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2017-09 |
Maps of preservation capacity of cultural artefacts and buried materials in soils in the EU: Strati |
Maps (2016) that indicate the preservation capacity of cultural artefacts and buried materials in soils in the EU, for bones, teeth and shells (bones), organic materials (organics), metals (Cu,...
Full Abstract
Maps (2016) that indicate the preservation capacity of cultural artefacts and buried materials in soils in the EU, for bones, teeth and shells (bones), organic materials (organics), metals (Cu, bronze and Fe) (metals), stratigraphic evidence (strati).
This is the Strati layer.
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2017-09 |
Maps of preservation capacity of cultural artefacts and buried materials in soils in the EU: Bones |
Maps (2016) that indicate the preservation capacity of cultural artefacts and buried materials in soils in the EU, for bones, teeth and shells (bones), organic materials (organics), metals (Cu,...
Full Abstract
Maps (2016) that indicate the preservation capacity of cultural artefacts and buried materials in soils in the EU, for bones, teeth and shells (bones), organic materials (organics), metals (Cu, bronze and Fe) (metals), stratigraphic evidence (strati).
This is the Bones layer
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2017-09 |
Maps of preservation capacity of cultural artefacts and buried materials in soils in the EU: Metals |
Maps (2016) that indicate the preservation capacity of cultural artefacts and buried materials in soils in the EU, for bones, teeth and shells (bones), organic materials (organics), metals (Cu,...
Full Abstract
Maps (2016) that indicate the preservation capacity of cultural artefacts and buried materials in soils in the EU, for bones, teeth and shells (bones), organic materials (organics), metals (Cu, bronze and Fe) (metals), stratigraphic evidence (strati).
This is the Metals layer.
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2017-09 |
Maps of preservation capacity of cultural artefacts and buried materials in soils in the EU: Organics |
Maps (2016) that indicate the preservation capacity of cultural artefacts and buried materials in soils in the EU, for bones, teeth and shells (bones), organic materials (organics), metals (Cu,...
Full Abstract
Maps (2016) that indicate the preservation capacity of cultural artefacts and buried materials in soils in the EU, for bones, teeth and shells (bones), organic materials (organics), metals (Cu, bronze and Fe) (metals), stratigraphic evidence (strati).
This layer is for organic materials
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2017-08 |
N2O emissions from agricultural soils in Europe: MT2 model |
This dataset derives from the integration of the LUCAS soil survey with the bio-geochemistry process-based model DayCent. The model was ran for more than 11,000 LUCAS sampling points under...
Full Abstract
This dataset derives from the integration of the LUCAS soil survey with the bio-geochemistry process-based model DayCent. The model was ran for more than 11,000 LUCAS sampling points under agricultural use, assessing also the model uncertainty. Meta-models based on model outcomes and the Random Forest algorithm were used to upscale the N2O emissions at 1km resolution.
MT2 meta-model
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2017-08 |
N2O emissions from agricultural soils in Europe: MT1 model |
This dataset derives from the integration of the LUCAS soil survey with the bio-geochemistry process-based model DayCent. The model was ran for more than 11,000 LUCAS sampling points under...
Full Abstract
This dataset derives from the integration of the LUCAS soil survey with the bio-geochemistry process-based model DayCent. The model was ran for more than 11,000 LUCAS sampling points under agricultural use, assessing also the model uncertainty. Meta-models based on model outcomes and the Random Forest algorithm were used to upscale the N2O emissions at 1km resolution.
MT1 meta-model
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2017-08 |
Soil Biomass Productivity maps of grasslands and pasture, of croplands and of forest areas in the European Union (EU27): croplands |
This dataset consists of 3 GIS maps that indicate the soil biomass productivity of grasslands and pasture, of croplands and of forest areas in the European Union (EU27): this layer displays Soil...
Full Abstract
This dataset consists of 3 GIS maps that indicate the soil biomass productivity of grasslands and pasture, of croplands and of forest areas in the European Union (EU27): this layer displays Soil biomass productivity of croplands, values normalized to a range [0..10]
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2017-07 |
Potential threats to soil biodiversity in Europe: Biological Functions |
Three major components of soil biodiversity are assesed: a) soil microorganisms, b) fauna, and c) biological functions. The maps were developed based on 13 potential threats to soil biodiversity...
Full Abstract
Three major components of soil biodiversity are assesed: a) soil microorganisms, b) fauna, and c) biological functions. The maps were developed based on 13 potential threats to soil biodiversity which were proposed to experts with different backgrounds in order to assess biodiversity threat.
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2017-07 |
Potential threats to soil biodiversity in Europe: Fauna |
Three major components of soil biodiversity are assesed: a) soil microorganisms, b) fauna, and c) biological functions. The maps were developed based on 13 potential threats to soil biodiversity...
Full Abstract
Three major components of soil biodiversity are assesed: a) soil microorganisms, b) fauna, and c) biological functions. The maps were developed based on 13 potential threats to soil biodiversity which were proposed to experts with different backgrounds in order to assess biodiversity threat.
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2017-07 |
Potential threats to soil biodiversity in Europe: Microorganisms |
Three major components of soil biodiversity are assessed: a) soil microorganisms, b) fauna, and c) biological functions. The maps were developed based on 13 potential threats to soil biodiversity...
Full Abstract
Three major components of soil biodiversity are assessed: a) soil microorganisms, b) fauna, and c) biological functions. The maps were developed based on 13 potential threats to soil biodiversity which were proposed to experts with different backgrounds in order to assess biodiversity threat.
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2017-05 |
Dominant Soil Typological Unit (STU), WRBFU |
Full soil code of the STU from the World Reference Base for Soil Resources
Full Abstract
Full soil code of the STU from the World Reference Base for Soil Resources
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2017-05 |
Dominant Soil Typological Unit (STU), WRBLV1 |
Soil Reference Group code of the STU from the World Reference Base for Soil Resources.
Full Abstract
Soil Reference Group code of the STU from the World Reference Base for Soil Resources.
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2017-01 |
Global Rainfall Erosivity |
Rainfall erosivity dataset (2017) is one of the input layers when calculating the Revised Universal Soil Loss Equation (RUSLE) model, which is the most frequently used model for soil erosion risk...
Full Abstract
Rainfall erosivity dataset (2017) is one of the input layers when calculating the Revised Universal Soil Loss Equation (RUSLE) model, which is the most frequently used model for soil erosion risk estimation; for the whole World; R-factor map at resolutions of 30 arc-sec ((~1 km at the Equator).
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2016-01 |
Maps of the Storing and Filtering Capacity of Soils in Europe: non-polar organic chemicals filtering capacity (FILT_CAPNP) |
This dataset (2016) contains 10 maps that relate to the soil's storing and filtering capacity in Europe (the EU only): cation storing capacity (STOR_CAPCA), cation filtering capacity (FILT_CAPCA...
Full Abstract
This dataset (2016) contains 10 maps that relate to the soil's storing and filtering capacity in Europe (the EU only): cation storing capacity (STOR_CAPCA), cation filtering capacity (FILT_CAPCA), anion storing capacity (STOR_CAPAN), anion filtering capacity (FILT_CAPAN), solids and pathogenic microorganisms storing capacity (STOR_CAPSO), solids and pathogenic microorganisms filtering capacity (FILT_CAPSO), non-polar organic chemicals storing capacity (STOR_CAPNP), non-polar organic chemicals filtering capacity (FILT_CAPNP), nonaqueous Phase Liquids (NAPL) storing capacity (STOR_NAPL), nonaqueous Phase Liquids (NAPL), filtering capacity (FILT_NAPL). As input, variables from the European Soil Database have been used.
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2016-01 |
Maps of the Storing and Filtering Capacity of Soils in Europe: anion filtering capacity (FILT_CAPAN) |
This dataset (2016) contains 10 maps that relate to the soil's storing and filtering capacity in Europe (the EU only): cation storing capacity (STOR_CAPCA), cation filtering capacity (FILT_CAPCA...
Full Abstract
This dataset (2016) contains 10 maps that relate to the soil's storing and filtering capacity in Europe (the EU only): cation storing capacity (STOR_CAPCA), cation filtering capacity (FILT_CAPCA), anion storing capacity (STOR_CAPAN), anion filtering capacity (FILT_CAPAN), solids and pathogenic microorganisms storing capacity (STOR_CAPSO), solids and pathogenic microorganisms filtering capacity (FILT_CAPSO), non-polar organic chemicals storing capacity (STOR_CAPNP), non-polar organic chemicals filtering capacity (FILT_CAPNP), nonaqueous Phase Liquids (NAPL) storing capacity (STOR_NAPL), nonaqueous Phase Liquids (NAPL), filtering capacity (FILT_NAPL). As input, variables from the European Soil Database have been used.
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2016-01 |
Maps of the Storing and Filtering Capacity of Soils in Europe: nonaqueous Phase Liquids (NAPL) storing capacity (STOR_NAPL) |
This dataset (2016) contains 10 maps that relate to the soil's storing and filtering capacity in Europe (the EU only): cation storing capacity (STOR_CAPCA), cation filtering capacity (FILT_CAPCA...
Full Abstract
This dataset (2016) contains 10 maps that relate to the soil's storing and filtering capacity in Europe (the EU only): cation storing capacity (STOR_CAPCA), cation filtering capacity (FILT_CAPCA), anion storing capacity (STOR_CAPAN), anion filtering capacity (FILT_CAPAN), solids and pathogenic microorganisms storing capacity (STOR_CAPSO), solids and pathogenic microorganisms filtering capacity (FILT_CAPSO), non-polar organic chemicals storing capacity (STOR_CAPNP), non-polar organic chemicals filtering capacity (FILT_CAPNP), nonaqueous Phase Liquids (NAPL) storing capacity (STOR_NAPL), nonaqueous Phase Liquids (NAPL), filtering capacity (FILT_NAPL). As input, variables from the European Soil Database have been used.
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2016-01 |
Maps of the Storing and Filtering Capacity of Soils in Europe: solids and pathogenic microorganisms filtering capacity (FILT_CAPSO) |
This dataset (2016) contains 10 maps that relate to the soil's storing and filtering capacity in Europe (the EU only): cation storing capacity (STOR_CAPCA), cation filtering capacity (FILT_CAPCA...
Full Abstract
This dataset (2016) contains 10 maps that relate to the soil's storing and filtering capacity in Europe (the EU only): cation storing capacity (STOR_CAPCA), cation filtering capacity (FILT_CAPCA), anion storing capacity (STOR_CAPAN), anion filtering capacity (FILT_CAPAN), solids and pathogenic microorganisms storing capacity (STOR_CAPSO), solids and pathogenic microorganisms filtering capacity (FILT_CAPSO), non-polar organic chemicals storing capacity (STOR_CAPNP), non-polar organic chemicals filtering capacity (FILT_CAPNP), nonaqueous Phase Liquids (NAPL) storing capacity (STOR_NAPL), nonaqueous Phase Liquids (NAPL), filtering capacity (FILT_NAPL). As input, variables from the European Soil Database have been used.
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2016-01 |
European map of soil suitability to provide a platform for most human activities (EU28) |
This dataset (map)(2016) presents the suitability of soil as a platform for most human activities in the EU. Calculation of suitability was done using vaious properties of the European Soil database...
Full Abstract
This dataset (map)(2016) presents the suitability of soil as a platform for most human activities in the EU. Calculation of suitability was done using vaious properties of the European Soil database (soil type, soil water regime, limitation to agricultural use, depth to rock, land use) and slope of the terrain.
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2016-01 |
Predicted distribution of SOC content in Europe (based on LUCAS, BioSoil and CZO) in the context of the EU-funded SoilTrEC project: SOC LUCAS only |
These maps of predicted distribution of SOC content in Europe (2016) are based on aggregated 23,835 soil samples collected from the LUCAS Project (samples from agricultural soil), BioSoil Project (...
Full Abstract
These maps of predicted distribution of SOC content in Europe (2016) are based on aggregated 23,835 soil samples collected from the LUCAS Project (samples from agricultural soil), BioSoil Project (samples from forest soil), and Soil Transformations in European Catchments (SoilTrEC) Project (samples from local soil data coming from five different critical zone observatories (CZOs) in Europe).
This layer is SOC LUCAS only.
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2016-01 |
Maps of indicators of soil hydraulic properties for Europe: water content at wilting point |
Soil hydraulic properties maps (2016) for Europe: for Water retention of topsoil: saturated water content (cm3/cm3), water content at field capacity (cm3/cm3), water content at wilting point (cm3/cm3...
Full Abstract
Soil hydraulic properties maps (2016) for Europe: for Water retention of topsoil: saturated water content (cm3/cm3), water content at field capacity (cm3/cm3), water content at wilting point (cm3/cm3); for Hydraulic conductivity of topsoil: saturated hydraulic conductivity (cm/day). Besides the true values in the units mentioned values scaled between 1 and 10 without measurement units were also calculated. This layer shows the 'true values' for the water content at wilting point.
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2016-01 |
Soil Organic Carbon (SOC) Projections for Europe: hd26 |
This dataset consists of a number of data layers (raster GRID maps) that are associated to the peer-reviewed publication "Assessment of soil organic carbon stocks under future climate and land...
Full Abstract
This dataset consists of a number of data layers (raster GRID maps) that are associated to the peer-reviewed publication "Assessment of soil organic carbon stocks under future climate and land cover changes in Europe" . Layers cover the current Soil Organic Carbon Stocks (2016) and the projected Soil Organic Carbon Stocks by 2050, for various Climate Scenarios (CCSM4, HadGEM2-AO , IPSL-CM5A-LR MRI-CGCM3) and Representative Concentration Pathways (RCPs).
hd26: HadGEM2-AO, RCP 2.6
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2016-01 |
Maps indicating the availability of Raw Material from soils in the European Union: soil material for construction |
This dataset (maps) indicates the availability of Raw Material (organic soil material and soil material for constructions) from soils in the European Union, and corresponds to the figures 7a and 7b...
Full Abstract
This dataset (maps) indicates the availability of Raw Material (organic soil material and soil material for constructions) from soils in the European Union, and corresponds to the figures 7a and 7b from the publication "Continental-scale assessment of provisioning soil functions in Europe", Gergely Tóth, Ciro Gardi, Katalin Bódis, Éva Ivits, Ece Aksoy, Arwyn Jones, Simon Jeffrey, Thorum Petursdottir and Luca Montanarella, Ecological Processes 2013 2:32; DOI: 10.1186/2192-1709-2-32
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2016-01 |
Maps of the Storing and Filtering Capacity of Soils in Europe: nonaqueous Phase Liquids (NAPL) filtering capacity (FILT_NAPL) |
This dataset (2016) contains 10 maps that relate to the soil's storing and filtering capacity in Europe (the EU only): cation storing capacity (STOR_CAPCA), cation filtering capacity (FILT_CAPCA...
