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).
Global Rainfall Erosivity
Registration is requested: 
Yes
Country: 
Ispra
Italy
Author - Contributors: 
Panos Panagos
Cristiano Ballabio
Publisher: 
European Commission, Joint research Centre
Year: 
2017
Language: 
Keywords: 

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. In addition, we explore an approach to derive an R-factor based on satellite data.
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

Global R-factor

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.

 

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.

GloREDa Database: You can also download the updated GloREDa 1.2 includes measured erosivity (R-factor) data for 3939 stations. In addition, we added the monthly component to GloREDa and we calculated the mean monthly R-factor per station. For 94% (3702 stations) of GloREDa, it was possible to add the monthly R-factor values summarizing 44,424 monthly records. This also includes the he derived twelve (12) global monthly erosivity maps.

 

Additional - derived datasets 

The Global R-factor data and the poin data have contributed to develop additional datasets such as a) Satellite-based R-factor b) Global assessment of storm disaster-prone areas.

a) Satellite-based R-factor

In addition, we developed two alternatives for erosivity map based on a) satellite-based rainfall data and b)  erosivity density concept. We used the high spatial and temporal resolution global precipitation estimates obtained with the National Oceanic and Atmospheric Administration (NOAA) Climate Data Record (CDR) Climate Prediction Center MORPHing (CMORPH) technique. Such high spatial and temporal (30 min) resolution data have not yet been used for the estimation of rainfall erosivity on a global scale. Alternatively, the erosivity density (ED) concept was also used to estimate global rainfall erosivity.The obtained global estimates of rainfall erosivity were validated against the pluviograph data included in the Global Rainfall Erosivity Database (GloREDa). Overall, results indicated that the CMORPH estimates have a marked tendency to underestimate rainfall erosivity when compared to the GloREDa estimates. The most substantial underestimations were observed in areas with the highest rainfall erosivity values.

The global erosivity map and the satellite derived one  are publicly available and can be used by other research groups to perform national, continental and global soil erosion modelling.

b) Global assessment of storm disaster-prone areas

Rainfall erosivity density (RED), i.e. rainfall erosivity (MJ mm hm-2 h-1 yr-1) per rainfall unit (mm), is a measure of rainstorm aggressiveness and a proxy indicator of damaging hydrological events. By using measured Ranfall Erosivity Density (RED) for 3,625 raingauges worldwide and applying kriging methodologies, we identify the damaging hydrological hazard-prone areas that exceed warning and alert thresholds (1.5 and 3.0 hm-2 h-1 yr-1, respectively). We have analysed for the first time the spatial pattern of hydrological hazard associated with rainfall erosivity in a global-scale visualisation. The results indicated that about 31% and 19% of the world’s land area have a greater than 50% probability of exceeding the warning and alert thresholds of 1.5 and 3.0 hm-2 h-1 yr-1, respectively.

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. We also share the recenlty developed global erosivity maps based on satellite high resolution temporal data (CMORPH) and the erosivity density concept.

GloREDa calcualted erosivity values can be shared in case of scientific collaborations. The point measured data for 3,625 stations can be requested from contact author (for scientific developments).

GloREDa has contributed in developing the Global Rainfall Projections for 2050 and 2070.

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.

GloREDa reference: Panagos, P., Hengl, T., Wheeler, I., Marcinkowski, P., Rukeza, M.B., Yu, B., Yang, J.E., Miao, C., Chattopadhyay, N., Sadeghi, S.H. and Levi, Y., et al. 2023. Global Rainfall Erosivity database (GloREDa) and monthly R-factor data at 1km spatial resolution. Data in Brief, 50, Art.no.109482. DOI: 10.1016/j.dib.2023.109482

Global Rainfall Erosivity

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