OverviewThis data set delineates peatlands and other organic soils globally using five layers. Miettinen et al. 2016 was used for Indonesia and Malaysia, Hastie et al. 2022 was used in lowland Peru, Crezee et al. 2022 was used in the Congo basin, and Gumbricht et al. 2017 was used for all land between 40 degrees north and 60 degrees south (including areas covered by the aforementioned data sets). Xu et al. 2018 was used for all land above 40 degrees north. Miettinen et al. 2016, Xu et al. 2018 were rasterized to ~30x30 m resolution while Gumbricht et al. 2017, Crezee et al. 2022, and Hastie et al. 2022 were resampled from their native resolutions to ~30x30 m resolution in order to align with the Global Forest Change maps from Hansen et al. 2013. All layers were combined, i.e. Gumbricht et al. 2017 was also used in Indonesia/Malaysia, the Peruvian Amazon, and the Congo basin. All data sources have different methods for peatland delineation, which are described in their original publications. Crezee, B. et al. Mapping peat thickness and carbon stocks of the central Congo Basin using field data. Nature Geoscience 15: 639-644 (2022). https://www.nature.com/articles/s41561-022-00966-7. Data downloaded from https://congopeat.net/maps/, using classes 4 and 5 only (peat classes). Gumbricht, T. et al. An expert system model for mapping tropical wetlands and peatlands reveals South America as the largest contributor. Global Change Biology 23, 3581–3599 (2017). https://onlinelibrary.wiley.com/doi/full/10.1111/gcb.13689 Hastie, A. et al. Risks to carbon storage from land-use change revealed by peat thickness maps of Peru. Nature Geoscience 15: 369-374 (2022). https://www.nature.com/articles/s41561-022-00923-4 Miettinen, J., Shi, C. & Liew, S. C. Land cover distribution in the peatlands of Peninsular Malaysia, Sumatra and Borneo in 2015 with changes since 1990. Global Ecological Conservation. 6, 67– 78 (2016). https://www.sciencedirect.com/science/article/pii/S2351989415300470 Xu et al. PEATMAP: Refining estimates of global peatland distribution based on a meta-analysis. CATENA 160: 134-140 (2018). https://www.sciencedirect.com/science/article/pii/S0341816217303004 Resolution: 30 x 30mGeographic Coverage: GlobalFrequency of Updates: SporadicDate of Content: Range of yearsSources (by region):Crezee et al. 2022 (Congo basin) Gumbricht et al. 2017 (between 40 deg N and rest of southern hemisphere) Hastie et al. 2022 (Amazonian lowland Peru) Miettinen et al. 2016 (Indonesia and Malaysia) Xu et al. 2018 (temperate/boreal, north of 40 deg N)CautionsThis is a composite layer comprised of five data sets, each with their own methods and strengths and weaknesses. Refer to the original publications for each data set to learn more about specific cautions for each. All input layers have been converted from vector data or resampled from coarser raster dataLicenseCC-by-4.0

We present the first synthesis of global peatland extent through the last glacial cycle (130 ka) based on >975 detailed stratigraphic descriptions from exposures, soil pits, and sediment cores. Buried peats are defined as organic-rich sediments overlain by mineral sediments. Also included are deposits rich in wetland macrofossils indicated a local peatland environment. The dataset includes location (lat/long), chronologic information (when available), a description of the buried peat sediment, overlying and underlying sediments, whether geochemical information is available, and the original references.

Detailed information about methods and results can be found in the publication to which this dataset is a supplement.

We present the first synthesis of global peatland extent through the last glacial cycle (130 ka) based on >975 detailed stratigraphic descriptions from exposures, soil pits, and sediment cores. Buried peats are defined as organic-rich sediments overlain by mineral sediments. Also included are deposits rich in wetland macrofossils indicated a local peatland environment. The dataset includes location (lat/long), chronologic information (when available), a description of the buried peat sediment, overlying and underlying sediments, whether geochemical information is available, and the original references. Detailed information about methods and results can be found in the publication to which this dataset is a supplement.

