the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 26 Aug 2025
Seasonal patterns and diagnostic values of δ2H, δ18O, d-excess, and Δ17O in precipitation over Seoul, South Korea (2016–2020)
Abstract. Precipitation stable isotopes are critical tracers for understanding climate variabilities and the hydrological cycles, as they enable the tracing of moisture sources, air mass mixing, and evaporation-condensation mechanisms. In mid-latitude regions such as South Korea, which are influenced by tropical and extratropical circulation, highly resolved and long-term isotope records remain scarce. Here, we analyze stable isotopes in precipitation collected bi-weekly in Seoul, South Korea, from 2016 to 2020. The oxygen isotope ratios (δ18O) ranged widely from 1.15 to –18.21 ‰, deuterium (δ2H) ratios varied from 3.3 to –132.0 ‰, and the 17O-excess ranged from 69 to –28 ‰. All three primary isotopes exhibited a coherent sinusoidal seasonal cycle, with the most depleted values in winter, gradual enrichment through spring, and sharp depletion during the summer monsoon, reflecting the combined influence of temperature and the amount effect. The deuterium excess (d‑excess) was highest during cold, dry months and lowest in humid, rainy months, reflecting shifts in relative humidity and kinetic fractionation. Meanwhile, 17O-excess (Δ17O) exhibited a similar season trend with a smaller amplitude, suggesting that, beyond its known dependence on relative humidity and kinetic fractionation, it is also modulated by large‑scale transport and water vapor mixing. The local meteoric water line closely matches the global line but winter samples show a higher intercept and a slightly steeper δ17O–δ18O slope, suggesting enhanced kinetic fractionation under continental air masses. A consistently negative δ18O–Δ17O relationship was observed except in winter when it weakened. This integrated analysis of δ18O, d‑excess, and Δ17O provides a comprehensive picture of source humidity, transport dynamics, and seasonal precipitation processes in a mid‑latitude East Asia, and offers a valuable reference for refining isotope‑enabled climate models over East Asia.
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Status: open (until 02 Oct 2025)
- RC1: 'Comment on essd-2025-374', Anonymous Referee #1, 10 Sep 2025 reply
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RC2: 'Comment on essd-2025-374', Anonymous Referee #2, 12 Sep 2025
reply
In this paper, the authors presented precipitation hydrogen and triple oxygen isotope data of precipitation from South Korea and made some exploratory analysis on these data. I recognize that the authors have made great efforts to collect samples and data and put together a manuscript. However, I feel that it does fit with the scope of journal. The ESSD is a high-impact journal publishing flagship datasets for various applications with broad interest. Although it is indeed contributing to the emerging triple oxygen isotope study, this dataset does not make a significant contribution to the progress of this field. I suggest publishing the data in a substantially revised manuscript on a more specialized journal.
- L49: it is more common for using the prime symbol for ln(δ18O+1) as δ′18O. Also, most people (including IAEA authors) using “Δ′17O” notation (see Aron et al., 2021). The prime symbol is missing.
- L100: confusing… are you collecting event samples or biweekly samples?
- L106: is storing samples in freezing conditions problematic? I think most people store samples in liquid at 4 degree C.
- L122: what is the uncertainty in Δ′17O?
- Results section: some sentences are not results but are discussion. I suggest authors to have a better separation of results and discussion. For example, L146-153 and L169-196 are mostly interpretations of results, and better put into the discussion.
- L161: It’s inaccurate. A slope of 8 does not mean a governance of equilibrium fractionation. From the highly seasonal d-excess data, it is obvious that there is a large change in kinetic fractionation from winter to summer. A slope of 8 occurs in your dataset is because the low d18O data can have either high d-excess (winter) and low d-excess (summer). So in d2H-d18O space, the effect of d-excess variation on LMWL cancels out.
- Section 4.1: although a lot of people were doing this, but I am not advocate of correlation analysis of isotope data with environmental variables. It is reasonable to do this in 1960s… correlation analysis provides little insight into the process and mechanism and correlation is not causation. There have been many papers publishing new precipitation isotope data and analyzing their correlations with various variables, so here there is little novelty except the analysis of Δ′17O data.
- L200-205: low RH and high SST caused high d-excess data, according to MJ1979. Also, the RH here should be the “RH” referenced to ocean skin temperature, not atmospheric RH. Dry air may cause high d-excess in vapor due to kinetic fractionation but may also cause low d-excess in precipitation due to droplet re-evaporation.
- L243-L257: one mechanism not considered is the ice formation in winter snow. Ice-vapor fractionation may have very different impacts on d-excess and Δ′17O in winter precipitation, owing to equilibrium fractionation involved in this process.
- L258-259: this is a repeat of L243-L244.
- Section 4.3: This section is for comparing measured data with GCM simulations. However, this was not mentioned in the Introduction and Methods sections. There is little novelty of comparing d18O and d-excess outputs from GCMs with observations, as original authors have done this already. D-excess data are often use to “tune” the model. It’s great to mention the contribution of triple oxygen isotope data to benchmark GCM. I suggest authors to collaborate with GCM researchers who already have GCM outputs with triple oxygen isotope components.
