# Tradeoff of CO2 and CH4 emissions from global peatlands under water-table drawdown

Yuanyuan Huang, Phillipe Ciais, Yiqi Luo, Dan Zhu, Yingping Wang, Chunjing Qiu, Daniel Goll, Bertrand Guenet, David Makowski, Inge de Graaf, et al.

► To cite this version:

Yuanyuan Huang, Phillipe Ciais, Yiqi Luo, Dan Zhu, Yingping Wang, et al.. Tradeoff of CO2 and CH4 emissions from global peatlands under water-table drawdown. Nature Climate Change, 2021, 11, pp.618-622. 10.1038/s41558-021-01059-w. hal-03255991

HAL Id: hal-03255991

https://hal.inrae.fr/hal-03255991

Submitted on 25 Oct 2021

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

<figure><img src="image_1.png" /></figure>

Distributed under a Creative Commons Attribution 4.0 International License
1 Tradeoff of CO₂ and CH₄ emissions from global peatlands under water-table drawdown

2

3 Yuanyuan Huang¹,²*, Phillippe Ciais¹, Yiqi Luo³, Dan Zhu¹, Yingping Wang², Chunjing Qiu¹,

4 Daniel S. Goll¹, Bertrand Guenet⁴, David Makowski⁵,⁶, Inge De Graaf⁷, Jens Leifeld⁸, Min Jung

5 Kwon¹, Jing Hu⁹, Laiye Qu¹⁰,¹¹

6

7 ¹Laboratoire des Sciences du Climat et de l'Environnement, LSCE/IPSL, CEA-CNRS-UVSQ,

8 Université Paris-Saclay, 91191 Gif-sur-Yvette, France

9 ²CSIRO Oceans and Atmosphere, Aspendale 3195, Australia

10 ³Center for Ecosystem Science and Society, Department of Biological Sciences, Northern

11 Arizona University, AZ, USA

12 ⁴Laboratoire de Géologie, UMR 8538, Ecole Normale Supérieure, PSL Research University,

13 CNRS, IPSL, Paris, France.

14 ⁵INRAE, AgroParisTech, University Paris-Saclay, UMR MIA 518, 75231 Paris, France

15 ⁶CIRED, 45bis Avenue de la Belle Gabrielle, 94130 Nogent-sur-Marne, France

16 ⁷Chair of Environmental Hydrological Systems, Faculty of Environmental and Natural

17 Resources, University of Freiburg, Freiburg, Germany

18 ⁸Agroscope, Climate and Agriculture Group, Reckenholzstrasse 191, 8046, Zurich, Switzerland.

19 ⁹Wetland Biogeochemistry Laboratory, Soil and Water Science Department, University of

20 Florida, Gainesville, FL

21 ¹⁰State Key Laboratory of Urban and Regional Ecology, Research Center for Eco-environmental

22 Science, Chinese Academy of Sciences, 100085 Beijing, China

23 ¹¹University of Chinese Academy of Sciences, 100049 Beijing, China

24
**Abstract**

Water table drawdown across peatlands increases carbon dioxide (CO₂) and reduces methane (CH₄) emissions. The net climatic effect remains unclear. Based on observations from 130 sites around the globe, we found a positive (warming) net climate effect of water table drawdown.

Using a machine-learning based upscaling approach, we predict that peatland water table drawdown driven by climate drying and human activities will increase CO₂ emissions by 1.13 (95% interval: 0.88 – 1.50) Gt yr⁻¹ and reduce CH₄ by 0.26 (0.14 – 0.52) Gt CO₂-eq yr⁻¹, resulting in a net increase of greenhouse gas (GHG) of 0.86 (0.36 – 1.36) Gt CO₂-eq yr⁻¹ by the end of the 21st century under the RCP8.5 climate scenario. This net source drops to 0.73 (0.2 – 1.2) Gt CO₂-eq yr⁻¹ under RCP2.6. Our results point to an urgent need to preserve pristine and rehabilitate drained peatlands to decelerate the positive (more warming) feedback among water table drawdown, increased GHG emissions and climate warming.
Covering only ~3 percent of the Earth's land surface, peatlands store one-third of the global soil carbon¹. Peat is formed through a slow accumulation of detritus with litter input exceeding decomposition rates in waterlogged environments. In pristine peatlands, a shallow water table or permanently waterlogged condition causes oxygen deficiency, allowing the accumulation of organic matter over millennia. These anaerobic conditions favor methanogenesis, and peatlands thus act as a global source of methane (CH₄) of around 0.8 Gt CO₂-eq yr⁻¹ (1 Gt = 10¹⁵ g)². CH₄ is a greenhouse gas (GHG) with a global warming potential that is 25 times that of carbon dioxide (CO₂) over a 100-year time horizon³. Pristine peatlands are a sink of CO₂ of around 0.4 Gt CO₂ yr⁻¹ at the global scale². The balance between CO₂ sinks and CH₄ emissions determines the net climatic impact of peatlands. This balance is highly sensitive to changes in hydrology, particularly the water table position that regulates aerobic versus anaerobic conditions in the soil column and therefore the production and consumption processes of CO₂ and CH₄ in the soil profile⁴.

Human induced drainage, over-extraction of groundwater and climate drying have substantially altered peatland hydrology and resulted in a widespread downward movement of water tables. Around 51 Mha of the world's peatlands have been drained for agriculture or forestry⁵. Water table drawdown and associated land subsidence were observed in warm and wet peat regions such as Indonesia, Malaysia, Thailand, Florida (Everglades) and in specific summer dry regions such as California (Sacramento delta) and Israel (Lake Hula)⁶,⁷, or in temperate countries like the Netherlands⁸. Peatlands across Europe were also found to have undergone substantial and widespread drying in recent centuries⁹. Globally, drainage and subsequent conversion of natural peatlands to agriculture and forestry are estimated to emit 0.31–3.38 Gt CO₂-eq yr⁻¹ GHGs (see Supplementary Table 1 for a summary of GHG emissions on degraded peatlands). These estimates rely on peatland area and GHG emission factors. Both the area and emission factors and their upscaling are highly uncertain⁵,¹⁰,¹¹. It is unclear to what extent and how water table drawdown directly regulate changes of GHG emissions as it is challenging to separate compounding effects of other variables such as land clearing and carbon input to the soil from the new land use types.

Field manipulation experiments provide the opportunity to quantify the direct impact of lowering the water table on peatland GHG emissions. We compiled data from 376 pairs of data points measuring net ecosystem exchange of CO₂ (NEE), 532 pairs for CH₄ emissions, 209 pairs
for gross primary production (GPP) and 407 pairs for ecosystem respiration (or soil respiration in the absence of live plants, RES). The data were extracted from 130 field sites as documented in 96 publications (Supplementary Figure 1). NEE is jointly controlled by soil and vegetation (NEE = GPP + RES). Lowering water tables is expected to accelerate peat decomposition and soil CO₂ release by exposing C rich upper soil layers to oxygen. However, some studies measured either a decrease or no change in decomposition rates¹². Increased, no significant change and decreased vegetation CO₂ uptake (GPP) were also observed from individual studies when the water table was lowered. Correspondingly, the sign of NEE changes in response to water table drawdown varies among studies (Supplementary Figure 11). On the other hand, studies mostly reported reductions in CH₄ emissions by lowering the water table (Supplementary Figure 11). With highly uncertain soil emissions and plant uptake of CO₂ but generally lower CH₄ emissions, the net GHG balance, therefore the global climatic impact of water table drawdown remains highly variable¹³,¹⁴.

