Soil Organic Carbon (SOC) Projections for Europe

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).
Resource Type: 
Registration is requested: 
Yes
Author - Contributors: 
Yusuf Yigini and Panos Panagos, European Commission
Year: 
2016

A number of data layers are provided that accompany the publication "Assessment of soil organic carbon stocks under future climate and land cover changes in Europe" by Yusuf Yigini and Panos Panagos in "Science of The Total Environment, Volumes 557–558, 1 July 2016, Pages 838–850" (http://dx.doi.org/10.1016/j.scitotenv.2016.03.085)

Soil organic carbon plays an important role in the carbon cycling of terrestrial ecosystems, variations in soil organic carbon stocks are very important for the ecosystem. Soil is the largest organic carbon pool of the terrestrial ecosystems on earth which interacts strongly with climate, and land cover change. In this study, a geo-statistical model is used to estimate the current and the future soil organic carbon (SOC) stocks in Europe.

A geo-statistical approach is proposed to achieve spatiotemporal prediction of soil organic carbon stocks in Europe. The model consists of two sub-models (Figure 1). The base model predicts current soil organic carbon stocks at European scale using regression-kriging, and future model uses the regression coefficients and projects the estimation to the near future (2050). 

   

Figure 1. Prediction and Projection Workflow

 

Model and Model Outputs

It was hypothesized that soil organic carbon is driven largely by climate, land use and inherent soil properties. Moreover, it is anticipated that the complex relationship between soil organic carbon and its drivers is time independent and will remain in the future. From this point of view, the covariates which have been used to predict current soil organic carbon stocks in Europe can also help to predict future conditions by transferring the knowledge from today to the future.

The first phase of the study predicts current soil organic carbon content (Figure 2) by using stepwise multiple linear regression and ordinary kriging and the second phase of the study projects the soil organic carbon to the near future (2050) by using a set of environmental predictors (Table 1). An approach is demonstrated to predict present and future soil organic carbon stocks by using climate, land cover, terrain and soil data and their projections.  The covariates were selected for their role in the carbon cycle and their availability for the future model. The regression-kriging as a base model is predicting current SOC stocks in Europe by using a set of covariates and dense SOC measurements coming from LUCAS Soil Database. The base model delivers coefficients for each of the covariates to the future model.  The overall model produced soil organic carbon maps which reflect the present and the future predictions (2050) based on climate and land cover projections.  The data of the present climate conditions (long-term average (1950 - 2000)) and the future projections for 2050 were obtained from WorldClim data portal. The future climate projections are the recent climate projections mentioned in the Fifth Assessment IPCC report. These projections were extracted from the global climate models (GCMs) for four representative concentration pathways (RCPs). The results suggest an overall increase in SOC stocks by 2050 in Europe (EU26) under all climate and land cover scenarios, but the extent of the increase varies between the climate model and emissions scenarios.

Figure 2. Soil organic carbon prediction map which represents the present conditions simulated by the base model (background map: ESRI, USGS, NOAA).

 

Table 1. Present and Projected Soil organic Carbon Stocks (Pg) for EU26. (Cyprus and Croatia were excluded due to data unavailability)

 

Available Data:

  • Soil Organic Carbon Stocks (Current), tonnes.ha-1
  • Soil Organic Carbon Stocks (2050) by Climate Scenarios (CCSM4, HadGEM2-AO , IPSL-CM5A-LR  MRI-CGCM3) and Representative Concentration Pathways (RCPs). (EU26), tonnes.ha-1

Metadata for "Soil Organic Carbon Stocks (Current)"

Spatial Coverage: European Union, 26 Member States (no data for Cyprus and Croatia)

Resolution: 1000m

Format: Raster (GRID)

Projection: ETRS89 Lambert Azimuthal Equal Area

Input data:

Climate Data (Current) from WorldClim Data Portal:  Bio-climatic parameters, Annual Precipitation, Grid Size:  1000m

Land Cover 2010, European Commission, Joint Research Centre, Sustainability Assessment Unit: Pan-European Land Use Modelling Platform (LUMP), Grid Size:  1000m

Soil Data, Joint Research Centre European Soil Database, Ballabio et al. 2016: Clay, Silt, Sand, Soil Structure, Available Water Capacity, Grid Size:  1000m

Output Data Layers:

SOC_Stocks_EU26 : Current Predication of European Soil Organic Carbon Stocks (tonnes.ha-1)

 

Metadata for "Soil Organic Carbon Stocks (2050) by Climate Scenarios (CCSM4, HadGEM2-AO , IPSL-CM5A-LR  MRI-CGCM3) and Representative Concentration Pathways (RCPs). (EU26), tonnes.ha-1"

Spatial Coverage: European Union, 26 Member States (no data for Cyprus and Croatia)

Resolution: 1000m

Format: Raster (GRID)

Projection: ETRS89 Lambert Azimuthal Equal Area

Input data:

Climate Data (2050), WorldClim Data Portal:  Bio-climatic parameters, Annual Precipitation, Grid Size:  1000m

Land Cover 2050, European Commission, Joint Research Centre, Sustainability Assessment Unit: Pan-European Land Use Modelling Platform (LUMP), Grid Size:  1000m

Soil Data, Joint Research Centre European Soil Database, Ballabio et al. 2016: Clay, Silt, Sand, Soil Structure, Available Water Capacity, Grid Size:  1000m

Output Data Layers:  Projected Soil Organic Carbon Stocks (2050) by Climate Scenarios (CCSM4, HadGEM2-AO , IPSL-CM5A-LR  MRI-CGCM3), (tonnes.ha-1) - the names are self-explanatory after reading the paper (http://dx.doi.org/10.1016/j.scitotenv.2016.03.085)

  • cc426: CCSM4, RCP 2.6
  • cc445: CCSM4, RCP 4.5
  • cc460: CCSM4, RCP 6
  • cc485: CCSM4, RCP 8.5
  • hd26: HadGEM2-AO, RCP 2.6
  • hd45: HadGEM2-AO, RCP 4.5
  • hd60: HadGEM2-AO, RCP 6
  • hd85: HadGEM2-AO, RCP 8.5
  • ip26: IPSL-CM5A-LR, RCP 2.6
  • ip45: IPSL-CM5A-LR, RCP 4.5
  • ip60: IPSL-CM5A-LR, RCP 6
  • ip85: IPSL-CM5A-LR, RCP 8.5
  • mg26: MRI-CGCM3, RCP 2.6
  • mg45: MRI-CGCM3, RCP 4.5
  • mg60: MRI-CGCM3, RCP 6
  • mg85: MRI-CGCM3, RCP 8.5

 

 

 

 

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