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Evaluation of the SEdiment Delivery Distributed (SEDD) Model in the Shihmen Reservoir Watershed

Author

Listed:
  • Kent Thomas

    (Department of Civil Engineering, National Taipei University of Technology, Taipei 10608, Taiwan)

  • Walter Chen

    (Department of Civil Engineering, National Taipei University of Technology, Taipei 10608, Taiwan)

  • Bor-Shiun Lin

    (Disaster Prevention Technology Research Center, Sinotech Engineering Consultants, Taipei 11494, Taiwan)

  • Uma Seeboonruang

    (Faculty of Engineering, King Mongkut’s Institute of Technology Ladkrabang, Bangkok 10520, Thailand)

Abstract

The sediment delivery ratio (SDR) connects the weight of sediments eroded and transported from slopes of a watershed to the weight that eventually enters streams and rivers ending at the watershed outlet. For watershed management agencies, the estimation of annual sediment yield (SY) and the sediment delivery has been a top priority due to the influence that sedimentation has on the holding capacity of reservoirs and the annual economic cost of sediment-related disasters. This study establishes the SEdiment Delivery Distributed (SEDD) model for the Shihmen Reservoir watershed using watershed-wide SDR w and determines the geospatial distribution of individual SDR i and SY in its sub-watersheds. Furthermore, this research considers the statistical and geospatial distribution of SDR i across the two discretizations of sub-watersheds in the study area. It shows the probability density function (PDF) of the SDR i . The watershed-specific coefficient (β) of SDR i is 0.00515 for the Shihmen Reservoir watershed using the recursive method. The SY mean of the entire watershed was determined to be 42.08 t/ha/year. Moreover, maps of the mean SY by 25 and 93 sub-watersheds were proposed for watershed prioritization for future research and remedial works. The outcomes of this study can ameliorate future watershed remediation planning and sediment control by the implementation of geospatial SDR w /SDR i and the inclusion of the sub-watershed prioritization in decision-making. Finally, it is essential to note that the sediment yield modeling can be improved by increased on-site validation and the use of aerial photogrammetry to deliver more updated data to better understand the field situations.

Suggested Citation

  • Kent Thomas & Walter Chen & Bor-Shiun Lin & Uma Seeboonruang, 2020. "Evaluation of the SEdiment Delivery Distributed (SEDD) Model in the Shihmen Reservoir Watershed," Sustainability, MDPI, vol. 12(15), pages 1-21, August.
    • Handle: RePEc:gam:jsusta:v:12:y:2020:i:15:p:6221-:d:393597
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    References listed on IDEAS

    as
    1. Bor-Shiun Lin & Chun-Kai Chen & Kent Thomas & Chen-Kun Hsu & Hsing-Chuan Ho, 2019. "Improvement of the K-Factor of USLE and Soil Erosion Estimation in Shihmen Reservoir Watershed," Sustainability, MDPI, vol. 11(2), pages 1-16, January.
    2. Saskia Keesstra & Gerben Mol & Jan De Leeuw & Joop Okx & Co Molenaar & Margot De Cleen & Saskia Visser, 2018. "Soil-Related Sustainable Development Goals: Four Concepts to Make Land Degradation Neutrality and Restoration Work," Land, MDPI, vol. 7(4), pages 1-20, November.
    3. Kieu Anh Nguyen & Walter Chen & Bor-Shiun Lin & Uma Seeboonruang & Kent Thomas, 2019. "Predicting Sheet and Rill Erosion of Shihmen Reservoir Watershed in Taiwan Using Machine Learning," Sustainability, MDPI, vol. 11(13), pages 1-18, July.
    4. Kieu Anh Nguyen & Walter Chen & Bor-Shiun Lin & Uma Seeboonruang, 2020. "Using Machine Learning-Based Algorithms to Analyze Erosion Rates of a Watershed in Northern Taiwan," Sustainability, MDPI, vol. 12(5), pages 1-16, March.
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