Modelling fluvial responses to episodic sediment supply regimes in mountain streams

Large, episodically occurring sediment supply events may temporarily dominate channel morphology and sediment transport in mountain streams. Field studies of channel response to these events are challenging to undertake, as a long data record is needed to reasonably assess a system's state of response in the context of episodic supply. Greater confidence in the observed state of response of a system can be achieved with flume experiments where fluvial response can be observed in detail after episodic events are introduced in a controlled fashion. Yet, the amount of work necessary to carry out these experiments is large, which limits the number of experimental conditions that can be studied, and thus their utility for addressing applied problems of channel adjustment. To overcome this limitation, I developed the 1-D morphometric sediment transport model BESMo, which allows large numbers of simulations to be run in batches, generating ensemble results. This model was used to recreate results from flume experiments, after which the experimental conditions were extended to include a broader range of simulated pulse frequencies, magnitudes, and grain size compositions. It was shown that the sequencing of pulse events of different magnitudes has only a short term effect on the slope and grain size response of the channel. Furthermore, thresholds were identified that allow for the categorization of fluvial response to episodic sediment supply regimes into one of (a) constant-feed-like, or (b) pulse-dominated. The practical utility of BESMo for studying fluvial response to large sediment supply events was demonstrated through the study of potential geomorphic effects following the removal of a dam in the Carmel River, California, USA. This showed the advantage of BESMo for simulating many different future scenarios, as stochasticity could be explicitly included through varied hydrographs. This allowed results to be interpreted in light of the uncertainty in future flood occurrence. Finally, to overcome data limitations on surface grain size distributions, I developed machine-learning based methods to detect grain size distributions from images. Collectively, this work has advanced our understanding and ability to characterise downstream channel response to episodic supply events, and to better obtain data needed for this characterisation.Arts, Faculty ofGeography, Department ofGraduat