydrometeorological factors on the changes in the δ2HR between two contiguous samplings (Δδ2HR), which were calculated using the δ2HR differences between the δ2HR of the following and preceding samplings (i.e., Δδ2HR=δ2HR(t)-δ2HR(t-1)).

The 5 d runoff depth changes (i.e., ΔR=R(t)-R(t-1)) and Δδ2HR exhibited significant increases and decreases, respectively (p<0.001), in response to the 5 d accumulated precipitation (Fig. 5a and b). These findings indicate that precipitation input is the primary factor influencing the variations in runoff discharge and river water isotopes. Furthermore, based on the intersections between the linear fitting line and the x axis, the thresholds for precipitation amounts influencing the Δδ2HR and ΔR at a 5 d interval were determined to be 19.0 and 19.4 mm, respectively. This suggests that heavier precipitation events are more likely to alter the river water isotopes. The weak correlation (p>0.1) between the Δδ2HR and corresponding 5 d accumulated precipitation below the threshold value of 19.0 mm supports this observation (Fig. 5c). Conversely, when the 5 d accumulated precipitation exceeded 19.0 mm, a significant (p<0.001) negative correlation was observed between the Δδ2HR and the corresponding 5 d accumulated precipitation (Fig. 5d). In other words, greater 5 d accumulated precipitation led to more negative δ2H values in the subsequent river water sample, with a correlation coefficient of -0.34 and p<0.001. Therefore, it can be concluded that the variation in river water isotopes reflects the isotopic signal of the input precipitation, and the isotopic composition of river water exhibits a significant amount effect by the precipitation input.

Because of the confluence processes of the river water from upstream to downstream and the mixing processes between the new and old waters in the subsurface, river water may consist of a certain proportion of old water with a relatively long residence time (Xiao et al., 2022b, 2023), and thus we analyze the relationship between the river water isotopes and the hydrometeorological factors at a longer time interval. The relationships between the Δδ2HR and the corresponding accumulated evaporation at various time intervals (5, 10, 15, 20, 25, and 30 d) are shown in Fig. 6. As there is a strong consistency between evaporation and air temperature (Allen et al., 2005), the relationship between the Δδ2HR and air temperature was not analyzed in this paper. The results revealed a weak and statistically non-significant correlation (p>0.1) between the Δδ2HR and corresponding accumulated evaporation at the 5, 10, 15, and 20 d intervals (Fig. 6a–d). However, a significant (p<0.05) negative correlation with relatively low correlation coefficients was observed at the 25 and 30 d intervals (Fig. 6e and f). The negative relationship between the Δδ2HR and the corresponding accumulated evaporation (Fig. 6c–f) also contradicts the common sense that the river water generally becomes more enriched under the influences of evaporation. This may be due to the negative relationship between the δ2HP/δ2HR and the corresponding average air temperature and accumulated evaporation in the rainless period and major flood period (Figs. 2 and 3) as discussed earlier. In addition, the effect of dilution precipitation input on river water isotopes (Figs. 5 and S1) and the relatively low air temperature and high relative humidity in the heavy-precipitation events may be greater than the enrichment effect of evaporation (e.g., similar to the effect demonstrated in Xiao et al., 2022a).

To highlight the influences of evaporation on the river water isotopes and to eliminate the influence of precipitation input, we analyzed the relationship between the Δδ2HR and the corresponding accumulated evaporation at different intervals (5, 10, 15, 20, 25, and 30 d) without precipitation input (Fig. 7). The findings revealed a significant (p<0.01 or p<0.001) positive correlation between these variables at different time intervals. In other words, as the accumulated evaporation increased, the Δδ2HR also increased, with the correlation coefficient generally increasing with longer rainless intervals. Notably, the influence of greater evaporation on the Δδ2HR was particularly pronounced at the 30 d interval, exhibiting a high correlation coefficient of 0.35 and a p value of less than 0.001, while these intervals occurred exclusively in 2013 and 2022, which were characterized as very dry years with high evaporation and multiple 30 d intervals devoid of precipitation (Figs. 2 and S3 in the Supplement).

