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The partitioning of the total sediment load of a river into suspended load and bedload: a review of empirical data



The partitioning of the total sediment load of a river into suspended load and bedload is an important problem in fluvial geomorphology, sedimentation engineering and sedimentology. Bedload transport rates are notoriously hard to measure and, at many sites, only suspended load data are available. Often the bedload fraction is estimated with ‘rule of thumb’ methods such as Maddock’s Table, which are inadequately field-tested. Here, the partitioning of sediment load for the Pitzbach is discussed, an Austrian mountain stream for which high temporal resolution data on both bedload and suspended load are available. The available data show large scatter on all scales. The fraction of the total load transported in suspension may vary between zero and one at the Pitzbach, while its average decreases with rising discharge (i.e. bedload transport is more important during floods). Existing data on short-term and long-term partitioning is reviewed and an empirical equation to estimate bedload transport rates from measured suspended load transport rates is suggested. The partitioning averaged over a flood can vary strongly from event to event. Similar variations may occur in the year-to-year averages. Using published simultaneous short-term field measurements of bedload and suspended load transport rates, Maddock’s Table is reviewed and updated. Long-term average partitioning could be a function of the catchment geology, the fraction of the catchment covered by glaciers and the extent of forest, but the available data are insufficient to draw final conclusions. At a given drainage area, scatter is large, but the data show a minimal fraction of sediment transported in suspended load, which increases with increasing drainage area and with decreasing rock strength for gravel-bed rivers, whereby in large catchments the bedload fraction is insignificant at ca 1%. For sand-bed rivers, the bedload fraction may be substantial (30% to 50%) even for large catchments. However, available data are scarce and of varying quality. Long-term partitioning varies widely among catchments and the available data are currently not sufficient to discriminate control parameters effectively.