This data set provides a time series of global peatland carbon balance and carbon dioxide emissions from land use change throughout the Holocene (the past 11,000 yrs). Global peatland carbon balance was quantified using a) a continuous net carbon balance history throughout the Holocene derived from a data set of 64 dated peat cores, and b) global model simulations with the LPX-Bern model hindcasting the dynamics of past peatland distribution and carbon balance. CO2 emissions from land-use change are based on published scenarios for anthropogenic land use change (HYDE 3.1, HYDE 3.2, KK10) covering the last 10,000 years. This combination of model estimates with CO2 budget constraints narrows the range of past anthropogenic land use change emissions and their contribution to past carbon cycle changes.

PEATMAP is a GIS shapefile dataset that shows a distribution of peatlands that covers the entire world. It was produced by combining the most high quality available peatland map from a wide variety of sources that describe peatland distributions at global, regional and national levels. The following sequence of comparisons to discriminate between overlapping data sources were used: (1) Relevance. The most important criterion was that source data are able to identify peatlands faithfully and to distinguish them from other land cover types, especially non-peat forming wetlands. (2) Spatial resolution. In areas where two or more overlapping data sources were indistinguishable in terms of their relevance to peatlands, the dataset with the finest spatial resolution was selected. (3) Age. In any areas where two or more overlapping datasets were indistinguishable based on both their apparent relevance to peatlands and their spatial resolution, the data product that had been most recently updated was selected. Recently updated products commonly contain much older source data, the period over which the latest revision source data were collected as the primary measure of the age of a dataset.

Citation

In order to use these data, you must cite this data set with the following citation:

Xu, Jiren and Morris, Paul J. and Liu, Junguo and Holden, Joseph (2017) PEATMAP: Refining estimates of global peatland distribution based on a meta-analysis. University of Leeds. [Dataset] https://doi.org/10.5518/252

Ensemble estimate of the global distribution of peatlands / extent (peatland.extent_wri.gfw.peatgrids_p). This is a simple average from three (3) sources of data:

1. WRI Global Peatlands extent map at 30-m (250-m effective);
2. PEATGRIDS at 1-km;
3. Global Peatlands Map 2.0 produced by the Global Peatlands Initiative;

The average between the three sources is an extent map with value 0–100%. The refence period is 2000–2020, although probably most of data is based on pre 2010. For more details about the source data please refer to the cited references below.

Bare rock and bare sand estimates are based on the following two sources of data:

1. Copernicus GLC land cover at 100-m for 2015 and 2019;
2. Local Climate zones map at 100-m for 2018;

Two classes are considered: (1) probability of occurrence of bare rock (bare.rock_glc.gfz_p), (2) probability of occurrence of bare sand i.e. shifting sand (bare.soil.sand_glc.gfz_p). We recommend using only the 1-km data for spatial modeling.

The time-series of bare areas (bare.areas_esa.cci_p) are based on the ESA CCI Land Cover time-series (2000–2022) 300-m resolution data; also available at 1-km resolution based on "average" resampling.

This is the biomarker (GDGT) data measured in a global set of peatlands. One dataset provides the average per peatland. The second dataset (individual peats) contains the data for each individual sample as for many peatlands we analyzed more than one sample.

Peatlands play a crucial role in the global carbon cycle. Sphagnum mosses (peat mosses) are considered to be the peatland ecosystem engineers and contribute to the carbon accumulation in the peatland ecosystems. As cold-adapted species, the dominance of Sphagnum mosses in peatlands will be threatened by climate warming. The response of Sphagnum mosses to climate change is closely related to the future trajectory of carbon fluxes in peatlands. However, the impact of climate change on the habitat suitability of Sphagnum mosses on a global scale is poorly understood. To predict the potential impact of climate change on the global distribution of Sphagnum mosses, we used the MaxEnt model to predict the potential geographic distribution of six Sphagnum species that dominate peatlands in the future (2050 and 2070) under two greenhouse gas emission scenarios (SSP1-2.6 and SSP5-8.5). The results show that the mean temperature of the coldest quarter, precipitation of the driest month, and topsoi...

