Review Reports - Characteristics of Precipitation Stable Isotopes and Moisture Sources in the Qinghai Lake Basin

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

This study investigated the stable isotope composition of precipitation in the Qinghai Lake Basin (June 2023–October 2024) to better understand regional hydrological processes under a warming and humidifying climate. The results showed clear seasonal isotope variation, with enrichment in summer and depletion in spring and autumn. The Local Meteoric Water Line had higher slope and intercept values than the global standard, indicating specific regional climatic influences. Stable isotopes were positively correlated with air temperature, while the relationship with precipitation amount was weak and negative. Backward trajectory analysis further revealed that precipitation moisture is mainly supplied by westerly air masses, with additional contributions from local evaporation and continental sources. Overall, the study provides valuable insights into regional water cycling and atmospheric moisture transport.

A major limitation of the manuscript is the relatively short observation period (June 2023–October 2024). Given the strong interannual variability in precipitation regimes and atmospheric circulation over the Qinghai–Tibetan Plateau, the current dataset may not be sufficient to support broader climatic or hydrological generalizations. The authors should explicitly acknowledge the preliminary nature of these findings and discuss the limitations associated with the absence of a multi-year record.

The interpretation of the temperature effect appears overstated considering the very low coefficients of determination (R² = 0.075–0.079). Although the correlations are statistically significant, the explanatory power of the models is weak, suggesting that additional environmental and synoptic-scale factors likely play important roles. The discussion and conclusions should therefore be moderated to better reflect the limited predictive strength of the regression models.

The backward trajectory analysis requires a more rigorous methodological description. Important details are currently missing, including the rationale for selecting a single arrival height of 2000 m, the criteria used for determining the number of trajectory clusters, the number of trajectories analyzed per precipitation event, and the associated uncertainty of the clustering results. Since the moisture source interpretation strongly relies on the HYSPLIT outputs, a more transparent and reproducible methodological framework is essential.

Author Response

Comments 1:

Response 1:

Thank you very much to the reviewer for this important comment. We fully agree with the reviewer’s suggestion. In response to the key concerns raised, we have carried out systematic revisions as follows:

(1) We have systematically revised all overly generalized statements that extended beyond the observation period. The scope of the conclusions has been strictly limited to the observation period from June 2023 to October 2024. Expressions such as “atmospheric processes,” “significant seasonal variation characteristics,” and “seasonal differences,” which exceeded the support of the data, have been removed. The boundaries of the study conclusions have been clearly defined.

(2) In the Introduction, we have explicitly clarified that this study aims to obtain the latest in situ data for the basin based on short-term continuous observations and to analyze isotopic characteristics and moisture sources during the observation period, rather than providing a definitive explanation of long-term climate change(lines 71–78, pages 2).

(3) A new subsection on study limitations has been added to the Discussion. This section systematically elaborates that the study still has limitations and that the conclusions are preliminary in nature. Specifically, the observation period is clearly stated, emphasizing the preliminary nature of the results. In addition, considering the complex interannual variability of atmospheric circulation in the northeastern Qinghai–Tibet Plateau, we clarify that short-term datasets are insufficient to represent long-term climate variability(lines 511–526, pages 14).

Comments 2:

Response 2:

Thank you very much to the reviewer for this important comment. In response to the key concerns raised, we have carried out systematic revisions as follows:

(1) We have comprehensively revised the expressions throughout the manuscript, removing all statements that suggested significant temperature effects. The distinction between statistical significance and the actual explanatory power of the model has been clarified, and it is explicitly stated that temperature is not the dominant controlling factor of precipitation isotope fractionation in the basin during the study period(lines 338–351, pages 10).

(2) The relevant sections in the Discussion have been thoroughly rewritten, (lines 398–409, pages 12)with the following improvements: in response to the issue of low R², we explicitly state that single environmental factors (e.g., air temperature and precipitation amount) have limited explanatory and predictive power for isotopic variations; we further clarify that a single environmental factor cannot effectively predict isotopic changes. The lack of significant correlations between precipitation stable isotopes and individual meteorological variables is partly related to the length of the observational dataset. More fundamentally, the complexity and diversity of factors influencing precipitation isotopes lead to weak correlations with any single meteorological variable. In the Qinghai Lake Basin, precipitation is influenced by multiple moisture sources, including warm and humid air masses from the North Atlantic, monsoonal moisture, dry polar air masses, and recycled moisture from the lake surface. In addition, the pronounced temporal heterogeneity of hydrothermal conditions in inland basins further increases the complexity of precipitation isotope composition and its fractionation mechanisms.

(3) The Conclusion and Abstract have been revised accordingly. (lines 549–557, pages 15)All statements exaggerating the effect of temperature have been removed. The conclusions are now strictly based on the actual explanatory power of the regression models, clearly emphasizing that precipitation isotope fractionation is jointly regulated by multiple factors.

Comments 3:

Response 3:

Thank you very much to the reviewer for this important comment. In response to the key concerns raised, we have carried out systematic revisions as follows:

(1) In the revised manuscript, three gradients of initial air mass heights have been set at 500 m, 1000 m, and 2000 m. The rationale is that the maximum moisture transport in China generally occurs around 2000 m above ground level; therefore, 2000 m was selected as the upper limit in this study(lines 152–158, pages 4).

(2) In the revised manuscript, we have added the following: for each precipitation event, backward trajectories with a 1-hour simulation interval were employed to trace moisture sources, and the clustering module of the model was used to classify trajectories in order to determine the relative contributions of different moisture transport pathways. The method of backward trajectory clustering has also been supplemented. Specifically, clustering was conducted by calculating the spatial variance (SPVAR) between each pair of trajectories and the total spatial variance (TSV), thereby grouping all air mass trajectories reaching the receptor region(lines 164–170, pages 4–5).

(3) In the Discussion section, the backward trajectory clustering results have been cross-validated with the observed precipitation stable isotope data, thereby enhancing the reliability of the trajectory clustering results.

Author Response File: Author Response.pdf

Reviewer 2 Report

Comments and Suggestions for Authors

The manuscript entitled “Characteristics of Precipitation Stable Isotopes and Moisture Sources in the Qinghai Lake Basin” presents a relevant study on isotope hydrology and atmospheric moisture transport using δ²H, δ¹⁸O analysis and HYSPLIT modeling. The topic is scientifically valuable, and the dataset provides useful regional insights. However, the manuscript requires moderate revision to improve its scientific rigor, methodological transparency, and clarity of presentation.

Comments for author File: Comments.pdf

Comments on the Quality of English Language

The overall quality of English in the manuscript is acceptable; however, it requires improvement to ensure clarity, precision, and consistency with academic writing standards. Several grammatical issues are present, including incorrect capitalization (e.g., “june” instead of “June”), inconsistent verb tense usage, and occasional misuse of articles. In addition, some sentences are overly long and complex, which affects readability and clarity. Certain expressions are repetitive or lack conciseness, particularly in the Results and Discussion sections. It is recommended that the manuscript undergo professional English editing or careful linguistic revision to enhance fluency, coherence, and overall readability.

Author Response

Comments 1:

Conceptual Framework (Introduction, pp. 1–2, lines 12–34 and 80–90)

The manuscript provides adequate background; however, it lacks explicitly formulated research questions or hypotheses. The objectives are stated in general terms, which weakens the scientific framework and limits the coherence between the Introduction and subsequent sections. The authors should clearly define 2–3 research questions or testable hypotheses to guide the study.

Response 1:

We sincerely thank the reviewer for this important suggestion regarding the conceptual framework. We fully agree with the reviewer’s comment and have made systematic revisions as follows: (lines 71–78, pages 2)Based on the review of research progress in the Introduction, we have added a dedicated paragraph to clearly articulate the research questions. Two key issues in current studies on precipitation isotopes and moisture sources in the Qinghai Lake Basin are identified: although extensive research has been conducted on stable water isotopes and the hydrological cycle over the Qinghai–Tibet Plateau, there is still a lack of recent high–temporal-resolution continuous observational data of precipitation isotopes in the Qinghai Lake Basin. Consequently, the temporal variation patterns of precipitation isotopes and the influence of environmental factors (e.g., air temperature and precipitation amount) remain insufficiently understood. In addition, there is a lack of data support for quantitatively assessing the relative contributions of different moisture sources within the basin.

