Scholarly article on topic 'Assessment of soil organic carbon stocks under future climate and land cover changes in Europe'

Assessment of soil organic carbon stocks under future climate and land cover changes in Europe Academic research paper on "Earth and related environmental sciences"

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{"Soil organic carbon" / "Land cover change" / "Climate change" / Regression-kriging / "Climate scenarios" / "LUCAS Soil Survey"}

Abstract of research paper on Earth and related environmental sciences, author of scientific article — Yusuf Yigini, Panos Panagos

Abstract 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. In this study, a geostatistical model was used for predicting current and future soil organic carbon (SOC) stocks in Europe. The first phase of the study predicts current soil organic carbon content 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. We demonstrate here an approach 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.

Academic research paper on topic "Assessment of soil organic carbon stocks under future climate and land cover changes in Europe"

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Science of the Total Environment

journal homepage: www.elsevier.com/locate/scitotenv

Assessment of soil organic carbon stocks under future climate and land cover changes in Europe

Yusuf Yigini *, Panos Panagos

European Commission Joint Research Centre, Land Resource Management Unit, Via Enrico Fermi 2749,21027 Ispra, VA, Italy

CrossMark

HIGHLIGHTS

GRAPHICAL ABSTRACT

1 We predicted present and future SOC stocks using climate and land cover change scenarios.

1 The model produced two main outputs: present and future (2050) SOC stocks in Europe.

The results suggest an overall increase in SOC stocks by 2050 for selected Global Climate Models.

1 The extents of the increase in SOC stocks vary by different GCMs and their RCPs.

ARTICLE INFO

Article history:

Received 5 January 2016

Received in revised form 10 March 2016

Accepted 11 March 2016

Available online xxxx

Keywords: Soil organic carbon Land cover change Climate change Regression-kriging Climate scenarios LUCAS Soil Survey

ABSTRACT

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. In this study, a geostatistical model was used for predicting current and future soil organic carbon (SOC) stocks in Europe. The first phase of the study predicts current soil organic carbon content 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. We demonstrate here an approach 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.

© 2016 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license

(http://creativecommons.org/licenses/by/4.0/).

* Corresponding author.

E-mail addresses: yusuf.yigini@jrc.ec.europa.eu (Y. Yigini), panos.panagos@jrc.ec.europa.eu (P. Panagos).

http://dx.doi.org/10.10167j.scitotenv.2016.03.085

0048-9697/© 2016 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

1. Introduction

Soil is the largest organic carbon pool of the terrestrial ecosystems on earth which interacts strongly with atmospheric composition, climate, and land cover change (Jobbagy and Jackson, 2000). Soil organic carbon dynamics are driven by changes in climate and land cover or land use. In natural ecosystems, the balance of SOC is determined by the gains through plant and other organic inputs and losses due to the turnover of organic matter (Smith et al., 2008). In the soil ecosystem, soil organic carbon influences soil physical and chemical processes, and serves as a source of plant nutrients. The storage of organic carbon in the soil depends on the balance between gains and losses of C. Biotic characteristics such as biomass production and microbial abundance, mean annual precipitation and temperature, soil characteristics including texture and lithology and anthropogenic activities, like land use and management, influence the processes of SOC storage or losses. A clear description of the distribution and changes of SOC and its factors of control will help predict the consequences of climate change (Albaladejo et al., 2013).

Soil carbon stocks are strongly controlled by the climate and land cover and these main drivers, especially the land use patterns are changing rapidly by human activities. The climate and land-use changes are significantly visible, and their impacts on terrestrial ecosystems are increasingly being studied. There are numerous studies focused on the future climate and land-use change. One of the modelling platforms projecting the land use changes into the near future is LUISA (Land-Use-based Integrated Sustainability Assessment Modelling Platform). The platform is the Joint Research Centre's land use model which down-scales an aggregated amount of land use expected in the future (Maes et al., 2015). The LUISA is a modular modelling platform and land use change simulations take part in the allocation module. The platform's land cover projection data suggest an increase in forest cover and decrease in agricultural, pastures and wetland lands by 2050 in Europe. In much of continental Europe, the majority of forests are now growing faster than in the early 20th century (EEA, 2015).

During the last few decades, land use changes have largely affected the global warming process through emissions of CO2. However, C sequestration in terrestrial ecosystems could contribute to the decrease of atmospheric CO2 rates. Muñoz-Rojas (2012) studied impacts of LU changes on SOC stocks at a regional scale in Andalusia (Southern Spain). Muñoz-Rojas estimated SOC sequestration rates for different soil types and land cover flows for a period of 51 years, providing baseline information for future studies on C emissions, soil organic C modelling and mitigation scenarios associated with the land use change processes. While the intensification of agriculture between 1956 and 2007 has resulted in a general decrease of SOC stocks in Andalusia, soils like Arenosols have been largely affected by these transformations, in particular with changes from arable land to permanent crops. Remarkable positive rates of change of SOC stocks were found in Fluvisols and Luvisols as a result of the conversion to arable land or heterogeneous agricultural areas.

Another study by Qiu et al. (2013) carried out a study to understand spatial and temporal variations of soil organic carbon (SOC) under rapid urbanization and support soil and environmental management in Zhejiang Province, China. It is concluded that the average SOC in 2006 was 18.5 g-kg-1, significantly higher than 17.3 g-kg-1 in 1979. Although on average, this difference is small, it was greater in specific areas. The SOC measured in 2006 under peri-urban areas was higher than the under natural conditions. Extrinsic anthropogenic activities caused most of the spatial and temporal variations of the SOC. The study shows that the changes of agricultural use types and the transitions from agricultural to industrial or urbanised uses were the main factors influencing SOC (Qiu et al., 2013).

Another study by Poeplau and Axel (2013) was carried out in 24 paired study sites in Europe comprising the major European LUC types, cropland to grassland, grassland to cropland, cropland to forest and grassland to forest. The researchers found that the SOC

sequestration after grassland establishment on croplands equaled the SOC sequestration of cropland afforestation. Converting grassland to forest has no significant effect on the total SOC stock.

