Modelling volatility of growth rate in atmospheric carbon dioxide concentrations in a Bayesian approach

Atmospheric gases, such as carbon dioxide, ozone, methane, nitrous oxide, and etc., create a natural greenhouse effect and cause climate change. Therefore, modelling behavior of these gases could help policy makers to control greenhouse effects. In a Bayesian framework, we analyse and model conditional variance of growth rate in atmospheric carbon dioxide concentrations (ACDC) using monthly data from a subset of the well known Mauna Loa atmosphere carbon dioxide record. The conditional variance of ACDC monthly growth rate is modelled using the autoregressive conditional heteroscedasticity (ARCH), generalized ARCH model (GARCH) and a few variants of stochastic volatility (SV) models. Smooth transition ARCH and GARCH models are shown to be able to capture the dynamics in the conditional variance in ACDC level growth rate and to improve the forecast performance of ACDC growth rate.     

Generalized extreme value regression for ordinal response data

This paper introduces a flexible skewed link function for modeling ordinal response data with covariates based on the generalized extreme value (GEV) distribution. Commonly used probit, logit and complementary log-log links are prone to link misspecification because of their fixed skewness. The GEV link is flexible in fitting the skewness in the response curve with a free shape parameter. Using Bayesian methodology, it automatically detects the skewness in the response curve along with the model fitting. The flexibility of the proposed model is illustrated by its application to an ecological survey data about the coverage of Berberis thunbergii in New England. We employ the latent variable approach by Albert and Chib (J Am Stat Assoc 88:669–679, (1993) to develop computational schemes. For model selection, we employ the Deviance Information Criterion (DIC).     

Graphical spatial models: a new view on interpreting spatial pattern

Graphical models provide an important tool for facilitating communication between scientists, decision-makers, and statisticians—many complicated ecological processes can be described in terms of “box-and-arrow” conceptual diagrams (e.g., Shipley in Cause and correlation in biology: a user’s guide to path analysis, structural equations and causal inferences, Cambridge Universtiy Press, Cambridge, 2000; Clark and Gelfand TRENDS in Ecology and Evolution 21:375–380, 2006). In particular, problems in landscape ecology often involve modeling relationships among multiple physical and/or biological variables that may operate on differing spatial scales (e.g., Rossi et al. in Ecol Monographs 62:277–314, 1992; Legendre et al. in Ecography 25:601–615, 2002; Overmars et al. in Ecol Model 164:257–270, 2003; Brown and Spector in J Appl Ecol 45:1639–1648, 2008; Koniak and Noy-Meir in Ecol Model 220:1148–1158, 2008). These problems are inherently multivariate, though researchers commonly rely on univariate methods, such as spatial regression models, to address them. In this paper, we introduce a multivariate method—graphical spatial models—that extends path analysis to incorporate spatial autocorrelation in one or more variables in a directed graph. We show how both exogenous and endogenous ecological processes as defined by Legendre et al. (Ecography 25:601–615, 2002) and Lichstein et al. (Ecol Monographs 72:445–463, 2002) can be represented in a graph. Most importantly, we show how to translate graphs representing these ecological processes into statistically estimable models. We motivate our theoretical results using an example of stream health data from the Willamette Valley, Oregon. For these data we are interested in the spatial pattern within both riparian land use and an index of stream health, and whether there is an association between land use and stream health, after accounting for these spatial patterns. We use a graphical spatial model to address these ecological questions simultaneously. We find that the health of a stream decreases as the percent of developed land within a 120-m riparian buffer increases; interestingly, there is only evidence of spatial pattern within land use.     

Lack-of-fit tests for assessing mean structures for continuous dose-response data

We review a range of lack-of-fit tests suitable for assessing the appropriateness of the mean function in dose-response models. The review encompasses both well-known tests and new tests based on recent developments in statistics, which we have extended to the dose-response case. We argue that the classical methods are inadequate in certain situations, where the new tests may be applied. Power comparisons are carried out by means of extensive simulation studies, covering both designs with and without replicates at small and large sample sizes. Three datasets from dose-response applications illustrate differences and similarities between the tests. The results suggest that the new tests perform better and exhibit a wider applicability.     

Rank regression analysis of correlated water quality data from South East Queensland

With growing population and fast urbanization in Australia, it is a challenging task to maintain our water quality. It is essential to develop an appropriate statistical methodology in analyzing water quality data in order to draw valid conclusions and hence provide useful advices in water management. This paper is to develop robust rank-based procedures for analyzing nonnormally distributed data collected over time at different sites. To take account of temporal correlations of the observations within sites, we consider the optimally combined estimating functions proposed by Wang and Zhu (Biometrika, 93:459–464, 2006) which leads to more efficient parameter estimation. Furthermore, we apply the induced smoothing method to reduce the computational burden. Smoothing leads to easy calculation of the parameter estimates and their variance-covariance matrix. Analysis of water quality data from Total Iron and Total Cyanophytes shows the differences between the traditional generalized linear mixed models and rank regression models. Our analysis also demonstrates the advantages of the rank regression models for analyzing nonnormal data.     

