Annex 4 analytical methods

The revised version of the RUSLE is an empirical model that calculates soil loss due to sheet and rill erosion. The new soil loss by water erosion map of Europe uses a modified version of the RUSLE model (RUSLE2015, based on Renard et al., 1997), which calculates mean annual soil loss rates by sheet and rill erosion according to the following equation:

E = R * K * C * LS * P   

Where  E:  Annual average soil loss (t ha-¹ yr-¹),

R: Rainfall Erosivity factor (MJ mm ha-¹ h-1 yr-¹),

K: Soil Erodibility factor (t ha h ha-¹ MJ-¹ mm-¹),

C: Cover-Management factor (dimensionless),

LS:  Slope Length and Slope Steepness factor (dimensionless),

P: Support practices factor (dimensionless).   

Each of the input factors is modelled using pan-European harmonized datasets as inputs.

Rainfall erosivity factor (R).

Input for Rainfall erosivity factor: The Rainfall Erosivity Database at European Scale (REDES) (Panagos et al., 2015) which has been developed using high-temporal resolution rainfall data (5 min, 10-min, 15-min, 30-min, 60 min) from 1541 stations in European Union and Switzerland.

Methodology: The intensity of precipitations is one of the main factors affecting soil water erosion processes. R is a measure of the precipitation’s erosivity and indicates the climatic influence on the erosion phenomenon through the mixed effect of rainfall action and superficial runoff, both laminar and rill. Wischmeier (1959) identified a composite parameter, EI30, as the best indicator of rain erosivity. It is determined, for the ki-th rain event of the i-th year, by multiplying the kinetic energy of rain by the maximum rainfall intensity occurred within a temporal interval of 30 minutes.   In RUSLE20015, the R-factor is calculated based on high-resolution temporal rainfall data (5, 10, 15, 30 and 60 minutes) collected from 1,541 well-distributed precipitation stations across Europe (Panagos et al., 2015a). This first Rainfall Erosivity Database at the European Scale (REDES) was a major advancement in calculating rainfall erosivity in Europe. The precipitation time series used ranged from 7 to 56 years, with an average of 17.1 years. The time-series precipitation data of more than 75% of European Union (EU) countries cover the decade 2000-2010. Gaussian Process Regression(GPR) (Rasmussen and Williams, 2005) has been used to interpolate the R-factor station values to a European rainfall erosivity map at 500m 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-¹1 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.

Soil Erodibility factor (K).

Input for sol erodiblity factor: LUCAS topsoil database 2009 (and update 2012 for Romania and Bulgaria) and European Soil Database v.2 (Soil structure) (Orgiazzi et al., 2018).

Methodology: The greatest obstacle to soil erosion modelling at larger spatial scales is the lack of data on soil characteristics. One key parameter for modelling soil erosion is the soil erodibility, expressed as the K-factor in the widely used soil erosion model, the Universal Soil Loss Equation (USLE) and its revised version (RUSLE). 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.

Cover-Management factor (C)

Input for cover management factor: CORINE Land Cover 2000-2006-2012 , COPERNICUS vegetation density layer , Eurostat statistical data on crops distribution, Tillage practices, Cover crops and Plant residues. Detailed description of datasets can be found in the policy report Soil-related indicators to support agri-environmental policies (EUR30090).

Methodology: Land use and management influence the magnitude of soil loss. Among the different soil erosion risk factors, the cover-management factor (C-factor) is the one that policy makers and farmers can most readily influence in order to help reduce soil loss rates. The present study proposes a methodology for estimating 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 and practices. In arable lands, the C-factor was estimated using crop statistics (% of land per crop) and data on management practices such as conservation tillage, plant residues and winter crop cover. The C-factor in non-arable lands was estimated by weighting the range of literature values found according to fractional vegetation cover, which was estimated based on the remote sensing dataset Fcover (Panagos et al 2015). 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) reduce the 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 and crop rotation scenarios on soil erosion by water. The impact of land use changes (deforestation, arable land expansion) and the effect of policies (such as the Common Agricultural Policy and the push to grow more renewable energy crops) can potentially be quantified with the proposed model.

Slope Length and Slope Steepness factor (LS)

Input for Length and Slope Steepness factor: Digital Elevation model (DEM) at 25m resolution

Methodology: The Universal Soil Loss Equation (USLE) and the Revised USLE (RUSLE) 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) (https://esdac.jrc.ec.europa.eu/) 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.

Support practices factor (P)

Input for support practices factor: LUCAS Earth Observation database (stone walls, grass margins) , Good Agricultural Environmental Conditions (GAEC) database. A detailed description of datasets can be found in the policy report Soil-related indicators to support agri-environmental policies (EUR30090).

