The importance of spatial resolution in the modelling of methane emissions from natural wetlands

. An important uncertainty in the modelling of methane (CH 4 ) emissions from natural wetlands is the wetland area. It is difficult to model wetlands CH 4 emissions because of several factors, including its spatial heterogeneity on a large range 10 of scales. In this study, we investigate the impact of model resolution on the simulated wetland methane emission for the Fenno-Scandinavian Peninsula. This is done using a high-resolution wetland map (100x100m 2 ) and soil carbon map (250x250m 2 ) in combination with a highly simplified CH 4 emission model that is coarsened in six steps from 0.005 ° to 1 ° . We find a strong relation between wetland emissions and resolution, which is sensitive, however, to the sub-grid treatment of the wetland fraction. In our setup, soil carbon and soil moisture are positively correlated at high-resolution with wetland 15 location leading to increasing CH 4 emissions with increasing resolution. Keeping track of wetland fraction reduces the impact of resolution. However, uncertainties in CH 4 emissions remain high because of the large uncertainty in the representation of wetland area, as demonstrated using output of the WetChimp intercomparison over our study domain. Because of wetlands mapping uncertainties, the existing models are unlikely to realistically represent the correlation between soil moisture and soil carbon availability. The correlation is positive in our simplified model, but may be different 20 in more complex models depending on their method of representing substrate availability. Therefore, depending on the correlation, CH 4 emissions may be over or underestimated. As increasing the model resolution is an effective approach to mitigate the problem of accounting for the correlation between soil moisture and soil carbon and improve the accuracy of models, the main message of this study is that increasing the resolution of global wetland models, and especially the input datasets that are used, should receive high priority.


Introduction
Despite decades of research, the main drivers of variations in the growth rate of atmospheric methane (CH4) are still poorly understood (Saunois et al., 2020).This is a critical knowledge gap, since CH4 is the second most important anthropogenic greenhouse gas after carbon dioxide (CO2) (IPCC, 2013), and the increase of its recent growth rate introduces significant uncertainty in the scenarios that are used in climate projections (IPCC, 2007;Bloom et al., 2017).While those projections 30 are mainly concerned with anthropogenic emissions, natural emissions of CH4 are important also since they account for an important fraction of the growth rate uncertainty (Saunois et al., 2020;Bloom et al., 2016).This can be explained by the a wetland area W that can either be described at high-resolution by 2x2 tiles, each with area AT=1, or at low resolution by combining the 2x2 tiles into a single tile of AT=4 (see Figure 1).To quantify the CH4 emission in these tiles, we use the highly simplified model of wetland emissions,  $ = .. * (1) in which the CH4 emission (FCH4) is the product of the availability of soil carbon (SC), soil moisture (SM), and the area 90 of each wetland tile (AT).This model is a simplified version of CH4 emission equation that we will use in the remainder of this study, as will be explained in section 2.2.
In the first case, we set SC and SM both to unity.As a result, in the high-resolution representation (Figure 1.a) each cell has an emission of 1, and therefore the total emission over wetland area W equals to 4 (in an arbitrary unit).When aggregating the high-resolution tiles to coarse resolution (Figure 1.b), soil moisture and soil carbon are the average of the 95 2x2 tiles.In this case, the sum of emissions will again be 4, i.e. the high-and low-resolution representations are consistent.Alternatively, we assume that wetlands are located in two cells only (Figure 2.a), the other two cells being upland.For 100 upland tiles we assume soil carbon and soil moisture to be zero, so that wetland CH4 emissions are zero also.Then when applying the same principle, the total emission in the high-resolution case is 2 (Figure 2.a), whereas it is 1 (4x0.5x0.5) in the low-resolution case (Figure 2.b).Here the resolution-dependence of the CH4 emission arises because of the product SC x SM in Eq. (1), causing the impact of the averaging to coarse resolution on the CH4 emission to be squared.
This outcome can be generalized to larger steps in resolution as follows: 3-=  12  12  12  3- (3) where EHR and ELR are the emissions of the coarse resolution grid box evaluated at respectively high and low resolution.
AHR and ALR are the grid box areas at high and low resolution, and nwl is the number of high-resolution grid boxes that are

