the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 30 Sep 2025
Technical note: How well do evapotranspiration partitioning approaches perform in moss-covered wetlands?
Abstract. Evapotranspiration (ET) is the dominant hydrologic flux in wetlands, and partitioning into transpiration (T) and evaporation (E) is essential for understanding water and carbon dynamics, guiding sustainable water management practices, and predicting responses to climate change in these systems. However, the presence of moss layers in many wetlands challenges the assumptions of commonly used partitioning methods. This study evaluates the performance of nine eddy covariance (EC)-based ET partitioning approaches across multiple moss-covered wetland sites located in boreal and the Canadian Rocky Mountains. The partitioning results from each approach were compared against independent measurement-based estimates, which were obtained using flux chamber, micro-lysimeters, sap flow sensors, and EC systems. Results showed that none of the evaluated methods provided both accurate and precise estimates of ET partitioning (T:ET), and no single method emerged as the most suitable for studied ecosystems. Despite this, the general agreement between modelled and measured T:ET values indicates that many of these approaches still provide valuable insights. Applying multiple methods concurrently is recommended, where possible, to enhance confidence in partitioning results. For researchers with access to high-frequency EC data, priority should be given to high-frequency EC-based methods due to their more consistent performance across sites. The findings also highlight the limitations of current partitioning approaches under evaporation-dominated conditions, and underscore the need to examine the mechanistic role of mosses, as well as to improve how optimal stomatal conductance theory is conceptualized and implemented in model formulations.
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RC1: 'Comment on egusphere-2025-4252', Anonymous Referee #1, 31 Oct 2025
Review of "How well do evapotranspiration partitioning approaches perform in moss-covered wetlands?"This paper delves into an important topic of evapotranspiration (ET) partitioning in moss-covered wetlands, comparing different methods against "direct" measurements. The topic is relevant and the manuscript is generally well written and easy to follow. To my knowledge, this is the first manuscript that compares such a wide range of ET partitioning methods, including high-frequency EC and GPP-informed methods. While the study sites are not ideal given the high heterogeneous conditions, I believe it can still provide useful insights on the performance of the different methods. My only concern about the study sites is the presence of flooded areas, which might complicate the interpretation of the results or even invalidate some of the methods. Overall, I think the manuscript has merit and can be published after addressing the comments below.Line 106: Given that Eichelmann et al. (2022)'s method was developed specifically for flooded ecosystems, it is almost required to be included in this analyses. I would strongly encourage the authors to contact the group by Eichelmann et al. (2022) to get help using their code.Line 128: study sitesOne important aspect of the high-frequency partitioning methods is that the ground is a source of CO2 (respiration) and source of water vapor (evaporation). Moss photosynthesis at the ground level might not necessarily invalidate the methods if we consider the net effect (surface_level_respiration - moss_P), as long as the CO2 signal from the surface is positive and well mixed before it reaches the canopy top and sensor (i.e., different from being fully mixed with the plant canopy signal). What is unclear to me is the role of the water in flooded parts. If water is a sink of CO2, then the net CO2 flux from surface level is negative, and none of these methods would make sense given the decoupling between water vapor and co2.Line 225: Results and discussionI agree with the authors when they say that direct measurements cannot be directly compared to any of the approaches since they all use EC fluxes, covering a much larger and heterogeneous footprint. I would also add that even under ideal homogeneous conditions, direct measurements (sap flow, chambers, etc) still suffer from other limitations and cannot be considered as the "truth", but as proxies.Fig 1 and 2: Are "measurement-based" data an average including all "direct measurements"? Is there any measure of variance, say, across chambers/plants (for sap flow) or maybe across methods?Line 272: Is it possible that data quality was worse when T/ET < 0.5?Line 300: ConclusionI would refrain from talking about more or less "accurate" results. One of the main challenges of flux partitioning is that we really do not know the true values of any of the components, and while direct measurements are good proxies of trends, they have limitations and might even required some level of parameterization such as sap flow measurements.Citation: https://doi.org/
10.5194/egusphere-2025-4252-RC1 -
RC2: 'Comment on egusphere-2025-4252', Elke Eichelmann, 06 Jan 2026
Review: egusphere-2025-4252
Wang et al., Technical note: How well do evapotranspiration partitioning approaches perform in moss-covered wetlands?