Climate conditions strongly influence both the trends and rates of accumulation and transformation of organic compounds in the soil. There is constant interaction between soil organic carbon and atmospheric CO2. Moreover, CO2 is currently the main driver of the long-term climate change. According to European Environment Agency's "Climate change, impacts and vulnerability in Europe 2012" report (EEA, 2012), the projected changes in the climate during the 21st century will change the contribution of soil to the CO2 cycle in most areas of the EU. Adapted land-use and management practices could be implemented to counterbalance the climate-induced decline of carbon levels in soil (EEA, 2015). Smith et al. (2006) reported that the climate change was found to be an important driver of change in forest soil organic carbon over the 21st century, projected forest management and land-use change will have greater effects, leading to only small losses or increasing European forest SOC stocks. According to same study climate change may cause loss of soil organic carbon for most areas in Europe. This decline could be reversed if adaptation measures in the agricultural sector to enhance soil carbon were implemented. It should be noted that these modelled projected changes are very uncertain.

Environmental issues such as land degradation and global climate change, require assessing soils in the context of ecosystem change and environmental stressors impacting control on soil properties (Grunwald, 2010). However, it is hard to make accurate predictions in very dynamic and complex environments such as soils. The data on soils is very often outdated, limited in coverage, and fragmented in nature. Predicting and mapping the soil properties with limited data needs more sophisticated analysis. Digital soil mapping (DSM) is increasingly gaining worldwide acceptance as a means for fulfilling the demand for accurate soil information at different spatial resolutions and extent (Omuto and Vargas, 2014). Numerous environmental and socioeconomic models require soil parameters as inputs to estimate and forecast changes in our future life conditions. However, the availability of soil data is limited on both national and European scales. European countries are great reservoirs of existing large and medium scale soil maps, many still in paper form. The major limitation of such kind of data is the lack of exact geographic positioning (Jones et al., 2005a). In these existing data sources, soil information is either missing at the appropriate scale, its meaning is not well explained for reliable interpretation, or the quality of the data is questionable (Dobos et al., 2006a, 2006b). Digital soil mapping has evolved as a discipline linking field, laboratory, and proximal soil observations with quantitative methods to infer on spatial patterns of soils across various spatial and temporal scales. Studies use various approaches to predict soil properties or classes including univariate and multivariate statistical, geostatistical and hybrid methods, and process-based models that relate soils to environmental covariates considering spatial and temporal dimensions (Grunwald, 2010).

Statistical models are the functions that predict soil classes or soil properties from soil covariates or available soil data (Lagacherie and McBratney, 2007). These are the functions that predict soil properties or soil classes. Most of these models have been calibrated with soil samplings and have been tested over small areas. The limitation of soil sampling dense enough to capture the spatial variability and limit the use of numerical models to for large areas (Hartemink et al., 2008).

Prediction of soil organic carbon stocks has become a key issue over recent years, because of the potential impacts of carbon on climate change. Spatial prediction of soil organic carbon stocks has received significant attention because of the large variation of SOC at all scales from national to field, and also due to the expense of obtaining accurate measurements of SOC. As a result, research into approaches to improve spatial prediction of SOC stock is on-going (Minasny et al., 2013).

The method that we used in this study is regression-kriging which is a spatial interpolation technique that combines a regression of the

dependent variable on predictors with simple kriging of the regression residuals. In other words, Regression-Kriging is a hybrid method that combines either a simple or a multiple-linear regression model with ordinary, or simple kriging of the regression residuals (Odeh et al., 1995; McBratney et al., 2000).

In this study, we address two key challenges. First, predicting present soil organic carbon stocks by using a digital soil mapping technique. Moreover, the second challenge was, projecting our prediction into the near future by testing a new method which relies on a geostatistical concept.

2. Materials and methods

2.1. Study area and the point data

The point data derived from LUCAS Topsoil Database, which is a large dataset representing European soils. LUCAS Soil is a module of LUCAS Survey project. The objective of the soil module was to improve the availability of harmonized data on soil parameters in Europe. In the LUCAS Soil (2009) survey, 265,000 geo-referenced points were visited by more than 500 field surveyors (Toth et al., 2013). The survey points were selected from a standard 2 km x 2 km grid based on stratification information provided by Martino and Fritz (2008). LUCAS Soil topsoil samples (0-20 cm) were collected from 10% of the survey points, thus providing approximately 22,300 soil samples from European Countries. The selection of the LUCAS soil sampling sites has an inherent bias towards agricultural land (predominantly under arable cultivation), followed by grasslands and woodlands. This bias means that results based exclusively on LUCAS soil samples may over represent properties from the more heavily sampled conditions whiles underrepresenting other land cover types. Each soil sample was taken from the topsoil zone (top 20 cm) with a weight of 0.5 kg. The samples were analysed in a single ISO-certified laboratory that used harmonized chemical and physical analytical methods (ISO standards, or their equivalent) in order to obtain a coherent and harmonized dataset with pan-European coverage. The analysis results formed the LUCAS soil database, including, among other things, SOC in topsoils (0-20 cm) expressed in g-kg—1 (Panagos et al., 2013a). The dataset were divided into calibration (n = 20.056) and validation (n = 2228) datasets. The validation dataset is a subset of the main database and was put aside for validation phase and used to assess the performance of model built in the fitting phase. The basic statistics of the input dataset are shown in Table 1.

22. Climate data and climate scenarios

The data expressing current conditions were obtained from WorldClim Data Portal (Hijmans et al., 2005). These data layers were generated through interpolation of average monthly climate data from weather stations on a 30 arc-second resolution grid (often referred to as "1 km2" resolution). The study relies on a database consisted of precipitation records from 47,554 locations, mean temperature from 24,542 locations, and minimum and maximum temperatures from 14,835 locations (Hijmans et al., 2005). Variables included are total monthly precipitation, and monthly mean, minimum and maximum temperature, and 19 derived bioclimatic variables. The Bioclimatic layers are: annual mean temperature, mean diurnal range (mean of monthly (max temp — min temp)), isothermality, temperature seasonality, max temperature of warmest month, minimum temperature of

Table 1

Variation of soil organic carbon between the fitting and validation datasets (10%).

