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\begin{document}

\title{A High-Resolution Model for the Assessment and Forecasting of Wildfire
Susceptibility}



\author[1]{Esneider Zapata}%
\author[2]{Nicolás Velásquez}%
\author[3]{Sebastian Ospina}%
\author[4]{Carlos D. Hoyos}%
\author[5]{M.Alejandra Ochoa-Isaza}%
\author[6]{Gisel Guzmán}%
\author[7]{Julián Sepúlveda Berrío}%
\author[8]{Mauricio Zapata Henao}%
\affil[1]{SIATA, EAFIT}%
\affil[2]{The University of Iowaº,EAFIT,EAFIT,Universidad Nacional de Colombia, sede Medellín,Universidad Nacional de Colombia}%
\affil[3]{SIATA, Univerisdad Nacional de Colombia}%
\affil[4]{Universidad Nacional de Colombia,Universidad EAFIT,Corporación Clima}%
\affil[5]{Universidad EAFIT}%
\affil[6]{SIATA, Universidad Nacional de Colombia}%
\affil[7]{SIATA,Universidad Nacional de Colombia}%
\affil[8]{Universidad Nacional de Colombia Sede Medellín,Universidad Nacional de Colombia Sede Medellín}%


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  \date{May 08, 2024}


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\begin{abstract}
During the last decade, wildfires in the Aburr\selectlanguage{ngerman}á 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.%
\end{abstract}\selectlanguage{ngerman}%



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