thumbnail

GIS-based spatial regression and prediction of water quality in river networks: A case study in Iowa

Journal of Environmental Management

By:
,
DOI: 10.1016/j.jenvman.2010.04.011

Links

Abstract

Nonpoint source pollution is the leading cause of the U.S.'s water quality problems. One important component of nonpoint source pollution control is an understanding of what and how watershed-scale conditions influence ambient water quality. This paper investigated the use of spatial regression to evaluate the impacts of watershed characteristics on stream NO3NO2-N concentration in the Cedar River Watershed, Iowa. An Arc Hydro geodatabase was constructed to organize various datasets on the watershed. Spatial regression models were developed to evaluate the impacts of watershed characteristics on stream NO3NO2-N concentration and predict NO3NO2-N concentration at unmonitored locations. Unlike the traditional ordinary least square (OLS) method, the spatial regression method incorporates the potential spatial correlation among the observations in its coefficient estimation. Study results show that NO3NO2-N observations in the Cedar River Watershed are spatially correlated, and by ignoring the spatial correlation, the OLS method tends to over-estimate the impacts of watershed characteristics on stream NO3NO2-N concentration. In conjunction with kriging, the spatial regression method not only makes better stream NO3NO2-N concentration predictions than the OLS method, but also gives estimates of the uncertainty of the predictions, which provides useful information for optimizing the design of stream monitoring network. It is a promising tool for better managing and controlling nonpoint source pollution. ?? 2010 Elsevier Ltd.

Additional Publication Details

Publication type:
Article
Publication Subtype:
Journal Article
Title:
GIS-based spatial regression and prediction of water quality in river networks: A case study in Iowa
Series title:
Journal of Environmental Management
DOI:
10.1016/j.jenvman.2010.04.011
Volume
91
Issue:
10
Year Published:
2010
Language:
English
Larger Work Type:
Article
Larger Work Subtype:
Journal Article
First page:
1943
Last page:
1951
Number of Pages:
9