Wetlands CH4 Model
The CH4 emission scheme of Gedney et al. (2004) is used to compute CH4 emissions for the case study area described in section 3. It is a highly simplified representation of wetland CH4 emissions, but well suited for testing resolution 120 dependences because of its computational efficiency and ease of interpretation as the number of model parameters is only small.The CH4 flux from wetlands FCH4 [g CH4 m -2 yr -1 ] is calculated from the basic CH4 controls of soil temperature (Tsoil), soil moisture (SM) and soil carbon (SC), as follows: Where Tsoil is the average soil surface temperature in Kelvin [K] for the top 5 cm.Q10 is the temperature sensitivity of the 125 CH4 emission to a 10 K temperature change relative to T0 = 273.15K. Since FCH4 is now expressed as the CH4 flux per unit area, this will be used for soil carbon also (SC in [g.m -2 ]).KCH4 is a calibration constant relating the driving variables to a CH4 flux in units of [g CH4 m -2 yr -1 ].We want to note here that the input data used in Eq.5 are for year 2015 as will be described in section 3.2.
Different scenarios are used (Sn.1 -Sn.4) representing wetlands, uplands, and combinations between them (Table 1).In 130 Sn.1, we use the high-resolution wetlands map (see section 3.2) as a mask for wetlands to distinguish wetlands from the upland surroundings.CH4 emissions are only calculated for the wetland fraction Fwl.This is because equation 5 does not apply to aerobic upland soils, where CH4 oxidation by methanotrophic bacteria dominates methanogenic CH4 production.
Despite that, in Sn.2 uplands are treated as the wetlands in Sn.1.CH4 oxidation in upland soils may show a resolutiondependence following the logic of section 2.1 also.However, since the upland fraction is generally substantially larger than 135 the wetland fraction at spatial resolutions that are common in global wetland modelling, the sensitivity of the sink to resolution is expected to be less important (see equation 4).The setup of Sn.2 is meant to isolate the impact of the difference between wetland and upland fraction on the resolution dependence, which explains why the method to compute the flux is wetlands only, like in Sn.1, but using spatially varying SM and SC data (section 3.2.).The aim is to test the extent to which the results of Sn.1 and Sn.2 might have been influenced by the simplifying assumptions on SC and SM that are made, and how sensitive the resolution-dependence may be to a more realistic representation of their spatial variations.
The first three scenarios used to test the resolution-dependence were aggregated from original high-resolution wetlands datasets described in the data section to 6 different resolutions; 0.005º, 0.01º, 0.05º, 0.1º, 0.5º and 1º.For the remained 145 scenario, we aggregate from the finest available resolution of the global hydrological model PCR-GLOBWB (PCRG) (5 arcmin) to 0.1º, 0.5º and 1º.
We acknowledge that our wetland 'model' provides only a highly simplified representation of the processes controlling CH4 emissions in wetlands.However, the main objective is to demonstrate the principle and provide a first order estimate of its importance, suitable to provide a basic discussion to be refined further using more sophisticated models in the future. 150

Study area
The Fennoscandinavian peninsula, excluding the Russian sector, is used as the domain of our computations (see Figure 3).
It is chosen as a favorable compromise between size, importance for high-latitude CH4 emissions, ecosystems diversity,

Wetland map
To localize wetlands at high resolution, the Corine Land Cover map is used (CLC2018).These data are made available by the Copernicus Land Monitoring Service from (https://land.copernicus.eu/pan-european/corine-land-cover/clc2018).
CLC2018 represents the year 2018 at 100x100m 2 resolution for the European continent (Büttner et al., 2017), and provides 165 information about the physical state of the landscape (Faltan et al., 2020).The land-cover classification is based on satellite images with a spatial resolution in the order of meters, from sensors onboard Landsat, RapidEye, Sentinel-2, and Landsat-8.This information is extended using various auxiliary data, e.g., aerial photographs, thematic maps, etc. yielding a high resolution land cover map suitable for large scale research and land cover/use mapping (Faltan et al., 2020).CLC2018 classifies wetlands into major categories; inland wetlands and coastal wetlands.Inland wetlands are inland marches and 170 peatbogs (class number 411 and 412 respectively), which we use in our study as decribed in the CLC2018 user guide (Kosztra et al., 2017).

