Data Driven Landscapes

Abstract

Urban neighbourhoods face climate risks from pluvial flooding and heatwaves which can be tackled using blue green infrastructure (BGI) interventions, yet existing BGI design tools lack the capacity to evaluate multi-hazard performance parametrically at the design stage.

This study develops and deploys an integrated parametric workflow combining FastFlood flood simulation and ENVI-met microclimate modelling within a Grasshopper environment, enabling systematic variation of BGI design parameters and simultaneous assessment of flood depth, flow velocity, and daytime and evening outdoor thermal comfort assess PET across four BGI feature types: trees, shrubs, surfaces, and water bodies. Performance assessment is structured around the IPCC risk framework covering sensitivity to flood, sensitivity to heat, and multi-hazard adaptive capacity. This identifies
which BGI design parameters most influence multi-hazard performance and under what conditions. Results show that surface roughness is the dominant flood parameter, trees are the dominant daytime heat mitigation feature, and trade-offs exist between hazard domains. These trade-offs confirm that BGI design decisions cannot be reduced to single-hazard optimization. A TOPSIS-based multicriteria strategy ranking framework is applied to translate performance evidence into comparative design guidance, demonstrating that strategy preferences are sensitive to criterion weights, particularly when adaptive capacity indicators are introduced.

The research concludes that a parametric BGI workflow is both necessary and feasible for evidence-informed neighbourhood-scale design, and that the integration of a digital workflow, multi-hazard performance analysis, and multi-criteria decision analysis constitutes a replicable methodological foundation for climate-adaptive urban design.