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This work used the measured data during the whole test period to study the law of temperature change in the steel rail beams, and the distribution characteristics of the sunshine temperature field, in straddle-type monorail tourist transportation systems, employing a field-test and a numerical simulation. The curve form of the temperature gradient was determined by a comparative analysis of the existing domestic and foreign norms. Finally, the generalized extreme value distribution model was used to predict the extreme value of the representative value of the temperature difference, and the value of the temperature base, for different return periods, and the complete temperature gradient model were determined. Results: During the whole test period, the maximum vertical positive temperature difference of the steel box girder was 15.21 °C, and the negative temperature difference was −5.07 °C. In addition, the effect of the ambient temperature, considering solar radiation, was found to be an important factor affecting the distribution of the vertical temperature difference. The analysis determined that the positive and negative temperature difference curves in the unpaved steel box girder were multi-segment polylines and linear straight lines, respectively. The extreme value predicts that the representative temperature differences between T1 and T2 of the 450 mm beam during the 50-year return period were 17.2 °C and 4.58 °C, respectively, and T1 and T2 under the 100-year return period were 17.38 °C and 4.62 °C, respectively.

The paper aims at introducing an advanced delivery tour planner to support operators in urban delivery operations through a combined approach which chooses delivery bays and delivery time windows while optimizing the delivery routes. After a literature review on tools for the management and the control of the delivery system implemented for optimizing the usage of on-street delivery bays, a prototypical tour delivery planner is described. The tool allows transport and logistics operators to book the delivery bays and to have real-time suggestions on the delivery tour to follow, through the minimization of the total delivery time. Currently, at development phase, the tool has been tested in a target zone, considering the road network and time/city delivering constraints and real-time data about vehicles location, traffic and delivery bay availability. The tool identifies the possible tours based on the delivery preferences, ranks the possible solutions according to the total route time based on information on the road network (i.e. travel time forecasts), performs a further optimization to reduce the total travel times and presents the user the best alternative along with the indications of which delivery bay to use in each delivery stop. The developed prototype is composed by two main parts: a web application that manages communication between the database and the road network simulation, and, an Android mobile App that supports transport and logistic operators in managing their delivering, pre trip and en route, showing and updating routing based on real-time information.

In this work, we introduce a method of estimating stochastic freeway capacity using elements of both extreme value theory and survival analysis. First, we define capacity data, or estimates of the capacity of the roadway, as the daily maximum flow values. Then, under a survival analysis premise, we introduce censoring into our definition. That is, on days when flows are sufficiently high and congestion occurs, corresponding flow maxima are considered true estimates of capacity; otherwise, for those days that do not observe high flows or congestion, flow maxima are deemed censored observations and capacities must be higher than the observations. By extreme value theory, the collection of flow maxima (block maxima) can be appropriately approximated with a generalized extreme value (GEV) distribution. Because of small sample sizes and the presence of censoring, a Bayesian framework is pursued for model fitting and parameter estimation. To lend credence to our proposed methodology, the procedure is applied to real-world traffic stream data collected by the New Hampshire Department of Transportation (NHDOT) at a busy location on Interstate I-93 near Salem, New Hampshire. Data were collected over a period of 11 months and raw data were aggregated into 15-minute intervals. To assess our procedure, and to provide proof of concept, several validation procedures are presented. First, using distinct training and validation subsets of our data, the procedure yields accurate predictions of highway capacity. Next, our procedure is applied to a training set to yield random capacities which are then used to predict breakdown in the validation set. The frequency of these predicted breakdowns is found to be statistically similar to observed breakdowns observed in our validation set. Lastly, after comparing our methodology to other methods of stochastic capacity estimation, we find our procedure to be highly successful.

The block maxima approach is an important method in univariate extreme value analysis. While assuming that block maxima are independent results in straightforward analysis, the resulting inferences maybe invalid when a series of block maxima exhibits dependence. We propose a model, based on a first‐order Markov assumption, that incorporates dependence between successive block maxima through the use of a bivariate logistic dependence structure while maintaining generalized extreme value (GEV) marginal distributions. Modeling dependence in this manner allows us to better estimate extreme quantiles when block maxima exhibit short‐ranged dependence. We demonstrate via a simulation study that our first‐order Markov GEV model performs well when successive block maxima are dependent, while still being reasonably robust when maxima are independent. We apply our method to two polar annual minimum air temperature data sets that exhibit short‐ranged dependence structures, and find that the proposed model yields modified estimates of high quantiles. [ABSTRACT FROM AUTHOR]

We consider forecasting functional time series of extreme values within a generalized extreme value distribution (GEV). The GEV distribution can be characterized using the three parameters (location, scale, and shape). As a result, the forecasts of the GEV density can be accomplished by forecasting these three latent parameters. Depending on the underlying data structure, some of the three parameters can either be modeled as scalars or functions. We provide two forecasting algorithms to model and forecast these parameters. To assess the forecast uncertainty, we apply a sieve bootstrap method to construct pointwise and simultaneous prediction intervals of the forecasted extreme values. Illustrated by a daily maximum temperature dataset, we demonstrate the advantages of modeling these parameters as functions. Further, the finite-sample performance of our methods is quantified using several Monte Carlo simulated data under a range of scenarios. [ABSTRACT FROM AUTHOR]

