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Heavy metals can play an important biological role as micronutrients but also as potentially toxic elements (PTEs). Understanding the natural concentrations of PTEs-Pb and Zn included-in soils allows for the identification and monitoring of contaminated areas and their role in environmental risk assessment. In this study, we aim to determine semi-total or natural and available concentrations of Pb and Zn in topsoils (0–20 cm depth) from 337 samples under native vegetation in the State of Minas Gerais, Brazil. Additionally, we sought to interpret the spatial geochemical variability using geostatistical techniques and quality reference values for these elements in soils were established. The semi-total concentrations were determined by flame and graphite furnace atomic absorption after microwave-assisted nitric acid digestion method. The available concentrations were extracted using the Mehlich-I extractor and determined by atomic absorption spectrometer. Spatial variability was modeled using semivariance estimators: Matheron's classic, Cressie and Hawkins' robust, and Cressie median estimators, the last two being less sensitive to extreme values. This allowed the construction of digital maps through kriging of semi-total Pb and Zn contents using the median estimator, as well as other soil properties by the robust estimator. The dominance of acidic pH and low CEC values reflects highly weathered low-fertility soils. Semi-total Pb contents ranged from 2.1 to 278 mg kg−1 (median: 9.35 mg kg−1) whereas semi-total Zn contents ranged from 2.7 to 495 mg kg−1 (median: 7.7 mg kg−1). The available Pb contents ranged from 0.1 to 6.92 mg kg−1 (median: 0.54 mg kg−1) whereas available Zn contents ranged from 0.1 to 78.2 mg kg−1 (median: 0.32 mg kg−1). The highest Pb and Zn concentrations were observed near Januária, in the northern part of the territory, probably on limestone rocks from the Bambuí group. Finally, the QRVs for Pb and Zn in natural soils were lower than their background values from other Brazilian region and below the prevention values suggested by Brazilian environmental regulations. [ABSTRACT FROM AUTHOR]

Dengue fever, a prevalent and rapidly spreading arboviral disease, poses substantial public health and economic challenges in tropical and sub-tropical regions worldwide. Predicting infectious disease outbreaks on a countrywide scale is complex due to spatiotemporal variations in dengue incidence across administrative areas. To address this, we propose a machine learning ensemble model for forecasting the dengue incidence rate (DIR) in Brazil, with a focus on the population under 19 years old. The model integrates spatial and temporal information, providing one-month-ahead DIR estimates at the state level. Comparative analyses with a dummy model and ablation studies demonstrate the ensemble model's qualitative and quantitative efficacy across the 27 Brazilian Federal Units. Furthermore, we showcase the transferability of this approach to Peru, another Latin American country with differing epidemiological characteristics. This timely forecast system can aid local governments in implementing targeted control measures. The study advances climate services for health by identifying factors triggering dengue outbreaks in Brazil and Peru, emphasizing collaborative efforts with intergovernmental organizations and public health institutions. The innovation lies not only in the algorithms themselves but in their application to a domain marked by data scarcity and operational scalability challenges. We bridge the gap by integrating well-curated ground data with advanced analytical methods, addressing a significant deficiency in current practices. The successful transfer of the model to Peru and its consistent performance during the 2019 outbreak in Brazil showcase its scalability and practical application. While acknowledging limitations in handling extreme values, especially in regions with low DIR, our approach excels where accurate predictions are critical. The study not only contributes to advancing DIR forecasting but also represents a paradigm shift in integrating advanced analytics into public health operational frameworks. This work, driven by a collaborative spirit involving intergovernmental organizations and public health institutions, sets a precedent for interdisciplinary collaboration in addressing global health challenges. It not only enhances our understanding of factors triggering dengue outbreaks but also serves as a template for the effective implementation of advanced analytical methods in public health. [ABSTRACT FROM AUTHOR]

