Practical tools for quantitative analysis of coastal vulnerability and sea level rise impacts—application in a Caribbean island and assessment of the 1.5 °C threshold

The quantitative analysis of hurricane impacts on coastal development in the Caribbean is surprisingly infrequent and many tools to assess physical vulnerability to sea level rise (SLR) are insufficient to evaluate risk in coastal areas exposed to wave attack during extreme events. This paper proposes a practical methodology to quantify coastal hazards and evaluate SLR impact scenarios in coastal areas, providing quantitative input for coastal vulnerability analysis. We illustrate the implementation of the proposed methodology with results from a site-specific analysis. We quantify how storm wave impacts penetrate farther inland and reach higher elevations for increasing SLR conditions. We also show that the increase in elevation of storm wave impacts is more than the nominal increase in mean sea level, and that elevation increase may be on the order of up to twice the nominal SLR. By developing design parameters for multiple scenarios, as opposed to the determination of a single SLR value for design established by consensus, this approach generates information that we argue encourages resilient design and embedding future adaptation in coastal design. We discuss how government planners and regulators, as well as real estate developers, lenders, and investors, can improve coastal planning and resilient design of coastal projects by using this approach.     

Telemetric System for Hydrology and Water Quality Monitoring in Watersheds of Northern New Mexico, Usa

This study utilized telemetric systems to sample microbes and pathogens in forest, burned forest, rangeland, and urban watersheds to assess surface water quality in northern New Mexico. Four sites included remote mountainous watersheds, prairie rangelands, and a small urban area. The telemetric system was linked to dataloggers with automated event monitoring equipment to monitor discharge, turbidity, electrical conductivity, water temperature, and rainfall during base flow and storm events. Site data stored in dataloggers was uploaded to one of three types of telemetry: 1) radio in rangeland and urban settings; 2) a conventional phone/modem system with a modem positioned at the urban/forest interface; and 3) a satellite system used in a remote mountainous burned forest watershed. The major variables affecting selection of each system were site access, distance, technology, and cost. The systems were compared based on operation and cost. Utilization of telecommunications systems in this varied geographic area facilitated the gathering of hydrologic and water quality data on a timely basis.     

Systematic monitoring of heathy woodlands in a Mediterranean climate—a practical assessment of methods

Practical and useful vegetation monitoring methods are needed, and data compatibility and validation of remotely sensed data are desirable. Methods have not been adequately tested for heathy woodlands. We tested the feasibility of detecting species composition shifts in remnant woodland in South Australia, comparing historical (1986) plot data with temporal replicates (2010). We compared the uniformity of species composition among spatially scattered versus spatially clustered plots. At two sites, we compared visual and point-intercept estimation of cover and species diversity. Species composition (presence/absence) shifted between 1986 and 2010. Species that significantly shifted in frequency had low cover. Observations of decreasing species were consistent with predictions from temperature response curves (generalised additive models) for climate change over the period. However, long-term trends could not be distinguished from medium-term dynamics or short-term changes in visibility from this dataset. Difficulties were highlighted in assessing compositional change using historical baselines established for a different purpose in terms of spatial sampling and accuracy of replicate plots, differences in standard plot methods and verification of species identifications. Spatially clustered replicate plots were more similar in species composition than spatially scattered plots, improving change detection potential but decreasing area of inference. Visual surveys detected more species than point-intercepts. Visual cover estimates differed little from point-intercepts although underestimating cover in some instances relative to intercepts. Point-intercepts provide more precise cover estimates of dominant species but took longer and were difficult in steep, heathy terrain. A decision tree based on costs and benefits is presented assessing monitoring options based on data presented. The appropriate method is a function of available resources, the need for precise cover estimates versus adequate species detection, replication and practical considerations such as access and terrain.     

Regional data refine local predictions: modeling the distribution of plant species abundance on a portion of the central plains

Species distribution models are frequently used to predict species occurrences in novel conditions, yet few studies have examined the consequences of extrapolating locally collected data to regional landscapes. Similarly, the process of using regional data to inform local prediction for species distribution models has not been adequately evaluated. Using boosted regression trees, we examined errors associated with extrapolating models developed with locally collected abundance data to regional-scale spatial extents and associated with using regional data for predictions at a local extent for a native and non-native plant species across the northeastern central plains of Colorado. Our objectives were to compare model results and accuracy between those developed locally and extrapolated regionally, those developed regionally and extrapolated locally, and to evaluate extending species distribution modeling from predicting the probability of presence to predicting abundance. We developed models to predict the spatial distribution of plant species abundance using topographic, remotely sensed, land cover and soil taxonomic predictor variables. We compared model predicted mean and range abundance values to observed values between local and regional. We also evaluated model prediction performance based on Pearson's correlation coefficient. We show that: (1) extrapolating local models to regional extents may restrict predictions, (2) regional data can help refine and improve local predictions, and (3) boosted regression trees can be useful to model and predict plant species abundance. Regional sampling designed in concert with large sampling frameworks such as the National Ecological Observatory Network may improve our ability to monitor changes in local species abundance.     

