Chlorine in submarine glasses from the Lau Basin: seawater contamination and constraints on the composition of slab-derived fluids

Measurements of chlorine concentrations in matrix glasses from 18 primitive (>6 wt% MgO) and eight evolved lavas from active spreading centers in the Lau Basin back-arc system provide insight into the processes which control chlorine concentrations in subduction-related magmas, and can be used to investigate chlorine enrichment related to fluids derived from the underlying subducted slab. Chlorine contents of the glasses are highly variable (0.008-0.835 wt%) and generally high with respect to uncontaminated mid-ocean ridge basalt. Chlorine contents are highest in fractionated lavas from propagating ridge tips and lowest in more primitive basaltic lavas. Two different styles of enrichment in chlorine (relative to other incompatible elements) are recognized. Glasses from the Central Lau Spreading and Eastern Lau Spreading Center typically have low Ba/Nb ratios indicating minimal input of slab-derived components, and high to very high ratios of chlorine relative to K₂O, H₂O, and TiO₂. This style of chlorine enrichment is highest in the most fractionated samples and is consistent with crustal assimilation of chlorine-rich altered ocean crust material. Data from the literature suggest that contamination by chlorine-rich seawater-derived components also characterizes the Woodlark Basin and North Fiji Basin back-arc systems. The second style of chlorine enrichment reflects input from slab-derived fluid(s) to the mantle wedge from the adjacent Tonga subduction zone. Basaltic glasses from the Valu Fa Ridge and Mangatolu Triple Junction show correlations between ratios of chlorine and K₂O, H₂O, and TiO₂ and indices of slab-derived fluid input such as Ba/Nb, Ba/Th and U/Th, consistent with chlorine in these lavas originating from a saline fluid added to the mantle wedge. Within the Valu Fa Ridge the measured range of chlorine contents equates to a chlorine flux of 224-1120 kg/m/yr to the back-arc crust. Using a simple melting model and additional data from other back-arc and arc sample suites we conclude that chlorine is a major component within the slab fluids that contribute to many arc and back-arc melting systems, and probably plays an important role in regulating trace element transport by slab fluids in the mantle wedge. For the back-arc suites we have examined the estimated Cl/H₂O and Cl/K₂O ratios in the slab fluid component correlate with proximity to the arc front, suggesting that progressive dehydration of the slab and/or re-equilibration and transport within the mantle wedge may influence the overall degree of chlorine enrichment within the slab fluid component. The degree of chlorine enrichment observed in most back-arc lavas also appears too great to be explained solely by melting of amphibole, phlogopite or apatite within the mantle source and suggests that chlorine must be present in another phase, possibly a chlorine-rich fluid.     

GIMA ground penetrating radar system for monitoring concrete bridge decks

Ground Penetrating Radar (GPR) has been investigated as a non-destructive method for evaluating damage in concrete structures. However, the commercially available techniques are limited to detection of gross quantities of deterioration, due to the limited resolution of the system. The objective of this research is to evaluate a ground penetrating radar system with a novel Good Impedance Match Antenna (GIMA) for concrete structural assessment. This system has the capacity to detect concrete cracks as small as 1 mm thick, while being able to reflect from and detect features at depths of up to 360 mm. Laboratory results of testing of the GIMA antenna by using a step-frequency and a high-frequency impulse system are presented. The experimental results reveal that the GIMA antenna is capable for use in frequency ranges, at least as broad as 500 Mhz to 6 GHz for the step-frequency and 1 to 16 GHz for the high-frequency impulse system.     

Detecting communities with the multi-scale Louvain method: robustness test on the metropolitan area of Brussels