Fitting dataset (n = 20.056) Validation dataset (n = 2228)

Mean (SOCg-kg-1) StD (SOCg-kg-1 ) Mean (SOCg-kg-1) StD (SOCg-kg-1)

47.46 88.8 46.83 84.7

coldest month, temperature annual range, mean temperature of wettest quarter, mean temperature of driest quarter, mean temperature of warmest quarter, mean temperature of coldest quarter, annual precipitation, precipitation of wettest month, precipitation of driest month, precipitation seasonality (coefficient of variation), precipitation of wettest quarter, precipitation of driest quarter, precipitation of warmest quarter, precipitation of coldest quarter. Bioclimatic variables are the derivatives and were included in the base model as well as in the projection phase.

Climate projections used in the study were taken from global climate models (GCMs) for four representative concentration pathways (RCPs) which are available on WorldClim data portal. These are the most recent GCM climate projections that are used in the Fifth Assessment IPCC report (IPCC, 2013). The GCM output was downscaled and calibrated (bias corrected) using WorldClim 1.4 as baseline 'current' climate (Hijmans et al., 2005). A set of scenarios known as Representative Concentration Pathways (RCPs) has been adopted by climate researchers to provide a range of possible futures for the evolution of atmospheric composition (Moss et al., 2008, 2010). The RCPs are being used to drive climate model simulations planned as part of the World Climate Research Programme's Fifth Coupled Model Intercomparison Project (CMIP5) (Taylor et al., 2009). These are called the representative concentration pathways and are denoted as RCP 2.6, RCP 4.5, RCP 6.0 and RCP 8.5. Each RCP was developed by an Integrated Assessment Modelling (IAM) group; whose published scenario papers were consistent with the base criteria for a particular RCP. For the Fifth Assessment Report of IPCC, the scientific community has defined a set of four new scenarios, denoted Representative Concentration Pathways. They are identified according to radiative forcing target level for 2100 relative to 1750: 2.6 W m— 2 for RCP 2.6, 4.5 W m—2 for RCP 4.5, 6.0 W m—2 for RCP 6.0, and 8.5 W m—2 for RCP 8.5 (IPCC, 2013).

2.3. Projected land cover data

Current (2010) and future (2050) land cover simulation data have been obtained from the Land Use Modelling Platform (LUMP) which is the predecessor of the Land-Use based Integrated Sustainability Assessment (LUISA) modelling platform and both developed by the Sustainability Assessment Unit of EC Joint Research Centre (Baranzelli et al., 2014). LUMP and LUISA are the pan-European platforms developed to provide projected land use maps at a detailed geographical scale (100 m2, regional or country level). These platforms translate policy scenarios into land-use changes such as afforestation and deforestation; pressure on natural areas; abandonment of productive agricultural areas; and urbanization (Lavalle et al., 2013).

The land cover data used in this study are the outputs of the "Land Allocation Module" of the LUMP platform. The main output of the allocation module is a yearly land use map, from 2010 to 2050 at 100 m resolution for the EU28. The platform relies on the CORINE land cover (CLC) 2006 dataset for complete and consistent information on land use/cover across Europe (Baranzelli et al., 2014). In this study, the projected land cover data were re-classified into four main land cover classes which are agricultural lands, forest and semi-natural areas, pastures and wetlands. The LUMP modelling platform output data suggest an increase in forest and semi-natural lands (3.08%) and a decrease in agricultural lands ( — 4.16), pastures ( — 5.18) and wetlands ( — 0.31) by 2050 (Table 2). The trends calculated from the projected land cover changes are shown in Fig. 1.

2.4. EU-DEM and derivatives

The elevation, slope and aspect layers were derived from EU-DEM (EEA, 2014). The EU-DEM is a hybrid product based on SRTM and ASTER GDEM data fused by a weighted averaging approach, and it has been generated as a contiguous dataset divided into 1-degree by 1-degree tiles, corresponding to the SRTM naming convention. The Digital

Projected land cover and land cover changes by 2050 (LUMP, EU-28, processing cell size: 100 m).

Land cover

x1000 km2

Change (2010 to 2050)%

Agricultural lands

Forest and semi-natural areas and other nature

Pastures

Wetlands

1612 1917 447 85

1602 1931

435 85

1583 1950 429 85

1566 1964 425 85

1548 1978 425 85

-4.16 3.08 -5.18 -0.31

Elevation Model over Europe from the GMES RDA project (EU-DEM) is a Digital Surface Model (DSM) representing the first surface as illuminated by the sensors. The EU-DEM dataset is a realisation of the Copernicus programme, managed by the European Commission, DG Enterprise and Industry (EEA, 2015). The terrain data and its derivatives (elevation, slope and aspect) were resampled to 1000 m similarly to what was done for the other predictors.

2.5. Auxiliary soil data

The soil covariates were obtained from European Soil Data Centre (ESDAC) (Panagos et al., 2012, 2013a,b) and the study by Ballabio et al. (2016) (Table 3). The Soil Geographical Database of Eurasia at scale 1:1,000,000 is part of the European Soil Information System (EUSIS). It is the resulting product of a collaborative project involving all the European Union and neighbouring countries. It is a simplified representation of the diversity and spatial variability of the soil coverage. The European Soil Database consists of a number of databases which are the Soil Geographical Database of Eurasia (SGDBE), Pedotransfer Rules Database (PTRDB), Soil Profile Analytical Database of Europa (SPADBE) and Database of Hydraulic Properties of European Soils (HYPRES). The soil structure, available water capacity, soil classification, cation exchange capacity and parent material layers were taken from the European Soil Database and included in the linear regression model.

The soil texture data used in this study is from Ballabio etal. (2016). The researchers made several predictions including soil texture which was also produced using geostatistical methods.

Present and future SOC stocks were calculated by multiplying soil bulk density, sampling depth and SOC concentration. The bulk density data was taken from the European Soil Data Centre (ESDAC) (Jones et al., 2005a).

3. Methods

We tested in this study a geostatistical approach to achieve spatiotemporal prediction of soil organic carbon in Europe. The method consists of two main steps (Fig. 2). The base model predicts current soil organic carbon concentrations at European scale using the regression-kriging technique. The future model projects the prediction to 2050 by applying the fitting regression coming from the base model. The base model is developed by using regression kriging geostatistical technique which is an interpolation method that combines a regression of the dependent variable on predictors with simple kriging of the regression residuals.

