Soil Themes > Soil Erosion > Rainfall Erosivity Factor
Rainfall erosivity is the kinetic energy of raindrop's impact and the rate of associated runoff. The R-factor is a multi-annual average index that measures rainfall's kinetic energy and intensity to describe the effect of rainfall on sheet and rill erosion. Among the factors used within RUSLE and its earlier version, the Universal Soil Loss Equation (USLE), rainfall erosivity is of high importance as precipitation is the driving force of erosion and has a direct impact on the detachment of soil particles, the breakdown of aggregates and the transport of eroded particles via runoff. A precise assessment of rainfall erosivity requires recordings of precipitation at short time intervals (1 – 60 minutes) for a period of at least several years. The rainfall erosivity is calculated by multiplying the kinetic energy by the maximum rainfall intensity during a period of 30-minutes for each rainstorm. The R-factor accumulates the rainfall erosivity of individual rainstorm events and averages this value over multiple years.
REDES: Rainfall Erosivity Database on the European Scale
The Rainfall Erosivity Database on the European Scale (REDES) includes high temporal resolution precipitation data and the claculated R-factor from 1,541 precipitation stations within the European Union (EU) and Switzerland. The Rainfall Erosivity Database on European Scale (REDES) of precipitation stations is the result of calculating the R-factor for a total of 26,394 years with a mean value of 17.1 years per station. The data collection exercise of high temporal resolution data began in March 2013 and was concluded in May 2014. For the present rainfall erosivity data collection exercise, a participatory approach has been followed in order to collect data from all Member States (Aknowledgments). The precipitation data collected from the 28 countries across Europe have different temporal resolutions: 60-min, 30-min, 15-min, 10-min and 5-min. In order to homogenise the R-factor results calculated using different time-step data, conversion factors were established to have the data at the 30-min temporal resolution (reference).
R-factor in Europe
The purpose of this study is to assess rainfall erosivity in Europe in the form of the RUSLE R-factor, based on the best available datasets in Europe. We used the Rainfall Erosivity Database on the European Scale(REDES) which contains 1,541 precipitation stations in all European Union(EU) Member States and Switzerland, with temporal resolutions of 5 to 60 minutes. The R-factor values calculated from precipitation data of different temporal resolutions were normalised to R-factor values with temporal resolutions of 30 minutes using linear regression functions. Precipitation time series ranged from a minimum of 5 years to maximum of 40 years. The average time series per precipitation station is around 17.1 years, the most datasets including the first decade of the 21st century. Gaussian Process Regression(GPR) has been used to interpolate the R-factor station values to a European rainfall erosivity map at 1 km 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-1 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 (Less than 500 MJ mm ha-1 h-1 yr-1) in the Nordic countries. The erosivity density (erosivity normalised to annual precipitation amounts) was also highest in Mediterranean regions which implies high risk for erosive events and floods.
Rainfall erosivity (R-factor) in Europe is a key parameter for estimating soil erosion loss and soil erosion risk, but the use of this dataset can be widely extended to other applications: landslide risk assessment, flood risk forecasting, Hydrology, post-fire conservation measures, agricultural management and design of crop rotation scenarios.
Rainfall erosivity in Europe
Title: Rainfall erosivity in Europe
Description: This map provides a complete rainfall erosivity dataset for European Union (28 member States) and Switzerland based on 1541 precipitation stations and 26,394 years of measurements. Gaussian Process Regression(GPR) model was used to interpolate the rainfall erosivity values of single stations and to generate the R-factor map.
Spatial coverage: European Union (28 Countries) & Switzerland
Pixel size: 500m
Measurement Unit: MJ mm ha-1 h-1 yr-1
Projection: ETRS89 Lambert Azimuthal Equal Area
Temporal coverage: 40 years - Predominant in the last decade: 2000 - 2010
Information: Panos Panagos, Pasquale Borrelli, Katrin Meusburger*
European Commission, Institute of Environment and Sustainability, Land Resource Management Unit AND *Institute of Environmental Geosciences, University of Basel
A complete description of the methodology and the application in Europe is described in the paper:
Panagos, P., Ballabio, C., Borrelli, P., Meusburger, K., Klik, A., Rousseva, S., Tadic, M.P., Michaelides, S., Hrabalíková, M., Olsen, P., Aalto, J., Lakatos, M., Rymszewicz, A., Dumitrescu, A., Beguería, S., Alewell, C.
