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Unsteady flow events, such as those caused by extreme precipitation events or reservoir flushing, can result in hysteresis of sediment transport rates in alluvial streams. Over the past 20 years, several experimental studies have been conducted that monitored sediment transport rates in response to unsteady flow event hydrographs. Previous literature has identified numerous morphological and hydraulic factors, including sediment composition, sediment supply, hydrograph characteristics, bed morphology, and mode of sediment transport, that affect hysteresis of sediment transport rates. This manuscript reviews and evaluates the degree of influence of these factors on hysteresis in order to develop a comprehensive understanding of the dominant factors responsible for this phenomenon. This systematic evaluation suggests that the mode of sediment transport and sediment composition are the most dominant factors influencing the resulting type of hysteresis. Further research is required to investigate the effect of other factors, such as non-uniform stream bed composition and planform geometry, and develop predictive models to assess the sediment transport response to unsteady flow events.

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Accurate estimate of precipitation is of paramount importance for assessing the hydrologic response of a river basin. Weather radar data integrated with rain gauge measurements are applied to characterize the spatial feature of the storm event producing precipitation over the basin. Ordinary kriging of rain gauge data, mean field bias, Brandes spatial adjustment, conditional merging (CM), and local bias techniques are applied in this study to evaluate the performance of these radar-rain gauge merging methods for hydrologic modelling of the Upper Thames River basin (UTRb), southwestern Ontario, Canada. Singularity-sensitive Bayesian merging method (SSBM) with a fine spatial resolution was also applied to retain the singularity character of the rainfall event. Rainfall-runoff simulations were carried out for three major storm events recorded in the UTRb using the HEC-HMS 4.0 hydrologic model. River flow analysis was performed for the comparison of results of HEC-RAS 4.1 hydraulic model with the observed rating curve. A novel methodology involving a dual-storage system is proposed to model three sub-basins of UTRb which displayed skewed and spiked observed runoff hydrographs. Using this dual-storage system for the three sub-basins it is found that CM and SSBM merging methods yielded optimal Nash-Sutcliffe efficiency coefficients for the prediction of runoff from these sub-basins.