SciELO - Scientific Electronic Library Online

 
vol.15 número6Reviewers of Volume 15, 2020 of Revista Ambiente & ÁguaDesempenho de filtros lentos de areia no pós-tratamento de efluente do polo têxtil do estado de Pernambuco índice de autoresíndice de assuntospesquisa de artigos
Home Pagelista alfabética de periódicos  

Serviços Personalizados

Journal

Artigo

Indicadores

Links relacionados

Compartilhar


Revista Ambiente & Água

versão On-line ISSN 1980-993X

Rev. Ambient. Água vol.15 no.6 Taubaté  2020  Epub 16-Nov-2020

http://dx.doi.org/10.4136/ambi-agua.2544 

ARTICLES

Extreme rainfall and IDF equations for Alagoas State, Brazil

Chuvas intensas e equações IDF para o estado de Alagoas, Brasil

Álvaro José Back1  * 
http://orcid.org/0000-0002-0057-2186

Sabrina Baesso Cadorin2 
http://orcid.org/0000-0001-5001-4986

Sérgio Luciano Galatto3 
http://orcid.org/0000-0002-4325-7936

1Estação Experimental de Urussanga. Empresa de Pesquisa Agropecuária e Extensão Rural de Santa Catarina (EPAGRI), Rodovia SC 108, km 353, nº 1563, CEP: 88840-000, Urussanga, SC, Brazil. E-mail: ajb@epagri.sc.gov.br

2Programa de Pós-Graduação em Ciências Ambientais. Universidade do Extremo Sul Catarinense (UNESC), Avenida Universitária, nº 1105, CEP: 88806-000, Criciúma, SC, Brazil. E-mail: bcadorin.sabrina@gmail.com

3Instituto de Pesquisas Ambientais e Tecnológicas. Centro de Pesquisa e Estudos Ambientais. Universidade do Extremo Sul Catarinense (UNESC), Rodovia Governador Jorge Lacerda, km 4,5, nº 3800, CEP: 88807-400, Criciúma, SC, Brazil. E-mail: sga@unesc.net


Abstract

Intensity-duration-frequency (IDF) equations have important applications in several engineering areas such as urban drainage designs, hydrological modeling, and soil conservation projects. This study analyzes the annual maximum series and fits IDF equations for 44 rainfall stations in Alagoas State, Brazil. We adjusted parameters of the Gumbel distribution (GD) and the Generalized Extreme Value (GEV) distribution. The fitting of the observed data to the probability distributions, as well as the selection of the best distribution, were based on the Kolmogorov-Smirnov and Anderson-Darling tests at a 5% significance level. The GEV distribution with parameters obtained by the L-moments method was considered the best in 73% of rainfall stations. The estimated IDF equations showed a good fit, with determination coefficients above 0.991. The maximum rainfall intensities have spatial variation following the climatic zones of the state. The fitted equations allow estimating rainfall intensities from 5 minutes to 24 hours with a return period of 2 to 100 years, and standard error of less than 6.83 mm h-1.

Keywords: drainage; probabilities; water resources

Resumo

As equações IDF tem importante aplicação em diversas áreas da engenharia como nos projetos de drenagem urbana, na modelagem hidrológica e em projetos de conservação do solo. Este trabalho teve como objetivo analisar as séries de máximas anuais e ajustar as equações IDF para 44 estações pluviométricas do estado de Alagoas, Brasil. Foram ajustados os parâmetros da distribuição de Gumbel e da distribuição Generalizada de Valores Extremos. A avaliação da aderência dos dados observados às distribuições de probabilidade bem como a seleção da melhor distribuição foi baseada nos testes de Kolmogorov-Smirnov e Anderson-Darling, ao nível de significância de 5%. A distribuição GEV com parâmetros obtidos pelo método dos L-Momentos foi considerada a melhor em 73% das estações pluviométricas. As equações IDF estimadas apresentaram um bom ajuste, com coeficientes de determinação acima de 0,991. As intensidades de chuvas máximas têm variação espacial acompanhando as zonas climáticas do estado. As equações ajustadas permitem estimativa da intensidade da chuva com duração de 5 minutos a 24 horas e período de retorno de 2 a 100 anos, com erro padrão de estimativa inferior 6,83 mm h-1.

