SWAT Literature Database for Peer-Reviewed Journal Articles

Title:Fitting of Time Series Models to Forecast Streamflow and Groundwater Using Simulated Data from SWAT 
Authors:Vazquez-Amabile, G. and B.A. Engel 
Year:2008 
Journal:Journal of Hydrologic Engineering 
Volume:Vol. 13, No. 7 
Pages:554–562 
Article ID: 
DOI:10.1061/(ASCE)1084-0699(2008)13:7(554) 
URL (non-DOI journals): 
Model:SWAT 
Broad Application Category:hydrologic only 
Primary Application Category:hydrologic assessment 
Secondary Application Category:calibration, sensitivity, and/or uncertainty analysis 
Watershed Description:Six watersheds that ranged between 244.3 and 2,553.1 km^2 in size and were located in either northeast or southeast Indiana (within the larger St. Joseph and Muscatatuck River watersheds) 
Calibration Summary: 
Validation Summary: 
General Comments: 
Abstract:Time series models provide a valuable tool for simulation and forecasting hydrologic variables. However, time series models require fitting long series of records. This study explores the applicability of soil water assessment tool (SWAT), a deterministic hydrologic model, to generate long data series to fit autoregressive and autoregressive moving average models, in order to perform short-term forecasting of monthly streamflow and groundwater table depth in areas that lack long historical records. SWAT performed well in reproducing the statistical structure of the variables making it possible to fit time series models to simulated series. Time series models fitted to SWAT simulated data and to historical records showed a similar but poor performance to forecast monthly streamflow in all watersheds. However, time series fitted to SWAT data for groundwater table depth showed good performance for forecasting this variable with correlation coefficients between 0.58 to 0.70 and Nash-Sutcliffe model efficiencies from 0.22 to 0.46 in the validation period. 
Language:English 
Keywords:Time series analysis, Auto-regressive models, Hydrologic models, Ground-water management, Streamflow, Forecasting, Simulation