Assessing improvements in models used to operationally predict wildland fire rate of spread
| Autor: | Martin E. Alexander, Jim Gould, Musa Kilinc, Andrew L. Sullivan, Miguel G. Cruz |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
040101 forestry
geography Environmental Engineering geography.geographical_feature_category 010504 meteorology & atmospheric sciences Ecological Modeling Fire propagation 04 agricultural and veterinary sciences Atmospheric sciences 01 natural sciences Shrubland Fire spread Rate of spread Temperate climate 0401 agriculture forestry and fisheries Environmental science Software 0105 earth and related environmental sciences |
| Zdroj: | Environmental Modelling & Software. 105:54-63 |
| ISSN: | 1364-8152 |
| DOI: | 10.1016/j.envsoft.2018.03.027 |
| Popis: | The prediction of fire propagation across landscapes is necessary for safe and effective fire management. We analyzed the predictive accuracy of models currently used operationally in Australia for predicting fire spread rates in five different fuel types (grasslands, temperate and semi-arid shrublands, dry eucalypt and conifer forests) compared to their previous counterparts. We calculated error statistics and contrasted model predictions against observed spread rates of field observations of wildfires and prescribed fires. We then compared the changes in error metrics of older models to newer ones. Evaluation results show newer models to have improved prediction accuracy. Mean absolute errors were reduced by 56%, 68% and 70% in dry eucalypt forests, grasslands and crown fires in conifer forests, respectively. The most significant improvement was the reversion of under-prediction bias achieved with newer models. This study has highlighted the value of continuous improvement when it comes to developing operational wildland fire spread models. |
| Databáze: | OpenAIRE |
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