Rainfall erosivity in Europe. Sci Total Environ. 511 (2015), pp. 801-814. DOI: 10.1016/j.scitotenv.2015.01.008
Download the article: 10.1016/j.scitotenv.2015.01.008
A reply to the comment (misunderstanding or misinterpretations) of Auerswald et al can be found in our recent paper:
Panagos, P., Meusburger K., Ballabio C., Borrelli P., Begueria S., Klik A., Rymszewicz A., Michaelides, S, Olsen, P., Tadic, M.P., P., Aalto, J., Lakatos, M., Dumitrescu, Rousseva, S., Montanarella, L., Alewell C. 2015.
Reply to the comment on "Rainfall erosivity in Europe" by Auerswald et al. Science of the Total Environment, In Press.
Download the Article:10.1016/j.scitotenv.2015.05.020.
Data - Maps
The Rainfall Erosivity and the other climatic data is in Raster format. The public user can download Datasets a) Rainfall erosivity in Europe (R-factor) b) Erosivity Density c) The standard error of the estimates d) The R-factor in Switzerland (as calculated in 2012) and the code for calculating R-factor. To get access to the data and the code, please compile the online form; instructions will then follow how to download the datasets.
The References to the source are always necessary!
Fig. 1: R-factor high resolution(2015)
|Fig. 2: Erosivity Density||Fig. 3: Mean annual precipitation & R-factor Stations|
The authors would also like to acknowledge the following services for providing access to their data:
Austria: Hydrographic offices of Upper Austria, Lower Austria, Burgenland, Styria, Salzburg
Belgium - Flanders: Flemish Environmental Agency (VMM), Operational Water Management.
Belgium - Wallonia: Service public de Wallonie, Direction générale Mobilité et Voies hydrauliques, Direction de la Gestion hydrologique intégrée, Namur.
Bulgaria: Rousseva et al. (2010)
Cyprus: Cyprus Department of Meteorology.
Germany: Deutscher Wetterdienst (DWD), WebWerdis Service
Denmark: Aarhus University, Department of Agroecology
Estonia: Client service department, Estonian Environment Agency, Tallinn
Spain: Confederaciones Hidrográficas del Ebro, Tajo, Duero, Guadalquivir, Segura, Júcar, Miño-Sil, Cantábrico and Sur, Servei Meteorològic de Catalunya, and Meteo Navarra.
France: Météo-France DP/SERV/FDP, Division Fourniture de Données Publiques
Croatia: Meteorological and Hydrological Service
Hungary: Hungarian Meteorological Service
Ireland: Data from Met Éireann, financial support from Irish EPA STRIVE Programme - SILTFLUX (2010-W-LS-4) and UCD Earth Institute
Italy: the Servizio Idrografico Abruzzo, Protezione Civile Regione Basilicata, Ufficio idrografico Bolzano, Servizio Idrografico Friuli-Venezia Giulia, Centro funzionale regione Lazio, Meteotrentino, Agenzia Regionale per lo Sviluppo e l'Innovazione dell'Agricoltura nel Molise, Servizio Meteo-Idro-Pluviometrico Marche, Associazione Regionale dei Consorzi di Difesa della Puglia, Osservatorio delle Acque Sicilia, Servizio Idrologico Regionale Toscana, Servizio Risorse idriche e rischio idraulico Umbria, Diodato Nazzareno from Regione Campagna, Centro funzionale regionale Valle d'Aosta and the Hydro-Meteo-Climate Service of the Environmental Agency ARPA Calabria, ARPA Emilia Romagna, ARPA Liguria, ARPA Lombardia, ARPA Piemonte, ARPA Veneto.
Latvia: Latvian Environment, Geology and Meteorology Centre, Riga
Lithuania: Mazvila et al. (2010)
Luxembourg: Agrarmeteorologisches Messnetz Luxembourg
Netherlands: KNMI, Royal Netherlands Meteorological Institute
Portugal: Agência Portuguesa do Ambiente, Departamento de Monitorização de Recursos Hídricos
Poland: Banasik et al. (2001)
Romania: National Meteorological Administration
Slovakia: Malisek et al. (1992) , Jan Styk and Jozef Kobza from Soil Science and Conservation Research Institute Bratislava
Slovenia: Slovenian Environment Agency, Petan et al. (2010)
Sweden: Swedish Meteorological and Hydrological Institute (SMHI)
Switzerland : MeteoSchweiz, Meusburger et al. (2012)
United Kingdom: NERC & UK Environmental Change Network (ECN), and British Atmospheric Data Centre (BADC)
|Important legal notice
© European Communities, 1995-
| European Commission - Joint
Institute for Environment and Sustainability
Marc Van Liedekerke(tel. +39-0332-785179)
Panos Panagos (tel. +39-0332-785574)