Details

Autor(en) / Beteiligte

• Panagos, Panos
• Hengl, Tomislav
• Wheeler, Ichsani
• Marcinkowski, Pawel
• Rukeza, Montfort Bagalwa
• Yu, Bofu
• Yang, Jae E.
• Miao, Chiyuan
• Chattopadhyay, Nabansu
• Sadeghi, Seyed Hamidreza
• Levi, Yoav
• Erpul, Gunay
• Birkel, Christian
• Hoyos, Natalia
• Oliveira, Paulo Tarso S.
• Bonilla, Carlos A.
• Nel, Werner
• Al Dashti, Hassan
• Bezak, Nejc
• Van Oost, Kristof
• Petan, Sašo
• Fenta, Ayele Almaw
• Haregeweyn, Nigussie
• Pérez-Bidegain, Mario
• Liakos, Leonidas
• Ballabio, Cristiano
• Borrelli, Pasquale

Titel

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

Ist Teil von

• Data in brief, 2023-10, Vol.50, p.109482-109482, Article 109482

Ort / Verlag

Elsevier

Erscheinungsjahr

2023

Quelle

Alma/SFX Local Collection

Beschreibungen/Notizen

• 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.