Sea level rise can bring disastrous outcomes to people living in coastal regions by increasing flood risk or inducing stronger storm surges. We study long‐term linear trends in monthly maximum sea levels by applying extreme value methods. The monthly maximum sea levels are extracted from multiple tide gauges around the coastal regions of the world over a period of as long as 169 years. Due to instrument changes, location changes, earthquakes, land reclamation, dredging, etc., the sea level data could contain inhomogeneous shifts in their means, which can substantially impact trend estimates if ignored. To rigorously quantify the long‐term linear trends and return levels for the monthly maximum sea level data, we use a genetic algorithm to estimate the number and times of changepoints in the data. As strong periodicity and temporal correlation are pertinent to the data, bootstrap techniques are used to obtain more realistic confidence intervals to the estimated trends and return levels. We find that the consideration of changepoints changed the estimated linear trends of 89 tide gauges (approximately 30% of tide gauges considered) by more than 20cmcentury‐1. Our results are summarized in maps with estimated extreme sea level trends and 50‐year return levels. [ABSTRACT FROM AUTHOR]

The objective of the present study was to examine and predict the annual maximum temperature in the northeast of Thailand by using data from 25 stations and employing spatial extreme modeling which is based on max-stable process (MSP) using schlatter’s method. We analyzed extreme temperature data using the MSP using latitude, longitude, and altitude variables. Our result showed that the maximum temperature has an increasing trend. The return level estimates of the study areas from both the local generalized extreme value (GEV) model and MSP models show that the Nong Khai, Maha Sarakham, and Khon Kaen stations had higher return levels than the other stations for every return period, whereas Pak Chong Agromet had the lowest return levels. Furthermore, the results showed that MSP modeling is more suitable than point-wise GEV distribution. We realize that the spatial extreme modeling based on MSP provides more precise and robust return levels as well as some indices of the maximum temperatures for both the observation stations and the locations with no observed data. The results of this study are consistent with those of some previous studies. The increasing trend in return levels could affect agriculture and the surrounding environment in northeast Thailand. Spatial extreme modeling can be beneficial in the impact management and vulnerability assessment under extreme event scenarios caused by climate change.

This article compares the performances of three fitting methods (SLmom, S1NCM, and S3NCM) to account for temporal characteristics of Annual Maximum Precipitations (AMPs) on daily and sub‐daily time scales using scaling General Extreme Value (GEV) distribution at a local site. Based on simple scaling properties of AMPs, the temporal downscaling model (called Scaling‐GEV) with parameter estimation methods are used to estimate sub‐daily AMPs from observed daily data. The feasibility and accuracy of the suggested method were assessed using rainfall data available from Dorval in Quebec (Canada) and Seoul (South Korea) for the period 1961–1990. Presence of simple scaling properties of AMPs for two stations has shown that it is feasible to use the temporal downscaling method for describing the linkage between AMPs of different time scales. Numerical and graphical analyses revealed that the Scaling‐GEV distribution by the Three‐Non central moments (NCM) method (S3NCM) provides the most accurate estimates compared to observed data amongst three fitting methods. In addition, this study suggested a modified bootstrap technique to determine confidence intervals (CIs) CIs of extreme rainfall series using the simple scaling properties of extreme rainfalls and only daily AMPs. Although the CIs were constructed by only daily AMPs and the simple scaling properties, the observed sub‐daily AMPs are generally within the 95% CI estimated. [ABSTRACT FROM AUTHOR]

This study aims to overcome the problem of dimensionality, accurate estimation, and forecasting Value-at-Risk (VaR) and Expected Shortfall (ES) uncertainty intervals in high frequency data. A Bayesian bootstrapping and backtest density forecasts, which are based on a weighted threshold and quantile of a continuously ranked probability score, are developed. Developed backtesting procedures revealed that an estimated Seasonal autoregressive integrated moving average-generalized autoregressive score-generalized extreme value distribution (SARIMA–GAS–GEVD) with a skewed student-t distribution had the best prediction performance in forecasting and bootstrapping VaR and ES. Extension of this non-stationary distribution in literature is quite complicated since it requires specifications not only on how the usual Bayesian parameters change over time but also those with bulk distribution components. This implies that the combination of a stochastic econometric model with extreme value theory (EVT) procedures provides a robust basis necessary for the statistical backtesting and bootstrapping density predictions for VaR and ES.

