09 - Crewed and Uncrewed Aircraft Operations and Traffic Management
CLIMATE HAZARD TIMING AND FLIGHT DEMAND COOCCURRENCE: A DEMAND-WEIGHTED EXPOSURE FRAMEWORK
K. Sumaja¹, T.-R.T. Koo¹, S. Mooney, School of Biological, Earth and Environmental Sciences, UNSW, Australia; T.H. Rashidi¹; ¹School of Civil and Environmental Engineering, UNSW, Australia
Airport climate-risk assessments typically evaluate hazards using frequency, intensity, or projected trends, but these indicators do not capture whether hazardous conditions coincide with periods of high aviation demand. This study develops a demand-linked climate exposure framework to analyse the temporal co-occurrence between aviation climate hazards and flight activity at Sydney Airport. Daily maximum temperature, minimum temperature, precipitation, and wind variables are analysed using observational and reanalysis data for 1991– 2024 and bias-corrected climate projections for 2025–2050 under SSP1–2.6 and SSP3–7.0. Hazard indicators are derived using month-specific percentile thresholds and structured into three tiers: frequency, severity, and accumulated hazard load. These metrics are combined with monthly flight activity to quantify exposed flights and flight-weighted hazard burden. The analysis focuses on exposure rather than realised disruption and excludes delay, cancellation, diversion, and vulnerability effects. Results demonstrate that aviation exposure is governed by seasonal timing rather than hazard intensity alone. A primary exposure window occurs from November to January, when heat, rainfall, and wind coincide with peak demand, with December generating the highest exposure, particularly for heat. A secondary exposure window occurs in July and is dominated by cold minimum-temperature events aligning with winter traffic. These findings demonstrate that hazard trends alone are insufficient to characterise airport climate exposure. Integrating hazard timing with aviation demand establishes a demand-weighted exposure framework for identifying operationally critical periods and supporting seasonal adaptation planning.
DOI: 10.71945/icas2026_0870