Palavras-chave: drenagem; probabilidades; recursos hídricos

1. INTRODUCTION

Excessive rainfall can cause agricultural losses, soil erosion, and flooding. In addition to causing material damage, these events represent a major risk to life, especially in urban areas. Thus, knowledge about extreme rainfall in a given location has great application in urban and agricultural planning, besides being used in environmental risk analysis, water infrastructure projects, irrigation, and dimensioning of engineering drawings (Deng et al., 2017; Coelho Filho et al., 2017).

To characterize extreme rainfall, one must know the intensity, duration of the event, and the frequency of occurrence, which can be represented by intensity-duration-frequency (IDF) curves and equations (Silveira, 2016). These equations have great application in the hydrological dimensioning of urban drainage structures, in hydrological models for flow estimation, and in agricultural drainage and soil conservation (Marra et al., 2017; Ouali and Cannon, 2018).

Intensity-duration-frequency equations are determined using traditional methods based on data from rainfall stations (Martins et al., 2017; Tfwala et al., 2017). In the absence or scarcity of long data series, information from rainfall stations is gathered, and the maximum 1-day rainfall is clipped to shorter rainfall events, thus allowing the fitting of equations (Penner and Lima, 2016; Rangel and Hartwig, 2017; Dias and Penner, 2019). Some studies assess the possibility of using satellite or radar observations to obtain IDF equations (Marra et al., 2017).

Mirzaei et al. (2014) claim that is important to assess the uncertainties related to extreme rainfall estimates and to propagate those uncertainties into design decisions and risk assessment, and point out that uncertainty in depth-duration-frequency curves is usually disregarded in the view of difficulties associated in assigning a value to it. Mirzaei et al. (2015) investigated uncertainties incorporated in the distribution function of the series of annual maximum daily rainfall.

There are different statistical distributions of extreme events that can be used to fit a set of hydrological data. Among them is Gumbel, which is the most used for fitting data in studies of extreme rainfall (Gonçalves et al., 2019; Petrucci and Oliveira, 2019). Another distribution that has been shown to be quite adequate to represent extreme natural events such as heavy rainfall is the Generalized Extreme Value (GEV) distribution (Quadros et al., 2011; Tfwala et al., 2017). Olofintoye et al. (2009) point out that many statistical distributions can be applied to describe extreme annual rainfall events in a given location. However, choosing the appropriate model is one of the biggest problems in engineering practice, as there is no general agreement on which distribution or distributions to use in the analysis of extreme rainfall frequency. The selection of the appropriate model depends mainly on the characteristics of the available rainfall data for a particular station. That is why it is necessary to evaluate several distributions to find the model that allows obtaining the best extreme rainfall estimates.

In Alagoas State there is a lack of information on IDF equations (Dias and Penner, 2019). Thus, the present study analyzes the maximum annual rainfall and determines IDF equations in 44 rainfall stations distributed throughout Alagoas State, Brazil.

2. MATERIALS AND METHODS

Alagoas State is bathed by the Atlantic Ocean and borders the states of Pernambuco, Sergipe, and Bahia. The state is located between latitudes 8°54’57” S and 9°19’50” S and longitudes 35°09’08” W and 38°13’38” W. The relief is divided into three major types, starting from east to west through the coastal plain, followed by the tablelands region, and the plateau that corresponds to most of the Alagoas territory. According to Barros et al. (2012), the annual rainfall averages in Alagoas State vary from 2,000 mm, on the coast, to 400 mm in the hinterlands (Sertão). These values gradually decrease from east to west.

Climatic variation throughout the state is quite significant. According to the Köppen climate classification, Alagoas State is divided into four zones. There is the occurrence of humid tropical climate (Ams) and semi humid tropical climate (As) in the most coastal region of the state, which corresponds to the forest zone, the most humid part of the Agreste region, and the coast. Such climates are characterized by abundant rains throughout the year and a well-defined dry season. To the west of Alagoas, in the Agreste and Sertão, climatic classification comprises the driest types, with a hot semi arid climate (BSsh), in which evaporation exceeds rainfall. There is also the presence of As climate in a small area northwest of the state (Barros et al., 2012).

The study included rainfall data from 44 rainfall stations located in Alagoas State, belonging to the Hydrological Network of the National Water Agency (ANA). We selected stations that presented at least 20 years of data. Table 1 contains the stations used, their respective coordinates, historical series, and climate data. Figure 1 shows the spatial distribution of these stations.

Figure 1. Location map of the rainfall stations used. 

