↓ Skip to main content

The Regionalization of National‐Scale SPARROW Models for Stream Nutrients1

Overview of attention for article published in Journal of the American Water Resources Association, August 2011
Altmetric Badge

Citations

dimensions_citation
17 Dimensions

Readers on

mendeley
30 Mendeley
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Title
The Regionalization of National‐Scale SPARROW Models for Stream Nutrients1
Published in
Journal of the American Water Resources Association, August 2011
DOI 10.1111/j.1752-1688.2011.00581.x
Pubmed ID
Authors

Gregory E. Schwarz, Richard B. Alexander, Richard A. Smith, Stephen D. Preston

Abstract

This analysis modifies the parsimonious specification of recently published total nitrogen (TN) and total phosphorus (TP) national-scale SPAtially Referenced Regressions On Watershed attributes models to allow each model coefficient to vary geographically among three major river basins of the conterminous United States. Regionalization of the national models reduces the standard errors in the prediction of TN and TP loads, expressed as a percentage of the predicted load, by about 6 and 7%. We develop and apply a method for combining national-scale and regional-scale information to estimate a hybrid model that imposes cross-region constraints that limit regional variation in model coefficients, effectively reducing the number of free model parameters as compared to a collection of independent regional models. The hybrid TN and TP regional models have improved model fit relative to the respective national models, reducing the standard error in the prediction of loads, expressed as a percentage of load, by about 5 and 4%. Only 19% of the TN hybrid model coefficients and just 2% of the TP hybrid model coefficients show evidence of substantial regional specificity (more than ±100% deviation from the national model estimate). The hybrid models have much greater precision in the estimated coefficients than do the unconstrained regional models, demonstrating the efficacy of pooling information across regions to improve regional models.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 30 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 2 7%
Unknown 28 93%

Demographic breakdown

Readers by professional status Count As %
Researcher 7 23%
Student > Ph. D. Student 4 13%
Other 3 10%
Student > Master 3 10%
Professor 2 7%
Other 5 17%
Unknown 6 20%
Readers by discipline Count As %
Environmental Science 11 37%
Engineering 4 13%
Earth and Planetary Sciences 4 13%
Computer Science 1 3%
Chemical Engineering 1 3%
Other 2 7%
Unknown 7 23%