Governmental research agencies from Germany, Italy, Spain, and UK have suggested the use of specific two- and three-parameter probability density functions (PDFs) for estimating the magnitude and frequency of annual maximum streamflow (AMS). In Brazil, there are no guidelines concerning the use of multiparameter PDFs to model AMS, with most applications relying on two-parameter distributions. Considering the worldwide promising results when using multiparameter PDFs, here we focused on the evaluation of ten PDFs to model AMS over all gauged streams of Brazil. The methodology developed for this study consisted of the: (i) acquisition of streamflow data; (ii) organization of the AMS series; (iii) screening of AMS series considering temporal and statistical criteria; (iv) fit of the following PDFs to the AMS series based on the L-moments method: Gumbel, Gamma, Generalized Logistic, Generalized Normal, Generalized Pareto, three-parameter Log-Normal, Pearson type 3, Generalized Extreme Value, Kappa, and Wakeby; (v) quantile estimation; and (vi) PDF performance assessment according to the Filliben test and the relative absolute error (RAE). Based on the almost 4 thousand AMS series considered on this study, we concluded that: (i) Gumbel and Gamma provided poor performance (more than 17% of non-satisfactory fits); (ii) the multiparameter PDFs (Wakeby and Kappa) outperformed all other PDFs; (iii) Gumbel and Generalized Extreme Value had the highest RAE values for quantile estimate; and (iv) this study contributes to the scientific advances reported in the recent statistical hydrology literature and can provide local decision makers with the necessary technical information for developing national design flood guidelines.. [ABSTRACT FROM AUTHOR]

The El Niño episode between September 2015 and February 2016 caused abundant rainfall in the state of Paraná in southern Brazil. In this study, we map the greatest magnitude of this climatic event and the respective geosystem responses in the landscape. For this purpose, 32 precipitation series were analyzed (1981–2016). The parameter most indicative of extreme rainfall events during the El Niño period was the six-month accumulated precipitation. The return period (RP) of this parameter was calculated and spatialized using kriging. The results show that a longitudinal strip of 450 km by 140 km had rainfall with RP > 30 years, reaching maximums greater than 150 years. Surveys performed in the field, with aerial images, official sources and media news show a great diversity of effects caused by rainfall, such as road blockages, interruption of water and power supply, and erosion in urban and rural areas. It is concluded that the stability of regional geosystems was disrupted because of extreme rainfall values, so that the northwest region of Paraná had less resilience compared to the north-central and Pioneer North regions, due to the differences between the lithologies and soils of these regions. [ABSTRACT FROM AUTHOR]

Previous studies depicted that the global gridded hydroclimatic products mostly lack precision and are inconsistent throughout the real-world water cycle. The current study evaluates the efficiency of the eight streamflow datasets (FLDAS, GLDAS 2.1, GLDAS 2.0, ERA5, TERRA, ERA5_Land, MERRA2, and GRUN) in two large-scale watersheds with different climate conditions (Karkhe River basin in Iran and Rio Itapecuru River basin in Brazil). To correct the products via different statistical metrics, two tuning procedures (including scale factors and the network-based Muskingum method) have been used. Based on the results showing the paramount impacts on the correction procedures of the products, GRUN, ERA_land, and GLDAS 2 perform the best accuracy in the Karkhe River basin; however, the worst product in the watershed is GLDAS 2.1. In the Rio Itapecuru River basin, MERRA2 and TERRA have the best and worst performance, respectively. In the first watershed, the KGE and TaylorS in GLDAS 2.1 improved by 0.85 and 0.23 in the case of correction using scale factor, and these statistics also significantly increased using coupled scale factor and Muskingum routing methods by 0.84 and 0.21, respectively. In the second watershed, these statistics increased by 9.16 and 0.56 in the worst case using scale factors; these metrics also levelled up by 9.32 and 0.62 via coupled scale factor and Muskingum routing methods. In addition, it appeared that the corrected products could better simulate the streamflow in terms of oscillation time series and the extreme temporal values of the watersheds. [ABSTRACT FROM AUTHOR]

