Contribution of the adjusted empirical analysis of the RSULE and FAO models in the estimation of soil losses in the watershed of Wadi El Hayat (Saudi Arabia)

Azaiez, Naima; Baazaoui, Narjes; Blel, Ilhem; Hammami, Besma Sghaier

doi:10.1007/s12517-021-08561-7

Cited by 4 publications

(1 citation statement)

References 24 publications

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“…Elhag et al conducted recent research on two arid basins (Yalamlam and Al-Lith) of KSA and found that the annual soil losses ranged from 10 t/ha/year to 40 t/ha/year in both basins. Azaiez et al [69] used FAO and RUSLE equations to monitor soil loss in Wadi El Hayat in the Jizan region and found the soil loss to be between 36.1 tons/ha/year for FAO and 40 tons/ha/year for the RUSLE equation. Bahrawi et al [20] used the RUSLE and RS techniques to assess soil erosion in the Wadi Yalamlam basin southeast of Jeddah, KSA, and calculated annual soil loss of 40 t ha −1 yr −1 .…”

Section: Discussionmentioning

confidence: 99%

Soil Erosion Modelling and Accumulation Using RUSLE and Remote Sensing Techniques: Case Study Wadi Baysh, Kingdom of Saudi Arabia

et al. 2023

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This study examines the sediment retention in Wadi Baysh using the Revised Universal Soil Loss Equation (RUSLE) and TerrSet models, accompanied by integrated remote sensing and Geographic Information System (GIS) techniques. The contribution of this study is mainly associated with the application of TerrSet integrated with high resolution datasets to precisely estimate sediments load, which provide useful information to operate dams and improve the operational efficiency of dams. The Advanced Land Observing Satellite (ALOS) Phased Array type L-band Synthetic Aperture Radar (PALSAR) data are utilized to delineate the basin and have been used as an input to the TerrSet model. The rainfall erosivity (R factor) was calculated using the Climate Hazards Center Infrared Precipitation with Stations (CHIRPS) in the research area during 2015–2020. The soil erodibility (K factor) and LULC categorization are calculated using the digital soil map of the world (DSMW) and Sentinel-2 datasets, respectively. The R factor calculated for Wadi Baysh ranges between 91.35 and 115.95 MJ mm/ha/h/year, while the estimated K factor ranges from 0.139 to 0.151 t ha h/ha M. The Support Vector Machine (SVM) method categorized LULC of the study area into four major classes including barren land (81% of the total area), built-up area (11%), vegetation (8%), and water bodies (1%). Results from the sediment retention module (TerrSet) indicated that each year, 57.91 million tons of soil loss occurred in the basin. The data show that soil loss is greater in the northeast and south, whereas it is typical in the middle of Wadi Baysh. It is concluded from the current analyses that the dam lake of Wadi Baysh, located downstream, will be filled soon in the coming few years if sediment loads are carried to the lake at the same rate. Surface dam operators can obtain a full understanding of sedimentation and take proactive measures to reduce its influence on dam operations by leveraging TerrSet’s sophisticated capabilities.

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Section: Discussionmentioning

confidence: 99%

Soil Erosion Modelling and Accumulation Using RUSLE and Remote Sensing Techniques: Case Study Wadi Baysh, Kingdom of Saudi Arabia

et al. 2023

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Integrating USLE in Geospatial Platform for Preparing Catchment Area Treatment Plan for Chandil Dam, Jharkhand, India

Avishek,

Kanungo,

Majumdar

et al. 2023

J Indian Soc Remote Sens

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GIS-based revised universal soil loss equation for estimating annual soil erosion: a case of lower Kulsi basin, India

Thakuriah

2023

SN Appl. Sci.

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Assessment and estimation of soil loss is a fundamental aspect of land and water resource conservation and management practices as it provides necessary information in the course of watershed-level development of a region. The soil loss model of Wischmeier and Smith, popularly known as the Revised Universal Soil Loss Equation, was selected to estimate soil loss in the lower Kulsi river basin due to its simplicity, versatility, and flexibility nature method in the Geographic information system platform. Most original governmental datasets, mainly daily gauge rainfall from 2009 to 2018, satellite images for land use land cover, digital elevation model of Shuttle Radar Topographic Mission for topographic factor, and National Bureau of Soil Survey and Land-use Planning, India soil map were utilized to estimate the average annual soil erosion. The estimated average annual soil erosion ranges from 0.0 to 6.45 thousand t ha−1y−1, grouped into low, moderate, high, and very high risk of soil erosion. A basin area of 36.235 km2 (1.85%) basin area was identified as high to very high zones of soil erosion risk and needed immediate conservation measures to reduce the erosion risk.

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Contribution of the adjusted empirical analysis of the RSULE and FAO models in the estimation of soil losses in the watershed of Wadi El Hayat (Saudi Arabia)

Cited by 4 publications

References 24 publications

Soil Erosion Modelling and Accumulation Using RUSLE and Remote Sensing Techniques: Case Study Wadi Baysh, Kingdom of Saudi Arabia

Soil Erosion Modelling and Accumulation Using RUSLE and Remote Sensing Techniques: Case Study Wadi Baysh, Kingdom of Saudi Arabia

Integrating USLE in Geospatial Platform for Preparing Catchment Area Treatment Plan for Chandil Dam, Jharkhand, India

GIS-based revised universal soil loss equation for estimating annual soil erosion: a case of lower Kulsi basin, India

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