SOFT-CLIFF RETREAT, SELF-ORGANIZED CRITICAL PHENOMENA IN THE LIMIT OF PREDICTABILITY?

The coastal erosion along the world's coastlines is a natural process that occurs through the actions of marine and subaerial physico-chemical phenomena, waves, tides, and currents. The development of cliff erosion predictive models is limited due to the complex interactions between environmental processes and material properties over a wide range of temporal and spatial scales. As a result of this erosive action, gravity driven mass movements occur and the coastline moves inland. Like other studied earth natural and synthetically modelled phenomena characterized as self-organized critical (SOC), the recession of the cliff has a seemingly random, sporadic behavior, with a wide range of yearly recession rate values probabilistically distributed by a power-law. Usually, SOC systems are defined by a number of scaling features in the size distribution of its parameters and on its spatial and/or temporal pattern. Particularly, some previous studies of derived parameters from slope movements catalogues, have allowed detecting certain SOC features in this phenomenon, which also shares the recession of cliffs. Due to the complexity of the phenomenon and, as for other natural processes, there is no definitive model of recession of coastal cliffs. In this work, various analysis techniques have been applied to identify SOC features in the distribution and pattern to a particular case: the Holderness shoreline. This coast is a great case study to use when examining coastal processes and the structures associated with them. It is one of World's fastest eroding coastlines (2m/yr in average, max observed 22 m/yr). Cliffs, ranging from 2 m up to 35m in height, and made up of glacial tills, mainly compose this coast. It is this soft boulder clay that is being rapidly eroded and where coastline recession measurements have been recorded by the Cliff Erosion Monitoring Program (East Riding of Yorkshire Council, UK). The original database has been filtered by grouping contiguous sections, with similar geomorphological and dynamic features, to configure a complete and suitable catalogue of yearly recession rates (in m/yr) to analyze. Results show SOC fingerprint characteristics in the limited range scaling of the probability distribution function, in the variographic analysis and in the zero-mean Gaussian distribution of the Fourier coefficients. Therefore it cannot be neglected the possibility that Holderness cliffs behave as a SOC system. According to the discussed results, predictability possibilities of sea-cliff recession phenomena have been concluded.