Preprints
https://doi.org/10.5194/egusphere-2025-3449
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/egusphere-2025-3449
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
08 Aug 2025
| 08 Aug 2025
Status: this preprint is open for discussion and under review for Biogeosciences (BG).
Upscaling of soil methane fluxes from topographic attributes derived from a digital elevation model in a cold temperate mountain forest
Abstract. Forest soils are generally considered a sink for atmospheric methane (CH4), but their uptake rate can vary considerably in space and time. This study aimed to investigate the temporal patterns of spatially distributed soil CH4 fluxes in a topographically complex cold-temperate mountain forest in central Japan. Soil CH4 fluxes were measured nine times during the snow-free season at multiple locations within a 40-ha area in a forested watershed. A machine-learning approach was developed to upscale measured upland fluxes to the landscape scale, using topographic attributes derived from a digital elevation model and vegetation types. Upland soils were a sink of CH4, while small wetland patches emitted CH4 consistently throughout the study period. The accuracy of predicted upland fluxes varied seasonally, with the highest model performance observed in early autumn (R² = 0.67) and the lowest in mid-summer (R² = 0.28). Within the study landscape, predicted upland CH4 fluxes varied significantly across topographic positions, with greater uptake on ridges and slopes than on the plain and foot slopes. Predicted upland CH4 fluxes ranged from −0.35 to −0.60 g CH4 ha−1 h−1 in spring, −0.41 to −1.25 g CH4 ha−1 h−1 in summer, and −0.50 to −0.89 g CH4 ha−1 h−1 in autumn. Seasonal upland fluxes were highly correlated with the 20-day antecedent precipitation index (R² = 0.71), revealing the importance of seasonal moisture conditions in regulating CH4 flux dynamics. This study highlighted the importance of topography in controlling the soil CH4 fluxes and the efficiency of remote sensing and machine learning approaches in scaling field measurements to the landscape level, enabling visualization of spatial patterns of fluxes across the landscape over time.
How to cite. Paul, S. K., Yuasa, K., Dannoura, M., and Epron, D.: Upscaling of soil methane fluxes from topographic attributes derived from a digital elevation model in a cold temperate mountain forest, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2025-3449, 2025.
Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this paper. While Copernicus Publications makes every effort to include appropriate place names, the final responsibility lies with the authors. Views expressed in the text are those of the authors and do not necessarily reflect the views of the publisher.
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Status: open (until 17 Oct 2025)
Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor
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- RC1: 'Comment on egusphere-2025-3449', Anonymous Referee #1, 01 Sep 2025 reply
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RC2: 'Comment on egusphere-2025-3449', Anonymous Referee #2, 11 Sep 2025
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The manuscript upscales soil methane fluxes with a digital terrain model in a forested landscape. It is nicely written, and the topic is relatively novel and worth investigating. However, there are certain issues that should be covered better.
- The current analysis and methodology seems to have a double structure: there is a quantile regression forest analysis for upscaling methane fluxes for different dates and then there is a mix of different traditional statistical techniques (e.g., ANOVA, linear mixed models) for looking at relationships between different environmental characteristics (including topography and methane fluxes). I feel that this structure is a bit complex and some of the issues are done twice but with different methods. Therefore, I suggest simplifying the methodological approach and justifying better why certain analyses are conducted. For instance, why is linear regression conducted between measured and predicted fluxes? Isn’t it sufficient to provide observed-predicted plots? Why is there a need to conduct separate linear regression between topography and methane fluxes in addition to quantile regression forests?
- In relation to the first point, there could also be additional analyses that have not been conducted. It is a bit unclear to me what is the logic in predicting temporally dynamic methane fluxes with temporally static topographic variables. Why not to test also a model with both temporally static but spatially distributed topographic variables and temporally dynamic but spatially uniform climate/weather variables such as API (there could be possibilities for including also other weather-related variables)? Could the soil variables be included also in the quantile regression forest to test their strength in addition to the topographic variables? Why is vegetation (or actually tree) information condensed into one categorical variable (forest type based on tree types? You could also have continuous variables about the tree species presence and abundance.
