SUMMARY Studies of driving and sleepiness indicators have mainly focused on prior sleep reduction. The present study sought to identify sleepiness indicators responsive to several potential regulators of sleepiness: sleep loss, time of day (TOD) and time on task (TOT) during simulator driving. Thirteen subjects drove a high-fidelity moving base simulator in six 1-h sessions across a 24-h period, after normal sleep duration (8 h) and after partial sleep deprivation (PSD; 4 h). The results showed clear main effects of TOD (night) and TOT but not for PSD, although the latter strongly interacted with TOD. The most sensitive variable was subjective sleepiness, the standard deviation of lateral position (SDLAT) and measures of eye closure [duration, speed (slow), amplitude (low)]. Measures of electroencephalography and line crossings (LCs) showed only modest responses. For most variables individual differences vastly exceeded those of the fixed effects, except for subjective sleepiness and SDLAT. In a multiple regression analysis, SDLAT, amplitude ⁄ peak eye-lid closing velocity and blink duration predicted subjective sleepiness bouts with a sensitivity and specificity of about 70%, but were mutually redundant. The prediction of LCs gave considerably weaker, but similar results. In summary, SDLAT and eye closure variables could be candidates for use in sleepiness-monitoring devices. However, individual differences are considerable and there is need for research on how to identify and predict individual differences in susceptibility to sleepiness.k e y w o r d s accidents,
[1] Wildfires represent one of the most common disturbances in boreal regions, and have the potential to reduce C, N, and Hg stocks in soils while contributing to atmospheric emissions. Organic soil layers of the forest floor were sampled before and after the FROSTFIRE experimental burn in interior Alaska, and were analyzed for bulk density, major and trace elements, and organic compounds. Concentrations of carbon, nutrients, and several major and trace elements were significantly altered by the burn. Emissions of C, N, and Hg, estimated from chemical mass balance equations using Fe, Al, and Si as stable constituents, indicated that 500 to 900 g C and up to 0 to 4 Â 10 À4 g Hg/m 2 were lost from the site. Calculations of nitrogen loss range from À4 to +6 g/m 2 but were highly variable (standard deviation 19), with some samples showing increased N concentrations post-burn potentially from canopy ash. Noncombustible major nutrients such as Ca and K also were inherited from canopy ash. Thermogravimetry indicates a loss of thermally labile C and increase of lignin-like C in char and ash relative to unburned counterparts. Overall, atmospheric impacts of boreal fires include large emissions of C, N and Hg that vary greatly as a function of severe fire weather and its access to deep organic layers rich in C, N, and Hg. In terrestrial systems, burning rearranges the vertical distribution of nutrients in fuels and soils, the proximity of nutrients and permafrost to surface biota, and the chemical composition of soil including its nutrient and organic constituents, all of which impact C cycling.
We present an overview of the Fuel Characteristic Classification System (FCCS), a tool that enables land managers, regulators, and scientists to create and catalogue fuelbeds and to classify those fuelbeds for their capacity to support fire and consume fuels. The fuelbed characteristics and fire classification from this tool will provide inputs for current and future sophisticated models for the quantification of fire behavior, fire effects, and carbon accounting and enable assessment of fuel treatment effectiveness. The system was designed from requirements provided by land managers, scientists, and policy makers gathered through six regional workshops. The FCCS contains a set of fuelbeds representing the United States, which were compiled from scientific literature, fuels photo series, fuels data sets, and expert opinion. The system enables modification and enhancement of these fuelbeds to represent a particular scale of interest. The FCCS then reports assigned and calculated fuel characteristics for each existing fuelbed stratum including the canopy, shrubs, nonwoody, woody, litter–lichen–moss, and duff. Finally, the system classifies each fuelbed by calculating fire potentials that provide an index of the intrinsic capacity of each fuelbed to support surface fire behavior, support crown fire, and provide fuels for flaming, smoldering, and residual consumption. The FCCS outputs are being used in a national wildland fire emissions inventory and in the development of fuelbed, fire hazard, and treatment effectiveness maps on several national forests. Although the FCCS was built for the United States, the conceptual framework is applicable worldwide.
This paper was presented at the conference ‘Integrating spatial technologies and ecological principles for a new age in fire management’, Boise, Idaho, USA, June 1999 The ongoing development of sophisticated fire behavior and effects models has demonstrated the need for a comprehensive system of fuel classification that more accurately captures the structural complexity and geographic diversity of fuelbeds. The Fire and Environmental Research Applications Team (FERA) of the USDA Forest Service, Pacific Northwest Research Station, is developing a national system of fuel characteristic classification (FCC). The system is designed to accommodate researchers and managers operating at a variety of scales, and who have access to a variety of kinds of input data. Users can generate fuel characteristics by accessing existing fuelbed descriptions (fuelbed prototypes) using generic information such as cover type or vegetation form. Fuelbed prototypes will provide the best available predictions of the kind, quality and abundance of fuels. Users can accept these default settings or modify some or all of them using more detailed information about vegetation structure and fuel biomass. When the user has completed editing the fuelbed data, the FCC system calculates or infers quantitative fuel characteristics (physical, chemical, and structural properties) and probable fire parameters specific to that fuelbed. Each user-described fuelbed is also assigned to one of approximately 192 stylized fuel characteristic classes.
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