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Radar remote sensing reveals potential underestimation of rainfall erosivity at the global scale
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 regiona...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
American Association for the Advancement of Science
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10411884/ https://www.ncbi.nlm.nih.gov/pubmed/37556540 http://dx.doi.org/10.1126/sciadv.adg5551 |
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author | Dai, Qiang Zhu, Jingxuan Lv, Guonian Kalin, Latif Yao, Yuanzhi Zhang, Jun Han, Dawei |
author_facet | Dai, Qiang Zhu, Jingxuan Lv, Guonian Kalin, Latif Yao, Yuanzhi Zhang, Jun Han, Dawei |
author_sort | Dai, Qiang |
collection | PubMed |
description | 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 (RKE(MPH)) flux retrieved from radar reflectivity at different frequencies. The results suggest that RKE(MPH) flux outperforms the RKE estimates derived from a widely used empirical KE-I relation (RKE(KE-I)) validated using ground disdrometers. We found a potentially widespread underestimation of RKE(KE-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. |
format | Online Article Text |
id | pubmed-10411884 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | American Association for the Advancement of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-104118842023-08-10 Radar remote sensing reveals potential underestimation of rainfall erosivity at the global scale Dai, Qiang Zhu, Jingxuan Lv, Guonian Kalin, Latif Yao, Yuanzhi Zhang, Jun Han, Dawei Sci Adv Earth, Environmental, Ecological, and Space Sciences 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 (RKE(MPH)) flux retrieved from radar reflectivity at different frequencies. The results suggest that RKE(MPH) flux outperforms the RKE estimates derived from a widely used empirical KE-I relation (RKE(KE-I)) validated using ground disdrometers. We found a potentially widespread underestimation of RKE(KE-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. American Association for the Advancement of Science 2023-08-09 /pmc/articles/PMC10411884/ /pubmed/37556540 http://dx.doi.org/10.1126/sciadv.adg5551 Text en Copyright © 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 NonCommercial License 4.0 (CC BY-NC). https://creativecommons.org/licenses/by-nc/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution-NonCommercial license (https://creativecommons.org/licenses/by-nc/4.0/) , which permits use, distribution, and reproduction in any medium, so long as the resultant use is not for commercial advantage and provided the original work is properly cited. |
spellingShingle | Earth, Environmental, Ecological, and Space Sciences Dai, Qiang Zhu, Jingxuan Lv, Guonian Kalin, Latif Yao, Yuanzhi Zhang, Jun Han, Dawei Radar remote sensing reveals potential underestimation of rainfall erosivity at the global scale |
title | Radar remote sensing reveals potential underestimation of rainfall erosivity at the global scale |
title_full | Radar remote sensing reveals potential underestimation of rainfall erosivity at the global scale |
title_fullStr | Radar remote sensing reveals potential underestimation of rainfall erosivity at the global scale |
title_full_unstemmed | Radar remote sensing reveals potential underestimation of rainfall erosivity at the global scale |
title_short | Radar remote sensing reveals potential underestimation of rainfall erosivity at the global scale |
title_sort | radar remote sensing reveals potential underestimation of rainfall erosivity at the global scale |
topic | Earth, Environmental, Ecological, and Space Sciences |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10411884/ https://www.ncbi.nlm.nih.gov/pubmed/37556540 http://dx.doi.org/10.1126/sciadv.adg5551 |
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