Comments 2:

Study Area and Sampling Design (Section 2.1–2.2, pp. 3–4, lines 92–121; Figure 1, p. 3)

The study area is well presented in Figure 1; however, the representativeness of a single sampling site within the basin is not discussed. Additionally, the sampling design lacks justification (frequency and duration), and no information is provided on analytical precision, calibration, or quality control of isotope measurements. These aspects should be clarified to ensure reproducibility.

Response 2:

We sincerely thank the reviewer for the detailed and important comments on the Methods section. We fully agree with the reviewer’s suggestions and have made systematic additions and improvements in the revised manuscript as follows:

(1) We have added clarifications in both the Methods and Discussion sections: the sampling site is located in a representative area of the Qinghai Lake Basin and can, to some extent, reflect the isotopic characteristics of precipitation at the basin scale. At the same time, we explicitly acknowledge that the relatively short observation period and the use of a single sampling site limit the analysis of interannual and spatial variability in the regional climate system. Therefore, the results of this study are mainly applicable for characterizing regional-scale average features(lines 511–519, pages 14).

(2) Regarding the sampling design (frequency and duration), we have supplemented the underlying principles in the revised manuscript. (lines 100–107, pages 3)The sampling period spans from June 2023 to October 2024. Precipitation samples were collected following meteorological observation standards for defining precipitation events, whereby all precipitation occurring between 20:00 of the current day and 20:00 of the following day is defined as a single event. In addition, the limitations section now clarifies that this duration represents a short-term observation period and cannot fully capture multi-year variability.

(3) We have added a detailed description of isotope analysis and quality control procedures in the Methods section. Specifically: (lines 114–127, pages 2)Samples were sealed immediately after collection and transported to the laboratory for low-temperature storage. Prior to analysis, water was extracted without fractionation using a fully automated vacuum condensation extraction system (LI-2100). The extracted water samples were transferred into 2 mL vials and sealed with Parafilm. After filtration through a 0.22 μm membrane, δ²H and δ¹⁸O values were measured using a liquid water isotope analyzer (TLWIA-912). During measurement, three standard samples were selected, and three unknown samples were analyzed after each standard. Every 12 injections (3 standards and 9 unknown samples) constituted one cycle, with a total of 72 injections. At the end of each cycle, a calibration curve was generated based on the standard samples to correct the results. Each sample was injected six times; the first two injections were discarded to eliminate isotopic memory effects, and the average of the last four injections was taken as the final value.

All isotopic results are reported in per mil (‰) relative to Vienna Standard Mean Ocean Water (V-SMOW). The analytical uncertainties for δ¹⁸O and δ²H are ±0.05‰ and ±0.2‰, respectively. The calculation is as follows:

where _Rsample and R_V-SMOW represent the isotope ratios of the sample and the standard, respectively.

These additions ensure the completeness of the Methods section and the reproducibility of the data.

Comments 3:

HYSPLIT Model Configuration (Section 2.3, p. 4, lines 125–140)

The application of the HYSPLIT model is appropriate, but the configuration lacks justification (e.g., 240 h trajectory length, 2000 m height). Moreover, uncertainties and limitations of backward trajectory modeling are not discussed. This reduces the robustness of the moisture source analysis presented later in the manuscript.

Response 3:

We sincerely thank the reviewer for the professional comments on the configuration of the HYSPLIT model and its associated uncertainties. We fully agree with the reviewer’s suggestions and have made the following additions and revisions in the manuscript:

(1) In the Methods section, we have added a justification for the 240-hour trajectory length, noting that it corresponds to the average residence time of water vapor in the troposphere (~240 h). Accordingly, a 10-day backward trajectory was adopted, which can capture continuous precipitation processes and improve the accuracy of tracing moisture source pathways. We have also included a comparison of different initial air mass heights, setting them at 500 m, 1000 m, and 2000 m based on previous studies in the Qilian Mountains. In addition, the resolution of the meteorological reanalysis data has been specified(lines 150–157, pages 4).

(2) In the Discussion section, we have added a dedicated subsection titled “Uncertainty in the HYSPLIT Model and Moisture Source Analysis,”which addresses uncertainties associated with meteorological input data resolution and model assumptions, and discusses their potential impacts on the accuracy of moisture source identification(lines 522–526, pages 14).

(3) Furthermore, in the limitations subsection, we have supplemented a discussion on the influence of the short observation period on the results of moisture source analysis.

Comments 4:

Temporal Isotope Variability (Section 3.1, pp. 4–6; Figure 2, p. 6)

Figure 2 clearly illustrates temporal variations of δ²H, δ¹⁸O, and d-excess. However, the discussion is partially repetitive and lacks deeper physical interpretation of extreme values and variability. The linkage between isotopic patterns and atmospheric processes should be strengthened.

Response 4:

We sincerely thank the reviewer for the detailed comments on the Results and Discussion sections. We fully agree with the reviewer’s suggestions and have carried out systematic improvements in the revised manuscript as follows:

(1) Reduction of redundant descriptions: we have restructured the Discussion section by removing repetitive descriptive content that overlapped with the Results, thereby strengthening the interpretation of the physical mechanisms underlying isotopic variations.

(2) Supplementation of physical explanations for extreme values and variability: we have added explanations for isotopic extremes and variability. In summer, higher temperatures enhance surface evaporation, and the vapor derived from evaporating water bodies is typically enriched in d-excess. When this evaporative vapor mixes with atmospheric moisture, it can significantly increase the d-excess of the air mass. In addition, extreme isotope values are interpreted as responses to different weather processes(lines 464–465, pages 13).

(3) We have further linked isotopic variations to atmospheric circulation patterns and moisture transport pathways. (lines 483–487, pages 14)Specifically, moisture originating from distant oceanic sources travels long distances with limited replenishment along the pathway and undergoes multiple precipitation events. During transport, heavy isotopes are progressively removed (“rainout effect”), leading to isotopic depletion and consequently lower precipitation isotope values in autumn. In contrast, moisture from central and northern Asia is generally sourced from relatively dry regions. Combined with lower spring temperatures at high altitudes and weaker secondary evaporation effects, this results in generally lower d-excess values in spring compared to summer and autumn.

Comments 5:

Meteoric Water Line Analysis (Section 3.2, pp. 6–7; Figure 3 and Table 1)

The Local Meteoric Water Line and seasonal variations are well presented in Figure 3 and Table 1. Nevertheless, the discussion lacks sufficient comparison with regional and global studies, and deviations from the GMWL are not fully explained. A more critical and comparative analysis is required.

Response 5:

We sincerely thank the reviewer for the valuable suggestion. We fully agree with the reviewer’s comment and have made the following improvements in the revised manuscript:

(lines 243–252, pages 7)The slope and intercept of the Local Meteoric Water Line (LMWL) in the Qinghai Lake Basin are both relatively high. This can be attributed to the long-distance transport of water vapor into the interior of the Qinghai–Tibet Plateau, during which multiple in-cloud condensation and fractionation processes occur. As a result, heavy isotopes are progressively depleted, leading to a notably elevated intercept. Meanwhile, in high-altitude regions, low temperatures, combined with the influence of lake surface evaporation from Qinghai Lake and relatively high atmospheric humidity, result in weak sub-cloud secondary evaporation, causing the slope to approach the global average level. Furthermore, the Qinghai–Tibet Plateau is influenced by multiple moisture sources and a continental climate, leading to substantial spatial variability in precipitation isotope composition between the northern and southern regions. With increasing latitude, both the slope and intercept of the meteoric water line exhibit an increasing trend.

