Using multiple watershed models to assess the water quality impacts of alternate land development scenarios for a small community

CATENA
2017
Sharifi, Amirreza; Yen, Haw; Boomer, Kathleen M. B.; Kalin, Latif; Li, Xuyong; Weller, Donald E.
PublisherElsevier
SourceWeb of Science
Volume / Issue150
Pages87 - 99
Total Pages12 pages
Article Link
ISBN N/A
DOI10.1016/j.catena.2016.11.009
Editor(s) N/A
Conference / Book Title N/A
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Tagswatershed modeling; multiple model comparisons; SWAT; GWLF; CBP-CWM; Chesapeake Bay; agriculture
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Conference Date N/A
Publication DateNovember 14, 2016
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AbstractChesapeake Bay, the largest estuary in North America, is impaired by excess nutrient discharges, especially from urban and agricultural land. Watershed simulation models have provided key insights to understanding land-to-water connections, but rarely are these models applied to guide local land management to explore and communicate uncertainty in the model predictions. In this study, three watershed simulation models; the Soil and Water Assessment Tool (SWAT), the Generalized Watershed Loading Function (GWLF) model, and the Chesapeake Bay Program's Chesapeake Watershed Model (CBP-CWM) were implemented to predict water, total nitrogen, and total phosphorus discharges from small tributaries in the town of Queenstown, Maryland, USA. Based on our evaluation metrics, none of the models consistently provided better results. In general, there was a good agreement on annual average water flow between the SWAT and CBP-CWM models, and the GWLF and CBP-CWM models predicted similar TN and TP loads. Each model has strengths and weaknesses in flow and nutrient predictions, and predictions differed among models even when models were initialized with the same data. Using multiple models may enhance the quality of model predictions and the decision making process. However, it could also be the case that the complexity of implemented watershed models and resolution of our understanding currently are not yet suited to provide scientifically credible solutions.
Created: 12/14/2017 10:31 AM (ET)
Modified: 1/3/2019 1:24 PM (ET)
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