Interpolation framework to speed up near-surface wind simulations for data-driven wildfire applications

Abstract

Local wind fields that account for topographic interaction are a key element for any wildfire spread simulator. Currently available tools to generate near-surface winds with acceptable accuracy do not meet the tight time constraints required for data-driven applications. This article presents the specific problem of data-driven wildfire spread simulation (with a strategy based on using observed data to improve results), for which wind diagnostic models must be run iteratively during an optimisation loop. An interpolation framework is proposed as a feasible alternative to keep a positive lead time while minimising the loss of accuracy. The proposed methodology was compared with the WindNinja solver in eight different topographic scenarios with multiple resolutions and reference – pre-run– wind map sets. Results showed a major reduction in computation time (~100 times once the reference fields are available) with average deviations of 3% in wind speed and 3° in direction. This indicates that high-resolution wind fields can be interpolated from a finite set of base maps previously computed. Finally, wildfire spread simulations using original and interpolated maps were compared showing minimal deviations in the fire shape evolution. This methodology may have an important effect on data assimilation frameworks and probabilistic risk assessment where high-resolution wind fields must be computed for multiple weather scenarios.

A Data-Driven Fire Spread Simulator: Validation in Vall-llobrega's Fire

“…SmartQFire is a data-driven wildfire spread modeling system explained in detail in Rios et al (2014aRios et al ( , 2016Rios et al ( , 2018. The system is based on the Rothermel (1972) model to estimate the Rate of Spread of a fire front given the fuel, terrain, and wind characteristics.…”

“…As the wind speed and direction are part of the key parameters to be resolved by the assimilation process, every time that a new parameter set is estimated, the wind field needs to be updated. In order to work around this limitation, our modeling system integrates the interpolation approach described in Rios et al (2018). This strategy makes use of a set of pre-run wind field maps within the fire domain and all new values of wind magnitude and direction are automatically interpolated.…”

“…This paper presents the validation of the simulator presented in Rios et al (2014bRios et al ( , 2016Rios et al ( , 2018 in a large-scale wildfire scenario of real complexity with intricate terrain. We employed, as reference data, the event reconstruction performed by the Catalan Fire Service and validated with operational observations.…”

Recognizing Women Leaders in Fire Science: Revisited

“…Her notable works include assessing hydrocarbon pool fires [345,346], the development of mathematical models to assess wildland fire behavior [347], and a review of the effectiveness of long-term forest fire retardants [348]. Her recent works include further development of wildland fire behavior models [349].…”

“…Her notable works include the development of mathematical models to assess wildland fire behavior [347], and a review of the effectiveness of long-term forest fire retardants [348]. Her recent works include an introduction to a special issue focused on vulnerability and resilience of socio-ecological systems [496], and further development of wildland fire behavior models [349].…”