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Original Article 

Biosci. J., Uberlândia, v. 33, n. 3, p. 747-753, May/June. 2017 

PROBABLE MONTHLY RAINFALL ASSOCIATED WITH DISTINCT BIOMES 
OF MATO GROSSO DO SUL STATE 

 
PRECIPITAÇÃO MENSAL PROVÁVEL ASSOCIADA AOS DIFERENTES BIOMAS DO 

ESTADO DE MATO GROSSO DO SUL 
 

Paulo Eduardo TEODORO¹; Carlos Antonio da SILVA JUNIOR²;  
José Francisco OLIVEIRA-JUNIOR³; Rafael Coll DELGADO4; Givanildo de GOIS4; 

 Caio Cezar Guedes CORREA5; Francisco Eduardo TORRES6 
1. Universidade Federal de Viçosa, Viçosa, MG, Brasil. eduteodoro@hotmail.com; 2. Universidade do Estado de Mato Grosso, Alta Floresta, MT, 

Brasil; 3. Universidade Federal de Alagoas, Maceió, AL, Brasil; 4. Universidade Federal Rural do Rio de Janeiro, Seropédica, RJ, Brasil; 5. 
Universidade Estadual do Norte Fluminense, Campos dos Goytacazes, RJ, Brasil; 6. Universidade Estadual de Mato Grosso do Sul, Aquidauana, MS, 

Brasil. 

 
ABSTRACT: The aim of this study was to determine the probable monthly rainfall for the state of Mato Grosso do Sul, 

considering the level of 75% probability, and study the spatial distribution associated with its different biomes. The rainfall data of 32 
stations (sites) in the state of Mato Grosso do Sul were collected in the period 1954-2013. In each of the 384 series, the average 
monthly rainfall was calculated, for at least 30 years of observation. The Kolmogorov-Smirnov adhesion test was applied to the 
rainfall time series to check the fit of the data to a normal distribution. The likely fallout was estimated at 75% probability, using the 
normal probability distribution and, subsequently, it was adopted the method of Ordinary Kriging interpolation mathematics to spatial 
data. Based on the likely monthly precipitation estimated, the State of Mato Grosso do Sul possess three distinct periods, with the 
precipitation associated with different biomes: the rainy season (between the months November to March, where increased 
precipitation occurred in the Savanna biome), dry season (between the months from June to August, when the highest rainfall 
occurred in the Atlantic Forest) and transition period (April and May and September and October). 
 

KEYWORDS: Atlantic Forest. Pantanal. Savanna. Normal distribution. 
 
INTRODUCTION 
 

Mato Grosso do Sul State (MS) covers an area 
of approximately 350,000 km2, of which 13,000 km2 are 
exploited by agriculture, especially soybean, corn, 
cotton, sugarcane sugar and irrigated rice (CONAB, 
2014), which are distributed differently in three biomes 
of peculiar climatic attributes: Pantanal, Savanna and the 
Atlantic Forest (TEODORO et al., 2016a,b). Among the 
main factors that influence crop production, the rainfall 
and its variation are the most relevant for planning 
agricultural activities, allowing for better approaches and 
more reliable decisions (ARAI et al. 2010). 

Correa et al. (2014) emphasize that it is possible 
to plan the best time of tillage, harvest, sowing and 
fertilizer and defensive application from probable 
monthly rainfall estimates. Besides the influence in 
agriculture, very long dry seasons affect the level of 
water sources and reservoirs of hydroelectric power 
plants, causing problems to the urban water supply and 
the electricity generation (RODRIGUES et al. 2013). 
According to Togashi et al. (2012), Moura et al. (2009) 
and Arcova et al. (2003) research on this subject is 
critical for the management of areas subject to flooding, 
as well for preventing potential problems caused by 
climate change. 

