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Water and Nitrate‐Nitrogen Losses From a Small, Upland, Coastal Plain Watershed
Author(s) -
Hubbard R. K.,
Sheridan J. M.
Publication year - 1983
Publication title -
journal of environmental quality
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.888
H-Index - 171
eISSN - 1537-2537
pISSN - 0047-2425
DOI - 10.2134/jeq1983.00472425001200020028x
Subject(s) - surface runoff , subsurface flow , hydrology (agriculture) , watershed , environmental science , base flow , nitrate , surface water , soil water , soil science , geology , groundwater , drainage basin , environmental engineering , chemistry , ecology , geography , geotechnical engineering , cartography , organic chemistry , machine learning , computer science , biology
Abstract Surface runoff (SRO) and shallow‐subsurface flow (SSF) from a small, upland, Coastal Plain watershed underlain by plinthite were monitored for a 10‐y period. Samples collected during surface runoff and subsurface‐flow events were analyzed for NO 3 ‐N. The major water runoff loss from the system was found to be subsurface flow, accounting for 79% of total runoff loss, which occurred primarily from December through May. Nitrate‐N concentrations in surface runoff and subsurface flow were relatively uniform over the period, averaging 0.47 and 8.75 mg/L, respectively. The combination of high volume of subsurface flow and its relatively high NO 3 ‐N content, resulted in 99% of total NO 3 ‐N loss via subsurface flow. Predictive equations for monthly surface and subsurface water runoff losses from this watershed were developed using multiple linear regression. The equations contain seasonal and climatic parameters, including pan evaporation. Tests of the equations with observed results gave significant ( P ≤ 0.01) correlation coefficients, r , of 0.82 and 0.85 for surface runoff and subsurface flow, respectively. Mean surface and subsurface NO 3 ‐N concentrations were multiplied by predicted monthly flows to compute predicted monthly NO 3 ‐N losses. Comparison of predicted and actual monthly NO 3 ‐N losses gave significant ( P ≤ 0.01) correlation coefficients, r , of 0.59 and 0.86 for surface and subsurface flows, respectively. The good fit of predicted and observed results can be partially attributed to the seasonal and climatic data base collected over a relatively long period of 10 y.