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\begin{document}

\title{Integration of a deep-learning-based fire model into a global land
surface model}



\author[1]{Rackhun Son}%
\author[2]{Tobias Stacke}%
\author[2]{Veronika Gayler}%
\author[2]{Julia Esther Marlene Sophia Nabel}%
\author[3]{Reiner Schnur}%
\author[1]{Lazaro Alonso Silva}%
\author[4]{Christian Requena Mesa}%
\author[1]{Alexander Winkler}%
\author[5]{Stijn Hantson}%
\author[1]{Sönke Zaehle}%
\author[6]{Ulrich Weber}%
\author[7]{Nuno Carvalhais}%
\affil[1]{Max Planck Institute for Biogeochemistry}%
\affil[2]{Max Planck Institute for Meteorology}%
\affil[3]{Max Planck  Institute for Meteorology}%
\affil[4]{Unknown}%
\affil[5]{University of California, Irvine}%
\affil[6]{Max-Planck Institute for Biogeochemistry}%
\affil[7]{MPI-Jena}%


\vspace{-1em}



  
  \date{May 08, 2024}


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\begin{abstract}
Fire is a crucial factor in terrestrial ecosystems playing a role in
disturbance for vegetation dynamics. Process-based fire models quantify
fire disturbance effects in stand-alone dynamic global vegetation models
(DGVMs) and their advances have incorporated both descriptions of
natural processes and anthropogenic drivers. Nevertheless, these models
show limited skill in modeling fire events at the global scale, due to
stochastic characteristics of fire occurrence and behavior as well as
the limits in empirical parameterizations in process-based models. As an
alternative, machine learning has shown the capability of providing
robust diagnostics of fire regimes. Here, we develop a
deep-learning-based fire model (DL-fire) to estimate daily burnt area
fraction at the global scale and couple it within JSBACH4, the land
surface model used in the ICON ESM. The stand-alone DL-fire model forced
with meteorological, terrestrial and socio-economic variables is able to
simulate global total burnt area, showing 0.8 of monthly correlation
(rm) with GFED4 during the evaluation period (2011-15). The performance
remains similar with the hybrid modeling approach JSB4-DL-fire (rm=0.79)
outperforming the currently used uncalibrated standard fire model in
JSBACH4 (rm=-0.07). We further quantify the importance of each predictor
by applying layer-wise relevance propagation (LRP). Overall, land
properties, such as fuel amount and water content in soil layers, stand
out as the major factors determining burnt fraction in DL-fire,
paralleled by meteorological conditions over tropical and high latitude
regions. Our study demonstrates the potential of hybrid modeling in
advancing fire prediction in ESMs by integrating deep learning
approaches in physics-based dynamical models.%
\end{abstract}%



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\textbf{Hosted file}

\verb`958429_0_art_file_10796588_rrt49t.docx`  available at \url{https://authorea.com/users/596143/articles/629772-integration-of-a-deep-learning-based-fire-model-into-a-global-land-surface-model}

\textbf{Hosted file}

\verb`958429_0_supp_10796633_rrt4yl.docx`  available at \url{https://authorea.com/users/596143/articles/629772-integration-of-a-deep-learning-based-fire-model-into-a-global-land-surface-model}

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