In order to deal with the heterogeneity of experimental results, we conducted a meta-analysis based on random effect models to quantitatively summarize results across multiple studies. Our sign convention is a positive sign for CO₂ or CH₄ emissions to the atmosphere, and a positive sign for a water table depth (WTD) becoming deeper. ΔCO₂_WTD represents the difference of NEE resulting from a drawdown of WTD, and ΔCH₄_WTD is the same for the difference of CH₄ emissions. ΔCH₄_WTD is expressed as its CO₂ equivalent assuming its global warming potential is 25 times of CO₂ over the 100-year time span³. The net greenhouse gas (GHG) balance is defined by ΔGHG_WTD = ΔCO₂_WTD + ΔCH₄_WTD. Note here that ΔGHG_WTD, ΔCO₂_WTD, ΔCH₄_WTD vary with the magnitude of water table drawdown.

The estimated mean value of ΔCO₂_WTD (Figure 1) is 62 mg CO₂ m⁻² h⁻¹ (47 to 77), all ranges being defined as 95% confidence intervals (CI), meaning an increase of CO₂ emissions (or a decreased sink) for a water table becoming deeper. This estimated mean value is significantly positive since the 95% CI does not overlap zero (Methods; Supplementary Figure 3) despite individual values of ΔCO₂_WTD varying from -497 to 1234 mg CO₂ m⁻² h⁻¹ across sites (Supplementary Figure 11). Complex responses and interactions of biotic and abiotic processes make it difficult to identify a unifying mechanism for NEE responses. Vegetation coverage, species composition, photosynthetic capacity, biomass allocation, substrate quality, nutrient availability, environmental conditions (e.g., soil temperature, water availability, aeration status),
peat physicochemical properties, microtopography, extent of changes in water level and experimental duration (short-term vs. long-term) are possible factors that have dominant influences on NEE responses from individual experiments. Overall, water table drawdown induced an increase in CO₂ emissions from respiration exceeding that of GPP uptake (Figure 1b). In contrast, the estimated mean value of ΔCH₄, WTD shown in Figure 1 is -26 mg CO₂-eq m⁻² h⁻¹ (95% CI: -35, -20), revealing a significant reduction of CH₄ emissions or an increase in the CH₄ sink resulting from deceased methanogenesis and/or enhanced methanotrophy (Figure 1; Supplementary Figure 3). ΔCH₄, WTD across sites go from -1120 to 484 mg CO₂-eq m⁻² h⁻¹ (Supplementary Figure 11). Using data from experiments that measured both NEE and CH₄ emissions, we estimated a significantly positive mean value of ΔGHG, WTD equal to 33 mg CO₂-eq m⁻² h⁻¹ (9 to 57), which implies that lowering WTD leads to a net increase of radiative forcing. The result of an overall positive ΔGHG, WTD is robust and consistent among different estimating methods (Supplementary Figure 3).

We then quantified the sensitivities of GHG fluxes to the magnitude of water table drawdown (ΔWTD), and found that the overall average sensitivity to a 1 cm water table drawdown was 4.1 (95% CI: 3.3 to 5.0) mg CO₂ m⁻² h⁻¹ for CO₂ (NEE) and -2.9 (95% CI: -3.6 to -2.2) mg CO₂-eq m⁻² h⁻¹ for CH₄ (Methods, Supplementary Figure 4). The average sensitivity of ΔGHG, WTD was 1.6 (95% CI: 0.8 to 2.3) mg CO₂-eq m⁻² h⁻¹ cm⁻¹ based on a subset of experiments that measured both NEE and CH₄. No significant pattern in the regional values of ΔGHG, WTD was found (Supplementary Figures 5-10; Supplementary Discussion), because of the large inter-site variability of the observed fluxes, small sample sizes in arctic, tropical and coastal regions, and nonlinear responses to ΔWTD. The average sensitivity of ΔCH₄, WTD to unit water table drawdown was smaller (less reduction) in tropical than boreal and temperate peatlands (Supplementary Figure 6). The difference between these regions was significant (95% CI not overlap) according to one of the weighting approaches considered. Respiration of coastal regions had a greater average sensitivity than non-coastal regions, while differences of NEE (or CH₄) were inconsistent (Supplementary Figure 8). Undisturbed coastal regions are more likely to experience frequent flooding and anoxic conditions, leaving more labile peat susceptible to decomposition if the water table was lowered. Among different peatland types, the mean ΔCO₂, WTD per unit ΔWTD was higher in swamps than bogs and fens (not significant, Supplementary Figure 10). Fens had higher mean ΔCO₂, WTD and mean ΔGHG, WTD per unit ΔWTD than bogs.
Responses of GHG emissions to WTD were non-linear and covaried with peatland types, regions, land use and management histories, hydrology, vegetation characteristics, climate, and physicochemical properties of peat⁴,¹³,¹⁵⁻¹⁷. Therefore, upscaling above-estimates to the global scale can be problematic. To further understand how different factors regulated ΔCO₂, WTD and ΔCH₄, WTD, we built random forest models¹⁸ for these two quantities (see Methods). The random forests were built against site data using as predictors ΔWTD, WTD, CO₂ (NEE) and CH₄ emissions under the high (shallow) water table treatment (WTDinitial, CO₂initial and CH₄initial in short), climatic, topographic, edaphic, biotic, management and experimental factors (Methods; Supplementary Tables 2, 3). We show in Figure 2 that CO₂initial, ΔWTD and WTDinitial are the most important predictors of ΔCO₂, WTD, accounting for 53% of the Gini-based relative importance (Supplementary Table 4; Figure 2; Supplementary Figures 13, 15). Variations in ΔCH₄, WTD are mostly explained by CH₄initial, ΔWTD and WTDinitial (relative importance: 88%; Supplementary Table 5; Figure 2; Supplementary Figures 14, 15). The models predict that peatlands with a stronger initial capacity of being CO₂ sink (CO₂initial), a shallower WTDinitial, and experiencing a larger ΔWTD have a more positive ΔCO₂, WTD value (Figure 2, red lines), and that peatlands with a larger CH₄initial flux to the atmosphere and a bigger ΔWTD experience a stronger reduction in their CH₄ emissions, i.e., a more negative ΔCH₄ WTD value (Figure 2, red lines).

