SWAT Literature Database for Peer-Reviewed Journal Articles

Title:Remote sensing and ground-based weather forcing data analysis for streamflow simulation 
Authors:Infante Corona, J.A., T. Lakhankar, S. Pradhanang and R. Khanbilvardi 
Year:2014 
Journal:Hydrology 
Volume:1(1) 
Pages:89-111 
Article ID: 
DOI:10.1080/02626667.2014.968571 
URL (non-DOI journals): 
Model:SWAT 
Broad Application Category:hydrologic only 
Primary Application Category:climate data effects 
Secondary Application Category:calibration, sensitivity, and/or uncertainty analysis 
Watershed Description:849.9 km^2 Cannonsville Reservoir (West Branch Delaware) in southern New York, 86.5 km^2 West Branch Neversink in southern New York; 4.260 km^2 Upper Hudson River in northwest New York; 4,234 km^2 Aroostook River in northern Maine, all in U.S. 
Calibration Summary: 
Validation Summary: 
General Comments: 
Abstract:Hydrological simulation, based on weather inputs and the physical characterization of the watershed, is a suitable approach to predict the corresponding streamflow. This work, carried out on four different watersheds, analyzed the impacts of using three different meteorological data inputs in the same model to compare the model’s accuracy when simulated and observed streamflow are compared. Meteorological data from the Daily Global Historical Climatology Network (GHCN-D), National Land Data Assimilation Systems (NLDAS) and the National Operation Hydrological Remote Sensing Center’s Interactive Snow Information (NOHRSC-ISI) were used as an input into the Soil and Water Assessment Tool (SWAT) hydrological model and compared as three different scenarios on each watershed. The results showed that meteorological data from an assimilation system like NLDAS achieved better results than simulations performed with ground-based meteorological data, such as GHCN-D. However, further work needs to be done to improve both the datasets and model capabilities, in order to better predict streamflow. 
Language:English 
Keywords:NLDAS; NOHRSC-ISI; GHCN-D; SWAT; meteorological data; streamflow simulation