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: All Member States of the European Union where data available. 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.
Pixel size: 500m
Projection: ETRS89 Lambert Azimuthal Equal Area
Temporal coverage: 2014
Input data source: LUCAS point data, European Soil Database
K-factor in Europe
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. The new proposed dataset has also been verified against local/regional/national studies with very good results. Soil erosion modellers (and not only) may use it for their applications at any scale. 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 and then for the 28 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 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
The Soil Erodibility Dataset is in Raster format. The public user can download 3 different datasets: a) Soil erodibility in Europe (K-factor), b) Soil Erodibility incorporating Stoniness (Kst Factor) and c) the Effect of Stoniness in K-factor (% reduction). To get access to the data, please compile the online form; instructions will then follow how to download the data.
(Mar 2015) K-factor values are available for the 28 European Union Member States (including Bulgaria, Romania, Croatia). 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.
More information about Soil Erodibility in the corresponding section.
A complete description of the methodogoly (High resolution - 2014 version) 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