  Time series and extreme value analyses are two statistical approaches usually
applied to study hydrological data. Classical techniques, such as ARIMA models
(in the case of mean flow predictions), and parametric generalised extreme
value (GEV) fits and nonparametric extreme value methods (in the case of
extreme value theory) have been usually employed in this context. In this
paper, nonparametric functional data methods are used to perform mean monthly
flow predictions and extreme value analysis, which are important for flood risk
management. These are powerful tools that take advantage of both, the
functional nature of the data under consideration and the flexibility of
nonparametric methods, providing more reliable results. Therefore, they can be
useful to prevent damage caused by floods and to reduce the likelihood and/or
the impact of floods in a specific location. The nonparametric functional
approaches are applied to flow samples of two rivers in the U.S. In this way,
monthly mean flow is predicted and flow quantiles in the extreme value
framework are estimated using the proposed methods. Results show that the
nonparametric functional techniques work satisfactorily, generally
outperforming the behaviour of classical parametric and nonparametric
estimators in both settings.
