Mingrui Liu , Gregory Greene , Daniel D.B. Perrakis , Dominik Roeser
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引用次数: 0
Abstract
Fire behaviour models are increasingly used to guide wildfire management decisions, yet few have been rigorously validated with field-based evidence. Following a 2017 wildfire, we evaluated four fire behaviour models in natural and irregular shelterwood–treated lodgepole pine stands in interior British Columbia by comparing modelled against field-reconstructed head fire intensity (HFI) under recorded fire-weather scenarios. The Canadian Conifer Pyrometrics (ConPyro) model, when ladder fuels were included, produced predictions that closely matched reconstructed head fire intensity at the 75th wind percentile, with a mean absolute quantile distance (MAQD) of 1407 kW m−1 across quantiles. Crown Fire Initiation and Spread (CFIS) predictions exhibited higher variance, with MAQD values ranging from 4545 to 7470 kW m−1. The Canadian Forest Fire Behaviour Prediction (FBP) System (C2 and C3 fuel types) showed strong sensitivity to wind speed, resulting in large variability in predicted intensity. In contrast, BehavePlus consistently underestimated HFI (MAQD = 11,387 kW m−1, p < 0.01). In treated stands, all models either over- or underestimated HFI relative to reconstructed values, reflecting limited applicability in forests with discontinuous canopy structure. Overall, ConPyro performed adequately in natural conifer stands when ladder fuels were included, whereas all current models inadequately represented the spatial discontinuity and structural complexity created by treatments. These findings highlight the need to explicitly incorporate canopy structure into future model development to improve fire behaviour predictions in both natural and managed forests.
期刊介绍:
The journal Ecological Informatics is devoted to the publication of high quality, peer-reviewed articles on all aspects of computational ecology, data science and biogeography. The scope of the journal takes into account the data-intensive nature of ecology, the growing capacity of information technology to access, harness and leverage complex data as well as the critical need for informing sustainable management in view of global environmental and climate change.
The nature of the journal is interdisciplinary at the crossover between ecology and informatics. It focuses on novel concepts and techniques for image- and genome-based monitoring and interpretation, sensor- and multimedia-based data acquisition, internet-based data archiving and sharing, data assimilation, modelling and prediction of ecological data.