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

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.

Current Status of Research on Wildland Fire Impacts on Soil Environment and Soil Organisms and Hotspots Visualization Analysis

“…It has been shown that low and moderate intensities of fire will not directly affect the soil structure whereas, under severe fire generating high temperatures, organic matter and roots will decompose, exposing the inorganic soil, which alters the soil structure and results in soil compaction and hardening, thereby reducing the air permeability and water retention of the soil [20]. Rainfall is the primary driving force of soil erosion [21]. Vegetation and litter function to intercept and absorb rainwater [22,23].…”

Rainfall erosivity mapping in mainland China using 1-minute precipitation data from densely distributed weather stations

“…Studies have indicated that E values derived from 1-hourly in-situ precipitation data tend to underestimate those obtained from 1minute data by approximately 10% (Agnese et al, 2006;Yin et al, 2007). In 2023, Dai et al (2023) introduced the first global rainfall microphysics-based E, utilizing parameters retrieved from radar reflectivity. Compared to the radar remote sensing-based E values, the multi-year averaged annual rainfall kinetic energy calculating using E-I method was smaller with biases ranging from -6.17% to -12.5% across distinct regions worldwide.…”