Full Abstract
This dataset (2016) contains 10 maps that relate to the soil's storing and filtering capacity in Europe (the EU only): cation storing capacity (STOR_CAPCA), cation filtering capacity (FILT_CAPCA), anion storing capacity (STOR_CAPAN), anion filtering capacity (FILT_CAPAN), solids and pathogenic microorganisms storing capacity (STOR_CAPSO), solids and pathogenic microorganisms filtering capacity (FILT_CAPSO), non-polar organic chemicals storing capacity (STOR_CAPNP), non-polar organic chemicals filtering capacity (FILT_CAPNP), nonaqueous Phase Liquids (NAPL) storing capacity (STOR_NAPL), nonaqueous Phase Liquids (NAPL), filtering capacity (FILT_NAPL). As input, variables from the European Soil Database have been used.
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2016-01 |
Maps of the Storing and Filtering Capacity of Soils in Europe: solids and pathogenic microorganisms storing capacity (STOR_CAPSO) |
This dataset (2016) contains 10 maps that relate to the soil's storing and filtering capacity in Europe (the EU only): cation storing capacity (STOR_CAPCA), cation filtering capacity (FILT_CAPCA...
Full Abstract
This dataset (2016) contains 10 maps that relate to the soil's storing and filtering capacity in Europe (the EU only): cation storing capacity (STOR_CAPCA), cation filtering capacity (FILT_CAPCA), anion storing capacity (STOR_CAPAN), anion filtering capacity (FILT_CAPAN), solids and pathogenic microorganisms storing capacity (STOR_CAPSO), solids and pathogenic microorganisms filtering capacity (FILT_CAPSO), non-polar organic chemicals storing capacity (STOR_CAPNP), non-polar organic chemicals filtering capacity (FILT_CAPNP), nonaqueous Phase Liquids (NAPL) storing capacity (STOR_NAPL), nonaqueous Phase Liquids (NAPL), filtering capacity (FILT_NAPL). As input, variables from the European Soil Database have been used.
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2016-01 |
Maps of indicators of soil hydraulic properties for Europe: saturated hydraulic conductivity |
Soil hydraulic properties maps (2016) for Europe: for Water retention of topsoil: saturated water content (cm3/cm3), water content at field capacity (cm3/cm3), water content at wilting point (cm3/cm3...
Full Abstract
Soil hydraulic properties maps (2016) for Europe: for Water retention of topsoil: saturated water content (cm3/cm3), water content at field capacity (cm3/cm3), water content at wilting point (cm3/cm3); for Hydraulic conductivity of topsoil: saturated hydraulic conductivity (cm/day). Besides the true values in the units mentioned values scaled between 1 and 10 without measurement units were also calculated. This layer shows the 'true values' for the saturated hydraulic conductivity.
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2016-01 |
Maps of the Storing and Filtering Capacity of Soils in Europe: anion storing capacity (STOR_CAPAN) |
This dataset (2016) contains 10 maps that relate to the soil's storing and filtering capacity in Europe (the EU only): cation storing capacity (STOR_CAPCA), cation filtering capacity (FILT_CAPCA...
Full Abstract
This dataset (2016) contains 10 maps that relate to the soil's storing and filtering capacity in Europe (the EU only): cation storing capacity (STOR_CAPCA), cation filtering capacity (FILT_CAPCA), anion storing capacity (STOR_CAPAN), anion filtering capacity (FILT_CAPAN), solids and pathogenic microorganisms storing capacity (STOR_CAPSO), solids and pathogenic microorganisms filtering capacity (FILT_CAPSO), non-polar organic chemicals storing capacity (STOR_CAPNP), non-polar organic chemicals filtering capacity (FILT_CAPNP), nonaqueous Phase Liquids (NAPL) storing capacity (STOR_NAPL), nonaqueous Phase Liquids (NAPL), filtering capacity (FILT_NAPL). As input, variables from the European Soil Database have been used.
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2016-01 |
Soil Biomass Productivity maps of grasslands and pasture, of croplands and of forest areas in the European Union (EU27): forest areas |
This dataset consists of 3 GIS maps that indicate the soil biomass productivity of grasslands and pasture, of croplands and of forest areas in the European Union (EU27); this layer displays Soil...
Full Abstract
This dataset consists of 3 GIS maps that indicate the soil biomass productivity of grasslands and pasture, of croplands and of forest areas in the European Union (EU27); this layer displays Soil biomass productivity of forest areas, values normalized to a range [0..10]
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2016-01 |
Soil Organic Carbon (SOC) Projections for Europe: hd85 |
This dataset consists of a number of data layers (raster GRID maps) that are associated to the peer-reviewed publication "Assessment of soil organic carbon stocks under future climate and land...
Full Abstract
This dataset consists of a number of data layers (raster GRID maps) that are associated to the peer-reviewed publication "Assessment of soil organic carbon stocks under future climate and land cover changes in Europe" . Layers cover the current Soil Organic Carbon Stocks (2016) and the projected Soil Organic Carbon Stocks by 2050, for various Climate Scenarios (CCSM4, HadGEM2-AO , IPSL-CM5A-LR MRI-CGCM3) and Representative Concentration Pathways (RCPs).
hd85: HadGEM2-AO, RCP 8.5
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2016-01 |
Predicted distribution of SOC content in Europe (based on LUCAS, BioSoil and CZO) in the context of the EU-funded SoilTrEC project: SOC Lucas + CZO |
These maps of predicted distribution of SOC content in Europe (2016) are based on aggregated 23,835 soil samples collected from the LUCAS Project (samples from agricultural soil), BioSoil Project (...
Full Abstract
These maps of predicted distribution of SOC content in Europe (2016) are based on aggregated 23,835 soil samples collected from the LUCAS Project (samples from agricultural soil), BioSoil Project (samples from forest soil), and Soil Transformations in European Catchments (SoilTrEC) Project (samples from local soil data coming from five different critical zone observatories (CZOs) in Europe)"
This layer is SOC Lucas + CZO
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2016-01 |
Maps indicating the availability of Raw Material from soils in the European Union: organic soil material |
This dataset (maps) indicates the availability of Raw Material (organic soil material and soil material for constructions) from soils in the European Union, and corresponds to the figures 7a and 7b...
Full Abstract
This dataset (maps) indicates the availability of Raw Material (organic soil material and soil material for constructions) from soils in the European Union, and corresponds to the figures 7a and 7b from the publication "Continental-scale assessment of provisioning soil functions in Europe", Gergely Tóth, Ciro Gardi, Katalin Bódis, Éva Ivits, Ece Aksoy, Arwyn Jones, Simon Jeffrey, Thorum Petursdottir and Luca Montanarella, Ecological Processes 2013 2:32; DOI: 10.1186/2192-1709-2-32.
Values scaled to the range [0..10]
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2016-01 |
Predicted distribution of SOC content in Europe (based on LUCAS, BioSoil and CZO) in the context of the EU-funded SoilTrEC project: SOC LUCAS + CZO + Biosoil |
These maps of predicted distribution of SOC content in Europe (2016) are based on aggregated 23,835 soil samples collected from the LUCAS Project (samples from agricultural soil), BioSoil Project (...
Full Abstract
These maps of predicted distribution of SOC content in Europe (2016) are based on aggregated 23,835 soil samples collected from the LUCAS Project (samples from agricultural soil), BioSoil Project (samples from forest soil), and Soil Transformations in European Catchments (SoilTrEC) Project (samples from local soil data coming from five different critical zone observatories (CZOs) in Europe).
This layer is SOC LUCAS + CZO + Biosoil
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2016-01 |
Soil Organic Carbon - Saturation Capacity in Europe |
This dataset (map) shows the Soil Organic Carbon (SOC) saturation capacity, expressed as the ratio between the actual and the potential SOC stock in each pixel. Values close to 0 indicate a great...
Full Abstract
This dataset (map) shows the Soil Organic Carbon (SOC) saturation capacity, expressed as the ratio between the actual and the potential SOC stock in each pixel. Values close to 0 indicate a great potential of soil to store more carbon.
The actual SOC stock was derived from the Pan-European simulation using the biogeochemical CENTURY model (a detailed explanation can be found in the references below). The associated data can be found in ESDAC: "Pan-European SOC stock of agricultural soils"
The potential SOC stock was obtained simulating a grassland land use without nitrogen limitation, since it was considered a good scenario for SOC accumulation. The scenario set-up was analogous to that described in Lugato et al (2014b, see below) for the grassland land use, namely ‘AR_GR_LUC’. However to obtain a potential SOC stock, the model was ran for 2000 years with repeated actual climate, in order to reach an equilibrium condition. The simulation involved only the agricultural soils, according to the Corine Land Cover. A value of 1 was arbitrary attributed to forest soils.
The data come as a single ESRI Grid (250m resolution) in the ETRS_LAEA_10_52 Coordinate System + the two referenced articles
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2016-01 |
Global Soil Biodiversity Atlas Maps: soil threats |
The Soil Biodiversity threats showing the potential rather than the actual level of threat to soil organisms. For the development of this map, a number of diverse threats and corresponding proxies...
Full Abstract
The Soil Biodiversity threats showing the potential rather than the actual level of threat to soil organisms. For the development of this map, a number of diverse threats and corresponding proxies were chosen
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2016-01 |
Maps of indicators of soil hydraulic properties for Europe: saturated water content |
Soil hydraulic properties maps (2016) for Europe: for Water retention of topsoil: saturated water content (cm3/cm3), water content at field capacity (cm3/cm3), water content at wilting point (cm3/cm3...
Full Abstract
Soil hydraulic properties maps (2016) for Europe: for Water retention of topsoil: saturated water content (cm3/cm3), water content at field capacity (cm3/cm3), water content at wilting point (cm3/cm3); for Hydraulic conductivity of topsoil: saturated hydraulic conductivity (cm/day). Besides the true values in the units mentioned values scaled between 1 and 10 without measurement units were also calculated. This layer shows the 'true values' for the saturated water content.
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2016-01 |
Global Soil Biodiversity Atlas Maps: soil biodiversity |
The Soil Biodiversity map showing a simple index describing the potential level of diversity living in soils (with the use of two other datasets: distribution of microbial soil carbon used as a proxy...
Full Abstract
The Soil Biodiversity map showing a simple index describing the potential level of diversity living in soils (with the use of two other datasets: distribution of microbial soil carbon used as a proxy for soil microbial diversity, and the distribution of the main groups of soil macrofauna used as a proxy for soil fauna diversity.
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2016-01 |
Maps of indicators of soil hydraulic properties for Europe: water content at field capacity |
Soil hydraulic properties maps (2016) for Europe: for Water retention of topsoil: saturated water content (cm3/cm3), water content at field capacity (cm3/cm3), water content at wilting point (cm3/cm3...
Full Abstract
Soil hydraulic properties maps (2016) for Europe: for Water retention of topsoil: saturated water content (cm3/cm3), water content at field capacity (cm3/cm3), water content at wilting point (cm3/cm3); for Hydraulic conductivity of topsoil: saturated hydraulic conductivity (cm/day). Besides the true values in the units mentioned values scaled between 1 and 10 without measurement units were also calculated. This layer shows the 'true values' for the water content at field capacity.
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2016-01 |
Soil Biomass Productivity maps of grasslands and pasture, of croplands and of forest areas in the European Union (EU27): grasslands and pastures |
This dataset consists of 3 GIS maps that indicate the soil biomass productivity of grasslands and pasture, of croplands and of forest areas in the European Union (EU27); this layer displays Soil...
Full Abstract
This dataset consists of 3 GIS maps that indicate the soil biomass productivity of grasslands and pasture, of croplands and of forest areas in the European Union (EU27); this layer displays Soil biomass productivity of grasslands and pastures, values normalized to a range [0..10]
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2016-01 |
Soil Organic Carbon (SOC) Projections for Europe: hd45 |
This dataset consists of a number of data layers (raster GRID maps) that are associated to the peer-reviewed publication "Assessment of soil organic carbon stocks under future climate and land...
Full Abstract
This dataset consists of a number of data layers (raster GRID maps) that are associated to the peer-reviewed publication "Assessment of soil organic carbon stocks under future climate and land cover changes in Europe" . Layers cover the current Soil Organic Carbon Stocks (2016) and the projected Soil Organic Carbon Stocks by 2050, for various Climate Scenarios (CCSM4, HadGEM2-AO , IPSL-CM5A-LR MRI-CGCM3) and Representative Concentration Pathways (RCPs).
hd45: HadGEM2-AO, RCP 4.5
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2016-01 |
Soil Organic Carbon (SOC) Projections for Europe: hd60 |
This dataset consists of a number of data layers (raster GRID maps) that are associated to the peer-reviewed publication "Assessment of soil organic carbon stocks under future climate and land...
Full Abstract
This dataset consists of a number of data layers (raster GRID maps) that are associated to the peer-reviewed publication "Assessment of soil organic carbon stocks under future climate and land cover changes in Europe" . Layers cover the current Soil Organic Carbon Stocks (2016) and the projected Soil Organic Carbon Stocks by 2050, for various Climate Scenarios (CCSM4, HadGEM2-AO , IPSL-CM5A-LR MRI-CGCM3) and Representative Concentration Pathways (RCPs).
hd60: HadGEM2-AO, RCP 6
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2016-01 |
Maps of the Storing and Filtering Capacity of Soils in Europe: cation filtering capacity (FILT_CAPCA) |
This dataset (2016) contains 10 maps that relate to the soil's storing and filtering capacity in Europe (the EU only): cation storing capacity (STOR_CAPCA), cation filtering capacity (FILT_CAPCA...
Full Abstract
This dataset (2016) contains 10 maps that relate to the soil's storing and filtering capacity in Europe (the EU only): cation storing capacity (STOR_CAPCA), cation filtering capacity (FILT_CAPCA), anion storing capacity (STOR_CAPAN), anion filtering capacity (FILT_CAPAN), solids and pathogenic microorganisms storing capacity (STOR_CAPSO), solids and pathogenic microorganisms filtering capacity (FILT_CAPSO), non-polar organic chemicals storing capacity (STOR_CAPNP), non-polar organic chemicals filtering capacity (FILT_CAPNP), nonaqueous Phase Liquids (NAPL) storing capacity (STOR_NAPL), nonaqueous Phase Liquids (NAPL), filtering capacity (FILT_NAPL). As input, variables from the European Soil Database have been used.
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2016-01 |
Maps of the Storing and Filtering Capacity of Soils in Europe: cation storing capacity (STOR_CAPCA) |
This dataset (2016) contains 10 maps that relate to the soil's storing and filtering capacity in Europe (the EU only): cation storing capacity (STOR_CAPCA), cation filtering capacity (FILT_CAPCA...
Full Abstract
This dataset (2016) contains 10 maps that relate to the soil's storing and filtering capacity in Europe (the EU only): cation storing capacity (STOR_CAPCA), cation filtering capacity (FILT_CAPCA), anion storing capacity (STOR_CAPAN), anion filtering capacity (FILT_CAPAN), solids and pathogenic microorganisms storing capacity (STOR_CAPSO), solids and pathogenic microorganisms filtering capacity (FILT_CAPSO), non-polar organic chemicals storing capacity (STOR_CAPNP), non-polar organic chemicals filtering capacity (FILT_CAPNP), nonaqueous Phase Liquids (NAPL) storing capacity (STOR_NAPL), nonaqueous Phase Liquids (NAPL), filtering capacity (FILT_NAPL). As input, variables from the European Soil Database have been used.
