This page summarises the datasets needed for a complete emission of Germany. The pages also give time estimates for updates (technical & scientific) and raise constraints the data currently faces.

Coding: GNFR (Gridded Nomenclature For Reporting) is an aggregated version of the NFR (Nomenclature For Reporting) used by individual country emission reporting to EMEP and EU. CRT is the Common Reporting Table

v2 - 2024 - based on the submission 2024

1. CO₂: Data-driven NEE prior estimates from data-driven statistical upscaling

This dataset contains estimates of the terrestrial biogenic CO2 exchange between the land surface and the atmosphere, called Net Ecosystem Exchange (NEE). NEE is defined negative when more CO2 is taken from the atmosphere into the land surface than the other way around. The NEE estimates are prior estimates in the atmospheric inversions in ITMS module M.

The NEE estimates here are based on a statistical upscaling approach that marries eddy-covariance measurements of in-situ NEE with satellite observations of land surface properties via machine learning. The data originate from the so-called X-BASE product version from the modelling framework FluxCom-X (Nelson* & Walther* et al. 2024 Biogeosciences Discussions, submitted). Satellite observations from the MODIS instruments provided in ITMS B deliverable 1.7.1 drive the simulations. The data are provided in netcdf format in separate files for each year between 2001 and 2021 for every hour and in 0.05deg resolution for the European domain (33.82- 70.18N, -21.3- 59.32E, according to agreements here). NEE is given in units of mumolCO2 m-2 s-1.

Included Gases: CO2

Time Extent: 2001 - 2021

Geographic Extent:

• A dataset containing Europe from 2001 - 2021 (NEE_Europe_XBASE_deliverableI_3_1_20**.nc)

• A dataset containing a cut out of Germany from 2017 - 2021 for comparisons with QS_I WP4 (NEE_Germany_XBASE_20**.nc)

• A test dataset ~430 MB (NEE_Germany_XBASE_2021growingSeason_smalltestfile_.nc)

Uncertainties: not yet available, will be implemented by QS_II TORCH

Included Sectors (CRT): Biogenic CO2, Net Ecosystem Exchange Rate (NEE)

Data Access: https://itmsgermany.atlassian.net/l/cp/DdYf5Do5

2. NIR data (UBA) from Greta gridding tool (submission 2024):

Greta documentation: https://iir.umweltbundesamt.de/2024/general/gridded_data/start
great version: 1.2.0.1

Included Gases: CH4, CO2, N2O

Included Sectors (NFR):

• 1. Energy

• 2. Industrial processes and product use

• 3. Agriculture (delivered by Thünen to UBA and included)

  • Agriculture Livestock

    • A Enteric fermentation (CH4)

    • B Manure Management (CH4, N2O)

  • Agriculture Other (Emission produced due to management)

    • C Rice cultivation (CH 4) - not relevant in Germany, no Rice cultivation

    • D Agricultural soils (N2O) - CO2 and CH4 is located in the LULUCF Dataset

    • E Prescribed burning of savannas (CH4, N2O) - not relevant in Germany, no prescribed burning, forest fire is included in LULUCF

    • F Field burning of agricultural residues (CH4, N2O) - not relevant in Germany; Field burning of agricultural residues

    • G Liming (CO2)

    • H Urea application (CO 2

    • I Other carbon-containing fertilizers (CO2)

    • J Other (CH4 , N2O)

• 4. Land use, Land-use change, and forestry

  • Agriculture

    • 4 A Forest

    • 4 B Cropland

    • 4 C Grazing Land

    • 4 D Wetlands (wet1, wet3, wet4, wet5)

    • 4 E Settlements

    • 4 F other Land

• 5. Waste and wastewater

Documentation Sector 1, 2, 5: https://itmsgermany.atlassian.net/l/cp/dF5TWoN0

Documentation Sector 4: https://itmsgermany.atlassian.net/l/cp/vuPL1aRp

Data Access: https://itmsgermany.atlassian.net/l/cp/dF5TWoN0

Remarks CRT 4 Land use, Land-use change, and forestry & CRT 3 Agriculture
(delivered by Thünen to UBA and included)

Modifications of the ITMS LULUCF Dataset in comparison to the NIR

• Forest fire emissions are distributed over the federal states

• Artificial water emissions are georeferenced (in contrast to submission 2023)

• Natural inland water bodies are calculated by rough emission factor and distributed over all german waterbodies (river and lakes). Important: originally not reported in NIR, for ITMS only

• Emissions from / sequestration in harvested wood products are distributed over Germany

• A rough estimate of (unreported in NIR) emissions from natural inland waters is distributed over Germany

• The spatial distribution of carbon sequestration in forest biomass has a systematic error. Forest data should, therefore, only be used at national aggregation.