People often need precipitation data when building hydraulic engineering, especially the maximum daily precipitation data, so it is very important to study the maximum daily precipitation for hydraulic engineering. The spatiotemporal distribution characteristics of annual and seasonal maximum daily precipitation in the Hengduan Mountains were analyzed based on the maximum daily precipitation data collected at 30 meteorological stations between 1971 and 2013. The following results were obtained. The annual and seasonal maximum daily precipitation gradually increase from north to south; in comparison, the temporal variations in the annual and seasonal maximum daily precipitation are not significant. Except for winter, the standard deviations and coefficient of variation values of maximum daily precipitation in the other time scales are negatively correlated with both elevation and latitude, and most of those correlations are significant. Shape parameters of maximum daily precipitation have no significant correlation with either elevation or latitude, whereas the scale and location parameters have significant correlation with both elevation and latitude at different confidence levels. In addition, except for summer and autumn, there are significant negative correlations between maximum daily precipitation and elevation and latitude at different confidence levels during different return periods in the other time scales. [ABSTRACT FROM AUTHOR]

In order to cope with traffic accidents efficiently, the maximum number of traffic accidents, deaths and serious injuries that can occur during the day should be presented quantitatively. In order to examine the characteristics of traffic accidents in different regions, it was divided into the Seoul metropolitan area, Chungcheong area, Gyeongbuk area, Honam area, and Gyeongnam area and was suitable for the generalized extreme value distribution (GEV). The parameters of the GEV distribution were estimated by the L-moments, and the Anderson-Darling test and the Cramer-von Mises test confirmed the suitability of the distribution. According to the analysis, the maximum number of traffic accidents that can occur once every 50 years is 401 in the Seoul metropolitan area, 168 in the South Gyeongsang region, 455 in the North Gyeongsang region, 136 in the Chungcheong region and 205 in the South Jeolla region. Compared to the Seoul metropolitan area, which has a large population and car registration, the number of traffic accidents is relatively high due to the large area, mountainous areas, and logistics movement caused by the industrial complex. [ABSTRACT FROM AUTHOR]

A major winter storm brought up to 42 inches of snow in parts of the Mid-Atlantic and Northeast states for January 22–24, 2016. The blizzard of January 2016 impacted about 102.8 million people, claiming at least 55 lives and $500 million to $3 billion in economic losses. This article studies two important aspects of extreme snowfall events: 1. trends in annual maxima and threshold exceedances and 2. return levels for extreme snowfall. Applying extreme value methods to the extreme snow data in the New York City area, we quantify linear trends in extreme snowfall and assess how severe the 2016 blizzard is in terms of return levels. To find a more realistic standard error for the extreme value methods, we extend Smith's method to adapt to both spatial and temporal correlations in the snow data. Our results show increasing, but insignificant trends in the annual maximum snowfall series. However, we find that the 87.5th percentile snowfall has significantly increased by 0.564 inches per decade, suggesting that, while the maximum snowfall is not significantly increasing, there have been increases in the snowfall among the larger storms. We also find that the 2016 blizzard is indeed an extreme snow event equivalent to about a 40-year return level in the New York City area. The extreme value methods used in this study are thoroughly illustrated for general readers. Data and modularized programming codes are to be available online to aid practitioners in using extreme value methods in applications. [ABSTRACT FROM AUTHOR]

We analyze annual extremes of daily maximum and minimum surface air temperature and of daily rainfall in East Asia and the Korean peninsula. This study made intensive use of the simulation data available from the CMIP5 (Coupled Model Intercomparison Project Phase 5) multimodels in historical and future experiments up to the year 2100, employing three different radiative forcings: RCP2.6, RCP4.5, and RCP8.5 (representative concentration pathways). Several reanalysis datasets are used to compare and evaluate the simulated climate extremes in the late twentieth century. We estimate the future changes in precipitation and temperature extremes in East Asia and Korea, and compare them to the global result, for the reference period 1986–2005. The rising rate of future cold extremes over East Asia and Korea is faster than that of warm extremes. This phenomenon appears more distinctly in Korea as a local scale, indicating more sensitivity of the Korean peninsula to global warming. The increase of the 20-year return level of maximum precipitation in the CMIP5 over East Asia by the end of twenty-first century is about 7% in the RCP2.6, 15% in the RCP4.5, and 35% in the RCP8.5 experiments, which exceed the corresponding global values. We also estimate the changes in precipitation extremes across East Asia as a function of the annual mean temperature variation at the same location. The CMIP5 sensitivity in maximum precipitation across East Asia is 5.5%/∘C, which is lower than the global figure (5.8%/∘C). The sensitivity for the Korean peninsula is 7.38%/∘C, indicating the strong impact of global warming to Korea. The results will be important in mitigating the detrimental effects of variations of climatic extremes and in improving the regional strategy for water resource and eco-environmental management, particularly for such areas in East Asia under significant changes in temperature and rainfall extremes. [ABSTRACT FROM AUTHOR]