By scaling up using the random forest models, we found that arctic peatlands were more likely to have both positive and negative ΔCO₂, WTD when conditions varied (Supplementary Figure 16). The average response curves showed that arctic peatlands were more sensitive to ΔWTD and CO₂initial over the whole predictor space (Supplementary Figure 17). ΔCH₄, WTD of tropical peatlands could be less or greater than boreal peatlands when CH₄initial varied (Supplementary Figures 16, 17). Overall, both ΔCO₂, WTD and ΔCH₄, WTD were highly sensitive to ΔWTD when ΔWTD was small (<10 cm), and also became highly sensitive to WTDinitial when WTDinitial was around the surface. ΔCO₂, WTD stayed relatively constant when WTDinitial was 10 cm above the surface or 80 cm below the surface. ΔCH₄, WTD was not responsive to WTDinitial for strong drying or wetting when WTDinitial got typically more than 33 cm below the surface or 21 cm above the surface (Figure 2, Supplementary Figures 16, 17). That being said, the above-mentioned broad patterns emerged from our analyses may not hold under some specific environmental conditions, due to the strong nonlinearity of response curves. Apart from these average responses, each individual paired experiment carried a unique response pattern as an
outcome of complex interactions involving peatland characteristics, climate and other environmental factors (Figure 2, grey lines; see Supplementary Figures 13, 14 for the responses to different factors).

Based on future WTD predicted by de Graaf, et al. ¹⁹ under their “business as usual” water demand scenario and the RCP8.5 climate change scenario (Methods), we used the trained random forest models to compute global gridded CO₂ (NEE) and CH₄ emissions in response to future water table drawdown. We found both an increase of global peatland NEE, that is, a larger CO₂ source or a smaller sink by 1.13 (95% probability interval: 0.88 – 1.50) Gt CO₂ yr⁻¹ and a reduction of peatland CH₄ emissions by 0.26 (0.14 – 0.52) Gt CO₂-eq yr⁻¹, which together amounts to a net increase of GHG of 0.86 (0.36 – 1.36) Gt CO₂-eq yr⁻¹ by 2100 (see Figure 3 and Supplementary Figures 21, 23, 25, 27). This estimated net GHG budget was 0.73 (0.2 – 1.2) Gt CO₂-eq yr⁻¹ under the RCP2.6 climate scenario by 2100 (see Figure 3 and Supplementary Figures 22, 24, 26, 28). Under the scenario assuming a 40% less reduction of WTD than the de Graaf, et al. ¹⁹ prediction, the global ΔGHG,WTD reached 0.74 (0.5 – 1.29) Gt CO₂-eq yr⁻¹. A 80% less reductions of WTD than the de Graaf, et al. ¹⁹ prediction yields a global ΔGHG,WTD of 0.53 (0.34 – 0.85) Gt CO₂-eq yr⁻¹. The RCP2.6 climate scenario and a 80% less reduction of water table drawdown together bring the global ΔGHG,WTD down to 0.42 (0.22 – 0.74) Gt CO₂-eq yr⁻¹.
Note these estimates do not account for anthropogenic impacts other than water table drawdown, such as land use change or fires.

Across different latitudes, regions with high CH₄ reductions generally have high ΔCO₂, WTD and ΔGHG,WTD. Mid- to high-northern latitudes and tropics dominate the global response of GHG budgets to water table drawdown due to their large areas (Figure 3; Supplementary Figures 21, 22). Inferred from several drained peat sites in Finland, Laine, et al. ¹⁴ suggested a reduced GHG emission from northern peatlands under future drying because of lower CH₄ emissions and enhanced vegetation CO₂ uptake offsetting peat CO₂ emissions. We found negative ΔGHG, WTD over Finland (Supplementary Figures 23,24). Across northern peatlands, positive ΔGHG, WTD outweighs negative ΔGHG, WTD, resulting in the positive (warming) feedback on future climate. We acknowledge large uncertainties in predicting future GHG emissions over northern peatlands. In particular, permafrost thawing, a critical process that has dramatic impacts on the climate system²⁰, was not included as a predictor in our model. Arctic warming and permafrost thaw can alter peatland hydrology. Thermokarst peatlands form as a result of permafrost thaw.
Thermokarst peatlands are known for their localized patchy landscape with distinguished dry-wet zones and irregular hummocks and hollows. Considering the big sensitivity of GHG to $\Delta_{WTD}$ in arctic peatlands, widespread alterations of hydrology could dramatically change arctic GHG budgets.

Peatlands in Scandinavia, coastal region or along river networks are predicted to experience high reductions in CH$_4$ emissions in response to future water table drawdown (Supplementary Figures 25,26). Latitudinal average CH$_4$ reductions are higher in tropics than high latitudes. High CH$_4$ fluxes, relatively large change of WTD, and its warm environment make Amazonian peatlands the largest contributor to total CH$_4$ reduction in tropics. This prediction is subject to large uncertainties because of few field observations in Amazonian peatlands. Few field studies in Amazonian peatlands documented high CH$_4$ fluxes$^{21,22}$. The spatiotemporal variability, hydrologic and biogeochemical controls of CH$_4$ emissions across Amazonian peatlands remain poorly understood. Southeast Asian peatlands show low CH$_4$ emissions in comparison to temperate and boreal peatlands$^{13}$, most likely due to poorer substrate quality (lower carbohydrate and greater aromatic content) of tropical peats$^{23}$. It remains largely unclear whether the low CH$_4$ fluxes are widespread across tropics, or biases from limited sample size and coverage. Boreal and temperate peatlands had experienced widespread drainage and peat conversion before the 21st century, while tropic peatlands are subject to large scale disturbance in the future$^{24}$. The recent discovery of the world's largest tropical peatland in Congo basin$^{25}$ highlights the need for additional field observations in tropics to understand hydrological controls on CH$_4$ emissions.

A less controversial issue in tropics is the higher CO$_2$ emissions following water table drawdown. Tropical peatlands contain about 5-10% of global soil carbon$^{26}$. Earlier$^{13}$ and our syntheses (Supplementary Figure 6) found an increase in emission of at least 10 mg CO$_2$ m$^{-2}$ h$^{-1}$ by respiration for each 1cm water table drawdown. Many tropical peatlands are occupied by swamp forests, and some of them were converted into agricultural land uses with a lowering of the WTD, which would result in an increase in CO$_2$ emissions$^{27}$. In Southeast Asia, 25% of deforestation occurs in peat swamp forests$^{27}$. Water table management and conservation of tropical swamp forests are critical for climate mitigation in the tropics, due to the high CO$_2$ emissions from swamp forests and the positive feedbacks among water table drawdown, GHG emissions and climate warming. Under RCP8.5 where CO$_2$ emissions continue to rise throughout
the 21st century with warmer climate conditions, global $\Delta_{GHG,WTD}$ is predicted to increase by 225
18% more than under the less warm RCP2.6 scenario. These estimates are rather conservative, 226
because we did not account for the effect of lowering the water table under a warm and drying 227
climate on $\Delta_{GHG,WTD}$. Positive contributions of water table drawdown to GHGs accelerate future 228
GHG emissions through climate feedback. 229

