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

Qiang DaiJingxuan ZhuGuonian LvLatif KalinYuanzhi YaoJun ZhangDawei Han
Published in: Science advances (2023)
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.
Keyphrases
  • healthcare
  • primary care
  • climate change
  • machine learning
  • high intensity
  • artificial intelligence
  • big data
  • electronic health record
  • deep learning