Radar remote sensing reveals potential underestimation of rainfall erosivity at the global scale
Affiliation
Department of Hydrology and Atmospheric Sciences, The University of ArizonaIssue Date
2023-08-09
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Qiang 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.adg5551Journal
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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.Collection Information
This 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.Abstract
Rainfall 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.Note
Open access journalISSN
2375-2548PubMed ID
37556540Version
Final Published Versionae974a485f413a2113503eed53cd6c53
10.1126/sciadv.adg5551
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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.