138 resultados para Extreme values


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The Cévennes–Vivarais Mediterranean Hydrometeorological Observatory (OHM-CV) is a research initiative aimed at improving the understanding and modeling of the Mediterranean intense rain events that frequently result in devastating flash floods in southern France. A primary objective is to bring together the skills of meteorologists and hydrologists, modelers and instrumentalists, researchers and practitioners, to cope with these rather unpredictable events. In line with previously published flash-flood monographs, the present paper aims at documenting the 8–9 September 2002 catastrophic event, which resulted in 24 casualties and an economic damage evaluated at 1.2 billion euros (i.e., about 1 billion U.S. dollars) in the Gard region, France. A description of the synoptic meteorological situation is first given and shows that no particular precursor indicated the imminence of such an extreme event. Then, radar and rain gauge analyses are used to assess the magnitude of the rain event, which was particularly remarkable for its spatial extent with rain amounts greater than 200 mm in 24 h over 5500 km2. The maximum values of 600–700 mm observed locally are among the highest daily records in the region. The preliminary results of the postevent hydrological investigation show that the hydrologic response of the upstream watersheds of the Gard and Vidourle Rivers is consistent with the marked space–time structure of the rain event. It is noteworthy that peak specific discharges were very high over most of the affected areas (5–10 m3 s−1 km−2) and reached locally extraordinary values of more than 20 m3 s−1 km−2. A preliminary analysis indicates contrasting hydrological behaviors that seem to be related to geomorphological factors, notably the influence of karst in part of the region. An overview of the ongoing meteorological and hydrological research projects devoted to this case study within the OHM-CV is finally presented.

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Leading patterns of observed monthly extreme rainfall variability in Australia are examined using an Empirical Orthogonal Teleconnection (EOT) method. Extreme rainfall variability is more closely related to mean rainfall variability during austral summer than in winter. The leading EOT patterns of extreme rainfall explain less variance in Australia-wide extreme rainfall than is the case for mean rainfall EOTs. We illustrate that, as with mean rainfall, the El Niño-Southern Oscillation (ENSO) has the strongest association with warm-season extreme rainfall variability, while in the cool-season the primary drivers are atmospheric blocking and the subtropical ridge. The Indian Ocean Dipole and Southern Annular Mode also have significant relationships with patterns of variability during austral winter and spring. Leading patterns of summer extreme rainfall variability have predictability several months ahead from Pacific sea surface temperatures (SSTs) and as much as a year in advance from Indian Ocean SSTs. Predictability from the Pacific is greater for wetter than average summer months than for months that are drier than average, whereas for the Indian Ocean the relationship has greater linearity. Several cool-season EOTs are associated with mid-latitude synoptic-scale patterns along the south and east coasts. These patterns have common atmospheric signatures denoting moist onshore flow and strong cyclonic anomalies often to the north of a blocking anti-cyclone. Tropical cyclone activity is observed to have significant relationships with some warm season EOTs. This analysis shows that extreme rainfall variability in Australia can be related to remote drivers and local synoptic-scale patterns throughout the year.

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Dynamical downscaling is frequently used to investigate the dynamical variables of extra-tropical cyclones, for example, precipitation, using very high-resolution models nested within coarser resolution models to understand the processes that lead to intense precipitation. It is also used in climate change studies, using long timeseries to investigate trends in precipitation, or to look at the small-scale dynamical processes for specific case studies. This study investigates some of the problems associated with dynamical downscaling and looks at the optimum configuration to obtain the distribution and intensity of a precipitation field to match observations. This study uses the Met Office Unified Model run in limited area mode with grid spacings of 12, 4 and 1.5 km, driven by boundary conditions provided by the ECMWF Operational Analysis to produce high-resolution simulations for the Summer of 2007 UK flooding events. The numerical weather prediction model is initiated at varying times before the peak precipitation is observed to test the importance of the initialisation and boundary conditions, and how long the simulation can be run for. The results are compared to raingauge data as verification and show that the model intensities are most similar to observations when the model is initialised 12 hours before the peak precipitation is observed. It was also shown that using non-gridded datasets makes verification more difficult, with the density of observations also affecting the intensities observed. It is concluded that the simulations are able to produce realistic precipitation intensities when driven by the coarser resolution data.