According to Bernardo et al. (2006), 
probable rainfall can be defined as the minimum 

amount of precipitation within a given probability of 
occurrence. Usually, the probability of 75% is used 
in irrigation, ie, is the minimum monthly rainfall 
that should occur in 75% of the folloing years. 
These statiscal analyses of rainfall are important for 
planning of agricultural activities, but also of other 
activities such as construction, tourism and transport 
(ULIANA et al. 2013). 

Researches investigating the climate in the 
State of Mato Grosso do Sul are still scarce. There is 
a variety of meteorological phenomena in this 
region, from micro-scale to large-scale, which 
require a better understanding, as for example, soil–
vegetation–atmosphere interaction processes during 
dry spells and flood in the Pantanal, which have 
local and wider climatic effects, especially on 
rainfall patterns (MESQUITA et al. 2013). Given 
the above, the aim of this study was to determine the 
probable monthly rainfall for Mato Grosso do Sul 
State, considering the level of 75% probability, and 
studying the spatial distribution associated with the 
different biomes of the region. 

 
MATERIAL AND METHODS 
 
Characterization of the study area 

Mato Grosso do Sul State lays in the center of 
the South American continent and has several 

Received: 28/06/16 
Accepted: 20/11/16 



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Biosci. J., Uberlândia, v. 33, n. 3, p. 747-753, May/June. 2017 

edaphoclimatic features distributed among three biomes: 
Savanna, Atlantic Forest and Pantanal. Altitudes vary 
from 24 to 1,000 m (TEODORO et al., 2015). 

Savanna (or Cerrado) is the second largest 
biome in South America (SA) and covers 22% of 
the Brazilian territory. It is this biome that is the 
source of three major watersheds of South America 
(Amazônica/Tocantins, São Francisco and Prata), 
which results in high aquifer potential and great 
biodiversity. Due to its latitudinal position, the 
region is characterized by the transition between the 
warm low latitude climates and mesothermal type 
climates of mid-latitudes (NIMER, 1989). 

Atlantic Forest is an environmental complex 
that includes mountain ranges, valleys and plateaus. 
It was already one of the richest and most varied 
rainforests of South America, but is currently 
recognized as the most disturbed and threatened 
Brazilian biome (Teodoro et al., 2015). 

Pantanal covers 25% of Mato Grosso do Sul 
State and it is a biome almost exclusive to Brazil. It 

is characterized by long-term flooding (due to low 
permeability soil) that occur each year on the plain, 
and cause changes in the environment, wildlife and 
local populations daily life. Pantanal climate is 
Tropical of Savanna (AW), with rainfall totals 
ranging between 1,000 and 2,000 mm, and two 
distinct seasons: a dry (May to September) and a 
rainy (October to April), this latter accounting for 
more than 80% of the total annual rainfall 
(MESQUITA et al. 2013). 

 
Historical Series of Monthly Rainfall 

Rainfall data of 32 stations (sites) of Mato 
Grosso do Sul (MS) were obtained of the Agência 
Nacional de Águas (ANA 2014), collected from 1954 to 
2013 (Table 1). At each site and year, the rainfall daily 
data were added up to obtain the monthly rainfall (mm 
monthly-1), of each month of the year. Thus, they formed 
384 time series (12 months × 32 sites), with different 
numbers of observations years in each series, defined 
according to the availability of meteorological data. 

 
Table 1. Altitude (m), latitude and longitude (°), and observation period of monthly rainfall of 32 municipalities in Mato 

Grosso do Sul State, Brazil. 
Site Altitude (m) Latitude (S, °) Longitude (W, °) Period (years) 