The decrease in runoff discharge or water level in the basin can be attributed to evaporation within the basin, as evaporation increases the outflow component in the river water balance, leading to a reduction in the amount of water flowing out of the basin. Additionally, as runoff discharge can increase due to precipitation input, we examined the relationship between the Δδ2HR and the corresponding changes in the runoff (ΔR) at different intervals (5, 10, 15, 20, 25, and 30 d) without precipitation input (Fig. 8). The results indicated a negative correlation between the Δδ2HR and the corresponding ΔR at the 10, 15, 20, and 25 d intervals, demonstrating relatively high correlation coefficients around -0.4 and p values of less than 0.001. Considering the higher correlation coefficients observed between the Δδ2HR and the corresponding ΔR (Fig. 8) compared to those between the Δδ2HR and the corresponding accumulated evaporation (Fig. 7), it can be inferred that the ΔR serves as a suitable proxy to represent the effects of evaporation on river water isotopes. However, the correlation between the Δδ2HR and the corresponding ΔR was relatively weak at the 5 d intervals, with correlation coefficients of -0.11 (Fig. 8a). Thus, this could be attributed to the short time interval and the limited impact of accumulated evaporation.

Analysis of the annual accumulated evaporation, average air temperature, accumulated precipitation, and average runoff discharge in the major flood period and summer drought period reveals that 2013 and 2022 experienced severe summer drought conditions (Fig. S3). These periods were characterized by exceptionally high temperatures and evaporation and low precipitation levels compared to the 13-year observations, particularly in 2022. The summer drought period of 2022 recorded only 16.3 mm of precipitation, the lowest among the 13-year observations and significantly lower than the highest precipitation recorded in the summer drought period of 2010 (i.e., 300.5 mm). Accumulated evaporation and average air temperature in the summer drought period of 2022 reached 385.1 mm and 28.8 ∘C, second only to the 393.7 mm and 29.8 ∘C recorded in the summer drought period of 2013 (Fig. 3). Furthermore, the extreme drought events in 2013 and 2022 have been extensively reported, indicating widespread meteorological, hydrological, and soil droughts that pose a significant threat to water resources for domestic, agricultural, ecological, and human needs (Ma et al., 2022; Bonaldo et al., 2023). Therefore, this section primarily focuses on these two extreme drought processes in 2013 and 2022.

As depicted in Fig. 9, the period from late June to mid-August 2013 witnessed rare precipitation, high evaporation rates, and elevated air temperatures. Consequently, the runoff discharge gradually decreased from 2699 m3s-1 on 30 June to 415 m3s-1 on 16 August. Simultaneously, the δ2HR progressively increased from -42.0 % to -34.8 % (Fig. 9e). Similarly, from mid-July to early November 2022, only 72.6 mm of precipitation was recorded, resulting in a gradual decline in runoff discharge until early September. In this process, the δ2HR increased from -53.1 % on 16 July to -37.9 % on 6 September (Fig. 9f). Subsequently, the Xiangjiang River maintained low runoff discharge and raised δ2HR levels until the end of December, and the δ2HR increased by up to 20.2 ‰ from -53.1 % on 16 July to -32.9 ‰ on 26 November 2022 (Fig. 9f). These findings align with the results obtained in the previous section, indicating that decreases in runoff discharge and higher evaporation rates on long rainless days contribute to the gradual enrichment of river water isotopes. However, it is noteworthy that the δ2HR range (-63.7 ‰ to -21.7 ‰) mentioned earlier includes the most positive δ2HR values influenced by the extreme drought events in 2013 and 2022 (Fig. 2d), while the most isotope-enriched river water occurred on 21 May 2014 (Fig. 2d) and the most isotope-enriched precipitation occurred in the spring flood period (Fig. 3b). This indicates that the input of relatively enriched spring precipitation isotopes plays a crucial role in controlling the isotopic enrichment of river water.

By examining the 13-year observations and ranking the 5 d accumulated precipitation, we found that the maximum accumulated precipitation, totaling 301.6 mm, occurred from 27 June to 1 July 2017 (Fig. 2). Notably, on 30 June and 1 July 2017, daily precipitation reached 146.4 and 130.3 mm, respectively. Additionally, between 21 and 26 June 2017, the precipitation amount reached 185.5 mm. Another significant precipitation event took place in June 2011, when the total precipitation for the month reached 340.3 mm, with a single-day rainfall of 110.4 mm on 28 June. To analyze the impact of extreme precipitation on river water isotopes in the major flood period, the temperature, evaporation, precipitation, runoff discharge, δ2HP, and δ2HR in these two extreme-precipitation processes are presented in Fig. 10.

