Evidence of Self‐Organized Criticality in riverbank mass failures: a matter of perspective?. Issue 7 (23rd December 2014) - Croke, Jacky; Denham, Robert; Thompson, Chris; Grove, James
Abstract A growing body of field, theoretical and numerical modelling studies suggests that predicting river response to even major changes in input variables is difficult. Rivers are seen to adjust rapidly and variably through time and space as well as changing independently of major driving variables. Concepts such as Self‐Organized Criticality (SOC) are considered to better reflect the complex interactions and adjustments occurring in systems than traditional approaches of cause and effect. This study tests the hypothesis that riverbank mass failures which occurred both prior to, and during, an extreme flood event in southeast Queensland (SEQ) in 2011 are a manifestation of SOC. Each wet‐flow failure is somewhat analogous to the 'avalanche' described in the initial sand‐pile experiments of Bak et al. (Physical Review Letters, 1987, 59(4), 381–384) and, due to the use of multitemporal LiDAR, the time period of instability can be effectively constrained to that surrounding the flood event. The data is examined with respect to the key factors thought to be significant in evaluating the existence of SOC including; non‐linear temporal dynamics in the occurrence of disturbance events within the system; an inverse power‐law relation between the magnitude and frequency of the events; the existence of a critical state to which the system readjusts after a disturbance; the existence of a cascading processes mechanism by which the same process can initiate both low‐magnitude and high‐magnitude events. While there was a significant change in the frequency of mass failures pre‐ and post‐flood, suggesting non‐linear temporal dynamics in the occurrence of disturbance events, the data did not fit an inverse power‐law within acceptable probability and other models were found to fit the data better. Likewise, determining a single 'critical' state is problematic when a variety of feedbacks and multiple modes of adjustment are likely to have operated throughout this high magnitude event. Overall, the extent to which the data supports a self‐organized critical state is variable and highly dependent upon inferential arguments. Investigating the existence of SOC, however, provided results and insights that are useful to the management and future prediction of these features. Copyright © 2014 John Wiley & Sons, Ltd.