Occurrence Of Extreme Values Research Articles

Variabilidad, tendencia y eventos extremos en los rendimientos agrícolas a nivel de partidos en la provincia de Buenos Aires

Climate variability is the main determinant of fluctuations in the productive and economic results of agriculture. Extreme events are expected to occur with greater frequency and intensity in the future. In this scenario, the effects of climate variability on agricultural production are of special interest. This study analyzes the time series of yields at the county level of the main crops in the province of Buenos Aires, in the period 2000/01-2020/21. The trend and occurrence of extreme values ””of wheat, corn and soybean yields are identified. The frequencies of extreme values ””are related to the phases of the ENSO -El Niño-Southern Oscillation phenomenon, for each crop year. Yields show significant positive trends in 78%, 46% and 30% of the counties for wheat, corn and soybean, respectively. There is a significant relationship between the frequencies of extreme yield values ””and the ENSO phases, this relationship being more important in summer crops. In particular, there is a relative frequency of extremely low or very low yields of 38 and 41%, in second consecutive La Niña crop years, for corn and soybean, respectively. While the frequencies of extremely low or very low yields in neutral or El Niño crop years are between 0% - 3%. Regarding the economic value of production, the differences between obtained vs. expected values, accumulated in the period, are positive values of +3285 and +872 million USD in “El Niño” years for the north and south regions, respectively, and negative values of -3387 and -388 million USD, in “La Niña” years for both regions, respectively. The results provide evidence on the potential value of ENSO-based seasonal forecasts for agriculture. However, it is necessary to deepen the analysis of the effects of ENSO and other seasonal phenomena on yields. It is also necessary more information about the attitudes of the farmers in the Pampas and the different management practices that can be adjusted based on these forecasts.

Análise espacial da mortalidade por acidentes de motocicleta nos municípios do Brasil

Analysis of the distribution of motorcycle-related mortality rates in Brazilian municipalities is fundamental to understand and seek to minimize the occurrence of this growing phenomenon. The main objective of this work is to analyze the spatial distribution of motorcycle rider mortality rates in Brazil, based on more robust and reliable estimates. An attempt was also made to identify the presence of spatial clusters in the distribution of such mortality rates in given municipalities. The rates were estimated based on the average number of motorcyclist deaths recorded in the years 2014, 2015 and 2016. These rates were then directly standardized and graduated based on the local empirical Bayesian estimator. A Local Indicator of Spatial Autocorrelation (LISA) indicated the presence of spatial patterns. The Northeast and Mid-West regions concentrated most of the municipalities with high mortality rates as well and most of the clusters of municipalities with a high-high distribution pattern. Graduated Bayesian estimation was effective to deal with the occurrence of extreme values, thereby improving the reliability of the estimates and enhancing the visualization of the rates on the map.

Stationary and non‐stationary detection of extreme precipitation events and trends of average precipitation from 1980 to 2010 in the Paraná River basin, Brazil

AbstractThe main objective of this study was to investigate the trends on average and extreme events in time series of daily precipitation from 1980 to 2010 in the Paraná River basin, Brazil. The nonparametric Mann–Kendall test was applied to detect monotonic trend in the precipitation series. The occurrence of extreme values was analysed based on three generalized extreme values (GEV) models: Model 1 (stationary), Model 2 (non‐stationary for location parameter), and Model 3 (non‐stationary for location and scale parameters). The GEV parameters were estimated by the Generalized Maximum Likelihood method (GMLE) and for the non‐stationary models, the parameters were estimated as linear functions of time. To choose the most suitable model, the maximum likelihood ratio test (D) was used. From the results observed at the monthly scale, it was possible to infer that the months with the highest probability of an extreme weather event occurrence are February (climates Aw and Cfa), July (Cfa and Cfb), and October (Aw, Cfa, and Cfb). Approximately 90% of the 1,112 stations presented no trend regarding the GEV parameters. The non‐stationarity showed by other stations (Models 2 and 3) might be associated with several factors, such as the alteration of land use due to the north expansion of the agricultural border of the Paraná River basin.

Part I: Temporal and spatial distribution of multiclass pesticide residues in lake waters of Northern Greece: application of an optimized SPE-UPLC-MS/MS pretreatment and analytical method

The present work describes the application of an analytical procedure, utilizing ultra performance liquid chromatography (UPLC) coupled with mass spectrometry instrumentation, for the determination of 253 multiclass pesticides, classified in six different groups. Solid phase extraction was applied for the isolation and pre-concentration of target compounds in water samples. Surface waters of the lakes located in Northern Greece (Volvi, Doirani, and Kerkini), were collected in two time periods (fall/winter 2010 and spring/summer 2011) and analyzed, applying the developed analytical methods. Spatial distribution of detected pesticides was visualized using interpolation methods and geographical information systems (GIS). Pesticides with maximum concentrations were amitrole, propoxur, simazine, chlorpyrifos, carbendazim, triazophos, disulfoton-sulfone, pyridaben, sebuthylazine, terbuthylazine, atrazine, atrazine-desethyl, bensulfuron-methyl, metobromuron, metribuzin, rotenone, pyriproxyfen, and rimsulfuron. In Lake Kerkini, mainly carbamates and triazines were determined at elevated concentrations, near the coastal point of the NW side of the lake. Seasonal variations were strong among the applied pesticide classes and determined concentrations, indicating the contribution of pesticide application patterns and rainfall. Lake Doirani exhibited organophosphate pesticides at higher concentrations mainly at coastal points, while triazines emerged as the main pollutant during spring sampling. Lake Volvi exhibited the highest pesticide concentrations, mostly triazines and ureas at the central part of the lake. The occurrence of extreme values and nonconstant seasonal variations indicated that the concentrations were increased disproportionately during the second sampling, as a result of the varying contribution of pollution sources right after the application period. In all cases, the total concentration of pesticides increased during the second sampling period.

