Estudos sobre a probabilidade de chuvas e suas variações espaço-temporais são importantes para o planejamento de recursos hídricos e otimização do calendário de atividades agrícolas. O presente estudo identifica a ocorrência de chuvas pela cadeia de Markov de primeira ordem (CM) e por dois estados da bacia hidrográfica do rio Tapajós (BHRT), Amazônia, Brasil. A análise de agrupamento (AC), baseada no método de Ward, classificou regiões homogêneas e selecionou a amostra para verificar a probabilidade de ocorrência de chuvas por estação. A série histórica de dados diários de precipitação de 80 estações compreendendo o período de 1990-2014 foi usada. A técnica AC identificou 8 regiões homogêneas e suas probabilidades de ocorrência de chuvas, auxiliando assim no estabelecimento de períodos com maior necessidade de agricultura irrigada de acordo com a região. Os resultados sobre a probabilidade de ocorrência de períodos secos e chuvosos na bacia definiram as estações seca (maio a setembro) e chuvosa (outubro a abril). Elementos das probabilidades da matriz de transição mostraram variabilidade em relação ao tempo e, além disso, a influência da posição geográfica da precipitação sazonal na determinação dos períodos seco e chuvoso em locais específicos na bacia do rio Tapajós.
Palavras-chave: análise de agrupamentos, cadeias de Markov, dias secos e chuvosos.
Studies on the probability of rainfall and its spatiotemporal variations are important for the planning of water resources and optimization of the calendar of agricultural activities. This study identifies the occurrence of rain by first-order Markov Chain (MC) and by two states in the Tapajos River Basin (TRB), Amazon, Brazil. Cluster analysis (CA), based on the Ward method, was used to classify homogeneous regions and select samples for checking the probability of rainfall occurrence by season. The historical series of daily rainfall data of 80 stations were used for the period 1990-2014. The CA technique identified 8 homogeneous regions and their probability of occurrence of rainfall, helping to determine which regions and periods have greater need of irrigation. Results of the probability of occurrence of dry and rainy periods in the TRB were used to define the dry (May thru September) and rainy seasons (October thru April). Elements of the matrix transition probabilities showed variability in relation to time and, in addition, the influence of geographical position of seasonal rainfall in determining dry and rainy periods at specific sites in the TRB.
Studies on the probability of rainfall and its spatiotemporal variations are important for the planning of water resources and optimization of the calendar of agricultural activities. This study identifies the occurrence of rain by first-order Markov Chain (MC) and by two states in the Tapajos River Basin (TRB), Amazon, Brazil. Cluster analysis (CA), based on the Ward method, was used to classify homogeneous regions and select samples for checking the probability of rainfall occurrence by season. The historical series of daily rainfall data of 80 stations were used for the period 1990-2014. The CA technique identified 8 homogeneous regions and their probability of occurrence of rainfall, helping to determine which regions and periods have greater need of irrigation. Results of the probability of occurrence of dry and rainy periods in the TRB were used to define the dry (May thru September) and rainy seasons (October thru April). Elements of the matrix transition probabilities showed variability in relation to time and, in addition, the influence of geographical position of seasonal rainfall in determining dry and rainy periods at specific sites in the TRB.
