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zenodo40/100

Data and R-scripts for estimating carbon dioxide emissions from drained peatland forest soils for the greenhouse gas inventory of Finland

<p><strong>&nbsp;Introduction</strong></p> <p>A new method for estimating carbon dioxide emissions from rained peatland forest soils was developed for the Greenhouse Gas Inventory of Finland (GHG inventory). The method is based on a set of models (Ojanen et al. 2014, Tuomi et al., 2009) that dynamically compile all relevant carbon inputs and outputs into a time series of soil CO<sub>2</sub> emission. A complete description of the method is described in Alm et al. (2023). Here we present the input data and R-scripts (R Core Team, 2020) for computing the time series from year 1990 to 2022 of CO<sub>2</sub> emission from soil in forest land on drained organic soil, like it was reported by the Finnish GHG inventory (Statistics Finland, 2023).</p> <p><strong>Time series data </strong></p> <p>The source of forest and area data is the Finnish National Forest Inventory (NFI) as a part of Luke Statutory Services. The NFI standing forest data in the data files includes annual country-wide estimates of mean basal area and standing biomass of Scots pine (<em>Pinus sylvestris</em> L.), Norway spruce (Picea abies (L.) H. Karst) and all the broadleaved forest trees combined. The data concerns forest land on drained organic soil only (class FRA 1 according to the FAO forest land definition).</p> <p>The NFI data for each year has been averaged by different drained peatland forest site types (FTYPE) and by inventory regions of southern and northern Finland. The areas and proportions of FTYPEs of all drained peatland &ldquo;forests remaining forests&rdquo; (i.e., forests that have not undergone another change in land use in the past 20 years) in southern and northern Finland (Alm et al., 2023), derived from NFI12 (2014&ndash;2018).</p> <p>Annual litter input from harvest residues was estimated using statistics of harvested stem volumes by species, collected and published by Luke (Luke statistics). The stem volumes were converted to whole trees and further to litter fractions and further to The share of residues remaining in forest is estimated by subtracting the amount of the logging residues collected for energy use, the data obtained from Luke statistics/energy. The biomass of live trees, annual litterfall from live trees aboveground and root litter belowground are derived from the National Forest Inventory of Finland (inventory rounds NFI8 to NFI13). The R-code also includes calculation of annual litter production from the harvesting residues.</p> <p>The regression-based transfer models, implemented in the R-code, also need meteorological time series inputs: The soil organic matter decomposition model (Ojanen et al. 2014) uses May-October mean temperature. Decomposition model yasso07 (Tuomi et al., 2009), applied for estimating the CO<sub>2</sub> release by decomposition of harvesting residues and above ground litter from natural mortality, is constrained by annual temperature, annual temperature amplitude and annual precipitation. Starting from the original country-wide grid produced by the Finnish Meteorological Institute (FMI) the weather time series were spatially averaged so that the FMI weather grid values were collected from those locations where peatlands representing each FTYPE in southern and northern Finland were observed by the NFI, respectively.</p> <p>The pre-prepared input data are given in files, see Table 1 for descriptions.</p> <p>&nbsp;</p> <p>&nbsp;</p> <p>Table 1. Description of input data files.</p> <table> <tbody> <tr> <td> <p><strong>File</strong></p> </td> <td> <p><strong>Description of data</strong></p> </td> </tr> <tr> <td> <p>basal.areas.csv</p> </td> <td> <p>Time series of years 1990-2022 for annual average basal area (m<sup>2</sup> ha<sup>-1</sup>) by year, by peatland forest site type (peat_type) and by tree species or group (tree_type).</p> <p>&nbsp;</p> <p>Values of peat_type correspond to FTYPE:</p> <p>1&nbsp; Herb-rich type</p> <p>2&nbsp; <em>Vaccinium myrtillus</em> type</p> <p>4&nbsp; <em>Vaccinium vitis-idaea</em> type</p> <p>6&nbsp; Dwarf shrub type</p> <p>7&nbsp; <em>Cladina</em> type</p> <p>&nbsp;</p> <p>Values of tree species or group correspond to:</p> <p>1&nbsp; Scots pine</p> <p>2&nbsp; Norway spruce</p> <p>3&nbsp; Broadleaved species</p> </td> </tr> <tr> <td> <p>biomass.csv</p> </td> <td> <p>Time series of years 1990-2022 for annual biomass (biomass, t ha<sup>-1</sup> of dry mass) by year, by biomass component, by tree species and by peatland forest site type (tkg).</p> <p>&nbsp;</p> <p>Values of peat_type correspond to FTYPE:</p> <p>1&nbsp; Herb-rich type</p> <p>2&nbsp; <em>Vaccinium myrtillus</em> type</p> <p>4&nbsp; <em>Vaccinium vitis-idaea</em> type</p> <p>6&nbsp; Dwarf shrub type</p> <p>7&nbsp; <em>Cladina</em> type</p> <p>&nbsp;</p> </td> </tr> <tr> <td> <p>dead_litter.csv</p> </td> <td> <p>Time series of years 1990-2022 of annual aboveground litter from dead wood: Harvesting residues and natural mortality combined (C, t ha<sup>-1</sup> of dry mass; lognat_litter).