939 resultados para extreme events


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The last 30 years have seen Fuzzy Logic (FL) emerging as a method either complementing or challenging stochastic methods as the traditional method of modelling uncertainty. But the circumstances under which FL or stochastic methods should be used are shrouded in disagreement, because the areas of application of statistical and FL methods are overlapping with differences in opinion as to when which method should be used. Lacking are practically relevant case studies comparing these two methods. This work compares stochastic and FL methods for the assessment of spare capacity on the example of pharmaceutical high purity water (HPW) utility systems. The goal of this study was to find the most appropriate method modelling uncertainty in industrial scale HPW systems. The results provide evidence which suggests that stochastic methods are superior to the methods of FL in simulating uncertainty in chemical plant utilities including HPW systems in typical cases whereby extreme events, for example peaks in demand, or day-to-day variation rather than average values are of interest. The average production output or other statistical measures may, for instance, be of interest in the assessment of workshops. Furthermore the results indicate that the stochastic model should be used only if found necessary by a deterministic simulation. Consequently, this thesis concludes that either deterministic or stochastic methods should be used to simulate uncertainty in chemical plant utility systems and by extension some process system because extreme events or the modelling of day-to-day variation are important in capacity extension projects. Other reasons supporting the suggestion that stochastic HPW models are preferred to FL HPW models include: 1. The computer code for stochastic models is typically less complex than a FL models, thus reducing code maintenance and validation issues. 2. In many respects FL models are similar to deterministic models. Thus the need for a FL model over a deterministic model is questionable in the case of industrial scale HPW systems as presented here (as well as other similar systems) since the latter requires simpler models. 3. A FL model may be difficult to "sell" to an end-user as its results represent "approximate reasoning" a definition of which is, however, lacking. 4. Stochastic models may be applied with some relatively minor modifications on other systems, whereas FL models may not. For instance, the stochastic HPW system could be used to model municipal drinking water systems, whereas the FL HPW model should or could not be used on such systems. This is because the FL and stochastic model philosophies of a HPW system are fundamentally different. The stochastic model sees schedule and volume uncertainties as random phenomena described by statistical distributions based on either estimated or historical data. The FL model, on the other hand, simulates schedule uncertainties based on estimated operator behaviour e.g. tiredness of the operators and their working schedule. But in a municipal drinking water distribution system the notion of "operator" breaks down. 5. Stochastic methods can account for uncertainties that are difficult to model with FL. The FL HPW system model does not account for dispensed volume uncertainty, as there appears to be no reasonable method to account for it with FL whereas the stochastic model includes volume uncertainty.

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© 2014, Springer-Verlag Berlin Heidelberg.The frequency and severity of extreme events are tightly associated with the variance of precipitation. As climate warms, the acceleration in hydrological cycle is likely to enhance the variance of precipitation across the globe. However, due to the lack of an effective analysis method, the mechanisms responsible for the changes of precipitation variance are poorly understood, especially on regional scales. Our study fills this gap by formulating a variance partition algorithm, which explicitly quantifies the contributions of atmospheric thermodynamics (specific humidity) and dynamics (wind) to the changes in regional-scale precipitation variance. Taking Southeastern (SE) United States (US) summer precipitation as an example, the algorithm is applied to the simulations of current and future climate by phase 5 of Coupled Model Intercomparison Project (CMIP5) models. The analysis suggests that compared to observations, most CMIP5 models (~60 %) tend to underestimate the summer precipitation variance over the SE US during the 1950–1999, primarily due to the errors in the modeled dynamic processes (i.e. large-scale circulation). Among the 18 CMIP5 models analyzed in this study, six of them reasonably simulate SE US summer precipitation variance in the twentieth century and the underlying physical processes; these models are thus applied for mechanistic study of future changes in SE US summer precipitation variance. In the future, the six models collectively project an intensification of SE US summer precipitation variance, resulting from the combined effects of atmospheric thermodynamics and dynamics. Between them, the latter plays a more important role. Specifically, thermodynamics results in more frequent and intensified wet summers, but does not contribute to the projected increase in the frequency and intensity of dry summers. In contrast, atmospheric dynamics explains the projected enhancement in both wet and dry summers, indicating its importance in understanding future climate change over the SE US. The results suggest that the intensified SE US summer precipitation variance is not a purely thermodynamic response to greenhouse gases forcing, and cannot be explained without the contribution of atmospheric dynamics. Our analysis provides important insights to understand the mechanisms of SE US summer precipitation variance change. The algorithm formulated in this study can be easily applied to other regions and seasons to systematically explore the mechanisms responsible for the changes in precipitation extremes in a warming climate.