At the end of the first phase of the overall process; current soil organic carbon map of Europe (EU26) was produced by applying the regression-kriging. Cyprus and Croatia were excluded from the analysis due to data unavailability. The application of regression kriging generates coefficients for each of the predictors. In practice, the regression coefficients are the knowledge of different processes affecting soil organic carbon and taking place at European level. In the second step, the future model receives the mathematical relations (regression coefficients) from the base model and projects the SOC prediction to 2050. The year, 2050 was selected for the future projection, as the data availability of the future climate and land cover simulations. Regression-kriging is increasingly popular because it achieves lower prediction errors at the control points and because a multitude of explanatory variables is available today at high resolutions. The second advantage of regression-kriging is that it uses explanatory variables that are recognised by pe-dologists as causal factors, also known as CLORPT (Climate, Organisms, Relief, Parent material, Time) factors (Hengl and Heuvelink, 2004a). In regression analysis, residuals are the deviations between the measured and simulated values. The kriging of the residuals provides correction for applying to the regression estimates to improve the model performance (Prudhomme and Reed, 1999).

Land Cover Change Trends (2010 - 2050 km2 - LUMP)

2000 _;__

1600 -—--

o 1400 o 1200 x 1000 800 600 400 200

u 2010 2020 2030 2040 2050

Agricultural Lands 1612273 1601828 1583280 3566003 1547869

-Forest and Semi-natural areas and other nature 1916841 1931328 1950024 1964152 1977753

-Pastures 446665 435075 428711 425122 424673

Wetlands 84937 84674 84655 84649 84671

Fig. 1. Land cover changes for the period 2010-2050 (Lavalle et al., 2013).

BASE MODEL FUTURE PROJECTION

-» Regression Equation + Covariates » —¡

Fig. 2. Soil organic carbon prediction workflow.

3.1. Base model

Soil organic carbon levels are determined mainly by the balance between net primary production (NPP) from vegetation and the rate of decomposition of the organic material. While climate change is expected to have an impact on soil carbon in the long term, changes in the short term will more likely be driven by land management practices and land-use change which can mask the evidence of climate change impacton soil carbon stocks (EEA, 2015,2012). The base model predicts soil organic carbon under current climate and land use conditions as well as projects to the near future (2050). Linear regression method was used to identify and to determine the predictive power of each of the explanatory variables. The soil classification, cation exchange capacity and parent material layers were excluded from the model by a step-wise procedure. Moreover, the regression residuals were interpolated by ordinary kriging to create an error map and this map incorporated to propagate the prediction error on the soil organic carbon prediction map into the process. In other words, the final soil organic carbon

map was created by summing the regression map and error map. The base model's regression equation contains predictors and their coefficients which are also used in the future soil organic carbon prediction.

32. Projection phase

The another challenge addressed in this study was to examine how, and to what extent the natural processes can be projected to the future and how the information is transferable to the future using geostatistical methods. Here, we hypothesized that soil organic carbon is driven largely by climate, land 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 between today and the future.

Table 3

Environmental predictors used in the model (base model and projection).

Base model (B), projection model (P) Predictors Source Resolution (m)

BP Terrain Slope (%), elevation (m), aspect (deg) EU-DEM 1000 m (resampled from 30 m)

B Climate (current) Bio-climatic parameters,a annual WorldClim 1000 m (resampled from 30 arc sec)

precipitation

P Climate (2050) Bio-climatic parameters,a annual WorldClim 1000 m (resampled from 30 arc sec)

precipitation

B Land cover (current, reclassified as Pan-European Land Use Modelling European Commission, Joint Research 1000 m

arable lands, forest lands, pastures Platform (LUMP) Centre, Sustainability Assessment Unit

and wetlands)

P Land cover (2050, (reclassified as Pan-European Land Use Modelling European Commission, Joint Research 1000 m

arable lands, forest lands, pastures Platform (LUMP) Centre, Sustainability Assessment Unit

and wetlands)

BP Soil Clay, silt, sand, soil structure, Joint Research Centre European Soil 1000 m (texture layers were

available water capacity Database (Ballabio et al., 2016) resampled from 500 m)

a Climate data derivatives (WorldClim BioClimatic Parameters, Current and 2050): annual mean temperature, mean diurnal range (mean of monthly (max temp — min temp)), iso-thermality, temperature seasonality (standard deviation»100), max temperature of warmest month, minimum temperature of coldest month, temperature annual range, mean temperature of wettest quarter, mean temperature of driest quarter, mean temperature of warmest quarter, mean temperature of coldest quarter, annual precipitation, precipitation of wettest month, precipitation of driest month, precipitation seasonality (coefficient of variation), precipitation of wettest quarter, precipitation of driest quarter, precipitation of warmest quarter, and precipitation of coldest quarter.

3.3. Calculation of SOC stocks

The current and the future soil organic carbon stocks are calculated by using predicted soil organic carbon contents, sampling depth which is 20 cm and bulk density which is a derivative of European Soil Database. The SOC stocks were calculated with the following equation;

(Mean Error), MAE (Mean Absolute Error) and Root Mean Square Error (RMSE). Atotal of2228 samples were used to assess the performance of the spatial interpolation. The predicted SOC values were validated by the measured values from the validation data set and the calculated indices are mean error (ME), mean absolute error (MAE), R-squared (R2), and root mean squared error (RMSE) as seen in the following equations,

SOCstock = (SOCX x pb x d) (1)

where SOCstock represents soil organic carbon stock in tonnes - ha— 1, SOC the soil organic carbon content (%), pb the bulk density (g-cm3) and d the sampling depth which is 20 cm for the LUCAS Sampling Campaign.

3.4. Validation and comparison

The predictive ability of the model was assessed by using a validation data set which is a subset of the main database. The main dataset was randomly split into two parts; calibration dataset (90%) to be used to model the spatial structure and produce a surface, the other is the validation dataset (10%) to be used to compare and validate the output surface. The predictive performance of the geostatistical model is reported in the results section by the relevant statistical indices of R2, ME

ME = ^E (Ppi-P°0 (2)

MAE = |Ppi-Poi | (3)

R2 = (^MX^)) (4)

EL (Pi-P)2E 0i-O2 RMSE = y1 XLCp-o )2 (5)

where Pi represents the predicted values, Oi the observed SOC values, and n the total number of observations. The output maps and the data

Fig. 3. Soil organic carbon prediction map which represents the present conditions simulated by the base model (background map: ESRI, USGS, NOAA).

were also compared against recent soil organic carbon studies and the comparison figures are shown in the results section.

