Title

Model Predictions of Postwildfire Woody Fuel Succession and Fire Behavior Are Sensitive to Fuel Dynamics Parameters

Publication Date

9-26-2020

Document Type

Article

Abstract

Computer models used to predict forest and fuels dynamics and wildfire behavior inform decisionmaking in contexts such as postdisturbance management. It is imperative to understand possible uncertainty in model predictions. We evaluated sensitivity of the Fire and Fuels Extension to the Forest Vegetation Simulator predictions to parameters that determine dynamics of standing dead trees (snags) and surface woody fuels. Predicted peak coarse and fine woody fuels were not sensitive to the decomposition rate of snags but were sensitive to decomposition rate of surface fuels regardless of initial snag density. Predictions of coarse woody fuel were sensitive to the snag fall rate when there was a higher initial density of snags. Fire behavior predictions were most sensitive to whether stylized fuel models or modeled fuels were used in calculations. When modeled fuels were used, fire behavior predictions were sensitive to the decomposition rate of surface fuels. Although this analysis does not inform the accuracy of model predictions, it does show where there is potential uncertainty in predictions of woody fuels succession and associated fire behavior. It is likely that any model that predicts postdisturbance fuel succession will also be sensitive to parameters that control snag dynamics and fuel decomposition. Study Implications

Forest managers use computer models to help decide which actions to take to meet management objectives. Computer models have many settings and rules that affect predicted outcomes, and the values these settings should take are often uncertain. This study evaluates the consequences of such uncertainty on model predictions of future fuels following a high-severity fire. We show that model predictions are sensitive to the decomposition rate of small fuels and the snag fall rate. These results provide guidance for managers for which settings they should focus on when using these models and point to potential model improvements.

Publication Title

Forest Science

DOI

10.1093/forsci/fxaa036

Open Access Status

Licensed

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