Multivariate Technique for Baseflow Separation Using Water Quality Data
Sanjay Kumar Shukla, Saied Mostaghimi, B. Petrauskas, Mohammad Alsmadi
Abstract
Sanjay Kumar Shukla, Saied Mostaghimi, B. Petrauskas, Mohammad Alsmadi
Abstract
A procedure for baseflow separation was developed. The uniqueness of the procedure is that it uses water quantity (flow rate) as well as the quality (NO 3 , total Kjeldahl nitrogen and total suspended solids) data for baseflow separation. Cluster analysis (CA), a multivariate statistical procedure, was used to group the water quality samples on the storm hydrograph to identify the end point of the baseflow separation line. The proposed procedure was applied to 30 storms in two watersheds, located in the Coastal Plain and Piedmont regions of Virginia, with different hydrologic characteristics. In addition to being a more physically based approach, the CA method also provided consistently better baseflow estimates, compared to two traditional approaches. One of the unique features of the CA method was that it conformed to the premise that baseflow increases after a storm event. For improving the performance of the CA method, regular time interval sampling and inclusion of additional water quality variables (e.g., Ca ++ and HCO 3 - ) are suggested.
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A procedure for baseflow separation was developed. The uniqueness of the procedure is that it uses water quantity (flow rate) as well as the quality (NO 3 , total Kjeldahl nitrogen and total suspended solids) data for baseflow separation. Cluster analysis (CA), a multivariate statistical procedure, was used to group the water quality samples on the storm hydrograph to identify the end point of the baseflow separation line. The proposed procedure was applied to 30 storms in two watersheds, located in the Coastal Plain and Piedmont regions of Virginia, with different hydrologic characteristics. In addition to being a more physically based approach, the CA method also provided consistently better baseflow estimates, compared to two traditional approaches. One of the unique features of the CA method was that it conformed to the premise that baseflow increases after a storm event. For improving the performance of the CA method, regular time interval sampling and inclusion of additional water quality variables (e.g., Ca ++ and HCO 3 - ) are suggested.
Key concepts: Baseflow, Hydrograph, Hydrology (agriculture), Environmental science, Water quality, Streamflow, Multivariate statistics, Storm