SWAT Literature Database for Peer-Reviewed Journal Articles

Title:Using NEXRAD and rain gauge precipitation data for hydrologic calibration of SWAT in a northeastern watershed 
Authors:Sexton, A.M., A.M. Sadeghi, X. Zhang, R. Srinivasan and A. Shirmohammadi 
Year:2010 
Journal:Transactions of the ASABE 
Volume:53(5) 
Pages:1501-1510 
Article ID: 
DOI:10.13031/2013.34900 
URL (non-DOI journals):http://ddr.nal.usda.gov/handle/10113/46663 
Model:SWAT 
Broad Application Category:hydrologic only 
Primary Application Category:climate data effects 
Secondary Application Category:hydrologic assessment 
Watershed Description:50 km^2 German Branch in eastern Maryland, USA 
Calibration Summary: 
Validation Summary: 
General Comments: 
Abstract:The value of watershed‐scale, hydrologic and water quality models to ecosystem management is increasingly evident as more programs adopt these tools to evaluate the effectiveness of different management scenarios and their impact on the environment. Quality of precipitation data is critical for appropriate application of watershed models. In small watersheds, where no dense rain gauge network is available, modelers are faced with a dilemma to choose between different data sets. In this study, we used the German Branch (GB) watershed (~50 km^2), which is included in the USDA Conservation Effects Assessment Project (CEAP), to examine the implications of using surface rain gauge and next‐generation radar (NEXRAD) precipitation data sets on the performance of the Soil and Water Assessment Tool (SWAT). The GB watershed is located in the Coastal Plain of Maryland on the eastern shore of Chesapeake Bay. Stream flow estimation results using surface rain gauge data seem to indicate the importance of using rain gauges within the same direction as the storm pattern with respect to the watershed. In the absence of a spatially representative network of rain gauges within the watershed, NEXRAD data produced good estimates of stream flow at the outlet of the watershed. Three NEXRAD datasets, including (1)non‐corrected (NC), (2) bias‐corrected (BC), and (3) inverse distance weighted (IDW) corrected NEXRAD data, were produced. Nash‐Sutcliffe efficiency coefficients for daily stream flow simulation using these three NEXRAD data ranged from 0.46 to 0.58 during calibration and from 0.68 to 0.76 during validation. Overall, correcting NEXRAD with rain gauge data is promising to produce better hydrologic modeling results. Given the multiple precipitation datasets and corresponding simulations, we explored the combination of the multiple simulations using Bayesian model averaging. The results show that this Bayesian scheme can produce better deterministic prediction than any single simulation and can provide reasonable uncertainty estimation. The optimal water balance obtained in this study is an essential precursor to acquiring realistic estimates of sediment and nutrient loads in future GB modeling efforts. The results presented in this study are expected to provide insights into selecting precipitation data for watershed modeling in small Coastal Plain catchments. 
Language:English 
Keywords:Hydrologic modeling, Model calibration, MPE, NEXRAD, Rain gauge, SWAT