Soil Erosion Estimation in Arid Area by USLE Model Applying GIS and RS: Case of Oued El Hamma Catchment, South-Eastern Tunisia

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Abstract

Soil erosion risk is one of the most severe environmental problems that can affect water availability, agricultural productivity and consequently countries’ sustainable development worldwide. The current study was performed in the Oued El Hamma Catchment, South-Eastern Tunisia to estimate the soil loss, applying the Universal Soil Loss Equation (USLE) integrated with Geographic Information System (GIS) and Remote Sensing (RS) approaches. The main objective of this research was to identify the vulnerable zones to elaborate a soil erosion risk map in the study catchment. The adopted approach was built essentially on five factors including rainfall erosivity, topography, soil erodibility, field management practice and vegetation cover factors. In addition, the soil erodibility factor was simulated based on soil properties obtained via in-situ measurements of the soil sampling and analysis campaigns. The spatial erosion risk map of Oued El Hamma Catchment was established with four distinct erosion classes based on the intensity of soil loss. The mean soil loss rate is approximately 0.2 t/ha/yr. The maximum soil loss in the study area was 17 t/ha/yr in Matmata mountainous regions. The results obtained showed that erosion risk was minimal in most of the catchment (99%). The erosion risk was moderate to high in a limited coverage observed especially in South sector (< 1%) that is subjected to a greater soil loss risk than 5 t/ha/yr. Finally, it is important to note that the developed soil erosion risk map represents an efficient tool to help decision makers evolving strategies for water and soil conservation works in such arid areas where farmlands are vulnerable and extremely scarce.

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CITATION STYLE

APA

Jemai, S., Kallel, A., Agoubi, B., & Abida, H. (2021). Soil Erosion Estimation in Arid Area by USLE Model Applying GIS and RS: Case of Oued El Hamma Catchment, South-Eastern Tunisia. Journal of the Indian Society of Remote Sensing, 49(6), 1293–1305. https://doi.org/10.1007/s12524-021-01320-x

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