Toward Probabilistic Risk Assessment of Wildland–Urban Interface Communities for Wildfires

Masoudvaziri, Nima; Khorasani, Negar Elhami; Sun, Kang

doi:10.1007/s10694-023-01382-y

Cited by 3 publications

(1 citation statement)

References 40 publications

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“…WRF and SWUIFT are, respectively, well-validated models for simulating downslope windstorm-driven fires and WUI fire spread. The models' capabilities have been recently demonstrated simulating the Marshall Fire (Juliano et al, 2023), the Tubbs Fire (Masoudvaziri et al, 2023), and the Camp Fire (Shamsaei et al, 2023;Szasdi-Bardales et al, 2023), to name a few. The fire spread simulation does not consider the effects of structure hardening and suppression.…”

Section: Methodsmentioning

confidence: 99%

Brief communication: The Lahaina Fire disaster: How models can be used to understand and predict wildfires

Juliano,

Szasdi-Bardales,

Lareau

et al. 2023

Preprint

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Abstract. Following the destructive Lahaina Fire in Hawaii, our team has modeled the wind and fire spread processes to understand the drivers of this devastating event. The results are in good agreement with observations recorded during the event. Extreme winds with high variability, a fire ignition close to the community, and construction characteristics led to continued fire spread in multiple directions. Our results suggest that available modeling capabilities can provide vital information to guide decision-making and emergency response management during wildfire events.

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Section: Methodsmentioning

confidence: 99%

Brief communication: The Lahaina Fire disaster: How models can be used to understand and predict wildfires

Juliano,

Szasdi-Bardales,

Lareau

et al. 2023

Preprint

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Multi-source tri-environmental conceptual framework for fire impact analysis

Li,

et al. 2024

Urban Info

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The summer of 2023 highlighted the growing threat of wildfires in isolated areas, with Maui Island in Hawaii experiencing one of its largest wildfires. Our study employs a multi-source approach, integrating high-resolution optical data, thermal infrared data, and demographic information to assess the environmental, built, and social impacts of this event. Our innovative tri-environmental framework reveals significant vegetation degradation, land cover change, and disproportionate effects on various demographic groups. The fire caused extensive damage, with residential properties incurring 77.6% of the total losses, equating to approximately $563.2 million. Social impacts were profound, particularly among females, children, and the elderly, with employment and commuting disruptions affecting both low- and high-income groups. The study highlights the effectiveness of combining dasymetric mapping with real-time satellite data to refine population distribution estimates in affected areas. Our findings are applicable beyond wildfires, offering valuable insights into disaster response and mitigation strategies across various natural hazards like floods and earthquakes.

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Brief communication: The Lahaina Fire disaster – how models can be used to understand and predict wildfires

Juliano,

Szasdi-Bardales,

Lareau

et al. 2024

Nat. Hazards Earth Syst. Sci.

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Toward Probabilistic Risk Assessment of Wildland–Urban Interface Communities for Wildfires

Cited by 3 publications

References 40 publications

Brief communication: The Lahaina Fire disaster: How models can be used to understand and predict wildfires

Brief communication: The Lahaina Fire disaster: How models can be used to understand and predict wildfires

Multi-source tri-environmental conceptual framework for fire impact analysis

Brief communication: The Lahaina Fire disaster – how models can be used to understand and predict wildfires

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