This study reveals that despite water table drawdown reduces global peatland CH$_4$ emissions, increased CO$_2$ flux outweighs the climate benefits of reduced CH$_4$ in terms of global warming potential. Many other adverse environmental and ecological impacts associated with peatland water table drawdown have already motivated national and international actions to preserve pristine peatlands and rewet drained peatlands. Controlling the magnitude of future water table drawdown is an effective measure as future $\Delta_{CO2, WTD}$ and $\Delta_{CH4, WTD}$ are largely regulated by $\Delta_{WTD}$ (Supplementary Tables 6-8). Rewetting to ~10 cm above-surface greatly reduces CO$_2$ emission, it may also increase CH$_4$ emissions, especially in regions where pristine peatlands are strong CH$_4$ emitters. Instead of rewetting all drained peatlands, care must be taken in regional implementation, as the tradeoff between CO$_2$ decrease and CH$_4$ increase is dependent on many local factors. Climate change mitigation strategies outside peatlands that aim to limit global warming are also critical for lowering peatland GHG emissions. Finally, despite significant progresses in peatland studies over recent decades, there are still large uncertainties in quantifying peatland CO$_2$ (NEE)$^{28,29}$, CH$_4$ emissions$^{30}$ and WTD$^{19}$ dynamics over large spatial scales. Arctic, coastal and tropical regions are highly vulnerable, but largely understudied, especially in the area of long-term vegetation adaptation. Dominant control on the response of peatland carbon to water table drawdown may also vary with timescales. As a first step, we assessed the uncertainty of our prediction through combining different datasets to account for currently known major uncertainty sources, which is yet to be all inclusive. Additional observations especially from those under-sampled regions will enable us to reduce the uncertainty in the estimated response of peatland to climate change and developing appropriate mitigation strategies in the future. 230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252

253 References

1 Yu, Z. C., Loisel, J., Brosseau, D. P., Beilman, D. W. & Hunt, S. J. Global peatland dynamics since the Last Glacial Maximum. *Geophysical Research Letters* **37**, doi:10.1029/2010gl043584 (2010). 254
255
256
257 2 Frolking, S. et al. Peatlands in the Earth's 21st century climate system. *Environmental Reviews* **19**, 371-396, doi:10.1139/a11-014 (2011).
258
259 3 Myhre, G., D. Shindell, F.-M. Bréon, W. Collins, J. Fuglestvedt, J. Huang, D. Koch, J.-F.
260
261 Lamarque, D. Lee, B. Mendoza, T. Nakajima, A. Robock, G. Stephens, T. Takemura and
262 H. Zhang. Anthropogenic and Natural Radiative Forcing. (Cambridge University Press,
263 4 Ise, T., Dunn, A. L., Wofsy, S. C. & Moorcroft, P. R. High sensitivity of peat
decomposition to climate change through water-table feedback. *Nature Geoscience* **1**,
264
265 763-766, doi:10.1038/ngeo331 (2008).
266 5 Leifeld, J. & Menichetti, L. The underappreciated potential of peatlands in global climate
267
268 change mitigation strategies. *Nature Communications* **9**, doi:10.1038/s41467-018-03406-6 (2018).
269 6 Hooijer, A. et al. Subsidence and carbon loss in drained tropical peatlands.
*Biogeosciences* **9**, 1053-1071, doi:10.5194/bg-9-1053-2012 (2012).
270
271 7 Nagano, T. et al. Subsidence and soil CO2 efflux in tropical peatland in southern
272 Thailand under various water table and management conditions. *Mires and Peat* **11**
273 (2013).
274 8 Erkens, G., van der Meulen, M. J. & Middelkoop, H. Double trouble: subsidence and
275 CO2 respiration due to 1,000 years of Dutch coastal peatlands cultivation. *Hydrogeology
276 Journal* **24**, 551-568, doi:10.1007/s10040-016-1380-4 (2016).
277 9 Swindles, G. T. et al. Widespread drying of European peatlands in recent centuries.
278 *Nature Geoscience*, doi:10.1038/s41561-019-0462-z (2019).
279 10 IPCC. in IPCC, *Wetlands, 2014* (2013).
280 11 Wilson, D. et al. Greenhouse gas emission factors associated with rewetting of organic
soils. *Mires and Peat* **17**, doi:10.19189/MaP.2016.OMB.222 (2016).
281
282 12 Laiho, R. Decomposition in peatlands: Reconciling seemingly contrasting results on the
impacts of lowered water levels. *Soil Biology & Biochemistry* **38**, 2011-2024,
283
284 doi:10.1016/j.soilbio.2006.02.017 (2006).
285 13 Couwenberg, J., Dommain, R. & Joosten, H. Greenhouse gas fluxes from tropical
286 peatlands in south-east Asia. *Global Change Biology* **16**, 1715-1732, doi:10.1111/j.1365-2486.2009.02016.x (2010).
287
288 14 Laine, J. et al. Effect of water-level drawdown on global climatic warming: Northern
peatlands. *Ambio* **25**, 179-184 (1996).
289
290 15 Prananto, J. A., Minasny, B., Comeau, L. P., Rudiyanto, R. & Grace, P. Drainage
increases CO2 and N2O emissions from tropical peat soils. *Global Change Biology*,
291
292 4583-4600, doi:10.1111/gcb.15147 (2020).
293 16 Turetsky, M. R. et al. A synthesis of methane emissions from 71 northern, temperate, and
subtropical wetlands. *Global Change Biology* **20**, 2183-2197, doi:10.1111/gcb.12580
294
295 (2014).
296 17 Jungkunst, H. F. & Fiedler, S. Latitudinal differentiated water table control of carbon
297 dioxide, methane and nitrous oxide fluxes from hydromorphic soils: feedbacks to climate
298 change. *Global Change Biology* **13**, 2668-2683, doi:10.1111/j.1365-2486.2007.01459.x
299
300 18 Breiman, L. Random forests. *Machine Learning* **45**, 5-32, doi:10.1023/a:1010933404324
301 (2001).
302 19 de Graaf, I. E. M., Gleeson, T., van Beek, L. P. H., Sutanudjaja, E. H. & Bierkens, M. F.

303 P. Environmental flow limits to global groundwater pumping. *Nature* **574**, 90-94, doi:10.1038/s41586-019-1594-4 (2019).

305 20 Schuur, E. A. G. et al. Climate change and the permafrost carbon feedback. *Nature* **520**, 171-179, doi:10.1038/nature14338 (2015).

307 21 Winton, R. S., Flanagan, N. & Richardson, C. J. Neotropical peatland methane emissions along a vegetation and biogeochemical gradient. *Plos One* **12**, doi:10.1371/journal.pone.0187019 (2017).

310 22 Teh, Y. A., Murphy, W. A., Berrio, J. C., Boom, A. & Page, S. E. Seasonal variability in methane and nitrous oxide fluxes from tropical peatlands in the western Amazon basin. *Biogeosciences* **14**, 3669-3683, doi:10.5194/bg-14-3669-2017 (2017).

313 23 Hodgkins, S. B. et al. Tropical peatland carbon storage linked to global latitudinal trends in peat recalcitrance. *Nature Communications* **9**, doi:10.1038/s41467-018-06050-2 (2018).