We investigate the challenges to be faced by quality practitioners to implement a control chart for individuals aimed at monitoring the stability of maximum/minimum realizations of profile quality features. We consider the situation where only a limited amount of inspections can be scheduled, which is typical of the most recent manufacturing applications for the production of small lots of customized products or processes characterized by frequent changeovers. A quantitative study on simulated profiles described by functional data modelled as random Gaussian fields is provided. Finally, we discuss how the proposed control chart can be implemented in a canning process. [ABSTRACT FROM AUTHOR]

• A new change-point model for the r -largest order statistics is proposed. • The proposed model can be used for any application that involves an extreme time series with a structural break. • A Bayesian adaptation for the estimation of the optimal number of order statistics is proposed. • Simulations and real data sets shows the good performance of this new model. • The application in three examples verifies the performance of the new model is better than that the usual model. This study makes a new contribution to extreme value theory by proposing a change-point model of the distribution of the r -larger order statistics. In some situations, using only the maxima of grouped data results in a small sample size that may require a larger dataset. In this sense, using the joint distribution of the r -largest order statistics provides more information and, consequently, better estimators. We perform a comprehensive simulation to show the advantage of this method over other competitive models that approach the change-point model in extremes. Finally, the proposed model is fitted to river quota data (environmental data) and NASDAQ daily returns data (financial data) to demonstrate its potential for practical application. [ABSTRACT FROM AUTHOR]

Copyright of Radio Electronics, Computer Science, Control is the property of Zaporizhzhia National Technical University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The extreme value theory (EVT) is used to assess the risk of extreme events caused by natural calamities or untoward circumstances in the social and economic sectors. The theory can be used to study the frequency of rare events and to build up a predictive model so that one can attempt to forecast the frequency of such future extreme events such as a financial collapse and the amount of damage from such a collapse. Even though many statistical techniques have been used to analyze the manner in which the Federal Reserve determines the level of the Federal Fund Rates, no known study has used EVT to analyze and predict the extreme fund rates. In this study, the US Federal Funds Rate, one of the most publicized and important economic indicators in the financial world, from 1954–2019 has been analyzed. The contributions of this study are: (1) to provide an appropriate model for the normalized Federal Funds Rate data; (2) to compare several estimation techniques in estimating parameters for two possible models; (3) to predict the maximum economic return rate from a Federal Funds Rate in the future by using the concept of the return period; and (4) to investigate the bias of estimated parameters applying a simulation study. Simulated data and real financial data are used for the study, and the outcome satisfies the efficiency of its application. [ABSTRACT FROM AUTHOR]

Confidence evaluation algorithms for the analysis of binary phase-shift keying (BPSK) signals are discussed. The phase of the product of the observed signal and constructed reference signal is extracted, and its spectrum is utilized to verify confidence. Two extreme statistics of the phase spectrum are developed for the confidence evaluation, which depend on goodness-of-fit tests based on the generalized extreme value distribution and generalized Pareto distribution respectively. Via extensive simulations, we verify the effectiveness of the proposed algorithms. [ABSTRACT FROM AUTHOR]

Maximum analysis consists of modeling the maximums of a data set by considering a specific distribution. Extreme value theory (EVT) shows that, for a sufficiently large block size, the maxima distribution is approximated by the generalized extreme value (GEV) distribution. Under EVT, it is important to observe the high quantiles of the distribution. In this sense, quantile regression techniques fit the data analysis of maxima by using the GEV distribution. In this context, this work presents the quantile regression extension for the GEV distribution. In addition, a time‐varying quantile regression model is presented, and the important properties of this approach are displayed. The parameter estimation of these new models is carried out under the Bayesian paradigm. The results of the temperature data and river quota application show the advantage of using this model, which allows us to estimate directly the quantiles as a function of the covariates. This shows which of them influences the occurrence of extreme temperature and the magnitude of this influence. [ABSTRACT FROM AUTHOR]

Hurricane Harvey brought extreme levels of rainfall to the Houston, Texas, area over a 7‐day period in August 2017, resulting in catastrophic flooding that caused loss of human life and damage to personal property and public infrastructure. In the wake of this event, there has been interest in understanding the degree to which this event was unusual and estimating the probability of experiencing a similar event in other locations. Additionally, researchers have aimed to better understand the ways in which the sea surface temperature (SST) in the Gulf of Mexico (GoM) is associated with precipitation extremes in this region. This work addresses all of these issues through the development of a multivariate spatial extreme value model. Our analysis indicates that warmer GoM SSTs are associated with higher precipitation extremes in the western Gulf Coast region during hurricane season and that the precipitation totals observed during Hurricane Harvey are less unusual based on the warm GoM SST in 2017. As SSTs in the GoM are expected to steadily increase over the remainder of this century, this analysis suggests that western Gulf Coast locations may experience more severe precipitation extremes during hurricane season. [ABSTRACT FROM AUTHOR]