Comments 6:

Statistical Analysis (Section 4.1, pp. 8–10; Figure 4)

The relationships between isotopes and meteorological variables are clearly visualized; however, the reported low R² values (0.075–0.079) are not critically discussed. This may lead to overinterpretation. Additional controlling variables (e.g., humidity, atmospheric circulation) should be considered or at least discussed.

Response 6:

We sincerely thank the reviewer for the critical comments on the statistical analysis. We fully agree with the reviewer’s suggestions and have carried out systematic revisions as follows:

(1) The Discussion section has been revised to remove all statements implying significant temperature effects. We have clarified the distinction between statistical significance and the actual explanatory power of the model, and explicitly stated that temperature is not the dominant controlling factor of precipitation isotope fractionation in the basin during the study period(lines 346–347, pages 10).

(2) The relevant parts of the Discussion have been rewritten with the following improvements: (lines 402–409, pages 12)in response to the issue of low R², we clearly state that single environmental factors (e.g., air temperature and precipitation amount) have limited explanatory and predictive power for isotopic variations. We further emphasize that a single environmental factor cannot effectively predict isotopic changes. In the Qinghai Lake Basin, precipitation is influenced by multiple moisture sources, including warm and humid air masses from the North Atlantic, monsoonal moisture, dry polar air masses, and recycled moisture from the lake surface. In addition, the pronounced temporal heterogeneity of hydrothermal conditions in inland basins further increases the complexity of precipitation isotope composition and its fractionation mechanisms.

Comments 7:

Moisture Source Analysis (Section 4.2, pp. 10–12; Figures 5 and 6)

Figures 5 and 6 effectively present moisture transport pathways and seasonal variability. However, the interpretation remains largely descriptive and lacks integration with isotopic indicators such as d-excess. Furthermore, no uncertainty analysis is provided for trajectory clustering results.

Response 7:

We sincerely thank the reviewer for the professional comments on the analysis of Figures 5 and 6. We fully agree with the reviewer’s suggestions and have made the following improvements in the revised manuscript:

(1) In the Discussion section, we have incorporated a combined analysis of d-excess and backward trajectory clustering. Specifically, (lines 417–426, pages 12)deuterium excess (d-excess) contains important information on moisture sources and serves as a key tracer in identifying the origin of stable isotopes in precipitation. The mean d-excess in the Qinghai Lake Basin is 37.02‰, which is significantly higher than the average value of 10‰ observed in inland arid regions of northwestern China. This elevated value indicates, on the one hand, the contribution of local moisture, as evaporation from Qinghai Lake enhances d-excess through local moisture recycling processes. On the other hand, it may also reflect interactions between oceanic moisture and locally evaporated vapor, potentially associated with stronger convective activity. The large variation range of d-excess (up to 37.72‰) further indicates the complexity of moisture sources, as well as the variability in evaporation conditions at source regions and precipitation processes over the study area.

(2) The related discussion has been rewritten to reduce simple descriptive accounts of trajectory pathways and to focus more on the underlying physical processes associated with different air mass origins.

(3) We have also supplemented a discussion on the potential uncertainties of the trajectory clustering method, including uncertainties related to the resolution of meteorological input data and model assumptions, which may influence the accuracy of moisture source identification(lines 522–527, pages 14).

Comments 8:

Redundancy and Section Integration (pp. 4–12)

There is noticeable redundancy in the explanation of isotopic patterns across the Results and Discussion sections. Additionally, figures are not always fully integrated into the text. The manuscript would benefit from improved conciseness and stronger cross-referencing between sections.

Response 8:

We sincerely thank the reviewer for the valuable comments on the manuscript structure and expression. We fully agree with the reviewer’s suggestions and have made systematic revisions in the manuscript as follows:

(1) Redundant descriptions in the Results and Discussion sections have been removed. The Results section now focuses strictly on the objective presentation of key findings, while the Discussion is dedicated to interpreting underlying mechanisms and implications. The analysis of d-excess has been integrated into the Discussion under moisture source analysis, whereas the results of moisture source pathways and their contributions derived from the backward trajectory model have been incorporated into the Results and Analysis section.

(2) The integration between figures and the main text has been strengthened: figure citation formats have been standardized, and the placement of figures has been adjusted so that they appear immediately after the paragraphs where they are first referenced.

(3) Similar discussion paragraphs throughout the manuscript have been consolidated, terminology has been standardized, and the overall readability of the manuscript has been further improved.

Comments 9:

Equation Formatting (pp. 9–10, lines 350–353)

Regression equations are inconsistently formatted. The authors should standardize notation, spacing, and symbols according to scientific conventions and guidelines.

Response 9:

We sincerely thank the reviewer for the detailed comments on the formatting of regression equations. We fully agree with the reviewer’s suggestions and have conducted a comprehensive review and standardization of all relevant formulas and statistical expressions throughout the manuscript, as follows:

(1) Following the common conventions in isotope hydrology, we have established a unified formatting standard for regression equations across the manuscript, including consistent notation for isotope symbols, statistical parameters, spacing of operators, and the standardized presentation of statistical values within parentheses.

(2) All regression equations in the manuscript have been revised one by one. The notation of statistical indicators such as R² and p has been standardized, and the spacing of operators (e.g., equals signs, plus signs, commas) has been corrected to resolve inconsistencies in formatting.

(3) The number of significant digits for all regression coefficients (slopes and intercepts) has been unified, ensuring complete consistency among the main text, tables, and figure captions.

Comments 10:

Conclusions and Uncertainty (Section 5, p. 13, lines 473–501)

The conclusions are consistent with the results; however, the manuscript lacks a discussion of uncertainties and limitations (sampling, model assumptions, temporal representativeness). A dedicated subsection on limitations is strongly recommended.

Response 10:

We sincerely thank the reviewer for this important suggestion. We fully agree and have added a dedicated section titled “Uncertainties and Limitations” in the revised manuscript, in which the main sources of uncertainty in this study are discussed as follows:

(1) Uncertainty in sampling design: the relatively short observation period and the use of a single sampling site limit the assessment of interannual and spatial variability in the regional climate system.

(2) Uncertainty in model assumptions and methods: this study employs the HYSPLIT model for moisture source analysis, and the results depend on the accuracy of meteorological reanalysis data and are influenced by parameter settings such as trajectory length and initial height. In addition, uncertainties related to the resolution of meteorological input data and model assumptions may also affect the accuracy of moisture source identification(lines 524–526, pages 14).

(3) Limitations in temporal representativeness: (lines 511–519, pages 14)the observation period (June 2023 to October 2024) covers less than two complete hydrological years. The Qinghai Lake Basin is strongly influenced by large-scale atmospheric circulation, and interannual variability associated with large-scale climate modes, such as the El Niño–Southern Oscillation (ENSO), can significantly alter precipitation patterns, moisture transport pathways, and isotope fractionation processes. Therefore, analyses based on a single-year (or short-term) dataset may introduce systematic biases related to anomalous climatic conditions, making it difficult to quantify the interannual variability of precipitation isotopes and to distinguish the relative contributions of interannual fluctuations to isotope composition.

Comments 11:

References (pp. 14–15, lines 520–570)

The references are generally relevant but include some outdated sources and formatting inconsistencies. The authors should incorporate more recent literature (2020–2025) and ensure full compliance with formatting standards.

Response 11:

We thank the reviewer for pointing this out and fully agree with the comment. Accordingly, we have carefully checked and revised the formatting of all references. The reference list now fully complies with the formatting requirements of the Sustainability journal.

Author Response File: Author Response.pdf

Reviewer 3 Report

Comments and Suggestions for Authors

This manuscript presents hydrogen and oxygen stable isotope data from precipitation samples collected at a single station in the Qinghai Lake Basin over approximately 17 months (June 2023–October 2024). The study characterizes temporal variations in δ²H, δ¹⁸O, and d-excess, examines correlations with temperature and precipitation amount, and applies HYSPLIT backward trajectory analysis to identify moisture sources.

