Articles | Volume 21, issue 12
https://doi.org/10.5194/nhess-21-3663-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/nhess-21-3663-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
03 Dec 2021
Research article |
| 03 Dec 2021
| 03 Dec 2021
Data-based wildfire risk model for Mediterranean ecosystems – case study of the Concepción metropolitan area in central Chile
Edilia Jaque Castillo
Departamento de Geografía, Universidad de Concepción,
Concepción, Chile
Departamento de Geografía, Universidad de Concepción,
Concepción, Chile
Rodrigo Fuentes Robles
Laboratorio de Ecología de Paisaje (LEP), Facultad de Ciencias Forestales, Universidad de Concepción, Concepción,
Chile
Carolina G. Ojeda
Facultad de Comunicación, Historia y Cs. Sociales, Universidad
Católica de la Santísima Concepción, Concepción, Chile
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Through a physical model, we explored how lacier geometry and topography configuration constrains glacier thinning in the Patagonian Icefields, the world's main glacial freshwater reservoir after Antarctica and Greenland. Our results indicate that about 53 % of the Patagonian Icefield ice flow is susceptible to thinning. Our findings allow for identifying priority glaciers for future research considering climate change projections.
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Short summary
Wildfires pose risks to lives and livelihoods in many regions of the world. Particularly in Chile's central-south region, climate change, widespread land use change, and urban growth tend to increase the likelihood of fire occurrence. Our work focused on the Concepción metropolitan area, where we developed a model using machine learning in order to map wildfire risks. We found that the interface between urban areas and forestry plantations presents the highest risks.
Wildfires pose risks to lives and livelihoods in many regions of the world. Particularly in...
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