The annual maximum flood records of the Danjiangkou reservoir displayed significant decreasing trends. The upper stream of the reservoir was characterized by minor changes in urbanization in recent decades, while several large reservoirs were built since 1975. To better explain the changes in floods, this paper used the nonstationary generalized extreme value (GEV) distribution by considering the impacts of climate change and human activity. The GEV parameters were expressed as functions of time, climate indices, and climate-reservoir index (CRI). By using CRI as the covariate, the performance of the nonstationary GEV distribution was much better than others. This paper also analyzed the risk of floods using design life level and annual average reliability methods. The reliability obtained from these methods can describe the effects of external indices on floods. Finally, the uncertainty analysis indicated that a nonstationary model might not be practical, and the large confidence intervals of the design flood implied that the results were meaningless. It is essential to build a deterministic relationship between parameters and covariates, and we should be prudent when using general circulation models outputs in extreme statistics. [ABSTRACT FROM AUTHOR]

Estimating the probability of the occurrence of hazardous winds is crucial for their impact in human activities; however, this is inherently affected by the shortage of observations. This becomes critical in poorly sampled regions, such as the northwestern Sahara, where this work is focused. The selection of any single methodological variant contributes with additional uncertainty. To gain robustness in the estimates, we expand the uncertainty space by applying a large body of methodologies. The methodological uncertainty is constrained afterward by keeping only the reliable experiments. In doing so, we considerably narrow the uncertainty associated with the wind return levels. The analysis suggest that not necessarily all methodologies are equally robust. The highest 10-min speed (wind gust) for a return period of 50 years is about 45 ms−1 (56 ms−1). The intensity of the expected extreme winds is closely related to orography. The study is based on wind and wind gust observations that were collected and quality controlled for the specific purposes herein. We also make use of a 12-year high-resolution regional simulation to provide simulation-based wind return level maps that endorse the observation-based results. Such an exhaustive methodological sensitivity analysis with a long high-resolution simulation over this region was lacking in the literature.

Thermal effect is an essential factor in the durability and safety of concrete bridges. Therefore, this paper mainly studied the concrete bridge box girder temperature distribution and thermal effect under solar radiation and the thermal load. With a concrete rigid frame bridge as the engineering background, the temperature distribution of the box girder on a clear summer day was observed. Then, according to the solar physics and heat transfer theory, the different surfaces of the box girder cross-section are classified based on the heat transfer conditions, and the variation of solar radiation on different surfaces is investigated. The temperature field of the box girder is simulated by ANSYS. To obtain the extreme thermal condition, the meteorological data of the bridge site from 1990 to 2020 are collected. The data are fitted by generalized extreme value distribution to obtain the extreme temperature and average wind factors in the bridge design lifetime. Combined with the solar radiation, temperature, and wind factors, the extreme thermal condition of the concrete box girder is obtained. Lastly, the thermal effect of the box girder under the extreme condition is analyzed, and the thermal stress is compared with the allowable stress in the design code. The results show that the girder temperature difference is closely related to the solar radiation intensity and heat transfer conditions, and the solar radiation intensity is the more critical factor. The tensile stress caused by the extreme thermal load is more significant than the design strength value in the girder cross-section. The results also provide a method to obtain the extreme thermal condition and evaluate the impact of the thermal effect on concrete box girder bridges.

A Bayesian hierarchical framework with a Gaussian copula and a generalized extreme value (GEV) marginal distribution is proposed for the description of spatial dependencies in data. This spatial copula model was applied to extreme summer temperatures over the Extremadura Region, in the southwest of Spain, during the period 1980–2015, and compared with the spatial noncopula model. The Bayesian hierarchical model was implemented with a Monte Carlo Markov Chain (MCMC) method that allows the distribution of the model’s parameters to be estimated. The results show the GEV distribution’s shape parameter to take constant negative values, the location parameter to be altitude dependent, and the scale parameter values to be concentrated around the same value throughout the region. Further, the spatial copula model chosen presents lower deviance information criterion (DIC) values when spatial distributions are assumed for the GEV distribution’s location and scale parameters than when the scale parameter is taken to be constant over the region.

Daily global solar radiation (DGSR) is a renewable energy source that cannot be depleted. Many researchers have used the sample method to collect the dataset of DGSR to obtain the statistical characteristics of the population. However, since this is difficult, descriptive statistics and statistical inference became the primary objective of obtaining the characteristics of the statistical population. In this study, a statistical model combined with generalized extreme value (GEV) distribution is proposed to represent the dataset of DGSR. The moments method, the Kolmogorov-Smirnov test, and the properties of GEV were performed to estimate the parameters of GEV and check the validity of the estimated parameters with the actual dataset of DGSR. Nonlinear regression and multiple nonlinear regression of GEV with its corresponding days during a year were produced. Eventually, a flowchart was designed to obtain the close probability distribution of the DGSR. [ABSTRACT FROM AUTHOR]

