Local Extremes in the Lidar‐Derived Snow Cover of Alpine Glaciers

Snow deposition and redistribution are major drivers of snow cover dynamics in mountainous terrain and contribute to the mass balance of alpine glaciers. The quantitative understanding of inhomogeneous snow distribution in mountains has recently benefited from advances in measuring technologies, such as airborne laser scanning (ALS). This contribution further advances the quantitative understanding of snow distribution by analysing the areas of maximum surface elevation changes in a mountain catchment with large and small glaciers. Using multi‐annual ALS observations, we found extreme surface elevation changes on rather thin borders along the glacier margins. While snow depth distribution patterns in less extreme terrain have presented high inter‐annual persistence, there is little persistence of those extreme glacier accumulations between winters. We therefore interpret the lack of persistence as the result of a predominance of gravity‐driven redistribution, which has an inherently higher random component because it does not occur with all conditions in all winters. In highly crevassed zones, the lidar‐derived surface elevation changes are caused by a complex interaction of ice flux divergence, the propagation of crevasses and snow accumulation. In general, the relative contribution of gravitational mass transport to glacier snow cover volume was found to decrease for glaciers larger than 5 km² in the investigated region. We therefore suggest that extreme accumulations caused by gravitational snow transport play a significant role in the glacier mass balance of small to medium‐size glaciers and that they may be successfully parameterized by simple mass redistribution algorithms, which have been presented in the literature.     

Distribution, magnitude and characterization of the toxicity of Ukrainian estuarine sediments

During the Soviet era, Ukraine was an important industrial and agricultural region of the Soviet Union. This industrial and agricultural activity resulted in contamination of Ukraine’s estuaries with legacy anthropogenic pollutants. Investigations on the toxicological effects of this estuarine contamination have been limited. For this research, we measured the toxicity of contaminated sediments from four Ukrainian estuaries to several aquatic organisms over 3 years. Sediment chemical analyses and whole sediment toxicity identification evaluations (TIEs) were also performed to determine the classes of contaminants contributing to toxicity. Toxic sediments were observed in several of the Ukrainian estuaries and chemical analyses of the sediments demonstrated anthropogenic contaminants were widely distributed. Contaminants were also detected in macrobenthic organisms collected from the sediments. Several lines of evidence, including TIEs, indicated hydrophobic organic chemicals (HOCs) were contributing substantially to observed toxicity. This information can guide environmental managers to prioritize portions of the estuaries requiring remediation.     

Bronfenbrenner’s bioecological theory for modelling community resilience to natural disasters

This paper advocates the use of Bronfenbrenner’s bioecological theory as a framework to analyse resilience at diverse scales. Bronfenbrenner’s bioecological theory can be employed to (a) benchmark social resilience, (b) target the priority interventions required and (c) measure progress arising from these interventions to enhance resilience to natural disasters. First, the paper explores resilience to natural disasters in the context of climatic change as building resilience is seen as a way to mitigate impacts of natural disasters. Second, concepts of resilience are systematically examined and documented, outlining resilience as a trait and resilience as a process. Third, issues arising in relation to the measurement of resilience are discussed. Fourth, Bronfenbrenner’s bioecological systems theory is described and proffered to model and assess resilience at different scales. Fifth, studies are described which have supported the use of the bioecological systems theory for the study of resilience. Sixth, an example of the use of Bronfenbrenner’s theory is offered and the paper concludes with suggestions for future research using Bronfenbrenner’s theory.     

Ground motion characteristics and shaking damage of the 11th March 2011 M w9.0 Great East Japan earthquake

A catastrophic M _w9.0 earthquake and subsequent giant tsunami struck the Tōhoku and Kanto regions of Japan on 11th March 2011, causing tremendous casualties, massive damage to structures and infrastructure, and huge economic loss. This event has revealed weakness and vulnerability of urban cities and modern society in Japan, which were thought to be one of the most earthquake-prepared nations in the world. Nevertheless, recorded ground motion data from this event offer invaluable information and opportunity; their unique features include very strong short-period spectral content, long duration, and effects due to local asperities as well as direction of rupture/wave propagation. Aiming at gaining useful experience from this tragic event, Earthquake Engineering Field Investigation Team (EEFIT) organised and dispatched a team to the Tōhoku region of Japan. During the EEFIT mission, ground shaking damage surveys were conducted in Sendai, Shirakawa, and Sukagawa, where the Japan Meteorological Agency intensity of 6+ was observed and instrumentally recorded ground motion data were available. The damage survey results identify the key factors for severe shaking damage, such as insufficient lateral reinforcement and detailing in structural columns from structural capacity viewpoint and rich spectral content of ground shaking in the intermediate vibration period range from seismic demand viewpoint. Importantly, inclusion of several ground motion parameters, such as nonlinear structural response, in shaking damage surveys, can improve the correlation of observed ground motion with shaking damage and therefore enhance existing indicators of potential damage.     

Classification of kangaroo habitat distribution using three GIS models

Abstract

In this paper, we compare three techniques for mapping wildlife habitat, termed BIOCLIM, CART and a new classification method based on nonparametric techniques. These techniques model dependent map layers of species distributions, where the areas to be mapped are large and the plot data is sparse. The techniques recognise pattern in the (independent) plot data, available to natural resource managers. In this case, the independent data set comprised 12 climate surfaces, that attempt to represent the range of temperature and precipitation important in determining the habitat of kangaroos across Australia. With this particular data set, the CART (decision tree) model was most accurate, but more time consuming to initialise. The relative performance of these models depends on the quality of the data set, and skill of the GIS analyst. Where possible, GIS analysts should implement all available methods, and compare output.     

Can simulations reproduce the observed temperature–mass relation for clusters of galaxies?

It has become increasingly apparent that traditional hydrodynamical simulations of galaxy clusters are unable to reproduce the observed properties of galaxy clusters, in particular overpredicting the mass corresponding to a given cluster temperature. Such overestimation may lead to systematic errors in results using galaxy clusters as cosmological probes, such as constraints on the density perturbation normalization σ ₈. In this paper we demonstrate that inclusion of additional gas physics, namely radiative cooling and a possible pre-heating of gas prior to cluster formation, is able to bring the temperature–mass relation in the innermost parts of clusters into good agreement with recent determinations by Allen, Schmidt & Fabian using Chandra data.     

A comparison of three approaches to spatial generalization of rainfall–runoff models

The generalization of the parameters of rainfall–runoff models, to enable application at ungauged sites, is an important and ongoing area of research. This paper compares the performance of three alternative methods of generalization, for two parameter‐sparse conceptual models (PDM and TATE), specifically for use in flood frequency estimation using continuous simulation. Two of the methods are based on fitting regression relationships between catchment properties and calibrated parameter values, using weighted or sequential regression (with weights based on estimates of calibration uncertainty), and the third is based on the use of pooling groups, defined through measures of site‐similarity based on catchment properties. The study uses a relatively large sample of catchments in Britain. For the PDM, the site‐similarity method performs best, but not greatly better than either regression method, so there may be cases where the use of regression would be preferable. For the TATE model, weighted regression performs best (with a very similar level of performance to that of the PDM with site‐similarity), whereas site‐similarity performs worst (due to poor performance for catchments with higher baseflow), indicating that the choice of model and generalization method should not be separated. The use of sequential regression, which was developed to try to allow for parameter interdependence, shows no clear advantage for either model. Other than the poor performance of the TATE model with site‐similarity for catchments with a higher baseflow index, there are no clear relationships between performance of any model/method and catchment type. Copyright © 2006 John Wiley & Sons, Ltd.