Água Clara 376  20º6’7’’  52º55’33” 1975-2013 
Amambai 395 22º55’59’’ 55º13’0’’ 1973-2013 
Anastácio 106 19º34’0’’ 56º12’0’’ 1960-2000 
Anaurilândia 284 22º11’11’’ 52º42’48’’ 1975-2013 
Aparecida do Taboado 375 20º1’6’’ 51º6’13’’ 1983-2013 
Aquidauana 155 20º27’24’’ 55º40’17’’ 1960-2000 
Bataguassu 293 21º43’33’’ 52º20’3’’ 1975-2013 
Bodoquena 133 19º52’15’’ 56º59’1’’ 1954-2013 
Caarapó 454 22º37’28’’ 54º49’29’’ 1973-2013 
Camapuã 404 19º29’48’’ 53º59’48’’ 1973-2013 
Campo Grande 559 20º28’0’’ 54º40’0’’ 1975-2013 
Chapadão do Sul 570 18º59’52’’ 52º35’17’’ 1983-2013 
Corumbá 101 17º37’24’’ 56º57’54’’ 1983-2013 
Costa Rica 635 18º32’50’’ 53º8’7’’ 1983-2013 
Coxim 250 18º38’57’’ 54º21’26’’ 1973-2013 
Dourados 293 22º23’53’’ 54º47’30’’ 1973-2013 
Glória de Dourados 422 22º24’21’’ 54º14’7’’ 1973-2013 
Iguatemi 333 23º40’55’’ 54º33’42’’ 1975-2013 
Inocência 502 19º44’11’’ 51º56’1’’ 1983-2013 
Maracaju 356 21º37’7’’ 55º8’13’’ 1973-2013 
Miranda 140 20º6’7’’ 56º47’43’’ 1960-2000 
Navirai 366 23º3’28’’ 54º11’38’’ 1975-2013 
Nova Andradina 271 21º36’55’’ 53º3’8’’ 1975-2013 
Paranaíba 458 19º23’27’’ 51º36’32’’ 1983-2013 
Ponta Porã 650 22º32’0’’ 55º43’0’’ 1973-2013 
Porto Murtinho 83 21º42’5’’ 57º53’30’’ 1983-2013 
Ribas do Rio Pardo 373 20º26’41’’ 53º45’29’’ 1975-2013 
Rio Brilhante 287 21º38’50’’ 54º25’31’’ 1973-2013 
Rio Negro 233 19º26’23’’ 54º59’0’’ 1975-2013 
Santa Rita do Pardo 393 21º17’43’’ 52º48’38’’ 1975-2013 
Selviria 348 20º21’49’’ 51º25’26’’ 1983-2013 
Três Lagoas 313  20º47’41’’ 51º42’46’’ 1975-2013 

 



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The processing and the organization of rainfall 
data of 32 sites in the State were performed. The 
historical series with failures were eliminated and filled 
by the climatologically normal from each of the 11 
regions of Mato Grosso do Sul State according to the 
performed by Teodoro et al. (2015). 

 
Estimate of the Probable Rainfall 

In each of the 384 time series of monthly 
rainfall was calculated the average (m), at least 30 years 
of observation. The Kolmogorov-Smirnov adhesion test 
(ASSIS et al., 1996) was applied on 384 monthly rainfall 
time series to verify their fit to normal distribution. 

Probable rainfall was estimated at 75% 
probability, using the normal probability distribution 
according Torres et al. (2016), also named Gaussian 
curve, which shows two parameters and its probability 
density function is defined by the equation 
(HASTINGS; PEACOCK, 1975): 











 −
−

=
2

2

2σ

µ)(x

e
2πσ

1
f(x)

 
where: µ is the average and σ the standard 

deviation. 
Statistical analyzes were performed with the 

software Genes (CRUZ, 2013) and Microsoft Office 
Excel®. 

 
Geographical Distribution of the Probable Rainfall 

In this study was adopted the ordinary 
kriging mathematical interpolation method (OK) 
which allows to calculate local averages, limiting 
the average stationary domain to local neighborhood 

centered on the point to be estimated. Values of z 
dimension are estimated at spatial locations (xj, yj) 
unobserved, without the need to know the stationary 
average, from a linear combination of the values of 
a local sampling subset. The condition set out in this 
study was the sum of the weights of OK λi (xj, yj) to 
be equal to 1 (Equation 2).  