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Purpose – The aim of this paper is to investigate how values from within Abrahamic religions could be adopted to improve liberal market economies’ (LMEs’) corporate governance business practices. Design/methodology/approach – The concept of spiritual capitalism is explained from an Islamic perspective by adopting three universal Abrahamic values to critically analyse LMEs and offer an ethical alternative to current capitalism concerns. Findings – It is found that LMEs can be improved by considering all stakeholders, putting ethics before economics, and introducing shared risk/reward plus lower debt. Originality/value – The paper compares LMEs/Co-ordinated market economies (CMEs)/Islamic countries economies (ICEs) within an ethical framework for LMEs.

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The ability of the HiGEM climate model to represent high-impact, regional, precipitation events is investigated in two ways. The first focusses on a case study of extreme regional accumulation of precipitation during the passage of a summer extra-tropical cyclone across southern England on 20 July 2007 that resulted in a national flooding emergency. The climate model is compared with a global Numerical Weather Prediction (NWP) model and higher resolution, nested limited area models. While the climate model does not simulate the timing and location of the cyclone and associated precipitation as accurately as the NWP simulations, the total accumulated precipitation in all models is similar to the rain gauge estimate across England and Wales. The regional accumulation over the event is insensitive to horizontal resolution for grid spacings ranging from 90km to 4km. Secondly, the free-running climate model reproduces the statistical distribution of daily precipitation accumulations observed in the England-Wales precipitation record. The model distribution diverges increasingly from the record for longer accumulation periods with a consistent under-representation of more intense multi-day accumulations. This may indicate a lack of low-frequency variability associated with weather regime persistence. Despite this, the overall seasonal and annual precipitation totals from the model are still comparable to those from ERA-Interim.

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Extreme temperature during reproductive development affects rice (Oryza sativa L.) yield and seed quality. A controlled-environment reciprocal-transfer experiment was designed where plants from two japonica cultivars were grown at 28/24 ⁰C and moved to 18/14 ⁰C and vice versa, or from 28/24 to 38/34 ⁰C and vice versa, for 7-d periods to determine the respective temporal pattern of sensitivity of spikelet fertility, yield, and seed viability to each temperature extreme. Spikelet fertility and seed yield per panicle were severely reduced by extreme temperature in the 14 d period prior to anthesis; and both cultivars were affected at 38/34 ⁰C while only cv. Gleva was affected at 18/14 ºC. The damage was greater the earlier the panicles were stressed within this period. Later-exserted panicles compensated only partly for yield loss. Seed viability was significantly reduced by 7-d exposure to 38/34 ⁰C or 18/14 ⁰C at 1 to 7 and 1 to 14 d after anthesis, respectively, in cv. Gleva. Cultivar Taipei 309 was not affected by 7 d exposure at 18/14 ⁰C; and no consistent temporal pattern of sensitivity was evident at 38/34 ⁰C. Hence, brief exposure to low or high temperature was most damaging to spikelet fertility and yield 14 to 7 d before anthesis, coinciding with microsporogenesis; and it was almost as damaging around anthesis. Seed viability was most vulnerable to low or high temperature in the 7 or 14 d after anthesis, when histodifferentiation occurs.

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Background: Symbiotic relationships have contributed to major evolutionary innovations, the maintenance of fundamental ecosystem functions, and the generation and maintenance of biodiversity. However, the exact nature of host/symbiont associations, which has important consequences for their dynamics, is often poorly known due to limited understanding of symbiont taxonomy and species diversity. Among classical symbioses, figs and their pollinating wasps constitute a highly diverse keystone resource in tropical forest and savannah environments. Historically, they were considered to exemplify extreme reciprocal partner specificity (one-to-one host-symbiont species relationships), but recent work has revealed several more complex cases. However, there is a striking lack of studies with the specific aims of assessing symbiont diversity and how this varies across the geographic range of the host. Results: Here, we use molecular methods to investigate cryptic diversity in the pollinating wasps of a widespread Australian fig species. Standard barcoding genes and methods were not conclusive, but incorporation of phylogenetic analyses and a recently developed nuclear barcoding gene (ITS2), gave strong support for five pollinator species. Each pollinator species was most common in a different geographic region, emphasising the importance of wide geographic sampling to uncover diversity, and the scope for divergence in coevolutionary trajectories across the host plant range. In addition, most regions had multiple coexisting pollinators, raising the question of how they coexist in apparently similar or identical resource niches. Conclusion: Our study offers a striking example of extreme deviation from reciprocal partner specificity over the full geographical range of a fig-wasp system. It also suggests that superficially identical species may be able to co-exist in a mutualistic setting albeit at different frequencies in relation to their fig host’s range. We show that comprehensive sampling and molecular taxonomic techniques may be required to uncover the true structure of cryptic biodiversity underpinning intimate ecological interactions.

