the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 24 Jun 2026
Groundwater-Level Response of Annual CO2 Budgets from a Dutch Eddy Covariance Network: Comparison with European Temperate Peatlands across Land Use and Sites
Abstract. Peatlands in the Netherlands hold significant cultural, agricultural, and ecological value but also contribute substantially to national greenhouse gas (GHG) emissions as a result of drainage. This necessitates urgent mitigation, leading to the implementation of various land and water management strategies. As part of the Dutch national GHG research program (NOBV), eddy covariance (EC) measurements of carbon dioxide (CO2) fluxes were conducted at 20 sites, encompassing managed peat meadows, wet nature areas, and paludiculture systems over periods of 1 to 3 years. A novel mobile EC set-up was utilized at some locations to enhance spatial coverage.
Net Ecosystem Exchange (NEE) of CO2 demonstrated pronounced seasonal dynamics across different land-use types. Wet and highly productive systems exhibited both elevated uptake and emissions, leading to intervals of net CO2 uptake. Managed pastures were typically net emitters on a daily timescale, except during early summer when assimilation peaked. Although annual net ecosystem carbon balance (NECB) estimates were subject to uncertainty due to data gaps and adjustments for harvest, grazing, and manure application, systematic differences between land-use types remained evident. To assess the sensitivity of CO2 emissions to groundwater dynamics, ecological response functions (ERFs) were employed to relate NECB to groundwater depth, and these relationships were compared with findings from the literature. Across all sites, NECB exhibited only a weak association with mean annual groundwater level when referenced to the soil surface (ERF slope approximately 0.5 t CO2 ha-1 yr-1 cm-1, R2 = -0.08), and an even weaker association with mean summer groundwater level. When groundwater depth was referenced to the clay layer, the inferred ERF sensitivity increased, resulting in steeper slopes of about 0.8 t CO2 ha-1 yr-1 cm-1 (R2 = 0.26), although the overall explanatory power remained limited. These ERF-based sensitivities align with previously published ERFs, including studies that report non-linear groundwater–CO2 responses and threshold behavior.
Groundwater management interventions produced mixed effects on NECB. Of the pasture-oriented measures, only the Active Water Infiltration System (AWIS) resulted in a detectable shift in NECB beyond background interannual variability, while other interventions were not distinguishable from within-land-use variability. By contrast, clearer categorical patterns emerged across land-use and soil classes, and paludiculture systems showed lower net CO2 emissions than drained managed grasslands, and sites with a clay cap consistently emitted less CO2 than pure peat under comparable management. Overall, these results indicate that although mean groundwater metrics have limited predictive power at the site-year level, CO2 emissions at landscape scales are primarily governed by land use and the presence of a clay-layer, which defines the baseline against which water-management measures operate.
- Preprint
(7219 KB) - Metadata XML
- BibTeX
- EndNote
Status: open (until 26 Aug 2026)
- RC1: 'Comment on egusphere-2026-3151', Anonymous Referee #1, 19 Aug 2026 reply
-
AC1: 'Comment on egusphere-2026-3151', Laurent Bataille, 19 Aug 2026
reply
Erratum for one site - After submission, we noted that an incorrect data file had been used for the Ilperveld site. The analyses and results reported for this site will therefore change, and corrected results will be provided in the final version of the manuscript. Because this site represents only a very small fraction of the dataset, the general results and conclusions are not significantly impacted.
Citation: https://doi.org/10.5194/egusphere-2026-3151-AC1
Viewed
| HTML | XML | Total | BibTeX | EndNote | |
|---|---|---|---|---|---|
| 85 | 62 | 8 | 155 | 8 | 9 |
- HTML: 85
- PDF: 62
- XML: 8
- Total: 155
- BibTeX: 8
- EndNote: 9
Viewed (geographical distribution)
| Country | # | Views | % |
|---|
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1
Summary: The manuscript titled “Groundwater-Level Response of Annual CO2 Budgets from a Dutch Eddy Covariance Network: Comparison with European Temperate Peatlands across Land Use and Sites” uses a large set of Eddy Covariance measurements, ground water level measurements and additional site information to relate net ecosystem CO2 exchange with hydrological conditions of peatlands in the Netherlands. The manuscript thus addresses an important topic in large scale emission modelling, land use transformation, emission reduction and agricultural policy. Herewith, the manuscript fits well to the aims and scope of EGUsphere.