Uncertainty in return level estimates for rare events, like the intensity of large rainfall events, makes it difficult to develop strategies to mitigate related hazards, like flooding. Latent spatial extremes models reduce the uncertainty by exploiting spatial dependence in statistical characteristics of extreme events to borrow strength across locations. However, these estimates can have poor properties due to model misspecification: Many latent spatial extremes models do not account for extremal dependence, which is spatial dependence in the extreme events themselves. We improve estimates from latent spatial extremes models that make conditional independence assumptions by proposing a weighted likelihood that uses the extremal coefficient to incorporate information about extremal dependence during estimation. This approach differs from, and is simpler than, directly modeling the spatial extremal dependence; for example, by fitting a max-stable process, which is challenging to fit to real, large datasets. We adopt a hierarchical Bayesian framework for inference, use simulation to show the weighted model provides improved estimates of high quantiles, and apply our model to improve return level estimates for Colorado rainfall events with 1% annual exceedance probability. Supplementary materials accompanying this paper appear online. [ABSTRACT FROM AUTHOR]

Incorrect estimation of extreme values of daily precipitation can have severe consequences in hydrological and engineering applications. Recent advances in the study of extreme precipitation have shown that the Metastatistical Extreme Value Distribution (MEV) is superior to the Generalized Extreme Value Distribution (GEV) whenever the length of the available record is small compared to the average recurrence time. This paper provides a detailed examination of the relative performance of MEV and GEV for both point estimates and spatial modeling. An analysis for a large number of sample years and return periods for daily precipitation in Austria shows that the MEV exceeds the GEV if the number of sample years is smaller, and the estimated return period is larger than 35 years. This advantage disappears almost entirely if the MEV is used for spatially smooth extreme value modeling instead of the GEV. However, the computational effort is drastically reduced in comparison to spatial modeling with the GEV if a simplified version of the MEV is used. Key Points: We compare the performance of the metastatistical and generalized extreme value distributions (MEV, GEV)The MEV uncertainty is significantly increased when used for smooth spatial modeling of extremesThe MEV should be preferred over the GEV for point estimates and smooth modeling [ABSTRACT FROM AUTHOR]

The October 2015 precipitation event in the Southeastern United States brought large amounts of rainfall to South Carolina, with particularly heavy amounts in Charleston and Columbia. The subsequent flooding resulted in numerous casualties and hundreds of millions of dollars in property damage. Precipitation levels were so severe that media outlets and government agencies labeled this storm as a 1 in 1000-year event in parts of the state. Two points of discussion emerged as a result of this event. The first was related to understanding the degree to which this event was anomalous; the second was related to understanding whether precipitation extremes in South Carolina have changed over recent time. In this work, 50 years of daily precipitation data at 28 locations are used to fit a spatiotemporal hierarchical model, with the ultimate goal of addressing these two points of discussion. Bayesian inference is used to estimate return levels and to perform a severity-area-frequency analysis, and it is determined that precipitation levels related to this event were atypical throughout much of the state, but were particularly unusual in the Columbia area. This analysis also finds marginal evidence in favor of the claim that precipitation extremes in the Carolinas have become more intense over the last 50 years. [ABSTRACT FROM AUTHOR]

We present an approach to estimate intensity-duration-frequency (IDF) curves based on max-stable processes. The proposed method has been inspired by the seminal study of Nadarajah et al. (J R Stat Soc B 60(2):473-496, 1998), who used a multivariate extreme value distribution (MEVD) to estimate (IDF) curves from rainfall records. Max-stable processes are continuous extensions of MEVD (i.e. the marginal distributions of rainfall maxima at different durations are generalized extreme valued), which are more flexible, allow for extreme rainfall estimation at any arbitrary duration d (i.e. not just a discrete set, as is the case of MEVD), while preserving asymptotic dependencies. The latter characteristic of IDF estimates results from the combined effect of the statistical structure of rainfall (i.e. temporal dependencies), as well as the IDF construction process, which involves averaging of the original series to obtain rainfall maxima at different temporal resolutions. We apply the method to hourly precipitation data, and compare it to empirical estimates and the results produced by a semiparametric approach. Our findings indicate that max-stable processes fit well the statistical structure and inter-dependencies of annual rainfall maxima at different durations, produce slightly more conservative estimates relative to semiparametric methods, while allowing for extrapolations to durations and return periods beyond the range of the available data. The proposed statistical model is fully parametric and likelihood based, providing a theoretically consistent basis in solving the problem at hand. [ABSTRACT FROM AUTHOR]

Spatial climate data are often presented as summaries of areal regions such as grid cells, either because they are the output of numerical climate models or to facilitate comparison with numerical climate model output. Extreme value analysis can benefit greatly from spatial methods that borrow information across regions. For Gaussian outcomes, a host of methods that respect the areal nature of the data are available, including conditional and simultaneous autoregressive models. However, to our knowledge, there is no such method in the spatial extreme value analysis literature. In this article, we propose a new method for areal extremes that accounts for spatial dependence using latent clustering of neighboring regions. We show that the proposed model has desirable asymptotic dependence properties and leads to relatively simple computation. Applying the proposed method to North American climate data reveals several local and continental-scale changes in the distribution of precipitation and temperature extremes over time. Supplementary material for this article is available online. [ABSTRACT FROM AUTHOR]