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2016-01 |
Maps of the Storing and Filtering Capacity of Soils in Europe: non-polar organic chemicals storing capacity (STOR_CAPNP) |
This dataset (2016) contains 10 maps that relate to the soil's storing and filtering capacity in Europe (the EU only): cation storing capacity (STOR_CAPCA), cation filtering capacity (FILT_CAPCA...
Full Abstract
This dataset (2016) contains 10 maps that relate to the soil's storing and filtering capacity in Europe (the EU only): cation storing capacity (STOR_CAPCA), cation filtering capacity (FILT_CAPCA), anion storing capacity (STOR_CAPAN), anion filtering capacity (FILT_CAPAN), solids and pathogenic microorganisms storing capacity (STOR_CAPSO), solids and pathogenic microorganisms filtering capacity (FILT_CAPSO), non-polar organic chemicals storing capacity (STOR_CAPNP), non-polar organic chemicals filtering capacity (FILT_CAPNP), nonaqueous Phase Liquids (NAPL) storing capacity (STOR_NAPL), nonaqueous Phase Liquids (NAPL), filtering capacity (FILT_NAPL). As input, variables from the European Soil Database have been used.
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2015-01 |
Rainfall Erosivity in the EU and Switzerland (R-factor) |
Rainfall is one the main drivers of soil erosion. The erosive force of rainfall is expressed as rainfall erosivity. Rainfall erosivity considers the rainfall amount and intensity, and is most...
Full Abstract
Rainfall is one the main drivers of soil erosion. The erosive force of rainfall is expressed as rainfall erosivity. Rainfall erosivity considers the rainfall amount and intensity, and is most commonly expressed as the R-factor in the USLE model and its revised version, RUSLE. At national and continental levels, the scarce availability of data obliges soil erosion modellers to estimate this factor based on rainfall data with only low temporal resolution (daily, monthly, annual averages). The purpose of this study is to assess rainfall erosivity in Europe in the form of the RUSLE R-factor, based on the best available datasets. Data have been collected from 1,541 precipitation stations in all European Union(EU) Member States and Switzerland, with temporal resolutions of 5 to 60 minutes. The R-factor values calculated from precipitation data of different temporal resolutions were normalised to R-factor values with temporal resolutions of 30 minutes using linear regression functions. Precipitation time series ranged from a minimum of 5 years to maximum of 40 years. The average time series per precipitation station is around 17.1 years, the most datasets including the first decade of the 21st century. Gaussian Process Regression(GPR) has been used to interpolate the R-factor station values to a European rainfall erosivity map at 1 km resolution. The covariates used for the R-factor interpolation were climatic data (total precipitation, seasonal precipitation, precipitation of driest/wettest months, average temperature), elevation and latitude/longitude. The mean R-factor for the EU plus Switzerland is 722 MJ mm ha-1 h-1 yr-1, with the highest values (>1,000 MJ mm ha-1 h-1 yr-1) in the Mediterranean and alpine regions and the lowest (<500 MJ mm ha-1 h-1 yr-1) in the Nordic countries. The erosivity density (erosivity normalised to annual precipitation amounts) was also highest in Mediterranean regions which implies high risk for erosive events and floods. More information about the Methodology and the results an be found in : Panagos, P., Ballabio, C., Borrelli, P., Meusburger, K., Klik, A., Rousseva, S., Tadic, M.P., Michaelides, S., Hrabalíková, M., Olsen, P., Aalto, J., Lakatos, M., Rymszewicz, A., Dumitrescu, A., Beguería, S., Alewell, C. Rainfall erosivity in Europe. Sci Total Environ. 511 (2015), pp. 801-814
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2015-01 |
Support Practices factor (P-factor) for the EU |
The USLE/RUSLE support practice factor (P-factor) is rarely taken into account in soil erosion
risk modelling at sub-continental scale, as it is difficult to estimate for large areas. This study...
Full Abstract
The USLE/RUSLE support practice factor (P-factor) is rarely taken into account in soil erosion
risk modelling at sub-continental scale, as it is difficult to estimate for large areas. This study
attempts to model the P-factor in the European Union. For this, it considers the latest policy
developments in the Common Agricultural Policy, and applies the rules set by Member
States for contour farming over a certain slope. The impact of stone walls and grass margins
is also modelled using the more than 226,000 observations from the Land use/cover area
frame statistical survey (LUCAS) carried out in 2012 in the European Union.
The mean P-factor considering contour farming, stone walls and grass margins in the
European Union is estimated at 0.9702. The support practices accounted for in the P-factor
reduce the risk of soil erosion by 3%, with grass margins having the largest impact (57% of the
total erosion risk reduction) followed by stone walls (38%). Contour farming contributes very
little to the P-factor given its limited application; it is only used as a support practice in eight
countries and only on very steep slopes. Support practices have the highest impact in Malta,
Portugal, Spain, Italy, Greece, Belgium, The Netherlands and United Kingdom where they
reduce soil erosion risk by at least 5%. The P-factor modelling tool can potentially be used by
policy makers to run soil-erosion risk scenarios for a wider application of contour farming in
areas with slope gradients less than 10%, maintaining stone walls and increasing the number
of grass margins under the forthcoming reform of the Common Agricultural Policy. More information about the methodology of P-factor estimation in: Panagos, P., Borrelli, P., Meusburger, K., van der Zanden, E.H., Poesen, J., Alewell, C. 2015. Modelling the effect of support practices (P-factor) on the reduction of soil erosion by water at European Scale. Environmental Science & Policy 51: 23-34
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2015-01 |
Rainfall Erosivity in the EU and Switzerland (Erosivity Density) |
Rainfall is one the main drivers of soil erosion. The erosive force of rainfall is expressed as rainfall erosivity. Rainfall erosivity considers the rainfall amount and intensity, and is most...
Full Abstract
Rainfall is one the main drivers of soil erosion. The erosive force of rainfall is expressed as rainfall erosivity. Rainfall erosivity considers the rainfall amount and intensity, and is most commonly expressed as the R-factor in the USLE model and its revised version, RUSLE. At national and continental levels, the scarce availability of data obliges soil erosion modellers to estimate this factor based on rainfall data with only low temporal resolution (daily, monthly, annual averages). The purpose of this study is to assess rainfall erosivity in Europe in the form of the RUSLE R-factor, based on the best available datasets. Data have been collected from 1,541 precipitation stations in all European Union(EU) Member States and Switzerland, with temporal resolutions of 5 to 60 minutes. The R-factor values calculated from precipitation data of different temporal resolutions were normalised to R-factor values with temporal resolutions of 30 minutes using linear regression functions. Precipitation time series ranged from a minimum of 5 years to maximum of 40 years. The average time series per precipitation station is around 17.1 years, the most datasets including the first decade of the 21st century. Gaussian Process Regression(GPR) has been used to interpolate the R-factor station values to a European rainfall erosivity map at 1 km resolution. The covariates used for the R-factor interpolation were climatic data (total precipitation, seasonal precipitation, precipitation of driest/wettest months, average temperature), elevation and latitude/longitude. The mean R-factor for the EU plus Switzerland is 722 MJ mm ha-1 h-1 yr-1, with the highest values (>1,000 MJ mm ha-1 h-1 yr-1) in the Mediterranean and alpine regions and the lowest (<500 MJ mm ha-1 h-1 yr-1) in the Nordic countries. The erosivity density (erosivity normalised to annual precipitation amounts) was also highest in Mediterranean regions which implies high risk for erosive events and floods. More information about the Methodology and the results an be found in : Panagos, P., Ballabio, C., Borrelli, P., Meusburger, K., Klik, A., Rousseva, S., Tadic, M.P., Michaelides, S., Hrabalíková, M., Olsen, P., Aalto, J., Lakatos, M., Rymszewicz, A., Dumitrescu, A., Beguería, S., Alewell, C. Rainfall erosivity in Europe. Sci Total Environ. 511 (2015), pp. 801-814
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2015-01 |
Cover Management factor (C-factor) for the EU |
Land use and management influence the magnitude of soil loss. Among the different soil erosion riskfactors, the cover-management factor (C-factor) is the one that policy makers and farmers can...
Full Abstract
Land use and management influence the magnitude of soil loss. Among the different soil erosion riskfactors, the cover-management factor (C-factor) is the one that policy makers and farmers can mostreadily influence in order to help reduce soil loss rates. The present study proposes a methodology forestimating the C-factor in the European Union (EU), using pan-European datasets (such as CORINE LandCover), biophysical attributes derived from remote sensing, and statistical data on agricultural crops andpractices. In arable lands, the C-factor was estimated using crop statistics (% of land per crop) and data onmanagement practices such as conservation tillage, plant residues and winter crop cover. The C-factor innon-arable lands was estimated by weighting the range of literature values found according to fractionalvegetation cover, which was estimated based on the remote sensing dataset Fcover. The mean C-factor inthe EU is estimated to be 0.1043, with an extremely high variability; forests have the lowest mean C-factor(0.00116), and arable lands and sparsely vegetated areas the highest (0.233 and 0.2651, respectively).Conservation management practices (reduced/no tillage, use of cover crops and plant residues) reducethe C-factor by on average 19.1% in arable lands.The methodology is designed to be a tool for policy makers to assess the effect of future land use andcrop rotation scenarios on soil erosion by water. The impact of land use changes (deforestation, arableland expansion) and the effect of policies (such as the Common Agricultural Policy and the push to growmore renewable energy crops) can potentially be quantified with the proposed model. The C-factor dataand the statistical input data used are available from the European Soil Data Centre. More information about the LANDUM model and the data on C-factor in: Panagos, P., Borrelli, P., Meusburger, C., Alewell, C., Lugato, E., Montanarella, L., 2015. Estimating the soil erosion cover-management factor at European scale. Land Use policy 48C: 38-50.
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2015-01 |
LS-factor (Slope Length and Steepness factor) for the EU |
The Universal Soil Loss Equation (USLE) model is the most frequently used.
model for soil erosion risk estimation. Among the six input layers, the combined slope length
and slope angle (LS-factor...
Full Abstract
The Universal Soil Loss Equation (USLE) model is the most frequently used.
model for soil erosion risk estimation. Among the six input layers, the combined slope length
and slope angle (LS-factor) has the greatest influence on soil loss at the European scale.
The S-factor measures the effect of slope steepness, and the L-factor defines the impact of
slope length. The combined LS-factor describes the effect of topography on soil erosion. The
European Soil Data Centre (ESDAC) developed a new pan-European high-resolution soil
erosion assessment to achieve a better understanding of the spatial and temporal patterns of
soil erosion in Europe. The LS-calculation was performed using the original equation
proposed by Desmet and Govers (1996) and implemented using the System for Automated
Geoscientific Analyses (SAGA), which incorporates a multiple flow algorithm and
contributes to a precise estimation of flow accumulation. The LS-factor dataset was
calculated using a high-resolution (25 m) Digital Elevation Model (DEM) for the whole
European Union, resulting in an improved delineation of areas at risk of soil erosion as
compared to lower-resolution datasets. This combined approach of using GIS software
tools with high-resolution DEMs has been successfully applied in regional assessments in
the past, and is now being applied for first time at the European scale. More information about the LS-factor methodology and the data in: Panagos, P., Borrelli, P., Meusburger, K. 2015. A New European Slope Length and Steepness Factor (LS-Factor) for Modeling Soil Erosion by Water. Geosciences, 5: 117-126
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2015-01 |
Soil erosion by water (RUSLE2015) |
Soil erosion by water is one of the major threats to soils in the European Union, with a negative impact on ecosystem services, crop production, drinking water and carbon stocks. The European...
Full Abstract
Soil erosion by water is one of the major threats to soils in the European Union, with a negative impact on ecosystem services, crop production, drinking water and carbon stocks. The European Commission’s Soil Thematic Strategy has identified soil erosion as a relevant issue for the European Union, and has proposed an approach to monitor soil erosion. This paper presents the application of a modified version of the Revised Universal Soil Loss Equation (RUSLE) model (RUSLE2015) to estimate soil loss in Europe for the reference year 2010, within which the input factors (Rainfall erosivity, Soil erodibility, Cover- Management, Topography, Support practices) are modelled with the most recently available pan- European datasets. While RUSLE has been used before in Europe, RUSLE2015 improves the quality of estimation by introducing updated (2010), high-resolution (100 m), peer-reviewed input layers. The mean soil loss rate in the European Union’s erosion-prone lands (agricultural, forests and semi-natural areas) was found to be 2.46 t ha 1 yr 1, resulting in a total soil loss of 970 Mt annually. A major benefit of RUSLE2015 is that it can incorporate the effects of policy scenarios based on land- use changes and support practices. The impact of the Good Agricultural and Environmental Condition (GAEC) requirements of the Common Agricultural Policy (CAP) and the EU’s guidelines for soil protection can be grouped under land management (reduced/no till, plant residues, cover crops) and support practices (contour farming, maintenance of stone walls and grass margins). The policy interventions (GAEC, Soil Thematic Strategy) over the past decade have reduced the soil loss rate by 9.5% on average in Europe, and by 20% for arable lands. Special attention is given to the 4 million ha of croplands which currently have unsustainable soil loss rates of more than 5 t ha 1 yr 1, and to which policy measures should be targeted. More information about the RUSLE2015 model and the data in: Panagos, P., Borrelli, P., Poesen, J., Ballabio, C., Lugato, E., Meusburger, K., Montanarella, L., Alewell, .C. 2015. The new assessment of soil loss by water erosion in Europe. Environmental Science & Policy. 54: 438-447.
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2014-01 |
Pan-European SOC stock of agricultural soils |
Proposed European policy in the agricultural sector will place higher emphasis on soil organic carbon (SOC), both as an indicator of soil quality and as a means to offset CO2 emissions through soil...