The dataset representing a compilation of species lists from published literature on fungal diversity of peatlands globally. Totally 127 literature sources were found, digitized and information on fungal occurrences was extracted in the dataset. About 20% of the published works used cultivation technique (summing in about 1000 records in the dataset), while 80% represent direct observation of fruiting structures of larger fungi or micro-fungi (totally about 4700 records in the dataset). The table has 15 fields, including scientificName, habitat (vegetation type), occurrenceRemarks (substrate or other field notes), bibliographicCitation, eventDate, country, and locality. The taxonomic structure of fungal diversity represented by the dataset (after synonimization using GBIF species matching tool) includes 3 kingdoms (Fungi, Chromista, Protozoa), 7 phyla, 27 classes, 87 orders, 239 families, 616 genera and about 1500 species. The larger fungi represent about 80% of occurrences and 1100 species, while microfungi only about 400 species.

This database contains the basal ages from peatlands worldwide. See details at Yu et al. 2010.

This file contains carbon, nitrogen, organic matter and pH data from peat samples collected in 24 countries. Samples were collected between 2018 and 2020.

This data set provides a time series of global peatland carbon balance and carbon dioxide emissions from land use change throughout the Holocene (the past 11,000 yrs). Global peatland carbon balance was quantified using a) a continuous net carbon balance history throughout the Holocene derived from a data set of 64 dated peat cores, and b) global model simulations with the LPX-Bern model hindcasting the dynamics of past peatland distribution and carbon balance. CO2 emissions from land-use change are based on published scenarios for anthropogenic land use change (HYDE 3.1, HYDE 3.2, KK10) covering the last 10,000 years. This combination of model estimates with CO2 budget constraints narrows the range of past anthropogenic land use change emissions and their contribution to past carbon cycle changes.

This is the archaeal biomarker dataset for global peatlands. The first dataset contains biomarker data for each individual sample as for many peatlands we analysed more than one sample. The second dataset contains the averaged data for isoGDGT isomers in all sites that contains both associated pH and temperature measurements.

Peatlands cover 3% of the global land surface, yet store 25% of the world’s soil organic carbon. These organic-rich soils are widespread across permafrost regions, representing nearly 18% of land surface and storing between 500 and 600 petagrams of carbon (PgC). Peat (i.e., partially decomposed thick organic layers) accumulates due to the imbalance between plant production and decomposition often within saturated, nutrient deficient, and acidic soils, which limit decomposition. As warmer and drier conditions become more prevalent across northern ecosystems, the vulnerability of peatland soils may increase with the susceptibility of peat-fire ignitions, yet the distribution of peatlands across Alaska remains uncertain. Here we develop a new high-resolution (20 meter (m) resolution) wall-to-wall ~1.5 million square kilometer (km2) peatland map of Alaska, using a combination of Sentinel-1 (Dual-polarized Synthetic Aperture Radar), Sentinel-2 (Multi-Spectral Imager), and derivatives from the Arctic Digital Elevation Model (ArcticDEM). Machine learning classifiers were trained and tested using peat cores, ground observations, and sub-meter resolution image interpretation, which was spatially constrained by a peatland suitability model that described the extent of terrain suitable for peat accumulation. This product identifies peatlands in Polar, Boreal, and Maritime ecoregions in Alaska to cover 26,842 (4.6%), 69,783 (10.4%), and 13,506 (5.3%) km2, respectively.