Organic Soil Carbon SC
To specify SC in equation 2, the soil carbon dataset from the International Soil Reference and Information Centre (Hengl et al., 2017) at 250x250m 2 resolution is used.According to the ISRIC map, the SC for the study area ranges between (10-175 110 g.m -2 ).This information is used in different ways as specified in

Soil Moisture SM
The study of Schaufler et al. (2010) hypothesized that CH4 fluxes peak at soil moistures between 30% and 70% of waterfilled pore space and decline below 20% and above 80%.Following this hypothesis, for Sn.1, Sn.2 and Sn.3 we minimize 185 SM for uplands to be 0.10 cm 3 .cm - again as an attempt to lower the impact of upland resolution-dependence when following the coarsening steps explained in the following sub-section.SM is maximized at wetlands locations to 0.70 cm 3 .cm - .
For Sn.4, we use soil moisture data modelled by PCRG.PCRG is grid-based global hydrology and water resources model (Sutanudjaja et al., 2018).We run the model to simulate soil moisture for the study area using the two available versions 190 with spatial resolutions of 5 arcmin (»10km) and 30 arcmin (»50km).PCRG has three different soil depth layer, 0-5 cm (top layer), 5-30 cm and 30-150 cm.A simulation has been carried out for the year 2015 and output has been used for the top 5 cm soil layer, after remapping the data to 0.1º and 0.5º resolution.

Temperature T
Temperature in equation 5 has been taken from the European Centre for Medium-range Weather Forecasts reanalysis 195 (ECMWF-ERA5), using daily soil surface temperature for the top 5 cm of the soil for the year 2015.Data at a spatial resolution of 7x7 km 2 were downloaded from (https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era5).We acknowledge that this resolution should preferably have been higher.Although air temperature variations may be represented adequately enough at this resolution, the surface energy balance of wetlands and upland ecosystems is expected to be different.This gives rise to variations in soil surface temperature that we are unable to account for but are assumed 200 to be second order in importance compared to variations in soil carbon and soil moisture.

Q10 and KCH4
For the temperature sensitivity of methane emissions from natural wetlands, previous studies derived Q10 values varying in the range of 1.7-16 (Walter and Heimann, 2000).This wide range is explained by the difficulty of separating co-varying environmental drivers (Gedney et al., 2004).We use a Q10 of 3.0, following the studies of Wania et al., (2013), used also 205 in the WetChimp simulations (Melton et al., 2013).Ringeval et al., (2010) derived this value in an attempt to optimize the agreement between the LPX model and site measurements under inundated conditions.
To estimate the KCH4 emission calibration factor, we use daily CH4 flux measurements for the year 2015 from sites located within the study area (Table 1).The KCH4 that is used brings our simulations approximately to the same annual emission for the year 2015 as measured at the FLUXNET sites Siikaneva, Finland, and Degero, Sweden (see section 4.4).CH4 flux 210 measurements have been downloaded from (https://fluxnet.org/download-data/).This section presents the modelled CH4 emissions over our Fenno-Scandinavian domain for the scenarios in Table 2, using the datasets described in section 3.2, and how they vary with spatial resolution.Annual CH4 emissions integrated over the full domain span a wide range when coarsened from the highest resolution of 0.001º, used as reference, to progressively coarser resolutions up to 1º (Tables B-1 and B-2).

Scenario Sn.1 220
Figure 4 compares the spatial distribution of annual CH4 emissions from wetlands over the study area.Significant differences are seen across the wide range of scales from the reference resolution to the coarsest resolution of 1ºx1º.
Towards the finer resolutions, the spatial pattern gradually converges, however, the reference resolution integrated CH4 emissions is ~1.68 Tg CH4 yr -1 which about two times higher than at 0.005º resolution (~0.96Tg CH4 yr -1 ).The total emission difference increases aggregating to coarser resolutions.The total emissions decrease from 1.68 Tg CH4 yr -1 at the 225 finest resolution to ~0.13 Tg CH4 yr -1 at the coarsest resolution of 1ºx1º.To eliminate coastal area effects on the resolution dependence, due to coarsening the land sea mask, we exclude all cells nearby the shoreline such that grid boxes at the coarsest resolution are still entirely over land.This takes care in the same way for the border with Russia.

Scenario Sn.2 235
The resolution dependence for CH4 emissions (uptake in reality) in uplands soils is less strong than for wetlands.Figure A.1 compares total CH4 emission for the study area obtained using prescribed values for SC and SM in Table 2.The emission ratio of each test resolution remains much closer to unity compared to Sn.1 (Table B.1).The total emission at the uplands reference-resolution (0.12 Tg CH4 yr -1 ) is only a factor of 1.04 higher than at the coarsest resolution of 1ºx1º (0.115 Tg CH4 yr -1 ).Since the study area is dominated by 240 uplands, the impact of averaging to coarser resolution is expected to be less than for wetlands.