Australian agriculture: coping with dangerous climate change

Australian agriculture has operated successfully in one of the world’s most hostile environments for two centuries. However, climate change is posing serious challenges to its ongoing success. Determining what might constitute dangerous climate change for Australian agriculture is not an easy task, as most climate-related risks are associated with changes in the highly uncertain hydrological cycle rather than directly to more predictable changes in temperature. In addition, the adaptive capacity of Australian producers is generally high, as they have had to cope with a highly variable climate in which periodic, severe droughts are the norm. As the underlying global trends in climate interact with the continent’s patterns of natural variability, producers can generally deal with gradual changes in climate but are most concerned about high rates of change in regional and local climates and with abrupt, unexpected shifts in climate patterns. Perhaps the best indicator of dangerous climate change for Australian agriculture is the persistence, or not, of the drying trends in many of the Country’s most productive regions and the strength of the linkage between these trends and global climate change.     

Gasification of refuse derived fuel in a fixed bed reactor for syngas production

Steam gasification of two different refuse derived fuels (RDFs), differing slightly in composition as well as thermal stability, was carried out in a fixed-bed reactor at atmospheric pressure. The proximate and ultimate analyses reveal that carbon and hydrogen are the major components in RDFs. The thermal analysis indicates the presence of cellulose and plastic based materials in RDFs. H2 and CO are found to be the major products, along with CO2 and hydrocarbons resulting from gasification of RDFs. The effect of gasification temperature on H2 and CO selectivities was studied, and the optimum temperature for better H2 and CO selectivity was determined to be 725 degrees C. The calorific value of product gas produced at lower gasification temperature is significantly higher than that of gas produced at higher process temperature. Also, the composition of RDF plays an important role in distribution of products gas. The RDF with more C and H content is found to produce more amounts of CO and H2 under similar experimental conditions. The steam/waste ratio showed a notable effect on the selectivity of syngas as well as calorific value of the resulting product gas. The flow rate of carrier gas did not show any significant effect on products yield or their distribution.     

Investigating differential binding of polychlorinated dibenzo-p-dioxins/dibenzofurans in soil and soil components using selective supercritical fluid extraction

Supercritical fluid extraction (SFE) with pure carbon dioxide was performed at increasingly strong conditions to investigate differential binding of polychlorinated dibenzo-p-dioxins/dibenzofurans (PCDD/Fs) in two impacted soils, in their sieved size fractions, and in small (a few mg) samples of industry-related waste products separated from impacted soil. The binding strengths of PCDD/Fs were shown to be different in the two soils, and in their different soil particle size fractions. As might be expected based on surface area considerations, one soil showed the strongest binding in the smallest (<5 μm) sieved fraction. However, the other soil showed the strongest binding in the larger sized fractions, possibly indicating that process-related particles could be controlling PCDD/F binding. Selective SFE of various types of particles including black carbon and charcoal (separated from soil), and from a suspected process anode residue did show different PCDD/F binding behavior ranging from quite weak binding (charcoal) to very strong binding (anode particles). Shifts to the stronger SFE fractions in the soils after activated carbon treatment agreed well with the decreases previously found in the uptake of PCDD/Fs by earthworms, as well as decreases in their freely-dissolved aqueous concentrations in soil/water slurries. These results show that, as previously demonstrated for PAHs and PCBs, selective SFE can be a useful tool to investigate differences in PCDD/F binding behaviors in impacted soils and sediments and their component parts, as well as a rapid tool for estimating the effectiveness of activated carbon treatments on decreasing the bioavailability of PCDD/Fs in soils and sediments.     

Statistical modelling of district-level residential electricity use in NSW, Australia

Electricity network investment and asset management require accurate estimation of future demand in energy consumption within specified service areas. For this purpose, simple models are typically developed to predict future trends in electricity consumption using various methods and assumptions. This paper presents a statistical model to predict electricity consumption in the residential sector at the Census Collection District (CCD) level over the state of New South Wales, Australia, based on spatial building and household characteristics. Residential household demographic and building data from the Australian Bureau of Statistics (ABS) and actual electricity consumption data from electricity companies are merged for 74 % of the 12,000 CCDs in the state. Eighty percent of the merged dataset is randomly set aside to establish the model using regression analysis, and the remaining 20 % is used to independently test the accuracy of model prediction against actual consumption. In 90 % of the cases, the predicted consumption is shown to be within 5 kWh per dwelling per day from actual values, with an overall state accuracy of ?1.15 %. Given a future scenario with a shift in climate zone and a growth in population, the model is used to identify the geographical or service areas that are most likely to have increased electricity consumption. Such geographical representation can be of great benefit when assessing alternatives to the centralised generation of energy; having such a model gives a quantifiable method to selecting the ‘most’ appropriate system when a review or upgrade of the network infrastructure is required.     

Numerical investigation of the flow profiles in the electrically enhanced cyclone

A numerical model for simulation of the electrohydrodynamic flow in an electrically enhanced cyclone is presented. A finite element approach was applied to solve the coupled equations for the positive corona-induced electric field. Three-dimensional simulations of gas flow were carried using Reynolds-Averaged Navier-Stokes equations including the Reynolds stress model and the electrohydrodynamic effect. Numerical results show that the change in the flow profile because of the influence of the corona-induced electric field is apparent when the inlet flow rate is low but is negligible at higher flow rates.