In the heavy-precipitation period from 5 to 16 June 2011, the runoff discharge increased from 1410 to 9010 m3s-1, while the δ2HR decreased from -30.8 ‰ to -46.5 ‰ (Fig. 10e). Similarly, from 21 June to 1 July 2017, the runoff discharge and river water isotopes exhibited significant fluctuations and depletion under the influence of precipitation input (Fig. 10f). For instance, the runoff discharge increased from 3961 to 18 237 m3s-1, while the δ2HR decreased from -29.3 ‰ to -56.8 ‰, which represents the third-lowest δ2HR value among the 13-year observations. In both extreme-precipitation processes, the precipitation isotopes were relatively depleted (Fig. 10c and d). Specifically, the volume-weighted δ2HP was -50.9 ‰ from 5 to 16 June 2011, and the value was -76.7 ‰ from 21 June to 1 July 2017. This indicates that the input of extreme precipitation leads to rapid decreases in δ2HR. For instance, in the extreme-precipitation process of June 2011, the δ2HR was reduced by -15.7 ‰ under a precipitation input of 144.7 mm from 5 to 16 June, while the δ2HR decreased by -27.5 ‰ with a precipitation input of 487.1 mm in the extreme-precipitation process from 21 June to 1 July 2017. Subsequently, in the summer drought periods of 2011 and 2017, the river water isotope was gradually enriched to approximately -38 % (Fig. 10e and f).

River water isotopes serve as a valuable record not only for detecting the isotopic depletion signal of extreme-precipitation input in the major flood period (Fig. 10), but also for capturing the influence of moderate precipitation in the summer drought period. In the latter case, the river water isotopes gradually become enriched with the decrease in the runoff discharge in prolonged periods of drought. When the basin is relatively dry, i.e., the reserves of soil water, groundwater, and river water are limited, the river water may be influenced by a medium precipitation amount resulting in a highly depleted river water isotope signal observed in this extended river water sample series. For instance, following a precipitation input of 53.5 mm with a δ2HP of -68.1 ‰ on 23 August 2013 (Fig. 9c), the river water isotopes exhibited a significant depletion (Fig. 9e). Notably, the δ2HR rapidly declined from -35.7 ‰ on 21 August to -63.7 ‰ on 1 September, representing the most negative δ2HR value observed over the 13-year observations. This δ2HR signal differs significantly from rapid decreases in δ2HR caused by extreme-precipitation input in the river water (Fig. 10), highlighting the importance of careful consideration when reconstructing precipitation based on isotopic signals derived from river water.

By examining the relationship between the Δδ2HR and the corresponding accumulated evaporation, it becomes evident that a stronger correlation between the two variables emerges at time intervals exceeding 10 d (Fig. 6). This suggests that the influence of evaporation on river water isotopes manifests over relatively long time intervals, spanning from tens of days to even several months, particularly in long dry periods without precipitation input (Fig. 9). The influences of evaporation on river water isotopes occur gradually, making it challenging to capture using short-term analyses. Conversely, as indicated by Xiao et al. (2022b, 2023), the relatively weak influence of evaporation on river water isotopes observed at short intervals (Figs. 6 and 7) can be attributed to the significant influx of precipitation input (i.e., new water) rapidly flowing into the river network, which experiences limited evaporation effects in the relatively short residence time. Furthermore, the variation in runoff discharge exhibits a notable relationship with the Δδ2HR (Fig. 8); however, it is important to note that the decline in runoff discharge may not be solely due to evaporation but could also be influenced by water transport from the Xiangjiang River to Dongting Lake due to rapid downstream drainage (Zhan et al., 2015). This introduces uncertainties in the analysis based on the changes in runoff discharge and river water isotopes between different time intervals.

