SWAT Literature Database for Peer-Reviewed Journal Articles

Title:Estimating watershed-scale precipitation by combining gauge- and radar-derived observations 
Authors:Ercan, M.B. and J.L. Goodall 
Year:2013 
Journal:Journal of Hydrologic Engineering 
Volume:18(8) 
Pages:983-994 
Article ID: 
DOI:10.1061/(ASCE)HE.1943-5584.0000687 
URL (non-DOI journals): 
Model:SWAT 
Broad Application Category:hydrologic only 
Primary Application Category:climate data effects 
Secondary Application Category:hydrologic assessment 
Watershed Description:171 km^2 Eno, located in Orange County, in north central North Carolina, U.S. 
Calibration Summary: 
Validation Summary: 
General Comments: 
Abstract:Watershed modeling requires accurate estimates of precipitation; however, in some cases it is necessary to simulate streamflow in a watershed for which there is no precipitation gauge records within close proximity to the watershed. For such cases, we propose an approach to estimating watershed-scale precipitation by combining (or fusing) gauge-based precipitation time series with radar-based precipitation time series in a way that seeks to match input precipitation for the watershed model with observed streamflow at the watershed outlet. We test the proposed data fusion approach through a case study where the Soil and Water Assessment Tool (SWAT) model is used to simulate streamflow for a portion of the Eno River Watershed located in Orange County, North Carolina. Results of this case study show that the proposed approach improved model accuracy (E ¼ 0.60; R2 ¼ 0.74; PB ¼ −10.2) when compared to a model driven by gauge data only (E ¼ 0.50; R2 ¼ 0.54; PB ¼ −25.5) or radar data only (E ¼ 0.33; R2 ¼ 0.61; PB ¼ −13.7). While this result is limited to a single watershed case study, it suggests that the proposed approach could be a useful tool for hydrologic engineers in need of retrospective precipitation estimates for watersheds that suffer from inadequate gauge coverage. 
Language:English 
Keywords:Precipitation; Watersheds; Hydrologic models; Radar