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Volatile halogenated organic compounds containing bromine and iodine, which are naturally produced in the ocean, are involved in ozone depletion in both the troposphere and stratosphere. Three prominent compounds transporting large amounts of marine halogens into the atmosphere are bromoform (CHBr3), dibromomethane (CH2Br2) and methyl iodide (CH3I). The input of marine halogens to the stratosphere has been estimated from observations and modelling studies using low-resolution oceanic emission scenarios derived from top-down approaches. In order to improve emission inventory estimates, we calculate data-based high resolution global sea-to-air flux estimates of these compounds from surface observations within the HalOcAt (Halocarbons in the Ocean and Atmosphere) database (https://halocat.geomar.de/). Global maps of marine and atmospheric surface concentrations are derived from the data which are divided into coastal, shelf and open ocean regions. Considering physical and biogeochemical characteristics of ocean and atmosphere, the open ocean water and atmosphere data are classified into 21 regions. The available data are interpolated onto a 1 degrees x 1 degrees grid while missing grid values are interpolated with latitudinal and longitudinal dependent regression techniques reflecting the compounds' distributions. With the generated surface concentration climatologies for the ocean and atmosphere, global sea-to-air concentration gradients and sea-to-air fluxes are calculated. Based on these calculations we estimate a total global flux of 1.5/2.5 Gmol Br yr(-1) for CHBr3, 0.78/0.98 Gmol Br yr(-1) for CH2Br2 and 1.24/1.45 Gmol Br yr(-1) for CH3I (robust fit/ordinary least squares regression techniques). Contrary to recent studies, negative fluxes occur in each sea-to-air flux climatology, mainly in the Arctic and Antarctic regions. "Hot spots" for global polybromomethane emissions are located in the equatorial region, whereas methyl iodide emissions are enhanced in the subtropical gyre regions. Inter-annual and seasonal variation is contained within our flux calculations for all three compounds. Compared to earlier studies, our global fluxes are at the lower end of estimates, especially for bromoform. An under-representation of coastal emissions and of extreme events in our estimate might explain the mismatch between our bottom-up emission estimate and top-down approaches.

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Extreme climatic events, including heat waves (HWs) and severe storms, influence the structure of marine and terrestrial ecosystems. Despite growing consensus that anthropogenic climate change will increase the frequency, duration and magnitude of extreme events, current understanding of their impact on communities and ecosystems is limited. Here, we used sessile invertebrates on settlement panels as model assemblages to examine the influence of HW magnitude, duration and timing on marine biodiversity patterns. Settlement panels were deployed in a marina in southwest UK for ≥5 weeks, to allow sufficient time for colonisation and development of sessile fauna, before being subjected to simulated HWs in a mesocosm facility. Replicate panel assemblages were held at ambient sea temperature (∼17 °C), or +3 °C or +5 °C for a period of 1 or 2 weeks, before being returned to the marina for a recovery phase of 2–3 weeks. The 10-week experiment was repeated 3 times, staggered throughout summer, to examine the influence of HW timing on community impacts. Contrary to our expectations, the warming events had no clear, consistent impacts on the abundance of species or the structure of sessile assemblages. With the exception of 1 high-magnitude long-duration HW event, warming did not alter not assemblage structure, favour non-native species, nor lead to changes in richness, abundance or biomass of sessile faunal assemblages. The observed lack of effect may have been caused by a combination of (1) the use of relatively low magnitude, realistic heat wave treatments compared to previous studies (2), the greater resilience of mature adult sessile fauna compared to recruits and juveniles, and (3) the high thermal tolerance of the model organisms (i.e., temperate fouling species, principally bryozoans and ascidians). Our study demonstrates the importance of using realistic treatments when manipulating climate change variables, and also suggests that biogeographical context may influence community-level responses to short-term warming events, which are predicted to increase in severity in the future.

