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The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.

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

Open access
Panagos, Panos, Borrelli, Pasquale, Meusburger, Katrin, Yu, Bofu, Klik, Andreas, Jae Lim, Kyoung, Yang, Jae E., Ni, Jinren, Miao, Chiyuan, Chattopadhyay, Nabansu, Sadeghi, Seyed Hamidreza, Hazbavi, Zeinab, Zabihi, Mohsen, Larionov, Gennady A., Krasnov, Sergey F., Gorobets, Andrey V., Levi, Yoav, Erpul, Gunay, Birkel, Christian, Hoyos, Natalia, Naipal, Victoria, Oliveira, Paulo Tarso S., Bonilla, Carlos A., Meddi, Mohamed, Nel, Werner, Al Dashti, Hassan, Boni, Martino, Diodato, Nazzareno, Van Oost, Kristof, Nearing, Mark, Ballabio, Cristiano·2017·Scientific Reports

Abstract

The exposure of the Earth’s surface to the energetic input of rainfall is one of the key factors controlling water erosion. While water erosion is identified as the most serious cause of soil degradation globally, global patterns of rainfall erosivity remain poorly quantified and estimates have large uncertainties. This hampers the implementation of effective soil degradation mitigation and restoration strategies. Quantifying rainfall erosivity is challenging as it requires high temporal resolution(<30 min) and high fidelity rainfall recordings. We present the results of an extensive global data collection effort whereby we estimated rainfall erosivity for 3,625 stations covering 63 countries. This first ever Global Rainfall Erosivity Database was used to develop a global erosivity map at 30 arc-seconds(~1 km) based on a Gaussian Process Regression(GPR). Globally, the mean rainfall erosivity was estimated to be 2,190 MJ mm ha −1 h −1 yr −1 , with the highest values in South America and the Caribbean countries, Central east Africa and South east Asia. The lowest values are mainly found in Canada, the Russian Federation, Northern Europe, Northern Africa and the Middle East. The tropical climate zone has the highest mean rainfall erosivity followed by the temperate whereas the lowest mean was estimated in the cold climate zone.

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Metadata

Authors
Panagos, P., Borrelli, P., Meusburger, K., Yu, B., Klik, A., Jae Lim, K., Yang, J. E., Ni, J., Miao, C., Chattopadhyay, N., Sadeghi, S. H., Hazbavi, Z., Zabihi, M., Larionov, G. A., Krasnov, S. F., Gorobets, A. V., Levi, Y., Erpul, G., Birkel, C., Hoyos, N., Naipal, V., Oliveira, P. T. S., Bonilla, C. A., Meddi, M., Nel, W., Al Dashti, H., Boni, M., Diodato, N., Van Oost, K., Nearing, M., & Ballabio, C.
Venue
Scientific Reports
Published
2017-06-23
Volume
7
Issue
1
Pages
4175-4175
ISSN
2045-2322 (ISSN-L)
OpenAlex
W2674211945
Cited by
651
References
3
FWCI
46.91
Type
journal article
Language
en
Open access
gold

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CoChat. (2026). Citation check: Global rainfall erosivity assessment based on high-temporal resolution rainfall records. https://app.cochat.ai/papers/10.1038/s41598-017-04282-8
The paper
Panagos, P., Borrelli, P., Meusburger, K., Yu, B., Klik, A., Jae Lim, K., Yang, J. E., Ni, J., Miao, C., Chattopadhyay, N., Sadeghi, S. H., Hazbavi, Z., Zabihi, M., Larionov, G. A., Krasnov, S. F., Gorobets, A. V., Levi, Y., Erpul, G., Birkel, C., . . . Ballabio, C. (2017). Global rainfall erosivity assessment based on high-temporal resolution rainfall records. Scientific Reports. https://doi.org/10.1038/s41598-017-04282-8

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

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