Towards a process-based estimation of global lake methane emissions using LAKE2.6

Li, Xinyu; Peng, Shushi; Stepanenko, Victor M.; Liu, Liu; Zhu, Dan

doi:10.5194/egusphere-2026-2349

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https://doi.org/10.5194/egusphere-2026-2349

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10 Jun 2026

| 10 Jun 2026

Status: this preprint is open for discussion and under review for Geoscientific Model Development (GMD).

Towards a process-based estimation of global lake methane emissions using LAKE2.6

Xinyu Li, Shushi Peng, Victor M. Stepanenko, Liu Liu, and Dan Zhu

Abstract. While lakes play an important role in global methane (CH₄) budget, the present meta-analysis based global estimates produce large uncertainties (16.5 to 185 Tg CH₄ yr^-1), which were often due to lacking sufficient geographical and spatio-temporal representations. Here, we applied a one-dimensional process-based CH₄ emission model (LAKE2.6) to simulate global lake CH₄ emissions. We first calibrated the model in 10 boreal and temperate lakes and 5 tropical (24 °S–24 °N) lakes with continuous flux observations spanning 2 months to 8 years, and subsequently proposed a novel parameterization scheme for global lake CH₄ simulation based on these site-level calibrations. For global model validation, flux observations in 155 lakes from boreal and temperate regions and 21 lakes from tropical regions were collected, ranging in depth from 0.1 to 572 m and in size from 6 m² to 67,075 km². We found that simulated CH₄ fluxes in 85 % of boreal and temperate lakes and 38 % of tropical lakes were consistent with observations, with relative biases within ±50 %. Based on these model calibration and validation results, we established a global parameterization framework and applied it to simulate global CH₄ simulations. Our estimates show that global lakes (>10 ha) emitted 17.7–20.1 Tg CH₄ yr^-1 during the period 1979–2023. This approach improves the reliability of model extrapolations from site-level measurements to the global-scale, thus strengthening our ability to assess historical and future changes in global lake CH₄ emissions.

Received: 23 Apr 2026 – Discussion started: 10 Jun 2026

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Xinyu Li, Shushi Peng, Victor M. Stepanenko, Liu Liu, and Dan Zhu

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RC1: 'Comment on egusphere-2026-2349', Anonymous Referee #1, 06 Jul 2026 reply