- The selection of the topographic variables for upscaling is relatively arbitrary. Why were these specific variables chosen and not others (listed e.g., in Ågren et al 2021, https://doi.org/10.1016/j.geoderma.2021.115280). Furthermore, it is unclear why SAGA wetness index was not used instead of the traditional topographic wetness index, as the SAGA version spreads high values in the flat areas. Similarly, topographic position index should have been calculated with multiple neighborhood radiuses and vertical distance to streams with multiple stream networks. Now it is even unclear how the stream network was calculated and streams initiated when calculating the layer.
- Research hypotheses and result section are not well aligned with each other. Particularly, section 3.1 does not seem to address any of the hypotheses. I would suggest phrasing the hypotheses/research questions so that they are answered one by one in the results section. Furthermore, also the methods section could be organized in the same way. Now result section starts with research for such methods that were described in the end of the methods section.
- Novelty value of the research is not entirely clear yet. Is the main novelty about analyzing the role of topography on methane fluxes at different times of snow-free season? If yes, this could be highlighted more in the introduction and also in the conclusions section.
More detailed/minor comments:
- l14: “aimed to investigate” -> “investigated”; i.e., you can use stronger language
- l79: should it be “have been” instead of “were” to be more consistent with tenses. Also otherwise, it is best to write the introduction in present tense.
- l82: can the km2 be written in ha so that same unit is used for all referenced studies
- l85: Can you start just by writing “We assess”. Overall, it would be best if you would use active voice throughout. Now, you use partly passive and partly active voice in the methods section.
- Figure 1: Can you also show the location of the area within Japan/Honshu?
- l117: how were the coverages for the different land cover types estimated?
- How was the measurement point sampling designed? Purposeful sampling or somehow randomized designed? How were the wetland measurement points sampled? Did you use boardwalks when measuring methane fluxes from wetlands?
- l145/147: maybe better to use “spatial resolution”, “pixel size” or “grid” instead of “mesh”
- l148: you write in parentheses both “less than” and “≤”. Either or is sufficient. Actually, it should be “less than or equal to”.
- l161: What method was used to fill the DEM?
- Did you upscale the vegetation/tree classification for the whole study area?
- l195: you can delete “In this study”. It is self-evident that you are describing “this study”
- l203: How were the vegetation types used as predictors? One categorical predictor with three different values? Why not to use continuous predictors related to vegetation and soil?
- VSURF: did you employ all three steps of the method?
- l217: Why did you use a separate package for variable importance? They can be obtained from random forest directly. What metric was used to assess the importance?
- l235: How about temporal autocorrelation in the models?
- l242: What does “scaled” mean here?
- l288: How were the importance scores quantified?
- Figure 3: Is the line 1:1-line?
- Figure 5: Can you have the measured fluxes in the same figure?
- l458: Can you provide some results about the models including wetland points in the supplementary material? Now this feels like speculation.
- l516: You did not really quantify the dominant role of topography as your quantile regression models had mostly just topography predictors.
Citation: https://doi.org/10.5194/egusphere-2025-3449-RC2
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Graduate School of Agriculture, Kyoto University, 606-8502, Japan
Keisuke Yuasa
Graduate School of Agriculture, Kyoto University, 606-8502, Japan
Masako Dannoura
Graduate School of Agriculture, Kyoto University, 606-8502, Japan
Daniel Epron
CORRESPONDING AUTHOR
Graduate School of Agriculture, Kyoto University, 606-8502, Japan
Short summary
This study used a machine learning approach to scale soil CH4 fluxes over time in a topographically complex mountain forest. Within the landscape, predicted upland CH4 fluxes varied significantly across topographic positions, with the greater uptake on ridges and slopes than in the plain and foot slopes. Recent past precipitations significantly influenced seasonal CH4 uptake. Our findings highlight the role of topography and the potential of remote sensing and machine learning to map CH4 fluxes.
This study used a machine learning approach to scale soil CH4 fluxes over time in a...
