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

• DOI: 10.1016/j.dib.2023.109482
• Publication Date: 2023-08-09T16:30:48Z
• Authors: Panos Panagos; Tomislav Hengl; Ichsani Wheeler; Pawel Marcinkowski; Montfort Bagalwa ...; Bofu Yu; Jae E. Yang; Chiyuan Miao; Nabansu Chattopad...; Seyed Hamidreza S...; Yoav Levi; Gunay Erpul; Christian Birkel; Natalia Hoyos; Paulo Tarso S. Ol...; 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
• Keywords: Raster data

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