The authors applied the one-dimensional process-based CH₄ emission model LAKE2.6 to simulate global lake CH₄ emissions. They first calibrated the model using 10 boreal and temperate lakes and 5 tropical lakes, with continuous flux observations spanning from 2 months to 8 years. Based on these site-level calibrations, they proposed a new parameterization scheme for global lake CH₄ simulations. They then validated the model at the global scale using flux observations from 155 boreal and temperate lakes and 21 tropical lakes. Finally, they applied the calibrated and validated parameterization scheme to estimate CH₄ emissions from global lakes.
Overall, the manuscript is well organized and clearly written. However, I have several major concerns that should be carefully addressed before publication.
1. I am not fully convinced by the proposed parameterization of P0 and αnew, because the statistical relationships fitted in Figure 7 are based on only 10 lakes. It is highly challenging to upscale such relationships to more than one million lakes globally. I understand that the relationship between P0 and mean air temperature during the ice-free period may be reasonable to some extent, but there are likely important exceptions. For example, in lakes dominated by allochthonous carbon inputs from surrounding catchments, P0 may be more strongly controlled by carbon fluxes transported by streamflow, and precipitation or runoff may therefore be more relevant drivers than temperature. In addition, the authors included thermokarst lakes (shown in the global maps of results), where CH₄ production may be strongly controlled by permafrost thaw and the release of permafrost-derived organic carbon. In such systems, higher mean air temperature may lead to a longer thawing period and greater carbon availability for methanogenesis, which is a different mechanism from the general temperature–P0 relationship proposed here. Therefore, the current upscaling strategy for P0 may not be applicable to all global lakes.
Similarly, although the relationship between αnew and the product of mean temperature and lake geometry may be statistically fitted, 10 data points are insufficient to support a robust global-scale parameterization. The authors should provide a clearer mechanistic explanation for this relationship. My concern is that these relationships are derived from calibrated parameters and may partly reflect numerical artifacts or parameter compensation rather than true ecological or biogeochemical controls. Do the authors have independent observations or parameters directly derived from measurements to support these relationships? For example, in Langenegger et al. (2019; https://doi.org/10.1002/lno.11133), similar parameters were derived from regression analysis based on laboratory measurements.
2. LAKE2.0 is able to represent a valley-shaped lake geometry. I am wondering whether the simulations in this study used a valley-shaped or bucket-shaped lake configuration. If a bucket-shaped configuration was used, how did the authors distinguish CH₄ processes in pelagic and benthic zones? If a valley-shaped configuration was used, how were sediment CH₄ emissions distributed into different water layers along the sediment–water interface?
3. The authors mention that LAKE2.6 simulates photosynthesis and respiration. More details should be provided on the parameterizations of these carbon-related processes, because they may be key controls on CH₄ production and emissions. Does the model explicitly simulate the growth of phytoplankton or macrophytes, especially rooted macrophytes with deep roots in lake sediments?
4. The model was calibrated and validated mainly using datasets from Rosentreter et al. (2021), in which most sites are located in subarctic, boreal, and temperate regions of the Northern Hemisphere. However, in many subarctic and boreal lakes, allochthonous carbon inputs from surrounding catchments are important sources of substrate for CH₄ production. How are these external carbon inputs represented in the current model framework?
5. I noticed that the authors included seven non-anthropogenic reservoirs in the model calibration and validation. What are the differences between the model settings for reservoirs and natural lakes when simulating CH₄ emissions?
6. The authors state that the light extinction coefficient is an important parameter. For lakes without observations, empirical equations from Håkanson were used to estimate the light extinction coefficient based on lake depth. I agree with the general need for such an approximation, but the light extinction coefficient is also strongly related to inorganic suspended particulate matter, dissolved and particulate organic matter, nutrient status, and allochthonous carbon inputs. Therefore, using lake depth alone may introduce substantial biases in simulated thermal regimes and nutrient/light conditions. I suggest that the authors add a sensitivity test for this parameterization and explicitly discuss this limitation.
7. Please add some explanation for the accumulated CH₄ emissions with increasing lake depth in Figure 3. The pattern for ebullition is intuitive, but the pattern for diffusive emissions is less clear. Why does the accumulated diffusive emission show this particular shape?
8. In lines 333–334, the authors state that “LAKE2.6 could not simulate ebullition events with comparably high fluxes, which might be due to the uncertain mechanism of the pulse of CH₄ emissions.” I agree that gas bubble release can be stochastic to some extent, but the mechanistic distinction between diffusion and ebullition is relatively clear in process-based models, for example as described in Eqs. (16)–(17) of Maisonnier et al. (2025). The authors should explain how CH₄ production is partitioned between diffusive and ebullitive pathways in LAKE2.6, and why the model fails to reproduce high-flux ebullition events.
9. In Section 4.1, too many numerical values are listed in the text for comparison purposes. A clearer approach would be to summarize the comparison between simulations and observations in a table, especially if the authors want to emphasize agreement in mean or total fluxes. Since many of these comparisons are already shown in figures, the text could also be simplified to avoid redundancy.
10. Line 383: the authors state that they “hypothesized that a universal q10 value of 2.5 applies to all global lakes.” Was this hypothesis validated? If not, “assumed” would be more appropriate than “hypothesized.”
11. I am confused by the parameterization of P0, which represents the CH₄ production rate. The authors state that LAKE2.6 simulates photosynthesis and respiration, which control carbon dynamics in lakes and the availability of substrates for CH₄ production. If so, why is P0 not directly linked to the simulated carbon dynamics within the lake?
12. In Figure 8b, the comparison based on only four points seems to provide limited information and may not be meaningful.
13. In Figure 10a, the horizontal axis indicates lake names rather than “observed.” Please revise the axis label and add a legend to distinguish observations and simulations.
14. The model validation shown in Figure 10 seems to suggest that the proposed parameterization does not perform as well as expected. In particular, the results in panel a are difficult to accept unless the authors provide a convincing explanation.
15. Readers may be interested in the computational cost of applying LAKE2.6 to global lake CH₄ simulations. Please add a sentence or short paragraph describing the computational requirements.
16. Line 466: “hypothesized” should be changed to “assumed,” unless the hypothesis was explicitly tested.
17. I am not sure whether LAKE2.6 is suitable for simulating CH₄ emissions from thermokarst lakes. Does the model include permafrost thaw and the release of permafrost-derived organic carbon? If not, the applicability of the model to thermokarst lakes should be discussed more carefully.
18. Lines 485–490 are confusing. Do the authors mean that permafrost-affected lakes were excluded from the global analysis? If so, why do thermokarst lakes still appear in the figures? Please clarify whether thermokarst and permafrost-affected lakes were included or excluded, and how they were treated in the global simulations.

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Citation: https://doi.org/10.5194/egusphere-2026-2349-RC1

Xinyu Li, Shushi Peng, Victor M. Stepanenko, Liu Liu, and Dan Zhu

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https://doi.org/10.5194/egusphere-2026-2349-supplement

Xinyu Li, Shushi Peng, Victor M. Stepanenko, Liu Liu, and Dan Zhu

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Short summary

Lakes release methane, but global totals are uncertain. We used a process-based computer model to simulate how methane forms and escapes. We calibrated the model in 15 lakes with continuous observations, and validated the model in 176 lakes. The model showed global lakes over ten hectares emit 17.7 to 20.1 million metric tons annually. Most gas escapes through bubbles, particularly from shallow areas. This study helps us track these emissions and predict how they might rise as the world warms.


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