Global Rainfall Erosivity

Rainfall erosivity dataset (2017) is one of the input layers when calculating the Revised Universal Soil Loss Equation (RUSLE) model, which is the most frequently used model for soil erosion risk estimation; for the whole World; R-factor map at resolutions of 30 arc-sec ((~1 km at the Equator).
Registration is requested: 
Yes
Country: 
Ispra
Italy
Author - Contributors: 
Panos Panagos
Cristiano Ballabio
Publisher: 
European Commission, Joint research Centre
Year: 
2017
Language: 

Metadata:

Title: Rainfall Erosivity in the World
Description: This map provides a complete rainfall erosivity dataset for the whole World based on 3625 precipitation stations and around 60,000 years of rainfall records at high temporal resolution (1 to 60 minutes). 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: World
Pixel size: 30 arc-seconds (~1 km at the Equator).
Measurement Unit: MJ mm ha-1 h-1 yr-1
Projection: ETRS89 Lambert Azimuthal Equal Area
Temporal coverage: 30-40 years - Predominant in the last decade: 2000 - 2010

R-factor in the World

The purpose of this study is to assess rainfall erosivity inthe World in the form of the RUSLE R-factor, based on the best available datasets in the Globe. We used the Global Rainfall Erosivity Database (GloREDa) which contains 3,625 precipitation stations from 63 countires in the Globe  with temporal resolutions of 1 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 52 years. The average time series per precipitation station is around 16.8 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 30 arc-seconds (~1 km at the Equator). 

Globally, the mean rainfall erosivity is estimated to be 2,190 MJ mm ha-1 h-1 yr-1 and broadly reflects climatic patterns, with the highest values, (which are 3 three times highergreater than the mean) are found in South America (especially around the Amazon Basin) and the Caribbean countries, Central and parts of east Western Africa and South East Asia. The lowest values are mainly found in mid and high latitude regions such as Canada, the Russian Federation, Northern Europe, Northern Africa, the and  Middle East and southern Australia. It should be noted that high rainfall erosivity does not necessarily mean high erosion as factors such as soil characteristics, vegetative cover and land use are also important factors.The new global erosivity map is a critical input to global and continental assessments of soil erosion by water, flood risk and natural hazard prevention. Current global estimates of soil erosion by water are very uncertain, ranging over one order of magnitude (from around 20 to over 200 Pg per year). More accurate global predictions of rill and interrill soil erosion rates can only be achieved when the rainfall erosivity factor is thoroughly computed.

The global erosivity map is publicly available and can be used by other research groups to perform national, continental and global soil erosion modelling.

 

GloREDa: Global Rainfall Erosivity Database

At global scale, this is the first time ever that an erosivity database of such dimension is compiled. The Global Rainfall Erosivity Database, named hereafter as GloREDa, contains erosivity values estimated as R-factors (refer to the method section) from 3,625 stations distributed in 63 countries worldwide. This is the result of an extensive data collection of high temporal resolution rainfall data from the maximum possible number of countries in order to have a representative sample across different climatic and geographic gradients. GloREDa has three components, which are described in the relevant publication:

  • The Rainfall Erosivity database at European Scale (REDES) 
  • 1,865 stations from 23 countries outside Europe (Australia, New Zealand, South Korea, Japan, China, India, Malaysia, Iran, Kuwait, Israel, Turkey, Russian Federation, United States of America, Mexico, Costa Rica, Jamaica, Colombia, Suriname, Chile, Brazil, Algeria, South Africa, Mauritius).
  • 85 stations collected from a literature review (12 countries)

The number of GloREDa stations varied greatly among continents. Europe had the largest contribution to the dataset, with 1,725 stations (48% of total), while South America had the lowest number of stations (141 stations or ~4% of total). Africa has very low density of GloREDa stations (5% of the total). In North America and the Caribbean, we collected erosivity values from 146 stations located in 6 countries (Unites States, Canada, Mexico, Cuba, Jamaica and Costa Rica). Finally, Asia and the Middle East were well represented in GloREDa, with 1,220 stations (34% of the total) distributed in 10 countries including the Siberian part of the Russian Federation, China, India, Japan.

Data

The Global erosivity map (GeoTIFF format) at 30 arc-seconds (~1 km) resolution is available for free download in the European Soil Data Centre (ESDAC). The calculated erosivity values per station in GloREDa will become available in the future pending on the agreed copyright issues with data providers. GloREDa calcualted erosivity values can be shared in case of scientific collaborations.

To get access to the all datasets and the code, please compile the request form ; instructions will then follow how to download the datasets. More information about Global Rainfall erosivity in the corresponding section.

References

A complete description of the methodology and the application in World is described in the paper:
Panagos P., Borrelli P., Meusburger K., Yu B., Klik A., Lim K.J., Yang J.E, Ni J., Miao C., Chattopadhyay N., Sadeghi S.H., Hazbavi Z., Zabihi M., Larionov G.A., Krasnov S.F., Garobets A., Levi Y., Erpul G., Birkel C., Hoyos N., Naipal V., Oliveira P.T.S., Bonilla C.A., Meddi M., Nel W., Dashti H., Boni M., Diodato N., Van Oost K., Nearing M.A., Ballabio C., 2017. Global rainfall erosivity assessment based on high-temporal resolution rainfall records. Scientific Reports 7: 4175. DOI: 10.1038/s41598-017-04282-8.

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