The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
BEHAVE: fire behavior prediction and fuel modeling system-BURN Subsystem, part 1
Abstract
Describes BURN Subsystem, Part 1, the operational fire behavior prediction subsystem of the BEHAVE fire behavior prediction and fuel modeling system. The manual covers operation of the computer program, assumptions of the mathematical models used in the calculations, and application of the predictions.
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Citations
Cites
How to Predict the Spread and Intensity of Forest and Range Fires
Rothermel, R. C. (1983) · doi:10.2737/int-gtr-143 · Cited by 742
Cited by
A review of prescribed burning effectiveness in fire hazard reduction.
Fernandes, P. M., & Botelho, H. S. (2003) · International Journal of Wildland Fire · doi:10.1071/wf02042 · Cited by 718
Scott, J. H., & Burgan, R. E. (2005) · doi:10.2737/rmrs-gtr-153 · Cited by 636
Science basis for changing forest structure to modify wildfire behavior and severity
Graham, R. T., McCaffrey, S., & Jain, T. B. (2004) · doi:10.2737/rmrs-gtr-120 · Cited by 366
Uncertainty and risk in wildland fire management: A review
Thompson, M. P., & Calkin, D. E. (2011) · Journal of Environmental Management · doi:10.1016/j.jenvman.2011.03.015 · Cited by 343
Firebrands and spotting ignition in large-scale fires
Koo, E., Pagni, P. J., Weise, D. R., & Woycheese, J. P. (2010) · International Journal of Wildland Fire · doi:10.1071/wf07119 · Cited by 336
Metadata
- Authors
- Andrews, P. L.
- Published
- 1986
- OpenAlex
- W1602392695
- Cited by
- 460
- References
- 1
- Type
- report
- Language
- en
- Open access
- closed
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- Andrews, P. L. (1986). BEHAVE: fire behavior prediction and fuel modeling system-BURN Subsystem, part 1. https://doi.org/10.2737/int-gtr-194
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
