ORCID

Abstract

Coastal Change Management Areas (CCMAs) were introduced into the English planning system in 2012 to enable anticipatory management of long-term coastal change. However, their implementation has been limited and inconsistent, reflecting uncertainty in how future shoreline change should be defined and translated into statutory planning boundaries. This thesis addresses the gap between national coastal adaptation policy and the scientific evidence required to support its practical application.The research combines policy analysis, geomorphological classification and probabilistic modelling to develop a structured framework for delineating areas of future coastal change. Chapter 2 evaluates the national implementation of CCMAs and demonstrates substantial variability in uptake, evidence bases and methodological approaches. The findings indicate that ambiguity in datasets and modelling guidance alongside limited resource within local planning authorities has constrained local authority confidence in boundary delineation.Chapter 3 develops a high-resolution coastal typology for the south west of England, distinguishing 896 coastal units across four principal coastal types and associated sub-types. The typology captures spatial variability in coastal form and process at a scale compatible with planning decisions and provides the foundation for type specific modelling assumptions.Chapter 4 operationalises this typology through probabilistic modelling of future shoreline change under UKCP18 sea-level rise scenarios. Cliff retreat, beach translation and gravel barrier rollover are modelled using coastal type appropriate process response relationships, with uncertainty represented through Monte Carlo simulation. Results demonstrate that significant landward change—often tens to hundreds of metres within a single planning horizon—can occur across multiple coastal types. These findings challenge reliance on cliff-focused national datasets for CCMA designation.The thesis contributes a repeatable, typology informed, methodology for projecting coastal change with quantified uncertainty that is compatible with statutory spatial planning. By integrating geomorphological differentiation with probabilistic modelling, the research provides a structured evidence-chain linking coastal form, process and future change to CCMA delineation. The framework demonstrates how coastal science can be translated into reliable planning outputs under conditions of accelerating sea-level rise.

Awarding Institution(s)

University of Plymouth

Supervisor

Gerd Masselink, Tim Scott, Tim Poate, Stephen Essex

Document Type

Thesis

Publication Date

2026

Embargo Period

2026-07-06

Deposit Date

July 2026

Creative Commons License

Creative Commons Attribution-NonCommercial 4.0 International License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License

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