Rainfall Erosivity and Its Estimation: Conventional and Machine Learning Methods

Abstract

Rainfall erosivity concerns the ability of rainfall to cause erosion on the surface of the earth. The difficulty in modeling the distribution, the size, and the terminal velocity of raindrops in relation to the detachment of soil particles led to the use of more tractable rainfall indices. Thus, in the universal soil loss equation (USLE), the coefficient of rainfall erosivity, R, was introduced. This coefficient is based on the product of the rainfall kinetic energy of a storm and its maximum 30-minute intensity. An important problem in the application of USLE and its revisions in various parts of the world concerns the computation of R, which requires pluviograph records with a length of at least 20 years. For this reason, empirical equations have been developed that are based on coarser rainfall data, such as daily, monthly, or yearly, which are available on larger spatial and temporal extents. However, the lack of denser data is dealt more effectively by means of machine learning methods. Computational systems for this purpose were recently developed based on feed-forward neural networks, yielding significantly better results.

Impacts of Land Cover and Greenness Change on Soil Loss and Erosion Risk in Damota Area Districts, Southern Ethiopia

“…e Damota area of southern Ethiopia is characterized by high population density, erosive rainfall, rugged topography, and intensively cultivated land for a long period. Erosive rainfall is rainfall with a cumulative value of greater than 12.7 mm [44]. Because of these and other related factors, the area has been experiencing high soil erosion.…”

Estimating Current and Future Rainfall Erosivity in Greece Using Regional Climate Models and Spatial Quantile Regression Forests

“…The highest values are calculated at the mountain range of Pindos and the lowest at central Greece; (b) the monthly R maps adhere to the spatiotemporal characteristics of precipitation depth and intensities over the country; (c) the projected R values, as an average over Greece, follow the projected changes of precipitation of climatic models, but not in a spatially homogenous way.Water 2020, 12, 687 2 of 20 to simulate changes in future rainfall erosivity, land uses and the application of policies on land management [10].Universal Soil Loss Equation (USLE) [11], which is the most widely used soil erosion prediction model in the world [12], is an empirical equation that estimates the long-term, average, rate of soil loss involving the product of six factors. The USLE erosivity factor, R, is calculated using high frequency or break-point precipitation data with a duration of over 20 years [13,14], as a function of rainfall intensity and depth. In the second revised version of USLE, RUSLE2 [15], monthly Erosivity Density (ED) was introduced, as a measure of rainfall erosivity per unit rainfall, which requires shorter precipitation record lengths.…”

“…Universal Soil Loss Equation (USLE) [11], which is the most widely used soil erosion prediction model in the world [12], is an empirical equation that estimates the long-term, average, rate of soil loss involving the product of six factors. The USLE erosivity factor, R, is calculated using high frequency or break-point precipitation data with a duration of over 20 years [13,14], as a function of rainfall intensity and depth. In the second revised version of USLE, RUSLE2 [15], monthly Erosivity Density (ED) was introduced, as a measure of rainfall erosivity per unit rainfall, which requires shorter precipitation record lengths.…”

Spatial and seasonal patterns of rainfall erosivity in the Lake Kivu region: Insights from a meteorological observatory network

“…Rainfall erosivity is therefore typically described as the product of rainfall energy and maximum 30-minute intensity, such as in the R -factor (MJ mm ha −1 h −1 yr −1 ) of the RUSLE (Wischmeier and Smith, 1978) and its predecessor the USLE (Wischmeier and Smith, 1958). It is well established that a few erosive events can contribute to a significant share of erosion (Vantas et al, 2019). However, long-term dense time series, i.e.…”

“…Therefore, models have been developed that correlate rainfall erosivity with more readily available data (such as daily, monthly and annual precipitation amounts) to estimate rainfall erosivity (e.g. Bonilla and Vidal, 2011; Vantas et al, 2019). For Africa, parametric equations relating R -factor estimates to the monthly values of rainfall depth have been developed for North Africa (Smithen and Schulze, 1982), Morocco (Arnoldus, 1977), Nigeria (Igwe et al, 1999) and Sudan (Elagib, 2011).…”