Full Abstract
Proposed European policy in the agricultural sector will place higher emphasis on soil organic carbon (SOC), both as an indicator of soil quality and as a means to offset CO2 emissions through soil carbon (C) sequestration. Despite detailed national SOC data sets in several European Union (EU) Member States, a consistent C stock estimation at EU scale remains problematic. Data are often not directly comparable, different methods have been used to obtain values (e.g. sampling, laboratory analysis) and access may be restricted. Therefore, any evolution of EU policies on C accounting and sequestration may be constrained by a lack of an accurate SOC estimation and the availability of tools to carry out scenario analysis, especially for agricultural soils. In this context, a comprehensive model platform was established at a pan-European scale (EU + Serbia, Bosnia and Herzegovina, Croatia, Montenegro, Albania, Former Yugoslav Republic of Macedonia and Norway) using the agro-ecosystem SOC model CENTURY. Almost 164 000 combinations of soil-climate-land use were computed, including the main arable crops, orchards and pasture. The model was implemented with the main management practices (e.g. irrigation, mineral and organic fertilization, tillage) derived from official statistics. The model results were tested against inventories from the European Environment and Observation Network (EIONET) and approximately 20 000 soil samples from the 2009 LUCAS survey, a monitoring project aiming at producing the first coherent, comprehensive and harmonized top-soil data set of the EU based on harmonized sampling and analytical methods. The CENTURY model estimation of the current 0–30 cm SOC stock of agricultural soils was 17.63 Gt; the model uncertainty estimation was below 36% in half of the NUTS2 regions considered. The model predicted an overall increase of this pool according to different climate-emission scenarios up to 2100, with C loss in the south and east of the area (involving 30% of the whole simulated agricultural land) compensated by a gain in central and northern regions. Generally, higher soil respiration was offset by higher C input as a consequence of increased CO2 atmospheric concentration and favourable crop growing conditions, especially in northern Europe. Considering the importance of SOC in future EU policies, this platform of simulation appears to be a very promising tool to orient future policymaking decisions. More information about the model and the data in: Lugato E., Panagos P., Bampa, F., Jones A., Montanarella L. (2014). A new baseline of organic carbon stock in European agricultural soils using a modelling approach. Global change biology. 20 (1), pp. 313-326
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2014-01 |
Soil Erodibility (K- Factor) High Resolution dataset for Europe |
This map provides a complete picture of the soil erodibility in the European Union member states. It is derived from the LUCAS 2009 point survey (20,000 SOIL SAMPLES) and the European Soil Database...
Full Abstract
This map provides a complete picture of the soil erodibility in the European Union member states. It is derived from the LUCAS 2009 point survey (20,000 SOIL SAMPLES) and the European Soil Database. The K-factor, which expresses the susceptibility of a soil to erode, is related to soil properties such as organic matter content, soil texture, soil structure and permeability. With the Land Use/Cover Area frame Survey (LUCAS) soil survey in 2009 a pan-European soil dataset is available for the first time, consisting of around 20,000 points across 25 Member States of the European Union. The aim of this study is the generation of a harmonised high-resolution soil erodibility map (with a grid cell size of 500 m) for the 25 EU Member States. Soil erodibility was calculated for the LUCAS survey points using the nomograph of Wischmeier and Smith (1978). A Cubist regression model was applied to correlate spatial data such as latitude, longitude, remotely sensed and terrain features in order to develop a high-resolution soil erodibility map. The mean K-factor for Europe was estimated at 0.032 t ha h ha−1 MJ−1 mm−1 with a standard deviation of 0.009 t ha h ha−1 MJ−1 mm−1. The yielded soil erodibility dataset compared well with the published local and regional soil erodibility data. However, the incorporation
of the protective effect of surface stone cover, which is usually not considered for the soil erodibility calculations, resulted in an average 15% decrease of the K-factor. The exclusion of this effect in K-factor calculations is likely to result in an overestimation of soil erosion, particularly for the Mediterranean countries, where highest percentages of surface stone cover were observed. More information about the Methodology and the results an be found in : Panagos P., Meusburger K., Ballabio C., Borrelli P., Alewell C. (2014). Soil erodibility in Europe: A high-resolution dataset based on LUCAS . Science of the Total Environment, 479-480 (1) , pp. 189-200.
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2014-01 |
Soil Erodibility (K- Factor) High Resolution dataset for Europe (incorporating Stoniness) |
This map provides a complete picture of the soil erodibility in the European Union member states. It is derived from the LUCAS 2009 point survey (20,000 SOIL SAMPLES) and the European Soil Database...
Full Abstract
This map provides a complete picture of the soil erodibility in the European Union member states. It is derived from the LUCAS 2009 point survey (20,000 SOIL SAMPLES) and the European Soil Database. The K-factor, which expresses the susceptibility of a soil to erode, is related to soil properties such as organic matter content, soil texture, soil structure and permeability. With the Land Use/Cover Area frame Survey (LUCAS) soil survey in 2009 a pan-European soil dataset is available for the first time, consisting of around 20,000 points across 25 Member States of the European Union. The aim of this study is the generation of a harmonised high-resolution soil erodibility map (with a grid cell size of 500 m) for the 25 EU Member States. Soil erodibility was calculated for the LUCAS survey points using the nomograph of Wischmeier and Smith (1978). A Cubist regression model was applied to correlate spatial data such as latitude, longitude, remotely sensed and terrain features in order to develop a high-resolution soil erodibility map. The mean K-factor for Europe was estimated at 0.032 t ha h ha−1 MJ−1 mm−1 with a standard deviation of 0.009 t ha h ha−1 MJ−1 mm−1. The yielded soil erodibility dataset compared well with the published local and regional soil erodibility data. However, the incorporation
of the protective effect of surface stone cover, which is usually not considered for the soil erodibility calculations, resulted in an average 15% decrease of the K-factor. The exclusion of this effect in K-factor calculations is likely to result in an overestimation of soil erosion, particularly for the Mediterranean countries, where highest percentages of surface stone cover were observed. More information about the Methodology and the results an be found in : Panagos P., Meusburger K., Ballabio C., Borrelli P., Alewell C. (2014). Soil erodibility in Europe: A high-resolution dataset based on LUCAS . Science of the Total Environment, 479-480 (1) , pp. 189-200.
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2013-02 |
Pan-European Landslide Susceptibility (ELSUS 1000 version 1) |
The European Landslide Susceptibility Map ELSUS 1000 version 1 shows levels of spatial probability of generic landslide occurrence at 1 km cell size. It covers all European Union’s Member States,...
Full Abstract
The European Landslide Susceptibility Map ELSUS 1000 version 1 shows levels of spatial probability of generic landslide occurrence at 1 km cell size. It covers all European Union’s Member States, except Cyprus, and Albania, Bosnia and Herzegovina, Croatia, Kosovo, FYR Macedonia, Montenegro, Norway, Serbia and Switzerland. The map has been produced by regionalizing the study area based on elevation and climatic conditions, followed by spatial multi-criteria evaluation modelling using pan-European slope gradient, soil parent material and land cover spatial datasets as the main landslide conditioning factors. In addition, over 100,000 landslide locations across Europe, provided by various national organizations or collected by the authors, have been used for model calibration and map validation. The map has been made jointly by the Federal Institute for Geosciences and Natural Resources (BGR, Hannover, Germany), the Joint Research Centre (JRC, Ispra, Italy), the Institute of Physics of the Globe (CNRS-EOST, Strasbourg, France), and the Research Institute for Hydrogeological Protection (CNR-IRPI, Perugia, Italy).
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2010-01 |
ph |
The JRC created a quantitative map of estimated soil pH values across Europe from a compilation of 12,333 soil pH measurements from 11 different sources, and using a geo-statistical framework based...
Full Abstract
The JRC created a quantitative map of estimated soil pH values across Europe from a compilation of 12,333 soil pH measurements from 11 different sources, and using a geo-statistical framework based on Regression-Kriging. Fifty-four (54) auxiliary variables in the form of raster maps at 1km resolution were used to explain the differences in the distribution of soil pHCaCl2 and the kriged map of the residuals from the regression model was added. The goodness of fit of the regression model was satisfactory (R2adj = 0.43) and its residuals follow a Gaussian distribution. The lowest values correspond to the soils developed on acid rock (granites, quartzite’s, sandstones, etc), while the higher values are related to the presence of calcareous sediments and basic rocks. The validation of the model shows that the model is quite accurate (R2adj = 0.56). This shows the validity of Regression-Kriging in the estimation of the distribution of soil properties when a large and adequately documented number of soil measurements are available.
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2004-01 |
OCTOP: Topsoil Organic Carbon Content for Europe |
Soil organic carbon, the major component of soil organic matter, is extremely important in all soil processes. Organic material in the soil is essentially derived from residual plant and animal...
Full Abstract
Soil organic carbon, the major component of soil organic matter, is extremely important in all soil processes. Organic material in the soil is essentially derived from residual plant and animal material, synthesised by microbes and decomposed under the influence of temperature, moisture and ambient soil conditions. The annual rate of loss of organic matter can vary greatly, depending on cultivation practices, the type of plant/crop cover, drainage status of the soil and weather conditions. There are two groups of factors that influence inherent organic matter content: natural factors (climate, soil parent material, land cover and/or vegetation and topography), and human-induced factors (land use, management and degradation).
At the European level, there is a serious lack of geo-referenced, measured and harmonised data on soil organic carbon available from systematic sampling programmes. The European Soil Database, at a scale of 1:1,000,000, is the only comprehensive source of data on the soils of Europe harmonised according to a standard international classification (FAO). At the present time, the most homogeneous and comprehensive data on the organic carbon/matter content of European soils remain those that can be extracted and/or derived from the European Soil Database in combination with associated databases on land cover, climate and topography.
The Soil Portal makes available the Maps of Organic carbon content (%) in the surface horizon of soils in Europe. The data are in ESRI GRID format and are available as an ASCII raster file or in native ESRI GRID format. In addition, an interactive application allows the user to navigate in the Organic Carbon data with OCTOP Map Server and print his own customized map. The list of authorised codes and their corresponding meanings is given in the following table:
Organic carbon content (%)
Value Ranges
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0 - 0.01
0.01 - 1.00
1.0 - 2.0
2.0 - 6.0
6.0 - 12.5
12.5 - 25.0
25.0 - 35.0
> 35.0
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2003-01 |
PESERA: Pan European Soil Erosion Risk Assessment |
The Pan-European Soil Erosion Risk Assessment - PESERA - uses a process-based and spatially distributed model to quantify soil erosion by water and assess its risk across Europe. The conceptual basis...
Full Abstract
The Pan-European Soil Erosion Risk Assessment - PESERA - uses a process-based and spatially distributed model to quantify soil erosion by water and assess its risk across Europe. The conceptual basis of the PESERA model can also be extended to include estimates of tillage and wind erosion. The model is intended as a regional diagnostic tool, replacing comparable existing methods, such as the Universal Soil Loss Equation (USLE), which are less suitable for European conditions and lack compatibility with higher resolution models.
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2001-01 |
USEDO:Dominant Land Use |
USEDO:Dominant Land Use
USE-DOM: Code for dominant land use of the STU.
USE DOM describes the dominant and most apparent land use for an STU. A second type of land use can be taken into account...
Full Abstract
USEDO:Dominant Land Use
USE-DOM: Code for dominant land use of the STU.
USE DOM describes the dominant and most apparent land use for an STU. A second type of land use can be taken into account in USE SEC. The map co-ordinator must use his expert judgement to determine what are the dominant and secondary land uses for an STU, as the soil can cover extensive surfaces in regions with different agricultural practices and crops.
If there is only one land use or if the variability is unknown, then the value of USE DOM must be copied to USE SEC. Land uses that do not involve much human intervention, such as wasteland, or wildlife refuse, or land above timberline, are also listed here. The list of authorised codes and their corresponding meanings is given in the following table for attributes USE-DOM :
USE-DOM: Code for dominant land use of the STU
Code Value
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0 No information
1 Pasture, grassland, grazing land
2 Poplars
3 Arable land, cereals
4 Wasteland, shrub
5 Forest, coppice
6 Horticulture
7 Vineyards
8 Garrigue
9 Bush, macchia
10 Moor
11 Halophile grassland
12 Arboriculture, orchard
13 Industrial crops
14 Rice
15 Cotton
16 Vegetables
17 Olive trees
18 Recreation
19 Extensive pasture, grazing, rough pasture
20 Dehesa (extensive pastoral system in forest parks
in Spain)
21 Cultivos enarenados (artificial soils for orchards
in SE Spain)
22 Wildlife refuge, land above timberline
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2001-01 |
WM2: Type of Water Management System |
WM2:Type of Water Management System
WM2: Code for the type of an existing water management system.
A water management system is intended to palliate the lack of water (dry conditions), correct a...
Full Abstract
WM2:Type of Water Management System
WM2: Code for the type of an existing water management system.
A water management system is intended to palliate the lack of water (dry conditions), correct a soil condition preventing agricultural use (salinity), or drain excess water in waterlogged or frequently flooded areas. In some cases, it has a double purpose, for example in zones with contrasting seasonal conditions, alternatively flooded or experiencing droughts. The most obvious, apparent, or dominant type of water management system must be chosen from the list according to the contributor's expertise.
Obviously, WM1 and WM2 are inter-dependant. For example, if WM1 = 2 (no water management system) then WM2 can only have value 2. As another example, WM1 = 3 (drainage) is clearly incompatible with WM2 = 9 (flooding).
The list of authorised codes and their corresponding meanings is given in the following tables for attributes WM2:
WM2: Code for the type of an existing water management system
Code Value
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0 No information
1 Not applicable (no agriculture)
2 No water management system
3 Pumping
4 Ditches
5 Pipe under drainage (network of drain pipes)
6 Mole drainage
7 Deep loosening (subsoiling)
8 'Bed' system (ridge-funow or steching)
9 Flood irrigation (system of irrigation by
controlled flooding as for rice)
10 Overhead sprinkler (system of irrigation by
sprinkling)
11 Trickle irrigation
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2001-01 |
WR: Annual average soil water regime |
WR:Annual average soil water regime
Dominant annual average soil water regime class of the soil profile of the STU.
The annual average soil water regime is an estimate of the soil moisture...
Full Abstract
WR:Annual average soil water regime
Dominant annual average soil water regime class of the soil profile of the STU.
The annual average soil water regime is an estimate of the soil moisture conditions throughout the year. It is based on time series of matrix suction profiles, or groundwater table depths, or soil morphological attributes, or a combination of these characteristics. The annual soil water regime is expressed in terms of the duration of the state of soil wetness during the year. A soil is wet when it is saturated and has a matrix suction less than 10 cm, or a matrix potential over 1 kPa. Time is counted in cumulative days and not as successive days of wet conditions.
“Wet” means waterlogged and is defined as: a matrix suction of less than 10 cm, or a matrix potential over 1 kPa. The WR attribute is used to describe the dominant annual average soil water regime class of the soil profile of the STU. The list of authorised codes and their corresponding meaning is given in the following table for attribute WR:
Dominant annual average soil water regime class of the soil profile of the STU
Code Value
--------------
0 No information
1 Not wet within 80 cm for over 3 months, nor wet
within 40 cm for over 1 month
2 Wet within 80 cm for 3 to 6 months, but not wet
within 40 cm for over 1 month
3 Wet within 80 cm for over 6 months, but not wet
within 40 cm for over 11 months
4 Wet within 40 cm depth for over 11 months
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2001-01 |
TXEROD: Textural factor of soil erodibility |
TXEROD:Textural factor of soil erodibility
The list of authorised codes and their corresponding meanings is given in the following table:
TXEROD:Textural factor of soil erodibility
Code...
Full Abstract
TXEROD:Textural factor of soil erodibility
The list of authorised codes and their corresponding meanings is given in the following table:
TXEROD:Textural factor of soil erodibility
Code Value
--------------
1 = Very favourable
2 = Favourable
3 = Medium
4 = Unfavourable
5 = Very unfavourable
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2001-01 |
PD_TOP: Topsoil packing density |
PD_TOP:Topsoil packing density
The list of authorised codes and their corresponding meanings is given in the following table:
PD_TOP:Topsoil packing density
Code Value
--------------
L...