)y,Z(x )y,(xλ)y,Z(x ii
n(j)

1i
jjijj ∑

=

×= , 

where:  
)y,Z(x jj is the value estimated by kriging 

in the point )y,(x jj ; )y,Z(x ii is the value of the 

sample in the point )y,(x ii ; λi (xj, yj) is the weight 
of the OK to the specified location. 

It was utilized the circular theoretical 
mathematical model, as recommended by Correa et 
al. (2014). The spatial distribution of the probable 
monthly rainfall data was analyzed with the 
assistance of geostatistical software ArcGis 10.1®. 

 
RESULTS AND DISCUSSION 
 

Kolmogorov-Smirnov test revealed that the data 
of 359 monthly rainfall time series (93.49%) fit to 
normal distribution (P>0.01) (Table 2). Given these high 
percentages with fit to normal distribution, it can be 
concluded that the use of normal distribution has 
credibility for estimating the monthly rainfall in the Mato 
Grosso do Sul State, agreeing with the results obtained 
by Torres et al. (2016). 

 
Table 2. Normality  tests (Kolmogorov-Smirnov, KS) regarding to 384 monthly rainfall time series in sites of Mato 

Grosso do Sul State. 
City JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC 
Água Clara S S S S S S N N S S S S 
Amambai S S S S S S S S S S S S 
Anastácio S S S S S S S S S S S S 
Anaurilândia S S S S S S S S S S S S 
Aparecida do Taboado S S S S S N S S S S S S 
Aquidauana S S S S S S S S S S S S 
Bataguassu S S S S S S S S S S S S 
Bodoquena S S N S S N S N S S S S 
Caarapó S S S S S S S S S S S S 
Camapuã S S S S S S S N S S S S 
Campo Grande S S S S S S S S S S S S 
Chapadão do Sul S S S S S S S S S S S S 
Corumbá S S S S S S S S S S S S 
Costa Rica S S S S S S S S S S S S 
Coxim S S S S S S S S S S S S 
Dourados S S S S S S S N N S S S 
Glória de Dourados S S S S S S S S S S S S 
Iguatemi S S S S S S S S S S S S 
Inocência S S S S S S S S S S S S 



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Maracaju S S S S S S S S S S S S 
Miranda N N N N N N N N N N N N 
Navirai S S S S S S S S S S S S 
Nova Andradina S S S S S S S S S S S S 
Paranaíba S S S S S S S S S S S S 
Ponta Porã S N S S N S S S S S N S 
Porto Murtinho S S S S S S S S S S S S 
Ribas do Rio Pardo S S S S S S S S S S S S 
Rio Brilhante S S S S S S S S S S S S 
Rio Negro S S S S S S S S S S S S 
Santa Rita do Pardo S S S S S S S S S S S S 
Selviria S S S S S N S S S S S S 
Três Lagoas S S S S S S S S S S S S 
S = Normal distribution, by Kolmogorov-Smirnov test (P>0.01). N = non-normal (P≤0.01). 

 
The greatest probable precipitation values at 

75% probability by normal distribution occurred 
between the months of November to April, which allows 

us to classify these months as the rainiest months of the 
year in Mato Grosso do Sul (Figure 1).  

 

 
 
Figure 1. Probable rainfall for Mato Grosso do Sul State from January to December, estimated by normal distribution at 

75% probability of occurrence. 



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The lowest probable precipitation values at 75% 
probability by normal distribution occurred between the 
months of June, July and August, demonstrating that this 
is the driest period of the year in the Mato Grosso do Sul 
State. In addition to this, April, May, September and 
October showed intermediary probable monthly rainfall, 
being characterized as transitional periods between the 
rainy and the dry season of the year. 