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A new generation of reanalysis products is currently being produced that provides global gridded atmospheric data spanning more than a century. Such data may be useful for characterising the observed long-term variability of extreme precipitation events, particularly in regions where spatial coverage of surface observations is limited, and in the pre-satellite era. An analysis of extreme precipitation events is performed over England and Wales, investigating the ability of Twentieth Century Reanalysis and ERA-Interim to represent extreme precipitation accumulations as recorded in the England and Wales Precipitation dataset on accumulation time-scales from 1 to 7 days. Significant correlations are found between daily precipitation accumulation observations and both reanalysis products. A hit-rate analysis indicates that the reanalyses have hit rates (as defined by an event above the 98th percentile) of approximately 40–65% for extreme events in both summer (JJA) and winter (DJF). This suggests that both ERA-Interim and Twentieth Century Reanalysis are difficult to use for representing individual extreme precipitation events over England and Wales.

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Recent research into flood modelling has primarily concentrated on the simulation of inundation flow without considering the influences of channel morphology. River channels are often represented by a simplified geometry that is implicitly assumed to remain unchanged during flood simulations. However, field evidence demonstrates that significant morphological changes can occur during floods to mobilise the boundary sediments. Despite this, the effect of channel morphology on model results has been largely unexplored. To address this issue, the impact of channel cross-section geometry and channel long-profile variability on flood dynamics is examined using an ensemble of a 1D-2D hydraulic model (LISFLOOD-FP) of the 1:2102 year recurrence interval floods in Cockermouth, UK, within an uncertainty framework. A series of hypothetical scenarios of channel morphology were constructed based on a simple velocity based model of critical entrainment. A Monte-Carlo simulation framework was used to quantify the effects of channel morphology together with variations in the channel and floodplain roughness coefficients, grain size characteristics, and critical shear stress on measures of flood inundation. The results showed that the bed elevation modifications generated by the simplistic equations reflected a good approximation of the observed patterns of spatial erosion despite its overestimation of erosion depths. The effect of uncertainty on channel long-profile variability only affected the local flood dynamics and did not significantly affect the friction sensitivity and flood inundation mapping. The results imply that hydraulic models generally do not need to account for within event morphodynamic changes of the type and magnitude modelled, as these have a negligible impact that is smaller than other uncertainties, e.g. boundary conditions. Instead morphodynamic change needs to happen over a series of events to become large enough to change the hydrodynamics of floods in supply limited gravel-bed rivers like the one used in this research.

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This paper presents a novel approach to the automatic classification of very large data sets composed of terahertz pulse transient signals, highlighting their potential use in biochemical, biomedical, pharmaceutical and security applications. Two different types of THz spectra are considered in the classification process. Firstly a binary classification study of poly-A and poly-C ribonucleic acid samples is performed. This is then contrasted with a difficult multi-class classification problem of spectra from six different powder samples that although have fairly indistinguishable features in the optical spectrum, they also possess a few discernable spectral features in the terahertz part of the spectrum. Classification is performed using a complex-valued extreme learning machine algorithm that takes into account features in both the amplitude as well as the phase of the recorded spectra. Classification speed and accuracy are contrasted with that achieved using a support vector machine classifier. The study systematically compares the classifier performance achieved after adopting different Gaussian kernels when separating amplitude and phase signatures. The two signatures are presented as feature vectors for both training and testing purposes. The study confirms the utility of complex-valued extreme learning machine algorithms for classification of the very large data sets generated with current terahertz imaging spectrometers. The classifier can take into consideration heterogeneous layers within an object as would be required within a tomographic setting and is sufficiently robust to detect patterns hidden inside noisy terahertz data sets. The proposed study opens up the opportunity for the establishment of complex-valued extreme learning machine algorithms as new chemometric tools that will assist the wider proliferation of terahertz sensing technology for chemical sensing, quality control, security screening and clinic diagnosis. Furthermore, the proposed algorithm should also be very useful in other applications requiring the classification of very large datasets.