Full Abstract
PD_TOP:Topsoil packing density
The list of authorised codes and their corresponding meanings is given in the following table:
PD_TOP:Topsoil packing density
Code Value
--------------
L = Low
M = Medium
H = High
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2001-01 |
STR_SUB: Subsoil structure |
STR_SUB:Subsoil structure
The list of authorised codes and their corresponding meanings is given in the following table:
STR_SUB:Subsoil structure
Code Value
--------------
G = Good...
Full Abstract
STR_SUB:Subsoil structure
The list of authorised codes and their corresponding meanings is given in the following table:
STR_SUB:Subsoil structure
Code Value
--------------
G = Good
N = Normal
P = Poor
O = Peaty subsoil
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2001-01 |
IL: Impermeable Layer |
IL:Impermeable Layer
IL: Code for the presence of an impermeable layer within the soil profile of the STU
An impermeable layer is a subsoil horizon restricting water penetration. The...
Full Abstract
IL:Impermeable Layer
IL: Code for the presence of an impermeable layer within the soil profile of the STU
An impermeable layer is a subsoil horizon restricting water penetration. The impermeability can be of lithological origin (lithic contact), or pedogenic origin (claypan, duripan, petrocalcic or petroferric horizons,…). The IL attribute holds the code for the presence of an impermeable layer within the soil profile.
The list of authorised codes and their corresponding meaning is given in the following table for attribute IL:
IL: Code for the presence of an impermeable layer within the soil profile of the STU
Code Value
--------------
0 No information
1 No impermeable layer within 150 cm
2 Impermeable layer between 80 and 150 cm
3 Impermeable layer between 40 and 80 cm
4 Impermeable layer within 40 cm
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2001-01 |
VS: Volume of stones |
VS:Volume of stones
The list of authorised codes and their corresponding meanings is given in the following table:
VS:Volume of stones
Code Value
--------------
00 = 0 % stones
10 = 10...
Full Abstract
VS:Volume of stones
The list of authorised codes and their corresponding meanings is given in the following table:
VS:Volume of stones
Code Value
--------------
00 = 0 % stones
10 = 10 % stones
15 = 15 % stones
20 = 20 % stones
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2001-01 |
CEC_TOP: Topsoil Cation Exchange Capacity |
CEC_TOP: Topsoil Cation Exchange Capacity.
The list of authorised codes and their corresponding meanings is given in the following table:
CEC_TOP: Topsoil cation exchange capacity.
Code...
Full Abstract
CEC_TOP: Topsoil Cation Exchange Capacity.
The list of authorised codes and their corresponding meanings is given in the following table:
CEC_TOP: Topsoil cation exchange capacity.
Code Value
--------------
H = High ( > 40 cmol(+)/kg)
M = Medium (15-40 cmol(+)/kg)
L = Low ( < 15 cmol(+)/kg)
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2001-01 |
Natural Soil Susceptibility to Compaction |
The map of natural soil susceptibility to compaction was created from the evaluation of selected parameters from the European Soil Database. The soil susceptibility to compaction was divided into 4...
Full Abstract
The map of natural soil susceptibility to compaction was created from the evaluation of selected parameters from the European Soil Database. The soil susceptibility to compaction was divided into 4 categories. Two additional categories represent the data concerning places where this evaluation was either not relevant or could not been provided because of lack of information. In total there are 6 categories:
Natural Soil Susceptibility to Compaction
Code Value
--------------
0 - no soil. This represents water bodies, glaciers and rock outcrops
1 - low susceptibility to compaction
2 - medium susceptibility to compaction
3 - high susceptibility to compaction
4 - very high susceptibility to compaction
9 - no evaluation possible. This was the case of towns including also soils, soils disturbed by man and marsh.
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2001-01 |
ZMIN: Minimun elevetaion above sea |
ZMIN: Minimum elevation above sea level of the STU (in metres).
It is often difficult to fill in the information concerning ZMIN and ZMAX attributes. This is particularly true when the map coverage...
Full Abstract
ZMIN: Minimum elevation above sea level of the STU (in metres).
It is often difficult to fill in the information concerning ZMIN and ZMAX attributes. This is particularly true when the map coverage is a generalisation of previous maps that were not very detailed themselves. The use of a Digital Elevation Model (DEM) will often palliate the lack of information for these attributes. Using a DEM will however apply to the whole Soil Mapping Unit, and not to the individual STU components, as required here. Hence the information should be provided if available from the soil survey. The range of authorised values for attributes ZMIN and ZMAX is an integer number selected in [ 999, and the interval -400 ,9999]. –999 is the code used when no information is available. The negative values in the interval are given since some areas, such as the Caspian or Dead Seas, are below the general ocean level. The following table holds the coding scheme for attributes ZMIN:
ZMIN: Minimum elevation above sea level of the STU (in metres)
values
--------------
-999 No information
-2 metres
-1 metres
0 metres
1 metres
2 metres
5000 metres
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2001-01 |
EAWC_SUB: Subsoil easily available water capacity |
EAWC_SUB: Subsoil easily available water capacity.
The list of authorised codes and their corresponding meanings is given in the following table:
EAWC_SUB: Subsoil easily available water...
Full Abstract
EAWC_SUB: Subsoil easily available water capacity.
The list of authorised codes and their corresponding meanings is given in the following table:
EAWC_SUB: Subsoil easily available water capacity.
Code Value
--------------
VL = Very low ( ~ 0 mm/m)
L = Low ( < 100 mm/m)
M = Medium (100 - 140 mm/m)
H = High (140 - 190 mm/m)
VH = Very high ( > 190 mm/m)
# = No data or not applicable
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2001-01 |
AWC_TOP: Topsoil available water capacity |
AWC_TOP:Topsoil available water capacity.
The list of authorised codes and their corresponding meanings is given in the following table:
AWC_TOP: Topsoil available water capacity.
Code...
Full Abstract
AWC_TOP:Topsoil available water capacity.
The list of authorised codes and their corresponding meanings is given in the following table:
AWC_TOP: Topsoil available water capacity.
Code Value
--------------
L = Low ( < 100 mm/m)
M = Medium (100 - 140 mm/m)
H = High (140 - 190 mm/m)
VH = Very high ( > 190 mm/m)
# = No data or not applicable
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2001-01 |
AGLIM2NNI: 2nd Limitation to agricultural use (without no information) |
AGLIM2NNI:2nd Limitation to agricultural use (without no information)
The list of authorised codes and their corresponding meanings is given in the following table:
AGLIM2NNI:Se2nd Limitation to...
Full Abstract
AGLIM2NNI:2nd Limitation to agricultural use (without no information)
The list of authorised codes and their corresponding meanings is given in the following table:
AGLIM2NNI:Se2nd Limitation to agricultural use (without no information)
Code Value
--------------
0 No information
1 No limitation to agricultural use
2 Gravelly (over 35% gravels diameter < 7.5 cm)
3 Stony (presence of stones diameter > 7.5 cm, impracticable mechanization)
4 Lithic (coherent and hard rock within 50 cm)
5 Concretionary (over 35% concretions diameter < 7.5 cm near the surface)
6 Petrocalcic (cemented or indurated calcic horizon within 100 cm)
7 Saline (electric conductivity > 4 mS.cm-1 within 100 cm)
8 Sodic (Na/T > 6% within 100 cm)
9 Glaciers and snow-caps
10 Soils disturbed by man
20 Fragic
21 Drained
22 Quasi permanently flooded
30 Eroded phase, erosion
31 Phreatic phase
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2001-01 |
TD: Rule inferred subsoil texture |
No abstract provided
Full Abstract
No abstract provided
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2001-01 |
WM1: Presence of Water Management System |
WM1:Presence of Water Management System
WM1 : Code for normal presence and purpose of an existing water management system in agricultural land on more than 50% of the STU.
A water management...
Full Abstract
WM1:Presence of Water Management System
WM1 : Code for normal presence and purpose of an existing water management system in agricultural land on more than 50% of the STU.
A water management system is intended to palliate the lack of water (dry conditions), correct a soil condition preventing agricultural use (salinity), or drain excess water in waterlogged or frequently flooded areas. In some cases, it has a double purpose, for example in zones with contrasting seasonal conditions, alternatively flooded or experiencing droughts. The most obvious, apparent, or dominant type of water management system must be chosen from the list according to the contributor's expertise.
Obviously, WM1 and WM2 are inter-dependant. For example, if WM1 = 2 (no water management system) then WM2 can only have value 2. As another example, WM1 = 3 (drainage) is clearly incompatible with WM2 = 9 (flooding).
The list of authorised codes and their corresponding meanings is given in the following tables for attributes WM1:
WM1 : Code for normal presence and purpose of an existing water management system in agricultural land on more than 50% of the STU
Code Value
--------------
0 No information
1 Not applicable (no agriculture)
2 No water management system
3 A water management system exists to alleviate
waterlogging (drainage)
4 A water management system exists to alleviate
drought stress (irrigation)
5 A water management system exists to alleviate
salinity (drainage)
6 A water management system exists to alleviate both
waterlogging and drought stress
7 A water management system exists to alleviate both
waterlogging and salinity
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2001-01 |
PARMASE1:Major group code for the secondary parent material |
PARMASE1:Major group code for the secondary parent material
PAR-MAT-SEC1: Major group code for the secondary parent material of the STU.
PAR-MAT-SEC1: Major group code for the secondary parent...
Full Abstract
PARMASE1:Major group code for the secondary parent material
PAR-MAT-SEC1: Major group code for the secondary parent material of the STU.
PAR-MAT-SEC1: Major group code for the secondary parent material of the STU
Code Value
--------------
0 No information
1 consolidated-clastic-sedimentary rocks
2 sedimentary rocks (chemically precipitated,
evaporated, or organogenic or biogenic in origin)
3 igneous rocks
4 metamorphic rocks
5 unconsolidated deposits (alluvium, weathering
residuum and slope deposits)
6 unconsolidated glacial deposits/glacial drift
7 eolian deposits
8 organic materials
9 anthropogenic deposits
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2001-01 |
SLOPEDO:Dominant Slope Class |
SLOPE-DOM: Dominant slope class of the STU.
The list of authorised codes and their corresponding meanings is given in the following tables for attributes SLOPE DOM:
SLOPE-DOM: Dominant...
Full Abstract
SLOPE-DOM: Dominant slope class of the STU.
The list of authorised codes and their corresponding meanings is given in the following tables for attributes SLOPE DOM:
SLOPE-DOM: Dominant slope class of the STU
Code Value
--------------
0 No information
1 Level (dominant slope ranging from 0 to 8 %)
2 Sloping (dominant slope ranging from 8 to 15 %)
3 Moderately steep (dominant slope ranging from 15
to 25 %)
4 Steep (dominant slope over 25 %)
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2001-01 |
TXSUBDO:Dominant sub-surface textural class |
TXSUBDO:Dominant sub-surface textural class
A Soil Typological Unit (STU) can have contrasted sub-surface textures that fall in two different textural classes. The secondary sub-surface textural...
Full Abstract
TXSUBDO:Dominant sub-surface textural class
A Soil Typological Unit (STU) can have contrasted sub-surface textures that fall in two different textural classes. The secondary sub-surface textural class (TEXT-SUB-SEC) is used to indicate which sub-surface texture is less extensive than the dominant one. Together the TEXT-SUB-DOM and the TEXT-SUB-SEC attributes reflect the lateral variability of the sub-surface horizon texture within the STU. If there is no such variability or if there is no information, the value of TEXT-SUB-DOM must also be entered for TEXT-SUB-SEC.
>TEXT-SUB-DOM: Dominant sub-surface textural class of the STU
----------------
0 No information
9 No mineral texture (Peat soils)
1 Coarse (18% < clay and > 65% sand)
2 Medium (18% < clay < 35% and >= 15% sand, or 18% < clay and 15% < sand < 65%)
3 Medium fine (< 35% clay and < 15% sand)
4 Fine (35% < clay < 60%)
5 Very fine (clay > 60 %)
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2001-01 |
TEXT-SRF-DOM: Dominant surface textural class of the STU |
A Soil Typological Unit (STU) can have surface textures that fall in two different textural classes. The secondary surface textural class (TXSRFSE) is used to indicate the surface texture less...
Full Abstract
A Soil Typological Unit (STU) can have surface textures that fall in two different textural classes. The secondary surface textural class (TXSRFSE) is used to indicate the surface texture less extensive than the dominant one. Together the TXSRFDO and the TXSRFSE (Sec.surface text.class) attributes describe the lateral variability of the surface horizon texture within the STU. If there is no such variability or if information is unavailable, then the value of TXSRFDO must also be entered for TXSRFSE .
The list of authorised codes and their corresponding meanings is given in the following table:
TEXT-SRF-DOM: Dominant surface textural class of the STU
Code Value
----------------
0 No information
9 No mineral texture (Peat soils)
1 Coarse (18% < clay and > 65% sand)
2 Medium (18% < clay < 35% and >= 15% sand, or 18% <
clay and 15% < sand < 65%)
3 Medium fine (< 35% clay and < 15% sand)
4 Fine (35% < clay < 60%)
5 Very fine (clay > 60 %)
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2001-01 |
AGLIM1NNI: Dominant limitation to agricultural use (without no information) |
AGLIM1NNI: Dominant limitation to agricultural use
The list of authorised codes and their corresponding meanings is given in the following table:
AGLIM1NNI: Dominant limitation to agricultural...
Full Abstract
AGLIM1NNI: Dominant limitation to agricultural use
The list of authorised codes and their corresponding meanings is given in the following table:
AGLIM1NNI: Dominant limitation to agricultural use (without no information)
Code Value
--------------
0 No information
1 No limitation to agricultural use
2 Gravelly (over 35% gravels diameter < 7.5 cm)
3 Stony (presence of stones diameter > 7.5 cm, impracticable mechanization)
4 Lithic (coherent and hard rock within 50 cm)
5 Concretionary (over 35% concretions diameter < 7.5 cm near the surface)
6 Petrocalcic (cemented or indurated calcic horizon within 100 cm)
7 Saline (electric conductivity > 4 mS.cm-1 within 100 cm)
8 Sodic (Na/T > 6% within 100 cm)
9 Glaciers and snow-caps
10 Soils disturbed by man
20 Fragic
21 Drained
22 Quasi permanently flooded
30 Eroded phase, erosion
31 Phreatic phase
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2001-01 |
TXSRFSE: Secondary surface textural class of the STU |
TXSRFSE:Secondary surface textural class
A Soil Typological Unit (STU) can have surface textures that fall in two different textural classes. The secondary surface textural class (TXSRFSE) is used...
Full Abstract
TXSRFSE:Secondary surface textural class
A Soil Typological Unit (STU) can have surface textures that fall in two different textural classes. The secondary surface textural class (TXSRFSE) is used to indicate the surface texture less extensive than the dominant one. Together the TXSRFDO and the TXSRFSE (Sec.surface text.class) attributes describe the lateral variability of the surface horizon texture within the STU. If there is no such variability or if information is unavailable, then the value of TXSRFDO must also be entered for TXSRFSE .