316 24 Leifeld, J., Wust-Galley, C. & Page, S. Intact and managed peatland soils as a source and sink of GHGs from 1850 to 2100. *Nature Climate Change* **9**, 945+, doi:10.1038/s41558-019-0615-5 (2019).

319 25 Dargie, G. C. et al. Age, extent and carbon storage of the central Congo Basin peatland complex. *Nature* **542**, 86+, doi:10.1038/nature21048 (2017).

321 26 Minasny, B. et al. Digital mapping of peatlands - A critical review. *Earth-Science Reviews* **196**, doi:10.1016/j.earscirev.2019.05.014 (2019).

323 27 Miettinen, J., Shi, C. H. & Liew, S. C. Deforestation rates in insular Southeast Asia between 2000 and 2010. *Global Change Biology* **17**, 2261-2270, doi:10.1111/j.1365-2486.2011.02398.x (2011).

326 28 Gallego-Sala, A. V. et al. Latitudinal limits to the predicted increase of the peatland carbon sink with warming. *Nature Climate Change* **8**, 907+, doi:10.1038/s41558-018-0271-1 (2018).

329 29 Jung, M. et al. The FLUXCOM ensemble of global land-atmosphere energy fluxes. *Scientific Data* **6**, doi:10.1038/s41597-019-0076-8 (2019).

331 30 Melton, J. R. et al. Present state of global wetland extent and wetland methane modelling: conclusions from a model inter-comparison project (WETCHIMP). *Biogeosciences* **10**, 753-788, doi:10.5194/bg-10-753-2013 (2013).

334

335 Methods

336 Data collection

337 We extracted values of GHG fluxes for NEE, ecosystem respiration (or soil respiration in the absence of live plant), GPP, CH₄ emissions, WTD and ancillary environmental variables from water table manipulation experiments carried out through mesocosms or/and *in-situ* field conditions. Mesocosm experiments normally enclose relatively large intact peat monoliths to manipulate WTD in well-controlled conditions. *in-situ* field experiments alter WTD through draining, ditching, precipitation exclusion, flooding, building dams or groundwater extraction.

343 Difference in ΔCO₂_WTD and ΔCH₄_WTD between these two types of experiments is not significant
344 (95% CI overlap). We do not separate mesocosm experiments from experiments without peat
345 enclosure. We treat them as different approaches to manipulate water table depth. We used the
346 ISI Web of Science database to conduct literature search for the papers published before October
347 2020 with query terms including "water table", "carbon", "methane", "respiration", "NEE",
348 "primary production", "drain" and "peatland". More papers were identified through the Chinese
349 CNKI platform. Studies included in our database were selected according to several criteria. The
350 study should measure at least WTD and one flux of CO2 or CH4 under both low- and high-water
351 table treatments over the same time period in the same geographic region and have the same
352 natural background and land use type. Studies that compare ecosystem responses under altered
353 water table during different time periods, for example, from Merbold, et al. 31, were not
354 incorporated. Studies using laboratory peat columns, with synthetic/repacked soils, artificial
355 additions (ameliorant, biochar or compost) or through incubations or have a treatment less than 1
356 month were also excluded. Around 1/3 of studies experienced water table disturbance 10 years
357 earlier. Some papers reported ecosystem responses to several water table treatments at the same
358 location. In these cases, we rearranged and paired the datasets to have different combinations of
359 low vs. high water table treatments. For those papers reporting multiple values across several
360 years, we compared results from meta-analysis that separates vs. lumps each year's mean
361 response. Differences are minor, and we reported results without lumping. In total, we obtained
362 96 papers that cover 130 locations, mostly in the northern hemisphere (Supplementary Figures 1,
363 2). A pair of data points reflecting ΔWTD effects on carbon flux includes a target GHG data from
364 two treatments corresponding to a low and high water table depth in a specific site. In total, we
365 have 376 pairs on NEE (CO2), 532 pairs of CH4, 209 pairs of gross primary production (GPP)
366 and 407 pairs of ecosystem respiration (or soil in the absence of live plants, RES) measurements.

367 For carbon fluxes, we extracted mean values of emission for each treatment, standard
368 deviations (SD), and sample sizes from each published study. If standard error (SE) rather than
369 SD was reported, SD was calculated from SE. For experiments that did not document SD or SE
370 (3-20% of the experiments), we estimated the variance through scaling the mean of each
371 experiment by the average coefficient of variation within each treatment and each GHG. We also
372 extracted mean WTD before and after water table manipulation and other ancillary information
373 (Supplementary Tables 2, 3).

374 **Response quantification**
For each pair of data points, we use the difference ($\Delta_{C,WTD}$, in unit of mg CO₂-eq m⁻² h⁻¹) in the mean value (over experimental replicates) of each CO₂ or CH₄ flux under high (shallow)-($\overline{C}_h$) and low (deep) -water table ($\overline{C}_l$) (Equation 1) as a metric to quantify the effect of water table drawdown. We chose the difference instead of the odds ratio (log) to incorporate experiments in which $\overline{C}_h$ and $\overline{C}_l$ vary in sign.

$$ \Delta_{C,WTD} = \overline{C}_h - \overline{C}_l \quad (1) $$

We conducted the meta-analysis with the random-effect model (assuming that between study variations are randomly distributed) and the inverse variance weighting scheme³² based on the extracted values of SD and sample sizes through the Metafor package in R version 3.6.2 ³³. The between study variance (heterogeneity, $\tau^2$) was quantified through the restricted maximum-likelihood method³³. 95% confidence interval (CI) was estimated as the Wald-type (i.e., normal) CI if the standardized residuals of observations are not strongly deviated from theoretical quantiles of a normal distribution. Otherwise, we applied a bootstrapping CI estimation. We randomly sampled 90% of the original datasets with replacement and estimated the mean effect 1000 times. 95% CI was calculated as the 2.5% and 97.5% quantiles of the 1000 estimates in R 3.6.2 ³³. To test the robustness of our conclusion towards an overall positive or negative response, we conducted additional meta-analyses under alternative assumptions. First, we assessed whether the conclusions depended on how individual studies are weighted ³⁴. We applied the fixed effect model with weighting based on the number of replicates and with a uniform weighting. For studies with sample size not published (~20%), we used the average sample size from our database for corresponding gases. For the fixed effect model, we estimated the 95% CIs of the mean response through 10000 times bootstrapping using the bootES library³⁵. A significant asymmetry of the funnel plot indicates the bias in compiled studies which tend to report more results with a significant response compared to studies without. We conducted an asymmetry test through the regtest function of the Metafor package. We did not detect the publication bias in the combined effect of CO₂ and CH₄ (p=0.31). For CO₂ or CH₄ alone, the funnel plot (p < 0.01) is significantly asymmetry. We corrected the publication bias through the trimfill function in Metafor. These tests, the fixed effect models and with correction of potential publication bias, pointed to consistent signs of the overall effects. The average responses are robust (Supplementary Figure 3).
The average sensitivity of $\Delta_{C,WTD}$ to unit change of $\Delta_{WTD}$ ($\frac{\Delta_{C,WTD}}{\Delta_{WTD}}$, mg CO₂-eq m⁻² h⁻¹ cm⁻¹) was quantified similarly with the random effect model and the inverse variance weighting scheme. Note that we do not account for variance in WTD, assuming it is relatively well measured in manipulation experiments. For regional analyses, we grouped samples into arctic (north of 66.5 °N), boreal and temperate (30°N – 66.5°N, 66.5°S – 30°S) and tropical regions (30°S – 30°N). We also compared coastal vs. non-coastal regions, and among peatland types (bog, fen, marsh, swamp).

