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MODELING DEVELOPED COASTAL WATERSHEDS WITH THE AGRICULTURAL NON‐POINT SOURCE MODEL 1
Author(s) -
Choi Kyoung Sik,
Blood Elizabeth
Publication year - 1999
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.1752-1688.1999.tb03585.x
Subject(s) - impervious surface , environmental science , hydrology (agriculture) , watershed , surface runoff , nonpoint source pollution , drainage basin , land use , geography , ecology , geology , geotechnical engineering , cartography , machine learning , computer science , biology
Many coastal states are facing increasing urban growth along their coast lines. The growth has caused urban non‐point source nitrogen runoff to be a major contributor to coastal and estuarine enrichment. Water resource managers are responsible for evaluating the impacts from point and non‐point sources in developed watersheds and developing strategies to manage future growth. Non‐point source models provide an effective approach to these management challenges. The Agricultural Non‐Point Source Model (AGNPS) permits the incorporation of important spatial information (soils, landuse, topography, hydrology) in simulating surface hydrology and nitrogen non‐point source runoff. The AGNPS model was adapted for developed coastal watersheds by deriving urban coefficients that reflect urban landuse classes and the amount of impervious surface area. Popperdam Creek watershed was used for model parameter development and model calibration. Four additional watersheds were simulated to validate the model. The model predictions of the peak flow and total nitrogen concentrations were close to the field measurements for the five sub‐basins simulated. Measured peak flow varied by 30 fold among the sub‐basins. The average simulated peak flow was within 14 percent of the average measured peak flow. Measured total nitrogen loads varied over an order of magnitude among the sub‐basins yet error between the measured and simulated loads for a given sub‐basin averaged 5 percent. The AGNPS model provided better estimates of nitrogen loads than widely used regression methods. The spatial distribution of important watershed characteristics influenced the impacts of urban landuse and projecting future residential expansion on runoff, sediment and nitrogen yields. The AGNPS model provides a useful tool to incorporate these characteristics, evaluate their importance, and evaluate fieldscale to watershed‐scale urban impacts.