Effective policies promoting diversity in geoscience require understanding of how the values and practices of the community support the inclusion of different social groups. As sites of knowledge exchange and professional development, academic conferences are important culturing institutions that can alleviate or reproduce barriers to diversity in geoscience. This study examines diversity at a 2017 geoscience conference, the joint Canadian Geophysical Union and Canadian Society of Agricultural and Forest Meteorology annual meeting, through observation of participation, presentation content, and behaviour in conference sessions. Across 256 observed presentations, women constituted 28% of speakers, whereas women of colour made up only 5%. Participation rates differed between disciplinary sections, with the most populous sessions (Hydrology and Earth Surface) having the lowest percentage of women. Examination of presentation content reveals that the methods and scholarly contributions of both women and people of colour differed from the majority, suggesting an intellectual division of labour in geoscience. Examination of audience behaviours between presenters reveals how a "chilly climate" can be experienced by women and other marginalized demographics in conferences. We argue that there is more to be done than simply increasing numbers of women or other minorities in geoscientific spaces, and we suggest pathways to making geoscience a more inclusive and democratic pursuit.

While the influence of large grains on the morphodynamics of gravel‐bed rivers has long been recognized, nothing dominates our collective efforts to model such rivers like the bed surface D50, which turns up in virtually all the relevant equations. While researchers interested in flow resistance have recognized the relative importance of large grains and have modified flow resistance equations accordingly, there have been few attempts to quantify the effects of large grains on gravel‐bed river morphodynamics. However, there is little evidence that D50 exerts first‐order control over the physics occurring along the channel boundary, and its prevalence seems to be primarily based on the untested, a priori assumption that the best description of a distribution is the mean or median value. This commentary questions the long‐standing assumption that D50 is the best choice for characteristic grain size, and uses evidence from previous studies to show that mobilization of the largest grains in the bed likely controls morphological stability, and possibly sediment transport. © 2018 John Wiley & Sons, Ltd.

While the stabilizing function of large grains in step‐pool streams has long been recognized, the role they play in gravel‐bed streams is less clear. Most researchers have ignored the role of large grains in gravel‐bed streams, and have assumed that the median bed surface size controls the erodibility of alluvial boundaries. The experiments presented herein challenge this convention. Two experiments were conducted that demonstrate the significant morphodynamic implications of a slight change to the coarse tail of the bed material. The two distributions had the same range of particle sizes, and nearly identical bulk d50 values (1.6 mm); however the d90 of experiment GSD1 was slightly finer (3.7 mm) than that for experiment GSD2 (3.9 mm). Transport rates during GSD1 were nearly four times greater than during GSD2 (even though the dimensionless shear stress was slightly lower), and the channel developed a sinuous pattern with well‐developed riffles, pools and bars. During GSD2 the initial rectangular channel remained virtually unchanged for the duration of the experiment. The relative stability of GSD2 seems to be associated with a slightly larger proportion of stable (large) grains on the bed surface: at the beginning of GSD1, 3.5% of the bed was immobile, while almost twice as much of it (6.1%) was immobile at the beginning of GSD2. The results demonstrate that the largest grains (not the median size) exert first‐order control on channel stability. Copyright © 2017 John Wiley & Sons, Ltd.

Large wood has historically been removed from streams, resulting in the depletion of instream wood in waterways worldwide. As wood increases morphological and hydraulic complexity, the addition of large wood is commonly employed as a means to rehabilitate in-stream habitat. At present, however, the scientific understanding of wood mobilization and transport is incomplete. This paper presents results from a series of four flume experiments in which wood was added to a reach to investigate the piece and reach characteristics that determine wood stability and transport, as well as the time scale required for newly recruited wood to self-organize into stable jams. Our results show that wood transitions from a randomly distributed newly recruited state to a self-organized, or jam-stabilized state, over the course of a single bankfull flow event. Statistical analyses of piece mobility during this transitional period indicate that piece irregularities, especially rootwads, dictate the stability of individual wood pieces; rootwad presence or absence accounts for up to 80% of the variance explained by linear regression models for transport distance. Furthermore, small pieces containing rootwads are especially stable. Large ramped pieces provide nuclei for the formation of persistent wood jams, and the frequency of these pieces in the reach impacts the travel distance of mobile wood. This research shows that the simulation of realistic wood dynamics is possible using a simplified physical model, and also has management implications, as it suggests that randomly added wood may organize into persistent, stable jams, and characterizes the time scale for this transition. Key Points:Unstable newly recruited wood adopts a stable configuration during a single flood event Stable jams form around large key members and ramped wood pieces Realistic wood dynamics can be reproduced in a physical model

Most studies of gravel bed rivers present at least one bed surface grain size distribution, but there is almost never any information provided about the uncertainty in the percentile estimates. We present a simple method for estimating the grain size confidence intervals about sample percentiles derived from standard Wolman or pebble count samples of bed surface texture. The width of a grain size confidence interval depends on the confidence level selected by the user (e.g., 95 %), the number of stones sampled to generate the cumulative frequency distribution, and the shape of the frequency distribution itself. For a 95 % confidence level, the computed confidence interval would include the true grain size parameter in 95 out of 100 trials, on average. The method presented here uses binomial theory to calculate a percentile confidence interval for each percentile of interest, then maps that confidence interval onto the cumulative frequency distribution of the sample in order to calculate the more useful grain size confidence interval. The validity of this approach is confirmed by comparing the predictions using binomial theory with estimates of the grain size confidence interval based on repeated sampling from a known population. We also developed a two-sample test of the equality of a given grain size percentile (e.g., D 50 ), which can be used to compare different sites, sampling methods, or operators. The test can be applied with either individual or binned grain size data. These analyses are implemented in the freely available GSDtools package, written in the R language. A solution using the normal approximation to the binomial distribution is implemented in a spreadsheet that accompanies this paper. Applying our approach to various samples of grain size distributions in the field, we find that the standard sample size of 100 observations is typically associated with uncertainty estimates ranging from about ±15 % to ±30 %, which may be unacceptably large for many applications. In comparison, a sample of 500 stones produces uncertainty estimates ranging from about ±9 % to ±18 %. In order to help workers develop appropriate sampling approaches that produce the desired level of precision, we present simple equations that approximate the proportional uncertainty associated with the 50th and 84th percentiles of the distribution as a function of sample size and sorting coefficient; the true uncertainty in any sample depends on the shape of the sample distribution and can only be accurately estimated once the sample has been collected.