Prediction of sustainable management of sediment in rivers and reservoirs

• Published in: Chemosphere (Oxford) Vol. 309; no. Pt 1; p. 136369
• Main Authors: Aldrees, Ali, Bakheit Taha, Abubakr Taha, Mustafa Mohamed, Abdeliazim
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.12.2022
• Subjects: famine; geometry; geomorphology; Management; parameter uncertainty; prediction; Reservoirs; Rivers; roughness; Sediment; sediment contamination; sediments; Sustainability; sustainable development; water flow; watersheds; Reservoirs; Sediment; Management; Rivers; Sustainability
• ISSN: 0045-6535; 1879-1298; 1879-1298
• DOI: 10.1016/j.chemosphere.2022.136369

Water structure construction has been the subject of heated discussion for more than a decade. The following concepts include integrated management of water basins or continued sustainable development of water basins. Although there are not a lot of underdeveloped water basins, there have been 150 years of dam development. Dams prevent sediment from moving continuously down rivers by trapping it in reservoirs, which reduces the storage capacity and useable life of the reservoirs and prevents downstream reaches of materials necessary for channel form and aquatic ecosystems. Restricted availability of hydrological and geomorphological data hinders current knowledge of sediment characteristics and dynamics, which affects prediction models required for management. This research tested current models for estimating sediment output and transport based on data mining categorization and prediction approaches and assessed their applicability. This work brings together experience from five continents in controlling reservoir sediments and reducing downstream sediment famine of data on the distribution of sediment particle sizes, and manning's roughness coefficient were shown to have the largest influences on the predicted sediment load. Additionally, parameter uncertainty for river geometry and water flow almost doubled, underlining the fact that more knowledge of these variables might significantly enhance our ability to forecast and control present and future sediment dynamics in the river basin. [Display omitted] •Prediction of sustainable management of sediment in rivers and reservoirs investigated.•Model is based on data mining categorization and prediction approaches and assessed their applicability.•Experience of five continents in controlling reservoir sediments and reducing downstream sediment famine are used.•Distribution of sediment particle sizes, and Manning's roughness coefficient were shown.•The largest influences on the predicted sediment load is discussed.