To analyze the historical series of extreme rainfall data, we used the Gumbel distribution (GD) (Type I distribution of extremes) and the Generalized Extreme Value (GEV) distribution, whose probability density functions (PDFs) are given, respectively, by Equations 1 and 2:

f(x)=1αExp-X-βα--X-βα (1)

fx=1α1-kX-βα1k-1Exp-1-kX-βα1k (2)

Where x is the maximum annual daily rainfall; α, β, and k are parameters of the probability distribution (De Alcântara et al., 2019).

Table 1. Location and data of the selected stations. 

No. Code City Latitude (°S) Longitude (°W) Period No. years Climate
1 835139 JacuÍpe 8.8419 35.4475 1990-2018 29 Am
2 935001 Flexeiras 9.2833 35.7167 1963-2000 31 As
3 935010 Maragogi 9.0167 35.2333 1963-1991 26 Am
4 935012 Murici 9.3136 35.9497 1963-2018 53 As
5 935013 Passo de Camaragibe 9.2333 35.4833 1957-1991 34 Am
6 935023 Satuba 9.5833 35.8167 1963-1991 28 Am
7 935056 Rio Largo 9.4675 35.8564 1990-2018 27 As
8 935057 Marechal Deodoro 9.7164 35.8917 1991-2018 28 Am
9 936014 Capela 9.4333 36.0833 1963-1988 26 As
10 936031 Mar Vermelho 9.4500 36.3833 1963-1994 30 As
11 936032 Palmeira dos Índios 9.3167 36.8667 1963-1989 26 As
12 936045 Santana do Mundaú 9.1667 36.2167 1963-2000 34 As
13 936048 São Miguel Dos Campos 9.7833 36.1000 1921-1984 61 As
14 936051 Traipu 9.9667 36.9833 1946-1998 49 As
15 936052 Tanque D’arca 9.5333 36.4333 1963-2000 32 As
16 936053 União dos Palmares 9.1667 36.0500 1913-1991 71 As
17 936057 Viçosa 9.3833 36.2500 1913-1989 74 As
18 936066 Arapiraca 9.7500 36.6500 1964-1991 24 As
19 936070 Anadia 9.6836 36.3036 1913-1987 72 As
20 936076 Traipu 9.9728 37.0033 1973-2018 41 As
21 936110 Atalaia 9.5072 36.0233 1990-2018 28 As
22 936111 Viçosa 9.3792 36.2492 1990-2018 28 As
23 936112 São José da Laje 9.0042 36.0511 1991-2018 28 As
24 936113 União dos Palmares 9.1544 36.0358 1991-2018 28 As
25 936115 Quebrangulo 9.3192 36.4731 1991-2010 20 As
26 937004 Poço das Trincheiras 9.2167 37.2833 1921-1989 64 As
27 937005 Santana do Ipanema 9.4667 37.4667 1964-1994 28 As
28 937006 Santana do Ipanema 9.3672 37.2292 1913-1991 69 As
29 937012 Canapi 9.1833 37.4333 1938-1991 50 As
30 937013 Delmiro Gouvéia 9.3928 37.9942 1937-2018 77 BSh
31 937016 Olho D’água das Flores 9.5333 37.2833 1963-1989 25 As
32 937017 Olho D’água do Casado 9.5167 37.8500 1963-1991 29 As
33 937018 Pão de Açúcar 9.7486 37.4497 1982-2018 36 BSh
34 937019 Pão de Açúcar 9.7333 37.4333 1913-1985 63 BSh
35 937023 Piranhas 9.6261 37.7561 1935-2018 73 BSh
36 937032 Santana do Ipanema 9.3728 37.2453 1979-2018 36 As
37 1036003 Igreja Nova 10.1167 36.6500 1964-1999 32 As
38 1036005 Penedo 10.2850 36.5564 1935-2018 82 As
39 1036007 Piaçabuçú 10.4064 36.4261 1929-2018 80 As
40 1036008 Piaçabuçú 10.4053 36.4219 1929-2000 60 As
41 1036009 Porto Real do Colégio 10.1833 36.8333 1913-1991 74 As
42 1036011 Coruripe 10.1167 36.4000 1963-1991 27 As
43 1036013 Coruripe 10.1167 36.1667 1937-1984 45 As
44 1036062 Coruripe 10.0314 36.3039 1990-2018 27 As

The parameters of the Gumbel distribution were estimated using the Moments (MM), Maximum Likelihood (MLM), and L-moments (MML) methods (Naghettini and Pinto, 2007), in addition to the method proposed by Chow (CH) (Back and Bonetti, 2014). The parameters of the GEV distribution were adjusted by the Moments (MM) and L-moments (MML) methods (Naghettini and Pinto, 2007).

