Adaptation of QES-Fire, a dynamically coupled fast response wildfire model for heterogeneous environments

Moody, Matthew J.; Stoll, Rob; Bailey, Brian N.

doi:10.1071/wf22190

Cited by 5 publications

(2 citation statements)

References 44 publications

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“…In other words, we need to quantify how homogeneous a vegetation patch must be to allow the application of volumeaveraging operators, an essential transformation in the canopy wind modeling framework. This question is now made relevant thanks to the increased availability of high-resolution LiDAR instruments or satellite data, and its answer will help to address the ever-growing need to model how forest heterogeneity impacts wildfire rate of spread (as studied by Moody et al, 2023) or the wind velocity field, as examined in the present article. Finally, it would be interesting to simulate the wind field in vegetated areas in urban environments, like large parks in cities or nearby forests.…”

Section: Discussionmentioning

confidence: 99%

A rapid method for computing 3-D high-resolution vegetative canopy winds in weakly complex terrain

Renault,

Bailey,

Stoll

et al. 2024

Front. Earth Sci.

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To determine near-surface winds within and above vegetation canopies for operational environmental applications, a wind model must run at high-resolution (O(1–10 m)), in a few minutes, using limited input information, and requiring minimal computing resources (e.g., personal computers). Current research models simulate large domains at coarse resolution or small domains at fine scale, but canopy simulations can take days. Fast-modeling approaches are used to solve large complex wind fields, but they oversimplify the roughness elements’ distribution impact on momentum exchanges. To overcome these deficits, the fast-running wind model QUIC-URB (Quick Urban and Industrial Complex) was augmented with a high-resolution canopy wind solver. The wind model includes a non-local factor that describes how momentum propagates through the canopy and how sub-canopy jets appear under certain conditions. QUIC-URB was also coupled with the mesoscale WRF (Weather Research and Forecasting) model to downscale wind fields from a few kilometers to a meter. The new QUIC Canopy Model resolves 3-D wind fields over hundreds of millions of cells in less than 30 s per time step on a personal computer. It was compared to two canopy models for real quasi-homogeneous and heterogeneous canopies. An error analysis shows that the model was relatively accurate with a normalized root-mean-square error of about 0.2 m s−1 in the quasi-homogeneous canopy, and a mean absolute error of 0.3 m s−1. The new model is suitable for coupling with pollution dispersion, wildfire spread, and numerical weather prediction models over weakly complex terrain, defined here as a mildly undulating environment with gradual changes in elevation and a heterogeneous distribution of plants.

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Section: Discussionmentioning

confidence: 99%

A rapid method for computing 3-D high-resolution vegetative canopy winds in weakly complex terrain

Renault,

Bailey,

Stoll

et al. 2024

Front. Earth Sci.

Self Cite

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“…This is an important finding given that updraft mergers typically lead to deeper convection and are still not well understood (Glenn & Krueger, 2017). Updraft mergers are an understudied topic in fire weather that have recently been explored numerically (Moody et al, 2023), and it is hypothesized that individual sources of convection that produce these updrafts are interacting during ascent and trigger deeper plume rise along with enhanced convergence and convection.…”

Section: Interacting Updraftsmentioning

confidence: 99%

Isolating and Investigating Updrafts Induced by Wildland Fires Using an Airborne Doppler Lidar During FIREX‐AQ

et al. 2023

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Understanding the complex dynamical interactions that occur during the evolution of a wildland fire still remains a challenge within the research community. Processes such as entrainment, the incident wind field, the interaction between neighboring updrafts, among other processes, motivates the need for more intensive measurement campaigns to determine the mechanisms responsible for the processes observed as wildland fire updrafts evolve. A recent opportunity to examine processes associated with wildland fires was made possible with a Doppler lidar (DL) mounted on a Twin Otter aircraft to measure horizontal and vertical winds during the 2019 Fire Influence on Regional‐to‐Global Environments and Air Quality campaign. Using the data collected, a technique was developed to isolate updrafts over designated hotspots with the intention of statistically analyzing the updraft samples as well as assessing specific cases that underscore the complex dynamical interactions generated by wildland fire behavior. Strong evidence in support of statistically derived relationships between the updraft characteristics and downdraft extrema at the flanks of updrafts are shown using methods developed herein. In addition to the influence of entrainment/detrainment on the updraft structure are features such as updraft displacement and interacting updrafts that could only be resolved by a high along‐ and cross‐beam resolution DL. Lastly, it was found that a high degree of symmetry between profiles to the left and right of core updrafts was preserved for most of the cases, which validates some of the assumptions typically used in idealized updraft formulations.

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Trending and emerging prospects of physics-based and ML-based wildfire spread models: a comprehensive review

Singh,

Ang,

Lewis

et al. 2024

J. For. Res.

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The significant threat of wildfires to forest ecology and biodiversity, particularly in tropical and subtropical regions, underscores the necessity for advanced predictive models amidst shifting climate patterns. There is a need to evaluate and enhance wildfire prediction methods, focusing on their application during extended periods of intense heat and drought. This study reviews various wildfire modelling approaches, including traditional physical, semi-empirical, numerical, and emerging machine learning (ML)-based models. We critically assess these models’ capabilities in predicting fire susceptibility and post-ignition spread, highlighting their strengths and limitations. Our findings indicate that while traditional models provide foundational insights, they often fall short in dynamically estimating parameters and predicting ignition events. Cellular automata models, despite their potential, face challenges in data integration and computational demands. Conversely, ML models demonstrate superior efficiency and accuracy by leveraging diverse datasets, though they encounter interpretability issues. This review recommends hybrid modelling approaches that integrate multiple methods to harness their combined strengths. By incorporating data assimilation techniques with dynamic forecasting models, the predictive capabilities of ML-based predictions can be significantly enhanced. This review underscores the necessity for continued refinement of these models to ensure their reliability in real-world applications, ultimately contributing to more effective wildfire mitigation and management strategies. Future research should focus on improving hybrid models and exploring new data integration methods to advance predictive capabilities.

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Adaptation of QES-Fire, a dynamically coupled fast response wildfire model for heterogeneous environments

Cited by 5 publications

References 44 publications

A rapid method for computing 3-D high-resolution vegetative canopy winds in weakly complex terrain

A rapid method for computing 3-D high-resolution vegetative canopy winds in weakly complex terrain

Isolating and Investigating Updrafts Induced by Wildland Fires Using an Airborne Doppler Lidar During FIREX‐AQ

Trending and emerging prospects of physics-based and ML-based wildfire spread models: a comprehensive review

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