## 1. Introduction

[2] In the last several years, the development and use of mixture distribution models has increased due to their flexibility [*Evin et al.*, 2011], in that they allow the user to model series of data from different populations. These models have been used with success for various geophysical applications, e.g., statistical downscaling of precipitation [*Vrac and Naveau*, 2007], simulating extreme rainfall events [*Furrer and Katz*, 2008], flow analysis [*Evin et al.*, 2011], and time series simulations of sea state parameters [*Solari and Losada*, 2011].

[3] In general, the mixture models used have been composed of a central distribution and one (or two) distribution(s) of the tail(s). For these models, either the transition (threshold) values between the central and tail distributions are left undefined (e.g., *Vrac and Naveau* [2007] and *Hundecha et al.* [2009] use dynamic models imposing the threshold equal to zero], or these are defined a priori using a different method from that used to estimate the other parameters of the model [*Furrer and Katz*, 2008]. Recently, *Carreau et al.* [2009] expressed threshold values as a function of the other parameters of the model. Therefore, it should be investigated whether it is possible to include the determination of threshold values in the estimation method used for the distribution parameters. If so, it seems reasonable to explore whether the threshold value of the upper tail is a good choice as the estimate of the threshold value required to apply the peaks over threshold (POT) method. This would provide another way to estimate threshold values that is complementary to that described by *Coles* [2001], which unfortunately cannot be automated and requires user intervention in the process. The aim of this paper is to analyze the potential of applying a mixture model to parametrically model the entire range of values of hydrological variables and use the obtained upper thresholds values to define the series of peaks in the POT method.

[4] As an alternative to previously used models, we propose the use of a mixture model that is composed of a truncated central distribution, representative of the central regime, and two generalized Pareto distributions (GPD) for the upper and lower tails representing the maximum and minimum regimes, respectively. The transition thresholds between the three distributions are parameters of the model and are calculated by maximum likelihood (ML) simultaneously with the other parameters in the model. The former objective (i.e., to obtain a distribution for the entire range of values of a variable) has been found to be particularly useful when long term simulations of both, central and extreme values, is required [see, e.g., *Solari and Losada*, 2011; *Solari and van Gelder*, 2011].

[5] This paper is organized as follows. In section 2 the background, the models, and methodologies used are presented, their advantages and disadvantages are discussed. In section 3 an alternative mixture model and a working methodology are introduced to solve the main problems identified in section 2. The behavior of the proposed model is analyzed in section 4 by means of a simulation study. In section 5 the results from applying the model to four data sets are presented, analyzing its capacity to fit the entire ranges of the values of the variables. We focused particularly in the tails and the potential use of the upper threshold when applying the POT method. Finally, section 6 summarizes the conclusions. In the Appendix, specific aspects concerning the estimation methodology of the model parameters are discussed.