Krista M. Gebert

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Controlling wildfire suppression expenditures has become a major public policy concern in the United States. However, most policy remedies have focused on the biophysical determinants of suppression costs: fuel loads and weather, for example. We show that two non-biophysical variables—newspaper coverage and political pressure—have a significant effect on(More)
Approaches for forecasting wildfire suppression costs in advance of a wildfire season are demonstrated for two lead times: fall and spring of the current fiscal year (Oct. 1–Sept. 30). Model functional forms are derived from aggregate expressions of a least cost plus net value change model. Empirical estimates of these models are used to generate(More)
In the last decade, increases in fire activity and suppression expenditures have caused budgetary problems for federal land management agencies. Spatial forecasts of upcoming fire activity and costs have the potential to help reduce expenditures, and increase the efficiency of suppression efforts, by enabling them to focus resources where they have the(More)
costs, pure autoregressive models of historical costs, and more complex regression models that may include historical costs but also external information. The first approach is the simplest, and one example is the 10-year moving average of the most recent observed costs. This model gives each year an equal weight when making forecasts and implicitly assumes(More)
suppression costs focusing on climatic, demographic, and physical characteristics of the fire area (Liang et al. in press, Donovan et al. 2004, Gebert et al. 2007) leave approximately one-half or more of the expenditure variation unexplained. Fires that from the outside appear quite similar in characteristics such as proximity to communities, fire behavior,(More)
A quantitative tool was developed to predict USDA Forest Service fire suppression expenditures by fiscal year on the basis of fire activity data (i.e., number of fires and acres burned) from Incident Management Situation Reports. Regional regression models were developed with adjusted rsquares ranging from 0.696 to 0.969. National predictions result from(More)
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