Non-stationary extreme value analysis in a changing climate

This paper introduces a framework for estimating stationary and non-stationary return levels, return periods, and risks of climatic extremes using Bayesian inference. This framework is implemented in the Non-stationary Extreme Value Analysis (NEVA) software package, explicitly designed to facilitate analysis of extremes in the geosciences. In a Bayesian approach, NEVA estimates the extreme value parameters with a Differential Evolution Markov Chain (DE-MC) approach for global optimization over the parameter space. NEVA includes posterior probability intervals (uncertainty bounds) of estimated return levels through Bayesian inference, with its inherent advantages in uncertainty quantification. The software presents the results of non-stationary extreme value analysis using various exceedance probability methods. We evaluate both stationary and non-stationary components of the package for a case study consisting of annual temperature maxima for a gridded global temperature dataset. The results show that NEVA can reliably describe extremes and their return levels.     

Climate change links fate of glaciers and an endemic alpine invertebrate

Climate warming in the mid- to high-latitudes and high-elevation mountainous regions is occurring more rapidly than anywhere else on Earth, causing extensive loss of glaciers and snowpack. However, little is known about the effects of climate change on alpine stream biota, especially invertebrates. Here, we show a strong linkage between regional climate change and the fundamental niche of a rare aquatic invertebrate—the meltwater stonefly Lednia tumana—endemic to Waterton-Glacier International Peace Park, Canada and USA. L. tumana has been petitioned for listing under the U.S. Endangered Species Act due to climate-change-induced glacier loss, yet little is known on specifically how climate impacts may threaten this rare species and many other enigmatic alpine aquatic species worldwide. During 14 years of research, we documented that L. tumana inhabits a narrow distribution, restricted to short sections (~500 m) of cold, alpine streams directly below glaciers, permanent snowfields, and springs. Our simulation models suggest that climate change threatens the potential future distribution of these sensitive habitats and the persistence of L. tumana through the loss of glaciers and snowfields. Mountaintop aquatic invertebrates are ideal early warning indicators of climate warming in mountain ecosystems. Research on alpine invertebrates is urgently needed to avoid extinctions and ecosystem change.     

Economic impacts on key Barents Sea fisheries arising from changes in the strength of the Atlantic thermohaline circulation

A bioeconomic model of key fisheries of the Barents Sea is run with scenarios generated by an earth system model of intermediate complexity to assess how the Barents Sea fisheries of cod (Gadus morhua) and capelin (Mallotus villosus) are affected by changes in the Atlantic thermohaline circulation (THC) arising from anthropogenic climate change. Changes in hydrographic conditions have an impact on recruitment success and survival rates, which constitute a lasting effect on the stocks. The economic development of the fisheries is determined for the 21st century, considering a purely stock size based and a coupled stock size-hydrography based harvesting strategy. Results show that a substantial weakening of the THC leads to impaired cod stock development, causing the associated fishery to become unprofitable in the long run. Simultaneous improvements in capelin stock development help the capelin fishery, but are insufficient to offset the losses incurred by the cod fishery. A comparison of harvest strategies reveals that in times of high variability in stock development, coupled stock size-hydrography based management leads to more stable economic results of these fisheries than the stock size based fishing strategy.     

AN HISTORICAL APPROACH TO MOBILITY RESEARCH *

Most existing work on residential mobility has assumed that the household relocation decision is an intrinsically significant object of inquiry. In contrast, we argue that mobility derives its significance primarily from the particular historical and locational contexts within which it occurs. We suggest, therefore, that future mobility research should be directed away from the development of a theory of mobility per se and toward a more explicit articulation of mobility studies to existing theories of urbanization and social change.     

COMPARING TRADITIONAL METHODS FOR SELECTING CLASS INTERVALS ON CHOROPLETH MAPS*

The most common goal when classing data for choropleth maps is to create homogeneous classes which contain similar data values. None of the four traditional data classing methods examined here (quartile, equal interval, standard deviation, and natural breaks) consistently generalized the experimental data sets into homogeneous classes. These methods were most accurate for data sets with specific distributional characteristics, but none classed all of any type of distribution accurately. Only the optimization method produced reliable and accurate results for all of the experimental data.     

EMPLOYMENT DATA AND THE CLASSIFICATION OF URBAN SETTLEMENTS

Classifications of urban settlements have traditionally used total employment data in place of basic (export-oriented) employment data. While urban analysis agree upon the superiority of basic employment data for these classifications, usually only total employment data are available. This paper uses the Arizona Community Data Set, a unique survey-generated body of information that describes the major economic characteristics of numerous Southwestern communities and includes both basic and total employment data, to explore the extent to which misclassification results when using total employment data.     

High spatial resolution hyperspectral mapping of in-stream habitats, depths, and woody debris in mountain streams

This article evaluates the potential of 1-m resolution, 128-band hyperspectral imagery for mapping in-stream habitats, depths, and woody debris in third- to fifth-order streams in the northern Yellowstone region. Maximum likelihood supervised classification using principal component images provided overall classification accuracies for in-stream habitats (glides, riffles, pools, and eddy drop zones) ranging from 69% for third-order streams to 86% for fifth-order streams. This scale dependency of classification accuracy was probably driven by the greater proportion of transitional boundary areas in the smaller streams. Multiple regressions of measured depths (y) versus principal component scores (x₁, x₂,…, x_n) generated R² values ranging from 67% for high-gradient riffles to 99% for glides in a fifth-order reach. R² values were lower in third-order reaches, ranging from 28% for runs and glides to 94% for pools. The less accurate depth estimates obtained for smaller streams probably resulted from the relative increase in the number of mixed pixels, where a wide range of depths and surface turbulence occurred within a single pixel. Matched filter (MF) mapping of woody debris generated overall accuracies of 83% in the fifth-order Lamar River. Accuracy figures for the in-stream habitat and wood mapping may have been misleadingly low because the fine-resolution imagery captured fine-scale variations not mapped by field teams, which in turn generated false “misclassifications” when the image and field maps were compared.The use of high spatial resolution hyperspectral (HSRH) imagery for stream mapping is limited by the need for clear water to measure depth, by any tree cover obscuring the stream, and by the limited availability of airborne hyperspectral sensors. Nonetheless, the high accuracies achieved in northern Yellowstone streams indicate that HSRH imagery can be a powerful tool for watershed-wide mapping, monitoring, and modeling of streams.     

THE NEW INTERNATIONAL DIVISION OF LABOR AND DEBT CRISIS IN THE THIRD WORLD

The advanced capitalist ccuntries are undergoing an industrial devolution as remarkable as the industrial revolution of the nineteenth century. The removal of high-paying jobs through automation and geographical migration destroys the main market of the center and precipitates debt crises in newly industrialized countries of the periphery which have followed export-oriented growth policies. This results in a new, internationalized form of the crises of iate capitalism and provides a new institutional foucs for crisis in the international banks. The paper examines this global process from the perspective of the geography of class struggle.