Scenario-based urban growth modelling for Dominica Island

Urbanisation and climate change are converging challenges, particularly in Small Island Developing States (SIDS), where limited land availability, fragile ecosystems, and high hazard exposure converge. Dominica exemplifies these challenges, having experienced severe impacts from tropical storms and hurricanes in recent decades. This research addresses the need for spatially informed, climate-resilient urban planning by developing and analysing urban growth scenarios for Dominica using a GIS-based modelling approach.

The study assessed how different planning strategies could influence future urban expansion under hazard and climate constraints. Historically built-up area data from the Global Human Settlement Layer (GHSL) for 1985–2015 were used to project urban growth to 2050 using linear regression. A land suitability analysis incorporating ten spatial drivers, multiple hazard layers, field work-based participatory mapping, and sea level rise projection was procured to allocate urban growth across four future scenarios: Business-as-Usual, Low-exposure to local hazards, Stakeholder perspective, and Climate-Resilient Planning.

Findings indicate a strong pattern of urban clustering around existing built-up areas, particularly near Roseau, with topography and hazard exposure significantly limiting outward expansion. Integrating hazard data into the modelling process highlighted spatial trade-offs between development potential and disaster risk. The Climate-Resilient scenario, which excluded high-hazard zones, demonstrated the most balanced spatial outcome but reduced available land for expansion.

A key assumption of this study is that the past built-up growth trend will continue linearly, which may not hold under dynamic policy or demographic changes. Nevertheless, the scenario-based approach offers valuable insights for decision-makers, reinforcing the importance of hazard-informed, anticipatory planning in SIDS. Future work should incorporate recent land-use data and stakeholder input to refine model accuracy and policy relevance.