Following De Alcântara et al. (2019), two tests were used to analyze the fitting to the distribution: Kolmogorov-Smirnov (KS) and Anderson-Darling (AD), considering the ranking of distributions and the respective methods of estimating parameters. The distribution with the lowest sum of the ranks of the two tests was selected.

Using the selected distribution for each data series, the values of maximum daily rainfall with return periods of 2, 5, 10, 15, 25, 50, and 100 years were determined. The breakdown of daily rainfall into shorter duration rainfall followed the methodology proposed by CETESB (1986), estimating maximum rainfall intensities for 5, 10, 15, 20, 25, 30, 60, 360, 720, and 1440 minutes.

With the values obtained from maximum rainfall intensities for different durations and return times, we determined the parameters of the Equation 3 that expresses IDF:

I=KTmt+bn (3)

Where: I is the average maximum rainfall intensity (mm h-1); K, m, b, n are the coefficients to be fitted; T is the return period (years); t is the rainfall duration (minutes).

To fit the Equation 4, we minimized the standard error (RMSE), expressed by:

RMSE= j=1nGTt-ITt2n (4)

With the IDF equations obtained for each station, we determined rainfall intensity for different durations (15, 30, and 60 minutes, and maximum daily rainfall with a 10-year return period). To represent the spatial distribution of extreme rainfall, data were interpolated in ArcGIS 10.3 software using the ordinary kriging method with spherical model.

3. RESULTS AND DISCUSSION

For all data series, neither GD nor GEV distributions were rejected by the KS and AD adhesion tests. Although all distributions were adequate, the one with the best adherence was chosen (Table 2). In general, the GEV distribution showed better results, with this distribution being chosen for approximately 80% of stations. The GEV distribution obtained by the L-moments method was highlighted with the best fitting in 32 (73%) historical series. The Gumbel distribution is widely used for maximum annual rainfall (Ottero et al., 2018; Mistry and Suryanarayana, 2019). Notwithstanding, there are studies indicating that the GEV distribution has been shown to be superior to the Gumbel distribution (Beskow et al., 2015; Namitha and Vinothkumar, 2019).

Table 2 also presents the values of the coefficients of the IDF equation fitted for each station, the standard error values, and the coefficients of determination (R²). For all stations, correlation coefficients greater than 0.991 and RMSE values ranging from 1.94 to 6.82 mm h-1 were obtained. Sabino et al. (2020) evaluated the fitting of the IDF equation for 14 rainfall stations in Mato Grosso State. The authors obtained a correlation coefficient (R2) ranging from 0.8665 to 0.9596, and RMSE ranging from 8.40 to 15.69 mm h-1. These data show the good quality of the fitting of IDF equations for Alagoas State.

The K coefficient values ranged from 268.5 to 1107.4, and the m coefficient values ranged from 0.092 to 0.324. Moreover, b values approached 9.19 for all rainfall stations, and the n coefficient values were equal to 0.706. Other studies have already reported values practically constant for parameters b and n in fitting the coefficients of the IDF equation (Caldeira et al., 2015; Souza et al., 2012). Sabino et al. (2020) fitted IDF equations for 14 rainfall stations in Mato Grosso State and also observed a higher coefficient of variation in coefficients K and b.

The K parameter is directly proportional to the rainfall intensity. The places where the highest values of this parameter were found coincide with the regions with the highest rainfall values, corresponding to the eastern/coastal region of tropical climate. In turn, the lowest K values are observed in the interior of the state, since in this region there is a dry climate. Therefore, there are coincidences with the characteristics of the Köppen climate classification, as already noted by Silva and Oliveira (2017).

Table 2. Coefficients of the fitted IDF equation with the respective RMSE and R² values. 