The main findings of this manuscript are, that water level is only a weak predictor for peatland emissions, strongly impacted by site-specific conditions like land use and clay cover. Impacts of specific water management measures were characterized as ambiguous.
General comments:
The main concern about this manuscript is, that it is based entirely on large, but yet unpublished data sets, unpublished methods for green house gas measurements and gap filling routines, making it impossible to evaluate the quality of the finding presented. In some data sets, biases are introduced by the data acquisition and processing (dry dip wells, completely omitted incorporation of grazing into CO2 balances, although 10 sites out of 21 were dairy pastures). Sites were further misclassified by ignoring the actual state of the hydrologic conditions or vegetation cover. Some of the presented findings cannot be backed up by the data presented. Finally, the major outcome, that water level in peatland soils is only a weak predictor for its CO2 balance, is a strong claim and contradicting current process understanding in the vast universe of literature on this topic. If true, this requires a comprehensive discussion and plausible explanations, which the authors omit. Please see the detailed comments below.
Specific comments:
L 15: The correlation to summer water levels were nowhere shown in the manuscript nor discussed.
L 34: The most obvious measure, restoration, is omitted from the discussion. It would be valuable for the reader if the national context of restoration, e.g. in conjunction with the upcoming demands of the EU restoration law, would be discussed as well. It would be valuable to know in the context of this paper, if the Dutch government is not taking into account this land use for peatlands in its national strategies.
L 42: Here the authors state, that ground water management plays a crucial role in peat degradation and emissions, which they clearly contradict in their findings. Please clarify that.
L 50: “…, whereby capturing emissions caused by deeper soil drainage is the primary motivation for introducing this non-linearity.” This sentence is not clear to me, as you state in the next sentence, when using non-linear functions, emissions from deeper soil layers are smaller (since not the same amount of oxygen can reach down there as a result of diffusion through the reactive porous media, compared to upper layers). Please make that clearer.
Fig 1: It is not explained how you calculated “effective linear slope”, is it the arithmetic mean slope between the given intervals? If so, state clearly.
Fig 3: The sentence “NEE measured by eddy-covariance integrates plant-driven fluxes…” omits a closing bracket, leaving unclear which fluxes are considered plant- or peat-based, in particular short-term microbial respiration. Further, the explanation omits peat based fluxes from all upper layers, e.g. the root zone, since only peat oxidation “from deeper degraded pools” is mentioned, leaving this description unclear if not highly misleading. According to this figure, no long term peat oxidation occurs in the root zone, which is incorrect.
L 90: Lateral exports were estimated only at fully inundated sites and set elsewhere to zero. Although it is desired to use all available data at hand, this introduces an artificial bias when comparing NEC balances among sites, rendering these comparisons corrupt. Please treat lateral fluxes the same way for the purpose of site comparisons. Further, Appendix F details that the impact of grazing on NECB was fully ignored in this study, although 10 sites out of 21 were dairy pastures. This further adds to erroneous site comparisons.
L 96: The no-accumulation assumption of pastures is valid here for annual, and even more, multi-annual balances and in line with international standards, e.g. IPCC guidelines. These could be cited here to back-up this assumptions.
L 101: “The addition of manure might imply the addition of carbon to the system..” The addition of manure is always an addition of carbon to the system.
L 132: The entire data analysis is based on large, but yet unpublished data and the data (e.g. flux and water level time series) is not at all shown or discussed in this paper. Thus, the quality of the underlying data remains completely unknown. This strongly violates established scientific practice and the standards of EGUsphere, giving rise to possibly tremendous misinterpretation of the data.
L 140 / 165 / 200: The new approach of using intermittend mobile eddy towers is not an established scientific strategy for conducting tower-based flux observations, due to the resulting, extremely large data gaps. This method was not published or validated befor this manuscript, similar to the newly proposed, ML-based gap filling method. Thus, this manuscript, similar to the underlying data sets, relies on unpublished methods, questioning the quality of the outcomes.
L 249: Water levels exceeding this range (0 to 1-1.5 m) were recorded at the installation depth limit. This means, that whenever the water level fell below this value, recorded values were set to e.g. 1 m. This of course introduces a bias in the annual mean water levels (towards higher water levels). Were dip wells deepened after data retrieval discovered this incorrect installation to reduce this type of bias? Why were these times not filled with more realistic values, e.g. by some ML algorithm as the authors apparently are used to these types of methods? This artificial bias could substantially lower the correlation strenght to emissions and need to be elaborated, since this is one of the main outcomes of this manuscript, and also making non-linear behaviour more likely in fig 7, altering the NECB=0 intersection.
