## 1. Introduction

[2] Synthetic streamflow data are needed in water resources studies for the evaluation of alternative designs and policies against the range of sequences that are likely to occur in the future [*Loucks et al.*, 1981]. It is desired that synthetic streamflow is similar to historical streamflow and preserves moment statistics (such as mean, standard deviation, and skewness) and dependence structure (such as lag-one correlation). The interannual dependence is important for the simulation of long wet and dry periods [*Sivakumar and Berndtsson*, 2010], and also needs to be preserved. *Sivakumar and Berndtsson* [2010] provided a review of streamflow simulation models.

[3] The copula method has been extensively applied for hydrologic modeling mainly due to its flexibility in constructing the joint distribution to describe the dependence structure between random variables. One of the most common applications is frequency analysis of hydrological variables [*Favre et al.*, 2004; *Salvadori and De Michele*, 2004; *Genest et al.*, 2007; *Kao and Govindaraju*, 2008; *Chebana and Ouarda*, 2009; *Salvadori and De Michele*, 2010; *Renard*, 2011; *Vandenberghe et al.*, 2011]. Recent years have been witnessing an upsurge in applications of the copula method. *Bardossy and Li* [2008] introduced a copula-based model to describe spatial variability for the interpolation of groundwater quality parameters. *Serinaldi* [2009] employed the bivariate copula-based mixed distribution to deduce the multisite Markov model for modeling and generating daily rainfall series. *Chowdhary and Singh* [2010] developed a copula-based approach for reducing uncertainty in the parameter estimation of frequency distributions. *Gyasi-Agyei* [2011] used a copula to model the dependence structure of daily rainfall properties for daily rainfall disaggregation.

[4] One of the important applications of entropy theory is to derive the maximum entropy-based distribution of random variables [*Kapur*, 1989; *Kesavan and Kapur*, 1992]. *Hao and Singh* [2011] proposed the entropy method for single-site monthly streamflow simulation with the entropy-based joint distribution and *Lee and Salas* [2011] proposed the copula method for annual streamflow simulation with the copula-based joint distribution. This study proposes an entropy-copula method for monthly streamflow simulation in which the joint distribution is constructed using the copula method with the marginal distribution derived using the entropy method. The entropy-copula method is simpler than the previous work by *Hao and Singh* [2011], since less parameters are determined simultaneously and is able to model different (nonlinear) dependence structures of streamflow due to the copula component. To model the interannual dependence of monthly streamflow, an aggregate variable is used to guide the simulation. The proposed method is applied to the monthly streamflow of the Colorado River at Lees Ferry, Arizona, and its performance is evaluated by comparing generated and observed statistics.