No. Distribution Parameter Coefficient of the IDF equation RMSE R2
α β k K m b n
1 GD-MMV 0.048 74.21 - 742.1 0.171 9.19 0.706 3.15 0.9975
2 GEV-MML 33.24 90.99 0.278 992.5 0.121 9.19 0.706 5.26 0.9946
3 GEV-MML 39.09 67.30 0.189 815.1 0.178 9.19 0.706 6.82 0.9910
4 GEV-MML 23.18 68.09 0.007 704.9 0.188 9.19 0.706 3.75 0.9966
5 GEV-MML 40.27 80.62 0.223 937.9 0.157 9.19 0.706 6.77 0.9922
6 GEV-MML 39.62 100.59 0.211 1107.4 0.142 9.19 0.706 6.49 0.9942
7 GEV-MML 24.13 80.03 -0.243 734.9 0.282 9.19 0.706 2.81 0.9991
8 GEV-MM 26.83 93.82 0.143 968.7 0.133 9.19 0.706 4.27 0.9965
9 GD-CH 0.049 64.67 - 660.6 0.184 9.19 0.706 3.27 0.9970
10 GEV-MML 29.20 67.42 -0.009 732.0 0.217 9.19 0.706 5.04 0.9954
11 GEV-MML 17.35 47.09 0.026 497.2 0.189 9.19 0.706 2.88 0.9960
12 GEV-MML 18.21 63.85 -0.020 637.3 0.179 9.19 0.706 2.77 0.9976
13 GEV-MML 17.76 68.13 -0.093 656.9 0.197 9.20 0.706 2.44 0.9984
14 GD-CH 0.053 42.69 - 466.8 0.215 9.19 0.706 3.27 0.9952
15 GEV-MML 25.10 66.02 0.460 730.3 0.092 9.19 0.706 3.62 0.9943
16 GEV-MML 23.50 54.33 -0.126 569.6 0.265 9.19 0.706 3.93 0.9968
17 GEV-MML 16.96 60.53 -0.203 561.9 0.252 9.19 0.706 2.03 0.9990
18 GEV-MML 15.94 36.06 -0.168 374.6 0.287 9.20 0.706 2.63 0.9972
19 GEV-MML 15.44 60.88 0.007 602.7 0.159 9.19 0.706 2.31 0.9978
20 GD-CH 0.064 38.28 - 411.0 0.208 9.19 0.706 2.67 0.9956
21 GEV-MML 33.71 59.51 0.026 702.1 0.228 9.19 0.706 6.23 0.9930
22 GEV-MML 18.20 51.53 -0.069 525.8 0.219 9.19 0.706 2.88 0.9971
23 GD-MMV 0.043 59.27 - 630.7 0.204 9.19 0.706 3.92 0.9959
24 GEV-MML 18.09 56.40 -0.305 504.4 0.324 9.20 0.706 1.96 0.9994
25 GEV-MML 20.41 56.74 0.163 610.0 0.146 9.19 0.706 3.35 0.9951
26 GEV-MML 19.51 56.06 0.076 591.4 0.167 9.19 0.706 3.21 0.9959
27 GEV-MML 11.35 25.25 -0.116 268.5 0.265 9.19 0.706 1.94 0.9965
28 GEV-MML 20.34 52.04 0.061 559.9 0.183 9.19 0.706 3.43 0.9953
29 GEV-MML 12.53 35.86 -0.065 365.7 0.217 9.20 0.706 1.98 0.9971
30 GEV-MML 20.28 50.05 -0.004 535.5 0.208 9.19 0.706 3.44 0.9957
31 GEV-MML 16.97 39.95 0.070 438.8 0.187 9.19 0.706 2.91 0.9947
32 GEV-MML 25.24 40.10 -0.103 478.2 0.291 9.20 0.706 4.90 0.9943
33 GEV-MML 16.15 42.03 0.004 445.5 0.200 9.19 0.706 2.70 0.9960
34 GD-MMV 0.048 52.10 - 555.5 0.205 9.20 0.706 3.48 0.9958
35 GEV-MML 20.24 42.74 -0.045 471.3 0.239 9.20 0.706 3.58 0.9953
36 GEV-MM 18.60 43.89 0.096 483.7 0.179 9.19 0.706 3.18 0.9945
37 GEV-MML 22.43 61.15 0.130 657.6 0.157 9.20 0.706 3.71 0.9952
38 GEV-MML 23.91 71.38 0.044 742.8 0.174 9.19 0.706 3.88 0.9963
39 GEV-MML 29.07 62.97 0.138 712.5 0.174 9.19 0.706 5.00 0.9935
40 GD-MM 0.038 56.79 - 627.0 0.218 9.19 0.706 4.57 0.9949
41 GD-MMV 0.047 53.39 - 570.6 0.206 9.19 0.706 3.63 0.9957
42 GD-MMV 0.040 78.50 - 800.2 0.183 9.19 0.706 3.92 0.9970
43 GEV-MML 30.94 82.40 -0.076 850.0 0.228 9.20 0.706 5.00 0.9969
44 GEV-MM 29.32 72.39 0.035 781.2 0.194 9.19 0.706 4.98 0.9953

