Uncertainty quantification of forecast error in coupled fire–atmosphere wildfire spread simulations: sensitivity to the spatial resolution
U. Ciri A B , M. M. Garimella A , F. Bernardoni A , R. L. Bennett A and S. Leonardi A
+ Author Affiliations
- Author Affiliations
A Department of Mechanical Engineering, The University of Texas at Dallas, 800 W Campbell Rd, Richardson, TX 75080, USA.
B Corresponding author. Email: umberto.ciri@utdallas.edu
International Journal of Wildland Fire 30(10) 790-806 https://doi.org/10.1071/WF20149
Submitted: 15 September 2020 Accepted: 13 July 2021 Published: 18 August 2021
Abstract
A methodology to quantify uncertainty in wildfire forecast using coupled fire-atmosphere computational models is presented. In these models, an atmospheric solver is coupled with a fire-spread module. In order to maintain a low computational cost, the atmospheric simulation is limited to a coarse numerical resolution, which increases the uncertainty in the wildfire spread prediction. Generalised polynomial chaos is proposed to quantify this uncertainty and obtain a response function for the forecast error in terms of the atmospheric resolution and the forecast horizon. The response is obtained from a set of simulations of a grassland fire with an in-house coupled fire-atmosphere model at varying degrees of resolution. Global sensitivity analysis of the response shows that the resolution is the primary parameter affecting the error. However, due to the strongly coupled fire-atmosphere dynamics in the initial fire development, the forecast time horizon locally becomes the dominant variable. A parametric study on the effect of the fire spreading regime suggests that the forecast uncertainty is large in plume-dominated spreading conditions: coarse resolution forecasts accumulate a large error because they cannot capture the intense small-scale vortical motion at the fire front.
Keywords: coupled fire-atmosphere models, fire-spreading regime, large-eddy simulations, level-set method, polynomial chaos, Rothermel’s model, uncertainty quantification, wildfire spread forecast.
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