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

Qiang Dai; Jingxuan Zhu; Guonian Lv; Latif Kalin; Yuanzhi Yao; Jun Zhang; Dawei Han

doi:10.1126/sciadv.adg5551

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

Sci Adv. 2023 Aug 9;9(32):eadg5551. doi: 10.1126/sciadv.adg5551. Epub 2023 Aug 9.

Authors

Qiang Dai¹, Jingxuan Zhu^{1

2}, Guonian Lv¹, Latif Kalin³, Yuanzhi Yao^{3

4}, Jun Zhang^{1

5}, Dawei Han²

Affiliations

¹ Key Laboratory of VGE of Ministry of Education, Nanjing Normal University, Nanjing, China.
² Department of Civil Engineering, University of Bristol, Bristol, UK.
³ College of Forestry, Wildlife and Environment, Auburn University, Auburn, AL, USA.
⁴ School of Geographic Science, East China Normal University, Shanghai, China.
⁵ Department of Hydrology and Atmospheric Sciences, The University of Arizona, Tucson, USA.

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 (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.