The probable rainfall is an excellent climatic parameter of information since it consists of the highest/lowest expected rainfall for a particular period of the year considering a certain level of probability. This study aims to evaluate traditional PDF performance using different goodness-of-fit tests to establish a criterion for choosing them on a monthly rainfall scale, in Mato Grosso do Sul, Brazil. The Cramer-von Misses (CVM) and Anderson–Darling (AD) tests were the most rigorous in rejecting the hypothesis of PFD suitable for monthly rainfall data, while the Kolmogorov–Smirnov (KS) was the least rigorous. The application of stricter goodness-of-fit tests as the CVM implies the use of up to 60% fewer series compared to the KS test. However, suitable series by the KS test presented erroneous estimates of probable monthly rainfall. The CVM and AD tests indicate the PDF with the best statistical performance (higher precision and accuracy between the observed and estimated frequency by the PDF) in more than 60% of situations. The most suitable PDFs for total monthly rainfall by the goodness-of-fit tests was gamma (12 months of the year). The exponential and Generalized Extreme Values (GEV) can be used for both the dry and rainy periods, respectively. The parameters of PDF are correlated with geographical variables, describing the total monthly rainfall distribution such as, for example, the influence of the South Atlantic Subtropical Anticyclone, the South Atlantic Convergence Zone in the rainy period, and the orographic effect in the dry period. [ABSTRACT FROM AUTHOR]

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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]

This paper aims to find probabilities of extreme values of the air temperature for the Cerrado, Pantanal and Atlantic Forest biomes in Mato Grosso do Sul in Brazil. In this case a maximum likelihood estimation was employed for the probability distributions fitting the extreme monthly air temperatures for 2007–2018. Using the Extreme Value Theory approach this work estimates three probability distributions: the Generalized Distribution of Extreme Values (GEV), the Gumbel (GUM) and the Log-Normal (LN). The Kolmogorov–Smirnov test, the corrected Akaike criterion AICc, the Bayesian information criterion BIC, the root of the mean square error RMSE and the determination coefficient R2 were applied to measure the goodness-of-fit. The estimated distributions were used to calculate the probabilities of occurrence of maximum monthly air temperatures over 28–32 °C. Temperature predictions were done for the 2-, 5-, 10-, 30-, 50- and 100-year return periods. The GEV and GUM distributions are recommended to be used in the warmer months. In the coldest months, the LN distribution gave a better fit to a series of extreme air temperatures. Deforestation, combustion and extensive fires, and the related aerosol emissions contribute, alongside climate change, to the generation of extreme air temperatures in the studied biomes. [ABSTRACT FROM AUTHOR]

The number of natural disasters triggered by extreme events is increasing worldwide and significantly impacts modern society. Extreme rainfall is one of the most important factors contributing to these events. A better understanding of the physical process that causes extreme rainfall can allow rapid responses from decision-makers to lessen the impact of natural disasters on the local population. Satellite monitoring is widely used for this purpose and is essential for regions where terrestrial observations are limited or non-existent. The primary purpose of this study is to describe the performance of satellite products for extreme rainfall events that caused natural disasters in various climate regimes in Brazil and discuss the contribution of mesoscale convective systems (MCS) to these events. We defined regions based on the climatological rainfall distribution. Cases with rain values above the 99th percentile during 2012–2016 were considered statistically extreme. Our analysis is based on three datasets, with precipitation from (i) rain gauge stations, (ii) different satellite-based estimates, and (iii) mesoscale convective tracking data. The methodology was based on identifying extreme rainfall events, analyzing the performance of satellite precipitation estimates and, finally, quantifying the influence of convective systems on extreme rain. Although all regions of Brazil may be affected by natural disasters caused by extreme rains, the results suggest that the impacts caused in each region are different in magnitude. Convective systems explained over 90% of extreme rains in the case analyzed in Brazil's south and about 60% to 90% of extreme rains in the case analyzed in the Northeast. In general, satellite products have identified rain events; however, in the southern region of Brazil, products have tended to overestimate rainfall, while other regions have tended to underestimate extreme rain values. The methods used in satellite precipitation estimation products have limitations to accurately identifying specific extreme rain events. [ABSTRACT FROM AUTHOR]

Copyright of Journal Public Administration & Social Management / Administração Pública e Gestão Social is the property of Administracao Publica e Gestao Social 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.)