The topic falls within an established research framework, and the data themselves have regional value. However, the manuscript suffers from several significant weaknesses: (1) the observational record is too short to support the climatological conclusions drawn; (2) the statistical analyses are superficial and at times misinterpreted; (3) the novelty relative to existing studies in the same region is not clearly articulated; and (4) the discussion is largely descriptive and does not advance mechanistic understanding beyond what has already been published for neighboring sites. Major revisions are required before this manuscript can be considered for publication.

Major Issues

Insufficient Observational Period and Missing Winter Data

The dataset covers June 2023 to October 2024, a span of roughly 17 months with only one complete summer season and no winter (December–February) data at all. The paper nonetheless draws conclusions about "seasonal variability" and "seasonal patterns" that implicitly require multi-year coverage. With n = 86 total samples distributed across fewer than two annual cycles, the results are vulnerable to interannual anomalies and cannot be treated as representative of the basin's climatological behavior.

Furthermore, the absence of winter data is a critical gap. Winter precipitation on the northeastern Tibetan Plateau, though small in amount, is isotopically distinct and often associated with different moisture sources. The seasonal analysis only covers spring, summer, and autumn (Table 1), yet the text does not acknowledge this limitation or explain why winter is excluded. Was there no precipitation during winter months, or were samples not collected? This must be explicitly stated.

Recommendation: Either extend the observational period to at least two full annual cycles, or substantially revise all claims about seasonal patterns to reflect the limited temporal coverage. Add a dedicated limitations paragraph discussing the short record length and missing winter data.

Weak Statistical Relationships Overstated

The regression analyses in Section 4.1 report R² values of 0.075 (δ²H vs. temperature), 0.079 (δ¹⁸O vs. temperature), 0.001 (δ²H vs. precipitation amount), and 0.009 (δ¹⁸O vs. precipitation amount). The multiple regression models yield R² = 0.059 and 0.077 for δ²H and δ¹⁸O, respectively.

These R² values indicate that the models explain less than 8% of the total variance in isotopic composition. While a statistically significant p-value (driven by sample size) indicates the relationship is unlikely to be zero, the practical explanatory power is negligible. Claiming that "temperature is the dominant factor controlling the variability of precipitation stable isotopes" (Conclusion 2) based on R² = 0.075 is not defensible. Temperature explains 7.5% of the variance; 92.5% is unexplained. The word "dominant" is misleading here.

Additionally, the partial correlation between δ²H and temperature yields p = 0.09 (line 342), which does not reach the conventional significance threshold of 0.05. The text conflates this with the significant result for δ¹⁸O (p = 0.005), creating a misleading impression.

Recommendation: Rewrite Section 4.1 and Conclusion (2) to accurately represent the weakness of these relationships. Discuss what other factors (sub-cloud evaporation, moisture source mixing, synoptic-scale variability) may account for the large unexplained variance. Do not use "dominant" to describe a factor explaining <8% of variance.

Internal Inconsistency in LMWL R² Value

The abstract and Section 3.2 report the LMWL as δ²H = 8.15δ¹⁸O + 38.71 with R² = 0.93, while Conclusion (1) states R² = 0.96. One of these is incorrect. This type of numerical inconsistency undermines confidence in the data handling throughout the manuscript.

Recommendation: Verify the correct R² value from the original regression output and correct all occurrences.

Anomalously High d-excess Values Insufficiently Discussed

The reported d-excess values range from 15.67‰ to 53.39‰, with a weighted mean of 37.09‰. These values are remarkably high, even by continental Tibetan Plateau standards. Most published d-excess values for northern Tibetan Plateau stations fall in the range of 10–25‰. A weighted mean of 37‰ is an outlier that demands careful scrutiny.

The paper attributes this to westerly transport and local evaporation from Qinghai Lake, but does not consider alternative explanations such as: (a) kinetic fractionation during snow formation or sublimation (relevant given the altitude); (b) potential analytical artifacts from the liquid water isotope analyzer (especially for samples with low water content or organic contamination); (c) re-evaporation of collected samples before sealing.

Recommendation: Provide a more rigorous discussion of possible causes for the unusually high d-excess values. Compare quantitatively with published d-excess data from nearby stations (not just qualitative statements). If analytical uncertainty can be quantified, present it.

Limited Novelty

The study area is adjacent to well-studied basins (Heihe River headwaters, Qilian Mountains, source region of the Yellow River), and the methods employed (LMWL construction, temperature/amount effect analysis, HYSPLIT clustering) are identical to those used in dozens of prior studies on the Tibetan Plateau. The manuscript's own discussion repeatedly confirms that its findings are "consistent with" previous results from neighboring regions.

The introduction (lines 72–73) claims that "systematic investigations of precipitation stable isotope characteristics and moisture sources in the Qinghai Lake Basin remain relatively limited," but Jing et al. (2023), already cited as reference [15], specifically studied daily to interannual isotopic variations of summer precipitation in the Qinghai Lake Basin. The paper does not adequately distinguish itself from this prior work.

Recommendation: Strengthen the novelty statement. If the primary contribution is extending the spatial coverage of isotope monitoring on the plateau, state this explicitly and frame the paper as a data contribution rather than a process study. Alternatively, if the authors believe they offer new mechanistic insight, the discussion must go beyond confirming known patterns.

Minor Issues

Analytical Methods Incompletely Reported
The make and model of the liquid water isotope analyzer are not specified. Was it a Picarro, Los Gatos Research, or another instrument?
Analytical precision (typically reported as ±0.1‰ for δ¹⁸O and ±1.0‰ for δ²H) is not stated anywhere.
The laboratory standards used for calibration (VSMOW, SLAP, or working standards) are not mentioned.
Memory effects and drift corrections for laser-based analyzers should be described.
Precipitation Weighting Scheme Not Defined

The text refers to "weighted mean" values for δ²H, δ¹⁸O, and d-excess but does not state what weighting was applied. Precipitation-amount weighting is standard practice, but this must be made explicit. If event-based samples were collected, were all 86 events used for weighting, or were some excluded (e.g., trace precipitation)?

Seasonal Definitions Missing

The paper analyzes spring, summer, and autumn separately but never defines the months assigned to each season. For the northeastern Tibetan Plateau, the conventional meteorological seasons may not align with the actual precipitation climatology. State the month ranges explicitly.

HYSPLIT Configuration Details
Only one arrival height (2000 m AGL) was tested. Sensitivity to arrival height (e.g., 500 m, 1000 m, 3000 m) should be examined or at least discussed.
The criterion for determining the optimal number of clusters is not described. Was the total spatial variance method used? What was the percent change in TSV for different cluster numbers?
GDAS data resolution (1° × 1° or 0.5° × 0.5°) is not specified.
Manuscript Type Mislabeled

The header classifies this manuscript as an "Essay." For an original research article presenting new data and analysis, the correct type should be "Article." Confirm with the editorial office.

Formatting and Typographical Errors
"june" is not capitalized in multiple locations (lines 15, 82, 107, 382). Should be "June."
Section numbering is inconsistent: "2.1." has a period, while "2.2", "2.3", "2.4" lack trailing periods.
Conclusion section uses a non-standard numbered paragraph format rather than continuous prose or proper numbered conclusions with consistent structure.
Table 1 uses Chinese parentheses "（）" around R² values instead of standard parentheses.
Figure Quality

Figure 1: The DEM color ramp legend shows only two elevation values (3036 and 5299). A continuous scale with intermediate ticks would be more informative.

Figure 2: The time axis has a gap between October 2023 and April 2024 that is not visually distinguished from the data-bearing periods. Consider shading or annotating the data gap.

Figures 5 and 6: Trajectory maps lack topographic context. Adding elevation shading or at least national boundaries for Central Asian countries would improve readability.

Discussion Structure

Section 4.2 (Moisture Source Analysis) mixes results presentation (HYSPLIT percentages, seasonal breakdowns) with discussion. The seasonal trajectory clustering results (Figure 6) are presented here for the first time rather than in Section 3. Consider restructuring so that all results appear in Section 3 and all interpretation appears in Section 4.

