Please use this identifier to cite or link to this item: http://dspace2020.uniten.edu.my:8080/handle/123456789/21004
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dc.contributor.authorAbdul Razad Z.en_US
dc.contributor.authorAzwin.razad@tnb.com.myen_US
dc.contributor.authorSidek L.M.en_US
dc.contributor.authorBasri H.en_US
dc.contributor.authorJung K.S.en_US
dc.date.accessioned2021-08-25T00:58:15Z-
dc.date.available2021-08-25T00:58:15Z-
dc.date.issued2019-
dc.identifier.urihttp://dspace2020.uniten.edu.my:8080/handle/123456789/21004-
dc.description.abstractSediment inflow prediction is needed for the development of sediment management strategies to ensure the sustainability of hydropower. There are many methods to predict and forecast reservoir sedimentation, focusing on the sediment yield catchment, sediment transport along the river network and sediment deposition inside the reservoir. This study compare three main methods in predicting the sediment inflow into Ringlet Reservoir, a hydropower reservoir located in active agricultural highland area in Cameron Highlands, Pahang. It compares sediment inflow prediction using 1) soil loss and sediment delivery ratio (RUSLE-SDR); 2) integration of rainfall runoff and sediment discharge rating curves (RR-SRC) and 3) processed based sediment yield model using SWAT. Accuracy of the annual prediction from each method is assessed based on the available bathymetry survey results, proving that SWAT performs the best. © 2019 Mattingley Publishing. All rights reserved.en_US
dc.language.isoenen_US
dc.titleDifferentiation of reservoir sediment inflow forecasting using RUSLE-SDR, Rainfall - Runoff - Sediment Discharge Rating Curve (RR-SRC) and SWATen_US
dc.typearticleen_US
item.cerifentitytypePublications-
item.languageiso639-1en-
item.fulltextWith Fulltext-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.grantfulltextreserved-
item.openairetypearticle-
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