</p> <p>&nbsp;</p> <p>Values of region correspond to GHG inventory region:</p> <p>south&nbsp; South Finland</p> <p>north&nbsp; North Finland</p> </td> </tr> <tr> <td> <p>ghgi_litter.csv</p> </td> <td> <p>Time series of years 1990-2022 for litter AWEN-fractions (A=acid soluble, W=water soluble, E=ethanol soluble, N=non-soluble; C, t ha<sup>-1</sup>) by different litter types: Above-ground coarse woody litter (coarse_woody_litter), fine woody litter (fine_woody_litter), non-woody litter (non_woody_litter) by litter source and deposition type by region. &ldquo;org&rdquo; denotes organic soil.</p> <p>&nbsp;</p> <p>Values of region correspond to GHG inventory region:</p> <p>south&nbsp; South Finland</p> <p>north&nbsp; North Finland</p> <p>&nbsp;</p> <p>Values of ground correspond to litter deposition environment:</p> <p>above&nbsp; Above-ground litter</p> <p>below&nbsp; Below-ground litter</p> </td> </tr> <tr> <td> <p>lognat_decomp.csv</p> </td> <td> <p>Time series of years 1990-2022 for C, t ha<sup>-1</sup> of dry mass, decomposed from logging residues and natural mortality by region.</p> <p>&nbsp;</p> <p>Values of variable &ldquo;region&rdquo; correspond to GHG inventory region:</p> <p>south&nbsp; South Finland</p> <p>north&nbsp; North Finland</p> </td> </tr> <tr> <td> <p>logyasso_weather_data.csv</p> </td> <td> <p>Time series of years 1990-2022 for regional (region) precipitation sum (mm, sum_P), average annual temperature (&deg;C, mean_T) and amplitude of the annual temperature (&deg;C , ampli_T).</p> <p>&nbsp;</p> <p>Values of region correspond to GHG inventory region:</p> <p>south&nbsp; South Finland</p> <p>north&nbsp; North Finland</p> <p>&nbsp;</p> </td> </tr> <tr> <td> <p>total_area.csv</p> </td> <td> <p>Areas (ha) of drained peatland forests remaining forest land by region and peat_type.</p> <p>&nbsp;</p> <p>Values of variable &ldquo;region&rdquo; correspond to GHG inventory region:</p> <p>south&nbsp; South Finland</p> <p>north&nbsp; North Finland</p> <p>&nbsp;</p> <p>Values of peat_type correspond to FTYPE:</p> <p>1&nbsp; Herb-rich type</p> <p>2&nbsp; <em>Vaccinium myrtillus</em> type</p> <p>4&nbsp; <em>Vaccinium vitis-idaea</em> type</p> <p>6&nbsp; Dwarf shrub type</p> <p>7&nbsp; <em>Cladina</em> type</p> <p>&nbsp;</p> </td> </tr> <tr> <td> <p>weather_data.csv</p> </td> <td> <p>Time series of years 1990-2022 for 30-year rolling mean temperature for the May-October period (roll_T) used by the soil decomposition models. The values are calculated for each FTYPE (peat_type) using their spatial distributions (see details in Alm et al., 2023).</p> <p>&nbsp;</p> <p>Values of variable &ldquo;region&rdquo; correspond to GHG inventory region:</p> <p>south&nbsp; South Finland</p> <p>north&nbsp; North Finland</p> <p>&nbsp;</p> <p>Values of peat_type correspond to FTYPE:</p> <p>1&nbsp; Herb-rich type</p> <p>2&nbsp; <em>Vaccinium myrtillus</em> type</p> <p>4&nbsp; <em>Vaccinium vitis-idaea</em> type</p> <p>6&nbsp; Dwarf shrub type</p> <p>7&nbsp; <em>Cladina</em> type</p> <p>&nbsp;</p> </td> </tr> </tbody> </table> <p>&nbsp;</p> <p><strong>The R-scripts</strong></p> <p>The scripts are an excerpt from the Finnish greenhouse gas inventory code set, applying the necessary pre-processed input data and producing the soil CO<sub>2</sub> emissions for each FTYPE separately. The necessary R-packages (R Core Team, 2020) are managed in the script LIBRARIES.R.</p> <p>Guidance for running the R-scripts is given in the README.txt.</p> <p><strong>References</strong></p> <p>Alm, J., Wall, A., Myllykangas, J-P., Ojanen, P., Heikkinen, J., Henttonen, H. M., Laiho, R., Minkkinen, K., Tuomainen, T. and Mikola, J. A new method for estimating carbon dioxide emissions from drained peatland forest soils for the greenhouse gas inventory of Finland. Biogeosciences https://doi.org/10.5194/bg-20-1-2023, 2023.</p> <p>LUKE Statistics</p> <ul> <li>https://www.luke.fi/en/statistics/total-roundwood-removals-and-drain, last access 8.12.2022.</li> </ul> <ul> <li>https://www.luke.fi/en/statistics/commercial-fellings/commercial-fellings-72023. last access 8.12.2022.</li> </ul> <p>Statistics Finland 2023. URL: https://unfccc.int/documents/627718 (last access 13.9.2023).</p> <p>Ojanen, P., Lehtonen, A., Heikkinen, J., Penttil&auml;, T., and Minkkinen, K.: Soil CO2 balance and its uncertainty in forestry drained peatlands in Finland, Forest Ecol. Manage., 325, 60&ndash;73, 2014.</p> <p>R Core Team: R: A language and environment for statistical computing. R Foundation forStatistical Computing, Vienna, Austria, URL https://www.R-project.org, 2020.</p> <p>Tuomi, M., Thum, T., J&auml;rvinen, H., Fronzek, S., Berg, B., Harmon, M., Trofymow, J.A., Sevanto, S. and Liski, J.: Leaf litter decomposition - Estimates of global variability based on Yasso07 model, Ecol. Modell. 220 (23):3362-3371, 2009.</p>