The fitting of IDF equations allowed estimating rainfall intensities for 15, 30, and 60 minutes with a 10-year return time, in addition to the maximum 1-day rainfall, using kriging to interpolate the data (Figure 2). The highest intensities occur on the coast, decreasing from east to west. Knowledge of IDF relationships, especially in places where hydrological monitoring is scarce, is an important tool for urban, agricultural, and environmental planning. Several engineering areas demand information about extreme rainfall, such as power generation, dams, civil construction, and urban drainage.

Figure 2. Rainfall intensity (mm h-1) for different durations with a 10-year return period, and maximum 1-day rainfall. 

There is a marked spatial variation in maximum rainfall intensity in Alagoas State. Knowledge of this variation in rainfall intensity is important for planning water resource management actions as well as for soil conservation and engineering projects. The 5-minute rainfall intensity is used in the dimensioning of gutters to capture rainwater (Back and Bonetti, 2014). For soil conservation and gradient terracing, it is common to use the 15-minute rainfall intensity and a 10-year return period (De Maria et al., 2016). For level terraces, the maximum 1-day rainfall intensity and a 10-year return period is recommended. These values can be obtained from the IDF equations established for the rainfall stations in Alagoas (Table 2). The maximum 30-minute rainfall intensity is used as an indicator of the rainfall erosive potential. Therefore, Figure 2 indicates the locations with the greatest rainfall erosive potential in Alagoas State.

4. CONCLUSIONS

Alagoas’ climate is quite varied, with tropical climate to the east and dry climate to the west. The highest averages of maximum annual rainfall coincide with the regions of tropical climate.

The series of maximum annual rainfall showed good fitting to Gumbel and GEV distributions, all of which were approved by the Kolmogorov-Smirnov and Anderson-Darling adhesion tests.

The GEV distribution with parameters obtained by the L-moments method was considered the best in 73% of rainfall stations.

The estimated IDF equations showed a good fit, with determination coefficients above 0.991. These equations allow estimating rainfall intensity from 5 minutes to 24 hours with a return period of 2 to 100 years, and standard error of 6.822 mm h-1.

There is a marked spatial variation in maximum rainfall intensity in Alagoas State, showing the need for hydrological studies addressing each climatic region of the state.

5. REFERENCES

BACK, Á. J.; BONETTI, A. V. Chuva de projeto para instalações prediais de águas pluviais de Santa Catarina. Revista Brasileira de Recursos Hídricos, v. 19, n. 4, p. 260-267, 2014. https://dx.doi.org/10.21168/rbrh.v19n4.p260-267Links ]

BARROS, A. H. C.; ARAÚJO, J. C. F.; SILVA, A. B.; SANTIAGO, G. A. C. F. Climatologia do Estado de Alagoas. Dados eletrônicos. Recife: Embrapa Solos, 2012. 32 p. (Boletim de Pesquisa e Desenvolvimento). [ Links ]

BESKOW, S.; CALDEIRA, T. L.; MELLO, C. R.; FARIA, L. C.; GUEDES, H. A. S. Multiparameter probability distributions for heavy rainfall modeling in extreme southern Brazil. Journal of Hydrology: Regional Studies, 4B, p. 123-133. 2015. https://dx.doi.org/10.1016/j.ejrh.2015.06.007 Links ]

CALDEIRA, T. L.; BESKOW, S.; MELLO, C. R. de; VARGAS, M. M. ; GUEDES, H. A. S.; FARIA, L. C. Daily rainfall disaggregation: an analysis for the Rio Grande do Sul state. Scientia Agraria, v. 16, n. 3, p. 1- 21, 2015. http://dx.doi.org/10.5380/rsa.v16i3.46320Links ]

CETESB. Drenagem urbana: manual de projetos. São Paulo: DAEE/CETESB, 1986. [ Links ]