This study provides the first estimates of the frequency distribution of observational and future maximum daily precipitation considering nonstationarity for the entire Brazilian territory. We assess observational data from 1980 to 2015 and projected data from 2020 to 2099 for two climate change scenarios and four downscaled climate models. We modelled extreme precipitation according to the extreme values theory and calculated the precipitation intensity associated with the return periods of 5, 10, 25, 50, and 100 years in nonstationary conditions. The results indicate that nonstationarity is identified in 17.5% of the study area during 1980–2015. The analysis of future climate projections indicated an increase in the return levels of extreme precipitation compared to the historical period in at least 90% of the national territory. It shows that engineering design must urgently consider the nonstationarity of extreme precipitation under the risk of increasingly unsafe infrastructure. [ABSTRACT FROM AUTHOR]

The popular approach to select a suitable distribution to characterize extreme rainfall events relies on the assessment of its descriptive performance. This study examines an alternative approach to this task that evaluates, in addition to the descriptive performance of the models, their performance in estimating out-of-sample events (predictive performance). With a numerical experiment and a study case in São Paulo state, Brazil, we evaluated the adequacy of seven probability distributions widely used in hydrological analysis to characterize extreme events in the region and compared the selection process of both popular and altenative frameworks. The results indicate that (1) the popular approach is not capable of selecting distributions with good predictive performance and (2) combining different predictive and descriptive tests can improve the reliability of extreme event prediction. The proposed framework allowed the assessment of model suitability from a regional perspective, identifying the Generalized Extreme Value (GEV) distribution as the most adequate to characterize extreme rainfall events in the region. [ABSTRACT FROM AUTHOR]

• Out-of-sample prediction of extreme precipitation using nonstationary models. • Significant trends in data of extreme precipitation are insufficient to warrant the selection of a nonstationary model. • Large prediction uncertainty reduces the support for nonstationary models of extreme precipitation. • (Non)stationary model selection should explicitly account for model complexity. Knowledge of the recurrence intervals of precipitation extremes is vital for infrastructure design, risk assessment, and insurance planning. However, trends and shifts in rainfall patterns globally pose challenges to the application of extreme value analysis (EVA) which relies critically on the assumption of stationarity. In this paper, we explore: (1) the suitability of nonstationary (NS) models in the presence of statistically significant trends, and (2) their potential in modeling out-of-sample data to improve frequency analysis of extreme precipitation. We analyze the benefits of using a nonstationary Generalized Extreme Value (GEV) model for annual extreme precipitation records from Southern Brazil. The location of the GEV distribution is allowed to change with time. The unknown GEV model parameters are estimated using Bayesian techniques coupled with Markov chain Monte Carlo simulation. Next, we use GAME sampling to compute the evidence (and their ratios, the so-called Bayes factors) for stationary and nonstationary models of annual maximum precipitation. Our results show that the presence of a statistically significant trend in annual maximum precipitation alone does not justify the use of a NS model. The location parameter of the GEV distribution must also be well defined, otherwise, stationary models of annual maximum precipitation receive more support by the data. These findings reiterate the importance of accounting for GEV model parameters and predictive uncertainty in frequency analysis and hypothesis testing of annual maximum precipitation data records. Furthermore, a meaningful EVA demands detailed knowledge about the origin and persistence of observed changes. [ABSTRACT FROM AUTHOR]

In extreme values, the block maxima procedure can provide a few observations to perform the inference using the generalized extreme values (GEV) distribution. In this case, an interesting alternative is considered to use the r-largest order statistics ( G E V r ). This study aims in obtaining the posterior distribution for the G E V r based on the Bayesian estimation using the Markov chain Monte Carlo method and the Metropolis–Hastings algorithm procedure. The novelty of this study is the introduction of a dynamic linear seasonal model to capture the parameters of the G E V r distribution over time. Also, we propose an adapted dynamic criterion for choosing the optimal value of r for each time point. The proposed model exhibits good performance in the simulations. Moreover, the application of the proposed model is performed on the dataset of daily maxima temperature ( ∘ C ) in Teresina, Piaui, Brazil. [ABSTRACT FROM AUTHOR]