Moreover, we verified that the 
municipalities located in the Savanna biome had the 
highest values. This region is under the influence of 
the South Atlantic Subtropical Anticyclone and the 
Polar migratory, Chaco’s Low and Bolivia’s High, 
which operate in the summer and spring and 
influence the weather conditions in the Midwest 
region. Another weather system that determines the 
rainfall pattern in the whole region is the South 
Atlantic Convergence Zone, favored by Low Level 
Jet, its draining comes from the channeling of the 
trade winds that enter the South American continent, 
carrying water vapor from the Equatorial Atlantic 
and the Amazon basin towards the southwest 
Atlantic (CARVALHO et al. 2004; KELLER 
FILHO et al., 2005). 

The municipalities located in the Atlantic Forest 
had higher probable rainfall at 75% probability by the 
normal distribution during the months of May to 
October. The Upper Level Low, acts from a subtropical 
origin, that causes rain and strong winds in this region 
(CASTRO et al., 1994) and finally the passage of 
Frontal Systems. The complex action of these weather 
systems explains the highest average monthly rainfall in 
dry seasons (KELLER FILHO et al., 2005). 

The municipalities located in Pantanal 
showed intermediate probable rainfall during most 
of the year, except for the months of August, 

September and October, which showed lower 
estimates at 75% probability of occurrence. In this 
biome, the main weather systems responsible for 
rainfall are the passages of Frontal Systems and 
heated branches, which hold 67% of the rainfall 
origins, followed by the South Atlantic 
Convergence Zone and Upper Level Low from the 
subtropical origin (KELLER FILHO et al., 2005). 

The topographic aligning, arranged in the 
longitudinal direction (NE-SW) shows 
morphological features clearly defined: the plain 
and the plateau. This behavior shows that despite 
the tropical features present in Mato Grosso do Sul, 
the altitude is among the physiographic factors, 
followed by the latitude, the most factor influential 
in the monthly rainfall during dry, transitional, and 
rainy seasons, regardless of the period of the year, 
resembling the results obtained in studies performed 
in other Brazilian States (BAÚ et al., 2006; ÁVILA 
et al., 2009; ULIANA et al., 2013). 

 
CONCLUSIONS 
 

The State of Mato Grosso do Sul possess three 
distinct rainfall periods, associated with different biomes: 
the rainy season (between the months November to 
March, where increased precipitation occur in the 
Savanna biome), dry season (between the months from 
June to August, when the highest rainfall occurring in 
the Atlantic Forest) and transition period (April and May 
and September and October). 

 
ACKNOWLEDGEMENTS 
 

To Agência Nacional de Águas (ANA) for data 
provision for present study. 

 
 

RESUMO: O objetivo estudo foi determinar a precipitação mensal provável para o Estado de Mato Grosso do Sul, 
considerando o nível de 75% probabilidade e estudar sua distribuição espacial associada aos seus diferentes biomas. Os dados de 
precipitação pluvial de 32 estações (locais) do Estado do Mato Grosso do Sul foram coletados do período de 1954 a 2013. Em cada 
uma das 384 séries temporais de precipitação pluvial mensal calculou-se a média, com no mínimo 30 anos de observação. Foi 
aplicado o teste de aderência de Kolmogorov-Smirnov nas 364 séries temporais de precipitação pluvial mensal para verificar o ajuste 
dos dados a distribuição normal. A precipitação provável foi estimada a 75% de probabilidade, utilizando-se a distribuição de 
probabilidade normal e, posteriormente, foi adotado o método de interpolação matemática da Krigagem Ordinária para espacialização 
dos dados. Com base na precipitação mensal provável, estimada pela distribuição normal a 75% de probabilidade, o Estado do Mato 
Grosso do Sul possuí três períodos distintos, estando à precipitação associada aos diferentes biomas: período chuvoso (entre os meses 
de novembro a março, onde as maiores precipitações ocorrem no bioma Cerrado), período seco (entre os meses de junho a agosto, 
onde as maiores precipitações ocorrem no bioma Mata Atlântica) e período de transição (meses de abril e maio e setembro e outubro). 

PALAVRAS-CHAVE: Série temporal Erro de estimação. Tamanho de amostra. Amostragem. 
 
 
 
 
 



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