Turbulent burst around experimental spur dike

Three-dimensional turbulent flow field was measured around an experimental spur dike by using a micro-acoustic Doppler velocimeter.Time fractions of turbulent burst events including outward interaction,ejection,inward interaction,and sweep were analyzed in each quadrant at the neighbor of the dike before and after the formation of scour hole.Over 80%of burst events near the bed have lower-order of magnitudes for both flat and scoured bed surfaces.Ejections and sweeps are prevalent before the local scour was initiated,and then outward interactions are dominant after the scour hole was formed.Conditional Reynolds stresses and high-order moments of turbulent velocities were analyzed along the thalweg.The magnitudes of u’-w’ pair were much larger than those of v’-vv’ pair in the scour zone.Among four burst events,ejections and sweeps are the higher order events contributing to the Reynolds stresses.Since sediment entrainment and transport are closely associated with turbulent bursts near bed,the development of scour hole greatly depends on the higher order event near the bed.     

Scattering properties of planetary regolith analogs

The physics of scattering of electromagnetic waves by media in which the particles are in contact, such as planetary regoliths, has been thought to be relatively well understood when the particles are larger than the wavelength. However, this is not true when the particles are comparable with or smaller than the wavelength. We have measured the scattering parameters of planetary regolith analogs consisting of suites of well-sorted abrasives whose particles ranged from larger to smaller than the wavelength. We measured the variation of reflectance as the phase angle varied from 0.05° to 140°. The following parameters of the media were then deduced: the single scattering albedo, single scattering phase function, transport mean free path, and scattering, absorption, and extinction coefficients. A scattering model based on the equation of radiative transfer was empirically able to describe quantitatively the variation of intensity with angle for each sample. Thus, such models can be used to characterize scattering from regoliths even when the particles are smaller than the wavelength. The scattering parameters were remarkably insensitive to particle size. These results are contrary to theoretical predictions, but are consistent with earlier measurements of alumina abrasives that were restricted to small phase angles. They imply that a basic assumption made by virtually all regolith scattering models, that the regolith particles are the fundamental scattering units of the medium, is incorrect. Our understanding of scattering by regoliths appears to be incomplete, even when the particles are larger than the wavelength.     

Leveraging modern climatology to increase adaptive capacity across protected area networks

Human-driven changes in the global environment pose an increasingly urgent challenge for the management of ecosystems that is made all the more difficult by the uncertain future of both environmental conditions and ecological responses. Land managers need strategies to increase regional adaptive capacity, but relevant and rapid assessment approaches are lacking. To address this need, we developed a method to assess regional protected area networks across biophysically important climatic gradients often linked to biodiversity and ecosystem function. We plot the land of the southwestern United States across axes of historical climate space, and identify landscapes that may serve as strategic additions to current protected area portfolios. Considering climate space is straightforward, and it can be applied using a variety of relevant climate parameters across differing levels of land protection status. The resulting maps identify lands that are climatically distinct from existing protected areas, and may be utilized in combination with other ecological and socio-economic information essential to collaborative landscape-scale decision-making. Alongside other strategies intended to protect species of special concern, natural resources, and other ecosystem services, the methods presented herein provide another important hedging strategy intended to increase the adaptive capacity of protected area networks.     

<Emphasis Type="Italic">Deepwater Horizon</Emphasis> Oil Spill Impacts on Salt Marsh Fiddler Crabs (<Emphasis Type="Italic">Uca</Emphasis> spp.)

The Deepwater Horizon oil spill was the largest marine oil spill in US waters to date and one of the largest worldwide. Impacts of this spill on salt marsh vegetation have been well documented, although impacts on marsh macroinvertebrates have received less attention. To examine impacts of the oil spill on an important marsh invertebrate and ecosystem engineer, we conducted a meta-analysis on fiddler crabs (Uca spp.) using published sources and newly available Natural Resources Damage Assessment (NRDA) and Gulf of Mexico Research Initiative (GoMRI) data. Fiddler crabs influence marsh ecosystem structure and function through their burrowing and feeding activities and are key prey for a number of marsh and estuarine predators. We tested the hypothesis that the spill affected fiddler crab burrow density (crab abundance), burrow diameter (crab size), and crab species composition. Averaged across multiple studies, sites, and years, our synthesis revealed a negative effect of oiling on all three metrics. Burrow densities were reduced by 39 % in oiled sites, with impacts and incomplete recovery observed over 2010–2014. Burrow diameters were reduced from 2010 to 2011, but appeared to have recovered by 2012. Fiddler crab species composition was altered through at least 2013 and only returned to reference conditions where marsh vegetation recovered, via restoration planting in one case. Given the spatial and temporal extent of data analyzed, this synthesis provides compelling evidence that the Deepwater Horizon spill suppressed populations of fiddler crabs in oiled marshes, likely affecting other ecosystem attributes, including marsh productivity, marsh soil characteristics, and associated predators.     

