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dc.contributor.advisorGuttorp, Peter
dc.contributor.authorPodschwit, Harry Richard
dc.date.accessioned2017-05-16T22:15:15Z
dc.date.available2017-05-16T22:15:15Z
dc.date.submitted2017-01
dc.identifier.otherPodschwit_washington_0250O_16908.pdf
dc.identifier.urihttp://hdl.handle.net/1773/38682
dc.descriptionThesis (Master's)--University of Washington, 2017-01
dc.description.abstractUnderstanding and quantifying wildfire behavior is of interest to the scientific community, as well as public health and fire management professionals. To achieve this end, there is a demand for statistical descriptions of wildfire behavior and its relationship to the environment. However, wildfire behavior can be complex, described by multiple characteristics such as final size, duration and growth rates, and influenced by processes that can be regionally dependent. Further challenges arise due to the poor quality and availability of cumulative burn area time series data, which often contain missing and erroneous measurements. To address these issues, a variety of methods are presented. Multiple wildfire behaviors are represented using a simple decomposition of cumulative burn area time series that measures four meaningful quantities from the growth curve. The relationship between wildfire activity and the environment are approximated using regionally specific generalized linear models. Weather and landscape data are used to predict various measures of wildfire behavior. Validation results suggested that most of the models generalized well to independent data, and have potentially useful applications in climatological research. Data quality issues common to cumulative burn area time series are addressed using Bayesian state-space models, which reconstruct growth curves from multiple corrupted burn area time series. Two state space models are presented, a stationary version that assumes idealized fire growth, and a non-stationary version that produces reconstructions with time-varying growth rates. The relative computational costs and goodness-of-fit is illustrated by reconstructing the growth curves of 13 wildfires from 2014 wildfire season using growth data coming from two sources, fire perimeters from the Geospatial Multi-Agency Coordination (GeoMAC) and cumulative hotspot detects from the Hazard Mapping System (HMS). The stationary model had minimal computational costs, but rarely produced adequate descriptions of the burn area observations. The non-stationary model had much higher computational costs, but produced realistic estimates of the time series. An informal sensitivity analysis suggested that the reconstructed curves would be robust to changes in the priors. The main application of the state-space models is to reconstruct burn area time series, which can in turn be used for statistical analysis or validation of currently existing growth models. The framework can be modified for other purposes as well including forecasting burn area, and predicting the extinguishment date of a fire.
dc.format.mimetypeapplication/pdf
dc.language.isoen_US
dc.rightsCC BY
dc.subjectburn area
dc.subjectgrowth model
dc.subjectmissing data
dc.subjectstate space
dc.subjecttime series
dc.subjectwildfire
dc.subjectEnvironmental science
dc.subjectStatistics
dc.subject.otherQuantitative ecology and resource management
dc.titleThe statistical analysis of wildfire growth
dc.typeThesis
dc.embargo.termsOpen Access


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