It is known that the mooring system response of floating production units subjected to environmental loads is nonlinear. Even though wave elevations can be assumed as Gaussian processes for short-term periods, corresponding line tension responses generally are not, due to second-order slow-drift floater motions and intrinsic nonlinearities of the system. In this work, short-term extreme responses are estimated based on two different approaches. In the first one, a number of probability distributions are fitted to the tension time histories' peaks samples and classic order statistics is applied to determine the most probable extreme line tension corresponding to a short-time period (3-h) in order to identify the one with best performance. The effect of correlation between consecutive peaks in the extremes estimation is investigated through the one-step Markov chain condition by using a Nataf transformation-based model. In the second approach, a more robust and recently developed method named average conditional exceedance rate (ACER) is investigated, where dependencies between maxima can be easily taken into account. Additionally, effects of major parameters in dynamic analyses, such as simulation length and discretization level of the wave spectrum, are evaluated. All time-series-based extreme estimates are compared with the estimates directly obtained from a sample of epochal maxima (Gumbel method). Numerical examples cover two study cases for mooring lines belonging to Floating Production Storage and Offloading (FPSO) units installed offshore Brazil. It is shown that the consideration of dependence between peaks leads to lower extreme estimates and that both approaches return accurate results. [ABSTRACT FROM AUTHOR]

This present study aims to evaluate the stratospheric ozone that was continuously measured during 2016 over Campo Grande, the capital of South Mato Grosso state, Brazil. To determine the best-adjusted distribution describing the ozone (O3) co-generation data in Campo Grande, 15 functions were used while modeling the numerical results. Five sets of data were used: the entire year, spring (September to December), summer (December to March, high solar radiation data), autumn (March to June), and winter (June to September, low solar radiation data) to study the seasonal variation in the statistical behavior of the probability distribution functions. The distribution performances are evaluated using three tests of quality, namely Kolmogorov–Smirnov (K-S), Anderson–Darling (A-D), and Chi-square tests. Finally, all the results of the fitted quality tests have been compared. It has been observed that the generalized extreme value distribution provides a good fit all along the year, while for specific seasons the best distributions vary. The best distributions, according to the seasons, are Gamma 3P for the winter, lognormal 3P for spring, Weibull for summer and Gamma 3P for autumn, respectively. There was a coincidence in the probability distribution function adjustment in winter and autumn, period with lower O3 concentrations, kurtosis, and skewness. In the summer and spring, it was observed higher concentrations of O3, kurtosis, and asymmetry and different probability distribution functions. [ABSTRACT FROM AUTHOR]

The generalized extreme value (GEV) distribution is known as the limiting result for the modeling of maxima blocks of size n, which is used in the modeling of extreme events. However, it is possible for the data to present an excessive number of zeros when dealing with extreme data, making it difficult to analyze and estimate these events by using the usual GEV distribution. The Zero-Inflated Distribution (ZID) is widely known in literature for modeling data with inflated zeros, where the inflator parameter w is inserted. The present work aims to create a new approach to analyze zero-inflated extreme values, that will be applied in data of monthly maximum precipitation, that can occur during months where there was no precipitation, being these computed as zero. An inference was made on the Bayesian paradigm, and the parameter estimation was made by numerical approximations of the posterior distribution using Markov Chain Monte Carlo (MCMC) methods. Time series of some cities in the northeastern region of Brazil were analyzed, some of them with predominance of non-rainy months. The results of these applications showed the need to use this approach to obtain more accurate and with better adjustment measures results when compared to the standard distribution of extreme value analysis. [ABSTRACT FROM AUTHOR]

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The 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. [ABSTRACT FROM AUTHOR]

