the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 17 Oct 2024
Soil and stem xylem water isotope data from two pan-European sampling campaigns
Abstract. Stable isotope ratios of hydrogen (δ2H) and oxygen (δ18O) are crucial for studying ecohydrological dynamics in forests. However, most studies are confined to single sites, resulting in a lack of large-scale isotope data for understanding tree water uptake. Here, we provide a first systematic isotope dataset of soil and stem xylem water collected during two pan-European sampling campaigns at 40 beech (Fagus sylvatica), spruce (Picea abies), or mixed beech-spruce forest sites in spring and summer 2023 (Lehmann et al., 2024). The dataset is complemented by additional site-, soil-, and tree-specific metadata. The samples and metadata were collected by different researchers across Europe following a standardized protocol. Soil samples were taken at up to 5 depths (ranging from 0 to 90 cm) and stem xylem samples from three beech and/or spruce trees per site. All samples were sent to a single laboratory, where all analytical work was conducted. Water was extracted using cryogenic vacuum distillation and analyzed with an isotope laser spectrometer. Additionally, a subset of the samples was analyzed with an isotope ratio mass spectrometer. Data quality checks revealed a high mean total extraction efficiency, mean absolute water amount (> 1 mL), as well as high analytical accuracy and precision. The water isotopic signature of soil and stem xylem water varied as a function of the geographic origin and changed from spring to summer across all sites. While δ2H and δ18O values were strongly correlated, the soil water data plotted closer to the Global Meteoric Water Line (GMWL) than the stem xylem water. Specifically, the δ2H values of the stem xylem were more enriched than those of the soil water, leading to a systematic deviation from the GMWL. Isotopic enrichment of the stem xylem water was larger for spruce than for beech trees at mixed forest sites. This dataset is particularly useful for large-scale studies on plant water use, ecohydrological model testing, and isotope mapping across Europe.
- Preprint
(2187 KB) - Metadata XML
-
Supplement
(648 KB) - BibTeX
- EndNote
Status: open (until 15 Jul 2025)
-
RC1: 'Comment on essd-2024-409', Anonymous Referee #1, 15 May 2025
reply
The manuscript "Soil and stem xylem water isotope data from two pan European sampling campaigns" presents a genuinely interesting data set as a result of an exemplary team effort. I can imagine that the presented data can help to answer some existing as well as to pose new questions regarding the investigation of tree water uptake with the help of stable water isotopes. I would recommend this manuscript to be accepted after minor revisions.
The follwoing issues should be resolved:
line 204: You requested 5 cm long sapwood samples. In the summer protocol you added 'Avoid sampling the heartwood'. Could you elaborate on why you added this to the protocoll and how well this instruction was subsequently followed by all contributors, especially the ones who sampled from trees with really small diameters (Tab 1 mentions minimum BHDs of 8 - 11 cm)? Do you think, that the isotopic samples of samples that included heartwood might skew your result? Do you think you could flag samples that might have inlcuded heartwood?
line 250: An average gravimetric water content (gwc) of 40.9% seems pretty high for soil, especially if drier summer soil samples are included.
For soils I am more familiar with typical values for volumetric water content (vwc), so maybe I'm just lacking an intuitive understanding of expectable gwc values. Could you explain why your average(!) gravimetric water contents ended up so high? Most of your soils are sandy loams, so if I'm not completely off with my gwc to vwc conversion, the average sample (including spring and summer) should have been very close to saturation. This does not seem right...
Fig. 2C: Where do the really high gravimetric water contents > 200% come from? Are that measurement errors or is there another explanation for them?Minor issues and some suggestions:
Fig.2: The caption refers to an inset in subfigure C, but the inset is shown in subfigure B
Fig.3: The legend item "Linear Model" in subfigure C confused me, since subfigure C does not contain such a line, but then I realized that this refers to the lines shown in subfigures A and B. Maybe you come up with a solution to improve this potentially confusing legend issue.
Table 2: The aspect of the slopes could also be of interest. In case you have this kind of information, I suggest you add it to the data set.
metadata.csv: Character encoding of the csv-files seems to be Latin3, my first guess of UTF-8 failed to properly display many of the special characters. Maybe add information on the proper encoding somewhere in the paper or within the repository.
Tab.5: The caption states that "Values in bold indicate the highest relative contribution...", but I do not see any bold values in the table...
Fig.6: The resolution of this Figure should be increased. The current version shows clear signs of compression artefacts. Better use a png-file, or even better better a vector graphic file format.
line 74: The numbers in δ2H and δ18O should be in superscript.
line 245-246: Apart from mean weight and maximum deviation, could you also specify the standard deviation?
line 271: "with [the] laser spectrometer"
line 277: "offset[s] between"
line 352: "than for spruce sites (41[%] in spring, 48% in summer)"
line 418: "standardized according [to] recently published"
Citation: https://doi.org/10.5194/essd-2024-409-RC1 -
RC2: 'Comment on essd-2024-409', Anonymous Referee #2, 12 Jun 2025
reply
The authors present an interesting dataset of soil moisture and plant xylem water (beech and spruce) isotopic compositions measured across 40 sites in the spring and summer seasons. The dataset documentation is clear and transparent. The potential uses of this dataset are also immediately clear to me. I fully support publication of this data release. I provide some minor comments below:
Specific comments:
Line 74: The “2” and “18” should be superscript.
