A hybrid stochastic model for multiseason streamflow simulation

Abstract

Abstract. A hybrid model is presented for stochastic simulation of multiseason streamflows. This involves partial prewhitening of the streamflows using a parsimonious linear periodic parametric model, followed by resampling the resulting residuals using moving block bootstrap to obtain innovations and subsequently postblackening these innovations to generate synthetic replicates. This model is simple and is efficient in reproducing both linear and nonlinear dependence inherent in the observed streamflows. The first part of this paper demonstrates the hybrid character of the model through stochastic simulations performed using monthly streamflows of Weber River (Utah) that exhibit a complex dependence structure. In the latter part of the paper the hybrid model is shown to be efficient in simulating multiseason streamflows, through an example of the San Juan River (New Mexico). This model ensures annual-to-monthly consistency without the need for any adjustment procedures. Furthermore, the hybrid model is able to preserve both within-year and cross-year monthly serial correlations for multiple lags. Also, it is seen to be consistent in predicting the reservoir storage (validation) statistic at low as well as high demand levels.

Copula-based stochastic simulation of hydrological data applied to Nile River flows

“…Srinivas & Srinivasan 2001, 2005a, b, 2006 or modelled with non-parametric density estimator (NPD) as in Kim & Valdes (2005). However, the generated values obtained using the technique of Srinivas & Srinivasan (2001,2005a, b, 2006 are still limited to the variability obtained from Equation (1) and may not produce expected extremes in the lower and upper tails. On the other hand, the marginal distribution of Y, obtained following the NPD (Kim & Valdes 2005) may not reproduce the marginal distribution of Y,.…”

“…In addition, semi-parametric models have been developed by combining parametric and non-parametric models to mitigate the drawbacks in both type of models (e.g. Srinivas & Srinivasan 2001, 2005a, b, 2006Kim & Valdes 2005). In these semi-parametric models, the Autoregressive (AR) model component is extracted first from the historical data as…”

Streamflow simulation and skewness preservation based on the bootstrapped stochastic models

“…Also, Cover and Unny(1986) used the bootstrap to estimate the parameter uncertainty in autoregressive moving average (ARMA) model for a streamflow series. Lall and Sharma (1996), Vogel and Shallcross (1996), Sharma et al (1997), and Srinivas and Srinivasan (2000, 2001a, 2001b applied the bootstrap to the flows instead of using the residuals from a model to generate a long time series of flows and they thereby avoided the assumptions for the dependent form of a stochastic model.…”

Serial dependence properties in multivariate streamflow simulation with independent decomposition analysis

“…Stochastic simulation of multivariate streamflow data has been employed for the evaluation of alternative designs and operation rules for water resources systems. Various alternatives have been developed by hydrologists and statisticians with parametric (Salas et al , 1980; Stedinger and Taylor, 1982a,b; Fernandez and Salas, 1990; Salas, 1993) and nonparametric (Lall and Sharma, 1996; Vogel and Shallcross, 1996; Ouarda et al , 1997; Salas and Lee, 2010) frameworks as well as their combined version (Koutsoyiannis, 2000; Srinivas and Srinivasan, 2001; Kim and Valdes, 2005; Srinivas and Srinivasan, 2005a, 2006b; Lee and Ouarda, 2010). Multivariate Autoregressive Moving average (MARMA) (Stedinger et al , 1985; Brockwell and Davis, 2003) has been commonly used as a representative parametric approach of multivariate hydrologic datasets.…”