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Assessment and quantification of sediment retention and dam management in arid environments using remote sensing techniques

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Abstract

The current study implements an empirical soil erosion model (RUSLE) along with remote sensing data, and TerrSet algorithms to analyze sediment retention in Wadi Baysh. The ALOS PALSAR digital elevation model was used to delineate watersheds. Rainfall erosivity (R factor) in the study area was calculated using the CHIRPS dataset from 2011 to 2020. K factor of soil erodibility and land use land cover categorization are derived using FAO soil data and a Sentinel-2 image, respectively. Generally, the R factor value is estimated between 91.35 to 115.95 MJ mm/ha/h/year, and the K factor from 0.139 to 0.151 t hah/ha M in the Wadi Baysh. The support vector machines approach divides the area into four major classes. Eighty-one percent of the overall area is barren, 11% is built up, 8% is vegetation, and 1% is water bodies. Using the TerrSet algorithms, the study area approximately losses 57.91 million tons of soil each year. The data show that soil loss is greater in the northeast and south, whereas it is typical in the middle of Wadi Baysh. In conclusion, with this level of sediment load due to the conceptual rainfall erosivity factor, the dam lake of Wadi Baysh, which is located on the downstream side, will be filled shortly in the upcoming 5 years; hence, the appropriate mitigating procedures must be performed.

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Funding

This research was funded by The Chinese Academy of Sciences (CAS), President’s International Fellowship Initiative (PIFI), grant number 2021VEA0007.

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Authors and Affiliations

  1. Department of Water Resources, Faculty of Environmental Sciences, King Abdulaziz University, 21589, Jeddah, Saudi Arabia

    Mohamed Elhag, Jarbou Bahrawi & Nuaman Ejaz

  2. The State Key Laboratory, of Remote Sensing, Aerospace Information Institute, Chinese Academy of Science, Beijing, 100101, China

    Mohamed Elhag & Lifu Zhang

  3. Department of Geoinformation in Environmental Management, CI-HEAM/Mediterranean Agronomic Institute of Chania, 73100, Chania, Greece

    Mohamed Elhag

  4. Department of Applied Geosciences, Faculty of Science, German University of Technology in Oman, Muscat, 1816, Oman

    Mohamed Elhag

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  1. Mohamed Elhag
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  2. Jarbou Bahrawi
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  4. Nuaman Ejaz
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Correspondence to Mohamed Elhag.

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Responsible Editor: Biswajeet Pradhan

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Elhag, M., Bahrawi, J., Zhang, L. et al. Assessment and quantification of sediment retention and dam management in arid environments using remote sensing techniques. Arab J Geosci 16, 559 (2023). https://doi.org/10.1007/s12517-023-11661-1

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