Modeling land use patterns and water quality: An evaluation of the pySPARROW model
Abstract
Modeling the effects of land use and land cover changes on water quality is important
for watershed managers to better understand how human modifications to land surfaces
may alter stream nutrient loads. One model available to resource managers for this
purpose is the U.S. Geological Survey's SPARROW (Spatially Referenced Regressions
on Watershed Attributes) model. SPARROW estimates total nitrogen and total phosphorus
loads for watersheds by relating water quality information to nutrient sources, land-surface
characteristics, stream connectivity, and downstream travel time. This project evaluates
the pySPARROW model, which is an application of SPARROW written in the Python programming
language for North Carolina's non-tidal stream network. By analyzing estimated nutrient
loads of the Falls Lake subbasin under current land uses, this project assesses how
well pySPARROW predicts the long term mean total nitrogen concentration. A regression
analysis of the observed versus predicted total nitrogen concentrations shows that
pySPARROW most likely needs to be recalibrated to improve its accuracy. The model
is also used to assess watershed impacts of a development scenario under which forests
and agricultural lands are converted to urban uses. Under this scenario, the total
nitrogen loading of the Falls Lake subbasin increases and the loading of most catchments
which experienced some development also increases. With the population of the Falls
Lake subbasin expected to increase by 50 percent from 2000 to 2025, it is especially
important that watershed managers have tools, such as pySPARROW, that may be used
to predict the impact of land use changes on water quality in this region.
Type
Master's projectPermalink
https://hdl.handle.net/10161/492Citation
Chambliss, Emily (2008). Modeling land use patterns and water quality: An evaluation of the pySPARROW model.
Master's project, Duke University. Retrieved from https://hdl.handle.net/10161/492.Collections
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