Scenario Sn.3
In this scenario, we combine Sn.1 and Sn.2 (i.e.wetlands and uplands) to account for the fact that SC and SM are both larger than zero for uplands.Accounting for the availability of soil moisture and soil carbon in upland soils reduces the difference between upland and wetlands values, which is expected to reduce the upland ecosystems, which usually is not the case.Nevertheless, the reason for including this scenario is to test the effect of assuming larger upland/peatland contrasts in SC and SM than will be the case in reality.As can be seen the resolution effect in this scenario is less than in Sn.1 but still large.

Scenario Sn.4
Here, the resolution dependence is computed using daily varying SC, SM and Tsoil data (see Table 2).Following the same 255 steps, using the finest resolution PCRG soil moisture data (5 arcmin).The latter is regridded to 0.1º(~10x10km) from which coarsened maps at 0.5º(~50x50km) and 1º(~100x100km) are derived by aggregation.To further improve the realism of the simulation, the emission model at 0.1º resolution has been calibrated using flux measurements.This is done in two ways; first, as the KCH4 values (Figure 6) are different for both Degero and Siikaneva sites at 0.1º, the KCH4 for both sites were averaged (value = 0.025) and applied to Eq.5 to simulate CH4 at 0.1 º ,0.5º and 1º as well as the simulated emissions in 260 scenarios 1-3.The results show considerable differences between the three modelled resolutions.At 0.1º resolution, we find an integrated CH4 emissions from wetlands of ~ 0.021 Tg yr -1 , which reduces by ~14% for the 0.5º to ~ 0.018 Tg yr - 1 and reduces by ~24% for the 1º to ~ 0.016 Tg yr -1 (Figure 7).
On the other hand, if we calibrate the results of each resolution used in this scenario with site measurements, so that each modelled resolution agrees with the measured annual total, this results in different KCH4 values for each of the tested 265 resolutions (Figure 6).KCH4 values at Degero and Siikaneva decrease with resolution, but not by much (about 10%).Using these KCH4 values, we find a domain integrated CH4 emissions from wetlands of ~0.021 Tg yr -1 at 0.1º resolution, which reduces by ~10% for the 0.5º to ~0.019 Tg yr -1 and reduces by ~19% for the 1º to ~0.017 Tg yr -1 .This means that the use of different KCH4 values partially mitigates the resolution dependence, but not enough to fully account for it.Note that this result will depend, among different factors, on the size of the wetland for which measurements are available.However, for 270 wetlands that are smaller than the coarsest resolution grid box, the impact is expected to be in the direction that we find for Siikaneva and Degero.This result favors the use of high-resolution models, for which the calibration will be most accurate.
However, it argues against to use of high-resolution KCH4 values in coarser resolution models.

