Flickering switches in the landscape: searching for tipping-points, system noise and bifurcation in Natural Flood Management catchments

Natural Flood Management (NFM) is an upcoming approach to managing flood risk in river basins – by modifying the hydrology through naturally sympathetic methods (such as small wooden dams, bunds and re-forestation). If widely adopted, it could present one of the largest wholesale changes in UK land use since the drive for agricultural self-sufficiency in World War 2. Such land use changes could lead to profound and extensive changes in our river basins – possibly shifting them from relative stability to unstable highly dynamic systems. Field experimentation of NFM measures has, so far, not had an impact detectable at the regional scale required for understanding any widespread implementation. In this project we will explore the use of landscape evolution modelling (LEM) combined with state-change detection techniques as a method for optimising – and understanding – local NFM implementation for catchment scale flood management.

Tipping points, or critical transitions, are a type of threshold phenomenon that result in major state changes and can have long lasting effects on various systems. Studies of geological, climate, and economic systems have shown that systems may ‘wobble’ before a critical transition. Such early warning may be due to the phenomenon of critical slowing down, which causes a system to recover slowly from small impacts, or to a flickering phenomenon, which causes a system to switch back and forth between alternative states in response to relatively large impacts. Such signals for transitions in geomorphic systems have been observed, such as a pebble vibrating violently shortly before entrainment. However, the lack of observations of earth surface systems at a time and spatial scales sufficient to identify flickering is sparse. Many geomorphic systems shift or bifurcate from one state to another, such as a river switching from braided to meandering or a river channel suddenly avulsing and changing course. During this switching period, a small external forcing applied at a key moment can determine which path the system takes, meaning a small change at the right time can have a large impact. If we can identify a ‘flicker’ that occurs before the shift we could then predict when natural systems will change.

Using re-analysis techniques, Landscape Evolution Models (LEMs) have the potential to represent hydrological and geomorphic change at the resolution and scales required to capture flickering switch signals in the landscape. In short, LEMs can generate data in sufficient temporal and spatial resolution to identify any flickering switch signals, without the need for extensive observational data. Any switches identified from LEM outputs can then be used to focus observations in the field to the right time and place. Using this technique, observational datasets from previous and current studies may be used to search for flickering switches and the forecasting of future tipping points. LEMs also allow us to explore the sensitivity of the switching/flickering signal and to assess what forcings are required to encourage or discourage the switching of a system from one state to another. This gives environmental managers and decision-makers the information required to decide on whether NFM measures may or may not have an adverse impact in different systems.