Statistic inversion of multi-zone transition probability models for aquifer characterization in alluvial fans

Understanding the heterogeneity arising from the complex architecture of sedimentary sequences in alluvial fans is challenging. This paper develops a statistical inverse framework in a multi-zone transition probability approach for characterizing the heterogeneity in alluvial fans. An analytical solution of the transition probability matrix is used to define the statistical relationships among different hydrofacies and their mean lengths, integral scales, and volumetric proportions. A statistical inversion is conducted to identify the multi-zone transition probability models and estimate the optimal statistical parameters using the modified Gauss–Newton–Levenberg–Marquardt method. The Jacobian matrix is computed by the sensitivity equation method, which results in an accurate inverse solution with quantification of parameter uncertainty. We use the Chaobai River alluvial fan in the Beijing Plain, China, as an example for elucidating the methodology of alluvial fan characterization. The alluvial fan is divided into three sediment zones. In each zone, the explicit mathematical formulations of the transition probability models are constructed with optimized different integral scales and volumetric proportions. The hydrofacies distributions in the three zones are simulated sequentially by the multi-zone transition probability-based indicator simulations. The result of this study provides the heterogeneous structure of the alluvial fan for further study of flow and transport simulations.     

Modeling acid-gas generation from boiling chloride brines

Background  

This study investigates the generation of HCl and other acid gases from boiling calcium chloride dominated waters at atmospheric pressure, primarily using numerical modeling. The main focus of this investigation relates to the long-term geologic disposal of nuclear waste at Yucca Mountain, Nevada, where pore waters around waste-emplacement tunnels are expected to undergo boiling and evaporative concentration as a result of the heat released by spent nuclear fuel. Processes that are modeled include boiling of highly concentrated solutions, gas transport, and gas condensation accompanied by the dissociation of acid gases, causing low-pH condensate.     

Great Powers and Geopolitical Change

Abstract

Most traditional segregation measures, such as the index of dissimilarity D, fail to distinguish spatial patterns effectively. Previously proposed spatial measures modifying D suffer from several shortcomings. This article describes a general spatial segregation index based upon the concept of composite population counts, which are derived from grouping people in neighboring areas together to account implicitly for spatial interaction of groups across unit boundaries. The suggested spatial index can overcome the disadvantages of previous indices and can assess the spatial extent of the segregated clusters. The results offer a more comprehensive depiction of spatial segregation of a region.

     

Phase difference analysis of temperature and vegetation phenology for beech forest: a wavelet approach

Temporal shifts in phenology or vegetation period of plants are seen as indicators of global warming with potentially severe impacts on ecosystem functioning. In spite of increasing knowledge on drivers, it is of utmost importance to disentangle the relationship between air temperatures, phenological events, potential temporal lags (phase shifts) and time scale for certain plant species. Assessing the phase shifts as well as the scale-dependent relationship between temperature and vegetation phenology requires the development of a nonlinear temporal model. Therefore, we use wavelet analysis and present a framework for identifying scale-dependent cross-phase coupling of bivariate time series. It allows the calculation of (a) scale-dependent decompositions of time series, (b) phase shifts of seasonal components in relation to the annual cycle, and (c) inter-annual phase differences between seasonal phases of different time series. The model is applied to air temperature data and remote sensing phenology data of a beech forest in Germany. Our study reveals that certain seasonal changes in amplitude and phase with respect to the normal annual rhythm of temperature and beech phenology are coupled time-delayed components, which are characterized by a time shift of about one year.     

Using Ground‐Penetrating Radar to Examine Spatial Organization at the Late Classic Maya Site of El Baúl,Cotzumalhuapa, Guatemala

The Cotzumalhuapa Nuclear Zone (CNZ) is a Late Classic site consisting of three large architectural compounds linked by a series of stone‐paved causeways and bridges. The majority of the site, however, lies beneath layers of recent tephra, leaving the structure of this settlement largely unknown. This study uses ground‐penetrating radar (GPR) to map the subsurface near El Baúl, one of the main compounds, in order to examine the spatial relationship among urban architectural features. Excavation within areas of the GPR survey provides fine‐grain detail of one major architectural feature in the region, the Gavarrete Causeway. The complimentary nature of these data sets characterizes the architectural organization of the settlement at two scales. Spatial patterning between large‐scale architectural features supports the hypothesis that the CNZ represents an integrated urban center with evidence of planned community organization characteristic of other Late Classic period centers across Mesoamerica.     

