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Author Posting. © American Geophysical Union, 2007. This article is posted here by permission of American Geophysical Union for personal use, not for redistribution. The definitive version was published in Journal of Geophysical Research 112 (2007): G04S54, doi:10.1029/2006JG000353., Dramatic changes have been observed in the Arctic over the last century. Many of these involve the storage and cycling of fresh water. On land, precipitation and river discharge, lake abundance and size, glacier area and volume, soil moisture, and a variety of permafrost characteristics have changed. In the ocean, sea ice thickness and areal coverage have decreased and water mass circulation patterns have shifted, changing freshwater pathways and sea ice cover dynamics. Precipitation onto the ocean surface has also changed. Such changes are expected to continue, and perhaps accelerate, in the coming century, enhanced by complex feedbacks between the oceanic, atmospheric, and terrestrial freshwater systems. Change to the arctic freshwater system heralds changes for our global physical and ecological environment as well as human activities in the Arctic. In this paper we review observed changes in the arctic freshwater system over the last century in terrestrial, atmospheric, and oceanic systems., The authors gratefully acknowledge the National Science Foundation (NSF) for funding this synthesis work. This paper is principally the work of authors funded under the NSF-funded Freshwater Integration (FWI) study.

This study aimed to determine whether the time-course of maximal resting H-reflex amplitude (H(max)) recovery after a prolonged moderate-intensity muscle contraction differs according to the optimal stimulation intensity used (predetermined vs. readjusted). Thirteen males performed a sustained isometric plantar flexion at 40% of their maximal voluntary contraction torque output until exhaustion. H(max) of the soleus muscle was recorded before and 2, 6, 10, and 14 min after the end of the contraction, then normalized by the respective maximal M-wave to form the H(max)/M(max) ratio. During recovery, pre- and redetermined optimal stimulation intensities (mini-recruitment curve drawn before each recovery measurement) were applied randomly to measure H(max). When using redetermined stimulation intensities, normalized H-reflex values were systematically greater (+11, +16, +15, and +15% after 2-, 6-, 10-, and 14-min recovery periods, respectively) than those obtained with the predetermined intensity. Keeping the stimulation intensity constant to evoke H(max) after a sustained muscle contraction can underestimate the H-reflex facilitation occurring after exhaustive exercise. It is therefore more appropriate to redefine the optimal stimulation intensity to evoke H(max) (using mini-recruitment curves) when the purpose is to analyze spinal modulation during the recovery phase.

The study objective was to determine whether sleep extension (a) improves alertness and performance during subsequent sleep restriction and (b) mediates the rate at which alertness and performance are restored by post-restriction recovery sleep. Twenty-four healthy adult participants (ages 18-39) were randomly assigned to an Extended [10 hours time in bed (TIB)] or Habitual [mean (SD) = 7.09 (0.7)] sleep group for one week, followed by one Baseline (10 hours or habitual TIB), seven Sleep Restriction (3 hours TIB), and five Recovery Sleep nights (8 hours TIB) with performance [Psychomotor Vigilance Task (PVT)] and alertness [Maintenance of Wakefulness Test (MWT); Stanford Sleepiness Scale (SSS)] tests administered hourly throughout. We conclude that the extent to which sleep restriction impairs alertness and performance, and the rate at which these impairments are subsequently reversed by recovery sleep, varies as a function of the amount of nightly sleep obtained prior to the sleep restriction period., See also ADM002187. Presented at the Army Science Conference (26th) held in Orlando, FL on 1-4 Dec 2008.

The purpose of this study was to compare the sensitivity of modelled area burned to environmental factors across a range of independently-developed landscape-fire-succession models. The sensitivity of area burned to variation in four factors, namely terrain (flat, undulating and mountainous), fuel pattern (finely and coarsely clumped), climate (observed, warmer & wetter, and warmer & drier) and weather (year-to-year variability) was determined for four existing landscape-fire-succession models (EMBYR, FIRESCAPE, LANDSUM and SEM-LAND) and a new model implemented in the LAMOS modelling shell (LAMOS(DS)). Sensitivity was measured as the variance in area burned explained by each of the four factors, and all of the interactions amongst them, in a standard generalised linear modelling analysis. Modelled area burned was most sensitive to climate and variation in weather, with four models sensitive to each of these factors and three models sensitive to their interaction. Models generally exhibited a trend of increasing area burned from observed, through warmer and wetter, to warmer and drier climates with a 23-fold increase in area burned, on average, from the observed to the warmer, drier climate. Area burned was sensitive to terrain for FIRESCAPE and fuel pattern for EMBYR. These results demonstrate that the models are generally more sensitive to variation in climate and weather as compared with terrain complexity and fuel pattern, although the sensitivity to these latter factors in a small number of models demonstrates the importance of representing key processes. The models that represented fire ignition and spread in a relatively complex fashion were more sensitive to changes in all four factors because they explicitly simulate the processes that link these factors to area burned.

A classification of spatial simulation models of fire and vegetation dynamics (landscape fire succession models or LFSMs) is presented. The classification was developed to provide a foundation for comparing models and to help identify the appropriate fire and vegetation processes and their simulation to include in coarse scale dynamic global vegetation models. Other uses include a decision tool for research and management applications and a vehicle to interpret differences between LFSMs. The classification is based on the four primary processes that influence fire and vegetation dynamics: fire ignition, fire spread, fire effects, and vegetation succession. Forty-four LFSMs that explicitly simulated the four processes were rated by the authors and the modelers on a scale from 0 to 10 for their inherent degree of stochasticity, complexity, and mechanism for each of the four processes. These ratings were then used to group LFSMs into similar classes using common ordination and clustering techniques. Another database was created to describe each LFSM using selected keywords for over 20 explanatory categories. This database and the ordination and clustering results were then used to create the final LFSM classification that contains 12 classes and a corresponding key. The database and analysis results were used to construct a second classification key so managers can pick the most appropriate model for their application based on computer resources, available modeling expertise, and management objective.

We investigate the properties of sparse matrix ensembles with particular regard for the spectral ergodicity hypothesis, which claims the identity of ensemble and spectral averages of spectral correlators. An apparent violation of the spectral ergodicity is observed. This effect is studied with the aid of the normal modes of the random matrix spectrum, which describe fluctuations of the eigenvalues around their average positions. This analysis reveals that spectral ergodicity is not broken, but that different energy scales of the spectra are examined by the two averaging techniques. Normal modes are shown to provide a useful complement to traditional spectral analysis with possible applications to a wide range of physical systems., Comment: 22 pages, 15 figures

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