Abstract
Abstract. Soil erosion can cause various ecological problems, such as land degradation, soil fertility loss, and river siltation. Rainfall is the primary water-driven force for soil erosion, and its potential effect on soil erosion is reflected by rainfall erosivity that relates to the raindrop kinetic energy. As it is difficult to observe large-scale dynamic characteristics of raindrops, all the current rainfall erosivity models use the function based on rainfall amount to represent the raindrops' kinetic energy. With the development of global atmospheric re-analysis data, numerical weather prediction techniques become a promising way to estimate rainfall kinetic energy directly at regional and global scales with high spatial and temporal resolutions. This study proposed a novel method for large-scale and long-term rainfall erosivity investigations based on the Weather Research and Forecasting (WRF) model, avoiding errors caused by inappropriate rainfall–energy relationships and large-scale interpolation. We adopted three microphysical parameterizations schemes (Morrison, WDM6, and Thompson aerosol-aware) to obtain raindrop size distributions, rainfall kinetic energy, and rainfall erosivity, with validation by two disdrometers and 304 rain gauges around the United Kingdom. Among the three WRF schemes, Thompson aerosol-aware had the best performance compared with the disdrometers at a monthly scale. The results revealed that high rainfall erosivity occurred in the west coast area at the whole country scale during 2013–2017. The proposed methodology makes a significant contribution to improving large-scale soil erosion estimation and for better understanding microphysical rainfall–soil interactions to support the rational formulation of soil and water conservation planning.
Highlights
Soil erosion plays a pivotal role in shaping the Earth’s physical landscape; it can threaten both ecosystems and human societies (Alewell et al, 2015)
All works show that rainfall erosivity decreases from west to east in United Kingdom (UK), previous studies (Panagos et al, 2015a; Naipal et al, 2015) using traditional methods lead to an overestimation of rainfall erosivity, which may be due to parameter a in the universal kinetic energy (KE)– I relationship being too high for the UK
This study presented a novel method for large-scale rainfall KE and erosivity estimation based on high-resolution, Weather Research and Forecasting (WRF)-derived DSDs
Summary
Soil erosion plays a pivotal role in shaping the Earth’s physical landscape; it can threaten both ecosystems and human societies (Alewell et al, 2015). The soil erosion rate is driven by a combination of factors, including rainfall, topography, soil characteristics, land cover, and land management applications (Wischmeier and Smith, 1958; Panagos et al, 2015b). Rainfall is a driving force that accounts for a large proportion of soil loss throughout most of the world (Panagos et al, 2015b). The erosive force of rainfall with consequent runoff is represented as erosivity of rainfall. This is a crucial factor for estimating soil loss in large-scale soil erosion models, for instance, the Universal Soil Loss Equation (USLE, Wischmeier and Smith, 1978; or RUSLE, Renard et al, 1997), Limburg Soil Erosion Model (LISEM) (De Roo et al, 1996), and USLE-M (Kinnell and Risse, 1998)
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