Distribution function estimation of the random variable of river flow is an important problem in hydrology. This issue is directly related to quantile estimation, and consequently to return level prediction. The estimation process can be complemented with the construction of confidence intervals (CIs) to perform a probabilistic assessment of the different variables and/or estimated functions. In this work, several methods for constructing CIs using bootstrap techniques, and parametric and nonparametric procedures in the estimation process are studied and compared. In the case that the target is the joint estimation of a vector of values, some new corrections to obtain joint coverage probabilities closer to the corresponding nominal values are also presented. A comprehensive simulation study compares the different approaches, and the application of the different procedures to real data sets from four rivers in the United States and one in Spain complete the paper.
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