Soil erosion modelling: A global review and statistical analysis

Abstract

To gain a better understanding of the global application of soil erosion prediction models, we comprehensively reviewed relevant peer-reviewed research literature on soil-erosion modelling 1994-2017. Our aim was to identify (i) processes and models most frequently addressed in the literature, (ii) regions within which models are primarily applied, (iii) what regions remain unaddressed and why, and (iv) how frequently studies are conducted to validate/evaluate model outcomes relative to measured data. To perform this task, we merged the knowledge of a group of 66 soil-erosion scientists from 67 research institutions and 25 countries. The resulting database ‘Global Applications of Soil Erosion Modelling Tracker (GASEMT)’ includes 3,030 individual modelling records from 126 counties encompassing all continents (except Antarctica). Out of 8,471 articles identified as potentially relevant, we reviewed 1,697 articles and transferred relevant information from each into the database. For each record reported in the GASEMT database, 42 attributes were evaluated. The GASEMT database provides insights into the state-of-the-art of soil- erosion models and model applications worldwide. The database is also intended to support the upcoming country-based United Nations global soil-erosion assessment. This database may help inform soil erosion research priorities in that it builds a foundation for future targeted in-depth analyses. GASEMT is an open-source database that anyone can use to develop research, rectify errors, and expand.

Comparison of Ensemble Machine Learning Methods for Soil Erosion Pin Measurements

“…Many theoretical/empirical models can be used to study soil erosion. According to Borrelli et al [6], 435 distinct models and model variants were used to analyze soil erosion from 1994 to 2017 in 1697 scientific articles in the Scopus database. The top five most applied models are RUSLE (Revised Universal Soil Loss Equation), USLE (Universal Soil Loss Equation), WEPP (Water Erosion Prediction Project), SWAT (Soil and Water Assessment Tool), and WaTEM/SEDEM (Water and Tillage Erosion Model and Sediment Delivery Model).…”

Geoinformatics of soil loss using GEE in Tigray regional state of Ethiopia.

“…Regional scale modeling are macroscale catchments of up to thousands of km2, thus require a spatial resolution from 30 meter to hundred meters (Mitasova et al 2013). Some of the papers that combined GIS and one of the RUSLE erosion models (Ahmed Harb Rabia 2012; Bagegnehu et al 2019;Borrelli et al 2021;K. et al 2020;Luvai et al 2022;Moges and Bhat 2017).…”

“…et al 2017;Van Remortel, Hamilton, and Hickey 2001). The RUSLE model have been applied enormously for the last 20 year and continued to be the most preferred soil erosion model used to estimate sheet & rill erosion caused by runoff (Borrelli, Alewell, and Alvarez 2021).…”

Can the Soil Erosion in Coastal Mountainous Areas Disturbed by Electric-Transmission-Line Construction be Estimated with a Deep Learning Model?

“…Soil erosion is one of the most widely distributed problems leading to land degradation. Currently, the universal soil loss equation (USLE), the revised universal soil loss equation (RUSLE) and its derived models are the most extensively used models among 435 types of soil erosion models (their representation in the literature accounts for 41%) (Ma et al, 2019;Xiong et al, 2019;Borrelli et al, 2021), and they are also commonly used in the soil loss research related to Electric-Transmission-Line (ETLs) (Li et al, 2021;Nie et al, 2022). In the RUSLE model, R (Rainfall erosivity factor), K (Soil erodibility factor), L (Slope length factor) and S (Slope steepness factor) are relatively stable in a region, while C (Cover and management factor) and P (Support practice factor) are very sensitive to land cover, vegetation cover and soil & water conservation activities (Feng and Zhao, 2014;Zhang et al, 2015;Huang et al, 2020).…”