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Temporal variability and memory in sediment transport in an experimental step‐pool channel
Author(s) -
Saletti Matteo,
Molnar Peter,
Zimmermann André,
Hassan Marwan A.,
Church Michael
Publication year - 2015
Publication title -
water resources research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.863
H-Index - 217
eISSN - 1944-7973
pISSN - 0043-1397
DOI - 10.1002/2015wr016929
Subject(s) - bed load , flume , sediment transport , sediment , hurst exponent , grain size , soil science , hydrology (agriculture) , autocorrelation , channel (broadcasting) , geology , environmental science , flow (mathematics) , geotechnical engineering , geomorphology , mechanics , mathematics , statistics , physics , engineering , electrical engineering
Temporal dynamics of sediment transport in steep channels using two experiments performed in a steep flume (8%) with natural sediment composed of 12 grain sizes are studied. High‐resolution (1 s) time series of sediment transport were measured for individual grain‐size classes at the outlet of the flume for different combinations of sediment input rates and flow discharges. Our aim in this paper is to quantify (a) the relation of discharge and sediment transport and (b) the nature and strength of memory in grain‐size‐dependent transport. None of the simple statistical descriptors of sediment transport (mean, extreme values, and quantiles) display a clear relation with water discharge, in fact a large variability between discharge and sediment transport is observed. Instantaneous transport rates have probability density functions with heavy tails. Bed load bursts have a coarser grain‐size distribution than that of the entire experiment. We quantify the strength and nature of memory in sediment transport rates by estimating the Hurst exponent and the autocorrelation coefficient of the time series for different grain sizes. Our results show the presence of the Hurst phenomenon in transport rates, indicating long‐term memory which is grain‐size dependent. The short‐term memory in coarse grain transport increases with temporal aggregation and this reveals the importance of the sampling duration of bed load transport rates in natural streams, especially for large fractions.