TEXT-SRF-SEC: Secondary surface textural class of the STU
----------------
0 No information
9 No mineral texture (Peat soils)
1 Coarse (18% < clay and > 65% sand)
2 Medium (18% < clay < 35% and >= 15% sand, or 18% < clay and 15% < sand < 65%)
3 Medium fine (< 35% clay and < 15% sand)
4 Fine (35% < clay < 60%)
5 Very fine (clay > 60 %)
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2001-01 |
HG: Hydro-geological class |
HG:Hydro-geological class
The list of authorised codes and their corresponding meanings is given in the following table:
HG:Hydro-geological class
Code Value
--------------
1R = 1R
1C...
Full Abstract
HG:Hydro-geological class
The list of authorised codes and their corresponding meanings is given in the following table:
HG:Hydro-geological class
Code Value
--------------
1R = 1R
1C = 1C
1S = 1S
1L = 1L
1H = 1H
1M = 1M
2 = 2
3 = 3
4W = 4W
4D = 4D
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2001-01 |
USE: Regrouped land use |
USE:Regrouped land use class.
The list of authorised codes and their corresponding meanings is given in the following table:
USE:Regrouped land use class.
Code Value
--------------
HG...
Full Abstract
USE:Regrouped land use class.
The list of authorised codes and their corresponding meanings is given in the following table:
USE:Regrouped land use class.
Code Value
--------------
HG = Halophile Grassland
MG = Managed Grassland
SN = Semi-natural
C = Cultivated
# = No information
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2001-01 |
ZMAX:Maximun elevetaion above sea |
ZMAX: Maximum elevation above sea level of the STU (in metres).
It is often difficult to fill in the information concerning ZMIN and ZMAX attributes. This is particularly true when the map coverage...
Full Abstract
ZMAX: Maximum elevation above sea level of the STU (in metres).
It is often difficult to fill in the information concerning ZMIN and ZMAX attributes. This is particularly true when the map coverage is a generalisation of previous maps that were not very detailed themselves. The use of a Digital Elevation Model (DEM) will often palliate the lack of information for these attributes. Using a DEM will however apply to the whole Soil Mapping Unit, and not to the individual STU components, as required here. Hence the information should be provided if available from the soil survey. The range of authorised values for attributes ZMIN and ZMAX is an integer number selected in [ 999, and the interval -400 ,9999]. –999 is the code used when no information is available. The negative values in the interval are given since some areas, such as the Caspian or Dead Seas, are below the general ocean level. The following table holds the coding scheme for attributes ZMAX:
ZMAX: Maximum elevation above sea level of the STU (in metres)
values
--------------
-999 No information
-2 metres
-1 metres
0 metres
1 metres
2 metres
5000 metres
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2001-01 |
TEXT: Dominant Surface Textural |
TEXT: Dominant surface textural class (completed from dominant STU)
The list of authorised codes and their corresponding meanings is given in the following table:
TEXT: Dominant surface...
Full Abstract
TEXT: Dominant surface textural class (completed from dominant STU)
The list of authorised codes and their corresponding meanings is given in the following table:
TEXT: Dominant surface textural class (completed from dominant STU)
Code Value
--------------
1 = Coarse (clay < 18 % and sand > 65 %)
2 = Medium (18% < clay < 35% and sand > 15%,\or clay < 18% and 15% < sand < 65%)
3 = Medium fine (clay < 35 % and sand < 15 %)
4 = Fine (35 % < clay < 60 %)
5 = Very fine (clay > 60 %)
7 = No texture (because of rock outcrop)
8 = No texture (because of organic layer)
6 = No texture (other cases)
0 = No information
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2001-01 |
AGLIM1:Dominant limitation to agricultural use |
AGLIM1:Dominant limitation to agricultural use
AGLIM1: Dominant limitation to agricultural use.
A STU can have more than one limitation for agricultural use. Only the two most important...
Full Abstract
AGLIM1:Dominant limitation to agricultural use
AGLIM1: Dominant limitation to agricultural use.
A STU can have more than one limitation for agricultural use. Only the two most important limitations are considered and ranked in order of their relative importance. Attribute AGLIM1 contains the code of the most important limitation and attribute AGLIM2 the code of the secondary limitation. If there is only one limitation or if the secondary limitation is unknown, then the value of AGLIM1 must also be entered for AGLIM2 . For example, a soil can be both shallow, with a lithic contact within the first 50 cm, and have more than 35% gravel. The pedologist may determine that shallowness is the dominant limiting factor and gravel content is the secondary limitation. Recently, duripans and petroferric horizons have been added to the list of limiting factors. These horizons are more often found in soils of the Mediterranean area than in northern Europe. The major types of chemical and physical limitations for agricultural use are listed below. Most limitations listed here, however, are physical. The list of authorised codes and their corresponding meanings is given in the following table for attribute AGLIM1:
AGLIM1: Dominant limitation to agricultural use
Code Value
--------------
0 No information
1 No limitation to agricultural use
2 Gravelly (over 35% gravel diameter < 7.5 cm)
3 Stony (presence of stones diameter > 7.5 cm,
impracticable mechanisation)
4 Lithic (coherent and hard rock within 50 cm)
5 Concretionary (over 35% concretions diameter < 7.5
cm near the surface)
6 Petrocalcic (cemented or indurated calcic horizon
within 100 cm)
7 Saline (electric conductivity > 4 mS.cm-1 within
100 cm)
8 Sodic (Na/T > 6% within 100 cm)
9 Glaciers and snow-caps
10 Soils disturbed by man (i.e. landfills, paved
surfaces, mine spoils)
11 Fragipans
12 Excessively drained
13 Almost always flooded
14 Eroded phase, erosion
15 Phreatic phase (shallow water table)
16 Duripan (silica and iron cemented subsoil horizon)
17 Petroferric horizon
18 Permafrost
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2001-01 |
PARMADO:Dominant Parent Material |
PARMADO:Dominant Parent Material
The parent material code must be selected from the list provided below. This list has evolved from a number of approximations using experiences from several...
Full Abstract
PARMADO:Dominant Parent Material
The parent material code must be selected from the list provided below. This list has evolved from a number of approximations using experiences from several pilot projects. The current version has been prepared by R. Hartwhich et al. (1999) as the reference list in the Manual for the Georeferenced Soil Database for Europe at 1 :250.000, version 1.1. It includes four levels: Major Class, Group, Type and Subtype.
PAR-MAT-DOM: Code for dominant parent material of the STU
Code Value
--------------
0 No information
1000 consolidated-clastic-sedimentary rocks
1100 psephite or rudite
1110 conglomerate
1111 pudding stone
1120 breccia
1200 psammite or arenite
1210 sandstone
1211 calcareous sandstone
1212 ferruginous sandstone
1213 clayey sandstone
1214 quartzitiic sandstone/orthoquartzite
1215 micaceous sandstone
1220 arkose
1230 graywacke
1231 feldspathic graywacke
1300 pelite, lutite or argilite
1310 claystone/mudstone
1311 kaolinite
1312 bentonite
1320 siltstone
1400 facies bound rock
1410 flysch
1411 sandy flisch
1412 clayey and silty flysch
1413 conglomeratic flysch
1420 molasse
2000 sedimentary rocks (chemically precipitated,
evaporated, or organogenic or biogenic in origin)
2100 calcareous rocks
2110 limestone
2111 hard limestone
2112 soft limestone
2113 marly limestone
2114 chalky limestone
2115 detrital limestone
2116 carbonaceous limestone
2117 lacustrine or freshwater limestone
2118 travertine/calcareous sinter
2119 cavernous limestone
2120 dolomite
2121 cavernous dolomite
2122 calcareous dolomite
2130 marlstone
2140 marl
2141 chalk marl
2142 gypsiferous marl
2150 chalk
2200 evaporites
2210 gypsum
2220 anhydrite
2230 halite
2300 siliceous rocks
2310 chert, hornstone, flint
2320 diatomite/radiolarite
3000 igneous rocks
3100 acid to intermediate plutonic rocks
3110 granite
3120 granodiorite
3130 diorite
3131 quartz diorite
3132 gabbro diorite
3140 syenite
3200 basic plutonic rocks
3210 gabbro
3300 ultrabasic plutonic rocks
3310 peridotite
3320 pyroxenite
3400 acid to intermediate volcanic rocks
3410 rhyolite
3411 obsidian
3412 quartz porphyrite
3420 dacite
3430 andesite
3431 porphyrite (interm,)
3440 phonolite
3441 tephritic phonolite
3450 trachyte
3500 basic to ultrabasic volcanic rocks
3510 basalt
3520 diabase
3530 pikrite
3600 dike rocks
3610 aplite
3620 pegmatite
3630 lamprophyre
3700 pyroclastic rocks (tephra)
3710 tuff/tuffstone
3711 agglomeratic tuff
3712 block tuff
3713 lapilli tuff
3720 tuffite
3721 sandy tuffite
3722 silty tuffite
3723 clayey tuffite
3730 volcanic scoria/volcanic breccia
3740 volcanic ash
3750 ignimbrite
3760 pumice
4000 metamorphic rocks
4100 weakly metamorphic rocks
4110 (meta-)shale/argilite
4120 slate
4121 graphitic slate
4200 acid regional metamorphic rocks
4210 (meta-)quartzite
4211 quartzite schist
4220 phyllite
4230 micaschist
4240 gneiss
4250 granulite (sensu stricto)
4260 migmatite
4300 basic regional metamorphic rocks
4310 greenschist
4311 prasinite
4312 chlorite
4313 talc schist
4320 amphibolite
4330 eclogite
4400 ultrabasic regional metamorphic rocks
4410 serpentinite
4411 greenstone
4500 calcareous regional metamorphic rocks
4510 marble
4520 calcschist, skam
4600 rocks formed by contact metamorphism
4610 contact slate
4611 nodular slate
4620 hornfels
4630 calsilicate rocks
4700 tectogenetic metamorphism rocks or cataclasmic
metamorphism
4710 tectonic breccia
4720 cataclasite
4730 mylonite
5000 unconsolidated deposits (alluvium, weathering
residuum and slope deposits)
5100 marine and estuarine sands
5110 pre-quaternary sand
5111 tertiary sand
5120 quaternary sand
5121 holocene coastal sand with shells
5122 delta sand
5200 marine and estuarine clays and silts
5210 pre-quaternary clay and silt
5211 tertiary clay
5212 tertiary silt
5220 quaternary clay and silt
5221 holocene clay
5222 holocene silt
5300 fluvial sands and gravels
5310 river terrace sand or gravel
5311 river terrace sand
5312 river terrace gravel
5320 floodplain sand or gravel
5321 floodplain sand
5322 floodplain gravel
5400 fluvial clays, silts and loams
5410 river clay and silt
5411 terrace clay and silt
5412 floodplain clay and silt
5420 river loam
5421 terrace loam
5430 overbank deposit
5431 floodplain clay and silt
5432 floodplain loam
5500 lake deposits
5510 lake sand and delta sand
5520 lake marl, bog lime
5530 lake silt
5600 residual and redeposited loams from silicate rocks
5610 residual loam
5611 stony loam
5612 clayey loam
5620 redeposited loam
5621 running-ground
5700 residual and redeposited clays from calcareous
rocks
5710 residual clay
5711 clay with flints
5712 ferruginous residual clay
5713 calcareous clay
5714 non-calcareous clay
5715 marly clay
5720 redeposited clay
5721 stony clay
5800 slope deposits
5810 slope-wash alluvium
5820 colluvial deposit
5830 talus scree
5831 stratified slope deposits
6000 unconsolidated glacial deposits/glacial drift
6100 morainic deposits
6110 glacial till
6111 boulder clay
6120 glacial debris
6200 glaciofluvial deposits
6210 outwash sand, glacial sand
6220 outwash gravels glacial gravels
6300 glaciolacustrine deposits
6310 varves
7000 eolian deposits
7100 loess
7110 loamy loess
7120 sandy loess
7200 eolian sands
7210 dune sand
7220 cover sand
8000 organic materials
8100 peat (mires)
8110 rainwater fed moor peat (raised bog)
8111 folic peat
8112 fibric peat
8113 terric peat
8120 groundwater fed bog peat
8200 slime and ooze deposits
8210 gyttja, sapropel
8300 carbonaceaous rocks (caustobiolite)
8310 lignite (brown coal)
8320 hard coal
8330 anthracite
9000 anthropogenic deposits
9100 redeposited natural materials
9110 sand and gravel fill
9120 loamy fill
9200 dump deposits
9210 rubble/rubbish
9220 industrial ashes and slag
9230 industrial sludge
9240 industrial waste
9300 anthropogenic organic materials
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2001-01 |
WRB-ADJ1: First soil adjective code |
The European Soil Database
WRBFU:Full soil Code
Soil Adjective code of the STU taken from the World Reference Base (WRB) for Soil Resources.
WRB-ADJ1: First soil adjective code of the STU...
Full Abstract
The European Soil Database
WRBFU:Full soil Code
Soil Adjective code of the STU taken from the World Reference Base (WRB) for Soil Resources.
WRB-ADJ1: First soil adjective code of the STU from the World Reference Base (WRB) for Soil Resources
Code Value
----------------
II Lamellic
Iv Luvic
Ix Lixic
ab Albic
ac Acric
ad Aridic
ae Aceric
ah Anthropic
ai Aric
al Alic
am Anthric
an Andic
ao Acroxic
ap Abruptic
aq Anthraquic
ar Arenic
au Alumic
ax Alcalic
az Arzic
ca Calcaric
cb Carbic
cc Calcic
ch Chernic
cl Chloridic
cn Carbonatic
cr Chromic
ct Cutanic
cy Cryic
dn Densic
du Duric
dy Dystric
es Eutrisilic
et Entic
eu Eutric
fg Fragic
fi Fibric
fl Ferralic
fo Folic
fr Ferric
fu Fulvic
fv Fluvic
ga Garbic
gc Glacic
ge Gelic
gi Gibbsic
gl Gleyic
gm Grumic
gp Gypsiric
gr Geric
gs Glossic
gt Gelistagnic
gy Gypsic
gz Greyic
ha Haplic
hg Hydragric
hi Histic
hk Hyperskeletic
ht Hortic
hu Humic
hy Hydric
ir Irragric
le Leptic
li Lithic
me Melanic
mg Magnesic
mo Mollic
ms Mesotrophic
mz Mazic
na Natric
ni Nitic
oa Oxyaquic
oh Ochric
om Ombric
or Orthic
pa Plaggic
pc Petrocalcic
pd Petroduric
pe Pellic
pf Profondic
pg Petrogypsic
ph Pachic
pi Placic
pl Plinthic
pn Planic
po Posic
pp Petroplinthic
pr Protic
ps Petrosalic
pt Petric
rd Reductic
rg Regic
rh Rheic
ro Rhodic
rp Ruptic
rs Rustic
ru Rubic
rz Rendzic
sa Sapric
sd Spodic
si Silic
sk Skeletic
sl Siltic
so Sodic
sp Spolic
st Stagnic
su Sulphatic
sz Salic
tf Tephric
ti Thionic
tr Terric
tu Turbic
tx Toxic
ty Takyric
ub Urbic
um Umbric
vi Vitric
vm Vermic
vr Vertic
vt Vetic
xa Xanthic
ye Yermic
1 Town
2 Soil disturbed by man
3 Water body
4 Marsh
5 Glacier
6 Rock outcrops
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2001-01 |
PHYSCHIM: Physi-chemical factor of soil |
PHYSCHIM: Physico-chemical factor of soil crusting & erodibility
The list of authorised codes and their corresponding meanings is given in the following table:
PHYSCHIM: Physico-chemical...