Copyright of Revista Brasileira de Agricultura Irrigada - RBAI is the property of Revista Brasileira de Agricultura Irrigada - RBAI and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Highlights • Extreme value theory approach is applied to before-after safety analysis. • Conflicts of different severity levels are used to estimate GEV distributions. • Odds ratio is calculated based on estimated crashes and most severe conflicts. • Apparent shape change in GEV and significant reduction in odds ratio are found. Abstract There is growing interest in the use of traffic conflicts in before and after safety evaluations because of well-recognized quality and quantity problems associated with historical crash records. Most of these studies apply statistical techniques to compare the number of conflicts before and after the implementation of safety countermeasures. However, to identify the number of conflicts, a specific threshold for various conflict indicators needs to be used and the results of the evaluation can vary significantly depending on the selection of this threshold. As well, there is an issue with how to account for conflict severity in the evaluation. This study proposes adopting the extreme value theory approach to overcome these two issues. The approach was applied to a case of left-turn bay extension at three signalized intersections, and the automated traffic conflict technique was used to identify conflicts with TTC values from the video data collected from treatment sites and matching control sites. Generalized extreme value (GEV) models with different covariates were developed and compared. The results show that there are apparent shape change in the GEV distribution (i.e., from narrow peak up to high severities to wide spread with fewer conflicts at high severity levels) after the treatment, indicating reduction in conflict severity. The safety improvement is further confirmed by the total reduction of 63.9% in estimated crashes. Moreover, with the aid of GEV model, the most severe conflicts that are also rare and random are included into the OR calculation, and a significant reduction of 73.2% is found in the estimated most severe conflicts. [ABSTRACT FROM AUTHOR]

Highlights • The SWH fields in SCS are simulated by WW-III model from 1976 to 2014. • Extreme wave climate variability is researched by GEV model. • Interannual climatology is analyzed by EOF and spectrum analysis method. Abstract Extreme wave climate variability in the South China Sea (SCS) was investigated using significant wave height (SWH) data simulated by the third generation wave model WAVEWATCH-III (WW-III) for the period 1976–2014. The wind forcing data was using the objective reanalysis wind dataset from the weather research and forecasting (WRF) model. The simulated SWH was well validated by observation data from satellite altimeter and in-situ buoys. A generalized extreme value (GEV) model was applied to analyze the extreme wave climate variability. Monthly significant wave height maxima is used to characterize the seasonal variability. The spatial distributions of positional and scale parameters are achieved. The positional parameter values reach the maximum in winter, while the scale parameter values are greater in summer and autumn due to the impact of typhoon. The regression analysis of harmonic functions was applied to give the annual cycle. Interannual climatology of extreme wave climate from Empirical Orthogonal Function (EOF) decomposition method and spectrum analysis method revealed a dominant 12 months period and a 22 months period, respectively. Results show that El Nino may significantly affect the extreme wave climate variability in the SCS. [ABSTRACT FROM AUTHOR]

Monthly maximum precipitation amounts for the period 1950–2010 were modelled for seven climatological stations in the western Black Sea subregion of Turkey using a distributional and time series analysis approach. First, the generalized extreme value (GEV) distribution was fitted using the location parameter of the GEV distribution as a function of several explanatory variables that affect the maximum precipitation. We quantified the change in extreme precipitation for each location and derived estimates of return levels for monthly precipitation amounts. We also considered a multivariate time series model with exogenous variables using the selected locations for a short‐term forecast of the maximum precipitation amount. It was discovered that the time series model that takes into account the autocorrelation structure of the series performed better than a probabilistic approach using the extreme value theory. The obtained forecast results are promising in terms of accurately defining future precipitation amounts. Maximum precipitation amount is analysed by extreme value theory and multivariate time series. The existence of autocorrelation and spatial dependence of stations are considered by vector ARIMA with exogenous variables model. Results compared with GEV distribution fitting and better results are obtained by VARMAX model. This is one of the prediction and forecast figures that we obtained using VARMAX procedure. [ABSTRACT FROM AUTHOR]

While the influence of the Pacific Decadal Oscillation (PDO) on precipitation has been extensively studied, its influence on precipitation extremes remains not well-known. Based on a series of generalized extreme value modeling experiments, this paper demonstrates some distinct regional patterns of the PDO’s influence on precipitation extremes worldwide. In eastern China, the well-known ‘southern flood (drought) and northern drought (flood)’ pattern in summer corresponds well to the positive (negative) phase of the PDO. In Australia, there tends to be a tripole pattern, with positive correlation between precipitation extremes and the PDO in the central region, and negative correlation in both eastern and western Australia. The precipitation extremes in northwestern Europe and western Russia roughly hold positive correlation with the PDO. These regional patterns of the PDO’s influences are explained via comparative analyses of the atmospheric circulation conditions between cold and warm PDO phases. Certain precipitation extremes tend to be missed or happen more than once during different phases of the PDO at more than 2/3 of stations in a typical region. The cold phase tends to exert more consistent influences than the warm phase in these typical regions. These findings not only indicate different risks of extreme precipitation for the typical regions during different phases of the PDO, but also have important implications for the near-term projection of variable regional climate extremes under global warming.