4. Results and discussion

The primary spatial product of the study is the soil organic carbon map which demonstrates the distribution of the current soil organic carbon stocks in Europe (EU26) (Fig. 3). Two European Countries were not included in the model since the LUCAS Database contains no samples from Cyprus and Croatia at the time of the analysis. Moreover, because of LUCAS Soil's sampling design, areas above 1000 m were not sampled but extrapolated by the model.

The fitting performance ofthe base model is 0.40 which is comparable to the similar studies in the literature. For example, De Brogniez et al. (2015) map the topsoil organic carbon content of Europe by using a generalized additive model and reported that the model fits the data with an R2 of 0.29. Meersmans et al. (2008) conducted a study and constructed a model to assess the spatial distribution of Soil Organic Carbon at the regional scale in Belgium, and their model has an R2 value of 0.36. The researchers believe that the low model performance may be due to other factors influencing the soil organic carbon status which were not included in the model (e.g. soil management, erosion). Similarly, Bell and Worrall (2009) produced a soil organic map at the National Trust Wallington estate in Northumberland, North East England. However, their model has an R2 value of 0.48 which shows more than 50% of the observed variation is unexplained, and it is suggested that stratification into a greater number of land-use categories is needed in order to take account of different land-use management practices.

The zonal (EU26) distribution of the soil organic carbon stocks is shown in Table 4 together with projected (2050) total organic carbon stocks (Pg) for each of the European Countries. Moreover, Fig. 4 visualises the stock values on the map. The base model predicts that the European (EU26) soils hold 37.94 Pg of organic carbon in the first 0-20 cm. Table 5 shows how present, and projected stocks are distributed among the main land cover types (Agricultural Areas, Forest and Semi-natural Areas, Pastures and Wetlands) in 26 European Countries.

These figures were calculated by using the base SOC prediction, future predictions by climate scenarios and LUMP Land Cover Scenarios (2010 and 2050). Our base model's carbon stock prediction seems consistent with the studies estimating soil organic stocks in the literature.

Lugato etal. (2014) reported a similar finding on soil organic carbon stock for the agricultural areas in Europe. The researchers constructed a modelling platform for estimating agricultural topsoil (0-30 cm) organic carbon stocks in continental Europe (EU-28 + Serbia, Bosnia and Herzegovina, Croatia, Montenegro, Albania, Former Yugoslav Republic of Macedonia and Norway) using the agroecosystem SOC model CENTURY. According to the study, the agricultural SOC stock is 17.63 Gigatonnes (0-30 cm) at Pan-European scale.

On the other side, European Environment Agency's soil organic carbon assessment which is based on OCTOP study by Jones et al., 2005a, Soil carbon stocks in the EU-27 are around 75 billion tonnes (75 Gigatonnes or 75 Pg) of carbon in the 0-30 cm; around 50% of which is located in Ireland, Finland, Sweden and the United Kingdom (because of the large area of peatlands in these countries) (EEA, 2015; Jones et al., 2005b). The big difference between our prediction and the OCTOP could be related to the different approaches, the prediction depth and the greater extent of the OCTOP's area which is the continental Europe. In addition, a study by Panagos et al., 2013b which estimates soil organic carbon in Europe based on collected data (EIONET-SOIL), reported that, in North-East Europe (Poland, Denmark), Central Europe (Austria, Slovakia) and The Netherlands, although the patterns of the spatial distribution of SOC content are similar between the OCTOP and EIONET-SOIL datasets, the values of OCTOP were almost double the values of EIONET-SOIL.

The EIONET-SOIL is a data collection network which is managed by the European Soil Data Centre (ESDAC). The project's primary objective is to develop the European datasets for soil erosion and Soil Organic Carbon (SOC). Panagos et al., 2013b estimated soil organic carbon stocks by using EINET-SOIL data (0-30 cm) and reported similar results for six of the European Countries (Table 6).

According to our results; the larger part of the present soil organic carbon stocks are held in Europe's forest and semi-natural soils which

Table 4

Present and projected soil organic carbon stocks (Pg) by European countries (Cyprus and Croatia were excluded due to data unavailability).

Country Base model MRI-CGCM3 IPSL-CM5A-LR HadGEM2-AO CCSM4

RCP85 RCP60 RCP45 RCP26 RCP85 RCP60 RCP45 RCP26 RCP85 RCP60 RCP45 RCP26 RCP85 RCP60 RCP45 RCP26

AT - Austria 0.79 1.3 0.97 1.13 1.12 1.07 1.24 1.09 1.15 1.08 1.21 1.07 1.16 1.22 1.29 1.3 1.14

BE — Belgium 0.18 0.21 0.22 0.21 0.21 0.24 0.25 0.26 0.22 0.19 0.23 0.16 0.2 0.2 0.24 0.25 0.23

BG - Bulgaria 0.54 0.51 0.62 0.63 0.62 0.67 0.73 0.69 0.74 0.6 0.59 0.55 0.65 0.65 0.73 0.58 0.61

CZ — Czech Republic 0.5 0.59 0.56 0.62 0.54 0.7 0.72 0.66 0.66 0.68 0.7 0.72 0.68 0.61 0.68 0.67 0.58

DE — Germany 2.72 3.16 3.08 3.36 3.17 3.63 3.68 3.53 3.43 3.08 3.2 3.08 3.11 3.19 3.63 3.73 3.22

DK — Denmark 0.33 0.39 0.38 0.36 0.39 0.5 0.48 0.43 0.52 0.32 0.38 0.35 0.33 0.39 0.42 0.45 0.45

EE — Estonia 0.5 0.58 0.58 0.61 0.71 0.94 0.63 0.87 0.64 0.54 0.61 0.64 0.57 0.62 0.64 0.69 0.61

ES — Spain 2.96 5.22 4.79 4.33 4.14 3.74 3.76 3.61 3.63 3.39 3.56 3.02 3.36 3.55 3.62 3.55 3.56