Detecting communities in large networks has become a common practice in socio-spatial analyses and has led to the development of numerous dedicated mathematical algorithms. Nowadays, however, researchers face a deluge of data and algorithms, and great care must be taken regarding methodological questions such as the values of the parameters and the geographical characteristics of the data. We aim here at testing the sensitivity of multi-scale modularity optimized by the Louvain method to the value of the resolution parameter (introduced by Reichardt and Bornholdt (Phys Rev Lett 93(21):218701, 2004.  https://doi.org/10.1103/PhysRevLett.93.218701) and controlling the size of the communities) and to a number of spatial issues such as the inclusion of internal loops and the delineation of the study area. We compare the community structures with those found by another well-known community detection algorithm (Infomap), and we further interpret the final results in terms of urban geography. Sensitivity analyses are conducted for commuting movements in and around Brussels. Results reveal slight effects of spatial issues (inclusion of the internal loops, definition of the study area) on the partition into job basins, while the resolution parameter plays a major role in the final results and their interpretation in terms of urban geography. Community detection methods seem to reveal a surprisingly strong spatial effect of commuting patterns: Similar partitions are obtained with different methods. This paper highlights the advantages and sensitivities of the multi-scale Louvain method and more particularly of defining communities of places. Despite these sensitivities, the method proves to be a valuable tool for geographers and planners.     

Field spectroradiometer and simulated multispectral bands for discriminating invasive species from morphologically similar cohabitant plants

One of the challenges in fighting plant invasions is the inefficiency of identifying their distribution using field inventory techniques. Remote sensing has the potential to alleviate this problem effectively using spectral profiling for species discrimination. However, little is known about the capability of remote sensing in discriminating between shrubby invasive plants with narrow leaf structures and other cohabitants with similar ecological niche. The aims of this study were therefore to (1) assess the classification performance of field spectroradiometer data among three bushy and shruby plants (Artemesia afra, Asparagus laricinus, and Seriphium plumosum) from the coexistent plant species largely dominated by acacia and grass species, and (2) explore the performance of simulated spectral bands of five space-borne images (Landsat 8, Sentinel 2A, SPOT 6, Pleiades 1B, and WorldView-3). Two machine-learning classifiers (boosted trees classification and support vector machines) were used to classify raw hyperspectral (n = 688) and simulated multispectral wavelengths. Relatively high classification accuracies were obtained for the invasive species using the original hyperspectral bands for both classifiers (overall accuracy, OA = 83–97%). The simulated data resulted in higher accuracies for Landsat 8, Sentinel 2A, and WorldView-3 compared to those computed for bands simulated to SPOT 6 and Pleiades 1B data. These findings suggest the potential of remote-sensing techniques in the discrimination of different plant species with similar morphological characteristics occupying the same niche.     

Mapping Cropping Practices Using MODIS Time Series: Harnessing the Data Explosion

The MODIS (Moderate Resolution Imaging Spectroradiometer) 250m EVI dataset provides a valuable ongoing means of characterising and monitoring changes in land use and resource condition. However the multiple factors that influence a time series of greenness data make the data difficult to analyse and interpret. Without prior knowledge, underlying models for time series in a given remote sensing image are often heterogeneous. So while conventional time series analysis methods such as wavelet transform and Fourier analysis may work well for part of the image, these models are either invalid or must be substantially re-parameterised for other parts of the image. To overcome these challenges we propose a new approach to distil information from earth observation time series data. The characteristics of a remote sensing time series are represented by a set of statistics (which we call coefficients) selected to correspond to the dynamics of a natural system. To ensure the coefficients are robust and generic, statistics are calculated independently by applying statistical models with less complexity on shorter segments within the time series. An International Standards Organization (ISO) Land Cover classification (Jansen 2000) was generated for cropping regions in the Gwydir and Namoi catchments, in Australia. Areas identified in the classification as irrigated and rain fed cropping were analysed using a tailored time series analysis tool. The crop analysis tool identifies time series features such as the number and duration of fallow periods, crop timing, presence/absence of a crop during a year for a specific growing season. This information is combined with paddock boundaries derived from Landsat imagery to provide detailed year-by-year insight into cropping practices in the Gwydir and Namoi catchments.     