Author Response

Comments 1:

1.Insufficient Observational Period and Missing Winter Data

Response 1:

We sincerely thank the reviewer for this important comment. We fully agree that the current dataset has limitations in temporal coverage. In the revised manuscript, we have comprehensively addressed this issue as follows:

(1) All statements related to seasonal patterns have been revised. Any expressions referring to multi-year “seasonal regularities” have been removed. All seasonal conclusions are now strictly confined to the observation period from June 2023 to October 2024, and the results only reflect characteristics of this specific period rather than long-term climatic behavior in the basin(lines 511–526, pages 14).

(2) In Section 2.2 (Sampling and Methods)(lines 108–112, pages 3), we have explicitly clarified the reason for the absence of winter data. Specifically, from December 1, 2023 to February 29, 2024 (winter), the Qinghai Lake Basin experienced cold and dry conditions, with only trace snowfall events (each < 0.5 mm), all in solid form. Due to sampling constraints, no valid samples were collected, and therefore winter was not included in the seasonal analysis.

(3) A dedicated limitations paragraph has been added at the end of the Discussion to address the impacts of the short observation period and missing winter data(lines 511–526, pages 14). The study includes only one complete summer, making it difficult to distinguish interannual variability from seasonal variation. In addition, the absence of winter data prevents a full-year assessment of moisture sources in precipitation. At the same time, we objectively clarify the scientific value of the study, balancing its limitations with its contributions.

(4) Due to the constraints of the completed study, it was not possible to extend the observation period to cover two full annual cycles. However, through the comprehensive revisions above, all analyses and conclusions are now strictly aligned with the actual data coverage, ensuring the rigor and reliability of the study.

Comments 2:

2.Weak Statistical Relationships Overstated

Response 2:

We sincerely thank the reviewer for these valuable comments. We fully agree with the reviewer’s suggestions and have carried out systematic revisions as follows:

(1) Section 4.1 of the manuscript has been comprehensively revised. All statements implying significant temperature effects have been removed. We have clarified the distinction between statistical significance and the actual explanatory power of the model, and explicitly stated that temperature is not the dominant controlling factor of precipitation isotope fractionation in the basin during the study period(lines 343–348, pages 10).

(2) The relevant parts of the Discussion have been rewritten with the following improvements: in response to the issue of low R²,(lines 398–409, pages 12) we clearly state that single environmental factors (e.g., air temperature and precipitation amount) have limited explanatory and predictive power for isotopic variations. We further emphasize that a single environmental factor cannot effectively predict isotopic changes. In the Qinghai Lake Basin, precipitation is influenced by multiple moisture sources, including warm and humid air masses from the North Atlantic, monsoonal moisture, dry polar air masses, and recycled moisture from the lake surface. In addition, the pronounced temporal heterogeneity of hydrothermal conditions in inland basins further increases the complexity of precipitation isotope composition and its fractionation mechanisms.

(3) The Conclusion and Abstract have been revised accordingly. All statements exaggerating the effect of temperature have been removed(lines 549–557, pages 15). The conclusions are now strictly based on the actual explanatory power of the regression models, clearly emphasizing that precipitation isotope fractionation is jointly regulated by multiple factors.

Comments 3:

3.Internal Inconsistency in LMWL R² Value

Recommendation: Verify the correct R² value from the original regression output and correct all occurrences.

Response 3:

We sincerely thank the reviewer for the valuable comment. In response to this key issue, we have made the following revision: We have re-extracted the regression results from the original SPSS output files and confirmed that the correct R² value is 0.93(lines 545–548, pages 15). The value of R² = 0.96 reported in the Conclusion section was a typographical error. We sincerely apologize for this mistake and have thoroughly checked all statistical values throughout the manuscript to ensure their accuracy.

Comments 4:

4.Anomalously High d-excess Values Insufficiently Discussed

Response 4:

We sincerely thank the reviewer for the valuable comment. In the revised manuscript, we have strengthened and refined this section as follows:

(1) We clarify that abnormally high d-excess values may result from multiple interacting factors. Specifically, (lines 418–426, pages 12)the mean d-excess in the Qinghai Lake Basin is 37.02‰, which is substantially higher than the average value of 10‰ in inland arid regions of northwestern China. This elevated value suggests, on the one hand, the contribution of local moisture, as evaporation from Qinghai Lake enhances d-excess through local moisture recycling processes. On the other hand, it may reflect the interaction between oceanic moisture and locally evaporated vapor during transport, potentially leading to stronger convective activity. Previous studies indicate that when moisture originates from high-latitude continental evaporation, d-excess values are typically greater than 10‰, whereas moisture derived from low-latitude oceanic evaporation generally exhibits d-excess values below 10‰. Therefore, the relatively high d-excess values observed in this study indicate a continental signature of moisture sources in the Qinghai Lake Basin. In addition, evaporation from the lake surface and soils contributes vapor to the local atmospheric cycle and participates in precipitation formation, which is one of the reasons for the elevated d-excess values in the region.

(2) Combined with moisture trajectory analysis, we further point out that high summer temperatures intensify surface evaporation. Vapor generated from water body evaporation is typically enriched in d-excess, and when this evaporative vapor mixes with atmospheric moisture, it can significantly increase the d-excess of the air mass(lines 470–472, pages 13).

Comments 5:

5.Limited Novelty

Response 5:

We sincerely thank the reviewer for the insightful comments on the novelty and academic positioning of this study. In the revised manuscript, we have systematically refined the research positioning and contributions as follows:

(1) We explicitly define this study as a data-driven investigation, with its primary contribution being the provision of continuous observational data on precipitation stable isotopes in the Qinghai Lake Basin, thereby extending the temporal coverage and improving the data foundation for isotope studies in this region(lines 71–78, pages 2).

(2) In response to the study by Jing et al. (2023) mentioned by the reviewer, we clarify that their work mainly focused on summer precipitation, whereas our study covers a relatively longer time span. In addition, this study integrates trajectory analysis with isotope observations to provide a more comprehensive assessment of moisture sources(lines 71–78, pages 2).

(3) We have revised the original statement that “systematic studies remain relatively limited” to avoid overstating the research gap and to present a more objective perspective. Specifically, although numerous studies have investigated stable water isotopes and moisture cycling over the Qinghai–Tibet Plateau, recent high–temporal-resolution continuous observational data of precipitation isotopes in the Qinghai Lake Basin remain relatively scarce. Consequently, the temporal variation patterns of precipitation isotopes and the influence of environmental factors (e.g., air temperature and precipitation amount) are still not fully understood. Moreover, there is a lack of data support for quantitatively assessing the relative contributions of different moisture sources within the basin(lines 71–78, pages 2).

Through these revisions, we have clarified the academic positioning of this study and improved the articulation of its contribution within the existing body of research.

Minor Issues

Comments 6:

6.Analytical Methods Incompletely Reported(lines 100–133, pages 3)

Response 6:

We sincerely thank the reviewer for the valuable suggestion. In response to this comment, we have supplemented and improved the analytical methods in the revised manuscript.

Comments 7:

7.The make and model of the liquid water isotope analyzer are not specified. Was it a Picarro, Los Gatos Research, or another instrument?

Response 7:

We sincerely thank the reviewer for the valuable suggestion. In response to this comment, we have made the following addition and revision: the brand and model of the liquid water isotope analyzer used in this study is TLWIA-912.

Comments 8:

8.Analytical precision (typically reported as ±0.1‰ for δ¹⁸O and ±1.0‰ for δ²H) is not stated anywhere.

Response 8:

We sincerely thank the reviewer for the valuable suggestion. In response to this comment, we have made the following addition and revision: the analytical uncertainties of δ¹⁸O and δ²H are ±0.05‰ and ±0.2‰, respectively.

Comments 9:

9.The laboratory standards used for calibration (VSMOW, SLAP, or working standards) are not mentioned.