FI — Finland 5.27 6.08 6.02 5.84 6.75 5.77 4.9 6.94 5.91 5.67 6.14 7.35 5.91 6.62 6.9 6.48 6.36

FR — France 3.81 5.16 5.12 4.65 4.41 4.89 5.31 4.81 4.77 3.95 4.64 2.95 4 4.36 4.86 4.83 4.56

GR — Greece 0.65 0.91 0.83 0.95 0.88 0.69 0.8 0.68 0.78 0.88 0.91 0.82 1.09 0.87 0.91 0.84 0.91

HU — Hungary 0.5 0.55 0.53 0.64 0.59 0.61 0.62 0.59 0.58 0.56 0.6 0.59 0.61 0.63 0.68 0.67 0.61

IE — Ireland 1.09 1.41 1.56 1.59 1.28 1.6 1.42 1.68 1.51 1.17 1.19 0.97 1.15 1.52 1.62 1.62 1.61

IT — Italy 1.96 2.98 2.44 2.96 2.53 2.36 2.53 2.33 2.47 2.65 3.29 2.66 2.7 2.8 2.83 2.55 2.68

LT — Lithuania 0.59 0.66 0.83 0.71 0.75 1.08 0.99 0.92 0.85 0.82 0.89 0.94 0.82 0.74 0.79 0.77 0.72

LU — Luxembourg 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02

LV — Latvia 0.63 0.74 0.87 0.78 0.91 1.26 0.94 1.06 0.87 0.76 0.86 0.9 0.78 0.78 0.84 0.85 0.78

MT — Malta < 0.003 <0.003 <0.003 <0.003 < 0.003 <0.003 <0.003 <0.003 <0.003 <0.003 <0.003 <0.003 <0.003 <0.003 <0.003 <0.003 <0.003

NL - The Netherlands 0.27 0.34 0.32 0.35 0.35 0.36 0.35 0.37 0.34 0.31 0.32 0.29 0.34 0.34 0.36 0.4 0.35

PL — Poland 2.2 2.5 2.76 2.85 2.61 3.47 3.19 3.13 3.05 2.95 3.19 3.35 2.96 2.57 2.9 2.85 2.52

PT — Portugal 0.56 1.77 1.29 1.3 1.01 0.94 0.84 0.91 1.22 1.02 0.86 0.78 0.98 0.83 0.83 0.86 0.77

RO — Romania 1.36 1.27 1.5 1.57 1.71 1.8 1.85 1.79 1.88 1.56 1.83 1.57 1.65 1.71 1.82 1.61 1.72

SE — Sweden 6.54 8.21 8.22 7.62 8.85 8.25 6.91 9.3 8.46 7.82 8.41 8.69 7.57 8.16 8.7 8.29 8.22

SI — Slovenia 0.17 0.27 0.18 0.27 0.24 0.21 0.24 0.23 0.23 0.18 0.22 0.22 0.2 0.27 0.27 0.28 0.26

SK — Slovakia 0.32 0.38 0.39 0.44 0.43 0.44 0.45 0.44 0.41 0.37 0.43 0.4 0.42 0.41 0.46 0.45 0.39

UK — United 3.48 4.48 4.83 4.64 4.59 5.32 4.98 5.35 4.45 4.35 4.5 3.4 3.9 4.69 4.9 5.11 5.1

Kingdom

Average/total 37.94 49.67 48.91 48.44 48.8 50.56 47.85 51.69 48.82 44.91 48.75 45.47 45.14 47.75 50.98 49.69 47.99

Fig. 4. Predicted topsoil (0-20 cm) SOC stocks in Europe (present conditions, in petagrams) (background map: ESRI, USGS, NOAA).

are around 16.40 Pg. While the agricultural areas stores around 12.79 Pg of soil organic carbon, the pastures and wetlands stores 8.53 Pg soil organic carbon in Europe (EU26).

Estimating current SOC stocks provides valuable information to assess the present conditions. However, in order to make appropriate

management decisions we need to be able to project how soil organic carbon stocks will change as a function of changes in land use/cover and climate. The transfer model relies heavily on base model's statistical output as well as the error map of the base model which is a result of the ordinary kriging of the regression residuals. The results suggested an

Table 5

Predicted soil organic carbon stocks (in petagrams — Pg) by land cover types.

Land cover scenario Climate scenario RCP Agricultural areas (Pg) Forest and semi-natural areas (Pg) Pastures (Pg) Wetlands (Pg)

LUMP 2010 Base Model (2010), WorldClim N/A 12.79 16.40 6.71 1.82

LUMP 2050 MRI-CGCM3 (2050) 2.6 13.87 22.75 8.77 2.40

4.5 13.86 21.25 9.11 2.30

6.0 13.86 21.57 9.34 2.30

8.5 13.9 22.07 9.53 2.35

LUMP 2050 IPSL-CM5A-LR (2050) 2.6 14.78 23.38 9.57 2.45

4.5 14.78 23.73 9.81 2.45

6.0 14.83 24.28 10.01 2.51

8.5 14.49 23.90 9.14 2.47

LUMP 2050 HadGEM2-AO (2050) 2.6 14.67 24.44 9.86 2.74

4.5 14.85 21.41 9.49 2.19

6.0 15.14 23.21 9.81 2.45

8.5 13.41 21.14 8.37 2.28

LUMP 2050 CCSM4 (2050) 2.6 12.99 22.44 7.65 2.35

4.5 14.25 22.87 9.25 2.44

6.0 13.17 20.98 8.57 2.23

8.5 13.64 22.28 9.52 2.59

Soil organic carbon stock estimation in six European countries (Panagos et al., 2013b).

Country Country coverage with SOC stock values Average OC 0-30 cm (tCha-1) SOC stock (Tg)

Bulgaria 100.0 28.0 315.2

Denmark 100.0 86.4 370.6

Italy 57.6 56.3 993.9

Netherlands 77.3 100.1 298.8

Poland 70.1 79.6 1752.7

Slovakia 54.0 45.3 122.3

overall increase in Europe's (EU26) SOC stocks by 2050 under all climate scenarios and projected land cover changes, but with a different extent of increase among the climate model and emissions scenarios.