COELHO FILHO, J. A. P.; MELO, D. C. R.; ARAÚJO, M.L.M. Estudo de chuvas intensas para a cidade de Goiânia/GO por meio da modelação de eventos máximos anuais pela aplicação das distribuições de Gumbel e Generalizada de Valores Extremos. Ambiência, v. 13, n. 1, p. 75-88, 2017. https://dx.doi.org/10.5935/ambiência.2017.01.05Links ]

DE ALCÂNTARA, L. R. P.; COUTINHO, A. P.; SANTOS NETO, S. M.; DE MELO, T. A. T.; COSTA, L. V.; RIBAS, L. V. S; ANTONINO, A. C. D; ALVES, E. M. Modelos probabilísticos para eventos de precipitações extremas na cidade de Palmares-PE. Revista Brasileira de Geografia Física, v. 12, n. 4, p. 1355-1369, 2019. https://dx.doi.org/10.26848/rbgf.v12.4.p1355-1369Links ]

DE MARIA, I. C.; DRUGOWICH, M. I.; BORTOLETTI, J. O.; VITTI, A. C.; ROSSETTO, R.; FONTES, J. L. ; TCATCHENCO, J. ; MARGATHO, S. F. Recomendações gerais para a conservação do solo na cultura da cana-de-açúcar. Campinas: IAC, 2016. 100 p. (Boletim Técnico IAC, 216). [ Links ]

DENG, S.; LI, M.; SUN, H.; CHEN, Y.; QU, L. ZHANG, X. Exploring temporal and spatial variability of precipitation of Weizhou Island, South China. Journal of Hydrology: Regional Studies, v. 9, p. 183-198, 2017. https://dx.doi.org/10.1016/j.ejrh.2016.12.079Links ]

DIAS, E. C.; PENNER, G. C. Contabilização de equações de Intensidade-Duração-Frequência disponíveis no Brasil. Anuário do Instituto de Geociências, v. 42, n. 1, p. 209-216, 2019. http://dx.doi.org/10.11137/2019_1_209_216Links ]

GONÇALVES, L. J.; TAGLIAFERRE, C.; CASTRO FILHO, M. N; BRITO NETO, R. L.; SILVA, B. L; ROCHA, F. A. Determination of intensity-duration-frequency equations for sites in Bahia state. Irriga, v. 1, n. 1, p. 109-115, 2019. https://dx.doi.org/10.15809/irriga.2019v1n1p109-115Links ]

MARRA, F.; MORIN, E.; PELEG, N.; MEI, Y.; ANAGNOSTOU, E. N. Intensity-duration-frequency curves from remote sensing rainfall estimates: comparing satellite and weather radar over the eastern Mediterranean. Hydrolog. Earth Syst. Sci, v. 21, p. 2389-2404, 2017. https://dx.doi.org/10.5194/hess-21-2389-2017Links ]

MARTINS, D.; KRUK, N. S.; MAGNI, N. L. G.; QUEIROZ, P. I. B. de. Comparação de duas metodologias de obtenção da equação de chuvas intensas para a cidade de Caraguatatuba (SP). Revista DAE, p. 34-49, 2017. https://dx.doi.org/10.4322/dae.2016.033Links ]

MIRZAEI, M.; HUANG, Y.; LEE, T. S.; EL-SHAFIE, A.; GHAZALI, A. H. Quantifying uncertainties associated with rainfall depth duration frequency curves. Natural Hazards-Springer, v. 71, n. 2, p. 1227-1239, 2014. https://dx.doi.org/10.1007/s11069-013-0819-3Links ]

MIRZAEI, M.; HUANG, Y. F.; EL-SHAFIE, A.; CHIMEH, T.; LEE, J.; VAIZADEH, N.; ADAMOWSKI, J. Uncertainty analysis for extreme flood events in a semi-arid region. Natural Hazards, v. 78, n. 3, p. 1947-1960, 2015. https://dx.doi.org/10.1007/s11069-015-1812-9Links ]

MISTRY, P. B.; SURYANARAYANA, T. M. V. Estimation of Annual One Day Maximum Rainfall using Probability Distributions for Waghodia Taluka, Vadodara. Global Research and Development Journal for Engineering, p. 296-300, 2019. [ Links ]

NAGHETTINI, M.; PINTO, É. J. A. Hidrologia Estatística. Belo Horizonte: CPRM, 2007. [ Links ]

NAMITHA, M. R.; VINOTHKUMAR, V. Development of empirical models from rainfall-intensity-durations-frequency y curves for consecutive Day maximum rainfall using GEC distribution. Journal of Pharmacognosy and Phytochemistry, v. 8, n. 1, p. 275-2709, 2019. [ Links ]

