The effect of sampling rate and anti-aliasing filters on high-frequency response spectra

The most commonly used intensity measure in ground-motion prediction equations is the pseudo-absolute response spectral acceleration (PSA), for response periods from 0.01 to 10 s (or frequencies from 0.1 to 100 Hz). PSAs are often derived from recorded ground motions, and these motions are usually filtered to remove high and low frequencies before the PSAs are computed. In this article we are only concerned with the removal of high frequencies. In modern digital recordings, this filtering corresponds at least to an anti-aliasing filter applied before conversion to digital values. Additional high-cut filtering is sometimes applied both to digital and to analog records to reduce high-frequency noise. Potential errors on the short-period (high-frequency) response spectral values are expected if the true ground motion has significant energy at frequencies above that of the anti-aliasing filter. This is especially important for areas where the instrumental sample rate and the associated anti-aliasing filter corner frequency (above which significant energy in the time series is removed) are low relative to the frequencies contained in the true ground motions. A ground-motion simulation study was conducted to investigate these effects and to develop guidance for defining the usable bandwidth for high-frequency PSA. The primary conclusion is that if the ratio of the maximum Fourier acceleration spectrum (FAS) to the FAS at a frequency $f_{saa} $ corresponding to the start of the anti-aliasing filter is more than about 10, then PSA for frequencies above $f_{saa} $ should be little affected by the recording process, because the ground-motion frequencies that control the response spectra will be less than $f_{saa} $ . A second topic of this article concerns the resampling of the digital acceleration time series to a higher sample rate often used in the computation of short-period PSA. We confirm previous findings that sinc-function interpolation is preferred to the standard practice of using linear time interpolation for the resampling.     

Trends in marine debris along the U.S. Pacific Coast and Hawai'i 1998-2007

We assessed amounts, composition, and trends of marine debris for the U.S. Pacific Coast and Hawai'i using National Marine Debris Monitoring Program data. Hawai'i had the highest debris loads; the North Pacific Coast region had the lowest debris loads. The Southern California Bight region had the highest land-based debris loads. Debris loads decreased over time for all source categories in all regions except for land-based and general-source loads in the North Pacific Coast region, which were unchanged. General-source debris comprised 30-40% of the items in all regions. Larger local populations were associated with higher land-based debris loads across regions; the effect declined at higher population levels. Upwelling affected deposition of ocean-based and general-source debris loads but not land-based loads along the Pacific Coast. LNSO decreased debris loads for both land-based and ocean-based debris but not general-source debris in Hawai'i, a more complex climate-ocean effect than had previously been found.     

A review of EU bio-economic models for fisheries: The value of a diversity of models

The lessons learned from a review of thirteen existing European bio-economic models used in the evaluation of EU policies are presented. How these models compare and differ in terms of their biological and economic components, the integration between the components, which indicators are selected and how they are used, are described and analysed. The article concludes that the multitude of construction differences reflects the necessity of adapting the modelling approach to answer different questions. Since real life questions in fisheries are so diverse, answering them requires a diversity of models.     

Platiniferous gold–tourmaline aggregates in the gold–palladium belt of Minas Gerais,Brazil: implications for regional boron metasomatism

Mineralogy and Petrology - The platiniferous gold–palladium belt of Minas Gerais, Brazil, forms an approximately 240-km-long, roughly north–south-trending domain that includes numerous...     

Spatial dimensions of New Zealand's environmental management

New Zealand's regional council boundaries drawn as part of the 1989 local government reforms privilege integrated catchment management. This privilege is now challenged by calls to both fragment and amalgamate councils and centralise decision‐making but with little analytic assessment of the spatial differentiation to support them. The spatial underpinnings of these calls were explored using a case study that assessed the spatial dimensions of environment management. Analysis of the Land Environments and River Environment Classification databases and councils' planning documents highlights challenges local governments face in applying integrated natural resource management within single purpose jurisdictions and implications for wider sustainable development policy.     

A land-use-based modelling chain to assess the impacts of Natural Water Retention Measures on Europe’s Green Infrastructure

This article details the process of integrating models to answer a specific policy-driven question: ‘What could be the impact of proposed Natural Water Retention Measures (NWRMs) on Europe’s Green Infrastructure (GI)?’ It describes the new Land Use Modelling Platform (LUMP), now enabling a high spatial scale (100-m) and large coverage (pan-European), whereby several sector-specific models contribute to assessing the impact of regional-level policy on a given spatial topic of concern. The configuration (land claims and land allocations modules) and calibration (accessibility and biophysical suitability) of the LUMP are explained. Four NWRM scenarios (riparian areas, afforestation, grassland and baseline scenario) are configured to run the simulations. For the reference: year 2006, the spatial representation of GI is based on land-use features of a refined version of the CORINE Land Cover (CLC), and resumed as connected components made of nodes and links.

