Wildland fire behavior and effects models are playing an increasingly vital role in wildland fire science and management. For example, land managers commonly use fire behavior and effects models to support prescribed fire planning, stand and landscape-level fuel treatment effectiveness assessments, and decision-making during fire suppression operations. Models have also increased our fundamental knowledge of wildland fire behavior and effects by supporting numerical experiments that would be otherwise impossible. In the real world these are often too costly, too time-consuming, or hazardous to conduct. Furthermore, models can complement traditional experimentation and observation by suggesting new hypotheses and aid in interpreting potential cause and effect relationships associated with observations. In parallel with the increased use of models is the rapid growth of data collection methods, innovative approaches for data analysis, data quality assurance, and the ability to organize and openly share large data sets. These new methods, analyses, and data sets provide a considerable opportunity to evaluate and refine existing models and assist in developing new models.
In this Research Topic, we encourage submissions that highlight recent efforts and advancements in data acquisition, integration, delivery, storage, and analysis, which increase our understanding of fire behavior or fire effects and, thus, increases our ability to model wildland fire.
We encourage the following types of submissions:
• Original research reports on the use of observation to support model refinement and evaluation.
• Descriptions of new methods, protocols, and techniques that are of specific interest to fire behavior and effects model development, refinement, and evaluation.
• Review articles and perspectives that highlight recent advances and future directions.
• Lessons learned or requirements for creation, delivery, and maintenance of observational data sets that can be used to evaluate and advance fire behavior models.
• We especially want to encourage Data Report submissions, which present a description of publicly available research datasets that can be used for future model development refinement and evaluation, and Technology and Code submissions, which describe new openly available software.
Wildland fire behavior and effects models are playing an increasingly vital role in wildland fire science and management. For example, land managers commonly use fire behavior and effects models to support prescribed fire planning, stand and landscape-level fuel treatment effectiveness assessments, and decision-making during fire suppression operations. Models have also increased our fundamental knowledge of wildland fire behavior and effects by supporting numerical experiments that would be otherwise impossible. In the real world these are often too costly, too time-consuming, or hazardous to conduct. Furthermore, models can complement traditional experimentation and observation by suggesting new hypotheses and aid in interpreting potential cause and effect relationships associated with observations. In parallel with the increased use of models is the rapid growth of data collection methods, innovative approaches for data analysis, data quality assurance, and the ability to organize and openly share large data sets. These new methods, analyses, and data sets provide a considerable opportunity to evaluate and refine existing models and assist in developing new models.
In this Research Topic, we encourage submissions that highlight recent efforts and advancements in data acquisition, integration, delivery, storage, and analysis, which increase our understanding of fire behavior or fire effects and, thus, increases our ability to model wildland fire.
We encourage the following types of submissions:
• Original research reports on the use of observation to support model refinement and evaluation.
• Descriptions of new methods, protocols, and techniques that are of specific interest to fire behavior and effects model development, refinement, and evaluation.
• Review articles and perspectives that highlight recent advances and future directions.
• Lessons learned or requirements for creation, delivery, and maintenance of observational data sets that can be used to evaluate and advance fire behavior models.
• We especially want to encourage Data Report submissions, which present a description of publicly available research datasets that can be used for future model development refinement and evaluation, and Technology and Code submissions, which describe new openly available software.
