Copula-based modeling and stochastic simulation of seasonal intermittent streamflows for arid regions

Streamflow is often intermittent in arid and semi-arid regions. Stochastically simulated data play a key role in managing water resources with intermittent streamfiows. The stochastic modeling of intermittent streamflow that incorporates the seasonality of key statistics is a difficult task. In the current study, the product model was tested to simulate the intermittent monthly streamflow by employing the periodic Markov chain (PMC) model for occurrence and the periodic gamma autoregressive (PGAR) and copula models for amount. The copula models were tested in a previous study for the simulation of yearly streamflow, resulting in successful replication of the key and operational statistics of historical data; however, the copula models have never been tested on a monthly time scale. The intermittent models were applied to the Colorado River system in the present study. A few drawbacks of the PGAR model were identified, such as significant underestimation of minimum values on an aggregated yearly time scale and restrictions of the parameter boundaries. Conversely, the copula models do not present such drawbacks but show feasible reproduction of key and operational statistics. We concluded that the copula models combined with the PMC model is a feasible method for the simulation of intermittent monthly streamflow time series. (C) 2014 International Association for Hydro-environment Engineering and Research, Asia Pacific Division. Published by Elsevier B.V. All rights reserved.