A map of global peatland extent created using machine learning (Peat-ML)

Melton, Joe R.; Chan, Ed; Millard, Koreen; Fortier, Matthew; Winton, R. Scott; Martín-López, Javier M.; Cadillo‐Quiroz, Hinsby; Kidd, Darren; Verchot, Louis

doi:10.5194/gmd-15-4709-2022

Cited by 32 publications

(20 citation statements)

References 80 publications

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“…Uncertainties in the peatland C stock partially arise from uncertainties in peatland coverage. Since PTEM 2.1 does not simulate peatland coverage, we use three different maps covering the pan‐Arctic region (Hugelius et al., 2020; Melton et al., 2022; Xu et al., 2018). All maps were aggregated into 0.5° × 0.5° grid cells with spatially explicit peatland abundance, and their average was used as a fourth map.…”

Section: Methodsmentioning

confidence: 99%

“…Although multiple process‐based models have simulated the dynamic peatland spatial extent with TOPMODEL, the uncertainty remains an issue (Qiu et al., 2019; Stocker et al., 2014). To quantify these uncertainties, three northern peatland coverage maps are selected (Hugelius et al., 2020; Melton et al., 2022; Xu et al., 2018), and the soil C stock is estimated based on different observation‐based data sets and the mean of these data sets, respectively.…”

Section: Introductionmentioning

confidence: 99%

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Modeling Carbon Accumulation and Permafrost Dynamics of Northern Peatlands Since the Holocene

2022

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Northern peatlands (north of 45°N) comprise a soil C stock of 300-1,045 Pg C (Table 1). The Arctic region has been experiencing around three times the global average warming (Allen et al., 2018;GISTEMP-Team, 2021) which might increase regional peatland C release due to the acceleration of peat decomposition (Frolking et al., 2011;Gallego-Sala et al., 2018). In addition to the direct influence of temperature on decomposition, peatlands are also susceptible to indirect influences of climate change and anthropogenic activities. For example, peatland drainage lowers peatland water table and increases decomposition (Huang et al., 2021;Qiu et al., 2021) and peatland fires result in large carbon emissions (Turetsky et al., 2015).Peatland C accumulation in permafrost-affected regions under a changing climate is influenced by permafrost dynamics. As permafrost thaws, C in newly thawed soil becomes susceptible to anaerobic and aerobic decomposition (O'Donnell et al., 2012;Turetsky et al., 2002). Permafrost thaw can also enhance soil C loss via the outflow of dissolved organic C (Hugelius et al., 2020;Plaza et al., 2019). Meanwhile, permafrost thaw also shifts the microbial community to one that benefits methane emissions (McCalley et al., 2014). Several studies have

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Modeling Carbon Accumulation and Permafrost Dynamics of Northern Peatlands Since the Holocene

2022

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“…There are various other approaches available in the literature, including (1) soil map reclassification methods similar to the default approach used in this study (Hiederer and Köchy 2011), ( 2) assessments based on inventories (Tanneberger et al 2017, Humpenöder et al 2020, (3) expert-based modeling (Gumbricht et al 2017) and ( 4) machine-learning algorithms (Melton et al 2022). In addition to our default map, we select five global spatial-explicit estimates of peatland area that are integrated in the IMAGE model to analyze the related uncertainty and its effect on GHG emissions estimates.…”

Section: Sensitivity Analysismentioning

confidence: 99%

“…In addition to our default map, we select five global spatial-explicit estimates of peatland area that are integrated in the IMAGE model to analyze the related uncertainty and its effect on GHG emissions estimates. These are the meta-studies by (1) Leifeld and Manichetti (2018) and (2) Xu et al (2018) that both combine multiple data sources to compile peatland maps; the inventory-based approach by (3) Humpenöder et al (2020) who downscale inventory data from Joosten (2010) to the grid level; the machine-learning based approach from (4) Melton et al (2022); and the expert-based model approach by (5) Gumbricht et al (2017), which is complemented by the default S-world data for the temperate and boreal zones because the data is only available for the tropical zones.…”

Section: Sensitivity Analysismentioning

confidence: 99%

The role of peatland degradation, protection and restoration for climate change mitigation in the SSP scenarios