Scientists occasionally predict projected changes in extreme climate using multi-model ensemble methods that combine predictions from individual simulation models. To predict future changes in precipitation extremes in the Korean peninsula, we examined the observed data and 21 models of the Coupled Model Inter-Comparison Project Phase 6 (CMIP6) over East Asia. We applied generalized extreme value distribution (GEVD) to a series of annual maximum daily precipitation (AMP1) data. Multivariate bias-corrected simulation data under three shared socioeconomic pathway (SSP) scenarios—namely, SSP2-4.5, SSP3-7.0, and SSP5-8.5—were used. We employed a model weighting method that accounts for both performance and independence (PI-weighting). In calculating the PI-weights, two shape parameters should be determined, but usually, a perfect model test method requires a considerable amount of computing time. To address this problem, we suggest simple ways for selecting two shape parameters based on the chi-square statistic and entropy. Variance decomposition was applied to quantify the uncertainty of projecting the future AMP1. Return levels spanning over 20 and 50 years, as well as the return periods relative to the reference years (1973–2010), were estimated for three overlapping periods in the future, namely, period 1 (2021–2050), period 2 (2046–2075), and period 3 (2071–2100). From these analyses, we estimated that the relative increases in the observations for the spatial median 20-year return level will be approximately 18.4% in the SSP2-4.5, 25.9% in the SSP3-7.0, and 41.7% in the SSP5-8.5 scenarios, respectively, by the end of the 21st century. We predict that severe rainfall will be more prominent in the southern and central parts of the Korean peninsula.

In the absence of wave measuring buoys operating over extended periods, wave hindcast data or satellite observations are indispensable for estimating global extreme wave heights. However, the results may depend on the analysed wind wave sources and the period's length. The sensitivity of the estimated extreme significant wave heights (SWH) to the analysed data sources and periods is investigated in this study. Global extreme wave heights are estimated using ECMWF Reanalysis v5 data (ERA5), global wave hindcast developed based on Simulating WAves Nearshore forced by the Japanese 55-year Reanalysis (SWAN-JRA55), satellite altimeter observations, and long-term wave buoy measurements. Both Annual Maximum fitting to the Generalized Extreme Value Distribution (AM-GEV) and Peaks Over Threshold fitted to the Generalized Pareto Distribution (POT-GPD) models are used. The results show that the global extreme SWH estimates considerably depend on the analysed data sources. The relative differences observed between the analysed data sources are >20% in large parts of the world. Thus, the relative differences in extreme SWH are mainly lower by increasing the analysed data periods. However, they can reach 30% and are more critical using AM-GEV. Besides, by comparing the extreme values from reanalysis and hindcast wave data to those from long-term wave measurements, underestimations of up to 2 m are observed for a return period of 100 years in the North-West Atlantic and North-East Pacific. • Extreme waves estimates are highly dependent on wave sources in the global ocean. • Changes extreme wave estimates are up to ±30% among different analysed periods. • Satellite observations are more appropriate for an EVA using AM-GEV. • POT-GEV is more recommended for EVA in tropical cyclone areas. • The accuracy of wave storm peaks should be considered for extreme wave estimates. [ABSTRACT FROM AUTHOR]

• Definition of PIGI and SPIGI via enhanced envelope impulses extraction. • Introduction of GEVD-based dynamic alarm thresholds by fitting available PIGIs. • Proposal of a novel framework based on PIGI, SPIGI, and dynamic alarm thresholds. • Validation of the proposed framework by simulations and experimental applications. Rolling bearing dynamic fault alarm and identification is essential in condition-based maintenance and can prevent serious accidents. However, an integrated framework with dynamic adaptability and full interpretability is rarely reported for this issue based on sparsity measures and statistical properties. Therefore, a framework via impulses-oriented Gini index and extreme value distribution is developed in this paper. First, for attenuating normal-phase amplitude oscillations of the Gini index, periodical impulses-oriented Gini index (PIGI) and sparsity-smoothed periodical impulses-oriented Gini index (SPIGI) are defined successively through enhanced envelope impulses extraction to improve incipient fault sensitivity and robustness against interferences. Next, a dynamic alarm threshold setting strategy is built by fitting the available PIGIs using generalized extreme value distribution (GEVD). Further, a novel framework, without prior knowledge and complex signal processing algorithms, is established based on PIGI, SPIGI, and GEVD-based thresholds for dynamic fault alarm and identification. The verification results on simulation and experimental data indicate that the proposed framework is effective in balancing false alarms and missed alarms and detecting incipient faults, and thus would have favorable application prospects. [ABSTRACT FROM AUTHOR]