Response 9:

We sincerely thank the reviewer for the valuable suggestion. In response to this comment, we have made the following addition and revision: all isotopic results are reported in per mil (‰) relative to Vienna Standard Mean Ocean Water (V-SMOW), using the following equation:

where R_sample and R_V-SMOW represent the isotope ratios of the sample and the standard, respectively.

Comments 10:

Memory effects and drift corrections for laser-based analyzers should be described.

Response 10:

We sincerely thank the reviewer for the valuable suggestion. In response to this comment, we have made the following additions and revisions: After filtration through a 0.22 μm membrane, the samples were analyzed for δ²H and δ¹⁸O using a liquid water isotope analyzer (TLWIA-912). During measurement, three standard samples were selected, and three unknown samples were analyzed after each standard. Every 12 injections (3 standards and 9 unknown samples) constituted one analytical cycle, with a total of 72 injections. At the end of each cycle, a calibration curve was generated based on the standard samples to correct the results. Each sample was injected six times; the first two injections were discarded to eliminate isotopic memory effects, and the average of the last four injections was taken as the final value.

Comments 11:

11.Precipitation Weighting Scheme Not Defined

Response 11:

We sincerely thank the reviewer for the detailed comment on the calculation of weighted averages. In the revised manuscript, we have clarified this issue as follows: (lines 183–184, pages 5)A total of 86 event-based precipitation samples were collected in this study. The values reported in the manuscript are arithmetic means, which equally represent the isotopic composition of each precipitation event. The use of the term “weighted mean” in the original manuscript was a typographical error. We sincerely apologize for this mistake. All instances of “weighted mean” have now been consistently corrected to “arithmetic mean.” All 86 precipitation events were included in the calculation, and no minor precipitation events were excluded. The arithmetic mean values have been recalculated and carefully verified, confirming full consistency with the original data and all 86 precipitation samples.

Comments 8:

8.Seasonal Definitions Missing

Response 8:

We sincerely thank the reviewer for the valuable suggestion. In response, Specifically(lines 108–116, pages 3), all samples are grouped according to the standard meteorological seasons: spring (March–May), summer (June–August), and autumn (September–November). Based on this classification, seasonal differences in isotopic characteristics and moisture sources are systematically analyzed, providing a consistent framework for all subsequent seasonal-scale analyses.

In addition, we have rechecked all seasonal statistical results, including the arithmetic mean values of isotopes and the seasonal differences in moisture source analysis. All results have been verified to be fully consistent with the above seasonal classification and the corresponding samples.

Comments 9-10:

9.HYSPLIT Configuration Details (lines 142–170, pages 4–5)

10.Only one arrival height (2000 m AGL) was tested. Sensitivity to arrival height (e.g., 500 m, 1000 m, 3000 m) should be examined or at least discussed.

Response 9-10:

We sincerely thank the reviewer for the valuable suggestion. In response, we have supplemented Section 2.4 (Backward Trajectory Model) by introducing gradient settings for the initial air mass heights and clarifying the rationale. Specifically, the maximum moisture transport in China generally occurs around 2000 m above ground level; therefore, the initial air mass heights were set at 500 m, 1000 m, and 2000 m.

Comments 11:

The criterion for determining the optimal number of clusters is not described. Was the total spatial variance method used? What was the percent change in TSV for different cluster numbers?

Response 11:

We sincerely thank the reviewer for the valuable suggestion. In response, we have supplemented Section 2.4 (Backward Trajectory Model) with a detailed description of the trajectory clustering methodology, including the calculation of spatial similarity. Specifically, the principle of trajectory clustering in this study is to group trajectories with similar paths. For each cluster, the mean trajectory is obtained by calculating the average latitude and longitude of air mass positions at each hourly time step. The clustering process is based on the calculation of spatial variance between trajectories. In particular, backward trajectories are grouped by evaluating the spatial variance (SPVAR) between each pair of trajectories and the total spatial variance (TSV), thereby classifying all air mass trajectories arriving at the receptor region into distinct clusters.

Comments 12:

GDAS data resolution (1° × 1° or 0.5° × 0.5°) is not specified.

Response 12:

We sincerely thank the reviewer for the valuable suggestion. In response, we have supplemented the description of the GDAS dataset in the section on the backward trajectory model.

Specifically, the meteorological data used in this study are from the Global Data Assimilation System (GDAS) provided by the National Centers for Environmental Prediction (NCEP), with a spatial resolution of 1° × 1°. This dataset includes key meteorological variables such as air pressure, wind speed, wind direction, temperature, and relative humidity.

Comments 13:

13.Manuscript Type Mislabeled

The header classifies this manuscript as an "Essay." For an original research article presenting new data and analysis, the correct type should be "Article." Confirm with the editorial office.

Response 13:We sincerely thank the reviewer for the valuable suggestion. All relevant labels throughout the manuscript have been corrected accordingly. Specifically, the manuscript type indicated above the title on the first page has been revised from “Essay” to “Article.”

Comments 11-12:

11.Formatting and Typographical Errors

12."june" is not capitalized in multiple locations (lines 15, 82, 107, 382). Should be "June."

Response 11-12:We sincerely thank the reviewer for the valuable suggestion. All instances of “june” in the manuscript have been corrected to the proper capitalization “June.”

Comments 13:

Section numbering is inconsistent: "2.1." has a period, while "2.2", "2.3", "2.4" lack trailing periods.

Response 13:

We sincerely thank the reviewer for the valuable suggestion. (lines 99, pages 3)The incorrect “2.1..” has been corrected to the standard “2.1” by removing the redundant period. In addition, all section and subsection numbering throughout the manuscript has been standardized according to the journal requirements, ensuring that no numbering includes trailing periods and that the entire numbering system is fully consistent.

Comments 14:

Conclusion section uses a non-standard numbered paragraph format rather than continuous prose or proper numbered conclusions with consistent structure.

Response 14:

We sincerely thank the reviewer for the valuable suggestion. The original non-standard formatting of the Conclusion section has been revised into a structured and standardized format, using consecutive numbering (1), (2), and (3), with each number corresponding to an independent key finding of the study.

Comments 15:

Table 1 uses Chinese parentheses "（）" around R² values instead of standard parentheses.

Response 15:

We sincerely thank the reviewer for the valuable suggestion. In Table 1, all Chinese full-width parentheses “（）” surrounding the R² values have been replaced with standard English half-width parentheses “()”. In addition, we have conducted a comprehensive check of all tables, main text, and figure captions throughout the manuscript, and have consistently standardized the use of English half-width parentheses for statistical values, units, and annotations.

Comments 16:

16.Figure Quality

Figure 1: The DEM color ramp legend shows only two elevation values (3036 and 5299). A continuous scale with intermediate ticks would be more informative.

Response:We sincerely thank the reviewer for the detailed suggestion regarding the figure legend. In the revised manuscript, we have optimized the DEM legend as follows: the original color bar, which displayed only endpoint values, has been replaced with a continuous gradient scale to more intuitively represent spatial variations in elevation. In addition, multiple intermediate elevation ticks have been added to the color bar.

Figure 2: The time axis has a gap between October 2023 and April 2024 that is not visually distinguished from the data-bearing periods. Consider shading or annotating the data gap.

Response:We sincerely thank the reviewer for the detailed suggestion. In the revised manuscript, we have optimized the relevant figures as follows: in the time series plots, shaded regions have been added for June–October 2023 and March–October 2024, while the period from November 2023 to February 2024 is shown without shading. A corresponding note has been added to the figure caption to clarify that unshaded regions indicate periods with no observational data.

Figures 5 and 6: Trajectory maps lack topographic context. Adding elevation shading or at least national boundaries for Central Asian countries would improve readability.

Response: We sincerely thank the reviewer for the valuable suggestion regarding the figures. In the revised manuscript, we have optimized the figures by adding the national boundary of China and regional outlines, thereby enhancing the spatial reference and interpretability of the trajectory pathways.