Enhancing submerged coastal constructions by incorporating multifunctional purposes

Surfing has becoming more and more attractive in the past few decades, constituting nowadays an important source of revenue for many countries with extensive coastlines. For this purpose and also for environmental reasons, the conventional ways of protecting a coastline appear to entail some disadvantages. An innovative and interesting way of improving surfing capacities and contributing to protect a local coastal zone is by means of multifunctional artificial reefs. A multifunctional artificial reef (MFAR) is a submerged structure that serves several purposes; in particular, it may enhance the surfing possibilities and the environmental value of the local area. This structure has some promising new aspects, too: first, it provides an unimpaired visual amenity; second, it offers tourist and economic benefits by improving the surfing conditions; third, it can enhance the environmental value of the area where it is built, and fourth, if designed properly, the down drift erosion can be minimal. An appropriate reef design in terms of ‘surfability’, i.e. the possibility to surf a wave, for the Leirosa beach, located to the south of Figueira da Foz, midway along Portugal’s West Atlantic coast, has been investigated. In order to achieve the best design several steps were conducted. First, the performance of the Boussinesq-type COULWAVE model is tested with experimental data. Next, this numerical model is used to define the best values for three design parameters: reef angle; geometry of the reef (without or with a platform), and horizontal dimensions for the appropriate geometry. A preliminary design was achieved step by step, making use of the theory and of the state of the art of multifunctional reefs. This reef geometry is used in the numerical study. In terms of ‘surfability’ and for the conditions of the local coastline of Leirosa, the following values were found for the main parameters: a reef angle of 66°; a structure height of 3.20 m; a reef geometry composed of a delta without a platform; a reef submergence of 1.50 m, and a structure seaward slope of 1:10.     

A coupled numerical model to investigate the air–sea interaction at the coastal upwelling area of Cabo Frio,Brazil

This work investigates the capability of an oceanic numerical model dynamic and thermodynamically coupled to a three-dimensional mesoscale atmospheric numerical model to simulate the basic features of the air–sea interaction in the coastal upwelling area of Cabo Frio (RJ, Brazil). The upwelling/downwelling regime is an important feature in the oceanic circulation of Cabo Frio and determines the sustainability of local ecosystems. This regime is predominantly driven by the atmospheric circulation and is well documented, being suitable to be used as test reference for atmospheric and oceanic coupled and uncoupled models. The oceanic boundary conditions, coastline shape and coupling effect have been tested. The uncoupled oceanic model forced by a NE (SW) wind field generates a realistic upwelling (downwelling) phenomenon regardless of the proximity of the lateral boundary and how realistic is the shape of the coastline. The atmospheric-oceanic coupled model generates an upwelling location and intensity similar to the uncoupled simulation, but the upwelling is gradually enhanced by the sea-breeze circulation. It also generates vertical profiles of mixing ratio that compare better to the observations than the uncoupled simulation and air potential temperature and wind vertical profiles that represent particular features of the atmospheric circulation at Cabo Frio.     

Integrated remote sensing and GIS techniques for landslide hazard zonation: a case study Wadi Watier area,South Sinai,Egypt

Natural hazard analysis involves mapping and identifying future hazardous zones through the analysis of the controls influencing hazard initiation and occurrence. One of such natural hazard is the landslide. Landslides are amongst the most costly and damaging natural hazards especially in mountain regions and are triggered mainly by seismic activity and/or rainfall. The aim of the present study is to integrate Remote Sensing (RS) and Geographic Information System (GIS) tools to create thematic layers for assessment and the estimation of landslide hazard zones in and surrounding the Wadi Watier area, South Sinai, Egypt. Various factors, variables and/or parameters can be derived from thematic layers such as lithology, structural lineaments, land-cover/land-use, terrain analysis and earthquakes. Intensity risk layers were created by using ERDAS Imagine 9.2, ARC GIS 9.2 and ARC INFO 7.2.1 software. Enhanced Thematic Mapper (ETM+7) Landsat satellite images were used to discriminate and extract structural lineaments, lithology and land-use/land-cover variables for the study area. The Digital Elevation Model (DEM) was generated from digitized topographic maps to produce terrain analysis maps such as; slope, aspect, height elevation, and 3D. The weighting score rating system based on the relative importance of various causal factors derived from RS data and other thematic layers was used for landslide hazard zonation (LHZ). Based on these data, a simple algorithm was created to classify the area into different risk zones. By overlaying all hazard layers a final landslide hazard map was produced. Using trial and error and statistical methods the weight score rating values have been readjusted. GIS integration with RS data can greatly facilitate classifying landslide hazard zones into low risk, moderate risk and high risk by using a slicing operation. Seismic data are integrated with final the LHZ to generate a LHZ scenario map for the future and to draw up an action plan of mitigation measures to avoid the damage, loss of life and socio-economic impacts in the study area.     

Functional autoregressive forecasting of long-term seabed evolution

There is a need for decadal predictions of the seabed evolution, for example to inform resurvey strategies when maintaining navigation channels. The understanding of the physical processes involved in morphological evolution, and the viability of process models to accurately model evolution over these time scales, are currently limited. As a result, statistical approaches are used to supply long-term forecasts. In this paper, we introduce a novel statistical approach for this problem: the autoregressive Hilbertian model (ARH). This model naturally assesses the time evolution of spatially-distributed measurements. We apply the technique to a coastal area in the East Anglian coast over the period 1846 to 2002, and compare with two other statistical methods used recently for seabed prediction: the autoregressive model and the EOF model. We evaluate the performance of the three methods by comparing observations and predictions for 2002. The ARH model enables a reduction of 10% of the root mean squared errors. Finally, we compute the variability in the predictions related to time sampling using the jackknife, a method that uses subsamples to quantify uncertainties.