K-Factor: Background information

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 high-resolution soil erodibility map (500m) version 2014 incorporates certain improvements over the coarse-resolution map (10km) version 2011:

• High resolution dataset (500m) and application of Cubist regression-interpolation (better spatial accuracy)
• Soil structure was for the first time included in the K-factor estimation
• Coarse fragments were taken into account for the better estimation of soil permeability
• Surface stone content, which acts as protection against soil erosion was for the first time included in the K-factor estimation. This correction is of great interest for the Mediterranean countries where stoniness is an important regulating parameter of soil erosion
• The estimated soil erodibility dataset is verified against local, regional and national data found in the literature (21 Studies)
• Cyprus and Malta have been included in the analysis

K-factor high-resolution dataset (500m) - Version 2014

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.

The soil erodibility dataset overcomes the problems of limited data availability for K-factor assessment and presents a high quality resource for modellers who aim at soil erosion estimation on local/regional, national or European scale.

Title: Soil Erodibility in Europe High Resolution dataset (500m)
Description: 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 exercise and the European Soil Database.
Spatial coverage: 25 Member States of the European Union where data available (All EU member states except BG, RO, HR).
Pixel size: 500m
Projection: ETRS89 Lambert Azimuthal Equal Area
Temporal coverage: 2014
Input data source: LUCAS point data, European Soil Database

Download the OFFICIAL datasets (as published in the Peer-review Journal Science of the Total Environment in 2014):

• K-factor dataset [72 MB]
• Kst-factor (incorporating Stoniness) dataset [72 MB]
• Effect of Stoniness in K-factor (% reduction) [67 MB]

Update 03/2015: For the European Union (EU28) assessments, we also make available the EU-28 datasets covering all the EU28 Member States:

• K-factor (EU-28) dataset [78 MB]
• Kst-factor (EU-28) dataset (incorporating Stoniness) [80 MB]

Update 09/2018: Due to a number of requests from non-EU users, we also make available the Extrapolated datasets covering also Norway, Switzerland, Balkan states, Moldova and Ukraine:

• K-factor extrapolated dataset [133 MB]
• Kst-factor extrapolated (incorporating Stoniness) dataset [136 MB]
• Please note that the uncertainty is higher in the extrapolated areas due to non-sampled points. However, the results are coming from spatial interpolations which performed very well.

Update 12/2025: We make available the measured K-factor point data (circa 21,680 records) for the LUCAS 2009/12 according to the  Wischmeier and Smith (1978) and Renard et al. (1997) equation as presented in the paper Panagos et al. 2014 - Please look at README file for fields reference :

• K-factor point data (Excel Format)
• K-factor Shape file (GIS)
• K-factor Geopackage

References:
A complete description of the methodogoly and the application in Europe is described in the paper:
Panagos, P., Meusburger, K., Ballabio, C., Borrelli, P., Alewell, C.
Soil erodibility in Europe: A high-resolution dataset based on LUCAS, Science of Total Environment, 479–480 (2014) pp. 189–200
Download the article (Open Access): 10.1016/j.scitotenv.2014.02.010

K-factor coarse-resolution dataset (10Km) - Version 2011 (Users are advised to use the latest (2014) high resolution dataset)

Title: Soil Erodibility in Europe
Description: 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 exercise.
Spatial coverage: 23 Member States of the European Union where data available (All EU member states except CY, MT, BG, RO).
Pixel size: 10km
Projection: ETRS89 Lambert Azimuthal Equal Area
Temporal coverage: 2009

Input data source: LUCAS point data

Information: Panos Panagos, Luca Montanarella,
European Commission, Institute of Environment and Sustainability,
Land Management and Natural Hazards Unit, Ispra, Italy.

References:
A complete description of the methodogoly and the application in Europe is described in the paper:
Panagos, P., Meusburger, K., Alewell, C., Montanarella, L.
Soil erodibility estimation using LUCAS point survey data of Europe, Environmental Modelling & Software, Volume 30, April 2012, Pages 143-145, doi:10.1016/j.envsoft.2011.11.002
Download the article: 10.1016/j.envsoft.2011.11.002

Data

K-factor data: TIFF format or Raster format

Find attached the LYR (Legend file)

The estimation method of soil erodibility is based on the LUCAS point data. Since the density of points has a variety, we have performed a first assessment of Uncertainty based on the nymber of points in the 10km Grid Cell.
For the estimation of uncertainty the following rules have been followed:
- High: Where no LUCAS point falls into the 10kmx10km cell and the IDW (see paper above) methodology has been used (around 24.000 cells)
- Medium: Where 1 LUCAS point falls into the 10kmx10km cell (around 12.000 cells)
- Low: Where more than 1 LUCAS points fall into the 10kmx10km cell (around 4.000 cells)

Uncertainty data: TIFF format or Raster format