OLOFINTOYE, O. O.; SULE, B.F.; SALAMI, A.W. Best-fit probability model for peak daily rainfall of selected Cities in Nigeria. New York Science Journal, v. 2, n. 3, p. 1-12, 2009. [ Links ]

OTTERO, C. R.; CHARGEL, L. T.; HORA, M. A. G. M. Análise de frequência dos dados pluviométricos observados em 2011 a 2013 na região Serrana do Rio de Janeiro. Revista Brasileira de Meteorologia, v. 33, n. 1, p.131-139, 2018. https://dx.doi.org/10.1590/0102-7786331007Links ]

OUALI, D.; CANNON, A. J. Estimation of rainfall intensity-duration-frequency curves at ungauged locations using quantile regression methods. Stochastic Environmental Research and Risk Assessment, v. 32, p. 2821-2836, 2018. https://dx.doi.org/10.1007/s00477-018-1564-7.0123Links ]

PENNER, G. C.; LIMA, M. P. Comparação entre métodos de determinação da equação de chuvas intensas para a cidade de Ribeirão Preto. Geociências, v. 35, n. 4, p. 542-559, 2016. [ Links ]

PETRUCCI, E.; OLIVEIRA, L. A. Relações entre intensidade, duração e frequência das precipitações máximas de 24 horas e equação de chuvas intensas para a cidade de Uberlândia-MG. Revista Brasileira de Climatologia, ano 15, v. 25, p. 337-354, 2019. https://dx.doi.org/10.5380/abclima.v25i0.57767 [ Links ]

QUADROS, L. E.; QUEIROZ, M. M. F.; VILAS BOAS, M. A. Distribuição de frequência e temporal de chuvas intensas. Acta Scientiarum, v. 33, n. 3, p. 401-410, 2011. https://dx.doi.org/10.4025/actasciagron.v33i3.6021Links ]

RANGEL, E. M.; HARTWIG, M. P. Análise das curvas de intensidade-duração-frequência para a cidade de Pelotas através de uma função de desagregação. Revista Thema, v. 14, n. 2, p. 63-77, 2017. https://dx.doi.org/10.15536/thema.14.2017.63-77.353Links ]

SABINO, M.; SOUZA, A. P.; ULIANA, E. M.; LISBOA, L.; ALMEIDA, F. T.; ZOLIN, C. A. Intensity-duration-frequency of maximum rainfall in Mato Grosso State. Revista Ambiente & Água, v. 15, n. 1, e2373, 2020. https://dx.doi.org/10.4136/ambi-agua.2373Links ]

SILVA, C. B.; OLIVEIRA, L. F. C. Relação Intensidade-Duração-Frequência de chuvas extremas na região Nordeste do Brasil. Revista Brasileira de Climatologia, ano 13, v. 20, p. 267-283, 2017. https://dx.doi.org/10.5380/abclima.v20i0.49286Links ]

SILVEIRA, A. L. L da. Equações cumulativas sequenciais do hietograma do método de Chicago. Revista Brasileira de Recursos Hídricos, v. 21, n. 3, p. 646-651, 2016. https://dx.doi.org/10.1590/2318-0331.011615094 [ Links ]

SOUZA, R. O. R. M.; SCARAMUSSA, P. H. M.; AMARAL, M. A. C. M.; NETO, J. A. P.; PANTOJA, A. V.; SADECK, L. W. R. Equações de chuvas intensas para o estado do Pará. Revista Brasileira de Engenharia Agrícola e Ambiental, v. 16, n. 9, p. 999-1005, 2012. https://dx.doi.org/10.1590/S1415-43662012000900011Links ]

TFWALA, C. M.; VAN RENSBURG, L. D.; SCHALL, R.; MOSIAM S. M.; DLAMINI, P. Precipitation intensity-duration-frequency curves and their uncertainties for Ghaap plateau. Climate Risk Management, v. 16, p. 1-9, 2017. https://dx.doi.org/doi.org/10.1016/j.crm.2017.04.004Links ]

Received: March 17, 2020; Accepted: September 11, 2020

*Corresponding author: Álvaro José Back, e-mail: ajb@epagri.sc.gov.br

Creative Commons License This is an open-access article distributed under the terms of the Creative Commons Attribution License