| 26 Nov 2025
Modelling wetland methane emission estimates by leveraging new observations of anthropogenic point-source plumes
Abstract. Satellite retrieval capabilities for detecting methane (CH4) diffuse and point sources have increased drastically over the last decade. These observations are playing an important role in atmospheric inversion systems to optimize emissions from anthropogenic and natural sources. A critical component of atmospheric inverse modelling is the prior estimate of CH4 emissions, which impact both the magnitude of posterior emissions and the distribution between sectors, including wetland emissions. Here we utilize point source retrievals from GHGSat to update prior emission estimate for fossil fuels. We demonstrate the effect on posterior emission using the Integrated Methane Inversion for the Western Siberian Lowlands. The updated GHGSat-informed fossil prior results in a reduction in wetland CH4 flux of 0.59 Tg CH4 yr-1. The approach demonstrates the potential impact of large point sources, that are typically not accounted for in bottom-up emission inventories. Due to modelling approaches adjusting anthropogenic and natural sources at the state vector level, input proportion of these sectors is critical for understanding regional methane budgets and the effect of climate change on wetland emissions.
Campbell et al. present an approach to incorporate high-resolution satellite data in the prior emission estimate used in a TROPOMI-based inversion. They show that posterior wetland emissions end up being lower because of the updated oil/gas prior emission estimate used in their inversion. While this is in principle a relevant topic and approach, the methodology used raises questions and the inversion itself is done over such a short time period that I have questions about the significance of the results. I would only recommend consideration for publication after major revisions.
My specific comments are included below.Â
P1L26-L27 I think the sentence on aquatic and wetland ecosystems appears to overstate their importance considering recent budget papers such as Jackson et al. (2024) and Sanois et al. (2025) show anthropogenic emissions make up two thirds of the global budget.
P2L42 Figure S1 seems to have some odd gaps, for example over Canada where I think wetland and oil/gas emissions are much more widespread than shown here?
P2L57 I think EDGAR does not use the UNFCCC-reported emissions, but calculates national emissions using a globally consistent approach based on IPCC methodology?
P3L66 It would be good to already in this sentence be clear that this consistency exists on the level of a state vector element.Â
P3L72-L73 This sentence seems to mix the varying definitions of a super-emitter between different papers. The aircraft data from the references would be able to detect much smaller emissions than GHGSat could capture.Â
P4L101 It would be good to clarify that these metrics are calculated between the prior/posterior simulation and the observations.Â
P4L103 Here and elsewhere, the description of the inversion system is very short, it should for example explain whether a scaled version of the geological seeps inventory is used, how the state vector compilation was done, and what the used uncertainties were.Â
P4L112 In Table S2, what are the latitude/longitudes given? Are they scene centers for the observations without detected emissions?
P4L118 Are all ‘oil and gas facilities’ (processing plants, wells, and so on) treated as equivalent? What exactly constitutes a facility?Â
P4L122 The use of flaring data is confusing to me, were emissions only found at locations with flares? Why would flares be a good predictor of emissions? The authors appear to share some of these doubts in Section 4.1 but do not argue why they did make this choice here.
P5L129 Is the use of flaring detections not inconsistent with the earlier counting of infrastructure (P4L126) that was used to get to emission factors?Â
P5L133 I guess the authors later argue that the GFEI emissions are very small compared to their estimates but how do they in general prevent double-counting of emissions between GFEI and the GHGSat-based estimates with this approach?
P6L149 Two months is a very short time to do an analysis over such a complicated region. The authors should show and explain that there is enough data to perform a meaningful analysis. Additionally, was a spin-up performed before this time window?
P8L178 Why do the authors put much more weight on the facilities in Observation 3 compared to the other ones? As indicated by their next sentence, this changes the outcome of the analysis by a factor 4.
P8L184 This result appears to be strongly inconsistent with the emission occurrence at the facilities/sites that were covered by GHGSat observations (P7L174)?
P10L213 A large part of the results appears to be driven by very large (spatially) state vector elements, is that correct?Â
P11Table1 The posterior RMSE is worse after the addition of GHGSat data, can the authors reflect on why this happens?Â
P11L227 It would be good to show prior and posterior model – observation mismatches to support this point as the constraints are relatively weak.Â
P11L228-L231 I think the authors should reflect on their confidence in this value (which is also included in the abstract). They strongly increased the prior oil/gas emissions and most of that increase was corrected away by the inversion, leaving a relatively small difference that then affected the wetland emissions. Do they have confidence in this 0.6 Tg/yr difference?
P12L260 I think it would be good if the authors evaluate whether there is a correlation between the TROPOMI data and surface albedo to make sure that does not drive (part of) the identified relationship. See (for example) Lorente et al. (2023).
P13L271 Several other papers have incorporated high-resolution satellite data in emission priors; examples include Naus et al. (2023) and East et al. (2025). This body of work should be reflected somewhere in the publication.Â
P14L284 Good to clarify ‘coarse resolution’ refers to ‘spectral resolution’ here.
P14L286 Do the authors mean the GHGSat plumes can be seen between the waterbodies?
P15L312 Based on the IMI v2.0 paper from Estrada et al. (2025), which should be cited, the IMI uses glint observations over water now, but I think these would not occur at high latitude so I am not sure what the authors are referring to here.
P15L331 The data given on the GHGSat analysis are rather shallow, only detected emissions are given. Could at least plume images/data be made available?
References
East, J.D., Jacob, D.J., Jervis, D., Balasus, N., Estrada, L.A., Hancock, S.E., Sulprizio, M.P., Thomas, J., Wang, X., Chen, Z. and Varon, D.J., 2025. Worldwide inference of national methane emissions by inversion of satellite observations with UNFCCC prior estimates. Nature Communications, 16(1), p.11004.
Estrada, L.A., Varon, D.J., Sulprizio, M., Nesser, H., Chen, Z., Balasus, N., Hancock, S.E., He, M., East, J.D., Mooring, T.A. and Oort Alonso, A., 2025. Integrated Methane Inversion (IMI) 2.0: an improved research and stakeholder tool for monitoring total methane emissions with high resolution worldwide using TROPOMI satellite observations. Geoscientific Model Development, 18(11), pp.3311-3330.
Jackson, R.B., Saunois, M., Martinez, A., Canadell, J.G., Yu, X., Li, M., Poulter, B., Raymond, P.A., Regnier, P., Ciais, P. and Davis, S.J., 2024. Human activities now fuel two-thirds of global methane emissions. Environmental Research Letters, 19(10), p.101002.
Lorente, A., Borsdorff, T., Martinez-Velarte, M.C. and Landgraf, J., 2022. Accounting for surface reflectance spectral features in TROPOMI methane retrievals. Atmospheric Measurement Techniques Discussions, 2022, pp.1-15.
Naus, S., Maasakkers, J.D., Gautam, R., Omara, M., Stikker, R., Veenstra, A.K., Nathan, B., Irakulis-Loitxate, I., Guanter, L., Pandey, S. and Girard, M., 2023. Assessing the relative importance of satellite-detected methane superemitters in quantifying total emissions for oil and gas production areas in algeria. Environmental Science & Technology, 57(48), pp.19545-19556.
Saunois, M., Martinez, A., Poulter, B., Zhang, Z., Raymond, P.A., Regnier, P., Canadell, J.G., Jackson, R.B., Patra, P.K., Bousquet, P. and Ciais, P., 2025. Global methane budget 2000–2020. Earth System Science Data, 17(5), pp.1873-1958.