This study aims to find evaporation (ET o) probabilities for the central west region of Brazil, including Campo Grande, Cuiabá, Goiânia and Brasilia. In this case, a maximum likelihood estimation was used for the probability distributions adjusting to the monthly ET o from 1960 to 2020. The work estimates three probability distributions, including the Generalized Extreme Values (GEV), Gumbel (GUM) and Log-Normal (LN). The Kolmogorov-Smirnov test, the Akaike information criterion (AIC), the Bayesian information criterion (BIC), the root mean square error (RMSE) and the coefficient of determination (R 2 ) were applied to measure the quality of the adjustment. The evapotranspiration forecasts were made for the return periods of 10, 30, 50, and 100 years. The GEV and GUM distributions are advised for use during the summer. Altering LN distribution to a sequence of ET o throughout the winter months is the best option. Moreover, deforestation, combustion, extensive fires, aerosol pollutants, and climate change are the primary causes of excessive ET o in the area under study. • Estimated the GEV, GUM and LN distributions for ETo. • Study found no positive trend or time dependency. • ETo estimates for return periods of 10, 30, 50 and 100 years. • Found that ETo is increasing with time. • Information obtained can help determine hydraulic tolerance limits. [ABSTRACT FROM AUTHOR]

We studied a benthic invertebrate assemblage of a stream that passes through pristine, rural, suburban and urban areas of a municipality located in southeastern Brazil to investigate a possible relationship between this assemblage structure and urbanization. The environmental variables and fauna structure were analyzed in a spatial and temporal scale, sampling the four sites in a dry and wet season. We found a clear spatial pattern, with higher similarity between sites from rural and suburban area that presented intermediate environmental characteristics. The pristine site showed in both seasons the lowest values of alkalinity and fecal coliform. On the other hand, the site located in the urban area showed the lowest concentration of dissolved oxygen and higher of suspended solids, ammonia and fecal coliform. The extreme values of these three variables occurred in the wet season, probably related to the high rainfall values. The benthic invertebrate fauna structure followed the same longitudinal and seasonal pattern found for the environmental variables. The site in urban area showed the lowest richness, diversity and evenness, with a dominance of two groups resistant to adverse environmental conditions (Oligochaeta and Orthocladiinae) and absence of more sensitive groups (Coleoptera, Ephemeroptera and Trichoptera). The increase drag of the substrate and associated invertebrates can be responsible for the lower abundance and richness observed in the wet season. The environmental variables that best defined the differentiation between sites (dissolved oxygen, organic suspended solids and fecal coliform) related directly to urbanization effects, like dump effluents and removal of riparian vegetation. [ABSTRACT FROM AUTHOR]

Copyright of Revista de Economia e Sociologia Rural is the property of Sociedade Brasileira de Economia e Sociologia Rural 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.)

This article presents an analysis for the estimation of economic capital concerning operational risk in a Brazilian banking industry case making use of Markov chains, Extreme Value Theory (EVT) and Peaks Over Threshold (POT) modelling. The findings denote that some existent methods present consistent results among institutions with similar characteristics of loss data. [ABSTRACT FROM AUTHOR]

The article examines the frequency of extreme values (EVs) of daily rainfall in Sao Paulo, Brazil from 1933-2005 using the peaks-over-threshold (POT) and Generalized Pareto Distribution (GPD) approach. It discusses the use of time-dependent thresholds composed of relatively large quantiles of daily rainfall amounts. According to the article, it is evident that high quantiles of daily rainfall in the city have been increasing in frequency and magnitude over time.

Copyright of Revista de Economia e Administração is the property of INSPER Instituto de Ensino e Pesquisa 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.)

