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Effects of Watershed Configuration and Composition on Downstream Lake Water Quality
Author(s) -
Gémesi Zsolt,
Downing John A.,
Cruse Richard M.,
Anderson Paul F.
Publication year - 2011
Publication title -
journal of environmental quality
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.888
H-Index - 171
eISSN - 1537-2537
pISSN - 0047-2425
DOI - 10.2134/jeq2010.0133
Subject(s) - watershed , environmental science , water quality , hydrology (agriculture) , land cover , secchi disk , land use , linear regression , nutrient , statistics , ecology , mathematics , computer science , biology , geotechnical engineering , machine learning , eutrophication , engineering
We evaluated the relationships between landscape characteristics and lake water quality in receiving waters by regressing four water quality responses on landscape variables that were measured for whole watersheds and three different buffer distances (30, 60, and 120 m). Classical percolation theory was used to conceptualize nutrient pathways and to explain nonlinear responses. The response variables were total nitrogen (TN), total phosphorus (TP), chlorophyll‐a (Chl‐a), and Secchi transparency (SD). Landscape data were obtained from satellite image‐derived maps of 130 watersheds in Iowa using geographic information systems software. We developed regression models with a stepwise protocol selecting the optimal number of significant explanatory variables. Configuration variables such as contagion, the cohesion of cropland and urban land, and the aggregation index of forest were very important and more important than variables assessing landscape composition (e.g., percentage farmland). Whole watershed models predicted between 15 and 67% of the variability in TN, TP, Chl‐a, and SD. Proximity‐explicit data offered only slightly improved statistical power over land cover data derived from the entire watershed for variables TN, Chl‐a, and SD, but not for TP.