Streamflow simulation and skewness preservation based on the bootstrapped stochastic models

Abstract

The stochastic model has been widely used for the simulation study. However, there was a difficulty in the reproduction of the skewness of observed series and so the stochastic model for the skewness preservation was appeared. While the skewness in the residuals of the stochastic model has been considered for the skewness preservation this study uses a random resampling technique of residuals from the stochastic models for the simulation study and for the investigation of the skewness coefficient. The main advantage of this resampling scheme, called the bootstrap method is that it does not rely on the assumption of population distribution and this study uses the combined model of the stochastic and bootstrapped models. The stochastic and bootstrapped stochastic (or combined) models are used for the investigations of skewness preservation and of the reproduction of probability density function between the simulated series. The models are applied to the annual and monthly streamflows of Yongdam site in Korea and Yakima river, Washington, USA for the streamflow simulation study then the statistics and probability density functions for the observed and simulated streamflows are compared. As the results the bootstrapped stochastic model reproduces the skewness and probability density function much better than the stochastic model. This evidences suggest that the bootstrapped stochastic model might be more appropriate than the stochastic model for the preservation of skewness and for simulation purposes of the series.

Numerical and multivariate stochastic approaches to characterize flow in a constructed wetland basin

“…Stochastic methods have been widely used in the fields of hydrology (Cantet et al 2011;Haan 1994;Lee and Yoo 2001), hydraulics (Chen et al 2009;Erik 2011;Kim et al 2004) and agriculture (Perreault et al 2000;Wu and Wang 1998;Young et al 2011). The nonlinear nature of dynamic pressure and dissipation rate is confirmed from Figs.…”

Streamflow Simulation Using Bayesian Regression with Multivariate Linear Spline to Estimate Future Changes

“…The SAR model parameters are estimated using the maximum likelihood method which auto-detects the appropriate order of seasonal differencing. The estimated model is denoted byf 1 . Running the optimization on our data, we discovered s = 10 lag maximizes the overall joint likelihood.…”

“…Decadal simulation of streamflow provides years-ahead insight on the capacity, adequacy, and reliability of a reservoir based on the anthropogenic changes in runoff [1][2][3]. Conceptual and physically based models of the watershed, for example, SWAT (Soil and Water Assessment Tool), VIC (Variable Infiltration Capacity Macroscale Hydrologic Model), and PIHM (Penn State Integrated Hydrologic Modeling System) remain a popular choice among water resources managers.…”

“…Random number generator was used by Hirsch [8] to simulate the traces of normally distributed innovations. Following that, studies [9][10][11] employed several random sampling algorithms for uncertainty analysis and comparison purposes of network design [1]. Recent studies focused on developing copula-based methods for monthly streamflow simulations [12,13].…”

Analysis of the Influence of Rainfall Spatial Uncertainty on Hydrological Simulations Using the Bootstrap Method

“…To this end, first, the bootstrap method, which has the advantages of simplicity (i.e., no need to make any assumption of normality) and high-accuracy (i.e., asymptotically more accurate than the results obtained using sample variance and assumption of normality) [35][36][37], was introduced in this study to quantitatively analyze the spatial uncertainty of rainfall in river basins at different scales [38][39][40]. Second, by employing a distributed hydrological model on a high-performance computing (HPC) system, the uncertainty of simulated runoff was also analyzed using the bootstrap method.…”