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CURVE NUMBER HYDROLOGY IN WATER QUALITY MODELING: USES, ABUSES, AND FUTURE DIRECTIONS 1
Author(s) -
Garen David C.,
Moore Daniel S.
Publication year - 2005
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/j.1752-1688.2005.tb03742.x
Subject(s) - hydrograph , nonpoint source pollution , runoff curve number , surface runoff , hydrology (agriculture) , hydrological modelling , water quality , environmental science , flood myth , streamflow , computer science , water resources , water resource management , watershed , drainage basin , geography , engineering , geology , ecology , geotechnical engineering , cartography , archaeology , climatology , machine learning , biology
Although the curve number method of the Natural Resources Conservation Service has been used as the foundation of the hydrology algorithms in many nonpoint source water quality models, there are significant problematic issues with the way it has been implemented and interpreted that are not generally recognized. This usage is based on misconceptions about the meaning of the runoff value that the method computes, which is a likely fundamental cause of uncertainty in subsequent erosion and pollutant loading predictions dependent on this value. As a result, there are some major limitations on the conclusions and decisions about the effects of management practices on water quality that can be supported with current nonpoint source water quality models. They also cannot supply the detailed quantitative and spatial information needed to address emerging issues. A key prerequisite for improving model predictions is to improve the hydrologic algorithms contained within them. The use of the curve number method is still appropriate for flood hydrograph engineering applications, but more physically based algorithms that simulate all streamflow generating processes are needed for nonpoint source water quality modeling. Spatially distributed hydrologic modeling has tremendous potential in achieving this goal.