Line 99: perhaps consider “resistance and resilience”. Deeper roots can help a plant recover after accumulating damage during drought, but deeper roots can also help individual trees avoid negative consequences during a drought in the first place.
Line 99: rather than pointing to the topic of “climate change” it could be more informative to list the changing hazards that impact trees where greater RWU would help to avoid or mitigate negative consequences (i.e., drought, fire). Climate change just seems to broad a term in this application. Ocean acidification falls under climate change but probably doesn't impact trees much.
Line 102: rather than “determination” I would suggest “estimation” as these isotope methods are constantly debated and refined. This paper and dataset highlight potential issues that still should be resolved.
Line 110: and possibly (iv) that all end-members have been measured with proper consideration for the locations of uptake and the transit time of water through the plant. The article states in the discussion the possibility of water sources beyond those that were measured.
Line 111-112: Water uptake is just one dimension of plant responses to drought though, right? Stomatal closure, xylem resistance to embolism, canopy position, internal water storage, etc. etc. etc. Roots are certainly important, but they do not paint a complete picture of drought resistance and resilience. As an example, cacti generally have shallow roots relative to forest trees but are capable of existing in much more arid regions.
Line 130: Possibly this is the only international network that explicitly states this as the primary goal. The LTER and CZO networks across the US measured soil and plant xylem water isotopic compositions at almost all of the sites similar to this study. The political boundary of the US makes it so that all exist in one country so they are not “international”, but the US is comparatively large by land area, spanning a diverse range of ecoregions. My point is that there have been other attempts at something similar, while this sentence makes it sound like this has never been done before. While the CZO is inactive (and LTER is threatened), NEON sites remain active, and isotopes are being measured at some.
Lines 174 – 175: It would be really interesting to share the lessons learned from these calls. My research group is constantly communicating with others regarding challenges in field sampling, storage, shipping, analysis, etc. This could be very helpful. Possibly this is already all documented in the Ceperley et al (2024) paper?
Line 189: Maximum of all sites, or the maximum at each site?
Line 189: Maybe clarify “canopy cover” here. I work with foresters who use lidar to construct 3D models of canopy structure. They might not call photos representative of canopy cover.
Fig 1: The country borders are showing up at a low resolution. Is there a way to improve this? Minor issue.
Line 202 – 203: I’m a little confused by the terminology. I typically think of “stem” as a < 3 cm diameter branch. Here it sounds like you are coring the trunk if you used an increment borer. Is it stems or trunks?
Line 205: was heartwood removed too?
Line 211: Why say “typically”? There was a protocol or not? What are those “other depths”?
Line 214: How close? The Goldsmith et al (2019) paper and my own sampling has me convinced that we need soil samples at least in triplicate due to high spatial heterogeneity. Was this disregarded to simplify data collection?
Line 217: was root mass with depth measured at any sites? Wouldn’t the root profile be informative for designing the soil sampling protocol of each site?
Line 222: How fast is “back in the laboratory”? We’ve done tests where we put samples into plastic containers and leave them at room temperature and stored cold for varied amounts of time. We observe no problematic effects for the first 6 hours, but we do often observe problems for samples held at longer storage times. This depends entirely on the container and ambient environmental conditions, but it might be good to quantify the potential issues related to sample storage and transport time.
Line 223: “shipped without cooling” and “4 weeks” is concerning to me. Did you perform a test to confirm that the shipping time didn’t result in problems? You could take a dried plant or soil sample, spike it with a standard, hold it for the duration, and then extract to estimate the potential bias.
Line 248: Does the final dataset exclude samples outside of the acceptable ranges, or does it include all values?
Line 371: Even if they were similar, they could still be used to study differences. It might just produce a null result, or maybe environmental conditions varied such that its interesting that the values were similar. More data is always good!
Line 400: What if the effect was similar across all containers and transportation routes? This doesn't rule out the problem for me. A small experiment to estimate the bias attributable to containers/transport might be worthwhile to at least rule this out entirely.
Citation: https://doi.org/10.5194/essd-2024-409-RC2
Data sets
Soil and stem xylem water isotope data from two pan-European sampling campaigns M. M. Lehmann et al. https://doi.org/10.16904/envidat.542
Viewed
| HTML | XML | Total | Supplement | BibTeX | EndNote | |
|---|---|---|---|---|---|---|
| 863 | 115 | 33 | 1,011 | 48 | 34 | 43 |
- HTML: 863
- PDF: 115
- XML: 33
- Total: 1,011
- Supplement: 48
- BibTeX: 34
- EndNote: 43
Viewed (geographical distribution)
| Country | # | Views | % |
|---|
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1