Turbidity and suspended sediment relationship based on sediment composition and particle size distribution
Abstract High turbidity in rivers, intensified by extreme rainfall associated with climate change, poses a great challenge to water resource management globally. To manage and control turbidity events, prediction models have been developed utilizing suspended sediment (SS) data. However, traditional...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-05-01
|
| Series: | Scientific Reports |
| Subjects: | |
| Online Access: | https://doi.org/10.1038/s41598-025-00435-2 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849312053227945984 |
|---|---|
| author | Jongmin Kim Siyoon Kwon Sewoong Chung Young Do Kim |
| author_facet | Jongmin Kim Siyoon Kwon Sewoong Chung Young Do Kim |
| author_sort | Jongmin Kim |
| collection | DOAJ |
| description | Abstract High turbidity in rivers, intensified by extreme rainfall associated with climate change, poses a great challenge to water resource management globally. To manage and control turbidity events, prediction models have been developed utilizing suspended sediment (SS) data. However, traditional methods for measuring SS data, such as water sampling and laboratory analysis, are time-consuming and impractical for real-time applications. Although the turbidity–SS relationship is widely used, its accuracy depends on sediment particle size distribution. To address this limitation, we developed turbidity–SS equations adaptive to various SS fractions, using data from controlled circulating flume experiments designed to reflect sediment characteristics of a natural river. The results showed significant improvements in the linear turbidity–SS relationship, with R2 values ranging from 0.60 to 0.99, depending on sediment fractions. These equations were applied to field data from the area upstream of Soyanggang Dam in South Korea, yielding error rates of 1–18%. This study highlights the importance of incorporating sediment fraction variability into turbidity–SS models, which significantly improves their accuracy and reliability. The proposed approach offers a practical and scalable solution for real-time and large-scale SS monitoring, contributing to improved water quality management in various riverine conditions. |
| format | Article |
| id | doaj-art-e0a25123d4f042c8a6afb0467e1893c8 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-e0a25123d4f042c8a6afb0467e1893c82025-08-20T03:53:12ZengNature PortfolioScientific Reports2045-23222025-05-0115111210.1038/s41598-025-00435-2Turbidity and suspended sediment relationship based on sediment composition and particle size distributionJongmin Kim0Siyoon Kwon1Sewoong Chung2Young Do Kim3Department of Civil and Environmental Engineering, Myongji UniversityDepartment of Civil and Environmental Engineering, Seoul National UniversityDepartment of Environmental Engineering, Chungbuk National UniversityDepartment of Civil and Environmental Engineering, Myongji UniversityAbstract High turbidity in rivers, intensified by extreme rainfall associated with climate change, poses a great challenge to water resource management globally. To manage and control turbidity events, prediction models have been developed utilizing suspended sediment (SS) data. However, traditional methods for measuring SS data, such as water sampling and laboratory analysis, are time-consuming and impractical for real-time applications. Although the turbidity–SS relationship is widely used, its accuracy depends on sediment particle size distribution. To address this limitation, we developed turbidity–SS equations adaptive to various SS fractions, using data from controlled circulating flume experiments designed to reflect sediment characteristics of a natural river. The results showed significant improvements in the linear turbidity–SS relationship, with R2 values ranging from 0.60 to 0.99, depending on sediment fractions. These equations were applied to field data from the area upstream of Soyanggang Dam in South Korea, yielding error rates of 1–18%. This study highlights the importance of incorporating sediment fraction variability into turbidity–SS models, which significantly improves their accuracy and reliability. The proposed approach offers a practical and scalable solution for real-time and large-scale SS monitoring, contributing to improved water quality management in various riverine conditions.https://doi.org/10.1038/s41598-025-00435-2TurbiditySuspended sedimentTurbidity–SS relationshipSediment fractionsParticle size distribution |
| spellingShingle | Jongmin Kim Siyoon Kwon Sewoong Chung Young Do Kim Turbidity and suspended sediment relationship based on sediment composition and particle size distribution Scientific Reports Turbidity Suspended sediment Turbidity–SS relationship Sediment fractions Particle size distribution |
| title | Turbidity and suspended sediment relationship based on sediment composition and particle size distribution |
| title_full | Turbidity and suspended sediment relationship based on sediment composition and particle size distribution |
| title_fullStr | Turbidity and suspended sediment relationship based on sediment composition and particle size distribution |
| title_full_unstemmed | Turbidity and suspended sediment relationship based on sediment composition and particle size distribution |
| title_short | Turbidity and suspended sediment relationship based on sediment composition and particle size distribution |
| title_sort | turbidity and suspended sediment relationship based on sediment composition and particle size distribution |
| topic | Turbidity Suspended sediment Turbidity–SS relationship Sediment fractions Particle size distribution |
| url | https://doi.org/10.1038/s41598-025-00435-2 |
| work_keys_str_mv | AT jongminkim turbidityandsuspendedsedimentrelationshipbasedonsedimentcompositionandparticlesizedistribution AT siyoonkwon turbidityandsuspendedsedimentrelationshipbasedonsedimentcompositionandparticlesizedistribution AT sewoongchung turbidityandsuspendedsedimentrelationshipbasedonsedimentcompositionandparticlesizedistribution AT youngdokim turbidityandsuspendedsedimentrelationshipbasedonsedimentcompositionandparticlesizedistribution |