## Response attribution

Water table manipulation studies differ in peatland types, nutrient status, background climate and other experimental designs (e.g., initial water table depth, drainage duration and the magnitude of drainage). Combining driving factors reported from individual studies and the availability of data across studies, we tested a list of factors to understand what drive $\Delta_{CO_2, WTD}$ and $\Delta_{CH_4, WTD}$ through the random forest method. These factors include WTD and carbon fluxes under high water table treatment (WTD<sub>initial</sub>, CO<sub>2</sub><sub>initial</sub> or CH<sub>4</sub><sub>initial</sub>), the magnitude of water table manipulation ($\Delta_{WTD}$), manipulation duration (short: <1 year; medium: 1-10 years; long: > 10 years), experimental type (mesocosm vs. *in-situ*), land management (managed or not), climatic, topographic (elevation) and edaphic properties (Supplementary Tables 2, 3). Climatic factors include mean annual precipitation, mean annual temperature, wind speed, solar radiation, vapor pressure, aridity (the ratio between potential evapotranspiration and precipitation), potential evapotranspiration and a range of other bioclimatic variables characterizing the annual trend, seasonality and extreme climatic conditions. Edaphic properties involve bulk density, pH, soil carbon content, soil nitrogen, soil phosphorus, soil potassium, cation exchange capacity, base saturation, clay content, sand content, silt content and volumetric moisture content.

Random forest is an ensemble machine learning approach that generates a number of decision trees¹⁸, and is capable of capturing non-linear interactions. We sequentially added explanatory variables one at a time and selected the random forest model that yielded the highest R² and the lowest root mean square error (RMSE) through leave-one-out cross-validation (LOOCV). Climatic, topographic and edaphic factors that are not documented in individual studies were extracted from high resolution data sources listed in Supplementary Table 3. For $\Delta_{CO_2, WTD}$, the selected random forest model (with LOOCV R²=0.52, RMSE= 134 mg CO₂ m⁻² h⁻¹, Supplementary Figure 11) was built through CO₂<sub>initial</sub>, $\Delta_{WTD}$, WTD<sub>initial</sub>, soil nitrogen, soil
carbon content, potential evapotranspiration, bulk density, volumetric water content at -10 kPa,
soil pH, wind speed, soil clay content, solar radiation and elevation (Figure 2 and Supplementary
Figure 13). The first three predictors accounted for 53% of the relative importance
(Supplementary Table 4). ΔCH₄, WTD are predictable (LOOCV R²=0.72, RMSE = 81 mg CO₂-eq
m⁻² h⁻¹, Supplementary Figure 11) through CH₄,initial, ΔWTD, WTD,initial, wind speed, soil nitrogen
content, aridity, manipulation duration (Figure 2 and Supplementary Figure 14). The first three
predictors accounted for 88% of the relative importance (Supplementary Table 5). Tropics
contribute significantly to CO₂ and CH₄ budgets. The small sample size of tropical studies makes
building regional random forest models infeasible. Despite being built over samples around the
world, the model performance is comparable between tropical samples (ΔCO₂, WTD, R²=0.49,
RMSE = 121 mg CO₂ m⁻² h⁻¹; ΔCH₄, WTD: R²=0.66, RMSE = 48 mg CO₂-eq m⁻² h⁻¹;
Supplementary Figures 11, 12) and the rest of the world. Earlier synthetic studies revealed that
relationships between water table and peatland greenhouse emissions were modified by peatland
types, region and disturbance etc ¹⁵,¹⁶. We reconstructed the functional relationship between
ΔCO₂, WTD (or ΔCH₄, WTD) and different predictors for each individual study, i.e., the Individual
Conditional Expectation (ICE)³⁶ (grey lines in Figure 2). Variations among ICE curves capture
the context-dependent response patterns.

Mapping future impact

ΔCO2, WTD and ΔCH4, WTD at the end of 2100 were predicted using the random forest models
built above (Methods: Response attribution), with predictors such as ΔWTD, WTDinitial, CO2,initial
(or CH4,initial) and future climatic conditions (wind speed, solar radiation, potential
evapotranspiration, aridity), assuming that edaphic and topographic factors (soil carbon, soil
nitrogen, bulk density, volumetric water content at -10 kPa, soil pH, soil clay content, and
elevation) remain equal to their current levels due to their relatively slow change rates. Average
ΔCO2, WTD and ΔCH4, WTD were estimated through different (if available) predictor datasets (see text
below and Supplementary Table 3). To verify our main results are not outcomes of overfitting,
we made predictions with the top three most important predictors, which yielded a global
ΔGHG, WTD of 1.47 Gt CO2-eq yr-1 (ΔCO2, WTD: 1.59, ΔCH4, WTD: -0.12). This value is larger than the
prediction from the random forest model built in previous section, and our conclusion of an
overall positive ΔGHG, WTD is robust. CO2,initial and CH4,initial from predicting datasets are within the
range spanned by observation datasets used to train the random forest models (Supplementary
Figure 20 and Supplementary Table 2). We set the upper limit of $\Delta_{WTD}$ to be 300 cm. We varied this boundary value from 100 to 400cm, and our results stayed the same as $\Delta_{CO_2, WTD}$ and $\Delta_{CH_4, WTD}$ are not responsive to further increasing $\Delta_{WTD}$ beyond 100cm (Figure 3b, e). Similarly, we set the upper limit of $WTD_{initial}$ close to the upper limit of the training dataset (around 100cm) to avoid extrapolating. $\Delta_{CO_2, WTD}$ and $\Delta_{CH_4, WTD}$ are not sensitive to $WTD_{initial}$ when water table depth is deep (Figure 3c, f). We checked that increasing this boundary value did not change our results.

Future WTDs were projected through a physically based global hydrology and water-resources model PCR-GLOBWB that was coupled to the global groundwater flow model (MODFLOW) with future climate forcing (HadGEM2-ES) under RCP8.5 GHG emission scenario and ‘business-as-usual’ water consumptions from de Graaf, et al. ¹⁹,³⁷. By ‘business-as-usual’, per capita water demand for industry, domestic and livestock uses as well as irrigated area were assumed to remain constant after 2010. Per unit irrigation demands vary with the projected climate change. Total future water consumption varies with the projected trends in population growth and economic development. HadGem2-ES was chosen to capture the average climatic conditions predicted from GCMs within the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP, https://www.isimip.org). RCP8.5 was used to represent climatic conditions under the worst-case scenario for future GHG emissions. This coupled modeling tracks a range of key processes that are critical in global hydrology and water table dynamics, particularly precipitation, evapotranspiration, runoff, infiltration, surface-groundwater interactions, capillary rise, groundwater discharge, recharge and lateral flows, water-use by agriculture irrigation, industries, households and livestock, and return flows of unconsumed withdrawn water, and showed robust estimates, as compared to observations³⁷. This is considered as the best available dataset on future WTD while we acknowledge potentially large uncertainties. $\Delta_{WTD}$ is the difference between the average WTD during 2050-2100 (future) vs. 1960-2010 (historical). To assess the impact of uncertainties in future $\Delta_{WTD}$ quantifications, we conducted additional predictions with future $\Delta_{WTD}$ being 0.2, 0.4, 0.6, 0.8, 1.2, 1.4, 1.6 and 1.8 of previous quantifications (Supplementary Table 8).

