| Title: | Comparison of short-term streamflow forecasting using stochastic time series, neural networks, process-based, and Bayesian models |
| Authors: | Wagena, M.B., D. Goering, A.S. Collick, E. Bock, D.R. Fuka, A. Buda and Z.M. Easton |
| Year: | 2020 |
| Journal: | Environmental Modelling & Software |
| Volume (Issue): | 126 |
| Pages: | |
| Article ID: | 104669 |
| DOI: | 10.1016/j.envsoft.2020.104669 |
| URL (non-DOI journals): | |
| Model: | SWAT-VSA |
| Broad Application Category: | hydrologic only |
| Primary Application Category: | model and/or data comparison |
| Secondary Application Category: | extreme low and/or high flows/events |
| Watershed Description: | 7.38 km^2 WE-38 experimental, a tributary of Mahantango Creek located in east central Pennsylvania, U.S. |
| Calibration Summary: | |
| Validation Summary: | |
| General Comments: | |
| Language: | English |
| Keywords: | SWAT-VSA, ANNs, ARMA, Forecasting, Stochastic model, Process-based model, Bayesian model |