Case Study of the Integrated Model for Estimation of Sediment Load in Artificial River Channel

被引:0
|
作者
Zhou, Hong [1 ,2 ]
Chang, Tiao J. [1 ]
机构
[1] Ohio Univ, Dept Civil Engn, Athens, OH 45701 USA
[2] IDesign Engn Inc, 4041 Powder Mill Rd,Suite 204, Beltsville, MD 20705 USA
基金
美国国家科学基金会;
关键词
Sedimentation; Soil erosion; Total suspended sediment (TSS); Bedload; Revised universal soil loss equation (RUSLE); ArcGIS; SOIL-EROSION; TRANSPORT MODELS; DELIVERY RATIO; SCALE;
D O I
10.1061/(ASCE)HE.1943-5584.0001642
中图分类号
TU [建筑科学];
学科分类号
0813 ;
摘要
Sedimentation is one of the most important factors affecting stream channel stability. A proposed model was developed to estimate the sediment load of an artificial channel by the integration of the revised universal soil loss equation (RUSLE) and Watershed Assessment of River Stability and Sediment Supply (WARSSS). The developed model was tested in the channelized portion of the Hocking River near Athens, Ohio. It was estimated that the gross erosion from the watershed was 7.29x1010kg/year, of which 96.64% resulted from surface erosion and 3.36% from bank erosion. A field measurement of total sediment yield in the channel, assumed to be the sum of suspended sediment and bedload, was conducted. The total annual sediment yield was estimated as 8.09x109kg, of which 98.29% was suspended sediments and 1.71% bedload sediments. It was concluded that the sediment delivery ratio of the studied watershed was estimated to be 11.11%, which is consistent with those of the watersheds having similar sizes in the region. Based on these results, the authors believe that the proposed model can reasonably well estimate the sediment load in the studied portion of the Hocking River. (C) 2018 American Society of Civil Engineers.
引用
收藏
页数:8
相关论文
共 50 条
  • [1] Application of Artificial Neural Network Model for the Prediction of Suspended Sediment Load in the Large River
    Gaur, Shishir
    Mishra, Aryan
    Gupta, Anurag
    Jain, Arihant
    Dave, Apurve
    Eslamian, Saied
    Dwivedi, S. B.
    Graillot, Didier
    WATER RESOURCES, 2021, 48 (04) : 565 - 575
  • [2] Application of Artificial Neural Network Model for the Prediction of Suspended Sediment Load in the Large River
    Shishir Gaur
    Aryan Mishra
    Anurag Gupta
    Arihant Jain
    Apurve Dave
    Saied Eslamian
    S. B. Dwivedi
    Didier Graillot
    Water Resources, 2021, 48 : 565 - 575
  • [3] ESTIMATION OF SUSPENDED SEDIMENT LOAD BY ARTIFICIAL NEURAL NETWORK
    Kumcu, S. Y.
    Tumer, A. E.
    INTERNATIONAL JOURNAL OF ECOSYSTEMS AND ECOLOGY SCIENCE-IJEES, 2019, 9 (04): : 665 - 670
  • [4] CHANNEL SEDIMENT VARIABILITY ALONG A RIVER - A CASE-STUDY OF THE SIRET RIVER (ROMANIA)
    ICHIM, I
    RADOANE, M
    EARTH SURFACE PROCESSES AND LANDFORMS, 1990, 15 (03) : 211 - 225
  • [5] Estimation of Suspended Sediment Load Using Artificial Intelligence-Based Ensemble Model
    Nourani, Vahid
    Gokcekus, Huseyin
    Gelete, Gebre
    COMPLEXITY, 2021, 2021 (2021)
  • [6] Case Study: Sediment Transport in Proposed Geomorphic Channel for Napa River
    Neary, V.S.
    Wright, S.A.
    Bereciartua, P.
    2001, American Society of Civil Engineers (ASCE) (127)
  • [7] Case study: Sediment transport in proposed geomorphic channel for Napa River
    Neary, VS
    Wright, SA
    Bereciartua, P
    JOURNAL OF HYDRAULIC ENGINEERING-ASCE, 2001, 127 (11): : 901 - 910
  • [8] Case study: Sediment transport in proposed geomorphic channel for Napa River
    Neary, V.S.
    Wright, S.A.
    Bereciartua, P.
    Journal of Hydraulic Engineering, 2001, 127 (11) : 901 - 910
  • [9] Estimation of sediment load for Himalayan Rivers: Case study of Kaligandaki in Nepal
    Pennan Chinnasamy
    Aditya Sood
    Journal of Earth System Science, 2020, 129
  • [10] Evaluation of different types of artificial intelligence methods to model the suspended sediment load in Tigris River
    Al-Mukhtar, Mustafa
    3RD INTERNATIONAL CONFERENCE ON BUILDINGS, CONSTRUCTION AND ENVIRONMENTAL ENGINEERING, BCEE3-2017, 2018, 162