We used FLUXCOM NEE to estimate $CO_2$ (NEE) before water table drawdown ($CO_{2,initial}$). FLUXCOM NEE merged eddy covariance and remote-sensing observations through three machine learning techniques (MARS, ANN, RF) ²⁹. In addition, we incorporated an ensemble (18 in total) estimation of NEE generated by land models LPJ-GUESS, LPJML,
ORCHIDEE-DGVM, ORCHIDEE, VEGAS and VISIT driven by different climate forcing within the ISIMIP framework (Supplementary Table 3). CH₄ emissions are higher over wetland compared to upland soils ³⁸. We used gridded dataset from the Wetland CH₄ Inter-comparison of Models Project (WETCHIMP) which quantified CH₄ emission rate per wetland area from 7 models (LPJ-Bern, CLM4Me, DLEM ORCHIDEE-ALT, ORCHIDEE, SDGVM and LPJ-WSL) to cover uncertainties in CH₄, initial ³⁰,³⁹. Peatland is defined through the PEATMAP⁴⁰, which combines geospatial information from a variety of peatland-specific databases and histosol distributions from the Harmonized World Soil Database V1.2 (HWSD) in the regions where peatland-specific information are not available. The total global peatland area is 4.23 million km² from PEATMAP. We assume no changes in future peatland distribution while acknowledging uncertainties in peatland area and that future peatland area may expand or shrink with new discoveries, under future climate change or land use. We tested the duration of water table manipulation with the manipulation variable going from long (>10 years), medium (1-10 years) to short-terms (<= 1 year). The impact of manipulation duration is not big, and we reported results with long-term duration.

Future climatic conditions were predicted from GCM runs driven by RCP8.5 (worst) and RCP2.6 (optimistic) emission scenarios (see ISIMIP). We chose simulations from three GCM models i.e., the GFDL-ESM2M (wettest), the HadGEM2-ES (average) and the MIROC-ESM-CHEM (driest) to account for climate uncertainties. Future potential evapotranspiration (PET) and the aridity index (the ratio between precipitation and PET) were estimated using the Penman-Monteith equation for a hypothetical short grass as the reference surface (python package, PyETo, https://github.com/Evapotranspiration/ETo).

We applied bootstrap resampling and ensemble prediction to estimate prediction uncertainties. For ΔCO₂, WTD, we randomly sampled 80% of our observation samples to build one random forest model. This random model was then used to make future predictions with different combinations of predictor datasets. We repeated this bootstrap resampling, random forest model building and future prediction 200 times. In total, we had 25200 (200 x 21 CO₂, initial x 2 WTD, initial x 3 Climate) ensemble members and we calculated the 95% probability interval as the indicator of prediction uncertainty. Bootstrap resampling provides reasonable estimation of prediction uncertainty for random forest models⁴¹ and the ensemble approach can take into account of both uncertainties from random forest algorithms and from predictor
variables. Similarly, we conducted 8400 predictions for ΔCH₄, WTD through 200 times bootstrap resampling, 7 CH₄,initial datasets, 2 WTD,initial datasets and 3 climate datasets (Supplementary Table 3). To quantify contributions to future ΔCO₂, WTD and ΔCH₄, WTD, we conducted a series of predictions (Supplementary Tables 6, 7) through sequentially replacing climate, ΔWTD, WTD,initial and CO₂,initial (or CH₄,initial) by corresponding reference level datasets listed in Supplementary Table 3.