Citation: https://doi.org/10.5194/essd-2025-374-RC2 -
RC3: 'Reply on RC2', Anonymous Referee #2, 12 Sep 2025
reply
Citation: https://doi.org/
10.5194/essd-2025-374-RC3
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RC4: 'Comment on essd-2025-374', Anonymous Referee #3, 13 Sep 2025
reply
General comments:
Kim et al. present a unique data set of stable isotopes (δ2H, δ17O, δ18O, d-excess and Δ17O) of precipitation sampled bi-weekly between February 2016 and December 2020 in Seoul, South Korea. Such data sets can help to better constrain the drivers of isotope variability in precipitation, improve the interpretation of paleoclimate records, and tune isotope-enabled global climate models. In particular, data sets combining d-excess and 17O-excess remain scarce so far. Therefore, the data set is new and will be useful for future studies. The data set is accessible, however, does not contain uncertainties for each variable. Also, no meteorological data is given in the file, where especially precipitation amounts, but also T and RH data would be useful for the interpretation of the data set and have been used in the manuscript. If these data were derived from a different data base, this should be mentioned in the data availability section.
Overall, the manuscript is clearly structured and well written. However, the methodological section needs more detail, some discussion points appear already in the results and interpretations are often not justified by data. The manuscript is worth publication in ESSD, but needs major revision as outlined below.
Specific comments:
Methodology:
- Missing description of the meteorological data.
- Missing description of how secondary order parameters (d-excess and 17O-excess) are calculated.
- Give analytical precision for d-excess and 17O-excess.
- Details on the comparison of the GSM with observational data presented in the discussion section are missing in the methods. For example, model input parameters, but also more details about the model simulations should be given. I think that this model-data comparison could be a bit over the scope of this journal. Instead of adding mor details to the model, the authors may consider removing this part from the manuscript.
- In line 99 the authors state that precipitation has been collected from January 2016 to December 2020. However, the data set starts in February 2016. This should be corrected.
- Also, they state that sampling was performed bi-weekly. They should make clear that their interpretation is based on amount-weighted monthly values as bi-weekly data is not presented.
Results:
The results are mixed up with discussion points. A better separation of both is needed. Discussion parts that should be shifted to the discussion section: Line 163-164, Line 169-171, Line 173-174, Line 178-179, Line 189-196
Discussion:
Interpretations in the discussion section are often not justified by the presented data. For example, in the first discussion section, correlation between isotope data and local meteorological parameters are investigated. For example, line 204-206 that “lower relative humidity and temperature at the moisture source enhance kinetic fractionation during evaporation, thereby increasing d-excess” However, no information on relative humidity and temperature at the moisture source region is provided nor differences between different moisture source regions are discussed. –Further, more explanation is need in Line 211-212. Here, the authors state that the negative correlation between d-excess and local temperature is controlled by the moisture sources and isotope fractionation during precipitation. This is very general too. Can you explain how this correlation relates to these factors? Also more explanation and justification is needed in line 215-217 and line 222-224.
Line-by-Line comments:
Line 45-47: δ18O and δ2H are influenced by both equilibrium and kinetic fractionation and thus it is difficult to disentangle these two. The secondary parameters, d-excess and 17O-excess are primarily sensitive to kinetic fractionations and thus help to disentangle them. Clarify this in the text.
Line 47-49: δ17O has not been introduced yet. Consider adding a sentence on the value of additional analysis of the 17O isotope before.
Line 79: Be more specific: 5-year record of monthly triple oxygen and hydrogen precipitation isotope data.
Section 2: There is no reference to Figure 2 in the main text. This could be added to a sentence describing the meteorological data.
Line 97-98: I don’t expect this phrase at the end of the paragraph. It should be introduced more at the beginning of the paragraph and then all four seasons need to be described. For now, only summer and winter are described, but which conditions persist in spring and autumn?
Line 137: Why a sine function has been fitted to the data? This should be explained in the text.
Line 147: Which other moisture sources than the ocean are important. Specify this.
Line 149-151: I was first confused by the “unlike” but looking at the figure I understood that the difference between 17O-excess and d-excess is that 17O-excess is highest in spring, while d-excess is highest in winter. Can you make this clearer in the text. Also, Quantify give values for 17O-excess’ seasonal variability (highest value, lowest value).
Line 153-155: During which process kinetic fractionation is more pronounced? As I understood from the previous, this is due to evaporation from the ocean. Is this correct? Be more specific here.
Line 161: Can you add uncertainties for the slope and the intercept of the GMWL?
Line 167-168: Winter precipitation is mainly in the form of snow? Do you see differences between snow and rain samples?
Line 180: the 17O-excess is defined based on the prime values of δ17O and δ18O. This should be defined in the methods and clarified in the main text and the figures.
Line 187: You should refer here to Figure 4.
Figure 4: What is shown in B? It does not make any sense to me. Suggestion to illustrate 17O-excess vs d'18O as no difference will be visible in d'17O vs d'18O, when plotted to scale. The purple line is not the GMWL, should be dashed line, I guess.