Mathematical morphological image processing and network graph theory model, available from the free software package GUIDOS (the Joint Research Center of the European Commission), enabled the measurement of the GI connectivity and identified most critical links. Results show that the competition for land claimed by different economic sectors, combined with policy-driven rule-sets for the implementation of different NWRMs, yields very different results for the 2030 land-use projections, and subsequently for the morphology of GI. Three indicators associated with the morphology of GI are computed in order to assess the model outputs for 2030. The indicators are computed to answer the following questions: (1) How is the quantity of GI affected by each of the NWRM, and what proportion of that GI is most valuable? (2) What is the location of the most critical nodes and connectors of GI, and what land-use conversions occur under these? (3) Are the average components getting larger or smaller?

Whereas the grassland measure results in the largest net increase of GI, the afforestation measure results in the overall largest number of hectares of key nodes and links within the network. Land conversions occur under the critical GI nodes and links, with a large increase in agricultural areas, especially for the riparian measure under critical nodes and the grassland measure under critical links. Also predominant is the swapping of land from pasture to forest under critical links with the afforestation measure. The riparian measure most increases the average size of GI components, and all three measures contribute to bridging two large components which were divided in the 2006 land-use map, thus increasing the size of the largest component by more than 50%.     

Assessing future risk: quantifying the effects of sea level rise on storm surge risk for the southern shores of Long Island, New York

Sea level rise threatens to increase the impacts of future storms and hurricanes on coastal communities. However, many coastal hazard mitigation plans do not consider sea level rise when assessing storm surge risk. Here we apply a GIS-based approach to quantify potential changes in storm surge risk due to sea level rise on Long Island, New York. We demonstrate a method for combining hazard exposure and community vulnerability to spatially characterize risk for both present and future sea level conditions using commonly available national data sets. Our results show that sea level rise will likely increase risk in many coastal areas and will potentially create risk where it was not before. We find that even modest and probable sea level rise (.5 m by 2080) vastly increases the numbers of people (47% increase) and property loss (73% increase) impacted by storm surge. In addition, the resulting maps of hazard exposure and community vulnerability provide a clear and useful example of the visual representation of the spatial distribution of the components of risk that can be helpful for developing targeted hazard mitigation and climate change adaptation strategies. Our results suggest that coastal agencies tasked with managing storm surge risk must consider the effects of sea level rise if they are to ensure safe and sustainable coastal communities in the future.     

Spatial scan statistics in vulnerability assessment: an application to mountain hazards

In the European Alps, the concept of risk has increasingly been applied in order to reduce the susceptibility of society to mountain hazards. Risk is defined as a function of the magnitude and frequency of a hazard process times consequences; the latter being quantified by the value of elements at risk exposed and their vulnerability. Vulnerability is defined by the degree of loss to a given element at risk resulting from the impact of a natural hazard. Recent empirical studies suggested a dependency of the degree of loss on the hazard impact, and respective vulnerability (or damage-loss) functions were developed. However, until now, only little information is available on the spatial characteristics of vulnerability on a local scale; considerable ranges in the loss ratio for medium process intensities only provide a hint that there might be mutual reasons for lower or higher loss rates. In this paper, we therefore focus on the spatial dimension of vulnerability by searching for spatial clusters in the damage ratio of elements at risk exposed. By using the software SaTScan, we applied an ordinal data model and a normal data model in order to detect spatial distribution patterns of five individual torrent events in Austria. For both models, we detected some significant clusters of high damage ratios, and consequently high vulnerability. Moreover, secondary clusters of high and low values were found. Based on our results, the assumption that lower process intensities result in lower damage ratios, and therefore in lower vulnerability, and vice versa, has to be partly rejected. The spatial distribution of vulnerability is not only dependent on the process intensities but also on the overall land use pattern and the individual constructive characteristics of the buildings exposed. Generally, we suggest the use of a normal data model for test sites exceeding a minimum of 30 elements at risk exposed. As such, the study enhanced our understanding of spatial vulnerability patterns on a local scale.     

Climate change,theory of planned behavior and values: a structural equation model with mediation analysis

An online survey about climate change was conducted 2008/2009 among all university members (N = 3541). Using the Theory of Planned Behavior and Cultural Theory within a structural equation modeling approach, one main goal was to explain climate-friendly behavioral intentions and the underlying psychological processes comprehensively and to show the interdependencies between both approaches. The model explained 72% of the variance in Intentions to use public transport. Attitude towards public transport (beta = 0.67, p < .001) most strongly influenced Intentions followed by Subjective Norms (beta = 0.23, p < .001). In turn, Attitudes and Norm were explained by the value types Egalitarian, Individualist and Fatalist (24% and 14% respectively). These value types are mediated through Attitudes and Subjective Norms. Recommendations regarding the support of climate friendly behavior are formulated.     

Systematic under- and overestimation of GHG reductions in renewable biomass systems

This paper identifies a critical systematic error in greenhouse gas accounting in renewable biomass systems. While CO₂ emissions from renewable biomass energy systems are generally considered to have a net impact of 0, no similar adjustment is made for carbon-based products of incomplete combustion, such as methane, in renewable systems. This results in an under- or overestimation of the impact of CH₄ by 12.3% and CO by ∼478% in renewable systems. This error is propagated both in scientific studies and in carbon accounting policies. We advocate first for full-carbon accounting of biomass-derived emissions, but also provide adjusted global warming impacts for emissions from proven renewable systems.