Discussion 280
The results of our simplified wetland experiments show a strong dependence of regionally integrated CH4 emissions on the spatial resolution that is used.The question, however, is whether this resolution dependence is representative of wetlands models that are used to estimate wetland CH4 emissions or that it arises because of the simplicity of the setup that has been chosen.One obvious simplification is the use of grid box averaged soil carbon and soil moisture values.Wetland models commonly keep track of the sub grid fraction that is covered by wetlands.In our simplified experiment, that 285 approach fully accounts for the resolution dependence.This can readily be understood from Eq. ( 4), indicating that the resolution dependence scales with the inverse wetland fraction (the right-hand-side being 1/Fwl).Therefore, if the soil carbon and soil moisture are averaged over the wetland fraction rather than the whole grid box, then the EHL/ELR ratio becomes 1 and the resolution dependence vanishes.
However, a few problems remain.The first is that the wetland fraction is determined from a hydrological model or satellite 290 data with a limited horizontal resolution, compromising the ability to determine the wetland fraction.Secondly, the representation of wetland area in models is associated with large uncertainties.
To assess the uncertainty in wetland area, we have plotted the wetlands extent maps used by the WetChimp model intercomparison (Melton et al. 2013) (Table B. 3) for the Fennoscandinavian Peninsula in Figure 9.For reference, the highresolution CLC2018 wetland map is included at the same resolution of 0.5 o x0.5 o to match with the resolution of wetlands 295 maps of WetChimp.Depending on the type of information that is used to determine where the wetlands are, the wetland map looks very different.Integrated over our domain, the total wetland areas represented by the models (Figure 10) are significantly different and range between 53x10 3 and 171x10 3 km 2 .The Swedish Wetland Survey (VMI) reports a total wetland area of approximately 34x10 3 km 2 for Sweden (Gunnarsson and Löfroth, 2014).According to Ramsar, however, the Swedish wetland areal extent amounts only to 6655 km 2 .If the VMI estimate from Sweden is combined with Ramsar 300 estimates for Finland and Norway of 7795 km 2 and 9091 km 2 (Ramsar, 2021) respectively, this leads to a total wetland area for the Fennoscandinavian peninsula of 51x10 3 km 2 .which is in close agreement with the Corine land cover map (53x10 3 km 2 ).Looking at the overall pattern of modelled wetland extent, most of the models simulate greater wetland area than CLC2018 (Figure 10).LPJ-WHyMe is in closest agreement with CLC2018 for the total wetland area (see Figure 10).However, its 310 spatial distribution of wetlands is very different.The maps in Figure 9 and corresponding correlation matrix in Figure 11, show large disagreements in magnitude and spatial distribution of wetland extent among the WetChimp datasets.This is primarily due to inconsistencies in 1) the definition and classification of wetland types (e.g.peatland or inundated area), 2) the time window represented by the wetland datasets, 3) the purpose of the wetland data set and the method from which it was derived (Zhang et al., 2017a).The importance of uncertainties in wetland area have been reported before (Wania et al., 2013).The reason for mentioning it here is the implication for the correlation between wetland location and other variables, such as soil carbon and soil moisture, which are multiplied to compute CH4 emissions as in Eq. ( 2).It is the correlation between these terms that 320 determines the resolution dependence.To show this, we simplify Eq. ( 2) further so that only variations in soil moisture and soil carbon are considered.In this case, Eq. ( 4) can be reformulated, expressing local soil moisture and soil carbon as sums of their coarse resolution mean ( .... ,  .... ) and the local deviation (ΔS[], ΔS [𝑖]).This leads to with n the number of high-resolution grid boxes in each low-resolution grid box.Eq. ( 6) shows that for uncorrelated soil carbon and soil moisture, the second right-hand-side term becomes small and the ratio approaches 1.For a positive correlation, the emission increases with resolution.The effect is large if local deviations are large compared with the coarse resolution mean.Likewise, negative correlations lead to emissions that decrease with increasing resolution.This equation explains why emission scenarios with smaller differences between upland and wetland soil carbon and soil moisture lead 330 to smaller resolution dependences.To avoid resolution dependent errors, it is important to get the correlation between soil carbon and soil moisture right.The same is true for temperature variations, following the same logic.The challenge of getting the spatial correlation right is highlighted in Figure 11, which shows the limited correlation (-0.12 on average) in wetland area between the WetChimp models over Fennyscandinavia.between sub grid wetland regions -the opposite is generally the case for wetlands, as their CH4 emission is known to be highly heterogeneous.We have tried to quantify the resolution dependence that might arise from variations within the wetland fraction.The results (not shown) indicate that the impact is only small (2%).However, it is questionable how well the ISRIC and PCR-GLOBWB (corr.= 0.89 at 0.1 o resolution) datasets capture the variability at their native resolutions.
The role of soil carbon requires special attention, because many models rather use soil respiration or vegetation productivity 340 as measure of the amount of available degradable carbon.However, here no distinction is made between wetland and upland productivity, whereas in reality the productivity in wetlands is usually much lower than in uplands due to oxygen limitation.As a result, important errors are to be expected from models failing to capture the correlation of the parameters that drive CH4 emissions from wetlands.
A solution to mitigate resolution dependent errors is to increase resolution up to Eddy Covariance tower (EC) resolution, 345 which is 100x100m in order to calibrate model results to EC measurements.As shown in this study, advanced datasets are available for doing this.Equally important to get the correlation right is it for these datasets to be mutually consistent.Note that this is true not only for the distinction between wetland and upland ecosystems.Large variations occur also within a single wetland.
Multivariant Probability Density Functions (PFDs) might be useful to mitigate this problem by determining the correlation 350 between SM and SC at high resolution maps then identifying the multi-variant PDF of SM and SC at the course resolution.
We do not provide a solution for that, but argue that an important step in the right direction can be made using highresolution datasets that are available.

