Modeling nitrate-nitrogen load reduction strategies for the des moines river, iowa using SWAT

Environmental Management
By:  and 

Links

Abstract

The Des Moines River that drains a watershed of 16,175 km2 in portions of Iowa and Minnesota is impaired for nitrate-nitrogen (nitrate) due to concentrations that exceed regulatory limits for public water supplies. The Soil Water Assessment Tool (SWAT) model was used to model streamflow and nitrate loads and evaluate a suite of basin-wide changes and targeting configurations to potentially reduce nitrate loads in the river. The SWAT model comprised 173 subbasins and 2,516 hydrologic response units and included point and nonpoint nitrogen sources. The model was calibrated for an 11-year period and three basin-wide and four targeting strategies were evaluated. Results indicated that nonpoint sources accounted for 95% of the total nitrate export. Reduction in fertilizer applications from 170 to 50 kg/ha achieved the 38% reduction in nitrate loads, exceeding the 34% reduction required. In terms of targeting, the most efficient load reductions occurred when fertilizer applications were reduced in subbasins nearest the watershed outlet. The greatest load reduction for the area of land treated was associated with reducing loads from 55 subbasins with the highest nitrate loads, achieving a 14% reduction in nitrate loads achieved by reducing applications on 30% of the land area. SWAT model results provide much needed guidance on how to begin implementing load reduction strategies most efficiently in the Des Moines River watershed. ?? 2009 Springer Science+Business Media, LLC.
Publication type Article
Publication Subtype Journal Article
Title Modeling nitrate-nitrogen load reduction strategies for the des moines river, iowa using SWAT
Series title Environmental Management
DOI 10.1007/s00267-009-9364-y
Volume 44
Issue 4
Year Published 2009
Language English
Larger Work Type Article
Larger Work Subtype Journal Article
Larger Work Title Environmental Management
First page 671
Last page 682
Google Analytic Metrics Metrics page
Additional publication details