Line 206: “mixing” of what? Air masses?
Line 208: This is very general. Can you name the multiple meteorological factors that are interacting?
Line 215: lower δ18O values compared to what? Compared to other months of the year? Is this a rainout effect or an amount effect?
Line 244-246: This is referring to evaporation from the ocean or re-evaporation of precipitation? Specify!
Line 249: evaporation of what? Precipitation?
Technical comments:
- Throughout the manuscript. The unit of 17O-excess is per meg not per mil. Please correct in text and in figures.
- Line 37-38: repetition of the previous sentence. Consider removing it.
- Line 95: Korean Peninsula
- Line 177: Repetition of slope and intercept not necessary here. Remove.
- Line 258-264: Repetition of previous paragraph. Remove.
- The Summary should be stated before the data availability statement, isn’t it?
Citation: https://doi.org/10.5194/essd-2025-374-RC4
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- 1
Dear Authors, Dear Editor,
The manuscript presents a valuable dataset on triple stable isotope composition of precipitation from an East Asian locality, namely from the capital of South Korea. The manuscript is principally well-written, however there are some points in the methodology which require more details and it seems that a key reference avoided Authors’ attention which definitely deserves consideration during the revision stage.
Anyway, I think that this dataset deserves publication, and I encourage the Authors to revise their study which can provide a valuable reference dataset for isotope hydro logical research of the Korean Peninsula.
I note that I cannot provide a detailed linguistic revision since I’m not a native English speaker.
General comments:
-I suggest Authors considering the following paper in the revision: Terzer-Wassmuth, S., Araguás-Araguás, L.J., Wassenaar, L.I. et al. Global and local meteoric water lines for δ17O/δ18O and the spatiotemporal distribution of Δ′17O in Earth’s precipitation. Sci Rep 13, 19056 (2023). https://doi.org/10.1038/s41598-023-45920-8
This global review presents comparable data from Cheongju locating from ~100 km south from Seoul from a partially overlapping period (2015-2018) compared to the Seoul record. So comparing the main features must be included in this study. For instance, the δ17O/δ18O regression reported for Cheongju (δ′17O = 0.5283 × δ′18O + 0.0216 ) definitely can be compared to the equation derived from the Seoul dataset. In addition, the seasonal variation for the overlapping period should be compared in a plot to confirm the spatial consistency. This might bring some major change in section 4.2.
- I missed very much a brief methodological description on the derivation of the local meteoric water line (LMWL). There are a set of methods which can be applied to approximate the linear covariance between δ18O and δ2H (see Crawford et al., 2014 https://doi.org/10.1016/j.jhydrol.2014.10.033 ). Ordinary least square (OLS) regression is more sensitive to the evaporatively enriched compositions typically accompanied with small precip amount, while reduced major axis (RMA) is theoretically more suited to development of a MWL than OLS because they consider errors in both δ18O and δ2H. Precipitation-weighted least squared regression can be the most suitable to derive a LMWL for reference in isotope hydrological comparisons. So, it would be necessary to describe how the LMWL was calculated in this study.
Specific comments:
line 13: I suggest rephrasing in this way “The oxygen isotope composition (δ18O) ranged widely from 1.15 to –18.21‰, hydrogen isotope composition (δ2H) varied from…”
lines 56-57: I suggest citing the study of Terzer-Wassmuth et al., 2023 mentioned in the general comment section.
line 104: Have you applied oil to prevent evaporation? If not please report it in the appropriate paragraph describing methodology, if yes, please report if you experienced any complication during analysis.
lines 106-107: To verify the evaporation proof storage in HDPE bottle Authors might consider citing the following study: Spangenberg, J.E. (2012). Caution on the storage of waters and aqueous solutions in plastic containers for hydrogen and oxygen stable isotope analysis. – Rapid Communications in Mass Spectrometry, 26, 2627–2636.
lines 120-123: I'm confused. I think that the long-term analytical precision should be estimated based on the repeated measurement results of your laboratory standards rather than based on the calibration standards. see e.g https://doi.org/10.1002/rcm.5037 and https://doi.org/10.1556/24.2023.00134
line 133: Have you experienced a threshold regarding precipitation amount? I mean a minimum amount of precipitation below which the collected water was insufficient for the analysis. For instance, this study reported a ≥0.56 mm/day during the rainy season, and 0.5 mm/day during the snowy season: https://doi.org/10.1038/s41597-022-01148-1
lines 136-137: The sentence sounds like figure caption. I suggest omitting this sentence and referring to Fig 3 at the end of the next sentence (in line 139)
line 159: I suggest writing „The linear relationship...” instead of „The relationship...” at the beginning of this sentence.
lines 161 & 185: I think that double brackets are not needed when referring to panels of certain figures.
line 206: I think that “lower humidity” instead of “humidity”
lines 209&213: I suggest writing “δ18O values” instead of simply the delta notation
lines 225-235: Please add relevant citations in this paragraph.
lines 269-271: This sounds like figure caption. I suggest removing this sentence and simply referring to Fig7 after the relevant statements.