Globally consistent assessment of economic impacts of wildfires in CLIMADA v2.2

<p>In light of the dramatic increase in economic impacts due to wildfires over recent years, the need for globally consistent impact modelling of wildfire damages is ever increasing. Insurance companies, individual households, humanitarian organizations, governmental authorities, and investors...

Descripción completa

Guardado en:
Detalles Bibliográficos
Autores principales: S. Lüthi, G. Aznar-Siguan, C. Fairless, D. N. Bresch
Formato: article
Lenguaje:EN
Publicado: Copernicus Publications 2021
Materias:
Acceso en línea:https://doaj.org/article/9c363fa5977c4081ae60005a1a4b2824
Etiquetas: Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
Descripción
Sumario:<p>In light of the dramatic increase in economic impacts due to wildfires over recent years, the need for globally consistent impact modelling of wildfire damages is ever increasing. Insurance companies, individual households, humanitarian organizations, governmental authorities, and investors and portfolio owners are increasingly required to account for climate-related physical risks. In response to these societal challenges, we present an extension to the open-source and open-access risk modelling platform CLIMADA (CLImate ADAptation) for modelling economic impacts of wildfires in a globally consistent and spatially explicit approach. All input data are free, public and globally available, ensuring applicability in data-scarce regions of the Global South. The model was calibrated at resolutions of 1, 4 and 10 km using information on past wildfire damage reported by the disaster database EM-DAT. Despite the large remaining uncertainties, the model yields sound damage estimates with a model performance well in line with the results of other natural catastrophe impact models, such as for tropical cyclones. To complement the global perspective of this study, we conducted two case studies on the recent megafires in Chile (2017) and Australia (2020). The model is made available online as part of a Python package, ready for application in practical contexts such as disaster risk assessment, near-real-time impact estimates or physical climate risk disclosure.</p>