Simple linear regression models have been widely employed in the analysis of suspended-sediment concentration
(SSC) time series from glacierized catchments, although they have many limitations. This paper builds regression
models which address these shortcomings and permit inferences concerning the controls on suspended-sediment
transfer from a glacier at 788N in the Svalvard archipelago. A bivariate regression model, deterministically predicting
SSC from discharge alone, explained less than 15 per cent of the variance in SSC. A multivariate model, incorporating
additional potentially explanatory variables, offered little improvement. Diurnal hysteresis in the data gives rise to
quasi-autocorrelation in the residual series from regression models. This was effectively removed by incorporating
dummy diurnal variables into the multivariate model. The presence of a first-order autoregressive, stochastic process
gives rise to true autocorrelation in the residual series from regression models. This was accommodated by
incorporating an ARIMA (1,0,0) term into a multivariate autoregression model. The model-building process yielded a
systematic progression in the explanation of variance in SSC, stripping away pattern in the autocorrelation function of
the residual series; mean model error was reduced from 54 per cent to 6 per cent. The dependence of SSC on the
magnitude of discharge is weak and highly variable, whereas the dependence of current SSC on recent values of SSC,
revealed through the stochastic term, is an order of magnitude greater and relatively constant during the melt season.
The dominant control on SSC throughout the melt season is therefore short-term sediment availability. The simple
and largely unchanging stochastic process generally responsible for generating the observed SSC series implies a
simple and unchanging glacier drainage system.
History
School
Social Sciences
Department
Geography and Environment
Citation
HODGKINS, R. ... et al, 1999. Controls on suspended-sediment transfer at a High-Arctic glacier, determined from statistical modelling. Earth Surface Processes and Landforms, 24, pp. 1-21