In the interest of transparency, I would like to disclose that some of the work discussed in this manuscript and my review relates to some of my own publications (e.g. Eichelmann et al., 2020; Stapleton et al., 2022).
General remarks:
In this manuscript, Wang et al. conduct a comparative study to evaluate nine different evapotranspiration partitioning tools/models for eddy covariance data at four moss-covered wetland sites over one growing season at each site (total of four site-years).
They conclude that none of the tools captures all the temporal and spatial variability, but that in combination they can provide useful information on T:ET estimates for these sites.
Overall the manuscript investigates an interesting and relevant subject. Cross-site and cross-tool comparisons of eddy covariance based ET partitioning methods are still very rare and are much needed. This is especially true for some of the more unique and challenging ecosystems such as those investigated in this manuscript, where models aren’t routinely evaluated. The manuscript is well written and the science is generally sound.
That all being said, I think that the manuscript at the moment, unfortunately, falls short of being actually useful for other researchers in a concrete way. The overall conclusions are quite broad and vague and don’t go much beyond what existing knowledge already told us. I think there are two main reasons for this:
1) My main concern is the lack of quantitative assessment/comparison of the ET partitioning performance. I acknowledge that it is extremely difficult if not impossible to get ‘ground truth’ data to validate model performance against, and commend the authors on their efforts to obtain some validation data for their sites. While I agree with the authors that the micro-lysimeter and Shuttleworth-Wallace model validation data is not a true ground truth and any comparison between them has to be taken with a grain of salt, I still think that there should be a quantitative evaluation of the model performance, rather than just a qualitative assessment. At the end of the day, there is a set of independent data, which can be used as a baseline for comparison across the models. While we might not expect a perfect fit with these baseline data, we can still get quantifiable differences across models for a more robust discussion. See my specific comments further below for more detail on this.
2) I also would have liked to see a bit more detail on the environmental/meteorological conditions at these sites across the study periods and how they could be influencing some of the observed patterns (or lack thereof). There are no high temporal resolution data presented (only an overall range or average for each site across the measurement period) on water table depth, temperature (soil/water/air), VPD, or precipitation. As the authors state, some of these parameters exert strong influences on biosphere-atmosphere exchange of water, and some of the tools/models are better or worse at incorporating these impacts. Providing some of these meteorological data alongside the partitioning results and/or evaluating under what conditions certain tools perform better/worse would improve the manuscript and make it more useful for other researchers to help decide which tools might be best in their context. But I recognize that the limited data availability might restrict how much can be learned from this.
Overall this study is a very worthwhile effort, but requires a bit more quantitative analysis to realize its full potential.
Specific comments:
Introduction: Well written, concise introduction with a clear justification for the study and sufficient background information provided. Relevant literature is cited, but it might be interesting to have a look at Speranskaya et al. (2024) also.
Methods
Line 106:
While the original ANN based partitioning code from our 2020 paper unfortunately cannot be made publicly available, a subsequent publication using different machine learning algorithms, but based on the same underlying concept, has since been published (Stapleton et al., 2022). The python code for this is publicly accessible and linked in that publication. It might be an interesting comparison to include, given that it works on evaporation prediction and doesn’t require ecosystem carbon-water coupling. As far as I can see all other non-high-frequency based methods in this manuscript rely on carbon-water coupling, so it could be interesting to compare with the machine learning based method and see if different patterns emerge.
