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Global rainfall erosivity database (GloREDa) and monthly R-factor data at 1 km spatial resolution

Authors :
Panos Panagos
Tomislav Hengl
Ichsani Wheeler
Pawel Marcinkowski
Montfort Bagalwa Rukeza
Bofu Yu
Jae E. Yang
Chiyuan Miao
Nabansu Chattopadhyay
Seyed Hamidreza Sadeghi
Yoav Levi
Gunay Erpul
Christian Birkel
Natalia Hoyos
Paulo Tarso S. Oliveira
Carlos A. Bonilla
Werner Nel
Hassan Al Dashti
Nejc Bezak
Kristof Van Oost
Sašo Petan
Ayele Almaw Fenta
Nigussie Haregeweyn
Mario Pérez-Bidegain
Leonidas Liakos
Cristiano Ballabio
Pasquale Borrelli
Source :
Data in Brief, Vol 50, Iss , Pp 109482- (2023)
Publication Year :
2023
Publisher :
Elsevier, 2023.

Abstract

Here, we present and release the Global Rainfall Erosivity Database (GloREDa), a multi-source platform containing rainfall erosivity values for almost 4000 stations globally. The database was compiled through a global collaboration between a network of researchers, meteorological services and environmental organisations from 65 countries. GloREDa is the first open access database of rainfall erosivity (R-factor) based on hourly and sub-hourly rainfall records at a global scale. This database is now stored and accessible for download in the long-term European Soil Data Centre (ESDAC) repository of the European Commission's Joint Research Centre. This will ensure the further development of the database with insertions of new records, maintenance of the data and provision of a helpdesk.In addition to the annual erosivity data, this release also includes the mean monthly erosivity data for 94% of the GloREDa stations. Based on these mean monthly R-factor values, we predict the global monthly erosivity datasets at 1 km resolution using the ensemble machine learning approach (ML) as implemented in the mlr package for R. The produced monthly raster data (GeoTIFF format) may be useful for soil erosion prediction modelling, sediment distribution analysis, climate change predictions, flood, and natural disaster assessments and can be valuable inputs for Land and Earth Systems modelling.

Details

Language :
English
ISSN :
23523409
Volume :
50
Issue :
109482-
Database :
Directory of Open Access Journals
Journal :
Data in Brief
Publication Type :
Academic Journal
Accession number :
edsdoj.72d8cd9277d244678f3fefb05b34ea1b
Document Type :
article
Full Text :
https://doi.org/10.1016/j.dib.2023.109482