Likewise, Lugato et al. (2014) used the CENTURY model for predicting soil organic carbon stocks at pan-European scale. The model predicted an overall increase in soil organic carbon stocks according to different climate-emission scenarios up to 2100, with C loss in the south and east of the area compensated by a gain in central and northern European regions. Cao and Woodward (1998), predicted a strong enhancement in net primary production (NPP) and carbon stocks of

■ r-

-4 I A r L * ■ W ^^ifeM JÊ

OL r f ^JL S L , „. ', & fc v.

3) Changes In Soil Organic Carbon Stocks by 2050 by Climate Scenarios and Representative Concentration Pathways (RCPs), 1st row: CC5M4 (RCP 2.6,4.51 2nd Row: CC5M4 |RCP 6.0 and 8,5). Red areas represent decrease and green areas represent Increase in SOC Stocks (tonnes.ha-1) compared to present conditions (Background map: ESRI, USGS, NOAA].

- HV t •'"-'-. Je-'- L ¥ i I f.' P .JW»-^ .......£

13 Ék JTk r 1

C) Changes In Soil Organic Carbon Stocks by 2050 by Climate Scenarios and Representative Concentration Pathways (RCPsl. 1st row: IPSL-CM5A-LR |RCP 2.6,4.5| 2nd Row: IP5L-CM5A-LR [RCP 6.0 and 8.5). Red areas represent decrease and green areas represent increase in SOC Stocks [tonnes.ha-lj compared to present conditions (Background map: ESRI, USGS, NOAA).

1 / v % Hf »jft .-•

< '' '-.ii JÉ • F ^ i ç f t t 'irLt-',

b) Changes in Soil Organic Carbon Stocks by 2050 by Climate Scenarios and Representative Concentration Pathways (RCPs). 1st row: HadGEMZ-AO (RCP 2.6,4.5) 2nd Row: HadGEM2-AO {RCP 6.0 and 8.5}. Red areas represent decrease and green areas represent increase in SOC Stocks (tonnes.ha-l) compared to present conditions (Background map: ESRI, USGS, NOAA).

■4" k ' J^ f f KjJ?!, ■kk. IB - - 1 ^MM*» kii-a,--.. ■ if-'-'

-t A ' 1 * ■ ' ^ ¿g ' . ■4- w m i' * I fc • JCT^ m

d) changes in Soil Organic Carbon Stocks by 2050 by Climate Scenarios and Representative

Concentration Pathways |RCPs|. 1st row: MRI-C6CM3 (RCP 2.6,4.5] 2nd Row: MRI-CGCM3 (RCP 6.0 and S.5). Red areas represent decrease and green areas represent increase in SOC Stocks (tonnes.ha-1) compared to present conditions (Background map: ESRI, USGS, NOAA).

Fig. 5. (a) Changes in Soil Organic Carbon Stocks by 2050 by Climate Scenarios and Representative Concentration Pathways (RCPs). 1st row: CCSM4 (RCP 2.6,4.5). 2nd row: CCSM4 (RCP 6.0 and 8.5). Red areas represent decrease and green areas represent increase in SOC Stocks (tonnes-ha-1) compared to present conditions (background map: ESRI, USGS, NOAA). (b) Changes in Soil Organic Carbon Stocks by 2050 by Climate Scenarios and Representative Concentration Pathways (RCPs). 1st row: HadGEM2-AO (RCP 2.6, 4.5). 2nd row: HadGEM2-AO (RCP 6.0 and 8.5). Red areas represent decrease and green areas represent increase in SOC stocks (tonnes-ha-1) compared to present conditions (Background map: ESRI, USGS, NOAA). (c) Changes in Soil Organic Carbon Stocks by 2050 by Climate Scenarios and Representative Concentration Pathways (RCPs). 1st row: IPSL-CM5A-LR (RCP 2.6,4.5) 2nd row: IPSL-CM5A-LR (RCP 6.0 and 8.5). Red areas represent decrease and green areas represent increase in SOC stocks (tonnes-ha-1) compared to present conditions (background map: ESRI, USGS, NOAA). (d) Changes in soil organic carbon stocks by 2050 by Climate Scenarios and Representative Concentration Pathways (RCPs). 1st row: MRI-CGCM3 (RCP 2.6, 4.5). 2nd row: MRI-CGCM3 (RCP 6.0 and 8.5). Red areas represent decrease and green areas represent increase in SOC stocks (tonnes-ha-1) compared to present conditions (background map: ESRI, USGS, NOAA). (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

Validation indices of the base model predicting current conditions (MEE, mean estimation error; MAEE, mean absolute estimation error; RMSE, root mean square error).

Model R2 RMSE (g-kg-1) MEE (g-kg-1) MAEE (g-kg-1)

Base (current) 0.40 96.80 4.984 36.35

organic carbon stocks in Europe, we also observed slight decreases mainly in southern Europe. Fig. 5a, b, c and d show the changes including decreases (red areas) in Soil Organic Carbon Stocks by 2050 by Climate Scenarios and Representative Concentration Pathways (RCPs).

4.1. Model performance and validation

terrestrial ecosystems by combining the effects of CO2 doubling, climate change, and the consequent redistribution of vegetation. The researchers stated that doubling of atmospheric CO2 without climate change might enhance NPP by 25% and result in a substantial increase in carbon stocks in vegetation and soils. Climate change without CO2 elevation will reduce the global NPP and soil carbon stocks but leads to an increase in vegetation carbon because of a forest extension and NPP enhancement in the north.

In our study, the projected increase in soil organic carbon stocks is 713 Pg by 2050. The highest increase was predicted by IPSL-CM5A-LR (RCP4.5) by 13.7 Pg, and the lowest increase was by HadGEM2-AO (RCP4.5) 6.9 Pg. Despite the results show a significant increase in soil

The first phase of the study is building a base which predicts the current soil organic carbon stocks and delivers the regression coefficients (regression equation) to the projection phase. This base model had explanatory power of R2 = 0.40 and indicated that the model can explain up to 40% of total SOC variability in Europe. The regression-kriging approach yielded prediction error indicators as shown in Table 7. A typical result of geostatistical interpolation is a map of predictions and prediction error, which is an estimate of prediction uncertainty (Hengl et al., 2004b). The prediction uncertainty map is shown in Fig. 6.

