Enhancement of Sediment Routing Method of <scp>SWAT</scp> Model Considering Sediment Occurrence Trends of Rise and Fall of Streamflow

ABSTRACT Accurate quantification of sediment occurrence in river basins is essential for establishing sediment management strategies. This study enhanced the simplified Bagnold method within the SWAT model to enhance simulation of sediment dynamics due to the rise and fall of streamflow. The sediment simulation efficacy of the enhanced Bagnold method in the SWAT was evaluated by comparison of estimated daily sediment with the original Bagnold method in the SWAT model. During the rise of streamflow, the original Bagnold method in the SWAT model yielded an R 2 of 0.86 and an NSE of 0.85, but these indicators declined to an R 2 of 0.49 and an NSE of −14.88 in simulations of sediment during the fall of streamflow. In contrast, the enhanced Bagnold method in the SWAT showed superior calibration performance, achieving an R 2 of 0.94 and an NSE of 0.92 during the rise of streamflow, and an improved R 2 of 0.68 and an NSE of 0.6 during the fall of streamflow. Notably, the enhanced Bagnold method in the SWAT provided a more accurate sediment prediction during increases in streamflow, with an overestimation of only 25.1% relative to observed data. This marks a significant improvement over the original model, which overestimated sediment by 443.5%. As shown in this study, streamflow variability (changes in rise/fall) needs to be considered in sediment simulations, enhancing model accuracy and informing effective sediment management strategy development.

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Publication Details

Journal
JAWRA Journal of the American Water Resources Association
Published
2025-08-01
DOI
https://doi.org/10.1111/1752-1688.70023
Primary Topic
Hydrology and Watershed Management Studies
Type
article
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article

Enhancement of Sediment Routing Method of SWAT Model Considering Sediment Occurrence Trends of Rise and Fall of Streamflow

Kyoung Jae Lim, Gwanjae Lee, S. N. Bak, Yonghun Choi et al.
JAWRA Journal of the American Water Resources Association
Hydrology and Watershed Management Studies
article

Enhancement of Sediment Routing Method of SWAT Model Considering Sediment Occurrence Trends of Rise and Fall of Streamflow

Kyoung Jae Lim, Gwanjae Lee, S. N. Bak, Yonghun Choi, Seoro Lee, Bernard A. Engel, Jeongho Han, Yeon Ho Jeong
article en

Abstract

ABSTRACT Accurate quantification of sediment occurrence in river basins is essential for establishing sediment management strategies. This study enhanced the simplified Bagnold method within the SWAT model to enhance simulation of sediment dynamics due to the rise and fall of streamflow. The sediment simulation efficacy of the enhanced Bagnold method in the SWAT was evaluated by comparison of estimated daily sediment with the original Bagnold method in the SWAT model. During the rise of streamflow, the original Bagnold method in the SWAT model yielded an R 2 of 0.86 and an NSE of 0.85, but these indicators declined to an R 2 of 0.49 and an NSE of −14.88 in simulations of sediment during the fall of streamflow. In contrast, the enhanced Bagnold method in the SWAT showed superior calibration performance, achieving an R 2 of 0.94 and an NSE of 0.92 during the rise of streamflow, and an improved R 2 of 0.68 and an NSE of 0.6 during the fall of streamflow. Notably, the enhanced Bagnold method in the SWAT provided a more accurate sediment prediction during increases in streamflow, with an overestimation of only 25.1% relative to observed data. This marks a significant improvement over the original model, which overestimated sediment by 443.5%. As shown in this study, streamflow variability (changes in rise/fall) needs to be considered in sediment simulations, enhancing model accuracy and informing effective sediment management strategy development.

JAWRA Journal of the American Water Resources AssociationVol. 61(4)
Kangwon National University (KR), Purdue University West Lafayette (US), Chuncheon National Hospital (KR)
Openalex Percentile: Top 80%
Hydrology and Watershed Management Studies
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