L 280: The authors state, that the vegetation composition of the Sphagnum site (ILP_PT) was in the early state of establishment an resembles more of an extensive grassland. Attributing the flux results to Sphagnum paludiculture is thereby wrong and incites erronous interpretation by the reader. The authors should either name the site properly reflecting its conditions, e.g. semi-natural grassland, or remove it from the analysis. The authors need to make sure to use an appropriate classification and naming as well for all other sites. See also caption of Fig 6: “Sphagnum-based paludiculture is comparable to extensive grasslands”, this a strongly misleading and false statement. See also Fig 7, which shows a wet pasture, but with an annual mean water level of -50 cm?
Fig 6: Authors attribute the reduced summer emission balances at sites with a clay cover to reduced peat oxidation. This claim is nowhere backed up by, e.g. biomass data, since a reduced biomass growth on these sites could also be a cause with the same impact on the seasonal balance, bearing the strong risk of mixing fossil peat emissions with recent biomass turn-over.
Fig 7: How do the authors explain net uptake or near to neutral emissions at deeply drained sites, since no other synthesis publications like Evans et al., (2021) or Tiemeyer et al., (2020) show this behaviour. This strongly contradicts current process understanding.
L 294: As it is also common in the field (see Table 2), was there ever a non-linear function tested against the presented data set, e.g. parabolic or compertz, to see if this dependency would yield a better representation of the data than a linear one and thus increasing the likelihood of a more realistic process understanding? Otherwise, the conclusion of weak correlation of NECB to WLEV could be a matter of choice instead of inference.
L 311: Section 3.4, set out to display outcomes with relation to Land-use and management, consists of just two sentences and seems thus, fairly incomplete. Maybe some text went missing during the writing?
L 316: Section 3.5 is set out to display comparisons between pairs of control and treatment sites. However, may be because the entire section is just two sentences long, it omits completely to explain the different treatments, e.g. what is the difference between the ALB_RF and the ALB_MS sites. I could not find it in the entire manuscript nor the appendix tables. This should be part of the Methods section, however, the site description part there is also only 4 sentences long. Conclusively, with out the necessary information the entire control-treatment comparison remains pointless.
L 333: This important claim cannot be backed up by data in this paper, since the observed annual mean water levels often violates the given land use classification (see comment to Fig 7.) and the range of water level changes in Fig 9 (~25 cm) is way to small to conclude findings on saturation effects (in the wet or dry range) given the actual range observed (~80 cm, compare Fig 7).
L 351: Have the authors taken into account the uncertainty behind all slope values given, when comparing their outcome with previous studies? If done so, is a significant distinction even possible or are the majority of slopes so close, that they can not be distinguished from one another?
Table 2 and Fig 10, 11, 12: It is unusual to present data outside the result section (like here, in the discussion). This way, there is no place to thoroughly describe these findings in detail there, and the necessary background for the reader is missing at this point. Please move Table 2 and Figure 10, 11, 12 into the results section.
L 388: Here you state, that you have fitted various slopes, apparently for different land use types, however, in the entire manuscript, there is just the fit over all sites given (Fig 7.). Were these additional fits not included into the manuscript? Why have these never mentioned before?
L 398: The authors derive the conclusion, that type of measurement (e.g. Eddy Covariance vs. chamber) is likely to contribute to observed differences in data sets. This conclusion cannot be derived, since reported EF, as they state themselves, strongly depend on various site conditions as well as the specific site selection included in each data set. Given this variability, systematic differences between measurement techniques cannot be derived. Which the authors state themselves further down in L 405.
L 412: The authors speculate, that biomass removal impacts NECB, in this case of restored and paludiculture systems. From Eq. 1 and Fig 8, this should rather be a derived outcome of this paper (the authors take biomass export into account), than a guess. Why where the authors unable to derive that conclusion based on their data?
L 426: See comment to L333. Additionally, the authors showed differences in observed water levels among the different country specific studies. Consequently, in each country, e.g. “deeply drained” refers to different water levels. To guarantee a comparability, the authors need to use absolute values instead of inconclusive terms.
L 427: See comment on L 412.
L 445: The authors present the water level as a weak predictor for NECB, omitting that they characterized it as the first-order descriptor (L 363), omitting a better predictor available for large scale modelling or mentioning the vast universe of literature identifying water level as the main driver for emissions from peatlands. This appears to be a biased opinion rather than an objective assessment.
Technical corrections: None