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An Optimization Method for Estimating Constituent Mean Concentrations in Base Flow‐Dominated Flow
Author(s) -
Ahiablame Laurent,
Engel Bernard,
Chaubey Indrajeet
Publication year - 2013
Publication title -
jawra journal of the american water resources association
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.957
H-Index - 105
eISSN - 1752-1688
pISSN - 1093-474X
DOI - 10.1111/jawr.12076
Subject(s) - baseflow , base flow , pollutant , environmental science , nonpoint source pollution , surface runoff , streamflow , watershed , water quality , pollution , hydrology (agriculture) , computer science , drainage basin , engineering , ecology , geography , cartography , geotechnical engineering , machine learning , biology
Pollutant coefficients have been widely used to assess runoff nonpoint source pollution from individual land uses (e.g., agricultural, residential) of a watershed. Pollutant coefficients, known as event mean concentrations ( EMC s), were developed by the U.S. Environmental Protection Agency's Nationwide Urban Runoff Program ( NURP ) to serve as a national measure for characterizing pollutant loading in a receiving water body. The term “baseflow pollutant coefficient ( BPC )” is used in this study as a surrogate for EMC to describe mean concentration of pollutants in base flow‐dominated flow. A method for characterizing base flow quantity and quality for different land uses was explored using inverse modeling with two optimization techniques (a least square method and a genetic algorithm [ GA ] optimization), land use information, and streamflow quantity and quality data. The inverse model was formulated as a constrained minimization problem and demonstrated with data for 15 watersheds in Indiana. Results showed that estimated pollutant coefficients are comparable to the published literature. This indicates that the proposed method has the potential to effectively estimate constituent mean concentrations for pollutant load determination in gauged and ungauged watersheds, albeit more analysis with larger and more robust datasets is desirable to further refine and validate the accuracy of the approach.