Full Abstract
PHYSCHIM: Physico-chemical factor of soil crusting & erodibility
The list of authorised codes and their corresponding meanings is given in the following table:
PHYSCHIM: Physico-chemical factor of soil crusting & erodibility
Code Value
--------------
1 = Very favourable
2 = Favourable
3 = Medium
4 = Unfavourable
5 = Very unfavourable
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2001-01 |
OC_TOP: Topsoil Organic Carbon. |
OC_TOP = Topsoil organic carbon content.
The list of authorised codes and their corresponding meanings is given in the following table:
OC_TOP = Topsoil organic carbon content.
Code Value...
Full Abstract
OC_TOP = Topsoil organic carbon content.
The list of authorised codes and their corresponding meanings is given in the following table:
OC_TOP = Topsoil organic carbon content.
Code Value
--------------
H = High ( > 6 %)
M = Medium (2 - 6 %)
L = Low (1 - 2 %)
V = Very low ( < 1 %)
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2001-01 |
PMH: Parent material hydro-geological |
PMH:Parent material hydro-geological type
The list of authorised codes and their corresponding meanings is given in the following table:
PMH: Parent material hydro-geological type
Code...
Full Abstract
PMH:Parent material hydro-geological type
The list of authorised codes and their corresponding meanings is given in the following table:
PMH: Parent material hydro-geological type
Code Value
--------------
R = Porous - Stor. ~ Perm. +
C = Porous 2 Stor. ~ Perm. +
S = Porous 1 Stor. + Perm. +
L = Stor. - Perm. -
H = Hard. Stor. -- Perm. --
M = Soft. Stor. -- Perm. --
# = No information
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2001-01 |
USESE:Secondary Land Use |
USESE:Secondary Land Use
USE-SEC : Code for Secondary land use of the STU Land Use
USE DOM describes the dominant and most apparent land use for an STU. A second type of land use can be taken...
Full Abstract
USESE:Secondary Land Use
USE-SEC : Code for Secondary land use of the STU Land Use
USE DOM describes the dominant and most apparent land use for an STU. A second type of land use can be taken into account in USE SEC. The map co-ordinator must use his expert judgement to determine what are the dominant and secondary land uses for an STU, as the soil can cover extensive surfaces in regions with different agricultural practices and crops.
If there is only one land use or if the variability is unknown, then the value of USE DOM must be copied to USE SEC. Land uses that do not involve much human intervention, such as wasteland, or wildlife refuse, or land above timberline, are also listed here. The list of authorised codes and their corresponding meanings is given in the following table for attributes USE-SEC :
USE-SEC : Code for Secondary land use of the STU Land Use
Code Value
--------------
0 No information
1 Pasture, grassland, grazing land
2 Poplars
3 Arable land, cereals
4 Wasteland, shrub
5 Forest, coppice
6 Horticulture
7 Vineyards
8 Garrigue
9 Bush, macchia
10 Moor
11 Halophile grassland
12 Arboriculture, orchard
13 Industrial crops
14 Rice
15 Cotton
16 Vegetables
17 Olive trees
18 Recreation
19 Extensive pasture, grazing, rough pasture
20 Dehesa (extensive pastoral system in forest parks
in Spain)
21 Cultivos enarenados (artificial soils for orchards
in SE Spain)
22 Wildlife refuge, land above timberline
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2001-01 |
DR: Depth to rock |
DR:Depth to rock
The list of authorised codes and their corresponding meanings is given in the following table:
DR:Depth to rock
Code Value
--------------
S = Shallow ( < 40 cm)
M...
Full Abstract
DR:Depth to rock
The list of authorised codes and their corresponding meanings is given in the following table:
DR:Depth to rock
Code Value
--------------
S = Shallow ( < 40 cm)
M = Moderate (40 - 80 cm)
D = Deep (80 - 120 cm)
V = Very deep ( > 120 cm)
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2001-01 |
SLOPESE: Secondary Slope class |
SLOPE-SEC: Secondary Slope class of the STU.
The SLOPE SEC attribute provides an option to indicate a secondary slope class when slope variability within an STU is important and some parts of the...
Full Abstract
SLOPE-SEC: Secondary Slope class of the STU.
The SLOPE SEC attribute provides an option to indicate a secondary slope class when slope variability within an STU is important and some parts of the STU fall into a different slope class than that of the dominant one. If there is no variability or if the variability is unknown, the value of SLOPE DOM must be copied to SLOPE SEC . The list of authorised codes and their corresponding meanings is given in the following tables for attributes SLOPE SEC:
SLOPE-SEC: Secondary Slope class of the STU
Code Value
--------------
0 No information
1 Level (dominant slope ranging from 0 to 8 %)
2 Sloping (dominant slope ranging from 8 to 15 %)
3 Moderately steep (dominant slope ranging from 15
to 25 %)
4 Steep (dominant slope over 25 %)
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2001-01 |
MIN_TOP: Topsoil Mineralogy |
MIN_TOP:Topsoil Mineralogy
The list of authorised codes and their corresponding meanings is given in the following table:
MIN_TOP:Topsoil Mineralogy
Code Value
--------------
KQ = 1/1...
Full Abstract
MIN_TOP:Topsoil Mineralogy
The list of authorised codes and their corresponding meanings is given in the following table:
MIN_TOP:Topsoil Mineralogy
Code Value
--------------
KQ = 1/1 Minerals + Quartz
KX = 1/1 Min. + Oxy. & Hydroxy.
MK = 2/1 & 1/1 Minerals
M = 2/1 & 2/1/1 non swel. Min.
MS = Swel. & non swel. 2/1 Min.
S = Swelling 2/1 Minerals
TV = Vitric Minerals
TO = Andic Minerals
NA = Not applicable
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2001-01 |
TXDEPCHG:Depth class to a textural change |
TXDEPCHG:Depth class to a textural change
If a textural contrast is present within the soil profile, then this change of textural class for the STU must be recorded in attribute TEXT-DEP-CHG....
Full Abstract
TXDEPCHG:Depth class to a textural change
If a textural contrast is present within the soil profile, then this change of textural class for the STU must be recorded in attribute TEXT-DEP-CHG. The textural contrast is recorded for both areas with dominant and secondary surface textures, when necessary.
TEXT-DEP-CHG: Depth class to a textural change of the dominant and/or secondary surface texture of the STU
----------------
0 No information
1 Textural change between 20 and 40 cm depth
2 Textural change between 40 and 60 cm depth
3 Textural change between 60 and 80 cm depth
4 Textural change between 80 and 120 cm depth
5 No textural change between 20 and 120 cm depth
6 Textural change between 20 and 60 cm depth
7 Textural change between 60 and 120 cm depth
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2001-01 |
ERODI: Soil erodibility class |
ERODI:Soil erodibility class
The list of authorised codes and their corresponding meanings is given in the following table:
ERODI:Soil erodibility class
Code Value
--------------
1 =...
Full Abstract
ERODI:Soil erodibility class
The list of authorised codes and their corresponding meanings is given in the following table:
ERODI:Soil erodibility class
Code Value
--------------
1 = Very weak
2 = Weak
3 = Moderate
4 = Strong
5 = Very strong
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2001-01 |
BS_SUB: Base Subsoil Saturation |
BS_SUB: Base saturation of the subsoil
The list of authorised codes and their corresponding meanings is given in the following table:
BS_SUB: Base saturation of the subsoil
Code Value...
Full Abstract
BS_SUB: Base saturation of the subsoil
The list of authorised codes and their corresponding meanings is given in the following table:
BS_SUB: Base saturation of the subsoil
Code Value
--------------
H = High ( > 50 %)
L = Low ( < 50 %)
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2001-01 |
ALT: Elevation |
ALT:Elevation
The list of authorised codes and their corresponding meanings is given in the following table:
ALT:Elevation
Code Value
--------------
# = No information
L = Lowlands...
Full Abstract
ALT:Elevation
The list of authorised codes and their corresponding meanings is given in the following table:
ALT:Elevation
Code Value
--------------
# = No information
L = Lowlands & intermediate
U = Uplands & mountains
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2001-01 |
EAWC_TOP: Topsoil easily available water capacity |
EAWC_TOP: Topsoil easily available water capacity.
The list of authorised codes and their corresponding meanings is given in the following table:
EAWC_TOP: Topsoil easily available water...
Full Abstract
EAWC_TOP: Topsoil easily available water capacity.
The list of authorised codes and their corresponding meanings is given in the following table:
EAWC_TOP: Topsoil easily available water capacity.
Code Value
--------------
L = Low ( < 100 mm/m)
M = Medium (100 - 140 mm/m)
H = High (140 - 190 mm/m)
VH = Very high ( > 190 mm/m)
# = No data or not applicable
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2001-01 |
WRB-ADJ2: Second soil adjective code |
WRBADJ2:2nd Soil Adjective Code
WRB-ADJ2: Second soil adjective code of the STU from the World Reference Base (WRB) for Soil Resources
WRB-ADJ2: Second soil adjective code of the STU from...
Full Abstract
WRBADJ2:2nd Soil Adjective Code
WRB-ADJ2: Second soil adjective code of the STU from the World Reference Base (WRB) for Soil Resources
WRB-ADJ2: Second soil adjective code of the STU from the World Reference Base (WRB) for Soil Resources
Code Value
--------------
II Lamellic
Iv Luvic
Ix Lixic
ab Albic
ac Acric
ad Aridic
ae Aceric
ah Anthropic
ai Aric
al Alic
am Anthric
an Andic
ao Acroxic
ap Abruptic
aq Anthraquic
ar Arenic
au Alumic
ax Alcalic
az Arzic
ca Calcaric
cb Carbic
cc Calcic
ch Chernic
cl Chloridic
cn Carbonatic
cr Chromic
ct Cutanic
cy Cryic
dn Densic
du Duric
dy Dystric
es Eutrisilic
et Entic
eu Eutric
fg Fragic
fi Fibric
fl Ferralic
fo Folic
fr Ferric
fu Fulvic
fv Fluvic
ga Garbic
gc Glacic
ge Gelic
gi Gibbsic
gl Gleyic
gm Grumic
gp Gypsiric
gr Geric
gs Glossic
gt Gelistagnic
gy Gypsic
gz Greyic
ha Haplic
hg Hydragric
hi Histic
hk Hyperskeletic
ht Hortic
hu Humic
hy Hydric
ir Irragric
le Leptic
li Lithic
me Melanic
mg Magnesic
mo Mollic
ms Mesotrophic
mz Mazic
na Natric
ni Nitic
oa Oxyaquic
oh Ochric
om Ombric
or Orthic
pa Plaggic
pc Petrocalcic
pd Petroduric
pe Pellic
pf Profondic
pg Petrogypsic
ph Pachic
pi Placic
pl Plinthic
pn Planic
po Posic
pp Petroplinthic
pr Protic
ps Petrosalic
pt Petric
rd Reductic
rg Regic
rh Rheic
ro Rhodic
rp Ruptic
rs Rustic
ru Rubic
rz Rendzic
sa Sapric
sd Spodic
si Silic
sk Skeletic
sl Siltic
so Sodic
sp Spolic
st Stagnic
su Sulphatic
sz Salic
tf Tephric
ti Thionic
tr Terric
tu Turbic
tx Toxic
ty Takyric
ub Urbic
um Umbric
vi Vitric
vm Vermic
vr Vertic
vt Vetic
xa Xanthic
ye Yermic
1 Town
2 Soil disturbed by man
3 Water body
4 Marsh
5 Glacier
6 Rock outcrops
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2001-01 |
PD_SUB: Subsoil Packing Density |
PD_SUB:Subsoil Packing Density
The list of authorised codes and their corresponding meanings is given in the following table:
PD_SUB:Subsoil Packing Density
Code Value
--------------
L...
Full Abstract
PD_SUB:Subsoil Packing Density
The list of authorised codes and their corresponding meanings is given in the following table:
PD_SUB:Subsoil Packing Density
Code Value
--------------
L = Low
M = Medium
H = High
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2001-01 |
CEC_SUB: Subsoil Cation Exchange Capacity |
CEC_SUB: Subsoil cation exchange capacity
The list of authorised codes and their corresponding meanings is given in the following table:
CEC_SUB: Subsoil cation exchange capacity
Code Value...
Full Abstract
CEC_SUB: Subsoil cation exchange capacity
The list of authorised codes and their corresponding meanings is given in the following table:
CEC_SUB: Subsoil cation exchange capacity
Code Value
--------------
H = High ( > 40 cmol(+)/kg)
M = Medium (15-40 cmol(+)/kg)
L = Low ( < 15 cmol(+)/kg)
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2001-01 |
Saline and Sodic Soils |
The Saline and Sodic Soils Map shows the area distribution of saline, sodic and potentially salt affected areas within the European Union. The accuracy of input input data only allows the designation...
Full Abstract
The Saline and Sodic Soils Map shows the area distribution of saline, sodic and potentially salt affected areas within the European Union. The accuracy of input input data only allows the designation of salt affected areas with a limited level of reliability (e.g. < 50 or > 50% of the area); therefore the results represented in the map should only be used for orientating purposes.In total there are 5 categories:
Saline and Sodic Soils
Code Value
--------------
1 - Saline > 50% of the area
2 - Sodic > 50% of the area
3 - Saline < 50% of the area
4 - Sodic < 50% of the area
5 - Potentially salt affected soils.
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2001-01 |
AWC_SUB: Subsoil available water capacity |
AWC_SUB: Subsoil available water capacity
The list of authorised codes and their corresponding meanings is given in the following table:
AWC_SUB: Subsoil available water capacity
Code...
Full Abstract
AWC_SUB: Subsoil available water capacity
The list of authorised codes and their corresponding meanings is given in the following table:
AWC_SUB: Subsoil available water capacity
Code Value
--------------
VL = Very low ( ~ 0 mm/m)
L = Low ( < 100 mm/m)
M = Medium (100 - 140 mm/m)
H = High (140 - 190 mm/m)
VH = Very high ( > 190 mm/m)
# = No data or not applicable
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2001-01 |
DIMP: Depth to an impermeable layer |
DIMP:Depth to an impermeable layer
The list of authorised codes and their corresponding meanings is given in the following table:
DIMP:Depth to an impermeable layer
Code Value...
Full Abstract
DIMP:Depth to an impermeable layer
The list of authorised codes and their corresponding meanings is given in the following table:
DIMP:Depth to an impermeable layer
Code Value
--------------
S = Shallow ( < 80 cm)
D = Deep ( > 80 cm)
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2001-01 |
MIN_SUB: Subsoil Mineralogy |
MIN_SUB:Subsoil Mineralogy
The list of authorised codes and their corresponding meanings is given in the following table:
MIN_SUB:Subsoil Mineralogy
Code Value
--------------
KQ = 1/1...
