Regionalization of precipitation with determination of homogeneous regions via fuzzy c-means

• Autor(a) principal: Gomes,Evanice Pinheiro
• Data de Publicação: 2018
• Outros Autores: Blanco,Claudio José Cavalcante, Pessoa,Francisco Carlos Lira
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: RBRH (Online)
• Texto Completo: http://old.scielo.br/scielo.php?script=sci_arttext&pid=S2318-03312018000100247
• topic: PBM index; Probability distribution models; Multiple regression models; Tocantins-Araguaia Hydrographic Region

ABSTRACT Knowledge about precipitation is indispensable for hydrological and climatic studies because precipitation subsidizes projects related to water supply, sanitation, drainage, flood and erosion control, reservoirs, agricultural production, hydroelectric facilities, and waterway transportation and other projects. In this context, methodologies are used to estimate precipitation in unmonitored locations. Thus, the objectives of this work are to i) identify homogeneous regions of precipitation in the Tocantins-Araguaia Hydrographic Region (TAHR) via the fuzzy c-means method, ii) regionalize and estimate the probability of occurrence of monthly and annual average precipitation using probability distribution models, and iii) regionalize and estimate the precipitation height using multiple regression models. Three homogeneous regions of precipitation were identified, and the results of the performance indices from the regional models of probability distribution were satisfactory for estimating average monthly and annual precipitation. The results of the regional multiple regression models showed that the annual mean precipitation was satisfactorily estimated. For the average monthly precipitation, the estimates of multiple regression models were only satisfactory when the months used were distributed in the dry and rainy seasons. Therefore, our results show that the methodology developed can be used to estimate precipitation in unmonitored locations in the TAHR.