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 i...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.展开更多
Background： Australia＇s energy future is at the crossroads and the role of renewable sources is in focus. Biomass from sustainably managed forests provide a significant opportunity for electricity and heat generatio...Background： Australia＇s energy future is at the crossroads and the role of renewable sources is in focus. Biomass from sustainably managed forests provide a significant opportunity for electricity and heat generation and production of liquid fuels. Australia has extensive native forests of which a significant proportion are on private land. However, there is limited knowledge on the potential capacity of this resource to contribute to the expansion of a biomass for bioenergy industry. In addition, there are concerns on how to reconcile biomass harvesting with environmental protection. Methods： We used regional ecosystem vegetation mapping for Queensland to stratify harvestable forests within the 1.8 m hectares of private native forests present in the Southeast Queensland bioregion in 2014. We used a dataset of 52,620 individual tree measurements from 541 forest inventory plots collected over the last 10 years. Tree biomass was estimated using current biomass allometric equations for Australia. Biomass potentially available from selective sawlog harvesting and silvicultural treatment across the bioregion was calculated and mapped. Results： Current sawlog harvesting extracts 41.4% of the standing tree biomass and a biomass for bioenergy harvest would retain on average 36% of felled tree biomass on site for the protection of environmental and fauna habitat values. The estimated area extent of harvestable private native forests in the bioregion in 2013 was 888,000 ha and estimated available biomass for bioenergy in living trees was 13.6 million tonnes （t）. The spotted gum （Corymbio citriodora subsp, variegata） forests were the most extensive, covering an area of 379,823 ha and with a biomass for bioenergy yield of 14.2 t-ha-1 （with approximately 11.2 t.ha-1 of the biomass harvested from silvicultural thinning and 3 t.ha-1 recovered from sawlog harvest residual）. Conclusions： Silvicultural treatment of private native forests in the Southeast Queensland bioregion, has the capacity to supply a large quantity of biomass for bioenergy. The availability of a biomass for bioenergy market, and integration of sawlog harvesting and silvicultural treatment operations, could provide land owners with additional commercial incentive to improve the management of private native forests. This could potentially promote restoration of degraded forests, ecological sustainability and continued provision of wood products.展开更多
基金funding enabled and organized by CAUL and its Member Institutions.
文摘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.
基金supported by the Australian Biomass for Bioenergy Assessment(ABBA)Project,Queensland Government
文摘Background： Australia＇s energy future is at the crossroads and the role of renewable sources is in focus. Biomass from sustainably managed forests provide a significant opportunity for electricity and heat generation and production of liquid fuels. Australia has extensive native forests of which a significant proportion are on private land. However, there is limited knowledge on the potential capacity of this resource to contribute to the expansion of a biomass for bioenergy industry. In addition, there are concerns on how to reconcile biomass harvesting with environmental protection. Methods： We used regional ecosystem vegetation mapping for Queensland to stratify harvestable forests within the 1.8 m hectares of private native forests present in the Southeast Queensland bioregion in 2014. We used a dataset of 52,620 individual tree measurements from 541 forest inventory plots collected over the last 10 years. Tree biomass was estimated using current biomass allometric equations for Australia. Biomass potentially available from selective sawlog harvesting and silvicultural treatment across the bioregion was calculated and mapped. Results： Current sawlog harvesting extracts 41.4% of the standing tree biomass and a biomass for bioenergy harvest would retain on average 36% of felled tree biomass on site for the protection of environmental and fauna habitat values. The estimated area extent of harvestable private native forests in the bioregion in 2013 was 888,000 ha and estimated available biomass for bioenergy in living trees was 13.6 million tonnes （t）. The spotted gum （Corymbio citriodora subsp, variegata） forests were the most extensive, covering an area of 379,823 ha and with a biomass for bioenergy yield of 14.2 t-ha-1 （with approximately 11.2 t.ha-1 of the biomass harvested from silvicultural thinning and 3 t.ha-1 recovered from sawlog harvest residual）. Conclusions： Silvicultural treatment of private native forests in the Southeast Queensland bioregion, has the capacity to supply a large quantity of biomass for bioenergy. The availability of a biomass for bioenergy market, and integration of sawlog harvesting and silvicultural treatment operations, could provide land owners with additional commercial incentive to improve the management of private native forests. This could potentially promote restoration of degraded forests, ecological sustainability and continued provision of wood products.