The Exact Distributions of FIS under Partial Asexuality in Small Finite Populations with Mutation

Reproductive systems like partial asexuality participate to shape the evolution of genetic diversity within populations, which is often quantified by the inbreeding coefficient F IS. Understanding how those mating systems impact the possible distributions of F IS values in theoretical populations helps to unravel forces shaping the evolution of real populations. We proposed a population genetics model based on genotypic states in a finite population with mutation. For populations with less than 400 individuals, we assessed the impact of the rates of asexuality on the full exact distributions of F IS, the probabilities of positive and negative F IS, the probabilities of fixation and the probabilities to observe changes in the sign of F IS over one generation. After an infinite number of generations, we distinguished three main patterns of effects of the rates of asexuality on genetic diversity that also varied according to the interactions of mutation and genetic drift. Even rare asexual events in mainly sexual populations impacted the balance between negative and positive F IS and the occurrence of extreme values. It also drastically modified the probability to change the sign of F IS value at one locus over one generation. When mutation prevailed over genetic drift, increasing rates of asexuality continuously increased the variance of F IS that reached its highest value in fully asexual populations. In consequence, even ancient asexual populations showed the entire F IS spectrum, including strong positive F IS. The prevalence of heterozygous loci only occurred in full asexual populations when genetic drift dominated.

Black swans in the brazilian stock market

This study analyzes extreme values in the daily returns of 45 Brazilian stocks between 2 January 1995 and 18 March 2009. The incidence of observations outside the range of three standard deviationsfrom the mean is at least five times greater than under the normal distribution. The occurrence of extreme values in the upper tail is 1.13 times higher than in the lower. The average of the extreme positive returns is higher than that of extreme negative returns. Half percent of the days determined the outcome of the investment. Extreme values are at least ± 7%. Investors should assess whether they will keep their holdings when returns of such magnitude occur. The characteristics of empirical distributions of stock returns favor the passive investor and the use of weight constraints in portfolio allocation models.

A Nonlinear Threshold Model for the Dependence of Extremes of Stationary Sequences

We propose a TAR(3,1)-GARCH(1,1) model able to describe two different types of extreme events: a first type generated by large uncertainty regimes and a second type where extremes come from isolated dread/joy events. The novelty of this model resides on the definition of the regimes, motivated by the occurrence of extreme values, and of the threshold variable, defined by the shock affecting the process one period lagged. The model is able to uncover dependence and clustering of extremes in high and low volatility periods. A Wald type test to detect nonlinearities on the conditional mean process defined by an unobservable threshold variable is introduced. In the empirical application, we find evidence of predictability for extreme returns on SPDR S&P500 fund during the recent crisis period, July 2008 to March 2011. This finding seems to support the presence of some persistence and mean reversion in the dynamics of returns after the occurrence of extreme shocks.

Smoothing Sample Extremes with Dynamic Models

The study of extreme values is of crucial interest in many contexts. The concentration of pollutants, the sea-level and the closing prices of stock indexes are only a few examples in which the occurrence of extreme values may lead to important consequences. In the present paper we are interested in detecting trend in sample extremes. A common statistical approach used to identify trend in extremes is based on the generalized extreme value distribution, which constitutes a building block for parametric models. However, semiparametric procedures imply several advantages when exploring data and checking the model. This paper outlines a semiparametric approach for smoothing sample extremes, based on nonlinear dynamic modelling of the generalized extreme value distribution. The relative merits of this approach are illustrated through two real examples.

The effect of ENSO on the intraseasonal variance of surface temperatures in winter

The effect of the El Niño–Southern Oscillation (ENSO) on the probability distribution of daily surface air temperature over the Pacific–North American sector is investigated using the US National Centers for Environmental Prediction (NCEP) Reanalysis data for 1959–1998. The El Niño response is characterized by reduced intraseasonal variance over most of the US, western Canada and the Gulf of Alaska. Conversely, there is an increase of variance during La Niña over the US and the west coast, northward to Alaska. The sign of this response is consistent for most individual El Niño/La Niña years in regions with a strong signal. The response is also robust with respect to differing definitions of ENSO or choice of dataset. Finally, a similar response is evident in station data for an earlier period. The change of variance is associated both with altered skewness, and a change in high and low extremes. Extremes of both signs are reduced during El Niño, and are slightly increased during La Niña. These results are consistent with other studies, suggesting an increased incidence of blocking along the west coast of North America during El Niño, leading to less storm activity and less incursions of warm and cold air over the eastern US. While an understanding of the changed variance is important in itself, it also has implications for changes in exceedence statistics (e.g. heating degree days) and the occurrence of extreme values. Copyright © 2000 Royal Meteorological Society