Full Abstract
MIN_SUB:Subsoil Mineralogy
The list of authorised codes and their corresponding meanings is given in the following table:
MIN_SUB:Subsoil Mineralogy
Code Value
--------------
KQ = 1/1 Minerals + Quartz
KX = 1/1 Min. + Oxy. & Hydroxy.
MK = 2/1 & 1/1 Minerals
M = 2/1 & 2/1/1 non swel. Min.
MS = Swel. & non swel. 2/1 Min.
S = Swelling 2/1 Minerals
TV = Vitric Minerals
TO = Andic Minerals
NA = Not applicable
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2001-01 |
peat |
Peat
The list of authorised codes and their corresponding meanings is given in the following table:
Peat
Code Value
--------------
N = No
Y = Yes
Full Abstract
Peat
The list of authorised codes and their corresponding meanings is given in the following table:
Peat
Code Value
--------------
N = No
Y = Yes
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2001-01 |
DGH: Depth to a gleyed horizon |
DGH:Depth to a gleyed horizon
The list of authorised codes and their corresponding meanings is given in the following table:
DGH:Depth to a gleyed horizon
Code Value
--------------
S =...
Full Abstract
DGH:Depth to a gleyed horizon
The list of authorised codes and their corresponding meanings is given in the following table:
DGH:Depth to a gleyed horizon
Code Value
--------------
S = Shallow ( < 40 cm)
M = Moderate (40 - 80 cm)
D = Deep (80 - 120 cm)
V = Very deep ( > 120 cm)
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2001-01 |
TXSUBSE:Sec.Sub-surface text.class |
TXSUBSE:Sec.Sub-surface text.class
A Soil Typological Unit (STU) can have contrasted sub-surface textures that fall in two different textural classes. The secondary sub-surface textural class (...
Full Abstract
TXSUBSE:Sec.Sub-surface text.class
A Soil Typological Unit (STU) can have contrasted sub-surface textures that fall in two different textural classes. The secondary sub-surface textural class (TEXT-SUB-SEC) is used to indicate which sub-surface texture is less extensive than the dominant one. Together the TEXT-SUB-DOM and the TEXT-SUB-SEC attributes reflect the lateral variability of the sub-surface horizon texture within the STU. If there is no such variability or if there is no information, the value of TEXT-SUB-DOM must also be entered for TEXT-SUB-SEC.
TEXT-SUB-SEC :Secondary sub-surface textural class of the STU
----------------
0 No information
9 No mineral texture (Peat soils)
1 Coarse (18% < clay and > 65% sand)
2 Medium (18% < clay < 35% and >= 15% sand, or 18% <
clay and 15% < sand < 65%)
3 Medium fine (< 35% clay and < 15% sand)
4 Fine (35% < clay < 60%)
5 Very fine (clay > 60 %)
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2001-01 |
PARMADO1:Major group code for the dominant parent material |
PARMADO1:Major group code for the dominant parent material
PAR-MAT-DOM1: Major group code for the dominant parent material of the STU.
PAR-MAT-DOM1: Major group code for the dominant parent...
Full Abstract
PARMADO1:Major group code for the dominant parent material
PAR-MAT-DOM1: Major group code for the dominant parent material of the STU.
PAR-MAT-DOM1: Major group code for the dominant parent material of the STU
Code Value
--------------
0 No information
1 consolidated-clastic-sedimentary rocks
2 sedimentary rocks (chemically precipitated,
evaporated, or organogenic or biogenic in origin)
3 igneous rocks
4 metamorphic rocks
5 unconsolidated deposits (alluvium, weathering
residuum and slope deposits)
6 unconsolidated glacial deposits/glacial drift
7 eolian deposits
8 organic materials
9 anthropogenic deposits
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2001-01 |
BS_TOP: Base Topsoil Saturation |
BS_TOP: Base saturation of the topsoil
The list of authorised codes and their corresponding meanings is given in the following table:
BS_TOP: Base saturation of the topsoil)
Code Value...
Full Abstract
BS_TOP: Base saturation of the topsoil
The list of authorised codes and their corresponding meanings is given in the following table:
BS_TOP: Base saturation of the topsoil)
Code Value
--------------
H = High ( > 75 %)
M = Medium (50 - 75 %)
L = Low ( < 50 %)
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2001-01 |
CRUSTING: Soil crusting class |
CRUSTING:Soil crusting class
The list of authorised codes and their corresponding meanings is given in the following table:
CRUSTING:Soil crusting class
Code Value
--------------
1 =...
Full Abstract
CRUSTING:Soil crusting class
The list of authorised codes and their corresponding meanings is given in the following table:
CRUSTING:Soil crusting class
Code Value
--------------
1 = Very weak
2 = Weak
3 = Moderate
4 = Strong
5 = Very strong
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2001-01 |
AGLIM2:Secondary limitation to agricultural use |
No abstract provided
Full Abstract
No abstract provided
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2001-01 |
ROO: Depth Class of obstacle to roots |
ROO:Depth Class of obstacle to roots
ROO: Depth class of an obstacle to roots within the STU
An obstacle to roots is defined as a subsoil horizon restricting root penetration. It can be of...
Full Abstract
ROO:Depth Class of obstacle to roots
ROO: Depth class of an obstacle to roots within the STU
An obstacle to roots is defined as a subsoil horizon restricting root penetration. It can be of lithologic origin (lithic contact), or pedogenic origin (fragipan, duripan, petrocalcic or petroferric horizons), or can result from the accumulation of toxic elements, or from waterlogging.
The ROO attribute holds the depth class of an obstacle to roots within the STU. The list of authorised codes and their corresponding meanings is given in the following table for attribute ROO:
ROO: Depth class of an obstacle to roots within the STU
Code Value
--------------
0 No information
1 No obstacle to roots between 0 and 80 cm
2 Obstacle to roots between 60 and 80 cm depth
3 Obstacle to roots between 40 and 60 cm depth
4 Obstacle to roots between 20 and 40 cm depth
5 Obstacle to roots between 0 and 80 cm depth
6 Obstacle to roots between 0 and 20 cm depth
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2001-01 |
ATC: Accumulated temperature class |
ATC: Regrouped accumulated mean annual temperature class (ATC) (source: JRC-MARS)
The list of authorised codes and their corresponding meanings is given in the following table:
ATC: Regrouped...
Full Abstract
ATC: Regrouped accumulated mean annual temperature class (ATC) (source: JRC-MARS)
The list of authorised codes and their corresponding meanings is given in the following table:
ATC: Regrouped accumulated mean annual temperature class (ATC) (source: JRC-MARS)
Code Value
--------------
H: High (> 3000°C)
M: Medium (1800-3000°C)
L: Low (< 1800°C)
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2001-01 |
DIFF: Soil profile differentiation |
DIFF: Soil profile differentiation
The list of authorised codes and their corresponding meanings is given in the following table:
DIFF: Soil profile differentiation
Code Value...
Full Abstract
DIFF: Soil profile differentiation
The list of authorised codes and their corresponding meanings is given in the following table:
DIFF: Soil profile differentiation
Code Value
--------------
H = High differentiation
L = Low differentiation
O = No differentiation
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2001-01 |
PARMASE:Secondary Parent Material |
PARMASE:Secondary Parent Material
The PAR-MAT-SEC attribute provides the option to indicate a secondary parent material code when parent material variability within an STU is important and some...
Full Abstract
PARMASE:Secondary Parent Material
The PAR-MAT-SEC attribute provides the option to indicate a secondary parent material code when parent material variability within an STU is important and some parts of the STU fall into a different parent material class than that of the dominant one.
PAR-MAT-SEC:Code for secondary parent material of the STU
Code Value
--------------
0 No information
1000 consolidated-clastic-sedimentary rocks
1100 psephite or rudite
1110 conglomerate
1111 pudding stone
1120 breccia
1200 psammite or arenite
1210 sandstone
1211 calcareous sandstone
1212 ferruginous sandstone
1213 clayey sandstone
1214 quartzitiic sandstone/orthoquartzite
1215 micaceous sandstone
1220 arkose
1230 graywacke
1231 feldspathic graywacke
1300 pelite, lutite or argilite
1310 claystone/mudstone
1311 kaolinite
1312 bentonite
1320 siltstone
1400 facies bound rock
1410 flysch
1411 sandy flisch
1412 clayey and silty flysch
1413 conglomeratic flysch
1420 molasse
2000 sedimentary rocks (chemically precipitated,
evaporated, or organogenic or biogenic in origin)
2100 calcareous rocks
2110 limestone
2111 hard limestone
2112 soft limestone
2113 marly limestone
2114 chalky limestone
2115 detrital limestone
2116 carbonaceous limestone
2117 lacustrine or freshwater limestone
2118 travertine/calcareous sinter
2119 cavernous limestone
2120 dolomite
2121 cavernous dolomite
2122 calcareous dolomite
2130 marlstone
2140 marl
2141 chalk marl
2142 gypsiferous marl
2150 chalk
2200 evaporites
2210 gypsum
2220 anhydrite
2230 halite
2300 siliceous rocks
2310 chert, hornstone, flint
2320 diatomite/radiolarite
3000 igneous rocks
3100 acid to intermediate plutonic rocks
3110 granite
3120 granodiorite
3130 diorite
3131 quartz diorite
3132 gabbro diorite
3140 syenite
3200 basic plutonic rocks
3210 gabbro
3300 ultrabasic plutonic rocks
3310 peridotite
3320 pyroxenite
3400 acid to intermediate volcanic rocks
3410 rhyolite
3411 obsidian
3412 quartz porphyrite
3420 dacite
3430 andesite
3431 porphyrite (interm,)
3440 phonolite
3441 tephritic phonolite
3450 trachyte
3500 basic to ultrabasic volcanic rocks
3510 basalt
3520 diabase
3530 pikrite
3600 dike rocks
3610 aplite
3620 pegmatite
3630 lamprophyre
3700 pyroclastic rocks (tephra)
3710 tuff/tuffstone
3711 agglomeratic tuff
3712 block tuff
3713 lapilli tuff
3720 tuffite
3721 sandy tuffite
3722 silty tuffite
3723 clayey tuffite
3730 volcanic scoria/volcanic breccia
3740 volcanic ash
3750 ignimbrite
3760 pumice
4000 metamorphic rocks
4100 weakly metamorphic rocks
4110 (meta-)shale/argilite
4120 slate
4121 graphitic slate
4200 acid regional metamorphic rocks
4210 (meta-)quartzite
4211 quartzite schist
4220 phyllite
4230 micaschist
4240 gneiss
4250 granulite (sensu stricto)
4260 migmatite
4300 basic regional metamorphic rocks
4310 greenschist
4311 prasinite
4312 chlorite
4313 talc schist
4320 amphibolite
4330 eclogite
4400 ultrabasic regional metamorphic rocks
4410 serpentinite
4411 greenstone
4500 calcareous regional metamorphic rocks
4510 marble
4520 calcschist, skam
4600 rocks formed by contact metamorphism
4610 contact slate
4611 nodular slate
4620 hornfels
4630 calsilicate rocks
4700 tectogenetic metamorphism rocks or cataclasmic
metamorphism
4710 tectonic breccia
4720 cataclasite
4730 mylonite
5000 unconsolidated deposits (alluvium, weathering
residuum and slope deposits)
5100 marine and estuarine sands
5110 pre-quaternary sand
5111 tertiary sand
5120 quaternary sand
5121 holocene coastal sand with shells
5122 delta sand
5200 marine and estuarine clays and silts
5210 pre-quaternary clay and silt
5211 tertiary clay
5212 tertiary silt
5220 quaternary clay and silt
5221 holocene clay
5222 holocene silt
5300 fluvial sands and gravels
5310 river terrace sand or gravel
5311 river terrace sand
5312 river terrace gravel
5320 floodplain sand or gravel
5321 floodplain sand
5322 floodplain gravel
5400 fluvial clays, silts and loams
5410 river clay and silt
5411 terrace clay and silt
5412 floodplain clay and silt
5420 river loam
5421 terrace loam
5430 overbank deposit
5431 floodplain clay and silt
5432 floodplain loam
5500 lake deposits
5510 lake sand and delta sand
5520 lake marl, bog lime
5530 lake silt
5600 residual and redeposited loams from silicate rocks
5610 residual loam
5611 stony loam
5612 clayey loam
5620 redeposited loam
5621 running-ground
5700 residual and redeposited clays from calcareous
rocks
5710 residual clay
5711 clay with flints
5712 ferruginous residual clay
5713 calcareous clay
5714 non-calcareous clay
5715 marly clay
5720 redeposited clay
5721 stony clay
5800 slope deposits
5810 slope-wash alluvium
5820 colluvial deposit
5830 talus scree
5831 stratified slope deposits
6000 unconsolidated glacial deposits/glacial drift
6100 morainic deposits
6110 glacial till
6111 boulder clay
6120 glacial debris
6200 glaciofluvial deposits
6210 outwash sand, glacial sand
6220 outwash gravels glacial gravels
6300 glaciolacustrine deposits
6310 varves
7000 eolian deposits
7100 loess
7110 loamy loess
7120 sandy loess
7200 eolian sands
7210 dune sand
7220 cover sand
8000 organic materials
8100 peat (mires)
8110 rainwater fed moor peat (raised bog)
8111 folic peat
8112 fibric peat
8113 terric peat
8120 groundwater fed bog peat
8200 slime and ooze deposits
8210 gyttja, sapropel
8300 carbonaceaous rocks (caustobiolite)
8310 lignite (brown coal)
8320 hard coal
8330 anthracite
9000 anthropogenic deposits
9100 redeposited natural materials
9110 sand and gravel fill
9120 loamy fill
9200 dump deposits
9210 rubble/rubbish
9220 industrial ashes and slag
9230 industrial sludge
9240 industrial waste
9300 anthropogenic organic materials
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2001-01 |
TXTCRUST: Textural factor of soil crusting |
TXTCRUST:Textural factor of soil crusting
The list of authorised codes and their corresponding meanings is given in the following table:
TXTCRUST: Textural factor of soil crusting
Code Value...
Full Abstract
TXTCRUST:Textural factor of soil crusting
The list of authorised codes and their corresponding meanings is given in the following table:
TXTCRUST: Textural factor of soil crusting
Code Value
--------------
1 = Very favourable
2 = Favourable
3 = Medium
4 = Unfavourable
5 = Very unfavourable
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2001-01 |
STR_TOP: Topsoil structure |
STR_TOP:Topsoil structure
The list of authorised codes and their corresponding meanings is given in the following table:
STR_TOP:Topsoil structure
Code Value
--------------
G = Good
N...
Full Abstract
STR_TOP:Topsoil structure
The list of authorised codes and their corresponding meanings is given in the following table:
STR_TOP:Topsoil structure
Code Value
--------------
G = Good
N = Normal
P = Poor
H = Humic or Peaty topsoil
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