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ARS Home » Pacific West Area » Tucson, Arizona » SWRC » Research » Publications at this Location » Publication #337837

Research Project: ARS-Tucson IAG for RHEM and Remote Sensing Products

Location: Southwest Watershed Research Center

Title: Global rainfall erosivity assessment based on high-temporal resolution rainfall records

Author
item Panagos, P. - European Commission-Joint Research Centre (JRC)
item Borrelli, P. - University Of Basel
item Meusburger, K. - University Of Basel
item Yu, B. - Griffiths University
item Klik, A. - University Of Natural Resources & Applied Life Sciences - Austria
item Lim, K.j. - Kangwon National University
item Yang, J.e. - Kangwon National University
item Ni, J. - Peking University
item Miao, C. - Beijing Normal University
item Chattopadhyay, N. - India Meteorological Department
item Sadeghi, S.h. - Tarbiat Modares University
item Hazbavi, Z. - Tarbiat Modares University
item Zabihi, M. - Tarbiat Modares University
item Larionov, G.a. - Moscow State University
item Krasnov, S.f. - Moscow State University
item Gorobets, A.v. - Moscow State University
item Levi, G. - Israel Meteorological Service
item Erpul, G. - Ankara University Of Turkey
item Birkel, C. - Universidad De Costa Rica
item Hoyos, N. - Universidad Del Norte
item Naipal, V. - Laboratoire Des Sciences Du Climat Et De L'Environnement (LSCE)
item Oliveria, P.t.s. - Universidade Federal Do Mato Grosso Do Sul
item Bonilla, C.a. - Pontifical Catholic University Of Chile
item Meddi, M. - Ecole Nationale Supérieure D’Hydraulique De Blida
item Nei, W. - University Of Fort Hare
item Dashti, H.a. - Kuwait University
item Boni1, M. - European Commission-Joint Research Centre (JRC)
item Diodata, N. - Met European Research Observatory
item Van Oost, K. - Universite Catholique
item Sadeghi, S.h. - Tarbiat Modares University
item Nearing, Mark
item Ballabio, C. - European Commission-Joint Research Centre (JRC)

Submitted to: Scientific Reports
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 6/2/2017
Publication Date: 6/23/2017
Citation: Panagos, P., Borrelli, P., Meusburger, K., Yu, B., Klik, A., Lim, K., Yang, J., Ni, J., Miao, C., Chattopadhyay, N., Sadeghi, S., Hazbavi, Z., Zabihi, M., Larionov, G., Krasnov, S., Gorobets, A., Levi, G., Erpul, G., Birkel, C., Hoyos, N., Naipal, V., Oliveria, P., Bonilla, C., Meddi, M., Nei, W., Dashti, H., Boni1, M., Diodata, N., Van Oost, K., Sadeghi, S., Nearing, M.A., Ballabio, C. 2017. Global rainfall erosivity assessment based on high-temporal resolution rainfall records. Scientific Reports. 7:4175. https://doi.org/10.1038/s41598-017-04282-8.
DOI: https://doi.org/10.1038/s41598-017-04282-8

Interpretive Summary: Rainfall erosivity defines the power of rainfall to cause soil erosion. Quantification of rainfall erosivity is necessary when applying USDA models (USLE, RUSLE, RUSLE2) for estimating soil erosion rates used in conservation planning. In this study data was collected on rainfall from around the world to develop an international map of rainfall erosivity. Detailed rainfall records from 3,625 stations from 63 countries were used in the analysis. The map will provide users of erosion models a first cut at estimating erosivity for use in nearly every part of the globe. This will make the USDA tools for erosion prediction model readily usable across the world.

Technical Abstract: Rainfall erosivity quantifies the climatic effect on water erosion. In the framework of the Universal Soil Loss Equation, rainfall erosivity, also known as the R-factor, is defined as the mean annual sum of event erosivity values. For a new global soil erosion assessment, also in the broad context of natural hazard assessment and prevention, an inventory of rainfall erovisity and its spatial distribution at the global scale is considered to be both extremely valuable and challenging. Literature suggests that the rainfall erosivity is best computed using high temporal resolution rainfall data. For the presented global assessment 30 scientists all around the world have collected high temporal resolution rainfall data from more than 100 data providers during the last three years. This extensive data collection resulted in the computed rainfall erosivity for 3,625 stations covering 63 countries. This first ever Global Erosivity Database was interpolated using the Gaussian Process Regression (GPR) geo-statistical model to develop the global erosivity map at 30 arc-seconds (~1 km). Globally, the mean rainfall erosivity was estimated to be 2,190 MJ mm ha-1 h-1 yr-1, with the highest values (>5,200 MJ mm ha-1 h-1 yr-1) in major parts of South America and Caribbean countries, Central east Africa and South east Asia. The lowest values (< 200 MJ mm ha-1 h-1 yr-1) are mainly found in Canada, Russian Federation, North Europe, North Africa and Middle East. The tropical climate zone has by far the highest mean rainfall erosivity (7,104 MJ mm ha-1 h-1 yr-1) followed by the temperate climate zone (3,729 MJ mm ha-1 h-1 yr-1), whereas the lowest mean (493 MJ mm ha-1 h-1 yr-1) was estimated in the cold climate zone.