Doelman

Verhagen

Stehfest

et al. 2023

Environ. Res.: Climate

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Peatlands only cover a small fraction of the global land surface (∼3%) but store large amounts of carbon (∼600 GtC). Drainage of peatlands for agriculture results in the decomposition of organic matter, leading to greenhouse gas (GHG) emissions. As a result, degraded peatlands are currently responsible for 2%–3% of global anthropogenic emissions. Preventing further degradation of peatlands and restoration (i.e. rewetting) are therefore important for climate change mitigation. In this study, we show that land-use change in three SSP scenarios with optimistic, recent trends, and pessimistic assumptions leads to peatland degradation between 2020 and 2100 ranging from −7 to +10 Mha (−23% to +32%), and a continuation or even an increase in annual GHG emissions (−0.1 to +0.4 GtCO2-eq yr−1). In default mitigation scenarios without a specific focus on peatlands, peatland degradation is reduced due to synergies with forest protection and afforestation policies. However, this still leaves large amounts of GHG emissions from degraded peatlands unabated, causing cumulative CO2 emissions from 2020 to 2100 in an SSP2-1.5 °C scenario of 73 GtCO2. In a mitigation scenario with dedicated peatland restoration policy, GHG emissions from degraded peatlands can be reduced to nearly zero without major effects on projected land-use dynamics. This underlines the opportunity of peatland protection and restoration for climate change mitigation and the need to synergistically combine different land-based mitigation measures. Peatland location and extent estimates vary widely in the literature; a sensitivity analysis implementing various spatial estimates shows that especially in tropical regions degraded peatland area and peatland emissions are highly uncertain. The required protection and mitigation efforts are geographically unequally distributed, with large concentrations of peatlands in Russia, Europe, North America and Indonesia (33% of emission reductions are located in Indonesia). This indicates an important role for only a few countries that have the opportunity to protect and restore peatlands with global benefits for climate change mitigation.

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“…Tropical peatlands are globally important carbon reservoirs that span across continents (Melton et al, 2022), but are vulnerable to deterioration from anthropogenic influences, such as land conversion for agriculture and the changing climate (Ribeiro et al, 2021). Two critical roles of tropical peatlands are their capacity to store soil organic matter (SOM) and the emission of greenhouse gases (GHG) when SOM is decomposed (Page et al, 2011; Page & Baird, 2016).…”

Section: Introductionmentioning

confidence: 99%

Genes and genome‐resolved metagenomics reveal the microbial functional make up of Amazon peatlands under geochemical gradients

Pavia

Finn

Macedo‐Tafur

et al. 2023

Environmental Microbiology

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The Pastaza‐Marañón Foreland Basin (PMFB) holds the most extensive tropical peatland area in South America. PMFB peatlands store ~7.07 Gt of organic carbon interacting with multiple microbial heterotrophic, methanogenic, and other aerobic/anaerobic respirations. Little is understood about the contribution of distinct microbial community members inhabiting tropical peatlands. Here, we studied the metagenomes of three geochemically distinct peatlands spanning minerotrophic, mixed, and ombrotrophic conditions. Using gene‐ and genome‐centric approaches, we evaluate the functional potential of the underlying microbial communities. Abundance analyses show significant differences in C, N, P, and S acquisition genes. Furthermore, community interactions mediated by toxin–antitoxin and CRISPR‐Cas systems were enriched in oligotrophic soils, suggesting that non‐metabolic interactions may exert additional controls in low‐nutrient environments. Additionally, we reconstructed 519 metagenome‐assembled genomes spanning 28 phyla. Our analyses detail key differences across the geochemical gradient in the predicted microbial populations involved in degradation of organic matter, and the cycling of N and S. Notably, we observed differences in the nitric oxide (NO) reduction strategies between sites with high and low N2O fluxes and found phyla putatively capable of both NO and sulfate reduction. Our findings detail how gene abundances and microbial populations are influenced by geochemical differences in tropical peatlands.

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A map of global peatland extent created using machine learning (Peat-ML)

Cited by 32 publications

References 80 publications

Modeling Carbon Accumulation and Permafrost Dynamics of Northern Peatlands Since the Holocene

Modeling Carbon Accumulation and Permafrost Dynamics of Northern Peatlands Since the Holocene

The role of peatland degradation, protection and restoration for climate change mitigation in the SSP scenarios

Genes and genome‐resolved metagenomics reveal the microbial functional make up of Amazon peatlands under geochemical gradients

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