Modeling and Assessing Potential Soil Erosion Hazards Using USLE and Wind Erosion Models in Integration with GIS Techniques: Dakhla Oasis, Egypt

Salman A. H. Selmy, Salah H. Abd Al-Aziz, Raimundo Jiménez-Ballesta, Francisco Jesús García-Navarro and Mohamed E. Fadl
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Salman A. H. Selmy: Department of Soils and Water, Faculty of Agriculture, Assiut University, Assiut 71526, Egypt
Salah H. Abd Al-Aziz: Department of Soils and Water, Faculty of Agriculture, Assiut University, Assiut 71526, Egypt
Raimundo Jiménez-Ballesta: Department of Geology and Geochemistry, Autonomous University of Madrid, 28019 Madrid, Spain
Francisco Jesús García-Navarro: Higth Technical School of Agricultural Engineers, University of Castilla-La Mancha, 13007 Ciudad Real, Spain
Mohamed E. Fadl: Division of Scientific Training and Continuous Studies, National Authority for Remote Sensing and Space Sciences (NARSS), Cairo 11769, Egypt

Agriculture, 2021, vol. 11, issue 11, 1-29

Abstract: Soil erosion modeling is becoming more significant in the development and implementation of soil management and conservation policies. For a better understanding of the geographical distribution of soil erosion, spatial-based models of soil erosion are required. The current study proposed a spatial-based model that integrated geographic information systems (GIS) techniques with both the universal soil loss equation (USLE) model and the Index of Land Susceptibility to Wind Erosion (ILSWE). The proposed Spatial Soil Loss Model (SSLM) was designed to generate the potential soil erosion maps based on water erosion and wind erosion by integrating factors of the USLE and ILSWE models into the GIS environment. Hence, the main objective of this study is to predict, quantify, and assess the soil erosion hazards using the SSLM in the Dakhla Oasis as a case study. The water soil loss values were computed by overlaying the values of five factors: the rainfall factor ( R -Factor), soil erodibility ( K -Factor), topography ( LS -Factor), crop types ( C -Factor), and conservation practice ( P -Factor). The severity of wind-driven soil loss was calculated by overlaying the values of five factors: climatic erosivity ( CE -Factor), soil erodibility (E-Factor), soil crust ( SC -Factor), vegetation cover ( VC -Factor), and surface roughness ( SR -Factor). The proposed model was statistically validated by comparing its outputs to the results of USLE and ILSWE models. Soil loss values based on USLE and SSLM varied from 0.26 to 3.51 t ha −1 yr −1 with an average of 1.30 t ha −1 yr −1 and from 0.26 to 3.09 t ha −1 yr −1 with a mean of 1.33 t ha −1 yr −1 , respectively. As a result, and according to the assessment of both the USLE and the SSLM, one soil erosion class, the very low class (<6.7 t ha −1 yr −1 ), has been reported to be the prevalent erosion class in the study area. These findings indicate that the Dakhla Oasis is slightly eroded and more tolerable against water erosion factors under current management conditions. Furthermore, the study area was classified into four classes of wind erosion severity: very slight, slight, moderate, and high, representing 1.0%, 25.2%, 41.5%, and 32.3% of the total study area, respectively, based on the ILSWE model and 0.9%, 25.4%, 43.9%, and 29.9%, respectively, according to the SSLM. Consequently, the Dakhla Oasis is qualified as a promising area for sustainable agriculture when appropriate management is applied. The USLE and ILSWE model rates had a strong positive correlation ( r = 0.97 and 0.98, respectively), with the SSLM rates, as well as a strong relationship based on the average linear regression ( R 2 = 0.94 and 0.97, respectively). The present study is an attempt to adopt a spatial-based model to compute and map the potential soil erosion. It also pointed out that designing soil erosion spatial models using available data sources and the integration of USLE and ILSWE with GIS techniques is a viable option for calculating soil loss rates. Therefore, the proposed soil erosion spatial model is fit for calculating and assessing soil loss rates under this study and is valid for use in other studies under arid regions with the same conditions.

Keywords: soil erosion hazard; USLE; ILSWE; GIS techniques; soil erosion spatial modeling; soil erosion mapping; soil conservation; erosion factors (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
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