『Abstract
Most sediment loads are estimated from sediment-rating curves
created by performing a linear least-square regression on log-transformed
sediment load-discharge data. when log-transformed sediment load-discharge
data plots result in concave or convex curves, such regressions
under- or overestimate sediment loads. Conflicting results exist
regarding the accuracy/utility of using nonlinear regression to
estimate loads. A nonlinear regression technique (optimized/constrained
two different ways) was compared with the linear regression method
at 26 United States Geological Survey gaging stations throughout
the Upper Mississippi River basin. Sensitivity analyses were conducted
at two stations, one having a concave sediment load-discharge
plot and one having a convex sediment load-discharge plot, to
determine each rating curve's ability, based on varying amounts
of data, to predict annual and cumulative suspended sediment yields.
With a 5-year calibration dataset, a nonlinear maximized r2
statistic curve produced the best estimates for a station with
a convex sediment load-discharge relationship, while a nonlinear
load-constrained curve produced the best estimates for a station
with a concave sediment load-discharge relationship. At both stations
(using 5-year calibration datasets), annual yield errors ranged
from -54％ to 112％, while 15- and 18-year cumulative yield errors
ranged from about -21％ to 13％.
Keywords: Sediment-rating curve; Regression analysis; Suspended
sediment; Upper Mississippi River basin; Linear regression; Nonlinear
regression』
Introduction
Study methodology
Suspended sediment and discharge data
Sediment-rating curves
Linear regression
‘Nonlinear’ r2 optimized curves
Nonlinear load-constrained curves
Comparison of rating curve shapes and parameters
Computation and comparison of suspended sediment yield estimates
Sensitivity analysis
Results
Comparison of rating curves
Annual and cumulative yield estimates for 26 USGS sites
Sensitivity analyses
Discussion
Conclusions
Acknowledgements
References