Abstract

Water erosion is a natural fact in the cycle of shaping the earth’s landforms and the most evident form of land degradation on a planetary scale (Roose & De Noni, 2004; Dautrebande & Sohier, 2006; Toumi et al., 2013; Azaiez, 2020a). Its effects have largely affected the rural landscape. Although it is the result of an ancient and primeval climatic and tectonic forcing, man’s footprint in its intensification remains a reality. A real awareness of this problem on the part of scientists is reflected in the many studies on understanding the risks. Experiments have been carried out, equations and models have been developed with the aim of preserving the soil. The watershed of wadi El Badalah, the subject of this study, is not excluded from this risk. The present research is a new comparative contribution to the quantification of soil loss based on four empirical models (Universal Soil Loss Equation USLE, Revised Universal Soil Loss Equation RUSLE, Modified Universal Soil Loss Equation MUSLE and the adjusted model). The main objective of this research project is to test several empirical models of quantification of soil loss, extensively tested on a global scale, to discuss the potential of each model in order to choose the most appropriate for the El Badalah basin. The method is based on geotechnical and geophysical investigations. It consists of determining the anomalies of the subsoil based on the difference in density and the resistivity of the heterogeneous constituents of the subsoil. This is in order to look for sectors potentially favorable to infiltration at the expense of runoff.

Highlights

  • Today, the problem of erosion is taking on a social dimension as the little viable agricultural land is at risk of degradation to varying degrees due to changes in land use and the adoption of farming methods that are not appropriate for certain types of soil, especially those located in areas at high risk of landslides (Roose and De Noni, 2004; Felah, 2010; Azaiez, 2016, 2020a; Azaiez et al, 2020)

  • The present research is a new comparative contribution to the quantification of soil loss based on four empirical models (Universal Soil Loss Equation USLE, Revised Universal Soil Loss Equation RUSLE, Modified Universal Soil Loss Equation MUSLE and the adjusted model)

  • It was chosen to assess soil losses through a comparative test between the three models (USLE, RUSLE and MUSLE) in order to understand the different circumstances and processes that are at the origin of land degradation in the El Badalah watershed

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Summary

Introduction

The problem of erosion is taking on a social dimension as the little viable agricultural land is at risk of degradation to varying degrees due to changes in land use and the adoption of farming methods that are not appropriate for certain types of soil, especially those located in areas at high risk of landslides (Roose and De Noni, 2004; Felah, 2010; Azaiez, 2016, 2020a; Azaiez et al, 2020). It would not be advisable to assess them by their statistical weight, but rather by their causal relationship due to their various aspects and behaviors, especially with respect to exceptional rainfall events and the ancient volcanic substratum worn out by the weathering mechanisms (mechanical and chemical) In this perspective, it was chosen to assess soil losses through a comparative test between the three models (USLE, RUSLE and MUSLE) in order to understand the different circumstances and processes that are at the origin of land degradation in the El Badalah watershed. Using existing knowledge and findings related to magnetic and gravity prospecting, can provide new insights into water erosion by seeking out the most favorable areas to infiltration and to identify potentially favorable sectors for the proliferation and deepening of the horizon of biological weathering as being a natural mechanism for the restitution of the soil layer recently opposed to erosion process

Study Area
Methodology of Work
The RUSLE Equation
Parameters for Adapting the Models to Local Conditions in the Watershed
Modeling Soil Losses
Findings
Conclusion

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