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Using NEXRAD and Rain Gauge Precipitation Data for Hydrologic Calibration of SWAT in a Northeastern Watershed

Journal Article · · Transactions of the ASABE
DOI:https://doi.org/10.13031/2013.34900· OSTI ID:1000610
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 km2), 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.
Research Organization:
Pacific Northwest National Laboratory (PNNL), Richland, WA (US)
Sponsoring Organization:
USDOE
DOE Contract Number:
AC05-76RL01830
OSTI ID:
1000610
Report Number(s):
PNNL-SA-72621
Journal Information:
Transactions of the ASABE, Journal Name: Transactions of the ASABE Journal Issue: 5 Vol. 53; ISSN 2151-0040
Country of Publication:
United States
Language:
English