Line 149, Table 2: Please add information on the measurement period for each site (start and end date)
Line 161: Maybe add the Pastorello et al. (2020) reference for the Fluxnet data processing protocol
Line 173-175: I understand that the detailed upscaling procedures have been described elsewhere, but I still think the authors should provide a short (couple of sentences) summary of the main steps to help the reader get a sense of what is involved here without having to dig up the other publications and reading up on the detail.
Results and Discussion
Figure 1 and 2: For Bonsai, it looks as though there are more ‘Measurement-based’ black circles in Figure 2 compared to Figure 1, especially in the late growing season after Aug 15, but there are also some of the higher values missing earlier in the growing season (e.g. a measurement based value close to 1 around August 1). Why are some of those measurements omitted from Figure 1?
There doesn’t seem to be a lot of temporal variability in the measured T:ET values and most of the models don’t capture the temporal variability well. I am wondering, are any of these models actually better than just assuming a constant T:ET ratio? I think it would be good to add that in for comparison.
I would also like to see a bit more of a quantitative analysis on the performance of the models. At the moment, everything is based on reading off approximate performance from the graphs and is quite qualitative, e.g. lines 290-292: “Both CP and CWSC [...] produced a narrower spread of T:ET estimates than uWUE ...” I understand that the ‘ground truth’ data based on Micro-lysimeters and the Shuttleworth-Wallace model isn’t really a ground truth and fully comparable 1:1, but I still think some level of quantitative analysis would be beneficial. While we maybe wouldn’t expect the graphs in Fig 3 to fall on the 1:1 line, even just providing R2 values (within and across sites) for how well the models track temporal and spatial patterns would be interesting and will make the resulting discussion more robust. The authors may also want to explore other measures of goodness of fit, which might be more appropriate in this particular situation (e.g. Nash–Sutcliffe coefficient).
Line 296-298: I think it is a bit of a stretch to say these models still offer valuable insights. Just from looking at the graphs in Fig 3, it seems to me that assuming a constant T:ET ratio based on an approximate global average of 0.6 (e.g. Wei et al., 2017; Liu et al., 2022) would perform just as well across sites and in some cases also within sites (especially at Brustall). So this statement really needs to be backed up with some quantitative analysis.
Summary and closing thoughts
Line 305: Again, here it says that the models captured similar temporal patterns of T:ET. I’m not sure what this statement is really based on given that most sites don’t exhibit much temporal variation at all, and and at the one site that does (Brustall) only one out of 9 models manages to capture that temporal trend.
References:
Liu, Y., Zhang, Y., Shan, N., Zhang, Z., & Wei, Z., 2022. Global assessment of partitioning transpiration from evapotranspiration based on satellite solar-induced chlorophyll fluorescence data. Journal of Hydrology, 612. https://doi.org/10.1016/j.jhydrol.2022.128044
Pastorello, G., Trotta, C., Canfora, E. et al., 2020. The FLUXNET2015 dataset and the ONEFlux processing pipeline for eddy covariance data. Sci Data 7, 225. https://doi.org/10.1038/s41597-020-0534-3
Speranskaya, L., Campbell, D. I., Lafleur, P. M., and Humphreys, E. R., 2024. Peatland evaporation across hemispheres: contrasting controls and sensitivity to climate warming driven by plant functional types. Biogeosciences, 21, 1173–1190. https://doi.org/10.5194/bg-21-1173-2024
Stapleton, A., Eichelmann, E., Roantree, M., 2022. A framework for constructing machine learning models with feature set optimisation for evapotraspiration partitioning. Applied Computing and Geoscience, 100105. https://doi.org/10.1016/j.acags.2022.100105
Wei, Z., Yoshimura, K., Wang, L., Miralles, D. G., Jasechko, S., & Lee, X., 2017. Revisiting the contribution of transpiration to global terrestrial evapotranspiration. Geophysical Research Letters, 44(6), 2792–2801. https://doi.org/10.1002/2016GL072235
Citation: https://doi.org/10.5194/egusphere-2025-4252-RC2
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