Cluster analysis applied to the spatial and temporal variability of monthly rainfall in Mato Grosso do Sul State, Brazil

Teodoro, Paulo Eduardo; Júnior, José Francisco Oliveira; Cunha, Elias Rodrigues da; Corrêa, Caio Cézar Guedes; Torres, Francisco Eduardo; Bacani, Vitor Matheus; Góis, Givanildo de; Ribeiro, Larissa Pereira

doi:10.1007/s00703-015-0408-y

Cited by 68 publications

(60 citation statements)

References 35 publications

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“…This behavior demonstrates that despite tropical features present in the State, altitude is among the physiographic factors, followed by the latitude, which most influences the monthly rainfall behavior in dry, transition and rainy periods, resembling to results obtained by researches carried out in other Brazilian states (BAÚ et al 2006;ANDRÉ et al 2008;ÁVILA et al 2009;TEODORO et al, 2015b).…”

Section: Resultssupporting

confidence: 85%

Altitude and geographic coordinates to estimate monthly rainfall in the state of Mato Grosso do Sul

Teodoro

Cunha²,

Corrêa³

et al. 2016

Biosci. J.

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Adjustment of multiple linear regression equations has allowed estimating the value of a certain climatological variable according to geographical coordinates with acceptable degree of accuracy. The aim of this study was to verify if the average monthly rainfall could be estimated according to the altitude, latitude and longitude in Mato Grosso do Sul State (MS). Rainfall data of 32 stations of MS were collected from 1954 to 2013. It were formed 384 time series (12 months × 32 sites), with different numbers of years of observations in each series. On each of the 384 monthly rainfall time series it was calculated the average (a), at least 30 years of observation, forming 12 matrices 32 x 4 (32 sites x 4 variables: altitude, latitude, longitude and monthly rainfall). It was estimated for each matrix the Pearson's linear correlation coefficient among the variables, performing the multicollinearity diagnosis for each matrix. Correlations were unfolded by path analysis in direct and indirect effects and in each month it was used the multiple linear regression model. The altitude and latitude have greater effect on the spatial distribution of rainfall in MS. The multiple linear regression equations generated in this study will subsidize researches of crop zoning, indication for sowing times, irrigation, determination of yield potential, climate risks zoning and credit and agricultural insurance.

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Section: Resultssupporting

confidence: 85%

Altitude and geographic coordinates to estimate monthly rainfall in the state of Mato Grosso do Sul

Teodoro

Cunha²,

Corrêa³

et al. 2016

Biosci. J.

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“…As a measure of proximity, the Euclidean distance was used for representing the similarity and dissimilarity of the pluviometric stations [9,12,25]. The Euclidean distance is commonly used for the analysis of quantitative variables and, according to Freitas et al [26], corresponds to the geometric distance in the multidimensional space between observations.…”

Section: Rainfallmentioning

confidence: 99%

Identification of Homogeneous Rainfall Regions in the Eastern Watersheds of the State of Paraná, Brazil

Terassi

Galvani

2017

Climate

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Abstract:The objectives of this study are to use a clustering technique to identify homogeneous rainfall regions in the watersheds of the eastern region of the state of Paraná and to associate the spatial and temporal distribution of rainfall with the influences of orography, the ocean and regional atmospheric dynamics. Rainfall data were obtained from 54 pluviometric stations and from eight meteorological stations, which comprise the historical series from 1976 to 2015. A cluster analysis technique was used with the Euclidean distance for measuring proximity and Ward's method for hierarchical grouping. The Litorânea watershed exhibited the highest rainfall totals in the study area, and the annual average was 2551 mm for the homogeneous group with the highest rainfall. The Ribeira river watershed exhibited the lowest total rainfall (1488 mm); therefore, it was considered a rain shadow region with a more tropical climate due to the concentration of rainfall in the period from September to March. The Alto Iguaçu watershed was characterized by the smallest spatiotemporal variation in rainfall due to its flatter relief and the influence of the subtropical climate.

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“…These techniques have been frequently employed, in Brazil. The determination of sites with similar behaviour was performed by Diniz et al (2003), Keller Filho et al (2005, Lyra et al (2006), Martin et al (2008), Lyra et al (2014) and Teodoro et al (2015b). These authors concluded that, once determined, these homogeneous regions, once determined, will provide subsidies for more detailed studies on climatic and agro climatic zonings for the State.…”

Section: Introductionmentioning

confidence: 89%

Cluster analysis identified rainfall homogeneous regions in Tocantins state, Brazil

Oliveira‐Júnior¹,

Xavier²,

Teodoro³

et al. 2017

Biosci. J.

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ABSTRACT:Identifying homogeneous regions regarding the monthly rainfall is relevant in agricultural planning, especially in relation to the installation of crops. Thus, the aim of this study was to apply the cluster analysis using Ward's algorithm to identify homogeneous regions in Tocantins State. Rainfall database of 34 stations (sites) of Tocantins, from 1976 to 2012, it were obtained of the Database of the Agência Nacional de Águas, Instituto Nacional de Meteorologia and Serviço Meteorológico do Brasil. Thus, were formed 408 time series (12 months × 34 sites) wherein AA technique was applied in conjunction with Ward's algorithm. Three homogeneous regions in relation to monthly rainfall were identified. Group 1, located in the extreme north of the state, has intermediate average values (135.58 mm) to the other groups. Group 2 showed the greatest variance (13,543.68 mm²) and higher average (162.19 mm) for the studied period. On the other hand, group 3 has the lowest average rainfall (117.93 mm) among the homogeneous groups. Separating the groups followed a north-south alignment which suggests that latitude is the physiographic factor that most influences the occurrence of monthly rainfall in the State of Tocantins.

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Cluster analysis applied to the spatial and temporal variability of monthly rainfall in Mato Grosso do Sul State, Brazil

Cited by 68 publications

References 35 publications

Altitude and geographic coordinates to estimate monthly rainfall in the state of Mato Grosso do Sul

Altitude and geographic coordinates to estimate monthly rainfall in the state of Mato Grosso do Sul

Identification of Homogeneous Rainfall Regions in the Eastern Watersheds of the State of Paraná, Brazil

Cluster analysis identified rainfall homogeneous regions in Tocantins state, Brazil

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