Extreme Flood Sediment Production and Export Controlled by Reach‐Scale Morphology

Abstract

Rapid earth surface evolution is discrete in nature, with long periods of quiescence punctuated by relatively short-duration events when the majority of geomorphic work takes place (Baynes et al., 2015;Jones et al., 2021). In extreme cases, infrequent high-magnitude events can leave a long-lasting legacy on the landscape and dominate long-term evolution by contributing to widespread erosion, sediment production, transport and deposition within catchments (Benda & Dunne, 1997;Duller et al., 2014). Such events are typically characterized by very high river runoff, large volumes of sediment production and transport, leading to rapid landscape change and potentially hazardous conditions for local communities (Cook et al., 2018). Following rare extreme events, landscapes enter a period of recovery during which they gradually return to background conditions and sediment export rates (Brunsden & Thornes, 1979;Hovius et al., 2011;Marc et al., 2015). The balance between the recovery timescale and the magnitude and frequency of extreme events determines whether a landscape is in a transient state (e.g., continually eroding or aggrading) over the long-term (Allen, 2008;Duller et al., 2014). Despite this, our understanding of the fundamental mechanisms that control the impact of extreme events on landscapes remains limited, as it is based on observations of the local morphological impact of extreme events that typically occur within single catchments (e.g., Licciardello et al., 2019;Milan, 2012;Scorpio et al., 2022). Therefore, it is difficult to develop a broadly-applicable predictive tool that can establish the expected future impact of extreme events in diverse catchments. Over-reliance on identifying the impacts of extreme events from single catchments means subtle controls of catchment morphology could be overlooked. With the frequency and magnitude of rainfall-induced extreme events expected to increase in the future as a result of climate changes (Madsen et al., 2014), it is therefore critical to increase the quantitative understanding of the key controls that govern the impact of extreme events on landscapes.