Methodology:

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 (Orgiazzi et al., 2018). 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.

Additional references for the methdology

Desmet, P., Govers, G., 1996. A GIS procedure for automatically calculating the ULSE LS factor on topographically complex landscape units. Journal of Soil and Water Conservation 51 (5), 427–433.

Renard, K.G., et al., 1997. Predicting Soil Erosion by Water: A Guide to Conservation Planning with the Revised Universal Soil Loss Equation (RUSLE) (Agricultural Handbook 703). US Department of Agriculture, Washington, DC, pp. 404.

Inputs are:

• R: Rainfall Erosivity factor (MJ mm ha-¹ h-¹ yr-¹),
• K: Soil Erodibility factor (t ha h ha-¹ MJ-¹ mm-¹),
• C: Cover-Management factor (dimensionless),
• LS:  Slope Length and Slope Steepness factor (dimensionless),
• P: Support practices factor (dimensionless).   

Each of the input factors is modelled using pan-European harmonized datasets as inputs.

• R: The Rainfall Erosivity Database at European Scale (REDES) which has been developed using high-temporal resolution rainfall data (5 min, 10-min, 15-min, 30-min, 60 min) from 1541 stations in European Union and Switzerland.
• K: LUCAS topsoil database 2009 (and update 2012 for Romania and Bulgaria) and European Soil Database v.2 (Soil structure)
• C: CORINE Land Cover 2000-2006-2012 , COPERNICUS vegetation density layer , Eurostat statistical data on crops distribution, Tillage practices, Cover crops and Plant residues.
• LS: Digital Elevation model (DEM) at 25m resolution
• P: LUCAS Earth Observation database (stone walls, grass margins) , Good Agricultural Environmental Conditions (GAEC) database.

Detailed description of datasets can be found in the policy report Soil-related indicators to support agri-environmental policies (EUR30090).

 The model output is

• Soil erosion by water in Europe in t ha-¹ yr-¹ for cells of 100 m x 100 m

The model outputs are aggregated of further analysed to produce a a number of indicators derived from this map:

• The soil erosion can be aggregated at NUTS0, NUTS2 and NUTS Level 3 administrative areas
• Soil erosion of more than 11 tonnes per ha annually in Agricultural areas
• Soil Erosion indicator for the scoreboard of the Roadmap to a Resource Efficient Europe  EUROPE2020 Scoreboard (Number of Square Km with soil erosion more than 10 tonnes per ha), see: https://ec.europa.eu/eurostat/databrowser/view/t2020_rn300/default/table?lang=en

The RUSLE model can contribute to impact assessment of European Policies by identifying the impact of land use/cover change to soil erosion and furthermore to environment. Land Cover is an important factor in RUSLE soil erosion estimation and possible changes in land cover will have direct influence to the model results.

The RUSLE model contributes to the following policies: 

1. Impact Assessment of the post 2020 Common Agricultural Policy (CAP) - SWD(2018) 301 final
2. Sustainable Development Goals (SDGs) COM(2016) 739 final 
3. Thematic strategy for soil protection (COM/2006/231). Commission of the European Communities, Brussels. 
4. Common Agricultural Policy 2014-2020 (context indicator No 42) 
5. Resource Efficiency Flagship Initiative - EU Roadmap to a Resource Efficient Europe.

by providing e.g. statistics for the Sustainable Development Goal (SDGs), Eurostat Agro-environmental Indicator No 21, Eurostat Regional Statistics edition 2015, and the Eurostat Europe2020 Resource Efficiency scoreboard.

Potential applications at a global scale include United Nations Convention to Combat Desertification (UNCCD), FAO, Status of the World’s Soil Resources, The Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem (IPBES) and the OECD Agri-Environmental Indicators.

A major development in 2018 was the use of RUSLE2015 for the Impact Assessment of the post2020 Common Agricultural Policy (CAP). RUSLE2015 scenarios and references can be found in the Impact assessment SWD (2018) 301 final.

Policy Document: Impact assessment SWD (2018) 301 final Accompanying the document for a Regulation on the Post 2020 CAP
Reference: European Commission proposal COM(2018) 392 Final for the post2020 Common Agricultural Policy (CAP).

The model has contributed to run policy scenario analysis for the soil erosion projections in 2030. Those projections have been included in the policy document EU Agricultural outlook for markets and income 2018-2030:  https://ec.europa.eu/agriculture/markets-and-prices/medium-term-outlook_en

An additional developmnt was the use of RUSLE2015 for the Sustainable development in the European Union — Monitoring report on progress towards the SDGs in an EU context — 2018 edition .

• Agriculture and rural development 
• Environment