This data set consists of 1 degree x 1 degree gridded monthly burned area, fuel loads, combustion completeness, and fire emissions of carbon (C), carbon dioxide (CO2), carbon monoxide (CO), methane (CH4), non-methane hydrocarbons (NMHC), molecular hydrogen (H2), nitrogen oxides (NOx), nitrous oxide (N2O), particulate matter (PM2.5), total particulate matter (TPM), total carbon (TC), organic carbon (OC), and black carbon (BC) for the time period January 1997 through December 2005. Emission estimates for the 2001-2005 period are also available with an 8-day time step. The data set was compiled using satellite data and the Carnegie-Ames-Stanford Approach (CASA) biogeochemical model. Burned area from 2001-2004 was derived from active fire and 500-m burned area data from MODIS (Giglio et al., 2006). ATSR (Along Track Scanning Radiometer) and VIRS (Visible and Infrared Scanner) satellite data were used to extend the burned area time series back to 1997 (Arino et al., 1999; Giglio et al., 2003; Van der Werf et al., 2004). Fuel loads and net flux from terrestrial ecosystems were estimated as the balance between net primary production, heterotrophic respiration, and biomass burning, using time varying inputs of precipitation, temperature, solar radiation, and satellite-derived fractional absorbed photosynthetically active radiation. Tropical and boreal peatland emissions were also considered, using a global wetland cover map (Matthews and Fung, 1987) to modify surface and belowground fuel availability. The data set also includes monthly estimates of the C4 fraction of carbon emissions that can be used to construct the 13C isotope ratio (Randerson et al., 2005).The data files are in space delimited ASCII format. For each subject (e.g., burned area, fuel loads, combustion completeness, or individual fire emission species), all monthly files for the 9-year period are combined in one zipped file. Similarly, the emission estimates with an 8-day time step for the 2001-2005 period are combined in one zipped file by subject.Additional information about the methodology, data format, and parameters measured is found in the companion file: ftp://daac.ornl.gov/data/global_vegetation/fire_emission_v2/comp/global_fire_emissions_v2_1_readme.pdf. Version 2.1 Note: This data set is intended for use for large-scale modeling studies. It supersedes and replaces the Global Fire Emissions Database, Version 2 (GFEDv2) which was archived by the Oak Ridge National Laboratory Distributed Active Archive Center in 2006.

This dataset is about: Peatland sites, a global review: catchment and climatic characteristics.

Peatland ecosystems are defined by soils with sufficient under-decomposed organic layer, called peat, formed under anoxic conditions. Peatlands are widespread around the world, with several highly paludified regions, one of which is the Western Siberian Plain. Peatlands store large amounts of carbon and are important in their intact state to counteract climate change, as well as for a variety of other ecosystem functions. From the practical aspect, these ecosystems are used as a source of peat for fuel, peat-based fertilisers and growing media, berries and Sphagnum plantations. Fungi are the key part of the decomposer community of peatlands, playing a critical role in the aerobic decomposition in the upper peat layer. The community of peatland fungi is adapted to decomposition of peat and dead parts of Sphagnum in wet acidic conditions; they form specific mycorrhizal associations with a variety of plants. Thus, the research of fungal diversity of peatlands is important for several reasons: 1) adding knowledge of peatland fungal diversity to local or global biodiversity databases; 2) studying carbon cycling in peatlands; 3) using peat and peatlands for different applications, such as cultivation of Sphagnum with regards to some parasitic species of fungi and 4) peatland restoration and conservation, to mention a few.The community of macromycetes of the raised bog “Mukhrino” in Western Siberia was studied using plot-based monitoring throughout a 9-year observation period. The revealed species diversity is represented by approximately 500 specimens in the Fungarium of Yugra State University collection. Selected specimens were used for barcoding of the ITS region to reveal a total of 95 species from 33 genera and three classes. The barcoding effort confirmed morphological identifications for most specimens and identified a number of cryptic species and several potentially new taxa. Based on regular all-season observations, we describe the phenology of the community sporophore production. The quantitative community structure, based on sporophores, revealed a difference in abundance between species by four orders of magnitude, with rare species representing nearly half of the species list. The inter-annual fruiting abundance varied several times by the total number of sporophores per year. To make the comparisons with global studies, we created an open access database of literature-based observations of fungi in peatlands, based on about 120 published papers (comprising about 1300 species) and compared our species list with this database.As a result, the study created an accurate representation of taxonomic and quantitative structure of the community of macromycetes in raised bogs in the region. The raw data of plot-based counts was published as a sampling-event dataset and the sequenced specimens with the sequence information as an DNA-derived extension dataset in GBIF.

