Leveraging national forestry data repositories to advocate wildfire modeling towards simulation-driven risk assessment

J.L. Gómez, A. Cantizano, R. Caro, M. Castro

Ecological Indicators Vol. 158, pp. 111306-1 - 111306-15

Summary:

Modeling wildfire dynamics is complex and challenging due to the multiple scales involved in fire propagation, from physical–chemical processes to the interaction with topography and meteorological conditions. To provide reliable indicators of the risk of an ongoing wildfire, models aimed at informing policy-making should quantify the primary sources of uncertainty in their predictions. In this paper, we introduce a novel methodology built on top of Cellular Automata to assess the impact of uncertainty by implementing wildfire ensemble modeling using data from the Spanish National Forestry Data Repositories. Uncertainty is embedded in the model considering the ±2σ  deviations from the medians of linear regressions of the canopy stratum with LiDAR metrics as explainable variables. The relevance of dynamic meteorological conditions in contrast to static environment conditions is analyzed. Our results suggest that an accurate account of the fuel model, including time-dependent wind and moisture maps, is mandatory to provide reliable predictions. Using a real case study (Concentaina’s extreme wildfire), we also illustrate the importance of assessing the impact of the firefighters’ mitigation efforts.

Spanish layman's summary:

Los incendios forestales son más disruptivos año tras año. El incremento de su impacto es acompañado de un desarrollo paralelo en los recursos computacionales y el acceso público a bases de datos forestales masivas. En este estudio se propone una metodología de "enjambre de modelos" para estudiar los incendios haciendo hincapié en los datos existentes en España. Como contribución, se ha desarrollado un Autómata Celular que modela la evolución espacio temporal de un incendio real (GIF Cocentaina, 2012), del que se derivan intervalos de confianza de la superficie quemada empleando para el modelado de la incertidumbre los datos del Mapa Forestal Español, la cobertura LiDAR del Proyecto Nacional de Ortofotografía Aérea y el Inventariado Forestal Nacional.

English layman's summary:

Forest fires are becoming more disruptive year after year. As the impact of fires increases, there is a parallel development in available computational resources and public access to massive forest databases. This study proposes an "ensemble modeling" approach to study fires, emphasizing existing data in Spain. As a contribution of the study, a Cellular Automaton has been developed to model the spatial-temporal evolution of a real case study wildfire (GIF Cocentaina, 2012), from which confidence intervals of the burned area are derived using the uncertainty modeling of the data of the Spanish Forest Map, the LiDAR coverage provided by the National Aerial Orthophotography Project, and the National Forest Inventory.

Keywords: Ensemble modeling; Uncertainty propagation; Forestry raster data; Rothermel; Cellular automata; Wildfires

JCR Impact Factor and WoS quartile: 7,000 - Q1 (2023)

DOI reference: https://doi.org/10.1016/j.ecolind.2023.111306

Published on paper: January 2024.

Published on-line: December 2023.

Citation:
J.L. Gómez, A. Cantizano, R. Caro, M. Castro, Leveraging national forestry data repositories to advocate wildfire modeling towards simulation-driven risk assessment. Ecological Indicators. Vol. 158, pp. 111306-1 - 111306-15, January 2024. [Online: December 2023]