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Nonlinear regression modeling of nutrient loads in streams: A Bayesian approach

Water Resources Research

By:
, , , and
DOI: 10.1029/2005WR003986

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Abstract

A Bayesian nonlinear regression modeling method is introduced and compared with the least squares method for modeling nutrient loads in stream networks. The objective of the study is to better model spatial correlation in river basin hydrology and land use for improving the model as a forecasting tool. The Bayesian modeling approach is introduced in three steps, each with a more complicated model and data error structure. The approach is illustrated using a data set from three large river basins in eastern North Carolina. Results indicate that the Bayesian model better accounts for model and data uncertainties than does the conventional least squares approach. Applications of the Bayesian models for ambient water quality standards compliance and TMDL assessment are discussed. Copyright 2005 by the American Geophysical Union.

Additional Publication Details

Publication type:
Article
Publication Subtype:
Journal Article
Title:
Nonlinear regression modeling of nutrient loads in streams: A Bayesian approach
Series title:
Water Resources Research
DOI:
10.1029/2005WR003986
Volume
41
Issue:
7
Year Published:
2005
Language:
English
Larger Work Type:
Article
Larger Work Subtype:
Journal Article
Larger Work Title:
Water Resources Research
First page:
1
Last page:
10