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The atmosphere and ocean are two components of the Earth system that are essential for life, yet humankind is altering both. Contemporary climate change is now a well-identified problem: anthropogenic causes, disturbance in extreme events patterns, gradual environmental changes, widespread impacts on life and natural resources, and multiple threats to human societies all around the world. But part of the problem remains largely unknown outside the scientific community: significant changes are also occurring in the ocean, threatening life and its sustainability on Earth. This Policy Brief explains the significance of these changes in the ocean. It is based on a scientific paper recently published in Science (Gattuso et al., 2015), which synthesizes recent and future changes to the ocean and its ecosystems, as well as to the goods and services they provide to humans. Two contrasting CO2 emission scenarios are considered: the high emissions scenario (also known as “business-as-usual” and as the Representative Concentration Pathway 8.5, RCP8.5) and a stringent emissions scenario (RCP2.6) consistent with the Copenhagen Accord1 of keeping mean global temperature increase below 2°C in 2100.

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The photometric and spectroscopic properties of 26 well-observed Type Ia Supernovae (SNe Ia) were analyzed with the aim of exploring SN Ia diversity. The sample includes (Branch) normal SNe, as well as extreme events such as SN 1991T and SN 1991bg, while the truly peculiar SNe Ia, SN 2000cx and SN 2002cx, are not included in our sample. A statistical treatment reveals the existence of three different groups. The first group (FAINT) consists of faint SNe Ia similar to SN 1991bg, with low expansion velocities and rapid evolution of Si II velocity. A second group consists of normal SNe Ia, also with high temporal velocity gradient (HVG), but with brighter mean absolute magnitude =-19.3 and higher expansion velocities than the FAINT SNe. The third group includes both normal and SN 1991T-like SNe Ia: these SNe populate a narrow strip in the Si II velocity evolution plot, with a low-velocity gradient (LVG), but have absolute magnitudes similar to HVGs. While the FAINT and HVG SNe Ia together seem to define a relation between R(Si II) and ���m15(B), the LVG SNe either do not conform to that relation or define a new, looser one. The R(Si II) premaximum evolution of HVGs is strikingly different from that of LVGs. We discuss the impact of this evidence on the understanding of SN Ia diversity, in terms of explosion mechanisms, degree of ejecta mixing, and ejecta-circumstellar material interaction.

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‘Risk’ in social work is typically read as risk-of-bads, and specifically extreme bads. This paper develops the implications of the logical objection to attempts to predict low frequency extreme events (such as child homicides). Our argument is that if we focus on these low probability high cost outcomes—these heart wrenching, but unpredictable, tragedies—we take social work away from the good that it can do, leave it open to inappropriate disapprobation, and, in terms of outcomes, do less well by the vulnerable. This point is reinforced by discussion of developments in other academic fields, and by further examination of the logic (and the morality) of protection under uncertainty. We explore the implications for the way social work should be evaluated. A proper academic understanding of risk, and decision making under uncertainty, has, we argue clear practical implications.

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Tese de doutoramento, Ciências Geofísicas e da Geoinformação (Deteção Remota), Universidade de Lisboa, Faculdade de Ciências, 2015

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In the current context of serious climate changes, where the increase of the frequency of some extreme events occurrence can enhance the rate of periods prone to high intensity forest fires, the National Forest Authority often implements, in several Portuguese forest areas, a regular set of measures in order to control the amount of fuel mass availability (PNDFCI, 2008). In the present work we’ll present a preliminary analysis concerning the assessment of the consequences given by the implementation of prescribed fire measures to control the amount of fuel mass in soil recovery, in particular in terms of its water retention capacity, its organic matter content, pH and content of iron. This work is included in a larger study (Meira-Castro, 2009(a); Meira-Castro, 2009(b)). According to the established praxis on the data collection, embodied in multidimensional matrices of n columns (variables in analysis) by p lines (sampled areas at different depths), and also considering the quantitative data nature present in this study, we’ve chosen a methodological approach that considers the multivariate statistical analysis, in particular, the Principal Component Analysis (PCA ) (Góis, 2004). The experiments were carried out in a soil cover over a natural site of Andaluzitic schist, in Gramelas, Caminha, NW Portugal, who was able to maintain itself intact from prescribed burnings from four years and was submit to prescribed fire in March 2008. The soils samples were collected from five different plots at six different time periods. The methodological option that was adopted have allowed us to identify the most relevant relational structures inside the n variables, the p samples and in two sets at the same time (Garcia-Pereira, 1990). Consequently, and in addition to the traditional outputs produced from the PCA, we have analyzed the influence of both sampling depths and geomorphological environments in the behavior of all variables involved.