Comments 13:

13.Discussion Structure

Comments 13:

We sincerely thank the reviewer for the valuable suggestion regarding the optimization of the manuscript structure. We fully agree with the reviewer’s comment and have carried out a systematic reorganization of the relevant sections as follows:

(1) All result-related content on HYSPLIT trajectory analysis originally presented in Section 4.2 (including trajectory distribution characteristics, clustering results, and their proportional statistics) has been moved to Section 3 for consolidated presentation, retaining only objective descriptions.

(2) The analysis of d-excess has been relocated to Section 4 (Discussion) for integrated interpretation.

(3) Redundant descriptions of results in the Discussion section have been removed. The focus has been strengthened on explaining the mechanisms of moisture sources, with a comprehensive analysis combining precipitation isotope characteristics and d-excess.

Author Response File: Author Response.pdf

Reviewer 4 Report

Comments and Suggestions for Authors

This study analyzes the local meteoric water line and its influencing factors based on 17-month precipitation isotope data from the Qinghai Lake watershed, and investigates moisture sources using the HYSPLIT model. The study area (Qinghai Lake) is of great significance for understanding the water cycle in the northeastern Tibetan Plateau.

1 In introduction, it is basically a history of isotope hydrology research. The research motivation of "filling a geographical gap" is generally considered a relatively weak research goal. A truly compelling introduction should identify contradictions or unknown pain points in current understanding. For example, if the authors could elaborate on: "Qinghai Lake water level has risen sharply, and the academic community still has disputes on whether it is dominated by intensified local moisture recycling or increased external monsoon/westerly moisture input, therefore isotope data are urgently needed to 'solve the case'." Such research significance would be very prominent. However, the authors merely describe in a plain and straightforward manner that "water level has risen, precipitation is the main source, therefore isotopes can provide important evidence," which makes it look more like a routine regional data monitoring report. Anb the introduction emphasizes the long-term trends of "warm-humid climate change" and "21-year water level rise," but the actual sampling only covers June 2023 to October 2024. Precipitation isotopes are significantly influenced by interannual climate variability. Using less than two years of short-term data to explain decades of regional hydrological change represents seriously insufficient sample representativeness, and the conclusions show obvious over-generalization problems. Precipitation years include El Niño and La Niña years, and such analysis carries randomness. The discussion section could explain the possible influences, or collect corresponding inferences.

2 the HYSPLIT model sets the air mass arrival height at 2,000 meters above ground. On the Tibetan Plateau, this reflects the large-scale circulation transport characteristics of the middle troposphere, while water vapor from local lake surface evaporation is mainly concentrated within the boundary layer. So, using trajectories at this height to argue that "local evaporation from Qinghai Lake contributes significantly" has a fundamental physical mechanism mismatch.

Besides, the paper directly attributes the high d-excess value of 37.09‰ to "local evaporation from Qinghai Lake," yet fails to separate the upstream evaporation signals mixed in by westerly air masses passing through arid regions (e.g. Central Asia and the northwestern Gobi Desert). Whether the high value is locally produced or carried from upstream lacks necessary differentiation, and the attribution chain is not sufficiently rigorous.

Author Response

Comments 1:

In introduction, it is basically a history of isotope hydrology research. The research motivation of "filling a geographical gap" is generally considered a relatively weak research goal. A truly compelling introduction should identify contradictions or unknown pain points in current understanding. For example, if the authors could elaborate on: "Qinghai Lake water level has risen sharply, and the academic community still has disputes on whether it is dominated by intensified local moisture recycling or increased external monsoon/westerly moisture input, therefore isotope data are urgently needed to 'solve the case'." Such research significance would be very prominent. However, the authors merely describe in a plain and straightforward manner that "water level has risen, precipitation is the main source, therefore isotopes can provide important evidence," which makes it look more like a routine regional data monitoring report. Anb the introduction emphasizes the long-term trends of "warm-humid climate change" and "21-year water level rise," but the actual sampling only covers June 2023 to October 2024. Precipitation isotopes are significantly influenced by interannual climate variability. Using less than two years of short-term data to explain decades of regional hydrological change represents seriously insufficient sample representativeness, and the conclusions show obvious over-generalization problems. Precipitation years include El Niño and La Niña years, and such analysis carries randomness. The discussion section could explain the possible influences, or collect corresponding inferences.

Response 1:

We sincerely thank the reviewer for these valuable comments. In response, we have made the following important revisions in the manuscript:

(1) We fully acknowledge that the original Introduction did not sufficiently highlight the research motivation, placing excessive emphasis on filling a regional research gap rather than clearly identifying key scientific questions in hydrology. To address this issue, we have systematically revised the Introduction(lines 79–89, pages 2), shifting the research motivation from “filling a geographical gap” to focusing on the uncertainty of moisture source mechanisms underlying increasing precipitation in the Qinghai Lake Basin. Specifically, the basin has experienced an average annual increase in precipitation of 1.68 mm. As precipitation is the primary water source for Qinghai Lake, its variation is jointly regulated by multiple moisture processes. Among these, the relative contributions of enhanced oceanic moisture input and increased local moisture recycling driven by lake expansion have become key scientific questions in current regional hydrological cycle research.

(2) We fully agree with the reviewer’s assessment regarding data representativeness and have clarified the scientific positioning of this study(lines 71–78, pages 2). Rather than attempting to directly explain long-term hydrological changes over decades using short-term data, this study uses high-resolution observations from June 2023 to October 2024 to provide process-based insights. These results offer critical evidence to help address the long-standing debate on whether increased precipitation in the Qinghai Lake Basin is primarily driven by intensified local moisture recycling or by enhanced external inputs from monsoonal and westerly moisture. Accordingly, we have systematically revised all overgeneralized statements in the manuscript, removed any direct linkage between short-term results and long-term lake level rise, and strictly confined all conclusions to the observation period (June 2023–October 2024), ensuring that all conclusions are fully supported by the data.

(3) A dedicated paragraph has been added to the Discussion section to comprehensively address study limitations. This includes the short observation period(lines 508–537, pages 14–15), the influence of specific climatic backgrounds such as El Niño and La Niña events, the absence of winter data, and the inability to explain long-term trends. These limitations are now integrated into a coherent subsection, where their impacts on the study conclusions are systematically analyzed. We explicitly acknowledge that the results may not represent multi-year regional hydrological variability, while also objectively clarifying the scientific value of this study.

Comments 2:

The HYSPLIT model sets the air mass arrival height at 2,000 meters above ground. On the Tibetan Plateau, this reflects the large-scale circulation transport characteristics of the middle troposphere, while water vapor from local lake surface evaporation is mainly concentrated within the boundary layer. So, using trajectories at this height to argue that "local evaporation from Qinghai Lake contributes significantly" has a fundamental physical mechanism mismatch.

Response 2:

We sincerely thank the reviewer for this critical comment. We fully agree with the reviewer’s point regarding the differences in physical processes represented by air mass trajectories at different heights. In response, we have made the following important revisions in the manuscript:

(1) In the Methods section, we have added a dedicated description introducing gradients of initial air mass heights. Specifically(lines 152–155, pages 4), the initial heights were set at 500 m, 1000 m, and 2000 m to better quantify the contribution of local moisture recycling.

(2) Based on the characteristics of precipitation stable isotopes, we have provided qualitative evidence for the significant influence of local evaporation on basin precipitation. This includes relatively high intercepts of the Local Meteoric Water Line and elevated d-excess values, both of which support the contribution of locally evaporated moisture to precipitation sources(lines 243–252, pages 7).

(3) In the Discussion section, we explicitly address the methodological limitations and outline future research directions(lines 522–526, pages 14). These include incorporating boundary layer gradient observations, high-resolution numerical simulations, and multi-isotope tracing approaches to more comprehensively resolve moisture processes across different spatial scales.