Special Remarks of the Submission 2024:

• All time series are recalculated for each submission. This ensures methodological consistency within the time series but needs to be considered for further use!

• Emission of agricultural soils: According to the CRF tables, CO2 and CH4 emissions and N2O emissions from mineral grassland soils are reported in CRT sector 4 (LULUCF), and N2O from mineral cropland soils and organic cropland and grassland soils in CRT sector 3 (Agriculture)

Inland Waters (4 D Wetlands - wet2) - Natural water bodies and watercourses.

Inland Waters are one of the considerable uncertainties in detecting GHG emissions on a spatial and temporal scale. Recent estimates from the NIR reporting 2023 for artificial waters show relatively high methane emissions. This is in concordance with recent literature. Inland waters, comprising lotic (streams, rivers, and canals) and lentic (reservoirs, lakes, and ponds) ecosystems, are increasingly recognized as significant sources of greenhouse gases (GHG), contributing (~25 %; 13.5 (9.9–20.1) Pg CO2-eq) of the global 39 CO2-equivalent emissions (Lauerwald et al., 2023). In comparison, lotic ecosystems 40 contribute disproportionately higher CO2-eq emissions (>60%) than lentic ecosystems, linked to their close connectivity with terrestrial landscapes and their flowing and turbulent nature that favours gas evasion to the atmosphere (Raymond et al., 2013; Rocher-Ros et al., 2023; 43 Yao et al., 2020).

Inland Waters will be covered by side projects of the upcoming project CoastGEm and WP4, but currently, no complete gridded datasets from ITMS are available.

1. Natural inland water bodies are now calculated by rough emission factor and distributed over all German waterbodies (rivers and lakes).

Important: originally not reported in NIR, for ITMS only

2. Rivers: Rocher-Ros 2023 - Methane emissions of rivers (monthly):

From the Publication Gerard Rocher-Ros et al. 2023 "Global Methane emissions from running waters," (https://doi.org/10.1038/s41586-023-06344-6 ).
Methane emissions of rivers can be derived as gridded data for yearly and monthly data at 0.25 degrees or approximately 27 km. The Methanee emission is provided in Mega grams of C-CH4 (for each pixel). Recalculation of x CH4 to y C-Ch4 is performed by “y = x/12*(12+4)”.
The data of the dataset is Based on information from the Global River Methane Database GRiMeDB: https://essd.copernicus.org/articles/15/2879/2023/essd-15-2879-2023.pdf (1973 - 2021), the River Width and River Location by Global Hydrodynamics Lab (Yamazaki Lab - Home )
The NetCDF for this dataset is in-kind provided by Module Q&S:
River methane yearly monthly - Ros et al 2023

v1 - 2023

1. UBA NIR data (UBA) from Greta gridding tool (submission 2023):

For a detailed description, see: https://itmsgermany.atlassian.net/l/cp/XKkj0G3x

For the last Data Deliverable, including TestData here: https://itmsgermany.atlassian.net/l/cp/910LtHE5

Coding: GNFR, NFR, SNAP

Included Gases: CH4, CO2, N2O

Time Extent: 1990 to submission year -2 (Submission 2023: 1990-2021)

Uncertainties: not in the dataset, available in NIR, the yearly number for the entire Germany

Included Sectors (NFR):

• 1. Energy (CO2, CH4, N2O)

• 2. Industrial processes and product use (CO2, CH4, N2O)

• 3. Agriculture (delivered by Thünen to UBA and included)

  • Agriculture Livestock

    • A Enteric fermentation (CH4)

    • B Manure Management (CH4, N2O)

  • Agriculture Other (Emission produced due to management) D, F & I

  • C Rice cultivation (CH4) - not relevant in Germany, no Rive cultivation

  • D Agricultural soils (N2O) - (CO2 and CH4 are located in the LULUCF Dataset - 4B)

  • E Prescribed burning of savannas (CH4, N2O) - not relevant in Germany, no prescribed burning, forest fire is included in LULUCF

  • F Field burning of agricultural residues (CH4, N2O) - not relevant in Germany; Field burning of agricultural residues

  • G Liming (CO2)

  • H Urea application (CO2)

  • I Other carbon-containing fertilizers (CO2)

  • J Other (CH4 , N2O)

• 5. Waste and wastewater (CH4 , N2O)

Constrains/Planned Improvements:

• Yearly Emissions (will be improved to monthly/weekly during ITMS)

2. Thünen LULUCF Database (submission 2023)

For a detailed description, see https://itmsgermany.atlassian.net/l/cp/hc43Bmsg

Included Gases: CH4, CO2, N2O

Coding: GNFR

Included Gases: CH4, CO2, N2O

Time Extent: 1990 - year -2 (Submission 2023: 1990-2021)

Uncertainties: not in the dataset, available in NIR, the yearly number for the entire Germany

Included Sectors (CRT):

• 4. Land use, Land-use change and forestry

  • Agriculture

    • 4 A Forest

    • 4 B Cropland

    • 4 C Grazing Land

    • 4 D Wetlands (wet1, wet3, wet4, wet5)

    • 4 E Settlements

    • 4 F other Land

Constraints:

• All time series are recalculated for each submission. This ensures methodological consistency within the time series but needs to be considered for further use!

• Emission of agricultural soils: According to the CRF tables, CO2 and CH4 emissions and N2O emissions from mineral grassland soils are reported in CRT sector 4 (LULUCF), and N2O from mineral cropland soils and organic cropland and grassland soils in CRT sector 3 (Agriculture)

• some emissions without a direct georeference are currently not included in the spatial dataset:

  • Forest fire (forest fires have a minor contribution to Methane and will be neglected in the first inversion experiment)

  • harvested wood products

  • emissions from peat products

• Inland water "natural" emissions are not included.
  However, as many artificial waterbodies (e.g., fishponds) have no georeference, they are included in the inland waters.
  --> In wet2 (natural waterbodies), emissions of ponds are included.

• The spatial distribution of carbon sequestration in forest biomass has a systematic error. Forest data should, therefore, only be used at national aggregation.

• Sinks, only CO2 & N2O sinks are included. No Methane sinks are calculated and reported.

3. Inland Waters (4 D Wetlands - wet2) - Natural water bodies and watercourses.

Inland Waters are one of the considerable uncertainties in detecting GHG emissions on a spatial and temporal scale.
Recent estimates from the NIR reporting 2023 for artificial waters show relatively high methane emissions. This is in concordance with recent literature. Inland waters, comprising lotic (streams, rivers, and canals) and lentic (reservoirs, lakes, and ponds) ecosystems, are increasingly recognized as significant sources of greenhouse gases (GHG), contributing (~25 %; 13.5 (9.9–20.1) Pg CO2-eq) of the global 39 CO2-equivalent emissions (Lauerwald et al., 2023). In comparison, lotic ecosystems 40 contribute disproportionately higher CO2-eq emissions (>60%) than lentic ecosystems, linked to their close connectivity with terrestrial landscapes and their flowing and turbulent nature that favours gas evasion to the atmosphere (Raymond et al., 2013; Rocher-Ros et al., 2023; 43 Yao et al., 2020).

Inland Waters will be covered by side projects of the upcoming project CoastGem and WP4, but currently, no complete gridded datasets from ITMS are available.

• 1) Standing waters (e.g Lakes) Usage of Thünen preliminary estimate for lakes
  The current solution provides preliminary Methane emission estimates by the Thünen Institute for inland waters based on the estimates for artificial waterbodies (fishponds, Stauhaltungen) https://itmsgermany.atlassian.net/l/cp/PYYYmQ8f . This preliminary estimate can be spread over lakes and used as an a-priori estimate.
  Georeference:

  • ATKIS Basis DLM

  • wet2 from LULUCF Dataset (not working as wet2 includes running waters and lakes)
    
    

• 2) Rivers: Rocher-Ros 2023 - Methane emissions of rivers (monthly):
  From the Publication Gerard Rocher-Ros et al. 2023 "Global Methane emissions from running waters," (https://doi.org/10.1038/s41586-023-06344-6 ).
  Methane emissions of rivers can be derived as gridded data for yearly and monthly data at 0.25 degrees or approximately 27 km. The Methanee emission is provided in Mega grams of C-CH4 (for each pixel). Recalculation of x CH4 to y C-Ch4 is performed by “y = x/12*(12+4)”.
  The data of the dataset is Based on information from the Global River Methane Database GRiMeDB: https://essd.copernicus.org/articles/15/2879/2023/essd-15-2879-2023.pdf (1973 - 2021), the River Width and River Location by Global Hydrodynamics Lab (Yamazaki Lab - Home )
  The NetCDF for this dataset is in-kind provided by Module Q&S:
  River methane yearly monthly - Ros et al 2023

Go back to ITMS Module Q&S or ITMS Evaluation Report_Home