In developing countries, accurate rainfall estimation with adequate spatial distribution is limited due to sparse rain gauge networks. One way to solve this problem is the use of satellite-based precipitation products. These satellite products have significant spatial coverage of rainfall estimates and it is of fundamental importance to investigate their performance across space–time scales and the factors that affect their uncertainties. In the open literature, some studies have already analyzed the ability of satellite-based rain estimation products to estimate average rainfall values. These investigations have found very close agreement between the estimates and observed data. However, further evaluation of the satellite precipitation products is necessary to improve their reliability to estimate extreme values. In this scenario, the main goal of this work is to evaluate the ability of satellite-based precipitation products to capture the characteristics of extreme precipitation over the tropical region of South America. The products evaluated in this investigation were 3B42 RT v7.0, 3B42 RT v7.0 uncalibrated, CMORPH V1.0 RAW, CMORPH V1.0 CRT, GSMAP-NRT-no gauge v6.0, GSMAP-NRT- gauge v6.0, CHIRP V2.0, CHIRPS V2.0, PERSIANN CDR v1 r1, CoSch and TAPEER v1.5 from Frequent Rainfall Observations on GridS (FROGS) database. Some products considered in this investigation are adjusted with rain gauge values and others only with satellite information. In this study, these two sets of products were considered. In addition, gauge-based daily precipitation data, provided by Brazil's National Institute for Space Research, were used as reference in the analyses. In order to compare gauge-based daily precipitation and satellite-based data for extreme values, statistical techniques were used to evaluate the performance the selected satellite products over the tropical region of South America. According to the results, the threshold for rain to be considered an extreme event in South America presented high variability, ranging from 20 to 150 mm/day, depending on the region and the percentile threshold chosen for analysis. In addition, the results showed that the ability of the satellite estimates to retrieve rainfall extremes depends on the geographical location and large-scale rainfall regimes. [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]

The devastating effects of floods, combined with scarce data sets, have stimulated the development of hydrological regionalisation techniques. The present study proposed an evaluation of the L‐moments based index‐flood procedure, coupled with watershed grouping based on geographical convenience for regionalisation of maximum streamflows. A pioneer analysis for South America, addressing the geographical classification method adopted by National Water Agency of Brazil (ANA), was conducted considering over 100 watersheds in southern Brazil. Nonstationary and discordant maximum annual streamflow (MAS) series were removed with the aid of the Mann–Kendall test and discordancy measure, whereas the heterogeneity measure was used to check regional homogeneity. The best regional distribution was identified by the ZDIST goodness‐of‐fit measure. Finally, multiple nonlinear regressions, considering morphological and meteorological watershed characteristics, were performed to obtain better index‐flood estimates. It was concluded that: (a) the proposed methodological strategy provided satisfactory estimation of design floods; (b) area, mean slope, stream gradient, and flow length, were the most satisfactory explanatory variables; (c) the fitted equations stand out as a state‐of‐art alternative for the scarce hydrological monitoring in southern Brazil; and (d) the hydrological boundaries defined by ANA might not be the most adequate approach from a regional point of view. [ABSTRACT FROM AUTHOR]

Recent studies have focused on characterizing different aspects of drought (i.e., meteorological, soil moisture, hydrological, or even, socio-economic drought). However, the complexity of the phenomenon and the uncertainty associated to the data analyzed makes it difficult to predict its associated risk and resulting impacts. In this work, we used the severity-duration-frequency approach for hydrological drought to analyze the risk of the Cantareira System in Brazil, the main water source for the Metropolitan Region of Sao Paulo, which has been recently affected by drought. Our approach combined water demand and supply conditions. For water demand, we set up one stationary and one non-stationary threshold. For water supply, we used: (a) hydrologic modeling simulations using as climatic input historical series (1962-2015) from the regional climate models Eta-INPE, HadGEM-ES, and MIROC5; (b) monthly historical reconstructed streamflow data (1930-2016). We used the Generalized Extreme Value distribution to fit the hydrological deficit data for return periods of 2, 10, 20, and 100 years. Results showed greater water deficits for the cases that used the reconstructed hydrologic time series (compared to the cases using hydrologic modeling) but, for the simulations using the hydrologic modeling, results showed greater water deficit for the MIROC5 model. Greater water deficits existed for the non-stationary threshold water demand case. Furthermore, the diagnostic plots found that the longest droughts showed better fit for the Gumbel distribution. This approach may be useful for regional water resources management, helping on the quantification of the impacts of hydrological drought and its uncertainty.Key words: Hydrological droughts, climate change, urban water, SDF approach. [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]