The effect of uncertainty on estimates of hurricane surge hazards

It is shown here that uncertainty can significantly affect estimated surge levels over a wide range of annual exceedance probabilities (AEPs). For AEPs in the range of 1 × 10^?2–5 × 10^?2 in the New Orleans area, estimated surge values with and without consideration of uncertainty differ by about 0.5–1.0 m. Similarly, suppression of natural variability, such as using a single value for Mississippi River discharge in surge simulations, rather than allowing the discharge to vary probabilistically, is shown to produce deviations up to 1 m for the 1 × 10^?2 AEP in locations within the mainline river levees in this area. It is also shown that uncertainty can play a critical role in the analysis of very low probability events in the AEP range 1 × 10^?4–1 × 10^?6. Such events are typically used in designs of structures with major societal impacts. It is shown here that, for this range of AEPs along the west coast of Florida, the neglect of uncertainty can under-predict design surge levels by about 20 % compared to estimated surge levels that include uncertainty.     

A hurricane surge risk assessment framework using the joint probability method and surge response functions

Hurricane surge events have caused devastating damage in active-hurricane areas all over the world. The ability to predict surge elevations and to use this information for damage estimation is fundamental for saving lives and protecting property. In this study, we developed a framework for evaluating hurricane flood risk and identifying areas that are more prone to them. The approach is based on the joint probability method with optimal sampling (JPM-OS) using surge response functions (SRFs) (JPM-OS-SRF). Derived from a discrete set of high-fidelity storm surge simulations, SRFs are non-dimensional, physics-based empirical equations with an algebraic form, used to rapidly estimate surge as a function of hurricane parameters (i.e., central pressure, radius, forward speed, approach angle and landfall location). The advantage of an SRF-based approach is that a continuum of storm scenarios can be efficiently evaluated and used to estimate continuous probability density functions for surge extremes, producing more statistically stable surge hazard assessments without adding measurably to epistemic uncertainty. SRFs were developed along the coastline and then used to estimate maximum surge elevations with respect to a set of hurricane parameters. Integrating information such as ground elevation, property value and population with the JPM-OS-SRF allows quantification of storm surge-induced hazard impacts over the continuum of storm possibilities, yielding a framework to create the following risk-based products, which can be used to assist in hurricane hazard management and decision making: (1) expected annual loss maps; (2) flood damage versus return period relationships; and (3) affected business (e.g., number of business, number of employees) versus return period relationships. By employing several simplifying assumptions, the framework is demonstrated at three northern Gulf of Mexico study sites exhibiting similar surge hazard exposure. The framework results reveal Gulfport, MS, USA is at relatively more risk of economic loss than Corpus Christi, TX, USA, and Panama City, FL, USA. Note that economic processes are complex and very interrelated to most other human activities. Our intention here is to present a methodology to quantify the flood damage (i.e., infrastructure economic loss, number of businesses affected, number of employees in these affected businesses and sales volume in these affected businesses) but not to discuss the complex interactions of these damages with other economic activities and recovery plans.

     

Variable Grid Method: An Intuitive Approach for Simultaneously Quantifying and Visualizing Spatial Data and Uncertainty

Efforts to develop applications and methods that effectively quantify and communicate uncertainty associated with spatial data remains a focus within many scientific communities. However, the inherent complexity of uncertainty makes it difficult to define, characterize, and represent. Frequently, the products of spatial and spatio‐temporal data are presented without a clear explanation of the inherent uncertainty underlying the data. As uses and applications for spatial data and their products continues to increase, so does the importance for utilizing reliable approaches to effectively communicate spatial data along with their inherent uncertainties. To address this need, the Variable Grid Method (VGM) was developed as an intuitive approach that simultaneously communicates both spatial patterns and trends and the uncertainty associated with data or their analyses. This article details the VGM approach and demonstrates the utility of the VGM to provide critical information about the relationship between uncertainty and spatial data, necessary to support the increasing utilization of spatial information for a wide range of research and other needs.     

Mapping the Globalization of the Temporary Staffing Industry*

Very little is currently known about the globalization of the temporary staffing industry, a strategically significant sector given its role in promulgating wider labor market flexibility. This article starts to rectify this research lacuna in four ways: by conceptualizing the international expansion of temporary staffing and comparing it to other business service sectors, by identifying and mapping the top twenty transnational staffing agencies, by offering a typology of the leading transnational agencies based on their functional and geographic characteristics, and by charting a research agenda for future work on this sector.     

Surficial and Deep Earth Material Prediction from Geochemical Compositions

Prediction of true classes of surficial and deep earth materials using multivariate spatial data is a common challenge for geoscience modelers. Most geological processes leave a footprint that can be explored by geochemical data analysis. These footprints are normally complex statistical and spatial patterns buried deep in the high-dimensional compositional space. This paper proposes a spatial predictive model for classification of surficial and deep earth materials derived from the geochemical composition of surface regolith. The model is based on a combination of geostatistical simulation and machine learning approaches. A random forest predictive model is trained, and features are ranked based on their contribution to the predictive model. To generate potential and uncertainty maps, compositional data are simulated at unsampled locations via a chain of transformations (isometric log-ratio transformation followed by the flow anamorphosis) and geostatistical simulation. The simulated results are subsequently back-transformed to the original compositional space. The trained predictive model is used to estimate the probability of classes for simulated compositions. The proposed approach is illustrated through two case studies. In the first case study, the major crustal blocks of the Australian continent are predicted from the surface regolith geochemistry of the National Geochemical Survey of Australia project. The aim of the second case study is to discover the superficial deposits (peat) from the regional-scale soil geochemical data of the Tellus Project. The accuracy of the results in these two case studies confirms the usefulness of the proposed method for geological class prediction and geological process discovery.