Tsunami hazard and exposure on the global scale

In the aftermath of the 2004 Indian Ocean tsunami, a large increase in the activity of tsunami hazard and risk mapping is observed. Most of these are site-specific studies with detailed modelling of the run-up locally. However, fewer studies exist on the regional and global scale. Therefore, tsunamis have been omitted in previous global studies comparing different natural hazards. Here, we present a first global tsunami hazard and population exposure study. A key topic is the development of a simple and robust method for obtaining reasonable estimates of the maximum water level during tsunami inundation. This method is mainly based on plane wave linear hydrostatic transect simulations, and validation against results from a standard run-up model is given. The global hazard study is scenario based, focusing on tsunamis caused by megathrust earthquakes only, as the largest events will often contribute more to the risk than the smaller events. Tsunamis caused by non-seismic sources are omitted. Hazard maps are implemented by conducting a number of tsunami scenario simulations supplemented with findings from literature. The maps are further used to quantify the number of people exposed to tsunamis using the Landscan population data set. Because of the large geographical extents, quantifying the tsunami hazard assessment is focusing on overall trends.     

Trace metal assessment in soils in a small city and its rural surroundings,Pensacola, FL,USA

This study assesses the origin and pollution of trace metals in surface soils in a region with contrasting land uses (urban vs. rural). Principal component analysis (PCA) reveals that Cr and Ni are predominantly of geogenic origin. These two trace metals are also the only ones with statistically significant correlations with soil particle size and organic matter content. Copper, Pb and Zn, and possibly Hg, are of anthropogenic origin, but their concentrations rarely reach levels described as strongly polluted by pollution indexes. The concentrations of these anthropogenic trace metals are statistically not different in urban and rural areas, except for Pb that is higher in the urban area. This general lack of a difference between the two land uses indicates that the influence of this small urban area on trace metal concentrations in soils is minor. Lead and Zn have the most, but still a modest number, of strongly polluted sites, mainly in an industrial part of the city. GIS analysis shows that, based on the pollution load index (PLI), overall concentrations of trace metals also reach their highest levels in that industrial area. These observations indicate that the influence of industry on trace metal pollution in soil exceeds that of other urban activities in the region. Local background concentrations were found to be very different from general crustal background concentrations. This demonstrates the importance of the careful selection of a background type in studies like this.     

Responding to change: Using scenarios to understand how socioeconomic factors may influence amplifying or dampening exploitation feedbacks among Tanzanian fishers

Environmental change often requires societies to adapt. In some instances, these adaptations can create feedbacks that amplify the change. Alternatively, other adaptations may dampen the change. We used semi-structured interviews with 240 fishers from nine Tanzanian coastal communities to explore responses to four hypothetical scenarios of increasingly severe declines in their average catch (10%, 20%, 30% and 50%). Overall, a higher proportion of fishers said they would respond to decline using amplifying adaptations (such as fishing harder) than dampening adaptations (such as reducing effort), particularly in the scenarios with lower levels of decline. We used a redundancy analysis to explore whether certain types of responses were related to the fishers’ socioeconomic characteristics. Fishers that would employ amplifying responses had greater economic wealth but lacked options. Fishers who would adopt dampening responses possessed characteristics associated with having livelihood options. Fishers who would adopt neither amplifying nor dampening responses were less likely to belong to community groups and sold the largest proportion of their catch. This study provides novel contributions by differentiating aspects of adaptive capacity that will amplify versus dampen environmental change and by highlighting what the resource users’ themselves say regarding responding to environmental change. Although direct policy application is limited by the study's hypothetical scenario nature, it provides a good beginning to incorporating resource users’ voices into such policy discussions.     

Actually achieving marine sustainability demands a radical re-think in approach, not 'more of the same'

The UK is committed to meeting a series of ‘high-level marine objectives’ which are required to satisfy national obligations and to meet international commitments for the marine environment. There are more than 16 such international high-level policy driven obligations and commitments, together with more than 18 European and more than 12 of national origin. In the UK, there is an assumption that the current and planned monitoring will provide evidence to demonstrate achievement against these high-level objectives.     

Analyzing spatial ecological data using linear regression and wavelet analysis

Spatial (two-dimensional) distributions in ecology are often influenced by spatial autocorrelation. In standard regression models, however, observations are assumed to be statistically independent. In this paper we present an alternative to other methods that allow for autocorrelation. We show that the theory of wavelets provides an efficient method to remove autocorrelations in regression models using data sampled on a regular grid. Wavelets are particularly suitable for data analysis without any prior knowledge of the underlying correlation structure. We illustrate our new method, called wavelet-revised model, by applying it to multiple regression for both normal linear models and logistic regression. Results are presented for computationally simulated data and real ecological data (distribution of species richness and distribution of the plant species Dianthus carthusianorum throughout Germany). These results are compared to those of generalized linear models and models based on generalized estimating equations. We recommend wavelet-revised models, in particular, as a method for logistic regression using large datasets.