The independent variables, including volume-weighted precipitation isotopes (δ2HP), accumulated precipitation (P), accumulated evaporation (E), changes in runoff depth (ΔR), average air temperature (Tave), maximum air temperature (Tmax), and minimum air temperature (Tave), were used to build the prediction model of the dependent variable (i.e., the river water isotopes). According to the correlations between the river water isotopes and the hydrometeorological factors (Figs. 4–8), the changes in the δ2HR (Δδ2HR) and a 15 d time interval were used as the dependent variable and the time step, respectively, of all the variables in the MLRs. As indicated by the regression equations in the different runoff periods (Eqs. 2–5), the river water isotopes showed strong seasonality and were controlled by different factors. For instance, in the major flood period and summer drought period, the δ2HR usually reflected heavy-precipitation inputs with depleted isotopes, as supported by the negative correlation between the Δδ2HR and ΔR or accumulated precipitation (Eqs. 2 and 3), indicating the amount effect by the precipitation input. The amount effect of water stable isotopes was widely observed around the world, especially in the regions with flash input of depleted precipitation (e.g., Dansgaard, 1964; H. Zhou et al., 2019). Specifically, the precipitation isotopes were relatively depleted in these two periods (Fig. 3b), while the river water isotopes captured the precipitation input signal, particularly when the accumulated precipitation exceeded the threshold precipitation amount (Fig. 5). Additionally, the extreme-precipitation events mainly occurred in the major flood period, resulting in relatively isotope-depleted precipitation that was reflected as negative records in the δ2HR (Figs. 2 and 10, Eq. 2). 2δ2HRinMF=(0.098±0.012)δ2HP+(0.098±0.022)E-(0.027±0.007)ΔR-(3.01±0.861),r=0.56,p<0.001,n=2263δ2HRinSD=-(0.064±0.01)ΔR-(0.02±0.008)P+(0.497±0.479),r=0.51,p<0.001,n=1614δ2HRinRL=-(0.041±0.008)ΔR+(0.01±0.004)δ2HP+(0.957±0.152),r=0.29,p<0.001,n=3845δ2HRinSF=(0.055±0.013)δ2HP+(0.013±0.003)P-(0.092±0.031)Tmax+(2.773±0.702),r=0.43,p<0.001,n=155

In the rainless period, the δ2HR values were more positive compared to the summer drought period (Fig. 3b), possibly influenced by the input of more enriched precipitation and evaporation enrichment along with the decreases in ΔR (Eq. 4). In addition, the δ2HR reached its highest positive values in the spring flood period, influenced by the precipitation input with relatively depleted isotopes (Figs. 2 and 3), while an “inverse amount effect” and an “inverse temperature effect” were found in this period, as indicated by the positive correlation between the Δδ2HR and accumulated precipitation and the negative correlation between the Δδ2HR and air temperature (Tmax) (Eq. 5), and the reasons can be attributed to the seasonality of precipitation isotopes and air temperature as discussed in Sect. 4.3. Overall, the river water isotopes in the Xiangjiang River basin are controlled by various complex factors in different runoff periods, while such findings in the controlling factors that influence river water isotopes may be beneficial in paleoclimate reconstruction and establishment of isotope hydrologic models.

The Xiangjiang River, serving as a significant inflow water source, exerts an influence on the hydrologic and isotope mass balance of Dongting Lake, the second-largest freshwater lake in China (Zhan et al., 2015; H. Zhou et al., 2019). The isotopic composition of lake water primarily reflects the input waters, including lake surface precipitation and inflowing river water (Steinman and Abbott, 2013; Gibson et al., 2016; Xiao et al., 2022a), while the isotopic information in lake water can also influence the stable isotopic signatures preserved in lake sediment. Consequently, proxy indicators recorded in lake sediments can be utilized for paleoclimate reconstruction, benefiting from the relationships between the input water isotopes and the local environments.

Through the analysis of river water isotopes and various hydrometeorological factors on a seasonal scale, it becomes evident that the Δδ2HR can reflect the corresponding accumulated evaporation and precipitation input (Figs. 5–7) and the decline in runoff discharge (Fig. 8) at the observed time intervals. Moreover, river water isotopes entering the lakes can record signals of extreme precipitation (Fig. 10) or exhibit gradual isotopic enrichment under the influence of evaporation in relatively dry periods spanning tens of days or even several months without precipitation (Fig. 9). In addition, the isotopic characteristics of precipitation are governed by large-scale factors such as moisture sources, upstream effects, and circulation patterns and are less influenced by local meteorological factors (Aggarwal et al., 2016; H. Zhou et al., 2019), and thus the river water isotopes are better suited to reflecting local environments. Consequently, in comparison to the isotopic characteristics of precipitation, the river water isotopes may provide valuable insights into the relationship between the proxy indicators and the local environments.

