SWAT Literature Database for Peer-Reviewed Journal Articles

Title:A generic system dynamics model for simulating and evalutating the hydrological performance of reconstructed watersheds 
Authors:Keshta, N., A. Elshorbagy and S. Carey 
Journal:Hydrology and Earth System Sciences 
Article ID: 
URL (non-DOI journals):http://www.hydrol-earth-syst-sci.net/13/865/2009/hess-13-865-2009.html 
Broad Application Category:hydrologic only 
Primary Application Category:model comparison 
Secondary Application Category:hydrologic assessment 
Watershed Description:Reconstructed watersheds in northern Alberta that cover a total area of approximately 2 km^2 
Calibration Summary: 
Validation Summary: 
General Comments:This study also reports some comparisons between the GSDW model and SWAT. 
Abstract:A generic system dynamics watershed (GSDW) model is developed and applied to five reconstructed watersheds located in the Athabasca mining basin, Alberta, Canada, and one natural watershed (boreal forest) located in Saskatchewan, Canada, to simulate various hydrological processes in reconstructed and natural watersheds. This paper uses the root mean square error (RMSE), the mean absolute relative error (MARE), and the correlation coefficient (R) as the main performance indicators, in addition to the visual comparison. For the South Bison Hills (SBH), South West Sand Storage (SWSS) and Old Aspen (OA) simulated soil moisture, the RMSE values ranges between 2.5–4.8 mm, and the MARE ranges from 7% to 18%, except for the D2- cover it was 26% for the validation year. The R statistics ranges from 0.3 to 0.77 during the validation period. The error between the measured and simulated cumulative actual evapotranspiration (AET) flux for the SWSS, SBH, and the OA sites were 2%, 5%, and 8%, respectively. The developed GSDW model enables the investigation of the utility of different soil cover designs and evaluation of their performance. The model is capable of capturing the dynamics of water balance components, and may used to conduct short- and longterm predictions under different climate scenarios.