Geostatistical interpretation of paleoceanographic data over large ocean basins - Reality and fiction

A promising approach to reconstruct oceanographic scenarios of past time slices is to drive numerical ocean circulation models with sea surface temperatures, salinities, and ice distributions derived from sediment core data. Set up properly, this combination of boundary conditions provided by the data and physical constraints represented by the model can yield physically consistent sets of three-dimensional water mass distribution and circulation patterns. This idea is not only promising but dangerous, too. Numerical models cannot be fed directly with data from single core locations distributed unevenly and, as it is the common case, scarcely in space. Conversely, most models require forcing data sets on a regular grid with no missing points, and some method of interpolation between punctual source data and model grid has to be employed. An ideal gridding scheme must retain as much of the information present in the sediment core data while generating as few artifacts in the interpolated field as possible. Based on a set of oxygen isotope ratios, we discuss several standard interpolation strategies, namely nearest neighbour schemes, bicubic splines, Delaunay triangulation, and ordinary and indicator kriging. We assess the gridded fields with regard to their physical consistence and their implications for the oceanic circulation

Scale effect in hazard assessment - application to daily rainfall

International audienceDaily precipitation is recorded as the total amount of water collected by a rain-gauge in 24h. Events are modelled as a Poisson process and the 24h precipitation by a Generalized Pareto Distribution (GPD) of excesses. Hazard assessment is complete when estimates of the Poisson rate and the distribution parameters, together with a measure of their uncertainty, are obtained. The shape parameter of the GPD determines the support of the variable: Weibull domain of attraction (DA) corresponds to finite support variables, as should be for natural phenomena. However, Fréchet DA has been reported for daily precipitation, which implies an infinite support and a heavy-tailed distribution. We use the fact that a log-scale is better suited to the type of variable analyzed to overcome this inconsistency, thus showing that using the appropriate natural scale can be extremely important for proper hazard assessment. The approach is illustrated with precipitation data from the Eastern coast of the Iberian Peninsula affected by severe convective precipitation. The estimation is carried out by using Bayesian techniques

The effect of scale in daily precipitation hazard assessment

Daily precipitation is recorded as the total amount of water collected by a rain-gauge in 24 h. Events are modelled as a Poisson process and the 24 h precipitation by a Generalised Pareto Distribution (GPD) of excesses. Hazard assessment is complete when estimates of the Poisson rate and the distribution parameters, together with a measure of their uncertainty, are obtained. The shape parameter of the GPD determines the support of the variable: Weibull domain of attraction (DA) corresponds to finite support variables as should be for natural phenomena. However, Fréchet DA has been reported for daily precipitation, which implies an infinite support and a heavy-tailed distribution. Bayesian techniques are used to estimate the parameters. The approach is illustrated with precipitation data from the Eastern coast of the Iberian Peninsula affected by severe convective precipitation. The estimated GPD is mainly in the Fréchet DA, something incompatible with the common sense assumption of that precipitation is a bounded phenomenon. The bounded character of precipitation is then taken as a priori hypothesis. Consistency of this hypothesis with the data is checked in two cases: using the raw-data (in mm) and using log-transformed data. As expected, a Bayesian model checking clearly rejects the model in the raw-data case. However, log-transformed data seem to be consistent with the model. This fact may be due to the adequacy of the log-scale to represent positive measurements for which differences are better relative than absolute

Nova metodologia para a construção de Índices Composicionais de Poluição em solos: um estudo de caso em Langreo, Espanha

O complexo comportamento geoquímico de Elementos Potencialmente Tóxicos (EPT) em solos pode afetar severamente os ecossistemas e a saúde humana. No entanto, é necessário compreender primeiro o que se entende por um solo “limpo” para depois poder avaliar um solo “poluído “e correspondente grau de severidade associado. Tendo em conta que a composição química de um solo é composicional, os atributos variam juntos, é, neste trabalho, introduzida uma nova metodologia para a construção de Indicadores Composicionais com base no formalismo da Análise de Dados Composicionais (CoDa). Na identificação de um equilíbrio composicional é necessário definir os elementos poluentes e os não poluentes para depois construir um Indicador de Poluição Composicional (IPC). Apresenta-se como estudo de caso a região contaminada de Langreo, noroeste de Espanha, onde num conjunto de 150 amostras de solo georreferenciadas foram analisados, em cada amostra, 37 elementos químicos. Calcularam-se depois, três IPCs, com base no formalismo CoDa e no conhecimento pericial. Num último passo, foram construídos mapas de distribuição espacial e de probabilidade, de exceder um dado limite, para os três novos indicadores de poluição, através de um processo estocástico de simulação sequencial – Simulação Sequencial Gaussiana (SSG). Os padrões espaciais obtidos e correspondente variabilidade associada, permitiu uma melhor compreensão dos processos associados à distribuição e ao enriquecimento relativo em EPTs.info:eu-repo/semantics/publishedVersio

water chemistry are new challenges possible from coda compositional data analysis point of view

John Aitchison died in December 2016 leaving behind an important inheritance: to continue to explore the fascinating world of compositional data. However, notwithstanding the progress that we have made in this field of investigation and the diffusion of the CoDA theory in different researches, a lot of work has still to be done, particularly in geochemistry. In fact most of the papers published in international journals that manage compositional data ignore their nature and their consequent peculiar statistical properties. On the other hand, when CoDA principles are applied, several efforts are often made to continue to consider the log-ratio transformed variables, for example the centered log-ratio ones, as the original ones, demonstrating a sort of resistance to thinking in relative terms. This appears to be a very strange behavior since geochemists are used to ratios and their analysis is the base of the experimental calibration when standards are evolved to set the instruments. In this chapter some challenges are presented by exploring water chemistry data with the aim to invite people to capture the essence of thinking in a relative and multivariate way since this is the path to obtain a description of natural processes as complete as possible

Combined effects of time spent in physical activity, sedentary behaviors and sleep on obesity and cardio-metabolic health markers: a novel compositional data analysis approach

<div><p>The associations between time spent in sleep, sedentary behaviors (SB) and physical activity with health are usually studied without taking into account that time is finite during the day, so time spent in each of these behaviors are codependent. Therefore, little is known about the combined effect of time spent in sleep, SB and physical activity, that together constitute a composite whole, on obesity and cardio-metabolic health markers. Cross-sectional analysis of NHANES 2005–6 cycle on N = 1937 adults, was undertaken using a compositional analysis paradigm, which accounts for this intrinsic codependence. Time spent in SB, light intensity (LIPA) and moderate to vigorous activity (MVPA) was determined from accelerometry and combined with self-reported sleep time to obtain the 24 hour time budget composition. The distribution of time spent in sleep, SB, LIPA and MVPA is significantly associated with BMI, waist circumference, triglycerides, plasma glucose, plasma insulin (all p<0.001), and systolic (p<0.001) and diastolic blood pressure (p<0.003), but not HDL or LDL. Within the composition, the strongest positive effect is found for the proportion of time spent in MVPA. Strikingly, the effects of MVPA replacing another behavior and of MVPA being displaced by another behavior are asymmetric. For example, re-allocating 10 minutes of SB to MVPA was associated with a lower waist circumference by 0.001% but if 10 minutes of MVPA is displaced by SB this was associated with a 0.84% higher waist circumference. The proportion of time spent in LIPA and SB were detrimentally associated with obesity and cardiovascular disease markers, but the association with SB was stronger. For diabetes risk markers, replacing SB with LIPA was associated with more favorable outcomes. Time spent in MVPA is an important target for intervention and preventing transfer of time from LIPA to SB might lessen the negative effects of physical inactivity.</p></div