Landscape Recovery following Extreme Events: Morphodynamic timescales in the face of a changing climate

Rapid earth surface evolution is discrete in nature, with short-duration extreme events having a widespread impact on landscapes through producing and transporting large volumes of sediment. These impacts can be profound and long-lasting despite these events occurring relatively infrequently (e.g. Baynes et al., 2015).

The immediate impact of an extreme event on a landscape (e.g., an earthquake, a flood, or a cyclone) is usually pretty clear, but the legacy of the event can be visible in the landscape for decades to centuries following the perturbation (e.g., dynamic channel adjustment in response to the high sediment load; Hovius et al., 2011; Westoby et al., 2023). Following a period of elevated geomorphic activity, the landscape can return and recover to more normal 'background' conditions, but associated geohazards (e.g., flooding/landsliding) can remain a sustained threat to local populations during the 'recovery timescale' (e.g., Marc et al., 2015).

The key factors that control the recovery timescale of landscapes following extreme events at the catchment to regional scale remain poorly understood and unquantified. This quantification is critical as both the frequency and magnitude of the extreme events will be exacerbated as a result of climate change, with future events potentially occurring before the landscape has fully recovered (Baynes et al. 2018) and therefore potentially shifting the landscape into a new, more hazardous, state.

This PhD will harness innovative GIS-based remote sensing, topographic analysis and numerical modelling methods to explore and quantify the timescales and dynamics of landscapes that are recovering from extreme events over the past 100 to 102-years. Study landscapes will include a spectrum of environments from upland UK catchments impacted by flash floods (e.g., Baynes et al., 2023) to tectonically active locations impacted by earthquakes and/or cyclones such as New Zealand (Tunnicliffe et al., 2018).

Rates of landscape recovery will be calculated using time-series of satellite imagery before and after the occurrence of the extreme event, will be baselined with fieldwork and compared to output from numerical models that simulate a range of events of different magnitudes.

The findings from this project will lead to a step-change in the understanding of dynamic landscapes that are under threat from a changing climate. There are exciting implications for associated analysis of geohazards, risk to communities and populations and new insights into expected future rates of landscape change.