Abstract
Abstract. Published suspended sediment data for Arctic rivers is scarce. Suspended sediment rating curves for three medium to large rivers of the Russian Arctic were obtained using various curve-fitting techniques. Due to the biased sampling strategy, the raw datasets do not exhibit log-normal distribution, which restricts the applicability of a log-transformed linear fit. Non-linear (power) model coefficients were estimated using the Levenberg-Marquardt, Nelder-Mead and Hooke-Jeeves algorithms, all of which generally showed close agreement. A non-linear power model employing the Levenberg-Marquardt parameter evaluation algorithm was identified as an optimal statistical solution of the problem. Long-term annual suspended sediment loads estimated using the non-linear power model are, in general, consistent with previously published results.
Highlights
The sediment rating curve is widely employed as an empirical technique for relating suspended sediment concentrations C (g m-3) with water discharge Q (m3 s-1) (Colby, 1956)
Alternative formulations of the sediment rating curve equation include the use of a power function with a constant (Asselman, 2000) and a simple linear fit (Mount & Abrahart, 2011)
Fitting procedures based on discharge classes (Jansson, 1996), seasonal rating curve equations (Khanchoul & Jansson, 2008) and dataset separation by rising and falling limb stages of the hydrograph (Aquino et al, 2009) enhance the performance of rating curves in these circumstances, if such distinctions can be clearly drawn on the basis of the data collected
Summary
The sediment rating curve is widely employed as an empirical technique for relating suspended sediment concentrations C (g m-3) with water discharge Q (m3 s-1) (Colby, 1956). Alternative formulations of the sediment rating curve equation include the use of a power function with a constant (Asselman, 2000) and a simple linear fit (Mount & Abrahart, 2011). Each of these alternatives has its benefits and limitations. Fitting procedures based on discharge classes (Jansson, 1996), seasonal rating curve equations (Khanchoul & Jansson, 2008) and dataset separation by rising and falling limb stages of the hydrograph (Aquino et al, 2009) enhance the performance of rating curves in these circumstances, if such distinctions can be clearly drawn on the basis of the data collected
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