Modelagem probabilística de eventos de precipitação extrema no estado do Rio Grande do Sul

Abstract

R E S U M OA gestão de cheias em bacias hidrográficas brasileiras deve ser discutida e priorizada, porém o cenário atual indica lacunas quanto às informações hidrológicas com variabilidade espacial e temporal condizentes. A modelagem probabilística de eventos extremos de precipitação, buscando a extrapolação para uma frequência e duração, pode servir como excelente ferramenta de análise e tomada de decisões. O objetivo principal deste trabalho foi analisar o ajuste de diferentes modelos probabilísticos a séries de precipitação máxima diária anual no Rio Grande do Sul. Séries pluviométricas de 342 estações foram ajustadas às distribuições Log-Normal a 2 e 3 parâmetros e Gumbel e a adequação foi realizada pelos testes de Kolmogorov-Smirnov e Qui-Quadrado. Todas as distribuições de probabilidade consideradas foram adequadas; entretanto, a distribuição Log-Normal a 3 parâmetros apresentou os melhores ajustes segundo os resultados do teste Qui-Quadrado. Os parâmetros das distribuições de probabilidades apresentaram variabilidade ao longo do estado e forte relação com a localização, sugerindo que a regionalização de chuvas intensas pode ser empregada no Rio Grande do Sul como excelente ferramenta de gestão.Probabilistic modelling of extreme rainfall events in the Rio Grande do Sul state A B S T R A C T Flood management in Brazilian watersheds must be discussed and prioritized, however, the current scenario indicates that there are gaps in hydrological information with respect to its spatial and temporal variability. The probabilistic modelling of extreme rainfall events, having as goal to extrapolate values for a given frequency and duration, can be used as an excellent tool for analysis and decision-making. The main objective of this study was to analyse the adjustment of different probabilistic models for series of annual maximum daily rainfall in Rio Grande do Sul. Series of 342 rain gauges were adjusted to 2-parameter Log-Normal, 3-parameter Log-Normal and Gumbel probability distributions and goodnessof-fit tests were based on the Kolmogorov-Smirnov and Chi-Square procedures. It was found that all the distributions presented adequate results, however, 3-parameter LogNormal distribution had the best performance in accordance with the Chi-Square test. The parameters of probability distribution presented variability over the state and a pronounced relationship with the location of rain gauges. This suggests that regionalization of highintensity rainfall can be employed in Rio Grande do Sul as an excellent management tool. Palavras-chave:chuvas intensas distribuição de probabilidade regionalização hidrológica

Standardizing germination tests for quinoa seeds

“…These results show a visible influence of high temperature on germination velocity of quinoa seeds, varying between 15°C low and 30°C high. Low temperatures could reduce enzymatic activities involving seed metabolism, reducing rate and delaying germination (Caldeira et al, 2015). The germination velocity index (GVI) and the germination mean time, at all temperatures, within sand substrate had the lowest values (Table 1 and Figure 1).…”

“…Therefore, the ideal date for first count should be on the 2 nd day and for the second count on the 4 th day after sowing. The experiment followed similar criteria to define standard germination test with other crops (Alves et al, 2014(Alves et al, , 2015Caldeira et al, 2015;Oliveira et al, 2014). On soybean and maize, first and second count occurs at 5 to 8 and 4 to 7 days, respectively.…”

Multiparameter probability distributions for heavy rainfall modeling in extreme southern Brazil

“…However, due to the lack of sub-daily data, studies conducted in Brazil have commonly applied daily rainfall data sets through annual maximum daily rainfall series (Silva and Clarke, 2004;Souza et al, 2012;Aragão et al, 2013;Franco et al, 2014;Caldeira et al, 2015).…”

“…In Brazil, the fit of more simplified theoretical probability models has been commonly observed, such as 2 and 3 parameter Log-Normal distribution, and Asymptotic Extreme Value Type I, also known as Gumbel (Silva et al, 2002;Sansigolo, 2008;Santos et al, 2009;Back et al, 2011;Souza et al, 2012;Caldeira et al, 2015). However, several studies associated with heavy rainfall events have sought also evaluate other probability distributions, such as the 2-parameter Gamma (Franco et al, 2014), Generalized Extreme Value (Durrans and Kirby, 2004;Nadarajah and Choi, 2007;Blain and Camargo, 2012;Blain and Meschiatti, 2014), Kappa (Parida, 1999;Park and Jung, 2002;Norbiato et al, 2007;Ahmad et al, 2013;Blain and Meschiatti, 2014), Wakeby (Park et al, 2001;Blain and Meschiatti, 2014), Generalized Logistic (Norbiato et al, 2007;Hailegeorgis et al, 2013;Rahman et al, 2013) and Generalized Pareto (Hailegeorgis et al, 2013;Rahman et al, 2013).…”

A Stochastic Multi-Objective Chance-Constrained Programming Model for Water Supply Management in Xiaoqing River Watershed

“…In fact, many parameters involved in practical water management systems may have other forms of probabilistic distributions, such as the log-normal type. As reported by Caldeira et al [23], the observed statistical rainfall amounts are suitable to be expressed as log-normal distributions rather than normal ones. Moreover, the traditional SCCP method mostly aims to achieve a single economic objective, such as minimization of total system cost or maximization of total revenue.…”