Response to reviewer 2

Abstract

In steep headwater reaches, episodic mass movements can deliver large volumes of sediment to fluvial channels. If these inputs of sediment occur with a high frequency and magnitude, the capacity of the stream to rework the supplied material can be exceeded for a significant amount of time. To study the equilibrium conditions in a channel following different episodic sediment supply regimes (defined by grain size distribution, frequency, and magnitude of events), we simulate sediment transport through an idealized reach with our numerical 1-D model "BESMo" (Bedload Scenario Model). The model performs well in replicating flume experiments of a similar scope (where sediment was fed constantly, in one, two, or four pulses) and allowed the exploration of alternative event sequences. We show that in these experiments, the order of events is not important in the long term, as the channel quickly recovers even from high magnitude events. In longer equilibrium simulations, we imposed different supply regimes on a channel, which after some time leads to an adjustment of slope, grain size, and sediment transport that is in equilibrium with the respective forcing conditions. We observe two modes of channel adjustment to episodic sediment supply. (1) High-frequency supply regimes lead to equilibrium slopes and armouring ratios that are like conditions in constant-feed simulations. In these cases, the period between pulses is shorter than a "fluvial evacuation time", which we approximate as the time it takes to export a pulse of sediment under average transport conditions. (2) In low-frequency regimes the pulse period (i.e., recurrence interval) exceeds the "fluvial evacuation time", leading to higher armouring ratios due to the longer exposure of the bed surface to flow. If the grain size distribution of the bed is fine and armouring weak, the model predicts a decrease in the average channel slope. The ratio between the "fluvial evacuation time" and the pulse period constitutes a threshold that can help to quantify how a system responds to episodic disturbances.