535

536
<table>
  <tbody>
    <tr>
      <td>537</td>
      <td>538</td>
      <td>Code availability</td>
    </tr>
    <tr>
      <td>539</td>
      <td>540</td>
      <td>Calculations were conducted through Python 3.7.3, R 3.6.2 and ferret 6.72. Data processing code and code used to generate figures are provided through</td>
    </tr>
    <tr>
      <td>541</td>
      <td>542</td>
      <td>https://doi.org/10.6084/m9 figshare.13139906.v3 <sup>42</sup>.</td>
    </tr>
    <tr>
      <td>543</td>
      <td>544</td>
      <td>Data availability</td>
    </tr>
    <tr>
      <td>545</td>
      <td>546</td>
      <td>Source datasets and global maps generated in this study are available at</td>
    </tr>
    <tr>
      <td>547</td>
      <td>547</td>
      <td>https://doi.org/10.6084/m9 figshare.13139906.v3 <sup>42</sup>.</td>
    </tr>
    <tr>
      <td>548</td>
      <td>548</td>
      <td>References</td>
    </tr>
    <tr>
      <td>549</td>
      <td>549</td>
      <td>Merbold, L. et al. Artificial drainage and associated carbon fluxes (CO2/CH4) in a tundra ecosystem. Global Change Biology 15, 2599-2614, doi:10.1111/j.1365-2486.2009.01962.x (2009).</td>
    </tr>
    <tr>
      <td>550</td>
      <td>550</td>
      <td>Borenstein, M., Hedges, L. V., Higgins, J. P. T. & Rothstein, H. R. A basic introduction to fixed-effect and random-effects models for meta-analysis. Research Synthesis Methods 1, 97-111, doi:10.1002/jrsm.12 (2010).</td>
    </tr>
    <tr>
      <td>551</td>
      <td>551</td>
      <td>Viechtbauer, W. Conducting Meta-Analyses in R with the metafor Package. Journal of Statistical Software 36, 1-48 (2010).</td>
    </tr>
    <tr>
      <td>552</td>
      <td>552</td>
      <td>van Groenigen, K. J., Osenberg, C. W. & Hungate, B. A. Increased soil emissions of potent greenhouse gases under increased atmospheric CO2. Nature 475, 214-U121, doi:10.1038/nature10176 (2011).</td>
    </tr>
    <tr>
      <td>553</td>
      <td>553</td>
      <td>Kirby, K. N. & Gerlanc, D. BootES: An R package for bootstrap confidence intervals on effect sizes. Behavior Research Methods 45, 905-927, doi:10.3758/s13428-013-0330-5 (2013).</td>
    </tr>
    <tr>
      <td>554</td>
      <td>554</td>
      <td>Goldstein, A., Kapelner, A., Bleich, J. & Pitkin, E. Peeking Inside the Black Box: Visualizing Statistical Learning With Plots of Individual Conditional Expectation. Journal of Computational and Graphical Statistics 24, 44-65, doi:10.1080/10618600.2014.907095 (2015).</td>
    </tr>
    <tr>
      <td>555</td>
      <td>555</td>
      <td>de Graaf, I. E. M., Sutanudjaja, E. H., van Beek, L. P. H. & Bierkens, M. F. P. A high-resolution global-scale groundwater model. Hydrology and Earth System Sciences 19, 823-837, doi:10.5194/hess-19-823-2015 (2015).</td>
    </tr>
    <tr>
      <td>556</td>
      <td>556</td>
      <td>Saunois, M. et al. The global methane budget 2000-2012. Earth System Science Data 8, 697-751, doi:10.5194/essd-8-697-2016 (2016).</td>
    </tr>
    <tr>
      <td>557</td>
      <td>557</td>
      <td>Wania, R. et al. Present state of global wetland extent and wetland methane modelling: methodology of a model inter-comparison project (WETCHIMP). Geoscientific Model Development 6, 617-641, doi:10.5194/gmd-6-617-2013 (2013).</td>
    </tr>
    <tr>
      <td>558</td>
      <td>558</td>
      <td>Xu, J. R., Morris, P. J., Liu, J. G. & Holden, J. PEATMAP: Refining estimates of global peatland distribution based on a meta-analysis. Catena 160, 134-140, doi:10.1016/j.catena.2017.09.010 (2018).</td>
    </tr>
    <tr>
      <td>559</td>
      <td>559</td>
      <td></td>
    </tr>
    <tr>
      <td>560</td>
      <td>560</td>
      <td></td>
    </tr>
    <tr>
      <td>561</td>
      <td>561</td>
      <td></td>
    </tr>
    <tr>
      <td>562</td>
      <td>562</td>
      <td></td>
    </tr>
    <tr>
      <td>563</td>
      <td>563</td>
      <td></td>
    </tr>
    <tr>
      <td>564</td>
      <td>564</td>
      <td></td>
    </tr>
    <tr>
      <td>565</td>
      <td>565</td>
      <td></td>
    </tr>
    <tr>
      <td>566</td>
      <td>566</td>
      <td></td>
    </tr>
    <tr>
      <td>567</td>
      <td>567</td>
      <td></td>
    </tr>
    <tr>
      <td>568</td>
      <td>568</td>
      <td></td>
    </tr>
    <tr>
      <td>569</td>
      <td>569</td>
      <td></td>
    </tr>
    <tr>
      <td>570</td>
      <td>570</td>
      <td></td>
    </tr>
    <tr>
      <td>571</td>
      <td>571</td>
      <td></td>
    </tr>
    <tr>
      <td>572</td>
      <td>572</td>
      <td></td>
    </tr>
    <tr>
      <td>573</td>
      <td>573</td>
      <td></td>
    </tr>
    <tr>
      <td>574</td>
      <td>574</td>
      <td></td>
    </tr>
    <tr>
      <td>575</td>
      <td>575</td>
      <td></td>
    </tr>
    <tr>
      <td>576</td>
      <td>576</td>
      <td></td>
    </tr>
  </tbody>
</table>
577 41 Coulston, J. W., Blinn, C. E., Thomas, V. A. & Wynne, R. H. Approximating Prediction
578 Uncertainty for Random Forest Regression Models. *Photogrammetric Engineering and
579 Remote Sensing 82*, 189-197, doi:10.14358/pers.82.3.189 (2016).

580 42 Huang, Y. et al. Supporting data for Tradeoff of CO₂ and CH₄ emissions from global peatlands under water-table drawdown. figshare. Dataset. doi: 10.6084/m9 figshare.13139906 (2020).

583

584 **Corresponding author**

585 Correspondence to Yuanyuan Huang (yuanyuanhuang2011@gmail.com)

586 **Acknowledgements**

587 Y.H., P.C., D.Z., C. Q. and D.S.G received support from the European Research Council

588 Synergy project SyG-2013-610028 IMBALANCE-P and Y.H., P.C., D.Z., C.Q., D.S.G., B.G.

589 and D.M. from the ANR CLAND Convergence Institute (ANR-16-CONV-0003).

590 **Author contributions**

591 Y.H., P.C., L.Q. designed this study. Y. H., L.Q. and I.G.G. contributed the data. Y.H., P.C., Y.

592 L., D. Z. and L.Q. discussed analyzing methods. Y.H. conducted the analysis and drafted the

593 manuscript. All authors discussed the results and contributed to writing the manuscript.

594 **Competing interests**

595 The authors declare no competing interests.

596

597 **Figure legends**

598 Figure 1. Effects of water table drawdown on peatland CO₂ and CH₄ fluxes. a, the net exchange
599 of CO₂ (NEE), CH₄ and their combined response (GHG). b, ecosystem respiration (or soil
600 respiration in the absence of live plants, RES) and photosynthetic CO₂ uptake (GPP). *n* is the
601 number of experiments. Mean effect sizes were obtained through the meta-analysis. Error bars
602 correspond to 95% confidence intervals. The unit of CH₄ is expressed as its CO₂ equivalent
603 assuming its global warming potential is 25 times of CO₂. We define a positive sign for
604 emissions to the atmosphere and *vice versa*.

605

606 Figure 2. ΔCO₂,WTD and ΔCH₄,WTD in response to predictors. One grey line captures responses per
607 one pair of field studies to a gradual increase of the corresponding predictor while holding other
608 predictors constant. Red lines are the averages across individual studies. We show the top three
609 most important predictors ordered in declining importance from left to right (See Supplementary
Figures 13, 14 for other predictors). WTD<sub>initial</sub>, CO<sub>2</sub><sub>initial</sub> and CH<sub>4</sub><sub>initial</sub> are water table depth, net ecosystem exchange of CO<sub>2</sub> and CH<sub>4</sub> under high water table. Δ<sub>WTD</sub> is the magnitude of water table drawdown. Rugs at the bottom indicate the distributions of predictors.

Figure 3. GHG changes ($\Delta_{\text{GHG,WTD}}=\Delta_{\text{CO2,WTD}}+\Delta_{\text{CH4,WTD}}$) in response to water table drawdown by 2100. Panels a, b show results with RCP8.5 climatic variables, and c, d under RCP2.6. Latitudinal totals (b, d) were estimated through the average values per area across 0.1 degree latitude band and peatland areas (Supplementary Figures 21, 22), and were smoothed with a window size of 5 degrees. Shading areas are 95% intervals. White region show locations with small $\Delta_{\text{GHG,WTD}}$ or with negligible water table drawdown. Spatial distributions of $\Delta_{\text{CO2,WTD}}$ and $\Delta_{\text{CH4,WTD}}$ are provided in Supplementary Figures 25, 26 and the 95% intervals in Supplementary Figures 27, 28.
<figure><img src="image_2.png" /></figure>

625

<figure><img src="image_3.png" /></figure>

627

628
<figure><img src="image_4.png" /></figure>

<figure><img src="image_5.png" /></figure>

<figure><img src="image_6.png" /></figure>

<figure><img src="image_7.png" /></figure>