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Radar remote sensing reveals potential underestimation of rainfall erosivity at the global scale

dc.contributor.authorDai, Q.
dc.contributor.authorZhu, J.
dc.contributor.authorLv, G.
dc.contributor.authorKalin, L.
dc.contributor.authorYao, Y.
dc.contributor.authorZhang, J.
dc.contributor.authorHan, D.
dc.date.accessioned2024-08-06T03:50:09Z
dc.date.available2024-08-06T03:50:09Z
dc.date.issued2023-08-09
dc.identifier.citationQiang Dai et al. ,Radar remote sensing reveals potential underestimation of rainfall erosivity at the global scale.Sci. Adv.9,eadg5551(2023).DOI:10.1126/sciadv.adg5551
dc.identifier.issn2375-2548
dc.identifier.pmid37556540
dc.identifier.doi10.1126/sciadv.adg5551
dc.identifier.urihttp://hdl.handle.net/10150/673855
dc.description.abstractRainfall kinetic energy (RKE) constitutes one of the most critical factors that drive rainfall erosivity on surface soil. Direct measurements of RKE are limited, relying instead on the empirical relations between kinetic energy and rainfall intensity (KE-I relation), which have not been well regionalized for data-scarce regions. Here, we present the first global rainfall microphysics–based RKE (RKEMPH) flux retrieved from radar reflectivity at different frequencies. The results suggest that RKEMPH flux outperforms the RKE estimates derived from a widely used empirical KE-I relation (RKEKE-I) validated using ground disdrometers. We found a potentially widespread underestimation of RKEKE-I, which is especially prominent in some low-income countries with ~20% underestimation of RKE and the resultant rainfall erosivity. Given the evidence that these countries are subject to greater rainfall-induced soil erosion, these underestimations would mislead conservation practices for sustainable development of terrestrial ecosystems. Copyright © 2023 The Authors, some rights reserved.
dc.language.isoen
dc.publisherAmerican Association for the Advancement of Science
dc.rights© 2023 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works. Distributed under a Creative Commons Attribution License 4.0.
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.titleRadar remote sensing reveals potential underestimation of rainfall erosivity at the global scale
dc.typeArticle
dc.typetext
dc.contributor.departmentDepartment of Hydrology and Atmospheric Sciences, The University of Arizona
dc.identifier.journalScience Advances
dc.description.noteOpen access journal
dc.description.collectioninformationThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at repository@u.library.arizona.edu.
dc.eprint.versionFinal Published Version
dc.source.journaltitleScience Advances
refterms.dateFOA2024-08-06T03:50:09Z


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© 2023 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works. Distributed under a Creative Commons Attribution License 4.0.
Except where otherwise noted, this item's license is described as © 2023 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works. Distributed under a Creative Commons Attribution License 4.0.