Impacts of land use/land cover metrics and patterns on phosphorus loading from watersheds in Southeastern USA
Abstract
Non-point source (NPS) pollutions are considered the leading threat to water quality and major cause of aquatic ecosystem degradation. NPS pollution is recognized as a landscape-level phenomenon. Hence, quantifying landscape metrics is increasingly relevant to examine the relationship between landscape pattern and measures of stream health. The SPARROW (Spatially Referenced Regressions on Watershed attributes) water quality model is a watershed modeling technique that uses a hybrid statistical and process-based approach to estimate pollutant sources and contaminant transport in watersheds and surface water. It has been applied in the Southeastern United States, but it does not integrate the LU/LC effect based on landscape metrics. The present study was conducted to integrate selected landscape metrics into the current SPARROW model to improve model prediction accuracy. The 2001 LU/LC data was used to calculate four class levels namely: area weighed mean patch size (AREA_AM), area weighted edge contrast (ECON_AM), percent of land (PLAND) and normalized landscape shape index (NLSI). The resulting data used as source and land to-water delivery variables to run the SPARROW model. The overall model prediction accuracy was 67%, 69, 70, and 71%, respectively. Incorporation of ECON_AM, PLAND and NLSI significantly improved the model accuracy compared to the original model which is 67%. This study suggests that combining landscape metrics with conventional SPARROW model could considerably improve the predictive power of Phosphorous loading and has the potential to be applied to other regional and local landscape metrics. Key words: NPS, phosphorus, landscape metrics, SPARROW
Subject Area
Geographic information science|Natural Resource Management|Environmental science
Recommended Citation
Tigist G Jima,
"Impacts of land use/land cover metrics and patterns on phosphorus loading from watersheds in Southeastern USA"
(2014).
ETD Collection for Tennessee State University.
Paper AAI1584217.
https://digitalscholarship.tnstate.edu/dissertations/AAI1584217