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United States Department of Agriculture

Agricultural Research Service

Research Project: USING REMOTE SENSING & MODELING FOR EVALUATING HYDROLOGIC FLUXES, STATES, & CONSTITUENT TRANSPORT PROCESSES WITHIN AGRICULTURAL LANDSCAPES Title: Using NEXRAD and Rain Gauge Precipitation Data for Hydrologic Calibration of SWAT in a Northeastern Watershed

Authors
item Sexton, Aisha -
item Sadeghi, Ali
item Zhang, Xuesong -
item Srinivasan, Raghavan -
item Shirmohammadi, Adel -

Submitted to: Transactions of the ASABE
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: March 12, 2009
Publication Date: March 12, 2009
Citation: Sexton, A.M., Sadeghi, A.M., Zhang, X., Srinivasan, R., Shirmohammadi, A. 2009. Using NEXRAD and rain gauge precipitation data for hydrologic calibration of SWAT in a Northeastern watershed. Transactions of the American Society of Agriculture and Biological Engineers. 53(5):1501-1510.

Interpretive Summary: Precipitation is one of the most important inputs to any hydrologic model. Except for several experimental watersheds, the National Climatic Data Center (NCDC) rain gauge data (approximately one gauge per 800 km2) is the major source of observed precipitation data for most watersheds in the U.S. Another important source of precipitation data is Next Generation Radar (NEXRAD), which provides spatially continuous estimations at approximately 4×4 km2 resolution. In watersheds, where no adequate rain gauge network is available, we are faced with a dilemma to choose between different data sets. The goal of this study was to evaluate the ability of SWAT watershed model to estimate stream flow in a watershed containing no rain gauges using proximal rain gauge and NEXRAD MPE precipitation data. Results indicated that NEXRAD rainfall data can be a viable alternative to using rainfall data collected from surface rain gauges located outside of the watershed or where gauges are not located within the storm path of the watershed. The use of NEXRAD data in model calibration produced comparable and, in most cases, better estimates of flow than gauge data. As the quality of NEXRAD data is further improved, it is increasingly more suitable for use in hydrologic modeling, especially where gauge data is lacking.

Technical Abstract: The value of watershed-scale, hydrologic/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 Costal Plain of Maryland on the Eastern Shore of the Chesapeake Bay. Stream flow estimation results using surface rain gauge data revealed 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 Distanced 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 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 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.

Last Modified: 11/25/2014
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