Abstract
Alternative methods to the intensity-duration-frequency equations have been used for intense rainfall study in places with no pluviographic data. A pioneering study, by CETESB (1986), established rainfall disaggregation coefficients for Brazil; however, the use of these coefficients take into account neither the rainfall characteristics of the interest locality nor the return period influence in obtaining the precipitation intensities. This study aimed to determine rainfall disaggregation coefficients for Doce river basin; to verify the validity of the regional coefficients use for the entire basin; and to evaluate the return period influence on these coefficients. Data from 19 gauges located in the basin, with rainfall associated with durations of 10 to 1440 min and return periods of 2, 5, 10, 20, 50 and 100 years, were used to obtain the coefficients. Student's t-test was used to assess the regional applicability of these coefficients and the return period influence. The coefficients do not provide regional validity for the entire basin; therefore, regional coefficients were estimated for five hydrologically homogeneous regions, confirming the validity of these coefficients use and the influence of the return period. We conclude that the disaggregation coefficients must be specific for the homogeneous regions and for each return period.
Highlights
Intensive rainfall is one of the most important meteorological variables in climate studies, as it generates a considerable volume of water in short intervals (Bielenki Júnior et al, 2016; Pereira et al, 2017; Silva et al, 2003)
The database used belongs to the hydrometeorological network of the National Water and Sanitation Agency of Brazil (ANA), which consists of records from 19 pluviograph gauge stations located in the Doce River basin (Table 1)
The regional rainfall disaggregation coefficients obtained for the entire Doce river basin were not adequate for the use in daily rainfall disaggregation studies
Summary
Intensive rainfall is one of the most important meteorological variables in climate studies, as it generates a considerable volume of water in short intervals (Bielenki Júnior et al, 2016; Pereira et al, 2017; Silva et al, 2003). The pluviographic series, besides a smaller number, have generally shorter periods and with more failures than the pluviometric series (Back, 2020) This has led to the use of relations that allow the estimation of critical rainfall based on pluviometric data, i.e., using daily rainfall data (Silva & Oliveira, 2017), since in Brazil, there is an extensive pluviometric monitoring network (Dorneles et al, 2019)
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