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Over the past decade, scientists have been called to participate more actively in public education and outreach (E&O). This is particularly true in fields of significant societal impact, such as earthquake science. Local earthquake risk culture plays a role in the way that the public engages in educational efforts. In this article, we describe an adapted E&O program for earthquake science and risk. The program is tailored for a region of slow tectonic deformation, where large earthquakes are extreme events that occur with long return periods. The adapted program has two main goals: (1) to increase the awareness and preparedness of the population to earthquake and related risks (tsunami, liquefaction, fires, etc.), and (2) to increase the quality of earthquake science education, so as to attract talented students to geosciences. Our integrated program relies on activities tuned for different population groups who have different interests and abilities, namely young children, teenagers, young adults, and professionals.

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The momentum and carry anomalies have been extensively documented in the literature. However, there are still many issues relating to the risks associated to them that are left unexplained. One is the fact that an investor holds for too long the most volatile assets, both under momentum and carry strategies. Therefore, they present a level of risk and a probability of extreme events to happen inconsistent. This work project hypothesizes and proves the introduction of risk parity rules on the weights of the portfolios do increase risk rewarding of carry strategies. However, it fails under momentum strategies.

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In this work project we study the tail properties of currency returns and analyze whether changes in the tail indices of these series have occurred over time as a consequence of turbulent periods. Our analysis is based on the methods introduced by Quintos, Fan and Phillips (2001), Candelon and Straetmans (2006, 2013), and their extensions. Specifically, considering a sample of daily data from December 31, 1993 to February 13, 2015 we apply the recursive test in calendar time (forward test) and in reverse calendar time (backward test) and indeed detect falls and rises in the tail indices, signifying increases and decreases in the probability of extreme events.

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Le Sénégal est situé dans une zone soudano-sahélienne particulièrement exposée aux changements du climat, ce dernier rendant l’agriculture, activité principale du pays, précaire. La modification des conditions climatiques, en particulier depuis la fin des années 1960, a fortement affaibli le secteur agricole, majoritairement vivrier et pluvial. Face à l’importance de l’activité agraire vivrière du pays, il apparaît primordial de savoir comment les agriculteurs vivriers du Sénégal ont modifié ou prévoit modifier leurs pratiques en vue de satisfaire leurs besoins alimentaires dans un contexte de changement et de variabilité du climat. Cette étude a été effectuée au sein de la communauté rurale de Sessène selon une approche qualitative et à l’aide d’entretiens, de l’observation participante et d’analyse phénoménologique. Elle a permis de mettre en avant les caractéristiques générales des familles agraires et des exploitations de cette zone, de montrer comment les agriculteurs ont vécu le changement climatique et comment ils envisagent les prochaines années et enfin de discuter de leur capacité d’adaptation. Face au raccourcissement de la saison des pluies, à la diminution des précipitations, à l’intensification des évènements extrêmes et aux impacts de ces modifications sur l’environnement naturel, les agriculteurs vivriers adoptent des mesures aussi bien techniques que socio-économiques.

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It is generally agreed that changing climate variability, and the associated change in climate extremes, may have a greater impact on environmentally vulnerable regions than a changing mean. This research investigates rainfall variability, rainfall extremes, and their associations with atmospheric and oceanic circulations over southern Africa, a region that is considered particularly vulnerable to extreme events because of numerous environmental, social, and economic pressures. Because rainfall variability is a function of scale, high-resolution data are needed to identify extreme events. Thus, this research uses remotely sensed rainfall data and climate model experiments at high spatial and temporal resolution, with the overall aim being to investigate the ways in which sea surface temperature (SST) anomalies influence rainfall extremes over southern Africa. Extreme rainfall identification is achieved by the high-resolution microwave/infrared rainfall algorithm dataset. This comprises satellite-derived daily rainfall from 1993 to 2002 and covers southern Africa at a spatial resolution of 0.1° latitude–longitude. Extremes are extracted and used with reanalysis data to study possible circulation anomalies associated with extreme rainfall. Anomalously cold SSTs in the central South Atlantic and warm SSTs off the coast of southwestern Africa seem to be statistically related to rainfall extremes. Further, through a number of idealized climate model experiments, it would appear that both decreasing SSTs in the central South Atlantic and increasing SSTs off the coast of southwestern Africa lead to a demonstrable increase in daily rainfall and rainfall extremes over southern Africa, via local effects such as increased convection and remote effects such as an adjustment of the Walker-type circulation.