In previous studies, due to limited data availability, the inflow water isotopes of the lake were often represented by the volume-weighted precipitation isotopes in hydrologic and isotope-mass-balance models (e.g., Steinman and Abbott, 2013; Skrzypek et al., 2015; Jones et al., 2016). However, based on the 13-year observations conducted in this study, the annual volume-weighted δ2HR and δ2HP were found to closely match only in the period of 2012–2015, with differences within 2 ‰ (Table 2). In other years, the annual volume-weighted δ2HP was either more negative or more positive compared to the annual volume-weighted δ2HR. These differences in the annual δ2HR and δ2HP were mainly influenced by the seasonality of δ2HP and precipitation amount. For instance, significant variations were observed between the volume-weighted δ2HP and δ2HR in the different runoff periods (Table 2). Therefore, representing the inflow water of the lake solely by the annual volume-weighted precipitation isotopes can only serve as a rough estimation. To accurately depict the detailed variations in the lake hydrologic and isotope mass balance, more comprehensive observations of the inflowing river water are required.

Nevertheless, according to the analysis on an annual scale based on the 13-year observations, the volume-weighted δ2HR values in the different runoff periods exhibited non-significant correlations (0.1>p>0.05 or p>0.1) with the corresponding total precipitation, average runoff discharge, average air temperature, and total evaporation (Fig. S4 in the Supplement). Although this study encompasses 13-year observations with a sampling interval of 5 d, there is a need for longer and systematic observations of various water types and hydrometeorological factors, spanning decadal or longer timescales, to better elucidate the relationships between river water isotopes and local environments on the annual scale.

The increases in runoff discharge and water level in the Changsha section of the Xiangjiang River are primarily attributed to the precipitation recharge in the middle and upper reaches, while the influences of evaporation on river water isotopes mainly occur within the basin. It should be noted that the sampling and observation sites for precipitation, air temperature, and evaporation in this study, located at Hunan Normal University and the National Meteorological Reference Station in Changsha, respectively, only represent the local conditions in Changsha. Therefore, the extent of their influence on the runoff discharge and water stable isotopes of the entire Xiangjiang River may be limited. To assess whether the sampling and observations in Changsha can adequately represent the Xiangjiang River basin, the spatial correlation analysis based on the iAWBM was conducted, and the results of air temperature (Fig. 11a), evaporation (Fig. 11b), precipitation amount (Fig. 11c), and precipitation isotopes (Fig. 11d), respectively, were shown.

The results of spatial correlation analysis revealed a high correlation between the reanalysis data of air temperature and evaporation at the Changsha site and those in the surrounding regions, with correlation coefficients above 0.8 and p<0.001 for the grid points in the Xiangjiang River basin (Fig. 11a and b). Furthermore, while the relationship between the reanalysis data of precipitation amount and simulated precipitation isotopes at the Changsha site and in the surrounding regions is not as strong as that for air temperature and evaporation, the correlation is still high (Fig. 11c and d). For instance, the correlation coefficients between the Changsha site and the grid points in the Xiangjiang River basin exceed 0.7 with p<0.001 for both the reanalysis data of the precipitation amount and the simulated precipitation isotopes (Fig. 11c and d).

Overall, the high correlation coefficients for these factors support the time consistency between the Changsha site and the surrounding regions, while the high spatial consistency is characterized by the large area of the high correlation coefficients, which covers the whole Xiangjiang River basin. These may be related to the fact that the Xiangjiang River basin as a whole is located in the subtropical zone of southern China, which makes the meteorological elements (precipitation, evaporation, temperature) of the basin have a certain consistency. Moreover, the precipitation isotope of the basin also has a high correlation, which can be attributed to the uniform water vapor source, precipitation form, and atmospheric circulation within the Xiangjiang River basin (H. Zhou et al., 2019; Liu et al., 2023). These spatial correlation analyses provide support for the study basis of using the observations at the Changsha site as representative of the Xiangjiang River basin.