the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Satellite-based modeling of wetland methane emissions on a global scale (SatWetCH4 1.0)
Abstract. Wetlands are major contributors to global methane emissions. However, their budget and temporal variability remain subject to large uncertainties. This study develops the Satellite-based Wetland CH4 model (SatWetCH4), which simulates global wetland methane emissions at 0.25°x0.25° and monthly temporal resolution, relying mainly on remote sensing products. In particular, a new approach is derived to assess the substrate availability, based on Moderate-Resolution Imaging Spectroradiometer data. The model is calibrated using eddy covariance flux data from 58 sites, allowing for independence from other estimates. At the site level, the model effectively reproduces the magnitude and seasonality of the fluxes in the boreal and temperate regions, but shows limitations in capturing the seasonality of tropical sites. Despite its simplicity, the model provides global simulations over decades and produces consistent spatial patterns and seasonal variations comparable to more complex Land Surface Models. In addition, our study highlights uncertainties and issues in wetland extent datasets and the need for new seamless satellite-based wetland extent products. In the future, there is potential to integrate this one-step model into atmospheric inversion frameworks, thereby allowing optimization of the model parameters using atmospheric methane concentrations as constraints, and hopefully better estimates of wetland emissions.
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Status: open (until 19 Jul 2024)
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RC1: 'Comment on egusphere-2024-1331', Anonymous Referee #1, 31 May 2024
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The article titled "Satellite-based modeling of wetland methane emissions on a global scale (SatWetCH4 1.0)" presents a pioneering approach to understanding and quantifying methane emissions from wetlands across the globe. The authors have developed an innovative model that leverages satellite-based data to simulate methane emissions with a high degree of spatial and temporal resolution. This study is particularly significant given the substantial contribution of wetlands to global methane emissions and the critical role of methane as a greenhouse gas.
The manuscript is well-structured, with a clear abstract that succinctly summarizes the study's objectives, methods, and findings. Overall, this study represents a significant advancement in the field of wetland methane emissions modeling. It is my pleasure to recommend this paper for publication in GMD journal.
My comments here are relatively minor, but I hope may be useful for the authors to consider.
- L165-L171: Is the reason for using ERA5's lay2 soil moisture because of the high correlation coefficient between the site data and lay2 soil moisture? In this article (DOI: 10.1126/sciadv.aba2724), the soil moisture used is from 0-289cm depth, and I haven't seen how the interannual variation of soil moisture at other layers correlates with the site observations. As far as I know, the interannual variation of soil moisture in lay1 and lay2 is similar, and lay1 is closer to the surface soil moisture. Why not use the data from lay1?
- L206-L210: Are you suggesting that the green single-peaked line (Albuhaisi et al., (2023))is more in line with physical laws but the formula is more complex and prone to non-convergence, which is why such a complex approach was not adopted? So, your point is that the methods of Albuhaisi et al., (2023) should be more reasonable, rather than the others?
- Fig 2: By the way, could you explain why there are three lines in WetCHARTs, and what do they represent, respectively? What is the significance of such classification? Your results seem to be mainly close to the one with Q10=3.
- L221: I understand that when you were assessing with the site data, you set fwto 1, and later when generating the product, you utilized WAD2W and TOPMODEL? Or after considering the two different wetland areas, why didn't you compare with this quantity? Was it because you considered the spatial resolution of the gridded data to be too coarse?
- In Figure 3c, the simulation appears to be quite similar to the observations in the low-value area, but there is a significant difference in the high-value area. Could you briefly explain the reason for this? Is it related to the wetland areafw being set to 1?
- L227-L230: From a statistical relationship perspective, it is true that the correlation coefficient between the tropical region and the site data is low. Could you briefly explain the reason? Since your model uses MODIS observations similar to NPP, there should theoretically be more data in the tropical regions. Is it because the emissions are high in the tropics and the mechanisms of methane production activities are not well understood?
- In Figure 4, should the time period range of the annual average be indicated in the figure title? Is it 2003-2020? In line 249, it is mentioned that WAD2M is from 2003 to 2020, is TOPMODEL climatological? Please clarify.
- Line 261: The absolute magnitude of the Csubstratevalue is small, and the text provides the corresponding explanation. My question is, after normalization, compared to the other two products (SoilGrids and HWSD), the high-value areas of Csubstrate in the region north of 60°N and more northerly, could you briefly explain this reason?
- L293-294: The overall smaller values of Csubstrateare due to the lower values obtained from MODIS. Do you think these comparatively smaller values are reasonable? Please explain.
- L356-358: Why did you previously mention that the global distribution is related to the distribution of wetland areas, yet the wetland distributions in the Southern Hemisphere are opposite for the two, yet the methane emission fluxes from wetlands are similar? In Equation 1, temperature and wetland area are directly proportional to methane emission fluxes; why is temperature considered the dominant parameter in the Southern Hemisphere?
Citation: https://doi.org/10.5194/egusphere-2024-1331-RC1
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