Abstract
Detecting trends in streamflow and rainfall series can have great significance for proper water resource management. Thus, the objective of this study was to analyze the trends of historical streamflow and rainfall series using nonparametric statistical tests. A historical series of pluviometric and fluviometric gauges, which belong to the hydrometeorological network of the Brazilian Water National Agency in the Santo Antônio River Basin, Brazil, from 1985 to 2014 were used. By applying statistical tests, it was found that the time series are independent and random, and from the total 24 rainfall gauges evaluated, 12 presented nonstationary behavior, exhibiting mostly decreasing trends. Based on the six fluviometric gauges used for the annual streamflow series, only the annual data of one gauge tended to decrease to the minimum streamflow. However, for the monthly series, three gauges showed decreasing trends between July and September. This decrease in streamflow may be a consequence of rainfall reductions, high water demand, and changes in land use and cover.
Highlights
As water is a fundamental natural element for the existence of life on Earth, it is necessary to develop strategies for its resource management
By analyzing the Q7 occurrence data for the historical series of the base period of the fluviometric gauges, it was found that the highest incidence occurred in September and October
The hydrological year for the Santo Antônio River Basin was defined to occur from November to October, with a rainy semester occurring from November to April and a dry semester from May to October
Summary
As water is a fundamental natural element for the existence of life on Earth, it is necessary to develop strategies for its resource management. Research is required to better understand the hydrological variables that interfere with water dynamics. In this sense, the streamflow of watercourses is characterized as one of the most important variables as it represents the support capacity of a water basin (Uliana et al, 2015). Water resource management systems worldwide face problems related to these events, and they are generally designed and operated with the hypothesis of stationarity (Jiang et al, 2015; Milly et al, 2008; VerdonKidd & Kiem, 2015)
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