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[1] The Brune and Churchill curves have long been used to predict sediment trapping efficiencies for reservoirs in the USA which typically experience winter and springdominant runoff. Their suitability for reservoirs receiving highly variable summer-dominant inflows has not previously been evaluated. This study compares sediment trapping efficiency (TE) data with the predictions of the two established curves for the Burdekin Falls Dam, a large reservoir in northern tropical Australia which receives highly variable summer-dominant runoff. The measured TE of the reservoir ranged between 50% and 85% and was considerably less than estimates using the Brune and Churchill curves over the 5 year study period. We modified the original equations so that daily trapping can be calculated and weighted based on daily flow volumes. This modification better accounts for shorter residence times experienced by such systems characterized by relatively high intraannual flow variability. The modification to the Churchill equation reasonably predicted sediment TEs for the Burdekin Dam for four of the five monitored years and over the whole monitoring period. We identified four key sediment particle classes: (1) <0.5 mm which exclusively passes over the dam spillway ; (2) 0.5-5.0 mm which, on average, 50% is trapped in the reservoir ; (3) 5.0-30 mm most (75%) of which is trapped; and (4) >30 mm which is almost totally (95%) trapped in the dam reservoir. We show that the modification to the Churchill equation has broader application to predict reservoir TE provided that daily flow data are available.

Abstract. The pH is one of the fundamental soil properties governing nutrient availability, metal mobility, elemental toxicity, microbial activity, and plant growth. The field pH of topsoil (0-0.10 m depth) and subsoil (~0.60-0.80 m depth) was measured on floodplain soils collected near the outlet of 1186 catchments covering >6 Mkm 2 (6 Â10 12 m 2 ) or~80% of Australia. Field pH duplicate data, obtained at 124 randomly selected sites, indicate a precision of 0.5 pH unit (or 7%), and mapped pH patterns are consistent and meaningful. The median topsoil pH is 6.5, while the subsoil pH has a median of 7 but is strongly bimodal (6-6.5 and 8-8.5). In most cases (64%) the topsoil and subsoil pH values are similar; among the sites exhibiting a pH contrast, those with more acidic topsoils are more common (28%) than those with more alkaline topsoils (7%). The distribution of soil pH at the national scale indicates the strong controls exerted by precipitation and ensuing leaching (e.g. low pH along the coastal fringe, high pH in the dry centre), aridity (e.g. high pH where calcrete is common in the regolith), vegetation (e.g. low pH reflecting abundant soil organic matter), and subsurface lithology (e.g. high pH over limestone bedrock). The new data, together with existing soil pH datasets, can support regional-scale decision-making relating to agricultural, environmental, infrastructural, and mineral exploration decisions.

Abstract:The National Geochemical Survey of Australia (NGSA) was carried out to bridge a vast knowledge gap about the concentration and distribution of chemical elements at the Earth's surface in Australia and consequent poor understanding of processes controlling their distribution here. The aim of the project was to contribute to improving exploration for energy and mineral resources through the pre-competitive delivery of a new spatial layer of compositional data and information.Surface (0-10 cm depth) and shallow (c. 60-80 cm) samples of catchment outlet sediments were collected from 1315 sites located near the outlet of 1186 catchments (c. 10% of which were sampled in duplicate) from across Australia. The total area covered by the survey was 6.174 million km 2 , or c. 81% of Australia, at an average sampling density of 1 site per c. 5200 km 2 . A number of field parameters (e.g. soil colour, pH), bulk parameters (e.g. electrical conductivity, particle size distribution) and geochemical parameters (i.e. multi-element composition of dry sieved <2 mm and <75 μm grain-size fractions) were determined. The grain-size fractions were analysed to determine (1) total, (2) aqua regia soluble, and (3) Mobile Metal Ion (MMI®) extractable element contents.These data were collated into a spreadsheet and graphically represented as a series of 529 geochemical maps (http://www. ga.gov.au/ngsa). These constitute the first continental-scale series of geochemical maps for Australia based on internally consistent, state-of-the-art data pertaining to the same sampling medium collected, prepared and analysed in a uniform and thoroughly documented manner and over a short time period. They are being used to better understand the accumulation, mobility and significance of chemical elements in the near-surface environment. Applications to date and ongoing and future directions are discussed.Supplementary material: Appendices 1-4 of summary statistics are available at http://www.geolsoc.org.uk/SUP18853

The conservation profession is increasingly seeking effective ways to reduce societal impact on biodiversity, including through targeted behavior change interventions. Multiple conservation behavior change programs exist, but there is also great uncertainty regarding which behaviors are most strategic to target. Behavioral prioritization is a tool that has been used effectively to support behavior change decision‐making in other environmental disciplines and more recently for a small sub‐set of biodiversity behavior change challenges. Here, we use behavioral prioritization to identify individual behaviors that could be modified to achieve biodiversity benefits in the state of Victoria, Australia. We use an adapted nominal group technique method to identify potential biodiversity behaviors and, for each behavior, estimate the corresponding plasticity (or capacity for change) and positive impact on biodiversity outcomes. We elicited 27 behaviors that individuals could undertake to benefit or reduce their negative impact on biodiversity. This list was then used to prioritize 10 behaviors as determined by their likely effect(s) on biodiversity, plasticity, and current prevalence in Victoria. We take a first step in outlining a list of behaviors that can direct Victorian decision‐makers toward increasing positive and reducing negative impacts of society on biodiversity, guide motivated individuals to reduce their own biodiversity footprint, and more broadly, develop a behavior change research agenda for behaviors most likely to benefit biodiversity.