New constraints on UHT metamorphism in the Eastern Ghats Province through the application of phase equilibria modelling and in situ geochronology

High Mg–Al granulites from the Sunki locality in the central portion of the Eastern Ghats Province record evidence for the high-temperature peak and retrograde evolution. Peak metamorphic phase assemblages from two samples are garnet + orthopyroxene + quartz + ilmenite + melt and orthopyroxene + spinel + sillimanite + melt, respectively. Isochemical phase diagrams (pseudosections) based on bulk rock compositions calculated in the chemical system Na₂O–CaO–K₂O–FeO–MgO–Al₂O₃–SiO₂–H₂O–TiO₂–Fe₂O₃ (NCKFMASHTO) and Al contents in orthopyroxene indicate peak UHT metamorphic conditions in excess of 960 °C and 9.7 kbar. Microstructures and the presence of cordierite interpreted to record the post-peak evolution show that the rocks underwent decompression and minor cooling from conditions of peak UHT metamorphism to conditions of ~ 900 °C at ~ 7.5 kbar. In situ U–Pb isotope analyses of monazite associated with garnet and cordierite using the Sensitive High Resolution Ion Microprobe (SHRIMP) yield a weighted mean ²⁰⁷Pb/²³⁵U age of ca. 980 Ma, which is interpreted to broadly constrain the timing of high-temperature monazite growth during decompression and melt crystallization at ~ 900–890 °C and 7.5 kbar. However, the range of ²⁰⁷Pb/²³⁵U monazite ages (from ca. 1014 Ma to 959 Ma for one sample and ca. 1043 Ma to 922 Ma for the second sample) suggest protracted monazite growth during the high-temperature retrograde evolution, and possibly diffusive lead loss during slow cooling after decompression. The results of the integrated petrologic and geochronologic approach presented here are inconsistent with a long time gap between peak conditions and the formation of cordierite-bearing assemblages at lower pressure, as proposed in previous studies, but are consistent with a simple evolution of a UHT peak followed by decompression and cooling.     

Statistical analysis of the radon-222 potential of rocks in Virginia,U.S.A.

More than 3,200 indoor radon-222 (²²²Rn) measurements were made seasonally in an area of about 1,000 square kilometers of the Coastal Plain and Piedmont physiographic provinces in Virginia, U.S.A. Results of these measurements indicate that some geological units are associated, on the average, with twice as much indoor²²²Rn as other geological units, and that indoor²²²Rn varies seasonally. The Kruskal-Wallis test was used to test whether indoor²²²Rn concentrations for data gathered over the winter and summer seasons differ significantly by rock unit. The tests concluded that indoor²²²Rn concentrations for different rock units were not equal at the 5-percent significance level. The rocks associated with the highest median indoor²²²Rn concentration are specific rocks in the Mesozoic Culpeper basin, including shale and siltstone units with Jurassic diabase intrusives, and mica schists in the Piedmont physiographic province. The pre-Triassic Peters Creek Schist has the highest ranking in terms of indoor²²²Rn concentration. The rocks associated with the lowest indoor²²²Rn concentrations include coastal plain sediments, the Occoquan Granite, Falls Church Tonalite, Piney Branch Mafic and Ultramafic complex, and unnamed mafic and ultramafic inclusions, respectively. The rocks have been ranked according to observed²²²Rn concentration by transforming the average rank of indoor²²²Rn concentrations to z scores.     

Landslide detection and susceptibility mapping using LiDAR and an artificial neural network approach: a case study in the Cuyahoga Valley National Park,Ohio

The purpose of this study was to detect shallow landslides using hillshade maps derived from light detection and ranging (LiDAR)-based digital elevation model (DEM) derivatives. The landslide susceptibility mapping used an artificial neural network (ANN) approach and backpropagation method that was tested in the northern portion of the Cuyahoga Valley National Park (CVNP) located in northeast Ohio. The relationship between landslides and predictor attributes, which describe landform classes using slope, profile and plan curvatures, upslope drainage area, annual solar radiation, and wetness index, was extracted from LiDAR-based DEM using geographic information system (GIS). The approach presented in this paper required a training study area for the development of the susceptibility model and a validation study area to test the model. The results from the validation showed that within the very high susceptibility class, a total of 42.6 % of known landslides that were associated with 1.56 % of total area were correctly predicted. In contrast, the very low susceptibility class that represented 82.68 % of the total area was associated with 1.20 % of known landslides. The results suggest that the majority of the known landslides occur within a small portion of the study area, consistent with field investigation and other studies. Sample probabilistic maps of landslide susceptibility potential and other products from this approach are summarized and presented for visualization to help park officials in effective management and planning.     

The Christiansen Effect in Saturn’s narrow dusty rings and the spectral identification of clumps in the F ring

