Cluster analysis identified rainfall homogeneous regions in Tocantins state, Brazil

Oliveira‐Júnior, José Francisco de; Xavier, Fábio Miranda Gomes; Teodoro, Paulo Eduardo; Góis, Givanildo de; Delgado, Rafael Coll

doi:10.14393/bj-v33n2-32739

Cited by 19 publications

(10 citation statements)

References 12 publications

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“…However, the station (649000) did not present any occurrence of the reduction of the mixed exponential (Figure 5), and this one is also inserted in the Amazon–Cerrado ecotone biome, however, the station is further north of HR II. According to Oliveira Junior et al (2017), this region is more influenced by the rainfall regimes of the Amazon region. Thus, the ecotone regions should be analyzed more carefully.…”

Section: Resultsmentioning

confidence: 99%

“…What motivates this study is the importance of the TAHR in the Brazilian context because, among its resources, the hydroelectric and mineral production and its vocation for agribusiness are outstanding; thus, knowledge of the temporal and spatial variability of the precipitation is indispensable (Oliveira Junior, Xavier, Teodoro, Gois, & Delgado, 2017; Silva Junior et al, 2019). Thus, this study aims to evaluate the applicability of four CDFs (exponential, two‐parameter exponential, mixed exponential, and gamma) to estimate the probability of occurrence of daily rainfall in the TAHR.…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Resultsmentioning

confidence: 99%

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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“…Many studies have opted for cluster analysis in identifying homogeneous rainfall regions, e.g., Terassi et al [23], Lyra et al [24], Goyal et al [25] and Oliveira-Junior et al [26]. However, the approaches and the algorithms used to perform the cluster analysis are inherently different.…”

Section: Cluster Analysismentioning

confidence: 99%

A Novel Approach for Delineation of Homogeneous Rainfall Regions for Water Sensitive Urban Design—A Case Study in Southeast Queensland

Rasheed

Egodawatta

Goonetilleke

et al. 2019

Water

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The delineation of homogeneous regions is primarily based on long-term overall rainfall characteristics and therefore does not necessarily consider the homogeneity of event-based rainfall characteristics. However, event-based rainfall characteristics including antecedent dry days, rainfall intensity, total rainfall and total duration of rainfall events are critical for Water Sensitive Urban Design (WSUD). Accordingly, this study presents a novel approach to objectively identify homogeneous rainfall regions based on event-based rainfall characteristics. This approach uses cluster analysis and Hosking–Wallis heterogeneous tests collectively to test the homogeneity of event-based rainfall characteristics. A case study conducted for southeast Queensland (SEQ), Australia is also presented in this article. This study compares the results of the novel modified approach against results of the conventional approach for the delineation of homogeneous regions. It was evident from the results that the entire SEQ could be treated as a homogeneous rainfall region based on the conventional approach. In contrast, based on the modified approach, the coast and the inland of SEQ were identified as separate homogeneous regions. Further, antecedent dry days and rainfall intensity were recognized as the deciding rainfall characteristics in the delineation of homogeneous rainfall regions.

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“…2019 series (Dourado et al, 2013). Oliveira-Júnior et al (2017) reported that the identification of homogeneous zones was fundamental for agricultural planning. Marzban and Sandgathe (2006) showed that clustering is an effective algorithm when using observed meteorological data.…”

Section: Introductionmentioning

confidence: 99%

Identification of Homogeneous Rainfall Zones During Grain Crops in Paraná, Brazil

et al. 2019

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The aim of this study is to identify homogeneous rainfall zones in the winter and summer 1 st and 2 nd crops, in the state of Paraná, Brazil. The zones were defined by clustering using the expectation-maximization (EM) algorithm to transform seasonal rainfall series. Monthly average rainfall data collected from 157 weather stations for 20 years (1996 to 2015) were employed. The results show that the number of homogeneous zones varied among growing seasons. The summer crop presented two clusters, with rainfall averages of 1489 and 1925 mm; the second crop presented four clusters, with averages of 1849, 1004, 1454, and 1182 mm; and the winter crop had three clusters, with averages of 969, 1498, and 1171 mm. Clustering was a useful instrument to identify geographical regions with similar rainfall regimes during different growing seasons in the state of Paraná. Rainfall distribution was more homogeneous in the summer crop. In all crops analyzed, the clusters with the lowest rainfall rate were present in the northwestern, northern center, and northern pioneer of the state of Paraná, whereas the clusters with the highest rainfall rate were found in the coastal regions.

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Cluster analysis identified rainfall homogeneous regions in Tocantins state, Brazil

Cited by 19 publications

References 12 publications

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

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

A Novel Approach for Delineation of Homogeneous Rainfall Regions for Water Sensitive Urban Design—A Case Study in Southeast Queensland

Identification of Homogeneous Rainfall Zones During Grain Crops in Paraná, Brazil

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