This dataset contains all supporting data for the research paper "Microbial responses to changing plant community protect peatland carbon stores during Holocene drying", including:Plant macrofossils, hopane and fatty acid concentration, hopane and fatty acid carbon and hydrogen isotopes, bulk carbon isotope, peat organic matter composition, and carbon accumulation rate from the ZGT peat core (China).Compiled apparent carbon accumulation rate (aCAR) and long-term apparent rate of carbon accumulation (LORCA) data from 155 global peatlands.

The datasets archived here include simulation results shown in the peer-reviewed article “Tropical peatland hydrology simulated with a global land surface model“, published in the open access AGU Journal of Advances in Modeling Earth Systems (JAMES; Apers et al., 2022). The output was produced using the Catchment land surface model (CLSM), the land model component of the NASA Goddard Earth Observing System (GEOS) modeling framework, and various versions of peatland-specific adaptations of CLSM, i.e. PEATCLSM. Here, we provide netCDF files (*.nc or .nc4c) for CLSM, the natural (PEATCLSMTrop,Nat), and drained (PEATCLSMTrop,Drain) tropical versions of PEATCLSM. The simulations are at a 9-km spatial resolution (EASEv2 grid) for the three major tropical peatland regions in Central and South America, the Congo Basin, and Southeast Asia, using a peat grid cell distribution that is a combination of the PEATMAP distribution from Xu et al. (2018) and the peat distribution from De Lannoy et al. (2014). Simulations with the northern version of PEATCLSM (PEATCLSMNorth,Nat) are not included in the archived dataset but can be obtained upon request. We provide three types of netCDF files: • daily_images_.nc4c: daily land states and fluxes for variables discussed in Apers et al., (2022; Table 1), provided as netCDF image-chunked image stack; • daily_mean_*.nc: 20-year mean of the land states and fluxes (Table 1), provided as a single netCDF image; • daily_std_*.nc: 20-year standard deviation of the land states and fluxes (Table 1), provided as a single netCDF image.

The file content is described in the file PEATCLSM_Trop-Simulations.pdf.

Please contact Sebastian Apers (sebastian.apers@kuleuven.be) or Michel Bechtold (michel.bechtold@kuleuven.be) for any questions.

References: Apers, S., De Lannoy, G. J. M., Baird, A. J., Cobb, A. R., Dargie, G. C., del Aguila Pasquel, J., … others (2022). Tropical peatland hydrology simulated with a global land surface model. Journal of Advances in Modeling Earth Systems. https://doi.org/10.1029/2021MS002784 Bechtold, M., De Lannoy, G. J. M., Koster, R. D., Reichle, R. H., Mahanama, S. P., Bleuten, W., ... others (2019). PEAT-CLSM: A specific treatment of peatland hydrology in the NASA Catchment Land Surface Model. Journal of Advances in Modeling Earth Systems, 11(7), 2130–2162. https://doi.org/10.1029/2018MS001574 De Lannoy, G. J. M., Koster, R. D., Reichle, R. H., Mahanama, S. P. P., & Liu, Q. (2014). An updated treatment of soil texture and associated hydraulic properties in a global land modeling system. Journal of Advances in Modeling Earth Systems, 6(4), 957– 979. https://doi.org/10.1002/2014MS000330 Xu, J., Morris, P. J., Liu, J., & Holden, J. (2018). PEATMAP: Refining estimates of global peatland distribution based on a meta-analysis. Catena, 160, 134–140. https://doi.org/10.1016/j.catena.2017.09.010