Coniferization of the mixed‐wood boreal forests under warm climate

Mixed‐wood boreal forests are characterized by a heterogeneous landscape dominated by coniferous or deciduous species depending on stand moisture and fire activity. Our study highlights the long‐term drivers of these differences between landscapes across mixed‐wood boreal forests to improve simulated vegetation dynamics under predicted climate changes. We investigate the effects of main climate trends and wildfire activities on the vegetation dynamics of two areas characterized by different stand moisture regimes during the last 9000 years. We performed paleofire and pollen analyses in the mixed‐wood boreal forest of north‐western Ontario, derived from lacustrine sediment deposits, to reconstruct historical vegetation dynamics, which encompassed both the Holocene climatic optimum (ca. 8000–4000 a bp ) and the Neoglacial period (ca. 4000 a bp ). The past warm and dry period (Holocene climatic optimum) promoted higher fire activity that resulted in an increase in coniferous species abundance in the xeric area. The predicted warmer climate and an increase in drought events should lead to a coniferization of the xeric areas affected by high fire activity while the mesic areas may retain a higher broadleaf abundance, as these areas are not prone to an increase in fire activity. Copyright © 2019 John Wiley & Sons, Ltd.     

Large wood inhibits debris flow runout in forested southeast Alaska

Due to their potentially long runout, debris flows are a major hazard and an important geomorphic process in mountainous environments. Understanding runout is therefore essential to minimize risk in the near-term and interpret the pace and pattern of debris flow erosion and deposition over geomorphic timescales. Many debris flows occur in forested landscapes where they mobilize large volumes of large woody debris (LWD) in addition to sediment, but few studies have quantitatively documented the effects of LWD on runout. Here, we analyze recent and historic debris flows in southeast Alaska, a mountainous, forested system with minimal human alteration. Sixteen debris flows near Sitka triggered on August 18, 2015 or more recently had volumes of 80 to 25 000 m³ and limited mobility compared to a global compilation of similarly-sized debris flows. Their deposits inundated 31% of the planimetric area, and their runout lengths were 48% of that predicted by the global dataset. Depositional slopes were 6°–26°, and mobility index, defined as the ratio of horizontal runout to vertical elevation change, ranged from 1.2 to 3, further indicating low mobility. In the broader southeast Alaskan region consisting of Chichagof and Baranof Islands, remote sensing-based analysis of 1061 historic debris flows showed that mobility index decreased from 2.3–2.5 to 1.4–1.8 as average forest age increased from 0 to 416 years. We therefore interpret that the presence of LWD within a debris flow and standing trees, stumps, and logs in the deposition zone inhibit runout, primarily through granular phenomena such as jamming due to force chains. Calibration of debris flow runout models should therefore incorporate the ecologic as well as geologic setting, and feedbacks between debris flows and vegetation likely control the transport of sediment and organic material through steep, forested catchments over geomorphic time. © 2020 John Wiley & Sons, Ltd.     

Predicting site-specific storm wave run-up

Natural Hazards - Storm wave run-up causes beach erosion, wave overtopping, and street flooding. Extreme runup estimates may be improved, relative to predictions from general empirical formulae...     

Computational validation of static and dynamic plate load testing

In this paper, the static load plate test and the dynamic load plate test by means of the light falling weight device are assessed utilizing numerical simulations. Simplified computational models of the tested subsoil and of the testing devices are developed, which capture the main effects of both the static and the dynamic load plate test. In extensive parametric studies, the impact of various subsoil conditions on the test results and several sources of error are evaluated and discussed. Computational test results of the static load plate test and of the dynamic load plate test are set in contrast to an effort to demonstrate the differences and the common features of the outcomes.     

College students’ consumption, contribution, and risk awareness related to online mapping services and social media outlets: does geography and GIS knowledge matter?

Location-enabled online tools and/or services (i.e. Google Earth/Maps, Flickr, Facebook check-ins, etc.) have been widely used for distributing and processing geospatial-related data. They attract diverse users to consume and contribute geographic information (GI) in many different forms. This study examines college students’ consumption and contribution of GI through these tools and/or services as well as their perception of risk and privacy. It evaluates the impact of Geographic information systems (GIS) and geography knowledge on the related behavior and perception. Through conducting a survey, it was found that college students’ consumption frequency of GI through the investigated tools and services is positively related to their knowledge in GIS and geography, but their GI contribution is not related. GIS knowledge was found to help raise students’ awareness level of risk. However, this relationship does not translate into students’ concern about potential privacy disclosure or their willingness to share personal location information through using location-enabled online tools and/or services. Discussions on the gap between students’ consumption and contribution of GI through the tools and/or services are shared in the paper as well as possible explanations on the disconnection between their risk awareness, privacy concern, and willingness to share personal information. Future research directions are discussed.