The scatter plot for the validation dataset is shown in Fig. 7a, b and c. The figures show the correlations between predicted and measured SOC values (g-kg—1) in EU26, Northern Europe and Middle and Southern

i Kilometers

Fig. 6. Prediction uncertainty map (base layer: ESRI, USGS, NOAA).

Europe (EU26)

600 500 400 300 200 100 0

<> 0 o

« 0 0 o o o o o

o < ° ° ° ° ° o o

o > ¿x>

300 Measured

a) Scatterplots of observed versus predicted SOC Values (g.kgJ)from validation procedure (EU26)

South and Middle Europe (Latitude < N50)

600 500 400 300 200 100 0

<______■—

CC><J> 0 o 0 ___ o— o

O % O o

300 Measured

b) Scatterplot of observed versus predicted 50C Values (g.kg1) - South and Middle Europe.

Northern Europe (Latitude > N50)

450 400 350 300 I 250

"J 200

150 100 50 0

$ a % /vO o

300 Measured

c) Scatterplot of observed versus predicted SOC Values (g.kg1) - Northern Europe.

Fig. 7. (a) Scatterplots of observed versus predicted SOC values (g-kg-1) from validation procedure (EU26). (b) Scatterplot of observed versus predicted SOC values (g-kg-1) — South and Middle Europe. (c) Scatterplot of observed versus predicted SOC values (g-kg-1) — Northern Europe.

Europe. The validation dataset was split into two spatial proportions (Northern Europe, Middle and Southern Europe) by setting 50th Latitude as the reference line for how the model also behaves in the northern part of the Europe since organic soils are relatively abundant in the Northern part of Europe.

4.2. Comparisons to the other datasets

The base model's soil organic carbon predictions and two previous studies (De Brogniez et al., 2015 and Jones et al., 2005a) are compared by using their descriptive statistics in Table 8, and the correlation matrix of these predictions are shown in Table 9.

5. Discussion

Predicting environmental processes need complex approaches most of the times because of their dynamic and multi-dimensional nature.

Table 8

Comparison of basic statistics of present SOC predictions.

Layer Min Max Mean Std

(g-kg-1) (g-kg-1) (g-kg-1) (g-kg-1)

Base Model (EU26) 9.21 911.7 55.58 51.66

De Brogniez et al. (2015) (EU25) 6.96 1000 59.79 67.65

Jones et al. (2005a) (EU26 mask) 0 630 66.4 11.32

Correlation matrix (on matching extent of the raster layers) of present SOC predictions.

Layer Base model De Brogniez et al. Jones et al. (2005a)

(EU26) (2015) (EU25) (extracted by EU26)

Base model (EU26) 1 0.64 0.57

De Brogniez et al. 0.64 1 0.52

(2015) (EU25)

Jones et al. (2005a) 0.57 0.52 1

(EU26 Mask)

Soil organic carbon is one of the most important soil properties strongly influenced by other soil and non-soil dynamics. To set the appropriate environmental or agricultural strategies at regional or at a global scale, we need to address SOC dynamics.

We highlighted two key challenges in this study. First, predicting present soil organic carbon stocks by using a digital soil mapping technique which is studied extensively by many researchers. And the second challenge is projecting our prediction into the near future by testing a relatively new approach which uses mathematical relations (regression coefficients) to predict soil organic carbon stocks under present day conditions and to project them into the future by using the same regression coefficients as we assume that the nature of the carbon cycle, the drivers and their degree of influence will remain the same in the future. Land cover, climate and their projections were placed in the centre of the model. The soil and terrain parameters were included in the model as constant layers (temporo-spatial).

The model results suggest an overall increase in carbon stocks by 2050 in consequence of changes in land cover and climate. Whether soil gains or loses organic carbon depends upon the balance between carbon inputs and decomposition. Changes in net primary production will change the carbon input to the soil. According to Gottschalk et al., 2012, decomposition usually increases by warmer temperatures, but can also be slowed by decreased soil moisture. Underlying the global trend of increasing SOC under future climate is a complex pattern of regional SOC change. Rusu (2013) stated that, in terrestrial environments, increasing temperature determines an increase in the amount of natural atmospheric CO2, which would significantly boost photosynthesis, and enhance metabolism as well as increase the amount of vegetation biomass. On the other hand, the Land Cover Scenario of LUMP platform implies an increase in forest cover by 2050. Increasing forest cover will lead to an increase in biomass production and levels of soil organic carbon (SOC) content.

Despite the differences in atmospheric CO2 concentrations among the RCPs are relatively smaller until 2050 than for the period of 20502100, their combined effects with global climate models and land cover changes are noticeably high in this study.

6. Conclusion

This study represents a unique and new assessment of SOC at European scale based on spatial and temporal data and builds a generic framework to be used by further studies on other environmental parameters. Our study provides two cascaded outputs and a generic framework for further applications. The first output is the prediction of soil organic carbon map and the stock figures reflecting the present conditions in Europe and the second output provides projected soil organic carbon data for the near future (2050). The results suggest a significant increase in SOC stocks by 2050 under the future climate and land cover projections. The extent of the increase in soil organic carbon stocks varies in different RCPs and climate models. The predictive power of the base model is relatively good (R2 = 0.40) when compared to studies using similar statistical approaches. Even though the model fits the data at an acceptable level and the outputs are consistent with the previous studies, the uncertainty is very high in Northern Europe especially in Northern UK, Ireland, and Scandinavia and the areas above 1000 m were extrapolated. The LUCAS dataset does not have samples

above 1000 m, and the selection of the soil sampling sites has an inherent bias towards agricultural land (predominantly under arable cultivation), followed by grasslands and woodlands. This bias means our results based exclusively on LUCAS soil samples may over represent properties from the more heavily sampled conditions whiles underrepresenting others (Toth et al., 2013). The modelling platform presented here can be improved by incorporating additional datasets and more representative data to reach the highest possible representability for all land cover types.

Acknowledgments

Funding for this work was provided in part by the European Commission FP 7 Collaborative Project "Soil Transformations in European Catchments" (SoilTrEC) (Grant Agreement no. 244118).

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