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EVALUATION OF Ann AGNPS NITROGEN LOADING IN AN AGRICULTURAL WATERSHED 1
Author(s) -
Yuan Yongping,
Bingner Ronald L.,
Rebich Richard A.
Publication year - 2003
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.1752-1688.2003.tb04398.x
Subject(s) - watershed , pollutant , nonpoint source pollution , environmental science , nutrient pollution , agriculture , nitrogen , surface runoff , pollution , environmental engineering , hydrology (agriculture) , engineering , computer science , ecology , chemistry , machine learning , geotechnical engineering , organic chemistry , biology
Pollutants entering a water system can be very destructive to the health of that system. Best Management Practices (BMPs) are used to reduce these pollutants, but understanding the most effective practices is very difficult. Watershed models are an effective tool to aid in the decision‐making process of selecting the BMPs that are most effective in reducing the pollutant loading and are also the most cost effective. The Annualized Agricultural Nonpoint Source Pollution model (AnnAGNPS 2.0) is a technological tool that can be used to estimate watershed response to agricultural management practices. The main purpose of this paper is to test the performance of AnnAGNPS 2.0 on nitrogen loading using comparisons with measurements from the Deep Hollow watershed of the Mississippi Delta Management Systems Evaluation Area (MDMSEA) project. Previous work has demonstrated the capability of the model to simulate runoff and sediment. From sensitivity analyses in this study, initial nitrogen concentration in the soil and crop nitrogen uptake had the most impact on the nitrogen loadings. AnnAGNPS simulations of monthly nitrogen loadings are poor. However, statistical test (t‐test) showed that the predicted nitrogen loading is not significantly different from observed nitrogen loading at the 95 percent level of confidence.