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Exceedance analysis aims to model extreme data that are in the tail of the distribution, with these values being greater than a threshold. In this type of modelling, the tail can usually have different types of shapes. For exceedance, the extreme value theory shows that generalized Pareto distribution (GPD) is the limit distribution of sufficiently large thresholds. The studies on extreme analysis primarily focus on estimating the probability of the occurrence of extreme events, which are the values that are in the highest quantiles of the distribution. Based on this, this work proposed a quantile regression model for the GPD distribution, where the high quantiles of the distribution are written as a function of covariates. The proposed model also allows the regression parameters to vary over time. The parameter estimation of these new models is conducted under the Bayesian paradigm. Simulations are conducted to analyze the performance of our proposed models. Results of temperature applications in the USA and Brazil (environmental data) and consumer price index in Brazil (financial data) have efficiently explained how covariates influence the quantile behaviour over time. [ABSTRACT FROM AUTHOR]

Knowing the temporal and spatial variability of the probability of rainfall occurrence is indispensable for the planning and management of agricultural and agroindustrial activities. This study aims to evaluate the applicability of four cumulative distribution functions (CDFs; exponential, two‐parameter exponential, mixed exponential, and gamma) to estimate the probability of daily rainfall in the Tocantins–Araguaia hydrographic region (TAHR). The CDFs were applied to 196 rainfall gauge stations distributed in three homogeneous regions (HRs) of rainfall in the TAHR. In relation to the CDFs, the Kolmogorov–Smirnov test and the probability–probability plot showed that the mixed exponential best adhered to the observed data for most of the months of the year in the three HRs that were analyzed. In the few months that this function did not have a good statistical performance, the gamma function was the one with the best fit quality. This study can be used as a guide for studies in other river basins in Brazil and around the world to estimate CDFs of daily rainfall. Recommendations for Resource Managers Stochastic models using cumulative distribution functions (CDFs) allow the simulation of rainfall data by means of their frequency of occurrence. Some CDFs have been used to verify the behavior and variability of precipitation over the years. For example, the mixed exponential, the Weibull with two parameters, the generalized Pareto, the lognormal, and the gamma with two parameters.Daily rainfall modeling is important to recognize the occurrence patterns for the prediction of the climatic behavior of a region. Sectors such as agriculture, flood control, and human supply projects need knowledge of precipitation for planning, management, and operation.The knowledge of the rainfall behavior is very important in Tocantins–Araguaia hydrographic region because it is located in the arc of deforestation in the Amazon. In this case, the dry season and temperatures have been increasing due to global climate change and deforestation itself. [ABSTRACT FROM AUTHOR]

The northeast of Brazil is characterized by a semiarid climate, in which there is a considerable temporal and spatial variability in the distribution of rainfall. In this region, the occurrence of intense rainfall events (IREs) causes severe damage to the population, given that the rampant urbanization and land use processes make cities more susceptible to harmful consequences. Thus, the objective of this study is to estimate the period and the level of return of IREs in the capital cities of the northeast Brazil. For that end, observed data from weather stations of the National Institute of Meteorology for the period from 1988 to 2017 were used. We defined thresholds based on the excesses above the mean and inserted the data in the generalized Pareto distribution in order to obtain diagnostics of future estimates. Results showed that return periods were surpassed in seven of the nine capital cities in 2018. Furthermore, the estimates for IREs with a return period of 5, 10, and 100 years were higher if compared to the return period of 1 year. The results found in this study are extremely relevant for the understanding of the occurrence of these events and also serve as a tool for decision‐making and the elaboration of policies aimed at minimizing the impacts of such events. [ABSTRACT FROM AUTHOR]

Extreme rainfall is one of the most severe weather hazards affecting all the globe, especially thetropical region. Some studies use statistical approaches for interpreting extreme events bychoosing a constant threshold based on different empirical rainfall distributions. The main goal ofthis work is to fit different Extreme value distributions (EVDs) like the Generalized Extreme Value(GEV) and Generalized Pareto Distribution (GPD) for extreme rainfall in different time scales tounderstand their behaviour. The objectives of this study are: (i) ​ analyse the extreme precipitationdistribution at 1 degree daily (1DD) using rain gauges data and different well-known satellite-basedretrievals like TAPEER (Tropical Amount of ​ Precipitation with an Estimate of ERrors) over Brazil;(ii) characterize the Mesoscale Convective Systems (MCS) life cycle properties like duration andminimum temperature (among others) associated with extreme events using TOOCAN (TrackingOf Organized Convection through a 3 dimensional segmentatioN) tool. [ABSTRACT FROM AUTHOR]