Atmospheric forcing of episodic wildfire activity in KwaZulu-Natal: Implications for fire risk modelling

Vollständiger Abstract

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Abstract Wildfires are increasingly driven by extreme weather and climate variability, posing risks to ecosystems, infrastructure, and human health. This study examines the severe KwaZulu-Natal wildfire of August 4, 2024, using an integrated, process-based framework that combines satellite fire detections, tree cover change data, atmospheric reanalysis, and air quality observations. This study provides a new integrated event-scale assessment of Berg Wind-driven wildfire forcing, atmospheric air-quality impacts, and ecological disturbance associated with an extreme wildfire event in KwaZulu-Natal, South Africa. Analysis of NASA FIRMS and Global Forest Watch data shows that wildfire activity in KwaZulu-Natal is highly episodic, with short-lived but intense fire peaks dominating the seasonal regime. The August 2024 event occurred under anomalously warm winter conditions, extremely low relative humidity, and strong downslope Berg Winds associated with an advancing dry cold front. Reanalysis reveals a coherent atmospheric structure, including a strong surface pressure gradient, upper-level ridging, subsidence, and gusty winds exceeding 20 m s⁻¹, creating conditions conducive to rapid fire ignition and spread. The wildfire substantially degraded air quality, with elevated PM₂.₅ concentrations and enhanced column-integrated organic carbon transported downslope. Tree cover loss analysis indicates that wildfire is an episodic yet significant contributor to long-term forest disturbance, adding pressure to remaining natural ecosystems. These findings highlight the critical role of short-term atmospheric extremes in amplifying wildfire risk, underscore the influence of Berg Wind–dry cold front interactions on fire behaviour, and demonstrate the value of integrating atmospheric forcing into ecological wildfire models, early warning systems, and mitigation strategies in fire-prone regions. By linking drivers, dynamics, and impacts, this work advances process-based ecological modelling of extreme wildfire events.