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A High-Resolution Model for the Assessment and Forecasting of Wildfire Susceptibility
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  • Esneider Zapata,
  • Nicolás Velásquez,
  • Sebastian Ospina,
  • Carlos D. Hoyos,
  • M.Alejandra Ochoa-Isaza,
  • Gisel Guzmán,
  • Julián Sepúlveda Berrío,
  • Mauricio Zapata Henao
Esneider Zapata

Corresponding Author:[email protected]

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Nicolás Velásquez
The University of Iowaº,EAFIT,EAFIT,Universidad Nacional de Colombia, sede Medellín,Universidad Nacional de Colombia
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Sebastian Ospina
SIATA, Univerisdad Nacional de Colombia
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Carlos D. Hoyos
Universidad Nacional de Colombia,Universidad EAFIT,Corporación Clima
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M.Alejandra Ochoa-Isaza
Universidad EAFIT
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Gisel Guzmán
SIATA, Universidad Nacional de Colombia
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Julián Sepúlveda Berrío
SIATA,Universidad Nacional de Colombia
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Mauricio Zapata Henao
Universidad Nacional de Colombia Sede Medellín,Universidad Nacional de Colombia Sede Medellín
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During the last decade, wildfires in the Aburrá Valley watershed, located in northwestern Colombia, have caused significant forest and ecosystem losses, health issues in nearby communities associated with aerosols from biomass burning, and increases in the CO2 emissions. Human activities, along with weather variability, modulate the occurrence of forest fires during the dry seasons, and the efforts to reduce them have shown limited success, highlighting the need for the development of holistic prevention strategies. We implemented a general strategy involving real-time monitoring, modeling, and warning based on a distributed Bayesian model coupled with a distributed hydrological model and a regional weather model (WRF) to estimate wildfire susceptibility in the basin. The model operates with a spatial resolution of 60m and an hourly temporal resolution. The model uses static and time-dependent (dynamic) information. Static variables include land use, urban-rural fringe area, historical fire occurrence, and are updated occasionally. The dynamic variables change at each time step, and they depend on meteorological conditions and include soil moisture, cumulative rainfall during the last ten days, and an estimation of the surface temperature. These variables are obtained from in-situ rain gauges and quantitative precipitation estimation (QPE) techniques using C-band weather radar reflectivity, in-situ pyranometers and automatic weather stations, and output from a distributed hydrological model and WRF-based weather forecasts. The Bayesian model allows the generation of fire susceptibility predictions that help optimize prevention strategies implemented by the fire departments in the region. The model has been evaluated using the location of historical wildfires showing high skill. Along with the model, there are efforts in the region implemented for early-detection, and quantification of forest fires using in-situ and drone-borne thermal and high-definition cameras, a continuous monitoring strategy is established.