Distribution of rainfall probability in the Tapajos River Basin, Amazonia, Brazil

Santos, Vanessa Conceição dos; Blanco, Cláudio José Cavalcante; Júnior, José Francisco de Oliveira

doi:10.4136/ambi-agua.2284

Cited by 9 publications

(6 citation statements)

References 28 publications

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“…One of the concerns about rain is the intensity and frequency of its occurrence, due to its potentially harmful effects, when in excess or due to scarcity. Thus, the knowledge of the probabilities of occurrence of rain becomes of paramount importance in planning related activities or in monitoring hydrological processes concerning hydrographic basins, being important for the planning of water resources and optimization of the calendar of agricultural activities (Santos, Blanco, & Oliveira Junior, 2019). In this context, the rainfall of a given location can be estimated, among other ways, in probabilistic terms, using theoretical distribution models adjusted to a historical series (Lyra, Garcia, Piedade, Sediyama, & Sentelhas, 2006;Teodoro et al, 2017).…”

Section: Probability Distributions Of Seasonal and Annual Rainfallmentioning

confidence: 99%

The impacts of climate change on rainfall modeling in the Pantanal of Mato Grosso do Sul

Pobocikova

Souza²,

Abreu

et al. 2021

Acta Sci Technol

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The most significant and influential meteorological element in environmental conditions and human activities is precipitation. The objective of this study was to adjust eight probability distributions to monthly, seasonal and annual rainfall data in the Pantanal of Mato Grosso do Sul, Brazil, using a time series of data (1983-2013) by the National Meteorological Water Agency (ANA). The performance evaluation of different probability distribution models was assessed by the quality of fit of the selected probability distributions for precipitation data. Quality tests as chi-square, Kolmogorov-Smirnov (KS) and Anderson-Darling (AD), the information criteria as Akaike (AIC) and the Bayesian criterion (BIC) were used. Then the mean root square error (RMSE) and the coefficient of determination (R2) were applied. The analyzes were made monthly, annually and by seasons. The 3-parameter Lognormal distribution performs the best for all twelve months and provides the best-fit to the monthly rainfall data. Thus characterizing a dry period that runs from May to September and a rainy period between the months of October and April, it was observed that the 3-parameter Lognormal distribution has best adjustment for spring and summer, and for winter and autumn the 2-parameter Gamma and 3-parameter Gamma distribution performed better. For annual observations, the function that best fits is 3-parameter Weibull distribution.

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Section: Probability Distributions Of Seasonal and Annual Rainfallmentioning

confidence: 99%

The impacts of climate change on rainfall modeling in the Pantanal of Mato Grosso do Sul

Pobocikova

Souza²,

Abreu

et al. 2021

Acta Sci Technol

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“…In the selection of the stations, those in which the historical series had data that were consisted and with few failures were considered. To define the failure interval, the criterion proposed by Santos et al (2019) was applied, which used series with 1.8% missing data. In addition, it was decided not to fill the gaps, following the recommendations of Detzel and Mine (2011a) and Kigobe, Mcintyre, Wheater, and Chandler (2011).…”

Section: Data Usedmentioning

confidence: 99%

“…Rainfall is variable in space and time; thus, it is important to recognize its occurrence patterns for a good prediction of the climatic behavior of a region (Santos, Blanco, & Oliveira Junior, 2019). Sectors such as agriculture, flood control, and human supply projects need knowledge of precipitation for planning, management and operation (Jale et al, 2019; Soares, Paz, & Piccilli, 2016).…”

Section: Introductionmentioning

confidence: 99%

Cumulative distribution function of daily rainfall in the Tocantins–Araguaia hydrographic region, Amazon, Brazil

Progênio

Blanco

2020

Natural Resource Modeling

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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. K E Y W O R D S adherence test, agroindustrial activities, ecotone, homogeneous regions, water resources

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“…Methods based on unsupervised neural networks have been used as an alternative approach to identify the multi-dimensional patterns, based on cluster algorithms (Xu & Tian, 2015), such as self-organizing maps (Kohonen, 2013), the Ward method (Ward, 1963) and K-means (Lloyd, 1982). Cluster analysis has been applied in meteorological studies to classify regions with similar climate conditions (Netzel & Stepinski, 2016) and identify weather patterns from two-dimensional maps of surface air pressure (Sheridan & Lee, 2011), sea surface temperature (Johnson, 2013), precipitation (Santos et al, 2019) and geopotential height (Liu et al, 2016). In Brazil, studies involving clustering-based procedures are usually related to improving our general understanding of the weather and climate conditions (Anunciação et al, 2014;Lyra et al, 2014;Brito et al, 2017;Ferreira & Reboita, 2022) and more applications focused on the Brazilian energy sector are needed.…”

Section: Introductionmentioning

confidence: 99%

Use of self organizing map to identify precipitation patterns and assess their impact on hydrographic basins in Brazil

Sakagami

Folganes

Penz

et al. 2022

RBRH

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In this study, we used neural networks known as self-organizing maps (SOMs) to identify clusters of spatial synoptic precipitation patterns. These clusters were compared with the precipitation regime of the ten main hydrographic sub-basins in Brazil. Sixty years of daily precipitation data obtained from over 389 weather station in Brazil were used as input data for the SOMs, with a number of six clusters being prescribed as the optimal number according to the elbow and silhouette methods. The six precipitation patterns identified by the SOMs reflect the typical synoptic conditions associated mainly with the cold frontal systems (CF), South American Monsoon System (SAMS) and Inter-tropical Convergence Zone (ITCZ). In conclusion, SOMs perform well using interpolated precipitation data as the input data to identify synoptic precipitation patterns, which could be used to monitor the spatial distribution of precipitation, which affects the hydrographic basins in Brazil and hence hydropower plant performance.

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Distribution of rainfall probability in the Tapajos River Basin, Amazonia, Brazil

Cited by 9 publications

References 28 publications

The impacts of climate change on rainfall modeling in the Pantanal of Mato Grosso do Sul

The impacts of climate change on rainfall modeling in the Pantanal of Mato Grosso do Sul

Cumulative distribution function of daily rainfall in the Tocantins–Araguaia hydrographic region, Amazon, Brazil

Use of self organizing map to identify precipitation patterns and assess their impact on hydrographic basins in Brazil

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