Assessing the hydrologic performance of the EPA’s nonpoint source water quality assessment decision support tool using North American Land Data Assimilation System (NLDAS) products

• Published in: Journal of hydrology (Amsterdam) 2010-06, Vol.387 (3), p.212-220
• Main Authors: Lee, Shihyan, Ni-Mesister, Wenge, Toll, David, Nigro, Joseph, Gutierrez-Magness, Angelica L., Engman, Ted
• Format: Article
• Language: English
• Subjects: Assessments; BASINS; BASINS model; Better Assessme; Data assimilation; decision support systems; Earth sciences; Earth, ocean, space; Evapotranspiration; Exact sciences and technology; hydrologic data; hydrologic models; Hydrological and watershed modeling; Hydrological simulation program FORTRAN; Hydrology; Hydrology. Hydrogeology; meteorological data; model validation; North American Land Data Assimilation System; North American Land Data Assimulation System; precipitation; prediction; simulation models; Stations; stream flow; Water quality; watershed hydrology; Watersheds
• ISSN: 0022-1694; 1879-2707
• DOI: 10.1016/j.jhydrol.2010.04.009

The accuracy of streamflow predictions in the EPA’s BASINS (Better Assessment Science Integrating Point and Nonpoint Sources) decision support tool is affected by the sparse meteorological data contained in BASINS. The North American Land Data Assimilation System (NLDAS) data with high spatial and temporal resolutions provide an alternative to the NOAA National Climatic Data Center (NCDC)’s station data. This study assessed the improvement of streamflow prediction of the Hydrological Simulation Program-FORTRAN (HSPF) model contained within BASINS using the NLDAS 1/8 degree hourly precipitation and evapotranspiration estimates in seven watersheds of the Chesapeake Bay region. Our results demonstrated consistent improvements of daily streamflow predictions in five of the seven watersheds when NLDAS precipitation and evapotranspiration data was incorporated into BASINS. The improvement of using NLDAS data is significant when the watershed’s meteorological station is either far away or not in a similar climatic region. When the station is nearby, using NLDAS data produces similar results. The correlation coefficients of the analyses using NLDAS data were greater than 0.8, the Nash–Sutcliffe (NS) model fit efficiency greater than 0.6, and the error in the water balance was less than 5%. Our analyses also showed that the streamflow improvements were mainly contributed by NLDAS precipitation data and that the improvement from using NLDAS evapotranspiration data was not significant; partially due to the constraints of current BASINS-HSPF settings. However, NLDAS evapotranspiration data did improve the baseflow prediction. This study demonstrates NLDAS data has the potential to improve stream flow predictions, thus aid the water quality assessment in the EPA nonpoint water quality assessment decision tool.