Stellar occultations by Saturn’s rings observed with the Visual and Infrared Mapping Spectrometer (VIMS) onboard the Cassini spacecraft reveal that dusty features such as the F ring and the ringlets in the Encke and the Laplace Gaps have distinctive infrared transmission spectra. These spectra show a narrow optical depth minimum at wavelengths around 2.87 μm. This minimum is likely due to the Christiansen Effect, a reduction in the extinction of small particles when their (complex) refractive index is close to that of the surrounding medium. Simple Mie-scattering models demonstrate that the strength of this opacity dip is sensitive to the size distribution of particles between 1 and 100 μm across. Furthermore, the spatial resolution of the occultation data is sufficient to reveal variations in the transmission spectra within and among these rings. In both the Encke Gap ringlets and F ring, the opacity dip weakens with increasing local optical depth, which is consistent with the larger particles being concentrated near the cores of these rings. The Encke Gap ringlets also show systematically weaker opacity dips than the F ring and Laplace Gap ringlet, implying that the former has a smaller fraction of grains less than ∼30 μm across. However, the strength of the opacity dip varies most dramatically within the F ring; certain compact regions of enhanced optical depth lack an opacity dip and therefore appear to have a greatly reduced fraction of grains in the few-micron size range. Such spectrally-identifiable structures probably represent a subset of the compact optically-thick clumps observed by other Cassini instruments. These variations in the ring’s particle size distribution can provide new insights into the processes of grain aggregation, disruption and transport within dusty rings. For example, the unusual spectral properties of the F-ring clumps could perhaps be ascribed to small grains adhering onto the surface of larger particles in regions of anomalously low velocity dispersion.     

INDUSTRIAL PHOTOGRAMMETRY: NEW DEVELOPMENTS AND RECENT APPLICATIONS

Over the past decade, photogrammetry has been increasingly applied as a precise three dimensional measuring tool in industrial and engineering works. Analytical photogrammetry is now routinely employed in tasks of measurement as diverse as machine tool inspection and fixture checking, structural deformation monitoring, the provision of control databases to guide industrial robots and the measurement of structures in earth orbit. Traditional photogrammetric techniques and instrumentation are usually inappropriate for industrial work and the development of complete, fully integrated close range photogrammetric systems has recently gained considerable impetus. This paper details some of the significant new developments in industrial photogrammetry, with particular reference to technological advances in the Simultaneous Triangulation and Resection System (STARS) designed by Geodetic Services, Inc. for industrial photograininetry. Practical aspects of industrial measurement are also presented in a review of some of the varying tasks that GSI has carried out in recent years. Emphasis is placed on demonstrating the flexibility, accuracy, reliability and economy of photogrammetry, as well as the progress made in automating this measurement technique.     

Use of a reservoir water quality model to simulate global climate change effects on fish habitat

A case study was conducted on the potential impacts of climate change on fish habitat in a southeastern reservoir. A reservoir water quality model and one year of baseline meteorologic, hydrologic, and inflow water quality input were used to simulate current reservoir water quality. Total adult striped bass habitat, defined by specific quantitative temperature and dissolved oxygen criteria, was simulated. Daily reservoir volumes with optimal, suboptimal, and unsuitable temperature and DO were predicted for the year. Output from recent runs of atmospheric general circulation models (GCMs), in which atmospheric carbon dioxide concentrations have been doubled, was then used to adjust the baseline inputs to the water quality model. New sets of input data were created for two grid cells for each of three GCMs. All six climate scenarios are predicted to cause overall declines in the available summer striped bass habitat, mostly due to lake water temperatures exceeding striped bass tolerance levels. These predictions are believed to result from the consensus among GCM scenarios that air temperatures and humidity will rise, and the sensitivity of the reservoir model to these parameters. The reservoir model was found to be a promising tool for examining potential climate-change impacts. Some of the assumptions required to apply GCM output to the reservoir model, however, illustrate the problems in using large-scale gridcell output to assess small-scale impacts.     

Assessing spatial associations between perceptions of landscape value and climate change risk for use in climate change planning

This study examines spatially referenced perceived landscape values and climate change risks collected through public participation geographic information systems for potential use in climate change planning. Using survey data from the Southern Fleurieu Peninsula, South Australia, we present a method for identifying perceived landscape values and climate change risks to describe and quantify their spatial associations. Two spatial data models??vector and raster??and two analytical methods??Jaccard coefficients and spatial cross-correlations were used to describe the spatial associations. Results indicate that perceptions of climate change risk are driven, in part, by the values people assign or hold for places on the landscape. Biodiversity and intrinsic landscape values have strong spatial association with biodiversity loss risk while recreation values have strong spatial association with riparian flooding, sea-level rise and wave action risks. Other landscape values show weak to no spatial association with perceived climate change risks. The methodology described in this research provides a mechanism for government agencies to develop place-based adaptation strategies based on these associations.     

Regionalisation of population growth projections in coastal exposure analysis

Large-area coastal exposure and impact analysis has focussed on using sea-level rise (SLR) scenarios and has placed little emphasis on socioeconomic scenarios, while neglecting spatial variations of population dynamics. We use the Dynamic Interactive Vulnerability Assessment (DIVA) Framework to assess the population exposed to 1 in 100-year coastal flood events under different population scenarios, that are consistent with the shared socioeconomic pathways (SSPs); and different SLR scenarios, derived from the representative concentration pathways (RCPs); and analyse the effect of accounting for regionalised population dynamics on population exposure until 2100. In a reference approach, we use homogeneous population growth on national level. In the regionalisation approaches, we test existing spatially explicit projections that also account for urbanisation, coastal migration and urban sprawl. Our results show that projected global exposure in 2100 ranges from 100 million to 260 million, depending on the combination of SLR and population scenarios and method used for regionalising the population projections. The assessed exposure based on the regionalised approaches is higher than that derived from the reference approach by up to 60 million people (39%). Accounting for urbanisation and coastal migration leads to an increase in exposure, whereas considering urban sprawl leads to lower exposure. Differences between the reference and the regionalised approaches increase with higher SLR. The regionalised approaches show highest exposure under SSP5 over most of the twenty-first century, although total population in SSP5 is the second lowest overall. All methods project the largest absolute growth in exposure for Asia and relative growth for Africa.