| 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. |
| Genre: | Article |
| ProdID: | 70031463 |
| Citation Author: | Qian, S. S.; Reckhow, K. H.; Zhai, J.; McMahon, G. |
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| Citation End Page: | 10 |
| Citation Issue: | 7 |
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| Citation Language: | English |
| Citation Larger Work Title: | Water Resources Research |
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| Citation Number Of Pages: | 10 |
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| Citation Search Results Text: | Nonlinear regression modeling of nutrient loads in streams: A Bayesian approach; 2005; Article; Journal; Water Resources Research; Qian, S. S.; Reckhow, K. H.; Zhai, J.; McMahon, G. |
| Citation Start Page: | 1 |
| Citation Volume: | 41 |
| Citation Year: | 2005 |
| Type: | citation/reference |
| Text: | Nonlinear regression modeling of nutrient loads in streams: A Bayesian approach; 2005; Article; Journal; Water Resources Research; Qian, S. S.; Reckhow, K. H.; Zhai, J.; McMahon, G. |
| URL (THUMBNAIL): | http://pubs.er.usgs.gov/thumbnails/outside_thumb.jpg |
| URL (DIGITAL OBJECT IDENTIFIER): | http://dx.doi.org/10.1029/2005WR003986 |
| Date Other: | Sat, 1 Jan 2005 00:00 -0600 |
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