Conclusion
This study investigates the dependence of regionally integrated CH4 emissions on spatial resolution.Simulations are 355 performed for the Fennoscandinavian domain at resolutions ranging from 100x100 m 2 to 1 o x1 o .The results of our simplified wetland experiments show that this dependence can be strong (up to 13 times greater between high "in meters" and coarse resolution).In the model that is used, the effect arises from the correlation between soil moisture and soil carbon.
In our experiments, the impact is effectively mitigated by accounting for the sub grid wetland fraction.How well this works dependents on how well the true wetland fraction is represented, which is a key uncertainty in wetland models.In addition, 360 correlated variations between soil moisture and soil carbon within the wetland fraction lead to resolution dependent errors, which are more difficult to quantify using the available datasets.The results suggest that macroscale biogeochemical models might underestimate regional CH4 emissions due to a coarse representation of the correlation between input parameters that drive the methane emission (such as soil moisture and soil carbon).Our solution is not a straightforward recipe; however, we strongly recommend to make use as much as possible of existing high-resolution datasets.

Figure 1 :
Figure 1: A hypothetical case of wetland CH4 emissions, represented at high-resolution (a) and low resolution (b).The CH4 emission is calculated using input data fields of soil moisture, soil carbon and a wetlands mask, each at the same resolution.
110covered by wetland (note the use of the subscript wl to indicate a wetland grid box).If Fwl is the wetland fraction, then the right-hand-side term in equation 4 is 1/Fwl.As long as the wetland fraction remains the same, the impact of a change in resolution will remain the same also.However, if the wetland fraction becomes lower, because part of coarse resolution wetland grid box happens to be dry at high resolution, then the impact of a change in resolution increases.https://doi.org/10.5194/bg-2022-55Preprint.Discussion started: 2 March 2022 c Author(s) 2022.CC BY 4.0 License.

Figure 2 :
Figure 2: As Figure 1 for a case in which wetlands occupy 50% of the total area.
155 and data availability.In this domain, CH4 fluxes are monitored at a few sites that are reporting to FLUXNET-CH4 (https://fluxnet.org).Despite the limited number of sites (2 sites in this study), the network density is still relatively high for the circumpolar boreal/arctic region.https://doi.org/10.5194/bg-2022-55Preprint.Discussion started: 2 March 2022 c Author(s) 2022.CC BY 4.0 License.

Figure 3 :
Figure 3: Study area domain and land cover classification (for the color legend see Figure A-3).160

Figure 4 :
Figure 4: CH4 emissions of Sn. 1, spanning the full range of resolutions from 0.005 o (top left) to 1 o (bottom right).
245 resolution dependence.Note that in this scenario, the results assume that emissions are always positive in https://doi.org/10.5194/bg-2022-55Preprint.Discussion started: 2 March 2022 c Author(s) 2022.CC BY 4.0 License.
3 have been plotted together to show the difference in resolution dependence between 250 them.

Figure 7 :
Figure 7: Integrated CH4 emissions for wetlands over the study area using PCR-GLOBWB soil moisture inputs at 0.1º (left) and 0.5º (middle) and 1º (right) for Sn.4.

Figure 8 :
Figure 8: Wetland extent maps used by the participated models of WetChimp intercomparison (from b to i) in comparison to 305 CLC2018 wetland extent map (a).

Figure 10 :
Figure 10: Correlation matrix for the tested wetlands datasets used in the study.
the same.Sn.3 combines wetlands and uplands to test the impact of changing the contrast between uplands and wetlands emissions, using the same threshold values of SC and SM in Sn.1 and Sn.2.Sn.4 represents emissions from 140 kept

Table 2
. In scenario 4 and 5(Sn.4 & Sn.5), the full soil carbon map is used.In Sn.1 and Sn.3, however, we use the maximum value in the ISRIC map to represent peat.The underlying assumption is that soil carbon in the ISRIC map is limited by the peat fraction at 250x250m resolution, and that the highest values represent grid boxes that are fully covered by peat.In Sn.2 the lowest value is used to represent uplands https://doi.org/10.5194/bg-2022-55Preprint.Discussion started: 2 March 2022 c Author(s) 2022.CC BY 4.0 License.conditions, following the same logic.Note that this is a simplifying assumption since different land cover types have 180 different soil carbon contents, but this choice guarantees the expected insignificant contribution of methane emissions from upland ecosystems.The ISRIC data were downloaded from (https://files.isric.org/soilgrids/former/2017-03-10/data).