Overall Assessment
This manuscript presents a machine learning approach to upscale soil CH4 flux measurements across a topographically complex forest landscape using quantile regression forest models with topographic predictors. While the study addresses important questions about spatial controls on soil CH4 fluxes, there is room to improve methodologies to better differentiate between mechanistic and predictive statistics, and to contextualize the landscape-scale conclusions.
Major Comments:
1. The study assumes that topographic indices (TWI, TPI, VDCN) accurately represent soil moisture patterns that drive CH4 fluxes, but never measures soil moisture or temperature at sampling locations to validate this assumption. While the topographic predictors successfully predict CH4 fluxes, the mechanistic pathway (topography → soil moisture → CH4 flux) remains unverified. Without ground-truthing, it's unclear whether the correlations reflect the proposed moisture mechanisms or other covarying factors.
Please either acknowledge this limitation more explicitly or provide basic validation by measuring volumetric water content at a subset of locations to demonstrate that topographic predictors correlate with actual soil moisture conditions.
2. The study excludes wetlands from their predictive framework, leaving wetland pixels unmapped, but provides insufficient guidance on how their upland-only predictions should be applied to real forest landscapes. Most forests contain wet patches, seeps, or seasonally saturated areas that may not be classified as "wetlands" in standard remote sensing products but could function as significant CH4 sources. The authors' approach of simply excluding these areas creates uncertainty about how their upland flux predictions should be applied when: (a) wet patches exist but aren't formally classified as wetlands, (b) the boundary between "upland" and "wetland" conditions varies seasonally or with precipitation, and (c) their results are used to parameterize larger-scale models that need to handle mixed hydrologic conditions.
Provide clearer guidance on how to classify and handle hydrologically diverse areas when applying these results. Discuss what topographic or hydrologic thresholds define the boundaries of their "upland" predictions, and suggest approaches for handling wet patches that fall between clear upland and wetland classifications. This would help users appropriately apply their upland flux relationships while avoiding systematic underestimation of emissions from hydrologically complex forest landscapes.
3. Table A2 shows a significant three-way interaction (Position × Vegetation × Date, p = 0.04), yet the authors conclude that position and vegetation have no effects based on their lack of selection in the later random forest models. This understates the importance of the interaction effects in their mechanistic descriptive modeling, even if it does not provide additional predictive power in the landscape scaling.
The authors should acknowledge that the significant interaction indicates vegetation and position effects are present but depend on specific combinations and timing. In the discussion of Jevon et al. (lines 372-382), note that while vegetation interactions were significant in the LMM, vegetation wasn't selected in RF models because continuous topographic variables captured relevant gradients more effectively for prediction.
4. The LMM results (Table A2) report only p-values without effect sizes, making it impossible to assess practical significance. Similarly, while RF variable importance scores are reported in a table, the magnitude and direction of predictor effects aren't clear in text/discussion.
Please rreport standardized coefficients for LMM factors to show effect magnitudes alongside statistical significance. For RF models, clarify the interpretation of relationships for key predictors (e.g., whether higher TPI increases or decreases CH4 uptake).
5. Scale mismatch in validation approach The authors validate their predictions by comparing point measurements (20 cm diameter chambers) with pixel-level predictions (5m resolution), despite using predictors calculated at even coarser scales (e.g., 30m radius for TPI). This scale mismatch may actually understate the model's true predictive accuracy by forcing landscape-scale predictors to explain fine-scale chamber measurements that inevitably include local variability beyond what topographic indices can capture. The current validation approach tests whether coarse-resolution environmental variables can predict point-level flux heterogeneity, rather than testing the model's ability to capture the landscape-scale flux patterns it's designed to represent.
Consider validating at aggregated scales that better match the conceptual basis of the predictors. Compare predicted vs. observed mean fluxes within topographic position classes (ridge/slope/foot slope/plain) or other meaningful landscape units to test whether the model captures the spatial patterns it's intended to represent. This approach would provide a more appropriate assessment of model performance for landscape-scale applications. Additionally, measuring soil moisture at chamber locations would help validate the mechanistic assumption that topographic predictors accurately represent the moisture conditions driving CH4 fluxes, allowing separation of prediction errors due to scale mismatch from errors due to invalid mechanistic assumptions.