Comparison of short-term streamflow forecasting using stochastic time series, neural networks, process-based, and Bayesian models. (April 2020)

• Record Type: Journal Article
• Authors: Wagena, Moges B.; Goering, Dustin; Collick, Amy S.; Bock, Emily; Fuka, Daniel R.; Buda, Anthony; Easton, Zachary M.
• Is Part Of: Environmental modelling & software. Volume 126(2020)
• Publication Date: 2020-04
• Subjects: SWAT-VSA -- ANNs -- ARMA -- Forecasting -- Stochastic model -- Process-based model -- Bayesian model; Environmental monitoring -- Computer programs -- Periodicals; Ecology -- Computer simulation -- Periodicals; Digital computer simulation -- Periodicals; Computer software -- Periodicals; Environmental Monitoring -- Periodicals; Computer Simulation -- Periodicals; Environnement -- Surveillance -- Logiciels -- Périodiques; Écologie -- Simulation, Méthodes de -- Périodiques; Simulation par ordinateur -- Périodiques; Logiciels -- Périodiques; Computer software; Digital computer simulation; Ecology -- Computer simulation; Environmental monitoring -- Computer programs; Periodicals; Electronic journals; 363.70015118
• DOI: 10.1016/j.envsoft.2020.104669
• Languages: English
• ISSNs: 1364-8152

Abstract

Abstract: Streamflow forecasts are essential for water resources management. Although there are many methods for forecasting streamflow, real-time forecasts remain challenging. This study evaluates streamflow forecasts using a process-based model (Soil and Water Assessment Tool-Variable Source Area model-SWAT-VSA), a stochastic model (Artificial Neural Network -ANN), an Auto-Regressive Moving-Average (ARMA) model, and a Bayesian ensemble model that utilizes the SWAT-VSA, ANN, and ARMA results. Streamflow is forecast from 1 to 8 d, forced with Quantitative Precipitation Forecasts from the US National Weather Service. Of the individual models, SWAT-VSA and the ANN provide better predictions of total streamflow (NSE 0.60–0.70) and peak flow, but underpredicted low flows. During the forecast period the ANN had the highest predictive power (NSE 0.44–0.64), however all three models underpredicted peak flow. The Bayesian ensemble forecast streamflow with the most skill for all forecast lead times (NSE 0.49–0.67) and provided a quantification of prediction uncertainty. Highlights: Several modeling techniques are developed and forced with Quantitative Precipitation Forecasts to predict streamflow. Both stochastic and process-based models are capable of providing valuable streamflow forecast information. An ensemble model forecast streamflow with the greatest predictive power and quantified uncertainty in predictions.