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Abstract The concept of values “fit” has been a significant theme in the management literature for many years. It is argued that where there is alignment of staff and organizational values a range of positive outcomes are encountered. What is unclear is how this translates into the charity sector. This study explores the phenomenon of values alignment in two UK charities. Questionnaires were used to measure staff values, perceptions of organization values and staff commitment. Drawing on the work of Finegan (2000), an interaction term is used as a proxy for fit. Analyses of data from 286 participants indicated that it was the perceptions of organization values that had the greatest impact on staff commitment. The alignment of staff values and perceptions of organization values only had a degree of effect within one of the charities. This challenges the dominant view on such alignment and the implications of this are discussed. Keywords staff, values fit, commitment, organizational identification

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The XWS (eXtreme WindStorms) catalogue consists of storm tracks and model-generated maximum 3 s wind-gust footprints for 50 of the most extreme winter windstorms to hit Europe in the period 1979–2012. The catalogue is intended to be a valuable resource for both academia and industries such as (re)insurance, for example allowing users to characterise extreme European storms, and validate climate and catastrophe models. Several storm severity indices were investigated to find which could best represent a list of known high-loss (severe) storms. The best-performing index was Sft, which is a combination of storm area calculated from the storm footprint and maximum 925 hPa wind speed from the storm track. All the listed severe storms are included in the catalogue, and the remaining ones were selected using Sft. A comparison of the model footprint to station observations revealed that storms were generally well represented, although for some storms the highest gusts were underestimated. Possible reasons for this underestimation include the model failing to simulate strong enough pressure gradients and not representing convective gusts. A new recalibration method was developed to estimate the true distribution of gusts at each grid point and correct for this underestimation. The recalibration model allows for storm-to-storm variation which is essential given that different storms have different degrees of model bias. The catalogue is available at www.europeanwindstorms.org.

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This study examines the atmospheric circulation patterns and surface features associated with the seven coldest winters in the U.K. since 1870, using the 20th Century Reanalysis. Six of these winters are outside the scope of previous reanalysis datasets; we examine them here for the first time. All winters show a marked lack of the climatological southwesterly flow over the UK, displaying easterly and northeasterly anomalies. Six of the seven winters (all except 1890) were associated with a negative phase of the North Atlantic Oscillation; 1890 was characterised by a blocking anticyclone over and northeast of the UK.

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At the most recent session of the Conference of the Parties (COP19) in Warsaw (November 2013) the Warsaw international mechanism for loss and damage associated with climate change impacts was established under the United Nations Framework Convention on Climate Change (UNFCCC). The mechanism aims at promoting the implementation of approaches to address loss and damage associated with the adverse effects of climate change. Specifically, it aims to enhance understanding of risk management approaches to address loss and damage. Understanding risks associated with impacts due to highly predictable (slow onset) events like sea-level rise is relatively straightforward whereas assessing the effects of climate change on extreme weather events and their impacts is much more difficult. However, extreme weather events are a significant cause of loss of life and livelihoods, particularly in vulnerable countries and communities in Africa. The emerging science of probabilistic event attribution is relevant as it provides scientific evidence on the contribution of anthropogenic climate change to changes in risk of extreme events. It thus provides the opportunity to explore scientifically-backed assessments of the human influence on such events. However, different ways of framing attribution questions can lead to very different assessments of change in risk. Here we explain the methods of, and implications of different approaches to attributing extreme weather events with a focus on Africa. Crucially, it demonstrates that defining the most appropriate attribution question to ask is not a science decision but needs to be made in dialogue with those stakeholders who will use the answers.

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The susceptibility of a catchment to flooding is affected by its soil moisture prior to an extreme rainfall event. While soil moisture is routinely observed by satellite instruments, results from previous work on the assimilation of remotely sensed soil moisture into hydrologic models have been mixed. This may have been due in part to the low spatial resolution of the observations used. In this study, the remote sensing aspects of a project attempting to improve flow predictions from a distributed hydrologic model by assimilating soil moisture measurements are described. Advanced Synthetic Aperture Radar (ASAR) Wide Swath data were used to measure soil moisture as, unlike low resolution microwave data, they have sufficient resolution to allow soil moisture variations due to local topography to be detected, which may help to take into account the spatial heterogeneity of hydrological processes. Surface soil moisture content (SSMC) was measured over the catchments of the Severn and Avon rivers in the South West UK. To reduce the influence of vegetation, measurements were made only over homogeneous pixels of improved grassland determined from a land cover map. Radar backscatter was corrected for terrain variations and normalized to a common incidence angle. SSMC was calculated using change detection. To search for evidence of a topographic signal, the mean SSMC from improved grassland pixels on low slopes near rivers was compared to that on higher slopes. When the mean SSMC on low slopes was 30–90%, the higher slopes were slightly drier than the low slopes. The effect was reversed for lower SSMC values. It was also more pronounced during a drying event. These findings contribute to the scant information in the literature on the use of high resolution SAR soil moisture measurement to improve hydrologic models.