Comments 3:

Response 3:

We sincerely thank the reviewer for this critical comment. We fully agree with the reviewer’s analysis regarding the multiple sources of d-excess. In response, we have made the following revisions in the manuscript:

(1) The original statement that “high d-excess indicates a significant contribution from local evaporation” has been revised to a more cautious interpretation. Specifically, (lines 417–425, pages 12)the mean d-excess in the Qinghai Lake Basin is 37.02‰, which is notably higher than the average value of 10‰ in inland arid regions of northwestern China. This elevated value suggests, on the one hand, the contribution of local moisture, as evaporation from Qinghai Lake enhances d-excess through local moisture recycling. On the other hand, it may also reflect the interaction between oceanic moisture and locally evaporated vapor during transport, potentially leading to stronger convective processes.

(2) In combination with HYSPLIT trajectory results, (lines 524–526, pages 14)we have further clarified in the Discussion that when air masses originate from locally evaporated moisture, the vapor from water body evaporation is typically enriched in d-excess. When this evaporative vapor mixes with atmospheric moisture, it can significantly increase the d-excess of the air mass.

(3) We have added statements in the Discussion and Limitations sections noting that uncertainties in meteorological input data resolution and model assumptions in the trajectory analysis may affect the accuracy of moisture source identification(lines 243–252, pages 7). Through these revisions, the interpretation of d-excess is now more consistent with the physical mechanisms of moisture transport and isotopic fractionation, while avoiding uncertainties associated with single-factor attribution.

Author Response File: Author Response.pdf

Round 2

Reviewer 3 Report

Comments and Suggestions for Authors

I thank the authors for their thorough and systematic revisions. Most of the original concerns have been adequately addressed. However, several issues require further attention before the manuscript can be accepted.

Comment 4 (d-excess): The revision remains insufficient. My original review specifically requested consideration of three alternative explanations for the anomalously high d-excess values: (a) kinetic fractionation during snow formation or sublimation, (b) potential analytical artifacts from the laser analyzer, and (c) re-evaporation of collected samples before sealing. None of these were addressed. The revised discussion still attributes high d-excess solely to moisture source characteristics and lake evaporation. The authors must explicitly evaluate and rule out (or acknowledge) these alternative explanations, particularly given that some samples may derive from solid precipitation at high altitude. A quantitative comparison with published d-excess values from nearby stations (e.g., Delingha, Yeniugou, Waliguan) is also still missing.

Comment 11 (arithmetic vs. weighted mean): Replacing precipitation-amount-weighted means with arithmetic means is a substantive methodological change that requires justification. Arithmetic averaging treats a 0.5 mm drizzle event identically to a 30 mm convective event, systematically overweighting small events that are isotopically distinct due to sub-cloud evaporation. The standard practice in isotope hydrology is precipitation-amount weighting. If the authors lack event-specific precipitation amounts, this should be stated as a limitation. If amounts are available, weighted means should be used.

Comment 5 (novelty): The repositioning as a data contribution is appropriate, but the distinction from Jing et al. (2023) remains vague. The authors should state concretely what temporal coverage Jing et al. provided (e.g., summer-only, which years) versus what this study adds (spring and autumn data, specific year range), and whether the trajectory analysis offers any findings that differ from prior work in the basin.

Comments 9-10 (HYSPLIT arrival heights): The addition of 500 m and 1000 m arrival heights is welcome. However, the response does not report whether the clustering results are consistent across the three heights. A brief statement on the sensitivity of the results to arrival height is needed, either in the text or as supplementary material.

All other revisions are satisfactory. I recommend acceptance after these remaining issues are resolved.

Author Response

Response 4:We sincerely thank the reviewer for the insightful suggestion to further analyze the sources of high d-excess values. We fully agree with this comment and have addressed the issue in detail in the revised manuscript as follows(lines 464–471, pages 14):

(1) We have clarified in the revised manuscript that, in high-altitude regions, some precipitation events may occur in solid form, and their formation and subsequent sublimation processes can introduce significant kinetic fractionation effects, potentially leading to elevated d-excess values. For low-precipitation events, partial re-evaporation may occur during sample collection or transfer. In addition, a certain degree of analytical uncertainty may exist during instrumental measurements. To minimize these potential influences, we applied a precipitation amount-weighted averaging method and excluded events with precipitation amounts lower than 0.5 mm, thereby reducing their impact on the calculated d-excess values.

(2) In response to the reviewer’s suggestion regarding comparisons with nearby observational sites, we have incorporated a comparative analysis using data from the Mustagh Ata region in the revised manuscript(lines 484–491, pages 14). Previous studies report a mean d-excess value of 25.9‰ in this region, which is slightly lower than that observed in the Qinghai Lake Basin. When moisture originating from distant regions such as the Mediterranean or the North Atlantic is transported to the Tibetan Plateau via the westerlies, d-excess values tend to increase significantly. Therefore, the relatively high d-excess values observed in the Qinghai Lake Basin likely reflect not only local evaporative recycling processes but also the important role of long-range moisture transport by the westerlies in precipitation formation.

Response 11:We sincerely thank the reviewer for the critical and constructive comment regarding the calculation of mean values. We fully agree with this suggestion and have made substantial revisions accordingly in the manuscript(lines 202–209, pages 5):

(1) In the Methods section, we have clearly specified the calculation procedure and data sources for the weighted mean. A precipitation amount-weighted approach was applied to recalculate δ²H, δ¹⁸O, and d-excess, using the following equation:

where P_irepresents the precipitation amount (mm) of the i-th event, δ_i denotes the corresponding δ¹⁸O, δ²H, or d-excess value (‰), and n is the total number of precipitation events. To avoid instability in weighting caused by extremely low precipitation amounts, events with precipitation < 0.5 mm were excluded from the calculation. A total of 79 precipitation events were retained for the weighted analysis.

(2) Based on the recalculated weighted means, the corresponding Results and Discussion sections have been revised accordingly to ensure the robustness and accuracy of the analysis.

Comment 5 (novelty): The repositioning as a data contribution is appropriate, but the distinction from Jing et al. (2023) remains vague. The authors should state concretely what temporal coverage Jing et al. provided (e.g., summer-only, which years) versus what this study adds (spring and autumn data, specific year range), and whether the trajectory analysis offers any findings that differ from prior work in the basin.

Response 5:We sincerely thank the reviewer for the valuable suggestion to further clarify the novelty of this study. We fully agree with this comment and have revised the Introduction accordingly to better highlight the originality of our work(lines 74–82, pages 2):

(1) We have supplemented the Introduction by noting that previous studies on precipitation stable isotopes in the Qinghai Lake Basin have primarily focused on the summer season. For example, Jing et al. analyzed precipitation isotope data during the summer period (June–July) from 2012 to 2018 and revealed intra-seasonal variations in δ¹⁸O and δ²H, as well as a significant positive correlation between δ¹⁸O and the El Niño–Southern Oscillation (ENSO) index. Wu et al. collected event-based precipitation samples during summer and investigated the temporal variability of isotope composition, highlighting the influence of atmospheric circulation and local moisture recycling on precipitation isotopes.

(2) In contrast, the present study is based on continuous observations and integrates HYSPLIT trajectory analysis to comprehensively examine moisture transport pathways across different periods. This approach enables a more systematic characterization of both temporal variability and moisture source dynamics in the Qinghai Lake Basin.

Response 9: We sincerely thank the reviewer for the valuable suggestion regarding the sensitivity of trajectory analysis(lines 359–363, pages 10). We fully agree with this comment and have conducted additional analyses in the revised manuscript by comparing trajectory clustering results at three arrival heights (500 m, 1000 m, and 2000 m). The results indicate that, in spring and summer, the dominant moisture transport pathways and their relative contributions are generally consistent across different arrival heights, with only minor differences in the proportional contributions of individual trajectories. In autumn, some variability in transport pathways is observed; however, the overall pattern and dominant moisture sources remain largely consistent. These findings suggest that the main conclusions regarding moisture sources in this study are not sensitive to variations in arrival height and demonstrate a high level of robustness. Relevant descriptions have been added to the manuscript accordingly.

Author Response File: Author Response.pdf