opencc-by-4.0Sep 2023View details →
zenodo40/100

Direct evidence for atmospheric carbon dioxide removal via enhanced weathering in cropland soil: Supporting data

<p>Datasets (climate, alkalinity, moisture sensors) associated with the manuscript "Direct evidence for atmospheric carbon dioxide removal via enhanced weathering in cropland soil."</p>

opencc-by-4.0Oct 2023View details →
dryad40/100

Data from: Soil microbes influence the ecology and evolution of plant plasticity

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publicDec 2024View details →
dryad40/100

Data and code from: Soil decomposer can regulate the legacy effect of photodegradation on forest marcescent litter decomposition, but emerging microplastics disrupt this

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publicJan 2025View details →
dryad40/100

Data for: The interactive effects of soil fertility and tree mycorrhizal association explain spatial variation of diversity-biomass relationships in a subtropical forest

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publicJan 2023View details →
dryad40/100

Data from: Fungal energy channeling sustains soil animal communities across forest types and regions

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publicApr 2025View details →
dryad40/100

Data from: Temporal variation of soil microarthropods in different forest types and regions of Central Europe

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publicJul 2024View details →
dryad40/100

Data from: Controlled drainage and subirrigation suitability in the United States: A meta-analysis of crop yield and soil moisture effects

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publicJul 2022View details →
dryad40/100

Data from: Long-term changes in soil carbon and nitrogen fractions in switchgrass, native grasses, and no-till corn bioenergy production systems

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publicFeb 2025View details →
dryad40/100

Data from: Specific leaf area is lower on ultramafic than on neighbouring non-ultramafic soils

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publicDec 2022View details →
dryad40/100

Data from: Ectomycorrhizal fungi are more sensitive to high soil nitrogen levels in forests exposed to nitrogen deposition

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publicJan 2024View details →
dryad40/100

Data from: Large, climate-sensitive soil carbon stocks mapped with pedology-informed machine learning in the North Pacific coastal temperate rainforest

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publicOct 2024View details →
dryad40/100

Data collected for: The contrasted impacts of grasshoppers on soil microbial activities in function of ecosystem productivity and herbivore diet

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publicJul 2022View details →
dryad40/100

Data from: Fecal biomarkers in soils record landscape-scale wild herbivore abundance

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publicOct 2025View details →
dryad40/100

Data for: Soil legacy effects of plants and drought on aboveground insects in native and range-expanding plant communities

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publicOct 2022View details →
dryad40/100

Raw data of the microarthropods and decomposition from: Effects of microarthropod density on soil fungal community composition in nutrient-poor ecosystems

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publicDec 2024View details →
dryad40/100

Data from: Soil organic carbon stability in forests: distinct effects of tree species identity and traits

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publicJan 2019View details →
dryad40/100

Data from: Reduced snow cover increases wintertime nitrous oxide (N2O) emissions from an agricultural soil in the upper U.S. Midwest

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publicNov 2019View details →
dryad40/100

Data from: The effect of drainage on the fine root biomass, production, and turnover in hemiboreal old-growth forests on organic soils

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publicFeb 2024View details →
dryad40/100

Data from: Differential pulse sensitivity of nitric and nitrous oxide emissions to temperature, carbon, and nitrogen following wetting of desert soils

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publicNov 2025View details →

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